MGMT Of Production And Oprtns#3 Discussion
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Operations_Management_12_Edition.pdf
ISBN-13: 978-0-13-413042-2 ISBN-10: 0-13-413042-1
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OPE R AT IONS M A NAGE M E N T Sustainability and Supply Chain Management
T W E L F T H E D I T I O N
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T W E L F T H E D I T I O N
JAY HEIZER | BARRY RENDER | CHUCK MUNSON
HEIZER RENDER MUNSON
www.pearsonhighered.com
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O P E R A T I O N S M A N A G E M E N T Sustainability and Supply Chain Management
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T W E L F T H E D I T I O N
O P E R A T I O N S M A N A G E M E N T Sustainability and Supply Chain Management
HEIZER J A Y
RENDER B A R R Y
Jesse H. Jones Professor of Business Administration Texas Lutheran University
Charles Harwood Professor of Operations Management Graduate School of Business Rollins College
Boston Columbus Indianapolis New York San Francisco Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
C H U C K
MUNSON Professor of Operations Management Carson College of Business Washington State University
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Library of Congress Cataloging-in-Publication Data
Heizer, Jay. [Production and operations management] Operations management; sustainability and supply chain management / Jay Heizer, Jesse H. Jones Professor of Business Administration, Texas Lutheran University, Barry Render, Charles Harwood Professor of Operations Management, Crummer Graduate School of Business, Rollins College, Chuck Munson, Professor of Operations Management, Carson College of Business, Washington State University. -- Twelfth edition. pages cm Original edition published under the Title: Production and operations management. Includes bibliographical references and index. ISBN 978-0-13-413042-2 -- ISBN 0-13-413042-1 1. Production management. I. Render, Barry. II. Munson, Chuck. III. Title. TS155.H3725 2015 658.5--dc23 2015036857
10 9 8 7 6 5 4 3 2 1
ISBN 10: 0-13-413042-1
ISBN 13: 978-0-13-413042-2
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To Karen Heizer Herrmann, all a sister could ever be
To Donna, Charlie, and Jesse
J.H.
B.R.
To Kim, Christopher, and Mark Munson for their unwavering support, and to Bentonville High School teachers Velma Reed and Cheryl Gregory,
who instilled in me the importance of detail and a love of learning C.M.
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A BO U T T HE A U T H O R S
JAY HEIZER
BARRY RENDER
Professor Emeritus, the Jesse H. Jones Chair of Business Administration, Texas Lutheran University, Seguin, Texas. He received his B.B.A. and M.B.A. from the University of North Texas and his Ph.D. in Management and Statistics from Arizona State University. He was previously a member of the faculty at the University of Memphis, the University of Oklahoma, Virginia Commonwealth University, and the University of Richmond. He has also held visiting positions at Boston University, George Mason University, the Czech Management Center, and the Otto-Von-Guericke University, Magdeburg.
Dr. Heizer’s industrial experience is extensive. He learned the practical side of operations management as a machinist apprentice at Foringer and Company, as a production planner for Westinghouse Airbrake, and at General Dynamics, where he worked in engineering administration. In addition, he has been actively involved in consulting in the OM and MIS areas for a variety of organizations, includ- ing Philip Morris, Firestone, Dixie Container Corporation, Columbia Industries, and Tenneco. He holds the CPIM certification from APICS—the Association for Operations Management.
Professor Heizer has co-authored 5 books and has published more than 30 arti- cles on a variety of management topics. His papers have appeared in the Academy of Management Journal , Journal of Purchasing , Personnel Psychology , Production & Inventory Control Management , APICS—The Performance Advantage , Journal of Management History , IIE Solutions, and Engineering Management , among others. He has taught operations management courses in undergraduate, graduate, and executive programs.
Professor Emeritus, the Charles Harwood Professor of Operations Management, Crummer Graduate School of Business, Rollins College, Winter Park, Florida. He received his B.S. in Mathematics and Physics at Roosevelt University, and his M.S. in Operations Research and Ph.D. in Quantitative Analysis at the University of Cincinnati. He previously taught at George Washington University, University of New Orleans, Boston University, and George Mason University, where he held the Mason Foundation Professorship in Decision Sciences and was Chair of the Decision Sciences Department. Dr. Render has also worked in the aerospace indus- try for General Electric, McDonnell Douglas, and NASA.
Professor Render has co-authored 10 textbooks for Pearson, including Managerial Decision Modeling with Spreadsheets , Quantitative Analysis for Management , Service Management , Introduction to Management Science , and Cases and Readings in Management Science . Quantitative Analysis for Management, now in its 13th edition, is a leading text in that discipline in the United States and globally. Dr. Render’s more than 100 articles on a variety of management topics have appeared in Decision Sciences , Production and Operations Management , Interfaces , Information and Management , Journal of Management Information Systems , Socio-Economic Planning Sciences , IIE Solutions , and Operations Management Review , among others.
Dr. Render has been honored as an AACSB Fellow and was twice named a Senior Fulbright Scholar. He was Vice President of the Decision Science Institute Southeast Region and served as Software Review Editor for Decision Line for six years and as Editor of the New York Times Operations Management special issues for five years. For nine years, Dr. Render was President of Management Service Associates of Virginia, Inc., whose technology clients included the FBI, NASA, the U.S. Navy, Fairfax County, Virginia, and C&P Telephone. He is currently Consulting Editor to Pearson Press .
Dr. Render has received Rollins College’s Welsh Award as leading Professor and was selected by Roosevelt University as the recipient of the St. Claire Drake Award for Outstanding Scholarship. Dr. Render also received the Rollins College MBA Student Award for Best Overall Course, and was named Professor of the Year by full-time MBA students.
vi
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Professor of Operations Management, Carson College of Business, Washington State University, Pullman, Washington. He received his BSBA summa cum laude in finance, along with his MSBA and Ph.D. in operations management, from Washington University in St. Louis. For two years, he served as Associate Dean for Graduate Programs in Business at Washington State. He also worked for three years as a financial analyst for Contel Telephone Corporation.
Professor Munson serves as a senior editor for Production and Operations Management , and he serves on the editorial review board of four other journals . He has published more than 25 articles in such journals as Production and Operations Management , IIE Transactions, Decision Sciences , Naval Research Logistics , European Journal of Operational Research , Journal of the Operational Research Society , and Annals of Operations Research. He is editor of the book The Supply Chain Management Casebook: Comprehensive Coverage and Best Practices in SCM , and he has co-authored the research monograph Quantity Discounts: An Overview and Practical Guide for Buyers and Sellers . He is also coauthor of Managerial Decision Modeling with Spreadsheets (4th edition), published by Pearson.
Dr. Munson has taught operations management core and elective courses at the undergraduate, MBA, and Ph.D. levels at Washington State University. He has also conducted several teaching workshops at international conferences and for Ph.D. students at Washington State University. His major awards include being a Founding Board Member of the Washington State University President’s Teaching Academy (2004); winning the WSU College of Business Outstanding Teaching Award (2001 and 2015), Research Award (2004), and Service Award (2009 and 2013); and being named the WSU MBA Professor of the Year (2000 and 2008).
CHUCK MUNSON
A B O U T T H E A U T H O R S vii
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PART ONE Introduction to Operations Management 1
Chapter 1 Operations and Productivity 1 Chapter 2 Operations Strategy in a Global Environment 29 Chapter 3 Project Management 59 Chapter 4 Forecasting 105
PART TWO Designing Operations 159
Chapter 5 Design of Goods and Services 159 ◆
Supplement 5 Sustainability in the Supply Chain 193
Chapter 6 Managing Quality 213 ◆
Supplement 6 Statistical Process Control 245
Chapter 7 Process Strategy 279 ◆
Supplement 7 Capacity and Constraint Management 307
Chapter 8 Location Strategies 337 Chapter 9 Layout Strategies 367 Chapter 10 Human Resources, Job Design, and Work Measurement 407
PART THREE Managing Operations 441
Chapter 11 Supply Chain Management 441 ◆
Supplement 11 Supply Chain Management Analytics 471
Chapter 12 Inventory Management 487 Chapter 13 Aggregate Planning and S&OP 529 Chapter 14 Material Requirements Planning (MRP) and ERP 563 Chapter 15 Short-Term Scheduling 599 Chapter 16 Lean Operations 635 Chapter 17 Maintenance and Reliability 659
PART FOUR Business Analytics Modules 677
Module A Decision-Making Tools 677 Module B Linear Programming 699 Module C Transportation Models 729 Module D Waiting-Line Models 747 Module E Learning Curves 775 Module F Simulation 791
ONLINE TUTORIALS
1. Statistical Tools for Managers T1-1 2. Acceptance Sampling T2-1 3. The Simplex Method of Linear Programming T3-1 4. The MODI and VAM Methods of Solving Transportation Problems T4-1 5. Vehicle Routing and Scheduling T5-1
Brief Table of Contents
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Table of Contents
About the Authors vi
Preface xxiii
Chapter 1 Operations and Productivity 1
GLOBAL COMPANY PROFILE: HARD ROCK CAFE 2
What Is Operations Management? 4
Organizing to Produce Goods and Services 4
The Supply Chain 6
Why Study OM? 6
What Operations Managers Do 7
The Heritage of Operations Management 8
Operations for Goods and Services 11
Growth of Services 11
Service Pay 12
The Productivity Challenge 13
Productivity Measurement 14
Productivity Variables 15
Productivity and the Service Sector 17
Current Challenges in Operations Management 18
Ethics, Social Responsibility, and Sustainability 19
Summary 20
Key Terms 20
Ethical Dilemma 20
Discussion Questions 20
Using Software for Productivity Analysis 21
Solved Problems 21
Problems 22
CASE STUDIES 24
Uber Technologies, Inc. 24
Frito-Lay: Operations Management in Manufacturing
Video Case 25
Hard Rock Cafe: Operations Management in Services
Video Case 25
Endnotes 26
Rapid Review 27
Self Test 28
Chapter 2 Operations Strategy in a Global Environment 29
GLOBAL COMPANY PROFILE: BOEING 30
A Global View of Operations and Supply Chains 32
Cultural and Ethical Issues 35
Developing Missions and Strategies 35
Mission 36
Strategy 36
Achieving Competitive Advantage Through Operations 36
Competing on Diff erentiation 37
Competing on Cost 38
Competing on Response 39
Issues in Operations Strategy 40
Strategy Development and Implementation 41
Key Success Factors and Core Competencies 41
Integrating OM with Other Activities 43
Building and Staffi ng the Organization 43
Implementing the 10 Strategic OM Decisions 44
Strategic Planning, Core Competencies, and Outsourcing 44
The Theory of Comparative Advantage 46
Risks of Outsourcing 46
Rating Outsource Providers 47
Global Operations Strategy Options 49
Summary 50
Key Terms 50
Ethical Dilemma 51
Discussion Questions 51
Using Software to Solve Outsourcing Problems 51
Solved Problems 52
Problems 53
CASE STUDIES 55
Rapid-Lube 55
Strategy at Regal Marine Video Case 55
Hard Rock Cafe’s Global Strategy Video Case 55
Outsourcing Off shore at Darden Video Case 56
Endnotes 56
Rapid Review 57
Self Test 58
Chapter 3 Project Management 59
GLOBAL COMPANY PROFILE: BECHTEL GROUP 60
The Importance of Project Management 62
PART ONE Introduction to Operations Management 1
xi
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xii TA B L E O F C O N T E N T S
Project Planning 62
The Project Manager 63
Work Breakdown Structure 64
Project Scheduling 65
Project Controlling 66
Project Management Techniques: PERT and CPM 67
The Framework of PERT and CPM 67
Network Diagrams and Approaches 68
Activity-on-Node Example 69
Activity-on-Arrow Example 71
Determining the Project Schedule 71
Forward Pass 72
Backward Pass 74
Calculating Slack Time and Identifying the Critical
Path(s) 75
Variability in Activity Times 77
Three Time Estimates in PERT 77
Probability of Project Completion 79
Cost-Time Trade-Off s and Project Crashing 82
A Critique of PERT and CPM 85
Using Microsoft Project to Manage Projects 86
Summary 88
Key Terms 88
Ethical Dilemma 89
Discussion Questions 89
Using Software to Solve Project Management Problems 89
Solved Problems 90
Problems 93
CASE STUDIES 98
Southwestern University: (A) 98
Project Management at Arnold Palmer Hospital
Video Case 99
Managing Hard Rock’s Rockfest Video Case 100
Endnotes 102
Rapid Review 103
Self Test 104
Chapter 4 Forecasting 105
GLOBAL COMPANY PROFILE: WALT DISNEY PARKS & RESORTS 106
What is Forecasting? 108
Forecasting Time Horizons 108
Types of Forecasts 109
The Strategic Importance of Forecasting 109
Supply-Chain Management 109
Human Resources 110
Capacity 110
Seven Steps in the Forecasting System 110
Forecasting Approaches 111
Overview of Qualitative Method 111
Overview of Quantitative Methods 112
Time-Series Forecasting 112
Decomposition of a Time Series 112
Naive Approach 113
Moving Averages 114
Exponential Smoothing 116
Measuring Forecast Error 117
Exponential Smoothing with Trend Adjustment 120
Trend Projections 124
Seasonal Variations in Data 126
Cyclical Variations in Data 131
Associative Forecasting Methods: Regression and Correlation Analysis 131
Using Regression Analysis for Forecasting 131
Standard Error of the Estimate 133
Correlation Coeffi cients for Regression Lines 134
Multiple-Regression Analysis 136
Monitoring and Controlling Forecasts 138
Adaptive Smoothing 139
Focus Forecasting 139
Forecasting in the Service Sector 140
Summary 141
Key Terms 141
Ethical Dilemma 141
Discussion Questions 142
Using Software in Forecasting 142
Solved Problems 144
Problems 146
CASE STUDIES 153
Southwestern University: (B) 153
Forecasting Ticket Revenue for Orlando Magic
Basketball Games Video Case 154
Forecasting at Hard Rock Cafe Video Case 155
Endnotes 156
Rapid Review 157
Self Test 158
PART TWO Designing Operations 159
Chapter 5 Design of Goods and Services 159
GLOBAL COMPANY PROFILE: REGAL MARINE 160
Goods and Services Selection 162
Product Strategy Options Support Competitive
Advantage 163
Product Life Cycles 164
Life Cycle and Strategy 164
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TA B L E O F C O N T E N T S xiii
Product-by-Value Analysis 165
Generating New Products 165
Product Development 166
Product Development System 166
Quality Function Deployment (QFD) 166
Organizing for Product Development 169
Manufacturability and Value Engineering 170
Issues for Product Design 171
Robust Design 171
Modular Design 171
Computer-Aided Design (CAD) and Computer-Aided
Manufacturing (CAM) 171
Virtual Reality Technology 172
Value Analysis 173
Sustainability and Life Cycle Assessment (LCA) 173
Product Development Continuum 173
Purchasing Technology by Acquiring a Firm 174
Joint Ventures 174
Alliances 175
Defi ning a Product 175
Make-or-Buy Decisions 176
Group Technology 177
Documents for Production 178
Product Life-Cycle Management (PLM) 178
Service Design 179
Process–Chain–Network (PCN) Analysis 179
Adding Service Effi ciency 181
Documents for Services 181
Application of Decision Trees to Product Design 182
Transition to Production 184
Summary 184
Key Terms 185
Ethical Dilemma 185
Discussion Questions 185
Solved Problem 186
Problems 186
CASE STUDIES 189
De Mar’s Product Strategy 189
Product Design at Regal Marine Video Case 189
Endnotes 190
Rapid Review 191
Self Test 192
Supplement 5 Sustainability in the Supply Chain 193
Corporate Social Responsibility 194
Sustainability 195
Systems View 195
Commons 195
Triple Bottom Line 195
Design and Production for Sustainability 198
Product Design 198
Production Process 200
Logistics 200
End-of-Life Phase 203
Regulations and Industry Standards 203
International Environmental Policies and
Standards 204
Summary 205
Key Terms 205
Discussion Questions 205
Solved Problems 206
Problems 207
CASE STUDIES 208
Building Sustainability at the Orlando Magic’s
Amway Center Video Case 208
Green Manufacturing and Sustainability at Frito-Lay
Video Case 209
Endnotes 210
Rapid Review 211
Self Test 212
Chapter 6 Managing Quality 213
GLOBAL COMPANY PROFILE: ARNOLD PALMER HOSPITAL 214
Quality and Strategy 216
Defi ning Quality 217
Implications of Quality 217
Malcolm Baldrige National Quality Award 218
ISO 9000 International Quality Standards 218
Cost of Quality (COQ) 218
Ethics and Quality Management 219
Total Quality Management 219
Continuous Improvement 220
Six Sigma 221
Employee Empowerment 222
Benchmarking 222
Just-in-Time (JIT) 224
Taguchi Concepts 224
Knowledge of TQM Tools 225
Tools of TQM 226
Check Sheets 226
Scatter Diagrams 227
Cause-and-Eff ect Diagrams 227
Pareto Charts 227
Flowcharts 228
Histograms 229
Statistical Process Control (SPC) 229
The Role of Inspection 230
When and Where to Inspect 230
Source Inspection 231
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xiv TA B L E O F C O N T E N T S
Service Industry Inspection 232
Inspection of Attributes versus Variables 233
TQM in Services 233
Summary 235
Key Terms 235
Ethical Dilemma 235
Discussion Questions 236
Solved Problems 236
Problems 237
CASE STUDIES 239
Southwestern University: (C) 239
The Culture of Quality at Arnold Palmer Hospital
Video Case 240
Quality Counts at Alaska Airlines Video Case 240
Quality at the Ritz-Carlton Hotel Company
Video Case 242
Endnotes 242
Rapid Review 243
Self Test 244
Supplement 6 Statistical Process Control 245
Statistical Process Control (SPC) 246
Control Charts for Variables 248
The Central Limit Theorem 248
Setting Mean Chart Limits ( x -Charts) 250
Setting Range Chart Limits ( R-Charts) 253
Using Mean and Range Charts 254
Control Charts for Attributes 256
Managerial Issues and Control Charts 259
Process Capability 260
Process Capability Ratio ( C p ) 260
Process Capability Index ( C pk
) 261
Acceptance Sampling 262
Operating Characteristic Curve 263
Average Outgoing Quality 264
Summary 265
Key Terms 265
Discussion Questions 265
Using Software for SPC 266
Solved Problems 267
Problems 269
CASE STUDIES 274
Bayfi eld Mud Company 274
Frito-Lay’s Quality-Controlled Potato Chips
Video Case 275
Farm to Fork: Quality at Darden Restaurants
Video Case 276
Endnotes 276
Rapid Review 277
Self Test 278
Chapter 7 Process Strategy 279
GLOBAL COMPANY PROFILE: HARLEY-DAVIDSON 280
Four Process Strategies 282
Process Focus 282
Repetitive Focus 283
Product Focus 284
Mass Customization Focus 284
Process Comparison 286
Selection of Equipment 288
Process Analysis and Design 288
Flowchart 289
Time-Function Mapping 289
Process Charts 289
Value-Stream Mapping 290
Service Blueprinting 292
Special Considerations for Service Process Design 293
Production Technology 294
Machine Technology 294
Automatic Identifi cation Systems (AISs) and RFID 295
Process Control 295
Vision Systems 296
Robots 296
Automated Storage and Retrieval Systems
(ASRSs) 296
Automated Guided Vehicles (AGVs) 296
Flexible Manufacturing Systems (FMSs) 297
Computer-Integrated Manufacturing (CIM) 297
Technology in Services 298
Process Redesign 298
Summary 299
Key Terms 299
Ethical Dilemma 300
Discussion Questions 300
Solved Problem 300
Problems 301
CASE STUDIES 302
Rochester Manufacturing’s Process Decision 302
Process Strategy at Wheeled Coach Video Case 302
Alaska Airlines: 20-Minute Baggage Process—
Guaranteed! Video Case 303
Process Analysis at Arnold Palmer Hospital
Video Case 304
Endnotes 304
Rapid Review 305
Self Test 306
Supplement 7 Capacity and Constraint Management 307
Capacity 308
Design and Eff ective Capacity 309
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TA B L E O F C O N T E N T S xv
Capacity and Strategy 311
Capacity Considerations 311
Managing Demand 312
Service-Sector Demand and Capacity
Management 313
Bottleneck Analysis and the Theory of Constraints 314
Theory of Constraints 317
Bottleneck Management 317
Break-Even Analysis 318
Single-Product Case 319
Multiproduct Case 320
Reducing Risk with Incremental Changes 322
Applying Expected Monetary Value (EMV) to Capacity Decisions 323
Applying Investment Analysis to Strategy-Driven Investments 324
Investment, Variable Cost, and Cash Flow 324
Net Present Value 324
Summary 326
Key Terms 327
Discussion Questions 327
Using Software for Break-Even Analysis 327
Solved Problems 328
Problems 330
CASE STUDY 333
Capacity Planning at Arnold Palmer Hospital
Video Case 333
Endnote 334
Rapid Review 335
Self Test 336
Chapter 8 Location Strategies 337
GLOBAL COMPANY PROFILE: FEDEX 338
The Strategic Importance of Location 340
Factors That Aff ect Location Decisions 341
Labor Productivity 342
Exchange Rates and Currency Risk 342
Costs 342
Political Risk, Values, and Culture 343
Proximity to Markets 343
Proximity to Suppliers 344
Proximity to Competitors (Clustering) 344
Methods of Evaluating Location Alternatives 344
The Factor-Rating Method 345
Locational Cost–Volume Analysis 346
Center-of-Gravity Method 348
Transportation Model 349
Service Location Strategy 350
Geographic Information Systems 351
Summary 353
Key Terms 353
Ethical Dilemma 354
Discussion Questions 354
Using Software to Solve Location Problems 354
Solved Problems 355
Problems 357
CASE STUDIES 362
Southern Recreational Vehicle Company 362
Locating the Next Red Lobster Restaurant
Video Case 362
Where to Place the Hard Rock Cafe Video Case 363
Endnote 364
Rapid Review 365
Self Test 366
Chapter 9 Layout Strategies 367
GLOBAL COMPANY PROFILE: McDONALD’S 368
The Strategic Importance of Layout Decisions 370
Types of Layout 370
Offi ce Layout 371
Retail Layout 372
Servicescapes 375
Warehouse and Storage Layouts 375
Cross-Docking 376
Random Stocking 377
Customizing 377
Fixed-Position Layout 377
Process-Oriented Layout 378
Computer Software for Process-Oriented Layouts 382
Work Cells 383
Requirements of Work Cells 383
Staffi ng and Balancing Work Cells 384
The Focused Work Center and the Focused
Factory 386
Repetitive and Product-Oriented Layout 386
Assembly-Line Balancing 387
Summary 392
Key Terms 392
Ethical Dilemma 392
Discussion Questions 392
Using Software to Solve Layout Problems 393
Solved Problems 394
Problems 396
CASE STUDIES 402
State Automobile License Renewals 402
Laying Out Arnold Palmer Hospital’s New Facility
Video Case 402
Facility Layout at Wheeled Coach Video Case 404
Endnotes 404
Rapid Review 405
Self Test 406
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xvi TA B L E O F C O N T E N T S
Chapter 10 Human Resources, Job Design, and Work Measurement 407
GLOBAL COMPANY PROFILE: RUSTY WALLACE’S NASCAR RACING TEAM 408
Human Resource Strategy for Competitive Advantage 410
Constraints on Human Resource Strategy 410
Labor Planning 411
Employment-Stability Policies 411
Work Schedules 411
Job Classifi cations and Work Rules 412
Job Design 412
Labor Specialization 412
Job Expansion 413
Psychological Components of Job Design 413
Self-Directed Teams 414
Motivation and Incentive Systems 415
Ergonomics and the Work Environment 415
Methods Analysis 417
The Visual Workplace 420
Labor Standards 420
Historical Experience 421
Time Studies 421
Predetermined Time Standards 425
Work Sampling 427
Ethics 430
Summary 430
Key Terms 430
Ethical Dilemma 431
Discussion Questions 431
Solved Problems 432
Problems 434
CASE STUDIES 437
Jackson Manufacturing Company 437
The “People” Focus: Human Resources at Alaska
Airlines Video Case 437
Hard Rock’s Human Resource Strategy
Video Case 438
Endnotes 438
Rapid Review 439
Self Test 440
PART THREE Managing Operations 441
Chapter 11 Supply Chain Management 441
GLOBAL COMPANY PROFILE: DARDEN RESTAURANTS 442
The Supply Chain’s Strategic Importance 444
Sourcing Issues: Make-or-Buy and Outsourcing 446
Make-or-Buy Decisions 447
Outsourcing 447
Six Sourcing Strategies 447
Many Suppliers 447
Few Suppliers 447
Vertical Integration 448
Joint Ventures 448
Keiretsu Networks 448
Virtual Companies 449
Supply Chain Risk 449
Risks and Mitigation Tactics 450
Security and JIT 451
Managing the Integrated Supply Chain 451
Issues in Managing the Integrated Supply Chain 451
Opportunities in Managing the Integrated Supply
Chain 452
Building the Supply Base 454
Supplier Evaluation 454
Supplier Development 454
Negotiations 455
Contracting 455
Centralized Purchasing 455
E-Procurement 456
Logistics Management 456
Shipping Systems 456
Warehousing 457
Third-Party Logistics (3PL) 458
Distribution Management 459
Ethics and Sustainable Supply Chain Management 460
Supply Chain Management Ethics 460
Establishing Sustainability in Supply
Chains 460
Measuring Supply Chain Performance 461
Assets Committed to Inventory 461
Benchmarking the Supply Chain 463
The SCOR Model 463
Summary 464
Key Terms 465
Ethical Dilemma 465
Discussion Questions 465
Solved Problems 465
Problems 466
CASE STUDIES 467
Darden’s Global Supply Chains Video Case 467
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TA B L E O F C O N T E N T S xvii
Supply Chain Management at Regal Marine
Video Case 467
Arnold Palmer Hospital’s Supply Chain
Video Case 468
Endnote 468
Rapid Review 469
Self Test 470
Supplement 11 Supply Chain Management Analytics 471
Techniques for Evaluating Supply Chains 472
Evaluating Disaster Risk in the Supply Chain 472
Managing the Bullwhip Eff ect 474
A Bullwhip Eff ect Measure 475
Supplier Selection Analysis 476
Transportation Mode Analysis 477
Warehouse Storage 478
Summary 479
Discussion Questions 480
Solved Problems 480
Problems 482
Rapid Review 485
Self Test 486
Chapter 12 Inventory Management 487
GLOBAL COMPANY PROFILE: AMAZON.COM 488
The Importance of Inventory 490
Functions of Inventory 490
Types of Inventory 490
Managing Inventory 491
ABC Analysis 491
Record Accuracy 493
Cycle Counting 493
Control of Service Inventories 494
Inventory Models 495
Independent vs. Dependent Demand 495
Holding, Ordering, and Setup Costs 495
Inventory Models for Independent Demand 496
The Basic Economic Order Quantity (EOQ)
Model 496
Minimizing Costs 497
Reorder Points 501
Production Order Quantity Model 502
Quantity Discount Models 505
Probabilistic Models and Safety Stock 508
Other Probabilistic Models 511
Single-Period Model 513
Fixed-Period (P) Systems 514
Summary 515
Key Terms 515
Ethical Dilemma 515
Discussion Questions 515
Using Software to Solve Inventory Problems 516
Solved Problems 517
Problems 520
CASE STUDIES 524
Zhou Bicycle Company 524
Parker Hi-Fi Systems 525
Managing Inventory at Frito-Lay Video Case 525
Inventory Control at Wheeled Coach Video Case 526
Endnotes 526
Rapid Review 527
Self Test 528
Chapter 13 Aggregate Planning and S&OP 529
GLOBAL COMPANY PROFILE: FRITO-LAY 530
The Planning Process 532
Sales and Operations Planning 533
The Nature of Aggregate Planning 534
Aggregate Planning Strategies 535
Capacity Options 535
Demand Options 536
Mixing Options to Develop a Plan 537
Methods for Aggregate Planning 538
Graphical Methods 538
Mathematical Approaches 543
Aggregate Planning in Services 545
Restaurants 546
Hospitals 546
National Chains of Small Service Firms 546
Miscellaneous Services 546
Airline Industry 547
Revenue Management 547
Summary 550
Key Terms 550
Ethical Dilemma 551
Discussion Questions 551
Using Software for Aggregate Planning 552
Solved Problems 554
Problems 555
CASE STUDIES 559
Andrew-Carter, Inc. 559
Using Revenue Management to Set Orlando Magic
Ticket Prices Video Case 560
Endnote 560
Rapid Review 561
Self Test 562
Chapter 14 Material Requirements Planning (MRP) and ERP 563
GLOBAL COMPANY PROFILE: WHEELED COACH 564
Dependent Demand 566
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xviii TA B L E O F C O N T E N T S
Dependent Inventory Model Requirements 566
Master Production Schedule 567
Bills of Material 568
Accurate Inventory Records 570
Purchase Orders Outstanding 570
Lead Times for Components 570
MRP Structure 571
MRP Management 575
MRP Dynamics 575
MRP Limitations 575
Lot-Sizing Techniques 576
Extensions of MRP 580
Material Requirements Planning II (MRP II) 580
Closed-Loop MRP 581
Capacity Planning 581
MRP in Services 583
Distribution Resource Planning (DRP) 584
Enterprise Resource Planning (ERP) 584
ERP in the Service Sector 587
Summary 587
Key Terms 587
Ethical Dilemma 587
Discussion Questions 588
Using Software to Solve MRP Problems 588
Solved Problems 589
Problems 592
CASE STUDIES 595
When 18,500 Orlando Magic Fans Come to Dinner
Video Case 595
MRP at Wheeled Coach Video Case 596
Endnotes 596
Rapid Review 597
Self Test 598
Chapter 15 Short-Term Scheduling 599
GLOBAL COMPANY PROFILE: ALASKA AIRLINES 600
The Importance of Short-Term Scheduling 602
Scheduling Issues 602
Forward and Backward Scheduling 603
Finite and Infi nite Loading 604
Scheduling Criteria 604
Scheduling Process-Focused Facilities 605
Loading Jobs 605
Input–Output Control 606
Gantt Charts 607
Assignment Method 608
Sequencing Jobs 611
Priority Rules for Sequencing Jobs 611
Critical Ratio 614
Sequencing N Jobs on Two Machines: Johnson’s
Rule 615
Limitations of Rule-Based Sequencing Systems 616
Finite Capacity Scheduling (FCS) 617
Scheduling Services 618
Scheduling Service Employees with Cyclical
Scheduling 620
Summary 621
Key Terms 621
Ethical Dilemma 621
Discussion Questions 622
Using Software for Short-Term Scheduling 622
Solved Problems 624
Problems 627
CASE STUDIES 630
Old Oregon Wood Store 630
From the Eagles to the Magic: Converting the Amway
Center Video Case 631
Scheduling at Hard Rock Cafe Video Case 632
Endnotes 632
Rapid Review 633
Self Test 634
Chapter 16 Lean Operations 635
GLOBAL COMPANY PROFILE: TOYOTA MOTOR CORPORATION 636
Lean Operations 638
Eliminate Waste 638
Remove Variability 639
Improve Throughput 640
Lean and Just-in-Time 640
Supplier Partnerships 640
Lean Layout 642
Lean Inventory 643
Lean Scheduling 646
Lean Quality 649
Lean and the Toyota Production System 649
Continuous Improvement 649
Respect for People 649
Processes and Standard Work Practice 650
Lean Organizations 650
Building a Lean Organization 650
Lean Sustainability 652
Lean in Services 652
Summary 653
Key Terms 653
Ethical Dilemma 653
Discussion Questions 653
Solved Problem 653
Problems 654
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TA B L E O F C O N T E N T S xix
CASE STUDIES 655
Lean Operations at Alaska Airlines Video Case 655
JIT at Arnold Palmer Hospital Video Case 656
Endnote 656
Rapid Review 657
Self Test 658
Chapter 17 Maintenance and Reliability 659
GLOBAL COMPANY PROFILE: ORLANDO UTILITIES COMMISSION 660
The Strategic Importance of Maintenance and Reliability 662
Reliability 663
System Reliability 663
Providing Redundancy 665
Maintenance 667
Implementing Preventive Maintenance 667
Increasing Repair Capabilities 670
Autonomous Maintenance 670
Total Productive Maintenance 671
Summary 671
Key Terms 671
Ethical Dilemma 671
Discussion Questions 671
Using Software to Solve Reliability Problems 672
Solved Problems 672
Problems 672
CASE STUDY 674
Maintenance Drives Profi ts at Frito-Lay
Video Case 674
Rapid Review 675
Self Test 676
PART FOUR Business Analytics Modules 677
Module A Decision-Making Tools 677
The Decision Process in Operations 678
Fundamentals of Decision Making 679
Decision Tables 680
Types of Decision-Making Environments 681
Decision Making Under Uncertainty 681
Decision Making Under Risk 682
Decision Making Under Certainty 683
Expected Value of Perfect Information (EVPI) 683
Decision Trees 684
A More Complex Decision Tree 686
The Poker Decision Process 688
Summary 689
Key Terms 689
Discussion Questions 689
Using Software for Decision Models 689
Solved Problems 691
Problems 692
CASE STUDY 696
Warehouse Tenting at the Port of Miami 696
Endnote 696
Rapid Review 697
Self Test 698
Module B Linear Programming 699
Why Use Linear Programming? 700
Requirements of a Linear Programming Problem 701
Formulating Linear Programming Problems 701
Glickman Electronics Example 701
Graphical Solution to a Linear Programming Problem 702
Graphical Representation of Constraints 702
Iso-Profi t Line Solution Method 703
Corner-Point Solution Method 705
Sensitivity Analysis 705
Sensitivity Report 706
Changes in the Resources or Right-Hand-Side
Values 706
Changes in the Objective Function Coeffi cient 707
Solving Minimization Problems 708
Linear Programming Applications 710
Production-Mix Example 710
Diet Problem Example 711
Labor Scheduling Example 712
The Simplex Method of LP 713
Integer and Binary Variables 713
Creating Integer and Binary Variables 713
Linear Programming Applications with Binary
Variables 714
A Fixed-Charge Integer Programming Problem 715
Summary 716
Key Terms 716
Discussion Questions 716
Using Software to Solve LP Problems 716
Solved Problems 718
Problems 720
CASE STUDIES 725
Quain Lawn and Garden, Inc. 725
Scheduling Challenges at Alaska Airlines
Video Case 726
Endnotes 726
Rapid Review 727
Self Test 728
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xx TA B L E O F C O N T E N T S
Module C Transportation Models 729
Transportation Modeling 730
Developing an Initial Solution 732
The Northwest-Corner Rule 732
The Intuitive Lowest-Cost Method 733
The Stepping-Stone Method 734
Special Issues in Modeling 737
Demand Not Equal to Supply 737
Degeneracy 737
Summary 738
Key Terms 738
Discussion Questions 738
Using Software to Solve Transportation Problems 738
Solved Problems 740
Problems 741
CASE STUDY 743
Custom Vans, Inc. 743
Rapid Review 745
Self Test 746
Module D Waiting-Line Models 747
Queuing Theory 748
Characteristics of a Waiting-Line System 749
Arrival Characteristics 749
Waiting-Line Characteristics 750
Service Characteristics 751
Measuring a Queue’s Performance 752
Queuing Costs 753
The Variety of Queuing Models 754
Model A (M/M/1): Single-Server Queuing Model with
Poisson Arrivals and Exponential Service Times 754
Model B (M/M/S): Multiple-Server Queuing
Model 757
Model C (M/D/1): Constant-Service-Time Model 762
Little’s Law 763
Model D (M/M/1 with Finite Source): Finite-Population
Model 763
Other Queuing Approaches 765
Summary 765
Key Terms 765
Discussion Questions 765
Using Software to Solve Queuing Problems 766
Solved Problems 766
Problems 768
CASE STUDIES 771
New England Foundry 771
The Winter Park Hotel 772
Endnotes 772
Rapid Review 773
Self Test 774
Module E Learning Curves 775
What Is a Learning Curve? 776
Learning Curves in Services and Manufacturing 777
Applying the Learning Curve 778
Doubling Approach 778
Formula Approach 779
Learning-Curve Table Approach 779
Strategic Implications of Learning Curves 782
Limitations of Learning Curves 783
Summary 783
Key Term 783
Discussion Questions 783
Using Software for Learning Curves 784
Solved Problems 784
Problems 785
CASE STUDY 787
SMT’s Negotiation with IBM 787
Endnote 788
Rapid Review 789
Self Test 790
Module F Simulation 791
What Is Simulation? 792
Advantages and Disadvantages of Simulation 793
Monte Carlo Simulation 794
Simulation with Two Decision Variables: An Inventory Example 797
Summary 799
Key Terms 799
Discussion Questions 799
Using Software in Simulation 800
Solved Problems 801
Problems 802
CASE STUDY 805
Alabama Airlines’ Call Center 805
Endnote 806
Rapid Review 807
Self Test 808
Appendix A1 Bibliography B1 Name Index I1 General Index I7
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TA B L E O F C O N T E N T S xxi
ONLINE TUTORIALS
1. Statistical Tools for Managers T1-1
Discrete Probability Distributions T1-2
Expected Value of a Discrete Probability
Distribution T1-3
Variance of a Discrete Probability Distribution T1-3
Continuous Probability Distributions T1-4
The Normal Distribution T1-4
Summary T1-7
Key Terms T1-7
Discussion Questions T1-7
Problems T1-7
Bibliography T1-7
2. Acceptance Sampling T2-1
Sampling Plans T2-2
Single Sampling T2-2
Double Sampling T2-2
Sequential Sampling T2-2
Operating Characteristic (OC) Curves T2-2
Producer’s and Consumer’s Risk T2-3
Average Outgoing Quality T2-5
Summary T2-6
Key Terms T2-6
Solved Problem T2-7
Discussion Questions T2-7
Problems T2-7
3. The Simplex Method of Linear Programming T3-1
Converting the Constraints to Equations T3-2
Setting Up the First Simplex Tableau T3-2
Simplex Solution Procedures T3-4
Summary of Simplex Steps for Maximization Problems T3-6
Artifi cial and Surplus Variables T3-7
Solving Minimization Problems T3-7
Summary T3-8
Key Terms T3-8
Solved Problem T3-8
Discussion Questions T3-8
Problems T3-9
4. The MODI and VAM Methods of Solving Transportation Problems T4-1
MODI Method T4-2
How to Use the MODI Method T4-2
Solving the Arizona Plumbing Problem with
MODI T4-2
Vogel’s Approximation Method: Another Way to Find an Initial Solution T4-4
Discussion Questions T4-8
Problems T4-8
5. Vehicle Routing and Scheduling T5-1
Introduction T5-2
Service Delivery Example: Meals-for-ME T5-2
Objectives of Routing and Scheduling Problems T5-2
Characteristics of Routing and Scheduling Problems T5-3
Classifying Routing and Scheduling Problems T5-3
Solving Routing and Scheduling Problems T5-4
Routing Service Vehicles T5-5
The Traveling Salesman Problem T5-5
Multiple Traveling Salesman Problem T5-8
The Vehicle Routing Problem T5-9
Cluster First, Route Second Approach T5-10
Scheduling Service Vehicles T5-11
The Concurrent Scheduler Approach T5-13
Other Routing and Scheduling Problems T5-13
Summary T5-14
Key Terms T5-15
Discussion Questions T5-15
Problems T5-15
Case Study: Routing and Scheduling of Phlebotomists T5-17
Bibliography T5-17
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Welcome to your operations management (OM) course. In this book, we present a state-of-the- art view of the operations function. Operations is an exciting area of management that has a profound effect on productivity. Indeed, few other activities have as much impact on the quality of our lives. The goal of this text is to present a broad introduction to the field of operations in a realistic, practical manner. Even if you are not planning on a career in the operations area, you will likely be working with people in operations. Therefore, having a solid understanding of the role of operations in an organization will be of substantial benefit to you. This book will also help you understand how OM affects society and your life. Certainly, you will better understand what goes on behind the scenes when you attend a concert or major sports event; purchase a bag of Frito-Lay potato chips; buy a meal at an Olive Garden or a Hard Rock Cafe; place an order through Amazon.com; board a flight on Alaska Airlines; or enter a hospital for medical care. More than one and a half million readers of our earlier editions seem to have endorsed this premise.
We welcome comments by email from our North American readers and from students using the International edition, the Indian edition, the Arabic edition, and our editions in Portuguese, Spanish, Turkish, Indonesian, and Chinese. Hopefully, you will find this material useful, interest- ing, and even exciting.
New to This Edition We’ve made significant revisions to this edition, and want to share some of the changes with you.
Five New Video Case Studies Featuring Alaska Airlines In this edition, we take you behind the scenes of Alaska Airlines, consistently rated as one of the top carriers in the country. This fascinating organization opened its doors—and planes— so we could examine leading edge OM in the airlines industry. We observe: the quality pro- gram at Alaska Air (Chapter 6); the process analysis behind the airline’s 20-minute baggage retrieval guarantee (Chapter 7); how Alaska empowers its employees (Chapter 10); the air- line’s use of Lean, 5s, kaizen, and Gemba walks (Chapter 16); and the complexities of sched- uling (Module B).
Our prior editions focused on integrated Video Case Studies for the Orlando Magic basketball team, Frito-Lay, Darden Restaurants, Hard Rock Cafe, Arnold Palmer Hospital, Wheeled Coach Ambulances, and Regal Marine. These Video Case Studies appear in this edition as well, along with the five new ones for Alaska Airlines. All of our videos are created by the authors, with the outstanding coauthorship of Beverly Amer at Northern Arizona University, to explicitly match with text content and terminology.
Preface
xxiii
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xxiv P R E FAC E
Creating Your Own Excel Spreadsheets We continue to provide two free decision support software programs, Excel OM for Windows and Mac and POM for Windows, to help you and your students solve homework problems and case studies. These excellent packages are found in MyOMLab and at our text’s Student Download Page.
Many instructors also encourage students to develop their own Excel spreadsheet models to tackle OM issues. With this edition, we provide numerous examples at chapter end on how to do so. “Creating Your Own Excel Spreadsheets” examples now appear in Chapters 1, 2, 4, 8, 12, and 13, Supplement 6, Supplement 7, and Modules A, B, and F. We hope these eleven samples will help expand students’ spreadsheet capabilities.
Video Case Alaska Airlines: 20-Minute Baggage Process—Guaranteed! Alaska Airlines is unique among the nine major U.S. carriers not only for its extensive flight coverage of remote towns throughout Alaska (it also covers the U.S., Hawaii, and Mexico from its pri- mary hub in Seattle). It is also one of the smallest independent airlines, with 10,300 employees, including 3,000 flight attendants and 1,500 pilots. What makes it really unique, though, is its abil- ity to build state-of-the-art processes, using the latest technology, that yield high customer satisfaction. Indeed, J. D. Power and Associates has ranked Alaska Airlines highest in North America for seven years in a row for customer satisfaction.
Alaska Airlines was the first to sell tickets via the Internet, first to offer Web check-in and print boarding passes online, and first with kiosk check-in. As Wayne Newton, Director of System Operation Control, states, “We are passionate about our pro- cesses. If it’s not measured, it’s not managed.”
One of the processes Alaska is most proud of is its baggage han- dling system. Passengers can check in at kiosks, tag their own bags with bar code stickers, and deliver them to a customer service agent at the carousel, which carries the bags through the vast under- ground system that eventually delivers the bags to a baggage han- dler. En route, each bag passes through TSA automated screening and is manually opened or inspected if it appears suspicious. With the help of bar code readers, conveyer belts automatically sort and transfer bags to their location (called a “pier”) at the tarmac level. A baggage handler then loads the bags onto a cart and takes it to Al
as ka
A irl
in es
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Using Software for Productivity Analysis
This section presents three ways to solve productivity problems with computer software. First, you can create your own Excel spreadsheets to conduct productivity analysis. Second, you can use the Excel OM software that comes with this text. Third, POM for Windows is another program that is available with this text .
Program 1.1
Actions Copy C7 to B7, Copy B14 to C14, Copy C15 to B15, and Copy D14 to D15
Create a row for each of the inputs used for the productivity measure. Put the output in the last row.
=C5*C6
=B10/B7
Enter the values for the old system in column B and the new system in Column C.
Productivity = Output/Input
=(C14-B14)/B14=C10/(C8+C9)
X USING EXCEL OM Excel OM is an Excel “add-in” with 24 Operations Management decision support “Templates.” To access the templates, double- click on the Excel OM tab at the top of the page, then in the menu bar choose the appropriate chapter (in this case Chapter 1 ), from either the “Chapter” or “Alphabetic” tab on the left. Each of Excel OM’s 24 modules includes instructions for that particular module. The instructions can be turned on or off via the “instruction” tab in the menu bar.
P USING POM FOR WINDOWS POM for Windows is decision support software that includes 24 Operations Management modules. The modules are accessed by double-clicking on Module in the menu bar, and then double-clicking on the appropriate (in this case Productivity ) item. Instructions are provided for each module just below the menu bar.
CREATING YOUR OWN EXCEL SPREADSHEETS Program 1.1 illustrates how to build an Excel spreadsheet for the data in Example 2.
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P R E FAC E xxv
Expanding and Reordering Our Set of Homework Problems We believe that a vast selection of quality homework problems, ranging from easy to challeng- ing (denoted by one to four dots), is critical for both instructors and students. Instructors need a broad selection of problems to choose from for homework, quizzes, and exams—without reus- ing the same set from semester to semester. We take pride in having more problems—by far, with 807—than any other OM text. We added dozens of new problems this edition. The following table illustrates the selection by chapter.
Further, with the majority of our adopters now using the MyOMLab learning system in their classes, we have reorganized all the homework problems—both those appearing in the printed text, as well as the Additional Homework Problems that are available in MyOMLab—by topic heading. We are identifying all problems by topic (see the following example).
The list of all problems by topic also appears at the end of each boxed example, as well as in the Rapid Review that closes each chapter. These handy references should make it easier to assign problems for homework, quizzes, and exams. A rich set of assignable problems and cases makes the learning experience more complete and pedagogically sound.
C H A P T E R 5 | D E S I G N O F G O O D S A N D S E RV I C E S 187
Problems 5.4–5.8 relate to Product Development
• • 5.4 Construct a house of quality matrix for a wrist- watch. Be sure to indicate specific customer wants that you think the general public desires. Then complete the matrix to show how an operations manager might identify specific attributes that can be measured and controlled to meet those customer desires.
• • 5.5 Using the house of quality, pick a real product (a good or service) and analyze how an existing organization satis- fies customer requirements.
• • 5.6 Prepare a house of quality for a mousetrap.
• • 5.7 Conduct an interview with a prospective purchaser of a new bicycle and translate the customer’s wants into the specific hows of the firm.
• • • • 5.8 Using the house of quality sequence, as described in Figure 5.4 on page 169, determine how you might deploy resources to achieve the desired quality for a product or service whose production process you understand.
Problems 5.9–5.17 relate to Defining a Product
• • 5.9 Prepare a bill of material for (a) a pair of eyeglasses and its case or (b) a fast-food sandwich (visit a local sandwich
Problems 5.21–5.28 relate to the Application of Decision Trees to Product Design
• • 5.21 The product design group of Iyengar Electric Supplies, Inc., has determined that it needs to design a new series of switches. It must decide on one of three design strategies. The market forecast is for 200,000 units. The better and more sophisticated the design strategy and the more time spent on value engineering, the less will be the variable cost. The chief of engineering design, Dr. W. L. Berry, has decided that the following costs are a good estimate of the initial and variable costs connected with each of the three strategies: a) Low-tech: A low-technology, low-cost process consisting of
hiring several new junior engineers. This option has a fixed cost of $45,000 and variable-cost probabilities of .3 for $.55 each, .4 for $.50, and .3 for $.45.
b) Subcontract: A medium-cost approach using a good outside design staff. This approach would have a fixed cost of $65,000 and variable-cost probabilities of .7 of $.45, .2 of $.40, and .1 of $.35.
c) High-tech: A high-technology approach using the very best of the inside staff and the latest computer-aided design technol- ogy. This approach has a fixed cost of $75,000 and variable- cost probabilities of .9 of $.40 and .1 of $.35.
What is the best decision based on an expected monetary value (EMV) criterion? ( Note: We want the lowest EMV, as we are dealing with costs in this problem.) PX
• • 5.22 MacDonald Products, Inc., of Clarkson, New York, has the option of (a) proceeding immediately with production of
Problem 5.3 is available in MyOMLab.
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Chapter Number of
Problems
15 27
16 12
17 24
Module A 32
Module B 42
Module C 18
Module D 39
Module E 33
Module F 25
Chapter Number of
Problems
Supplement 7 45
8 34
9 27
10 46
11 8
Supplement 11 20
12 53
13 26
14 32
Chapter Number of
Problems
1 18
2 12
3 33
4 59
5 28
Supplement 5 19
6 21
Supplement 6 55
7 17
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Jay, Barry, and Chuck’s OM Blog As a complement to this text, we have created a companion blog, with coordinated features to help teach the OM course. There are teaching tips, highlights of OM items in the news (along with class discussion questions and links), video tips, guest posts by instructors using our text, sample OM syllabi from dozens of colleges, and much more—all arranged by chapter. To learn more about any chapter topics, visit www.heizerrenderOM.wordpress.com . As you prepare your lectures and syllabus, scan our blog for discussion ideas, teaching tips, and classroom exercises.
Lean Operations In previous editions, we sought to explicitly differentiate the concepts of just-in-time, Lean, and Toyota Production System in Chapter 16. However, there is significant overlap and interchangea- bility among those three concepts, so we have revised Chapter 16 to incorporate the three concepts into an overall concept of “Lean.” The chapter suggests that students view Lean as a comprehen- sive integrated operations strategy that sustains competitive advantage and results in increased returns to all stakeholders.
Chapter-by-Chapter Changes To highlight the extent of the revisions in this edition, here are a few of the changes, on a chapter- by-chapter basis.
Chapter 1 : Operations and Productivity We updated Table 1.4 to reflect employment in various sectors and expanded our discussion of Lean operations. Our new case, Uber Technologies, introduces productivity by discussing the dis- ruptive nature of the Uber business model. In addition, there is a new “Creating Your Own Excel Spreadsheets” example for both labor productivity and multifactor productivity.
Chapter 2 : Operations Strategy in a Global Environment We have updated Figure 2.1 to better reflect changes in the growth of world trade and Figure 2.5 to reflect product life cycle changes. The Minute Lube case has been revised as Rapid Lube. Example 1 (National Architects) has been expanded to clarify factor rating calculations and is also demonstrated with a “Creating Your Own Excel Spreadsheets” presentation.
Chapter 3 : Project Management We rewrote and updated the Bechtel Global Company Profile and added a new section on well- defined projects with the “agile” and “waterfall” approaches. There are two new OM in Action boxes: “Agile Project Management at Mastek,” and “Behind the Tour de France.”
Chapter 4 : Forecasting We created a new table comparing the MAD, MSE, and MAPE forecasting error measures. There is also a new OM in Action box called “NYC’s Potholes and Regression Analysis.”
Chapter 5 : Design of Goods and Services We expanded our treatment of concurrent engineering and added two new discussion questions. Solved Problem 5.1 has been revised.
Supplement 5: Sustainability in the Supply Chain We wrote a new introductory section on Corporate Social Responsibility. There is also a new OM in Action box called “Blue Jeans and Sustainability” and 10 new homework problems.
Chapter 6 : Managing Quality We added new material to expand our discussion of Taguchi’s quality loss function. There is a new sec- tion on SERVQUAL, and a new video case study, “Quality Counts at Alaska Airlines,” appears here.
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Supplement 6: Statistical Process Control We added a figure on the relationship between sample size and sampling distribution. We also added raw data to Examples S2 and S3 to illustrate how ranges are computed. There is a new Excel spreadsheet to show students how to make their own c -chart, and we have added three new homework problems.
Chapter 7 : Process Strategy We wrote a new section on machine technology and additive manufacturing. There are two new discussion questions and three new homework problems. Our second new video case study is called “Alaska Airlines: 20-Minute Baggage Process—Guaranteed!”
Supplement 7: Capacity and Constraint Management We added a new Table S7.1, which compares and clarifies three capacity measurements, with an example of each. There is a new treatment of expected output and actual output in Example S2. The discussion of bottleneck time versus throughput time has also been expanded. Example S3, capacity analysis with parallel processes, has been revised. We have also added a new “Creating Your Own Excel Spreadsheets” example for a break-even model. Finally, we updated the Arnold Palmer Hospital capacity planning case with recent data.
Chapter 8 : Location Strategies We added two new OM in Action boxes: “Iowa—Home of Corn and Facebook” and “Denmark’s Meat Cluster.” We changed the notation for the center-of-gravity model to simplify the equa- tion and provided a new “Creating Your Own Excel Spreadsheets” presentation for the center-of- gravity example.
Chapter 9 : Layout Strategies We created a new Muther grid for office relationship charting and added a spread of five layouts showing how offices have evolved over time. There is a new OM in Action box called “Amazon Lets Loose the Robots,” and there is a new graphic example of Proplanner’s Flow Path Calculator. We have included a formula for idle time as a second measure of balance assignment efficiency and added new technology issues to the Arnold Palmer Hospital video case.
Chapter 10 : Human Resources, Job Design, and Work Measurement We added a new OM in Action box, “The Missing Perfect Chair,” and revised the Operations Chart as a service example. Our third new video case study is “The ‘People’ Focus: Human Resources at Alaska Airlines.”
Chapter 11 : Supply Chain Management We added “outsourcing” as a supply chain risk in Table 11.3.
Supplement 11: Supply Chain Management Analytics We added a major section on the topic of Warehouse Storage, with a new model for allocating inven- tory to storage locations. There is a new discussion question and three new homework problems.
Chapter 12 : Inventory Management New Programs 12.1 and 12.2 illustrate “Creating Your Own Excel Spreadsheets” for both the production run model and the single-period inventory model. The Excel function NORMSINV is introduced throughout the chapter. The Quantity Discount Model section is totally rewritten to illustrate the feasible solution shortcut. Solved Problem 12.5 is likewise redone with the new approach.
Chapter 13 : Aggregate Planning and S&OP We added a new OM in Action box, “Revenue Management Makes Disney the ‘King’ of the Broadway Jungle.” We also provided a new “Creating Your Own Excel Spreadsheets” example for the transportation method for aggregate planning, using the Solver approach.
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Chapter 14 : Material Requirements Planning (MRP) and ERP The MRP II example now includes greenhouse gasses.
Chapter 15 : Short-Term Scheduling We begin this chapter with a new Global Company Profile featuring Alaska Airlines and the scheduling issues it faces in its northern climate. We have added two new graphics to help illus- trate Forward and Backward Scheduling. There is also a new section called Performance Criteria, detailing how the choice of priority rule depends on four quantifiable criteria. We now explicitly define the performance criteria for sequencing jobs as separate numbered equations. Also, we provide an explicit formula for job lateness. There is a new OM in Action box called “Starbucks’ Controversial Scheduling Software.”
Chapter 16 : Lean Operations This chapter saw a major reorganization and rewrite with an enhanced focus on Lean operations. There is more material on supplier partnerships and building lean organizations. A new OM in Action box describes the use of kaizen at San Francisco General Hospital, and we have added a new video case study called “Lean Operations at Alaska Airlines.”
Chapter 17 : Maintenance and Reliability There are no major changes in this chapter.
Module A: Decision-Making Tools We added a discussion of “big data” and a new “Creating Your Own Excel Spreadsheets” example on how to evaluate a decision table.
Module B: Linear Programming There is a new section on integer and binary programming, two new homework problems, and a new video case study called “Using LP to Meet Scheduling Challenges at Alaska Airlines.” The corner point method is now covered before the iso-profit line approach.
Module C: Transportation Models There are no major changes to Module C.
Module D: Waiting-Line Models The limited population model (Model D) has been replaced by the finite population model, M/M/1 with finite source. This standardizes the queuing notation to match the M/M/1, M/M/s, and M/D/1. We have also expanded the coverage of Little’s Law and added six new homework problems.
Module E: Learning Curves There are no major changes to Module E.
Module F: Simulation We added a new “Creating Your Own Excel Spreadsheets” example for a simulation problem.
Student Resources To liven up the course and help students learn the content material, we have made available the following resources:
◆ Forty-one exciting Video Case Studies (videos located at MyOMLab ): These Video Case Studies feature real companies (Alaska Airlines, The Orlando Magic, Frito-Lay, Darden Restaurants, Regal Marine, Hard Rock Cafe, Ritz-Carlton, Wheeled Coach, and Arnold Palmer Hospital) and
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allow students to watch short videos, read about the key topics, and answer questions. These case studies can also be assigned without using class time to show the videos. Each of them was developed and written by the text authors to specifi cally supplement the book’s content. Instruc- tors who wish to use these in class, and who don’t have access to MyOMLab, should contact their Pearson Publishing Representative for access to the MyOMLab materials.
◆ POM for Windows software (located at MyOMLab and at the Student Download Page, www .pearsonhighered.com/heizer): POM for Windows is a powerful tool for easily solving OM problems. Its 24 modules can be used to solve most of the homework problems in the text.
◆ Excel OM problem-solving software (located at MyOMLab and at the Student Download Page, www.pearsonhighered.com/heizer): Excel OM is our exclusive user-friendly Excel add-in. Excel OM automatically creates worksheets to model and solve problems. Users select a topic from the pull-down menu and fi ll in the data, and then Excel will display and graph (where appropri- ate) the results. This software is great for student homework, what-if analysis, and classroom demonstrations. This edition includes a new version of Excel OM that is compatible with Microsoft Excel 2013 for Windows, Excel 2011 and 2016 for Mac, and earlier versions of Excel. Professor Howard Weiss, Temple University, developed both Excel OM for Windows and Mac, and POM for Windows to accompany our text and its problem set.
◆ Excel OM data fi les (located at MyOMLab and at the Student Download Page, www .pearsonhighered.com/heizer): These data fi les are prepared for specifi c examples and allow users to solve all the marked text examples without reentering any data.
◆ Active Models (located at MyOMLab and at the Student Download Page, www.pearsonhighered .com/heizer): These 28 Active Models are Excel-based OM simulations, designed to help students understand the quantitative methods shown in the textbook examples. Students may change the data in order to see how the changes aff ect the answers.
◆ Virtual tours (located at MyOMLab): These company tours provide direct links to companies— ranging from a hospital to an auto manufacturer—that practice key OM concepts. After touring each Web site, students are asked questions directly related to the concepts discussed in the chapter.
◆ Online Tutorial Chapters (located at MyOMLab and at the Student Download Page, www .pearsonhighered.com/heizer ): “Statistical Tools for Managers,” “Acceptance Sampling,” “The Simplex Method of Linear Programming,” “The MODI and VAM Methods of Solving Trans- portation Problems,” and “Vehicle Routing and Scheduling” are provided as additional material.
◆ Additional practice problems (located at MyOMLab): These problems provide problem-solving experience. They supplement the examples and solved problems found in each chapter.
◆ Additional case studies (located at MyOMLab and at the Student Download Page, www .pearsonhighered.com/heizer ): Over two dozen additional case studies supplement the ones in the text. Detailed solutions appear in the Solutions Manual.
◆ Virtual offi ce hours (located at MyOMLab): Professors Heizer, Render, and Munson walk stu- dents through all 89 Solved Problems in a series of 5- to 20-minute explanations. These have been updated with this new edition.
Instructor Resources At the Instructor Resource Center, www.pearsonhighered.com/irc , instructors can easily register to gain access to a variety of instructor resources available with this text in downloadable format. If assistance is needed, our dedicated technical support team is ready to help with the media sup- plements that accompany this text. Visit http://247.pearsoned.com for answers to frequently asked questions and toll-free user support phone numbers.
The following supplements are available with this text:
Instructor’s Resource Manual The Instructor’s Resource Manual, updated by co-author Chuck Munson, contains many useful resources for instructors—PowerPoint presentations with annotated notes, course outlines, video notes, blog highlights, learning techniques, Internet exercises and sample answers, case analysis ideas, additional teaching resources, and faculty notes.
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Instructor’s Solutions Manual The Instructor’s Solutions Manual, written by the authors, contains the answers to all of the dis- cussion questions, Ethical Dilemmas , Active Models, and cases in the text, as well as worked-out solutions to all the end-of-chapter problems, additional homework problems, and additional case studies.
PowerPoint Presentations An extensive set of PowerPoint presentations, created by Professor Jeff Heyl of Lincoln University, is available for each chapter. With well over 2,000 slides, this set has excellent color and clarity.
Test Bank / TestGen® Computerized Test Bank The test bank, updated by James Roh, contains a variety of true/false, multiple-choice, short-answer, and essay questions, along with a selection of written problems, for each chapter. Test questions are annotated with the following information:
◆ Diffi culty level ◆ Type: multiple-choice, true/false, short-answer, essay, problem ◆ Learning objective ◆ AACSB (see the description that follows)
TestGen®, Pearson Education’s test-generating software, is PC/MAC compatible and preloaded with all the test bank questions. The test program permits instructors to edit, add, and delete ques- tions from the test bank to create customized tests.
The Association to Advance Collegiate Schools of Business (AACSB)
The test bank has connected select questions to the general knowledge and skill guidelines found in the AACSB Assurance of Learning standards.
AACSB is a not-for-profit corporation of educational institutions, corporations, and other organizations devoted to the promotion and improvement of higher education in business admin- istration and accounting. A collegiate institution offering degrees in business administration or accounting may volunteer for AACSB accreditation review. The AACSB makes initial accredi- tation decisions and conducts periodic reviews to promote continuous quality improvement in management education. Pearson Education is a proud member of the AACSB and is pleased to provide advice to help you apply AACSB assurance of learning standards.
What are AACSB assurance of learning standards? One of the criteria for AACSB accredita- tion is quality of the curricula. Although no specific courses are required, the AACSB expects a curriculum to include learning experiences in the following areas:
◆ Written and oral communication ◆ Ethical understanding and reasoning ◆ Analytical thinking ◆ Information technology ◆ Interpersonal relations and teamwork ◆ Diverse and multicultural work environments ◆ Refl ective thinking ◆ Application of knowledge
Questions that test skills relevant to these guidelines are appropriately tagged. For example, a question regarding clothing manufactured for U.S. firms by 10-year olds in Asia would receive the Ethical understanding and reasoning tag.
Tagged questions help you measure whether students are grasping the course content that aligns with the AACSB guidelines noted. In addition, the tagged questions may help instructors identify potential applications of these skills. This in turn may suggest enrichment activities or other educational experiences to help students achieve these skills.
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AACSB
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Video Package Designed and created by the authors specifically for their Heizer/Render/Munson texts, the video package contains the following 41 videos:
◆ Frito-Lay: Operations Management in Manufacturing ( Chapter 1 ) ◆ Hard Rock Cafe: Operations Management in Services ( Chapter 1 ) ◆ Strategy at Regal Marine ( Chapter 2 ) ◆ Hard Rock Cafe’s Global Strategy ( Chapter 2 ) ◆ Outsourcing Off shore at Darden ( Chapter 2 ) ◆ Project Management at Arnold Palmer Hospital ( Chapter 3 ) ◆ Managing Hard Rock’s Rockfest ( Chapter 3 ) ◆ Forecasting Ticket Revenue for Orlando Magic Basketball Games ( Chapter 4 ) ◆ Forecasting at Hard Rock Cafe ( Chapter 4 ) ◆ Product Design at Regal Marine ( Chapter 5 ) ◆ Building Sustainability at the Orlando Magic’s Amway Center ( Supplement 5 ) ◆ Green Manufacturing and Sustainability at Frito-Lay ( Supplement 5 ) ◆ Quality Counts at Alaska Airlines ( Chapter 6 ) ◆ The Culture of Quality at Arnold Palmer Hospital ( Chapter 6 ) ◆ Quality at the Ritz-Carlton Hotel Company ( Chapter 6 ) ◆ Frito-Lay’s Quality-Controlled Potato Chips ( Supplement 6 ) ◆ Farm to Fork: Quality at Darden Restaurants ( Supplement 6 ) ◆ Alaska Airlines: 20-Minute Baggage Process—Guaranteed! ( Chapter 7 ) ◆ Process Strategy at Wheeled Coach ( Chapter 7 ) ◆ Process Analysis at Arnold Palmer Hospital ( Chapter 7 ) ◆ Capacity Planning at Arnold Palmer Hospital ( Supplement 7 ) ◆ Locating the Next Red Lobster Restaurant ( Chapter 8 ) ◆ Where to Place the Hard Rock Cafe ( Chapter 8 ) ◆ Facility Layout at Wheeled Coach ( Chapter 9 ) ◆ Laying Out Arnold Palmer Hospital’s New Facility ( Chapter 9 ) ◆ The “People” Focus: Human Resources at Alaska Airlines ( Chapter 10 ) ◆ Hard Rock’s Human Resource Strategy ( Chapter 10 ) ◆ Darden’s Global Supply Chains ( Chapter 11 ) ◆ Supply Chain Management at Regal Marine ( Chapter 11 ) ◆ Arnold Palmer Hospital’s Supply Chain ( Chapter 11 ) ◆ Managing Inventory at Frito-Lay ( Chapter 12 ) ◆ Inventory Control at Wheeled Coach ( Chapter 12 ) ◆ Using Revenue Management to Set Orlando Magic Ticket Prices ( Chapter 13 ) ◆ When 18,500 Orlando Magic Fans Come to Dinner ( Chapter 14 ) ◆ MRP at Wheeled Coach ( Chapter 14 ) ◆ From the Eagles to the Magic: Converting the Amway Center ( Chapter 15 ) ◆ Scheduling at Hard Rock Cafe ( Chapter 15 ) ◆ Lean Operations at Alaska Airlines ( Chapter 16 ) ◆ JIT at Arnold Palmer Hospital ( Chapter 16 ) ◆ Maintenance Drives Profi ts at Frito-Lay ( Chapter 17 ) ◆ Scheduling Challenges at Alaska Airlines (Module B)
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ALABAMA John Mittenthal University of Alabama Philip F. Musa University of Alabama at Birmingham William Petty University of Alabama Doug Turner Auburn University
ALASKA Paul Jordan University of Alaska
ARIZONA Susan K. Norman Northern Arizona University Scott Roberts Northern Arizona University Vicki L. Smith-Daniels Arizona State University Susan K. Williams Northern Arizona University
CALIFORNIA Jean-Pierre Amor University of San Diego Moshen Attaran California State University–Bakersfi eld Ali Behnezhad California State University–Northridge Joe Biggs California Polytechnic State University Lesley Buehler Ohlone College Rick Hesse Pepperdine Ravi Kathuria Chapman University Richard Martin California State University–Long Beach Ozgur Ozluk San Francisco State University Zinovy Radovilsky California State University–Hayward Robert J. Schlesinger San Diego State University
V. Udayabhanu San Francisco State University Rick Wing San Francisco State University
COLORADO Peter Billington Colorado State University–Pueblo Gregory Stock University of Colorado at Colorado Springs
CONNECTICUT David Cadden Quinnipiac University Larry A. Flick Norwalk Community Technical College
FLORIDA Joseph P. Geunes University of Florida Rita Gibson Embry-Riddle Aeronautical University Jim Gilbert Rollins College Donald Hammond University of South Florida Wende Huehn-Brown St. Petersburg College Adam Munson University of Florida Ronald K. Satterfi eld University of South Florida Theresa A. Shotwell Florida A&M University Jeff Smith Florida State University
GEORGIA John H. Blackstone University of Georgia Johnny Ho Columbus State University John Hoft Columbus State University John Miller Mercer University
Nikolay Osadchiy Emory University Spyros Reveliotis Georgia Institute of Technology
ILLINOIS Suad Alwan Chicago State University Lori Cook DePaul University Matt Liontine University of Illinois–Chicago Zafar Malik Governors State University
INDIANA Barbara Flynn Indiana University B.P. Lingeraj Indiana University Frank Pianki Anderson University Stan Stockton Indiana University Jerry Wei University of Notre Dame Jianghua Wu Purdue University Xin Zhai Purdue University
IOWA Debra Bishop Drake University Kevin Watson Iowa State University Lifang Wu University of Iowa
KANSAS William Barnes Emporia State University George Heinrich Wichita State University Sue Helms Wichita State University Hugh Leach Washburn University
xxxii P R E FAC E
Acknowledgments We thank the many individuals who were kind enough to assist us in this endeavor. The following professors provided insights that guided us in this edition (their names are in bold) and in prior editions:
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M.J. Riley Kansas State University Teresita S. Salinas Washburn University Avanti P. Sethi Wichita State University
KENTUCKY Wade Ferguson Western Kentucky University Kambiz Tabibzadeh Eastern Kentucky University
LOUISIANA Roy Clinton University of Louisiana at Monroe L. Wayne Shell (retired) Nicholls State University
MARYLAND Eugene Hahn Salisbury University Samuel Y. Smith, Jr. University of Baltimore
MASSACHUSETTS Peter Ittig University of Massachusetts Jean Pierre Kuilboer University of Massachusetts–Boston Dave Lewis University of Massachusetts–Lowell Mike Maggard (retired) Northeastern University Peter Rourke Wentworth Institute of Technology Daniel Shimshak University of Massachusetts–Boston Ernest Silver Curry College Yu Amy Xia Northeastern University
MICHIGAN Darlene Burk Western Michigan University Damodar Golhar Western Michigan University Dana Johnson Michigan Technological University Doug Moodie Michigan Technological University
MINNESOTA Rick Carlson Metropolitan State University John Nicolay University of Minnesota Michael Pesch St. Cloud State University Manus Rungtusanatham University of Minnesota Kingshuk Sinha University of Minnesota Peter Southard University of St. Thomas
MISSOURI Shahid Ali Rockhurst University Stephen Allen Truman State University Sema Alptekin University of Missouri–Rolla Gregory L. Bier University of Missouri–Columbia James Campbell University of Missouri–St. Louis Wooseung Jang University of Missouri–Columbia Mary Marrs University of Missouri–Columbia A. Lawrence Summers University of Missouri
NEBRASKA Zialu Hug University of Nebraska–Omaha
NEVADA Joel D. Wisner University of Nevada, Las Vegas
NEW JERSEY Daniel Ball Monmouth University Leon Bazil Stevens Institute of Technology Mark Berenson Montclair State University Grace Greenberg Rider University Joao Neves The College of New Jersey Leonard Presby William Paterson University
Faye Zhu Rowan University
NEW MEXICO William Kime University of New Mexico
NEW YORK Theodore Boreki Hofstra University John Drabouski DeVry University Richard E. Dulski Daemen College Jonatan Jelen Baruch College Beate Klingenberg Marist College Donna Mosier SUNY Potsdam Elizabeth Perry SUNY Binghamton William Reisel St. John’s University Kaushik Sengupta Hofstra University Girish Shambu Canisius College Rajendra Tibrewala New York Institute of Technology
NORTH CAROLINA Coleman R. Rich Elon University Ray Walters Fayetteville Technical Community College
OHIO Victor Berardi Kent State University Andrew R. Thomas University of Akron
OKLAHOMA Wen-Chyuan Chiang University of Tulsa
OREGON Anne Deidrich Warner Pacifi c College Gordon Miller Portland State University
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John Sloan Oregon State University
PENNSYLVANIA Henry Crouch Pittsburgh State University Jeff rey D. Heim Pennsylvania State University James F. Kimpel University of Pittsburgh Ian M. Langella Shippensburg University Prafulla Oglekar LaSalle University David Pentico Duquesne University Stanford Rosenberg LaRoche College Edward Rosenthal Temple University Susan Sherer Lehigh University Howard Weiss Temple University
RHODE ISLAND Laurie E. Macdonald Bryant College John Swearingen Bryant College Susan Sweeney Providence College
SOUTH CAROLINA Jerry K. Bilbrey Anderson University Larry LaForge Clemson University Emma Jane Riddle Winthrop University
TENNESSEE Joseph Blackburn Vanderbilt University Hugh Daniel Lipscomb University
Cliff Welborn Middle Tennessee State University
TEXAS Warren W. Fisher Stephen F. Austin State University Garland Hunnicutt Texas State University Gregg Lattier Lee College Henry S. Maddux III Sam Houston State University Arunachalam Narayanan Texas A&M University Ranga V. Ramasesh Texas Christian University Victor Sower San Houston State University Cecelia Temponi Texas State University John Visich-Disc University of Houston Dwayne Whitten Texas A&M University Bruce M. Woodworth University of Texas–El Paso
UTAH William Christensen Dixie State College of Utah Shane J. Schvaneveldt Weber State University Madeline Thimmes (retired) Utah State University
VIRGINIA Andy Litteral University of Richmond Arthur C. Meiners, Jr. Marymount University Michael Plumb Tidewater Community College
WASHINGTON Mark McKay University of Washington
Chris Sandvig Western Washington University John Stec Oregon Institute of Technology
WASHINGTON, DC Narendrea K. Rustagi Howard University
WEST VIRGINIA Charles Englehardt Salem International University Daesung Ha Marshall University John Harpell West Virginia University James S. Hawkes University of Charleston
WISCONSIN James R. Gross University of Wisconsin–Oshkosh Marilyn K. Hart (retired) University of Wisconsin–Oshkosh Niranjan Pati University of Wisconsin–La Crosse X. M. Saff ord Milwaukee Area Technical College Rao J. Taikonda University of Wisconsin–Oshkosh
WYOMING Cliff Asay University of Wyoming
INTERNATIONAL Steven Harrod Technical University of Denmark Robert D. Klassen University of Western Ontario Ronald Lau Hong Kong University of Science and Technology
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In addition, we appreciate the wonderful people at Pearson Education who provided both help and advice: Stephanie Wall, our superb editor-in-chief; Lenny Ann Kucenski, our dynamo mar- keting manager; Linda Albelli, our editorial assistant; Courtney Kamauf and Andra Skaalrud for their fantastic and dedicated work on MyOMLab; Jeff Holcomb, our project manager team lead; Claudia Fernandes, our program manager; Jacqueline Martin, our senior project manager; and Heidi Allgair, our project manager at Cenveo® Publisher Services. We are truly blessed to have such a fantastic team of experts directing, guiding, and assisting us.
In this edition, we were thrilled to be able to include one of the country’s premier airlines, Alaska Airlines, in our ongoing Video Case Study series. This was possible because of the wonderful efforts of COO/EVP-Operations Ben Minicucci, and his superb management team. This included John Ladner (Managing Director, Seattle Station Operations), Wayne Newton (Managing Director, Station Operations Control), Mike McQueen (Director, Schedule Planning), Chad Koehnke (Director, Planning and Resource Allocation), Cheryl Schulz (Executive Assistant to EVP Minicucci), Jeffrey Butler (V.P. Airport Operations & Customer Service), Dan Audette (Manager of Operations Research and Analysis), Allison Fletcher (Process Improvement Manager), Carlos Zendejas (Manager Line-Flying Operations, Pilots), Robyn Garner (Flight Attendant Trainer), and Nikki Meier and Sara Starbuck (Process Improvement Facilitators). We are grateful to all of these fine people, as well as the many others that participated in the develop- ment of the videos and cases during our trips to the Seattle headquarters.
We also appreciate the efforts of colleagues who have helped to shape the entire learning pack- age that accompanies this text. Professor Howard Weiss (Temple University) developed the Active Models, Excel OM, and POM for Windows software; Professor Jeff Heyl (Lincoln University) created the PowerPoint presentations; and Professor James Roh (Rowan University) updated the test bank. Beverly Amer (Northern Arizona University) produced and directed the video series; Professors Keith Willoughby (Bucknell University) and Ken Klassen (Brock University) contrib- uted the two Excel-based simulation games; and Professor Gary LaPoint (Syracuse University) developed the Microsoft Project crashing exercise and the dice game for SPC. We have been fortu- nate to have been able to work with all these people.
We wish you a pleasant and productive introduction to operations management.
JAY HEIZER
Texas Lutheran University
1000 W. Court Street
Seguin, TX 78155
Email: [email protected]
BARRY RENDER
Graduate School of Business
Rollins College
Winter Park, FL 32789
Email: [email protected]
CHUCK MUNSON
Carson College of Business
Washington State University
Pullman, WA 99164-4746
Email: [email protected]
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OPERATIONS MANAGEMENT,
12TH EDITION
ISBN: 0-13-413042-1
PART I INTRODUCTION TO OPERATIONS
MANAGEMENT 1. Operations and Productivity 2. Operations Strategy in a Global
Environment 3. Project Management 4. Forecasting
PART II DESIGNING OPERATIONS 5. Design of Goods and Services S5. Sustainability in the Supply Chain 6. Managing Quality S6. Statistical Process Control 7. Process Strategy S7. Capacity and Constraint Management 8. Location Strategies 9. Layout Strategies 10. Human Resources, Job Design, and
Work Measurement
PART III MANAGING OPERATIONS 11. Supply Chain Management S11. Supply Chain Management Analytics 12. Inventory Management 13. Aggregate Planning and S&OP 14. Material Requirements Planning (MRP)
and ERP 15. Short-Term Scheduling 16. Lean Operations 17. Maintenance and Reliability
PART IV BUSINESS ANALYTICS MODULES A. Decision-Making Tools B. Linear Programming C. Transportation Models D. Waiting-Line Models E. Learning Curves F. Simulation
ONLINE TUTORIALS 1. Statistical Tools for Managers 2. Acceptance Sampling 3. The Simplex Method of Linear
Programming 4. The MODI and VAM Methods of
Solving Transportation Problems 5. Vehicle Routing and Scheduling
PRINCIPLES OF OPERATIONS
MANAGEMENT, 10TH EDITION
ISBN: 0-13-418198-0
PART I INTRODUCTION TO OPERATIONS
MANAGEMENT 1. Operations and Productivity 2. Operations Strategy in a Global
Environment 3. Project Management 4. Forecasting
PART II DESIGNING OPERATIONS 5. Design of Goods and Services S5. Sustainability in the Supply Chain 6. Managing Quality S6. Statistical Process Control 7. Process Strategy S7. Capacity and Constraint Management 8. Location Strategies 9. Layout Strategies 10. Human Resources, Job Design, and
Work Measurement
PART III MANAGING OPERATIONS 11. Supply Chain Management S11. Supply Chain Management Analytics 12. Inventory Management 13. Aggregate Planning and S&OP 14. Material Requirements Planning (MRP)
and ERP 15. Short-Term Scheduling 16. Lean Operations 17. Maintenance and Reliability
ONLINE TUTORIALS 1. Statistical Tools for Managers 2. Acceptance Sampling 3. The Simplex Method of Linear
Programming 4. The MODI and VAM Methods of
Solving Transportation Problems 5. Vehicle Routing and Scheduling
TWO VERSIONS ARE AVAILABLE This text is available in two versions: Operations Management , 12th edition, a hardcover, and Principles of Operations Management , 10th edition, a paperback. Both books include the identi- cal core Chapters 1 – 17 . However, Operations Management , 12th edition also includes six business analytics modules in Part IV .
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O P E R A T I O N S M A N A G E M E N T Sustainability and Supply Chain Management
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11
C H A P T E R O U T L I N E
1 ◆
What Is Operations Management? 4
◆
Organizing to Produce Goods and Services 4
◆ The Supply Chain 6
◆
Why Study OM? 6
◆
What Operations Managers Do 7
◆
The Heritage of Operations Management 8
◆
Operations for Goods and Services 11
◆
The Productivity Challenge 13
◆
Current Challenges in Operations Management 18
◆
Ethics, Social Responsibility, and Sustainability 19
GLOBAL COMPANY PROFILE: Hard Rock Cafe
C H
A P
T E
R
PART ONE Introduction to Operations Management
Operations and Productivity
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
A la
sk a A
ir lin
e s
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O perations managers throughout the world are producing products every day to provide
for the well-being of society. These products take on a multitude of forms. They may be
washing machines at Whirlpool, motion pictures at DreamWorks, rides at Disney World, or
food at Hard Rock Cafe. These firms produce thousands of complex products every day—to be
delivered as the customer ordered them, when the customer wants them, and where the cus-
tomer wants them. Hard Rock does this for over 35 million guests worldwide every year. This is a
challenging task, and the operations manager’s job, whether at Whirlpool, DreamWorks, Disney,
or Hard Rock, is demanding.
Operations Management at Hard Rock Cafe
GLOBAL COMPANY PROFILE Hard Rock Cafe
C H A P T E R 1
2
Hard Rock Cafe in Orlando, Florida,
prepares over 3,500 meals each day.
Seating more than 1,500 people, it is
one of the largest restaurants in the
world. But Hard Rock’s operations
managers serve the hot food hot and
the cold food cold.
A n d re
J e n n y/
A la
m y
A n d re
J e n n y/
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M01_HEIZ0422_12_SE_C01.indd 2M01_HEIZ0422_12_SE_C01.indd 2 01/12/15 2:18 PM01/12/15 2:18 PM
3
Orlando-based Hard Rock Cafe opened its first restau-
rant in London in 1971, making it over 45 years old and the
granddaddy of theme restaurants. Although other theme
restaurants have come and gone, Hard Rock is still going
strong, with 150 restaurants in more than 53 countries—and
new restaurants opening each year. Hard Rock made its
name with rock music memorabilia, having started when Eric
Clapton, a regular customer, marked his favorite bar stool
by hanging his guitar on the wall in the London cafe. Now
Hard Rock has 70,000 items and millions of dollars invested
in memorabilia. To keep customers coming back time and
again, Hard Rock creates value in the form of good food and
entertainment.
The operations managers at Hard Rock Cafe at Uni-
versal Studios in Orlando provide more than 3,500 custom
products—in this case meals—every day. These products
are designed, tested, and then analyzed for cost of
Lots of work goes into designing, testing, and costing
meals. Then suppliers deliver quality products on time,
every time, for well-trained cooks to prepare quality
meals. But none of that matters unless an enthusiastic
waitstaff, such as the one shown here, holding guitars
previously owned by members of U2, is doing its job.
Efficient kitchen layouts, motivated
personnel, tight schedules, and the right
ingredients at the right place at the right time
are required to delight the customer.
Lots of work goes into designing testing and costing
Pr e ss
e le
ct /A
la m
y
Ja ck
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o n e /A
la m
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ingredients, labor requirements, and customer satisfaction.
On approval, menu items are put into production—and then
only if the ingredients are available from qualified suppliers.
The production process, from receiving, to cold storage,
to grilling or baking or frying, and a dozen other steps, is
designed and maintained to yield a quality meal. Operations
managers, using the best people they can recruit and train,
also prepare effective employee schedules and design
efficient layouts.
Managers who successfully design and deliver goods
and services throughout the world understand operations.
In this text, we look not only at how Hard Rock’s manag-
ers create value but also how operations managers in other
services, as well as in manufacturing, do so. Operations
management is demanding, challenging, and exciting. It
affects our lives every day. Ultimately, operations managers
determine how well we live.
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4
What Is Operations Management? Operations management (OM) is a discipline that applies to restaurants like Hard Rock Cafe as well as to factories like Ford and Whirlpool. The techniques of OM apply throughout the world to virtually all productive enterprises. It doesn’t matter if the application is in an office, a hospital, a restaurant, a department store, or a factory—the production of goods and ser- vices requires operations management. And the efficient production of goods and services requires effective applications of the concepts, tools, and techniques of OM that we introduce in this book.
As we progress through this text, we will discover how to manage operations in an economy in which both customers and suppliers are located throughout the world. An array of informa- tive examples, charts, text discussions, and pictures illustrates concepts and provides informa- tion. We will see how operations managers create the goods and services that enrich our lives.
In this chapter, we first define operations management , explaining its heritage and exploring the exciting role operations managers play in a huge variety of organizations. Then we discuss production and productivity in both goods- and service-producing firms. This is followed by a discussion of operations in the service sector and the challenge of managing an effective and efficient production system.
Production is the creation of goods and services. Operations management (OM) is the set of activi- ties that creates value in the form of goods and services by transforming inputs into outputs. Activities creating goods and services take place in all organizations. In manufacturing firms, the production activities that create goods are usually quite obvious. In them, we can see the creation of a tangible product such as a Sony TV or a Harley-Davidson motorcycle.
In an organization that does not create a tangible good or product, the production func- tion may be less obvious. We often call these activities services . The services may be “hidden” from the public and even from the customer. The product may take such forms as the transfer of funds from a savings account to a checking account, the transplant of a liver, the filling of an empty seat on an airplane, or the education of a student. Regardless of whether the end product is a good or service, the production activities that go on in the organization are often referred to as operations, or operations management .
Organizing to Produce Goods and Services To create goods and services, all organizations perform three functions (see Figure 1.1 ). These functions are the necessary ingredients not only for production but also for an organization’s survival. They are:
1. Marketing , which generates the demand, or at least takes the order for a product or ser- vice (nothing happens until there is a sale).
2. Production/operations , which creates, produces, and delivers the product. 3. Finance/accounting , which tracks how well the organization is doing, pays the bills, and
collects the money.
Universities, churches or synagogues, and businesses all perform these functions. Even a vol- unteer group such as the Boy Scouts of America is organized to perform these three basic
L E A R N I N G OBJEC TI V ES
LO 1.1 Defi ne operations management 4
LO 1.2 Explain the distinction between goods and services 11
LO 1.3 Explain the diff erence between production and productivity 13
LO 1.4 Compute single-factor productivity 14
LO 1.5 Compute multifactor productivity 15
LO 1.6 Identify the critical variables in enhancing productivity 16
STUDENT TIP Let’s begin by defining what
this course is about.
LO 1.1 Define operations management
VIDEO 1.1 Operations Management
at Hard Rock
VIDEO 1.2 Operations Management at
Frito-Lay
Production
The creation of goods and
services.
Operations management (OM)
Activities that relate to the creation
of goods and services through
the transformation of inputs to
outputs.
STUDENT TIP Operations is one of the
three functions that every
organization performs.
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C H A P T E R 1 | O P E R AT I O N S A N D P R O D U C T I V I T Y 5
Figure 1.1
Organization Charts for Two
Service Organizations and One
Manufacturing Organization
(A) a bank, (B) an airline, and
(C) a manufacturing organization.
The blue areas are OM activities.
Manufacturing
Operations
Facilities Construction; maintenance
Production and inventory control Scheduling; materials control
Quality assurance and control
Manufacturing Tooling; fabrication; assembly
Supply-chain management
Design Product development and design Detailed product specifications
Industrial engineering Efficient use of machines, space, and personnel
Process analysis Development and installation of production tools and equipment
Finance/accounting
Disbursements/credits Accounts receivable Accounts payable General ledger
Funds management Money market International exchange
Capital requirements Stock issue Bond issue and recall
Marketing
Sales promotion
Market research Sales Advertising
(C)
Airline
Operations
Ground support equipment
Maintenance
Ground operations Facility maintenance Catering
Flight operations Crew scheduling Flying Communications Dispatching
Management science
Finance/accounting
Accounting Accounts payable Accounts receivable General ledger
Finance Cash control International
exchange
Marketing
Traffic administration Reservations Schedules Tariffs (pricing)
Advertising
Sales
(B)
Operations
Ground suppportrt rtrtt equipment
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Marketing
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STUDENT TIP The areas in blue indicate the
significant role that OM plays in both
manufacturing and service firms.
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6 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
functions. Figure 1.1 shows how a bank, an airline, and a manufacturing firm organize them- selves to perform these functions. The blue-shaded areas show the operations functions in these firms.
The Supply Chain Through the three functions—marketing, operations, and finance—value for the customer is created. However, firms seldom create this value by themselves. Instead, they rely on a variety of suppliers who provide everything from raw materials to accounting services. These suppli- ers, when taken together, can be thought of as a supply chain. A supply chain (see Figure 1.2 ) is a global network of organizations and activities that supply a firm with goods and services.
As our society becomes more technologically oriented, we see increasing specialization. Specialized expert knowledge, instant communication, and cheaper transportation also foster specialization and worldwide supply chains. It just does not pay for a firm to try to do every- thing itself. The expertise that comes with specialization exists up and down the supply chain, adding value at each step. When members of the supply chain collaborate to achieve high levels of customer satisfaction, we have a tremendous force for efficiency and competitive advantage. Competition in the 21st century is not between companies; it is between supply chains.
Why Study OM? We study OM for four reasons:
1. OM is one of the three major functions of any organization, and it is integrally related to all the other business functions. All organizations market (sell), finance (account), and produce (operate), and it is important to know how the OM activity functions. Therefore, we study how people organize themselves for productive enterprise .
2. We study OM because we want to know how goods and services are produced . The produc- tion function is the segment of our society that creates the products and services we use.
3. We study OM to understand what operations managers do . Regardless of your job in an organization, you can perform better if you understand what operations managers do. In addition, understanding OM will help you explore the numerous and lucrative career opportunities in the field.
4. We study OM because it is such a costly part of an organization . A large percentage of the revenue of most firms is spent in the OM function. Indeed, OM provides a major oppor- tunity for an organization to improve its profitability and enhance its service to society. Example 1 considers how a firm might increase its profitability via the production function.
Figure 1.2
Soft Drink Supply Chain
A supply chain for a bottle of
Coke requires a beet or sugar
cane farmer, a syrup producer, a
bottler, a distributor, and a retailer,
each adding value to satisfy a
customer. Only with collaborations
between all members of the supply
chain can efficiency and customer
satisfaction be maximized. The
supply chain, in general, starts
with the provider of basic raw
materials and continues all the
way to the final customer at the
retail store.
Farmer Syrup producer
Bottler Distributor Retailer
Supply chain
A global network of organizations
and activities that supplies a firm
with goods and services.
STUDENT TIP Good OM managers are
scarce and, as a result,
career opportunities and
pay are excellent.
Example 1 EXAMINING THE OPTIONS FOR INCREASING CONTRIBUTION Fisher Technologies is a small firm that must double its dollar contribution to fixed cost and profit in order to be profitable enough to purchase the next generation of production equipment. Management has determined that if the firm fails to increase contribution, its bank will not make the loan and the equipment cannot be purchased. If the firm cannot purchase the equipment, the limitations of the old equipment will force Fisher to go out of business and, in doing so, put its employees out of work and discontinue producing goods and services for its customers.
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C H A P T E R 1 | O P E R AT I O N S A N D P R O D U C T I V I T Y 7
Example 1 underscores the importance of the effective operations activity of a firm. Devel- opment of increasingly effective operations is the approach taken by many companies as they face growing global competition.
What Operations Managers Do All good managers perform the basic functions of the management process. The management process consists of planning , organizing , staffing , leading , and controlling . Operations manag- ers apply this management process to the decisions they make in the OM function. The 10 strategic OM decisions are introduced in Table 1.2 . Successfully addressing each of these decisions requires planning, organizing, staffing, leading, and controlling.
Where Are the OM Jobs? How does one get started on a career in operations? The 10 strategic OM decisions identified in Table 1.2 are made by individuals who work in the dis- ciplines shown in the blue areas of Figure 1.1 . Business students who know their accounting,
APPROACH c Table 1.1 shows a simple profit-and-loss statement and three strategic options (mar- keting, finance/accounting, and operations) for the firm. The first option is a marketing option , where excellent marketing management may increase sales by 50%. By increasing sales by 50%, contribution will in turn increase 71%. But increasing sales 50% may be difficult; it may even be impossible.
TABLE 1.1 Options for Increasing Contribution
MARKETING OPTION a
FINANCE/ ACCOUNTING
OPTION b OM
OPTION c
CURRENT INCREASE SALES REVENUE 50%
REDUCE FINANCE COSTS 50%
REDUCE PRODUCTION COSTS 20%
Sales $100,000 $150,000 $100,000 $100,000
Costs of goods −80,000 −120,000 −80,000 −64,000
Gross margin 20,000 30,000 20,000 36,000
Finance costs −6,000 −6,000 −3,000 −6,000
Subtotal 14,000 24,000 17,000 30,000
Taxes at 25% −3,500 −6,000 −4,250 −7,500
Contribution d $ 10,500 $ 18,000 $ 12,750 $ 22,500
a Increasing sales 50% increases contribution by $7,500, or 71% (7,500/10,500).
b Reducing fi nance costs 50% increases contribution by $2,250, or 21% (2,250/10,500).
c Reducing production costs 20% increases contribution by $12,000, or 114% (12,000/10,500).
d Contribution to fi xed cost (excluding fi nance costs) and profi t.
The second option is a finance/accounting option , where finance costs are cut in half through good financial management. But even a reduction of 50% is still inadequate for generating the necessary increase in contribution. Contribution is increased by only 21%.
The third option is an OM option , where management reduces production costs by 20% and increases contribution by 114%.
SOLUTION c Given the conditions of our brief example, Fisher Technologies has increased contribu- tion from $10,500 to $22,500. It may now have a bank willing to lend it additional funds.
INSIGHT c The OM option not only yields the greatest improvement in contribution but also may be the only feasible option. Increasing sales by 50% and decreasing finance cost by 50% may both be virtu- ally impossible. Reducing operations cost by 20% may be difficult but feasible.
LEARNING EXERCISE c What is the impact of only a 15% decrease in costs in the OM option? [Answer: A $19,500 contribution; an 86% increase.]
10 Strategic OM Decisions
Design of goods and services
Managing quality
Process strategy
Location strategies
Layout strategies
Human resources
Supply-chain management
Inventory management
Scheduling
Maintenance
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8 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
statistics, finance, and OM have an opportunity to assume entry-level positions in all of these areas. As you read this text, identify disciplines that can assist you in making these decisions. Then take courses in those areas. The more background an OM student has in accounting, statistics, information systems, and mathematics, the more job opportunities will be available. About 40% of all jobs are in OM.
The following professional organizations provide various certifications that may enhance your education and be of help in your career:
◆ APICS, the Association for Operations Management ( www.apics.org ) ◆ American Society for Quality (ASQ) ( www.asq.org ) ◆ Institute for Supply Management (ISM) ( www.ism.ws ) ◆ Project Management Institute (PMI) ( www.pmi.org ) ◆ Council of Supply Chain Management Professionals ( www.cscmp.org )
Figure 1.3 shows some recent job opportunities.
The Heritage of Operations Management The field of OM is relatively young, but its history is rich and interesting. Our lives and the OM discipline have been enhanced by the innovations and contributions of numerous indi- viduals. We now introduce a few of these people, and we provide a summary of significant events in operations management in Figure 1.4 .
STUDENT TIP An operations manager must
successfully address the 10
decisions around which this text is
organized.
TABLE 1.2 Ten Strategic Operations Management Decisions
DECISION CHAPTER(S)
1. Design of goods and services: Defi nes much of what is required of operations in each of the other OM decisions. For instance, product design usually determines the lower limits of cost and the upper limits of quality, as well as major implications for sustainability and the human resources required.
5, Supplement 5
2. Managing quality: Determines the customer’s quality expectations and establishes policies and procedures to identify and achieve that quality.
6, Supplement 6
3. Process and capacity strategy: Determines how a good or service is produced (i.e., the process for production) and commits management to specifi c technology, quality, human resources, and capital investments that determine much of the fi rm’s basic cost structure.
7, Supplement 7
4. Location strategy: Requires judgments regarding nearness to customers, suppliers, and talent, while considering costs, infrastructure, logistics, and government.
8
5. Layout strategy: Requires integrating capacity needs, personnel levels, technology, and inventory requirements to determine the effi cient fl ow of materials, people, and information.
9
6. Human resources and job design: Determines how to recruit, motivate, and retain personnel with the required talent and skills. People are an integral and expensive part of the total system design.
10
7. Supply chain management: Decides how to integrate the supply chain into the fi rm’s strategy, including decisions that determine what is to be purchased, from whom, and under what conditions.
11, Supplement 11
8. Inventory management: Considers inventory ordering and holding decisions and how to optimize them as customer satisfaction, supplier capability, and production schedules are considered.
12, 14, 16
9. Scheduling: Determines and implements intermediate- and short-term schedules that effectively and effi ciently utilize both personnel and facilities while meeting customer demands.
13, 15
10. Maintenance: Requires decisions that consider facility capacity, production demands, and personnel necessary to maintain a reliable and stable process.
17
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C H A P T E R 1 | O P E R AT I O N S A N D P R O D U C T I V I T Y 9
1/15 Plant Manager Division of Fortune 1000 company seeks plant manager for plant located in the upper Hudson Valley area. This
plant manufactures loading dock equipment for commercial markets. The candidate must be experienced in plant
management including expertise in production planning, purchasing, and inventory management. Good written
and oral communication skills are a must, along with excellent application of skills in managing people.
2/23 Operations Analyst Expanding national coffee shop: top 10 “Best Places to Work” wants junior level systems analyst to join our
excellent store improvement team. Business or I.E. degree, work methods, labor standards, ergonomics, cost
accounting knowledge a plus. This is a hands-on job and excellent opportunity for a team player with good people
skills. West Coast location. Some travel required.
3/18 Quality Manager
Several openings exist in our small package processing facilities in the Northeast, Florida, and Southern California
for quality managers. These highly visible positions require extensive use of statistical tools to monitor all aspects
of service, timeliness, and workload measurement. The work involves (1) a combination of hands-on applications
and detailed analysis using databases and spreadsheets, (2) processing of audits to identify areas for improvement,
and (3) management of implementation of changes. Positions involve night hours and weekends.
4/6 Supply-Chain Manager and Planner Responsibilities entail negotiating contracts and establishing long-term relationships with suppliers. We will
rely on the selected candidate to maintain accuracy in the purchasing system, invoices, and product returns. A
bachelor's degree and up to 2 years related experience are required. Working knowledge of MRP, ability to use
feedback to master scheduling and suppliers and consolidate orders for best price and delivery are necessary.
Proficiency in all PC Windows applications, particularly Excel and Word, is essential. Effective verbal and written
communication skills are essential.
5/14 Process Improvement Consultants An expanding consulting firm is seeking consultants to design and implement lean production and cycle time
reduction plans in both service and manufacturing processes. Our firm is currently working with an international
bank to improve its back office operations, as well as with several manufacturing firms. A business degree
required; APICS certification a plus.
Eli Whitney (1800) is credited for the early popularization of interchangeable parts, which was achieved through standardization and quality control. Through a contract he signed with the U.S. government for 10,000 muskets, he was able to command a premium price because of their interchangeable parts.
Frederick W. Taylor (1881), known as the father of scientific management, contributed to personnel selection, planning and scheduling, motion study, and the now popular field of ergo- nomics. One of his major contributions was his belief that management should be much more resourceful and aggressive in the improvement of work methods. Taylor and his colleagues, Henry L. Gantt and Frank and Lillian Gilbreth, were among the first to systematically seek the best way to produce.
Another of Taylor’s contributions was the belief that management should assume more responsibility for:
1. Matching employees to the right job. 2. Providing the proper training. 3. Providing proper work methods and tools. 4. Establishing legitimate incentives for work to be accomplished.
Figure 1.3
Many Opportunities Exist for Operations Managers
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10 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
By 1913, Henry Ford and Charles Sorensen combined what they knew about standardized parts with the quasi-assembly lines of the meatpacking and mail-order industries and added the revolutionary concept of the assembly line, where men stood still and material moved.
Quality control is another historically significant contribution to the field of OM. Walter Shewhart (1924) combined his knowledge of statistics with the need for quality control and provided the foundations for statistical sampling in quality control. W. Edwards Deming (1950) believed, as did Frederick Taylor, that management must do more to improve the work environ- ment and processes so that quality can be improved.
Operations management will continue to progress as contributions from other disciplines, including industrial engineering, statistics, management, and economics , improve decision making.
Innovations from the physical sciences (biology, anatomy, chemistry, physics) have also contributed to advances in OM. These innovations include new adhesives, faster integrated circuits, gamma rays to sanitize food products, and specialized glass for iPhones and plasma TVs. Innovation in products and processes often depends on advances in the physical sciences.
Especially important contributions to OM have come from information technology , which we define as the systematic processing of data to yield information. Information technology—with wireless links, Internet, and e-commerce—is reducing costs and accelerating communication.
Decisions in operations management require individuals who are well versed in analyti- cal tools, in information technology, and often in one of the biological or physical sciences. In this textbook, we look at the diverse ways a student can prepare for a career in operations management.
Figure 1.4
Significant Events in Operations Management
E ve
re tt
C o lle
ct io
n /N
e w
sc o m
Early Concepts 1776–1880 Labor Specialization (Smith, Babbage) Standardized Parts (Whitney)
Scientific Management Era 1880–1910 Gantt Charts (Gantt) Motion & Time Studies (Gilbreth) Process Analysis (Taylor) Queuing Theory (Erlang)
Mass Production Era 1910–1980 Moving Assembly Line (Ford/Sorensen) Statistical Sampling (Shewhart) Economic Order Quantity (Harris) Linear Programming PERT/CPM (DuPont) Material Requirements Planning (MRP)
Mass Customization Era 1995–2005 Internet/E-Commerce Enterprise Resource Planning International Quality Standards (ISO) Finite Scheduling Supply Chain Management Mass Customization Build-to-Order Radio Frequency Identification (RFID)
Globalization Era 2005–2020 Global Supply Chains Growth of Transnational Organizations Instant Communications Sustainability Ethics in a Global Workforce Logistics
Lean Production Era 1980–1995 Just-in-Time (JIT) Computer-Aided Design (CAD) Electronic Data Interchange (EDI) Total Quality Management (TQM) Baldrige Award Empowerment Kanbans
Globalization FocusCustomization FocusQuality FocusCost Focus
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Operations for Goods and Services Manufacturers produce a tangible product, while service products are often intangible. But many products are a combination of a good and a service, which complicates the definition of a service. Even the U.S. government has trouble generating a consistent definition. Because definitions vary, much of the data and statistics generated about the service sector are incon- sistent. However, we define services as including repair and maintenance, government, food and lodging, transportation, insurance, trade, financial, real estate, education, legal, medical, entertainment, and other professional occupations.
The operation activities for both goods and services are often very similar. For instance, both have quality standards, are designed and produced on a schedule that meets customer de- mand, and are made in a facility where people are employed. However, some major differences do exist between goods and services. These are presented in Table 1.3 .
We should point out that in many cases, the distinction between goods and services is not clear-cut. In reality, almost all services and almost all goods are a mixture of a service and a tangible product. Even services such as consulting may require a tangible report. Similarly, the sale of most goods includes a service. For instance, many products have the service components of financing and delivery (e.g., automobile sales). Many also require after-sale training and maintenance (e.g., office copiers and machinery). “Service” activities may also be an integral part of production. Human resource activities, logistics, accounting, training, field service, and repair are all service activities, but they take place within a manufacturing organization. Very few services are “pure,” meaning they have no tangible component. Counseling may be one of the exceptions.
Growth of Services Services constitute the largest economic sector in postindustrial societies. Until about 1900, most Americans were employed in agriculture. Increased agricultural productivity allowed people to leave the farm and seek employment in the city. Similarly, manufactur- ing employment has decreased for the past 60 years. The changes in agriculture, manufac- turing, and service employment as a percentage of the workforce are shown in Figure 1.5 . Although the number of people employed in manufacturing has decreased since 1950, each person is now producing almost 20 times more than in 1950. Services became the dominant
STUDENT TIP Services are especially
important because almost 80%
of all jobs are in service firms.
Services
Economic activities that typically
produce an intangible product
(such as education, entertainment,
lodging, government, financial,
and health services).
LO 1.2 Explain the distinction between
goods and services
TABLE 1.3 Differences Between Goods and Services
CHARACTERISTICS OF SERVICES CHARACTERISTICS OF GOODS
Intangible: Ride in an airline seat Tangible: The seat itself
Produced and consumed simultaneously: Beauty salon produces a haircut that is consumed as it is produced
Product can usually be kept in inventory (beauty care products)
Unique: Your investments and medical care are unique
Similar products produced (iPods)
High customer interaction: Often what the customer is paying for (consulting, education)
Limited customer involvement in production
Inconsistent product defi nition: Auto insurance changes with age and type of car
Product standardized (iPhone)
Often knowledge based: Legal, education, and medical services are hard to automate
Standard tangible product tends to make automation feasible
Services dispersed: Service may occur at retail store, local offi ce, house call, or via Internet.
Product typically produced at a fi xed facility
Quality may be hard to evaluate: Consulting, education, and medical services
Many aspects of quality for tangible products are easy to evaluate (strength of a bolt)
Reselling is unusual: Musical concert or medical care Product often has some residual value
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employer in the early 1920s, with manufacturing employment peaking at about 32% in 1950. The huge productivity increases in agriculture and manufacturing have allowed more of our economic resources to be devoted to services. Consequently, much of the world can now enjoy the pleasures of education, health services, entertainment, and myriad other things that we call services. Examples of firms and percentage of employment in the U.S. service sector are shown in Table 1.4 . Table 1.4 also provides employment percentages for the nonservice sectors of manufacturing, construction, agriculture, and mining on the bottom four lines.
Service Pay Although there is a common perception that service industries are low paying, in fact, many service jobs pay very well. Operations managers in the maintenance facility of an airline are very well paid, as are the operations managers who supervise computer services to the finan- cial community. About 42% of all service workers receive wages above the national average. However, the service-sector average is driven down because 14 of the U.S. Department of
Figure 1.5
U.S. Agriculture,
Manufacturing, and Service
Employment
Source: U.S. Bureau of Labor Statistics.
1800
0
20
40
P e rc
e n
t o
f w
o rk
fo rc
e
60
80
100
1825 1850
1875
U.S. Agriculture, Manufacturing, and Service Employment
1900 1925
1950 1975
2000 2025 (est.)
Agriculture
Services
Manufacturing
Service sector
The segment of the economy that
includes trade, financial, lodging,
education, legal, medical, and
other professional occupations.
TABLE 1.4 Examples of Organizations in Each Sector
SECTOR EXAMPLE PERCENT OF
ALL JOBS
Service Sector
Education, Medical, Other San Diego State University, Arnold Palmer Hospital
15.3
Trade (retail, wholesale), Transportation
Walgreen’s, Walmart, Nordstrom, Alaska Airlines
15.8
Information, Publishers, Broadcast IBM, Bloomberg, Pearson, ESPN 1.9
Professional, Legal, Business Services, Associations
Snelling and Snelling, Waste Management, American Medical Association, Ernst & Young
13.6 85.2
Finance, Insurance, Real Estate Citicorp, American Express, Prudential, Aetna 9.6
Leisure, Lodging, Entertainment Olive Garden, Motel 6, Walt Disney 10.4
Government (Fed, State, Local) U.S., State of Alabama, Cook County 15.6
Manufacturing Sector General Electric, Ford, U.S. Steel, Intel 8.6
Construction Sector Bechtel, McDermott 4.3
Agriculture King Ranch 1.4
Mining Sector Homestake Mining .5
Grand Total 100.0
Source: Bureau of Labor Statistics, 2015.
( ' ' ' ' ' ' )
' ' ' ' ' ' *
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Commerce categories of the 33 service industries do indeed pay below the all-private industry average. Of these, retail trade, which pays only 61% of the national private industry average, is large. But even considering the retail sector, the average wage of all service workers is about 96% of the average of all private industries.
The Productivity Challenge The creation of goods and services requires changing resources into goods and services. The more efficiently we make this change, the more productive we are and the more value is added to the good or service provided. Productivity is the ratio of outputs (goods and services) divided by the inputs (resources, such as labor and capital) (see Figure 1.6 ). The operations manager’s job is to enhance (improve) this ratio of outputs to inputs. Improving productivity means improving efficiency. 1
This improvement can be achieved in two ways: reducing inputs while keeping output constant or increasing output while keeping inputs constant. Both represent an improvement in productivity. In an economic sense, inputs are labor, capital, and management, which are integrated into a production system. Management creates this production system, which provides the conversion of inputs to outputs. Outputs are goods and services, including such diverse items as guns, butter, education, improved judicial systems, and ski resorts. Production is the making of goods and services. High production may imply only that more people are working and that employment levels are high (low unemployment), but it does not imply high productivity .
Measurement of productivity is an excellent way to evaluate a country’s ability to provide an improving standard of living for its people. Only through increases in productivity can the standard of living improve. Moreover, only through increases in productivity can labor, capital, and management receive additional payments. If returns to labor, capital, or management are increased without increased productivity, prices rise. On the other hand, downward pressure is placed on prices when productivity increases because more is being produced with the same resources.
The benefits of increased productivity are illustrated in the OM in Action box “Improving Productivity at Starbucks.”
For well over a century (from about 1869), the U.S. has been able to increase productiv- ity at an average rate of almost 2.5% per year. Such growth has doubled U.S. wealth every 30 years. The manufacturing sector, although a decreasing portion of the U.S. economy, has on occasion seen annual productivity increases exceeding 4%, and service sector increases of almost 1%. However, U.S. annual productivity growth in the early part of the 21st century is slightly below the 2.5% range for the economy as a whole and in recent years has been trending down. 2
In this text, we examine how to improve productivity through operations management. Productivity is a significant issue for the world and one that the operations manager is uniquely qualified to address.
STUDENT TIP Why is productivity important?
Because it determines our
standard of living.
Productivity
The ratio of outputs (goods and
services) divided by one or more
inputs (such as labor, capital, or
management).
Figure 1.6
The Economic System Adds
Value by Transforming Inputs
to Outputs
An effective feedback loop
evaluates performance against
a strategy or standard. It also
evaluates customer satisfaction
and sends signals to managers
controlling the inputs and
transformation process.
LO 1.3 Explain the difference between
production and
productivity
Inputs Transformation Outputs
Feedback loop
Goods and services
The U.S. economic system transforms inputs
to outputs at about an annual 2.5% increase in
productivity per year. The productivity increase is
the result of a mix of capital (38% of 2.5%), labor (10% of 2.5%),
and management (52% of 2.5%).
Labor, capital,
management
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Productivity Measurement The measurement of productivity can be quite direct. Such is the case when productivity is measured by labor-hours per ton of a specific type of steel. Although labor-hours is a common measure of input, other measures such as capital (dollars invested), materials (tons of ore), or energy (kilowatts of electricity) can be used. 3 An example of this can be summarized in the following equation:
Productivity = Units produced
Input used (1-1)
For example, if units produced = 1,000 and labor-hours used is 250, then:
Single@factor productivity = Units produced
Labor@hours used =
1,000 250
= 4 units per labor@hour
The use of just one resource input to measure productivity, as shown in Equation (1-1) , is known as single-factor productivity . However, a broader view of productivity is multifactor productivity , which includes all inputs (e.g., capital, labor, material, energy). Multifactor productivity is also known as total factor productivity . Multifactor productivity is calculated by combining the input units as shown here:
Multifactor productivity = Output
Labor + Material + Energy + Capital + Miscellaneous (1-2)
To aid in the computation of multifactor productivity, the individual inputs (the denominator) can be expressed in dollars and summed as shown in Example 2 .
OM in Action Improving Productivity at Starbucks “This is a game of seconds …” says Silva Peterson, whom Starbucks has put
in charge of saving seconds. Her team of 10 analysts is constantly asking
themselves: “How can we shave time off this?”
Peterson’s analysis suggested that there were some obvious opportunities.
First, stop requiring signatures on credit-card purchases under $25. This sliced
8 seconds off the transaction time at the cash register.
Then analysts noticed that Starbucks’ largest cold beverage, the Venti size,
required two bending and digging motions to scoop up enough ice. The scoop
was too small. Redesign of the scoop provided the proper amount in one mo-
tion and cut 14 seconds off the average time of 1 minute.
Third were new espresso machines; with the push of a button, the
machines grind coffee beans and brew. This allowed the server, called a
“barista” in Starbucks’s vocabulary, to do other things. The savings: about
12 seconds per espresso shot.
As a result, operations improve-
ments at Starbucks outlets have
increased the average transactions
per hour to 11.7—a 46% increase—
and yearly volume by $250,000, to
about $1 million. The result: a 27%
improvement in overall productivity—
about 4.5% per year. In the service
industry, a 4.5% per year increase is
very tasty.
Sources: BusinessWeek (August
23–30, 2012) and The Wall Street
Journal (October 13, 2010 and
August 4, 2009).
LO 1.4 Compute single-factor productivity
Single-factor productivity
Indicates the ratio of goods and
services produced (outputs) to one
resource (input).
Multifactor productivity
Indicates the ratio of goods and
services produced (outputs) to
many or all resources (inputs).
Example 2 COMPUTING SINGLE-FACTOR AND MULTIFACTOR GAINS IN PRODUCTIVITY Collins Title Insurance Ltd. wants to evaluate its labor and multifactor productivity with a new comput- erized title-search system. The company has a staff of four, each working 8 hours per day (for a payroll cost of $640/day) and overhead expenses of $400 per day. Collins processes and closes on 8 titles each day. The new computerized title-search system will allow the processing of 14 titles per day. Although the staff, their work hours, and pay are the same, the overhead expenses are now $800 per day.
APPROACH c Collins uses Equation (1-1) to compute labor productivity and Equation (1-2) to com- pute multifactor productivity.
K o n d o r8
3 /S
h u tt
e rs
to ck
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Use of productivity measures aids managers in determining how well they are doing. But results from the two measures can be expected to vary. If labor productivity growth is entirely the result of capital spending, measuring just labor distorts the results. Multifactor productivity is usually better, but more complicated. Labor productivity is the more popular measure. The multifactor-productivity measures provide better information about the trade-offs among fac- tors, but substantial measurement problems remain. Some of these measurement problems are:
1. Quality may change while the quantity of inputs and outputs remains constant. Compare an HDTV of this decade with a black-and-white TV of the 1950s. Both are TVs, but few people would deny that the quality has improved. The unit of measure—a TV—is the same, but the quality has changed.
2. External elements may cause an increase or a decrease in productivity for which the sys- tem under study may not be directly responsible. A more reliable electric power service may greatly improve production, thereby improving the firm’s productivity because of this support system rather than because of managerial decisions made within the firm.
3. Precise units of measure may be lacking. Not all automobiles require the same inputs: Some cars are subcompacts, others are 911 Turbo Porsches.
Productivity measurement is particularly difficult in the service sector, where the end product can be hard to define. For example, economic statistics ignore the quality of your haircut, the outcome of a court case, or the service at a retail store. In some cases, adjustments are made for the quality of the product sold but not the quality of the sales presentation or the advantage of a broader product selection. Productivity measurements require specific inputs and out- puts, but a free economy is producing worth—what people want—which includes convenience, speed, and safety. Traditional measures of outputs may be a very poor measure of these other measures of worth. Note the quality-measurement problems in a law office, where each case is different, altering the accuracy of the measure “cases per labor-hour” or “cases per employee.”
Productivity Variables As we saw in Figure 1.6 , productivity increases are dependent on three productivity variables : 1. Labor, which contributes about 10% of the annual increase. 2. Capital, which contributes about 38% of the annual increase. 3. Management, which contributes about 52% of the annual increase. These three factors are critical to improved productivity. They represent the broad areas in which managers can take action to improve productivity.
SOLUTION c
Labor productivity with the old system: 8 titles per day 32 labor@hours
= .25 titles per labor@hour
Labor productivity with the new system: 14 titles per day 32 labor@hours
= .4375 titles per labor@hour
Multifactor productivity with the old system: 8 titles per day
$640 + 400 = .0077 titles per dollar
Multifactor productivity with the new system: 14 titles per day
$640 + 800 = .0097 titles per dollar
Labor productivity has increased from .25 to .4375. The change is ( .4375 - .25)>.25 = 0.75, or a 75% increase in labor productivity. Multifactor productivity has increased from .0077 to .0097. This change is ( .0097 - .0077)>.0077 = 0.26, or a 26% increase in multifactor productivity.
INSIGHT c Both the labor (single-factor) and multifactor productivity measures show an increase in productivity. However, the multifactor measure provides a better picture of the increase because it includes all the costs connected with the increase in output.
LEARNING EXERCISE c If the overhead goes to $960 (rather than $800), what is the multifactor productivity? [Answer: .00875.]
RELATED PROBLEMS c 1.1, 1.2, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.10, 1.11, 1.13, 1.14, 1.17
LO 1.5 Compute multifactor productivity
Productivity variables
The three factors critical to
productivity improvement—labor,
capital, and the art and science of
management.
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Which of the following is true about 84% of 100?
It is greater than 100
It is less than 100
It is equal to 100
What is the area of this rectangle?
6 yds
4 yds
4 square yds
6 square yds
10 square yds
20 square yds
24 square yds
If 9y + 3 = 6y + 15 then y =
1
2
4
6
Labor Improvement in the contribution of labor to productivity is the result of a healthier, better- educated, and better-nourished labor force. Some increase may also be attributed to a shorter workweek. Historically, about 10% of the annual improvement in productivity is attributed to improvement in the quality of labor. Three key variables for improved labor productivity are:
1. Basic education appropriate for an effective labor force. 2. Diet of the labor force. 3. Social overhead that makes labor available, such as transportation and sanitation.
Illiteracy and poor diets are a major impediment to productivity, costing countries up to 20% of their productivity. Infrastructure that yields clean drinking water and sanitation is also an opportunity for improved productivity, as well as an opportunity for better health, in much of the world.
In developed nations, the challenge becomes maintaining and enhancing the skills of labor in the midst of rapidly expanding technology and knowledge. Recent data suggest that the average American 17-year-old knows significantly less mathematics than the average Japanese at the same age, and about half cannot answer the questions in Figure 1.7 . Moreover, about one-third of American job applicants tested for basic skills were deficient in reading, writing, or math.
Overcoming shortcomings in the quality of labor while other countries have a better labor force is a major challenge. Perhaps improvements can be found not only through increasing competence of labor but also via better utilized labor with a stronger commitment . Training, motivation, team building, and the human resource strategies discussed in Chapter 10 , as well as improved education, may be among the many techniques that will contribute to increased labor productivity. Improvements in labor productivity are possible; however, they can be expected to be increasingly difficult and expensive.
Capital Human beings are tool-using animals. Capital investment provides those tools. Capital investment has increased in the U.S. every year except during a few very severe reces- sion periods. Annual capital investment in the U.S. has increased at an annual rate of 1.5% after allowances for depreciation.
Inflation and taxes increase the cost of capital, making capital investment increasingly ex- pensive. When the capital invested per employee drops, we can expect a drop in productivity. Using labor rather than capital may reduce unemployment in the short run, but it also makes economies less productive and therefore lowers wages in the long run. Capital investment is often a necessary, but seldom a sufficient, ingredient in the battle for increased productivity.
The trade-off between capital and labor is continually in flux. The higher the cost of capital or perceived risk, the more projects requiring capital are “squeezed out”: they are not pursued because the potential return on investment for a given risk has been reduced. Managers adjust their investment plans to changes in capital cost and risk.
Management Management is a factor of production and an economic resource. Manage- ment is responsible for ensuring that labor and capital are effectively used to increase produc- tivity. Management accounts for over half of the annual increase in productivity. This increase includes improvements made through the use of knowledge and the application of technology.
Using knowledge and technology is critical in postindustrial societies. Consequently, post- industrial societies are also known as knowledge societies . Knowledge societies are those in which much of the labor force has migrated from manual work to technical and information-processing
LO 1.6 Identify the critical variables in
enhancing productivity
Figure 1.7
About Half of the 17-Year-Olds
in the U.S. Cannot Correctly
Answer Questions of This Type
Knowledge society
A society in which much of the
labor force has migrated from
manual work to work based on
knowledge.
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tasks requiring ongoing education. The required education and training are important high- cost items that are the responsibility of operations managers as they build organizations and workforces. The expanding knowledge base of contemporary society requires that managers use technology and knowledge effectively.
More effective use of capital also contributes to productivity. It falls to the operations man- ager, as a productivity catalyst, to select the best new capital investments as well as to improve the productivity of existing investments.
The productivity challenge is difficult. A country cannot be a world-class competitor with second-class inputs. Poorly educated labor, inadequate capital, and dated technology are second-class inputs. High productivity and high-quality outputs require high-quality inputs, including good operations managers.
Productivity and the Service Sector The service sector provides a special challenge to the accurate measurement of productivity and productivity improvement. The traditional analytical framework of economic theory is based primarily on goods-producing activities. Consequently, most published economic data relate to goods production. But the data do indicate that, as our contemporary service econ- omy has increased in size, we have had slower growth in productivity.
The effective use of capital often means finding the proper trade-off between investment in capital assets (automation, left) and
human assets (a manual process, right). While there are risks connected with any investment, the cost of capital and physical
investments is fairly clear-cut, but the cost of employees has many hidden costs including fringe benefits, social insurance, and
legal constraints on hiring, employment, and termination.
An d rz
e j T h ie
l/ F o to
lia
Siemens, a multi-billion-dollar German conglomerate, has
long been known for its apprentice programs in its home
country. Because education is often the key to efficient
operations in a technological society, Siemens has spread
its apprentice-training programs to its U.S. plants. These
programs are laying the foundation for the highly skilled
workforce that is essential for global competitiveness. Ola f
Ja n d ke
/A g e n cj
a F
o to
g ra
fi cz
n a C
a ro
/A la
m y
G u y
S h a p ir a /S
h u tt
e rs
to ck
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Productivity of the service sector has proven difficult to improve because service-sector work is:
1. Typically labor intensive (e.g., counseling, teaching). 2. Frequently focused on unique individual attributes or desires (e.g., investment advice). 3. Often an intellectual task performed by professionals (e.g., medical diagnosis). 4. Often difficult to mechanize and automate (e.g., a haircut). 5. Often difficult to evaluate for quality (e.g., performance of a law firm).
The more intellectual and personal the task, the more difficult it is to achieve increases in pro- ductivity. Low-productivity improvement in the service sector is also attributable to the growth of low-productivity activities in the service sector. These include activities not previously a part of the measured economy, such as child care, food preparation, house cleaning, and laundry service. These activities have moved out of the home and into the measured economy as more and more women have joined the workforce. Inclusion of these activities has probably resulted in lower measured productivity for the service sector, although, in fact, actual productivity has probably increased because these activities are now more efficiently produced than previously.
However, despite the difficulty of improving productivity in the service sector, improve- ments are being made. And this text presents a multitude of ways to make these improvements. Indeed, what can be done when management pays attention to how work actually gets done is astonishing!
Although the evidence indicates that all industrialized countries have the same problem with service productivity, the U.S. remains the world leader in overall productivity and service productivity. Retailing is twice as productive in the U.S. as in Japan, where laws protect shop- keepers from discount chains. The U.S. telephone industry is at least twice as productive as Germany’s. The U.S. banking system is also 33% more efficient than Germany’s banking oli- gopolies. However, because productivity is central to the operations manager’s job and because the service sector is so large, we take special note in this text of how to improve productivity in the service sector. (See, for instance, the OM in Action box “Taco Bell Improves Productivity and Goes Green to Lower Costs.”)
Current Challenges in Operations Management Operations managers work in an exciting and dynamic environment. This environment is the result of a variety of challenging forces, from globalization of world trade to the transfer of ideas, products, and money at electronic speeds. Let’s look at some of these challenges:
OM in Action Taco Bell Improves Productivity and Goes Green to Lower Costs Founded in 1962 by Glenn Bell, Taco Bell seeks competitive advantage via low
cost. Like many other services, Taco Bell relies on its operations management
to improve productivity and reduce cost.
Its menu and meals are designed to be easy to prepare. Taco Bell has
shifted a substantial portion of food preparation to suppliers who could perform
food processing more efficiently than a stand-alone restaurant. Ground beef is
precooked prior to arrival and then reheated, as are many dishes that arrive in
plastic boil bags for easy sanitary reheating. Similarly, tortillas arrive already
fried and onions prediced. Efficient layout and automation has cut to 8 seconds
the time needed to prepare tacos and burritos and has cut time in the drive-
through lines by 1 minute. These advances have been combined with training
and empowerment to increase the span of management from one supervisor
for 5 restaurants to one supervisor for 30 or more.
Operations managers at Taco Bell have cut in-store labor by 15 hours per
day and reduced floor space by more than 50%. The result is a store that can
average 164 seconds for each customer, from drive-up to pull-out.
In 2010, Taco Bell completed the rollout
of its new Grill-to-Order kitchens by install-
ing water- and energy-saving grills that
conserve 300 million gallons of water and
200 million kilowatt hours of electricity each
year. This “green”-inspired cooking method
also saves the company’s 5,800 restaurants
$17 million per year.
Effective operations management has
resulted in productivity increases that sup-
port Taco Bell’s low-cost strategy. Taco Bell
is now the fast-food low-cost leader with a
58% share of the Mexican fast-food market. Bob
P a rd
u e -S
ig n s/
A la
m y
Sources: Business Week (May 5, 2011); Harvard Business Review (July/August
2008); and J. Hueter and W. Swart, Interfaces (Vol. 28; issue 1).
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◆ Globalization: The rapid decline in the cost of communication and transportation has made markets global. Similarly, resources in the form of capital, materials, talent, and labor are also now global. As a result, countries throughout the world are contributing to globaliza- tion as they vie for economic growth. Operations managers are rapidly seeking creative designs, efficient production, and high-quality goods via international collaboration.
◆ Supply-chain partnering: Shorter product life cycles, demanding customers, and fast changes in technology, materials, and processes require supply-chain partners to be in tune with the needs of end users. And because suppliers may be able to contribute unique expertise, operations managers are outsourcing and building long-term partnerships with critical players in the supply chain.
◆ Sustainability: Operations managers’ continuing battle to improve productivity is con- cerned with designing products and processes that are ecologically sustainable. This means designing green products and packaging that minimize resource use, can be recycled or re- used, and are generally environmentally friendly.
◆ Rapid product development: Technology combined with rapid international communica- tion of news, entertainment, and lifestyles is dramatically chopping away at the life span of products. OM is answering with new management structures, enhanced collaboration, digi- tal technology, and creative alliances that are more responsive and effective.
◆ Mass customization: Once managers recognize the world as the marketplace, the cultural and individual differences become quite obvious. In a world where consumers are increas- ingly aware of innovation and options, substantial pressure is placed on firms to respond in a creative way. And OM must rapidly respond with product designs and flexible production processes that cater to the individual whims of consumers. The goal is to produce custom- ized products, whenever and wherever needed.
◆ Lean operations: Lean is the management model sweeping the world and providing the standard against which operations managers must compete. Lean can be thought of as the driving force in a well-run operation, where the customer is satisfied, employees are respected, and waste does not exist. The theme of this text is to build organizations that are more efficient, where management creates enriched jobs that help employees engage in continuous improvement, and where goods and services are produced and delivered when and where the customer desires them. These ideas are also captured in the phrase Lean .
These challenges must be successfully addressed by today’s operations managers. This text will provide you with the foundations necessary to meet those challenges.
Ethics, Social Responsibility, and Sustainability The systems that operations managers build to convert resources into goods and services are complex. And they function in a world where the physical and social environment is evolving, as are laws and values. These dynamics present a variety of challenges that come from the con- flicting perspectives of stakeholders , such as customers, distributors, suppliers, owners, lenders, employees, and community. Stakeholders, as well as government agencies at various levels, require constant monitoring and thoughtful responses.
Identifying ethical and socially responsible responses while developing sustainable processes that are also effective and efficient productive systems is not easy. Managers are also challenged to: ◆ Develop and produce safe, high-quality green products ◆ Train, retain, and motivate employees in a safe workplace ◆ Honor stakeholder commitments Managers must do all this while meeting the demands of a very competitive and dynamic world marketplace. If operations managers have a moral awareness and focus on increasing productivity in this system , then many of the ethical challenges will be successfully addressed. The organiza- tion will use fewer resources, the employees will be committed, the market will be satisfied, and the ethical climate will be enhanced. Throughout this text, we note ways in which operations managers can take ethical and socially responsible actions while successfully addressing these challenges of the market. We also conclude each chapter with an Ethical Dilemma exercise.
STUDENT TIP One of the reasons OM is
such an exciting discipline
is that an operations manager is
confronted with ever-changing
issues, from technology, to global
supply chains, to sustainability.
Stakeholders
Those with a vested interest in an
organization, including customers,
distributors, suppliers, owners,
lenders, employees, and com-
munity members.
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Summary Operations, marketing, and finance/accounting are the three functions basic to all organizations. The operations function creates goods and services. Much of the progress of opera- tions management has been made in the twentieth century, but since the beginning of time, humankind has been attempt- ing to improve its material well-being. Operations managers are key players in the battle to improve productivity.
As societies become increasingly affluent, more of their resources are devoted to services. In the U.S., more than 85% of the workforce is employed in the service sector. Productivity improvements and a sustainable environment are difficult to achieve, but operations managers are the primary vehicle for making improvements.
Key Terms
Production (p. 4 ) Operations management (OM) (p. 4 ) Supply chain (p. 6 ) 10 strategic OM decisions (p. 7 )
Services (p. 11 ) Service sector (p. 12 ) Productivity (p. 13 ) Single-factor productivity (p. 14 )
Multifactor productivity (p. 14 ) Productivity variables (p. 15 ) Knowledge society (p. 16 ) Stakeholders (p. 19 )
Ethical Dilemma The American car battery industry boasts that its recycling rate now exceeds 95%, the highest rate for any commodity. However, with changes brought about by specialization and globalization, parts of the recycling system are moving offshore. This is particularly true of automobile batteries, which contain lead. The Environmental Protection Agency (EPA) is contributing to the offshore fl ow with newly implemented standards that make domestic battery recycling increasingly difficult and expensive. The result is a major increase in used batteries going to Mexico, where environmental standards and control are less demanding than they are in the U.S. One in fi ve batteries is now exported to Mexico. There is seldom diffi culty fi nding buyers because lead is expensive and in worldwide demand. While U.S.
recyclers operate in sealed, mechanized plants, with smokestacks equipped with scrubbers and plant surroundings monitored for traces of lead, this is not the case in most Mexican plants. The harm from lead is legendary, with long-run residual effects. Health issues include high blood pressure, kidney damage, detrimental effects on fetuses during pregnancy, neurological problems, and arrested development in children.
Given the two scenarios below, what action do you take?
a) You own an independent auto repair shop and are trying to safely dispose of a few old batteries each week. (Your battery supplier is an auto parts supplier who refuses to take your old batteries.)
b) You are manager of a large retailer responsible for disposal of thousands of used batteries each day.
Discussion Questions
1. Why should one study operations management? 2. Identify four people who have contributed to the theory and
techniques of operations management. 3. Briefly describe the contributions of the four individuals
identified in the preceding question. 4. Figure 1.1 outlines the operations, finance/accounting, and
marketing functions of three organizations. Prepare a chart similar to Figure 1.1 outlining the same functions for one of the following:
a. a newspaper b. a drugstore c. a college library d. a summer camp e. a small costume-jewelry factory 5. Answer Question 4 for some other organization, perhaps an
organization where you have worked. 6. What are the three basic functions of a firm? 7. Identify the 10 strategic operations management decisions. 8. Name four areas that are significant to improving labor
productivity.
9. The U.S., and indeed much of the rest of the world, has been described as a “knowledge society.” How does this affect pro- ductivity measurement and the comparison of productivity between the U.S. and other countries?
10. What are the measurement problems that occur when one attempts to measure productivity?
11. Mass customization and rapid product development were identified as challenges to modern manufacturing operations. What is the relationship, if any, between these challenges? Can you cite any examples?
12. What are the five reasons productivity is difficult to improve in the service sector?
13. Describe some of the actions taken by Taco Bell to increase productivity that have resulted in Taco Bell’s ability to serve “twice the volume with half the labor.”
14. As a library or Internet assignment, find the U.S. produc- tivity rate (increase) last year for the (a) national economy, (b) manufacturing sector, and (c) service sector.
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Using Software for Productivity Analysis
This section presents three ways to solve productivity problems with computer software. First, you can create your own Excel spreadsheets to conduct productivity analysis. Second, you can use the Excel OM software that comes with this text. Third, POM for Windows is another program that is available with this text .
Program 1.1
Actions Copy C7 to B7, Copy B14 to C14, Copy C15 to B15, and Copy D14 to D15
Create a row for each of the inputs used for the productivity measure. Put the output in the last row.
=C5*C6
=B10/B7
Enter the values for the old system in column B and the new system in Column C.
Productivity = Output/Input
=(C14-B14)/B14=C10/(C8+C9)
X USING EXCEL OM Excel OM is an Excel “add-in” with 24 Operations Management decision support “Templates.” To access the templates, double- click on the Excel OM tab at the top of the page, then in the menu bar choose the appropriate chapter (in this case Chapter 1 ), from either the “Chapter” or “Alphabetic” tab on the left. Each of Excel OM’s 24 modules includes instructions for that particular module. The instructions can be turned on or off via the “instruction” tab in the menu bar.
P USING POM FOR WINDOWS POM for Windows is decision support software that includes 24 Operations Management modules. The modules are accessed by double-clicking on Module in the menu bar, and then double-clicking on the appropriate (in this case Productivity ) item. Instructions are provided for each module just below the menu bar.
Solved Problems Virtual Office Hours help is available in MyOMLab .
SOLVED PROBLEM 1.1 Productivity can be measured in a variety of ways, such as by labor, capital, energy, material usage, and so on. At Modern Lumber, Inc., Art Binley, president and producer of apple crates sold to growers, has been able, with his current equip- ment, to produce 240 crates per 100 logs. He currently pur- chases 100 logs per day, and each log requires 3 labor-hours to process. He believes that he can hire a professional buyer who can buy a better-quality log at the same cost. If this is the case, he can increase his production to 260 crates per 100 logs. His labor-hours will increase by 8 hours per day.
What will be the impact on productivity (measured in crates per labor-hour) if the buyer is hired?
SOLUTION
(a) Current labor productivity = 240 crates
100 logs * 3 hours>log
= 240 300
= .8 crates per labor@hour (b) Labor productivitywith buyer =
260 crates ( 100 logs * 3 hours>log) + 8 hours
= 260 308
= .844 crates per labor@hour Using current productivity (.80 from [a]) as a base, the
increase will be 5.5% (.844/.8 = 1.055, or a 5.5% increase).
CREATING YOUR OWN EXCEL SPREADSHEETS Program 1.1 illustrates how to build an Excel spreadsheet for the data in Example 2.
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Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
SOLVED PROBLEM 1.2 Art Binley has decided to look at his productivity from a multi- factor (total factor productivity) perspective (refer to Solved Problem 1.1). To do so, he has determined his labor, capital, energy, and material usage and decided to use dollars as the common denominator. His total labor-hours are now 300 per
day and will increase to 308 per day. His capital and energy costs will remain constant at $350 and $150 per day, respec- tively. Material costs for the 100 logs per day are $1,000 and will remain the same. Because he pays an average of $10 per hour (with fringes), Binley determines his productivity increase as follows:
SOLUTION
CURRENT SYSTEM
Labor: 300 hrs. @10 = 3,000
Material: 100 logs/day 1,000
Capital: 350
Energy: 150
Total Cost: $4,500
Multifactor productivity of current system: = 240 crates>$4,500 = .0533 crates/dollar
SYSTEM WITH PROFESSIONAL BUYER
308 hrs. @10 = $3,080
1,000
350
150
$4,580
Multifactor productivity of proposed system: = 260 crates>$4,580 = .0568 crates/dollar
Using current productivity (.0533) as a base, the increase will be .066. That is, .0568>.0533 = 1.066, or a 6.6% increase.
Problems 1.1 to 1.17 relate to The Productivity Challenge
• 1.1 Chuck Sox makes wooden boxes in which to ship motorcycles. Chuck and his three employees invest a total of 40 hours per day making the 120 boxes. a) What is their productivity? b) Chuck and his employees have discussed redesigning the pro-
cess to improve efficiency. If they can increase the rate to 125 per day, what will be their new productivity?
c) What will be their unit increase in productivity per hour? d) What will be their percentage change in productivity? PX
• 1.2 Carbondale Casting produces cast bronze valves on a 10-person assembly line. On a recent day, 160 valves were pro- duced during an 8-hour shift. a) Calculate the labor productivity of the line. b) John Goodale, the manager at Carbondale, changed the layout
and was able to increase production to 180 units per 8-hour shift. What is the new labor productivity per labor-hour?
c) What is the percentage of productivity increase? PX
• 1.3 This year, Druehl, Inc., will produce 57,600 hot water heaters at its plant in Delaware, in order to meet expected global demand. To accomplish this, each laborer at the plant will work 160 hours per month. If the labor productivity at the plant is 0.15 hot water heaters per labor-hour, how many laborers are employed at the plant?
• 1.4 Lori Cook produces “Final Exam Care Packages” for resale by her sorority. She is currently working a total of 5 hours per day to produce 100 care packages. a) What is Lori’s productivity? b) Lori thinks that by redesigning the package, she can increase
her total productivity to 133 care packages per day. What will be her new productivity?
c) What will be the percentage increase in productivity if Lori makes the change? PX
• • 1.5 George Kyparisis makes bowling balls in his Miami plant. With recent increases in his costs, he has a newfound interest in efficiency. George is interested in determining the productivity of his organization. He would like to know if his organization is maintaining the manufacturing average of 3% increase in productivity per year? He has the following data representing a month from last year and an equivalent month this year:
LAST YEAR NOW
Units produced 1,000 1,000
Labor (hours) 300 275
Resin (pounds) 50 45
Capital invested ($) 10,000 11,000
Energy (BTU) 3,000 2,850
Show the productivity percentage change for each category and then determine the improvement for labor-hours, the typical standard for comparison. PX
• • 1.6 George Kyparisis (using data from Problem 1.5) determines his costs to be as follows:
◆ Labor: $10 per hour
◆ Resin: $5 per pound
◆ Capital expense: 1% per month of investment
◆ Energy: $0.50 per BTU
Show the percent change in productivity for one month last year versus one month this year, on a multifactor basis with dollars as the common denominator. PX
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• 1.7 Hokey Min’s Kleen Karpet cleaned 65 rugs in October, consuming the following resources:
Labor: 520 hours at $13 per hour
Solvent: 100 gallons at $5 per gallon
Machine rental: 20 days at $50 per day
a) What is the labor productivity per dollar? b) What is the multifactor productivity? PX
• • 1.8 Lillian Fok is president of Lakefront Manufacturing, a producer of bicycle tires. Fok makes 1,000 tires per day with the following resources:
Labor: 400 hours per day @ $12.50 per hour
Raw material: 20,000 pounds per day @ $1 per pound
Energy: $5,000 per day
Capital costs: $10,000 per day
a) What is the labor productivity per labor-hour for these tires at Lakefront Manufacturing?
b) What is the multifactor productivity for these tires at Lakefront Manufacturing?
c) What is the percent change in multifactor productivity if Fok can reduce the energy bill by $1,000 per day without cutting production or changing any other inputs? PX
• • • 1.9 Brown’s, a local bakery, is worried about increased costs—particularly energy. Last year’s records can provide a fairly good estimate of the parameters for this year. Wende Brown, the owner, does not believe things have changed much, but she did invest an additional $3,000 for modifications to the bakery’s ovens to make them more energy efficient. The modifi- cations were supposed to make the ovens at least 15% more effi- cient. Brown has asked you to check the energy savings of the new ovens and also to look over other measures of the bakery’s productivity to see if the modifications were beneficial. You have the following data to work with:
LAST YEAR NOW
Production (dozen) 1,500 1,500
Labor (hours) 350 325
Capital investment ($) 15,000 18,000
Energy (BTU) 3,000 2,750
T a ra
s V ys
h n ya
/S h u tt
e rs
to ck
• • 1.10 Munson Performance Auto, Inc., modifies 375 autos per year. The manager, Adam Munson, is interested in obtain- ing a measure of overall performance. He has asked you to pro- vide him with a multifactor measure of last year’s performance as a benchmark for future comparison. You have assembled the following data. Resource inputs were labor, 10,000 hours; 500 suspension and engine modification kits; and energy, 100,000 kilowatt-hours. Average labor cost last year was $20 per hour, kits cost $1,000 each, and energy costs were $3 per kilowatt-hour. What do you tell Mr. Munson? PX
• • 1.11 Lake Charles Seafood makes 500 wooden packing boxes for fresh seafood per day, working in two 10-hour shifts. Due to increased demand, plant managers have decided to oper- ate three 8-hour shifts instead. The plant is now able to produce 650 boxes per day. a) Calculate the company’s productivity before the change in
work rules and after the change. b) What is the percentage increase in productivity? c) If production is increased to 700 boxes per day, what is the
new productivity? PX
• • • 1.12 Charles Lackey operates a bakery in Idaho Falls, Idaho. Because of its excellent product and excellent location, demand has increased by 25% in the last year. On far too many occasions, customers have not been able to purchase the bread of their choice. Because of the size of the store, no new ovens can be added. At a staff meeting, one employee suggested ways to load the ovens differently so that more loaves of bread can be baked at one time. This new process will require that the ovens be loaded by hand, requiring additional manpower. This is the only thing to be changed. If the bakery makes 1,500 loaves per month with a labor productivity of 2.344 loaves per labor-hour, how many workers will Lackey need to add ? ( Hint: Each worker works 160 hours per month.)
• • 1.13 Refer to Problem 1.12. The pay will be $8 per hour for employees. Charles Lackey can also improve the yield by purchasing a new blender. The new blender will mean an increase in his investment. This added investment has a cost of $100 per month, but he will achieve the same output (an increase to 1,875) as the change in labor-hours. Which is the better decision? a) Show the productivity change, in loaves per dollar, with an
increase in labor cost (from 640 to 800 hours). b) Show the new productivity, in loaves per dollar, with only an
increase in investment ($100 per month more). c) Show the percent productivity change for labor and
investment.
• • • 1.14 Refer to Problems 1.12 and 1.13. If Charles Lackey’s utility costs remain constant at $500 per month, labor at $8 per hour, and cost of ingredients at $0.35 per loaf, but Charles does not purchase the blender suggested in Problem 1.13, what will the productivity of the bakery be? What will be the percent increase or decrease?
• • 1.15 In December, General Motors produced 6,600 cus- tomized vans at its plant in Detroit. The labor productivity at this plant is known to have been 0.10 vans per labor-hour during that month. 300 laborers were employed at the plant that month. a) How many hours did the average laborer work that month? b) If productivity can be increased to 0.11 vans per labor-
hour, how many hours would the average laborer work that month?
PX
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• • 1.16 Susan Williams runs a small Flagstaff job shop where garments are made. The job shop employs eight workers. Each worker is paid $10 per hour. During the first week of March, each worker worked 45 hours. Together, they produced a batch of 132 garments. Of these garments, 52 were “seconds” (meaning that they were flawed). The seconds were sold for $90 each at a factory outlet store. The remaining 80 garments were sold to retail outlets at a price of $198 per garment. What was the labor productivity, in dollars per labor-hour, at this job shop during the first week of March?
• • • 1.17 As part of a study for the Department of Labor Statistics, you are assigned the task of evaluating the improve- ment in productivity of small businesses. Data for one of the small businesses you are to evaluate are shown at right. The data are the monthly average of last year and the monthly average this year. Determine the multifactor productivity with dollars as the common denominator for:
a) Last year. b) This year. c) Then determine the percent change in productivity for the
monthly average last year versus the monthly average this year on a multifactor basis.
◆ Labor: $8 per hour
◆ Capital: 0.83% per month of investment
◆ Energy: $0.60 per BTU
LAST YEAR THIS YEAR
Production (dozen) 1,500 1,500
Labor (hours) 350 325
Capital investment ($) 15,000 18,000
Energy (BTU) 3,000 2,700
CASE STUDIES Uber Technologies, Inc.
The $41 billion dollar firm Uber Technology, Inc., is unsettling the traditional taxi business. In over 40 countries and 240 mar- kets around the world, Uber and similar companies are chal- lenging the existing taxi business model. Uber and its growing list of competitors, Lyft, Sidecar, and Flywheel in America, and fledging rivals in Europe, Asia, and India, think their smart phone apps can provide a new and improved way to call a taxi. This disruptive business model uses an app to arrange rides between riders and cars, theoretically a nearby car, which is tracked by the app. The Uber system also provides a history of rides, routes, and fees as well as automatic billing. In addition, driver and rider are also allowed to evaluate each other. The services are increasingly popular, worrying established taxi ser- vices in cities from New York to Berlin, and from Rio de Janeiro to Bangkok. In many markets, Uber has proven to be the best, fastest, and most reliable way to find a ride. Consumers world- wide are endorsing the system as a replacement for the usual taxi ride. As the most established competitor in the field, Uber is putting more cars on the road, meaning faster pickup times, which should attract even more riders, which in turn attracts even more drivers, and so on. This growth cycle may speed the demise of the existing taxi businesses as well as provide sub- stantial competition for firms with a technology-oriented model similar to Uber’s.
The Uber business model initially attempts to bypass a number of regulations and at the same time offer better service and lower fees than traditional taxis. However, the traditional taxi industry is fighting back, and regulations are mounting. The regulations vary by country and city, but increasingly spe- cial licensing, testing, and inspections are being imposed. Part of the fee charged to riders does not go to the driver, but to
Uber, as there are real overhead costs. Uber’s costs, depending on the locale, may include insurance, background checks for drivers, vetting of vehicles, software development and mainte- nance, and centralized billing. How these overhead costs com- pare to traditional taxi costs is yet to be determined. Therefore, improved efficiency may not be immediately obvious, and contract provisions are significant (see www.uber.com/legal/ usa/terms ).
In addition to growing regulations, a complicating factor in the model is finding volunteer drivers at inopportune times. A sober driver and a clean car at 1:00 a.m. New Year’s Eve does cost more. Consequently, Uber has introduced “surge” pricing. Surge pricing means a higher price, sometimes much higher, than normal. Surge pricing has proven necessary to ensure that cars and drivers are available at unusual times. These higher surge prices can be a shock to riders, making the “surge price” a conten- tious issue.
Discussion Questions
1. The market has decided that Uber and its immediate competi- tors are adding efficiency to our society. How is Uber providing that added efficiency?
2. Do you think the Uber model will work in the trucking industry?
3. In what other areas/industries might the Uber model be used?
Sources: Wall Street Journal (January 2, 2015), B3, and (Dec. 18, 2014), D1; and www.bloombergview.com/articles/2014-12-11/can-uber-rule-the-world .
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Frito-Lay, the massive Dallas-based subsidiary of PepsiCo, has 38 plants and 48,000 employees in North America. Seven of Frito-Lay’s 41 brands exceed $1 billion in sales: Fritos, Lay’s, Cheetos, Ruffles, Tostitos, Doritos, and Walker’s Potato Chips. Operations is the focus of the firm—from designing products for new markets, to meeting changing consumer preferences, to adjusting to rising commodity costs, to subtle issues involving fla- vors and preservatives—OM is under constant cost, time, quality, and market pressure. Here is a look at how the 10 decisions of OM are applied at this food processor.
In the food industry, product development kitchens experi- ment with new products, submit them to focus groups, and per- form test marketing. Once the product specifications have been set, processes capable of meeting those specifications and the nec- essary quality standards are created. At Frito-Lay, quality begins at the farm, with onsite inspection of the potatoes used in Ruffles and the corn used in Fritos. Quality continues throughout the manufacturing process, with visual inspections and with statis- tical process control of product variables such as oil, moisture, seasoning, salt, thickness, and weight. Additional quality evalu- ations are conducted throughout shipment, receipt, production, packaging, and delivery.
The production process at Frito-Lay is designed for large vol- umes and small variety, using expensive special-purpose equip- ment, and with swift movement of material through the facility. Product-focused facilities, such as Frito-Lay’s, typically have high capital costs, tight schedules, and rapid processing. Frito- Lay’s facilities are located regionally to aid in the rapid delivery of products because freshness is a critical issue. Sanitary issues and necessarily fast processing of products put a premium on an effi- cient layout. Production lines are designed for balanced through- put and high utilization. Cross-trained workers, who handle a variety of production lines, have promotion paths identified for their particular skill set. The company rewards employees with medical, retirement, and education plans. Its turnover is very low.
The supply chain is integral to success in the food industry; vendors must be chosen with great care. Moreover, the finished food product is highly dependent on perishable raw materials. Consequently, the supply chain brings raw material (potatoes, corn, etc.) to the plant securely and rapidly to meet tight pro- duction schedules. For instance, from the time that potatoes are picked in St. Augustine, Florida, until they are unloaded at the Orlando plant, processed, packaged, and shipped from the plant is under 12 hours. The requirement for fresh product requires on- time, just-in-time deliveries combined with both low raw mate- rial and finished goods inventories. The continuous-flow nature of the specialized equipment in the production process permits little work-in-process inventory. The plants usually run 24/7. This means that there are four shifts of employees each week.
Tight scheduling to ensure the proper mix of fresh finished goods on automated equipment requires reliable systems and effective maintenance. Frito-Lay’s workforce is trained to recognize problems early, and professional maintenance per- sonnel are available on every shift. Downtime is very costly and can lead to late deliveries, making maintenance a high priority.
Discussion Questions *
1. From your knowledge of production processes and from the case and the video, identify how each of the 10 decisions of OM is applied at Frito-Lay.
2. How would you determine the productivity of the production process at Frito-Lay?
3. How are the 10 decisions of OM different when applied by the operations manager of a production process such as Frito-Lay versus a service organization such as Hard Rock Cafe (see the Hard Rock Cafe video case below)?
Video Case Frito-Lay: Operations Management in Manufacturing
Video Case Hard Rock Cafe: Operations Management in Services
In its 45 years of existence, Hard Rock has grown from a mod- est London pub to a global power managing 150 cafes, 13 hotels/ casinos, and live music venues. This puts Hard Rock firmly in the service industry—a sector that employs over 75% of the people in the U.S. Hard Rock moved its world headquarters to Orlando, Florida, in 1988 and has expanded to more than 40 locations throughout the U.S., serving over 100,000 meals each day. Hard Rock chefs are modifying the menu from classic American— burgers and chicken wings—to include higher-end items such as stuffed veal chops and lobster tails. Just as taste in music changes over time, so does Hard Rock Cafe, with new menus, layouts, memorabilia, services, and strategies.
At Orlando’s Universal Studios, a traditional tourist des- tination, Hard Rock Cafe serves over 3,500 meals each day. The cafe employs about 400 people. Most are employed in the restaurant, but some work in the retail shop. Retail is now a standard and increasingly prominent feature in Hard Rock Cafes (since close to 48% of revenue comes from this source).
Cafe employees include kitchen and waitstaff, hostesses, and bar- tenders. Hard Rock employees are not only competent in their job skills but are also passionate about music and have engag- ing personalities. Cafe staff is scheduled down to 15-minute intervals to meet seasonal and daily demand changes in the tourist environment of Orlando. Surveys are done on a regular basis to evaluate quality of food and service at the cafe. Scores are rated on a 1-to-7 scale, and if the score is not a 7, the food or service is a failure.
Hard Rock is adding a new emphasis on live music and is rede- signing its restaurants to accommodate the changing tastes. Since Eric Clapton hung his guitar on the wall to mark his favorite bar stool, Hard Rock has become the world’s leading collector and exhibitor of rock ‘n’ roll memorabilia, with changing exhibits at its cafes throughout the world. The collection includes 70,000 pieces, valued at $40 million. In keeping with the times, Hard Rock also maintains a Web site, www.hardrock.com , which receives over 100,000 hits per week, and a weekly cable television
* You may wish to view the video that accompanies this case before addressing these questions.
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program on VH1. Hard Rock’s brand recognition, at 92%, is one of the highest in the world.
Discussion Questions *
1. From your knowledge of restaurants, from the video, from the Global Company Profile that opens this chapter, and from the case itself, identify how each of the 10 OM strategy decisions is applied at Hard Rock Cafe.
2. How would you determine the productivity of the kitchen staff and waitstaff at Hard Rock?
3. How are the 10 OM strategy decisions different when applied to the operations manager of a service operation such as Hard Rock versus an automobile company such as Ford Motor Company?
* You may wish to view the video that accompanies this case before addressing these questions.
• Additional Case Study: Visit MyOMLab for these case studies: National Air Express: Introduces the issue of productivity, productivity improvement, and measuring productivity. Zychol Chemicals Corp.: The production manager must prepare a productivity report, which includes multifactor analysis.
Endnotes
1. Efficiency means doing the job well—with a minimum of resources and waste. Note the distinction between being effi- cient , which implies doing the job well, and effective , which means doing the right thing. A job well done—say, by applying the 10 strategic decisions of operations management—helps us
2. U.S. Dept. of Labor, 2015: www.bls.gov/lpc/ 3. The quality and time period are assumed to remain constant.
be efficient ; developing and using the correct strategy helps us be effective .
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1
R ap
id R
ev ie
w Chapter 1 Rapid Review
Main Heading Review Material MyOMLab WHAT IS OPERATIONS MANAGEMENT? (p. 4 )
j Production —The creation of goods and services j Operations management (OM) —Activities that relate to the creation of goods
and services through the transformation of inputs to outputs
Concept Questions: 1.1–1.4
VIDEOS 1.1 and 1.2 OM at Hard Rock OM at Frito-Lay
ORGANIZING TO PRODUCE GOODS AND SERVICES (pp. 4 – 6 )
All organizations perform three functions to create goods and services: 1. Marketing , which generates demand 2. Production/operations, which creates the product 3. Finance/accounting, which tracks how well the organization is doing, pays the
bills, and collects the money
Concept Questions: 2.1–2.4
THE SUPPLY CHAIN (p. 6 )
j Supply chain—A global network of organizations and activities that supply a firm with goods and services
Concept Questions: 3.1–3.4
WHY STUDY OM? (pp. 6 – 7 )
We study OM for four reasons: 1. To learn how people organize themselves for productive enterprise 2. To learn how goods and services are produced 3. To understand what operations managers do 4. Because OM is a costly part of an organization
Concept Questions: 4.1–4.2
WHAT OPERATIONS MANAGERS DO (pp. 7 – 8 )
Ten OM strategic decisions are required of operations managers: 1. Design of goods and services 2. Managing quality 3. Process strategy 4. Location strategies 5. Layout strategies 6. Human resources 7. Supply chain management 8. Inventory management 9. Scheduling 10. Maintenance About 40% of all jobs are in OM. Operations managers possess job titles such as plant manager, quality manager, process improvement consultant, and operations analyst.
Concept Questions: 5.1–5.4
THE HERITAGE OF OPERATIONS MANAGEMENT (pp. 8 – 10 )
Significant events in modern OM can be classified into six eras: 1. Early concepts (1776–1880)—Labor specialization (Smith, Babbage), standard-
ized parts (Whitney) 2. Scientific management (1880–1910)—Gantt charts (Gantt), motion and time
studies (Gilbreth), process analysis (Taylor), queuing theory (Erlang) 3. Mass production (1910–1980)—Assembly line (Ford/Sorensen), statistical
sampling (Shewhart), economic order quantity (Harris), linear programming (Dantzig), PERT/CPM (DuPont), material requirements planning
4. Lean production (1980–1995)—Just-in-time, computer-aided design, electronic data interchange, total quality management, Baldrige Award, empowerment, kanbans
5. Mass customization (1995–2005)—Internet/e-commerce, enterprise resource planning, international quality standards, finite scheduling, supply-chain management, mass customization, build-to-order, radio frequency identification (RFID)
6. Globalization era (2005–2020)—Global supply chains, growth of transnational organizations, instant communications, sustainability, ethics in a global work force, logistics and shipping
Concept Questions: 6.1–6.4
OPERATIONS FOR GOODS AND SERVICES (pp. 11 – 13 )
j Services —Economic activities that typically produce an intangible product (such as education, entertainment, lodging, government, financial, and health services). Almost all services and almost all goods are a mixture of a service and a tangible product.
j Service sector —The segment of the economy that includes trade, financial, lodg- ing, education, legal, medical, and other professional occupations. Services now constitute the largest economic sector in postindustrial societies. The huge pro- ductivity increases in agriculture and manufacturing have allowed more of our economic resources to be devoted to services. Many service jobs pay very well.
Concept Questions: 7.1–7.4
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Main Heading Review Material MyOMLab THE PRODUCTIVITY CHALLENGE (pp. 13 – 18 )
j Productivity —The ratio of outputs (goods and services) divided by one or more inputs (such as labor, capital, or management)
High production means producing many units, while high productivity means producing units efficiently. Only through increases in productivity can the standard of living of a country im- prove. U.S. productivity has averaged a 2.5% increase per year for over a century.
Single@factor productivity = Units produced
Input used (1-1)
j Single-factor productivity —Indicates the ratio of goods and services produced (outputs) to one resource (input).
j Multifactor productivity —Indicates the ratio of goods and services produced (outputs) to many or all resources (inputs).
Multifactor productivity
= Output
Labor + Material + Energy + Capital + Miscellaneous (1-2)
Measurement problems with productivity include: (1) the quality may change, (2) external elements may interfere, and (3) precise units of measure may be lacking. j Productivity variables —The three factors critical to productivity improvement
are labor (10%), capital (38%), and management (52%). j Knowledge society —A society in which much of the labor force has migrated
from manual work to work based on knowledge
Concept Questions: 8.1–8.4 Problems: 1.1–1.17 Virtual Office Hours for Solved Problems: 1.1, 1.2
CURRENT CHALLENGES IN OPERATIONS MANAGEMENT (pp. 18 – 19 )
Some of the current challenges for operations managers include: j Global focus; international collaboration j Supply chain partnering; joint ventures; alliances j Sustainability; green products; recycle, reuse j Rapid product development; design collaboration j Mass customization; customized products j Lean operations; continuous improvement and elimination of waste
Concept Questions: 9.1–9.4
ETHICS, SOCIAL RESPONSIBILITY, AND SUSTAINABILITY (p. 19 )
Among the many ethical challenges facing operations managers are (1) efficiently developing and producing safe, quality products; (2) maintaining a clean environ- ment; (3) providing a safe workplace; and (4) honoring stakeholder commitments. j Stakeholders —Those with a vested interest in an organization
Concept Question: 10.1
1 R
ap id
R ev
ie w
Chapter 1 Rapid Review continued
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 1.1 Productivity increases when: a) inputs increase while outputs remain the same. b) inputs decrease while outputs remain the same. c) outputs decrease while inputs remain the same. d) inputs and outputs increase proportionately. e) inputs increase at the same rate as outputs. LO 1.2 Services often: a) are tangible. b) are standardized. c) are knowledge based. d) are low in customer interaction. e) have consistent product definition. LO 1.3 Productivity: a) can use many factors as the numerator. b) is the same thing as production. c) increases at about 0.5% per year. d) is dependent upon labor, management, and
capital. e) is the same thing as effectiveness.
LO 1.4 Single-factor productivity: a) remains constant. b) is never constant. c) usually uses labor as a factor. d) seldom uses labor as a factor. e) uses management as a factor. LO 1.5 Multifactor productivity: a) remains constant. b) is never constant. c) usually uses substitutes as common variables for the
factors of production. d) seldom uses labor as a factor. e) always uses management as a factor. LO 1.6 Productivity increases each year in the U.S. are a result of
three factors: a) labor, capital, management b) engineering, labor, capital c) engineering, capital, quality control d) engineering, labor, data processing e) engineering, capital, data processing
Answers: LO 1.1. b; LO 1.2. c; LO 1.3. d; LO 1.4. c; LO 1.5. c; LO 1.6. a.
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29
C H A P T E R O U T L I N E
2 ◆
A Global View of Operations and Supply Chains 32
◆
Developing Missions and Strategies 35
◆
Achieving Competitive Advantage Through Operations 36
◆
Issues in Operations Strategy 40
◆
Strategy Development and Implementation 41
◆
Strategic Planning, Core Competencies, and Outsourcing 44
◆
Global Operations Strategy Options 49
GLOBAL COMPANY PROFILE: Boeing
C H
A P
T E
R
Operations Strategy in a Global Environment
A la
sk a A
ir lin
e s
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B oeing’s strategy for its 787 Dreamliner is unique for its technologically advanced product de-
sign and vast global supply chain.
The Dreamliner incorporates the latest in a wide range of aerospace technologies, from
airframe and engine design to super-lightweight titanium-graphite laminate and carbon-fiber
composites. The electronic monitoring system that allows the airplane to report maintenance
Boeing’s Global Supply-Chain Strategy Yields Competitive Advantage
C H A P T E R 2
30
GLOBAL COMPANY PROFILE Boeing
requirements in real time to ground-based
computer systems is another product innova-
tion. Boeing’s collaboration with General Electric
and Rolls-Royce has resulted in the develop-
ment of more efficient engines and an emis-
sions reduction of 20%. The advances in engine
technology contribute as much as 8% of the
increased fuel/payload efficiency of the new
airplane, representing a nearly two-generation
jump in technology.
Boeing’s design group at its Everett,
Washington, facility led an international team of
aerospace companies in development of this
state-of-the-art plane. Technologically advanced
design, new manufacturing processes, and
a committed international supply chain have
helped Boeing and its partners achieve unprec-
edented levels of performance in design and
manufacture.
With the 787’s state-of-the-art design, more spacious interior, and global suppliers, Boeing has garnered record sales worldwide.
P e te
r C
a re
y/ A
la m
y
D a n L
a m
o n t/
A la
m y
Some of the International Suppliers of Boeing 787 Components
SUPPLIER HQ COUNTRY COMPONENT
Latecoere France Passenger doors Labinel France Wiring Dassault France Design and product life cycle
management software Messier-Bugatti France Electric brakes Thales France Electrical power conversion system Messier-Dowty France Landing gear structure Diehl Germany Interior lighting Cobham UK Fuel pumps and valves Rolls-Royce UK Engines Smiths Aerospace UK Central computer system BAE Systems UK Electronics Alenia Aeronautica Italy Upper center fuselage Toray Industries Japan Carbon fi ber for wing and tail units Fuji Heavy Industries Japan Center wing box Kawasaki Heavy Ind. Japan Forward fuselage, fi xed sections of wing Teijin Seiki Japan Hydraulic actuators Mitsubishi Heavy Ind. Japan Wing box Chengdu Aircraft China Rudder Hafei Aviation China Parts Korean Airlines South Korea Wingtips Saab Sweden Cargo and access doors
d d l ld idd
D a n
L a m
o n t/
A la
m y
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31
The 787 is global not only because it has a range of
8,300 miles, but also because it is built all over the world.
With a huge financial commitment of over $5 billion, Boeing
needed partners. The global nature of both the technology
and the aircraft market meant finding exceptional engineering
talent and suppliers, wherever they might be. It also meant
developing a culture of collaboration and integration with
firms willing to step up to the risk associated with this revolu-
tionary and very expensive new product.
State-of-the-art technology, multinational aircraft certi-
fications, the cross-culture nature of the communications,
and logistical challenges all added to the supply chain risk.
In the end, Boeing accepted the challenge of teaming with
more than 300 suppliers in over a dozen countries. Twenty
of these suppliers developed technologies, design concepts,
and major systems for the 787. Some of them are shown
in the table. The partners brought commitment to the table.
The expectation is that countries that have a stake in the
Dreamliner are more likely to buy from Boeing than from its
European competitor, Airbus.
Japanese companies are producing over 35% of the
project, and Italy’s Alenia Aeronautica is building an addi-
tional 10% of the plane.
The innovative Dreamliner, with its global range and
worldwide supply chain, is setting new levels of operational
efficiency. As a result, it is the fastest-selling commercial jet
in history with over 1,100 planes sold. Boeing’s Dreamliner
reflects the global nature of business in the 21st century.
State-of-the-art composite sections of the 787 are built around the world and
shipped to Boeing for final assembly.
Components from Boeing’s worldwide supply chain come together on assembly
lines in Everett, Washington, and Charleston, South Carolina. Although
components come from throughout the world, about 35% of the 787 structure
comes from Japanese companies.
Boeing’s collaborative technology enables
a “virtual workspace” that allows Everett,
Washington-based engineers, as well as
partners in Australia, Japan, Italy, Canada, and
across the United States, to make concurrent
design changes to the airplane in real time.
Digitally designing, building, and testing before
production not only reduces design time
and errors, but also improves efficiencies in
component manufacturing and assembly. C o p yr
ig h t
B o e in
g
T im
K e lly
/R e u te
rs
C o p yr
ig h t
B o e in
g
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32
A Global View of Operations and Supply Chains Today’s successful operations manager has a global view of operations strategy. Since the early 1990s, nearly 3 billion people in developing countries have overcome the cultural, reli- gious, ethnic, and political barriers that constrain productivity. And now they are all play- ers on the global economic stage. As these barriers disappear, simultaneous advances are being made in technology, reliable shipping, and inexpensive communication. These changes mean that, increasingly, firms find their customers and suppliers located around the world. The unsurprising result is the growth of world trade (see Figure 2.1 ), global capital markets, and the international movement of people. This means increasing economic integration and interdependence of countries—in a word, globalization. In response, organizations are hastily extending their distribution channels and supply chains globally. The result is innovative strat- egies where firms compete not just with their own expertise but with the talent in their entire global supply chain. For instance:
◆ Boeing is competitive because both its sales and supply chain are worldwide. ◆ Italy’s Benetton moves inventory to stores around the world faster than its competition
with rapid communication and by building exceptional flexibility into design, production, and distribution.
◆ Sony purchases components from a supply chain that extends to Thailand, Malaysia, and elsewhere around the world for assembly of its electronic products, which in turn are dis- tributed around the world.
◆ Volvo, considered a Swedish company, was purchased by a Chinese company, Geely. But the current Volvo S40 is assembled in Belgium, South Africa, Malaysia, and China, on a platform shared with the Mazda 3 (built in Japan) and the Ford Focus (built in Europe).
◆ China’s Haier (pronounced “higher”) is now producing compact refrigerators (it has one- third of the U.S. market) and refrigerated wine cabinets (it has half of the U.S. market) in South Carolina.
L E A R N I N G OBJEC TI V ES
LO 2.1 Defi ne mission and strategy 36
LO 2.2 Identify and explain three strategic approaches to competitive advantage 36
LO 2.3 Understand the signifi cance of key success factors and core competencies 42
LO 2.4 Use factor rating to evaluate both country and outsource providers 47
LO 2.5 Identify and explain four global operations strategy options 49
0
10
20
30
40
50
60
70
1970 1980 1990 2000 2010 2020
W o
rl d
t ra
d e a
s a
% o
f w
o rl
d G
D P
Year
Figure 2.1
Growth of World Trade as
a Percent of World GDP
Sources: World Bank; World Trade
Organization; and IMF.
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C H A P T E R 2 | O P E R AT I O N S S T R AT E G Y I N A G L O B A L E N V I R O N M E N T 33
Globalization means customers, talent, and suppliers are worldwide. The new standards of global competitiveness impact quality, variety, customization, convenience, timeliness, and cost. Globalization strategies contribute efficiency, adding value to products and services, but they also complicate the operations manager’s job. Complexity, risk, and competition are in- tensified, forcing companies to adjust for a shrinking world.
We have identified six reasons domestic business operations decide to change to some form of international operation. They are:
1. Improve the supply chain. 2. Reduce costs and exchange rate risk. 3. Improve operations. 4. Understand markets. 5. Improve products. 6. Attract and retain global talent.
Let us examine, in turn, each of the six reasons.
Improve the Supply Chain The supply chain can often be improved by locating facili- ties in countries where unique resources are available. These resources may be human resource expertise, low-cost labor, or raw material. For example, auto-styling studios from throughout the world have migrated to the auto mecca of southern California to ensure the necessary ex- pertise in contemporary auto design. Similarly, world athletic shoe production has migrated from South Korea to Guangzhou, China; this location takes advantage of the low-cost labor and production competence in a city where 40,000 people work making athletic shoes for the world. And a perfume manufacturer wants a presence in Grasse, France, where much of the world’s perfume essences are prepared from the flowers of the Mediterranean.
Reduce Costs and Exchange Rate Risk Many international operations seek to reduce risks associated with changing currency values (exchange rates) as well as take advan- tage of the tangible opportunities to reduce their direct costs. (See the OM in Action box “U.S. Cartoon Production at Home in Manila.”) Less stringent government regulations on a wide variety of operations practices (e.g., environmental control, health and safety) can also reduce indirect costs.
Shifting low-skilled jobs to another country has several potential advantages. First, and most obviously, the firm may reduce costs. Second, moving the lower-skilled jobs to a lower- cost location frees higher-cost workers for more valuable tasks. Third, reducing wage costs allows the savings to be invested in improved products and facilities (and the retraining of existing workers, if necessary) at the home location. Finally, having facilities in countries with different currencies can allow firms to finesse currency risk (and related costs) as economic conditions dictate.
U.S. Cartoon Production at Home in Manila
Fred Flintstone is not from Bedrock. He is actually from Manila, capital of
the Philippines. So are Tom and Jerry, Aladdin, and Donald Duck. More
than 90% of American television cartoons are produced in Asia and India,
with the Philippines leading the way. With their natural advantage of English
as an official language and a strong familiarity with U.S. culture, animation
companies in Manila now employ more than 1,700 people. Filipinos under-
stand Western culture, and “you need to have a group of artists that can
understand the humor that goes with it,” says Bill Dennis, a Hanna-Barbera
executive.
Major studios like Disney, Marvel, Warner Brothers, and Hanna-Barbera
send storyboards —cartoon action outlines—and voice tracks to the Philippines.
OM in Action Artists there draw, paint, and film about
20,000 sketches for a 30-minute episode.
The cost of $130,000 to produce an episode
in the Philippines compares with $160,000 in
Korea and $500,000 in the United States.
a rt
is ti cc
o /F
o to
lia
Sources: Animation Insider (March
30, 2011); The New York Times
(February 26, 2004): and The Wall
Street Journal (August 9, 2005).
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34 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
The United States and Mexico have created maquiladoras (free trade zones) that allow manu- facturers to cut their costs by paying only for the value added by Mexican workers. If a U.S. manufacturer, such as Caterpillar, brings a $1,000 engine to a maquiladora operation for as- sembly work costing $200, tariff duties will be charged only on the $200 of work performed in Mexico.
Trade agreements also help reduce tariffs and thereby reduce the cost of operating facili- ties in foreign countries. The World Trade Organization (WTO) has helped reduce tariffs from 40% in 1940 to less than 3% today. Another important trade agreement is the North American Free Trade Agreement (NAFTA) . NAFTA seeks to phase out all trade and tariff barriers among Canada, Mexico, and the U.S. Other trade agreements that are accelerating global trade include APEC (the Pacific Rim countries), SEATO (Australia, New Zealand, Japan, Hong Kong, South Ko- rea, New Guinea, and Chile), MERCOSUR (Argentina, Brazil, Paraguay, and Uruguay), and CAFTA (Central America, Dominican Republic, and United States).
Another trading group is the European Union (EU) . 1 The European Union has reduced trade barriers among the participating European nations through standardization and a common currency, the euro. However, this major U.S. trading partner, with over 500 million people, is also placing some of the world’s most restrictive conditions on products sold in the EU. Every- thing from recycling standards to automobile bumpers to hormone-free farm products must meet EU standards, complicating international trade.
Improve Operations Operations learn from better understanding of management innovations in different countries. For instance, the Japanese have improved inventory man- agement, the Germans are aggressively using robots, and the Scandinavians have contributed to improved ergonomics throughout the world.
Another reason to have international operations is to reduce response time to meet custom- ers’ changing product and service requirements. Customers who purchase goods and services from U.S. firms are increasingly located in foreign countries. Providing them with quick and adequate service is often improved by locating facilities in their home countries.
Understand Markets Because international operations require interaction with for- eign customers, suppliers, and other competitive businesses, international firms inevitably learn about opportunities for new products and services. Europe led the way with cell phone innovations, and then the Japanese and Indians led with cell phone fads. Knowl- edge of markets not only helps firms understand where the market is going but also helps firms diversify their customer base, add production flexibility, and smooth the business cycle.
Another reason to go into foreign markets is the opportunity to expand the life cycle (i.e., stages a product goes through; see Chapter 5 ) of an existing product. While some products in the U.S. are in a “mature” stage of their product life cycle, they may represent state-of-the-art products in less-developed countries.
Improve Products Learning does not take place in isolation. Firms serve themselves and their customers well when they remain open to the free flow of ideas. For example, Toy- ota and BMW will manage joint research and share development costs on battery research for the next generation of green cars. Their relationship also provides Toyota with BMW’s highly regarded diesel engines for its European market, where diesel-powered vehicles make up more than half of the market. The payoff is reduced risk in battery development for both, a state-of-the-art diesel engine for Toyota in Europe, and lower per-unit diesel engine cost for BMW. Similarly, international learning in operations is taking place as South Korea’s Samsung and Germany’s Robert Bosch join to produce lithium-ion batteries to the benefit of both.
Attract and Retain Global Talent Global organizations can attract and retain better employees by offering more employment opportunities. They need people in all functional areas and areas of expertise worldwide. Global firms can recruit and retain good employees because they provide both greater growth opportunities and insulation against
Maquiladoras
Mexican factories located along
the U.S.–Mexico border that re-
ceive preferential tariff treatment.
World Trade Organization (WTO)
An international organization that
promotes world trade by lowering
barriers to the free flow of goods
across borders.
North American Free Trade Agreement (NAFTA)
A free trade agreement between
Canada, Mexico, and the United
States.
European Union (EU)
A European trade group that has
28 member states.
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C H A P T E R 2 | O P E R AT I O N S S T R AT E G Y I N A G L O B A L E N V I R O N M E N T 35
unemployment during times of economic downturn. During economic downturns in one country or continent, a global firm has the means to relocate unneeded personnel to more prosperous locations.
So, to recap, successfully achieving a competitive advantage in our shrinking world means maximizing all the possible opportunities, from tangible to intangible, that international opera- tions can offer.
Cultural and Ethical Issues While there are great forces driving firms toward globalization, many challenges remain. One of these challenges is reconciling differences in social and cultural behavior. With issues rang- ing from bribery, to child labor, to the environment, managers sometimes do not know how to respond when operating in a different culture. What one country’s culture deems acceptable may be considered unacceptable or illegal in another. It is not by chance that there are fewer female managers in the Middle East than in India.
In the last decade, changes in international laws, agreements, and codes of conduct have been applied to define ethical behavior among managers around the world. The WTO, for ex- ample, helps to make uniform the protection of both governments and industries from foreign firms that engage in unethical conduct. Even on issues where significant differences between cultures exist, as in the area of bribery or the protection of intellectual property, global unifor- mity is slowly being accepted by most nations.
Despite cultural and ethical differences, we live in a period of extraordinary mobility of capital, information, goods, and even people. We can expect this to continue. The financial sector, the telecommunications sector, and the logistics infrastructure of the world are healthy institutions that foster efficient and effective use of capital, information, and goods. Globaliza- tion, with all its opportunities and risks, is here. It must be embraced as managers develop their missions and strategies.
Developing Missions and Strategies An effective operations management effort must have a mission so it knows where it is going and a strategy so it knows how to get there. This is the case for a small domestic organization as well as a large international organization.
STUDENT TIP Getting an education and managing
an organization both require a
mission and strategy.
A worldwide strategy places added
burdens on operations management.
Because of economic and lifestyle
differences, designers must target
products to each market. For instance,
clothes washers sold in northern
countries must spin-dry clothes much
better than those in warmer climates,
where consumers are likely to line-dry
them. Similarly, as shown here, Whirlpool
refrigerators sold in Bangkok are
manufactured in bright colors because
they are often put in living rooms.
K ra
ip it P
h a n vu
t/ S ip
a P
re ss
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36 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
Mission Economic success, indeed survival, is the result of identifying missions to satisfy a customer’s needs and wants. We define the organization’s mission as its purpose—what it will contribute to society. Mission statements provide boundaries and focus for organizations and the con- cept around which the firm can rally. The mission states the rationale for the organization’s existence. Developing a good strategy is difficult, but it is much easier if the mission has been well defined. Figure 2.2 provides examples of mission statements.
Once an organization’s mission has been decided, each functional area within the firm de- termines its supporting mission. By functional area we mean the major disciplines required by the firm, such as marketing, finance/accounting, and production/operations. Missions for each function are developed to support the firm’s overall mission. Then within that function lower-level supporting missions are established for the OM functions. Figure 2.3 provides such a hierarchy of sample missions.
Strategy With the mission established, strategy and its implementation can begin. Strategy is an organi- zation’s action plan to achieve the mission. Each functional area has a strategy for achieving its mission and for helping the organization reach the overall mission. These strategies exploit opportunities and strengths, neutralize threats, and avoid weaknesses. In the following sec- tions, we will describe how strategies are developed and implemented.
Firms achieve missions in three conceptual ways: (1) differentiation, (2) cost leadership, and (3) response. This means operations managers are called on to deliver goods and services that are (1) better , or at least different, (2) cheaper , and (3) more responsive . Operations manag- ers translate these strategic concepts into tangible tasks to be accomplished. Any one or com- bination of these three strategic concepts can generate a system that has a unique advantage over competitors.
Achieving Competitive Advantage Through Operations Each of the three strategies provides an opportunity for operations managers to achieve com- petitive advantage. Competitive advantage implies the creation of a system that has a unique advantage over competitors. The idea is to create customer value in an efficient and sustain- able way. Pure forms of these strategies may exist, but operations managers will more likely
Mission
The purpose or rationale for an
organization’s existence.
LO 2.1 Define mission and strategy
Strategy
How an organization expects to
achieve its missions and goals.
LO 2.2 Identify and explain three
strategic approaches to
competitive advantage
VIDEO 2.1 Operations Strategy at Regal Marine
Competitive advantage
The creation of a unique advan-
tage over competitors.
Merck
The mission of Merck is to provide society with superior products and services—innova- tions and solutions that improve the quality of life and satisfy customer needs—to provide employees with meaningful work and advancement opportunities and investors with a superior rate of return.
PepsiCo
Our mission is to be the world's premier consumer products company focused on convenient foods and beverages. We seek to produce financial rewards to investors as we provide opportunities for growth and enrichment to our employees, our business partners and the communities in which we operate. And in everything we do, we strive for honesty, fairness and integrity.
Arnold Palmer Hospital
Arnold Palmer Hospital for Children provides state of the art, family-centered healthcare focused on restoring the joy of childhood in an environment of compassion, healing, and hope.
Figure 2.2
Mission Statements for Three
Organizations
Source: Mission statement from Merck.
Copyright © by Merck & Co., Inc.
Reprinted with permission.
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be called on to implement some combination of them. Let us briefly look at how managers achieve competitive advantage via differentiation , low cost , and response .
Competing on Differentiation Safeskin Corporation is number one in latex exam gloves because it has differentiated itself and its products. It did so by producing gloves that were designed to prevent allergic reac- tions about which doctors were complaining. When other glove makers caught up, Safeskin developed hypoallergenic gloves. Then it added texture to its gloves. Then it developed a synthetic disposable glove for those allergic to latex—always staying ahead of the compe- tition. Safeskin’s strategy is to develop a reputation for designing and producing reliable state-of-the-art gloves, thereby differentiating itself.
Differentiation is concerned with providing uniqueness . A firm’s opportunities for creating uniqueness are not located within a particular function or activity but can arise in virtually ev- erything the firm does. Moreover, because most products include some service, and most services
STUDENT TIP For many organizations, the
operations function provides the
competitive advantage.
Sample Company Mission
Sample OM Department Missions
To manufacture and service an innovative, growing, and profitable worldwide microwave communications business that exceeds our customers’ expectations.
Sample Operations Management Mission
To produce products consistent with the company’s mission as the worldwide low-cost manufacturer.
Process design To determine, design, and develop the production process and equipment that will be compatible with low-cost product, high quality, and a good quality of work life.
Location To locate, design, and build efficient and economical facilities that will yield high value to the company, its employees, and the community.
Layout design To achieve, through skill, imagination, and resourcefulness in layout and work methods, production effectiveness and efficiency while supporting a high quality of work life.
Human resources To provide a good quality of work life, with well-designed, safe, rewarding jobs, stable employment, and equitable pay, in exchange for outstanding individual contribution from employees at all levels.
Supply-chain management To collaborate with suppliers to develop innovative products from stable, effective, and efficient sources of supply.
Inventory To achieve low investment in inventory consistent with high customer service levels and high facility utilization.
Scheduling To achieve high levels of throughput and timely customer delivery through effective scheduling.
Maintenance To achieve high utilization of facilities and equipment by effective preventive maintenance and prompt repair of facilities and equipment.
quality and inherent customer value.
Quality management To attain the exceptional value that is consistent with our company mission and marketing objectives by close attention to design, supply chain, production, and field service opportunities.
Product design To design and produce products and services with outstanding
Figure 2.3
Sample Missions for a
Company, the Operations
Function, and Major OM
Departments
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include some product, the opportunities for creating this uniqueness are limited only by imagina- tion. Indeed, differentiation should be thought of as going beyond both physical characteristics and service attributes to encompass everything about the product or service that influences the value that the customers derive from it. Therefore, effective operations managers assist in defin- ing everything about a product or service that will influence the potential value to the customer. This may be the convenience of a broad product line, product features, or a service related to the product. Such services can manifest themselves through convenience (location of distribution centers, stores, or branches), training, product delivery and installation, or repair and mainte- nance services.
In the service sector, one option for extending product differentiation is through an expe- rience . Differentiation by experience in services is a manifestation of the growing “experience economy.” The idea of experience differentiation is to engage the customer—to use people’s five senses so they become immersed, or even an active participant, in the product. Disney does this with the Magic Kingdom. People no longer just go on a ride; they are immersed in the Magic Kingdom—surrounded by dynamic visual and sound experiences that complement the physical ride. Some rides further engage the customer with changing air flow and smells, as well as having them steer the ride or shoot at targets or villains. Even movie theaters are moving in this direction with surround sound, moving seats, changing “smells,” and mists of “rain,” as well as multimedia inputs to story development.
Theme restaurants, such as Hard Rock Cafe, likewise differentiate themselves by provid- ing an “experience.” Hard Rock engages the customer with classic rock music, big-screen rock videos, memorabilia, and staff who can tell stories. In many instances, a full-time guide is available to explain the displays, and there is always a convenient retail store so the guest can take home a tangible part of the experience. The result is a “dining experience” rather than just a meal. In a less dramatic way, both Starbucks and your local supermarket deliver an experience when they provide music and the aroma of fresh coffee or freshly baked bread.
Competing on Cost Southwest Airlines has been a consistent moneymaker while other U.S. airlines have lost bil- lions. Southwest has done this by fulfilling a need for low-cost and short-hop flights. Its opera- tions strategy has included use of secondary airports and terminals, first-come, first-served seating, few fare options, smaller crews flying more hours, snacks-only or no-meal flights, and no downtown ticket offices.
In addition, and less obviously, Southwest has very effectively matched capacity to demand and effectively utilized this capacity. It has done this by designing a route struc- ture that matches the capacity of its Boeing 737, the only plane in its fleet. Second, it achieves more air miles than other airlines through faster turnarounds—its planes are on the ground less.
One driver of a low-cost strategy is a facility that is effectively utilized. Southwest and oth- ers with low-cost strategies understand this and use financial resources effectively. Identifying the optimum size (and investment) allows firms to spread overhead costs, providing a cost advantage. For instance, Walmart continues to pursue its low-cost strategy with superstores, open 24 hours a day. For 20 years, it has successfully grabbed market share. Walmart has driven down store overhead costs, shrinkage, and distribution costs. Its rapid transportation of goods, reduced warehousing costs, and direct shipment from manufacturers have resulted in high inventory turnover and made it a low-cost leader.
Likewise, Franz Colruyt, a Belgian discount food retailer, is also an aggressive cost cutter. Colruyt cuts overhead by using converted factory warehouses, movie theaters, and garages as outlets. Customers find no background music, shopping bags, or bright lights: all have been eliminated to cut costs. Walmart and Colruyt are winning with a low-cost strategy.
Low-cost leadership entails achieving maximum value as defined by your customer. It re- quires examining each of the 10 OM decisions in a relentless effort to drive down costs while meeting customer expectations of value. A low-cost strategy does not imply low value or low quality.
Differentiation
Distinguishing the offerings of
an organization in a way that the
customer perceives as adding
value.
Experience differentiation
Engaging a customer with a
product through imaginative use of
the five senses, so the customer
“experiences” the product.
VIDEO 2.2 Hard Rock’s Global Strategy
Low-cost leadership
Achieving maximum value, as
perceived by the customer.
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Competing on Response The third strategy option is response. Response is often thought of as flexible response, but it also refers to reliable and quick response. Indeed, we define response as including the entire range of values related to timely product development and delivery, as well as reliable schedul- ing and flexible performance.
Flexible response may be thought of as the ability to match changes in a marketplace where design innovations and volumes fluctuate substantially.
Hewlett-Packard is an exceptional example of a firm that has demonstrated flexibility in both design and volume changes in the volatile world of personal computers. HP’s products often have a life cycle of months, and volume and cost changes during that brief life cycle are dramatic. However, HP has been successful at institutionalizing the ability to change products and volume to respond to dramatic changes in product design and costs—thus building a sus- tainable competitive advantage .
The second aspect of response is the reliability of scheduling. One way the German ma- chine industry has maintained its competitiveness despite having the world’s highest labor costs is through reliable response. This response manifests itself in reliable scheduling. German ma- chine firms have meaningful schedules—and they perform to these schedules. Moreover, the results of these schedules are communicated to the customer, and the customer can, in turn, rely on them. Consequently, the competitive advantage generated through reliable response has value to the end customer.
The third aspect of response is quickness . Johnson Electric Holdings, Ltd., with headquar- ters in Hong Kong, makes 83 million tiny motors each month. The motors go in cordless tools, household appliances, and personal care items such as hair dryers; dozens are found in each automobile. Johnson’s major competitive advantage is speed: speed in product development, speed in production, and speed in delivery.
Whether it is a production system at Johnson Electric or a pizza delivered in 5 minutes by Pizza Hut, the operations manager who develops systems that respond quickly can have a competitive advantage.
In practice, differentiation, low cost, and response can increase productivity and generate a sustainable competitive advantage. Proper implementation of the ten decisions by operations managers (see Figure 2.4 ) will allow these advantages to be achieved.
Response
A set of values related to rapid,
flexible, and reliable performance.
10 Operations Decisions Strategy Example
Competitive Advantage
Product
Quality
Process
Location
Layout
Human resources
Supply chain
Inventory
Scheduling
Maintenance
Innovative design . . . . . . . . . . . . . . . . . . . . . . . Safeskin’s innovative gloves Broad product line . . . . . . . . . . . . . . . . . . . . .Fidelity Security’s mutual funds After-sales service . . . . . . . . . . . . . . . . Caterpillar’s heavy equipment service Experience . . . . . . . . . . . . . . . . . . . . . . . . . Hard Rock Cafe’s dining experience
COST LEADERSHIP: Low overhead . . . . . . . . . . . . . . . . . . . . . Franz-Colruyt’s warehouse-type stores
Effective capacity use . . . . . . . . . . . . Southwest Airlines' high aircraft utilization Inventory management . . . . . . . . . . Walmart's sophisticated distribution system
RESPONSE: Flexibility . . . . . . . . . . . . . Hewlett-Packard’s response to volatile world market
Reliability . . . . . . . . . . . . . . . . . . . . . . . FedEx’s “absolutely, positively on time” Quickness . . . . . . . . . . . . . Pizza Hut’s five-minute guarantee at lunchtime
DIFFERENTIATION:
Differentiation (better)
Cost leadership (cheaper)
Response (faster)
Figure 2.4
Achieving Competitive Advantage Through Operations
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Issues in Operations Strategy Whether the OM strategy is differentiation, cost, or response (as shown in Figure 2.4 ), OM is a critical player. Therefore, prior to establishing and attempting to implement a strategy, some alternate perspectives may be helpful. One perspective is to take a resources view . This means thinking in terms of the financial, physical, human, and technological resources avail- able and ensuring that the potential strategy is compatible with those resources. Another per- spective is Porter’s value-chain analysis. 2 Value-chain analysis is used to identify activities that represent strengths, or potential strengths, and may be opportunities for developing competi- tive advantage. These are areas where the firm adds its unique value through product research, design, human resources, supply-chain management, process innovation, or quality manage- ment. Porter also suggests analysis of competitors via what he calls his five forces model . 3 These potential competing forces are immediate rivals, potential entrants, customers, suppliers, and substitute products.
In addition to the competitive environment, the operations manager needs to understand that the firm is operating in a system with many other external factors. These factors range from economic, to legal, to cultural. They influence strategy development and execution and require constant scanning of the environment.
The firm itself is also undergoing constant change. Everything from resources, to tech- nology, to product life cycles is in flux. Consider the significant changes required within the firm as its products move from introduction, to growth, to maturity, and to decline (see Figure 2.5 ). These internal changes, combined with external changes, require strategies that are dynamic.
In this chapter’s Global Company Profile , Boeing provides an example of how strategy must change as technology and the environment change. Boeing can now build planes from carbon fiber, using a global supply chain. Like many other OM strategies, Boeing’s strategy has changed with technology and globalization. Microsoft has also had to adapt quickly to a changing environment. Faster processors, new computer languages, changing customer pref- erences, increased security issues, the Internet, the cloud, and Google have all driven changes at Microsoft. These forces have moved Microsoft’s product strategy from operating systems to office products, to Internet service provider, and now to integrator of computers, cell phones, games, and television via the cloud.
The more thorough the analysis and understanding of both the external and internal fac- tors, the more likely that a firm can find the optimum use of its resources. Once a firm under- stands itself and the environment, a SWOT analysis, which we discuss next, is in order.
Resources view
A method managers use to
evaluate the resources at their
disposal and manage or alter them
to achieve competitive advantage.
Value-chain analysis
A way to identify those elements
in the product/service chain that
uniquely add value.
Five forces model
A method of analyzing the
five forces in the competitive
environment.
Response strategy wins orders at Super Fast Pizza.
Using a wireless connection, orders are transmitted
to $20,000 kitchens in vans. The driver, who works
solo, receives a printed order, goes to the kitchen area,
pulls premade pizzas from the cooler, and places them
in the oven—it takes about 1 minute. The driver then
delivers the pizza—sometimes even arriving before the
pizza is ready.
D a rr
e n H
a u ck
/A P I m
a g e s
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Strategy Development and Implementation A SWOT analysis is a formal review of internal strengths and weaknesses and external opportuni- ties and threats. Beginning with SWOT analyses, organizations position themselves, through their strategy, to have a competitive advantage. A firm may have excellent design skills or great talent at identifying outstanding locations. However, it may recognize limitations of its manufacturing process or in finding good suppliers. The idea is to maximize opportunities and minimize threats in the environment while maximizing the advantages of the organization’s strengths and minimizing the weaknesses. Any preconceived ideas about mission are then reevaluated to ensure they are consistent with the SWOT analysis. Subsequently, a strategy for achieving the mission is developed. This strategy is continually evaluated against the value provided customers and competitive realities. The process is shown in Figure 2.6 . From this process, key success factors are identified.
Key Success Factors and Core Competencies Because no firm does everything exceptionally well, a successful strategy requires determining the firm’s key success factors and core competencies. Key success factors (KSFs) are those activi- ties that are necessary for a firm to achieve its goals. Key success factors can be so significant
STUDENT TIP A SWOT analysis provides an
excellent model for evaluating a
strategy.
SWOT analysis
A method of determining internal
strengths and weaknesses and
external opportunities and threats.
Key success factors (KSFs)
Activities or factors that are key to
achieving competitive advantage.
Product design and development critical
Frequent product and process design changes
Short production runs High production costs
Limited models
Attention to quality
Practical to change price or quality image
Strengthen niche
Poor time to change image, price, or quality
Competitive costs become critical
Defend market position
Cost control critical
Forecasting critical
Product and process reliability
Competitive product improvements and options
Increase capacity
Shift toward product focus
Enhance distribution
Standardization
Fewer rapid product changes, more minor changes
Optimum capacity
Increasing stability of process
Long production runs
Product improvement and cost cutting
Little product differentiation
Cost minimization
Overcapacity in the industry
Prune line to eliminate items not returning good margin
Reduce capacity
Best period to increase market share
R&D engineering is critical
Introduction Growth DeclineMaturity O
M S
tr a te
g y / I s s
u e s
C o
m p
a n
y S
tr a te
g y / I s s u
e s
Life Cycle Curve
Laptop computersHybrid engine vehicles
Video physical rentals
DVDs
Electric vehicles
3-D game players Apple SmartWatch
Xbox One Boeing 787
3D printers
Figure 2.5
Strategy and Issues During a Product’s Life
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that a firm must get them right to survive. A KSF for McDonald’s, for example, is layout. Without an effective drive-through and an efficient kitchen, McDonald’s cannot be successful. KSFs are often necessary, but not sufficient for competitive advantage. On the other hand, core competencies are the set of unique skills, talents, and capabilities that a firm does at a world-class standard. They allow a firm to set itself apart and develop a competitive advan- tage. Organizations that prosper identify their core competencies and nurture them. While McDonald’s KSFs may include layout, its core competency may be consistency and quality. Honda Motors’ core competence is gas-powered engines—engines for automobiles, motorcy- cles, lawn mowers, generators, snow blowers, and more. The idea is to build KSFs and core competencies that provide a competitive advantage and support a successful strategy and mis- sion. A core competency may be the ability to perform the KSFs or a combination of KSFs. The operations manager begins this inquiry by asking:
◆ “What tasks must be done particularly well for a given strategy to succeed?” ◆ “Which activities provide a competitive advantage?” ◆ “Which elements contain the highest likelihood of failure, and which require additional
commitment of managerial, monetary, technological, and human resources?”
Only by identifying and strengthening key success factors and core competencies can an organization achieve sustainable competitive advantage. In this text we focus on the 10 strate- gic OM decisions that typically include the KSFs. These decisions, plus major decision areas for marketing and finance, are shown in Figure 2.7 .
Core competencies
A set of skills, talents, and
capabilities in which a firm is
particularly strong.
LO 2.3 Understand the significance of key
success factors and core
competencies
Analyze the Environment
Determine the Corporate Mission
State the reason for the firm’s existence and identify the value it wishes to create.
Form a Strategy Build a competitive advantage, such as low price, design or volume flexibility, quality, quick delivery, dependability, after-sale services, or broad product lines.
Identify strengths, weaknesses, opportunities, and threats. Understand the environment, customers, industry, and competitors.
Figure 2.6
Strategy Development Process
Honda’s core competence
is the design and
manufacture of gas-
powered engines. This
competence has allowed
Honda to become a
leader in the design and
manufacture of a wide
range of gas-powered
products. Tens of millions
of these products are
produced and shipped
around the world.
Ju lie
L u ch
t/ S
h u tt
e rs
to ck
A m
e ri ca
n H
o n d a
M o to
r C
o ., I n c.
C o u rt
e sy
o f
w w
w .
H o n d a N
e w
s. co
m
A m
e ri ca
n H
o n d a
M o to
r C
o ., I n c.
A m
e ri ca
n H
o n d a
M o to
r C
o ., I n c.
A m
e ri ca
n H
o n d a
M o to
r C
o .,
I n c.
A m
e ri ca
n H
o n d a
M o to
r C
o .,
I n c.
A m
e ri ca
n H
o n d a
M o to
r C
o .,
I n c.
Automobiles Generators Motorcycles Water Pumps
Snow Blowers4-Wheel Scooters Race Cars
Marine Motors
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Integrating OM with Other Activities Whatever the KSFs and core competencies, they must be supported by the related activities. One approach to identifying the activities is an activity map , which links competitive advan- tage, KSFs, and supporting activities. For example, Figure 2.8 shows how Southwest Airlines, whose core competency is operations, built a set of integrated activities to support its low- cost competitive advantage. Notice how the KSFs support operations and in turn are sup- ported by other activities. The activities fit together and reinforce each other. In this way, all of the areas support the company’s objectives. For example, short-term scheduling in the airline industry is dominated by volatile customer travel patterns. Day-of-week preference, holidays, seasonality, college schedules, and so on all play roles in changing flight schedules. Consequently, airline scheduling, although an OM activity, is tied to marketing. Effective scheduling in the trucking industry is reflected in the amount of time trucks travel loaded. But maximizing the time trucks travel loaded requires the integration of information from deliver- ies completed, pickups pending, driver availability, truck maintenance, and customer priority. Success requires integration of all of these activities.
The better the activities are integrated and reinforce each other, the more sustain- able the competitive advantage. By focusing on enhancing its core competence and KSFs with a supporting set of activities, firms such as Southwest Airlines have built successful strategies.
Building and Staffing the Organization Once a strategy, KSFs, and the necessary integration have been identified, the second step is to group the necessary activities into an organizational structure. Then, managers must staff the organization with personnel who will get the job done. The manager works with subordinate managers to build plans, budgets, and programs that will successfully implement strategies that achieve missions. Firms tackle this organization of the operations function in a variety of
Activity map
A graphical link of competitive
advantage, KSFs, and supporting
activities.
Service Distribution Promotion Price Channels of distribution Product positioning (image, functions)
Leverage Cost of capital Working capital Receivables Payables Financial control Lines of credit
Product Quality
Process Location Layout Human resources Supply chain Inventory Schedule Maintenance
Marketing Finance/Accounting Operations
10 OM Decisions
Sample Options Chapter
Customized or standardized; sustainability Define customer quality expectations and how to achieve them Facility design, capacity, how much automation Near supplier or near customer Work cells or assembly line Specialized or enriched jobs Single or multiple suppliers When to reorder; how much to keep on hand Stable or fluctuating production rate Repair as required or preventive maintenance
5,S5
6,S6 7,S7
8 9 10
11, S11 12,14,16
13,15 17
Support a Core Competency and Implement Strategy by Identifying and Executing the Key Success Factors in the Functional Areas
Figure 2.7
Implement Strategy by
Identifying and Executing Key
Success Factors That Support
Core Competencies
STUDENT TIP These 10 decisions are used to
implement a specific strategy and
yield a competitive advantage.
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ways. The organization charts shown in Chapter 1 ( Figure 1.1 ) indicate the way some firms have organized to perform the required activities. The operations manager’s job is to implement an OM strategy, provide competitive advantage, and increase productivity.
Implementing the 10 Strategic OM Decisions As mentioned earlier, the implementation of the 10 strategic OM decisions is influenced by a variety of issues—from missions and strategy to key success factors and core competencies— while addressing such issues as product mix, product life cycle, and competitive environment. Because each product brings its own mix of attributes, the importance and method of imple- mentation of the 10 strategic OM decisions will vary. Throughout this text, we discuss how these decisions are implemented in ways that provide competitive advantage. How this might be done for two drug companies, one seeking competitive advantage via differentiation and the other via low cost, is shown in Table 2.1 .
Strategic Planning, Core Competencies, and Outsourcing As organizations develop missions, goals, and strategies, they identify their strengths—what they do as well as or better than their competitors—as their core competencies . By contrast, non-core activities , which can be a sizable portion of an organization’s total business, are good candidates for outsourcing. Outsourcing is transferring activities that have traditionally been internal to external suppliers.
Outsourcing is not a new concept, but it does add complexity and risk to the supply chain. Because of its potential, outsourcing continues to expand. The expansion is accelerating due to
Outsourcing
Transferring a firm’s activities that
have traditionally been internal to
external suppliers.
Courteous but Limited Passenger
Service
Short Haul, Point-to- Point Routes, Often to
Secondary Airports
Frequent, Reliable Schedules
Standardized Fleet of Boeing 737
Aircraft
High Aircraft Utilization
Lean, Productive Employees
“Bags fly free” and no baggage transfers
No seat assignmentsAutomated
ticketing machines Empowered employees
High employee compensation
Hire for attitude, then train
20-minute gate turnarounds
High level of stock ownership
Maintenance personnel trained on only one type
of aircraft Flexible employees/unions
and standard planes aid scheduling
Excellent supplier relations with
Boeing have aided financing
Pilot training required on only
one type of aircraft
Reduced maintenance inventory required because only one
type of aircraft is used
Saturate a city with flights, lowering
administrative costs per passenger
for that city
High number of flights reduces
employee idle time between flights
Lower gate costs at secondary airports
Competitive Advantage: Low Cost
No meals
Figure 2.8
Activity Mapping of Southwest Airlines’ Low-Cost Competitive Advantage
To achieve a low-cost competitive advantage, Southwest has identified a number of key success factors (connected by red arrows) and support
activities (shown by blue arrows). As this figure indicates, Southwest’s low-cost strategy is highly dependent on a very well-run operations function.
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three global trends: (1) increased technological expertise, (2) more reliable and cheaper trans- portation, and (3) the rapid development and deployment of advancements in telecommuni- cations and computers. This rich combination of economic advances is contributing to both lower cost and more specialization. As a result more firms are candidates for outsourcing of non-core activities.
Outsourcing implies an agreement (typically a legally binding contract) with an external organization. The classic make-or-buy decision, concerning which products to make and which to buy, is the basis of outsourcing. When firms such as Apple find that their core competency is in creativity, innovation, and product design, they may want to outsource manufacturing.
VIDEO 2.3 Outsourcing Offshore at Darden
TABLE 2.1 Operations Strategies of Two Drug Companies*
COMPETITIVE ADVANTAGE
BRAND NAME DRUGS, INC. GENERIC DRUG CORP.
PRODUCT DIFFERENTIATION STRATEGY LOW-COST STRATEGY
Product selection and design
Heavy R&D investment; extensive labs; focus on development in a broad range of drug categories
Low R&D investment; focus on development of generic drugs
Quality Quality is major priority, standards exceed regulatory requirements
Meets regulatory requirements on a country-by-country basis, as necessary
Process Product and modular production process; tries to have long product runs in specialized facilities; builds capacity ahead of demand
Process focused; general production processes; “job shop” approach, short-run production; focus on high utilization
Location Still located in city where it was founded Recently moved to low-tax, low-labor-cost environment
Layout Layout supports automated product-focused production Layout supports process-focused “job shop” practices
Human resources Hire the best; nationwide searches Very experienced top executives hired to provide direction; other personnel paid below industry average
Supply chain Long-term supplier relationships Tends to purchase competitively to fi nd bargains
Inventory Maintains high fi nished goods inventory primarily to ensure all demands are met
Process focus drives up work-in-process inventory; fi nished goods inventory tends to be low
Scheduling Centralized production planning Many short-run products complicate scheduling
Maintenance Highly trained staff; extensive parts inventory Highly trained staff to meet changing demands
* Notice how the 10 decisions are altered to build two distinct strategies in the same industry.
Contract manufacturers such as Flextronics provide outsourcing
service to IBM, Cisco Systems, HP, Microsoft, Sony, Nortel,
Ericsson, and Sun, among many others. Flextronics is a high-
quality producer that has won over 450 awards, including the
Malcolm Baldrige Award. One of the side benefits of outsourcing
is that client firms such as IBM can actually improve their
performance by using the competencies of an outstanding firm
like Flextronics. But there are risks involved in outsourcing. Ke it h D
a n n e m
ill e r/
A la
m y
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Outsourcing manufacturing is an extension of the long-standing practice of subcon- tracting production activities, which when done on a continuing basis is known as contract manufacturing . Contract manufacturing is becoming standard practice in many industries, from computers to automobiles. For instance, Johnson & Johnson, like many other big drug companies whose core competency is research and development, often farms out manufacturing to contractors. On the other hand, Sony’s core competency is electrome- chanical design of chips. This is its core competency, but Sony is also one of the best in the world when it comes to rapid response and specialized production of these chips. There- fore, Sony finds that it wants to be its own manufacturer , while specialized providers come up with major innovations in such areas as software, human resources, and distribution. These areas are the providers’ business, not Sony’s, and the provider may very well be bet- ter at it than Sony.
Other examples of outsourcing non-core activities include:
◆ DuPont’s legal services routed to the Philippines ◆ IBM’s handing of travel services and payroll and Hewlett-Packard’s provision of IT
services to P&G ◆ Production of the Audi A4 convertible and Mercedes CLK convertible by Wilheim
Karmann in Osnabruck, Germany ◆ Blue Cross sending hip resurfacing surgery patients to India
Managers evaluate their strategies and core competencies and ask themselves how to use the assets entrusted to them. Do they want to be the company that does low-margin work at 3%–4% or the innovative firm that makes a 30%–40% margin? PC and iPad contract manufac- turers in China and Taiwan earn 3%–4%, but Apple, which innovates, designs, and sells, has a margin 10 times as large.
The Theory of Comparative Advantage The motivation for international outsourcing comes from the theory of comparative advantage . This theory focuses on the economic concept of relative advantage. According to the theory, if an external provider, regardless of its geographic location, can perform activi- ties more productively than the purchasing firm, then the external provider should do the work. This allows the purchasing firm to focus on what it does best—its core competencies. Consistent with the theory of comparative advantage, outsourcing continues to grow. But outsourcing the wrong activities can be a disaster. And even outsourcing non-core activities has risks.
Risks of Outsourcing Risk management starts with a realistic analysis of uncertainty and results in a strategy that minimizes the impact of these uncertainties. Indeed, outsourcing is risky, with roughly half of all outsourcing agreements failing because of inadequate planning and analysis. Timely delivery and quality standards can be major problems, as can underestimating increases in inventory and logistics costs. Some potential advantages and disadvantages of outsourcing are shown in Table 2.2 . A survey of North American companies found that, as a group, those that outsourced customer service saw a drop in their score on the American Consumer Satisfaction Index. The declines were roughly the same whether companies outsourced domestically or overseas. 4
However, when outsourcing is overseas, additional issues must be considered. These issues include financial attractiveness, people skills and availability, and the general busi- ness environment. Another risk of outsourcing overseas is the political backlash that results from moving jobs to foreign countries. The perceived loss of jobs has fueled anti-outsourcing rhetoric. This rhetoric is contributing to a process known as reshoring , the return of business activity to the originating country. (See the OM in Action box “Reshoring to Small-Town U.S.A.”)
Theory of comparative advantage
A theory which states that coun-
tries benefit from specializing in
(and exporting) goods and services
in which they have relative advan-
tage, and they benefit from import-
ing goods and services in which
they have a relative disadvantage.
STUDENT TIP The substantial risk of outsourcing
requires managers to invest in
the effort to make sure they do it
right.
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In addition to the external risks, operations managers must deal with other issues that outsourcing brings. These include: (1) reduced employment levels, (2) changes in facil- ity requirements, (3) potential adjustments to quality control systems and manufactur- ing processes, and (4) expanded logistics issues, including insurance, tariffs, customs, and timing.
To summarize, managers can find substantial efficiencies in outsourcing non-core activities, but they must be cautious in outsourcing those elements of the product or service that provide a competitive advantage. The next section provides a methodology that helps analyze the out- sourcing decision process.
Rating Outsource Providers Research indicates that the most common reason for the failure of outsourcing agree- ments is that the decisions are made without sufficient analysis. The factor-rating method provides an objective way to evaluate outsource providers. We assign points for each factor to each provider and then importance weights to each of the factors. We now apply the technique in Example 1 to compare outsourcing providers being considered by a firm.
TABLE 2.2 Potential Advantages and Disadvantages of Outsourcing
ADVANTAGES DISADVANTAGES
Cost savings Increased logistics and inventory costs
Gaining outside expertise that comes with specialization
Loss of control (quality, delivery, etc.)
Improving operations and service Potential creation of future competition
Maintaining a focus on core competencies Negative impact on employees
Accessing outside technology Risks may not manifest themselves for years
LO 2.4 Use factor rating to evaluate both
country and outsource
providers
OM in Action Reshoring to Small-Town U.S.A. U.S. companies continue their global search for efficiency by outsourcing call
centers and back-office operations, but many find they need to look no farther
than a place like Dubuque, Iowa.
To U.S. firms facing quality problems with their outsourcing operations
overseas and bad publicity at home, small-town America is emerging as
a pleasant alternative. Dubuque (population 57,313), Nacogdoches, Texas
(population 29,914), or Twin Falls, Idaho (population 34,469), may be the
perfect call center location. Even though the pay is low, the jobs are some of
the best available to small-town residents.
By moving out of big cities to the cheaper labor and real estate of small
towns, companies can save millions and still increase productivity. A call
center in a town that just lost its major manufacturing plant finds the jobs
easy to fill.
IBM, which has been criticized in the past for moving jobs to India and
other offshore locations, picked Dubuque for its new remote computer-servic-
es center with 1,300 jobs.
Taking advantage of even cheaper wages in other countries will not
stop soon, though. Is India the unstoppable overseas call center capital
that people think it is? Not
at all. Despite its population
of 1.3 billion, only a small
percentage of its workers
have the language skills and
technical education to work in
Western-style industries. Al-
ready, India has been warned
that if call centers can’t recruit
at reasonable wages, its jobs
will move to the Philippines,
South Africa, and Ghana.
And indeed, Dell, Apple, and
Britain’s Powergen are reshoring from Indian call centers, claiming their
costs had become too high.
K e it h D
a n n e m
ill e r/
A la
m y
Sources: Industry Week (August 5, 2014) and The Wall Street Journal ,
(November 27, 2013).
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Example 1 RATING PROVIDER SELECTION CRITERIA National Architects, Inc., a San Francisco–based designer of high-rise office buildings, has decided to outsource its information technology (IT) function. Three outsourcing providers are being actively con- sidered: one in the U.S., one in India, and one in Israel.
APPROACH c National’s VP–Operations, Susan Cholette, has made a list of seven criteria she con- siders critical. After putting together a committee of four other VPs, she has rated each firm (boldface type, on a 1–5 scale, with 5 being highest) and has also placed an importance weight on each of the fac- tors, as shown in Table 2.3 .
TABLE 2.3 Factor Ratings Applied to National Architects’ Potential IT Outsourcing Providers
FACTOR (CRITERION)*
OUTSOURCE PROVIDERS
IMPORTANCE WEIGHT BIM (U.S.) S.P.C. (INDIA)
TELCO (ISRAEL)
1. Can reduce operating costs .2 .2 × 3 = .6 .2 × 3 = .6 .2 × 5 = 1.0
2. Can reduce capital investment .2 .2 × 4 = .8 .2 × 3 = .6 .2 × 3 = .6
3. Skilled personnel .2 .2 × 5 = 1.0 .2 × 4 = .8 .2 × 3 = .6
4. Can improve quality .1 .1 × 4 = .4 .1 × 5 = .5 .1 × 2 = .2
5. Can gain access to technology not in company
.1 .1 × 5 = .5 .1 × 3 = .3 .1 × 5 = .5
6. Can create additional capacity .1 .1 × 4 = .4 .1 × 2 = .2 .1 × 4 = .4
7. Aligns with policy/philosophy/culture .1 .1 × 2 = .2 .1 × 3 = .3 .1 × 5 = .5
Total Weighted Score 3.9 3.3 3.8
SOLUTION c Susan multiplies each rating by the weight and sums the products in each column to gen- erate a total score for each outsourcing provider. She selects BIM, which has the highest overall rating.
INSIGHT c When the total scores are as close (3.9 vs. 3.8) as they are in this case, it is important to examine the sensitivity of the results to inputs. For example, if one of the importance weights or factor scores changes even marginally, the final selection may change. Management preference may also play a role here.
LEARNING EXERCISE c Susan decides that “Skilled personnel” should instead get a weight of 0.1 and “Aligns with policy/philosophy/culture” should increase to 0.2. How do the total scores change? [Answer: BIM = 3.6, S.P.C. = 3.2, and Telco = 4.0, so Telco would be selected.]
RELATED PROBLEMS c 2.8–2.12
EXCEL OM Data File Ch02Ex1.xls can be found in MyOMLab.
* These seven major criteria are based on a survey of 165 procurement executives, as reported in J. Schildhouse, Inside Supply Management (December 2005): 22–29.
Most U.S. toy companies now outsource their production to
Chinese manufacturers. Cost savings are significant, but there are
several downsides, including loss of control over such issues as
quality. A few years ago, Mattel had to recall 10.5 million Elmos,
Big Birds, and SpongeBobs. These made-in-China toys contained
excessive levels of lead in their paint. More recently, quality issues
have dealt with poisonous pet food, tainted milk products, and
contaminated sheetrock. A. R
a m
e y/
P h o to
E d it , In
c.
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Global Operations Strategy Options As we suggested early in this chapter, many operations strategies now require an international dimension. An international business is any firm that engages in international trade or invest- ment. A multinational corporation (MNC) is a firm with extensive international business involve- ment. MNCs buy resources, create goods or services, and sell goods or services in a variety of countries. The term multinational corporation applies to most of the world’s large, well-known businesses. Certainly IBM is a good example of an MNC. It imports electronics components to the U.S. from over 50 countries, exports to over 130 countries, has facilities in 45 countries, and earns more than half its sales and profits abroad.
Operations managers of international and multinational firms approach global oppor- tunities with one of four strategies: international , multidomestic , global , or transnational (see Figure 2.9 ). The matrix of Figure 2.9 has a vertical axis of cost reduction and a horizontal axis of local responsiveness. Local responsiveness implies quick response and/or the differentiation necessary for the local market. The operations manager must know how to position the firm in this matrix. Let us briefly examine each of the four strategies.
An international strategy uses exports and licenses to penetrate the global arena. This strategy is the least advantageous, with little local responsiveness and little cost advantage. But an inter- national strategy is often the easiest, as exports can require little change in existing operations, and licensing agreements often leave much of the risk to the licensee.
The multidomestic strategy has decentralized authority with substantial autonomy at each busi- ness. These are typically subsidiaries, franchises, or joint ventures with substantial independence. The advantage of this strategy is maximizing a competitive response for the local market; how- ever, the strategy has little or no cost advantage. Many food producers, such as Heinz, use a multi- domestic strategy to accommodate local tastes because global integration of the production pro- cess is not critical. The concept is one of “we were successful in the home market; let’s export the management talent and processes, not necessarily the product, to accommodate another market.”
A global strategy has a high degree of centralization, with headquarters coordinating the orga- nization to seek out standardization and learning between plants, thus generating economies of scale. This strategy is appropriate when the strategic focus is cost reduction but has little to recom- mend it when the demand for local responsiveness is high. Caterpillar, the world leader in earth- moving equipment, and Texas Instruments, a world leader in semiconductors, pursue global strat- egies. Caterpillar and Texas Instruments find this strategy advantageous because the end products are similar throughout the world. Earth-moving equipment is the same in Nigeria as in Iowa.
International business
A firm that engages in cross-
border transactions.
Multinational corporation (MNC)
A firm that has extensive involve-
ment in international business,
owning or controlling facilities in
more than one country.
Figure 2.9
Four International Operations
Strategies
Source: See a similar presentation in
M. Hitt, R. D. Ireland, and R. E. Hoskis-
son, Strategic Management: Concepts,
Competitiveness, and Globalization,
8th ed. (Cincinnati: Southwestern College
Publishing).
International strategy
A strategy in which global markets
are penetrated using exports and
licenses.
Multidomestic strategy
A strategy in which operating
decisions are decentralized to
each country to enhance local
responsiveness.
Global strategy
A strategy in which operating
decisions are centralized and
headquarters coordinates the
standardization and learning
between facilities.
LO 2.5 Identify and explain four global
operations strategy
options
Low
HighLow
High
Local Responsiveness (Quick Response and/or Differentiation)
C o
s t
R e d
u c
ti o
n
• Use existing domestic model globally • Franchise, joint ventures, subsidiaries
Multidomestic strategy • Import/export or license existing product
International strategy
Global strategy
• Standardized product • Economies of scale • Cross-cultural learning
Examples: Texas Instruments Caterpillar Otis Elevator
Examples: Coca-Cola Nestlé
Examples: U.S. Steel Harley-Davidson
Examples: Heinz McDonald’s The Body Shop Hard Rock Cafe
Transnational strategy
• Move material, people, or ideas across national boundaries • Economies of scale • Cross-cultural learning
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A transnational strategy exploits the economies of scale and learning, as well as pressure for responsiveness, by recognizing that core competence does not reside in just the “home” coun- try but can exist anywhere in the organization. Transnational describes a condition in which material, people, and ideas cross—or transgress —national boundaries. These firms have the potential to pursue all three operations strategies (i.e., differentiation, low cost, and response). Such firms can be thought of as “world companies” whose country identity is not as important as their interdependent network of worldwide operations. Nestlé is a good example of such a company. Although it is legally Swiss, 95% of its assets are held and 98% of its sales are made outside Switzerland. Fewer than 10% of its workers are Swiss.
Transnational strategy
A strategy that combines the
benefits of global-scale efficien-
cies with the benefits of local
responsiveness.
In a continuing fierce worldwide
battle, both Komatsu and
Caterpillar seek global advantage
in the heavy equipment market. As
Komatsu (left) moved west to the
UK, Caterpillar (right) moved east,
with 13 facilities and joint ventures
in China. Both firms are building
equipment throughout the world
as cost and logistics dictate. Their
global strategies allow production
to move as markets, risk, and
exchange rates suggest.
W a sh
in g to
n I m
a g in
g /A
la m
y
B e rn
d W
ü st
n e ck
/d p a /p
ic tu
re -a
lli a n ce
/N e w
sc o m
Summary Global operations provide an increase in both the challenges and opportunities for operations managers. Although the task is difficult, operations managers can and do improve productivity. They build and manage global OM functions and supply chains that contribute in a significant way to competitiveness. Organizations identify their strengths and weaknesses. They then develop effective missions and strat- egies that account for these strengths and weaknesses and complement the opportunities and threats in the environ- ment. If this procedure is performed well, the organization can have competitive advantage through some combination of product differentiation, low cost, and response.
Increasing specialization provides economic pressure to build organizations that focus on core competencies and to outsource the rest. But there is also a need for plan- ning outsourcing to make it beneficial to all participants. In this increasingly global world, competitive advantage is often achieved via a move to international, multidomestic, global, or transnational strategies.
Effective use of resources, whether domestic or interna- tional, is the responsibility of the professional manager, and professional managers are among the few in our society who can achieve this performance. The challenge is great, and the rewards to the manager and to society are substantial.
Key Terms
Maquiladoras (p. 34 ) World Trade Organization (WTO) (p. 34 ) North American Free Trade Agreement
(NAFTA) (p. 34 ) European Union (EU) (p. 34 ) Mission (p. 36 ) Strategy (p. 36 ) Competitive advantage (p. 36 ) Differentiation (p. 38 )
Experience differentiation (p. 38 ) Low-cost leadership (p. 38 ) Response (p. 39 ) Resources view (p. 40 ) Value-chain analysis (p. 40 ) Five forces model (p. 40 ) SWOT analysis (p. 41 ) Key success factors (KSFs) (p. 41 ) Core competencies (p. 42 )
Activity map (p. 43 ) Outsourcing (p. 44 ) Theory of comparative advantage (p. 46 ) International business (p. 49 ) Multinational corporation (MNC) (p. 49 ) International strategy (p. 49 ) Multidomestic strategy (p. 49 ) Global strategy (p. 49 ) Transnational strategy (p. 50 )
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Ethical Dilemma As a manufacturer of athletic shoes whose image—indeed performance—is widely regarded as socially responsible, you fi nd your costs increasing. Traditionally, your athletic shoes have been made in Indonesia and South Korea. Although the ease of doing business in those countries has been improving, wage rates have also been increasing. The labor-cost differential between your current suppliers and a contractor who will get the shoes made in China now exceeds $1 per pair. Your sales next year are projected to be 10 million pairs, and your analysis suggests that this cost differential is not offset by any other tangible costs; you face only the political risk and potential damage to your commitment to social responsibility. Thus, this $1 per pair savings should fl ow directly to your bottom line. There is no doubt that the Chinese government engages in censorship, remains repressive, and is a long way from a democracy. Moreover, you will have little or no control over working conditions, sexual harassment, and pollution. What do you do, and on what basis do you make your decision?
M ic
h a e l S . Y a m
a sh
it a /C
o rb
is
Discussion Questions
1. Based on the descriptions and analyses in this chapter, would Boeing be better described as a global firm or a transnational firm? Discuss.
2. List six reasons to internationalize operations. 3. Coca-Cola is called a global product. Does this mean that
Coca-Cola is formulated in the same way throughout the world? Discuss.
4. Define mission . 5. Define strategy . 6. Describe how an organization’s mission and strategy have
different purposes. 7. Identify the mission and strategy of your automobile repair
garage. What are the manifestations of the 10 strategic OM decisions at the garage? That is, how is each of the 10 deci- sions accomplished?
8. As a library or Internet assignment, identify the mission of a firm and the strategy that supports that mission.
9. How does an OM strategy change during a product’s life cycle?
10. There are three primary ways to achieve competitive advan- tage. Provide an example, not included in the text, of each. Support your choices.
11. Given the discussion of Southwest Airlines in the text, define an operations strategy for that firm now that it has purchased AirTran.
12. How must an operations strategy integrate with marketing and accounting?
13. How would you summarize outsourcing trends? 14. What potential cost-saving advantages might firms experi-
ence by using outsourcing? 15. What internal issues must managers address when outsourc-
ing? 16. How should a company select an outsourcing provider? 17. What are some of the possible consequences of poor out-
sourcing? 18. What global operations strategy is most descriptive of
McDonald’s?
Using Software to Solve Outsourcing Problems
Excel, Excel OM, and POM for Windows may be used to solve many of the problems in this chapter.
CREATING YOUR OWN EXCEL SPREADSHEETS Program 2.1 illustrates how to build an Excel spreadsheet for the data in Example 1 . In this example the factor rating method is used to compare National Architects’ three potential outsourcing providers.
This program provides the data inputs for seven important factors, including their weights (0.0–1.0) and ratings (1–5 scale where 5 is the highest rating) for each country. As we see, BIM is most highly rated, with a 3.9 score, versus 3.3 for S.P.C. and 3.8 for Telco.
X USING EXCEL OM Excel OM (free with your text and also found in MyOMLab) may be used to solve Example 1 (with the Factor Rating module).
P USING POM FOR WINDOWS POM for Windows also includes a factor rating module. For details, refer to Appendix IV. POM for Windows is also found in MyOMLab and can solve all problems labeled with a P .
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Solved Problems Virtual Office Hours help is available in MyOMLab .
Program 2.1
Using Excel to Develop a Factor Rating Analysis, With Data from Example 1 .
Compute the weighted scores as the sum of the product of the weights and the scores for each option using the SUMPRODUCT function.
=SUMPRODUCT($B$6:$B$12,C6:C12)
Enter factor names and weights in columns A and B.
Enter scores (that come from manager ratings) for BIM, S.P.C., and Telco on each factor in columns C, D, and E.
Actions Copy C14 to D14 and E14
SOLVED PROBLEM 2.1 The global tire industry continues to consolidate. Michelin buys Goodrich and Uniroyal and builds plants throughout the world. Bridgestone buys Firestone, expands its research budget, and focuses on world markets. Goodyear spends almost 4% of its sales revenue on research. These three aggressive firms have come to dominate the world tire market, with total market share approaching 60%. And the German tire maker Continental AG has strengthened its position as fourth in the world, with a dominant presence in Germany and a research budget of 6%. Against this formidable array, the old-line Italian tire company Pirelli SpA is challenged to respond effectively. Although Pirelli still has almost 5% of the market, it is a relatively small player in a tough, competitive business.
And although the business is reliable even in recessions, as motorists still need replacement tires, the competition is get- ting stronger. The business rewards companies that have large market shares and long production runs. Pirelli, with its small market share and 1,200 specialty tires, has neither. However, Pirelli has some strengths: an outstanding reputation for tire research and excellent high-performance tires, including sup- plying specially engineered tires for performance automobiles, Ducati motorcycles, and Formula 1 racing teams. In addition, Pirelli’s operations managers complement the creative engineer- ing with world-class innovative manufacturing processes that allow rapid changeover to different models and sizes of tires.
Use a SWOT analysis to establish a feasible strategy for Pirelli.
SOLUTION First, find an opportunity in the world tire market that avoids the threat of the mass-market onslaught by the big-three tire makers. Second, use the internal marketing strength represented by Pirelli’s strong brand name supply- ing Formula 1 racing and a history of winning World Rally Championships. Third, maximize the innovative capabilities of an outstanding operations function. This is a classic dif- ferentiation strategy, supported by activity mapping that ties Pirelli’s marketing strength to research and its innovative operations function.
To implement this strategy, Pirelli is differentiating itself with a focus on higher-margin performance tires and away from the low-margin standard tire business. Pirelli has established deals with luxury brands Jaguar, BMW, Maserati, Ferrari, Bentley, and Lotus Elise and established itself as a provider of a large share of the tires on new Porsches and S-class Mercedes. Pirelli also made a strategic decision to divest itself of other businesses. As a result, the vast majority of the company’s tire production is now high-performance tires. People are willing to pay a premium for Pirellis.
The operations function continued to focus its design efforts on performance tires and developing a system of modular tire manufacture that allows much faster switching between models. This modular system, combined with billions of dollars in new manufacturing investment, has driven batch sizes down to as small as 150 to 200, making small-lot perfor- mance tires economically feasible. Manufacturing innovations
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at Pirelli have streamlined the production process, moving it from a 14-step process to a 3-step process.
Pirelli still faces a threat from the big three going after the performance market, but the company has bypassed its weak- ness of having a small market share with a substantial research budget and an innovative operations function. The firm now
has 19 plants in 13 countries and a presence in more than 160 countries, with sales approaching $8 billion.
Sources: Based on The Economist (January 8, 2011): 65; www.pirelli.com; and RubberNews.com .
SOLVED PROBLEM 2.2 DeHoratius Electronics, Inc., is evaluating several options for sourcing a critical processor for its new modem. Three sources are being considered: Hi-Tech in Canada, Zia in Hong Kong,
and Zaragoza in Spain. The owner, Nicole DeHoratius, has determined that only three criteria are critical. She has rated each firm on a 1–5 scale (with 5 being highest) and has also placed an importance weight on each of the factors, as shown below:
OUTSOURCE PROVIDERS
FACTOR (CRITERION) IMPORTANCE WEIGHT
HI-TECH (CANADA)
ZIA (HONG KONG)
ZARAGOZA (SPAIN)
Rating Wtd. Score Rating Wtd.score Rating Wtd. Score
1. Cost .5 3 1.5 3 1.5 5 2.5
2. Reliability .2 4 .8 3 .6 3 .6
3. Competence .3 5 1.5 4 1.2 3 .9
Totals 1.0 3.8 3.3 4.0
SOLUTION Nicole multiplies each rating by the weight and sums the prod- ucts in each column to generate a total score for each outsourcing
provider. For example the weighted score for Hi-Tech equals (.5 * 3) + (.2 * 4) + (.3 * 5) = 1.5 + .8 + 1.5 = 3.8. She selects Zaragoza, which has the highest overall rating.
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems 2.1–2.3 relate to A Global View of Operations and Supply Chains
• • 2.1 Match the product with the proper parent company and country in the table below:
PRODUCT PARENT COMPANY COUNTRY
Arrow Shirts a. Volkswagen 1. France
Braun Household Appliances
b. Bidermann International
2. Great Britain
Volvo Autos c. Bridgestone 3. Germany
Firestone Tires d. Campbell Soup 4. Japan
Godiva Chocolate e. Credit Lyonnais 5. U.S.
Häagen-Dazs Ice Cream (USA)
f. Tata 6. Switzerland
Jaguar Autos g. Procter & Gamble 7. China
MGM Movies h. Michelin 8. India
Lamborghini Autos i. Nestlé
Goodrich Tires j. Geely
Alpo Pet Foods
• • 2.2 Based on the corruption perception index developed by Transparency International ( www.transparency.org ), rank the following countries from most corrupt to least: Venezuela, Denmark, the U.S., Switzerland, and China.
• • 2.3 Based on the competitiveness ranking developed by the Global Competitiveness Index ( www.weforum.org ), rank the following countries from most competitive to least: Mexico, Switzerland, the U.S., and China.
Problems 2.4 and 2.5 relate to Achieving Competitive Advantage Through Operations
• 2.4 The text provides three primary strategic approaches (differentiation, cost, and response) for achieving competitive advantage. Provide an example of each not given in the text. Support your choices. ( Hint: Note the examples provided in the text.)
• • 2.5 Within the food service industry (restaurants that serve meals to customers, but not just fast food), find examples of firms that have sustained competitive advantage by competing on the basis of (1) cost leadership, (2) response, and (3) differen- tiation. Cite one example in each category; provide a sentence or two in support of each choice. Do not use fast-food chains for all categories. ( Hint: A “99¢ menu” is very easily copied and is not a good source of sustained advantage.)
Problem 2.6 relates to Issues in Operations Strategy
• • • 2.6 Identify how changes within an organization affect the OM strategy for a company. For instance, discuss what impact the following internal factors might have on OM strategy: a) Maturing of a product. b) Technology innovation in the manufacturing process. c) Changes in laptop computer design that builds in wireless
technology.
Problem 2.7 relates to Strategy Development and Implementation
• • • 2.7 Identify how changes in the external environment affect the OM strategy for a company. For instance, discuss what impact the following external factors might have on OM strategy: a) Major increases in oil prices. b) Water- and air-quality legislation.
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54 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
c) Fewer young prospective employees entering the labor market. d) Inflation versus stable prices. e) Legislation moving health insurance from a pretax benefit to
taxable income.
Problems 2.8–2.12 relate to Strategic Planning, Core Competencies, and Outsourcing
• • 2.8 Claudia Pragram Technologies, Inc., has narrowed its choice of outsourcing provider to two firms located in dif- ferent countries. Pragram wants to decide which one of the two countries is the better choice, based on risk-avoidance criteria. She has polled her executives and established four crite- ria. The resulting ratings for the two countries are presented in the table below, where 1 is a lower risk and 3 is a higher risk.
SELECTION CRITERION ENGLAND CANADA
Price of service from outsourcer 2 3
Nearness of facilities to client 3 1
Level of technology 1 3
History of successful outsourcing 1 2
The executives have determined four criteria weightings: Price, with a weight of 0.1; Nearness, with 0.6; Technology, with 0.2; and History, with 0.1. a) Using the factor-rating method, which country would you
select? b) Double each of the weights used in part (a) (to 0.2, 1.2, 0.4,
and 0.2, respectively). What effect does this have on your answer? Why? PX
• • 2.9 Ranga Ramasesh is the operations manager for a firm that is trying to decide which one of four countries it should research for possible outsourcing providers. The first step is to select a country based on cultural risk factors, which are criti- cal to eventual business success with the provider. Ranga has reviewed outsourcing provider directories and found that the four countries in the table that follows have an ample number of providers from which they can choose. To aid in the country selection step, he has enlisted the aid of a cultural expert, John Wang, who has provided ratings of the various criteria in the table. The resulting ratings are on a 1 to 10 scale, where 1 is a low risk and 10 is a high risk.
John has also determined six criteria weightings: Trust, with a weight of 0.4; Quality, with 0.2; Religious, with 0.1; Individualism, with 0.1; Time, with 0.1; and Uncertainty, with 0.1. Using the factor-rating method, which country should Ranga select? PX
CULTURE SELECTION CRITERION MEXICO PANAMA
COSTA RICA PERU
Trust 1 2 2 1
Society value of quality work 7 10 9 10
Religious attitudes 3 3 3 5
Individualism attitudes 5 2 4 8
Time orientation attitudes 4 6 7 3
Uncertainty avoidance attitudes
3 2 4 2
• • 2.10 Fernando Garza’s firm wishes to use factor rating to help select an outsourcing provider of logistics services.
a) With weights from 1–5 (5 highest) and ratings 1–100 (100 high- est), use the following table to help Garza make his decision:
RATING OF LOGISTICS PROVIDERS
CRITERION WEIGHT OVERNIGHT
SHIPPING WORLDWIDE
DELIVERY UNITED FREIGHT
Quality 5 90 80 75
Delivery 3 70 85 70
Cost 2 70 80 95
b) Garza decides to increase the weights for quality, delivery, and cost to 10, 6, and 4, respectively. How does this change your conclusions? Why?
c) If Overnight Shipping’s ratings for each of the factors increase by 10%, what are the new results? PX
• • • 2.11 Walker Accounting Software is marketed to small accounting firms throughout the U.S. and Canada. Owner George Walker has decided to outsource the company’s help desk and is considering three providers: Manila Call Center (Philippines), Delhi Services (India), and Moscow Bell (Russia). The following table summarizes the data Walker has assembled. Which outsourc- ing firm has the best rating? (Higher weights imply higher impor- tance and higher ratings imply more desirable providers.) PX
PROVIDER RATINGS
CRITERION IMPORTANCE
WEIGHT MANILA DELHI MOSCOW
Flexibility 0.5 5 1 9
Trustworthiness 0.1 5 5 2
Price 0.2 4 3 6
Delivery 0.2 5 6 6
• • • • 2.12 Rao Technologies, a California-based high-tech man- ufacturer, is considering outsourcing some of its electronics pro- duction. Four firms have responded to its request for bids, and CEO Mohan Rao has started to perform an analysis on the scores his OM team has entered in the table below.
RATINGS OF OUTSOURCE PROVIDERS
FACTOR WEIGHT A B C D
Labor w 5 4 3 5 Quality procedures 30 2 3 5 1 Logistics system 5 3 4 3 5 Price 25 5 3 4 4
Trustworthiness 5 3 2 3 5 Technology in place 15 2 5 4 4 Management team 15 5 4 2 1
Weights are on a scale from 1 through 30, and the outsourcing provider scores are on a scale of 1 through 5. The weight for the labor factor is shown as a w because Rao’s OM team cannot agree on a value for this weight. For what range of values of w , if any, is company C a recommended outsourcing provider, according to the factor-rating method?
Problem 2.13 relates to Global Operations Strategy Options
• • 2.13 Does Boeing practice a multinational operations strat- egy, a global operations strategy, or a transnational operations strategy? Support your choice with specific references to Boeing’s operations and the characteristics of each type of organization.
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C H A P T E R 2 | O P E R AT I O N S S T R AT E G Y I N A G L O B A L E N V I R O N M E N T 55
CASE STUDIES Rapid-Lube
A huge market exists for automobile tune-ups, oil changes, and lubrication service for more than 250 million vehicles on U.S. roads. Some of this demand is filled by full-service auto dealer- ships, some by Walmart and Firestone, and some by other tire/ service dealers. However, Rapid-Lube, Mobil-Lube, Jiffy-Lube and others have also developed strategies to accommodate this opportunity.
Rapid-Lube stations perform oil changes, lubrication, and interior cleaning in a spotless environment. The buildings are clean, usually painted white, and often surrounded by neatly trimmed landscaping. To facilitate fast service, cars can be driven through three abreast. At Rapid-Lube, the customer is greeted by service representatives who are graduates of Rapid- Lube U. The Rapid-Lube school is not unlike McDonald’s Hamburger University near Chicago or Holiday Inn’s training school in Memphis. The greeter takes the order, which typically includes fluid checks (oil, water, brake fluid, transmission fluid, differential grease) and the necessary lubrication, as well as fil- ter changes for air and oil. Service personnel in neat uniforms then move into action. The standard three-person team has one
person checking fluid levels under the hood, another assigned interior vacuuming and window cleaning, and the third in the garage pit, removing the oil filter, draining the oil, checking the differential and transmission, and lubricating as necessary. Precise task assignments and good training are designed to move the car into and out of the bay in 10 minutes. The business model is to charge no more, and hopefully less, than gas stations, auto- motive repair chains, and auto dealers, while providing better and faster service.
Discussion Questions
1. What constitutes the mission of Rapid-Lube? 2. How does the Rapid-Lube operations strategy provide compet-
itive advantage? ( Hint: Evaluate how Rapid-Lube’s traditional competitors perform the 10 decisions of operations manage- ment vs. how Rapid-Lube performs them.)
3. Is it likely that Rapid-Lube has increased productivity over its more traditional competitors? Why? How would we measure productivity in this industry?
Video Case Strategy at Regal Marine Regal Marine, one of the U.S.’s 10 largest power-boat manu- facturers, achieves its mission—providing luxury performance boats to customers worldwide—using the strategy of differentia- tion. It differentiates its products through constant innovation, unique features, and high quality. Increasing sales at the Orlando, Florida, family-owned firm suggest that the strategy is working.
As a quality boat manufacturer, Regal Marine starts with continuous innovation, as reflected in computer-aided design (CAD), high-quality molds, and close tolerances that are con- trolled through both defect charts and rigorous visual inspection. In-house quality is not enough, however. Because a product is only as good as the parts put into it, Regal has established close ties with a large number of its suppliers to ensure both flexibil- ity and perfect parts. With the help of these suppliers, Regal can profitably produce a product line of 22 boats, ranging from the $14,000 19-foot boat to the $500,000 44-foot Commodore yacht.
“We build boats,” says VP Tim Kuck, “but we’re really in the ‘fun’ business. Our competition includes not only 300 other boat, canoe, and yacht manufacturers in our $17 billion industry, but home theaters, the Internet, and all kinds of alternative family
entertainment.” Fortunately Regal has been paying down debt and increasing market share.
Regal has also joined with scores of other independent boat makers in the American Boat Builders Association. Through economies of scale in procurement, Regal is able to navigate against billion-dollar competitor Brunswick (makers of the Sea Ray and Bayliner brands). The Global Company Profile featur- ing Regal Marine (which opens Chapter 5 ) provides further back- ground on Regal and its strategy.
Discussion Questions *
1. State Regal Marine’s mission in your own words. 2. Identify the strengths, weaknesses, opportunities, and threats
that are relevant to the strategy of Regal Marine. 3. How would you define Regal’s strategy? 4. How would each of the 10 operations management decisions
apply to operations decision making at Regal Marine?
*You may wish to view the video that accompanies the case before addressing these questions.
Video Case Hard Rock Cafe’s Global Strategy Hard Rock brings the concept of the “experience economy” to its cafe operation. The strategy incorporates a unique “experi- ence” into its operations. This innovation is somewhat akin to mass customization in manufacturing. At Hard Rock, the experi- ence concept is to provide not only a custom meal from the menu but a dining event that includes a unique visual and sound experi- ence not duplicated anywhere else in the world. This strategy is succeeding. Other theme restaurants have come and gone while Hard Rock continues to grow. As Professor C. Markides of the London Business School says, “The trick is not to play the game
* Constantinos Markides, “Strategic Innovation,” MIT Sloan Manage- ment Review 38, no. 3: 9.
better than the competition, but to develop and play an altogether different game.” * At Hard Rock, the different game is the experi- ence game.
From the opening of its first cafe in London in 1971, during the British rock music explosion, Hard Rock has been serving food and rock music with equal enthusiasm. Hard Rock Cafe has 40 U.S. locations, about a dozen in Europe, and the remainder
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56 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
scattered throughout the world, from Bangkok and Beijing to Beirut. New construction, leases, and investment in remodeling are long term; so a global strategy means special consideration of political risk, currency risk, and social norms in a context of a brand fit. Although Hard Rock is one of the most recognized brands in the world, this does not mean its cafe is a natural eve- rywhere. Special consideration must be given to the supply chain for the restaurant and its accompanying retail store. About 48% of a typical cafe’s sales are from merchandise.
The Hard Rock Cafe business model is well defined, but because of various risk factors and differences in business prac- tices and employment law, Hard Rock elects to franchise about half of its cafes. Social norms and preferences often suggest some tweaking of menus for local taste. For instance, Hard Rock focuses less on hamburgers and beef and more on fish and lobster in its British cafes.
Because 70% of Hard Rock’s guests are tourists, recent years have found it expanding to “destination” cities. While this has been a winning strategy for decades, allowing the firm to grow
from one London cafe to 145 facilities in 60 countries, it has made Hard Rock susceptible to economic fluctuations that hit the tour- ist business hardest. So Hard Rock is signing a long-term lease for a new location in Nottingham, England, to join recently opened cafes in Manchester and Birmingham—cities that are not standard tourist destinations. At the same time, menus are being upgraded. Hopefully, repeat business from locals in these cities will smooth demand and make Hard Rock less dependent on tourists.
Discussion Questions *
1. Identify the strategy changes that have taken place at Hard Rock Cafe since its founding in 1971.
2. As Hard Rock Cafe has changed its strategy, how has its responses to some of the 10 decisions of OM changed?
3. Where does Hard Rock fit in the four international operations strategies outlined in Figure 2.9 ? Explain your answer.
*You may wish to view the video that accompanies the case before addressing these questions.
Video Case Outsourcing Offshore at Darden
Darden Restaurants, owner of popular brands such as Olive Garden, Bahama Breeze, and Longhorn Grill, serves more than 320 million meals annually in over 1,500 restaurants across the U.S. and Canada. To achieve competitive advantage via its sup- ply chain, Darden must achieve excellence at each step. With pur- chases from 35 countries, and seafood products with a shelf life as short as 4 days, this is a complex and challenging task.
Those 320 million meals annually mean 40 million pounds of shrimp and huge quantities of tilapia, swordfish, and other fresh purchases. Fresh seafood is typically flown to the U.S. and moni- tored each step of the way to ensure that 34°F is maintained.
Darden’s purchasing agents travel the world to find competi- tive advantage in the supply chain. Darden personnel from sup- ply chain and development, quality assurance, and environmental relations contribute to developing, evaluating, and checking sup- pliers. Darden also has seven native-speaking representatives living on other continents to provide continuing support and evaluation of suppliers. All suppliers must abide by Darden’s food standards, which typically exceed FDA and other industry standards. Darden expects continuous improvement in durable relationships that increase quality and reduce cost.
Darden’s aggressiveness and development of a sophisticated supply chain provide an opportunity for outsourcing. Much food preparation is labor intensive and is often more efficient when handled in bulk. This is particularly true where large volumes may justify capital investment. For instance, Tyson and Iowa Beef pre- pare meats to Darden’s specifications much more economically than can individual restaurants. Similarly, Darden has found that it can outsource both the cutting of salmon to the proper portion size and the cracking/peeling of shrimp more cost-effectively off- shore than in U.S. distribution centers or individual restaurants.
Discussion Questions *
1. What are some outsourcing opportunities in a restaurant? 2. What supply chain issues are unique to a firm sourcing from 35
countries? 3. Examine how other firms or industries develop international
supply chains as compared to Darden. 4. Why does Darden outsource harvesting and preparation of
much of its seafood? *You may wish to view the video that accompanies this case study before answering these questions .
• Additional Case Study: Visit MyOMLab for this free case study: Outsourcing to Tata: The Indian outsourcing fi rm is hired by New Mexico.
1. The 28 members of the European Union (EU) as of 2015 were Austria, Belgium, Bulgaria, Cyprus, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and United Kingdom. Not all have adopted the euro. In addition, Iceland, Macedonia, Montenegro, and Turkey are candidates for entry into the European Union.
2. M. E. Porter, Competitive Advantage: Creating and Sustaining Superior Performance. New York: The Free Press, 1985.
3. M. E. Porter, Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: The Free Press, 1980, 1998.
4. J. Whitaker, M. S. Krishnan, and C. Fornell. “How Offshore Outsourcing Affects Customer Satisfaction.” The Wall Street Journal (July 7, 2008): R4.
Endnotes
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Chapter 2 Rapid Review 2
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Main Heading Review Material MyOMLab A GLOBAL VIEW OF OPERATIONS AND SUPPLY CHAINS (pp. 32 – 35 )
Domestic business operations decide to change to some form of international opera- tions for six main reasons: 1. Improve supply chain 2. Reduce costs and exchange rate risks 3. Improve operations 4. Understand markets 5. Improve products 6. Attract and retain global talent j Maquiladoras —Mexican factories located along the U.S.–Mexico border that re-
ceive preferential tariff treatment. j World Trade Organization (WTO) —An international organization that promotes
world trade by lowering barriers to the free flow of goods across borders. j NAFTA —A free trade agreement between Canada, Mexico, and the United States. j European Union (EU) —A European trade group that has 28 member states.
Concept Questions: 1.1–1.4 Problems: 2.1–2.3
DEVELOPING MISSIONS AND STRATEGIES (pp. 35 – 36 )
An effective operations management effort must have a mission so it knows where it is going and a strategy so it knows how to get there. j Mission —The purpose or rationale for an organization’s existence. j Strategy —How an organization expects to achieve its missions and goals. The three strategic approaches to competitive advantage are: 1. Differentiation 2. Cost leadership 3. Response
Concept Questions: 2.1–2.4
VIDEO 2.1 Operations Strategy at Regal Marine
ACHIEVING COMPETITIVE ADVANTAGE THROUGH OPERATIONS (pp. 36 – 40 )
j Competitive advantage —The creation of a unique advantage over competitors. j Differentiation —Distinguishing the offerings of an organization in a way that the
customer perceives as adding value. j Experience differentiation —Engaging the customer with a product through imagina-
tive use of the five senses, so the customer “experiences” the product. j Low-cost leadership —Achieving maximum value, as perceived by the customer. j Response —A set of values related to rapid, flexible, and reliable performance.
Concept Questions: 3.1–3.4
Problems: 2.4–2.5 VIDEO 2.2 Hard Rock’s Global Strategy
ISSUES IN OPERATIONS STRATEGY (pp. 40 – 41 )
j Resources view —A view in which managers evaluate the resources at their disposal and manage or alter them to achieve competitive advantage.
j Value-chain analysis —A way to identify the elements in the product/service chain that uniquely add value.
j Five forces model —A way to analyze the five forces in the competitive environment. Forces in Porter’s five forces model are (1) immediate rivals, (2) potential entrants, (3) customers, (4) suppliers, and (5) substitute products. Different issues are emphasized during different stages of the product life cycle: j Introduction —Company strategy: Best period to increase market share, R&D engi-
neering is critical. OM strategy: Product design and development critical, frequent product and process design changes, short production runs, high production costs, limited models, attention to quality.
j Growth —Company strategy: Practical to change price or quality image, strengthen niche. OM strategy: Forecasting critical, product and process reliability, competitive product improvements and options, increase capacity, shift toward product focus, enhance distribution.
j Maturity —Company strategy: Poor time to change image or price or quality, competitive costs become critical, defend market position. OM strategy: Stand- ardization, less rapid product changes (more minor changes), optimum capacity, increasing stability of process, long production runs, product improvement and cost cutting.
j Decline —Company strategy: Cost control critical. OM strategy: Little product dif- ferentiation, cost minimization, overcapacity in the industry, prune line to eliminate items not returning good margin, reduce capacity.
Concept Questions: 4.1–4.4 Problem: 2.6
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Main Heading Review Material MyOMLab STRATEGY DEVELOPMENT AND IMPLEMENTATION (pp. 41 – 44 )
j SWOT analysis —A method of determining internal strengths and weaknesses and external opportunities and threats.
j Key success factors (KSFs) —Activities or factors that are key to achieving competi- tive advantage.
j Core competencies —A set of unique skills, talents, and activities that a firm does particularly well. A core competence may be a combination of KSFs.
j Activity map —A graphical link of competitive advantage, KSFs, and supporting activities.
Concept Questions: 5.1–5.4 Problem: 2.7 Virtual Office Hours for Solved Problem: 2.1
STRATEGIC PLANNING, CORE COMPETENCIES, AND OUTSOURCING (pp. 44 – 48 )
j Outsourcing —Procuring from external sources services or products that are nor- mally part of an organization.
j Theory of comparative advantage —The theory which states that countries benefit from specializing in (and exporting) products and services in which they have rela- tive advantage and importing goods in which they have a relative disadvantage.
Perhaps half of all outsourcing agreements fail because of inappropriate planning and analysis. Potential risks of outsourcing include: j A drop in quality or customer service j Political backlash that results from outsourcing to foreign countries j Negative impact on employees j Potential future competition j Increased logistics and inventory costs The most common reason given for outsourcing failure is that the decision was made without sufficient understanding and analysis. The factor-rating method is an excellent tool for dealing with both country risk assess- ment and provider selection problems.
Concept Questions: 6.1–6.4 Problems: 2.8–2.12 Virtual Office Hours for Solved Problem: 2.2
VIDEO 2.3 Outsourcing Offshore at Darden
GLOBAL OPERATIONS STRATEGY OPTIONS (pp. 49 – 50 )
j International business —A firm that engages in cross-border transactions. j Multinational corporation (MNC) —A firm that has extensive involvement in inter-
national business, owning or controlling facilities in more than one country. The four operations strategies for approaching global opportunities can be classified according to local responsiveness and cost reduction: j International strategy —A strategy in which global markets are penetrated using
exports and licenses with little local responsiveness. j Multidomestic strategy —A strategy in which operating decisions are decentralized to
each country to enhance local responsiveness. j Global strategy —A strategy in which operating decisions are centralized and head-
quarters coordinates the standardization and learning between facilities. j Transnational strategy —A strategy that combines the benefits of global-scale ef-
ficiencies with the benefits of local responsiveness. These firms transgress national boundaries.
Concept Questions: 7.1–7.4 Problem 2.13
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Chapter 2 Rapid Review continued
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 2.1 A mission statement is beneficial to an organization because it:
a) is a statement of the organization’s purpose. b) provides a basis for the organization’s culture. c) identifies important constituencies. d) details specific income goals. e) ensures profitability. LO 2.2 The three strategic approaches to competitive advantage are ____, ____, and _____. LO 2.3 Core competencies are those strengths in a firm that include: a) specialized skills. b) unique production methods. c) proprietary information/knowledge. d) things a company does better than others. e) all of the above.
LO 2.4 Evaluating outsourcing providers by comparing their weighted average scores involves:
a) factor-rating analysis. b) cost-volume analysis. c) transportation model analysis. d) linear regression analysis. e) crossover analysis. LO 2.5 A company that is organized across international boundaries,
with decentralized authority and substantial autonomy at each business via subsidiaries, franchises, or joint ventures, has:
a) a global strategy. b) a transnational strategy. c) an international strategy. d) a multidomestic strategy.
Answers: LO 2.1. a; LO 2.2. differentiation, cost leadership, response; LO 2.3. e; LO 2.4. a; LO 2.5. c.
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59
CHAPTER OUTLINE
Project Management 3
◆
The Importance of Project Management 62
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Project Planning 62
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Project Scheduling 65
◆
Project Controlling 66
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Project Management Techniques: PERT and CPM 67
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Determining the Project Schedule 71
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Variability in Activity Times 77
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Cost-Time Trade-Offs and Project Crashing 82
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A Critique of PERT and CPM 85
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Using Microsoft Project to Manage Projects 86
GLOBAL COMPANY PROFILE: Bechtel Group
C H
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O ver a century old, the San Francisco–based Bechtel Group ( www.bechtel.com ) is the
world’s premier manager of massive construction and engineering projects. Known for
billion-dollar projects, Bechtel is famous for its construction feats on the Hoover Dam, the
Boston Central Artery/Tunnel project, the Riyadh, Saudi Arabia Metro, and over 25,000 other
projects in 160 countries. With 53,000 employees and revenues over $39 billion, Bechtel is the
U.S.’s largest project manager.
Project Management Provides a Competitive Advantage for Bechtel
GLOBAL COMPANY PROFILE Bechtel Group
C H A P T E R 3
60
Conditions weren’t what Bechtel ex-
pected when it won a series of billion-dollar
contracts from the U.S. government to help
reconstruct war-torn Iraq in the last decade.
That country’s defeat by Allied forces hadn’t
caused much war damage. Instead, what
Bechtel found was a nation that had been
crumbling for years. None of the sewage
plants in Baghdad worked. Power flicked
on and off. Towns and cities had been left
to decay. And scavengers were stealing
everything from museum artifacts to electric
power lines. Bechtel’s job was to oversee
electric power, sewage, transportation,
and airport repairs.
Bechtel’s crews travelled under armed
escort and slept in trailers surrounded by
razor wire. But the company’s efforts have
paid off. Iraq’s main seaport, Umm Qasr,
has opened. Electrical generation is back to
prewar levels, and Bechtel has refurbished
more than 1,200 schools.
With a global procurement program,
Bechtel easily tapped the company’s
network of suppliers and buyers world-
wide to help rebuild Iraq’s infrastructure.
A massive dredge hired by Bechtel removes silt from Iraq’s port at Umm Qasr. This paved the way
for large-scale deliveries of U.S. food and the return of commercial shipping.
In addition to major construction projects, Bechtel used its project management skills to provide
emergency response to major catastrophes as it did here in the wake of Hurricane Katrina.
P h ili
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/B e ch
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sc o m
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61
Other interesting recent Bechtel projects
include: ◆ Constructing 30 high-security data centers
worldwide for Equinix, Inc. ($1.2 billion).
◆ Building and running a rail line between
London and the Channel Tunnel
($4.6 billion).
◆ Developing an oil pipeline from the Caspian
Sea region to Russia ($850 million).
◆ Expanding the Dubai Airport in the United
Arab Emirates ($600 million) and the Miami
International Airport ($2 billion).
◆ Building liquefi ed natural gas plants in Trini-
dad, West Indies ($1 billion).
◆ Building a new subway for Athens, Greece
($2.6 billion).
◆ Constructing a natural gas pipeline in Thai-
land ($700 million).
◆ Building 30 plants for iMotors.com, a
company that sells refurbished autos online
($300 million).
◆ Building a highway to link the north and
south of Croatia ($303 million).
When companies or countries seek out
firms to manage massive projects, they go
to Bechtel, which, again and again, through
outstanding project management, has dem-
onstrated its competitive advantage.
Managing massive construction projects such as this is the strength of Bechtel. With large penalties for late
completion and incentives for early completion, a good project manager is worth his or her weight in gold.
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o f
B e ch
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Reconstructed terminal at Baghdad International Airport.
T h o m
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Bechtel was the construction contractor for the Hoover Dam. This dam,
on the Colorado River, is the highest in the Western Hemisphere.
Jo e C
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62
The Importance of Project Management When Bechtel, the subject of the opening Global Company Profile, begins a project, it quickly has to mobilize substantial resources, often consisting of manual workers, con- struction professionals, cooks, medical personnel, and even security forces. Its project management team develops a supply chain to access materials to build everything from ports to bridges, dams, and monorails. Bechtel is just one example of a firm that faces modern phenomena: growing project complexity and collapsing product/service life cycles. This change stems from awareness of the strategic value of time-based competition and a quality mandate for continuous improvement. Each new product/service introduction is a unique event—a project. In addition, projects are a common part of our everyday life. We may be planning a wedding or a surprise birthday party, remodeling a house, or preparing a semester-long class project.
Scheduling projects can be a difficult challenge for operations managers. The stakes in proj- ect management are high. Cost overruns and unnecessary delays occur due to poor scheduling and poor controls.
Projects that take months or years to complete are usually developed outside the normal production system. Project organizations within the firm may be set up to handle such jobs and are often disbanded when the project is complete. On other occasions, managers find projects just a part of their job. The management of projects involves three phases (see Figure 3.1 ):
1. Planning: This phase includes goal setting, defining the project, and team organization. 2. Scheduling: This phase relates people, money, and supplies to specific activities and relates
activities to each other. 3. Controlling: Here the firm monitors resources, costs, quality, and budgets. It also revises
or changes plans and shifts resources to meet time and cost demands.
We begin this chapter with a brief overview of these functions. Three popular techniques to allow managers to plan, schedule, and control—Gantt charts, PERT, and CPM—are also described.
Project Planning Projects can be defined as a series of related tasks directed toward a major output. In some firms a project organization is developed to make sure existing programs continue to run smoothly on a day-to-day basis while new projects are successfully completed.
For companies with multiple large projects, such as a construction firm, a project organiza- tion is an effective way of assigning the people and physical resources needed. It is a tempo- rary organization structure designed to achieve results by using specialists from throughout the firm.
The project organization may be most helpful when:
1. Work tasks can be defined with a specific goal and deadline. 2. The job is unique or somewhat unfamiliar to the existing organization. 3. The work contains complex interrelated tasks requiring specialized skills.
VIDEO 3.1 Project Management at Hard Rock’s
Rockfest
Project organization
An organization formed to ensure
that programs (projects) receive
the proper management and
attention.
L E A R N I N G OBJEC TI V ES
LO 3.1 Use a Gantt chart for scheduling 65
LO 3.2 Draw AOA and AON networks 69
LO 3.3 Complete forward and backward passes for a project 72
LO 3.4 Determine a critical path 76
LO 3.5 Calculate the variance of activity times 78
LO 3.6 Crash a project 83
STUDENT TIP Wherever your career takes you,
one of the most useful tools you can
have, as a manager, is the ability to
manage a project.
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 63
Planning the Project (Before project)
Scheduling the Project
Controlling the Project (During project)
Set the goals Performance
1.1 1.2 2.0 2.1
2.11
Define the project
Develop work breakdown
structure Identify team/
resources
Sequence activities Assign people Schedule deliverables Schedule resources
T im
e Cost
1.1 1.2 2.0 2.1
2.11
Monitor resources, costs, quality
June Adams
Smith
Jones
S M T W T F S
1 2 3 4 5 6
7 8 9 10 11 12 13
Shift resources
Revise and change plans
Adams
Smith
Jones
Figure 3.1
Project Planning, Scheduling,
and Controlling
4. The project is temporary but critical to the organization. 5. The project cuts across organizational lines.
The Project Manager An example of a project organization is shown in Figure 3.2 . Project team members are tem- porarily assigned to a project and report to the project manager. The manager heading the project coordinates activities with other departments and reports directly to top management. Project managers receive high visibility in a firm and are responsible for making sure that (1) all necessary activities are finished in proper sequence and on time; (2) the project comes in within budget; (3) the project meets its quality goals; and (4) the people assigned to the project receive the motivation, direction, and information needed to do their jobs. This means that project managers should be good coaches and communicators, and be able to organize activi- ties from a variety of disciplines.
Project No.1
Project No. 2
Mechanical Engineer
Test Engineer
Production
Project Manager
Technician Electrical Engineer
Computer Engineer
Project Manager
Technician
Quality Mgt.
Human Resources Marketing Finance Design
President Figure 3.2
A Sample Project Organization
STUDENT TIP Managers must “make the plan
and then work the plan.”
STUDENT TIP Project organizations can
be temporary or permanent.
A permanent organization
is usually called a matrix
organization.
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Ethical Issues Faced in Project Management Project managers not only have high visibility but they also face ethical decisions on a daily basis. How they act establishes the code of conduct for the project. Project managers often deal with (1) offers of gifts from contractors, (2) pressure to alter status reports to mask the reality of delays, (3) false reports for charges of time and expenses, and (4) pressures to compromise quality to meet bonuses or avoid penalties related to schedules.
Using the Project Management Institute’s ( www.pmi.org ) ethical codes is one means of try- ing to establish standards. These codes need to be accompanied by good leadership and a strong organizational culture, with its ingrained ethical standards and values.
Work Breakdown Structure The project management team begins its task well in advance of project execution so that a plan can be developed. One of its first steps is to carefully establish the project’s objectives, then break the project down into manageable parts. This work breakdown structure (WBS) defines the project by dividing it into its major subcomponents (or tasks), which are then subdivided into more detailed components, and finally into a set of activities and their related costs. The division of the project into smaller and smaller tasks can be difficult, but is critical to manag- ing the project and to scheduling success. Gross requirements for people, supplies, and equip- ment are also estimated in this planning phase.
The work breakdown structure typically decreases in size from top to bottom and is in- dented like this:
Level 1. Project 2. Major tasks in the project 3. Subtasks in major tasks 4. Activities (or “work packages”) to be completed
This hierarchical framework can be illustrated with the development of Microsoft’s operating system Windows 8. As we see in Figure 3.3 , the project, creating a new operating system, is labeled 1.0. The first step is to identify the major tasks in the project (level 2). Three examples would be software design (1.1), cost management plan (1.2), and system testing (1.3). Two major subtasks for 1.1 are development of graphical user interfaces (GUIs) (1.1.1) and creat- ing compatibility with previous versions of Windows (1.1.2). The major subtasks for 1.1.2 are level-4 activities, such as creating a team to handle compatibility with Windows 7 (1.1.2.1),
Work breakdown structure (WBS)
A hierarchical description of
a project into more and more
detailed components.
Level 2
Level 3
Level 4
Level 1 Develop Windows 8 Operating System
Software Design
Cost Management Plan
System Testing
Develop GUIs
1.0
1.1 1.2 1.3
1.1.1
1.1.2
1.1.2.1
1.1.2.2
1.1.2.3
1.2.2 1.3.2
1.2.1 1.3.1 Module Testing
Ensure Compatibility with Earlier Versions
Defect Tracking
Develop Cost/ Schedule Interface
Compatible with Windows 7(Work packages)
Compatible with Windows Vista
Compatible with Windows XP
Design Cost Tracking Reports
Figure 3.3
Work Breakdown Structure
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 65
creating a team for Windows Vista (1.1.2.2), and creating a team for Windows XP (1.1.2.3). There are usually many level-4 activities.
Project Scheduling Project scheduling involves sequencing and allotting time to all project activities. At this stage, managers decide how long each activity will take and compute the resources needed at each stage of production. Managers may also chart separate schedules for personnel needs by type of skill (management, engineering, or pouring concrete, for example) and material needs.
One popular project scheduling approach is the Gantt chart. Gantt charts are low-cost means of helping managers make sure that (1) activities are planned, (2) order of performance is documented, (3) activity time estimates are recorded, and (4) overall project time is developed. As Figure 3.4 shows, Gantt charts are easy to understand. Horizontal bars are drawn for each project activity along a time line. This illustration of a routine servicing of a Delta jetliner during a 40-minute layover shows that Gantt charts also can be used for scheduling repetitive operations. In this case, the chart helps point out potential delays. The OM in Action box on Delta provides additional insights.
On simple projects, scheduling charts such as these permit managers to observe the progress of each activity and to spot and tackle problem areas. Gantt charts, though, do not adequately illustrate the interrelationships between the activities and the resources.
PERT and CPM, the two widely used network techniques that we shall discuss shortly, do have the ability to consider precedence relationships and interdependency of activities. On complex projects, the scheduling of which is almost always computerized, PERT and CPM thus have an edge over the simpler Gantt charts. Even on huge projects, though, Gantt charts can be used as summaries of project status and may complement the other network approaches.
To summarize, whatever the approach taken by a project manager, project scheduling serves several purposes:
1. It shows the relationship of each activity to others and to the whole project. 2. It identifies the precedence relationships among activities. 3. It encourages the setting of realistic time and cost estimates for each activity. 4. It helps make better use of people, money, and material resources by identifying critical
bottlenecks in the project.
Gantt charts
Planning charts used to schedule
resources and allocate time.
STUDENT TIP Gantt charts are simple and visual,
making them widely used.
LO 3.1 Use a Gantt chart for scheduling
0 10 20 30 40
Time, minutes
Passengers
Baggage
Fueling
Lavatory servicing
Galley servicing
Cargo and mail
Drinking water
Flight service
Cabin cleaning
Cargo and mail
Operating crew Baggage Passengers
Baggage claim Deplaning
Container offload
Engine injection water Pumping
Container offload Main cabin door Aft cabin door
Loading Aft, center, forward
Economy section First-class section
Container/bulk loading
Galley/cabin check
Aircraft check Receive passengers
Loading Boarding
Figure 3.4
Gantt Chart of Service
Activities for a Delta Jet during
a 40-Minute Layover
Delta saves $50 million a year
with this turnaround time, which
is a reduction from its traditional
60-minute routine.
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Project Controlling The control of projects, like the control of any management system, involves close moni- toring of resources, costs, quality, and budgets. Control also means using a feedback loop to revise the project plan and having the ability to shift resources to where they are needed most. Computerized PERT/CPM reports and charts are widely available today from scores of competing software firms. Some of the more popular of these programs are Oracle Primavera (by Oracle), MindView (by Match Ware), HP Project (by Hewlett-Packard), Fast Track (by AEC Software), and Microsoft Project (by Microsoft Corp.), which we illustrate in this chapter.
These programs produce a broad variety of reports, including (1) detailed cost breakdowns, (2) labor requirements, (3) cost and hour summaries, (4) raw material and expenditure fore- casts, (5) variance reports, (6) time analysis reports, and (7) work status reports.
VIDEO 3.2 Project Management at Arnold Palmer
Hospital
STUDENT TIP To use project management
software, you first need to
understand the next two sections in
this chapter.
Delta’s Ground Crew Orchestrates a Smooth Takeoff
Flight 574’s engines screech its arrival as the jet lumbers down Richmond’s
taxiway with 140 passengers arriving from Atlanta. In 40 minutes, the plane is
to be airborne again.
However, before this jet can depart, there is business to attend to: passen-
gers, luggage, and cargo to unload and load; thousands of gallons of jet fuel
and countless drinks to restock; cabin and restrooms to clean; toilet holding
tanks to drain; and engines, wings, and landing gear to inspect.
The 10-person ground crew knows that a miscue anywhere—a broken
cargo loader, lost baggage, misdirected passengers—can mean a late depar-
ture and trigger a chain reaction of headaches from Richmond to Atlanta to
every destination of a connecting flight.
Carla Sutera, the operations manager for Delta’s Richmond Interna-
tional Airport, views the turnaround operation like a pit boss awaiting a
race car. Trained crews are in place for Flight 574 with baggage carts
OM in Action and tractors, hydraulic cargo loaders, a truck to load food and drinks,
another to lift the cleanup crew, another to put fuel on, and a fourth to
take water off. The “pit crew” usually performs so smoothly that most
passengers never suspect the pro-
portions of the effort. Gantt charts,
such as the one in Figure 3.4 , aid
Delta and other airlines with the
staffing and scheduling that are
needed for this task.
Jef f
T o p p in
g /G
e tt
y Im
a g e s
Sources: Knight Ridder Tribune
Business News (July 16, 2005) and
(November 21, 2002).
Cou rt
e sy
A rn
o ld
P a lm
e r
M e d ic
a l C
e n te
r
C o u rt
e sy
A rn
o ld
P a lm
e r
M e d ic
a l C
e n te
r
Construction of the new 11-story building at Arnold Palmer Hospital in Orlando, Florida, was an enormous project for the hospital
administration. The photo on the left shows the first six floors under construction. The photo on the right shows the building as
completed two years later. Prior to beginning actual construction, regulatory and funding issues added, as they do with most
projects, substantial time to the overall project. Cities have zoning and parking issues; the EPA has drainage and waste issues;
and regulatory authorities have their own requirements, as do issuers of bonds. The $100 million, 4-year project at Arnold Palmer
Hospital is discussed in the Video Case Study at the end of this chapter.
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 67
Controlling projects can be difficult. The stakes are high; cost overruns and unnecessary delays can occur due to poor planning, scheduling, and controls. Some projects are “well-defined,” whereas others may be “ill-defined.” Projects typically only become well-defined after detailed extensive initial planning and careful definition of required inputs, resources, processes, and outputs. Well-established projects where constraints are known (e.g., buildings and roads) and engineered products (e.g., airplanes and cars) with well-defined specifications and drawings may fall into this category. Well-defined projects are assumed to have changes small enough to be managed without substantially revising plans. They use what is called a waterfall approach, where the project progresses smoothly, in a step-by-step manner, through each phase to completion.
But many projects, such as software development (e.g., 3-D games) and new technology (e.g., landing the Mars land rover) are ill-defined. These projects require what is known as an agile style of management with collaboration and constant feedback to adjust to the many unknowns of the evolving technology and project specifications. The OM in Action box “Agile Project Management at Mastek” provides such an example. Most projects fall somewhere be- tween waterfall and agile.
Project Management Techniques: PERT and CPM Program evaluation and review technique (PERT) and the critical path method (CPM) were both developed in the 1950s to help managers schedule, monitor, and control large and complex projects. CPM arrived first, as a tool developed to assist in the building and maintenance of chemical plants at duPont. Independently, PERT was developed in 1958 for the U.S. Navy.
The Framework of PERT and CPM PERT and CPM both follow six basic steps:
1. Define the project and prepare the work breakdown structure. 2. Develop the relationships among the activities. Decide which activities must precede and
which must follow others. 3. Draw the network connecting all the activities. 4. Assign time and/or cost estimates to each activity. 5. Compute the longest time path through the network. This is called the critical path . 6. Use the network to help plan, schedule, monitor, and control the project.
Step 5, finding the critical path, is a major part of controlling a project. The activities on the critical path represent tasks that will delay the entire project if they are not completed on time. Managers can gain the flexibility needed to complete critical tasks by identifying
Program evaluation and review technique (PERT)
A project management technique
that employs three time estimates
for each activity.
Critical path method (CPM)
A project management technique
that uses only one time factor per
activity.
Critical path
The computed longest time path(s)
through a network.
Agile Project Management at Mastek
Agile project management has changed the way that Mastek Corp., in Mumbai,
India, develops its educational software products. On a traditional well-defined
project, managers are actively involved in directing work and telling their team
what needs to be done—a style often referred to as a step-by-step waterfall
style of project management.
Agile project management is different. In the early stages, the project
manager creates a high-level plan, based on outline requirements and a
high-level view of the solution. From that point, the end project is created
iteratively and incrementally, with each increment building on the output of
steps preceding it.
OM in Action The principles of agile are essentially communication and transparency. Instead
of waiting for something to be delivered, with limited understanding of the desired
end result, there are numerous checkpoints and feedback loops to track progress.
Agile provides Mastek the ability to keep costs under control. Without agile,
the cost of quality increases. “It’s much harder to correct mistakes when a
software product is nearing its final phase of development,” says a company
executive. “It’s much better to develop it as you go along. I think agile project
management would help any software developer.”
Sources: AMPG International (2015) and www.cprime.com (2012).
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68 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
noncritical activities and replanning, rescheduling, and reallocating labor and financial resources.
Although PERT and CPM differ to some extent in terminology and in the construction of the network, their objectives are the same. Furthermore, the analysis used in both techniques is very similar. The major difference is that PERT employs three time estimates for each activ- ity. These time estimates are used to compute expected values and standard deviations for the activity. CPM makes the assumption that activity times are known with certainty and hence requires only one time factor for each activity.
For purposes of illustration, the rest of this section concentrates on a discussion of PERT. Most of the comments and procedures described, however, apply just as well to CPM.
PERT and CPM are important because they can help answer questions such as the follow- ing about projects with thousands of activities:
1. When will the entire project be completed? 2. What are the critical activities or tasks in the project—that is, which activities will delay
the entire project if they are late? 3. Which are the noncritical activities—the ones that can run late without delaying the whole
project’s completion? 4. What is the probability that the project will be completed by a specific date? 5. At any particular date, is the project on schedule, behind schedule, or ahead of schedule? 6. On any given date, is the money spent equal to, less than, or greater than the budgeted
amount? 7. Are there enough resources available to finish the project on time? 8. If the project is to be finished in a shorter amount of time, what is the best way to accom-
plish this goal at the least cost?
Network Diagrams and Approaches The first step in a PERT or CPM network is to divide the entire project into significant activi- ties in accordance with the work breakdown structure. There are two approaches for drawing a project network: activity on node (AON) and activity on arrow (AOA) . Under the AON convention, nodes designate activities. Under AOA, arrows represent activities. Activities consume time and resources. The basic difference between AON and AOA is that the nodes in an AON diagram represent activities. In an AOA network, the nodes represent the starting and finish- ing times of an activity and are also called events . So nodes in AOA consume neither time nor resources.
Although both AON and AOA are popular in practice, many of the project management software packages, including Microsoft Project, use AON networks. For this reason, although we illustrate both types of networks in the next examples, we focus on AON networks in sub- sequent discussions in this chapter.
Activity-on-node (AON)
A network diagram in which nodes
designate activities.
Activity-on-arrow (AOA)
A network diagram in which
arrows designate activities.
Milwaukee Paper Manufacturing had long delayed the expense of installing advanced computerized air pollution control equipment in its facility. But when the board of directors adopted a new proactive policy on sustainability, it did not just authorize the budget for the state-of-the-art equipment. It directed the plant manager, Julie Ann Williams, to complete the installation in time for a major announcement of the policy, on Earth Day, exactly 16 weeks away! Under strict deadline from her bosses, Williams needs to be sure that installation of the filtering system progresses smoothly and on time.
Given the following information, develop a table showing activity precedence relationships.
APPROACH c Milwaukee Paper has identified the eight activities that need to be performed in order for the project to be completed. When the project begins, two activities can be simultaneously started: building the internal components for the device (activity A) and the modifications necessary for the floor and roof (activity B). The construction of the collection stack (activity C) can begin when
Example 1 PREDECESSOR RELATIONSHIPS FOR POLLUTION CONTROL AT MILWAUKEE PAPER
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 69
Activity-on-Node Example Note that in Example 1 , we only list the immediate predecessors for each activity. For instance, in Table 3.1 , because activity A precedes activity C, and activity C precedes activity E, the fact that activity A precedes activity E is implicit . This relationship need not be explicitly shown in the activity precedence relationships.
When there are many activities in a project with fairly complicated precedence relationships, it is difficult for an individual to comprehend the complexity of the project from just the tabular information. In such cases, a visual representation of the project, us- ing a project network , is convenient and useful. A project network is a diagram of all the activities and the precedence relationships that exist between these activities in a project. Example 2 illustrates how to construct an AON project network for Milwaukee Paper Manufacturing.
It is convenient to have the project network start and finish with a unique node. In the Milwaukee Paper example, it turns out that a unique activity, H, is the last activity in the proj- ect. We therefore automatically have a unique ending node.
In situations in which a project has multiple ending activities, we include a “dummy” ending activity. We illustrate this type of situation in Solved Problem 3.1 at the end of this chapter.
LO 3.2 Draw AOA and AON networks
the internal components are completed. Pouring the concrete floor and installation of the frame (activ- ity D) can be started as soon as the internal components are completed and the roof and floor have been modified.
After the collection stack has been constructed, two activities can begin: building the high-tempera- ture burner (activity E) and installing the pollution control system (activity F). The air pollution device can be installed (activity G) after the concrete floor has been poured, the frame has been installed, and the high-temperature burner has been built. Finally, after the control system and pollution device have been installed, the system can be inspected and tested (activity H).
SOLUTION c Activities and precedence relationships may seem rather confusing when they are pre- sented in this descriptive form. It is therefore convenient to list all the activity information in a table, as shown in Table 3.1 . We see in the table that activity A is listed as an immediate predecessor of activity C. Likewise, both activities D and E must be performed prior to starting activity G.
TABLE 3.1 Milwaukee Paper Manufacturing’s Activities and Predecessors
ACTIVITY DESCRIPTION IMMEDIATE PREDECESSORS
A Build internal components —
B Modify roof and fl oor —
C Construct collection stack A
D Pour concrete and install frame A, B
E Build high-temperature burner C
F Install pollution control system C
G Install air pollution device D, E
H Inspect and test F, G
INSIGHT c To complete a network, all predecessors must be clearly defined.
LEARNING EXERCISE c What is the impact on this sequence of activities if Environmental Protec- tion Agency (EPA) approval is required after Inspect and Test? [Answer: The immediate predecessor for the new activity would be H, Inspect and Test , with EPA approval as the last activity.]
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70 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
Draw the AON network for Milwaukee Paper, using the data in Example 1 .
APPROACH c In the AON approach, we denote each activity by a node. The lines, or arrows, repre- sent the precedence relationships between the activities.
SOLUTION c In this example, there are two activities (A and B) that do not have any predecessors. We draw separate nodes for each of these activities, as shown in Figure 3.5 . Although not required, it is usually convenient to have a unique starting activity for a project. We have therefore included a dummy activity called Start in Figure 3.5 . This dummy activity does not really exist and takes up zero time and resources. Activity Start is an immediate predecessor for both activities A and B, and it serves as the unique starting activity for the entire project.
Example 2 AON GRAPH FOR MILWAUKEE PAPER
Dummy activity
An activity having no time that is
inserted into a network to maintain
the logic of the network.
We now show the precedence relationships using lines with arrow symbols. For example, an arrow from activity Start to activity A indicates that Start is a predecessor for activity A. In a similar fashion, we draw an arrow from Start to B.
Next, we add a new node for activity C. Because activity A precedes activity C, we draw an arrow from node A to node C (see Figure 3.6 ). Likewise, we first draw a node to represent activity D. Then, because activities A and B both precede activity D, we draw arrows from A to D and from B to D (see Figure 3.6 ).
A
B
Activity A (Build Internal Components)
Activity B (Modify Roof and Floor)
Start
Start Activity
Figure 3.5
Beginning AON Network
for Milwaukee Paper
We proceed in this fashion, adding a separate node for each activity and a separate line for each precedence relationship that exists. The complete AON project network for the Milwaukee Paper Manufacturing project is shown in Figure 3.7 .
D
C
Activity A Precedes Activity C
Activities A and B Precede Activity D
Start
B
A
Figure 3.6
Intermediate AON Network
for Milwaukee Paper
INSIGHT c Drawing a project network properly takes some time and experience. We would like the lines to be straight and arrows to move to the right when possible.
LEARNING EXERCISE c If EPA Approval occurs after Inspect and Test , what is the impact on the graph? [Answer: A straight line is extended to the right beyond H (with a node I added) to reflect the additional activity.]
RELATED PROBLEMS c 3.4a, 3.5, 3.8, 3.9, 3.10, 3.11a, 3.12 (3.13–3.14 are available in MyOMLab)
D G
H
Arrows Show Precedence
Relationships
Start
B
A
F
E
C
Figure 3.7
Complete AON Network
for Milwaukee Paper
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 71
Activity-on-Arrow Example In an AOA project network we can represent activities by arrows. A node represents an event , which marks the start or completion time of an activity. We usually identify an event (node) by a number.
Draw the complete AOA project network for Milwaukee Paper’s problem.
APPROACH c Using the data from Table 3.1 in Example 1 , draw one activity at a time, starting with A.
SOLUTION c We see that activity A starts at event 1 and ends at event 2. Likewise, activity B starts at event 1 and ends at event 3. Activity C, whose only immediate predecessor is activity A, starts at node 2 and ends at node 4. Activity D, however, has two predecessors (i.e., A and B). Hence, we need both activities A and B to end at event 3, so that activity D can start at that event. However, we cannot have multiple activities with common starting and ending nodes in an AOA network. To overcome this dif- ficulty, in such cases, we may need to add a dummy line (activity) to enforce the precedence relationship. The dummy activity, shown in Figure 3.8 as a dashed line, is inserted between events 2 and 3 to make the diagram reflect the precedence between A and D. The remainder of the AOA project network for Milwaukee Paper’s example is also shown.
Example 3 ACTIVITY-ON-ARROW FOR MILWAUKEE PAPER
STUDENT TIP The dummy activity consumes
no time, but note how it changes
precedence. Now activity D
cannot begin until both B and the
dummy are complete.
INSIGHT c Dummy activities are common in AOA networks. They do not really exist in the project and take zero time.
LEARNING EXERCISE c A new activity, EPA Approval , follows activity H. Add it to Figure 3.8 . [Answer: Insert an arrowed line from node 7, which ends at a new node 8, and is labeled I (EPA Approval).]
RELATED PROBLEMS c 3.4b, 3.6, 3.7
C
(Construct Stack)
(Pour Concrete/Install Frame)
D
(B u ild
B u rn
e r)
E
A
(B uil
d In
te rn
al
Co m
po ne
nt s)
B (M
odify Roof/Floor)
(Install Controls)
F
G
(In st
al l P
ol lu
tio n
De vic
e)
(Inspect/Test)
HDummy Activity
2 4
5
6 7
3
1
Figure 3.8
Complete AOA Network
(with Dummy Activity) for
Milwaukee Paper
Determining the Project Schedule Look back at Figure 3.7 (in Example 2 ) for a moment to see Milwaukee Paper’s completed AON project network. Once this project network has been drawn to show all the activities and their precedence relationships, the next step is to determine the project schedule. That is, we need to identify the planned starting and ending time for each activity.
Let us assume Milwaukee Paper estimates the time required for each activity, in weeks, as shown in Table 3.2 . The table indicates that the total time for all eight of the company’s activi- ties is 25 weeks. However, because several activities can take place simultaneously, it is clear that the total project completion time may be less than 25 weeks. To find out just how long the project will take, we perform the critical path analysis for the network.
Critical path analysis
A process that helps determine a
project schedule.
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As mentioned earlier, the critical path is the longest time path through the network. To find the critical path, we calculate two distinct starting and ending times for each activity. These are defined as follows:
Earliest start (ES) = earliest time at which an activity can start, assuming all predecessors have been completed
Earliest finish (EF) = earliest time at which an activity can be finished Latest start (LS) = latest time at which an activity can start so as to not delay
the completion time of the entire project Latest finish (LF) = latest time by which an activity has to finish so as to not
delay the completion time of the entire project
We use a two-pass process, consisting of a forward pass and a backward pass, to deter- mine these time schedules for each activity. The early start and finish times (ES and EF) are determined during the forward pass . The late start and finish times (LS and LF) are determined during the backward pass.
Forward Pass To clearly show the activity schedules on the project network, we use the notation shown in Figure 3.9 . The ES of an activity is shown in the top left corner of the node denoting that activity. The EF is shown in the top right corner. The latest times, LS and LF, are shown in the bottom-left and bottom-right corners, respectively.
Forward pass
A process that identifies all the
early times.
LO 3.3 Complete forward and backward
passes for a project
TABLE 3.2 Time Estimates for Milwaukee Paper Manufacturing
ACTIVITY DESCRIPTION TIME (WEEKS)
A Build internal components 2
B Modify roof and fl oor 3
C Construct collection stack 2
D Pour concrete and install frame 4
E Build high-temperature burner 4
F Install pollution control system 3
G Install air pollution device 5
H Inspect and test 2
Total time (weeks) 25
STUDENT TIP Does this mean the project will take
25 weeks to complete? No. Don’t
forget that several of the activities
are being performed at the same
time. It would take 25 weeks if they
were done sequentially.
ES
Earliest Start
Earliest Finish
Activity Name or Symbol
Activity Duration
Latest Start
Latest Finish
A
2
LS
EF
LF
Figure 3.9
Notation Used in Nodes for
Forward and Backward Pass
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 73
Earliest Start Time Rule Before an activity can start, all its immediate predecessors must be finished:
◆ If an activity has only a single immediate predecessor, its ES equals the EF of the predecessor.
◆ If an activity has multiple immediate predecessors, its ES is the maximum of all EF values of its predecessors. That is:
ES = Max {EF of all immediate predecessors} (3-1)
Earliest Finish Time Rule The earliest finish time (EF) of an activity is the sum of its earliest start time (ES) and its activity time. That is:
EF = ES + Activity time (3-2)
STUDENT TIP All predecessor activities must be
completed before an acitivity can
begin.
Calculate the earliest start and finish times for the activities in the Milwaukee Paper Manufacturing project.
APPROACH c Use Table 3.2 , which contains the activity times. Complete the project network for the company’s project, along with the ES and EF values for all activities.
SOLUTION c With the help of Figure 3.10 , we describe how these values are calculated. Because activity Start has no predecessors, we begin by setting its ES to 0. That is, activity Start can
begin at time 0, which is the same as the beginning of week 1. If activity Start has an ES of 0, its EF is also 0, since its activity time is 0.
Next, we consider activities A and B, both of which have only Start as an immediate predecessor. Using the earliest start time rule, the ES for both activities A and B equals zero, which is the EF of activity Start. Now, using the earliest finish time rule, the EF for A is 2 (= 0 + 2), and the EF for B is 3 (= 0 + 3).
Since activity A precedes activity C, the ES of C equals the EF of A (= 2). The EF of C is therefore 4 (= 2 + 2).
We now come to activity D. Both activities A and B are immediate predecessors for D. Whereas A has an EF of 2, activity B has an EF of 3. Using the earliest start time rule, we compute the ES of activity D as follows:
ES of D = Max{EF of A, EF of B} = Max (2, 3) = 3
The EF of D equals 7 (= 3 + 4). Next, both activities E and F have activity C as their only immediate predecessor. Therefore, the ES for both E and F equals 4 (= EF of C). The EF of E is 8 (= 4 + 4), and the EF of F is 7 (= 4 + 3).
Activity G has both activities D and E as predecessors. Using the earliest start time rule, its ES is therefore the maximum of the EF of D and the EF of E. Hence, the ES of activity G equals 8 (= maxi- mum of 7 and 8), and its EF equals 13 (= 8 + 5).
Finally, we come to activity H. Because it also has two predecessors, F and G, the ES of H is the max- imum EF of these two activities. That is, the ES of H equals 13 (= maximum of 13 and 7). This implies that the EF of H is 15 (= 13 + 2). Because H is the last activity in the project, this also implies that the earliest time in which the entire project can be completed is 15 weeks.
INSIGHT c The ES of an activity that has only one predecessor is simply the EF of that predecessor. For an activity with more than one predecessor, we must carefully examine the EFs of all immediate predecessors and choose the largest one.
LEARNING EXERCISE c A new activity I, EPA Approval , takes 1 week. Its predecessor is activity H. What are I’s ES and EF? [Answer: 15, 16]
RELATED PROBLEMS c 3.15, 3.16, 3.19c
EXCEL OM Data File Ch03Ex4.xls can be found in MyOMLab.
Example 4 COMPUTING EARLIEST START AND FINISH TIMES FOR MILWAUKEE PAPER
Although the forward pass allows us to determine the earliest project completion time, it does not identify the critical path. To identify this path, we need to now conduct the backward pass to determine the LS and LF values for all activities.
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Backward Pass Just as the forward pass began with the first activity in the project, the backward pass begins with the last activity in the project. For each activity, we first determine its LF value, followed by its LS value. The following two rules are used in this process.
Latest Finish Time Rule This rule is again based on the fact that before an activity can start, all its immediate predecessors must be finished:
◆ If an activity is an immediate predecessor for just a single activity, its LF equals the LS of the activity that immediately follows it.
◆ If an activity is an immediate predecessor to more than one activity, its LF is the minimum of all LS values of all activities that immediately follow it. That is:
LF = Min{LS of all immediate following activities} (3-3)
Latest Start Time Rule The latest start time (LS) of an activity is the difference of its latest finish time (LF) and its activity time. That is:
LS = LF − Activity time (3-4)
Backward pass
An activity that finds all the late
start and late finish times.
Calculate the latest start and finish times for each activity in Milwaukee Paper’s pollution project.
APPROACH c Use Figure 3.10 as a beginning point. Overlay 1 of Figure 3.10 shows the complete project network for Milwaukee Paper, along with added LS and LF values for all activities. In what fol- lows, we see how these values were calculated.
Example 5 COMPUTING LATEST START AND FINISH TIMES FOR MILWAUKEE PAPER
0 A
ES of A
ES EF ES of D= Max(2,3)
LS LF
Activity Name
Activity Duration
ES = Max{EF of D, EF of E} = Max(7, 8) = 8
EF of A = ES of A + 2
ES of C = EF of A
2
2
0 B
3
3 8 G
5
13
13 H
2
15
4 F
3
7
2 C
2
4
3 D
4
7
4 E
4
80 Start
0
0
Figure 3.10 Earliest Start and Earliest Finish Times for Milwaukee Paper
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 75
Calculating Slack Time and Identifying the Critical Path(s) After we have computed the earliest and latest times for all activities, it is a simple matter to find the amount of slack time that each activity has. Slack is the length of time an activity can be delayed without delaying the entire project. Mathematically:
Slack = LS − ES or Slack = LF − EF (3-5)
Slack time
Free time for an activity. Also
referred to as free float or free
slack.
SOLUTION c We begin by assigning an LF value of 15 weeks for activity H. That is, we specify that the latest finish time for the entire project is the same as its earliest finish time. Using the latest start time rule, the LS of activity H is equal to 13 (= 15 − 2).
Because activity H is the lone succeeding activity for both activities F and G, the LF for both F and G equals 13. This implies that the LS of G is 8 (= 13 − 5), and the LS of F is 10 (= 13 − 3).
Proceeding in this fashion, we see that the LF of E is 8 (= LS of G), and its LS is 4 (= 8 − 4). Likewise, the LF of D is 8 (= LS of G), and its LS is 4 (= 8 − 4).
We now consider activity C, which is an immediate predecessor to two activities: E and F. Using the latest finish time rule, we compute the LF of activity C as follows:
LF of C = Min{LS of E, LS of F} = Min(4, 10) = 4
The LS of C is computed as 2 (= 4 − 2). Next, we compute the LF of B as 4 (= LS of D) and its LS as 1 (= 4 − 3).
We now consider activity A. We compute its LF as 2 (= minimum of LS of C and LS of D). Hence, the LS of activity A is 0 (= 2 − 2). Finally, both the LF and LS of activity Start are equal to 0.
INSIGHT c The LF of an activity that is the predecessor of only one activity is just the LS of that fol- lowing activity. If the activity is the predecessor to more than one activity, its LF is the smallest LS value of all activities that follow immediately.
LEARNING EXERCISE c A new activity I, EPA Approval , takes 1 week. Its predecessor is activity H. What are I’s LS and LF? [Answer: 15, 16]
RELATED PROBLEMS c 3.15, 3.19c
Calculate the slack for the activities in the Milwaukee Paper project.
APPROACH c Start with the data in Overlay 1 of Figure 3.10 in Example 5 and develop Table 3.3 one line at a time.
SOLUTION c Table 3.3 summarizes the ES, EF, LS, LF, and slack time for all of the firm’s activities. Activity B, for example, has 1 week of slack time because its LS is 1 and its ES is 0 (alternatively, its LF is 4 and its EF is 3). This means that activity B can be delayed by up to 1 week, and the whole project can still be finished in 15 weeks.
On the other hand, activities A, C, E, G, and H have no slack time. This means that none of them can be delayed without delaying the entire project. Conversely, if plant manager Julie Ann Williams wants to reduce the total project times, she will have to reduce the length of one of these activities.
Overlay 2 of Figure 3.10 shows the slack computed for each activity.
INSIGHT c Slack may be computed from either early/late starts or early/late finishes. The key is to find which activities have zero slack.
Example 6 CALCULATING SLACK TIMES FOR MILWAUKEE PAPER
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The activities with zero slack are called critical activities and are said to be on the critical path. The critical path is a continuous path through the project network that:
◆ Starts at the first activity in the project (Start in our example). ◆ Terminates at the last activity in the project (H in our example). ◆ Includes only critical activities (i.e., activities with no slack time).
LO 3.4 Determine a critical path
LEARNING EXERCISE c A new activity I, EPA Approval , follows activity H and takes 1 week. Is it on the critical path? [Answer: Yes, it’s LS – ES = 0]
RELATED PROBLEMS c 3.8d, 3.15d, 3.19c
ACTIVE MODEL 3.1 This example is further illustrated in Active Model 3.1 in MyOMLab.
TABLE 3.3 Milwaukee Paper’s Schedule and Slack Times
ACTIVITY ACTIVITY
TIME EARLIEST START ES
EARLIEST FINISH EF
LATEST START LS
LATEST FINISH LF
SLACK LS – ES
ON CRITICAL PATH
A 2 0 2 0 2 0 Yes
B 3 0 3 1 4 1 No
C 2 2 4 2 4 0 Yes
D 4 3 7 4 8 1 No
E 4 4 8 4 8 0 Yes
F 3 4 7 10 13 6 No
G 5 8 13 8 13 0 Yes
H 2 13 15 13 15 0 Yes
Total Slack Time Look again at the project network in Overlay 3 of Figure 3.10 . Consider activities B and D, which have slack of 1 week each. Does it mean that we can delay each activity by 1 week, and still complete the project in 15 weeks? The answer is no.
Let’s assume that activity B is delayed by 1 week. It has used up its slack of 1 week and now has an EF of 4. This implies that activity D now has an ES of 4 and an EF of 8. Note that these are also its LS and LF values, respectively. That is, activity D also has no slack time now. Essentially, the slack of 1 week that activities B and D had is, for that path, shared between them. Delaying either activity by 1 week causes not only that activity, but also the other activity, to lose its slack. This type of a slack time is referred to as total slack . Typically, when two or more noncritical activities appear successively in a path, they share total slack.
Show Milwaukee Paper’s critical path and find the project completion time.
APPROACH c We use Table 3.3 and Overlay 3 of Figure 3.10 . Overlay 3 of Figure 3.10 indicates that the total project completion time of 15 weeks corresponds to the longest path in the network. That path is Start-A-C-E-G-H in network form. It is shown with thick blue arrows.
INSIGHT c The critical path follows the activities with slack = 0. This is considered the longest path through the network.
LEARNING EXERCISE c Why are activities B, D, and F not on the path with the thick blue line? [Answer: They are not critical and have slack values of 1, 1, and 6 weeks, respectively.]
RELATED PROBLEMS c 3.5–3.11, 3.16, 3.19b, 3.21a
Example 7 SHOWING CRITICAL PATH WITH BLUE ARROWS
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 77
Variability in Activity Times In identifying all earliest and latest times so far, and the associated critical path(s), we have adopted the CPM approach of assuming that all activity times are known and fixed constants. That is, there is no variability in activity times. However, in practice, it is likely that activity completion times vary depending on various factors.
For example, building internal components (activity A) for Milwaukee Paper Manufactur- ing is estimated to finish in 2 weeks. Clearly, supply-chain issues such as late arrival of materi- als, absence of key personnel, and so on could delay this activity. Suppose activity A actually ends up taking 3 weeks. Because A is on the critical path, the entire project will now be delayed by 1 week to 16 weeks. If we had anticipated completion of this project in 15 weeks, we would obviously miss our Earth Day deadline.
Although some activities may be relatively less prone to delays, others could be extremely susceptible to delays. For example, activity B (modify roof and floor) could be heavily dependent on weather conditions. A spell of bad weather could significantly affect its completion time.
This means that we cannot ignore the impact of variability in activity times when deciding the schedule for a project. PERT addresses this issue.
Three Time Estimates in PERT In PERT, we employ a probability distribution based on three time estimates for each activity, as follows:
Optimistic time ( a ) = time an activity will take if everything goes as planned. In estimating this value, there should be only a small probability (say, 1/100) that the activity time will be < a .
Pessimistic time ( b ) = time an activity will take assuming very unfavorable conditions. In estimating this value, there should also be only a small prob- ability (also 1/100) that the activity time will be > b .
Most likely time ( m ) = most realistic estimate of the time required to complete an activity.
When using PERT, we often assume that activity time estimates follow the beta probability distribution (see Figure 3.11 ). This continuous distribution is often appropriate for determin- ing the expected value and variance for activity completion times.
STUDENT TIP PERT’s ability to handle three time
estimates for each activity enables
us to compute the probability that
we can complete the project by a
target date.
Optimistic time
The “best” activity completion time
that could be obtained in a PERT
network.
Pessimistic time
The “worst” activity time that could
be expected in a PERT network.
Most likely time
The most probable time to
complete an activity in a PERT
network.
To plan, monitor, and control the huge
number of details involved in sponsoring
a rock festival attended by more than
100,000 fans, managers use Microsoft
Project and the tools discussed in this
chapter. The Video Case Study “Managing
Hard Rock’s Rockfest,” at the end of the
chapter, provides more details of the
management task.
T im
C o g g in
/A la
m y
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To find the expected activity time , t , the beta distribution weights the three time estimates as follows:
t = ( a + 4 m + b ) ∕ 6 (3-6)
That is, the most likely time ( m ) is given four times the weight as the optimistic time ( a ) and pessimistic time ( b ). The time estimate t computed using Equation (3-6) for each activity is used in the project network to compute all earliest and latest times.
To compute the dispersion or variance of activity completion time , we use the formula: 1
Variance = [( b − a ) ∕ 6] 2 (3-7)
Optimistic Time (a)
Most Likely Time (m)
Pessimistic Time (b)
Activity Time P
ro b a b ili
ty
Probability of 1 in 100 of occurring< a
Probability of 1 in 100 of occurring> b
Figure 3.11
Beta Probability Distribution
with Three Time Estimates
Julie Ann Williams and the project management team at Milwaukee Paper want an expected time and variance for Activity F (Installing the Pollution Control System) where:
a = 1 week, m = 2 weeks, b = 9 weeks
APPROACH c Use Equations (3–6) and (3–7) to compute the expected time and variance for F.
SOLUTION c The expected time for Activity F is:
t = a + 4m + b
6 =
1 + 4(2) + 9 6
= 18 6
= 3 weeks
The variance for Activity F is:
Variance = c (b - a)
6 d
2 = c
(9 - 1) 6
d 2
= a 8 6 b
2 =
64 36
= 1.78
INSIGHT c Williams now has information that allows her to understand and manage Activity F. The expected time is, in fact, the activity time used in our earlier computation and identification of the critical path.
LEARNING EXERCISE c Review the expected times and variances for all of the other activities in the project. These are shown in Table 3.4 .
LO 3.5 Calculate the variance of activity times
Example 8 EXPECTED TIMES AND VARIANCES FOR MILWAUKEE PAPER
STUDENT TIP Can you see why the variance
is higher in some activities
than in others? Note the spread
between the optimistic and
pessimistic times.
RELATED PROBLEMS c 3.17a, b, 3.18, 3.19a, 3.20a (3.26b, 3.27 are available in MyOMLab) EXCEL OM Data File Ch03Ex8.xls can be found in MyOMLab.
TABLE 3.4 Time Estimates (in weeks) for Milwaukee Paper’s Project
ACTIVITY OPTIMISTIC
a
MOST LIKELY
m PESSIMISTIC
b EXPECTED TIME
t 5 ( a 1 4 m 1 b )/6 VARIANCE [( b 2 a )/6] 2
A 1 2 3 2 [(3 2 1)/6] 2 = 4/36 = .11 B 2 3 4 3 [(4 2 2)/6] 2 = 4/36 = .11 C 1 2 3 2 [(3 2 1)/6] 2 = 4/36 = .11 D 2 4 6 4 [(6 2 2)/6] 2 = 16/36 = .44 E 1 4 7 4 [(7 2 1)/6] 2 = 36/36 = 1.00 F 1 2 9 3 [(9 2 1)/6] 2 = 64/36 = 1.78 G 3 4 11 5 [(11 2 3)/6] 2 = 64/36 = 1.78 H 1 2 3 2 [(3 2 1)/6] 2 = 4/36 = .11
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 79
Probability of Project Completion The critical path analysis helped us determine that Milwaukee Paper’s expected project com- pletion time is 15 weeks. Julie Ann Williams knows, however, that there is significant variation in the time estimates for several activities. Variation in activities that are on the critical path can affect the overall project completion time—possibly delaying it. This is one occurrence that worries the plant manager considerably.
PERT uses the variance of critical path activities to help determine the variance of the over- all project. Project variance is computed by summing variances of critical activities:
s2p = Project variance = Σ(variances of activities on critical path) (3-8)
Here we see a ship being built at the Hyundai
shipyard, Asia’s largest shipbuilder, in Korea.
Managing this project uses the same techniques as
managing the remodeling of a store, installing a new
production line, or implementing a new computer
system.
K im
H o n g -J
i/ R
e u te
rs
Milwaukee Paper’s managers now wish to know the project’s variance and standard deviation.
APPROACH c Because the activities are independent, we can add the variances of the activities on the critical path and then take the square root to determine the project’s standard deviation.
SOLUTION c From Example 8 ( Table 3.4 ), we have the variances of all of the activities on the critical path. Specifically, we know that the variance of activity A is 0.11, variance of activity C is 0.11, variance of activity E is 1.00, variance of activity G is 1.78, and variance of activity H is 0.11.
Compute the total project variance and project standard deviation:
Project variance (s2p) = 0.11 + 0.11 + 1.00 + 1.78 + 0.11 = 3.11
which implies:
Project standard deviation (sp) = 2Project variance = 23.11 = 1.76 weeks
INSIGHT c Management now has an estimate not only of expected completion time for the project but also of the standard deviation of that estimate.
LEARNING EXERCISE c If the variance for activity A is actually 0.30 (instead of 0.11), what is the new project standard deviation? [Answer: 1.817.]
RELATED PROBLEMS c 3.17e (3.24 is available in MyOMLab)
Example 9 COMPUTING PROJECT VARIANCE AND STANDARD DEVIATION FOR MILWAUKEE PAPER
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How can this information be used to help answer questions regarding the probability of fin- ishing the project on time? PERT makes two more assumptions: (1) total project completion times follow a normal probability distribution, and (2) activity times are statistically inde- pendent. With these assumptions, the bell-shaped normal curve shown in Figure 3.12 can be used to represent project completion dates. This normal curve implies that there is a 50% chance that the manufacturer’s project completion time will be less than 15 weeks and a 50% chance that it will exceed 15 weeks.
Standard Deviation = 1.76 Weeks
15 Weeks
(Expected Completion Time)
Figure 3.12
Probability Distribution
for Project Completion Times
at Milwaukee Paper
Julie Ann Williams would like to find the probability that her project will be finished on or before the 16-week Earth Day deadline.
APPROACH c To do so, she needs to determine the appropriate area under the normal curve. This is the area to the left of the 16th week.
SOLUTION c The standard normal equation can be applied as follows:
Z = (Due date - Expected date of completion)>sp = (16 weeks - 15 weeks)>1.76 weeks = 0.57 (3-9)
where Z is the number of standard deviations the due date or target date lies from the mean or expected date.
Referring to the Normal Table in Appendix I (alternatively using the Excel formula =NORMSDIST(0.57)), we find a Z -value of 0.57 to the right of the mean indicates a probability of 0.7157. Thus, there is a 71.57% chance that the pollution control equipment can be put in place in 16 weeks or less. This is shown in Figure 3.13 .
Example 10 PROBABILITY OF COMPLETING A PROJECT ON TIME
15 Weeks
16 Weeks
0.57 Standard Deviations
Time
Probability (T … 16 Weeks) is 71.57%
INSIGHT c The shaded area to the left of the 16th week (71.57%) represents the probability that the project will be completed in less than 16 weeks.
LEARNING EXERCISE c What is the probability that the project will be completed on or before the 17th week? [Answer: About 87.2%.]
RELATED PROBLEMS c 3.17f, 3.19d, 3.20d, 3.21b, 3.23 (3.25, 3.26e,f,g are available in MyOMLab)
Figure 3.13
Probability That Milwaukee
Paper Will Meet the 16-Week
Deadline
STUDENT TIP Here is a chance to review
your statistical skills and use
of a normal distribution table
(Appendix I).
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 81
Determining Project Completion Time for a Given Confidence Level Let’s say Julie Ann Williams is worried that there is only a 71.57% chance that the pollution control equipment can be put in place in 16 weeks or less. She thinks that it may be possible to plead with the board of directors for more time. However, before she approaches the board, she wants to arm herself with sufficient information about the project. Specifically, she wants to find the deadline by which she has a 99% chance of completing the project. She hopes to use her analysis to convince the board to agree to this extended deadline, even though she is aware of the public relations damage the delay will cause.
Clearly, this due date would be greater than 16 weeks. However, what is the exact value of this new due date? To answer this question, we again use the assumption that Milwau- kee Paper’s project completion time follows a normal probability distribution with a mean of 15 weeks and a standard deviation of 1.76 weeks.
Julie Ann Williams wants to find the due date that gives her company’s project a 99% chance of on-time completion.
APPROACH c She first needs to compute the Z -value corresponding to 99%, as shown in Figure 3.14 . Mathematically, this is similar to Example 10 , except the unknown is now the due date rather than Z .
Example 11 COMPUTING PROBABILITY FOR ANY COMPLETION DATE
SOLUTION c Referring again to the Normal Table in Appendix I (alternatively using the Excel formula =NORMSINV(0.99)), we identify a Z -value of 2.33 as being closest to the probability of 0.99. That is, Julie Ann Williams’s due date should be 2.33 standard deviations above the mean project com- pletion time. Starting with the standard normal equation [see Equation (3-9) ], we can solve for the due date and rewrite the equation as:
Due date = Expected completion time + (Z * sp) = 15 + (2.33 * 1.76) = 19.1 weeks (3-10)
INSIGHT c If Williams can get the board to agree to give her a new deadline of 19.1 weeks (or more), she can be 99% sure of finishing the project by that new target date.
LEARNING EXERCISE c What due date gives the project a 95% chance of on-time completion? [Answer: About 17.9 weeks.]
RELATED PROBLEMS c 3.21c, 3.23e
0 2.33 Z2.33 Standard
Deviations
Probability of 0.99
Probability of 0.01
Figure 3.14
Z -Value for 99% Probability
of Project Completion at
Milwaukee Paper
Variability in Completion Time of Noncritical Paths In our discussion so far, we have focused exclusively on the variability in the completion times of activities on the critical path. This seems logical because these activities are, by definition, the more important activi- ties in a project network. However, when there is variability in activity times, it is important that we also investigate the variability in the completion times of activities on noncritical paths.
Consider, for example, activity D in Milwaukee Paper’s project. Recall from Overlay 3 in Figure 3.10 (in Example 7 ) that this is a noncritical activity, with a slack time of 1 week. We have therefore not considered the variability in D’s time in computing the probabilities of project completion times. We observe, however, that D has a variance of 0.44 (see Table 3.4 in
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Example 8 ). In fact, the pessimistic completion time for D is 6 weeks. This means that if D ends up taking its pessimistic time to finish, the project will not finish in 15 weeks, even though D is not a critical activity.
For this reason, when we find probabilities of project completion times, it may be neces- sary for us to not focus only on the critical path(s). Indeed, some research has suggested that expending project resources to reduce the variability of activities not on the critical path can be an effective element in project management. We may need also to compute these probabilities for noncritical paths, especially those that have relatively large variances. It is possible for a noncritical path to have a smaller probability of completion within a due date, when compared with the critical path. Determining the variance and probability of completion for a noncritical path is done in the same manner as Examples 9 and 10 .
What Project Management Has Provided So Far Project management tech- niques have thus far been able to provide Julie Ann Williams with several valuable pieces of management information:
1. The project’s expected completion date is 15 weeks. 2. There is a 71.57% chance that the equipment will be in place within the 16-week deadline.
PERT analysis can easily find the probability of finishing by any date Williams is inter- ested in.
3. Five activities (A, C, E, G, and H) are on the critical path. If any one of these is delayed for any reason, the entire project will be delayed.
4. Three activities (B, D, F) are not critical and have some slack time built in. This means that Williams can borrow from their resources, and, if necessary, she may be able to speed up the whole project.
5. A detailed schedule of activity starting and ending dates, slack, and critical path activities has been made available (see Table 3.3 in Example 6 ).
Cost-Time Trade-Offs and Project Crashing While managing a project, it is not uncommon for a project manager to be faced with either (or both) of the following situations: (1) the project is behind schedule, and (2) the sched- uled project completion time has been moved forward. In either situation, some or all of the remaining activities need to be speeded up (usually by adding resources) to finish the project by the desired due date. The process by which we shorten the duration of a project in the cheapest manner possible is called project crashing .
CPM is a technique in which each activity has a normal or standard time that we use in our computations. Associated with this normal time is the normal cost of the activity. However, another time in project management is the crash time , which is defined as the shortest duration required to complete an activity. Associated with this crash time is the crash cost of the activity. Usually, we can shorten an activity by adding extra resources (e.g., equipment, people) to it. Hence, it is logical for the crash cost of an activity to be higher than its normal cost.
The amount by which an activity can be shortened (i.e., the difference between its normal time and crash time) depends on the activity in question. We may not be able to shorten some activities at all. For example, if a casting needs to be heat-treated in the furnace for 48 hours, adding more resources does not help shorten the time. In contrast, we may be able to shorten some activities significantly (e.g., frame a house in 3 days instead of 10 days by using three times as many workers).
Likewise, the cost of crashing (or shortening) an activity depends on the nature of the activ- ity. Managers are usually interested in speeding up a project at the least additional cost. Hence, when choosing which activities to crash, and by how much, we need to ensure the following:
◆ The amount by which an activity is crashed is, in fact, permissible ◆ Taken together, the shortened activity durations will enable us to finish the project by the
due date ◆ The total cost of crashing is as small as possible
Crashing
Shortening activity time in a net-
work to reduce time on the critical
path so total completion time is
reduced.
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Crashing a project involves four steps:
STEP 1: Compute the crash cost per week (or other time period) for each activity in the net- work. If crash costs are linear over time, the following formula can be used:
Crash cost per period = (Crash cost - Normal cost) (Normal time - Crash time)
(3-11)
STEP 2: Using the current activity times, fi nd the critical path(s) in the project network. Iden- tify the critical activities.
STEP 3: If there is only one critical path, then select the activity on this critical path that (a) can still be crashed and (b) has the smallest crash cost per period. Crash this activity by one period.
If there is more than one critical path, then select one activity from each critical path such that (a) each selected activity can still be crashed and (b) the total crash cost per period of all selected activities is the smallest. Crash each activity by one period. Note that the same activity may be common to more than one critical path.
STEP 4: Update all activity times. If the desired due date has been reached, stop. If not, re- turn to Step 2.
We illustrate project crashing in Example 12 .
LO 3.6 Crash a project
Suppose the plant manager at Milwaukee Paper Manufacturing has been given only 13 weeks (instead of 16 weeks) to install the new pollution control equipment. As you recall, the length of Julie Ann Williams’s critical path was 15 weeks, but she must now complete the project in 13 weeks.
APPROACH c Williams needs to determine which activities to crash, and by how much, to meet this 13-week due date. Naturally, Williams is interested in speeding up the project by 2 weeks, at the least additional cost.
SOLUTION c The company’s normal and crash times, and normal and crash costs, are shown in Table 3.5 . Note, for example, that activity B’s normal time is 3 weeks (the estimate used in computing the critical path), and its crash time is 1 week. This means that activity B can be shortened by up to 2 weeks if extra resources are provided. The cost of these additional resources is $4,000 (= difference between the crash cost of $34,000 and the normal cost of $30,000). If we assume that the crashing cost is linear over time (i.e., the cost is the same each week), activity B’s crash cost per week is $2,000 (= $4,000/2).
Example 12 PROJECT CRASHING TO MEET A DEADLINE AT MILWAUKEE PAPER
TABLE 3.5 Normal and Crash Data for Milwaukee Paper Manufacturing
TIME (WEEKS) COST ($)
ACTIVITY NORMAL CRASH NORMAL CRASH CRASH COST PER
WEEK ($) CRITICAL PATH?
A 2 1 22,000 22,750 750 Yes
B 3 1 30,000 34,000 2,000 No
C 2 1 26,000 27,000 1,000 Yes
D 4 3 48,000 49,000 1,000 No
E 4 2 56,000 58,000 1,000 Yes
F 3 2 30,000 30,500 500 No
G 5 2 80,000 84,500 1,500 Yes
H 2 1 16,000 19,000 3,000 Yes
This calculation for Activity B is shown in Figure 3.15 . Crash costs for all other activities can be com- puted in a similar fashion.
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Steps 2, 3, and 4 can now be applied to reduce Milwaukee Paper’s project completion time at a mini- mum cost. We show the project network for Milwaukee Paper again in Figure 3.16 .
1
$30,000
Activity Cost
$31,000
$32,000
$33,000
$34,000
2 3
Crash
Normal
Crash Cost/Week = Crash Cost – Normal Cost Normal Time – Crash Time
= $34,000 – $30,000 3 – 1
= $4,000 2 Weeks
= $2,000/Week
Time (Weeks)
Crash Time Normal Time
Normal Cost
Crash Cost
Figure 3.15
Crash and Normal Times and
Costs for Activity B
The current critical path (using normal times) is Start–A–C–E–G–H, in which Start is just a dummy starting activity. Of these critical activities, activity A has the lowest crash cost per week of $750. Julie Ann Williams should therefore crash activity A by 1 week to reduce the project completion time to 14 weeks. The cost is an additional $750. Note that activity A cannot be crashed any further, since it has reached its crash limit of 1 week.
At this stage, the original path Start–A–C–E–G–H remains critical with a completion time of 14 weeks. However, a new path Start–B–D–G–H is also critical now, with a completion time of 14 weeks. Hence, any further crashing must be done to both critical paths.
On each of these critical paths, we need to identify one activity that can still be crashed. We also want the total cost of crashing an activity on each path to be the smallest. We might be tempted to simply pick the activities with the smallest crash cost per period in each path. If we did this, we would select activity C from the first path and activity D from the second path. The total crash cost would then be $2,000 (= $1,000 + $1,000).
0 A
Activity Name
2
2
0 B
3
3 8 G
5
13
4 E
4
8 13 H
2
15
4 F
3
7
2 C
2
4
3 D
4
7
0 2
EFES
2 4
10 13
4 8
1 4 4 8 8 13
13 15
LS
Slack = 0 LF
Slack = 0
Slack = 6
Activity Duration
Slack = 1 Slack = 1 Slack = 0
Slack = 0 Slack = 0
0 Start
0
0
0 0
Figure 3.16
Critical Path and Slack Times
for Milwaukee Paper
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A Critique of PERT and CPM As a critique of our discussions of PERT, here are some of its features about which operations managers need to be aware:
Advantages
1. Especially useful when scheduling and controlling large projects. 2. Straightforward concept and not mathematically complex. 3. Graphical networks help highlight relationships among project activities. 4. Critical path and slack time analyses help pinpoint activities that need to be closely
watched. 5. Project documentation and graphs point out who is responsible for various activities. 6. Applicable to a wide variety of projects. 7. Useful in monitoring not only schedules but costs as well.
But we spot that activity G is common to both paths. That is, by crashing activity G, we will simul- taneously reduce the completion time of both paths. Even though the $1,500 crash cost for activity G is higher than that for activities C and D, we would still prefer crashing G because the total crashing cost will now be only $1,500 (compared with the $2,000 if we crash C and D).
INSIGHT c To crash the project down to 13 weeks, Williams should crash activity A by 1 week and activity G by 1 week. The total additional cost will be $2,250 (= $750 + $1,500). This is important because many contracts for projects include bonuses or penalties for early or late finishes.
LEARNING EXERCISE c Say the crash cost for activity B is $31,000 instead of $34,000. How does this change the answer? [Answer: no change.]
RELATED PROBLEMS c 3.28–3.32 (3.33 is available in MyOMLab)
EXCEL OM Data File Ch03Ex12.xls can be found in MyOMLab.
Behind the Tour de France
The large behind-the-scenes operations that support a football World Cup
or Formula One racing team are well-known, but a Tour de France team
also needs major support. “A Tour de France team is like a large traveling
circus,” says the coach of the Belkin team. “The public only sees the riders,
but they could not function without the unseen support staff.” The base to
the team’s cycling pyramid includes everything from osteopaths to mechan-
ics, from logistics staff to PR people. Their project management skills
require substantial know-how, as well as the ability to guarantee that riders
are in peak physical, nutritional, and psychological condition. This can mean
deciding which snack bars to give the cyclists before, during, and after race
stages, while ensuring there are scientifically based cooling regimens in
place for the riders. The team’s huge truck, coach, three vans, and five cars
resemble the sort of traveling convoy more associated with an international
music act. Here are just some of the supplies the project management team
for Belkin handles:
◆ 11 mattresses
◆ 36 aero suits, 45 bib shorts, 54 race jerseys, 250 podium caps
◆ 63 bikes
◆ 140 wheels, 220 tires
◆ 250 feeding bags, 3,000 water bottles
◆ 2,190 nutrition gels, 3,800 nutrition bars
OM in Action
◆ 10 jars of peanut butter, 10 boxes of chocolate sprinkles, 20 bags of wine
gums, 20 jars of jam
◆ 80 kg of nuts, raisins, apricots, and fi gs, plus 50 kg of cereals
The project management behind a world-tour team is complex: These top
teams often compete in two to three races simultaneously, in different
countries and sometimes on different continents. Each team has 25–35 riders
(9 compete in any single race), coming from different parts of the world, going
to different races at different times, each with his own physique and strengths.
They have customized bikes, uniforms, and food preferences. The support staff
can include another 30 people.
Sources: BBC News (July 6, 2014) and The Operations Room (June 24,2013).
M a rc
P a g a n i P h o to
g ra
p h y/
S h u tt
e rs
to ck
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Limitations
1. Project activities have to be clearly defined, independent, and stable in their relationships. 2. Precedence relationships must be specified and networked together. 3. Time estimates tend to be subjective and are subject to fudging by managers who fear the
dangers of being overly optimistic or not pessimistic enough. 4. There is the inherent danger of placing too much emphasis on the longest, or critical,
path. Near-critical paths need to be monitored closely as well.
Using Microsoft Project to Manage Projects The approaches discussed so far are effective for managing small projects. However, for large or complex projects, specialized project management software is much preferred. In this sec- tion, we provide a brief introduction to the most popular example of such specialized soft- ware, Microsoft Project. A time-limited version of Microsoft Project may be requested with this text.
Microsoft Project is extremely useful in drawing project networks, identifying the project schedule, and managing project costs and other resources.
Entering Data Let us again consider the Milwaukee Paper Manufacturing project. Recall that this project has eight activities (repeated in the margin). The first step is to define the ac- tivities and their precedence relationships. To do so, we select File|New to open a blank project. We type the project start date (as July 1), then enter all activity information (see Program 3.1). For each activity (or task, as Microsoft Project calls it), we fill in the name and duration. The description of the activity is also placed in the Task Name column in Program 3.1. As we enter activities and durations, the software automatically inserts start and finish dates.
The next step is to define precedence relationships between these activities. To do so, we enter the relevant activity numbers (e.g., 1, 2) in the Predecessors column.
Viewing the Project Schedule When all links have been defined, the complete project schedule can be viewed as a Gantt chart. We can also select View|Network Diagram to view the schedule as a project network (shown in Program 3.2). The critical path is shown in red on the
Program 3.1
Gantt Chart in Microsoft Project for Milwaukee Paper Manufacturing
Milwaukee Paper Co. Activities
ACTIVITY TIME (WKS)
PREDE- CESSORS
A 2 —
B 3 —
C 2 A
D 4 A, B
E 4 C
F 3 C
G 5 D, E
H 2 F, G
Project will finish on Friday, 10/14.
View has been zoomed out to show weeks.
Click here to select different views.
Gantt chart view.
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 87
screen in the network diagram. We can click on any of the activities in the project network to view details of the activities. Likewise, we can easily add or remove activities from the project network. Each time we do so, Microsoft Project automatically updates all start dates, finish dates, and the critical path(s). If desired, we can manually change the layout of the network (e.g., reposition activities) by changing the options in Format|Layout .
Programs 3.1 and 3.2 show that if Milwaukee Paper’s project starts July 1, it can be finished on October 14. The start and finish dates for all activities are also clearly identified. Project management software, we see, can greatly simplify the scheduling procedures discussed earlier in this chapter.
PERT Analysis Microsoft Project does not perform the PERT probability calculations discussed in Examples 10 and 11 . However, by clicking View|Toolbars|PERT Analysis , we can get Microsoft Project to allow us to enter optimistic, most likely, and pessimistic times for each activity. We can then choose to view Gantt charts based on any of these three times for each activity.
Tracking the Time Status of a Project Perhaps the biggest advantage of using software to manage projects is that it can track the progress of the project. In this regard, Microsoft Project has many features available to track individual activities in terms of time, cost, resource usage, and so on.
An easy way to track the time progress of tasks is to enter the percent of work completed for each task. One way to do so is to double-click on any activity in the Task Name column in Program 3.1 . A window is displayed that allows us to enter the percent of work completed for each task.
The table in the margin provides data regarding the percent of each of Milwaukee Paper’s activities that are completed as of today. (Assume that today is Friday, August 12, i.e., the end of the sixth week of the project schedule.)
As shown in Program 3.3 , the Gantt chart immediately reflects this updated information by drawing a thick line within each activity’s bar. The length of this line is proportional to the percent of that activity’s work that has been completed.
How do we know if we are on schedule? Notice that there is a vertical line shown on the Gantt chart corresponding to today’s date. Microsoft Project will automatically move this line to correspond with the current date. If the project is on schedule, we should see all bars to the left
STUDENT TIP Now that you understand the
workings of PERT and CPM, you are
ready to master this useful program.
Knowing such software gives you an
edge over others in the job market.
Critical path and activities (A, C, E, G, and H) are shown in red.
Click activity to see details regarding the activity.
Project network view.
Program 3.2
Project Network in Microsoft Project for Milwaukee Paper Manufacturing
Pollution Project Percentage Completed
on Aug. 12
ACTIVITY COMPLETED
A 100
B 100
C 100
D 10
E 20
F 20
G 0
H 0
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of today’s line indicate that they have been completed. For example, Program 3.3 shows that activities A, B, and C are on schedule. In contrast, activities D, E, and F appear to be behind schedule. These activities need to be investigated further to determine the reason for the delay. This type of easy visual information is what makes such software so useful in practice for proj- ect management.
We encourage you to load the copy of Microsoft Project that may be ordered with your text and to create a project network for work you are currently doing.
Activity F is behind schedule, as are activities D and E.
Check mark indicates activity is 100% complete.
This is the indicator for today’s date (Aug. 12).
Bar indicates activity progress.
Program 3.3
Tracking Project Progress in Microsoft Project
Summary PERT, CPM, and other scheduling techniques have proven to be valuable tools in controlling large and complex pro- jects. Managers use such techniques to segment projects into discrete activities (work breakdown structures), inden- tifying specific resources and time requirements for each. With PERT and CPM, managers can understand the status of each activity, including its earliest start, latest start, ear- liest finish, and latest finish (ES, LS, EF, and LF) times. By controlling the trade-off between ES and LS, managers can identify the activities that have slack and can address resource allocation, perhaps by smoothing resources. Effective project management also allows managers to focus on the activities that are critical to timely project
completion. By understanding the project’s critical path, they know where crashing makes the most economic sense.
Good project management also allows firms to effi- ciently create products and services for global markets and to respond effectively to global competition. Microsoft Project, illustrated in this chapter, is one of a wide vari- ety of software packages available to help managers handle network modeling problems.
The models described in this chapter require good man- agement practices, detailed work breakdown structures, clear responsibilities assigned to activities, and straightfor- ward and timely reporting systems. All are critical parts of project management.
Key Terms
Project organization (p. 62 ) Work breakdown structure (WBS) (p. 64 ) Gantt charts (p. 65 ) Program evaluation and review technique
(PERT) (p. 67 ) Critical path method (CPM) (p. 67 )
Critical path (p. 67 ) Activity-on-node (AON) (p. 68 ) Activity-on-arrow (AOA) (p. 68 ) Dummy activity (p. 70 ) Critical path analysis (p. 71 ) Forward pass (p. 72 )
Backward pass (p. 74 ) Slack time (p. 75 ) Optimistic time (p. 77 ) Pessimistic time (p. 77 ) Most likely time (p. 77 ) Crashing (p. 82 )
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 89
Ethical Dilemma Two examples of massively mismanaged projects are TAURUS and the “Big Dig.” The fi rst, formally called the London Stock Exchange Automation Project, cost $575 million before it was fi nally abandoned. Although most IT projects have a reputation for cost overruns, delays, and underperformance, TAURUS set a new standard.
But even TAURUS paled next to the biggest, most expensive public works project in U.S. history—Boston’s 15-year-long Central Artery/Tunnel Project. Called the Big Dig, this was
perhaps the poorest and most felonious case of project mismanagement in decades. From a starting $2 billion budget to a fi nal price tag of $15 billion, the Big Dig cost more than the Panama Canal, Hoover Dam, or Interstate 95, the 1,919-mile highway between Maine and Florida.
Read about one of these two projects (or another of your choice) and explain why it faced such problems. How and why do project managers allow such massive endeavors to fall into such a state? What do you think are the causes?
1. Give an example of a situation in which project management is needed.
2. Explain the purpose of project organization. 3. What are the three phases involved in the management of a
large project? 4. What are some of the questions that can be answered with
PERT and CPM? 5. Define work breakdown structure . How is it used? 6. What is the use of Gantt charts in project management? 7. What is the difference between an activity-on-arrow (AOA)
network and an activity-on-node (AON) network? Which is primarily used in this chapter?
8. What is the significance of the critical path? 9. What would a project manager have to do to crash an
activity? 10. Describe how expected activity times and variances can be
computed in a PERT network. 11. Define earliest start, earliest finish, latest finish , and latest
start times.
12. Students are sometimes confused by the concept of critical path, and want to believe that it is the shortest path through a network. Convincingly explain why this is not so.
13. What are dummy activities? Why are they used in activity-on- arrow (AOA) project networks?
14. What are the three time estimates used with PERT? 15. Would a project manager ever consider crashing a noncritical
activity in a project network? Explain convincingly. 16. How is the variance of the total project computed in
PERT? 17. Describe the meaning of slack, and discuss how it can be
determined. 18. How can we determine the probability that a project will be
completed by a certain date? What assumptions are made in this computation?
19. Name some of the widely used project management software programs.
20. What is the difference between the waterfall approach and agile project management?
Discussion Questions
Using Software to Solve Project Management Problems
In addition to the Microsoft Project software illustrated earlier, both Excel OM and POM for Windows are available to readers of this text as project management tools.
X USING EXCEL OM Excel OM has a Project Scheduling module. Program 3.4 uses the data from the Milwaukee Paper Manufacturing example in this chapter (see Examples 4 and 5 ). The PERT/CPM analysis also handles activities with three time estimates.
P USING POM FOR WINDOWS POM for Window’s Project Scheduling module can also find the expected project completion time for a CPM and PERT net- work with either one or three time estimates. POM for Windows also performs project crashing. For further details refer to Appendix IV.
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Program 3.4
Excel OM’s Use of Milwaukee
Paper Manufacturing’s Data
from Examples 4 and 5
Early start is the maximum of the computations below.
Late finishes depend on the tasks that precede the given task. The late finish is the earliest of the dependencies.
Enter the task names, times, and the names of the precedences. Be careful that the precedence names match the task names.
EF = ES + task time.
Late start is the late finish (from below) minus the task time.
SOLVED PROBLEM 3.1 Construct an AON network based on the following:
ACTIVITY IMMEDIATE
PREDECESSOR(S)
A —
B —
C —
D A, B
E C
SOLUTION
End
D
E
B
A
C
Start
Solved Problems Virtual Office Hours help is available in MyOMLab .
SOLVED PROBLEM 3.2 Insert a dummy activity and event to correct the following AOA network:
3 days
5 days
2
3
4
51
SOLUTION Because we cannot have two activities starting and ending at the same node, we add the following dummy activity and dummy event to obtain the correct AOA network:
5 Dummy activity
2 3
4
1
Dummy event
(0 days)
3 d ays
5 days
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SOLVED PROBLEM 3.3 Calculate the critical path, project completion time T , and project variance s2p, based on the following AON network information:
ACTIVITY TIME VARIANCE ES EF LS LF SLACK
A 2 2 6
0 2 0 2 0
B 3 2 6
0 3 1 4 1
C 2 4 6
2 4 2 4 0
D 4 4 6
3 7 4 8 1
E 4 2 6
4 8 4 8 0
F 3 1 6
4 7 10 13 6
G 5 1 6
8 13 8 13 0
Start End E G
A
B
FC
D
SOLUTION We conclude that the critical path is Start–A–C–E–G–End:
Total project time = T = 2 + 2 + 4 + 5 = 13 and
s2p = ∑ Variances on the critical path
= 2 6
+ 4 6
+ 2 6
+ 1 6
= 9 6
= 1.5
SOLVED PROBLEM 3.4 To complete the wing assembly for an experimental aircraft, Jim Gilbert has laid out the seven major activities involved. These activities have been labeled A through G in the following table, which also shows their estimated completion times (in weeks) and immediate predecessors. Determine the expected time and variance for each activity.
ACTIVITY a m b IMMEDIATE
PREDECESSORS
A 1 2 3 —
B 2 3 4 —
C 4 5 6 A
D 8 9 10 B
E 2 5 8 C, D
F 4 5 6 D
G 1 2 3 E
SOLUTION Expected times and variances can be computed using Equations (3–6) and (3–7) presented on page 78 in this chapter. The results are summarized in the following table:
ACTIVITY EXPECTED TIME (IN WEEKS) VARIANCE
A 2 1 9
B 3 1 9
C 5 1 9
D 9 1 9
E 5 1
F 5 1 9
G 2 1 9
SOLVED PROBLEM 3.5 Referring to Solved Problem 3.4, now Jim Gilbert would like to determine the critical path for the entire wing assembly project as well as the expected completion time for the total project. In addition, he would like to determine the earliest and latest start and finish times for all activities.
SOLUTION The AON network for Gilbert’s project is shown in Figure 3.17 . Note that this project has multiple activities (A and B) with no immediate predecessors, and multiple activities (F and G) with no successors. Hence, in addition to a unique starting activity (Start), we have included a unique finishing activity (End) for the project.
Figure 3.17 shows the earliest and latest times for all activities. The results are also summarized in the following table:
ACTIVITY TIME
ACTIVITY ES EF LS LF SLACK
A 0 2 5 7 5
B 0 3 0 3 0
C 2 7 7 12 5
D 3 12 3 12 0
E 12 17 12 17 0
F 12 17 14 19 2
G 17 19 17 19 0
Expected project length = 19 weeks Variance of the critical path = 1.333 Standard deviation of the critical path = 1.155 weeks
The activities along the critical path are B, D, E, and G. These activities have zero slack as shown in the table.
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SOLVED PROBLEM 3.6 The following information has been computed from a project:
Expected total project time = T = 62 weeks Project variance (s2p ) = 81 What is the probability that the project will be completed 18 weeks before its expected completion date?
SOLUTION The desired completion date is 18 weeks before the expected completion date, 62 weeks. The desired completion date is 44 (or 62–18) weeks:
sp = 2Project variance
Z = Due date - Expected completion date
sp
= 44 - 62
9 =
- 18 9
= - 2.0
Because the normal curve is symmetrical and table values are calculated for positive values of Z , the area desired is equal to 1– (table value). For Z = + 2.0 the area from the table is .97725. Thus, the area corresponding to a Z -value of –2.0 is .02275 (or 1 – .97725). Hence, the probability of completing the project 18 weeks before the expected completion date is approximately .023, or 2.3%.
Activity Duration
Dummy Ending Activity
17 G
2
19
17 190 Start
0
0
0 A
Activity Name
2
2
0 B
3
3 12 F
5
17
19 End
0
19
12 E
5
172 C
5
7
3 D
9
12
0 0
5 7 7 12 12 17
0 3 3 12 14 19
19 19 Dummy Starting Activity
ES EF
LS LF
Figure 3.17
Critical Path for Solved
Problem 3.5
T = 62Due date = 44
The normal curve appears as follows:
SOLVED PROBLEM 3.7 Determine the least cost of reducing the project completion date by 3 months based on the following information:
SOLUTION The first step in this problem is to compute ES, EF, LS, LF, and slack for each activity.
ACTIVITY ES EF LS LF SLACK
A 0 6 9 15 9
B 0 7 0 7 0
C 6 13 15 22 9
D 7 13 7 13 0
E 13 22 13 22 0
The critical path consists of activities B, D, and E.
Start
B
C
D E
End
A
ACTIVITY NORMAL TIME
(MONTHS) CRASH TIME (MONTHS)
NORMAL COST
CRASH COST
A 6 4 $2,000 $2,400 B 7 5 3,000 3,500 C 7 6 1,000 1,300 D 6 4 2,000 2,600 E 9 8 8,800 9,000
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Next, crash cost/month must be computed for each activity:
ACTIVITY
NORMAL TIME–CRASH
TIME
CRASH COST– NORMAL
COST
CRASH COST/
MONTH CRITICAL PATH?
A 2 $400 $200/month No B 2 500 250/month Yes C 1 300 300/month No D 2 600 300/month Yes E 1 200 200/month Yes
Finally, we will select that activity on the critical path with the smallest crash cost/month. This is activity E. Thus, we can reduce the total project completion date by 1 month for an
additional cost of $200. We still need to reduce the project com- pletion date by 2 more months. This reduction can be achieved at least cost along the critical path by reducing activity B by 2 months for an additional cost of $500. Neither reduction has an effect on noncritical activities. This solution is summarized in the following table:
ACTIVITY MONTHS REDUCED COST
E 1 $200
B 2 500
Total: $700
Problems 3.1–3.2 relate to Project Planning • 3.1 The work breakdown structure (WBS) for building a house (levels 1 and 2) is shown below:
Help Lawson by providing details where the blank lines appear. Are there any other major (level-2) activities to create? If so, add an ID no. 1.6 and insert them.
Problem 3.3 relates to Project Scheduling
• • 3.3 The City Commission of Nashville has decided to build a botanical garden and picnic area in the heart of the city for the recreation of its citizens. The precedence table for all the activities required to construct this area successfully is given. Draw the Gantt chart for the whole construction activity.
CODE ACTIVITY DESCRIPTION TIME (IN HOURS)
IMMEDIATE PREDECESSOR(S)
A Planning Find location; determine resource requirements
20 None
B Purchasing Requisition of lumber and sand
60 Planning
C Excavation Dig and grade 100 Planning D Sawing Saw lumber into
appropriate sizes 30 Purchasing
E Placement Position lumber in correct locations
20 Sawing, excavation
F Assembly Nail lumber together
10 Placement
G Infi ll Put sand in and under the equipment
20 Assembly
H Outfi ll Put dirt around the equipment
10 Assembly
I Decoration Put grass all over the garden, landscape, paint
30 Infi ll, outfi ll
Problems 3.4–3.14 relate to Project Management Techniques
• • 3.4 Refer to the table in Problem 3.3. a) Draw the AON network for the construction activity. b) Draw the AOA network for the construction activity.
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Site Preparation
Masonry Carpentry Plumbing Finishing
House
Level 2
Level 1
a) Add two level-3 activities to each of the level-2 activities to
provide more detail to the WBS. b) Select one of your level-3 activities and add two level-4 activi-
ties below it.
• • 3.2 James Lawson has decided to run for a seat as Congressman from the House of Representatives, District 34, in Florida. He views his 8-month campaign for office as a major project and wishes to create a work breakdown structure (WBS) to help control the detailed scheduling. So far, he has developed the following pieces of the WBS:
LEVEL LEVEL ID NO. ACTIVITY
1 1.0 Develop political campaign 2 1.1 Fund-raising plan 3 1.1.1 ________________________________ 3 1.1.2 ________________________________ 3 1.1.3 ________________________________ 2 1.2 Develop a position on major issues 3 1.2.1 ________________________________ 3 1.2.2 ________________________________ 3 1.2.3 ________________________________ 2 1.3 Staffi ng for campaign 3 1.3.1 ________________________________ 3 1.3.2 ________________________________ 3 1.3.3 ________________________________ 3 1.3.4 ________________________________ 2 1.4 Paperwork compliance for candidacy 3 1.4.1 ________________________________ 3 1.4.2 ________________________________ 2 1.5 Ethical plan/issues 3 1.5.1 ________________________________
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• 3.5 Draw the activity-on-node (AON) project network associated with the following activities for Carl Betterton’s construction project. How long should it take Carl and his team to complete this project? What are the critical path activities?
ACTIVITY IMMEDIATE
PREDECESSOR(S) TIME
(DAYS)
A — 3
B A 4
C A 6
D B 6
E B 4
F C 4
G D 6
H E, F 8
• 3.6 Given the activities whose sequence is described by the following table, draw the appropriate activity-on-arrow (AOA) network diagram. a) Which activities are on the critical path? b) What is the length of the critical path?
ACTIVITY IMMEDIATE
PREDECESSOR(S) TIME (DAYS)
A — 5
B A 2
C A 4
D B 5
E B 5
F C 5
G E, F 2
H D 3
I G, H 5
• 3.7 Using AOA, diagram the network described below for Lillian Fok’s construction project. Calculate its critical path. How long is the minimum duration of this network?
ACTIVITY NODES TIME
(WEEKS) ACTIVITY NODES TIME
(WEEKS)
J 1–2 10 N 3–4 2
K 1–3 8 O 4–5 7
L 2–4 6 P 3–5 5
M 2–3 3
• • 3.8 Roger Ginde is developing a program in supply chain management certification for managers. Ginde has listed a num- ber of activities that must be completed before a training program of this nature could be conducted. The activities, immediate pre- decessors, and times appear in the accompanying table:
ACTIVITY IMMEDIATE
PREDECESSOR(S) TIME (DAYS)
A — 2
B — 5
C — 1
D B 10
E A, D 3
F C 6
G E, F 8
a) Develop an AON network for this problem. b) What is the critical path? c) What is the total project completion time? d) What is the slack time for each individual activity? PX
•• 3.9 Task time estimates for the modification of an assembly line at Jim Goodale’s Carbondale, Illinois, factory are as follows:
ACTIVITY TIME (IN HOURS)
IMMEDIATE PREDECESSORS
A 6.0 —
B 7.2 —
C 5.0 A
D 6.0 B, C
E 4.5 B, C
F 7.7 D
G 4.0 E, F
a) Draw the project network using AON. b) Identify the critical path. c) What is the expected project length? d) Draw a Gantt chart for the project. PX
• 3.10 The activities described by the following table are given for the Howard Corporation in Kansas:
ACTIVITY IMMEDIATE
PREDECESSOR(S) TIME
A — 9
B A 7
C A 3
D B 6
E B 9
F C 4
G E, F 6
H D 5
I G, H 3
a) Draw the appropriate AON PERT diagram for J.C. Howard’s management team.
b) Find the critical path. c) What is the project completion time? PX
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 95
• • 3.11 The following is a table of activities associated with a project at Rafay Ishfaq’s software firm in Chicago, their dura- tions, and what activities each must precede:
ACTIVITY DURATION (WEEKS) PRECEDES
A (start) 1 B, C
B 1 E
C 4 F
E 2 F
F (end) 2 —
a) Draw an AON diagram of the project, including activity durations.
b) Define the critical path, listing all critical activities in chrono- logical order.
c) What is the project duration (in weeks)? d) What is the slack (in weeks) associated with any and all non-
critical paths through the project?
• 3.12 The activities needed to build a prototype laser scan- ning machine at Dave Fletcher Corp. are listed in the following table. Construct an AON network for these activities.
ACTIVITY IMMEDIATE
PREDECESSOR(S) ACTIVITY IMMEDIATE
PREDECESSOR(S)
A — E B
B — F B
C A G C, E
D A H D, F
Additional problems 3.13–3.14 are available in MyOMLab.
Problems 3.15–3.16 relate to Determining the Project Schedule
• 3.15 Dave Fletcher (see Problem 3.12) was able to deter- mine the activity times for constructing his laser scanning machine. Fletcher would like to determine ES, EF, LS, LF, and slack for each activity. The total project completion time and the critical path should also be determined. Here are the activity times:
ACTIVITY TIME (WEEKS) ACTIVITY TIME (WEEKS)
A 6 E 4
B 7 F 6
C 3 G 10
D 2 H 7
• • • 3.16 The Rover 6 is a new custom-designed sports car. An analysis of the task of building the Rover 6 reveals the following list of relevant activities, their immediate predecessors, and their duration: 2
JOB LETTER DESCRIPTION
IMMEDIATE PREDECESSOR(S)
NORMAL TIME (DAYS)
A Start — 0
B Design A 8
C Order special accessories B 0.1
D Build frame B 1
E Build doors B 1
F Attach axles, wheels, gas tank
D 1
G Build body shell B 2
H Build transmission and drivetrain
B 3
I Fit doors to body shell G, E 1
J Build engine B 4
K Bench-test engine J 2
L Assemble chassis F, H, K 1
M Road-test chassis L 0.5
N Paint body I 2
O Install wiring N 1
P Install interior N 1.5
Q Accept delivery of special accessories
C 5
R Mount body and accessories on chassis
M, O, P, Q 1
S Road test car R 0.5
T Attach exterior trim S 1
U Finish T 0
a) Draw a network diagram for the project. b) Mark the critical path and state its length. c) If the Rover 6 had to be completed 2 days earlier, would it
help to: i) Buy preassembled transmissions and drivetrains? ii) Install robots to halve engine-building time? iii) Speed delivery of special accessories by 3 days? d) How might resources be borrowed from activities on the non-
critical path to speed activities on the critical path? PX
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Problems 3.17–3.27 relate to Variability in Activity Times
• • • 3.17 Ross Hopkins, president of Hopkins Hospitality, has developed the tasks, durations, and predecessor relationships in the following table for building new motels. Draw the AON net- work and answer the questions that follow.
TIME ESTIMATES (IN WEEKS)
ACTIVITY IMMEDIATE
PREDECESSOR(S) OPTIMISTIC MOST LIKELY PESSIMISTIC
A — 4 8 10
B A 2 8 24
C A 8 12 16
D A 4 6 10
E B 1 2 3
F E, C 6 8 20
G E, C 2 3 4
H F 2 2 2
I F 6 6 6
J D, G, H 4 6 12
K I, J 2 2 3
a) What is the expected (estimated) time for activity C? b) What is the variance for activity C? c) Based on the calculation of estimated times, what is the critical
path? d) What is the estimated time of the critical path? e) What is the activity variance along the critical path? f ) What is the probability of completion of the project before
week 36? PX
• 3.18 A renovation of the gift shop at Orlando Amway Center has six activities (in hours). For the following estimates of a , m , and b , calculate the expected time and the standard devia- tion for each activity:
ACTIVITY a m b
A 11 15 19
B 27 31 41
C 18 18 18
D 8 13 19
E 17 18 20
F 16 19 22
• • 3.19 Kelle Carpet and Trim installs carpet in commercial offices. Peter Kelle has been very concerned with the amount of time it took to complete several recent jobs. Some of his workers are very unreliable. A list of activities and their optimistic com- pletion time, the most likely completion time, and the pessimistic completion time (all in days) for a new contract are given in the following table:
PX
TIME (DAYS)
ACTIVITY a m b IMMEDIATE
PREDECESSOR(S)
A 3 6 8 —
B 2 4 4 —
C 1 2 3 —
D 6 7 8 C
E 2 4 6 B, D
F 6 10 14 A, E
G 1 2 4 A, E
H 3 6 9 F
I 10 11 12 G
J 14 16 20 C
K 2 8 10 H, I
a) Determine the expected completion time and variance for each activity.
b) Determine the total project completion time and the critical path for the project.
c) Determine ES, EF, LS, LF, and slack for each activity. d) What is the probability that Kelle Carpet and Trim will finish
the project in 40 days or less? PX
• • • 3.20 The estimated times and immediate predecessors for the activities in a project at George Kyparis’s retinal scanning company are given in the following table. Assume that the activ- ity times are independent.
TIME (WEEKS)
ACTIVITY IMMEDIATE
PREDECESSOR a m b
A — 9 10 11
B — 4 10 16
C A 9 10 11
D B 5 8 11
ja m
st o ck
fo to
/F o to
lia
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 97
a) Calculate the expected time and variance for each activity. b) What is the expected completion time of the critical path?
What is the expected completion time of the other path in the network?
c) What is the variance of the critical path? What is the variance of the other path in the network?
d) If the time to complete path A–C is normally distributed, what is the probability that this path will be finished in 22 weeks or less?
e) If the time to complete path B–D is normally distributed, what is the probability that this path will be finished in 22 weeks or less?
f ) Explain why the probability that the critical path will be fin- ished in 22 weeks or less is not necessarily the probability that the project will be finished in 22 weeks or less. PX
• • • 3.21 Rich Cole Control Devices, Inc., produces custom- built relay devices for auto makers. The most recent project undertaken by Cole requires 14 different activities. Cole’s man- agers would like to determine the total project completion time (in days) and those activities that lie along the critical path. The appropriate data are shown in the following table. a) What is the probability of being done in 53 days? b) What date results in a 99% probability of completion?
ACTIVITY IMMEDIATE
PREDECESSOR(S) OPTIMISTIC
TIME
MOST LIKELY TIME
PESSIMISTIC TIME
A — 4 6 7
B — 1 2 3
C A 6 6 6
D A 5 8 11
E B, C 1 9 18
F D 2 3 6
G D 1 7 8
H E, F 4 4 6
I G, H 1 6 8
J I 2 5 7
K I 8 9 11
L J 2 4 6
M K 1 2 3
N L, M 6 8 10
• • • 3.22 Four Squares Productions, a firm hired to coordinate the release of the movie Pirates of the Caribbean: On Stranger Tides (starring Johnny Depp), identified 16 activities to be com- pleted before the release of the film. a) How many weeks in advance of the film release should
Four Squares have started its marketing campaign? What is the critical path? The tasks (in time units of weeks) are as follows:
PX
ACTIVITY IMMEDIATE
PREDECESSOR(S) OPTIMISTIC
TIME
MOST LIKELY TIME
PESSIMISTIC TIME
A — 1 2 4
B — 3 3.5 4
C — 10 12 13
D — 4 5 7
E — 2 4 5
F A 6 7 8
G B 2 4 5.5
H C 5 7.7 9
I C 9.9 10 12
J C 2 4 5
K D 2 4 6
L E 2 4 6
M F, G, H 5 6 6.5
N J, K, L 1 1.1 2
O I, M 5 7 8
P N 5 7 9
b) What is the probability of completing the marketing campaign in the time (in weeks) noted in part a?
c) If activities I and J were not necessary, what impact would this have on the critical path and the number of weeks needed to complete the marketing campaign? PX
• • 3.23 Using PERT, Adam Munson was able to determine that the expected project completion time for the construction of a pleasure yacht is 21 months, and the project variance is 4. a) What is the probability that the project will be completed in
17 months? b) What is the probability that the project will be completed in
20 months? c) What is the probability that the project will be completed in
23 months? d) What is the probability that the project will be completed in
25 months? e) What is the due date that yields a 95% chance of completion?
PX
T ra
cy W
h it e si
d e /S
h u tt
e rs
to ck
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Additional problems 3.24–3.27 are available in MyOMLab.
Problems 3.28–3.33 relate to Cost-Time Trade-Offs and Project Crashing
• • 3.28 Assume that the activities in Problem 3.11 have the following costs to shorten: A, $300/week; B, $100/week; C, $200/ week; E, $100/week; and F, $400/week. Assume also that you can crash an activity down to 0 weeks in duration and that every week you can shorten the project is worth $250 to you. What activities would you crash? What is the total crashing cost?
• • • 3.29 What is the minimum cost of crashing the following project that Roger Solano manages at Slippery Rock University by 4 days?
ACTIVITY
NORMAL TIME
(DAYS)
CRASH TIME
(DAYS) NORMAL
COST CRASH COST
IMMEDIATE PREDECESSOR(S)
A 6 5 $ 900 $1,000 —
B 8 6 300 400 —
C 4 3 500 600 —
D 5 3 900 1,200 A
E 8 5 1,000 1,600 C • • 3.30 Three activities are candidates for crashing on a pro- ject network for a large computer installation (all are, of course, critical). Activity details are in the following table:
ACTIVITY PREDE- CESSOR
NORMAL TIME
NORMAL COST
CRASH TIME
CRASH COST
A — 7 days $6,000 6 days $6,600
B A 4 days 1,200 2 days 3,000
C B 11 days 4,000 9 days 6,000
a) What action would you take to reduce the critical path by 1 day?
b) Assuming no other paths become critical, what action would you take to reduce the critical path one additional day?
c) What is the total cost of the 2-day reduction? PX
• • • 3.31 Development of Version 2.0 of a particular account- ing software product is being considered by Jose Noguera’s technology firm in Baton Rouge. The activities necessary for the completion of this project are listed in the following table:
PX
ACTIVITY
NORMAL TIME
(WEEKS)
CRASH TIME
(WEEKS) NORMAL
COST CRASH COST
IMMEDIATE PREDECESSOR(S)
A 4 3 $2,000 $2,600 —
B 2 1 2,200 2,800 —
C 3 3 500 500 —
D 8 4 2,300 2,600 A
E 6 3 900 1,200 B
F 3 2 3,000 4,200 C
G 4 2 1,400 2,000 D, E
a) What is the project completion date? b) What is the total cost required for completing this project on
normal time? c) If you wish to reduce the time required to complete this project
by 1 week, which activity should be crashed, and how much will this increase the total cost?
d) What is the maximum time that can be crashed? How much would costs increase? PX
• • • 3.32 Kimpel Products makes pizza ovens for commercial use. James Kimpel, CEO, is contemplating producing smaller ovens for use in high school and college kitchens. The activities necessary to build an experimental model and related data are given in the following table:
ACTIVITY
NORMAL TIME
(WEEKS)
CRASH TIME
(WEEKS) NORMAL COST ($)
CRASH COST ($)
IMMEDIATE PREDECESSOR(S)
A 3 2 1,000 1,600 —
B 2 1 2,000 2,700 —
C 1 1 300 300 —
D 7 3 1,300 1,600 A
E 6 3 850 1,000 B
F 2 1 4,000 5,000 C
G 4 2 1,500 2,000 D, E
a) What is the project completion date? b) Crash this project to 10 weeks at the least cost. c) Crash this project to 7 weeks (which is the maximum it can be
crashed) at the least cost. PX
Additional problem 3.33 is available in MyOMLab.
A longtime football powerhouse, SWU is a member of the Big Eleven conference and is usually in the top 20 in college foot- ball rankings. To bolster its chances of reaching the elusive and long-desired number-one ranking, in 2009, SWU hired the leg- endary Phil Flamm as its head coach.
CASE STUDIES Southwestern University: (A) *
Southwestern University (SWU), a large state college in Stephenville, Texas, 30 miles southwest of the Dallas/Fort Worth metroplex, enrolls close to 20,000 students. In a typical town–gown relationship, the school is a dominant force in the small city, with more students during fall and spring than permanent residents.
* This integrated study runs throughout the text. Other issues facing Southwestern’s football expansion include (B) forecasting game attendance ( Chapter 4 ); (C) quality of facilities ( Chapter 6 ); (D) break-even analysis for food services (Supplement 7); (E) location of the new stadium ( Chapter 8 ); (F) inventory planning of football programs ( Chapter 12 ); and (G) scheduling of campus security offi cers/staff for game days ( Chapter 13 ).
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One of Flamm’s demands on joining SWU had been a new stadium. With attendance increasing, SWU administrators began to face the issue head-on. After 6 months of study, much politi- cal arm wrestling, and some serious financial analysis, Dr. Joel Wisner, president of Southwestern University, had reached a decision to expand the capacity at its on-campus stadium.
Adding thousands of seats, including dozens of luxury sky- boxes, would not please everyone. The influential Flamm had argued the need for a first-class stadium, one with built-in dormi- tory rooms for his players and a palatial office appropriate for the coach of a future NCAA champion team. But the decision was made, and everyone , including the coach, would learn to live with it.
The job now was to get construction going immediately after the 2015 season ended. This would allow exactly 270 days until the 2016 season opening game. The contractor, Hill Construction (Bob Hill being an alumnus, of course), signed his contract. Bob Hill looked at the tasks his engineers had outlined and looked President Wisner in the eye. “I guarantee the team will be able to take the field on schedule next year,” he said with a sense of con- fidence. “I sure hope so,” replied Wisner. “The contract penalty
of $10,000 per day for running late is nothing compared to what Coach Flamm will do to you if our opening game with Penn State is delayed or canceled.” Hill, sweating slightly, did not need to respond. In football-crazy Texas, Hill Construction would be mud if the 270-day target was missed.
Back in his office, Hill again reviewed the data (see Table 3.6 ) and noted that optimistic time estimates can be used as crash times. He then gathered his foremen. “Folks, if we’re not 75% sure we’ll finish this stadium in less than 270 days, I want this project crashed! Give me the cost figures for a target date of 250 days—also for 240 days. I want to be early , not just on time!”
Discussion Questions
1. Develop a network drawing for Hill Construction and deter- mine the critical path. How long is the project expected to take?
2. What is the probability of finishing in 270 days? 3. If it is necessary to crash to 250 or 240 days, how would Hill do
so, and at what costs? As noted in the case, assume that opti- mistic time estimates can be used as crash times.
TABLE 3.6 Southwestern University Project
TIME ESTIMATES (DAYS)
ACTIVITY DESCRIPTION PREDECESSOR(S) OPTIMISTIC MOST LIKELY PESSIMISTIC CRASH COST/DAY
A Bonding, insurance, tax structuring — 20 30 40 $1,500
B Foundation, concrete footings for boxes A 20 65 80 3,500
C Upgrading skybox stadium seating A 50 60 100 4,000
D Upgrading walkways, stairwells, elevators C 30 50 100 1,900
E Interior wiring, lathes B 25 30 35 9,500
F Inspection approvals E 0.1 0.1 0.1 0
G Plumbing D, F 25 30 35 2,500
H Painting G 10 20 30 2,000
I Hardware/AC/metal workings H 20 25 60 2,000
J Tile/carpet/windows H 8 10 12 6,000
K Inspection J 0.1 0.1 0.1 0
L Final detail work/cleanup I, K 20 25 60 4,500
The equivalent of a new kindergarten class is born every day at Orlando’s Arnold Palmer Hospital. With more than 13,000 births in the mid-2000s in a hospital that was designed 15 years earlier for a capacity of 6,500 births a year, the newborn intensive care unit was stretched to the limit. Moreover, with continuing strong population growth in central Florida, the hospital was often full. It was clear that new facilities were needed. After much analy- sis, forecasting, and discussion, the management team decided to build a new 273-bed building across the street from the existing hospital. But the facility had to be built in accordance with the hospital’s Guiding Principles and its uniqueness as a health center dedicated to the specialized needs of women and infants. Those
Video Case Guiding Principles are: Family-centered focus, a healing environ- ment where privacy and dignity are respected, sanctuary of car- ing that includes warm, serene surroundings with natural lighting, sincere and dedicated staff providing the highest quality care, and patient-centered flow and function .
The vice president of business development, Karl Hodges, wanted a hospital that was designed from the inside out by the people who understood the Guiding Principles, who knew most about the current system, and who were going to use the new sys- tem, namely, the doctors and nurses. Hodges and his staff spent 13 months discussing expansion needs with this group, as well as with patients and the community, before developing a proposal
Project Management at Arnold Palmer Hospital
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100 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
for the new facility. An administrative team created 35 user groups, which held over 1,000 planning meetings (lasting from 45 minutes to a whole day). They even created a “Supreme Court” to deal with conflicting views on the multifaceted issues facing the new hospital.
Funding and regulatory issues added substantial complexity to this major expansion, and Hodges was very concerned that the project stay on time and within budget. Tom Hyatt, director of facility development, was given the task of onsite manager of the $100 million project, in addition to overseeing ongoing renova- tions, expansions, and other projects. The activities in the multi- year project for the new building at Arnold Palmer are shown in Table 3.7 .
Discussion Questions *
1. Develop the network for planning and construction of the new hospital at Arnold Palmer.
2. What is the critical path, and how long is the project expected to take?
3. Why is the construction of this 11-story building any more complex than construction of an equivalent office building?
4. What percent of the whole project duration was spent in plan- ning that occurred prior to the proposal and reviews? Prior to the actual building construction? Why?
TABLE 3.7 Expansion Planning and Arnold Palmer Hospital Construction Activities and Times a
ACTIVITY SCHEDULED TIME PRECEDENCE ACTIVITY(IES)
1. Proposal and review 1 month —
2. Establish master schedule 2 weeks 1
3. Architect selection process 5 weeks 1
4. Survey whole campus and its needs 1 month 1
5. Conceptual architect’s plans 6 weeks 3
6. Cost estimating 2 months 2, 4, 5
7. Deliver plans to board for consideration/decision 1 month 6
8. Surveys/regulatory review 6 weeks 6
9. Construction manager selection 9 weeks 6
10. State review of need for more hospital beds (“Certifi cate of Need”) 3.5 months 7, 8
11. Design drawings 4 months 10
12. Construction documents 5 months 9, 11
13. Site preparation/demolish existing building 9 weeks 11
14. Construction start/building pad 2 months 12, 13
15. Relocate utilities 6 weeks 12
16. Deep foundations 2 months 14
17. Building structure in place 9 months 16
18. Exterior skin/roofi ng 4 months 17
19. Interior buildout 12 months 17
20. Building inspections 5 weeks 15, 19
21. Occupancy 1 month 20
a This list of activities is abbreviated for purposes of this case study. For simplifi cation, assume each week 5 .25 months (i.e., 2 weeks 5 .5 month, 6 weeks 5 1.5 months, etc.).
* You may wish to view the video accompanying this case before address- ing these questions.
At the Hard Rock Cafe, like many organizations, project manage- ment is a key planning tool. With Hard Rock’s constant growth in hotels and cafes, remodeling of existing cafes, scheduling for Hard Rock Live concert and event venues, and planning the annual Rockfest, managers rely on project management techniques and software to maintain schedule and budget performance.
Video Case “Without Microsoft Project,” says Hard Rock Vice-President
Chris Tomasso, “there is no way to keep so many people on the same page.” Tomasso is in charge of the Rockfest event, which is attended by well over 100,000 enthusiastic fans. The chal- lenge is pulling it off within a tight 9-month planning horizon. As the event approaches, Tomasso devotes greater energy to its
Managing Hard Rock’s Rockfest
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C H A P T E R 3 | P R O J E C T M A N AG E M E N T 101
activities. For the first 3 months, Tomasso updates his Microsoft Project charts monthly. Then at the 6-month mark, he updates his progress weekly. At the 9-month mark, he checks and corrects his schedule twice a week.
Early in the project management process, Tomasso iden- tifies 10 major tasks (called level-2 activities in a work breakdown structure, or WBS): † talent booking, ticketing, marketing/PR, online promotion, television, show production, travel, sponsorships, operations, and merchandising. Using a WBS, each of these is further divided into a series of subtasks. Table 3.8 identifies 26 of the major activities and subactivities, their immediate predecessors, and time estimates. Tomasso enters all these into the Microsoft Project software. ‡ Tomasso alters the Microsoft Project document and the time line as the
project progresses. “It’s okay to change it as long as you keep on track,” he states.
The day of the rock concert itself is not the end of the project planning. “It’s nothing but surprises. A band not being able to get to the venue because of traffic jams is a surprise, but an ‘antici- pated’ surprise. We had a helicopter on stand-by ready to fly the band in,” says Tomasso.
On completion of Rockfest in July, Tomasso and his team have a 3-month reprieve before starting the project planning pro- cess again.
† The level-1 activity is the Rockfest concert itself. ‡ There are actually 127 activities used by Tomasso; the list is abbreviated for this case study.
TABLE 3.8 Some of the Major Activities and Subactivities in the Rockfest Plan
ACTIVITY DESCRIPTION PREDECESSOR(S) TIME (WEEKS)
A Finalize site and building contracts — 7
B Select local promoter A 3
C Hire production manager A 3
D Design promotional Web site B 5
E Set TV deal D 6
F Hire director E 4
G Plan for TV camera placement F 2
H Target headline entertainers B 4
I Target support entertainers H 4
J Travel accommodations for talent I 10
K Set venue capacity C 2
L Ticketmaster contract D, K 3
M On-site ticketing L 8
N Sound and staging C 6
O Passes and stage credentials G, R 7
P Travel accommodations for staff B 20
Q Hire sponsor coordinator B 4
R Finalize sponsors Q 4
S Defi ne/place signage for sponsors R, X 3
T Hire operations manager A 4
U Develop site plan T 6
V Hire security director T 7
W Set police/fi re security plan V 4
X Power, plumbing, AC, toilet services U 8
Y Secure merchandise deals B 6
Z Online merchandise sales Y 6
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Discussion Questions §
1. Identify the critical path and its activities for Rockfest. How long does the project take?
2. Which activities have a slack time of 8 weeks or more? 3. Identify five major challenges a project manager faces in events
such as this one.
4. Why is a work breakdown structure useful in a project such as this? Take the 26 activities and break them into what you think should be level-2, level-3, and level-4 tasks.
§ You may wish to view the video accompanying this case before addressing these questions.
• Additional Case Study: Visit MyOMLab for this free case study: Shale Oil Company: This oil refi nery must shut down for maintenance of a major piece of equipment.
Endnotes
1. This formula is based on the statistical concept that from one end of the beta distribution to the other is 6 standard devia- tions (±3 standard deviations from the mean). Because ( b – a) is 6 standard deviations, the variance is [(b – a )�6] 2 .
2. Source: Adapted from James A. D. Stoner, Management , 6th ed. (Upper Saddle River, NJ: Pearson).
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3
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Main Heading Review Material MyOMLab THE IMPORTANCE OF PROJECT MANAGEMENT (p. 62 )
The management of projects involves three phases: 1. Planning —This phase includes goal setting, defining the project, and team organization. 2. Scheduling —This phase relates people, money, and supplies to specific activities and
relates activities to each other. 3. Controlling —Here the firm monitors resources, costs, quality, and budgets. It also revises
or changes plans and shifts resources to meet time and cost demands.
Concept Questions: 1.1–1.4 VIDEO 3.1 Project Management at Hard Rock’s Rockfest
PROJECT PLANNING (pp. 62–65 )
Projects can be defined as a series of related tasks directed toward a major output. j Project organization —An organization formed to ensure that programs (projects) receive
the proper management and attention. j Work breakdown structure (WBS) —Defines a project by dividing it into more and more
detailed components.
Concept Questions: 2.1–2.4 Problems: 3.1–3.2
PROJECT SCHEDULING (pp. 65 – 66 )
j Gantt charts —Planning charts used to schedule resources and allocate time. Project scheduling serves several purposes: 1. It shows the relationship of each activity to others and to the whole project. 2. It identifies the precedence relationships among activities. 3. It encourages the setting of realistic time and cost estimates for each activity. 4. It helps make better use of people, money, and material resources by identifying critical
bottlenecks in the project.
Concept Questions: 3.1–3.4 Problem: 3.3
PROJECT CONTROLLING (pp. 66–67 )
Computerized programs produce a broad variety of PERT/CPM reports, including (1) detailed cost breakdowns for each task, (2) total program labor curves, (3) cost distri- bution tables, (4) functional cost and hour summaries, (5) raw material and expenditure forecasts, (6) variance reports, (7) time analysis reports, and (8) work status reports.
Concept Questions: 4.1–4.2 VIDEO 3.2 Project Management at Arnold Palmer Hospital
PROJECT MANAGEMENT TECHNIQUES: PERT AND CPM (pp. 67 – 71 )
j Program evaluation and review technique (PERT) —A project management technique that employs three time estimates for each activity.
j Critical path method (CPM) —A project management technique that uses only one esti- mate per activity.
j Critical path —The computed longest time path(s) through a network. PERT and CPM both follow six basic steps. The activities on the critical path will delay the entire project if they are not completed on time. j Activity-on-node (AON) —A network diagram in which nodes designate activities. j Activity-on-arrow (AOA) —A network diagram in which arrows designate activities. In an AOA network, the nodes represent the starting and finishing times of an activity and are also called events . j Dummy activity —An activity having no time that is inserted into a network to maintain
the logic of the network. A dummy ending activity can be added to the end of an AON diagram for a project that has multiple ending activities.
Concept Questions: 5.1–5.4 Problems: 3.4–3.14 Virtual Office Hours for Solved Problems: 3.1, 3.2
DETERMINING THE PROJECT SCHEDULE (pp. 71 – 77 )
j Critical path analysis —A process that helps determine a project schedule. To find the critical path, we calculate two distinct starting and ending times for each activity: j Earliest start (ES) = Earliest time at which an activity can start, assuming that all prede-
cessors have been completed j Earliest finish (EF) = Earliest time at which an activity can be finished j Latest start (LS) = Latest time at which an activity can start, without delaying the com-
pletion time of the entire project j Latest finish (LF) = Latest time by which an activity has to finish so as to not delay the
completion time of the entire project j Forward pass —A process that identifies all the early start and early finish times. ES = Max {EF of all immediate predecessors} (3-1) EF = ES + Activity time (3-2) j Backward pass —A process that identifies all the late start and late finish times. LF = Min {LS of all immediate following activities} (3-3) LS = LF – Activity time (3-4)
Concept Questions: 6.1–6.4 Problems: 3.15, 3.16
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Main Heading Review Material MyOMLab j Slack time —Free time for an activity. Slack = LS − ES or Slack = LF − EF (3-5) The activities with zero slack are called critical activities and are said to be on the critical path. The critical path is a continuous path through the project network that starts at the first activity in the project, terminates at the last activity in the project, and includes only critical activities.
Virtual Office Hours for Solved Problem: 3.3 ACTIVE MODEL 3.1
VARIABILITY IN ACTIVITY TIMES (pp. 77 – 82 )
j Optimistic time ( a ) —The “best” activity completion time that could be obtained in a PERT network.
j Pessimistic time ( b ) —The “worst” activity time that could be expected in a PERT network.
j Most likely time ( m ) —The most probable time to complete an activity in a PERT network.
When using PERT, we often assume that activity time estimates follow the beta distribution. Expected activity time t = ( a + 4 m + b ) / 6 (3-6) Variance of activity completion time = [( b – a ) / 6] 2 (3-7) s2p = Project variance = ∑ (variances of activities on critical path) (3-8) Z = (Due date − Expected date of completion) / sp (3-9) Due date = Expected completion time + ( Z × sp) (3-10)
Concept Questions: 7.1–7.4 Problems: 3.17–3.27 Virtual Office Hours for Solved Problems: 3.4, 3.5, 3.6
COST-TIME TRADE-OFFS AND PROJECT CRASHING (pp. 82 – 85 )
j Crashing —Shortening activity time in a network to reduce time on the critical path so total completion time is reduced.
Crash cost per period = (Crash cost – Normal cost) (Normal time – Crash time)
(3-11)
Concept Questions: 8.1–8.4 Problems: 3.28–3.33 Virtual Office Hours for Solved Problem: 3.7
A CRITIQUE OF PERT AND CPM (pp. 85 – 86 )
As with every technique for problem solving, PERT and CPM have a number of advantages as well as several limitations.
Concept Questions: 9.1–9.4
USING MICROSOFT PROJECT TO MANAGE PROJECTS (pp. 86 – 88 )
Microsoft Project, the most popular example of specialized project management software, is extremely useful in drawing project networks, identifying the project schedule, and man- aging project costs and other resources.
Concept Questions: 10.1–10.4
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Chapter 3 Rapid Review continued
LO 3.1 Which of the following statements regarding Gantt charts is true? a) Gantt charts give a timeline and precedence relationships
for each activity of a project. b) Gantt charts use the four standard spines: Methods,
Materials, Manpower, and Machinery. c) Gantt charts are visual devices that show the duration of
activities in a project. d) Gantt charts are expensive. e) All of the above are true. LO 3.2 Which of the following is true about AOA and AON networks? a) In AOA, arrows represent activities. b) In AON, nodes represent activities. c) Activities consume time and resources. d) Nodes are also called events in AOA. e) All of the above. LO 3.3 Slack time equals: a) ES + t . b) LS − ES. c) zero. d) EF − ES.
LO 3.4 The critical path of a network is the: a) shortest-time path through the network. b) path with the fewest activities. c) path with the most activities. d) longest-time path through the network. LO 3.5 PERT analysis computes the variance of the total project com-
pletion time as: a) the sum of the variances of all activities in the project. b) the sum of the variances of all activities on the critical path. c) the sum of the variances of all activities not on the critical
path. d) the variance of the final activity of the project. LO 3.6 The crash cost per period: a) is the difference in costs divided by the difference in times
(crash and normal). b) is considered to be linear in the range between normal and
crash. c) needs to be determined so that the smallest cost values on
the critical path can be considered for time reduction first. d) all of the above.
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
Answers: LO 3.1. c; LO 3.2. e; LO 3.3. b; LO 3.4. d; LO 3.5. b; LO 3.6. d.
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105
C H A P T E R O U T L I N E
Forecasting 4
◆
What Is Forecasting? 108
◆
The Strategic Importance of Forecasting 109
◆
Seven Steps in the Forecasting System 110
◆
Forecasting Approaches 111
◆
Time-Series Forecasting 112
◆
Associative Forecasting Methods: Regression and Correlation Analysis 131
◆
Monitoring and Controlling Forecasts 138
◆
Forecasting in the Service Sector 140
GLOBAL COMPANY PROFILE: Walt Disney Parks & Resorts
C H
A P
T E
R
Ala sk
a A
ir lin
e s
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W hen it comes to the world’s most respected global brands, Walt Disney Parks & Resorts
is a visible leader. Although the monarch of this magic kingdom is no man but a mouse—
Mickey Mouse—it’s CEO Robert Iger who daily manages the entertainment giant.
Disney’s global portfolio includes Shanghai Disney (2016), Hong Kong Disneyland (2005),
Disneyland Paris (1992), and Tokyo Disneyland (1983). But it is Walt
Disney World Resort (in Florida) and Disneyland Resort (in California)
that drive profits in this $50 billion corporation, which is ranked in the
top 100 in both the Fortune 500 and Financial Times Global 500.
Revenues at Disney are all about people—how many visit the
parks and how they spend money while there. When Iger receives
a daily report from his four theme parks and two water parks near
Orlando, the report contains only two numbers: the forecast of yes-
terday’s attendance at the parks (Magic Kingdom, Epcot, Disney’s
Animal Kingdom, Disney-Hollywood Studios, Typhoon Lagoon, and
Blizzard Beach) and the actual attendance. An error close to zero is
expected. Iger takes his forecasts very seriously.
The forecasting team at Walt Disney World Resort doesn’t just do
a daily prediction, however, and Iger is not its only customer. The team
also provides daily, weekly, monthly, annual, and 5-year forecasts to the labor management,
maintenance, operations, finance, and park scheduling departments. Forecasters use judgmental
models, econometric models, moving-average models, and regression analysis.
Forecasting Provides a Competitive Advantage for Disney
GLOBAL COMPANY PROFILE Walt Disney Parks & Resorts
C H A P T E R 4
Donald Duck, Goofy, and Mickey Mouse provide the public image of
Disney to the world. Forecasts drive the work schedules of 72,000
cast members working at Walt Disney World Resort near Orlando.
The giant sphere is the symbol of Epcot,
one of Disney’s four Orlando parks, for
which forecasts of meals, lodging,
entertainment, and transportation must be
made. This Disney monorail moves guests
among parks and the 28 hotels on the
massive 47-square-mile property (about the
size of San Francisco and twice the size of
Manhattan).
T ra
ve ls
h o ts
/P e te
r P h ip
p /T
ra ve
ls h o ts
.c o m
/A la
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106
Tr a ve
ls h o ts
/P e te
r P h ip
p /T
ra ve
ls h o ts
.c o m
/A la
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y
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107
With 20% of Walt Disney World Resort’s customers
coming from outside the United States, its economic model
includes such variables as gross domestic product (GDP),
cross-exchange rates, and arrivals into the U.S. Disney also
uses 35 analysts and 70 field people to survey 1 million
people each year. The surveys, administered to guests at the
parks and its 20 hotels, to employees, and to travel industry
professionals, examine future travel plans and experiences at
the parks. This helps forecast not only attendance but also
behavior at each ride (e.g., how long people will wait, how
many times they will ride). Inputs to the monthly forecast-
ing model include airline specials, speeches by the chair of
the Federal Reserve, and Wall Street trends. Disney even
monitors 3,000 school districts inside and outside the U.S.
for holiday/vacation schedules. With this approach, Disney’s
5-year attendance forecast yields just a 5% error on average.
Its annual forecasts have a 0% to 3% error.
Attendance forecasts for the parks drive a whole slew of
management decisions. For example, capacity on any day
can be increased by opening at 8 A.M. instead of the usual
9 A.M., by opening more shows or rides, by adding more food/
beverage carts (9 million hamburgers and 50 million Cokes
are sold per year!), and by bringing in more employees (called
“cast members”). Cast members are scheduled in 15-minute
intervals throughout the parks for flexibility. Demand can be
managed by limiting the number of guests admitted to the
A daily forecast of attendance is made by adjusting Disney’s annual operating
plan for weather forecasts, the previous day’s crowds, conventions, and
seasonal variations. One of the two water parks at Walt Disney World Resort,
Typhoon Lagoon, is shown here.
Cinderella’s iconic castle is a focal point for meeting up with
family and friends in the massive park. The statue of Walt
Disney greets visitors to the open plaza.
Ci d ll ’ i i l i f l i f i i h
M e lv
yn L
o n g h u rs
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is
Forecasts are critical to making sure rides are not overcrowded.
Disney is good at “managing demand” with techniques such as
adding more street activities to reduce long lines for rides. On
slow days, Disney calls fewer cast members to work.
d m
a c/
A la
m y
parks, with the “FAST PASS” reservation system, and by shift-
ing crowds from rides to more street parades.
At Disney, forecasting is a key driver in the company’s
success and competitive advantage.
K e vi
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108
What Is Forecasting? Every day, managers like those at Disney make decisions without knowing what will happen in the future. They order inventory without knowing what sales will be, purchase new equip- ment despite uncertainty about demand for products, and make investments without knowing what profits will be. Managers are always trying to make better estimates of what will hap- pen in the future in the face of uncertainty. Making good estimates is the main purpose of forecasting.
In this chapter, we examine different types of forecasts and present a variety of forecasting models. Our purpose is to show that there are many ways for managers to forecast. We also provide an overview of business sales forecasting and describe how to prepare, monitor, and judge the accuracy of a forecast. Good forecasts are an essential part of efficient service and manufacturing operations.
Forecasting is the art and science of predicting future events. Forecasting may involve taking historical data (such as past sales) and projecting them into the future with a math- ematical model. It may be a subjective or an intuitive prediction (e.g., “this is a great new product and will sell 20% more than the old one”). It may be based on demand-driven data, such as customer plans to purchase, and projecting them into the future. Or the forecast may involve a combination of these, that is, a mathematical model adjusted by a manager’s good judgment.
As we introduce different forecasting techniques in this chapter, you will see that there is seldom one superior method. Forecasts may be influenced by a product’s position in its life cycle—whether sales are in an introduction, growth, maturity, or decline stage. Other prod- ucts can be influenced by the demand for a related product—for example, navigation systems may track with new car sales. Because there are limits to what can be expected from forecasts, we develop error measures. Preparing and monitoring forecasts can also be costly and time consuming.
Few businesses, however, can afford to avoid the process of forecasting by just waiting to see what happens and then taking their chances. Effective planning in both the short run and long run depends on a forecast of demand for the company’s products.
Forecasting Time Horizons A forecast is usually classified by the future time horizon that it covers. Time horizons fall into three categories:
1. Short-range forecast: This forecast has a time span of up to 1 year but is generally less than 3 months. It is used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels.
2. Medium-range forecast: A medium-range, or intermediate, forecast generally spans from 3 months to 3 years. It is useful in sales planning, production planning and budgeting, cash budgeting, and analysis of various operating plans.
3. Long-range forecast: Generally 3 years or more in time span, long-range forecasts are used in planning for new products, capital expenditures, facility location or expansion, and research and development.
STUDENT TIP An increasingly complex world
economy makes forecasting
challenging.
Forecasting
The art and science of predicting
future events.
LO 4.1 Understand the three time horizons and
which models apply for
each
L E A R N I N G OBJEC TI V ES
LO 4.1 Understand the three time horizons and which models apply for each 108
LO 4.2 Explain when to use each of the four qualitative models 111
LO 4.3 Apply the naive, moving-average, exponential smoothing, and trend methods 113
LO 4.4 Compute three measures of forecast accuracy 118
LO 4.5 Develop seasonal indices 127
LO 4.6 Conduct a regression and correlation analysis 131
LO 4.7 Use a tracking signal 138
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C H A P T E R 4 | F O R E C A S T I N G 109
Medium- and long-range forecasts are distinguished from short-range forecasts by three features:
1. First, intermediate and long-range forecasts deal with more comprehensive issues sup- porting management decisions regarding planning and products, plants, and processes. Implementing some facility decisions, such as GM’s decision to open a new Brazilian manufacturing plant, can take 5 to 8 years from inception to completion.
2. Second, short-term forecasting usually employs different methodologies than longer-term forecasting. Mathematical techniques, such as moving averages, exponential smoothing, and trend extrapolation (all of which we shall examine shortly), are common to short- run projections. Broader, less quantitative methods are useful in predicting such issues as whether a new product, like the optical disk recorder, should be introduced into a com- pany’s product line.
3. Finally, as you would expect, short-range forecasts tend to be more accurate than longer- range forecasts. Factors that influence demand change every day. Thus, as the time hori- zon lengthens, it is likely that forecast accuracy will diminish. It almost goes without saying, then, that sales forecasts must be updated regularly to maintain their value and integrity. After each sales period, forecasts should be reviewed and revised.
Types of Forecasts Organizations use three major types of forecasts in planning future operations:
1. Economic forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators.
2. Technological forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment.
3. Demand forecasts are projections of demand for a company’s products or services. Forecasts drive decisions, so managers need immediate and accurate information about real demand. They need demand-driven forecasts , where the focus is on rapidly identifying and tracking customer desires. These forecasts may use recent point-of-sale (POS) data, retailer-generated reports of customer preferences, and any other information that will help to forecast with the most current data possible. Demand-driven forecasts drive a company’s production, capacity, and scheduling systems and serve as inputs to financial, marketing, and personnel planning. In addition, the payoff in reduced inventory and obsolescence can be huge.
Economic and technological forecasting are specialized techniques that may fall outside the role of the operations manager. The emphasis in this chapter will therefore be on demand forecasting.
The Strategic Importance of Forecasting Good forecasts are of critical importance in all aspects of a business: The forecast is the only estimate of demand until actual demand becomes known. Forecasts of demand therefore drive decisions in many areas. Let’s look at the impact of product demand forecast on three activi- ties: (1) supply-chain management, (2) human resources, and (3) capacity.
Supply-Chain Management Good supplier relations and the ensuing advantages in product innovation, cost, and speed to market depend on accurate forecasts. Here are just three examples:
◆ Apple has built an effective global system where it controls nearly every piece of the supply chain, from product design to retail store. With rapid communication and accurate data shared up and down the supply chain, innovation is enhanced, inventory costs are reduced, and speed to market is improved. Once a product goes on sale, Apple tracks demand by the
Economic forecasts
Planning indicators that are
valuable in helping organizations
prepare medium- to long-range
forecasts.
Technological forecasts
Long-term forecasts concerned
with the rates of technological
progress.
Demand forecasts
Projections of a company’s sales
for each time period in the plan-
ning horizon.
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110 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
hour for each store and adjusts production forecasts daily. At Apple, forecasts for its supply chain are a strategic weapon.
◆ Toyota develops sophisticated car forecasts with input from a variety of sources, includ- ing dealers. But forecasting the demand for accessories such as navigation systems, custom wheels, spoilers, and so on is particularly difficult. And there are over 1,000 items that vary by model and color. As a result, Toyota not only reviews reams of data with regard to vehi- cles that have been built and wholesaled but also looks in detail at vehicle forecasts before it makes judgments about the future accessory demand. When this is done correctly, the result is an efficient supply chain and satisfied customers.
◆ Walmart collaborates with suppliers such as Sara Lee and Procter & Gamble to make sure the right item is available at the right time in the right place and at the right price. For instance, in hurricane season, Walmart’s ability to analyze 700 million store–item combi- nations means it can forecast that not only flashlights but also Pop-Tarts and beer sell at seven times the normal demand rate. These forecasting systems are known as collaborative planning, forecasting, and replenishment (CPFR). They combine the intelligence of multiple supply-chain partners. The goal of CPFR is to create significantly more accurate informa- tion that can power the supply chain to greater sales and profits.
Human Resources Hiring, training, and laying off workers all depend on anticipated demand. If the human resources department must hire additional workers without warning, the amount of training declines, and the quality of the workforce suffers. A large Louisiana chemical firm almost lost its biggest customer when a quick expansion to around-the-clock shifts led to a total break- down in quality control on the second and third shifts.
Capacity When capacity is inadequate, the resulting shortages can lead to loss of customers and market share. This is exactly what happened to Nabisco when it underestimated the huge demand for its new Snackwell Devil’s Food Cookies. Even with production lines working overtime, Nabisco could not keep up with demand, and it lost customers. Nintendo faced this problem when its Wii was introduced and exceeded all forecasts for demand. Amazon made the same error with its Kindle. On the other hand, when excess capacity exists, costs can skyrocket.
Seven Steps in the Forecasting System Forecasting follows seven basic steps. We use Disney World, the focus of this chapter’s Global Company Profile , as an example of each step:
1. Determine the use of the forecast: Disney uses park attendance forecasts to drive decisions about staffing, opening times, ride availability, and food supplies.
2. Select the items to be forecasted: For Disney World, there are six main parks. A forecast of daily attendance at each is the main number that determines labor, maintenance, and scheduling.
3. Determine the time horizon of the forecast: Is it short, medium, or long term? Disney devel- ops daily, weekly, monthly, annual, and 5-year forecasts.
4. Select the forecasting model(s): Disney uses a variety of statistical models that we shall discuss, including moving averages, econometrics, and regression analysis. It also employs judgmental, or nonquantitative, models.
5. Gather the data needed to make the forecast: Disney’s forecasting team employs 35 ana- lysts and 70 field personnel to survey 1 million people/businesses every year. Disney also uses a firm called Global Insights for travel industry forecasts and gathers data on exchange rates, arrivals into the U.S., airline specials, Wall Street trends, and school vacation schedules.
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6. Make the forecast. 7. Validate and implement the results: At Disney, forecasts are reviewed daily at the highest
levels to make sure that the model, assumptions, and data are valid. Error measures are applied; then the forecasts are used to schedule personnel down to 15-minute intervals.
These seven steps present a systematic way of initiating, designing, and implementing a fore- casting system. When the system is to be used to generate forecasts regularly over time, data must be routinely collected. Then actual computations are usually made by computer.
Regardless of the system that firms like Disney use, each company faces several realities:
◆ Outside factors that we cannot predict or control often impact the forecast. ◆ Most forecasting techniques assume that there is some underlying stability in the system.
Consequently, some firms automate their predictions using computerized forecasting soft- ware, then closely monitor only the product items whose demand is erratic.
◆ Both product family and aggregated forecasts are more accurate than individual product forecasts. Disney, for example, aggregates daily attendance forecasts by park. This approach helps balance the over- and underpredictions for each of the six attractions.
Forecasting Approaches There are two general approaches to forecasting, just as there are two ways to tackle all deci- sion modeling. One is a quantitative analysis; the other is a qualitative approach. Quantitative forecasts use a variety of mathematical models that rely on historical data and/or associative variables to forecast demand. Subjective or qualitative forecasts incorporate such factors as the decision maker’s intuition, emotions, personal experiences, and value system in reaching a forecast. Some firms use one approach and some use the other. In practice, a combination of the two is usually most effective.
Overview of Qualitative Methods In this section, we consider four different qualitative forecasting techniques:
1. Jury of executive opinion : Under this method, the opinions of a group of high-level experts or managers, often in combination with statistical models, are pooled to arrive at a group estimate of demand. Bristol-Myers Squibb Company, for example, uses 220 well-known research scientists as its jury of executive opinion to get a grasp on future trends in the world of medical research.
2. Delphi method : There are three different types of participants in the Delphi method: decision makers, staff personnel, and respondents. Decision makers usually consist of a group of 5 to 10 experts who will be making the actual forecast. Staff personnel assist decision mak- ers by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results. The respondents are a group of people, often located in different places, whose judgments are valued. This group provides inputs to the decision makers before the forecast is made.
The state of Alaska, for example, has used the Delphi method to develop its long- range economic forecast. A large part of the state’s budget is derived from the million-plus barrels of oil pumped daily through a pipeline at Prudhoe Bay. The large Delphi panel of experts had to represent all groups and opinions in the state and all geographic areas.
3. Sales force composite : In this approach, each salesperson estimates what sales will be in his or her region. These forecasts are then reviewed to ensure that they are realistic. Then they are combined at the district and national levels to reach an overall forecast. A variation of this approach occurs at Lexus, where every quarter Lexus dealers have a “make meeting.” At this meeting, they talk about what is selling, in what colors, and with what options, so the factory knows what to build.
4. Market survey : This method solicits input from customers or potential customers regarding future purchasing plans. It can help not only in preparing a forecast but also in improving
Quantitative forecasts
Forecasts that employ mathemati-
cal modeling to forecast demand.
Qualitative forecasts
Forecasts that incorporate such
factors as the decision maker’s
intuition, emotions, personal expe-
riences, and value system.
Jury of executive opinion
A forecasting technique that uses
the opinion of a small group of
high-level managers to form a
group estimate of demand.
Delphi method
A forecasting technique using a
group process that allows experts
to make forecasts.
LO 4.2 Explain when to use each of the four
qualitative models
Sales force composite
A forecasting technique based
on salespersons’ estimates of
expected sales.
Market survey
A forecasting method that solicits
input from customers or potential
customers regarding future
purchasing plans.
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product design and planning for new products. The consumer market survey and sales force composite methods can, however, suffer from overly optimistic forecasts that arise from customer input.
Overview of Quantitative Methods 1 Five quantitative forecasting methods, all of which use historical data, are described in this chapter. They fall into two categories:
1. Naive approach 2. Moving averages 3. Exponential smoothing
( ' )
' *
Time-series models
4. Trend projection 5. Linear regression
" Associative model
Time-Series Models Time-series models predict on the assumption that the future is a function of the past. In other words, they look at what has happened over a period of time and use a series of past data to make a forecast. If we are predicting sales of lawn mowers, we use the past sales for lawn mowers to make the forecasts.
Associative Models Associative models, such as linear regression, incorporate the vari- ables or factors that might influence the quantity being forecast. For example, an associative model for lawn mower sales might use factors such as new housing starts, advertising budget, and competitors’ prices.
Time-Series Forecasting A time series is based on a sequence of evenly spaced (weekly, monthly, quarterly, and so on) data points. Examples include weekly sales of Nike Air Jordans, quarterly earnings reports of Microsoft stock, daily shipments of Coors beer, and annual consumer price indices. Forecasting time-series data implies that future values are predicted only from past values and that other variables, no matter how potentially valuable, may be ignored.
Decomposition of a Time Series Analyzing time series means breaking down past data into components and then projecting them forward. A time series has four components:
1. Trend is the gradual upward or downward movement of the data over time. Changes in income, population, age distribution, or cultural views may account for movement in trend.
2. Seasonality is a data pattern that repeats itself after a period of days, weeks, months, or quarters. There are six common seasonality patterns:
PERIOD LENGTH “SEASON” LENGTH NUMBER OF “SEASONS” IN PATTERN
Week Day 7
Month Week 4–4 12
Month Day 28–31
Year Quarter 4
Year Month 12
Year Week 52
Restaurants and barber shops, for example, experience weekly seasons, with Saturday being the peak of business. See the OM in Action box “Forecasting at Olive Garden.” Beer distributors forecast yearly patterns, with monthly seasons. Three “seasons”—May, July, and September—each contain a big beer-drinking holiday.
Time series
A forecasting technique that uses
a series of past data points to
make a forecast.
STUDENT TIP Here is the meat of this
chapter. We now show you a
wide variety of models that use
time-series data.
STUDENT TIP The peak “seasons” for sales
of Frito-Lay chips are the Super
Bowl, Memorial Day, Labor
Day, and the Fourth of July.
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3. Cycles are patterns in the data that occur every several years. They are usually tied into the business cycle and are of major importance in short-term business analysis and plan- ning. Predicting business cycles is difficult because they may be affected by political events or by international turmoil.
4. Random variations are “blips” in the data caused by chance and unusual situations. They follow no discernible pattern, so they cannot be predicted.
Figure 4.1 illustrates a demand over a 4-year period. It shows the average, trend, seasonal components, and random variations around the demand curve. The average demand is the sum of the demand for each period divided by the number of data periods.
Naive Approach The simplest way to forecast is to assume that demand in the next period will be equal to demand in the most recent period. In other words, if sales of a product—say, Nokia cell phones—were 68 units in January, we can forecast that February’s sales will also be 68 phones.
LO 4.3 Apply the naive, moving-average,
exponential smoothing,
and trend methods
Forecasting at Olive Garden
It’s Friday night in the college town of Gainesville, Florida, and the local Olive
Garden restaurant is humming. Customers may wait an average of 30 minutes
for a table, but they can sample new wines and cheeses and admire scenic
paintings of Italian villages on the Tuscan-style restaurant’s walls. Then comes
dinner with portions so huge that many people take home a doggie bag. The
typical bill: under $15 per person.
Crowds flock to the Darden restaurant chain’s Olive Garden, Seasons 52,
and Bahama Breeze for value and consistency— and they get it.
Every night, Darden’s computers crank out forecasts that tell store manag-
ers what demand to anticipate the next day. The forecasting software gener-
ates a total meal forecast and breaks that down into specific menu items. The
system tells a manager, for instance, that if 625 meals will be served the next
day, “you will serve these items in these quantities. So before you go home,
pull 25 pounds of shrimp and 30 pounds of crab out, and tell your operations
people to prepare 42 portion packs of chicken, 75 scampi dishes, 8 stuffed
flounders, and so on.” Managers often fine-tune the quantities based on local
conditions, such as weather or a convention, but they know what their custom-
ers are going to order.
OM in Action
By relying on demand history, the forecasting system has cut millions of
dollars of waste out of the system. The forecast also reduces labor costs by pro-
viding the necessary information for improved scheduling. Labor costs decreased
almost a full percent in the first year, translating into additional millions in savings
for the Darden chain. In the low-margin restaurant business, every dollar counts.
Sources: InformationWeek (April 1, 2014); USA Today (Oct. 13, 2014); and
FastCompany (July-August 2009).
Seasonal peaks
Random variation
Actual demand line
Average demand over 4 years
Trend component
D e m
a n
d f
o r
p ro
d u
c t
o r
s e rv
ic e
Time (years) 1 2 3 4
Figure 4.1
Demand Charted over 4 Years,
with a Growth Trend and
Seasonality Indicated
STUDENT TIP Forecasting is easy when
demand is stable. But with
trend, seasonality, and cycles
considered, the job is a lot
more interesting.
B o b P
a rd
u e -
S ig
n s/
A la
m y
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Does this make any sense? It turns out that for some product lines, this naive approach is the most cost-effective and efficient objective forecasting model. At least it provides a starting point against which more sophisticated models that follow can be compared.
Moving Averages A moving-average forecast uses a number of historical actual data values to generate a forecast. Moving averages are useful if we can assume that market demands will stay fairly steady over time . A 4-month moving average is found by simply summing the demand during the past 4 months and dividing by 4. With each passing month, the most recent month’s data are added to the sum of the previous 3 months’ data, and the earliest month is dropped. This practice tends to smooth out short-term irregularities in the data series.
Mathematically, the simple moving average (which serves as an estimate of the next period’s demand) is expressed as:
Moving average = g demand in previous n periods
n (4-1)
where n is the number of periods in the moving average—for example, 4, 5, or 6 months, respectively, for a 4-, 5-, or 6-period moving average.
Example 1 shows how moving averages are calculated.
Naive approach
A forecasting technique that
assumes that demand in the next
period is equal to demand in the
most recent period.
Moving averages
A forecasting method that uses
an average of the n most recent
periods of data to forecast the next
period.
Donna’s Garden Supply wants a 3-month moving-average forecast, including a forecast for next January, for shed sales.
APPROACH c Storage shed sales are shown in the middle column of the following table. A 3-month moving average appears on the right.
Example 1 DETERMINING THE MOVING AVERAGE
MONTH ACTUAL SHED SALES 3-MONTH MOVING AVERAGE
January 10 February 12 March 13 April 16 (10 + 12 + 13)/3 = 1123 May 19 (12 + 13 + 16)/3 = 1323 June 23 (13 + 16 + 19)/3 = 16 July 26 (16 + 19 + 23)/3 = 1913 August 30 (19 + 23 + 26)/3 = 2223 September 28 (23 + 26 + 30)/3 = 2613 October 18 (26 + 30 + 28)/3 = 28 November 16 (30 + 28 + 18)/3 = 2513 December 14 (28 + 18 + 16)/3 = 2023
SOLUTION c The forecast for December is 2023 . To project the demand for sheds in the coming January, we sum the October, November, and December sales and divide by 3: January forecast = (18 + 16 + 14)/3 = 16.
INSIGHT c Management now has a forecast that averages sales for the last 3 months. It is easy to use and understand.
LEARNING EXERCISE c If actual sales in December were 18 (rather than 14), what is the new January forecast? [Answer: 1713. ]
RELATED PROBLEMS c 4.1a, 4.2b, 4.5a, 4.6, 4.8a, b, 4.10a, 4.13b, 4.15, 4.33 (4.35, 4.38 are available in MyOMLab)
EXCEL OM Data File Ch04Ex1.xls can be found in MyOMLab.
ACTIVE MODEL 4.1 This example is further illustrated in Active Model 4.1 in MyOMLab.
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When a detectable trend or pattern is present, weights can be used to place more emphasis on recent values. This practice makes forecasting techniques more responsive to changes because more recent periods may be more heavily weighted. Choice of weights is somewhat arbitrary because there is no set formula to determine them. Therefore, deciding which weights to use requires some experience. For example, if the latest month or period is weighted too heavily, the forecast may reflect a large unusual change in the demand or sales pattern too quickly.
A weighted moving average may be expressed mathematically as:
Weighted moving average = g ((Weight for period n)(Demand in period n))
g Weights (4-2)
Example 2 shows how to calculate a weighted moving average.
Example 2 DETERMINING THE WEIGHTED MOVING AVERAGE Donna’s Garden Supply (see Example 1 ) wants to forecast storage shed sales by weighting the past 3 months, with more weight given to recent data to make them more significant.
APPROACH c Assign more weight to recent data, as follows:
WEIGHTS APPLIED PERIOD
3 Last month 2 Two months ago
1 6
Three months ago Sum of weights
Forecast for this month =
3 * Sales last mo. + 2 * Sales 2 mos. ago + 1 * Sales 3 mos. ago
Sum of the weights
SOLUTION c The results of this weighted-average forecast are as follows:
MONTH ACTUAL SHED SALES 3-MONTH WEIGHTED MOVING AVERAGE
January 10
February 12
March 13
April 16 [(3 * 13) + (2 * 12) + (10)]/6 = 1216
May 19 [(3 * 16) + (2 * 13) + (12)]/6 = 1413
June 23 [(3 * 19) + (2 * 16) + (13)]/6 = 17
July 26 [(3 * 23) + (2 * 19) + (16)]/6 = 2012
August 30 [(3 * 26) + (2 * 23) + (19)]/6 = 2356
September 28 [(3 * 30) + (2 * 26) + (23)]/6 = 2712
October 18 [(3 * 28) + (2 * 30) + (26)]/6 = 2813
November 16 [(3 * 18) + (2 * 28) + (30)]/6 = 2313
December 14 [(3 * 16) + (2 * 18) + (28)]/6 = 1823
The forecast for January is 1513. Do you see how this number is computed? INSIGHT c In this particular forecasting situation, you can see that more heavily weighting the latest month provides a more accurate projection.
LEARNING EXERCISE c If the assigned weights were 0.50, 0.33, and 0.17 (instead of 3, 2, and 1), what is the forecast for January’s weighted moving average? Why? [Answer: There is no change. These are the same relative weights. Note that g weights = 1 now, so there is no need for a denominator. When the weights sum to 1, calculations tend to be simpler.]
RELATED PROBLEMS c 4.1b, 4.2c, 4.5c, 4.6, 4.7, 4.10b (4.38 is available in MyOMLab)
EXCEL OM Data File Ch04Ex2.xls can be found in MyOMLab.
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Both simple and weighted moving averages are effective in smoothing out sudden fluctuations in the demand pattern to provide stable estimates. Moving averages do, however, present three problems:
1. Increasing the size of n (the number of periods averaged) does smooth out fluctuations better, but it makes the method less sensitive to changes in the data.
2. Moving averages cannot pick up trends very well. Because they are averages, they will always stay within past levels and will not predict changes to either higher or lower levels. That is, they lag the actual values.
3. Moving averages require extensive records of past data.
Figure 4.2 , a plot of the data in Examples 1 and 2 , illustrates the lag effect of the moving- average models. Note that both the moving-average and weighted-moving-average lines lag the actual demand. The weighted moving average, however, usually reacts more quickly to demand changes. Even in periods of downturn (see November and December), it more closely tracks the demand.
Exponential Smoothing Exponential smoothing is another weighted-moving-average forecasting method. It involves very little record keeping of past data and is fairly easy to use. The basic exponential smoothing formula can be shown as follows:
New forecast = Last period’s forecast + a (Last period’s actual demand − Last period’s forecast) (4-3)
where a is a weight, or smoothing constant , chosen by the forecaster, that has a value greater than or equal to 0 and less than or equal to 1. Equation (4-3) can also be written mathemati- cally as:
Ft = Ft91 + a (At91 - Ft91) (4-4)
where F t = new forecast Ft91 = previous period’s forecast a = smoothing (or weighting) constant (0 … a … 1) At91 = previous period’s actual demand
STUDENT TIP Moving-average methods
always lag behind when there
is a trend present, as shown by
the blue line (actual sales) for
January through August.
Exponential smoothing
A weighted-moving-average
forecasting technique in which
data points are weighted by an
exponential function.
Smoothing constant
The weighting factor used in an
exponential smoothing forecast, a
number greater than or equal to 0
and less than or equal to 1.
Weighted moving average (from Example 2)
Actual sales
Moving average (from Example 1)
Month Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.
S a le
s d
e m
a n
d
20
15
10
5
25
30
Figure 4.2
Actual Demand vs. Moving-
Average and Weighted-
Moving-Average Methods for
Donna’s Garden Supply
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The concept is not complex. The latest estimate of demand is equal to the old forecast adjusted by a fraction of the difference between the last period’s actual demand and last period’s fore- cast. Example 3 shows how to use exponential smoothing to derive a forecast.
In January, a car dealer predicted February demand for 142 Ford Mustangs. Actual February demand was 153 autos. Using a smoothing constant chosen by management of a = .20, the dealer wants to fore- cast March demand using the exponential smoothing model.
APPROACH c The exponential smoothing model in Equations (4-3) and (4-4) can be applied.
SOLUTION c Substituting the sample data into the formula, we obtain:
New forecast (for March demand) = 142 + .2(153 - 142) = 142 + 2.2 = 144.2
Thus, the March demand forecast for Ford Mustangs is rounded to 144.
INSIGHT c Using just two pieces of data, the forecast and the actual demand, plus a smoothing con- stant, we developed a forecast of 144 Ford Mustangs for March.
LEARNING EXERCISE c If the smoothing constant is changed to .30, what is the new forecast? [Answer: 145.3]
RELATED PROBLEMS c 4.1c, 4.3, 4.4, 4.5d, 4.6, 4.9d, 4.11, 4.12, 4.13a, 4.17, 4.18, 4.31, 4.33, 4.34 (4.36, 4.61a are available in MyOMLab)
Example 3 DETERMINING A FORECAST VIA EXPONENTIAL SMOOTHING
The smoothing constant , a , is generally in the range from .05 to .50 for business applications. It can be changed to give more weight to recent data (when a is high) or more weight to past data (when a is low). When a reaches the extreme of 1.0, then in Equation (4-4) , F t 5 1.0 A t 21 . All the older values drop out, and the forecast becomes identical to the naive model mentioned earlier in this chapter. That is, the forecast for the next period is just the same as this period’s demand.
The following table helps illustrate this concept. For example, when a = .5 , we can see that the new forecast is based almost entirely on demand in the last three or four periods. When a = .1 , the forecast places little weight on recent demand and takes many periods (about 19) of historical values into account.
WEIGHT ASSIGNED TO
SMOOTHING CONSTANT
MOST RECENT PERIOD ( A )
2ND MOST RECENT PERIOD A (12A )
3RD MOST RECENT PERIOD
A (12 A ) 2
4TH MOST RECENT PERIOD
A (12A ) 3
5TH MOST RECENT PERIOD
A (12A ) 4
a = .1 .1 .09 .081 .073 .066
a = .5 .5 .25 .125 .063 .031
Selecting the Smoothing Constant Exponential smoothing has been successfully applied in virtually every type of business. However, the appropriate value of the smoothing constant, a , can make the difference between an accurate forecast and an inaccurate forecast. High values of a are chosen when the underlying average is likely to change. Low values of a are used when the underlying average is fairly stable. In picking a value for the smoothing constant, the objective is to obtain the most accurate forecast.
Measuring Forecast Error The overall accuracy of any forecasting model—moving average, exponential smoothing, or other—can be determined by comparing the forecasted values with the actual or observed
STUDENT TIP Forecasts tend to be more
accurate as they become
shorter. Therefore, forecast
error also tends to drop with
shorter forecasts.
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values. If F t denotes the forecast in period t , and A t denotes the actual demand in period t , the forecast error (or deviation) is defined as: Forecast error = Actual demand - Forecast value = At - Ft Several measures are used in practice to calculate the overall forecast error. These measures can be used to compare different forecasting models, as well as to monitor forecasts to ensure they are performing well. Three of the most popular measures are mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percent error (MAPE). We now describe and give an example of each.
Mean Absolute Deviation The first measure of the overall forecast error for a model is the mean absolute deviation (MAD) . This value is computed by taking the sum of the absolute values of the individual forecast errors (deviations) and dividing by the number of periods of data ( n ):
MAD = g � Actual - Forecast �
n (4-5)
Example 4 applies MAD, as a measure of overall forecast error, by testing two values of a .
LO 4.4 Compute three measures of forecast
accuracy
Mean absolute deviation (MAD)
A measure of the overall forecast
error for a model.
During the past 8 quarters, the Port of Baltimore has unloaded large quantities of grain from ships. The port’s operations manager wants to test the use of exponential smoothing to see how well the technique works in predicting tonnage unloaded. He guesses that the forecast of grain unloaded in the first quarter was 175 tons. Two values of a are to be examined: a = .10 and a = .50.
APPROACH c Compare the actual data with the data we forecast (using each of the two a values) and then find the absolute deviation and MADs.
SOLUTION c The following table shows the detailed calculations for a = .10 only:
QUARTER ACTUAL TONNAGE
UNLOADED FORECAST WITH A = .10 FORECAST WITH
A = .50
1 180 175 175 2 168 175.50 = 175.00 + .10(180 - 175) 177.50 3 159 174.75 = 175.50 + .10(168 - 175.50) 172.75 4 175 173.18 = 174.75 + .10(159 - 174.75) 165.88 5 190 173.36 = 173.18 + .10(175 - 173.18) 170.44 6 205 175.02 = 173.36 + .10(190 - 173.36) 180.22 7 180 178.02 = 175.02 + .10(205 - 175.02) 192.61 8 182 178.22 = 178.02 + .10(180 - 178.02) 186.30 9 ? 178.59 = 178.22 + .10(182 - 178.22) 184.15
To evaluate the accuracy of each smoothing constant, we can compute forecast errors in terms of abso- lute deviations and MADs:
QUARTER ACTUAL TONNAGE
UNLOADED FORECAST WITH
A = .10
ABSOLUTE DEVIATION FOR A = .10
FORECAST WITH
A = .50
ABSOLUTE DEVIATION FOR A = .50
1 180 175 5.00 175 5.00 2 168 175.50 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30
Sum of absolute deviations: 82.45 98.62
MAD = g � Deviations �
n 10.31 12.33
Example 4 DETERMINING THE MEAN ABSOLUTE DEVIATION (MAD)
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Most computerized forecasting software includes a feature that automatically finds the smoothing constant with the lowest forecast error. Some software modifies the a value if errors become larger than acceptable.
Mean Squared Error The mean squared error (MSE) is a second way of measuring overall forecast error. MSE is the average of the squared differences between the forecasted and observed values. Its formula is:
MSE = g(Forecast errors)2
n (4-6)
Example 5 finds the MSE for the Port of Baltimore problem introduced in Example 4 .
Mean squared error (MSE)
The average of the squared differ-
ences between the forecasted and
observed values.
INSIGHT c On the basis of this comparison of the two MADs, a smoothing constant of a = .10 is preferred to a = .50 because its MAD is smaller.
LEARNING EXERCISE c If the smoothing constant is changed from a = .10 to a = .20, what is the new MAD? [Answer: 10.21.]
RELATED PROBLEMS c 4.5b, 4.8c, 4.9c, 4.14, 4.23, 4.59b (4.35d, 4.37a, 4.38c, 4.61b are available in MyOMLab)
EXCEL OM Data File Ch04Ex4a.xls and Ch04Ex4b.xls can be found in MyOMLab.
ACTIVE MODEL 4.2 This example is further illustrated in Active Model 4.2 in MyOMLab.
Example 5 DETERMINING THE MEAN SQUARED ERROR (MSE) The operations manager for the Port of Baltimore now wants to compute MSE for a = .10.
APPROACH c Using the same forecast data for a = .10 from Example 4 , compute the MSE with Equation (4-6) .
SOLUTION c
QUARTER ACTUAL TONNAGE
UNLOADED FORECAST FOR
A = .10 (ERROR) 2
1 180 175 52 = 25 2 168 175.50 ( - 7.5)2 = 56.25 3 159 174.75 ( - 15.75)2 = 248.06 4 175 173.18 (1.82)2 = 3.31 5 190 173.36 (16.64)2 = 276.89 6 205 175.02 (29.98)2 = 898.80 7 180 178.02 (1.98)2 = 3.92 8 182 178.22 (3.78)2 = 14.29
Sum of errors squared = 1,526.52
MSE = g(Forecast errors)2
n = 1,526.52/8 = 190.8
INSIGHT c Is this MSE = 190.8 good or bad? It all depends on the MSEs for other forecasting approaches. A low MSE is better because we want to minimize MSE. MSE exaggerates errors because it squares them.
LEARNING EXERCISE c Find the MSE for a = .50. [Answer: MSE = 195.24. The result indicates that a = .10 is a better choice because we seek a lower MSE. Coincidentally, this is the same conclusion we reached using MAD in Example 4 .]
RELATED PROBLEMS c 4.8d, 4.11c, 4.14, 4.15c, 4.16c, 4.20 (4.35d, 4.37b are available in MyOMLab)
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The MSE tends to accentuate large deviations due to the squared term. For example, if the forecast error for period 1 is twice as large as the error for period 2, the squared error in period 1 is four times as large as that for period 2. Hence, using MSE as the measure of forecast error typically indicates that we prefer to have several smaller deviations rather than even one large deviation.
Mean Absolute Percent Error A problem with both the MAD and MSE is that their values depend on the magnitude of the item being forecast. If the forecast item is measured in thousands, the MAD and MSE values can be very large. To avoid this problem, we can use the mean absolute percent error (MAPE) . This is computed as the average of the absolute difference between the forecasted and actual values, expressed as a percentage of the actual values. That is, if we have forecasted and actual values for n periods, the MAPE is calculated as:
MAPE = a
n
i = 1 100 � Actuali - Forecasti � >Actuali
n (4-7)
Example 6 illustrates the calculations using the data from Examples 4 and 5 .
Mean absolute percent error (MAPE)
The average of the absolute
differences between the forecast
and actual values, expressed as a
percent of actual values.
The Port of Baltimore wants to now calculate the MAPE when a = .10.
APPROACH c Equation (4-7) is applied to the forecast data computed in Example 4 .
SOLUTION c
QUARTER ACTUAL TONNAGE
UNLOADED FORECAST FOR
A = .10 ABSOLUTE PERCENT ERROR
100 (|ERROR|/ACTUAL)
1 180 175.00 100(5/180) = 2.78%
2 168 175.50 100(7.5/168) = 4.46% 3 159 174.75 100(15.75/159) = 9.90% 4 175 173.18 100(1.82/175) = 1.05% 5 190 173.36 100(16.64/190) = 8.76% 6 205 175.02 100(29.98/205) = 14.62% 7 180 178.02 100(1.98/180) = 1.10% 8 182 178.22 100(3.78/182) = 2.08%
Sum of % errors = 44.75%
MAPE = g absolute percent error
n =
44.75% 8
= 5.59%
INSIGHT c MAPE expresses the error as a percent of the actual values, undistorted by a single large value.
LEARNING EXERCISE c What is MAPE when a is .50? [Answer: MAPE = 6.75%. As was the case with MAD and MSE, the a = .1 was preferable for this series of data.]
RELATED PROBLEMS c 4.8e, 4.29c
Example 6 DETERMINING THE MEAN ABSOLUTE PERCENT ERROR (MAPE)
The MAPE is perhaps the easiest measure to interpret. For example, a result that the MAPE is 6% is a clear statement that is not dependent on issues such as the magnitude of the input data.
Table 4.1 summarizes how MAD, MSE, and MAPE differ.
Exponential Smoothing with Trend Adjustment Simple exponential smoothing, the technique we just illustrated in Examples 3 to 6 , is like any other moving-average technique: It fails to respond to trends. Other forecasting techniques that can deal with trends are certainly available. However, because exponential smoothing is such a popular modeling approach in business, let us look at it in more detail.
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C H A P T E R 4 | F O R E C A S T I N G 121
Here is why exponential smoothing must be modified when a trend is present. Assume that demand for our product or service has been increasing by 100 units per month and that we have been forecasting with a = 0.4 in our exponential smoothing model. The following table shows a severe lag in the second, third, fourth, and fifth months, even when our initial estimate for month 1 is perfect:
MONTH ACTUAL DEMAND FORECAST ( F t ) FOR MONTHS 1–5
1 100 F1 = 100 (given)
2 200 F2 = F1 + a(A1 - F1) = 100 + .4(100 - 100) = 100
3 300 F3 = F2 + a(A2 - F2) = 100 + .4(200 - 100) = 140
4 400 F4 = F3 + a(A3 - F3) = 140 + .4(300 - 140) = 204
5 500 F5 = F4 + a(A 4 - F4) = 204 + .4(400 - 204) = 282
To improve our forecast, let us illustrate a more complex exponential smoothing model, one that adjusts for trend. The idea is to compute an exponentially smoothed average of the data and then adjust for positive or negative lag in trend. The new formula is:
Forecast including trend (FITt) = Exponentially smoothed forecast average (Ft) + Exponentially smoothed trend (Tt) (4-8)
With trend-adjusted exponential smoothing, estimates for both the average and the trend are smoothed. This procedure requires two smoothing constants: a for the average and b for the trend. We then compute the average and trend each period:
Ft = a(Actual demand last period) + (1 - a)(Forecast last period + Trend estimate last period)
or: Ft = a(At - 1) + (1 - a)(Ft - 1 + Tt - 1) (4-9)
Tt = b(Forecast this period - Forecast last period) + (1 - b)(Trend estimate last period)
or:
Tt = b(Ft - Ft - 1) + (1 - b)Tt - 1 (4-10)
where F t = exponentially smoothed forecast average of the data series in period t T t = exponentially smoothed trend in period t A t = actual demand in period t a = smoothing constant for the average (0 … a … 1) b = smoothing constant for the trend (0 … b … 1)
TABLE 4.1 Comparison of Measures of Forecast Error
MEASURE MEANING EQUATION APPLICATION TO CHAPTER EXAMPLE
Mean absolute deviation (MAD)
How much the forecast missed the target
MAD = g 0Actual - Forecast 0
n (4-5)
For a = .10 in Example 4 , the forecast for grain unloaded was off by an average of 10.31 tons.
Mean squared error (MSE)
The square of how much the forecast missed the target
MSE = g(Forecast errors)2
n (4-6)
For a = .10 in Example 5 , the square of the forecast error was 190.8. This number does not have a physical meaning but is useful when compared to the MSE of another forecast.
Mean absolute percent error (MAPE)
The average percent error
MAPE = a n
i = 1 100 � Actuali - Forecasti � >Actuali
n (4-7)
For a = .10 in Example 6 , the forecast is off by 5.59% on average. As in Examples 4 and 5 , some forecasts were too high, and some were low.
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So the three steps to compute a trend-adjusted forecast are:
STEP 1: Compute F t , the exponentially smoothed forecast average for period t , using Equation (4-9) .
STEP 2: Compute the smoothed trend, T t , using Equation (4-10) . STEP 3: Calculate the forecast including trend, FIT t , by the formula FIT t = F t + T t [from
Equation (4-8) ].
Example 7 shows how to use trend-adjusted exponential smoothing.
A large Portland manufacturer wants to forecast demand for a piece of pollution-control equipment. A review of past sales, as shown below, indicates that an increasing trend is present:
MONTH ( t ) ACTUAL DEMAND ( A t ) MONTH ( t ) ACTUAL DEMAND ( A t )
1 12 6 21
2 17 7 31
3 20 8 28
4 19 9 36
5 24 10 ?
Smoothing constants are assigned the values of a = .2 and b = .4. The firm assumes the initial forecast average for month 1 ( F 1 ) was 11 units and the trend over that period ( T 1 ) was 2 units.
APPROACH c A trend-adjusted exponential smoothing model, using Equations (4-9) , (4-10) , and (4-8) and the three steps above, is employed.
SOLUTION c Step 1: Forecast average for month 2:
F2 = aA1 + (1 - a)(F1 + T1) F2 = (.2)(12) + (1 - .2)(11 + 2) = 2.4 + (.8)(13) = 2.4 + 10.4 = 12.8 units
Step 2: Compute the trend in period 2:
T2 = b(F2 - F1) + (1 - b)T1 = .4(12.8 - 11) + (1 - .4)(2) = (.4)(1.8) + (.6)(2) = .72 + 1.2 = 1.92
Step 3: Compute the forecast including trend ( FIT t ):
FIT2 = F2 + T2 = 12.8 + 1.92 = 14.72 units
We will also do the same calculations for the third month:
Step 1: F3 = aA2 + (1 - a)(F2 + T2) = (.2)(17) + (1 - .2)(12.8 + 1.92) = 3.4 + (.8)(14.72) = 3.4 + 11.78 = 15.18
Step 2: T3 = b(F3 - F2) + (1 - b)T2 = (.4)(15.18 - 12.8) + (1 - .4)(1.92) = (.4)(2.38) + (.6)(1.92) = .952 + 1.152 = 2.10
Step 3: FIT3 = F3 + T3 = 15.18 + 2.10 = 17.28.
Example 7 COMPUTING A TREND-ADJUSTED EXPONENTIAL SMOOTHING FORECAST
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Table 4.2 completes the forecasts for the 10-month period.
TABLE 4.2 Forecast with A 5 .2 and B 5 .4
MONTH ACTUAL DEMAND
SMOOTHED FORECAST AVERAGE, F t
SMOOTHED TREND, T t
FORECAST INCLUDING TREND, FIT t
1 12 11 2 13.00 2 17 12.80 1.92 14.72 3 20 15.18 2.10 17.28 4 19 17.82 2.32 20.14 5 24 19.91 2.23 22.14 6 21 22.51 2.38 24.89 7 31 24.11 2.07 26.18 8 28 27.14 2.45 29.59 9 36 29.28 2.32 31.60
10 — 32.48 2.68 35.16
INSIGHT c Figure 4.3 compares actual demand ( A t ) to an exponential smoothing forecast that includes trend ( FIT t ). FIT picks up the trend in actual demand. A simple exponential smoothing model (as we saw in Examples 3 and 4 ) trails far behind.
LEARNING EXERCISE c Using the data for actual demand for the 9 months, compute the exponen- tially smoothed forecast average without trend [using Equation (4-4) as we did earlier in Examples 3 and 4 ]. Apply a = .2, and assume an initial forecast average for month 1 of 11 units. Then plot the months 2–10 forecast values on Figure 4.3 . What do you notice? [Answer: Month 10 forecast = 24.65. All the points are below and lag the trend-adjusted forecast.]
RELATED PROBLEMS c 4.19, 4.20, 4.21, 4.22, 4.32
ACTIVE MODEL 4.3 This example is further illustrated in Active Model 4.3 in MyOMLab.
EXCEL OM Data File Ch04Ex7.xis can be found in MyOMLab.
Actual demand (At )
40
35
30
25
20
15
10
5
0
Time (months)
Forecast including trend (FITt ) with c = .2 and d = .4
P ro
d u
c t
d e m
a n
d
1 2 3 4 5 6 7 8 9
Figure 4.3
Exponential Smoothing with
Trend-Adjustment Forecasts
Compared to Actual Demand
Data
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The value of the trend-smoothing constant, b , resembles the a constant because a high b is more responsive to recent changes in trend. A low b gives less weight to the most recent trends and tends to smooth out the present trend. Values of b can be found by the trial-and-error approach or by using sophisticated commercial forecasting software, with the MAD used as a measure of comparison.
Simple exponential smoothing is often referred to as first-order smoothing , and trend- adjusted smoothing is called second-order smoothing or double smoothing . Other advanced exponential-smoothing models are also used, including seasonal-adjusted and triple smoothing.
Trend Projections The last time-series forecasting method we will discuss is trend projection . This technique fits a trend line to a series of historical data points and then projects the slope of the line into the future for medium- to long-range forecasts. Several mathematical trend equations can be developed (for example, exponential and quadratic), but in this section, we will look at linear (straight-line) trends only.
If we decide to develop a linear trend line by a precise statistical method, we can apply the least-squares method . This approach results in a straight line that minimizes the sum of the squares of the vertical differences or deviations from the line to each of the actual observations. Figure 4.4 illustrates the least-squares approach.
A least-squares line is described in terms of its y -intercept (the height at which it intercepts the y -axis) and its expected change (slope). If we can compute the y -intercept and slope, we can express the line with the following equation:
ny = a + bx (4-11)
where ny (called “ y hat”) = computed value of the variable to be predicted (called the dependent variable )
a = y -axis intercept b = slope of the regression line (or the rate of change in y for given
changes in x ) x = the independent variable (which in this case is time )
Statisticians have developed equations that we can use to find the values of a and b for any regression line. The slope b is found by:
b = gxy - nx y gx2 - nx2
(4-12)
Trend projection
A time-series forecasting method
that fits a trend line to a series
of historical data points and then
projects the line into the future for
forecasts.
Time period
îTrend line, y = a + bx
V a lu
e s o
f d
e p
e n
d e n
t v a ri
a b
le (
y -v
a lu
e s )
Deviation 3
Deviation (error)
1
Deviation
Deviation 5 Deviation 6
Deviation 7
Deviation 2
Actual observation (y -value)
4
1 2 3 4 5 6 7
Figure 4.4
The Least-Squares Method
for Finding the Best-Fitting
Straight Line, Where the
Asterisks Are the Locations of
the Seven Actual Observations
or Data Points
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C H A P T E R 4 | F O R E C A S T I N G 125
where b = slope of the regression line g = summation sign
x = known values of the independent variable y = known values of the dependent variable x = average of the x -values
y = average of the y -values n = number of data points or observations
We can compute the y -intercept a as follows:
a = y - bx (4-13)
Example 8 shows how to apply these concepts.
Example 8 FORECASTING WITH LEAST SQUARES The demand for electric power at N.Y. Edison over the past 7 years is shown in the following table, in megawatts. The firm wants to forecast next year’s demand by fitting a straight-line trend to these data.
YEAR ELECTRICAL
POWER DEMAND YEAR ELECTRICAL
POWER DEMAND
1 74 5 105 2 79 6 142 3 80 7 122 4 90
APPROACH c Equations (4-12) and (4-13) can be used to create the trend projection model.
SOLUTION c
YEAR ( x ) ELECTRIC POWER
DEMAND ( y ) x 2 xy
1 74 1 74 2 79 4 158 3 80 9 240 4 90 16 360 5 105 25 525 6 142 36 852 7 122 49 854
gx = 28 gy = 692 gx2 = 140 gxy = 3,063
x = gx n
= 28 7
= 4 y = gy n
= 692 7
= 98.86
b = gxy - nx y gx2 - nx2
= 3,063 - (7)(4)(98.86)
140 - (7)(42) =
295 28
= 10.54
a = y - bx = 98.86 - 10.54(4) = 56.70
Thus, the least-squares trend equation is ny = 56.70 + 10.54x. To project demand next year, x = 8:
Demand in year 8 = 56.70 + 10.54(8) = 141.02, or 141 megawatts
INSIGHT c To evaluate the model, we plot both the historical demand and the trend line in Figure 4.5 . In this case, we may wish to be cautious and try to understand the year 6 to year 7 swing in demand.
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Notes on the Use of the Least-Squares Method Using the least-squares method implies that we have met three requirements:
1. We always plot the data because least-squares data assume a linear relationship. If a curve appears to be present, curvilinear analysis is probably needed.
2. We do not predict time periods far beyond our given database. For example, if we have 20 months’ worth of average prices of Microsoft stock, we can forecast only 3 or 4 months into the future. Forecasts beyond that have little statistical validity. Thus, you cannot take 5 years’ worth of sales data and project 10 years into the future. The world is too uncertain.
3. Deviations around the least-squares line (see Figure 4.4 ) are assumed to be random and normally distributed, with most observations close to the line and only a smaller number farther out.
Seasonal Variations in Data Seasonal variations in data are regular movements in a time series that relate to recurring events such as weather or holidays. Demand for coal and fuel oil, for example, peaks during cold winter months. Demand for golf clubs or sunscreen may be highest in summer.
Seasonality may be applied to hourly, daily, weekly, monthly, or other recurring patterns. Fast-food restaurants experience daily surges at noon and again at 5 p.m. Movie theaters see higher demand on Friday and Saturday evenings. The post office, Toys “ R” Us, The Christ- mas Store, and Hallmark Card Shops also exhibit seasonal variation in customer traffic and sales.
Similarly, understanding seasonal variations is important for capacity planning in organi- zations that handle peak loads. These include electric power companies during extreme cold and warm periods, banks on Friday afternoons, and buses and subways during the morning and evening rush hours.
Seasonal variations
Regular upward or downward
movements in a time series that
tie to recurring events.
LEARNING EXERCISE c Estimate demand for year 9. [Answer: 151.56, or 152 megawatts.]
RELATED PROBLEMS c 4.6, 4.13c, 4.16, 4.24, 4.30, 4.34 (4.39, 4.42 are available in MyOMLab)
EXCEL OM Data File Ch04Ex8.xls can be found in MyOMLab.
ACTIVE MODEL 4.4 This example is further illustrated in Active Model 4.4 in MyOMLab.
Figure 4.5
Electrical Power and the
Computed Trend Line
î
1 2 3 4 5 6 7 8 9
160
150
140
130
120
110
100
90
80
70
60
50
Year
P o
w e r
d e m
a n
d (
m e g
a w
a tt
s )
Trend line, y = 56.70 + 10.54x
STUDENT TIP John Deere understands
seasonal variations: It has been
able to obtain 70% of its orders
in advance of seasonal use so
it can smooth production.
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C H A P T E R 4 | F O R E C A S T I N G 127
Time-series forecasts like those in Example 8 involve reviewing the trend of data over a series of time periods. The presence of seasonality makes adjustments in trend-line forecasts necessary. Seasonality is expressed in terms of the amount that actual values differ from aver- age values in the time series. Analyzing data in monthly or quarterly terms usually makes it easy for a statistician to spot seasonal patterns. Seasonal indices can then be developed by several common methods.
In what is called a multiplicative seasonal model , seasonal factors are multiplied by an esti- mate of average demand to produce a seasonal forecast. Our assumption in this section is that trend has been removed from the data. Otherwise, the magnitude of the seasonal data will be distorted by the trend.
Here are the steps we will follow for a company that has “seasons” of 1 month:
1. Find the average historical demand each season (or month in this case) by summing the demand for that month in each year and dividing by the number of years of data avail- able. For example, if, in January, we have seen sales of 8, 6, and 10 over the past 3 years, average January demand equals (8 + 6 + 10)/3 = 8 units.
2. Compute the average demand over all months by dividing the total average annual demand by the number of seasons. For example, if the total average demand for a year is 120 units and there are 12 seasons (each month), the average monthly demand is 120/12 = 10 units.
3. Compute a seasonal index for each season by dividing that month’s historical average demand (from Step 1) by the average demand over all months (from Step 2). For example, if the average historical January demand over the past 3 years is 8 units and the aver- age demand over all months is 10 units, the seasonal index for January is 8/10 = .80. Likewise, a seasonal index of 1.20 for February would mean that February’s demand is 20% larger than the average demand over all months.
4. Estimate next year’s total annual demand. 5. Divide this estimate of total annual demand by the number of seasons, then multiply it by
the seasonal index for each month. This provides the seasonal forecast .
Example 9 illustrates this procedure as it computes seasonal indices from historical data.
LO 4.5 Develop seasonal indices
Demand for many products
is seasonal. Yamaha, the
manufacturer of this jet ski and
snowmobile, produces products
with complementary demands to
address seasonal fluctuations.
C it yF
ile s/
G e tt
y Im
a g e s
D ic
k L o e k/
G e tt
y Im
a g e s
A Des Moines distributor of Sony laptop computers wants to develop monthly indices for sales. Data from the past 3 years, by month, are available.
APPROACH c Follow the five steps listed above.
Example 9 DETERMINING SEASONAL INDICES
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For simplicity, only 3 periods (years) are used for each monthly index in the preceding exam- ple. Example 10 illustrates how indices that have already been prepared can be applied to adjust trend-line forecasts for seasonality.
SOLUTION c DEMAND
MONTH YEAR 1 YEAR 2 YEAR 3 AVERAGE PERIOD
DEMAND AVERAGE MONTHLY
DEMAND a SEASONAL
INDEX b
Jan. 80 85 105 90 94 .957 ( = 90>94) Feb. 70 85 85 80 94 .851 ( = 80>94) Mar. 80 93 82 85 94 .904 ( = 85>94) Apr. 90 95 115 100 94 1.064 ( = 100>94) May 113 125 131 123 94 1.309 ( = 123>94) June 110 115 120 115 94 1.223 ( = 115>94) July 100 102 113 105 94 1.117 ( = 105>94) Aug. 88 102 110 100 94 1.064 ( = 100>94) Sept. 85 90 95 90 94 .957 ( = 90>94) Oct. 77 78 85 80 94 .851 ( = 80>94) Nov. 75 82 83 80 94 .851 ( = 80>94) Dec. 82 78 80 80 94 .851 ( = 80>94)
Total average annual demand = 1,128
aAverage monthly demand = 1,128
12 months = 94. bSeasonal index =
Average monthly demand for past 3 years
Average monthly demand .
If we expect the annual demand for computers to be 1,200 units next year, we would use these seasonal indices to forecast the monthly demand as follows:
MONTH DEMAND MONTH DEMAND
Jan. 1,200
12 * .957 = 96
July 1,200
12 * 1.117 = 112
Feb. 1,200
12 * .851 = 85
Aug. 1,200
12 * 1.064 = 106
Mar. 1,200
12 * .904 = 90
Sept. 1,200
12 * .957 = 96
Apr. 1,200
12 * 1.064 = 106
Oct. 1,200
12 * .851 = 85
May 1,200
12 * 1.309 = 131
Nov. 1,200
12 * .851 = 85
June 1,200
12 * 1.223 = 122
Dec. 1,200
12 * .851 = 85
INSIGHT c Think of these indices as percentages of average sales. The average sales (without seasonal- ity) would be 94, but with seasonality, sales fluctuate from 85% to 131% of average.
LEARNING EXERCISE c If next year’s annual demand is 1,150 laptops (instead of 1,200), what will the January, February, and March forecasts be? [Answer: 91.7, 81.5, and 86.6, which can be rounded to 92, 82, and 87.]
RELATED PROBLEMS c 4.26, 4.27 (4.40, 4.41a are available in MyOMLab)
EXCEL OM Data File Ch04Ex9.xls can be found in MyOMLab.
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Example 10 APPLYING BOTH TREND AND SEASONAL INDICES San Diego Hospital wants to improve its forecasting by applying both trend and seasonal indices to 66 months of data it has collected. It will then forecast “patient-days” over the coming year.
APPROACH c A trend line is created; then monthly seasonal indices are computed. Finally, a multi- plicative seasonal model is used to forecast months 67 to 78.
SOLUTION c Using 66 months of adult inpatient hospital days, the following equation was computed:
ny = 8,090 + 21.5x where
ny = patient days x = time, in months
Based on this model, which reflects only trend data, the hospital forecasts patient days for the next month (period 67) to be:
Patient days = 8,090 + (21.5)(67) = 9,530 (trend only)
While this model, as plotted in Figure 4.6 , recognized the upward trend line in the demand for inpatient services, it ignored the seasonality that the administration knew to be present.
Jan. 67
Feb. 68
9,000
9,600
9,800
10,000
10,200
9,400
9,200
In p
a ti
e n
t d
ay s
Mar. 69
Apr. 70
9,594 9,530
9,551
9,573
May 71
9,616
June 72
9,637
July 73
9,659
Aug. 74
Sept. 75
9,680
9,702
Oct. 76
9,724
Dec. 78
9,766
Nov. 77
9,745
Month (period = 67 for Jan. through 78 for Dec.)
Figure 4.6
Trend Data for San Diego
Hospital
Source: From “Modern Methods Improve
Hospital Forecasting” by W. E. Sterk and
E. G. Shryock from Healthcare Financial
Management 41, no. 3, p. 97 . Reprinted
by permission of Healthcare Financial
Management Association.
The following table provides seasonal indices based on the same 66 months. Such seasonal data, by the way, were found to be typical of hospitals nationwide.
Seasonality Indices for Adult Inpatient Days at San Diego Hospital
MONTH SEASONALITY INDEX MONTH SEASONALITY INDEX
January 1.04 July 1.03 February 0.97 August 1.04 March 1.02 September 0.97 April 1.01 October 1.00 May 0.99 November 0.96 June 0.99 December 0.98
These seasonal indices are graphed in Figure 4.7 . Note that January, March, July, and August seem to exhibit significantly higher patient days on average, while February, September, November, and December experience lower patient days.
However, neither the trend data nor the seasonal data alone provide a reasonable forecast for the hospital. Only when the hospital multiplied the trend-adjusted data by the appropriate seasonal index did it obtain good forecasts. Thus, for period 67 (January):
Patient days = (Trend@adjusted forecast)(Monthly seasonal index) = (9,530)(1.04) = 9,911
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Example 11 further illustrates seasonality for quarterly data at a wholesaler.
The patient-days for each month are:
Period 67 68 69 70 71 72 73 74 75 76 77 78 Month Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. Forecast with Trend & Seasonality
9,911 9,265 9,764 9,691 9,520 9,542 9,949 10,068 9,411 9,724 9,355 9,572
A graph showing the forecast that combines both trend and seasonality appears in Figure 4.8 .
0.94
0.96
0.92
0.98
1.00
1.02
1.04
1.06
Month (period = 67 for Jan. through 78 for Dec.)
In d
e x f
o r
in p
a ti
e n
t d
a y s
1.01
1.04
0.97
1.02
0.99
0.99
1.03 1.04
0.97
1.00
0.98
Jan. 67
Feb. 68
Mar. 69
Apr. 70
May 71
June 72
July 73
Aug. 74
Sept. 75
Oct. 76
Dec. 78
Nov. 77
0.96
Figure 4.7
Seasonal Index for San Diego
Hospital
9,400
9,200
9,800
9,000
10,200
10,000
9,600
Month (period = 67 for Jan. through 78 for Dec.)
In p
a ti
e n
t d
a y s
Jan. 67
Feb. 68
Mar. 69
Apr. 70
May 71
June 72
July 73
Aug. 74
Sept. 75
Oct. 76
Dec. 78
Nov. 77
9,691
9,911
9,265
9,764
9,520 9,542
9,949
10,068
9,411
9,724
9,572
9,355
Figure 4.8
Combined Trend and Seasonal
Forecast
INSIGHT c Notice that with trend only, the September forecast is 9,702, but with both trend and sea- sonal adjustments, the forecast is 9,411. By combining trend and seasonal data, the hospital was better able to forecast inpatient days and the related staffing and budgeting vital to effective operations.
LEARNING EXERCISE c If the slope of the trend line for patient-days is 22.0 (rather than 21.5) and the index for December is .99 (instead of .98), what is the new forecast for December inpatient days? [Answer: 9,708.]
RELATED PROBLEMS c 4.25, 4.28
Management at Jagoda Wholesalers, in Calgary, Canada, has used time-series regression based on point- of-sale data to forecast sales for the next 4 quarters. Sales estimates are $100,000, $120,000, $140,000, and $160,000 for the respective quarters. Seasonal indices for the four quarters have been found to be 1.30, .90, .70, and 1.10, respectively.
Example 11 ADJUSTING TREND DATA WITH SEASONAL INDICES
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C H A P T E R 4 | F O R E C A S T I N G 131
Cyclical Variations in Data Cycles are like seasonal variations in data but occur every several years , not weeks, months, or quarters. Forecasting cyclical variations in a time series is difficult. This is because cycles include a wide variety of factors that cause the economy to go from recession to expansion to recession over a period of years. These factors include national or industrywide overexpansion in times of euphoria and contraction in times of concern. Forecasting demand for individual products can also be driven by product life cycles—the stages products go through from introduction through decline. Life cycles exist for virtually all products; striking examples include floppy disks, video recorders, and the original Game Boy. We leave cyclical analysis to forecasting texts.
Developing associative techniques of variables that affect one another is our next topic.
Associative Forecasting Methods: Regression and Correlation Analysis Unlike time-series forecasting, associative forecasting models usually consider several variables that are related to the quantity being predicted. Once these related variables have been found, a statistical model is built and used to forecast the item of interest. This approach is more pow- erful than the time-series methods that use only the historical values for the forecast variable.
Many factors can be considered in an associative analysis. For example, the sales of Dell PCs may be related to Dell’s advertising budget, the company’s prices, competitors’ prices and promotional strategies, and even the nation’s economy and unemployment rates. In this case, PC sales would be called the dependent variable , and the other variables would be called independent variables . The manager’s job is to develop the best statistical relationship between PC sales and the independent variables . The most common quantitative associative forecasting model is linear-regression analysis .
Using Regression Analysis for Forecasting We can use the same mathematical model that we employed in the least-squares method of trend projection to perform a linear-regression analysis. The dependent variables that we want to forecast will still be ny . But now the independent variable, x , need no longer be time. We use the equation: ny = a + bx
where ny = value of the dependent variable (in our example, sales) a = y -axis intercept b = slope of the regression line x = independent variable
Example 12 shows how to use linear regression.
Cycles
Patterns in the data that occur
every several years.
STUDENT TIP We now deal with the same
mathematical model that we
saw earlier, the least-squares
method. But we use any
potential “cause-and-effect”
variable as x .
Linear-regression analysis
A straight-line mathematical
model to describe the functional
relationships between independent
and dependent variables.
LO 4.6 Conduct a regression and correlation
analysis
APPROACH c To compute a seasonalized or adjusted sales forecast, we just multiply each seasonal index by the appropriate trend forecast:
nyseasonal = Index * nytrend forecast
SOLUTION c Quarter I: nyI = (1.30)(+100,000) = +130,000 Quarter II: nyII = (.90)(+120,000) = +108,000 Quarter III: nyIII = (.70)(+140,000) = +98,000 Quarter IV: nyIV = (1.10)(+160,000) = +176,000
INSIGHT c The straight-line trend forecast is now adjusted to reflect the seasonal changes.
LEARNING EXERCISE c If the sales forecast for Quarter IV was $180,000 (rather than $160,000), what would be the seasonally adjusted forecast? [Answer: $198,000.]
RELATED PROBLEMS c 4.25, 4.28 (4.41b is available in MyOMLab)
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132 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
Nodel Construction Company renovates old homes in West Bloomfield, Michigan. Over time, the com- pany has found that its dollar volume of renovation work is dependent on the West Bloomfield area payroll. Management wants to establish a mathematical relationship to help predict sales.
APPROACH c Nodel’s VP of operations has prepared the following table, which lists company rev- enues and the amount of money earned by wage earners in West Bloomfield during the past 6 years:
NODEL’S SALES (IN $ MILLIONS), y
AREA PAYROLL (IN $ BILLIONS), x
NODEL’S SALES (IN $ MILLIONS), y
AREA PAYROLL (IN $ BILLIONS), x
2.0 1 2.0 2
3.0 3 2.0 1
2.5 4 3.5 7
The VP needs to determine whether there is a straight-line (linear) relationship between area payroll and sales. He plots the known data on a scatter diagram:
Example 12 COMPUTING A LINEAR REGRESSION EQUATION
STUDENT TIP A scatter diagram is a powerful
data analysis tool. It helps
quickly size up the relationship
between two variables.
Area payroll (in $ billions)
1.0
2.0
3.0
4.0
0 1 2 3 54 6 7
N o
d e l’ s s
a le
s (i
n $
m il li o
n s )
From the six data points, there appears to be a slight positive relationship between the independent variable (payroll) and the dependent variable (sales): As payroll increases, Nodel’s sales tend to be higher.
SOLUTION c We can find a mathematical equation by using the least-squares regression approach:
SALES, y PAYROLL, x x 2 xy
2.0 1 1 2.0 3.0 3 9 9.0 2.5 4 16 10.0 2.0 2 4 4.0 2.0 1 1 2.0
3.5 7 49 24.5
g y = 15.0 g x = 18 g x 2 = 80 g xy = 51.5
x = gx 6
= 18 6
= 3
y = gy 6
= 15 6
= 2.5
b = gxy - nx y gx2 - nx2
= 51.5 - (6)(3)(2.5)
80 - (6)(32) = .25
a = y - bx = 2.5 - (.25)(3) = 1.75
The estimated regression equation, therefore, is:
ny = 1.75 + .25x or:
Sales = 1.75 + .25 (payroll)
VIDEO 4.1 Forecasting Ticket Revenue for
Orlando Magic Basketball Games
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C H A P T E R 4 | F O R E C A S T I N G 133
If the local chamber of commerce predicts that the West Bloomfield area payroll will be $6 billion next year, we can estimate sales for Nodel with the regression equation:
Sales (in + millions) = 1.75 + .25(6) = 1.75 + 1.50 = 3.25
or:
Sales = +3,250,000
INSIGHT c Given our assumptions of a straight-line relationship between payroll and sales, we now have an indication of the slope of that relationship: on average, sales increase at the rate of 14 million dol- lars for every billion dollars in the local area payroll. This is because b = .25.
LEARNING EXERCISE c What are Nodel’s sales when the local payroll is $8 billion? [Answer: $3.75 million.]
RELATED PROBLEMS c 4.34, 4.43–4.48, 4.50–4.54 (4.56a, 4.57, 4.58 are available in MyOMLab)
EXCEL OM Data File Ch04Ex12.xls can be found in MyOMLab.
The final part of Example 12 shows a central weakness of associative forecasting methods like regression. Even when we have computed a regression equation, we must provide a forecast of the independent variable x —in this case, payroll—before estimating the dependent variable y for the next time period. Although this is not a problem for all forecasts, you can imagine the difficulty of determining future values of some common independent variables (e.g., unem- ployment rates, gross national product, price indices, and so on).
Standard Error of the Estimate The forecast of $3,250,000 for Nodel’s sales in Example 12 is called a point estimate of y . The point estimate is really the mean , or expected value , of a distribution of possible values of sales. Figure 4.9 illustrates this concept.
To measure the accuracy of the regression estimates, we must compute the standard error of the estimate , Sy, x . This computation is called the standard deviation of the regression: It mea- sures the error from the dependent variable, y , to the regression line, rather than to the mean. Equation (4-14) is a similar expression to that found in most statistics books for computing the standard deviation of an arithmetic mean:
Sy, x = B
g( y - yc)2
n - 2 (4-14)
where y = y -value of each data point yc = computed value of the dependent variable, from the regression equation n = number of data points
Standard error of the estimate
A measure of variability around
the regression line—its standard
deviation.
î
1 2 3 4 5 6 7
3.25
4.0
3.0
2.0
1.0
Area payroll (in $ billions)
N o
d e l’ s s
a le
s (i
n $
m il li o
n s )
Regression line, y = 1.75 + .25x
x
y Figure 4.9
Distribution about the Point
Estimate of $3.25 Million Sales
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134 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
Equation (4-15) may look more complex, but it is actually an easier-to-use version of Equation (4-14) . Both formulas provide the same answer and can be used in setting up prediction inter- vals around the point estimate: 2
Sy,x = B
gy2 - agy - bgxy n - 2
(4-15)
Example 13 shows how we would calculate the standard error of the estimate in Example 12 .
Glidden Paints’ assembly lines require thousands of gallons every hour.
To predict demand, the firm uses associative forecasting methods such
as linear regression, with independent variables such as disposable
personal income and GNP. Although housing starts would be a natural
variable, Glidden found that it correlated poorly with past sales. It turns
out that most Glidden paint is sold through retailers to customers who
already own homes or businesses.
M ic
h a e l R
o se
n fe
ld /M
a xi
m ili
a n S
/R G
B V
e n tu
re s/
S u p e rS
to ck
/A la
m y
Nodel’s VP of operations now wants to know the error associated with the regression line computed in Example 12 .
APPROACH c Compute the standard error of the estimate, S y , x , using Equation (4-15) .
SOLUTION c The only number we need that is not available to solve for S y , x is gy 2. Some quick addi-
tion reveals gy2 = 39.5. Therefore:
Sy,x = B
gy2 - agy - bgxy n - 2
= B
39.5 - 1.75(15.0) - .25(51.5) 6 - 2
= 2.09375 = .306 (in $ millions)
The standard error of the estimate is then $306,000 in sales.
INSIGHT c The interpretation of the standard error of the estimate is similar to the standard devia- tion; namely, {1 standard deviation = .6827. So there is a 68.27% chance of sales being { $306,000 from the point estimate of $3,250,000.
LEARNING EXERCISE c What is the probability sales will exceed $3,556,000? [Answer: About 16%.]
RELATED PROBLEMS c 4.52e, 4.54b (4.56c, 4.57 are available in MyOMLab)
Example 13 COMPUTING THE STANDARD ERROR OF THE ESTIMATE
Correlation Coefficients for Regression Lines The regression equation is one way of expressing the nature of the relationship between two variables. Regression lines are not “cause-and-effect” relationships. They merely describe the relationships among variables. The regression equation shows how one variable relates to the value and changes in another variable.
Another way to evaluate the relationship between two variables is to compute the coefficient of correlation . This measure expresses the degree or strength of the linear relationship (but note
Coefficient of correlation
A measure of the strength of
the relationship between two
variables.
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C H A P T E R 4 | F O R E C A S T I N G 135
that correlation does not necessarily imply causality). Usually identified as r , the coefficient of correlation can be any number between + 1 and - 1 . Figure 4.10 illustrates what different values of r might look like.
To compute r , we use much of the same data needed earlier to calculate a and b for the regression line. The rather lengthy equation for r is:
r = ngxy - gxgy
2[ngx2 - (gx)2][ngy2 - (gy)2] (4-16)
Example 14 shows how to calculate the coefficient of correlation for the data given in Examples 12 and 13 .
(e) Perfect positive correlation: r = 1
x
y
(a) Perfect negative correlation: r = –1
x
y
(b) Negative correlation
High
–1.0 –0.8 –0.6 –0.4 –0.2 0 Correlation coefficient values
0.2 0.4 0.6 0.8 1.0
HighModerate ModerateLow Low
x
y
(c) No correlation: r = 0
x
y
(d) Positive correlation x
y
Figure 4.10
Five Values of the Correlation
Coefficient
In Example 12 , we looked at the relationship between Nodel Construction Company’s renovation sales and payroll in its hometown of West Bloomfield. The VP now wants to know the strength of the associa- tion between area payroll and sales.
APPROACH c We compute the r value using Equation (4-16) . We need to first add one more column of calculations—for y 2 .
SOLUTION c The data, including the column for y 2 and the calculations, are shown here:
y x x 2 xy y 2
2.0 1 1 2.0 4.0 3.0 3 9 9.0 9.0 2.5 4 16 10.0 6.25 2.0 2 4 4.0 4.0 2.0 1 1 2.0 4.0
3.5 7 49 24.5 12.25
g y = 15.0 g x = 18 g x 2 = 80 g xy = 51.5 g y 2 = 39.5
Example 14 DETERMINING THE COEFFICIENT OF CORRELATION
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136 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
Although the coefficient of correlation is the measure most commonly used to describe the relationship between two variables, another measure does exist. It is called the coefficient of determination and is simply the square of the coefficient of correlation—namely, r 2 . The value of r 2 will always be a positive number in the range 0 … r2 … 1. The coefficient of determina- tion is the percent of variation in the dependent variable ( y ) that is explained by the regression equation. In Nodel’s case, the value of r 2 is .81, indicating that 81% of the total variation is explained by the regression equation.
Multiple-Regression Analysis Multiple regression is a practical extension of the simple regression model we just explored. It allows us to build a model with several independent variables instead of just one variable. For example, if Nodel Construction wanted to include average annual interest rates in its model for forecasting renovation sales, the proper equation would be:
ny = a + b1x1 + b2x2 (4-17)
where y = dependent variable, sales a = a constant, the y intercept
x 1 and x 2 = values of the two independent variables, area payroll and interest rates, respectively
b1 and b 2 = coefficients for the two independent variables
The mathematics of multiple regression becomes quite complex (and is usually tackled by com- puter), so we leave the formulas for a , b 1 , and b 2 to statistics textbooks. However, Example 15 shows how to interpret Equation (4-17) in forecasting Nodel’s sales.
Coefficient of determination
A measure of the amount of
variation in the dependent variable
about its mean that is explained by
the regression equation.
Multiple regression
An associative forecasting method
with more than one independent
variable.
r = (6)(51.5) - (18)(15.0)
2[(6)(80) - (18)2][(6)(39.5) - (15.0)2]
= 309 - 270 2(156)(12)
= 39
21,872
= 39
43.3 = .901
INSIGHT c This r of .901 appears to be a significant correlation and helps confirm the closeness of the relationship between the two variables.
LEARNING EXERCISE c If the coefficient of correlation was - .901 rather than + .901 , what would this tell you? [Answer: The negative correlation would tell you that as payroll went up, Nodel’s sales went down—a rather unlikely occurrence that would suggest you recheck your math.]
RELATED PROBLEMS c 4.43d, 4.48d, 4.50c, 4.52f, 4.54b (4.56b, 4.57 are available in MyOMLab)
Nodel Construction wants to see the impact of a second independent variable, interest rates, on its sales.
APPROACH c The new multiple-regression line for Nodel Construction, calculated by computer soft- ware, is:
ny = 1.80 + .30x1 - 5.0x2
We also find that the new coefficient of correlation is .96, implying the inclusion of the variable x 2 , inter- est rates, adds even more strength to the linear relationship.
Example 15 USING A MULTIPLE-REGRESSION EQUATION
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C H A P T E R 4 | F O R E C A S T I N G 137
SOLUTION c We can now estimate Nodel’s sales if we substitute values for next year’s payroll and interest rate. If West Bloomfield’s payroll will be $6 billion and the interest rate will be .12 (12%), sales will be forecast as:
Sales($ millions) = 1.80 + .30(6) - 5.0(.12) = 1.8 + 1.8 - .6 = 3.00
or:
Sales = $3,000,000
INSIGHT c By using both variables, payroll and interest rates, Nodel now has a sales forecast of $3 million and a higher coefficient of correlation. This suggests a stronger relationship between the two variables and a more accurate estimate of sales.
LEARNING EXERCISE c If interest rates were only 6%, what would be the sales forecast? [Answer: 1.8 + 1.8 - 5.0(.06) = 3.3, or $3,300,000.]
RELATED PROBLEMS c 4.47, 4.49 (4.59 is available in MyOMLab)
NYC’s Potholes and Regression Analysis
New York is famous for many things, but one it does not like to be known for
is its large and numerous potholes. David Letterman used to joke: “There is a
pothole so big on 8th Avenue, it has its own Starbucks in it.” When it comes to
potholes, some years seem to be worse than others. The winter of 2014 was
an exceptionally bad year. City workers filled a record 300,000 potholes during
the first 4 months of the year. That’s an astounding accomplishment.
But potholes are to some extent a measure of municipal competence—and
they are costly. NYC’s poor streets cost the average motorist an estimated
$800 per year in repair work and new tires. There has been a steady and
dramatic increase in potholes from around 70,000–80,000 in the 1990s to the
devastatingly high 200,000–300,000 range in recent years. One theory is that
bad weather causes the potholes. Using inches of snowfall as a measure of the
severity of the winter, the graph below shows a plot of the number of potholes
versus the inches of snow each winter.
OM in Action Any amount below that would contribute to a “gap” or backlog of streets need-
ing repair. The graph below shows the plot of potholes versus the gap. With an
r 2 of .81, there is a very strong relationship between the increase in the “gap”
and the number of potholes. It is obvious that the real reason for the steady
and substantial increase in the number of potholes is due to the increasing gap
in road resurfacing.
350,000
300,000
250,000
200,000
150,000
100,000
50,000
0 10 20 30 40 50 60 70
y = 115,860 + 2,246.1x r 2 = .32
0
N o
. o
f p
o th
o le
s
Inches of snow
y = 15,495 + 91.1x r 2 = .81
350,000
300,000
250,000
200,000
150,000
100,000
50,000
0
N o
. o
f p
o th
o le
s
Backlog of streets needing repair 0 500 1,000 1,500 2,000 2,500 3,000
A third model performs a regression analysis using the resurfacing gap and
inches of snow as two independent variables and number of potholes as the
dependent variable. That regression model’s r 2 is .91.
Potholes = 7,801.5 + 80.6 * Resurfacing gap + 930.1 * Inches of snow
Sources: OR/MS Today (June, 2014) and New York Daily News (March 5, 2014).
Research showed that the city would need to resurface at least
1,000 miles of roads per year just to stay even with road deterioration.
The OM in Action box, “NYC’s Potholes and Regression Analysis,” provides an interesting example of one city’s use of regression and multiple regression.
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Monitoring and Controlling Forecasts Once a forecast has been completed, it should not be forgotten. No manager wants to be reminded that his or her forecast is horribly inaccurate, but a firm needs to determine why actual demand (or whatever variable is being examined) differed significantly from that pro- jected. If the forecaster is accurate, that individual usually makes sure that everyone is aware of his or her talents. Very seldom does one read articles in Fortune , Forbes , or The Wall Street Journal , however, about money managers who are consistently off by 25% in their stock mar- ket forecasts.
One way to monitor forecasts to ensure that they are performing well is to use a tracking signal. A tracking signal is a measurement of how well a forecast is predicting actual values. As forecasts are updated every week, month, or quarter, the newly available demand data are com- pared to the forecast values.
The tracking signal is computed as the cumulative error divided by the mean absolute devia- tion (MAD) :
Tracking signal = Cumulative error
MAD
= g(Actual demand in period i - Forecast demand in period i)
MAD
(4-18)
where MAD = g � Actual - Forecast �
n
as seen earlier, in Equation (4-5) . Positive tracking signals indicate that demand is greater than forecast. Negative signals
mean that demand is less than forecast. A good tracking signal—that is, one with a low cumu- lative error—has about as much positive error as it has negative error. In other words, small deviations are okay, but positive and negative errors should balance one another so that the tracking signal centers closely around zero. A consistent tendency for forecasts to be greater or less than the actual values (that is, for a high absolute cumulative error) is called a bias error. Bias can occur if, for example, the wrong variables or trend line are used or if a seasonal index is misapplied.
Once tracking signals are calculated, they are compared with predetermined control limits. When a tracking signal exceeds an upper or lower limit, there is a problem with the forecasting method, and management may want to reevaluate the way it forecasts demand. Figure 4.11 shows the graph of a tracking signal that is exceeding the range of acceptable variation. If the model being used is exponential smoothing, perhaps the smoothing constant needs to be readjusted.
How do firms decide what the upper and lower tracking limits should be? There is no single answer, but they try to find reasonable values—in other words, limits not so low as to be triggered with every small forecast error and not so high as to allow bad forecasts to be regularly overlooked. One MAD is equivalent to approximately .8 standard deviations,
Tracking signal
A measurement of how well
a forecast is predicting actual
values.
Bias
A forecast that is consistently
higher or consistently lower than
actual values of a time series.
LO 4.7 Use a tracking signal
STUDENT TIP Using a tracking signal is a good way
to make sure the forecasting system
is continuing to do a good job.
+
–
0 MADs
Upper control limit
Lower control limit
Time
Signal exceeded limit
Tracking signal
Acceptable range
*
Figure 4.11
A Plot of Tracking Signals
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{ 2 MADs = { 1.6 standard deviations, { 3 MADs = { 2.4 standard deviations, and { 4 MADs = { 3.2 standard deviations. This fact suggests that for a forecast to be “in control,” 89% of the errors are expected to fall within { 2 MADs, 98% within { 3 MADs, or 99.9% within { 4 MADs. 3
Example 16 shows how the tracking signal and cumulative error can be computed.
Carlson’s Bakery wants to evaluate performance of its croissant forecast.
APPROACH c Develop a tracking signal for the forecast, and see if it stays within acceptable limits, which we define as { 4 MADs.
SOLUTION c Using the forecast and demand data for the past 6 quarters for croissant sales, we develop a tracking signal in the following table:
QUARTER ACTUAL DEMAND
FORECAST DEMAND ERROR
CUMULATIVE ERROR
ABSOLUTE FORECAST
ERROR
CUMULATIVE ABSOLUTE FORECAST
ERROR MAD
TRACKING SIGNAL
(CUMULATIVE ERROR/MAD)
1 90 100 210 210 10 10 10.0 210/10 5 21 2 95 100 25 215 5 15 7.5 215/7.5 5 22 3 115 100 115 0 15 30 10.0 0/10 5 0 4 100 110 210 210 10 40 10.0 210/10 5 21 5 125 110 115 15 15 55 11.0 15/11 5 10.5 6 140 110 130 135 30 85 14.2 135/14.2 5 12.5
At the end of quarter 6, MAD = g � Forecast errors �
n =
85 6
= 14.2
and Tracking signal = Cumulative error
MAD =
35 14.2
= 2.5 MADs
INSIGHT c Because the tracking signal drifted from - 2 MAD to + 2.5 MAD (between 1.6 and 2.0 standard deviations), we can conclude that it is within acceptable limits.
LEARNING EXERCISE c If actual demand in quarter 6 was 130 (rather than 140), what would be the MAD and resulting tracking signal? [Answer: MAD for quarter 6 would be 12.5, and the tracking signal for period 6 would be 2 MADs.]
RELATED PROBLEMS c 4.59, 4.60 (4.61c is available in MyOMLab)
Example 16 COMPUTING THE TRACKING SIGNAL AT CARLSON’S BAKERY
Adaptive Smoothing Adaptive forecasting refers to computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit. For example, when applied to exponential smoothing, the a and b coefficients are first selected on the basis of values that minimize error forecasts and then adjusted accordingly whenever the computer notes an errant tracking signal. This process is called adaptive smoothing .
Focus Forecasting Rather than adapt by choosing a smoothing constant, computers allow us to try a variety of forecasting models. Such an approach is called focus forecasting. Focus forecasting is based on two principles:
1. Sophisticated forecasting models are not always better than simple ones. 2. There is no single technique that should be used for all products or services.
Adaptive smoothing
An approach to exponential
smoothing forecasting in which the
smoothing constant is automati-
cally changed to keep errors to a
minimum.
Focus forecasting
Forecasting that tries a variety of
computer models and selects the
best one for a particular
application.
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Bernard Smith, inventory manager for American Hardware Supply, coined the term focus forecasting . Smith’s job was to forecast quantities for 100,000 hardware products pur- chased by American’s 21 buyers. 4 He found that buyers neither trusted nor understood the exponential smoothing model then in use. Instead, they used very simple approaches of their own. So Smith developed his new computerized system for selecting forecasting methods.
Smith chose to test seven forecasting methods. They ranged from the simple ones that buy- ers used (such as the naive approach) to statistical models. Every month, Smith applied the forecasts of all seven models to each item in stock. In these simulated trials, the forecast values were subtracted from the most recent actual demands, giving a simulated forecast error. The forecast method yielding the least error is selected by the computer, which then uses it to make next month’s forecast. Although buyers still have an override capability, American Hardware finds that focus forecasting provides excellent results.
Forecasting in the Service Sector Forecasting in the service sector presents some unusual challenges. A major technique in the retail sector is tracking demand by maintaining good short-term records. For instance, a bar- bershop catering to men expects peak flows on Fridays and Saturdays. Indeed, most barber- shops are closed on Sunday and Monday, and many call in extra help on Friday and Saturday. A downtown restaurant, on the other hand, may need to track conventions and holidays for effective short-term forecasting.
Specialty Retail Shops Specialty retail facilities, such as flower shops, may have other unusual demand patterns, and those patterns will differ depending on the holiday. When Val- entine’s Day falls on a weekend, for example, flowers can’t be delivered to offices, and those romantically inclined are likely to celebrate with outings rather than flowers. If a holiday falls on a Monday, some of the celebration may also take place on the weekend, reducing flower sales. However, when Valentine’s Day falls in midweek, busy midweek schedules often make flowers the optimal way to celebrate. Because flowers for Mother’s Day are to be delivered on Saturday or Sunday, this holiday forecast varies less. Due to special demand patterns, many service firms maintain records of sales, noting not only the day of the week but also unusual events, including the weather, so that patterns and correlations that influence demand can be developed.
Fast-Food Restaurants Fast-food restaurants are well aware not only of weekly, daily, and hourly but even 15-minute variations in demands that influence sales. Therefore, detailed forecasts of demand are needed. Figure 4.12(a) shows the hourly forecast for a typical fast- food restaurant. Note the lunchtime and dinnertime peaks. This contrasts to the mid-morning and mid-afternoon peaks at FedEx’s call center in Figure 4.12(b) .
Firms like Taco Bell now use point-of-sale computers that track sales every quarter hour. Taco Bell found that a 6-week moving average was the forecasting technique that minimized its mean squared error (MSE) of these quarter-hour forecasts. Building this forecasting methodology into each of Taco Bell’s 6,500 U.S. stores’ computers, the model makes weekly projections of customer transactions. These in turn are used by store man- agers to schedule staff, who begin in 15-minute increments, not 1-hour blocks as in other industries. The forecasting model has been so successful that Taco Bell has increased cus- tomer service while documenting more than $50 million in labor cost savings in 4 years of use.
STUDENT TIP Forecasting at McDonald’s,
FedEx, and Walmart is as
important and complex as it
is for manufacturers such as
Toyota and Dell.
VIDEO 4.2 Forecasting at Hard Rock Cafe
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11–12
5%
Hour of day
P e rc
e n
t o
f s a le
s b
y h
o u
r o
f d
ay
12–1 (Lunchtime)
1–2 2–3
3–4 4–5
5–6 6–7
7–8 8–9
9–10
10%
15%
20%
10–11 (Dinnertime)
Hourly sales at a fast-food restaurant
(a)
1 0%
Hour of day
2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 1210 11
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12% Monday calls at a FedEx call center*
(b)
A.M. P.M.
Figure 4.12
Forecasts Are Unique: Note the Variations between (a) Hourly Sales at a Fast-Food Restaurant and (b) Hourly Call Volume at FedEx
* Based on historical data: see Journal of Business Forecasting (Winter 1999–2000): 6–11.
Summary Forecasts are a critical part of the operations manager’s function. Demand forecasts drive a firm’s production, capacity, and scheduling systems and affect the financial, marketing, and personnel planning functions.
There are a variety of qualitative and quantitative fore- casting techniques. Qualitative approaches employ judg- ment, experience, intuition, and a host of other factors that are difficult to quantify. Quantitative forecasting uses historical data and causal, or associative, relations to pro- ject future demands. The Rapid Review for this chapter
summarizes the formulas we introduced in quantitative forecasting. Forecast calculations are seldom performed by hand. Most operations managers turn to software packages such as Forecast PRO, NCSS, Minitab, Systat, Statgraphics, SAS, or SPSS.
No forecasting method is perfect under all conditions. And even once management has found a satisfactory approach, it must still monitor and control forecasts to make sure errors do not get out of hand. Forecasting can often be a very challenging, but rewarding, part of managing.
Key Terms
Forecasting (p. 108 ) Economic forecasts (p. 109 ) Technological forecasts (p. 109 ) Demand forecasts (p. 109 ) Quantitative forecasts (p. 111 ) Qualitative forecasts (p. 111 ) Jury of executive opinion (p. 111 ) Delphi method (p. 111 ) Sales force composite (p. 111 ) Market survey (p. 111 )
Time series (p. 112 ) Naive approach (p. 114 ) Moving averages (p. 114 ) Exponential smoothing (p. 116 ) Smoothing constant (p. 116 ) Mean absolute deviation (MAD) (p. 118 ) Mean squared error (MSE) (p. 119 ) Mean absolute percent error (MAPE) (p. 120 ) Trend projection (p. 124 ) Seasonal variations (p. 126 )
Cycles (p. 131 ) Linear-regression analysis (p. 131 ) Standard error of the estimate (p. 133 ) Coefficient of correlation (p. 134 ) Coefficient of determination (p. 136 ) Multiple regression (p. 136 ) Tracking signal (p. 138 ) Bias (p. 138 ) Adaptive smoothing (p. 139 ) Focus forecasting (p. 139 )
Ethical Dilemma We live in a society obsessed with test scores and maximum performance. Think of the SAT, ACT, GRE, GMAT, and LSAT. Though they take only a few hours, they are supposed to give schools and companies a snapshot of a student’s abiding talents.
But these tests are often spectacularly bad at forecasting performance in the real world. The SAT does a decent job ( r 2 = .12) of predicting the grades of a college freshman. It is, however, less effective at predicting achievement after graduation.
LSAT scores bear virtually no correlation to career success as measured by income, life satisfaction, or public service.
What does the r 2 mean in this context? Is it ethical for colleges to base admissions and fi nancial aid decisions on scores alone? What role do these tests take at your own school?
R o b e rt
K n e sc
h ke
/F o to
lia
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1. What is a qualitative forecasting model, and when is its use appropriate?
2. Identify and briefly describe the two general forecasting approaches.
3. Identify the three forecasting time horizons. State an approx- imate duration for each.
4. Briefly describe the steps that are used to develop a forecast- ing system.
5. A skeptical manager asks what medium-range forecasts can be used for. Give the manager three possible uses/purposes.
6. Explain why such forecasting devices as moving averages, weighted moving averages, and exponential smoothing are not well suited for data series that have trends.
7. What is the basic difference between a weighted moving aver- age and exponential smoothing?
8. What three methods are used to determine the accuracy of any given forecasting method? How would you determine whether time-series regression or exponential smoothing is better in a specific application?
9. Research and briefly describe the Delphi technique. How would it be used by an employer you have worked for?
10. What is the primary difference between a time-series model and an associative model?
11. Define time series . 12. What effect does the value of the smoothing constant have on
the weight given to the recent values? 13. Explain the value of seasonal indices in forecasting. How are
seasonal patterns different from cyclical patterns? 14. Which forecasting technique can place the most emphasis on
recent values? How does it do this? 15. In your own words, explain adaptive forecasting. 16. What is the purpose of a tracking signal? 17. Explain, in your own words, the meaning of the correlation
coefficient. Discuss the meaning of a negative value of the correlation coefficient.
18. What is the difference between a dependent and an independ- ent variable?
19. Give examples of industries that are affected by seasonality. Why would these businesses want to filter out seasonality?
20. Give examples of industries in which demand forecasting is dependent on the demand for other products.
21. What happens to the ability to forecast for periods farther into the future?
22. CEO John Goodale, at Southern Illinois Power and Light, has been collecting data on demand for electric power in its western subregion for only the past 2 years. Those data are shown in the table below.
To plan for expansion and to arrange to borrow power from neighboring utilities during peak periods, Goodale needs to be able to forecast demand for each month next year. However, the standard forecasting models discussed in this chapter will not fit the data observed for the 2 years.
a) What are the weaknesses of the standard forecasting tech- niques as applied to this set of data?
b) Because known models are not appropriate here, propose your own approach to forecasting. Although there is no perfect solution to tackling data such as these (in other words, there are no 100% right or wrong answers), justify your model.
c) Forecast demand for each month next year using the model you propose.
DEMAND IN MEGAWATTS
MONTH LAST YEAR THIS YEAR
January 5 17
February 6 14
March 10 20
April 13 23
May 18 30
June 15 38
July 23 44
August 26 41
September 21 33
October 15 23
November 12 26
December 14 17
Discussion Questions
Using Software in Forecasting
This section presents three ways to solve forecasting problems with computer software. First, you can create your own Excel spreadsheets to develop forecasts. Second, you can use the Excel OM software that comes with the text. Third, POM for Windows is another program that is located in MyOMLab .
CREATING YOUR OWN EXCEL SPREADSHEETS Excel spreadsheets (and spreadsheets in general) are frequently used in forecasting. Exponential smoothing, trend analysis, and regression analysis (simple and multiple) are supported by built-in Excel functions.
Program 4.1 illustrates how to build an Excel forecast for the data in Example 8 . The goal for N.Y. Edison is to create a trend analysis of the year 1 to year 7 data.
As an alternative, you may want to experiment with Excel’s built-in regression analysis. To do so, under the Data menu bar selec- tion choose Data Analysis , then Regression . Enter your Y and X data into two columns (say A and B). When the regression window appears, enter the Y and X ranges, then select OK . Excel offers several plots and tables to those interested in more rigorous analysis of regression problems.
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X USING EXCEL OM Excel OM’s forecasting module has five components: (1) moving averages, (2) weighted moving averages, (3) exponential smooth- ing, (4) regression (with one variable only), and (5) decomposition. Excel OM’s error analysis is much more complete than that available with the Excel add-in.
Program 4.2 illustrates Excel OM’s input and output, using Example 2 ’s weighted-moving-average data.
=B$16+A5*B$17
=INTERCEPT(B5:B11,A5:A11)
=SLOPE(B5:B11,A5:A11)
=STEYX(B5:B11,A5:A11)
=CORREL(B5:B11,A5:A11)
Actions Copy C5 to C6:C13
To create the graph, select A5:C13 and choose Insert Line Chart
Program 4.1
Using Excel to Develop Your Own Forecast, with Data from Example 8
Enter the weights to be placed on each of the last three periods at the top of column C. Weights must be entered from oldest to most recent.
Forecast is the weighted sum of past sales (SUMPRODUCT) divided by the sum of the weights (SUM) because weights do not sum to 1.
Error (B11 – E11) is the difference between the demand and the forecast.
= AVERAGE(H11: H19)
The standard error is given by the square root of the total error divided by n – 2 , where n is the number of periods for which forecasts exist, i.e., 9.
= SUMPRODUCT(B17:B19, $C$8:$C$10)/SUM($C$8:$C$10)
Program 4.2
Analysis of Excel OM’s Weighted-Moving-Average Program, Using Data from Example 2 as Input
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P USING POM FOR WINDOWS POM for Windows can project moving averages (both simple and weighted), handle exponential smoothing (both simple and trend adjusted), forecast with least squares trend projection, and solve linear regression (associative) models. A summary screen of error analysis and a graph of the data can also be generated. As a special example of exponential smoothing adaptive forecasting, when using an a of 0, POM for Windows will find the a value that yields the minimum MAD.
Appendix IV provides further details.
SOLVED PROBLEM 4.1 Sales of Volkswagen’s popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). The sales manager had predicted before the new model was introduced that first year sales would be 410 VWs. Using exponential smoothing with a weight of a 5 .30, develop fore- casts for years 2 through 6.
YEAR SALES FORECAST
1 450 410 2 495 3 518 4 563 5 584 6 ?
SOLUTION
YEAR FORECAST
1 410.0
2 422.0 = 410 + .3 (450 - 410)
3 443.9 = 422 + .3 (495 - 422)
4 466.1 = 443.9 + .3 (518 - 443.9)
5 495.2 = 466.1 + .3 (563 - 466.1)
6 521.8 = 495.2 + .3 (584 - 495.2)
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM 4.3 Sales of hair dryers at the Walgreens stores in Youngstown, Ohio, over the past 4 months have been 100, 110, 120, and 130 units (with 130 being the most recent sales).
Develop a moving-average forecast for next month, using these three techniques:
a) 3-month moving average. b) 4-month moving average. c) Weighted 4-month moving average with the most recent
month weighted 4, the preceding month 3, then 2, and the oldest month weighted 1.
d) If next month’s sales turn out to be 140 units, forecast the following month’s sales (months) using a 4-month mov- ing average.
SOLUTION a) 3-month moving average
= 110 + 120 + 130
3 =
360 3
= 120 dryers
b) 4-month moving average
= 100 + 110 + 120 + 130
4 =
460 4
= 115 dryers
c) Weighted moving average
= 4(130) + 3(120) + 2(110) + 1(100)
10 =
1,200 10
= 120 dryers
d) Now the four most recent sales are 110, 120, 130, and 140.
4@month moving average = 110 + 120 + 130 + 140
4 =
500 4
= 125 dryers
We note, of course, the lag in the forecasts, as the moving- average method does not immediately recognize trends.
SOLVED PROBLEM 4.2 In Example 7 , we applied trend-adjusted exponential smooth- ing to forecast demand for a piece of pollution-control equip- ment for months 2 and 3 (out of 9 months of data provided). Let us now continue this process for month 4. We want to con- firm the forecast for month 4 shown in Table 4.2 (p. 123 ) and Figure 4.3 (p. 123 ).
For month 4, A 4 = 19, with a = .2, and b = .4 .
SOLUTION
F4 = aA3 + (1 - a)(F3 + T3) = (.2)(20) + (1 - .2)(15.18 + 2.10) = 4.0 + (.8)(17.28) = 4.0 + 13.82 = 17.82 T4 = b(F4 - F3) + (1 - b)T3 = (.4)(17.82 - 15.18) + (1 - .4)(2.10) = (.4)(2.64) + (.6)(2.10) = 1.056 + 1.26 = 2.32 FIT4 = 17.82 + 2.32 = 20.14
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SOLVED PROBLEM 4.4 The following data come from regression line projections:
PERIOD FORECAST VALUES ACTUAL VALUES
1 410 406 2 419 423 3 428 423 4 435 440
Compute the MAD and MSE.
SOLVED PROBLEM 4.5 Room registrations in the Toronto Towers Plaza Hotel have been recorded for the past 9 years. To project future occu- pancy, management would like to determine the mathemati- cal trend of guest registration. This estimate will help the hotel determine whether future expansion will be needed. Given the following time-series data, develop a regression equation relat- ing registrations to time (e.g., a trend equation). Then forecast year 11 registrations. Room registrations are in the thousands:
Year 1: 17 Year 2: 16 Year 3: 16 Year 4: 21 Year 5: 20
Year 6: 20 Year 7: 23 Year 8: 25 Year 9: 24
SOLVED PROBLEM 4.6 Quarterly demand for Ford F150 pickups at a New York auto dealer is forecast with the equation:
yn = 10 + 3x where x = quarters, and:
Quarter I of year 1 = 0 Quarter II of year 1 = 1
Quarter III of year 1 = 2 Quarter IV of year 1 = 3
Quarter I of year 2 = 4 and so on
and:
yn = quarterly demand
The demand for trucks is seasonal, and the indices for Quarters I, II, III, and IV are 0.80, 1.00, 1.30, and 0.90, respectively. Forecast demand for each quarter of year 3. Then, seasonalize each forecast to adjust for quarterly variations.
SOLUTION
MAD = g 0 Actual - Forecast 0
n
= 0 406 - 410 0 + 0423 - 419 0 + 0423 - 428 0 + 0440 - 435 0
4
= 4 + 4 + 5 + 5
4 =
18 4
= 4.5
MSE = g(Forecast errors)2
n
= (406 - 410)2 + (423 - 419)2 + (423 - 428)2 + (440 - 435)2
4
= 42 + 42 + 52 + 52
4 =
16 + 16 + 25 + 25 4
= 20.5
SOLUTION
YEAR REGISTRANTS, y (IN THOUSANDS) x 2 xy
1 17 1 17 2 16 4 32 3 16 9 48 4 21 16 84
5 20 25 100 6 20 36 120 7 23 49 161 8 25 64 200 9 24 81 216
g x = 45 g y = 182 g x 2 = 285 g xy = 978
b = gxy - nx y gx2 - nx2
= 978 - (9)(5)(20.22)
285 - (9)(25)
= 978 - 909.9 285 - 225
= 68.1 60
= 1.135
a = y - bx = 20.22 - (1.135)(5) = 20.22 - 5.675 = 14.545 yn = (registrations) = 14.545 + 1.135 x The projection of registrations in year 11 is: yn = 14.545 + (1.135)(11) = 27.03 or 27,030 guests in year 11.
SOLUTION Quarter II of year 2 is coded x = 5; Quarter III of year 2, x = 6; and Quarter IV of year 2, x = 7. Hence, Quarter I of year 3 is coded x = 8; Quarter II, x = 9; and so on.
yn (Year 3 Quarter I) = 10 + 3(8) = 34 yn (Year 3 Quarter II) = 10 + 3(9) = 37
yn (Year 3 Quarter III) = 10 + 3(10) = 40 yn (Year 3 Quarter IV) = 10 + 3(11) = 43
Adjusted forecast = (.80)(34) = 27.2 Adjusted forecast = (1.00)(37) = 37 Adjusted forecast = (1.30)(40) = 52 Adjusted forecast = (.90)(43) = 38.7
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SOLVED PROBLEM 4.7 Cengiz Haksever runs an Istanbul high-end jewelry shop. He advertises weekly in local Turkish newspapers and is thinking of increasing his ad budget. Before doing so, he decides to eval- uate the past effectiveness of these ads. Five weeks are sampled, and the data are shown in the table below:
SALES ($1,000s)
AD BUDGET THAT WEEK
($100s)
11 5
6 3
10 7
6 2
12 8
Develop a regression model to help Cengiz evaluate his advertising.
SOLUTION We apply the least-squares regression model as we did in Example 12 .
SALES, y ADVERTISING, x x 2 xy
11 5 25 55 6 3 9 18
10 7 49 70 6 2 4 12
12 8 64 96
gy = 45 gx = 25 gx2 = 151 gxy = 251
y = 45 5
= 9 x = 25 5
= 5
b = gxy - nx y gx2 - nx2
= 251 - (5)(5)(9) 151 - (5)(52)
= 251 - 225 151 - 125
= 26 26
= 1
a = y - bx = 9 - (1)(5) = 4
So the regression model is yn = 4 + 1x, or Sales (in $1,000s) = 4 + 1 (Ad budget in $100s)
This means that for each 1-unit increase in x (or $100 in ads), sales increase by 1 unit (or $1,000).
SOLVED PROBLEM 4.8 Using the data in Solved Problem 4.7, find the coefficient of determination, r 2 , for the model.
SOLUTION
To find r 2 , we need to also compute gy2 .
gy2 = 112 + 62 + 102 + 62 + 122
= 121 + 36 + 100 + 36 + 144 = 437
The next step is to find the coefficient of correlation, r :
r = ngxy - gxgy
2[ngx2 - (gx)2][ngy2 - (gy)2]
= 5(251) - (25)(45)
2[5(151) - (25)2][5(437) - (45)2]
= 1,255 - 1,125
2(130)(160) =
130
220, 800 =
130 144.22
= .9014
Thus, r2 = (.9014)2 = .8125, meaning that about 81% of the variability in sales can be explained by the regression model with advertising as the independent variable.
Problems 4.1–4.42 relate to Time-Series Forecasting • 4.1 The following gives the number of pints of type B blood used at Woodlawn Hospital in the past 6 weeks:
WEEK OF PINTS USED
August 31 360
September 7 389
September 14 410
September 21 381
September 28 368
October 5 374
a) Forecast the demand for the week of October 12 using a 3-week moving average.
b) Use a 3-week weighted moving average, with weights of .1, .3, and .6, using .6 for the most recent week. Forecast demand for the week of October 12.
c) Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 and a = .2. PX
• • 4.2
YEAR 1 2 3 4 5 6 7 8 9 10 11
DEMAND 7 9 5 9 13 8 12 13 9 11 7
a) Plot the above data on a graph. Do you observe any trend, cycles, or random variations?
b) Starting in year 4 and going to year 12, forecast demand using a 3-year moving average. Plot your forecast on the same graph as the original data.
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
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c) Starting in year 4 and going to year 12, forecast demand using a 3-year moving average with weights of .1, .3, and .6, using .6 for the most recent year. Plot this forecast on the same graph.
d) As you compare forecasts with the original data, which seems to give the better results? PX
• • 4.3 Refer to Problem 4.2. Develop a forecast for years 2 through 12 using exponential smoothing with a = .4 and a fore- cast for year 1 of 6. Plot your new forecast on a graph with the actual data and the naive forecast. Based on a visual inspection, which forecast is better? PX
• 4.4 A check-processing center uses exponential smooth- ing to forecast the number of incoming checks each month. The number of checks received in June was 40 million, while the fore- cast was 42 million. A smoothing constant of .2 is used. a) What is the forecast for July? b) If the center received 45 million checks in July, what would be
the forecast for August? c) Why might this be an inappropriate forecasting method for
this situation? PX
• • 4.5 The Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest partly on the antici- pated mileage to be driven next year. The miles driven during the past 5 years are as follows:
YEAR MILEAGE
1 3,000
2 4,000
3 3,400
4 3,800
5 3,700
a) Forecast the mileage for next year (6th year) using a 2-year moving average.
b) Find the MAD based on the 2-year moving average. ( Hint: You will have only 3 years of matched data.)
c) Use a weighted 2-year moving average with weights of .4 and .6 to forecast next year’s mileage. (The weight of .6 is for the most recent year.) What MAD results from using this approach to forecasting? ( Hint: You will have only 3 years of matched data.)
d) Compute the forecast for year 6 using exponential smoothing, an initial forecast for year 1 of 3,000 miles, and a = .5. PX
• • 4.6 The monthly sales for Yazici Batteries, Inc., were as follows:
MONTH SALES
January 20
February 21
March 15
April 14
May 13
June 16
July 17
August 18
September 20
October 20
November 21
December 23
a) Plot the monthly sales data. b) Forecast January sales using each of the following: i) Naive method. ii) A 3-month moving average. iii) A 6-month weighted average using .1, .1, .1, .2, .2, and .3,
with the heaviest weights applied to the most recent months. iv) Exponential smoothing using an a = .3 and a September
forecast of 18. v) A trend projection. c) With the data given, which method would allow you to fore-
cast next March’s sales? PX
• • 4.7 The actual demand for the patients at Omaha Emergency Medical Clinic for the first 6 weeks of this year fol- lows:
WEEK ACTUAL NO. OF
PATIENTS
1 65
2 62
3 70
4 48
5 63
6 52
Clinic administrator Marc Schniederjans wants you to forecast patient demand at the clinic for week 7 by using this data. You decide to use a weighted moving average method to find this fore- cast. Your method uses four actual demand levels, with weights of 0.333 on the present period, 0.25 one period ago, 0.25 two peri- ods ago, and 0.167 three periods ago. a) What is the value of your forecast? PX b) If instead the weights were 20, 15, 15, and 10, respectively, how
would the forecast change? Explain why. c) What if the weights were 0.40, 0.30, 0.20, and 0.10, respec-
tively? Now what is the forecast for week 7?
• 4.8 Daily high temperatures in St. Louis for the last week were as follows: 93, 94, 93, 95, 96, 88, 90 (yesterday). a) Forecast the high temperature today, using a 3-day moving
average. b) Forecast the high temperature today, using a 2-day moving
average. c) Calculate the mean absolute deviation based on a 2-day mov-
ing average. d) Compute the mean squared error for the 2-day moving average. e) Calculate the mean absolute percent error for the 2-day mov-
ing average. PX
• • • 4.9 Lenovo uses the ZX-81 chip in some of its laptop computers. The prices for the chip during the past 12 months were as follows:
MONTH PRICE PER
CHIP MONTH PRICE PER
CHIP
January $1.80 July 1.80
February 1.67 August 1.83
March 1.70 September 1.70
April 1.85 October 1.65
May 1.90 November 1.70
June 1.87 December 1.75
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148 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
a) Use exponential smoothing, first with a smoothing constant of .6 and then with one of .9, to develop forecasts for years 2 through 6.
b) Use a 3-year moving average to forecast demand in years 4, 5, and 6.
c) Use the trend-projection method to forecast demand in years 1 through 6.
d) With MAD as the criterion, which of the four forecasting methods is best? PX
• • 4.14 Following are two weekly forecasts made by two dif- ferent methods for the number of gallons of gasoline, in thou- sands, demanded at a local gasoline station. Also shown are actual demand levels, in thousands of gallons.
FORECASTS
WEEK METHOD 1 METHOD 2 ACTUAL DEMAND
1 0.90 0.80 0.70
2 1.05 1.20 1.00
3 0.95 0.90 1.00
4 1.20 1.11 1.00
What are the MAD and MSE for each method?
• 4.15 Refer to Solved Problem 4.1 on page 144 . a) Use a 3-year moving average to forecast the sales of
Volkswagen Beetles in Nevada through year 6. b) What is the MAD? PX c) What is the MSE?
• 4.16 Refer to Solved Problem 4.1 on page 144. a) Using the trend projection (regression) method, develop a
forecast for the sales of Volkswagen Beetles in Nevada through year 6.
b) What is the MAD? PX c) What is the MSE?
• 4.17 Refer to Solved Problem 4.1 on page 144. Using smoothing constants of .6 and .9, develop forecasts for the sales of VW Beetles. What effect did the smoothing constant have on the forecast? Use MAD to determine which of the three smooth- ing constants (.3, .6, or .9) gives the most accurate forecast. PX
• • • • 4.18 Consider the following actual ( A t ) and forecast ( F t ) demand levels for a commercial multiline telephone at Office Max:
a) Use a 2-month moving average on all the data and plot the averages and the prices.
b) Use a 3-month moving average and add the 3-month plot to the graph created in part (a).
c) Which is better (using the mean absolute deviation): the 2-month average or the 3-month average?
d) Compute the forecasts for each month using exponential smoothing, with an initial forecast for January of $1.80. Use a
= .1, then a = .3, and finally a = .5. Using MAD, which a is the best? PX
• • 4.10 Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table:
YEAR 1 2 3 4 5 6 7 8 9 10 11
REGISTRATIONS (000) 4 6 4 5 10 8 7 9 12 14 15
a) Develop a 3-year moving average to forecast registrations from year 4 to year 12.
b) Estimate demand again for years 4 to 12 with a 3-year weighted moving average in which registrations in the most recent year are given a weight of 2, and registrations in the other 2 years are each given a weight of 1.
c) Graph the original data and the two forecasts. Which of the two forecasting methods seems better? PX
• 4.11 Use exponential smoothing with a smoothing con- stant of 0.3 to forecast the registrations at the seminar given in Problem 4.10. To begin the procedure, assume that the forecast for year 1 was 5,000 people signing up. a) What is the MAD? PX b) What is the MSE?
• • 4.12 Consider the following actual and forecast demand levels for Big Mac hamburgers at a local McDonald’s restaurant:
DAY ACTUAL DEMAND FORECAST DEMAND
Monday 88 88
Tuesday 72 88
Wednesday 68 84
Thursday 48 80
Friday
The forecast for Monday was derived by observing Monday’s demand level and setting Monday’s forecast level equal to this demand level. Subsequent forecasts were derived by using expo- nential smoothing with a smoothing constant of 0.25. Using this exponential smoothing method, what is the forecast for Big Mac demand for Friday? PX
• • • 4.13 As you can see in the following table, demand for heart transplant surgery at Washington General Hospital has increased steadily in the past few years:
YEAR 1 2 3 4 5 6
HEART TRANSPLANTS 45 50 52 56 58 ?
The director of medical services predicted 6 years ago that demand in year 1 would be 41 surgeries.
D m
it ry
K a lin
o vs
ky /S
h u tt
e rs
to ck
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C H A P T E R 4 | F O R E C A S T I N G 149
a) Compute MAD and MAPE for management’s technique. b) Do management’s results outperform (i.e., have smaller MAD
and MAPE than) a naive forecast? c) Which forecast do you recommend, based on lower forecast
error?PX
• 4.24 The following gives the number of accidents that occurred on Florida State Highway 101 during the past 4 months:
MONTH NUMBER OF ACCIDENTS
January 30
February 40
March 60
April 90
Forecast the number of accidents that will occur in May, using least-squares regression to derive a trend equation. PX
• 4.25 In the past, Peter Kelle’s tire dealership in Baton Rouge sold an average of 1,000 radials each year. In the past 2 years, 200 and 250, respectively, were sold in fall, 350 and 300 in winter, 150 and 165 in spring, and 300 and 285 in summer. With a major expansion planned, Kelle projects sales next year to increase to 1,200 radials. What will be the demand during each season?
• • 4.26 George Kyparisis owns a company that manufactures sailboats. Actual demand for George’s sailboats during each of the past four seasons was as follows:
YEAR
SEASON 1 2 3 4
Winter 1,400 1,200 1,000 900
Spring 1,500 1,400 1,600 1,500
Summer 1,000 2,100 2,000 1,900
Fall 600 750 650 500
George has forecasted that annual demand for his sailboats in year 5 will equal 5,600 sailboats. Based on this data and the multiplicative seasonal model, what will the demand level be for George’s sailboats in the spring of year 5?
• • 4.27 Attendance at Orlando’s newest Disneylike attrac- tion, Lego World, has been as follows:
QUARTER GUESTS (IN
THOUSANDS) QUARTER GUESTS
(IN THOUSANDS)
Winter Year 1 73 Summer Year 2 124
Spring Year 1 104 Fall Year 2 52
Summer Year 1 168 Winter Year 3 89
Fall Year 1 74 Spring Year 3 146
Winter Year 2 65 Summer Year 3 205
Spring Year 2 82 Fall Year 3 98
Compute seasonal indices using all of the data. PX
• 4.28 North Dakota Electric Company estimates its demand trend line (in millions of kilowatt hours) to be:
D = 77 + 0.43Q
TIME PERIOD, t
ACTUAL DEMAND, A t
FORECAST DEMAND, F t
1 50 50 2 42 50 3 56 48 4 46 50 5
The first forecast, F 1 , was derived by observing A 1 and setting F 1 equal to A 1 . Subsequent forecast averages were derived by expo- nential smoothing. Using the exponential smoothing method, find the forecast for time period 5. ( Hint: You need to first find the smoothing constant, a.)
• • • 4.19 Income at the architectural firm Spraggins and Yunes for the period February to July was as follows:
MONTH FEBRUARY MARCH APRIL MAY JUNE JULY
Income (in $ thousand)
70.0 68.5 64.8 71.7 71.3 72.8
Use trend-adjusted exponential smoothing to forecast the firm’s August income. Assume that the initial forecast average for February is $65,000 and the initial trend adjustment is 0. The smoothing constants selected are a = .1 and b = .2. PX
• • • 4.20 Resolve Problem 4.19 with a = .1 and b = .8. Using MSE, determine which smoothing constants provide a better forecast. PX
• 4.21 Refer to the trend-adjusted exponential smoothing illustration in Example 7 on pages 122 – 123 . Using a = .2 and b = .4, we forecast sales for 9 months, showing the detailed cal- culations for months 2 and 3. In Solved Problem 4.2, we contin- ued the process for month 4.
In this problem, show your calculations for months 5 and 6 for F t , T t , and FIT t . PX
• 4.22 Refer to Problem 4.21. Complete the trend-adjusted exponential-smoothing forecast computations for periods 7, 8, and 9. Confirm that your numbers for F t , T t , and FIT t match those in Table 4.2 (p. 123 ). PX
• • 4.23 Sales of quilt covers at Bud Banis’s department store in Carbondale over the past year are shown below. Management prepared a forecast using a combination of exponential smooth- ing and its collective judgment for the 4 months (March, April, May, and June):
MONTH UNIT SALES MANAGEMENT’S FORECAST
July 100 August 93 September 96 October 110 November 124 December 119 January 92 February 83 March 101 120 April 96 114 May 89 110 June 108 108
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• • • 4.32 Using the 911 call data in Problem 4.31, forecast calls for weeks 2 through 25 with a trend-adjusted exponential smoothing model. Assume an initial forecast for 50 calls for week 1 and an initial trend of zero. Use smoothing constants of a = .3 and b = .2. Is this model better than that of Problem 4.31? What adjustment might be useful for further improvement? (Again, assume that actual calls in week 25 were 85.) PX
• • • 4.33 Storrs Cycles has just started selling the new Cyclone mountain bike, with monthly sales as shown in the table. First, co-owner Bob Day wants to forecast by exponential smoothing by initially setting February’s forecast equal to January’s sales with a = .1. Co-owner Sherry Snyder wants to use a three-period moving average.
SALES BOB SHERRY BOB’S ERROR
SHERRY’S ERROR
JANUARY 400 —
FEBRUARY 380 400
MARCH 410
APRIL 375
MAY
a) Is there a strong linear trend in sales over time? b) Fill in the table with what Bob and Sherry each forecast for
May and the earlier months, as relevant. c) Assume that May’s actual sales figure turns out to be 405.
Complete the table’s columns and then calculate the mean absolute deviation for both Bob’s and Sherry’s methods.
d) Based on these calculations, which method seems more accurate? PX
• • • • 4.34 Boulanger Savings and Loan is proud of its long tra- dition in Winter Park, Florida. Begun by Michelle Boulanger 22 years after World War II, the S&L has bucked the trend of financial and liquidity problems that has repeatedly plagued the industry. Deposits have increased slowly but surely over the years, despite recessions in 1983, 1988, 1991, 2001, and 2010. Ms. Boulanger believes it is necessary to have a long-range strategic plan for her firm, including a 1-year forecast and preferably even a 5-year forecast of deposits. She examines the past deposit data and also peruses Florida’s gross state product (GSP) over the same 44 years. (GSP is analogous to gross national product [GNP] but on the state level.) The resulting data are in the following table.
YEAR DEPOSITS a GSP b YEAR DEPOSITS a GSP b
1 .25 .4 13 .50 1.2
2 .24 .4 14 .95 1.2
3 .24 .5 15 1.70 1.2
4 .26 .7 16 2.3 1.6
5 .25 .9 17 2.8 1.5
6 .30 1.0 18 2.8 1.6
7 .31 1.4 19 2.7 1.7
8 .32 1.7 20 3.9 1.9
9 .24 1.3 21 4.9 1.9
10 .26 1.2 22 5.3 2.3
11 .25 1.1 23 6.2 2.5
12 .33 .9 24 4.1 2.8
where Q refers to the sequential quarter number and Q 5 1 for winter of Year 1. In addition, the multiplicative seasonal factors are as follows:
QUARTER FACTOR (INDEX)
Winter .8
Spring 1.1
Summer 1.4
Fall .7
Forecast energy use for the four quarters of year 26 (namely quar- ters 101 to 104), beginning with winter.
• 4.29 The number of disk drives (in millions) made at a plant in Taiwan during the past 5 years follows:
YEAR DISK DRIVES
1 140
2 160
3 190
4 200
5 210
a) Forecast the number of disk drives to be made next year, using linear regression.
b) Compute the mean squared error (MSE) when using linear regression.
c) Compute the mean absolute percent error (MAPE). PX
• • 4.30 Dr. Lillian Fok, a New Orleans psychologist, spe- cializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year:
YEAR 1 2 3 4 5 6 7 8 9 10
NUMBER OF PATIENTS 36 33 40 41 40 55 60 54 58 61
ROBBERY RATE PER 1,000
POPULATION 58.3 61.1 73.4 75.7 81.1 89.0 101.1 94.8 103.3 116.2
Using trend (linear regression) analysis, predict the number of patients Dr. Fok will see in years 11 and 12 as a function of time. How well does the model fit the data? PX
• • • 4.31 Emergency calls to the 911 system of Durham, North Carolina, for the past 24 weeks are shown in the following table:
WEEK 1 2 3 4 5 6 7 8 9 10 11 12
CALLS 50 35 25 40 45 35 20 30 35 20 15 40
WEEK 13 14 15 16 17 18 19 20 21 22 23 24
CALLS 55 35 25 55 55 40 35 60 75 50 40 65
a) Compute the exponentially smoothed forecast of calls for each week. Assume an initial forecast of 50 calls in the first week, and use a = .2. What is the forecast for week 25?
b) Reforecast each period using a = .6. c) Actual calls during week 25 were 85. Which smoothing con-
stant provides a superior forecast? Explain and justify the measure of error you used. PX (continued)
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C H A P T E R 4 | F O R E C A S T I N G 151
PRICE NUMBER SOLD
$2.70 760
$3.50 510
$2.00 980
$4.20 250
$3.10 320
$4.05 480
Using these data, how many mocha latte coffees would be fore- cast to be sold according to simple linear regression if the price per cup were $2.80? PX
• 4.46 The following data relate the sales figures of the bar in Mark Kaltenbach’s small bed-and-breakfast inn in Portand, to the number of guests registered that week:
WEEK GUESTS BAR SALES
1 16 $330
2 12 270
3 18 380
4 14 300
a) Perform a linear regression that relates bar sales to guests (not to time).
b) If the forecast is for 20 guests next week, what are the sales expected to be? PX
• 4.47 The number of auto accidents in Athens, Ohio, is related to the regional number of registered automobiles in thou- sands ( X 1 ), alcoholic beverage sales in $10,000s ( X 2 ), and rainfall in inches ( X 3 ). Furthermore, the regression formula has been cal- culated as:
Y = a + b1X1 + b2X2 + b3X3 where
Y 5 number of automobile accidents a 5 7.5
b 1 5 3.5 b 2 5 4.5 b 3 5 2.5
Calculate the expected number of automobile accidents under conditions a, b, and c:
X 1 X 2 X 3
(a) 2 3 0 (b) 3 5 1 (c) 4 7 2
• • 4.48 Rhonda Clark, a Slippery Rock, Pennsylvania, real estate developer, has devised a regression model to help determine residential housing prices in northwestern Pennsylvania. The model was developed using recent sales in a particular neighbor- hood. The price ( Y ) of the house is based on the size (square foot- age 5 X ) of the house. The model is:
Y = 13,473 + 37.65X The coefficient of correlation for the model is 0.63. a) Use the model to predict the selling price of a house that is
1,860 square feet. b) An 1,860-square-foot house recently sold for $95,000. Explain
why this is not what the model predicted.
YEAR DEPOSITSa GSPb YEAR DEPOSITSa GSPb
25 4.5 2.9 35 31.1 4.1
26 6.1 3.4 36 31.7 4.1
27 7.7 3.8 37 38.5 4.0
28 10.1 4.1 38 47.9 4.5
29 15.2 4.0 39 49.1 4.6
30 18.1 4.0 40 55.8 4.5
31 24.1 3.9 41 70.1 4.6
32 25.6 3.8 42 70.9 4.6
33 30.3 3.8 43 79.1 4.7
34 36.0 3.7 44 94.0 5.0
a In $ millions. b In $ billions.
a) Using exponential smoothing, with a 5 .6, then trend analysis, and finally linear regression, discuss which forecasting model fits best for Boulanger’s strategic plan. Justify the selection of one model over another.
b) Carefully examine the data. Can you make a case for exclud- ing a portion of the information? Why? Would that change your choice of model? PX
Additional problems 4.35–4.42 are available in MyOMLab.
Problems 4.43–4.58 relate to Associative Forecasting Methods
• • 4.43 Mark Gershon, owner of a musical instrument dis- tributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Mark has collected the data shown in the following table:
DEMAND FOR GUITARS 3 6 7 5 10 7
MAROON 5 TV APPEARANCES 3 4 7 6 8 5
a) Graph these data to see whether a linear equation might describe the relationship between the group’s television shows and guitar sales.
b) Use the least-squares regression method to derive a forecasting equation.
c) What is your estimate for guitar sales if Maroon 5 performed on TV nine times last month?
d) What are the correlation coefficient ( r ) and the coefficient of determination ( r 2 ) for this model, and what do they mean? PX
• 4.44 Lori Cook has developed the following forecasting model:
ny = 36 + 4.3x
where ny = demand for Kool Air conditioners and x 5 the outside temperature (°F) PX
a) Forecast demand for the Kool Air when the temperature is 70°F. b) What is demand when the temperature is 80°F? c) What is demand when the temperature is 90°F? PX
• • 4.45 Café Michigan’s manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of these coffees sold over six different price values:
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152 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
YEAR (SUMMER MONTHS)
NUMBER OF TOURISTS (IN MILLIONS)
RIDERSHIP (IN MILLIONS)
1 7 1.5 2 2 1.0 3 6 1.3 4 4 1.5 5 14 2.5 6 15 2.7 7 16 2.4 8 12 2.0 9 14 2.7 10 20 4.4 11 15 3.4 12 7 1.7
a) Plot these data and decide if a linear model is reasonable. b) Develop a regression relationship. c) What is expected ridership if 10 million tourists visit London
in a year? d) Explain the predicted ridership if there are no tourists at all. e) What is the standard error of the estimate? f) What is the model’s correlation coefficient and coefficient of
determination? PX
• • 4.53 Thirteen students entered the business program at Sante Fe College 2 years ago. The following table indicates what each student scored on the high school SAT math exam and their grade-point averages (GPAs) after students were in the Sante Fe program for 2 years:
STUDENT A B C D E F G SAT SCORE 421 377 585 690 608 390 415 GPA 2.90 2.93 3.00 3.45 3.66 2.88 2.15 STUDENT H I J K L M SAT SCORE 481 729 501 613 709 366 GPA 2.53 3.22 1.99 2.75 3.90 1.60
a) Is there a meaningful relationship between SAT math scores and grades?
b) If a student scores a 350, what do you think his or her GPA will be?
c) What about a student who scores 800?
• • 4.54 Dave Fletcher, the general manager of North Carolina Engineering Corporation (NCEC), thinks that his firm’s engineer- ing services contracted to highway construction firms are directly related to the volume of highway construction business contracted with companies in his geographic area. He wonders if this is really so, and if it is, can this information help him plan his operations better by forecasting the quantity of his engineering services required by construction firms in each quarter of the year? The following table presents the sales of his services and total amounts of contracts for highway construction over the past eight quarters:
QUARTER 1 2 3 4 5 6 7 8
Sales of NCEC Services (in $ thousands)
8 10 15 9 12 13 12 16
Contracts Released (in $ thousands)
153 172 197 178 185 199 205 226
a) Using this data, develop a regression equation for predicting the level of demand of NCEC’s services.
c) If you were going to use multiple regression to develop such a model, what other quantitative variables might you include?
d) What is the value of the coefficient of determination in this problem? PX
• 4.49 Accountants at the Tucson firm, Larry Youdelman, CPAs, believed that several traveling executives were submitting unusually high travel vouchers when they returned from business trips. First, they took a sample of 200 vouchers submitted from the past year. Then they developed the following multiple-regres- sion equation relating expected travel cost to number of days on the road ( x 1 ) and distance traveled ( x 2 ) in miles:
ny = +90.00 + +48.50x1 + +.40x2
The coefficient of correlation computed was .68. a) If Donna Battista returns from a 300-mile trip that took her
out of town for 5 days, what is the expected amount she should claim as expenses?
b) Battista submitted a reimbursement request for $685. What should the accountant do?
c) Should any other variables be included? Which ones? Why? PX
• • 4.50 City government has collected the following data on annual sales tax collections and new car registrations:
ANNUAL SALES TAX COLLECTIONS (IN MILLIONS)
1.0 1.4 1.9 2.0 1.8 2.1 2.3
NEW CAR REGISTRATIONS (IN THOUSANDS)
10 12 15 16 14 17 20
Determine the following: a) The least-squares regression equation. b) Using the results of part (a), find the estimated sales tax collec-
tions if new car registrations total 22,000. c) The coefficients of correlation and determination. PX
• • 4.51 Using the data in Problem 4.30, apply linear regres- sion to study the relationship between the robbery rate and Dr. Fok’s patient load. If the robbery rate increases to 131.2 in year 11, how many phobic patients will Dr. Fok treat? If the rob- bery rate drops to 90.6, what is the patient projection? PX
• • • 4.52 Bus and subway ridership for the summer months in London, England, is believed to be tied heavily to the number of tourists visiting the city. During the past 12 years, the data on the next page have been obtained:
L ig
h t
T h ru
M y
L e n s
P h o to
g ra
p h y/
G e tt
y Im
a g e s
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C H A P T E R 4 | F O R E C A S T I N G 153
b) Compute the MAD. c) Compute the tracking signal. PX
• • • 4.60 The following are monthly actual and forecast demand levels for May through December for units of a product manufactured by the D. Bishop Company in Des Moines:
MONTH ACTUAL DEMAND FORECAST DEMAND
May 100 100 June 80 104 July 110 99 August 115 101 September 105 104 October 110 104 November 125 105 December 120 109
What is the value of the tracking signal as of the end of December?
Additional problem 4.61 is available in MyOMLab.
b) Determine the coefficient of correlation and the standard error of the estimate. PX
Additional problems 4.55-4.58 are available in MyOMLab.
Problems 4.59–4.61 relate to Monitoring and Controlling Forecasts
• • 4.59 Sales of tablet computers at Ted Glickman’s electron- ics store in Washington, D.C., over the past 10 weeks are shown in the table below:
WEEK DEMAND WEEK DEMAND
1 20 6 29
2 21 7 36
3 28 8 22
4 37 9 25
5 25 10 28
a) Forecast demand for each week, including week 10, using exponential smoothing with a 5 .5 (initial forecast 5 20).
CASE STUDIES Southwestern University: (B) *
Southwestern University (SWU), a large state college in Stephenville, Texas, enrolls close to 20,000 students. The school is a dominant force in the small city, with more students during fall and spring than permanent residents.
Always a football powerhouse, SWU is usually in the top 20 in college football rankings. Since the legendary Phil Flamm was
Southwestern University Football Game Attendance, 2010–2015
2010 2011 2012
GAME ATTENDEES OPPONENT ATTENDEES OPPONENT ATTENDEES OPPONENT
1 34,200 Rice 36,100 Miami 35,900 USC
2 a 39,800 Texas 40,200 Nebraska 46,500 Texas Tech
3 38,200 Duke 39,100 Ohio State 43,100 Alaska
4 b 26,900 Arkansas 25,300 Nevada 27,900 Arizona
5 35,100 TCU 36,200 Boise State 39,200 Baylor
2013 2014 2015
GAME ATTENDEES OPPONENT ATTENDEES OPPONENT ATTENDEES OPPONENT
1 41,900 Arkansas 42,500 Indiana 46,900 LSU
2 a 46,100 Missouri 48,200 North Texas 50,100 Texas
3 43,900 Florida 44,200 Texas A&M 45,900 South Florida
4 b 30,100 Central Florida
33,900 Southern 36,300 Montana
5 40,500 LSU 47,800 Oklahoma 49,900 Arizona State
a Homecoming games. b During the fourth week of each season, Stephenville hosted a hugely popular southwestern crafts fes- tival. This event brought tens of thousands of tourists to the town, especially on weekends, and had an obvious negative impact on game attendance.
hired as its head coach in 2009 (in hopes of reaching the elusive number 1 ranking), attendance at the five Saturday home games each year increased. Prior to Flamm’s arrival, attendance generally averaged 25,000 to 29,000 per game. Season ticket sales bumped up by 10,000 just with the announcement of the new coach’s arrival. Stephenville and SWU were ready to move to the big time!
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154 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
For its first 2 decades of existence, the NBA’s Orlando Magic basketball team set seat prices for its 41-game home schedule the same for each game. If a lower-deck seat sold for $150, that was the price charged, regardless of the opponent, day of the week, or time of the season. If an upper-deck seat sold for $10 in the first game of the year, it likewise sold for $10 for every game.
But when Anthony Perez, director of business strategy, fin- ished his MBA at the University of Florida, he developed a valu- able database of ticket sales. Analysis of the data led him to build a forecasting model he hoped would increase ticket revenue. Perez hypothesized that selling a ticket for similar seats should differ based on demand.
Studying individual sales of Magic tickets on the open Stub Hub marketplace during the prior season, Perez determined the additional potential sales revenue the Magic could have made had they charged prices the fans had proven they were willing to pay on Stub Hub. This became his dependent variable, y , in a multiple-regression model.
He also found that three variables would help him build the “true market” seat price for every game. With his model, it was possible that the same seat in the arena would have as many as seven different prices created at season onset—sometimes higher than expected on average and sometimes lower.
The major factors he found to be statistically significant in determining how high the demand for a game ticket, and hence, its price, would be were:
◆ The day of the week ( x 1 ) ◆ A rating of how popular the opponent was ( x 2 ) ◆ The time of the year ( x 3 )
For the day of the week, Perez found that Mondays were the least-favored game days (and he assigned them a value of 1). The rest of the weekdays increased in popularity, up to a Saturday game, which he rated a 6. Sundays and Fridays received 5 ratings, and holidays a 3 (refer to the footnote in Table 4.3 ).
His ratings of opponents, done just before the start of the sea- son, were subjective and range from a low of 0 to a high of 8. A very high-rated team in that particular season may have had one or more superstars on its roster, or have won the NBA finals the prior season, making it a popular fan draw.
Video Case
Finally, Perez believed that the NBA season could be divided into four periods in popularity:
◆ Early games (which he assigned 0 scores) ◆ Games during the Christmas season (assigned a 3) ◆ Games until the All-Star break (given a 2) ◆ Games leading into the play-offs (scored with a 3)
The first year Perez built his multiple-regression model, the dependent variable y , which was a “potential premium revenue score,” yielded an r 2 = .86 with this equation:
y = 14,996 + 10,801x1 + 23,397x2 + 10,784x3
Table 4.3 illustrates, for brevity in this case study, a sample of 12 games that year (out of the total 41 home game regular season), including the potential extra revenue per game ( y ) to be expected using the variable pricing model.
A leader in NBA variable pricing, the Orlando Magic have learned that regression analysis is indeed a profitable forecasting tool.
Discussion Questions *
1. Use the data in Table 4.3 to build a regression model with day of the week as the only independent variable.
F e rn
a n d o M
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a
Forecasting Ticket Revenue for Orlando Magic Basketball Games
The immediate issue facing SWU, however, was not NCAA ranking. It was capacity. The existing SWU stadium, built in 1953, has seating for 54,000 fans. The following table indicates attendance at each game for the past 6 years.
One of Flamm’s demands upon joining SWU had been a sta- dium expansion, or possibly even a new stadium. With attendance increasing, SWU administrators began to face the issue head-on. Flamm had wanted dormitories solely for his athletes in the sta- dium as an additional feature of any expansion.
SWU’s president, Dr. Joel Wisner, decided it was time for his vice president of development to forecast when the existing stadium would “max out.” The expansion was, in his mind, a given. But Wisner needed to know how long he could wait. He also sought a revenue projection, assuming an average ticket price of $50 in 2016 and a 5% increase each year in future prices.
Discussion Questions
1. Develop a forecasting model, justifying its selection over other techniques, and project attendance through 2017.
2. What revenues are to be expected in 2016 and 2017? 3. Discuss the school’s options.
* This integrated case study runs throughout the text. Other issues fac- ing Southwestern’s football stadium include (A) managing the stadium project ( Chapter 3 ); (C) quality of facilities ( Chapter 6 ); (D) break-even analysis of food services (Supplement 7 Web site); (E) locating the new stadium ( Chapter 8 Web site); (F) inventory planning of football programs ( Chapter 12 Web site); and (G) scheduling of campus security offi cers/staff for game days ( Chapter 13 Web site).
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C H A P T E R 4 | F O R E C A S T I N G 155
2. Use the data to build a model with rating of the opponent as the sole independent variable.
3. Using Perez’s multiple-regression model, what would be the additional sales potential of a Thursday Miami Heat game played during the Christmas holiday?
4. What additional independent variables might you suggest to include in Perez’s model?
TABLE 4.3 Data for Last Year’s Magic Ticket Sales Pricing Model
TEAM DATE * DAY OF WEEK * TIME OF YEAR RATING OF OPPONENT ADDITIONAL SALES POTENTIAL
Phoenix Suns November 4 Wednesday 0 0 $12,331
Detroit Pistons November 6 Friday 0 1 $29,004
Cleveland Cavaliers November 11 Wednesday 0 6 $109,412
Miami Heat November 25 Wednesday 0 3 $75,783
Houston Rockets December 23 Wednesday 3 2 $42,557
Boston Celtics January 28 Thursday 1 4 $120,212
New Orleans Pelicans
February 3 Monday 1 1 $20,459
L. A. Lakers March 7 Sunday 2 8 $231,020
San Antonio Spurs March 17 Wednesday 2 1 $28,455
Denver Nuggets March 23 Sunday 2 1 $110,561
NY Knicks April 9 Friday 3 0 $44,971
Philadelphia 76ers April 14 Wednesday 3 1 $30,257
*Day of week rated as 1 5 Monday, 2 5 Tuesday, 3 5 Wednesday, 4 5 Thursday, 5 5 Friday, 6 5 Saturday, 5 5 Sunday, 3 5 holiday.
With the growth of Hard Rock Cafe—from one pub in London in 1971 to more than 145 restaurants in 60 countries today—came a corporatewide demand for better forecasting. Hard Rock uses long-range forecasting in setting a capacity plan and intermedi- ate-term forecasting for locking in contracts for leather goods (used in jackets) and for such food items as beef, chicken, and pork. Its short-term sales forecasts are conducted each month, by cafe, and then aggregated for a headquarters view.
The heart of the sales forecasting system is the point-of-sale (POS) system, which, in effect, captures transaction data on nearly every person who walks through a cafe’s door. The sale of each entrée represents one customer; the entrée sales data are transmit- ted daily to the Orlando corporate headquarters’ database. There, the financial team, headed by Todd Lindsey, begins the forecast process. Lindsey forecasts monthly guest counts, retail sales, ban- quet sales, and concert sales (if applicable) at each cafe. The general managers of individual cafes tap into the same database to prepare a daily forecast for their sites. A cafe manager pulls up prior years’ sales for that day, adding information from the local Chamber of Commerce or Tourist Board on upcoming events such as a major convention, sporting event, or concert in the city where the cafe is located. The daily forecast is further broken into hourly sales, which drives employee scheduling. An hourly forecast of $5,500 in sales translates into 19 workstations, which are further broken down into a specific number of waitstaff, hosts, bartenders, and kitchen staff. Computerized scheduling software plugs in people based on their availability. Variances between forecast and actual sales are then examined to see why errors occurred.
Video Case Hard Rock doesn’t limit its use of forecasting tools to sales.
To evaluate managers and set bonuses, a 3-year weighted moving average is applied to cafe sales. If cafe general managers exceed their targets, a bonus is computed. Todd Lindsey, at corporate headquarters, applies weights of 40% to the most recent year’s sales, 40% to the year before, and 20% to sales 2 years ago in reaching his moving average.
An even more sophisticated application of statistics is found in Hard Rock’s menu planning. Using multiple regression, manag- ers can compute the impact on demand of other menu items if the price of one item is changed. For example, if the price of a cheeseburger increases from $7.99 to $8.99, Hard Rock can pre- dict the effect this will have on sales of chicken sandwiches, pork sandwiches, and salads. Managers do the same analysis on menu placement, with the center section driving higher sales volumes. When an item such as a hamburger is moved off the center to one of the side flaps, the corresponding effect on related items, say french fries, is determined.
HARD ROCK’S MOSCOW CAFE a
MONTH 1 2 3 4 5 6 7 8 9 10
Guest count (in thousands)
21 24 27 32 29 37 43 43 54 66
Advertising (in $ thousand)
14 17 25 25 35 35 45 50 60 60
a These fi gures are used for purposes of this case study.
Forecasting at Hard Rock Cafe
* You may wish to view the video that accompanies this case before answering these questions.
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156 P A R T 1 | I N T R O D U C T I O N T O O P E R AT I O N S M A N AG E M E N T
Discussion Questions *
1. Describe three different forecasting applications at Hard Rock. Name three other areas in which you think Hard Rock could use forecasting models.
2. What is the role of the POS system in forecasting at Hard Rock?
3. Justify the use of the weighting system used for evaluating managers for annual bonuses.
4. Name several variables besides those mentioned in the case that could be used as good predictors of daily sales in each cafe.
5. At Hard Rock’s Moscow restaurant, the manager is trying to evaluate how a new advertising campaign affects guest counts. Using data for the past 10 months (see the table), develop a least-squares regression relationship and then forecast the expected guest count when advertising is $65,000.
* You may wish to view the video that accompanies this case before answering these questions.
• Additional Case Studies: Visit MyOMLab for these free case studies: North-South Airlines: Refl ects the merger of two airlines and addresses their maintenance costs. Digital Cell Phone, Inc.: Uses regression analysis and seasonality to forecast demand at a cell phone manufacturer.
Endnotes
1. For a good review of statistical terms, refer to Tutorial 1, “Statistical Review for Managers,” in MyOMLab .
2. When the sample size is large ( n 7 30), the prediction inter- val value of y can be computed using normal tables. When the number of observations is small, the t -distribution is appropri- ate. See D. Groebner et al., Business Statistics , 9th ed. (Upper Saddle River, NJ: Prentice Hall, 2014).
3. To prove these three percentages to yourself, just set up a normal curve for { 1.6 standard deviations ( z -values). Using the normal table in Appendix I , you find that the area under the curve is .89. This represents { 2 MADs. Likewise, { 3 MADs = { 2.4 standard deviations encompass 98% of the area, and so on for { 4 MADs.
4. Bernard T. Smith, Focus Forecasting: Computer Techniques for Inventory Control (Boston: CBI Publishing, 1978).
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4
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Main Heading Review Material MyOMLab WHAT IS FORECASTING? (pp. 108 – 109 )
j Forecasting— The art and science of predicting future events. j Economic forecasts— Planning indicators that are valuable in helping organiza-
tions prepare medium- to long-range forecasts. j Technological forecasts— Long-term forecasts concerned with the rates of tech-
nological progress. j Demand forecasts— Projections of a company’s sales for each time period in the
planning horizon.
Concept Questions: 1.1–1.4
THE STRATEGIC IMPORTANCE OF FORECASTING (pp. 109 – 110 )
The forecast is the only estimate of demand until actual demand becomes known. Forecasts of demand drive decisions in many areas, including: human resources, capacity, and supply chain management.
Concept Questions: 2.1–2.3
SEVEN STEPS IN THE FORECASTING SYSTEM (pp. 110 – 111 )
j Forecasting follows seven basic steps: (1) Determine the use of the forecast; (2) Select the items to be forecasted; (3) Determine the time horizon of the forecast; (4) Select the forecasting model(s); (5) Gather the data needed to make the forecast; (6) Make the forecast; (7) Validate and implement the results.
Concept Questions: 3.1–3.4
FORECASTING APPROACHES (pp. 111 – 112 )
j Quantitative forecasts —Forecasts that employ mathematical modeling to fore- cast demand.
j Qualitative forecast —Forecasts that incorporate such factors as the decision maker’s intuition, emotions, personal experiences, and value system.
j Jury of executive opinion —Takes the opinion of a small group of high-level managers and results in a group estimate of demand.
j Delphi method —Uses an interactive group process that allows experts to make forecasts.
j Sales force composite —Based on salespersons’ estimates of expected sales. j Market survey —Solicits input from customers or potential customers regarding
future purchasing plans. j Time series —Uses a series of past data points to make a forecast.
Concept Questions: 4.1–4.4
TIME-SERIES FORECASTING (pp. 112 – 131 )
j Naive approach —Assumes that demand in the next period is equal to demand in the most recent period.
j Moving average —Uses an average of the n most recent periods of data to fore- cast the next period.
Moving average = gdemand in previous n periods
n (4-1)
Weighted moving average = g((Weight for period n)(Demand in period n))
gWeights (4-2)
j Exponential smoothing —A weighted-moving-average forecasting technique in which data points are weighted by an exponential function.
j Smoothing constant —The weighting factor, a, used in an exponential smooth- ing forecast, a number between 0 and 1.
Exponential smoothing formula: Ft = Ft - 1 + a(At - 1 - Ft - 1) (4-4) j Mean absolute deviation (MAD) —A measure of the overall forecast error for a
model.
MAD = g 0Actual - Forecast 0
n (4-5)
j Mean squared error (MSE) —The average of the squared differences between the forecast and observed values.
MSE = g(Forecast errors)2
n (4-6)
j Mean absolute percent error (MAPE) —The average of the absolute differences between the forecast and actual values, expressed as a percentage of actual values.
MAPE = a
n
i = 1 100 0 Actuali - Forecasti 0 /Actuali
n (4-7)
Concept Questions: 5.1–5.4 Problems: 4.1–4.42 Virtual Office Hours for Solved Problems: 4.1–4.4
ACTIVE MODELS 4.1–4.4
Chapter 4 Rapid Review
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Main Heading Review Material MyOMLab Exponential smoothing with trend adjustment Forecast including trend (FITt) = Exponentially smoothed forecast average (Ft) + Exponentially smoothed trend (Tt) (4-8) j Trend projection —A time-series forecasting method that fits a trend line to a series
of historical data points and then projects the line into the future for forecasts. Trend projection and regression analysis
ny = a + bx, where b = gxy - nx y gx2 - nx 2
and a = y - bx (4-11), (4-12), (4-13)
j Seasonal variations —Regular upward or downward movements in a time series that tie to recurring events.
j Cycles —Patterns in the data that occur every several years.
Virtual Office Hours for Solved Problems: 4.5–4.6
ASSOCIATIVE FORECASTING METHODS: REGRESSION AND CORRELATION ANALYSIS (pp. 131 – 137 )
j Linear-regression analysis —A straight-line mathematical model to describe the functional relationships between independent and dependent variables.
j Standard error of the estimate —A measure of variability around the regression line.
j Coefficient of correlation —A measure of the strength of the relationship between two variables.
j Coefficient of determination —A measure of the amount of variation in the dependent variable about its mean that is explained by the regression equation.
j Multiple regression —An associative forecasting method with . 1 independent variable.
Multiple regression forecast: yn = a + b1x1 + b2x2 (4-17)
Concept Questions: 6.1–6.4 Problems: 4.43-4.58 VIDEO 4.1 Forecasting Ticket Rev- enue for Orlando Magic Basketball Games Virtual Office Hours for Solved Problems: 4.7–4.8
MONITORING AND CONTROLLING FORE- CASTS (pp. 138 – 140 )
j Tracking signal —A measurement of how well the forecast is predicting actual values.
Tracking signal = g(Actual demand in period i - Forecast demand in period i )
MAD (4-18)
j Bias —A forecast that is consistently higher or lower than actual values of a time series.
j Adaptive smoothing —An approach to exponential smoothing forecasting in which the smoothing constant is automatically changed to keep errors to a minimum.
j Focus forecasting —Forecasting that tries a variety of computer models and selects the best one for a particular application.
Concept Questions: 7.1–7.4 Problems: 4.59–4.61
FORECASTING IN THE SERVICE SECTOR (pp. 140 – 141 )
Service-sector forecasting may require good short-term demand records, even per 15-minute intervals. Demand during holidays or specific weather events may also need to be tracked.
Concept Question: 8.1 VIDEO 4.2 Forecasting at Hard Rock Cafe
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Chapter 4 Rapid Review continued
LO 4.1 Forecasting time horizons include: a) long range. b) medium range. c) short range. d) all of the above. LO 4.2 Qualitative methods of forecasting include: a) sales force composite. b) jury of executive opinion. c) consumer market survey. d) exponential smoothing. e) all except (d). LO 4.3 The difference between a moving-average model and an
exponential smoothing model is that . LO 4.4 Three popular measures of forecast accuracy are: a) total error, average error, and mean error. b) average error, median error, and maximum error. c) median error, minimum error, and maximum absolute error. d) mean absolute deviation, mean squared error, and mean
absolute percent error.
LO 4.5 Average demand for iPods in the Rome, Italy, Apple store is 800 units per month. The May monthly index is 1.25. What is the seasonally adjusted sales forecast for May?
a) 640 units b) 798.75 units c) 800 units d) 1,000 units e) cannot be calculated with the information given LO 4.6 The main difference between simple and multiple regression is
. LO 4.7 The tracking signal is the: a) standard error of the estimate. b) cumulative error. c) mean absolute deviation (MAD). d) ratio of the cumulative error to MAD. e) mean absolute percent error (MAPE).
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
Answers: LO 4.1. d; LO 4.2. e; LO 4.3. exponential smoothing is a weighted moving-average model in which all prior values are weighted with a set of exponentially declining weights; LO 4.4. d; LO 4.5. d; LO 4.6. simple regression has only one independent variable; LO 4.7. d.
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159159
C H A P TE R O U T L I N E
5 ◆
Goods and Services Selection 162
◆
Generating New Products 165 ◆
Product Development 166 ◆
Issues for Product Design 171 ◆
Product Development Continuum 173
◆
Defining a Product 175
◆
Documents for Production 178
◆
Service Design 179
◆
Application of Decision Trees to Product Design 182
◆
Transition to Production 184
GLOBAL COMPANY PROFILE: Regal Marine
C
H A
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E R
PART TWO Designing Operations
Design of Goods and Services
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
A la
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F orty years after its founding by potato farmer Paul Kuck, Regal Marine has become a power-
ful force on the waters of the world. The world’s third-largest boat manufacturer (by global
sales), Regal exports to 30 countries, including Russia and China. Almost one-third of its
sales are overseas.
Product design is critical in the highly competitive pleasure boat business: “We keep in
touch with our customers and we respond to the marketplace,” says Kuck. “We’re intro-
ducing six new models this year alone. I’d say we’re definitely on the aggressive end of the
spectrum.”
With changing consumer tastes, compounded by material changes and ever–improving
marine engineering, the design function is under constant pressure. Added to these pressures
Product Strategy Provides Competitive Advantage at Regal Marine
GLOBAL COMPANY PROFILE Regal Marine
C H A P T E R 5
160
CAD/CAM is used to design the rain
cover of a new product. This process
results in faster and more efficient
design and production.
Here the deck, suspended from
ceiling cranes, is being finished
prior to being moved to join
the hull. Regal is one of the
first boat builders in the world
to earn the ISO 9001 quality
certification.
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161
is the constant issue of cost competitiveness
combined with the need to provide good value
for customers.
Consequently, Regal Marine is a frequent
user of computer-aided design (CAD).
New designs come to life via Regal’s three-
dimensional CAD system, borrowed from au-
tomotive technology. Regal’s naval architect’s
goal is to continue to reduce the time from
concept to prototype to production. The so-
phisticated CAD system not only has reduced
product development time and cost, but also
has reduced problems with tooling and pro-
duction, resulting in a superior product.
All of Regal’s products, from its $14,000
19-foot boat to the $500,000 52-foot Sports
yacht, follow a similar production process.
Hulls and decks are separately hand-produced
by spraying preformed molds with three to five
layers of a fiberglass laminate. The hulls and
decks harden and are removed to become the
lower and upper structure of the boat. As they
move to the assembly line, they are joined and
components added at each workstation.
Wooden components, precut in-house
by computer-driven routers, are delivered
on a just-in-time basis for installation at one
station. Engines—one of the few purchased
components—are installed at another. Racks
of electrical wiring harnesses, engineered
and rigged in-house, are then installed. An
in-house upholstery department delivers
customized seats, beds, dashboards, or
other cushioned components. Finally, chrome
fixtures are put in place, and the boat is sent
to Regal’s test tank for watertight, gauge, and
system inspection.
Here the finishing touches are being put on a mold used for forming the hull.
Once a hull has been pulled from the mold, it travels down a monorail assembly
path. JIT inventory delivers engines, wiring, seats, flooring, and interiors when
needed.
At the final stage, smaller boats, such as this one, are placed in this test tank,
where a rain machine ensures watertight fits.
H th fi i hi t h b i t ld d f f i th h ll
O h ll h b ll d f th ld it t l d il bl
At th fi l t ll b t h thi l d i thi t t t k
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162
Goods and Services Selection Global firms like Regal Marine know that the basis for an organization’s existence is the good or service it provides society. Great products are the keys to success. Anything less than an excellent product strategy can be devastating to a firm. To maximize the potential for suc- cess, many companies focus on only a few products and then concentrate on those products. For instance, Honda’s focus, its core competency, is engines. Virtually all of Honda’s sales (autos, motorcycles, generators, lawn mowers) are based on its outstanding engine technol- ogy. Likewise, Intel’s focus is on microprocessors, and Michelin’s is on tires.
However, because most products have a limited and even predictable life cycle, companies must constantly be looking for new products to design, develop, and take to market. Opera- tions managers insist on strong communication among customer, product, processes, and sup- pliers that results in a high success rate for their new products. 3M’s goal is to produce 30% of its profit from products introduced in the past 4 years. Apple generates almost 60% of its revenue from products launched in the past 4 years. Benchmarks, of course, vary by industry; Regal introduces six new boats a year, and Rubbermaid introduces a new product each day!
The importance of new products cannot be overestimated. As Figure 5.1 shows, leading companies generate a substantial portion of their sales from products less than 5 years old. The need for new products is why Gillette developed its multiblade razors, in spite of continuing high sales of its phenomenally successful Sensor razor, and why Disney continues to innovate with new rides and new parks even though it is already the world’s leading family entertainment company.
Despite constant efforts to introduce viable new products, many new products do not suc- ceed. Product selection, definition, and design occur frequently—perhaps hundreds of times
L E A R N I N G OBJEC TI V ES
LO 5.1 Defi ne product life cycle 164
LO 5.2 Describe a product development system 166
LO 5.3 Build a house of quality 167
LO 5.4 Explain how time-based competition is implemented by OM 173
LO 5.5 Describe how goods and services are defi ned by OM 175
LO 5.6 Describe the documents needed for production 179
LO 5.7 Explain how the customer participates in the design and delivery of services 180
LO 5.8 Apply decision trees to product issues 182
STUDENT TIP Product strategy is critical
to achieving competitive
advantage.
VIDEO 5.1 Product Strategy at Regal Marine
Figure 5.1
Innovation and New Products
P e rc
e n
t o
f s a le
s f
ro m
n e w
p ro
d u
c ts
Position of firm in its industry
50%
Industry leader
The higher the percentage of sales from the last 5 years, the more likely the firm is to be a leader.
Top third
Middle third
Bottom third
40%
30%
20%
10%
0%
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C H A P T E R 5 | D E S I G N O F G O O D S A N D S E RV I C E S 163
for each financially successful product. DuPont estimates that it takes 250 ideas to yield one marketable product. Operations managers and their organizations build cultures that accept this risk and tolerate failure. They learn to accommodate a high volume of new product ideas while maintaining the production activities to which they are already committed.
Although the term products often refers to tangible goods, it also refers to offerings by ser- vice organizations. For instance, when Allstate Insurance offers a new homeowner’s policy, it is referred to as a new “product.” Similarly, when Citicorp opens a mortgage department, it offers a number of new mortgage “products.”
An effective product strategy links product decisions with investment, market share, and product life cycle, and defines the breadth of the product line. The objective of the product decision is to develop and implement a product strategy that meets the demands of the marketplace with a competitive advantage . As one of the 10 decisions of OM, product strategy may focus on devel- oping a competitive advantage via differentiation, low cost, rapid response, or a combination of these.
Product Strategy Options Support Competitive Advantage A world of options exists in the selection, definition, and design of products. Product selection is choosing the good or service to provide customers or clients. For instance, hospitals special- ize in various types of patients and medical procedures. A hospital’s management may decide to operate a general-purpose hospital or a maternity hospital or, as in the case of the Canadian hospital Shouldice, to specialize in hernias. Hospitals select their products when they decide what kind of hospital to be. Numerous other options exist for hospitals, just as they exist for Taco Bell and Toyota.
Service organizations like Shouldice Hospital differentiate themselves through their prod- uct. Shouldice differentiates itself by offering a distinctly unique and high-quality product. Its world-renowned specialization in hernia-repair service is so effective it allows patients to return
STUDENT TIP Motorola went through 3,000
working models before it
developed its first pocket cell
phone.
Product decision
The selection, definition, and
design of products.
Product Innovation Can Be Driven By Markets, Technology, and Packaging. Whether it is design focused on changes in the market (a), the application of technology at Samsung (b), or a new container at Sherwin-Williams (c), operations managers need to remind themselves that the creative process is ongoing
with major production implications.
(a) Markets: In its creative way, the market has moved athletic shoes from utilitarian
footwear into fashionable accessories.
(b) Technology: Samsung’s latest technology: radical new smart phones that are bendable.
(c) Packaging: Sherwin-Williams’ Dutch Boy has revolutionized the paint industry with its square
Twist & Pour paint container.
G a n g /F
o to
lia
D u tc
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to normal living in 8 days as opposed to the average 2 weeks—and with very few complica- tions. The entire production system is designed for this one product. Local anesthetics are used; patients enter and leave the operating room on their own; meals are served in a common dining room, encouraging patients to get out of bed for meals and join fellow patients in the lounge. As Shouldice demonstrates, product selection affects the entire production system.
Taco Bell has developed and executed a low-cost strategy through product design. By de- signing a product (its menu) that can be produced with a minimum of labor in small kitchens, Taco Bell has developed a product line that is both low cost and high value. Successful product design has allowed Taco Bell to increase the food content of its products from 27¢ to 45¢ of each sales dollar.
Toyota’s strategy is rapid response to changing consumer demand. By executing the fastest automobile design in the industry, Toyota has driven the speed of product development down to well under 2 years in an industry whose standard is still over 2 years. The shorter design time allows Toyota to get a car to market before consumer tastes change and to do so with the latest technology and innovations.
Product decisions are fundamental to an organization’s strategy and have major im- plications throughout the operations function. For instance, GM’s steering columns are a good example of the strong role product design plays in both quality and efficiency. The redesigned steering column is simpler, with about 30% fewer parts than its predecessor. The result: Assembly time is one-third that of the older column, and the new column’s quality is about seven times higher. As an added bonus, machinery on the new line costs a third less than that on the old line.
Product Life Cycles Products are born. They live and they die. They are cast aside by a changing society. It may be helpful to think of a product’s life as divided into four phases. Those phases are introduction, growth, maturity, and decline.
Product life cycles may be a matter of a few days (a concert t-shirt), months (seasonal fashions), years (Madden NFL football video game), or decades (Boeing 737). Regardless of the length of the cycle, the task for the operations manager is the same: to design a system that helps introduce new products successfully. If the operations function cannot perform effectively at this stage, the firm may be saddled with losers—products that cannot be produced efficiently and perhaps not at all.
Figure 5.2 shows the four life cycle stages and the relationship of product sales, cash flow, and profit over the life cycle of a product. Note that typically a firm has a negative cash flow while it develops a product. When the product is successful, those losses may be recovered. Eventually, the successful product may yield a profit prior to its decline. However, the profit is fleeting—hence, the constant demand for new products.
Life Cycle and Strategy Just as operations managers must be prepared to develop new products, they must also be prepared to develop strategies for new and existing products. Periodic examination of
LO 5.1 Define product life cycle
Figure 5.2
Product Life Cycle, Sales, Cost,
Profit, and Loss
Phase of life cycle
Cost of development and production$ Sales revenue
ProfitLoss
Introduction Growth Maturity Decline
Loss
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products is appropriate because strategies change as products move through their life cycle . Successful product strategies require determining the best strategy for each product based on its position in its life cycle. A firm, therefore, identifies products or families of products and their position in the life cycle. Let us review some strategy options as products move through their life cycles.
Introductory Phase Because products in the introductory phase are still being “fine- tuned” for the market, as are their production techniques, they may warrant unusual expen- ditures for (1) research, (2) product development, (3) process modification and enhancement, and (4) supplier development. For example, when the iPhone was first introduced, the features desired by the public were still being determined. At the same time, operations managers were still groping for the best manufacturing techniques.
Growth Phase In the growth phase, product design has begun to stabilize, and effec- tive forecasting of capacity requirements is necessary. Adding capacity or enhancing existing capacity to accommodate the increase in product demand may be necessary.
Maturity Phase By the time a product is mature, competitors are established. So high- volume, innovative production may be appropriate. Improved cost control, reduction in options, and a paring down of the product line may be effective or necessary for profitability and market share.
Decline Phase Management may need to be ruthless with those products whose life cycle is at an end. Dying products are typically poor products in which to invest resources and managerial talent. Unless dying products make some unique contribution to the firm’s reputation or its product line or can be sold with an unusually high contribution, their production should be terminated. 1
Product-by-Value Analysis The effective operations manager selects items that show the greatest promise. This is the Pareto principle applied to product mix: Resources are to be invested in the critical few and not the trivial many. Product-by-value analysis lists products in descending order of their indi- vidual dollar contribution to the firm. It also lists the total annual dollar contribution of the product. Low contribution on a per-unit basis by a particular product may look substantially different if it represents a large portion of the company’s sales.
A product-by-value report allows management to evaluate possible strategies for each prod- uct. These may include increasing cash flow (e.g., increasing contribution by raising selling price or lowering cost), increasing market penetration (improving quality and/or reducing cost or price), or reducing costs (improving the production process). The report may also tell man- agement which product offerings should be eliminated and which fail to justify further invest- ment in research and development or capital equipment. Product-by-value analysis focuses attention on the strategic direction for each product.
Generating New Products Because products die; because products must be weeded out and replaced; because firms gen- erate most of their revenue and profit from new products—product selection, definition, and design take place on a continuing basis. Consider recent product changes: DVDs to video streaming, coffee shops to Starbucks lifestyle coffee, traveling circuses to Cirque du Soleil, landlines to cell phones, cell phone to smart phones, and an Internet of digital information to an Internet of “things” that connects you and your smart phone to your home, car, and doctor. And the list goes on. Knowing how to successfully find and develop new products is a requirement.
Product-by-value analysis
A list of products, in descending
order of their individual dollar
contribution to the firm, as well as
the total annual dollar contribution
of the product.
STUDENT TIP Societies reward those who
supply new products that
reflect their needs.
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Aggressive new product development requires that organizations build structures internally that have open communication with customers, innovative product develop- ment cultures, aggressive R&D, strong leadership, formal incentives, and training. Only then can a firm profitably and energetically focus on specific opportunities such as the following:
1. Understanding the customer is the premier issue in new-product development. Many com- mercially important products are initially thought of and even prototyped by users rather than producers. Such products tend to be developed by “lead users”—companies, organi- zations, or individuals that are well ahead of market trends and have needs that go far beyond those of average users. The operations manager must be “tuned in” to the market and particularly these innovative lead users.
2. Economic change brings increasing levels of affluence in the long run but economic cycles and price changes in the short run. In the long run, for instance, more and more people can afford automobiles, but in the short run, a recession may weaken the demand for automobiles.
3. Sociological and demographic change may appear in such factors as decreasing family size. This trend alters the size preference for homes, apartments, and automobiles.
4. Technological change makes possible everything from smart phones to iPads to artificial hearts.
5. Political and legal change brings about new trade agreements, tariffs, and government requirements.
6. Other changes may be brought about through market practice, professional standards, suppliers , and distributors .
Operations managers must be aware of these dynamics and be able to anticipate changes in product opportunities, the products themselves, product volume, and product mix.
Product Development Product Development System An effective product strategy links product decisions with other business functions, such as R&D, engineering, marketing, and finance. A firm requires cash for product development, an understanding of the marketplace, and the necessary human talents. The product develop- ment system may well determine not only product success but also the firm’s future. Figure 5.3 shows the stages of product development. In this system, product options go through a series of steps, each having its own screening and evaluation criteria, but providing a continuing flow of information to prior steps.
Optimum product development depends not only on support from other parts of the firm but also on the successful integration of all 10 of the OM decisions, from product design to maintenance. Identifying products that appear likely to capture market share, be cost-effective, and be profitable but are, in fact, very difficult to produce may lead to failure rather than success.
Quality Function Deployment (QFD) Quality function deployment (QFD) refers to both (1) determining what will satisfy the customer and (2) translating those customer desires into the target design. The idea is to capture a rich understanding of customer wants and to identify alternative process solutions. This information is then integrated into the evolving product design. QFD is used early in the design process to help determine what will satisfy the customer and where to deploy quality efforts .
One of the tools of QFD is the house of quality , a graphic technique for defining the relation- ship between customer desires and product (or service). Only by defining this relationship in a rigorous way can managers design products and processes with features desired by customers.
LO 5.2 Describe a product development
system
Quality function deployment (QFD)
A process for determining
customer requirements (customer
“wants”) and translating them
into the attributes (the “hows”)
that each functional area can
understand and act on.
House of quality
A part of the quality function
deployment process that utilizes a
planning matrix to relate customer
“wants” to “how” the firm is going
to meet those “wants.”
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Defining this relationship is the first step in building a world-class production system. To build the house of quality, we perform seven basic steps:
1. Identify customer wants . (What do customers want in this product?) 2. Identify how the good/service will satisfy customer wants. (Identify specific prod-
uct characteristics, features, or attributes and show how they will satisfy customer wants .)
3. Relate customer wants to product hows . (Build a matrix, as in Example 1 , that shows this relationship.)
4. Identify relationships between the firm’s hows . (How do our hows tie together? For instance, in the following example, there is a high relationship between low electricity requirements and auto focus, auto exposure, and number of pixels because they all require electricity. This relationship is shown in the “roof ” of the house in Example 1 .)
5. Develop importance ratings. (Using the customer’s importance ratings and weights for the relationships shown in the matrix, compute our importance ratings, as in Example 1 .)
6. Evaluate competing products. (How well do competing products meet customer wants? Such an evaluation, as shown in the two columns on the right of the figure in Example 1 , would be based on market research.)
7. Determine the desirable technical attributes, your performance, and the competitor’s performance against these attributes. (This is done at the bottom of the figure in Example 1 .)
Figure 5.3
Product Development Stages
Product concepts are developed from
a variety of sources, both external and
internal to the fi rm. Concepts that survive
the product idea stage progress through
various stages, with nearly constant
review, feedback, and evaluation in
a highly participative environment to
minimize failure.
Scope for
design and
engineering teams
Scope of
product development
team
Design review: Are these product specifications the best way to meet
customer requirements?
Functional specifications: How the product will work
Customer requirements to win orders
Feasibility: Does firm have ability to carry out idea?
Concept: Ideas from many sources
Test market: Does product meet customer expectations?
Introduction to market: Training, promotion and channel decisions
Evaluation: Success?
Product specifications and manufacturability: How the product will be made
LO 5.3 Build a house of quality
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The following series of overlays for Example 1 show how to construct a house of quality.
Example 1 CONSTRUCTING A HOUSE OF QUALITY Great Cameras, Inc., wants a methodology that strengthens its ability to meet customer desires with its new digital camera.
APPROACH c Use QFD’s house of quality.
SOLUTION c Build the house of quality for Great Cameras, Inc. We do so here using Overlays 1, 2, 3, and 4.
Quality Function Deployment’s (QFD) House of Quality
Relationship between the things we can do
What we can do (how the organization is going to translate customer wants into product and process attributes and design targets)
G = good F = fair P = poor
How well what we do meets the customer’s wants (relationship matrix)
Customer importance
ratings (5 = highest)
What the customer
wants
Weighted rating
Competitive assessment
Target values (technical attributes)
Technical evaluation
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Figure 5.4
House of Quality Sequence Indicates How to Deploy Resources to Achieve Customer Requirements
Design characteristics
C u st
o m
e r
re q u ir e m
e n ts
Specific components
D e si
g n
ch a ra
ct e ri
st ic
s
Production process
S p e ci
fic co
m p o n e n ts
House 4
Quality plan
P ro
d u
c ti
o n
p ro
c e s s
House 1
House 2
House 3
INSIGHT c QFD provides an analytical tool that structures design features and technical issues, as well as providing importance rankings and competitor comparison.
LEARNING EXERCISE c If the market research for another country indicates that “lightweight” has the most important customer ranking (5), and reliability a 3, what is the new total importance ranking for low electricity requirements, aluminum components, and ergonomic design? [Answer: 18, 15, 27, respectively.]
RELATED PROBLEMS c 5.4, 5.5, 5.6, 5.7, 5.8
Another use of quality function deployment (QFD) is to show how the quality effort will be deployed . As Figure 5.4 shows, design characteristics of House 1 become the inputs to House 2, which are satisfied by specific components of the product. Similarly, the concept is carried to House 3, where the specific components are to be satisfied through particular production pro- cesses . Once those production processes are defined, they become requirements of House 4 to be satisfied by a quality plan that will ensure conformance of those processes. The quality plan is a set of specific tolerances, procedures, methods, and sampling techniques that will ensure that the production process meets the customer requirements.
The QFD effort is devoted to meeting customer requirements. The sequence of houses is a very effective way of identifying, communicating, and deploying production resources. In this way we produce quality products, meet customer requirements, and win orders.
Organizing for Product Development Let’s look at four approaches to organizing for product development. First , the traditional U.S. approach to product development is an organization with distinct departments: a research and development department to do the necessary research; an engineering depart- ment to design the product; a manufacturing engineering department to design a product that can be produced; and a production department that produces the product. The distinct advantage of this approach is that fixed duties and responsibilities exist. The distinct disad- vantage is lack of forward thinking: How will downstream departments in the process deal with the concepts, ideas, and designs presented to them, and ultimately what will the cus- tomer think of the product?
A second and popular approach is to assign a product manager to “champion” the prod- uct through the product development system and related organizations. However, a third , and perhaps the best, product development approach used in the U.S. seems to be the use of teams.
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Such teams are known variously as product development teams, design for manufacturability teams , and value engineering teams .
The Japanese use a fourth approach. They bypass the team issue by not subdividing organi- zations into research and development, engineering, production, and so forth. Consistent with the Japanese style of group effort and teamwork, these activities are all in one organization. Japanese culture and management style are more collegial and the organization less structured than in most Western countries. Therefore, the Japanese find it unnecessary to have “teams” provide the necessary communication and coordination. However, the typical Western style, and the conventional wisdom, is to use teams.
Product development teams are charged with the responsibility of moving from market re- quirements for a product to achieving a product success (refer to Figure 5.3 on page 167 ). Such teams often include representatives from marketing, manufacturing, purchasing, qual- ity assurance, and field service personnel. Many teams also include representatives from ven- dors. Regardless of the formal nature of the product development effort, research suggests that success is more likely in an open, highly participative environment where those with potential contributions are allowed to make them. The objective of a product development team is to make the good or service a success. This includes marketability, manufacturability, and serviceability.
Concurrent engineering implies speedier product development through simultaneous perfor- mance of the various stages of product development (as we saw earlier in Figure 5.3 ). Often the concept is expanded to include all elements of a product’s life cycle, from customer require- ments to disposal and recycling. Concurrent engineering is facilitated by teams representing all affected areas (known as cross-functional teams).
Manufacturability and Value Engineering Manufacturability and value engineering activities are concerned with improvement of design and specifications at the research, development, design, and preproduction stages of product development. In addition to immediate, obvious cost reduction, design for manufacturability and value engineering may produce other benefits. These include:
1. Reduced complexity of the product. 2. Reduction of environmental impact. 3. Additional standardization of components. 4. Improvement of functional aspects of the product. 5. Improved job design and job safety. 6. Improved maintainability (serviceability) of the product. 7. Robust design.
Manufacturability and value engineering activities may be the best cost-avoidance technique available to operations management. They yield value improvement by focusing on achiev- ing the functional specifications necessary to meet customer requirements in an optimal way. Value engineering programs typically reduce costs between 15% and 70% without reducing quality, with every dollar spent yielding $10 to $25 in savings. The cost reduction achieved for a specific bracket via value engineering is shown in Figure 5.5 .
Product development teams
Teams charged with moving from
market requirements for a product
to achieving product success.
Concurrent engineering
Simultaneous performance of
the various stages of product
development.
Manufacturability and value engineering
Activities that help improve a
product’s design, production,
maintainability, and use.
STUDENT TIP Each time the bracket is
redesigned and simplified, we
are able to produce it for less.
Figure 5.5
Cost Reduction of a Bracket
via Value Engineering
1 3
$3.50 $2.00
2
$.80
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Issues for Product Design In addition to developing an effective system and organization structure for product develop- ment, several considerations are important to the design of a product. We will now review six of these: (1) robust design, (2) modular design, (3) computer-aided design/computer- aided manufacturing (CAD/CAM), (4) virtual reality technology, (5) value analysis, and (6) sustainability/life cycle assessment (LCA).
Robust Design Robust design means that the product is designed so that small variations in production or assembly do not adversely affect the product. For instance, Lucent developed an integrated circuit that could be used in many products to amplify voice signals. As originally designed, the circuit had to be manufactured very expensively to avoid variations in the strength of the signal. But after testing and analyzing the design, Lucent engineers realized that if the resistance of the circuit was reduced—a minor change with no associated costs—the circuit would be far less sensitive to manufacturing variations. The result was a 40% improvement in quality.
Modular Design Products designed in easily segmented components are known as modular designs . Modular designs offer flexibility to both production and marketing. Operations managers find modu- larity helpful because it makes product development, production, and subsequent changes easier. Marketing may like modularity because it adds flexibility to the ways customers can be satisfied. For instance, virtually all premium high-fidelity sound systems are produced and sold this way. The customization provided by modularity allows customers to mix and match to their own taste. This is also the approach taken by Harley-Davidson, where relatively few different engines, chassis, gas tanks, and suspension systems are mixed to produce a huge vari- ety of motorcycles. It has been estimated that many automobile manufacturers can, by mixing the available modules, never make two cars alike. This same concept of modularity is carried over to many industries, from airframe manufacturers to fast-food restaurants. Airbus uses the same wing modules on several planes, just as McDonald’s and Burger King use relatively few modules (cheese, lettuce, buns, sauces, pickles, meat patties, french fries, etc.) to make a variety of meals.
Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) Computer-aided design (CAD) is the use of computers to interactively design products and prepare engineering documentation. CAD uses three-dimensional drawing to save time and money by shortening development cycles for virtually all products (see the 3-D design photo in the Regal Marine Global Company Profile that opens this chapter). The speed and ease with which sophisticated designs can be manipulated, analyzed, and modified with CAD makes review of numerous options possible before final commitments are made. Faster development, better products, and accurate flow of information to other departments all contribute to a tremen- dous payoff for CAD. The payoff is particularly significant because most product costs are determined at the design stage.
One extension of CAD is design for manufacture and assembly (DFMA) software, which focuses on the effect of design on assembly. For instance, DFMA allows Ford to build new vehicles in a virtual factory where designers examine how to put a transmission in a car on the production line, even while both the transmission and the car are still in the design stage.
CAD systems have moved to the Internet through e-commerce, where they link computer- ized design with purchasing, outsourcing, manufacturing, and long-term maintenance. This move also speeds up design efforts, as staff around the world can work on their unique work schedules. Rapid product change also supports the trend toward “mass customization” and,
Robust design
A design that can be produced to
requirements even with unfavora-
ble conditions in the production
process.
Modular design
A design in which parts or
components of a product are
subdivided into modules that are
easily interchanged or replaced.
Computer-aided design (CAD)
Interactive use of a computer to
develop and document a product.
Design for manufacture and assembly (DFMA)
Software that allows designers
to look at the effect of design on
manufacturing of the product.
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when carried to an extreme, allows customers to enter a supplier’s design libraries and make changes. The result is faster and less expensive customized products. As product life cycles shorten, designs become more complex, and global collaboration has grown, the European Community (EU) has developed a standard for the exchange of product data (STEP; ISO 10303) . STEP permits 3-D product information to be expressed in a standard format so it can be exchanged internationally.
Computer-aided manufacturing (CAM) refers to the use of specialized computer programs to direct and control manufacturing equipment. When CAD information is translated into instructions for CAM, the result of these two technologies is CAD/CAM. The combination is a powerful tool for manufacturing efficiency. Fewer defective units are produced, translating into less re- work and lower inventory. More precise scheduling also contributes to less inventory and more efficient use of personnel.
A related extension of CAD is 3-D printing . This technology is particularly useful for proto- type development and small lot production (as shown in the photo above). 3-D printing speeds development by avoiding a more lengthy and formal manufacturing process, as we see in the OM in Action box “3-D Printers Hit the Mainstream.”
Virtual Reality Technology Virtual reality is a visual form of communication in which images substitute for the real thing but still allow the user to respond interactively. The roots of virtual reality technology in opera- tions are in CAD. Once design information is in a CAD system, it is also in electronic digital form for other uses, such as developing 3-D layouts of everything from retail stores and res- taurant layouts to amusement parks. Procter & Gamble, for instance, builds walk-in virtual
Standard for the exchange of product data (STEP)
A standard that provides a format
allowing the electronic transmis-
sion of three-dimensional data.
Computer-aided manufacturing (CAM)
The use of information technology
to control machinery.
3-D printing
An extension of CAD that builds
prototypes and small lots.
Virtual reality
A visual form of communication in
which images substitute for reality
and typically allow the user to
respond interactively.
OM in Action 3-D Printers Hit the Mainstream 3-D printers are revolutionizing the product design process. With instructions
from 3-D CAD models, these printers “build” products by laying down succes-
sive thin layers of plastic, metal, glass, or ceramics. Indeed, for many firms,
3-D printers have become indispensable.
The medical field uses the machines to make custom hearing aids.
Invisalign Corp. produces individualized braces for teeth. Architects use the
technology to produce models of buildings, and consumer electronics com-
panies build prototypes of their latest gadgets. Microsoft uses 3-D printers to
help design computer mouse devices and keyboards, while Mercedes, Honda,
Boeing, and Lockheed Martin use them to fashion prototypes and to make
parts that go into final products. Eventually, “a person who buys a BMW will
want a part of the car with their name on it or to customize the seats to the
contours of their bodies,” says 3-D Systems’s CEO. And currently 3-D printing
at Hershey’s Chocolate World attraction means customers can order their
likeness or wedding cake decoration in chocolate.
The cost of 3-D printing continues to drop. Now anyone can buy a 3-D
printer, hook it up to a Wi-Fi network, and begin downloading files that will
turn into real objects. Another beauty and value of 3-D printing is that it
has the power to unleash a world of creative energy: People who previously
only thought about an invention or improved product can now quickly make
it real.
Sources: Advertising Age (January 28, 2015); BusinessWeek (April 30, 2012);
and The Wall Street Journal (July 16, 2011).
For prototypes, spares, and in the case of
Jay Leno’s classic car collection, difficult-to-
replace parts, 3D printing is often the answer.
By scanning the original part, creating a digital
file, making the necessary modifications, and
feeding that data into a 3D printer, Jay’s shop
can make parts not otherwise available for his
1906 Stanley Steamer.
Pa u l D
ri n kw
a te
r/ N
B C
/N B
C U
P h o to
B a n k/
G e tt
y Im
a g e s
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stores to rapidly generate and test ideas. Changes to mechanical design, layouts, and even amusement park rides are much less expensive at the design stage than they are later.
Value Analysis Although value engineering (discussed on page 170 ) focuses on preproduction design and manufacturing issues, value analysis, a related technique, takes place during the production process, when it is clear that a new product is a success. Value analysis seeks improvements that lead to either a better product, or a product made more economically, or a product with less environmental impact. The techniques and advantages for value analysis are the same as for value engineering, although minor changes in implementation may be necessary because value analysis is taking place while the product is being produced.
Sustainability and Life Cycle Assessment (LCA) Product design requires that managers evaluate product options. Addressing sustainability and life cycle assessment (LCA) are two ways of doing this. Sustainability means meeting the needs of the present without compromising the ability of future generations to meet their needs. An LCA is a formal evaluation of the environmental impact of a product. Both sustain- ability and LCA are discussed in depth in the supplement to this chapter.
Product Development Continuum As product life cycles shorten, the need for faster product development increases. And as tech- nological sophistication of new products increases, so do the expense and risk. For instance, drug firms invest an average of 12 to 15 years and $1 billion before receiving regulatory approval for a new drug. And even then, only 1 of 5 will actually be a success. Those operations manag- ers who master this art of product development continually gain on slower product developers. To the swift goes the competitive advantage. This concept is called time-based competition .
Often, the first company into production may have its product adopted for use in a variety of applications that will generate sales for years. It may become the “standard.” Consequently, there is often more concern with getting the product to market than with optimum product design or process efficiency. Even so, rapid introduction to the market may be good management because until competition begins to introduce copies or improved versions, the product can sometimes be priced high enough to justify somewhat inefficient production design and methods.
Because time-based competition is so important, instead of developing new products from scratch (which has been the focus thus far in this chapter), a number of other strategies can be used. Figure 5.6 shows a continuum that goes from new, internally developed products (on the lower left) to “alliances.” Enhancements and migrations use the organization’s existing product strengths for innovation and therefore are typically faster while at the same time being less risky than developing entirely new products.
Enhancements may be changes in color, size, weight, taste, or features, such as are taking place in fast-food menu items (see the OM in Action box “Product Development at Taco Bell” on the next page), or even changes in commercial aircraft. Boeing’s enhancements of the 737 since its introduction in 1967 has made the 737 the largest-selling commercial aircraft in history.
Boeing also uses its engineering prowess in air frames to migrate from one model to the next. This allows Boeing to speed development while reducing both cost and risk for new designs. This approach is also referred to as building on product platforms . Similarly, Volk- swagen is using a versatile automobile platform (the MQB chassis) for small to midsize front- wheel-drive cars. This includes VW’s Polo, Golf, Passat, Tiguan, and Skoda Octavia, and it may eventually include 44 different vehicles. The advantages are downward pressure on cost as well as faster development. Hewlett-Packard has done the same in the printer business. Enhance- ments and platform migrations are a way of building on existing expertise, speeding product development, and extending a product’s life cycle.
The product development strategies on the lower left of Figure 5.6 are internal develop- ment strategies, while the three approaches we now introduce can be thought of as external
Value analysis
A review of successful products
that takes place during the pro-
duction process.
STUDENT TIP Fast communication, rapid
technological change, and short
product life cycles push product
development.
Time-based competition
Competition based on time; rapidly
developing products and moving
them to market.
LO 5.4 Explain how time-based competition
is implemented by OM
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174 P A R T 2 | D E S I G N I N G O P E R AT I O N S
development strategies. Firms use both. The external strategies are (1) purchase the technology, (2) establish joint ventures, and (3) develop alliances.
Purchasing Technology by Acquiring a Firm Microsoft and Cisco Systems are examples of companies on the cutting edge of technology that often speed development by acquiring entrepreneurial firms that have already developed the technology that fits their mission. The issue then becomes fitting the purchased organiza- tion, its technology, its product lines, and its culture into the buying firm, rather than a product development issue.
Joint Ventures In an effort to reduce the weight of new cars, GM is in a joint venture with Tokyo-based Teijin Ltd. to bring lightweight carbon fiber to GM’s customers. Joint ventures such as this are
Figure 5.6
Product Development
Continuum
STUDENT TIP Managers seek a variety of
approaches to obtain speed to
market. As the president of one
U.S. firm said: “If I miss one
product cycle, I’m dead.”
Internal Lengthy High
Shared Rapid and/or Existing
Shared
External development strategies
Product Development Continuum
Internal development strategies Migrations of existing products
Enhancements to existing products New internally developed products
Alliances Joint ventures
Purchase technology or expertise by acquiring the developer
Cost of product development Speed of product development Risk of product development
Joint ventures
Firms establishing joint owner-
ship to pursue new products or
markets.
OM in Action Product Development at Taco Bell
Taco Bell’s New Waffle Taco
Jon a th
a n L
e ib
so n /G
e tt
y Im
a g e s
Chains such as Chipotle, Carl’s Jr., and In-N-Out Burger may rely on a stable
menu of popular items, but Taco Bell creates a constant rotation of products
in hopes of not only keeping consumers coming back, but also uncovering
the next big seller. Taco Bell seeks to be the leader in fast-food innovation
and believes there is no finish line when it comes to being first and staying
relevant. Breakfast is the fastest-growing part of the fast-food market—with
dinner sales declining and lunch sales flat. Moreover, breakfast items tend to
have good margins, making the crafting of breakfast hits, such as Taco Bell’s
new A.M. Crunchwrap and Waffle Taco, lucrative additions.
In search of ideas, the product developers mine social media, consider
new ingredients, and track rivals. Some Fridays, the team does what they’ve
dubbed a “grocery store hustle” to see what’s new in retail. But the basic
pillars of anything they develop remain taste, value, and speed—all of which
must be attainable within the constraints and operations capability of the
Taco Bell kitchen. The less a restaurant has to change its kitchen operations,
ingredients, or equipment, the better.
Taco Bell’s 40-person product innovation team looks at 4,000 to 4,500 ideas
every year. From there developers come up with 300 to 500 prototypes, which
they then test on consumers in the
lab and in test restaurants. From
this huge array, Taco Bell selects
dozens of items in various permuta-
tions for further review. Usually, only
8 to 10 of the new product ideas
make the Taco Bell menu.
The typical product goes
through about 100 iterations by
the time it is launched. The Waffle
Taco, for instance, was changed
80 times through various charac-
teristics such as shape, weight,
thickness, intensity of vanilla flavor
in the shell, and fillings.
Sources: BusinessWeek (June 2–9, 2014); The Wall Street Journal (Dec. 4,
2014); www.grubgrade.com ; investorplace.com/2014/03.
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combined ownership, usually between just two firms, to form a new entity. Ownership can be 50–50, or one owner can assume a larger portion to ensure tighter control. Joint ventures are often appropriate for exploiting specific product opportunities that may not be central to the firm’s mission. Such ventures are more likely to work when the risks are known and can be equitably shared.
Alliances When new products are central to the mission, but substantial resources are required and siz- able risk is present, then alliances may be a good strategy for product development. Alliances are cooperative agreements that allow firms to remain independent but use complementing strengths to pursue strategies consistent with their individual missions. Alliances are particu- larly beneficial when the products to be developed also have technologies that are in ferment. For example, Microsoft is pursuing alliances with a variety of companies to deal with the convergence of computing, the Internet, and television broadcasting. Alliances in this case are appropriate because the technological unknowns, capital demands, and risks are significant. Similarly, three firms, Mercedes-Benz, Ford Motor, and Ballard Power Systems, have formed an alliance to develop “green” cars powered by fuel cells. Alliances are much more difficult to achieve and maintain than joint ventures because of the ambiguities associated with them. It may be helpful to think of an alliance as an incomplete contract between the firms. The firms remain separate.
Enhancements, migration, acquisitions, joint ventures, and alliances are all strategies for speeding product development. Moreover, they typically reduce the risk associated with prod- uct development while enhancing the human and capital resources available.
Defining a Product Once new goods or services are selected for introduction, they must be defined. First, a good or service is defined in terms of its functions —that is, what it is to do . The product is then designed, and the firm determines how the functions are to be achieved. Management typi- cally has a variety of options as to how a product should achieve its functional purpose. For instance, when an alarm clock is produced, aspects of design such as the color, size, or loca- tion of buttons may make substantial differences in ease of manufacture, quality, and market acceptance.
Rigorous specifications of a product are necessary to ensure efficient production. Equip- ment, layout, and human resources cannot be determined until the product is defined, de- signed, and documented. Therefore, every organization needs documents to define its products. This is true of everything from meat patties, to cheese, to computers, to a medical procedure. In the case of cheese, a written specification is typical. Indeed, written specifications or stan- dard grades exist and provide the definition for many products. For instance, Monterey Jack cheese has a written description that specifies the characteristics necessary for each Depart- ment of Agriculture grade. A portion of the Department of Agriculture grade for Monterey Jack Grade AA is shown in Figure 5.7 . Similarly, McDonald’s Corp. has 60 specifications for potatoes that are to be made into french fries.
Most manufactured items, as well as their components, are defined by a drawing, usually referred to as an engineering drawing. An engineering drawing shows the dimensions, toler- ances, materials, and finishes of a component. The engineering drawing will be an item on a bill of material. An engineering drawing is shown in Figure 5.8 . The bill of material (BOM) lists the hierarchy of components, their description, and the quantity of each required to make one unit of a product. A bill of material for a manufactured item is shown in Figure 5.9 (a). Note that subassemblies and components (lower-level items) are indented at each level to indicate their subordinate position. An engineering drawing shows how to make one item on the bill of material.
Alliances
Cooperative agreements that allow
firms to remain independent, but
pursue strategies consistent with
their individual missions.
STUDENT TIP Before anything can be produced,
a product’s functions and attributes
must be defined.
LO 5.5 Describe how products and services are
defined by OM
Engineering drawing
A drawing that shows the dimen-
sions, tolerances, materials, and
finishes of a component.
Bill of material (BOM)
A list of the hierarchy of compo-
nents, their description, and the
quantity of each required to make
one unit of a product.
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In the food-service industry, bills of material manifest themselves in portion-control stan- dards . The portion-control standard for Hard Rock Cafe’s hickory BBQ bacon cheeseburger is shown in Figure 5.9 (b). In a more complex product, a bill of material is referenced on other bills of material of which they are a part. In this manner, subunits (subassemblies) are part of the next higher unit (their parent bill of material) that ultimately makes a final product. In addition to being defined by written specifications, portion-control documents, or bills of material, products can be defined in other ways. For example, products such as chemicals, paints, and petroleums may be defined by formulas or proportions that describe how they are to be made. Movies are defined by scripts, and insurance coverage by legal documents known as policies.
Make-or-Buy Decisions For many components of products, firms have the option of producing the components them- selves or purchasing them from outside sources. Choosing between these options is known as the make-or-buy decision. The make-or-buy decision distinguishes between what the firm wants to produce and what it wants to purchase . Because of variations in quality, cost, and deliv- ery schedules, the make-or-buy decision is critical to product definition. Many items can be purchased as a “standard item” produced by someone else. Examples are the standard bolts listed twice on the bill of material shown in Figure 5.9 (a), for which there will be SAE (Society
Figure 5.7
Monterey Jack
A portion of the general
requirements for the U.S. grades
of Monterey Jack cheese is shown
here.
Source: Based on 58.2469 Specifi cations
for U.S. grades of Monterey (Monterey
Jack) cheese, (May 10, 1996).
§ 58.2469 Specifications for U.S. grades of Monterey (Monterey Jack) cheese
(2) Body and texture. A plug drawn from the cheese shall be reasonably firm. It shall have numerous small mechanical openings evenly distributed throughout the plug. It shall not possess sweet holes, yeast holes, or other gas holes.
(4) Finish and appearance —bandaged and paraffin-dipped. The rind shall be
sound, firm, and smooth, providing a good protection to the cheese.
(a) U.S. grade AA. Monterey Cheese shall conform to the following requirements:
(1) Flavor. Is fine and highly pleasing, free from undesirable flavors and odors. May possess a very slight acid or feed flavor.
(3) Color. Shall have a natural, uniform, bright, attractive appearance.
Code of Federal Regulation, Parts 53 to 109, General Service Administration.
Make-or-buy decision
The choice between producing a
component or a service and pur-
chasing it from an outside source.
Figure 5.8
Engineering Drawings Such as
This One Show Dimensions,
Tolerances, Materials, and
Finishes
.250
.251 DIA. THRU
FINE KNURL
.250
.093
5-40 TAP THRU
1/64 R X .010 DP. AFTER KNURL
.050
.055. 3 7 5
.6 2 4
.6 2 5
AUX. VIEW
MARK PART NO.
REVISIONS
Tolerance Unless Specified:
DRIVE ROLLER
D. PHILLIPS
Material Heat Treat Finish
Scale: Checked: Drawn: Date:
A- Bryce D. Jewett
Machine Mfg. Co., Inc.
A 2 58-60 RC
Fractional:
Decimal:
1— 64
+– +– .005
No. By Date
D av
id M
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ay /D
or lin
g K
in d er
sl ey
, Lt
d .
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C H A P T E R 5 | D E S I G N O F G O O D S A N D S E RV I C E S 177
of Automotive Engineers) specifications. Therefore, there typically is no need for the firm to duplicate this specification in another document.
Group Technology Engineering drawings may also include codes to facilitate group technology. Group technology identifies components by a coding scheme that specifies size, shape, and the type of process- ing (such as drilling). This facilitates standardization of materials, components, and pro- cesses as well as the identification of families of parts. As families of parts are identified, activities and machines can be grouped to minimize setups, routings, and material handling. An example of how families of parts may be grouped is shown in Figure 5.10 . Group tech- nology provides a systematic way to review a family of components to see if an existing com- ponent might suffice on a new project. Using existing or standard components eliminates all the costs connected with the design and development of the new part, which is a major cost reduction.
STUDENT TIP Hard Rock’s recipe here serves
the same purpose as a bill of
material in a factory: It defines
the product for production.
Figure 5.9
Bills of Material Take Different
Forms in a (a) Manufacturing
Plant and (b) Restaurant, but
in Both Cases, the Product
Must Be Defined
Bill of Material for a Panel Weldment
A 60-7 R 60-17 R 60-428 P 60-2
A 60-72 R 60-57-1 A 60-4 02-50-1150
A 60-73 A 60-74 R 60-99 02-50-1150
LOWER ROLLER ASSM. ROLLER PIN LOCKNUT
GUIDE ASSM. REAR SUPPORT ANGLE ROLLER ASSEM. BOLT
GUIDE ASSM. FRONT SUPPORT WELDM’T WEAR PLATE BOLT
1 1 1 1
1 1 1 1
1 1 1 1
(a) Hard Rock Cafe’s Hickory BBQ Bacon Cheeseburger
Bun Hamburger patty Cheddar cheese Bacon BBQ onions Hickory BBQ sauce Burger set Lettuce Tomato Red onion Pickle French fries Seasoned salt 11- inch plate HRC flag
1 8 oz. 2 slices 2 strips 1/2 cup 1 oz.
1 leaf 1 slice 4 rings 1 slice 5 oz. 1 tsp. 1 1
(b)
PANEL WELDM’T 1A 60-71
NUMBER DESCRIPTION QTY DESCRIPTION QTY
Group technology
A product and component coding
system that specifies the size,
shape, and type of processing;
it allows similar products to be
grouped.
Figure 5.10
A Variety of Group Technology
Coding Schemes Move
Manufactured Components
from (a) Ungrouped to
(b) Grouped (families of parts)
(a) Ungrouped Parts (b) Grouped Cylindrical Parts (families of parts)
Grooved Slotted Threaded Drilled Machined
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Documents for Production Once a product is selected, designed, and ready for production, production is assisted by a variety of documents. We will briefly review some of these.
An assembly drawing simply shows an exploded view of the product. An assembly drawing is usually a three-dimensional drawing, known as an isometric drawing ; the relative locations of components are drawn in relation to each other to show how to assemble the unit [see Figure 5.11 (a)].
The assembly chart shows in schematic form how a product is assembled. Manufactured components, purchased components, or a combination of both may be shown on an assembly chart. The assembly chart identifies the point of production at which components flow into subassemblies and ultimately into a final product. An example of an assembly chart is shown in Figure 5.11 (b).
The route sheet lists the operations necessary to produce the component with the material specified in the bill of material. The route sheet for an item will have one entry for each opera- tion to be performed on the item. When route sheets include specific methods of operation and labor standards, they are often known as process sheets .
The work order is an instruction to make a given quantity of a particular item, usually to a given schedule. The ticket that a waiter writes in your favorite restaurant is a work order. In a hospital or factory, the work order is a more formal document that provides authorization to draw items from inventory, to perform various functions, and to assign personnel to perform those functions.
Engineering change notices (ECNs) change some aspect of the product’s definition or documen- tation, such as an engineering drawing or a bill of material. For a complex product that has a long manufacturing cycle, such as a Boeing 777, the changes may be so numerous that no two 777s are built exactly alike—which is indeed the case. Such dynamic design change has fostered the development of a discipline known as configuration management, which is concerned with product identification, control, and documentation. Configuration management is the system by which a product’s planned and changing configurations are accurately identified and for which control and accountability of change are maintained.
Product Life-Cycle Management (PLM) Product life-cycle management (PLM) is an umbrella of software programs that attempts to bring together phases of product design and manufacture—including tying together many of
STUDENT TIP Production personnel need
clear, specific documents to
help them make the product.
Assembly drawing
An exploded view of the product.
Figure 5.11
Assembly Drawing
and Assembly Chart
Source: Assembly drawing and assembly
chart produced by author.
Assembly chart
A graphic means of identifying
how components flow into subas-
semblies and final products.
Route sheet
A listing of the operations neces-
sary to produce a component with
the material specified in the bill of
material.
Work order
An instruction to make a given
quantity of a particular item.
Engineering change notice (ECN)
A correction or modification of
an engineering drawing or bill of
material.
Configuration management
A system by which a product’s
planned and changing compo-
nents are accurately identified.
Product life-cycle management (PLM)
Software programs that tie
together many phases of product
design and manufacture.
R 209 Angle
R 207 Angle
Bolts w/nuts (2)
Left bracket
assembly
R 209 Angle
R 207 Angle
Bolts w/nuts (2)
Right bracket
assembly
Bolt w/nut
Part number tag
R 404 Roller
Lock washer
Box w/packing material
Poka-yoke inspection
A1
A2
A3
A5
A4
(b) Assembly Chart
R 207
31/2" * 3/8" Hex head bolt
3/8" Hex nut
R 404
R 209
11/2" * 3/8" Hex head bolt
R 207
3/8" Lock washer
3/8" Hex nut
(a) Assembly Drawing
1
2
3
4
5
6
7
8
9
10
11
SA 2
SA 1
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the techniques discussed in the prior two sections, Defining a Product and Documents for Production . The idea behind PLM software is that product design and manufacture decisions can be performed more creatively, faster, and more economically when the data are integrated and consistent.
Although there is not one standard, PLM products often start with product design (CAD/ CAM); move on to design for manufacture and assembly (DFMA); and then into product routing, materials, layout, assembly, maintenance, and even environmental issues. Integration of these tasks makes sense because many of these decision areas require overlapping pieces of data. PLM software is now a tool of many large organizations, including Lockheed Martin, GE, Procter & Gamble, Toyota, and Boeing. Boeing estimates that PLM will cut final assem- bly of its 787 jet from 2 weeks to 3 days. PLM is now finding its way into medium and small manufacture as well.
Shorter life cycles, more technologically challenging products, more regulations regard- ing materials and manufacturing processes, and more environmental issues all make PLM an appealing tool for operations managers. Major vendors of PLM software include SAP PLM ( www.mySAP.com ), Parametric Technology Corp. ( www.ptc.com ), Siemens ( www.plm .automation.siemens.com ), and Proplanner ( www.proplanner.com ).
Service Design Much of our discussion so far has focused on what we can call tangible products—that is, goods. On the other side of the product coin are, of course, services. Service indus- tries include banking, finance, insurance, transportation, and communications. The prod- ucts offered by service firms range from a medical procedure that leaves only the tiniest scar after an appendectomy, to a shampoo and cut at a hair salon, to a great sandwich. Designing services is challenging because they have a unique characteristic—customer interaction.
Process–Chain–Network (PCN) Analysis Process–chain–network (PCN) analysis , developed by Professor Scott Sampson, focuses on the ways in which processes can be designed to optimize interaction between firms and
Each year the JR Simplot potato-processing facility in
Caldwell, Idaho, produces billions of french fries for quick-
service restaurant chains and many other customers, both
domestically and overseas (left photo). Sixty specifications
(including a special blend of frying oil, a unique steaming
process, and exact time and temperature for prefrying and
drying) define how these potatoes become french fries.
Further, 40% of all french fries must be 2 to 3 inches long,
40% must be over 3 inches long, and a few stubby ones
constitute the final 20%. Quality control personnel use a
micrometer to measure the fries (right photo).
J. R
. S im
p lo
t C
o m
p a n y
J. R
. S im
p lo
t C
o m
p a n y
LO 5.6 Describe the documents needed for
production
STUDENT TIP Services also need to be
defined and documented.
Process–chain–network (PCN) analysis
Analysis that focuses on the
ways in which processes can be
designed to optimize interaction
between firms and their customers.
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their customers. 2 A process chain is a sequence of steps that accomplishes an activity, such as building a home, completing a tax return, or preparing a sandwich. A process participant can be a manufacturer, a service provider, or a customer. A network is a set of participants.
Each participant has a process domain that includes the set of activities over which it has control. The domain and interactions between two participants for sandwich preparation are shown in the PCN diagram ( Figure 5.12 ). The activities are organized into three process regions for each participant:
1. The direct interaction region includes process steps that involve interaction between par- ticipants. For example, a sandwich buyer directly interacts with employees of a sandwich store (e.g., Subway, in the middle of Figure 5.12 ).
2. The surrogate (substitute) interaction region includes process steps in which one partici- pant is acting on another participant’s resources, such as their information, materials, or technologies. This occurs when the sandwich supplier is making sandwiches in the res- taurant kitchen (left side of Figure 5.12 ) or, alternately, when the customer has access to buffet ingredients and assembles the sandwich himself (right side of the figure). Under surrogate interaction, direct interaction is limited.
3. The independent processing region includes steps in which the sandwich supplier and/or the sandwich customer is acting on resources where each has maximum control. Most make-to-stock production fits in this region (left side of Figure 5.12 ; think of the firm that assembles all those prepackaged sandwiches available in vending machines and conveni- ence stores). Similarly, those sandwiches built at home occur to the right, in the custom- er’s independent processing domain.
All three process regions have similar operating issues—quality control, facility location and layout, job design, inventory, and so on—but the appropriate way of handling the issues differs across regions. Service operations exist only within the area of direct and surrogate interaction .
From the operations manager’s perspective, the valuable aspect of PCN analysis is insight to aid in positioning and designing processes that can achieve strategic objectives. A firm’s operations are strategic in that they can define what type of business the firm is in and what value proposition it desires to provide to customers. For example, a firm may assume a low-cost strategy, operating on the left of Figure 5.12 as a manufacturer of premade sandwiches. Other firms (e.g., Subway) adopt a differentiation strategy with high customer interaction. Each of the process regions depicts a unique operational strategy.
LO 5.7 Explain how the customer participates in
the design and delivery of
services
Process chain
A sequence of steps that
accomplishes an identifiable
purpose (of providing value to
process participants).
Figure 5.12
Customer Interaction Is a Strategic Choice
Sandwich supplier Assemble sandwich
Supplier’s process domain
Prepare sandwiches at factory for resale at convenience stores
Make sandwich in restau- rant kitchen from menu offerings with modest modifications
Assemble custom sandwich at Subway as customer orders
Customer assembles sandwich from buffet offerings
Assemble sandwich at home using ingredients from refrigerator
Independent processing
Independent processing
Surrogate interaction
Surrogate interaction
Direct interaction
Direct interaction
Sandwich consumer
Consumer’s process domain
P e te
r T it m
u ss
/A la
m y
R E D
A &
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la m
y
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n e ss
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a g e s/
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to ck
p h o to
sb ye
h le
rs /F
o to
lia
Z u ri je
ta /S
h u tt
e rs
to ck
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Firms wanting to achieve high economies of scale or more control in their operations should probably position toward the independent processing region of their process domain. Firms intending to provide a value offering that focuses on customization should be positioned more toward the consumer’s process domain. PCN analysis can be applied in a wide variety of business settings.
Adding Service Efficiency Service productivity is notoriously low, in part because of customer involvement in the design or delivery of the service, or both. This complicates the product design challenge. We will now discuss a number of ways to increase service efficiency and, among these, several ways to limit this interaction.
Limit the Options Because customers may participate in the design of the service (e.g., for a funeral or a hairstyle), design specifications may take the form of everything from a menu (in a restaurant), to a list of options (for a funeral), to a verbal description (a hairstyle). However, by providing a list of options (in the case of the funeral) or a series of photographs (in the case of the hairstyle), ambiguity may be reduced. An early resolution of the product’s definition can aid efficiency as well as aid in meeting customer expectations.
Delay Customization Design the product so that customization is delayed as late in the process as possible. This is the way a hair salon operates. Although shampoo and condition are done in a standard way with lower-cost labor, the color and styling (customizing) are done last. It is also the way most restaurants operate: How would you like that cooked? Which dressing would you prefer with your salad?
Modularization Modularize the service so that customization takes the form of chang- ing modules. This strategy allows for “custom” services to be designed as standard modular entities. Just as modular design allows you to buy a high-fidelity sound system with just the features you want, modular flexibility also lets you buy meals, clothes, and insurance on a mix- and-match (modular) basis. Investments (portfolios of stocks and bonds) and education (col- lege curricula) are examples of how the modular approach can be used to customize a service.
Automation Divide the service into small parts, and identify those parts that lend them- selves to automation. For instance, by isolating check-cashing activity via ATM, banks have been very effective at designing a product that both increases customer service and reduces costs. Similarly, airlines have moved to ticketless service via kiosks. A technique such as kiosks reduces both costs and lines at airports—thereby increasing customer satisfaction—and pro- viding a win–win “product” design.
Moment of Truth High customer interaction means that in the service industry there is a moment of truth when the relationship between the provider and the customer is crucial. At that moment, the customer’s satisfaction with the service is defined. The moment of truth is the moment that exemplifies, enhances, or detracts from the customer’s expectations. That moment may be as simple as a smile from a Starbucks barista or having the checkout clerk focus on you rather than talking over his shoulder to the clerk at the next counter. Moments of truth can occur when you order at McDonald’s, get a haircut, or register for college courses. The operations manager’s task is to identify moments of truth and design operations that meet or exceed the customer’s expectations.
Documents for Services Because of the high customer interaction of most services, the documents for moving the product to production often take the form of explicit job instructions or script . For instance, regardless of how good a bank’s products may be in terms of checking, savings, trusts, loans, mortgages, and so forth, if the interaction between participants is not done well, the product may be poorly received. Example 2 shows the kind of documentation a bank may use to move
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a product (drive-up window banking) to “production.” Similarly, a telemarketing service has the product design communicated to production personnel in the form of a telephone script , while a manuscript is used for books, and a storyboard is used for movie and TV production.
Example 2 SERVICE DOCUMENTATION FOR PRODUCTION First Bank Corp. wants to ensure effective delivery of service to its drive-up customers.
APPROACH c Develop a “production” document for the tellers at the drive-up window that provides the information necessary to do an effective job.
SOLUTION c
Documentation for Tellers at Drive-up Windows
Customers who use the drive-up teller windows rather than walk-in lobbies require a different customer relations technique. The distance and machinery between the teller and the customer raises communica- tion barriers. Guidelines to ensure good customer relations at the drive-up window are:
◆ Be especially discreet when talking to the customer through the microphone. ◆ Provide written instructions for customers who must fill out forms you provide. ◆ Mark lines to be completed or attach a note with instructions. ◆ Always say “please” and “thank you” when speaking through the microphone. ◆ Establish eye contact with the customer if the distance allows it. ◆ If a transaction requires that the customer park the car and come into the lobby, apologize for the
inconvenience.
Source: Adapted with permission from Teller Operations (Chicago, IL: The Institute of Financial Education, 1999): 32.
INSIGHT c By providing documentation in the form of a script/guideline for tellers, the likelihood of effective communication and a good product/service is improved.
LEARNING EXERCISE c Modify the guidelines above to show how they would be different for a drive-through restaurant. [Answer: Written instructions, marking lines to be completed, or coming into the store are seldom necessary, but techniques for making change and proper transfer of the order should be included.]
RELATED PROBLEM c 5.11
Application of Decision Trees to Product Design Decision trees can be used for new-product decisions as well as for a wide variety of other management problems when uncertainty is present. They are particularly help- ful when there are a series of decisions and various outcomes that lead to subsequent decisions followed by other outcomes. To form a decision tree, we use the following procedure:
1. Be sure that all possible alternatives and states of nature (beginning on the left and moving right) are included in the tree. This includes an alternative of “doing nothing.”
2. Payoffs are entered at the end of the appropriate branch. This is the place to develop the payoff of achieving this branch.
3. The objective is to determine the expected monetary value (EMV) of each course of action. We accomplish this by starting at the end of the tree (the right-hand side) and working toward the beginning of the tree (the left), calculating values at each step and “pruning” alternatives that are not as good as others from the same node.
Example 3 shows the use of a decision tree applied to product design.
STUDENT TIP A decision tree is a great tool
for thinking through a problem.
LO 5.8 Apply decision trees to product issues
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Example 3 DECISION TREE APPLIED TO PRODUCT DESIGN Silicon, Inc., a semiconductor manufacturer, is investigating the possibility of producing and market- ing a microprocessor. Undertaking this project will require either purchasing a sophisticated CAD system or hiring and training several additional engineers. The market for the product could be either favorable or unfavorable. Silicon, Inc., of course, has the option of not developing the new product at all.
With favorable acceptance by the market, sales would be 25,000 processors selling for $100 each. With unfavorable acceptance, sales would be only 8,000 processors selling for $100 each. The cost of CAD equipment is $500,000, but that of hiring and training three new engineers is only $375,000. However, manufacturing costs should drop from $50 each when manufacturing without CAD to $40 each when manufacturing with CAD.
The probability of favorable acceptance of the new microprocessor is .40; the probability of unfa- vorable acceptance is .60.
APPROACH c Use of a decision tree seems appropriate as Silicon, Inc., has the basic ingredients: a choice of decisions, probabilities, and payoffs.
SOLUTION c In Figure 5.13 we draw a decision tree with a branch for each of the three decisions, assign the respective probabilities and payoff for each branch, and then compute the respective EMVs. The expected monetary values (EMVs) have been circled at each step of the decision tree. For the top branch:
EMV (Purchase CAD system) = (.4)(+1,000,000) + (.6)(9+20,000) = +388,000
This figure represents the results that will occur if Silicon, Inc., purchases CAD. The expected value of hiring and training engineers is the second series of branches:
EMV (Hire>train engineers) = (.4)($875,000) + (.6)($25,000) = $365,000
Figure 5.13
Decision Tree for Development
of a New Product
STUDENT TIP The manager’s options are
to purchase CAD, hire/train
engineers, or do nothing.
Purchasing CAD has the
highest EMV.
$2,500,000 –1,000,000 – 500,000 ––––––––– $1,000,000
Revenue Mfg. cost ($40 * 25,000) CAD cost Net
(.4)
High sales
$800,000 –320,000 –500,000 ––––––– –$20,000
Revenue Mfg. cost ($40 * 8,000) CAD cost Net loss
(.6)
Low sales
$2,500,000 –1,250,000 – 375,000 –––––––––
$875,000
Revenue Mfg. cost ($50 * 25,000) Hire and train cost Net
(.4)
High sales
$800,000 –400,000 –375,000 ––––––– $25,000
Revenue Mfg. cost ($50 * 8,000) Hire and train cost Net
(.6)
Low sales
$0 Net
Do nothing $0
Hire and train engineers $365,000
Purchase CAD $388,000
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Transition to Production Eventually, a product, whether a good or service, has been selected, designed, and defined. It has progressed from an idea to a functional definition, and then perhaps to a design. Now, management must make a decision as to further development and production or ter- mination of the product idea. One of the arts of management is knowing when to move a product from development to production; this move is known as transition to production . The product development staff is always interested in making improvements in a product. Because this staff tends to see product development as evolutionary, they may never have a completed product, but as we noted earlier, the cost of late product introduction is high. Although these conflicting pressures exist, management must make a decision—more devel- opment or production.
Once this decision is made, there is usually a period of trial production to ensure that the design is indeed producible. This is the manufacturability test. This trial also gives the operations staff the opportunity to develop proper tooling, quality control procedures, and training of personnel to ensure that production can be initiated successfully. Finally, when the product is deemed both marketable and producible, line management will assume responsibility.
To ensure that the transition from development to production is successful, some compa- nies appoint a project manager ; others use product development teams . Both approaches allow a wide range of resources and talents to be brought to bear to ensure satisfactory production of a product that is still in flux. A third approach is integration of the product development and manufacturing organizations . This approach allows for easy shifting of resources between the two organizations as needs change. The operations manager’s job is to make the transition from R&D to production seamless.
The EMV of doing nothing is $0. Because the top branch has the highest expected monetary value (an EMV of $388,000 vs. $365,000
vs. $0), it represents the best decision. Management should purchase the CAD system.
INSIGHT c Use of the decision tree provides both objectivity and structure to our analysis of the Silicon, Inc., decision.
LEARNING EXERCISE c If Silicon, Inc., thinks the probabilities of high sales and low sales may be equal, at .5 each, what is the best decision? [Answer: Purchase CAD remains the best decision, but with an EMV of $490,000.]
RELATED PROBLEMS c 5.21–5.27 (5.28 is available in MyOMLab)
ACTIVE MODEL 5.1 This example is further illustrated in Active Model 5.1 in MyOMLab.
STUDENT TIP One of the arts of management
is knowing when a product
should move from development
to production.
Summary Effective product strategy requires selecting, design- ing, and defining a product and then transitioning that product to production. Only when this strategy is car- ried out effectively can the production function contrib- ute its maximum to the organization. The operations manager must build a product development system that has the ability to conceive, design, and produce products that will yield a competitive advantage for the firm. As products move through their life cycle (intro- duction, growth, maturity, and decline), the options that the operations manager should pursue change.
Both manufactured and service products have a variety of techniques available to aid in performing this activ- ity efficiently.
Written specifications, bills of material, and engineer- ing drawings aid in defining products. Similarly, assem- bly drawings, assembly charts, route sheets, and work orders are often used to assist in the actual production of the product. Once a product is in production, value anal- ysis is appropriate to ensure maximum product value. Engineering change notices and configuration management provide product documentation.
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Key Terms
Product decision (p. 163 ) Product-by-value analysis (p. 165 ) Quality function deployment (QFD) (p. 166 ) House of quality (p. 166 ) Product development teams (p. 170 ) Concurrent engineering (p. 170 ) Manufacturability and value
engineering (p. 170 ) Robust design (p. 171 ) Modular design (p. 171 ) Computer-aided design (CAD) (p. 171 ) Design for manufacture and assembly
(DFMA) (p. 171 )
Standard for the exchange of product data (STEP) (p. 172 )
Computer-aided manufacturing (CAM) (p. 172 )
3-D printing (p. 172 ) Virtual reality (p. 172 ) Value analysis (p. 173 ) Time-based competition (p. 173 ) Joint ventures (p. 174 ) Alliances (p. 175 ) Engineering drawing (p. 175 ) Bill of material (BOM) (p. 175 ) Make-or-buy decision (p. 176 )
Group technology (p. 177 ) Assembly drawing (p. 178 ) Assembly chart (p. 178 ) Route sheet (p. 178 ) Work order (p. 178 ) Engineering change notice
(ECN) (p. 178 ) Configuration management (p. 178 ) Product life-cycle management
(PLM) (p. 178 ) Process–chain–network (PCN)
analysis (p. 179 ) Process chain (p. 179 )
Ethical Dilemma John Sloan, president of Sloan Toy Company, Inc., in Oregon, has just reviewed the design of a new pull-toy locomotive for 1- to 3-year-olds. John’s design and marketing staff are very enthusiastic about the market for the product and the potential of follow-on circus train cars. The sales manager is looking forward to a very good reception at the annual toy show in Dallas next month. John, too, is delighted, as he is faced with a layoff if orders do not improve.
John’s production people have worked out the manufacturing issues and produced a successful pilot run. However, the quality assessment staff suggests that under certain conditions, a hook to attach cars to the locomotive and the crank for the bell can be broken off. This is an issue because children can choke on small parts such as these. In the quality test, 1- to 3-year-olds were unable to break off these parts; there were no failures. But when the test simulated the force of an adult tossing the locomotive into a toy box or a 5-year-old throwing it on the fl oor, there were failures. The estimate is that one of the two parts can be broken off 4 times out of 100,000 throws. Neither the design
nor the material people know how to make the toy safer and still perform as designed. The failure rate is low and certainly normal for this type of toy, but not at the Six Sigma level that John’s fi rm strives for. And, of course, someone, someday may sue. A child choking on the broken part is a serious matter. Also, John was recently reminded in a discussion with legal counsel that U.S. case law suggests that new products may not be produced if there is “actual or foreseeable knowledge of a problem” with the product.
The design of successful, ethically produced new products, as suggested in this chapter, is a complex task. What should John do?
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Discussion Questions
1. Why is it necessary to document a product explicitly? 2. What techniques do we use to define a product? 3. In what ways is product strategy linked to product decisions? 4. Once a product is defined, what documents are used to assist
production personnel in its manufacture? 5. What is time-based competition? 6. Describe the differences between joint ventures and alliances. 7. Describe four organizational approaches to product develop-
ment. Which of these is generally thought to be best? 8. Explain what is meant by robust design. 9. What are three specific ways in which computer-aided design
(CAD) benefits the design engineer? 10. What information is contained in a bill of material? 11. What information is contained in an engineering drawing?
12. What information is contained in an assembly chart? In a process sheet?
13. Explain what is meant in service design by the “moment of truth.”
14. Explain how the house of quality translates customer desires into product/service attributes.
15. What strategic advantages does computer-aided design provide?
16. What is a process chain? 17. Why are the direct interaction and surrogate interaction
regions in a PCN diagram important in service design? 18. Why are documents for service useful? Provide examples of
four types.
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Solved Problem Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM 5.1 Sarah King, president of King Electronics, Inc., has two design options for her new line of high-resolution monitors for CAD workstations. The production run is for 100,000 units.
Design option A has a .90 probability of yielding 60 good monitors per 100 and a .10 probability of yielding 65 good monitors per 100. This design will cost $1,000,000.
Design option B has a .80 probability of yielding 64 good units per 100 and a .20 probability of yielding 59 good units per 100. This design will cost $1,350,000.
Good or bad, each monitor will cost $75. Each good moni- tor will sell for $150. Bad monitors are destroyed and have no salvage value. We ignore any disposal costs in this problem.
SOLUTION We draw the decision tree to reflect the two decisions and the probabilities associated with each decision. We then determine the payoff associated with each branch. The resulting tree is shown in Figure 5.14 .
For design A: EMV(design A) = (.9)(+500,000) + (.1)(+1,250,000)
= +575,000 For design B:
EMV(design B) = (.8)(+750,000) + (.2)(+0) = +600,000
The highest payoff is design option B, at $600,000.
Figure 5.14
Decision Tree for
Solved Problem 5.1 EMV = $575,000
(.9)
(.1)
$9,000,000 –7,500,000 –1,000,000 –––––––––
Sales 60,000 at $150 Mfg. cost 100,000 at $75 Design cost
$500,000
Mean Yield 60
Mean Yield 65
EMV = $600,000
(.8)
(.2)
Mean Yield 64
Mean Yield 59
$9,750,000 –7,500,000 –1,000,000 –––––––––
Sales 65,000 at $150 Mfg. cost 100,000 at $75 Design cost
$1,250,000
$9,600,000 –7,500,000 –1,350,000 –––––––––
Sales 64,000 at $150 Mfg. cost 100,000 at $75 Design cost
$750,000
$8,850,000 –7,500,000 –1,350,000 –––––––––
Sales 59,000 at $150 Mfg. cost 100,000 at $75 Design cost
0
Design A
Design B
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems 5.1–5.3 relate to Goods and Services Selection
• • • 5.1 Prepare a product-by-value analysis for the follow- ing products, and given the position in its life cycle, identify the issues likely to confront the operations manager and his or her possible actions. Product Alpha has annual sales of 1,000 units and a contribution of $2,500; it is in the introductory stage. Product Bravo has annual sales of 1,500 units and a contribu- tion of $3,000; it is in the growth stage. Product Charlie has annual sales of 3,500 units and a contribution of $1,750; it is in the decline stage.
• • 5.2 Given the contribution made on each of the three products in the following table and their position in
the life cycle, identify a reasonable operations strategy for each:
PRODUCT
PRODUCT CONTRIBUTION (% OF SELLING
PRICE)
COMPANY CONTRIBUTION
(%: TOTAL ANNUAL CONTRIBUTION
DIVIDED BY TOTAL ANNUAL SALES)
POSITION IN LIFE CYCLE
Smart watch 30 40 Introduction
Tablet 30 50 Growth
Hand calculator 50 10 Decline
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Problems 5.4–5.8 relate to Product Development
• • 5.4 Construct a house of quality matrix for a wrist- watch. Be sure to indicate specific customer wants that you think the general public desires. Then complete the matrix to show how an operations manager might identify specific attributes that can be measured and controlled to meet those customer desires.
• • 5.5 Using the house of quality, pick a real product (a good or service) and analyze how an existing organization satis- fies customer requirements.
• • 5.6 Prepare a house of quality for a mousetrap.
• • 5.7 Conduct an interview with a prospective purchaser of a new bicycle and translate the customer’s wants into the specific hows of the firm.
• • • • 5.8 Using the house of quality sequence, as described in Figure 5.4 on page 169, determine how you might deploy resources to achieve the desired quality for a product or service whose production process you understand.
Problems 5.9–5.17 relate to Defining a Product
• • 5.9 Prepare a bill of material for (a) a pair of eyeglasses and its case or (b) a fast-food sandwich (visit a local sandwich shop like Subway, McDonald’s, Blimpie, Quizno’s; perhaps a clerk or the manager will provide you with details on the quan- tity or weight of various ingredients—otherwise, estimate the quantities).
• • 5.10 Draw an assembly chart for a pair of eyeglasses and its case.
• • 5.11 Prepare a script for telephone callers at the univer- sity’s annual “phone-a-thon” fund raiser.
• • 5.12 Prepare an assembly chart for a table lamp.
Problems 5.18–5.20 relate to Service Design
• • 5.18 Draw a two-participant PCN diagram (similar to Figure 5.12 ) for one of the following processes: a) The process of having your computer repaired. b) The process of pizza preparation. c) The process of procuring tickets for a concert. • • 5.19 Review strategic process positioning options for the regions in Figure 5.12 , discussing the operational impact (in terms of the 10 strategic OM decisions) for: a) Manufacturing the sandwiches. b) Direct interaction. c) Establishing a sandwich buffet.
• • • 5.20 Select a service business that involves interaction between customers and service providers, and create a PCN dia- gram similar to Figure 5.12 . Pick a key step that could be per- formed either by the service provider or by the customers. Show process positioning options for the step. Describe how the options compare in terms of efficiency, economies of scale, and opportu- nity for customization. Ro
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Problems 5.21–5.28 relate to the Application of Decision Trees to Product Design
• • 5.21 The product design group of Iyengar Electric Supplies, Inc., has determined that it needs to design a new series of switches. It must decide on one of three design strategies. The market forecast is for 200,000 units. The better and more sophisticated the design strategy and the more time spent on value engineering, the less will be the variable cost. The chief of engineering design, Dr. W. L. Berry, has decided that the following costs are a good estimate of the initial and variable costs connected with each of the three strategies: a) Low-tech: A low-technology, low-cost process consisting of
hiring several new junior engineers. This option has a fixed cost of $45,000 and variable-cost probabilities of .3 for $.55 each, .4 for $.50, and .3 for $.45.
b) Subcontract: A medium-cost approach using a good outside design staff. This approach would have a fixed cost of $65,000 and variable-cost probabilities of .7 of $.45, .2 of $.40, and .1 of $.35.
c) High-tech: A high-technology approach using the very best of the inside staff and the latest computer-aided design technol- ogy. This approach has a fixed cost of $75,000 and variable- cost probabilities of .9 of $.40 and .1 of $.35.
What is the best decision based on an expected monetary value (EMV) criterion? ( Note: We want the lowest EMV, as we are dealing with costs in this problem.) PX
• • 5.22 MacDonald Products, Inc., of Clarkson, New York, has the option of (a) proceeding immediately with production of a new top-of-the-line stereo TV that has just completed prototype testing or (b) having the value analysis team complete a study. If Ed Lusk, VP for operations, proceeds with the existing prototype (option a), the firm can expect sales to be 100,000 units at $550 each, with a probability of .6, and a .4 probability of 75,000 at $550. If, however, he uses the value analysis team (option b), the firm expects sales of 75,000 units at $750, with a probability of .7, and a .3 probability of 70,000 units at $750. Value analysis, at a cost of $100,000, is only used in option b. Which option has the highest expected monetary value (EMV)? PX
Problems 5.13–5.17 are available in MyOMLab.
Problem 5.3 is available in MyOMLab.
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• • 5.23 Residents of Mill River have fond memories of ice skating at a local park. An artist has captured the experience in a drawing and is hoping to reproduce it and sell framed copies to current and former residents. He thinks that if the market is good he can sell 400 copies of the elegant version at $125 each. If the market is not good, he will sell only 300 at $90 each. He can make a deluxe version of the same drawing instead. He feels that if the market is good he can sell 500 copies of the deluxe version at $100 each. If the market is not good, he will sell only 400 copies at $70 each. In either case, production costs will be approximately $35,000. He can also choose to do nothing. If he believes there is a 50% probability of a good market, what should he do? Why? PX
• • 5.24 Ritz Products’s materials manager, Tej Dhakar, must determine whether to make or buy a new semiconductor for the wrist TV that the firm is about to produce. One million units are expected to be produced over the life cycle. If the product is made, start-up and production costs of the make decision total $1 million, with a probability of .4 that the product will be sat- isfactory and a .6 probability that it will not. If the product is not satisfactory, the firm will have to reevaluate the decision. If the decision is reevaluated, the choice will be whether to spend another $1 million to redesign the semiconductor or to purchase. Likelihood of success the second time that the make decision is made is .9. If the second make decision also fails, the firm must purchase. Regardless of when the purchase takes place, Dhakar’s best judgment of cost is that Ritz will pay $.50 for each purchased semiconductor plus $1 million in vendor development cost. a) Assuming that Ritz must have the semiconductor (stopping or
doing without is not a viable option), what is the best decision? b) What criteria did you use to make this decision? c) What is the worst that can happen to Ritz as a result of this
particular decision? What is the best that can happen? PX
• • 5.25 Sox Engineering designs and constructs air condi- tioning and heating systems for hospitals and clinics. Currently, the company’s staff is overloaded with design work. There is a major design project due in 8 weeks. The penalty for completing the design late is $14,000 per week, since any delay will cause the facility to open later than anticipated and cost the client signifi- cant revenue. If the company uses its inside engineers to complete the design, it will have to pay them overtime for all work. Sox has estimated that it will cost $12,000 per week (wages and over- head), including late weeks, to have company engineers complete the design. Sox is also considering having an outside engineering firm do the design. A bid of $92,000 has been received for the completed design. Yet another option for completing the design is to conduct a joint design by having a third engineering company
complete all electromechanical components of the design at a cost of $56,000. Sox would then complete the rest of the design and control systems at an estimated cost of $30,000.
Sox has estimated the following probabilities of completing the project within various time frames when using each of the three options. Those estimates are shown in the following table:
PROBABILITY OF COMPLETING THE DESIGN
OPTION ON
TIME 1 WEEK
LATE 2 WEEKS
LATE 3 WEEKS
LATE
Internal Engineers .4 .5 .1 —
External Engineers .2 .4 .3 .1
Joint Design .1 .3 .4 .2
What is the best decision based on an expected monetary value criterion? ( Note: You want the lowest EMV because we are deal- ing with costs in this problem.) PX
• • • 5.26 Use the data in Solved Problem 5.1 to examine what happens to the decision if Sarah King can increase all of Design B yields from 59,000 to 64,000 by applying an expensive phosphorus to the screen at an added manufacturing cost of $250,000. Prepare the modified decision tree. What are the pay- offs, and which branch has the greatest EMV?
• • • • 5.27 McBurger, Inc., wants to redesign its kitchens to improve productivity and quality. Three designs, called designs K1, K2, and K3, are under consideration. No matter which design is used, daily production of sandwiches at a typical McBurger res- taurant is for 500 sandwiches. A sandwich costs $1.30 to produce. Non-defective sandwiches sell, on the average, for $2.50 per sand- wich. Defective sandwiches cannot be sold and are scrapped. The goal is to choose a design that maximizes the expected profit at a typical restaurant over a 300-day period. Designs K1, K2, and K3 cost $100,000, $130,000, and $180,000, respectively. Under design K1, there is a .80 chance that 90 out of each 100 sandwiches are non-defective and a .20 chance that 70 out of each 100 sandwiches are non-defective. Under design K2, there is a .85 chance that 90 out of each 100 sandwiches are non-defective and a .15 chance that 75 out of each 100 sandwiches are non-defective. Under design K3, there is a .90 chance that 95 out of each 100 sandwiches are non-defective and a .10 chance that 80 out of each 100 sand- wiches are non-defective. What is the expected profit level of the design that achieves the maximum expected 300-day profit level?
Problem 5.28 is available in MyOMLab.
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CASE STUDIES De Mar’s Product Strategy
De Mar, a plumbing, heating, and air-conditioning company located in Fresno, California, has a simple but powerful prod- uct strategy: Solve the customer’s problem no matter what, solve the problem when the customer needs it solved, and make sure the customer feels good when you leave . De Mar offers guaranteed, same-day service for customers requiring it. The company pro- vides 24-hour-a-day, 7-day-a-week service at no extra charge for customers whose air conditioning dies on a hot summer Sunday or whose toilet overflows at 2:30 A.M. As assistant service coor- dinator Janie Walter puts it: “We will be there to fix your A/C on the fourth of July, and it’s not a penny extra. When our competi- tors won’t get out of bed, we’ll be there!”
De Mar guarantees the price of a job to the penny before the work begins. Whereas most competitors guarantee their work for 30 days, De Mar guarantees all parts and labor for one year. The company assesses no travel charge because “it’s not fair to charge customers for driving out.” Owner Larry Harmon says: “We are in an industry that doesn’t have the best reputation. If we start making money our main goal, we are in trouble. So I stress cus- tomer satisfaction; money is the by-product.”
De Mar uses selective hiring, ongoing training and education, performance measures, and compensation that incorporate cus- tomer satisfaction, strong teamwork, peer pressure, empower- ment, and aggressive promotion to implement its strategy. Says credit manager Anne Semrick: “The person who wants a nine-to- five job needs to go somewhere else.”
De Mar is a premium pricer. Yet customers respond because De Mar delivers value—that is, benefits for costs. In 8 years, annual sales increased from about $200,000 to more than $3.3 million.
Discussion Questions
1. What is De Mar’s product? Identify the tangible parts of this product and its service components.
2. How should other areas of De Mar (marketing, finance, per- sonnel) support its product strategy?
3. Even though De Mar’s product is primarily a service product, how should each of the 10 strategic OM decisions in the text be managed to ensure that the product is successful?
Source: Reprinted with the permission of The Free Press, from On Great Service: A Framework for Action by Leonard L. Berry.
Video Case Product Design at Regal Marine With hundreds of competitors in the boat business, Regal Marine must work to differentiate itself from the flock. As we saw in the Global Company Profile that opened this chapter, Regal con- tinuously introduces innovative, high-quality new boats. Its dif- ferentiation strategy is reflected in a product line consisting of 22 models.
To maintain this stream of innovation, and with so many boats at varying stages of their life cycles, Regal constantly seeks design input from customers, dealers, and consultants. Design ideas rapidly find themselves in the styling studio, where they are placed onto CAD machines in order to speed the development process. Existing boat designs are always evolving as the company tries to stay stylish and competitive. Moreover, with life cycles as short as 3 years, a steady stream of new products is required. A few years ago, the new product was the three-passenger $11,000 Rush, a small but powerful boat capable of pulling a water-skier. This was followed with a 20-foot inboard–outboard performance boat with so many innovations that it won prize after prize in the industry. Another new boat is a redesigned 52-foot sports yacht that sleeps six in luxury staterooms. With all these models and innovations, Regal designers and production personnel are under pressure to respond quickly.
By getting key suppliers on board early and urging them to participate at the design stage, Regal improves both innovations and quality while speeding product development. Regal finds that
the sooner it brings suppliers on board, the faster it can bring new boats to the market. After a development stage that constitutes concept and styling, CAD designs yield product specifications. The first stage in actual production is the creation of the “plug,” a foam-based carving used to make the molds for fiberglass hulls and decks. Specifications from the CAD system drive the carv- ing process. Once the plug is carved, the permanent molds for each new hull and deck design are formed. Molds take about 4 to 8 weeks to make and are all handmade. Similar molds are made for many of the other features in Regal boats—from galley and
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stateroom components to lavatories and steps. Finished molds can be joined and used to make thousands of boats.
Discussion Questions *
1. How does the concept of product life cycle apply to Regal Marine products?
2. What strategy does Regal use to stay competitive?
3. What kind of engineering savings is Regal achieving by using CAD technology rather than traditional drafting techniques?
4. What are the likely benefits of the CAD design technology?
* You may wish to view the video accompanying this case before addressing these questions.
Endnotes
2. See Scott Sampson, “Visualizing Service Operations,” Journal of Service Research (May 2012). More details about PCN anal- ysis are available at services.byu.edu.
1. Contribution is defined as the difference between direct cost and selling price. Direct costs are directly attributable to the product, namely labor and material that go into the product.
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Chapter 5 Rapid Review 5
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Main Heading Review Material MyOMLab GOODS AND SERVICES SELECTION (pp. 162 – 165 )
Although the term products may often refer to tangible goods, it also refers to offerings by service organizations. The objective of the product decision is to develop and implement a product strategy that meets the demands of the marketplace with a competitive advantage. j Product Decision —The selection, definition, and design of products. The four phases of the product life cycle are introduction, growth, maturity, and decline. j Product-by-value analysis —A list of products, in descending order of their
individual dollar contribution to the firm, as well as the total annual dollar contribution of the product.
Concept Questions: 1.1–1.4 Problems: 5.1–5.3
VIDEO 5.1 Product Strategy at Regal Marine
GENERATING NEW PRODUCTS (pp. 165 – 166 )
Product selection, definition, and design take place on a continuing basis. Changes in product opportunities, the products themselves, product volume, and product mix may arise due to understanding the customer, economic change, sociological and demographic change, technological change, political/legal change, market practice, professional standards, suppliers, or distributors.
Concept Question: 2.1
PRODUCT DEVELOPMENT (pp. 166 – 170 )
j Quality function deployment (QFD) —A process for determining customer requirements (customer “wants”) and translating them into attributes (the “hows”) that each functional area can understand and act on.
j House of quality —A part of the quality function deployment process that utilizes a planning matrix to relate customer wants to how the firm is going to meet those wants.
j Product development teams —Teams charged with moving from market requirements for a product to achieving product success.
j Concurrent engineering —Simultaneous performance of the various stages of product development.
j Manufacturability and value engineering —Activities that help improve a product’s design, production, maintainability, and use.
Concept Questions: 3.1–3.4
ISSUES FOR PRODUCT DESIGN (pp. 171 – 173 )
j Robust design —A design that can be produced to requirements even with unfavorable conditions in the production process.
j Modular design —A design in which parts or components of a product are subdivided into modules that are easily interchanged or replaced.
j Computer-aided design (CAD) —Interactive use of a computer to develop and document a product.
j Design for manufacture and assembly (DFMA) —Software that allows designers to look at the effect of design on manufacturing of a product.
j Standard for the exchange of product data (STEP) —A standard that provides a format allowing the electronic transmission of three-dimensional data.
j Computer-aided manufacturing (CAM) —The use of information technology to control machinery.
j 3-D printing —An extension of CAD that builds prototypes and small lots. j Virtual reality —A visual form of communication in which images substitute for
reality and typically allow the user to respond interactively. j Value analysis —A review of successful products that takes place during the
production process. Sustainability is meeting the needs of the present without compromising the ability of future generations to meet their needs. Life cycle assessment (LCA) is part of ISO 14000; it assesses the environmental impact of a product from material and energy inputs to disposal and environmen- tal releases. Both sustainability and LCA are discussed in depth in Supplement 5.
Concept Questions: 4.1–4.4
PRODUCT DEVELOPMENT CONTINUUM (pp. 173 – 175 )
j Time-based competition —Competition based on time; rapidly developing products and moving them to market.
Internal development strategies include (1) new internally developed products, (2) enhancements to existing products, and (3) migrations of existing products. External development strategies include (1) purchase the technology or expertise by acquiring the developer, (2) establish joint ventures, and (3) develop alliances. j Joint ventures —Firms establishing joint ownership to pursue new products or
markets. j Alliances —Cooperative agreements that allow firms to remain independent but
pursue strategies consistent with their individual missions.
Concept Questions: 5.1–5.4
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Main Heading Review Material MyOMLab DEFINING A PRODUCT (pp. 175 – 177 )
j Engineering drawing —A drawing that shows the dimensions, tolerances, materials, and finishes of a component.
j Bill of material (BOM) —A list of the components, their description, and the quantity of each required to make one unit of a product.
j Make-or-buy decision —The choice between producing a component or a service and purchasing it from an outside source.
j Group technology —A product and component coding system that specifies the size, shape, and type of processing; it allows similar products to be grouped.
Concept Questions: 6.1–6.4 Problems: 5.9, 5.10, 5.12–5.17
DOCUMENTS FOR PRODUCTION (pp. 178 – 179 )
j Assembly drawing —An exploded view of a product. j Assembly chart —A graphic means of identifying how components flow into
subassemblies and final products. j Route sheet —A list of the operations necessary to produce a component with the
material specified in the bill of material. j Work order —An instruction to make a given quantity of a particular item. j Engineering change notice (ECN) —A correction or modification of an
engineering drawing or bill of material. j Configuration management —A system by which a product’s planned and
changing components are accurately identified. j Product life cycle management (PLM) —Software programs that tie together
many phases of product design and manufacture.
Concept Questions: 7.1–7.4
SERVICE DESIGN (pp. 179 – 182 )
j Process-chain-network (PCN) analysis —A way to design processes to optimize interaction between firms and their customers.
j Process chain —A sequence of steps that provide value to process participants. To enhance service efficiency, companies: (1) limit options, (2) delay customization, (3) modularize, (4) automate, and (5) design for the “moment of truth.”
Concept Questions: 8.1–8.4
APPLICATION OF DECISION TREES TO PRODUCT DESIGN (pp. 182 – 184 )
To form a decision tree, (1) include all possible alternatives (including “do nothing”) and states of nature; (2) enter payoffs at the end of the appropriate branch; and (3) determine the expected value of each course of action by starting at the end of the tree and working toward the beginning, calculating values at each step and “pruning” inferior alternatives.
Concept Questions: 9.1–9.2 Problems: 5.21–5.25, 5.27–5.28 Virtual Office Hours for Solved Problem: 5.1 ACTIVE MODEL 5.1
TRANSITION TO PRODUCTION (p. 184 )
One of the arts of management is knowing when to move a product from development to production; this move is known as transition to production .
Concept Questions: 10.1–10.2
Self Test
LO 5.1 A product’s life cycle is divided into four stages, including: a) introduction. b) growth. c) maturity. d) all of the above. LO 5.2 Product development systems include: a) bills of material. b) routing charts. c) functional specifications. d) product-by-values analysis. e) configuration management. LO 5.3 A house of quality is: a) a matrix relating customer “wants” to the firm’s “hows.” b) a schematic showing how a product is put together. c) a list of the operations necessary to produce a component. d) an instruction to make a given quantity of a particular item. e) a set of detailed instructions about how to perform a task. LO 5.4 Time-based competition focuses on: a) moving new products to market more quickly. b) reducing the life cycle of a product. c) linking QFD to PLM. d) design database availability. e) value engineering.
LO 5.5 Products are defined by: a) value analysis. b) value engineering. c) routing sheets. d) assembly charts. e) engineering drawings. LO 5.6 A route sheet: a) lists the operations necessary to produce a component. b) is an instruction to make a given quantity of a particular item. c) is a schematic showing how a product is assembled. d) is a document showing the flow of product components. e) all of the above. LO 5.7 The three process regions in a process–chain–network diagram are: a) manufacture, supplier, customer b) direct and surrogate, customer, provider c) independent, dependent, customer interaction d) direct interaction, surrogate interaction, independent processing LO 5.8 Decision trees use: a) probabilities. b) payoffs. c) logic. d) options. e) all of the above.
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Chapter 5 Rapid Review continued
j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
Answers: LO 5.1. d; LO 5.2. c; LO 5.3. a; LO 5.4. a; LO 5.5. e; LO 5.6. a; LO 5.7. d; LO 5.8. e.
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193
SUPPLEMENT OUTLINE
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Corporate Social Responsibility 194
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Sustainability 195
Sustainability in the Supply Chain
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Design and Production for Sustainability 198
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Regulations and Industry Standards 203
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194
Corporate Social Responsibility 1 Managers must consider how the products and services they provide affect both people and the environment. Certainly, firms must provide products and services that are innovative and attractive to buyers. But today’s technologies allow consumers, communities, public interest groups, and regulators to be well informed about all aspects of an organization’s performance. As a result, stakeholders can have strong views about firms that fail to respect the environ- ment or that engage in unethical conduct. Firms need to consider all the implications of a product—from design to disposal.
Many companies now realize that “doing what’s right” and doing it properly can be beneficial to all stakeholders. Companies that practice corporate social responsibility (CSR) introduce policies that consider environmental, societal, and financial impacts in their decision making. As managers consider approaches to CSR, they find it helpful to consider the concept of creating shared value . Shared value suggests finding policies and practices that enhance the organization’s competitive- ness while simultaneously advancing the economic and social conditions in the communities in which it operates. For instance, note how automakers Tesla, Toyota, and Nissan find shared value in low-emission vehicles . . . vehicles that enhance their competiveness in a global market while meeting society’s interest in low-emission vehicles. Similarly, Dow Chemical finds social benefits and profit in Nexera canola and sunflower seeds. These seeds yield twice as much cooking oil as soybeans, enhancing profitability to the grower. They also have a longer shelf life, which reduces operating costs throughout the supply chain. As an added bonus, the oils have lower levels of saturated fat than traditional products and contain no trans fats. A win–win for Dow and society.
Operations functions—from supply chain management to product design to production to packaging and logistics—provide an opportunity for finding shared value and meeting CSR goals. 2
L E A R N I N G OBJEC TI V ES
LO S5.1 Describe corporate social responsibility 194
LO S5.2 Describe sustainability 195
LO S5.3 Explain the 3 R s for sustainability 198 LO S5.4 Calculate design for disassembly 199
LO S5.5 Explain the impact of sustainable regulations on operations 203
Airlines from around the world,
including Air China, Virgin
Atlantic Airways, KLM, Alaska,
Air New Zealand, and Japan
Airlines, are experimenting
with alternative fuels to power
their jets in an effort to reduce
greenhouse gas emissions and
to reduce their dependence on
traditional petroleum-based jet
fuel. Alternative biofuels are being
developed from recycled cooking
oil, sewage sludge, municipal
waste, coconuts, sugar cane, and
genetically modified algae that
feed on plant waste.
Lex V a n L
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sc o m
LO S5.1 Describe corporate social
responsibility
Corporate social responsibility (CSR)
Managerial decision making that
considers environmental, societal,
and financial impacts.
Shared value
Developing policies and practices
that enhance the competitiveness
of an organization while advancing
the economic and social condi-
tions in the communities in which
it operates.
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Sustainability Sustainability is often associated with corporate social responsibility. The term sustainability refers to meeting the needs of the present without compromising the ability of future genera- tions to meet their needs. Many people who hear of sustainability for the first time think of green products or “going green”—recycling, global warming, and saving rainforests. This is certainly part of it. However, it is more than this. True sustainability involves thinking not only about environmental resources but also about employees, customers, community, and the company’s reputation. Three concepts may be helpful as managers consider sustainability decisions: a systems view, the commons , and the triple bottom line .
Systems View Managers may find that their decisions regarding sustainability improve when they take a systems view. This means looking at a product’s life from design to disposal, including all the resources required. Recognizing that both raw materials and human resources are sub- systems of any production process may provide a helpful perspective. Similarly, the product or service itself is a small part of much larger social, economic, and environmental systems. Indeed, managers need to understand the inputs and interfaces between the interacting sys- tems and identify how changes in one system affect others. For example, hiring or laying off employees can be expected to have morale implications for internal systems (within an organization), as well as socioeconomic implications for external systems. Similarly, dump- ing chemicals down the drain has implications on systems beyond the firm. Once managers understand that the systems immediately under their control have interactions with systems below them and above them, more informed judgments regarding sustainability can be made.
Commons Many inputs to a production system have market prices, but others do not. Those that do not are those held by the public, or in the common . Resources held in the common are often misallocated. Examples include depletion of fish in international waters and polluted air and waterways. The attitude seems to be that just a little more fishing or a little more pollution will not matter, or the adverse results may be perceived as someone else’s problem. Society is still groping for solutions for use of those resources in the common . The answer is slowly being found in a number of ways: (1) moving some of the common to private property (e.g., selling radio frequency spectrum), (2) allocation of rights (e.g., establishing fishing boundaries), and (3) allocation of yield (e.g., only a given quantity of fish can be harvested). As managers understand the issues of the commons , they have further insight about sustainability and the obligation of caring for the commons .
Triple Bottom Line Firms that do not consider the impact of their decisions on all their stakeholders see reduced sales and profits. Profit maximization is not the only measure of success. A one-dimensional bottom line, profit, will not suffice; the larger socioeconomic systems beyond the firm demand more. One way to think of sustainability is to consider the systems necessary to support the triple bottom line of the three P s: people, planet , and profit (see Figure S5.1 ), which we will now discuss.
People Companies are becoming more aware of how their decisions affect people—not only their employees and customers but also those who live in the communities in which they operate. Most employers want to pay fair wages, offer educational opportunities, and pro- vide a safe and healthy workplace. So do their suppliers. But globalization and the reliance on outsourcing to suppliers around the world complicate the task. This means companies must create policies that guide supplier selection and performance. Sustainability suggests that supplier selection and performance criteria evaluate safety in the work environment, whether living wages are paid, if child labor is used, and whether work hours are excessive. Apple, GE, Procter & Gamble, and Walmart are examples of companies that conduct supplier audits to uncover any harmful or exploitative business practices that are counter to their sustainability goals and objectives.
Sustainability
Meeting the needs of the present
without compromising the ability
of future generations to meet their
needs.
LO S5.2 Describe sustainability
VIDEO S5.1 Building Sustainability at the Orlando
Magic’s Amway Center
STUDENT TIP Profit is now just one of the
three P s: people, planet, and
profit.
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196 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Recognizing that customers increasingly want to know that the materials in the products they buy are safe and produced in a responsible way, Walmart initiated the development of the worldwide sustainable product index for evaluating the sustainability of products. The goals of that initiative are to create a more transparent supply chain, accelerate the adoption of best practices, and drive product innovation.
Walmart found a correlation between supply-chain transparency, positive labor practices, community involvement, and quality, efficiency, and cost. Walmart is committed to working with its suppliers to sell quality products that are safe, that create value for customers, and that are produced in a sustainable way. The firm is accomplishing this in four ways:
1. Improving livelihoods through the creation of productive, healthy, and safe workplaces and promoting quality of life
2. Building strong communities through access to affordable, high-quality services such as education and job training that support workers and their families
3. Preventing exposure to substances that are considered harmful or toxic to human health 4. Promoting health and wellness by increasing access to nutritious products, encouraging
healthy lifestyles, and promoting access to health care
Walmart’s CEO has said that companies that are unfair to their people are also likely to skimp on quality and that he will not continue to do business with those suppliers. Accordingly, operations managers must consider the working conditions in which they place their employees. This includes training and safety orientations, before-shift exer- cises, earplugs, safety goggles, and rest breaks to reduce the possibility of worker fatigue and injury. Operations managers must also make decisions regarding the disposal of mate- rial and chemical waste, including hazardous materials, so they don’t harm employees or the community.
Planet When discussing the subject of sustainability, our planet’s environment is the first thing that comes to mind, so it understandably gets the most attention from managers. Opera- tions managers look for ways to reduce the environmental impact of their operations, whether from raw material selection, process innovation, alternative product delivery methods, or disposal of products at their end-of-life. The overarching objective for operations managers is to conserve scarce resources, thereby reducing the negative impact on the environment. Here are a few exam- ples of how organizations creatively make their operations more environmentally friendly:
◆ S.C. Johnson, the company that makes Windex, Saran Wrap, Pledge, Ziploc bags, and Raid, developed Greenlist , a classification system that evaluates the impact of raw materials on human and environmental health. By using Greenlist , S.C. Johnson has eliminated millions of pounds of pollutants from its products.
Raw material
Concept Design Raw material
Transport Transport TransportConsume DisposalManufacture
Energy Water
Minimize
Planet People Profit
Waste
Maximize the triple bottom line
Figure S5.1
Improving the Triple Bottom Line with Sustainability
STUDENT TIP Walmart has become a global
leader in sustainability. Read
Force of Nature: The Unlikely
Story of Walmart’s Green
Revolution.
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◆ Thirty-one public school districts across the state of Kentucky operate hybrid electric school buses. They estimate fuel savings as high as 40%, with fuel mileage of 7.5 mpg increasing to 12 miles per gallon, relative to standard diesel buses.
◆ Levi has started a campaign to save water in the creation of jeans, as seen in the OM in Action box “Blue Jeans and Sustainability.”
To gauge their environmental impact on the planet, many companies are measuring their carbon footprint. Carbon footprint is a measure of the total greenhouse gas (GHG) emissions caused directly and indirectly by an organization, a product, an event, or a person. A substan- tial portion of greenhouse gases are released naturally by farming, cattle, and decaying forests and, to a lesser degree, by manufacturing and services. The most common greenhouse gas pro- duced by human activities is carbon dioxide, primarily from burning fossil fuels for electricity generation, heating, and transport. Operations managers are being asked to do their part to reduce GHG emissions.
Industry leaders such as Frito-Lay have been able to break down the carbon emissions from various stages in the production process. For instance, in potato chip production, a 34.5-gram (1.2-ounce) bag of chips is responsible for about twice its weight in emissions—75 grams per bag (see Figure S5.2 ).
Profit Social and environmental sustainability do not exist without economic sustainability. Economic sustainability refers to how companies remain in business. Staying in business requires making investments, and investments require making profits. Though profits may be relatively easy to determine, other measures can also be used to gauge economic sustainability. The al- ternative measures that point to a successful business include risk profile, intellectual property, employee morale, and company valuation. To support economic sustainability, firms may supplement standard financial accounting and reporting with some version of social accounting . Social accounting can include brand equity, management talent, human capital development and benefits, research and development, productivity, philanthropy, and taxes paid.
Carbon footprint
A measure of total greenhouse
gas emissions caused directly or
indirectly by an organization, a
product, an event, or a person.
Economic sustainability
Appropriately allocating scarce
resources to make a profit.
VIDEO S5.2 Green Manufacturing and Sustainability at Frito-Lay
OM in Action The recent drought in California is hurting more than just farmers. It is also
having a significant impact on the fashion industry and spurring changes in
how jeans are made and how they should be laundered. Southern California is
estimated to be the world’s largest supplier of so-called premium denim, the
$100 to $200-plus-a-pair of designer jeans. Water is a key component in the
various steps of the processing and repeated washing with stones, or bleaching
and dyeing that create that “distressed” vintage look. Southern California
produces 75% of the high-end denim in the U.S. that is sold worldwide.
The area employs about 200,000 people, making it the largest U.S. fashion
manufacturing hub.
Now that water conservation is a global priority, major denim brands
are working to cut water use. Levi, with sales of $5 billion, is using ozone
machines to replace the bleach traditionally used to lighten denim. It is
also reducing the number of times it washes jeans. The company has
saved more than a billion liters of water since 2011 with its Levi’s
Water Less campaign. By 2020, the company plans to have 80% of
Levi’s brand products made using the Water Less process, up from
about 25% currently.
Traditionally, about 34 liters of water are used in the cutting, sewing, and
finishing process to make a pair of Levi’s signature 501 jeans. Nearly 3,800
liters of water are used throughout the lifetime of a pair of Levi’s 501. A
study found cotton cultivation represents 68% of that and consumer washing
another 23%. So Levi is promoting the idea that jeans only need washing
Blue Jeans and Sustainability
Fiber 68%
Consumer care 23%
Cradle to grave water consumption percentage
Sundries & Packaging 2%
Cut, Sew, Finish 1%
Fabric production 6%
after 10 wears . (The average American consumer washes after 2 wears.)
Levi’s CEO recently urged people to stop washing their jeans, saying he hadn’t
washed his one-year-old jeans at the time. “You can air dry and spot clean
instead,” he said.
Sources: The Wall Street Journal (April 10, 2015) and New York Times
(March 31, 2015).
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198 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Design and Production for Sustainability The operations manager’s greatest opportunity to make substantial contributions to the company’s environmental objectives occurs during product life cycle assessment. Life cycle assessment evaluates the environmental impact of a product, from raw material and energy inputs all the way to the disposal of the product at its end-of-life. The goal is to make decisions that help reduce the environmental impact of a product throughout its entire life. Focusing on the 3 R s— reduce, reuse, and recycle— can help accomplish this goal. By incorporating the 3 R s, product design teams, process managers, and supply-chain personnel can make great strides toward reducing the environmental impact of products—to the benefit of all stakeholders.
Product Design Product design is the most critical phase in product life cycle assessment. The decisions that are made during this phase greatly affect materials, quality, cost, processes, related packaging and logistics, and ultimately how the product will be processed when discarded. During design, one of the goals is to incorporate a systems view in the product or service design that lowers the environmental impact. This is the first R . Such an approach reduces waste and energy costs at the supplier, in the logistics system, and for the end user. For instance, by taking a systems view, Procter & Gamble developed Tide Coldwater , a detergent that gets clothes clean with cold water, saving the consumer about three-fourths of the energy used in a typical wash.
Other successful design efforts include:
◆ Boston’s Park Plaza Hotel eliminated bars of soap and bottles of shampoo by installing pump dispensers in its bathrooms, saving the need for 1 million plastic containers a year.
◆ UPS reduced the amount of materials it needs for its envelopes by developing its reusable express envelopes, which are made from 100% recycled fiber. These envelopes are designed to be used twice, and after the second use, the envelope can be recycled.
◆ Coca-Cola’s redesigned Dasani bottle reduced the amount of plastic needed and is now 30% lighter than when it was introduced.
Product design teams also look for alternative materials from which to make their products. Innovating with alternative materials can be expensive, but it may make autos, trucks, and air- craft more environmentally friendly while improving payload and fuel efficiency. Aircraft and auto makers, for example, constantly seek lighter materials to use in their products. Lighter materials translate into better fuel economy, fewer carbon emissions, and reduced operating cost. For instance:
◆ Mercedes is building some car exteriors from a banana fiber that is both biodegradable and lightweight.
◆ Some Fords have seat upholstery made from recycled plastic soda bottles and old clothing.
Figure S5.2
Carbon Footprint of a
34.5-gram Bag of Frito-Lay
Chips
Shipping 9%
Packaging 15%
Manufacture 30%
Farming 44%
Total carbon footprint
Disposal 2%
75 g
Life cycle assessment
Analysis of environmental impacts
of products from the design stage
through end-of-life.
LO S5.3 Explain the 3 R s for sustainability
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◆ Boeing is using carbon fiber, epoxy composites, and titanium graphite laminate to reduce weight in its new 787 Dreamliner.
Product designers often must decide between two or more environmentally friendly design alternatives. Example S1 deals with a design for disassembly cost–benefit analysis. This process focuses on the second and third R s: reuse and recycle. The design team analyzes the amount of revenue that might be reclaimed against the cost of disposing of the product at its end-of-life.
An excellent place for operations managers to begin the sustainability challenge is with good product design. Here Tom Malone, CEO, of MicroGreen Polymers,
discusses the company’s new ultra light cup with production personnel (left). The cup can be recycled over and over and never go to a landfill. Another new design
is the “winglet” (right). These wing tip extensions increase climb speed, reduce noise by 6.5%, cut CO 2 emissions by 5%, and save 6% in fuel costs. Alaska Air has
retrofitted its entire 737 fleet with winglets, saving $20 million annually.
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STUDENT TIP A fourth R , improved reputation,
follows the success of reduce,
reuse, and recycle.
Example S1 DESIGN FOR DISASSEMBLY Sound Barrier, Inc., needs to decide which of two speaker designs is better environmentally.
APPROACH c The design team collected the following information for two audio speaker designs, the Harmonizer and the Rocker: 1. Resale value of the components minus the cost of transportation to the disassembly facility 2. Revenue collected from recycling 3. Processing costs, which include disassembly, sorting, cleaning, and packaging 4. Disposal costs, including transportation, fees, taxes, and processing time
SOLUTION c The design team developed the following revenue and cost information for the two speaker design alternatives:
Harmonizer
PART RESALE REVENUE
PER UNIT RECYCLING
REVENUE PER UNIT PROCESSING
COST PER UNIT DISPOSAL COST
PER UNIT
Printed circuit board $5.93 $1.54 $3.46 $0.00
Laminate back 0.00 0.00 4.53 1.74
Coil 8.56 5.65 6.22 0.00
Processor 9.17 2.65 3.12 0.00
Frame 0.00 0.00 2.02 1.23
Aluminum case 11.83 2.10 2.98 0.00
Total $35.49 $11.94 $22.33 $2.97
LO S5.4 Calculate design for disassembly
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Rocker
PART RESALE REVENUE
PER UNIT RECYCLING
REVENUE PER UNIT PROCESSING
COST PER UNIT DISPOSAL COST
PER UNIT
Printed circuit board $7.88 $3.54 $2.12 $0.00
Coil 6.67 4.56 3.32 0.00
Frame 0.00 0.00 4.87 1.97
Processor 8.45 4.65 3.43 0.00
Plastic case 0.00 0.00 4.65 3.98
Total $23.00 $12.75 $18.39 $5.95
Using the Equation (S5-1) , the design team can compare the two design alternatives:
Revenue retrieval = Total resale revenue + Total recycling revenue - Total processing cost - Total disposal cost (S5-1)
Revenue retrieval for Harmonizer 5 $35.49 1 $11.94 − $22.33 − $2.97 5 $22.13
Revenue retrieval for Rocker 5 $23.00 1 $12.75 − $18.39 − $5.95 5 $11.41
INSIGHT c After analyzing both environmental revenue and cost components of each speaker design, the design team finds that the Harmonizer is the better environmental design alternative as it achieves a higher revenue retrieval opportunity. Note that the team is assuming that both products have the same market acceptance, profitability, and environmental impact.
LEARNING EXERCISE c What would happen if there was a change in the supply chain that caused the processing and disposal costs to triple for the laminate back part of the Harmonizer? [Answer: The revenue retrieval from the Harmonizer is $35.49 1 $11.94 − $31.39 − $6.45 5 $9.59. This is less than the Rocker’s revenue retrieval of $11.41, so the Rocker becomes the better environmental design alternative, as it achieves a higher revenue retrieval opportunity.]
RELATED PROBLEMS c S5.1, S5.2, S5.3, S5.9, S5.12, S5.13, S5.14
Production Process Manufacturers look for ways to reduce the amount of resources in the production process. Opportunities to reduce environmental impact during production typically revolve around the themes of energy, water, and environmental contamination. Conservation of energy and improving energy efficiency come from the use of alternative energy and more energy-efficient machinery. For example:
◆ S.C. Johnson built its own power plant that runs on natural gas and methane piped in from a nearby landfill, cutting back its reliance on coal-fired power.
◆ PepsiCo developed Resource Conservation (ReCon) , a diagnostic tool for understanding and reducing in-plant water and energy usage. In its first 2 years, ReCon helped sites across the world identify 2.2 billion liters of water savings, with a corresponding cost savings of nearly $2.7 million.
◆ Frito-Lay decided to extract water from potatoes, which are 80% water. Each year, a single factory processes 350,000 tons of potatoes, and as those potatoes are processed, the com- pany reuses the extracted water for that factory’s daily production.
These and similar successes in the production process reduce both costs and environmental concerns. Less energy is consumed, and less material is going to landfills.
Logistics As products move along in the supply chain, managers strive to achieve efficient route and delivery networks, just as they seek to drive down operating cost. Doing so reduces envi- ronmental impact. Management analytics (such as linear programming, queuing, and vehicle routing software) help firms worldwide optimize elaborate supply-chain and distribution networks. Networks of container ships, airplanes, trains, and trucks are being analyzed to
STUDENT TIP Las Vegas, always facing a
water shortage, pays residents
$40,000 an acre to take out
lawns and replace them with
rocks and native plants.
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reduce the number of miles traveled or the number of hours required to make deliveries. For example:
◆ UPS has found that making left turns increases the time it takes to make deliveries. This in turn increases fuel usage and carbon emissions. So UPS plans its delivery truck routes with the fewest possible left turns. Likewise, airplanes fly at different altitudes and routes to take advantage of favorable wind conditions in an effort to reduce fuel use and carbon emissions.
◆ Food distribution companies now have trucks with three temperature zones (frozen, cool, and nonrefrigerated) instead of using three different types of trucks.
◆ Whirlpool radically revised its packaging to reduce “dings and dents” of appliances during delivery, generating huge savings in transportation and warranty costs.
To further enhance logistic efficiency, operations managers also evaluate equipment alterna- tives, taking into account cost, payback period, and the firm’s stated environmental objectives. Example S2 deals with decision making that takes into account life cycle ownership costs. A firm must decide whether to pay more up front for vehicles to further its sustainability goals or to pay less up front for vehicles that do not.
Three key success factors in the trucking industry are (1) getting shipments to customers promptly (rapid response), (2) keeping trucks busy (capacity utilization),
and (3) buying inexpensive fuel (driving down costs). Many firms have now developed devices like the one shown on the right to track location of trucks and facilitate
communication between drivers and dispatchers. Some systems use global positioning satellites (shown on the left), to speed shipment response, maximize utilization of
the truck, and ensure purchase of fuel at the most economical location. Sensors are also being added inside trailers. These sensors communicate whether the trailer is
empty or full and detect if the trailer is connected to a truck or riding on a railroad car.
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T e d F
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Example S2 LIFE CYCLE OWNERSHIP AND CROSSOVER ANALYSIS Blue Star is starting a new distribution service that delivers auto parts to the service departments of auto dealerships in the local area. Blue Star has found two light-duty trucks that would do the job well, so now it needs to pick one to perform this new service. The Ford TriVan costs $28,000 to buy and uses regular unleaded gasoline, with an average fuel efficiency of 24 miles per gallon. The TriVan has an operating cost of $.20 per mile. The Honda CityVan, a hybrid truck, costs $32,000 to buy and uses regular unleaded gasoline and battery power; it gets an average of 37 miles per gallon. The CityVan has an operating cost of $.22 per mile. The distance traveled annually is estimated to be 22,000 miles, with the life of either truck expected to be 8 years. The average gas price is $4.25 per gallon.
APPROACH c Blue Star applies Equation (S5-2) to evaluate total life cycle cost for each vehicle:
Total life cycle cost = Cost of vehicle + Life cycle cost of fuel + Life cycle operating cost (S5-2)
a) Based on life cycle cost, which model truck is the best choice? b) How many miles does Blue Star need to put on a truck for the costs to be equal? c) What is the crossover point in years?
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SOLUTION c
a) Ford TriVan:
Total life@ cycle cost
= $28,000 + ≥ 22,000
miles year
24 miles gallon
¥($4.25>gallon) (8 years) + a22,000 miles year
b($.20>mile)(8 years)
= $28,000 + $31,167 + $35,200 = $94,367
Honda CityVan:
Total life@ cycle cost
= $32,000 + ≥ 22, 000
miles year
37 miles gallon
¥($4.25>gallon)(8 years) + a22,000 miles year
b($.22>mile)(8 years)
= $32,000 + $20,216 + $38,720 = $90,936
b) Blue Star lets M be the crossover (break-even) point in miles, sets the two life cycle cost equations equal to each other, and solves for M :
Total cost for Ford TriVan = Total cost for Honda CityVan
$28,000 + ≥ 4.25
$ gallon
24 miles gallon
+ .20 $
mile ¥(M miles) = $32,000 + ≥
4.25 $
gallon
37 miles gallon
+ .22 $
mile ¥(M miles)
or,
$28,000 + a.3770 $
mile b(M) = $32,000 + a.3349
$ mile b (M)
or,
a.0421 $
mile b(M ) = $4,000
M = $4,000
.0421 $
mile
= 95,012 miles
c) The crossover point in years is:
Crossover point = 95,012 miles
22,000 miles year
= 4.32 years
INSIGHTS c
a) Honda CityVan is the best choice, even though the initial fi xed cost and variable operating cost per mile are higher. The savings comes from the better fuel mileage (more miles per gallon) for the Honda CityVan. b) The crossover (break-even) point is at 95,012 miles, which indicates that at this mileage point, the cost for either truck is the same. c) It will take 4.32 years to recoup the cost of purchasing and operating either vehicle. It will cost Blue Star approximately $.03 per mile less to operate the Honda CityVan than the Ford TriVan over the 8-year expected life.
LEARNING EXERCISE c If the cost of gasoline drops to $3.25, what will be the total life-cycle cost of each van, the break-even point in miles, and the crossover point in years? [Answer: The cost of the Ford TriVan is $87,033; the Honda CityVan costs $86,179; the break-even is 144,927 miles; and the crossover point is 6.59 years.]
RELATED PROBLEMS c S5.4, S5.5, S5.6, S5.10, S5.11, S5.15, S5.16, S5.17, S5.18, S5.19
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End-of-Life Phase We noted earlier that during product design, managers need to consider what happens to a product or its materials after the product reaches its end-of-life stage. Products with less mate- rial, with recycled material, or with recyclable materials all contribute to sustainability efforts, reducing the need for the “burn or bury” decision and conserving scarce natural resources.
Innovative and sustainability-conscious companies are now designing closed-loop supply chains , also called reverse logistics . Firms can no longer sell a product and then forget about it. They need to design and implement end-of-life systems for the physical return of products that facilitate recycling or reuse.
Caterpillar, through its expertise in remanufacturing technology and processes, has devised Cat Reman, a remanufacturing initiative, in an effort to show its commitment to sustainabil- ity. Caterpillar remanufactures parts and components that provide same-as-new performance and reliability at a fraction of new cost, while reducing the impact on the environment. The remanufacturing program is based on an exchange system where customers return a used com- ponent in exchange for a remanufactured product. The result is lower operating costs for the customer, reduced material waste, and less need for raw material to make new products. In a 1-year period, Caterpillar took back 2.1 million end-of-life units and remanufactured over 130 million pounds of material from recycled iron.
The OM in Action box “From Assembly Lines to Green Disassembly Lines” describes one automaker’s car design philosophy to facilitate the disassembly, recycling, and reuse of its autos that have reached their end-of-life.
Regulations and Industry Standards Government, industry standards, and company policies are all important factors in opera- tional decisions. Failure to recognize these constraints can be costly. Over the last 100 years, we have seen development of regulations, standards, and policies to guide managers in prod- uct design, manufacturing/assembly, and disassembly/disposal.
To guide decisions in product design , U.S. laws and regulations, such as those of the Food and Drug Administration, Consumer Product Safety Commission, and National Highway Safety Administration, provide guidance and often explicit regulations.
Manufacturing and assembly activities have their own set of regulatory agencies provid- ing guidance and standards of operations. These include the Occupational Safety and Health
Closed-loop supply chains
Supply chains that consider
forward and reverse product flows
over the entire life cycle.
LO S5.5 Explain the impact of sustainable
regulations on operations
OM in Action From Assembly Lines to Green Disassembly Lines A century has passed since assembly lines were developed to make
automobiles—and now we’re developing disassembly lines to take them
apart. So many automobiles are disassembled that recycling is the 16th-
largest industry in the U.S. The motivation for this comes from many sources,
including mandated industry recycling standards and a growing consumer
interest in purchasing cars based on how “green” they are.
New car designs have traditionally been unfriendly to recyclers, with little
thought given to disassembly. Some components, such as air bags, are hard to
handle and dangerous, and they take time to disassemble. However, manufac-
turers now design in such a way that materials can be easily reused in the next
generation of cars. The 2015 Mercedes S-class is 95% recyclable. BMW has
disassembly plants in Europe, Japan, New York, Los Angeles, and Orlando. A giant 200,000-square-foot facility in Baltimore (called CARS) can disas-
semble up to 30,000 vehicles per year. At CARS’s initial “greening station,”
special tools puncture tanks and drain fluids and remove the battery and gas
tank. Then wheels, doors, hood, and trunk are removed; next come the interior
items; plastic parts are removed and sorted for recycling; then glass and interior
and trunk materials. Eventually the chassis is a bale and sold as a commodity to
minimills that use scrap steel. Reusable parts are bar-coded and entered into a
database. The photo shows an operator controlling the car recycling plant.
Sources: Wall Street Journal (April 29, 2008) and Time (February 4, 2010).
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Administration (OSHA), Environmental Protection Agency (EPA), and many state and local agencies that regulate workers’ rights and employment standards.
U.S. agencies that govern the disassembly and disposal of hazardous products include the EPA and the Department of Transportation. As product life spans shorten due to ever- changing trends and innovation, product designers are under added pressure to design for dis- assembly . This encourages designers to create products that can be disassembled and whose components can be recovered, minimizing impact on the environment.
Organizations are obliged by society and regulators to reduce harm to consumers, employ- ees, and the environment. The result is a proliferation of community, state, federal, and even international laws that often complicate compliance. The lack of coordination of regulations and reporting requirements between jurisdictions adds not just complexity but cost.
From the following examples it is apparent that nearly all industries must abide by regula- tions in some form or another: ◆ Commercial homebuilders are required not just to manage water runoff but to have a pol-
lution prevention plan for each site. ◆ Public drinking water systems must comply with the Federal Safe Drinking Water Act’s
arsenic standard, even for existing facilities. ◆ Hospitals are required to meet the terms of the Resource Conservation and Recovery Act,
which governs the storage and handling of hazardous material. The consequences of ignoring regulations can be disastrous and even criminal. The EPA
investigates environmental crimes in which companies and individuals are held accountable. Prison time and expensive fines can be handed down. (British Petroleum paid billions of dol- lars in fines in the past few years for breaking U.S. environmental and safety laws.) Even if a crime has not been committed, the financial impacts and customer upheaval can be disastrous to companies that do not comply with regulations. Due to lack of supplier oversight, Mattel, Inc., the largest U.S. toymaker, has recalled over 10 million toys in recent years because of consumer health hazards such as lead paint.
International Environmental Policies and Standards Organizations such as the U.N. Framework Convention on Climate Change (UNFCCC), International Organization for Standardization (ISO), and governments around the globe are guiding businesses to reduce environmental impacts from disposal of materials to reductions in greenhouse gas (GHG) emissions. Some governments are implementing laws that mandate the outright reduction of GHG emissions by forcing companies to pay taxes based on the amount of GHG emissions that are emitted. We now provide an overview of some of the inter- national standards that apply to how businesses operate, manufacture, and distribute goods and services.
European Union Emissions Trading System The European Union has developed and implemented the EU Emissions Trading System (EUETS) to combat climate change. This is the key tool for reducing industrial greenhouse gas emissions in the EU. The EUETS works on the “cap-and-trade” principle. This means there is a cap, or limit, on the total amount of certain greenhouse gases that can be emitted by factories, power plants, and airlines in EU airspace. Within this cap, companies receive emission allowances, which they can sell to, or buy from, one another as needed.
ISO 14000 The International Organization for Standardization (ISO) is widely known for its contributions in ISO 9000 quality assurance standards (discussed in Chapter 6 ). The ISO 14000 family grew out of the ISO’s commitment to support the 1992 U.N. objective of sustainable development. ISO 14000 is a series of environmental management standards that contain five core elements: (1) environmental management, (2) auditing, (3) performance evaluation, (4) labeling, and (5) life cycle assessment. Companies that demonstrate these ele- ments may apply for certification. ISO 14000 has several advantages:
◆ Positive public image and reduced exposure to liability ◆ Good systematic approach to pollution prevention through minimization of ecological
impact of products and activities
STUDENT TIP A group of 100 apparel brands
and retailers have created the Eco
Index to display an eco-value
on a tag, like the Energy Star rating
does for appliances.
ISO 14000
A series of environmental man-
agement standards established by
the International Organization for
Standardization (ISO).
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◆ Compliance with regulatory requirements and opportunities for competitive advantage ◆ Reduction in the need for multiple audits
ISO 14000 standards have been implemented by more than 200,000 organizations in 155 countries. Companies that have implemented ISO 14000 standards report environmental and economic benefits such as reduced raw material/resource use, reduced energy consumption, lower distribution costs, improved corporate image, improved process efficiency, reduced waste generation and disposal costs, and better utilization of recoverable resources.
ISO 14001, which addresses environmental management systems, gives guidance to com- panies to minimize harmful effects on the environment caused by their activities. The OM in Action box “Subaru’s Clean, Green Set of Wheels with ISO 14001” illustrates the growing application of the ISO 14000 standards.
OM in Action Subaru’s Clean, Green Set of Wheels with ISO 14001 “Going green” had humble beginnings. First, it was newspapers, soda cans
and bottles, and corrugated packaging—the things you typically throw into
your own recycling bins. Similarly, at Subaru’s Lafayette, Indiana, plant,
the process of becoming the first completely waste-free auto plant in North
America began with employees dropping these items in containers throughout
the plant. Then came employee empowerment. “We had 268 suggestions for
different things to improve our recycling efforts,” said Denise Coogan, plant
ISO 14001 environmental compliance leader.
Some ideas were easy to handle. “With plastic shrink wrap, we found
some (recyclers) wouldn’t take colored shrink wrap. So we went back to our
vendors and asked for only clear shrink wrap,” Coogan said. Some sugges-
tions were a lot dirtier. “We went dumpster diving to see what we were
throwing away and see what we could do with it.”
The last load of waste generated by Subaru made its way to a landfill
7 years ago. Since then, everything that enters the plant eventually exits as
a usable product. Coogan adds, “We didn’t redefine ‘zero.’ Zero means zero.
Nothing from our manufacturing process goes to the landfill.”
Last year alone, the Subaru plant recycled 13,142 tons of steel, 1,448
tons of paper products, 194 tons of plastics, 10 tons of solvent-soaked rags,
and 4 tons of light bulbs. Doing so conserved 29,200 trees, 670,000 gallons
of oil, 34,700 gallons of gas, 10 million gallons of water, and 53,000 million
watts of electricity. “Going green” isn’t easy, but it can be done! Sources: IndyStar (May 10, 2014) and BusinessWeek (June 6, 2011).
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Summary If a firm wants to be viable and competitive, it must have a strategy for corporate social responsibility and sustainability. Operations and supply-chain managers understand that they have a critical role in a firm’s sustainability objectives. Their actions impact all the stakeholders. They must continually seek
new and innovative ways to design, produce, deliver, and dis- pose of profitable, customer-satisfying products while adher- ing to many environmental regulations. Without the expertise and commitment of operations and supply-chain managers, firms are unable to meet their sustainability obligations.
Key Terms
Corporate social responsibility (CSR) (p. 194 )
Shared value (p. 194 )
Sustainability (p. 195 ) Carbon footprint (p. 197 ) Economic sustainability (p. 197 )
Life cycle assessment (p. 198 ) Closed-loop supply chains (p. 203 ) ISO 14000 (p. 204 )
Discussion Questions
1. Why must companies practice corporate social responsibility? 2. Find statements of sustainability for a well-known company
online and analyze that firm’s policy. 3. Explain sustainability.
4. Discuss the 3 R s. 5. Explain closed-loop supply chains. 6. How would you classify a company as green? 7. Why are sustainable business practices important?
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Solved Problems Virtual Office Hours help is available in MyOMLab .
SOLVED PROBLEM S5.1 The design team for Superior Electronics is creating a mobile audio player and must choose between two design alternatives. Which is the better environmental design alternative, based on achieving a higher revenue retrieval opportunity?
SOLUTION Collecting the resale revenue per unit, recycling revenue per unit, processing cost per unit, and the disposal cost per unit, the design team computes the revenue retrieval for each design:
Design 1
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Tuner $4.93 $2.08 $2.98 $0.56
Speaker 0.00 0.00 4.12 1.23
Case 6.43 7.87 4.73 0.00
Total $11.36 $9.95 $11.83 $1.79
Design 2
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Tuner $6.91 $4.92 $3.41 $2.13
Case 5.83 3.23 2.32 1.57
Amplifi er 1.67 2.34 4.87 0.00
Speaker 0.00 0.00 3.43 1.97
Total $14.41 $10.49 $14.03 $5.67
Using the following formula [ Equation (S5-1) ], compare the two design alternatives:
Revenue retrieval = Total resale revenue + Total recycling revenue - Total processing cost - Total disposal cost Revenue retrieval Design 1 = +11.36 + +9.95 - +11.83 - +1.79 = +7.69 Revenue retrieval Design 2 = +14.41 + +10.49 - +14.03 - +5.67 = +5.20
Design 1 brings in the most revenue from its design when the product has reached its end-of-life.
SOLVED PROBLEM S5.2 The City of High Point is buying new school buses for the local school system. High Point has found two models of school buses that it is interested in. Eagle Mover costs $80,000 to buy and uses diesel fuel, with an average fuel efficiency of 10 miles per gallon. Eagle Mover has an operating cost of $.28 per mile. Yellow Transport, a hybrid bus, costs $105,000 to buy and uses diesel fuel and battery power, getting an average of 22 miles per gallon. Yellow Transport has an operating cost of $.32 per mile. The distance traveled annu- ally is determined to be 25,000 miles, with the expected life of either bus to be 10 years. The average diesel price is $3.50 per gallon.
SOLUTION a) Based on life cycle cost, which bus is the better choice? Eagle Mover:
$80,000 + ≥ 25,000
miles year
10 miles gallon
¥($3.50>gallon)(10 years) + a25,000 miles year
b($.28>mile)(10 years)
= $80,000 + $87,500 + $70,000 = $237,500 Yellow Transport:
$105,000 + ≥ 25,000
miles year
22 miles gallon
¥($3.50>gallon)(10 years) + a25,000 miles year
b($.32>mile)(10 years)
= $105,000 + $39,773 + $80,000 = $224,773 Yellow Transport is the better choice. b) How many miles does the school district need to put on a bus for costs to be equal? Let M be the break-even point in miles, set the equations equal to each other, and solve for M: Total cost for Eagle Mover 5 Total cost for Yellow Transport
$80,000 + ≥ 3.50
$ gallon
10 miles gallon
+ .28 $
mile ¥(M miles) = $105,000 + ≥
3.50 $
gallon
22 miles gallon
+ .32 $
mile ¥(M miles)
$80,000 + a.630 $
mile b(M ) = $105,000 + a.479
$ mile b(M )
a.151 $
mile b(M ) = $25,000
M = $25,000
.151 $
mile
= 165,563 miles
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c) What is the crossover point in years?
Crossover point = 165,563 miles
25,000 miles year
= 6.62 years
Problems
Problems S5.1–S5.19 relate to Design and Production for Sustainability
• • S5.1 The Brew House needs to decide which of two coffee maker designs is better environmentally. Using the follow- ing tables, determine which model is the better design alternative. Brew Master
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Metal frame $1.65 $2.87 $1.25 $0.75
Timer 0.50 0.00 1.53 1.45
Plug/cord 4.25 5.65 6.22 0.00
Coffee pot 2.50 2.54 2.10 1.35
Brew Mini
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Plastic frame $1.32 $3.23 $0.95 $0.95
Plug/cord 3.95 4.35 5.22 0.00
Coffee pot 2.25 2.85 2.05 1.25
• • S5.2 Using the information in Problem S5.1, which design alternative is the better environmental choice if the Brew House decided to add a timer to the Brew Mini model? The timer rev- enue and costs are identical to those of the Brew Master.
• • S5.3 Using the information in Problem S5.1, which design alternative is the better environmental choice if the Brew House decided to remove the timer from the Brew Master model?
• • S5.4 What is the total vehicle life cycle cost of this hybrid car, given the information provided in the following table?
VEHICLE PURCHASE COST $17,000
VEHICLE OPERATING COST PER MILE $0.12
USEFUL LIFE OF VEHICLE 15 years
MILES PER YEAR 14,000
MILES PER GALLON 32
AVERAGE FUEL PRICE PER GALLON $3.75
• • S5.5 What is the crossover point in miles between the hybrid vehicle in Problem S5.4 and this alternative vehicle from a competing auto manufacturer?
VEHICLE PURCHASE COST $19,000
VEHICLE OPERATING COST PER MILE $0.09
USEFUL LIFE OF VEHICLE 15 years
MILES PER YEAR 14,000
MILES PER GALLON 35
AVERAGE FUEL PRICE PER GALLON $3.75
• • S5.6 Given the crossover mileage in Problem S5.5, what is the crossover point in years?
• • S5.7 In Problem S5.5, if gas prices rose to $4.00 per gallon, what would be the new crossover point in miles?
• • S5.8 Using the new crossover mileage in Problem S5.7, what is the crossover point in years?
• • S5.9 Mercedes is assessing which of two windshield sup- pliers provides a better environmental design for disassembly. Using the tables below, select between PG Glass and Glass Unlimited.
PG Glass
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Glass $12 $10 $6 $2
Steel frame 2 1 1 1
Rubber insulation 1 2 1 1
Glass Unlimited
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Refl ective glass $15 $12 $7 $3
Aluminium frame 4 3 2 2
Rubber insulation 2 2 1 1
• • S5.10 Environmentally conscious Susan has been told that a new electric car will only generate 6 ounces of greenhouse gases (GHG) per mile, but that a standard internal combustion car is double that at 12 ounces per mile. However, the nature of electric cars is such that the new technology and electric batteries gener- ate 30,000 lbs. of GHG to manufacture and another 10,000 lbs. to recycle. A standard car generates only 14,000 lbs. of GHG to manufacture, and recycling with established technology is only 1,000 lbs. Susan is interested in taking a systems approach that considers the life-cycle impact of her decision. How many miles must she drive the electric car for it to be the preferable decision in terms of reducing greenhouse gases?
• • • S5.11 A Southern Georgia school district is considering ordering 53 propane-fueled school buses. “They’re healthier, they’re cleaner burning, and they’re much quieter than the die- sel option,” said a school administrator. Propane-powered buses also reduce greenhouse gasses by 22% compared to gasoline- powered buses and 6% compared to diesel ones. But they come at a premium—$103,000 for a propane model, $15,000 more than the diesel equivalent.
The propane bus operating cost (above and beyond fuel cost) is 30 cents/mile, compared to 40 cents for the diesel. Diesel fuel costs about $2/gallon in Georgia, about $1 more than propane.
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208 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Bus mileage is 12 mpg for the propane model vs. 10 mpg for die- sel. The life of a school bus in the district averages 9 years, and each bus travels an average of 30,000 miles per year because the district is so large and rural.
Which bus is the better choice based on a life-cycle analysis?
• • S5.12 Green Forever, a manufacturer of lawn equipment, has preliminary drawings for two grass trimmer designs. Charla Fraley’s job is to determine which is better environmentally. Specifically, she is to use the following data to help the company determine: a) The revenue retrieval for the GF Deluxe b) The revenue retrieval for the Premium Mate c) Which model is the better design alternative based on revenue
retrieval
GF Deluxe
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Metal drive $3.27 $4.78 $1.05 $0.85
Battery 0.00 3.68 6.18 3.05
Motor housing 3.93 2.95 2.05 1.25
Trimmer head 1.25 0.75 1.00 0.65
Premium Mate
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Metal drive $3.18 $3.95 $1.15 $0.65
Battery 0.00 2.58 4.98 2.90
Motor housing 4.05 3.45 2.45 1.90
Trimmer head 1.05 0.85 1.10 0.75
• • S5.13 Green Forever (see Problem S5.12) has decided to add an automatic string feeder system with cost and revenue estimates as shown below to the GF Deluxe model. a) What is the new revenue retrieval value for each model? b) Which model is the better environmental design alternative?
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
String feeder system
$1.05 $1.25 $1.50 $1.40
• • S5.14 Green Forever’s challenge (see Problem S5.12) is to determine which design alternative is the better environmental choice if it uses a different battery for the Premium Mate. The alternate battery revenue and costs are as follows:
PART
RESALE REVENUE PER UNIT
RECYCLING REVENUE PER UNIT
PROCESSING COST PER
UNIT
DISPOSAL COST PER
UNIT
Battery $0.00 $3.68 $4.15 $3.00
a) What is the revenue retrieval for the GF Deluxe? b) What is the revenue retrieval for the Premium Mate? c) Which is the better environmental design alternative?
• • S5.15 Hartley Auto Supply delivers parts to area auto ser- vice centers and is replacing its fleet of delivery vehicles. What is the total vehicle life-cycle cost of this gasoline engine truck given the information provided in the following table?
VEHICLE PURCHASE COST $25,000
VEHICLE OPERATING COST PER MILE $0.13
USEFUL LIFE OF VEHICLE 10 years
MILES PER YEAR 18,000
MILES PER GALLON 25
AVERAGE FUEL PRICE PER GALLON $2.55
• • S5.16 Given the data in Problem S5.15 and an alternative hybrid vehicle with the specifications shown below: a) What is the crossover point in miles? b) Which vehicle is has the lowest cost until the crossover point is
reached? VEHICLE PURCHASE COST $29,000
VEHICLE OPERATING COST PER MILE $0.08
USEFUL LIFE OF VEHICLE 10 years
MILES PER YEAR 18,000
MILES PER GALLON 40
AVERAGE FUEL PRICE PER GALLON $2.55
• S5.17 Based the crossover point in miles found in Problem S5.16, what is this point in years?
• • S5.18 Using the data from Problem S5.16, if gas prices rose to $3.00 per gallon, what would be the new crossover point in miles?
• S5.19 Using the new crossover point in Problem S5.18, how many years does it take to reach that point?
CASE STUDIES Video Case Building Sustainability at the Orlando Magic’s Amway Center
When the Amway Center opened in Orlando in 2011, it became the first LEED (Leadership in Energy and Environmental Design) gold–certified professional basketball arena in the country. It took 10 years for Orlando Magic’s management to develop a plan for the new state-of-the-art sports and entertain- ment center. The community received not only an entertainment center but an environmentally sustainable building to showcase
in its revitalized downtown location. “We wanted to make sure we brought the most sustainable measures to the construction, so in operation we can be a good partner to our community and our environment,” states CEO Alex Martins. The new 875,000-square foot facility—almost triple the size of the Amway Arena it replaced—is now the benchmark for other sports facilities.
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S U P P L E M E N T 5 | S U S TA I N A B I L I T Y I N T H E S U P P LY C H A I N 209
Here are a few of the elements in the Amway Center project that helped earn the LEED certification:
◆ The roof of the building is designed to minimize daytime heat gain by using reflective and insulated materials.
◆ Rainwater and air-conditioning condensation are captured and used for irrigation.
◆ There is 40% less water usage than in similar arenas (sav- ing 800,000 gallons per year), mostly through use of high- efficiency restrooms, including low-flow, dual-flush toilets.
◆ There is 20% energy savings (about $750,000 per year) with the use of high-efficiency heating and cooling systems.
◆ The center used environmentally friendly building materials and recycled 83% of the wood, steel, and concrete construction waste that would have ended up in a landfill.
◆ There is preferred parking for hybrids and other energy- efficient cars.
◆ The center is maintained using green-friendly cleaning products.
LEED certification means five environmental measures and one design measure must be met when a facility is graded by the U.S. Green Building Council, which is a nationally accepted benchmark program. The categories are sustainability of site, water efficiency, energy, materials/resources, indoor environmen- tal quality, and design innovation.
Other Amway Center design features include efficient receiv- ing docks, food storage layouts, and venue change-over systems. Massive LED electronic signage controlled from a central control room also contributes to lower operating costs. From an opera- tions management perspective, combining these savings with the
significant ongoing savings from reduced water and energy usage will yield a major reduction in annual operating expenses. “We think the LEED certification is not only great for the environ- ment but good business overall,” says Martins.
Discussion Questions *
1. Find a LEED-certified building in your area and compare its features to those of the Amway Center.
2. What does a facility need to do to earn the gold LEED rating? What other ratings exist?
3. Why did the Orlando Magic decide to “go green” in its new building?
Video Case Green Manufacturing and Sustainability at Frito-Lay Frito-Lay, the multi-billion-dollar snack food giant, requires vast amounts of water, electricity, natural gas, and fuel to pro- duce its 41 well-known brands. In keeping with growing envi- ronmental concerns, Frito-Lay has initiated ambitious plans to produce environmentally friendly snacks. But even envi- ronmentally friendly snacks require resources. Recognizing the environmental impact, the firm is an aggressive “green manufacturer,” with major initiatives in resource reduction and sustainability.
For instance, the company’s energy management program includes a variety of elements designed to engage employees in reducing energy consumption. These elements include scorecards and customized action plans that empower employees and recog- nize their achievements.
At Frito-Lay’s factory in Casa Grande, Arizona, more than 500,000 pounds of potatoes arrive every day to be washed, sliced, fried, seasoned, and portioned into bags of Lay’s and Ruffles chips. The process consumes enormous amounts of energy and creates vast amounts of wastewater, starch, and potato peelings. Frito-Lay plans to take the plant off the power grid and run it almost entirely on renewable fuels and recycled water. The man- agers at the Casa Grande plant have also installed skylights in conference rooms, offices, and a finished goods warehouse to reduce the need for artificial light. More fuel-efficient ovens recapture heat from exhaust stacks. Vacuum hoses that pull
moisture from potato slices to recapture the water and to reduce the amount of heat needed to cook the potato chips are also being used.
Frito-Lay has also built over 50 acres of solar concentra- tors behind its Modesto, California, plant to generate solar power. The solar power is being converted into heat and used to cook Sun Chips. A biomass boiler, which will burn agricul- tural waste, is also planned to provide additional renew- able fuel.
Frito-Lay is installing high-tech filters that recycle most of the water used to rinse and wash potatoes. It also recycles corn by- products to make Doritos and other snacks; starch is reclaimed and sold, primarily as animal feed, and leftover sludge is burned to create methane gas to run the plant boiler.
There are benefits besides the potential energy savings. Like many other large corporations, Frito-Lay is striving to estab- lish its green credentials as consumers become more focused on environmental issues. There are marketing opportunities, too. The company, for example, advertises that its popular Sun Chips snacks are made using solar energy.
At Frito-Lay’s Florida plant, only 3.5% of the waste goes to landfills, but that is still 1.5 million pounds annually. The goal is zero waste to landfills. The snack food maker earned its spot in the National Environmental Performance Task Program by maintaining a sustained environmental compliance record and
* You may wish to view the video that accompanies this case before addressing these questions.
F e rn
a n d o M
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a
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210 P A R T 2 | D E S I G N I N G O P E R AT I O N S
making new commitments to reduce, reuse, and recycle at this facility.
Substantial resource reductions have been made in the produc- tion process, with an energy reduction of 21% across Frito-Lay’s 34 U.S. plants. But the continuing battle for resource reduction continues. The company is also moving toward biodegradable packaging and seasoning bags and cans and bottles. While these multiyear initiatives are expensive, they have the backing at the highest levels of Frito-Lay as well as corporate executives at PepsiCo, the parent company.
Discussion Questions *
1. What are the sources of pressure on firms such as Frito-Lay to reduce their environmental footprint?
2. Identify the specific techniques that Frito-Lay is using to become a “green manufacturer.”
3. Select another company and compare its green policies to those of Frito-Lay.
1. The authors wish to thank Dr. Steve Leon, University of Central Florida, for his contributions to this supplement.
2. See related discussions in M. E. Porter and M. R. Kramer, “Creating Shared Value,” Harvard Business Review
• Additional Case Study: Visit MyOMLab for this free case study: Environmental Sustainability at Walmart : Walmart’s experiment with global sustainability.
Endnotes
* You may wish to view the video that accompanies this case before answering these questions.
(Jan.–Feb. 2011) and M. Pfitzer, V. Bockstette, and M. Stamp, “Innovating for Shared Values,” Harvard Business Review (Sept. 2013).
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Supplement 5 Rapid Review S5
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Main Heading Review Material MyOMLab CORPORATE SOCIAL RESPONSIBILITY (p. 194)
Managers must consider how the products and services they make affect people and the environment in which they operate. j Corporate social responsibility (CSR) —Managerial decision making that
considers environmental, societal, and financial impacts. j Shared value —Developing policies and practices that enhance the competitive-
ness of an organization, while advancing the economic and social conditions in the communities in which it operates.
Concept Question: 1.1
SUSTAINABILITY (pp. 195 – 197 )
j Sustainability —Meeting the needs of the present without compromising the ability of future generations to meet their needs.
Systems view—Looking at a product’s life from design to disposal, including all of the resources required. The commons—Inputs or resources for a production system that are held by the public. Triple bottom line—Systems needed to support the three Ps: people, planet , and profit . To support their people, many companies evaluate safety in the work environment, the wages paid, work hours/week. Apple, GE, P&G, and Walmart conduct audits of their suppliers to make sure sustainability goals are met. To support the planet, operation managers look for ways to reduce the environmental impact of their operations. j Carbon footprint —A measure of the total GHG emissions caused directly and
indirectly by an organization, product, event or person. To support their profits, company investments must be sustainable economically. Firms may supplement standard accounting with social accounting.
Concept Questions: 2.1–2.4
VIDEO S5.1 Building Sustainability at the Orlando Magic’s Amway Center
VIDEO S5.2 Green Manufacturing and Sustainability at Frito-Lay
DESIGN AND PRODUCTION FOR SUSTAINABILITY (pp. 198 – 203 )
j Life cycle assessment —Analysis of environmental impacts of products from the design stage through end-of-life.
The 3 R s: reduce, reuse, and recycle . These must be incorporated by design teams, process managers, and supply-chain personnel. Product design is the most critical phase in the product life cycle assessment. Design for disassembly focuses on reuse and recycle. Revenue retrieval 5 Total resale revenue 1 Total recycling revenue 2 Total processing cost 2 Total disposal cost (S5-1) Manufacturers also look for ways to reduce the amount of scarce resources in the production process. As products move along the supply chain, logistics managers strive to achieve efficient route and delivery networks, which reduce environmental impact. Vehicles are also evaluated on a life cycle ownership cost basis. A firm must decide whether to pay more up front for sustainable vehicles or pay less up front for vehicles that may be less sustainable. Total life cycle cost 5 Cost of vehicle 1 Life cycle cost of fuel 1 Life cycle operating cost (S5-2) j Closed-loop supply chains, also called reverse logistics —Supply chains that
consider the product or its materials after the product reaches its end-of-life stage. This includes forward and reverse product flows. Green disassembly lines help take cars apart so that parts can be recycled. Recycling is the 16th-largest industry in the U.S.
Concept Questions: 3.1–3.4 Problems: S5.1–S5.19 Virtual Office Hours for Solved Problems S5.1–S5.2
REGULATIONS AND INDUSTRY STANDARDS (pp. 203 – 205 )
To guide product design decisions, U.S. laws and regulations often provide explicit regulations. Manufacturing and assembly activities are guided by OSHA, EPA, and many state and local agencies. There are also U.S. agencies that govern the disassembly and disposal of hazardous products. International environmental policies and standards come from the U.N., ISO, the EU, and governments around the globe. The EU has implemented the Emissions Trading System to help reduce greenhouse gas emissions. It works on a “cap-and- trade” principle. j ISO 14000 —The International Organization of Standardization family of
guidelines for sustainable development. ISO 14000 has been implemented by more than 200,000 organizations in 155 countries. ISO 14001 addresses environmental management systems.
Concept Questions: 4.1–4.4
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Self Test
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Supplement 5 Rapid Review continued
j Before taking the self-test, refer to the learning objectives listed at the beginning of the supplement and the key terms listed at the end of the supplement.
LO S5.1 Corporate social responsibility includes: a) doing what’s right. b) having policies that consider environmental, societal,
and financial impact. c) considering a product from design to disposal. d) all of the above. e) a and b only. LO S5.2 Sustainability deals: a) solely with green products, recycling, global warming,
and rain forests. b) with keeping products that are not recyclable. c) with meeting the needs of present and future
generations. d) with three views—systems, commons, and defects. e) with not laying off older workers. LO S5.3 The 3 R s of sustainability are: a) reputation, reuse, reduce. b) reputation, recycle, reuse.
c) reputation, reverse logistics, renewal. d) reuse, reduce, recycle. e) recycle, review, reuse. LO S5.4 Design for disassembly is: a) cost–benefit analysis for old parts. b) analysis of the amount of revenue that might be
reclaimed versus the cost of disposing of a product. c) a means of recycling plastic parts in autos. d) the use of lightweight materials in products. LO S5.5 U.S. and international agencies provide policies and
regulations to guide managers in product design, manufacturing/assembly, and disassembly/disposal. They include:
a) U.N. Commission on Resettlement. b) World Health Organization (WHO). c) OSHA, FDA, EPA, and NHSA. d) EPA, ISO, and British High Commission. e) GHG Commission, UN, and ISO.
Answers: LO S5.1. d; LO S5.2. c; LO S5.3. d; LO S5.4. b; LO S5.5. c.
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C H A P T E R O U T L I N E
Managing Quality 6
◆
Quality and Strategy 216
◆
Defining Quality 217
◆
Total Quality Management 219
◆
Tools of TQM 226
◆
The Role of Inspection 230
◆
TQM in Services 233
GLOBAL COMPANY PROFILE: Arnold Palmer Hospital
C H
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• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
1010 OMOM STRATEGY DECISIONS
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
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214
S ince 1989, Arnold Palmer Hospital, named after its famous golfing benefactor, has touched
the lives of over 7 million children and women and their families. Its patients come not
only from its Orlando location but from all 50 states and around the world. More than
12,000 babies are delivered every year at Arnold Palmer, and its huge neonatal intensive care
unit boasts one of the highest survival rates in the U.S.
Every hospital professes quality health care, but at Arnold Palmer quality is the mantra—
practiced in a fashion like the Ritz-Carlton practices it in the hotel industry. The hospital typically
scores in the top 10% of national benchmark studies in terms of patient satisfaction. And its
managers follow patient questionnaire results daily. If anything is
amiss, corrective action takes place immediately.
Virtually every quality management technique we present in
this chapter is employed at Arnold Palmer Hospital:
◆ Continuous improvement: The hospital constantly seeks
new ways to lower infection rates, readmission rates, deaths,
costs, and hospital stay times.
Managing Quality Provides a Competitive Advantage at Arnold Palmer Hospital
GLOBAL COMPANY PROFILE Arnold Palmer Hospital
C H A P T E R 6
The Storkboard is a visible chart of the status of each baby about to be
delivered, so all nurses and doctors are kept up to date at a glance.
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The lobby of Arnold Palmer Hospital, with its
20-foot-high Genie, is clearly intended as a
warm and friendly place for children.
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215
◆ Employee empowerment: When employees
see a problem, they are trained to take care of
it; staff are empowered to give gifts to patients
displeased with some aspect of service.
◆ Benchmarking: The hospital belongs to
a 2,000-member organization that moni-
tors standards in many areas and provides
monthly feedback to the hospital.
This PYXIS inventory station gives nurses quick
access to medicines and supplies needed in their
departments. When the nurse removes an item for
patient use, the item is automatically billed to that
account, and usage is noted at the main supply area.
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The hospital has redesigned its neonatal rooms. In the old
system, there were 16 neonatal beds in an often noisy and large
room. The new rooms are semiprivate, with a quiet simulated-
night atmosphere. These rooms have proven to help babies
develop and improve more quickly.
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◆ Just-in-time: Supplies are delivered to Arnold Palmer on
a JIT basis. This keeps inventory costs low and keeps
quality problems from hiding.
◆ Tools such as Pareto charts and fl owcharts: These tools
monitor processes and help the staff graphically spot
problem areas and suggest ways they can be improved.
From their first day of orientation, employees from janitors
to nurses learn that the patient comes first. Staff standing
in hallways will never be heard discussing their personal
lives or commenting on confidential issues of health care.
This culture of quality at Arnold Palmer Hospital makes a
hospital visit, often traumatic to children and their parents, a
warmer and more comforting experience.
When Arnold Palmer Hospital began planning for a new 11-story hospital across
the street from its existing building, it decided on a circular pod design, creating a
patient-centered environment. Rooms use warm colors, have pull-down Murphy beds
for family members, 14-foot ceilings, and natural lighting with oversized windows.
The pod concept also means there is a nursing station within a few feet of each
10-bed pod, saving much wasted walking time by nurses to reach the patient.
The Video Case Study in Chapter 9 examines this layout in detail.
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216
Quality and Strategy As Arnold Palmer Hospital and many other organizations have found, quality is a won- derful tonic for improving operations. Managing quality helps build successful strategies of differentiation , low cost , and response . For instance, defining customer quality expectations has helped Bose Corp. successfully differentiate its stereo speakers as among the best in the world. Nucor has learned to produce quality steel at low cost by developing efficient pro- cesses that produce consistent quality. And Dell Computers rapidly responds to customer orders because quality systems, with little rework, have allowed it to achieve rapid through- put in its plants. Indeed, quality may be the key success factor for these firms, just as it is at Arnold Palmer Hospital.
As Figure 6.1 suggests, improvements in quality help firms increase sales and reduce costs, both of which can increase profitability. Increases in sales often occur as firms speed response, increase or lower selling prices, and improve their reputation for quality products. Similarly, improved quality allows costs to drop as firms increase productivity and lower rework, scrap, and warranty costs. One study found that companies with the highest quality were five times as productive (as measured by units produced per labor-hour) as companies with the poorest quality. Indeed, when the implications of an organization’s long-term costs and the potential for increased sales are considered, total costs may well be at a minimum when 100% of the goods or services are perfect and defect free.
Quality, or the lack of quality, affects the entire organization from supplier to customer and from product design to maintenance. Perhaps more important, building an organization that can achieve quality is a demanding task. Figure 6.2 lays out the flow of activities for an organization to use to achieve total quality management (TQM). A successful quality strategy begins with an organizational culture that fosters quality, followed by an understanding of the principles of quality, and then engaging employees in the necessary activities to implement quality. When these things are done well, the organization typically satisfies its customers and obtains a competitive advantage. The ultimate goal is to win customers. Because quality causes so many other good things to happen, it is a great place to start.
L E A R N I N G OBJEC TI V ES
LO 6.1 Defi ne quality and TQM 217
LO 6.2 Describe the ISO international quality standards 218
LO 6.3 Explain Six Sigma 221
LO 6.4 Explain how benchmarking is used in TQM 223
LO 6.5 Explain quality robust products and Taguchi concepts 225
LO 6.6 Use the seven tools of TQM 226
VIDEO 6.1 The Culture of Quality at Arnold
Palmer Hospital
STUDENT TIP High-quality products and
services are the most
profitable.
Improved Quality Increased Profits Reduced Costs via
Increased productivity Lower rework and scrap costs Lower warranty costs
Sales Gains via
Improved response Flexible pricing Improved reputation
Two Ways Quality Improves Profitability
Figure 6.1
Ways Quality Improves
Profitability
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 217
Defining Quality The operations manager’s objective is to build a total quality management system that iden- tifies and satisfies customer needs . Total quality management takes care of the customer. Consequently, we accept the definition of quality as adopted by the American Society for Quality (ASQ; www.asq.org ): “The totality of features and characteristics of a product or ser- vice that bears on its ability to satisfy stated or implied needs.”
Others, however, believe that definitions of quality fall into several categories. Some defini- tions are user based . They propose that quality “lies in the eyes of the beholder.” Marketing people like this approach and so do customers. To them, higher quality means better perfor- mance, nicer features, and other (sometimes costly) improvements. To production managers, quality is manufacturing based . They believe that quality means conforming to standards and “making it right the first time.” Yet a third approach is product based , which views quality as a precise and measurable variable. In this view, for example, really good ice cream has high butterfat levels.
This text develops approaches and techniques to address all three categories of quality. The characteristics that connote quality must first be identified through research (a user-based approach to quality). These characteristics are then translated into specific product attributes (a product-based approach to quality). Then, the manufacturing process is organized to en- sure that products are made precisely to specifications (a manufacturing-based approach to quality). A process that ignores any one of these steps will not result in a quality product.
Implications of Quality In addition to being a critical element in operations, quality has other implications. Here are three other reasons why quality is important:
1. Company reputation: An organization can expect its reputation for quality—be it good or bad—to follow it. Quality will show up in perceptions about the firm’s new products, employment practices, and supplier relations. Self-promotion is not a substitute for qual- ity products.
Organizational practices Leadership, Mission statement, Effective operating procedures,
Staff support, Training
Yields: What is important and what is to be accomplished.
Quality principles Customer focus, Continuous improvement, Benchmarking, Just-in-time, Tools of TQM
Yields: How to do what is important and to be accomplished.
Employee fulfillment Empowerment, Organizational commitment Yields: Employee attitudes that can accomplish what is important.
Customer satisfaction Winning orders, Repeat customers Yields: An effective organization with a competitive advantage.
Figure 6.2
The Flow of Activities Necessary to Achieve Total Quality Management
STUDENT TIP To create a quality good or
service, operations managers
need to know what the customer
expects.
Quality
The ability of a product or service
to meet customer needs.
LO 6.1 Define quality and TQM
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218 P A R T 2 | D E S I G N I N G O P E R AT I O N S
2. Product liability: The courts increasingly hold organizations that design, produce, or distribute faulty products or services liable for damages or injuries resulting from their use. Legislation such as the Consumer Product Safety Act sets and enforces product standards by banning products that do not reach those standards. Impure foods that cause illness, nightgowns that burn, tires that fall apart, or auto fuel tanks that explode on impact can all lead to huge legal expenses, large settlements or losses, and terrible publicity.
3. Global implications: In this technological age, quality is an international, as well as OM, concern. For both a company and a country to compete effectively in the global economy, products must meet global quality, design, and price expectations. Inferior products harm a firm’s profitability and a nation’s balance of payments.
Malcolm Baldrige National Quality Award The global implications of quality are so important that the U.S. has established the Malcolm Baldrige National Quality Award for quality achievement. The award is named for former Secretary of Commerce Malcolm Baldrige. Winners include such firms as Motorola, Milliken, Xerox, FedEx, Ritz-Carlton Hotels, AT&T, Cadillac, and Texas Instruments. (For details about the Baldrige Award and its 1,000-point scoring system, visit www.nist.gov/baldrige/ .)
The Japanese have a similar award, the Deming Prize, named after an American, Dr. W. Edwards Deming.
ISO 9000 International Quality Standards The move toward global supply chains has placed so much emphasis on quality that the world has united around a single quality standard, ISO 9000 . ISO 9000 is the quality standard with international recognition. Its focus is to enhance success through eight quality man- agement principles: (1) top management leadership, (2) customer satisfaction, (3) continual improvement, (4) involvement of people, (5) process analysis, (6) use of data-driven deci- sion making, (7) a systems approach to management, and (8) mutually beneficial supplier relationships.
The ISO standard encourages establishment of quality management procedures, detailed documentation, work instructions, and recordkeeping. Like the Baldrige Awards, the assess- ment includes self-appraisal and problem identification. Unlike the Baldrige, ISO certified organizations must be reaudited every three years.
The latest modification of the standard, ISO 9001: 2015, follows a structure that makes it more compatible with other management systems. This version gives greater emphasis to risk- based thinking, attempting to prevent undesirable outcomes.
Over one million certifications have been awarded to firms in 206 countries, including about 30,000 in the U.S. To do business globally, it is critical for a firm to be certified and listed in the ISO directory.
Cost of Quality (COQ) Four major categories of costs are associated with quality. Called the cost of quality (COQ) , they are:
◆ Prevention costs: costs associated with reducing the potential for defective parts or services (e.g., training, quality improvement programs).
◆ Appraisal costs: costs related to evaluating products, processes, parts, and services (e.g., test- ing, labs, inspectors).
◆ Internal failure costs: costs that result from production of defective parts or services before delivery to customers (e.g., rework, scrap, downtime).
◆ External failure costs: costs that occur after delivery of defective parts or services (e.g., rework, returned goods, liabilities, lost goodwill, costs to society).
ISO 9000
A set of quality standards devel-
oped by the International Organi-
zation for Standardization (ISO).
LO 6.2 Describe the ISO international quality
standards
STUDENT TIP International quality standards
grow in prominence every year.
See www.iso.ch .
Cost of quality (COQ)
The cost of doing things
wrong—that is, the price of
nonconformance.
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The first three costs can be reasonably estimated, but external costs are very hard to quantify. When GE had to recall 3.1 million dishwashers (because of a defective switch alleged to have started seven fires), the cost of repairs exceeded the value of all the machines. This leads to the belief by many experts that the cost of poor quality is consistently underestimated.
Observers of quality management believe that, on balance, the cost of quality products is only a fraction of the benefits. They think the real losers are organizations that fail to work aggressively at quality. For instance, Philip Crosby stated that quality is free. “What costs money are the unquality things—all the actions that involve not doing it right the first time.” 1
Leaders in Quality Besides Crosby there are several other giants in the field of quality management, including Deming, Feigenbaum, and Juran. Table 6.1 summarizes their philoso- phies and contributions.
Ethics and Quality Management For operations managers, one of the most important jobs is to deliver healthy, safe, and qual- ity products and services to customers. The development of poor-quality products, because of inadequate design and production processes, not only results in higher production costs but also leads to injuries, lawsuits, and increased government regulation.
If a firm believes that it has introduced a questionable product, ethical conduct must dic- tate the responsible action. This may be a worldwide recall, as conducted by both Johnson & Johnson (for Tylenol) and Perrier (for sparkling water), when each of these products was found to be contaminated. A manufacturer must accept responsibility for any poor-quality product released to the public.
There are many stakeholders involved in the production and marketing of poor-quality products, including stockholders, employees, customers, suppliers, distributors, and creditors. As a matter of ethics, management must ask if any of these stakeholders are being wronged. Every company needs to develop core values that become day-to-day guidelines for everyone from the CEO to production-line employees.
Total Quality Management Total quality management (TQM) refers to a quality emphasis that encompasses the entire organiza- tion, from supplier to customer. TQM stresses a commitment by management to have a con- tinuing companywide drive toward excellence in all aspects of products and services that are
LEADER PHILOSOPHY/CONTRIBUTION
W. Edwards Deming Deming insisted management accept responsibility for building good systems. The employee cannot produce products that on average exceed the quality of what the process is capable of producing. His 14 points for implementing quality improvement are presented in this chapter.
Joseph M. Juran A pioneer in teaching the Japanese how to improve quality, Juran believed strongly in top-management commitment, support, and involvement in the quality effort. He was also a believer in teams that continually seek to raise quality standards. Juran varies from Deming somewhat in focusing on the customer and defi ning quality as fi tness for use, not necessarily the written specifi cations.
Armand Feigenbaum His 1961 book Total Quality Control laid out 40 steps to quality improvement processes. He viewed quality not as a set of tools but as a total fi eld that integrated the processes of a company. His work in how people learn from each other’s successes led to the fi eld of cross-functional teamwork.
Philip B. Crosby Quality Is Free was Crosby’s attention-getting book published in 1979. Crosby believed that in the traditional trade-off between the cost of improving quality and the cost of poor quality, the cost of poor quality is understated. The cost of poor quality should include all of the things that are involved in not doing the job right the fi rst time. Crosby coined the term zero defects and stated, “There is absolutely no reason for having errors or defects in any product or service.”
Source: Based on Quality Is Free by Philip B. Crosby (New York, McGraw-Hill, 1979) p. 58 .
TABLE 6.1 Leaders in the Field of Quality Management
Takumi is a Japanese character
that symbolizes a broader
dimension than quality, a deeper
process than education, and
a more perfect method than
persistence.
Total quality management (TQM)
Management of an entire
organization so that it excels in all
aspects of products and services
that are important to the customer.
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220 P A R T 2 | D E S I G N I N G O P E R AT I O N S
important to the customer. Each of the 10 decisions made by operations managers deals with some aspect of identifying and meeting customer expectations. Meeting those expectations requires an emphasis on TQM if a firm is to compete as a leader in world markets.
Quality expert W. Edwards Deming used 14 points (see Table 6.2 ) to indicate how he implemented TQM. We develop these into seven concepts for an effective TQM program: (1) continuous improvement, (2) Six Sigma, (3) employee empowerment, (4) benchmarking, (5) just-in-time (JIT), (6) Taguchi concepts, and (7) knowledge of TQM tools.
Continuous Improvement Total quality management requires a never-ending process of continuous improvement that covers people, equipment, suppliers, materials, and procedures. The basis of the philosophy is that every aspect of an operation can be improved. The end goal is perfection, which is never achieved but always sought.
Plan-Do-Check-Act Walter Shewhart, another pioneer in quality management, de- veloped a circular model known as PDCA (plan, do, check, act) as his version of continuous improvement. Deming later took this concept to Japan during his work there after World War II. The PDCA cycle (also called a Deming circle or a Shewhart circle) is shown in Figure 6.3 as a circle to stress the continuous nature of the improvement process.
TABLE 6.2 Deming’s 14 Points for Implementing Quality Improvement
1. Create consistency of purpose.
2. Lead to promote change.
3. Build quality into the product; stop depending on inspections to catch problems.
4. Build long-term relationships based on performance instead of awarding business on the basis of price.
5. Continuously improve product, quality, and service.
6. Start training.
7. Emphasize leadership.
8. Drive out fear.
9. Break down barriers between departments.
10. Stop haranguing workers.
11. Support, help, and improve.
12. Remove barriers to pride in work.
13. Institute a vigorous program of education and self-improvement.
14. Put everybody in the company to work on the transformation.
Source: Deming, W. Edwards. Out of the Crisis , pp. 23 – 24 , © 2000 W. Edwards Deming Institute, published by The MIT Press. Reprinted by permission.
STUDENT TIP Here are 7 concepts that make
up the heart of an effective TQM
program.
3. Check Is the plan working?
2. Do Test the
plan.
4. Act Implement the plan,
document.
1. Plan Identify the
problem and make a plan.
Figure 6.3
PDCA Cycle
PDCA
A continuous improvement model
of plan, do, check. act.
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 221
The Japanese use the word kaizen to describe this ongoing process of unending improvement—the setting and achieving of ever-higher goals. In the U.S., TQM and zero defects are also used to describe continuous improvement efforts. But whether it’s PDCA, kaizen, TQM, or zero defects, the operations manager is a key player in building a work culture that endorses continuous improvement.
Six Sigma The term Six Sigma , popularized by Motorola, Honeywell, and General Electric, has two mean- ings in TQM. In a statistical sense, it describes a process, product, or service with an extremely high capability (99.9997% accuracy). For example, if 1 million passengers pass through the St. Louis Airport with checked baggage each month, a Six Sigma program for baggage han- dling will result in only 3.4 passengers with misplaced luggage. The more common three-sigma program (which we address in the supplement to this chapter) would result in 2,700 passengers with misplaced bags every month. See Figure 6.4 .
The second TQM definition of Six Sigma is a program designed to reduce defects to help lower costs, save time, and improve customer satisfaction. Six Sigma is a comprehensive system— a strategy, a discipline, and a set of tools—for achieving and sustaining business success:
◆ It is a strategy because it focuses on total customer satisfaction. ◆ It is a discipline because it follows the formal Six Sigma Improvement Model known as
DMAIC . This five-step process improvement model (1) D efines the project’s purpose, scope, and outputs and then identifies the required process information, keeping in mind the customer’s definition of quality; (2) M easures the process and collects data; (3) A nalyzes the data, ensuring repeatability (the results can be duplicated) and reproduc- ibility (others get the same result); (4) I mproves , by modifying or redesigning, existing processes and procedures; and (5) C ontrols the new process to make sure performance levels are maintained.
◆ It is a set of seven tools that we introduce shortly in this chapter: check sheets, scatter diagrams, cause-and-effect diagrams, Pareto charts, flowcharts, histograms, and statistical process control.
Motorola developed Six Sigma in the 1980s, in response to customer complaints about its products and in response to stiff competition. The company first set a goal of reducing defects by 90%. Within one year, it had achieved such impressive results—through bench- marking competitors, soliciting new ideas from employees, changing reward plans, adding training, and revamping critical processes—that it documented the procedures into what it called Six Sigma. Although the concept was rooted in manufacturing, GE later expanded Six Sigma into services, including human resources, sales, customer services, and financial/credit services. The concept of wiping out defects turns out to be the same in both manufacturing and services.
Six Sigma
A program to save time, improve
quality, and lower costs.
Upper limits
Lower limits
2,700 defects/million
Mean
;3u
;6u
3.4 defects/million
Figure 6.4
Defects per Million
for t 3S vs. t 6S
LO 6.3 Explain Six Sigma
STUDENT TIP Recall that { 3s provides
99.73% accuracy, while { 6s
is 99.9997%.
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222 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Implementing Six Sigma Implementing Six Sigma is a big commitment. Indeed, suc- cessful Six Sigma programs in every firm, from GE to Motorola to DuPont to Texas Instru- ments, require a major time commitment, especially from top management. These leaders have to formulate the plan, communicate their buy-in and the firm’s objectives, and take a visible role in setting the example for others.
Successful Six Sigma projects are clearly related to the strategic direction of a company. It is a management-directed, team-based, and expert-led approach. 2
Employee Empowerment Employee empowerment means involving employees in every step of the production process. Consistently, research suggests that some 85% of quality problems have to do with materials and processes, not with employee performance. Therefore, the task is to design equipment and processes that produce the desired quality. This is best done with a high degree of involvement by those who understand the shortcomings of the system. Those dealing with the system on a daily basis understand it better than anyone else. One study indicated that TQM programs that delegate responsibility for quality to shop-floor employees tend to be twice as likely to succeed as those implemented with “top-down” directives. 3
When nonconformance occurs, the worker is seldom at fault. Either the product was designed wrong, the process that makes the product was designed wrong, or the employee was improperly trained. Although the employee may be able to help solve the problem, the employee rarely causes it.
Techniques for building employee empowerment include (1) building communication networks that include employees; (2) developing open, supportive supervisors; (3) moving responsibility from both managers and staff to production employees; (4) building high- morale organizations; and (5) creating such formal organization structures as teams and qual- ity circles.
Teams can be built to address a variety of issues. One popular focus of teams is quality. Such teams are often known as quality circles. A quality circle is a group of employees who meet regularly to solve work-related problems. The members receive training in group plan- ning, problem solving, and statistical quality control. They generally meet once a week (usually after work but sometimes on company time). Although the members are not rewarded finan- cially, they do receive recognition from the firm. A specially trained team member, called the facilitator , usually helps train the members and keeps the meetings running smoothly. Teams with a quality focus have proven to be a cost-effective way to increase productivity as well as quality.
Benchmarking Benchmarking is another ingredient in an organization’s TQM program. Benchmarking involves selecting a demonstrated standard of products, services, costs, or practices that represent
Employee empowerment
Enlarging employee jobs so that
the added responsibility and
authority is moved to the lowest
level possible in the organization.
Quality circle
A group of employees meeting
regularly with a facilitator to solve
work-related problems in their
work area.
Benchmarking
Selecting a demonstrated standard
of performance that represents
the very best performance for a
process or an activity.
Workers at this TRW airbag
manufacturing plant in Marshall,
Illinois, are their own inspectors.
Empowerment is an essential part
of TQM. This man is checking the
quality of a crash sensor he built.
T R
W A
u to
m o ti ve
/G e n e ra
l M
a n le
y F o rd
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 223
the very best performance for processes or activities very similar to your own. The idea is to develop a target at which to shoot and then to develop a standard or benchmark against which to compare your performance. The steps for developing benchmarks are:
1. Determine what to benchmark. 2. Form a benchmark team. 3. Identify benchmarking partners. 4. Collect and analyze benchmarking information. 5. Take action to match or exceed the benchmark.
Typical performance measures used in benchmarking include percentage of defects, cost per unit or per order, processing time per unit, service response time, return on investment, customer satisfaction rates, and customer retention rates.
In the ideal situation, you find one or more similar organizations that are leaders in the particular areas you want to study. Then you compare yourself (benchmark yourself) against them. The company need not be in your industry. Indeed, to establish world-class standards, it may be best to look outside your industry. If one industry has learned how to compete via rapid product development while yours has not, it does no good to study your industry.
This is exactly what Xerox and Mercedes-Benz did when they went to L.L. Bean for order- filling and warehousing benchmarks. Xerox noticed that L.L. Bean was able to “pick” orders three times faster. After benchmarking, Xerox was immediately able to pare warehouse costs by 10%. Mercedes-Benz observed that L.L. Bean warehouse employees used flowcharts to spot wasted motions. The auto giant followed suit and now relies more on problem solving at the worker level.
Benchmarks often take the form of “best practices” found in other firms or in other divi- sions. Table 6.3 illustrates best practices for resolving customer complaints.
Likewise, Britain’s Great Ormond Street Hospital benchmarked the Ferrari Racing Team’s pit stops to improve one aspect of medical care. (See the OM in Action box “A Hospital Bench- marks Against the Ferrari Racing Team?”)
Internal Benchmarking When an organization is large enough to have many divisions or business units, a natural approach is the internal benchmark. Data are usually much more accessible than from outside firms. Typically, one internal unit has superior performance worth learning from.
Xerox’s almost religious belief in benchmarking has paid off not only by looking outward to L.L. Bean but by examining the operations of its various country divisions. For example, Xerox Europe, a $6 billion subsidiary of Xerox Corp., formed teams to see how better sales could result through internal benchmarking. Somehow, France sold five times as many color copiers as did other divisions in Europe. By copying France’s approach, namely, better sales training and use of dealer channels to supplement direct sales, Norway increased sales by 152%, Holland by 300%, and Switzerland by 328%!
Benchmarks can and should be established in a variety of areas. Total quality management requires no less.
TABLE 6.3 Best Practices for Resolving Customer Complaints
BEST PRACTICE JUSTIFICATION
Make it easy for clients to complain. It is free market research.
Respond quickly to complaints. It adds customers and loyalty.
Resolve complaints on the fi rst contact. It reduces cost.
Use computers to manage complaints. Discover trends, share them, and align your services.
Recruit the best for customer service jobs. It should be part of formal training and career advancement.
Source: Based on Canadian Government Guide on Complaint Mechanism.
LO 6.4 Explain how benchmarking is used in
TQM
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224 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Just-in-Time (JIT) The philosophy behind just-in-time (JIT) is one of continuing improvement and enforced problem solving. JIT systems are designed to produce or deliver goods just as they are needed. JIT is related to quality in three ways:
◆ JIT cuts the cost of quality: This occurs because scrap, rework, inventory investment, and damage costs are directly related to inventory on hand. Because there is less inventory on hand with JIT, costs are lower. In addition, inventory hides bad quality, whereas JIT imme- diately exposes bad quality.
◆ JIT improves quality: As JIT shrinks lead time, it keeps evidence of errors fresh and limits the number of potential sources of error. JIT creates, in effect, an early warning system for quality problems, both within the firm and with vendors.
◆ Better quality means less inventory and a better, easier-to-employ JIT system: Often the purpose of keeping inventory is to protect against poor production performance resulting from unreliable quality. If consistent quality exists, JIT allows firms to reduce all the costs associated with inventory.
Taguchi Concepts Most quality problems are the result of poor product and process design. Genichi Taguchi has provided us with three concepts aimed at improving both product and process quality: quality robustness , target-oriented quality, and the quality loss function .
Quality robust products are products that can be produced uniformly and consistently in adverse manufacturing and environmental conditions. Taguchi’s idea is to remove the effects of adverse conditions instead of removing the causes. Taguchi suggests that removing the effects
OM in Action A Hospital Benchmarks Against the Ferrari Racing Team? After surgeons successfully completed a 6-hour operation to fix a hole in a
3-year-old boy’s heart, Dr. Angus McEwan supervised one of the most
dangerous phases of the procedure: the boy’s transfer from surgery to the
intensive care unit.
Thousands of such “handoffs” occur in hospitals every day, and devastating
mistakes can happen during them. In fact, at least 35% of preventable hospital
mishaps take place because of handoff problems. Risks come from many
sources: using temporary nursing staff, frequent shift changes for interns,
surgeons working in larger teams, and an ever-growing tangle of wires and
tubes connected to patients.
Using an unlikely benchmark, Britain’s largest children’s hospital turned
to Italy’s Formula One Ferrari racing team for help in revamping patient
handoff techniques. Armed with videos and slides, the racing team described
how they analyze pit crew performance. It also explained how its system
for recording errors stressed the small ones that go unnoticed in pit-stop
handoffs.
To move forward, Ferrari invited a team of doctors to attend practice
sessions at the British Grand Prix in order to get closer looks at pit stops.
Ferrari’s technical director, Nigel Stepney, then watched a video of a hospital
handoff. Stepney was not impressed. “In fact, he was amazed at how
clumsy, chaotic, and informal the process appeared,” said one hospital
official. At that meeting, Stepney described how each Ferrari crew member
is required to do a specific job, in a specific sequence, and in silence.
The hospital handoff, in contrast, had several conversations going on at
once, while different members of its team disconnected or reconnected
patient equipment, but in no particular order.
Results of the benchmarking process: handoff errors fell over 40%, with
a bonus of faster handoff time.
Sources: The Wall Street Journal (December 3, 2007) and (November 14, 2006).
O liv
e r
M u lt h a u p /A
P I m
a g e s
Quality robust
Products that are consistently built
to meet customer needs despite
adverse conditions in the
production process.
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 225
is often cheaper than removing the causes and more effective in producing a robust product. In this way, small variations in materials and process do not destroy product quality.
A study found that U.S. consumers preferred Sony TVs made in Japan to Sony TVs made in the U.S., even though both factories used the exact same designs and specifications. The dif- ference in approaches to quality generated the difference in consumer preferences. In particu- lar, the U.S. factory was conformance-oriented , accepting all components that were produced within specification limits. On the other hand, the Japanese factory strove to produce as many components as close to the actual target as possible (see Figure 6.5 (a)).
This suggests that even though components made close to the boundaries of the specifi- cation limits may technically be acceptable, they may still create problems. For example, TV screens produced near their diameter’s lower spec limit may provide a loose fit with screen frames produced near their upper spec limit, and vice versa. This implies that a final prod- uct containing many parts produced near their specification boundaries may contain numer- ous loose and tight fits, which could cause assembly, performance, or aesthetic concerns. Customers may be dissatisfied, resulting in possible returns, service work, or decreased future demand.
Taguchi introduced the concept of target-oriented quality as a philosophy of continuous im- provement to bring the product exactly on target. As a measure, Taguchi’s quality loss function (QLF) attempts to estimate the cost of deviating from the target value. Even though the item is produced within specification limits, the variation in quality can be expected to increase costs as the item output moves away from its target value. (These quality-related costs are estimates of the average cost over many such units produced.)
The QLF is an excellent way to estimate quality costs of different processes. A process that produces closer to the actual target value may be more expensive, but it may yield a more valuable product. The QLF is the tool that helps the manager determine if this added cost is worthwhile. The QLF takes the general form of a simple quadratic equation (see Figure 6.5 (b)).
Knowledge of TQM Tools To empower employees and implement TQM as a continuing effort, everyone in the organiza- tion must be trained in the techniques of TQM. In the following section, we focus on some of the diverse and expanding tools that are used in the TQM crusade.
Target-oriented quality brings products toward the target value.
Conformance-oriented quality keeps products within 3 standard deviations.
Distribution of Specifications for
Products Produced (a)
Quality Loss Function (b)
High loss
Low loss
Loss (to producing organization, customer, and society)
Frequency
Lower
Specification
Target Upper
Poor
Fair
Good
Best
Unacceptable
Target-oriented quality yields more product in the “best” category.
Figure 6.5
(a) Distribution of Products
Produced and (b) Quality Loss
Function
Taguchi aims for the target
because products produced near
the upper and lower acceptable
specifications result in a higher
quality loss.
Target-oriented quality
A philosophy of continuous
improvement to bring a product
exactly on target.
Quality loss function (QLF)
A mathematical function that
identifies all costs connected with
poor quality and shows how these
costs increase as output moves
away from the target value.
LO 6.5 Explain quality robust products and
Taguchi concepts
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226 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Tools of TQM Seven tools that are particularly helpful in the TQM effort are shown in Figure 6.6 . We will now introduce these tools.
Check Sheets A check sheet is any kind of a form that is designed for recording data. In many cases, the recording is done so the patterns are easily seen while the data are being taken [see Figure 6.6 (a)]. Check sheets help analysts find the facts or patterns that may aid subsequent analysis. An exam- ple might be a drawing that shows a tally of the areas where defects are occurring or a check sheet showing the type of customer complaints.
Tools for Generating Ideas
Tools for Organizing the Data
Tools for Identifying Problems (f) Histogram: A distribution that shows the frequency of occurrences of a variable
(g) Statistical Process Control Chart: A chart with time on the horizontal axis for plotting values of a statistic
(b) Scatter Diagram: A graph of the value of one variable vs. another variable
(a) Check Sheet: An organized method of recording data
(c) Cause-and-Effect Diagram: A tool that identifies process elements (causes) that may affect an outcome
Defect 1
l l l
l l
l l l l l l l l l
l l l l l l l l
l l l l l l l l
2 3 4 5 6 7 8
A
B
C
Hour
Absenteeism
P ro
d u
c ti
v it
y
Effect MethodsMaterials
MachineryManpower
Cause
P e rc
e n
t
F re
q u
e n
c y
A B C D E
Repair time (minutes)
Distribution
F re
q u e n cy
Upper control limit
Lower control limit
Time
Target value
(d) Pareto Chart: A graph that identifies and plots problems or defects in descending order of frequency
(e) Flowchart (Process Diagram): A chart that describes the steps in a process
Figure 6.6
Seven Tools of TQM
STUDENT TIP These tools will prove useful
in many of your courses and
throughout your career.
LO 6.6 Use the seven tools of TQM
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 227
Scatter Diagrams Scatter diagrams show the relationship between two measurements. An example is the positive relationship between length of a service call and the number of trips a repair person makes back to the truck for parts. Another example might be a plot of productivity and absenteeism, as shown in Figure 6.6 (b). If the two items are closely related, the data points will form a tight band. If a random pattern results, the items are unrelated.
Cause-and-Effect Diagrams Another tool for identifying quality issues and inspection points is the cause-and-effect diagram, also known as an Ishikawa diagram or a fish-bone chart . Figure 6.7 illustrates a chart (note the shape resembling the bones of a fish) for a basketball quality control problem—missed free- throws. Each “bone” represents a possible source of error.
The operations manager starts with four categories: material, machinery/equipment, man- power, and methods. These four M s are the “causes.” They provide a good checklist for initial analysis. Individual causes associated with each category are tied in as separate bones along that branch, often through a brainstorming process. For example, the method branch in Figure 6.7 has problems caused by hand position, follow-through, aiming point, bent knees, and balance. When a fish-bone chart is systematically developed, possible quality problems and inspection points are highlighted.
Pareto Charts Pareto charts are a method of organizing errors, problems, or defects to help focus on problem- solving efforts. They are based on the work of Vilfredo Pareto, a 19th-century economist. Joseph M. Juran popularized Pareto’s work when he suggested that 80% of a firm’s problems are a result of only 20% of the causes.
Example 1 indicates that of the five types of complaints identified, the vast majority were of one type—poor room service.
Cause-and-effect diagram
A schematic technique used to
discover possible locations of
quality problems.
Material (ball)
Rim alignment
Size of ball
Lopsidedness
Method (shooting process)
Hand position
Follow-through
Missed free-throws
Conditioning
Consistency
Manpower (shooter)
Rim size
Machine (hoop & backboard)
Concentration
Motivation
Training
Balance
Bend knees
Aiming pointGrain/feel (grip)
Air pressure
Rim height
Backboard stability
Figure 6.7
Fish-Bone Chart (or Cause-and-Effect Diagram) for Problems with Missed Free-Throws
Source: Adapted from MoreSteam.com, 2007.
Pareto charts
A graphic way of classifying
problems by their level of
importance, often referred to as
the 80–20 rule.
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Pareto analysis indicates which problems may yield the greatest payoff. Pacific Bell discov- ered this when it tried to find a way to reduce damage to buried phone cable, the number-one cause of phone outages. Pareto analysis showed that 41% of cable damage was caused by con- struction work. Armed with this information, Pacific Bell was able to devise a plan to reduce cable cuts by 24% in one year, saving $6 million.
Likewise, Japan’s Ricoh Corp., a copier maker, used the Pareto principle to tackle the “call- back” problem. Callbacks meant the job was not done right the first time and that a second visit, at Ricoh’s expense, was needed. Identifying and retraining only the 11% of the customer engineers with the most callbacks resulted in a 19% drop in return visits.
Flowcharts Flowcharts graphically present a process or system using annotated boxes and interconnected lines [see Figure 6.6 (e)]. They are a simple but great tool for trying to make sense of a process or explain a process. Example 2 uses a flowchart to show the process of completing an MRI at a hospital.
Example 1 A PARETO CHART AT THE HARD ROCK HOTEL The Hard Rock Hotel in Bali has just collected the data from 75 complaint calls to the general manager during the month of October. The manager wants to prepare an analysis of the complaints. The data provided are room service, 54; check-in delays, 12; hours the pool is open, 4; minibar prices, 3; and miscellaneous, 2.
APPROACH c A Pareto chart is an excellent choice for this analysis.
SOLUTION c The Pareto chart shown below indicates that 72% of the calls were the result of one cause: room service. The majority of complaints will be eliminated when this one cause is corrected.
Room service Check-in Pool hours Minibar Misc.
F re
q u
e n
c y (
n u
m b
e r)
0
10
20
30
40
50
60
70
Causes as a percentage of the total
72% 16% 5% 4% 3%
54
12
4 3 2
C u
m u
la ti
v e p
e rc
e n
ta g
e
72
88 93 100
Data for October
Pareto Analysis of Hotel Complaints
Number of occurrences
INSIGHT c This visual means of summarizing data is very helpful—particularly with large amounts of data, as in the Southwestern University case study at the end of this chapter. We can immediately spot the top problems and prepare a plan to address them.
LEARNING EXERCISE c Hard Rock’s bar manager decides to do a similar analysis on complaints she has collected over the past year: too expensive, 22; weak drinks, 15; slow service, 65; short hours, 8; unfriendly bartender, 12. Prepare a Pareto chart. [Answer: slow service, 53%; expensive, 18%; drinks, 12%; bartender, 10%; hours, 7%.]
RELATED PROBLEMS c 6.1, 6.3, 6.7b, 6.12, 6.13, 6.16c, 6.17b
ACTIVE MODEL 6.1 This example is further illustrated in Active Model 6.1 in MyOMLab.
Flowcharts
Block diagrams that graphically
describe a process or system.
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 229
Histograms Histograms show the range of values of a measurement and the frequency with which each value occurs [see Figure 6.6 (f)]. They show the most frequently occurring readings as well as the variations in the measurements. Descriptive statistics, such as the average and standard deviation, may be calculated to describe the distribution. However, the data should always be plotted so the shape of the distribution can be “seen.” A visual presentation of the distribution may also provide insight into the cause of the variation.
Statistical Process Control (SPC) Statistical process control (SPC) monitors standards, makes measurements, and takes corrective action as a product or service is being produced. Samples of process outputs are examined; if they are within acceptable limits, the process is permitted to continue. If they fall outside certain specific ranges, the process is stopped and, typically, the assignable cause located and removed.
Example 2 A FLOWCHART FOR HOSPITAL MRI SERVICE Arnold Palmer Hospital has undertaken a series of process improvement initiatives. One of these is to make the MRI service efficient for patient, doctor, and hospital. The first step, the administrator believes, is to develop a flowchart for this process.
APPROACH c A process improvement staffer observed a number of patients and followed them (and information flow) from start to end. Here are the 11 steps:
1. Physician schedules MRI after examining patient (START). 2. Patient taken from the examination room to the MRI lab with test order and copy of medical
records. 3. Patient signs in, completes required paperwork. 4. Patient is prepped by technician for scan. 5. Technician carries out the MRI scan. 6. Technician inspects film for clarity. 7. If MRI not satisfactory (20% of time), Steps 5 and 6 are repeated. 8. Patient taken back to hospital room. 9. MRI is read by radiologist and report is prepared. 10. MRI and report are transferred electronically to physician. 11. Patient and physician discuss report (END).
SOLUTION c Here is the flowchart:
2 3 4
1 112 3 4 5 6
9
8
10
7 80%
20%
STUDENT TIP Flowcharting any
process is an
excellent way to
understand and then
try to improve that
process.
INSIGHT c With the flowchart in hand, the hospital can analyze each step and identify value-added activities and activities that can be improved or eliminated.
LEARNING EXERCISE c A new procedure requires that if the patient’s blood pressure is over 200/120 when being prepped for the MRI, she is taken back to her room for 2 hours and the process returns to Step 2. How does the flowchart change? Answer:
RELATED PROBLEMS c 6.6, 6.15
Statistical process control (SPC)
A process used to monitor stand-
ards, make measurements, and
take corrective action as a product
or service is being produced.
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Control charts are graphic presentations of data over time that show upper and lower limits for the process we want to control [see Figure 6.6 (g)]. Control charts are constructed in such a way that new data can be quickly compared with past performance data. We take samples of the process output and plot the average of each of these samples on a chart that has the limits on it. The upper and lower limits in a control chart can be in units of temperature, pressure, weight, length, and so on.
Figure 6.8 shows the plot of sample averages in a control chart. When the samples fall within the upper and lower control limits and no discernible pattern is present, the process is said to be in control with only natural variation present. Otherwise, the process is out of con- trol or out of adjustment.
The supplement to this chapter details how control charts of different types are developed. It also deals with the statistical foundation underlying the use of this important tool.
The Role of Inspection To make sure a system is producing as expected, control of the process is needed. The best processes have little variation from the standard expected. In fact, if variation were completely eliminated, there would be no need for inspection because there would be no defects. The operations manager’s challenge is to build such systems. However, inspection must often be performed to ensure that processes are performing to standard. This inspection can involve measurement, tasting, touching, weighing, or testing of the product (sometimes even destroy- ing it when doing so). Its goal is to detect a bad process immediately. Inspection does not correct deficiencies in the system or defects in the products, nor does it change a product or increase its value. Inspection only finds deficiencies and defects. Moreover, inspections are expensive and do not add value to the product.
Inspection should be thought of as a vehicle for improving the system. Operations manag- ers need to know critical points in the system: (1) when to inspect and (2) where to inspect .
When and Where to Inspect Deciding when and where to inspect depends on the type of process and the value added at each stage. Inspections can take place at any of the following points:
1. At your supplier’s plant while the supplier is producing. 2. At your facility upon receipt of goods from your supplier. 3. Before costly or irreversible processes. 4. During the step-by-step production process. 5. When production or service is complete. 6. Before delivery to your customer. 7. At the point of customer contact.
Control charts
Graphic presentations of
process data over time, with
predetermined control limits.
1
Game number
0%
20%
40%
2 3 4 5 6 7 8 9
Upper control limit
Plot of the percentage of free-throws missed
Lower control limit
Coach’s target value
Inspection
A means of ensuring that an
operation is producing at the
quality level expected.
Figure 6.8
Control Chart for Percentage
of Free-throws Missed by the
Orlando Magic in Their First
Nine Games of the New Season
M cC
la tc
h y/
Tr ib
u n e C
o n te
n t
A g e n cy
L LC
/A la
m y
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 231
The seven tools of TQM discussed in the previous section aid in this “when and where to inspect” decision. However, inspection is not a substitute for a robust product produced by well- trained employees in a good process. In one well-known experiment conducted by an independent research firm, 100 defective pieces were added to a “perfect” lot of items and then subjected to 100% inspection. The inspectors found only 68 of the defective pieces in their first inspection. It took another three passes by the inspectors to find the next 30 defects. The last two defects were never found. So the bottom line is that there is variability in the inspection process. In addition, inspectors are only human: They become bored, they become tired, and the inspection equipment itself has variability. Even with 100% inspection, inspectors cannot guarantee perfection. There- fore, good processes, employee empowerment, and source control are a better solution than trying to find defects by inspection. You cannot inspect quality into the product.
For example, at Velcro Industries, as in many other organizations, quality was viewed by machine operators as the job of “those quality people.” Inspections were based on random sampling, and if a part showed up bad, it was thrown out. The company decided to pay more attention to the system (operators, machine repair and design, measurement methods, com- munications, and responsibilities) and to invest more money in training. Over time as defects declined, Velcro was able to pull half its quality control people out of the process.
Source Inspection The best inspection can be thought of as no inspection at all; this “inspection” is always done at the source—it is just doing the job properly with the operator ensuring that this is so. This may be called source inspection (or source control) and is consistent with the concept of employee empowerment, where individual employees self-check their own work. The idea is that each supplier, process, and employee treats the next step in the process as the customer , ensuring perfect product to the next “customer.” This inspection may be assisted by the use of checklists and controls such as a fail-safe device called a poka-yoke , a name borrowed from the Japanese.
A poka-yoke is a foolproof device or technique that ensures production of good units every time. These special devices avoid errors and provide quick feedback of problems. A simple ex- ample of a poka-yoke device is the diesel gas pump nozzle that will not fit into the “unleaded” gas tank opening on your car. In McDonald’s, the french fry scoop and standard-size container used to measure the correct quantity are poka-yokes. Similarly, in a hospital, the prepackaged surgical coverings that contain exactly the items needed for a medical procedure are poka-yokes.
Checklists are a type of poka-yoke to help ensure consistency and completeness in carrying out a task. A basic example is a to-do list. This tool may take the form of preflight checklists used by airplane pilots, surgical safety checklists used by doctors, or software quality assurance lists used by programmers. The OM in Action box “Safe Patients, Smart Hospitals” illustrates the important role checklists have in hospital quality.
The idea of source inspection, poka-yokes, and checklists is to guarantee 100% good prod- uct or service at each step of a process.
Good methods analysis and the proper tools can result in
poka-yokes that improve both quality and speed. Here,
two poka-yokes are demonstrated. First, the aluminum
scoop automatically positions the french fries vertically,
and second, the properly sized container ensures that the
portion served is correct. McDonald’s thrives by bringing
rigor and consistency to the restaurant business.
M a tt
h ia
s S ch
ra d e r/
d p a p
ic tu
re a
lli a n ce
a rc
h iv
e /A
la m
y
STUDENT TIP One of our themes of quality
is that “quality cannot be
inspected into a product.”
Source inspection
Controlling or monitoring at the
point of production or purchase—
at the source.
Poka-yoke
Literally translated, “mistake
proofing”; it has come to mean a
device or technique that ensures
the production of a good unit
every time.
Checklist
A type of poka-yoke that lists the
steps needed to ensure consist-
ency and completeness in a task.
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Service Industry Inspection In service -oriented organizations, inspection points can be assigned at a wide range of loca- tions, as illustrated in Table 6.4 . Again, the operations manager must decide where inspections are justified and may find the seven tools of TQM useful when making these judgments.
OM in Action Safe Patients, Smart Hospitals Simple and avoidable errors are made in hospitals each day, causing patients
to die. Inspired by two tragic medical mistakes—his father’s misdiagnosed
cancer and sloppiness that killed an 18-month-old child at Johns Hopkins—
Dr. Peter Pronovost has made it his mission, often swimming upstream against
the medical culture, to improve patient safety and prevent deaths.
He began by developing a basic 5-step checklist to reduce catheter infec-
tions. Inserted into veins in the groin, neck, or chest to administer fluids and
medicines, catheters can save lives. But every year, 80,000 Americans get
infections from central venous catheters (or lines), and over 30,000 of these
patients die. Pronovost’s checklist has dropped infection rates at hospitals that
use it down to zero, saving thousands of lives and tens of millions of dollars.
His steps for doctors and nurses are simple: (1) wash your hands; (2) use
sterile gloves, masks, and drapes; (3) use antiseptic on the area being opened
for the catheter; (4) avoid veins in the arms and legs; and (5) take the catheter
out as soon as possible. He also created a special cart, where all supplies
needed are stored.
Dr. Provonost believes that many hospital errors are due to lack of standard-
ization, poor communications, and a noncollaborative culture that is “antiquated
and toxic.” He points out that checklists in the airline industry are a science,
and every crew member works as part of the safety team. Provonost’s book has
shown that one person, with small changes, can make a huge difference.
Sources: Safe Patients, Smart Hospitals (Penguin Publishers, 2011); and
The Wall Street Journal (December 13, 2014).
D a vi
d J
o e l/ G
e tt
y Im
a g e s
TABLE 6.4 Examples of Inspection in Services
ORGANIZATION WHAT IS INSPECTED STANDARD
Alaska Airlines Last bag on carousel Less than 20 minutes after arrival at the gate
Airplane door opened Less than 2 minutes after arrival at the gate
Jones Law Offi ces Receptionist performance Phone answered by the second ring
Billing Accurate, timely, and correct format
Attorney Promptness in returning calls
Hard Rock Hotel Reception desk Use customer’s name
Doorman Greet guest in less than 30 seconds
Room All lights working, spotless bathroom
Minibar Restocked and charges accurately posted to bill
Arnold Palmer Billing Accurate, timely, and correct format
Hospital Pharmacy Prescription accuracy, inventory accuracy
Lab Audit for lab-test accuracy
Nurses Charts immediately updated
Admissions Data entered correctly and completely
Olive Garden Busboy Serves water and bread within one minute
Restaurant Busboy Clears all entrèe items and crumbs prior to dessert
Waiter Knows and suggests specials, desserts
Nordstrom Department Display areas Attractive, well organized, stocked, good lighting
Store Stockrooms Rotation of goods, organized, clean
Salesclerks Neat, courteous, very knowledgeable
VIDEO 6.2 Quality Counts at Alaska Airlines
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 233
Inspection of Attributes versus Variables When inspections take place, quality characteristics may be measured as either attributes or variables . Attribute inspection classifies items as being either good or defective. It does not address the degree of failure. For example, the lightbulb burns or it does not. Variable inspection measures such dimensions as weight, speed, size, or strength to see if an item falls within an acceptable range. If a piece of electrical wire is supposed to be 0.01 inch in diameter, a micrometer can be used to see if the product is close enough to pass inspection.
Knowing whether attributes or variables are being inspected helps us decide which statisti- cal quality control approach to take, as we will see in the supplement to this chapter.
TQM in Services The personal component of services is more difficult to measure than the quality of the tangible component. Generally, the user of a service, like the user of a good, has features in mind that form a basis for comparison among alternatives. Lack of any one feature may eliminate the service from further consideration. Quality also may be perceived as a bundle of attributes in which many lesser characteristics are superior to those of competitors. This approach to prod- uct comparison differs little between goods and services. However, what is very different about the selection of services is the poor definition of the (1) intangible differences between prod- ucts and (2) the intangible expectations customers have of those products . Indeed, the intangible attributes may not be defined at all. They are often unspoken images in the purchaser’s mind. This is why all of those marketing issues such as advertising, image, and promotion can make a difference.
The operations manager plays a significant role in addressing several major aspects of ser- vice quality. First, the tangible component of many services is important . How well the service is designed and produced does make a difference. This might be how accurate, clear, and com- plete your checkout bill at the hotel is, how warm the food is at Taco Bell, or how well your car runs after you pick it up at the repair shop.
Second, another aspect of service and service quality is the process. Notice in Table 6.5 that 9 out of 10 of the determinants of service quality are related to the service process . Such things as reliability and courtesy are part of the process. An operations manager can
Attribute inspection
An inspection that classifies items
as being either good or defective.
Variable inspection
Classifications of inspected items
as falling on a continuum scale,
such as dimension or strength.
TABLE 6.5 Determinants of Service Quality
Reliability involves consistency of performance and dependability. It means that the fi rm performs the service right the fi rst time and that the fi rm honors its promises.
Responsiveness concerns the willingness or readiness of employees to provide service. It involves timeliness of service.
Competence means possession of the required skills and knowledge to perform the service.
Access involves approachability and ease of contact.
Courtesy involves politeness, respect, consideration, and friendliness of contact personnel (including receptionists, telephone operators, etc.).
Communication means keeping customers informed in language they can understand and listening to them. It may mean that the company has to adjust its language for different consumers—increasing the level of sophistication with a well-educated customer and speaking simply and plainly with a novice.
Credibility involves trustworthiness, believability, and honesty. It involves having the customer’s best interests at heart.
Security is the freedom from danger, risk, or doubt.
Understanding/knowing the customer involves making the effort to understand the customer’s needs.
Tangibles include the physical evidence of the service.
Sources: Adapted from A. Parasuranam, Valarie A. Zeithaml, and Leonard L. Berry, “A Conceptual Model of Service Quality and Its Implications for Future
Research,” Journal of Marketing (1985): 49. Copyright © 1985 by the American Marketing Association. Reprinted with permission.
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234 P A R T 2 | D E S I G N I N G O P E R AT I O N S
design processes that have these attributes and can ensure their quality through the TQM techniques discussed in this chapter. (See the Alaska Airlines photo.)
Third, the operations manager should realize that the customer’s expectations are the standard against which the service is judged. Customers’ perceptions of service quality result from a comparison of their “before-service expectations” with their “actual-service experi- ence.” In other words, service quality is judged on the basis of whether it meets expectations. The manager may be able to influence both the quality of the service and the expectation . Don’t promise more than you can deliver.
Fourth, the manager must expect exceptions. There is a standard quality level at which the regular service is delivered, such as the bank teller’s handling of a transaction. However, there are “exceptions” or “problems” initiated by the customer or by less-than-optimal operating conditions (e.g., the computer “crashed”). This implies that the quality control system must recognize and have a set of alternative plans for less-than-optimal operating conditions .
Well-run companies have service recovery strategies. This means they train and empower frontline employees to immediately solve a problem. For instance, staff at Marriott Hotels are drilled in the LEARN routine— L isten, E mpathize, A pologize, R eact, N otify—with the final step ensuring that the complaint is fed back into the system. And at the Ritz-Carlton, staff members are trained not to say merely “sorry” but “please accept my apology.” The Ritz gives them a budget for reimbursing upset guests. Similarly, employees at Alaska Airlines are em- powered to soothe irritated travelers by drawing from a “toolkit” of options at their disposal.
Managers of service firms may find SERVQUAL useful when evaluating performance. SERVQUAL is a widely used instrument that provides direct comparisons between customer service expectations and the actual service provided. SERVQUAL focuses on the gaps between the customer service expectations and the service provided on 10 service quality determinants. The most common version of the scale collapses the 10 service quality determinants shown in Table 6.5 into five factors for measurement: reliability, assurance, tangibles, empathy, and responsiveness.
Designing the product, managing the service process, matching customer expectations to the product, and preparing for the exceptions are keys to quality services. The OM in Action box “Richey International’s Spies” provides another glimpse of how OM managers improve quality in services.
Service recovery
Training and empowering frontline
workers to solve a problem
immediately.
SERVQUAL
A popular measurement scale
for service quality that compares
service expectations with service
performance.
VIDEO 6.3 TQM at Ritz-Carlton Hotels
First passenger boarded 40 min. before departure
Flight attendants on-board 45 min. before departure
Final load closeout 2 min. before departure
On board check-in count 5 min. before departure
All doors closed 2 min. before departure
Cargo door opened 1 min. after arrival
First bag to conveyor belt 15 min. after arrival
Aircraft 97% boarded 10 min. before departure time
A la
sk a A
ir lin
e s
Like many service organizations, Alaska Airlines sets quality standards in areas such as courtesy, appearance, and time. Shown here are some of Alaska
Airlines 50 quality checkpoints, based on a timeline for each departure.
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 235
OM in Action Richey International’s Spies How do luxury hotels maintain quality? They inspect. But when the product
is one-on-one service, largely dependent on personal behavior, how do you
inspect? You hire spies!
Richey International is the spy. Preferred Hotels and Resorts Worldwide and
Intercontinental Hotels have both hired Richey to do quality evaluations via spy-
ing. Richey employees posing as customers perform the inspections. However,
even then management must have established what the customer expects
and specific services that yield customer satisfaction. Only then do managers
know where and how to inspect. Aggressive training and objective inspections
reinforce behavior that will meet those customer expectations.
The hotels use Richey’s undercover inspectors to ensure performance to
exacting standards. The hotels do not know when the evaluators will arrive. Nor
what aliases they will use. Over 50 different standards are evaluated before
the inspectors even check in at a luxury hotel. Over the next 24 hours, using
checklists, tape recordings, and photos, written reports are prepared. The
reports include evaluation of standards such as:
◆ Does the doorman greet each guest in less than 30 seconds?
◆ Does the front-desk clerk use the guest’s name during check-in?
◆ Are the bathroom tub and shower spotlessly clean?
◆ How many minutes does it take to get coff ee after the guest sits down for
breakfast?
◆ Did the waiter make eye contact?
◆ Were minibar charges posted correctly on the bill?
Established standards, aggressive training, and inspections are part of
the TQM effort at these hotels. Quality does not happen by accident.
Sources: Hotelier (Feb. 6, 2010); Hotel and Motel Management (August 2002);
and The Wall Street Journal (May 12, 1999).
Summary Quality is a term that means different things to different people. We define quality as “the totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs.” Defining quality expectations is critical to effective and efficient operations.
Quality requires building a total quality management (TQM) environment because quality cannot be inspected
into a product. The chapter also addresses seven TQM concepts : continuous improvement, Six Sigma, employee empowerment, benchmarking, just-in-time, Taguchi con- cepts, and knowledge of TQM tools. The seven TQM tools introduced in this chapter are check sheets, scatter diagrams, cause-and-effect diagrams, Pareto charts, flow- charts, histograms, and statistical process control (SPC).
Key Terms
Quality (p. 217 ) ISO 9000 (p. 218 ) Cost of quality (COQ) (p. 218 ) Total quality management (TQM) (p. 219 ) PDCA (p. 220 ) Six Sigma (p. 221 ) Employee empowerment (p. 222 ) Quality circle (p. 222 ) Benchmarking (p. 222 )
Quality robust (p. 224 ) Target-oriented quality (p. 225 ) Quality loss function (QLF) (p. 225 ) Cause-and-effect diagram, Ishikawa
diagram, or fish-bone chart (p. 227 ) Pareto charts (p. 227 ) Flowcharts (p. 228 ) Statistical process control (SPC) (p. 229 ) Control charts (p. 230 )
Inspection (p. 230 ) Source inspection (p. 231 ) Poka-yoke (p. 231 ) Checklist (p. 231 ) Attribute inspection (p. 233 ) Variable inspection (p. 233 ) Service recovery (p. 234 ) SERVQUAL (p. 234 )
Ethical Dilemma A lawsuit a few years ago made headlines worldwide when a McDonald’s drive-through customer spilled a cup of scalding hot coffee on herself. Claiming the coffee was too hot to be safely consumed in a car, the badly burned 80-year-old woman won $2.9 million in court. (The judge later reduced the award to $640,000.) McDonald’s claimed the product was served to the correct specifi cations and was of proper quality. Further, the cup read “Caution—Contents May Be Hot.” McDonald’s coffee, at 180°, is substantially hotter (by corporate rule)
than typical restaurant coffee, despite hundreds of coffee- scalding complaints in the past 10 years. Similar court cases, incidentally, resulted in smaller verdicts, but again in favor of the plaintiffs. For example, Motor City Bagel Shop was sued for a spilled cup of coffee by a drive-through patron, and Starbucks by a customer who spilled coffee on her own ankle.
Are McDonald’s, Motor City, and Starbucks at fault in situations such as these? How do quality and ethics enter into these cases?
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Solved Problems Virtual Office Hours help is available in MyOMLab .
SOLVED PROBLEM 6.1 Northern Airlines’s frequent flyer complaints about redeeming miles for free, discounted, and upgraded travel are summarized below, in five categories, from 600 letters received this year.
COMPLAINT FREQUENCY
Could not get through to customer service to make requests
125
Seats not available on date requested 270
Had to pay fees to get “free” seats 62
Seats were available but only on fl ights at odd hours 110
Rules kept changing whenever customer called 33
Develop a Pareto chart for the data.
SOLUTION
Seats not available
45%
Customer service
21%
Available odd hours only 18%
Fees 10%
Rules 6%
F re
q u
e n
c y
0
50
100
150
200
250
300
Causes as a percentage of the total
270
125 110
62
33 C
u m
u la
ti v e p
e rc
e n
ta g
e
66
84
95 100
45
Number of complaints
Discussion Questions
1. Explain how improving quality can lead to reduced costs. 2. As an Internet exercise, determine the Baldrige Award crite-
ria. See the Web site www.nist.gov/baldrige/ . 3. Which 3 of Deming’s 14 points do you think are most critical
to the success of a TQM program? Why? 4. List the seven concepts that are necessary for an effective
TQM program. How are these related to Deming’s 14 points? 5. Name three of the important people associated with the qual-
ity concepts of this chapter. In each case, write a sentence about each one summarizing his primary contribution to the field of quality management.
6. What are seven tools of TQM? 7. How does fear in the workplace (and in the classroom) inhibit
learning? 8. How can a university control the quality of its output (that is,
its graduates)? 9. Philip Crosby said that quality is free. Why? 10. List the three concepts central to Taguchi’s approach. 11. What is the purpose of using a Pareto chart for a given
problem?
12. What are the four broad categories of “causes” to help initially structure an Ishikawa diagram or cause-and-effect diagram?
13. Of the several points where inspection may be necessary, which apply especially well to manufacturing?
14. What roles do operations managers play in addressing the major aspects of service quality?
15. Explain, in your own words, what is meant by source inspection . 16. What are 10 determinants of service quality? 17. Name several products that do not require high quality. 18. In this chapter, we have suggested that building quality into
a process and its people is difficult. Inspections are also dif- ficult. To indicate just how difficult inspections are, count the number of E s (both capital E and lowercase e ) in the OM in Action box “Richey International’s Spies” on page 235 (include the title but not the source note). How many did you find? If each student does this individually, you are very likely to find a distribution rather than a single number!
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 237
Problems
Problems 6.1–6.20 relate to Tools of TQM
• 6.1 An avant-garde clothing manufacturer runs a series of high-profile, risqué ads on a billboard on Highway 101 and regu- larly collects protest calls from people who are offended by them. The company has no idea how many people in total see the ads, but it has been collecting statistics on the number of phone calls from irate viewers:
TYPE DESCRIPTION NUMBER OF COMPLAINTS
R Offensive racially/ethnically 10
M Demeaning to men 4
W Demeaning to women 14
I Ad is incomprehensible 6
O Other 2
a) Depict this data with a Pareto chart. Also depict the cumula- tive complaint line.
b) What percent of the total complaints can be attributed to the most prevalent complaint?
• 6.2 Develop a scatter diagram for two variables of inter- est [say pages in the newspaper by day of the week; see the exam- ple in Figure 6.6 (b)].
• 6.3 Develop a Pareto chart of the following causes of poor grades on an exam:
REASON FOR POOR GRADE FREQUENCY
Insuffi cient time to complete 15
Late arrival to exam 7
Diffi culty understanding material 25
Insuffi cient preparation time 2
Studied wrong material 2
Distractions in exam room 9
Calculator batteries died during exam 1
Forgot exam was scheduled 3
Felt ill during exam 4
• 6.4 Develop a histogram of the time it took for you or your friends to receive six recent orders at a fast-food restaurant.
• • 6.5 Kathleen McFadden’s restaurant in Boston has recorded the following data for eight recent customers:
CUSTOMER NUMBER, i
MINUTES FROM TIME FOOD ORDERED UNTIL
FOOD ARRIVED ( y i )
NO. OF TRIPS TO KITCHEN BY WAITRESS ( x i )
1 10.50 4
2 12.75 5
3 9.25 3
4 8.00 2
5 9.75 3
6 11.00 4
7 14.00 6
8 10.75 5
a) McFadden wants you to graph the eight points ( x i , y i ), i 5 1, 2, … 8. She has been concerned because customers have been waiting too long for their food, and this graph is intended to help her find possible causes of the problem.
b) This is an example of what type of graph?
• • 6.6 Develop a flowchart [as in Figure 6.6 (e) and Example 2 ] showing all the steps involved in planning a party.
• • 6.7 Consider the types of poor driving habits that might occur at a traffic light. Make a list of the 10 you consider most likely to happen. Add the category of “other” to that list. a) Compose a check sheet [like that in Figure 6.6 (a)] to collect
the frequency of occurrence of these habits. Using your check sheet, visit a busy traffic light intersection at four different times of the day, with two of these times being during high- traffic periods (rush hour, lunch hour). For 15 to 20 minutes each visit, observe the frequency with which the habits you listed occurred.
b) Construct a Pareto chart showing the relative frequency of occurrence of each habit.
• • 6.8 Draw a fish-bone chart detailing reasons why an air- line customer might be dissatisfied.
• • 6.9 Consider the everyday task of getting to work on time or arriving at your first class on time in the morning. Draw a fish-bone chart showing reasons why you might arrive late in the morning.
• • 6.10 Construct a cause-and-effect diagram to reflect “student dissatisfied with university registration process.” Use the “four M s” or create your own organizing scheme. Include at least 12 causes.
• • 6.11 Draw a fish-bone chart depicting the reasons that might give rise to an incorrect fee statement at the time you go to pay for your registration at school.
• • • 6.12 Mary Beth Marrs, the manager of an apartment complex, feels overwhelmed by the number of complaints she is receiving. Below is the check sheet she has kept for the past 12 weeks. Develop a Pareto chart using this information. What recommendations would you make?
WEEK GROUNDS PARKING/
DRIVES POOL TENANT ISSUES
ELECTRICAL/ PLUMBING
1 ✓✓✓ ✓✓ ✓ ✓✓✓
2 ✓ ✓✓✓ ✓✓ ✓✓ ✓
3 ✓✓✓ ✓✓✓ ✓✓ ✓
4 ✓ ✓✓✓✓ ✓ ✓ ✓✓
5 ✓✓ ✓✓✓ ✓✓✓✓ ✓✓
6 ✓ ✓✓✓✓ ✓✓
7 ✓✓✓ ✓✓ ✓✓
8 ✓ ✓✓✓✓ ✓✓ ✓✓✓ ✓
9 ✓ ✓✓ ✓
10 ✓ ✓✓✓✓ ✓✓ ✓✓
11 ✓✓✓ ✓✓ ✓
12 ✓✓ ✓✓✓ ✓✓✓ ✓
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238 P A R T 2 | D E S I G N I N G O P E R AT I O N S
• 6.13 Use Pareto analysis to investigate the following data collected on a printed-circuit-board assembly line:
DEFECT NUMBER OF DEFECT
OCCURRENCES
Components not adhering 143
Excess adhesive 71
Misplaced transistors 601
Defective board dimension 146
Mounting holes improperly positioned 12
Circuitry problems on fi nal test 90
Wrong component 212
a) Prepare a graph of the data. b) What conclusions do you reach?
• • 6.14 A list of 16 issues that led to incorrect formulations in Tuncey Bayrak’s jam manufacturing unit in New England is provided below:
List of Issues
1. Incorrect measurement 9. Variability in scale accuracy
2. Antiquated scales 10. Equipment in disrepair
3. Lack of clear instructions 11. Technician calculation off
4. Damaged raw material 12. Jars mislabeled
5. Operator misreads display 13. Temperature controls off
6. Inadequate cleanup 14. Incorrect weights
7. Incorrect maintenance 15. Priority miscommunication
8. Inadequate fl ow controls 16. Inadequate instructions
Create a fish-bone diagram and categorize each of these issues correctly, using the “four M s” method.
• • 6.15 Develop a flowchart for one of the following: a) Filling up with gasoline at a self-serve station. b) Determining your account balance and making a withdrawal
at an ATM. c) Getting a cone of yogurt or ice cream from an ice cream store.
• • • • 6.16 Boston Electric Generators has been getting many complaints from its major customer, Home Station, about the quality of its shipments of home generators. Daniel Shimshak, the plant manager, is alarmed that a customer is providing him with the only information the company has on shipment quality. He decides to collect information on defective shipments through a form he has asked his drivers to complete on arrival at custom- ers’ stores. The forms for the first 279 shipments have been turned in. They show the following over the past 8 weeks:
WEEK
NO. OF SHIP-
MENTS
NO. OF SHIP-
MENTS WITH
DEFECTS
REASON FOR DEFECTIVE SHIPMENT
INCORRECT BILL OF LADING
INCORRECT TRUCK- LOAD
DAMAGED PRODUCT
TRUCKS LATE
1 23 5 2 2 1
2 31 8 1 4 1 2
3 28 6 2 3 1
4 37 11 4 4 1 2
5 35 10 3 4 2 1
6 40 14 5 6 3
7 41 12 3 5 3 1
8 44 15 4 7 2 2
Even though Daniel increased his capacity by adding more work- ers to his normal contingent of 30, he knew that for many weeks he exceeded his regular output of 30 shipments per week. A review of his turnover over the past 8 weeks shows the following:
WEEK NO. OF
NEW HIRES NO. OF
TERMINATIONS TOTAL NO. OF
WORKERS
1 1 0 30
2 2 1 31
3 3 2 32
4 2 0 34
5 2 2 34
6 2 4 32
7 4 1 35
8 3 2 36
a) Develop a scatter diagram using total number of shipments and number of defective shipments. Does there appear to be any relationship?
b) Develop a scatter diagram using the variable “turnover” (num- ber of new hires plus number of terminations) and the number of defective shipments. Does the diagram depict a relationship between the two variables?
c) Develop a Pareto chart for the type of defects that have occurred. d) Draw a fish-bone chart showing the possible causes of the
defective shipments.
• • • 6.17 A recent Gallup poll of 519 adults who flew in the past year found the following number of complaints about flying: cramped seats (45), cost (16), dislike or fear of flying (57), security measures (119), poor service (12), connecting flight problems (8), overcrowded planes (42), late planes/waits (57), food (7), lost lug- gage (7), and other (51). a) What percentage of those surveyed found nothing they disliked? b) Draw a Pareto chart summarizing these responses. Include the
“no complaints” group. c) Use the “four M s” method to create a fish-bone diagram for
the 10 specific categories of dislikes (exclude “other” and “no complaints”).
d) If you were managing an airline, what two or three specific issues would you tackle to improve customer service? Why?
Problems 6.18–6.20 are available in MyOMLab.
C h ri st
o p h e T
e st
i/ S
h u tt
e rs
to ck
Problem 6.21 (available in MyOMLab) relates to TQM in Services
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 239
CASE STUDIES
The popularity of Southwestern University’s football program under its new coach Phil Flamm surged in each of the 5 years since his arrival at the Stephenville, Texas, college. (See Southwestern University: (A) in Chapter 3 and (B) in Chapter 4 .) With a foot- ball stadium close to maxing out at 54,000 seats and a vocal coach pushing for a new stadium, SWU president Joel Wisner faced some difficult decisions. After a phenomenal upset victory over its archrival, the University of Texas, at the homecoming game in the fall, Dr. Wisner was not as happy as one would think. Instead of ecstatic alumni, students, and faculty, all Wisner heard were complaints. “The lines at the concession stands were too long”; “Parking was harder to find and farther away than in the old days” (that is, before the team won regularly); “Seats weren’t comfortable”; “Traffic was backed up halfway to Dallas”; and
on and on. “A college president just can’t win,” muttered Wisner to himself.
At his staff meeting the following Monday, Wisner turned to his VP of administration, Leslie Gardner. “I wish you would take care of these football complaints, Leslie,” he said. “See what the real problems are and let me know how you’ve resolved them.” Gardner wasn’t surprised at the request. “I’ve already got a han- dle on it, Joel,” she replied. “We’ve been randomly surveying 50 fans per game for the past year to see what’s on their minds. It’s all part of my campuswide TQM effort. Let me tally things up and I'll get back to you in a week.”
When she returned to her office, Gardner pulled out the file her assistant had compiled (see Table 6.6 ). “There’s a lot of infor- mation here,” she thought.
Southwestern University: (C)*
TABLE 6.6 Fan Satisfaction Survey Results ( N = 250)
OVERALL GRADE
A B C D F
Game Day A. Parking 90 105 45 5 5
B. Traffi c 50 85 48 52 15
C. Seating 45 30 115 35 25
D. Entertainment 160 35 26 10 19
E. Printed Program 66 34 98 22 30
Tickets A. Pricing 105 104 16 15 10
B. Season Ticket Plans 75 80 54 41 0
Concessions A. Prices 16 116 58 58 2
B. Selection of Foods 155 60 24 11 0
C. Speed of Service 35 45 46 48 76
Respondents
Alumnus
Student
Faculty/Staff
None of the above
113
83
16
38
Open-Ended Comments on Survey Cards:
Parking a mess Add a skybox Get better cheerleaders Double the parking attendants Everything is okay Too crowded Seats too narrow Great food Phil F. for President! I smelled drugs being smoked Stadium is ancient Seats are like rocks Not enough cops for traffi c Game starts too late Hire more traffi c cops Need new band Great!
More hot dog stands Seats are all metal Need skyboxes Seats stink Go SWU! Lines are awful Seats are uncomfortable I will pay more for better view Get a new stadium Student dress code needed I want cushioned seats Not enough police Students too rowdy Parking terrible Toilets weren’t clean Not enough handicap spots in lot Well done, SWU
Put in bigger seats Friendly ushers Need better seats Expand parking lots Hate the bleacher seats Hot dogs cold $3 for a coffee? No way! Get some skyboxes Love the new uniforms Took an hour to park Coach is terrifi c More water fountains Better seats Seats not comfy Bigger parking lot I’m too old for bench seats Cold coffee served at game
My company will buy a skybox— build it! Programs overpriced Want softer seats Beat those Longhorns! I’ll pay for a skybox Seats too small Band was terrifi c Love Phil Flamm Everything is great Build new stadium Move games to Dallas No complaints Dirty bathroom
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240 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Quality Counts at Alaska Airlines Video Case
Alaska Airlines, with nearly 100 destinations, including regular service to Alaska, Hawaii, Canada, and Mexico, is the seventh- largest U.S. carrier. Alaska Airlines has won the J.D. Power and Associates Award for highest customer satisfaction in the indus- try for eight years in a row while being the number one on-time airline for five years in a row.
Management’s unwavering commitment to quality has driven much of the firm’s success and generated an extremely loyal cus- tomer base. Executive V.P. Ben Minicucci exclaims, “We have rewritten our DNA.” Building an organization that can achieve quality is a demanding task, and the management at Alaska Airlines accepted the challenge. This is a highly participative quality cul- ture, reinforced by leadership training, constant process improve- ment, comprehensive metrics, and frequent review of those metrics. The usual training of flight crews and pilots is supplemented with
The Culture of Quality at Arnold Palmer Hospital Video Case
Founded in 1989, Arnold Palmer Hospital is one of the largest hospitals for women and children in the U.S., with 431 beds in two facilities totaling 676,000 square feet. Located in downtown Orlando, Florida, and named after its famed golf benefactor, the hospital, with more than 2,000 employees, serves an 18-county area in central Florida and is the only Level 1 trauma center for children in that region. Arnold Palmer Hospital provides a broad range of medical services including neonatal and pediatric inten- sive care, pediatric oncology and cardiology, care for high-risk pregnancies, and maternal intensive care.
The Issue of Assessing Quality Health Care Quality health care is a goal all hospitals profess, but Arnold Palmer Hospital has actually developed comprehensive and sci- entific means of asking customers to judge the quality of care they receive. Participating in a national benchmark comparison against other hospitals, Arnold Palmer Hospital consistently scores in the top 10% in overall patient satisfaction. Executive Director Kathy Swanson states, “Hospitals in this area will be dis- tinguished largely on the basis of their customer satisfaction. We must have accurate information about how our patients and their families judge the quality of our care, so I follow the questionnaire results daily. The in-depth survey helps me and others on my team to gain quick knowledge from patient feedback.” Arnold Palmer Hospital employees are empowered to provide gifts in value up to $200 to patients who find reason to complain about any hospital service such as food, courtesy, responsiveness, or cleanliness.
Swanson doesn’t focus just on the customer surveys, which are mailed to patients one week after discharge, but also on a variety of internal measures. These measures usually start at the grass- roots level, where the staff sees a problem and develops ways to
track performance. The hospital’s longstanding philosophy sup- ports the concept that each patient is important and respected as a person. That patient has the right to comprehensive, compas- sionate family-centered health care provided by a knowledgeable physician-directed team.
Some of the measures Swanson carefully monitors for con- tinuous improvement are morbidity, infection rates, readmission rates, costs per case, and length of stays. The tools she uses daily include Pareto charts, flowcharts, and process charts, in addition to benchmarking against hospitals both nationally and in the southeast region.
The result of all of these efforts has been a quality culture as manifested in Arnold Palmer’s high ranking in patient sat- isfaction and one of the highest survival rates of critically ill babies.
Discussion Questions *
1. Why is it important for Arnold Palmer Hospital to get a patient’s assessment of health care quality? Does the patient have the expertise to judge the health care she receives?
2. How would you build a culture of quality in an organization such as Arnold Palmer Hospital?
3. What techniques does Arnold Palmer Hospital practice in its drive for quality and continuous improvement?
4. Develop a fish-bone diagram illustrating the quality variables for a patient who just gave birth at Arnold Palmer Hospital (or any other hospital).
* You may wish to view the video that accompanies this case before answering these questions.
Discussion Questions
1. Using at least two different quality tools, analyze the data and present your conclusions.
2. How could the survey have been more useful? 3. What is the next step?
* This integrated case study runs throughout the text. Other issues facing Southwestern’s football stadium include: (A) Managing the renovation project ( Chapter 3 ); (B) Forecasting game attendance ( Chapter 4 ); (D) Break-even analysis of food services (Supplement 7 Web site); (E) Locating the new stadium ( Chapter 8 Web site); (F) Inventory plan- ning of football programs ( Chapter 12 Web site); and (G) Scheduling of campus security offi cers/staff for game days ( Chapter 13 Web site).
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sk a A
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C H A P T E R 6 | M A N AG I N G Q U A L I T Y 241
classroom training in areas such as Six Sigma. Over 200 managers have obtained Six Sigma Green Belt certification.
Alaska collects more than 100 quality and performance metrics every day. For example, the accompanying picture tells the crew that it has 6 minutes to close the door and back away from the gate to meet the “time to pushback” target. Operations personnel review each airport hub’s performance scorecard daily and the overall operations scorecard weekly. As Director of System Operations Control, Wayne Newton proclaims, “If it is not measured, it is not managed.” The focus is on identifying problem areas or trends, determining causes, and working on preventive measures.
Within the operations function there are numerous detailed input metrics for station operations (such as the percentage of time that hoses are free of twists, the ground power cord is stowed, and no vehicles are parked in prohibited zones). Management operates under the assumption that if all the detailed input met- rics are acceptable, the major key performance indicators, such as Alaska’s on-time performance and 20-minute luggage guarantee, will automatically score well.
The accompanying table displays a sample monthly scorecard for Alaska’s ground crew provider in Seattle. The major evalua- tion categories include process compliance, staffing (degree that crew members are available when needed), MAP rate (minimum acceptable performance for mishandled bags), delays, time to carousel, safety compliance, and quality compliance. The qual- ity compliance category alone tracks 64 detailed input metrics using approximately 30,000 monthly observations. Each of the major categories on the scorecard has an importance weight, and the provider is assigned a weighted average score at the end of each month. The contract with the supplier provides for up to a 3.7% bonus for outstanding performance and as much as a 5.0%
penalty for poor performance. The provider’s line workers receive a portion of the bonus when top scores are achieved.
As a company known for outstanding customer service, ser- vice recovery efforts represent a necessary area of emphasis. When things go wrong, employees mobilize to first communi- cate with, and in many cases compensate, affected customers. “It doesn’t matter if it’s not our fault,” says Minicucci. Front-line workers are empowered with a “toolkit” of options to offer to inconvenienced customers, including the ability to provide up to 5,000 frequent flyer miles and/or vouchers for meals, hotels, lug- gage, and tickets. When an Alaska flight had to make an emer- gency landing in Eugene, Oregon, due to a malfunctioning oven, passengers were immediately texted with information about what happened and why, and they were told that a replacement plane would be arriving within one hour. Within that hour, an apology letter along with a $450 ticket voucher were already in the mail to each passenger’s home. No customer complaints subsequently appeared on Twitter or Facebook. It’s no wonder why Alaska’s customers return again and again.
Discussion Questions *
1. What are some ways that Alaska can ensure that quality and performance metric standards are met when the company out- sources its ground operations to a contract provider?
2. Identify several quality metrics, in addition to those identified earlier, that you think Alaska tracks or should be tracking.
3. Think about a previous problem that you had when flying, for example, a late flight, a missed connection, or lost luggage. How, if at all, did the airline respond? Did the airline ade- quately address your situation? If not, what else should they
ELEMENTS WEIGHTING PERFORMANCE SCORE BONUS POINTS TOTAL GRADE
Process Compliance 20 15 15 B
Staffi ng 15 15 5 20 A+
MAP Rate (for bags) 20 15 15 B
Delays 10 9 9 A
Time to Carousel (total weight 5 10) Percentage of fl ights scanned Percentage of bags scanned 20 Minutes all bags dropped (% compliance) Outliers (>25mins)
2 2 4 2
98.7% 70.9% 92.5%
2
10 10 A
Safety Compliance 15 15 5 20 A+
Quality Compliance 10 10 10 A
Total - 100% 100 89 10 99 A+
Time to Carousel
Points 2 1.5 1 0 Percentage of fl ights scanned 95%–100% 90%–94.9% 89.9%–85% < 84.9%
Points 2 0 Percentage of bags scanned 60% or above ≤ 59.9%
Points 0 4 Last bag percent compliance Below 89.9% 90%–100%
Points 0 1 1.5 2 Last Bag >25 min. (Outliers) 20 15 10 5
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242 P A R T 2 | D E S I G N I N G O P E R AT I O N S
• Additional Case Study: Visit MyOMLab for this free case study: Westover Electrical, Inc.: This electric motor manufacturer has a large log of defects in its wiring process.
2. To train employees in how to improve quality and its relationship to customers, there are three other key players in the Six Sigma program: Master Black Belts, Black Belts, and Green Belts.
3. “The Straining of Quality,” The Economist (January 14, 1995): 55. We also see that this is one of the strengths of Southwest Airlines, which offers bare-bones domestic service but whose friendly and humorous employees help it obtain number-one ranking for quality. (See Fortune [March 6, 2006]: 65–69.)
Endnotes
1. Philip B. Crosby, Quality Is Free (New York: McGraw-Hill, 1979). Further, J. M. Juran states, in his book Juran on Quality by Design (The Free Press 1992, p. 119 ), that costs of poor quality “are huge, but the amounts are not known with preci- sion. In most companies the accounting system provides only a minority of the information needed to quantify this cost of poor quality. It takes a great deal of time and effort to extend the accounting system so as to provide full coverage.”
Source: Adapted from C. T. Horngren, S. M. Datar, and G. Foster, Cost Accounting , 15th ed. (Upper Saddle River, NJ: Prentice Hall, 2014).
Quality at the Ritz-Carlton Hotel Company Video Case
Ritz-Carlton. The name alone evokes images of luxury and qual- ity. As the first hotel company to win the Malcolm Baldrige National Quality Award, the Ritz treats quality as if it is the heartbeat of the company. This means a daily commitment to meeting customer expectations and making sure that each hotel is free of any deficiency.
In the hotel industry, quality can be hard to quantify. Guests do not purchase a product when they stay at the Ritz: They buy an experience. Thus, creating the right combination of elements to make the experience stand out is the challenge and goal of every employee, from maintenance to management.
Before applying for the Baldrige Award, company manage- ment undertook a rigorous self-examination of its operations in an attempt to measure and quantify quality. Nineteen processes were studied, including room service delivery, guest reservation and registration, message delivery, and breakfast service. This period of self-study included statistical measurement of process work flows and cycle times for areas ranging from room service delivery times and reservations to valet parking and housekeeping efficiency. The results were used to develop performance bench- marks against which future activity could be measured.
With specific, quantifiable targets in place, Ritz-Carlton man- agers and employees now focus on continuous improvement. The goal is 100% customer satisfaction: If a guest’s experience does not meet expectations, the Ritz-Carlton risks losing that guest to competition.
One way the company has put more meaning behind its qual- ity efforts is to organize its employees into “self-directed” work teams. Employee teams determine work scheduling, what work needs to be done, and what to do about quality problems in their own areas. In order to see the relationship of their specific area to the overall goals, employees are also given the opportunity to take additional training in hotel operations. Ritz-Carlton believes that a more educated and informed employee is in a better posi- tion to make decisions in the best interest of the organization.
Discussion Questions *
1. In what ways could the Ritz-Carlton monitor its success in achieving quality?
2. Many companies say that their goal is to provide quality prod- ucts or services. What actions might you expect from a company that intends quality to be more than a slogan or buzzword?
3. Why might it cost the Ritz-Carlton less to “do things right” the first time?
4. How could control charts, Pareto diagrams, and cause-and- effect diagrams be used to identify quality problems at a hotel?
5. What are some nonfinancial measures of customer satisfaction that might be used by the Ritz-Carlton?
*You may wish to view the video that accompanies this case before addressing these questions.
* You may wish to view the video that accompanies this case before addressing these questions.
have done? Did your experience affect your desire (positively or negative) to fly with that airline in the future?
4. See the accompanying table. The contractor received a perfect Time to Carousel score of 10 total points, even though its per- formance was not “perfect.” How many total points would the contractor have received with the following performance
scores: 93.2% of flights scanned, 63.5% of bags scanned, 89.6% of all bags dropped within 20 minutes, and 15 bags arriving longer than 25 minutes?
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6
R ap
id R
ev ie
w Chapter 6 Rapid Review
Main Heading Review Material MyOMLab QUALITY AND STRATEGY (pp. 216–217 )
Managing quality helps build successful strategies of differentiation, low cost, and response.
Two ways that quality improves profitability are: j Sales gains via improved response, price flexibility, increased market share,
and/or improved reputation j Reduced costs via increased productivity, lower rework and scrap costs, and/
or lower warranty costs
Concept Questions: 1.1–1.4 VIDEO 6.1 The Culture and Qual- ity at Arnold Palmer Hospital
DEFINING QUALITY (pp. 217 – 219 )
An operations manager’s objective is to build a total quality management system that identifies and satisfies customer needs. j Quality —The ability of a product or service to meet customer needs. The American Society for Quality (ASQ) defines quality as “the totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs.” The two most well-known quality awards are:
j U.S .: Malcolm Baldrige National Quality Award, named after a former secretary of commerce
j Japan : Deming Prize, named after an American, Dr. W. Edwards Deming j ISO 9000 —A set of quality standards developed by the International Organiza-
tion for Standardization (ISO). ISO 9000 is the only quality standard with international recognition. To do busi- ness globally, being listed in the ISO directory is critical.
j Cost of quality (COQ) —The cost of doing things wrong; that is, the price of nonconformance.
The four major categories of costs associated with quality are prevention costs, appraisal costs, internal failure costs, and external failure costs. Four leaders in the field of quality management are W. Edwards Deming, Joseph M. Juran, Armand Feigenbaum, and Philip B. Crosby.
Concept Questions: 2.1–2.4
TOTAL QUALITY MANAGEMENT (pp. 219 – 226 )
j Total quality management (TQM) —Management of an entire organization so that it excels in all aspects of products and services that are important to the customer.
Seven concepts for an effective TQM program are (1) continuous improvement, (2) Six Sigma, (3) employee empowerment, (4) benchmarking, (5) just-in-time (JIT), (6) Taguchi concepts, and (7) knowledge of TQM tools. j PDCA —A continuous improvement model that involves four stages: plan, do,
check, and act. The Japanese use the word kaizen to describe the ongoing process of unending improvement—the setting and achieving of ever-higher goals. j Six Sigma —A program to save time, improve quality, and lower costs. In a statistical sense, Six Sigma describes a process, product, or service with an extremely high capability—99.9997% accuracy, or 3.4 defects per million. j Employee empowerment —Enlarging employee jobs so that the added responsibil-
ity and authority are moved to the lowest level possible in the organization. Business literature suggests that some 85% of quality problems have to do with materials and processes, not with employee performance. j Quality circle —A group of employees meeting regularly with a facilitator to
solve work-related problems in their work area. j Benchmarking —Selecting a demonstrated standard of performance that repre-
sents the very best performance for a process or an activity. The philosophy behind just-in-time (JIT) involves continuing improvement and enforced problem solving. JIT systems are designed to produce or deliver goods just as they are needed. j Quality robust —Products that are consistently built to meet customer needs,
despite adverse conditions in the production process. j Target-oriented quality —A philosophy of continuous improvement to bring the
product exactly on target. j Quality loss function (QLF) —A mathematical function that identifies all costs
connected with poor quality and shows how these costs increase as output moves away from the target value.
Concept Questions: 3.1–3.4
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6 R
ap id
R ev
ie w
Main Heading Review Material MyOMLab TOOLS OF TQM (pp. 226 – 230 )
TQM tools that generate ideas include the check sheet (organized method of re- cording data), scatter diagram (graph of the value of one variable vs. another vari- able), and cause-and-effect diagram . Tools for organizing the data are the Pareto chart and flowchart . Tools for identifying problems are the histogram (distribution showing the frequency of occurrences of a variable) and statistical process control chart . j Cause-and-effect diagram —A schematic technique used to discover possible
locations of quality problems. (Also called an Ishikawa diagram or a fish-bone chart.)
The 4 M s (material, machinery/equipment, manpower, and methods) may be broad “causes.” j Pareto chart —A graphic that identifies the few critical items as opposed to many
less important ones. j Flowchart —A block diagram that graphically describes a process or system. j Statistical process control (SPC) —A process used to monitor standards,
make measurements, and take corrective action as a product or service is being produced.
j Control chart —A graphic presentation of process data over time, with predeter- mined control limits.
Concept Questions: 4.1–4.4 Problems: 6.1, 6.3, 6.5, 6.8–6.14, 6.16–6.20 ACTIVE MODEL 6.1
Virtual Office Hours for Solved Problem: 6.1
THE ROLE OF INSPECTION (pp. 230 – 233 )
j Inspection —A means of ensuring that an operation is producing at the quality level expected.
j Source inspection —Controlling or monitoring at the point of production or purchase: at the source.
j Poka-yoke —Literally translated, “mistake proofing”; it has come to mean a device or technique that ensures the production of a good unit every time.
j Checklist —A type of poka-yoke that lists the steps needed to ensure consistency and completeness in a task.
j Attribute inspection —An inspection that classifies items as being either good or defective.
j Variable inspection —Classifications of inspected items as falling on a continuum scale, such as dimension, size, or strength.
Concept Questions: 5.1–5.4
VIDEO 6.2 Quality Counts at Alaska Airlines
TQM IN SERVICES (pp. 233 – 235 )
Determinants of service quality: reliability, responsiveness, competence, access, courtesy, communication, credibility, security, understanding/knowing the customer, and tangibles. j Service recovery —Training and empowering frontline workers to solve a
problem immediately. j SERVQUAL—A popular measurement scale for service quality that compares
service expectations with service performance.
Concept Questions: 6.1–6.4
Problem: 6.21
VIDEO 6.3 TQM at Ritz-Carlton Hotels
Chapter 6 Rapid Review continued
Answers: LO 6.1. c; LO 6.2. quality management systems; LO 6.3. a; LO 6.4. c; LO 6.5. a; LO 6.6. check sheets, scatter diagrams, cause-and-effect diagrams, Pareto charts, flowcharts, histograms, SPC charts.
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 6.1 In this chapter, quality is defined as: a) the degree of excellence at an acceptable price and the
control of variability at an acceptable cost. b) how well a product fits patterns of consumer preferences. c) the totality of features and characteristics of a product or
service that bears on its ability to satisfy stated or implied needs.
d) being impossible to define, but you know what it is. LO 6.2 ISO 9000 is an international standard that addresses _____. LO 6.3 If 1 million passengers pass through the Jacksonville Airport
with checked baggage each year, a successful Six Sigma pro- gram for baggage handling would result in how many passen- gers with misplaced luggage?
a) 3.4 b) 6.0 c) 34 d) 2,700 e) 6 times the monthly standard deviation of passengers
LO 6.4 The process of identifying other organizations that are best at some facet of your operations and then modeling your organization after them is known as:
a) continuous improvement. b) employee empowerment. c) benchmarking. d) copycatting. e) patent infringement. LO 6.5 The Taguchi method includes all except which of the follow-
ing major concepts? a) Employee involvement b) Remove the effects of adverse conditions c) Quality loss function d) Target specifications LO 6.6 The seven tools of total quality management are ______,
______, ______, ______, ______, ______, and ______.
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245
SUPPLEMENT OUTLINE
Statistical Process Control 6
◆
Statistical Process Control (SPC) 246
◆
Acceptance Sampling 262
◆
Process Capability 260
S U
P P
L E
M E
N T
A la
sk a A
ir lin
e s
A la
sk a A
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246
Statistical Process Control (SPC) In this supplement, we address statistical process control—the same techniques used at BetzDearborn, at Arnold Palmer Hospital, at GE, and at Southwest Airlines to achieve qual- ity standards. Statistical process control (SPC) is the application of statistical techniques to ensure that processes meet standards. All processes are subject to a certain degree of variability. While studying process data in the 1920s, Walter Shewhart of Bell Laboratories made the distinction between the common (natural) and special (assignable) causes of variation. He developed a simple but powerful tool to separate the two—the control chart .
A process is said to be operating in statistical control when the only source of variation is common (natural) causes. The process must first be brought into statistical control by detecting and eliminating special (assignable) causes of variation. 1 Then its performance is predictable, and its ability to meet customer expectations can be assessed. The objective of a process control system is to provide a statistical signal when assignable causes of variation are present . Such a signal can quicken appropriate action to eliminate assignable causes.
Natural Variations Natural variations affect almost every process and are to be expected. Natural variations are the many sources of variation that occur within a process, even one that is in statistical control. Natural variations form a pattern that can be described as a distribution .
As long as the distribution (output measurements) remains within specified limits, the pro- cess is said to be “in control,” and natural variations are tolerated.
L E A R N I N G OBJEC TI V ES
LO S6.1 Explain the purpose of a control chart 247
LO S6.2 Explain the role of the central limit theorem in SPC 248
LO S6.3 Build x -charts and R -charts 250 LO S6.4 List the fi ve steps involved in building control charts 254
LO S6.5 Build p -charts and c -charts 256 LO S6.6 Explain process capability and compute C p and C pk 260
LO S6.7 Explain acceptance sampling 262
As part of its statistical process
control system, Flowers Bakery, in
Georgia, uses a digital camera to
inspect just-baked sandwich buns as
they move along the production line.
Items that don’t measure up in terms
of color, shape, seed distribution,
or size are identified and removed
automatically from the conveyor.
G e o rg
ia T
e ch
Statistical process control (SPC)
A process used to monitor stand-
ards by taking measurements and
corrective action as a product or
service is being produced.
Control chart
A graphical presentation of pro-
cess data over time.
Natural variations
Variability that affects every
production process to some
degree and is to be expected;
also known as common cause.
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S U P P L E M E N T 6 | S TAT I S T I C A L P R O C E S S C O N T R O L 247
Assignable Variations Assignable variation in a process can be traced to a specific reason. Factors such as machine wear, misadjusted equipment, fatigued or untrained workers, or new batches of raw material are all potential sources of assignable variations.
Natural and assignable variations distinguish two tasks for the operations manager. The first is to ensure that the process is capable of operating under control with only natural varia- tion. The second is, of course, to identify and eliminate assignable variations so that the pro- cesses will remain under control.
Samples Because of natural and assignable variation, statistical process control uses averages of small samples (often of four to eight items) as opposed to data on individual parts. Individual pieces tend to be too erratic to make trends quickly visible.
Figure S6.1 provides a detailed look at the important steps in determining process varia- tion. The horizontal scale can be weight (as in the number of ounces in boxes of cereal) or length (as in fence posts) or any physical measure. The vertical scale is frequency. The samples of five boxes of cereal in Figure S6.1 (a) are weighed, (b) form a distribution, and (c) can vary. The distributions formed in (b) and (c) will fall in a predictable pattern (d) if only natural varia- tion is present. If assignable causes of variation are present, then we can expect either the mean to vary or the dispersion to vary, as is the case in (e) .
Control Charts The process of building control charts is based on the concepts pre- sented in Figure S6.2 . This figure shows three distributions that are the result of outputs from three types of processes. We plot small samples and then examine characteristics of the result- ing data to see if the process is within “control limits.” The purpose of control charts is to help distinguish between natural variations and variations due to assignable causes. As seen in Figure S6.2 , a process is (a) in control and the process is capable of producing within estab- lished control limits , (b) in control but the process is not capable of producing within established
Assignable variation
Variation in a production process
that can be traced to specific
causes.
LO S6.1 Explain the purpose of a control chart
F re
q u e n cy
Weight
(a)
(b)
(c)
(e)
(d)
Samples of the product, say five boxes of cereal taken off the filling machine line, vary from one another in weight.
After enough sample means are taken from a stable process, they form a pattern called a distribution.
There are many types of distributions, including the normal (bell-shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape.
If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable.
If assignable causes of variation are present, the process output is not stable over time and is not predictable. That is, when causes that are not an expected part of the process occur, the samples will yield unexpected distributions that vary by central tendency, standard deviation, and shape.
F re
q u e n cy
Weight
F re
q u e n cy
Weight
Measure of central tendency (mean)
Variation (std. deviation)
Each of these represents one
sample of five boxes of cereal.
The solid line represents
the distribution.
Shape
F re
q u e n cy
Weight
Prediction
Weight Weight
Time
F re
q u e n cy
Weight
Prediction ??
? ?
? ??
???? ????
Time
Figure S6.1
Natural and Assignable
Variation
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248 P A R T 2 | D E S I G N I N G O P E R AT I O N S
limits , or (c) out of control. We now look at ways to build control charts that help the opera- tions manager keep a process under control.
Control Charts for Variables The variables of interest here are those that have continuous dimensions. They have an infinite number of possibilities. Examples are weight, speed, length, or strength. Control charts for the mean, x or x -bar, and the range, R , are used to monitor processes that have continuous dimensions. The x -chart tells us whether changes have occurred in the central tendency (the mean, in this case) of a process. These changes might be due to such factors as tool wear, a gradual increase in temperature, a different method used on the second shift, or new and stronger materials. The R -chart values indicate that a gain or loss in dispersion has occurred. Such a change may be due to worn bearings, a loose tool, an erratic flow of lubricants to a machine, or to sloppiness on the part of a machine operator. The two types of charts go hand in hand when monitoring variables because they measure the two critical parameters: central tendency and dispersion.
The Central Limit Theorem
The theoretical foundation for x -charts is the central limit theorem . This theorem states that regardless of the distribution of the population, the distribution of x s (each of which is a mean of a sample drawn from the population) will tend to follow a normal curve as the num- ber of samples increases. Fortunately, even if each sample ( n ) is fairly small (say, 4 or 5), the distributions of the averages will still roughly follow a normal curve. The theorem also states that: (1) the mean of the distribution of the x s (called x ) will equal the mean of the overall population (called m ); and (2) the standard deviation of the sampling distribution , sx , will be the population (process) standard deviation , divided by the square root of the sample size, n . In other words: 2
x = m (S6-1)
and
sx = s
1n (S6-2)
x -chart
A quality control chart for variables
that indicates when changes
occur in the central tendency of a
production process.
R -chart
A control chart that tracks the
“range” within a sample; it
indicates that a gain or loss in uni-
formity has occurred in dispersion
of a production process.
Frequency
Size (weight, length, speed, etc.)
Upper control limitLower control limit
A process with only natural causes of variation and capable of producing within the specified control limits
(a) In statistical control and capable of producing within control limits
(b) In statistical control but not capable of producing within control limits
(c) Out of control
A process in control (only natural causes of variation are present) but not capable of producing within the specified control limits
A process out of control having assignable causes of variation
Figure S6.2
Process Control: Three Types
of Process Outputs
LO S6.2 Explain the role of the central limit
theorem in SPC
Central limit theorem
The theoretical foundation for
x -charts, which states that
regardless of the distribution of the
population of all parts or services,
the distribution of x s tends to fol-
low a normal curve as the number
of samples increases.
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S U P P L E M E N T 6 | S TAT I S T I C A L P R O C E S S C O N T R O L 249
Figure S6.3 shows three possible population distributions, each with its own mean, m, and standard deviation, s. If a series of random samples ( x1, x2, x3, x4, and so on), each of size n , is drawn from any population distribution (which could be normal, beta, uniform, and so on), the resulting distribution of xi s will approximate a normal distribution (see Figure S6.3 ).
Moreover, the sampling distribution, as is shown in Figure S6.4 (a), will have less vari- ability than the process distribution. Because the sampling distribution is normal, we can state that:
◆ 95.45% of the time, the sample averages will fall within {2sx if the process has only natural variations.
◆ 99.73% of the time, the sample averages will fall within {3sx if the process has only natural variations.
If a point on the control chart falls outside of the {3sx control limits, then we are 99.73% sure the process has changed. Figure S6.4 (b) shows that as the sample size increases, the sam- pling distribution becomes narrower. So the sample statistic is closer to the true value of the population for larger sample sizes. This is the theory behind control charts.
95.45% fall within ;2ux
99.73% of all x s fall within ;3ux
(mean)
Beta
Normal
Uniform
Population distributions
Distribution of sample means
+2ux +3ux+1ux–1ux–2ux–3ux x
Mean of sample means = x
Standard deviation of the sample means =ux
u
Un =
Figure S6.3
The Relationship Between
Population and Sampling
Distributions
Even though the population
distributions will differ (e.g., normal,
beta, uniform), each with its own
mean (m) and standard deviation (s), the distribution of sample means always approaches a
normal distribution.
n = 50
n = 100
As the sample size increases, the sampling distribution narrows
n = 25
Mean
(b)
Mean
x = m
Sampling distribution of means
Process distribution
The sampling distribution has less variability than the process distribution
(a)
Figure S6.4
The Sampling Distribution of Means Is Normal
The process distribution from which the sample was drawn was also normal, but it could have been any distribution.
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250 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Example S1 SETTING CONTROL LIMITS USING SAMPLES The weights of boxes of Oat Flakes within a large production lot are sampled each hour. Managers want to set control limits that include 99.73% of the sample means.
APPROACH c Randomly select and weigh nine (n = 9) boxes each hour. Then find the overall mean and use Equations (S6-3) and (S6-4) to compute the control limits. Here are the nine boxes chosen for Hour 1:
17 oz.
Oat Flakes
13 oz. 16 oz. 18 oz. 17 oz. 16 oz. 15 oz. 17 oz. 16 oz.
Oat Flakes
Oat Flakes
Oat Flakes
Oat Flakes
Oat Flakes
Oat Flakes
Oat Flakes
Oat Flakes
STUDENT TIP If you want to see an example
of such variability in your
supermarket, go to the soft
drink section and line up a few
2-liter bottles of Coke or Pepsi.
SOLUTION c
The average weight in the first hourly sample = 17 + 13 + 16 + 18 + 17 + 16 + 15 + 17 + 16
9
= 16.1 ounces.
Also, the population (process) standard deviation (s) is known to be 1 ounce. We do not show each of the boxes randomly selected in hours 2 through 12, but here are all 12 hourly samples:
WEIGHT OF SAMPLE WEIGHT OF SAMPLE WEIGHT OF SAMPLE
HOUR (AVG. OF 9 BOXES) HOUR (AVG. OF 9 BOXES) HOUR (AVG. OF 9 BOXES)
1 16.1 5 16.5 9 16.3
2 16.8 6 16.4 10 14.8
3 15.5 7 15.2 11 14.2
4 16.5 8 16.4 12 17.3
The average mean x of the 12 samples is calculated to be exactly 16 ounces £
x = a 12
i = 1 (Avg. of 9 Boxes)
12 § .
LO S6.3 Build x -charts and R -charts
Setting Mean Chart Limits ( x -Charts) If we know, through past data, the standard deviation of the population (process), s, we can set upper and lower control limits 3 by using these formulas:
Upper control limit (UCL) = x + zsx (S6-3)
Lower control limit (LCL) = x - zsx (S6-4)
where x = mean of the sample means or a target value set for the process z = number of normal standard deviations (2 for 95.45% confidence, 3 for 99.73%) sx = standard deviation of the sample means = s>1n s = population (process) standard deviation n = sample size
Example S1 shows how to set control limits for sample means using standard deviations.
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S U P P L E M E N T 6 | S TAT I S T I C A L P R O C E S S C O N T R O L 251
We therefore have x = 16 ounces, s = 1 ounce, n = 9, and z = 3. The control limits are:
UCL x = x + zsx = 16 + 3¢ 1 19
≤ = 16 + 3¢ 1 3 ≤ = 17 ounces
LCL x = x - zsx = 16 - 3¢ 1 19
≤ = 16 - 3¢ 1 3 ≤ = 15 ounces
The 12 samples are then plotted on the following control chart:
Control chart for samples of 9 boxes
17 = UCL
Out of control
Out of controlSample number
1 2 3 4 5 6 7 8 9 10 11 12
Variation due to assignable
causes
Variation due to assignable
causes
Variation due to natural causes
15 = LCL
16 = Mean
INSIGHT c Because the means of recent sample averages fall outside the upper and lower control limits of 17 and 15, we can conclude that the process is becoming erratic and is not in control.
LEARNING EXERCISE c If Oat Flakes’s population standard deviation (s) is 2 (instead of 1), what is your conclusion? [Answer: LCL = 14, UCL = 18; the process would be in control.]
RELATED PROBLEMS c S6.1, S6.2, S6.4, S6.8, S6.10a,b (S6.28 is available in MyOMLab)
ACTIVE MODEL S6.1 This example is further illustrated in Active Model S6.1 in MyOMLab.
EXCEL OM Data File CH06ExS1.XLS can be found in MyOMLab.
Because process standard deviations are often not available, we usually calculate control limits based on the average range values rather than on standard deviations. Table S6.1 provides the necessary conversion for us to do so. The range ( R i ) is defined as the difference between the largest and smallest items in one sample. For example, the heaviest box of Oat Flakes in Hour 1 of Example S1 was 18 ounces and the lightest was 13 ounces, so the range for that hour is 5 ounces. We use Table S6.1 and the equations:
UCLx = x + A2R (S6-5)
and:
LCLx = x - A2R (S6-6)
where R = a k
i = 1 Ri
k = average range of all the samples; Ri = range for sample i
A2 = value found in Table S6.1 k = total number of samples
x = mean of the sample means
Example S2 shows how to set control limits for sample means by using Table S6.1 and the average range.
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252 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Super Cola bottles soft drinks labeled “net weight 12 ounces.” Indeed, an overall process average of 12 ounces has been found by taking 10 samples, in which each sample contained 5 bottles. The OM team wants to determine the upper and lower control limits for averages in this process.
APPROACH c Super Cola first examines the 10 samples to compute the average range of the process. Here are the data and calculations:
SAMPLE WEIGHT OF LIGHTEST BOTTLE
IN SAMPLE OF n 5 5 WEIGHT OF HEAVIEST BOTTLE
IN SAMPLE OF n 5 5 RANGE (Ri ) 5 DIFFERENCE
BETWEEN THESE TWO
1 11.50 11.72 .22
2 11.97 12.00 .03
3 11.55 12.05 .50
4 12.00 12.20 .20
5 11.95 12.00 .05
6 10.55 10.75 .20
7 12.50 12.75 .25
8 11.00 11.25 .25
9 10.60 11.00 .40
10 11.70 12.10 .40
∑ R i 5 2.50
Average Range = 2.50
10 samples 5 .25 ounces
Now Super Cola applies Equations (S6-5) and (S6-6) and uses the A2 column of Table S6.1 .
SOLUTION c Looking in Table S6.1 for a sample size of 5 in the mean factor A2 column, we find the value .577. Thus, the upper and lower control chart limits are:
UCL x = x + A2R
= 12 + (.577)(.25)
= 12 + .144
= 12.144 ounces
Example S2 SETTING MEAN LIMITS USING TABLE VALUES
TABLE S6.1 Factors for Computing Control Chart Limits (3 sigma)
SAMPLE SIZE, n MEAN FACTOR, A 2 UPPER RANGE, D 4 LOWER RANGE, D 3
2 1.880 3.268 0
3 1.023 2.574 0
4 .729 2.282 0
5 .577 2.115 0
6 .483 2.004 0
7 .419 1.924 0.076
8 .373 1.864 0.136
9 .337 1.816 0.184
10 .308 1.777 0.223
12 .266 1.716 0.284
Source: Reprinted by permission of American Society for Testing Materials. Copyright 1951. Taken from Special Technical Publication 15–C,
“Quality Control of Materials,” pp. 63 and 72 . Copyright ASTM INTERNATIONAL. Reprinted with permission.
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S U P P L E M E N T 6 | S TAT I S T I C A L P R O C E S S C O N T R O L 253
LCL x = x - A2R
= 12 - .144
= 11.856 ounces
INSIGHT c The advantage of using this range approach, instead of the standard deviation, is that it is easy to apply and may be less confusing.
LEARNING EXERCISE c If the sample size was n = 4 and the average range = .20 ounces, what are the revised UCLx and LCLx ? [Answer: 12.146, 11.854.]
RELATED PROBLEMS c S6.3a, S6.5, S6.6, S6.7, S6.9, S6.10b,c,d, S6.11, S6.26 (S6.29a, S6.30a, S6.31a, S6.32a, S6.33a are available in MyOMLab)
EXCEL OM Data File CH06ExS2.xls can be found in MyOMLab.
Setting Range Chart Limits ( R -Charts) In Examples S1 and S2, we determined the upper and lower control limits for the process aver- age . In addition to being concerned with the process average, operations managers are inter- ested in the process dispersion , or range . Even though the process average is under control, the dispersion of the process may not be. For example, something may have worked itself loose in a piece of equipment that fills boxes of Oat Flakes. As a result, the average of the samples may remain the same, but the variation within the samples could be entirely too large. For this reason, operations managers use control charts for ranges to monitor the process variability, as well as control charts for averages, which monitor the process central tendency. The theory behind the control charts for ranges is the same as that for process average control charts. Limits are established that contain {3 standard deviations of the distribution for the aver- age range R. We can use the following equations to set the upper and lower control limits for ranges:
UCLR = D4R (S6-7)
LCLR = D3R (S6-8)
where UCLR = upper control chart limit for the range LCLR = lower control chart limit for the range D4 and D3 = values from Table S6.1
11.5 UCL=11.524
UCL=0.6943
R=0.2125
LCL=10.394
LCL=0
x=10.959
1 3 5 7 9 11 13 15 17
10.5
11.0
S a m
p le
M e a n
0.8
1 3 5 7 9 11 13 15 17 0.0
0.4
R (Range) Chart
S a m
p le
R a n g e
x (mean) Chart–
Salmon filets are monitored by Darden Restaurant’s SPC software, which includes x -(mean) charts and R -(range) charts. Darden
uses average weight as a measure of central tendency for salmon filets. The range is the difference between the heaviest and the
lightest filets in each sample. The video case study “Farm to Fork,” at the end of this supplement, asks you to interpret these figures.
VIDEO S6.1 Farm to Fork: Quality of Darden
Restaurants
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254 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Example S3 shows how to set control limits for sample ranges using Table S6.1 and the aver- age range.
Example S3 SETTING RANGE LIMITS USING TABLE VALUES Roy Clinton’s mail-ordering business wants to measure the response time of its operators in taking customer orders over the phone. Clinton lists below the time recorded (in minutes) from five different samples of the ordering process with four customer orders per sample. He wants to determine the upper and lower range control chart limits.
APPROACH c Looking in Table S6.1 for a sample size of 4, he finds that D4 = 2.282 and D3 = 0.
SOLUTION c
SAMPLE OBSERVATIONS (MINUTES) SAMPLE RANGE ( R i )
1 5, 3, 6, 10 10 – 3 5 7
2 7, 5, 3, 5 7 – 3 5 4
3 1, 8, 3, 12 12 – 1 5 11
4 7, 6, 2, 1 7 – 1 5 6
5 3, 15, 6, 12 15 – 3 5 12
∑ R i 5 40
R 5 40 5
5 8
UCL R 5 2.282(8) 5 18.256 minutes
LCL R 5 0(8) 5 0 minutes
INSIGHT c Computing ranges with Table S6.1 is straightforward and an easy way to evaluate dispersion. No sample ranges are out of control.
LEARNING EXERCISE c Clinton decides to increase the sample size to n = 6 (with no change in average range, R ). What are the new UCLR and LCLR values? [Answer: 16.032, 0.]
RELATED PROBLEMS c S6.3b, S6.5, S6.6, S6.7, S6.9, S6.10c, S6.11, S6.12, S6.26 (S6.29b, S6.30b, S6.31b, S6.32b, S6.33b are available in MyOMLab)
Using Mean and Range Charts The normal distribution is defined by two parameters, the mean and standard deviation . The x (mean)-chart and the R -chart mimic these two parameters. The x -chart is sensitive to shifts in the process mean, whereas the R -chart is sensitive to shifts in the process standard deviation. Consequently, by using both charts we can track changes in the process distribution.
For instance, the samples and the resulting x -chart in Figure S6.5 (a) show the shift in the process mean, but because the dispersion is constant, no change is detected by the R -chart. Conversely, the samples and the x -chart in Figure S6.5 (b) detect no shift (because none is pres- ent), but the R -chart does detect the shift in the dispersion. Both charts are required to track the process accurately.
Steps to Follow When Building Control Charts There are five steps that are gen- erally followed in building x - and R -charts:
1. Collect 20 to 25 samples, often of n = 4 or n = 5 observations each, from a stable process, and compute the mean and range of each.
2. Compute the overall means ( x and R ), set appropriate control limits, usually at the 99.73% level, and calculate the preliminary upper and lower control limits. Refer to Table S6.2 for other control limits. If the process is not currently stable and in control , use the desired mean, m, instead of x to calculate limits.
LO S6.4 List the five steps involved in building
control charts
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S U P P L E M E N T 6 | S TAT I S T I C A L P R O C E S S C O N T R O L 255
3. Graph the sample means and ranges on their respective control charts, and determine whether they fall outside the acceptable limits.
4. Investigate points or patterns that indicate the process is out of control. Try to assign causes for the variation, address the causes, and then resume the process.
5. Collect additional samples and, if necessary, revalidate the control limits using the new data.
UCL
UCL
LCL
LCL
These sampling distributions result in the charts below.
These sampling distributions result in the charts below.
(a)
(b)
UCL
UCL
LCL
LCL
x-chart (x-chart detects shift in central tendency
(Sampling mean is shifting upward, but range is consistent.)
(Sampling mean is constant, but dispersion is increasing.)
(R-chart indicates no change in dispersion [mean].)
(x-chart indicates no change in central tendency.)
(R-chart detects increase in dispersion [range].)
x-chart
R-chart
R-chart
.)
Figure S6.5
Mean and Range Charts
Complement Each Other
by Showing the Mean and
Dispersion of the Normal
Distribution
STUDENT TIP Mean ( x ) charts are a measure
of central tendency , while
range ( R ) charts are a measure
of dispersion . SPC requires
both charts for a complete
assessment because a sample
mean could be out of control
while the range is in control
and vice versa.
TABLE S6.2
Common z Values
DESIRED CONTROL LIMIT (%)
Z-VALUE (STANDARD DEVIATION
REQUIRED FOR DESIRED LEVEL OF
CONFIDENCE)
90.0 1.65
95.0 1.96
95.45 2.00
99.0 2.58
99.73 3.00
WEIGHTED SINGLE SAMPLE CENTERLINING CHART
IN ST
R U
C TI
O N
S
FL-5885 (Rev. 11/03)
SHIFT
1st SHIFT
2nd SHIFT
3rd SHIFT
RED ABORT SPECIFICATION LIMITS
NUMBER OF CHECKS MAX. TIME GAP BETWEEN CHECKS
1. CURRENT VALUE MUST BE WITHIN SPECIFICATION LIMITS. IF NOT, FOLLOW PRESCRIBED PROCEDURES (ABORT, HOLD, ETC.). RECORD CORRECTIVE ACTION AT BOTTOM OF CHART.
2. PREDICTED VALUE = AVERAGE OF CURRENT VALUE AND PREVIOUS PREDICTED VALUE. THE PREDICTED VALUE IS ALWAYS PLOTTED ON THE CHART.
3. DECISION RULE: ANY PREDICTED VALUE IN THE YELLOW (RED TAKE ACTION) INDICATES IMMEDIATE CORRECTIVE ACTION IS REQUIRED. RECORD CORRECTIVE ACTION AT BOTTOM OF CHART.
4. AT STARTUP OR AFTER MAJOR PROCESS ADJUSTMENTS THE CURRENT VALUE WILL ALSO BE USED AS THE PREDICTED VALUE.
TIME:
CURRENT VALUE
216
204
ADJUST
ADJUST
UCL = 2.22
AIM = 2.10
LCL = 1.98
710 750 830 910 950 1000 1035 1115 1155 1235 115 218 182 210 215 194 Break 1.99 193 193 205 191
218
218
200
200
205
205
210
210
202
202
201
201
197
197
193
193
199
199
195
195
10 Checks 40 min
PREDICTED VALUE (PLOT ON CHART)
D o n n a M
cW ill
ia m
/A P
I m
a g e s
Frito-Lay uses x charts to control production quality at critical points in the process. About every 40 minutes, three batches of chips are taken from the
conveyor (on the left) and analyzed electronically to get an average salt content, which is plotted on an x −
-chart (on the right). Points plotted in the green zone
are “in control,” while those in the yellow zone are “out of control.” The SPC chart is displayed where all production employees can monitor process stability.
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256 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Example S4 SETTING CONTROL LIMITS FOR PERCENT DEFECTIVE Clerks at Mosier Data Systems key in thousands of insurance records each day for a variety of client firms. CEO Donna Mosier wants to set control limits to include 99.73% of the random variation in the data entry process when it is in control.
APPROACH c Samples of the work of 20 clerks are gathered (and shown in the table). Mosier care- fully examines 100 records entered by each clerk and counts the number of errors. She also computes the fraction defective in each sample. Equations (S6-9) , (S6-10) , and (S6-11) are then used to set the control limits.
SAMPLE NUMBER
NUMBER OF ERRORS
FRACTION DEFECTIVE
SAMPLE NUMBER
NUMBER OF ERRORS
FRACTION DEFECTIVE
1 6 .06 11 6 .06
2 5 .05 12 1 .01
3 0 .00 13 8 .08
4 1 .01 14 7 .07
5 4 .04 15 5 .05
6 2 .02 16 4 .04
7 5 .05 17 11 .11
8 3 .03 18 3 .03
9 3 .03 19 0 .00
10 2 .02 20 4 .04
80
Control Charts for Attributes Control charts for x and R do not apply when we are sampling attributes , which are typically classified as defective or nondefective . Measuring defectives involves counting them (for exam- ple, number of bad lightbulbs in a given lot, or number of letters or data entry records typed with errors), whereas variables are usually measured for length or weight. There are two kinds of attribute control charts: (1) those that measure the percent defective in a sample—called p -charts—and (2) those that count the number of defects—called c -charts.
p-Charts Using p -charts is the chief way to control attributes. Although attributes that are either good or bad follow the binomial distribution, the normal distribution can be used to calculate p -chart limits when sample sizes are large. The procedure resembles the x -chart approach, which is also based on the central limit theorem.
The formulas for p -chart upper and lower control limits follow:
UCLp = p + zsp (S6-9)
LCLp = p - zsp (S6-10)
where p = mean fraction (percent) defective in the samples 5 total number of defects
sample size * number of samples z = number of standard deviations ( z = 2 for 95.45% limits; z = 3 for 99.73% limits) sp = standard deviation of the sampling distribution
sp is estimated by the formula:
nsp = B
p(1 - p) n
(S6-11)
where n = number of observations in each sample 4 Example S4 shows how to set control limits for p -charts for these standard deviations.
LO S6.5 Build p -charts and c -charts
p -chart
A quality control chart that is used
to control attributes.
VIDEO S6.2 Frito-Lay’s Quality-Controlled Potato
Chips
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The OM in Action box “Trying to Land a Seat with Frequent Flyer Miles” provides a real- world follow-up to Example S4 .
c -Charts In Example S4 , we counted the number of defective records entered. A defective record was one that was not exactly correct because it contained at least one defect. However, a bad record may contain more than one defect. We use c -charts to control the number of defects per unit of output (or per insurance record, in the preceding case).
SOLUTION c
p = Total number of errors
Total number of records examined =
80 (100) (20)
= .04
nsp = B
(.04)(1 - .04) 100
= .02 (rounded up from .0196)
( Note: 100 is the size of each sample = n .)
UCLp = p + z nsp = .04 + 3(.02) = .10
LCLp = p - z nsp = .04 - 3(.02) = 0
(because we cannot have a negative percentage defective)
INSIGHT c When we plot the control limits and the sample fraction defectives, we find that only one data-entry clerk (number 17) is out of control. The firm may wish to examine that individual’s work a bit more closely to see if a serious problem exists (see Figure S6.6 ).
.11
.10
.09
.08
.07
.06
.05
.04
.03
.02
.01
.00 1 2 3 4 5 6 7 8 9 10 11 12
Sample number
13 14 15 16 17 18 19 20
F ra
ct io
n d
e fe
ct iv
e
UCLp = 0.10
p = 0.04–
LCLp = 0.00
LEARNING EXERCISE c Mosier decides to set control limits at 95.45% instead. What are the new UCLp and LCLp? [Answer: 0.08, 0]
RELATED PROBLEMS c S6.13–S6.20, S6.25, S6.27 (S6.35–S6.39 are available in MyOMLab)
ACTIVE MODEL S6.2 This example is further illustrated in Active Model S6.2 in MyOMLab.
EXCEL OM Data File Ch06ExS4.xls can be found in MyOMLab.
STUDENT TIP We are always pleased to be at
zero or below the center line in
a p -chart.
Figure S6.6
p -Chart for Data Entry for
Example S4
c -chart
A quality control chart used to
control the number of defects per
unit of output.
Trying to Land a Seat with Frequent Flyer Miles
How hard is it to redeem your 25,000 frequent flyer points for airline tickets?
That depends on the airline. (It also depends on the city. Don’t try to get into
or out of San Francisco!) When the consulting firm Idea Works made 280
requests for a standard mileage award to each of 24 airlines’ Web sites (a total
of 6,720 requests), the success rates ranged from a low of 25.7% and 27.1%
(at US Airways and Delta, respectively) to a high of 100% at GOL-Brazil and
99.3% at Southwest.
The overall average of 68.6% for the two dozen carriers provides the
center line in a p -chart. With 3-sigma upper and lower control limits of
82.5% and 54.7%, the other top and bottom performers are easily spotted.
“Out of control” (but in a positive outperforming way) are GOL and Southwest,
OM in Action Lufthansa (85.0%), Singapore (90.7%), Virgin Australia (91.4%), and Air
Berlin (96.4%).
Out of control on the negative side are US Airways and Delta, plus Emirates
(35.7%), AirTran (47.1%), Turkish (49.3%), and SAS (52.9%).
Control charts can help airlines see where they stand relative to competi-
tors in such customer service activities as lost bags, on-time rates, and ease
of redeeming mileage points. “I think airlines are getting the message that
availability is important. Are airlines where they need to be? I don’t think so,”
says the president of Idea Works.
Sources: Wall Street Journal (May 26, 2011); and Consumer Reports
(November 2014).
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258 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Control charts for defects are helpful for monitoring processes in which a large number of potential errors can occur, but the actual number that do occur is relatively small. Defects may be errors in newspaper words, bad circuits in a microchip, blemishes on a table, or missing pickles on a fast-food hamburger.
The Poisson probability distribution, 5 which has a variance equal to its mean, is the basis for c -charts. Because c is the mean number of defects per unit, the standard deviation is equal to 2c . To compute 99.73% control limits for c , we use the formula:
Control limits = c { 32c (S6-12)
Example S5 shows how to set control limits for a c -chart.
Sampling wine from these wooden
barrels, to make sure it is aging
properly, uses both SPC (for alcohol
content and acidity) and subjective
measures (for taste).
C h a rl e s
O ’R
e a r/
C o rb
is
Example S5 SETTING CONTROL LIMITS FOR NUMBER OF DEFECTS Red Top Cab Company receives several complaints per day about the behavior of its drivers. Over a 9-day period (where days are the units of measure), the owner, Gordon Hoft, received the following numbers of calls from irate passengers: 3, 0, 8, 9, 6, 7, 4, 9, 8, for a total of 54 complaints. Hoft wants to compute 99.73% control limits.
APPROACH c He applies Equation (S6–12) .
SOLUTION c c = 54 9
= 6 complaints per day
Thus:
UCLc = c + 32c = 6 + 316 = 6 + 3(2.45) = 13.35, or 13
LCLc = c - 32c = 6 - 316 = 6 - 3(2.45) = 0 d (since it cannot be negative)
INSIGHT c After Hoft plotted a control chart summarizing these data and posted it prominently in the drivers’ locker room, the number of calls received dropped to an average of three per day. Can you explain why this occurred?
LEARNING EXERCISE c Hoft collects 3 more days’ worth of complaints (10, 12, and 8 complaints) and wants to combine them with the original 9 days to compute updated control limits. What are the revised UCLc and LCLc ? [Answer: 14.94, 0.]
RELATED PROBLEMS c S6.21, S6.22, S6.23, S6.24
EXCEL OM Data File Ch06SExS5.xls can be found in MyOMLab.
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Managerial Issues and Control Charts In an ideal world, there is no need for control charts. Quality is uniform and so high that employees need not waste time and money sampling and monitoring variables and attributes. But because most processes have not reached perfection, managers must make three major decisions regarding control charts.
First, managers must select the points in their process that need SPC. They may ask “Which parts of the job are critical to success?” or “Which parts of the job have a tendency to become out of control?”
Second, managers need to decide if variable charts (i.e., x and R ) or attribute charts (i.e., p and c ) are appropriate. Variable charts monitor weights or dimensions. Attribute charts are more of a “yes–no” or “go–no go” gauge and tend to be less costly to implement. Table S6.3 can help you understand when to use each of these types of control charts.
Third, the company must set clear and specific SPC policies for employees to follow. For example, should the data-entry process be halted if a trend is appearing in percent defective records being keyed? Should an assembly line be stopped if the average length of five successive samples is above the centerline? Figure S6.7 illustrates some of the patterns to look for over time in a process.
STUDENT TIP This is a really useful table.
When you are not sure which
control chart to use, turn here
for clarification.
TABLE S6.3 Helping You Decide Which Control Chart to Use
VARIABLE DATA USING AN x -CHART AND AN R -CHART
1. Observations are variables , which are usually products measured for size or weight. Examples are the width or length of a wire and the weight of a can of Campbell’s soup.
2. Collect 20 to 25 samples, usually of n = 4, n = 5, or more, each from a stable process, and compute the means for an x -chart and the ranges for an R -chart.
3. We track samples of n observations each, as in Example S1 .
ATTRIBUTE DATA USING A p-CHART
1. Observations are attributes that can be categorized as good or bad (or pass–fail, or functional–broken); that is, in two states.
2. We deal with fraction, proportion, or percent defectives. 3. There are several samples, with many observations in each. For example, 20 samples of n = 100
observations in each, as in Example S4 .
ATTRIBUTE DATA USING A c -CHART
1. Observations are attributes whose defects per unit of output can be counted. 2. We deal with the number counted, which is a small part of the possible occurrences. 3. Defects may be: number of blemishes on a desk; fl aws in a bolt of cloth; crimes in a year; broken seats
in a stadium; typos in a chapter of this text; or complaints in a day, as is shown in Example S5 .
Upper control limit
Target
Lower control limit
Upper control limit
Target
Lower control limit
Normal behavior. Process is “in control.”
One point out above (or below). Investigate for cause. Process is “out of control.”
Run of 5 points above (or below) central line. Investigate for cause.
Two points very near lower (or upper) control. Investigate for cause.
Trends in either direction, 5 points. Investigate for cause of progressive change. This could be the result of gradual tool wear.
Erratic behavior. Investigate.
Figure S6.7
Patterns to Look for on Control
Charts
Source: Adapted from Bertrand L. Hansen,
Quality Control: Theory and Applications
(1991): 65. Reprinted by permission of
Prentice Hall, Upper Saddle River, NJ.
STUDENT TIP Workers in companies such as
Frito-Lay are trained to follow
rules like these.
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260 P A R T 2 | D E S I G N I N G O P E R AT I O N S
A tool called a run test is available to help identify the kind of abnormalities in a process that we see in Figure S6.7 . In general, a run of 5 points above or below the target or centerline may suggest that an assignable, or nonrandom, variation is present. When this occurs, even though all the points may fall inside the control limits, a flag has been raised. This means the process may not be statisti- cally in control. A variety of run tests are described in books on the subject of quality methods.
Process Capability Statistical process control means keeping a process in control. This means that the natural variation of the process must be stable. However, a process that is in statistical control may not yield goods or services that meet their design specifications (tolerances). In other words, the variation should be small enough to produce consistent output within specifications. The ability of a process to meet design specifications, which are set by engineering design or cus- tomer requirements, is called process capability . Even though that process may be statistically in control (stable), the output of that process may not conform to specifications.
For example, let’s say the time a customer expects to wait for the completion of a lube job at Quik Lube is 12 minutes, with an acceptable tolerance of {2 minutes. This tolerance gives an upper specification of 14 minutes and a lower specification of 10 minutes. The lube process has to be capable of operating within these design specifications—if not, some customers will not have their requirements met. As a manufacturing example, the tolerances for Harley-Davidson cam gears are extremely low, only 0.0005 inch—and a process must be designed that is capable of achieving this tolerance.
There are two popular measures for quantitatively determining if a process is capable: process capability ratio ( Cp ) and process capability index ( Cpk ).
Process Capability Ratio (C p )
For a process to be capable, its values must fall within upper and lower specifications. This typically means the process capability is within {3 standard deviations from the process mean. Because this range of values is 6 standard deviations, a capable process tolerance, which is the difference between the upper and lower specifications, must be greater than or equal to 6.
The process capability ratio, C p , is computed as:
Cp = Upper specification - Lower specification
6s (S6-13)
Example S6 shows the computation of Cp.
Run test
A test used to examine the points
in a control chart to see if
nonrandom variation is present.
STUDENT TIP Here we deal with whether a
process meets the specification
it was designed to yield.
Process capability
The ability to meet design
specifications.
LO S6.6 Explain process capability and
compute C p and C
pk
C p
A ratio for determining whether a
process meets design specifica-
tions; a ratio of the specification to
the process variation.
Example S6 PROCESS CAPABILITY RATIO (C P )
In a GE insurance claims process, x = 210.0 minutes, and s = .516 minutes. The design specification to meet customer expectations is 210 { 3 minutes. So the Upper
Specification is 213 minutes and the lower specification is 207 minutes. The OM manager wants to compute the process capability ratio.
APPROACH c GE applies Equation (S6-13) .
SOLUTION c Cp = Upper specification - Lower specification
6s =
213 - 207 6(.516)
= 1.938
INSIGHT c Because a ratio of 1.00 means that 99.73% of a process’s outputs are within specifications, this ratio suggests a very capable process, with nonconformance of less than 4 claims per million.
LEARNING EXERCISE c If s = .60 (instead of .516), what is the new Cp ? [Answer: 1.667, a very capable process still.]
RELATED PROBLEMS c S6.40, S6.41 (S6.50 is available in MyOMLab)
ACTIVE MODEL S6.3 This example is further illustrated in Active Model S6.3 in MyOMLab.
EXCEL OM Data File Ch06SExS6.xls can be found in MyOMLab.
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A capable process has a Cp of at least 1.0. If the Cp is less than 1.0, the process yields prod- ucts or services that are outside their allowable tolerance. With a Cp of 1.0, 2.7 parts in 1,000 can be expected to be “out of spec.” 6 The higher the process capability ratio, the greater the likelihood the process will be within design specifications. Many firms have chosen a Cp of 1.33 (a 4-sigma standard) as a target for reducing process variability. This means that only 64 parts per million can be expected to be out of specification.
Recall that in Chapter 6 we mentioned the concept of Six Sigma quality, championed by GE and Motorola. This standard equates to a Cp of 2.0, with only 3.4 defective parts per million (very close to zero defects) instead of the 2.7 parts per 1,000 with 3-sigma limits.
Although Cp relates to the spread (dispersion) of the process output relative to its tolerance, it does not look at how well the process average is centered on the target value.
Process Capability Index (C pk
) The process capability index, C
pk , measures the difference between the desired and actual
dimensions of goods or services produced. The formula for Cpk is:
Cpk = Minimum of J Upper specification limit - X3s , X - Lower specification limit
3s R
(S6-14) where X = process mean
s = standard deviation of the process population
When the Cpk index for both the upper and lower specification limits equals 1.0, the process variation is centered and the process is capable of producing within {3 standard deviations (fewer than 2,700 defects per million). A C pk of 2.0 means the process is capable of producing fewer than 3.4 defects per million. For C pk to exceed 1, s must be less than
1 3 of the difference
between the specification and the process mean ( X ). Figure S6.8 shows the meaning of various measures of C pk , and Example S7 shows an application of C pk .
C pk
A proportion of variation (3 s )
between the center of the process
and the nearest specification limit.
Example S7 PROCESS CAPABILITY INDEX (C pk
)
You are the process improvement manager and have developed a new machine to cut insoles for the company’s top-of-the-line running shoes. You are excited because the company’s goal is no more than 3.4 defects per million, and this machine may be the innovation you need. The insoles cannot be more than {.001 of an inch from the required thickness of .250 ″ . You want to know if you should replace the existing machine, which has a Cpk of 1.0.
Mean of the new process X = .250 inches. Standard deviation of the new process = s = .0005 inches.
APPROACH c You decide to determine the Cpk , using Equation (S6-14) , for the new machine and make a decision on that basis.
SOLUTION c Upper specification limit = .251 inches
Lower specification limit = .249 inches
Cpk = Minimum of J Upper specification limit - X3s , X - Lower specification limit
3s R
Cpk = Minimum of J .251 - .250(3).0005 , .250 - .249
(3).0005 R
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262 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Note that Cp and Cpk will be the same when the process is centered. However, if the mean of the process is not centered on the desired (specified) mean, then the smaller numerator in Equation (S6-14) is used (the minimum of the difference between the upper specification limit and the mean or the lower specification limit and the mean). This application of Cpk is shown in Solved Problem S6.4. Cpk is the standard criterion used to express process performance.
Acceptance Sampling 7 Acceptance sampling is a form of testing that involves taking random samples of “lots,” or batches, of finished products and measuring them against predetermined standards. Sampling is more economical than 100% inspection. The quality of the sample is used to judge the qual- ity of all items in the lot. Although both attributes and variables can be inspected by accept- ance sampling, attribute inspection is more commonly used, as illustrated in this section.
Acceptance sampling can be applied either when materials arrive at a plant or at final inspection, but it is usually used to control incoming lots of purchased products. A lot of items rejected, based on an unacceptable level of defects found in the sample, can (1) be returned to the supplier or (2) be 100% inspected to cull out all defects, with the cost of this screening usually billed to the supplier. However, acceptance sampling is not a substitute for adequate process controls. In fact, the current approach is to build statistical quality controls at suppliers so that acceptance sampling can be eliminated.
Cpk = negative number (Process does not meet specifications.)
Cpk = zero (Process does not meet specifications.)
Cpk = between 0 and 1 (Process does not meet specifications.)
Cpk = 1 (Process meets specifications.)
Cpk greater than 1 (Process is better than the specification requires.) Lower
specification limit
Upper specification
limit
Both calculations result in: .001 .0015
= .67.
INSIGHT c Because the new machine has a Cpk of only 0.67, the new machine should not replace the existing machine.
LEARNING EXERCISE c If the insoles can be {.002″ (instead of .001″ ) from the required .250″ , what is the new Cpk ? [Answer: 1.33 and the new machine should replace the existing one.]
RELATED PROBLEMS c S6.41–S6.45 (S6.46–S6.49 are available in MyOMLab)
ACTIVE MODEL S6.2 This example is further illustrated in Active Model S6.2 in MyOMLab.
EXCEL OM Data File Ch06SExS7.xls can be found in MyOMLab.
Acceptance sampling
A method of measuring random
samples of lots or batches of
products against predetermined
standards.
LO S6.7 Explain acceptance sampling
Figure S6.8
Meanings of C pk
Measures
A C pk
index of 1.0 for both the
upper and lower specification
limits indicates that the process
variation is within the upper and
lower specification limits. As the
C pk
index goes above 1.0, the
process becomes increasingly
target oriented, with fewer defects.
If the C pk
is less than 1.0, the
process will not produce within
the specified tolerance. Because
a process may not be centered,
or may “drift,” a C pk
above 1 is
desired.
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Operating Characteristic Curve The operating characteristic (OC) curve describes how well an acceptance plan discriminates between good and bad lots. A curve pertains to a specific plan—that is, to a combination of n (sample size) and c (acceptance level). It is intended to show the probability that the plan will accept lots of various quality levels.
With acceptance sampling, two parties are usually involved: the producer of the product and the consumer of the product. In specifying a sampling plan, each party wants to avoid costly mistakes in accepting or rejecting a lot. The producer usually has the responsibility of replacing all defects in the rejected lot or of paying for a new lot to be shipped to the customer. The producer, therefore, wants to avoid the mistake of having a good lot rejected ( producer’s risk ). On the other hand, the customer or consumer wants to avoid the mistake of accepting a bad lot because defects found in a lot that has already been accepted are usually the responsibil- ity of the customer ( consumer’s risk ). The OC curve shows the features of a particular sampling plan, including the risks of making a wrong decision. The steeper the curve, the better the plan distinguishes between good and bad lots. 8
Figure S6.9 can be used to illustrate one sampling plan in more detail. Four concepts are illustrated in this figure.
The acceptable quality level (AQL) is the poorest level of quality that we are willing to accept. In other words, we wish to accept lots that have this or a better level of quality, but no worse. If an acceptable quality level is 20 defects in a lot of 1,000 items or parts, then AQL is 20>1,000 = 2, defectives.
The lot tolerance percentage defective (LTPD) is the quality level of a lot that we consider bad. We wish to reject lots that have this or a poorer level of quality. If it is agreed that an unacceptable quality level is 70 defects in a lot of 1,000, then the LTPD is 70>1,000 = 7, defective.
To derive a sampling plan, producer and consumer must define not only “good lots” and “bad lots” through the AQL and LTPD, but they must also specify risk levels.
Producer’s risk ( a ) is the probability that a “good” lot will be rejected. This is the risk that a random sample might result in a much higher proportion of defects than the population of all items. A lot with an acceptable quality level of AQL still has an a chance of being rejected. Sampling plans are often designed to have the producer’s risk set at a = .05, or 5%.
Consumer’s risk ( b ) is the probability that a “bad” lot will be accepted. This is the risk that a random sample may result in a lower proportion of defects than the overall population of items. A common value for consumer’s risk in sampling plans is b = .10, or 10,.
The probability of rejecting a good lot is called a type I error . The probability of accepting a bad lot is a type II error .
Sampling plans and OC curves may be developed by computer (as seen in the software available with this text), by published tables, or by calculation, using binomial or Poisson distributions.
Raw data for Statistical Process
Control is collected in a wide
variety of ways. Here physical
measures using a micrometer
(on the left) and a microscope
(on the right) are being made.
Operating characteristic (OC) curve
A graph that describes how well
an acceptance plan discriminates
between good and bad lots.
Producer’s risk
The mistake of having a producer’s
good lot rejected through sampling.
Consumer’s risk
The mistake of a customer’s ac-
ceptance of a bad lot overlooked
through sampling.
Acceptable quality level (AQL)
The quality level of a lot
considered good.
Lot tolerance percentage defective (LTPD)
The quality level of a lot
considered bad.
Type I error
Statistically, the probability of
rejecting a good lot.
Type II error
Statistically, the probability of
accepting a bad lot.
R ic
h a rd
T . N
o w
it z/
F lir
t/ A
la m
y
L yr
o ky
/A la
m y
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264 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Average Outgoing Quality In most sampling plans, when a lot is rejected, the entire lot is inspected and all defective items replaced. Use of this replacement technique improves the average outgoing quality in terms of percent defective. In fact, given (1) any sampling plan that replaces all defective items encountered and (2) the true incoming percent defective for the lot, it is possible to determine the average outgoing quality (AOQ) in percentage defective. The equation for AOQ is:
AOQ = (Pd) (Pa) (N - n)
N (S6-15)
where Pd = true percentage defective of the lot Pa = probability of accepting the lot for a given sample size and quantity defective N = number of items in the lot n = number of items in the sample
The maximum value of AOQ corresponds to the highest average percentage defective or the lowest average quality for the sampling plan. It is called the average outgoing quality limit (AOQL) .
0
Indifference zone
1.00
.95
.75
.50
.25
.10
0 2 4 6 81 3 5 7
Bad lotsGood lots
Consumer’s risk
for LTPD AQL LTPD
Probability of
acceptance
d = .10 Percentage defective
c = .05 Producer’s risk for AQL
This laser tracking device, by Faro
Technologies, enables quality control
personnel to measure and inspect
parts and tools during production.
The portable tracker can measure objects
from 262 feet away and takes to up
1,000 accurate readings per second. Fa ro
T e ch
n o lo
g ie
s
Figure S6.9
An Operating Characteristic
(OC) Curve Showing
Producer’s and Consumer’s
Risks
A good lot for this particular
acceptance plan has less than or
equal to 2% defectives. A bad lot
has 7% or more defectives.
Average outgoing quality (AOQ)
The percentage defective in an
average lot of goods inspected
through acceptance sampling.
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Acceptance sampling is useful for screening incoming lots. When the defective parts are replaced with good parts, acceptance sampling helps to increase the quality of the lots by reducing the outgoing percent defective.
Figure S6.10 compares acceptance sampling, SPC, and Cpk . As the figure shows, (a) accep- tance sampling by definition accepts some bad units, (b) control charts try to keep the process in control, but (c) the C pk index places the focus on improving the process. As operations man- agers, that is what we want to do—improve the process.
Lower specification
limit
Process mean, m
Upper specification
limit (a) Acceptance sampling (Some bad units accepted; the “lot” is good or bad.)
(b) Statistical process control (Keep the process “in control.”)
(c) Cpk > 1 (Design a process that is within specification.)
Figure S6.10
The Application of Statistical
Process Control Techniques
Contributes to the Identification
and Systematic Reduction of
Process Variability
Summary Statistical process control is a major statistical tool of qual- ity control. Control charts for SPC help operations manag- ers distinguish between natural and assignable variations. The x -chart and the R -chart are used for variable sampling, and the p -chart and the c -chart for attribute sampling.
The Cpk index is a way to express process capability. Operating characteristic (OC) curves facilitate acceptance sampling and provide the manager with tools to evaluate the quality of a production run or shipment.
Key Terms
Statistical process control (SPC) (p. 246 ) Control chart (p. 246 ) Natural variations (p. 246 ) Assignable variation (p. 247 ) x -chart (p. 248 ) R -chart (p. 248 ) Central limit theorem (p. 248 ) p -chart (p. 255 )
c -chart (p. 257 ) Run test (p. 260 ) Process capability (p. 260 ) C p (p. 260 ) C pk (p. 261 ) Acceptance sampling (p. 262 ) Operating characteristic (OC) curve (p. 263 ) Producer’s risk (p. 263 ) Consumer’s risk (p. 263 )
Acceptable quality level (AQL) (p. 263 ) Lot tolerance percentage defective (LTPD)
(p. 263 ) Type I error (p. 263 ) Type II error (p. 263 ) Average outgoing quality (AOQ)
(p. 264 )
Discussion Questions
1. List Shewhart’s two types of variation. What are they also called?
2. Define “in statistical control.” 3. Explain briefly what an x -chart and an R -chart do. 4. What might cause a process to be out of control? 5. List five steps in developing and using x -charts and R -charts.
6. List some possible causes of assignable variation. 7. Explain how a person using 2-sigma control charts will more
easily find samples “out of bounds” than 3-sigma control charts. What are some possible consequences of this fact?
8. When is the desired mean, m , used in establishing the center- line of a control chart instead of x ?
9. Can a production process be labeled as “out of control” because it is too good? Explain.
10. In a control chart, what would be the effect on the control limits if the sample size varied from one sample to the next?
11. Define Cpk and explain what a Cpk of 1.0 means. What is Cp ? 12. What does a run of 5 points above or below the centerline in
a control chart imply? 13. What are the acceptable quality level (AQL) and the lot toler-
ance percentage defective (LTPD)? How are they used? 14. What is a run test, and when is it used? 15. Discuss the managerial issues regarding the use of control charts. 16. What is an OC curve?
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17. What is the purpose of acceptance sampling? 18. What two risks are present when acceptance sampling is
used?
19. Is a capable process a perfect process? That is, does a capa- ble process generate only output that meets specifications? Explain.
Using Software for SPC
Excel, Excel OM, and POM for Windows may be used to develop control charts for most of the problems in this chapter.
X CREATING YOUR OWN EXCEL SPREADSHEETS TO DETERMINE CONTROL LIMITS FOR A C -CHART Excel and other spreadsheets are extensively used in industry to maintain control charts. Program S6.1 is an example of how to use Excel to determine the control limits for a c -chart. A c -chart is used when the number of defects per unit of output is known. The data from Example S5 are used here. In this example, 54 complaints occurred over 9 days. Excel also contains a built-in graphing ability with Chart Wizard.
Enter the desired number of standard deviations.
Do not change this cell without changing the number of rows in the data table.
Enter the mean weight for each of the 12 samples.
Calculate x—the overall average weight of all the samples = AVERAGE (B10:B21).
Use the overall average as the center line; add and subtract the product of the desired number of standard deviations and sigma x-bar in order to create upper and lower control limits (e.g., LCL = F10 – F11*F12).
= B7/SQRT(B6)
= B22
Enter the size for each of the hourly samples taken.
=SUM(B7:B15)
=AVERAGE(B7:B15)
=SQRT(C18)
=C18+C4*C19
=MAX(0,C18-C4*C19)
Use z = 3 for 99.73% limits.
Program S6.1
An Excel Spreadsheet for
Creating a c -Chart for
Example S5
X USING EXCEL OM Excel OM’s Quality Control module has the ability to develop x -charts, p -charts, and c -charts. It also handles OC curves, accept- ance sampling, and process capability. Program S6.2 illustrates Excel OM’s spreadsheet approach to computing the x control limits for the Oat Flakes company in Example S1 .
Program S6.2
Excel OM Input and Selected
Formulas for the Oat Flakes
Company in Example S1
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P USING POM FOR WINDOWS The POM for Windows Quality Control module has the ability to compute all the SPC control charts we introduced in this supplement, as well as OC curves, acceptance sampling, and process capability. See Appendix IV for further details.
Solved Problems Virtual Office Hours help is available in MyOMLab .
SOLVED PROBLEM S6.1 A manufacturer of precision machine parts produces round shafts for use in the construction of drill presses. The average diameter of a shaft is .56 inch. Inspection samples contain 6 shafts each. The average range of these samples is .006 inch. Determine the upper and lower x control chart limits.
SOLUTION The mean factor A2 from Table S6.1 , where the sample size is 6, is seen to be .483. With this factor, you can obtain the upper and lower control limits:
UCLx = .56 + (.483) (.006) = .56 + .0029 = .5629 inch
LCLx = .56 - .0029 = .5571 inch
SOLVED PROBLEM S6.2 Nocaf Drinks, Inc., a producer of decaffeinated coffee, bottles Nocaf. Each bottle should have a net weight of 4 ounces. The machine that fills the bottles with coffee is new, and the opera- tions manager wants to make sure that it is properly adjusted. Bonnie Crutcher, the operations manager, randomly selects and weighs n = 8 bottles and records the average and range in ounces for each sample. The data for several samples is given in the following table. Note that every sample consists of 8 bottles.
SAMPLE SAMPLE RANGE
SAMPLE AVERAGE SAMPLE
SAMPLE RANGE
SAMPLE AVERAGE
A .41 4.00 E .56 4.17
B .55 4.16 F .62 3.93
C .44 3.99 G .54 3.98
D .48 4.00 H .44 4.01
Is the machine properly adjusted and in control?
SOLUTION We first find that x = 4.03 and R = .505. Then, using Table S6.1 , we find:
UCLx = x + A2R = 4.03 + (.373) (.505) = 4.22
LCLx = x - A2R = 4.03 - (.373) (.505) = 3.84
UCLR = D4R = (1.864) (.505) = .94
LCLR = D3R = (.136) (.505) = .07
It appears that the process average and range are both in sta- tistical control.
The operations manager needs to determine if a process with a mean (4.03) slightly above the desired mean of 4.00 is satisfactory; if it is not, the process will need to be changed.
SOLVED PROBLEM S6.3 Altman Distributors, Inc., fills catalog orders. Samples of size n = 100 orders have been taken each day over the past 6 weeks. The average defect rate was .05. Determine the upper and lower limits for this process for 99.73% confidence.
SOLUTION z = 3, p = .05. Using Equations (S6-9) , (S6-10) , and (S6-11) :
UCLp = p + 3 B
p(1 - p) n
= .05 + 3 B
(.05) (1 - .05) 100
= .05 + 3(0.0218) = .1154
LCLp = p - 3 B
p(1 - p) n
= .05 - 3(0.0218)
= .05 - .0654 = 0 (because percentage defective cannot be negative)
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SOLVED PROBLEM S6.4 Ettlie Engineering has a new catalyst injection system for your countertop production line. Your process engineering department has conducted experiments and determined that the mean is 8.01 grams with a standard deviation of .03. Your specifications are: m = 8.0 and s = .04, which means an upper specification limit of 8.12 [ = 8.0 + 3(.04)] and a lower specification limit of 7.88 [= 8.0 - 3(.04)].
What is the Cpk performance of the injection system?
SOLUTION Using Equation (S6-14) :
Cpk = Minimum of J Upper specification limit - X3s , X - Lower specification limit
3s R
where X = process mean s = standard deviation of the process population
Cpk = Minimum of J 8.12 - 8.01(3) (.03) , 8.01 - 7.88
(3) (.03) R
J .11 .09
= 1.22 , .13 .09
= 1.44 R The minimum is 1.22, so the Cpk is within specifications and has an implied error rate of less than 2,700 defects per million.
SOLVED PROBLEM S6.5 Airlines lose thousands of checked bags every day, and America South Airlines is no exception to the industry rule. Over the past 6 weeks, the number of bags “misplaced” on America South flights has been 18, 10, 4, 6, 12, and 10. The head of customer service wants to develop a c -chart at 99.73% levels.
SOLUTION
She first computes c = 18 + 10 + 4 + 6 + 12 + 10
6 =
60 6
= 10 bags> week
Then, using Equation (S6-12) :
UCLc = c + 32c = 10 + 3210 = 10 + 3(3.16) = 19.48 bags
LCLC = c - 32c = 10 - 3210 = 10 - 3(3.16) = .52 bag
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Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM/Excel.
Problems S6.1–S6.39 relate to Statistical Process Control (SPC)
• S6.1 Boxes of Honey-Nut Oatmeal are produced to con- tain 14 ounces, with a standard deviation of .1 ounce. Set up the 3-sigma x -chart for a sample size of 36 boxes. PX
• S6.2 The overall average on a process you are attempting to monitor is 50 units. The process population standard deviation is 1.72. Determine the upper and lower control limits for a mean chart, if you choose to use a sample size of 5. PX a) Set z = 3. b) Now set z = 2. How do the control limits change?
• S6.3 Thirty-five samples of size 7 each were taken from a fertilizer-bag-filling machine. The results were overall mean = 57.75 lb; average range = 1.78 lb. a) Determine the upper and lower control limits of the x -chart,
where s = 3. b) Determine the upper and lower control limits of the R -chart,
where s = 3. PX
• S6.4 Rosters Chicken advertises “lite” chicken with 30% fewer calories than standard chicken. When the process for “lite” chicken breast production is in control, the average chicken breast contains 420 calories, and the standard deviation in caloric content of the chicken breast population is 25 calories.
Rosters wants to design an x -chart to monitor the caloric con- tent of chicken breasts, where 25 chicken breasts would be chosen at random to form each sample. a) What are the lower and upper control limits for this chart if
these limits are chosen to be four standard deviations from the target?
b) What are the limits with three standard deviations from the target? PX
• S6.5 Ross Hopkins is attempting to monitor a filling pro- cess that has an overall average of 705 cc. The average range is 6 cc. If you use a sample size of 10, what are the upper and lower control limits for the mean and range?
• • S6.6 Sampling four pieces of precision-cut wire (to be used in computer assembly) every hour for the past 24 hours has pro- duced the following results:
HOUR X R HOUR X R
1 3.25” .71” 13 3.11” .85”
2 3.10 1.18 14 2.83 1.31
3 3.22 1.43 15 3.12 1.06
4 3.39 1.26 16 2.84 .50
5 3.07 1.17 17 2.86 1.43
6 2.86 .32 18 2.74 1.29
7 3.05 .53 19 3.41 1.61
8 2.65 1.13 20 2.89 1.09
9 3.02 .71 21 2.65 1.08
10 2.85 1.33 22 3.28 .46
11 2.83 1.17 23 2.94 1.58
12 2.97 .40 24 2.64 .97
Develop appropriate control charts and determine whether there is any cause for concern in the cutting process. Plot the informa- tion and look for patterns. PX
• • S6.7 Auto pistons at Wemming Chung’s plant in Shanghai are produced in a forging process, and the diameter is a critical factor that must be controlled. From sample sizes of 10 pistons produced each day, the mean and the range of this diameter have been as follows:
DAY MEAN (MM) RANGE (MM)
1 156.9 4.2
2 153.2 4.6
3 153.6 4.1
4 155.5 5.0
5 156.6 4.5
a) What is the value of x ? b) What is the value of R ? c) What are the UCLx and LCLx , using 3s ? Plot the data. d) What are the UCLR and LCLR , using 3s ? Plot the data. e) If the true diameter mean should be 155 mm and you want
this as your center (nominal) line, what are the new UCLx and LCLx ? PX
• • S6.8 A. Choudhury’s bowling ball factory in Illinois makes bowling balls of adult size and weight only. The standard devia- tion in the weight of a bowling ball produced at the factory is known to be 0.12 pounds. Each day for 24 days, the average weight, in pounds, of nine of the bowling balls produced that day has been assessed as follows:
DAY AVERAGE (lb) DAY AVERAGE (lb)
1 16.3 13 16.3
2 15.9 14 15.9
3 15.8 15 16.3
4 15.5 16 16.2
5 16.3 17 16.1
6 16.2 18 15.9
7 16.0 19 16.2
8 16.1 20 15.9
9 15.9 21 15.9
10 16.2 22 16.0
11 15.9 23 15.5
12 15.9 24 15.8
a) Establish a control chart for monitoring the average weights of the bowling balls in which the upper and lower control lim- its are each two standard deviations from the mean. What are the values of the control limits?
b) If three standard deviations are used in the chart, how do these values change? Why? PX
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• • S6.9 Organic Grains LLC uses statistical process control to ensure that its health-conscious, low-fat, multigrain sandwich loaves have the proper weight. Based on a previously stable and in-control process, the control limits of the x - and R -charts are UCLx = 6.56. LCLx = 5.84, UCLR = 1.141, LCLR = 0. Over the past few days, they have taken five random samples of four loaves each and have found the following:
SAMPLE
NET WEIGHT
LOAF #1 LOAF #2 LOAF #3 LOAF #4
1 6.3 6.0 5.9 5.9
2 6.0 6.0 6.3 5.9
3 6.3 4.8 5.6 5.2
4 6.2 6.0 6.2 5.9
5 6.5 6.6 6.5 6.9
Is the process still in control? Explain why or why not. PX
• • • S6.10 A process that is considered to be in control measures an ingredient in ounces. Below are the last 10 samples (each of size n = 5 ) taken. The population process standard deviation, s, is 1.36.
SAMPLES
1 2 3 4 5 6 7 8 9 10
10 9 13 10 12 10 10 13 8 10
9 9 9 10 10 10 11 10 8 12
10 11 10 11 9 8 10 8 12 9
9 11 10 10 11 12 8 10 12 8
12 10 9 10 10 9 9 8 9 12
a) What is sx ? b) If z = 3, what are the control limits for the mean chart? c) What are the control limits for the range chart? d) Is the process in control? PX
• • • S6.11 Twelve samples, each containing five parts, were taken from a process that produces steel rods at Emmanuel Kodzi’s factory. The length of each rod in the samples was determined. The results were tabulated and sample means and ranges were computed. The results were:
SAMPLE SAMPLE MEAN (in.) RANGE (in.)
1 10.002 0.011
2 10.002 0.014
3 9.991 0.007
4 10.006 0.022
5 9.997 0.013
6 9.999 0.012
7 10.001 0.008
8 10.005 0.013
9 9.995 0.004
10 10.001 0.011
11 10.001 0.014
12 10.006 0.009
a) Determine the upper and lower control limits and the overall means for x -charts and R -charts.
b) Draw the charts and plot the values of the sample means and ranges.
c) Do the data indicate a process that is in control? d) Why or why not? PX
• • S6.12 Eagletrons are all-electric automobiles produced by Mogul Motors, Inc. One of the concerns of Mogul Motors is that the Eagletrons be capable of achieving appropriate maximum speeds. To monitor this, Mogul executives take samples of eight Eagletrons at a time. For each sample, they determine the aver- age maximum speed and the range of the maximum speeds within the sample. They repeat this with 35 samples to obtain 35 sample means and 35 ranges. They find that the average sample mean is 88.50 miles per hour, and the average range is 3.25 miles per hour. Using these results, the executives decide to establish an R chart. They would like this chart to be established so that when it shows that the range of a sample is not within the control limits, there is only approximately a 0.0027 probability that this is due to natural variation. What will be the upper control limit (UCL) and the lower control limit (LCL) in this chart? PX
• • S6.13 The defect rate for data entry of insurance claims has historically been about 1.5%. a) What are the upper and lower control chart limits if you wish
to use a sample size of 100 and 3-sigma limits? b) What if the sample size used were 50, with 3s ? c) What if the sample size used were 100, with 2s ? d) What if the sample size used were 50, with 2s ? e) What happens to nsp when the sample size is larger? f) Explain why the lower control limit cannot be less than 0. PX
• • S6.14 You are attempting to develop a quality monitoring system for some parts purchased from Charles Sox Manufacturing Co. These parts are either good or defective. You have decided to take a sample of 100 units. Develop a table of the appropri- ate upper and lower control chart limits for various values of the average fraction defective in the samples taken. The values for p in this table should range from 0.02 to 0.10 in increments of 0.02. Develop the upper and lower control limits for a 99.73% confi- dence level.
N 5 100
P UCL LCL
0.02
0.04
0.06
0.08
0.10
PX
• • S6.15 The results of an inspection of DNA samples taken over the past 10 days are given below. Sample size is 100.
DAY 1 2 3 4 5 6 7 8 9 10
DEFECTIVES 7 6 6 9 5 6 0 8 9 1
a) Construct a 3-sigma p -chart using this information. b) Using the control chart in part (a), and finding that the num-
ber of defectives on the next three days are 12, 5, and 13, is the process in control? PX
• S6.16 In the past, the defective rate for your product has been 1.5%. What are the upper and lower control chart limits if you wish to use a sample size of 500 and z = 3 ? PX
• S6.17 Refer to Problem S6.16. If the defective rate was 3.5% instead of 1.5%, what would be the control limits ( z = 3 )? PX
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• • S6.18 Five data entry operators work at the data process- ing department of the Birmingham Bank. Each day for 30 days, the number of defective records in a sample of 250 records typed by these operators has been noted, as follows:
SAMPLE NO.
NO. DEFECTIVE
SAMPLE NO.
NO. DEFECTIVE
SAMPLE NO.
NO. DEFECTIVE
1 7 11 18 21 17
2 5 12 5 22 12
3 19 13 16 23 6
4 10 14 4 24 7
5 11 15 11 25 13
6 8 16 8 26 10
7 12 17 12 27 14
8 9 18 4 28 6
9 6 19 6 29 12
10 13 20 16 30 3
a) Establish 3s upper and lower control limits. b) Why can the lower control limit not be a negative number? c) The industry standards for the upper and lower control lim-
its are 0.10 and 0.01, respectively. What does this imply about Birmingham Bank’s own standards? PX
• • S6.19 Houston North Hospital is trying to improve its image by providing a positive experience for its patients and their relatives. Part of the “image” program involves providing tasty, inviting patient meals that are also healthful. A questionnaire accompanies each meal served, asking the patient, among other things, whether he or she is satisfied or unsatisfied with the meal. A 100-patient sample of the survey results over the past 7 days yielded the following data:
DAY NO. OF UNSATISFIED PATIENTS SAMPLE SIZE
1 24 100
2 22 100
3 8 100
4 15 100
5 10 100
6 26 100
7 17 100
Construct a p -chart that plots the percentage of patients unsat- isfied with their meals. Set the control limits to include 99.73% of the random variation in meal satisfaction. Comment on your results. PX
• • S6.20 Jamison Kovach Supply Company manufactures paper clips and other office products. Although inexpen- sive, paper clips have provided the firm with a high margin of profitability. Sample size is 200. Results are given for the last 10 samples:
SAMPLE 1 2 3 4 5 6 7 8 9 10
DEFECTIVES 5 7 4 4 6 3 5 6 2 8
a) Establish upper and lower control limits for the control chart and graph the data.
b) Has the process been in control? c) If the sample size were 100 instead, how would your limits and
conclusions change? PX
• S6.21 Peter Ittig’s department store, Ittig Brothers, is Amherst’s largest independent clothier. The store receives an average of six returns per day. Using z = 3, would nine returns in a day warrant action? PX
• • S6.22 An ad agency tracks the complaints, by week received, about the billboards in its city:
WEEK NO. OF COMPLAINTS
1 4
2 5
3 4
4 11
5 3
6 9
a) What type of control chart would you use to monitor this pro- cess? Why?
b) What are the 3-sigma control limits for this process? Assume that the historical complaint rate is unknown.
c) Is the process in control, according to the control limits? Why or why not?
d) Assume now that the historical complaint rate has been four calls a week. What would the 3-sigma control limits for this process be now? Has the process been in control according to the control limits? PX
• • S6.23 The school board is trying to evaluate a new math program introduced to second-graders in five elementary schools across the county this year. A sample of the student scores on standardized math tests in each elementary school yielded the fol- lowing data:
SCHOOL NO. OF TEST ERRORS
A 52
B 27
C 35
D 44
E 55
Construct a c -chart for test errors, and set the control limits to contain 99.73% of the random variation in test scores. What does the chart tell you? Has the new math program been effective? PX Co
rb is
S u p e r
R F /A
la m
y
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• • S6.24 Telephone inquiries of 100 IRS “customers” are monitored daily at random. Incidents of incorrect information or other nonconformities (such as impoliteness to customers) are recorded. The data for last week follow:
DAY NO. OF NONCONFORMITIES
1 5
2 10
3 23
4 20
5 15
a) Construct a 3-standard deviation c -chart of nonconformities. b) What does the control chart tell you about the IRS telephone
operators? PX
• • • S6.25 The accounts receivable department at Rick Wing Manufacturing has been having difficulty getting customers to pay the full amount of their bills. Many customers complain that the bills are not correct and do not reflect the materials that arrived at their receiving docks. The department has decided to implement SPC in its billing process. To set up control charts, 10 samples of 50 bills each were taken over a month’s time and the items on the bills checked against the bill of lading sent by the company’s shipping department to determine the number of bills that were not correct. The results were:
SAMPLE NO.
NO. OF INCORRECT
BILLS SAMPLE
NO.
NO. OF INCORRECT
BILLS
1 6 6 5
2 5 7 3
3 11 8 4
4 4 9 7
5 0 10 2
a) Determine the value of p -bar, the mean fraction defective. Then determine the control limits for the p -chart using a 99.73% confidence level (3 standard deviations). Has this process been in control? If not, which samples were out of control?
b) How might you use the quality tools discussed in Chapter 6 to determine the source of the billing defects and where you might start your improvement efforts to eliminate the causes? PX
• • • S6.26 West Battery Corp. has recently been receiving com- plaints from retailers that its 9-volt batteries are not lasting as long as other name brands. James West, head of the TQM program at West’s Austin plant, believes there is no problem because his batteries have had an average life of 50 hours, about 10% longer than competitors’ models. To raise the lifetime above this level would require a new level of technology not available to West. Nevertheless, he is concerned enough to set up hourly assembly line checks. Previously, after ensuring that the process was running properly, West took size n 5 5 samples of 9-volt batteries for each of 25 hours to establish the standards for control chart limits. Those samples are shown in the follow- ing table:
West Battery Data—Battery Lifetimes (in hours)
HOUR SAMPLE TAKEN
SAMPLE
1 2 3 4 5 X R
1 51 50 49 50 50 50.0 2
2 45 47 70 46 36 48.8 34
3 50 35 48 39 47 43.8 15
4 55 70 50 30 51 51.2 40
5 49 38 64 36 47 46.8 28
6 59 62 40 54 64 55.8 24
7 36 33 49 48 56 44.4 23
8 50 67 53 43 40 50.6 27
9 44 52 46 47 44 46.6 8
10 70 45 50 47 41 50.6 29
11 57 54 62 45 36 50.8 26
12 56 54 47 42 62 52.2 20
13 40 70 58 45 44 51.4 30
14 52 58 40 52 46 49.6 18
15 57 42 52 58 59 53.6 17
16 62 49 42 33 55 48.2 29
17 40 39 49 59 48 47.0 20
18 64 50 42 57 50 52.6 22
19 58 53 52 48 50 52.2 10
20 60 50 41 41 50 48.4 19
21 52 47 48 58 40 49.0 18
22 55 40 56 49 45 49.0 16
23 47 48 50 50 48 48.6 3
24 50 50 49 51 51 50.2 2
25 51 50 51 51 62 53.0 12
With these limits established, West now takes 5 more hours of data, which are shown in the following table:
SAMPLE
HOUR 1 2 3 4 5
26 48 52 39 57 61
27 45 53 48 46 66
28 63 49 50 45 53
29 57 70 45 52 61
30 45 38 46 54 52
a) Determine means and the upper and lower control limits for x and R (using the first 25 hours only).
b) Has the manufacturing process been in control? c) Comment on the lifetimes observed. PX
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• • • • S6.27 One of New England Air’s top competitive priorities is on-time arrivals. Quality VP Clair Bond decided to personally monitor New England Air’s performance. Each week for the past 30 weeks, Bond checked a random sample of 100 flight arrivals for on-time performance. The table that follows contains the number of flights that did not meet New England Air’s definition of “on time”:
SAMPLE (WEEK)
LATE FLIGHTS
SAMPLE (WEEK)
LATE FLIGHTS
1 2 16 2
2 4 17 3
3 10 18 7
4 4 19 3
5 1 20 2
6 1 21 3
7 13 22 7
8 9 23 4
9 11 24 3
10 0 25 2
11 3 26 2
12 4 27 0
13 2 28 1
14 2 29 3
15 8 30 4
a) Using a 95% confidence level, plot the overall percentage of late flights ( p ) and the upper and lower control limits on a con- trol chart.
b) Assume that the airline industry’s upper and lower control limits for flights that are not on time are .1000 and .0400, respectively. Draw them on your control chart.
c) Plot the percentage of late flights in each sample. Do all sam- ples fall within New England Air’s control limits? When one falls outside the control limits, what should be done?
d) What can Clair Bond report about the quality of service? PX
• S6.40 The difference between the upper specification and the lower specification for a process is 0.6". The standard devia- tion is 0.1". What is the process capability ratio, Cp ? Interpret this number. PX
• • S6.41 Meena Chavan Corp.’s computer chip production process yields DRAM chips with an average life of 1,800 hours and s = 100 hours. The tolerance upper and lower specification limits are 2,400 hours and 1,600 hours, respectively. Is this pro- cess capable of producing DRAM chips to specification? PX
• • S6.42 Linda Boardman, Inc., an equipment manufacturer in Boston, has submitted a sample cutoff valve to improve your manufacturing process. Your process engineering department has conducted experiments and found that the valve has a mean ( m ) of 8.00 and a standard deviation ( s ) of .04. Your desired perfor- mance is m = 8.0 {3s, where s = .045. What is the Cpk of the Boardman valve? PX
Problems S6.40–S6.50 relate to Process Capability
Additional problems S6.28–S6.39 are available in MyOMLab.
• • S6.43 The specifications for a plastic liner for concrete high- way projects calls for a thickness of 3.0 mm {.1 mm. The stand- ard deviation of the process is estimated to be .02 mm. What are the upper and lower specification limits for this product? The pro- cess is known to operate at a mean thickness of 3.0 mm. What is the Cpk for this process? About what percentage of all units of this liner will meet specifications? PX
• • S6.44 Frank Pianki, the manager of an organic yogurt processing plant, desires a quality specification with a mean of 16 ounces, an upper specification limit of 16.5, and a lower specification limit of 15.5. The process has a mean of 16 ounces and a standard deviation of 1 ounce. Determine the Cpk of the process. PX
• • S6.45 A process filling small bottles with baby formula has a target of 3 ounces {0.150 ounce. Two hundred bottles from the process were sampled. The results showed the average amount of formula placed in the bottles to be 3.042 ounces. The standard deviation of the amounts was 0.034 ounce. Determine the value of Cpk . Roughly what proportion of bottles meet the specifications? PX
• • S6.51 As the supervisor in charge of shipping and receiv- ing, you need to determine the average outgoing quality in a plant where the known incoming lots from your assembly line have an average defective rate of 3%. Your plan is to sample 80 units of every 1,000 in a lot. The number of defects in the sample is not to exceed 3. Such a plan provides you with a probability of acceptance of each lot of .79 (79%). What is your average outgoing quality? PX
• • S6.52 An acceptance sampling plan has lots of 500 pieces and a sample size of 60. The number of defects in the sample may not exceed 2. This plan, based on an OC curve, has a probability of .57 of accepting lots when the incoming lots have a defective rate of 4%, which is the historical average for this process. What do you tell your customer the average outgoing quality is? PX
• • S6.53 The percent defective from an incoming lot is 3%. An OC curve showed the probability of acceptance to be 0.55. Given a lot size of 2,000 and a sample of 100, determine the average out- going quality in percent defective.
• • S6.54 In an acceptance sampling plan developed for lots containing 1,000 units, the sample size n is 85. The percent defec- tive of the incoming lots is 2%, and the probability of acceptance is 0.64. What is the average outgoing quality?
• • S6.55 We want to determine the AOQ for an acceptance sampling plan when the quality of the incoming lots in percent defective is 1.5%, and then again when the incoming percent defec- tive is 5%. The sample size is 80 units for a lot size of 550 units. Furthermore, P a at 1.5% defective levels is 0.95. At 5% incoming defective levels, the P a is found to be 0.5. Determine the average outgoing quality for both incoming percent defective levels.
Additional problems S6.46–S6.50 are available in MyOMLab.
Problems S6.51–S6.55 relate to Acceptance Sampling
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CASE STUDIES Bayfield Mud Company
In November 2015, John Wells, a customer service representa- tive of Bayfield Mud Company, was summoned to the Houston warehouse of Wet-Land Drilling, Inc., to inspect three boxcars of mudtreating agents that Bayfield had shipped to the Houston firm. (Bayfield’s corporate offices and its largest plant are located in Orange, Texas, which is just west of the Louisiana–Texas bor- der.) Wet-Land had filed a complaint that the 50-pound bags of treating agents just received from Bayfield were short-weight by approximately 5%.
The short-weight bags were initially detected by one of Wet- Land’s receiving clerks, who noticed that the railroad scale tick- ets indicated that net weights were significantly less on all three boxcars than those of identical shipments received on October 25, 2015. Bayfield’s traffic department was called to determine if lighter-weight pallets were used on the shipments. (This might explain the lighter net weights.) Bayfield indicated, however, that no changes had been made in loading or palletizing procedures. Thus, Wet-Land engineers randomly checked 50 bags and discov- ered that the average net weight was 47.51 pounds. They noted from past shipments that the process yielded bag net weights averaging exactly 50.0 pounds, with an acceptable standard devi- ation s of 1.2 pounds. Consequently, they concluded that the sample indicated a significant short-weight. (The reader may wish to verify this conclusion.) Bayfield was then contacted, and Wells was sent to investigate the complaint. Upon arrival, Wells verified the complaint and issued a 5% credit to Wet-Land.
Wet-Land management, however, was not completely satis- fied with the issuance of credit. The charts followed by their mud engineers on the drilling platforms were based on 50-pound bags of treating agents. Lighter-weight bags might result in poor chem- ical control during the drilling operation and thus adversely affect drilling efficiency. (Mud-treating agents are used to control the pH and other chemical properties of the core during drilling oper- ation.) This defect could cause severe economic consequences because of the extremely high cost of oil and natural gas well- drilling operations. Consequently, special-use instructions had to accompany the delivery of these shipments to the drilling plat- forms. Moreover, the short-weight shipments had to be isolated in Wet-Land’s warehouse, causing extra handling and poor space utilization. Thus, Wells was informed that Wet-Land might seek a new supplier of mud-treating agents if, in the future, it received bags that deviated significantly from 50 pounds.
The quality control department at Bayfield suspected that the lightweight bags might have resulted from “growing pains” at the Orange plant. Because of the earlier energy crisis, oil and natural gas exploration activity had greatly increased. In turn, this increased activity created increased demand for prod- ucts produced by related industries, including drilling muds. Consequently, Bayfield had to expand from a one-shift (6:00 a.m. to 2:00 p.m.) to a two-shift (2:00 p.m. to 10:00 p.m.) operation in mid-2010, and finally to a three-shift operation (24 hours per day) in the fall of 2015.
RANGE RANGE
TIME
AVERAGE WEIGHT
(POUNDS) SMALLEST LARGEST TIME
AVERAGE WEIGHT
(POUNDS) SMALLEST LARGEST
6:00 A.M. 49.6 48.7 50.7 6:00 P.M. 46.8 41.0 51.2 7:00 50.2 49.1 51.2 7:00 50.0 46.2 51.7 8:00 50.6 49.6 51.4 8:00 47.4 44.0 48.7 9:00 50.8 50.2 51.8 9:00 47.0 44.2 48.9 10:00 49.9 49.2 52.3 10:00 47.2 46.6 50.2 11:00 50.3 48.6 51.7 11:00 48.6 47.0 50.0 12 noon 48.6 46.2 50.4 12 midnight 49.8 48.2 50.4 1:00 P.M. 49.0 46.4 50.0 1:00 A.M. 49.6 48.4 51.7 2:00 49.0 46.0 50.6 2:00 50.0 49.0 52.2 3:00 49.8 48.2 50.8 3:00 50.0 49.2 50.0 4:00 50.3 49.2 52.7 4:00 47.2 46.3 50.5 5:00 51.4 50.0 55.3 5:00 47.0 44.1 49.7 6:00 51.6 49.2 54.7 6:00 48.4 45.0 49.0 7:00 51.8 50.0 55.6 7:00 48.8 44.8 49.7 8:00 51.0 48.6 53.2 8:00 49.6 48.0 51.8 9:00 50.5 49.4 52.4 9:00 50.0 48.1 52.7 10:00 49.2 46.1 50.7 10:00 51.0 48.1 55.2 11:00 49.0 46.3 50.8 11:00 50.4 49.5 54.1 12 midnight 48.4 45.4 50.2 12 noon 50.0 48.7 50.9 1:00 A.M. 47.6 44.3 49.7 1:00 P.M. 48.9 47.6 51.2 2:00 47.4 44.1 49.6 2:00 49.8 48.4 51.0 3:00 48.2 45.2 49.0 3:00 49.8 48.8 50.8 4:00 48.0 45.5 49.1 4:00 50.0 49.1 50.6 5:00 48.4 47.1 49.6 5:00 47.8 45.2 51.2
(cont’d)
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RANGE RANGE
TIME
AVERAGE WEIGHT
(POUNDS) SMALLEST LARGEST TIME
AVERAGE WEIGHT
(POUNDS) SMALLEST LARGEST
6:00 A.M. 48.6 47.4 52.0 6:00 P.M. 46.4 44.0 49.7 7:00 50.0 49.2 52.2 7:00 46.4 44.4 50.0 8:00 49.8 49.0 52.4 8:00 47.2 46.6 48.9 9:00 50.3 49.4 51.7 9:00 48.4 47.2 49.5 10:00 50.2 49.6 51.8 10:00 49.2 48.1 50.7 11:00 50.0 49.0 52.3 11:00 48.4 47.0 50.8 12 noon 50.0 48.8 52.4 12 midnight 47.2 46.4 49.2 1:00 P.M. 50.1 49.4 53.6 1:00 A.M. 47.4 46.8 49.0 2:00 49.7 48.6 51.0 2:00 48.8 47.2 51.4 3:00 48.4 47.2 51.7 3:00 49.6 49.0 50.6 4:00 47.2 45.3 50.9 4:00 51.0 50.5 51.5 5:00 46.8 44.1 49.0 5:00 50.5 50.0 51.9
The additional night-shift bagging crew was staffed entirely by new employees. The most experienced foremen were tem- porarily assigned to supervise the night-shift employees. Most emphasis was placed on increasing the output of bags to meet ever-increasing demand. It was suspected that only occasional reminders were made to double-check the bag weight-feeder. (A double-check is performed by systematically weighing a bag on a scale to determine if the proper weight is being loaded by the weight-feeder. If there is significant deviation from 50 pounds, corrective adjustments are made to the weight-release mechanism.)
To verify this expectation, the quality control staff randomly sampled the bag output and prepared the chart on the previous page. Six bags were sampled and weighed each hour.
Discussion Questions
1. What is your analysis of the bag-weight problem? 2. What procedures would you recommend to maintain proper
quality control?
Source: Professor Jerry Kinard, Western Carolina University. Reprinted with permission.
Frito-Lay’s Quality-Controlled Potato Chips Video Case
Frito-Lay, the multi-billion-dollar snack food giant, produces bil- lions of pounds of product every year at its dozens of U.S. and Canadian plants. From the farming of potatoes—in Florida, North Carolina, and Michigan—to factory and to retail stores, the ingredients and final product of Lay’s chips, for example, are inspected at least 11 times: in the field, before unloading at the plant, after washing and peeling, at the sizing station, at the fryer, after seasoning, when bagged (for weight), at carton filling, in the warehouse, and as they are placed on the store shelf by Frito- Lay personnel. Similar inspections take place for its other famous products, including Cheetos, Fritos, Ruffles, and Tostitos.
In addition to these employee inspections, the firm uses pro- prietary vision systems to look for defective potato chips. Chips are pulled off the high-speed line and checked twice if the vision system senses them to be too brown.
The company follows the very strict standards of the American Institute of Baking (AIB), standards that are much tougher than those of the U.S. Food and Drug Administration. Two unan- nounced AIB site visits per year keep Frito-Lay’s plants on their toes. Scores, consistently in the “excellent” range, are posted, and every employee knows exactly how the plant is doing.
There are two key metrics in Frito-Lay’s continuous improve- ment quality program: (1) total customer complaints (measured on a complaints per million bag basis) and (2) hourly or daily sta- tistical process control scores (for oil, moisture, seasoning, and salt content, for chip thickness, for fryer temperature, and for weight).
In the Florida plant, Angela McCormack, who holds engineering and MBA degrees, oversees a 15-member quality
assurance staff. They watch all aspects of quality, including training employees on the factory floor, monitoring automated processing equipment, and developing and updating statistical process control (SPC) charts. The upper and lower control lim- its for one checkpoint, salt content in Lay’s chips, are 2.22% and 1.98%, respectively. To see exactly how these limits are created using SPC, watch the video that accompanies this case.
Discussion Questions *
1. Angela is now going to evaluate a new salt process delivery sys- tem and wants to know if the upper and lower control limits at 3 standard deviations for the new system will meet the upper and lower control specifications noted earlier.
The data (in percents) from the initial trial samples are: Sample 1: 1.98, 2.11, 2.15, 2.06 Sample 2: 1.99, 2.0, 2.08, 1.99 Sample 3: 2.20, 2.10. 2.20, 2.05 Sample 4: 2.18, 2.01, 2.23, 1.98 Sample 5: 2.01, 2.08, 2.14, 2.16 Provide the report to Angela. 2. What are the advantages and disadvantages of Frito-Lay driv-
ers stocking their customers’ shelves? 3. Why is quality a critical function at Frito-Lay?
* You may wish to view the video that accompanies this case before answering these questions.
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Farm to Fork: Quality at Darden Restaurants Video Case
Darden Restaurants, the $6.3 billion owner of such popular brands as Olive Garden, Seasons 52, and Bahama Breeze, serves more than 320 million meals annually in its 1,500 restaurants across the U.S. and Canada. Before any one of these meals is placed before a guest, the ingredients for each recipe must pass quality control inspections at the source, ranging from measure- ment and weighing to tasting, touching, or lab testing. Darden has differentiated itself from its restaurant peers by developing the gold standard in continuous improvement.
To assure both customers and the company that quality expectations are met, Darden uses a rigorous inspection process, employing statistical process control (SPC) as part of its “Farm to Fork” program. More than 50 food scientists, microbiologists, and public health professionals report to Ana Hooper, vice presi- dent of quality assurance.
As part of Darden’s Point Source program, Hooper’s team, based in Southeast Asia (in China, Thailand, and Singapore) and Latin America (in Equador, Honduras, and Chile), approves and inspects—and works with Darden buyers to purchase—more than 50 million pounds of seafood each year for restaurant use. Darden used to build quality in at the end by inspecting ship- ments as they reached U.S. distribution centers. Now, thanks to coaching and partnering with vendors abroad, Darden needs but a few domestic inspection labs to verify compliance to its exacting standards. Food vendors in source countries know that when sup- plying Darden, they are subject to regular audits that are stricter than U.S. Food and Drug Administration (FDA) standards.
Two Quality Success Stories Quality specialists’ jobs include raising the bar and improving quality and safety at all plants in their geographic area. The Thai quality representative, for example, worked closely with several of Darden’s largest shrimp vendors to convert them to a produc- tion-line-integrated quality assurance program. The vendors were
able to improve the quality of shrimp supplied and reduce the percentage of defects by 19%.
Likewise, when the Darden quality teams visited fields of growers/ shippers in Mexico recently, it identified challenges such as low employee hygiene standards, field food safety problems, lack of portable toilets, child labor, and poor working conditions. Darden addressed these concerns and hired third-party independent food safety verification firms to ensure continued compliance to standards.
SPC Charts SPC charts, such as the one shown on page 253 in this supplement, are particularly important. These charts document precooked food weights; meat, seafood and poultry temperatures; blemishes on produce; and bacteria counts on shrimp—just to name a few. Quality assurance is part of a much bigger process that is key to Darden’s success—its supply chain (see Chapters 2 and 11 for discussion and case studies on this topic). That’s because quality comes from the source and flows through distribution to the res- taurant and guests.
Discussion Questions *
1. How does Darden build quality into the supply chain? 2. Select two potential problems—one in the Darden supply chain
and one in a restaurant—that can be analyzed with a fish-bone chart. Draw a complete chart to deal with each problem.
3. Darden applies SPC in many product attributes. Identify where these are probably used.
4. The SPC chart on page 253 illustrates Darden’s use of control charts to monitor the weight of salmon filets. Given these data, what conclusion do you, as a Darden quality control inspector, draw? What report do you issue to your supervisor? How do you respond to the salmon vendor?
* You might want to view the video that accompanies this case before answering these questions.
• Additional Case Study: Visit MyOMLab for this free case study: Green River Chemical Company: Involves a company that needs to set up a control chart to monitor sulfate content because of customer complaints.
Endnotes
1. Removing assignable causes is work. Quality expert W. Edwards Deming observed that a state of statistical control is not a nat- ural state for a manufacturing process. Deming instead viewed it as an achievement, arrived at by elimination, one by one, by determined effort, of special causes of excessive variation.
2. The standard deviation is easily calculated as
s = T
a n
i = 1 (xi - x)2
n - 1 . For a good review of this and other
statistical terms, refer to Tutorial 1, “Statistical Review for Managers,” in MyOMLab.
3. Lower control limits cannot take negative values in control charts. So the LCL 5 max (0, x - zsx).
4. If the sample sizes are not the same, other techniques must be used. 5. A Poisson probability distribution is a discrete distribution
commonly used when the items of interest (in this case, defects) are infrequent or occur in time or space.
6. This is because a Cp of 1.0 has 99.73% of outputs within speci- fications. So 1.00 − .9973 5 .0027; with 1,000 parts, there are .0027 5 1,000 5 2.7 defects.
For a Cp of 2.0, 99.99966% of outputs are “within spec.” So 1.00 − .9999966 5 .0000034; with 1 million parts, there are 3.4 defects.
7. Refer to Tutorial 2 in MyOMLab for an extended discussion of acceptance sampling.
8. Note that sampling always runs the danger of leading to an erroneous conclusion. Let us say that in one company the total population under scrutiny is a load of 1,000 computer chips, of which in reality only 30 (or 3%) are defective. This means that we would want to accept the shipment of chips, because for this particular firm 4% is the allowable defect rate. However, if a random sample of n 5 50 chips was drawn, we could con- ceivably end up with 0 defects and accept that shipment (that is, it is okay), or we could find all 30 defects in the sample. If the latter happened, we could wrongly conclude that the whole population was 60% defective and reject them all.
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S6
R ap
id R
ev ie
w
Supplement 6 Rapid Review Main Heading Review Material MyOMLab STATISTICAL PROCESS CONTROL (SPC) (pp. 246 – 260 )
j Statistical process control (SPC) —A process used to monitor standards by taking measurements and corrective action as a product or service is being produced.
j Control chart —A graphical presentation of process data over time. A process is said to be operating in statistical control when the only source of vari- ation is common (natural) causes. The process must first be brought into statistical control by detecting and eliminating special (assignable) causes of variation. The objective of a process control system is to provide a statistical signal when assignable causes of variation are present. j Natural variations —The variability that affects every production process to some
degree and is to be expected; also known as common cause. When natural variations form a normal distribution, they are characterized by two parameters: j Mean, m (the measure of central tendency—in this case, the average value) j Standard deviation, s (the measure of dispersion) As long as the distribution (output measurements) remains within specified limits, the process is said to be “in control,” and natural variations are tolerated. j Assignable variation —Variation in a production process that can be traced to
specific causes. Control charts for the mean, x , and the range, R , are used to monitor variables (out- puts with continuous dimensions), such as weight, speed, length, or strength. j x -chart —A quality control chart for variables that indicates when changes occur in
the central tendency of a production process. j R -chart —A control chart that tracks the range within a sample; it indicates that a
gain or loss in uniformity has occurred in dispersion of a production process. j Central limit theorem —The theoretical foundation for x -charts, which states that
regardless of the distribution of the population of all parts or services, the x distri- bution will tend to follow a normal curve as the number of samples increases:
x = m (S6-1)
sx = s
1n (S6-2)
The x-chart limits, if we know the true standard deviation s of the process population, are: Upper control limit (UCL) = x + zsx (S6-3)
Lower control limit (LCL) = x - zsx (S6-4)
where z = confidence level selected (e.g., z = 3 is 99.73% confidence). The range , R , of a sample is defined as the difference between the largest and
smallest items. If we do not know the true standard deviation, s , of the population, the x -chart limits are: UCLx = x + A2R (S6-5) LCLx = x - A2R (S6-6) In addition to being concerned with the process average, operations managers are interested in the process dispersion, or range. The R -chart control limits for the range of a process are: UCLR = D4R (S6-7) LCLR = D3R (S6-8) Attributes are typically classified as defective or nondefective . The two attribute charts are (1) p -charts (which measure the percent defective in a sample), and (2) c -charts (which count the number of defects in a sample). j p -chart —A quality control chart that is used to control attributes:
UCLp = p + zsp (S6-9) LCLp = p - zsp (S6-10)
n sp = B
p(1 - p) n
(S6-11)
j c -char t—A quality control chart used to control the number of defects per unit of output. The Poisson distribution is the basis for c -charts, whose 99.73% limits are computed as:
Control limits = c { 31c (S6-12) j Run test —A test used to examine the points in a control chart to determine whether
nonrandom variation is present.
Concept Questions: 1.1–1.4 Problems: S6.1–S6.39
VIDEO S6.1 Farm to Fork: Quality at Darden Restaurants
Virtual Office Hours for Solved Problems: S6.1–S6.3
ACTIVE MODELS S6.1 and S6.2
VIDEO S6.2 Frito-Lay’s Quality- Controlled Potato Chips
Virtual Office Hours for Solved Problem: S6.5
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Self Test
S6 R
ap id
R ev
ie w
Main Heading Review Material MyOMLab PROCESS CAPABILITY (pp. 260 – 262 )
j Process capability —The ability to meet design specifications. j C p —A ratio for determining whether a process meets design specifications.
Cp = (Upper specification - Lower specification)
6s (S6-13)
j C pk —A proportion of variation (3s) between the center of the process and the nearest specification limit:
Cpk = Minimum of J Upper spec limit - X3s , X - Lower spec limit
3s R (S6-14)
Concept Questions: 2.1–2.4 Problems: S6.40-S6.50
Virtual Office Hours for Solved Problems: S6.4
ACTIVE MODEL S6.3
ACCEPTANCE SAMPLING (pp. 262 – 265 )
j Acceptance sampling— A method of measuring random samples of lots or batches of products against predetermined standards.
j Operating characteristic (OC) curve— A graph that describes how well an acceptance plan discriminates between good and bad lots.
j Producer’s risk— The mistake of having a producer’s good lot rejected through sampling.
j Consumer’s risk— The mistake of a customer’s acceptance of a bad lot overlooked through sampling.
j Acceptable quality level (AQL)— The quality level of a lot considered good. j Lot tolerance percent defective (LTPD)— The quality level of a lot considered bad. j Type I error— Statistically, the probability of rejecting a good lot. j Type II error— Statistically, the probability of accepting a bad lot. j Average outgoing quality (AOQ)— The percent defective in an average lot of goods
inspected through acceptance sampling:
AOQ = (Pd) (Pa) (N - n)
N (S6-15)
Concept Questions: 3.1–3.4 Problems: S6.51–S6.55
j Before taking the self-test, refer to the learning objectives listed at the beginning of the supplement and the key terms listed at the end of the supplement.
LO S6.1 If the mean of a particular sample is within control limits and the range of that sample is not within control limits:
a) the process is in control, with only assignable causes of variation.
b) the process is not producing within the established con- trol limits.
c) the process is producing within the established control limits, with only natural causes of variation.
d) the process has both natural and assignable causes of variation.
LO S6.2 The central limit theorem: a) is the theoretical foundation of the c -chart. b) states that the average of assignable variations is zero. c) allows managers to use the normal distribution as the
basis for building some control charts. d) states that the average range can be used as a proxy for
the standard deviation. e) controls the steepness of an operating characteristic
curve. LO S6.3 The type of chart used to control the central tendency of
variables with continuous dimensions is: a) x -chart. b) R -chart. c) p -chart. d) c -chart. e) none of the above.
LO S6.4 If parts in a sample are measured and the mean of the sample measurement is outside the control limits:
a) the process is out of control, and the cause should be established.
b) the process is in control but not capable of producing within the established control limits.
c) the process is within the established control limits, with only natural causes of variation.
d) all of the above are true. LO S6.5 Control charts for attributes are: a) p -charts. b) c -charts. c) R -charts. d) x -charts. e) both a and b. LO S6.6 The ability of a process to meet design specifications is
called: a) Taguchi. b) process capability. c) capability index. d) acceptance sampling. e) average outgoing quality. LO S6.7 The _______ risk is the probability that a lot will be rejected
despite the quality level exceeding or meeting the _______.
Supplement 6 Rapid Review continued
Answers: LO S6.1. b; LO S6.2. c; LO S6.3. a; LO S6.4. a; LO S6.5. e; LO S6.6. b; LO S6.7. producer’s, AQL
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279279
C H A P T E R O U T L I N E
Process Strategy 7
◆
Four Process Strategies 282 ◆
Selection of Equipment 288 ◆
Process Analysis and Design 288
◆
Special Considerations for Service Process Design 293
◆
Production Technology 294
◆
Technology in Services 298
◆
Process Redesign 298
GLOBAL COMPANY PROFILE: Harley-Davidson
C H
A P
T E
R
10 OM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
•• Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
A la
sk a A
ir lin
e s
A la
sk a A
ir lin
e s
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S ince Harley-Davidson’s founding in Milwaukee in 1903, it has competed with hundreds of
manufacturers, foreign and domestic. The competition has been tough. Recent competitive
battles have been with the Japanese, and earlier battles were with the German, English, and
Italian manufacturers. But after over 110 years, Harley is the only major U.S. motorcycle com-
pany. The company now has five U.S. facilities and an assembly plant in Brazil. The Sportster
powertrain is manufactured in Wauwatosa, Wisconsin, and the sidecars, saddlebags, wind-
shields, and other specialty items are produced in Tomahawk, Wisconsin. The Touring and Softail
bikes are assembled in York, Pennsylvania, while the Sportster models, Dyna models, and VRSC
models of motorcycles are produced in Kansas City, Missouri.
As a part of management’s lean manufacturing effort, Harley groups production of parts that
require similar processes together. The result is work cells. Using the latest technology, work
cells perform in one location all the operations necessary for production of a specific module.
Raw materials are moved to the work cells for module production. The modules then proceed
Repetitive Manufacturing Works at Harley-Davidson
GLOBAL COMPANY PROFILE Harley-Davidson
C H A P T E R 7
280
Frame tube bending
Frame-building work cells
Frame machining
28 tests
THE ASSEMBLY LINE
TESTING Incoming parts
Hot-paint frame painting
Roller testing
Oil tank work cell
Shocks and forks
Crating
Handlebars
Fender work cell
Air cleaners
Fluids and mufflers
Fuel tank work cell
Wheel work cell
Engines and transmissions
Engines arrive on a JIT schedule from a 10-station work cell in Milwaukee.
In less than 3 hours, 450 parts and subassemblies go into a Harley motorcycle.
Flowchart Showing the Production Process at
Harley-Davidson’s York
Assembly Plant
fckncg/Alamy
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281
to the assembly line. As a double check on quality, Harley
has also installed “light curtain” technology, which uses an
infrared sensor to verify the bin from which an operator is
taking parts. Materials go to the assembly line on a just-in-
time basis, or as Harley calls it, using a Materials as Needed
(MAN) system. The 12.5-million-square-foot York facility includes manu-
facturing cells that perform tube bending, frame-building,
machining, painting, and polishing. Innovative manufacturing
techniques use robots to load machines and highly auto-
mated production to reduce machining time. Automation and
precision sensors play a key role in maintaining tolerances
and producing a quality product. Each day the York facility
produces up to 600 heavy-duty factory-custom motorcycles.
Bikes are assembled with different engine displacements,
multiple wheel options, colors, and accessories. The result
is a huge number of variations in the motorcycles available,
which allows customers to individualize their purchase. (See
www.Harley-Davidson.com for an example of modular
customization.) The Harley-Davidson production system
works because high-quality modules are brought together on
a tightly scheduled repetitive production line.
Wheel assembly modules are prepared in a work cell for JIT delivery to the
assembly line.
Wheel assembly modules are prepared in a work cell for JIT delivery to the
R ic
k F ri e d m
a n /C
o rb
is
For manufacturers like Harley-Davidson, which produces a large number
of end products from a relatively small number of options, modular bills of
material provide an effective solution.
For manufacturers like Harley Davidson which produces a large number
R ic
k F ri e d m
a n /C
o rb
is
Engines are assembled in Memomonee Falls, Wisconsin, and placed in their own
protective containers for shipment to the York facility. Upon arrival in York, engines
are placed on an overhead conveyor for movement directly to the assembly line.
N u cc
io D
iN u zz
o /K
R T /N
e w
sc o m
It all comes together on the line. Any employee who spots a problem has the
authority to stop the line until the problem in corrected. The multicolored “andon”
light above the line signals the severity of the problem.
R ic
k F ri e d m
a n /C
o rb
is
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282
Four Process Strategies In Chapter 5 , we examined the need for the selection, definition, and design of goods and services. Our purpose was to create environmentally friendly goods and services that could be delivered in an ethical, sustainable manner. We now turn to their production. A major deci- sion for an operations manager is finding the best way to produce so as not to waste our plan- et’s resources. Let’s look at ways to help managers design a process for achieving this goal.
A process strategy is an organization’s approach to transforming resources into goods and ser- vices. The objective is to create a process that can produce offerings that meet customer requirements within cost and other managerial constraints . The process selected will have a long-term effect on efficiency and flexibility of production, as well as on cost and quality of the goods produced.
Virtually every good or service is made by using some variation of one of four process strategies: (1) process focus, (2) repetitive focus, (3) product focus, and (4) mass customization. The relationship of these four strategies to volume and variety is shown in Figure 7.1 . We ex- amine Arnold Palmer Hospital as an example of a process-focused firm, Harley-Davidson as a repetitive producer, Frito-Lay as a product-focused operation, and Dell as a mass customizer.
Process Focus The vast majority of global production is devoted to making low-volume , high-variety products in places called “job shops.” Such facilities are organized around specific activities or processes. In a factory, these processes might be departments devoted to welding, grinding, and painting. In an office, the processes might be accounts payable, sales, and payroll. In a restaurant, they might be bar, grill, and bakery. Such facilities are process focused in terms of equipment, layout, and supervision. They provide a high degree of product flexibility as products move between the specialized processes. Each process is designed to perform a variety of activities and handle frequent changes. Consequently, they are also called intermittent processes .
L E A R N I N G OBJEC TI V ES
LO 7.1 Describe four process strategies 282
LO 7.2 Compute crossover points for diff erent processes 286
LO 7.3 Use the tools of process analysis 289
LO 7.4 Describe customer interaction in service processes 293
LO 7.5 Identify recent advances in production technology 294
Process strategy
An organization’s approach to
transforming resources into goods
and services.
V a ri
e ty
( fle
xi b ili
ty )
Changes in Modules modest runs, standardized modules
Repetitive Process
Volume
Changes in Attributes (such as grade, quality, size, thickness, etc.) long runs only
Low Volume
High Variety one or few units per run (allows customization)
High Volume
Process Focus projects, job shops
(machine, print, hospitals, restaurants) Arnold Palmer Hospital
Repetitive (autos, motorcycles, home appliances)
Harley-Davidson
Poor Strategy (Both fixed and variable costs
are high.)
Product Focus (commercial baked goods,
steel, glass, beer) Frito-Lay
Mass Customization (difficult to achieve but huge rewards)
Dell Computer
LO 7.1 Describe four process strategies
Figure 7.1
Process Selected Must Fit
with Volume and Variety
Process focus
A production facility organized
around processes to facilitate low-
volume, high-variety production.
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C H A P T E R 7 | P R O C E S S S T R AT E G Y 283
Referring to Figure 7.2 (a), imagine a diverse group of patients entering Arnold Palmer Hospital, a process-focused facility, to be routed to specialized departments, treated in a dis- tinct way, and then exiting as uniquely cared-for individuals.
Process-focused facilities have high variable costs with extremely low utilization of facilities, as low as 5%. This is the case for many restaurants, hospitals, and machine shops. However, facilities that lend themselves to electronic controls can do somewhat better.
Repetitive Focus Repetitive processes, as we saw in the Global Company Profile on Harley-Davidson, use modules (see Figure 7.2 b). Modules are parts or components previously prepared, often in a product- focused (continuous) process.
The repetitive process is the classic assembly line. Widely used in the assembly of virtually all automobiles and household appliances, it has more structure and consequently less flexibility than a process-focused facility.
Fast-food firms are another example of a repetitive process using modules. This type of produc- tion allows more customizing than a product-focused facility; modules (for example, meat, cheese, sauce, tomatoes, onions) are assembled to get a quasi-custom product, a cheeseburger. In this man- ner, the firm obtains both the economic advantages of the product-focused model (where many of the modules are prepared) and the custom advantage of the low-volume, high-variety model.
Many inputs
Process Focus (low-volume, high-variety,
intermittent process) Arnold Palmer Hospital
Many different outputs (uniquely treated patients)
Many departments and many routings
Many departments and many routings
Raw material and module inputs
Repetitive Focus (modular)
Harley-Davidson
Modules combined for many outputs
(many combinations of motorcycles)
Few modules
Many part and component inputs
Mass Customization (high-volume, high-variety)
Dell Computer
Many output versions (custom PCs and notebooks)
Many modules
Few inputs
(surgeries, sick patients, baby deliveries, emergencies)
(multiple engines and wheel modules)
(chips, hard drives, software, cases)
(corn, potatoes, water, seasoning)
Product Focus (high-volume, low-variety,
continuous process) Frito-Lay
(a) (b) (d)(c)
Output variations in size, shape, and packaging
(3-oz, 5-oz, 24-oz packages labeled for each market)
Figure 7.2
Four Process Options with an Example of Each
B ra
si lia
o /S
h u tt
e rs
to ck
3 0
0 d p i/ S
h u tt
e rs
to ck
Tu n d /S
h u tt
e rs
to ck
A rc
h m
a n /S
h u tt
e rs
to ck
VIDEO 7.1 Process Strategy at Wheeled Coach
Ambulance
Modules
Parts or components of a product
previously prepared, often in a
continuous process.
Repetitive process
A product-oriented production
process that uses modules.
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284 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Product Focus High-volume, low-variety processes are product focused . The facilities are organized around prod- ucts . They are also called continuous processes because they have very long, continuous production runs. Products such as glass, paper, tin sheets, lightbulbs, beer, and potato chips are made via a continuous process. Some products, such as lightbulbs, are discrete; others, such as rolls of paper, are made in a continuous flow. Still others, such as repaired hernias at Canada’s famous Shouldice Hospital, are services. It is only with standardization and effective quality control that firms have established product-focused facilities. An organization producing the same lightbulb or hot dog bun day after day can organize around a product. Such an organization has an inherent ability to set standards and maintain a given quality, as opposed to an organization that is producing unique products every day, such as a print shop or general-purpose hospital. For example, Frito- Lay’s family of products is also produced in a product-focused facility [see Figure 7.2 (c)]. At Frito- Lay, corn, potatoes, water, and seasoning are the relatively few inputs, but outputs (like Cheetos, Ruffles, Tostitos, and Fritos) vary in seasoning and packaging within the product family.
A product-focused facility produces high volume and low variety. The specialized nature of the facility requires high fixed cost, but low variable costs reward high facility utilization.
Mass Customization Focus Our increasingly wealthy and sophisticated world demands individualized goods and services. A peek at the rich variety of goods and services that operations managers are called on to sup- ply is shown in Table 7.1 . The explosion of variety has taken place in automobiles, movies, breakfast cereals, and thousands of other areas. Despite this proliferation of products, opera- tions managers have improved product quality while reducing costs. Consequently, the variety of products continues to grow. Operations managers use mass customization to produce this vast array of goods and services. Mass customization is the rapid, low-cost production of goods and services that fulfill increasingly unique customer desires. But mass customization (see the upper-right section of Figure 7.1 ) is not just about variety; it is about making precisely what the customer wants when the customer wants it economically.
Mass customization brings us the variety of products traditionally provided by low- volume manufacture (a process focus) at the cost of standardized high-volume (product-focused) production. However, achieving mass customization is a challenge that requires sophisticated operational capabilities. Building agile processes that rapidly and inexpensively produce custom products requires a limited product line and modular design. The link between sales, design, production, supply chain, and logistics must be tight.
Dell Computer [see Figure 7.2 (d)] has demonstrated that the payoff for mass customization can be substantial. More traditional manufacturers include Toyota, which recently announced
Product focus
A facility organized around
products; a product-oriented,
high-volume, low-variety process.
Mass customization
Rapid, low-cost production that
caters to constantly changing
unique customer desires.
TABLE 7.1 Mass Customization Provides More Choices Than Ever
NUMBER OF CHOICES a
ITEM 1970s 21ST CENTURY
Vehicle styles 18 1,212
Bicycle types 8 211,000 c
iPhone mobile game apps 0 1,200,000 g
Web sites 0 634,000,000 d
Movie releases per year 267 1,551 e
New book titles 40,530 300,0001
Houston TV channels 5 185
Breakfast cereals 160 340
Items (SKUs) in supermarkets 14,000 b 150,000 f
High-defi nition TVs 0 102
Source: Various; however, many of the data are from the Federal Reserve Bank of Dallas. a Variety available in America; worldwide the variety increases even more. b 1989. c Possible combinations for one manufacturer. d Royal Pingdom Estimate (2015). e www.the-numbers.com/movies/year/2014 . f SKUs managed by H. E. Butts grocery chain. g Business Week, April 26, 2015.
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C H A P T E R 7 | P R O C E S S S T R AT E G Y 285
delivery of custom-ordered cars in 5 days. Similarly, electronic controls allow designers in the textile industry to rapidly revamp their lines and respond to changes.
The service industry is also moving toward mass customization. For instance, not very many years ago, most people had the same telephone service. Now, not only is the phone ser- vice full of options, from caller ID to voice mail, but contemporary phones are hardly phones. They may also be part camera, computer, game player, GPS, and Web browser. Insurance companies are adding and tailoring new products with shortened development times to meet the unique needs of their customers. And firms like iTunes, Spotify, Rhapsody, Amazon, and eMusic maintain a music inventory on the Internet that allows customers to select a dozen songs of their choosing and have them made into a custom playlist. Similarly, the number of new books and movies increases each year. Mass customization places new demands on opera- tions managers who must create and align the processes that provide this expanding variety of goods and services.
Making Mass Customization Work Mass customization suggests a high-volume system in which products are built-to-order. Build-to-order (BTO) means producing to customer orders, not forecasts. But high-volume build-to-order is difficult. Some major challenges are:
◆ Product design must be imaginative. Successful build-to-order designs include a limited product line and modules. Ping Inc., a premier golf club manufacturer, uses different com- binations of club heads, grips, shafts, and angles to make 20,000 variations of its golf clubs.
◆ Process design must be flexible and able to accommodate changes in both design and tech- nology. For instance, postponement allows for customization late in the production process. Toyota installs unique interior modules very late in production for its popular Scion, a process also typical with customized vans. Postponement is further discussed in Chapter 11 .
◆ Inventory management requires tight control. To be successful with build-to-order, a firm must avoid being stuck with unpopular or obsolete components. With virtually no raw material, Dell puts custom computers together in less than a day.
◆ Tight schedules that track orders and material from design through delivery are another requirement of mass customization. Align Technology, a well-known name in orthodontics, figured out how to achieve competitive advantage by delivering custom-made clear plastic aligners within 3 weeks of the first visit to the dentist’s office (see the OM in Action box “Mass Customization for Straight Teeth”).
◆ Responsive partners in the supply chain can yield effective collaboration. Forecasting, inven- tory management, and ordering for JCPenney shirts are all handled for the retailer by its supplier in Hong Kong.
Mass customization/build-to-order is the new imperative for operations. There are ad- vantages to mass customization and building to order: first, by meeting the demands of the marketplace, firms win orders and stay in business; in addition, they trim costs (from personnel to inventory to facilities) that exist because of inaccurate sales forecasting.
Build-to-order (BTO)
Produce to customer order rather
than to a forecast.
Postponement
The delay of any modifications
or customization to a product as
long as possible in the production
process.
OM in Action Mass Customization for Straight Teeth Align Technology of Santa Clara, California, wants to straighten your teeth
with a clear plastic removable aligner. The company is a mass customizer for
orthodontic treatments. Each patient is very custom, requiring a truly unique
product; no two patients are alike. Based on dental impressions, X-rays, and
photos taken at the dentist’s office and sent to Align headquarters, the firm
builds a precise 3-D computer model and file of the patient’s mouth. This
digitized file is then sent to Costa Rica, where technicians develop a compre-
hensive treatment plan, which is then returned to the dentist for approval. After
approval, data from the virtual models and treatment plan are used to program
3-D printers to form molds. The molds are then shipped to Juarez, Mexico,
where a series of customized teeth aligners—usually about 19 pairs—are
made. The time required for this process: about 3 weeks from start to finish.
The clear aligners take the
place of the traditional “wire
and brackets.” Align calls the
product “complex to make,
easy to use.” With good OM,
mass customization works,
even for a very complex, very
individualized product, such as
teeth aligners. Sources: BusinessWeek (April 30, 2012); Laura Rock Kopezak and M. Eric
Johnson, “Aligning the Supply Chain,” Case #6-0024, Dartmouth College, 2006;
and www.invisalign.com .
H u g h G
ra n n u m
/K R
T /N
e w
sc o m
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286 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Process Comparison The characteristics of the four processes are shown in Table 7.2 and Figure 7.2 (on page 283 ), and each may provide a strategic advantage. For instance, unit costs will be less in the prod- uct (continuous) or repetitive case when high volume (and high utilization) exists. However, a low-volume differentiated product is likely to be produced more economically under pro- cess focus. And mass customization requires exceptional competence in product and process design, scheduling, supply chain, and inventory management. Proper evaluation and selection of process strategies are critical. Crossover Charts The comparison of processes can be further enhanced by looking at the point where the total cost of the processes changes. For instance, Figure 7.3 shows three al- ternative processes compared on a single chart. Such a chart is sometimes called a crossover chart . Process A has the lowest cost for volumes below V1, process B has the lowest cost between V1 and V2, and process C has the lowest cost at volumes above V2.
Example 1 illustrates how to determine the exact volume where one process becomes more expensive than another.
TABLE 7.2 Comparison of the Characteristics of Four Types of Processes
PROCESS FOCUS (LOW VOLUME, HIGH
VARIETY; e.g., ARNOLD PALMER HOSPITAL)
REPETITIVE FOCUS (MODULAR; e.g.,
HARLEY-DAVIDSON)
PRODUCT FOCUS (HIGH VOLUME, LOW VARIETY;
e.g., FRITO-LAY)
MASS CUSTOMIZATION (HIGH VOLUME, HIGH VARIETY; e.g., DELL
COMPUTER)
1. Small quantity and large variety of products
1. Long runs, a standardized product from modules
1. Large quantity and small variety of products
1. Large quantity and large variety of products
2. Broadly skilled operators
2. Moderately trained employees
2. Less broadly skilled operators
2. Flexible operators
3. Instructions for each job
3. Few changes in job instructions
3. Standardized job instructions
3. Custom orders requiring many job instructions
4. High inventory 4. Low inventory 4. Low inventory 4. Low inventory relative to the value of the product
5. Finished goods are made to order and not stored
5. Finished goods are made to frequent forecasts
5. Finished goods are made to a forecast and stored
5. Finished goods are build-to-order (BTO)
6. Scheduling is complex
6. Scheduling is routine 6. Scheduling is routine 6. Sophisticated scheduling accommodates custom orders
7. Fixed costs are low and variable costs high
7. Fixed costs are dependent on fl exibility of the facility
7. Fixed costs are high, and variable costs low
7. Fixed costs tend to be high and variable costs low
Crossover chart
A chart of costs at the possible
volumes for more than one
process.
Example 1 CROSSOVER CHART Kleber Enterprises would like to evaluate three accounting software products (A, B, and C) to support changes in its internal accounting processes. The resulting processes will have cost structures similar to those shown in Figure 7.3 . The costs of the software for these processes are:
TOTAL FIXED COST DOLLARS REQUIRED PER
ACCOUNTING REPORT
Software A $200,000 $60 Software B $300,000 $25 Software C $400,000 $10
APPROACH c Solve for the crossover point for software A and B and then the crossover point for software B and C.
LO 7.2 Compute crossover points for
different processes
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C H A P T E R 7 | P R O C E S S S T R AT E G Y 287
To ta
l p ro
ce ss
A c
os ts
To tal
pr oc
es s B
co sts
Tota l pro
cess C c
osts
Fixed costs
$
$
Low volume, high variety Process A
Variable costs
Fixed costs
$
Repetitive Process B
Variable costs
Fixed costs $
High volume, low variety Process C
Volume
400,000
300,000
200,000
(2,857) (6,666) V2V1
Variable costs
Fixed cost
Process A
Fixed cost
Process B
Fixed cost
Process C
SOLUTION c Software A yields a process that is most economical up to V1, but to exactly what number of reports (volume)? To determine the volume at V1, we set the cost of software A equal to the cost of software B. V1 is the unknown volume:
200,000 + (60)V1 = 300,000 + (25)V1 35V1 = 100,000
V1 = 2,857
This means that software A is most economical from 0 reports to 2,857 reports ( V1 ).
Similarly, to determine the crossover point for V2, we set the cost of software B equal to the cost of software C:
300,000 + (25)V2 = 400,000 + (10)V2 15V2 = 100,000
V2 = 6,666
This means that software B is most economical if the number of reports is between 2,857 ( V1 ) and 6,666 ( V2 ) and that software C is most economical if reports exceed 6,666 ( V2 ).
INSIGHT c As you can see, the software and related process chosen is highly dependent on the forecasted volume.
LEARNING EXERCISE c If the vendor of software A reduces the fixed cost to $150,000, what is the new crossover point between A and B? [Answer: 4,286.]
RELATED PROBLEMS c 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 7.10, 7.11, 7.12 ACTIVE MODEL 7.1 This example is further illustrated in Active Model 7.1 in MyOMLab.
EXCEL OM Data File Ch07Ex1.xls can be found in MyOMLab.
Figure 7.3
Crossover Charts
STUDENT TIP Different processes can be expected
to have different costs. However, at
any given volume, only one will have
the lowest cost.
Focused Processes In an ongoing quest for efficiency, industrialized societies con- tinue to move toward specialization. The focus that comes with specialization contributes to efficiency. Managers who focus on a limited number of activities, products, and technologies do better. As the variety of products in a facility increase, overhead costs increase even faster.
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Similarly, as the variety of products, customers, and technology increases, so does complexity. The resources necessary to cope with the complexity expand disproportionately. A focus on depth of product line as opposed to breadth is typical of outstanding firms, of which Intel, L.M. Ericsson, and Bosch are world-class examples. Focus , defined here as specialization, simplifi- cation, and concentration, yields efficiency. Focus also contributes to building a core compe- tence that fosters market and financial success. The focus can be:
◆ Customers (such as Winterhalter Gastronom, a German company that focuses on dishwashers for hotels and restaurants, for whom spotless glasses and dishes are critical)
◆ Products with similar attributes (such as Nucor Steel’s Crawford, Ohio, plant, which processes only high-quality sheet steels, and Gallagher, a New Zealand company, which has 45% of the world market in electric fences)
◆ Service (such as Orlando’s Arnold Palmer Hospital, with a focus on children and women; or Shouldice Hospital, in Canada, with a focus on hernia repair)
◆ Technology (such as Texas Instruments, with a focus on only certain specialized kinds of semi- conductors; and SAP, which despite a world of opportunities, remains focused on software)
The key for the operations manager is to move continuously toward specialization, focusing on the core competence necessary to excel at that speciality.
Selection of Equipment Ultimately, selection of a particular process strategy requires decisions about equipment and technology. These decisions can be complex, as alternative methods of production are present in virtually all operations functions, from hospitals, to restaurants, to manufacturing facili- ties. Picking the best equipment requires understanding the specific industry and available processes and technology. The choice of equipment, be it an X-ray machine for a hospital, a computer-controlled lathe for a factory, or a new computer for an office, requires consider- ing cost, cash flow, market stability, quality, capacity, and flexibility. To make this decision, operations managers develop documentation that indicates the capacity, size, tolerances, and maintenance requirements of each option.
In this age of rapid technological change and short product life cycles, adding flexibility to the production process can be a major competitive advantage. Flexibility is the ability to respond with little penalty in time, cost, or customer value. This may mean modular, movable, or digitally con- trolled equipment. Honda’s process flexibility, for example, has allowed it to become the industry leader at responding to market dynamics by modifying production volume and product mix.
Building flexibility into a production process can be difficult and expensive, but if it is not present, change may mean starting over. Consider what would be required for a rather sim- ple change—such as McDonald’s adding the flexibility necessary to serve you a charbroiled hamburger. What appears to be rather straightforward would require changes in many of the 10 OM decisions. For instance, changes may be necessary in (1) purchasing (a different quality of meat, perhaps with more fat content, and supplies such as charcoal), (2) quality standards (how long and at what temperature the patty will cook), (3) equipment (the charbroiler), (4) lay- out (space for the new process and for new exhaust vents), (5) training, and (6) maintenance. You may want to consider the implications of another simple change, such as a change from paper menus to iPad menus as discussed in the OM in Action box “The iPad Menu . . . A New Process.”
Changing processes or equipment can be difficult and expensive. It is best to get this critical decision right the first time.
Process Analysis and Design When analyzing and designing processes, we ask questions such as the following:
◆ Is the process designed to achieve competitive advantage in terms of differentiation, response, or low cost?
◆ Does the process eliminate steps that do not add value?
Flexibility
The ability to respond with little
penalty in time, cost, or customer
value.
STUDENT TIP A process that is going to win orders
often depends on the selection of the
proper equipment.
STUDENT TIP Here we look at five tools that help
understand processes.
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◆ Does the process maximize customer value as perceived by the customer? ◆ Will the process win orders?
Process analysis and design not only addresses these issues, but also related OM issues such as throughput, cost, and quality. Process is key. Examine the process; then continuously improve the process.
The following tools help us understand the complexities of process design and redesign. They are simply ways of making sense of what happens or must happen in a process. We now look at: flowcharts, time-function mapping, process charts, value-stream mapping, and service blueprinting.
Flowchart The first tool is the flowchart , which is a schematic or drawing of the movement of material, product, or people. For instance, the flowchart in the Global Company Profile for this chapter shows the assembly processes for Harley-Davidson. Such charts can help understanding, anal- ysis, and communication of a process.
Time-Function Mapping A second tool for process analysis and design is a modified flowchart with time added on the horizontal axis. Such charts are sometimes called time-function mapping, or process mapping . With time-function mapping, nodes indicate the activities, and the arrows indicate the flow direction, with time on the horizontal axis. This type of analysis allows users to identify and eliminate waste such as extra steps, duplication, and delay. Figure 7.4 shows the use of process mapping before and after process improvement at American National Can Company. In this example, substantial reduction in waiting time and process improvement in order processing contributed to a savings of 46 days.
Process Charts The third tool is the process chart . Process charts use symbols, time, and distance to provide an objective and structured way to analyze and record the activities that make up a process. 1 They allow us to focus on value-added activities. For instance, the process chart shown in Figure 7.5 , which includes the present method of hamburger assembly at a fast-food restaurant, includes a value-added line to help us distinguish between value-added activities and waste. Identifying all value-added operations (as opposed to inspection, storage, delay, and transportation, which add no value) allows us to determine the percent of value added to total activities. 2 We can see from the computation at the bottom of Figure 7.5 that the percentage of value added in this case is 85.7%.
OM in Action OM in Action The iPad Menu . . . A New Process Mass customization begins with the order. And at restaurants from California
to Boston, the order now starts with an iPad. Stacked Restaurants lets
customers choose ingredients for their sandwiches using an iPad on the table.
Diners also get a great photo of the menu item (which stimulates sales), a
list of ingredients and nutritional information (a plus for those with allergies
or watching their diet), and an opportunity to build their own meal (mass
customization).
Some restaurants, in addition to having the enticing photo of the meal,
find that they can add a description and photo of just what a medium-rare
steak looks like. They can further enrich the dining experience by adding a
“recipe” tab or “history” tab with descriptions of the item’s origins and tradi-
tion. Steakhouses, a chain in San Francisco, Atlanta, and Chicago, finds
the tabs great for its lengthy wine lists. Others program the system to
remember the guest’s meal preferences. And some customers love the
ability to order immediately, scan
coupons, and swipe credit cards
at the table. The instantaneous
placement of the order to the
kitchen is a significant advan-
tage for those restaurants pursu-
ing a response strategy .
Using iPads means develop-
ing a new process. iPads are not
cheap, but they are accurate and
fast, with lots of options. Restaurants using the new process find customer
retention, frequency of visits, and average check size all increasing.
S ta
ck e d R
e st
a u ra
n ts
Sources: New York Times (June 21, 2014) and USA Today (February 16, 2011)
and (July 25, 2012).
LO 7.3 Use the tools of process analysis
VIDEO 7.2 Alaska Airlines: 20-minute Baggage
Process—Guaranteed!
Flowchart
A drawing used to analyze
movement of people or material.
Time-function mapping (or process mapping)
A flowchart with time added on the
horizontal axis.
Process charts
Charts that use symbols to analyze
the movement of people or
material.
VIDEO 7.3 Process Analysis at Arnold Palmer
Hospital
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The operations manager’s job is to reduce waste and increase the percent of value added. The non- value-added items are a waste; they are resources lost to the firm and to society forever.
Value-Stream Mapping A variation of time-function mapping is value-stream mapping (VSM) ; however, value-stream map- ping takes an expanded look at where value is added (and not added) in the entire production process, including the supply chain. As with time-function mapping, the idea is to start with the customer and understand the production process, but value-stream mapping extends the analysis back to suppliers.
“Baseline” Time-Function Map “Target” Time-Function Map
12 days 13 days
52 days
1 day 4 days 1 day 10 days 1 day 9 days 1 day
Customer
Sales
Production control
Plant A
Warehouse
Plant B
Transport
Order product
Receive product
Process order
Wait
Wait Wait Wait
Move Move
Extrude
O rd
e r
O rd
e r
W IP
W IP
W IP
W IP
P ro
d u ct
P ro
d u ct
P ro
d u ct
1 day
6 days
2 days 1 day 1 day 1 day
Customer
Sales
Production control
Plant
Warehouse
Transport
Order product
Receive product
Process order
Wait
Wait
Move
Extrude
O rd
e r
O rd
e r
WIP
P ro
d u ct
P ro
d u ct
P ro
d u ct
(a) (b)
Figure 7.4
Time-Function Mapping (Process Mapping) for a Product Requiring Printing and Extruding Operations at American
National Can Company
This technique clearly shows that waiting and order processing contributed substantially to the 46 days that can be eliminated in
this operation.
Source: Excerpted from Elaine J. Labach, “Faster, Better, and Cheaper,” Target no. 5:43 with permission of the Association for Manufacturing Excellence,
380 West Palatine Road, Wheeling, IL 60090-5863, 847/520-3282. www.ame.org . Reprinted with permission of Target Magazine.
Value-stream mapping (VSM)
A process that helps managers
understand how to add value in the
flow of material and information
through the entire production
process.
Present Method Proposed Method
SUBJECT CHARTED
DEPARTMENT CHART BY
DIST. IN
FEET
TIME IN
MINS.
CHART SYMBOLS
DATE
SHEET NO. OF
PROCESS CHART
PROCESS DESCRIPTION
TOTALS Value-added time = Operation time/Total time = (2.50+.20)/3.15 = 85.7%
= operation; = transport; = inspect; = delay; = storage.
X Hamburger Assembly Process
1.5
1.0
.5
.5
3.5 3.15
.05
.20 .10 .15 .05 .05 2.50 .05
2 4 1 – 2
Meat Patty in Storage
Assemble Order Obtain Buns, Lettuce, etc.
Place in Finish Rack
Transfer to Broiler Broiler Visual Inspection Transfer to Rack Temporary Storage
12 / 1 / 15 1KH 1
Figure 7.5
Process Chart Showing a
Hamburger Assembly Process
at a Fast-Food Restaurant
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C H A P T E R 7 | P R O C E S S S T R AT E G Y 291
Value-stream mapping takes into account not only the process but, as shown in Example 2 , also the management decisions and information systems that support the process.
Manufacturing Management
Production Supervisor
500 needed each day
Weekly Orders 2,500
Weekly Orders 2,500
Monthly Forecast = 11,000 Monthly Forecast
Weekly
Daily Communication
1 operator
Machine
1 operator
Ship 500
Package
1 operator
Test
2 operators
Assemble
1 operator
45 seconds 20 seconds 4 days4 days4 days6 days3 days5 days
20 seconds15 seconds
Non-value-added time = 26 days Value-added time = 140 seconds
40 seconds
Component Mounting
Supplier Customer
1,500 2,500 2,000 2,000
2,0002,500
Weekly Daily
Example 2 VALUE-STREAM MAPPING Motorola has received an order for 11,000 cell phones per month and wants to understand how the order will be processed through manufacturing.
APPROACH c To fully understand the process from customer to supplier, Motorola prepares a value- stream map.
SOLUTION c Although value-stream maps appear complex, their construction is easy. Here are the steps needed to complete the value-stream map shown in Figure 7.6 . 1. Begin with symbols for customer, supplier, and production to ensure the big picture. 2. Enter customer order requirements. 3. Calculate the daily production requirements. 4. Enter the outbound shipping requirements and delivery frequency. 5. Determine inbound shipping method and delivery frequency. 6. Add the process steps (i.e., machine, assemble) in sequence, left to right. 7. Add communication methods, add their frequency, and show the direction with arrows. 8. Add inventory quantities (shown with ) between every step of the entire flow. 9. Determine total working time (value-added time) and delay (non-value-added time).
Figure 7.6
Value-Stream Mapping (VSM)
INSIGHT c From Figure 7.6 we note that large inventories exist in incoming raw material and between processing steps, and that the value-added time is low as a proportion of the entire process.
LEARNING EXERCISE c How might raw material inventory be reduced? [Answer: Have deliveries twice per week rather than once per week.]
RELATED PROBLEM c 7.17
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Service Blueprinting Products with a high service content may warrant use of yet a fifth process technique. Service blueprinting is a process analysis technique that focuses on the customer and the provider’s interaction with the customer. For instance, the activities at level one of Figure 7.7 are under the control of the customer. In the second level are activities of the service provider interact- ing with the customer. The third level includes those activities that are performed away from, and not immediately visible to, the customer. Each level suggests different management issues. For instance, the top level may suggest educating the customer or modifying expectations, whereas the second level may require a focus on personnel selection and training. Finally, the third level lends itself to more typical process innovations. The service blueprint shown in Figure 7.7 also notes potential failure points and shows how poka-yoke techniques can be added to improve quality. The consequences of these failure points can be greatly reduced if identified at the design stage when modifications or appropriate poka-yokes can be included. A time dimension is included in Figure 7.7 to aid understanding, extend insight, and provide a focus on customer service.
Service blueprinting
A process analysis technique
that lends itself to a focus on
the customer and the provider’s
interaction with the customer.
Level #1 Customer is in control.
Physical Attributes to Support Service
Employee appearance Forms
Shop cleanliness Technology
Car delivered clean Employee appearance
Level #2 Customer interacts with service provider.
Level #3 Service is removed from customer’s control and interaction.
Customer arrives for service.
(3 min)
Customer departs.
Customer pays bill. (4 min)
Warm greeting and obtain
service request. (10 sec)
Determine specifics. (5 min)
Direct customer to waiting room.
Perform required work.
(varies)
Prepare invoice. (3 min)
Standard request. (3 min)
Can service be
done and does customer approve? (5 min)
No
No
F
FF
F
YesYes
Poka-yoke: Bell in driveway in case customer arrival was unnoticed. Poka-yoke: If customer remains in the work area, offer coffee and reading material in waiting room.
Personal Greeting
Poka-yoke: Conduct dialog with customer to identify customer expectation and assure customer acceptance.
Service Diagnosis
Poka-yoke: Review checklist for compliance. Poka-yoke: Service personnel review invoice for accuracy.
Perform Service
Poka-yoke: Customer approves invoice.
Poka-yoke: Customer inspects car.
Friendly Close
Notify customer that car is ready. (3 min)
Notify customer
and recommend an alternative
provider. (7 min)
F
F
F
F
F
Poka-yokes to address potential failure points
Parking adequate Signage clear
Waiting room amenities
STUDENT TIP Service blueprinting helps evaluate
the impact of customer interaction
with the process.
Figure 7.7
Service Blueprint for
Service at Speedy
Lube, Inc.
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C H A P T E R 7 | P R O C E S S S T R AT E G Y 293
Each of these five process analysis tools has strengths and variations. Flowcharts provide a quick way to view the big picture and try to make sense of the entire system. Time-function mapping adds some rigor and a time element to the macro analysis. Value-stream mapping extends beyond the immediate organization to customers and suppliers. Process charts are designed to provide a much more detailed view of the process, adding items such as value- added time, delay, distance, storage, and so forth. Service blueprinting, on the other hand, is designed to help us focus on the customer interaction part of the process. Because customer interaction is often an important variable in process design, we now examine some additional aspects of service process design.
Special Considerations for Service Process Design Interaction with the customer often affects process performance adversely. But a service, by its very nature, implies that some interaction and customization is needed. Recognizing that the customer’s unique desires tend to play havoc with a process, the more the manager designs the process to accommodate these special requirements, the more effective and efficient the process will be. The trick is to find the right combination.
The four quadrants of Figure 7.8 provide additional insight on how operations managers modify service processes to find the best level of specialization and focus while maintaining the necessary customer interaction and customization. The 10 OM decisions we introduced in Chapters 1 and 2 are used with a different emphasis in each quadrant. For instance: ◆ In the upper sections (quadrants) of mass service and professional service , where labor
content is high , we expect the manager to focus extensively on human resources. This is often done with personalized services, requiring high labor involvement and therefore significant personnel selection and training issues. This is particularly true in the profes- sional service quadrant.
◆ The quadrants with low customization tend to (1) standardize or restrict some offerings, as do fast-food restaurants, (2) automate, as have airlines with ticket-vending machines, or (3) remove some services, such as seat assignments, as has Southwest Airlines. Offloading some aspect of the service through automation may require innovations in process design. Such is the case with airline ticket vending, self-checkout at Home Depot, and bank ATMs. This move to standardization and automation may also require changes in other areas, such as added capital expenditure and new OM skills for the purchase and mainte- nance of equipment. A reduction in a customization capability will require added strength in other areas.
STUDENT TIP Customer interaction within service
processes increases the design
challenge.
Low High
Low
D e g re
e o
f L a b o r
High
Professional ServiceMass Service
Service ShopService Factory
Commercial banking
Limited-service stockbroker
Private banking
Specialized hospitals
Digitized orthodontics
Fast-food restaurants
No-frills airlines
Fine-dining restaurants
Traditional orthodontics
Airlines
Hospitals
General- purpose law firms
Retailing
Full-service stockbroker
Boutiques
Degree of Customization
Warehouse and catalog stores
Law clinics
Figure 7.8
Services Moving Toward
Specialization and Focus
Within the Service Process
Matrix
See related discussions in: Gary J.
Salegna and Farzanch Fazel, “An Integra-
tive Approach for Classifying Services,”
Journal of Global Business Management
(vol. 9, no. 1), 2013; and Roger Schmen-
ner, “Services Moving toward Specializa-
tion and Focus with the Service Matrix,”
MIT Sloan Management Review , 1986.
LO 7.4 Describe customer interaction in
service processes
STUDENT TIP Notice how services find a
competitive opportunity by moving
from the rectangles to the ovals.
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◆ Because customer feedback is lower in the quadrants with low customization , tight control may be required to maintain quality standards.
◆ Operations with low labor intensity may lend themselves particularly well to innovations in process technology and scheduling.
Table 7.3 shows some additional techniques for innovative process design in services. Managers focus on designing innovative processes that enhance the service. For instance, supermarket self-service reduces cost while it allows customers to check for the specific features they want, such as freshness or color. Dell Computer provides another version of self-service by allowing customers to design their own product on the Web. Customers seem to like this, and it is cheaper and faster for Dell.
Production Technology Advances in technology that enhance production and productivity are changing how things are designed, made, and serviced around the world. In this section, we introduce nine areas of technology: (1) machine technology, (2) automatic identification systems (AIS), (3) process control, (4) vision systems, (5) robots, (6) automated storage and retrieval systems (ASRSs), (7) automated guided vehicles (AGVs), (8) flexible manufacturing systems (FMSs), and (9) computer-integrated manufacturing (CIM). Consider the impact on operations managers as we digitally link these technologies within the firm. Then consider the implications when they are combined and linked globally in a seamless chain that can immediately respond to changing consumer demands, supplier dynamics, and producer innovations. The implications for the world economy and OM are huge.
Machine Technology Much of the world’s machinery performs operations by removing material, performing opera- tions such as cutting, drilling, boring, and milling. This technology is undergoing tremendous progress in both precision and control. Machinery now turns out metal components that vary less than a micron—1/76 the width of a human hair. They can accelerate water to three times the speed of sound to cut titanium for surgical tools. Such machinery is often five times more productive than that of previous generations while being smaller and using less power. And continuing advances in lubricants now allow the use of water-based lubricants rather than oil-based. Water-based lubricants enhance sustainability by eliminating hazardous waste and allowing shavings to be easily recovered and recycled.
TABLE 7.3 Techniques for Improving Service Productivity
STRATEGY TECHNIQUE EXAMPLE
Separation Structuring service so customers must go where the service is offered
Bank customers go to a manager to open a new account, to loan offi cers for loans, and to tellers for deposits
Self-service Self-service so customers examine, compare, and check out at their own pace
Supermarkets and department stores Internet ordering
Postponement Customizing at delivery Customizing vans at delivery rather than at production
Focus Restricting the offerings Limited-menu restaurant
Modules Modular selection of service Modular production
Investment and insurance selection Prepackaged food modules in restaurants
Automation Separating services that may lend themselves to some type of automation
Automatic teller machines
Scheduling Precise personnel scheduling Scheduling airline ticket counter personnel at 15-minute intervals
Training Clarifying the service options Explaining how to avoid problems
Investment counselor, funeral directors After-sale maintenance personnel
STUDENT TIP Here are nine technologies that can
improve employee safety, product
quality, and productivity.
LO 7.5 Identify recent advances in production
technology
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Computer intelligence often controls this new machinery, allowing more complex and pre- cise items to be made faster. Such machinery, with its own computer and memory, is referred to as having computer numerical controls (CNC) . Electronic controls increase speed by cutting change- over time, reducing waste (because of fewer mistakes), and enhancing flexibility.
Advanced versions of such technology are used on Pratt & Whitney’s turbine blade plant in Connecticut. The machinery has improved the loading and alignment task so much that Pratt has cut the total time for the grinding process of a turbine blade from 10 days to 2 hours. The new machinery has also contributed to process improvements that mean the blades now travel just 1,800 feet in the plant, down from 8,100 feet, cutting throughput time from 22 days to 7 days.
New advances in machinery suggest that rather than removing material as has traditionally been done, adding material may in many cases be more efficient. Additive manufacturing or, as it is commonly called, 3D printing, is frequently used for design testing, prototypes, and custom products. The technology continues to advance and now supports innovative product design (variety and complexity), minimal custom tooling (little tooling is needed), minimal assembly (integrated assemblies can be “printed”), low inventory (make-to-order systems), and reduced time to market. As a result, additive manufacturing is being increasingly used to enhance pro- duction efficiency for high-volume products. In addition, production processes using numer- ous materials including plastics, ceramics, and even a paste of living cells are being developed. The convergence of software advances, computer technology, worldwide communication, and 3D printing seems to be putting us on the cusp of true mass customization. We can expect per- sonalized mass markets via additive manufacturing to bring enormous changes to operations.
Automatic Identification Systems (AISs) and RFID New equipment, from numerically controlled manufacturing machinery to ATMs, is con- trolled by digital electronic signals. Electrons are a great vehicle for transmitting informa- tion, but they have a major limitation—most OM data does not start out in bits and bytes. Therefore, operations managers must get the data into an electronic form. Making data digital is done via computer keyboards, bar codes, radio frequencies, optical characters, and so forth. These automatic identification systems (AISs) help us move data into electronic form, where it is easily manipulated.
Because of its decreasing cost and increasing pervasiveness, radio frequency identification (RFID) warrants special note. RFID is integrated circuitry with its own tiny antennas that use radio waves to send signals a limited range—usually a matter of yards. These RFID tags provide unique identification that enables the tracking and monitoring of parts, pallets, people, and pets—virtually everything that moves. RFID requires no line of sight between tag and reader.
Process Control Process control is the use of information technology to monitor and control a physical process. For instance, process control is used to measure the moisture content and thickness of paper as it travels over a paper machine at thousands of feet per minute. Process control is also used to deter- mine and control temperatures, pressures, and quantities in petroleum refineries, petrochemical processes, cement plants, steel mills, nuclear reactors, and other product-focused facilities.
Computer numerical control (CNC)
Machinery with its own computer
and memory.
Additive manufacturing
The production of physical items
by adding layer upon layer, much
in the same way an inkjet printer
lays down ink.
Automatic identification system (AIS)
A system for transforming data
into electronic form, for example,
bar codes.
With RFID, a cashier could scan the entire contents of a shopping cart in seconds.
Pharmaceutical companies are
counting on RFID to aid the tracking
and tracing of drugs in the distribution
system to reduce losses that total
over $30 billion a year.
K ru
e ll/
la if /R
e d u x
P ic
tu re
s
The Safe Place® Infant Security
Solution from RF Technologies®
monitors infants with small,
lightweight transmitters and soft,
comfortable banding. When a
protected infant approaches a
monitored exit, the transmitter
triggers the exit’s lock and notifies
staff to ensure a fast response.
Process control
The use of information technology
to control a physical process.
Radio frequency identification (RFID)
A wireless system in which
integrated circuits with antennas
send radio waves.
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Vision systems
Systems that use video cameras
and computer technology in
inspection roles.
Robot
A flexible machine with the ability
to hold, move, or grab items. It
functions through electronic
impulses that activate motors and
switches.
Automated storage and retrieval system (ASRS)
Computer-controlled warehouses
that provide for the automatic
placement of parts into and from
designated places in a warehouse.
Process control systems operate in a number of ways, but the following are typical:
◆ Sensors collect data, which is read on some periodic basis, perhaps once a minute or second.
◆ Measurements are translated into digital signals, which are transmitted to a computer.
◆ Computer programs read the file and analyze the data.
◆ The resulting output may take numerous forms. These include messages on computer consoles or printers, signals to motors to change valve settings, warning lights or horns, or statistical process control charts.
Vision Systems Vision systems combine video cameras and com- puter technology and are often used in inspec- tion roles. Visual inspection is an important task
in most food-processing and manufacturing organizations. Moreover, in many applica- tions, visual inspection performed by humans is tedious, mind-numbing, and error prone. Thus vision systems are widely used when the items being inspected are very similar. For instance, vision systems are used to inspect Frito-Lay’s potato chips so that imperfections can be identified as the chips proceed down the production line. The systems are also used to ensure that sealant is present and in the proper amount on Whirlpool’s washing- machine transmissions. Vision systems are consistently accurate, do not become bored, and are of modest cost. These systems are vastly superior to individuals trying to perform these tasks.
Robots When a machine is flexible and has the ability to hold, move, and perhaps “grab” items, we tend to use the word robot . Robots are mechanical devices that use electronic impulses to acti- vate motors and switches. Robots may be used effectively to perform tasks that are especially monotonous or dangerous or those that can be improved by the substitution of mechanical for human effort. Such is the case when consistency, accuracy, speed, strength, or power can be enhanced by the substitution of machines for people. The automobile industry, for exam- ple, uses robots to do virtually all the welding and painting on automobiles. And a new, more sophisticated, generation of robots are fitted with sensors and cameras that provide enough dexterity to assemble, test, and pack small parts.
Automated Storage and Retrieval Systems (ASRSs) Because of the tremendous labor involved in error-prone warehousing, computer-controlled warehouses have been developed. These systems, known as automated storage and retrieval systems (ASRSs) , provide for the automatic placement and withdrawal of parts and products into and from designated places in a warehouse. Such systems are commonly used in distribution facili- ties of retailers such as Walmart, Tupperware, and Benetton. These systems are also found in inventory and test areas of manufacturing firms.
Automated Guided Vehicles (AGVs) Automated material handling can take the form of monorails, conveyors, robots, or auto- mated guided vehicles. Automated guided vehicles (AGVs) are electronically guided and con- trolled carts used in manufacturing and warehousing to move parts and equipment. They are also used in agriculture to distribute feed, in offices to move mail, and in hospitals and jails to deliver supplies and meals.
Sophisticated process control is required to monitor complex processes that vary—from beer
at Anheuser-Busch, to steel at Nucor, to nuclear reactors at Dominion Resources (shown here).
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Automated guided vehicle (AGV)
Electronically guided and
controlled cart used to move
materials.
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Flexible Manufacturing Systems (FMSs) When a central computer provides instructions to each workstation and to the material- handling equipment such as robots, ASRSs, and AGVs (as just noted), the system is known as an automated work cell or, more commonly, a flexible manufacturing system (FMS) . An FMS is flexible because both the material-handling devices and the machines themselves are controlled by easily changed electronic signals (computer programs). Operators simply load new programs, as necessary, to produce different products. The result is a system that can economically produce low volume but high variety. For example, the Lockheed Martin facility, near Dallas, efficiently builds one-of-a-kind spare parts for military aircraft. The costs associated with changeover and low utilization have been reduced substantially. FMSs bridge the gap between product-focused and process-focused facilities.
Computer-Integrated Manufacturing (CIM) Flexible manufacturing systems can be extended backward electronically into the engi- neering and inventory control departments and forward to the warehousing and shipping departments. In this way, computer-aided design (CAD) generates the necessary electronic instructions to run a numerically controlled machine. In a computer-integrated manu- facturing environment, a design change initiated at a CAD terminal can result in that change being made in the part produced on the shop floor in a matter of minutes. When this capability is integrated with inventory control, warehousing, and shipping as a part of a flexible manufacturing system, the entire system is called computer-integrated manufacturing (CIM) ; ( Figure 7.9 ).
Flexible manufacturing system (FMS)
A system that uses elec-
tronic signals from a centralized
computer to automate production
and material flow.
Management decides to make a product
OM runs production process, purchasing components, coordinating suppliers, planning and scheduling operations, overseeing quality and the workforce, and shipping to customers.
Computer-aided manufacturing (CAM) converts raw materials into components or products
Robots and specialized equipment weld, insert, and assemble components.
Robots test, package, and ship the finished product.
Information flows
Material flows
ASRS (above) and AGVs move incoming materials and parts, work-in-process, and complete product.
Computer-aided design (CAD) designs the product and programs the automated production equipment.
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Computer-Integrated
Manufacturing (CIM)
CIM includes computer-aided
design (CAD), computer-aided
manufacturing (CAM), flexible
manufacturing systems (FMSs),
automated storage and retrieval
systems (ASRSs), automated
guided vehicles (AGVs), and robots
to provide an integrated and
flexible manufacturing process.
Computer-integrated manufacturing (CIM)
A manufacturing system in which
CAD, FMS, inventory control,
warehousing, and shipping are
integrated.
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Flexible manufacturing systems and computer-integrated manufacturing are reducing the distinction between low-volume/high-variety and high-volume/low-variety production. Infor- mation technology is allowing FMS and CIM to handle increasing variety while expanding to include a growing range of volumes.
Technology in Services Just as we have seen rapid advances in technology in the manufacturing sector, so we also find dramatic changes in the service sector. These range from electronic diagnostic equipment at auto repair shops, to blood- and urine-testing equipment in hospitals, to retinal security scanners at airports. The hospitality industry provides other examples, as discussed in the OM in Action box “Technology Changes the Hotel Industry.” The McDonald’s approach is to use self-serve kiosks. The labor savings when ordering and speedier checkout service provide valuable productivity increases for both the restaurant and the customer.
In retail stores, POS terminals download prices quickly to reflect changing costs or market conditions, and sales are tracked in 15-minute segments to aid scheduling. Drug companies, such as Purdue Pharma LP, track critical medications with radio frequency identification (RFID) tags to reduce counterfeiting and theft.
Table 7.4 provides a glimpse of the impact of technology on services. Operations managers in services, as in manufacturing, must be able to evaluate the impact of technology on their firm. This ability requires particular skill when evaluating reliability, investment analysis, human resource requirements, and maintenance/service.
Process Redesign Often a firm finds that the initial assumptions of its process are no longer valid. The world is a dynamic place, and customer desires, product technology, and product mix change. Consequently, processes are redesigned. Process redesign (sometimes called process reengineer- ing) is the fundamental rethinking of business processes to bring about dramatic improve- ments in performance. Effective process redesign relies on reevaluating the purpose of the process and questioning both purpose and underlying assumptions. It works only if the basic process and its objectives are reexamined.
Process redesign also focuses on those activities that cross functional lines. Because man- agers are often in charge of specific “functions” or specialized areas of responsibility, those
OM in Action Technology Changes the Hotel Industry Technology is introducing “intelligent rooms” to the hotel industry. Hotel man-
agement can now precisely track a maid’s time through the use of a security
system. When a maid enters a room, a card is inserted that notifies the front-
desk computer of the maid’s location. “We can show her a printout of how long
she takes to do a room,” says one manager.
Security systems also enable guests to use their own credit cards as keys
to unlock their doors. There are also other uses for the system. The computer
can bar a guest’s access to the room after checkout time and automati-
cally control the air conditioning or heat, turning it on at check-in and off at
checkout.
Minibars are now equipped with sensors that alert the central computer
system at the hotel when an item is removed. Such items are immediately
billed to the room. And now, with a handheld infrared unit, housekeeping staff
can check, from the hallway, to see if a room is physically occupied. This both
eliminates the embarrassment of having a hotel staffer walk in on a guest and
improves security for housekeepers.
At Loew’s Portofino Bay Hotel at Universal Studios, Orlando, guest smart
cards act as credit cards in both the theme park and the hotel, and staff smart
cards (programmed for different levels of security access) create an audit trail
of employee movement. At the Mandarin Oriental Hotel in Las Vegas, guests
arriving in their rooms after check-in are greeted by the drapes opening, lights
turning on, and the TV displaying a customized message with the guest’s name.
Not to be outdone, Aloft hotel in Cupertino, California, has a robot that will
hustle razors, toothbrushes, snacks, or the morning paper to any of the hotel’s
150 rooms in 2 to 3 minutes. And when finished it returns to the lobby for the
next chore or recharging. As a result, the staff spends more time with guests.
Sources: New York Times (August 12, 2014) and (November 10, 2008); The Wall
Street Journal (October 28, 2014); and Hotel Marketing.com (March 28, 2011).
Process redesign
The fundamental rethinking of
business processes to bring
about dramatic improvements in
performance.
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activities (processes) that cross from one function or specialty to another may be neglected. Redesign casts aside all notions of how the process is currently being done and focuses on dra- matic improvements in cost, time, and customer value. Any process is a candidate for radical redesign. The process can be a factory layout, a purchasing procedure, a new way of processing credit applications, or a new order-fulfillment process.
Shell Lubricants, for example, reinvented its order-fulfillment process by replacing a group of people who handled different parts of an order with one individual who does it all. As a result, Shell has cut the cycle time of turning an order into cash by 75%, reduced operating expenses by 45%, and boosted customer satisfaction 105%—all by introducing a new way of handling orders. Time, cost, and customer satisfaction—the dimension of performance shaped by operations—get major boosts from operational innovation.
TABLE 7.4 Examples of Technology’s Impact on Services
SERVICE INDUSTRY EXAMPLE
Financial Services Debit cards, electronic funds transfer, automatic teller machines, Internet stock trading, online banking via cell phone
Education Online newspapers and journals, interactive assignments via WebCT, Blackboard, and smartphones
Utilities and government
Automated one-person garbage trucks, optical mail scanners, fl ood-warning systems, meters that allow homeowners to control energy usage and costs
Restaurants and foods
Wireless orders from waiters to the kitchen, robot butchering, transponders on cars that track sales at drive-throughs
Communications Interactive TV, e-books via Kindle
Hotels Electronic check-in/check-out, electronic key/lock systems, mobile Web bookings
Wholesale/retail trade
Point-of-sale (POS) terminals, e-commerce, electronic communication between store and supplier, bar-coded data, RFID
Transportation Automatic toll booths, satellite-directed navigation systems, Wi-Fi in automobiles
Health care Online patient-monitoring systems, online medical information systems, robotic surgery
Airlines Ticketless travel, scheduling, Internet purchases, boarding passes downloaded as two- dimensional bar codes on smartphones
Summary Effective operations managers understand how to use pro- cess strategy as a competitive weapon. They select a pro- duction process with the necessary quality, flexibility, and cost structure to meet product and volume requirements. They also seek creative ways to combine the low unit cost of high-volume, low-variety manufacturing with the cus- tomization available through low-volume, high-variety
facilities. Managers use the techniques of lean production and employee participation to encourage the development of efficient equipment and processes. They design their equipment and processes to have capabilities beyond the tolerance required by their customers, while ensuring the flexibility needed for adjustments in technology, features, and volumes.
Key Terms
Process strategy (p. 282 ) Process focus (p. 282 ) Modules (p. 283 ) Repetitive process (p. 283 ) Product focus (p. 284 ) Mass customization (p. 284 ) Build-to-order (BTO) (p. 285 ) Postponement (p. 285 ) Crossover chart (p. 286 ) Flexibility (p. 288 ) Flowchart (p. 289 )
Time-function mapping (or process mapping) (p. 289 )
Process charts (p. 289 ) Value-stream mapping (VSM) (p. 290 ) Service blueprinting (p. 292 ) Computer numerical control (CNC) (p. 295 ) Additive manufacturing (3D Printing)
(p. 295 ) Automatic identification system (AIS) (p. 295 ) Radio frequency identification (RFID)
(p. 295 )
Process control (p. 295 ) Vision systems (p. 296 ) Robot (p. 296 ) Automated storage and retrieval system
(ASRS) (p. 296 ) Automated guided vehicle (AGV) (p. 296 ) Flexible manufacturing system (FMS)
(p. 297 ) Computer-integrated manufacturing
(CIM) (p. 297 ) Process redesign (p. 298 )
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Discussion Questions
Ethical Dilemma For the sake of effi ciency and lower costs, Premium Standard Farms of Princeton, Missouri, has turned pig production into a standardized product-focused process. Slaughterhouses have done this for a hundred years—but after the animal was dead. Doing it while the animal is alive is a relatively recent innovation. Here is how it works.
Impregnated female sows wait for 40 days in metal stalls so small that they cannot turn around. After an ultrasound test, they wait 67 days in a similar stall until they give birth. Two weeks after delivering 10 or 11 piglets, the sows are moved back to breeding rooms for another cycle. After 3 years, the sow is slaughtered. Animal-welfare advocates say such confi nement drives pigs crazy. Premium Standard replies that its hogs are in fact comfortable, arguing that only 1% die before Premium Standard wants them to and that their system helps reduce the cost of pork products.
Discuss the productivity and ethical implications of this industry and these two divergent opinions.
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1. What is process strategy? 2. What type of process is used for making each of the following
products? (a) beer (b) wedding invitations (c) automobiles (d) paper (e) Big Macs (f ) custom homes (g) motorcycles
3. What is service blueprinting? 4. What is process redesign? 5. What are the techniques for improving service productivity? 6. Name the four quadrants of the service process matrix.
Discuss how the matrix is used to classify services into categories.
7. What is CIM? 8. What do we mean by a process-control system, and what are
the typical elements in such systems?
9. Identify manufacturing firms that compete on each of the four processes shown in Figure 7.1 .
10. Identify the competitive advantage of each of the four firms identified in Discussion Question 9.
11. Identify service firms that compete on each of the four processes shown in Figure 7.1 .
12. Identify the competitive advantage of each of the four firms identified in Discussion Question 11.
13. What are numerically controlled machines? 14. Describe briefly what an automatic identification system
(AIS) is and how service organizations could use AIS to increase productivity and at the same time increase the variety of services offered.
15. Name some of the advances being made in technology that enhance production and productivity.
16. Explain what a flexible manufacturing system (FMS) is. 17. In what ways do CAD and FMS connect? 18. What is additive manufacturing? 19. Discuss the advantages and disadvantages of 3D printing.
Solved Problem Virtual Office Hours help is available in MyOMLab .
SOLVED PROBLEM 7.1 Bagot Copy Shop has a volume of 125,000 black-and-white copies per month. Two salespeople have made presentations to Gordon Bagot for machines of equal quality and reliability. The Print Shop 5 has a cost of $2,000 per month and a variable cost of $.03. The other machine (a Speed Copy 100 ) will cost only $1,500 per month, but the toner is more expensive, driving the cost per copy up to $.035. If cost and volume are the only considerations, which machine should Bagot purchase?
SOLUTION
2,000 + .03X = 1,500 + .035X 2,000 - 1,500 = .035X - .03X 500 = .005X 100,000 = X
Because Bagot expects his volume to exceed 100,000 units, he should choose the Print Shop 5 .
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Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems 7.1–7.12 relate to Four Process Strategies
• 7.1 Borges Machine Shop, Inc., has a 1-year contract for the production of 200,000 gear housings for a new off-road vehicle. Owner Luis Borges hopes the contract will be extended and the volume increased next year. Borges has developed costs for three alternatives. They are general-purpose equipment (GPE), flexible manufacturing system (FMS), and expensive, but efficient, dedicated machine (DM). The cost data follow:
GENERAL- PURPOSE
EQUIPMENT (GPE)
FLEXIBLE MANUFACTURING
SYSTEM (FMS)
DEDICATED MACHINE
(DM)
Annual contracted units
200,000 200,000 200,000
Annual fi xed cost $100,000 $200,000 $500,000
Per unit variable cost $ 15.00 $ 14.00 $ 13.00
Which process is best for this contract? PX
• 7.2 Using the data in Problem 7.1, determine the most economical volume for each process. PX
• 7.3 Using the data in Problem 7.1, determine the best pro- cess for each of the following volumes: (1) 75,000, (2) 275,000, and (3) 375,000.
• 7.4 Refer to Problem 7.1. If a contract for the second and third years is pending, what are the implications for process selection?
• • 7.5 Stan Fawcett’s company is considering producing a gear assembly that it now purchases from Salt Lake Supply, Inc. Salt Lake Supply charges $4 per unit, with a minimum order of 3,000 units. Stan estimates that it will cost $15,000 to set up the process and then $1.82 per unit for labor and materials. a) Draw a graph illustrating the crossover (or indifference) point. b) Determine the number of units where either choice has the
same cost. PX
• • 7.6 Ski Boards, Inc., wants to enter the market quickly with a new finish on its ski boards. It has three choices: (a) Refurbish the old equipment at a cost of $800, (b) make major modifications at a cost of $1,100, or (c) purchase new equipment
at a net cost of $1,800. If the firm chooses to refurbish the equip- ment, materials and labor will be $1.10 per board. If it chooses to make modifications, materials and labor will be $0.70 per board. If it buys new equipment, variable costs are estimated to be $0.40 per board. a) Graph the three total cost lines on the same chart. b) Which alternative should Ski Boards, Inc., choose if it thinks it
can sell more than 3,000 boards? c) Which alternative should the firm use if it thinks the market
for boards will be between 1,000 and 2,000? PX
• • 7.7 Tim Urban, owner/manager of Urban’s Motor Court in Key West, is considering outsourcing the daily room cleanup for his motel to Duffy’s Maid Service. Tim rents an average of 50 rooms for each of 365 nights (365 3 50 equals the total rooms rented for the year). Tim’s cost to clean a room is $12.50. The Duffy’s Maid Service quote is $18.50 per room plus a fixed cost of $25,000 for sundry items such as uniforms with the motel’s name. Tim’s annual fixed cost for space, equipment, and supplies is $61,000. Which is the preferred process for Tim, and why? PX
• • 7.8 Matthew Bailey, as manager of Designs by Bailey, is upgrading his CAD software. The high-performance (HP) software rents for $3,000 per month per workstation. The standard-performance (SP) software rents for $2,000 per month per workstation. The productivity figures that he has available suggest that the HP software is faster for his kind of design. Therefore, with the HP software he will need five engineers and with the SP software he will need six. This translates into a vari- able cost of $200 per drawing for the HP system and $240 per drawing for the SP system. At his projected volume of 80 draw- ings per month, which system should he rent? PX
• • • 7.9 Metters Cabinets, Inc., needs to choose a production method for its new office shelf, the Maxistand. To help accomplish this, the firm has gathered the following production cost data:
PROCESS TYPE
ANNUALIZED FIXED COST OF PLANT & EQUIP.
VARIABLE COSTS (PER UNIT) ($)
LABOR MATERIAL ENERGY
Mass Customization
$1,260,000 30 18 12
Intermittent $1,000,000 24 26 20
Repetitive $1,625,000 28 15 12
Continuous $1,960,000 25 15 10
Metters Cabinets projects an annual demand of 24,000 units for the Maxistand. The Maxistand will sell for $120 per unit. a) Which process type will maximize the annual profit from pro-
ducing the Maxistand? b) What is the value of this annual profit? PX
• • 7.10 California Gardens, Inc., prewashes, shreds, and dis- tributes a variety of salad mixes in 2-pound bags. Doug Voss, Operations VP, is considering a new Hi-Speed shredder to replace the old machine, referred to in the shop as “Clunker.” Hi-Speed will have a fixed cost of $85,000 per month and a variable cost of $1.25 per bag. Clunker has a fixed cost of only $44,000 per month, but a variable cost of $1.75. Selling price is $2.50 per bag. a) What is the crossover point in units (point of indifference) for
the processes? Eri c
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b) What is the monthly profit or loss if the company changes to the Hi-Speed shredder and sells 60,000 bags per month?
c ) What is the monthly profit or loss if the company stays with Clunker and sells 60,000 bags per month?
• • 7.11 Nagle Electric, Inc., of Lincoln, Nebraska, must replace a robotic Mig welder and is evaluating two alternatives. Machine A has a fixed cost for the first year of $75,000 and a variable cost of $16, with a capacity of 18,000 units per year. Machine B is slower, with a speed of one-half of A’s, but the fixed cost is only $60,000. The variable cost will be higher, at $20 per unit. Each unit is expected to sell for $28. a) What is the crossover point (point of indifference) in units for
the two machines? b) What is the range of units for which machine A is prefer-
able? c) What is the range of units for which machine B is preferable?
• • 7.12 Stapleton Manufacturing intends to increase capac- ity through the addition of new equipment. Two vendors have presented proposals. The fixed cost for proposal A is $65,000, and for proposal B, $34,000. The variable cost for A is $10, and for B, $14. The revenue generated by each unit is $18.
a) What is the crossover point in units for the two options? b) At an expected volume of 8,300 units, which alternative should
be chosen?
Problems 7.13–7.17 relate to Process Analysis and Design
• 7.13 Prepare a flowchart for one of the following: a) the registration process at a school b) the process at the local car wash c) a shoe shine d) some other process with the approval of the instructor
• 7.14 Prepare a process chart for one of the activities in Problem 7.13.
• • 7.15 Prepare a time-function map for one of the activities in Problem 7.13.
• • 7.16 Prepare a service blueprint for one of the activities in Problem 7.13.
• • 7.17 Using Figure 7.6 in the discussion of value-stream mapping as a starting point, analyze an opportunity for improve- ment in a process with which you are familiar and develop an improved process.
CASE STUDIES
Rochester Manufacturing Corporation (RMC) is considering moving some of its production from traditional numerically con- trolled machines to a flexible manufacturing system (FMS). Its computer numerical control machines have been operating in a high-variety, low-volume manner. Machine utilization, as near as it can determine, is hovering around 10%. The machine tool sales- people and a consulting firm want to put the machines together in an FMS. They believe that a $3 million expenditure on machinery and the transfer machines will handle about 30% of RMC’s work. There will, of course, be transition and startup costs in addition to this.
The firm has not yet entered all its parts into a comprehensive group technology system, but believes that the 30% is a good esti- mate of products suitable for the FMS. This 30% should fit very nicely into a “family.” A reduction, because of higher utilization, should take place in the number of pieces of machinery. The firm should be able to go from 15 to about 4 machines, and per- sonnel should go from 15 to perhaps as low as 3. Similarly, floor space reduction will go from 20,000 square feet to about 6,000.
Throughput of orders should also improve with processing of this family of parts in 1 to 2 days rather than 7 to 10. Inventory reduction is estimated to yield a one-time $750,000 savings, and annual labor savings should be in the neighborhood of $300,000.
Although the projections all look very positive, an analysis of the project’s return on investment showed it to be between 10% and 15% per year. The company has traditionally had an expecta- tion that projects should yield well over 15% and have payback periods of substantially less than 5 years.
Discussion Questions
1. As a production manager for RMC, what do you recommend? Why?
2. Prepare a case by a conservative plant manager for maintaining the status quo until the returns are more obvious.
3. Prepare the case for an optimistic sales manager that you should move ahead with the FMS now.
Rochester Manufacturing’s Process Decision
Process Strategy at Wheeled Coach
Wheeled Coach, based in Winter Park, Florida, is the world’s largest manufacturer of ambulances. Working four 10-hour days each week, 350 employees make only custom-made ambulances; virtually every vehicle is unique. Wheeled Coach accommodates the marketplace by providing a wide variety of options and an engineering staff accustomed to innovation and custom design. Continuing growth, which now requires that more than 20 ambu- lances roll off the assembly line each week, makes process design a continuing challenge. Wheeled Coach’s response has been to build a focused factory: Wheeled Coach builds nothing but
ambulances. Within the focused factory, Wheeled Coach estab- lished work cells for every major module feeding an assembly line, including aluminum bodies, electrical wiring harnesses, interior cabinets, windows, painting, and upholstery.
Labor standards drive the schedule so that every work cell feeds the assembly line on schedule, just-in-time for installations. The chassis, usually that of a Ford truck, moves to a station at which the aluminum body is mounted. Then the vehicle is moved to painting. Following a custom paint job, it moves to the assembly line, where it will spend 7 days. During each of these 7 workdays,
Video Case
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each work cell delivers its respective module to the appropriate position on the assembly line. During the first day, electrical wir- ing is installed; on the second day, the unit moves forward to the station at which cabinetry is delivered and installed, then to a win- dow and lighting station, on to upholstery, to fit and finish, to fur- ther customizing, and finally to inspection and road testing. The Global Company Profile featuring Wheeled Coach, which opens Chapter 14 , provides further details about this process.
Discussion Questions *
1. Why do you think major auto manufacturers do not build ambulances?
2. What is an alternative process strategy to the assembly line that Wheeled Coach currently uses?
3. Why is it more efficient for the work cells to prepare “mod- ules” and deliver them to the assembly line than it would be to produce the component (e.g., interior upholstery) on the line?
4. How does Wheeled Coach manage the tasks to be performed at each work station?
Video Case Alaska Airlines: 20-Minute Baggage Process—Guaranteed! Alaska Airlines is unique among the nine major U.S. carriers not only for its extensive flight coverage of remote towns throughout Alaska (it also covers the U.S., Hawaii, and Mexico from its pri- mary hub in Seattle). It is also one of the smallest independent airlines, with 10,300 employees, including 3,000 flight attendants and 1,500 pilots. What makes it really unique, though, is its abil- ity to build state-of-the-art processes, using the latest technology, that yield high customer satisfaction. Indeed, J. D. Power and Associates has ranked Alaska Airlines highest in North America for seven years in a row for customer satisfaction.
Alaska Airlines was the first to sell tickets via the Internet, first to offer Web check-in and print boarding passes online, and first with kiosk check-in. As Wayne Newton, Director of System Operation Control, states, “We are passionate about our pro- cesses. If it’s not measured, it’s not managed.”
One of the processes Alaska is most proud of is its baggage han- dling system. Passengers can check in at kiosks, tag their own bags with bar code stickers, and deliver them to a customer service agent at the carousel, which carries the bags through the vast under- ground system that eventually delivers the bags to a baggage han- dler. En route, each bag passes through TSA automated screening and is manually opened or inspected if it appears suspicious. With the help of bar code readers, conveyer belts automatically sort and transfer bags to their location (called a “pier”) at the tarmac level. A baggage handler then loads the bags onto a cart and takes it to the plane for loading by the ramp team waiting inside the cargo hold. There are different procedures for “hot bags” (bags that have less than 30 minutes between transfer) and for “cold bags” (bags with over 60 minutes between plane transfers). Hot bags are deliv- ered directly from one plane to another (called “tail-to-tail”). Cold bags are sent back into the normal conveyer system.
The process continues on the destination side with Alaska’s unique guarantee that customer luggage will be delivered to the terminal’s carousel within 20 minutes of the plane’s arrival at the gate. If not, Alaska grants each passenger a 2,000 frequent-flier mile bonus!
The airline’s use of technology includes bar code scanners to check in the bag when a passenger arrives, and again before it is placed on the cart to the plane. Similarly, on arrival, the time the passenger door opens is electronically noted and bags are again scanned as they are placed on the baggage carousel at the destination—tracking this metric means that the “time to carou- sel” (TTC) deadline is seldom missed. And the process almost guarantees that the lost bag rate approaches zero. On a recent day, only one out of 100 flights missed the TTC mark. The baggage
process relies not just on technology, though. There are detailed, documented procedures to ensure that bags hit the 20-minute timeframe. Within one minute of the plane door opening at the gate, baggage handlers must begin the unloading. The first bag must be out of the plane within three minutes of parking the plane. This means the ground crew must be in the proper location—with their trucks and ramps in place and ready to go.
Largely because of technology, flying on Alaska Airlines is remarkably reliable—even in the dead of an Alaska winter with only two hours of daylight, 50 mph winds, slippery run- ways, and low visibility. Alaska Airlines has had the industry’s best on-time performance, with 87% if its flights landing on time.
Discussion Questions*
1. Prepare a flowchart of the process a passenger’s bag follows from kiosk to destination carousel. (See Example 2 in Chapter 6 for a sample flowchart.) Include the exception process for the TSA opening of selected bags.
* You may wish to view the video that accompanies this case before addressing these questions.
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2. What other processes can an airline examine? Why is each important?
3. How does the kiosk alter the check-in process?
4. What metrics (quantifiable measures) are needed to track bag- gage?
5. What is the role of scanners in the baggage process?
• Additional Case Study: Visit MyOMLab for this free case study: Matthew Yachts, Inc.: Examines a possible process change as the market for yachts changes.
Video Case Process Analysis at Arnold Palmer Hospital The Arnold Palmer Hospital (APH) in Orlando, Florida, is one of the busiest and most respected hospitals for the medical treatment of children and women in the U.S. Since its opening on golfing legend Arnold Palmer’s birthday September 10, 1989, more than 1.6 million children and women have passed through its doors. It is the fourth busiest labor and delivery hospital in the U.S. and one of the largest neonatal intensive care units in the Southeast. APH ranks in the top 10% of hospitals nationwide in patient sat- isfaction.
“Part of the reason for APH’s success,” says Executive Director Kathy Swanson, “is our continuous improvement pro- cess. Our goal is 100% patient satisfaction. But getting there means constantly examining and reexamining everything we do, from patient flow, to cleanliness, to layout space, to a work- friendly environment, to speed of medication delivery from the pharmacy to a patient. Continuous improvement is a huge and never-ending task.”
One of the tools the hospital uses consistently is process charts [like those in Figures 7.4 to 7.7 in this chapter and Figure 6.6 (e) in Chapter 6 ]. Staffer Diane Bowles, who carries the title “clini- cal practice improvement consultant,” charts scores of processes. Bowles’s flowcharts help study ways to improve the turnaround of a vacated room (especially important in a hospital that has pushed capacity for years), speed up the admission process, and deliver warm meals warm.
Lately, APH has been examining the flow of maternity patients (and their paperwork) from the moment they enter the hospital until they are discharged, hopefully with their healthy baby, a day or two later. The flow of maternity patients follows these steps:
1. Enter APH’s Labor & Delivery (L&D) check-in desk entrance.
2. If the baby is born en route or if birth is imminent, the mother and baby are taken directly to Labor & Delivery on the sec- ond floor and registered and admitted directly at the bedside. If there are no complications, the mother and baby go to Step 6.
3. If the baby is not yet born, the front desk asks if the mother is pre-registered. (Most do preregister at the 28- to 30-week pregnancy mark.) If she is not, she goes to the registration office on the first floor.
4. The pregnant woman is then taken to L&D Triage on the 8th floor for assessment. If she is in active labor, she is taken to an L&D room on the 2nd floor until the baby is born. If she is not ready, she goes to Step 5.
5. Pregnant women not ready to deliver (i.e., no contractions or false alarms) are either sent home to return on a later date and reenter the system at that time, or if contractions are not yet close enough, they are sent to walk around the hospital grounds (to encourage progress) and then return to L&D Triage at a prescribed time.
6. When the baby is born, if there are no complications, after 2 hours the mother and baby are transferred to a “mother– baby care unit” room on floors 3, 4, or 5 for an average of 40–44 hours.
7. If there are complications with the mother, she goes to an operating room and/or intensive care unit. From there, she goes back to a mother–baby care room upon stabilization—or is discharged at another time if not stabilized. Complications for the baby may result in a stay in the neonatal intensive care unit (NICU) before transfer to the baby nursery near the mother’s room. If the baby is not stable enough for discharge with the mother, the baby is discharged later.
8. Mother and/or baby, when ready, are discharged and taken by wheelchair to the discharge exit for pickup to travel home.
Discussion Questions *
1. As Diane’s new assistant, you need to flowchart this process. Explain how the process might be improved once you have completed the chart.
2. If a mother is scheduled for a Caesarean-section birth (i.e., the baby is removed from the womb surgically), how would this flowchart change?
3. If all mothers were electronically (or manually) preregistered, how would the flowchart change? Redraw the chart to show your changes.
4. Describe in detail a process that the hospital could analyze, besides the ones mentioned in this case.
Endnotes
1. An additional example of a process chart is shown in Chapter 10 .
2. Waste includes inspection (if the task is done properly, then inspection is unnecessary); transportation (movement of
material within a process may be a necessary evil, but it adds no value); delay (an asset sitting idle and taking up space is waste); storage (unless part of a “curing” process, storage is waste).
* You may wish to view the video that accompanies this case before addressing these questions.
* You may wish to view the video that accompanies this case before addressing these questions.
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Chapter 7 Rapid Review Main Heading Review Material MyOMLab FOUR PROCESS STRATEGIES (pp. 282–288)
j Process strategy —An organization’s approach to transforming resources into goods and services.
The objective of a process strategy is to build a production process that meets customer requirements and product specifications within cost and other managerial constraints. Virtually every good or service is made by using some variation of one of four process strategies. j Process focus —A facility organized around processes to facilitate low- volume,
high-variety production. The vast majority of global production is devoted to making low-volume, high-variety products in process-focused facilities, also known as job shops or intermittent process facilities. Process-focused facilities have high variable costs with extremely low utilization (5% to 25%) of facilities. j Modules —Parts or components of a product previously prepared, often in a
continuous process. j Repetitive process —A product-oriented production process that uses modules. The repetitive process is the classic assembly line. It allows the firm to use modules and combine the economic advantages of the product-focused model with the customization advantages of the process-focus model. j Product focus —A facility organized around products; a product-oriented, high-
volume, low-variety process. Product-focused facilities are also called continuous processes because they have very long, continuous production runs. The specialized nature of a product-focused facility requires high fixed cost; however, low variable costs reward high facility utilization. j Mass customization —Rapid, low-cost production that caters to constantly
changing unique customer desires. j Build-to-order (BTO) —Produce to customer order rather than to a forecast. Major challenges of a build-to-order system include: Product design, Process design, Inventory management, Tight schedules , and Responsive partners. j Postponement —The delay of any modifications or customization to a product as
long as possible in the production process. j Crossover chart —A chart of costs at the possible volumes for more than one
process.
Concept Questions: 1.1–1.4 Problems: 7.1–7.12
ACTIVE MODEL 7.1
VIDEO 7.1 Process Strategy at Wheeled Coach Ambulance
Virtual Office Hours for Solved Problem: 7.1
SELECTION OF EQUIPMENT (p. 288)
Picking the best equipment involves understanding the specific industry and available processes and technology. The choice requires considering cost, quality, capacity, and flexibility. j Flexibility —The ability to respond with little penalty in time, cost, or customer
value.
Concept Questions: 2.1–2.3
PROCESS ANALYSIS AND DESIGN (pp. 288 – 293 )
Five tools of process analysis are (1) flowcharts, (2) time-function mapping, (3) process charts, (4) value-stream mapping, and (5) service blueprinting. j Flowchart —A drawing used to analyze movement of people or materials. j Time-function mapping (or process mapping )—A flowchart with time added on
the horizontal axis. j Process charts —Charts that use symbols to analyze the movement of people or
material. Process charts allow managers to focus on value-added activities and to compute the percentage of value-added time (5 operation time/total time). j Value-stream mapping (VSM) —A tool that helps managers understand how to
add value in the flow of material and information through the entire production process.
j Service blueprinting —A process analysis technique that lends itself to a focus on the customer and the provider’s interaction with the customer.
Concept Questions: 3.1–3.4 Problems: 7.14–7.15
VIDEO 7.2 Alaska Airlines 20-Minute Baggage Process–Guaranteed!
VIDEO 7.3 Process Analysis at Arnold Palmer Hospital
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Chapter 7 Rapid Review continued
Main Heading Review Material MyOMLab SPECIAL CONSIDERA- TIONS FOR SERVICE PROCESS DESIGN (pp. 293 – 294 )
Services can be classified into one of four quadrants, based on relative degrees of labor and customization: 1. Service factory 2. Service shop 3. Mass service 4. Professional service Techniques for improving service productivity include: j Separation —Structuring service so customers must go where the service is offered j Self-service —Customers examining, comparing, and evaluating at their own pace j Postponement —Customizing at delivery j Focus —Restricting the offerings j Modules —Modular selection of service; modular production j Automation —Separating services that may lend themselves to a type of automation j Scheduling —Precise personnel scheduling j Training —Clarifying the service options; explaining how to avoid problems
Concept Questions: 4.1–4.4
PRODUCTION TECHNOLOGY (pp. 294 – 298 )
j Computer numerical control (CNC) —Machinery with its own computer and memory.
j Additive manufacturing —The production of physical items by adding layer upon layer, much in the same way an ink jet printer lays down ink; often referred to as 3D printing.
j Automatic identification system (AIS) —A system for transforming data into electronic form (e.g., bar codes).
j Radio frequency identification (RFID) —A wireless system in which integrated circuits with antennas send radio waves.
j Process control —The use of information technology to control a physical process. j Vision systems —Systems that use video cameras and computer technology in
inspection roles. j Robot —A flexible machine with the ability to hold, move, or grab items. j Automated storage and retrieval systems (ASRS) —Computer-controlled
warehouses that provide for the automatic placement of parts into and from designated places within a warehouse.
j Automated guided vehicle (AGV) —Electronically guided and controlled cart used to move materials.
j Flexible manufacturing system (FMS) —Automated work cell controlled by electronic signals from a common centralized computer facility.
j Computer-integrated manufacturing (CIM) —A manufacturing system in which CAD, FMS, inventory control, warehousing, and shipping are integrated.
Concept Questions: 5.1–5. 4
TECHNOLOGY IN SERVICES (p. 298 )
Many rapid technological developments have occurred in the service sector. These range from POS terminals and RFID to online newspapers and e-books.
Concept Questions: 6.1–6.2
PROCESS REDESIGN (pp. 298–299 )
j Process redesign —The fundamental rethinking of business processes to bring about dramatic improvements in performance.
Process redesign often focuses on activities that cross functional lines.
Concept Questions: 7.1–7.2
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 7.1 Low-volume, high-variety processes are also known as: a) continuous processes. b) process focused. c) repetitive processes. d) product focused. LO 7.2 A crossover chart for process selection focuses on: a) labor costs. b) material cost. c) both labor and material costs. d) fixed and variable costs. e) fixed costs. LO 7.3 Tools for process analysis include all of the following except: a) flowchart. b) vision systems. c) service blueprinting. d) time-function mapping. e) value-stream mapping.
LO 7.4 Customer feedback in process design is lower as: a) the degree of customization is increased. b) the degree of labor is increased. c) the degree of customization is lowered. d) both a and b. e) both b and c. LO 7.5 Computer-integrated manufacturing (CIM) includes manufac-
turing systems that have: a) computer-aided design, direct numerical control
machines, and material-handling equipment controlled by automation.
b) transaction processing, a management information system, and decision support systems.
c) automated guided vehicles, robots, and process control. d) robots, automated guided vehicles, and transfer equipment.
Answers: LO 7.1. b; LO 7.2. d; LO 7.3. b; LO 7.4. c; LO 7.5. a.
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SUPPLEMENT OUTLINE
Capacity and Constraint Management 7
◆
Capacity 308
◆
Bottleneck Analysis and the Theory of Constraints 314
◆ Break-Even Analysis 318
◆
Reducing Risk with Incremental Changes 322
◆
Applying Expected Monetary Value (EMV) to Capacity Decisions 323
◆
Applying Investment Analysis to Strategy-Driven Investments 324
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L E A R N I N G OBJEC TI V ES
LO S7.1 Defi ne capacity 308
LO S7.2 Determine design capacity, eff ective capacity, and utilization 310
LO S7.3 Perform bottleneck analysis 315
LO S7.4 Compute break-even 319
LO S7.5 Determine expected monetary value of a capacity decision 323
LO S7.6 Compute net present value 324
Capacity What should be the seating capacity of a concert hall? How many customers per day should an Olive Garden or a Hard Rock Cafe be able to serve? How large should a Frito-Lay plant be to produce 75,000 bags of Ruffles in an 8-hour shift? In this supplement we look at tools that help a manager make these decisions.
After selection of a production process ( Chapter 7 ), managers need to determine capacity. Capacity is the “throughput,” or the number of units a facility can hold, receive, store, or produce in a given time. Capacity decisions often determine capital requirements and therefore a large portion of fixed cost. Capacity also determines whether demand will be satisfied or whether facilities will be idle. If a facility is too large, portions of it will sit unused and add cost to existing production. If a facility is too small, customers—and perhaps entire markets—will be lost. Determining facility size, with an objective of achieving high levels of utilization and a high return on investment, is critical.
Capacity planning can be viewed in three time horizons. In Figure S7.1 we note that long- range capacity (generally greater than 3 years) is a function of adding facilities and equipment that have a long lead time. In the intermediate range (usually 3 to 36 months), we can add equipment, personnel, and shifts; we can subcontract; and we can build or use inventory. This is the “aggregate planning” task. In the short run (usually up to 3 months), we are primarily concerned with scheduling jobs and people, as well as allocating machinery. Modifying capacity in the short run is difficult, as we are usually constrained by existing capacity.
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When designing a concert hall,
management hopes that the forecasted
capacity (the product mix—opera,
symphony, and special events—and
the technology needed for these events)
is accurate and adequate for operation
above the break-even point. However,
in many concert halls, even when
operating at full capacity, break-even is
not achieved, and supplemental funding
must be obtained.
LO S7.1 Define capacity
Capacity
The “throughput,” or number of
units a facility can hold, receive,
store, or produce in a period
of time.
STUDENT TIP Too little capacity loses customers
and too much capacity is expensive.
Like Goldilocks’s porridge, capacity
needs to be just right.
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Design and Effective Capacity Design capacity is the maximum theoretical output of a system in a given period under ideal condi- tions. It is normally expressed as a rate, such as the number of tons of steel that can be produced per week, per month, or per year. For many companies, measuring capacity can be straightfor- ward: it is the maximum number of units the company is capable of producing in a specific time. However, for some organizations, determining capacity can be more difficult. Capacity can be measured in terms of beds (a hospital), active members (a church), or billable hours (a CPA firm). Other organizations use total work time available as a measure of overall capacity.
Most organizations operate their facilities at a rate less than the design capacity. They do so because they have found that they can operate more efficiently when their resources are not stretched to the limit. For example, Ian’s Bistro has tables set with 2 or 4 chairs seating a total of 270 guests. But the tables are never filled that way. Some tables will have 1 or 3 guests; tables can be pulled together for parties of 6 or 8. There are always unused chairs. Design capacity is 270, but effective capacity is often closer to 220, which is 81% of design capacity.
Effective capacity is the capacity a firm expects to achieve given the current operating con- straints. Effective capacity is often lower than design capacity because the facility may have been designed for an earlier version of the product or a different product mix than is currently being produced. Table S7.1 further illustrates the relationship between design capacity, effective capacity, and actual output.
Long-range planning
Options for Adjusting Capacity
Time Horizon
Intermediate-range planning
Short-range planning
(aggregate planning)
(scheduling)
Modify capacity Use capacity
Design new production processes. Add (or sell existing) long-lead-time equipment. Acquire or sell facilities. Acquire competitors.
Subcontract. Add or sell equipment. Add or reduce shifts.
Build or use inventory. More or improved training. Add or reduce personnel.
Schedule jobs. Schedule personnel. Allocate machinery.
*
*
* Difficult to adjust capacity, as limited options exist
Figure S7.1
Time Horizons and Capacity
Options
Design capacity
The theoretical maximum output
of a system in a given period
under ideal conditions.
Effective capacity
The capacity a firm can expect
to achieve, given its product mix,
methods of scheduling, mainte-
nance, and standards of quality.
TABLE S7.1 Capacity Measurements
MEASURE DEFINITION EXAMPLE
Design capacity Ideal conditions exist during the time that the system is available.
If machines at Frito-Lay are designed to produce 1,000 bags of chips/hr., and the plant operates 16 hrs./day. Design Capacity 5 1,000 bags/hr. 3 16 hrs. 5 16,000 bags/day
Effective capacity Design capacity minus lost output because of planned resource unavailability (e.g., preventive maintenance, machine setups/ changeovers, changes in product mix, scheduled breaks)
If Frito-Lay loses 3 hours of output per day (namely 0.5 hrs./day on preventive maintenance + 1 hr./day on employee breaks + 1.5 hrs./day setting up machines for different products). Effective Capacity 5 16,000 bags/day 2 (1,000 bags/hr.)(3 hrs./day) 5 16,000 bags/day 2 3,000 bags/day 5 13,000 bags/day
Actual output Effective capacity minus lost output during unplanned resource idleness (e.g., absenteeism, machine breakdowns, unavailable parts, quality problems)
On average, if machines at Frito-Lay are not running 1 hr./day due to late parts and machine breakdowns. Actual Output 5 13,000 bags/day 2 (1,000 bags/hr.)(1 hr./day) 5 13,000 bags/day 2 1,000 bags/day 5 12,000 bags/day
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Two measures of system performance are particularly useful: utilization and efficiency. Utilization is simply the percent of design capacity actually achieved. Efficiency is the percent of effective capacity actually achieved. Depending on how facilities are used and managed, it may be difficult or impossible to reach 100% efficiency. Operations managers tend to be evaluated on efficiency. The key to improving efficiency is often found in correcting quality problems and in effective scheduling, training, and maintenance. Utilization and efficiency are computed below:
Utilization = Actual output>Design capacity (S7-1)
Efficiency = Actual output>Effective capacity (S7-2)
In Example S1 we determine these values.
Utilization
Actual output as a percent
of design capacity.
Efficiency
Actual output as a percent
of effective capacity.
LO S7.2 Determine design capacity, effective
capacity, and utilization
Example S1 DETERMINING CAPACITY UTILIZATION AND EFFICIENCY Sara James Bakery has a plant for processing Deluxe breakfast rolls and wants to better understand its capability. Last week the facility produced 148,000 rolls. The effective capacity is 175,000 rolls. The production line operates 7 days per week, with three 8-hour shifts per day. The line was designed to process the nut-filled, cinnamon-flavored Deluxe roll at a rate of 1,200 per hour. Determine the design capacity, utilization, and efficiency for this plant when producing this Deluxe roll.
APPROACH c First compute the design capacity and then use Equation (S7-1) to determine utiliza- tion and Equation (S7-2) to determine efficiency.
SOLUTION c Design capacity = (7 days * 3 shifts * 8 hours) * (1,200 rolls per hour) = 201,600 rolls
Utilization = Actual output>Design capacity = 148,000>201,600 = 73.4,
Efficiency = Actual output>Effective capacity = 148,000>175,000 = 84.6,
INSIGHT c The bakery now has the information necessary to evaluate efficiency. LEARNING EXERCISE c If the actual output is 150,000, what is the efficiency? [Answer: 85.7%.] RELATED PROBLEMS c S7.1, S7.2, S7.3, S7.4, S7.5, S7.6, S7.7, S7.8 ACTIVE MODEL S7.1 This example is further illustrated in Active Model S7.1 in MyOMLab.
In Example S2 we see how the effectiveness of new capacity additions depends on how well management can perform on the utilization and efficiency of those additions.
Example S2 EXPANDING CAPACITY The manager of Sara James Bakery (see Example S1 ) now needs to increase production of the increas- ingly popular Deluxe roll. To meet this demand, she will be adding a second production line. The second line has the same design capacity (201,600) and effective capacity (175,000) as the first line; however, new workers will be operating the second line. Quality problems and other inefficiencies stemming from the inexperienced workers are expected to reduce output on the second line to 130,000 (compared to 148,000 on the first). The utilization and efficiency were 73.4% and 84.6%, respectively, on the first line. Determine the new utilization and efficiency for the Deluxe roll operation after adding the second line.
APPROACH c First, determine the new design capacity, effective capacity, and actual output after adding the second line. Then, use Equation (S7-1) to determine utilization and Equation (S7-2) to deter- mine efficiency.
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Actual output, as used in Equation (S7-2) , represents current conditions. Alternatively, with a knowledge of effective capacity and a current or target value for efficiency, the future expected out- put can be computed by reversing Equation (S7-2) :
Expected output 5 Effective capacity 3 Efficiency If the expected output is inadequate, additional capacity may be needed. Much of the remain-
der of this supplement addresses how to effectively and efficiently add that capacity.
Capacity and Strategy Sustained profits come from building competitive advantage, not just from a good financial return on a specific process. Capacity decisions must be integrated into the organization’s mis- sion and strategy. Investments are not to be made as isolated expenditures, but as part of a coor- dinated plan that will place the firm in an advantageous position. The questions to be asked are, “Will these investments eventually win profitable customers?” and “What competitive advan- tage (such as process flexibility, speed of delivery, improved quality, and so on) do we obtain?”
All 10 OM decisions we discuss in this text, as well as other organizational elements such as marketing and finance, are affected by changes in capacity. Change in capacity will have sales and cash flow implications, just as capacity changes have quality, supply chain, human resource, and maintenance implications. All must be considered.
Capacity Considerations In addition to tight integration of strategy and investments, there are four special considera- tions for a good capacity decision: 1. Forecast demand accurately: Product additions and deletions, competition actions,
product life cycle, and unknown sales volumes all add challenge to accurate forecasting. 2. Match technology increments and sales volume: Capacity options are often constrained
by technology. Some capacity increments may be large (e.g., steel mills or power plants), while others may be small (hand-crafted Louis Vuitton handbags). Large capacity incre- ments complicate the difficult but necessary job of matching capacity to sales.
3. Find the optimum operating size (volume): Economies and diseconomies of scale often dictate an optimal size for a facility. Economies of scale exist when average cost declines as size increases, whereas diseconomies of scale occur when a larger size raises the average cost. As Figure S7.2 suggests, most businesses have an optimal size—at least until some- one comes along with a new business model. For decades, very large integrated steel mills were considered optimal. Then along came Nucor, CMC, and other minimills, with a new process and a new business model that radically reduced the optimum size of a steel mill.
4. Build for change: Managers build flexibility into facilities and equipment; changes will occur in processes, as well as products, product volume, and product mix.
Next, we note that rather than strategically manage capacity, managers may tactically man- age demand.
STUDENT TIP Each industry and technology has an
optimum size.
SOLUTION c Design capacity 5 201,600 3 2 5 403,200 rolls Effective capacity 5 175,000 3 2 5 350,000 rolls Actual output 5 148,000 1 130,000 5 278,000 rolls Utilization 5 Actual output /Design capacity 5 278,000 / 403,200 5 68.95%
Efficiency 5 Actual output /Effective capacity 5 278,000 / 350,000 5 79.43%
INSIGHT c Although adding equipment increases capacity, that equipment may not be operated as efficiently with new employees as might be the case with experienced employees. For Sara James Bakery, a doubling of equipment investment did not result in a doubling of output; other variables drove both utilization and efficiency lower.
LEARNING EXERCISE c Suppose that Sara James reduces changeover time (setup time) by three fewer hours per week.What will be the new values of utilization and efficiency? [Answer: utilization is still 68.95%, efficiency now increases to 81.10%]
RELATED PROBLEMS c S7.1, S7.2, S7.3, S7.4, S7.5, S7.6, S7.7, S7.8
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Managing Demand Even with good forecasting and facilities built to accomodate that forecast, there may be a poor match between the actual demand that occurs and available capacity. A poor match may mean demand exceeds capacity or capacity exceeds demand. However, in both cases, firms have options.
Demand Exceeds Capacity When demand exceeds capacity , the firm may be able to curtail demand simply by raising prices, scheduling long lead times (which may be inevita- ble), and discouraging marginally profitable business. However, because inadequate facilities reduce revenue below what is possible, the long-term solution is usually to increase capacity.
Capacity Exceeds Demand When capacity exceeds demand , the firm may want to stimulate demand through price reductions or aggressive marketing, or it may accommodate the market through product changes. When decreasing customer demand is combined with old and inflexible processes, layoffs and plant closings may be necessary to bring capacity in line with demand.
Adjusting to Seasonal Demands A seasonal or cyclical pattern of demand is another capacity challenge. In such cases, management may find it helpful to offer products with com- plementary demand patterns—that is, products for which the demand is high for one when low for the other. For example, in Figure S7.3 the firm is adding a line of snowmobile motors to its line of jet skis to smooth demand. With appropriate complementing of products, perhaps the utilization of facility, equipment, and personnel can be smoothed (as we see in the OM in Action box “Matching Airline Capacity to Demand”).
Capacity Considerations for Krispy Kreme Stores
1,300 sq.ft store
2,600 sq.ft store
Economies of scale
8,000 sq.ft store
Diseconomies of scale
Number of square feet in store
A v e ra
g e u
n it
c o
s t
(s a le
s p
e r
s q
u a re
f o
o t)
1,300 2,600 8,000
Krispy Kreme originally had
8,000-square-foot stores but
found them too large and too
expensive for many markets. Then
they tried tiny 1,300-square-
foot stores, which required less
investment, but such stores were
too small to provide the mystique
of seeing and smelling Krispy
Kreme doughnuts being made.
Krispy Kreme finally got it right
with a 2,600-foot-store.
Figure S7.2
Economies and Diseconomies
of Scale
Combining the two demand patterns reduces the variation.
Snowmobile motor sales
Jet ski engine sales
1,000
2,000
3,000
4,000
S a le
s in
u n its
Time (months)
J F M A M J J A S O N D J F M A M J J A S O N D J
Figure S7.3
By Combining Products That
Have Complementary Seasonal
Patterns, Capacity Can Be
Better Utilized
C h it o se
S u zu
ki /A
P I m
a g e s
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Tactics for Matching Capacity to Demand Various tactics for adjusting capacity to demand include:
1. Making staffing changes (increasing or decreasing the number of employees or shifts) 2. Adjusting equipment (purchasing additional machinery or selling or leasing out existing
equipment) 3. Improving processes to increase throughput (e.g., reducing setup times at M2 Global
Technology added the equivalent of 17 shifts of capacity) 4. Redesigning products to facilitate more throughput 5. Adding process flexibility to better meet changing product preferences 6. Closing facilities
The foregoing tactics can be used to adjust demand to existing facilities. The strategic issue is, of course, how to have a facility of the correct size.
Service-Sector Demand and Capacity Management In the service sector, scheduling customers is demand management , and scheduling the work- force is capacity management .
Recessions (e.g., 2008–2010) and terrorist attacks
(e.g., September 11, 2001) can make even the
best capacity decision for an airline look bad. And
excess capacity for an airline can be very expensive,
with storage costs running as high as $60,000 per
month per aircraft. Here, as a testimonial to excess
capacity, aircraft sit idle in the Mojave Desert. Jo e M
cN a lly
/G e tt
y Im
a g e s
OM in Action Matching Airline Capacity to Demand Airlines constantly struggle to control their capital expenditures and to adapt to
unstable demand patterns.
Southwest and Lufthansa have each taken their own approach to increas-
ing capacity while holding down capital investment. To manage capacity
constraints on the cheap, Southwest squeezes seven flight segments out of its
typical plane schedule per day—one more than most competitors. Its opera-
tions personnel find that quick ground turnaround, long a Southwest strength,
is a key to this capital-saving technique.
Lufthansa has cut hundreds of millions of dollars in new jet purchases by
squashing rows of seats 2 inches closer together. On the A320, for example,
Lufthansa added two rows of seats, giving the plane 174 seats instead of 162.
For its European fleet, this is the equivalent of having 12 more Airbus A320
jets. But Lufthansa will tell you that squeezing in more seats is not quite as bad
as it sounds, as the new generation of ultra-thin seats provides passengers
with more leg room. Using a strong mesh, similar to that in fancy office chairs
(instead of inches of foam padding), and moving magazine pockets to the top
of seat backs, there is actually more knee room than with the old chairs.
Unstable demands in the airline industry provide another capacity
challenge. Seasonal patterns (e.g., fewer people fly in the winter),
compounded by spikes in demand during major holidays and summer
vacations, play havoc with efficient use of capacity. Airlines attack costly
seasonality in several ways. First, they schedule more planes for maintenance
and renovations during slow winter months, curtailing winter capacity; second,
they seek out contra-seasonal routes. And when capacity is substantially
above demand, placing planes in storage (as shown in the photo) may be the
most economical answer.
Airlines also use revenue management (see Chapter 13 ) to maximize per-
seat pricing of available capacity, regardless of current demand patterns.
Sources: The Wall Street Journal (February 29, 2012) and (October 6, 2011).
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Demand Management When demand and capacity are fairly well matched, demand management can often be handled with appointments, reservations, or a first-come, first- served rule. In some businesses, such as doctors’ and lawyers’ offices, an appointment system is the schedule and is adequate. Reservations systems work well in rental car agencies, hotels, and some restaurants as a means of minimizing customer waiting time and avoiding disappoint- ment over unfilled service. In retail shops, a post office, or a fast-food restaurant, a first-come, first-served rule for serving customers may suffice. Each industry develops its own approaches to matching demand and capacity. Other more aggressive approaches to demand manage- ment include many variations of discounts: “early bird” specials in restaurants, discounts for matinee performances or for seats at odd hours on an airline, and cheap weekend hotel rooms.
Capacity Management When managing demand is not feasible, then managing capac- ity through changes in full-time, temporary, or part-time staff may be an option. This is the approach in many services. For instance, hospitals may find capacity limited by a shortage of board-certified radiologists willing to cover the graveyard shifts. Getting fast and reliable ra- diology readings can be the difference between life and death for an emergency room patient. As the photo above illustrates, when an overnight reading is required (and 40% of CT scans are done between 8 p.m. and 8 a.m.), the image can be sent by e-mail to a doctor in Europe or Australia for immediate analysis.
Bottleneck Analysis and the Theory of Constraints As managers seek to match capacity to demand, decisions must be made about the size of specific operations or work areas in the larger system. Each of the interdependent work areas can be expected to have its own unique capacity. Capacity analysis involves determining the throughput capacity of workstations in a system and ultimately the capacity of the entire system.
A key concept in capacity analysis is the role of a constraint or bottleneck . A bottleneck is an operation that is the limiting factor or constraint. The term bottleneck refers to the literal neck of a bottle that constrains flow or, in the case of a production system, constrains throughput. A bottleneck has the lowest effective capacity of any operation in the system and thus limits the system’s output. Bottlenecks occur in all facets of life—from job shops where a machine is constraining the work flow to highway traffic where two lanes converge into one inadequate lane, resulting in traffic congestion.
We define the process time of a station as the time to produce a unit (or a specified batch size of units) at that workstation. For example, if 16 customers can be checked out in a supermarket line every 60 minutes, then the process time at that station is 3.75 minutes per customer (5 60/16). (Process time is simply the inverse of capacity, which in this case is 60 minutes per hour/3.75 minutes per customer 5 16 customers per hour.)
Many U.S. hospitals use services abroad to manage
capacity for radiologists during night shifts. Night
Hawk, an Idaho-based service with 50 radiologists
in Zurich and Sydney, contracts with 900 facilities
(20% of all U.S. hospitals). These trained experts,
wide awake and alert in their daylight hours, usually
return a diagnosis in 10 to 20 minutes, with a
guarantee of 30 minutes. Al e ks
e y
K h ri p u n ko
v/ F o to
lia
Capacity analysis
A means of determining through-
put capacity of workstations or an
entire production system.
Bottleneck
The limiting factor or constraint in
a system.
Process time
The time to produce a unit
(or specified batch of units) at
a workstation.
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Bottleneck time
The process time of the longest
(slowest) process, i.e., the
bottleneck.
Throughput time
The time it takes for a product to
go through the production process
with no waiting . It is the time
of the longest path through the
system.
3 min/unit
Station C
4 min/unit
Station B
2 min/unit
Station A
Figure S7.4
Three-Station Assembly Line
A box represents an operation,
a triangle represents inventory,
and arrows represent precedence
relationships
To determine the bottleneck in a production system, simply identify the station with the slowest process time. The bottleneck time is the process time of the slowest workstation (the one that takes the longest) in a production system. For example, the flowchart in Figure S7.4 shows a simple assembly line. Individual station process times are 2, 4, and 3 minutes, respectively. The bottleneck time is 4 minutes. This is because station B is the slowest station. Even if we were to speed up station A, the entire production process would not be faster. Inventory would simply pile up in front of station B even more than now. Likewise, if station C could work faster, we could not tap its excess capacity because station B will not be able to feed products to it any faster than 1 every 4 minutes.
Example S3 CAPACITY ANALYSIS WITH PARALLEL PROCESSES
Howard Kraye’s sandwich shop provides healthy sandwiches for customers. Howard has two identical sandwich assembly lines. A customer first places an order, which takes 30 seconds. The order is then sent to one of the two assembly lines. Each assembly line has two workers and three operations: (1) assembly worker 1 retrieves and cuts the bread (15 seconds/sandwich), (2) assembly worker 2 adds ingredients and places the sandwich onto the toaster conveyor belt (20 seconds/sandwich), and (3) the toaster heats the sandwich (40 seconds/sandwich). Finally, another employee wraps the heated sandwich coming out of the toaster and delivers it to the customer (37.5 seconds/sandwich). A flowchart of the process is shown below. Howard wants to determine the bottleneck time and throughput time of this process.
LO S7.3 Perform bottleneck analysis
Toaster
15 sec/sandwich
Bread
20 sec/sandwich
Fill
15 sec/sandwich
Bread
20 sec/sandwich
Fill
40 sec/sandwich
40 sec/sandwich
Toaster30 sec/sandwich
Order
37.5 sec/sandwich
Wrap/Deliver
Second assembly line
First assembly line
APPROACH c Clearly the toaster is the single-slowest resource in the five-step process, but is it the bottleneck? Howard should first determine the bottleneck time of each of the two assembly lines sepa- rately, then the bottleneck time of the combined assembly lines, and finally the bottleneck time of the entire operation. For throughput time, each assembly line is identical, so Howard should just sum the process times for all five operations.
The throughput time , on the other hand, is the time it takes a unit to go through pro- duction from start to end, with no waiting . (Throughput time describes the behavior in an empty system. In contrast, flow time describes the time to go through a production process from beginning to end, including idle time waiting for stations to finish working on other units.) The throughput time to produce a new completed unit in Figure S7.4 is 9 minutes (= 2 minutes + 4 minutes + 3 minutes).
Bottleneck time and throughput time may be quite different. For example, a Ford assembly line may roll out a new car every minute (bottleneck time), but it may take 25 hours to actu- ally make a car from start to finish (throughput time). This is because the assembly line has many workstations, with each station contributing to the completed car. Thus, bottleneck time determines the system’s capacity (one car per minute), while its throughput time determines potential ability to produce a newly ordered product from scratch in 25 hours.
The following two examples illustrate capacity analysis for slightly more complex systems. Example S3 introduces the concept of parallel processes, and Example S4 introduces the concept of simultaneous processing.
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In Example S3 , how could we claim that the process time of the toaster was 20 seconds per sandwich when it takes 40 seconds to toast a sandwich? The reason is that we had two toasters; thus, two sandwiches could be toasted every 40 seconds, for an average of one sandwich every 20 seconds. And that time for a toaster can actually be achieved if the start times for the two are staggered (i.e., a new sandwich is placed in a toaster every 20 seconds). In that case, even though each sandwich will sit in the toaster for 40 seconds, a sandwich could emerge from one of the two toasters every 20 seconds. As we see, doubling the number of resources effectively cuts the process time at that station in half, resulting in a doubling of the capacity of those resources.
SOLUTION c Because each of the three assembly-line operations uses a separate resource (worker or machine), different partially completed sandwiches can be worked on simultaneously at each sta- tion. Thus, the bottleneck time of each assembly line is the longest process time of each of the three operations. In this case, the 40-second toasting time represents the bottleneck time of each assembly line. Next, the bottleneck time of the combined assembly line operations is 40 seconds per two sandwiches, or 20 seconds per sandwich. Therefore, the wrapping and delivering operation, with a process time of 37.5 seconds, appears to be the bottleneck for the entire operation. The capacity per hour equals 3,600 seconds per hour/37.5 seconds per sandwich = 96 sandwiches per hour. The throughput time equals 30 + 15 + 20 + 40 + 37.5 = 142.5 seconds (or 2 minutes and 22.5 seconds), assuming no wait time in line to begin with.
INSIGHT c Doubling the resources at a workstation effectively cuts the time at that station in half. (If n parallel [redundant] operations are added, the process time of the combined workstation operation will equal 1 > n times the original process time.)
LEARNING EXERCISE c If Howard hires an additional wrapper, what will be the new hourly capacity? [Answer: The new bottleneck is now the order-taking station: Capacity = 3,600 seconds per hour/30 seconds per sandwich = 120 sandwiches per hour]
RELATED PROBLEMS c S7.9, S7.10, S7.11, S7.12, S7.13
Example S4 CAPACITY ANALYSIS WITH SIMULTANEOUS PROCESSES Dr. Cynthia Knott’s dentistry practice has been cleaning customers’ teeth for decades. The process for a basic dental cleaning is relatively straightforward: (1) the customer checks in (2 minutes); (2) a lab techni- cian takes and develops X-rays (2 and 4 minutes, respectively); (3) the dentist processes and examines the X-rays (5 minutes) while the hygienist cleans the teeth (24 minutes); (4) the dentist meets with the patient to poke at a few teeth, explain the X-ray results, and tell the patient to floss more often (8 minutes); and (5) the customer pays and books her next appointment (6 minutes). A flowchart of the customer visit is shown below. Dr. Knott wants to determine the bottleneck time and throughput time of this process.
24 min/unit
Hygienist cleaning
5 min/unit
X-ray exam4 min/unit
Develops X-ray
2 min/unit2 min/unit
Check in
6 min/unit
Check out
8 min/unit
Dentist Takes X-ray
APPROACH c With simultaneous processes, an order or a product is essentially split into different paths to be rejoined later on. To find the bottleneck time, each operation is treated separately, just as though all operations were on a sequential path. To find the throughput time, the time over all paths must be computed, and the throughput time is the time of the longest path.
SOLUTION c The bottleneck in this system is the hygienist cleaning operation at 24 minutes per pa- tient, resulting in an hourly system capacity of 60 minutes>24 minutes per patient = 2.5 patients. The throughput time is the maximum of the two paths through the system. The path through the X-ray
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Theory of constraints (TOC)
A body of knowledge that deals
with anything that limits an
organization’s ability to achieve
its goals.
exam is 2 + 2 + 4 + 5 + 8 + 6 = 27 minutes, while the path through the hygienist cleaning operation is 2 + 2 + 4 + 24 + 8 + 6 = 46 minutes. Thus a patient should be out the door after 46 minutes (i.e., the maximum of 27 and 46).
INSIGHT c With simultaneous processing, all operation times in the entire system are not simply added together to compute throughput time because some operations are occurring simultaneously. Instead, the time of the longest path through the system is deemed the throughput time.
LEARNING EXERCISE c Suppose that the same technician now has the hygienist start immediately after the X-rays are taken (allowing the hygienist to start 4 minutes sooner). The technician then develops the X-rays while the hygienist is cleaning teeth. The dentist still examines the X-rays while the teeth clean- ing is occurring. What would be the new system capacity and throughput time? [Answer: The X-ray de- velopment operation is now on the parallel path with cleaning and X-ray exam, reducing the total patient visit duration by 4 minutes, for a throughput time of 42 minutes (the maximum of 27 and 42). However, the hygienist cleaning operation is still the bottleneck, so the capacity remains 2.5 patients per hour.]
RELATED PROBLEMS c S7.14, S7.15
To summarize: (1) the bottleneck is the operation with the longest (slowest) process time, after dividing by the number of parallel (redundant) operations, (2) the system capacity is the inverse of the bottleneck time , and (3) the throughput time is the total time through the longest path in the system, assuming no waiting.
Theory of Constraints The theory of constraints (TOC) has been popularized by the book The Goal: A Process of Ongoing Improvement, by Goldratt and Cox. 1 TOC is a body of knowledge that deals with anything that limits or constrains an organization’s ability to achieve its goals. Constraints can be phys- ical (e.g., process or personnel availability, raw materials, or supplies) or nonphysical (e.g., procedures, morale, and training). Recognizing and managing these limitations through a five-step process is the basis of TOC.
STEP 1: Identify the constraints. STEP 2: Develop a plan for overcoming the identifi ed constraints. STEP 3: Focus resources on accomplishing Step 2. STEP 4: Reduce the eff ects of the constraints by offl oading work or by expanding capability. Make
sure that the constraints are recognized by all those who can have an impact on them. STEP 5: When one set of constraints is overcome, go back to Step 1 and identify new con-
straints.
Bottleneck Management A crucial constraint in any system is the bottleneck, and managers must focus significant attention on it. We present four principles of bottleneck management:
1. Release work orders to the system at the pace set by the bottleneck’s capacity: The theory of constraints utilizes the concept of drum, buffer, rope to aid in the implementation of bottleneck and nonbottleneck scheduling. In brief, the drum is the beat of the system. It provides the schedule—the pace of production. The buffer is the resource, usually inventory, which may be helpful to keep the bottleneck operating at the pace of the drum. Finally, the rope provides the synchronization or communication necessary to pull units through the system. The rope can be thought of as signals between workstations.
2. Lost time at the bottleneck represents lost capacity for the whole system: This principle implies that the bottleneck should always be kept busy with work. Well-trained and cross- trained employees and inspections prior to the bottleneck can reduce lost capacity at a bottleneck.
3. Increasing the capacity of a nonbottleneck station is a mirage: Increasing the capacity of nonbottleneck stations has no impact on the system’s overall capacity. Working faster on
STUDENT TIP There are always bottlenecks;
a manager must identify and
manage them.
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a nonbottleneck station may just create extra inventory, with all of its adverse effects. This implies that nonbottlenecks should have planned idle time. Extra work or setups at nonbottleneck stations will not cause delay, which allows for smaller batch sizes and more frequent product changeovers at nonbottleneck stations.
4. Increasing the capacity of the bottleneck increases capacity for the whole system: Managers should focus improvement efforts on the bottleneck. Bottleneck capacity may be improved by various means, including offloading some of the bottleneck operations to another workstation (e.g., let the beer foam settle next to the tap at the bar, not under it, so the next beer can be poured), increasing capacity of the bottleneck (adding resources, working longer or working faster), subcontracting, developing alternative routings, and reducing setup times.
Even when managers have process and quality variability under control, changing technol- ogy, personnel, products, product mixes, and volumes can create multiple and shifting bottle- necks. Identifying and managing bottlenecks is a required operations task, but by definition, bottlenecks cannot be “eliminated.” A system will always have at least one.
Break-Even Analysis Break-even analysis is the critical tool for determining the capacity a facility must have to achieve profitability. The objective of break-even analysis is to find the point, in dollars and units, at which costs equal revenue. This point is the break-even point. Firms must operate above this level to achieve profitability. As shown in Figure S7.5 , break-even analysis requires an estimation of fixed costs, variable costs, and revenue.
Fixed costs are costs that continue even if no units are produced. Examples include depre- ciation, taxes, debt, and mortgage payments. Variable costs are those that vary with the volume of units produced. The major components of variable costs are labor and materials. However, other costs, such as the portion of the utilities that varies with volume, are also variable costs. The difference between selling price and variable cost is contribution . Only when total contribu- tion exceeds total fixed cost will there be profit.
Another element in break-even analysis is the revenue function . In Figure S7.5 , revenue be- gins at the origin and proceeds upward to the right, increasing by the selling price of each unit. Where the revenue function crosses the total cost line (the sum of fixed and variable costs) is the break-even point, with a profit corridor to the right and a loss corridor to the left.
Assumptions A number of assumptions underlie the basic break-even model. Notably, costs and revenue are shown as straight lines. They are shown to increase linearly—that is,
Break-even analysis
A means of finding the point, in
dollars and units, at which costs
equal revenues.
0 Volume (units per period)
C o st
( d o lla
rs ) Break-even point:
Total cost = Total revenue
100
200
300
400
500
600
700
800
900
100 200 300 400 500 600 700 800 900 1000 1100
Total revenue line
Total cost line
Fixed cost
Variable cost
Pr ofi
t C or
rid or
Lo ss
Co rri
do r
Figure S7.5
Basic Break-Even Point
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in direct proportion to the volume of units being produced. However, neither fixed costs nor variable costs (nor, for that matter, the revenue function) need be a straight line. For example, fixed costs change as more capital equipment or warehouse space is used; labor costs change with overtime or as marginally skilled workers are employed; the revenue function may change with such factors as volume discounts.
Single-Product Case The formulas for the break-even point in units and dollars for a single product are shown below. Let:
BEPx = break@even point in units TR = total revenue = Px BEP+ = break@even point in dollars F = fixed costs
P = price per unit (after all discounts) V = variable costs per unit x = number of units produced TC = total costs = F + Vx
The break-even point occurs where total revenue equals total costs. Therefore:
TR = TC or Px = F + Vx
Solving for x , we get:
Break@even point in units (BEPx) = F
P - V
and:
Break@even point in dollars (BEP+) = BEPxP = F
P - V P =
F (P - V )>P
= F
1 - V>P
Profit = TR - TC = Px - (F + Vx) = Px - F - Vx = (P - V )x - F
Using these equations, we can solve directly for break-even point and profitability. The two break-even formulas of particular interest are:
Break@even in units (BEPx) = Total fixed cost
Price - Variable cost =
F P - V
(S7-3)
Break@even in dollars (BEP$) = Total fixed cost
1 - Variable cost
Price
= F
1 - V P
(S7-4)
In Example S5 , we determine the break-even point in dollars and units for one product.
LO S7.4 Compute break- even
Example S5 SINGLE-PRODUCT BREAK-EVEN ANALYSIS Stephens, Inc., wants to determine the minimum dollar volume and unit volume needed at its new facility to break even.
APPROACH c The firm first determines that it has fixed costs of $10,000 this period. Direct labor is $1.50 per unit, and material is $.75 per unit. The selling price is $4.00 per unit.
SOLUTION c The break-even point in dollars is computed as follows:
BEP+ = F
1 - (V>P) =
+10,000 1 - 3(1.50 + .75)>(4.00)4
= +10,000 .4375
= +22,857.14
The break-even point in units is:
BEPx = F
P - V =
+10,000 4.00 - (1.50 + .75)
= 5,714
Note that we use total variable costs (that is, both labor and material).
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Multiproduct Case Most firms, from manufacturers to restaurants, have a variety of offerings. Each offering may have a different selling price and variable cost. Utilizing break-even analysis, we modify Equation (S7-4) to reflect the proportion of sales for each product. We do this by “weighting” each product’s contribution by its proportion of sales. The formula is then:
Break@even point in dollars (BEP$) = F
a c1 - a Vi Pi b * (Wi) d
(S7-5)
where V = variable cost per unit P = price per unit F = fixed cost
W = percent each product is of total dollar sales i = each product
Paper machines such as the one
shown here require a high capital
investment. This investment results in
a high fixed cost but allows production
of paper at a very low variable cost.
The production manager’s job is to
maintain utilization above the break-
even point to achieve profitability. Im a g e I d e a s/
S to
ck b yt
e /G
e tt
y Im
a g e s
Example S6 shows how to determine the break-even point for the multiproduct case at the Le Bistro restaurant.
Example S6 MULTIPRODUCT BREAK-EVEN ANALYSIS Le Bistro, like most other resturants, makes more than one product and would like to know its break- even point in dollars. Information for Le Bistro follows. Fixed costs are $3,000 per month.
ITEM
ANNUAL FORECASTED SALES UNITS PRICE COST
Sandwich 9,000 $5.00 $3.00
Drinks 9,000 1.50 0.50
Baked potato 7,000 2.00 1.00
INSIGHT c The management of Stevens, Inc., now has an estimate in both units and dollars of the volume necessary for the new facility.
LEARNING EXERCISE c If Stevens finds that fixed cost will increase to $12,000, what happens to the break-even in units and dollars? [Answer: The break-even in units increases to 6,857, and break-even in dollars increases to $27,428.57.]
RELATED PROBLEMS c S7.16–S7.25 (S7.28–S7.31 are available in MyOMLab) EXCEL OM Data File Ch07SExS3.xls can be found in MyOMLab.
ACTIVE MODEL S7.2 This example is further illustrated in Active Model S7.2 in MyOMLab.
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Break-even figures by product provide the manager with added insight as to the realism of his or her sales forecast. They indicate exactly what must be sold each day, as we illustrate in Example S7 .
APPROACH c With a variety of offerings, we proceed with break-even analysis just as in a single-product case, except that we weight each of the products by its proportion of total sales using Equation (S7-5) .
SOLUTION c Multiproduct Break-Even: Determining Contribution
1 2 3 4 5 6 7 8 9
ITEM (i)
ANNUAL FORECASTED SALES UNITS
SELLING PRICE
(Pi) VARIABLE COST (Vi) (Vi/Pi)
CONTRI- BUTION 1− ( V i / P i )
ANNUAL FORECASTED
SALES $
% OF SALES ( W i )
WEIGHTED CONTRIBUTION
(COL. 6 3 COL. 8)
Sandwich 9,000 $5.00 $3.00 .60 .40 $45,000 .621 .248
Drinks 9,000 1.50 0.50 .33 .67 13,500 .186 .125
Baked potato
7,000 2.00 1.00 .50 .50 14,000 .193 .097
$72,500 1.000 .470
Note: Revenue for sandwiches is $45,000 (5 5.00 * 9,000 ), which is 62.1% of the total revenue of $72,500. Therefore, the contribution for sandwiches is “weighted” by .621. The weighted contribution is .621 * .40 = .248. In this manner, its relative contribution is properly reflected.
Using this approach for each product, we find that the total weighted contribution is .47 for each dollar of sales, and the break-even point in dollars is $76,596:
BEP$ = F
a c1 - a Vi Pi b * (Wi) d
= $3,000 * 12
.47 =
$36,000 .47
= $76,596
The information given in this example implies total daily sales (52 weeks at 6 days each) of:
+76,596 312 days
= +245.50
INSIGHT c The management of Le Bistro now knows that it must generate average sales of $245.50 each day to break even. Management also knows that if the forecasted sales of $72,500 are correct, Le Bistro will lose money, as break-even is $76,596.
LEARNING EXERCISE c If the manager of Le Bistro wants to make an additional $1,000 per month in salary, and considers this a fixed cost, what is the new break-even point in average sales per day? [Answer: $327.33.]
RELATED PROBLEMS c S7.26, S7.27
Example S7 UNIT SALES AT BREAK-EVEN Le Bistro also wants to know the break-even for the number of sandwiches that must be sold every day.
APPROACH c Using the data in Example S6 , we take the forecast sandwich sales of 62.1% times the daily break-even of $245.50 divided by the selling price of each sandwich ($5.00).
SOLUTION c At break-even, sandwich sales must then be:
.621 * $245.50
5.00 = Number of sandwiches = 30.5 L 31 sandwiches each day
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Once break-even analysis has been prepared, analyzed, and judged to be reasonable, deci- sions can be made about the type and capacity of equipment needed. Indeed, a better judgment of the likelihood of success of the enterprise can now be made.
Reducing Risk with Incremental Changes When demand for goods and services can be forecast with a reasonable degree of precision, determining a break-even point and capacity requirements can be rather straightforward. But, more likely, determining the capacity and how to achieve it will be complicated, as many factors are difficult to measure and quantify. Factors such as technology, competi- tors, building restrictions, cost of capital, human resource options, and regulations make the decision interesting. To complicate matters further, demand growth is usually in small units, while capacity additions are likely to be both instantaneous and in large units. This contradiction adds to the capacity decision risk. To reduce risk, incremental changes that hedge demand forecasts may be a good option. Figure S7.6 illustrates four approaches to new capacity.
Alternative Figure S7.6 (a) leads capacity—that is, acquires capacity to stay ahead of demand, with new capacity being acquired at the beginning of period 1. This capacity handles increased demand until the beginning of period 2. At the beginning of period 2, new capacity is again acquired, allowing the organization to stay ahead of demand until the beginning of period 3. This process can be continued indefinitely into the future. Here capacity is acquired incrementally —at the beginning of period 1 and at the beginning of period 2.
But managers can also elect to make a larger increase at the beginning of period 1 [ Figure S7.6 (b)]—an increase that may satisfy expected demand until the beginning of period 3. Excess capacity gives operations managers flexibility. For instance, in the hotel industry, added (extra) capacity in the form of rooms can allow a wider variety of room options and perhaps flexibility in room cleanup schedules. In manufacturing, excess capacity can be used to do more setups, shorten production runs, and drive down inventory costs.
STUDENT TIP Capacity decisions require matching
capacity to forecasts, which is
always difficult.
VIDEO S7.1 Capacity Planning at Arnold Palmer
Hospital
D e
m a
n d
1 2 3
Time (years)
New capacity
New capacity
Expected demand
(a) Leading demand with an incremental expansion
(b) Leading demand with a one-step expansion
(c) Lagging demand with incremental expansion
(d) Attempts to have an average capacity that straddles demand with incremenal expansion
D e
m a
n d
1 2 3
Time (years)
1 2 3
Time (years)
New capacity
Expected demandExpected
demand
New capacity
D e
m a
n d
D e
m a
n d
1 2 3
Time (years)
Expected demand
Figure S7.6
Four Approaches to Capacity Expansion
INSIGHT c With knowledge of individual product sales, the manager has a basis for determining material and labor requirements.
LEARNING EXERCISE c At a dollar break-even of $327.33 per day, how many sandwiches must Le Bistro sell each day? [Answer: < 41. ]
RELATED PROBLEMS c S7.26b, S7.27b
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Figure S7.6 (c) shows an option that lags capacity, perhaps using overtime or subcontracting to accommodate excess demand. Finally, Figure S7.6 (d) straddles demand by building capacity that is “average,” sometimes lagging demand and sometimes leading it. Both the lag and strad- dle option have the advantage of delaying capital expenditure.
In cases where the business climate is stable, deciding between alternatives can be relatively easy. The total cost of each alternative can be computed, and the alternative with the least total cost can be selected. However, when capacity requirements are subject to significant unknowns, “probabilistic” models may be appropriate. One technique for making successful capacity plan- ning decisions with an uncertain demand is decision theory, including the use of expected monetary value.
Applying Expected Monetary Value (EMV) to Capacity Decisions Determining expected monetary value (EMV) requires specifying alternatives and various states of nature. For capacity-planning situations, the state of nature usually is future demand or market favorability. By assigning probability values to the various states of nature, we can make decisions that maximize the expected value of the alternatives. Example S8 shows how to apply EMV to a capacity decision.
STUDENT TIP Uncertainty in capacity decisions
makes EMV a helpful tool.
LO S7.5 Determine expected monetary value
of a capacity decision
Example S8 EMV APPLIED TO CAPACITY DECISION Southern Hospital Supplies, a company that makes hospital gowns, is considering capacity expansion. APPROACH: c Southern’s major alternatives are to do nothing, build a small plant, build a medium plant, or build a large plant. The new facility would produce a new type of gown, and currently the po- tential or marketability for this product is unknown. If a large plant is built and a favorable market ex- ists, a profit of $100,000 could be realized. An unfavorable market would yield a $90,000 loss. However, a medium plant would earn a $60,000 profit with a favorable market. A $10,000 loss would result from an unfavorable market. A small plant, on the other hand, would return $40,000 with favorable market conditions and lose only $5,000 in an unfavorable market. Of course, there is always the option of doing nothing.
Recent market research indicates that there is a .4 probability of a favorable market, which means that there is also a .6 probability of an unfavorable market. With this information, the alternative that will result in the highest expected monetary value (EMV) can be selected.
SOLUTION c Compute the EMV for each alternative:
EMV (large plant) = (.4) (+100,000) + (.6) (-+90,000) = - +14,000 EMV (medium plant) = (.4) (+60,000) + (.6) ( - +10,000) = + +18,000
EMV (small plant) = (.4) (+40,000) + (.6) ( - +5,000) = + +13,000 EMV (do nothing) = +0
Based on EMV criteria, Southern should build a medium plant.
INSIGHT c If Southern makes many decisions like this, then determining the EMV for each alternative and selecting the highest EMV is a good decision criterion.
LEARNING EXERCISE c If a new estimate of the loss from a medium plant in an unfavorable market increases to –$20,000, what is the new EMV for this alternative? [Answer: $12,000, which changes the decision because the small plant EMV is now higher.]
RELATED PROBLEMS c S7.32, S7.33
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Applying Investment Analysis to Strategy-Driven Investments Once the strategy implications of potential investments have been considered, traditional investment analysis is appropriate. We introduce the investment aspects of capacity next.
Investment, Variable Cost, and Cash Flow Because capacity and process alternatives exist, so do options regarding capital investment and variable cost. Managers must choose from among different financial options as well as capacity and process alternatives. Analysis should show the capital investment, variable cost, and cash flows as well as net present value for each alternative.
Net Present Value Determining the discount value of a series of future cash receipts is known as the net present value technique. By way of introduction, let us consider the time value of money. Say you invest $100.00 in a bank at 5% for 1 year. Your investment will be worth $100.00 + (+100.00)(.05) = +105.00. If you invest the $105.00 for a second year, it will be worth +105.00 + (+105.00)(.05) = $110.25 at the end of the second year. Of course, we could calculate the future value of $100.00 at 5% for as many years as we wanted by simply extending this analysis. However, there is an easier way to express this relationship mathematically. For the first year:
+105 = +100(1 + .05)
For the second year:
+110.25 = +105(1 + .05) = +100(1 + .05)2
In general:
F = P(1 + i)N (S7-6)
where F = future value (such as +110.25 or +105) P = present value (such as +100.00) i = interest rate (such as .05) N = number of years (such as 1 year or 2 years)
In most investment decisions, however, we are interested in calculating the present value of a series of future cash receipts. Solving for P , we get:
P = F
(1 + i)N (S7-7)
When the number of years is not too large, the preceding equation is effective. However, when the number of years, N , is large, the formula is cumbersome. For 20 years, you would have to compute (1 + i)20. Interest-rate tables, such as Table S7.2 , can help. We restate the present value equation:
P = F
(1 + i)N = FX (S7-8)
where X = a factor from Table S7.2 defined as = 1>(1 + i)N and F = future value
Thus, all we have to do is find the factor X and multiply it by F to calculate the present value, P . The factors, of course, are a function of the interest rate, i , and the number of years, N . Table S7.2 lists some of these factors.
Equations (S7-7) and (S7-8) are used to determine the present value of one future cash amount, but there are situations in which an investment generates a series of uniform and equal
STUDENT TIP An operations manager may be
held responsible for return on
investment (ROI).
Net present value
A means of determining the
discounted value of a series of
future cash receipts.
LO S7.6 Compute net present value
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cash amounts. This type of investment is called an annuity . For example, an investment might yield $300 per year for 3 years. Easy-to-use factors have been developed for the present value of annuities. These factors are shown in Table S7.3 . The basic relationship is:
S = RX
where X = factor from Table S7.3 S = present value of a series of uniform annual receipts R = receipts that are received every year for the life of the investment (the annuity)
The present value of a uniform annual series of amounts is an extension of the present value of a single amount, and thus Table S7.3 can be directly developed from Table S7.2 . The fac- tors for any given interest rate in Table S7.3 are the cumulative sum of the values in Table S7.2 . In Table S7.2 , for example, .943, .890, and .840 are the factors for years 1, 2, and 3 when the interest rate is 6%. The cumulative sum of these factors is 2.673. Now look at the point in Table S7.3 where the interest rate is 6% and the number of years is 3. The factor for the present value of an annuity is 2.673, as you would expect. Alternatively, the PV formula in Microsoft Excel can be used: 52PV(interest rate,year,1), e.g., 52PV(.06,3,1) 5 2.673.
TABLE S7.2 Present Value of $1
YEAR 5% 6% 7% 8% 9% 10% 12% 14%
1 .952 .943 .935 .926 .917 .909 .893 .877
2 .907 .890 .873 .857 .842 .826 .797 .769
3 .864 .840 .816 .794 .772 .751 .712 .675
4 .823 .792 .763 .735 .708 .683 .636 .592
5 .784 .747 .713 .681 .650 .621 .567 .519
6 .746 .705 .666 .630 .596 .564 .507 .456
7 .711 .665 .623 .583 .547 .513 .452 .400
8 .677 .627 .582 .540 .502 .467 .404 .351
9 .645 .592 .544 .500 .460 .424 .361 .308
10 .614 .558 .508 .463 .422 .386 .322 .270
15 .481 .417 .362 .315 .275 .239 .183 .140
20 .377 .312 .258 .215 .178 .149 .104 .073
TABLE S7.3 Present Value of an Annuity of $1
YEAR 5% 6% 7% 8% 9% 10% 12% 14%
1 .952 .943 .935 .926 .917 .909 .893 .877
2 1.859 1.833 1.808 1.783 1.759 1.736 1.690 1.647
3 2.723 2.673 2.624 2.577 2.531 2.487 2.402 2.322
4 3.546 3.465 3.387 3.312 3.240 3.170 3.037 2.914
5 4.329 4.212 4.100 3.993 3.890 3.791 3.605 3.433
6 5.076 4.917 4.766 4.623 4.486 4.355 4.111 3.889
7 5.786 5.582 5.389 5.206 5.033 4.868 4.564 4.288
8 6.463 6.210 5.971 5.747 5.535 5.335 4.968 4.639
9 7.108 6.802 6.515 6.247 5.985 5.759 5.328 4.946
10 7.722 7.360 7.024 6.710 6.418 6.145 5.650 5.216
15 10.380 9.712 9.108 8.559 8.060 7.606 6.811 6.142
20 12.462 11.470 10.594 9.818 9.128 8.514 7.469 6.623
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The net present value method is straightforward: You simply compute the present value of all cash flows for each investment alternative. When deciding among investment alternatives, you pick the investment with the highest net present value. Similarly, when making several investments, those with higher net present values are preferable to investments with lower net present values.
Solved Problem S7.4 shows how to use the net present value to choose between investment alternatives.
Although net present value is one of the best approaches to evaluating investment alterna- tives, it does have its faults. Limitations of the net present value approach include the following:
1. Investments with the same net present value may have significantly different projected lives and different salvage values.
2. Investments with the same net present value may have different cash flows. Different cash flows may make substantial differences in the company’s ability to pay its bills.
3. The assumption is that we know future interest rates, which we do not. 4. Payments are always made at the end of the period (week, month, or year), which is not
always the case.
Summary Managers tie equipment selection and capacity decisions to the organization’s missions and strategy. Four additional considerations are critical: (1) accurately forecasting demand; (2) understanding the equipment, processes, and capacity increments; (3) finding the opti- mum operating size; and (4) ensuring the flexibility needed for adjustments in technology, product features and mix, and volumes.
Techniques that are particularly useful to operations managers when making capacity decisions include good forecasting, bottleneck analysis, break-even analysis, expected monetary value, cash flow, and net present value.
The single most important criterion for investment deci- sions is the contribution to the overall strategic plan and the winning of profitable orders. Successful firms select the correct process and capacity.
Example S9 shows how to determine the present value of an annuity.
Example S9 DETERMINING NET PRESENT VALUE OF FUTURE RECEIPTS OF EQUAL VALUE River Road Medical Clinic is thinking of investing in a sophisticated new piece of medical equipment. It will generate $7,000 per year in receipts for 5 years.
APPROACH c Determine the present value of this cash flow; assume an interest rate of 6%.
SOLUTION c The factor from Table S7.3 (4.212) is obtained by finding that value when the interest rate is 6% and the number of years is 5 (alternatively using the Excel formula 52PV (.06,5,1) ):
S = RX = +7,000(4.212) = +29,484
INSIGHT c There is another way of looking at this example. If you went to a bank and took a loan for $29,484 today, your payments would be $7,000 per year for 5 years if the bank used an interest rate of 6% compounded yearly. Thus, $29,484 is the present value.
LEARNING EXERCISE c If the interest rate is 8%, what is the present value? [Answer: $27,951.] RELATED PROBLEMS c S7.34–S7.39 (S7.40–S7.45 are available in MyOMLab) EXCEL OM Data File Ch07SExS9.xls can be found in MyOMLab.
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Key Terms
Capacity (p. 308 ) Design capacity (p. 309 ) Effective capacity (p. 309 ) Utilization (p. 310 ) Efficiency (p. 310 )
Capacity analysis (p. 314 ) Bottleneck (p. 314 ) Process time (p. 314 ) Bottleneck time (p. 315 ) Throughput time (p. 315 )
Theory of constraints (TOC) (p. 317 ) Break-even analysis (p. 318 ) Net present value (p. 324 )
Discussion Questions
1. Distinguish between design capacity and effective capacity. 2. What is effective capacity? 3. What is efficiency? 4. Distinguish between effective capacity and actual output. 5. Explain why doubling the capacity of a bottleneck may not
double the system capacity. 6. Distinguish between bottleneck time and throughput time. 7. What is the theory of constraints? 8. What are the assumptions of break-even analysis? 9. What keeps plotted revenue data from falling on a straight
line in a break-even analysis?
10. Under what conditions would a firm want its capacity to lag demand? to lead demand?
11. Explain how net present value is an appropriate tool for comparing investments.
12. Describe the five-step process that serves as the basis of the theory of constraints.
13. What are the techniques available to operations managers to deal with a bottleneck operation? Which of these does not decrease throughput time?
Using Software for Break-Even Analysis
Excel, Excel OM, and POM for Windows all handle break-even and cost–volume analysis problems.
CREATING YOUR OWN EXCEL SPREADSHEETS It is a straightforward task to develop the formulas to conduct a single-product break-even analysis in Excel. Although we do not demonstrate the basics here, Active Model S7.2 provides a working example. Program S7.1 illustrates how you can make an Excel model to solve Example S6 , which is a multiproduct break-even analysis.
=D6/C6 =B6*C6 =G6/$G$9
=F6*H6
=(C11*C12)/I9
=SUM(G6:G8) =ROUND(SUM(I6:I8),3)
=ROUND(1-E6,2)
Actions Copy E6:I6 to E7:I8, and Copy I9 to H9
Program S7.1
An Excel Spreadsheet for Performing Break-Even Analysis for Example S6
X USING EXCEL OM Excel OM’s Break-Even Analysis module provides the Excel formulas needed to compute the break-even points, and the solution and graphical output.
P USING POM FOR WINDOWS Similar to Excel OM, POM for Windows also contains a break-even/cost–volume analysis module.
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Solved Problems Virtual Office Hours help is available in MyOMLab .
SOLVED PROBLEM S7.1 Sara James Bakery, described in Examples S1 and S2 , has decided to increase its facilities by adding one additional process line. The firm will have two process lines, each working 7 days a week, 3 shifts per day, 8 hours per shift, with effective capacity of 300,000 rolls. This addition, however, will reduce overall system efficiency to 85%. Compute the expected pro- duction with this new effective capacity.
SOLUTION Expected production = (Effective capacity) (Efficiency)
= 300,000(.85) = 255,000 rolls per week
SOLVED PROBLEM S7.2 Marty McDonald has a business packaging software in Wisconsin. His annual fixed cost is $10,000, direct labor is $3.50 per package, and material is $4.50 per package. The sell- ing price will be $12.50 per package. What is the break-even point in dollars? What is break-even in units?
SOLUTION
BEP+ = F
1 - (V>P) =
+10,000 1 - (+8.00>+12.50)
= +10,000
.36 = +27,777
BEPx = F
P - V =
+10,000 +12.50 - +8.00
= +10,000 +4.50
= 2,222 units
SOLVED PROBLEM S7.3 John has been asked to determine whether the $22.50 cost of tickets for the community dinner theater will allow the group to achieve break-even and whether the 175 seating capacity is adequate. The cost for each performance of a 10-performance run is $2,500. The facility rental cost for the entire 10 performances is $10,000. Drinks and parking are extra charges and have their own price and variable costs, as shown below:
1 2 3 4 5 6 7 8 9
SELLING PRICE ( P )
VARIABLE COST ( V )
PERCENT VARIABLE COST ( V/P )
CONTRIBUTION 1 2 ( V/P )
ESTIMATED QUANTITY OF SALES UNITS
(SALES)
DOLLAR SALES
(SALES 3 P) PERCENT OF
SALES
CONTRIBUTION WEIGHTED BY
PERCENT SALES (COL.5 3 COL. 8)
Tickets with dinner $22.50 $10.50 0.467 0.533 175 $3,938 0.741 0.395
Drinks $ 5.00 $ 1.75 0.350 0.650 175 $ 875 0.165 0.107
Parking $ 5.00 $ 2.00 0.400 0.600 100 $ 500 0.094 0.056
450 $5,313 1.000 0.558
SOLUTION
BEP$ = F
a ca1 - Vi Pi b * (Wi )
= $(10 * 2,500) + $10,000
0.558 =
$35,000 0.558
= $62,724
Revenue for each performance (from column 7) = +5,313 Total forecasted revenue for the 10 performances = (10 * +5,313) = +53,130 Forecasted revenue with this mix of sales shows a break-even of $62,724
Thus, given this mix of costs, sales, and capacity John determines that the theater will not break even.
SOLVED PROBLEM S7.4 Your boss has told you to evaluate the cost of two machines. After some questioning, you are assured that they have the costs shown at the right. Assume:
a) The life of each machine is 3 years. b) The company thinks it knows how to make 14% on
investments no riskier than this one.
Determine via the present value method which machine to purchase.
MACHINE A MACHINE B
Original cost $13,000 $20,000
Labor cost per year 2,000 3,000
Floor space per year 500 600
Energy (electricity) per year 1,000 900
Maintenance per year 2,500 500
Total annual cost $ 6,000 $ 5,000
Salvage value $ 2,000 $ 7,000
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SOLUTION
MACHINE A MACHINE B
COLUMN 1 COLUMN 2 COLUMN 3 COLUMN 4 COLUMN 5 COLUMN 6
Now Expense 1.000 $13,000 $13,000 1.000 $20,000 $20,000
1 yr. Expense .877 6,000 5,262 .877 5,000 4,385
2 yr. Expense .769 6,000 4,614 .769 5,000 3,845
3 yr. Expense .675 6,000 4,050 .675 5,000 3,375
$26,926 $31,605
3 yr. Salvage revenue .675 $ 2,000 21,350 .675 $ 7,000 24,725
$25,576 $26,880
We use 1.0 for payments with no discount applied against them (that is, when payments are made now, there is no need for a discount). The other values in columns 1 and 4 are from the 14% column and the respective year in Table S7.2 (for example, the intersection of 14% and 1 year is .877, etc.). Columns 3 and 6 are the products of the present value figures times the com- bined costs. This computation is made for each year and for the salvage value.
The calculation for machine A for the first year is:
.877 * (+2,000 + +500 + +1,000 + +2,500) = +5,262
The salvage value of the product is subtracted from the summed costs, because it is a receipt of cash. Because the sum of the net costs for machine B is larger than the sum of the net costs for machine A, machine A is the low-cost purchase, and your boss should be so informed.
SOLVED PROBLEM S7.5 T. Smunt Manufacturing Corp. has the process displayed below. The drilling operation occurs separately from and simultaneously with the sawing and sanding operations. The product only needs to go through one of the three assembly operations (the assembly operations are “parallel”).
a) Which operation is the bottleneck? b) What is the throughput time for the overall system?
c) If the firm operates 8 hours per day, 22 days per month, what is the monthly capacity of the manufacturing process?
d) Suppose that a second drilling machine is added, and it takes the same time as the original drilling machine. What is the new bottleneck time of the system?
e) Suppose that a second drilling machine is added, and it takes the same time as the original drilling machine. What is the new throughput time?
SOLUTION
a) The time for assembly is 78 minutes/3 operators 5 26 minutes per unit, so the station that takes the longest time, hence the bottleneck, is drilling , at 27 minutes.
b) System throughput time is the maximum of (15 + 15 + 25 + 78), (27 + 25 + 78) = maximum of (133, 130) = 133 minutes c) Monthly capacity = (60 minutes)(8 hours)(22 days)>27 minutes per unit = 10,560 minutes per month>27 minutes per unit =
391.11 units>month. d) The bottleneck shifts to Assembly , with a time of 26 minutes per unit. e) Redundancy does not affect throughput time. It is still 133 minutes.
15 min/unit
Sanding
27 min/unit
Drilling
15 min/unit
Sawing 78 min/unit
Assembly
78 min/unit
Assembly
78 min/unit
Assembly
25 min/unit
Welding
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Problems Note : PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems S7.1–S7.8 relate to Capacity • S7.1 Amy Xia’s plant was designed to produce 7,000 hammers per day but is limited to making 6,000 hammers per day because of the time needed to change equipment between styles of hammers. What is the utilization?
• S7.2 For the past month, the plant in Problem S7.1, which has an effective capacity of 6,500, has made only 4,500 hammers per day because of material delay, employee absences, and other problems. What is its efficiency?
• • S7.3 If a plant has an effective capacity of 6,500 and an efficiency of 88%, what is the actual (planned) output?
• S7.4 A plant has an effective capacity of 900 units per day and produces 800 units per day with its product mix; what is its efficiency?
• S7.5 Material delays have routinely limited production of household sinks to 400 units per day. If the plant efficiency is 80%, what is the effective capacity?
• • S7.6 The effective capacity and efficiency for the next quarter at MMU Mfg. in Waco, Texas, for each of three depart- ments are shown:
DEPARTMENT EFFECTIVE CAPACITY RECENT EFFICIENCY
Design 93,600 .95
Fabrication 156,000 1.03
Finishing 62,400 1.05
Compute the expected production for next quarter for each department.
• • S7.7 Southeastern Oklahoma State University’s busi- ness program has the facilities and faculty to handle an enroll- ment of 2,000 new students per semester. However, in an effort to limit class sizes to a “reasonable” level (under 200, generally), Southeastern’s dean, Holly Lutze, placed a ceiling on enrollment of 1,500 new students. Although there was ample demand for business courses last semester, conflicting schedules allowed only 1,450 new students to take business courses. What are the utiliza- tion and efficiency of this system?
• • S7.8 Under ideal conditions, a service bay at a Fast Lube can serve 6 cars per hour. The effective capacity and efficiency of a Fast Lube service bay are known to be 5.5 and 0.880, respectively. What is the minimum number of service bays Fast Lube needs to achieve an anticipated servicing of 200 cars per 8-hour day?
Problems S7.9–S7.15 relate to Bottleneck Analysis and the Theory of Constraints
• S7.9 A production line at V. J. Sugumaran’s machine shop has three stations. The first station can process a unit in 10 min- utes. The second station has two identical machines, each of which can process a unit in 12 minutes. (Each unit only needs to be pro- cessed on one of the two machines.) The third station can process a unit in 8 minutes. Which station is the bottleneck station?
• • S7.10 A work cell at Chris Ellis Commercial Laundry has a workstation with two machines, and each unit produced at the
Capacity: 20 units/hr
Station 1 Machine A
Capacity: 20 units/hr
Capacity: 12 units/hr
Station 3
Capacity: 5 units/hr
Station 2
Station 1 Machine B
station needs to be processed by both of the machines. (The same unit cannot be worked on by both machines simultaneously.) Each machine has a production capacity of 4 units per hour. What is the throughput time of the work cell?
• • S7.11 The three-station work cell illustrated in Figure S7.7 has a product that must go through one of the two machines at station 1 (they are parallel) before proceeding to station 2.
a) What is the bottleneck time of the system? b) What is the bottleneck station of this work cell? c) What is the throughput time? d) If the firm operates 10 hours per day, 5 days per week, what is
the weekly capacity of this work cell?
• • S7.12 The three-station work cell at Pullman Mfg., Inc. is illustrated in Figure S7.8 . It has two machines at station 1 in parallel (i.e., the product needs to go through only one of the two machines before proceeding to station 2).
20 min/unit
Station 1 Machine A
20 min/unit
8 min/unit
Station 3
12 min/unit
Station 2
Station 1 Machine B
Figure S7.8
a) What is the throughput time of this work cell? b) What is the bottleneck time of this work cell? c) What is the bottleneck station? d) If the firm operates 8 hours per day, 6 days per week, what is
the weekly capacity of this work cell? • • S7.13 The Pullman Mfg., Inc., three-station work cell illus- trated in Figure S7.8 has two machines at station 1 in parallel. (The product needs to go through only one of the two machines before proceeding to station 2.) The manager, Ms. Hartley, has asked you to evaluate the system if she adds a parallel machine at station 2. a) What is the throughput time of the new work cell? b) What is the bottleneck time of the new work cell? c) If the firm operates 8 hours per day, 6 days per week, what is
the weekly capacity of this work cell? d) How did the addition of the second machine at workstation 2
affect the performance of the work cell from Problem S7.12?
• S7.14 Klassen Toy Company, Inc., assembles two parts ( parts 1 and 2 ): Part 1 is first processed at workstation A for 15 minutes per unit and then processed at workstation B for
Figure S7.7
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10 minutes per unit. Part 2 is simultaneously processed at work- station C for 20 minutes per unit. Work stations B and C feed the parts to an assembler at workstation D, where the two parts are assembled. The time at workstation D is 15 minutes. a) What is the bottleneck of this process? b) What is the hourly capacity of the process?
• • S7.15 A production process at Kenneth Day Manufacturing is shown in Figure S7.9 . The drilling operation occurs separately from, and simultaneously with, sawing and sanding, which are
independent and sequential operations. A product needs to go through only one of the three assembly operations (the operations are in parallel). a) Which operation is the bottleneck? b) What is the bottleneck time? c) What is the throughput time of the overall system? d) If the firm operates 8 hours per day, 20 days per month, what
is the monthly capacity of the manufacturing process?
6 units/hr
Sanding
2.4 units/hr
Drilling
6 units/hr
Sawing 0.7 units/hr
Assembly
0.7 units/hr
Assembly
0.7 units/hr
Assembly
2 units/hr
Welding
Figure S7.9
Problems S7.16–S7.31 relate to Break-Even Analysis • S7.16 Smithson Cutting is opening a new line of scissors for supermarket distribution. It estimates its fixed cost to be $500.00 and its variable cost to be $0.50 per unit. Selling price is expected to average $0.75 per unit. a) What is Smithson’s break-even point in units? b) What is the break-even point in dollars? PX
• S7.17 Markland Manufacturing intends to increase capac- ity by overcoming a bottleneck operation by adding new equip- ment. Two vendors have presented proposals. The fixed costs for proposal A are $50,000, and for proposal B, $70,000. The variable cost for A is $12.00, and for B, $10.00. The revenue generated by each unit is $20.00. a) What is the break-even point in units for proposal A? b) What is the break-even point in units for proposal B? PX
• S7.18 Using the data in Problem S7.17: a) What is the break-even point in dollars for proposal A if you
add $10,000 installation to the fixed cost? b) What is the break-even point in dollars for proposal B if you
add $10,000 installation to the fixed cost? PX
• S7.19 Given the data in Problem S7.17, at what volume (units) of output would the two alternatives yield the same profit? PX
• • S7.20 Janelle Heinke, the owner of Ha’Peppas!, is consider- ing a new oven in which to bake the firm’s signature dish, vegetar- ian pizza. Oven type A can handle 20 pizzas an hour. The fixed costs associated with oven A are $20,000 and the variable costs are $2.00 per pizza. Oven B is larger and can handle 40 pizzas an hour. The fixed costs associated with oven B are $30,000 and the variable costs are $1.25 per pizza. The pizzas sell for $14 each. a) What is the break-even point for each oven? b) If the owner expects to sell 9,000 pizzas, which oven should she
purchase?
c) If the owner expects to sell 12,000 pizzas, which oven should she purchase?
d) At what volume should Janelle switch ovens? PX
• • S7.21 Given the following data, calculate a) BEP x ; b) BEP $ ; and c) the profit at 100,000 units:
P = +8>unit V = +4>unit F = +50,000 PX
• • S7.22 You are considering opening a copy service in the student union. You estimate your fixed cost at $15,000 and the variable cost of each copy sold at $.01. You expect the selling price to average $.05. a) What is the break-even point in dollars? b) What is the break-even point in units? PX
• • S7.23 An electronics firm is currently manufacturing an item that has a variable cost of $.50 per unit and a selling price of $1.00 per unit. Fixed costs are $14,000. Current volume is
C o rb
is
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30,000 units. The firm can substantially improve the product quality by adding a new piece of equipment at an additional fixed cost of $6,000. Variable cost would increase to $.60, but volume should jump to 50,000 units due to a higher-quality product. Should the company buy the new equipment? PX
• • S7.24 The electronics firm in Problem S7.23 is now con- sidering the new equipment and increasing the selling price to $1.10 per unit. With the higher-quality product, the new volume is expected to be 45,000 units. Under these circumstances, should the company purchase the new equipment and increase the selling price? PX
• • • • S7.25 Zan Azlett and Angela Zesiger have joined forces to start A&Z Lettuce Products, a processor of packaged shred- ded lettuce for institutional use. Zan has years of food processing experience, and Angela has extensive commercial food prepa- ration experience. The process will consist of opening crates of lettuce and then sorting, washing, slicing, preserving, and finally packaging the prepared lettuce. Together, with help from vendors, they think they can adequately estimate demand, fixed costs, rev- enues, and variable cost per 5-pound bag of lettuce. They think a largely manual process will have monthly fixed costs of $37,500 and variable costs of $1.75 per bag. A more mechanized process will have fixed costs of $75,000 per month with variable costs of $1.25 per 5-pound bag. They expect to sell the shredded lettuce for $2.50 per 5-pound bag. a) What is the break-even quantity for the manual process? b) What is the revenue at the break-even quantity for the manual
process? c) What is the break-even quantity for the mechanized process? d) What is the revenue at the break-even quantity for the mecha-
nized process? e) What is the monthly profit or loss of the manual process if they
expect to sell 60,000 bags of lettuce per month? f ) What is the monthly profit or loss of the mechanized process if
they expect to sell 60,000 bags of lettuce per month? g) At what quantity would Zan and Angela be indifferent to the
process selected? h) Over what range of demand would the manual process be
preferred over the mechanized process? Over what range of demand would the mechanized process be preferred over the manual process? PX
• • • • S7.26 As a prospective owner of a club known as the Red Rose, you are interested in determining the volume of sales dollars necessary for the coming year to reach the break-even point. You have decided to break down the sales for the club into four categories, the first category being beer. Your estimate of the beer sales is that 30,000 drinks will be served. The selling price for each unit will average $1.50; the cost is $.75. The second major category is meals, which you expect to be 10,000 units with an average price of $10.00 and a cost of $5.00. The third major cat- egory is desserts and wine, of which you also expect to sell 10,000 units, but with an average price of $2.50 per unit sold and a cost of $1.00 per unit. The final category is lunches and inexpensive sandwiches, which you expect to total 20,000 units at an average price of $6.25 with a food cost of $3.25. Your fixed cost (i.e., rent, utilities, and so on) is $1,800 per month plus $2,000 per month for entertainment. a) What is your break-even point in dollars per month? b) What is the expected number of meals each day if you are open
30 days a month?
• • • S7.27 As manager of the St. Cloud Theatre Company, you have decided that concession sales will support themselves. The following table provides the information you have been able to put together thus far:
ITEM SELLING PRICE VARIABLE COST % OF REVENUE
Soft drink $1.00 $.65 25
Wine 1.75 .95 25
Coffee 1.00 .30 30
Candy 1.00 .30 20
Last year’s manager, Jim Freeland, has advised you to be sure to add 10% of variable cost as a waste allowance for all categories.
You estimate labor cost to be $250.00 (5 booths with 2 people each). Even if nothing is sold, your labor cost will be $250.00, so you decide to consider this a fixed cost. Booth rental, which is a contractual cost at $50.00 for each booth per night, is also a fixed cost. a) What is the break-even volume per evening performance? b) How much wine would you expect to sell each evening at the
break-even point?
Additional problems S7.28–S7.31 are available in MyOMLab.
Problems S7.32–S7.33 relate to Applying Expected Monetary Value (EMV) to Capacity Decisions
• • S7.32 James Lawson’s Bed and Breakfast, in a small his- toric Mississippi town, must decide how to subdivide (remodel) the large old home that will become its inn. There are three alternatives: Option A would modernize all baths and combine rooms, leaving the inn with four suites, each suitable for two to four adults. Option B would modernize only the second floor; the results would be six suites, four for two to four adults, two for two adults only. Option C (the status quo option) leaves all walls intact. In this case, there are eight rooms available, but only two are suitable for four adults, and four rooms will not have private baths. Below are the details of profit and demand patterns that will accompany each option:
ANNUAL PROFIT UNDER VARIOUS DEMAND PATTERNS
ALTERNATIVES HIGH P AVERAGE P
A (modernize all) $90,000 .5 $25,000 .5
B (modernize 2nd) $80,000 .4 $70,000 .6
C (status quo) $60,000 .3 $55,000 .7
Which option has the highest expected monetary value? PX
• • • • S7.33 As operations manager of Holz Furniture, you must make a decision about adding a line of rustic furniture. In discussing the possibilities with your sales manager, Steve Gilbert, you decide that there will definitely be a market and that your firm should enter that market. However, because rustic furniture has a different finish than your standard offering, you decide you need another process line. There is no doubt in your mind about the deci- sion, and you are sure that you should have a second process. But you do question how large to make it. A large process line is going to cost $400,000; a small process line will cost $300,000. The ques- tion, therefore, is the demand for rustic furniture. After extensive discussion with Mr. Gilbert and Tim Ireland of Ireland Market Research, Inc., you determine that the best estimate you can make
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is that there is a two-out-of-three chance of profit from sales as large as $600,000 and a one-out-of-three chance as low as $300,000.
With a large process line, you could handle the high figure of $600,000. However, with a small process line you could not and would be forced to expand (at a cost of $150,000), after which time your profit from sales would be $500,000 rather than the $600,000 because of the lost time in expanding the process. If you do not expand the small process, your profit from sales would be held to $400,000. If you build a small process and the demand is low, you can handle all of the demand.
Should you open a large or small process line?
Problems S7.34–S7.45 relate to Applying Investment Analysis to Strategy-Driven Investments
• • S7.34 What is the net present value of an investment that costs $75,000 and has a salvage value of $45,000? The annual profit from the investment is $15,000 each year for 5 years. The cost of capital at this risk level is 12%. PX
• S7.35 The initial cost of an investment is $65,000 and the cost of capital is 10%. The return is $16,000 per year for 8 years. What is the net present value? PX
• S7.36 What is the present value of $5,600 when the interest rate is 8% and the return of $5,600 will not be received for 15 years? PX
• • S7.37 Tim Smunt has been asked to evaluate two machines. After some investigation, he determines that they have the costs shown in the following table. He is told to assume that: 1. The life of each machine is 3 years. 2. The company thinks it knows how to make 12% on investments
no more risky than this one. 3. Labor and maintenance are paid at the end of the year.
MACHINE A MACHINE B
Original cost $10,000 $20,000
Labor per year 2,000 4,000
Maintenance per year 4,000 1,000
Salvage value 2,000 7,000
Determine, via the present value method, which machine Tim should recommend.
• • • • S7.38 Your boss has told you to evaluate two ovens for Tink-the-Tinkers, a gourmet sandwich shop. After some ques- tioning of vendors and receipt of specifications, you are assured that the ovens have the attributes and costs shown in the follow- ing table. The following two assumptions are appropriate: 1. The life of each machine is 5 years. 2. The company thinks it knows how to make 14% on invest-
ments no more risky than this one. a) Determine via the present value method which machine to tell
your boss to purchase. b) What assumption are you making about the ovens? c) What assumptions are you making in your methodology?
THREE SMALL OVENS
AT $1,250 EACH
TWO LARGE OVENS
AT $2,500 EACH
Original cost $3,750 $5,000
Labor per year in excess of
larger models
$ 750 (total)
Cleaning/ maintenance
$ 750 ($250 each) $ 400 ($200 each)
Salvage value $ 750 ($250 each) $1,000 ($500 each)
• • • • S7.39 Bold’s Gym, a health club chain, is consider- ing expanding into a new location: the initial investment would be $1 million in equipment, renovation, and a 6-year lease, and its annual upkeep and expenses would be $75,000 (paid at the beginning of the year). Its planning horizon is 6 years out, and at the end, it can sell the equipment for $50,000. Club capacity is 500 members who would pay an annual fee of $600. Bold’s expects to have no problems filling membership slots. Assume that the interest rate is 10%. (See Table S7.2 .) a) What is the present value profit/loss of the deal? b) The club is considering offering a special deal to the mem-
bers in the first year. For $3,000 upfront they get a full 6-year membership (i.e., 1 year free). Would it make financial sense to offer this deal?
Additional problems S7.40–S7.45 are available in MyOMLab.
CASE STUDY Capacity Planning at Arnold Palmer Hospital V ideo Case
Since opening day, Arnold Palmer Hospital has experienced an explosive growth in demand for its services. One of only six hospitals in the U.S. to specialize in health care for women and children, Arnold Palmer Hospital has cared for over 1,500,000 patients who came to the Orlando facility from all 50 states and more than 100 other countries. With patient satisfaction scores in the top 10% of U.S. hospitals surveyed (over 95% of patients would recommend the hospital to others), one of Arnold Palmer Hospital’s main focuses is delivery of babies. Originally built with
281 beds and a capacity for 6,500 births per year, the hospital steadily approached and then passed 10,000 births. Looking at Table S7.4 , Executive Director Kathy Swanson knew an expan- sion was necessary.
With continuing population growth in its market area serving 18 central Florida counties, Arnold Palmer Hospital was deliver- ing the equivalent of a kindergarten class of babies every day and still not meeting demand. Supported with substantial additional demographic analysis, the hospital was ready to move ahead with
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a capacity expansion plan and a new 11-story hospital building across the street from the existing facility.
Thirty-five planning teams were established to study such issues as (1) specific forecasts, (2) services that would transfer to the new facility, (3) services that would remain in the existing facility, (4) staffing needs, (5) capital equipment, (6) pro forma accounting data, and (7) regulatory requirements. Ultimately, Arnold Palmer Hospital was ready to move ahead with a budget of $100 million and a commitment to an additional 150 beds. But given the growth of the central Florida region, Swanson decided to expand the hospital in stages: the top two floors would be empty interiors (“shell”) to be completed at a later date, and the fourth-floor operating room could be doubled in size when needed. “With the new facility in place, we are now able to handle up to 16,000 births per year,” says Swanson.
Discussion Questions *
1. Given the capacity planning discussion in the text (see Figure S7.6 ), what approach is being taken by Arnold Palmer Hospital toward matching capacity to demand?
2. What kind of major changes could take place in Arnold Palmer Hospital’s demand forecast that would leave the hospital with an underutilized facility (namely, what are the risks connected with this capacity decision)?
3. Use regression analysis to forecast the point at which Swanson needs to “build out” the top two floors of the new building, namely, when demand will exceed 16,000 births.
• Additional Case Study: Visit MyOMLab for this free case study: Southwestern University (D): Requires the development of a multiproduct break-even solution.
* You may wish to view the video that accompanies the case before ad- dressing these questions.
TABLE S7.4 Births at Arnold Palmer Hospital
YEAR BIRTHS
1995 6,144
1996 6,230
1997 6,432
1998 6,950
1999 7,377
2000 8,655
2001 9,536
2002 9,825
2003 10,253
2004 10,555
2005 12,316
2006 13,070
2007 14,028
2008 14,241
2009 13,050
2010 12,571
2011 12,978
2012 13,529
2013 13,576
2014 13,994
Endnote
1. See E. M. Goldratt and J. Cox, The Goal: A Process of Ongoing Improvement , 3rd rev. ed., Great Barrington, MA: North River Press, 2004.
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Supplement 7 Rapid Review Main Heading Review Material MyOMLab CAPACITY (pp. 308–314)
j Capacity— The “throughput,” or number of units a facility can hold, receive, store, or produce in a period of time.
Capacity decisions often determine capital requirements and therefore a large portion of fixed cost. Capacity also determines whether demand will be satis- fied or whether facilities will be idle. Determining facility size, with an objective of achieving high levels of utilization and a high return on investment, is critical. Capacity planning can be viewed in three time horizons: 1. Long-range (. 1 year)—Adding facilities and long lead-time equipment 2. Intermediate-range (3–18 months)—“Aggregate planning” tasks, including
adding equipment, personnel, and shifts; subcontracting; and building or using inventory
3. Short-range (, 3 months)—Scheduling jobs and people, and allocating machinery
j Design capacity —The theoretical maximum output of a system in a given period, under ideal conditions.
Most organizations operate their facilities at a rate less than the design capacity. j Effective capacity —The capacity a firm can expect to achieve, given its prod-
uct mix, methods of scheduling, maintenance, and standards of quality. j Utilization —Actual output as a percent of design capacity. j Efficiency —Actual output as a percent of effective capacity. Utilization = Actual output>Design capacity (S7-1)
Efficiency = Actual output>Effective capacity (S7-2) When demand exceeds capacity, a firm may be able to curtail demand simply by raising prices, increasing lead times (which may be inevitable), and discour- aging marginally profitable business.
When capacity exceeds demand, a firm may want to stimulate demand through price reductions or aggressive marketing, or it may accommodate the market via product changes. In the service sector, scheduling customers is demand management, and sched- uling the workforce is capacity management. When demand and capacity are fairly well matched, demand management in services can often be handled with appointments, reservations, or a first-come, first-served rule. Otherwise, discounts based on time of day may be used (e.g., “early bird” specials, matinee pricing). When managing demand in services is not feasible, managing capacity through changes in full-time, temporary, or part-time staff may be an option.
Concept Questions: 1.1–1.4 Problems: S7.1–S7.8
Virtual Office Hours for Solved Problem: S7.1
ACTIVE MODEL S7.1
BOTTLENECK ANALYSIS AND THE THEORY OF CONSTRAINTS (pp. 314–318)
j Capacity analysis —Determining throughput capacity of workstations or an entire production system.
j Bottleneck —The limiting factor or constraint in a system. j Process time —The time to produce a unit (or batch) at a workstation. j Bottleneck time —The process time of the longest (slowest) process. j Throughput time —The time it takes for a product to go through the
production process with no waiting , i.e., the time of the longest path through the system.
If n parallel (redundant) operations are added, the process time of the combined operations will equal 1 > n times the process time of the original. With simultaneous processing, an order or product is essentially split into different paths to be rejoined later on. The longest path through the system is deemed the throughput time. j Theory of constraints (TOC) —A body of knowledge that deals with anything
limiting an organization’s ability to achieve its goals.
Concept Questions: 2.1–2.4 Problems: S7.9–S7.15
Virtual Office Hours for Solved Problem: S7.5
S7
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S 7 Supplement 7 Rapid Review continued
Main Heading Review Material MyOMLab BREAK-EVEN ANALYSIS (pp. 318–322)
j Break-even analysis —A means of finding the point, in dollars and units, at which costs equal revenues.
Fixed costs are costs that exist even if no units are produced. Variable costs are those that vary with the volume of units produced. In the break-even model, costs and revenue are assumed to increase linearly.
Break@even in units = Total Fixed cost
Price - Variable cost =
F P - V
(S7-3)
Break@even in dollars = Total Fixed cost
1 - Variable cost
Price
= F
1 - a V P b
(S7-4)
Multiproduct break@even point in dollars = BEP$ = F
a ca1 - Vi Pi b * (Wi ) d
(S7-5)
Concept Questions: 3.1–3.4 Problems: S7.16–S7.31 Virtual Office Hours for Solved Problem: S7.3
ACTIVE MODEL S7.2
REDUCING RISK WITH INCREMENTAL CHANGES (pp. 322–323)
Demand growth is usually in small units, while capacity additions are likely to be both instantaneous and in large units. To reduce risk, incremental changes that hedge demand forecasts may be a good option. Four approaches to capac- ity expansion are (1) leading strategy, with incremental expansion, (2) leading strategy with one step expansion, (3) lag strategy, and (4) straddle strategy. Both lag strategy and straddle strategy delay capital expenditure.
Concept Questions: 4.1–4.4 VIDEO S7.1 Capacity Planning at Arnold Palmer Hospital
APPLYING EXPECTED MONETARY VALUE (p. 323)
Determining expected monetary value requires specifying alternatives and various states of nature (e.g., demand or market favorability). By assigning probability values to the various states of nature, we can make decisions that maximize the expected value of the alternatives.
Concept Questions: 5.1–5.4 Problems: S7.32–S7.33
APPLYING INVESTMENT ANALYSIS TO STRATEGY-DRIVEN INVESTMENTS (pp. 324–326)
j Net present value —A means of determining the discounted value of a series of future cash receipts.
F = P(1 + i)N (S7-6)
P = F
(1 + i)N (S7-7)
P = F
(1 + i)N = FX (S7-8)
When making several investments, those with higher net present values are preferable to investments with lower net present values.
Concept Questions: 6.1–6.4 Problems: S7.34–S7.45
Virtual Office Hours for Solved Problem: S7.4
j Before taking the self-test , refer to the learning objectives listed at the beginning of the supplement and the key terms listed at the end of the supplement.
LO S7.1 Capacity decisions should be made on the basis of: a) building sustained competitive advantage. b) good financial returns. c) a coordinated plan. d) integration into the company’s strategy. e) all of the above. LO S7.2 Effective capacity is: a) the capacity a firm expects to achieve, given the current
operating constraints. b) the percentage of design capacity actually achieved. c) the percentage of capacity actually achieved. d) actual output. e) efficiency. LO S7.3 System capacity is based on: a) the bottleneck. b) throughput time. c) time of the fastest station. d) throughput time plus waiting time. e) none of the above.
LO S7.4 The break-even point is: a) adding processes to meet the point of changing product
demands. b) improving processes to increase throughput. c) the point in dollars or units at which cost equals revenue. d) adding or removing capacity to meet demand. e) the total cost of a process alternative. LO S7.5 Expected monetary value is most appropriate: a) when the payoffs are equal. b) when the probability of each decision alternative is known. c) when probabilities are the same. d) when both revenue and cost are known. e) when probabilities of each state of nature are known. LO S7.6 Net present value: a) is greater if cash receipts occur later rather than earlier. b) is greater if cash receipts occur earlier rather than later. c) is revenue minus fixed cost. d) is preferred over break-even analysis. e) is greater if $100 monthly payments are received in a
lump sum ($1,200) at the end of the year.
Answers: LO S7.1. e; LO S7.2. a; LO S7.3. a; LO S7.4. c; LO S7.5. b; LO S7.6. b.
Self Test
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337337
C H A P TE R O U T L I N E
8 ◆
The Strategic Importance of Location 340
◆
Factors That Affect Location Decisions 341
◆
Methods of Evaluating Location Alternatives 344
◆
Service Location Strategy 350
◆
Geographic Information Systems 351
GLOBAL COMPANY PROFILE: FedEx
C H
A P
T E
R
Location Strategies
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
•• Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
A l a s k a A i r l i n e s A l a s k a A i r l i n e s Alas
ka A
ir lin
e s
A la
sk a A
ir lin
e s
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O vernight-delivery powerhouse FedEx has believed in the hub concept for its 46-year exis-
tence. Even though Fred Smith, founder and CEO, got a C on his college paper proposing a
hub for small-package delivery, the idea has proven extremely successful. Starting with one
central location in Memphis, Tennessee (now called its superhub ), the $45 billion firm has added
a European hub in Paris, an Asian hub in Guangzhou, China, a Latin American hub in Miami, and
a Canadian hub in Toronto. FedEx’s fleet of 667 planes flies into 375 airports worldwide, then
delivers to the door with more than 80,000 vans and trucks.
Location Provides Competitive Advantage for FedEx
GLOBAL COMPANY PROFILE FedEx
C H A P T E R 8
338
At the FedEx hub in Memphis,
Tennessee, approximately 100 FedEx
aircraft converge each night around
midnight with more than 5 million
documents and packages.
O liv
e r
B e rg
/E PA
/N e w
sc o m
At the preliminary sorting area, packages
and documents are sorted and sent to
a secondary sorting area. The Memphis
facility covers 1.5 million square feet; it
is big enough to hold 33 football fields.
Packages are sorted and exchanged
until 4 A.M.
T ro
y G
la sg
o w
/A P
I m
a g e s
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339
Why was Memphis picked as FedEx’s central location?
(1) It is located in the middle of the U.S. (2) It has very few
hours of bad weather closures, perhaps contributing to the
firm’s excellent flight-safety record. (3) It provided FedEx with
generous tax incentives.
Each night, except Sunday, FedEx brings to Memphis
packages from throughout the world that are going to cities
for which FedEx does not have direct flights. The central hub
permits service to a far greater number of points with fewer
aircraft than the traditional City-A-to-City-B system. It also
allows FedEx to match aircraft flights with package loads
each night and to reroute flights when load volume requires
it, a major cost savings. Moreover, FedEx also believes that
the central hub system helps reduce mishandling and delay in
transit because there is total control over the packages from
pickup point through delivery.
Packages and documents that have already gone through
the primary and secondary sorts are checked by city, state,
and zip code. They are then placed in containers that are
loaded onto aircraft for delivery to their final destinations in
236 countries.
L a n ce
M u rp
h e y/
R e u te
rs /N
e w
sc o m
M a tt
Y o rk
/A P I m
a g e s
FedEx’s fleet of 667 planes makes it the
largest airline in the world. More than
80,000 trucks complete the delivery process.
S h i L i/ sh
zq /I m
a g in
e C
h in
a
The $150 million hub opened in Guangzhou in
2009 lies in the heart of one of China’s fastest-
growing manufacturing districts. FedEx controls
39% of the China-to-U.S. air express market.
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340
The Strategic Importance of Location World markets continue to expand, and the global nature of business is accelerating. Indeed, one of the most important strategic decisions made by many companies, including FedEx, Mercedes-Benz, and Hard Rock, is where to locate their operations. When FedEx opened its Asian hub in Guangzhou, China, it set the stage for “round-the-world” flights linking its Paris and Memphis package hubs to Asia. When Mercedes-Benz announced its plans to build its first major overseas plant in Vance, Alabama, it completed a year of competition among 170 sites in 30 states and two countries. When Hard Rock Cafe opened in Moscow, it ended 3 years of advance preparation of a Russian food-supply chain. The strategic impact, cost, and international aspect of these decisions indicate how significant location decisions are.
Firms throughout the world are using the concepts and techniques of this chapter to address the location decision because location greatly affects both fixed and variable costs. Location has a major impact on the overall risk and profit of the company. For instance, depending on the product and type of production or service taking place, transportation costs alone can total as much as 25% of the product’s selling price. That is, one-fourth of a firm’s total revenue may be needed just to cover freight expenses of the raw materials coming in and finished products going out. Other costs that may be influenced by location include taxes, wages, raw material costs, and rents. When all costs are considered, location may alter total operating expenses as much as 50%.
The economics of transportation are so significant that companies—and even cities— have coalesced around a transportation advantage. For centuries, rivers and ports, and more recently rail hubs and then interstate highways, were a major ingredient in the location deci- sion. Today airports are often the deciding factor, providing fast, low-cost transportation of goods and people.
Companies make location decisions relatively infrequently, usually because demand has outgrown the current plant’s capacity or because of changes in labor productivity, exchange rates, costs, or local attitudes. Companies may also relocate their manufacturing or service facilities because of shifts in demographics and customer demand.
Location options include (1) expanding an existing facility instead of moving, (2) maintain- ing current sites while adding another facility elsewhere, or (3) closing the existing facility and moving to another location.
The location decision often depends on the type of business. For industrial location deci- sions, the strategy is usually minimizing costs, although locations that foster innovation and creativity may also be critical. For retail and professional service organizations, the strategy focuses on maximizing revenue. Warehouse location strategy, however, may be driven by a combination of cost and speed of delivery. The objective of location strategy is to maximize the benefit of location to the firm.
Location and Costs Because location is such a significant cost and revenue driver, loca- tion often has the power to make (or break) a company’s business strategy. Key multinationals in every major industry, from automobiles to cellular phones, now have or are planning a pres- ence in each of their major markets. Location decisions to support a low-cost strategy require particularly careful consideration.
L E A R N I N G OBJEC TI V ES
LO 8.1 Identify and explain seven major factors that aff ect location decisions 342
LO 8.2 Compute labor productivity 342
LO 8.3 Apply the factor-rating method 345
LO 8.4 Complete a locational cost–volume analysis graphically and mathematically 347
LO 8.5 Use the center-of-gravity method 348
LO 8.6 Understand the diff erences between service- and industrial-sector location analysis 351
VIDEO 8.1 Hard Rock’s Location Selection
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C H A P T E R 8 | L O C AT I O N S T R AT E G I E S 341
Once management is committed to a specific location, many costs are firmly in place and difficult to reduce. For instance, if a new factory location is in a region with high energy costs, even good management with an outstanding energy strategy is starting at a disadvantage. Man- agement is in a similar bind with its human resource strategy if labor in the selected location is expensive, ill-trained, or has a poor work ethic. Consequently, hard work to determine an optimal facility location is a good investment.
Factors That Affect Location Decisions Selecting a facility location is becoming much more complex with globalization. As we saw in Chapter 2 , globalization has taken place because of the development of (1) market eco- nomics; (2) better international communications; (3) more rapid, reliable travel and shipping; (4) ease of capital flow between countries; and (5) high differences in labor costs. Many firms now consider opening new offices, factories, retail stores, or banks outside their home coun- try. Location decisions transcend national borders. In fact, as Figure 8.1 shows, the sequence of location decisions often begins with choosing a country in which to operate.
One approach to selecting a country is to identify what the parent organization believes are key success factors (KSFs) needed to achieve competitive advantage. Six possible country KSFs are listed at the top of Figure 8.1 . Using such factors (including some negative ones, such as crime) the World Economic Forum biannually ranks the global competitiveness of 144 countries (see Table 8.1 ). Switzerland placed first because of its high rates of saving and investment, openness to trade, quality education, and efficient government.
Once a firm decides which country is best for its location, it focuses on a region of the chosen country and a community. The final step in the location decision process is choosing a specific site within a community. The company must pick the one location that is best suited for shipping and receiving, zoning, utilities, size, and cost. Again, Figure 8.1 summarizes this series of decisions and the factors that affect them.
Political risks, government rules, attitudes, incentives Cultural and economic issues Location of markets Labor talent, attitudes, productivity, costs Availability of supplies, communications, energy Exchange rates and currency risk
1. 2. 3. 4. 5. 6.
Country Decision Key Success Factors
Region/Community Decision
Site Decision
12
4
3
Corporate desires Attractiveness of region (culture, taxes, climate, etc.) Labor availability, costs, attitudes toward unions Cost and availability of utilities Environmental regulations of state and town Government incentives and fiscal policies Proximity to raw materials and customers Land/construction costs
1. 2. 3. 4. 5. 6. 7. 8.
Site size and cost Air, rail, highway, and waterway systems Zoning restrictions Proximity of services/supplies needed Environmental impact issues Customer density and demographics
1. 2. 3. 4. 5. 6.
MN
WI
IL IN OH
MI
465
465
465
465
70
65
65
69
70
Indianapolis
TABLE 8.1
Competitiveness of 144 Selected Countries, Based on Annual Surveys of 13,000 Business Executives
COUNTRY 2015
RANKING
Switzerland 1
Singapore 2
U.S. 3
Finland 4
Germany 5
Japan 6 f
Canada 15 f
Israel 27
China 28 f
Russia 53 f
Mexico 61 f
Vietnam 68 f
Haiti 137
f
Chad 143
Guinea 144
Source: www.weforum.org , 2015. Used
with permission of World Economic
Forum.
Figure 8.1
Some Considerations and
Factors That Affect Location
Decisions
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342 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Besides globalization, a number of other factors affect the location decision. Among these are labor productivity, foreign exchange, culture, changing attitudes toward the industry, and proximity to markets, suppliers, and competitors.
Labor Productivity When deciding on a location, management may be tempted by an area’s low wage rates. However, wage rates cannot be considered by themselves, as Otis Elevator discovered when it opened its plant in Mexico in 1998. But by 2011, Otis found a move to an auto- mated plant in South Carolina more advantageous. Management must also consider productivity.
As discussed in Chapter 1 , differences exist in productivity in various countries. What management is really interested in is the combination of production and the wage rate. For example, if Otis Elevator pays $70 per day with 60 units produced per day in South Carolina, it will spend less on labor than at a Mexican plant that pays $25 per day with production of 20 units per day:
Labor cost per day
Production (units per day) = Labor cost per unit
1. Case 1: South Carolina plant:
+70 Wages per day
60 Units produced per day = +70 60
= +1.17 per unit
2. Case 2: Juarez, Mexico, plant:
+25 Wages per day
20 Units produced per day = +25 20
= +1.25 per unit
Employees with poor training, poor education, or poor work habits may not be a good buy even at low wages. By the same token, employees who cannot or will not always reach their places of work are not much good to the organization, even at low wages. (Labor cost per unit is sometimes called the labor content of the product.)
Exchange Rates and Currency Risk Although wage rates and productivity may make a country seem economical, unfavorable exchange rates may negate any savings. Sometimes, though, firms can take advantage of a particularly favorable exchange rate by relocating or exporting to a foreign country. However, the values of foreign currencies continually rise and fall in most countries. Such changes could well make what was a good location in 2015 a disastrous one in 2019. Operational hedging describes the situation where firms have excess capacity in multiple countries and then shift production levels from location to location as exchange rates change.
Costs We can divide location costs into two categories, tangible and intangible. Tangible costs are those costs that are readily identifiable and precisely measured. They include utilities, labor, mate- rial, taxes, depreciation, and other costs that the accounting department and management can identify. In addition, such costs as transportation of raw materials, transportation of finished goods, and site construction are all factored into the overall cost of a location. Government incentives, as we see in the OM in Action box “Iowa—Home of Corn and Facebook,” also affect a location’s cost.
Intangible costs are less easily quantified. They include quality of education, public transpor- tation facilities, community attitudes toward the industry and the company, and quality and attitude of prospective employees. They also include quality-of-life variables, such as climate and sports teams, that may influence personnel recruiting.
LO 8.1 Identify and explain seven major
factors that affect location
decisions
LO 8.2 Compute labor productivity
STUDENT TIP Final cost is the critical factor,
and low productivity can negate
low wages.
Tangible costs
Readily identifiable costs that can
be measured with some precision.
Intangible costs
A category of location costs that
cannot be easily quantified, such
as quality of life and government.
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C H A P T E R 8 | L O C AT I O N S T R AT E G I E S 343
OM in Action Iowa—Home of Corn and Facebook Among the big draws in Altoona, Iowa, population 15,000, are Adventureland,
a Bass Pro Shop, and the Prairie Meadows casino. And now, it has Facebook’s
new data center. The social network recently opened the $300 million facility,
a move that highlights the intense competition and lavish tax breaks available
from small communities looking for technology bragging rights. The Altoona
facility was built on millions of dollars of tax breaks and about 18 months of
negotiation.
Facebook isn’t Iowa’s first high-tech catch. Microsoft is spending $2 billion on
a data center nearby in Des Moines. Google is expanding a facility in Council Bluffs.
States and cities long have vied against each other to lure factories, sports
teams and corporate headquarters. Iowa, the country’s largest producer of
corn, is among many states rolling out a green carpet for those farming bits
and bytes. Officials say data centers broaden their tax base, create well-
paying technical and construction jobs, and confer bragging rights that will
lure companies with bigger hiring plans. They also contribute to the local
economy without stressing infrastructure such as roads and sewage plants.
But it remains an open question whether the cost of these facilities, in tax
breaks and services, works out in their favor. Altoona provided Facebook a
20-year exemption on paying property taxes, and Iowa agreed to $18 million
in sales-tax refunds or investment-tax credits through 2023. “For the tax
Political Risk, Values, and Culture The political risk associated with national, state, and local governments’ attitudes toward private and intellectual property, zoning, pollution, and employment stability may be in flux. Governmental positions at the time a location decision is made may not be lasting ones. However, management may find that these attitudes can be influenced by their own leadership.
Worker values may also differ from country to country, region to region, and small town to city. Worker views regarding turnover, unions, and absenteeism are all relevant factors. In turn, these values can affect a company’s decision whether to make offers to current workers if the firm relocates to a new location. The case study at the end of this chapter, “Southern Recre- ational Vehicle Company,” describes a St. Louis firm that actively chose not to relocate any of its workers when it moved to Mississippi.
One of the greatest challenges in a global operations decision is dealing with another coun- try’s culture. Cultural variations in punctuality by employees and suppliers make a marked difference in production and delivery schedules. Bribery and other forms of corruption also create substantial economic inefficiency, as well as ethical and legal problems in the global arena. As a result, operations managers face significant challenges when building effective sup- ply chains across cultures. Table 8.2 provides one ranking of corruption in countries around the world.
Proximity to Markets For many firms, locating near customers is extremely important. Particularly, service organi- zations, like drugstores, restaurants, post offices, or barbers, find that demographics and proximity to market are the primary location factors. Manufacturing firms find it useful to be close to customers when transporting finished goods is expensive or difficult (perhaps because they are bulky, heavy, or fragile). To be near U.S. markets, foreign-owned auto giants such as Mercedes, Honda, Toyota, and Hyundai are building millions of cars each year in the U.S.
breaks they often receive, the
centers produce few jobs or spinoff
benefits,” said an Iowa State Uni-
versity professor. Tech companies
aren’t looking for incentives alone.
Availability and pricing of electric-
ity, which can exceed two-thirds of
the cost to run a data center, are
among the most important factors.
Proponents argue that busi-
nesses expect to trade tax cuts
for jobs. But a report by the John
Locke foundation summarized the results of 55 studies on the impact of
targeted tax incentives. More than 70% of the studies found that incentives
either did not substantially contribute to economic performance or produced
mixed results. Often the giveaways add up to cronyism, a misallocation of
resources, and a huge bill for taxpayers.
Sources: Wall Street Journal (Nov. 15–16, 2014) and (March 13, 2015); and
New York Times (Dec. 1, 2012).
gdb re
kk e /F
o to
lia
TABLE 8.2
Ranking Corruption in Selected Countries (Score of 100 Represents a Corruption-Free Country
RANK SCORE
1 Denmark 92
2 New Zealand 91
3 Finland 89
f
10 Canada 81
f
17 U.S., Hong Kong
74 (tie)
f
37 Israel 60
f
69 Brazil, Greece
43 (tie)
f
136 Russia 27
f
161 Haiti 19
f
174 Somalia, North Korea
8 (tie)
Source: Transparency International’s 2014
survey, at www.transparency.org . Used
with permission of Transparency International.
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344 P A R T 2 | D E S I G N I N G O P E R AT I O N S
In addition, with just-in-time production, suppliers want to locate near users. For a firm like Coca-Cola, whose product’s primary ingredient is water, it makes sense to have bottling plants in many cities rather than shipping heavy (and sometimes fragile glass) containers cross country.
Proximity to Suppliers Firms locate near their raw materials and suppliers because of (1) perishability, (2) transporta- tion costs, or (3) bulk. Bakeries, dairy plants, and frozen seafood processors deal with perish- able raw materials, so they often locate close to suppliers. Companies dependent on inputs of heavy or bulky raw materials (such as steel producers using coal and iron ore) face expensive inbound transportation costs , so transportation costs become a major factor. And goods for which there is a reduction in bulk during production (e.g., trees to lumber) typically need facili- ties near the raw material.
Proximity to Competitors (Clustering) Both manufacturing and service organizations also like to locate, somewhat surprisingly, near competitors. This tendency, called clustering , often occurs when a major resource is found in that region. Such resources include natural resources, information resources, venture capital resources, and talent resources. Table 8.3 presents nine examples of industries that exhibit clustering, and the reasons why.
Italy may be the true leader when it comes to clustering, however, with northern zones of that country holding world leadership in such specialties as ceramic tile (Modena), gold jew- elry (Vicenza), machine tools (Busto Arsizio), cashmere and wool (Biella), designer eyeglasses (Belluma), and pasta machines (Parma). When it comes to clusters for innovations in slaugh- tering, however (see the OM in Action box), Denmark is the leader.
Methods of Evaluating Location Alternatives Four major methods are used for solving location problems: the factor-rating method, loca- tional cost–volume analysis, the center-of-gravity method, and the transportation model. This section describes these approaches.
Clustering
The location of competing companies
near each other, often because of a
critical mass of information, talent,
venture capital, or natural resources.
TABLE 8.3 Clustering of Companies
INDUSTRY LOCATIONS REASON FOR CLUSTERING
Wine making Napa Valley (U.S.), Bordeaux region (France)
Natural resources of land and climate
Software fi rms Silicon Valley, Boston, Bangalore, Israel
Talent resources of bright graduates in scientifi c /technical areas, venture capitalists nearby
Clean energy Colorado Critical mass of talent and information, with 1,000 companies
Theme parks (e.g., Disney World, Universal Studios, and Sea World)
Orlando, Florida A hot spot for entertainment, warm weather, tourists, and inexpensive labor
Electronics fi rms (e.g., Sony, IBM, HP, Motorola, and Panasonic)
Northern Mexico NAFTA, duty-free export to U.S. (24% of all TVs are built here)
Computer hardware manufacturing Singapore, Taiwan High technological penetration rates and per capita GDP, skilled/educated workforce with large pool of engineers
Fast-food chains (e.g., Wendy’s, McDonald’s, Burger King, Pizza Hut)
Sites within 1 mile of one another Stimulate food sales, high traffi c fl ows
General aviation aircraft (e.g., Cessna, Learjet, Boeing, Raytheon)
Wichita, Kansas Mass of aviation skills (60–70% of world’s small planes/jets are built here)
Athletic footwear, outdoor wear Portland, Oregon 300 companies, many spawned by Nike, deep talent pool and outdoor culture
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C H A P T E R 8 | L O C AT I O N S T R AT E G I E S 345
The Factor-Rating Method There are many factors, both qualitative and quantitative, to consider in choosing a location. Some of these factors are more important than others, so managers can use weightings to make the decision process more objective. The factor-rating method is popu- lar because a wide variety of factors, from education to recreation to labor skills, can be objectively included. Figure 8.1 listed a few of the many factors that affect location decisions.
The factor-rating method (which we introduced in Chapter 2 ) has six steps:
1. Develop a list of relevant factors called key success factors (such as those in Figure 8.1 ). 2. Assign a weight to each factor to reflect its relative importance in the company’s
objectives. 3. Develop a scale for each factor (for example, 1 to 10 or 1 to 100 points). 4. Have management score each location for each factor, using the scale in Step 3. 5. Multiply the score by the weights for each factor and total the score for each location. 6. Make a recommendation based on the maximum point score, considering the results of
other quantitative approaches as well.
Factor-rating method
A location method that instills
objectivity into the process of
identifying hard-to-evaluate costs.
Example 1 FACTOR-RATING METHOD FOR AN EXPANDING THEME PARK Five Flags over Florida, a U.S. chain of 10 family-oriented theme parks, has decided to expand overseas by opening its first park in Europe. It wishes to select between France and Denmark.
APPROACH c The ratings sheet in Table 8.4 lists key success factors that management has decided are important; their weightings and their rating for two possible sites—Dijon, France, and Copenhagen, Denmark—are shown.
LO 8.3 Apply the factor-rating method
OM in Action Denmark’s Meat Cluster Every day, 20,000 pigs are delivered to the Danish Crown company’s slaughter-
house in central Denmark. The pigs trot into the stunning room, guided by
workers armed with giant fly swats. The animals are hung upside down,
divided in two, shaved, and scalded clean. A machine cuts them into pieces,
which are then cooled, boned, and packed.
The slaughterhouse is enormous: 10 football fields long with 7 miles
of conveyor belts. Its managers attend to the tiniest detail. The workers
wear green rather than white because this puts the pigs in a better
mood. The cutting machine photographs a carcass before adjusting its
blades to the exact carcass contours. The company calibrates not only
how to carve the flesh, but also where the various parts will fetch the
highest prices.
Denmark is a tiny country, with 5.6 million people and wallet-draining
labor costs. But it is an agricultural giant, home to 30 million pigs and numer-
ous global brands. Farm products make up over 20% of its goods exports—
and the value of these exports is expected to grow from $5.5 billion in 2001 to
$31 billion by 2020.
How is this meat-processing cluster still thriving? It is because clustering
can be applied to ancient industries like slaughtering as well as to new ones.
The cluster includes several big companies: Danish Crown, Arla, Rose Poultry,
and DuPont Danisco, as well as
plenty of smaller firms, which act
as indicators of nascent trends
and incubators of new ideas. Other
firms are contributing information
technology tools for the clus-
ter. Among these are LetFarm
for fields, Bovisoft for stables,
Agrosoft for pigs, Webstech for
grain, and InOMEGA for food.
The cluster also has a collec-
tion of productivity-spurring institu-
tions (the Cattle Research Center,
for example, creates ways to boost
pork productivity through robotics)
and Danish Tech University, where
1,500 people work on food-related
subjects.
Sources: The Economist (Jan. 4, 2014); and GlobalMeatNews.com (Nov. 1, 2013).
Rac o rn
/1 2 3 rf
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346 P A R T 2 | D E S I G N I N G O P E R AT I O N S
TABLE 8.4 Weights, Scores, and Solution
KEY SUCCESS FACTOR WEIGHT
SCORES (OUT OF 100) WEIGHTED SCORES
FRANCE DENMARK FRANCE DENMARK
Labor availability and attitude .25 70 60 (.25)(70) 5 17.50 (.25)(60) 5 15.00
People-to-car ratio .05 50 60 (.05)(50) 5 2.50 (.05)(60) 5 3.00
Per capita income .10 85 80 (.10)(85) 5 8.50 (.10)(80) 5 8.00
Tax structure .39 75 70 (.39)(75) 5 29.25 (.39)(70) 5 27.30
Education and health .21 60 70 (.21)(60) 5 12.60 (.21)(70) 5 14.70
Totals 1.00 70.35 68.00
STUDENT TIP These weights do not need to
be on a 0–1 scale or total to 1.
We can use a 1–10 scale,
1–100 scale, or any other
scale we prefer.
SOLUTION c Table 8.4 uses weights and scores to evaluate alternative site locations. Given the option of 100 points assigned to each factor, the French location is preferable.
INSIGHT c By changing the points or weights slightly for those factors about which there is some doubt, we can analyze the sensitivity of the decision. For instance, we can see that changing the scores for “labor availability and attitude” by 10 points can change the decision. The numbers used in factor weight- ing can be subjective, and the model’s results are not “exact” even though this is a quantitative approach.
LEARNING EXERCISE c If the weight for “tax structure” drops to .20 and the weight for “education and health” increases to .40, what is the new result? [Answer: Denmark is now chosen, with a 68.0 vs. a 67.5 score for France.]
RELATED PROBLEMS c 8.5–8.15, 8.24, 8.25 (8.26, 8.27, 8.28, 8.33, 8.34 are available in MyOMLab)
EXCEL OM Data File Ch08Ex1.xls can be found in MyOMLab.
When a decision is sensitive to minor changes, further analysis of the weighting and the points assigned may be appropriate. Alternatively, management may conclude that these intangible factors are not the proper criteria on which to base a location decision. Managers therefore place primary weight on the more quantitative aspects of the decision.
Locational Cost–Volume Analysis Locational cost–volume analysis is a technique for making an economic comparison of location alternatives. By identifying fixed and variable costs and graphing them for each location, we can determine which one provides the lowest cost. Locational cost–volume analysis can be done mathematically or graphically. The graphic approach has the advantage of providing the range of volume over which each location is preferable.
The three steps to locational cost–volume analysis are as follows:
1. Determine the fixed and variable cost for each location. 2. Plot the costs for each location, with costs on the vertical axis of the graph and annual
volume on the horizontal axis. 3. Select the location that has the lowest total cost for the expected production volume.
Locational cost–volume analysis
A method of making an economic
comparison of location alternatives.
Example 2 LOCATIONAL COST–VOLUME ANALYSIS FOR A PARTS MANUFACTURER Esmail Mohebbi, owner of European Ignitions Manufacturing, needs to expand his capacity. He is con- sidering three locations—Athens, Brussels, and Lisbon—for a new plant. The company wishes to find the most economical location for an expected volume of 2,000 units per year.
APPROACH c Mohebbi conducts locational cost–volume analysis. To do so, he determines that fixed costs per year at the sites are $30,000, $60,000, and $110,000, respectively; and variable costs are $75 per unit, $45 per unit, and $25 per unit, respectively. The expected selling price of each ignition system produced is $120.
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SOLUTION c For each of the three locations, Mohebbi can plot the fixed costs (those at a volume of zero units) and the total cost (fixed costs + variable costs) at the expected volume of output. These lines have been plotted in Figure 8.2 .
$10,000
$30,000
$60,000
$80,000
$110,000
$130,000
$150,000 $160,000
$180,000
0 500 1,000 1,500 2,000 2,500 3,000
Lisbo n cos
t curv e
Bru sse
ls
cos t cu
rve
At he
ns c os
t c ur
veA n n u a l c
o st
Volume
Athens lowest cost
Brussels lowest cost
Lisbon lowest cost
LO 8.4 Complete a locational cost–volume
analysis graphically and
mathematically
Figure 8.2
Crossover Chart for Locational
Cost–Volume Analysis
For Athens:
Total cost = +30,000 + +75(2,000) = +180,000
For Brussels: Total cost = +60,000 + +45(2,000) = +150,000
For Lisbon: Total cost = +110,000 + +25(2,000) = +160,000
With an expected volume of 2,000 units per year, Brussels provides the lowest cost location. The expected profit is:
Total revenue - Total cost = +120(2,000) - +150,000 = +90,000 per year
The crossover point for Athens and Brussels is:
30,000 + 75(x) = 60,000 + 45(x) 30(x) = 30,000
x = 1,000
and the crossover point for Brussels and Lisbon is:
60,000 + 45(x) = 110,000 + 25(x) 20(x) = 50,000
x = 2,500
INSIGHT c As with every other OM model, locational cost–volume analysis can be sensitive to input data. For example, for a volume of less than 1,000, Athens would be preferred. For a volume greater than 2,500, Lisbon would yield the greatest profit.
LEARNING EXERCISE c The variable cost for Lisbon is now expected to be $22 per unit. What is the new crossover point between Brussels and Lisbon? [Answer: 2,174 units.]
RELATED PROBLEMS c 8.16–8.19 (8.29, 8.30 are available in MyOMLab)
EXCEL OM Data File Ch08Ex2.xls can be found in MyOMLab.
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Center-of-Gravity Method The center-of-gravity method is a mathematical technique used for finding the location of a distribution center that will minimize distribution costs. The method takes into account the location of markets, the volume of goods shipped to those markets, and shipping costs in finding the best location for a distribution center.
The first step in the center-of-gravity method is to place the locations on a coordinate sys- tem. This will be illustrated in Example 3 . The origin of the coordinate system and the scale used are arbitrary, just as long as the relative distances are correctly represented. This can be done easily by placing a grid over an ordinary map. The center of gravity is determined using Equations (8-1) and (8-2) :
x@coordinate of the center of gravity = a
i xiQi
a i
Qi (8-1)
y@coordinate of the center of gravity = a
i yiQi
a i
Qi (8-2)
where x i = x -coordinate of location i y i = y -coordinate of location i
Q i = Quantity of goods moved to or from location i
Note that Equations (8-1) and (8-2) include the term Qi, the quantity of supplies transferred to or from location i .
Because the number of containers shipped each month affects cost, distance alone should not be the principal criterion. The center-of-gravity method assumes that cost is directly pro- portional to both distance and volume shipped. The ideal location is that which minimizes the weighted distance between sources and destinations, where the distance is weighted by the number of containers shipped. 1
Center-of-gravity method
A mathematical technique used for
finding the best location for a sin-
gle distribution point that services
several stores or areas.
LO 8.5 Use the center- of-gravity method
Example 3 CENTER OF GRAVITY Quain’s Discount Department Stores, a chain of four large Target-type outlets, has store locations in Chicago, Pittsburgh, New York, and Atlanta; they are currently being supplied out of an old and inad- equate warehouse in Pittsburgh, the site of the chain’s first store. The firm wants to find some “central” location in which to build a new warehouse.
APPROACH c Quain’s will apply the center-of-gravity method. It gathers data on demand rates at each outlet (see Table 8.5 ).
TABLE 8.5 Demand for Quain’s Discount Department Stores
STORE LOCATION NUMBER OF CONTAINERS
SHIPPED PER MONTH
Chicago 2,000
Pittsburgh 1,000
New York 1,000
Atlanta 2,000
Its current store locations are shown in Figure 8.3 . For example, location 1 is Chicago, and from Table 8.5 and Figure 8.3 , we have:
x1 = 30
y1 = 120
Q1 = 2,000
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SOLUTION c Using the data in Table 8.5 and Figure 8.3 for each of the other cities, and Equations (8-1) and (8-2) , we find:
x -coordinate of the center of gravity:
= (30)(2000) + (90)(1000) + (130)(1000) + (60)(2000)
2000 + 1000 + 1000 + 2000 =
400,000 6,000
= 66.7
y -coordinate of the center of gravity:
= (120)(2000) + (110)(1000) + (130)(1000) + (40)(2000)
2000 + 1000 + 1000 + 2000 =
560,000 6,000
= 93.3
This location (66.7, 93.3) is shown by the crosshairs in Figure 8.3 .
INSIGHT c By overlaying a U.S. map on Figure 8.3 , we find this location (66.7, 93.3) is near central Ohio. The firm may well wish to consider Columbus, Ohio, or a nearby city as an appropriate location. But it is important to have both north–south and east–west interstate highways near the city selected to make delivery times quicker.
LEARNING EXERCISE c The number of containers shipped per month to Atlanta is expected to grow quickly to 3,000. How does this change the center of gravity, and where should the new warehouse be located? [Answer: (65.7, 85.7), which is closer to Cincinnati, Ohio.]
RELATED PROBLEMS c 8.20–8.23 (8.31, 8.32 are available in MyOMLab)
EXCEL OM Data File Ch08Ex3.xls can be found in MyOMLab.
ACTIVE MODEL 8.1 This example is further illustrated in Active Model 8.1 in MyOMLab.
30
60
90
120
30 60 90 120 150 Arbitrary origin
North–South
East–West
Chicago (30, 120)
Pittsburgh (90, 110)
New York (130, 130)
Atlanta (60, 40)
Center of gravity (66.7, 93.3)
Figure 8.3
Coordinate Locations of Four
Quain’s Department Stores
and Center of Gravity
Transportation Model The objective of the transportation model is to determine the best pattern of shipments from sev- eral points of supply (sources) to several points of demand (destinations) so as to minimize total production and transportation costs. Every firm with a network of supply-and-demand points faces such a problem. The complex Volkswagen supply network (shown in Figure 8.4 ) provides one such illustration. We note in Figure 8.4 , for example, that VW of Mexico ships vehicles for assembly and parts to VW of Nigeria, sends assemblies to VW of Brasil, and receives parts and assemblies from headquarters in Germany.
Transportation model
A technique for solving a class of
linear programming problems.
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Although the linear programming (LP) technique can be used to solve this type of problem, more efficient, special-purpose algorithms have been developed for the transportation applica- tion. The transportation model finds an initial feasible solution and then makes step-by-step improvement until an optimal solution is reached.
Service Location Strategy While the focus in industrial-sector location analysis is on minimizing cost, the focus in the service sector is on maximizing revenue. This is because manufacturing firms find that costs tend to vary substantially among locations, while service firms find that location often has more impact on revenue than cost. Therefore, the location focus for service firms should be on determining the volume of customers and revenue.
There are eight major determinants of volume and revenue for the service firm:
1. Purchasing power of the customer-drawing area 2. Service and image compatibility with demographics of the customer-drawing area 3. Competition in the area 4. Quality of the competition 5. Uniqueness of the firm’s and competitors’ locations 6. Physical qualities of facilities and neighboring businesses 7. Operating policies of the firm 8. Quality of management
Realistic analysis of these factors can provide a reasonable picture of the revenue expected. The techniques used in the service sector include regression analysis (see the OM in Action box, “How La Quinta Selects Profitable Hotel Sites”), traffic counts, demographic analysis, purchasing power analysis, the factor-rating method, the center-of-gravity method, and geo- graphic information systems. Table 8.6 provides a summary of location strategies for both service and goods-producing organizations.
VW Canada
VW America
VW de Mexico
VW do Brasil
VW Argentina
VW Nigeria
Volkswagen
VW Asia
Shanghai-VW
VW South Africa
Engines, other assemblies
Finished vehicles
Parts
Vehicles for assembly
Figure 8.4
Volkswagen, the Third Largest Automaker in the World, Finds It Advantageous to Locate Its Plants Throughout the World
This graphic shows a portion of VW’s supply network. There are 61 plants in Europe, along with nine countries in the Americas, Asia, and Africa.
STUDENT TIP Retail stores often attract more
shoppers when competitors
are close.
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Geographic Information Systems Geographic information systems are an important tool to help firms make successful, analyti- cal decisions with regard to location. A geographic information system (GIS) stores, accesses, dis- plays, and links demographic information to a geographical location. For instance, retailers,
STUDENT TIP This table helps differentiate
between service- and
manufacturing-sector decisions.
LO 8.6 Understand the differences between
service- and industrial-
sector location analysis
OM in Action How La Quinta Selects Profi table Hotel Sites One of the most important decisions a lodging chain makes is location. Those
that pick good sites more accurately and quickly than competitors have a
distinct advantage. La Quinta Inns, headquartered in San Antonio, Texas, is a
moderately priced chain of 800 inns. To model motel selection behavior and
predict success of a site, La Quinta turned to regression analysis.
The hotel started by testing 35 independent variables, trying to find
which of them would have the highest correlation with predicted profitability,
the dependent variable. Variables included: the number of hotel rooms in
the vicinity and their average room rates; local attractions such as office
buildings and hospitals that drew potential customers to a 4-mile-radius
trade area; local population and unemployment rate; the number of inns in
a region; and physical characteristics of the site, such as ease of access or
sign visibility.
In the end, the regression model chosen, with an R 2 of 51%, included
four predictive variables: (1) the price of the inn, (2) median income levels,
(3) the state population per inn, and (4) the location of nearby colleges
(which serves as a proxy for
other demand generators).
La Quinta then used the
regression model to predict
profitability and developed a
cutoff that gave the best results
for predicting success or failure
of a site. A spreadsheet is now
used to implement the model,
which applies the decision rule
and suggests “build” or “don’t build.” The CEO likes the model so much that
he no longer feels obliged to personally select new sites.
Sources: S. Kimes and J. Fitzsimmons, Interfaces 20, no. 2: 12–20; and G. Keller,
Statistics for Management and Economics , 8th ed. Cincinnati-Cengage,
2008: 679.
M ik
e B
o o th
/A la
m y
TABLE 8.6 Location Strategies—Service vs. Goods-Producing Organizations
SERVICE/RETAIL/PROFESSIONAL GOODS-PRODUCING
REVENUE FOCUS COST FOCUS
Volume/revenue Drawing area; purchasing power Competition; advertising/pricing
Physical quality Parking/access; security/lighting; appearance/ image
Cost determinants Rent Management caliber Operation policies (hours, wage rates)
Tangible costs Transportation cost of raw material Shipment cost of fi nished goods Energy and utility cost; labor; raw material;
taxes, and so on Intangible and future costs
Attitude toward union Quality of life Education expenditures by state Quality of state and local government
TECHNIQUES TECHNIQUES
Regression models to determine importance of various factors
Factor-rating method Traffi c counts Demographic analysis of drawing area Purchasing power analysis of area Center-of-gravity method Geographic information systems
Transportation method Factor-rating method Locational cost–volume analysis Crossover charts
ASSUMPTIONS ASSUMPTIONS
Location is a major determinant of revenue High customer-interaction issues are critical Costs are relatively constant for a given area;
therefore, the revenue function is critical
Location is a major determinant of cost Most major costs can be identifi ed explicitly for
each site Low customer contact allows focus on the identifi able
costs Intangible costs can be evaluated
Geographic information system (GIS)
A system that stores and displays
information that can be linked to a
geographic location.
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banks, food chains, gas stations, and print shop franchises can all use geographically coded files from a GIS to conduct demographic analyses. By combining population, age, income, traffic flow, and density figures with geography, a retailer can pinpoint the best location for a new store or restaurant.
Here are some of the geographic databases available in many GISs:
◆ Census data by block, tract, city, county, congressional district, metropolitan area, state, and zip code
◆ Maps of every street, highway, bridge, and tunnel in the U.S. ◆ Utilities such as electrical, water, and gas lines ◆ All rivers, mountains, lakes, and forests ◆ All major airports, colleges, and hospitals
For example, airlines use GISs to identify airports where ground services are the most effec- tive. This information is then used to help schedule and to decide where to purchase fuel, meals, and other services.
Commercial office building developers use GISs in the selection of cities for future con- struction. Building new office space takes several years; therefore, developers value the database approach that a GIS can offer. GIS is used to analyze factors that influence the location deci- sions by addressing five elements for each city: (1) residential areas, (2) retail shops, (3) cultural and entertainment centers, (4) crime incidence, and (5) transportation options.
Here are five examples of how location-scouting GIS software is turning commercial real estate into a science.
◆ Carvel Ice Cream: This 80-year-old chain of ice cream shops uses GIS to create a demo- graphic profile of what a typically successful neighborhood for a Carvel looks like—mostly in terms of income and ages.
◆ Saber Roofing: Rather than send workers out to estimate the costs for reroofing jobs, this Redwood City, California, firm pulls up aerial shots of the building via Google Earth. The
Geographic information systems
(GISs) are used by a variety
of firms, including Darden
Restaurants, to identify target
markets by income, ethnicity,
product use, age, etc. Here,
data from MapInfo helps with
competitive analysis for a retailer.
Three concentric blue rings,
each representing various mile
radii, were drawn around the
competitor’s store. The heavy red
line indicates the “drive” time to
the firm’s own central store (the
red dot).
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owner can measure roofs, evaluate their condition, and e-mail the client an estimate, sav- ing hundreds of miles of driving daily. In one case, while on the phone, a potential client was told her roof was too steep for the company to tackle after the Saber employee quickly looked up the home on Google Earth.
◆ Arby’s: As this fast-food chain learned, specific products can affect behavior. Using MapInfo, Arby’s discovered that diners drove up to 20% farther for their roast beef sandwich (which they consider a “destination” product) than for its chicken sandwich.
◆ Home Depot: Wanting a store in New York City, even though Home Depot demo- graphics are usually for customers who own big homes, the company opened in Queens when GIS software predicted it would do well. Although most people there live in apartments and very small homes, the store has become one of the chain’s highest-volume outlets. Similarly, Home Depot thought it had saturated Atlanta two decades ago, but GIS analysis suggested expansion. There are now over 40 Home Depots in that area.
◆ Jo-Ann Stores: This fabric and craft retailer’s 70 superstores were doing well a few years ago, but managers were afraid more big-box stores could not justify building expenses. So Jo-Ann used its GIS to create an ideal customer profile—female home- owners with families—and mapped it against demographics. The firm found it could build 700 superstores, which in turn increased the sales from $105 to $150 per square foot.
Other packages similar to MapInfo are Atlas GIS (from Strategic Mapping), ArcGIS (by Esri), SAS/GIS (by SAS Institute), Maptitude (by Caliper), and GeoMedia (by Intergraph).
These GISs can be extensive, including comprehensive sets of map and demographic data. The maps have millions of miles of streets and points of interest to allow users to locate restau- rants, airports, hotels, gas stations, ATMs, museums, campgrounds, and freeway exits. Demo- graphic data include statistics for population, age, income, education, and housing. These data can be mapped by state, county, city, zip code, or census tract.
The Video Case Study “Locating the Next Red Lobster Restaurant” that appears at the end of this chapter describes how that chain uses its GIS to define trade areas based on market size and population density.
VIDEO 8.2 Locating the Next Red Lobster
Restaurant
Summary Location may determine up to 50% of operating expense. Location is also a critical element in determining reve- nue for the service, retail, or professional firm. Industrial firms need to consider both tangible and intangible costs. Industrial location problems are typically addressed via a factor-rating method, locational cost–volume analysis, the center-of-gravity method, and the transportation method of linear programming.
For service, retail, and professional organizations, anal- ysis is typically made of a variety of variables including purchasing power of a drawing area, competition, advertis- ing and promotion, physical qualities of the location, and operating policies of the organization.
Key Terms
Tangible costs (p. 342 ) Intangible costs (p. 342 ) Clustering (p. 344 )
Factor-rating method (p. 345 ) Locational cost–volume analysis (p. 346 ) Center-of-gravity method (p. 348 )
Transportation model (p. 349 ) Geographic information system
(GIS) (p. 351 )
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Ethical Dilemma In this chapter, we have discussed a number of location decisions. Consider another: United Airlines announced its competition to select a town for a new billion-dollar aircraft-repair base. The bidding for the prize of 7,500 jobs paying at least $25 per hour was fast and furious, with Orlando offering $154 million in incentives and Denver more than twice that amount. Kentucky’s governor angrily rescinded Louisville’s offer of $300 million, likening the bidding to “squeezing every drop of blood out of a turnip.”
When United finally selected from among the 93 cities bidding on the base, the winner was Indianapolis and its $320 million offer of taxpayers’ money.
But a few years later, with United near bankruptcy, and having fulfilled its legal obligation, the company walked away from the massive center. This left the city and state governments out all that money, with no new tenant in sight. The city now even owns the tools, neatly arranged in each of the 12 elaborately equipped hangar bays. United outsourced its maintenance to mechanics at a southern firm (which pays one-third of what United paid in salary and benefi ts in Indianapolis).
What are the ethical, legal, and economic implications of such location bidding wars? Who pays for such giveaways? Are local citizens allowed to vote on offers made by their cities, counties, or states? Should there be limits on these incentives?
Discussion Questions
1. How is FedEx’s location a competitive advantage? Discuss. 2. Why do so many U.S. firms build facilities in other countries? 3. Why do so many foreign companies build facilities in the
U.S.? 4. What is clustering? 5. How does factor weighting incorporate personal preference
in location choices? 6. What are the advantages and disadvantages of a qualitative
(as opposed to a quantitative) approach to location decision making?
7. Provide two examples of clustering in the service sector. 8. What are the major factors that firms consider when choos-
ing a country in which to locate? 9. What factors affect region/community location decisions?
10. Although most organizations may make the location deci- sion infrequently, there are some organizations that make the decision quite regularly and often. Provide one or two exam- ples. How might their approach to the location decision differ from the norm?
11. List factors, other than globalization, that affect the location decision.
12. Explain the assumptions behind the center-of-gravity method. How can the model be used in a service facility location?
13. What are the three steps to locational cost–volume analysis? 14. “Manufacturers locate near their resources, retailers locate
near their customers.” Discuss this statement, with reference to the proximity-to-markets arguments covered in the text. Can you think of a counter-example in each case? Support your choices.
15. Why shouldn’t low wage rates alone be sufficient to select a location?
16. List the techniques used by service organizations to select locations.
17. Contrast the location of a food distributor and a supermar- ket. (The distributor sends truckloads of food, meat, produce, etc., to the supermarket.) Show the relevant considerations (factors) they share; show those where they differ.
18. Elmer’s Fudge Factory is planning to open 10 retail outlets in Oregon over the next 2 years. Identify (and weight) those fac- tors relevant to the decision. Provide this list of factors and weights.
Using Software to Solve Location Problems
This section presents three ways to solve location problems with computer software. First, you can create your own spreadsheets to compute factor ratings, the center of gravity, and locational cost–volume analysis. Second, Excel OM (free with your text and found in MyOMLab) is programmed to solve all three models. Third, POM for Windows is also found in MyOMLab and can solve all problems labeled with a P .
CREATING YOUR OWN EXCEL SPREADSHEETS Excel spreadsheets are easily developed to solve most of the problems in this chapter. Consider the Quain’s Department Store center-of-gravity analysis in Example 3 . You can see from Program 8.1 how the formulas are created.
X USING EXCEL OM Excel OM may be used to solve Example 1 (with the Factor Rating module), Example 2 (with the Cost–Volume Analysis module), and Example 3 (with the Center-of-Gravity module), as well as other location problems. The factor-rating method was illustrated in Chapter 2 .
P USING POM FOR WINDOWS POM for Windows also includes three different facility location models: the factor-rating method, the center-of-gravity model, and locational cost–volume analysis. For details, refer to Appendix IV.
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Solved Problems Virtual Office Hours help is available in MyOMLab .
=SUM(B5:B8)
=SUMPRODUCT(D5:D8,$B5:$B8)/$B9
Action Copy D11 to C11
Program 8.1
An Excel Spreadsheet for Creating a Center-of-Gravity Analysis for Example 3,
Quain’s Discount Department Stores
SOLVED PROBLEM 8.1 Just as cities and communities can be compared for location selection by the weighted approach model, as we saw earlier in this chapter, so can actual site decisions within those cities. Table 8.7 illustrates four factors of importance to Washington, DC, and the health officials charged with opening that city’s first public drug treatment clinic. Of primary concern (and given a weight of 5) was location of the clinic so it would be as accessible as possible to the largest number of patients. Due to a tight budget, the annual lease cost was also of some concern. A suite in the city hall, at 14th and U Streets, was highly rated because its rent would be free. An old office building near the downtown bus station received a much lower rating because of its cost. Equally important as lease cost was the need for
confidentiality of patients and, therefore, for a relatively incon- spicuous clinic. Finally, because so many of the staff at the clinic would be donating their time, the safety, parking, and accessibility of each site were of concern as well.
Using the factor-rating method, which site is preferred?
SOLUTION From the three rightmost columns in Table 8.7 , the weighted scores are summed. The bus terminal area has a low score and can be excluded from further consideration. The other two sites are virtually identical in total score. The city may now want to consider other factors, including political ones, in selecting between the two remaining sites.
TABLE 8.7 Potential Clinic Sites in Washington, DC
POTENTIAL LOCATIONS * WEIGHTED SCORES
FACTOR IMPORTANCE
WEIGHT
HOMELESS SHELTER (2 ND AND
D, SE)
CITY HALL (14 TH AND
U, NW)
BUS TERMINAL AREA (7 TH
AND H, NW) HOMELESS SHELTER
CITY HALL
BUS TERMINAL
AREA
Accessibility for addicts 5 9 7 7 45 35 35
Annual lease cost 3 6 10 3 18 30 9
Inconspicuous 3 5 2 7 15 6 21
Accessibility for health staff 2 3 6 2 6 12 4
Total scores: 84 83 69
* All sites are rated on a 1 to 10 basis, with 10 as the highest score and 1 as the lowest.
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SOLVED PROBLEM 8.2 Ching-Chang Kuo is considering opening a new foundry in Denton, Texas; Edwardsville, Illinois; or Fayetteville, Arkansas, to produce high-quality rifle sights. He has assem- bled the following fixed-cost and variable-cost data:
PER-UNIT COSTS
LOCATION FIXED COST PER YEAR MATERIAL
VARIABLE LABOR OVERHEAD
Denton $200,000 $ .20 $ .40 $ .40
Edwardsville $180,000 $ .25 $ .75 $ .75
Fayetteville $170,000 $1.00 $1.00 $1.00
a) Graph the total cost lines. b) Over what range of annual volume is each facility going
to have a competitive advantage? c) What is the volume at the intersection of the Edwardsville
and Fayetteville cost lines?
SOLUTION
a) A graph of the total cost lines is shown in Figure 8.5 . b) Below 8,000 units, the Fayetteville facility will have a
competitive advantage (lowest cost); between 8,000 units and 26,666 units, Edwardsville has an advantage; and above 26,666, Denton has the advantage. (We have made the assumption in this problem that other costs—that is, delivery and intangible factors—are constant regardless of the decision.)
c) From Figure 8.5 , we see that the cost line for Fayetteville and the cost line for Edwardsville cross at about 8,000. We can also determine this point with a little algebra:
+180,000 + 1.75Q = +170,000 + 3.00Q +10,000 = 1.25Q 8,000 = Q
$250,000
0 5,000
Fayetteville lowest cost
Edwardsville lowest cost
Units (or rifle sights)
T o ta
l c o st
Denton lowest cost
10,000 15,000 20,000 25,000 30,000 35,000
$225,000
$200,000
$175,000
$150,000
0
8,000 26,666
Denton
Edwa rdsvil
le
Fa yet
tev ille
Figure 8.5
Graph of Total Cost Lines for
Ching-Chang Kuo
SOLVED PROBLEM 8.3 The Metropolis Public Library plans to expand with its first major branch library in the city’s growing north side. The branch will serve six census tracts. Here are the coordinates of each tract and the population within it:
CENSUS TRACT CENTER OF TRACT POPULATION IN TRACT
503—Logan Square (3, 4) 45,000
519—Albany Park (4, 5) 25,000
522—Rogers Park (3, 6) 62,000
538—Kentwood (4, 7) 51,000
540—Roosevelt (2, 3) 32,000
561—Western (5, 2) 29,000
Using the center-of-gravity method, what should be the coordinate location of the branch library?
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SOLUTION
x@coordinate = a
i xiQi
a i
Qi =
3(45,000) + 4(25,000) + 3(62,000) + 4(51,000) + 2(32,000) + 5(29,000) 244,000
= 3.42
y@coordinate = a
i yiQi
a i
Qi =
4(45,000) + 5(25,000) + 6(62,000) + 7(51,000) + 3(32,000) + 2(29,000) 244,000
= 4.87
The new branch library will sit just west of Logan Square and Rogers Park, at the (3.42, 4.87) tract location.
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems 8.1–8.4 relate to Factors That Affect Location Decisions
• 8.1 In Myanmar (formerly Burma), 6 laborers, each mak- ing the equivalent of $3 per day, can produce 40 units per day. In rural China, 10 laborers, each making the equivalent of $2 per day, can produce 45 units. In Billings, Montana, 2 laborers, each making $60 per day, can make 100 units. Based on labor costs only, which location would be most economical to produce the item?
• 8.2 Refer to Problem 8.1. Shipping cost from Myanmar to Denver, Colorado, the final destination, is $1.50 per unit. Shipping cost from China to Denver is $1 per unit, while the ship- ping cost from Billings to Denver is $.25 per unit. Considering both labor and transportation costs, which is the most favorable production location?
• • 8.3 You have been asked to analyze the bids for 200 polished disks used in solar panels. These bids have been sub- mitted by three suppliers: Thailand Polishing, India Shine, and Sacramento Glow. Thailand Polishing has submitted a bid of 2,000 baht. India Shine has submitted a bid of 2,000 rupees. Sacramento Glow has submitted a bid of $200. You check with your local bank and find that +1 = 10 baht and +1 = 8 rupees. Which company should you choose?
• 8.4 Refer to Problem 8.3. If the final destination is New Delhi, India, and there is a 30% import tax, which firm should you choose?
Problems 8.5–8.34 relate to Methods of Evaluating Location Alternatives
• • 8.5 Subway, with more than 25,000 outlets in the U.S., is planning for a new restaurant in Buffalo, New York. Three loca- tions are being considered. The following table gives the factors for each site.
FACTOR WEIGHT MAITLAND BAPTIST CHURCH
NORTHSIDE MALL
Space .30 60 70 80
Costs .25 40 80 30
Traffi c density .20 50 80 60
Neighborhood income .15 50 70 40
Zoning laws .10 80 20 90
a) At which site should Subway open the new restaurant? b) If the weights for Space and Traffic density are reversed, how
would this affect the decision? PX
A n d re
a C
a te
n a ro
/S h u tt
e rs
to ck
• 8.6 Ken Gilbert owns the Knoxville Warriors, a minor league baseball team in Tennessee. He wishes to move the Warriors south, to either Mobile (Alabama) or Jackson (Mississippi). The table below gives the factors that Gilbert thinks are important, their weights, and the scores for Mobile and Jackson.
FACTOR WEIGHT MOBILE JACKSON
Incentive .4 80 60
Player satisfaction .3 20 50
Sports interest .2 40 90
Size of city .1 70 30
a) Which site should he select? b) Jackson just raised its incentive package, and the new score is
75. Why doesn’t this impact your decision in part (a)? PX
• • 8.7 Northeastern Insurance Company is considering opening an office in the U.S. The two cities under consideration are Philadelphia and New York. The factor ratings (higher scores are better) for the two cities are given in the following table. In which city should Northeastern locate?
FACTOR WEIGHT PHILADELPHIA NEW YORK
Customer convenience .25 70 80
Bank accessibility .20 40 90
Computer support .20 85 75
Rental costs .15 90 55
Labor costs .10 80 50
Taxes .10 90 50
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• • 8.8 Marilyn Helm Retailers is attempting to decide on a location for a new retail outlet. At the moment, the firm has three alternatives—stay where it is but enlarge the facility; locate along the main street in nearby Newbury; or locate in a new shopping mall in Hyde Park. The company has selected the four factors listed in the following table as the basis for evaluation and has assigned weights as shown:
FACTOR FACTOR DESCRIPTION WEIGHT
1 Average community income .30
2 Community growth potential .15
3 Availability of public transportation .20
4 Labor availability, attitude, and cost .35
Helm has rated each location for each factor, on a 100-point basis. These ratings are given below:
LOCATION
FACTOR PRESENT LOCATION NEWBURY HYDE PARK
1 40 60 50
2 20 20 80
3 30 60 50
4 80 50 50
a) What should Helm do? b) A new subway station is scheduled to open across the street from
the present location in about a month, so its third factor score should be raised to 40. How does this change your answer? PX
• • 8.9 A location analysis for Cook Controls, a small manu- facturer of parts for high-technology cable systems, has been nar- rowed down to four locations. Cook will need to train assemblers, testers, and robotics maintainers in local training centers. Lori Cook, the president, has asked each potential site to offer training programs, tax breaks, and other industrial incentives. The critical factors, their weights, and the ratings for each location are shown in the following table. High scores represent favorable values.
LOCATION
FACTOR WEIGHT AKRON,
OH BILOXI,
MS CARTHAGE,
TX DENVER,
CO
Labor availability .15 90 80 90 80
Technical school quality .10 95 75 65 85
Operating cost .30 80 85 95 85
Land and construction cost .15 60 80 90 70
Industrial incentives .20 90 75 85 60
Labor cost .10 75 80 85 75
a) Compute the composite (weighted average) rating for each location.
b) Which site would you choose? c) Would you reach the same conclusion if the weights for oper-
ating cost and labor cost were reversed? Recompute as neces- sary and explain. PX
• • • 8.10 Pan American Refineries, headquartered in Houston, must decide among three sites for the construction of a new oil- processing center. The firm has selected the six factors listed
below as a basis for evaluation and has assigned rating weights from 1 to 5 on each factor:
FACTOR FACTOR NAME RATING WEIGHT
1 Proximity to port facilities 5
2 Power-source availability and cost 3
3 Workforce attitude and cost 4
4 Distance from Houston 2
5 Community desirability 2
6 Equipment suppliers in area 3
Subhajit Chakraborty, the CEO, has rated each location for each factor on a 1- to 100-point basis.
FACTOR LOCATION A LOCATION B LOCATION C
1 100 80 80
2 80 70 100
3 30 60 70
4 10 80 60
5 90 60 80
6 50 60 90
a) Which site will be recommended based on total weighted scores?
b) If location B’s score for Proximity to port facilities was reset at 90, how would the result change?
c) What score would location B need on Proximity to port facili- ties to change its ranking? PX
• • 8.11 A company is planning on expanding and building a new plant in one of three Southeast Asian countries. Chris Ellis, the manager charged with making the decision, has determined that five key success factors can be used to evaluate the pro- spective countries. Ellis used a rating system of 1 (least desirable country) to 5 (most desirable) to evaluate each factor.
KEY SUCCESS FACTOR WEIGHT
CANDIDATE COUNTRY RATINGS
TAIWAN THAILAND SINGAPORE
Technology 0.2 4 5 1
Level of education 0.1 4 1 5
Political and legal aspects 0.4 1 3 3
Social and cultural aspects 0.1 4 2 3
Economic factors 0.2 3 3 2
a) Which country should be selected for the new plant? b) Political unrest in Thailand results in a lower score, 2, for
Political and legal aspects. Does your conclusion change? c) What if Thailand’s score drops even further, to a 1, for
Political and legal aspects? PX
• 8.12 Harden College is contemplating opening a European campus where students from the main campus could go to take courses for 1 of the 4 college years. At the moment, it is con- sidering five countries: The Netherlands, Great Britain, Italy, Belgium, and Greece. The college wishes to consider eight factors in its decision. The first two factors are given weights of 0.2, while the rest are assigned weights of 0.1. The following table illustrates its assessment of each factor for each country (5 is best).
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FACTOR FACTOR DESCRIPTION
THE NETHER- LANDS
GREAT BRITAIN ITALY BELGIUM GREECE
1 Stability of government 5 5 3 5 4
2 Degree to which the population can converse in English 4 5 3 4 3
3 Stability of the monetary system 5 4 3 4 3
4 Communications infrastructure 4 5 3 4 3
5 Transportation infrastructure 5 5 3 5 3
6 Availability of historic/cultural sites 3 4 5 3 5
7 Import restrictions 4 4 3 4 4
8 Availability of suitable quarters 4 4 3 4 3
a) In which country should Harden College choose to set up its European campus?
b) How would the decision change if the “degree to which the population can converse in English” was not an issue? PX
• • 8.13 Daniel Tracy, owner of Martin Manufacturing, must expand by building a new factory. The search for a location for this factory has been narrowed to four sites: A, B, C, or D. The following table shows the results thus far obtained by Tracy by using the factor-rating method to analyze the problem. The scale used for each factor scoring is 1 through 5.
FACTOR WEIGHT
SITE SCORES
A B C D
Quality of labor 10 5 4 4 5 Construction cost 8 2 3 4 1 Transportation costs 8 3 4 3 2 Proximity to markets 7 5 3 4 4 Taxes 6 2 3 3 4 Weather 6 2 5 5 4 Energy costs 5 5 4 3 3
a) Which site should Tracy choose? b) If site D’s score for Energy costs increases from a 3 to a 5, do
results change? c) If site A’s Weather score is adjusted to a 4, what is the impact?
What should Tracy do at this point? PX
• • • 8.14 An American consulting firm is planning to expand globally by opening a new office in one of four countries: Germany, Italy, Spain, or Greece. The chief partner entrusted with the decision, L. Wayne Shell, has identified eight key success factors that he views as essential for the success of any consul- tancy. He used a rating system of 1 (least desirable country) to 5 (most desirable) to evaluate each factor.
KEY SUCCESS FACTOR WEIGHT
CANDIDATE COUNTRY RATINGS
GERMANY ITALY SPAIN GREECE
Level of education
Number of consultants .05 5 5 5 2 National literacy rate .05 4 2 1 1
Political aspects Stability of government 0.2 5 5 5 2 Product liability laws 0.2 5 2 3 5 Environmental regulations 0.2 1 4 1 3
Social and cultural aspects
Similarity in language 0.1 4 2 1 1 Acceptability of consultants 0.1 1 4 4 3
Economic factors Incentives 0.1 2 3 1 5
a) Which country should be selected for the new office? b) If Spain’s score were lowered in the Stability of government
factor, to a 4, how would its overall score change? On this fac- tor, at what score for Spain would the rankings change? PX
• • 8.15 A British hospital chain wishes to make its first entry into the U.S. market by building a medical facility in the Midwest, a region with which its director, Doug Moodie, is comfortable because he got his medical degree at Northwestern University. After a preliminary analysis, four cities are chosen for further consideration. They are rated and weighted according to the fac- tors shown below:
FACTOR WEIGHT
CITY
CHICAGO MILWAUKEE MADISON DETROIT
Costs 2.0 8 5 6 7 Need for a
facility 1.5 4 9 8 4 Staff availability 1.0 7 6 4 7 Local incentives 0.5 8 6 5 9
a) Which city should Moodie select? b) Assume a minimum score of 5 is now required for all factors.
Which city should be chosen? PX
• • 8.16 The fixed and variable costs for three potential manu- facturing plant sites for a rattan chair weaver are shown:
SITE FIXED COST PER YEAR VARIABLE COST PER UNIT
1 $ 500 $11 2 1,000 7 3 1,700 4
a) Over what range of production is each location optimal? b) For a production of 200 units, which site is best? PX
• 8.17 Peter Billington Stereo, Inc., supplies car radios to auto manufacturers and is going to open a new plant. The com- pany is undecided between Detroit and Dallas as the site. The
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fixed costs in Dallas are lower due to cheaper land costs, but the variable costs in Dallas are higher because shipping distances would increase. Given the following costs:
COST DALLAS DETROIT
Fixed costs $600,000 $800,000 Variable costs $28/radio $22/radio
a) Perform an analysis of the volume over which each location is preferable.
b) How does your answer change if Dallas’s fixed costs increase by 10%? PX
• • • 8.18 Hyundai Motors is considering three sites—A, B, and C—at which to locate a factory to build its new-model automo- bile, the Hyundai Sport C150. The goal is to locate at a minimum- cost site, where cost is measured by the annual fixed plus variable costs of production. Hyundai Motors has gathered the following data:
SITE ANNUALIZED FIXED COST
VARIABLE COST PER AUTO PRODUCED
A $10,000,000 $2,500 B $20,000,000 $2,000 C $25,000,000 $1,000
The firm knows it will produce between 0 and 60,000 Sport C150s at the new plant each year, but, thus far, that is the extent of its knowledge about production plans. a) For what values of volume, V, of production, if any, is site C a
recommended site? b) What volume indicates site A is optimal? c) Over what range of volume is site B optimal? Why? PX
• • 8.19 Peggy Lane Corp., a producer of machine tools, wants to move to a larger site. Two alternative locations have been identified: Bonham and McKinney. Bonham would have fixed costs of $800,000 per year and variable costs of $14,000 per standard unit produced. McKinney would have annual fixed costs of $920,000 and variable costs of $13,000 per standard unit. The finished items sell for $29,000 each. a) At what volume of output would the two locations have the
same profit? b) For what range of output would Bonham be superior (have
higher profits)? c) For what range would McKinney be superior? d) What is the relevance of break-even points for these
cities? PX
• • 8.20 The following table gives the map coordinates and the shipping loads for a set of cities that we wish to connect through a central hub.
CITY MAP COORDINATE ( X, Y ) SHIPPING LOAD
A (5, 10) 5 B (6, 8) 10 C (4, 9) 15 D (9, 5) 5 E (7, 9) 15 F (3, 2) 10 G (2, 6) 5
a) Near which map coordinates should the hub be located? b) If the shipments from city A triple, how does this change the
coordinates? PX
• • 8.21 A chain of home health care firms in Louisiana needs to locate a central office from which to conduct internal audits and other periodic reviews of its facilities. These facilities are scat- tered throughout the state, as detailed in the following table. Each site, except for Houma, will be visited three times each year by a team of workers, who will drive from the central office to the site. Houma will be visited five times a year. Which coordinates rep- resent a good central location for this office? What other factors might influence the office location decision? Where would you place this office? Explain. PX
CITY
MAP COORDINATES
x y
Covington 9.2 3.5 Donaldsonville 7.3 2.5 Houma 7.8 1.4 Monroe 5.0 8.4 Natchitoches 2.8 6.5 New Iberia 5.5 2.4 Opelousas 5.0 3.6 Ruston 3.8 8.5
• • 8.22 A small rural county has experienced unprecedented growth over the past 6 years, and as a result, the local school dis- trict built the new 500-student North Park Elementary School. The district has three older and smaller elementary schools: Washington, Jefferson, and Lincoln. Now the growth pressure is being felt at the secondary level. The school district would like to build a centrally located middle school to accommodate students and reduce busing costs. The older middle school is adjacent to the high school and will become part of the high school campus. a) What are the coordinates of the central location? b) What other factors should be considered before building a
school? PX
• • 8.23 Todd’s Direct, a major TV sales chain headquar- tered in New Orleans, is about to open its first outlet in Mobile, Alabama, and wants to select a site that will place the new out- let in the center of Mobile’s population base. Todd examines the seven census tracts in Mobile, plots the coordinates of the center of each from a map, and looks up the population base in each to use as a weighting. The information gathered appears in the following table.
1
1
2
3
4
5
6
7
8
9
10
11
12
2 3 4 5
Jefferson (300)
Highway
Lincoln (300)
Washington (200)
North Park (500)
6 7 8 9 10 11 12
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CENSUS TRACT POPULATION IN CENSUS TRACT
X, Y MAP COORDINATES
101 2,000 (25, 45) 102 5,000 (25, 25) 103 10,000 (55, 45) 104 7,000 (50, 20) 105 10,000 (80, 50) 106 20,000 (70, 20) 107 14,000 (90, 25)
a) At what center-of-gravity coordinates should the new store be located?
b) Census tracts 103 and 105 are each projected to grow by 20% in the next year. How will this influence the new store’s coordinates? PX
• • • • 8.24 Eagle Electronics must expand by building a second facility. The search has been narrowed down to locating the new facility in one of four cities: Atlanta (A), Baltimore (B), Chicago (C), or Dallas (D). The factors, scores, and weights follow:
SCORES BY SITE
I FACTOR WEIGHT ( W I ) A B C D
1 Labor quality 20 5 4 4 5 2 Quality of life 16 2 3 4 1 3 Transportation 16 3 4 3 2 4 Proximity to
markets 14 5 3 4 4 5 Proximity to
suppliers 12 2 3 3 4 6 Taxes 12 2 5 5 4 7 Energy supplies 10 5 4 3 3
a) Using the factor-rating method, what is the recommended site for Eagle Electronics’s new facility?
b) For what range of values for the weight (currently w 7 = 10) does the site given as the answer to part (a) remain a recom- mended site?
• • • • 8.25 The EU has made changes in airline regulation that dramatically affect major European carriers such as British International Air (BIA), KLM, Air France, Alitalia, and Swiss International Air. With ambitious expansion plans, BIA has decided it needs a second service hub on the continent, to comple- ment its large Heathrow (London) repair facility. The location selection is critical, and with the potential for 4,000 new skilled blue-collar jobs on the line, virtually every city in western Europe is actively bidding for BIA’s business.
After initial investigations by Holmes Miller, head of the Operations Department, BIA has narrowed the list to 9 cities. Each is then rated on 12 factors, as shown in the table below. a) Help Miller rank the top three cities that BIA should consider
as its new site for servicing aircraft. b) After further investigation, Miller decides that an existing set
of hangar facilities for repairs is not nearly as important as earlier thought. If he lowers the weight of that factor to 30, does the ranking change?
c) After Miller makes the change in part (b), Germany announces it has reconsidered its offer of financial incentives, with an additional 200-million-euro package to entice BIA. Accordingly, BIA has raised Germany’s rating to 10 on that factor. Is there any change in top rankings in part (b)? PX
DATA FOR PROBLEM 8.25 LOCATION
FACTOR IMPORTANCE
WEIGHT
ITALY FRANCE GERMANY
MILAN ROME GENOA PARIS LYON NICE MUNICH BONN BERLIN
Financial incentives 85 8 8 8 7 7 7 7 7 7 Skilled labor pool 80 4 6 5 9 9 7 10 8 9 Existing facility 70 5 3 2 9 6 5 9 9 2 Wage rates 70 9 8 9 4 6 6 4 5 5 Competition for jobs 70 7 3 8 2 8 7 4 8 9 Ease of air traffi c access 65 5 4 6 2 8 8 4 8 9 Real estate cost 40 6 4 7 4 6 6 3 4 5 Communication links 25 6 7 6 9 9 9 10 9 8 Attractiveness to relocating executives 15 4 8 3 9 6 6 2 3 3 Political considerations 10 6 6 6 8 8 8 8 8 8 Expansion possibilities 10 10 2 8 1 5 4 4 5 6 Union strength 10 1 1 1 5 5 5 6 6 6
Additional problems 8.26–8.34 are available in MyOMLab.
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CASE STUDIES Southern Recreational Vehicle Company
In October 2015, the top management of Southern Recreational Vehicle Company of St. Louis, Missouri, announced its plans to relocate its manufacturing and assembly operations to a new plant in Ridgecrest, Mississippi. The firm, a major producer of pickup campers and camper trailers, had experienced 5 consecu- tive years of declining profits as a result of spiraling production costs. The costs of labor and raw materials had increased alarm- ingly, utility costs had gone up sharply, and taxes and transpor- tation expenses had steadily climbed upward. Despite increased sales, the company suffered its first net loss since operations were begun in 1982.
When management initially considered relocation, it closely scrutinized several geographic areas. Of primary importance to the relocation decision were the availability of adequate transpor- tation facilities, state and municipal tax structures, an adequate labor supply, positive community attitudes, reasonable site costs, and financial inducements. Although several communities offered essentially the same incentives, the management of Southern Recreational Vehicle Company was favorably impressed by the efforts of the Mississippi Power and Light Company to attract “clean, labor-intensive” industry and the enthusiasm exhibited by state and local officials, who actively sought to bolster the state’s economy by enticing manufacturing firms to locate within its boundaries.
Two weeks prior to the announcement, management of Southern Recreational Vehicle Company finalized its relocation plans. An existing building in Ridgecrest’s industrial park was selected (the physical facility had previously housed a mobile home manufacturer that had gone bankrupt due to inadequate financing and poor management); initial recruiting was begun through the state employment office; and efforts to lease or sell the St. Louis property were initiated. Among the inducements offered Southern Recreational Vehicle Company to locate in Ridgecrest were:
1. Exemption from county and municipal taxes for 5 years 2. Free water and sewage services 3. Construction of a second loading dock—free of cost—at the
industrial site
4. An agreement to issue $500,000 in industrial bonds for future expansion
5. Public-financed training of workers in a local industrial trade school
In addition to these inducements, other factors weighed heav- ily in the decision to locate in the small Mississippi town. Labor costs would be significantly less than those incurred in St. Louis; organized labor was not expected to be as powerful (Mississippi is a right-to-work state); and utility costs and taxes would be moderate. All in all, the management of Southern Recreational Vehicle Company felt that its decision was sound.
On October 15, the following announcement was attached to each employee’s paycheck:
To: Employees of Southern Recreational Vehicle Company From: Gerald O’Brian, President
The Management of Southern Recreational Vehicle Company regretfully announces its plans to cease all manufacturing opera- tions in St. Louis on December 31. Because of increased operating costs and the unreasonable demands forced upon the company by the union, it has become impossible to operate profitably. I sin- cerely appreciate the fine service that each of you has rendered to the company during the past years. If I can be of assistance in helping you find suitable employment with another firm, please let me know. Thank you again for your cooperation and past service.
Discussion Questions
1. Evaluate the inducements offered Southern Recreational Vehicle Company by community leaders in Ridgecrest, Mississippi.
2. What problems would a company experience in relocating its executives from a heavily populated industrialized area to a small rural town?
3. Evaluate the reasons cited by O’Brian for relocation. Are they justifiable?
4. What legal and ethical responsibilities does a firm have to its employees when a decision to cease operations is made?
Source: Reprinted by permission of Professor Jerry Kinard, Western Carolina University.
Video Case Locating the Next Red Lobster Restaurant From its first Red Lobster in 1968, the chain has grown to 705 loca- tions, with over $2.6 billion in U.S. sales annually. The casual din- ing market may be crowded, with competitors such as Chili’s, Ruby Tuesday, Applebee’s, TGI Friday’s, and Outback, but Red Lobster’s continuing success means the chain thinks there is still plenty of room to grow. Robert Reiner, director of market development, is charged with identifying the sites that will maximize new store sales without cannibalizing sales at the existing Red Lobster locations.
Characteristics for identifying a good site have not changed in 40 years; they still include real estate prices, customer age, competition, ethnicity, income, family size, population density, nearby hotels, and buying behavior, to name just a few. What has changed is the powerful software that allows Reiner to analyze a
new site in 5 minutes, as opposed to the 8 hours he spent just a few years ago.
Red Lobster has partnered with MapInfo Corp., whose geo- graphic information system (GIS) contains a powerful module for analyzing a trade area (see the discussion of GIS in the chapter). With the U.S. geo-coded down to the individual block, MapInfo allows Reiner to create a psychographic profile of existing and potential Red Lobster trade areas. “We can now target areas with greatest sales potential,” says Reiner.
The U.S. is segmented into 72 “clusters” of customer profiles by MapInfo. If, for example, cluster #7, Equestrian Heights (see MapInfo description below), represents 1.7% of a household base within a Red Lobster trade area, but this segment also accounts
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for 2.4% of sales, Reiner computes that this segment is effectively spending 1.39 times more than average (Index = 2.4/1.7) and adjusts his analysis of a new site to reflect this added weight.
CLUSTER PSYTE 2003 SNAP SHOT DESCRIPTION
7 Equestrian Heights
They may not have a stallion in the barn, but they likely pass a corral on the way home. These families with teens live in older, larger homes adjacent to, or between, suburbs but not usually tract housing. Most are married with teenagers, but 40% are empty nesters. They use their graduate and professional school education—56% are dual earners. Over 90% are white, non-Hispanic. Their mean family income is $99,000, and they live within commuting distance of central cities. They have white- collar jobs during the week but require a riding lawn mower to keep the place up on weekends.
When Reiner maps the U.S., a state, or a region for a new site, he wants one that is at least 3 miles from the nearest Red Lobster and won’t negatively impact its sales by more than 8%; MapInfo pinpoints the best spot. The software also recognizes the nearness of non-Red Lobster competition and assigns a probability of suc- cess (as measured by reaching sales potential).
The specific spot selected depends on Red Lobster’s seven real estate brokers, whose list of considerations include proximity to a vibrant retail area, proximity to a freeway, road visibility, nearby hotels, and a corner location at a primary intersection.
“Picking a new Red Lobster location is one of the most criti- cal functions we can do,” says Reiner. “And the software we use
serves as an independent voice in assessing the quality of an exist- ing or proposed location.”
Discussion Questions *
1. Visit the Web site for PSTYE 2003 ( www.gemapping.com /downloads/targetpro_brochure.pdf ). Describe the psychological profiling (PSYTE) clustering system. Select an industry, other than restaurants, and explain how the software can be used for that industry.
2. What are the major differences in site location for a restaurant versus a retail store versus a manufacturing plant?
3. Red Lobster also defines its trade areas based on market size and population density. Here are its seven density classes:
DENSITY CLASS DESCRIPTION
HOUSEHOLDS PER SQ. MILE
1 Super Urban 8,000+ 2 Urban 4,000−7,999 3 Light Urban 2,000−3,999 4 First Tier Suburban 1,000−1,999 5 Second Tier Suburban 600−999 6 Exurban/Small 100−599 7 Rural 0−99
Note: Density classes are based on the households and land area within 3 miles of the geography
(e.g., census tract) using population-weighted centroids.
The majority (92%) of the Red Lobster restaurants fall into three of these classes. Which three classes do you think the chain has the most restaurants in? Why?
Video Case Where to Place the Hard Rock Cafe Some people would say that Oliver Munday, Hard Rock’s vice president for cafe development, has the best job in the world. Travel the world to pick a country for Hard Rock’s next cafe, select a city, and find the ideal site. It’s true that selecting a site involves lots of incognito walking around, visiting nice restau- rants, and drinking in bars. But that is not where Mr. Munday’s work begins, nor where it ends. At the front end, selecting the country and city first involves a great deal of research. At the back end, Munday not only picks the final site and negotiates the deal but then works with architects and planners and stays with the project through the opening and first year’s sales.
Munday is currently looking heavily into global expansion in Europe, Latin America, and Asia. “We’ve got to look at political risk, currency, and social norms—how does our brand fit into the country,” he says. Once the country is selected, Munday focuses on the region and city. His research checklist is extensive, as seen in the accompanying table.
Site location now tends to focus on the tremendous resur- gence of “city centers,” where nightlife tends to concentrate. That’s what Munday selected in Moscow and Bogota, although in both locations he chose to find a local partner and franchise the operation. In these two political environments, “Hard Rock wouldn’t dream of operating by ourselves,” says Munday. The location decision also is at least a 10- to 15-year commitment by Hard Rock, which employs tools such as locational cost–volume
Hard Rock’s Standard Market Report (for offshore sites)
A. Demographics (local, city, region, SMSA), with trend analysis
1. Population of area
2. Economic indicators
B. Visitor market, with trend analysis
1. Tourists/business visitors
2. Hotels
3. Convention center
4. Entertainment
5. Sports
6. Retail
C. Transportation
1. Airport
2. Rail
3. Road
4. Sea/river
D. Restaurants and nightclubs (a selection in key target market
areas)
E. Political risk
F. Real estate market
G. Hard Rock Cafe comparable market analysis
subcategories
include:
(a) age of airport
(b) no. of passengers
(c) airlines
(d) direct flights
(e) hubs
* You may wish to view the video that accompanies this case before answering the questions.
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364 P A R T 2 | D E S I G N I N G O P E R AT I O N S
analysis to help decide whether to purchase land and build, or to remodel an existing facility.
Currently, Munday is considering four European cities for Hard Rock’s next expansion. Although he could not provide the names, for competitive reasons, the following is known:
FACTOR
EUROPEAN CITY UNDER CONSIDERATION
IMPORTANCE OF THIS FACTOR
AT THIS TIMEA B C D
A. Demographics 70 70 60 90 20 B. Visitor market 80 60 90 75 20 C. Transportation 100 50 75 90 20 D. Restaurants/
nightclubs 80 90 65 65 10 E. Low political risk 90 60 50 70 10 F. Real estate
market 65 75 85 70 10 G. Comparable
market analysis 70 60 65 80 10
Discussion Questions *
1. From Munday’s Standard Market Report checklist, select any other four categories, such as population (A1), hotels (B2), or restaurants/nightclubs (D), and provide three sub- categories that should be evaluated. (See item C1 [airport] for a guide.)
2. Which is the highest rated of the four European cities under consideration, using the table?
3. Why does Hard Rock put such serious effort into its location analysis?
4. Under what conditions do you think Hard Rock prefers to franchise a cafe?
• Additional Case Study: Visit MyOMLab for this free case study: Southwestern University (E): The university faces three choices as to where to locate its football stadium.
Endnote
1. Equations (8-1) and (8-2) compute a center of gravity (COG) under “squared Euclidean” distances and may actually result in transportation costs slightly (less than 2%) higher than an optimal COG computed using “Euclidean” (straight-line) dis- tances. The latter, however, is a more complex and involved
procedure mathematically, so the formulas we present are gen- erally used as an attractive substitute. See C. Kuo and R. E. White, “A Note on the Treatment of the Center-of-Gravity Method in Operations Management Textbooks,” Decision Sciences Journal of Innovative Education 2: 219–227.
* You may wish to view the video case before answering the questions.
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Chapter 8 Rapid Review 8
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Main Heading Review Material MyOMLab THE STRATEGIC IMPORTANCE OF LOCATION (pp. 340 – 341 )
Location has a major impact on the overall risk and profit of the company. Trans- portation costs alone can total as much as 25% of the product’s selling price. When all costs are considered, location may alter total operating expenses as much as 50%. Companies make location decisions relatively infrequently, usually because demand has outgrown the current plant’s capacity or because of changes in labor productivity, exchange rates, costs, or local attitudes. Companies may also relocate their manufacturing or service facilities because of shifts in demographics and customer demand. Location options include (1) expanding an existing facility instead of moving, (2) maintaining current sites while adding another facility elsewhere, and (3) closing the existing facility and moving to another location. For industrial location decisions, the location strategy is usually minimizing costs. For retail and professional service organizations, the strategy focuses on maximiz- ing revenue. Warehouse location strategy may be driven by a combination of cost and speed of delivery. The objective of location strategy is to maximize the benefit of location to the firm. When innovation is the focus, overall competitiveness and innovation are affected by (1) the presence of high-quality and specialized inputs such as scientific and technical talent, (2) an environment that encourages investment and intense local rivalry, (3) pressure and insight gained from a sophisticated local market, and (4) local presence of related and supporting industries.
Concept Questions: 1.1–1.4 VIDEO 8.1 Hard Rock’s Location Selection
FACTORS THAT AFFECT LOCATION DECISIONS (pp. 341 – 344 )
Globalization has taken place because of the development of (1) market economics; (2) better international communications; (3) more rapid, reliable travel and ship- ping; (4) ease of capital flow between countries; and (5) large differences in labor costs. Labor cost per unit is sometimes called the labor content of the product:
Labor cost per unit = Labor cost per day ÷ Production (that is, units per day) Sometimes firms can take advantage of a particularly favorable exchange rate by relocating or exporting to (or importing from) a foreign country. j Tangible costs —Readily identifiable costs that can be measured with some
precision. j Intangible costs —A category of location costs that cannot be easily quantified,
such as quality of life and government. Many service organizations find that proximity to market is the primary location factor. Firms locate near their raw materials and suppliers because of (1) perish- ability, (2) transportation costs, or (3) bulk. j Clustering —Location of competing companies near each other, often because
of a critical mass of information, talent, venture capital, or natural resources.
Concept Questions: 2.1–2.4 Problems: 8.1–8.4
METHODS OF EVALUATING LOCATION ALTERNATIVES (pp. 344 – 350 )
j Factor-rating method —A location method that instills objectivity into the pro- cess of identifying hard-to-evaluate costs.
The six steps of the factor-rating method are: 1. Develop a list of relevant factors called key success factors. 2. Assign a weight to each factor to reflect its relative importance in the company’s
objectives. 3. Develop a scale for each factor (for example, 1 to 10 or 1 to 100 points). 4. Have management score each location for each factor, using the scale in step 3. 5. Multiply the score by the weight for each factor and total the score for each
location. 6. Make a recommendation based on the maximum point score, considering the
results of other quantitative approaches as well. j Locational cost–volume analysis —A method used to make an economic compari-
son of location alternatives. The three steps to locational cost–volume analysis are: 1. Determine the fixed and variable cost for each location. 2. Plot the costs for each location, with costs on the vertical axis of the graph and
annual volume on the horizontal axis. 3. Select the location that has the lowest total cost for the expected production
volume.
Concept Questions: 3.1–3.4 Problems: 8.5–8.34 Virtual Office Hours for Solved Problems: 8.1, 8.2 ACTIVE MODEL 8.1
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Main Heading Review Material MyOMLab
SERVICE LOCATION STRATEGY (pp. 350 – 351 )
The eight major determinants of volume and revenue for the service firm are: 1. Purchasing power of the customer-drawing area 2. Service and image compatibility with demographics of the customer-drawing area 3. Competition in the area 4. Quality of the competition 5. Uniqueness of the firm’s and competitors’ locations 6. Physical qualities of facilities and neighboring businesses 7. Operating policies of the firm 8. Quality of management
Concept Questions: 4.1–4.4
GEOGRAPHIC INFORMATION SYSTEMS (pp. 351 – 353 )
j Geographic information system (GIS) —A system that stores and displays information that can be linked to a geographic location.
Some of the geographic databases available in many GISs include (1) census data by block, tract, city, county, congressional district, metropolitan area, state, and zip code; (2) maps of every street, highway, bridge, and tunnel in the U.S.; (3) utilities such as electrical, water, and gas lines; (4) all rivers, mountains, lakes, and forests; and (5) all major airports, colleges, and hospitals.
Concept Questions: 5.1–5.4 VIDEO 8.2 Locating the Next Red Lobster Restaurant
8 Chapter 8 Rapid Review continued
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 8.1 The factors involved in location decisions include a) foreign exchange. b) attitudes. c) labor productivity. d) all of the above. LO 8.2 If Fender Guitar pays $30 per day to a worker in its
Ensenada, Mexico, plant, and the employee completes four instruments per 8-hour day, the labor cost/unit is
a) $30.00. b) $3.75. c) $7.50. d) $4.00. e) $8.00. LO 8.3 Evaluating location alternatives by comparing their composite
(weighted-average) scores involves a) factor-rating analysis. b) cost–volume analysis. c) transportation model analysis. d) linear regression analysis. e) crossover analysis. LO 8.4 On the cost–volume analysis chart where the costs of two or
more location alternatives have been plotted, the quantity at which two cost curves cross is the quantity at which:
a) fixed costs are equal for two alternative locations. b) variable costs are equal for two alternative locations. c) total costs are equal for all alternative locations. d) fixed costs equal variable costs for one location. e) total costs are equal for two alternative locations. LO 8.5 A regional bookstore chain is about to build a distribution
center that is centrally located for its eight retail outlets. It will most likely employ which of the following tools of analysis?
a) Assembly-line balancing b) Load–distance analysis c) Center-of-gravity model d) Linear programming e) All of the above LO 8.6 What is the major difference in focus between location deci-
sions in the service sector and in the manufacturing sector? a) There is no difference in focus. b) The focus in manufacturing is revenue maximization,
while the focus in service is cost minimization. c) The focus in service is revenue maximization, while the
focus in manufacturing is cost minimization. d) The focus in manufacturing is on raw materials, while the
focus in service is on labor.
Answers: LO 8.1. d; LO 8.2. c; LO 8.3. a; LO 8.4. e; LO 8.5. c; LO 8.6. c.
j Center-of-gravity method —A mathematical technique used for finding the best location for a single distribution point that services several stores or areas.
The center-of-gravity method chooses the ideal location that minimizes the weighted distance between itself and the locations it serves, where the distance is weighted by the number of containers shipped, Q i :
x -coordinate of the center of gravity = a i
x i Q i ÷ a i
Q i (8-1)
y -coordinate of the center of gravity = a i
y i Q i ÷ a i
Q i (8-2)
j Transportation model —A technique for solving a class of linear programming problems.
The transportation model determines the best pattern of shipments from several points of supply to several points of demand to minimize total production and transportation costs.
Virtual Office Hours for Solved Problem: 8.3
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C H A P T E R O U T L I N E
Layout Strategies 9
◆
The Strategic Importance of Layout Decisions 370
◆
Types of Layout 370
◆
Office Layout 371
◆
Retail Layout 372
◆
Warehouse and Storage Layouts 375
◆
Fixed-Position Layout 377
◆
Process-Oriented Layout 378
◆
Work Cells 383
◆
Repetitive and Product-Oriented Layout 386
GLOBAL COMPANY PROFILE: McDonald’s
C H
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R
367
10 10 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
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I n its over half-century of existence, McDonald’s has revolutionized the restaurant industry by
inventing the limited-menu fast-food restaurant. It has also made seven major innovations.
The first, the introduction of indoor seating (1950s), was a layout issue, as was the second,
drive-through windows (1970s). The third, adding breakfasts to the menu (1980s), was a product
strategy. The fourth, adding play areas (late 1980s), was again a layout decision.
In the 1990s, McDonald’s completed its fifth innovation, a radically new redesign of the kitch-
ens in its 14,000 North American outlets to facilitate a mass customization process. Dubbed the
“Made by You” kitchen system, sandwiches were assembled to order with the revamped layout.
In 2004, the chain began the rollout of its sixth innovation, a new food ordering layout: the
self-service kiosk . Self-service kiosks have been infiltrating the service sector since the introduction
of ATMs in 1985 (there are over 1.5 million ATMs in banking). Alaska Airlines was the first airline to
provide self-service airport check-in, in 1996. Most passengers of the major airlines now check
themselves in for flights. Kiosks take up less space than an employee and reduce waiting line time.
Now, McDonald’s is working on its seventh innovation, and not surprisingly, it also deals with
restaurant layout. The company, on an unprecedented scale, is redesigning all 30,000 eateries
around the globe to take on a 21st-century look . The dining area will be separated into three
sections with distinct personalities: (1) the “linger” zone focuses on young adults and offers
McDonald’s Looks for Competitive Advantage Through Layout
GLOBAL COMPANY PROFILE McDonald’s
C H A P T E R 9
368
McDonald’s finds that kiosks reduce both space requirements and waiting; order taking is faster. An added benefit is that customers like them. Also,
kiosks are reliable—they don’t call in sick. And, most important, sales are up 10%–15% (an average of $1) when a customer orders from a kiosk,
which consistently recommends the larger size and other extras.
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369
comfortable furniture and Wi-Fi connections; (2) the
“grab and go” zone features tall counters, bar
stools, and flat-screen TVs; and (3) the “flexible”
zone has colorful family booths, flexible seating, and
kid-oriented music. The cost per outlet: a whopping
$300,000–$400,000 renovation fee. As McDonald’s has discovered, facility layout is
indeed a source of competitive advantage.
The redesigned kitchen of a McDonald’s
in Manhattan. The more efficient layout
requires less labor, reduces waste, and
provides faster service. A graphic of this
“assembly line” is shown in Figure 9.11 . N a n cy
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T im
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Flexible Zone
This area is geared for family and larger groups, with movable tables and chairs.
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Grab & Go Zone
This section has tall counters with bar stools for customers who
eat alone. Flat-screen TVs keep them company.
Linger Zone
Cozy booths, plus Wi-Fi connections, make these areas attractive to those who want to hang
out and socialize.
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370
The Strategic Importance of Layout Decisions Layout is one of the key decisions that determines the long-run efficiency of operations. Layout has strategic implications because it establishes an organization’s competitive priori- ties in regard to capacity, processes, flexibility, and cost, as well as quality of work life, cus- tomer contact, and image. An effective layout can help an organization achieve a strategy that supports differentiation, low cost, or response. Benetton, for example, supports a differentia- tion strategy by heavy investment in warehouse layouts that contribute to fast, accurate sorting and shipping to its 5,000 outlets. Walmart store layouts support a strategy of low cost , as do its warehouse layouts. Hallmark’s office layouts, where many professionals operate with open communication in work cells, support rapid development of greeting cards. The objective of layout strategy is to develop an effective and efficient layout that will meet the firm’s competitive requirements . These firms have done so.
In all cases, layout design must consider how to achieve the following: ◆ Higher utilization of space, equipment, and people ◆ Improved flow of information, materials, and people ◆ Improved employee morale and safer working conditions ◆ Improved customer/client interaction ◆ Flexibility (whatever the layout is now, it will need to change) In our increasingly short-life-cycle, mass-customized world, layout designs need to be viewed as dynamic.This means considering small, movable, and flexible equipment. Store displays need to be movable, office desks and partitions modular, and warehouse racks prefabricated. To make quick and easy changes in product models and in production rates, operations man- agers must design flexibility into layouts. To obtain flexibility in layout, managers cross-train their workers, maintain equipment, keep investments low, place workstations close together, and use small, movable equipment. In some cases, equipment on wheels is appropriate, in anticipation of the next change in product, process, or volume.
Types of Layout Layout decisions include the best placement of machines (in production settings), offices and desks (in office settings), or service centers (in settings such as hospitals or department stores). An effective layout facilitates the flow of materials, people, and information within and between areas. To achieve these objectives, a variety of approaches has been developed. We will discuss seven of them in this chapter:
1. Office layout: Positions workers, their equipment, and spaces/offices to provide for move- ment of information.
2. Retail layout: Allocates display space and responds to customer behavior. 3. Warehouse layout: Addresses trade-offs between space and material handling. 4. Fixed-position layout: Addresses the layout requirements of large, bulky projects such as
ships and buildings.
L E A R N I N G OBJEC TI V ES
LO 9.1 Discuss important issues in offi ce layout 372
LO 9.2 Defi ne the objectives of retail layout 374
LO 9.3 Discuss modern warehouse management and terms such as ASRS, cross-docking, and random stocking 375
LO 9.4 Identify when fi xed-position layouts are appropriate 378
LO 9.5 Explain how to achieve a good process-oriented facility layout 379
LO 9.6 Defi ne work cell and the requirements of a work cell 383
LO 9.7 Defi ne product-oriented layout 386
LO 9.8 Explain how to balance production fl ow in a repetitive or product-oriented facility 387
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C H A P T E R 9 | L AYO U T S T R AT E G I E S 371
5. Process-oriented layout: Deals with low-volume, high-variety production (also called “job shop,” or intermittent production).
6. Work-cell layout: Arranges machinery and equipment to focus on production of a single product or group of related products.
7. Product-oriented layout: Seeks the best personnel and machine utilization in repetitive or continuous production.
Examples for each of these classes of layouts are noted in Table 9.1 . Because only a few of these seven classes can be modeled mathematically, layout and design
of physical facilities are still something of an art. However, we do know that a good layout requires determining the following:
◆ Material handling equipment: Managers must decide about equipment to be used, including conveyors, cranes, automated storage and retrieval systems, and automatic carts to deliver and store material.
◆ Capacity and space requirements: Only when personnel, machines, and equipment require- ments are known can managers proceed with layout and provide space for each compo- nent. In the case of office work, operations managers must make judgments about the space requirements for each employee. They must also consider allowances for require- ments that address safety, noise, dust, fumes, temperature, and space around equipment and machines.
◆ Environment and aesthetics: Layout concerns often require decisions about windows, planters, and height of partitions to facilitate air flow, reduce noise, and provide privacy.
◆ Flows of information: Communication is important to any organization and must be facili- tated by the layout. This issue may require decisions about proximity, as well as decisions about open spaces versus half-height dividers versus private offices.
◆ Cost of moving between various work areas: There may be unique considerations related to moving materials or to the importance of having certain areas next to each other. For example, moving molten steel is more difficult than moving cold steel.
Office Layout Office layouts require the grouping of workers, their equipment, and spaces to provide for comfort, safety, and movement of information. The main distinction of office layouts is the importance placed on the flow of information. Office layouts are in constant flux as the tech- nological changes sweeping society alter the way offices function.
Office layout
The grouping of workers, their
equipment, and spaces/offices to
provide for comfort, safety, and
movement of information.
TABLE 9.1 Layout Strategies
OBJECTIVES EXAMPLES
Offi ce Locate workers requiring frequent contact close to one another
Allstate Insurance Microsoft Corp.
Retail Expose customer to high-margin items Kroger’s Supermarket Walgreens Bloomingdale’s
Warehouse (storage) Balance low-cost storage with low cost material handling
Federal-Mogul’s warehouse The Gap’s distribution center
Project (fi xed position) Move material to the limited storage areas around the site
Ingall Ship Building Corp. Trump Plaza Pittsburgh Airport
Job shop (process oriented)
Manage varied material fl ow for each product
Arnold Palmer Hospital Hard Rock Cafe Olive Garden
Work cell (product families)
Identify a product family, build teams, cross-train team members
Hallmark Cards Wheeled Coach Ambulances
Repetitive/continuous (product oriented)
Equalize the task time at each workstation Sony’s TV assembly line Toyota Scion
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372 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Even though the movement of information is increasingly electronic, analysis of office layouts still requires a task-based approach. Managers therefore examine both electronic and conventional communication patterns, separation needs, and other conditions affecting employee effectiveness. A useful tool for such an analysis is the relationship chart (also called a Muther Grid) shown in Figure 9.1 . This chart, prepared for a software firm, indicates that operations must be near accounting and marketing, but it does not need to be near the graphic arts staff.
On the other hand, some layout considerations are universal (many of which apply to fac- tories as well as to offices). They have to do with working conditions, teamwork, authority, and status. Should offices be private or open cubicles, have low file cabinets to foster informal communication or high cabinets to reduce noise and contribute to privacy?
Workspace can inspire informal and productive encounters if it balances three physical and social aspects 1 :
◆ Proximity : Spaces should naturally bring people together. ◆ Privacy : People must be able to control access to their conversations. ◆ Permission : The culture should signal that nonwork interactions are encouraged.
As a final comment on office layout, we note two major trends. First, technology, such as smart phones, scanners, the Internet, laptop computers, and tablets, allows increasing lay- out flexibility by moving information electronically and allowing employees to work offsite. Second, modern firms create dynamic needs for space and services.
Here are two examples:
◆ When Deloitte & Touche found that 30% to 40% of desks were empty at any given time, the firm developed its “hoteling programs.” Consultants lost their permanent offices; anyone who plans to be in the building (rather than out with clients) books an office through a “concierge,” who hangs that consultant’s name on the door for the day and stocks the space with requested supplies.
◆ Cisco Systems cut rent and workplace service costs by 37% and saw productivity benefits of $2.4 billion per year by reducing square footage, reconfiguring space, creating movable, everything-on-wheels offices, and designing “get away from it all” innovation areas.
Retail Layout Retail layouts are based on the idea that sales and profitability vary directly with customer exposure to products. Thus, most retail operations managers try to expose customers to as many products as possible. Studies do show that the greater the rate of exposure, the greater the sales and the higher the return on investment. The operations manager can change
LO 9.1 Discuss important issues in office
layout
Retail layout
An approach that addresses flow,
allocates space, and responds to
customer behavior.
CLOSENESSCode
A
E
I
O
U
X
Absolutely necessary
Especially important
Important
Ordinary OK
Unimportant
Not desirable
1 Accounting
2 Marketing
3 Operations
4 Graphic Arts
5 Information technology
6 Social media relations
7 Lounge area
1 2
3 4
5 6
7
U
A A
A
E E
I I
I
I I
I
I
O
O
O
O U
A
U
O
Figure 9.1
Office Relationship Chart
The Muther Grid for a software
firm.
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C H A P T E R 9 | L AYO U T S T R AT E G I E S 373
Managers and architects have pondered how to design an office to encourage
productivity for more than 100 years. In the early 20th century, large offices re-
sembled factories (see the photo of the Jack Lemmon film The Apartment, where
clerical workers sat in long rows, often performing repetitive tasks).
Starting in the 1960s, layouts changed to foster teamwork where managers and
support staff sat together, and groupings were geared toward specific tasks.
With computers, more individual work was possible and the “Cube Farm” era be-
came ubiquitous through the ’80s and ’90s. An office full of high-walled cubicles
offered both an open environment and personal office space.
By the turn of the century, looking for innovation and creativity to recruit and inspire
college grads, technology firms created the “fun” office. Bright, casual, open office
spaces, with amenities such as beanbag chairs, foosball tables, and coffee bars
became the fad.
The buzzwords today are serendipity and collaboration, as companies design
office space to engineer encounters between employees. Steve Jobs designed
his Pixar headquarters with the cafeteria and bathrooms in a central atrium away
from work areas to encourage intermingling and collaboration. Skype achieves
similar goals with open lounges.
Sources: Wall Street Journal (April 28, 2014); USA Today (Feb. 28, 2013); and Harvard Business Review (Oct. 2014).
Here are five versions of the office layout. E ve
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374 P A R T 2 | D E S I G N I N G O P E R AT I O N S
exposure with store arrangement and the allocation of space to various products within that arrangement.
Five ideas are helpful for determining the overall arrangement of many stores:
1. Locate the high-draw items around the periphery of the store. Thus, we tend to find dairy products on one side of a supermarket and bread and bakery products on another. An example of this tactic is shown in Figure 9.2 .
2. Use prominent locations for high-impulse and high-margin items. Best Buy puts fast- growing, high-margin digital goods—such as cameras and printers—in the front and center of its stores.
3. Distribute what are known in the trade as “power items”—items that may dominate a purchasing trip—to both sides of an aisle, and disperse them to increase the viewing of other items.
4. Use end-aisle locations because they have a very high exposure rate. 5. Convey the mission of the store by carefully selecting the position of the lead-off depart-
ment. For instance, if prepared foods are part of a supermarket’s mission, position the bakery and deli up front to appeal to convenience-oriented customers. Walmart’s push to increase sales of clothes means those departments are in broad view upon entering a store .
Once the overall layout of a retail store has been decided, products need to be arranged for sale. Many considerations go into this arrangement. However, the main objective of retail lay- out is to maximize profitability per square foot of floor space (or, in some stores, on linear foot of shelf space). Big-ticket, or expensive, items may yield greater dollar sales, but the profit per square foot may be lower. Computer programs are available to assist managers in evaluating the profitability of various merchandising plans for hundreds of categories: this technique is known as category management.
An additional, and somewhat controversial, issue in retail layout is called slotting. Slotting fees are fees manufacturers pay to get their goods on the shelf in a retail store or supermarket chain. The result of massive new-product introductions, retailers can now demand up to $25,000 to place an item in their chain. During the last decade, marketplace economics, consolidations, and technology have provided retailers with this leverage. The competition for shelf space is advanced by POS systems and scanner technology, which improve supply-chain management and inventory control. Many small firms question the legality and ethics of slotting fees, claiming the fees stifle new products, limit their ability to expand, and cost consumers money. Walmart is one of the few major retailers that does not demand slotting fees, removing a barrier to entry. (See the Ethical Dilemma at the end of this chapter.)
LO 9.2 Define the objectives of retail layout
Slotting fees
Fees manufacturers pay to get
shelf space for their products.
WINE
BEER
DELI
FAST
FOOD
VIDEO
BAKERY
W A
L L O
F V
A L U
E S
PHOTO
LAB
DAIRY
PRODUCE
MEAT/FISH
CHEESE
SEA
FOOD ETHNIC
FOOD
CHECKSTANDS
Figure 9.2
Store Layout with Dairy and
Bakery, High-Draw Items, in
Different Areas of the Store
STUDENT TIP The goal in a retail layout is to
maximize profit per square foot of
store space.
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C H A P T E R 9 | L AYO U T S T R AT E G I E S 375
Servicescapes Although a major goal of retail layout is to maximize profit through product exposure, there are other aspects of the service that managers consider. The term servicescape describes the physical surroundings in which the service is delivered and how the surroundings have a humanistic effect on customers and employees. To provide a good service layout, a firm con- siders three elements:
1. Ambient conditions , which are background characteristics such as lighting, sound, smell, and temperature. All these affect workers and customers and can affect how much is spent and how long a person stays in the building.
2. Spatial layout and functionality , which involve customer circulation path planning, aisle characteristics (such as width, direction, angle, and shelf spacing), and product grouping.
3. Signs, symbols, and artifacts , which are characteristics of building design that carry social significance (such as carpeted areas of a department store that encourage shoppers to slow down and browse).
Examples of each of these three elements of servicescape are:
◆ Ambient conditions: Fine-dining restaurants with linen tablecloths and candlelit atmo- sphere; Mrs. Field’s Cookie bakery smells permeating the shopping mall; leather chairs at Starbucks.
◆ Layout/functionality: Kroger’s long aisles and high shelves; Best Buy’s wide center aisle. ◆ Signs, symbols, and artifacts: Walmart’s greeter at the door; Hard Rock Cafe’s wall of
guitars; Disneyland’s entrance looking like hometown heaven.
Warehouse and Storage Layouts The objective of warehouse layout is to find the optimum trade-off between handling cost and costs associated with warehouse space . Consequently, management’s task is to maximize the utiliza- tion of the total “cube” of the warehouse—that is, utilize its full volume while maintaining low material handling costs. We define material handling costs as all the costs related to the trans- action. This consists of incoming transport, storage, and outgoing transport of the materials to be warehoused. These costs include equipment, people, material, supervision, insurance, and depreciation. Effective warehouse layouts do, of course, also minimize the damage and spoilage of material within the warehouse.
Servicescape
The physical surroundings in
which a service takes place, and
how they affect customers and
employees.
LO 9.3 Discuss modern warehouse management
and terms such as ASRS,
cross-docking, and
random stocking
Warehouse layout
A design that attempts to minimize
total cost by addressing trade-offs
between space and material
handling.
A critical element contributing to the bottom line at Hard Rock
Cafe is the layout of each cafe’s retail shop space. The retail
space, from 600 to 1,300 square feet in size, is laid out in
conjunction with the restaurant area to create the maximum
traffic flow before and after eating. The payoffs for cafes like this
one in London are huge. Almost half of a cafe’s annual sales are
generated from these small shops, which have very high retail
sales per square foot. im a g e B
R O
K E R
/A la
m y
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376 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Management minimizes the sum of the resources spent on finding and moving material plus the deterioration and damage to the material itself. The variety of items stored and the number of items “picked” has direct bearing on the optimum layout. A warehouse storing a few unique items lends itself to higher density than a warehouse storing a variety of items. Modern ware- house management is, in many instances, an automated procedure using automated storage and retrieval systems (ASRSs).
The Stop & Shop grocery chain, with 350 supermarkets in New England, has recently completed the largest ASRS in the world. The 1.3-million-square-foot distribution center in Freetown, Massachusetts, employs 77 rotating-fork automated storage and retrieval machines. These 77 ASRS machines each access 11,500 pick slots on 90 aisles—a total of 64,000 pallets of food. The OM in Action box , “Amazon Lets Loose the Robots,” shows another way that technology can help minimize warehouse costs.
An important component of warehouse layout is the relationship between the receiving/unloading area and the shipping/loading area. Facility design depends on the type of supplies unloaded, what they are unloaded from (trucks, rail cars,
barges, and so on), and where they are unloaded. In some companies, the receiv- ing and shipping facilities, or docks , as they are called, are even in the same area;
sometimes they are receiving docks in the morning and shipping docks in the afternoon.
Cross-Docking Cross-docking means to avoid placing materials or supplies in storage by processing
them as they are received. In a manufacturing facility, product is received directly by the assembly line. In a distribution center, labeled and presorted loads arrive at
the shipping dock for immediate rerouting, thereby avoiding formal receiving, stock- ing/storing, and order-selection activities. Because these activities add no value to the product, their elimination is 100% cost savings. Walmart, an early advocate of cross-
docking, uses the technique as a major component of its continuing low-cost strategy. With cross-docking, Walmart reduces distribution costs and speeds restocking of stores, thereby improving customer service. Although cross-docking reduces product handling, inventory, and facility costs, it requires both (1) tight scheduling and (2) accurate inbound product identification.
STUDENT TIP In warehouse layout, we want
to maximize use of the whole
building—from floor to ceiling.
Cross-docking
Avoiding the placement of
materials or supplies in storage
by processing them as they are
received for shipment.
Amazon Lets Loose the Robots
Amazon’s robot army is falling into place.The Seattle online retailer has outfitted
several U.S. warehouses with over 10,000 short, orange, wheeled Kiva robots
that move stocked shelves to workers, instead of having employees seek items
amid long aisles of merchandise. This is similar to the introduction of the mov-
ing assembly line with cars moving down the line, rather than the employee
moving from workstation to workstation.
At a 1.2-million-square-foot warehouse in Tracy, California, Amazon has
replaced 4 floors of fixed shelving with the robots. Now, “pickers” at the
facility stand in one place, and robots bring 4-foot-by-6-foot shelving units
to them, sparing them what amounted to as much as 20 miles a day of
walking through the warehouse. Employees at robot-equipped warehouses
are now expected to pick and scan at least 300 items an hour, compared
with 100 under the old system.
At the heart of the robot rollout is Amazon’s relentless drive to compete
with the immediacy of shopping at brick-and-mortar retailers by improving the
efficiency of its logistics. If Amazon can shrink the time it takes to sort and
pack goods at its 80 U.S. warehouses, it can guarantee same-day or overnight
delivery for more products to more customers.
OM in Action
The robots save Amazon $400–$900 million a year in fulfillment costs by
reducing the number of times a product is “touched.” The Kiva robots pare
20% to 40% from the average $3.50-to-$3.75 cost of sorting, picking, and
boxing an order.
Sources: The Wall Street Journal (Nov. 20, 2014) and (Dec. 9, 2013).
INBOUND
OUTBOUND
No delay
No storage
System in place for information
exchange and product movement
N o a h B
e rg
e r/
R e u te
rs
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C H A P T E R 9 | L AYO U T S T R AT E G I E S 377
Random Stocking Automatic identification systems (AISs), usually in the form of bar codes, allow accurate and rapid item identification. When automatic identification systems are combined with effec- tive management information systems, operations managers know the quantity and location of every unit. This information can be used with human operators or with automatic stor- age and retrieval systems to load units anywhere in the warehouse— randomly. Accurate inventory quantities and locations mean the potential utilization of the whole facility because space does not need to be reserved for certain stock-keeping units (SKUs) or part fami- lies. Computerized random stocking systems often include the following tasks:
1. Maintaining a list of “open” locations 2. Maintaining accurate records of existing inventory and its
locations 3. Sequencing items to minimize the travel time required to “pick”
orders 4. Combining orders to reduce picking time 5. Assigning certain items or classes of items, such as high-usage
items, to particular warehouse areas so that the total distance trave- led within the warehouse is minimized
Random stocking systems can increase facility utilization and decrease l abor cost, but they require accurate records.
Customizing Although we expect warehouses to store as little product as pos- sible and hold it for as short a time as possible, we are now asking warehouses to customize products. Warehouses can be places where value is added through customizing . Warehouse customization is a particularly useful way to generate competitive advantage in markets where products have multiple configurations. For instance, a ware- house can be a place where computer components are put together, software loaded, and repairs made. Warehouses may also provide customized labeling and packaging for retailers so items arrive ready for display.
Increasingly, this type of work goes on adjacent to major airports, in facilities such as the FedEx terminal in Memphis. Adding value at warehouses adjacent to major airports also facilitates overnight delivery. For example, if your computer has failed, the replace- ment may be sent to you from such a warehouse for delivery the next morning. When your old machine arrives back at the warehouse, it is repaired and sent to someone else. These value-added activities at “ quasi-warehouses” contribute to strategies of differentiation, low cost, and rapid response.
Fixed-Position Layout In a fixed-position layout , the project remains in one place, and workers and equipment come to that one work area. Examples of this type of project are a ship, a highway, a bridge, a house, and an operating table in a hospital operating room.
The techniques for addressing the fixed-position layout are complicated by three factors. First, there is limited space at virtually all sites. Second, at different stages of a project, different materials are needed; therefore, different items become critical as the project develops. Third, the volume of materials needed is dynamic. For example, the rate of use of steel panels for the hull of a ship changes as the project progresses.
Random stocking
Used in warehousing to locate
stock wherever there is an open
location.
Customizing
Using warehousing to add value
to a product through component
modification, repair, labeling, and
packaging.
Fixed-position layout
A system that addresses the
layout requirements of stationary
projects.
A n d re
w H
e th
e ri n g to
n /R
e d u x
At Ikea’s distribution center in Almhult, Sweden, pallets are
stacked and retrieved through a fully automated process.
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378 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Because problems with fixed-position layouts are so difficult to solve well onsite, an alter- native strategy is to complete as much of the project as possible offsite. This approach is used in the shipbuilding industry when standard units—say, pipe-holding brackets—are assembled on a nearby assembly line (a product-oriented facility). In an attempt to add efficiency to ship- building, Ingall Ship Building Corporation has moved toward product-oriented production when sections of a ship (modules) are similar or when it has a contract to build the same sec- tion of several similar ships. Also, as the first photo on this page shows, many home builders are moving from a fixed-position layout strategy to one that is more product oriented. About one-third of all new homes in the U.S. are built this way. In addition, many houses that are built onsite (fixed position) have the majority of components such as doors, windows, fixtures, trusses, stairs, and wallboard built as modules in more efficient offsite processes.
Process-Oriented Layout A process-oriented layout can simultaneously handle a wide variety of products or services. This is the traditional way to support a product differentiation strategy. It is most efficient when making products with different requirements or when handling customers, patients, or clients with different needs. A process-oriented layout is typically the low-volume, high-variety strat- egy discussed in Chapter 7 . In this job-shop environment, each product or each small group of products undergoes a different sequence of operations. A product or small order is produced
LO 9.4 Identify when fixed-position layouts are
appropriate
Process-oriented layout
A layout that deals with low-volume,
high-variety production in which
like machines and equipment are
grouped together.
Here are three versions of the fixed-position layout. A house built via traditional fixed-position layout would be constructed
onsite, with equipment, materials, and workers brought to the site.
Then a “meeting of the trades” would assign space for various time
periods. However, the home pictured here can be built at a much
lower cost. The house is built in two movable modules in a factory.
Scaffolding and hoists make the job easier, quicker, and cheaper,
and the indoor work environment aids labor productivity.
C ra
ig R
u tt
le /A
P I m
a g e s
A service example of a fixed-position layout is an operating
room; the patient remains stationary on the table, and medical
personnel and equipment are brought to the site.
In shipbuilding, there is limited space next to the fixed-position layout.
Shipyards call these loading areas platens, and they are assigned for various
time periods to each contractor. Ti m
A lt e vo
g t/
R e u te
rs /C
o rb
is
D ic
k B
lu m
e /T
h e I m
a g e W
o rk
s
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C H A P T E R 9 | L AYO U T S T R AT E G I E S 379
by moving it from one department to another in the sequence required for that product. A good example of the process-oriented layout is a hospital or clinic. Figure 9.3 illustrates the process for two patients, A and B, at an emergency clinic in Chicago. An inflow of patients, each with his or her own needs, requires routing through admissions, laboratories, operating rooms, radiology, pharmacies, nursing beds, and so on. Equipment, skills, and supervision are organized around these processes.
A big advantage of process-oriented layout is its flexibility in equipment and labor assign- ments. The breakdown of one machine, for example, need not halt an entire process; work can be transferred to other machines in the department. Process-oriented layout is also especially good for handling the manufacture of parts in small batches, or job lots , and for the production of a wide variety of parts in different sizes or forms.
The disadvantages of process-oriented layout come from the general-purpose use of the equipment. Orders take more time to move through the system because of difficult schedul- ing, changing setups, and unique material handling. In addition, general-purpose equipment requires high labor skills, and work-in-process inventories are higher because of imbalances in the production process. High labor-skill needs also increase the required level of training and experience, and high work-in-process levels increase capital investment.
When designing a process layout, the most common tactic is to arrange departments or work centers so as to minimize the costs of material handling. In other words, departments with large flows of parts or people between them should be placed next to one another. Mate- rial handling costs in this approach depend on (1) the number of loads (or people) to be moved between two departments during some period of time and (2) the distance-related costs of moving loads (or people) between departments. Cost is assumed to be a function of distance between departments. The objective can be expressed as follows:
Minimize cost = a n
i = 1 a
n
j = 1 XijCij (9-1)
where n = total number of work centers or departments i, j = individual departments X ij = number of loads moved from department i to department j C ij = cost to move a load between department i and department j
Process-oriented facilities (and fixed-position layouts as well) try to minimize loads, or trips, multiplied by distance-related costs. The term C ij combines distance and other costs into one factor. We thereby assume not only that the difficulty of movement is equal but also that the pickup and setdown costs are constant. Although they are not always constant, for simplic- ity’s sake we summarize these data (that is, distance, difficulty, and pickup and setdown costs) in this one variable, cost. The best way to understand the steps involved in designing a process layout is to look at an example.
Job lots
Groups or batches of parts pro-
cessed together.
LO 9.5 Explain how to achieve a good process-
oriented facility layout
Surgery
Radiology
ER triage room Patient A–broken leg
Patient B–erratic heart pacemaker
Emergency room admissions
Laboratories
ER beds Pharmacy Billing/exit
STUDENT TIP Patient A (broken leg) proceeds (blue
arrow) to ER triage, to radiology,
to surgery, to a bed, to pharmacy,
to billing. Patient B (pacemaker
problem) moves (red arrow) to ER
triage, to surgery, to pharmacy, to
lab, to a bed, to billing.
Figure 9.3
An Emergency Room Process
Layout Showing the Routing of
Two Patients
VIDEO 9.1 Laying Out Arnold Palmer Hospital’s
New Facility
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380 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Walters Company management wants to arrange the six departments of its factory in a way that will minimize interdepartmental material-handling costs. They make an initial assumption (to simplify the problem) that each department is 20 × 20 feet and that the building is 60 feet long and 40 feet wide.
APPROACH AND SOLUTION c The process layout procedure that they follow involves six steps: Step 1: Construct a “from–to matrix” showing the flow of parts or materials from department to
department (see Figure 9.4 ).
Example 1 DESIGNING A PROCESS LAYOUT
Assembly (1)
Painting (2)
Machine Shop (3)
Receiving (4)
Shipping (5)
Testing (6)
50 100 0 0 20
30 50 10 0
0
0
20 0 100
Number of loads per week
Department
50
Assembly (1)
Painting (2)
Machine Shop (3)
Receiving (4)
Shipping (5)
Testing (6)
STUDENT TIP The high flows between 1
and 3 and between 3 and 6
are immediately apparent.
Departments 1, 3, and 6,
therefore, should be close
together.
Step 3: Develop an initial schematic diagram showing the sequence of departments through which parts must move. Try to place departments with a heavy flow of materials or parts next to one another. (See Figure 9.6 .)
Figure 9.4
Interdepartmental Flow
of Parts
Step 2: Determine the space requirements for each department. ( Figure 9.5 shows available plant space.)
Area A
Assembly Department
(1)
Receiving Department
(4)
Area B
Painting Department
(2)
Shipping Department
(5)
Area C
Machine Shop Department
(3)
Testing Department
(6)
Area D Area E Area F
60'
40'
Figure 9.5
Building Dimensions and One
Possible Department Layout
STUDENT TIP Think of this as a starting, initial,
layout. Our goal is to improve it,
if possible.
STUDENT TIP This shows that 100 loads also
move weekly between Assembly
and the Machine Shop. We will
probably want to move these
two departments closer to one
another to minimize the flow of
parts through the factory.
Receiving (4)
100
50 30
50
10
100
20
50
20
Assembly (1)
Painting (2)
Machine Shop (3)
Shipping (5)
Testing (6)
Figure 9.6
Interdepartmental Flow Graph
Showing Number of Weekly
Loads
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Step 4: Determine the cost of this layout by using the material-handling cost equation:
Cost = a n
i = 1 a
n
j = 1 XijCij
For this problem, Walters Company assumes that a forklift carries all interdepartmental loads. The cost of moving one load between adjacent departments is estimated to be $1. Moving a load between nonadjacent departments costs $2. Looking at Figures 9.4 and 9.5 , we thus see that the handling cost between departments 1 and 2 is $50 ($1 × 50 loads), $200 between departments 1 and 3 ($2 × 100 loads), $40 between departments 1 and 6 ($2 × 20 loads), and so on. Work areas that are diagonal to one another, such as 2 and 4, are treated as adjacent. The total cost for the layout shown in Figure 9.6 is:
Cost = $50 + $200 + $40 + $30 + $50 (1 and 2) (1 and 3) (1 and 6) (2 and 3) (2 and 4)
+ $10 + $40 + $100 + $50 (2 and 5) (3 and 4) (3 and 6) (4 and 5)
= $570
Step 5: By trial and error (or by a more sophisticated computer program approach that we discuss shortly), try to improve the layout pictured in Figure 9.5 to establish a better arrangement of departments.
By looking at both the flow graph ( Figure 9.6 ) and the cost calculations, we see that placing departments 1 and 3 closer together appears desirable. They currently are nonadjacent, and the high volume of flow between them causes a large handling expense. Looking the situation over, we need to check the effect of shifting departments and possibly raising, instead of lowering, overall costs.
One possibility is to switch departments 1 and 2. This exchange produces a second departmental flow graph ( Figure 9.7 ), which shows a reduction in cost to $480, a savings in material handling of $90:
Cost = $50 + $100 + $20 + $60 + $50 (1 and 2) (1 and 3) (1 and 6) (2 and 3) (2 and 4)
+ $10 + $40 + $100 + $50 (2 and 5) (3 and 4) (3 and 6) (4 and 5)
= $480
4 5 6
30
50 100
50
100
10
50 20 20
Receiving (4)
Assembly (1)
Painting (2)
Machine Shop (3)
Shipping (5)
Testing (6)
Figure 9.7
Second Interdepartmental
Flow Graph
STUDENT TIP Notice how Assembly and
Machine Shop are now adjacent.
Testing stayed close to the
Machine Shop also.
Suppose Walters Company is satisfied with the cost figure of $480 and the flow graph of Figure 9.7 . The problem may not be solved yet. Often, a sixth step is necessary:
Step 6: Prepare a detailed plan arranging the departments to fit the shape of the building and its nonmovable areas (such as the loading dock, washrooms, and stairways). Often this step involves ensuring that the final plan can be accommodated by the electrical system, floor loads, aesthetics, and other factors.
In the case of Walters Company, space requirements are a simple matter (see Figure 9.8 ).
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STUDENT TIP Here we see the departments
moved to areas A–F to try to
improve the flow.
Area A
Painting Department
(2)
Receiving Department
(4)
Area B
Assembly Department
(1)
Shipping Department
(5)
Area C
Machine Shop Department
(3)
Testing Department
(6)
Area D Area E Area F
INSIGHT c This switch of departments is only one of a large number of possible changes. For a six-department problem, there are actually 720 (or 6! = 6 × 5 × 4 × 3 × 2 × 1) potential arrangements! In layout problems, we may not find the optimal solution and may have to be satisfied with a “reason- able” one.
LEARNING EXERCISE c Can you improve on the layout in Figures 9.7 and 9.8 ? [Answer: Yes, it can be lowered to $430 by placing Shipping in area A, Painting in area B, Assembly in area C, Receiving in area D (no change), Machine Shop in area E, and Testing in area F (no change).]
RELATED PROBLEMS c 9.1–9.9 (9.10 is available in MyOMLab)
EXCEL OM Data File Ch09Ex1.xls can be found in MyOMLab.
ACTIVE MODEL 9.1 Example 1 is further illustrated in Active Model 9.1 in MyOMLab.
Figure 9.8
A Feasible Layout for Walters
Company
Computer Software for Process-Oriented Layouts The graphic approach in Example 1 is fine for small problems. It does not, however, suf- fice for larger problems. When 20 departments are involved in a layout problem, more than 600 trillion different department configurations are possible. Fortunately, computer programs have been written to handle large layouts. These programs (see the Flow Path Calculator graphic on the next page) often add sophistication with flowcharts, multiple-story capabil- ity, storage and container placement, material volumes, time analysis, and cost comparisons. These programs tend to be interactive—that is, require participation by the user. And most only claim to provide “good,” not “optimal,” solutions.
Ju lia
n S
tr a te
n sc
h u lt e /p
ic tu
re -a
lli a n ce
/d p a /A
P I m
a g e s
Siemens Corp. software such as this allows operations
managers to quickly place factory equipment for
a full three-dimensional view of the layout. Such
presentations provide added insight into the issues of
facility layout in terms of process, material handling,
efficiency, and safety. (Images created with Tecnomatix
Plant Simulation software, courtesy of Siemens PLM
Software)
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Work Cells A work cell reorganizes people and machines that would ordinarily be dispersed in various departments into a group so that they can focus on making a single product or a group of related products ( Figure 9.9 ). Cellular work arrangements are used when volume warrants a special arrangement of machinery and equipment. These work cells are reconfigured as prod- uct designs change or volume fluctuates. The advantages of work cells are:
1. Reduced work-in-process inventory because the work cell is set up to provide one-piece flow from machine to machine.
2. Less floor space required because less space is needed between machines to accommodate work-in-process inventory.
3. Reduced raw material and finished goods inventories because less work-in-process allows more rapid movement of materials through the work cell.
4. Reduced direct labor cost because of improved communication among employees, better material flow, and improved scheduling.
5. Heightened sense of employee participation in the organization and the product: employees accept the added responsibility of product quality because it is directly associated with them and their work cell.
6. Increased equipment and machinery utilization because of better scheduling and faster material flow.
7. Reduced investment in machinery and equipment because good utilization reduces the num- ber of machines and the amount of equipment and tooling.
Requirements of Work Cells The requirements of cellular production include: ◆ Identification of families of products, often through the use of group technology codes or
equivalents ◆ A high level of training, flexibility, and empowerment of employees ◆ Being self-contained, with its own equipment and resources ◆ Testing (poka-yoke) at each station in the cell
Work cell
An arrangement of machines and
personnel that focuses on making
a single product or family of
related products.
LO 9.6 Define work cell and the requirements of a
work cell
Proplanner Software for Process-Oriented Layouts
Working with computer-aided design software, analysts with the click of a mouse can use Proplanner’s Flow Path Calculator to
generate material flow diagrams and calculate material handling distances, time, and cost. Variable-width flow lines, color-coded by
product, part, or material handling method, allow users to identify how layouts should be arranged and where to eliminate excessive
material handling.
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Work cells have at least five advantages over assembly lines and focused process facilities: (1) because tasks are grouped, inspection is often immediate; (2) fewer workers are needed; (3) workers can reach more of the work area; (4) the work area can be more ef- ficiently balanced; and (5) communication is enhanced. Work cells are sometimes organized in a U shape, as shown on the right side of Figure 9.9 . The shape of the cell is secondary to the process flow. The focus should be on a flow that optimizes people, material, and communication.
Why did Canon’s copier factories in Japan switch from assembly lines to work cells? First, the move freed up 12 miles of conveyor-belt space, at 54 plants, saving $280 million in real estate costs. Second, the cells enabled Canon to change its product mix more quickly, to ac- commodate short life cycles. And third, morale increased because workers can now assemble a whole copier, not just one part. Some of Canon’s fastest workers are so admired that they have become TV celebrities!
Staffing and Balancing Work Cells Once the work cell has the appropriate equipment located in the proper sequence, the next task is to staff and balance the cell. Efficient production in a work cell requires appropriate staffing.
This involves two steps. First, determine the takt time , 2 which is the pace (frequency) of pro- duction units necessary (time per unit) to meet customer orders:
Takt time = Total work time available>Units required to satisfy customer demand (9-2)
Second, determine the number of operators required. This requires dividing the total operation time in the work cell by the takt time:
Workers required = Total operation time required>Takt time (9-3)
Takt time
Pace of production to meet
customer demands.
Current layout—workers are in small closed areas.
(a)
(b)
Improved layout—cross-trained workers can assist each other. May be able to add a third worker as added output is needed.
Improved layout—in U shape, workers have better access. Four cross-trained workers were reduced to three.
Material
Note in both (a) and (b) that U-shaped work cells can reduce material and employee movement. The U shape may also reduce space requirements, enhance communication, cut the number of workers, and make inspection easier.
Current layout—straight lines make it hard to balance tasks because work may not be divided evenly.
Figure 9.9
Improving Layouts by Moving
to the Work Cell Concept
STUDENT TIP Using work cells is a big step toward
manufacturing efficiency. They can
make jobs more interesting, save
space, and cut inventory.
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Stephen Hall’s company in Dayton makes auto mirrors. The major customer is the Honda plant nearby. Honda expects 600 mirrors delivered daily, and the work cell producing the mirrors is scheduled for 8 hours. Hall wants to determine the takt time and the number of workers required.
APPROACH c Hall uses Equations (9-2) and (9-3) and develops a work balance chart to help deter- mine the time for each operation in the work cell, as well as total time.
SOLUTION c
Takt time = (8 hours * 60 minutes)>600 units = 480>600 = .8 minute = 48 seconds
Therefore, the customer requirement is one mirror every 48 seconds. The work balance chart in Figure 9.10 shows that 5 operations are necessary, for a total operation
time of 140 seconds: Workers required = Total operation time required>Takt time
= (50 + 45 + 10 + 20 + 15)>48 = 140>48 = 2.92
Example 2 STAFFING WORK CELLS
Operations
50
40
30
20
10
Assemble Paint Test Label
S ta
n d
a rd
t im
e re
q u
ir e d
( s e c o
n d
s )
60
Pack for shipping
Figure 9.10
Work Balance Chart for Mirror
Production
INSIGHT c To produce one unit every 48 seconds will require 2.92 people. With three operators this work cell will be producing one unit each 46.67 seconds (140 seconds/3 employees = 46.67) and 617 units per day (480 minutes available × 60 seconds�46.67 seconds for each unit = 617).
LEARNING EXERCISE c If testing time is expanded to 20 seconds, what is the staffing requirement? [Answer: 3.125 employees.]
RELATED PROBLEM c 9.11
A work balance chart (like the one in Example 2 ) is also valuable for evaluating the opera- tion times in work cells. Some consideration must be given to determining the bottleneck operation. Bottleneck operations can constrain the flow through the cell. Imbalance in a work cell is seldom an issue if the operation is manual, as cell members by definition are part of a cross-trained team. Consequently, the inherent flexibility of work cells typically overcomes modest imbalance issues within a cell. However, if the imbalance is a machine constraint, then an adjustment in machinery, process, or operations may be necessary. In such situations the use of traditional assembly-line-balancing analysis, the topic of our next section, may be helpful.
The success of work cells is not limited to manufacturing. Kansas City’s Hallmark, which has over half the U.S. greeting card market and produces some 40,000 different cards, has modified the offices into a cellular design. In the past, its 700 creative professionals would take up to 2 years to develop a new card. Hallmark’s decision to create work cells consisting of artists, writers, lithographers, merchandisers, and accountants, all located in the same
Example 2 considers these two steps when staffing work cells.
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area, has resulted in card preparation in a fraction of the time that the old layout required. Work cells have also yielded higher performance and better service for the American Red Cross blood donation process.
The Focused Work Center and the Focused Factory When a firm has identified a family of similar products that have a large and stable demand , it may organize a focused work center. A focused work center (also called a “plant within a plant”) moves production to a large work cell that remains part of the present facility. For example, bumpers and dashboards in Toyota’s Texas plant are produced in a focused work center, and the Levi’s departments in JCPenney are managed and run in a stand-alone bou- tique setting.
If the focused work center is in a separate facility, it is often called a focused factory . For example, separate plants that produce seat belts, fuel tanks, and exhaust systems for Toyota are focused factories. A fast-food restaurant is also a focused factory—most are easily reconfig- ured for adjustments to product mix and volume. Burger King changes the number of person- nel and task assignments rather than moving machines and equipment. In this manner, Burger King balances the assembly line to meet changing production demands. In effect, the “layout” changes numerous times each day.
The term focused factories may also refer to facilities that are focused in ways other than by product line or layout. For instance, facilities may focus on their core competence, such as low cost, quality, new product introduction, or flexibility.
Focused facilities in both manufacturing and services appear to be better able to stay in tune with their customers, to produce quality products, and to operate at higher margins. This is true whether they are auto manufacturers like Toyota; restaurants like McDonald’s and Burger King; or a hospital like Arnold Palmer.
Repetitive and Product-Oriented Layout Product-oriented layouts are organized around products or families of similar high-volume, low-variety products. Repetitive production and continuous production, which are discussed in Chapter 7 , use product layouts. The assumptions are that:
1. Volume is adequate for high equipment utilization 2. Product demand is stable enough to justify high investment in specialized equipment 3. Product is standardized or approaching a phase of its life cycle that justifies investment in
specialized equipment 4. Supplies of raw materials and components are adequate and of uniform quality ( adequately
standardized) to ensure that they will work with the specialized equipment
Two types of a product-oriented layout are fabrication and assembly lines. The fabrication line builds components, such as automobile tires or metal parts for a refrigerator, on a series of machines, while an assembly line puts the fabricated parts together at a series of workstations. However, both are repetitive processes, and in both cases, the line must be “balanced”; that is, the time spent to perform work on one machine must equal or “balance” the time spent to perform work on the next machine in the fabrication line, just as the time spent at one worksta- tion by one assembly-line employee must “balance” the time spent at the next workstation by the next employee. The same issues arise when designing the “disassembly lines” of slaughter- houses and automobile recyclers.
A well-balanced assembly line has the advantage of high personnel and facility utilization and equity among employees’ workloads. Some union contracts require that workloads be nearly equal among those on the same assembly line. The term most often used to describe this process is assembly-line balancing . Indeed, the objective of the product-oriented layout is to mini- mize imbalance in the fabrication or assembly line .
Focused work center
A permanent or semipermanent
product-oriented arrangement of
machines and personnel.
Focused factory
A facility designed to produce
similar products or components.
Fabrication line
A machine-paced, product-orient-
ed facility for building components.
Assembly line
An approach that puts fabri-
cated parts together at a series
of workstations; used in repetitive
processes.
Assembly-line balancing
Obtaining output at each worksta-
tion on a production line so delay
is minimized.
LO 9.7 Define product- oriented layout
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The main advantages of product-oriented layout are:
1. The low variable cost per unit usually associated with high-volume, standardized products
2. Low material-handling costs 3. Reduced work-in-process inventories 4. Easier training and supervision 5. Rapid throughput
The disadvantages of product layout are:
1. The high volume required because of the large investment needed to establish the process
2. Work stoppage at any one point can tie up the whole operation 3. The process flexibility necessary for a variety of products and production rates can be a
challenge
Because the problems of fabrication lines and assembly lines are similar, we focus our dis- cussion on assembly lines. On an assembly line, the product typically moves via automated means, such as a conveyor, through a series of workstations until completed. This is the way fast-food hamburgers are made (see Figure 9.11 ), automobiles and some planes (see the photo of the Boeing 737 on the next page) are assembled, and television sets and ovens are pro- duced. Product-oriented layouts use more automated and specially designed equipment than do process layouts.
Assembly-Line Balancing Line balancing is usually undertaken to minimize imbalance between machines or personnel while meeting a required output from the line. To produce at a specified rate, management must know the tools, equipment, and work methods used. Then the time requirements for each assembly task (e.g., drilling a hole, tightening a nut, or spray-painting a part) must be determined. Management also needs to know the precedence relationship among the activities—that is, the sequence in which various tasks must be performed. Example 3 shows how to turn these task data into a precedence diagram.
LO 9.8 Explain how to balance production flow
in a repetitive or product-
oriented facility
VIDEO 9.2 Facility Layout at Wheeled Coach
Ambulances
2
1
5
43
2. Bun toasting1. Order
11 20 14 0 45
0:11
Task
Elapsed time
Task time (seconds)
0:00 0:31 0:45 1:30
Order read on a video screen
Toaster Condiments
More personnel added during busy periods
5. Order picked up immediately to keep it fresh
Heated cabinet for the grilled patties
Buns
3. Assembly with condiments
4. Wrapping of patty with bun
6. Customer service (order and payment)
Heated landing pad
6
Point-of-sale (POS) terminal that tracks each order (payment, time, cashier, etc.)
Figure 9.11
McDonald’s Hamburger Assembly Line
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The Boeing 737, the world’s most popular commercial airplane, is
produced on a moving production line, traveling at 2 inches a minute
through the final assembly process. The moving line, one of several
lean manufacturing innovations at the Renton, Washington, facility, has
enhanced quality, reduced flow time, slashed inventory levels, and cut
space requirements. Final assembly is only 11 days—a time savings of
50%—and inventory is down more than 55%.
C o p yr
ig h t
B o e in
g
Boeing wants to develop a precedence diagram for an electrostatic wing component that requires a total assembly time of 65 minutes.
APPROACH c Staff gather tasks, assembly times, and sequence requirements for the component in Table 9.2 .
Example 3 DEVELOPING A PRECEDENCE DIAGRAM FOR AN ASSEMBLY LINE
SOLUTION c Figure 9.12 shows the precedence diagram.
11
11
4
5
3
11
7
F
10 minutes
I
3
A B
E
D
C
H
G Figure 9.12
Precedence Diagram
TABLE 9.2 Precedence Data for Wing Component
TASK ASSEMBLY TIME
(MINUTES) TASK MUST FOLLOW TASK
LISTED BELOW
A 10 — This means that tasks B and E cannot be done until task A has been completed.
B 11 A C 5 B D 4 B E 11 A F 3 C, D G 7 F H 11 E I 3 G, H
Total time 65
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Once we have constructed a precedence chart summarizing the sequences and performance times, we turn to the job of grouping tasks into job stations so that we can meet the specified production rate. This process involves three steps:
1. Take the units required (demand or production rate) per day and divide them into the productive time available per day (in minutes or seconds). This operation gives us what is called the cycle time 3 —namely, the maximum time allowed at each workstation if the production rate is to be achieved:
Cycle time = Production time available per day
Units required per day (9-4)
2. Calculate the theoretical minimum number of workstations. This is the total task-dura- tion time (the time it takes to make the product) divided by the cycle time. Fractions are rounded to the next higher whole number:
Minimum number of workstations = a
n
i = 1 Time for task i
Cycle time (9-5)
where n is the number of assembly tasks. 3. Balance the line by assigning specific assembly tasks to each workstation. An efficient bal-
ance is one that will complete the required assembly, follow the specified sequence, and keep the idle time at each workstation to a minimum. A formal procedure for doing this is the following: a. Identify a master list of tasks. b. Eliminate those tasks that have been assigned. c. Eliminate those tasks whose precedence relationship has not been satisfi ed. d. Eliminate those tasks for which inadequate time is available at the workstation. e. Use one of the line-balancing “heuristics” described in Table 9.3 . The fi ve choices
are (1) longest task time, (2) most following tasks, (3) ranked positional weight, (4) shortest task time, and (5) least number of following tasks. You may wish to test several of these heuristics to see which generates the “best” solution—that is, the smallest number of workstations and highest effi ciency. Remember, however, that although heuristics provide solutions, they do not guarantee an optimal solution.
Cycle time
The maximum time that a product
is allowed at each workstation.
Heuristic
Problem solving using procedures
and rules rather than mathemati-
cal optimization.
INSIGHT c The diagram helps structure an assembly line and workstations, and it makes it easier to visualize the sequence of tasks.
LEARNING EXERCISE c If task D had a second preceding task (C), how would Figure 9.12 change? [Answer: There would also be an arrow pointing from C to D.]
RELATED PROBLEMS c 9.13a, 9.15a, 9.16a, 9.17a, 9.20a (9.25a,d, 9.26a, 9.27 are available in MyOMLab)
TABLE 9.3
Layout Heuristics That May Be Used to Assign Tasks to Workstations in Assembly-Line Balancing
1. Longest task (operation) time From the available tasks, choose the task with the largest (longest) time.
2. Most following tasks From the available tasks, choose the task with the largest number of following tasks.
3. Ranked positional weight From the available tasks, choose the task for which the sum of the times for each following task is longest. (In Example 4 we see that the ranked positional weight of task C = 5(C) + 3(F) + 7(G) + 3(I) = 18, whereas the ranked positional weight of task D = 4(D) + 3(F) + 7(G) + 3(I) = 17; therefore, C would be chosen fi rst, using this heuristic.)
4. Shortest task (operations) time From the available tasks, choose the task with the shortest task time.
5. Least number of following tasks From the available tasks, choose the task with the least number of subsequent tasks.
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Example 4 illustrates a simple line-balancing procedure.
On the basis of the precedence diagram and activity times given in Example 3 , Boeing determines that there are 480 productive minutes of work available per day. Furthermore, the production schedule requires that 40 units of the wing component be completed as output from the assembly line each day. It now wants to group the tasks into workstations.
APPROACH c Following the three steps above, we compute the cycle time using Equation (9-4) and minimum number of workstations using Equation (9-5) , and we assign tasks to workstations—in this case using the most following tasks heuristic.
SOLUTION c
Cycle time (in minutes) = 480 minutes
40 units
= 12 minutes>unit
Minimum number of workstations = Total task time
Cycle time =
65 12
= 5.42, or 6 stations
Figure 9.13 shows one solution that does not violate the sequence requirements and that groups tasks into six one-person stations. To obtain this solution, activities with the most following tasks were moved into workstations to use as much of the available cycle time of 12 minutes as possible. The first worksta- tion consumes 10 minutes and has an idle time of 2 minutes.
Example 4 BALANCING THE ASSEMBLY LINE
STUDENT TIP Tasks C, D, and F can be grouped
together in one workstation,
provided that the physical
facilities and skill levels meet the
work requirements.
INSIGHT c This is a reasonably well-balanced assembly line. The second and third workstations use 11 minutes. The fourth workstation groups three small tasks and balances perfectly at 12 minutes. The fifth has 1 minute of idle time, and the sixth (consisting of tasks G and I) has 2 minutes of idle time per cycle. Total idle time for this solution is 7 minutes per cycle.
LEARNING EXERCISE c If task I required 6 minutes (instead of 3 minutes), how would this change the solution? [Answer: The cycle time would not change, and the theoretical minimum num- ber of workstations would still be 6 (rounded up from 5.67), but it would take 7 stations to balance the line.]
RELATED PROBLEMS c 9.12–9.24 (9.25–9.27 are available in MyOMLab)
EXCEL OM Data File Ch09Ex4.xls can be found in MyOMLab.
Station 2
Station 1
Station 3 Station 5
Station 6
Station 4
10 min
A
11 min
B
3 min
F
7 min
5 min
C
4 min
D
11 min
E
11 min
H
3 min
I
G
Figure 9.13
A Six-Station Solution to the
Line-Balancing Problem
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There are two measures of effectiveness of a balance assignment. The first measure com- putes the efficiency of a line balance by dividing the total task times by the product of the number of workstations required times the assigned (actual) cycle time of the longest workstation:
Efficiency = g Task times
(Actual number of workstations) * (Largest assigned cycle time) (9-6)
Operations managers compare different levels of efficiency for various numbers of work- stations. In this way, a firm can determine the sensitivity of the line to changes in the produc- tion rate and workstation assignments.
The second measure computes the idle time for the line.
Idle time = (Actual number of workstations × Largest assigned cycle time) 2 ∑ Task times (9-7)
Boeing needs to calculate the efficiency for Example 4 .
APPROACH c Equation (9-6) is applied.
SOLUTION c
Efficiency = 65 minutes
(6 stations) * (12 minutes) =
65 72
= 90.3%
Note that opening a seventh workstation, for whatever reason, would decrease the efficiency of the bal- ance to 77.4% (assuming that at least one of the workstations still required 12 minutes):
Efficiency = 65 minutes
(7 stations) * (12 minutes) = 77.4%
INSIGHT c Increasing efficiency may require that some tasks be divided into smaller elements and reassigned to other tasks. This facilitates a better balance between workstations and means higher effi- ciency. Note that we can also compute efficiency as 1 − (% Idle time), that is, [1 − (Idle time)/(Total time in workstations)].
LEARNING EXERCISE c What is the efficiency if an eighth workstation is opened? [Answer: Effi- ciency = 67.7%.]
RELATED PROBLEMS c 9.13f, 9.14c, 9.15f, 9.17c, 9.18b, 9.19b, 9.20e,g (9.25e, 9.26c, 9.27 are avail- able in MyOMLab)
Example 5 DETERMINING LINE EFFICIENCY
Large-scale line-balancing problems, like large process-layout problems, are often solved by computers. Computer programs such as Assembly Line Pro, Proplanner, Timer Pro, Flexible Line Balancing, and Promodel are available to handle the assignment of workstations on as- sembly lines with numerous work activities. Such software evaluates the thousands, or even millions, of possible workstation combinations much more efficiently than could ever be done by hand.
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Summary Layouts make a substantial difference in operating effi- ciency. The seven layout situations discussed in this chapter are (1) office, (2) retail, (3) warehouse, (4) fixed position, (5) process oriented, (6) work cells, and (7) product ori- ented. A variety of techniques have been developed to solve these layout problems. Office layouts often seek to maxi- mize information flows, retail firms focus on product expo- sure, and warehouses attempt to optimize the trade-off between storage space and material handling cost.
The fixed-position layout problem attempts to minimize material handling costs within the constraint of limited
space at the site. Process layouts minimize travel distances times the number of trips. Product layouts focus on reduc- ing waste and the imbalance in an assembly line. Work cells are the result of identifying a family of products that justify a special configuration of machinery and equipment that reduces material travel and adjusts imbalances with cross-trained personnel.
Often, the issues in a layout problem are so wide-rang- ing that finding an optimal solution is not possible. For this reason, layout decisions, although the subject of sub- stantial research effort, remain something of an art.
Key Terms
Office layout (p. 371 ) Retail layout (p. 372 ) Slotting fees (p. 374 ) Servicescape (p. 375 ) Warehouse layout (p. 375 ) Cross-docking (p. 376 ) Random stocking (p. 377 )
Customizing (p. 377 ) Fixed-position layout (p. 377 ) Process-oriented layout (p. 378 ) Job lots (p. 379 ) Work cell (p. 383 ) Takt time (p. 384 ) Focused work center (p. 386 )
Focused factory (p. 386 ) Fabrication line (p. 386 ) Assembly line (p. 386 ) Assembly-line balancing (p. 386 ) Cycle time (p. 389 ) Heuristic (p. 389 )
Ethical Dilemma Although buried by mass customization and a proliferation of new products of numerous sizes and variations, grocery chains continue to seek to maximize payoff from their layout. Their layout includes a marketable commodity—shelf space—and they charge for it. This charge is known as a slotting fee . Recent estimates are that food manufacturers now spend some 13% of sales on trade promotions, which is paid to grocers to get them to promote and discount the manufacturer’s products. A portion of these fees is for slotting, but slotting fees drive up the manufacturer’s cost. They also put the small company with a new product at a disadvantage because small companies with limited resources may be squeezed out of the marketplace. Slotting fees may also mean that customers may no longer be able to fi nd the special local brand. How ethical are slotting fees?
Im a g e S
o u rc
e /A
la m
y
1. What are the seven layout strategies presented in this chapter? 2. What are the three factors that complicate a fixed-position
layout? 3. What are the advantages and disadvantages of process layout? 4. How would an analyst obtain data and determine the number
of trips in: (a) a hospital? (b) a machine shop? (c) an auto-repair shop? 5. What are the advantages and disadvantages of product layout?
6. What are the four assumptions (or preconditions) of estab- lishing layout for high-volume, low-variety products?
7. What are the alternative forms of work cells discussed in this textbook?
8. What are the advantages and disadvantages of work cells? 9. What are the requirements for a focused work center or
focused factory to be appropriate? 10. What are the two major trends influencing office layout? 11. What layout variables would you consider particularly impor-
tant in an office layout where computer programs are written?
Discussion Questions
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Using Software to Solve Layout Problems
In addition to the many commercial software packages available for addressing layout problems, Excel OM and POM for Windows, both of which accompany this text, contain modules for the process problem and the assembly-line-balancing problem.
X USING EXCEL OM Excel OM can assist in evaluating a series of department work assignments like the one we saw for the Walters Company in Example 1 . The layout module can generate an optimal solution by enumeration or by computing the “total movement” cost for each layout you wish to examine. As such, it provides a speedy calculator for each flow–distance pairing.
Program 9.1 illustrates our inputs in the top two tables. We first enter department flows, then provide distances between work areas. Entering area assignments on a trial-and-error basis in the upper left of the top table generates movement computations at the bottom of the screen. Total movement is recalculated each time we try a new area assignment. It turns out that the assignment shown is optimal at 430 feet of movement.
12. What layout innovations have you noticed recently in retail establishments?
13. What are the variables that a manager can manipulate in a retail layout?
14. Visit a local supermarket and sketch its layout. What are your observations regarding departments and their locations?
15. What is random stocking? 16. What information is necessary for random stocking to
work? 17. Explain the concept of cross-docking. 18. What is a heuristic? Name several that can be used in assembly-
line balancing.
= C28*F28
Look up the cost as = INDEX ($D$16: $I$21, D28, E28).
Get the loads from the load table above using = INDEX ($D$8: $I$13, A28, B28).
Calculations continue below row 30.
Columns A and B together contain all possible 6 by 6 = 36 combinations of pairs of areas.
Program 9.1
Using Excel OM’s
Process Layout
Module to Solve the
Walters Company
Problem in
Example 1
P USING POM FOR WINDOWS The POM for Windows facility layout module can be used to place up to 10 departments in 10 rooms to minimize the total distance traveled as a function of the distances between the rooms and the flow between departments. The program exchanges departments until no exchange will reduce the total amount of movement, meaning an optimal solution has been reached.
The POM for Windows and Excel OM modules for line balancing can handle a line with up to 99 tasks, each with up to six imme- diate predecessors. In this program, cycle time can be entered as either (1) given , if known, or (2) the demand rate can be entered with time available as shown. All five “heuristic rules” are used: (1) longest operation (task) time, (2) most following tasks, (3) ranked positional weight, (4) shortest operation (task) time, and (5) least number of following tasks. No one rule can guarantee an optimal solution, but POM for Windows displays the number of stations needed for each rule.
Appendix IV discusses further details regarding POM for Windows.
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SOLVED PROBLEM 9.1 Aero Maintenance is a small aircraft engine maintenance facility located in Wichita, Kansas. Its new administrator, Ann Daniel, decides to improve material flow in the facility, using the process layout method she studied at Wichita State University. The current layout of Aero Maintenance’s eight departments is shown in Figure 9.14 .
The only physical restriction perceived by Daniel is the need to keep the entrance in its current location. All other depart- ments can be moved to a different work area (each 10 feet square) if layout analysis indicates a move would be beneficial.
First, Daniel analyzes records to determine the number of material movements among departments in an average month. These data are shown in Figure 9.15 . Her objective, Daniel
decides, is to lay out the departments so as to minimize the total movement (distance traveled) of material in the facility. She writes her objective as:
Minimize material movement = a 8
i = 1 a
8
j = 1 XijCij
where X ij = number of material movements per month (loads or trips) moving from department i to department j
C ij = distance in feet between departments i and j (which, in this case, is the equivalent of cost per load to move between departments)
Note that this is only a slight modification of the cost-objec- tive equation shown earlier in the chapter.
Daniel assumes that adjacent departments, such as entrance (now in work area A) and receiving (now in work area B), have a walking distance of 10 feet. Diagonal departments are also considered adjacent and assigned a distance of 10 feet. Nonadjacent departments, such as the entrance and parts (now in area C) or the entrance and inspection (area G) are 20 feet apart, and nonadjacent rooms, such as entrance and metallurgy (area D), are 30 feet apart. (Hence, 10 feet is con- sidered 10 units of cost, 20 feet is 20 units of cost, and 30 feet is 30 units of cost.)
Given the above information, redesign Aero Maintenance’s layout to improve its material flow efficiency.
Solved Problems Virtual Office Hours help is available in MyOMLab .
Entrance (1)
Receiving (2)
Parts (3)
Metallurgy (4)
Breakdown (5)
Assembly (6)
Inspection (7)
Test (8)
10'
10'
Current Aero Maintenance Layout Area A Area B Area C Area D
40' Area E Area F Area G Area H
Figure 9.14
Aero Maintenance Layout
Department
Entrance (1)
Receiving (2)
Parts (3)
Entrance (1)
Receiving (2)
Parts (3)
Metallurgy (4)
Breakdown (5)
Assembly (6)
Inspection (7)
Test (8)
Metallurgy (4)
Breakdown (5)
Assembly (6)
Inspection (7)
Test (8)
100
0
30
100 0 0 0 0 0
50 20 0 0 0
30 0 0 0
20 0 0 20
20 0 10
30 0
0
Figure 9.15
Number of Material
Movements (Loads) Between
Departments in 1 Month
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C H A P T E R 9 | L AYO U T S T R AT E G I E S 395
SOLUTION First, establish Aero Maintenance’s current layout, as shown in Figure 9.16 . Then, by analyzing the current layout, compute mate- rial movement:
Total movement = (100 * 10′) + (100 * 20′) + (50 * 20′) + (20 * 10′) 1 to 2 1 to 3 2 to 4 2 to 5 + (30 * 10′) + (30 * 20′) + (20 * 30′) + (20 * 10′) 3 to 4 3 to 5 4 to 5 4 to 8 + (20 * 10′) + (10 * 30′) + (30 * 10′) 5 to 6 5 to 8 5 to 7 = 1,000 + 2,000 + 1,000 + 200 + 300 + 600 + 600 + 200 + 200 + 300 + 300 = 6,700 feet
100 trips
100
20
30
20 30
20
50
10
20 30
Entrance (1)
Receiving (2)
Parts (3)
Metallurgy (4)
Breakdown (5)
Assembly (6)
Inspection (7)
Test (8)
Figure 9.16
Current Material Flow
Propose a new layout that will reduce the current figure of 6,700 feet. Two useful changes, for example, are to switch departments 3 and 5 and to interchange departments 4 and 6. This change would result in the schematic shown in Figure 9.17 :
Total movement = (100 * 10′) + (100 * 10′) + (50 * 10′) + (20 * 10′) 1 to 2 1 to 3 2 to 4 2 to 5 + (30 * 10′) + (30 * 20′) + (20 * 10′) + (20 * 20′) 3 to 4 3 to 5 4 to 5 4 to 8 + (20 * 10′) + (10 * 10′) + (30 * 10′) 5 to 6 5 to 8 6 to 7 = 1,000 + 1,000 + 500 + 200 + 300 + 600 + 200 + 400 + 200 + 100 + 300 = 4,800 feet
Do you see any room for further improvement?
20
30
20
100 20
30
20
100 50
10 30
Entrance (1)
Receiving (2)
Parts (3)
Metallurgy (4)
Breakdown (5)
Assembly (6)
Inspection (7)
Test (8)
Figure 9.17
Improved Layout
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396 P A R T 2 | D E S I G N I N G O P E R AT I O N S
A
35
B
4
C
3
D
1
F
6
E
G
2
H
4
Workstation 1
Workstation 2
Workstation 3
Workstation 4
SOLVED PROBLEM 9.2 The assembly line whose activities are shown in Figure 9.18 has an 8-minute cycle time. Draw the precedence graph, and find the minimum possible number of one-person workstations. Then arrange the work activities into workstations so as to bal- ance the line. What is the efficiency of your line balance?
TASK PERFORMANCE TIME
(MINUTES) TASK MUST FOLLOW
THIS TASK
A 5 —
B 3 A
C 4 B
D 3 B
E 6 C
F 1 C
G 4 D, E, F
H 2 G
28
Figure 9.18
Four-Station Solution to the
Line-Balancing Problem
SOLUTION The theoretical minimum number of workstations is:
gti Cycle time
= 28 minutes 8 minutes
= 3.5, or 4 stations
The precedence graph and one good layout are shown in Figure 9.18 :
Efficiency = Total task time
(Actual number of workstations) * (Largest assigned cycle time) =
28 (4)(8)
= 87.5%
Problems 9.1–9.10 relate to Process-Oriented Layout
• • 9.1 Gordon Miller’s job shop has four work areas, A, B, C, and D. Distances in feet between centers of the work areas are:
A B C D
A — 4 9 7
B — — 6 8
C — — — 10
D — — — —
Workpieces moved, in hundreds of workpieces per week, between pairs of work areas, are:
A B C D
A — 8 7 4
B — — 3 2
C — — — 6
D — — — —
It costs Gordon $1 to move 1 work piece 1 foot. What is the weekly total material handling cost of the layout? PX
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
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• • 9.2 A Missouri job shop has four departments—machin- ing (M), dipping in a chemical bath (D), finishing (F), and plating (P)—assigned to four work areas. The operations manager, Mary Marrs, has gathered the following data for this job shop as it is currently laid out (Plan A).
100s of Workpieces Moved Between Work Areas Each Year Plan A
M D F P
M — 6 18 2 D — — 4 2 F — — — 18 P — — — —
Distances Between Work Areas (Departments) in Feet
M D F P
M — 20 12 8 D — — 6 10 F — — — 4 P — — — —
It costs $0.50 to move 1 workpiece 1 foot in the job shop. Marrs’s goal is to find a layout that has the lowest material han- dling cost. a) Determine cost of the current layout, Plan A, from the data above. b) One alternative is to switch those departments with the high
loads, namely, finishing (F) and plating (P), which alters the distance between them and machining (M) and dipping (D), as follows:
Distances Between Work Areas (Departments) in Feet Plan B
M D F P
M — 20 8 12
D — — 10 6
F — — — 4
P — — — —
What is the cost of this layout? c) Marrs now wants you to evaluate Plan C, which also switches
milling (M) and drilling (D), below. Distance Between Work Areas (Departments) in Feet Plan C
M D F P
M — 20 10 6
D — — 8 12
F — — — 4
P — — — —
What is the cost of this layout? d) Which layout is best from a cost perspective? PX
• 9.3 Three departments—milling (M), drilling (D), and sawing (S)—are assigned to three work areas in Victor Berardis’s machine shop in Vent, Ohio. The number of workpieces moved per day and the distances between the centers of the work areas, in feet, follow.
Pieces Moved Between Work Areas Each Day
M D S
M — 23 32
D — — 20
S — — —
Distances Between Centers of Work Areas (Departments) in Feet
M D S
M — 10 5
D — — 8
S — — —
It costs $2 to move 1 workpiece 1 foot. What is the cost?
• • 9.4 Roy Creasey Enterprises, a machine shop, is planning to move to a new, larger location. The new building will be 60 feet long by 40 feet wide. Creasey envisions the building as having six distinct production areas, roughly equal in size. He feels strongly about safety and intends to have marked pathways throughout the building to facilitate the movement of people and materials. See the following building schematic.
His foreman has completed a month-long study of the number of loads of material that have moved from one process to another in the current building. This information is contained in the follow- ing flow matrix.
Flow Matrix Between Production Processes
TO FROM MATERIALS WELDING DRILLS LATHES GRINDERS BENDERS
Materials 0 100 50 0 0 50
Welding 25 0 0 50 0 0
Drills 25 0 0 0 50 0
Lathes 0 25 0 0 20 0
Grinders 50 0 100 0 0 0
Benders 10 0 20 0 0 0
Building Schematic (with work areas 1– 6)
1 2 3
4 5 6
Finally, Creasey has developed the following matrix to indicate distances between the work areas shown in the building schematic.
Distance Between Work Areas
6
60
40
20
40
20
5
40
20
40
20
4
20
40
60
3
40
20
2
20
1
1
2
3
4
5
6
What is the appropriate layout of the new building? PX
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a) What is the “load × distance,” or “movement cost,” of the layout shown?
b) Provide an improved layout and compute its movement cost. PX
• • • 9.6 You have just been hired as the director of opera- tions for Reid Chocolates, a purveyor of exceptionally fine candies. Reid Chocolates has two kitchen layouts under consid- eration for its recipe making and testing department. The strat- egy is to provide the best kitchen layout possible so that food scientists can devote their time and energy to product improve- ment, not wasted effort in the kitchen. You have been asked to evaluate these two kitchen layouts and to prepare a recommen- dation for your boss, Mr. Reid, so that he can proceed to place the contract for building the kitchens. [See Figure 9.20(a) , and Figure 9.20(b) .] PX
• • • 9.8 Reid Chocolates (see Problems 9.6 and 9.7) has yet two more layouts to consider. a) Layout 4 is shown below. What is the total trip distance? b) Layout 5, which also follows, has what total trip distance?
• • 9.5 Adam Munson Manufacturing, in Gainesville, Florida, wants to arrange its four work centers so as to minimize interdepartmental parts handling costs. The flows and existing facility layout are shown in Figure 9.19 . For example, to move a part from Work Center A to Work Center C is a 60-foot move- ment distance. It is 90 feet from A to D.
A
Existing Layout
30'
B C D
30' 30'
Parts Moved Between Work Centers
A
B
C
D
—
350
0
0
A B C D
450
—
0
0
550
200
—
0
50
0
750
—
Figure 9.19
Munson Manufacturing
• • • 9.7 Reid Chocolates (see Problem 9.6) is considering a third layout, as shown below. Evaluate its effectiveness in trip- distance feet. PX
1
0
5
3
3
0
2
8
0
12
0
8
3
13
3
0
0
4
4
0
3
4
0
10
5
0
8
0
5
0
1
2
3
4
5
To:
Number of trips between work centers:
From:
Refrig.
Counter
Sink
Storage
Stove
Re fri
ge ra
to r
Co un
te r
Si nk
St or
ag e
St ov
e
Figure 9.20(a)
Layout Options
4 4 4 4
Kitchen layout #1 Walking distance in feet
CounterRefrig.
Sink Storage
Stove
2
3 4
Sink Storage
3 4
Kitchen layout #2 Walking distance in feet
1 5
CounterRefrig. Stove
2
8
7 12
5 6
7 9 6
4
1 5
Figure 9.20(b)
Kitchen layout #4 Walking distance in feet
5 8 5
1111 8
13
Refrig.
1
Counter
2
Stove
5
StorageSink
3 4 4
4
Kitchen layout #3 Walking distance in feet
2 Storage
4
Sink
3
Counter
2
Refrig.
Stove
5
1 44
8 12
14 10 8 4
StorageSink
3 4
Refrig.
1
Stove
5
44 4
4
4
1212
4 33
Counter
2
Kitchen layout #5 Walking distance in feet
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Additional problem 9.10 is available in MyOMLab.
Problem 9.11 relates to Work Cells
• • 9.11 After an extensive product analysis using group tech- nology, Leon Bazil has identified a product he believes should be pulled out of his process facility and handled in a work cell. Leon has identified the following operations as necessary for the work cell. The customer expects delivery of 250 units per day, and the workday is 420 minutes. a) What is the takt time? b) How many employees should be cross-trained for the cell? c) Which operations may warrant special consideration?
OPERATION STANDARD TIME (min)
Shear 1.1 Bend 1.1 Weld 1.7 Clean 3.1 Paint 1.0
Problems 9.12–9.27 relate to Repetitive and Product-Oriented Layout
• • 9.12 Stanford Rosenberg Computing wants to establish an assembly line for producing a new product, the Personal Digital Assistant (PDA). The tasks, task times, and immediate predeces- sors for the tasks are as follows:
TASK TIME (sec) IMMEDIATE PREDECESSORS
A 12 —
B 15 A
C 8 A
D 5 B, C
E 20 D
Rosenberg’s goal is to produce 180 PDAs per hour. a) What is the cycle time? b) What is the theoretical minimum for the number of work-
stations that Rosenberg can achieve in this assembly line? c) Can the theoretical minimum actually be reached when work-
stations are assigned? PX
• • 9.9 Six processes are to be laid out in six areas along a long corridor at Rita Gibson Accounting Services in Daytona Beach. The distance between adjacent work centers is 40 feet. The number of trips between work centers is given in the following table:
TRIPS BETWEEN PROCESSES
TO
FROM A B C D E F
A 18 25 73 12 54
B 96 23 31 45
C 41 22 20
D 19 57
E 48
F
a) Assign the processes to the work areas in a way that minimizes the total flow, using a method that places processes with high- est flow adjacent to each other.
b) What assignment minimizes the total traffic flow? PX
• • • 9.13 Illinois Furniture, Inc., produces all types of office furniture. The “Executive Secretary” is a chair that has been designed using ergonomics to provide comfort during long work hours. The chair sells for $130. There are 480 minutes available during the day, and the average daily demand has been 50 chairs. There are eight tasks:
TASK PERFORMANCE TIME (min) TASK MUST FOLLOW TASK
LISTED BELOW
A 4 — B 7 — C 6 A, B D 5 C E 6 D F 7 E G 8 E H 6 F, G
a) Draw a precedence diagram of this operation. b) What is the cycle time for this operation? c) What is the theoretical minimum number of workstations? d) Assign tasks to workstations. e) What is the idle time per cycle? f ) How much total idle time is present in an 8-hour shift? g) What is the efficiency of the assembly line, given your answer
in (d)? PX
• • 9.14 Sue Helms Appliances wants to establish an assem- bly line to manufacture its new product, the Micro Popcorn Popper. The goal is to produce five poppers per hour. The tasks, task times, and immediate predecessors for producing one Micro Popcorn Popper are as follows:
TASK TIME (min) IMMEDIATE
PREDECESSORS
A 10 — B 12 A C 8 A, B D 6 B, C E 6 C F 6 D, E
a) What is the theoretical minimum for the smallest number of workstations that Helms can achieve in this assembly line?
b) Graph the assembly line, and assign workers to workstations. Can you assign them with the theoretical minimum?
c) What is the efficiency of your assignment? PX
• • 9.15 The Action Toy Company has decided to manu- facture a new train set, the production of which is broken into six steps. The demand for the train is 4,800 units per 40-hour workweek:
TASK PERFORMANCE TIME (sec) PREDECESSORS
A 20 None
B 30 A
C 15 A
D 15 A
E 10 B, C
F 30 D, E
a) Draw a precedence diagram of this operation. b) Given the demand, what is the cycle time for this operation? c) What is the theoretical minimum number of workstations? d) Assign tasks to workstations.
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400 P A R T 2 | D E S I G N I N G O P E R AT I O N S
Iv a n S
m u k/
S h u tt
e rs
to ck
e) How much total idle time is present each cycle? f ) What is the efficiency of the assembly line with five stations?
With six stations? PX
• • 9.16 The following table details the tasks required for Indiana-based Frank Pianki Industries to manufacture a fully portable industrial vacuum cleaner. The times in the table are in minutes. Demand forecasts indicate a need to operate with a cycle time of 10 minutes.
ACTIVITY ACTIVITY DESCRIPTION IMMEDIATE
PREDECESSORS TIME
A Attach wheels to tub — 5
B Attach motor to lid — 1.5
C Attach battery pack B 3
D Attach safety cutoff C 4
E Attach fi lters B 3
F Attach lid to tub A, E 2
G Assemble attachments — 3
H Function test D, F, G 3.5
I Final inspection H 2
J Packing I 2
a) Draw the appropriate precedence diagram for this production line.
b) Assign tasks to workstations and determine how much idle time is present each cycle.
c) Discuss how this balance could be improved to 100%. d) What is the theoretical minimum number of workstations? PX
• • 9.17 Tailwind, Inc., produces high-quality but expensive training shoes for runners. The Tailwind shoe, which sells for $210, contains both gas- and liquid-filled compartments to pro- vide more stability and better protection against knee, foot, and back injuries. Manufacturing the shoes requires 10 separate tasks. There are 400 minutes available for manufacturing the shoes in the plant each day. Daily demand is 60. The information for the tasks is as follows:
TASK PERFORMANCE TIME (min) TASK MUST FOLLOW TASK
LISTED BELOW
A 1 —
B 3 A
C 2 B
D 4 B
E 1 C, D
F 3 A
G 2 F
H 5 G
I 1 E, H
J 3 I
a) Draw the precedence diagram. b) Assign tasks to the minimum feasible number of workstations
according to the “ranked positioned weight” decision rule. c) What is the efficiency of the process you completed in (b)? d) What is the idle time per cycle? PX
• • 9.18 The Mach 10 is a one-person sailboat manufactured by Creative Leisure. The final assembly plant is in Cupertino, California. The assembly area is available for production of the
Mach 10 for 200 minutes per day. (The rest of the time it is busy making other products.) The daily demand is 60 boats. Given the information in the table, a) Draw the precedence diagram and assign tasks using five
workstations. b) What is the efficiency of the assembly line, using your answer
to (a)? c) What is the theoretical minimum number of workstations? d) What is the idle time per boat produced? PX
TASK PERFORMANCE TIME (min) TASK MUST FOLLOW TASK
LISTED BELOW
A 1 —
B 1 A
C 2 A
D 1 C
E 3 C
F 1 C
G 1 D, E, F
H 2 B
I 1 G, H
• • 9.19 Because of the expected high demand for Mach 10, Creative Leisure has decided to increase manufacturing time available to produce the Mach 10 (see Problem 9.18). a) If demand remained the same but 300 minutes were available
each day on the assembly line, how many workstations would be needed?
b) What would be the efficiency of the new system, using the actual number of workstations from (a)?
c) What would be the impact on the system if 400 minutes were available? PX
• • • 9.20 Dr. Lori Baker, operations manager at Nesa Electro- nics, prides herself on excellent assembly-line balancing. She has been told that the firm needs to complete 96 instruments per 24-hour day. The assembly-line activities are:
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a) Develop a layout and balance the line. b) How many people can be processed per hour? c) Which activity accounts for the current bottleneck? d) What is the total idle time per cycle? e) If one more physician and one more paramedic can be placed
on duty, how would you redraw the layout? What is the new throughput?
• • • 9.23 Samuel Smith’s company wants to establish an assembly line to manufacture its new product, the iStar phone. Samuel’s goal is to produce 60 iStars per hour. Tasks, task times, and immediate predecessors are as follows:
TASK TIME (sec)
IMMEDIATE PREDECESSORS TASK
TIME (sec)
IMMEDIATE PREDECESSORS
A 40 — F 25 C
B 30 A G 15 C
C 50 A H 20 D, E
D 40 B I 18 F, G
E 6 B J 30 H, I
a) What is the theoretical minimum for the number of worksta- tions that Samuel can achieve in this assembly line?
b) Use the most following tasks heuristic to balance an assembly line for the iStar phone.
c) How many workstations are in your answer to (b)? d) What is the efficiency of your answer to (b)? PX
• • • • 9.24 As the Cottrell Bicycle Co. of St. Louis completes plans for its new assembly line, it identifies 25 different tasks in the production process. VP of Operations Jonathan Cottrell now faces the job of balancing the line. He lists precedences and pro- vides time estimates for each step based on work-sampling tech- niques. His goal is to produce 1,000 bicycles per standard 40-hour workweek.
TASK TIME (sec) PRECEDENCE
TASKS TASK TIME (sec)
PRECEDENCE TASKS
K3 60 — E3 109 F3
K4 24 K3 D6 53 F4
K9 27 K3 D7 72 F9, E2, E3
J1 66 K3 D8 78 E3, D6
J2 22 K3 D9 37 D6
J3 3 — C1 78 F7
G4 79 K4, K9 B3 72 D7, D8, D9, C1
G5 29 K9, J1 B5 108 C1
F3 32 J2 B7 18 B3
F4 92 J2 A1 52 B5
F7 21 J3 A2 72 B5
F9 126 G4 A3 114 B7, A1, A2
E2 18 G5, F3
a) Balance this operation, using various heuristics. Which is best and why?
b) What happens if the firm can change to a 41-hour work- week? PX
TASK TIME (min) PREDECESSORS
A 3 —
B 6 —
C 7 A
D 5 A, B
E 2 B
F 4 C
G 5 F
H 7 D, E
I 1 H
J 6 E
K 4 G, I, J
50
a) Draw the precedence diagram. b) If the daily (24-hour) production rate is 96 units, what is the
highest allowable cycle time? c) If the cycle time after allowances is given as 10 minutes, what
is the daily (24-hour) production rate? d) With a 10-minute cycle time, what is the theoretical minimum
number of stations with which the line can be balanced? e) With a 10-minute cycle time and six workstations, what is the
efficiency? f) What is the total idle time per cycle with a 10-minute cycle
time and six workstations? g) What is the best workstation assignment you can make
without exceeding a 10-minute cycle time, and what is its efficiency? PX
• • 9.21 Suppose production requirements in Solved Problem 9.2 (see page 396 ) increase and require a reduction in cycle time from 8 minutes to 7 minutes. Balance the line once again, using the new cycle time. Note that it is not possible to combine task times so as to group tasks into the minimum number of workstations. This condition occurs in actual balancing problems fairly often. PX
• • 9.22 The preinduction physical examination given by the U.S. Army involves the following seven activities:
ACTIVITY AVERAGE TIME (min)
Medical history 10
Blood tests 8
Eye examination 5
Measurements (e.g., weight, height, blood pressure)
7
Medical examination 16
Psychological interview 12
Exit medical evaluation 10
These activities can be performed in any order, with two excep- tions: Medical history must be taken first, and Exit medical evaluation is last. At present, there are three paramedics and two physicians on duty during each shift. Only physicians can perform exit evaluations and conduct psychological inter- views. Other activities can be carried out by either physicians or paramedics. Additional problems 9.25–9.27 are available in MyOMLab.
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CASE STUDIES State Automobile License Renewals
Henry Coupe, the manager of a metropolitan branch office of the state department of motor vehicles, attempted to analyze the driver’s license-renewal operations. He had to perform several steps. After examining the license-renewal process, he identified those steps and associated times required to perform each step, as shown in the following table:
State Automobile License Renewal Process Times
STEP AVERAGE TIME TO
PERFORM (sec)
1. Review renewal application for correctness 15
2. Process and record payment 30
3. Check fi le for violations and restrictions 60
4. Conduct eye test 40
5. Photograph applicant 20
6. Issue temporary license 30
Coupe found that each step was assigned to a different person. Each application was a separate process in the sequence shown. He determined that his office should be prepared to accommo- date a maximum demand of processing 120 renewal applicants per hour.
He observed that work was unevenly divided among clerks and that the clerk responsible for checking violations tended to shortcut her task to keep up with the others. Long lines built up during the maximum-demand periods.
Coupe also found that Steps 1 to 4 were handled by general clerks who were each paid $12 per hour. Step 5 was performed by a photographer paid $16 per hour. (Branch offices were charged $10 per hour for each camera to perform photography.)
When Orlando’s Arnold Palmer Hospital began plans to create a new 273-bed, 11-story hospital across the street from its exist- ing facility, which was bursting at the seams in terms of capac- ity, a massive planning process began. The $100 million building, opened in 2006, was long overdue, according to Executive Director Kathy Swanson: “We started Arnold Palmer Hospital in 1989, with a mission to provide quality services for children and women in a comforting, family-friendly environment. Since then we have served well over 1.5 million women and children and now deliver more than 12,000 babies a year. By 2001, we simply ran out of room, and it was time for us to grow.”
The new hospital’s unique, circular pod design provides a maximally efficient layout in all areas of the hospital, creating a patient-centered environment. Servicescape design features include a serene environment created through the use of warm colors, private rooms with pull-down Murphy beds for family members, 14-foot ceilings, and natural lighting with oversized windows in patient rooms. But these radical new features did not come easily. “This pod concept with a central nursing area and pie-shaped rooms resulted from over 1,000 planning meetings of 35 user groups, extensive motion and time studies, and computer simulations of the daily movements of nurses,” says Swanson.
Video Case In a traditional linear hospital layout, called the racetrack
design, patient rooms line long hallways, and a nurse might walk 2.7 miles per day serving patient needs at Arnold Palmer. “Some nurses spent 30% of their time simply walking. With the nursing shortage and the high cost of health care professionals, efficiency is a major concern,” added Swanson. With the nursing station in the center of 10- or 12-bed circular pods, no patient room is more than 14 feet from a station. The time savings are in the 20% range. Swanson pointed to Figures 9.21 and 9.22 as examples of the old and new walking and trip distances. *
“We have also totally redesigned our neonatal rooms,” says Swanson. “In the old system, there were 16 neonatal beds in a large and often noisy rectangular room. The new building features semiprivate rooms for these tiny babies. The rooms are much improved, with added privacy and a quiet, simulated night atmosphere, in addition to pull-down beds for parents to use. Our research shows that babies improve and develop much more quickly with this layout design. Layout and environment indeed impact patient care!”
Laying Out Arnold Palmer Hospital’s New Facility
* Layout and walking distances, including some of the numbers in Figures 9.21 and 9.22 , have been simplifi ed for purposes of this case.
Step 6, issuing temporary licenses, was required by state policy to be handled by uniformed motor vehicle officers. Officers were paid $18 per hour but could be assigned to any job except photography.
A review of the jobs indicated that Step 1, reviewing applica- tions for correctness, had to be performed before any other step could be taken. Similarly, Step 6, issuing temporary licenses, could not be performed until all the other steps were completed.
Henry Coupe was under severe pressure to increase productiv- ity and reduce costs, but he was also told by the regional director that he must accommodate the demand for renewals. Otherwise, “heads would roll.”
Discussion Questions
1. What is the maximum number of applications per hour that can be handled by the present configuration of the process?
2. How many applications can be processed per hour if a second clerk is added to check for violations?
3. If the second clerk could be added anywhere you choose (and not necessarily to check for violations, as in Question 2), what is the maximum number of applications the process can han- dle? What is the new configuration?
4. How would you suggest modifying the process to accommo- date 120 applications per hour? What is the cost per application of this new configuration?
Source: Modifi ed from a case by W. Earl Sasser, Paul R. Olson, and D. Daryl Wyckoff , Management of Services Operations: Text, Cases, and Readings (Boston: Allyn & Bacon).
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C H A P T E R 9 | L AYO U T S T R AT E G I E S 403
Room 507
Room 508
Room 509
Room 510
Room 511
Room 512
Room 501
Room 502
Room 503
Room 504
Room 505
Room 506
Linens Break room
Medical supply
Electrical/ mechanical
room Nurses’ station
20' 30' 40' 50' 60' 70'
20' 30' 40' 50'
30' 40' 50'
60' 70' Hallway
Hallway
Distance from nurses’ station (feet)
Figure 9.21
Traditional Hospital Layout
Patient rooms are on two linear
hallways with exterior windows.
Supply rooms are on interior
corridors. This layout is called a
“racetrack” design.
Pie-shaped rooms
Central nursing station for 34 rooms in the 3 pods
Break and central medical supply rooms
Local supply for pod’s linens
Local nursing station pod
Figure 9.22
New Pod Design for Hospital
Layout
Note that each room is 14 feet
from the pod’s local nursing
station. The break rooms and
the central medical station are
each about 60 feet from the local
nursing pod. Pod linen supply
rooms are also 14 feet from the
local nursing station.
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P a lm
e r
M e d ic
a l C
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r
Discussion Questions *
1. Identify the many variables that a hospital needs to consider in layout design.
2. What are the advantages of the circular pod design over the traditional linear hallway layout found in most hospitals?
3. Figure 9.21 illustrates a sample linear hallway layout. During a period of random observation, nurse Thomas Smith’s day includes 6 trips from the nursing station to each of the 12 patient rooms (back and forth), 20 trips to the medical sup- ply room, 5 trips to the break room, and 12 trips to the linen supply room. What is his total distance traveled in miles?
4. Figure 9.22 illustrates an architect’s drawing of Arnold Palmer Hospital’s new circular pod system. If nurse Susan Jones’s day includes 7 trips from the nursing pod to each of the 12 rooms (back and forth), 20 trips to central medical supply, 6 trips to the break room, and 12 trips to the pod linen supply, how many miles does she walk during her shift? What are the dif- ferences in the travel times between the two nurses for this ran- dom day?
5. The concept of servicescapes is discussed in this chapter. Describe why this is so important at Arnold Palmer Hospital, and give examples of its use in layout design.
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404 P A R T 2 | D E S I G N I N G O P E R AT I O N S
* You may wish to view the video that accompanies this case before addressing these questions.
When President Bob Collins began his career at Wheeled Coach, the world’s largest manufacturer of ambulances, there were only a handful of employees. Now the firm’s Florida plant has a work- force of 350. The physical plant has also expanded, with offices, R&D, final assembly, and wiring, cabinetry, and upholstery work cells in one large building. Growth has forced the painting work cell into a separate building, aluminum fabrication and body installation into another, inspection and shipping into a fourth, and warehousing into yet another.
Like many other growing companies, Wheeled Coach was not able to design its facility from scratch. And although man- agement realizes that material handling costs are a little higher than an ideal layout would provide, Collins is pleased with the way the facility has evolved and employees have adapted. The aluminum cutting work cell lies adjacent to body fabrication, which, in turn, is located next to the body-installation work cell. And while the vehicle must be driven across a street to one building for painting and then to another for final assembly, at least the ambulance is on wheels. Collins is also satisfied with the flexibility shown in the design of the work cells. Cell con- struction is flexible and can accommodate changes in product mix and volume. In addition, work cells are typically small and movable, with many work benches and staging racks borne on
Video Case wheels so that they can be easily rearranged and products trans- ported to the assembly line.
Assembly-line balancing is one key problem facing Wheeled Coach and every other repetitive manufacturer. Produced on a schedule calling for four 10-hour work days per week, once an ambulance is on one of the six final assembly lines, it must move forward each day to the next workstation. Balancing just enough workers and tasks at each of the seven workstations is a never- ending challenge. Too many workers end up running into each other; too few can’t finish an ambulance in seven days. Constant shifting of design and mix and improved analysis has led to fre- quent changes.
Discussion Questions *
1. What analytical techniques are available to help a company like Wheeled Coach deal with layout problems?
2. What suggestions would you make to Bob Collins about his layout?
3. How would you measure the “efficiency” of this layout?
Facility Layout at Wheeled Coach
• Additional Case Study: Visit MyOMLab for this free case study: Microfi x, Inc.: This company needs to balance its PC manufacturing assembly line and deal with sensitivity analysis of time estimates.
Endnotes
1. Fayurd, A. L., and J. Weeks. “Who Moved My Cube?” Harvard Business Review (July–August, 2011): 102.
2. Takt is German for “time,” “measure,” or “beat” and is used in this context as the rate at which completed units must be pro- duced to satisfy customer demand.
3. Cycle time is the maximum time allowed to accomplish a task or process step. Several process steps may be necessary to com- plete the product. Takt time , discussed earlier, is determined by the customer and is the speed at which completed units must be produced to satisfy customer demand.
* You may wish to view the video that accompanies this case before addressing these questions.
6. As technology and costs change, hospitals continue to inno- vate. The reduced cost of computers means some hospitals have moved from a central computer at the nurse’s station to computers in the room or on carts (see photo). What changes in overall hospital layout would these innovations suggest?
R u b b e rm
a id
H e a lt h ca
re .
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9
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Main Heading Review Material MyOMLab THE STRATEGIC IMPORTANCE OF LAYOUT DECISIONS (p. 370 )
Layout has numerous strategic implications because it establishes an organization’s competitive priorities in regard to capacity, processes, flexibility, and cost, as well as quality of work life, customer contact, and image. The objective of layout strategy is to develop an effective and efficient layout that will meet the firm’s competitive requirements.
Concept Questions: 1.1–1.4
TYPES OF LAYOUT (pp. 370–371 )
Types of layout and examples of their typical objectives include: 1. Office layout : Locate workers requiring frequent contact close to one another. 2. Retail layout : Expose customers to high-margin items. 3. Warehouse layout : Balance low-cost storage with low-cost material handling. 4. Fixed-position layout : Move material to the limited storage areas around the site. 5. Process-oriented layout : Manage varied material flow for each product. 6. Work-cell layout : Identify a product family, build teams, and cross-train team
members. 7. Product-oriented layout : Equalize the task time at each workstation.
Concept Questions: 2.1–2.4
OFFICE LAYOUT (pp. 371 – 372 )
j Office layout —The grouping of workers, their equipment, and spaces/offices to provide for comfort, safety, and movement of information.
A relationship chart displays a “closeness value” between each pair of people and/or departments that need to be placed in the office layout.
Concept Questions: 3.1–3.4
RETAIL LAYOUT (pp. 372 – 375 )
j Retail layout —An approach that addresses flow, allocates space, and responds to customer behavior.
Retail layouts are based on the idea that sales and profitability vary directly with customer exposure to products. The main objective of retail layout is to maximize profitability per square foot of floor space (or, in some stores, per linear foot of shelf space). j Slotting fees —Fees manufacturers pay to get shelf space for their products. j Servicescape —The physical surroundings in which a service takes place and how
they affect customers and employees.
Concept Questions: 4.1–4.4
WAREHOUSE AND STORAGE LAYOUTS (pp. 375 – 377 )
j Warehouse layout —A design that attempts to minimize total cost by addressing trade-offs between space and material handling.
The variety of items stored and the number of items “picked” has direct bearing on the optimal layout. Modern warehouse management is often an automated proce- dure using automated storage and retrieval systems (ASRSs). j Cross-docking —Avoiding the placement of materials or supplies in storage by
processing them as they are received for shipment. Cross-docking requires both tight scheduling and accurate inbound product identi- fication. j Random stocking —Used in warehousing to locate stock wherever there is an
open location. j Customizing —Using warehousing to add value to a product through component
modification, repair, labeling, and packaging.
Concept Questions: 5.1–5.4
FIXED-POSITION LAYOUT (pp. 377 – 378 )
j Fixed-position layout —A system that addresses the layout requirements of sta- tionary projects.
Fixed-position layouts involve three complications: (1) there is limited space at virtually all sites, (2) different materials are needed at different stages of a project, and (3) the volume of materials needed is dynamic.
Concept Questions: 6.1–6.4
PROCESS-ORIENTED LAYOUT (pp. 378 – 383 )
j Process-oriented layout —A layout that deals with low-volume, high-variety production in which like machines and equipment are grouped together.
j Job lots —Groups or batches of parts processed together.
Minimize cost = a n
i = 1 a
n
j = 1 XijCij (9-1)
Concept Questions: 7.1–7.4 Problems: 9.1–9.10 Virtual Office Hours for Solved Problem: 9.1
VIDEO 9.1 Laying Out Arnold Palmer Hospital’s New Facility ACTIVE MODEL 9.1
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Main Heading Review Material MyOMLab WORK CELLS (pp. 383 – 386 )
j Work cell —An arrangement of machines and personnel that focuses on making a single product or family of related products.
j Takt time —Pace of production to meet customer demands. Takt time = Total work time available/ Units required to satisfy customer demand (9-2) Workers required = Total operation time required/Takt time (9-3) j Focused work center —A permanent or semipermanent product-oriented
arrangement of machines and personnel. j Focused factory —A facility designed to produce similar products or components.
Concept Questions: 8.1–8.4 Problem: 9.11
REPETITIVE AND PRODUCT-ORIENTED LAYOUT (pp. 386 – 391 )
j Fabrication line —A machine-paced, product-oriented facility for building components. j Assembly line —An approach that puts fabricated parts together at a series of
workstations; a repetitive process. j Assembly-line balancing —Obtaining output at each workstation on a production
line in order to minimize delay. j Cycle time —The maximum time that a product is allowed at each workstation. Cycle time = Production time available per day , Units required per day (9-4)
Minimum number of workstations = a n
i = 1 Time for task i>(Cycle time) (9-5)
j Heuristic —Problem solving using procedures and rules rather than mathematical optimization.
Line-balancing heuristics include longest task (operation) time, most following tasks, ranked positional weight, shortest task (operation) time, and least number of follow- ing tasks.
Efficiency = gTask times
(Actual number of workstations) * (Largest assigned cycle time) (9-6)
Idle time = (Actual number of workstations * Largest assigned cycle time) - gTask times (9-7)
Concept Questions: 9.1–9.4
Problems: 9.12–9.27
VIDEO 9.2 Facility Layout at Wheeled Coach Ambulances
Virtual Office Hours for Solved Problem: 9.2
9 R
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Chapter 9 Rapid Review continued
LO 9.1 Which of the statements below best describes office layout ? a) Groups workers, their equipment, and spaces/offices to
provide for movement of information. b) Addresses the layout requirements of large, bulky projects
such as ships and buildings. c) Seeks the best personnel and machine utilization in repeti-
tive or continuous production. d) Allocates shelf space and responds to customer behavior. e) Deals with low-volume, high-variety production. LO 9.2 Which of the following does not support the retail layout
objective of maximizing customer exposure to products? a) Locate high-draw items around the periphery of the store. b) Use prominent locations for high-impulse and high-margin
items. c) Maximize exposure to expensive items. d) Use end-aisle locations. e) Convey the store’s mission with the careful positioning of
the lead-off department. LO 9.3 The major problem addressed by the warehouse layout strat-
egy is: a) minimizing difficulties caused by material flow varying
with each product. b) requiring frequent contact close to one another. c) addressing trade-offs between space and material handling. c) balancing product flow from one workstation to the next. d) none of the above. LO 9.4 A fixed-position layout: a) groups workers to provide for movement of information. b) addresses the layout requirements of large, bulky projects
such as ships and buildings.
c) seeks the best machine utilization in continuous production. d) allocates shelf space based on customer behavior. e) deals with low-volume, high-variety production. LO 9.5 A process-oriented layout: a) groups workers to provide for movement of information. b) addresses the layout requirements of large, bulky projects
such as ships and buildings. c) seeks the best machine utilization in continuous production. d) allocates shelf space based on customer behavior. e) deals with low-volume, high-variety production. LO 9.6 For a focused work center or focused factory to be appropri-
ate, the following three factors are required: a) b) c) LO 9.7 Before considering a product-oriented layout, it is important
to be certain of: a) b) c) d) LO 9.8 An assembly line is to be designed for a product whose com-
pletion requires 21 minutes of work. The factory works 400 minutes per day. Can a production line with five workstations make 100 units per day?
a) Yes, with exactly 100 minutes to spare. b) No, but four workstations would be sufficient. c) No, it will fall short even with a perfectly balanced line. d) Yes, but the line’s efficiency is very low. e) Cannot be determined from the information given.
Self Test Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
Answers: LO 9.1. a; LO 9.2. c; LO 9.3. c; LO 9.4. b; LO 9.5. e; LO 9.6. family of products, stable forecast (demand), volume; LO 9.7. adequate volume, stable demand, standardized product, adequate/quality supplies; LO 9.8. c.
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407407
C H A P T E R O U T L I N E
10 ◆
Human Resource Strategy for Competitive Advantage 410
◆
Labor Planning 411 ◆
Job Design 412 ◆
Ergonomics and the Work Environment 415
◆
Methods Analysis 417
◆
The Visual Workplace 420
◆
Labor Standards 420
◆
Ethics 430
GLOBAL COMPANY PROFILE: Rusty Wallace’s NASCAR Racing Team
C H
A P
T E
R
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
•• Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
C H A P T E R GLOBAL COMPANY PROFILE Rusty Wallace’s NASCAR Racing Team
Human Resources, Job Design, and Work Measurement
A la
sk a A
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A la
sk a A
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A new century brought new popularity to NASCAR (National Association for Stock Car Auto
Racing). Hundreds of millions of TV and sponsorship dollars poured into the sport. With
more money, competition increased, as did the rewards for winning on Sunday. The teams,
headed by such names as Rusty Wallace, Jeff Gordon, Dale Earnhardt, Jr., and Tony Stewart,
are as famous as the New York Yankees, Atlanta Hawks, or Chicago Bears.
The race car drivers may be famous, but it’s the pit crews who
often determine the outcome of a race. Years ago, crews were auto
mechanics during the week who simply did double duty on Sundays
in the pits. They did pretty well to change four tires in less than 30
seconds. Today, because NASCAR teams find competitive advantage
wherever they can, taking more than 16 seconds can be disastrous.
A botched pit stop is the equivalent of ramming your car against the
wall—crushing all hopes for the day.
On Rusty Wallace’s team, as on all the top NASCAR squads, the
crewmen who go “over the wall” are now athletes, usually ex-college
football or basketball players with proven agility and strength. The Ever-
nham team, for example, includes a former defensive back from Fairleigh Dickinson (who is now
a professional tire carrier) and a 300-pound lineman from East Carolina University (who handles
the jack). The Chip Ganassi racing team includes baseball players from Wake Forest, football
players from University of Kentucky and North Carolina, and a hockey player from Dartmouth.
Tire changers—the guys who wrench lug nuts off and on—are a scarce human resource and
average $100,000 a year in salary. Jeff Gordon was reminded of the importance of coordinated
High-Performance Teamwork Makes the Difference Between Winning and Losing
GLOBAL COMPANY PROFILE Rusty Wallace’s NASCAR Racing Team
C H A P T E R 1 0
408
This Goodyear tire comes off Rusty Wallace’s car and is no longer
needed after going around the track for more than 40 laps in a
June 19 Michigan International Speedway race.
This Goodyear tire comes off Rusty Wallace’s car and is no longer
Co u rt
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o f
th e O
rl a n d o S
e n ti n e l, 2
0 0 5
Jamie Rolewicz takes tires from a pile of used tires and puts them
onto a cart. Lap 91—Gas is added and a tire is removed from Rusty Wallace’s car.
J i R l i t k ti f il f d ti d t th
L 91 G i dd d d ti i d f R t W ll ’
C o u rt
e sy
o f
th e O
rl a n d o S
e n ti n e l, 2
0 0
5
C o u rt
e sy
o f
th e O
rl a n d o S
e n ti n e l, 2
0 0
5
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409
teamwork when five of his “over-the-wall” guys jumped to
Dale Jarrett’s organization a few years ago; it was believed to
be a $500,000 per year deal.
A pit crew consists of seven men: a front-tire changer;
a rear-tire changer; front- and rear-tire carriers; a man who
jacks the car up; and two gas men with an 11-gallon can.
Every sport has its core competencies and key metrics—
for example, the speed of a pitcher’s fastball and a running
back’s time on the 40-yard dash. In NASCAR, a tire changer
should get 5 lug nuts off in 1.2 seconds. The jackman should
haul his 25-pound aluminum jack from the car’s right side to
left in 3.8 seconds. For tire carriers, it should take .7 seconds
to get a tire from the ground to mounted on the car.
The seven men who go over the wall are coached and
orchestrated. Coaches use the tools of OM and watch “game
tape” of pit stops and make intricate adjustments to the
choreography.
“There’s a lot of pressure,” says D. J. Richardson, a Rusty
Wallace team tire changer—and one of the best in the busi-
ness. Richardson trains daily with the rest of the crew in the
shop of the team owner. They focus on cardiovascular work
and two muscle groups daily. Twice a week, they simulate pit
stops—there can be from 12 to 14 variations—to work on
their timing.
In a recent race in Michigan, Richardson and the rest of
the Rusty Wallace team, with ergonomically designed gas
cans, tools, and special safety gear, were ready. On lap 43,
the split-second frenzy began, with Richardson—air gun in
hand—jumping over a 2-foot white wall and sprinting to the
right side of the team’s Dodge. A teammate grabbed the tire
and set it in place while Richardson secured it to the car. The
process was repeated on the left side while the front crew
followed the same procedure. Coupled with refueling, the pit
stop took 12.734 seconds.
After catching their breath for a minute, Richardson and
the other pit crew guys reviewed a videotape, looking for
split-second flaws.
The same process was repeated on lap 91. The Wallace
driver made a late charge on Jeff Burton and Kurt Busch on
the last lap and went from 14th place to a 10th place finish.
JM (Jackman) The jackman carries the hydraulic jack from the pit wall to raise the car’s right side. After new tires are bolted on, he drops the car to the ground and
repeats the process on the left side. His timing is crucial during this left side change, because when he drops the car again, it’s the signal for the driver to go. The jackman
has the most dangerous job of all the crew members; during the right-side change, he is exposed to oncoming traffic down pit row. FTC (Front tire carrier) Each tire
carrier hauls a new 75-pound tire to the car’s right side, places it on the wheel studs, and removes the old tire after the tire change. They repeat this process on the left
side of the car with a new tire rolled to them by crew members behind the pit wall. CFT (Changer front tire) Tire changers run to the car’s right side and, using an air
impact wrench, they remove five lug nuts off the old tire and bolt on a new tire. They repeat the process on the left side. RTC (Rear tire carrier) Same as front tire carrier,
except RTC may also adjust the rear jack bolt to alter the car’s handling. CRT (Changer rear tire) Same as FT but on two rear tires. Gas man #1 This gas man is usually
the biggest and strongest person on the team. He goes over the wall carrying a 75-pound, 11-gallon “dump can” whose nozzle he jams into the car’s fuel cell receptacle.
He is then handed (or tossed) another can, and the process is repeated. Gas man #2 Gets second gas can to Gas man #1 and catches excess fuel that spills out.
1 Wallace’s car pulls into the pit; the crew rushes tothe right side of the car to begin service.
FTC
CFT JM
CRT
RTC
GM#2 GM#1
Wall
2 Right side is jacked up, tire starts to come off; gasman is emptying his first can.
Wall
FTC CFT
JM CRT RTC
GM#2 GM#1
3 Action shifts to driver’s side of the car; gas mancarries second can of gas in.
Wall
FTC CFT CRTJM
RTC
GM#2
GM#1
4 The second can of gas is being emptied; driver’sside tires are being changed.
Wall
5 Service is complete. The jackman drops the car,which is the signal to the Wallace driver to exit the pit.
Wall
Movement of the pit crew members who go over the wall...
FTC CFT CRTJM RTC
GM#2
GM#1 FTC CFT CRTJM RTC
GM#2
GM#1
JM = Jackman
FTC = Front tire carrier
CFT = Changer front tire
RTC = Rear tire carrier
CRT = Changer rear tire
GM#1 = Gas man #1
GM#2 = Gas man #2
A good pit stop will take about 16 seconds.
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410
Human Resource Strategy for Competitive Advantage Good human resource strategies are expensive, difficult to achieve, and hard to sustain. But, like a NASCAR team, many organizations, from Hard Rock Cafe to Alaska Airlines, have demonstrated that sustainable competitive advantage can be built through a human resource strategy. The payoff can be significant and difficult for others to duplicate. Indeed, as the manager at London Four Seasons Hotel has noted, “We’ve identified that our key competitive difference is our people .” 1 In this chapter, we will examine some of the tools available to opera- tions managers for achieving competitive advantage via human resource management.
The objective of a human resource strategy is to manage labor and design jobs so people are effectively and efficiently utilized. As we focus on a human resource strategy, we want to ensure that people:
1. Are efficiently utilized within the constraints of other operations management decisions. 2. Have a reasonable quality of work life in an atmosphere of mutual commitment and trust.
By reasonable quality of work life we mean a job that is not only reasonably safe and for which the pay is equitable but that also achieves an appropriate level of both physical and psycho- logical requirements. Mutual commitment means that both management and employee strive to meet common objectives. Mutual trust is reflected in reasonable, documented employment policies that are honestly and equitably implemented to the satisfaction of both management and employee. When management has a genuine respect for its employees and their contribu- tions to the firm, establishing a reasonable quality of work life and mutual trust is not particu- larly difficult.
Constraints on Human Resource Strategy As Figure 10.1 suggests, many decisions made about people are constrained by other deci- sions. First, the product mix may determine seasonality and stability of employment. Second, technology, equipment, and processes may have implications for safety and job content. Third, the location decision may have an impact on the ambient environment in which the employees work. Finally, layout decisions, such as assembly line versus work cell, influence job content.
Technology decisions impose substantial constraints. For instance, some of the jobs in foundries are dirty, noisy, and dangerous; slaughterhouse jobs may be stressful and subject workers to stomach-crunching stench; assembly-line jobs are often boring and mind numbing; and high capital investments such as those required for manufacturing semiconductor chips may require 24-hour, 7-day-a-week operation in restrictive clothing.
We are not going to change these jobs without making changes in our other strategic deci- sions, so the trade-offs necessary to reach a tolerable quality of work life are difficult. Effective managers consider such decisions simultaneously. The result: a system in which both individual and team performance are enhanced through optimum job design.
We now look at three distinct decision areas of human resource strategy: labor planning , job design , and labor standards .
L E A R N I N G OBJEC TI V ES
LO 10.1 Describe labor-planning policies 411
LO 10.2 Identify the major issues in job design 412
LO 10.3 Identify major ergonomic and work environment issues 416
LO 10.4 Use the tools of methods analysis 418
LO 10.5 Identify four ways of establishing labor standards 421
LO 10.6 Compute the normal and standard times in a time study 423
LO 10.7 Find the proper sample size for a time study 424
VIDEO 10.1 The “People” Focus: Human Resources at Alaska Airlines
VIDEO 10.2 Human Resources at Hard Rock Cafe
STUDENT TIP An operations manager knows how
to build an effective human resource
strategy.
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C H A P T E R 1 0 | H U M A N R E S O U R C E S , J O B D E S I G N , A N D W O R K M E A S U R E M E N T 411
Labor Planning Labor planning is determining staffing policies that deal with (1) employment stability, (2) work schedules, and (3) work rules.
Employment-Stability Policies Employment stability deals with the number of employees maintained by an organization at any given time. There are two very basic policies for dealing with stability:
1. Follow demand exactly: Following demand exactly keeps direct labor costs tied to pro- duction but incurs other costs. These other costs include (a) hiring and layoff costs, (b) unemployment insurance, and (c) premium wages to entice personnel to accept unstable employment. This policy tends to treat labor as a variable cost.
2. Hold employment constant: Holding employment levels constant maintains a trained workforce and keeps hiring, layoff, and unemployment costs to a minimum. However, with employment held constant, employees may not be utilized fully when demand is low, and the firm may not have the human resources it needs when demand is high. This policy tends to treat labor as a fixed cost.
These policies are only two of many that can be efficient and provide a reasonable quality of work life. Firms must determine policies about employment stability.
Work Schedules Although the standard work schedule in the U.S. is still five 8-hour days, many variations exist. A popular variation is a work schedule called flextime. Flextime allows employees, within lim- its, to determine their own schedules. A flextime policy might allow an employee (with proper notification) to be at work at 8 a.m. plus or minus 2 hours. This policy allows more autonomy and independence on the part of the employee. Some firms have found flextime a low-cost fringe benefit that enhances job satisfaction. The problem from the OM perspective is that much production work requires full staffing for efficient operations. A machine that requires three people cannot run at all if only two show up. Having a waiter show up to serve lunch at 1:30 p.m. rather than 11:30 a.m. is not much help either.
Similarly, some industries find that their process strategies severely constrain their human resource scheduling options. For instance, paper manufacturing, petroleum refining, and power stations require around-the-clock staffing except for maintenance and repair shutdown.
Figure 10.1
Constraints on Human
Resource Strategy
Product strategy Skills needed Talents needed Materials used Safety
Process strategy Technology Machinery and equipment used Safety
HUMAN RESOURCE STRATEGY
Schedules Time of day Time of year (seasonal) Stability of schedules
Individual differences Strength and fatigue Information processing and response
Location strategy Climate Temperature Noise Light Air quality
Layout strategy Fixed position Process Assembly line Work cell Product
• • • •
• •
•
• •
•
• •
• • • • •
• • • • •
W hat
W he
re
H ow
Pr oc
ed ur
e
When Who
Labor planning
A means of determining staffing
policies dealing with employment
stability, work schedules, and
work rules.
LO 10.1 Describe labor-planning policies
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Another option is the flexible workweek . This plan often calls for fewer but longer days, such as four 10-hour days or, as in the case of light-assembly plants, 12-hour shifts. Work- ing 12-hour shifts usually means working 3 days one week and 4 the next. Such shifts are sometimes called compressed workweeks . These schedules are viable for many operations func- tions—as long as suppliers and customers can be accommodated.
Another option is shorter days rather than longer days. This plan often moves employees to part-time status . Such an option is particularly attractive in service industries, where staffing for peak loads is necessary. Banks and restaurants often hire part-time workers. Also, many firms reduce labor costs by reducing fringe benefits for part-time employees.
Job Classifications and Work Rules Many organizations have strict job classifications and work rules that specify who can do what, when they can do it, and under what conditions they can do it, often as a result of union pressure. These job classifications and work rules restrict employee flexibility on the job, which in turn reduces the flexibility of the operations function. Yet part of an operations manager’s task is to manage the unexpected. Therefore, the more flexibility a firm has when staffing and establishing work schedules, the more efficient and responsive it can be. This is particularly true in service organizations, where extra capacity often resides in extra or flexible staff. Building morale and meeting staffing requirements that result in an efficient, responsive operation are easier if managers have fewer job classifications and work-rule constraints. If the strategy is to achieve a competitive advantage by responding rapidly to the customer, a flexible workforce may be a prerequisite.
Job Design Job design specifies the tasks that constitute a job for an individual or a group. We examine five components of job design: (1) job specialization, (2) job expansion, (3) psychological compo- nents, (4) self-directed teams, and (5) motivation and incentive systems.
Labor Specialization The importance of job design as a management variable is credited to the 18th-century econo- mist Adam Smith. Smith suggested that a division of labor, also known as labor specialization (or job specialization), would assist in reducing labor costs of multiskilled artisans. This is accomplished in several ways:
1. Development of dexterity and faster learning by the employee because of repetition 2. Less loss of time because the employee would not be changing jobs or tools 3. Development of specialized tools and the reduction of investment because each employee
has only a few tools needed for a particular task
The 19th-century British mathematician Charles Babbage determined that a fourth considera- tion was also important for labor efficiency. Because pay tends to follow skill with a rather high correlation, Babbage suggested paying exactly the wage needed for the particular skill required . If the entire job consists of only one skill, then we would pay for only that skill. Otherwise, we would tend to pay for the highest skill contributed by the employee. These four advantages of labor specialization are still valid today.
A classic example of labor specialization is the assembly line. Such a system is often very efficient, although it may require employees to do short, repetitive, mind-numbing jobs. The wage rate for many of these jobs, however, is good. Given the relatively high wage rate for the modest skills required in many of these jobs, there is often a large pool of employees from which to choose.
From the manager’s point of view, a major limitation of specialized jobs is their failure to bring the whole person to the job. Job specialization tends to bring only the employee’s manual skills to work. In an increasingly sophisticated knowledge-based society, managers want em- ployees to bring their mind to work as well.
Job design
An approach that specifies the
tasks that constitute a job for an
individual or a group.
Labor specialization (or job specialization)
The division of labor into unique
(“special”) tasks.
LO 10.2 Identify the major issues in job design
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Job Expansion Moving from labor specialization toward more varied job design may improve the quality of work life. The theory is that variety makes the job “better” and that the employee therefore enjoys a higher quality of work life. This flexibility thus benefits the employee and the organization.
We modify jobs in a variety of ways. The first approach is job enlargement , which occurs when we add tasks requiring similar skill to an existing job. Job rotation is a version of job enlargement that occurs when the employee is allowed to move from one specialized job to another. Variety has been added to the employee’s perspective of the job. Another approach is job enrichment , which adds planning and control to the job. An example is to have department store salespeople responsible for ordering, as well as selling, their goods. Job enrichment can be thought of as vertical expansion , as opposed to job enlargement, which is horizontal . These ideas are shown in Figure 10.2 .
A popular extension of job enrichment, employee empowerment is the practice of enriching jobs so employees accept responsibility for a variety of decisions normally associated with staff specialists. Empowering employees helps them take “ownership” of their jobs so they have a personal interest in improving performance.
Psychological Components of Job Design An effective human resources strategy also requires consideration of the psychological com- ponents of job design. These components focus on how to design jobs that meet some mini- mum psychological requirements.
Hawthorne Studies The Hawthorne studies introduced psychology to the workplace. They were conducted in the 1920s at Western Electric’s Hawthorne plant near Chicago. These studies were initiated to determine the impact of lighting on productivity. Instead, they found the dynamic social system and distinct roles played by employees to be more important than the intensity of the lighting. They also found that individual differences may be dominant in what an employee expects from the job and what the employee thinks her or his contribution to the job should be.
Core Job Characteristics Substantial research regarding the psychological compo- nents of job design has taken place since the Hawthorne studies. Hackman and Oldham have incorporated much of that work into five desirable characteristics of job design. 2 They suggest that jobs should include the following characteristics:
1. Skill variety , requiring the worker to use a variety of skills and talents 2. Job identity , allowing the worker to perceive the job as a whole and recognize a start and
a finish 3. Job significance , providing a sense that the job has an impact on the organization and
society 4. Autonomy , offering freedom, independence, and discretion 5. Feedback , providing clear, timely information about performance
Figure 10.2
An Example of Job
Enlargement ( horizontal
job expansion) and Job
Enrichment ( vertical job
expansion)
Job enlargement
The grouping of a variety of tasks
about the same skill level; horizon-
tal enlargement.
Job rotation
A system in which an employee is
moved from one specialized job to
another.
Job enrichment
A method of giving an employee
more responsibility that includes
some of the planning and control
necessary for job accomplishment;
vertical expansion.
Enriched job Planning
(Participate in a cross-function quality improvement team.)
Task #2 (Adhere labels
to printed circuit board.)
Enlarged job Task #3
(Lock printed circuit board into fixture for
next operation.)
Control (Test circuits after assembly.)
Present job (Manually insert and solder six resistors.)
Employee empowerment
Enlarging employee jobs so that
the added responsibility and
authority are moved to the lowest
level possible.
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Specialization
Job expansion
S e lf-
d ir e ct
io n
Enlargement
Enrichment
Empowerment
Self-directed teams
Including these five ingredients in job design is consistent with job enlargement, job enrich- ment, and employee empowerment. We now want to look at some of the ways in which teams can be used to expand jobs and achieve these five job characteristics.
Self-Directed Teams Many world-class organizations have adopted teams to foster mutual trust and commitment, and provide the core job characteristics. One team concept of particular note is the self-directed team : a group of empowered individuals working together to reach a common goal. These teams may be organized for long- or short-term objectives. Teams are effective primarily because they can easily provide employee empowerment, ensure core job characteristics, and satisfy many of the psycho- logical needs of individual team members. A job design continuum is shown in Figure 10.3 .
Limitations of Job Expansion If job designs that enlarge, enrich, empower, and use teams are so good, why are they not universally used? Mostly it is because of costs. Here are a few limitations of expanded job designs:
◆ Higher capital cost: Job expansion may require additional equipment and facilities. ◆ Individual differences: Some employees opt for the less-complex jobs. ◆ Higher wage rates: Expanded jobs may well require a higher average wage. ◆ Smaller labor pool: Because expanded jobs require more skill and acceptance of more re-
sponsibility, job requirements have increased. ◆ Higher training costs: Job expansion requires training and cross-training. Therefore, train-
ing budgets need to increase.
Despite these limitations, firms are finding a substantial payoff in job expansion.
Figure 10.3
Job Design Continuum
An increasing reliance on the
employee’s contribution can
increase the responsibility
accepted by the employee.
Self-directed team
A group of empowered individuals
working together to reach
a common goal.
Southwest Airlines—consistently near the top of the airline pack in travel surveys, fewest lost bags and complaints, and highest
profits—hires people with enthusiasm and empowers them to excel. A barefoot co-founder and chairman emeritus, Herb Kelleher,
clings to the tail of a jet (left photo). Says Kelleher, “I’ve tried to create a culture of caring for people in the totality of their lives, not
just at work. Someone can go out and buy airplanes and ticket counters, but they can’t buy our culture, our esprit de corps. ”
So u th
w e st
A ir lin
e s
C o .
S o u th
w e st
A ir lin
e s
C o .
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Motivation and Incentive Systems Our discussion of the psychological components of job design provides insight into the factors that contribute to job satisfaction and motivation. In addition to these psychological factors, there are monetary factors. Money often serves as a psychological as well as financial motiva- tor. Monetary rewards take the form of bonuses, profit and gain sharing, and incentive systems.
Bonuses, in cash, stock ownership, or stock options, are often used to reward employees. Almost half of U.S. employees have one or more forms of profit sharing that distributes part of the profit to employees. A variation of profit sharing is gain sharing, which rewards employees for improvements made in an organization’s performance. The most popular of these is the Scanlon plan, in which any reduction in the cost of labor is shared between management and labor.
Incentive systems based on individual or group productivity are used throughout the world in a wide variety of applications, including nearly half of the manufacturing firms in America. Production incentives often require employees or crews to produce at or above a predetermined standard. The standard can be based on a “standard time” per task or number of pieces made. Both systems typically guarantee the employee at least a base rate. Incentives, of course, need not be monetary. Awards, recognition, and other kinds of preferences such as a preferred work schedule can be effective. (See the OM in Action box “Using Incentives to Unsnarl Traffic Jams in the OR.”) Hard Rock Cafe has successfully reduced its turnover by giving every employee—from the CEO to the busboys—a $10,000 gold Rolex watch on their 10th anniversary with the firm.
With the increasing use of teams, various forms of team-based pay are also being de- veloped. Many are based on traditional pay systems supplemented with some form of bo- nus or incentive system. However, because many team environments require cross training, knowledge-based pay systems have also been developed. Under knowledge-based (or skill- based) pay systems, a portion of the employee’s pay depends on demonstrated knowledge or skills. At Wisconsin’s Johnsonville Sausage Co., employees receive pay raises only by mastering new skills such as scheduling, budgeting, and quality control.
Ergonomics and the Work Environment With the foundation provided by Frederick W. Taylor, the father of the era of scientific man- agement, we have developed a body of knowledge about people’s capabilities and limitations. This knowledge is necessary because humans are hand/eye animals possessing exceptional capabilities and some limitations. Because managers must design jobs that can be done, we now introduce a few of the issues related to people’s capabilities and limitations. Ergonomics The operations manager is interested in building a good interface between humans, the environment, and machines. Studies of this interface are known as ergonomics . Ergonomics means “the study of work.” ( Ergon is the Greek word for “work.”) The term
Ergonomics
The study of the human interface
with the environment and
machines.
OM in Action Using Incentives to Unsnarl Traffi c Jams in the OR Hospitals have long offered surgeons a precious perk: scheduling the bulk of
their elective surgeries in the middle of the week so they can attend confer-
ences, teach, or relax during long weekends. But at Boston Medical Center,
St. John’s Health Center (in Missouri), and Elliot Health System (in New
Hampshire), this practice, one of the biggest impediments to a smooth-running
hospital, is changing. “Block scheduling” jams up operating rooms, overloads
nurses at peak times, and bumps scheduled patients for hours and even days.
Boston Medical Center’s delays and cancellations of elective surgeries were
nearly eliminated after surgeons agreed to stop block scheduling and to dedicate
one OR for emergency cases. Cancellations dropped to 3, from 334, in just one
6-month period. In general, hospitals changing to the new system of spreading
out elective surgeries during the week increase their surgery capacity by 10%,
move patients through the operating room faster, and reduce nursing overtime.
To get doctors on
board at St. John’s, the
hospital offered a carrot
and two sticks: Doctors
who were more than
10 minutes late 10% of
the time lost their cov-
eted 7:30 A.M. start times and were fined a portion of their fee—with proceeds
going to a kitty that rewarded the best on-time performers. Surgeons’ late start
times quickly dropped from 16% to 5% and then to less than 1% within a year.
Rob e rt
D a ly
/O JO
I m
a g e s
L td
/A la
m y
Sources: Executive Insight (October 4, 2011); The Wall Street Journal
(August 10, 2005); and Hospitals & Health Networks (September 2005).
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human factors is often substituted for the word ergonomics . Understanding ergonomic issues helps to improve human performance.
Male and female adults come in limited configurations and abilities. Therefore, design of tools and the workplace depends on the study of people to determine what they can and can- not do. Substantial data have been collected that provide basic strength and measurement data needed to design tools and the workplace. The design of the workplace can make the job easier or impossible. In addition, we now have the ability, through the use of computer modeling, to analyze human motions and efforts. The OM in Action box, “The Missing Perfect Chair,” discusses how the size of furniture can affect employees.
Operator Input to Machines Operator response to machines, be they hand tools, pedals, levers, or buttons, needs to be evaluated. Operations managers need to be sure that operators have the strength, reflexes, perception, and mental capacity to provide necessary control. Such problems as carpal tunnel syndrome may result when a tool as simple as a
With a commitment to efficiency and
an understanding of ergonomics,
UPS trains drivers in the company’s
“340 methods” that save seconds and
improve safety. Here a UPS driver learns
to walk on “ice” with the help of a “slip
and fall” simulator.
St e p h e n V
o ss
LO 10.3 Identify major ergonomic and work
environment issues
OM in Action The Missing Perfect Chair As you sit at your desk, are your feet dangling, or are they scrunched up un-
der the chair? In a perfectly fitting chair, your back is supported, your feet are
planted on the floor, your thighs are parallel to the floor, and your knees are
at a 90-degree angle. If your chair, as are many chairs, is 17.3 inches high
then you should be a 68.3-inch-tall male (the 50th percentile for men). For
women, the 50th percentile chair should be 15.7 inches high, and you should
be 62.9 inches tall. However, if you have an adjustable chair, you are in luck
as they are often designed for the 5th to the 95th percentiles. * But that still
leaves millions of unlucky people at both ends of the bell curve—too small or
too big for their chair.
Former Labor Secretary Robert Reich, who is 4 feet 10 inches tall, once
sawed off the legs of his office chair and desk to make them fit. While he
was working in the Justice Department in the 1970s, the General Services
Administration (GSA) refused his request to shorten his standard-sized
wooden desk and chair. “I snuck in one weekend with my saw and did it
myself, and sent the stubs to the GSA administrator,” Dr. Reich says. Later
as Labor Secretary, his chair left his legs sticking out, so he held meetings
standing up.
Managers may find that solving the “chair” problem is complicated
because special chairs for only some employees can foster resentment. In
addition, changing the height of a chair often means the desk must also be
higher or lower, complicating desk assignments. But, manufacturers are now * For men the 5th percentile is 63.6 in., and the 95th is 72.8 in. tall; for women it
is 62.9 in. and 76.1 in., respectively.
offering both adjustable
chairs and worktables.
Some desks now include
timers and a touch
screen that allow you
to change desk height.
Other offerings include
multiple work surfaces
and keyboard supports,
as well as repositionable
computer-monitor supports.
However, most operations managers are under heavy pressure to
hold down costs, so providing special items for a few workers presents
a conflict. Special chairs can list for $1,000 and adjustable desks for
much more. Nevertheless, the need for adjustable chairs and desks is
growing. Steelcase Inc. recently studied the body shapes and postures
of 2,000 workers in 11 countries and found that “extreme size” is on
the rise.
Sources: The Wall Street Journal (April 29, 2015), (May 20, 2014), and
(September 21, 2011).
Jo e M
a rq
u e tt
e /A
P I m
a g e s
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keyboard is poorly designed. The photo of the race car steering wheel above shows one inno- vative approach to critical operator input.
Feedback to Operators Feedback to operators is provided by sight, sound, and feel; it should not be left to chance. The mishap at the Three Mile Island nuclear facility, America’s worst nuclear experience, was in large part the result of poor feedback to the operators about reactor performance. Nonfunctional groups of large, unclear instruments and inaccessible con- trols, combined with hundreds of confusing warning lights, contributed to that failure. Such relatively simple issues make a difference in operator response and, therefore, performance.
An important human factor/ergonomic issue in the aircraft industry is cockpit design. Newer “glass cockpits” display information in more concise form than the traditional rows of round analog dials and gauges. These displays reduce the chance of human error, which is a factor in about two-thirds of commercial air accidents.
The Work Environment The physical environment in which employees work affects their performance, safety, and quality of work life. Illumination, noise and vibration, tem- perature, humidity, and air quality are work-environment factors under the control of the organization and the operations manager. The manager must approach them as controllable.
Illumination is necessary, but the proper level depends on the work being performed. Figure 10.4(a) provides some guidelines. However, other lighting factors are important. These include reflective ability, contrast of the work surface with surroundings, glare, and shadows.
Noise of some form is usually present in the work area, and most employees seem to adjust well. However, high levels of sound will damage hearing. Figure 10.4(b) provides indications of the sound generated by various activities. Extended periods of exposure to decibel levels above 85 dB are permanently damaging. The Occupational Safety and Health Administration (OSHA) requires ear protection above this level if exposure equals or exceeds eight hours. Even at low levels, noise and vibration can be distracting and can raise a person’s blood pressure, so managers make substantial effort to reduce noise and vibration through good machine design, enclosures, or insulation.
Temperature and humidity parameters have also been well established. Managers with activi- ties operating outside the established comfort zone should expect adverse effect on performance.
Methods Analysis Methods analysis focuses on how a task is accomplished. Whether controlling a machine or mak- ing or assembling components, how a task is done makes a difference in performance, safety, and quality. Using knowledge from ergonomics and methods analysis, methods engineers are charged with ensuring that quality and quantity standards are achieved efficiently and safely. Methods analysis and related techniques are useful in office environments as well as in the fac- tory. Methods techniques are used to analyze:
1. Movement of individuals or material. The analysis is performed using flow diagrams and process charts with varying amounts of detail.
Drivers of race cars have no time to grasp for
controls or to look for small hidden gauges.
Controls and instrumentation for modern
race cars have migrated to the steering
wheel itself—the critical interface between
man and machine.
In a ci
o P
ir e s/
S h u tt
e rs
to ck
Methods analysis
A system that involves developing
work procedures that are safe
and produce quality products
efficiently.
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2. Activity of human and machine and crew activity. This analysis is performed using activity charts (also known as man–machine charts and crew charts).
3. Body movement (primarily arms and hands). This analysis is performed using operations charts.
Flow diagrams are schematics (drawings) used to investigate movement of people or material. Britain’s Paddy Hopkirk Factory, which manufactures auto parts, demonstrates one version of a flow diagram in Figure 10.5 . Hopkirk’s old work flow is shown in Figure 10.5 (a), and a new method, with improved work flow and requiring less storage and space, is shown in Figure 10.5 (b). Process charts use symbols, as in Figure 10.5 (c), to help us understand the movement of people or material. In this way non-value-added activities can be recognized and operations made more efficient. Figure 10.5 (c) is a process chart used to supplement the flow diagrams shown in Figure 10.5 (b).
Activity charts are used to study and improve the utilization of an operator and a machine or some combination of operators (a “crew”) and machines. The typical approach is for the analyst to record the present method through direct observation and then propose the improve- ment on a second chart. Figure 10.6 is an activity chart to show a proposed improvement for a two-person crew at Quick Car Lube.
Body movement is analyzed by an operations chart . It is designed to show economy of motion by pointing out wasted motion and idle time (delay). The operations chart (also known as a right-hand/left-hand chart ) is shown in Figure 10.7 .
Normal Visual (office, classroom,
machining)
75–100
Assembly Tasks (parts assembly)
50–75
500 and up
Exacting Tasks (electronic and
watch assembly, dentistry)Small Details
(engraving, detail drafting)
100–200
Large Objects (warehouses,
hallways)
10–25
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile General Interiors
(conference, restrooms,
restaurants)
25–50
Figure 10.4(a)
Recommended Levels of Illumination (using foot-candles (ft-c) as the measure of illumination)
30
Soft whisper
Very quiet
Quiet Intrusive Ear protection needed if exposed 8 hours or more
Very annoying
Painful
Subway train
Printing press
Pneumatic hammer
Prop airplane
Jet take-off
Residential area of
Chicago at night
Near freeway
auto traffic
Light traffic
(100 feet)
Vacuum cleaner (10 feet)
40 50 60 70 80 90 100 110 120
Figure 10.4(b)
Decibel (dB) Levels for Various Sounds
Adapted from A. P. G. Peterson and E. E. Gross, Jr., Handbook of Noise Measurement , 7th ed. Copyright © by GenRad, LLC. Reprinted with permission.
LO 10.4 Use the tools of methods analysis
Flow diagram
A drawing used to analyze move-
ment of people or material.
Process chart
Graphic representations that
depict a sequence of steps for
a process.
Activity chart
A way of improving utilization of an
operator and a machine or some
combination of operators (a crew)
and machines.
Operations chart
A chart depicting right- and left-
hand motions.
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From press
machine
Mach. 2
Mach. 3
Storage bins
Storage bins
Paint shop
(a) (c)
(b)
Welding
Paint shop
Machine 2
Machine 3
Machine 4
Machine 1
Mach. 4
Present Method
Proposed Method
SUBJECT CHARTED
DEPARTMENT
DIST. IN
FEET
TIME IN
MINS.
CHART SYMBOLS
DATE
CHART BY
CHART NO.
SHEET NO. OF
PROCESS CHART
PROCESS DESCRIPTION
TOTAL
X Axle-stand Production
Work cell for axle stand
50
5
4
4
4
20
10
97
3
4
2.5
3.5
4
Poka- yoke
4
4
25
From press machine to storage bins at work cell
Move to machine 4
Move to welding Poka-yoke inspection at welding Weld Move to painting Paint
Operation at machine 4
Storage bins Move to machine 1 Operation at machine 1 Move to machine 2
Move to machine 3 Operation at machine 3
Operation at machine 2
5 / 1 / 15 JH
1 1 1
From press mach.
Machine 1
Welding
= operation; = transport; = inspect; = delay; = storage
Figure 10.5
Flow Diagrams and Process Chart of Axle-Stand Production at Paddy Hopkirk Factory
(a) Old method; (b) new method; (c) process chart of axle-stand production using Paddy Hopkirk’s new method (shown in (b)).
OPERATOR #1 OPERATOR #2
TIME % TIME %
WORK
IDLE
OPERATION:
EQUIPMENT:
OPERATOR:
STUDY NO.: ANALYST:
SUBJECT PRESENT PROPOSED DEPT.
SHEET OF
CHART BY
DATE
TIME TIME TIME
ACTIVITY CHART
Repeat cycle
12 100 12 100
Oil change & fluid check
Quick Car Lube 5-1-15 LSA
Move car to pitTake order
Drain oilVacuum car
Check transmission
Check transmission
Clean windows
Change oil filter Check under
hood Replace oil plugFill with oil
Move car to front for customerComplete bill
Move next car to pitGreet nextcustomer Drain oilVacuum car
Clean windows
Operator #1 Operator #2
1 1
One bay/pit Two-person crew
BR0 0 0 0
Figure 10.6
Activity Chart for Two-Person Crew Doing an Oil Change in 12
Minutes at Quick Car Lube
1
2
3
4
5
6
7
Reach for cup
Grasp cup
Move cup
Hold cup
Hold cup
Hold cup
Hold cup
OPERATION
TRANSPORT.
INSPECTION
DELAY
STORAGE
SYMBOLS PRESENT PROPOSED
LH RH LH RH
OPERATIONS CHART
Idle
Idle
Idle
Reach for scoop
Grasp scoop
Move scoop to ice
Scoop ice
LEFT-HAND ACTIVITY
METHODPresent RIGHT-HAND ACTIVITY
METHODPresentSYMBOLS SYMBOLS DIST.DIST.
PROCESS:
EQUIPMENT:
OPERATOR:
STUDY NO:
DATE:
METHOD ( PRESENT / PROPOSED )
REMARKS:
/ / SHEET NO.
Starbucks
Partial Study
CM 5 1 15 1
2 3
4 3
1 1 ANALYST:
6"
8"
Scooping Ice for Coffee Scoop
2of
Figure 10.7
Operations Chart (right-hand/left-hand chart) for Scooping Ice to Coffee Cup
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420 P A R T 2 | D E S I G N I N G O P E R AT I O N S
The Visual Workplace A visual workplace uses low-cost visual devices to share information quickly and accurately. Well-designed displays and graphs root out confusion and replace difficult-to-understand printouts and paperwork. Because workplace data change quickly and often, operations man- agers need to share accurate and up-to-date information. Changing customer requirements, specifications, schedules, and other details must be rapidly communicated to those who can make things happen.
The visual workplace can eliminate non-value-added activities by making standards, prob- lems, and abnormalities visual (see Figure 10.8 ). The visual workplace needs less supervision because employees understand the standard, see the results, and know what to do.
Labor Standards So far in this chapter, we have discussed labor planning and job design. The third requirement of an effective human resource strategy is the establishment of labor standards. Labor standards are the amount of time required to perform a job or part of a job, and they exist, formally or informally, for all jobs. Effective manpower planning is dependent on a knowledge of the labor required.
Modern labor standards originated with the works of Frederick W. Taylor and Frank and Lillian Gilbreth at the beginning of the 20th century. At that time, a large proportion of work was manual, and the resulting labor content of products was high. Little was known about what constituted a fair day’s work, so managers initiated studies to improve work methods and understand human effort. These efforts continue to this day. Although labor costs are often less than 10% of sales, labor standards remain important and continue to play a major role in both service and manufacturing organizations. They are often a beginning point for determining staffing requirements. With over half of the manufacturing plants in America using some form of labor incentive system, good labor standards are a requirement.
Visual workplace
Uses a variety of visual com-
munication techniques to rapidly
communicate information to stake-
holders.
Quantities in bins indicate ongoing daily requirements, and clipboards provide information on schedule changes.
A “3-minute service” clock reminds employees of the goal.
Company data, process specifications, and operating procedures are posted in each work area.
Andon
Visual signals at the machine notify support personnel.
Line/machine stoppage
Parts/ maintenance needed
All systems go
Reorder point
Visual kanbans reduce inventory and foster JIT.
Part A Part B Part C
Visual utensil holder encourages housekeeping.
GOAL
ACTUAL 3:00
2:10
Figure 10.8
The Visual Workplace
Labor standards
The amount of time required to
perform a job or part of a job.
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Effective operations management requires meaningful standards that help a firm determine:
1. Labor content of items produced (the labor cost) 2. Staffing needs (how many people it will take to meet required production) 3. Cost and time estimates prior to production (to assist in a variety of decisions, from cost
estimates to make-or-buy decisions) 4. Crew size and work balance (who does what in a group activity or on an assembly line) 5. Expected production (so that both manager and worker know what constitutes a fair
day’s work) 6. Basis of wage-incentive plans (what provides a reasonable incentive) 7. Efficiency of employees and supervision (a standard is necessary against which to deter-
mine efficiency)
Properly set labor standards represent the amount of time that it should take an average employee to perform specific job activities under normal working conditions. Labor standards are set in four ways:
1. Historical experience 2. Time studies 3. Predetermined time standards 4. Work sampling
Historical Experience Labor standards can be estimated based on historical experience —that is, how many labor- hours were required to do a task the last time it was performed. Historical standards have the advantage of being relatively easy and inexpensive to obtain. They are usually available from employee time cards or production records. However, they are not objective, and we do not know their accuracy, whether they represent a reasonable or a poor work pace, and whether unusual occurrences are included. Because these variables are unknown, their use is not recommended. Instead, time studies, predetermined time standards, and work sampling are preferred.
Time Studies The classical stopwatch study, or time study , originally proposed by Frederick W. Taylor in 1881, involves timing a sample of a worker’s performance and using it to set a standard. (See the OM in Action box, “Saving Seconds at Retail Boosts Productivity.”) Stopwatch studies are
LO 10.5 Identify four ways of establishing labor
standards
Time study
Timing a sample of a worker’s
performance and using it as a
basis for setting a standard time.
OM in Action Saving Seconds at Retail Boosts Productivity Retail services, like factory assembly lines, need labor standards. And the
Gap, Office Depot, Toys “R” Us, and Meijer are among the many firms that
use them. Labor is usually the largest single expense after purchases in retail-
ing, meaning it gets special attention. Labor standards are set for everything
from greeting customers, to number of cases loaded onto shelves, to scan-
ning merchandise at the cash register.
Meijer, a Midwestern chain of 213 stores, includes cashiers in its labor
standards. Since Meijer sells everything from groceries to clothes to automotive
goods, cashier labor standards include adjustments of allowances for the vast
variety of merchandise being purchased. This includes clothes with hard-to-find
bar codes and bulky items that are not usually removed from the shopping cart.
Allowances are also made for how customers pay, the number of customers re-
turning to an aisle for a forgotten item, and elderly and handicapped customers.
Employees are expected to meet 95% of the standard. Failure to do so
moves an employee to counseling, training, and other alternatives. Meijer has
added fingerprint readers
to cash registers, allowing
cashiers to sign in directly at
their register. This saves time
and boosts productivity by
avoiding a stop at the time
clock.
The bottom line: as retail
firms seek competitive
advantage via lower prices, they are finding that good labor standards are
not only shaving personnel costs by 5% to 15% but also contributing to more
accurate data for improved scheduling and customer service.
Sources: Supermarket News (January 26, 2015); The Wall Street Journal
(November 17, 2008); and www.Meijer.com .
T yl
e r
O ls
o n /S
h u tt
e rs
to ck
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422 P A R T 2 | D E S I G N I N G O P E R AT I O N S
the most widely used labor standard method. A trained and experienced person can establish a standard by following these eight steps:
1. Define the task to be studied (after methods analysis has been conducted). 2. Divide the task into precise elements (parts of a task that often take no more than a few
seconds). 3. Decide how many times to measure the task (the number of job cycles or samples
needed). 4. Time and record elemental times and ratings of performance. 5. Compute the average observed (actual) time. The average observed time is the arithmetic
mean of the times for each element measured, adjusted for unusual influence for each element:
Average observed time = Sum of the times recorded to perform each element
Number of observations (10-1)
6. Determine performance rating (work pace) and then compute the normal time for each element.
Normal time = Average observed time * Performance rating factor (10-2)
The performance rating adjusts the average observed time to what a trained worker could expect to accomplish working at a normal pace. For example, a worker should be able to walk 3 miles per hour. He or she should also be able to deal a deck of 52 cards into 4 equal piles in 30 seconds. A performance rating of 1.05 would indicate that the observed worker performs the task slightly faster than average. Numerous videos specify work pace on which professionals agree, and benchmarks have been established by the Society for the Advancement of Management. Performance rating, however, is still something of an art.
7. Add the normal times for each element to develop a total normal time for the task. 8. Compute the standard time . This adjustment to the total normal time provides for allow-
ances such as personal needs, unavoidable work delays , and worker fatigue :
Standard time = Total normal time
1 - Allowance factor (10-3)
Personal time allowances are often established in the range of 4% to 7% of total time, depending on nearness to restrooms, water fountains, and other facilities. Delay allowances are often set as a result of the actual studies of the delay that occurs. Fatigue allowances are based on our growing knowledge of human energy expenditure under various physical and environmental conditions. A sample set of personal and fatigue allowances is shown in Table 10.1 .
Average observed time
The arithmetic mean of the times
for each element measured,
adjusted for unusual influence for
each element.
Normal time
The average observed time,
adjusted for pace.
Standard time
An adjustment to the total normal
time; the adjustment provides
allowances for personal needs,
unavoidable work delays, and
fatigue.
TABLE 10.1 Allowance Factors (in percentage) for Various Classes of Work
1. Constant allowances: (A) Personal allowance . . . . . . . . . . . . . . . . . . . . . . . . . . 5 (B) Basic fatigue allowance . . . . . . . . . . . . . . . . . . . . . . . 4 2. Variable allowances: (A) Standing allowance . . . . . . . . . . . . . . . . . . . . . . . . . . 2 (B) Abnormal position allowance: (i) Awkward (bending) . . . . . . . . . . . . . . . . . . . . . . . . 2 (ii) Very awkward (lying, stretching) . . . . . . . . . . . . . . 7 (C) Use of force or muscular energy in
lifting, pulling, pushing
Weight lifted (pounds): 20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 40 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 60 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 (D) Bad light: (i) Well below recommended . . . . . . . . . . . . . . . . . . . . 2 (ii) Quite inadequate . . . . . . . . . . . . . . . . . . . . . . . . . . 5 (E) Noise level: (i) Intermittent—loud . . . . . . . . . . . . . . . . . . . . . . . . . . 2 (ii) Intermittent—very loud or high pitched . . . . . . . . . .5
Sources: George Kanawaty (ed.), Introduction to Work Study , International Labour Office, Geneva, 1992; B. W. Niebel, Motion and Time Study , 8th ed. (Homewood, IL: Richard D. Irwin), 1988; and
Stephan Konz, Work Design (Columbus, Ohio: Grid Publishing, Inc.), 1979.
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Example 1 illustrates the computation of standard time.
Example 1 DETERMINING NORMAL AND STANDARD TIME The time study of a work operation at a Red Lobster restaurant yielded an average observed time of 4.0 minutes. The analyst rated the observed worker at 85%. This means the worker performed at 85% of normal when the study was made. The firm uses a 13% allowance factor. Red Lobster wants to compute the normal time and the standard time for this operation.
APPROACH c The firm needs to apply Equations (10-2) and (10-3) .
SOLUTION c
Average observed time = 4.0 min
Normal time = (Average observed time) * (Performance rating factor)
= (4.0)(0.85)
= 3.4 min
Standard time = Normal time
1 - Allowance factor =
3.4 1 - 0.13
= 3.4 0.87
= 3.9 min
INSIGHT c Because the observed worker was rated at 85% (slower than average), the normal time is less than the worker’s 4.0-minute average time.
LEARNING EXERCISE c If the observed worker is rated at 115% (faster than average), what are the new normal and standard times? [Answer: 4.6 min, 5.287 min.]
RELATED PROBLEMS c 10.13–10.21, 10.33, 10.38 (10.39–10.40 are available in MyOMLab)
EXCEL OM Data File Ch10Ex1.xls can be found in MyOMLab.
LO 10.6 Compute the normal and standard
times in a time study
Example 2 uses a series of actual stopwatch times for each element.
Example 2 USING TIME STUDIES TO COMPUTE STANDARD TIME Management Science Associates promotes its management development seminars by mailing thousands of individually composed and typed letters to various firms. A time study has been conducted on the task of preparing letters for mailing. On the basis of the following observations, Management Science Associates wants to develop a time standard for this task. The firm’s personal, delay, and fatigue allow- ance factor is 15%.
JOB ELEMENT
OBSERVATIONS (MINUTES)
1 2 3 4 5 PERFORMANCE RATING
(A) Compose and type letter 8 10 9 21* 11 120% (B) Type envelope address 2 3 2 1 3 105% (C) Stuff, stamp, seal, and sort envelopes 2 1 5* 2 1 110%
APPROACH c Once the data have been collected, the procedure is to: 1. Delete unusual or nonrecurring observations. 2. Compute the average time for each element, using Equation (10-1) . 3. Compute the normal time for each element, using Equation (10-2) . 4. Find the total normal time. 5. Compute the standard time , using Equation (10-3) .
SOLUTION c 1. Delete observations such as those marked with an asterisk (*). (These may be due to business inter-
ruptions, conferences with the boss, or mistakes of an unusual nature; they are not part of the job element, but may be personal or delay time.)
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424 P A R T 2 | D E S I G N I N G O P E R AT I O N S
2. Average time for each job element:
Average time for A = 8 + 10 + 9 + 11
4 = 9.5 min
Average time for B = 2 + 3 + 2 + 1 + 3
5 = 2.2 min
Average time for C = 2 + 1 + 2 + 1
4 = 1.5 min
3. Normal time for each job element:
Normal time for A = (Average observed time) * (Performance rating)
= (9.5)(1.2) = 11.4 min
Normal time for B = (2.2)(1.05) = 2.31 min
Normal time for C = (1.5)(1.10) = 1.65 min
Note: Normal times are computed for each element because the performance rating factor (work pace) may vary for each element, as it did in this case. 4. Add the normal times for each element to fi nd the total normal time (the normal time for the whole
job): Total normal time = 11.40 + 2.31 + 1.65 = 15.36 min
5. Standard time for the job:
Standard time = Total normal time
1 - Allowance factor =
15.36 1 - 0.15
= 18.07 min
Thus, 18.07 minutes is the time standard for this job.
INSIGHT c When observed times are not consistent they need to be reviewed. Abnormally short times may be the result of an observational error and are usually discarded. Abnormally long times need to be analyzed to determine if they, too, are an error. However, they may include a seldom occurring but legiti- mate activity for the element (such as a machine adjustment) or may be personal, delay, or fatigue time.
LEARNING EXERCISE c If the two observations marked with an asterisk were not deleted, what would be the total normal time and the standard time? [Answer: 18.89 min, 22.22 min.]
RELATED PROBLEMS c 10.22–10.25, 10.28a,b, 10.29a, 10.30a (10.41–10.43 are available in MyOMLab)
Time study requires a sampling process; so the question of sampling error in the average observed time naturally arises. In statistics, error varies inversely with sample size. Thus, to determine just how many “cycles” we should time, we must consider the variability of each element in the study.
To determine an adequate sample size, three items must be considered:
1. How accurate we want to be (e.g., is {5% of observed time close enough?). 2. The desired level of confidence (e.g., the z -value; is 95% adequate or is 99% required?). 3. How much variation exists within the job elements (e.g., if the variation is large, a larger
sample will be required).
The formula for finding the appropriate sample size, given these three variables, is:
Required sample size = n = a zs hx b
2 (10-4)
where h = accuracy level (acceptable error) desired in percent of the job element, expressed as a decimal (5% = .05)
z = number of standard deviations required for desired level of confidence (90, confidence = 1.65; see Table 10.2 or Appendix I for more z -values) s = standard deviation of the initial sample x = mean of the initial sample n = required sample size
LO 10.7 Find the proper sample size for
a time study
TABLE 10.2
Common z -Values
DESIRED CONFIDENCE
(%)
Z -VALUE (STANDARD DEVIATION REQUIRED
FOR DESIRED LEVEL OF
CONFIDENCE)
90.0 1.65 95.0 1.96 95.45 2.00 99.0 2.58 99.73 3.00
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We demonstrate with Example 3 .
Example 3 COMPUTING SAMPLE SIZE Thomas W. Jones Manufacturing Co. has asked you to check a labor standard prepared by a recently terminated analyst. Your first task is to determine the correct sample size. Your accuracy is to be within {5% and your confidence level at 95%. The standard deviation of the sample is 1.0 and the mean 3.00.
APPROACH c You apply Equation (10-4) .
SOLUTION c h = 0.05 x = 3.00 s = 1.0 z = 1.96 (from Table 10.2 or Appendix I)
n = a zs hx b
2
n = a 1.96 * 1.0 0.05 * 3
b 2
= 170.74 ≈ 171
Therefore, you recommend a sample size of 171.
INSIGHT c Notice that as the confidence level required increases, the sample size also increases. Simi- larly, as the desired accuracy level increases (say, from 5% to 1%), the sample size increases.
LEARNING EXERCISE c The confidence level for Jones Manufacturing Co. can be set lower, at 90%, while retaining the same {5% accuracy levels. What sample size is needed now? [Answer: n = 121.]
RELATED PROBLEMS c 10.26, 10.27, 10.28c, 10.29b, 10.30b (10.44–10.46 are available in MyOMLab)
EXCEL OM Data File Ch10Ex3.xls can be found in MyOMLab.
Now let’s look at two variations of Example 3 . First, if h , the desired accuracy, is expressed as an absolute amount of error (say, {1 minute
of error is acceptable), then substitute e for hx, and the appropriate formula is:
n = a zs e b
2 (10-5)
where e is the absolute time amount of acceptable error. Second, for those cases when s , the standard deviation of the sample, is not provided (which
is typically the case outside the classroom), it must be computed. The formula for doing so is given in Equation (10-6) :
s = B
g(xi - x)2
n - 1 = B
g(Each sample observation - x)2
Number in sample - 1 (10-6)
where xi = value of each observation x = mean of the observations n = number of observations in the sample
An example of this computation is provided in Solved Problem 10.4 on page 433 . With the development of handheld computers, job elements, time, performance rates, and
statistical confidence intervals can be easily created, logged, edited, and managed. Although time studies provide accuracy in setting labor standards (see the OM in Action box “UPS: The Tightest Ship in the Shipping Business”), they have two disadvantages. First, they require a trained staff of analysts. Second, these standards cannot be set before tasks are actually per- formed. This leads us to two alternative work-measurement techniques that we discuss next.
Predetermined Time Standards In addition to historical experience and time studies, we can set production standards by using predetermined time standards. Predetermined time standards divide manual work into small basic elements that already have established times (based on very large samples of workers). To estimate the time for a particular task, the time factors for each basic element of that task are
Predetermined time standards
A division of manual work into
small basic elements that have
established and widely accepted
times.
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426 P A R T 2 | D E S I G N I N G O P E R AT I O N S
added together. Developing a comprehensive system of predetermined time standards would be prohibitively expensive for any given firm. Consequently, a number of systems are com- mercially available. The most common predetermined time standard is methods time measure- ment (MTM), which is a product of the MTM Association. 3
Predetermined time standards are an outgrowth of basic motions called therbligs. The term therblig was coined by Frank Gilbreth ( Gilbreth spelled backwards, with the t and h reversed). Therbligs include such activities as select, grasp, position, assemble, reach, hold, rest, and in- spect. These activities are stated in terms of time measurement units (TMUs) , which are equal to only .00001 hour, or .0006 minute each. MTM values for various therbligs are specified in very detailed tables. Figure 10.9 , for example, provides the set of time standards for the motion GET and PLACE. To use GET and PLACE, one must know what is “gotten,” its approximate weight, and where and how far it is supposed to be placed.
OM in Action UPS: The Tightest Ship in the Shipping Business United Parcel Service (UPS) employs 425,000 people and delivers an average of
16 million packages a day to locations throughout the U.S. and 220 other countries.
To achieve its claim of “running the tightest ship in the shipping business,” UPS me-
thodically trains its delivery drivers in how to do their jobs as efficiently as possible.
Industrial engineers at UPS have time-studied each driver’s route and set
standards for each delivery, stop, and pickup. These engineers have recorded
every second taken up by stoplights, traffic volume, detours, doorbells, walk-
ways, stairways, and coffee breaks. Even bathroom stops are factored into the
standards. All this information is then fed into company computers to provide
detailed time standards for every driver, every day.
To meet their objective of 200 deliveries and pickups each day (versus only
80 at FedEx), UPS drivers must follow procedures exactly. As they approach
a delivery stop, drivers unbuckle their seat belts, honk their horns, and cut
their engines. Ignition keys have been dispensed with and replaced by a digital
remote fob that turns off the engine and unlocks the bulkhead door that leads
to the packages. In one seamless motion, drivers are required to yank up their
emergency brakes and push their gearshifts into first. Then they slide to the
ground with their electronic clipboards under their right arm and their packages
in their left hand. They walk to the customer’s door at the prescribed 3 feet per
second and knock first to avoid lost seconds searching for the doorbell. After
making the delivery, they do the paperwork on the way back to the truck.
Productivity experts describe UPS as one of the most efficient companies
anywhere in applying effective labor standards.
Sources: Wall Street Journal (February 19, 2015), (December 26, 2011), and
(September 19, 2011); and G.Niemann, Big Brown : The Untold Story of UPS ,
New York: Wiley, 2007.
Therbligs
Basic physical elements of motion.
Time measurement units (TMUs)
Units for very basic micromotions
in which 1 TMU = .0006 min, or
100,000 TMUs = 1 hr.
GET and PLACE DISTANCE RANGE IN
IN. <8
WEIGHT CONDITIONS
OF GET PLACE ACCURACY
APPROXIMATE
LOOSE
TIGHT
APPROXIMATE
LOOSE
TIGHT
APPROXIMATE
APPROXIMATE
LOOSE
TIGHT
APPROXIMATE
LOOSE
TIGHT
MTM CODE
AA
AB
AC
AD
AE
AF
AG
AH
AJ
AK
AL
AM
AN
20
30
40
20
30
40
40
25
40
50
90
95
120
35
45
55
45
55
65
65
45
65
75
106
120
145
50
60
70
60
70
80
80
55
75
85
115
130
160
1
>8 <20
<2 LB
>2 LB <18 LB
>18 LB <45 LB
EASY
DIFFICULT
HANDFUL
2
>20 <32
3
Figure 10.9
Sample MTM Table for GET and PLACE Motion
Time values are in TMUs.
Source: Copyrighted by the MTM Association for Standards and Research. No reprint permission without consent from the MTM Association,
16–01 Broadway, Fair Lawn, NJ 07410. Used with permission of MTM Association for Standards & Research.
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Example 4 shows a use of predetermined time standards in setting service labor standards.
Example 4 USING PREDETERMINED TIME (MTM ANALYSIS) TO DETERMINE STANDARD TIME General Hospital wants to set the standard time for lab technicians to pour a tube specimen using MTM. 4
APPROACH c This is a repetitive task for which the MTM data in Table 10.3 may be used to develop standard times. The sample tube is in a rack and the centrifuge tubes in a nearby box. A technician removes the sample tube from the rack, uncaps it, gets the centrifuge tube, pours, and places both tubes in the rack.
TABLE 10.3 MTM-HC Analysis: Pouring Tube Specimen
ELEMENT DESCRIPTION ELEMENT TIME
Get tube from rack AA2 35 Uncap, place on counter AA2 35 Get centrifuge tube, place at sample tube AD2 45 Pour (3 sec) PT 83 Place tubes in rack (simo) PC2 40
Total TMU 238 .0006 * 238 = Total standard minutes = .143 or about 8.6 seconds
SOLUTION c The first work element involves getting the tube from the rack. The conditions for GETTING the tube and PLACING it in front of the technician are:
◆ Weight: (less than 2 pounds) ◆ Conditions of GET: (easy) ◆ Place accuracy: (approximate) ◆ Distance range: (8 to 20 inches)
Then the MTM element for this activity is AA2 (as seen in Figure 10.9 ). The rest of Table 10.3 is developed from similar MTM tables.
INSIGHT c Most MTM calculations are computerized, so the user need only key in the appropriate MTM codes, such as AA2 in this example.
LEARNING EXERCISE c General Hospital decides that the first step in this process really involves a distance range of 4 inches (getting the tube from the rack). The other work elements are unchanged. What is the new standard time? [Answer: .134 minutes, or just over 8 seconds]
RELATED PROBLEM c 10.36
STUDENT TIP Families of predetermined time
standards have been developed
for many occupations.
Predetermined time standards have several advantages over direct time studies. First, they may be established in a laboratory environment, where the procedure will not upset actual production activities (which time studies tend to do). Second, because the standard can be set before a task is actually performed, it can be used for planning. Third, no performance ratings are necessary. Fourth, unions tend to accept this method as a fair means of setting standards. Finally, predetermined time standards are particularly effective in firms that do substantial numbers of studies of similar tasks. To ensure accurate labor standards, some firms use both time studies and predetermined time standards.
Work Sampling The fourth method of developing labor or production standards, work sampling, was devel- oped in England by L. Tippet in the 1930s. Work sampling estimates the percent of the time that a worker spends on various tasks. Random observations are used to record the activity that a worker is performing. The results are primarily used to determine how employees allocate their time among various activities. Knowledge of this allocation may lead to staffing changes, reassignment of duties, estimates of activity cost, and the setting of delay allowances for labor standards. When work sampling is performed to establish delay allowances, it is sometimes called a ratio delay study .
Work sampling
An estimate, via sampling, of the
percentage of the time that a
worker spends on various tasks.
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The work-sampling procedure can be summarized in five steps:
1. Take a preliminary sample to obtain an estimate of the parameter value (e.g., percent of time a worker is busy).
2. Compute the sample size required. 3. Prepare a schedule for observing the worker at appropriate times. The con-
cept of random numbers is used to provide for random observation. For example, let’s say we draw the following five random numbers from a table: 07, 12, 22, 25, and 49. These can then be used to create an observation sched- ule of 9:07 a.m., 9:12, 9:22, 9:25, 9:49.
4. Observe and record worker activities. 5. Determine how workers spend their time (usually as a percentage).
To determine the number of observations required, management must decide on the desired confidence level and accuracy. First, however, the analyst must select a preliminary value for the parameter under study (Step 1 above). The choice is usually based on a small sample of perhaps 50 observations. The following for- mula then gives the sample size for a desired confidence and accuracy:
n = z2p(1 - p)
h2 (10-7)
where n = required sample size z = number of standard deviations for the desired confidence level ( z = 1
for 68.27% confidence, z = 2 for 95.45% confidence, and z = 3 for 99.73% confidence—these values are obtained from Table 10.2 or the normal table in Appendix I)
p = estimated value of sample proportion (of time worker is observed busy or idle)
h = acceptable error level, in percent (as a decimal)
Example 5 shows how to apply this formula.
Using the techniques of this chapter to develop labor
standards, operations managers at Orlando’s Arnold
Palmer Hospital determined that nurses walked an
average of 2.7 miles per day. This constitutes up to
30% of the nurse’s time, a terrible waste of critical
talent. Analysis resulted in a new layout design that
has reduced walking distances by 20%.
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Example 5 DETERMINING THE NUMBER OF WORK SAMPLE OBSERVATIONS NEEDED The manager of Michigan County’s welfare office, Dana Johnson, estimates that her employees are idle 25% of the time. She would like to take a work sample that is accurate within {3% and wants to have 95.45% confidence in the results.
APPROACH c Dana applies Equation (10-7) to determine how many observations should be taken.
SOLUTION c Dana computes n :
n = z2p(1 - p)
h2
where n = required sample size z = confidence level (2 for 95.45% confidence) p = estimate of idle proportion = 25% = .25
h = acceptable error of 3% = .03
She finds that
n = (2)2(.25)(.75)
(.03)2 = 833 observations
INSIGHT c Thus, 833 observations should be taken. If the percent of idle time observed is not close to 25% as the study progresses, then the number of observations may have to be recalculated and increased or decreased as appropriate.
LEARNING EXERCISE c If the confidence level increases to 99.73%, how does the sample size change? [Answer: n = 1,875.]
RELATED PROBLEMS c 10.31, 10.32, 10.35, 10.37
ACTIVE MODEL 10.1 This example is further illustrated in Active Model 10.1 in MyOMLab.
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The focus of work sampling is to determine how workers allocate their time among vari- ous activities. This is accomplished by establishing the percent of time individuals spend on these activities rather than the exact amount of time spent on specific tasks. The analyst simply records in a random, nonbiased way the occurrence of each activity. Example 6 shows the pro- cedure for evaluating employees at the state welfare office introduced in Example 5 .
Example 6 DETERMINING EMPLOYEE TIME ALLOCATION WITH WORK SAMPLING Dana Johnson, the manager of Michigan County’s welfare office, wants to be sure her employees have adequate time to provide prompt, helpful service. She believes that service to welfare clients who phone or walk in without an appointment deteriorates rapidly when employees are busy more than 75% of the time. Consequently, she does not want her employees to be occupied with client service activities more than 75% of the time.
APPROACH c The study requires several things: First, based on the calculations in Example 5 , 833 observations are needed. Second, observations are to be made in a random, nonbiased way over a period of 2 weeks to ensure a true sample. Third, the analyst must define the activities that are “work.” In this case, work is defined as all the activities necessary to take care of the client (filing, meetings, data entry, discussions with the supervisor, etc.). Fourth, personal time is to be included in the 25% of nonwork time. Fifth, the observations are made in a nonintrusive way so as not to distort the normal work patterns. At the end of the 2 weeks, the 833 observations yield the following results:
NO. OF OBSERVATIONS ACTIVITY
485 On the phone or meeting with a welfare client 126 Idle 62 Personal time 23 Discussions with supervisor
137 Filing, meeting, and computer data entry 833
SOLUTION c The analyst concludes that all but 188 observations (126 idle and 62 personal) are work related. Because 22.6% ( = 188/833) is less idle time than Dana believes necessary to ensure a high client service level, she needs to find a way to reduce current workloads. This could be done through a reassign- ment of duties or the hiring of additional personnel.
INSIGHT c Work sampling is particularly helpful when determining staffing needs or the reallocation of duties (see Figure 10.10 ).
LEARNING EXERCISE c The analyst working for Dana recategorizes several observations. There are now 450 “on the phone/meeting with client” observations, 156 “idle,” and 67 “personal time” observa- tions. The last two categories saw no changes. Do the conclusions change? [Answer: Yes; now about 27% of employee time is not work related—over the 25% Dana desires.]
RELATED PROBLEM c 10.34
The results of similar studies of salespeople and assembly-line employees are shown in Figure 10.10 .
Work sampling offers several advantages over time-study methods. First, because a single observer can observe several workers simultaneously, it is less expensive. Second, observers usually do not require much training, and no timing devices are needed. Third, the study can be temporarily delayed at any time with little impact on the results. Fourth, because work sampling uses instantaneous observations over a long period, the worker has little chance of affecting the study’s outcome. Fifth, the procedure is less intrusive and therefore less likely to generate objections.
The disadvantages of work sampling are (1) it does not divide work elements as completely as time studies, (2) it can yield biased or incorrect results if the observer does not follow ran- dom routes of travel and observation, and (3) because it is less intrusive, it tends to be less ac- curate; this is particularly true when job content times are short.
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Ethics Ethics in the workplace presents some interesting challenges. As we have suggested in this chap- ter, many constraints influence job design. The issues of fairness, equity, and ethics are perva- sive. Whether the issue is equal opportunity or safe working conditions, an operations manager is often the one responsible. Managers do have some guidelines. By knowing the law, working with OSHA, 5 MSDS, 6 state agencies, unions, trade associations, insurers, and employees, man- agers can often determine the parameters of their decisions. Human resource and legal depart- ments are also available for help and guidance through the labyrinth of laws and regulations.
Management’s role is to educate employees; specify the necessary equipment, work rules, and work environment; and then enforce those requirements, even when employees think it is not necessary to wear safety equipment. We began this chapter with a discussion of mutual trust and commitment, and that is the environment that managers should foster. Ethical man- agement requires no less.
Summary
Startup/pep talk 3%
Unscheduled tasks and downtime
4%
Breaks and lunch 10% Dead time
between tasks 13%
Productive work 67%
Cleanup 3%
Sales in person 20%
Lunch and personal
10%
Travel 20%
Paperwork 17%
Telephone sales 12%
Telephone within firm
13% Meetings and other
8%
Salespeople Assembly-Line Employees Figure 10.10
Work-Sampling Time Studies
These two work-sampling time
studies were done to determine
what salespeople do at a
wholesale electronics distributor
(left) and a composite of several
auto assembly-line employees
(right).
STUDENT TIP Mutual trust and commitment cannot
be achieved without ethical behavior.
Outstanding firms know that their human resource strat- egy can yield a competitive advantage. Often a large per- centage of employees and a large part of labor costs are under the direction of OM. Consequently, an operations manager usually has a major role to play in achieving human resource objectives. A requirement is to build an environment with mutual respect and commitment and a reasonable quality of work life. Successful organizations have designed jobs that use both the mental and physical capabilities of their employees. Regardless of the strategy
chosen, the skill with which a firm manages its human resources ultimately determines its success.
Labor standards are required for an efficient operations system. They are needed for production planning, labor planning, costing, and evaluating performance. They are used throughout industry—from the factory to finance, sales, and office. They can also be used as a basis for incen- tive systems. Standards may be established via historical data, time studies, predetermined time standards, and work sampling.
Labor planning (p. 411 ) Job design (p. 412 ) Labor specialization (or job
specialization) (p. 412 ) Job enlargement (p. 413 ) Job rotation (p. 413 ) Job enrichment (p. 413 ) Employee empowerment (p. 413 ) Self-directed team (p. 414 )
Ergonomics (p. 415 ) Methods analysis (p. 417 ) Flow diagram (p. 418 ) Process chart (p. 418 ) Activity chart (p. 418 ) Operations chart (p. 418 ) Visual workplace (p. 420 ) Labor standards (p. 420 ) Time study (p. 421 )
Average observed time (p. 422 ) Normal time (p. 422 ) Standard time (p. 422 ) Predetermined time standards (p. 425 ) Therbligs (p. 426 ) Time measurement units
(TMUs) (p. 426 ) Work sampling (p. 427 )
Key Terms
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Ethical Dilemma Johnstown Foundry, Inc., with several major plants, is one of the largest makers of cast-iron water and sewer pipes in the U.S. In one of the nation’s most dangerous industries, Johnstown is perhaps one of the most unsafe, with four times the injury rate of its six competitors combined. Its worker death rate is six times the industry average. In a recent 7-year period, Johnstown’s plants were also found to be in violation of pollution and emission limits 450 times.
Workers who protest dangerous work conditions claim they are “bull’s-eyed”—marked for termination. Supervisors have bullied injured workers and intimidated union leaders. Line workers who fail to make daily quotas get disciplinary actions. Managers have put up safety signs after a worker was injured to make it appear that the worker ignored posted policies. They doctor safety records and alter machines to cover up hazards. When the government investigated one worker’s death recently, inspectors found the Johnstown policy “was not to correct anything until OSHA found it.”
Johnstown plants have also been repeatedly fi ned for failing to stop production to repair broken pollution controls. Three plants have been designated “high-priority” violators by the EPA. Inside the plants, workers have repeatedly complained of blurred vision, severe headaches, and respiratory problems after being exposed, without training or protection, to chemicals used in the production process. Near one Pennsylvania plant, school crossing guards have had to wear gas masks; that location alone has averaged over a violation every month for 7 years. Johnstown’s “standard procedure,” according to a former plant manager, is to illegally dump industrial contaminants
into local rivers and creeks. Workers wait for night or heavy rainstorms before fl ushing thousands of gallons from their sump pumps.
Given the following scenarios, what is your position, and what action should you take?
a) On your spouse’s recent move to the area, you accepted a job, perhaps somewhat naively, as a company nurse in one of the Johnstown plants. After 2 weeks on the job, you became aware of the work environment noted above.
b) You are a contractor who has traditionally used Johnstown’s products, which meet specifi cations. Johnstown is consis- tently the low bidder. Your customers are happy with the product.
c) You are Johnstown’s banker. d) You are a supplier to Johnstown.
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Discussion Questions
1. How would you define a good quality of work life? 2. What are some of the worst jobs you know about? Why are
they bad jobs? Why do people want these jobs? 3. If you were redesigning the jobs described in Question 2, what
changes would you make? Are your changes realistic? Would they improve productivity (not just production but productivity )?
4. Can you think of any jobs that push the man–machine inter- face to the limits of human capabilities?
5. What are the five core characteristics of a good job design? 6. What are the differences among job enrichment, job enlarge-
ment, job rotation, job specialization, and employee empow- erment?
7. Define ergonomics. Discuss the role of ergonomics in job design.
8. List the techniques available for carrying out methods analysis. 9. Identify four ways in which labor standards are set. 10. What are some of the uses to which labor standards
are put?
11. How would you classify the following job elements? Are they personal, fatigue, or delay?
a) The operator stops to talk to you. b) The operator lights up a cigarette. c) The operator opens his lunch pail (it is not lunch time),
removes an apple, and takes an occasional bite. 12. How do you classify the time for a drill press operator who
is idle for a few minutes at the beginning of every job waiting for the setup person to complete the setup? Some of the setup time is used in going for stock, but the operator typically returns with stock before the setup person is finished with the setup.
13. How do you classify the time for a machine operator who, between every job and sometimes in the middle of jobs, turns off the machine and goes for stock?
14. The operator drops a part, which you pick up and hand to him. Does this make any difference in a time study? If so, how?
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Solved Problems Virtual Office Hours help is available in MyOMLab .
SOLVED PROBLEM 10.1 As pit crew manager for Rusty Wallace’s NASCAR team (see the Global Company Profile that opens this chapter), you would like to evaluate how your “Jackman” (JM) and “Gas Man #1” (GM #1) are utilized. Recent stopwatch studies have verified the fol- lowing times:
PIT CREW ACTIVITY TIME (SECONDS)
JM Move to right side of car and raise car 4.0 GM #1 Move to rear gas fi ller 2.5 JM Wait for tire 1.0 JM Move to left side of car and raise car 3.8 GM #1 Load fuel (per gallon) 0.5 JM Wait for tire 1.2 JM Move back over wall from left side 2.5 GM #1 Move back over the wall from gas fi ller 2.5
Use an activity chart similar to the one in Figure 10.6 as an aid.
SOLUTION
4.0
Jackman (Seconds)
Move to right side of car and raise car
Move to rear gas filler
Move back over the wall from gas filler
Load 11 gallons of fuel (one can of fuel)
Wait for tire exchange to finish
Wait for tire exchange to finish
Move to left side of car and raise car
Move back over wall from left side
Gas Man #1 (Seconds)
1.0
3.8
1.2
2.5
2.5
2.5
5.5
SOLVED PROBLEM 10.2 A work operation consisting of three elements has been subjected to a stopwatch time study. The recorded observa- tions are shown in the following table. By union contract, the allowance time for the operation is personal time 5%, delay 5%, and fatigue 10%. Determine the standard time for the work operation.
JOB ELEMENT
OBSERVATIONS (MINUTES) PERFORMANCE
RATING (%)1 2 3 4 5 6
A .1 .3 .2 .9 .2 .1 90 B .8 .6 .8 .5 3.2 .7 110 C .5 .5 .4 .5 .6 .5 80
SOLUTION First, delete the two observations that appear to be very unusual (.9 minute for job element A and 3.2 minutes for job element B). Then:
A’s average observed time = .1 + .3 + .2 + .2 + .1
5 = 0.18 min
B’s average observed time = .8 + .6 + .8 + .5 + .7
5 = 0.68 min
C’s average observed time = .5 + .5 + .4 + .5 + .6 + .5
6 = 0.50 min
A’s normal time = (0.18)(0.90) = 0.16 min
B’s normal time = (0.68)(1.10) = 0.75 min
C’s normal time = (0.50)(0.80) = 0.40 min
Normal time for job = 0.16 + 0.75 + 0.40 = 1.31 min Note, the total allowance factor = 0.05 + 0.05 + 0.10 = 0.20
Then: Standard time = 1.31
1 - 0.20 = 1.64 min
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SOLVED PROBLEM 10.3 The preliminary work sample of an operation indicates the fol- lowing:
Number of times operator working 60 Number of times operator idle 40 Total number of preliminary observations 100
What is the required sample size for a 99.73% confidence level with {4, precision?
SOLVED PROBLEM 10.4 Amor Manufacturing Co. of Geneva, Switzerland, has just observed a job in its laboratory in anticipation of releasing the job to the factory for production. The firm wants rather good accuracy for costing and labor forecasting. Specifically, it wants to provide a 99% confidence level and a cycle time that is within 3% of the true value. How many observations should it make? The data collected so far are as follows:
OBSERVATION TIME
1 1.7 2 1.6 3 1.4 4 1.4 5 1.4
SOLUTION First, solve for the mean, x , and the sample standard deviation, s :
s = B
g(Each sample observation - x)2
Number in sample - 1
SOLUTION
z = 3 for 99.73 confidence; p = 60 100
= 0.6; h = 0.04
So:
n = z2p(1 - p)
h2 =
(3)2(0.6)(0.4) (0.04)2
= 1,350 sample size
OBSERVATION x i x – xi2x
– (xi2x –)2
1 1.7 1.5 .2 0.04 2 1.6 1.5 .1 0.01 3 1.4 1.5 - .1 0.01 4 1.4 1.5 - .1 0.01 5 1.4 1.5 - .1 0.01
x– = 1.5 0.08 = g(x i - x –) 2
s = A
0.08 n - 1
= A
0.08 4
= 0.141
Then, solve for n = ¢ zs hx
≤2 = J (2.58)(0.141) (0.03)(1.5)
R 2 = 65.3 where x = 1.5 s = 0.141 z = 2.58 (from Table 10.2 ) h = 0.03 Therefore, you round up to 66 observations.
SOLVED PROBLEM 10.5 At Maggard Micro Manufacturing, Inc., workers press semicon- ductors into predrilled slots on printed circuit boards. The elemen- tal motions for normal time used by the company are as follows: Reach 6 inches for semiconductors 40 TMU
Grasp the semiconductor 10 TMU Move semiconductor to printed circuit board 30 TMU Position semiconductor 35 TMU Press semiconductor into slots 65 TMU Move board aside 20 TMU
(Each time measurement unit is equal to .0006 min.) Determine the normal time for this operation in minutes and in seconds.
SOLUTION Add the time measurement units:
40 + 10 + 30 + 35 + 65 + 20 = 200 Time in minutes = (200)(.0006 min.) = 0.12 min
Time in seconds = (0.12)(60 sec) = 7.2 sec
SOLVED PROBLEM 10.6 To obtain the estimate of time a worker is busy for a work
sampling study, a manager divides a typical workday into 480 minutes. Using a random-number table to decide what time to go to an area to sample work occurrences, the manager records observations on a tally sheet like the following:
STATUS TALLY
Productively working 0 0 0 0 0 0 0 0 0 0 0 0 0 Idle 0 0 0 0
SOLUTION In this case, the supervisor made 20 observations and found that employees were working 80% of the time. So, out of 480 minutes in an office workday, 20%, or 96 minutes, was idle time, and 384 minutes were productive. Note that this proce- dure describes that a worker is busy, not necessarily doing what he or she should be doing.
–
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Problems Note: PX means the problem may be solved with POM for Windows and/or Excel.
Problem 10.1 relates to Job Design
• 10.1 Rate a job you have had using Hackman and Oldham’s core job characteristics (see page 413 ) on a scale from 1 to 10. What is your total score? What about the job could have been changed to make you give it a higher score?
Problems 10.2–10.12 relate to Methods Analysis
• 10.2 Make a process chart for changing the right rear tire on an automobile. • 10.3 Draw an activity chart for a machine operator with the following operation. The relevant times are as follows: Prepare mill for loading (cleaning, oiling, and so on) .50 min Load mill 1.75 min Mill operating (cutting material) 2.25 min
Unload mill .75 min
• • • 10.4 Draw an activity chart (a crew chart similar to Figure 10.6 ) for a concert (for example, Tim McGraw, Linkin Park, Lil’ Wayne, or Bruce Springsteen) and determine how to put together the concert so the star has reasonable breaks. For instance, at what point is there an instrumental number, a visual effect, a duet, a dance moment, that allows the star to pause and rest physically or at least rest his or her voice? Do other members of the show have moments of pause or rest?
• • • 10.11 Your campus club is hosting a car wash. Due to demand, three people are going to be scheduled per wash line. (Three people have to wash each vehicle.) Design an activity chart for washing and drying a typical sedan. You must wash the wheels but ignore the cleaning of the interior, because this part of the operation will be done at a separate vacuum station.
• • • • 10.12 Design a process chart for printing a short docu- ment on a laser printer at an office. Unknown to you, the printer in the hallway is out of paper. The paper is located in a supply room at the other end of the hall. You wish to make five stapled copies of the document once it is printed. The copier, located next to the printer, has a sorter but no stapler. How could you make the task more efficient with the existing equipment?
Problems 10.13–10.46 relate to Labor Standards
• 10.13 If Charlene Brewster has times of 8.4, 8.6, 8.3, 8.5, 8.7, and 8.5 and a performance rating of 110%, what is the normal time for this operation? Is she faster or slower than normal? PX
• 10.14 If Charlene, the worker in Problem 10.13, has a per- formance rating of 90%, what is the normal time for the opera- tion? Is she faster or slower than normal? PX
• • 10.15 Refer to Problem 10.13. a) If the allowance factor is 15%, what is the standard time for
this operation? b) If the allowance factor is 18% and the performance rating is
now 90%, what is the standard time for this operation? PX
• • 10.16 Claudine Soosay recorded the following times assem- bling a watch. Determine (a) the average time, (b) the normal time, and (c) the standard time taken by her, using a performance rating of 95% and a personal allowance of 8%. Assembly Times Recorded
OBSERVATION NO. TIME (MINUTES) OBSERVATION NO. TIME (MINUTES)
1 0.11 9 0.12 2 0.10 10 0.09 3 0.11 11 0.12 4 0.10 12 0.11 5 0.14 13 0.10 6 0.10 14 0.12 7 0.10 15 0.14 8 0.09 16 0.09
• 10.17 A Northeast Airlines gate agent, Chip Gilliken, gives out seat assignments to ticketed passengers. He takes an average of 50 seconds per passenger and is rated 110% in performance. How long should a typical agent be expected to take to make seat assignments? PX
• 10.18 After being observed many times, Beverly Demarr, a hospital lab analyst, had an average observed time for blood tests of 12 minutes. Beverly’s performance rating is 105%. The hospital has a personal, fatigue, and delay allowance of 16%. a) Find the normal time for this process. b) Find the standard time for this blood test. PX
• 10.19 Jell Lee Beans is famous for its boxed candies, which are sold primarily to businesses. One operator had the following observed times for gift wrapping in minutes: 2.2, 2.6, 2.3, 2.5, 2.4. The operator has a performance rating of 105% and an allowance factor of 10%. What is the standard time for gift wrapping? PX
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a n d o M
e d in
a
• • 10.5 Make an operations chart of one of the following: a) Putting a new eraser in (or on) a pencil b) Putting a paper clip on two pieces of paper c) Putting paper in a printer • 10.6 Develop a process chart for installing a new memory board in your personal computer. • • 10.7 Using the data in Solved Problem 10.1, prepare an activity chart like the one in the Solved Problem, but a second Gas Man also delivers 11 gallons. • • 10.8 Prepare a process chart for the Jackman in Solved Problem 10.1. • • 10.9 Draw an activity chart for changing the right rear tire on an automobile with: a) Only one person working b) Two people working • • • 10.10 Draw an activity chart for washing the dishes in a double-sided sink. Two people participate, one washing, the other rinsing and drying. The rinser dries a batch of dishes from the drip rack as the washer fills the right sink with clean but unrinsed dishes. Then the rinser rinses the clean batch and places them on the drip rack. All dishes are stacked before being placed in the cabinets.
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• 10.20 After training, Mary Fernandez, a computer tech- nician, had an average observed time for memory-chip tests of 12 seconds. Mary’s performance rating is 100%. The firm has a personal fatigue and delay allowance of 15%. a) Find the normal time for this process. b) Find the standard time for this process. PX
• • 10.21 Susan Cottenden clocked the observed time for weld- ing a part onto truck doors at 5.3 minutes. The performance rat- ing of the worker timed was estimated at 105%. Find the normal time for this operation. Note: According to the local union contract, each welder is allowed 3 minutes of personal time per hour and 2 minutes of fatigue time per hour. Further, there should be an average delay allowance of 1 minute per hour. Compute the allowance factor and then find the standard time for the welding activity. PX
• • 10.22 A hotel housekeeper, Alison Harvey, was observed five times on each of four task elements, as shown in the following table. On the basis of these observations, find the standard time for the process. Assume a 10% allowance factor.
ELEMENT PERFORMANCE
RATING (%)
OBSERVATIONS (MINUTES PER CYCLE)
1 2 3 4 5
Check minibar 100 1.5 1.6 1.4 1.5 1.5 Make one bed 90 2.3 2.5 2.1 2.2 2.4 Vacuum fl oor 120 1.7 1.9 1.9 1.4 1.6 Clean bath 100 3.5 3.6 3.6 3.6 3.2
• • 10.23 Virginia College promotes a wide variety of executive- training courses for firms in the Arlington, Virginia, region. Director Wendy Tate believes that individually written letters add a personal touch to marketing. To prepare letters for mailing, she conducts a time study of her secretaries. On the basis of the obser- vations shown in the following table, she wishes to develop a time standard for the whole job.
The college uses a total allowance factor of 12%. Tate decides to delete all unusual observations from the time study. What is the standard time?
ELEMENT
OBSERVATIONS (MINUTES) PERFORMANCE
RATING (%)1 2 3 4 5 6
Typing letter 2.5 3.5 2.8 2.1 2.6 3.3 85 Typing envelope .8 .8 .6 .8 3.1 a .7 100 Stuffi ng envelope .4 .5 1.9 a .3 .6 .5 95 Sealing, sorting 1.0 2.9 b .9 1.0 4.4 b .9 125
a Disregard—secretary stopped to answer the phone. b Disregard—interruption by supervisor. PX
• 10.24 The results of a time study to perform a quality con- trol test are shown in the following table. On the basis of these observations, determine the normal and standard time for the test, assuming a 23% allowance factor. PX
C o m
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lt y
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iv is
io n
TASK ELEMENT
PERFORMANCE RATING (%)
OBSERVATIONS (MINUTES)
1 2 3 4 5
1 97 1.5 1.8 2.0 1.7 1.5 2 105 .6 .4 .7 3.7 a .5 3 86 .5 .4 .6 .4 .4 4 90 .6 .8 .7 .6 .7
a Disregard—employee is smoking a cigarette (included in personal time).
• • 10.25 Peter Rourke, a loan processor at Wentworth Bank, has been timed performing four work elements, with the results shown in the following table. The allowances for tasks such as this are personal, 7%; fatigue, 10%; and delay, 3%.
TASK ELEMENT
PERFORMANCE RATING (%)
OBSERVATIONS (MINUTES)
1 2 3 4 5
1 110 .5 .4 .6 .4 .4 2 95 .6 .8 .7 .6 .7 3 90 .6 .4 .7 .5 .5 4 85 1.5 1.8 2.0 1.7 1.5
a) What is the normal time? b) What is the standard time? PX
• • 10.26 Each year, Lord & Taylor, Ltd., sets up a gift- wrapping station to assist its customers with holiday shopping. Preliminary observations of one worker at the station produced the following sample time (in minutes per package): 3.5, 3.2, 4.1, 3.6, 3.9. Based on this small sample, what number of observations would be necessary to determine the true cycle time with a 95% confidence level and an accuracy of {5%? PX
• • 10.27 A time study of a factory worker has revealed an aver- age observed time of 3.20 minutes, with a standard deviation of 1.28 minutes. These figures were based on a sample of 45 obser- vations. Is this sample adequate in size for the firm to be 99% confident that the standard time is within {5% of the true value? If not, what should be the proper number of observations? PX
• • 10.28 Based on a careful work study in the Hofstetter Corp., the results shown in the following table have been observed:
ELEMENT
OBSERVATIONS (MINUTES) PERFORMANCE
RATING (%)1 2 3 4 5
Prepare daily reports 35 40 33 42 39 120 Photocopy results 12 10 36 a 15 13 110 Label and package reports 3 3 5 5 4 90 Distribute reports 15 18 21 17 45 b 85
a Photocopying machine broken; included as delay in the allowance factor. b Power outage; included as delay in the allowance factor.
a) Compute the normal time for each work element. b) If the allowance for this type of work is 15%, what is the stand-
ard time? c) How many observations are needed for a 95% confidence level
within {5% accuracy? ( Hint: Calculate the sample size of each element.)
• • 10.29 The Dubuque Cement Company packs 80-pound bags of concrete mix. Time-study data for the filling activity are shown in the following table. Because of the high physical demands of the job, the company’s policy is a 23% allowance for workers. a) Compute the standard time for the bag-packing task. b) How many observations are necessary for 99% confidence,
within {5% accuracy?
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436 P A R T 2 | D E S I G N I N G O P E R AT I O N S
ELEMENT
OBSERVATIONS (SECONDS) PERFORMANCE
RATING (%)1 2 3 4 5
Grasp and place bag 8 9 8 11 7 110 Fill bag 36 41 39 35 112 a 85 Seal bag 15 17 13 20 18 105 Place bag on conveyor 8 6 9 30 b 35 b 90
a Bag breaks open; included as delay in the allowance factor. b Conveyor jams; included as delay in the allowance factor.
• • 10.30 Installing mufflers at the O’Sullivan Garage in Golden, Colorado, involves five work elements. Jill O’Sullivan has timed workers performing these tasks seven times, with the results shown in the following table:
JOB ELEMENT
OBSERVATIONS (MINUTES)
PERFORMANCE RATING (%)1 2 3 4 5 6 7
1. Select correct muffl ers 4 5 4 6 4 15 a 4 110 2. Remove old muffl er 6 8 7 6 7 6 7 90 3. Weld/install new muffl er 15 14 14 12 15 16 13 105 4. Check/inspect work 3 4 24 a 5 4 3 18 a 100 5. Complete paperwork 5 6 8 — 7 6 7 130
a Employee has lengthy conversations with boss (not job related).
By agreement with her workers, Jill allows a 10% fatigue factor and a 10% personal-time factor, but no time for delay. To com- pute standard time for the work operation, Jill excludes all obser- vations that appear to be unusual or nonrecurring. She does not want an error of more than {5%. a) What is the standard time for the task? b) How many observations are needed to assure a 95% confi-
dence level? PX
• • 10.31 Bank manager Art Hill wants to determine the percent of time that tellers are working and idle. He decides to use work sam- pling, and his initial estimate is that the tellers are idle 15% of the time. How many observations should Hill take to be 95.45% confident that the results will not be more than {4% from the true result? PX
• • 10.32 Supervisor Kenneth Peterson wants to determine the percent of time a machine in his area is idle. He decides to use work sampling, and his initial estimate is that the machine is idle 20% of the time. How many observations should Peterson take to be 98% confident that the results will be less than 5% from the true results?
• • • 10.33 Tim Nelson’s job as an inspector for La-Z-Boy is to inspect 130 chairs per day. a) If he works an 8-hour day, how many minutes is he allowed
for each inspection (i.e., what is his “standard time”)? b) If he is allowed a 6% fatigue allowance, a 6% delay allowance,
and 6% for personal time, what is the normal time that he is assumed to take to perform each inspection?
• • • 10.34 A random work sample of operators taken over a 160- hour work month at Tele-Marketing, Inc., has produced the fol- lowing results. What is the percentage of time spent working?
On phone with customer 858 Idle time 220 Personal time 85
• • 10.35 A total of 300 observations of Bob Ramos, an assembly-line worker, were made over a 40-hour workweek. The sample also showed that Bob was busy working (assembling the parts) during 250 observations.
a) Find the percentage of time Bob was working. b) If you want a confidence level of 95%, and if {3% is an accept-
able error, what size should the sample be? c) Was the sample size adequate? PX
• 10.36 Sharpening your pencil is an operation that may be divided into eight small elemental motions. In MTM terms, each element may be assigned a certain number of TMUs:
Reach 4 inches for the pencil 6 TMU Grasp the pencil 2 TMU Move the pencil 6 inches 10 TMU Position the pencil 20 TMU Insert the pencil into the sharpener 4 TMU Sharpen the pencil 120 TMU Disengage the pencil 10 TMU Move the pencil 6 inches 10 TMU
What is the total normal time for sharpening one pencil? Convert your answer into minutes and seconds.
• • 10.37 Supervisor Tom Choi at Tempe Equipment Company is concerned that material is not arriving as promptly as needed at work cells. A new kanban system has been installed, but there seems to be some delay in getting the material moved to the work cells so that the job can begin promptly. Choi is interested in determining how much delay there is on the part of his highly paid machinists. Ideally, the delay would be close to zero. He has asked his assistant to determine the delay factor among his 10 work cells. The assistant collects the data on a random basis over the next 2 weeks and deter- mines that of the 1,200 observations, 105 were made while the oper- ators were waiting for materials. Use a 95% confidence level and a {3% acceptable error. What report does he give to Choi? PX
• • • • 10.38 The Miami Central Hotel has 400 rooms. Every day, the housekeepers clean any room that was occupied the night before. If a guest is checking out of the hotel, the housekeepers give the room a thorough cleaning to get it ready for the next guest. This takes 30 minutes. If a guest is staying another night, the housekeeper only “refreshes” the room, which takes 15 minutes.
Each day, each housekeeper reports for her 6-hour shift, then prepares her cart. She pushes the cart to her floor and begins work. She usually has to restock the cart once per day; then she pushes it back to the storeroom at the end of the day and delivers dirty laundry, etc. Here is a timetable: 1) Arrive at work and stock cart (0.10 hrs). 2) Push cart to floor (0.10 hrs). 3) Take morning break (0.33 hrs). 4) Stop for lunch (0.50 hrs). 5) Restock cart (0.30 hrs). 6) Take afternoon break (0.33 hrs). 7) Push cart back to laundry and store items (0.33 hrs).
Last night, the hotel was full (all 400 rooms were occupied). People are checking out of 200 rooms. Their rooms will need to be thoroughly cleaned. The other 200 rooms will need to be refreshed. a) How many minutes per day of actual room cleaning can each
housekeeper do? b) How many minutes of room cleaning will the Miami Central
Hotel need today? c) How many housekeepers will be needed to clean the hotel
today? d) If all the guests checked out this morning, how many house-
keepers would be needed to clean the 400 rooms?
Additional problems 10.39–10.46 are available in MyOMLab.
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C H A P T E R 1 0 | H U M A N R E S O U R C E S , J O B D E S I G N , A N D W O R K M E A S U R E M E N T 437
CASE STUDIES Jackson Manufacturing Company
Kathleen McFadden, vice president of operations at Jackson Manufacturing Company, has just received a request for quote (RFQ) from DeKalb Electric Supply for 400 units per week of a motor armature. The components are standard and either easy to work into the existing production schedule or readily available from established suppliers on a JIT basis. But there is some dif- ference in assembly. Ms. McFadden has identified eight tasks that Jackson must perform to assemble the armature. Seven of these tasks are very similar to ones performed by Jackson in the past; therefore, the average time and resulting labor standard of those tasks is known.
The eighth task, an overload test, requires performing a task that is very different from any performed previously, however. Kathleen has asked you to conduct a time study on the task to determine the standard time. Then an estimate can be made of the cost to assemble the armature. This information, combined with other cost data, will allow the firm to put together the information needed for the RFQ.
To determine a standard time for the task, an employee from an existing assembly station was trained in the new assembly pro- cess. Once proficient, the employee was then asked to perform the task 17 times so a standard could be determined. The actual times observed (in minutes) were as follows:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
2.05 1.92 2.01 1.89 1.77 1.80 1.86 1.83 1.93 1.96 1.95 2.05 1.79 1.82 1.85 1.85 1.99
The worker had a 115% performance rating. The task can be performed in a sitting position at a well-designed ergonomic work-station in an air-conditioned facility. Although the arma- ture itself weighs 10.5 pounds, there is a carrier that holds it so that the operator need only rotate the armature. But the detail work remains high; therefore, the fatigue allowance should be 8%. The company has an established personal allowance of 6%. Delay should be very low. Previous studies of delay in this department average 2%. This standard is to use the same figure.
The workday is 7.5 hours, but operators are paid for 8 hours at an average of $12.50 per hour.
Discussion Questions
In your report to Ms. McFadden, you realize you will want to address several factors:
1. How big should the sample be for a statistically accurate stand- ard (at, say, the 99.73% confidence level and accuracy of {5%)?
2. Is the sample size adequate? 3. How many units should be produced at this workstation per day? 4. What is the cost per unit for this task in direct labor cost?
The “People” Focus: Human Resources at Alaska Airlines Video Case With thousands of employees spread across nearly 100 locations in the United States, Mexico, and Canada, building a commit- ted and cohesive workforce is a challenge. Yet Alaska Airlines is making it work. The company’s “people” focus states:
While airplanes and technology enable us to do what we do, we recognize this is fundamentally a people business, and our future depends on how we work together to win in this extremely competitive environment. As we grow, we want to strengthen our small company feel . . . We will succeed where others fail because of our pride and passion, and because of the way we treat our customers, our suppliers and partners, and each other.
Managerial excellence requires a committed workforce. Alaska Airlines’ pledge of respect for people is one of the key ele- ments of a world-class operation.
Effective organizations require talented, committed, and trained personnel. Alaska Airlines conducts comprehensive training at all levels. Its “Flight Path” leadership training for all 10,000 employees is now being followed by “Gear Up” training for 800 front-line managers. In addition, training programs have been developed for Lean and Six Sigma as well as for the unique requirements for pilots, flight attendants, baggage, and ramp per- sonnel. Because the company only hires pilots into first officer positions—the right seat in the cockpit, it offers a program called
the “Fourth Stripe” to train for promotion into the captain’s seat on the left side, along with all the additional responsibility that entails (see exterior and interior photos of one of Alaska Airlines’ flight simulators on the opening page of this chapter).
Customer service agents receive specific training on the com- pany’s “Empowerment Toolkit.” Like the Ritz-Carlton’s famous customer service philosophy, agents have the option of awarding customers hotel and meal vouchers or frequent flier miles when the customer has experienced a service problem.
Because many managers are cross-trained in operational duties outside the scope of their daily positions, they have the ability to pitch in to ensure that customer-oriented processes go smoothly. Even John Ladner, Director of Seattle Airport Operations, who is a fully licensed pilot, has left his desk to cover a flight at the last minute for a sick colleague.
Along with providing development and training at all levels, managers recognize that inherent personal traits can make a huge difference. For example, when flight attendants are hired, the ones who are still engaged, smiling, and fresh at the end of a very long interview day are the ones Alaska wants on the team. Why? The job requires these behaviors and attitudes to fit with the Alaska Airlines team—and smiling and friendly flight attend- ants are particularly important at the end of a long flight.
Visual workplace tools also complement and close the loop that matches training to performance. Alaska Airlines makes
Source: Professor Hank Maddux, Sam Houston State University
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438 P A R T 2 | D E S I G N I N G O P E R AT I O N S
• Additional Case Studies: Visit MyOMLab for these free case studies: Chicago Southern Hospital: Examines the requirements for a work-sampling plan for nurses. The Fleet That Wanders: Requires a look at ergonomic issues for truck drivers.
Everyone—managers and hourly employees alike—who goes to work for Hard Rock Cafe takes Rock 101, an initial 2-day train- ing class. The Hard Rock value system is to bring a fun, healthy, nurturing environment into the Hard Rock Cafe culture. This initial course and many other courses help employees develop both personally and professionally. The human resource depart- ment plays a critical role in any service organization, but at Hard Rock, with its “experience strategy,” the human resource depart- ment takes on added importance.
Long before Jim Knight, manager of corporate training, begins the class, the human resource strategy of Hard Rock has had an impact. Hard Rock’s strategic plan includes building a culture that allows for acceptance of substantial diversity and individuality. From a human resource perspective, this has the benefit of enlarging the pool of applicants as well as contributing to the Hard Rock culture.
Creating a work environment above and beyond a paycheck is a unique challenge. Outstanding pay and benefits are a start, but the key is to provide an environment that works for the employees. This includes benefits that start for part-timers who work at least 19 hours per week (while others in the industry start at 35 hours per week); a unique respect for individuality; continuing training; and a high level of internal promotions—some 60% of the man- agers are promoted from hourly employee ranks. The company’s training is very specific, with job-oriented interactive DVDs cover- ing kitchen, retail, and front-of-the-house service. Outside volun- teer work is especially encouraged to foster a bond between the workers, their community, and issues of importance to them.
Video Case Hard Rock’s Human Resource Strategy Applicants also are screened on their interest in music and their
ability to tell a story. Hard Rock builds on a hiring criterion of bright, positive-attitude, self-motivated individuals with an employee bill of rights and substantial employee empowerment. The result is a unique culture and work environment, which no doubt contributes to the low turnover of hourly people—one-half the industry average.
The layout, memorabilia, music, and videos are important ele- ments in the Hard Rock “experience,” but it falls on the wait- ers and waitresses to make the experience come alive. They are particularly focused on providing an authentic and memorable dining experience. Like Alaska Airlines, Hard Rock is looking for people with a cause—people who like to serve. By succeeding with its human resource strategy, Hard Rock obtains a competi- tive advantage.
Discussion Questions *
1. What has Hard Rock done to lower employee turnover to half the industry average?
2. How does Hard Rock’s human resource department support the company’s overall strategy?
3. How would Hard Rock’s value system work for automo- bile assembly line workers? ( Hint: Consider Hackman and Oldham’s core job characteristics.)
4. How might you adjust a traditional assembly line to address more “core job characteristics”?
* Before answering these questions, you may wish to view the video that accompanies this case.
Endnotes
1. Four Seasons Magazine , Annabell Shaw, Jan. 3, 2011. 2. See “Motivation Through the Design of Work,” in Jay Richard
Hackman and Greg R. Oldham, eds., Work Redesign (Reading, MA: Addison-Wesley, 1980); and A. Thomas, W. C. Buboltz, and C. Winkelspecht, “Job Characteristics and Personality as Predictors of Job Satisfaction,” Organizational Analysis , 12, no. 2 (2004): 205–219.
3. MTM is really a family of products available from the Methods Time Measurement Association. For example, MTM-HC deals with the health care industry, MTM-C handles clerical activi- ties, MTM-M involves microscope activities, MTM-V deals with machine shop tasks, and so on.
4. A. S. Helms, B. W. Shaw, and C. A. Lindner, “The Development of Laboratory Workload Standards through Computer-Based Work Measurement Technique, Part I,” Journal of Methods- Time Measurement 12: 43. Used with permission of MTM Association for Standards and Research.
5. The Occupational Safety and Health Administration (OSHA) is a federal government agency whose task is to ensure the safety and health of U.S. workers.
6. Material safety data sheets (MSDSs) contain details of haz- ards associated with chemicals and give information on their safe use.
full use of color-coded graphs and charts to report performance against key metrics to employees. Twenty top managers gather weekly in an operations leadership meeting, run by Executive VP of Operations, Ben Minicucci, to review activity consolidated into visual summaries. Key metrics are color-coded and posted promi- nently in every work area.
A l a s k a ’ s t r a i n i n g a p p r o a c h r e s u l t s i n e m p o w e r e d employees who are willing to assume added responsibil- ity and accept the unknowns that come with that added responsibility.
Discussion Questions *
1. Summarize Alaska Airlines’ human resources focus in your own words.
2. Why is employee empowerment useful to companies such as Alaska Airlines?
3. What tools discussed in the chapter might be employed to enhance the company’s training and performance efforts? Why?
* Before answering these questions, you may wish to view the video that accompanies this case.
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Chapter 10 Rapid Review 10
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Main Heading Review Material MyOMLab HUMAN RESOURCE STRATEGY FOR COMPETITIVE ADVANTAGE (pp. 410–411)
The objective of a human resource strategy is to manage labor and design jobs so people are effectively and efficiently utilized. Quality of work life refers to a job that is not only reasonably safe with equitable pay but that also achieves an appropriate level of both physical and psychological requirements. Mutual commitment means that both management and employees strive to meet common objectives. Mutual trust is reflected in reasonable, documented employment policies that are honestly and equitably implemented to the satisfaction of both management and employees.
Concept Questions: 1.1–1.4 VIDEO 10.1 The “People” Focus: Human Resources at Alaska Airlines VIDEO 10.2 Human Resources at Hard Rock Cafe
LABOR PLANNING (pp. 411– 412 )
j Labor planning —A means of determining staffing policies dealing with employ- ment stability, work schedules, and work rules.
Flextime allows employees, within limits, to determine their own schedules. Flexible (or compressed ) workweeks often call for fewer but longer workdays. Part-time status is particularly attractive in service industries with fluctuating demand loads.
Concept Questions: 2.1–2.4
JOB DESIGN (pp. 412 – 415 )
j Job design —Specifies the tasks that constitute a job for an individual or group. j Labor specialization (or job specialization )—The division of labor into unique
(“special”) tasks. j Job enlargement —The grouping of a variety of tasks about the same skill level;
horizontal enlargement. j Job rotation —A system in which an employee is moved from one specialized job
to another. j Job enrichment —A method of giving an employee more responsibility that
includes some of the planning and control necessary for job accomplishment; vertical expansion.
j Employee empowerment —Enlarging employee jobs so that the added responsibil- ity and authority are moved to the lowest level possible.
j Self-directed team —A group of empowered individuals working together to reach a common goal.
Concept Questions: 3.1–3.4
ERGONOMICS AND THE WORK ENVIRONMENT (pp. 415 – 417 )
j Ergonomics —The study of the human interface with the environment and machines. The physical environment affects performance, safety, and quality of work life. Illumination, noise and vibration, temperature, humidity, and air quality are con- trollable by management.
Concept Questions: 4.1–4.4
METHODS ANALYSIS (pp. 417 – 419 )
j Methods analysis —A system that involves developing work procedures that are safe and produce quality products efficiently.
j Flow diagram —A drawing used to analyze movement of people or material. j Process chart —A graphic representation that depicts a sequence of steps for a
process. j Activity chart —A way of improving utilization of an operator and a machine or
some combination of operators (a crew) and machines. j Operations chart —A chart depicting right- and left-hand motions.
Concept Questions: 5.1–5.4 Problems: 10.2, 10.6, 10.8 Virtual Office Hours for Solved Problem: 10.1
THE VISUAL WORKPLACE (p. 420 )
j Visual workplace —Uses a variety of visual communication techniques to rapidly communicate information to stakeholders.
Concept Questions: 6.1–6.4
LABOR STANDARDS (pp. 420 – 430 )
j Labor standards —The amount of time required to perform a job or part of a job. Labor standards are set in four ways: (1) historical experience, (2) time studies, (3) predetermined time standards, and (4) work sampling. j Time study —Timing a sample of a worker’s performance and using it as a basis
for setting a standard time. j Average observed time —The arithmetic mean of the times for each element meas-
ured, adjusted for unusual influence for each element.
Average observed time = Sum of the times recorded to perform each element
Number of observations (10-1)
j Normal time —The average observed time, adjusted for pace: Normal time = (Average observed time) * (Performance rating factor) (10-2)
Concept Questions: 7.1–7.4 Problems: 10.13–10.46 Virtual Office Hours for Solved Problems: 10.2–10.6
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Main Heading Review Material MyOMLab j Standard time —An adjustment to the total normal time; the adjustment provides
allowances for personal needs, unavoidable work delays, and fatigue:
Standard time = Total normal time
1 - Allowance factor (10-3)
Personal time allowances are often established in the range of 4% to 7% of total time.
Required sample size = n = a zs hx b
2 (10-4)
n = a zs e b
2 (10-5)
s = B
g(xi - x)2
n - 1 = B
g(Each sample observation - x)2
Number in sample - 1 (10-6)
j Predetermined time standards —A division of manual work into small basic ele- ments that have established and widely accepted times.
The most common predetermined time standard is methods time measurement (MTM). j Therbligs —Basic physical elements of motion. j Time measurement units (TMUs) —Units for very basic micromotions in which 1
TMU = 0.0006 min or 100,000 TMUs = 1 hr. j Work sampling —An estimate, via sampling, of the percent of the time that a
worker spends on various tasks. Work sampling sample size for a desired confidence and accuracy:
n = z2p(1 - p)
h2 (10-7) ACTIVE MODEL 10.1
ETHICS (p. 430 )
Management’s role is to educate the employee; specify the necessary equipment, work rules, and work environment; and then enforce those requirements.
Concept Questions: 8.1–8.2
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Chapter 10 Rapid Review continued
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 10.1 When product demand fluctuates and yet you maintain a constant level of employment, some of your cost savings might include:
a) reduction in hiring costs. b) reduction in layoff costs and unemployment insurance
costs. c) lack of need to pay a premium wage to get workers to
accept unstable employment. d) having a trained workforce rather than having to retrain
new employees each time you hire for an upswing in demand.
e) all of the above. LO 10.2 The difference between job enrichment and job enlargement
is that: a) enlarged jobs contain a larger number of similar tasks,
while enriched jobs include some of the planning and control necessary for job accomplishment.
b) enriched jobs contain a larger number of similar tasks, while enlarged jobs include some of the planning and control necessary for job accomplishment.
c) enriched jobs enable an employee to do a number of boring jobs instead of just one.
d) all of the above. LO 10.3 The work environment includes these factors: a) Lighting, noise, temperature, and air quality b) Illumination, carpeting, and high ceilings c) Enough space for meetings and videoconferencing d) Noise, humidity, and number of coworkers e) Job enlargement and space analysis
LO 10.4 Methods analysis focuses on: a) the design of the machines used to perform a task. b) how a task is accomplished. c) the raw materials that are consumed in performing a
task. d) reducing the number of steps required to perform a task. LO 10.5 The least preferred method of establishing labor standards is: a) time studies. b) work sampling. c) historical experience. d) predetermined time standards. LO 10.6 The allowance factor in a time study: a) adjusts normal time for errors and rework. b) adjusts standard time for lunch breaks. c) adjusts normal time for personal needs, unavoidable
delays, and fatigue. d) allows workers to rest every 20 minutes. LO 10.7 To set the required sample size in a time study, you must
know: a) the number of employees. b) the number of parts produced per day. c) the desired accuracy and confidence levels. d) management’s philosophy toward sampling.
Answers: LO 10.1. e; LO 10.2. a; LO 10.3. a; LO 10.4. b; LO 10.5. c; LO 10.6. c; LO 10.7. c.
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C H A P T E R O U T L I N E
Supply Chain Management 11
◆
The Supply Chain’s Strategic Importance 444
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Sourcing Issues: Make-or-Buy and Outsourcing 446
◆ Six Sourcing Strategies 447
◆ Supply Chain Risk 449
◆
Managing the Integrated Supply Chain 451
◆
Building the Supply Base 454
◆
Logistics Management 456
◆
Distribution Management 459
◆
Ethics and Sustainable Supply Chain Management 460
◆
Measuring Supply Chain Performance 461
GLOBAL COMPANY PROFILE: Darden Restaurants
C
H A
P T
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PART THREE Managing Operations
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
A la
sk a A
ir lin
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D arden Restaurants, Inc., is one of the largest publicly traded casual dining restaurant
companies in the world with $6.3 billion in annual sales. It serves over 320 million meals
annually from more than 1,500 restaurants in North America. Its well-known flagship
brand—Olive Garden—generates sales of $3.6 billion annually. Darden’s other brands include
Bahama Breeze, Seasons 52, The Capital Grille, Eddie V’s, Yard House, and LongHorn Steak-
house. The firm employs more than 150,000 people and is the 33rd largest employer in the U.S.
“Operations is typically thought of as an execution of strategy. For us it is the strategy,”
Darden’s former chairman, Joe R. Lee, stated.
In the restaurant business, a winning strategy requires a winning supply chain. Nothing is
more important than sourcing and delivering healthy, high-quality food; and there are very few
Darden’s Supply Chain Yields a Competitive Edge
GLOBAL COMPANY PROFILE Darden Restaurants
C H A P T E R 1 1
442
Qualifying Worldwide Sources: Part of Darden’s
supply chain begins with a crab harvest in the frigid
waters off the coast of Alaska. But long before a
supplier is qualified to sell to Darden, a total quality
team is appointed. The team provides guidance,
assistance, support, and training to the suppliers to
ensure that overall objectives are understood and
desired results accomplished.
Aquaculture Certification: Shrimp in this Asian plant are certified to ensure
traceability. The focus is on quality control certified by the Aquaculture
Certification Council, of which Darden is a member. Farming and inspection
practices yield safe and wholesome shrimp.
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443
other industries where supplier performance is
so closely tied to the customer.
Darden sources its food from five conti-
nents and thousands of suppliers. To meet
Darden’s needs for fresh ingredients, the
company has developed four distinct sup-
ply chains: one for seafood; one for dairy/
produce/other refrigerated foods; a third for
other food items, like baked goods; and a
fourth for restaurant supplies (everything from
dishes to ovens to uniforms). Over $2 billion
is spent in these supply chains annually.
(See the Video Case Study at the end of this
chapter for details.)
Darden’s four supply channels have some
common characteristics. They all require
supplier qualification , have product tracking ,
are subject to independent audits , and employ
just-in-time delivery . With best-in-class
techniques and processes, Darden creates
worldwide supply chain partnerships and
alliances that are rapid, transparent, and effi-
cient. Darden achieves competitive advantage
through its superior supply chain.
Product tracking: Darden’s seafood inspection team developed an integral system
that uses a Lot ID to track seafood from its origin through shipping and receipt.
Darden uses a modified atmosphere packaging (MAP) process to extend the shelf
life and preserve the quality of its fresh fish. The tracking includes time temperature
monitoring.
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JIT Delivery: For many products, temperature monitoring begins immediately and is tracked through the entire supply chain, to the kitchen at each of Darden’s 1,500
restaurants, and ultimately to the guest.
rough the entire supply chain to the kitchen at each of Darden’s 1 500
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The Supply Chain’s Strategic Importance Like Darden, most firms spend a huge portion of their sales dollars on purchases. Because an increasing percentage of an organization’s costs are determined by purchasing, relationships with suppliers are increasingly integrated and long term. Combined efforts that improve inno- vation, speed design, and reduce costs are common. Such efforts, when part of a corporate- wide strategy, can dramatically improve all partners’ competitiveness. This integrated focus places added emphasis on managing supplier relationships.
Supply chain management describes the coordination of all supply chain activities, starting with raw materials and ending with a satisfied customer. Thus, a supply chain includes suppli- ers; manufacturers and/or service providers; and distributors, wholesalers, and/or retailers who deliver the product and/or service to the final customer. Figure 11.1 provides an example of the breadth of links and activities that a supply chain may cover.
The objective of supply chain management is to structure the supply chain to maximize its competitive advantage and benefits to the ultimate consumer. Just as with championship teams, a central feature of successful supply chains is members acting in ways that benefit the team (the supply chain).
L E A R N I N G OBJEC TI V ES
LO 11.1 Explain the strategic importance of the supply chain 445
LO 11.2 Identify six sourcing strategies 447
LO 11.3 Explain issues and opportunities in the supply chain 451
LO 11.4 Describe the steps in supplier selection 454
LO 11.5 Explain major issues in logistics management 456
LO 11.6 Compute the percentage of assets committed to inventory and inventory turnover 461
STUDENT TIP Competition today is not
between companies; it is
between supply chains.
Supply chain management
The coordination of all supply
chain activities involved in
enhancing customer value.
Distributor Sam’s GroceryFarm
Bottle manufacturing
Tier 3 suppliers
Tier 2 suppliers
Tier 1 suppliers
Can manufacturing
$1.18
Customer
$3.36
$4.62
6 12-oz beers
S3 S2
S3
S3
S2
S2
S1
S1
Brewer
Sam’s Grocery
Hops/grains
Hops, grain
$0.34
$6.99
Figure 11.1
A Supply Chain for Beer
The supply chain includes all the interactions among suppliers, manufacturers, distributors, and customers. A well-functioning
supply chain has information flowing between all partners. The chain includes transportation, scheduling information, cash and
credit transfers, as well as ideas, designs, and material transfers. Even can and bottle manufacturers have their own tiers of
suppliers providing components such as lids, labels, packing containers, etc. (Costs are approximate and include substantial taxes.)
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With collaboration, costs for both buyers and suppliers can drop. For example, when both parties are willing to share sales and cost information, profit can increase for both. Examples of supply chain coordination include:
◆ Walmart cooperates with its top 200 supplier factories in China to reach the goal of 20% energy efficiency improvement.
◆ Mercury Marine, the large boat-engine producer, uses the Internet to enhance design with boat builders and engine dealers as it fights off competition from Honda, Yamaha, and Volvo.
◆ Unifi, the leading U.S. maker of synthetic yarn, shares daily production-scheduling and quality-control information with raw materials supplier DuPont.
◆ Amazon, to reduce logistics costs, has moved its fulfillment activities for Procter and Gamble products directly into Procter and Gamble’s warehouse.
As Table 11.1 indicates, a huge part of a firm’s revenue is typically spent on purchases, so supply chains are a good place to look for savings. Example 1 further illustrates the amount of leverage available to the operations manager through the supply chain. These percentages indicate the strong role that supply chains play in potential profitability. Effective cost cutting may help a firm reach its profit goals more easily than would an increased sales effort.
LO 11.1 Explain the strategic importance of
the supply chain
TABLE 11.1
Supply Chain Costs as a Percentage of Sales
INDUSTRY %
PURCHASED
Automobile 67
Beverages 52
Chemical 62
Food 60
Lumber 61
Metals 65
Paper 55
Petroleum 79
Restaurants 35
Transportation 62
Example 1 SUPPLY CHAIN STRATEGY VS. SALES STRATEGY TO ACHIEVE A TARGET PROFIT Hau Lee Furniture, Inc., spends 60% of its sales dollars in the supply chain and has a current gross profit of $10,000. Hau wishes to increase gross profit by $5,000 (50%). He would like to compare two strategies: reducing material costs vs. increasing sales.
APPROACH c Use the table below to make the analysis.
SOLUTION c The current material costs and production costs are 60% and 20%, respectively, of sales dollars, with fixed cost at a constant $10,000. Analysis indicates that an improvement in the supply chain that would reduce material costs by 8.3% ($5,000/$60,000) would produce a 50% net profit gain for Hau, whereas a much larger 25% increase in sales ($25,000/$100,000) would be required to produce the same result.
CURRENT SITUATION SUPPLY CHAIN STRATEGY SALES STRATEGY
Sales $100,000 $100,000 $125,000
Cost of materials $60,000 (60%) $55,000 (55%) $75,000 (60%)
Production costs $20,000 (20%) $20,000 (20%) $25,000 (20%)
Fixed costs $10,000 (10%) $10,000 (10%) $10,000 (8%)
Profi t $10,000 (10%) $15,000 (15%) $15,000 (12%)
INSIGHT c Supply chain savings flow directly to the bottom line. In general, supply chain costs need to shrink by a much lower percentage than sales revenue needs to increase to attain a profit goal. Effec- tive management of the supply chain can generate substantial benefits.
LEARNING EXERCISE c If Hau wants to double the original gross profits (from $10,000 to $20,000), what would be required of the supply chain and sales strategies? [Answer: Supply chain strategy = 16.7% reduction in material costs; sales strategy = 50% increase in sales.]
RELATED PROBLEMS c 11.2, 11.3
As firms strive to increase their competitiveness via product customization, high quality, cost reductions, and speed to market, added emphasis is placed on the supply chain. Through long-term strategic relationships, suppliers become “partners” as they contribute to competitive advantage.
To ensure that the supply chain supports a firm’s strategy, managers need to consider the supply chain issues shown in Table 11.2 . Activities of supply chain managers cut across the accounting, finance, marketing, and operations disciplines. Just as the OM function supports the firm’s overall strategy, the supply chain must support the OM strategy. Strategies of low
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cost or rapid response demand different things from a supply chain than a strategy of differen- tiation. For instance, a low-cost strategy, as Table 11.2 indicates, requires suppliers be selected based primarily on cost. Such suppliers should have the ability to design low-cost products that meet the functional requirements, minimize inventory, and drive down lead times. However, if you want roses that are fresh, build a supply chain that focuses on response (see the OM in Action box “A Rose Is a Rose, but Only If It Is Fresh”).
Firms must achieve integration of strategy up and down the supply chain. And they must expect that strategy to be different for different products and to change as products move through their life cycle. Darden Restaurants, as noted in the opening Global Company Profile , has mastered worldwide product and service complexity by segmenting its supply chain and at the same time integrating four unique supply chains into its overall strategy.
Sourcing Issues: Make-or-Buy and Outsourcing As suggested in Table 11.2 , a firm needs to determine strategically how to design the supply chain. However, prior to embarking on supply chain design, operations managers must first consider the “make-or-buy” and outsourcing decisions.
OM in Action A Rose Is a Rose, but Only If It Is Fresh Supply chains for food and flowers must be fast, and they must be good.
When the food supply chain has a problem, the best that can happen is the
customer does not get fed on time; the worst that happens is the customer
gets food poisoning and dies. In the floral industry, the timing and tempera-
ture are also critical. Indeed, flowers are the most perishable agricultural
item—even more so than fish. Flowers not only need to move fast, but they
must also be kept cool, at a constant temperature of 33 to 37 degrees. And
they must be provided preservative-treated water while in transit. Roses are
especially delicate, fragile, and perishable.
Eighty percent of the roses sold in the U.S. market arrive by air from
rural Colombia and Ecuador. Roses move through this supply chain via an
intricate but fast transportation network. This network stretches from growers
who cut, grade, bundle, pack, and ship; to importers who make the deal;
to the U.S. Department of Agriculture personnel who quarantine and inspect
for insects, diseases, and parasites; to U.S. Customs agents who inspect
and approve; to facilitators
who provide clearance and
labeling; to wholesalers who
distribute; to retailers who
arrange and sell; and finally
to the customer. Each and
every minute the product is
deteriorating. The time and
temperature sensitivity of per-
ishables like roses requires sophistication and refined standards in the supply
chain. Success yields quality and low losses. After all, when it’s Valentine’s
Day, what good is a shipment of roses that arrives wilted or late? This is a
difficult supply chain; only an excellent one will get the job done.
Sources: NPR (Feb. 13, 2015); Supply Chain 24/7 (Feb. 13, 2014); and The
Star-Ledger (Feb. 6, 2011).
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VIDEO 11.1 Darden’s Global Supply Chain
TABLE 11.2 How Corporate Strategy Impacts Supply Chain Decisions*
LOW-COST STRATEGY RESPONSE STRATEGY DIFFERENTIATION STRATEGY
Primary supplier selection criteria
• Cost • Capacity • Speed • Flexibility
• Product development skills • Willing to share information • Jointly and rapidly develop
products
Supply chain inventory
• Minimize inventory to hold down costs
• Use buffer stocks to ensure speedy supply
• Minimize inventory to avoid product obsolescence
Distribution network
• Inexpensive transportation
• Sell through discount distributors/retailers
• Fast transportation • Provide premium
customer service
• Gather and communicate market research data
• Knowledgeable sales staff
Product design characteristics
• Maximize performance • Minimize cost
• Low setup time • Rapid production
ramp-up
• Modular design to aid product differentiation
*See related table and discussion in Marshall L. Fisher, “What Is the Right Supply Chain for Your Product?” Harvard Business Review (March–April 1997): 105.
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Make-or-Buy Decisions A wholesaler or retailer buys everything that it sells; a manufacturing operation hardly ever does. Manufacturers, restaurants, and assemblers of products buy components and subassem- blies that go into final products. As we saw in Chapter 5 , choosing products and services that can be advantageously obtained externally as opposed to produced internally is known as the make-or-buy decision . Supply chain personnel evaluate alternative suppliers and provide current, accurate, and complete data relevant to the buy alternative.
Outsourcing Outsourcing transfers some of what are traditional internal activities and resources of a firm to outside vendors, making it slightly different from the traditional make-or-buy decision. Outsourcing, discussed in Chapter 2 , is part of the continuing trend toward using the effi- ciency that comes with specialization. The vendor performing the outsourced service is an expert in that particular specialty. This leaves the outsourcing firm to focus on its key success factors and its core competencies.
Six Sourcing Strategies Having decided what to outsource, managers have six strategies to consider.
Many Suppliers With the many-suppliers strategy, a supplier responds to the demands and specifications of a “request for quotation,” with the order usually going to the low bidder. This is a common strategy when products are commodities. This strategy plays one supplier against another and places the burden of meeting the buyer’s demands on the supplier. Suppliers aggressively compete with one another. This approach holds the supplier responsible for maintaining the necessary technology, expertise, and forecasting abilities, as well as cost, quality, and delivery competencies. Long-term “partnering” relationships are not the goal.
Few Suppliers A strategy of few suppliers implies that rather than looking for short-term attributes, such as low cost, a buyer is better off forming a long-term relationship with a few dedicated suppli- ers. Long-term suppliers are more likely to understand the broad objectives of the procuring firm and the end customer. Using few suppliers can create value by allowing suppliers to have economies of scale and a learning curve that yields both lower transaction costs and lower production costs. This strategy also encourages those suppliers to provide design innovations and technological expertise.
Ford chooses suppliers even before parts are designed. Motorola evaluates suppliers on rig- orous criteria, but in many instances has eliminated traditional supplier bidding, placing added emphasis on quality and reliability. On occasion these relationships yield contracts that extend through the product’s life cycle. The British retailer Marks & Spencer finds that cooperation with its suppliers yields new products that win customers for the supplier and themselves. The move toward tight integration of the suppliers and purchasers is occurring in both manufactur- ing and services.
As with all other strategies, a downside exists. With few suppliers, the cost of changing partners is huge, so both buyer and supplier run the risk of becoming captives of the other. Poor supplier performance is only one risk the purchaser faces. The purchaser must also be concerned about trade secrets and suppliers that make other alliances or venture out on their own. This happened when the U.S. Schwinn Bicycle Co., needing additional capacity, taught Taiwan’s Giant Manufacturing Company to make and sell bicycles. Giant Manufacturing is now the largest bicycle manufacturer in the world, and Schwinn was acquired out of bank- ruptcy by Pacific Cycle LLC.
Make-or-buy decision
A choice between producing a
component or service in-house
or purchasing it from an outside
source.
Outsourcing
Transferring a firm’s activities that
have traditionally been internal to
external suppliers.
LO 11.2 Identify six sourcing strategies
STUDENT TIP Supply chain strategies come in
many varieties; choosing the correct
one is the trick.
VIDEO 11.2 Supply Chain Management at Regal
Marine
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Vertical Integration Purchasing can be extended to take the form of vertical integration. By vertical integration , we mean developing the ability to produce goods or services previously purchased or to actually buy a supplier or a distributor. As shown in Figure 11.2 , vertical integration can take the form of forward or backward integration .
Backward integration suggests a firm purchase its suppliers, as in the case of Apple decid- ing to manufacture its own semiconductors. Apple also uses forward integration by establishing its own revolutionary retail stores.
Vertical integration can offer a strategic opportunity for the operations manager. For firms with the capital, managerial talent, and required demand, vertical integration may provide sub- stantial opportunities for cost reduction, higher quality, timely delivery, and inventory reduc- tion. Vertical integration appears to work best when the organization has a large market share and the management talent to operate an acquired vendor successfully.
The relentless march of specialization continues, meaning that a model of “doing every- thing” or “vertical integration” is increasingly difficult. Backward integration may be particu- larly dangerous for firms in industries undergoing technological change if management cannot keep abreast of those changes or invest the financial resources necessary for the next wave of technology. Research and development costs are too high and technology changes too rapid for one company to sustain leadership in every component. Most organizations are better served concentrating on their own specialty and leveraging suppliers’ contributions.
Joint Ventures Because vertical integration is so dangerous, firms may opt for some form of formal collabo- ration. As we noted in Chapter 5 , firms may engage in collaboration to enhance their new product prowess or technological skills. But firms also engage in collaboration to secure sup- ply or reduce costs. One version of a joint venture is the current Daimler–BMW effort to develop and produce standard automobile components. Given the global consolidation of the auto industry, these two rivals in the luxury segment of the automobile market are at a disad- vantage in volume. Their relatively low volume means fewer units over which to spread fixed costs, hence the interest in consolidating to cut development and production costs. As in all other such collaborations, the trick is to cooperate without diluting the brand or conceding a competitive advantage.
Keiretsu Networks Many large Japanese manufacturers have found another strategy: it is part collaboration, part purchasing from few suppliers, and part vertical integration. These manufacturers are often financial supporters of suppliers through ownership or loans. The supplier becomes part of a company coalition known as a keiretsu . Members of the keiretsu are assured long-term rela- tionships and are therefore expected to collaborate as partners, providing technical expertise
Raw material (suppliers)
Vertical Integration Examples of Vertical Integration
Tree Harvesting
International Paper
Apple
Retail Stores
Pepsi
Bottling
Backward integration
Current transformation
Forward integration
Finished goods (customers)
PulpmakingChipmakers
End-User Paper Conversion
Figure 11.2
Vertical Integration Can Be
Forward or Backward
Vertical integration
Developing the ability to produce
goods or services previously
purchased or actually buying a
supplier or a distributor.
Keiretsu
A Japanese term that describes
suppliers who become part of a
company coalition.
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and stable quality production to the manufacturer. Members of the keiretsu can also have second- and even third-tier suppliers as part of the coalition.
Virtual Companies Virtual companies rely on a variety of good, stable supplier relationships to provide services on demand. Suppliers may provide a variety of services that include doing the payroll, hiring per- sonnel, designing products, providing consulting services, manufacturing components, con- ducting tests, or distributing products. The relationships may be short- or long-term and may include true partners, collaborators, or simply able suppliers and subcontractors. Whatever the formal relationship, the result can be exceptionally lean performance. The advantages of virtual companies include specialized management expertise, low capital investment, flexibil- ity, and speed. The result is efficiency.
The apparel business provides a traditional example of virtual organizations. The designers of clothes seldom manufacture their designs; rather, they license the manufacture. The manu- facturer may then rent space, lease sewing machines, and contract for labor. The result is an organization that has low overhead, remains flexible, and can respond rapidly to the market.
A contemporary example is exemplified by Vizio, Inc., a California-based producer of flat- screen TVs that has fewer than 100 employees but huge sales. Vizio uses modules to assemble its own brand of TVs. Because the key components of TVs are now readily available and sold almost as commodities, innovative firms such as Vizio can specify the components, hire a con- tract manufacturer, and market the TVs with very little startup cost. In a virtual company, the supply chain is the company. Managing it is dynamic and demanding.
Supply Chain Risk In this age of increasing specialization, low communication cost, and fast transportation, companies are making less and buying more. This means more reliance on supply chains and more risk. Managing integrated supply chains is a strategic challenge. Having fewer suppliers makes the supplier and customer more dependent on each other, increasing risk for both.
Virtual companies
Companies that rely on a variety
of supplier relationships to provide
services on demand. Also known
as hollow corporations or network
companies.
Supply chain risks arise in many ways. As this mishap illustrates, expected shipments can literally sink into the ocean.
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This risk is compounded by globalization and logistical complexity. In any supply chain, vendor reliability and quality may be challenging. But the new model of a tight, fast, low- inventory supply chain, operating across political and cultural boundaries, adds a new dimension to risk. As organizations go global, shipping time (lead time) may increase, logistics may be less reliable, and tariffs and quotas may block companies from doing business. In addition, international supply chains complicate information flows and increase political/currency risks.
Risks and Mitigation Tactics Supply chain risks arise in numerous ways, and you cannot outsource risk! Table 11.3 identi- fies major categories of risks and tactics to help manage them. The development of a successful strategic plan for supply chain management requires careful research, a thorough assessment of the risks involved, and innovative planning. Companies need to focus not only on reducing potential disruptions but also on how to prepare for responses to inevitable negative events. Flexible, secure supply chains and sufficient insurance against a variety of disruptions are a start. They may also choose to diversify their supplier base by using multiple sources for criti- cal components. Cross-sourcing represents a hybrid technique where two suppliers each provide a different component, but they have the capability of producing each other’s component— that is, each acting as a backup source. Another option is to create excess capacity that can be used in response to problems in the supply chain. Such contingency plans can reduce risk.
STUDENT TIP The environment, controls, and
process performance all affect
supply chain risk.
Cross-sourcing
Using one supplier for a component
and a second supplier for another
component, where each supplier
acts as a backup for the other.
TABLE 11.3 Supply Chain Risks and Tactics
RISK RISK REDUCTION TACTICS EXAMPLE
Supplier failure to deliver
Use multiple suppliers; effective contracts with penalties; subcontractors on retainer; preplanning
McDonald’s planned its supply chain 6 years before its opening in Russia. Every plant—bakery, meat, chicken, fi sh, and lettuce—is closely monitored to ensure strong links.
Supplier quality failures
Careful supplier selection, training, certifi cation, and monitoring
Darden Restaurants has placed extensive controls, including third-party audits, on supplier processes and logistics to ensure constant monitoring and reduction of risk.
Outsourcing Take over production; provide or perform the service yourself
Tyson took over chicken farm production in China to mitigate product quality and safety concerns related to using independent farmers.
Logistics delays or damage
Multiple/redundant transportation modes and warehouses; secure packaging; effective contracts with penalties
Walmart, with its own trucking fl eet and numerous distribution centers located throughout the U.S., fi nds alternative origins and delivery routes bypassing problem areas.
Distribution Careful selection, monitoring, and effective contracts with penalties
Toyota trains its dealers around the world, invoking principles of the Toyota Production System to help dealers improve customer service, used-car logistics, and body and paint operations.
Information loss or distortion
Redundant databases; secure IT systems; training of supply chain partners on the proper interpretations and uses of information
Boeing utilizes a state-of-the-art international communication system that transmits engineering, scheduling, and logistics data to Boeing facilities and suppliers worldwide.
Political Political risk insurance; cross-country diversifi cation; franchising and licensing
Hard Rock Café reduces political risk by franchising and licensing, rather than owning, when the political and cultural barriers seem signifi cant.
Economic Hedging to combat exchange rate risk; purchasing contracts that address price fl uctuations
Honda and Nissan are moving more manufacturing out of Japan as the exchange rate for the yen makes Japanese-made autos more expensive.
Natural catastrophes
Insurance; alternate sourcing; cross-country diversifi cation
Toyota, after its experience with fi res, earthquakes, and tsunamis, now attempts to have at least two suppliers, each in a different geographical region, for each component.
Theft, vandalism, and terrorism
Insurance; patent protection; security measures including RFID and GPS; diversifi cation
Domestic Port Radiation Initiative: The U.S. government has set up radiation portal monitors that scan nearly all imported containers for radiation.
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Security and JIT There is probably no society more open than the U.S. This includes its borders and ports—but they are swamped. Millions of containers enter U.S. ports each year, along with thousands of planes, cars, and trucks each day. Even under the best of conditions, some 5% of the container movements are misrouted, stolen, damaged, or excessively delayed.
Since the September 11, 2001, terrorist attacks, supply chains have become more complex. However, technological innovations in the supply chain are improving both security and inven- tory management, making logistics more reliable. Technology is now capable of knowing truck and container location, content, and condition. New devices can even detect broken container seals. Motion detectors can also be installed inside containers. Other sensors record interior data including temperature, shock, radioactivity, and whether a container is moving. Tracking lost containers, identifying delays, or just reminding individuals in the supply chain that a ship- ment is on its way will help expedite shipments.
Managing the Integrated Supply Chain As managers move toward integration of the supply chain, substantial efficiencies are pos- sible. The cycle of materials—as they flow from suppliers, to production, to warehousing, to distribution, to the customer—takes place among separate and often very independent organi- zations. It can lead to actions that may not optimize the entire chain. On the other hand, the supply chain is full of opportunities to reduce waste and enhance value. We now look at some of the significant issues and opportunities .
Issues in Managing the Integrated Supply Chain Three issues complicate development of an efficient, integrated supply chain: local optimiza- tion, incentives, and large lots.
Local Optimization Members of the chain are inclined to focus on maximizing local profit or minimizing immediate cost based on their limited knowledge. Slight upturns in demand are overcompensated for because no one wants to be caught short. Similarly, slight downturns are overcompensated for because no one wants to be caught holding excess inven- tory. So fluctuations are magnified. For instance, a pasta distributor does not want to run out of pasta for its retail customers; the natural response to an extra large order from the retailer
As this photo of the port of Charleston suggests,
with over 16 million containers entering the U.S.
annually, tracking location, content, and condition
of trucks and containers is a challenge. But new
technology may improve both security and JIT
shipments.
P ro
vi d e d b
y S o u th
C a ro
lin a S
ta te
P o rt
s A
u th
o ri ty
VIDEO 11.3 Arnold Palmer Hospital’s Supply
Chain
LO 11.3 Explain issues and opportunities in the
supply chain
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is to compensate with an even larger order to the manufacturer on the assumption that retail sales are picking up. Neither the distributor nor the manufacturer knows that the retailer had a major one-time promotion that moved a lot of pasta. This is exactly the issue that complicated the implementation of efficient distribution at the Italian pasta maker Barilla.
Incentives (Sales Incentives, Quantity Discounts, Quotas, and Promotions) Incentives push merchandise into the chain for sales that have not occurred. This generates fluctuations that are ultimately expensive to all members of the chain.
Large Lots There is often a bias toward large lots because large lots tend to reduce unit costs. A logistics manager wants to ship large lots, preferably in full trucks, and a production manager wants long production runs. Both actions drive down unit shipping and production costs, but they increase holding costs and fail to reflect actual sales.
These three common occurrences—local optimization, incentives, and large lots—contribute to distortions of information about what is really occurring in the supply chain. A well-running supply system needs to be based on accurate information about how many products are truly being pulled through the chain. The inaccurate information is unintentional, but it results in distortions and fluctuations, causing what is known as the bullwhip effect.
The bullwhip effect occurs as orders are relayed from retailers, to distributors, to wholesalers, to manufacturers, with fluctuations increasing at each step in the sequence. The “bullwhip” fluctuations in the supply chain increase the costs associated with inventory, transportation, shipping, and receiving, while decreasing customer service and profitability. A number of specific opportunities exist for reducing the bullwhip effect and improving supply chain per- formance. The bullwhip effect is discussed more thoroughly in the supplement to this chapter.
Opportunities in Managing the Integrated Supply Chain Opportunities for effective management in the supply chain include the following 10 items.
Accurate “Pull” Data Accurate pull data are generated by sharing (1) point-of-sales (POS) information so that each member of the chain can schedule effectively and (2) computer-assisted ordering (CAO). This implies using POS systems that collect sales data and then adjusting that data for market factors, inventory on hand, and outstanding orders. Then a net order is sent directly to the supplier, who is responsible for maintaining the finished-goods inventory.
Lot Size Reduction Lot sizes are reduced through aggressive management. This may in- clude (1) developing economical shipments of less than truckload lots; (2) providing discounts based on total annual volume rather than size of individual shipments; and (3) reducing the cost of ordering through techniques such as standing orders and various forms of electronic purchasing.
Single-Stage Control of Replenishment Single-stage control of replenishment means designating a member in the chain as responsible for monitoring and managing inventory in the supply chain based on the “pull” from the end user. This approach removes distorted infor- mation and multiple forecasts that create the bullwhip effect. Control may be in the hands of:
◆ A sophisticated retailer who understands demand patterns. Walmart does this for some of its inventory with radio frequency ID (RFID) tags.
◆ A distributor who manages the inventory for a particular distribution area. Distributors who handle grocery items, beer, and soft drinks may do this. Anheuser-Busch manages beer inventory and delivery for many of its customers.
◆ A manufacturer who has a well-managed forecasting, manufacturing, and distribution system. TAL Apparel Ltd., discussed in the OM in Action box, “The JCPenney Supply Chain for Dress Shirts,” does this for JCPenney.
Vendor-Managed Inventory Vendor-managed inventory (VMI) means the use of a local sup- plier (usually a distributor) to maintain inventory for the manufacturer or retailer. The supplier delivers directly to the purchaser’s using department rather than to a receiving dock or stock- room. If the supplier can maintain the stock of inventory for a variety of customers who use
Bullwhip effect
The increasing fluctuation in
orders that often occurs as orders
move through the supply chain.
Pull data
Accurate sales data that initiate
transactions to “pull” product
through the supply chain.
Single-stage control of replenishment
Fixing responsibility for monitoring
and managing inventory for the
retailer.
Vendor-managed inventory (VMI)
A system in which a supplier
maintains material for the buyer,
often delivering directly to the
buyer’s using department.
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the same product or whose differences are very minor (say, at the packaging stage), then there should be a net savings. These systems work without the immediate direction of the purchaser.
Collaborative Planning, Forecasting, and Replenishment (CPFR) As with single-stage control and vendor-managed inventory, collaborative planning, forecasting, and replen- ishment (CPFR) is another effort to manage inventory in the supply chain. With CPFR, members of the supply chain share planning, demand, forecasting, and inventory information. Partners in a CPFR effort begin with collaboration on product definition and a joint marketing plan. Promotion, advertising, forecasts, joint order commitments, and timing of shipments are all included in the plan in a concerted effort to drive down inventory and related costs. CPFR can help to significantly reduce the bullwhip effect.
Blanket Orders Blanket orders are unfilled orders with a vendor and are also called “open orders” or “incomplete orders.” A blanket order is a contract to purchase certain items from a vendor. It is not an authorization to ship anything. Shipment is made only on receipt of an agreed-on document, perhaps a shipping requisition or shipment release.
Standardization The purchasing department should make special efforts to increase lev- els of standardization. That is, rather than obtaining a variety of similar components with labeling, coloring, packaging, or perhaps even slightly different engineering specifications, the purchasing agent should try to have those components standardized.
Postponement Postponement withholds any modification or customization to the product (keeping it generic) as long as possible. The concept is to minimize internal variety while maxi- mizing external variety. For instance, after analyzing the supply chain for its printers, Hewlett- Packard (HP) determined that if the printer’s power supply was moved out of the printer itself and into a power cord, HP could ship the basic printer anywhere in the world. HP modified the printer, its power cord, its packaging, and its documentation so that only the power cord and documentation needed to be added at the final distribution point. This modification allowed the firm to manufacture and hold centralized inventories of the generic printer for shipment as demand changed. Only the unique power system and documentation had to be held in each country. This understanding of the entire supply chain reduced both risk and inventory invest- ment. Similarly, Benetton leaves a portion of each style of its sweaters white so that they can be dyed the color the market is demanding at the last possible moment.
Electronic Ordering and Funds Transfer Electronic ordering and bank transfers are traditional approaches to speeding transactions and reducing paperwork. Transactions
OM in Action The JCPenney Supply Chain for Dress Shirts Purchase a white Stafford wrinkle-free dress shirt, size 17 neck, 34/35 sleeve
at JCPenney at Atlanta’s Northlake Mall on a Tuesday, and the supply chain
responds. Within a day, TAL Apparel Ltd. in Hong Kong downloads a record
of the sale. After a run through its forecasting model, TAL decides how many
shirts to make and in what styles, colors, and sizes. By Wednesday afternoon,
the replacement shirt is packed to be shipped directly to the JCPenney North-
lake Mall store. The system bypasses the JCPenney warehouse—indeed all
warehouses—as well as the JCPenney corporate decision makers.
In a second instance, two shirts are sold, leaving none in stock. TAL, after
downloading the data, runs its forecasting model but comes to the decision
that this store needs to have two in stock. Without consulting JCPenney, a TAL
factory in Taiwan makes two new shirts. It sends one by ship, but because of
the outage, the other goes by air.
As retailers deal with mass customization, fads, and seasonal swings,
they also strive to cut costs—making a responsive supply chain critical.
Before globalization of the supply chain, JCPenney would have had thousands
of shirts warehoused across the country. Now JCPenney stores, like those of
many retailers, hold a very
limited inventory of shirts.
JCPenney’s supplier,
TAL, is providing both
sales forecasting and
inventory management, a
situation not acceptable to
many retailers. But what is
most startling is that TAL
also places its own orders!
A supply chain like this
works only when there is trust between partners. The rapid changes in supply
chain management not only place increasing technical demands on suppliers
but also increase demands for trust between the parties.
Sources: Fortune (June 10, 2013); Apparel (April 2006); and The Wall Street
Journal (September 11, 2003).
P a ve
l L P
h o to
a n d V
id e o /S
h u tt
e rs
to ck
Collaborative planning, forecasting, and replenishment (CPFR)
A system in which members of
a supply chain share information
in a joint effort to reduce supply
chain costs.
Blanket order
A long-term purchase commitment
to a supplier for items that are to
be delivered against short-term
releases to ship.
Postponement
Delaying any modifications or
customization to a product as
long as possible in the production
process.
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between firms often use electronic data interchange (EDI), which is a standardized data- transmittal format for computerized communications between organizations. EDI also provides for the use of advanced shipping notice (ASN), which notifies the purchaser that the vendor is ready to ship. Although some firms are still moving to EDI and ASN, the Internet’s ease of use and lower cost is proving more popular.
Drop Shipping and Special Packaging Drop shipping means the supplier will ship directly to the end consumer, rather than to the seller, saving both time and reshipping costs. Other cost-saving measures include the use of special packaging, labels, and optimal place- ment of labels and bar codes on containers. The final location down to the department and number of units in each shipping container can also be indicated. Substantial savings can be obtained through management techniques such as these. Some of these techniques can be of particular benefit to wholesalers and retailers by reducing shrinkage (lost, damaged, or stolen merchandise) and handling cost.
For instance, Walmart Marketplace ( Walmart.com ) provides customers access to hundreds of thousands of additional products through approved retailers. Although orders from these retailers are combined in a common payment to Walmart.com , all shipping and returns are handled by the affiliate retailers.
Building the Supply Base For those goods and services a firm buys, suppliers, also known as vendors, must be selected and actively managed. Supplier selection considers numerous factors, such as strategic fit, supplier competence, delivery, and quality performance. Because a firm may have some com- petence in all areas and may have exceptional competence in only a few, selection can be chal- lenging. Procurement policies also need to be established. Those might address issues such as percent of business done with any one supplier or with minority businesses. We now examine supplier selection as a four-stage process: (1) supplier evaluation, (2) supplier development, (3) negotiations, and (4) contracting.
Supplier Evaluation The first stage of supplier selection, supplier evaluation, involves finding potential suppliers and determining the likelihood of their becoming good suppliers. If good suppliers are not selected, then all other supply chain efforts are wasted. As firms move toward long-term sup- pliers, the issues of financial strength, quality, management, research, technical ability, and potential for a close, long-term relationship play an increasingly important role. Evaluation criteria critical to the firm might include these categories as well as production process capa- bility, location, and information systems. The supplement to this chapter provides an example of the commonly used factor weighting approach to supplier evaluation.
Supplier Certification International quality certifications such as ISO 9000 and ISO 14000 are designed to provide an external verification that a firm follows sound quality man- agement and environmental management standards. Buying firms can use such certifications to pre-qualify potential suppliers. Despite the existence of the ISO standards, firms often cre- ate their own supplier certification programs. Buyers audit potential suppliers and award a certified status to those that meet the specified qualification. A certification process often involves three steps: (1) qualification, (2) education, and (3) the certification performance process. Once certified, the supplier may be awarded special treatment and priority, allowing the buying firm to reduce or eliminate incoming inspection of materials. Such an arrangement may facilitate JIT production for the buying firm. Most large companies use some sort of supplier certification program.
Supplier Development The second stage of supplier selection is supplier development . Assuming that a firm wants to proceed with a particular supplier, how does it integrate this supplier into its system?
Drop shipping
Shipping directly from the supplier
to the end consumer rather than
from the seller, saving both time
and reshipping costs.
LO 11.4 Describe the steps in supplier selection
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The buyer makes sure the supplier has an appreciation of quality requirements, product specifications, schedules and delivery, and procurement policies. Supplier development may include everything from training, to engineering and production help, to procedures for infor- mation transfer.
Negotiations Although the prices that consumers pay are often inflexible (printed on the price tag, listed in the catalog, etc.), a significant number of final prices paid in business-to-business transac- tions are negotiated. In addition to the price itself, several other aspects of the full product “package” must be determined. These may include credit and delivery terms, quality stand- ards, and cooperative advertising agreements. In fact, negotiation represents a significant ele- ment in a purchasing manager’s job, and well-honed negotiation skills are highly valued.
Here are three classic types of negotiation strategies: the cost-based model, the market- based price model, and competitive bidding.
Cost-Based Price Model The cost-based price model requires that the supplier open its books to the purchaser. The contract price is then based on time and materials or on a fixed cost with an escalation clause to accommodate changes in the vendor’s labor and materials cost.
Market-Based Price Model In the market-based price model, price is based on a published, auction, or index price. Many commodities (agricultural products, paper, metal, etc.) are priced this way. Paperboard prices, for instance, are available via the Official Board Markets weekly publication ( www.advanstar.com ).
Competitive Bidding When suppliers are not willing to discuss costs or where near- perfect markets do not exist, competitive bidding is often appropriate. Competitive bidding is the typical policy in many firms for the majority of their purchases. Bidding policies usually require that the purchasing agent have several potential suppliers and quotations from each. The major disadvantage of this method, as mentioned earlier, is that the development of long- term relations between buyer and seller is hindered. It may also make difficult the communica- tion and performance that are vital for engineering changes, quality, and delivery.
Yet a fourth approach is to combine one or more of the preceding negotiation techniques. The supplier and purchaser may agree to review cost data, accept some form of market-based cost, or agree that the supplier will “remain competitive.”
Contracting Supply chain partners often develop contracts to spell out terms of the relationship. Contracts are designed to share risks, share benefits, and create incentive structures to encourage supply chain members to adopt policies that are optimal for the entire chain. The idea is to make the total pie (of supply chain profits) bigger and then divide the bigger pie among all participants. The goal is collaboration. Some common features of contracts include quantity discounts (lower prices for larger orders), buybacks (common in the magazine and book business where there is a buyback of unsold units), and revenue sharing (where both partners share the risk of uncertainty by sharing revenue).
Centralized Purchasing Companies with multiple facilities (e.g., multiple manufacturing plants or multiple retail out- lets) must determine which items to purchase centrally and which to allow local sites to pur- chase for themselves. Unmonitored decentralized purchasing can create havoc. For example, different plants for Nestle USA’s brands used to pay 29 different prices for its vanilla ingredi- ent to the same supplier ! Important cost, efficiency, and “single-voice” benefits often accrue from a centralized purchasing function. Typical benefits include:
◆ Leverage purchase volume for better pricing ◆ Develop specialized staff expertise ◆ Develop stronger supplier relationships ◆ Maintain professional control over the purchasing process
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◆ Devote more resources to the supplier selection and negotiation process ◆ Reduce the duplication of tasks ◆ Promote standardization
However, local managers enjoy having their own purchasing control, and decentralized pur- chasing can offer certain inventory control, transportation cost, or lead-time benefits. Often firms use a hybrid approach—using centralized purchasing for some items and/or sites while allowing local purchasing for others.
E-Procurement E-procurement speeds purchasing, reduces costs, and integrates the supply chain. It reduces the traditional barrage of paperwork and, at the same time, provides purchasing personnel with an extensive database of supplier, delivery, and quality data.
Online Catalogs and Exchanges Purchase of standard items is often accomplished via online catalogs. Such catalogs support cost comparisons and incorporate voice and video clips, making the process efficient for both buyers and sellers.
Online exchanges are typically industry-specific Internet sites that bring buyers and sellers together. Marriott and Hyatt created one of the first, Avendra ( www.avendra.com ), which facili- tates economic purchasing of the huge range of goods needed by the 5,000 hospitality industry customers now in the exchange. Online catalogs and exchanges can help move companies from a multitude of individual phone calls, faxes, and emails to a centralized system and drive bil- lions of dollars of waste out of the supply chain.
Online Auctions In addition to catalogs, some suppliers and buyers have established online auction sites. Operations managers find online auctions a fertile area for disposing of excess raw material and discontinued or excess inventory. Online auctions lower entry bar- riers, encourage sellers to join, and simultaneously increase the potential number of buyers. The key for intermediaries is to find and build a huge base of potential bidders, improve client buying procedures, and qualify new suppliers.
In a traditional auction, a seller offers a product or service and generates competition between bidders—bidding the price up. In contrast, buyers often utilize online reverse auctions (or Dutch auctions ). In reverse auctions, a buyer initiates the process by submitting a descrip- tion of the desired product or service. Potential suppliers then submit bids, which may include price and other delivery information. Thus, price competition occurs on the selling side of the transaction—bidding the price down. Note that, as with traditional supplier selection deci- sions, price is important but may not be the only factor in winning the bid.
Logistics Management Procurement activities may be combined with various shipping, warehousing, and inventory activities to form a logistics system. The purpose of logistics management is to obtain efficiency of operations through the integration of all material acquisition, movement, and storage activities. When transportation and inventory costs are substantial on both the input and output sides of the production process, an emphasis on logistics may be appropriate. Many firms opt for outsourc- ing the logistics function, as logistics specialists can often bring expertise not available in-house. For instance, logistics companies often have tracking technology that reduces transportation losses and supports delivery schedules that adhere to precise delivery windows. The potential for competitive advantage is found via both reduced costs and improved customer service.
Shipping Systems Firms recognize that the transportation of goods to and from their facilities can represent as much as 25% of the cost of products. Because of this high cost, firms constantly evaluate their means of shipping. Six major means of shipping are trucking, railroads, airfreight, waterways, pipelines, and multimodal.
E-procurement
Purchasing facilitated through the
Internet.
Logistics management
An approach that seeks efficiency
of operations through the integra-
tion of all material acquisition,
movement, and storage activities.
LO 11.5 Explain major issues in logistics
management
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Trucking The vast majority of manufactured goods moves by truck. The flexibility of ship- ping by truck is only one of its many advantages. Companies that have adopted JIT programs in recent years have put increased pressure on truckers to pick up and deliver on time, with no damage, with paperwork in order, and at low cost. Trucking firms are using computers to monitor weather, find the most effective route, reduce fuel cost, and analyze the most efficient way to unload. To improve logistics efficiency, the industry is establishing Web sites such as Schneider National’s connection ( www.schneider.com ), which lets shippers and truckers find each other to use some of this idle capacity.
Railroads Railroads in the U.S. employ 235,000 people and ship 40% of the ton-miles of all commodities, including 93% of coal, 57% of cereal grains, and 52% of basic chemicals. Containerization has made shipping of truck trailers on railroad flat cars a popular means of distribution. The equivalent of 47 million trailer loads are moved in the U.S. each year by rail.
Airfreight Airfreight represents less than 1% of tonnage shipped in the U.S. However, the proliferation of airfreight carriers such as FedEx, UPS, and DHL makes it a fast-growing mode of shipping. Clearly, for national and international movement of lightweight items, such as medical and emergency supplies, flowers, fruits, and electronic components, airfreight offers speed and reliability.
Waterways Waterways are one of the nation’s oldest means of freight transportation, dat- ing back to construction of the Erie Canal in 1817. Included in U.S. waterways are the nation’s rivers, canals, the Great Lakes, coastlines, and oceans connecting to other countries. The usual cargo on internal waterways is bulky, low-value cargo such as iron ore, grains, cement, coal, chemicals, limestone, and petroleum products. Internationally, millions of containers holding all sorts of industrial and consumer goods are shipped at very low cost via huge oceangoing ships each year. Water transportation is often preferred when cost is more important than speed.
Pipelines Pipelines are an important form of transporting crude oil, natural gas, and other petroleum and chemical products.
Multimodal Multimodal shipping combines shipping methods, and is a common means of getting a product to its final destination, particularly for international shipments. The use of standardized containers facilitates easy transport from truck to rail to ship and back again, without having to unload products from the containers until the very end.
While freight rates are often based on very complicated pricing systems, in general, clients pay for speed. Faster methods such as airfreight tend to be much more expensive, while slower methods, such as waterways, provide a much cheaper shipping rate per unit. The size of ship- ments follows a similar pattern. The faster methods tend to involve smaller shipment sizes, while the slower methods involve very large shipment sizes.
Warehousing Warehousing often adds 8–10% to the cost of a product, making warehousing a significant expense for many firms. Warehouses come in all shapes and sizes, from tiny rooms in the back of a store to enormous facilities that could fit multiple football fields. Warehouses may be extremely expensive to operate, but the alternatives (e.g., either no storage at all or storage at local operating facilities, along with the related logistics issues) may be much more costly.
The fundamental purpose of a warehouse is to store goods. However, some warehouses also provide other crucial functions. For example, a warehouse can serve as a consolidation point, gathering shipments from multiple sources to send outbound in one cheaper, fully loaded truck. Alternatively, a warehouse can provide a break-bulk function by accepting a cheaper full truckload inbound shipment and then dividing it for distribution to individual sites. Further, similar to a major airport hub, a warehouse can serve simply as a cross-docking facility— accepting shipments from a variety of sources and recombining them for distribution to a variety of destinations, often without actually storing any goods during the transition. Finally, a warehouse can serve as a point of postponement in the process, providing final customer- specific value-added processing to the product before final shipment.
Channel assembly represents one way to implement postponement. Channel assembly sends individual components and modules, rather than finished products, to the distributor.
STUDENT TIP Logistics represents a substantial
part of the economy, as logistics
cost comprises 11.3% of the
U.S. gross domestic product.
Channel assembly
Postpones final assembly of a
product so the distribution channel
can assemble it.
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The distributor then assembles, tests, and ships. Channel assembly treats distributors more as manufacturing partners than as distributors. This technique has proven successful in industries where products are undergoing rapid change, such as PCs. With this strategy, finished-goods inventory is reduced because units are built to a shorter, more accurate forecast. Consequently, market response is better, with lower investment—a nice combination.
Third-Party Logistics (3PL) Third-party logistics, as is the case with most specialization, tends to bring added innovation and expertise to the logistics system. Consequently, supply chain managers outsource logis- tics to meet three goals: (1) drive down inventory investment, (2) lower delivery costs, and (3) improve delivery reliability and speed. Specialized logistics firms support these goals by cre- atively coordinating the supplier’s inventory system with the service capabilities of the delivery firm. FedEx, for example, has a successful history of using the Internet for online tracking. At fedex.com , a customer can compute shipping costs, print labels, adjust invoices, and track package status. FedEx, UPS, and DHL play a core role in other firms’ logistics processes. For instance, UPS works with Nike at a shipping hub in Louisville, Kentucky, to store and immediately expedite shipments. The OM in Action box “DHL’s Role in the Supply Chain”
OM in Action DHL’s Role in the Supply Chain It’s the dead of night at DHL International’s air express hub in Brussels, yet
the massive building is alive with busy forklifts and sorting workers. The boxes
going on and off the DHL plane range from Dell computers and Cisco routers
to Caterpillar mufflers and Komatsu hydraulic pumps. Sun Microsystems
computers from California are earmarked for Finland; DVDs from Teac’s plant
in Malaysia are destined for Bulgaria.
The door-to-door movement of time-sensitive packages is key to the
global supply chain. JIT, short product life cycles, mass customization, and
reduced inventories depend on logistics firms such as DHL, FedEx, and UPS.
These powerhouses are in continuous motion.
With a decentralized network covering 220 countries and territories (more
than are in the UN), DHL is a true multinational. The Brussels headquarters
has under 2,000 of the company’s 325,000 employees but includes
26 nationalities.
DHL has assembled an extensive global network of express logistics
centers for strategic goods. In its Brussels logistics center, for instance, DHL
upgrades, repairs, and configures Fijitsu computers, InFocus projectors, and
Johnson & Johnson medical equipment. It stores and provides parts for EMC
and Hewlett-Packard and replaces Nokia and Philips phones. “If something
breaks down on a Thursday at 4 o’clock, the relevant warehouse knows at
4:05, and the part is on a DHL plane at 7 or 8 that evening,” says Robert
Kuijpers, DHL International’s CEO.
Sources : www.dhl.com (2015); The Wall Street Journal (July 19, 2012); Materials
Handling World (December 14, 2011); and www.dhlsupplychainmatters.com .
Speed and accuracy in the supply chain are supported by bar-code tracking of shipments. At each step of a journey, from initial
pickup (left) to final destination, bar codes are read and stored. Within seconds, this tracking information is available online to
customers worldwide (right).
Je ff
G re
e n b e rg
6 o
f 6
/A la
m y
N e tP
h o to
s/ A
la m
y
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provides another example of how outsourcing logistics can reduce costs while shrinking inven- tory and delivery times.
Distribution Management Management of the supply chain focuses on incoming materials, but just as important, dis- tribution management focuses on the outbound flow of products. Designing distribution net- works to meet customer expectations suggests three criteria: (1) rapid response, (2) product choice, and (3) service.
Office Depot, for example, addresses these customer concerns by having several stores in a town for convenience and quick response time. But it also offers an online shopping presence to accommodate customers requiring a much larger selection of products ( www.officedepot.com ). It may even offer delivery directly to large customers. These varying customer expectations sug- gest both different distribution channels and multiple outlets.
So how many stores should Office Depot open in a town? As Figure 11.3 (a) indicates, an increase in the number of facilities generally implies a quicker response and increased customer satisfaction. On the cost side, three logistics-related costs [see Figure 11.3 (b)] are shown: inventory costs , transportation costs , and facility costs. Taken together, total logistics costs tend to follow the top curve, first declining, and then rising. For this particular example, it appears that total logistics costs are minimized with three facilities. However, when revenue is considered [see Figure 11.3 (c)], we note that profit is maximized with four facilities.
Whether creating a network of warehouses or retail outlets, finding the optimal number of facilities represents a critical and often dynamic decision. For instance, barely a year after add- ing 3 million square feet of warehouse capacity, market dynamics caused Amazon.com to close three of its U.S. distribution centers.
Just as firms need an effective supplier management program, an effective distribution management program may make the difference between supply chain success and failure. For example, in addition to facilities, packaging and logistics are necessary for the network to perform well. Packaging and logistics are also important distribution decisions because the manufacturer is usually held responsible for breakages and serviceability. Further, selec- tion and development of dealers or retailers are necessary to ensure ethical and enthusias- tic representation of the firm’s products. Top-notch supply chain performance requires good downstream (distributors and retailers) management, just as it does good upstream (suppliers) management.
Response time
Lowest cost Total logistics cost
Facility costs
Inventory costs Transportation costs
$
Number of facilities
(a) Response Time
T im
e
1 2 3 4 5
Number of facilities
(b) Cost $ (c) Cost, Revenue, and Profit
Revenue
Total logistics cost Max profit
1 2 3 4 5
$
Number of facilities
1 2 3 4 5
Figure 11.3
Number of Facilities in a Distribution Network
The focus should be on profit maximization (c) rather than cost minimization (b).
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Ethics and Sustainable Supply Chain Management Let’s look at two issues that OM managers must address every day when dealing with supply chains: ethics and sustainability.
Supply Chain Management Ethics We consider three aspects of ethics: personal ethics, ethics within the supply chain, and ethical behavior regarding the environment. As the supply chain becomes increasingly international, each of these becomes even more significant.
Personal Ethics Ethical decisions are critical to the long-term success of any organi- zation. However, the supply chain is particularly susceptible to ethical lapses. With sales personnel anxious to sell and purchasing agents spending huge sums, temptations abound. Salespeople become friends with customers, do favors for them, take them to lunch, or present small (or large) gifts. Determining when tokens of friendship become bribes can be challenging. Many companies have strict rules and codes of conduct that limit what is acceptable.
Recognizing these issues, the Institute for Supply Management has developed the following principles and standards to be used as guidelines for ethical behavior:
◆ Promote and uphold responsibilities to one’s employer; positive supplier and customer relationships; sustainability and social responsibility; protection of confidential and propri- etary information; applicable laws, regulations, and trade agreements; and development of professional competence.
◆ Avoid perceived impropriety; conflicts of interest; behaviors that negatively influence supply chain decisions; and improper reciprocal agreements.
Ethics Within the Supply Chain In this age of hyper-specialization, much of any organization’s resources are purchased, putting great stress on ethics in the supply chain. Managers may be tempted to ignore ethical lapses by suppliers or offload pollution to sup- pliers. But firms must establish standards for their suppliers, just as they have established standards for themselves. Society expects ethical performance throughout the supply chain. For instance, Gap, Inc., reported that of its 3,000-plus factories worldwide, about 90% failed their initial evaluation. Gap found that 10% to 25% of its Chinese factories engaged in psycho- logical or verbal abuse, and more than 50% of the factories in sub-Saharan Africa operated without proper safety devices. The challenge of enforcing ethical standards is significant, but responsible firms such as Gap are finding ways to deal with this difficult issue.
Ethical Behavior Regarding the Environment While ethics on both a personal basis and in the supply chain are important, so is ethical behavior in regard to the environment. Good ethics extends to doing business in a way that supports conservation and renewal of resources. This requires evaluation of the entire environmental impact, from raw material, to manufacture, through use and final disposal. For instance, Darden Restaurants and Walmart both require their shrimp and fish suppliers in Southeast Asia to abide by the standards of the Global Aquaculture Alliance. These standards must be met if suppliers want to maintain the business relationship. Operations managers also ensure that sustainability is reflected in the performance of second- and third-tier suppliers. Enforcement can be done by in-house inspectors, third-party auditors, governmental agencies, or nongovernmental watchdog organizations. All four approaches are used.
Establishing Sustainability in Supply Chains The incoming supply chain garners most of the attention, but it is only part of the challenge of sustainability. The “return” supply chain is also significant. Reverse logistics involves the pro- cesses for sending returned products back up the supply chain for resale, repair, reuse, remanu- facture, recycling, or disposal. The operations manager’s goal should be to limit burning or
STUDENT TIP Because so much money
passes through the supply
chain, the opportunity for
ethical lapses is significant.
Reverse logistics
The process of sending returned
products back up the supply chain
for value recovery or disposal.
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burying of returned products and instead strive for reuse. Reverse logistics initiates a new set of challenges, as shown in Table 11.4 .
Although sometimes used as a synonym for reverse logistics, a closed-loop supply chain refers more to the proactive design of a supply chain that tries to optimize all forward and reverse flows. A closed-loop supply chain prepares for returns prior to product introduction. For instance, IBM has recognized that components often have much longer life cycles than the original products that they go into. So the company has established a systematic method for dismantling returns and used equipment to extract components that still have value, such as boards, cards, and hard-disk assemblies. IBM has realized millions of dollars of savings in procurement costs by exploiting its “dismantling channel” of used parts.
Measuring Supply Chain Performance Like all other managers, supply chain managers require standards (or metrics , as they are often called) to evaluate performance. For example, the large grocery chain HEB tracks met- rics such as total freight cost per $1 million of sales, errors and returns in distribution, and lead-time compliance. Lancers, a beverage dispenser manufacturer, tracks metrics such as on-time delivery percentage, defects per million, and lead time. We now introduce several financial-based inventory metrics.
Assets Committed to Inventory Supply chain managers make scheduling and quantity decisions that determine the assets committed to inventory. Three specific measures can be helpful here. The first is the amount of money invested in inventory, usually expressed as a percentage of assets, as shown in Equation (11-1) and Example 2 :
Percentage invested in inventory = (Average inventory investment>Total assets) × 100 (11-1)
TABLE 11.4 Management Challenges of Reverse Logistics
ISSUE FORWARD LOGISTICS REVERSE LOGISTICS
Forecasting Relatively straightforward More uncertain
Product quality Uniform Not uniform
Product packaging Uniform Often damaged
Pricing Relatively uniform Dependent on many factors
Speed Often very important Often not a priority
Distribution costs Easily visible Less directly visible
Inventory management Consistent Not consistent
Adapted from the Reverse Logistics Executive Council ( www.rlec.org ).
Closed-loop supply chain
A supply chain designed to
optimize both forward and
reverse flows.
STUDENT TIP If you can’t measure it, you
can’t control it.
LO 11.6 Compute the percentage of assets
committed to inventory
and inventory turnover
Example 2 TRACKING HOME DEPOT’S INVENTORY INVESTMENT Home Depot’s management wishes to track its investment in inventory as one of its performance meas- ures. Recently, Home Depot had $11.4 billion invested in inventory and total assets of $44.4 billion.
APPROACH c Determine the investment in inventory and total assets and then use Equation (11-1) .
SOLUTION c Percent invested in inventory = (11.4>44.4) × 100 = 25.7%
INSIGHT c Over one-fourth of Home Depot assets are committed to inventory.
LEARNING EXERCISE c If Home Depot can drive its investment down to 20% of assets, how much money will it free up for other uses? [Answer: 11.4 2 (44.4 × 0.2) = $2.52 billion.]
RELATED PROBLEMS c 11.5b, 11.6b
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Specific comparisons with competitors may assist evaluation. Total assets committed to inventory in manufacturing approach 15%, in wholesale 34%, and retail 27%—with wide variations, depending on the specific business model, the business cycle, and management (see Table 11.5 ).
The second common measure of supply chain performance is inventory turnover (see Table 11.6 ). Its reciprocal, weeks of supply , is the third. Inventory turnover is computed on an annual basis, using Equation (11-2) :
Inventory turnover 5 Cost of goods sold>Average inventory investment (11-2)
Cost of goods sold is the cost to produce the goods or services sold for a given period. Inven- tory investment is the average inventory value for the same period. This may be the average of several periods of inventory or beginning and ending inventory added together and divided by 2. Often, average inventory investment is based on nothing more than the inventory investment at the end of the period—typically at year-end. 1
In Example 3 , we look at inventory turnover applied to PepsiCo.
Inventory turnover
Cost of goods sold divided by
average inventory.
TABLE 11.5
Inventory as Percentage of Total Assets (with examples of exceptional performance)
Manufacturer (Toyota 5%)
15%
Wholesale (Coca-Cola 2.9%)
34%
Restaurants (McDonald’s .05%)
2.9%
Retail (Home Depot 25.7%)
27%
Example 3 INVENTORY TURNOVER AT PEPSICO, INC. PepsiCo, Inc., manufacturer and distributor of drinks, Frito-Lay, and Quaker Foods, provides the fol- lowing in a recent annual report (shown here in $ billions). Determine PepsiCo’s turnover.
Net revenue $32.5
Cost of goods sold $14.2
Inventory:
Raw material inventory $.74
Work-in-process inventory $.11
Finished goods inventory $.84
Total average inventory investment $1.69
APPROACH c Use the inventory turnover computation in Equation (11-2) to measure inventory performance. Cost of goods sold is $14.2 billion. Total inventory is the sum of raw material at $.74 billion, work-in-process at $.11 billion, and finished goods at $.84 billion, for total average inventory invest- ment of $1.69 billion.
SOLUTION c Inventory turnover = Cost of goods sold>Average inventory investment = 14.2>1.69 = 8.4
INSIGHT c We now have a standard, popular measure by which to evaluate performance.
LEARNING EXERCISE c If Coca-Cola’s cost of goods sold is $10.8 billion and inventory investment is $.76 billion, what is its inventory turnover? [Answer: 14.2.]
RELATED PROBLEMS c 11.5a, 11.6c, 11.7
Weeks of supply , as shown in Example 4 , may have more meaning in the wholesale and retail portions of the service sector than in manufacturing. It is computed below as the reciprocal of inventory turnover:
Weeks of supply = Average inventory investment>(Annual cost of goods sold>52 weeks) (11-3)
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Supply chain management is critical in driving down inventory investment. The rapid movement of goods is key. Walmart, for example, has set the pace in the retailing sector with its world-renowned supply chain management. By doing so, it has established a competitive advantage. With its own truck fleet, distribution centers, and a state-of-the-art communica- tion system, Walmart (with the help of its suppliers) replenishes store shelves an average of twice per week. Competitors resupply every other week. Economical and speedy resupply means both rapid response to product changes and customer preferences, as well as lower inventory investment. Similarly, while many manufacturers struggle to move inventory turn- over up to 10 times per year, Dell Computer has inventory turns exceeding 90 and supply measured in days —not weeks. Supply chain management provides a competitive advantage when firms effectively respond to the demands of global markets and global sources.
Benchmarking the Supply Chain While metric values convey their own meaning and are useful when compared to past data, another important use compares these values to those of benchmark firms. Several organi- zations and websites allow companies to submit their own data and receive reports on how they stack up against other firms in their own industry or against world-class firms from any industry. Table 11.7 provides a few examples of metric values for typical firms and for bench- mark firms in the consumer packaged goods industry. World-class benchmarks are the result of well-managed supply chains that drive down costs, lead times, late deliveries, and shortages while improving service levels.
Example 4 DETERMINING WEEKS OF SUPPLY AT PEPSICO Using the PepsiCo data in Example 3 , management wants to know the weeks of supply.
APPROACH c We know that inventory investment is $1.69 billion and that weekly sales equal annual cost of goods sold ($14.2 billion) divided by 52 = $14.2>52 = $.273 billion.
SOLUTION c Using Equation (11-3) , we compute weeks of supply as: Weeks of supply = (Average inventory investment>Average weekly cost of goods sold)
= 1.69>.273 = 6.19 weeks
INSIGHT c We now have a standard measurement by which to evaluate a company’s continuing per- formance or by which to compare companies.
LEARNING EXERCISE c If Coca-Cola’s average inventory investment is $.76 billion and its average weekly cost of goods sold is $.207 billion, what is the firm’s weeks of supply? [Answer: 3.67 weeks.]
RELATED PROBLEMS c 11.6a, 11.8
d h e - f y r - y e
, -
TABLE 11.6
Examples of Annual Inventory Turnover
FOOD, BEVERAGE, RETAIL
Anheuser Busch 15
Coca-Cola 14
Home Depot 5
McDonald’s 112
MANUFACTURING
Dell Computer 90
Johnson Controls 22
Toyota (overall) 13
Nissan (assembly) 150
TABLE 11.7 Supply Chain Metrics in the Consumer Packaged Goods Industry
TYPICAL FIRMS BENCHMARK FIRMS
Order fi ll rate 71% 98%
Order fulfi llment lead time (days) 7 3
Cash-to-cash cycle time (days) 100 30
Inventory days of supply 50 20
Source: Institute for Industrial Engineers
The SCOR Model Perhaps the best-known benchmarking system is the five-part Supply Chain Operations Reference (SCOR) model . As shown in Figure 11.4 , the five parts are Plan (planning activities for supply and demand), Source (purchasing activities), Make (production activities), Deliver
Supply Chain Operations Reference (SCOR) model
A set of processes, metrics, and
best practices developed by the
APICS Supply Chain Council.
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(distribution activities), and Return (closed-loop supply chain activities). The system is main- tained by the APICS Supply Chain Council ( www.apics.org/sites/apics-supply-chain-council ). Firms use SCOR to identify, measure, reorganize, and improve supply chain processes.
The SCOR model defines over 200 process elements, 550 measurable metrics, and 500 best practices. The best practices describe the techniques used by benchmark firms that have scored very well on the metrics. SCOR combines these metrics with “Performance Attributes” (see Table 11.8 ) to facilitate comparisons of companies that compete by using different strategies (for example, low cost vs. responsiveness).
Plan: Demand/Supply Planning and Management
Deliver: Invoice, warehouse, transport, and install
Make: Manage production execution, testing, and packaging
Source: Identify, select, manage, and assess sources
Return: Finished goodsReturn: Raw material
Figure 11.4
The Supply Chain Operations
Reference (SCOR) Model
TABLE 11.8 SCOR Model Metrics to Help Firms Benchmark Performance Against the Industry
PERFORMANCE ATTRIBUTE SAMPLE METRIC CALCULATION
Supply chain reliability Perfect order fulfi llment (Total perfect orders)/(Total number of orders)
Supply chain responsiveness
Average order fulfi llment cycle time
(Sum of actual cycle times for all orders delivered)/ (Total number of orders delivered)
Supply chain agility Upside supply chain fl exibility
Time required to achieve an unplanned 20% increase in delivered quantities
Supply chain costs Supply chain management cost
Cost to plan 1 Cost to source 1 Cost to deliver 1 Cost to return
Supply chain asset management
Cash-to-cash cycle time Inventory days of supply 1 Days of receivables outstanding 2 Days of payables outstanding
Summary Competition is no longer between companies but between supply chains. The key to success is to collaborate with members on both the supply side and the distribution side of the supply chain to make decisions that will benefit the whole channel. For many firms, the supply chain deter- mines a substantial portion of product cost and quality, as well as opportunities for responsiveness and differen- tiation. The challenge of building a great supply chain is
significant, but with good sourcing tactics, a thoughtful logistics plan, and active management of the distribution network, each link in the chain can be firmly forged. A number of metrics are available to help managers eval- uate their supply chain performance and benchmark against the industry. Skillful supply chain management provides a great strategic opportunity for competitive advantage.
Benchmarking can be very useful, but it is not always adequate for excellence in the supply chain. Audits based on continuing communication, understanding, trust, performance, and corporate strategy are necessary. The relationships should manifest themselves in the mutual belief that “we are in this together” and go well beyond written agreements.
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Key Terms
Supply chain management (p. 444 ) Make-or-buy decision (p. 447 ) Outsourcing (p. 447 ) Vertical integration (p. 448 ) Keiretsu (p. 448 ) Virtual companies (p. 449 ) Cross-sourcing (p. 450 ) Bullwhip effect (p. 452 ) Pull data (p. 452 )
Single-stage control of replenishment (p. 452 )
Vendor-managed inventory (VMI) (p. 452 )
Collaborative planning, forecasting, and replenishment (CPFR) (p. 453 )
Blanket order (p. 453 ) Postponement (p. 453 ) Drop shipping (p. 454 )
E-procurement (p. 456 ) Logistics management (p. 456 ) Channel assembly (p. 457 ) Reverse logistics (p. 460 ) Closed-loop supply chain (p. 461 ) Inventory turnover (p. 462 ) Supply Chain Operations Reference
(SCOR) model (p. 463 )
Ethical Dilemma As a buyer for a discount retail chain, you fi nd yourself caught in a maelstrom. Just last month, your chain began selling an economy-priced line of clothing endorsed by a famous movie star. To be price competitive, you have followed the rest of the industry and sourced the clothing from a low-wage region of Asia. Initial sales have been brisk; however, the movie star has recently called you screaming and crying because an investigative news outlet has reported that the clothes with her name on them are being made by children.
Outraged, you fl y to the outsourcing manufacturing facility only to fi nd that conditions are not quite as clear-cut as the news had reported. You feel uncomfortable riding through the streets. Poverty is everywhere. Children are chasing foreigners and begging for money. When you enter the plant, you observe a very clean facility. The completely female workforce appears to be very industrious, but many of them
do appear to be young. You confront the plant manager and explain your fi rm’s strict international sourcing policies. You demand to know why these girls aren’t in school. The manager provides the following response: “The truth is that some of these workers may be underage. We check IDs, but the use of falsifi ed records is commonplace in this country. Plus, you don’t understand the alternatives. If you shut this plant down, you will literally take food off the table for these families. There are no other opportunities in this town at this time, and there’s no comprehensive welfare system in our country. As for the young women, school is not an option. In this town, only boys receive an education past the sixth grade. If you shut us down, these girls will be out on the street, begging, stealing, or prostituting themselves. Your business offers them a better existence. Please don’t take that away!”
What do you say to your company, the movie star, the media, and the protestors picketing your stores? Is the best option to shut down and try someplace else?
Discussion Questions
1. Define supply chain management . 2. What are the objectives of supply chain management? 3. What is the objective of logistics management? 4. How do we distinguish between the types of risk in the
supply chain? 5. What is vertical integration? Give examples of backward and
forward integration. 6. What are three basic approaches to negotiations? 7. How does a traditional adversarial relationship with suppliers
change when a firm makes a decision to move to a few suppliers? 8. What is the difference between postponement and channel
assembly?
9. What is CPFR? 10. What is the value of online auctions in e-commerce? 11. Explain how FedEx uses the Internet to meet requirements
for quick and accurate delivery. 12. How does Walmart use drop shipping? 13. What are blanket orders? How do they differ from invoiceless
purchasing? 14. What can purchasing do to implement just-in-time deliveries? 15. What is e-procurement? 16. How does Darden Restaurants, described in the Global Company
Profile , find competitive advantage in its supply chain? 17. What is SCOR, and what purpose does it serve?
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM 11.1 Jack’s Pottery Outlet has total end-of-year assets of $5 million. The first-of-the-year inventory was $375,000, with a year-end inventory of $325,000. The annual cost of goods sold was
$7 million. The owner, Eric Jack, wants to evaluate his supply chain performance by measuring his percent of assets in inven- tory, his inventory turnover, and his weeks of supply. We use Equations (11-1) , (11-2) , and (11-3) to provide these measures.
SOLUTION First, determine average inventory :
($375,000 + $325,000)>2 = $350,000
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Then, use Equation (11-1) to determine percent invested in inventory: Percent invested in inventory 5 (Average inventory investment>Total assets) 3 100
5 (350,000>5,000,000) × 100 5 7% Third, determine inventory turnover, using Equation (11-2) :
Inventory turnover 5 Cost of goods sold>Average inventory investment 5 7,000,000>350,000 5 20 Finally, to determine weeks of inventory, use Equation (11-3) , adjusted to weeks:
Weeks of inventory 5 Average inventory investment>Weekly cost of goods sold 5 350,000>(7,000,000>52) 5 350,000>134,615 5 2.6 We conclude that Jack’s Pottery Outlet has 7% of its assets invested in inventory, that the inventory turnover is 20, and that weeks of supply is 2.6.
Problems
Problems 11.1–11.3 relate to The Supply Chain’s Strategic Importance
• • 11.1 Choose a local establishment that is a member of a relatively large chain. From interviews with workers and informa- tion from the Internet, identify the elements of the supply chain. Determine whether the supply chain supports a low-cost, rapid response, or differentiation strategy (refer to Chapter 2 ). Are the supply chain characteristics significantly different from one prod- uct to another?
• • • 11.2 Hau Lee Furniture, Inc., described in Example 1 of this chapter, finds its current profit of $10,000 inadequate. The bank is insisting on an improved profit picture prior to approval of a loan for some new equipment. Hau would like to improve the profit line to $25,000 so he can obtain the bank’s approval for the loan. a) What percentage improvement is needed in the supply chain
strategy for profit to improve to $25,000? What is the cost of material with a $25,000 profit?
b) What percentage improvement is needed in the sales strategy for profit to improve to $25,000? What must sales be for profit to improve to $25,000?
• • • • 11.3 Kamal Fatehl, production manager of Kennesaw Manufacturing, finds his profit at $15,000 (as shown in the state- ment below)—inadequate for expanding his business. The bank is insisting on an improved profit picture prior to approval of a loan for some new equipment. Kamal would like to improve the profit line to $25,000 so he can obtain the bank’s approval for the loan.
% OF SALES
Sales $250,000 100%
Cost of supply chain purchases 175,000 70%
Other production costs 30,000 12%
Fixed costs 30,000 12%
Profi t 15,000 6%
a) What percentage improvement is needed in a supply chain strategy for profit to improve to $25,000? What is the cost of material with a $25,000 profit?
b) What percentage improvement is needed in a sales strategy for profit to improve to $25,000? What must sales be for profit to improve to $25,000? ( Hint: See Example 1 .)
Problem 11.4 relates to Six Sourcing Strategies
• • 11.4 Using sources from the Internet, identify some of the problems faced by a company of your choosing as it moves toward, or operates as, a virtual organization. Does its operating as a virtual organization simply exacerbate old problems, or does it create new ones?
Problems 11.5–11.8 relate to Measuring Supply Chain Performance
• • 11.5 Baker Mfg. Inc. (see Table 11.9 ) wishes to compare its inventory turnover to those of industry leaders, who have turno- ver of about 13 times per year and 8% of their assets invested in inventory. a) What is Baker’s inventory turnover? b) What is Baker’s percent of assets committed to inventory? c) How does Baker’s performance compare to the industry leaders?
• • 11.6 Arrow Distributing Corp. (see Table 11.9 ) likes to track inventory by using weeks of supply as well as by inventory turnover. a) What is its weeks of supply? b) What percent of Arrow’s assets are committed to inventory? c) What is Arrow’s inventory turnover? d) Is Arrow’s supply chain performance, as measured by these
inventory metrics, better than that of Baker in Problem 11.5?
TABLE 11.9 For Problems 11.5 and 11.6
ARROW DISTRIBUTING CORP.
Net revenue $16,500
Cost of sales $13,500
Inventory $ 1,000
Total assets $ 8,600
BAKER MFG. INC.
Net revenue $27,500
Cost of sales $21,500
Inventory $ 1,250
Total assets $16,600
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• 11.7 The grocery industry has an annual inventory turnover of about 14 times. Organic Grocers, Inc. had a cost of goods sold last year of $10.5 million; its average inventory was $1.0 million. What was Organic Grocers’ inventory turno- ver, and how does that performance compare with that of the industry?
• • 11.8 Mattress Wholesalers, Inc., is constantly trying to reduce inventory in its supply chain. Last year, cost of goods sold was $7.5 million and inventory was $1.5 million. This year, cost of goods sold is $8.6 million and inventory investment is $1.6 million. a) What were the weeks of supply last year? b) What are the weeks of supply this year? c) Is Mattress Wholesalers making progress in its inventory-
reduction effort? Ty le
r O
ls o n /F
o to
lia
CASE STUDIES Video Case Darden’s Global Supply Chains
Darden Restaurants (subject of the Global Company Profile at the beginning of this chapter), owner of popular brands such as Olive Garden and LongHorn Steakhouse, requires unique supply chains to serve more than 300 million meals annually. Darden’s strategy is operations excellence, and Senior VP Jim Lawrence’s task is to ensure competitive advantage via Darden’s supply chains. For a firm with purchases exceeding $1.8 billion, manag- ing the supply chains is a complex and challenging task.
Darden, like other casual dining restaurants, has unique sup- ply chains that reflect its menu options. Darden’s supply chains are rather shallow, often having just one tier of suppliers. But it has four distinct supply chains.
First, “smallware” is a restaurant industry term for items such as linens, dishes, tableware and kitchenware, and silverware. These are purchased, with Darden taking title as they are received at the Darden Direct Distribution (DDD) warehouse in Orlando, Florida. From this single warehouse, smallware items are shipped via common carrier (trucking companies) to Olive Garden, Bahama Breeze, and Seasons 52 restaurants.
Second, frozen, dry, and canned food products are handled eco- nomically by Darden’s 11 distribution centers in North America, which are managed by major U.S. food distributors, such as MBM, Maines, and Sygma. This is Darden’s second supply line.
Third, the fresh food supply chain (not frozen and not canned), where product life is measured in days, includes dairy products, produce, and meat. This supply chain is B2B, where restaurant managers directly place orders with a preselected group of inde- pendent suppliers.
Fourth, Darden’s worldwide seafood supply chain is the final link. Here Darden has developed independent suppliers of salmon, shrimp, tilapia, scallops, and other fresh fish that are source inspected by Darden’s overseas representatives to ensure quality. These fresh products are flown to the U.S. and shipped to 16 dis- tributors, with 22 locations, for quick delivery to the restaurants. With suppliers in 35 countries, Darden must be on the cutting edge when it comes to collaboration, partnering, communication, and food safety. It does this with heavy travel schedules for purchas- ing and quality control personnel, native-speaking employees onsite, and aggressive communication. Communication is a critical ele- ment; Darden tries to develop as much forecasting transparency as possible. “Point of sale (POS) terminals,” says Lawrence, “feed actual sales every night to suppliers.”
Discussion Questions *
1. What are the advantages of each of Darden’s four supply chains?
2. What are the complications of having four supply chains? 3. Where would you expect ownership/title to change in each of
Darden’s four supply chains? 4. How do Darden’s four supply chains compare with those of
other firms, such as Dell or an automobile manufacturer? Why do the differences exist, and how are they addressed?
Supply Chain Management at Regal Marine Video Case
Like most other manufacturers, Regal Marine finds that it must spend a huge portion of its revenue on purchases. Regal has also found that the better its suppliers understand its end users, the better are both the supplier’s product and Regal’s final product. As one of the 10 largest U.S. power boat manufacturers, Regal is trying to differentiate its products from the vast number of boats supplied by 300 other companies. Thus, the firm works closely with suppliers to ensure innovation, quality, and timely delivery.
Regal has done a number of things to drive down costs while driving up quality, responsiveness, and innovation. First, work- ing on partnering relationships with suppliers ranging from pro- viders of windshields to providers of instrument panel controls, Regal has brought timely innovation at reasonable cost to its product. Key vendors are so tightly linked with the company that they meet with designers to discuss material changes to be incor- porated into new product designs.
* You may wish to view the video that accompanies this case before answering the questions.
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Second, the company has joined about 15 other boat manufac- turers in a purchasing group, known as American Boat Builders Association, to work with suppliers on reducing the costs of large purchases. Third, Regal is working with a number of local ven- dors to supply hardware and fasteners directly to the assembly line on a just-in-time basis. In some of these cases, Regal has worked out an arrangement with the vendor so that title does not transfer until parts are used by Regal. In other cases, title transfers when items are delivered to the property. This practice drives down total inventory and the costs associated with large-lot delivery.
Finally, Regal works with a personnel agency to outsource part of the recruiting and screening process for employees. In all these cases, Regal is demonstrating innovative approaches to supply
chain management that help the firm and, ultimately, the end user. The Global Company Profile featuring Regal Marine (which opens Chapter 5 ) provides further background on Regal’s operations.
Discussion Questions *
1. What other techniques might Regal use to improve supply chain management?
2. What kind of response might members of the supply chain expect from Regal because of their “partnering” in the supply chain?
3. Why is supply chain management important to Regal?
Arnold Palmer Hospital’s Supply Chain Video Case
Arnold Palmer Hospital, one of the nation’s top hospitals dedi- cated to serving women and children, is a large business with over 2,000 employees working in a 431-bed facility totaling 676,000 square feet in Orlando, Florida. Like many other hospitals, and other companies, Arnold Palmer Hospital had been a long-time member of a large buying group, one servicing 900 members. But the group did have a few limitations. For example, it might change suppliers for a particular product every year (based on a new lower-cost bidder) or stock only a product that was not familiar to the physicians at Arnold Palmer Hospital. The buying group was also not able to negotiate contracts with local manu- facturers to secure the best pricing.
So in 2003, Arnold Palmer Hospital, together with seven other partner hospitals in central Florida, formed its own much smaller, but still powerful (with $200 million in annual purchases) Healthcare Purchasing Alliance (HPA) corporation. The new alli- ance saved the HPA members $7 million in its first year with two main changes. First, it was structured and staffed to ensure that the bulk of the savings associated with its contracting efforts went to its eight members. Second, it struck even better deals with ven- dors by guaranteeing a committed volume and signing not 1-year deals but 3- to 5-year contracts. “Even with a new internal cost of $400,000 to run HPA, the savings and ability to contract for what our member hospitals really want makes the deal a winner,” says George DeLong, head of HPA.
Effective supply chain management in manufacturing often focuses on development of new product innovations and efficiency through buyer–vendor collaboration. However, the approach in a service industry has a slightly different emphasis. At Arnold Palmer Hospital, supply chain opportunities often manifest them- selves through the Medical Economic Outcomes Committee. This committee (and its subcommittees) consists of users (including the medical and nursing staff) who evaluate purchase options with
a goal of better medicine while achieving economic targets. For instance, the heart pacemaker negotiation by the cardiology sub- committee allowed for the standardization to two manufacturers, with annual savings of $2 million for just this one product.
Arnold Palmer Hospital is also able to develop custom prod- ucts that require collaboration down to the third tier of the sup- ply chain. This is the case with custom packs that are used in the operating room. The custom packs are delivered by a distributor, McKesson General Medical, but assembled by a pack company that uses materials the hospital wanted purchased from specific manufacturers. The HPA allows Arnold Palmer Hospital to be creative in this way. With major cost savings, standardization, blanket purchase orders, long-term contracts, and more control of product development, the benefits to the hospital are substantial.
Discussion Questions *
1. How does this supply chain differ from that in a manufacturing firm?
2. What are the constraints on making decisions based on eco- nomics alone at Arnold Palmer Hospital?
3. What role do doctors and nurses play in supply chain deci- sions in a hospital? How is this participation handled at Arnold Palmer Hospital?
4. Doctor Smith just returned from the Annual Physician’s Orthopedic Conference, where she saw a new hip joint replacement demonstrated. She decides she wants to start using the replacement joint at Arnold Palmer Hospital. What process will Dr. Smith have to go through at the hospital to introduce this new product into the supply chain for future surgical use?
Endnote
1. Inventory quantities often fluctuate wildly, and various types of inventory exist (e.g., raw material; work-in-process; finished goods; and maintenance, repair, and operating supplies [MRO]).
Therefore, care must be taken when using inventory values; they may reflect more than just supply chain performance.
* You may wish to view the video that accompanies this case before answering the questions.
* You may wish to view the video that accompanies this case before answering these questions.
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Chapter 11 Rapid Review 11
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Main Heading Review Material MyOMLab THE SUPPLY CHAIN’S STRATEGIC IMPORTANCE (pp. 444 – 446 )
Most firms spend a huge portion of their sales dollars on purchases. j Supply chain management —Management of activities related to procuring materials
and services, transforming them into intermediate goods and final products, and delivering them through a distribution system.
The objective is to build a chain of suppliers that focuses on maximizing value to the ultimate customer. Competition is no longer between companies; it is between supply chains.
Concept Questions: 1.1–1.4
Problems: 11.2–11.3
VIDEO 11.1 Darden’s Global Supply Chain
SOURCING ISSUES: MAKE-OR-BUY AND OUTSOURCING (pp. 446 – 447 )
j Make-or-buy decision —A choice between producing a component or service within the firm or purchasing it from an outside source.
j Outsourcing —Transferring to external suppliers a firm’s activities that have traditionally been internal.
Concept Questions: 2.1–2.4
SIX SOURCING STRATEGIES (pp. 447 – 449 )
Six supply chain strategies for goods and services to be obtained from outside sources are: 1. Negotiating with many suppliers and playing one supplier against another 2. Developing long-term partnering relationships with a few suppliers 3. Vertical integration 4. Joint ventures 5. Developing keiretsu networks 6. Developing virtual companies that use suppliers on an as-needed basis. j Vertical integration —Developing the ability to produce goods or services previously
purchased or actually buying a supplier or a distributor. j Keiretsu —A Japanese term that describes suppliers who become part of a company
coalition. j Virtual companies —Companies that rely on a variety of supplier relationships to pro-
vide services on demand. Also known as hollow corporations or network companies.
Concept Questions: 3.1–3.4
VIDEO 11.2 Supply Chain Management at Regal Marine
SUPPLY CHAIN RISK (pp. 449 – 451 )
The development of a supply chain plan requires a thorough assessment of the risks involved. j Cross-sourcing —Using one supplier for a component and a second supplier for
another component, where each supplier acts as a backup for the other.
Concept Questions: 4.1–4.4
MANAGING THE INTEGRATED SUPPLY CHAIN (pp. 451 – 454 )
Supply chain integration success begins with mutual agreement on goals, followed by mutual trust, and continues with compatible organizational cultures. Three issues complicate the development of an efficient, integrated supply chain: local optimization, incentives, and large lots. j Bullwhip effect —Increasing fluctuation in orders or cancellations that often occurs as
orders move through the supply chain. j Pull data —Accurate sales data that initiate transactions to “pull” product through
the supply chain. j Single-stage control of replenishment —Fixing responsibility for monitoring and
managing inventory for the retailer. j Vendor-managed inventory (VMI) —A system in which a supplier maintains material
for the buyer, often delivering directly to the buyer’s using department. j Collaborative planning, forecasting, and replenishment (CPFR) —A system in which
members of a supply chain share information in a joint effort to reduce supply chain costs. j Blanket order —A long-term purchase commitment to a supplier for items that are to
be delivered against short-term releases to ship. The purchasing department should make special efforts to increase levels of standardization. j Postponement —Delaying any modifications or customization to a product as long as
possible in the production process. Postponement strives to minimize internal variety while maximizing external variety. j Drop shipping —Shipping directly from the supplier to the end consumer rather than
from the seller, saving both time and reshipping costs. Online catalogs move companies from a multitude of individual phone calls, faxes, and e-mails to a centralized online system and drive billions of dollars of waste out of the supply chain.
Concept Questions: 5.1–5.4
VIDEO 11.3 Arnold Palmer Hospital’s Supply Chain
BUILDING THE SUPPLY BASE (pp. 454 – 456 )
Supplier selection is a four-stage process: (1) supplier evaluation, (2) supplier develop- ment, (3) negotiations, and (4) contracting. Supplier evaluation involves finding potential vendors and determining the likelihood of their becoming good suppliers. Supplier development may include everything from training, to engineering and production help, to procedures for information transfer.
Concept Questions: 6.1–6.4
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Main Heading Review Material MyOMLab
LOGISTICS MANAGEMENT (pp. 456 – 459 )
j Logistics management —An approach that seeks efficiency of operations through the integration of all material acquisition, movement, and storage activities.
Six major means of distribution are trucking, railroads, airfreight, waterways, pipelines, and multimodal. The vast majority of manufactured goods move by truck. Third-party logistics involves the outsourcing of the logistics function. j Channel assembly —A system that postpones final assembly of a product so the distri-
bution channel can assemble it.
Concept Questions: 7.1–7.4
DISTRIBUTION MANAGEMENT (p. 459 )
Distribution management focused on the outbound flow of final products. Total logistics costs are the sum of facility costs, inventory costs, and transportation costs ( Figure 11.3 ). The optimal number of distribution facilities focuses on maximizing profit.
Concept Questions: 8.1–8.4
ETHICS AND SUS- TAINABLE SUPPLY CHAIN MANAGEMENT (pp. 460 – 461 )
Ethics includes personal ethics, ethics within the supply chain, and ethical behavior regarding the environment. The Institute for Supply Management has developed a set of Principles and Standards for ethical conduct. j Reverse logistics —The process of sending returned products back up the supply chain
for value recovery or disposal. j Closed-loop supply chain —A supply chain designed to optimize all forward and
reverse flows.
Concept Questions: 9.1–9.4
MEASURING SUPPLY CHAIN PERFORMANCE (pp. 461 – 464 )
Typical supply chain benchmark metrics include lead time, time spent placing an order, percent of late deliveries, percent of rejected material, and number of shortages per year: Percent invested in inventory 5 (Average inventory investment>Total assets) 3 100 (11-1) j Inventory turnover —Cost of goods sold divided by average inventory: Inventory turnover 5 Cost of goods sold 4 Average inventory investment (11-2) Weeks of supply 5 Average inventory investment 4 (Annual cost of goods sold>52 weeks) (11-3) j Supply Chain Operations Reference (SCOR) model —A set of processes, metrics, and
best practices developed by the APICS Supply Chain Council. The five parts of the SCOR model are Plan, Source, Make, Deliver, and Return.
Concept Questions: 10.1–10.4 Problems: 11.5–11.8 Virtual Office Hours for Solved Problem: 11.1
Chapter 11 Rapid Review continued
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 11.1 The objective of supply chain management is to _________ . LO 11.2 The term vertical integration means to: a) develop the ability to produce products that complement
or supplement the original product. b) produce goods or services previously purchased. c) develop the ability to produce the specified good more
efficiently. d) all of the above. LO 11.3 The bullwhip effect can be aggravated by: a) local optimization. b) sales incentives. c) quantity discounts. d) promotions. e) all of the above. LO 11.4 Supplier selection requires: a) supplier evaluation and effective third-party logistics. b) supplier development and logistics.
c) negotiations, supplier evaluation, supplier development, and contracts.
d) an integrated supply chain. e) inventory and supply chain management. LO 11.5 A major issue in logistics is: a) cost of purchases. b) supplier evaluation. c) product customization. d) cost of shipping alternatives. e) excellent e-procurement. LO 11.6 Inventory turnover 5 a) Cost of goods sold 4 Weeks of supply b) Weeks of supply 4 Annual cost of goods sold c) Annual cost of goods sold 4 52 weeks d) Average inventory investment 4 Cost of goods sold e) Cost of goods sold 4 Average inventory investment
Answers: LO 11.1. build a chain of suppliers that focuses on maximizing value to the ultimate customer; LO 11.2. b; LO 11.3. e; LO 11.4. c; LO 11.5. d; LO 11.6. e.
Negotiations involve approaches taken by supply chain personnel to set prices. Three classic types of negotiation strategies are (1) the cost-based price model, (2) the market-based price model, and (3) competitive bidding. Contracting involves a design to share risks, share benefits, and create incentives so as to optimize the whole supply chain. j E-procurement —Purchasing facilitated through the Internet.
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471
SUPPLEMENT OUTLINE
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Techniques for Evaluating Supply Chains 472
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Evaluating Disaster Risk in the Supply Chain 472
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Managing the Bullwhip Effect 474
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Supplier Selection Analysis 476
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Transportation Mode Analysis 477
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Warehouse Storage 478
SUPPLEMENT ◆◆
Techniques for Evaluating Supply ◆◆
Supplier Selection Analys
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Techniques for Evaluating Supply Chains Many supply chain metrics exist that can be used to evaluate performance within a company as well as for its supply chain partners. This supplement introduces five techniques that are aimed at ways to build and evaluate performance of the supply chain.
Evaluating Disaster Risk in the Supply Chain Disasters that disrupt supply chains can take many forms, including tornadoes, fires, hur- ricanes, typhoons, tsunamis, earthquakes, and terrorism. When you are deciding whether to purchase collision insurance for your car, the amount of insurance must be weighed against the probability of a minor accident occurring and the potential financial worst-case scenario if an accident happens (e.g., “totaling” of the car). Similarly, firms often use multiple suppliers for important components to mitigate the risks of total supply disruption.
As shown in Example S1 , a decision tree can be used to help operations managers make this important decision regarding the number of suppliers. We will use the following notation for a given supply cycle:
S 5 the probability of a “super-event” that would disrupt all suppliers simultaneously U 5 the probability of a “unique-event” that would disrupt only one supplier L 5 the financial loss incurred in a supply cycle if all suppliers were disrupted C 5 the marginal cost of managing a supplier
L E A R N I N G OBJEC TI V ES
LO S11.1 Use a decision tree to determine the best number of suppliers to manage disaster risk 472
LO S11.2 Explain and measure the bullwhip eff ect 475
LO S11.3 Describe the factor-weighting approach to supplier evaluation 477
LO S11.4 Evaluate cost-of-shipping alternatives 478
LO S11.5 Allocate items to storage locations in a warehouse 479
The 2011 Tōhoku earthquake and tsunami devastated
eastern sections of Japan. The economic impact was
felt around the globe, as manufacturers had been
relying heavily—in some cases exclusively—on
suppliers located in the affected zones. In the month
immediately following the earthquake, the Japanese-
built vehicle outputs for both Toyota and Honda were
down 63%. Plants in other countries ceased or reduced
operations due to part shortages. Manufacturers in
several industries worldwide took 6 months or longer
before they saw their supply chains working normally
again. Although disasters such as this one occur
relatively infrequently, supply chain managers should
consider their probabilities and repercussions when
determining the makeup of the supply base.
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LO S11.1 Use a decision tree to determine
the best number of
suppliers to manage
disaster risk
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S U P P L E M E N T 1 1 | S U P P LY C H A I N M A N AG E M E N T A N A LY T I C S 473
All suppliers will be disrupted simultaneously if either the super-event occurs or the super- event does not occur but a unique-event occurs for all of the suppliers. Assuming that the probabilities are all independent of each other, the probability of all n suppliers being disrupted simultaneously equals:
P(n) = S + (1 - S)U n (S11-1)
Example S1 HOW MANY SUPPLIERS ARE BEST FOR MANAGING RISK? Xiaotian Geng, president of Shanghai Manufacturing Corp., wants to create a portfolio of suppliers for the motors used in her company’s products that will represent a reasonable balance between costs and risks. While she knows that the single-supplier approach has many potential benefits with respect to quality management and just-in-time production, she also worries about the risk of fires, natural disasters, or other catastrophes at supplier plants disrupting her firm’s performance. Based on histori- cal data and climate and geological forecasts, Xiaotian estimates the probability of a “super-event” that would negatively impact all suppliers simultaneously to be 0.5% (i.e., probability 5 0.005) during the supply cycle. She further estimates the “unique-event” risk for any of the potential suppliers to be 4% (probability 5 .04). Assuming that the marginal cost of managing an additional supplier is $10,000, and the financial loss incurred if a disaster caused all suppliers to be down simultaneously is $10,000,000, how many suppliers should Xiaotian use? Assume that up to three nearly identical suppliers are available.
APPROACH c Use of a decision tree seems appropriate, as Shanghai Manufacturing Corp. has the basic data: a choice of decisions, probabilities, and payoffs (costs).
SOLUTION c We draw a decision tree ( Figure S11.1 ) with a branch for each of the three decisions (one, two, or three suppliers), assign the respective probabilities [using Equation (S11-1) ] and payoffs for each branch, and then compute the respective expected monetary values (EMVs). The EMVs have been identified at each step of the decision tree.
Using Equation (S11-1) , the probability of a total disruption equals:
One supplier: 0.005 + (1 - 0.005)0.04 = 0.005 + 0.0398 = 0.044800, or 4.4800% Two suppliers: 0.005 + (1 - 0.005)0.042 = 0.005 + 0.001592 = 0.006592, or 0.6592% Three suppliers: 0.005 + (1 - 0.005)0.043 = 0.005 + 0.000064 = 0.005064, or 0.5064%
INSIGHT c Even with significant supplier management costs and unlikely probabilities of disaster, a large enough financial loss incurred during a total supplier shutdown will suggest that multiple suppliers may be needed.
P(1) = .044800
1–P(1) = .955200
No failure
Both fail
≤ 1 Fail
Failure
P(2) = .006592
1–P(2) = .993408
Three suppliers $80,640
1C = (1)$10,000 = $10,000
L + 1C = 10,000,000 + (1)$10,000 = $10,010,000
2C = (2)$10,000 = $20,000
L + 2C = $10,000,000 + (2)$10,000 = $10,020,000
All three fail
≤ 2 Fail
P(3) = .005064
1–P(3) = .994936 3C = (3)$10,000 = $30,000
L + 3C = $10,000,000 + (3)$10,000 = $10,030,000
One supplier $458,000
Two suppliers $85,920
Figure S11.1
Decision Tree for Selection of
Suppliers Under Risk
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474 P A R T 3 | M A N AG I N G O P E R AT I O N S
An interesting implication of Equation (S11-1) is that as the probability of a super-event ( S ) increases, the advantage of utilizing multiple suppliers diminishes (all would be knocked out anyway). On the other hand, large values of the unique event ( U ) increase the likelihood of needing more suppliers. These two phenomena taken together suggest that when multiple sup- pliers are used, managers may consider using ones that are geographically dispersed to lessen the probability of all failing simultaneously.
Managing the Bullwhip Effect Figure S11.2 provides an example of the bullwhip effect , which describes the tendency for larger order size fluctuations as orders are relayed to the supply chain from retailers. “Bullwhip” fluctuations create unstable production schedules, resulting in expensive capacity change adjustments such as overtime, subcontracting, extra inventory, backorders, hiring and laying off of workers, equipment additions, underutilization, longer lead times, or obsolescence of overproduced items.
Procter & Gamble found that although the use of Pampers diapers was steady and the retail-store orders had little fluctuation, as orders moved through the supply chain, fluctuations increased. By the time orders were initiated for raw material, the variability was substantial. Similar behavior has been observed and documented at many companies, including Campbell Soup, Hewlett-Packard, Barilla SpA, and Applied Materials.
The bullwhip effect can occur when orders decrease as well as when they increase. Table S11.1 identifies some of the major causes and remedies of the bullwhip effect. Often the human tendency to overreact to stimuli causes managers to make decisions that exacerbate the phenomenon. The overarching solution to the bullwhip effect is simply for supply-chain members to share informa- tion and work together, as in the OM in Action box “RFID Helps Control the Bullwhip.”
Supplier coordination can help with demand shifts. During the recent worldwide recession, but prior to experiencing the economic recovery and increasing sales, Caterpillar started order- ing more supplies. It also worked proactively with its suppliers to prepare them for a sharp increase in output. Caterpillar visited key suppliers individually. In some cases it helped suppli- ers obtain bank financing at favorable rates. As part of Caterpillar’s risk assessment activities, suppliers had to submit written plans describing their ability to ramp production back up once the economy improved. Careful, coordinated planning can help alleviate shortages and delays that might otherwise occur as the bullwhip snaps back upward.
LEARNING EXERCISE c Suppose that the probability of a super-event increases to 50%. How many suppliers are needed now? [Answer: 2.] Using the 50% probability of a super-event, suppose that the financial loss of a complete supplier shutdown drops to $500,000. Now how many suppliers are needed? [Answer: 1.]
RELATED PROBLEMS c S11.1, S11.2, S11.3, S11.4, S11.5
0 1 2 3 4 5
Retailers respond by ordering more
Suppliers believe sales are huge and respond accordingly
Wholesalers order even more to be sure retailers can be adequately supplied
A short-term increase in consumer demand
6
Day
7 8 9 10 11
10
20
30
O rd
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50
60
Consumers
Retailers
Wholesalers
Suppliers
Figure S11.2
The Bullwhip Effect
The bullwhip effect causes
members of the supply chain to
overreact to changes in demand
at the retail level. Minor demand
changes at the consumer level
may result in large ones at the
supplier level.
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S U P P L E M E N T 1 1 | S U P P LY C H A I N M A N AG E M E N T A N A LY T I C S 475
A Bullwhip Effect Measure A straightforward way to analyze the extent of the bullwhip effect at any link in the supply chain is to calculate the bullwhip measure :
Bullwhip = Variance of orders
Variance of demand =
s2orders
s2demand (S11-2)
Variance amplification (i.e., the bullwhip effect) is present if the bullwhip measure is greater than 1. This means the size of a company’s orders fluctuate more than the size of its incoming demand. If the measure equals 1, then no amplification is present. A value less than 1 would imply a smoothing or dampening scenario as orders move up the supply chain toward suppliers. Example S2 illustrates how to use Equation (S11-2) to analyze the extent of the bullwhip effect at each stage in the supply chain.
LO S11.2 Explain and measure the
bullwhip effect
TABLE S11.1 The Bullwhip Effect
CAUSE REMEDY
Demand forecast errors (cumulative uncertainty in the supply chain)
Share demand information throughout the supply chain.
Order batching (large, infrequent orders leading suppliers to order even larger amounts)
Channel coordination: Determine lot sizes as though the full supply chain was one company.
Price fl uctuations (buying in advance of demand to take advantage of low prices, discounts, or sales)
Price stabilization (everyday low prices).
Shortage gaming (hoarding supplies for fear of a supply shortage)
Allocate orders based on past demand.
OM in Action RFID Helps Control the Bullwhip Supply chains work smoothly when sales are steady, but often break down
when confronted by a sudden surge or rapid drop in demand. Radio frequency
ID (RFID) tags can change that by providing real-time information about what’s
happening on store shelves. Here’s how the system works for Procter &
Gamble’s (P&G’s) Pampers.
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P&G suppliers
P&G regional supply manager
Walmart distribution center
Walmart SHELF ALERT! NEED
PAMPERS!
STORE ALERT! NEED
PAMPERS! WAREHOUSE
ALERT! NEED
PAMPERS!
RESUPPLY
RESUPPLY
RESUPPLY
!
!
!
!
1. A special promotion causes Walmart shoppers to snap up boxes of Pampers Baby-Dry.
5. P&G’s logistics software tracks its trucks with GPS locators, and tracks their contents with RFID tag readers. Regional managers can reroute trucks to fill urgent needs.
6. P&G suppliers also use RFID tags and readers on their raw materials, giving P&G visibility several tiers down the supply chain, and giving suppliers the ability to accurately forecast demand and production.
2. Each box of Pampers has an RFID tag. Shelf-mounted scanners alert the stockroom of urgent need for restock.
3. Walmart’s inventory management system tracks and links its in-store stock and its warehouse stock, prompting quicker replenish- ment and providing accurate real-time data.
4. Walmart’s systems are linked to the P&G supply- chain management system. Demand spikes reported by RFID tags are immediately visible throughout the supply chain.
WAL*MART
RE-ROUTE # 237
237
Sources : Supply Chain Digest (July 21, 2012); Arkansas Business (July 2, 2012); and Business 2.0 (May 2002).
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476 P A R T 3 | M A N AG I N G O P E R AT I O N S
Supplier Selection Analysis Selecting suppliers from among a multitude of candidates can be a daunting task. Choosing suppliers simply based on the lowest bid has become a somewhat rare approach. Various, sometimes competing, factors often play a role in the decision. Buyers may consider such sup- plier characteristics as product quality, delivery speed, delivery reliability, customer service, and financial performance.
The U.S. Cash for Clunkers program produced an unintended bullwhip
effect in the automobile industry. In an effort to stimulate the economy and
improve fuel efficiency, the U.S. offered attractive rebates for trading old cars
in exchange for new, more fuel-efficient vehicles. The $3 billion, 8-week
program proved to be very popular with consumers. Fearing a shortage and
assuming that they would not receive 100% of their orders, some dealers
inflated orders for new cars to try to receive a larger pool of allocated vehicles.
In one month, Cash for Clunkers increased demand by 50% for automakers,
many of whom had already cut capacity significantly. Almost overnight,
manufacturers and parts suppliers had to transform from a shift reduction
mode to an overtime mode.
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Example S2 CALCULATING THE BULLWHIP EFFECT Chieh Lee Metals, Inc. orders sheet metal and transforms it into 50 formed tabletops that are sold to fur- niture manufacturers. The table below shows the weekly variance of demand and orders for each major company in this supply chain for tables. Each firm has one supplier and one customer, so the order vari- ance for one firm will equal the demand variance for its supplier. Analyze the relative contributions to the bullwhip effect in this supply chain.
FIRM VARIANCE OF DEMAND VARIANCE OF ORDERS BULLWHIP MEASURE
Furniture Mart, Inc. 100 110 110/100 5 1.10
Furniture Distributors, Inc. 110 180 180/110 5 1.64
Furniture Makers of America 180 300 300/180 5 1.67
Chieh Lee Metals, Inc. 300 750 750/300 5 2.50
Metal Suppliers Ltd. 750 2000 2000/750 5 2.67
APPROACH c Use Equation (S11-2) to calculate the bullwhip measure for each firm in the chain.
SOLUTION c The last column of the table displays the bullwhip measure for each firm.
INSIGHT c This supply chain exhibits a classic bullwhip effect. Despite what might be a very stable demand pattern at the retail level, order sizes to suppliers vary significantly. Chieh Lee should attempt to identifiy the causes for her own firm’s order amplification, and she should attempt to work with her supply chain partners to try to reduce amplification at every level of the chain.
LEARNING EXERCISE c Suppose that Chieh Lee is able to reduce her bullwhip measure from 2.50 to 1.20. If the measure for all other firms remained the same, what would be the new reduced variance of orders from Metal Suppliers? [Answer: 961.]
RELATED PROBLEMS c S11.6, S11.7, S11.8, S11.9
STUDENT TIP The factor-weighting model
adds objectivity to decision
making.
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S U P P L E M E N T 1 1 | S U P P LY C H A I N M A N AG E M E N T A N A LY T I C S 477
The factor-weighting technique, presented here, simultaneously considers multiple supplier criteria. Each factor must be assigned an importance weight, and then each potential supplier is scored on each factor. The weights typically sum to 100%. Factors are scored using the same scale (e.g., 1–10). Sometimes a key is provided for supplier raters that converts qualitative rat- ings into numerical scores (e.g., “Very good” 5 8). Example S3 illustrates the weighted criteria in comparing two competing suppliers.
LO S11.3 Describe the factor-weighting
approach to supplier
evaluation
Example S3 FACTOR-WEIGHTING APPROACH TO SUPPLIER EVALUATION Erick Davis, president of Creative Toys in Palo Alto, California, is interested in evaluating suppliers who will work with him to make nontoxic, environmentally friendly paints and dyes for his line of children’s toys. This is a critical strategic element of his supply chain, and he desires a firm that will contribute to his product.
APPROACH c Erick has narrowed his choices to two suppliers: Faber Paint and Smith Dye. He will use the factor-weighting approach to supplier evaluation to compare the two.
SOLUTION c Erick develops the following list of selection criteria. He then assigns the weights shown to help him perform an objective review of potential suppliers. His staff assigns the scores and computes the total weighted score.
FABER PAINT SMITH DYE
CRITERION WEIGHT SCORE (1–5) (5 HIGHEST)
WEIGHT 3 SCORE
SCORE (1–5) (5 HIGHEST)
WEIGHT 3 SCORE
Engineering/innovation skills .20 5 1.0 5 1.0
Production process capability .15 4 0.6 5 0.75
Distribution capability .05 4 0.2 3 0.15
Quality performance .10 2 0.2 3 0.3
Facilities/location .05 2 0.1 3 0.15
Financial strength .15 4 0.6 5 0.75
Information systems .10 2 0.2 5 0.5
Integrity .20 5 1.0 3 0.6
Total 1.00 3.9 4.2
Smith Dye received the higher score of 4.2 and, based on this analysis, would be the preferred vendor.
INSIGHT c The use of a factor-weighting approach can help firms systematically identify the features that are important to them and evaluate potential suppliers in an objective manner. A certain degree of subjectivity remains in the process, however, with regard to the criteria chosen, the weights applied to those criteria, and the supplier scores that are applied to each criterion.
LEARNING EXERCISE c If Erick believes that integrity should be twice as important while produc- tion process capability and financial strength should both only be 1/3 as important, how does the analysis change? [Answer: Faber Paint’s score becomes 4.1, while Smith Dye’s score becomes 3.8, so Faber Paint is now the preferred vendor.]
RELATED PROBLEMS c S11.10, S11.11, S11.12 (S11.13 is available in MyOMLab)
Transportation Mode Analysis The longer a product is in transit, the longer the firm has its money invested. But faster shipping is usually more expensive than slow shipping. A simple way to obtain some insight into this trade-off is to evaluate holding cost against shipping options. We do this in Example S4 .
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Example S4 looks only at holding cost versus shipping cost. For the operations or logistics manager there are many other considerations, including ensuring on-time delivery , coordinat- ing shipments to maintain a schedule, getting a new product to market, and keeping a cus- tomer happy. Estimates of these other costs can be added to the estimate of the daily holding cost. Determining the impact and cost of these considerations makes the evaluation of ship- ping alternatives a challenging OM task.
Warehouse Storage Storage represents a significant step for many items as they travel through their respective supply chains. The U.S. alone has more than 13,000 buildings dedicated to warehouse and storage. Some exceed the size of several connected football fields. In fact, more than 35% have over 100,000 square feet of floor space.
Care should be taken when determining which items to store in various locations in a ware- house. In large warehouses in particular, hundreds or thousands of trips are made each day along very long aisles. Proper placement of items can improve efficiency by shaving significant travel time for workers. In Example S5 , we observe a simple way to determine storage locations in a warehouse.
Example S4 DETERMINING DAILY COST OF HOLDING A shipment of new connectors for semiconductors needs to go from San Jose to Singapore for assembly. The value of the connectors is $1,750, and holding cost is 40% per year. One airfreight carrier can ship the connectors 1 day faster than its competitor, at an extra cost of $20.00. Which carrier should be selected?
APPROACH c First we determine the daily holding cost and then compare the daily holding cost with the cost of faster shipment.
SOLUTION c Daily cost of holding the product = (Annual holding cost * Product value)>365
= (.40 * $1,750)>365
= $1.92
Because the cost of saving one day is $20.00, which is much more than the daily holding cost of $1.92, we decide on the less costly of the carriers and take the extra day to make the shipment. This saves $18.08 ($20.00 − $1.92).
INSIGHT c The solution becomes radically different if the 1-day delay in getting the connectors to Singapore delays delivery (making a customer angry) or delays payment of a $150,000 final product. (Even 1 day’s interest on $150,000 or an angry customer makes a savings of $18.08 insignificant.)
LEARNING EXERCISE c If the holding cost is 100% per year, what is the decision? [Answer: Even with a holding cost of $4.79 per day, the less costly carrier is selected.]
RELATED PROBLEMS c S11.14, S11.15, S11.16, S11.17
LO S11.4 Evaluate cost-of-shipping
alternatives
Example S5 DETERMINING STORAGE LOCATIONS IN A WAREHOUSE Erika Marsillac manages a warehouse for a local chain of specialty hardware stores. As seen in Figure S11.3 , the single-aisle rectangular warehouse has a dock for pickup and delivery, along with 16 equal-sized storage blocks for inventory items.
1 3 5 7 9 11 13 15
Dock Aisle
2 4 6 8 10 12 14 16
Figure S11.3
Storage Locations in the
Warehouse
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Summary
The following table shows: (1) the category of each item stored in the warehouse, (2) the estimated number of times per month (trips) that workers need to either store or retrieve those items, and (3) the area (number of specialized blocks) required to store the items. Erika wishes to assign items to the stor- age blocks to minimize average distance traveled.
ITEM MONTHLY TRIPS TO STORAGE BLOCKS OF STORAGE SPACE NEEDED
Lumber 600 5
Paint 260 2
Tools 150 3
Small hardware 400 2
Chemical bags 90 3
Lightbulbs 220 1
APPROACH c For each item, calculate the ratio of the number of trips to blocks of storage area needed. Rank the items according to this ratio, and place the highest -ranked items closest to the dock.
SOLUTION c The following table calculates the ratio for each item and ranks the items from highest to lowest. Based on the ranking, items are assigned to the remaining blocks that are as close to the dock as possible. (Where applicable, given a choice between two equidistant blocks, items should be placed next to items of the same type rather than across the aisle from them.)
ITEM TRIPS/BLOCKS RANKING ASSIGNED BLOCKS
Lumber 600/5 5 120 4 6, 7, 8, 9, 10
Paint 260/2 5 130 3 3, 5
Tools 150/3 5 50 5 11, 12, 13
Small hardware 400/2 5 200 2 2, 4
Chemical bags 90/3 5 30 6 14, 15, 16
Lightbulbs 220/1 5 220 1 1
INSIGHT c This procedure allocates items with the highest “bang-for-the-buck” first. The “bang” (value) here is the number of trips. Because we want to minimize travel, we would like to place items with high-frequency visits near the front. The storage space represents the “buck” (cost). We want items that take up a lot of space moved toward the back because if they were placed near the front, we would have to travel past their multiple blocks every time we needed to store or retrieve an item from a different category. This bang versus buck trade-off is neatly accommodated by using the trips/blocks ratio (column 2 of the solution table). In this example, even though lumber has the highest number of trips, the lumber takes up so much storage space that it is placed further back, toward the middle of the warehouse.
LEARNING EXERCISE c Order frequency for paint is expected to increase to 410 trips per month. How will that change the storage plan? [Answer: Paint and small hardware will switch storage locations.]
RELATED PROBLEMS c S11.18, S11.19, S11.20
LO S11.5 Allocate items to storage locations
in a warehouse
Myriad tools have been developed to help supply-chain managers make well-informed decisions. We have pro- vided a small sampling in this supplement. A decision tree can help determine the best number of suppliers to protect against supply disruption from potential disasters. The bullwhip measure can identify each supply chain mem- ber’s contribution to exacerbating ordering fluctuations.
The factor-weighting approach can be used to help select suppliers based on multiple criteria. Inventory hold- ing costs can be computed for various shipping alterna- tives to better compare their overall cost impact. Finally, items can be ranked according to the ratio of (trips/ blocks of storage) to determine their best placement in a warehouse.
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Discussion Questions
1. What is the difference between “unique-event” risk and “super-event” risk?
2. If the probability of a “super-event” increases, does the “unique-event” risk increase or decrease in importance? Why?
3. If the probability of a “super-event” decreases, what happens to the likelihood of needing multiple suppliers?
4. Describe some ramifications of the bullwhip effect. 5. Describe causes of the bullwhip effect and their associated
remedies.
6. Describe how the bullwhip measure can be used to analyze supply chains.
7. Describe some potentially useful categories to include in a factor-weighting analysis for supplier selection.
8. Describe some potential pitfalls in relying solely on the results of a factor-weighting analysis for supplier selection.
9. Describe some disadvantages of using a slow shipping method. 10. Besides warehouse layout decisions, what are some other
applications where ranking items according to “bang/buck” might make sense?
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM S11.1 Jon Jackson Manufacturing is searching for suppliers for its new line of equipment. Jon has narrowed his choices to two sets of suppliers. Believing in diversification of risk, Jon would select two suppliers under each choice. However, he is still concerned about the risk of both suppliers failing at the same time. The “San Francisco option” uses both suppliers in San Francisco. Both are stable, reliable, and profitable firms, so Jon calculates the “unique-event” risk for either of them to be 0.5%. However, because San Francisco is in an earthquake zone, he estimates the probability of an event that would knock out both suppliers to be 2%. The “North American option” uses one supplier in Canada and another in Mexico. These are upstart firms; John calculates the “unique-event” risk for either of them to be 10%. But he estimates the “super-event” probability that would knock out both of these suppliers to be only 0.1%. Purchasing costs would be $500,000 per year using the San Francisco option and $510,000 per year using the North American option. A total disruption would create an annualized loss of $800,000. Which option seems best?
SOLUTION Using Equation (S11-1) , the probability of a total disruption (i.e., the probability of incurring the $800,000 loss) equals:
San Francisco option: 0.02 + (1 - 0.02)0.0052 = 0.02 + 0.0000245 = 0.0200245, or 2.00245% North American option: 0.001 + (1 - 0.001)0.12 = 0.001 + 0.0099 = 0.01099, or 1.099%
Total annual expected costs = Annual purchasing costs + Expected annualized disruption costs San Francisco option: $500,000 + $800,000(0.0200245) = $500,000 + $16,020 = $516,020 North American option: $510,000 + $800,000(0.01099) = $510,000 + $8,792 = $518,792
In this case, the San Francisco option appears to be slightly cheaper.
SOLVED PROBLEM S11.2 Over the past 10 weeks, demand for gears at Michael’s Metals has been 140, 230, 100, 175, 165, 220, 200, and 178. Michael has placed weekly orders of 140, 250, 90, 190, 140, 240, 190, and 168 units. The sample variance of a data set can be found by using the VAR.S function in Excel or by plugging each value ( x ) of the data
set into the formula: Variance = g(x - x )2
(n - 1) , where x is the mean of the data set and n is the number of values in the set. Using
Equation (S11-2) , calculate the bullwhip measure for Michael’s Metals over the 10-week period.
SOLUTION Mean demand = (140 + 230 + 100 + 175 + 165 + 220 + 200 + 178)>8 = 1,408>8 = 176
Variance of demand
= (140 - 176)2 + (230 - 176)2 + (100 - 176)2 + (175 - 176)2 + (165 - 176)2 + (220 - 176)2 + (200 - 176)2 + (178 - 176)2
(8 - 1)
= 362 + 542 + 762 + 12 + 112 + 442 + 242 + 22
7 =
1,296 + 2,916 + 5,776 + 1 + 121 + 1,936 + 576 + 4 7
= 12,626
7 = 1,804
Mean orders = (140 + 250 + 90 + 190 + 140 + 240 + 190 + 168)>8 = 1,408>8 = 176
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Variance of orders
= (140 - 176)2 + (250 - 176)2 + (90 - 176)2 + (190 - 176)2 + (140 - 176)2 + (240 - 176)2 + (190 - 176)2 + (168 - 176)2
(8 - 1)
= 362 + 742 + 862 + 142 + 362 + 642 + 142 + 82
7 =
1,296 + 5,476 + 7,396 + 196 + 1,296 + 4,096 + 196 + 64 7
= 20,016
7 = 2,859
From Equation (S11-2) , the bullwhip measure 5 2,859>1,804 5 1.58. Since 1.58 . 1, Michael’s Metals is contributing to the bullwhip eff ect in its supply chain.
SOLVED PROBLEM S11.3 Victor Pimentel, purchasing manager of Office Supply Center of Mexico, is searching for a new supplier for its paper. The most important supplier criteria for Victor include paper quality, delivery reliability, customer service, and financial condition, and he believes that paper quality is twice as important as each of the other three criteria. Victor has narrowed the choice to two suppliers, and his staff has rated each supplier on each criterion (using a scale of 1 to 100, with 100 being highest), as shown in the following table:
PAPER QUALITY DELIVERY RELIABILITY CUSTOMER SERVICE FINANCIAL CONDITION
Monterrey Paper 85 70 65 80
Papel Grande 80 90 95 75
Use the factor-weighting approach to determine the best supplier choice.
SOLUTION To determine the appropriate weights for each category, create a simple algebraic relationship: Let x = weight for criteria 2, 3, and 4.
Then 2x + x + x + x = 100%, i.e., 5x = 100%, or x = 0.2 = 20% Thus, paper quality has a weight of 2(20%) 5 40%, and the other three criteria each have a weight of 20%.
The following table presents the factor-weighting analysis:
MONTERREY PAPER PAPEL GRANDE
CRITERION WEIGHT SCORE (1–100) (100 HIGHEST) WEIGHT 3 SCORE
SCORE (1–100) (100 HIGHEST) WEIGHT 3 SCORE
Paper quality .40 85 34 80 32
Delivery reliability .20 70 14 90 18
Customer service .20 65 13 95 19
Financial condition .20 80 16 75 15
Total 1.00 77 84
Since 84 . 77, Papel Grande should be the chosen supplier according to the factor-weighting method.
SOLVED PROBLEM S11.4 A French car company ships 120,000 cars annually to the United Kingdom. The current method of shipment uses ferries to cross the English Channel and averages 10 days. The firm is considering shipping by rail through the Chunnel (the tunnel that goes through the English Channel) instead. That transport method would average approximately 2 days. Shipping through the Chunnel costs $80 more per vehicle. The firm has a holding cost of 25% per year. The average value of each car shipped is $20,000. Which transportation method should be selected?
SOLUTION
Daily cost of holding the product = (.25 * $20,000)>365 = $13.70 Total holding cost savings by using the Chunnel = (10 - 2) * $13.70 = $110 (rounded) Since the $110 savings exceeds the $80 higher shipping cost, the Chunnel option appears best. This switch would save the firm (120,000)($110 − $80) 5 $3,600,000 per year.
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Problems
Problems S11.1–S11.5 relate to Evaluating Disaster Risk in the Supply Chain
• S11.1 How would you go about attempting to come up with the probability of a “super-event” or the probability of a “unique- event?” What factors would you consider?
• • S11.2 Phillip Witt, president of Witt Input Devices, wishes to create a portfolio of local suppliers for his new line of key- boards. As the suppliers all reside in a location prone to hurri- canes, tornadoes, flooding, and earthquakes, Phillip believes that the probability in any year of a “super-event” that might shut down all suppliers at the same time for at least 2 weeks is 3%. Such a total shutdown would cost the company approximately $400,000. He estimates the “unique-event” risk for any of the suppliers to be 5%. Assuming that the marginal cost of managing an additional supplier is $15,000 per year, how many suppliers should Witt Input Devices use? Assume that up to three nearly identical local suppliers are available.
• • S11.3 Still concerned about the risk in Problem S11.2, sup- pose that Phillip is willing to use one local supplier and up to two more located in other territories within the country. This would reduce the probability of a “super-event” to 0.5%, but due to increased distance the annual costs for managing each of the dis- tant suppliers would be $25,000 (still $15,000 for the local sup- plier). Assuming that the local supplier would be the first one chosen, how many suppliers should Witt Input Devices use now?
• • S11.4 Johnson Chemicals is considering two options for its supplier portfolio. Option 1 uses two local suppliers. Each has a “unique-event” risk of 5%, and the probability of a “super- event” that would disable both at the same time is estimated to be 1.5%. Option 2 uses two suppliers located in different countries. Each has a “unique-event” risk of 13%, and the probability of a “super-event” that would disable both at the same time is esti- mated to be 0.2%. a) What is the probability that both suppliers will be disrupted
using option 1? b) What is the probability that both suppliers will be disrupted
using option 2? c) Which option would provide the lowest risk of a total shutdown?
• • S11.5 Bloom’s Jeans is searching for new suppliers, and Debbie Bloom, the owner, has narrowed her choices to two sets. Debbie is very concerned about supply disruptions, so she has chosen to use three suppliers no matter what. For option 1, the suppliers are well established and located in the same country. Debbie calculates the “unique-event” risk for each of them to be 4%. She estimates the probability of a nationwide event that would knock out all three suppliers to be 2.5%. For option 2, the suppliers are newer but located in three different countries. Debbie calculates the “unique-event” risk for each of them to be 20%. She estimates the “super-event” probability that would knock out all three of these suppliers to be 0.4%. Purchasing and transportation costs would be $1,000,000 per year using option 1 and $1,010,000 per year using option 2. A total disruption would create an annualized loss of $500,000. a) What is the probability that all three suppliers will be dis-
rupted using option 1? b) What is the probability that all three suppliers will be dis-
rupted using option 2?
Manufacturer Distributor Wholesaler Retailer
c) What is the total annual purchasing and transportation cost plus expected annualized disruption cost for option 1?
d) What is the total annual purchasing and transportation cost plus expected annualized disruption cost for option 2?
e) Which option seems best?
Problems S11.6–S11.9 relate to Managing the Bullwhip Effect
• • S11.6 Consider the supply chain illustrated below:
Last year, the retailer’s weekly variance of demand was 200 units. The variance of orders was 500, 600, 750, and 1,350 units for the retailer, wholesaler, distributor, and manufacturer, respectively. (Note that the variance of orders equals the variance of demand for that firm’s supplier.) a) Calculate the bullwhip measure for the retailer. b) Calculate the bullwhip measure for the wholesaler. c) Calculate the bullwhip measure for the distributor. d) Calculate the bullwhip measure for the manufacturer. e) Which firm appears to be contributing the most to the bull-
whip effect in this supply chain?
• • S11.7 Over the past 5 weeks, demand for wine at Winston’s Winery has been 1,000, 2,300, 3,200, 1,750, and 1,200 bottles. Winston has placed weekly orders for glass bottles of 1,100, 2,500, 4,000, 1,000, and 900 units. (Recall that the sample vari- ance of a data set can be found by using the VAR.S function in Excel or by plugging each x value of the data set into the
formula: Variance = g(x - x )2
(n - 1) , where x is the mean of the
data set and n is the number of values in the set.)
a) What is the variance of demand for Winston’s Winery? b) What is the variance of orders from Winston’s Winery for
glass bottles? c) What is the bullwhip measure for glass bottles for Winston’s
Winery? d) Is Winston’s Winery providing an amplifying or smoothing
effect?
• • S11.8 Over the past 12 months, Super Toy Mart has expe- rienced a demand variance of 10,000 units and has produced an order variance of 12,000 units. a) What is the bullwhip measure for Super Toy Mart? b) If Super Toy Mart had made a perfect forecast of demand
over the past 12 months and had decided to order 1/12 of that annual demand each month, what would its bullwhip measure have been?
• • • S11.9 Consider a three-firm supply chain consisting of a retailer, manufacturer, and supplier. The retailer’s demand over an 8-week period was 100 units each of the first 2 weeks, 200 units each of the second 2 weeks, 300 units each of the third 2 weeks, and 400 units each of the fourth 2 weeks. The following table pre- sents the orders placed by each firm in the supply chain. Notice, as is often the case in supply chains due to economies of scale, that total units are the same in each case, but firms further up the supply chain (away from the retailer) place larger, less frequent, orders.
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WEEK RETAILER MANUFACTURER SUPPLIER
1 100 200 600
2 100
3 200 400
4 200
5 300 600 1400
6 300
7 400 800
8 400
Recall that the sample variance of a data set can be found by using the VAR.S function in Excel or by plugging each x value of
the data set into the formula: Variance = g(x - x)2
(n - 1) , where x is
the mean of the data set and n is the number of values in the set. a) What is the bullwhip measure for the retailer? b) What is the bullwhip measure for the manufacturer? c) What is the bullwhip measure for the supplier? d) What conclusions can you draw regarding the impact that
economies of scale may have on the bullwhip effect?
Problems S11.10–S11.13 relate to Supplier Selection Analysis
• • S11.10 As purchasing agent for Eynan Enterprises in Richmond, Virginia, you ask your buyer to provide you with a ranking of “excellent,” “good,” “fair,” or “poor” for a variety of characteristics for two potential vendors. You suggest that the “Products” total be weighted 40% and the other three categories totals be weighted 20% each. The buyer has returned the rankings shown in Table S11.2 .
Which of the two vendors would you select? PX
• • S11.11 Using the data in Problem S11.10, assume that both Donna, Inc. and Kay Corp. are able to move all their “poor” rat- ings to “fair.” How would you then rank the two firms? PX
• • S11.12 Develop a vendor-rating form that represents your comparison of the education offered by universities in which you considered (or are considering) enrolling. Fill in the neces- sary data, and identify the “best” choice. Are you attending that “best” choice? If not, why not?
Problems S11.14–S11.17 relate to Transportation Mode Analysis
• • S11.14 Your options for shipping $100,000 of machine parts from Baltimore to Kuala Lumpur, Malaysia, are (1) use a ship that will take 30 days at a cost of $3,800 or (2) truck the parts to Los Angeles and then ship at a total cost of $4,800. The second option will take only 20 days. You are paid via a letter of credit the day the parts arrive. Your holding cost is estimated at 30% of the value per year. a) Which option is more economical? b) What customer issues are not included in the data presented?
• • S11.15 If you have a third option for the data in Problem S11.14 and it costs only $4,000 and also takes 20 days, what is your most economical plan?
• • S11.16 Monczka-Trent Shipping is the logistics vendor for Handfield Manufacturing Co. in Ohio. Handfield has daily ship- ments of a power-steering pump from its Ohio plant to an auto assembly line in Alabama. The value of the standard shipment is $250,000. Monczka-Trent has two options: (1) its standard 2-day shipment or (2) a subcontractor who will team drive overnight with an effective delivery of one day. The extra driver costs $175. Handfield’s holding cost is 35% annually for this kind of inventory. a) Which option is more economical? b) What production issues are not included in the data presented?
Additional problem S11.13 is available in MyOMLab.
TABLE S11.2 Vendor Rating for Problem S11.10
DONNA INC. = D KAY CORP. = K
Company Products
Financial Strength
Manufacturing Range
Research Facilities
Geographical Locations
Management
Labor Relations
Trade Relations
Quality Price Packaging
Product Knowledge Sales Calls Sales Service
Excellent
(4)
K
Good
(3)
K
K
Fair
(2)
K
KD
D
D
D
K
KD
Poor
(1)
D
D
KD
KD
K D
K
D K D
K D
Excellent
(4)
KD
Good
(3)
Fair
(2)
KD KD
Poor
(1)
Service
Sales
Deliveries on Time
Handling of Problems
Technical Assistance
VENDOR RATING
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• • • S11.17 Recently, Abercrombie & Fitch (A&F) began shifting a large portion of its Asian deliveries to the U.S. from air freight to slower but cheaper ocean freight. Shipping costs have been cut dramatically, but shipment times have gone from days to weeks. In addition to having less control over inventory and being less responsive to fashion changes, the holding costs have risen for the goods in transport. Meanwhile, Central America might offer an inexpensive manufacturing alternative that could reduce shipping time through the Panama Canal to, say, 6 days, compared to, say, 27 days from Asia. Suppose that A&F uses an annual holding rate of 30%. Suppose further that the product costs $20 to produce in Asia. Assuming that the transportation cost via ocean liner would be approximately the same whether coming from Asia or Central America, what would the maximum production cost in Central America need to be in order for that to be a competitive source compared to the Asian producer?
• S11.18 The items listed in the following table are stored in a warehouse.
ITEM WEEKLY TRIPS AREA NEEDED (BLOCKS)
A 300 60
B 219 3
C 72 1
D 90 10
E 24 3
a) Which item should be stored at the very front (closest to the dock)? b) Which item should be stored at the very back (furthest from
the dock)?
• • S11.19 Amy Zeng, owner of Zeng’s Restaurant Distributions, supplies nonperishable goods to restaurants around the metro area. She stores all the goods in a warehouse. The goods are divided into five categories according to the following table. The table indicates the number of trips per month to store or retrieve items in each category, as well as the number of storage blocks taken up by each.
1 3 5 7 9 11 13 15
Dock Aisle
2 4 6 8 10 12 14 16
Dock Aisle
ITEM CATEGORY MONTHLY TRIPS AREA NEEDED
(BLOCKS)
Paper Products 50 2
Dishes, Glasses, and Silverware 16 4
Cleaning Agents 6 2
Cooking Oils and Seasonings 30 2
Pots and Pans 12 6
The following picture of the warehouse provides an identification number for each of the 16 storage blocks. For each item category, indicate into which blocks it should be stored.
• • S11.20 The items listed in the following table are stored in a warehouse.
ITEM WEEKLY TRIPS AREA NEEDED (BLOCKS)
A 2 1
B 160 8
C 16 1
D 40 4
E 24 2
F 15 1
G 4 1
Using the following figure, indicate the best storage location for each item to minimize average distance traveled.
Problems S11.18–S11.20 relate to Warehouse Storage
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Supplement 11 Rapid Review Main Heading Review Material MyOMLab TECHNIQUES FOR EVALUATING SUPPLY CHAINS (p. 472 )
Many supply chain metrics exist that can be used to evaluate performance within a company and for its supply chain partners. The 2011 Tōhoku earthquake and tsunami devastated eastern sections of Japan. The economic impact was felt around the globe, as manufacturers had been rely- ing heavily, in some cases exclusively, on suppliers located in the affected zones. Manufacturers in several industries worldwide took 6 months or longer before they saw their supply chains working normally again.
Concept Question: 1.1
EVALUATING DISASTER RISK IN THE SUPPLY CHAIN (pp. 472 – 474 )
Disasters that disrupt supply chains can take on many forms, including tornadoes, fires, hurricanes, typhoons, tsunamis, earthquakes, and terrorism. Firms often use multiple suppliers for important components to mitigate the risks of total supply disruption. The probability of all n suppliers being disrupted simultaneously :
P(n) = S + (1 - S )U n (S11-1)
where: S = probability of a “super-event” disrupting all suppliers simultaneously U = probability of a “unique-event” disrupting only one supplier L = financial loss incurred in a supply chain if all suppliers were disrupted C = marginal cost of managing a supplier
All suppliers will be disrupted simultaneously if either the super-event occurs or the super-event does not occur but a unique-event occurs for all of the suppliers. As the probability of a super-event ( S ) increases, the advantage of utilizing multiple suppliers diminishes (all would be knocked out anyway). On the other hand, large values of the unique event ( U ) increase the likelihood of needing more suppliers. These two phenomena taken together suggest that when multiple suppliers are used, managers may consider using ones that are geographically dispersed to lessen the probability of all failing simultaneously. A decision tree can be used to help operations managers make this important deci- sion regarding number of suppliers.
P(1)
1–P(1)
No failure
Both fail
≤ 1 Fail
Failure
P(2)
1–P(2)
$1C
$L + $1C
$2C
$L + $2C
One supplier
Two suppliers
• • •
All N fail
≤ (N–1) Fail
P(N)
1–P(N) $NC
$L + $NC
N suppliers
Concept Questions: 2.1–2.4
Problems: S11.1–S11.5
Virtual Office Hours for Solved Problem: S11.1
MANAGING THE BULLWHIP EFFECT (pp. 474 – 476 )
Demand forecast updating, order batching, price fluctuations , and shortage gaming can all produce inaccurate information, resulting in distortions and fluctuations in the supply chain and causing the bullwhip effect . j Bullwhip effect —The increasing fluctuation in orders that often occurs as orders
move through the supply chain. “Bullwhip” fluctuations create unstable production schedules, resulting in expen- sive capacity change adjustments such as overtime, subcontracting, extra inventory, backorders, hiring and laying off of workers, equipment additions, equipment underutilization, longer lead times, or obsolescence of overproduced items. The bullwhip effect can occur when orders decrease as well as when they increase. Often the human tendency to overreact to stimuli causes managers to make decisions that exacerbate the phenomenon.
Concept Questions: 3.1–3.4
Problems: S11.6–S11.9
Virtual Office Hours for Solved Problem: S11.2
R ap
id R
ev ie
w
S11
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Supplement 11 Rapid Review continued
Main Heading Review Material MyOMLab
SUPPLIER SELECTION ANALYSIS (pp. 476 – 477 )
Choosing suppliers simply based on the lowest bid has become a somewhat rare approach. Various, sometimes competing, factors often play a role in the decision. Buyers may consider such supplier characteristics as product quality, delivery speed, delivery reliability, customer service, and financial performance. The factor-weighting technique simultaneously considers multiple supplier criteria. Each factor must be assigned an importance weight , and then each potential supplier is scored on each factor. The weights typically sum to 100%. Factors are scored us- ing the same scale (e.g., 1–10). Sometimes a key is provided for supplier raters that converts qualitative ratings into numerical scores (e.g., “Very good” = 8).
Concept Questions: 4.1–4.2 Problems: S11.10–S11.11, S11.13 Virtual Office Hours for Solved Problem: S11.3
TRANSPORTATION MODE ANALYSIS (pp. 477 – 478 )
The longer a product is in transit, the longer the firm has its money invested. But faster shipping is usually more expensive than slow shipping. A simple way to obtain some insight into this trade-off is to evaluate holding cost against shipping options.
Daily cost of holding the product : (Annual holding cost * Product value)>365
There are many other considerations beyond holding vs. shipping costs when choosing the appropriate transportation mode and carrier, including ensuring on-time delivery (whether fast or slow), coordinating shipments to maintain a schedule, getting a new product to market, and keeping a customer happy. Estimates of these other costs can be added to the estimate of the daily holding cost.
Concept Questions: 5.1–5.2 Problems: S11.14–S11.17 Virtual Office Hours for Solved Problem: S11.4
WAREHOUSE STORAGE (pp. 478–479 )
When determining storage locations for items in a warehouse, rank the items according to the ratio:
(Number of trips>Blocks of storage needed) Place the items with the highest ratios closest to the dock.
Concept Questions: 6.1–6.2 Problems: S11.18–S11.20
S11 R
ap id
R ev
ie w
The overarching solution to the bullwhip effect is simply for supply chain members to share information and work together. Specific remedies for the four primary causes include:
Demand forecast errors S Share demand information throughout the chain Order batching S Think of the supply chain as one firm when choosing order sizes Price fluctuations S Institute everyday low prices Shortage gaming S Allocate orders based on past demand
A straightforward way to measure the extent of the bullwhip effect at any link in the supply chain is to calculate the bullwhip measure :
Bullwhip = Variance of orders
Variance of demand =
s2orders
s2demand (S11-2)
Variance amplification (i.e., the bullwhip effect) is present if the bullwhip measure is greater than 1. That means the size of a company’s orders fluctuate more than the size of its incoming demand. If the measure equals 1, then no amplification is present. A value less than 1 would imply a smoothing or dampening scenario as orders move up the supply chain from the retailer toward suppliers.
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the supplement.
LO S11.1 Which of the following combinations would result in needing to utilize the largest number of suppliers?
a) a high value of S and high value of U b) a high value of S and low value of U c) a low value of S and high value of U d) a low value of S and low value of U LO S11.2 Typically, the bullwhip effect is most pronounced at which
level of the supply chain? a) consumers b) suppliers c) wholesalers d) retailers LO S11.3 Which of the following is not a characteristic of the
factor-weighting approach to supplier evalution? a) it applies quantitative scores to qualitative criteria b) the weights typically sum to 100% c) multiple criteria can be considered simultaneously d) subjective judgment is often involved e) it applies qualitative assessments to quantitative criteria
LO S11.4 A more expensive shipper tends to provide: a) faster shipments and lower holding costs b) faster shipments and higher holding costs c) slower shipments and lower holding costs d) slower shipments and higher holding costs LO S11.5 Which of the following items is most likely to be stored
at the back of a warehouse, furthest away from the shipping dock?
a) low number of trips and low number of storage blocks
b) low number of trips and high number of storage blocks
c) high number of trips and low number of storage blocks
d) high number of trips and high number of storage blocks
Answers: LO S11.1. c; LO S11.2. b; LO S11.3. e; LO S11.4. a; LO S11.5. b.
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487487
C H A P T E R O U T L I N E
12 ◆
The Importance of Inventory 490 ◆
Managing Inventory 491
◆
Inventory Models 495
◆
Inventory Models for Independent Demand 496
◆
Probabilistic Models and Safety Stock 508
◆
Single-Period Model 513
◆
Fixed-Period ( P ) Systems 514
GLOBAL COMPANY PROFILE: Amazon.com
C H
A P
T E
R
Inventory Management
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
jj Independent Demand ( Ch. 12 ) jj Dependent Demand ( Ch. 14 )
jj Lean Operations ( Ch. 16 )
• • Scheduling
• • Maintenance
A la
sk a A
ir lin
e s
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Inventory Management Provides Competitive Advantage at Amazon.com
GLOBAL COMPANY PROFILE Amazon.com
C H A P T E R 1 2
488
W hen Jeff Bezos opened his revolutionary business in 1995, Amazon.com was intended
to be a “virtual” retailer—no inventory, no warehouses, no overhead—just a bunch of
computers taking orders for books and authorizing others to fill them. Things clearly
didn’t work out that way. Now, Amazon stocks millions of items of inventory, amid hundreds of
thousands of bins on shelves in over 150 warehouses around the world. Additionally, Amazon’s
1. You order three items, and a computer in Seattle takes charge. A computer assigns your order—a book, a game, and a digital camera—to one of Amazon’s
massive U.S. distribution centers.
2. The “flow meister” at the distribution center receives your order. She determines which workers go where to fill your order.
M a ri ly
n N
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to n /R
e n o G
a ze
tt e -J
o u rn
a l
3. Amazon’s current system doubles the picking speed of manual operators and drops the error rate to nearly zero.
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B e n C
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4. Your items are put into crates on moving belts. Each item goes into a large yellow crate that contains many
customers’ orders. When full, the crates ride a series of
conveyor belts that wind more than 10 miles through the
plant at a constant speed of 2.9 feet per second. The bar
code on each item is scanned 15 times, by machines and
by many of the 600 workers. The goal is to reduce errors to
zero—returns are very expensive.
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489
R E X /N
e w
sc o m
5. All three items converge in a chute and then inside a box. All the crates arrive at a central point where bar codes
are matched with order numbers to
determine who gets what. Your three
items end up in a 3-foot-wide chute—
one of several thousand—and are placed
into a corrugated box with a new bar
code that identifies your order. Picking is
sequenced to reduce operator travel.
6. Any gifts you’ve chosen are wrapped by hand. Amazon trains an elite group of gift wrappers, each of
whom processes 30 packages an hour.
7. The box is packed, taped, weighed, and labeled before leaving the warehouse in a truck. A typical plant is designed to ship as many as 200,000 pieces a day. About 60% of
orders are shipped via the U.S. Postal Service;
nearly everything else goes through United
Parcel Service.
8. Your order arrives at your doorstep. In 1 or 2 days, your order is delivered. Ad
ri a n S
h e rr
a tt
/A la
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and management. The time to receive, process, and position
the stock in storage and to then accurately “pull” and pack-
age an order requires a labor investment of less than 3 min-
utes. And 70% of these orders are multiproduct orders. This
underlines the high benchmark that Amazon has achieved.
This is world-class performance.
When you place an order with Amazon
.com , you are doing business with
a company that obtains competitive
advantage through inventory management.
This Global Company Profile shows how
Amazon does it.
software is so good that
Amazon sells its order taking,
processing, and billing expertise
to others. It is estimated that
200 million items are now avail-
able via the Amazon Web site. Bezos expects the customer experience at Amazon to
be one that yields the lowest price, the fastest delivery, and
an error-free order fulfillment process so no other contact with
Amazon is necessary. Exchanges and returns are very expensive.
Managing this massive inventory precisely is the key for
Amazon to be the world-class leader in warehouse automation
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490
The Importance of Inventory As Amazon.com well knows, inventory is one of the most expensive assets of many compa- nies, representing as much as 50% of total invested capital. Operations managers around the globe have long recognized that good inventory management is crucial. On the one hand, a firm can reduce costs by reducing inventory. On the other hand, production may stop and customers become dissatisfied when an item is out of stock. The objective of inventory manage- ment is to strike a balance between inventory investment and customer service . You can never achieve a low-cost strategy without good inventory management.
All organizations have some type of inventory planning and control system. A bank has methods to control its inventory of cash. A hospital has methods to control blood supplies and pharmaceuticals. Government agencies, schools, and, of course, virtually every manufacturing and production organization are concerned with inventory planning and control.
In cases involving physical products, the organization must determine whether to produce goods or to purchase them. Once this decision has been made, the next step is to forecast demand, as discussed in Chapter 4 . Then operations managers determine the inventory necessary to service that demand. In this chapter, we discuss the functions, types, and management of inventory. We then address two basic inventory issues: how much to order and when to order.
Functions of Inventory Inventory can serve several functions that add flexibility to a firm’s operations. The four func- tions of inventory are:
1. To provide a selection of goods for anticipated customer demand and to separate the firm from fluctuations in that demand. Such inventories are typical in retail establishments.
2. To decouple various parts of the production process . For example, if a firm’s supplies fluctu- ate, extra inventory may be necessary to decouple the production process from suppliers.
3. To take advantage of quantity discounts , because purchases in larger quantities may reduce the cost of goods or their delivery.
4. To hedge against inflation and upward price changes.
Types of Inventory To accommodate the functions of inventory, firms maintain four types of inventories: (1) raw material inventory, (2) work-in-process inventory, (3) maintenance/repair/operating supply (MRO) inventory, and (4) finished-goods inventory.
Raw material inventory has been purchased but not processed. This inventory can be used to de- couple (i.e., separate) suppliers from the production process. However, the preferred approach is to eliminate supplier variability in quality, quantity, or delivery time so that separation is not needed. Work-in-process (WIP) inventory is components or raw material that have undergone some change but are not completed. WIP exists because of the time it takes for a product to be made (called cycle time ). Reducing cycle time reduces inventory. Often this task is not difficult: during most of the time a product is “being made,” it is in fact sitting idle. As Figure 12.1 shows, actual work time, or “run” time, is a small portion of the material flow time, perhaps as low as 5%.
MROs are inventories devoted to maintenance/repair/operating supplies necessary to keep ma- chinery and processes productive. They exist because the need and timing for maintenance and
L E A R N I N G OBJEC TI V E S
LO 12.1 Conduct an ABC analysis 492 LO 12.2 Explain and use cycle counting 493 LO 12.3 Explain and use the EOQ model for independent inventory demand 496 LO 12.4 Compute a reorder point and explain safety stock 502 LO 12.5 Apply the production order quantity model 503 LO 12.6 Explain and use the quantity discount model 505 LO 12.7 Understand service levels and probabilistic inventory models 511
VIDEO 12.1 Managing Inventory at Frito-Lay
Raw material inventory
Materials that are usually
purchased but have yet to enter
the manufacturing process.
Work-in-process (WIP) inventory
Products or components that are
no longer raw materials but have
yet to become finished products.
Maintenance/repair/operating (MRO) inventory
Maintenance, repair, and
operating materials.
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C H A P T E R 1 2 | I N V E N T O RY M A N AG E M E N T 491
repair of some equipment are unknown. Although the demand for MRO inventory is often a function of maintenance schedules, other unscheduled MRO demands must be anticipated. Finished-goods inventory is completed product awaiting shipment. Finished goods may be inven- toried because future customer demands are unknown.
Managing Inventory Operations managers establish systems for managing inventory. In this section, we briefly examine two ingredients of such systems: (1) how inventory items can be classified (called ABC analysis ) and (2) how accurate inventory records can be maintained. We will then look at inventory control in the service sector.
ABC Analysis ABC analysis divides on-hand inventory into three classifications on the basis of annual dollar volume. ABC analysis is an inventory application of what is known as the Pareto principle (named after Vilfredo Pareto, a 19th-century Italian economist). The Pareto principle states that there are a “critical few and trivial many.” The idea is to establish inventory policies that focus resources on the few critical inventory parts and not the many trivial ones. It is not real- istic to monitor inexpensive items with the same intensity as very expensive items.
To determine annual dollar volume for ABC analysis, we measure the annual demand of each inventory item times the cost per unit . Class A items are those on which the annual dollar volume is high. Although such items may represent only about 15% of the total inventory items, they represent 70% to 80% of the total dollar usage. Class B items are those inventory items of medium annual dollar volume. These items may represent about 30% of inventory items and 15% to 25% of the total value. Those with low annual dollar volume are Class C , which may represent only 5% of the annual dollar volume but about 55% of the total inventory items.
Graphically, the inventory of many organizations would appear as presented in Figure 12.2 .
95% 5%
Input Wait for inspection
Wait to be moved
Move time
Wait in queue for operator
Setup time
Run time
Output
Cycle time
Figure 12.1
The Material Flow Cycle
Most of the time that work is in-process (95% of the cycle time) is not productive time.
Finished-goods inventory
An end item ready to be sold, but
still an asset on the company’s
books.
ABC analysis
A method for dividing on-hand
inventory into three classifications
based on annual dollar volume.
STUDENT TIP A, B, and C categories need
not be exact. The idea is to
recognize that levels of control
should match the risk.
A items 80 70 60 50 40 30 20 10 0
P e rc
e n ta
g e o
f a n n u a l d
o lla
r u sa
g e
10 20 30 40 50 60 70 80 90 100
B items C items
Percentage of inventory items
Figure 12.2
Graphic Representation of ABC
Analysis
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492 P A R T 3 | M A N AG I N G O P E R AT I O N S
Criteria other than annual dollar volume can determine item classification. For in- stance, high shortage or holding cost, anticipated engineering changes, delivery problems, or quality problems may dictate upgrading items to a higher classification. The advantage of dividing inventory items into classes allows policies and controls to be established for each class.
Policies that may be based on ABC analysis include the following:
1. Purchasing resources expended on supplier development should be much higher for indi- vidual A items than for C items.
2. A items, as opposed to B and C items, should have tighter physical inventory control; perhaps they belong in a more secure area, and perhaps the accuracy of inventory records for A items should be verified more frequently.
3. Forecasting A items may warrant more care than forecasting other items.
Better forecasting, physical control, supplier reliability, and an ultimate reduction in inven- tory can all result from classification systems such as ABC analysis.
Example 1 ABC ANALYSIS FOR A CHIP MANUFACTURER Silicon Chips, Inc., maker of superfast DRAM chips, wants to categorize its 10 major inventory items using ABC analysis.
APPROACH c ABC analysis organizes the items on an annual dollar-volume basis. Shown below (in columns 1–4) are the 10 items (identified by stock numbers), their annual demands, and unit costs.
SOLUTION c Annual dollar volume is computed in column 5, along with the percentage of the total represented by each item in column 6. Column 7 groups the 10 items into A, B, and C categories.
ABC Calculation
(1) (2) (3) (4) (5) (6) (7)
ITEM STOCK
NUMBER
PERCENTAGE OF NUMBER
OF ITEMS STOCKED
ANNUAL VOLUME (UNITS) 3
UNIT COST 5
ANNUAL DOLLAR VOLUME
PERCENTAGE OF ANNUAL
DOLLAR VOLUME CLASS
#10286 #11526
20% 1,000
500 $ 90.00 154.00
$ 90,000 77,000
38.8% 33.2%
72% A A
#12760 #10867 #10500
30% 1,550
350 1,000
17.00 42.86 12.50
26,350 15,001 12,500
11.3% 6.4% 5.4%
23% B B B
#12572 #14075 #01036 #01307 #10572
50%
600 2,000
100 1,200
250
14.17 .60
8.50 .42 .60
8,502 1,200
850 504 150
3.7% .5% .4% .2% .1%
5%
C C C C C
8,550 $232,057 100.0%
LO 12.1 Conduct an ABC analysis
INSIGHT c The breakdown into A, B, and C categories is not hard and fast. The objective is to try to separate the “important” from the “unimportant.”
LEARNING EXERCISE c The unit cost for Item #10286 has increased from $90.00 to $120.00. How does this impact the ABC analysis? [Answer: The total annual dollar volume increases by $30,000, to $262,057, and the two A items now comprise 75% of that amount.]
RELATED PROBLEMS c 12.1, 12.2, 12.3 (12.5–12.6 are available in MyOMLab)
EXCEL OM Data File Ch12Ex1.xls can be found in MyOMLab.
J
6
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An example of the use of ABC analysis is shown in Example 1 .
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C H A P T E R 1 2 | I N V E N T O RY M A N AG E M E N T 493
Record Accuracy Record accuracy is a prerequisite to inventory management, production scheduling, and, ulti- mately, sales. Accuracy can be maintained by either periodic or perpetual systems. Periodic systems require regular (periodic) checks of inventory to determine quantity on hand. Some small retailers and facilities with vendor-managed inventory (the vendor checks quantity on hand and resupplies as necessary) use these systems. However, the downside is lack of control between reviews and the necessity of carrying extra inventory to protect against shortages.
A variation of the periodic system is a two-bin system. In practice, a store manager sets up two containers (each with adequate inventory to cover demand during the time required to receive another order) and places an order when the first container is empty.
Alternatively, perpetual inventory tracks both receipts and subtractions from inventory on a continuing basis. Receipts are usually noted in the receiving department in some semiauto- mated way, such as via a bar-code reader, and disbursements are noted as items leave the stock- room or, in retailing establishments, at the point-of-sale (POS) cash register.
Regardless of the inventory system, record accuracy requires good incoming and out- going record keeping as well as good security. Stockrooms will have limited access, good housekeeping, and storage areas that hold fixed amounts of inventory. In both manufactur- ing and retail facilities, bins, shelf space, and individual items must be stored and labeled accurately. Meaningful decisions about ordering, scheduling, and shipping, are made only when the firm knows what it has on hand. (See the OM in Action box, “Inventory Accuracy at Milton Bradley.”)
Cycle Counting Even though an organization may have made substantial efforts to record inventory accurately, these records must be verified through a continuing audit. Such audits are known as cycle counting . Historically, many firms performed annual physical inventories. This practice often meant shut- ting down the facility and having inexperienced people count parts and material. Inventory records should instead be verified via cycle counting. Cycle counting uses inventory classifications developed through ABC analysis. With cycle counting procedures, items are counted, records are verified, and inaccuracies are periodically documented. The cause of inaccuracies is then traced and appropriate remedial action taken to ensure integrity of the inventory system. A items will be counted frequently, perhaps once a month; B items will be counted less frequently, perhaps once a quarter; and C items will be counted perhaps once every 6 months. Example 2 illustrates how to compute the number of items of each classification to be counted each day.
Cycle counting
A continuing reconciliation of
inventory with inventory records.
OM in Action Inventory Accuracy at Milton Bradley Milton Bradley, a division of Hasbro, Inc., has been manufacturing toys for
150 years. Founded by Milton Bradley in 1860, the company started by making
a lithograph of Abraham Lincoln. Using his printing skills, Bradley developed
games, including The Game of Life, Chutes and Ladders, Candy Land, Scrab-
ble, and Lite Brite. Today, the company produces hundreds of games, requiring
billions of plastic parts.
Once Milton Bradley has determined the optimal quantities for each
production run, it must make them and assemble them as a part of the proper
game. Some games require literally hundreds of plastic parts, including spin-
ners, hotels, people, animals, cars, and so on. According to Gary Brennan,
director of manufacturing, getting the right number of pieces to the right toys
and production lines is the most important issue for the credibility of the com-
pany. Some orders can require 20,000 or more perfectly assembled games
delivered to their warehouses in a matter of days.
Games with the incorrect number of parts and pieces can result in some very
unhappy customers. It is also time-consuming and expensive for Milton Bradley to
supply the extra parts or to have
toys or games returned. When
shortages are found during
the assembly stage, the entire
production run is stopped until
the problem is corrected.
Counting parts by hand
or machine is not always
accurate. As a result, Milton Bradley now weighs pieces and completed games
to determine if the correct number of parts have been included. If the weight
is not exact, there is a problem that is resolved before shipment. Using highly
accurate digital scales, Milton Bradley is now able to get the right parts in the
right game at the right time. Without this simple innovation, the company’s
most sophisticated production schedule would be meaningless.
Sources: Forbes (February 7, 2011); and The Wall Street Journal (April 15, 1999).
A n th
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In this hospital, these vertically
rotating storage carousels provide
rapid access to hundreds of
critical items and at the same time
save floor space. This Omnicell
inventory management carousel
is also secure and has the added
advantage of printing bar code
labels.
O m
n ic
e ll
LO 12.2 Explain and use cycle counting
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494 P A R T 3 | M A N AG I N G O P E R AT I O N S
In Example 2 , the particular items to be cycle counted can be sequentially or randomly selected each day. Another option is to cycle count items when they are reordered.
Cycle counting also has the following advantages:
1. Eliminates the shutdown and interruption of production necessary for annual physical inventories.
2. Eliminates annual inventory adjustments. 3. Trained personnel audit the accuracy of inventory. 4. Allows the cause of the errors to be identified and remedial action to be taken. 5. Maintains accurate inventory records.
Example 2 CYCLE COUNTING AT COLE’S TRUCKS, INC. Cole’s Trucks, Inc., a builder of high-quality refuse trucks, has about 5,000 items in its inventory. It wants to determine how many items to cycle count each day.
APPROACH c After hiring Matt Clark, a bright young OM student, for the summer, the firm deter- mined that it has 500 A items, 1,750 B items, and 2,750 C items. Company policy is to count all A items every month (every 20 working days), all B items every quarter (every 60 working days), and all C items every 6 months (every 120 working days). The firm then allocates some items to be counted each day.
SOLUTION c ITEM
CLASS QUANTITY CYCLE-COUNTING POLICY NUMBER OF ITEMS COUNTED PER DAY
A 500 Each month (20 working days) 500Y20 5 25Yday
B 1,750 Each quarter (60 working days) 1,750Y60 5 29Yday
C 2,750 Every 6 months (120 working days) 2,750Y120 5 23Yday
77Yday
Each day, 77 items are counted.
INSIGHT c This daily audit of 77 items is much more efficient and accurate than conducting a massive inventory count once a year.
LEARNING EXERCISE c Cole’s reclassifies some B and C items so there are now 1,500 B items and 3,000 C items. How does this change the cycle count? [Answer: B and C both change to 25 items each per day, for a total of 75 items per day.]
RELATED PROBLEM c 12.4
Shrinkage
Retail inventory that is
unaccounted for between receipt
and sale.
Pilferage
A small amount of theft.
Pharmaceutical distributor McKesson Corp., which is one of Arnold
Palmer Hospital’s main suppliers of surgical materials, makes heavy
use of bar-code readers to automate inventory control. The device on
the warehouse worker’s arm combines a scanner, a computer, and a
two-way radio to check orders. With rapid and accurate data, items
are easily verified, improving inventory and shipment accuracy.
R o b in
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y
Control of Service Inventories Although we may think of the service sector of our economy as not having inventory, that is seldom the case. Extensive inventory is held in wholesale and retail businesses, making inventory management crucial. In the food-service business, control of inventory is often the difference between success and failure. Moreover, inventory that is in transit or idle in a warehouse is lost value. Similarly, inventory damaged or stolen prior to sale is a loss. In retailing, inventory that is unaccounted for between receipt and time of sale is known as shrinkage . Shrinkage occurs from damage and theft as well as from sloppy paperwork. Inventory theft is also known as pilferage . Retail inventory loss of 1% of sales is considered good, with losses in many stores exceeding 3%. Because the impact on profitability is substantial, inventory accuracy and control are critical. Applicable techniques include the following:
1. Good personnel selection, training, and discipline: These are never easy but very necessary in food-service, wholesale, and retail operations, where employees have access to directly consumable merchandise.
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2. Tight control of incoming shipments: This task is being addressed by many firms through the use of Universal Product Code (or bar code) and radio frequency ID (RFID) systems that read every incoming shipment and automatically check tallies against purchase orders. When properly designed, these systems—where each stock keeping unit (SKU; pronounced “skew”) has its own identifier—can be very hard to defeat.
3. Effective control of all goods leaving the facility: This job is accomplished with bar codes, RFID tags, or magnetic strips on merchandise, and via direct observation. Direct observation can be personnel stationed at exits (as at Costco and Sam’s Club wholesale stores) and in potentially high-loss areas or can take the form of one-way mirrors and video surveillance.
Successful retail operations require very good store-level control with accurate inventory in its proper location. Major retailers lose 10% to 25% of overall profits due to poor or inaccurate inventory records. 1 (See the OM in Action box, “Retail’s Last 10 Yards.”)
Inventory Models We now examine a variety of inventory models and the costs associated with them.
Independent vs. Dependent Demand Inventory control models assume that demand for an item is either independent of or depend- ent on the demand for other items. For example, the demand for refrigerators is independent of the demand for toaster ovens. However, the demand for toaster oven components is dependent on the requirements of toaster ovens.
This chapter focuses on managing inventory where demand is independent . Chapter 14 presents dependent demand management.
Holding, Ordering, and Setup Costs Holding costs are the costs associated with holding or “carrying” inventory over time. Therefore, holding costs also include obsolescence and costs related to storage, such as insurance, extra staffing, and interest payments. Table 12.1 shows the kinds of costs that need to be evalu- ated to determine holding costs. Many firms fail to include all the inventory holding costs. Consequently, inventory holding costs are often understated.
Ordering cost includes costs of supplies, forms, order processing, purchasing, clerical support, and so forth. When orders are being manufactured, ordering costs also exist, but they are a part
A handheld reader can scan RFID tags, aiding control of both incoming and outgoing shipments.
OM in Action Retail’s Last 10 Yards Retail managers commit huge resources to inventory and its management.
Even with retail inventory representing 36% of total assets, nearly 1 of 6 items
a retail store thinks it has available to its customers is not! Amazingly, close to
two-thirds of inventory records are wrong. Failure to have product available is
due to poor ordering, poor stocking, mislabeling, merchandise exchange er-
rors, and merchandise being in the wrong location. Despite major investments
in bar coding, RFID, and IT, the last 10 yards of retail inventory management is
a disaster.
The huge number and variety of stock keeping units (SKUs) at the retail
level adds complexity to inventory management. Does the customer really
need 32 different offerings of Crest toothpaste or 26 offerings of Colgate?
The proliferation of SKUs increases confusion, store size, purchasing,
inventory, and stocking costs, as well as subsequent markdown costs. With
so many SKUs, stores have little space to stock and display a full case of many
products, leading to labeling and “broken case” issues in the back room.
Supervalu, the nation’s 4th largest food retailer, is reducing the number of
SKUs by 25% as one way to cut costs and add focus to its own store-branded
items.
Reducing the variation in delivery lead time, improving forecasting ac-
curacy, and cutting the huge variety of SKUs may all help. But reducing the
number of SKUs may not improve customer service. Training and educating
employees about the importance of inventory management may be a better
way to improve the last 10 yards.
Sources: The Wall Street Journal (January 13, 2010); Management Science
(February 2005); and California Management Review (Spring 2001).
VIDEO 12.2 Inventory Control at Wheeled Coach
Ambulance
Holding cost
The cost to keep or carry inventory
in stock.
Ordering cost
The cost of the ordering process.
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of what is called setup costs. Setup cost is the cost to prepare a machine or process for manufac- turing an order. This includes time and labor to clean and change tools or holders. Operations managers can lower ordering costs by reducing setup costs and by using such efficient proce- dures as electronic ordering and payment.
In manufacturing environments, setup cost is highly correlated with setup time . Setups usually require a substantial amount of work even before a setup is actually performed at the work center. With proper planning, much of the preparation required by a setup can be done prior to shutting down the machine or process. Setup times can thus be reduced substantially. Machines and processes that traditionally have taken hours to set up are now being set up in less than a minute by the more imaginative world-class manufacturers. Reducing setup times is an excellent way to reduce inventory investment and to improve productivity.
Inventory Models for Independent Demand In this section, we introduce three inventory models that address two important questions: when to order and how much to order . These independent demand models are:
1. Basic economic order quantity (EOQ) model 2. Production order quantity model 3. Quantity discount model
The Basic Economic Order Quantity (EOQ) Model The economic order quantity (EOQ) model is one of the most commonly used inventory-control tech- niques. This technique is relatively easy to use but is based on several assumptions:
1. Demand for an item is known, reasonably constant, and independent of decisions for other items.
2. Lead time—that is, the time between placement and receipt of the order—is known and consistent.
3. Receipt of inventory is instantaneous and complete. In other words, the inventory from an order arrives in one batch at one time.
4. Quantity discounts are not possible. 5. The only variable costs are the cost of setting up or placing an order (setup or ordering
cost) and the cost of holding or storing inventory over time (holding or carrying cost). These costs were discussed in the previous section.
6. Stockouts (shortages) can be completely avoided if orders are placed at the right time.
With these assumptions, the graph of inventory usage over time has a sawtooth shape, as in Figure 12.3 . In Figure 12.3 , Q represents the amount that is ordered. If this amount is 500 dresses, all 500 dresses arrive at one time (when an order is received). Thus, the inventory
TABLE 12.1 Determining Inventory Holding Costs
CATEGORY
COST (AND RANGE) AS A PERCENTAGE OF INVENTORY VALUE
Housing costs (building rent or depreciation, operating cost, taxes, insurance) 6% (3–10%)
Material-handling costs (equipment lease or depreciation, power, operating cost) 3% (1–3.5%)
Labor cost (receiving, warehousing, security) 3% (3–5%)
Investment costs (borrowing costs, taxes, and insurance on inventory) 11% (6–24%)
Pilferage, scrap, and obsolescence (much higher in industries undergoing rapid change like tablets and smart phones)
3% (2–5%)
Overall carrying cost 26%
Note: All numbers are approximate, as they vary substantially depending on the nature of the business, location, and current interest rates.
Setup cost
The cost to prepare a machine or
process for production.
STUDENT TIP An overall inventory carrying cost of
less than 15% is very unlikely, but
this cost can exceed 40%, especially
in high-tech and fashion industries.
Setup time
The time required to prepare a
machine or process for production.
Economic order quantity (EOQ) model
An inventory-control technique
that minimizes the total of ordering
and holding costs.
LO 12.3 Explain and use the EOQ model for
independent inventory
demand
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level jumps from 0 to 500 dresses. In general, an inventory level increases from 0 to Q units when an order arrives.
Because demand is constant over time, inventory drops at a uniform rate over time. (Refer to the sloped lines in Figure 12.3 .) Each time the inventory is received, the inventory level again jumps to Q units (represented by the vertical lines). This process continues indefinitely over time.
Minimizing Costs The objective of most inventory models is to minimize total costs. With the assumptions just given, significant costs are setup (or ordering) cost and holding (or carrying) cost. All other costs, such as the cost of the inventory itself, are constant. Thus, if we minimize the sum of setup and holding costs, we will also be minimizing total costs. To help you visualize this, in Figure 12.4 we graph total costs as a function of the order quantity, Q . The optimal order size, Q *, will be the quantity that minimizes the total costs. As the quantity ordered increases, the total number of orders placed per year will decrease. Thus, as the quantity ordered increases, the annual setup or ordering cost will decrease [ Figure 12.4 (a)]. But as the order quantity increases, the holding cost will increase due to the larger average inventories that are main- tained [ Figure 12.4 (b)].
As we can see in Figure 12.4 (c), a reduction in either holding or setup cost will reduce the total cost curve. A reduction in the setup cost curve also reduces the optimal order quantity (lot size). In addition, smaller lot sizes have a positive impact on quality and production flexibility. At Toshiba, the $77 billion Japanese conglomerate, workers can make as few as 10 laptop com- puters before changing models. This lot-size flexibility has allowed Toshiba to move toward a “build-to-order” mass customization system, an important ability in an industry that has product life cycles measured in months, not years.
You should note that in Figure 12.4 (c), the optimal order quantity occurs at the point where the ordering-cost curve and the carrying-cost curve intersect. This was not by chance. With the EOQ model, the optimal order quantity will occur at a point where the total setup cost is equal
STUDENT TIP If the maximum we can ever
have is Q (say, 500 units) and
the minimum is zero, then if
inventory is used (or sold) at a
fairly steady rate, the average
5 ( Q 1 0)Y2 5 Q Y2.
In ve
n to
ry le
ve l
Order quantity = Q (maximum inventory
level)
Minimum inventory 0
Time
Average inventory on hand
Q— 2( )
Usage rate Total order received Figure 12.3
Inventory Usage over Time
Setup (order) cost
Order quantity (a) Annual setup (order) cost (b) Annual holding cost
A n n u a l c
o st
(c) Total costs
Order quantity
A n n u a l c
o st
Holding cost Holding cost
Setup (order) cost
Total cost for holding and setup (order)
Order quantityOptimal order quantity (Q *)
Minimum total cost
A n n u a l c
o st
Figure 12.4
Costs as a Function of Order Quantity
STUDENT TIP Figure 12.4 is the heart of EOQ
inventory modeling. We want to find
the smallest total cost (top curve),
which is the sum of the two curves
below it.
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to the total holding cost. 2 We use this fact to develop equations that solve directly for Q *. The necessary steps are:
1. Develop an expression for setup or ordering cost. 2. Develop an expression for holding cost. 3. Set setup (order) cost equal to holding cost. 4. Solve the equation for the optimal order quantity.
Using the following variables, we can determine setup and holding costs and solve for Q *:
Q 5 Number of units per order Q * 5 Optimum number of units per order (EOQ)
D 5 Annual demand in units for the inventory item S 5 Setup or ordering cost for each order H 5 Holding or carrying cost per unit per year
1. Annual setup cost 5 (Number of orders placed per year) 3 (Setup or order cost per order)
= ¢ Annual demand Number of units in each order
≤ (Setup or order cost per order) = ¢ D
Q ≤ (S) = D
Q S
2. Annual holding cost 5 (Average inventory level) 3 (Holding cost per unit per year)
= ¢ Order quantity 2
≤ (Holding cost per unit per year) = ¢ Q
2 ≤(H) = Q
2 H
3. Optimal order quantity is found when annual setup (order) cost equals annual holding cost, namely:
D Q
S = Q 2
H
4. To solve for Q *, simply cross-multiply terms and isolate Q on the left of the equal sign:
2DS = Q2H
Q2 = 2DS H
Q * = A
2DS H
(12-1)
Now that we have derived the equation for the optimal order quantity, Q *, it is possible to solve inventory problems directly, as in Example 3 .
Example 3 FINDING THE OPTIMAL ORDER SIZE AT SHARP, INC. Sharp, Inc., a company that markets painless hypodermic needles to hospitals, would like to reduce its inventory cost by determining the optimal number of hypodermic needles to obtain per order.
APPROACH c The annual demand is 1,000 units; the setup or ordering cost is $10 per order; and the holding cost per unit per year is $.50.
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SOLUTION c Using these figures, we can calculate the optimal number of units per order:
Q* = A
2DS H
Q* = A 2(1,000)(10)
0.50 = 240,000 = 200 units
INSIGHT c Sharp, Inc., now knows how many needles to order per order. The firm also has a basis for determining ordering and holding costs for this item, as well as the number of orders to be processed by the receiving and inventory departments.
LEARNING EXERCISE c If D increases to 1,200 units, what is the new Q *? [Answer: Q * 5 219 units.]
RELATED PROBLEMS c 12.7, 12.8, 12.9, 12.10, 12.11, 12.14, 12.15, 12.17, 12.29 (12.31, 12.32, 12.33a, 12.35a are available in MyOMLab)
EXCEL OM Data File Ch12Ex3.xls can be found in MyOMLab.
ACTIVE MODEL 12.1 This example is further illustrated in Active Model 12.1 in MyOMLab.
We can also determine the expected number of orders placed during the year ( N ) and the expected time between orders ( T ), as follows:
Expected number of orders = N = Demand
Order quantity =
D Q*
(12-2)
Expected time between orders = T = Number of working days per year
N (12-3)
Example 4 illustrates this concept.
Example 4 COMPUTING NUMBER OF ORDERS AND TIME BETWEEN ORDERS AT SHARP, INC. Sharp, Inc. (in Example 3 ) has a 250-day working year and wants to find the number of orders ( N ) and the expected time between orders ( T ).
APPROACH c Using Equations (12-2) and (12-3), Sharp enters the data given in Example 3 .
SOLUTION c N =
Demand Order quantity
= 1,000 200
= 5 orders per year
T = Number of working days per year
Expected number of orders
= 250 working days per year
5 orders = 50 days between orders
INSIGHT c The company now knows not only how many needles to order per order but that the time between orders is 50 days and that there are five orders per year.
LEARNING EXERCISE c If D 5 1,200 units instead of 1,000, find N and T . [Answer: N > 5.48, T 5 45.62.]
RELATED PROBLEMS c 12.14, 12.15, 12.17 (12.35c,d are available in MyOMLab)
As mentioned earlier in this section, the total annual variable inventory cost is the sum of setup and holding costs: Total annual cost = Setup (order) cost + Holding cost (12-4)
In terms of the variables in the model, we can express the total cost TC as:
TC = D Q
S + Q 2
H (12-5)
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Inventory costs may also be expressed to include the actual cost of the material purchased. If we assume that the annual demand and the price per hypodermic needle are known values (e.g., 1,000 hypodermics per year at P 5 $10) and total annual cost should include purchase cost, then Equation (12-5) becomes:
TC = D Q
S + Q 2
H + PD
Because material cost does not depend on the particular order policy, we still incur an annual material cost of D * P = (1,000)(+10) = +10,000. (Later in this chapter we will discuss the case in which this may not be true—namely, when a quantity discount is available.) 3
Robust Model A benefit of the EOQ model is that it is robust. By robust we mean that it gives satisfactory answers even with substantial variation in its parameters. As we have observed, determining accurate ordering costs and holding costs for inventory is often difficult. Consequently, a robust model is advantageous. The total cost of the EOQ changes little in the neighborhood of the minimum. The curve is very shallow. This means that variations in setup costs, holding costs, demand, or even EOQ make relatively modest differences in total cost. Example 6 shows the robustness of EOQ.
Example 5 COMPUTING COMBINED COST OF ORDERING AND HOLDING Sharp, Inc. (from Examples 3 and 4 ) wants to determine the combined annual ordering and holding costs.
APPROACH c Apply Equation (12-5), using the data in Example 3 .
SOLUTION c TC =
D Q
S + Q 2
H
= 1,000 200
(+10) + 200 2
(+.50)
= (5) (+10) + (100) (+.50) = +50 + +50 = +100
INSIGHT c These are the annual setup and holding costs. The $100 total does not include the actual cost of goods. Notice that in the EOQ model, holding costs always equal setup (order) costs.
LEARNING EXERCISE c Find the total annual cost if D 5 1,200 units in Example 3 . [Answer: $109.54.]
RELATED PROBLEMS c 12.11, 12.14, 12.15, 12.16 (12.33b,c; 12.35e; 12.36a,b are available in MyOMLab)
Robust
Giving satisfactory answers even
with substantial variation in the
parameters.
Example 6 EOQ IS A ROBUST MODEL Management in the Sharp, Inc., examples underestimates total annual demand by 50% (say demand is actually 1,500 needles rather than 1,000 needles) while using the same Q . How will the annual inventory cost be impacted?
APPROACH c We will solve for annual costs twice. First, we will apply the wrong EOQ; then we will recompute costs with the correct EOQ.
SOLUTION c If demand in Example 5 is actually 1,500 needles rather than 1,000, but management uses an order quantity of Q 5 200 (when it should be Q 5 244.9 based on D 5 1,500), the sum of holding and ordering cost increases to $125:
Annual cost = D Q
S + Q 2
H
= 1,500 200
(+10) + 200 2
(+.50)
= +75 + +50 = +125
Example 5 shows how to use this formula.
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However, had we known that the demand was for 1,500 with an EOQ of 244.9 units, we would have spent $122.47, as shown:
Annual cost = 1,500 244.9
(+10) + 244.9
2 (+.50)
= 6.125(+10) + 122.45(+.50) = +61.25 + +61.22 = +122.47
INSIGHT c Note that the expenditure of $125.00, made with an estimate of demand that was sub- stantially wrong, is only 2% ($2.52Y$122.47) higher than we would have paid had we known the actual demand and ordered accordingly. Note also that were it not due to rounding, the annual holding costs and ordering costs would be exactly equal.
LEARNING EXERCISE c Demand at Sharp remains at 1,000, H is still $.50, and we order 200 needles at a time (as in Example 5 ). But if the true order cost 5 S 5 $15 (rather than $10), what is the annual cost? [Answer: Annual order cost increases to $75, and annual holding cost stays at $50. So the total cost 5 $125.]
RELATED PROBLEMS c 12.10b, 12.16 (12.36a,b are available in MyOMLab)
We may conclude that the EOQ is indeed robust and that significant errors do not cost us very much. This attribute of the EOQ model is most convenient because our ability to accu- rately determine demand, holding cost, and ordering cost is limited.
Reorder Points Now that we have decided how much to order, we will look at the second inventory ques- tion, when to order. Simple inventory models assume that receipt of an order is instanta- neous. In other words, they assume (1) that a firm will place an order when the inventory level for that particular item reaches zero and (2) that it will receive the ordered items immediately. However, the time between placement and receipt of an order, called lead time , or delivery time, can be as short as a few hours or as long as months. Thus, the when- to-order decision is usually expressed in terms of a reorder point (ROP) —the inventory level at which an order should be placed (see Figure 12.5 ).
The reorder point (ROP) is given as:
ROP = Demand per day * Lead time for a new order in days ROP = d * L (12-6)
This equation for ROP assumes that demand during lead time and lead time itself are constant . When this is not the case, extra stock, often called safety stock ( ss ) , should be added. The reorder point with safety stock then becomes:
ROP = Expected demand during lead time + Safety stock
The demand per day, d , is found by dividing the annual demand, D , by the number of working days in a year:
d = D
Number of working days in a year
Lead time
In purchasing systems, the time
between placing an order and
receiving it; in production systems,
the wait, move, queue, setup, and
run times for each component
produced.
Reorder point (ROP)
The inventory level (point) at which
action is taken to replenish the
stocked item.
Slope = units/day = d
Resupply takes place as order arrives
Lead time = L
Q *
In ve
n to
ry le
ve l (
u n its
)
Time (days)
ROP (units)
0
Figure 12.5
The Reorder Point (ROP)
Q * is the optimum order quantity, and lead time represents the time between placing
and receiving an order.
Safety stock ( ss )
Extra stock to allow for uneven
demand; a buffer.
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When demand is not constant or variability exists in the supply chain, safety stock can be critical. We discuss safety stock in more detail later in this chapter.
Production Order Quantity Model In the previous inventory model, we assumed that the entire inventory order was received at one time. There are times, however, when the firm may receive its inventory over a period of time. Such cases require a different model, one that does not require the instantaneous-receipt assumption. This model is applicable under two situations: (1) when inventory continuously flows or builds up over a period of time after an order has been placed or (2) when units are produced and sold simultaneously. Under these circumstances, we take into account daily production (or inventory-flow) rate and daily demand rate. Figure 12.6 shows inventory levels as a function of time (and inventory dropping to zero between orders).
Because this model is especially suitable for the production environment, it is commonly called the production order quantity model . It is useful when inventory continuously builds up over time, and traditional economic order quantity assumptions are valid. We derive this model by setting ordering or setup costs equal to holding costs and solving for optimal order size, Q *. Using the following symbols, we can determine the expression for annual inventory holding cost for the production order quantity model:
Production order quantity model
An economic order quantity tech-
nique applied to production orders.
t
Demand part of cycle with no production (only usage takes place)
Part of inventory cycle during which production (and usage) takes place
Maximum inventory
In ve
n to
ry le
ve l
Time
Figure 12.6
Change in Inventory Levels
over Time for the Production
Model
Example 7 COMPUTING REORDER POINTS (ROP) FOR IPHONES WITH AND WITHOUT SAFETY STOCK An Apple store has a demand (D) for 8,000 iPhones per year. The firm operates a 250-day working year. On average, delivery of an order takes 3 working days, but has been known to take as long as 4 days. The store wants to calculate the reorder point without a safety stock and then with a one-day safety stock.
APPROACH c First compute the daily demand and then apply Equation (12-6) for the ROP. Then compute the ROP with safety stock.
SOLUTION c
d = D
Number of working days in a year =
8,000 250
= 32 units
ROP = Reorder point = d * L = 32 units per day * 3 days = 96 units
ROP with safety stock adds 1 day’s demand (32 units) to the ROP (for 128 units).
INSIGHT c When iPhone inventory stock drops to 96 units, an order should be placed. If the safety stock for a possible one-day delay in delivery is added, the ROP is 128 (5 96 1 32).
LEARNING EXERCISE c If there are only 200 working days per year, what is the correct ROP, without safety stock and with safety stock? [Answer: 120 iPhones without safety stock and 160 with safety stock.]
RELATED PROBLEMS c 12.11d, 12.12, 12.13, 12.15f (12.33d, 12.34, 12.35f, 12.36c are available in MyOMLab)
LO 12.4 Compute a reorder point and explain
safety stock
Computing the reorder point is demonstrated in Example 7 .
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Q = Number of units per order H = Holding cost per unit per year p = Daily production rate d = Daily demand rate, or usage rate t = Length of the production run in days
1. ¢Annual inventory holding cost
≤ = (Average inventory level) * ¢ Holding cost per unit per year
≤ 2. (Average inventory level) = (Maximum inventory level)>2
3. ¢ Maximum inventory level
≤ = ¢Total production during the production run
≤ - ¢ Total used during the production run
≤ = pt - dt
However, Q = total produced = pt, and thus t = Q>p. Therefore:
Maximum inventory level = p¢ Q p ≤ - d¢ Q
p ≤ = Q - d
p Q
= Q¢1 - d p ≤
4. Annual inventory holding cost (or simply holding cost) 5
Maximum inventory level
2 (H) =
Q 2 C 1 - ¢ d
p ≤ S H
Using this expression for holding cost and the expression for setup cost developed in the basic EOQ model, we solve for the optimal number of pieces per order by equating setup cost and holding cost:
Setup cost = (D>Q)S Holding cost = 12 HQ [1 - (d>p)]
Set ordering cost equal to holding cost to obtain Q*p :
D Q
S = 12 HQ[1 - (d>p)]
Q2 = 2DS
H[1 - (d>p)]
Q*p = A
2DS H[1 - (d>p)]
(12-7)
LO 12.5 Apply the production order quantity
model
STUDENT TIP Note in Figure 12.6 that inventory
buildup is not instantaneous but
gradual. So the formula reduces
the average inventory and thus the
holding cost by the ratio of that
buildup.
Each order may require a change in the way a machine
or process is set up. Reducing setup time usually
means a reduction in setup cost, and reductions in
setup costs make smaller batches (lots) economical
to produce. Increasingly, setup (and operation) is
performed by computer-controlled machines, such as
this one, operating from previously written programs. D m
it ry
K a lin
o vs
ky /S
h u tt
e rs
to ck
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In Example 8 , we use the above equation, Q*p, to solve for the optimum order or production quantity when inventory is consumed as it is produced.
Example 8 A PRODUCTION ORDER QUANTITY MODEL Nathan Manufacturing, Inc., makes and sells specialty hubcaps for the retail automobile aftermarket. Nathan’s forecast for its wire-wheel hubcap is 1,000 units next year, with an average daily demand of 4 units. However, the production process is most efficient at 8 units per day. So the company produces 8 per day but uses only 4 per day. The company wants to solve for the optimum number of units per order. ( Note: This plant schedules production of this hubcap only as needed, during the 250 days per year the shop operates.)
APPROACH c Gather the cost data and apply Equation (12-7):
Annual demand = D = 1,000 units Setup costs = S = +10 Holding cost = H = +0.50 per unit per year Daily production rate = p = 8 units daily Daily demand rate = d = 4 units daily
SOLUTION c
Q*p = A
2DS H31 - (d>p)4
Q*p = A 2(1,000)(10)
0.5031 - (4>8)4
= A
20,000 0.50(1>2)
= 280,000 = 282.8 hubcaps, or 283 hubcaps
INSIGHT c The difference between the production order quantity model and the basic EOQ model is that the effective annual holding cost per unit is reduced in the production order quantity model because the entire order does not arrive at once.
LEARNING EXERCISE c If Nathan can increase its daily production rate from 8 to 10, how does Q*p change? [Answer: Q*p 5 258.]
RELATED PROBLEMS c 12.18, 12.19, 12.20, 12.30 (12.37 is available in MyOMLab)
EXCEL OM Data File Ch12Ex8.xls can be found in MyOMLab.
ACTIVE MODEL 12.2 This example is further illustrated in Active Model 12.2 in MyOMLab.
You may want to compare this solution with the answer in Example 3 , which had identical D , S , and H values. Eliminating the instantaneous-receipt assumption, where p 5 8 and d 5 4, resulted in an increase in Q * from 200 in Example 3 to 283 in Example 8 . This increase in Q * occurred because holding cost dropped from $.50 to [$.50 3 (1 2 d Y p )], making a larger order quantity optimal. Also note that:
d = 4 = D
Number of days the plant is in operation =
1,000 250
We can also calculate Q*p when annual data are available. When annual data are used, we can express Q*p as:
Q*p =
H
2DS
H¢1 - Annual demand rate Annual production rate
≤ (12-8)
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Quantity Discount Models Quantity discounts appear everywhere—you cannot go into a grocery store without seeing them on nearly every shelf. In fact, researchers have found that most companies either offer or receive quantity discounts for at least some of the products that they sell or purchase. A quantity discount is simply a reduced price ( P ) for an item when it is purchased in larger quantities. A typical quantity discount schedule appears in Table 12.2 . As can be seen in the table, the normal price of the item is $100. When 120 to 1,499 units are ordered at one time, the price per unit drops to $98; when the quantity ordered at one time is 1,500 units or more, the price is $96 per unit. The 120 quantity and the 1,500 quantity are called price-break quan- tities because they represent the first order amount that would lead to a new lower price. As always, management must decide when and how much to order. However, given these quan- tity discounts, how does the operations manager make these decisions?
As with other inventory models, the objective is to minimize total cost. Because the unit cost for the second discount in Table 12.2 is the lowest, you may be tempted to order 1,500 units. Placing an order for that quantity, however, even with the greatest discount price, may not minimize total inventory cost. This is because holding cost increases. Thus, the major trade- off when considering quantity discounts is between reduced product cost and increased holding cost . When we include the cost of the product, the equation for the total annual inventory cost can be calculated as follows:
Total annual cost 5 Annual setup (ordering) cost 1 Annual holding cost 1 Annual product cost,
or
TC = D Q
S + Q 2
IP + PD (12-9)
where Q 5 Quantity ordered D 5 Annual demand in units S 5 Setup or ordering cost per order P 5 Price per unit I 5 Holding cost per unit per year expressed as a percent of price P
Note that holding cost is IP instead of H as seen in the regular EOQ model. Because the price of the item is a factor in annual holding cost, we do not assume that the holding cost is a constant when the price per unit changes for each quantity discount. Thus, it is common to express the holding cost as a percent ( I ) of unit price ( P ) when evaluating costs of quantity discount schedules.
The EOQ formula (12-1) is modified for the quantity discount problem as follows:
Q* = A
2DS IP
(12-10)
The solution procedure uses the concept of a feasible EOQ . An EOQ is feasible if it lies in the quantity range that leads to the same price P used to compute it in Equation (12-10). For ex- ample, suppose that D 5 5,200, S 5 $200, and I 5 28%. Using Table 12.2 and Equation (12-10), the EOQ for the $96 price equals 22(5,200)(200)>[(.28)(96)] = 278 units. Because 278 , 1,500 (the price-break quantity needed to receive the $96 price), the EOQ for the $96 price is not feasible . On the other hand, the EOQ for the $98 price equals 275 units. This amount is feasible because if 275 units were actually ordered, the firm would indeed receive the $98 purchase price.
Quantity discount
A reduced price for items
purchased in large quantities.
LO 12.6 Explain and use the quantity discount
model
TABLE 12.2 A Quantity Discount Schedule
PRICE RANGE QUANTITY ORDERED PRICE PER UNIT P
Initial price 1–119 $100
Discount price 1 120–1,499 $ 98
Discount price 2 1,500 and over $ 96
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Now we have to determine the quantity that will minimize the total annual inventory cost. Because there are a few discounts, this process involves two steps. In Step 1, we identify all possible order quantities that could be the best solution. In Step 2, we calculate the total cost of all possible best order quantities, and the least expensive order quantity is selected.
Solution Procedure
STEP 1: Starting with the lowest possible purchase price in a quantity discount schedule and working toward the highest price, keep calculating Q* from Equation (12-10) until the fi rst feasible EOQ is found. The fi rst feasible EOQ is a possible best order quan- tity, along with all price-break quantities for all lower prices.
STEP 2: Calculate the total annual cost TC using Equation (12-9) for each of the possible best order quantities determined in Step 1. Select the quantity that has the lowest total cost.
Note that no quantities need to be considered for any prices greater than the first feasible EOQ found in Step 1. This occurs because if an EOQ for a given price is feasible, then the EOQ for any higher price cannot lead to a lower cost ( TC is guaranteed to be higher).
Figure 12.7 provides a graphical illustration of Step 1 using the three price ranges from Table 12.2 . In that example, the EOQ for the lowest price is infeasible, but the EOQ for the second-lowest price is feasible. So the EOQ for the second-lowest price, along with the price-break quantity for the lowest price, are the possible best order quantities. Finally, the highest price (no discount) can be ignored because a feasible EOQ has already been found for a lower price.
Example 9 illustrates how the full solution procedure can be applied.
A n n u a l T
o ta
l C o st
550,000
540,000
530,000
520,053 517,155
510,000
500,000
120 1,500
Order Quantity
Not Feasible Possible Order Quantities
TC for Discount 2
TC for Discount 1
Initial Price Discount Price 1 Discount Price 2
TC for No Discount
Not Feasible
Feasible
Figure 12.7
EOQs and Possible Best Order
Quantities for the Quantity
Discount Problem with Three
Prices in Table 12.2
The solid black curves represent
the realized total annual setup plus
holding plus purchasing cost at the
applicable order quantities. The
black curve drops to the total cost
curve for the next discount level
when each price-break quantity is
reached.
Example 9 QUANTITY DISCOUNT MODEL Chris Beehner Electronics stocks toy remote control flying drones. Recently, the store has been offered a quantity discount schedule for these drones. This quantity schedule was shown in Table 12.2 . Furthermore, setup cost is $200 per order, annual demand is 5,200 units, and annual inventory carrying charge as a percent of cost, I , is 28%. What order quantity will minimize the total inventory cost?
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APPROACH c We will follow the two steps just outlined for the quantity discount model.
SOLUTION c First we calculate the Q * for the lowest possible price of $96, as we did earlier:
Q*$96 = A
2(5,200)($200) (.28)($96)
= 278 flying drones per order
Because 278 , 1,500, this EOQ is infeasible for the $96 price. So now we calculate Q * for the next-higher price of $98:
Q*$98 = A
2(5,200)($200) (.28)($98)
= 275 flying drones per order
Because 275 is between 120 and 1,499 units, this EOQ is feasible for the $98 price. Thus, the possible best order quantities are 275 (the first feasible EOQ) and 1,500 (the price-break quantity for the lower price of $96). We need not bother to compute Q * for the initial price of $100 because we found a feasible EOQ for a lower price.
Step 2 uses Equation (12-9) to compute the total cost for each of the possible best order quantities. This step is taken with the aid of Table 12.3 .
TABLE 12.3 Total Cost Computations for Chris Beehner Electronics
ORDER QUANTITY UNIT PRICE
ANNUAL ORDERING
COST ANNUAL
HOLDING COST ANNUAL
PRODUCT COST TOTAL ANNUAL
COST
275 $98 $3,782 $ 3,773 $509,600 $517,155
1,500 $96 $ 693 $20,160 $499,200 $520,053
Because the total annual cost for 275 units is lower, 275 units should be ordered. The costs for this exam- ple are shown in Figure 12.7 .
INSIGHT c Even though Beehner Electronics could save more than $10,000 in annual product costs, ordering 1,500 units (28.8% of annual demand) at a time would generate even more than that in increased holding costs. So in this example it is not in the store’s best interest to order enough to attain the lowest possible purchase price per unit. On the other hand, if the price-break quantity for the $96 had been 1,000 units rather than 1,500 units, then total annual costs would have been $513,680, which would have been cheaper than ordering 275 units at $98.
LEARNING EXERCISE c Resolve the problem with D 5 2,000, S 5 $5, I 5 50%, discount price 1 5 $99, and discount price 2 5 $98. [Answer: only 20 units should be ordered each time, which is the EOQ at the $100 price.]
RELATED PROBLEMS c 12.21–12.28 (12.38–12.40 are available in MyOMLab)
EXCEL OM Data file Ch12Ex9.xls can be found in MyOMLab.
In this section we have studied the most popular form of single-purchase quantity discount called the all-units discount . In practice, quantity discounts appear in a variety of forms. For example, i ncremental quantity discounts apply only to those units purchased beyond the price- break quantities rather than to all units. Fixed fees , such as a fixed shipping and processing cost for a catalog order or a $5,000 tooling setup cost for any order placed with a manufac- turer, encourage buyers to purchase more units at a time. Some discounts are aggregated over items or time. Item aggregation bases price breaks on total units or dollars purchased. Time aggregation applies to total items or dollars spent over a specific time period such as one year. Truckload discounts , buy-one-get-one-free offers , and one-time-only sales also represent types of quantity discounts in that they provide price incentives for buyers to purchase more units at one time. Most purchasing managers deal with some form of quantity discounts on a regular basis.
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Probabilistic Models and Safety Stock All the inventory models we have discussed so far make the assumption that demand for a product is constant and certain. We now relax this assumption. The following inventory mod- els apply when product demand is not known but can be specified by means of a probability distribution. These types of models are called probabilistic models . Probabilistic models are a real-world adjustment because demand and lead time won’t always be known and constant.
An important concern of management is maintaining an adequate service level in the face of uncertain demand. The service level is the complement of the probability of a stockout. For instance, if the probability of a stockout is 0.05, then the service level is .95. Uncertain demand raises the possibility of a stockout. One method of reducing stockouts is to hold extra units in inventory. As we noted earlier such inventory is referred to as safety stock. Safety stock in- volves adding a number of units as a buffer to the reorder point. As you recall:
Reorder point = ROP = d * L
where d 5 Daily demand L 5 Order lead time, or number of working days it takes to deliver an order
The inclusion of safety stock ( ss ) changed the expression to:
ROP = d * L + ss (12-11)
The amount of safety stock maintained depends on the cost of incurring a stockout and the cost of holding the extra inventory. Annual stockout cost is computed as follows:
Annual stockout costs = The sum of the units short for each demand level
* The probability of that demand level * The stockout cost>unit * The number of orders per year
(12-12)
Example 10 illustrates this concept.
Probabilistic model
A statistical model applicable
when product demand or any
other variable is not known but
can be specified by means of a
probability distribution.
Service level
The probability that demand will
not be greater than supply during
lead time. It is the complement of
the probability of a stockout.
Example 10 DETERMINING SAFETY STOCK WITH PROBABILISTIC DEMAND AND CONSTANT LEAD TIME David Rivera Optical has determined that its reorder point for eyeglass frames is 50 (d * L) units. Its carrying cost per frame per year is $5, and stockout (or lost sale) cost is $40 per frame. The store has experienced the following probability distribution for inventory demand during the lead time (reorder period). The optimum number of orders per year is six.
NUMBER OF UNITS PROBABILITY
30 .2
40 .2
ROP S 50 .3
60 .2
70 .1
1.0
How much safety stock should David Rivera keep on hand?
APPROACH c The objective is to find the amount of safety stock that minimizes the sum of the addi- tional inventory holding costs and stockout costs. The annual holding cost is simply the holding cost per unit multiplied by the units added to the ROP. For example, a safety stock of 20 frames, which implies that the new ROP, with safety stock, is 70 (5 50 1 20), raises the annual carrying cost by $5(20) 5 $100.
However, computing annual stockout cost is more interesting. For any level of safety stock, stockout cost is the expected cost of stocking out. We can compute it, as in Equation (12-12), by multiplying the number of frames short (Demand – ROP) by the probability of demand at that level, by the stockout cost, by the number of times per year the stockout can occur (which in our case is the number of orders per year). Then we add stockout costs for each possible stockout level for a given ROP. 4
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SOLUTION c We begin by looking at zero safety stock. For this safety stock, a shortage of 10 frames will occur if demand is 60, and a shortage of 20 frames will occur if the demand is 70. Thus the stockout costs for zero safety stock are:
(10 frames short) (.2) (+40 per stockout) (6 possible stockouts per year)
+ (20 frames short) (.1) (+40)(6) = +960
The following table summarizes the total costs for each of the three alternatives:
SAFETY STOCK
ADDITIONAL HOLDING COST STOCKOUT COST
TOTAL COST
20 (20) ($5) 5 $100 $ 0 $100
10 (10) ($5) 5 $ 50 (10) (.1) ($40) (6) 5 $240 $290
0 $ 0 (10) (.2) ($40) (6) 1 (20) (.1) ($40) (6) 5 $960 $960
The safety stock with the lowest total cost is 20 frames. Therefore, this safety stock changes the reorder point to 50 1 20 5 70 frames.
INSIGHT c The optical company now knows that a safety stock of 20 frames will be the most economi- cal decision.
LEARNING EXERCISE c David Rivera’s holding cost per frame is now estimated to be $20, while the stockout cost is $30 per frame. Does the reorder point change? [Answer: Safety stock 5 10 now, with a total cost of $380, which is the lowest of the three. ROP 5 60 frames.]
RELATED PROBLEMS c 12.43, 12.44, 12.45
When it is difficult or impossible to determine the cost of being out of stock, a manager may decide to follow a policy of keeping enough safety stock on hand to meet a prescribed customer service level. For instance, Figure 12.8 shows the use of safety stock when demand (for hospital resuscitation kits) is probabilistic. We see that the safety stock in Figure 12.8 is 16.5 units, and the reorder point is also increased by 16.5.
The manager may want to define the service level as meeting 95% of the demand (or, con- versely, having stockouts only 5% of the time). Assuming that demand during lead time (the reorder period) follows a normal curve, only the mean and standard deviation are needed to define the inventory requirements for any given service level. Sales data are usually adequate for computing the mean and standard deviation. Example 11 uses a normal curve with a known mean (m) and standard deviation (s) to determine the reorder point and safety stock necessary
ROP = 350 + safety stock of 16.5 = 366.5
Expected demand during lead time (350 kits)
Safety stock
Normal distribution probability of demand during lead time
Mean demand during lead time
Maximum demand satisfied during lead time
Minimum demand during lead time
ROP (reorder point)
In ve
n to
ry le
ve l
Lead time
Time 0
Receive order
Place order
16.5 units
Risk of stockout
Figure 12.8
Probabilistic Demand for
a Hospital Item
Expected number of kits needed
during lead time is 350, but for
a 95% service level, the reorder
point should be raised to 366.5.
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for a 95% service level. We use the following formula:
ROP = Expected demand during lead time + ZsdLT (12-13)
where Z 5 Number of standard deviations s dLT 5 Standard deviation of demand during lead time
Example 11 SAFETY STOCK WITH PROBABILISTIC DEMAND Memphis Regional Hospital stocks a “code blue” resuscitation kit that has a normally distributed demand during the reorder period. The mean (average) demand during the reorder period is 350 kits, and the standard deviation is 10 kits. The hospital administrator wants to follow a policy that results in stockouts only 5% of the time.
(a) What is the appropriate value of Z ? (b) How much safety stock should the hospital maintain? (c) What reorder point should be used?
APPROACH c The hospital determines how much inventory is needed to meet the demand 95% of the time. The figure in this example may help you visualize the approach. The data are as follows:
m = Mean demand = 350 kits sdLT = Standard deviation of demand during lead time = 10 kits Z = Number of standard normal deviations
Mean demand
350
ROP = ? kits Quantity
0 z
Safety stock
Number of standard deviations
Risk of a stockout (5% of area of normal curve)
No stockout in 95% of the
periods in the long run
SOLUTION c
a) We use the properties of a standardized normal curve to get a Z -value for an area under the normal curve of .95 (or 1 - .05). Using a normal table (see Appendix I ) or the Excel formula 5 NORMSINV(.95) , we find a Z -value of 1.645 standard deviations from the mean.
b) Because: Safety stock = x - m
and: Z = x - m sdLT
then: Safety stock = ZsdLT (12-14)
Solving for safety stock, as in Equation (12-14), gives: Safety stock = 1.645(10) = 16.5 kits
This is the situation illustrated in Figure 12.8 .
c) The reorder point is: ROP = Expected demand during lead time + Safety stock = 350 kits + 16.5 kits of safety stock = 366.5, or 367 kits
INSIGHT c The cost of the inventory policy increases dramatically (exponentially) with an increase in service levels.
LEARNING EXERCISE c What policy results in stockouts 10% of the time? [Answer: Z 5 1.28; safety stock 5 12.8; ROP 5 363 kits.]
RELATED PROBLEMS c 12.41, 12.42, 12.49 (12.50 is available in MyOMLab)
STUDENT TIP Recall that the service level is
1 minus the risk of a stockout.
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Other Probabilistic Models Equations (12-13) and (12-14) assume that both an estimate of expected demand during lead times and its standard deviation are available. When data on lead time demand are not avail- able, the preceding formulas cannot be applied. However, three other models are available. We need to determine which model to use for three situations:
1. Demand is variable and lead time is constant 2. Lead time is variable and demand is constant 3. Both demand and lead time are variable
All three models assume that demand and lead time are independent variables. Note that our examples use days, but weeks can also be used. Let us examine these three situations sepa- rately, because a different formula for the ROP is needed for each.
Demand Is Variable and Lead Time Is Constant (See Example 12 .) When only the demand is variable , then: 5
ROP = (Average daily demand * Lead time in days) + ZsdLT (12-15)
where s dLT 5 Standard deviation of demand during lead time 5 sd2Lead time and s d 5 Standard deviation of demand per day
LO 12.7 Understand service levels and
probabilistic inventory
models
Example 12 ROP FOR VARIABLE DEMAND AND CONSTANT LEAD TIME The average daily demand for Lenovo laptop computers at a Circuit Town store is 15, with a standard deviation of 5 units. The lead time is constant at 2 days. Find the reorder point if management wants a 90% service level (i.e., risk stockouts only 10% of the time). How much of this is safety stock?
APPROACH c Apply Equation (12-15) to the following data:
Average daily demand (normally distributed) 5 15
Lead time in days (constant) 5 2
Standard deviation of daily demand 5 s d 5 5
Service level 5 90%
SOLUTION c From the normal table ( Appendix I ) or the Excel formula = NORMSINV(.90) , we derive a Z -value for 90% of 1.28. Then:
ROP = (15 units * 2 days) + Zsd2Lead time = 30 + 1.28(5) (22) = 30 + 1.28(5) (1.41) = 30 + 9.02 = 39.02 _ 39
Thus, safety stock is about 9 Lenovo computers.
INSIGHT c The value of Z depends on the manager’s stockout risk level. The smaller the risk, the higher the Z .
LEARNING EXERCISE c If the Circuit Town manager wants a 95% service level, what is the new ROP? [Answer: ROP 5 41.63, or 42.]
RELATED PROBLEM c 12.46
Lead Time Is Variable and Demand Is Constant When the demand is constant and only the lead time is variable , then:
ROP = (Daily demand * Average lead time in days) + Z * Daily demand * sLT (12-16)
where s LT 5 Standard deviation of lead time in days
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Example 13 ROP FOR CONSTANT DEMAND AND VARIABLE LEAD TIME The Circuit Town store in Example 12 sells about 10 digital cameras a day (almost a constant quantity). Lead time for camera delivery is normally distributed with a mean time of 6 days and a standard devia- tion of 1 day. A 98% service level is set. Find the ROP.
APPROACH c Apply Equation (12-16) to the following data:
Daily demand 5 10
Average lead time 5 6 days
Standard deviation of lead time 5 sLT 5 1 day
Service level 5 98%, so Z (from Appendix I or the Excel formula = NORMSINV(.98)) 5 2.055
SOLUTION c From the equation we get:
ROP = (10 units * 6 days) + 2.055(10 units) (1) = 60 + 20.55 = 80.55
The reorder point is about 81 cameras.
INSIGHT c Note how the very high service level of 98% drives the ROP up.
LEARNING EXERCISE c If a 90% service level is applied, what does the ROP drop to? [Answer: ROP 5 60 1 (1.28) (10) (1) 5 60 1 12.8 5 72.8 because the Z -value is only 1.28.]
RELATED PROBLEM c 12.47
Both Demand and Lead Time Are Variable When both the demand and lead time are variable, the formula for reorder point becomes more complex: 6
ROP = (Average daily demand * Average lead time in days) + ZsdLT (12-17)
where
sd = Standard deviation of demand per day sLT = Standard deviation of lead time in days
and sdLT = 2(Average lead time * sd 2) + (Average daily demand)2sLT 2
Example 14 ROP FOR VARIABLE DEMAND AND VARIABLE LEAD TIME The Circuit Town store’s most popular item is six-packs of 9-volt batteries. About 150 packs are sold per day, following a normal distribution with a standard deviation of 16 packs. Batteries are ordered from an out-of-state distributor; lead time is normally distributed with an average of 5 days and a standard deviation of 1 day. To maintain a 95% service level, what ROP is appropriate?
APPROACH c Determine a quantity at which to reorder by applying Equation (12-17) to the following data:
Average daily demand 5 150 packs Standard deviation of demand 5 sd 5 16 packs Average lead time 5 5 days Standard deviation of lead time 5 sLT 5 1 day Service level 5 95%, so Z 51.645 (from Appendix I or the Excel formula = NORMSINV(.95))
SOLUTION c From the equation we compute:
ROP = (150 packs * 5 days) + 1.645 sdLT
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where sdLT = 2(5 days * 162) + (1502 * 12)
= 2(5 * 256) + (22,500 * 1)
= 21,280 + 22,500 = 223,780 _ 154
So ROP 5 (150 3 5) 1 1.645(154) > 750 1 253 5 1,003 packs
INSIGHT c When both demand and lead time are variable, the formula looks quite complex. But it is just the result of squaring the standard deviations in Equations (12-15) and (12-16) to get their variances, then summing them, and finally taking the square root.
LEARNING EXERCISE c For an 80% service level, what is the ROP? [Answer: Z 5 .84 and ROP 5 879 packs.]
RELATED PROBLEM c 12.48
Single-Period Model A single-period inventory model describes a situation in which one order is placed for a product. At the end of the sales period, any remaining product has little or no value. This is a typical problem for Christmas trees, seasonal goods, bakery goods, newspapers, and magazines. (Indeed, this inventory issue is often called the “newsstand problem.”) In other words, even though items at a newsstand are ordered weekly or daily, they cannot be held over and used as inventory in the next sales period. So our decision is how much to order at the beginning of the period.
Because the exact demand for such seasonal products is never known, we consider a prob- ability distribution related to demand. If the normal distribution is assumed, and we stocked and sold an average (mean) of 100 Christmas trees each season, then there is a 50% chance we would stock out and a 50% chance we would have trees left over. To determine the optimal stocking policy for trees before the season begins, we also need to know the standard deviation and consider these two marginal costs:
Cs = Cost of shortage (we underestimated) = Sales price per unit - Cost per unit Co = Cost of overage (we overestimated) = Cost per unit - Salvage value per unit
(if there is any)
The service level, that is, the probability of not stocking out, is set at:
Service level = Cs
Cs + Co (12-18)
Therefore, we should consider increasing our order quantity until the service level is equal to or more than the ratio of [ C s Y( C s 1 C o )].
This model, illustrated in Example 15 , is used in many service industries, from hotels to airlines to bakeries to clothing retailers.
Single-period inventory model
A system for ordering items that
have little or no value at the end of
a sales period (perishables).
Example 15 SINGLE-PERIOD INVENTORY DECISION Chris Ellis’s newsstand, just outside the Smithsonian subway station in Washington, DC, usually sells 120 copies of the Washington Post each day. Chris believes the sale of the Post is normally distributed, with a standard deviation of 15 papers. He pays 70 cents for each paper, which sells for $1.25. The Post gives him a 30-cent credit for each unsold paper. He wants to determine how many papers he should order each day and the stockout risk for that quantity.
APPROACH c Chris’s data are as follows:
Cs = cost of shortage = +1.25 - +.70 = +.55 Co = cost of overage = +.70 - +.30 (salvage value) = +.40
Chris will apply Equation (12-18) and the normal table, using m 5 120 and s 5 15.
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Fixed-quantity ( Q ) system
An ordering system with the same
order amount each time.
SOLUTION c a) Service level 5
Cs Cs + Co
= .55
.55 + .40 =
.55
.95 = .579
b) Chris needs to find the Z score for his normal distribution that yields a probability of .579.
o = 120
u = 15 copies
Service level
57.9%
Optimal stocking level
So 57.9% of the area under the normal curve must be to the left of the optimal stocking level.
c) Using Appendix I or the Excel formula = NORMSINV(.578) , for an area of .578, the Z value > .195.
Then, the optimal stocking level = 120 copies + (.195) (s) = 120 + (.195) (15) = 120 + 3 = 123 papers
The stockout risk if Chris orders 123 copies of the Post each day is 1 2 Service level 5 1 2 .578 5 .422 5 42.2%.
INSIGHT c If the service level is ever under .50, Chris should order fewer than 120 copies per day.
LEARNING EXERCISE c How does Chris’s decision change if the Post changes its policy and offers no credit for unsold papers, a policy many publishers are adopting?
[Answer: Service level 5 .44, Z 5 2.15. Therefore, stock 120 1 (2.15)(15) 5 117.75, or 118 papers.]
RELATED PROBLEMS c 12.51, 12.52, 12.53
Fixed-Period ( P ) Systems The inventory models that we have considered so far are fixed-quantity, or Q , systems . That is, the same fixed amount is added to inventory every time an order for an item is placed. We saw that orders are event triggered. When inventory decreases to the reorder point (ROP), a new order for Q units is placed.
To use the fixed-quantity model, inventory must be continuously monitored. 7 This requires a perpetual inventory system . Every time an item is added to or withdrawn from inventory, records must be updated to determine whether the ROP has been reached. In a fixed-period system (also called a periodic review, or P system ), on the other hand, inventory is ordered at the end of a given period. Then, and only then, is on-hand inventory counted. Only the amount necessary to bring total inventory up to a prespecified target level ( T ) is ordered. Figure 12.9 illustrates this concept.
Fixed-period systems have several of the same assumptions as the basic EOQ fixed- quantity system: ◆ The only relevant costs are the
ordering and holding costs. ◆ Lead times are known and constant. ◆ Items are independent of one another. The downward-sloped lines in Figure 12.9 again represent on-hand inventory levels. But now, when the time between orders ( P ) passes, we place an order to raise inventory up to the target quantity ( T ).
Perpetual inventory system
A system that keeps track of each
withdrawal or addition to inven-
tory continuously, so records are
always current.
Fixed-period ( P ) system
A system in which inventory orders
are made at regular time intervals.
P
Q1
Q2
Q 3
Q4
P
P
O n -h
a n d in
ve n to
ry
Time
Target quantity (T ) Figure 12.9
Inventory Level in a
Fixed-Period ( P ) System
Various amounts ( Q 1 , Q
2 , Q
3 ,
etc.) are ordered at regular time
intervals ( P ) based on the quantity
necessary to bring inventory up to
the target quantity ( T ).
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The amount ordered during the first period may be Q 1 , the second period Q 2 , and so on. The Q i value is the difference between current on-hand inventory and the target inventory level.
The advantage of the fixed-period system is that there is no physical count of inventory items after an item is withdrawn—this occurs only when the time for the next review comes up. This procedure is also convenient administratively.
A fixed-period (P) system is appropriate when vendors make routine (i.e., at fixed-time inter- val) visits to customers to take fresh orders or when purchasers want to combine orders to save ordering and transportation costs (therefore, they will have the same review period for similar in- ventory items). For example, a vending machine company may come to refill its machines every Tuesday. This is also the case at Anheuser-Busch, whose sales reps may visit a store every 5 days.
The disadvantage of the P system is that because there is no tally of inventory during the review period, there is the possibility of a stockout during this time. This scenario is possible if a large order draws the inventory level down to zero right after an order is placed. Therefore, a higher level of safety stock (as compared to a fixed-quantity system) needs to be maintained to provide protection against stockout during both the time between reviews and the lead time.
STUDENT TIP A fixed-period model potentially
orders a different quantity each time.
Summary Inventory represents a major investment for many firms. This investment is often larger than it should be because firms find it easier to have “just-in-case” inventory rather than “just-in-time” inventory. Inventories are of four types:
1. Raw material and purchased components 2. Work-in-process 3. Maintenance, repair, and operating (MRO)
4. Finished goods
In this chapter, we discussed independent inventory, ABC analysis, record accuracy, cycle counting, and inven- tory models used to control independent demands. The EOQ model, production order quantity model, and quantity discount model can all be solved using Excel, Excel OM, or POM for Windows software.
Key Terms
Raw material inventory (p. 490 ) Work-in-process (WIP) inventory (p. 490 ) Maintenance/repair/operating (MRO)
inventory (p. 490 ) Finished-goods inventory (p. 491 ) ABC analysis (p. 491 ) Cycle counting (p. 493 ) Shrinkage (p. 494 ) Pilferage (p. 494 )
Holding cost (p. 495 ) Ordering cost (p. 495 ) Setup cost (p. 496 ) Setup time (p. 496 ) Economic order quantity (EOQ)
model (p. 496 ) Robust (p. 500 ) Lead time (p. 501 ) Reorder point (ROP) (p. 501 )
Safety stock ( ss ) (p. 501 ) Production order quantity model (p. 502 ) Quantity discount (p. 505 ) Probabilistic model (p. 508 ) Service level (p. 508 ) Single-period inventory model (p. 513 ) Fixed-quantity ( Q ) system (p. 514 ) Perpetual inventory system (p. 514 ) Fixed-period ( P ) system (p. 514 )
Ethical Dilemma Wayne Hills Hospital in tiny Wayne, Nebraska, faces a problem common to large, urban hospitals as well as to small, remote ones like itself. That problem is deciding how much of each type of whole blood to keep in stock. Because blood is expensive and has a limited shelf life (up to 5 weeks under 1–6°C refrigeration), Wayne Hills naturally wants to keep its stock as low as possible. Unfortunately, past disasters such as a major tornado and a train wreck demonstrated that lives would be lost when not enough blood was available to handle massive needs. The hospital
administrator wants to set an 85% service level based on demand over the past decade. Discuss the implications of this decision. What is the hospital’s responsibility with regard to stocking lifesaving medicines with short shelf lives? How would you set the inventory level for a commodity such as blood?
G in
a sa
n d e rs
/F o to
lia
Discussion Questions
1. Describe the four types of inventory. 2. With the advent of low-cost computing, do you see alterna-
tives to the popular ABC classifications? 3. What is the purpose of the ABC classification system? 4. Identify and explain the types of costs that are involved in an
inventory system.
5. Explain the major assumptions of the basic EOQ model. 6. What is the relationship of the economic order quantity to
demand? To the holding cost? To the setup cost? 7. Explain why it is not necessary to include product cost (price
or price times quantity) in the EOQ model, but the quantity discount model requires this information.
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8. What are the advantages of cycle counting? 9. What impact does a decrease in setup time have on EOQ?
10. When quantity discounts are offered, why is it not necessary to check discount points that are below the EOQ or points above the EOQ that are not discount points?
11. What is meant by “service level”? 12. Explain the following: All things being equal, the production
order quantity will be larger than the economic order quantity.
13. Describe the difference between a fixed-quantity ( Q ) and a fixed-period ( P ) inventory system.
14. Explain what is meant by the expression “robust model.” Specifically, what would you tell a manager who exclaimed, “Uh-oh, we’re in trouble! The calculated EOQ is wrong; actual demand is 10% greater than estimated.”
15. What is “safety stock”? What does safety stock provide safety against?
16. When demand is not constant, the reorder point is a function of what four parameters?
17. How are inventory levels monitored in retail stores? 18. State a major advantage, and a major disadvantage, of a
fixed-period ( P ) system.
This section presents three ways to solve inventory problems with computer software. First, you can create your own Excel spread- sheets. Second, you can use the Excel OM software that comes free with this text. Third, POM for Windows, also free with this text, can solve all problems marked with a P .
CREATING YOUR OWN EXCEL SPREADSHEETS Program 12.1 illustrates how you can make an Excel model to solve Example 8 , which is a production order quantity model.
=B5/B9
=SQRT((2*B5*B6)/(B7*(1-B10/B8)))
=B12*(1-B10/B8)
=B16/2
=B5/B12
=B9/B18
=ROUND(B18*B6,2)
=ROUND(B17*B7,2)
=B21+B22
Program 12.1
Using Excel for a Production
Model, with Data from
Example 8
Program 12.2 illustrates how you can make an Excel model to solve Example 15, which is a single-period inventory model.
=B5-B6
=B6-B7
=B11/(B11+B12)
=NORMSINV(B13)
=B8+B14*B9
Program 12.2
Using Excel for a Single-
Period Inventory Model,
with Data from Example 15
Using Software to Solve Inventory Problems
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The cumulative dollar volumes in column G make sense only after the items have been sorted by dollar volume. Either use the copy and sort button, or, to sort by hand, highlight cells A7 through E17 and then use the Data and Sort commands.
Calculate the total dollar volume for each item. = B8*C8 Calculate the percentage of
the grand total dollar volume for each item. = E8/E18
= SUM(E8:E17)
= SUM($F$8:F8)
Enter the item name or number, its sales volume, and the unit cost in columns A, B, and C.
Program 12.3
Using Excel OM for an ABC Analysis, with Data from Example 1
X USING EXCEL OM Excel OM allows us to easily model inventory problems ranging from ABC analysis, to the basic EOQ model, to the production model, to quantity discount situations.
Program 12.3 shows the input data, selected formulas, and results for an ABC analysis, using data from Example 1 . After the data are entered, we use the Data and Sort Excel commands to rank the items from largest to smallest dollar volumes.
P USING POM FOR WINDOWS The POM for Windows Inventory module can also solve the entire EOQ family of problems. Please refer to Appendix IV for further details.
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM 12.1 David Alexander has compiled the following table of six items in inventory at Angelo Products, along with the unit cost and the annual demand in units:
IDENTIFICATION CODE UNIT COST ($) ANNUAL DEMAND
(UNITS)
XX1 5.84 1,200
B66 5.40 1,110
3CPO 1.12 896
33CP 74.54 1,104
R2D2 2.00 1,110
RMS 2.08 961
Use ABC analysis to determine which item(s) should be care- fully controlled using a quantitative inventory technique and which item(s) should not be closely controlled.
SOLUTION The item that needs strict control is 33CP, so it is an A item. Items that do not need to be strictly controlled are 3CPO, R2D2, and RMS; these are C items. The B items will be XX1 and B66.
CODE ANNUAL DOLLAR VOLUME 5 UNIT COST 3 DEMAND
XX1 $ 7,008.00
B66 $ 5,994.00
3CPO $ 1,003.52
33CP $82,292.16
R2D2 $ 2,220.00
RMS $ 1,998.88
Total cost 5 $100,516.56 70% of total cost 5 $70,347.92
SOLVED PROBLEM 12.2 The Warren W. Fisher Computer Corporation purchases 8,000 transistors each year as components in minicomputers. The unit cost of each transistor is $10, and the cost of carrying one tran- sistor in inventory for a year is $3. Ordering cost is $30 per order.
What are (a) the optimal order quantity, (b) the expected number of orders placed each year, and (c) the expected time between orders? Assume that Fisher operates on a 200-day working year.
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SOLUTION
a) Q* = A
2DS H
= A
2(8,000) (30) 3
= 400 units
b) N = D Q*
= 8,000 400
= 20 orders
c) Time between orders = T = Number of working days
N =
200 20
= 10 working days
With 20 orders placed each year, an order for 400 transistors is placed every 10 working days.
SOLVED PROBLEM 12.3 Annual demand for notebook binders at Meyer’s Stationery Shop is 10,000 units. Brad Meyer operates his business 300 days per year
and finds that deliveries from his supplier generally take 5 working days. Calculate the reorder point for the notebook binders.
SOLUTION L = 5 days
d = 10,000
300 = 33.3 units per day
ROP = d * L = (33.3 units per day) (5 days) = 166.7 units Thus, Brad should reorder when his stock reaches 167 units.
SOLVED PROBLEM 12.4 Leonard Presby, Inc., has an annual demand rate of 1,000 units but can produce at an average production rate of 2,000
units. Setup cost is $10; carrying cost is $1. What is the optimal number of units to be produced each time?
SOLUTION
Q*p =
H
2DS
H¢1 - Annual demand rate Annual production rate
≤ = A
2(1,000) (10) 131 - (1,000>2,000)4
= A
20,000 1>2
= 240,000 = 200 units
SOLVED PROBLEM 12.5 Whole Nature Foods sells a gluten-free product for which the annual demand is 5,000 boxes. At the moment, it is paying $6.40 for each box; carrying cost is 25% of the unit cost; order- ing costs are $25. A new supplier has offered to sell the same item for $6.00 if Whole Nature Foods buys at least 3,000 boxes per order. Should the firm stick with the old supplier, or take advantage of the new quantity discount?
SOLUTION Step 1, under the lowest possible price of $6.00 per box:
Economic order quantity, using Equation (12-10):
Q*$6.00 = A 2(5,000) (25) (0.25) (6.00)
= 408.25, or 408 boxes
Because 408 , 3,000, this EOQ is infeasible for the $6.00 price. So now we calculate Q * for the next-higher price of $6.40, which equals 395 boxes (and is feasible). Thus, the best possible order quantities are 395 (the first feasible EOQ) and 3,000 (the price-break quantity for the lower price of $6.00).
Step 2 uses Equation (12-9) to compute the total cost for both of the possible best order quantities:
TC395 = 5,000 395
($25) + 395 2
(0.25)($6.40) + $6.40(5,000)
= $316 + $316 + $32,000 = $32,632
And under the quantity discount price of $6.00 per box:
TC3,000 = 5,000 3,000
($25) + 3,000
2 (0.25)($6.00) + $6.00(5,000)
= $42 + $2,250 + $30,000 = $32,292
Therefore, the new supplier with which Whole Nature Foods would incur a total cost of $32,292 is preferable, but not by a large amount. If buying 3,000 boxes at a time raises problems of storage or freshness, the company may very well wish to stay with the current supplier.
SOLVED PROBLEM 12.6 Children’s art sets are ordered once each year by Ashok Kumar, Inc., and the reorder point, without safety stock ( dL ), is 100 art sets. Inventory carrying cost is $10 per set per year, and the cost of a stockout is $50 per set per year. Given the fol- lowing demand probabilities during the lead time, how much safety stock should be carried?
DEMAND DURING LEAD TIME PROBABILITY
0 .1 50 .2
ROP S 100 .4 150 .2 200 .1
1.0
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SOLUTION
INCREMENTAL COSTS
SAFETY STOCK CARRYING COST STOCKOUT COST TOTAL COST
0 0 50 3 (50 3 0.2 1 100 3 0.1) 5 1,000 $1,000
50 50 3 10 5 500 50 3 (0.1 3 50) 5 250 750
100 100 3 10 5 1,000 0 1,000
The safety stock that minimizes total incremental cost is 50 sets. The reorder point then becomes 100 sets 1 50 sets, or 150 sets.
SOLVED PROBLEM 12.7 What safety stock should Ron Satterfield Corporation maintain if mean sales are 80 during the reorder period, the
standard deviation is 7, and Ron can tolerate stockouts 10% of the time?
SOLUTION
o = 80 udLT = 7
10% area under the normal curve
Safety stock
From Appendix I , Z at an area of .9 (or 1 2 .10) 5 1.28, and Equation (12-14): Safety stock = ZsdLT = 1.28(7) = 8.96 units, or 9 units
SOLVED PROBLEM 12.8 The daily demand for 52” flat-screen TVs at Sarah’s Discount Emporium is normally distributed, with an average of 5 and a standard deviation of 2 units. The lead time for receiving a ship-
SOLUTION The ROP for this variable demand and constant lead time model uses Equation (12-15):
ROP = (Average daily demand * Lead time in days) + ZsdLT
where sdLT = sd2Lead time So, with Z 5 1.645,
ROP = (5 * 10) + 1.645(2)210 = 50 + 10.4 = 60.4 _ 60 TVs, or rounded up to 61 TVs
The safety stock is 10.4, which can be rounded up to 11 TVs.
ment of new TVs is 10 days and is fairly constant. Determine the reorder point and safety stock for a 95% service level.
SOLVED PROBLEM 12.9 The demand at Arnold Palmer Hospital for a special- ized surgery pack is 60 per week, virtually every week. The lead time from McKesson, its main supplier, is normally
distributed, with a mean of 6 weeks for this product and a standard deviation of 2 weeks. A 90% weekly service level is desired. Find the ROP.
SOLUTION Here the demand is constant and lead time is variable, with data given in weeks, not days. We apply Equation (12-16):
ROP = (Weekly demand * Average lead time in weeks) + Z (Weekly demand) sLT where sLT = standard deviation of lead time in weeks = 2
So, with Z 5 1.28, for a 90% service level: ROP = (60 * 6) + 1.28 (60) (2) = 360 + 153.6 = 513.6 _ 514 surgery packs
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Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems 12.1–12.6 relate to Managing Inventory
• • 12.1 L. Houts Plastics is a large manufacturer of injection- molded plastics in North Carolina. An investigation of the com- pany’s manufacturing facility in Charlotte yields the information presented in the table below. How would the plant classify these items according to an ABC classification system? PX
L. Houts Plastics’ Charlotte Inventory Levels
ITEM CODE # AVERAGE INVENTORY (UNITS) VALUE ($/UNIT)
1289 400 3.75
2347 300 4.00
2349 120 2.50
2363 75 1.50
2394 60 1.75
2395 30 2.00
6782 20 1.15
7844 12 2.05
8210 8 1.80
8310 7 2.00
9111 6 3.00
• • 12.2 Boreki Enterprises has the following 10 items in inventory. Theodore Boreki asks you, a recent OM graduate, to divide these items into ABC classifications.
ITEM ANNUAL DEMAND COST/UNIT
A2 3,000 $ 50
B8 4,000 12
C7 1,500 45
D1 6,000 10
E9 1,000 20
F3 500 500
G2 300 1,500
H2 600 20
I5 1,750 10
J8 2,500 5
a) Develop an ABC classification system for the 10 items. b) How can Boreki use this information? c) Boreki reviews the classification and then places item A2 into
the A category. Why might he do so? PX
• • 12.3 Jean-Marie Bourjolly’s restaurant has the following inventory items that it orders on a weekly basis:
INVENTORY ITEM $ VALUE/CASE # ORDERED/WEEK
Ribeye steak 135 3
Lobster tail 245 3
Pasta 23 12
Salt 3 2
Napkins 12 2
Tomato sauce 23 11
French fries 43 32
INVENTORY ITEM $ VALUE/CASE # ORDERED/WEEK
Pepper 3 3
Garlic powder 11 3
Trash can liners 12 3
Table cloths 32 5
Fish fi lets 143 10
Prime rib roasts 166 6
Oil 28 2
Lettuce (case) 35 24
Chickens 75 14
Order pads 12 2
Eggs (case) 22 7
Bacon 56 5
Sugar 4 2
a) Which is the most expensive item, using annual dollar volume? b) Which are C items? c) What is the annual dollar volume for all 20 items? PX
• 12.4 Lindsay Electronics, a small manufacturer of elec- tronic research equipment, has approximately 7,000 items in its inventory and has hired Joan Blasco-Paul to manage its inven- tory. Joan has determined that 10% of the items in inventory are A items, 35% are B items, and 55% are C items. She would like to set up a system in which all A items are counted monthly (every 20 working days), all B items are counted quarterly (every 60 work- ing days), and all C items are counted semiannually (every 120 working days). How many items need to be counted each day?
Additional problems 12.5–12.6 are available in MyOMLab.
Problems 12.7–12.40 relate to Inventory Models for Independent Demand
• 12.7 William Beville’s computer training school, in Richmond, stocks workbooks with the following characteristics:
Demand D = 19,500 units>year Ordering cost S = +25>order Holding cost H = +4>unit>year
a) Calculate the EOQ for the workbooks. b) What are the annual holding costs for the workbooks? c) What are the annual ordering costs? PX
• 12.8 If D 5 8,000 per month, S 5 $45 per order, and H 5 $2 per unit per month, a) What is the economic order quantity? b) How does your answer change if the holding cost doubles? c) What if the holding cost drops in half? PX
• • 12.9 Henry Crouch’s law office has traditionally ordered ink refills 60 units at a time. The firm estimates that carrying cost is 40% of the $10 unit cost and that annual demand is about 240 units per year. The assumptions of the basic EOQ model are thought to apply. a) For what value of ordering cost would its action be optimal? b) If the true ordering cost turns out to be much greater than your
answer to (a), what is the impact on the firm’s ordering policy?
• 12.10 Matthew Liotine’s Dream Store sells beds and assorted supplies. His best-selling bed has an annual demand of 400 units. Ordering cost is $40; holding cost is $5 per unit per year. cont’d
(cont’d)
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a) To minimize the total cost, how many units should be ordered each time an order is placed?
b) If the holding cost per unit was $6 instead of $5, what would be the optimal order quantity? PX
• 12.11 Southeastern Bell stocks a certain switch con- nector at its central warehouse for supplying field service offices. The yearly demand for these connectors is 15,000 units. Southeastern estimates its annual holding cost for this item to be $25 per unit. The cost to place and process an order from the supplier is $75. The company operates 300 days per year, and the lead time to receive an order from the supplier is 2 working days. a) Find the economic order quantity. b) Find the annual holding costs. c) Find the annual ordering costs. d) What is the reorder point? PX
• 12.12 Lead time for one of your fastest-moving products is 21 days. Demand during this period averages 100 units per day. a) What would be an appropriate reorder point? b) How does your answer change if demand during lead time
doubles? c) How does your answer change if demand during lead time
drops in half?
• 12.13 Annual demand for the notebook binders at Duncan’s Stationery Shop is 10,000 units. Dana Duncan operates her busi- ness 300 days per year and finds that deliveries from her supplier generally take 5 working days. a) Calculate the reorder point for the notebook binders that she
stocks. b) Why is this number important to Duncan?
• • 12.14 Thomas Kratzer is the purchasing manager for the headquarters of a large insurance company chain with a central inventory operation. Thomas’s fastest-moving inventory item has a demand of 6,000 units per year. The cost of each unit is $100, and the inventory carrying cost is $10 per unit per year. The aver- age ordering cost is $30 per order. It takes about 5 days for an order to arrive, and the demand for 1 week is 120 units. (This is a corporate operation, and there are 250 working days per year.) a) What is the EOQ? b) What is the average inventory if the EOQ is used? c) What is the optimal number of orders per year? d) What is the optimal number of days in between any two
orders? e) What is the annual cost of ordering and holding inventory? f ) What is the total annual inventory cost, including the cost of
the 6,000 units? PX
• • 12.15 Joe Henry’s machine shop uses 2,500 brackets during the course of a year. These brackets are purchased from a supplier 90 miles away. The following information is known about the brackets:
Annual demand: 2,500
Holding cost per bracket per year: $1.50
Order cost per order: $18.75
Lead time: 2 days
Working days per year: 250
a) Given the above information, what would be the economic order quantity (EOQ)?
b) Given the EOQ, what would be the average inventory? What would be the annual inventory holding cost?
c) Given the EOQ, how many orders would be made each year? What would be the annual order cost?
d) Given the EOQ, what is the total annual cost of managing the inventory?
e) What is the time between orders? f ) What is the reorder point (ROP)? PX
• • 12.16 Abey Kuruvilla, of Parkside Plumbing, uses 1,200 of a certain spare part that costs $25 for each order, with an annual holding cost of $24. a) Calculate the total cost for order sizes of 25, 40, 50, 60, and 100. b) Identify the economic order quantity and consider the impli-
cations for making an error in calculating economic order quantity. PX
• • • 12.17 M. Cotteleer Electronics supplies microcomputer circuitry to a company that incorporates microprocessors into refrigerators and other home appliances. One of the components has an annual demand of 250 units, and this is constant through- out the year. Carrying cost is estimated to be $1 per unit per year, and the ordering (setup) cost is $20 per order. a) To minimize cost, how many units should be ordered each
time an order is placed? b) How many orders per year are needed with the optimal policy? c) What is the average inventory if costs are minimized? d) Suppose that the ordering (setup) cost is not $20, and Cotteleer
has been ordering 150 units each time an order is placed. For this order policy (of Q 5 150) to be optimal, determine what the ordering (setup) cost would have to be. PX
• • 12.18 Race One Motors is an Indonesian car manufacturer. At its largest manufacturing facility, in Jakarta, the company produces subcomponents at a rate of 300 per day, and it uses these subcompo- nents at a rate of 12,500 per year (of 250 working days). Holding costs are $2 per item per year, and ordering (setup) costs are $30 per order. a) What is the economic production quantity? b) How many production runs per year will be made? c) What will be the maximum inventory level? d) What percentage of time will the facility be producing
components? e) What is the annual cost of ordering and holding inventory? PX
• • 12.19 Radovilsky Manufacturing Company, in Hayward, California, makes flashing lights for toys. The company operates its production facility 300 days per year. It has orders for about 12,000 flashing lights per year and has the capability of producing 100 per day. Setting up the light production costs $50. The cost of each light is $1. The holding cost is $0.10 per light per year. a) What is the optimal size of the production run? b) What is the average holding cost per year? c) What is the average setup cost per year? d) What is the total cost per year, including the cost of the
lights? PX
• • 12.20 Arthur Meiners is the production manager of Wheel- Rite, a small producer of metal parts. Wheel-Rite supplies Cal- Tex, a larger assembly company, with 10,000 wheel bearings each year. This order has been stable for some time. Setup cost for Wheel-Rite is $40, and holding cost is $.60 per wheel bearing per year. Wheel-Rite can produce 500 wheel bearings per day. Cal- Tex is a just-in-time manufacturer and requires that 50 bearings be shipped to it each business day. a) What is the optimum production quantity? b) What is the maximum number of wheel bearings that will be in
inventory at Wheel-Rite?
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c) How many production runs of wheel bearings will Wheel-Rite have in a year?
d) What is the total setup 1 holding cost for Wheel-Rite? PX
• • 12.21 Cesar Rego Computers, a Mississippi chain of com- puter hardware and software retail outlets, supplies both edu- cational and commercial customers with memory and storage devices. It currently faces the following ordering decision relating to purchases of very high-density disks:
D = 36,000 disks S = +25 H = +0.45 Purchase price = +.85 Discount price = +0.82
Quantity needed to qualify for the discount = 6,000 disks
Should the discount be taken? PX
• • 12.22 Bell Computers purchases integrated chips at $350 per chip. The holding cost is $35 per unit per year, the ordering cost is $120 per order, and sales are steady, at 400 per month. The company’s supplier, Rich Blue Chip Manufacturing, Inc., decides to offer price concessions in order to attract larger orders. The price structure is shown below.
Rich Blue Chip’s Price Structure
QUANTITY PURCHASED PRICE/UNIT
1–99 units $350
100–199 units $325
200 or more units $300
a) What is the optimal order quantity and the minimum annual cost for Bell Computers to order, purchase, and hold these integrated chips?
b) Bell Computers wishes to use a 10% holding cost rather than the fixed $35 holding cost in (a). What is the optimal order quantity, and what is the optimal annual cost? PX
• • 12.23 Wang Distributors has an annual demand for an air- port metal detector of 1,400 units. The cost of a typical detector to Wang is $400. Carrying cost is estimated to be 20% of the unit cost, and the ordering cost is $25 per order. If Ping Wang, the owner, orders in quantities of 300 or more, he can get a 5% discount on the cost of the detectors. Should Wang take the quantity discount? PX
• • 12.24 The catering manager of La Vista Hotel, Lisa Ferguson, is disturbed by the amount of silverware she is los- ing every week. Last Friday night, when her crew tried to set up for a banquet for 500 people, they did not have enough knives. She decides she needs to order some more silverware, but wants to take advantage of any quantity discounts her ven- dor will offer.
For a small order (2,000 or fewer pieces), her vendor quotes a price of $1.80Ypiece.
If she orders 2,001–5,000 pieces, the price drops to $1.60Ypiece. 5,001–10,000 pieces brings the price to $1.40Ypiece, and 10,001 and above reduces the price to $1.25.
Lisa’s order costs are $200 per order, her annual holding costs are 5%, and the annual demand is 45,000 pieces. For the best option: a) What is the optimal order quantity? b) What is the annual holding cost? c) What is the annual ordering (setup) cost?
d) What are the annual costs of the silverware itself with an opti- mal order quantity?
e) What is the total annual cost, including ordering, holding, and purchasing the silverware? PX
• • 12.25 Rocky Mountain Tire Center sells 20,000 go-cart tires per year. The ordering cost for each order is $40, and the holding cost is 20% of the purchase price of the tires per year. The purchase price is $20 per tire if fewer than 500 tires are ordered, $18 per tire if 500 or more—but fewer than 1,000—tires are ordered, and $17 per tire if 1,000 or more tires are ordered. a) How many tires should Rocky Mountain order each time it
places an order? b) What is the total cost of this policy? PX
• • 12.26 M. P. VanOyen Manufacturing has gone out on bid for a regulator component. Expected demand is 700 units per month. The item can be purchased from either Allen Manufacturing or Baker Manufacturing. Their price lists are shown in the table. Ordering cost is $50, and annual holding cost per unit is $5.
ALLEN MFG. BAKER MFG.
QUANTITY UNIT PRICE QUANTITY UNIT PRICE
1–499 $16.00 1–399 $16.10
500–999 15.50 400–799 15.60
1,0001 15.00 8001 15.10
a) What is the economic order quantity? b) Which supplier should be used? Why? c) What is the optimal order quantity and total annual cost of
ordering, purchasing, and holding the component? PX
• • • 12.27 Chris Sandvig Irrigation, Inc., has summarized the price list from four potential suppliers of an underground control valve. See the accompanying table. Annual usage is 2,400 valves; order cost is $10 per order; and annual inventory holding costs are $3.33 per unit.
Which vendor should be selected and what order quantity is best if Sandvig Irrigation wants to minimize total cost? PX
VENDOR A VENDOR B
QUANTITY PRICE QUANTITY PRICE
1–49 $35.00 1–74 $34.75
50–74 34.75 75–149 34.00
75–149 33.55 150–299 32.80
150–299 32.35 300–499 31.60
300–499 31.15 5001 30.50
5001 30.75
VENDOR C VENDOR D
QUANTITY PRICE QUANTITY PRICE
1–99 $34.50 1–199 $34.25
100–199 33.75 200–399 33.00
200–399 32.50 4001 31.00
4001 31.10
• • • 12.28 Emery Pharmaceutical uses an unstable chemical com- pound that must be kept in an environment where both tempera- ture and humidity can be controlled. Emery uses 800 pounds per month of the chemical, estimates the holding cost to be 50% of the purchase price (because of spoilage), and estimates order costs to be $50 per order. The cost schedules of two suppliers are as follows:
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VENDOR 1 VENDOR 2
QUANTITY PRICE/LB QUANTITY PRICE/LB
1–499 $17.00 1–399 $17.10
500–999 16.75 400–799 16.85
1,0001 16.50 800–1,199 16.60
1,2001 16.25
a) What is the economic order quantity for each supplier? b) What quantity should be ordered, and which supplier should
be used? c) What is the total cost for the most economic order size? d) What factor(s) should be considered besides total cost? PX
• • • 12.29 Kim Clark has asked you to help him determine the best ordering policy for a new product. The demand for the new product has been forecasted to be about 1,000 units annually. To help you get a handle on the carrying and ordering costs, Kim has given you the list of last year’s costs. He thought that these costs might be appropriate for the new product.
COST FACTOR COST ($) COST FACTOR COST ($)
Taxes for the warehouse
2,000 Warehouse supplies 280
Receiving and incoming inspection
1,500 Research and development
2,750
New product development
2,500 Purchasing salaries & wages
30,000
Acct. Dept. costs to pay invoices
500 Warehouse salaries & wages
12,800
Inventory insurance 600 Pilferage of inventory 800
Product advertising 800 Purchase order supplies
500
Spoilage 750 Inventory obsolescence 300
Sending purchasing orders
800 Purchasing Dept. overhead
1,000
He also told you that these data were compiled for 10,000 inven- tory items that were carried or held during the year. You have also determined that 200 orders were placed last year. Your job as a new operations management graduate is to help Kim determine the economic order quantity for the new product.
• • • • 12.30 Emarpy Appliance is a company that produces all kinds of major appliances. Bud Banis, the president of Emarpy, is concerned about the production policy for the company’s best- selling refrigerator. The annual demand has been about 8,000 units each year, and this demand has been constant throughout the year. The production capacity is 200 units per day. Each time production starts, it costs the company $120 to move materials into place, reset the assembly line, and clean the equipment. The holding cost of a refrigerator is $50 per year. The current produc- tion plan calls for 400 refrigerators to be produced in each pro- duction run. Assume there are 250 working days per year. a) What is the daily demand of this product? b) If the company were to continue to produce 400 units each time
production starts, how many days would production continue? c) Under the current policy, how many production runs per year
would be required? What would the annual setup cost be? d) If the current policy continues, how many refrigerators would
be in inventory when production stops? What would the average inventory level be?
e) If the company produces 400 refrigerators at a time, what would the total annual setup cost and holding cost be?
f ) If Bud Banis wants to minimize the total annual inventory cost, how many refrigerators should be produced in each production run? How much would this save the company in inventory costs compared to the current policy of producing 400 in each production run? PX
Additional problems 12.31–12.40 are available in MyOMLab.
Problems 12.41–12.50 relate to Probabilistic Models and Safety Stock
• • 12.41 Barbara Flynn is in charge of maintaining hospital supplies at General Hospital. During the past year, the mean lead time demand for bandage BX-5 was 60 (and was normally distributed). Furthermore, the standard deviation for BX-5 was 7. Ms. Flynn would like to maintain a 90% service level. a) What safety stock level do you recommend for BX-5? b) What is the appropriate reorder point? PX
• • 12.42 Based on available information, lead time demand for PC jump drives averages 50 units (normally distributed), with a stand- ard deviation of 5 drives. Management wants a 97% service level. a) What value of Z should be applied? b) How many drives should be carried as safety stock? c) What is the appropriate reorder point? PX
••• 12.43 Authentic Thai rattan chairs (shown in the photo) are delivered to Gary Schwartz’s chain of retail stores, called The Kathmandu Shop, once a year. The reorder point, without safety stock, is 200 chairs. Carrying cost is $30 per unit per year, and the cost of a stockout is $70 per chair per year. Given the following demand probabilities during the lead time, how much safety stock should be carried?
B a rr
y R
e n d e r
DEMAND DURING LEAD TIME PROBABILITY
0 0.2
100 0.2
200 0.2
300 0.2
400 0.2
• • 12.44 Tobacco is shipped from North Carolina to a ciga- rette manufacturer in Cambodia once a year. The reorder point, without safety stock, is 200 kilos. The carrying cost is $15 per kilo per year, and the cost of a stockout is $70 per kilo per year. Given the following demand probabilities during the lead time, how much safety stock should be carried?
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c) Explain what these two service levels mean. Which is preferable? PX
• • • • 12.49 A gourmet coffee shop in downtown San Francisco is open 200 days a year and sells an average of 75 pounds of Kona coffee beans a day. (Demand can be assumed to be distributed normally, with a standard deviation of 15 pounds per day.) After ordering (fixed cost 5 $16 per order), beans are always shipped from Hawaii within exactly 4 days. Per-pound annual holding costs for the beans are $3. a) What is the economic order quantity (EOQ) for Kona coffee
beans? b) What are the total annual holding costs of stock for Kona
coffee beans? c) What are the total annual ordering costs for Kona coffee beans? d) Assume that management has specified that no more than
a 1% risk during stockout is acceptable. What should the reorder point (ROP) be?
e) What is the safety stock needed to attain a 1% risk of stockout during lead time?
f ) What is the annual holding cost of maintaining the level of safety stock needed to support a 1% risk?
g) If management specified that a 2% risk of stockout during lead time would be acceptable, would the safety stock holding costs decrease or increase?
Additional problem 12.50 is available in MyOMLab.
Problems 12.51–12.53 relate to Single-Period Model
•• 12.51 Cynthia Knott’s oyster bar buys fresh Louisiana oysters for $5 per pound and sells them for $9 per pound. Any oysters not sold that day are sold to her cousin, who has a nearby grocery store, for $2 per pound. Cynthia believes that demand follows the normal distribution, with a mean of 100 pounds and a standard deviation of 15 pounds. How many pounds should she order each day?
•• 12.52 Henrique Correa’s bakery prepares all its cakes between 4 a.m. and 6 a.m. so they will be fresh when customers arrive. Day-old cakes are virtually always sold, but at a 50% dis- count off the regular $10 price. The cost of baking a cake is $6, and demand is estimated to be normally distributed, with a mean of 25 and a standard deviation of 4. What is the optimal stocking level?
••• 12.53 University of Florida football programs are printed 1 week prior to each home game. Attendance averages 90,000 screaming and loyal Gators fans, of whom two-thirds usually buy the program, following a normal distribution, for $4 each. Unsold programs are sent to a recycling center that pays only 10 cents per program. The standard deviation is 5,000 programs, and the cost to print each program is $1. a) What is the cost of underestimating demand for each program? b) What is the overage cost per program? c) How many programs should be ordered per game? d) What is the stockout risk for this order size?
CASE STUDIES Zhou Bicycle Company
Zhou Bicycle Company (ZBC), located in Seattle, is a whole- sale distributor of bicycles and bicycle parts. Formed in 1981 by University of Washington Professor Yong-Pin Zhou, the firm’s primary retail outlets are located within a 400-mile radius of the distribution center. These retail outlets receive the order
from ZBC within 2 days after notifying the distribution center, provided that the stock is available. However, if an order is not fulfilled by the company, no backorder is placed; the retailers arrange to get their shipment from other distributors, and ZBC loses that amount of business.
DEMAND DURING LEAD TIME (KILOS) PROBABILITY
0 0.1
100 0.1
200 0.2
300 0.4
400 0.2
• • • 12.45 Mr. Beautiful, an organization that sells weight training sets, has an ordering cost of $40 for the BB-1 set. (BB-1 stands for Body Beautiful Number 1.) The carrying cost for BB-1 is $5 per set per year. To meet demand, Mr. Beautiful orders large quantities of BB-1 seven times a year. The stockout cost for BB-1 is estimated to be $50 per set. Over the past several years, Mr. Beautiful has observed the following demand during the lead time for BB-1:
DEMAND DURING LEAD TIME PROBABILITY
40 .1
50 .2
60 .2
70 .2
80 .2
90 .1
1.0
The reorder point for BB-1 is 60 sets. What level of safety stock should be maintained for BB-1? PX
• • 12.46 Chicago’s Hard Rock Hotel distributes a mean of 1,000 bath towels per day to guests at the pool and in their rooms. This demand is normally distributed with a standard deviation of 100 towels per day, based on occupancy. The laundry firm that has the linens contract requires a 2-day lead time. The hotel expects a 98% service level to satisfy high guest expectations. a) What is the safety stock? b) What is the ROP? PX
•• 12.47 First Printing has contracts with legal firms in San Francisco to copy their court documents. Daily demand is almost constant at 12,500 pages of documents. The lead time for paper delivery is normally distributed with a mean of 4 days and a stand- ard deviation of 1 day. A 97% service level is expected. Compute First’s ROP. PX
• • • 12.48 Gainesville Cigar stocks Cuban cigars that have vari- able lead times because of the difficulty in importing the product: lead time is normally distributed with an average of 6 weeks and a standard deviation of 2 weeks. Demand is also a variable and normally distributed with a mean of 200 cigars per week and a standard deviation of 25 cigars. a) For a 90% service level, what is the ROP? b) What is the ROP for a 95% service level?
PX
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Parker Hi-Fi Systems
Parker Hi-Fi Systems, located in Wellesley, Massachusetts, a Boston suburb, assembles and sells the very finest home theater sys- tems. The systems are assembled with components from the best manufacturers worldwide. Although most of the components are procured from wholesalers on the East Coast, some critical items, such as LCD screens, come directly from their manufacturer. For instance, the LCD screens are shipped via air from Foxy, Ltd., in Taiwan, to Boston’s Logan airport, and the top-of-the-line speakers are purchased from the world-renowned U.S. manufacturer Boss.
Parker’s purchasing agent, Raktim Pal, submits an order release for LCD screens once every 4 weeks. The company’s annual requirements total 500 units (2 per working day), and Parker’s per unit cost is $1,500. (Because of Parker’s relatively low volume and the quality focus—rather than volume focus— of many of Parker’s suppliers, Parker is seldom able to obtain quantity discounts.) Because Foxy promises delivery within 1 week following receipt of an order release, Parker has never had a shortage of LCDs. (Total time between date of the release and date of receipt is 1 week or 5 working days.)
Parker’s activity-based costing system has generated the fol- lowing inventory-related costs. Procurement costs, which amount to $500 per order, include the actual labor costs involved in order- ing, customs inspection, arranging for airport pickup, delivery to the plant, maintaining inventory records, and arranging for the bank to issue a check. Parker’s holding costs take into account storage, damage, insurance, taxes, and so forth on a square-foot basis. These costs equal $150 per LCD per year.
With added emphasis being placed on efficiencies in the supply chain, Parker’s president has asked Raktim to seriously evaluate the purchase of the LCDs. One area to be closely scrutinized for possible cost savings is inventory procurement.
Discussion Questions
1. What is the optimal order number of LCDs that should be placed in each order?
2. What is the optimal reorder point (ROP) for LCDs? 3. What cost savings will Parker realize if it implements an order
plan based on EOQ?
The company distributes a wide variety of bicycles. The most popular model, and the major source of revenue to the company, is the AirWing. ZBC receives all the models from a single manufacturer in China, and shipment takes as long as 4 weeks from the time an order is placed. With the cost of com- munication, paperwork, and customs clearance included, ZBC estimates that each time an order is placed, it incurs a cost of $65. The purchase price paid by ZBC, per bicycle, is roughly 60% of the suggested retail price for all the styles available, and the inventory carrying cost is 1% per month (12% per year) of the purchase price paid by ZBC. The retail price (paid by the customers) for the AirWing is $170 per bicycle.
ZBC is interested in making an inventory plan for 2016. The firm wants to maintain a 95% service level with its customers to minimize the losses on the lost orders. The data collected for the past 2 years are summarized in the following table. A forecast for AirWing model sales in 2016 has been developed and will be used to make an inventory plan for ZBC.
Demands For Airwing Model
MONTH 2014 2015 FORECAST FOR 2016
January 6 7 8 February 12 14 15
MONTH 2014 2015 FORECAST FOR 2016
March 24 27 31 April 46 53 59 May 75 86 97 June 47 54 60 July 30 34 39 August 18 21 24 September 13 15 16 October 12 13 15 November 22 25 28 December 38 42 47 Total 343 391 439
Discussion Questions
1. Develop an inventory plan to help ZBC. 2. Discuss ROPs and total costs. 3. How can you address demand that is not at the level of the
planning horizon?
Source: Professor Kala Chand Seal, Loyola Marymount University.
Managing Inventory at Frito-Lay Video Case Frito-Lay has flourished since its origin—the 1931 purchase of a small San Antonio firm for $100 that included a recipe, 19 retail accounts, and a hand-operated potato ricer. The multi- billion- dollar company, headquartered in Dallas, now has 41 products—15 with sales of over $100 million per year and 7 at over $1 billion in sales. Production takes place in 36 product- focused plants in the U.S. and Canada, with 48,000 employees.
Inventory is a major investment and an expensive asset in most firms. Holding costs often exceed 25% of product value, but in Frito-Lay’s prepared food industry, holding cost can be much higher because the raw materials are perishable. In the food industry, inventory spoils. So poor inventory management is not only expensive but can also yield an unsatisfactory product that in the extreme can also ruin market acceptance.
Major ingredients at Frito-Lay are corn meal, corn, potatoes, oil, and seasoning. Using potato chips to illustrate rapid inventory
flow: potatoes are moved via truck from farm, to regional plants for processing, to warehouse, to the retail store. This happens in a matter of hours—not days or weeks. This keeps freshness high and holding costs low.
Frequent deliveries of the main ingredients at the Florida plant, for example, take several forms:
◆ Potatoes are delivered in 10 truckloads per day, with 150,000 lbs consumed in one shift: the entire potato storage area will only hold 7½ hours’ worth of potatoes.
◆ Oil inventory arrives by rail car, which lasts only 4½ days. ◆ Corn meal arrives from various farms in the Midwest, and
inventory typically averages 4 days’ production. ◆ Seasoning inventory averages 7 days. ◆ Packaging inventory averages 8 to 10 days.
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Inventory Control at Wheeled Coach
Controlling inventory is one of Wheeled Coach’s toughest prob- lems. Operating according to a strategy of mass customization and responsiveness, management knows that success is depend- ent on tight inventory control. Anything else results in an inabil- ity to deliver promptly, chaos on the assembly line, and a huge inventory investment. Wheeled Coach finds that almost 50% of the cost of every ambulance it manufactures is purchased materi- als. A large proportion of that 50% is in chassis (purchased from Ford), aluminum (from Reynolds Metal), and plywood used for flooring and cabinetry construction (from local suppliers). Wheeled Coach tracks these A inventory items quite carefully, maintaining tight security/control and ordering carefully so as to maximize quantity discounts while minimizing on-hand stock. Because of long lead times and scheduling needs at Reynolds, aluminum must actually be ordered as much as 8 months in advance.
In a crowded ambulance industry in which it is the only giant, its 45 competitors don’t have the purchasing power to draw the same discounts as Wheeled Coach. But this competitive cost advantage cannot be taken lightly, according to President Bob Collins. “Cycle
counting in our stockrooms is critical. No part can leave the locked stockrooms without appearing on a bill of materials.”
Accurate bills of material (BOM) are a requirement if prod- ucts are going to be built on time. Additionally, because of the custom nature of each vehicle, most orders are won only after a bidding process. Accurate BOMs are critical to cost estimation and the resulting bid. For these reasons, Collins was emphatic that Wheeled Coach maintain outstanding inventory control. The Global Company Profile featuring Wheeled Coach (which opens Chapter 14 ) provides further details about the ambulance inven- tory control and production process.
Discussion Questions *
1. Explain how Wheeled Coach implements ABC analysis. 2. If you were to take over as inventory control manager at
Wheeled Coach, what additional policies and techniques would you initiate to ensure accurate inventory records?
3. How would you go about implementing these suggestions?
* You may wish to view the video that accompanies this case before addressing these questions.
* You may wish to view the video that accompanies this case before addressing these questions.
• Additional Case Studies: Visit MyOMLab for these free case studies: Southwestern University (F): The university must decide how many football day programs to order, and from whom. LaPlace Power and Light: This utility company is evaluating its current inventory policies.
Endnotes
1. See E. Malykhina, “Retailers Take Stock,” Information Week (February 7, 2005): 20–22, and A. Raman, N. DeHoratius, and Z. Ton, “Execution: The Missing Link in Retail Operations,” California Management Review 43, no. 3 (Spring 2001): 136–141.
2. This is the case when holding costs are linear and begin at the origin—that is, when inventory costs do not decline (or they increase) as inventory volume increases and all holding costs are in small increments. In addition, there is probably some learning each time a setup (or order) is executed—a fact that lowers subsequent setup costs. Consequently, the EOQ model is probably a special case. However, we abide by the conven- tional wisdom that this model is a reasonable approximation.
3. The formula for the economic order quantity ( Q *) can also be determined by finding where the total cost curve is at a minimum (i.e., where the slope of the total cost curve is zero). Using calculus, we set the derivative of the total cost with respect to Q * equal to 0.
The calculations for finding the minimum of
TC = D Q
S + Q 2
H + PD
are d(TC)
dQ = ¢ 9DS
Q2 ≤ + H
2 + 0 = 0
Thus, Q* = A
2DS H
4. The number of units short, Demand–ROP, is true only when Demand–ROP is non-negative.
5. Equations (12-15), (12-16), and (12-17) are expressed in days; however, they could equivalently be expressed in weeks, months, or even years. Just be consistent, and use the same time units for all terms in the equations.
6. Note that Equation (12-17) can also be expressed as: ROP = Average daily demand * Average lead time +
Z2(Average lead time * sd 2) + d 2sLT 2 . 7. OM managers also call these continuous review systems .
Frito-Lay’s product-focused facility represents a major capital investment. That investment must achieve high utilization to be efficient. The capital cost must be spread over a substantial volume to drive down total cost of the snack foods produced. This demand for high utilization requires reliable equipment and tight schedules. Reliable machinery requires an inventory of critical components: this is known as MRO, or maintenance, repair, and operating sup- plies. MRO inventory of motors, switches, gears, bearings, and other critical specialized components can be costly but is necessary.
Frito-Lay’s non-MRO inventory moves rapidly. Raw mate- rial quickly becomes work-in-process, moving through the system and out the door as a bag of chips in about 112 shifts. Packaged finished products move from production to the distribution chain in less than 1.4 days.
Discussion Questions *
1. How does the mix of Frito-Lay’s inventory differ from those at a machine or cabinet shop (a process-focused facility)?
2. What are the major inventory items at Frito-Lay, and how rapidly do they move through the process?
3. What are the four types of inventory? Give an example of each at Frito-Lay.
4. How would you rank the dollar investment in each of the four types (from the most investment to the least investment)?
5. Why does inventory flow so quickly through a Frito-Lay plant? 6. Why does the company keep so many plants open? 7. Why doesn’t Frito-Lay make all its 41 products at each of its plants?
Video Case
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R ap
id R
ev ie
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12 Chapter 12 Rapid Review Main Heading Review Material MyOMLab THE IMPORTANCE OF INVENTORY (pp. 490–491)
Inventory is one of the most expensive assets of many companies. The objective of inventory management is to strike a balance between inventory investment and customer service. The two basic inventory issues are how much to order and when to order. j Raw material inventory —Materials that are usually purchased but have yet to
enter the manufacturing process. j Work-in-process (WIP) inventory —Products or components that are no longer
raw materials but have yet to become finished products. j MRO inventory —Maintenance, repair, and operating materials. j Finished-goods inventory —An end item ready to be sold but still an asset on the
company’s books.
Concept Questions: 1.1–1.4 VIDEO 12.1 Managing Inventory at Frito-Lay
MANAGING INVENTORY (pp. 491–495)
j ABC analysis —A method for dividing on-hand inventory into three classifications based on annual dollar volume.
j Cycle counting —A continuing reconciliation of inventory with inventory records. j Shrinkage —Retail inventory that is unaccounted for between receipt and sale. j Pilferage —A small amount of theft.
Concept Questions: 2.1–2.4 Problems: 12.1–12.6 Virtual Office Hours for Solved Problem: 12.1
INVENTORY MODELS (pp. 495–496)
j Holding cost —The cost to keep or carry inventory in stock. j Ordering cost —The cost of the ordering process. j Setup cost —The cost to prepare a machine or process for production. j Setup time —The time required to prepare a machine or process for production.
Concept Questions: 3.1–3.4 VIDEO 12.2 Inventory Control at Wheeled Coach Ambulance
INVENTORY MODELS FOR INDEPENDENT DEMAND (pp. 496–507)
j Economic order quantity (EOQ) model —An inventory-control technique that minimizes the total of ordering and holding costs:
Q* = A
2DS H
(12-1)
Expected number of orders = N = Demand
Order quantity =
D Q*
(12-2)
Expected time between orders = T = Number of working days per year
N (12-3)
Total annual cost = Setup (order) cost + Holding cost (12-4)
TC = D Q
S + Q 2
H (12-5)
j Robust —Giving satisfactory answers even with substantial variation in the parameters.
j Lead time —In purchasing systems, the time between placing an order and receiv- ing it; in production systems, the wait, move, queue, setup, and run times for each component produced.
j Reorder point (ROP) —The inventory level (point) at which action is taken to replenish the stocked item.
ROP for known demand: ROP = Demand per day * Lead time for a new order in days = d * L (12-6) j Safety stock ( ss ) —Extra stock to allow for uneven demand; a buffer. j Production order quantity model —An economic order quantity technique applied
to production orders:
Q*p = A
2DS H31 - (d>p)4
(12-7)
Q*p =
H
2DS
Ha1 - Annual demand rate
Annual production rate b
(12-8)
j Quantity discount —A reduced price for items purchased in large quantities:
TC = D Q
S + Q 2
H + PD (12-9)
Q* = A
2DS IP
(12-10)
Concept Questions: 4.1–4.4 Problems: 12.7–12.40 Virtual Office Hours for Solved Problems: 12.2–12.5 ACTIVE MODELS 12.1, 12.2
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R ap
id R
ev ie
w
12 Chapter 12 Rapid Review continued Main Heading Review Material MyOMLab PROBABILISTIC MODELS AND SAFETY STOCK (pp. 508–513)
j Probabilistic model —A statistical model applicable when product demand or any other variable is not known but can be specified by means of a probability distribution.
j Service level —The complement of the probability of a stockout. ROP for unknown demand: ROP = d * L + ss (12-11)
Annual stockout costs = The sum of the units short for each demand level
* The probability of that demand level * The stockout cost>unit * The number of orders per year
(12-12)
ROP for unknown demand and given service level: ROP = Expected demand during lead time + ZsdLT (12-13) Safety stock = ZsdLT (12-14) ROP for variable demand and constant lead time: ROP = (Average daily demand * Lead time in days) + ZsdLT (12-15) ROP for constant demand and variable lead time: ROP = (Daily demand * Average lead time in days) + Z * Daily demand * sLT
(12-16) ROP for variable demand and variable lead time: ROP = (Average daily demand * Average lead time in days) + ZsdLT (12-17) In each case, sdLT = 2(Average lead time * sd2) + d 2s2LT
but under constant demand: s2d = 0, and under constant lead time: s2LT = 0.
Concept Questions: 5.1–5.4 Problems: 12.41–12.50 Virtual Office Hours for Solved Problems: 12.6–12.9
SINGLE-PERIOD MODEL (pp. 513–514 )
j Single-period inventory model —A system for ordering items that have little or no value at the end of the sales period:
Service level = Cs
Cs + Co (12-18)
Concept Questions: 6.1–6.4 Problems: 12.51–12.53
FIXED-PERIOD ( P ) SYSTEMS (pp. 514–515)
j Fixed-quantity ( Q ) system —An ordering system with the same order amount each time. j Perpetual inventory system —A system that keeps track of each withdrawal or
addition to inventory continuously, so records are always current. j Fixed-period ( P ) system —A system in which inventory orders are made at regular
time intervals.
Concept Questions: 7.1–7.4
Self Test
LO 12.1 ABC analysis divides on-hand inventory into three classes, based on:
a) unit price. b) the number of units on hand. c) annual demand. d) annual dollar values. LO 12.2 Cycle counting: a) provides a measure of inventory turnover. b) assumes that all inventory records must be verified with
the same frequency. c) is a process by which inventory records are periodically
verified. d) all of the above. LO 12.3 The two most important inventory-based questions
answered by the typical inventory model are: a) when to place an order and the cost of the order. b) when to place an order and how much of an item
to order. c) how much of an item to order and the cost of the order. d) how much of an item to order and with whom the order
should be placed. LO 12.4 Extra units in inventory to help reduce stockouts are called: a) reorder point. b) safety stock. c) just-in-time inventory. d) all of the above.
LO 12.5 The difference(s) between the basic EOQ model and the pro- duction order quantity model is(are) that:
a) the production order quantity model does not require the assumption of known, constant demand.
b) the EOQ model does not require the assumption of negligible lead time.
c) the production order quantity model does not require the assumption of instantaneous delivery.
d) all of the above. LO 12.6 The EOQ model with quantity discounts attempts to
determine: a) the lowest amount of inventory necessary to satisfy a
certain service level. b) the lowest purchase price. c) whether to use a fixed-quantity or fixed-period order policy. d) how many units should be ordered. e) the shortest lead time. LO 12.7 The appropriate level of safety stock is typically determined by: a) minimizing an expected stockout cost. b) choosing the level of safety stock that assures a given
service level. c) carrying sufficient safety stock so as to eliminate all stockouts. d) annual demand.
Answers: LO 12.1. d; LO 12.2. c; LO 12.3. b; LO 12.4. b; LO 12.5. c; LO 12.6. d; LO 12.7. b.
j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
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C H A P T E R O U T L I N E
◆ The Planning Process 532
◆
Sales and Operations Planning 533
◆
The Nature of Aggregate Planning 534
◆
Aggregate Planning Strategies 535
◆
Methods for Aggregate Planning 538
◆
Aggregate Planning in Services 545
◆
Revenue Management 547
GLOBAL COMPANY PROFILE: Frito-Lay
C H
A P
T E
R
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
•• Scheduling
·· Aggregate/S&OP ( Ch. 13 ) ·· Short-Term ( Ch. 15 ) • • Maintenance
C H A P T E R GLOBAL COMPANY PROFILE Frito Lay
Aggregate Planning and S&OP 13
Al a sk
a A
ir lin
e s
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L ike other organizations throughout the world, Frito-Lay relies on effective aggregate planning
to match fluctuating multi-billion-dollar demand to capacity in its 36 North American plants.
Planning for the intermediate term (3 to 18 months) is the heart of aggregate planning.
Effective aggregate planning combined with tight scheduling, effective maintenance, and efficient
employee and facility scheduling are the keys to high plant utilization. High utilization is a critical
factor in facilities such as Frito-Lay, where capital investment is substantial.
Frito-Lay has more than three dozen brands of snacks and chips, 15 of which sell more than
$100 million annually and 7 of which sell over $1 billion. Its brands include such well-known
names as Fritos, Lay’s, Doritos, Sun Chips, Cheetos, Tostitos, Flat Earth, and Ruffles. Unique
processes using specially designed equipment are required to produce each of these products.
Because these specialized processes generate high fixed cost, they must operate at very high
volume. But such product-focused facilities benefit by having low variable costs. High utiliza-
tion and performance above the break-even point require a good match between demand and
capacity. Idle equipment is disastrous.
At Frito-Lay’s headquarters near Dallas, planners create a total demand profile. They use
historical product sales, forecasts of new products, product innovations, product promotions,
Aggregate Planning Provides a Competitive Advantage at Frito-Lay
GLOBAL COMPANY PROFILE Frito-Lay
C H A P T E R 1 3
530
The aggregate plan adjusts for farm location,
yield, and quantities for timely delivery of
Frito-Lay’s unique varieties of potatoes.
During harvest times, potatoes go directly
to the plant. During non-harvest months,
potatoes are stored in climate-controlled
environments to maintain quality, texture,
and taste.
F ri to
-L a y
N o rt
h A
m e ri ca
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531
and dynamic local demand data from account
managers to forecast demand. Planners then
match the total demand profile to existing
capacity, capacity expansion plans, and cost.
This becomes the aggregate plan. The aggre-
gate plan is communicated to each of the firm’s
17 regions and to the 36 plants. Every quarter,
headquarters and each plant modify the
respective plans to incorporate changing mar-
ket conditions and plant performance. Each plant uses its quarterly plan to develop
a 4-week plan, which in turn assigns specific
products to specific product lines for produc-
tion runs. Finally, each week raw materials and
labor are assigned to each process. Effective
aggregate planning is a major factor in high
utilization and low cost. As the company’s 60%
market share indicates, excellent aggregate
planning yields a competitive advantage at
Frito-Lay.
As potatoes arrive at the plant, they are promptly washed and peeled to ensure freshness and taste.
F ri to
-L a y
N o rt
h A
m e ri ca
F ri to
-L a y
N o rt
h A
m e ri ca
After peeling, potatoes are cut into thin
slices, rinsed of excess starch, and
cooked in sunflower and/or corn oil.
F ri to
-L a y
N o rt
h A
m e ri ca
After cooking is complete, inspection,
bagging, weighing, and packing
operations prepare Lay’s potato chips
for shipment to customers—all in a
matter of hours. Fr it o -L
a y
N o rt
h A
m e ri ca
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532
The Planning Process In Chapter 4 , we saw that demand forecasting can address long-, medium-, and short-range deci sions. Figure 13.1 illustrates how managers translate these forecasts into long-, interme- diate-, and short-range plans. Long-range forecasts, the responsibility of top management, provide data for a firm’s multi-year plans. These long-range plans require policies and strate- gies related to issues such as capacity and capital investment (Supplement 7), facility location ( Chapter 8 ), new products ( Chapter 5 ) and processes ( Chapter 7 ), and supply-chain develop- ment ( Chapter 11 ).
Intermediate plans are designed to be consistent with top management’s long-range plans and strategy, and work within the resource constraints determined by earlier strategic deci- sions. The challenge is to have these plans match production to the ever-changing demands of the market. Intermediate plans are the job of the operations manager, working with other functional areas of the firm. In this chapter we deal with intermediate plans, typically measured in months.
Short-range plans are usually for less than 3 months. These plans are also the responsibility of operations personnel. Operations managers work with supervisors and foremen to translate
L E A R N I N G OBJEC TI V ES
LO 13.1 Defi ne sales and operations planning 533
LO 13.2 Defi ne aggregate planning 534
LO 13.3 Identify optional strategies for developing an aggregate plan 535
LO 13.4 Prepare a graphical aggregate plan 538
LO 13.5 Solve an aggregate plan via the transportation method 544
LO 13.6 Understand and solve a revenue management problem 548
Long-range plans (over 1 year) Capacity decisions (Supplement 7) are critical to long-range plans.
Issues: Research and Development New product plans Capital investments Facility location/capacity
Top executives
Operations managers with sales and operations planning team
Operations managers, supervisors, foremen
Responsibility Planning tasks and time horizons
Short-range plans (up to 3 months) The scheduling techniques (Chapter 15) help managers prepare short- range plans.
Issues: Job assignments Ordering Job scheduling Dispatching Overtime Part-time help
Intermediate-range plans (3 to 18 months) The aggregate planning techniques of this chapter help managers build intermediate- range plans.
Issues: Sales and Operations Planning Production planning and budgeting Setting employment, inventory, subcontracting levels Analyzing operating plans
Figure 13.1
Planning Tasks and
Responsibilities
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C H A P T E R 1 3 | AG G R E G AT E P L A N N I N G A N D S & O P 533
the intermediate plan into short-term plans consisting of weekly, daily, and hourly schedules. Short-term planning techniques are discussed in Chapter 15 .
Intermediate planning is initiated by a process known as sales and operations planning (S&OP) .
Sales and Operations Planning Good intermediate planning requires the coordination of demand forecasts with functional areas of a firm and its supply chain. And because each functional part of a firm and the supply chain has its own limitations and constraints, the coordination can be difficult. This coordi- nated planning effort has evolved into a process known as sales and operations planning (S&OP) . As Figure 13.2 shows, S&OP receives input from a variety of sources both internal and external to the firm. Because of the diverse inputs, S&OP is typically done by cross-functional teams that align the competing constraints.
One of the tasks of S&OP is to determine which plans are feasible in the coming months and which are not. Any limitations, both within the firm and in the supply chain, must be reflected in an intermediate plan that brings day-to-day sales and operational realities together. When the resources appear to be substantially at odds with market expectations, S&OP provides advanced warning to top management. If the plan cannot be implemented in the short run, the planning exercise is useless. And if the plan cannot be supported in the long run, strategic changes need to be made. To keep aggregate plans current and to support its intermediate plan- ning role, S&OP uses rolling forecasts that are frequently updated—often weekly or monthly.
The output of S&OP is called an aggregate plan. The aggregate plan is concerned with de- termining the quantity and timing of production for the intermediate future, often from 3 to
Sales and operations planning (S&OP)
A process of balancing resources
and forecasted demand, aligning
an organization’s competing
demands from supply chain to
final customer, while linking
strategic planning with operations
over all planning horizons.
Aggregate plan
A plan that includes forecast levels
for families of products of finished
goods, inventory, shortages, and
changes in the workforce.
LO 13.1 Define sales and operations planning
Product decisions (Ch. 5)
1st Qtr
D e
m a
n d
2nd Qtr
3rd Qtr
4th Qtr
Demand forecasts, orders (Ch.4)
Process planning and
capacity decisions
(Ch. 7 and S7)
Marketplace
Master production
schedule and MRP systems
(Ch.14)
Detailed work
schedules (Ch.15)
Sales and operations planning develops the aggregate plan
for operations
Research and technology
Workforce (Ch.10)
Inventory on hand (Ch.12)
Supply-chain support (Ch.11)
External capacity (subcontractors)
Figure 13.2
Relationships of S&OP and the Aggregate Plan
G e o rg
e D
o yl
e /G
e tt
y Im
a g e s
D m
it ry
V e re
sh ch
a g in
/F o to
lia
M a rk
R ic
h a rd
s/ P
h o to
E d it I n c.
In d u st
ri e b lic
k/ F o to
lia
V a ri o i m
a g e s
G m
b H
& C
o .
K G
/A la
m yG u i Y o n g N
ia n /F
o to
lia
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534 P A R T 3 | M A N AG I N G O P E R AT I O N S
18 months ahead. Aggregate plans use information regarding product families or product lines rather than individual products. These plans are concerned with the total, or aggregate, of the individual product lines.
Rubbermaid, Office Max, and Rackspace have developed formal systems for S&OP, each with its own planning focus. Rubbermaid may use S&OP with a focus on production decisions; Office Max may focus S&OP on supply chain and inventory decisions; while Rackspace, a data storage firm, tends to have its S&OP focus on its critical and expensive investments in capac- ity. In all cases, though, the decisions must be tied to strategic planning and integrated with all areas of the firm over all planning horizons. Specifically, S&OP is aimed at (1) the coordination and integration of the internal and external resources necessary for a successful aggregate plan and (2) communication of the plan to those charged with its execution. The added advan- tage of S&OP and an aggregate plan is that they can be effective tools to engage members of the supply chain in achieving the firm’s goals.
Besides being representative, timely, and comprehensive, an effective S&OP process needs these four additional features to generate a useful aggregate plan: ◆ A logical unit for measuring sales and output, such as pounds of Doritos at Frito-Lay,
air-conditioning units at GE, or terabytes of storage at Rackspace ◆ A forecast of demand for a reasonable intermediate planning period in aggregate terms ◆ A method to determine the relevant costs ◆ A model that combines forecasts and costs so scheduling decisions can be made for the
planning period In this chapter we describe several techniques that managers use when developing an
aggregate plan for both manufacturing and service-sector firms. For manufacturers, an aggre- gate schedule ties a firm’s strategic goals to production plans. For service organizations, an aggregate schedule ties strategic goals to workforce schedules.
The Nature of Aggregate Planning An S&OP team builds an aggregate plan that satisfies forecasted demand by adjusting produc- tion rates, labor levels, inventory levels, overtime work, subcontracting rates, and other con- trollable variables. The plan can be for Frito-Lay, Whirlpool, hospitals, colleges, or Pearson Education, the company that publishes this textbook. Regardless of the firm, the objective of aggregate planning is usually to meet forecast demand while minimizing cost over the planning period. However, other strategic issues may be more important than low cost. These strate- gies may be to smooth employment, to drive down inventory levels, or to meet a high level of service, regardless of cost.
Let’s look at Snapper, which produces many different models of lawn mowers. Snapper makes walk-behind mowers, rear-engine riding mowers, garden tractors, and many more, for a total of 145 models. For each month in the upcoming 3 quarters, the aggregate plan for Snapper might have the following output (in units of production) for Snapper’s “family” of mowers:
QUARTER 1 QUARTER 2 QUARTER 3
Jan. Feb. March April May June July Aug. Sept. 150,000 120,000 110,000 100,000 130,000 150,000 180,000 150,000 140,000
LO 13.2 Define aggregate planning
Brig g s
& S
tr a tt
o n P
o w
e r
P ro
d u ct
s M
a rk
e ti n g
S&OP builds an aggregate plan using the total expected demand for all of the family products, such as 145 models at Snapper (a few of which are shown above). Only
when the forecasts are assembled in the aggregate plan does the company decide how to meet the total requirement with the available resources. These resource
constraints include facility capacity, workforce size, supply-chain limitations, inventory issues, and financial resources.
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C H A P T E R 1 3 | AG G R E G AT E P L A N N I N G A N D S & O P 535
Note that the plan looks at production in the aggregate (the family of mowers), not as a product- by-product breakdown. Likewise, an aggregate plan for BMW tells the auto manufacturer how many cars to make but not how many should be two-door vs. four-door or red vs. green. It tells Nucor Steel how many tons of steel to produce but does not differentiate grades of steel. (We extend the discussion of planning at Snapper in the OM in Action box “Building the Plan at Snapper.”)
In a manufacturing environment, the process of breaking the aggregate plan down into greater detail is called disaggregation . Disaggregation results in a master production schedule , which provides input to material requirements planning (MRP) systems. The master pro- duction schedule addresses the purchasing or production of major parts or components (see Chapter 14 ). It is not a sales forecast. Detailed work schedules for people and priority schedul- ing for products result as the final step of the production planning system (and are discussed in Chapter 15 ).
Aggregate Planning Strategies When generating an aggregate plan, the operations manager must answer several questions:
1. Should inventories be used to absorb changes in demand during the planning period? 2. Should changes be accommodated by varying the size of the workforce? 3. Should part-timers be used, or should overtime and idle time absorb fluctuations? 4. Should subcontractors be used on fluctuating orders so a stable workforce can be
maintained? 5. Should prices or other factors be changed to influence demand?
All of these are legitimate planning strategies. They involve the manipulation of inventory, production rates, labor levels, capacity, and other controllable variables. We will now examine eight options in more detail. The first five are called capacity options because they do not try to change demand but attempt to absorb demand fluctuations. The last three are demand options through which firms try to smooth out changes in the demand pattern over the planning period.
Capacity Options A firm can choose from the following basic capacity (production) options:
1. Changing inventory levels: Managers can increase inventory during periods of low demand to meet high demand in future periods. If this strategy is selected, costs associated with storage, insurance, handling, obsolescence, pilferage, and capital invested will increase.
Disaggregation
The process of breaking an
aggregate plan into greater detail.
Master production schedule
A timetable that specifies what is
to be made and when.
Building the Plan at Snapper
Every bright-red Snapper lawn mower sold anywhere in the world comes from
a factory in McDonough, Georgia. Ten years ago, the Snapper line had about
40 models of mowers, leaf blowers, and snow blowers. Today, reflecting the
demands of mass customization, the product line is much more complex. Snap-
per designs, manufactures, and sells 145 models. This means that aggregate
planning and the related short-term scheduling have become more complex, too.
In the past, Snapper met demand by carrying a huge inventory for
52 regional distributors and thousands of independent dealerships. It manu-
factured and shipped tens of thousands of lawn mowers, worth tens of millions
of dollars, without quite knowing when they would be sold—a very expensive
approach to meeting demand. Some changes were necessary. The new plan’s goal
is for each distribution center to receive only the minimum inventory necessary
to meet demand. Today, operations managers at Snapper evaluate production
OM in Action capacity and use frequent data from the field as inputs to sophisticated software
to forecast sales. The new system tracks customer demand and aggregates fore-
casts for every model in every region of the country. It even adjusts for holidays
and weather. And the number of distribution centers has been cut from 52 to 4.
Once evaluation of the aggregate plan against capacity determines the
plan to be feasible, Snapper’s planners break down the plan into production
needs for each model. Production by model is accomplished by building rolling
monthly and weekly plans. These plans track the pace at which various units
are selling. Then, the final step requires juggling work assignments to various
work centers for each shift, such as 265 lawn mowers in an 8-hour shift.
That’s a new Snapper every 109 seconds.
Sources: Fair Disclosure Wire (January 17, 2008); The Wall Street Journal (July 14,
2006); Fast Company (January/February 2006); and www.snapper.com .
STUDENT TIP Managers can meet aggregate
plans by adjusting either
capacity or demand.
LO 13.3 Identify optional strategies for developing
an aggregate plan
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536 P A R T 3 | M A N AG I N G O P E R AT I O N S
On the other hand, with low inventory on hand and increasing demand, shortages can occur, resulting in longer lead times and poor customer service.
2. Varying workforce size by hiring or layoffs: One way to meet demand is to hire or lay off production workers to match production rates. However, new employees need to be trained, and productivity drops temporarily as they are absorbed into the workforce. Layoffs or terminations, of course, lower the morale of all workers and also lead to lower productivity.
3. Varying production rates through overtime or idle time: Keeping a constant workforce while varying working hours may be possible. Yet when demand is on a large upswing, there is a limit on how much overtime is realistic. Overtime pay increases costs, and too much overtime can result in worker fatigue and a drop in productivity. Overtime also implies added overhead costs to keep a facility open. On the other hand, when there is a period of decreased demand, the company must somehow absorb workers’ idle time— often a difficult and expensive process.
4. Subcontracting: A firm can acquire temporary capacity by subcontracting work dur- ing peak demand periods. Subcontracting, however, has several pitfalls. First, it may be costly; second, it risks opening the door to a competitor. Third, developing the perfect subcontract supplier can be a challenge.
5. Using part-time workers: Especially in the service sector, part-time workers can fill labor needs. This practice is common in restaurants, retail stores, and supermarkets.
Demand Options The basic demand options are:
1. Influencing demand: When demand is low, a company can try to increase demand through advertising, promotion, personal selling, and price cuts. Airlines and hotels have long offered weekend discounts and off-season rates; theaters cut prices for matinees; some col- leges give discounts to senior citizens; and air conditioners are least expensive in winter. However, even special advertising, promotions, selling, and pricing are not always able to balance demand with production capacity.
2. Back ordering during high-demand periods: Back orders are orders for goods or services that a firm accepts but is unable (either on purpose or by chance) to fill at the moment.
S te
fa n K
ie fe
r/ im
a g e B
R O
K E R
/A la
m y
John Deere and Company, the
“granddaddy” of farm equipment
manufacturers, uses sales
incentives to smooth demand.
During the fall and winter off-
seasons, sales are boosted with
price cuts and other incentives.
About 70% of Deere’s big
machines are ordered in advance
of seasonal use—about double
the industry rate. Incentives hurt
margins, but Deere keeps its
market share and controls costs
by producing more steadily all year
long. Similarly, service businesses
like L.L. Bean offer customers free
shipping on orders placed before
the Christmas rush.
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C H A P T E R 1 3 | AG G R E G AT E P L A N N I N G A N D S & O P 537
If customers are willing to wait without loss of their goodwill or order, back ordering is a possible strategy. Many firms back order, but the approach often results in lost sales.
3. Counterseasonal product and service mixing: A widely used active smoothing technique among manufacturers is to develop a product mix of counterseasonal items. Examples include companies that make both furnaces and air conditioners or lawn mowers and snow- blowers. However, companies that follow this approach may find themselves involved in products or services beyond their area of expertise or beyond their target market.
These eight options, along with their advantages and disadvantages, are summarized in Table 13.1 .
Mixing Options to Develop a Plan Although each of the five capacity options and three demand options discussed above may produce an effective aggregate schedule, some combination of capacity options and demand options may be better.
Many manufacturers assume that the use of the demand options has been fully explored by the marketing department and those reasonable options incorporated into the demand fore- cast. The operations manager then builds the aggregate plan based on that forecast. However, using the five capacity options at his command, the operations manager still has a multitude of possible plans. These plans can embody, at one extreme, a chase strategy and, at the other, a level-scheduling strategy . They may, of course, fall somewhere in between.
Chase Strategy A chase strategy typically attempts to achieve output rates for each period that match the demand forecast for that period. This strategy can be accomplished in a variety of ways. For example, the operations manager can vary workforce levels by hiring or laying off or can vary output by means of overtime, idle time, part-time employees, or subcontract- ing. Many service organizations favor the chase strategy because the changing inventory levels option is difficult or impossible to adopt. Industries that have moved toward a chase strategy include education, hospitality, and construction.
Chase strategy
A planning strategy that sets
production equal to forecast
demand.
TABLE 13.1 Aggregate Planning Options: Advantages and Disadvantages
OPTION ADVANTAGES DISADVANTAGES COMMENTS
Changing inventory levels
Changes in human resources are gradual or none; no abrupt production changes.
Inventory holding costs may increase. Shortages may result in lost sales.
Applies mainly to production, not service, operations.
Varying workforce size by hiring or layoffs
Avoids the costs of other alternatives.
Hiring, layoff, and training costs may be signifi cant.
Used where size of labor pool is large.
Varying production rates through overtime or idle time
Matches seasonal fl uctuations without hiring/training costs.
Overtime premiums; tired workers; may not meet demand.
Allows fl exibility within the aggregate plan.
Subcontracting Permits fl exibility and smoothing of the fi rm’s output.
Loss of quality control; reduced profi ts; potential loss of future business.
Applies mainly in production settings.
Using part-time workers
Is less costly and more fl exible than full-time workers.
High turnover/training costs; quality suffers; scheduling diffi cult.
Good for unskilled jobs in areas with large temporary labor pools.
Infl uencing demand Tries to use excess capacity. Discounts draw new customers.
Uncertainty in demand. Hard to match demand to supply exactly.
Creates marketing ideas. Overbooking used in some businesses.
Back ordering during high-demand periods
May avoid overtime. Keeps capacity constant.
Customer must be willing to wait, but goodwill is lost.
Many companies back order.
Counterseasonal product and service mixing
Fully utilizes resources; allows stable workforce.
May require skills or equipment outside fi rm’s areas of expertise.
Risky fi nding products or services with opposite demand patterns.
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Level Strategy A level strategy (or level scheduling ) is an aggregate plan in which produc- tion is uniform from period to period. Firms like Toyota and Nissan attempt to keep production at uniform levels and may (1) let the finished-goods inventory vary to buffer the difference between demand and production or (2) find alternative work for employees. Their philoso- phy is that a stable workforce leads to a better-quality product, less turnover and absentee- ism, and more employee commitment to corporate goals. Other hidden savings include more experienced employees, easier scheduling and supervision, and fewer dramatic startups and shutdowns. Level scheduling works well when demand is reasonably stable.
For most firms, neither a chase strategy nor a level strategy is likely to prove ideal, so a com- bination of the eight options (called a mixed strategy ) must be investigated to achieve minimum cost. However, because there are a huge number of possible mixed strategies, managers find that aggregate planning can be a challenging task. Finding the one “optimal” plan is not always possible, but as we will see in the next section, a number of techniques have been developed to aid the aggregate planning process.
Methods for Aggregate Planning In this section, we introduce techniques that operations managers use to develop aggregate plans. They range from the widely used graphical method to the transportation method of linear programming.
Graphical Methods Graphical techniques are popular because they are easy to understand and use. These plans work with a few variables at a time to allow planners to compare projected demand with exist- ing capacity. They are trial-and-error approaches that do not guarantee an optimal produc- tion plan, but they require only limited computations and can be performed by clerical staff. Following are the five steps in the graphical method:
1. Determine the demand in each period. 2. Determine capacity for regular time, overtime, and subcontracting each period. 3. Find labor costs, hiring and layoff costs, and inventory holding costs. 4. Consider company policy that may apply to the workers or to stock levels. 5. Develop alternative plans and examine their total costs.
These steps are illustrated in Examples 1 through 4 .
Level scheduling
Maintaining a constant output rate,
production rate, or workforce level
over the planning horizon.
Mixed strategy
A planning strategy that uses two
or more controllable variables to
set a feasible production plan.
Graphical techniques
Aggregate planning techniques
that work with a few variables at a
time to allow planners to compare
projected demand with existing
capacity.
LO 13.4 Prepare a graphical aggregate plan
A Juarez, Mexico, manufacturer of roofing supplies has developed monthly forecasts for a family of products. Data for the 6-month period January to June are presented in Table 13.2 . The firm would like to begin development of an aggregate plan.
Example 1 GRAPHICAL APPROACH TO AGGREGATE PLANNING FOR A ROOFING SUPPLIER
TABLE 13.2 Monthly Forecasts
MONTH EXPECTED DEMAND PRODUCTION DAYS DEMAND PER DAY
(COMPUTED)
Jan. 900 22 41
Feb. 700 18 39
Mar. 800 21 38
Apr. 1,200 21 57
May 1,500 22 68
June 1,100 20 55
6,200 124
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APPROACH c Plot daily and average demand to illustrate the nature of the aggregate planning problem.
SOLUTION c First, compute demand per day by dividing the expected monthly demand by the num- ber of production days (working days) each month and drawing a graph of those forecast demands ( Figure 13.3 ). Second, draw a dotted line across the chart that represents the production rate required to meet average demand over the 6-month period. The chart is computed as follows:
Average requirement = Total expected demand
Number of production days =
6,200 124
= 50 units per day
INSIGHT c Changes in the production rate become obvious when the data are graphed. Note that in the first 3 months, expected demand is lower than average, while expected demand in April, May, and June is above average.
LEARNING EXERCISE c If demand for June increases to 1,200 (from 1,100), what is the impact on Figure 13.3 ? [Answer: The daily rate for June will go up to 60, and average production will increase to 50.8 ( 6,300>124 ).]
RELATED PROBLEM c 13.1
Level production, using average monthly forecast demand
Jan.
22
Forecast demand
70
60
50
40
30
0
P ro
d u ct
io n r
a te
p e r
w o rk
in g d
a y
Feb.
18
Mar.
21
Apr.
21
May
22
June
20
Month
Number of working days
=
=
Figure 13.3
Graph of Forecast and Average
Forecast Demand
The graph in Figure 13.3 illustrates how the forecast differs from the average demand. Some strategies for meeting the forecast were listed earlier. The firm, for example, might staff in order to yield a production rate that meets average demand (as indicated by the dashed line). Or it might produce a steady rate of, say, 30 units and then subcontract excess demand to other roofing suppliers. Other plans might combine overtime work with subcontracting to absorb demand or vary the workforce by hiring and laying off. Examples 2 to 4 illustrate three possible strategies.
One possible strategy (call it plan 1) for the manufacturer described in Example 1 is to maintain a con- stant workforce throughout the 6-month period. A second (plan 2) is to maintain a constant workforce at a level necessary to meet the lowest demand month (March) and to meet all demand above this level by subcontracting. Both plan 1 and plan 2 have level production and are, therefore, called level strate- gies . Plan 3 is to hire and lay off workers as needed to produce exact monthly requirements— a chase strategy . Table 13.3 provides cost information necessary for analyzing these three alternatives.
Example 2 PLAN 1 FOR THE ROOFING SUPPLIER—A CONSTANT WORKFORCE
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ANALYSIS OF PLAN 1 APPROACH c Here we assume that 50 units are produced per day and that we have a constant workforce, no overtime or idle time, no safety stock, and no subcontractors. The firm accumulates inventory during the slack period of demand, January through March, and depletes it during the higher-demand warm season, April through June. We assume beginning inventory = 0 and planned ending inventory = 0.
SOLUTION c We construct the table below and accumulate the costs:
MONTH PRODUCTION
DAYS PRODUCTION
AT 50 UNITS PER DAY DEMAND FORECAST
MONTHLY INVENTORY CHANGE
ENDING INVENTORY
Jan. 22 1,100 900 + 200 200
Feb. 18 900 700 + 200 400
Mar. 21 1,050 800 + 250 650
Apr. 21 1,050 1,200 2150 500
May 22 1,100 1,500 2400 100
June 20 1,000 1,100 2100 0
1,850
Total units of inventory carried over from one month to the next month = 1,850 units Workforce required to produce 50 units per day = 10 workers
Because each unit requires 1.6 labor-hours to produce, each worker can make 5 units in an 8-hour day. Therefore, to produce 50 units, 10 workers are needed.
Finally, the costs of plan 1 are computed as follows:
COST CALCULATIONS
Inventory carrying $ 9,250 (5 1,850 units carried 3 $5 per unit)
Regular-time labor 99,200 (5 10 workers 3 $80 per day 3 124 days)
Other costs (overtime, hiring, layoffs, subcontracting) 0
Total cost $108,450
INSIGHT c Note the significant cost of carrying the inventory.
LEARNING EXERCISE c If demand for June decreases to 1,000 (from 1,100), what is the change in cost? [Answer: Total inventory carried will increase to 1,950 at $5, for an inventory cost of $9,750 and total cost of $108,950.]
RELATED PROBLEMS c 13.2–13.12, 13.19 (13.23 is available in MyOMLab)
EXCEL OM Data File Ch13Ex2.xls can be found in MyOMLab.
ACTIVE MODEL 13.1 This example is further illustrated in Active Model 13.1 in MyOMLab.
TABLE 13.3 Cost Information
Inventory carrying cost $ 5 per unit per month
Subcontracting cost per unit $ 20 per unit
Average pay rate $ 10 per hour ($80 per day)
Overtime pay rate $ 17 per hour (above 8 hours per day)
Labor-hours to produce a unit 1.6 hours per unit
Cost of increasing daily production rate (hiring and training)
$300 per unit
Cost of decreasing daily production rate (layoffs) $600 per unit
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The graph for Example 2 was shown in Figure 13.3 . Some planners prefer a cumulative graph to display visually how the forecast deviates from the average requirements. Such a graph is provided in Figure 13.4 . Note that both the level production line and the forecast line produce the same total production.
7,000
C u m
u la
tiv e d
e m
a n d u
n its
6,000
5,000
4,000
3,000
2,000
1,000
Jan. Feb. Mar. Apr. May June Month
Cumulative forecast requirements
Cumulative level of production, using average monthly forecast requirements
Excess inventory
Reduction of inventory
6,200 units
Figure 13.4
Cumulative Graph for Plan 1
STUDENT TIP We saw another way to graph
this data in Figure 13.3 .
ANALYSIS OF PLAN 2 APPROACH c Although a constant workforce is also maintained in plan 2, it is set low enough to meet demand only in March, the lowest demand-per-day month. To produce 38 units per day (800/21) in-house, 7.6 workers are needed. (You can think of this as 7 full-time workers and 1 part-timer.) All other demand is met by subcontracting. Subcontracting is thus required in every other month. No inventory holding costs are incurred in plan 2.
SOLUTION c Because 6,200 units are required during the aggregate plan period, we must compute how many can be made by the firm and how many must be subcontracted:
In@house production = 38 units per day * 124 production days = 4,712 units Subcontract units = 6,200 - 4,712 = 1,488 units
The costs of plan 2 are computed as follows:
COST CALCULATIONS
Regular-time labor $ 75,392 (5 7.6 workers 3 $80 per day 3 124 days)
Subcontracting 29,760 (5 1,488 units 3 $20 per unit)
Total cost $105,152
INSIGHT c Note the lower cost of regular labor but the added subcontracting cost.
LEARNING EXERCISE c If demand for June increases to 1,200 (from 1,100), what is the change in cost? [Answer: Subcontracting requirements increase to 1,588 at $20 per unit, for a subcontracting cost of $31,760 and a total cost of $107,152.]
RELATED PROBLEMS c 13.2–13.12, 13.19 (13.23 is available in MyOMLab)
Example 3 PLAN 2 FOR THE ROOFING SUPPLIER—USE OF SUBCONTRACTORS WITHIN A CONSTANT WORKFORCE
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The final step in the graphical method is to compare the costs of each proposed plan and to select the approach with the least total cost. A summary analysis is provided in Table 13.5 . We see that because plan 2 has the lowest cost, it is the best of the three options.
ANALYSIS OF PLAN 3 APPROACH c The final strategy, plan 3, involves varying the workforce size by hiring and layoffs as necessary. The production rate will equal the demand, and there is no change in production from the previous month, December.
SOLUTION c Table 13.4 shows the calculations and the total cost of plan 3. Recall that it costs $600 per unit produced to reduce production from the previous month’s daily level and $300 per unit change to increase the daily rate of production through hirings.
Example 4 PLAN 3 FOR THE ROOFING SUPPLIER—HIRING AND LAYOFFS
Thus, the total cost, including production, hiring, and layoff, for plan 3 is $117,800.
INSIGHT c Note the substantial cost associated with changing (both increasing and decreasing) the production levels.
LEARNING EXERCISE c If demand for June increases to 1,200 (from 1,100), what is the change in cost? [Answer: Daily production for June is 60 units, which is a decrease of 8 units in the daily production rate from May’s 68 units, so the new June layoff cost is +4,800 (= 8 * +600), but an additional produc- tion cost for 100 units is $1,600 ( 100 * 1.6 * $10) with a total plan 3 cost of $116,400.]
RELATED PROBLEMS c 13.2–13.12, 13.19 (13.23 is available in MyOMLab)
TABLE 13.4 Cost Computations for Plan 3
MONTH FORECAST
(UNITS)
DAILY PRODUCTION
RATE
BASIC PRODUCTION
COST (DEMAND 31.6 HR PER UNIT 3
$10 PER HR)
EXTRA COST OF
INCREASING PRODUCTION (HIRING COST)
EXTRA COST OF
DECREASING PRODUCTION
(LAYOFF COST) TOTAL COST
Jan. 900 41 $14,400 — — $ 14,400
Feb. 700 39 11,200 — $1,200 (5 2 3 $600) 12,400
Mar. 800 38 12,800 — $ 600 (= 1 * +600 ) 13,400
Apr. 1,200 57 19,200 $5,700 (= 19 * +300) — 24,900
May 1,500 68 24,000 $3,300 (= 11 * +300) — 27,300
June 1,100 55 17,600 — $7,800 (5 13 3 $600) $ 25,400
$99,200 $9,000 $9,600 $117,800
TABLE 13.5 Comparison of the Three Plans
COST
PLAN 1 (CONSTANT
WORKFORCE OF 10 WORKERS)
PLAN 2 (WORKFORCE OF
7.6 WORKERS PLUS SUBCONTRACTORS)
PLAN 3 (HIRING AND LAYOFFS TO
MEET DEMAND)
Inventory carrying $ 9,250 $ 0 $ 0
Regular labor 99,200 75,392 99,200
Overtime labor 0 0 0
Hiring 0 0 9,000
Layoffs 0 0 9,600
Subcontracting 0 29,760 0
Total cost $108,450 $105,152 $117,800
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Of course, many other feasible strategies can be considered in a problem like this, includ- ing combinations that use some overtime. Although graphing is a popular management tool, its help is in evaluating strategies, not generating them. To generate strategies, a systematic approach that considers all costs and produces an effective solution is needed.
Mathematical Approaches This section briefly describes mathematical approaches to aggregate planning.
The Transportation Method of Linear Programming When an aggregate plan- ning problem is viewed as one of allocating operating capacity to meet forecast demand, it can be formulated in a linear programming format. The transportation method of linear programming is not a trial-and-error approach like graphing but rather produces an optimal plan for minimiz- ing costs. It is also flexible in that it can specify regular and overtime production in each time period, the number of units to be subcontracted, extra shifts, and the inventory carryover from period to period.
In Example 5 , the supply consists of on-hand inventory and units produced by regular time, overtime, and subcontracting. Costs per unit, in the upper-right corner of each cell of the matrix in Table 13.7 , relate to units produced in a given period or units carried in inventory from an earlier period.
Transportation method of linear programming
A way of solving for the optimal
solution to an aggregate planning
problem.
Farnsworth Tire Company would like to develop an aggregate plan via the transportation method. Data that relate to production, demand, capacity, and cost at its West Virginia plant are shown in Table 13.6 .
Example 5 AGGREGATE PLANNING WITH THE TRANSPORTATION METHOD
APPROACH c Solve the aggregate planning problem by minimizing the costs of matching production in various periods to future demands.
SOLUTION c Table 13.7 illustrates the structure of the transportation table and an initial feasible solution.
TABLE 13.6 Farnsworth’s Production, Demand, Capacity, and Cost Data
SALES PERIOD
MAR. APR. MAY
Demand 800 1,000 750
Capacity:
Regular 700 700 700
Overtime 50 50 50
Subcontracting 150 150 130
Beginning inventory 100 tires
COSTS
Regular time $40 per tire
Overtime $50 per tire
Subcontract $70 per tire
Carrying cost $ 2 per tire per month
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When setting up and analyzing this table, you should note the following:
1. Carrying costs are $2/tire per month. Tires produced in 1 period and held for 1 month will have a $2 higher cost. Because holding cost is linear, 2 months’ holdover costs $4. So when you move across a row from left to right, regular time, overtime, and subcontracting costs are lowest when output is used in the same period it is produced. If goods are made in one period and carried over to the next, holding costs are incurred. Beginning inventory, however, is generally given a unit cost of 0 if it is used to satisfy demand in period 1.
2. Transportation problems require that supply equals demand, so a dummy column called “unused capacity” has been added. Costs of not using capacity are zero.
3. Because back ordering is not a viable alternative for this particular company, no production is possible in those cells that represent production in a period to satisfy demand in a past period (i.e., those periods with an “3”). If back ordering is allowed, costs of expediting, loss of goodwill, and loss of sales revenues are summed to estimate backorder cost.
4. Quantities in red in each column of Table 13.7 designate the levels of inventory needed to meet demand requirements (shown in the bottom row of the table). Demand of 800 tires in March is met by using 100 tires from beginning inventory and 700 tires from regular time.
5. In general, to complete the table, allocate as much production as you can to a cell with the smallest cost without exceeding the unused capacity in that row or demand in that column. If there is still some demand left in that row, allocate as much as you can to the next-lowest-cost cell. You then repeat this process for periods 2 and 3 (and beyond, if necessary). When you are fi nished, the sum of all your entries in a row must equal the total row capacity, and the sum of all entries in a column must equal the demand for that period. (This step can be accomplished by the transportation method or by using POM for Windows or Excel OM software.)
TABLE 13.7 Farnsworth’s Transportation Table a
DEMAND FOR
TOTAL Unused CAPACITY
Period 1 Period 2 Period 3 Capacity AVAILABLE SUPPLY FROM (Mar.) (Apr.) (May) (dummy) (supply)
0 2 4 0
Beginning inventory 100 100 40 42 44 0
Regular time 700 700 50 52 54 0
Overtime 50 50 70 72 74 0
Subcontract 150 150 40 42 0
Regular time 700 700 50 52 0
Overtime 50 50 70 72 0
Subcontract 50 100 150 40 0
Regular time 700 700 50 0
Overtime 50 50 70 0
Subcontract 130 130 TOTAL DEMAND 800 1,000 750 230 2,780
P e r i o d
1
P e r i o d
2
P e r i o d
3
a Cells with an x indicate that back orders are not used at Farnsworth. When using Excel OM or POM for Windows to solve, you must insert a very high cost (e.g., 9999) in each cell that is not used for production.
LO 13.5 Solve an aggregate plan via the
transportation method
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The transportation method of linear programming described in the preceding example works well when analyzing the effects of holding inventories, using overtime, and subcontract- ing. However, it does not work when nonlinear or negative factors are introduced. Thus, when other factors such as hiring and layoffs are introduced, the more general method of linear programming must be used. Similarly, computer simulation models look for a minimum-cost combination of values.
A number of commercial S&OP software packages that incorporate the techniques of this chapter are available to ease the mechanics of aggregate planning. These include Arkieva’s S&OP Workbench for process industries, Demand Solutions’s S&OP Software, and Steel- wedge’s S&OP Suite.
Aggregate Planning in Services Some service organizations conduct aggregate planning in exactly the same way as we did in Examples 1 through 5 in this chapter, but with demand management taking a more active role. Because most services pursue combinations of the eight capacity and demand options dis- cussed earlier, they usually formulate mixed aggregate planning strategies. In industries such as banking, trucking, and fast foods, aggregate planning may be easier than in manufacturing.
Controlling the cost of labor in service firms is critical. Successful techniques include:
1. Accurate scheduling of labor-hours to ensure quick response to customer demand 2. An on-call labor resource that can be added or deleted to meet unexpected demand 3. Flexibility of individual worker skills that permits reallocation of available labor 4. Flexibility in rate of output or hours of work to meet changing demand
These options may seem demanding, but they are not unusual in service industries, in which labor is the primary aggregate planning vehicle. For instance:
◆ Excess capacity is used to provide study and planning time by real estate and auto salespersons.
◆ Police and fire departments have provisions for calling in off-duty personnel for major emergencies. Where the emergency is extended, police or fire personnel may work longer hours and extra shifts.
◆ When business is unexpectedly light, restaurants and retail stores send personnel home early.
◆ Supermarket stock clerks work cash registers when checkout lines become too lengthy. ◆ Experienced waitresses increase their pace and efficiency of service as crowds of customers
arrive.
Approaches to aggregate planning differ by the type of service provided. Here we discuss five service scenarios.
Try to confirm that the cost of this initial solution is $105,900. The initial solution is not optimal, however. See if you can find the production schedule that yields the least cost (which turns out to be $105,700) using software or by hand.
INSIGHT c The transportation method is flexible when costs are linear but does not work when costs are nonlinear.
LEARNING EXAMPLE c What is the impact on this problem if there is no beginning inventory? [Answer: Total capacity (units) available is reduced by 100 units and the need to subcontract increases by 100 units.]
RELATED PROBLEMS c 13.13–13.18 (13.20–13.22 are available in MyOMLab)
EXCEL OM Data File Ch13Ex5.xls can be found in MyOMLab.
STUDENT TIP The major variable in capacity
management for services is
labor.
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Restaurants In a business with a highly variable demand, such as a restaurant, aggregate scheduling is directed toward (1) smoothing the production rate and (2) finding the optimal size of the workforce. The general approach usually requires building very modest levels of inventory during slack periods and depleting inventory during peak periods, but using labor to accom- modate most of the changes in demand. Because this situation is very similar to those found in manufacturing, traditional aggregate planning methods may be applied to restaurants as well. One difference is that even modest amounts of inventory may be perishable. In addition, the relevant units of time may be much smaller than in manufacturing. For example, in fast- food restaurants, peak and slack periods may be measured in fractions of an hour, and the “product” may be inventoried for as little as 10 minutes.
Hospitals Hospitals face aggregate planning problems in allocating money, staff, and supplies to meet the demands of patients. Michigan’s Henry Ford Hospital, for example, plans for bed capac- ity and personnel needs in light of a patient-load forecast developed by moving averages. The necessary labor focus of its aggregate plan has led to the creation of a new floating staff pool serving each nursing pod.
National Chains of Small Service Firms With the advent of national chains of small service businesses such as funeral homes, oil change outlets, and photocopy/printing centers, the question of aggregate planning versus independent planning at each business establishment becomes an issue. Both purchases and production capacity may be centrally planned when demand can be influenced through special promotions. This approach to aggregate scheduling is often advantageous because it reduces costs and helps manage cash flow at independent sites.
Miscellaneous Services Most “miscellaneous” services—financial, transportation, and many communication and recreation services—provide intangible output. Aggregate planning for these services deals mainly with planning for human resource requirements and managing demand. The twofold goal is to level demand peaks and to design methods for fully utilizing labor resources during low-demand periods. Example 6 illustrates such a plan for a legal firm.
Klasson and Avalon, a medium-size Tampa law firm of 32 legal professionals, wants to develop an aggregate plan for the next quarter. The firm has developed 3 forecasts of billable hours for the next quarter for each of 5 categories of legal business it performs (column 1, Table 13.8 ). The 3 forecasts (best, likely, and worst) are shown in columns 2, 3, and 4 of Table 13.8 .
Example 6 AGGREGATE PLANNING IN A LAW FIRM
TABLE 13.8
Labor Allocation at Klasson and Avalon, Forecasts for Coming Quarter (1 lawyer 5 500 hours of labor)
FORECASTED LABOR-HOURS REQUIRED CAPACITY CONSTRAINTS
(1) CATEGORY OF LEGAL BUSINESS
(2) BEST
(HOURS)
(3) LIKELY
(HOURS)
(4) WORST (HOURS)
(5) MAXIMUM DEMAND
FOR PERSONNEL
(6) NUMBER OF
QUALIFIED PERSONNEL
Trial work 1,800 1,500 1,200 3.6 4
Legal research 4,500 4,000 3,500 9.0 32
Corporate law 8,000 7,000 6,500 16.0 15
Real estate law 1,700 1,500 1,300 3.4 6
Criminal law 3,500 3,000 2,500 7.0 12
Total hours 19,500 17,000 15,000
Lawyers needed 39 34 30
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Airline Industry Airlines and auto-rental firms also have unique aggregate scheduling problems. Consider an airline that has its headquarters in New York, two hub sites in cities such as Atlanta and Dallas, and 150 offices in airports throughout the country. This planning is considerably more complex than aggregate planning for a single site or even for a number of independent sites.
Aggregate planning consists of schedules for (1) number of flights into and out of each hub; (2) number of flights on all routes; (3) number of passengers to be serviced on all flights; (4) number of air personnel and ground personnel required at each hub and airport; and (5) determining the seats to be allocated to various fare classes. Techniques for determining seat allocation are called revenue (or yield) management, our next topic.
Revenue Management Most operations models, like most business models, assume that firms charge all customers the same price for a product. In fact, many firms work hard at charging different prices. The idea is to match capacity and demand by charging different prices based on the customer’s willing- ness to pay. The management challenge is to identify those differences and price accordingly. The technique for multiple price points is called revenue management.
Revenue (or yield) management is the aggregate planning process of allocating the company’s scarce resources to customers at prices that will maximize revenue. Popular use of the technique dates to the 1980s, when American Airlines’s reservation system (called SABRE) allowed the airline to alter ticket prices, in real time and on any route, based on demand information. If it looked like demand for expensive seats was low, more discounted seats were offered. If demand for full-fare seats was high, the number of discounted seats was reduced.
APPROACH c If we make some assumptions about the workweek and skills, we can provide an aggre- gate plan for the firm. Assuming a 40-hour workweek and that 100% of each lawyer’s hours are billed, about 500 billable hours are available from each lawyer this fiscal quarter.
SOLUTION c We divide hours of billable time (which is the demand) by 500 to provide a count of lawyers needed (lawyers represent the capacity) to cover the estimated demand. Capacity then is shown to be 39, 34, and 30 for the three forecasts, best, likely, and worst, respectively. For example, the best-case scenario of 19,500 total hours, divided by 500 hours per lawyer, equals 39 lawyers needed. Because all 32 lawyers at Klasson and Avalon are qualified to perform basic legal research, this skill has maximum scheduling flexibility (column 6). The most highly skilled (and capacity-constrained) categories are trial work and corporate law. The firm’s best-case forecast just barely covers trial work, with 3.6 lawyers needed (see column 5) and 4 qualified (column 6). And corporate law is short 1 full person.
Overtime may be used to cover the excess this quarter, but as business expands, it may be necessary to hire or develop talent in both of these areas. Available staff adequately covers real estate and criminal practice, as long as other needs do not use their excess capacity. With its current legal staff of 32, Klasson and Avalon’s best-case forecast will increase the workload by [( 39 - 32)>32 = ] 21.8% (assuming no new hires). This represents 1 extra day of work per lawyer per week. The worst-case scenario will result in about a 6% underutilization of talent. For both of these scenarios, the firm has determined that available staff will provide adequate service.
INSIGHT c While our definitions of demand and capacity are different than for a manufacturing firm, aggregate planning is as appropriate, useful, and necessary in a service environment as in manufacturing.
LEARNING EXERCISE c If the criminal law best-case forecast increases to 4,500 hours, what happens to the number of lawyers needed? [Answer: The demand for lawyers increases to 41.]
RELATED PROBLEMS c 13.24, 13.25 Source: Based on Glenn Bassett, Operations Management for Service Industries (Westport, CT: Quorum Books): 110.
STUDENT TIP Revenue management changes
the focus of aggregate planning
from capacity management to
demand management.
Revenue (or yield) management
Capacity decisions that determine
the allocation of resources to
maximize revenue or yield.
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American Airlines’ success in revenue management spurred many other companies and industries to adopt the concept. Revenue management in the hotel industry began in the late 1980s at Marriott International, which now claims an additional $400 million a year in profit from its management of revenue. The competing Omni hotel chain uses software that performs more than 100,000 calculations every night at each facility. The Dallas Omni, for example, charges its highest rates on weekdays but heavily discounts on weekends. Its sister hotel in San Antonio, which is in a more tourist-oriented destination, reverses this rating scheme, with better deals for its consumers on weekdays. Similarly, Walt Disney World has multiple prices: an annual admission “premium” pass for an adult was recently quoted at $779, but for a Florida resident, $691, with different discounts for AAA members and active-duty military. The OM in Action box, “Revenue Management Makes Disney the ‘King’ of the Broadway Jungle,” describes this practice in the live theatre industry. The video case at the end of this chapter addresses revenue management for the Orlando Magic.
Organizations that have perishable inventory , such as airlines, hotels, car rental agencies, cruise lines, and even electrical utilities, have the following shared characteristics that make yield management of interest: 1
1. Service or product can be sold in advance of consumption 2. Fluctuating demand 3. Relatively fixed resource (capacity) 4. Segmentable demand 5. Low variable costs and high fixed costs
Example 7 illustrates how revenue management works in a hotel.
Revenue Management Makes Disney the “King” of the Broadway Jungle
Disney accomplished the unthinkable for long-running Broadway musicals:
The Lion King transformed from a declining money-maker into the top-grossing
Broadway show. How? Hint: It’s not because the show added performances
after 16 years.
The show’s producers are using a previously undisclosed computer algorithm
to recommend the highest ticket prices that audiences would be likely to pay for
each of the 1,700 seats. Other shows also employ this dynamic pricing model
to raise seat prices during tourist-heavy holiday weeks, but only Disney has
reached the level of sophistication achieved in the airline and hotel industries.
By continually using its algorithm to calibrate prices based on ticket demand and
purchasing patterns, Disney was able to achieve the 2013 sales record.
By charging $10 more here, $20 more there, The Lion King stunned Broad-
way at year’s end as the No. 1 earner for the first time since 2003, bumping
off the champ, Wicked . And Disney even managed to do it by charging half as
much for top tickets as some rivals. “Credit the management science experts
at Disney’s corporate offices—a data army that no Broadway producer could ever
match—for helping develop the winning formula,” writes The New York Times .
Disney’s algorithm, a software tool that draws on Lion King data for 11.5 million
past customers, recommends prices for multiple categories of performances—
OM in Action
peak dates such as Christmas, off-peak dates such as a weeknight in February,
and various periods in between. “The Lion King” is widely believed to have sold
far more seats for $227 than most Broadway shows sell at their top rates, a
situation that bolsters its grosses.
Sources: NY Daily News (September 22, 2014); and The New York Times
(March 17, 2014).
VIDEO 13.1 Using Revenue Management to Set
Orlando Magic Ticket Prices
LO 13.6 Understand and solve a revenue
management problem
The Cleveland Downtown Inn is a 100-room hotel that has historically charged one set price for its rooms, $150 per night. The variable cost of a room being occupied is low. Management believes the cleaning, air-conditioning, and incidental costs of soap, shampoo, and so forth, are $15 per room per night. Sales average 50 rooms per night. Figure 13.5 illustrates the current pricing scheme. Net sales are $6,750 per night with a single price point.
Example 7 REVENUE MANAGEMENT
© J
T B
M E D
IA C
R E A
T IO
N , In
c. /
A la
m y
S to
ck P
h o to
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APPROACH c Analyze pricing from the perspective of revenue management. We note in Figure 13.5 that some guests would have been willing to spend more than $150 per room—“money left on the table.” Others would be willing to pay more than the variable cost of $15 but less than $150—“passed-up contribution.”
SOLUTION c In Figure 13.6 , the inn decides to set two price levels. It estimates that 30 rooms per night can be sold at $100 and another 30 rooms at $200, using revenue management software that is widely available.
INSIGHT c Revenue management has increased total contribution to $8,100 ($2,550 from $100 rooms and $5,550 from $200 rooms). It may be that even more price levels are called for at Cleveland Downtown Inn.
LEARNING EXERCISE c If the hotel develops a third price of $150 and can sell half of the $100 rooms at the increased rate, what is the contribution? [Answer: $8,850 5 (15 3 $85) 1 (15 3 $135) 1 (30 3 $185).]
RELATED PROBLEM c 13.26
Passed-up contribution
Money left on the table
Demand curve
100
50
Potential customers exist who are willing to pay more than the $15 variable cost of the room, but not $150.
Some customers who paid $150 were actually willing to pay more for the room.
Total $ contribution = (Price) * (50 rooms) = ($150 - $15)(50) = $6,750
$15 Variable cost
of room (e.g., cleaning, A/C)
$150 Price charged
for room
Price
Room Sales Figure 13.5
Hotel Sets Only One Price Level
Figure 13.6
Hotel with Two Price Levels
Demand curve Total $ contribution =
(1st price) * 30 rooms + (2nd price) * 30 rooms = ($100 - $15) * 30 + ($200 - $15) * 30 =
$2,550 + $5,550 = $8,100
$15 Variable
cost of room
100
60
30
$100 Price 1
for room
$200 Price 2
for room
Price
Room Sales
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Industries traditionally associated with revenue management include hotels, airlines, and rental cars. They are able to apply variable pricing for their product and control product use or availability (number of airline seats or hotel rooms sold at economy rates). Others, such as movie theaters, arenas, or performing arts centers have less pricing flexibility but still use time (evening or matinee) and location (orchestra, side, or balcony) to manage revenue. In both cases, management has control over the amount of the resource used—both the quantity and the duration of the resource.
The manager’s job is more difficult in facilities such as restaurants and on golf courses be- cause the duration and the use of the resource is less controllable. However, with imagination, managers are using excess capacity even for these industries. For instance, the golf course may sell less desirable tee times at a reduced rate, and the restaurant may have an “early bird” special to generate business before the usual dinner hour.
To make revenue management work, the company needs to manage three issues:
1. Multiple pricing structures: These structures must be feasible and appear logical (and pref- erably fair) to the customer. Such justification may take various forms, for example, first- class seats on an airline or the preferred starting time at a golf course. (See the Ethical Dilemma at the end of this chapter.)
2. Forecasts of the use and duration of the use: How many economy seats should be available? How much will customers pay for a room with an ocean view?
3. Changes in demand: This means managing the increased use as more capacity is sold. It also means dealing with issues that occur because the pricing structure may not seem logi- cal and fair to all customers. Finally, it means managing new issues, such as overbooking because the forecast was not perfect.
Precise pricing through revenue management has substantial potential, and several firms sell software available to address the issue. These include NCR’s Teradata, SPS, DemandTec, and Oracle with Profit Logic .
Summary Sales and operations planning (S&OP) can be a strong vehicle for coordinating the functional areas of a firm as well as for communication with supply-chain partners. The output of S&OP is an aggregate plan. An aggregate plan provides both manufacturing and service firms the ability to respond to changing customer demands and produce with a winning strategy.
Aggregate schedules set levels of inventory, production, subcontracting, and employment over an intermediate time range, usually 3 to 18 months. This chapter describes two aggregate planning techniques: the popular graphi- cal approach and the transportation method of linear programming.
The aggregate plan is an important responsibility of an operations manager and a key to efficient use of exist- ing resources. It leads to the more detailed master produc- tion schedule, which becomes the basis for disaggregation, detail scheduling, and MRP systems.
Restaurants, airlines, and hotels are all service systems that employ aggregate plans. They also have an opportu- nity to implement revenue management.
Regardless of the industry or planning method, the S&OP process builds an aggregate plan that a firm can implement and suppliers endorse.
Key Terms
Sales and operations planning (S&OP) (p. 533 )
Aggregate plan (p. 533 ) Disaggregation (p. 535 ) Master production schedule (p. 535 )
Chase strategy (p. 537 ) Level scheduling (p. 538 ) Mixed strategy (p. 538 ) Graphical techniques (p. 538 )
Transportation method of linear programming (p. 543 )
Revenue (or yield) management (p. 547 )
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Ethical Dilemma Airline passengers today stand in numerous lines, are crowded into small seats on mostly full airplanes, and often spend time on taxiways because of air-traffi c problems or lack of open gates. But what gripes travelers almost as much as these annoyances is fi nding out that the person sitting next to them paid a much lower fare than they did for their seat. This concept of revenue management results in ticket pricing that can range from free to thousands of dollars on the same plane. Figure 13.7
illustrates what passengers recently paid for various seats on an 11:35 A.M. fl ight from Minneapolis to Anaheim, California, on an Airbus A320.
Make the case for, and then against, this pricing system. Does the general public seem to accept revenue management? What would happen if you overheard the person in front of you in line getting a better room rate at a Hilton Hotel? How do customers manipulate the airline systems to get better fares?
First class $817 1
792 4
491 5
273 20
190 33
0 7
— 53
— 27
Full fare
Corporate discount
21-day advance
Deep discounts
Frequent flyer program
Connections
Empty
Sales Fare Seats $817
$792
FREE
$491
$190
$273
Figure 13.7
Revenue Management Seat Costs on a Typical Flight
1. Define sales and operations planning . 2. Why are S&OP teams typically cross-functional? 3. Define aggregate planning . 4. Explain what the term aggregate in “aggregate planning”
means. 5. List the strategic objectives of aggregate planning. Which
one of these is most often addressed by the quantitative tech- niques of aggregate planning? Which one of these is generally the most important?
6. Define chase strategy . 7. What is level scheduling? What is the basic philosophy under-
lying it? 8. Define mixed strategy . Why would a firm use a mixed strat-
egy instead of a simple pure strategy?
9. What are the advantages and disadvantages of varying the size of the workforce to meet demand requirements each period?
10. How does aggregate planning in service differ from aggregate planning in manufacturing?
11. What is the relationship between the aggregate plan and the master production schedule?
12. Why are graphical aggregate planning methods useful? 13. What are major limitations of using the transportation
method for aggregate planning? 14. How does revenue management impact an aggregate
plan?
Discussion Questions
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Using Software for Aggregate Planning
This section illustrates the use of Excel, Excel OM, and POM for Windows in aggregate planning.
CREATING YOUR OWN EXCEL SPREADSHEETS Program 13.1 illustrates how you can make an Excel model to solve Example 5 , which uses the transportation method for aggregate planning.
Enter a cost of 0 in the Cost Table for beginning inventory in Period 1 and for all unused capacity entries.
Enter an unacceptably large cost (9999) in the Cost Table for all entries that would result in a back order.
Enter decisions in the Transportation Table. For each row, the sum in Column G must equal the available capacity in Column I. For each column, the sum in Row 43 must equal the demand for that period in Row 45.
=$C$6
=$C$5 =C14+$C$8
=D14+$C$8
=SUM(C33:C42)
=SUM(I33:I42)-SUM(C45:E45)
=SUMPRODUCT(C14:F23,C33:F42)
=$C$7
Actions Copy C15:C17 to D18:D20 Copy C15:C17 to E21:E23 Copy D14 to D15:D17 Copy E14 to E15:E20
=SUM(C33:F33)
Actions Copy C43 to D43:F43 Copy G33 to G34:G42
Program 13.1
Using Excel for Aggregate Planning Via the Transportation Method, with Data from Example 5
Excel comes with an Add-In called Solver that offers the ability to analyze linear programs such as the transportation problem. To ensure that Solver always loads when Excel is loaded, go to: File →Options →Add-Ins. Next to Manage: at the bottom, make sure that Excel Add-ins is selected, and click on the <Go...> button. Check Solver Add-in, and click <OK>. Once in Excel, the Solver dialog box will appear by clicking on: Data→Analysis: Solver. The following screen shot shows how to use Solver to find the optimal (very best) solution to Example 4 . Click on <Solve>, and the solution will automatically appear in the Transportation Table, yielding a cost of $105,700.
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X USING EXCEL OM Excel OM’s Aggregate Planning module is demonstrated in Program 13.2 . Using data from Example 2 , Program 13.2 provides input and some of the formulas used to compute the costs of regular time, overtime, subcontracting, holding, shortage, and increase or decrease in production. The user must provide the production plan for Excel OM to analyze.
Enter the demands in column B and the number of units produced in each period in column C.
Enter the costs. Regular time and overtime costs must be computed based on production hours and labor rates, i.e., 10*1.6 and 17*1.6.
= SUM(B17:B22)
= SUM(B25:L25) Although the first period inventory relies on the initial inventory (B12), the others rely on the previous inventory in column G. Thus inventory in the first period is computed somewhat differently than the inventory in the other periods. The formula for G22 is = G21 + SUM(C22:E22) – B22.
The IF function is used [with the command = IF(G17> 0, –G17, 0)] to determine whether the inventory is positive (and therefore held) or negative (and therefore short).
$99,200 $108,450
16 27.2
20
Using Excel OM
for Aggregate
Planning, with
Example 2 Data
Program 13.2
P USING POM FOR WINDOWS The POM for Windows Aggregate Planning module performs aggregate or production planning for up to 90 time periods. Given a set of demands for future periods, you can try various plans to determine the lowest-cost plan based on holding, shortage, produc- tion, and changeover costs. Four methods are available for planning. More help is available on each after you choose the method. See Appendix IV for further details.
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SOLVED PROBLEM 13.1 The roofing manufacturer described in Examples 1 to 4 of this chapter wishes to consider yet a fourth planning strategy (plan 4). This one maintains a constant workforce of eight people and uses overtime whenever necessary to meet demand. Use the informa- tion found in Table 13.3 on page 540 . Again, assume beginning and ending inventories are equal to zero.
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLUTION Employ eight workers and use overtime when necessary. Note that carrying costs will be encountered in this plan.
MONTH PRODUCTION
DAYS
PRODUCTION AT 40
UNITS PER DAY
BEGINNING- OF-MONTH INVENTORY
FORECAST DEMAND THIS
MONTH
OVERTIME PRODUCTION
NEEDED ENDING
INVENTORY
Jan. 22 880 — 900 20 units 0 units
Feb. 18 720 0 700 0 units 20 units
Mar. 21 840 20 800 0 units 60 units
Apr. 21 840 60 1,200 300 units 0 units
May 22 880 0 1,500 620 units 0 units
June 20 800 0 1,100 300 units 0 units
1,240 units 80 units
Carrying cost totals = 80 units * $5>unit>month = $400
Regular pay:
8 workers * $80>day * 124 days = $79,360
Overtime pay: To produce 1,240 units at overtime rate requires 1,240 * 1.6 hours>unit = 1,984 hours.
Overtime cost = $17>hour * 1,984 hours = $33,728
Plan 4 COSTS (WORKFORCE OF 8 PLUS OVERTIME)
Carrying cost $ 400 (80 units carried 3 $5/unit)
Regular labor 79,360 (8 workers 3 $80/day 3 124 days)
Overtime 33,728 (1,984 hours 3 $17/hour)
Hiring or fi ring 0
Subcontracting 0
Total costs $113,488
Plan 2, at $105,152, is still preferable.
SOLVED PROBLEM 13.2 A Dover, Delaware, plant has developed the accompanying supply, demand, cost, and inventory data. The firm has a con- stant workforce and meets all its demand. Allocate production capacity to satisfy demand at a minimum cost. What is the cost of this plan?
Supply Capacity Available (units) PERIOD REGULAR TIME OVERTIME SUBCONTRACT
1 300 50 200
2 400 50 200
3 450 50 200
Demand Forecast
PERIOD DEMAND (UNITS)
1 450
2 550
3 750
Other data
Initial inventory 50 units
Regular-time cost per unit $50
Overtime cost per unit $65
Subcontract cost per unit $80
Carrying cost per unit per period $ 1
Back order cost per unit per period $ 4
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SOLUTION
DEMAND FOR TOTAL
Unused CAPACITY Capacity AVAILABLE
SUPPLY FROM Period 1 Period 2 Period 3 (dummy) (supply) 0 1 2 0
Beginning inventory 50 50
50 51 52 0
Regular time 300 300
65 66 67 0
Period Overtime 50 50
1 80 81 82 0
Subcontract 50 150 200
54 50 51 0
Regular time 400 400
69 65 66 0
Period Overtime 50 50
2 84 80 81 0
Subcontract 100 50 50 200
58 54 50 0
Regular time 450 450
73 69 65 0
Period Overtime 50 50
3 88 84 80 0
Subcontract 200 200
TOTAL DEMAND 450 550 750 200 1,950 Cost of plan:
Period 1: 50($0) + 300($50) + 50($65) + 50($80) = $22,250 Period 2: 400($50) + 50($65) + 100($80) = $31,250 Period 3: 50($81) + 450($50) + 50($65) + 200($80) = $45,800*
Total cost $99,300 *Includes 50 units of subcontract and carrying cost.
Problems 13.1–13.23 relate to Methods for Aggregate Planning
• 13.1 Prepare a graph of the monthly forecasts and average forecast demand for Chicago Paint Corp., a manufacturer of spe- cialized paint for artists.
MONTH PRODUCTION DAYS DEMAND FORECAST
January 22 1,000 February 18 1,100 March 22 1,200 April 21 1,300 May 22 1,350 June 21 1,350 July 21 1,300 August 22 1,200 September 21 1,100 October 22 1,100 November 20 1,050 December 20 900
• • 13.2 Develop another plan for the Mexican roofing manu- facturer described in Examples 1 to 4 (pages 538 – 542 ) and Solved Problem 13.1 (page 554 ). a) For this plan, plan 5, the firm wants to maintain a constant
workforce of six, using subcontracting to meet remaining demand. Is this plan preferable?
b) The same roofing manufacturer in Examples 1 to 4 and Solved Problem 13.1 has yet a sixth plan. A constant workforce of seven is selected, with the remainder of demand filled by subcontracting.
c) Is this better than plans 1–5? PX
• • • 13.3 The president of Hill Enterprises, Terri Hill, projects the firm’s aggregate demand requirements over the next 8 months as follows:
Jan. 1,400 May 2,200 Feb. 1,600 June 2,200 Mar. 1,800 July 1,800 Apr. 1,800 Aug. 1,800
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
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Her operations manager is considering a new plan, which begins in January with 200 units on hand. Stockout cost of lost sales is $100 per unit. Inventory holding cost is $20 per unit per month. Ignore any idle-time costs. The plan is called plan A.
Plan A: Vary the workforce level to execute a strategy that produces the quantity demanded in the prior month. The December demand and rate of production are both 1,600 units per month. The cost of hiring additional workers is $5,000 per 100 units. The cost of laying off workers is $7,500 per 100 units. Evaluate this plan. PX Note: Both hiring and layoff costs are incurred in the month of the change. For example, going from 1,600 in January to 1,400 in February incurs a cost of layoff for 200 units in February.
• • 13.4 Using the information in Problem 13.3, develop plan B. Produce at a constant rate of 1,400 units per month, which will meet minimum demands. Then use subcontracting, with addi- tional units at a premium price of $75 per unit. Evaluate this plan by computing the costs for January through August. PX
• • 13.5 Hill is now considering plan C: Keep a stable workforce by maintaining a constant production rate equal to the average requirements and allow varying inventory levels. Beginning inven- tory, stockout costs, and holding costs are provided in Problem 13.3.
Plot the demand with a graph that also shows average require- ments. Conduct your analysis for January through August. PX
• • • 13.6 Hill’s operations manager (see Problems 13.3 through 13.5) is also considering two mixed strategies for January– August: Produce in overtime or subcontracting only when there is no inventory.
◆ Plan D: Keep the current workforce stable at producing 1,600 units per month. Permit a maximum of 20% overtime at an additional cost of $50 per unit. A warehouse now constrains the maximum allowable inventory on hand to 400 units or less.
◆ Plan E: Keep the current workforce, which is producing 1,600 units per month, and subcontract to meet the rest of the demand.
Evaluate plans D and E and make a recommendation. PX Note: Do not produce in overtime if production or inventory are adequate to cover demand.
• • • 13.7 Consuelo Chua, Inc., is a disk drive manufacturer in need of an aggregate plan for July through December. The com- pany has gathered the following data:
COSTS
Holding cost $8/disk/month Subcontracting $80/disk Regular-time labor $12/hour Overtime labor $18/hour for hours above 8 hours/worker/day Hiring cost $40/worker Layoff cost $80/worker
DEMAND*
July 400 Aug. 500 Sept. 550 Oct. 700 Nov. 800 Dec. 700
*No costs are incurred for unmet demand, but unmet demand (backorders) must be handled in the following period. If half or more of a worker is needed, round up.
OTHER DATA
Current workforce (June) 8 people Labor-hours/disk 4 hours Workdays/month 20 days Beginning inventory 150 disks** No requirement for ending inventory 0 disks
**Note that there is no holding cost for June.
What will each of the two following strategies cost? a) Vary the workforce so that production meets demand. Chua
had eight workers on board in June. b) Vary overtime only and use a constant workforce of eight. PX
• • 13.8 You manage a consulting firm down the street from Consuelo Chua, Inc., and to get your foot in the door, you have told Ms. Chua (see Problem 13.7) that you can do a better job at aggregate planning than her current staff. She said, “Fine. You do that, and you have a one year contract.” You now have to make good on your boast using the data in Problem 13.7. You decide to hire 5 workers in August and 5 more in October. Your results?
• • • 13.9 The S&OP team at Kansas Furniture, has received the following estimates of demand requirements:
July Aug. Sept. Oct. Nov. Dec. 1,000 1,200 1,400 1,800 1,800 1,800
S te
p h a n ie
K le
in -D
a vi
s/ T h e R
o a n o ke
T im
e s/
A P I m
a g e s
a) Assuming one-time stockout costs for lost sales of $100 per unit, inventory carrying costs of $25 per unit per month, and zero beginning and ending inventory, evaluate these two plans on an incremental cost basis: ◆ Plan A: Produce at a steady rate (equal to minimum require-
ments) of 1,000 units per month and subcontract additional units at a $60 per unit premium cost.
◆ Plan B: Vary the workforce, to produce the prior month’s demand. The fi rm produced 1,300 units in June. The cost of hiring additional workers is $3,000 per 100 units produced. The cost of layoff s is $6,000 per 100 units cut back.
Note: Both hiring and layoff costs are incurred in the month of the change, (i.e. going from production of 1,300 in July to 1,000 in Au- gust requires a layoff (and related costs) of 300 units in August, just as going from production of 1,000 in August to 1,200 in September requires hiring (and related costs) of 200 units in September). PX
b) Which plan is best and why?
• • • 13.10 The S&OP team (see Problem 13.9) is considering two more mixed strategies. Using the data in Problem 13.9, compare plans C and D with plans A and B and make a recommendation.
◆ Plan C: Keep the current workforce steady at a level pro- ducing 1,300 units per month. Subcontract the remainder to meet demand. Assume that 300 units remaining from June are available in July.
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◆ Plan D: Keep the current workforce at a level capable of producing 1,300 units per month. Permit a maximum of 20% overtime at a premium of $40 per unit. Assume that warehouse limitations permit no more than a 180-unit car- ryover from month to month. This plan means that any time inventories reach 180, the plant is kept idle. Idle time per unit is $60. Any additional needs are subcontracted at a cost of $60 per incremental unit.
• • • 13.11 Deb Bishop Health and Beauty Products has devel- oped a new shampoo, and you need to develop its aggregate sched- ule. The cost accounting department has supplied you the costs relevant to the aggregate plan, and the marketing department has provided a four-quarter forecast. All are shown as follows:
QUARTER FORECAST
1 1,400 2 1,200 3 1,500 4 1,300
COSTS
Previous quarter’s output 1,500 units Beginning inventory 0 units Stockout cost for backorders $50 per unit Inventory holding cost $10 per unit for every unit held at
the end of the quarter Hiring workers $40 per unit
Layoff workers $80 per unit Unit cost $30 per unit Overtime $15 extra per unit Subcontracting Not available
Your job is to develop an aggregate plan for the next four quarters. a) First, try hiring and layoffs (to meet the forecast) as necessary. b) Then try a plan that holds employment steady. c) Which is the more economical plan for Deb Bishop Health
and Beauty Products? PX
• • • 13.12 Southeast Soda Pop, Inc., has a new fruit drink for which it has high hopes. John Mittenthal, the production planner, has assembled the following cost data and demand forecast:
QUARTER FORECAST
1 1,800 2 1,100 3 1,600 4 900
COSTS/OTHER DATA
Previous quarter’s output 5 1,300 cases
Beginning inventory 5 0 cases
Stockout cost 5 $150 per case
Inventory holding cost 5 $40 per case at end of quarter
Hiring employees 5 $40 per case
Terminating employees 5 $80 per case
Subcontracting cost 5 $60 per case
Unit cost on regular time 5 $30 per case
Overtime cost 5 $15 extra per case
Capacity on regular time 5 1,800 cases per quarter
A fr
ic a S
tu d io
/S h u tt
e rs
to ck
John’s job is to develop an aggregate plan. The three initial options he wants to evaluate are:
◆ Plan A: a strategy that hires and fi res personnel as necessary to meet the forecast.
◆ Plan B: a level strategy. ◆ Plan C: a level strategy that produces 1,200 cases per quar-
ter and meets the forecast demand with inventory and sub- contracting.
a) Which strategy is the lowest-cost plan? b) If you are John’s boss, the VP for operations, which plan do
you implement and why? PX
• • 13.13 Ram Roy’s firm has developed the following supply, demand, cost, and inventory data. Allocate production capac- ity to meet demand at a minimum cost using the transportation method. What is the cost? Assume that the initial inventory has no holding cost in the first period and backorders are not permitted.
Supply Available
PERIOD REGULAR
TIME OVERTIME SUBCONTRACT DEMAND FORECAST
1 30 10 5 40 2 35 12 5 50 3 30 10 5 40
Initial inventory 20 units Regular-time cost per unit $100 Overtime cost per unit $150 Subcontract cost per unit $200 Carrying cost per unit per month $ 4
• • 13.14 Jerusalem Medical Ltd., an Israeli producer of port- able kidney dialysis units and other medical products, develops a 4-month aggregate plan. Demand and capacity (in units) are forecast as follows:
CAPACITY SOURCE MONTH 1 MONTH 2 MONTH 3 MONTH 4
Labor Regular time 235 255 290 300 Overtime 20 24 26 24 Subcontract 12 15 15 17 Demand 255 294 321 301
The cost of producing each dialysis unit is $985 on regular time, $1,310 on overtime, and $1,500 on a subcontract. Inventory car- rying cost is $100 per unit per month. There is to be no beginning or ending inventory in stock and backorders are not permitted. Set up a production plan that minimizes cost using the transpor- tation method. PX
PX
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558 P A R T 3 | M A N AG I N G O P E R AT I O N S
Initial inventory 5 250 units Regular time cost 5 $1.00/unit Overtime cost 5 $1.50/unit Subcontracting cost 5 $2.00/unit Carrying cost 5 $0.20/unit/quarter Back order cost 5 $0.50/unit/quarter
Yu decides that the initial inventory of 250 units will incur the 20¢/unit cost from each prior quarter (unlike the situation in most other companies, where a 0 unit cost is assigned). a) Find the optimal plan using the transportation method. b) What is the cost of the plan? c) Does any regular time capacity go unused? If so, how much in
which periods? b) What is the extent of backordering in units and dollars? PX • • • 13.18 José Martinez of El Paso has developed a polished stainless steel tortilla machine that makes it a “showpiece” for display in Mexican restaurants. He needs to develop a 5-month aggregate plan. His forecast of capacity and demand follows:
MONTH
1 2 3 4 5
Demand 150 160 130 200 210 Capacity Regular 150 150 150 150 150 Overtime 20 20 10 10 10
Subcontracting: 100 units available over the 5-month period Beginning inventory: 0 units Ending inventory required: 20 units
COSTS
Regular-time cost per unit $100 Overtime cost per unit $125 Subcontract cost per unit $135 Inventory holding cost per unit per month $ 3
Assume that backorders are not permitted. Using the transporta- tion method, what is the total cost of the optimal plan? PX
•••• 13.19 Dwayne Cole, owner of a Florida firm that manu- factures display cabinets, develops an 8-month aggregate plan. Demand and capacity (in units) are forecast as follows:
CAPACITY SOURCE (UNITS) JAN. FEB. MAR. APR. MAY JUNE JULY AUG.
Regular time 235 255 290 300 300 290 300 290 Overtime 20 24 26 24 30 28 30 30 Subcontract 12 16 15 17 17 19 19 20 Demand 255 294 321 301 330 320 345 340
The cost of producing each unit is $1,000 on regular time, $1,300 on overtime, and $1,800 on a subcontract. Inventory carrying cost is $200 per unit per month. There is no beginning or ending inventory in stock, and no backorders are permitted from period to period.
Let the production (workforce) vary by using regular time first, then overtime, and then subcontracting. a) Set up a production plan that minimizes cost by producing
exactly what the demand is each month. This plan allows no backorders or inventory. What is this plan’s cost?
b) Through better planning, regular-time production can be set at exactly the same amount, 275 units, per month. If demand cannot be met there is no cost assigned to shortages and they will not be filled. Does this alter the solution?
• • 13.15 The production planning period for flat-screen moni- tors at Louisiana’s Roa Electronics, Inc., is 4 months. Cost data are as follows:
Regular-time cost per monitor $ 70
Overtime cost per monitor $110
Subcontract cost per monitor $120
Carrying cost per monitor per month $ 4
For each of the next 4 months, capacity and demand for flat- screen monitors are as follows:
PERIOD
MONTH 1 MONTH 2 MONTH 3 a MONTH 4
Demand 2,000 2,500 1,500 2,100 Capacity Regular time 1,500 1,600 750 1,600 Overtime 400 400 200 400 Subcontract 600 600 600 600
a Factory closes for 2 weeks of vacation.
CEO Mohan Roa expects to enter the planning period with 500 monitors in stock. Back ordering is not permitted (meaning, for example, that monitors produced in the second month cannot be used to cover first month’s demand). Develop a production plan that minimizes costs using the transportation method. PX
• • 13.16 A large St. Louis feed mill, Robert Orwig Processing, prepares its 6-month aggregate plan by forecasting demand for 50-pound bags of cattle feed as follows: January, 1,000 bags; February, 1,200; March, 1,250; April, 1,450; May, 1,400; and June, 1,400. The feed mill plans to begin the new year with no inventory left over from the previous year, and backorders are not permit- ted. It projects that capacity (during regular hours) for producing bags of feed will remain constant at 800 until the end of April, and then increase to 1,100 bags per month when a planned expansion is completed on May 1. Overtime capacity is set at 300 bags per month until the expansion, at which time it will increase to 400 bags per month. A friendly competitor in Sioux City, Iowa, is also available as a backup source to meet demand—but can provide only 500 bags total during the 6-month period. Develop a 6-month production plan for the feed mill using the transportation method.
Cost data are as follows:
Regular-time cost per bag (until April 30) $12.00
Regular-time cost per bag (after May 1) $11.00
Overtime cost per bag (during entire period) $16.00
Cost of outside purchase per bag $18.50
Carrying cost per bag per month $ 1.00 • • 13.17 Yu Amy Xia has developed a specialized airtight vacuum bag to extend the freshness of seafood shipped to restau- rants. She has put together the following demand cost data:
QUARTER FORECAST
(UNITS) REGULAR
TIME OVERTIME SUB-
CONTRACT
1 500 400 80 100 2 750 400 80 100 3 900 800 160 100 4 450 400 80 100
PX
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c) If overtime costs per unit rise from $1,300 to $1,400, will your answer to (a) change? What if overtime costs then fall to $1,200?
Cohen has an agreement with Forrester, his former partner, to help out during the busy tax season, if needed, for an hourly fee of $125. Cohen will not even consider laying off one of his col- leagues in the case of a slow economy. He could, however, hire another CPA at the same salary, as business dictates. a) Develop an aggregate plan for the 6-month period. b) Compute the cost of Cohen’s plan of using overtime and
Forrester. c) Should the firm remain as is, with a total of 4 CPAs?
• • 13.25 Refer to the CPA firm in Problem 13.24. In planning for next year, Cohen estimates that billable hours will increase by 10% in each of the 6 months. He therefore proceeds to hire a fifth CPA. The same regular time, overtime, and outside consultant (i.e., Forrester) costs still apply. a) Develop the new aggregate plan and compute its costs. b) Comment on the staffing level with five accountants. Was it a
good decision to hire the additional accountant?
Problem 13.26 relates to Revenue Management
• • 13.26 Southeastern Airlines’s daily flight from Atlanta to Charlotte uses a Boeing 737, with all-coach seating for 120 peo- ple. In the past, the airline has priced every seat at $140 for the one-way flight. An average of 80 passengers are on each flight. The variable cost of a filled seat is $25. Aysajan Eziz, the new operations manager, has decided to try a yield revenue approach, with seats priced at $80 for early bookings and at $190 for book- ings within 1 week of the flight. He estimates that the airline will sell 65 seats at the lower price and 35 at the higher price. Variable cost will not change. Which approach is preferable to Mr. Eziz?
Additional problems 13.20–13.23 are available in MyOMLab.
Problems 13.24–13.25 relate to Aggregate Planning in Services
• • • 13.24 Forrester and Cohen is a small accounting firm, managed by Joseph Cohen since the retirement in December of his partner Brad Forrester. Cohen and his 3 CPAs can together bill 640 hours per month. When Cohen or another accountant bills more than 160 hours per month, he or she gets an addi- tional “overtime” pay of $62.50 for each of the extra hours: this is above and beyond the $5,000 salary each draws during the month. (Cohen draws the same base pay as his employ- ees.) Cohen strongly discourages any CPA from working (bill- ing) more than 240 hours in any given month. The demand for billable hours for the firm over the next 6 months is estimated below:
MONTH ESTIMATE OF BILLABLE HOURS
Jan. 600
Feb. 500
Mar. 1,000
Apr. 1,200
May 650
June 590
Plant capacities, in units per week, are as follows:
Plant 1, regular time 27,000 units Plant 1, on overtime 7,000 Plant 2, regular time 20,000 Plant 2, on overtime 5,000 Plant 3, regular time 25,000 Plant 3, on overtime 6,000
If A-C shuts down any plants, its weekly costs will change, because fixed costs will be lower for a nonoperating plant. Table 13.9 shows production costs at each plant, both variable at regular time and overtime, and fixed when operating and shut down. Table 13.10 shows distribution costs from each plant to each distribution center.
Discussion Questions
1. Evaluate the various configurations of operating and closed plants that will meet weekly demand. Determine which con- figuration minimizes total costs.
2. Discuss the implications of closing a plant.
CASE STUDIES Andrew-Carter, Inc.
Andrew-Carter, Inc. (A-C), is a major Canadian producer and distributor of outdoor lighting fixtures. Its products are distrib- uted throughout South and North America and have been in high demand for several years. The company operates three plants to manufacture fixtures and distribute them to five distribution cent- ers (warehouses).
During the present global slowdown, A-C has seen a major drop in demand for its products, largely because the housing mar- ket has declined. Based on the forecast of interest rates, the head of operations feels that demand for housing and thus for A-C’s products will remain depressed for the foreseeable future. A-C is considering closing one of its plants, as it is now operating with a forecast excess capacity of 34,000 units per week. The forecast weekly demands for the coming year are as follows:
Warehouse 1 9,000 units Warehouse 2 13,000 Warehouse 3 11,000 Warehouse 4 15,000 Warehouse 5 8,000
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TABLE 13.10
Andrew-Carter, Inc., Distribution Costs per Unit
TABLE 13.9
Andrew-Carter, Inc., Variable Costs and Fixed Production Costs per Week
FIXED COST PER WEEK
PLANT VARIABLE COST
(PER UNIT) OPERATING NOT
OPERATING
1, regular time $2.80 $14,000 $6,000 1, overtime 3.52 —— —— 2, regular time 2.78 12,000 5,000 2, overtime 3.48 —— —— 3, regular time 2.72 15,000 7,500 3, overtime 3.42 —— ——
TO DISTRIBUTION CENTERS
FROM PLANTS W1 W2 W3 W4 W5
1 $.50 $.44 $.49 $.46 $.56 2 .40 .52 .50 .56 .57 3 .56 .53 .51 .54 .35
Revenue management was once the exclusive domain of the air- line industry. But it has since spread its wings into the hotel busi- ness, auto rentals, and now even professional sports, with the San Francisco Giants, Boston Celtics, and Orlando Magic as lead- ers in introducing dynamic pricing into their ticketing systems. Dynamic pricing means looking at unsold tickets for every single game, every day, to see if the current ticket price for a particu- lar seat needs to be lowered (because of slow demand) or raised (because of higher-than-expected demand).
Pricing can be impacted by something as simple as bad weather or by whether the team coming to play in the arena is on a winning streak or has just traded for a new superstar player. For example, a few years ago, a basketball star was traded in midsea- son to the Denver Nuggets; this resulted in an immediate runup in unsold ticket prices for the teams the Nuggets were facing on the road. Had the Nuggets been visiting the Orlando Magic 2 weeks after the trade and the Magic not raised prices, they would have been “leaving money on the table” (as shown in Figure 13.5 ).
As the Magic became more proficient in revenue management, they evolved from (l) setting the price for each seat at the start of the season and never changing it; to (2) setting the prices for each seat at season onset, based on the popularity of the opponent, the day of the week, and the time of season (see the Video Case in Chapter 4 )—but keeping the prices frozen once the season began (see Table 13.11 ); to (3) pricing tickets based on projected demand, but adjusting them frequently to match market demand as the season progressed.
To track market demand, the Magic use listed prices on Stub Hub and other online ticket exchange services. The key is to sell out all 18,500 seats every home game, keeping the pressure on Anthony Perez, the director of business strategy, and Chris Dorso, the Magic’s vice president of sales.
Perez and Dorso use every tool available to collect informa- tion on demand, including counting unique page views at the Ticketmaster Web site. If, for example, there are 5,000 page views for the Miami Heat game near Thanksgiving, it indicates enough demand that prices of unsold seats can be notched up. If there are only 150 Ticketmaster views for the Utah Jazz game 3 days later, there may not be sufficient information to make any changes yet.
Video Case
With a database of 650,000, the Magic can use e-mail blasts to react quickly right up to game day. The team may discount seat prices, offer other perks, or just point out that prime seats are still available for a game against an exciting opponent.
Using Revenue Management to Set Orlando Magic Ticket Prices
TABLE 13.11
An Example of Variable Pricing for a $68 Terrace V seat in Zone 103
OPPONENT POPULARITY RATING
NUMBER OF GAMES IN THIS CATEGORY PRICE
Tier I 3 $187 Tier II 3 $170 Tier III 4 $ 85 Tier IV 6 $ 75 Tier V 14 $ 60 Tier VI 9 $ 44 Tier VII 6 $ 40 Average $ 68
Source: Reprinted by permission of Professor Michael Ballot, University of the Pacific, Stockton, CA. Copyright © by Michael Ballot.
Discussion Questions*
1. After researching revenue (yield) management in airlines, describe how the Magic system differs from that of American or other air carriers.
2. The Magic used its original pricing systems of several years ago and set the price for a Terrace V, Zone 103 seat at $68 per game. There were 230 such seats not purchased as part of season ticket packages and thus available to the public. If the team switched to the 7-price dynamic system (illustrated in Table 13.11 ), how would the profit-contribution for the 45-game season change? (Note that the 45-game season includes 4 preseason games.)
3. What are some concerns the team needs to consider when using dynamic pricing with frequent changes in price?
* You may wish to view the video that accompanies this case before addressing these questions.
• Additional Case Studies: Visit MyOMLab for these free case studies: Cornwell Glass: Involves setting a production schedule for an auto glass producer. Southwestern University: (G) Requires developing an aggregate plan for a university police department.
Endnote
1. R. Oberwetter, “Revenue Management,” OR/MS Today (June 2001): 41–44.
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Main Heading Review Material MyOMLab THE PLANNING PROCESS (pp. 532 – 533 )
j Long-range plans develop policies and strategies related to location, capacity, products and process, supply chain, research, and capital investment.
j Intermediate planning develops plans that match production to demand. j Short-run planning translates intermediate plans into weekly, daily, and hourly
schedules.
Concept Questions: 1.1–1.4
SALES AND OPERATIONS PLANNING (pp. 533 – 534 )
j Sales and operation planning (S&OP)—Balances resources and forecasted de- mand, and aligns the organization’s competing demands, from supply chain to final customer, while linking strategic planning with operations over all planning horizons.
j Aggregate planning —An approach to determine the quantity and timing of pro- duction for the intermediate future (usually 3 to 18 months ahead).
Four things are needed for aggregate planning: 1. A logical unit for measuring sales and output 2. A forecast of demand for a reasonable intermediate planning period in these
aggregate terms 3. A method for determining the relevant costs 4. A model that combines forecasts and costs so that scheduling decisions can be
made for the planning period
Concept Questions: 2.1–2.4
THE NATURE OF AGGREGATE PLANNING (pp. 534 – 535 )
Usually, the objective of aggregate planning is to meet forecasted demand while minimizing cost over the planning period . An aggregate plan looks at production in the aggregate (a family of products), not as a product-by-product breakdown. j Disaggregation —The process of breaking an aggregate plan into greater detail. j Master production schedule —A timetable that specifies what is to be made and when.
Concept Questions: 3.1–3.4
AGGREGATE PLANNING STRATEGIES (pp. 535 – 538 )
The basic aggregate planning capacity (production) options are: j Changing inventory levels j Varying workforce size by hiring or layoffs j Varying production rates through overtime or idle time j Subcontracting j Using part-time workers The basic aggregate planning demand options are: j Influencing demand j Back ordering during high-demand periods j Counterseasonal product and service mixing j Chase strategy —A planning strategy that sets production equal to forecast demand. Many service organizations favor the chase strategy because the inventory option is difficult or impossible to adopt. j Level scheduling —Maintaining a constant output rate, production rate, or
workforce level over the planning horizon. Level scheduling works well when demand is reasonably stable. j Mixed strategy —A planning strategy that uses two or more controllable
variables to set a feasible production plan.
Concept Questions: 4.1–4.4
METHODS FOR AGGREGATE PLANNING (pp. 538 – 545 )
j Graphical techniques —Aggregate planning techniques that work with a few variables at a time to allow planners to compare projected demand with existing capacity.
Graphical techniques are trial-and-error approaches that do not guarantee an optimal production plan, but they require only limited computations. A cumulative graph displays visually how the forecast deviates from the average requirements. j Transportation method of linear programming —A way of solving for the optimal
solution to an aggregate planning problem. The transportation method of linear programming is flexible in that it can specify regular and overtime production in each time period, the number of units to be subcontracted, extra shifts, and the inventory carryover from period to period. Transportation problems require that supply equals demand, so when it does not, a dummy column called “unused capacity” may be added. Costs of not using capacity are zero.
Concept Questions: 5.1–5.4
Problems: 13.2–13.23
Virtual Office Hours for Solved Problems: 13.1, 13.2
ACTIVE MODEL 13.1
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Main Heading Review Material MyOMLab Demand requirements are shown in the bottom row of a transportation table. Total capacity available (supply) is shown in the far right column. In general, to complete a transportation table, allocate as much production as you can to a cell with the smallest cost, without exceeding the unused capacity in that row or demand in that column. If there is still some demand left in that row, allocate as much as you can to the next-lowest-cost cell. You then repeat this process for periods 2 and 3 (and beyond, if necessary). When you are finished, the sum of all your entries in a row must equal total row capacity, and the sum of all entries in a column must equal the demand for that period. The transportation method does not work when nonlinear or negative factors are introduced.
AGGREGATE PLANNING IN SERVICES (pp. 545 – 547 )
Successful techniques for controlling the cost of labor in service firms include: 1. Accurate scheduling of labor-hours to ensure quick response to customer
demand. 2. An on-call labor resource that can be added or deleted to meet unexpected
demand. 3. Flexibility of individual worker skills that permits reallocation of available
labor. 4. Flexibility in rate of output or hours of work to meet changing demand.
Concept Questions: 6.1–6.4
Problems: 13.24–13.25
REVENUE MANAGEMENT (pp. 547 – 550 )
j Revenue (or yield ) management —Capacity decisions that determine the alloca- tion of resources to maximize revenue.
Organizations that have perishable inventory , such as airlines, hotels, car rental agencies, and cruise lines, have the following shared characteristics that make revenue management of interest: 1. Service or product can be sold in advance of consumption 2. Fluctuating demand 3. Relatively fixed resource (capacity) 4. Segmentable demand 5. Low variable costs and high fixed costs To make revenue management work, the company needs to manage three issues: 1. Multiple pricing structures. 2. Forecasts of the use and duration of the use. 3. Changes in demand.
Concept Questions: 7.1–7.4
Problem: 13.26
VIDEO 13.1 Using Revenue Management to Set Orlando Magic Ticket Prices
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Chapter 13 Rapid Review continued
LO 13.1 The outputs from an S&OP process are: a) long-run plans. b) detail schedules. c) aggregate plans. d) revenue management plans. e) short-run plans. LO 13.2 Aggregate planning is concerned with determining the
quantity and timing of production in the: a) short term. b) intermediate term. c) long term. d) all of the above. LO 13.3 Aggregate planning deals with a number of constraints.
These typically are: a) job assignments, job ordering, dispatching, and overtime
help. b) part-time help, weekly scheduling, and SKU production
scheduling. c) subcontracting, employment levels, inventory levels, and
capacity. d) capital investment, expansion or contracting capacity, and
R&D. e) facility location, production budgeting, overtime, and
R&D.
LO 13.4 Which of the following is not one of the graphical method steps? a) Determine the demand in each period. b) Determine capacity for regular time, overtime, and
subcontracting each period. c) Find labor costs, hiring and layoff costs, and inventory
holding costs. d) Construct the transportation table. e) Consider company policy that may apply to the workers
or stock levels. f ) Develop alternative plans and examine their total costs. LO 13.5 When might a dummy column be added to a transportation
table? a) When supply does not equal demand b) When overtime is greater than regular time c) When subcontracting is greater than regular time d) When subcontracting is greater than regular time plus
overtime e) When production needs to spill over into a new period LO 13.6 Revenue management requires management to deal with: a) multiple pricing structures. b) changes in demand. c) forecasts of use. d) forecasts of duration of use. e) all of the above.
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
Answers: LO 13.1. c; LO 13.2. b; LO 13.3. c; LO 13.4. d; LO 13.5. a; LO 13.6. e.
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C H A P T E R O U T L I N E
14 ◆
Dependent Demand 566
◆
Dependent Inventory Model Requirements 566
◆
MRP Structure 571
◆
MRP Management 575
GLOBAL COMPANY PROFILE: Wheeled Coach
C H
A P
T E
R
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
•• Inventory Management
·· Independent Demand ( Ch. 12 ) ·· Dependent Demand ( Ch. 14 ) ·· Lean Operations ( Ch. 16 ) • • Scheduling
• • Maintenance
C H A P T E R GLOBAL COMPANY PROFILE Wheeled Coach
Material Requirements Planning (MRP) and ERP
◆
Lot-Sizing Techniques 576
◆
Extensions of MRP 580
◆
MRP in Services 583
◆
Enterprise Resource Planning (ERP) 584
A la
sk a A
ir lin
e s
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W heeled Coach, headquartered in Winter Park, Florida, is the largest manufacturer of
ambulances in the world. The $200 million firm is an international competitor that sells more
than 25% of its vehicles to markets outside the U.S. Twelve major ambulance designs are
produced on assembly lines (i.e., a repetitive process) at the Florida plant, using 18,000 different
MRP Provides a Competitive Advantage for Wheeled Coach
GLOBAL COMPANY PROFILE Wheeled Coach
C H A P T E R 1 4
564
This cutaway of one
ambulance interior
indicates the complexity
of the product, which for
some rural locations may
be the equivalent of a
hospital emergency room
in miniature. To complicate
production, virtually every
ambulance is custom
ordered. This customization
necessitates precise
orders, excellent bills of
materials, exceptional
inventory control from
supplier to assembly, and
an MRP system that works.
W h e e le
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I n d u st
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In co
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te d
Wheeled Coach uses work cells to feed
the assembly line. It maintains a complete
carpentry shop (to provide interior cabinetry),
a paint shop (to prepare, paint, and detail
each vehicle), an electrical shop (to provide
for the complex electronics in a modern
ambulance), an upholstery shop (to make
interior seats and benches), and as shown
here, a metal fabrication shop (to construct
the shell of the ambulance).
W h e e le
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I n d u st
ri e s
In co
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565
inventory items, of which 6,000 are manufactured and 12,000
purchased. Most of the product line is custom designed and
assembled to meet the specific and often unique require-
ments demanded by the ambulance’s application and
customer preferences.
This variety of products and the nature of the process
demand good material requirements planning (MRP). Effective use
of an MRP system requires accurate bills of material and inventory
records. The Wheeled Coach system provides daily updates and
has reduced inventory by more than 30% in just two years.
Wheeled Coach insists that four key tasks be performed
properly. First, the material plan must meet both the require-
ments of the master schedule and the capabilities of the
production facility. Second, the plan must be executed as
designed. Third, inventory investment must be minimized
through effective “time-phased” material deliveries, consign-
ment inventories, and a constant review of purchase meth-
ods. Finally, excellent record integrity must be maintained.
Record accuracy is recognized as a fundamental ingredient of
Wheeled Coach’s successful MRP program. Its cycle coun-
ters are charged with material audits that not only correct
errors but also investigate and correct problems.
Wheeled Coach Industries uses MRP as the catalyst for
low inventory, high quality, tight schedules, and accurate
records. Wheeled Coach has found competitive advantage
via MRP.
565
On five parallel lines, ambulances
move forward each day to the next
workstation. The MRP system makes
certain that just the materials needed
at each station arrive overnight for
assembly the next day.
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I n d u st
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In co
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Here an employee is installing the wiring for an ambulance. There are an average of 15 miles
of wire in a Wheeled Coach vehicle. This compares to 17 miles of wire in a sophisticated F-16
fighter jet.
W h e e le
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o a ch
I n d u st
ri e s
In co
rp o ra
te d
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566
Dependent Demand Wheeled Coach, the subject of the Global Company Profile , and many other firms have found important benefits in material requirements planning (MRP). These benefits include (1) bet- ter response to customer orders as the result of improved adherence to schedules, (2) faster response to market changes, (3) improved utilization of facilities and labor, and (4) reduced inventory levels. Better response to customer orders and to the market wins orders and mar- ket share. Better utilization of facilities and labor yields higher productivity and return on investment. Less inventory frees up capital and floor space for other uses. These benefits are the result of a strategic decision to use a dependent inventory scheduling system. Demand for every component of an ambulance is dependent.
Demand for items is dependent when the relationship between the items can be determined. Therefore, once management receives an order or makes a forecast for the final product, quan- tities for all components can be computed. All components are dependent items. The Boeing Aircraft operations manager who schedules production of one plane per week, for example, knows the requirements down to the last rivet. For any product, all components of that prod- uct are dependent demand items. More generally, for any product for which a schedule can be established, dependent techniques should be used.
When the requirements of MRP are met, dependent models are preferable to the models for independent demand (EOQ) described in Chapter 12 . 1 Dependent models are better not only for manufacturers and distributors but also for a wide variety of firms from restaurants to hospitals. The dependent technique used in a production environment is called material require- ments planning (MRP) .
Because MRP provides such a clean structure for dependent demand, it has evolved as the basis for Enterprise Resource Planning (ERP). ERP is an information system for identifying and planning the enterprise-wide resources needed to take, make, ship, and account for cus- tomer orders. We will discuss ERP in the latter part of this chapter.
Dependent Inventory Model Requirements Effective use of dependent inventory models requires that the operations manager know the following:
1. Master production schedule (what is to be made and when) 2. Specifications or bill of material (materials and parts required to make the product) 3. Inventory availability (what is in stock) 4. Purchase orders outstanding (what is on order, also called expected receipts) 5. Lead times (how long it takes to get various components)
We now discuss each of these requirements in the context of material requirements planning.
L E A R N I N G OBJEC TI V ES
LO 14.1 Develop a product structure 569
LO 14.2 Build a gross requirements plan 572
LO 14.3 Build a net requirements plan 573
LO 14.4 Determine lot sizes for lot-for-lot, EOQ, and POQ 577
LO 14.5 Describe MRP II 580
LO 14.6 Describe closed-loop MRP 582
LO 14.7 Describe ERP 584
STUDENT TIP “Dependent demand” means
the demand for one item is
related to the demand for
another item.
Material requirements planning (MRP)
A dependent demand technique
that uses a bill-of-material,
inventory, expected receipts, and
a master production schedule to
determine material requirements.
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Master Production Schedule A master production schedule (MPS) specifies what is to be made (e.g., the number of finished prod- ucts or items) and when. The schedule must be in accordance with an aggregate plan. The aggregate plan sets the overall level of output in broad terms (e.g., product families, standard hours, or dollar volume). The plan, usually developed by the sales and operations planning team, includes a variety of inputs, including financial data, customer demand, engineering capabilities, labor availability, inventory fluctuations, supplier performance, and other con- siderations. Each of these inputs contributes in its own way to the aggregate plan, as shown in Figure 14.1 .
As the planning process moves from the aggregate plan to execution, each of the lower-level plans must be feasible. When one is not, feedback to the next higher level is required to make the necessary adjustment. One of the major strengths of MRP is its ability to determine pre- cisely the feasibility of a schedule within aggregate capacity constraints. This planning process can yield excellent results. The aggregate plan sets the upper and lower bounds on the master production schedule.
The master production schedule tells us how to satisfy demand by specifying what items to make and when. It disaggregates the aggregate plan. While the aggregate plan (as discussed in Chapter 13 ) is established in gross terms such as families of products or tons of steel, the master production schedule is established in terms of specific products. Figure 14.2 shows the master production schedules for three stereo models that flow from the aggregate plan for a family of stereo amplifiers.
Sales & Operations Planning Generates an aggregate plan
Human Resources Staff planning
Finance Cash flow
Marketing Customer demand
Production Capacity Inventory
Supply Chain Procurement Supplier performance
Master production schedule
Material requirements plan
Schedule and execute plan
Change master production schedule?
Figure 14.1
The Planning Process
Master production schedule (MPS)
A timetable that specifies what
is to be made (usually finished
goods) and when.
MonthsAggregate Plan (Shows the total quantity of amplifiers) 1,500 1,200
Master Production Schedule (Shows the specific type and quantity of amplifier to be produced)
240-watt amplifier
150-watt amplifier
75-watt amplifier
100
500
100
500
100
450
100
450
300 100
Weeks 1
January February
2 3 4 5 6 7 8
Figure 14.2
The Aggregate Plan Is the Basis for Development of the Master Production Schedule
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Managers must adhere to the schedule for a reasonable length of time (usually a major portion of the production cycle—the time it takes to produce a product). Many organizations establish a master production schedule and establish a policy of not changing (“fixing”) the near-term portion of the plan. This near-term portion of the plan is then referred to as the “fixed,” “firm,” or “frozen” schedule. Wheeled Coach, the subject of the Global Company Profile for this chapter, fixes the last 14 days of its schedule. Only changes farther out, beyond the fixed schedule, are permitted. The master production schedule is a “rolling” production schedule. For example, a fixed 7-week plan has an additional week added to it as each week is completed, so a 7-week fixed schedule is maintained. Note that the master production schedule is a state- ment of what is to be produced; it is not a forecast. The master schedule can be expressed in the following terms:
◆ A customer order in a job shop (make-to-order) company (examples: print shops, machine shops, fine-dining restaurants)
◆ Modules in a repetitive (assemble-to-order or forecast) company (examples: Harley-Davidson motorcycles, TVs, fast-food restaurant)
◆ An end item in a continuous (stock-to-forecast) company (examples: steel, beer, bread, light bulbs, paper)
A master production schedule for Chef John’s “Buffalo Chicken Mac & Cheese” at the Orlando Magic’s Amway Center is shown in Table 14.1 .
Bills of Material Defining what goes into a product may seem simple, but it can be difficult in practice. As we noted in Chapter 5 , to aid this process, manufactured items are defined via a bill of material. A bill of material (BOM) is a list of quantities of components, ingredients, and materials required to make a product. Individual drawings describe not only physical dimensions but also any special processing as well as the raw material from which each part is made. Chef John’s recipe for Buffalo Chicken Mac & Cheese specifies ingredients and quantities, just as Wheeled Coach has a full set of drawings for an ambulance. Both are bills of material (although we call one a recipe, and they do vary somewhat in scope).
One way a bill of material defines a product is by providing a product structure. Example 1 shows how to develop the product structure and “explode” it to reveal the requirements for each component. A bill of material for item A in Example 1 consists of items B and C. Items above any level are called parents ; items below any level are called components or children . By convention, the top level in a BOM is the 0 level.
TABLE 14.1 Master Production Schedule for Chef John’s Buffalo Chicken Mac & Cheese
GROSS REQUIREMENTS FOR CHEF JOHN’S BUFFALO CHICKEN MAC & CHEESE
Day 6 7 8 9 10 11 12 13 14 and so on Quantity 450 200 350 525 235 375
VIDEO 14.1 When 18,500 Orlando Magic Fans
Come to Dinner
Bill of material (BOM)
A listing of the components, their
description, and the quantity of
each required to make one unit of
a product.
VIDEO 14.2 MRP at Wheeled Coach
Ambulances
Example 1 DEVELOPING A PRODUCT STRUCTURE AND GROSS REQUIREMENTS Speaker Kits, Inc., packages high-fidelity components for mail order. Components for the top-of-the- line speaker kit, “Awesome” (A), include 2 Bs and 3 Cs.
Each B consists of 2 Ds and 2 Es. Each of the Cs has 2 Fs and 2 Es. Each F includes 2 Ds and 1 G. It is an awesome sound system. (Most purchasers require hearing aids within 3 years, and at least one court case is pending because of structural damage to a men’s dormitory.) As we can see, the demand for B, C, D, E, F, and G is completely dependent on the master production schedule for A—the Awesome speaker kits.
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APPROACH c Given the preceding information, we construct a product structure and “explode” the requirements.
SOLUTION c This structure has four levels: 0, 1, 2, and 3. There are four parents: A, B, C, and F. Each parent item has at least one level below it. Items B, C, D, E, F, and G are components because each item has at least one level above it. In this structure, B, C, and F are both parents and components. The number in parentheses indicates how many units of that particular item are needed to make the item immediately above it. Thus, B(2) means that it takes two units of B for every unit of A, and F(2) means that it takes two units of F for every unit of C.
LO 14.1 Develop a product structure
A0
1
2
3
Product structure for “Awesome” (A)Level
B(2) C(3)
E(2) F(2)
G(1) D(2)D(2)
E(2)
D ra
g o n _
F a n g /S
h u tt
e rs
to ck
Once we have developed the product structure, we can determine the number of units of each item required to satisfy demand for a new order of 50 Awesome speaker kits. We “explode” the requirements as shown:
Part B: 2 * number of As = (2)(50) = 100 Part C: 3 * number of As = (3)(50) = 150 Part D: 2 * number of Bs + 2 * number of Fs = (2)(100) + (2)(300) = 800 Part E: 2 * number of Bs + 2 * number of Cs = (2)(100) + (2)(150) = 500 Part F: 2 * number of Cs = (2)(150) = 300 Part G: 1 * number of Fs = (1)(300) = 300
INSIGHT c We now have a visual picture of the Awesome speaker kit requirements and knowledge of the quantities required. Thus, for 50 units of A, we will need 100 units of B, 150 units of C, 800 units of D, 500 units of E, 300 units of F, and 300 units of G.
LEARNING EXERCISE c If there are 100 Fs in stock, how many Ds do you need? [Answer: 600.]
RELATED PROBLEMS c 14.1–14.4, 14.5a,b, 14.13a,b, 14.17a,b (14.20a,b are available in MyOMLab)
Bills of material not only specify requirements but also are useful for costing, and they can serve as a list of items to be issued to production or assembly personnel. When bills of material are used in this way, they are usually called pick lists .
Modular Bills Bills of material may be organized around product modules (see Chapter 5 ). Modules are not final products to be sold, but are components that can be produced and assembled into units. They are often major components of the final product or product options. Bills of material for modules are called modular bills . Modular bills are convenient because production scheduling and production are often facilitated by organizing around relatively few modules rather than a multitude of final assemblies. For instance, a firm may make 138,000 different final products but may have only 40 modules that are mixed and matched to produce those 138,000 final products. The firm builds an aggregate produc- tion plan and prepares its master production schedule for the 40 modules, not the 138,000
Modular bills
Bills of material organized by
major subassemblies or by
product options.
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configurations of the final product. This approach allows the MPS to be prepared for a reasonable number of items. The 40 modules can then be configured for specific orders at final assembly.
Planning Bills and Phantom Bills Two other special kinds of bills of material are planning bills and phantom bills. Planning bills (sometimes called “pseudo” bills, or super bills) are created in order to assign an artificial parent to the bill of material. Such bills are used (1) when we want to group subassemblies so the number of items to be scheduled is reduced and (2) when we want to issue “kits” to the production department. For instance, it may not be efficient to issue inexpensive items such as washers and cotter pins with each of numerous subassemblies, so we call this a kit and generate a planning bill. The planning bill specifies the kit to be issued. Consequently, a planning bill may also be known as kitted material, or kit . Phantom bills of material are bills of material for components, usually subassemblies, that exist only temporarily. These components go directly into another assembly and are never inventoried. Therefore, components of phantom bills of material are coded to receive special treatment; lead times are zero, and they are handled as an integral part of their parent item. An example is a transmission shaft with gears and bearings assembly that is placed directly into a transmission.
Low-Level Coding Low-level coding of an item in a BOM is necessary when identical items exist at various levels in the BOM. Low-level coding means that the item is coded at the lowest level at which it occurs. For example, item D in Example 1 is coded at the lowest level at which it is used. Item D could be coded as part of B and occur at level 2. However, because D is also part of F, and F is level 2, item D becomes a level-3 item. Low-level coding is a convention to allow easy computing of the requirements of an item.
Accurate Inventory Records As we saw in Chapter 12 , knowledge of what is in stock is the result of good inventory man- agement. Good inventory management is an absolute necessity for an MRP system to work. If the firm does not exceed 99% record accuracy, then material requirements planning will not work. 2
Purchase Orders Outstanding Knowledge of outstanding orders exists as a by-product of well-managed purchasing and inventory-control departments. When purchase orders are executed, records of those orders and their scheduled delivery dates must be available to production personnel. Only with good purchasing data can managers prepare meaningful production plans and effectively execute an MRP system.
Lead Times for Components Once managers determine when products are needed, they determine when to acquire them. The time required to acquire (that is, purchase, produce, or assemble) an item is known as lead time . Lead time for a manufactured item consists of move , setup , and assembly or run times for each component. For a purchased item, the lead time includes the time between recognition of need for an order and when it is available for production.
When the bill of material for Awesome speaker kits (As), in Example 1 , is turned on its side and modified by adding lead times for each component (see Table 14.2 ), we then have a time- phased product structure . Time in this structure is shown on the horizontal axis of Figure 14.3 with item A due for completion in week 8. Each component is then offset to accommodate lead times.
Planning bills (or kits)
Material groupings created in
order to assign an artificial parent
to a bill of material; also called
“pseudo” bills.
Phantom bills of material
Bills of material for components,
usually assemblies, that exist
only temporarily; they are never
inventoried.
Low-level coding
A number that identifies items
at the lowest level at which they
occur.
Lead time
In purchasing systems, the time
between recognition of the need
for an order and receiving it; in
production systems, it is the order,
wait, move, queue, setup, and run
times for each component.
TABLE 14.2
Lead Times for Awesome Speaker Kits (As)
COMPONENT LEAD TIME
A 1 week B 2 weeks C 1 week D 1 week E 2 weeks F 3 weeks G 2 weeks
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MRP Structure Although most MRP systems are computerized, the MRP procedure is straightforward, and we can illustrate a small one by hand. A master production schedule, a bill of material, inventory and purchase records, and lead times for each item are the ingredients of a material requirements planning system (see Figure 14.4 ).
Once these ingredients are available and accurate, the next step is to construct a gross mate- rial requirements plan. The gross material requirements plan is a schedule, as shown in Example 2 . It combines a master production schedule (that requires one unit of A in week 8) and the time- phased schedule ( Figure 14.3 ). It shows when an item must be ordered from suppliers if there is no inventory on hand or when the production of an item must be started to satisfy demand for the finished product by a particular date.
Time in weeks
1 2 3 4 5 6 7 8
D
G
F
E
C
B
A
E
D
2 weeks
2 weeks
2 weeks
2 weeks to produce
1 week
1 week
1 week
1 week
3 weeks
Must have D and E completed here so
production can begin on B
Start production of D
STUDENT TIP This is a product structure on
its side, with lead times.
Gross material requirements plan
A schedule that shows the total
demand for an item (prior to sub-
traction of on-hand inventory and
scheduled receipts) and (1) when
it must be ordered from suppliers,
or (2) when production must be
started to meet its demand by a
particular date.
STUDENT TIP MRP software programs are
popular because manual
approaches are slow and error
prone.
Figure 14.3
Time-Phased Product
Structure
MRP by period report
Planned order report
Purchase advice
MRP by date report
Order early or late or not needed
Order quantity too small or too large
Data Files
Material requirements
planning programs
(computer and software)
Master production schedule
Output Reports
Bill of material
Lead times
Inventory data
Purchasing data
(Item master file)
Exception reports
Figure 14.4
Structure of the MRP System
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Example 2 BUILDING A GROSS REQUIREMENTS PLAN Each Awesome speaker kit (item A of Example 1 ) requires all the items in the product structure for A. Lead times are shown in Table 14.2 .
APPROACH c Using the information in Example 1 and Table 14.2 , we construct the gross material requirements plan with a production schedule that will satisfy the demand of 50 units of A by week 8.
SOLUTION c We prepare a schedule as shown in Table 14.3 .
TABLE 14.3 Gross Material Requirements Plan for 50 Awesome Speaker Kits (As) with Order Release Dates Also Shown
WEEK
LEAD TIME1 2 3 4 5 6 7 8
A. Required date Order release date 50
50 1 week
B. Required date Order release date 100
100 2 weeks
C. Required date Order release date 150
150 1 week
E. Required date Order release date 200 300
200 300 2 weeks
F. Required date Order release date 300
300 3 weeks
D. Required date Order release date 600
600 200
200 1 week
G. Required date Order release date 300
300 2 weeks
You can interpret the gross material requirements shown in Table 14.3 as follows: If you want 50 units of A at week 8, you must start assembling A in week 7. Thus, in week 7, you will need 100 units of B and 150 units of C. These two items take 2 weeks and 1 week, respectively, to produce. Production of B, therefore, should start in week 5, and production of C should start in week 6 (lead time subtracted from the required date for these items). Working backward, we can perform the same computations for all of the other items. Because D and E are used in two different places in Awesome speaker kits, there are two entries in each data record.
INSIGHT c The gross material requirements plan shows when production of each item should begin and end in order to have 50 units of A at week 8. Management now has an initial plan.
LEARNING EXERCISE c If the lead time for G decreases from 2 weeks to 1 week, what is the new order release date for G? [Answer: 300 in week 2.]
RELATED PROBLEMS c 14.6, 14.8, 14.10a, 14.11a
EXCEL OM Data File Ch14Ex2.xls can be found in MyOMLab.
LO 14.2 Build a gross requirements plan
So far, we have considered gross material requirements , which assumes that there is no inventory on hand. A net requirements plan adjusts for on-hand inventory. When considering on-hand inventory, we must realize that many items in inventory contain subassemblies or parts. If the gross requirement for Awesome speaker kits (As) is 100 and there are 20 of those speakers on hand, the net requirement for As is 80 (that is, 100 – 20). However, each Awesome speaker kit on hand contains 2 Bs. As a result, the requirement for Bs drops by 40 Bs (20 A kits on hand × 2 Bs per A). Therefore, if inventory is on hand for a parent item, the requirements for the parent item and all its components decrease because each Awesome kit contains the components for lower-level items. Example 3 shows how to cre- ate a net requirements plan.
Net requirements plan
The result of adjusting gross
requirements for inventory on
hand and scheduled receipts.
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Example 3 DETERMINING NET REQUIREMENTS Speaker Kits, Inc., developed a product structure from a bill of material in Example 1 . Example 2 devel- oped a gross requirements plan. Given the following on-hand inventory, Speaker Kits, Inc., now wants to construct a net requirements plan. The gross requirement remains 50 units in week 8, and component requirements are as shown in the product structure in Example 1 .
ITEM ON HAND ITEM ON HAND
A 10 E 10 B 15 F 5 C 20 G 0 D 10
LO 14.3 Build a net requirements plan
1
15 1515 15 15 15 15 15
80A
120A
65 65
65
Gross RequirementsA0——101Lot- for- Lot
Scheduled Receipts Projected On Hand Net Requirements Planned Order Receipts Planned Order Releases
2 3 4 5
Week
6 7 8
Lot Size
Lead Time
(weeks)
On Hand
Safety Stock
Allo- cated
Low- Level Code
Item Identi- fication
200 120 120
120
195 195
195
20 2020 20 20 20 20 20 100 100
100
Gross RequirementsB1——152Lot- for- Lot
Scheduled Receipts Projected On Hand Net Requirements Planned Order Receipts Planned Order Releases
10 1010 10 10 10 10 10 10
50
40 40
40
Gross RequirementsC1——201Lot- for- Lot
Scheduled Receipts Projected On Hand Net Requirements Planned Order Receipts Planned Order Releases
10 1010 10 10 10
130B
130B390F
195F
200C
200C
200 200
Gross RequirementsE2——102Lot- for- Lot
Scheduled Receipts Projected On Hand Net Requirements Planned Order Receipts Planned Order Releases
5 55 5 5 5 5 195 195
195
Gross RequirementsF2——53Lot- for- Lot
Scheduled Receipts Projected On Hand Net Requirements Planned Order Receipts Planned Order Releases
10 1010 10 130 130
130
Gross RequirementsD3——101Lot- for- Lot
Scheduled Receipts Projected On Hand Net Requirements Planned Order Receipts Planned Order Releases
380 380
380
0
Gross RequirementsG3——02Lot- for- Lot
Scheduled Receipts Projected On Hand Net Requirements Planned Order Receipts Planned Order Releases
2 * number of As = 80
3 * number of As = 120
2 * number of Bs = 130 2 * number of Cs = 200
2 * number of Cs = 200
2 * number of Bs = 130 2 * number of Fs = 390
1 * number of Fs = 195
Net Material Requirements Plan for 50 Units of Product A in Week 8. ( The superscript is the source of the demand )
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APPROACH c A net material requirements plan includes gross requirements, on-hand inventory, net requirements, planned order receipt, and planned order release for each item. We begin with A and work backward through the components.
SOLUTION c Shown in the MRP format on the previous page is the net material requirements plan for product A.
Constructing a net requirements plan is similar to constructing a gross requirements plan. Starting with item A, we work backward to determine net requirements for all items. To do these computations, we refer to the product structure, on-hand inventory, and lead times. The gross requirement for A is 50 units in week 8. Ten items are on hand; therefore, the net requirements and the scheduled planned order receipt are both 40 items in week 8. Because of the one-week lead time, the planned order release is 40 items in week 7 (see the arrow connecting the order receipt and order release). Referring to week 7 and the product structure in Example 1 , we can see that 80 (2 × 40) items of B and 120 (3 × 40) items of C are required in week 7 to have a total for 50 items of A in week 8. The letter superscripted A to the right of the gross figure for items B and C was generated as a result of the demand for the parent, A. Performing the same type of analysis for B and C yields the net requirements for D, E, F, and G. Note the on-hand inventory in row E in week 6 is zero. It is zero because the on-hand inventory (10 units) was used to make B in week 5. By the same token, the inventory for D was used to make F in week 3.
INSIGHT c Once a net requirement plan is completed, management knows the quantities needed, an ordering schedule, and a production schedule for each component.
LEARNING EXERCISE c If the on-hand inventory quantity of component F is 95 rather than 5, how many units of G will need to be ordered in week 1? [Answer: 105 units.]
RELATED PROBLEMS c 14.9, 14.10b, 14.11b, 14.12, 14.13c, 14.14b, 14.15a,b,c, 14.16a,b, 14.17c (14.18–14.21 are available in MyOMLab)
ACTIVE MODEL 14.1 This example is further illustrated in Active Model 14.1 in MyOMLab.
EXCEL OM Data File Ch14Ex3.xls can be found in MyOMLab.
Planned order receipt
The quantity planned to be
received at a future date.
Planned order release
The scheduled date for an order
to be released.
Examples 2 and 3 considered only product A, the Awesome speaker kit, and its completion only in week 8. Fifty units of A were required in week 8. Normally, however, there is a demand for many products over time. For each product, management must prepare a master produc- tion schedule (as we saw earlier, in Table 14.1 ). Scheduled production of each product is added to the master schedule and ultimately to the net material requirements plan. Figure 14.5 shows how several product schedules, including requirements for components sold directly, can con- tribute to one gross material requirements plan.
Most inventory systems also note the number of units in inventory that have been assigned to specific future production but not yet used or issued from the stockroom. Such items are often
STUDENT TIP MRP gross requirements
can combine multiple
products, spare parts, and
items sold directly.
A
B C B C
S
5 6 7 8 9 10 11
40 50 15
8 9 10 11
40
12 13
3020
1
10
2
10
3
Master schedule for B
sold directly Lead time = 6 for S
Master schedule for S Lead time = 4 for A
Master schedule for A
7 8654321
15+30 =45
40+10 =50 20504010
Therefore, these are the gross requirements for B
Gross requirements: B
Periods
Periods
Figure 14.5
Several Schedules
Contributing to a Gross
Requirements Schedule for B
One B is in each A, and one B is
in each S; in addition, 10 Bs sold
directly are scheduled in week 1,
and 10 more that are sold directly
are scheduled in week 2.
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referred to as allocated items. Allocated items increase requirements as shown in Figure 14.6 , where gross requirements have been increased from 80 to 90 to reflect the 10 allocated items.
Safety Stock The continuing task of operations managers is to remove variability. This is the case in MRP systems as in other operations systems. Realistically, however, managers need to realize that bills of material and inventory records, like purchase and production quantities, as well as lead times, may not be perfect. This means that some consideration of safety stock may be prudent. Because of the significant domino effect of any change in requirements, safety stock should be minimized, with a goal of ultimate elimination. When safety stock is deemed absolutely necessary, the usual policy is to build it into (increase) the inventory requirement of the MRP logic. Distortion can be minimized when safety stock is held at the finished goods or module level and at the purchased component or raw material level.
MRP Management Now let’s look at the dynamics and limitations of MRP.
MRP Dynamics The inputs to MRP (the master schedule, BOM, lead times, purchasing, and inventory) fre- quently change. Conveniently, a central strength of MRP systems is timely and accurate replanning. However, many firms find they do not want to respond to minor scheduling or quantity changes even if they are aware of them. These frequent changes generate what is called system nervousness and can create havoc in purchasing and production departments if implemented. Consequently, OM personnel reduce such nervousness by evaluating the need and impact of changes prior to disseminating requests to other departments. Two tools are particularly helpful when trying to reduce MRP system nervousness.
The first is time fences. Time fences allow a segment of the master schedule to be designated as “not to be rescheduled.” This segment of the master schedule is therefore not changed dur- ing the periodic regeneration of schedules. The second tool is pegging. Pegging means tracing upward in the BOM from the component to the parent item. By pegging upward, the produc- tion planner can determine the cause for the requirement and make a judgment about the necessity for a change in the schedule.
With MRP, the operations manager can react to the dynamics of the real world. If the nervousness is caused by legitimate changes, then the proper response may be to investigate the production environment—not adjust via MRP.
MRP Limitations MRP does not do detailed scheduling—it plans. MRP is an excellent tool for product-focused and repetitive facilities, but it has limitations in process (make-to-order) environments. MRP will tell you that a job needs to be completed on a certain week or day but does not tell you
Lot Size
Lead Time
On Hand
Safety Stock
Allocated Low- Level Code
Item ID
1 2 3 4 5 6 7 8
Period
Gross Requirements Scheduled Receipts Projected On Hand Net Requirements Planned Order Receipts Planned Order Releases
Figure 14.6
Sample MRP Planning Sheet for Item Z
System nervousness
Frequent changes in an MRP
system.
Time fences
A means for allowing a segment of
the master schedule to be desig-
nated as “not to be rescheduled.”
Pegging
In material requirements planning
systems, tracing upward the bill
of material from the component to
the parent item.
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that Job X needs to run on Machine A at 10:30 a.m. and be completed by 11:30 a.m. so that Job X can then run on Machine B. MRP is also a planning technique with fixed lead times that loads work into infinite size “buckets.” The buckets are time units, usually one week. MRP puts work into these buckets without regard to capacity. Consequently, MRP is considered an infinite scheduling technique. Techniques for the alternative, finite scheduling, are discussed in Chapter 15 .
Lot-Sizing Techniques An MRP system is an excellent way to do production planning and determine net require- ments. But net requirements still demand a decision about how much and when to order. This decision is called a lot-sizing decision . There are a variety of ways to determine lot sizes in an MRP system; commercial MRP software usually includes the choice of several lot-sizing tech- niques. We now review a few of them.
Lot-for-Lot In Example 3 , we used a lot-sizing technique known as lot-for-lot , which pro- duced exactly what was required. This decision is consistent with the objective of an MRP system, which is to meet the requirements of dependent demand. Thus, an MRP system should produce units only as needed, with no safety stock and no anticipation of further orders. When frequent orders are economical (i.e., when setup costs are low) and just-in-time inventory tech- niques implemented, lot-for-lot can be very efficient. However, when setup costs are signifi- cant, lot-for-lot can be expensive. Example 4 uses the lot-for-lot criteria and determines cost for 10 weeks of demand.
Buckets
Time units in a material
requirements planning
system.
Lot-sizing decision
The process of, or techniques
used in, determining lot size.
Lot-for-lot
A lot-sizing technique that
generates exactly what is
required to meet the plan.
Example 4 LOT SIZING WITH LOT-FOR-LOT Speaker Kits, Inc., wants to compute its ordering and carrying cost of inventory on lot-for-lot criteria.
APPROACH c With lot-for-lot, we order material only as it is needed. Once we have the cost of order- ing (setting up), the cost of holding each unit for a given time period, and the production schedule, we can assign orders to our net requirements plan.
SOLUTION c Speaker Kits has determined that, for component B, setup cost is $100 and holding cost is $1 per period. The production schedule, as reflected in net requirements for assemblies, is as follows:
MRP Lot Sizing: Lot-for-Lot Technique*
WEEK 1 2 3 4 5 6 7 8 9 10
Gross requirements 35 30 40 0 10 40 30 0 30 55 Scheduled receipts Projected on hand 35 35 0 0 0 0 0 0 0 0 0 Net requirements 0 30 40 0 10 40 30 0 30 55 Planned order receipts 30 40 10 40 30 30 55 Planned order releases 30 40 10 40 30 30 55
* Holding costs = $1/unit/week; setup cost = $100; gross requirements average per week = 27; lead time = 1 week.
The lot-sizing solution using the lot-for-lot technique is shown in the table. The holding cost is zero as there is never any end-of-period inventory. (Inventory in the first period is used immediately and there- fore has no holding cost.) But seven separate setups (one associated with each order) yield a total cost of $700. (Holding cost = 0 × 1 = 0; ordering cost = 7 × 100 = 700.)
INSIGHT c When supply is reliable and frequent orders are inexpensive, but holding cost or obsoles- cence is high, lot-for-lot ordering can be very efficient.
LEARNING EXERCISE c What is the impact on total cost if holding cost is $2 per period rather than $1? [Answer: Total holding cost remains zero, as no units are held from one period to the next with lot-for-lot.]
RELATED PROBLEMS c 14.22, 14.25, 14.26a, 14.27a (14.28b is available in MyOMLab)
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Economic Order Quantity (EOQ) We now extend our discussion of EOQ in Chapter 12 to use it as a lot-sizing technique for MRP systems. As we indicated there, EOQ is useful when we have relatively constant demand. However, demand may change every period in MRP systems. Therefore, EOQ lot sizing often does not perform well in MRP. Operations managers should take advantage of demand information when it is known, rather than assuming a constant demand. EOQ is used to do lot sizing in Example 5 for comparison purposes.
LO 14.4 Determine lot sizes for lot-for-lot, EOQ,
and POQ
This Nissan line in Smyrna,
Tennessee, has little inventory
because Nissan schedules to a
razor’s edge. At Nissan, MRP
helps reduce inventory to world-
class standards. World-class
automobile assembly requires that
purchased parts have a turnover of
slightly more than once a day and
that overall turnover approaches
150 times per year.
Jo h n R
u ss
e ll/
A P I m
a g e s
Example 5 LOT SIZING WITH EOQ With a setup cost of $100 and a holding cost per week of $1, Speaker Kits, Inc., wants to examine its cost for component B, with lot sizes based on an EOQ criteria.
APPROACH c Using the same cost and production schedule as in Example 4 , we determine net requirements and EOQ lot sizes.
SOLUTION c Ten-week usage equals a gross requirement of 270 units; therefore, weekly usage equals 27, and 52 weeks (annual usage) equals 1,404 units. From Chapter 12 , the EOQ model is:
Q* = A
2DS H
where D = annual usage = 1,404 S = setup cost = $100 H = holding (carrying) cost, on an annual basis per unit = $1 * 52 weeks = $52 Q* = 73 units
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Therefore, place an order of 73 units, as necessary, to avoid a stockout.
MRP Lot Sizing: EOQ Technique*
WEEK 1 2 3 4 5 6 7 8 9 10
Gross requirements 35 30 40 0 10 40 30 0 30 55 Scheduled receipts Projected on hand 35 35 0 43 3 3 66 26 69 69 39 Net requirements 0 30 0 0 7 0 4 0 0 16 Planned order receipts 73 73 73 73 Planned order releases 73 73 73 73
* Holding costs = $1/unit/week; setup cost = $100; gross requirements average per week = 27; lead time = 1 week.
For the 10-week planning period:
Holding cost = 375 units * $1 = $375 (includes 57 remaining at the end of week 10) Ordering cost = 4 * $100 = $400 Total = $375 + $400 = $775
INSIGHT c EOQ can be a reasonable lot-sizing technique when demand is relatively constant. However, notice that actual holding cost will vary substantially depending on the rate of actual usage. If any stockouts had occurred, these costs too would need to be added to our actual EOQ cost of $775.
LEARNING EXERCISE c What is the impact on total cost if holding cost is $2 per period rather than $1? [Answer: The EOQ quantity becomes 52, the theoretical annual total cost becomes $5,404, and the 10-week cost is $1,039 ($5,404 × (10>52).]
RELATED PROBLEMS c 14.23, 14.25, 14.26b, 14.27c (14.28a is available in MyOMLab)
Periodic Order Quantity Periodic order quantity (POQ) is a lot-sizing technique that orders the quantity needed during a predetermined time between orders, such as every 3 weeks. We define the POQ interval as the EOQ divided by the average demand per period (e.g., one week). 3 The POQ is the order quantity that covers the specific demand for that interval. Each order quantity is recalculated at the time of the order release , never leaving extra inventory. An application of POQ is shown in Example 6 .
Periodic order quantity (POQ)
An inventory ordering technique
that issues orders on a predeter-
mined time interval, with the order
quantity covering the total of the
interval’s requirements.
Example 6 LOT SIZING WITH POQ With a setup cost of $100 and a holding cost per week of $1, Speaker Kits, Inc., wants to examine its cost for component B, with lot sizes based on POQ.
APPROACH c Using the same cost and production schedule as in Example 5 , we determine net requirements and POQ lot sizes.
SOLUTION c Ten-week usage equals a gross requirement of 270 units; therefore, average weekly usage equals 27, and from Example 5 , we know the EOQ is 73 units.
We set the POQ interval equal to the EOQ divided by the average weekly usage.
Therefore:
POQ interval = EOQ/Average weekly usage = 73/27 = 2.7, or 3 weeks.
The POQ order size will vary by the quantities required in the respective weeks, as shown in the following table, with first planned order release in week 1.
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Note : Orders are postponed if no demand exists, which is why week 7’s order is postponed until week 8.
MRP Lot Sizing: POQ Technique*
WEEK 1 2 3 4 5 6 7 8 9 10
Gross requirements 35 30 40 0 10 40 30 0 30 55 Scheduled receipts Projected on hand 35 35 0 40 0 0 70 30 0 0 55 Net requirements 0 30 0 0 10 0 0 0 55 0 Planned order receipts 70 80 0 85 0 Planned order releases 70 80 85
* Holding costs = $1/unit/week; setup cost = $100; gross requirements average per week = 27; lead time = 1 week.
Setups = 3 * $100 = $300 Holding cost = (40 + 70 + 30 + 55) units * $1 each = $195
The POQ solution yields a computed 10@week cost of $300 + $195 = $495
INSIGHT c Because POQ tends to produce a balance between holding and ordering costs with no excess inventory, POQ typically performs much better than EOQ. Notice that even with frequent recal- culations, actual holding cost can vary substantially, depending on the demand fluctuations. We are assuming no stockouts. In this and similar examples, we are also assuming no safety stock; such costs would need to be added to our actual cost.
LEARNING EXERCISE c What is the impact on total cost if holding cost is $2 per period rather than $1? [Answer: EOQ = 52; POQ interval = 52/27 = 1.93 ≈ 2 weeks; holding cost = $270; setups = $400. The POQ total cost becomes $670.]
RELATED PROBLEMS c 14.24, 14.25, 14.26c, 14.27b (14.28c is available in MyOMLab)
Other lot-sizing techniques, known as dynamic lot-sizing , are similar to periodic order quantity as they attempt to balance the lot size against the setup cost. These are part period bal- ancing (also called least total cost ), least unit cost, and least period cost (also called Silver-Meal ). Another technique, Wagner-Whitin , takes a different approach by using dynamic programming to optimize ordering over a finite time horizon. 4
Lot-Sizing Summary In the three speaker kits lot-sizing examples, we found the follow- ing costs:
COSTS
SETUP HOLDING TOTAL
Lot-for-lot $700 $0 $700 Economic order quantity (EOQ) $400 $375 $775 Periodic order quantity (POQ) $300 $195 $495
These examples should not, however, lead operations personnel to hasty conclusions about the preferred lot-sizing technique. In theory, new lot sizes should be computed whenever there is a schedule or lot-size change anywhere in the MRP hierarchy. In practice, such changes cause the instability and system nervousness referred to earlier in this chapter. Consequently, such frequent changes are not made. This means that all lot sizes are wrong because the pro- duction system cannot and should not respond to frequent changes. Note that there are no “shortage” (out of stock) charges in any of these lot-sizing techniques. This limitation places added demands on accurate forecasts and “time fences.”
In general, the lot-for-lot approach should be used whenever low-cost setup can be achieved. Lot-for-lot is the goal. Lots can be modified as necessary for scrap allowances, process constraints (for example, a heat-treating process may require a lot of a given size), or raw
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material purchase lots (for example, a truckload of chemicals may be available in only one lot size). However, caution should be exercised prior to any modification of lot size because the modification can cause substantial distortion of actual requirements at lower levels in the MRP hierarchy. When setup costs are significant and demand is reasonably smooth, POQ or even EOQ should provide satisfactory results. Too much concern with lot sizing yields false accuracy because of MRP dynamics. A correct lot size can be determined only after the fact, based on what actually happened in terms of requirements.
Extensions of MRP In this section, we review three extensions of MRP .
Material Requirements Planning II (MRP II) Material requirements planning II is an extremely powerful technique. Once a firm has MRP in place, requirements data can be enriched by resources other than just components. When MRP is used this way, resource is usually substituted for requirements , and MRP becomes MRP II . It then stands for material resource planning.
So far in our discussion of MRP, we have scheduled products and their components. How- ever, products require many resources, such as energy and money, beyond the product’s tangible components. In addition to these resource inputs, outputs can be generated as well. Outputs can include such things as scrap, packaging waste, effluent, and carbon emissions. As OM becomes increasingly sensitive to environmental and sustainability issues, identifying and managing by- products takes on more significance. MRP II provides a vehicle for doing so. Table 14.4 pro- vides an example of labor-hours, machine-hours, grams of greenhouse gas emissions, pounds of scrap, and cash, in the format of a gross requirements plan. With MRP II, management can identify both the inputs and outputs as well as the relevant schedule. MRP II provides another tool in OM’s battle for sustainable operations.
Material requirements planning II (MRP II)
A system that allows, with MRP
in place, inventory data to be
augmented by other resource vari-
ables; in this case, MRP becomes
material resource planning .
LO 14.5 Describe MRP II
Many MRP programs, such as
Resource Manager for Excel , are
commercially available. Resource
Manager’s initial menu screen is
shown here.
A demo program is available for
student use at
www.usersolutions.com .
Ji m
C o n vi
s, U
se r
S o lu
ti o n s,
I n c.
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MRP II systems are seldom stand-alone programs. Most are tied into other computer software. Purchasing, production scheduling, capacity planning, inventory, and warehouse management systems are a few examples of this data integration.
Closed-Loop MRP Closed-loop material requirements planning implies an MRP system that provides feedback to scheduling from the inventory control system. Specifically, a closed-loop MRP system provides information to the capacity plan, master production schedule, and ultimately to the produc- tion plan (as shown in Figure 14.7 ). Virtually all commercial MRP systems are closed-loop.
Capacity Planning In keeping with the definition of closed-loop MRP, feedback about workload is obtained from each work center. Load reports show the resource requirements in a work center for all work currently assigned to the work center, all work planned, and expected orders.
TABLE 14.4 Material Resource Planning (MRP II)
Weeks
LEAD TIME 5 6 7 8
Computer Labor-hours: .2 each Machine-hours: .2 each GHG Emissions : .25 each Scrap: 1 ounce fi berglass each Payables: $0
1 100 20 20 25 grams
6.25 lb $0
PC board (1 each) Labor-hours: .15 each Machine-hours: .1 each GHG Emissions : 2.5 each Scrap: .5 ounces copper each Payables: raw material at $5 each
2 100 15 10
250 grams 3.125 lb $500
Processors (5 each) Labor-hours: .2 each Machine-hours: .2 each GHG Emissions: .50 each Scrap: .01 ounces of acid waste each Payables: processor components at $10 each
4 500 100 100
25,000 grams 0.3125 lb $5,000
By utilizing the logic of MRP,
resources such as labor, machine-
hours, greenhouse gas emissions,
scrap, and cost can be accurately
determined and scheduled.
Weekly demand for labor,
machine-hours, greenhouse gas
emissions, scrap, and payables for
100 computers are shown.
Priority Management
Develop Master Production Schedule
Prepare Materials Requirements Plan
Detailed Production Activity Control (Shop Scheduling/Dispatching)
OK? YES
OK? YES
Capacity Management
Planning (see this chapter)
(see Chapter 13)
Execution (see Chapter 15) (in repetitive systems JIT techniques are used)
Evaluate Resource Availability (Rough Cut)
Determine Capacity Availability
Implement Input/Output Control
OK? NO
OK? NO
Aggregate Plan
Figure 14.7
Closed-Loop Material Requirements Planning
Closed-loop MRP system
A system that provides feedback
to the capacity plan, master pro-
duction schedule, and production
plan so planning can be kept valid
at all times.
Load report
A report showing the resource
requirements in a work center
for all work currently assigned
there as well as all planned and
expected orders.
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582 P A R T 3 | M A N AG I N G O P E R AT I O N S
Figure 14.8 (a) shows that the initial load in the milling center exceeds capacity on days 2, 3, and 5. Closed-loop MRP systems allow production planners to move the work between time periods to smooth the load or at least bring it within capacity. (This is the “capacity planning” part of Figure 14.7 .) The closed-loop MRP system can then reschedule all items in the net requirements plan (see Figure 14.8 [b]).
Tactics for smoothing the load and minimizing the impact of changed lead time include the following:
1. Overlapping, which reduces the lead time, sends pieces to the second operation before the entire lot is completed on the first operation.
2. Operations splitting sends the lot to two different machines for the same operation. This involves an additional setup, but results in shorter throughput times because only part of the lot is processed on each machine.
3. Order splitting, or lot splitting , involves breaking up the order and running part of it ear- lier (or later) in the schedule.
Example 7 shows a brief detailed capacity scheduling example using order splitting to improve utilization.
Capacity exceeded on days 2, 3, and 5
41 2 3 5
Days
14
8
6
4
2
0
10
12
S ta
n d a rd
L a b o r-
H o u rs
Available capacity
Days
(a) (b)
2 orders moved to day 1 from day 2 (a day early) 1 order forced to overtime or to day 6
2 orders moved to day 4 (a day early)
41 2 3 5
14
8
6
4
2
0
10
12
S ta
n d a rd
L a b o r-
H o u rs
Figure 14.8
(a) Initial Resource
Requirements Profile for a
Work Center
(b) Smoothed Resource
Requirements Profile for
a Work Center
LO 14.6 Describe closed-loop MRP
Example 7 ORDER SPLITTING Kevin Watson, the production planner at Wiz Products, needs to develop a capacity plan for a work center. He has the production orders shown below for the next 5 days. There are 12 hours available in the work cell each day. The parts being produced require 1 hour each.
Day 1 2 3 4 5
Orders 10 14 13 10 14
APPROACH c Compute the time available in the work center and the time necessary to complete the production requirements.
SOLUTION c
DAY UNITS
ORDERED
CAPACITY REQUIRED (HOURS)
CAPACITY AVAILABLE
(HOURS)
UTILIZATION: OVER/
(UNDER) (HOURS)
PRODUCTION PLANNER’S ACTION
NEW PRODUCTION
SCHEDULE
1 10 10 12 (2) 12
2 14 14 12 2 Split order: move 2 units to day 1 12 3 13 13 12 1 Split order: move 1 unit to day 6
or request overtime 13
4 10 10 12 (2) 12 5 14 14 12 2 Split order: move 2 units to day 4 12
61
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INSIGHT c By moving orders, the production planner is able to utilize capacity more effectively and still meet the order requirements, with only 1 order produced on overtime in day 3.
LEARNING EXERCISE c If the units ordered for day 5 increase to 16, what are the production plan- ner’s options? [Answer: In addition to moving 2 units to day 4, move 2 units of production to day 6, or request overtime.]
RELATED PROBLEMS c 14.29, 14.30
When the workload consistently exceeds work-center capacity, the tactics just discussed are not adequate. This may mean adding capacity via personnel, machinery, overtime, or subcontracting.
MRP in Services The demand for many services or service items is classified as dependent demand when it is directly related to or derived from the demand for other services. Such services often require product-structure trees, bills of material and labor, and scheduling. Variations of MRP sys- tems can make a major contribution to operational performance in such services. Examples from restaurants, hospitals, and hotels follow.
Restaurants In restaurants, ingredients and side dishes (bread, vegetables, and condiments) are typically meal components. These components are dependent on the demand for meals. The meal is an end item in the master schedule. Figure 14.9 shows (a) a product-structure tree and
Alpha
Garnish with Buffalo Chicken mix, Blue Cheese, Scallions
Baked Buffalo Chicken Mac & Cheese
Unbaked Buffalo Chicken Mac & Cheese Mix
Buffalo Chicken Mac & Cheese
Buffalo Sauce
Elbow Macaroni (large, uncooked) Cheese—Pepper Jack (grated) Mac and Cheese Base (from refrigerator) Milk Smoked Pulled Chicken Buffalo Sauce Blue Cheese Crumbles Scallions
20.00 10.00 32.00
4.00 2.00 8.00 4.00 2.00
oz. oz. oz. oz. lb.
oz. oz. oz.
$ 0.09 0.17 0.80 0.03 2.90 0.09 0.19 0.18
$ 1.80 1.70
25.60 0.12 5.80 0.72 0.76 0.36
Total Labor Hours 0.2 hrs
Ingredients Quantity Measure Unit Cost Total Cost Labor Hrs.
Production Specification Buffalo Chicken Mac & Cheese (6 portions)
Smoked Pulled
Chicken
Blue Cheese
Crumbles
Cooked Elbow
Macaroni
Grated Pepper Jack
Cheese
Chopped Scallions
Mac & Cheese Base
Milk
Buffalo Chicken Mix
(a) PRODUCT STRUCTURE TREE
(b) BILL OF MATERIALS
Figure 14.9
Product Structure Tree and
Bill of Material for Chef John’s
Buffalo Chicken Mac & Cheese
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(b) bill of material (here called a product specification ) for 6 portions of Buffalo Chicken Mac & Cheese, a popular dish prepared by Chef John for Orlando Magic fans at the Amway Center.
Hospitals MRP is also applied in hospitals, especially when dealing with surgeries that require known equipment, materials, and supplies. Houston’s Park Plaza Hospital and many hospital suppliers, for example, use the technique to improve the scheduling and management of expensive surgical inventory.
Hotels Marriott develops a bill of material and a bill of labor when it renovates each of its hotel rooms. Marriott managers explode the BOM to compute requirements for materials, furniture, and decorations. MRP then provides net requirements and a schedule for use by purchasing and contractors.
Distribution Resource Planning (DRP) When dependent techniques are used in the supply chain, they are called distribution resource planning (DRP). Distribution resource planning (DRP) is a time-phased stock-replenishment plan for all levels of the supply chain.
DRP procedures and logic are analogous to MRP. With DRP, expected demand becomes gross requirements. Net requirements are determined by allocating available inventory to gross requirements. The DRP procedure starts with the forecast at the retail level (or the most distant point of the distribution network being supplied). All other levels are computed. As is the case with MRP, inventory is then reviewed with an aim to satisfying demand. So that stock will arrive when it is needed, net requirements are offset by the necessary lead time. A planned order release quantity becomes the gross requirement at the next level down the distribution chain.
DRP pulls inventory through the system. Pulls are initiated when the retail level orders more stock. Allocations are made to the retail level from available inventory and production after being adjusted to obtain shipping economies. Effective use of DRP requires an integrated information system to rapidly convey planned order releases from one level to the next. The goal of the DRP system is small and frequent replenishment within the bounds of economical ordering and shipping.
Enterprise Resource Planning (ERP) Advances in MRP II systems that tie customers and suppliers to MRP II have led to the devel- opment of enterprise resource planning (ERP) systems. Enterprise resource planning (ERP) is soft- ware that allows companies to (1) automate and integrate many of their business processes, (2) share a common database and business practices throughout the enterprise, and (3) produce information in real time. A schematic showing some of these relationships for a manufactur- ing firm appears in Figure 14.10 .
The objective of an ERP system is to coordinate a firm’s entire business, from supplier evaluation to customer invoicing. This objective is seldom achieved, but ERP systems are um- brella systems that tie together a variety of specialized systems. This is accomplished by using a centralized database to assist the flow of information among business functions. Exactly what is tied together, and how, varies on a case-by-case basis. In addition to the traditional compo- nents of MRP, ERP systems usually provide financial and human resource (HR) management information. ERP systems may also include:
◆ Supply-chain management (SCM) software to support sophisticated vendor communica- tion, e-commerce, and those activities necessary for efficient warehousing and logistics. The idea is to tie operations (MRP) to procurement, to materials management, and to suppliers, providing the tools necessary for effective management of all four areas.
◆ Customer relationship management (CRM) software for the incoming side of the business. CRM is designed to aid analysis of sales, target the most profitable customers, and manage the sales force.
◆ Sustainability software to tie together sustainable workforce issues and provide transparency for supply-chain sustainability issues, as well as monitor health and safety activities, energy use and efficiency, emissions (carbon footprint, greenhouse gases), and environmental compliance.
Distribution resource planning (DRP)
A time-phased stock-
replenishment plan for all
levels of a distribution
network.
Enterprise resource planning (ERP)
An information system for identify-
ing and planning the enterprise-
wide resources needed to take,
make, ship, and account for
customer orders.
LO 14.7 Describe ERP
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In addition to data integration, ERP software promises reduced transaction costs and fast, accurate information. A strategic emphasis on just-in-time systems and supply chain integration drives the desire for enterprise-wide software. The OM in Action box “Managing Benetton with ERP Software” provides an example of how ERP software helps integrate company operations.
Inventory Management
Bills of Material
Routings and
Lead Times
Master Production Schedule
Sales Order (order entry, product configuration,
sales management)
Shipping Distributors,
retailers, and end users
General Ledger
Payroll
Accounts Payable
Invoicing
Supply-Chain Management Vendor Communication
(schedules, EDI, advanced shipping notice, e-commerce, etc.)
Accounts Receivable
MRP ERP
Purchasing and
Lead Times
Work Orders
Customer Relationship Management
Finance/ Accounting
STUDENT TIP ERP tries to integrate all of a
firm’s information to ensure
data integrity.
Figure 14.10
MRP and ERP Information
Flows, Showing Customer
Relationship Management
(CRM), Supply-Chain
Management (SCM), and
Finance/Accounting
Other functions such as human
resources and sustainability
are often also included in ERP
systems.
OM in Action Managing Benetton with ERP Software Thanks to ERP, the Italian sportswear company Benetton can probably claim to
have the world’s fastest factory and the most efficient distribution in the gar-
ment industry. Located in Ponzano, Italy, Benetton makes and ships 50 million
pieces of clothing each year. That is 30,000 boxes every day—boxes that must
be filled with exactly the items ordered going to the correct store of the 5,000
Benetton outlets in 60 countries. This highly automated distribution center uses
only 19 people. Without ERP, hundreds of people would be needed.
Here is how ERP software works:
1. Ordering: A salesperson in the south Boston store fi nds that she is
running out of a best-selling blue sweater. Using a laptop PC, her local
Benetton sales agent taps into the ERP sales module.
2. Availability: ERP’s inventory software simultaneously forwards the order
to the mainframe in Italy and fi nds that half the order can be fi lled im-
mediately from the Italian warehouse. The rest will be manufactured and
shipped in 4 weeks.
3. Production: Because the blue sweater was originally created by computer-
aided design (CAD), ERP manufacturing software passes the specifi ca-
tions to a knitting machine. The knitting machine makes the sweaters.
4. Warehousing: The blue sweaters are boxed with a radio frequency ID
(RFID) tag addressed to the Boston store and placed in one of the 300,000
slots in the Italian warehouse. A robot fl ies by, reading RFID tags, picks out
any and all boxes ready for the Boston store, and loads them for shipment.
5. Order tracking: The Boston salesperson logs onto the ERP system
through the Internet and sees that the sweater (and other items) are
completed and being shipped.
6. Planning: Based on data from ERP’s forecasting and fi nancial modules,
Benetton’s chief buyer decides that blue sweaters are in high demand
and quite profi table. She decides to add three new hues.
Sources: Forbes (December 2, 2011); The Wall Street Journal (April 10, 2007);
Information Week (June 13, 2005); and MIT Sloan Management Review (Fall 2001).
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586 P A R T 3 | M A N AG I N G O P E R AT I O N S
In an ERP system, data are entered only once into a common, complete, and consistent database shared by all applications. For example, when a Nike salesperson enters an order into his ERP system for 20,000 pairs of sneakers for Foot Locker, the data are instantly available on the manufacturing floor. Production crews start filling the order if it is not in stock, account- ing prints Foot Locker’s invoice, and shipping notifies Foot Locker of the future delivery date. The salesperson, or even the customer, can check the progress of the order at any point. This is all accomplished using the same data and common applications. To reach this consistency, however, the data fields must be defined identically across the entire enterprise. In Nike’s case, this means integrating operations at production sites from Vietnam to China to Mexico, at business units across the globe, in many currencies, and with reports in a variety of languages.
Each ERP vendor produces unique products. The major vendors, SAP AG (a German firm), BEA (Canada), SSAGlobal, American Software, PeopleSoft/Oracle, and CMS Software (all U.S. firms), sell software or modules designed for specific industries (a set of SAP’s mod- ules is shown in Figure 14.11 ). However, companies must determine if their way of doing business will fit the standard ERP module. If they determine that the product will not fit the standard ERP product, they can change the way they do business to accommodate the software. But such a change can have an adverse impact on their business process, reducing a competitive advantage.
Alternatively, ERP software can be customized to meet their specific process requirements. Although the vendors build the software to keep the customization process simple, many companies spend up to five times the cost of the software to customize it. In addition to the expense, the major downside of customization is that when ERP vendors provide an upgrade or enhancement to the software, the customized part of the code must be rewritten to fit into the new version. ERP programs cost from a minimum of $300,000 for a small company to
Covers all financial related activity:
Covers internal inventory management:
PROMOTE TO DELIVER Covers front-end customer-oriented activities:
DESIGN TO MANUFACTURE Covers internal production activities:
PROCURE TO PAY Covers sourcing activities:
RECRUIT TO RETIRE Covers all HR- and payroll-oriented activity:
Accounts receivable
Accounts payable
General ledger Treasury
Cash management Asset management
Shop floor reporting
Warehousing Distribution planning
Forecasting Replenishment planning
Physical inventory Material handling
Marketing
Quote and order processing
Transportation
Documentation and labeling
After sales service
Warranty and guarantees
Design engineering
Production engineering
Plant maintenance
Contract/project management
Subcontractor management
Vendor sourcing
Purchase requisitioning
Purchase ordering
Purchase contracts
Inbound logistics
Supplier invoicing/matching
Supplier payment/ settlement
Supplier performance Time and attendance Payroll
Travel and expenses
CASH TO CASH
DOCK TO DISPATCH
Figure 14.11
SAP’s Modules for ERP
Source: www.sap.com .
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C H A P T E R 1 4 | M AT E R I A L R E Q U I R E M E N T S P L A N N I N G ( M R P ) A N D E R P 587
hundreds of millions of dollars for global giants like Ford and Coca-Cola. It is easy to see, then, that ERP systems are expensive, full of hidden issues, and time-consuming to install.
ERP in the Service Sector ERP vendors have developed a series of service modules for such markets as health care, government, retail stores, and financial services. Springer-Miller Systems, for example, has created an ERP package for the hotel market with software that handles all front- and back- office functions. This system integrates tasks such as maintaining guest histories, booking room and dinner reservations, scheduling golf tee times, and managing multiple properties in a chain. PeopleSoft/Oracle combines ERP with supply chain management to coordinate airline meal preparation. In the grocery industry, these supply chain systems are known as efficient consumer response (ECR) systems. Efficient consumer response systems tie sales to buy- ing, to inventory, to logistics, and to production.
Efficient consumer response (ECR)
Supply chain management sys-
tems in the grocery industry that
tie sales to buying, to inventory, to
logistics, and to production.
Summary Material requirements planning (MRP) schedules produc- tion and inventory when demand is dependent. For MRP to work, management must have a master schedule, precise requirements for all components, accurate inventory and purchasing records, and accurate lead times.
When properly implemented, MRP can contribute in a major way to reduction in inventory while improving cus- tomer service levels. MRP techniques allow the operations manager to schedule and replenish stock on a “need-to- order” basis rather than simply a “time-to-order” basis.
Many firms using MRP systems find that lot-for-lot can be the low-cost lot-sizing option.
The continuing development of MRP systems has led to its use with lean manufacturing techniques. In addition, MRP can integrate production data with a variety of other activities, including the supply chain and sales. As a result, we now have integrated database-oriented enterprise resource planning (ERP) systems. These expensive and difficult-to-install ERP systems, when successful, support strategies of differentiation, response, and cost leadership.
Key Terms
Material requirements planning (MRP) (p. 566 )
Master production schedule (MPS) (p. 567 )
Bill of material (BOM) (p. 568 ) Modular bills (p. 569 ) Planning bills (or kits) (p. 570 ) Phantom bills of material (p. 570 ) Low-level coding (p. 570 ) Lead time (p. 570 )
Gross material requirements plan (p. 571 ) Net requirements plan (p. 572 ) Planned order receipt (p. 574 ) Planned order release (p. 574 ) System nervousness (p. 575 ) Time fences (p. 575 ) Pegging (p. 575 ) Buckets (p. 576 ) Lot-sizing decision (p. 576 ) Lot-for-lot (p. 576 )
Periodic order quantity (POQ) (p. 578 ) Material requirements planning II
(MRP II) (p. 580 ) Closed-loop MRP system (p. 581 ) Load report (p. 581 ) Distribution resource planning (DRP) (p. 584 ) Enterprise resource planning (ERP)
(p. 584 ) Efficient consumer response (ECR)
(p. 587 )
Ethical Dilemma For many months your prospective ERP customer has been analyzing the hundreds of assumptions built into the $900,000 ERP software you are selling. So far, you have knocked yourself out to try to make this sale. If the sale goes through, you will reach your yearly quota and get a nice bonus. On the other hand, loss of this sale may mean you start looking for other employment.
The accounting, human resource, supply chain, and marketing teams put together by the client have reviewed the specifi cations
and fi nally recommended purchase of the software. However, as you looked over their shoulders and helped them through the evaluation process, you began to realize that their purchasing procedures—with much of the purchasing being done at hundreds of regional stores—were not a good fit for the software. At the very least, the customizing will add $250,000 to the implementation and training cost. The team is not aware of the issue, and you know that the necessary $250,000 is not in the budget.
What do you do?
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588 P A R T 3 | M A N AG I N G O P E R AT I O N S
Discussion Questions
1. What is the difference between a gross requirements plan and a net requirements plan?
2. Once a material requirements plan (MRP) has been estab- lished, what other managerial applications might be found for the technique?
3. What are the similarities between MRP and DRP? 4. How does MRP II differ from MRP? 5. Which is the best lot-sizing policy for manufacturing organi-
zations? 6. What impact does ignoring carrying cost in the allocation of
stock in a DRP system have on lot sizes? 7. MRP is more than an inventory system; what additional
capabilities does MRP possess? 8. What are the options for the production planner who has:
a) scheduled more than capacity in a work center next week?
b) a consistent lack of capacity in that work center? 9. Master schedules are expressed in three different ways
depending on whether the process is continuous, a job shop, or repetitive. What are these three ways?
10. What functions of the firm affect an MRP system? How?
11. What is the rationale for (a) a phantom bill of material, (b) a planning bill of material, and (c) a pseudo bill of material?
12. Identify five specific requirements of an effective MRP system. 13. What are the typical benefits of ERP? 14. What are the distinctions between MRP, DRP, and ERP? 15. As an approach to inventory management, how does MRP
differ from the approach taken in Chapter 12 , dealing with economic order quantities (EOQ)?
16. What are the disadvantages of ERP? 17. Use the Web or other sources to: a) Find stories that highlight the advantages of an ERP system. b) Find stories that highlight the difficulties of purchasing,
installing, or failure of an ERP system. 18. Use the Web or other sources to identify what an ERP vendor
(SAP, PeopleSoft/Oracle, American Software, etc.) includes in these software modules:
a) Customer relationship management. b) Supply-chain management. c) Product life cycle management. 19. The structure of MRP systems suggests “buckets” and infi-
nite loading. What is meant by these two terms?
Using Software to Solve MRP Problems
There are many commercial MRP software packages, for com- panies of all sizes. MRP software for small and medium-size companies includes User Solutions, Inc., a demo of which is available at www.usersolutions.com , and MAX, from Exact Software North America, Inc. Software for larger systems is available from SAP, CMS, BEA, Oracle, i2 Technologies, and many others. The Excel OM software that accompanies this text includes an MRP module, as does POM for Windows. The use of both is explained in the following sections.
X USING EXCEL OM Using Excel OM’s MRP module requires the careful entry of several pieces of data. The initial MRP screen is where we enter (1) the total number of occurrences of items in the BOM (including the top item), (2) what we want the BOM items to be called (e.g., Item no., Part), (3) total number of periods to be scheduled, and (4) what we want the periods called (e.g., days, weeks).
Excel OM’s second MRP screen provides the data entry for an indented bill of material. Here we enter (1) the name of each item in the BOM, (2) the quantity of that item in the assem- bly, and (3) the correct indent (e.g., parent/child relationship) for each item. The indentations are critical, as they provide the logic for the BOM explosion. The indentations should follow the logic of the product structure tree with indents for each assembly item in that assembly.
Excel OM’s third MRP screen repeats the indented BOM and provides the standard MRP tableau for entries. This is shown in Program 14.1 using the data from Examples 1, 2, and 3.
P USING POM FOR WINDOWS The POM for Windows MRP module can also solve Examples 1 to 3. Up to 18 periods can be analyzed. Here are the inputs required:
1. Item names: The item names are entered in the left column. The same item name will appear in more than one row if the item is used by two parent items. Each item must follow its parents.
2. Item level: The level in the indented BOM must be given here. The item cannot be placed at a level more than one below the item immediately above.
3. Lead time: The lead time for an item is entered here. The default is 1 week.
4. Number per parent: The number of units of this subassem- bly needed for its parent is entered here. The default is 1.
5. On hand: List current inventory on hand once, even if the subassembly is listed twice.
6. Lot size: The lot size can be specifi ed here. A 0 or 1 will perform lot-for-lot ordering. If another number is placed here, then all orders for that item will be in integer multiples of that number.
7. Demands: The demands are entered in the end item row in the period in which the items are demanded.
8. Scheduled receipts: If units are scheduled to be received in the future, they should be listed in the appropriate time period (column) and item (row). (An entry here in level 1 is a demand; all other levels are receipts.)
Further details regarding POM for Windows are seen in Appendix IV .
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C H A P T E R 1 4 | M AT E R I A L R E Q U I R E M E N T S P L A N N I N G ( M R P ) A N D E R P 589
Solved Problems Virtual Office Hours help is available in MyOMLab.
Enter the quantity on hand.
Enter the lead time.
The data in columns A, B, C, D (down to row 15) are entered on the second screen and automatically transferred here.
Lot size must be ≥1.
Program 14.1
Using Excel OM’s MRP Module
to Solve Examples 1, 2, and 3
SOLVED PROBLEM 14.1 Determine the low-level coding and the quantity of each com- ponent necessary to produce 10 units of an assembly we will call Alpha. The product structure and quantities of each com- ponent needed for each assembly are noted in parentheses.
SOLUTION Redraw the product structure with low-level coding. Then mul- tiply down the structure until the requirements of each branch are determined. Then add across the structure until the total for each is determined.
B(1)
Alpha
C(1)B(1)
D(2) C(2)
Alpha
C(1)
E(1) F(1)
E(1) F(1)
B(1)
D(2) C(2)
Alpha
C(1)
E(1) F(1)E(1) F(1)
Level 0
Level 1
Level 2
Level 3
Alpha = 1
B = 1
D = 2
F = 3
C = 3
E = 3
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590 P A R T 3 | M A N AG I N G O P E R AT I O N S
Es required for left branch: (1alpha * 1B * 2 C * 1E) = 2 Es
and Es required for right branch: (1alpha * 1C * 1E) = 1 E
3 Es required in total
Then “explode” the requirement by multiplying each by 10, as shown in the table to the right:
LEVEL ITEM QUANTITY PER UNIT
TOTAL REQUIREMENTS FOR 10 ALPHA
0 Alpha 1 10 1 B 1 10 2 C 3 30 2 D 2 20 3 E 3 30 3 F 3 30
SOLVED PROBLEM 14.2 Using the product structure for Alpha in Solved Problem 14.1, and the following lead times, quantity on hand, and master production schedule, prepare a net MRP table for Alphas.
ITEM LEAD TIME
QUANTITY ON HAND
Alpha 1 10 B 2 20 C 3 0 D 1 100 E 1 10 F 1 50
Master Production Schedule for Alpha
PERIOD 6 7 8 9 10 11 12 13
Gross requirements 50 50 100
SOLUTION See the chart on following page.
SOLVED PROBLEM 14.3 Hip Replacements, Inc., has a master production schedule for its newest model, as shown below, a setup cost of $50, a holding cost per week of $2, beginning inventory of 0, and lead time of 1 week. What are the costs of using lot-for-lot for this l0-week period?
WEEK 1 2 3 4 5 6 7 8 9 10
Gross requirements 0 0 50 0 0 35 15 0 100 0 Scheduled receipts Projected on hand 0 0 0 0 0 0 0 0 0 0 0 Net requirements 0 0 50 0 0 35 15 0 100 Planned order receipts 50 35 15 100 Planned order releases 50 35 15 100
SOLUTION Holding cost = $0 (as there is never any end-of-period inventory)
Ordering costs = 4 orders * $50 = $200 Total cost for lot@for@lot = $0 + $200 = $200
SOLVED PROBLEM 14.4 Hip Replacements, Inc., has a master production schedule for its newest model, as shown on page 592 , a setup cost of $50, a holding cost per week of $2, beginning inventory of 0, and lead time of 1 week. What are the costs of using (a) EOQ and (b) POQ for this 10-week period?
SOLUTION a) For the EOQ lot size, first determine the EOQ. Annual usage = 200 units for 10 weeks; weekly usage = 200/10 weeks = 20 per week. Therefore, 20 units × 52 weeks
(annual demand) = 1,040 units. From Chapter 12 , the EOQ model is:
Q * = A
2DS H
where D = annual demand = 1,040 S = Setup cost = $50 H = holding (carrying) cost, on an annual basis per
unit = $2 × 52 = $104 Q * = 31.62 ≈ 32 units (order the EOQ or in multiples
of the EOQ)
(Continued on page 592)
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591
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592 P A R T 3 | M A N AG I N G O P E R AT I O N S
WEEK 1 2 3 4 5 6 7 8 9 10
Gross requirements 0 0 50 0 0 35 15 0 100 0 Scheduled receipts Projected on hand 0 0 0 0 14 14 14 11 28 28 24 24 Net requirements 0 0 50 0 0 21 0 0 72 0 Planned order receipts 64 32 32 96 Planned order releases 64 32 32 96
Holding cost = 157 units * $2 = $314 (note the 24 units available in period 11, for which there is an inventory charge as they are in on-hand inventory at the end of period 10) Ordering costs = 4 orders * $50 = $200 Total cost for EOQ lot sizing = $314 + $200 = $514 b) For the POQ lot size we use the EOQ computed above to find the time period between orders: Period interval = EOQ/average weekly usage = 32/20 = 1.6 ≈ 2 periods POQ order size = Demand required in the 2 periods, postponing orders in periods with no demand.
WEEK 1 2 3 4 5 6 7 8 9 10
Gross requirements 0 0 50 0 0 35 15 0 100 0 Scheduled receipts Projected on hand 0 0 0 0 0 0 0 15 0 0 Net requirements 0 0 50 0 0 50 0 0 100 0 Planned order receipts 50 50 100 Planned order releases 50 50 100
Holding cost = 15 units * $2 = $30 Ordering costs = 3 orders * $50 = $150 Total cost for POQ lot sizing = $30 + $150 = $180
Problems* Note: PX means the problem may be solved with POM for Windows and/or Excel OM. Many of the exercises in this chapter (14.1 through 14.16 and 14.29 through 14.32) can be done on Resource Manager for Excel, a commercial system
made available by User Solutions, Inc. Access to a trial version of the software and a set of notes for the user are available at
www.usersolutions.com .
Problems 14.1–14.4 relate to Dependent Inventory Model Requirements
• 14.1 You have developed the following simple prod- uct structure of items needed for your gift bag for a rush party for prospective pledges in your organization. You forecast 200 attendees. Assume that there is no inventory on hand of any of the items. Explode the bill of material. (Subscripts indicate the number of units required.)
K(1)
L(4) M(2)
J
• • 14.2 You are expected to have the gift bags in Problem 14.1 ready at 5 p.m. However, you need to personalize the items (mon- ogrammed pens, note pads, literature from the printer, etc.). The lead time is 1 hour to assemble 200 Js once the other items are prepared. The other items will take a while as well. Given the vol- unteers you have, the other time estimates are item K (2 hours), item L (1 hour), and item M (4 hours). Develop a time-phased assembly plan to prepare the gift bags. • • 14.3 As the production planner for Xiangling Hu Products, Inc., you have been given a bill of material for a bracket that is
made up of a base, two springs, and four clamps. The base is assembled from one clamp and two housings. Each clamp has one handle and one casting. Each housing has two bearings and one shaft. There is no inventory on hand. a) Design a product structure noting the quantities for each item
and show the low-level coding. b) Determine the gross quantities needed of each item if you are
to assemble 50 brackets. c) Compute the net quantities needed if there are 25 of the base
and 100 of the clamp in stock. PX
• • 14.4 Your boss at Xiangling Hu Products, Inc., has just provided you with the schedule and lead times for the bracket in Problem 14.3. The unit is to be prepared in week 10. The lead times for the components are bracket (1 week), base (1 week), spring (1 week), clamp (1 week), housing (2 weeks), handle (1 week), casting (3 weeks), bearing (1 week), and shaft (1 week). a) Prepare the time-phased product structure for the bracket. b) In what week do you need to start the castings? PX
Problems 14.5–14.21 relate to MRP Structure
• • 14.5 The demand for subassembly S is 100 units in week 7. Each unit of S requires 1 unit of T and 2 units of U. Each unit of T requires 1 unit of V, 2 units of W, and 1 unit of X. Finally, each unit of U requires 2 units of Y and 3 units of Z. One firm manu- factures all items. It takes 2 weeks to make S, 1 week to make T,
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C H A P T E R 1 4 | M AT E R I A L R E Q U I R E M E N T S P L A N N I N G ( M R P ) A N D E R P 593
2 weeks to make U, 2 weeks to make V, 3 weeks to make W, 1 week to make X, 2 weeks to make Y, and 1 week to make Z. a) Construct a product structure. Identify all levels, parents, and
components. b) Prepare a time-phased product structure.
• • 14.6 Using the information in Problem 14.5, construct a gross material requirements plan. PX
• • 14.7 Using the information in Problem 14.5, construct a net material requirements plan using the following on-hand inventory.
ITEM ON-HAND
INVENTORY ITEM ON-HAND
INVENTORY
S 20 W 30 T 20 X 25 U 40 Y 240 V 30 Z 40 PX
• • 14.8 Refer again to Problems 14.5 and 14.6. In addition to 100 units of S, there is also a demand for 20 units of U, which is a component of S. The 20 units of U are needed for maintenance purposes. These units are needed in week 6. Modify the gross material requirements plan to reflect this change. PX
• • 14.9 Refer again to Problems 14.5 and 14.7. In addition to 100 units of S, there is also a demand for 20 units of U, which is a component of S. The 20 units of U are needed for maintenance purposes. These units are needed in week 6. Modify the net mate- rial requirements plan to reflect this change. PX
• • 14.10 a) Given the product structure and master production schedule
( Figure 14.12 below), develop a gross requirements plan for all items. b) Given the preceding product structure, master production
schedule, and inventory status ( Figure 14.12 ), develop a net materials requirements (planned order release) for all items. PX
• • • 14.11 Given the product structure, master production schedule, and inventory status in Figure 14.13 on the next page and assuming the requirements for each BOM item is 1: a) develop a gross requirements plan for Item C; b) develop a net requirements plan for Item C. PX • • • • 14.12 Based on the data in Figure 14.13 , complete a net material requirements schedule for: a) All items (10 schedules in all), assuming the requirement for
each BOM item is 1. b) All 10 items, assuming the requirement for all items is 1, except
B, C, and F, which require 2 each . PX
• • • 14.13 Electro Fans has just received an order for one thou- sand 20-inch fans due week 7. Each fan consists of a housing assembly, two grills, a fan assembly, and an electrical unit. The housing assembly consists of a frame, two supports, and a handle. The fan assembly consists of a hub and five blades. The electri- cal unit consists of a motor, a switch, and a knob. The following table gives lead times, on-hand inventory, and scheduled receipts. a) Construct a product structure. b) Construct a time-phased product structure. c) Prepare a net material requirements plan. PX
Data Table for Problem 14.13
COMPONENT LEAD TIME
ON-HAND INVENTORY
LOT SIZE*
SCHEDULED RECEIPT
20” Fan 1 100 — Housing
Frame Supports (2) Handle
1 2 1 1
100 — 50
400
— —
100 500
Grills (2) 2 200 500 Fan Assembly
Hub Blades (5)
3 1 2
150 — —
— —
100 Electrical Unit
Motor Switch Knob
1 1 1 1
— — 20 —
— — 12 25 200 knobs
in week 2 * Lot-for-lot unless otherwise noted.
• • • 14.14 A part structure, lead time (weeks), and on-hand quantities for product A are shown in Figure 14.14 . From the information shown, generate: a) An indented bill of material for product A (see Figure 5.9 in
Chapter 5 as an example of a BOM). b) Net requirements for each part to produce 10 As in week 8
using lot-for-lot. PX
• • • 14.15 You are product planner for product A (in Problem 14.14 and Figure 14.14 ). The field service manager, Al Trostel, has just called and told you that the requirements for B and F should each be increased by 10 units for his repair requirements in the field. a) Prepare a list showing the quantity of each part required to
produce the requirements for the service manager and the pro- duction request of 10 Bs and Fs.
b) Prepare a net requirement plan by date for the new require- ments (for both production and field service), assuming that the field service manager wants his 10 units of B and F in week 6 and the 10 production units of A in week 8. PX
B1(1)
A1(1)
Subassembly X1
B2(2)
E(1) C(2) D(1)
E(2)
PERIOD 7 8 9 10 11 12
Gross requirements 50 20 100
LEAD ON LEAD ON ITEM TIME HAND ITEM TIME HAND
X1 1 50 C 1 0
B1 2 20 D 1 0
B2 2 20 E 3 10
A1 1 5
Master Production Schedule for X1
Figure 14.12
Information for Problem 14.10
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594 P A R T 3 | M A N AG I N G O P E R AT I O N S
• • • 14.16 You have just been notified via fax that the lead time for component G of product A (Problem 14.15 and Figure 14.14 ) has been increased to 4 weeks. a) Which items have changed, and why? b) What are the implications for the production plan? c) As production planner, what can you do? PX
• • 14.17 Heather Adams, production manager for a Colorado exercise equipment manufacturer, needs to schedule an order for 50 UltimaSteppers, which are to be shipped in week 8. Subscripts indicate quantity required for each parent. Assume lot-for-lot ordering. Below is information about the steppers:
ITEM LEAD TIME ON-HAND INVENTORY COMPONENTS
Stepper 2 20 A (1) , B (3) , C (2) A 1 10 D (1) , F (2) B 2 30 E (1) , F (3) C 3 10 D (2) , E (3) D 1 15 E 2 5 F 2 20
a) Develop a product structure for Heather. b) Develop a time-phased structure. c) Develop a net material requirements plan for F. PX
Additional problems 14.18–14.21 are available in MyOMLab.
Problems 14.22–14.28 relate to Lot-Sizing Techniques
PERIOD 8 9 10 11 12
Gross requirements: A 100 50 150
Gross requirements: H 100 50
ON LEAD ON LEAD ITEM HAND TIME ITEM HAND TIME
A 0 1 F 75 2
B 100 2 G 75 1
C 50 2 H 0 1
D 50 1 J 100 2
E 75 2 K 100 2
C(1)E(1)
H(1)
LT = 2LT = 1
LT = 1G(1) LT = 3
F(1) LT = 1
D(1) LT = 1C(1) LT = 2
B(1) LT = 1
A LT = 1 LT = lead time in weeks (1) = All quantities = 1
E(1) LT = 1
PART STRUCTURE TREEPART INVENTORY
ON HAND
A B C D E F G H
0 2
10 5 4 5 1
10
Figure 14.14
Information for Problems
14.14, 14.15, and 14.16
Data Table for Problems 14.22 through 14.25*
PERIOD 1 2 3 4 5 6 7 8 9 10 11 12
Gross requirements 30 40 30 70 20 10 80 50
* Holding cost = $2.50/unit/week; setup cost = $150; lead time = 1 week; beginning inventory = 40; stockout cost = $10.
• • • 14.22 Develop a lot-for-lot solution and calculate total rel- evant costs for the data in the preceding table. PX
• • • 14.23 Develop an EOQ solution and calculate total relevant costs for the data in the preceding table. PX
• • • 14.24 Develop a POQ solution and calculate total relevant costs for the data in the preceding table. PX
• • • 14.25 Using your answers for the lot sizes computed in Problems 14.22, 14.23, and 14.24, which is the best technique and why?
• • 14.26 M. de Koster, of Rene Enterprises, has the master production plan shown below:
Period (weeks) 1 2 3 4 5 6 7 8 9 Gross requirements 15 20 10 25
Lead time = 1 period; setup cost = $200; holding cost = $10 per week; stockout cost = $10 per week. Your job is to develop an ordering plan and costs for: a) Lot-for-lot. b) EOQ. c) POQ. d) Which plan has the lowest cost? PX
14.27 Grace Greenberg, production planner for Science and Technology Labs, in New Jersey, has the master production plan shown below:
Period (weeks) 1 2 3 4 5 6 7 8 9 10 11 12
Gross requirements 35 40 10 25 10 45
Lead time = 1 period; setup costs = $200; holding cost = $10 per week; stockout cost = $10 per week. Develop an ordering plan and costs for Grace, using these techniques: a) Lot-for-lot. b) EOQ. c) POQ. d) Which plan has the lowest cost? PX
GFGEFEGFED
CKJCB
HA
Figure 14.13
Information for Problems 14.11 and 14.12
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C H A P T E R 1 4 | M AT E R I A L R E Q U I R E M E N T S P L A N N I N G ( M R P ) A N D E R P 595
Additional problem 14.28 is available in MyOMLab.
Problems 14.29–14.32 relate to Extensions of MRP
• • • 14.29 Karl Knapps, Inc., has received the following orders:
Period 1 2 3 4 5 6 7 8 9 10 Order size 0 40 30 40 10 70 40 10 30 60
The entire fabrication for these units is scheduled on one machine. There are 2,250 usable minutes in a week, and each unit will take 65 minutes to complete. Develop a capacity plan, using lot split- ting, for the 10-week time period.
• • • 14.30 Coleman Rich, Ltd., has received the following orders:
Period 1 2 3 4 5 6 7 8 9 10 Order size 60 30 10 40 70 10 40 30 40 0
The entire fabrication for these units is scheduled on one machine. There are 2,250 usable minutes in a week, and each unit will take 65 minutes to complete. Develop a capacity plan, using lot split- ting, for the 10-week time period.
• • • 14.31 Courtney Kamauf schedules production of a popular Rustic Coffee Table at Kamauf Enterprises, Inc. The table requires a
top, four legs, 18 gallon of stain, 1 16 gallon of glue, 2 short braces between
the legs and 2 long braces between the legs, and a brass cap that goes on the bottom of each leg. She has 100 gallons of glue in inventory, but none of the other components. All items except the brass caps, stain, and glue are ordered on a lot-for-lot basis. The caps are pur- chased in quantities of 1,000, stain and glue by the gallon. Lead time is 1 day for each item. Schedule the order releases necessary to pro- duce 640 coffee tables on days 5 and 6, and 128 on days 7 and 8. PX
Stain Glue Base Assembly
COFFEE TABLE
Brass Caps
Top
LegsShort Braces
Long Braces
• • • • 14.32 Using the data for the coffee table in Problem 14.31, build a labor schedule when the labor standard for each top is 2 labor-hours; each leg including brass cap installation requires 14 hour, as does each pair of braces. Base assembly requires 1 labor-hour, and final assembly requires 2 labor-hours. What is the total number of labor-hours required each day, and how many employees are needed each day at 8 hours per day?
CASE STUDIES Video Case When 18,500 Orlando Magic Fans Come to Dinner
With vast experience at venues such as the American Airlines Arena (in Miami), the Kentucky Derby, and Super Bowls, Chef John Nicely now also plans huge culinary events at Orlando’s Amway Center, home of the Orlando Magic basketball team. With his unique talent and excep- tional operations skills, Nicely serves tens of thousands of cheering fans at some of the world’s largest events. And when more than 18,500 basketball fans show up for a game, expecting great food and great basketball, he puts his creative as well as operations talent to work.
Chef John must be prepared. This means determining not only a total demand for all 18,500 fans, but also translating that demand into specific menu items and beverages. He prepares a forecast from current ticket sales, history of similar events at other venues, and his own records, which reflect the demand with this particular opponent, night of week, time of year, and even time of day. He then breaks the demand for specific menu items and quantities into items to be available at each of the 22 conces- sion stands, 7 restaurants, and 68 suites. He must also be prepared to accommodate individual requests from players on both teams.
Chef John frequently changes the menu to keep it interesting for the fans who attend many of the 41 regular season home games each season. Even the culinary preference of the opponent’s fans who may be attending influences the menu. Additionally, when entertainment other than the Magic is using the Amway Center, the demographic mix is likely to be different, requiring additional tweaking of the menu. The size of the wait staff and the kitchen staff change to reflect the size of the crowd; Chef John may be super- vising as many as 90 people working in the kitchen. Similarly, the concessions stands, 40% of which have their own grills and fryers, present another challenge, as they are managed by volunteers from nonprofit organizations. The use of these volunteers adds the need for special training and extra enforcement of strict quality standards.
Once deciding on the overall demand and the menu, Chef John must prepare the production specifications (a bill of material) for each item. For the evening game with the Celtics, Chef John is pre- paring his unique Cheeto Crusted Mac & Cheese dish. The ingre- dients, quantity, costs, and labor requirements are shown below:
Production Specifi cations
CHEETO CRUSTED MAC & CHEESE (6 PORTIONS)
INGREDIENTS QUANTITY MEASURE UNIT COST TOTAL COST LABOR-HOURS
Elbow macaroni (large, uncooked) 20.00 oz. $0.09 $1.80 Cheese—cheddar shredded 10.00 oz. 0.16 1.60 Mac and cheese base (see recipe) 44.00 oz. 0.80 35.20 Milk 4.00 oz. 0.03 0.12 Cheetos, crushed 6.00 oz. 0.27 1.62 Sliced green onion—garnish 0.50 oz. 0.18 0.09 Whole Cheetos—garnish 2.00 oz. 0.27 0.54 Total labor hours 0.2 hours
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596 P A R T 3 | M A N AG I N G O P E R AT I O N S
The yield on this dish is 6 portions, and labor cost is $15 per hour, with fringes. The entire quantity required for the evening is prepared prior to the game and kept in warming ovens until needed. Demand for each basketball game is divided into 5 periods: prior to the game, first quarter, second quarter, half-time, and second half. At the Magic vs. Celtics game next week, the demand (number of portions) in each period is 60, 36, 48, 60, and 12 for the Cheeto Crusted Mac & Cheese dish, respectively.
Discussion Questions *
1. Prepare a bill of material explosion and total cost for the 216 portions of Cheeto Crusted Mac & Cheese.
2. What is the cost per portion? How much less expensive is the Cheeto Crusted Mac & Cheese than Chef John’s alternative creation, the Buffalo Chicken Mac & Cheese, shown in Figure 14.9 of this chapter?
3. Assuming that there is no beginning inventory of the Cheeto Crusted Mac & Cheese and cooking time for the entire 216 portions is 0.6 hours, when must preparation begin?
• Additional Case Studies: Visit MyOMLab for these free case studies: Ikon’s attempt at ERP: The giant offi ce technology fi rm faces hurdles with ERP implementation. Hill’s Automotive, Inc.: An after-market producer and distributor of auto replacement parts has trouble making MRP work.
Endnotes
* You may wish to view the video that accompanies this case before answering the questions.
Video Case MRP at Wheeled Coach Wheeled Coach, the world’s largest manufacturer of ambulances, builds thousands of different and constantly changing configu- rations of its products. The custom nature of its business means lots of options and special designs—and a potential scheduling and inventory nightmare. Wheeled Coach addressed such prob- lems, and succeeded in solving a lot of them, with an MRP system (described in the Global Company Profile that opens this chap- ter). As with most MRP installations, however, solving one set of problems uncovers a new set.
One of the new issues that had to be addressed by plant manager Lynn Whalen was newly discovered excess inventory. Managers discovered a substantial amount of inventory that was not called for in any finished products. Excess inventory was evi- dent because of the new level of inventory accuracy required by the MRP system. The other reason was a new series of inventory reports generated by the IBM MAPICS MRP system purchased by Wheeled Coach. One of those reports indicates where items are used and is known as the “Where Used” report. Interestingly, many inventory items were not called out on bills of material (BOMs) for any current products. In some cases, the reason some parts were in the stockroom remained a mystery.
The discovery of this excess inventory led to renewed efforts to ensure that the BOMs were accurate. With substantial work,
BOM accuracy increased and the number of engineering change notices (ECNs) decreased. Similarly, purchase-order accuracy, with regard to both part numbers and quantities ordered, was improved. Additionally, receiving department and stockroom accuracy went up, all helping to maintain schedule, costs, and ultimately, shipping dates and quality.
Eventually, Lynn Whalen concluded that the residual amounts of excess inventory were the result, at least in part, of rapid changes in ambulance design and technology. Another source was cus- tomer changes made after specifications had been determined and materials ordered. This latter excess occurs because, even though Wheeled Coach’s own throughput time is only 17 days, many of the items that it purchases require much longer lead times.
Discussion Questions *
1. Why is accurate inventory such an important issue at Wheeled Coach?
2. Why does Wheeled Coach have excess inventory, and what kind of a plan would you suggest for dealing with it?
3. Be specific in your suggestions for reducing inventory and how to implement them.
* You may wish to view the video that accompanies this case before answering the questions.
1. The inventory models (EOQ) discussed in Chapter 12 assumed that the demand for one item was independent of the demand for another item. For example, EOQ assumes the demand for refrigerator parts is independent of the demand for refrigerators and that demand for parts is constant. MRP makes neither of these assumptions.
2. Record accuracy of 99% may sound good, but note that even when each component has an availability of 99% and a product
3. Using EOQ is a convenient approach for determining the time between orders, but other rules can be used.
4. Part period balancing, Silver-Meal, and Wagner-Whitin are included in the software POM for Windows and ExcelOM , available with this text.
has only seven components, the likelihood of a product being completed is only .932 (because .99 7 = .932).
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Chapter 14 Rapid Review 14
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Main Heading Review Material MyOMLab DEPENDENT DEMAND (p. 566)
Demand for items is dependent when the relationship between the items can be determined. For any product, all components of that product are dependent demand items. j Material requirements planning (MRP) —A dependent demand technique that
uses a bill-of-material, inventory, expected receipts, and a master production schedule to determine material requirements.
Concept Questions: 1.1–1.4
DEPENDENT INVENTORY MODEL REQUIREMENTS (pp. 566–571)
Dependent inventory models require that the operations manager know the: (1) Master production schedule; (2) Specifications or bill of material; (3) Inventory availability; (4) Purchase orders outstanding; and (5) Lead times. j Master production schedule (MPS) —A timetable that specifies what is to be
made and when. The MPS is a statement of what is to be produced , not a forecast of demand. j Bill of material (BOM) —A listing of the components, their description, and the
quantity of each required to make one unit of a product. Items above any level in a BOM are called parents ; items below any level are called components , or children . The top level in a BOM is the 0 level. j Modular bills —Bills of material organized by major subassemblies or by product
options. j Planning bills (or kits) —Material groupings created in order to assign an artifi-
cial parent to a bill of material; also called “pseudo” bills. j Phantom bills of material —Bills of material for components, usually subassem-
blies, that exist only temporarily; they are never inventoried. j Low-level coding —A number that identifies items at the lowest level at which
they occur. j Lead time —In purchasing systems, the time between recognition of the need
for an order and receiving it; in production systems, it is the order, wait, move, queue, setup, and run times for each component.
When a bill of material is turned on its side and modified by adding lead times for each component, it is called a time-phased product structure .
Concept Questions: 2.1–2.4 Problems: 14.1–14.4 Virtual Office Hours for Solved Problem: 14.1
VIDEO 14.1 When 18,500 Orlando Magic Fans Come to Dinner
VIDEO 14.2 MRP at Wheeled Coach Ambulances
MRP STRUCTURE (pp. 571–575)
j Gross material requirements plan —A schedule that shows the total demand for an item (prior to subtraction of on-hand inventory and scheduled receipts) and (1) when it must be ordered from suppliers, or (2) when production must be started to meet its demand by a particular date.
j Net material requirements —The result of adjusting gross requirements for inven- tory on hand and scheduled receipts.
j Planned order receipt —The quantity planned to be received at a future date. j Planned order release —The scheduled date for an order to be released. Net requirements = Gross requirements + Allocations - (On hand + Scheduled receipts)
Concept Questions: 3.1–3.4 Problems: 14.5–14.9, 14.11, 14.16–14.21 Virtual Office Hours for Solved Problem: 14.2
ACTIVE MODEL 14.1
MRP MANAGEMENT (pp. 575–576)
j System nervousness —Frequent changes in an MRP system. j Time fences —A means for allowing a segment of the master schedule to be desig-
nated as “not to be rescheduled.” j Pegging —In material requirements planning systems, tracing upward the bill of
material from the component to the parent item. Four approaches for integrating MRP and JIT are (1) finite capacity scheduling, (2) small buckets, (3) balanced flow, and (4) supermarkets. j Buckets —Time units in a material requirements planning system. Finite capacity scheduling (FCS) considers department and machine capacity. FCS provides the precise scheduling needed for rapid material movement.
Concept Questions: 4.1–4.4
LOT-SIZING TECHNIQUES (pp. 576–580)
j Lot-sizing decision —The process of, or techniques used in, determining lot size. j Lot-for-lot —A lot-sizing technique that generates exactly what is required to
meet the plan. j Periodic order quantity (POQ) —A lot-sizing technique that issues orders on a
predetermined time interval with an order quantity equal to all of the interval’s requirements.
In general, the lot-for-lot approach should be used whenever low-cost deliveries setup can be achieved.
Concept Questions: 5.1–5.4 Problems: 14.22, 14.26, 14.28
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Main Heading Review Material MyOMLab EXTENSIONS OF MRP (pp. 580–583)
j Material requirements planning II (MRP II) —A system that allows, with MRP in place, inventory data to be augmented by other resource variables; in this case, MRP becomes material resource planning .
j Closed-loop MRP system —A system that provides feedback to the capacity plan, master production schedule, and production plan so planning can be kept valid at all times.
j Load report —A report for showing the resource requirements in a work center for all work currently assigned there as well as all planned and expected orders. Tactics for smoothing the load and minimizing the impact of changed lead time include: overlapping , operations splitting , and order splitting, or lot splitting .
Concept Questions: 6.1–6.4 Problem: 14.32
MRP IN SERVICES (pp. 583–584)
j Distribution resource planning (DRP) —A time-phased stock-replenishment plan for all levels of a distribution network.
Concept Questions: 7.1–7.4
ENTERPRISE RESOURCE PLANNING (ERP) (pp. 584–587)
j Enterprise resource planning (ERP) —An information system for identifying and planning the enterprise-wide resources needed to take, make, ship, and account for customer orders.
In an ERP system, data are entered only once into a common, complete, and con- sistent database shared by all applications. j Efficient consumer response (ECR) —Supply-chain management systems in the
grocery industry that tie sales to buying, to inventory, to logistics, and to production.
Concept Questions: 8.1–8.4
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Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 14.1 In a product structure diagram: a) parents are found only at the top level of the diagram. b) parents are found at every level in the diagram. c) children are found at every level of the diagram except
the top level. d) all items in the diagrams are both parents and children. e) all of the above. LO 14.2 The difference between a gross material requirements
plan (gross MRP) and a net material requirements plan (net MRP) is:
a) the gross MRP may not be computerized, but the net MRP must be computerized.
b) the gross MRP includes consideration of the inventory on hand, whereas the net MRP doesn’t include the inventory consideration.
c) the net MRP includes consideration of the inventory on hand, whereas the gross MRP doesn’t include the inventory consideration.
d) the gross MRP doesn’t take taxes into account, whereas the net MRP includes the tax considerations.
e) the net MRP is only an estimate, whereas the gross MRP is used for actual production scheduling.
LO 14.3 Net requirements = a) Gross requirements + Allocations − On-hand inventory
+ Scheduled receipts. b) Gross requirements − Allocations − On-hand inventory −
Scheduled receipts. c) Gross requirements − Allocations − On-hand inventory
+ Scheduled receipts. d) Gross requirements + Allocations − On-hand inventory −
Scheduled receipts.
LO 14.4 A lot-sizing procedure that orders on a predetermined time interval with the order quantity equal to the total of the interval’s requirement is:
a) periodic order quantity. b) part period balancing. c) economic order quantity. d) all of the above. LO 14.5 MRP II stands for: a) material resource planning. b) management requirements planning. c) management resource planning. d) material revenue planning. e) material risk planning. LO 14.6 A(n) ______ MRP system provides information to the
capacity plan, to the master production schedule, and ultimately to the production plan.
a) dynamic b) closed-loop c) continuous d) retrospective e) introspective LO 14.7 Which system extends MRP II to tie in customers and
suppliers? a) MRP III b) JIT c) IRP d) ERP e) Enhanced MRP II
Answers: LO 14.1. c; LO 14.2. c; LO 14.3. d; LO 14.4. a; LO 14.5. a; LO 14.6. b; LO 14.7. d.
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599599
C H A P T E R O U T L I N E
15 ◆
The Importance of Short-Term Scheduling 602
◆
Scheduling Issues 602
◆
Scheduling Process-Focused Facilities 605
GLOBAL COMPANY PROFILE: Alaska Airlines
C H
A P
T E
R
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
·· Aggregate/S&OP ( Ch. 13 ) ·· Short-Term ( Ch. 15 ) • • Maintenance
C H A P T E R GLOBAL COMPANY PROFILE Alaska Airlines
Short-Term Scheduling
◆
Loading Jobs 605
◆
Sequencing Jobs 611
◆
Finite Capacity Scheduling (FCS) 617
◆
Scheduling Services 618
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S eattle–Tacoma International Airport (SEA) is the 15th busiest in the U.S. in passenger traffic.
Served by 24 airlines that fly non-stop to 76 domestic and 19 international destinations, it is
a weather forecaster’s nightmare, raining 5 inches a month in the winter season. But it is also
the top-ranked U.S. airport in on-time departures, at 85.8%. Much of the credit goes to Alaska
Airlines, which dominates traffic at SEA with over 50% of all domestic flights. Alaska’s scheduling
is critical to efficiency and passenger service.
Scheduling Flights When Weather Is the Enemy
GLOBAL COMPANY PROFILE Alaska Airlines
C H A P T E R 1 5
600
12
6
3
1 2
11 10
5 4
7 8
12
3
1 210
5 4
7 8
9
6
3
111 10
5 4
7 8
9
4 A.M. FORECAST: Rain with a chance of light snow for Seattle.
ACTION: Discuss status of planes and possible need for cancellations.
10 A.M. FORECAST: Freezing rain after 5 P.M.
ACTION: Ready deicing trucks; develop plans to cancel 50% to 80% of flights after 6 P.M.
1:30 P.M. FORECAST: Rain changing to snow.
ACTION: Cancel half the flights from 6 P.M. to 10 A.M.; notify passengers and reroute planes.
5 P.M. FORECAST: Less snow than expected.
ACTION: Continue calling passengers and arrange alternate flights.
10 P.M. FORECAST: Snow tapering off.
ACTION: Find hotels for 600 passengers stranded by the storm.
12
6
3
1 2
11 10
5 4
7
9 8
12
6
3
1 2
11 10
5 4
7 8
6
11 2
12
99
This is typical of what Alaska Air officials had to do one December day when a storm bore down on Seattle.
Managers at airlines, such as Alaska,
learn to expect the unexpected. Events that
require rapid rescheduling are a regular part
of life. Throughout the ordeals of hurricanes,
tornadoes, ice storms, snow storms, and
more, airlines around the globe struggle to
cope with delays, cancellations, and furi-
ous passengers. The inevitable schedule
changes often create a ripple effect that
impacts passengers at dozens of airports.
To improve flight rescheduling efforts, Alaska Air employees monitor numerous screens that display flights in progress, meteorological charts,
and weather patterns at its Flight Operations Department in Seattle. Note the many andon signal lights used to indicate “status OK” (green),
“needs attention” (yellow), or “major issue—emergency” (red).
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ir lin
e s
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601
Alaska Air’s quest to provide
passenger and freight service to
the state of Alaska complicates its
scheduling even more than that of
other airlines. Here are just three
examples: (1) Juneau’s airport
is surrounded by mountains, so
the approach is often buffeted by
treacherous wind shears; (2) Sitka’s
one small runway is on a narrow
strip of land surrounded by water;
and (3) in Kodiak, the landing strip ends abruptly at a moun-
tainside. The airport approach is so tricky that first officers are
not allowed to land there—only captains are trusted to do so. Alaska Air takes the sting out of the scheduling night-
mares that come from weather-related problems by using
the latest technology on its planes and in its Flight Opera-
tions Department, located near the Seattle airport. From
computers to telecommunications systems to deicers, the
department reroutes flights, gets its jets in the air, and quickly
notifies customers of schedule changes. The department’s
job is to keep flights flowing despite the disruptions. Alaska
estimates that it saves $18 million a year by using its technol-
ogy to reduce cancellations and delays.
With mathematical scheduling models such as the ones
described in this text, Alaska quickly develops alternate
schedules and route changes. This may mean coordinating
incoming and outgoing aircraft, ensuring crews are on hand,
and making sure information gets to passengers as soon as
possible. Weather may be the enemy, but Alaska Airlines has
learned how to manage it.
M ik
e S
e g a r/
C o rb
is
Weather-related disruptions can
create major scheduling and
expensive snow removal issues
for airlines (left), just as they
create major inconveniences for
passengers (right).
J. D
a vi
d A
ke /A
P I m
a g e s
To maintain schedules, Alaska Airlines
uses elaborate equipment and motivated
personnel for snow and ice removal.
sh ip
fa ct
o ry
/S h u tt
e rs
to ck
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602
The Importance of Short-Term Scheduling Alaska Airlines doesn’t just schedule its 150 aircraft every day; it also schedules over 4,500 pilots and flight attendants to accommodate passengers seeking timely arrival at their destina- tions. This schedule, developed with huge computer programs, plays a major role in meeting customer expectations. Alaska finds competitive advantage with its ability to make last- minute adjustments to demand fluctuations and weather disruptions.
Scheduling decisions for five organizations—an airline, a hospital, a college, a sports arena, and a manufacturer—are shown in Table 15.1 . These decisions all deal with the timing of operations.
When manufacturing firms make schedules that match resources to customer demands, scheduling competence focuses on making parts on a just-in-time basis, with low setup times, little work-in-process, and high facility utilization. Efficient scheduling is how manufacturing companies drive down costs and meet promised due dates.
The strategic importance of scheduling is clear: ◆ Internally effective scheduling means faster movement of goods and services through a
facility and greater use of assets. The result is greater capacity per dollar invested, which translates into lower costs.
◆ Externally good scheduling provides faster throughput, added flexibility, and more depend- able delivery, improving customer service.
Scheduling Issues Figure 15.1 shows that a series of decisions affects scheduling. Schedule decisions begin with planning capacity, which defines the facility and equipment resources available (discussed in Supplement 7) . Capacity plans are usually made over a period of years as new equipment
L E A R N I N G OBJEC TI V ES
LO 15.1 Explain the relationship between short-term scheduling, capacity planning, aggregate planning, and a master schedule 603
LO 15.2 Draw Gantt loading and scheduling charts 607
LO 15.3 Apply the assignment method for loading jobs 608
LO 15.4 Name and describe each of the priority sequencing rules 613
LO 15.5 Use Johnson’s rule 616
LO 15.6 Defi ne fi nite capacity scheduling 617
LO 15.7 Use the cyclical scheduling technique 620
STUDENT TIP Scheduling decisions range
from years, for capacity
planning, to minutes/hours/
days, called short-term
scheduling. This chapter
focuses on the latter.
VIDEO 15.1 From the Eagles to the Magic: Converting the Amway Center
TABLE 15.1 Scheduling Decisions
ORGANIZATION MANAGERS SCHEDULE THE FOLLOWING
Alaska Airlines Maintenance of aircraft Departure timetables Flight crews, catering, gate, and ticketing personnel
Arnold Palmer Hospital Operating room use Patient admissions Nursing, security, maintenance staffs Outpatient treatments
University of Alabama Classrooms and audiovisual equipment Student and instructor schedules Graduate and undergraduate courses
Amway Center Ushers, ticket takers, food servers, security personnel Delivery of fresh foods and meal preparation Orlando Magic games, concerts, arena football
Lockheed Martin factory Production of goods Purchases of materials Workers
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 603
and facilities are designed, built, purchased, or shut down. Aggregate plans ( Chapter 13 ) are the result of a Sales and Operating Planning team that makes decisions regarding the use of facilities, inventory, people, and outside contractors. Aggregate plans are typically for 3 to 18 months, and resources are allocated in terms of an aggregate measure such as total units, tons, or shop hours. The master schedule breaks down the aggregate plan and develops weekly schedules for specific products or product lines. Short-term schedules then translate capacity decisions, aggregate (intermediate) plans, and master schedules into job sequences and specific assignments of personnel, materials, and machinery. In this chapter, we focus on this last step, scheduling goods and services in the short run (that is, matching daily or hourly demands to specific personnel and equipment capacity). See the OM in Action box “Prepping for the Orlando Magic Basketball Game.”
The objective of scheduling is to allocate and prioritize demand (generated by either forecasts or customer orders) to available facilities. Three factors are pervasive in scheduling: (1) gen- erating the schedule forward or backward, (2) finite and infinite loading, and (3) the criteria (priorities) for sequencing jobs. We discuss these topics next.
Forward and Backward Scheduling Scheduling can be initiated forward or backward. Forward scheduling starts the schedule as soon as the job requirements are known . Forward scheduling is used in organizations such as hospitals, clinics, restaurants, and machine tool manufacturers. In these facilities, jobs are performed to customer order, and delivery is typically scheduled at the earliest possible date.
Capacity Plan for New Facilities Adjust capacity to the demand suggested by strategic plan
Aggregate Production Plan for All Bikes (Determine personnel or subcontracting necessary to
match aggregate demand to existing facilities/capacity)
Master Production Schedule for Bike Models
Work Assigned to Specific Personnel and Work Centers
(Determine weekly capacity schedule)
Make finite capacity schedule by matching specific tasks to specific people and machines
Capacity Planning (Long term; years) Changes in facilities Changes in equipment See Chapter 7 and Supplement 7
Aggregate Planning (Intermediate term; quarterly or monthly) Facility utilization Personnel changes Subcontracting See Chapter 13
Master Schedule (Intermediate term; weekly) Material requirements planning Disaggregate the aggregate plan See Chapters 13 and 14
Month 1 2
Bike Production 800 850
1
100
100
2
200
Month 1
3
100
100
Week
Model 22
Model 24
Model 26
4
200
5
150
100
6
200
7
100
100
8
200
Assemble Model 22 in
work center 6
Month 2
Short-Term Scheduling (Short term; days, hours, minutes) Work center loading Job sequencing/dispatching See this chapter
LO 15.1 Explain the relationship between
short-term scheduling,
capacity planning,
aggregate planning,
and a master schedule
Figure 15.1
The Relationship Between
Capacity Planning, Aggregate
Planning, Master Schedule,
and Short-Term Scheduling for
a Bike Company
M yr
le e n P
e a rs
o n /A
la m
y P e te
r E n d ig
/d p a /L
a n d o v
Now Due date
Forward Scheduling
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604 P A R T 3 | M A N AG I N G O P E R AT I O N S
Backward scheduling begins with the due date, scheduling the final operation first. Steps within the job are then scheduled, one at a time, in reverse order. By subtracting the time needed for each item, the start time is obtained. Backward scheduling is used in manufacturing environments, as well as service environments such as catering a banquet or scheduling surgery. In practice, a combination of forward and backward scheduling is often used to find a reason- able trade-off between capacity constraints and customer expectations.
Finite and Infinite Loading Loading is the process of assigning jobs to work stations or processes. Scheduling techniques that load (or assign) work only up to the capacity of the process are called finite loading. The advantage of finite loading is that, in theory, all of the work assigned can be accomplished. However, because only work that can be accomplished is loaded into workstations—when in fact there may be more work than capacity—the due dates may be pushed out to an unaccep- table future time.
Techniques that load work without regard for the capacity of the process are infinite loading . All the work that needs to be accomplished in a given time period is assigned. The capacity of the process is not considered. Most material requirements planning (MRP) systems (discussed in Chapter 14 ) are infinite loading systems. The advantage of infinite loading is an initial schedule that meets due dates. Of course, when the workload exceeds capacity, either the capacity or the schedule must be adjusted.
Scheduling Criteria The correct scheduling technique depends on the volume of orders, the nature of operations, and the overall complexity of jobs, as well as the importance placed on each of four criteria:
1. Minimize completion time: Evaluated by determining the average completion time. 2. Maximize utilization: Evaluated by determining the percent of the time the facility is utilized. 3. Minimize work-in-process (WIP) inventory: Evaluated by determining the average num-
ber of jobs in the system. The relationship between the number of jobs in the system and
OM in Action Prepping for the Orlando Magic Basketball Game Tuesday. It’s time for John Nicely to make a grocery list. He is serving dinner
on Sunday, so he will need a few things . . . 200 pounds of chicken and
steak, ingredients for 800 servings of mac ’n’ cheese, 500 spring rolls, and
75 pounds of shrimp. Plus a couple hundred pizzas and a couple thousand
hot dogs—just enough to feed the Orlando Magic basketball players and the
18,500 guests expected. You see, Nicely is the executive chef of the Amway
Center in Orlando, and on Sunday the Magic are hosting the Boston Celtics.
How do you feed huge crowds good food in a short time? It takes good
scheduling, combined with creativity and improvisation. With 42 facilities serv-
ing food and beverages, “the Amway Center,” Nicely says, “is its own beast.”
Wednesday. Shopping Day.
Thursday–Saturday . The staff prepares whatever it can. Chopping vegeta-
bles, marinating meats, mixing salad dressings—everything but cooking the
food. Nicely also begins his shopping lists for next Tuesday’s game against the
Miami Heat and for a Lady Gaga concert 3 days later.
Sunday . 4 P.M. Crunch time. Suddenly the kitchen is a joke-free zone. In
20 minutes, Nicely’s first clients, 120 elite ticket holders who belong to the
Ritz Carlton Club, expect their meals—from a unique menu created for each
game.
5 P.M. As the Magic and Celtics start warming up, the chefs move their
operation in a brisk procession of hot boxes and cold-food racks to the
satellite kitchens.
6:12 P.M. Nicely faces
surprises at three conces-
sion stands: a shortage
of cashiers and a broken
cash register.
Halftime . There is
a run on rice pilaf in
the upscale Jernigan’s
restaurant. But Nicely has
thought ahead and has
anticipated. The backup
dishes arrive before
customers even notice.
For Nicely, success-
ful scheduling means
happy guests as a result
of a thousand details
having been identified, planned, and executed. Just another night of delivering
restaurant-quality meals and top-grade fast food to a sold-out arena crowd in
a span of a few hours.
Source: Interview with Chef John Nicely and Orlando Magic executives.
Now Due date
Backward Scheduling
F e rn
a n d o M
e d in
a
Loading
The assigning of jobs to work or
processing centers.
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 605
WIP inventory will be high. Therefore, the fewer the number of jobs that are in the sys- tem, the lower the inventory.
4. Minimize customer waiting time: Evaluated by determining the average number of late periods (e.g., days or hours).
These four criteria are used in this chapter, as they are in industry, to evaluate scheduling performance. In addition, good scheduling techniques should be simple, clear, easily under- stood, easy to carry out, flexible, and realistic.
Scheduling is further complicated by machine breakdowns, absenteeism, quality problems, shortages, and other factors. Consequently, assignment of a date does not ensure that the work will be performed according to the schedule. Many specialized techniques have been developed to aid in preparing reliable schedules. Table 15.2 provides an overview of approaches to sched- uling for three different processes.
In this chapter, we first examine the scheduling of process-focused facilities and then the challenge of scheduling employees in the service sector.
Scheduling Process-Focused Facilities Process-focused facilities (also known as intermittent , or job-shop, facilities ) are common in high-variety, low-volume manufacturing and service organizations. These facilities produce make-to-order products or services and include everything from auto repair garages and hos- pitals to beauty salons. The production items themselves differ considerably, as do the talents, material, and equipment required to make them. Scheduling requires that the sequence of work (its routing), time required for each item, and the capacity and availability of each work center be known. The variety of products and unique requirements means that scheduling is often complex. In this section we look at some of the tools available to managers for loading and sequencing work for these facilities.
Loading Jobs Operations managers assign jobs to work centers so that costs, idle time, or completion times are kept to a minimum. “Loading” work centers takes two forms. One is oriented to capacity; the second is related to assigning specific jobs to work centers.
First, we examine loading from the perspective of capacity via a technique known as input–output control. Then, we present two approaches used for loading: Gantt charts and the assignment method of linear programming.
TABLE 15.2 Different Processes Suggest Different Approaches to Scheduling
Process-focused facilities (job shops)
◆ Scheduling to customer orders where changes in both volume and variety of jobs/clients/patients are frequent. ◆ Schedules are often due-date focused, with loading refi ned by fi nite loading techniques. ◆ Examples: foundries, machine shops, cabinet shops, print shops, many restaurants, and the fashion industry. Repetitive facilities (assembly lines)
◆ Schedule module production and product assembly based on frequent forecasts. ◆ Finite loading with a focus on generating a forward-looking schedule. ◆ JIT techniques are used to schedule components that feed the assembly line. ◆ Examples: assembly lines for washing machines at Whirlpool and automobiles at Ford. Product-focused facilities (continuous)
◆ Schedule high-volume fi nished products of limited variety to meet a reasonably stable demand within existing fi xed capacity.
◆ Finite loading with a focus on generating a forward-looking schedule that can meet known setup and run times for the limited range of products.
◆ Examples: huge paper machines at International Paper, beer in a brewery at Anheuser-Busch, and potato chips at Frito-Lay.
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606 P A R T 3 | M A N AG I N G O P E R AT I O N S
Input–Output Control Many firms have difficulty scheduling (that is, achieving effective throughput) because they overload the production processes. This often occurs because they do not know actual perfor- mance in the work centers. Effective scheduling depends on matching the schedule to perfor- mance. Lack of knowledge about capacity and performance causes reduced throughput.
Input–output control is a technique that allows operations personnel to manage facility work flows. If the work is arriving faster than it is being processed, the facility is overloaded, and a backlog develops. Overloading causes crowding in the facility, leading to inefficiencies and quality problems. If the work is arriving at a slower rate than jobs are being performed, the facility is underloaded, and the work center may run out of work. Underloading the facility re- sults in idle capacity and wasted resources. Example 1 shows the use of input–output controls.
Input–output control
A system that allows operations
personnel to manage facility work
flows.
Example 1 INPUT–OUTPUT CONTROL Bronson Machining, Inc., manufactures driveway security fences and gates. It wants to develop an input–output control report for its welding work center for 5 weeks (weeks 6/6 through 7/4). The planned input is 280 standard hours per week. The actual input is close to this figure, varying between 250 and 285. Output is scheduled at 320 standard hours, which is the assumed capacity. A backlog exists in the work center.
APPROACH c Bronson uses schedule information to create Figure 15.2 , which monitors the workload–capacity relationship at the work center.
Week Ending
Planned Input
Actual Input
Planned Output
Actual Output
*Sum of actual inputs minus sum of actual outputs = cumulative change in backlog
6/6 6/13 6/20 6/27 7/4
280
270 250 280 285 280
280 280 280 280
320 320 320 320
270270270270
Explanation: 270 input, 270 output, implies 0 change.
7/11
Explanation: 250 input, 270 output, implies –20 change. (20 standard hours less work in the work center)
Welding Work Center (In standard hours)
–10 – 40 – 40 – 35 Cumulative Deviation
Cumulative Deviation
– 50 –100 –150 –200
Cumulative Change in Backlog*
0 – 20 –10 +5
Figure 15.2
Input–Output Control
SOLUTION c The deviations between scheduled input and actual output are shown in Figure 15.2 . Actual output (270 hours) is substantially less than planned. Therefore, neither the input plan nor the output plan is being achieved.
INSIGHT c The backlog of work in this work center has actually increased by 5 hours by week 6/27. This increases work-in-process inventory, complicating the scheduling task and indicating the need for manager action.
LEARNING EXERCISE c If actual output for the week of 6/27 was 275 (instead of 270), what changes? [Answer: Output cumulative deviation now is −195, and cumulative change in backlog is 0.]
RELATED PROBLEM c 15.10
ConWIP cards
Cards that control the amount
of work in a work center, aiding
input–output control.
Input–output control can be maintained by a system of ConWIP cards , which control the amount of work in a work center. ConWIP is an acronym for constant work-in-process . The ConWIP card travels with a job (or batch) through the work center. When the job is finished, the card is released and returned to the initial workstation, authorizing the entry of a new
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 607
batch into the work center. The ConWIP card effectively limits the amount of work in the work center, controls lead time, and monitors the backlog.
Gantt Charts Gantt charts are visual aids that are useful in loading and scheduling. The name is derived from Henry Gantt, who developed them in the late 1800s. The charts show the use of resources, such as work centers and labor.
When used in loading , Gantt charts show the loading and idle times of several departments, machines, or facilities. They display the relative workloads in the system so that the manager knows what adjustments are appropriate. For example, when one work center becomes over- loaded, employees from a low-load center can be transferred temporarily to increase the work- force. Or if waiting jobs can be processed at different work centers, some jobs at high-load centers can be transferred to low-load centers. Versatile equipment may also be transferred among centers. Example 2 illustrates a simple Gantt load chart.
Gantt charts
Planning charts used to schedule
resources and allocate time.
Example 2 GANTT LOAD CHART A New Orleans washing machine manufacturer accepts special orders for machines to be used in such unique facilities as submarines, hospitals, and large industrial laundries. The production of each machine requires varying tasks and durations. The company wants to build a load chart for the week of March 8.
APPROACH c The Gantt chart is selected as the appropriate graphical tool.
SOLUTION c Figure 15.3 shows the completed Gantt chart.
Processing
Work Center
Day
Metalworks
Mechanical
Electronics
Painting
Job 408
Job 295 Job 408 Job 349
Job 349
Job 349 Job 350
Job 349 Job 408
Monday Tuesday Wednesday Thursday Friday
Center not available (e.g., maintenance time, repairs, shortages)
Unscheduled
Figure 15.3
Gantt Load Chart for the Week
of March 8
INSIGHT c The four work centers process several jobs during the week. This particular chart indicates that the metalworks and painting centers are completely loaded for the entire week. The mechanical and electronic centers have some idle time scattered during the week. We also note that the metalworks center is unavailable on Tuesday, and the painting center is unavailable on Thursday, perhaps for preventive maintenance.
LEARNING EXERCISE c What impact results from the electronics work center closing on Tuesday for preventive maintenance? [Answer: None.]
RELATED PROBLEM c 15.1b
The Gantt load chart has a major limitation: it does not account for production variability such as unexpected breakdowns or human errors that require reworking a job. Consequently, the chart must also be updated regularly to account for new jobs and revised time estimates.
A Gantt schedule chart is used to monitor jobs in progress (and is also used for project scheduling). It indicates which jobs are on schedule and which are ahead of or behind schedule. In practice, many versions of the chart are found. The schedule chart in Example 3 places jobs in progress on the vertical axis and time on the horizontal axis.
LO 15.2 Draw Gantt loading and scheduling
charts
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608 P A R T 3 | M A N AG I N G O P E R AT I O N S
Assignment Method The assignment method involves assigning tasks or jobs to resources. Examples include assign- ing jobs to machines, contracts to bidders, people to projects, and salespeople to territories. The objective is most often to minimize total costs or time required to perform the tasks at hand. One important characteristic of assignment problems is that only one job (or worker) is assigned to one machine (or project).
Each assignment problem uses a table. The numbers in the table will be the costs or times associated with each particular assignment. For example, if First Printing has three available typesetters (A, B, and C) and three new jobs to be completed, its table might appear as follows. The dollar entries represent the firm’s estimate of what it will cost for each job to be completed by each typesetter.
JOB
TYPESETTER
A B C
R-34 $11 $14 $ 6 S-66 $ 8 $10 $11 T-50 $ 9 $12 $ 7
The assignment method involves adding and subtracting appropriate numbers in the table to find the lowest opportunity cost 1 for each assignment. There are four steps to follow:
1. Subtract the smallest number in each row from every number in that row and then, from the resulting matrix, subtract the smallest number in each column from every number in that column. This step has the effect of reducing the numbers in the table until a series
Example 3 GANTT SCHEDULING CHART First Printing in Winter Park, Florida, wants to use a Gantt chart to show the scheduling of three orders, jobs A, B, and C.
APPROACH c In Figure 15.4 , each pair of brackets on the time axis denotes the estimated starting and finishing of a job enclosed within it. The solid bars reflect the actual status or progress of the job. We are just finishing day 5.
SOLUTION c
Job Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Day 8
A
B
C
Now
Maintenance
Start of an activity
End of an activity
Scheduled activity time allowed
Actual work progress
Nonproduction time
Point in time when chart is reviewed
Gantt scheduling chart symbols:
Figure 15.4
Gantt Scheduling Chart for
Jobs A, B, and C at First
Printing
INSIGHT c Figure 15.4 illustrates that job A is about a half-day behind schedule at the end of day 5. Job B was completed after equipment maintenance. We also see that job C is ahead of schedule.
LEARNING EXERCISE c Redraw the Gantt chart to show that job A is a half-day ahead of schedule. [Answer: The orangish bar now extends all the way to the end of the activity.]
RELATED PROBLEMS c 15.1a, 15.2
Assignment method
A special class of linear
programming models that
involves assigning tasks or
jobs to resources.
LO 15.3 Apply the assignment method for
loading jobs
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 609
of zeros, meaning zero opportunity costs , appear. Even though the numbers change, this reduced problem is equivalent to the original one, and the same solution will be optimal.
2. Draw the minimum number of vertical and horizontal straight lines necessary to cover all zeros in the table. If the number of lines equals either the number of rows or the number of columns in the table, then we can make an optimal assignment (see Step 4). If the number of lines is less than the number of rows or columns, we proceed to Step 3.
3. Subtract the smallest number not covered by a line from every other uncovered number. Add the same number to any number(s) lying at the intersection of any two lines. Do not change the value of the numbers that are covered by only one line. Return to Step 2 and continue until an optimal assignment is possible.
4. Optimal assignments will always be at zero locations in the table. One systematic way of making a valid assignment is first to select a row or column that contains only one zero square. We can make an assignment to that square and then draw lines through its row and column. From the uncovered rows and columns, we choose another row or column in which there is only one zero square. We make that assignment and continue the procedure until we have assigned each person or machine to one task.
Example 4 shows how to use the assignment method.
Example 4 ASSIGNMENT METHOD First Printing wants to find the minimum total cost assignment of 3 jobs to 3 typesetters.
APPROACH c The cost table shown earlier in this section is repeated here, and steps 1 through 4 are applied.
STUDENT TIP You can also tackle assignment
problems with our Excel OM or
POM software or with Excel’s
Solver add-in.
TYPESETTER
A B C
JOB
R-34 $11 $14 $ 6
S-66 $ 8 $10 $11
T-50 $ 9 $12 $ 7 SOLUTION c Step 1A: Using the previous table, subtract the smallest number in each row from every number in the
row. The result is shown in the table on the left.
TYPESETTER
A B C
JOB
R-34 5 8 0
S-66 0 2 3
T-50 2 5 0
TYPESETTER
B CA
JOB
R-34 5 6 0
S-66 0 0 3
T-50 2 3 0 Step 1B: Using the above left table, subtract the smallest number in each column from every number in
the column. The result is shown in the table on the right. Step 2: Draw the minimum number of vertical and horizontal straight lines needed to cover all zeros.
Because two lines suffice, the solution is not optimal.
TYPESETTER
A B C
JOB
R-34 5 6 0
S-66 0 0 3
T-50 2 3 0
Smallest uncovered number
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610 P A R T 3 | M A N AG I N G O P E R AT I O N S
Step 3: Subtract the smallest uncovered number (2 in this table) from every other uncovered number and add it to numbers at the intersection of two lines.
TYPESETTER
A B C
JOB
R-34 3 4 0
S-66 0 0 5
T-50 0 1 0 Return to step 2. Cover the zeros with straight lines again.
TYPESETTER
A B C
JOB
R-34 3 4 0
S-66 0 0
010T-50 Because three lines are necessary, an optimal assignment can be made (see Step 4). Assign R-34 to
person C, S-66 to person B, and T-50 to person A. Referring to the original cost table, we see that: Minimum cost = $6 + $10 + $9 = $25
INSIGHT c If we had assigned S-66 to typesetter A, we could not assign T-50 to a zero location.
LEARNING EXERCISE c If it costs $10 for Typesetter C to complete Job R-34 (instead of $6), how does the solution change? [Answer: R-34 to A, S-66 to B, T-50 to C; cost = $28.]
RELATED PROBLEMS c 15.3–15.9 (15.11–15.14 are available in MyOMLab)
EXCEL OM Data File Ch15Ex4.xls can be found in MyOMLab.
Some assignment problems entail maximizing profit, effectiveness, or payoff of an assign- ment of people to tasks or of jobs to machines. An equivalent minimization problem can be obtained by converting every number in the table to an opportunity loss . To convert a maxi- mizing problem to an equivalent minimization problem, we create a minimizing table by sub- tracting every number in the original payoff table from the largest single number in that table. We then proceed to step 1 of the four-step assignment method. Minimizing the opportunity loss produces the same assignment solution as the original maximization problem.
The problem of scheduling major league baseball
umpiring crews from one series of games to the
next is complicated by many restrictions on travel.
The league strives to achieve two conflicting
objectives: (1) balance crew assignments relatively
evenly among all teams over the course of a season
and (2) minimize travel costs. Using the assignment
method, the time it takes the league to generate a
schedule has been significantly decreased, and the
quality of the schedule has improved. N ic
h o la
s D
. C
a cc
h io
n e /S
h u tt
e rs
to ck
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 611
Sequencing Jobs Once jobs are loaded in a work center, as we just discussed, managers decide the sequence in which they are to be completed. Sequencing (often called dispatching ) is accomplished by speci- fying the priority rules to use to release (dispatch) jobs to each work center.
Priority Rules for Sequencing Jobs Priority rules are especially applicable for process-focused facilities such as clinics, print shops, and manufacturing job shops. The most popular priority rules are:
◆ FCFS: first come, first served . Jobs are completed in the order they arrived. ◆ SPT: shortest processing time . Jobs with the shortest processing times are assigned first. ◆ EDD: earliest due date . Earliest due date jobs are assigned first. ◆ LPT: longest processing time . Jobs with the longest processing time are assigned first.
Performance Criteria The choice of which priority rule to choose depends in part on how each rule performs on four criteria: the priority rules try to minimize completion time, maximize facility utilization, minimize number of jobs in the system , and minimize job lateness . These performance criteria incorporate the concept of flow time , which measures the time each job spends waiting plus time being processed. For example, if Job B waits 6 days for Job A to be processed and then takes 2 more days of operation time itself, its flow time would be 6 + 2 = 8 days. The performance criteria are measured as:
Average completion time = Sum of total flow time
Number of jobs (15-1)
Utilization metric = Total job work (processing) time
Sum of total flow time (15-2)
Average number of jobs in the system = Sum of total flow time
Total job work (processing) time (15-3)
Average job lateness = Total late days Number of jobs
(15-4)
Computing the lateness of a particular job involves assumptions about the start time dur- ing the day and the timing of delivering a completed job. Equation (15-5) assumes that today is a work day, work has not yet begun today, and a job finished by the end of a day can be delivered to the customer that same day.
Job lateness = Max{0, yesterday + flow time - due date} (15-5)
For example, suppose that today is day 20 (thus yesterday was day 19). Job A is due tomor- row (day 21) and has a flow time of 1 day. That job would be considered to be completed on time, i.e., not late:
Max{0, 19 + 1 - 21} = Max{0, - 1} = 0 days late.
Meanwhile, Job B is due on day 32 and has a flow time of 15 days. The lateness of Job B would be:
Max{0, 19 + 15 - 32} = Max{0, 2} = 2 days late.
We will examine four of the most popular priority rules in Example 5 .
Sequencing
Determining the order in which
jobs should be done at each work
center.
Priority rules
Rules used to determine the
sequence of jobs in process-
oriented facilities.
Flow time
The time between the release of a
job to a work center until the job
is finished.
Example 5 PRIORITY RULES FOR DISPATCHING Five architectural rendering jobs are waiting to be assigned at Avanti Sethi Architects. Their work (pro- cessing) times and due dates are given in the following table. The firm wants to determine the sequence of processing according to (1) FCFS, (2) SPT, (3) EDD, and (4) LPT rules. Jobs were assigned a letter in the order they arrived. Today is day 1, and work begins today.
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612 P A R T 3 | M A N AG I N G O P E R AT I O N S
JOB WORK JOB DUE (PROCESSING) TIME DATE
JOB (DAYS) (DAYS)
A 6 8
B 2 6
C 8 18
D 3 15
E 9 23 APPROACH c Each of the four priority rules is examined in turn. Four measures of effectiveness can be computed for each rule and then compared to see which rule is best for the company.
SOLUTION c 1. The FCFS sequence shown in the next table is simply A–B–C–D–E.
JOB WORK FLOW JOB DUE JOB JOB SEQUENCE (PROCESSING) TIME TIME DATE LATENESS
A 6 6 8 0
B 2 8 6 2
C 8 16 18 0
D 3 19 15 4
E 9 28 23 5
28 77 11 The FCFS rule results in the following measures of eff ectiveness:
a. Average completion time = Sum of total flow time
Number of jobs
= 77 days
5 = 15.4 days
b. Utilization metric = Total job work (processing) time
Sum of total flow time
= 28 77
= 36.4,
c. Average number of jobs in the system = Sum of total flow time
Total job work (processing) time
= 77 days 28 days
= 2.75 jobs
d. Average job lateness = Total late days Number of jobs
= 11 5
= 2.2 days
2. The SPT rule shown in the next table results in the sequence B–D–A–C–E. Orders are sequenced according to processing time, with the highest priority given to the shortest job.
JOB WORK FLOW JOB DUE JOB JOB SEQUENCE (PROCESSING) TIME TIME DATE LATENESS
B 2 2 6 0
D 3 5
A 6 11 8 3
C 8 19 18 1
E 9 28 23 5
28 65 9
15 0
Measurements of eff ectiveness for SPT are:
a. Average completion time = 65 5
= 13 days
b. Utilization metric = 28 65
= 43.1,
c. Average number of jobs in the system = 65 28
= 2.32 jobs
d. Average job lateness = 9 5
= 1.8 days
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 613
3. The EDD rule shown in the next table gives the sequence B–A–D–C–E. Note that jobs are ordered by earliest due date fi rst.
JOB WORK FLOW JOB DUE JOB JOB SEQUENCE (PROCESSING) TIME TIME DATE LATENESS
B 2 2 6 0
A 6 8 8 0
D 3 11 15 0
C 8 19 18 1
E 9 28 23 5
28 68 6 Measurements of eff ectiveness for EDD are:
a. Average completion time = 68 5
= 13.6 days
b. Utilization metric = 28 68
= 41.2,
c. Average number of jobs in the system = 68 28
= 2.43 jobs
d. Average job lateness = 6 5
= 1.2 days
4. The LPT rule shown in the next table results in the order E–C–A–D–B.
JOB WORK FLOW JOB DUE JOB JOB SEQUENCE (PROCESSING) TIME TIME DATE LATENESS
E 9 9 23 0
C 8 17 18 0
A 6 23 8 15
D 3 26 15 11
B 2 28 6 22
28 103 48 Measures of eff ectiveness for LPT are:
a. Average completion time = 103 5
= 20.6 days
b. Utilization metric = 28 103
= 27.2,
c. Average number of jobs in the system = 103 28
= 3.68 jobs
d. Average job lateness = 48 5
= 9.6 days
The results of these four rules are summarized in the following table:
AVERAGE AVERAGE NUMBER AVERAGE COMPLETION
UTILIZATION METRIC OF JOBS IN LATENESS
RULE TIME (DAYS) (%) SYSTEM (DAYS)
FCFS 15.4 36.4 2.75 2.2
SPT 13.0 43.1 2.32 1.8
EDD 13.6 41.2 2.43 1.2
LPT 20.6 27.2 3.68 9.6 INSIGHT c LPT is the least effective measurement for sequencing for the Avanti Sethi firm. SPT is superior in 3 measures, and EDD is superior in the fourth (average lateness).
LEARNING EXERCISE c If job A takes 7 days (instead of 6), how do the 4 measures of effectiveness change under the FCFS rule? [Answer: 16.4 days, 35.4%, 2.83 jobs, 2.8 days late.]
RELATED PROBLEMS c 15.15, 15.17a–d, 15.18, 15.19 (15.15 alternate, 15.24 are available in MyOMLab)
EXCEL OM Data File Ch15Ex5.xls can be found in MyOMLab.
ACTIVE MODEL 15.1 This example is further illustrated in Active Model 15.1 in MyOMLab.
LO 15.4 Name and describe each of the
priority sequencing rules
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614 P A R T 3 | M A N AG I N G O P E R AT I O N S
The results in Example 5 are typically true in the real world also. No one sequencing rule always excels on all criteria. Experience indicates the following:
1. Shortest processing time is generally the best technique for minimizing job flow and mini- mizing the average number of jobs in the system. Its chief disadvantage is that long-duration jobs may be continuously pushed back in priority in favor of short-duration jobs. Customers may view this dimly, and a periodic adjustment for longer jobs must be made.
2. First come, first served does not score well on most criteria (but neither does it score par- ticularly poorly). It has the advantage, however, of appearing fair to customers, which is important in service systems.
3. Earliest due date minimizes maximum tardiness, which may be necessary for jobs that have a very heavy penalty after a certain date. In general, EDD works well when lateness is an issue.
Critical Ratio For organizations that have due dates (such as manufacturers and many firms like your local printer and furniture re-upholsterer), the critical ratio for sequencing jobs is beneficial. The critical ratio (CR) is an index number computed by dividing the time remaining until due date by the work time remaining. As opposed to the priority rules, critical ratio is dynamic and easily updated. It tends to perform better than FCFS, SPT, EDD, or LPT on the average job- lateness criterion.
The critical ratio gives priority to jobs that must be done to keep shipping on schedule. A job with a low critical ratio (less than 1.0) is one that is falling behind schedule. If CR is exactly 1.0, the job is on schedule. A CR greater than 1.0 means the job is ahead of schedule and has some slack.
The formula for critical ratio is:
CR = Time remaining
Workdays remaining =
Due date - Today>s date Work (lead) time remaining
(15-6)
Example 6 shows how to use the critical ratio.
Critical ratio (CR)
A sequencing rule that is an index
number computed by dividing the
time remaining until due date by
the work time remaining.
Your doctor may use a first-come, first-served priority rule
satisfactorily. However, such a rule may be less than optimal
for this emergency room. What priority rule might be best, and
why? What priority rule is often used on TV hospital dramas?
T yl
e r
O ls
o n /F
o to
lia
Example 6 CRITICAL RATIO Today is day 25 on Zyco Medical Testing Laboratories’ production schedule. Three jobs are on order, as indicated here:
JOB DUE DATE WORKDAYS REMAINING
A 30 4 B 28 5 C 27 2
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 615
APPROACH c Zyco wants to compute the critical ratios, using the formula for CR.
SOLUTION c JOB CRITICAL RATIO PRIORITY ORDER
A (30 2 25)>4 5 1.25 3
B (28 2 25)>5 5 .60 1
C (27 2 25)>2 5 1.00 2
INSIGHT c Job B has a critical ratio of less than 1, meaning it will be late unless expedited. Thus, it has the highest priority. Job C is on time, and job A has some slack. Once job B has been completed, we would recompute the critical ratios for jobs A and C to determine whether their priorities have changed.
LEARNING EXERCISE c Today is day 24 (a day earlier) on Zyco’s schedule. Recompute the CRs and determine the priorities. [Answer: 1.5, 0.8, 1.5; B is still number 1, but now jobs A and C are tied for second.]
RELATED PROBLEMS c 15.16, 15.17e, 15.21
In most production scheduling systems, the critical-ratio rule can help do the following:
1. Determine the status of a specific job. 2. Establish relative priority among jobs on a common basis. 3. Adjust priorities (and revise schedules) automatically for changes in both demand and job
progress. 4. Dynamically track job progress.
Sequencing N Jobs on Two Machines: Johnson’s Rule The next step in complexity is the case in which N jobs (where N is 2 or more) must go through two different machines or work centers in the same order. (Each work center only works on one job at a time.) This is called the N /2 problem.
Johnson’s rule can be used to minimize the time for sequencing a group of jobs through two work centers. It also minimizes total idle time on the machines. Johnson’s rule involves four steps:
1. All jobs are to be listed, and the time that each requires on a machine is to be shown. 2. Select the job with the shortest activity time. If the shortest time lies with the first machine,
the job is scheduled first. If the shortest time lies with the second machine, schedule the job last. Ties in activity times can be broken arbitrarily.
3. Once a job is scheduled, eliminate it. 4. Apply steps 2 and 3 to the remaining jobs, working toward the center of the sequence.
Example 7 shows how to apply Johnson’s rule.
Johnson’s rule
An approach that minimizes the
total time for sequencing a group
of jobs through two work centers
while minimizing total idle time in
the work centers.
Example 7 JOHNSON’S RULE Five specialty jobs at a La Crosse, Wisconsin, tool and die shop must be processed through two work centers (drill press and lathe). The time for processing each job follows:
Work (processing) Time for Jobs (hours)
JOB WORK CENTER 1
(DRILL PRESS) WORK CENTER 2
(LATHE)
A 5 2 B 3 6 C 8 4 D 10 7 E 7 12
The owner, Niranjan Pati, wants to set the sequence to minimize his total time for the five jobs.
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616 P A R T 3 | M A N AG I N G O P E R AT I O N S
APPROACH c Pati applies the four steps of Johnson’s rule.
SOLUTION c 1. The job with the shortest processing time is A, in work center 2 (with a time of 2 hours). Because it is
at the second center, schedule A last. Eliminate it from consideration.
A
2. Job B has the next shortest time (3 hours). Because that time is at the fi rst work center, we schedule it fi rst and eliminate it from consideration.
AB
3. The next shortest time is job C (4 hours) on the second machine. Therefore, it is placed as late as possible.
C AB
4. There is a tie (at 7 hours) for the shortest remaining job. We can place E, which was on the fi rst work center, fi rst. Then D is placed in the last sequencing position:
B E D C A
The sequential times are:
Work center 1 3 7 10 8 5
Work center 2 6 12 7 4 2
The time-phased flow of this job sequence is best illustrated graphically:
Time 0 1 3
B
105 7 11 12 13 17 19 21 22 23 25 27 29 31 33 35
E D C A
= Idle = Job completed
Time 0 3 10 20 28 33
9
Work center
1
Work center
2
B E D C A
B E D C A
Thus, the five jobs are completed in 35 hours.
INSIGHT c The second work center will wait 3 hours for its first job, and it will also wait 1 hour after completing job B.
LEARNING EXERCISE c If job C takes 8 hours in work center 2 (instead of 4 hours), what sequence is best? [Answer: B–E–C–D–A.]
RELATED PROBLEMS c 15.20, 15.22, 15.23 (15.25 is available in MyOMLab)
EXCEL OM Data File Ch15Ex7.xls can be found in MyOMLab.
LO 15.5 Use Johnson’s rule
Limitations of Rule-Based Sequencing Systems The scheduling techniques just discussed are rule-based techniques, but rule-based systems have a number of limitations. Among these are the following:
1. Scheduling is dynamic; therefore, rules need to be revised to adjust to changes in orders, process, equipment, product mix, and so forth.
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 617
2. Rules do not look upstream or downstream; idle resources and bottleneck resources in other departments may not be recognized.
3. Rules do not look beyond due dates. For instance, two orders may have the same due date. One order involves restocking a distributor and the other is a custom order that will shut down the customer’s factory if not completed. Both may have the same due date, but clearly the custom order is more important.
Despite these limitations, schedulers often use sequencing rules such as SPT, EDD, or critical ratio. They apply these methods at each work center and then modify the sequence to deal with a multitude of real-world variables. They may do this manually or with finite capacity scheduling software.
Finite Capacity Scheduling (FCS) Short-term scheduling systems are also called finite capacity scheduling. 2 Finite capacity sched- uling (FCS) overcomes the disadvantages of systems based exclusively on rules by providing the scheduler with interactive computing and graphic output. In dynamic scheduling envi- ronments such as job shops (with high variety, low volume, and shared resources) we expect changes. But changes disrupt schedules. Operations managers are moving toward FCS sys- tems that allow virtually instantaneous change by the operator. Improvements in communica- tion on the shop floor are also enhancing the accuracy and speed of information necessary for effective control in job shops. Computer-controlled machines can monitor events and collect information in near real-time. This means the scheduler can make schedule changes based on up-to-the-minute information. These schedules are often displayed in Gantt chart form. In addition to including priority rule options, many of the current FCS systems also combine an “expert system” or simulation techniques and allow the scheduler to assign costs to various options. The scheduler has the flexibility to handle any situation, including order, labor, or machine changes.
The combining of planning and FCS data, priority rules, models to assist analysis, and Gantt chart output is shown in Figure 15.5 .
Finite capacity scheduling allows delivery requirements to be based on today’s conditions and today’s orders, not according to some predefined rule. The scheduler determines what constitutes a “good” schedule. FCS software packages such as Lekin (shown in Figure 15.6 ), ProPlanner, Preactor, Asprova, Schedlyzer, and Jobplan are currently used at over 60% of U.S. plants.
Finite capacity scheduling (FCS)
Computerized short-term schedul-
ing that overcomes the disadvan-
tage of rule-based systems by
providing the user with graphical
interactive computing.
LO 15.6 Define finite capacity scheduling
Setups and run time
Interactive Finite Capacity Scheduling
Planning Data Routing files; work center information
Priority rules
• Expert systems • Simulation models
• Master schedule • BOM • Inventory
Tooling and other resources
Maintenance
Job
A
B
C
D
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Day 8
Figure 15.5
Finite Capacity Scheduling
Systems Use Production Data
to Generate Gantt Load Charts,
and Work-in-Process Data
That Can Be Manipulated by
the User to Evaluate Schedule
Alternatives
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618 P A R T 3 | M A N AG I N G O P E R AT I O N S
Scheduling Services Scheduling service systems differs from scheduling manufacturing systems in several ways:
◆ In manufacturing, the scheduling emphasis is on machines and materials; in services, it is on staffing levels.
◆ Inventories can help smooth demand for manufacturers, but many service systems do not maintain inventories.
◆ Services are labor intensive, and the demand for this labor can be highly variable. ◆ Legal considerations, such as wage and hour laws and union contracts that limit hours
worked per shift, week, or month, constrain scheduling decisions. ◆ Because services usually schedule people (rather than material), social, fatigue, seniority, and
status issues complicate scheduling.
The following examples note the complexity of scheduling services.
Hospitals A hospital is an example of a service facility that may use a scheduling system every bit as complex as one found in a job shop. Hospitals seldom use a machine shop priority system such as first come, first served (FCFS) for treating emergency patients, but they often use FCFS within a priority class, a “triage” approach. And they often schedule products (such as surgeries) just like a factory, maintaining excess capacity to meet wide variations in demand.
Banks Cross-training of the workforce in a bank allows loan officers and other managers to provide short-term help for tellers if there is a surge in demand. Banks also employ part- time personnel to provide a variable capacity.
Retail Stores Scheduling optimization systems, such as Workbrain, Cybershift, and Kronos, are used at retailers including Walmart, Payless Shoes, and Target. These systems track individual store sales, transactions, units sold, and customer traffic in 15-minute incre- ments to create work schedules. Walmart’s 2.2 million and Target’s 350,000 employees used to take thousands of managers’ hours to schedule; now staffing is drawn up nationwide in a few hours, and customer checkout experience has improved dramatically.
Starbucks’ scheduling software is discussed in the OM in Action box on the next page.
Figure 15.6
Finite Capacity Scheduling
(FCS) System
This Lekin ® finite capacity
scheduling software presents
a schedule of the five jobs and
the two work centers shown in
Example 7 (pages 615 – 616 ) in
Gantt chart form. The software
is capable of using a variety of
priority rules and many jobs. The
Lekin software is available for
free at http://community.stern
.nyu.edu/om/software/lekin
/download.html and can solve
many of the problems at the end
of this chapter.
S cr
e e n sh
o t
fr o m
L e ki
n ®
F in
it e C
a p a ci
ty S
ch e d u lin
g S
o ft
w a re
. R
e p ri n te
d w
it h p
e rm
is si
o n .
STUDENT TIP Scheduling people to perform
services can be even more
complex than scheduling
machines.
VIDEO 15.2 Scheduling at Hard Rock Cafe
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 619
Airlines Two of the constraints airlines face when scheduling flight crews are: (1) a complex set of FAA work-time limitations and (2) union contracts that guarantee crew pay for some number of hours each day or each trip. Planners must also make efficient use of their other expensive resource: aircraft. These schedules are typically built using linear programming models.
24/7 Operations Emergency hotlines, police/fire departments, telephone operations, and mail-order businesses (such as L.L.Bean) schedule employees 24 hours a day, 7 days a week. To allow management flexibility in staffing, sometimes part-time workers can be employed. This provides both benefits (in using odd shift lengths or matching anticipated workloads) and difficulties (from the large number of possible alternatives in terms of days off, lunch hour times, rest periods, starting times). Most companies use computerized scheduling systems to cope with these complexities.
Good scheduling in the health care industry can help
keep nurses happy and costs contained. Here, nurses
in Boston protest nurse-staffing levels in Massachusetts
hospitals. Shortages of qualified nurses is a chronic
problem.
P a tr
ic ia
M cD
o n n e ll/
A P
I m
a g e s
OM in Action Starbucks’ Controversial Scheduling Software Starbucks recently announced revisions to the way the company schedules
its 130,000 baristas, saying it wanted to improve “stability and consistency”
in work hours from week to week. The company intends to curb the much-
loathed practice of “clopening,” or workers closing the store late at night and
returning just a few hours later to reopen. All work hours must be posted at
least one week in advance, a policy that has been only loosely followed in the
past. Baristas with more than an hour’s commute will be given the option to
transfer to more convenient locations, and scheduling software will be revised
to allow more input from managers.
The revisions came in response to a newspaper article about a single mother
struggling to keep up with erratic hours set by automated software. A grow-
ing push to curb scheduling practices, enabled by sophisticated software, has
caused havoc in employees’ lives: giving only a few days’ notice of working
hours; sending workers home early when sales are slow; and shifting hours sig-
nificantly from week to week. Those practices have been common at Starbucks.
And many other chains use even more severe methods, such as requiring work-
ers to have “open availability,” or be able to work anytime they are needed, or to
stay “on call,” meaning they only find out that morning if they are needed.
Starbucks prides itself on progressive labor practices, such as offering
health benefits, free online degrees at Arizona State University, and stock.
But baristas across the country say that their actual working conditions vary
wildly, and that the company often fails to live up to its professed ideals, by
refusing to offer any guaranteed hours to part-time workers and keeping many
workers’ pay at minimum wage. Scheduling has been an issue for years. Said
a former company executive: “Labor is the biggest controllable cost for front-
line operators, who are under incredible pressure to hit financial targets.”
Sources: New York Times (September 24, 2015 and August 15, 2014) and
BloombergBusinessweek (August 15, 2014).
R o sa
Ir e n e B
e ta
n co
u rt
7 /A
la m
y
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620 P A R T 3 | M A N AG I N G O P E R AT I O N S
Scheduling Service Employees with Cyclical Scheduling A number of techniques and algorithms exist for scheduling service-sector employees when staffing needs vary. This is typically the case for police officers, nurses, restaurant staff, tellers, and retail sales clerks. Managers, trying to set a timely and efficient schedule that keeps per- sonnel happy, can spend substantial time each month developing employee schedules. Such schedules often consider a fairly long planning period (say, 6 weeks). One approach that is workable yet simple is cyclical scheduling .
Cyclical Scheduling Cyclical scheduling focuses on developing varying (inconsistent) schedules with the minimum number of workers. In these cases, each employee is assigned to a shift and has prescribed time off. Let’s look at Example 8 .
LO 15.7 Use the cyclical scheduling
technique
Example 8 CYCLICAL SCHEDULING Hospital administrator Doris Laughlin wants to staff the oncology ward using a standard 5-day work- week with two consecutive days off, but also wants to minimize the staff. However, as in most hospitals, she faces an inconsistent demand. Weekends have low usage. Doctors tend to work early in the week, and patients peak on Wednesday then taper off.
APPROACH c Doris must first establish staffing requirements. Then the following five-step process is applied.
SOLUTION c 1. Doris has determined that the necessary daily staffi ng requirements are:
DAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY
Staff required 5 5 6 5 4 3 3
2. Identify the two consecutive days that have the lowest total requirement and circle these. Assign these two days off to the fi rst employee. In this case, the fi rst employee has Saturday and Sunday off because 3 plus 3 is the lowest sum of any 2 days. In the case of a tie, choose the days with the lowest adjacent requirement, or by fi rst assigning Saturday and Sunday as an “off ” day. If there are more than one, make an arbitrary decision.
3. We now have an employee working each of the uncircled days; therefore, make a new row for the next employee by subtracting 1 from the fi rst row (because one day has been worked)—except for the circled days (which represent the days not worked) and any day that has a zero. That is, do not subtract from a circled day or a day that has a value of zero.
4. In the new row, identify the two consecutive days that have the lowest total requirement and circle them. Assign the next employee to the remaining days.
5. Repeat the process (Steps 3 and 4) until all staffi ng requirements are met.
Employee 1
Employee 2
Employee 3
Employee 4
Employee 5
Employee 6
Employee 7
Capacity
(measured in
number of
employees)
Excess capacity
5
4
3
2
1
1
5
4
3
2
1
1
6
5
4
3
2
1
5
4
3
2
2
1
4
3
2
2
2
1
3
3
3
3
2
1
1
3
3
3
2
1
0
5
0
5
0
6
0
5
0
4
0
3
1
3
0
MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 621
Doris needs six full-time employees to meet the staffing needs and one employee to work Saturday. Notice that capacity (number of employees) equals requirements, provided an employee works over-
time on Saturday, or a part-time employee is hired for Saturday.
INSIGHT c Doris has implemented an efficient scheduling system that accommodates 2 consecutive days off for every employee.
LEARNING EXERCISE c If Doris meets the staffing requirement for Saturday with a full-time employee, how does she schedule that employee? [Answer: That employee can have any 2 days off, except Saturday, and capacity will exceed requirements by 1 person each day the employee works (except Saturday).]
RELATED PROBLEMS c 15.26, 15.27
Using the approach in Example 8 , Colorado General Hospital saved an average of 10 to 15 hours a month and found these added advantages: (1) no computer was needed, (2) the nurses were happy with the schedule, (3) the cycles could be changed seasonally to accom- modate avid skiers, and (4) recruiting was easier because of predictability and flexibility. This approach yields an optimum, although there may be multiple optimal solutions.
Other cyclical scheduling techniques have been developed to aid service scheduling. Some approaches use linear programming: This is how Hard Rock Cafe schedules its services (see the Video Case Study at the end of this chapter). There is a natural bias in scheduling to use tools that are understood and yield solutions that are accepted.
Summary Scheduling involves the timing of operations to achieve the efficient movement of units through a system. This chapter addressed the issues of short-term scheduling in process- focused and service environments. We saw that process- focused facilities are production systems in which products are made to order and that scheduling tasks in them can become complex. Several aspects and approaches to schedul- ing, loading, and sequencing of jobs were introduced. These
ranged from Gantt charts and the assignment method of scheduling to a series of priority rules, the critical-ratio rule, Johnson’s rule for sequencing, and finite capacity scheduling.
Service systems generally differ from manufacturing sys- tems. This leads to the use of first-come, first-served rules and appointment and reservation systems, as well as linear programming for matching capacity to demand in service environments.
Key Terms
Loading (p. 604 ) Input–output control (p. 606 ) ConWIP cards (p. 606 ) Gantt charts (p. 607 )
Assignment method (p. 608 ) Sequencing (p. 611 ) Priority rules (p. 611 ) Flow time (p. 611 )
Critical ratio (CR) (p. 614 ) Johnson’s rule (p. 615 ) Finite capacity scheduling
(FCS) (p. 617 )
Ethical Dilemma Scheduling people to work second and third shifts (evening and “graveyard”) is a problem in almost every 24-hour company. Medical and ergonomic data indicate the body does not respond well to signifi cant shifts in its natural circadian rhythm of sleep.
There are also signifi cant long-run health issues with frequent changes in work and sleep cycles.
Consider yourself the manager of a nonunion steel mill that must operate 24-hour days, and where the physical demands are such that 8-hour days are preferable to 10- or 12-hour days.
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Discussion Questions
Your empowered employees have decided that they want to work weekly rotating shifts. That is, they want a repeating work cycle of 1 week, 7 A.M. to 3 P.M., followed by a second week from 3 P.M. to 11 P.M., and the third week from 11 P.M. to 7 P.M. You are sure this is not a good idea in terms of both productivity and the long-term health of the employees. If you do not accept their decision, you undermine the work empowerment program, generate a morale issue, and perhaps, more significantly, generate few more votes for a union. What is the ethical position and what do you do?
M a rc
e l M
o o ij/
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1. What is the overall objective of scheduling? 2. List the four criteria for determining the effectiveness of a
scheduling decision. How do these criteria relate to the four criteria for sequencing decisions?
3. Describe what is meant by “loading” work centers. What are the two ways work centers can be loaded? What are two tech- niques used in loading?
4. Name five priority sequencing rules. Explain how each works to assign jobs.
5. What are the advantages and disadvantages of the shortest processing time (SPT) rule?
6. What is a due date?
7. Explain the terms flow time and lateness . 8. Which shop-floor scheduling rule would you prefer to apply
if you were the leader of the only team of experts charged with defusing several time bombs scattered throughout your building? You can see the bombs; they are of different types. You can tell how long each one will take to defuse. Discuss.
9. When is Johnson’s rule best applied in job-shop scheduling? 10. State the four effectiveness measures for dispatching rules. 11. What are the steps of the assignment method of linear
programming? 12. What are the advantages to finite capacity scheduling? 13. What is input–output control?
Using Software for Short-Term Scheduling
In addition to the commercial software we noted in this chap- ter, short-term scheduling problems can be solved with the Excel OM software that comes free with this text. POM for Windows also includes a scheduling module. The use of each of these programs is explained next.
X USING EXCEL OM Excel OM has two modules that help solve short-term sched- uling problems: Assignment and Job Shop Scheduling. The Assignment module is illustrated in Programs 15.1 and 15.2 . The input screen, using the Example 4 data, appears first, as Program 15.1 . Once the data are all entered, we choose the Data tab command, followed by the Solver command. Excel’s Solver uses linear programming to optimize assignment problems. (So select Simplex LP.)
The constraints are also shown in Program 15.1 . We then select the Solve command; the solution appears in Program 15.2 .
Excel OM’s Job Shop Scheduling module is illustrated in Program 15.3 . Program 15.3 uses Example 5 ’s data. Because jobs are listed in the sequence in which they arrived (see col- umn A), the results are for the FCFS rule. Program 15.3 also shows some of the formulas (columns F, G, H, I, J) used in the calculations.
To solve with the SPT rule, we need four intermediate steps: (1) Select (that is, highlight) the data in columns A, B, C for all jobs; (2) invoke the Data command; (3) invoke the Sort command; and (4) sort by Time (column C) in ascending order. To solve for EDD, Step 4 changes to sort by Due Date (column D) in ascending order. Finally, for an LPT solution, Step 4 becomes sort by Due Date (column D) in descending order.
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B22 is where we placed our total costs on the data screen.
We need to create row and column totals in order to create the constraints.
These are the constraints for the linear programming representation of the assignment problem.
Nonnegativity constraints have been added through the checkbox.
Select Simplex LP as the solution method.
In Excel 2007 and later for PCs and Excel 2016 for Macs, Solver is in the Analysis section of the Data tab. In Excel 2011 for Macs, Solver is under the Tools menu.
Use the SUMPRODUCT function to calculate the total cost. Notice that this function is multiplying the data table by the assignment table.
The assignments will be filled in by Excel’s Solver.
Copy the names from the above table.
These are the cells that we will ask Excel’s Solver to fill in for us.
Program 15.1
Excel OM’s Assignment Module Using Example 4 ’s Data
After entering the problem data in the yellow area, select Data, then Solver.
Solver has filled in the assignments with 1s.
It is important to check the statement made by the Solver. In this case, it says that Solver found a solution. In other problems, this may not be the case. For some problems there may be no feasible solution, and for others more iterations may be required.
Program 15.2
Excel OM Output Screen for Assignment Problem Described in Program 15.1
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An IF function is used to determine whether or not the job was late. = IF(I13–D13>=0, I13–D13,0)
The results are for an FCFS schedule. To create other results, sort cells A9 through D13 based on a new criterion.
= AVERAGE(H9:H13)
Calculate the slack as = D9 – C9.
In this example, all work begins on Day 1 and all jobs are available on Day 1.
The completion times and the flow times are identical since work begins on Day 1= H14/C14
Program 15.3
Excel OM’s Job Shop Scheduling Module Applied to Example 5 ’s Data
P USING POM FOR WINDOWS POM for Windows can handle both categories of scheduling problems we see in this chapter. Its Assignment module is used to solve the traditional one-to-one assignment problem of people to tasks, machines to jobs, and so on. Its Job Shop Scheduling module can solve a one- or two-machine job-shop problem. Available priority rules include SPT, FCFS, EDD, and LPT. Each can be examined in turn once the data are all entered. Refer to Appendix IV for specifics regarding POM for Windows.
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM 15.1 King Finance Corporation, headquartered in New York, wants to assign three recently hired college graduates, Julie Jones, Al Smith, and Pat Wilson, to regional offices. However, the firm also has an opening in New York and would send one of the three there if it were more economical than a move to Omaha, Dallas, or Miami. It will cost $1,000 to relocate Jones to New York, $800 to relocate Smith there, and $1,500 to move Wilson. What is the optimal assignment of personnel to offices?
SOLUTION
a) The cost table has a fourth column to represent New York. To “balance” the problem, we add a “dummy” row (person) with a zero relocation cost to each city.
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones $800 $1,100 $1,200 $1,000
Smith $500 $1,600 $1,300 $ 800
Wilson $500 $1,000 $2,300 $1,500
Dummy 0 0 0 0
OFFICE
OMAHA MIAMI DALLAS
HIREE
Jones $800 $1,100 $1,200
Smith $500 $1,600 $1,300
Wilson $500 $1,000 $2,300
b) Subtract the smallest number in each row and cover all zeros (column subtraction of each column’s zero will give the same numbers and therefore is not necessary):
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones 0 300 400 200
Smith 0 1,100 800 300
Wilson 0 500 1,800 1,000
Dummy 0 0 0 0
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c) Only 2 lines cover, so subtract the smallest uncovered number (200) from all uncovered numbers, and add it to each square where two lines intersect. Then cover all zeros:
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones 0 100 200 0
Smith 0 900 600 100
Wilson 0 300 1,600 800
Dummy 200 0 0 0
d) Only 3 lines cover, so subtract the smallest uncovered number (100) from all uncovered numbers, and add it to each square where two lines intersect. Then cover all zeros:
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones 0 0 100 0
Smith 0 800 500 100
Wilson 0 200 1,500 800
Dummy 300 0 0 100
e) Still only 3 lines cover, so subtract the smallest uncovered number (100) from all uncovered numbers, add it to squares where two lines intersect, and cover all zeros:
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones 100 0 100 0
Smith 0 700 400 0
Wilson 0 100 1,400 700
Dummy 400 0 0 100
f) Because it takes four lines to cover all zeros, an optimal assignment can be made at zero squares. We assign: Wilson to Omaha Jones to Miami Dummy (no one) to Dallas Smith to New York
Cost = $500 + $1,100 + $0 + $800 = $2,400
SOLVED PROBLEM 15.2 A defense contractor in Dallas has six jobs awaiting processing. Processing time and due dates are given in the table. Assume that jobs arrive in the order shown. Set the processing sequence according to FCFS and evaluate. Start date is day 1.
JOB PROCESSING JOB DUE JOB TIME (DAYS) DATE (DAYS)
A 6 22
B 12 14
C 14 30
D 2 18
E 10 25
F 4 34
SOLUTION FCFS has the sequence A–B–C–D–E–F.
JOB JOB PROCESSING
SEQUENCE TIME FLOW TIME DUE DATE JOB LATENESS
A 6 6 22 0
B 12 18 14 4
C 14 32 30 2
D 2 34 18 16
E 10 44 25 19
F 4 48 34 14
48 182 55
1. Average completion time = 182>6 = 30.33 days 2. Average number of jobs in system = 182>48 = 3.79 jobs 3. Average job lateness = 55>6 = 9.16 days 4. Utilization = 48>182 = 26.4,
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SOLUTION SPT has the sequence D–F–A–E–B–C.
JOB JOB PROCESSING
SEQUENCE TIME FLOW TIME DUE DATE JOB LATENESS
D 2 2 18 0
F 4 6 34 0
A 6 12 22 0
E 10 22 25 0
B 12 34 14 20
C 14 48 30 18
48 124 38 1. Average completion time = 124>6 = 20.67 days 2. Average number of jobs in system = 124>48 = 2.58 jobs 3. Average job lateness = 38>6 = 6.33 days 4. Utilization = 48>124 = 38.7,
SPT is superior to FCFS in this case on all four measures. If we were to also analyze EDD, we would, however, find its average job lateness to be lowest at 5.5 days. SPT is a good recommendation. SPT’s major disadvantage is that it makes long jobs wait, sometimes for a long time.
SOLVED PROBLEM 15.4 Use Johnson’s rule to find the optimum sequence for processing the jobs shown through two work centers. Times at each center are in hours.
JOB WORK CENTER 1 WORK CENTER 2
A 6 12
B 3 7
C 18 9
D 15 14
E 16 8
F 10 15 SOLUTION
B A F D C E
The sequential times are:
Work center 1 3 0 15 18 16
Work center 2 7 12
6
15
1
14 9 8
SOLVED PROBLEM 15.5 Illustrate the throughput time and idle time at the two work centers in Solved Problem 15.4 by constructing a time-phased chart.
SOLUTION
0 10
A
37 51 52 68 76
D E Idle time
0 9 19 52 68
22
Work center
1 Work center
2
B A D C E
B F E
3
3
F
A
B F
34
D C
61
C
SOLVED PROBLEM 15.3 The Dallas firm in Solved Problem 15.2 also wants to consider job sequencing by the SPT priority rule. Apply SPT to the same data, and provide a recommendation.
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Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems 15.1–15.14 relate to Loading Jobs
• • 15.1 Ron Satterfield’s excavation company uses both Gantt scheduling charts and Gantt load charts. a) Today, which is the end of day 7, Ron is reviewing the Gantt
chart depicting these schedules: ◆ Job #151 was scheduled to begin on day 3 and to take
6 days. As of now, it is 1 day ahead of schedule. ◆ Job #177 was scheduled to begin on day 1 and take 4 days.
It is currently on time. ◆ Job #179 was scheduled to start on day 7 and take 2 days. It
actually got started on day 6 and is progressing according to plan.
◆ Job #211 was scheduled to begin on day 5, but miss- ing equipment delayed it until day 6. It is progressing as expected and should take 3 days.
◆ Job #215 was scheduled to begin on day 4 and take 5 days. It got started on time but has since fallen behind 2 days.
Draw the Gantt scheduling chart for the activities above. b) Ron now wants to use a Gantt load chart to see how much
work is scheduled in each of his three work teams: Able, Baker, and Charlie. Five jobs constitute the current workload for these three work teams: Job #250, requiring 48 hours and #275 requiring 32 hours for Work Team Able; Jobs #210 and #280, requiring 16 and 24 hours, respectively, for Team Baker; and Job #225, requiring 40 hours, for Team Charlie.
Prepare the Gantt load chart for these activities.
• • 15.2 First Printing and Copy Center has 4 more jobs to be scheduled, in addition to those shown in Example 3 in the chapter. Production scheduling personnel are reviewing the Gantt chart at the end of day 4. ◆ Job D was scheduled to begin early on day 2 and to end
on the middle of day 9. As of now (the review point after day 4), it is 2 days ahead of schedule.
◆ Job E should begin on day 1 and end on day 3. It is on time. ◆ Job F was to begin on day 3, but maintenance forced a delay
of 1½ days. The job should now take 5 full days. It is now on schedule.
◆ Job G is a day behind schedule. It started at the beginning of day 2 and should require 6 days to complete.
Develop a Gantt schedule chart for First Printing and Copy Center.
• 15.3 The Green Cab Company has a taxi waiting at each of four cabstands in Evanston, Illinois. Four customers have called and requested service. The distances, in miles, from the waiting taxis to the customers are given in the following table. Find the optimal assignment of taxis to customers so as to minimize total driving distances to the customers.
CAB SITE
CUSTOMER
A B C D
Stand 1 7 3 4 8 Stand 2 5 4 6 5 Stand 3 6 7 9 6 Stand 4 8 6 7 4
• 15.4 J.C. Howard’s medical testing company in Kansas wishes to assign a set of jobs to a set of machines. The follow- ing table provides the production data of each machine when performing the specific job:
PX
JOB
MACHINE
A B C D
1 7 9 8 10 2 10 9 7 6 3 11 5 9 6 4 9 11 5 8
a) Determine the assignment of jobs to machines that will maxi- mize total production.
b) What is the total production of your assignments? PX
• 15.5 The Johnny Ho Manufacturing Company in Columbus, Ohio, is putting out four new electronic components. Each of Ho’s four plants has the capacity to add one more prod- uct to its current line of electronic parts. The unit-manufacturing costs for producing the different parts at the four plants are shown in the accompanying table. How should Ho assign the new products to the plants to minimize manufacturing costs?
ELECTRONIC COMPONENT
PLANT
1 2 3 4
C53 $0.10 $0.12 $0.13 $0.11 C81 0.05 0.06 0.04 0.08 D5 0.32 0.40 0.31 0.30 D44 0.17 0.14 0.19 0.15
• 15.6 Jamison Day Consultants has been entrusted with the task of evaluating a business plan that has been divided into four sections—marketing, finance, operations, and human resources. Chris, Steve, Juana, and Rebecca form the evaluation team. Each of them has expertise in a certain field and tends to finish that section faster. The estimated times taken by each team member for each section have been outlined in the table below. Further information states that each of these individuals is paid $60/hour. a) Assign each member to a different section such that Jamison
Consultants’s overall cost is minimized. b) What is the total cost of these assignments?
Times Taken by Team Members for Different Sections (minutes)
MARKETING FINANCE OPERATIONS HR
Chris 80 120 125 140 Steve 20 115 145 160 Juana 40 100 85 45 Rebecca 65 35 25 75
• • 15.7 The Baton Rouge Police Department has five detec- tive squads available for assignment to five open crime cases. The chief of detectives, Jose Noguera, wishes to assign the squads so that the total time to conclude the cases is minimized. The average number of days, based on past performance, for each squad to complete each case is as follows:
CASE
SQUAD A B C D E
1 14 7 3 7 27 2 20 7 12 6 30 3 10 3 4 5 21 4 8 12 7 12 21 5 13 25 24 26 8
PX
PX
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Each squad is composed of different types of specialists, and whereas one squad may be very effective in certain types of cases, it may be almost useless in others. a) Solve the problem by using the assignment method. b) Assign the squads to the above cases, but with the constraint
that squad 5 cannot work on case E because of a conflict. PX
• 15.8 Tigers Sports Club has to select four separate co-ed doubles teams to participate in an inter-club table tennis tourna- ment. The pre-selection results in the selection of a group of four men—Raul, Jack, Gray, and Ajay—and four women—Barbara, Dona, Stella, and Jackie. Now, the task ahead lies in pairing these men and women in the best fashion. The table below shows a matrix that has been designed for this purpose, indicating how each of the men complements the game of each of the women. A higher score indicates a higher degree of compatibility in the games of the two individuals concerned. Find the best pairs.
Game Compatibility Matrix
BARBARA DONA STELLA JACKIE
Raul 30 20 10 40 Jack 70 10 60 70 Gray 40 20 50 40 Ajay 60 70 30 90
• • • 15.9 Daniel Glaser, chairman of the College of San Antonio’s business department, needs to assign professors to courses next semester. As a criterion for judging who should teach each course, Professor Glaser reviews the past 2 years’ teaching evaluations (which were filled out by students). Since each of the four profes- sors taught each of the four courses at one time or another during the 2-year period, Glaser is able to record a course rating for each instructor. These ratings are shown in the following table. a) Find the assignment of professors to courses to maximize the
overall teaching rating. b) Assign the professors to the courses with the exception that
Professor Fisher cannot teach statistics. PX
PROFESSOR
COURSE
STATISTICS MANAGEMENT FINANCE ECONOMICS
W. W. Fisher 90 65 95 40 D. Golhar 70 60 80 75 Z. Hug 85 40 80 60 N. K. Rustagi 55 80 65 55
• • 15.10 Lifang Wu owns an automated machine shop that makes precision auto parts. He has just compiled an input–output
PX
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report for the grinding work center. Complete this report and analyze the results.
Input–Output Report
PERIOD 1 2 3 4 TOTAL
Planned input 80 80 100 100 Actual input 85 85 85 85 Deviation Planned output 90 90 90 90 Actual output 85 85 80 80 Deviation Initial backlog: 30
Additional problems 15.11–15.14 are available in MyOMLab.
Problems 15.15–15.25 relate to Sequencing Jobs
• • 15.15 The following jobs are waiting to be processed at the same machine center. Jobs are logged as they arrive:
JOB DUE DATE DURATION (DAYS)
A 313 8 B 312 16 C 325 40 D 314 5 E 314 3
In what sequence would the jobs be ranked according to the follow- ing decision rules: (a) FCFS, (b) EDD, (c) SPT, and (d) LPT? All dates are specified as manufacturing planning calendar days. Assume that all jobs arrive on day 275. Which decision is best and why? PX
• 15.16 The following 5 overhaul jobs are waiting to be pro- cessed at Avianic’s Engine Repair Inc. These jobs were logged as they arrived. All dates are specified as planning calendar days. Assume that all jobs arrived on day 180; today’s date is 200.
JOB DUE DATE REMAINING TIME (DAYS)
103 214 10 205 223 7 309 217 11 412 219 5 517 217 15
Using the critical ratio scheduling rule, in what sequence would the jobs be processed? PX
• • 15.17 An Alabama lumberyard has four jobs on order, as shown in the following table. Today is day 205 on the yard’s schedule.
JOB DUE DATE REMAINING TIME (DAYS)
A 212 6 B 209 3 C 208 3 D 210 8
In what sequence would the jobs be ranked according to the fol- lowing decision rules: a) FCFS b) SPT c) LPT d) EDD e) Critical ratio
Which is best and why? Which has the minimum lateness?
• • 15.18 The following jobs are waiting to be processed at Rick Solano’s machine center. Solano’s machine center has a relatively
PX
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long backlog and sets a fresh schedule every 2 weeks, which does not disturb earlier schedules. Below are the jobs received during the previous 2 weeks. They are ready to be scheduled today, which is day 241 (day 241 is a work day). Job names refer to names of clients and contract numbers.
JOB DATE JOB RECEIVED PRODUCTION DAYS NEEDED DATE JOB DUE
BR-02 228 15 300 CX-01 225 25 270 DE-06 230 35 320 RG-05 235 40 360 SY-11 231 30 310
a) Complete the following table. (Show your supporting calculations.) b) Which dispatching rule has the best score for flow time? c) Which dispatching rule has the best score for utilization metric? d) Which dispatching rule has the best score for lateness? e) Which dispatching rule would you select? Support your decision.
DISPATCHING RULE
JOB SEQUENCE
FLOW TIME
UTILIZATION METRIC
AVERAGE NUMBER OF JOBS
AVERAGE LATENESS
EDD SPT LPT
FCFS
• • 15.19 The following jobs are waiting to be processed at Julie Morel’s machine center:
JOB DATE ORDER RECEIVED PRODUCTION DAYS NEEDED DATE ORDER DUE
A 110 20 180 B 120 30 200 C 122 10 175 D 125 16 230 E 130 18 210
In what sequence would the jobs be ranked according to the fol- lowing rules: (a) FCFS, (b) EDD, (c) SPT, and (d) LPT? All dates are according to shop calendar days. Today on the planning cal- endar is day 130, and none of the jobs have been started or sched- uled. Which rule is best? PX
• • 15.20 Sunny Park Tailors has been asked to make three dif- ferent types of wedding suits for separate customers. The table below highlights the time taken in hours for (1) cutting and sewing and (2) delivery of each of the suits. Which schedule finishes sooner: first come, first served (123) or a schedule using Johnson’s rule?
Times Taken for Different Activities (hours)
SUIT CUT AND SEW DELIVER
1 4 2 2 7 7 3 6 5
• • 15.21 The following jobs are waiting to be processed at Jeremy LaMontagne’s machine center. Today is day 250.
JOB DATE JOB RECEIVED PRODUCTION DAYS NEEDED DATE JOB DUE
1 215 30 260 2 220 20 290 3 225 40 300 4 240 50 320 5 250 20 340
PX
PX
Using the critical ratio scheduling rule, in what sequence would the jobs be processed? PX
• • • • 15.22 The following set of seven jobs is to be processed through two work centers at George Heinrich’s printing com- pany. The sequence is first printing, then binding. Processing time at each of the work centers is shown in the following table:
JOB PRINTING (HOURS) BINDING (HOURS)
T 15 3 U 7 9 V 4 10 W 7 6 X 10 9 Y 4 5 Z 7 8
a) What is the optimal sequence for these jobs to be scheduled? b) Chart these jobs through the two work centers. c) What is the total length of time of this optimal solution? d) What is the idle time in the binding shop, given the optimal
solution? e) How much would the binding machine’s idle time be cut by
splitting Job Z in half ? PX
• • • 15.23 Six jobs are to be processed through a two-step opera- tion. The first operation involves sanding, and the second involves painting. Processing times are as follows:
JOB OPERATION 1 (HOURS) OPERATION 2 (HOURS)
A 10 5 B 7 4 C 5 7 D 3 8 E 2 6 F 4 3
Determine a sequence that will minimize the total completion time for these jobs. Illustrate graphically. PX
Additional problems 15.24–15.25 are available in MyOMLab.
Problems 15.26–15.27 relate to Scheduling Services
• • 15.26 Daniel’s Barber Shop at Newark Airport is open 7 days a week but has fluctuating demand. Daniel Ball is inter- ested in treating his barbers as well as he can with steady work and preferably 5 days of work with two consecutive days off. His analysis of his staffing needs resulted in the following plan. Schedule Daniel’s staff with the minimum number of barbers.
DAY
MON. TUE. WED. THU. FRI. SAT. SUN.
Barbers needed
6 5 5 5 6 4 3
• • 15.27 Given the following demand for waiters and wait- resses at S. Ghosh Bar and Grill, determine the minimum wait staff needed with a policy of 2 consecutive days off.
DAY
MON. TUE. WED. THU. FRI. SAT. SUN.
Wait staff needed
3 4 4 5 6 7 4
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CASE STUDIES Old Oregon Wood Store
In 2015, George Wright started the Old Oregon Wood Store to manufacture Old Oregon tables. Each table is carefully constructed by hand using the highest-quality oak. Old Oregon tables can sup- port more than 500 pounds, and since the start of the Old Oregon Wood Store, not one table has been returned because of faulty work- manship or structural problems. In addition to being rugged, each table is beautifully finished using a urethane varnish that George developed over 20 years of working with wood-finishing materials.
The manufacturing process consists of four steps: preparation, assembly, finishing, and packaging. Each step is performed by one person. In addition to overseeing the entire operation, George does all of the finishing. Tom Surowski performs the preparation step, which involves cutting and forming the basic components of the tables. Leon Davis is in charge of the assembly, and Cathy Stark performs the packaging.
Although each person is responsible for only one step in the manufacturing process, everyone can perform any one of the steps. It is George’s policy that occasionally everyone should complete several tables on his or her own without any help or assistance. A small competition is used to see who can complete an entire table in the least amount of time. George maintains average total and intermediate completion times. The data are shown in Figure 15.7 .
It takes Cathy longer than the other employees to construct an Old Oregon table. In addition to being slower than the other employees, Cathy is also unhappy about her current responsibil- ity of packaging, which leaves her idle most of the day. Her first preference is finishing, and her second preference is preparation.
In addition to quality, George is concerned with costs and efficiency. When one of the employees misses a day, it causes major scheduling problems. In some cases, George assigns another employee overtime to complete the necessary work. At other times, George simply waits until the employee returns to work to complete his or her step in the manufacturing process. Both solutions cause problems. Overtime is expensive, and wait- ing causes delays and sometimes stops the entire manufacturing process.
To overcome some of these problems, Randy Lane was hired. Randy’s major duties are to perform miscellaneous jobs and to help out if one of the employees is absent. George has given Randy training in all phases of the manufacturing process, and he is pleased with the speed at which Randy has been able to learn how to completely assemble Old Oregon tables. Randy’s average total and intermediate completion times are given in Figure 15.8 .
Preparation 100
Assembly Finishing Packaging 160 250 275
(Tom)
Preparation 80
Assembly Finishing Packaging 160 220 230
(George)
Preparation 110
Assembly Finishing Packaging 200 280 290
(Leon)
Preparation 120
Assembly Finishing Packaging 190 290 315
(Cathy)
Preparation 110
Assembly Finishing Packaging 190 290 300
Figure 15.7
Manufacturing Time in Minutes
Figure 15.8
Randy’s Completion Times in Minutes
Discussion Questions
1. What is the fastest way to manufacture Old Oregon tables using the original crew? How many could be made per day?
2. Would production rates and quantities change signifi- cantly if George would allow Randy to perform one of the four functions and make one of the original crew the backup person?
3. What is the fastest time to manufacture a table with the original crew if Cathy is moved to either preparation or finishing?
4. Whoever performs the packaging function is severely under- utilized. Can you find a better way of utilizing the four- or five-person crew than either giving each a single job or allowing each to manufacture an entire table? How many tables could be manufactured per day with this scheme?
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C H A P T E R 1 5 | S H O R T - T E R M S C H E D U L I N G 631
The massive 875,000-square-foot Amway Center in Orlando, Florida, is a state-of-the-art sports entertainment center. While it is the home of the Orlando Magic basketball team, it is a flexible venue designed to accommodate a vast array of entertainment. The facility is used for everything from a concert by the Eagles or Britney Spears, to ice hockey, to arena football, to conven- tions, as well as 41 regular season home games played by its major tenant, the National Basketball Association’s Orlando Magic.
The building is a LEED-certified (Leadership in Energy and Environmental Design), sustainable, environmentally friendly design, with unmatched technology. Dispersed throughout the building are over 1,000 digital monitors, the latest in broadcasting technology, and the tallest high-definition video board in an NBA venue. To fully utilize this nearly $500 million complex, conversions from one event to the next must be done rapidly—often in a matter of hours. Letting the facility sit idle because of delays in conversion is not an option.
Well-executed conversions help maximize facility revenue and at the same time minimize expenses. Fast and efficient conversions are critical. Like any other process, a conversion can be analyzed and separated into its component activities, each requiring its own human and capital resources. The operations manager must determine when to do the conversion, how to train and schedule the crew, which tools and capital equipment are necessary, and the specific steps necessary to break down the current event and set up for the next. In addition to trying to maintain a stable crew (typically provided by local staffing companies) and to maintain
Video Case From the Eagles to the Magic: Converting the Amway Center
control during the frenzied pace of a conversion, managers divide the workforce into cross-trained crews, with each crew operating in its own uniquely colored shirt.
At the Amway Center, Charlie Leone makes it happen. Charlie is the operations manager, and as such, he knows that any conver- sion is loaded with complications and risks. Concerts add a special risk because each concert has its own idiosyncrasies—and the break- down for the Eagles concert will be unique. Charlie and his crews must anticipate and eliminate any potential problems. Charlie’s immediate issue is making a schedule for converting the Eagles’ con- cert venue to an NBA basketball venue. The activities and the time for various tasks have been determined and are shown in Table 15.3 .
TABLE 15.3 CONCERT-TO-BASKETBALL CONVERSION TASKS
TIME ALLOWED TASKS CREW AND TIME REQUIRED
3 to 4 hr 11:20 PM Performance crew begins teardown of concert stage & equipment Concert’s Responsibility 45 min 11:20 PM Clear Floor Crew
Get chair carts from storage 10 for 15 min Clear all chairs on fl oor, loading carts starting at south end, working north 16 for 30 min Move chair carts to north storage and stack as they become full (includes 1 fork truck operator)
15 min 11:50 PM (Or as soon as area under rigging is cleared) 6 for 15 min Set up retractable basketball seating on north end Take down railing above concert stage Place railings on cart and move to storage
2.5 hr 12:05 AM Basketball Floor Crew 8 Position 15 basketball fl oor carts on fl oor Mark out arena fl oor for proper placement of basketball fl oor Position basketball fl oor by section Assemble/join fl ooring/lay carpets over concrete Position basketball nets in place Set up scorer tables Install risers for all courtside seating Install 8-ft tables on east side of court
2.5 hr Seating Unit Crew Starts same time as Basketball Floor Crew 8 Set up retractable basketball seating on north end (includes 2 fork truck operators) Set up retractable basketball seating on south end (Can only be done after concert stage and equipment is out of way) Install stairs to Superstar Seating
2 hr Board Crew Starts after Seating Unit Crew fi nishes 4 Install dasher board on south end Move stairs to storage
Available crew size 5 16, including two fork truck drivers
(Continued)
F e rn
a n d o M
e d in
a
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632 P A R T 3 | M A N AG I N G O P E R AT I O N S
Discussion Questions *
1. Make a Gantt chart to help Charlie organize his crew to perform the concert-to-basketball conversion. Note : Do not include the teardown of the concert stage and equipment, as that is the responsibility of the concert crew.
TIME ALLOWED TASKS CREW AND TIME REQUIRED
2 hr Chair Crew Starts after Seating Unit Crew fi nishes 12 Get chair carts from storage Position chair carts on fl oor Position chairs behind goals, courtside, and scorer tables Clean, sweep, and place carts in order
45 min End-of-Shift Activities Starts after Chair Crew fi nishes 12 Perform checklist items Ensure that steps and stairways and railings are in place and tight Check all seats are in upright position and locked in place Report any damaged seats or armrests in need of repair Verify exact number of chairs behind goals, courtside, and scorer tables
15 min Check Out Starts after End-of-Shift Activities 16 Check for next conversion date and time and inform crew Report any injuries Punch out all employees before leaving 8:00 AM Floor ready for Magic practice
TABLE 15.3 Continued
2. What time will the floor be ready? 3. Does Charlie have any extra personnel or a shortage of personnel?
If so how many?
Video Case Scheduling at Hard Rock Cafe Whether it’s scheduling nurses at Mayo Clinic, pilots at Southwest Airlines, classrooms at UCLA, or servers at a Hard Rock Cafe, it’s clear that good scheduling is important. Proper schedules use an organization’s assets (1) more effectively, by serving customers promptly, and (2) more efficiently, by lowering costs.
Hard Rock Cafe at Universal Studios, Orlando, is the world’s largest restaurant, with 1,100 seats on two main levels. With typi- cal turnover of employees in the restaurant industry at 80% to 100% per year, Hard Rock General Manager Ken Hoffman takes scheduling very seriously. Hoffman wants his 160 servers to be effective, but he also wants to treat them fairly. He has done so with scheduling software and flexibility that has increased pro- ductivity while contributing to turnover that is half the industry average. His goal is to find the fine balance that gives employees financially productive daily work shifts while setting the schedule tight enough so as to not overstaff between lunch and dinner.
The weekly schedule begins with a sales forecast. “First, we examine last year’s sales at the cafe for the same day of the week,” says Hoffman. “Then we adjust our forecast for this year based on a variety of closely watched factors. For example, we call the Orlando Convention Bureau every week to see what major groups will be in town. Then we send two researchers out to check on the occupancy of nearby hotels. We watch closely to see what
concerts are scheduled at Hard Rock Live—the 3,000-seat con- cert stage next door. From the forecast, we calculate how many people we need to have on duty each day for the kitchen, the bar, as hosts, and for table service.”
Once Hard Rock determines the number of staff needed, serv- ers submit request forms, which are fed into the software’s linear programming mathematical model. Individuals are given priority rankings from 1 to 9, based on their seniority and how important they are to fill each day’s schedule. Schedules are then posted by day and by workstation. Trades are handled between employees, who understand the value of each specific shift and station.
Hard Rock employees like the system, as does the general man- ager, since sales per labor-hour are rising and turnover is dropping.
Discussion Questions *
1. Name and justify several factors that Hoffman could use in forecasting weekly sales.
2. What can be done to lower turnover in large restaurants? 3. Why is seniority important in scheduling servers? 4. How does the schedule impact productivity?
• Additional Case Study: Visit MyOMLab for this free case study: Payroll Planning, Inc.: Describes setting a schedule for handling the accounting for dozens of client fi rms.
* You may wish to view the video that accompanies this case before answering the questions.
* You may wish to view the video that accompanies this case before answering the questions.
Endnotes
2. Finite capacity scheduling (FCS) systems go by a number of names, including finite scheduling and advance planning systems
1. Opportunity costs are those profits forgone or not obtained. (APS). The name manufacturing execution systems (MES) may also be used, but MES tends to suggest an emphasis on the report- ing system from shop operations back to the scheduling activity.
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Chapter 15 Rapid Review 15
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Main Heading Review Material MyOMLab THE IMPORTANCE OF SHORT-TERM SCHEDULING (p. 602 )
The strategic importance of scheduling is clear: j Effective scheduling means faster movement of goods and services through a
facility. This means greater use of assets and hence greater capacity per dollar invested, which, in turn, lowers cost .
j Added capacity, faster throughput, and the related flexibility mean better customer service through faster delivery .
j Good scheduling contributes to realistic commitments, hence dependable delivery .
Concept Questions: 1.1–1.2
SCHEDULING ISSUES (pp. 602 – 605 )
The objective of scheduling is to allocate and prioritize demand (generated by either forecasts or customer orders) to available facilities. j Forward scheduling—Begins the schedule as soon as the requirements are known. j Backward scheduling—Begins with the due date by scheduling the final
operation first and the other job steps in reverse order. j Loading—The assigning of jobs to work or processing centers. The four scheduling criteria are (1) minimize completion time , (2) maximize utilization , (3) minimize work-in-process (WIP) inventory , and (4) minimize customer waiting time .
Concept Questions: 2.1–2.4 VIDEO 15.1 From the Eagles to the Magic: Converting the Amway Center
SCHEDULING PROCESS-FOCUSED FACILITIES (p. 605 )
A process-focused facility is a high-variety, low-volume system commonly found in manufacturing and services. It is also called an intermittent, or job shop, facility.
Concept Questions: 3.1–3.4
LOADING JOBS (pp. 605 – 610 )
j Input–output control —A system that allows operations personnel to manage facil- ity work flows by tracking work added to a work center and its work completed.
j ConWIP cards —Cards that control the amount of work in a work center, aiding input/output control.
ConWIP is an acronym for constant work-in-process. A ConWIP card travels with a job (or batch) through the work center. When the job is finished, the card is released and returned to the initial workstation, authorizing the entry of a new batch into the work center. j Gantt charts —Planning charts used to schedule resources and allocate time. The Gantt load chart shows the loading and idle times of several departments, machines, or facilities. It displays the relative workloads in the system so that the manager knows what adjustments are appropriate. The Gantt schedule chart is used to monitor jobs in progress (and is also used for project scheduling). It indicates which jobs are on schedule and which are ahead of or behind schedule. j Assignment method —A special class of linear programming models that involves
assigning tasks or jobs to resources. In assignment problems, only one job (or worker) is assigned to one machine (or project). The assignment method involves adding and subtracting appropriate numbers in the table to find the lowest opportunity cost for each assignment.
Concept Questions: 4.1–4.4 Problems: 15.1–15.14 Virtual Office Hours for Solved Problem: 15.1
SEQUENCING JOBS (pp. 611 – 617 )
j Sequencing —Determining the order in which jobs should be done at each work center.
j Priority rules —Rules used to determine the sequence of jobs in process-oriented facilities.
j First come, first served (FCFS)—Jobs are completed in the order in which they arrived.
j Shortest processing time (SPT)—Jobs with the shortest processing times are assigned first.
j Earliest due date—Earliest due date jobs are performed first. j Longest processing time (LPT)—Jobs with the longest processing time are
completed first.
Average completion time = Sum of total flow time
Number of jobs (15-1)
Utilization metric = Total job work (processing) time
Sum of total flow time (15-2)
Concept Questions: 5.1–5.4 Problems: 15.15–15.25 Virtual Office Hours for Solved Problems: 15.2–15.5 ACTIVE MODEL 15.1
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Main Heading Review Material MyOMLab
Average number of jobs in the system = Sum of total flow time
Total job work (processing) time (15-3)
Average job lateness = Total late days Number of jobs
(15-4)
Job lateness = Max{0, yesterday + flow time - due date} (15-5) SPT is the best technique for minimizing job flow and average number of jobs in the system. FCFS performs about average on most criteria, and it appears fair to customers. EDD minimizes maximum tardiness. j Flow time —The time each job spends waiting plus the time being processed. j Critical ratio (CR) —A sequencing rule that is an index number computed by
dividing the time remaining until due date by the work time remaining:
CR = Time remaining
Workdays remaining =
Due date - Today>s date Work (lead) time remaining
(15-6)
As opposed to the priority rules, the critical ratio is dynamic and easily updated. It tends to perform better than FCFS, SPT, EDD, or LPT on the average job- lateness criterion. j Johnson’s rule —An approach that minimizes processing time for sequencing a
group of jobs through two work centers while minimizing total idle time in the work centers.
Rule-based scheduling systems have the following limitations: (1) Scheduling is dynamic, (2) rules do not look upstream or downstream, and (3) rules do not look beyond due dates.
FINITE CAPACITY SCHEDULING (FCS) (pp. 617 – 618 )
j Finite capacity scheduling (FCS) —Computerized short-term scheduling that overcomes the disadvantage of rule-based systems by providing the user with graphical interactive computing.
Concept Questions: 6.1–6.2
SCHEDULING SERVICES (pp. 618 – 621 )
Cyclical scheduling with inconsistent staffing needs is often the case in services. The objective focuses on developing a schedule with the minimum number of workers. In these cases, each employee is assigned to a shift and has time off.
Concept Questions: 7.1–7.4 VIDEO 15.2 Scheduling at Hard Rock Cafe Problems: 15.26–15.27
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Chapter 15 Rapid Review continued
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 15.1 Which of the following decisions covers the longest time period?
a) Short-term scheduling b) Capacity planning c) Aggregate planning d) A master schedule LO 15.2 A visual aid used in loading and scheduling jobs is a: a) Gantt chart. b) planning file. c) bottleneck. d) load-schedule matrix. e) level material chart. LO 15.3 The assignment method involves adding and subtracting
appropriate numbers in the table to find the lowest _____ for each assignment.
a) profit b) number of steps c) number of allocations d) range per row e) opportunity cost
LO 15.4 The most popular priority rules include: a) FCFS. b) EDD. c) SPT. d) all of the above. LO 15.5 The job that should be scheduled last when using Johnson’s
rule is the job with the: a) largest total processing time on both machines. b) smallest total processing time on both machines. c) longest activity time if it lies with the first machine. d) longest activity time if it lies with the second machine. e) shortest activity time if it lies with the second machine. LO 15.6 What is computerized short-term scheduling that overcomes
the disadvantage of rule-based systems by providing the user with graphical interactive computing?
a) LPT b) FCS c) CSS d) FCFS e) GIC LO 15.7 Cyclical scheduling is used to schedule: a) jobs. b) machines. c) shipments. d) employees.
Answers: LO 15.1. b; LO 15.2. a; LO 15.3. e; LO 15.4. d; LO 15.5. e; LO 15.6. b; LO 15.7. d.
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C H A P T E R O U T L I N E
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Lean Operations 638
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Lean and Just-in-Time 640 ◆
Lean and the Toyota Production System 649
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Lean Organizations 650
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Lean in Services 652
GLOBAL COMPANY PROFILE: Toyota Motor Corporation
C H
A P
T E
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Lean Operations
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
•• Inventory Management
jj Independent Demand ( Ch. 12 )
jj Dependent Demand ( Ch. 14 )
jj Lean Operations ( Ch. 16 )
• • Scheduling
• • Maintenance
A la
sk a A
ir lin
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Reception entrance
Land available for Toyota expansion
Large supplier sites for future expansion.
Main assembly complex Tundras are built here.
Toyota Logistics Services coordinates the shipment of finished Tundras by truck or rail.
Supplier buildings surround main assembly complex.
Completed trucks exit here
Railway lines bring in engines from a Toyota plant in Alabama, axles from a supplier in Arkansas, and ship out finished trucks.
Tundras go from main assembly complex to test track or to staging area where they are shipped by truck or rail.
Metalsa Truck frames
Kautex Fuel tanks
Tenneco Automotive Exhaust systems
Curtis-Maruyasu America Inc. Tubing
Millenium Steel Service Texas LLC Steel processing
Green Metals Inc. Scrap steel recycling
Avanzar Interior Technologies Seats and interior parts
Toyotetsu Texas Stamped parts
Futaba Industrial Texas Corp. Stamped parts
14 Suppliers outside the main plant
Outside: Toyota has a 2,000-acre site with 14 of the 21 onsite suppliers, adjacent rail lines, and nearby interstate highway. The site provides expansion space for both Toyota and for its suppliers — and provides an environment for just-in-time.
Reyes-Amtex Interior parts
Toyoda-Gosei Texas LLC Interior/exterior parts
Vutex Inc. Assembly services
Takumi Stamping Texas Inc. Stamped parts
MetoKote E-coater
Achieving Competitive Advantage with Lean Operations at Toyota Motor Corporation
GLOBAL COMPANY PROFILE Toyota Motor Corporation
C H A P T E R 1 6
636
T oyota Motor Corporation, with $250 billion in annual
sales of over 9 million cars and trucks, is one of the
largest vehicle manufacturers in the world. Two Lean
techniques, just-in-time (JIT) and the Toyota Production
System (TPS), have been instrumental in its growth. Toyota,
with a wide range of vehicles, competes head-to-head with
successful, long-established companies in Europe and the
U.S. Taiichi Ohno, a former vice president of Toyota, created
the basic framework for two of the world’s most discussed
systems for improving productivity, JIT and TPS. These
two concepts provide much of the foundation for Lean
operations:
◆ Central to JIT is a philosophy of continuous problem solv-
ing. In practice, JIT means making only what is needed,
when it is needed. JIT provides an excellent vehicle for
finding and eliminating problems because problems are
easy to find in a system that eliminates the slack that in-
ventory generates. When excess inventory is eliminated,
shortcomings related to quality, layout, scheduling, and
supplier performance become immediately evident—as
does excess production.
◆ Central to TPS is employee learning and a continu-
ing effort to create and produce products under ideal
conditions. Ideal conditions exist only when management
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637
brings facilities, machines, and people together to add
value without waste. Waste undermines productivity by
diverting resources to excess inventory, unnecessary
processing, and poor quality. Respect for people, exten-
sive training, cross-training, and standard work practices
of empowered employees focusing on driving out waste
are fundamental to TPS.
Toyota’s implementation of TPS and JIT is present at its
2,000-acre San Antonio, Texas, facility, the largest Toyota
land site for an automobile assembly plant in the U.S.
Interestingly, despite its large site and annual production
capability of 200,000, a throughput time of 20 ½ hours, and
the output of a truck every 63 seconds, the building itself
is one of the smallest in the industry. Modern automobiles
have 30,000 parts, but at Toyota, independent suppliers
combine many of these parts into subassemblies. Twenty-
one of these suppliers are on site at the San Antonio facility
and transfer components to the assembly line on a JIT
basis.
Operations such as these taking place in the San Antonio
plant are why Toyota continues to perform near the top in
quality and maintain the lowest labor-hour assembly time in
the industry. Lean operations do work—and they provide a
competitive advantage for Toyota Motor Corporation.
122
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Level Schedules Models mixed on production lines to meet customer orders.
JIT Parts and supplies delivered just as needed in the quantity needed.
Standard Work Practices Rigorous, agreed upon, documented procedures for production.
Andon Problem display board that communicates abnormalities.
Minimal Machines Proprietary machines designed for specific Toyota applications.
Pull System Units produced only when more production is needed.
Jidoka Monitoring performance, making judgements, and even stopping the line as necessary.
Assembly Components Placed in cab for easy access rather than on shelves adjacent to the assembly line.
Respect for People Employees treated as knowledge workers.
Empowered Employees Can stop production, ideas solicited, quality circles, etc.
Kaizen Area An area where suggestions are tested and evaluated.
Kanban Signal that indicates production of small batches of components.
Toyota’s San Antonio plant has about 2 million interior sq. ft., providing facilities within the final assembly building for 7 of the 21 onsite suppliers, and capacity to build 200,000 pick-up trucks annually. But most importantly, Toyota practices the world-class Toyota Production System and expects its suppliers to do the same thing, wherever they are.
Seven suppliers inside the main plant
AGC Automotive Americas Glass assemblies
ARK Inc. Industrial waste management, recycling
HERO Assemblers LLP Assembly of tire onto wheel
HERO Logistics LLP Logistics
PPG Industries Inc. Glass assemblies
Reyes Automotive Group Interior/exterior parts
Tokai Rika Functional parts
1
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Lean Operations As shown in the Global Company Profile , the Toyota Production System (TPS) contributes to a world-class operation at Toyota Motor Corporation. In this chapter, we discuss Lean operations, including JIT and TPS, as approaches to continuous improvement that lead to world-class operations.
Lean operations supply the customer with exactly what the customer wants when the customer wants it, without waste, through continuous improvement. Lean operations are driven by workflow initiated by the “pull” of the customer’s order. Just-in-time (JIT) is an approach of continuous and forced problem solving via a focus on throughput and reduced inventory. The Toyota Production System (TPS) , with its emphasis on continuous improvement, respect for people, and standard work practices, is particularly suited for assembly lines.
In this chapter we use the term Lean operations to encompass all the related approaches and techniques of both JIT and TPS. When implemented as a comprehensive operations strategy, Lean sustains competitive advantage and results in increased overall returns to stakeholders.
Regardless of the approach and label, operations managers address three issues that are fundamental to operations improvement: eliminate waste, remove variability, and improve throughput. We now introduce these three issues and then discuss the major attributes of Lean operations. Finally, we look at Lean applied to services.
Eliminate Waste Lean producers set their sights on perfection: no bad parts, no inventory, only value-added activities, and no waste. Any activity that does not add value in the eyes of the customer is a waste. The customer defines product value. If the customer does not want to pay for it, it is a waste. Taiichi Ohno, noted for his work on the Toyota Production System, identified seven categories of waste. These categories have become popular in Lean organizations and cover many of the ways organizations waste or lose money. Ohno’s seven wastes are:
◆ Overproduction: Producing more than the customer orders or producing early (before it is demanded) is waste.
◆ Queues: Idle time, storage, and waiting are wastes (they add no value). ◆ Transportation: Moving material between plants or between work centers and handling it
more than once is waste. ◆ Inventory: Unnecessary raw material, work-in-process (WIP), finished goods, and excess
operating supplies add no value and are wastes. ◆ Motion: Movement of equipment or people that adds no value is waste. ◆ Overprocessing: Work performed on the product that adds no value is waste. ◆ Defective product: Returns, warranty claims, rework, and scrap are wastes.
A broader perspective—one that goes beyond immediate production—suggests that other resources, such as energy, water, and air, are often wasted but should not be. Efficient, sustain- able production minimizes inputs and maximizes outputs, wasting nothing.
L E A R N I N G OBJEC TI V ES
LO 16.1 Defi ne Lean operations 638
LO 16.2 Defi ne the seven wastes and the 5Ss 638
LO 16.3 Identify the concerns of suppliers when moving to supplier partnerships 642
LO 16.4 Determine optimal setup time 645
LO 16.5 Defi ne kanban 647
LO 16.6 Compute the required number of kanbans 648
LO 16.7 Identify six attributes of Lean organizations 651
LO 16.8 Explain how Lean applies to services 652
LO 16.1 Define Lean operations
Lean operations
Eliminates waste through continu-
ous improvement and focus on
exactly what the customer wants.
Just-in-time (JIT)
Continuous and forced problem
solving via a focus on throughput
and reduced inventory.
Toyota Production System (TPS)
Focus on continuous improve-
ment, respect for people, and
standard work practices.
Seven wastes
Overproduction
Queues
Transportation
Inventory
Motion
Overprocessing
Defective product
LO 16.2 Define the seven wastes and the 5Ss
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C H A P T E R 1 6 | L E A N O P E R AT I O N S 639
For over a century, managers have pursued “housekeeping” for a neat, orderly, and efficient workplace and as a means of reducing waste. Op- erations managers have embellished “housekeeping” to include a checklist—now known as the 5Ss. 1 The Japanese developed the initial 5Ss. Not only are the 5Ss a good checklist for Lean operations, but they also provide an easy vehicle with which to assist the culture change that is often necessary to bring about Lean operations. The 5Ss follow: ◆ Sort/segregate: Keep what is needed and remove
everything else from the work area; when in doubt, throw it out. Identify nonvalue items and remove them. Getting rid of these items makes space available and usually improves workflow.
◆ Simplify/straighten: Arrange and use methods analysis tools (see Chapter 7 and Chapter 10 ) to improve workflow and reduce wasted motion. Consider long-run and short-run ergonomic issues. Label and display for easy use only what is needed in the immediate work area. (For examples of visual displays, see Chapter 10 , Figure 10.8 and the adjacent photo of equip- ment located within prescribed lines on the tarmac at Seattle’s airport.)
◆ Shine/sweep: Clean daily; eliminate all forms of dirt, contamination, and clutter from the work area.
◆ Standardize: Remove variations from the process by developing standard operating procedures and checklists; good standards make the abnormal obvious. Standardize equipment and tooling so that cross-training time and cost are reduced. Train and retrain the work team so that when deviations occur, they are readily apparent to all.
◆ Sustain/self-discipline: Review periodically to recognize efforts and to motivate to sustain progress. Use visuals wherever possible to communicate and sustain progress.
U.S. managers often add two additional Ss that contribute to establishing and maintaining a Lean workplace:
◆ Safety: Build good safety practices into the preceding five activities. ◆ Support/maintenance: Reduce variability, unplanned downtime, and costs. Integrate daily
shine tasks with preventive maintenance. The Ss support continuous improvement and provide a vehicle with which employees can identify. Operations managers need think only of the examples set by a well-run hospital emergency room or the spit-and-polish of a fire department for a benchmark. Offices and retail stores, as well as manufacturers, have successfully used the 5Ss in their respective efforts to eliminate waste and move to Lean operations. A place for everything and everything in its place does make a difference in a well-run office. And retail stores successfully use the Ss to reduce misplaced merchandise and improve customer service. An orderly workplace reduces waste, releasing assets for other, more productive, purposes.
Remove Variability Managers seek to remove variability caused by both internal and external factors. Variability is any deviation from the optimum process that delivers a perfect product on time, every time. Variability is a polite word for problems. The less variability in a system, the less waste in the system. Most variability is caused by tolerating waste or by poor management. Among the many sources of variability are:
◆ Poor processes that allow employees and suppliers to produce improper quantities or non- conforming units
◆ Inadequate maintenance of facilities and processes ◆ Unknown and changing customer demands ◆ Incomplete or inaccurate drawings, specifications, and bills of material
5Ss
A Lean production checklist:
Sort
Simplify
Shine
Standardize
Sustain
In keeping with 5S, airports, like many other facilities, specify with painted guidelines exactly
where tools and equipment such as this fuel pump are to be positioned.
Variability
Any deviation from the optimum
process that delivers a perfect
product on time, every time.
A la
sk a A
ir lin
e s
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Inventory reduction via JIT is an effective tool for identifying causes of variability. The precise timing of JIT makes variability evident, just as reducing inventory exposes variability. Defeating variability allows managers to move good materials on schedule, add value at each step of the process, drive down costs, and ultimately win orders.
Improve Throughput Throughput is the rate at which units move through a process. Each minute that products remain on the books, costs accumulate, and competitive advantage is lost. Time is money. The time that an order is in the shop is called manufacturing cycle time . This is the time between the arrival of raw materials and the shipping of finished product. For example, phone-system manufac- turer Nortel had materials pulled directly from qualified suppliers to the assembly line. This effort reduced a segment of the manufacturing cycle time from 3 weeks to just 4 hours, the incoming inspection staff from 47 to 24, and problems on the shop floor caused by defective materials by 97%. Driving down manufacturing cycle time can make a major improvement in throughput.
A technique for increasing throughput is a pull system. A pull system pulls a unit to where it is needed just as it is needed. Pull systems are a standard tool of Lean. Pull systems use signals to request production and delivery from supplying stations to stations that have production capacity available. The pull concept is used both within the immediate production process and with suppliers. By pulling material through the system in very small lots—just as it is needed— waste and inventory are removed. As inventory is removed, clutter is reduced, problems become evident, and continuous improvement is emphasized. Removing the cushion of inventory also reduces both investment in inventory and manufacturing cycle time. A push system dumps orders on the next downstream workstation, regardless of timeliness and resource availability. Push systems are the antithesis of Lean. Pulling material through a production process as it is needed rather than in a “push” mode typically lowers cost and improves schedule performance, enhancing customer satisfaction.
Lean and Just-in-Time Just-in-time (JIT), with its focus on rapid through- put and reduced inventory, is a powerful compo- nent of Lean. With the inclusion of JIT in Lean, materials arrive where they are needed only when they are needed. When good units do not arrive just as needed, a “problem” has been identified. This is the reason this aspect of Lean is so power- ful—it focuses attention on problems . By driving out waste and delay, JIT reduces inventory, cuts variability and waste, and improves throughput. Every moment material is held, an activity that adds value should be occurring. Consequently, as Figure 16.1 suggests, JIT often yields a competi- tive advantage.
A well-executed Lean program requires a mean- ingful buyer–supplier partnership.
Supplier Partnerships Supplier partnerships exist when a supplier and a pur- chaser work together with open communication and a goal of removing waste and driving down costs. Trust and close collaboration are critical to
Throughput
The rate at which units move
through a process.
Manufacturing cycle time
The time between the arrival of
raw materials and the shipping of
finished products.
Pull system
A concept that results in mate-
rial being produced only when
requested and moved to where it
is needed just as it is needed.
STUDENT TIP JIT places added demands on
performance, but that is why it
pays off.
Supplier partnerships
Partnerships of suppliers and
purchasers that remove waste
and drive down costs for mutual
benefits.
Many services have adopted Lean techniques as a
normal part of their business. Restaurants like Olive
Garden expect and receive JIT deliveries. Both buyer
and supplier expect fresh, high-quality produce
delivered without fail just when it is needed. The system
doesn’t work any other way.
C u lin
a ry
I n st
it u te
o f
A m
e ri ca
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the success of Lean. Figure 16.2 shows the characteristics of supplier partnerships. Some spe- cific goals are: ◆ Removal of unnecessary activities , such as receiving, incoming inspection, and paperwork
related to bidding, invoicing, and payment. ◆ Removal of in-plant inventory by delivery in small lots directly to the using department as
needed.
Work cells; group technology; flexible machinery; organized workplace; reduced space for inventory
Few vendors; supportive supplier relationships; quality deliveries on time, directly to work areas
Small lot sizes; low setup time; specialized parts bins Zero deviation from schedules; level schedules; suppliers informed of schedules; kanban techniques
Scheduled; daily routine; operator involvement
Statistical process control; quality suppliers; quality within the firm
Empowered and cross-trained employees; training support; few job classifications to ensure flexibility of employees
Support of management, employees, and suppliers
Layout:
Suppliers:
JIT TECHNIQUES:
Inventory:
Scheduling:
Preventive maintenance:
Quality production:
Employee empowerment:
Commitment:
WHICH RESULTS IN:
WHICH WINS ORDERS BY:
Faster response to the customer at lower cost and higher quality—
A Competitive Advantage
Rapid throughput frees assets
Quality improvement reduces waste
Cost reduction adds pricing flexibility
Variability reduction
Rework reduction
Figure 16.1
Lean Contributes to
Competitive Advantage
Suppliers Locate near buyer Extend JIT techniques to their suppliers Include packaging and routing details Detail ID and routing labels Focus on core competencies
Quantities Produce small lots Deliver with little overage and underage Meet mutually developed quality requirements Produce with zero defects
Shipping Seek joint scheduling and shipping efficiencies Consider third-party logistics Use advance shipping notice (ASN) Ship frequent small orders
Buyers Share customer preferences and demand forecasts Minimize product specifications and encourage innovation Support supplier innovation and price competitiveness Develop long-term relationships Focus on core competencies Process orders with minimal paperwork
(Mutual Understanding
and Trust)
Collaboration
Figure 16.2
Characteristics of Supplier Partnerships
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◆ Removal of in-transit inventory by encouraging suppliers to locate nearby and provide frequent small shipments. The shorter the flow of material in the resource pipeline, the less inventory. Inventory can also be reduced through a technique known as consignment . Consignment inventory (see the OM in Action box, “Lean Production at Cessna Aircraft Company”), a variation of vendor-managed inventory ( Chapter 11 ) , means the supplier maintains the title to the inventory until it is used.
◆ Obtain improved quality and reliability through long-term commitments, communication, and cooperation.
Leading organizations view suppliers as extensions of their own organizations and expect suppliers to be fully committed to constant improvement. However, supplier concerns can be significant and must be addressed. These concerns include:
1. Diversification: Suppliers may not want to tie themselves to long-term contracts with one customer. The suppliers’ perception is that they reduce their risk if they have a variety of customers.
2. Scheduling: Many suppliers have little faith in the purchaser’s ability to produce orders to a smooth, coordinated schedule.
3. Lead time: Engineering or specification changes can play havoc with JIT because of inadequate lead time for suppliers to implement the necessary changes.
4. Quality: Suppliers’ capital budgets, processes, or technology may limit ability to respond to changes in product and quality.
5. Lot sizes: Suppliers may see frequent delivery in small lots as a way to transfer buyers’ holding costs to suppliers.
As the foregoing concerns suggest, good supplier partnerships require a high degree of trust and respect by both supplier and purchaser—in a word, collaboration. Many firms es- tablish this trust and collaborate very successfully. Two such firms are McKesson-General and Baxter International, who provide surgical supplies for hospitals on a JIT basis. They deliver prepackaged surgical supplies based on hospital operating schedules. Moreover, the surgical packages themselves are prepared so supplies are available in the sequence in which they will be used during surgery.
Lean Layout Lean layouts reduce another kind of waste—movement. The movement of material on a factory floor (or paper in an office) does not add value. Consequently, managers want flexible layouts that reduce the movement of both people and material. Lean layouts place material directly in the location where needed. For instance, an assembly line should be designed with delivery points next to the line so material need not be delivered first to a receiving department
Consignment inventory
An arrangement in which the
supplier maintains title to the
inventory until it is used.
OM in Action Lean Production at Cessna Aircraft Company When Cessna Aircraft opened its new plant in Independence, Kansas, it saw
the opportunity to switch from craftwork to a Lean manufacturing system. The
initial idea was to focus on three Lean concepts: (1) vendor-managed inven-
tory, (2) cross-training of employees, and (3) using technology and manufac-
turing cells to move away from batch processing.
After several years, with these goals accomplished, Cessna began working
on the next phase of Lean. This phase focuses on Team Build and Area Team
Development.
Team Build at Cessna empowers employees to expand their skills,
sequence their own work, and then sign off on it. This reduces wait time,
inventory, part shortages, rework, and scrap, all contributing to improved
productivity.
Area Team Development (ATD) provides experts when a factory em-
ployee cannot complete his or her standard work in the time planned. Team Sources: Interviews with Cessna executives, 2013.
members trained in the ATD
process are called Skill Coaches.
Skill Coaches provide support
throughout each area to improve
response time to problems.
Andon boards and performance
metrics are used for evaluating
daily performance.
These commitments to
Lean manufacturing are a major
contributor to Cessna being the world’s largest manufacturer of single-
engine aircraft.
C e ss
n a A
ir cr
a ft
C o m
p a n y
LO 16.3 Identify the concerns of suppliers
when moving to supplier
partnerships
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and then moved again. Toyota has gone one step further and places components in the chassis of each vehicle moving down the assembly line. This is not only convenient, but it also allows Toyota to save space and opens areas adjacent to the assembly line previously occupied by shelves. When a layout reduces distance, firms often save labor and space and may have the added bonus of eliminating potential areas for accumulation of unwanted inventory. Table 16.1 provides a list of Lean layout tactics.
Distance Reduction Reducing distance is a major contribution of work cells, work centers, and focused factories (see Chapter 9 ) . The days of long production lines and huge economic lots, with goods passing through monumental, single-operation machines, are gone. Now firms use work cells, often arranged in a U shape, containing several machines performing different operations. These work cells are often based on group technology codes (as discussed in Chapter 5 ) . Group technology codes help identify components with similar characteristics so they can be grouped into families. Once families are identified, work cells are built for them. The result can be thought of as a small product-oriented facility where the “product” is actually a group of similar products—a family of products. The cells produce one good unit at a time, and ideally, they produce the units only after a customer orders them.
Increased Flexibility Modern work areas are designed so they can be easily rearranged to adapt to changes in volume and product changes. Almost nothing is bolted down. This concept of layout flexibility applies to both factory and office environments. Not only is furniture and equipment movable, but so are walls, computer connections, and telecommu- nications. Equipment is modular. Layout flexibility aids the changes that result from prod- uct and process improvements that are inevitable at a firm with a philosophy of continuous improvement.
Impact on Employees When layouts provide for sequential operations, feedback, including quality issues, can be immediate, allowing employees working together to tell each other about problems and opportunities for improvement. When workers produce units one at a time, they test each product or component at each subsequent production stage. Work processes with self-testing poka-yoke functions detect defects automatically. Before Lean, defective products were replaced from inventory. Because surplus inventory is not kept in Lean facilities, there are no such buffers. Employees learn that getting it right the first time is critical. Indeed, Lean layouts allow cross-trained employees to bring flexibility and efficiency to the work area, reducing defects. Defects are waste.
Reduced Space and Inventory Because Lean layouts reduce travel distance, they also reduce inventory. When there is little space, inventory travels less and must be moved in very small lots or even single units. Units are always moving because there is no storage. For instance, each month a Bank of America focused facility sorts 7 million checks, processes 5 mil- lion statements, and mails 190,000 customer statements. With a Lean layout, mail- processing time has been reduced by 33%, annual salary costs by tens of thousands of dollars, floor space by 50%, and in-process waiting lines by 75% to 90%. Storage, including shelves and drawers, has been removed.
Lean Inventory Inventories in production and distribution systems often exist “just in case” something goes wrong. That is, they are used just in case some variation from the production plan occurs. The “extra” inventory is then used to cover variations or problems. Lean inventory tactics require “just in time,” not “just in case.” Lean inventory is the minimum inventory necessary to keep a perfect system running. With Lean inventory, the exact amount of goods arrives at the moment it is needed, not a minute before or a minute after. Some useful Lean inventory tactics are shown in Table 16.2 and discussed in more detail in the following sections.
Reduce Inventory and Variability Operations managers move toward Lean by first reducing inventory. The idea is to eliminate variability in the production system hidden by inventory. Reducing inventory uncovers the “rocks” in Figure 16.3 (a) that represent the variability and problems currently being tolerated. With reduced inventory, management chips away at the exposed problems. After the lake is lowered, managers make additional cuts
TABLE 16.1
LEAN LAYOUT TACTICS
Build work cells for families of products
Include a large number of operations in a small area
Minimize distance
Design little space for inventory
Improve employee communication
Use poka-yoke devices
Build fl exible or movable equipment
Cross-train workers to add fl exibility
STUDENT TIP Accountants book inventory as an
asset, but operations managers
know it is a cost.
Lean inventory
The minimum inventory necessary
to keep a perfect system running.
TABLE 16.2
LEAN INVENTORY TACTICS
Use a pull system to move inventory
Reduce lot size
Develop just-in-time delivery systems with suppliers
Deliver directly to the point of use
Perform to schedule
Reduce setup time
Use group technology
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in inventory and continue to chip away at the next level of exposed problems [see Figure 16.3 (b, c)]. Ultimately, there will be little inventory and few problems (variability).
Firms with technology-sensitive products estimate that the rapid product innovations can cost as much as 12 % to 2% of the values of inventory each week . Shigeo Shingo, codeveloper of the Toyota JIT system, says, “Inventory is evil.” He is not far from the truth. If inventory itself is not evil, it hides evil at great cost.
Reduce Lot Sizes Lean also reduces waste by cutting the investment in inventory. A key to slashing inventory is to produce good product in small lot sizes. Reducing the size of batches can be a major help in reducing inventory and inventory costs. As we saw in Chapter 12 , when inventory usage is constant, the average inventory level is the sum of the maximum inven- tory plus the minimum inventory divided by 2. Figure 16.4 shows that lowering the order size increases the number of orders, but drops inventory levels.
Ideally, in a Lean environment, order size is one and single units are being pulled from one adjacent process to another. More realistically, analysis of the process, transportation time, and physical attributes such as size of containers used for transport are considered when determining lot size. Such analysis typically results in a small lot size, but a lot size larger than one. Once a lot size has been determined, the EOQ production order quantity model can be modified to determine the desired setup time. We saw in Chapter 12 that the production order quantity model takes the form:
Q*p = A
2DS H[1 - (d>p)]
(16-1)
where D 5 Annual demand S 5 Setup cost H 5 Holding cost
Inventory level
Scrap
Setup time
Late deliveries
Quality problems
Process downtime
(a)
Inventory level
(c)
Inventory level
Scrap
Setup time
Late deliveries
Quality problems
Process downtime
(b)
No scrap
Setup time reduced
No late deliveries
Quality problems removed Process
downtime removed
Figure 16.3
High levels of inventory hide problems (a), but as we reduce inventory, problems are exposed (b), and finally after
reducing inventory and removing problems, we have lower inventory, lower costs, and smooth sailing (c).
200
100
In ve
n to
ry
Time
Q1 When average order size = 200 average inventory is 100
Q2 When average order size = 100 average inventory is 50
Figure 16.4
Frequent Orders Reduce
Average Inventory
A lower order size increases the
number of orders and total ordering
cost but reduces average inventory
and total holding cost.
Inventory
“Inventory is evil.”
S. Shingo
B o b D
a e m
m ri ch
/C O
R B
IS -N
Y
d 5 Daily demand p 5 Daily production
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Only two changes need to be made for small-lot material flow to work. First, material handling and work flow need to be improved. With short production cycles, there can be very little wait time. Improving material handling is usually easy and straightforward. The second change is more challenging, and that is a radical reduction in setup times. We discuss setup reduction next.
Reduce Setup Costs Both the quantity of inventory and the cost of holding it go down as the inventory-reorder quantity and the maximum inventory level drop. However, because inventory requires incurring an ordering or setup cost that is applied to the units produced, managers tend to purchase (or produce) large orders; the larger the order the less the cost to be absorbed by each unit. Consequently, the way to drive down lot sizes and reduce inventory cost is to reduce setup cost, which in turn lowers the optimum order size.
The effect of reduced setup costs on total cost and lot size is shown in Figure 16.5 . Moreover, smaller lot sizes hide fewer problems. In many environments, setup cost is highly correlated with setup time. In a manufacturing facility, setups usually require a substantial amount of preparation. Much of the preparation required by a setup can be done prior to shutting down
Example 1 DETERMINING OPTIMAL SETUP TIME Crate Furniture, Inc., a firm that produces rustic furniture, desires to move toward a reduced lot size. Crate Furniture’s production analyst, Aleda Roth, determined that a 2-hour production cycle would be acceptable between two departments. Further, she concluded that a setup time that would accommodate the 2-hour cycle time should be achieved.
APPROACH c Roth developed the following data and procedure to determine optimum setup time analytically:
D 5 Annual demand 5 400,000 units d 5 Daily demand 5 400,000 per 250 days 5 1,600 units per day p 5 Daily production rate 5 4,000 units per day Qp 5 EOQ desired 5 400 (which is the 2-hour demand; that is, 1,600 per day per four
2-hour periods) H 5 Holding cost 5 $20 per unit per year S 5 Setup cost (to be determined)
Hourly labor rate 5 $30.00
SOLUTION c Roth determines that the cost and related time per setup should be:
Qp = A
2DS H(1 - d >p)
Qp 2 =
2DS H(1 - d >p)
S = (Qp
2)(H)(1 - d >p) 2D
= (400)2(20)(1 - 1,600 > 4,000)
2(400,000) =
(3,200,000)(0.6) 800,000
= $2.40
Setup time = $2.40>(hourly labor rate) = $2.40 > ($30 per hour) = 0.08 hour, or 4.8 minutes
(16-2)
INSIGHT c Now, rather than produce components in large lots, Crate Furniture can produce in a 2-hour cycle with the advantage of an inventory turnover of four per day .
LEARNING EXERCISE c If labor cost goes to $40 per hour, what should be the setup time? [Answer: 0.06 hours, or 3.6 minutes.]
RELATED PROBLEMS c 16.1, 16.2, 16.3
LO 16.4 Determine optimal setup time
STUDENT TIP Reduced lot sizes must be
accompanied by reduced setup
times.
Example 1 shows how to determine the desired setup time.
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the machine or process. Setup times can be reduced substantially, as shown in Figure 16.6 . For example in one Kodak plant in Mexico, the setup time to change a bearing was reduced from 12 hours to 6 minutes! This is the kind of progress that is typical of world-class manufacturers.
Just as setup costs can be reduced at a machine in a factory, setup time can also be reduced during the process of getting the order ready in the office. Driving down factory setup time from hours to minutes does little good if orders are going to take weeks to process or “set up” in the office. This is exactly what happens in organizations that forget that Lean concepts have applications in offices as well as in the factory. Reducing setup time (and cost) is an excellent way to reduce inventory investment, improve productivity, and speed throughput.
Lean Scheduling Effective schedules, communicated to those within the organization as well as to outside suppliers, support Lean. Better scheduling also improves the ability to meet customer orders, drives down inventory by allowing smaller lot sizes, and reduces work-in- process. For instance, many com-
panies, such as Ford, now tie suppliers to their final assembly schedule. Ford communicates its schedules to bumper manufacturer Polycon Industries from the Ford production control system. The scheduling system describes the style and color of the bumper needed for each vehicle moving down the final assembly line. The scheduling system transmits the information to portable terminals carried by Polycon ware- house personnel, who load the bumpers onto conveyors leading to the loading dock. The bumpers are then trucked 50 miles to the Ford plant. Total time is 4 hours. However, as we saw in our open- ing Global Company Profile , Toyota has moved its bumper supplier inside the new Tundra plant; techniques such as this drive down delivery time even further.
Table 16.3 suggests several items that can contribute to achieving these goals, but two techniques (in addition to communicating schedules) are par- amount. They are level schedules and kanban .
C o st
Holding cost
T2
S2
T1
S1
Sum of ordering and holding cost
Setup cost curves (S1, S2)
Lot size
Figure 16.5
Lower Setup Costs Will Lower
Total Cost
More frequent orders require
reducing setup costs; otherwise,
inventory costs will rise. As the
setup costs are lowered (from S 1
to S 2 ), total inventory costs also
fall (from T 1 to T
2 ).
90 min
60 min
40 min
25 min
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
15 min
13 min
Train operators and standardize work procedures (save 2 minutes)
Repeat cycle until subminute setup is achieved
Use one-touch system to eliminate adjustments (save 10 minutes)
Separate setup into preparation and actual setup, doing as much as possible while the
machine/process is operating (save 30 minutes)
Initial Setup Time
Move material closer and improve material handling
(save 20 minutes)
Standardize and improve tooling
(save 15 minutes)
Figure 16.6
Steps for Reducing Setup Times
Reduced setup times are a major component of Lean.
STUDENT TIP Effective scheduling is required
for effective use of capital and
personnel.
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Level Schedules Level schedules process frequent small batches rather than a few large batches. Figure 16.7 contrasts a traditional large-lot approach using large batches with a level schedule using many small batches. The operations manager’s task is to make and move small lots so the level schedule is economical. This requires success with the issues discussed in this chapter that allow small lots. As lots get smaller, the constraints may change and become increasingly challenging. At some point, processing a unit or two may not be feasible. The constraint may be the way units are sold and shipped (four to a carton), or an expensive paint changeover (on an automobile assembly line), or the proper number of units in a sterilizer (for a food-canning line).
The scheduler may find that freezing , that is holding a portion of the schedule near due dates constant, allows the production system to function and the schedule to be met. Operations managers expect the schedule to be achieved with no deviations.
Kanban One way to achieve small lot sizes is to move inventory through the shop only as needed rather than pushing it on to the next workstation whether or not the personnel there are ready for it. As noted earlier, when inventory is moved only as needed, it is referred to as a pull system, and the ideal lot size is one. The Japanese call this system kanban . Kanbans allow arrivals at a work center to match (or nearly match) the processing time.
Kanban is a Japanese word for card . In their effort to reduce inventory, the Japanese use systems that “pull” inventory through work centers. They often use a “card” to signal the need for another container of material—hence the name kanban. The card is the authorization for the next container of material to be produced. Typically, a kanban signal exists for each con- tainer of items to be obtained. An order for the container is then initiated by each kanban and “pulled” from the producing department or supplier. A sequence of kanbans “pulls” the mate- rial through the plant.
The system has been modified in many facilities so that even though it is called a kanban , the card itself does not exist. In some cases, an empty position on the floor is sufficient indica- tion that the next container is needed. In other cases, some sort of signal, such as a flag or rag ( Figure 16.8 ), alerts that it is time for the next container.
When there is visual contact between producer and user, the process works like this:
1. The user removes a standard-size container of parts from a small storage area, as shown in Figure 16.8 .
2. The signal at the storage area is seen by the producing department as authorization to replenish the using department or storage area. Because there is an optimum lot size, the producing department may make several containers at a time.
A kanban system is similar to the resupply that occurs in your neighborhood supermarket: the customer buys; the stock clerk observes the shelf or receives notice from the end-of-day sales list and restocks. When the store’s limited supply is depleted, a “pull” signal is sent to the warehouse, distributor, or manufacturer for resupply, usually that night. The complicating factor in a manufacturing firm is the time needed for actual manufacturing (production) to take place.
TABLE 16.3
LEAN SCHEDULING TACTICS
Make level schedules Use kanbans Communicate schedules to suppliers Freeze part of the schedule Perform to schedule Seek one-piece-make and one-piece-move Eliminate waste Produce in small lots Each operation produces a perfect part
Level schedules
Scheduling products so that
each day’s production meets the
demand for that day.
AA BBB C AA BBB C AA BBB C AA BBB C AA BBB C AA BBB C AA BBB C AA BBB C
AAAAAA BBBBBBBBB CCC AAAAAA BBBBBBBBB CCC AAAAAA BBBBBBBBB CCC
JIT Level Material-Use Approach
Large-Lot Approach
Time
Figure 16.7
Scheduling Small Lots of Parts A, B, and C
Increases Flexibility to Meet Customer Demand and
Reduces Inventory
The Lean approach to scheduling, described as heijunka
by the Japanese, produces just as many of each model
per time period as the large-lot approach, provided setup
times are lowered.
Kanban
The Japanese word for card ,
which has come to mean “signal”;
a kanban system moves parts
through production via a “pull”
from a signal.
LO 16.5 Define kanban
X201
Y302
Z405
Z405
Y302
X201
Signal marker hanging on post for part Z405 shows that production should start for that part. The post is located so that workers in normal locations can easily see it.
Signal marker on stack of boxes.
Part numbers mark location of specific part.
Figure 16.8
Diagram of Storage Area with Warning-Signal Marker
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A kanban need not be as formal as signal lights or empty carts. The
cook in a fast-food restaurant knows that when six cars are in line,
eight meat patties and six orders of french fries should be cooking. D
o n n a S
h a d e r
Example 2 DETERMINING THE NUMBER OF KANBAN CONTAINERS Hobbs Bakery produces short runs of cakes that are shipped to grocery stores. The owner, Ken Hobbs, wants to try to reduce inventory by changing to a kanban system. He has developed the following data and asked you to finish the project.
Production lead time = Wait time + Material handling time + Processing time = 2 days
Daily demand = 500 cakes
Safety stock = 12 day Container size (determined on a production order size EOQ basis) = 250 cakes
APPROACH c Having determined that the EOQ size is 250, we then determine the number of kan- bans (containers) needed.
SOLUTION c Demand during lead time = Lead time * Daily demand = 2 days * 500 cakes = 1,000
Safety stock = 12 * Daily demand = 250
Number of kanbans (containers) needed =
Demand during lead time + Safety stock
Container size =
1,000 + 250 250
= 5
INSIGHT c Once the reorder point is hit, five containers should be released. LEARNING EXERCISE c If lead time drops to 1 day, how many containers are needed? [Answer: 3.] RELATED PROBLEMS c 16.4, 16.5, 16.6, 16.7, 16.8, 16.9, 16.10 (16.11, 16.12 are available in MyOMLab)
LO 16.6 Compute the required number of
kanbans
Several additional points regarding kanbans may be helpful:
◆ When the producer and user are not in visual contact, a card can be used; otherwise, a light, flag, or empty spot on the floor may be adequate.
◆ Usually each card controls a specific quantity of parts, although multiple card systems are used if the work cell produces several components or if the lot size is different from the move size.
◆ The kanban cards provide a direct control (limit) on the amount of work-in-process between cells.
Determining the Number of Kanban Cards or
Containers The number of kanban cards, or containers, sets the amount of authorized inventory. To determine the number of containers moving back and forth between the using area and the pro- ducing areas, management first sets the size of each container. This is done by computing the lot size, using a model such as the production order quantity model [ discussed in Chapter 1 2 and shown again on
page 644 in Equation (16-1) ]. Setting the number of containers involves knowing: (1) lead time needed to produce a container of parts and (2) the amount of safety stock needed to account for variability or uncertainty in the system. The number of kanban cards is computed as follows:
Number of kanbans (containers) = Demand during lead time + Safety stock
Size of container (16-3)
Example 2 illustrates how to calculate the number of kanbans needed.
Advantages of Kanban Containers are typically very small, usually a matter of a few hours’ worth of production. Such a system requires tight schedules, with small quantities being produced several times a day. The process must run smoothly with little variability in quality or lead time because any shortage has an almost immediate impact on the entire system. Kanban places added emphasis on meeting schedules, reducing the time and cost required by setups, and economical material handling.
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In-plant kanban systems often use standardized, reusable containers that protect the spe- cific quantities to be moved. Such containers are also desirable in the supply chain. Standard- ized containers reduce weight and disposal costs, generate less wasted space, and require less labor to pack, unpack, and prepare items.
Lean Quality There is no Lean without quality. And Lean’s “pull” production, smaller batch sizes, and low inventory all enhance quality by exposing bad quality. Savings occur because scrap, rework, inventory investment, and poor product are no longer buried in inventory. This means fewer bad units are produced. In short, whereas inventory hides bad quality, Lean exposes it.
As Lean shrinks queues and lead time, it keeps evidence of errors fresh and limits the number of potential sources of error. In effect, Lean creates an early warning system for quality problems so that fewer bad units are produced and feedback is immediate. This advantage accrues both within the firm and with goods received from outside vendors.
In addition, better quality means fewer buffers are needed, and therefore, a better, easier-to-maintain inventory system can exist. Often the purpose of keeping inventory is to protect against unreliable quality. But, when consistent quality exists, Lean firms can reduce all costs associated with inventory. Table 16.4 suggests some tactics for quality in a Lean environment.
Lean and the Toyota Production System Toyota Motor’s Eiji Toyoda and Taiichi Ohno are given credit for the Toyota Production System (TPS; see the Global Company Profile that opens this chapter). Three components of TPS are continuous improvement , respect for people , and standard work practice , which are now considered an integral part of Lean.
Continuous Improvement Continuous improvement under TPS means building an organizational culture and instilling in its people a value system stressing that processes can be improved—indeed, that improve- ment is an integral part of every employee’s job. This process is formalized in TPS by kaizen , the Japanese word for change for the good, or what is more generally known as continuous improvement. Kaizen is often implemented by a kaizen event. A kaizen event occurs when mem- bers of a work cell group or team meet to develop innovative ways to immediately implement improvements in the work area or process. In application, kaizen means making a multitude of small or incremental changes as one seeks elusive perfection. (See the OM in Action box, “Toyota’s New Challenge.”) Instilling the mantra of continuous improvement begins at per- sonnel recruiting and continues through extensive and continuing training. One of the reasons continuous improvement works at Toyota, we should note, is because of another core value at Toyota, Toyota’s respect for people.
Respect for People Toyota, like other Lean organizations, recruits, trains, and treats people as knowledge workers. Aided by aggressive cross-training and few job classifications, Lean firms engage the mental as well as physical capacities of employees in the challenging task of improving operations. Employees are empowered. They are empowered not only to make improvements, but also to stop machines and processes when quality problems exist. Indeed, empowered employees are an integral part of Lean. This means that those tasks that have traditionally been assigned to staff are moved to employees. Toyota recognizes that employees know more about their jobs than anyone else. Lean firms respect employees by giving them the opportunity to enrich both their jobs and their lives.
STUDENT TIP Good quality costs less.
TABLE 16.4
LEAN QUALITY TACTICS
Use statistical process control
Empower employees
Build fail-safe methods (poka-yoke, checklists, etc.)
Expose poor quality with small lots
Provide immediate feedback
Kaizen
A focus on continuous
improvement.
STUDENT TIP Respect for people brings the entire
person to work.
Kaizen event
Members of a work cell or team
meet to develop improvements in
the process.
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Processes and Standard Work Practice Building effective and efficient processes requires establishing what Toyota calls standard work practices. The underlying principles are:
◆ Work is completely specified as to content, sequence, timing, and outcome; this is fundamental to a good process.
◆ Supplier connections for both internal and external customers are direct, specifying personnel, methods, timing, and quantity.
◆ Material and service flows are simple and directed to a specific person or machine.
◆ Process improvements are made only after rigorous analysis at the lowest possible level in the organization.
Lean requires that activities, connections, and flows include built-in tests (or poka-yokes) to signal problems. When a problem or defect occurs, production is stopped. Japanese call the practice of stopping production because of a defect, jidoka . The dual focus on (1) education and training of employees and (2) the responsiveness of the system to problems make the seemingly rigid system flex- ible and adaptable. The result is continuous improvement.
Lean Organizations Lean organizations understand the customer and the customer’s expectations. Moreover, Lean organizations have functional areas that communicate and collaborate to verify that customer expectations are not only understood, but also met efficiently. This means iden- tifying and delivering the customer’s value expectation by implementing the tools of Lean throughout the organization.
Building a Lean Organization Building Lean organizations is difficult, requiring exceptional leadership. Such leaders imbue the organization not just with the tools of Lean, but with a culture of continuous improve- ment. Building such a culture requires open communication and destroying isolated functional
STUDENT TIP Lean drives out non-value-added
activities.
OM in Action Toyota’s New Challenge With the generally high value of the yen, making a profit on cars built
in Japan but sold in foreign markets is a challenge. As a result, Honda
and Nissan are moving plants overseas, closer to customers. But Toyota,
despite marginal profit on cars produced for export, is maintaining its
current Japanese capacity. Toyota, which led the way with JIT and the
TPS, is doubling down on its manufacturing prowess and continuous
improvement. For an organization that traditionally does things slowly and
step-by-step, the changes are radical. With its first new plant in Japan in
18 years, Toyota believes it can once again set new production bench-
marks. It is drastically reforming its production processes in a number
of ways:
◆ The assembly line has cars sitting side-by-side, rather than bumper-
to-bumper, shrinking the length of the line by 35% and requiring fewer
steps by workers.
◆ Instead of having car chassis dangling from overhead conveyors,
they are perched on raised platforms, reducing heating and cooling
costs by 40%.
◆ Retooling permits faster changeovers, allowing for shorter product runs
of components, supporting level scheduling.
◆ The assembly line uses quiet friction rollers with fewer moving parts, requir-
ing less maintenance than conventional lines and reducing worker fatigue.
These TPS innovations, efficient production with small lot sizes, rapid change-
over, level scheduling, half the workers, and half the square footage, are being
duplicated in Toyota’s new plant in Blue Springs, Mississippi.
Sources: Forbes (July 29, 2012); Automotive News (February, 2011); and The
Wall Street Journal (November 29, 2011).
Conventional
Toyota: Side-by-side
This Porsche assembly line, like most other Lean
facilities, empowers employees so they can stop
the entire production line, what the Japanese call
jidoka , if any quality problems are spotted.
B e rn
d W
e is
sb ro
d /d
p a /p
ic tu
re -a
lli a n ce
/N e w
sc o m
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disciplines that act as independent “silos.” There is no substitute for open two-way commu- nication that fosters effective and efficient processes. Such an organizational culture will have a demonstrated respect for people and a management willing to fully understand how and where the work is performed. Lean firms sometimes use the Japanese term Gemba or Gemba walk to refer to going to where the work is actually performed.
Building organizational cultures that foster ongoing improvement and that accept the con- stant change and improvement that makes improvement habitual is a challenge. However, such organizations exist. They understand the customer and drive out activities that do not add value in the eyes of the customer. They include industry leaders such as United Parcel Service, Alaska Airlines, and, of course, Toyota. Even traditionally idiosyncratic organizations such as hospitals (see the OM in Action box, “Lean Delivers the Medicine”) find improved productiv- ity with Lean operations. Lean operations adopt a philosophy of minimizing waste by striving for perfection through continuous learning, creativity, and teamwork. They tend to share the following attributes:
◆ Respect and develop employees by improving job design, providing constant training, instilling commitment, and building teamwork.
◆ Empower employees with jobs that are made challenging by pushing responsibility to the lowest level possible.
◆ Develop worker flexibility through cross-training and reducing job classifications. ◆ Build processes that destroy variability by helping employees produce a perfect product
every time. ◆ Develop collaborative partnerships with suppliers , helping them not only to understand the
needs of the ultimate customer, but also to accept responsibility for satisfying those needs. ◆ Eliminate waste by performing only value-added activities. Material handling, inspection,
inventory, travel time, wasted space, and rework are targets, as they do not add value.
Success requires leadership as well as the full commitment and involvement of managers, employees, and suppliers. The rewards that Lean producers reap are spectacular. Lean producers often become benchmark performers.
Gemba or Gemba walk
Going to where the work is actually
performed.
OM in Action Lean Delivers the Medicine Using kaizen techniques straight out of Lean, a team of employees at San
Francisco General Hospital target and then analyze a particular area within the
hospital for improvement. Hospitals today are focusing on throughput and quality
in the belief that excelling on these measures will drive down costs and push up
patient satisfaction. Doctors and nurses now work together in teams that im-
merse themselves in a weeklong kaizen event. These events generate plans that
make specific improvements in flow, quality, costs, or the patients’ experience.
One recent kaizen event focused on the number of minutes it takes from
the moment a patient is wheeled into the operating room to when the first
incision is made. A team spent a week coming up with ways to whittle 10 min-
utes off this “prep” time. Every minute saved reduces labor cost and opens
up critical facilities. Another kaizen event targeted the Urgent Care Center,
dropping the average wait from 5 hours down to 2.5, primarily by adding an
on-site X-ray machine instead of requiring patients to walk 15 minutes to the
main radiology department. Similarly, wait times in the Surgical Clinic dropped
from 2.5 hours to 70 minutes. The operating room now uses a 5S protocol and
has implemented Standard Work for the preoperation process.
As hospitals focus on improving medical quality and patient satisfaction,
they are exposed to some Japanese terms associated with Lean, many of
which do not have a direct English translation: Gemba, the place where work
is actually performed; Hansei, a period of critical self-reflection; Heijunka,
a level production schedule that provides balance and smooths day-to-day
F ra
n ck
B o st
o n /F
o to
lia
variation; Jidoka, using both human intelligence and technology to stop a pro-
cess at the first sign of a potential problem; Kaizen, continuous improvement;
and Muda, anything that consumes resources, but provides no value.
Lean systems are increasingly being adopted by hospitals as they try to
reduce costs while improving quality and increasing patient satisfaction—and
as San Francisco General has demonstrated, Lean techniques are working.
Sources: San Francisco Chronicle (Oct. 14, 2013) and San Francisco General
Hospital & Trauma Center Annual Report , 2012–2013.
VIDEO 16.1 Lean Operations at Alaska Airlines
LO 16.7 Identify six attributes of Lean
organizations
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Lean Sustainability Lean and sustainability are two sides of the same coin. Both seek to maximize resource and economic efficiency. However, if Lean focuses on only the immediate process and system, then managers may miss the sustainability issues beyond the firm. As we discussed in Supplement 5, sustainability requires examining the systems in which the firm and its stakeholders operate. When this is done, both Lean and sustainability achieve higher levels of performance.
Lean drives out waste because waste adds nothing for the customer. Sustainability drives out waste because waste is both expensive and has an adverse effect on the environment. Driving out waste is the common ground of Lean sustainability.
Lean in Services The features of Lean apply to services—from hospitals to amusement parks and airlines— directly influencing the customers’ received value. The Lean attributes of respect for people, efficient processes with rigorous standard practices that drive out waste, and a focus on continuous improvement are pervasive vehicles for consistently generating value for all stake- holders. If there is any change in focus of Lean between manufacturing and services, it may be that the high level of customer interaction places added emphasis on enabling people through training, motivation, and empowerment to contribute to their fullest. However, in addition to the customer interaction aspect of services, here are some specific applications of Lean applied to suppliers, layout, inventory, and scheduling in the service sector. Suppliers Virtually every restaurant deals with its suppliers on a JIT basis. Those that do not are usually unsuccessful. The waste is too evident—food spoils, and customers complain, get sick, and may die. Similarly, JIT is basic to the financial sector that processes your deposits, withdrawals, and brokerage activities on a JIT basis. That is the industry standard. Layouts Lean layouts are required in restaurant kitchens, where cold food must be served cold and hot food hot. McDonald’s, for example, has reconfigured its kitchen layout, at great expense, to drive seconds out of the production process, thereby speeding delivery to customers. With the new process, McDonald’s can produce made-to-order hamburgers in 45 seconds. Layouts also make a difference at Alaska Airline’s baggage claim, where customers expect their bags in 20 minutes or less. Inventory Stockbrokers drive inventory down to nearly zero every day. Most sell and buy orders occur on an immediate basis because an unexecuted sell or buy order is not acceptable to the client. A broker may be in serious trouble if left holding an unexecuted trade. Similarly, McDonald’s reduces inventory waste by maintaining a time-stamped finished-goods inventory of only a few minutes; after that, it is thrown away. Hospitals, such as Arnold Palmer (described in this chapter’s Video Case Study ), manage JIT inventory and low safety stocks for many items. For instance, critical supplies such as pharmaceuticals may be held to low levels by devel- oping community networks as backup. In this manner, if one pharmacy runs out of a needed drug, another member of the network can supply it until the next day’s shipment arrives. Scheduling Airlines must adjust to fluctuations in customer demand. But rather than adjusting by changes in inventory, demand is satisfied by personnel availability. Through elabo- rate scheduling, personnel show up just in time to cover peaks in customer demand. In other words, rather than “things” being inventoried, personnel are scheduled. At a salon, the focus is only slightly different: prompt service is assured by scheduling both the customer and the staff. At McDonald’s and Walmart, scheduling of personnel is down to 15-minute increments, based on precise forecasting of demand. Notice that in these organizations scheduling is a key ingredient of Lean. Excellent forecasts drive those schedules. Such forecasts may be very elaborate, with seasonal, daily, and even hourly components in the case of the airline ticket counter (holiday sales, flight time, etc.), seasonal and weekly components at the salon (holidays and Fridays create special problems), and down to a few minutes (to respond to the daily meal cycle) at McDonald’s.
To deliver goods and services to customers under continuously changing demand, suppliers need to be reliable, inventories low, cycle times short, and schedules nimble. Lean engages and empowers employees to create and deliver the customer’s perception of value, eliminating whatever does not contribute to this goal. Lean techniques are widely used in both goods- producing and service-producing firms; they just look different.
STUDENT TIP Lean began in factories, but is now
also used in services throughout
the world.
LO 16.8 Explain how Lean applies to services
VIDEO 16.2 JIT at Arnold Palmer Hospital
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Summary Lean operations, including JIT and TPS, focuses on con- tinuous improvement to eliminate waste. Because waste is found in anything that does not add value, organizations that implement these techniques are adding value more
efficiently than other firms. The expectation of lean firms is that empowered employees work with committed manage- ment to build systems that respond to customers with ever- increasing efficiency and higher quality.
Key Terms
Lean operations (p. 638 ) Just-in-time (JIT) (p. 638 ) Toyota Production System (TPS) (p. 638 ) Seven wastes (p. 638 ) 5Ss (p. 639 ) Variability (p. 639 )
Throughput (p. 640 ) Manufacturing cycle time (p. 640 ) Pull system (p. 640 ) Supplier partnerships (p. 640 ) Consignment inventory (p. 642 ) Lean inventory (p. 643 )
Level schedules (p. 647 ) Kanban (p. 647 ) Kaizen (p. 649 ) Kaizen event (p. 649 ) Gemba or Gemba walk (p. 651 )
Ethical Dilemma In this Lean operations world, in an effort to lower handling costs, speed delivery, and reduce inventory, retailers are forcing their suppliers to do more and more in the way of preparing their merchandise for their cross-docking warehouses, shipment to specific stores, and shelf presentation. Your company, a small manufacturer of aquarium decorations, is in a tough position. First, Mega-Mart wanted you to develop bar-code technology, then special packaging, then small individual shipments bar coded for each store. (This way when the merchandise hits the warehouse, it is cross-docked immediately to the truck destined for that store, and upon arrival the merchandise is ready for shelf placement.) And now Mega-Mart wants you to develop RFID—immediately.
Mega-Mart has made it clear that suppliers that cannot keep up with the technology will be dropped.
Earlier, when you didn’t have the expertise for bar codes, you had to borrow money and hire an outside fi rm to do the development, purchase the technology, and train your shipping clerk. Then, meeting the special packaging requirement drove you into a loss for several months, resulting in a loss for last year. Now it appears that the RFID request is impossible. Your business, under the best of conditions, is marginally profi table, and the bank may not be willing to bail you out again. Over the years, Mega-Mart has slowly become your major customer and without it, you are probably out of business. What are the ethical issues, and what do you do?
Discussion Questions
1. What is a Lean producer? 2. What is JIT? 3. What is TPS? 4. What is level scheduling? 5. JIT attempts to remove delays, which do not add value. How,
then, does JIT cope with weather and its impact on crop harvest and transportation times?
6. What are three ways in which Lean and quality are related? 7. What is kaizen, and what is a kaizen event? 8. What are the characteristics of supplier partnerships with
respect to suppliers?
9. Discuss how the Japanese word for card has application in the study of JIT.
10. Standardized, reusable containers have obvious benefits for shipping. What is the purpose of these devices within the plant?
11. Does Lean production work in the service sector? Provide an example.
12. Which Lean techniques work in both the manufacturing and service sectors?
Solved Problem Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM 16.1 Krupp Refrigeration, Inc., is trying to reduce inventory and wants you to install a kanban system for compressors on one of its assembly lines. Determine the size of the kanban and the number of kanbans (containers) needed.
Setup cost = $10 Annual holding cost per compressor = $100 Daily production = 200 compressors Annual usage = 25,000 (50 weeks * 5 days each * daily usage of 100 compressors) Lead time = 3 days Safety stock = 12 day’s production of compressors
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Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems 16.1–16.12 relate to Lean and Just-in-Time
• • • 16.1 Carol Cagle has a repetitive manufacturing plant pro- ducing trailer hitches in Arlington, Texas. The plant has an average inventory turnover of only 12 times per year. She has therefore deter- mined that she will reduce her component lot sizes. She has devel- oped the following data for one component, the safety chain clip:
Annual demand = 31,200 units Daily demand = 120 units
Daily production (in 8 hours) = 960 units Desired lot size (1 hour of production) = 120 units
Holding cost per unit per year = $12 Setup labor cost per hour = $20
How many minutes of setup time should she have her plant man- ager aim for regarding this component?
• • • 16.2 Given the following information about a product at Michael Gibson’s firm, what is the appropriate setup time?
Annual demand = 39,000 units Daily demand = 150 units
Daily production = 1,000 units Desired lot size = 150 units
Holding cost per unit per year = $10 Setup labor cost per hour = $40
• • • 16.3 Rick Wing has a repetitive manufacturing plant producing automobile steering wheels. Use the following data to pre- pare for a reduced lot size. The firm uses a work year of 305 days.
Annual demand for steering wheels 30,500
Daily demand 100
Daily production (8 hours) 800
Desired lot size (2 hours of production) 200
Holding cost per unit per year $10
a) What is the setup cost, based on the desired lot size? b) What is the setup time, based on $40 per hour setup labor?
• 16.4 Hartley Electronics, Inc., in Nashville, produces short runs of custom airwave scanners for the defense industry. The owner, Janet Hartley, has asked you to reduce inventory by introducing a kanban system. After several hours of analysis, you develop the following data for scanner connectors used in one work cell. How many kanbans do you need for this connector?
Daily demand 1,000 connectors
Lead time 2 days
Safety stock 12 day
Kanban size 500 connectors
• 16.5 Tej Dhakar’s company wants to establish kanbans to feed a newly established work cell. The following data have been provided. How many kanbans are needed?
Daily demand 250 units
Lead time 12 day
Safety stock 14 day
Kanban size 50 units
• • 16.6 Pauline Found Manufacturing, Inc., is moving to kanbans to support its telephone switching-board assembly lines. Determine the size of the kanban for subassemblies and the number of kanbans needed.
Setup cost = $30 Annual holding cost = $120 per subassembly
Daily production = 20 subassemblies Annual usage = 2,500 (50 weeks * 5 days each
* daily usage of 10 subassemblies) Lead time = 16 days
Safety stock = 4 days> production of subassemblies
PX
SOLUTION First, we must determine kanban container size. To do this, we determine the production order quantity [see discussion in Chapter 12 or Equation (16-1) ], which determines the kanban size:
Q*p =
H
2DS
H a1 - d p b
=
H
2(25,000)(10)
H a1 - d p b
=
H
500,000
100 a1 - 100 200 b
= A
500,000 50
= 210,000 = 100 compressors. So the production order size and the size of the kanban container = 100.
Then we determine the number of kanbans:
Demand during lead time = 300 ( = 3 days * daily usage of 100)
Safety stock = 100 ( = 12 * daily production of 200)
Number of kanbans = Demand during lead time + Safety stock
Size of container
= 300 + 100
100 =
400 100
= 4 containers
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• • 16.7 Maggie Moylan Motorcycle Corp. uses kanbans to support its transmission assembly line. Determine the size of the kanban for the mainshaft assembly and the number of kanbans needed.
Setup cost = $20 Annual holding cost
of mainshaft assembly = $250 per unit Daily production = 300 mainshafts
Annual usage = 20,000 ( = 50 weeks * 5 days each * daily usage of 80 mainshafts)
Lead time = 3 days Safety stock = 12 day>s production of mainshafts
• 16.8 Discount-Mart, a major East Coast retailer, wants to determine the economic order quantity (see Chapter 12 for EOQ formulas) for its halogen lamps. It currently buys all halogen lamps from Specialty Lighting Manufacturers in Atlanta. Annual demand is 2,000 lamps, ordering cost per order is $30, and annual carrying cost per lamp is $12.
a) What is the EOQ? b) What are the total annual costs of holding and ordering
(managing) this inventory? c) How many orders should Discount-Mart place with Specialty
Lighting per year? PX
• • • 16.9 Discount-Mart (see Problem 16.8), as part of its new Lean program, has signed a long-term contract with Specialty Lighting and will place orders electronically for its halogen lamps. Ordering costs will drop to $.50 per order, but Discount- Mart also reassessed its carrying costs and raised them to $20 per lamp. a) What is the new economic order quantity? b) How many orders will now be placed? c) What is the total annual cost of managing the inventory with
this policy? PX
• • 16.10 How do your answers to Problems 16.8 and 16.9 provide insight into a collaborative purchasing strategy?
Additional problems 16.11–16.12 are available in MyOMLab.
CASE STUDIES
Alaska Airlines operates in a land of rugged beauty, crystal clear lakes, spectacular glaciers, majestic mountains, and bright blue skies. But equally awesome is its operating performance. Alaska Airlines consistently provides the industry’s number one overall ranking and best on-time performance. A key ingredient of this excellent performance is Alaska Airlines’ Lean initiative.
With an aggressive implementation of Lean, Ben Minicucci, Executive VP for Operations, is finding ever-increasing levels of performance. He pushes this initiative throughout the company with: (1) a focus on continuous improvement, (2) metrics that measure performance against targets, and (3) making perfor- mance relevant to Alaska Airlines’ empowered employees.
With leadership training that includes a strong focus on participative management, Minicucci has created a seven-person Lean Department. The department provides extensive train- ing in Lean via one-week courses, participative workshops, and two-week classes that train employees to become a Six Sigma Green Belt. Some employees even pursue the next step, Black Belt certification.
A huge part of any airline’s operations is fuel cost, but capital utilization and much of the remaining cost is dependent upon ground equipment and crews that handle aircraft turnaround and maintenance, in-flight services, and customer service.
As John Ladner, Director of Seattle Airport Operations, has observed, “Lean eliminates waste, exposes non-standard work, and is forcing a focus on variations in documented best practices and work time.”
Lean is now part of the Alaska Airlines corporate culture, with some 60 ongoing projects. Kaizen events (called “Accelerated Improvement Workshops” at Alaska Airlines), Gemba Walks (called “waste walks” by Alaska Airlines), and 5S are now a part of every- day conversation at Alaska Airlines. Lean projects have included:
Lean Operations at Alaska Airlines
◆ Applying 5S to identify aircraft ground equipment and its location on the tarmac.
◆ Improving preparation for and synchronization of the arrival and departure sequences; time to open the front door after arrival has been reduced from 4.5 to 1 min.
◆ Redefining the disconnect procedure for tow bars used to “push back” aircraft at departure time; planes now depart 2–3 minutes faster.
◆ Revising the deicing process, meaning less time for the plane to be on the tarmac.
◆ Improving pilot staffing, making Alaska’s pilot productivity the highest in the industry. Every 1% improvement in pro- ductivity leads to a $5 million savings on a recurring basis. Alaska Airlines has achieved a 7% productivity improvement over the last five years.
PX
Video Case
A la
sk a A
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Another current Lean project is passenger unloading and loading. Lean instructor Allison Fletcher calls this “the most unique project I have worked on.” One exciting aspect of deplan- ing is Alaska’s solar-powered “switchback” staircase for unload- ing passengers through the rear door (see photo). Alaska is saving two minutes, or nearly 17%, off previous unloading time with this new process. Alaska Airlines’ Lean culture has made it a leader in the industry.
Discussion Questions*
1. What are the key ingredients of Lean, as identified at Alaska Airlines?
2. As an initial phase of a kaizen event, discuss the many ways passengers can be loaded and unloaded from airplanes.
3. Document the research that is being done on the aircraft passenger-loading problem.
JIT at Arnold Palmer Hospital Video Case
Orlando’s Arnold Palmer Hospital, founded in 1989, specializes in treatment of women and children and is renowned for its high- quality rankings (top 10% of 2000 benchmarked hospitals), its labor and delivery volume (more than 14,000 births per year), and its neonatal intensive care unit (one of the highest survival rates in the nation). But quality medical practices and high patient sat- isfaction require costly inventory—some $30 million per year and thousands of SKUs. * With pressure on medical care to manage and reduce costs, Arnold Palmer Hospital has turned toward con- trolling its inventory with just-in-time (JIT) techniques.
Within the hospital, for example, drugs are now distributed at the nursing stations via dispensing machines (almost like vending machines) that electronically track patient usage and post the related charge to each patient. Each night, based on patient demand and prescriptions written by doctors, the dispensing stations are refilled.
To address JIT issues externally, Arnold Palmer Hospital turned to a major distribution partner, McKesson General Medical, which as a first-tier supplier provides the hospital with about one-quarter of all its medical/surgical inventory. McKesson supplies sponges, basins, towels, Mayo stand covers, syringes, and hundreds of other medical/surgical items. To ensure coordinated daily delivery of inventory purchased from McKesson, an account executive has been assigned to the hospital on a full-time basis, as well as two other individuals who address customer service and product issues. The result has been a drop in Central Supply average daily inventory from $400,000 to $114,000 since JIT.
JIT success has also been achieved in the area of custom surgical packs . Custom surgical packs are the sterile coverings, dispos- able plastic trays, gauze, and the like, specialized to each type of surgical procedure. Arnold Palmer Hospital uses 10 different cus- tom packs for various surgical procedures. “Over 50,000 packs are used each year, for a total cost of about $1.5 million,” says George DeLong, head of Supply-Chain Management.
The packs are not only delivered in a JIT manner, but packed that way as well. That is, they are packed in the reverse order they are used so each item comes out of the pack in the sequence it is
needed. The packs are bulky, are expensive, and must remain sterile. Reducing the inventory and handling while maintaining an ensured sterile supply for scheduled surgeries presents a challenge to hospitals.
Here is how the supply chain works: Custom packs are assem- bled by a packing company with components supplied primar- ily from manufacturers selected by the hospital, and delivered by McKesson from its local warehouse. Arnold Palmer Hospital works with its own surgical staff (through the Medical Economics Outcome Committee) to identify and standardize the custom packs to reduce the number of custom pack SKUs. With this inte- grated system, pack safety stock inventory has been cut to one day.
The procedure to drive the custom surgical pack JIT system begins with a “pull” from the doctors’ daily surgical schedule. Then, Arnold Palmer Hospital initiates an electronic order to McKesson between 1:00 and 2:00 p.m. daily. At 4:00 a.m. the next day, McKesson delivers the packs. Hospital personnel arrive at 7:00 a.m. and stock the shelves for scheduled surgeries. McKesson then reor- ders from the packing company, which in turn “pulls” necessary inventory for the quantity of packs needed from the manufacturers.
Arnold Palmer Hospital’s JIT system reduces inventory investment, expensive traditional ordering, and bulky storage and supports quality with a sterile delivery.
Discussion Questions **
1. What do you recommend be done when an error is found in a pack as it is opened for an operation?
2. How might the procedure for custom surgical packs described here be improved?
3. When discussing JIT in services, the text notes that suppliers, layout, inventory, and scheduling are all used. Provide an example of each of these at Arnold Palmer Hospital.
4. When a doctor proposes a new surgical procedure, how do you recommend the SKU for a new custom pack be entered into the hospital’s supply-chain system?
• Additional Case Studies: Visit MyOMLab for these case studies: JIT after a Catastrophe: How Caterpillar responded after a tornado tore apart its Oxford plant. Mutual Insurance Company of Iowa: Applying JIT in an insurance offi ce.
Endnote
1. The term 5S comes from the Japanese words seiri ( sort and clear out), seiton ( straighten and configure), seiso ( scrub and clean up), seiketsu (maintain sanitation and cleanliness of self
and workplace), and shitsuke ( self-discipline and standardiza- tion of these practices).
*SKU 5 stock keeping unit **You may wish to view the video that accompanies this case before answering these questions.
* You may wish to view the video that accompanies this case before addressing these questions.
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16Chapter 16 Rapid Review Main Heading Review Material MyOMLab LEAN OPERATIONS (pp. 638–640)
j Lean operations —Eliminates waste through continuous improvement and focus on exactly what the customer wants.
j Just-in-time (JIT) —Continuous and forced problem solving via a focus on throughput and reduced inventory.
j Toyota Production System (TPS) —Focus on continuous improvement, respect for people, and standard work practices.
When implemented as a comprehensive manufacturing strategy, Lean, JIT, and TPS systems sustain competitive advantage and result in increased overall returns. j Seven wastes —Overproduction, queues, transportation, inventory, motion,
overprocessing, and defective product. j 5Ss —A Lean production checklist: sort, simplify, shine, standardize, and sustain. U.S. managers often add two additional S s to the 5 original ones: safety and support/maintenance . j Variability —Any deviation from the optimum process that delivers perfect
product on time, every time. Both JIT and inventory reduction are effective tools for identifying causes of variability. j Throughput —The rate at which units move through a process. j Manufacturing cycle time —The time between the arrival of raw materials and the
shipping of finished products. j Pull system —A concept that results in material being produced only when
requested and moved to where it is needed just as it is needed. Pull systems use signals to request production and delivery from supplying stations to stations that have production capacity available.
Concept Questions: 1.1–1.4
LEAN AND JUST-IN-TIME (pp. 640–649)
j Supplier partnerships —Suppliers and purchasers work together to remove waste and drive down costs for mutual benefit.
Some specific goals of supplier partnerships are removal of unnecessary activities , removal of in-plant inventory, removal of in-transit inventory, and obtain improved quality and reliability . j Consignment inventory —An arrangement in which the supplier maintains title to
the inventory until it is used. Concerns of suppliers in suppler partnerships include (1) diversification , (2) sched- uling , (3) lead time , (4) quality , and (5) lot sizes . Lean layout tactics include building work cells for families of products, including a large number of operations in a small area, minimizing distance, designing little space for inventory, improving employee communication, using poka-yoke devices, building flexible or movable equipment, and cross-training workers to add flexibility. j Lean inventory —The minimum inventory necessary to keep a perfect system running. The idea behind JIT is to eliminate inventory that hides variability in the production system. Lean inventory tactics include using a pull system to move inventory, reducing lot size, developing just-in-time delivery systems with suppliers, delivering directly to the point of use, performing to schedule, reducing setup time, and using group technology.
Q*p = A
2DS H [1 - (d /p)]
(16-1)
Using Equation (16-1) , for a given desired lot size, Q , we can solve for the optimal setup cost, S :
S = (Q2)(H)(1 - d /p)
2D (16-2)
Lean scheduling tactics include communicate schedules to suppliers, make level schedules, freeze part of the schedule, perform to schedule, seek one-piece-make and one-piece-move, eliminate waste, produce in small lots, use kanbans, and make each operation produce a perfect part. j Level schedules —Scheduling products so that each day’s production meets the
demand for that day. j Kanban —The Japanese word for card , which has come to mean “signal”; a
kanban system moves parts through production via a “pull” from a signal.
Number of kanbans (containers) = Demand during lead time + Safety stock
Size of container (16-3)
Lean quality —whereas inventory hides bad quality, Lean immediately exposes it. Lean quality tactics include using statistical process control, empowering employees, building fail-safe methods (poka-yoke, checklists, etc.), exposing poor quality with small lots, and providing immediate feedback.
Concept Questions: 2.1–2.4
Problems: 16.1–16.3
Problems: 16.4–16.9, 16.11, 16.12
Virtual Office Hours for Solved Problem: 16.1
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Chapter 16 Rapid Review continued16 Main Heading Review Material MyOMLab LEAN AND THE TOYOTA PRODUCTION SYSTEM (pp. 649–650)
j Kaizen —A focus on continuous improvement. j Kaizen event —Members of a work cell or team meet to develop improvements in
the process. Toyota recruits, trains, and treats people as knowledge workers. They are empow- ered. TPS employs aggressive cross-training and few job classifications.
Concept Questions: 3.1–3.4
LEAN ORGANIZATIONS (pp. 650–652)
Lean operations tend to share the following attributes: respect and develop employees by improving job design, providing constant training, instilling commit- ment, and building teamwork; empower employees by pushing responsibility to the lowest level possible; develop worker flexibility through cross-training and reducing job classifications; build processes that destroy variability; develop collaborative partnerships with suppliers to help them accept responsibility for satisfying end customer needs; and eliminate waste by performing only value-added activities. j Gemba or Gemba walk —Going to where the work is actually performed.
Concept Questions: 4.1–4.4
VIDEO 16.1 Lean Operations at Alaska Airlines
LEAN IN SERVICES (p. 652)
The features of Lean operations apply to services just as they do in other sectors. Forecasts in services may be very elaborate, with seasonal, daily, hourly, or even shorter components.
Concept Questions: 5.1–5.4 VIDEO 16.2 JIT at Arnold Palmer Hospital
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 16.1 Match Lean Operations, JIT, and TPS with the concepts shown below:
a) Continuous improvement and a focus on exactly what the customer wants, and when.
b) Supply the customer with exactly what the customer wants when the customer wants it, without waste, through continuous improvement.
c) Emphasis on continuous improvement, respect for people, and standard work practices.
LO 16.2 Define the seven wastes and the 5Ss. The seven wastes are ________ , ________ , ________ , ________ , ________ . ________ , and ________ , and the 5Ss are ________ , ________ , ________ , ________ , and ________ .
LO 16.3 Concerns of suppliers when moving to Supplier Partnerships include:
a) small lots sometimes seeming economically prohibitive. b) realistic quality demands. c) changes without adequate lead time. d) erratic schedules. e) all of the above. LO 16.4 What is the formula for optimal setup time? a) 22DQ>[H(1 - d>p)] b) 2Q2H(1 - d>p)>(2D) c) QH(1 - d>p)>(2D) d) Q2H(1 - d>p)>(2D) e) H(1 - d>p)
LO 16.5 Kanban is the Japanese word for: a) car. b) pull. c) card. d) continuous improvement. e) level schedule. LO 16.6 The required number of kanbans equals: a) 1. b) Demand during lead time / Q c) Size of container. d) Demand during lead time. e) Demand during lead time + Safety stock / Size of
container LO 16.7 The six attributes of Lean organizations are: ________ ,
________ , ________ , ________ , ________ , and ________ . LO 16.8 Lean applies to services: a) only in rare instances. b) except in terms of the supply chain. c) except in terms of employee issues. d) except in terms of both supply chain issues and
employee issues. e) just as it applies to manufacturing.
Answers: LO 16.1. Lean 5 a, JIT 5 b, TPS 5 c; LO 16.2. overproduction, queues, transportation, inventory, motion, overprocessing, defective product; sort, simplify, shine, standardize, sustain; LO 16.3. e; LO 16.4. d; LO 16.5. c; LO 16.6. e; LO 16.7. respect and develop people, empower employees, develop worker fl exibility, build excellent processes, develop collaborative partnerships with suppliers, eliminate waste; LO 16.8. e.
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C H A P T E R O U T L I N E
17 ◆
The Strategic Importance of Maintenance and Reliability 662
◆
Reliability 663
◆
Maintenance 667
◆
Total Productive Maintenance 671
GLOBAL COMPANY PROFILE: Orlando Utilities Commission
C H
A P
T E
R
1010 OMOM STRATEGY DECISIONS
• • Design of Goods and Services
• • Managing Quality
• • Process Strategy
• • Location Strategies
• • Layout Strategies
• • Human Resources
• • Supply-Chain Management
• • Inventory Management
• • Scheduling
• • Maintenance
C H A P T E R GLOBAL COMPANY PROFILE Orlando Utilities Commission
Maintenance and Reliability
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T he Orlando Utilities Commission (OUC) owns and operates power plants that supply power
to two central Florida counties. Every year, OUC takes each one of its power-generating
units off-line for 1 to 3 weeks to perform maintenance work.
In addition, each unit is also taken off-line every 3 years for a complete overhaul and turbine
generator inspection. Overhauls are scheduled for spring and fall, when the weather is mildest
and demand for power is low. These overhauls last from 6 to 8 weeks.
Units at OUC’s Stanton Energy Center require that maintenance personnel perform ap-
proximately 12,000 repair and preventive maintenance tasks a year. To accomplish these tasks
efficiently, many of these jobs are scheduled daily via a computerized maintenance management
program. The computer generates preventive maintenance work orders and lists of required
materials.
Every day that a plant is down for maintenance costs OUC about $110,000 extra for the
replacement cost of power that must be generated elsewhere. However, these costs pale beside
the costs associated with a forced outage. An unexpected outage could cost OUC an additional
$350,000 to $600,000 each day!
Scheduled overhauls are not easy; each one has 1,800 distinct tasks and requires 72,000
labor-hours. But the value of preventive maintenance was illustrated by the first overhaul of a
new turbine generator. Workers discovered a cracked rotor blade, which could have destroyed
a $27 million piece of equipment. To find such
cracks, which are invisible to the naked eye,
metals are examined using dye tests, X-rays,
and ultrasound.
At OUC, preventive maintenance is worth
its weight in gold. As a result, OUC’s electric
distribution system has been ranked number
one in the Southeast U.S. by PA Consulting
Group—a leading consulting firm. Effective
maintenance provides a competitive advan-
tage for the Orlando Utilities Commission.
Maintenance Provides a Competitive Advantage for the Orlando Utilities Commission
GLOBAL COMPANY PROFILE Orlando Utilities Commission
C H A P T E R 1 7
660
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The Stanton Energy Center in Orlando.
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Maintenance of capital-intensive facilities requires good planning to minimize
downtime. Here, turbine overhaul is under way. Organizing the thousands of
parts and pieces necessary for a shutdown is a major effort.
M o n ty
R a ku
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u lt u ra
/N e w
sc o m
This inspector is examining a low-pressure
section of turbine. The tips of these turbine
blades will travel at supersonic speeds of
1,300 miles per hour when the plant is in
operation. A crack in one of the blades can
cause catastrophic failure.
O rl a n d o U
ti lit
ie s
C o m
m is
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Two employees are on scaffolding near the top of
Stanton Energy Center’s 23-story high boiler, checking
and repairing super heaters.
O rl a n d o U
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C o m
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662
L E A R N I N G OBJEC TI V ES
LO 17.1 Describe how to improve system reliability 663
LO 17.2 Determine system reliability 664
LO 17.3 Determine mean time between failures (MTBF) 665
LO 17.4 Distinguish between preventive and breakdown maintenance 667
LO 17.5 Describe how to improve maintenance 668
LO 17.6 Compare preventive and breakdown maintenance costs 669
LO 17.7 Defi ne autonomous maintenance 670
The Strategic Importance of Maintenance and Reliability Managers at Orlando Utilities Commission (OUC), the subject of the chapter-opening Global Company Profile , fight for reliability to avoid the undesirable results of equipment failure. At OUC, a generator failure is very expensive for both the company and its customers. Power outages are instantaneous, with potentially devastating consequences. Similarly, managers at Frito-Lay, Walt Disney Company, and United Parcel Service (UPS) are intolerant of fail- ures or breakdowns. Maintenance is critical at Frito-Lay to achieve high plant utilization and excellent sanitation. At Disney, sparkling-clean facilities and safe rides are necessary to retain its standing as one of the most popular vacation destinations in the world. Likewise, UPS’s famed maintenance strategy keeps its delivery vehicles operating and looking as good as new for 20 years or more.
These companies, like most others, know that poor maintenance can be disruptive, incon- venient, wasteful, and expensive in dollars and even in lives. As Figure 17.1 illustrates, the interdependency of operator, machine, and mechanic is a hallmark of successful maintenance and reliability. Good maintenance and reliability management enhances a firm’s performance and protects its investment.
The objective of maintenance and reliability is to maintain the capability of the system . Good maintenance removes variability. Systems must be designed and maintained to reach expected performance and quality standards. Maintenance includes all activities involved in keeping a sys- tem’s equipment in working order. Reliability is the probability that a machine part or product will function properly for a specified time under stated conditions.
In this chapter, we examine four important tactics for improving the reliability and mainte- nance not only of products and equipment but also of the systems that produce them. The four tactics are organized around reliability and maintenance.
VIDEO 17.1 Maintenance Drives Profits at
Frito-Lay
Maintenance and Reliability Procedures
Yields
Results
Reduced variability
Reduced inventory
Improved quality
Improved capacity
Protecting investment in plant and equipment
Enhanced productivity
Winning products
Improved profitability
Clean and lubricate
Monitor and adjust
Make minor repairs
Keep accurate records
Employee Involvement
Autonomous maintenance (partnering with maintenance personnel)
Skill training
Reward system
Employee empowerment
Continuous improvement
STUDENT TIP If a system is not reliable, the other
OM decisions are more difficult.
Maintenance
The activities involved in keeping
a system’s equipment in working
order.
Reliability
The probability that a machine part
or product will function properly
for a specified time under stated
conditions.
Figure 17.1
Good Maintenance and Reliability Management Requires Employee Involvement and Good Procedures
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The reliability tactics are:
1. Improving individual components 2. Providing redundancy
The maintenance tactics are:
1. Implementing or improving preventive maintenance 2. Increasing repair capabilities or speed
We will now discuss these tactics.
Reliability Systems are composed of a series of individual interrelated components, each performing a specific job. If any one component fails to perform, for whatever reason, the overall system (for example, an airplane or machine) can fail. First, we discuss system reliability and then improvement via redundancy.
System Reliability Because failures do occur in the real world, understanding their occurrence is an important reliability concept. We now examine the impact of failure in a series. Figure 17.2 shows that as the number of components in a series increases, the reliability of the whole system declines very quickly. A system of n 5 50 interacting parts, each of which has a 99.5% reliability, has an overall reliability of 78%. If the system or machine has 100 interacting parts, each with an individual reliability of 99.5%, the overall reliability will be only about 60%!
To measure reliability in a system in which each component may have its own unique reliabil- ity, we cannot use the reliability curve in Figure 17.2 . However, the method of computing system reliability ( R s ) is simple. It consists of finding the product of individual reliabilities as follows:
Rs = R1 * R2 * R3 * c * Rn (17-1)
where R 1 5 reliability of component 1 R 2 5 reliability of component 2
and so on. Equation (17-1) assumes that the reliability of an individual component does not depend
on the reliability of other components (that is, each component is independent ). In addition, in this equation, as in most reliability discussions, reliabilities are presented as probabilities . Thus, a .90 reliability means that the unit will perform as intended 90% of the time. It also means that
Average reliability of each component (percent)
100 98 97 96 0
20
40
60
80
100
R e lia
b ili
ty o
f th
e s
ys te
m (
p e rc
e n t)
99
n = 300 n = 400
n = 100
n = 50
n = 10
n = 1
n = 200
Figure 17.2
Overall System Reliability
as a Function of Number of
n Components (Each with
the Same Reliability) and
Component Reliability with
Components in a Series
STUDENT TIP Designing for reliability is an
excellent place to start reducing
variability.
LO 17.1 Describe how to improve system
reliability
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664 P A R T 3 | M A N AG I N G O P E R AT I O N S
it will fail 1 2 .90 5 .10 5 10% of the time. We can use this method to evaluate the reliability of a service or a product, such as the one we examine in Example 1 .
Example 1 RELIABILITY IN A SERIES The National Bank of Greeley, Colorado, processes loan applications through three clerks (each check- ing different sections of the application in series), with reliabilities of .90, .80, and .99. It wants to find the system reliability.
APPROACH c Apply Equation (17-1) to solve for R s .
SOLUTION c The reliability of the loan process is: Rs = R1 * R2 * R3 = (.90)(.80)(.99) = .713, or 71.3,
INSIGHT c Because each clerk in the series is less than perfect, the error probabilities are cumulative and the resulting reliability for this series is .713, which is less than any one clerk.
LEARNING EXERCISE c If the lowest-performing clerk (.80) is replaced by a clerk performing at .95 reliability, what is the new expected reliability? [Answer: .846.]
RELATED PROBLEMS c 17.1, 17.2, 17.3, 17.9 (17.16 and 17.17 are available in MyOMLab)
ACTIVE MODEL 17.1 This example is further illustrated in Active Model 17.1 in MyOMLab.
EXCEL OM Data File Ch17Ex1.xls can be found in MyOMLab.
R2 R3
RS.80 .99.90
R1
LO 17.2 Determine system reliability
The basic unit of measure for reliability is the product failure rate (FR). Firms producing high-technology equipment often provide failure-rate data on their products. As shown in Equa- tions (17-2) and (17-3), the failure rate measures the percent of failures among the total number of products tested, FR(%), or a number of failures during a period of operating time, FR( N ):
FR(,) = Number of failures
Number of units tested * 100, (17-2)
FR(N ) = Number of failures
Number of unit@hours of operating time (17-3)
Perhaps the most common term in reliability analysis is the mean time between failures (MTBF) , which is the reciprocal of FR( N ):
MTBF = 1
FR(N ) (17-4)
In Example 2 , we compute the percentage of failure FR(%), number of failures FR( N ), and mean time between failures (MTBF).
Mean time between failures (MTBF)
The expected time between
a repair and the next failure of
a component, machine, process,
or product.
Example 2 DETERMINING MEAN TIME BETWEEN FAILURES Twenty air-conditioning systems designed for use by astronauts in Russia’s Soyuz spacecraft were operated for 1,000 hours at a Russian test facility. Two of the systems failed during the test—one after 200 hours and the other after 600 hours.
APPROACH c To determine the percent of failures [FR(%)], the number of failures per unit of time [FR( N )], and the mean time between failures (MTBF), we use Equations (17-2), (17-3), and (17-4), respectively.
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C H A P T E R 1 7 | M A I N T E N A N C E A N D R E L I A B I L I T Y 665
If the failure rate recorded in Example 2 is too high, Russia will have to increase systems reli- ability by either increasing the reliability of individual components or by redundancy.
Providing Redundancy To increase the reliability of systems, redundancy is added in the form of backup components or parallel paths . Redundancy is provided to ensure that if one component or path fails, the system has recourse to another.
Backup Redundancy Assume that reliability of a component is .80 and we back it up with another component with reliability of .75. The resulting reliability is the probability of the first component working plus the probability of the backup component working multiplied by the probability of needing the backup component (1 - .8 = .2). Therefore:
Rs = £ Probability
of first component
working ≥ + C £
Probability of second
component working ≥ * £
Probability of needing
second component
≥ S =
(.8) + 3(.75) * (1 - .8)4 = .8 + .15 = .95
(17-5)
LO 17.3 Determine mean time between
failures (MTBF)
Redundancy
The use of backup components
or parallel paths to raise reliability.
SOLUTION c Percentage of failures:
FR(,) = Number of failures
Number of units tested =
2 20
(100,) = 10,
Number of failures per operating hour:
FR(N) = Number of failures
Number of unit@hours of operating time
where Total time = (1,000 hr)(20 units) = 20,000 unit@hour
Nonoperating time = 800 hr for 1st failure + 400 hr for 2nd failure = 1,200 unit@hour Number of unit@hours of operating time = Total time - Nonoperating time
FR(N ) = 2
20,000 - 1,200 =
2 18,800
= .000106 failure/unit@hour
Because MTBF = 1
FR(N) :
MTBF = 1
.000106 = 9,434 hr
If the typical Soyuz shuttle trip to the International Space Station lasts 6 days, Russia may note that the failure rate per trip is:
Failure rate = (Failures/unit@hr)(24 hr>day)(6 days>trip) = (.000106)(24)(6) = .0153 failure>trip
INSIGHT c Mean time between failures (MTBF) is the standard means of stating reliability.
LEARNING EXERCISE c If nonoperating time drops to 800, what is the new MTBF? [Answer: 9,606 hr.]
RELATED PROBLEMS c 17.4, 17.5
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Example 3 shows how redundancy, in the form of backup components, can improve the reli- ability of the loan process presented in Example 1 .
Example 3 RELIABILITY WITH BACKUP
Parallel Redundancy Another way to enhance reliability is to provide parallel paths. In a parallel system, the paths are assumed to be independent; therefore, success on any one path allows the system to perform. In Example 4 , we determine the reliability of a process with three parallel paths.
Example 4 RELIABILITY WITH PARALLEL REDUNDANCY A new iPad design that is more reliable because of its parallel circuits is shown below. What is its reliability?
.95
.95
.975.975
R3
Rs R4
R2
R1
APPROACH c Identify the reliability of each path, then compute the likelihood of needing additional paths (likelihood of failure), and finally subtract the product of those failures from 1.
SOLUTION c
Reliability for the middle path 5 R 2 3 R 3 5 .975 3 .975 5 .9506
Then determine the probability of failure for all 3 paths 5 (1 2 0.95) 3 (1 2 .9506) 3 (1 2 0.95)
5 (.05) 3 (.0494) 3 (.05) 5 .00012
Therefore the reliability of the new design is 1 minus the probability of failures, or
= 1 - .00012 = .99988
The National Bank is disturbed that its loan-application process has a reliability of only .713 (see Example 1 ) and would like to improve this situation.
APPROACH c The bank decides to provide redundancy for the two least reliable clerks, with clerks of equal competence.
SOLUTION c This procedure results in the following system:
R1 R2 R3
0.90 0.80 T T
0.90 S 0.80 S 0.99
Rs = 3.9 + .9(1 - .9)4 * 3.8 + .8(1 - .8)4 * .99 = 3.9 + (.9)(.1)4 * 3.8 + (.8)(.2)4 * .99 = .99 * .96 * .99 = .94
INSIGHT c By providing redundancy for two clerks, National Bank has increased reliability of the loan process from .713 to .94.
LEARNING EXERCISE c What happens when the bank replaces both R 2 clerks with one new clerk who has a reliability of .90? [Answer: R s 5 .88.]
RELATED PROBLEMS c 17.7, 17.10, 17.12, 17.13, 17.14, 17.15
ACTIVE MODEL 17.2 This example is further illustrated in Active Model 17.2 in MyOMLab.
EXCEL OM Data File Ch17Ex3.xls can be found in MyOMLab.
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Managers often use a combination of backup components or parallel paths to improve reliability.
Maintenance There are two types of maintenance: preventive maintenance and breakdown maintenance. Preventive maintenance involves monitoring equipment and facilities, performing routine inspections, servicing, and keeping facilities in good repair. These activities are intended to build a system that will reduce variability, find potential failures, and make changes or repairs that will maintain efficient processes. The current generation of sophisticated sensors allows managers to build systems that can detect the slightest unusual vibration, minute changes in temperature or pressure, and slight changes in oil viscosity or chemical compo- nents. Preventive maintenance involves designing technical and human systems that will keep the productive process working within tolerance; it allows the system to perform as designed. Breakdown maintenance occurs when preventive maintenance fails and equipment/ facilities must be repaired on an emergency or priority basis.
Implementing Preventive Maintenance Preventive maintenance implies that we can determine when a system needs service or will need repair. Therefore, to perform preventive maintenance, we must know when a system requires service or when it is likely to fail. Failures occur at different rates during the life of a product. A high initial failure rate, known as infant mortality , may exist for many products. This is why many electronic firms “burn in” their products prior to shipment: that is to say, they execute a variety of tests (such as a full wash cycle at Whirlpool) to detect “startup” problems prior to shipment. Firms may also provide 90-day warranties. We should note that many infant mortality failures are not product failures per se, but rather failure due to improper use. This fact points up the importance in many industries of operations management’s building an after-sales service system that includes installing and training.
Once the product, machine, or process “settles in,” a study can be made of the MTBF (mean time between failures) distribution. Such distributions often follow a normal curve. When these distributions exhibit small standard deviations, then we know we have a candidate for preventive maintenance, even if the maintenance is expensive.
Once our firm has a candidate for preventive maintenance, we want to determine when preventive maintenance is economical. Typically, the more expensive the maintenance, the nar- rower must be the MTBF distribution (that is, have a small standard deviation). In addition, if the process is no more expensive to repair when it breaks down than the cost of preventive maintenance, perhaps we should let the process break down and then do the repair. However, the consequence of the breakdown must be fully considered. Even some relatively minor break- downs have catastrophic consequences. At the other extreme, preventive maintenance costs may be so incidental that preventive maintenance is appropriate even if the MTBF distribution is rather flat (that is, it has a large standard deviation).
With good reporting techniques, firms can maintain records of individual processes, ma- chines, or equipment. Such records can provide a profile of both the kinds of maintenance re- quired and the timing of maintenance needed. Maintaining equipment history is an important part of a preventive maintenance system, as is a record of the time and cost to make the repair. Such records can also provide information about the family of equipment and suppliers.
Preventive maintenance
A plan that involves monitoring,
routine inspections, servicing, and
keeping facilities in good repair.
Breakdown maintenance
Remedial maintenance that occurs
when preventive maintenance
fails and equipment/facilities must
be repaired on an emergency or
priority basis.
Infant mortality
The failure rate early in the life of
a product or process.
LO 17.4 Distinguish between preventive and
breakdown maintenance
INSIGHT c Even in a system where no component has reliability over .975, the parallel design increases reliability to over .999. Parallel paths can add substantially to reliability.
LEARNING EXERCISE c If reliability of all components is only .90, what is the new reliability? [Answer: .9981]
RELATED PROBLEMS c 17.6, 17.8, 17.11
ACTIVE MODEL 17.3 This example is further illustrated in Active Model 17.3 in MyOMLab.
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Reliability and maintenance are of such importance that most maintenance management systems are now computerized. Figure 17.3 shows the major components of such a system with files to be maintained on the left and reports generated on the right.
Companies from Boeing to Ford are improving product reliability via their maintenance information systems. Boeing monitors the health of planes in flight by relaying relevant infor- mation in real-time to the ground. This provides a head start on reliability and maintenance issues. Similarly, with wireless satellite service, millions of car owners are alerted to thousands of diagnostic issues, from faulty airbag sensors to the need for an oil change. These real-time systems provide immediate data that are used to head off quality issues before customers even notice a problem. The technology enhances reliability and customer satisfaction. And catching problems early saves millions of dollars in warranty costs.
Figure 17.4 (a) shows a traditional view of the relationship between preventive maintenance and breakdown maintenance. In this view, operations managers consider a balance between the two costs. Allocating more resources to preventive maintenance will reduce the number of breakdowns. At some point, however, the decrease in breakdown maintenance costs may be less than the increase in preventive maintenance costs. At this point, the total cost curve begins to rise. Beyond this optimal point, the firm will be better off waiting for breakdowns to occur and repairing them when they do.
Unfortunately, cost curves such as in Figure 17.4 (a) seldom consider the full costs of a break- down . Many costs are ignored because they are not directly related to the immediate breakdown. For instance, the cost of inventory maintained to compensate for downtime is not typically
Inventory and purchasing reports
Computer
Repair history file
Data entry • Work requests • Purchase requests • Time reporting • Contract work
Data Files Output Reports
Equipment parts list
Equipment history reports
Costs analysis (actual vs. standard)
Work orders
Maintenance and work order schedule
Equipment file with parts list
Inventory of spare parts
Personnel data with skills, wages, etc.
Figure 17.3
A Computerized Maintenance
System
LO 17.5 Describe how to improve maintenance
Total costs
Preventive maintenance costs
Preventive maintenance costs
Breakdown maintenance costs
Maintenance commitment
(a) Traditional View of Maintenance (b) Full Cost View of Maintenance
Optimal point (lowest- cost maintenance policy)
C o st
s
Total costs
Full cost of breakdowns
Maintenance commitment
Optimal point (lowest- cost maintenance policy)
C o st
s
Figure 17.4
Maintenance Costs
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considered. Moreover, downtime can have a devastating effect on safety and morale. Employees may also begin to believe that “performance to standard” and maintaining equipment are not important. Finally, downtime adversely affects delivery schedules, destroying customer relations and future sales. When the full impact of breakdowns is considered, Figure 17.4 (b) may be a better representation of maintenance costs. In Figure 17.4 (b), total costs are at a minimum when the system only breaks down due to unanticipated extraordinary events.
Assuming that all potential costs associated with downtime have been identified, the opera- tions staff can compute the optimal level of maintenance activity on a theoretical basis. Such analysis, of course, also requires accurate historical data on maintenance costs, breakdown probabilities, and repair times. Example 5 shows how to compare preventive and breakdown maintenance costs to select the least expensive maintenance policy.
STUDENT TIP When all breakdown costs are
considered, much more maintenance
may be advantageous.
Example 5 COMPARING PREVENTIVE AND BREAKDOWN MAINTENANCE COSTS Farlen & Halikman is a CPA firm specializing in payroll preparation. The firm has been successful in automating much of its work, using high-speed printers for check processing and report preparation. The computerized approach, however, has problems. Over the past 20 months, the printers have broken down at the rate indicated in the following table:
NUMBER OF BREAKDOWNS
NUMBER OF MONTHS THAT BREAKDOWNS OCCURRED
0 2
1 8
2 6
3 4
Total: 20
Each time the printers break down, Farlen & Halikman estimates that it loses an average of $300 in production time and service expenses. One alternative is to purchase a service contract for preventive maintenance. Even if Farlen & Halikman contracts for preventive maintenance, there will still be break- downs, averaging one breakdown per month. The price for this service is $150 per month.
APPROACH c To determine if the CPA firm should follow a “run until breakdown” policy or con- tract for preventive maintenance, we follow a 4-step process:
Step 1 Compute the expected number of breakdowns (based on past history) if the firm continues as is, without the service contract.
Step 2 Compute the expected breakdown cost per month with no preventive maintenance contract. Step 3 Compute the cost of preventive maintenance. Step 4 Compare the two options and select the one that will cost less.
SOLUTION c
Step 1
LO 17.6 Compare preventive and breakdown
maintenance costs
a Expected number of breakdowns
b = g JaNumber of breakdowns
b * a Corresponding frequency
bR = (0)(.1) + (1)(.4) + (2)(.3) + (3)(.2) = 0 + .4 + .6 + .6 = 1.6 breakdowns/month
NUMBER OF BREAKDOWNS FREQUENCY
NUMBER OF BREAKDOWNS FREQUENCY
0 2>20 5 .1 2 6>20 5 0.3
1 8>20 5 .4 3 4>20 5 0.2
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Using variations of the technique shown in Example 5 , operations managers can examine maintenance policies.
Increasing Repair Capabilities Because reliability and preventive maintenance are seldom perfect, most firms opt for some level of repair capability. Enlarging repair facilities or improving maintenance management may be an excellent way to get the system back in operation faster.
However, not all repairs can be done in the firm’s facility. Managers must, therefore, decide where repairs are to be performed. Figure 17.5 provides a continuum of options and how they rate in terms of speed, cost, and competence. Moving to the right in Figure 17.5 may improve the competence of the repair work, but at the same time it increases costs and replacement time.
Autonomous Maintenance Preventive maintenance policies and techniques must include an emphasis on employees accepting responsibility for the “observe, check, adjust, clean, and notify” type of equipment maintenance. Such policies are consistent with the advantages of employee empowerment. This approach is known as autonomous maintenance . Employees can predict failures, prevent breakdowns, and prolong equipment life. With autonomous maintenance, the manager is making a step toward both employee empowerment and maintaining system performance.
Operator (autonomous maintenance)
Increasing Operator Ownership Increasing Complexity
Manufacturer’s field service
Depot service (return equipment)
Competence is higher as we move to the right.
Maintenance department
Preventive maintenance costs less and is faster the more we move to the left.
Figure 17.5
The Operations Manager
Determines How Maintenance
Will Be Performed
Autonomous maintenance
Operators partner with main-
tenance personnel to observe,
check, adjust, clean, and notify.
LO 17.7 Define autonomous maintenance
Step 2
Expected breakdown cost = a Expected number of breakdowns
b * a Cost per breakdown
b
= (1.6)(+300) = +480>month
Step 3
a Preventive maintenance cost
b = £ Cost of expected breakdowns if service contract signed
≥ + aCost of service contract
b
= (1 breakdown>month)(+300) + +150>month = +450>month
Step 4 Because it is less expensive overall to hire a maintenance service firm ($450) than to not do so ($480), Farlen & Halikman should hire the service firm.
INSIGHT c Determining the expected number of breakdowns for each option is crucial to making a good decision. This typically requires good maintenance records.
LEARNING EXERCISE c What is the best decision if the preventive maintenance contract cost increases to $195 per month? [Answer: At $495 (5 $300 1 $195) per month, “run until breakdown” becomes less expensive (assuming that all costs are included in the $300 per breakdown cost).]
RELATED PROBLEMS c 17.18–17.21 (17.22–17.24 are available in MyOMLab)
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Total Productive Maintenance Many firms have moved to bring total quality management concepts to the practice of preven- tive maintenance with an approach known as total productive maintenance (TPM) . It involves the concept of reducing variability through autonomous maintenance and excellent maintenance practices. Total productive maintenance includes:
◆ Designing machines that are reliable, easy to operate, and easy to maintain ◆ Emphasizing total cost of ownership when purchasing machines, so that service and main-
tenance are included in the cost ◆ Developing preventive maintenance plans that utilize the best practices of operators, main-
tenance departments, and depot service ◆ Training for autonomous maintenance so operators maintain their own machines and part-
ner with maintenance personnel
High utilization of facilities, tight scheduling, low inventory, and consistent quality demand reliability. Total productive maintenance, which continues to improve with recent advances in the use of simulation, expert systems, and sensors, is the key to reducing variability and improving reliability.
Total productive maintenance (TPM)
Combines total quality manage-
ment with a strategic view of
maintenance from process and
equipment design to preventive
maintenance.
STUDENT TIP Maintenance improves
productivity.
Summary Operations managers focus on design improvements, backup components, and parallel paths to improve reliability. Reliability improvements also can be obtained through the use of preventive maintenance and excellent repair facilities.
Firms give employees “ownership” of their equipment. When workers repair or do preventive maintenance on their own machines, breakdowns are less common. Well-trained
and empowered employees ensure reliable systems through preventive maintenance. In turn, reliable, well-maintained equipment not only provides higher utilization but also improves quality and performance to schedule. Top firms build and maintain systems that drive out variability so that customers can rely on products and services to be produced to specifications and on time.
Key Terms
Maintenance (p. 662 ) Reliability (p. 662 ) Mean time between failures
(MTBF) (p. 664 )
Redundancy (p. 665 ) Preventive maintenance (p. 667 ) Breakdown maintenance (p. 667 ) Infant mortality (p. 667 )
Autonomous maintenance (p. 670 ) Total productive maintenance
(TPM) (p. 671 )
Ethical Dilemma The space shuttle Columbia disintegrated over Texas on its 2003 return to Earth. The Challenger exploded shortly after launch in 1986. And the Apollo 1 spacecraft imploded in fi re on the launch pad in 1967. In each case, the lives of all crew members were lost. The hugely complex shuttle may have looked a bit like an airplane but was very different. In reality, its overall statistical reliability is such that about 1 out of every 50 fl ights had a major malfunction. As one aerospace manager stated, “Of course, you can be perfectly safe and never get off the ground.”
Given the huge reliability and maintenance issues NASA faced (seals cracking in cold weather, heat shielding tiles falling off, tools left in the capsule), should astronauts have been allowed to fl y? (In earlier Atlas rockets, men were inserted not out of necessity but because test pilots and politicians thought they should be there.) What are the pros and cons of staffed space exploration from an ethical perspective? Should the U.S. spend billions of dollars to return an astronaut to the moon or send one to Mars?
Discussion Questions
1. What is the objective of maintenance and reliability? 2. How does one identify a candidate for preventive maintenance? 3. Explain the notion of “infant mortality” in the context of
product reliability. 4. How could simulation be a useful technique for maintenance
problems?
5. What is the trade-off between operator-performed mainte- nance versus supplier-performed maintenance?
6. How can a manager evaluate the effectiveness of the mainte- nance function?
7. How does machine design contribute to either increasing or alleviating the maintenance problem?
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Using Software to Solve Reliability Problems g
PX Excel OM and POM for Windows may be used to solve reliability problems. The reliability module allows us to enter (1) number of systems (components) in the series (1 through 10); (2) number of backup, or parallel, components (1 through 12); and (3) compo- nent reliability for both series and parallel data.
Solved Problems Virtual Office Hours help is available at MyOMLab.
SOLVED PROBLEM 17.1 The semiconductor used in the Sullivan Wrist Calculator has five circuits, each of which has its own reliability rate. Component 1 has a reliability of .90; component 2, .95; com- ponent 3, .98; component 4, .90; and component 5, .99. What is the reliability of one semiconductor?
SOLUTION Semiconductor reliability, Rs = R1 * R2 * R3 * R4 * R5 = (.90)(.95)(.98)(.90)(.99) = .7466
SOLVED PROBLEM 17.2 A recent engineering change at Sullivan Wrist Calculator places a backup component in each of the two least reliable transistor circuits. The new circuits will look like the following:
R4 R5R2 R3
.95 .98 .90 .99.90
R1
.90 .90
What is the reliability of the new system?
SOLUTION Reliability = 3.9 + (1 - .9) * .94 * .95 * .98 * 3.9 + (1 - .9) * .94 * .99 = 3.9 + .094 * .95 * .98 * 3.9 + .094 * .99 = .99 * .95 * .98 * .99 * .99 = .903
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems 17.1–17.17 relate to Reliability
• 17.1 The Beta II computer’s electronic processing unit contains 50 components in series. The average reliability of each component is 99.0%. Using Figure 17.2 , determine the overall reliability of the processing unit.
• 17.2. A testing process at Boeing Aircraft has 400 com- ponents in series. The average reliability of each component is 99.5%. Use Figure 17.2 to find the overall reliability of the whole testing process.
• • 17.3 A new aircraft control system is being designed that must be 98% reliable. This system consists of three components in series. If all three of the components are to have the same level of reliability, what level of reliability is required? PX
• • 17.4 Robert Klassan Manufacturing, a medical equipment manufacturer, subjected 100 heart pacemakers to 5,000 hours of testing. Halfway through the testing, 5 pacemakers failed. What was the failure rate in terms of the following: a) Percentage of failures? b) Number of failures per unit-hour?
c) Number of failures per unit-year? d) If 1,100 people receive pacemaker implants, how many units
can we expect to fail during the following year?
S ky
h a w
k x/
S h u tt
e rs
to ck
8. What roles can computerized maintenance management sys- tems play in the maintenance function?
9. During an argument as to the merits of preventive maintenance at Windsor Printers, the company owner asked, “Why fix it
before it breaks?” How would you, as the director of mainte- nance, respond?
10. Will preventive maintenance eliminate all breakdowns?
• • 17.5 A manufacturer of disk drives for notebook computers wants an MTBF of at least 50,000 hours. Recent test results for 10 units were one failure at 10,000 hours, another at 25,000 hours,
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and two more at 45,000 hours. The remaining units were still run- ning at 60,000 hours. Determine the following: a) Percent of failures b) Number of failures per unit-hour c) MTBF at this point in the testing
• • 17.6 What is the reliability of the following parallel pro- duction process? R 1 5 0.95, R 2 5 0.90, R 3 5 0.98.
• • 17.7 What is the overall reliability that bank loans will be processed accurately if each of the 5 clerks shown in the chart has the reliability shown?
R1
R3R2
Hint: The three paths are done in parallel, followed by an addi- tional independent step.
• • 17.8 Merrill Kim Sharp has a system composed of three com- ponents in parallel. The components have the following reliabilities:
R1 = 0.90, R2 = 0.95, R3 = 0.85
What is the reliability of the system? ( Hint: See Example 4 .) PX
• 17.9 A medical control system has three components in series with individual reliabilities ( R 1 , R 2 , R 3 ) as shown:
.95
.95
.95 .95.95
boards; this is a great concept, but the hot-air temperature con- trol lacks reliability. According to Wing, engineers at WSSI have improved the reliability of the critical temperature controls. The new system still has the four sensitive integrated circuits control- ling the temperature, but the new machine has a backup for each. The four integrated circuits have reliabilities of .90, .92, .94, and .96. The four backup circuits all have a reliability of .90. a) What is the reliability of the new temperature controller? b) If you pay a premium, Wing says he can improve all four of the
backup units to .93. What is the reliability of this option? PX
• • • • 17.14 As VP for operations at Méndez-Piñero Engineering, you must decide which product design, A or B, has the higher reli- ability. B is designed with backup units for components R 3 and R 4 . What is the reliability of each design? PX
0.99 0.95 0.998 0.995
Product Design A
R1 R2 R3 R4
0.99 0.95 0.985 0.99
0.95 0.99
Product Design B
R1 R2 R3 R4
What is the reliability of the system? PX
• • 17.10 What is the reliability of the system shown?
R2 R3
RS.98 .90.99
R1
• • • • 17.15 A typical retail transaction consists of several smaller steps, which can be considered components subject to failure. A list of such components might include:
COMPONENT DESCRIPTION DEFINITION OF FAILURE
1 Find product in proper size, color, etc.
Can’t fi nd product
2 Enter cashier line No lines open; lines too long; line experiencing diffi culty
3 Scan product UPC for name, price, etc.
Won’t scan; item not on fi le; scans incorrect name or price
4 Calculate purchase total
Wrong weight; wrong extension; wrong data entry; wrong tax
5 Make payment Customer lacks cash; check not acceptable; credit card refused
6 Make change Makes change incorrectly
7 Bag merchandise Damages merchandise while bagging; bag splits
8 Conclude transaction and exit
No receipt; unfriendly, rude, or aloof clerk
Let the eight probabilities of success be .92, .94, .99, .99, .98, .97, .95, and .96. What is the reliability of the system; that is, the prob- ability that there will be a satisfied customer? If you were the store manager, what do you think should be an acceptable value for this probability? Which components would be good candidates for backup, which for redesign?
Additional problems 17.16–17.17 are available in MyOMLab.
Problems 17.18–17.24 relate to Maintenance
• 17.18 What are the expected number of yearly breakdowns for the power generator at Orlando Utilities that has exhibited the following data over the past 20 years? PX Number of breakdowns 0 1 2 3 4 5 6
Number of years in which breakdown occurred 2 2 5 4 5 2 0
• 17.11 How much would reliability improve if the medical control system shown in Problem 17.9 changed to the redundant parallel system shown in Problem 17.10?
• • 17.12 Elizabeth Irwin’s design team has proposed the fol- lowing system with component reliabilities as indicated:
RS2 .98 .90.99
RS1
Rp
.98 .90.99
What is the reliability of the system? PX Hint: The system functions if either R 2 or R 3 work.
• • 17.13 Rick Wing, salesperson for Wave Soldering Systems, Inc. (WSSI), has provided you with a proposal for improving the temperature control on your present machine. The machine uses a hot-air knife to cleanly remove excess solder from printed circuit
R2 = 0.85
R1 = 0.90 R4 = 0.90
R3 = 0.85
PX
PX
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• 17.19 Each breakdown of a graphic plotter table at Airbus Industries costs $50. Find the expected daily breakdown cost, given the following data: PX Number of breakdowns 0 1 2 3 4
Daily breakdown probability .1 .2 .4 .2 .1
• • 17.20 David Hall, chief of the maintenance department at Mechanical Dynamics, has presented you with the following fail- ure curve. What does it suggest?
Time
N u m
b e r
o f fa
ilu re
s • • • 17.21 The fire department has a number of failures with its oxygen masks and is evaluating the possibility of outsourcing preventive maintenance to the manufacturer. Because of the risk associated with a failure, the cost of each failure is estimated at $2,000. The current maintenance policy (with station employees performing maintenance) has yielded the following history:
Number of breakdowns 0 1 2 3 4 5
Number of years in which breakdowns occurred 4 3 1 5 5 0
This manufacturer will guarantee repairs on any and all failures as part of a service contract. The cost of this service is $5,000 per year. a) What is the expected number of breakdowns per year with sta-
tion employees performing maintenance? b) What is the cost of the current maintenance policy? c) What is the more economical policy?
Additional problems 17.22–17.24 are available in MyOMLab.
CASE STUDY Video Case Maintenance Drives Profits at Frito-Lay
Frito-Lay, the multi-billion-dollar subsidiary of food and bever- age giant PepsiCo, maintains 36 plants in the U.S. and Canada. These facilities produce dozen of snacks, including the well- known Lay’s, Fritos, Cheetos, Doritos, Ruffles, and Tostitos brands, each of which sells over $1 billion per year.
Frito-Lay plants produce in the high-volume, low-variety process model common to commercial baked goods, steel, glass, and beer industries. In this environment, preventive maintenance of equip- ment takes a major role by avoiding costly downtime. Tom Rao, vice president for Florida operations, estimates that each 1% of downtime has a negative annual profit impact of $200,000. He is proud of the 112, unscheduled downtime his plant is able to reach—well below the 2% that is considered the “world-class” benchmark. This excel- lent performance is possible because the maintenance department takes an active role in setting the parameters for preventive main- tenance. This is done with weekly input to the production schedule.
Maintenance policy impacts energy use as well. The Florida plant’s technical manager, Jim Wentzel, states, “By reducing pro- duction interruptions, we create an opportunity to bring energy and utility use under control. Equipment maintenance and a solid production schedule are keys to utility efficiency. With every pro- duction interruption, there is substantial waste.”
As a part of its total productive maintenance (TPM) program, * Frito-Lay empowers employees with what it calls the “Run Right” system. Run Right teaches employees to “identify and do.” This
• Additional Case Studies: Visit MyOMLab for these free case studies: Cartak’s Department Store: Requires the evaluation of the impact of an additional invoice verifier. Worldwide Chemical Company: The maintenance department in this company is in turmoil.
** You may wish to view the video that accompanies this case before answering these questions.
* At Frito-Lay, preventive maintenance, autonomous maintenance, and total productive maintenance are part of a Frito-Lay program known as total productive manufacturing.
means each shift is responsible for identifying problems and making the necessary corrections, when possible. This is accomplished through (1) a “power walk” at the beginning of the shift to ensure that equipment and process settings are performing to standard, (2) mid-shift and post-shift reviews of standards and performance, and (3) posting of any issues on a large whiteboard in the shift office. Items remain on the whiteboard until corrected, which is seldom more than a shift or two.
With good manpower scheduling and tight labor control to hold down variable costs, making time for training is challeng- ing. But supervisors, including the plant manager, are available to fill in on the production line when that is necessary to free an employee for training.
The 30 maintenance personnel hired to cover 24>7 operations at the Florida plant all come with multi-craft skills (e.g., welding, electrical, plumbing). “Multi-craft maintenance personnel are harder to find and cost more,” says Wentzel, “but they more than pay for themselves.”
Discussion Questions **
1. What might be done to help take Frito-Lay to the next level of outstanding maintenance? Consider factors such as sophisti- cated software.
2. What are the advantages and disadvantages of giving more responsibility for machine maintenance to the operator?
3. Discuss the pros and cons of hiring multi-craft maintenance personnel.
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Chapter 17 Rapid Review Main Heading Review Material MyOMLab THE STRATEGIC IMPORTANCE OF MAINTENANCE AND RELIABILITY (pp. 662 – 663 )
Poor maintenance can be disruptive, inconvenient, wasteful, and expensive in dol- lars and even in lives. The interdependency of operator, machine, and mechanic is a hallmark of successful maintenance and reliability. Good maintenance and reliability management requires employee involvement and good procedures; it enhances a firm’s performance and protects its investment. The objective of maintenance and reliability is to maintain the capability of the system. j Maintenance —All activities involved in keeping a system’s equipment in work-
ing order. j Reliability —The probability that a machine part or product will function
properly for a specified time under stated conditions. The two main tactics for improving reliability are: 1. Improving individual components 2. Providing redundancy The two main tactics for improving maintenance are: 1. Implementing or improving preventive maintenance 2. Increasing repair capabilities or speed
Concept Questions: 1.1–1.4
VIDEO 17.1 Maintenance Drives Profits at Frito-Lay
RELIABILITY (pp. 663 – 667 )
A system is composed of a series of individual interrelated components, each performing a specific job. If any one component fails to perform, the overall system can fail. As the number of components in a series increases, the reliability of the whole system declines very quickly:
Rs = R1 * R2 * R3 * c * Rn (17-1)
where R 1 5 reliability of component 1, R 2 5 reliability of component 2, and so on. Equation (17-1) assumes that the reliability of an individual component does not depend on the reliability of other components. A .90 reliability means that the unit will perform as intended 90% of the time, and it will fail 10% of the time. The basic unit of measure for reliability is the product failure rate (FR). FR(%) is the percent of failures among the total number of products tested, and FR( N ) is the number of failures during a period of time:
FR(,) = Number of failures
Number of units tested * 100, (17-2)
FR(N) = Number of failures
Number of unit@hours of operating time (17-3)
j Mean time between failures (MTBF) —The expected time between a repair and the next failure of a component, machine, process, or product.
MTBF = 1
FR(N) (17-4)
j Redundancy —The use of components in parallel to raise reliability. The reli- ability of a component along with its backup equals:
(Probability that 1st component works) + 3(Prob. that backup works) * (Prob. that 1st fails)4 (17-5)
Concept Questions: 2.1–2.4
Problems: 17.1–17.17
Virtual Office Hours for Solved Problems: 17.1, 17.2
ACTIVE MODELS 17.1, 17.2, 17.3
MAINTENANCE (pp. 667 – 670 )
j Preventive maintenance —Involves routine inspections, monitoring, servicing, and keeping facilities in good repair.
j Breakdown maintenance —Remedial maintenance that occurs when preventive maintenance fails and equipment/facilities must be repaired on an emergency or priority basis.
j Infant mortality —The failure rate early in the life of a product or process. Consistent with job enrichment practices, machine operators must be held responsible for preventive maintenance of their own equipment and tools. Reliability and maintenance are of such importance that most maintenance systems are now computerized.
Concept Questions: 3.1–3.4
Problems: 17.18–17.24
17
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TOTAL PRODUCTIVE MAINTENANCE (p. 671 )
j Total productive maintenance (TPM) —Combines total quality management with a strategic view of maintenance from process and equipment design to preventive maintenance.
Total productive maintenance includes: 1. Designing machines that are reliable, easy to operate, and easy to maintain 2. Emphasizing total cost of ownership when purchasing machines, so that service
and maintenance are included in the cost 3. Developing preventive maintenance plans that utilize the best practices of
operators, maintenance departments, and depot service 4. Training for autonomous maintenance so operators maintain their own ma-
chines and partner with maintenance personnel Three techniques that have proven beneficial to effective maintenance are simula- tion, expert systems, and sensors.
Concept Questions: 4.1–4.4
17 R
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Chapter 17 Rapid Review continued
Costs of a breakdown that may get ignored include: 1. The cost of inventory maintained to compensate for downtime 2. Downtime, which can have a devastating effect on safety and morale and
which adversely affects delivery schedules, destroying customer relations and future sales
j Autonomous maintenance —Operators partner with maintenance personnel to observe, check, adjust, clean, and notify.
Employees can predict failures, prevent breakdowns, and prolong equipment life. With autonomous maintenance, the manager is making a step toward both employee empowerment and maintaining system performance.
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the chapter and the key terms listed at the end of the chapter.
LO 17.1 The two main tactics for improving reliability are _______ and _______ .
LO 17.2 The reliability of a system with n independent components equals:
a) the sum of the individual reliabilities. b) the minimum reliability among all components. c) the maximum reliability among all components. d) the product of the individual reliabilities. e) the average of the individual reliabilities. LO 17.3 What is the formula for the mean time between failures? a) Number of failures 4 Number of unit-hours of operat-
ing time b) Number of unit-hours of operating time ÷ Number
of failures c) (Number of failures 4 Number of units tested) 3 100% d) (Number of units tested 4 Number of failures) 3 100% e) 1 4 FR(%) LO 17.4 The process that is intended to find potential failures and
make changes or repairs is known as: a) breakdown maintenance. b) failure maintenance. c) preventive maintenance. d) all of the above.
LO 17.5 The two main tactics for improving maintenance are _______ and _______ .
LO 17.6 The appropriate maintenance policy is developed by balanc- ing preventive maintenance costs with breakdown mainte- nance costs. The problem is that:
a) preventive maintenance costs are very difficult to identify. b) full breakdown costs are seldom considered. c) preventive maintenance should be performed, regardless
of the cost. d) breakdown maintenance must be performed, regardless
of the cost. LO 17.7 _______ maintenance partners operators with maintenance
personnel to observe, check, adjust, clean, and notify. a) Partnering b) Operator c) Breakdown d) Six Sigma e) Autonomous
Answers: LO 17.1. improving individual components, providing redundancy; LO 17.2. d; LO 17.3. b; LO 17.4. c; LO 17.5. implementing or improv- ing preventive maintenance, increasing repair capabilities or speed; LO 17.6. b; LO 17.7. e.
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A ◆
The Decision Process in Operations 678
◆
Fundamentals of Decision Making 679
◆
Decision Tables 680
◆
Types of Decision-Making Environments 681
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Decision Trees 684
M O
D U
L E
PART FOUR Business Analytics Modules
Decision-Making Tools
Ala sk
a A
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A la
sk a A
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The Decision Process in Operations Operations managers are not gamblers. But they are decision makers. To achieve the goals of their organizations, managers must understand how decisions are made and know which decision-making tools to use. To a great extent, the success or failure of both people and companies depends on the quality of their decisions. Overcoming uncertainty is a manager’s challenge.
L E A R N I N G OBJEC TI V ES
LO A.1 Create a simple decision tree 680
LO A.2 Build a decision table 680
LO A.3 Explain when to use each of the three types of decision-making environments 681
LO A.4 Calculate an expected monetary value (EMV) 682
LO A.5 Compute the expected value of perfect information (EVPI) 683
LO A.6 Evaluate the nodes in a decision tree 686
LO A.7 Create a decision tree with sequential decisions 687
Of all the hands he might have opened with, 80% are likely worse than 5 5 or very high face cards. That means there’s an 80% chance he’ll fold and I’ll collect $99,000.
If I raise him “all in,” he’ll have to either bet all $422,000 of his chips or fold. My guess is, he’ll fold unless he has a 5 5 or better, or very high face cards.
So my overall expected value is $71,570, or nearly 5% of all chips in the game. I’m going for it.*
“ALL IN”
A call would put $853,000 on the table. Hmmm. But if I read him right, there’s only a 20% probability his cards are good enough for him to call, and even then, there’s a 45% chance my 7s win.
T.J. probably has good cards, or he wouldn’t have opened. But he doesn’t know I have a pair of 7s.
WILL T.J. FOLD?
WOULD YOU GO ALL IN? At the Legends of Poker tournament in Los Angeles, veteran T.J. Cloutier opens with a $60,000 bet. (Antes and required bets of $39,000 are already on the table.) Former Go2net CTO Paul Phillips ponders going “all in”—betting virtually all his chips. Using decision theory, here’s how he decided.
WHAT IF HE CALLS?
Source : Based on Business 2.0 (November 2003): 128–134.
*To see the details of Phillips’s decision, see Example A8.
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What makes the difference between a good decision and a bad decision? A “good” decision—one that uses analytic decision making—is based on logic and considers all available data and possible alternatives. It also follows these six steps:
1. Clearly define the problem and the factors that influence it. 2. Develop specific and measurable objectives. 3. Develop a model—that is, a relationship between objectives and variables (which are
measurable quantities). 4. Evaluate each alternative solution based on its merits and drawbacks. 5. Select the best alternative. 6. Implement and evaluate the decision and then set a timetable for completion.
So analytic decision making requires models, objectives, and quantifiable variables, often in the form of probabilities and payoffs. Such information is not always easy to obtain or derive from existing data. This challenge exists because of either a lack of data or an overabundance of data. However, because data are now easily generated and stored in digital form, we tend to have the latter—massive volumes of data. Data are collected automatically from produc- tion processes, as well as from websites, credit cards, point-of-sale records, and social media. Although this mass of data is a potential source of information, it requires sophistication in how it is stored, processed, and analyzed. Big data is the term used to describe this huge amount of data, which often cannot be efficiently processed by traditional data techniques.
Throughout this text, we have introduced a broad range of mathematical models and tools that help operations managers make better decisions. Because good decisions require data that can be analyzed and turned into information, decision makers appreciate the potential of big data. This module provides an introduction to the challenges facing managers by introducing two of the tools of decision making—decision tables and decision trees. These two tools are used in numerous OM situations, ranging from new-product analysis, to capacity planning, to location planning, to supply-chain disaster planning, to scheduling, and to maintenance planning.
Fundamentals of Decision Making Regardless of the complexity of a decision or the sophistication of the technique used to ana- lyze it, all decision makers are faced with alternatives and “states of nature.” The following notation will be used in this module:
1. Terms: a. Alternative —A course of action or strategy that may be chosen by a decision maker
(e.g., not carrying an umbrella tomorrow). b. State of nature —An occurrence or a situation over which the decision maker has little
or no control (e.g., tomorrow’s weather). 2. Symbols used in a decision tree:
a. —Decision node from which one of several alternatives may be selected. b. —A state-of-nature node out of which one state of nature will occur.
To present a manager’s decision alternatives, we can develop decision trees using the above symbols. When constructing a decision tree, we must be sure that all alternatives and states of nature are in their correct and logical places and that we include all possible alternatives and states of nature.
Big data
The huge amount of economic,
production, and consumer data
now being collected in digital form.
Getz Products Company is investigating the possibility of producing and marketing backyard storage sheds. Undertaking this project would require the construction of either a large or a small manufacturing plant. The market for the product produced—storage sheds—could be either favorable or unfavorable. Getz, of course, has the option of not developing the new product line at all.
APPROACH c Getz decides to build a decision tree.
Example A1 A SIMPLE DECISION TREE
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Decision Tables We may also develop a decision or payoff table to help Getz Products define its alternatives. For any alternative and a particular state of nature, there is a consequence or outcome , which is usually expressed as a monetary value. This is called a conditional value . Note that all of the alternatives in Example A2 are listed down the left side of the table, that states of nature (outcomes) are listed across the top, and that conditional values (payoffs) are in the body of the decision table .
SOLUTION c Figure A.1 illustrates Getz’s decision tree.
Unfavorable market
Favorable market
A decision node A state of nature node
1
2 Unfavorable market
Favorable market Co
nst ruc
t
lar ge
pla nt
Do nothing
Construct small plant
Figure A.1
Getz Products’ Decision Tree
INSIGHT c We never want to overlook the option of “doing nothing,” as that is usually a possible decision.
LEARNING EXERCISE c Getz now considers constructing a medium-sized plant as a fourth option. Redraw the tree in Figure A.1 to accommodate this. [Answer: Your tree will have a new node and branches between “Construct large plant” and “Construct small plant.”]
RELATED PROBLEMS c A.2e, A.8b, A.22–A.25
LO A.1 Create a simple decision tree
Decision table
A tabular means of analyzing
decision alternatives and states
of nature.
Getz Products now wishes to organize the following information into a table. With a favorable market, a large facility will give Getz Products a net profit of $200,000. If the market is unfavorable, a $180,000 net loss will occur. A small plant will result in a net profit of $100,000 in a favorable market, but a net loss of $20,000 will be encountered if the market is unfavorable.
APPROACH c These numbers become conditional values in the decision table. We list alternatives in the left column and states of nature across the top of the table.
SOLUTION c The completed table is shown in Table A.1 .
Example A2 A DECISION TABLE
LO A.2 Build a decision table
TABLE A.1 Decision Table with Conditional Values for Getz Products
STATES OF NATURE
ALTERNATIVES FAVORABLE MARKET UNFAVORABLE MARKET
Construct large plant $200,000 2$180,000 Construct small plant $100,000 2$ 20,000 Do nothing $ 0 $ 0
STUDENT TIP Decision tables force logic into
decision making.
INSIGHT c The toughest part of decision tables is obtaining the data to analyze.
LEARNING EXERCISE c In Examples A3 and A4, we see how to use decision tables to make decisions.
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Types of Decision-Making Environments The types of decisions people make depend on how much knowledge or information they have about the situation. There are three decision-making environments:
◆ Decision making under uncertainty ◆ Decision making under risk ◆ Decision making under certainty
Decision Making Under Uncertainty When there is complete uncertainty as to which state of nature in a decision environment may occur (i.e., when we cannot even assess probabilities for each possible outcome), we rely on three decision methods:
1. Maximax : This method finds an alternative that max imizes the max imum outcome for every alternative. First, we find the maximum outcome within every alternative, and then we pick the alternative with the maximum number. Because this decision criterion locates the alternative with the highest possible gain , it has been called an “optimistic” decision criterion.
2. Maximin : This method finds the alternative that max imizes the min imum outcome for every alternative. First, we find the minimum outcome within every alternative, and then we pick the alternative with the maximum number. Because this decision criterion locates the alternative that has the least possible loss , it has been called a “pessimistic” decision criterion.
3. Equally likely : This method finds the alternative with the highest average outcome. First, we calculate the average outcome for every alternative, which is the sum of all outcomes divided by the number of outcomes. We then pick the alternative with the maximum number. The equally likely approach assumes that each state of nature is equally likely to occur.
LO A.3 Explain when to use each of the three
types of decision-making
environments
Maximax
A criterion that finds an alternative
that maximizes the maximum
outcome.
Maximin
A criterion that finds an alternative
that maximizes the minimum
outcome.
Equally likely
A criterion that assigns equal
probability to each state of nature.
Getz Products Company would like to apply each of these three approaches now.
APPROACH c Given Getz’s decision table from Example A2, he determines the maximax, maximin, and equally likely decision criteria.
SOLUTION c Table A.2 provides the solution.
Example A3 A DECISION TABLE ANALYSIS UNDER UNCERTAINTY
TABLE A.2 Decision Table for Decision Making Under Uncertainty
STATES OF NATURE
ALTERNATIVES FAVORABLE
MARKET UNFAVORABLE
MARKET MAXIMUM
IN ROW MINIMUM
IN ROW ROW
AVERAGE
Construct large plant $200,000 –$180,000 $200,000 –180,000 $10,000
Construct small plant $100,000 –$ 20,000 $100,000 –$20,000 $40,000
Do nothing $ 0 $ 0 $ 0 $ 0 $ 0
Maximax Maximin Equally likely
1. The maximax choice is to construct a large plant. This is the max imum of the max imum number within each row, or alternative.
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Decision Making Under Risk Decision making under risk, a more common occurrence, relies on probabilities. Several pos- sible states of nature may occur, each with an assumed probability. The states of nature must be mutually exclusive and collectively exhaustive and their probabilities must sum to 1. 1 Given a decision table with conditional values and probability assessments for all states of nature, we can determine the expected monetary value (EMV) for each alternative. This figure represents the expected value or mean return for each alternative if we could repeat this decision (or similar types of decisions) a large number of times.
The EMV for an alternative is the sum of all possible payoffs from the alternative, each weighted by the probability of that payoff occurring:
EMV (Alternative i) = (Payoff of 1st state of nature) * (Probability of 1st state of nature)
+ (Payoff of 2nd state of nature) * (Probability of 2nd state of nature)
+ . . . + (Payoff of last state of nature) * (Probability of last state of nature)
Example A4 illustrates how to compute the maximum EMV.
2. The maximin choice is to do nothing. This is the max imum of the min imum number within each row, or alternative.
3. The equally likely choice is to construct a small plant. This is the maximum of the average outcome of each alternative. This approach assumes that all outcomes for any alternative are equally likely .
INSIGHT c There are optimistic decision makers (“maximax”) and pessimistic ones (“maximin”). Maximax and maximin present best case–worst case planning scenarios.
LEARNING EXERCISE c Getz reestimates the outcome for constructing a large plant when the market is favorable and raises it to $250,000. What numbers change in Table A.2 ? Do the decisions change? [Answer: The maximax is now $250,000, and the row average is $35,000 for large plant. No decision changes.]
RELATED PROBLEMS c A.1, A.2b–d, A.4, A.6 (A.15 is available in MyOMLab)
Expected monetary value (EMV)
The expected payout or value of a
variable that has different possible
states of nature, each with an
associated probability.
LO A.4 Calculate an expected monetary value
(EMV)
Getz would like to find the EMV for each alternative.
APPROACH c Getz Products’ operations manager believes that the probability of a favorable market is 0.6, and that of an unfavorable market is 0.4. He can now determine the EMV for each alternative (see Table A.3 ).
SOLUTION c
1. EMV (A1) = (0.6) (+200,000) + (0.4) (9+180,000) = +48,000 2. EMV (A2) = (0.6) (+100,000) + (0.4) (9+20,000) = +52,000 3. EMV (A3) = (0.6) (+0) + (0.4) (+0) = +0
Example A4 EXPECTED MONETARY VALUE
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Decision Making Under Certainty Now suppose that the Getz operations manager has been approached by a marketing research firm that proposes to help him make the decision about whether to build the plant to produce storage sheds. The marketing researchers claim that their technical analysis will tell Getz with certainty whether the market is favorable for the proposed product. In other words, it will change Getz’s environment from one of decision making under risk to one of decision making under certainty . This information could prevent Getz from making a very expensive mistake. The marketing research firm would charge Getz $65,000 for the informa- tion. What would you recommend? Should the operations manager hire the firm to make the study? Even if the information from the study is perfectly accurate, is it worth $65,000? What might it be worth? Although some of these questions are difficult to answer, determin- ing the value of such perfect information can be very useful. It places an upper bound on what you would be willing to spend on information, such as that being sold by a marketing consultant. This is the concept of the expected value of perfect information (EVPI), which we now introduce.
Expected Value of Perfect Information (EVPI) If a manager were able to determine which state of nature would occur, then he or she would know which decision to make. Once a manager knows which decision to make, the payoff increases because the payoff is now a certainty, not a probability. Because the payoff will increase with knowledge of which state of nature will occur, this knowledge has value. Therefore, we now look at how to determine the value of this information. We call this differ- ence between the payoff under perfect information and the payoff under risk the expected value of perfect information (EVPI) .
EVPI = Expected value with perfect information - Maximum EMV
To find the EVPI, we must first compute the expected value with perfect information (EVwPI) , which is the expected (average) return if we have perfect information before a decision has to be made.
INSIGHT c The maximum EMV is seen in alternative A 2 . Thus, according to the EMV decision crite- rion, Getz would build the small facility.
LEARNING EXERCISE c What happens to the three EMVs if Getz increases the conditional value on the “large plant/favorable market” result to $250,000? [Answer: EMV ( A 1 ) = $78,000. A 1 is now the preferable decision.]
RELATED PROBLEMS c A.2e, A.3a, A.5a, A.7a, A.8, A.9a, A.10, A.11, A.12, A.13a, A.14, A.22b,c (A.16, A.17, A.18, A.19, A.20 are available in MyOMLab)
EXCEL OM Data File ModAExA4.xls can be found in MyOMLab.
TABLE A.3 Decision Table for Getz Products
STATES OF NATURE
ALTERNATIVES FAVORABLE MARKET UNFAVORABLE MARKET
Construct large plant ( A 1 ) $200,000 –$180,000
Construct small plant ( A 2 ) $100,000 –$ 20,000
Do nothing ( A 3 ) $ 0 $ 0
Probabilities 0.6 0.4
Expected value of perfect information (EVPI)
The difference between the payoff
under perfect information and the
payoff under risk.
LO A.5 Compute the expected value of perfect
information (EVPI)
Expected value with perfect information (EVwPI)
The expected (average) return if
perfect information is available.
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To calculate this value, we choose the best alternative for each state of nature and multiply its payoff times the probability of occurrence of that state of nature:
Expected value with perfect information (EVwPI) = (Best outcome or consequence for 1st state of nature)
* (Probability of 1st state of nature)
+ (Best outcome for 2nd state of nature) * (Probability of 2nd state of nature)
+ . . . + (Best outcome for last state of nature) * (Probability of last state of nature)
In Example A5 we use the data and decision table from Example A4 to examine the expected value of perfect information.
The Getz operations manager would like to calculate the maximum that he would pay for information— that is, the expected value of perfect information, or EVPI.
APPROACH c Referring to Table A.3 in Example 4, he follows a two-stage process. First, the expected value with perfect information (EVwPI) is computed. Then, using this information, the EVPI is calculated.
SOLUTION c 1. The best outcome for the state of nature “favorable market” is “build a large facility” with a payoff of
$200,000. The best outcome for the state of nature “unfavorable market” is “do nothing” with a pay- off of $0. Expected value with perfect information = (+200,000)(0.6) + (+0)(0.4) = +120,000. Thus, if we had perfect information, we would expect (on the average) $120,000 if the decision could be repeated many times.
2. The maximum EMV is $52,000 for A 2 , which is the expected outcome without perfect information. Thus:
EVPI = EVwPI - Maximum EMV = +120,000 - +52,000 = +68,000
INSIGHT c The most Getz should be willing to pay for perfect information is $68,000. This conclusion, of course, is again based on the assumption that the probability of the first state of nature is 0.6 and the second is 0.4.
LEARNING EXERCISE c How does the EVPI change if the “large plant/favorable market” condi- tional value is $250,000? [Answer: EVPI = +72,000.]
RELATED PROBLEMS c A.3b, A.5b, A.7, A.9, A.13, A.22c
Example A5 EXPECTED VALUE OF PERFECT INFORMATION
STUDENT TIP EVPI places an upper limit
on what you should pay for
information.
Decision Trees Decisions that lend themselves to display in a decision table also lend themselves to display in a decision tree. We will therefore analyze some decisions using decision trees. Although the use of a decision table is convenient in problems having one set of decisions and one set of states of nature, many problems include sequential decisions and states of nature.
When there are two or more sequential decisions, and later decisions are based on the outcome of prior ones, the decision tree approach becomes appropriate. A decision tree is a graphic display of the decision process that indicates decision alternatives, states of nature and their respective probabilities, and payoffs for each combination of decision alternative and state of nature.
Expected monetary value (EMV) is the most commonly used criterion for decision tree analysis. One of the first steps in such analysis is to graph the decision tree and to specify the monetary consequences of all outcomes for a particular problem.
STUDENT TIP Decision trees can become
complex, so we illustrate two of
them in this section.
Decision tree
A graphical means of analyzing
decision alternatives and states
of nature.
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Analyzing problems with decision trees involves five steps:
1. Define the problem. 2. Structure or draw the decision tree. 3. Assign probabilities to the states of nature. 4. Estimate payoffs for each possible combination of decision alternatives and states of
nature. 5. Solve the problem by computing the expected monetary values (EMV) for each state-of-
nature node. This is done by working backward —that is, by starting at the right of the tree and working back to decision nodes on the left.
When Tomco Oil had to decide which of its new Kentucky lease areas to drill for oil, it turned to decision tree analysis. The 74 different factors, including
geological, engineering, economic, and political factors, became much clearer. Decision tree software such as DPL (shown here), Tree Plan, and Supertree allow
decision problems to be analyzed with less effort and greater depth than ever before.
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e e n sh
o t
fr o m
D P L S
o ft
w a re
. R
e p ri n te
d w
it h p
e rm
is si
o n .
S to
ck b yt
e /G
e tt
y Im
a g e s
Getz wants to develop a completed and solved decision tree.
APPROACH c The payoffs are placed at the right-hand side of each of the tree’s branches (see Figure A.2 ). The probabilities (first used by Getz in Example A4) are placed in parentheses next to each
Example A6 SOLVING A TREE FOR EMV
Unfavorable market (0.4)
Favorable market (0.6)
1
2 Unfavorable market (0.4)
Favorable market (0.6) Co
nst ruc
t la rge
pla nt
Do nothing
Construct small plant
EMV for node 1 = $48,000
EMV for node 2 = $52,000
= (0.6) ($200,000) + (0.4) (–$180,000)
= (0.6) ($100,000) + (0.4) (–$20,000)
Payoffs
$200,000
–$180,000
$100,000
20,000–$
$0
Figure A.2
Completed and Solved
Decision Tree for Getz
Products
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A More Complex Decision Tree When a sequence of decisions must be made, decision trees are much more powerful tools than are decision tables. Let’s say that Getz Products has two decisions to make, with the second decision dependent on the outcome of the first. Before deciding about building a new plant, Getz has the option of conducting its own marketing research survey, at a cost of $10,000. The information from this survey could help it decide whether to build a large plant, to build a small plant, or not to build at all. Getz recognizes that although such a survey will not provide it with perfect information, it may be extremely helpful.
Getz’s new decision tree is represented in Figure A.3 of Example A7. Take a careful look at this more complex tree. Note that all possible outcomes and alternatives are included in their logical sequence. This procedure is one of the strengths of using decision trees. The manager is forced to examine all possible outcomes, including unfavorable ones. He or she is also forced to make decisions in a logical, sequential manner.
state of nature. The expected monetary values for each state-of-nature node are then calculated and placed by their respective nodes. The EMV of the first node is $48,000. This represents the branch from the decision node to “construct a large plant.” The EMV for node 2, to “construct a small plant,” is $52,000. The option of “doing nothing” has, of course, a payoff of $0.
SOLUTION c The branch leaving the decision node leading to the state-of-nature node with the highest EMV will be chosen. In Getz’s case, a small plant should be built.
INSIGHT c This graphical approach is an excellent way for managers to understand all the options in making a major decision. Visual models are often preferred over tables.
LEARNING EXERCISE c Correct Figure A.2 to reflect a $250,000 payoff for “construct large plant/ favorable market.” [Answer: Change one payoff and recompute the EMV for node 1.]
RELATED PROBLEMS c A.2e, A.8b, A.22a,b, A.24, A.25
EXCEL OM Data File ModAExA6.xls can be found in MyOMLab.
Getz Products wishes to develop the new tree for this sequential decision.
APPROACH c Examining the tree in Figure A.3 , we see that Getz’s first decision point is whether to conduct the $10,000 market survey. If it chooses not to do the study (the lower part of the tree), it can either build a large plant, a small plant, or no plant. This is Getz’s second decision point. If the decision is to build, the market will be either favorable (0.6 probability) or unfavorable (0.4 probability). The payoffs for each of the possible consequences are listed along the right-hand side. As a matter of fact, this lower portion of Getz’s tree is identical to the simpler decision tree shown in Figure A.2 .
SOLUTION c The upper part of Figure A.3 reflects the decision to conduct the market survey. State- of-nature node number 1 has 2 branches coming out of it. Let us say there is a 45% chance that the survey results will indicate a favorable market for the storage sheds. We also note that the probability is 0.55 that the survey results will be negative.
The rest of the probabilities shown in parentheses in Figure A.3 are all conditional probabilities. For example, 0.78 is the probability of a favorable market for the sheds given a favorable result from the market survey. Of course, you would expect to find a high probability of a favorable market given that the research indicated that the market was good. Don’t forget, though: There is a chance that Getz’s $10,000 market survey did not result in perfect or even reliable information. Any market research study is subject to error. In this case, there remains a 22% chance that the market for sheds will be unfavorable given positive survey results.
Likewise, we note that there is a 27% chance that the market for sheds will be favorable given negative survey results. The probability is much higher, 0.73, that the market will actually be unfavorable given a negative survey.
Example A7 A DECISION TREE WITH SEQUENTIAL DECISIONS
LO A.6 Evaluate the nodes in a decision tree
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Finally, when we look to the payoff column in Figure A.3 , we see that $10,000—the cost of the marketing study—has been subtracted from each of the top 10 tree branches. Thus, a large plant con- structed in a favorable market would normally net a $200,000 profit. Yet because the market study was conducted, this figure is reduced by $10,000. In the unfavorable case, the loss of $180,000 would increase to $190,000. Similarly, conducting the survey and building no plant now results in a –$10,000 payoff.
With all probabilities and payoffs specified, we can start calculating the expected monetary value of each branch. We begin at the end or right-hand side of the decision tree and work back toward the origin. When we finish, the best decision will be known. 1. Given favorable survey results:
EMV (node 2) = (.78) (+190,000) + (.22) ( - +190,000) = +106,400 EMV (node 3) = (.78) (+90,000) + (.22) ( - +30,000) = +63,600
The EMV of no plant in this case is 2$10,000. Thus, if the survey results are favorable, a large plant should be built.
2. Given negative survey results:
EMV (node 4) = (.27) (+190,000) + (.73) (9+190,000) = - +87,400 EMV (node 5) = (.27) (+90,000) + (.73) (9+30,000) = +2,400
The EMV of no plant is again 2$10,000 for this branch. Thus, given a negative survey result, Getz should build a small plant with an expected value of $2,400.
3. Continuing on the upper part of the tree and moving backward, we compute the expected value of conducting the market survey:
EMV (node 1) = (.45) (+106,400) + (.55) (+2,400) = +49,200
STUDENT TIP The short parallel lines mean
“prune” that branch, as it is less
favorable than another available
option and may be dropped.
LO A.7 Create a decision tree with
sequential decisions
S ur
ve y
re su
lts fa
vo ra
bl e
First Decision Point
Second Decision Point
1
2
3 Unfavorable market
Favorable marketLa rge
pl an
t
No plant
Small plant
Unfavorable market
Favorable market $190,000
–$190,000
$ �90,000
–$ �30,000
–$ �10,000
Payoffs
4
5 Unfavorable market
Favorable market La
rge pl
an t
Small plant
Unfavorable market
$190,000
–$190,000
$ �90,000
–$ �30,000
–$ �10,000
6
7 Unfavorable market
Favorable market Small plant
Unfavorable market
Favorable market $200,000
–$180,000
$100,000
20,000–$
$0
S urvey
results
negative
C o n d u ct
m a rk
e t s
u rv
e y
D o not conduct survey
$ 1
0 6
,4 0
0
$ 5
2 ,0
0 0
$ 2
,4 0
0 $
5 2
,0 0
0
$49,200
No plant
No plant
La rge
pl an
t
$106,400
$63,600
–$87,400 Favorable market
$2,400
$48,000
$52,000
(.22)
(.78)
(.22)
(.78)
(.73)
(.27)
(.73)
(.27)
(.4)
(.6)
(.4)
(.6)
(. 45
)
(.55)
Figure A.3
Getz Products Decision Tree
with Probabilities and EMVs
Shown
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The Poker Decision Process We opened Module A with ex-dot-commer Paul Phillips’s decision to go “all in” at the Legends of Poker tournament in Los Angeles. Example A8 shows how he computed the expected value. Problem A.30 gives you a chance to create a decision tree for this process.
4. If the market survey is not conducted:
EMV (node 6) = (.6) (+200,000) + (.4) (9+180,000) = +48,000 EMV (node 7) = (.6) (+100,000) + (.4) (9+20,000) = +52,000
The EMV of no plant is $0. Thus, building a small plant is the best choice, given the marketing research is not performed.
5. Because the expected monetary value of not conducting the survey is $52,000—vs. an EMV of $49,200 for conducting the study—the best choice is to not seek marketing information . Getz should build the small plant.
INSIGHT c You can reduce complexity in a large decision tree by viewing and solving a number of smaller trees—start at the end branches of a large one. Take one decision at a time.
LEARNING EXERCISE c Getz estimates that if he conducts a market survey, there is really only a 35% chance the results will indicate a favorable market for the sheds. How does the tree change? [Answer: The EMV of conducting the survey = $38,800, so Getz should still not do it.]
RELATED PROBLEMS c A.21, A.25–A.29 (A.31, A.32 are available in MyOMLab)
As on the first page in this module, Paul Phillips is deciding whether to bet all his chips against poker star T.J. Cloutier. Phillips holds a pair of 7s. Phillips reasons that T.J. will fold (with 80% probability) if he does not have a pair of 5s or better, or very high cards like a jack, queen, king, or ace. But he also figures that a call would put $853,000 into the pot and surmises that even then, there is 45% chance his pair of 7s will win.
APPROACH c Phillips does an expected monetary analysis.
SOLUTION c If T.J. folds,
The amount of money already in the pot
EMV = (.80) (+99,000) = +79,200
If T.J. calls, the chance T.J. will call
EMV = .203(.45) (+853,000) - Phillips>s bet of +422,0004 = .20 3+383,850 - +422,0004
= .20 3 -+38,1504 = -+7,630 Overall EMV = $79,200 - $7,630 = $71,570
INSIGHT c The overall EMV of $71,570 indicates that if this decision were to be made many times, the average payoff would be large. So Phillips decides to bet almost all of his chips. As it turns out, T.J. was holding a pair of jacks. Even though Phillips’s decision in this instance did not work out, his analysis and procedure was the correct one.
LEARNING EXERCISE c What would happen if the amount of money already in the pot were only $39,000? [Answer: The overall EMV = $23,570.]
RELATED PROBLEM c A.30
Example A8 PHILLIPS’S POKER DECISION
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Summary This module examines two of the most widely used deci- sion techniques—decision tables and decision trees. These techniques are especially useful for making decisions under risk. Many decisions in research and development, plant and equipment, and even new buildings and structures can
be analyzed with these decision models. Problems in inven- tory control, aggregate planning, maintenance, scheduling, and production control also lend themselves to decision table and decision tree applications.
Key Terms
Big data (p. 679 ) Decision table (p. 680 ) Maximax (p. 681 ) Maximin (p. 681 )
Equally likely (p. 681 ) Expected monetary value (EMV) (p. 682 ) Expected value of perfect information
(EVPI) (p. 683 )
Expected value with perfect information (EVwPI) (p. 683 )
Decision tree (p. 684 )
1. Identify the six steps in the decision process. 2. Give an example of a good decision you made that resulted in
a bad outcome. Also give an example of a bad decision you made that had a good outcome. Why was each decision good or bad?
3. What is the equally likely decision model? 4. Discuss the differences between decision making under cer-
tainty, under risk, and under uncertainty. 5. What is a decision tree? 6. Explain how decision trees might be used in several of the
10 OM decisions.
7. What is the expected value of perfect information (EVPI)? 8. What is the expected value with perfect information
(EVwPI)? 9. Identify the five steps in analyzing a problem using a decision
tree. 10. Why are the maximax and maximin strategies considered to
be optimistic and pessimistic, respectively? 11. The expected value criterion is considered to be the rational
criterion on which to base a decision. Is this true? Is it rational to consider risk?
12. When are decision trees most useful?
Discussion Questions
Using Software for Decision Models
Analyzing decision tables is straightforward with Excel, Excel OM, and POM for Windows. When decision trees are involved, Excel OM or commercial packages such as DPL, Tree Plan, and Supertree provide flexibility, power, and ease. POM for Windows will also analyze trees but does not have graphic capabilities.
CREATING AN EXCEL SPREADSHEET TO EVALUATE A DECISION TABLE In Program A.1, we illustrate how you can build your own Excel spreadsheet to analyze decision making under uncertainty and under risk. The data from Getz Products in Examples A3 and A4 are used. Maximax, maximin, equally likely, and EMV are computed, along with EVPI.
X USING EXCEL OM Excel OM allows decision makers to evaluate decisions quickly and to perform sensitivity analysis on the results. Program A.2 uses Excel OM to create the decision tree for Getz Products shown earlier in Example A6. The tool to create the tree is seen in the window on the right.
P USING POM FOR WINDOWS POM for Windows can be used to calculate all of the information described in the decision tables and decision trees in this module. For details on how to use this software, please refer to Appendix IV .
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=MAX(B6:C6)
=G10
=D30-D31 =MAX(B6:B8)
=MIN(B6:C6) =AVERAGE(B6:C6)
=MAX(D6:D8)
=SUMPRODUCT(B28:C28,$B$9:$C$9)
=INDEX(A6:A8,MATCH(D10,D6:D8,0))
=INDEX(A6:A8,MATCH(E10,E6:E8,0))
=INDEX(A6:A8,MATCH(F10,F6:F8,0))
=INDEX(A6:A8,MATCH(G10,G6:G8,0))
=SUMPRODUCT(B6:C6,$B$9:$C$9)
Actions Copy D6:G6 to D7:G8 Copy D10 to E10:G10 Copy B28 to C28
Enter the Decision Table in B6:C9. The best values for the four decision criteria are in D10:G10, and the associated alternatives for those values are in B13:B16.
Use this Decision Tree Creation window to create the tree.
Expected profit for the small plant = F15*F13 + F19*F17
Maximum Profit = MAX(D7 + C9, D15 + C1)
Use the branch that leads to node 3 in order to achieve the maximum profit.
Program A.2
Getz Products’ Decision Tree Using Excel OM
Program A.1
An Excel Spreadsheet
for Analyzing Data in
Examples A3 and A4
for Getz Products
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SOLVED PROBLEM A.1 Stella Yan Hua is considering the possibility of opening a small dress shop on Fairbanks Avenue, a few blocks from the uni- versity. She has located a good mall that attracts students. Her options are to open a small shop, a medium-sized shop, or no shop at all. The market for a dress shop can be good, aver- age, or bad. The probabilities for these three possibilities are .2 for a good market, .5 for an average market, and .3 for a bad market. The net profit or loss for the medium-sized or small shops for the various market conditions are given in the adja- cent table. Building no shop at all yields no loss and no gain. What do you recommend?
STATES OF NATURE
ALTERNATIVES
GOOD MARKET
($)
AVERAGE MARKET
($)
BAD MARKET
($)
Small shop 75,000 25,000 –40,000
Medium-sized shop
100,000 35,000 –60,000
No shop 0 0 0
Probabilities .20 .50 .30
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLUTION The problem can be solved by computing the expected monetary value (EMV) for each alternative:
EMV (Small shop) = (.2) (+75,000) + (.5) (+25,000) + (.3) ( - +40,000) = +15,500 EMV (Medium@sized shop) = (.2) (+100,000) + (.5) (+35,000) + (.3) ( - +60,000) = +19,500
EMV (No shop) = (.2) (+0) + (.5) (+0) + (.3) (+0) = +0
As you can see, the best decision is to build the medium-sized shop. The EMV for this alternative is $19,500.
SOLVED PROBLEM A.2 T.S. Amer’s Ski Shop in Nevada has a 100-day season. T.S. has established the probability of various store traffic, based on his- torical records of skiing conditions, as indicated in the table to the right. T.S. has four merchandising plans, each focusing on a popular name brand. Each plan yields a daily net profit as noted in the table. He also has a meteorologist friend who, for a small fee, will accurately tell tomorrow’s weather so T.S. can implement one of his four merchandising plans. a) What is the expected monetary value (EMV) under risk? b) What is the expected value with perfect information
(EVwPI)? c) What is the expected value of perfect information (EVPI)?
TRAFFIC IN STORE BECAUSE OF SKI CONDITIONS (STATES OF NATURE)
DECISION ALTERNATIVES (MERCHANDISING PLAN FOCUSING ON:) 1 2 3 4
Patagonia $40 92 20 48
North Face 50 84 10 52
Cloud Veil 35 80 40 64
Columbia 45 72 10 60
Probabilities .20 .25 .30 .25
SOLUTION a) The highest expected monetary value under risk is:
EMV (Patagonia) = .20(40) + .25(92) + .30(20) + .25(48) = +49 EMV (North Face) = .20(50) + .25(84) + .30(10) + .25(52) = +47 EMV (Cloud Veil) = .20(35) + .25(80) + .30(40) + .25(64) = +55 EMV (Columbia) = .20(45) + .25(72) + .30(10) + .25(60) = +45
So the maximum EMV = $55 b) The expected value with perfect information is:
EVwPI = .20(50) + .25(92) + .30(40) + .25(64) = 10 + 23 + 12 + 16 = +61
c) The expected value of perfect information is:
EVPI = EVwPI - Maximum EMV = 61 - 55 = +6
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SOLVED PROBLEM A.3 Daily demand for cases of Tidy Bowl cleaner at Ravinder Nath’s Supermarket has always been 5, 6, or 7 cases. Develop a deci- sion tree that illustrates her decision alternatives as to whether to stock 5, 6, or 7 cases.
SOLUTION The decision tree is shown in Figure A.4 .
Demand is 5 cases
Demand is 6 cases
Demand is 7 cases
Demand is 5 cases
Demand is 6 casesStock 6 cases
Demand is 7 cases
Demand is 5 cases
Demand is 6 cases
Demand is 7 cases
St oc
k 5 ca
se s
Stock 7 cases
Figure A.4
Demand at Ravinder Nath’s Supermarket
Problems A.1–A.20 relate to Types of Decision-Making Environments
• A.1 Given the following conditional value table, deter- mine the appropriate decision under uncertainty using: a) Maximax b) Maximin c) Equally likely PX
STATES OF NATURE
ALTERNATIVES VERY FAVORABLE
MARKET AVERAGE MARKET
UNFAVORABLE MARKET
Build new plant $350,000 $240,000 –$300,000 Subcontract $180,000 $ 90,000 –$ 20,000 Overtime $110,000 $ 60,000 –$ 10,000 Do nothing $ 0 $ 0 $ 0
• • • A.2 Even though independent gasoline stations have been having a difficult time, Ian Langella has been thinking about starting his own independent gasoline station. Ian’s problem is to decide how large his station should be. The annual returns will depend on both the size of his station and a number of marketing factors related to the oil industry and demand for gasoline. After a careful analysis, Ian developed the following table:
SIZE OF FIRST STATION
GOOD MARKET ($)
FAIR MARKET ($)
POOR MARKET ($)
Small 50,000 20,000 –10,000
Medium 80,000 30,000 –20,000
Large 100,000 30,000 –40,000
Very large 300,000 25,000 –160,000
For example, if Ian constructs a small station and the market is good, he will realize a profit of $50,000. a) Develop a decision table for this decision, like the one illus-
trated in Table A.2 earlier. b) What is the maximax decision? c) What is the maximin decision?
d) What is the equally likely decision? e) Develop a decision tree. Assume each outcome is equally
likely, then find the highest EMV. PX
• A.3 Andrew Thomas, a sandwich vendor at Hard Rock Cafe’s annual Rockfest, created a table of conditional values for the various alternatives (stocking decision) and states of nature (size of crowd):
STATES OF NATURE (DEMAND)
ALTERNATIVES BIG AVERAGE SMALL
Large stock $22,000 $12,000 –$2,000 Average stock $14,000 $10,000 $6,000 Small stock $ 9,000 $ 8,000 $4,000
The probabilities associated with the states of nature are 0.3 for a big demand, 0.5 for an average demand, and 0.2 for a small demand. a) Determine the alternative that provides Andrew the greatest
expected monetary value (EMV). b) Compute the expected value of perfect information (EVPI).
• • A.4 Jeffrey Helm owns a health and fitness center called Bulk-Up in Harrisburg. He is considering adding more floor space to meet increasing demand. He will either add no floor space (N), a moderate area of floor space (M), a large area of floor space (L), or an area of floor space that doubles the size of the facility (D). Demand will either stay fixed, increase slightly, or increase greatly. The following are the changes in Bulk-Up’s annual profits under each combination of expansion level and demand change level:
EXPANSION LEVEL
DEMAND CHANGE N M L D
Fixed $ 0 –$4,000 –$10,000 –$50,000 Slight increase $2,000 $8,000 $ 6,000 $ 4,000 Major increase $3,000 $9,000 $20,000 $40,000
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
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Jeffrey is risk averse and wishes to use the maximin criterion. a) What are his decision alternatives and what are the states of
nature? b) What should he do? PX
• A.5 Howard Weiss, Inc., is considering building a sensi- tive new radiation scanning device. His managers believe that there is a probability of 0.4 that the ATR Co. will come out with a competitive product. If Weiss adds an assembly line for the prod- uct and ATR Co. does not follow with a competitive product, Weiss’s expected profit is $40,000; if Weiss adds an assembly line and ATR follows suit, Weiss still expects $10,000 profit. If Weiss adds a new plant addition and ATR does not produce a com- petitive product, Weiss expects a profit of $600,000; if ATR does compete for this market, Weiss expects a loss of $100,000. a) Determine the EMV of each decision. b) Compute the expected value of perfect information. PX
• • • A.6 Jerry Bildery’s factory is considering three approaches for meeting an expected increase in demand. These three approaches are increasing capacity, using overtime, and buying more equipment. Demand will increase either slightly (S), mod- erately (M), or greatly (G). The profits for each approach under each possible scenario are as follows:
DEMAND SCENARIO
APPROACH S M G
Increasing capacity $700,000 $700,000 $ 700,000 Using overtime $500,000 $600,000 $1,000,000 Buying equipment $600,000 $800,000 $ 800,000
Since the goal is to maximize, and Jerry is risk-neutral, he decides to use the equally likely decision criterion to make the decision as to which approach to use. According to this criterion, which approach should be used?
• A.7 The following payoff table provides profits based on various possible decision alternatives and various levels of demand at Robert Klassan’s print shop:
DEMAND
LOW HIGH
Alternative 1 $10,000 $30,000 Alternative 2 $ 5,000 $40,000 Alternative 3 –$ 2,000 $50,000
The probability of low demand is 0.4, whereas the probability of high demand is 0.6. a) What is the highest possible expected monetary value? b) What is the expected value with perfect information (EVwPI)? c) Calculate the expected value of perfect information for this
situation. PX
• A.8 Leah Johnson, director of Urgent Care of Brookline, wants to increase capacity to provide low-cost flu shots but must decide whether to do so by hiring another full-time nurse or by using part-time nurses. The table below shows the expected costs of the two options for three possible demand levels:
STATES OF NATURE
ALTERNATIVES LOW
DEMAND MEDIUM DEMAND
HIGH DEMAND
Hire full-time $300 $500 $ 700 Hire part-time $ 0 $350 $1,000 Probabilities .2 .5 .3
a) Using expected value, what should Ms. Johnson do? b) Draw an appropriate decision tree showing payoffs and
probabilities. PX
• • A.9 Zhu Manufacturing is considering the introduction of a family of new products. Long-term demand for the product group is somewhat predictable, so the manufacturer must be con- cerned with the risk of choosing a process that is inappropriate. Faye Zhu is VP of operations. She can choose among batch man- ufacturing or custom manufacturing, or she can invest in group technology. Faye won’t be able to forecast demand accurately until after she makes the process choice. Demand will be classi- fied into four compartments: poor, fair, good, and excellent. The table below indicates the payoffs (profits) associated with each process/demand combination, as well as the probabilities of each long-term demand level:
POOR FAIR GOOD EXCELLENT
Probability .1 .4 .3 .2 Batch –$ 200,000 $1,000,000 $1,200,000 $1,300,000 Custom $ 100,000 $ 300,000 $ 700,000 $ 800,000 Group technology –$1,000,000 –$ 500,000 $ 500,000 $2,000,000
a) Based on expected value, what choice offers the greatest gain? b) What would Faye Zhu be willing to pay for a forecast
that would accurately determine the level of demand in the future? PX
• • A.10 Consider the following decision table, which Joe Blackburn has developed for Vanderbilt Enterprises:
STATES OF NATURE
DECISION ALTERNATIVES LOW MEDIUM HIGH
A $40 $100 $60
B $85 $ 60 $70
C $60 $ 70 $70
D $65 $ 75 $70
E $70 $ 65 $80
Probability .40 .20 .40
Which decision alternative maximizes the expected value of the payoff ? PX
• • A.11 The University of Miami bookstore stocks textbooks in preparation for sales each semester. It normally relies on departmental forecasts and preregistration records to determine how many copies of a text are needed. Preregistration shows 90 operations management students enrolled, but bookstore man- ager Vaidy Jayaraman has second thoughts, based on his intuition and some historical evidence. Vaidy believes that the distribution of sales may range from 70 to 90 units, according to the following probability model:
Demand 70 75 80 85 90 Probability .15 .30 .30 .20 .05
This textbook costs the bookstore $82 and sells for $112. Any unsold copies can be returned to the publisher, less a restocking fee and shipping, for a net refund of $36. a) Construct the table of conditional profits. b) How many copies should the bookstore stock to achieve high-
est expected value? PX
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• • A.12 Palmer Jam Company is a small manufacturer of sev- eral different jam products. One product is an organic jam that has no preservatives, sold to retail outlets. Susan Palmer must decide how many cases of jam to manufacture each month. The probability that demand will be 6 cases is .1, for 7 cases it is .3, for 8 cases it is .5, and for 9 cases it is .1. The cost of every case is $45, and the price Susan gets for each case is $95. Unfortunately, any cases not sold by the end of the month are of no value as a result of spoilage. How many cases should Susan manufacture each month? PX
• • A.13 Deborah Hollwager, a concessionaire for the Amway Center in Orlando, has developed a table of conditional values for the various alternatives (stocking decisions) and states of nature (size of crowd):
STATES OF NATURE (SIZE OF CROWD)
ALTERNATIVES LARGE AVERAGE SMALL
Large inventory $20,000 $10,000 –$2,000
Average inventory $15,000 $12,000 $6,000
Small inventory $ 9,000 $ 6,000 $5,000
If the probabilities associated with the states of nature are 0.3 for a large crowd, 0.5 for an average crowd, and 0.2 for a small crowd, determine: a) The alternative that provides the greatest expected monetary
value (EMV). b) The expected value of perfect information (EVPI). PX
• • • • A.14 The city of Belgrade, Serbia, is contemplating building a second airport to relieve congestion at the main airport and is considering two potential sites, X and Y. Hard Rock Hotels would like to purchase land to build a hotel at the new airport. The value of land has been rising in anticipation and is expected to sky- rocket once the city decides between sites X and Y. Consequently, Hard Rock would like to purchase land now. Hard Rock will sell the land if the city chooses not to locate the airport nearby. Hard Rock has four choices: (1) buy land at X, (2) buy land at Y, (3) buy land at both X and Y, or (4) do nothing. Hard Rock has collected the following data (which are in millions of euros):
SITE X SITE Y
Current purchase price 27 15 Profi ts if airport and hotel built at this site* 45 30 Sale price if airport not built at this site 9 6
* The second row of the table represents net operating profi ts from the hotel, not including the upfront cost of land.
Hard Rock determines there is a 45% chance the airport will be built at X (hence, a 55% chance it will be built at Y). a) Set up the decision table. b) What should Hard Rock decide to do to maximize total net
profit? PX
The other option is to build a pilot plant and then decide whether to build a complete facility. The pilot plant would cost $10,000 to build. Lau estimates a 50–50 chance that the pilot plant will work. If the pilot plant works, there is a 90% probabil- ity that the complete plant, if it is built, will also work. If the pilot plant does not work, there is only a 20% chance that the com- plete project (if it is constructed) will work. Lau faces a dilemma. Should he build the plant? Should he build the pilot project and then make a decision? Help Lau by analyzing this problem. PX
• • A.22 Dwayne Whitten, president of Whitten Industries, is considering whether to build a manufacturing plant in north Texas. His decision is summarized in the following table:
ALTERNATIVES FAVORABLE MARKET UNFAVORABLE MARKET
Build large plant $400,000 –$300,000
Build small plant $ 80,000 –$ 10,000
Don’t build $ 0 $ 0
Market probabilities 0.4 0.6
a) Construct a decision tree. b) Determine the best strategy using expected monetary value
(EMV). c) What is the expected value of perfect information (EVPI)? PX
• • A.23 Deborah Kellogg buys Breathalyzer test sets for the Winter Park Police Department. The quality of the test sets from her two suppliers is indicated in the following table:
PERCENT DEFECTIVE
PROBABILITY FOR WINTER PARK TECHNOLOGY
PROBABILITY FOR DAYTON
ENTERPRISES
1 .70 .30 3 .20 .30 5 .10 .40
For example, the probability of getting a batch of tests that are 1% defective from Winter Park Technology is .70. Because Kellogg orders 10,000 tests per order, this would mean that there is a .70 probability of getting 100 defective tests out of the 10,000 tests if Winter Park Technology is used to fill the order. A defec- tive Breathalyzer test set can be repaired for $0.50. Although the quality of the test sets of the second supplier, Dayton Enterprises, is lower, it will sell an order of 10,000 test sets for $37 less than Winter Park.
Additional problems A.15–A.20 are available in MyOMLab.
Problems A.21–A.32 relate to Decision Trees
• • • A.21 Ronald Lau, chief engineer at South Dakota Electronics, has to decide whether to build a new state-of-the-art processing facility. If the new facility works, the company could realize a profit of $200,000. If it fails, South Dakota Electronics could lose $180,000. At this time, Lau estimates a 60% chance that the new process will fail.
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a) Develop a decision tree. b) Which supplier should Kellogg use? PX
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M O D U L E A | D E C I S I O N - M A K I N G T O O L S 695
• A.24 Joseph Biggs owns his own ice cream truck and lives 30 miles from a Florida beach resort. The sale of his products is highly dependent on his location and on the weather. At the resort, his profit will be $120 per day in fair weather, $10 per day in bad weather. At home, his profit will be $70 in fair weather and $55 in bad weather. Assume that on any particular day, the weather service suggests a 40% chance of foul weather. a) Construct Joseph’s decision tree. b) What decision is recommended by the expected value
criterion? PX
• • A.25 Jonatan Jelen is considering opening a bicycle shop in New York City. Jonatan enjoys biking, but this is to be a business endeavor from which he expects to make a living. He can open a small shop, a large shop, or no shop at all. Because there will be a 5-year lease on the building that Jonatan is thinking about using, he wants to make sure he makes the correct decision. Jonatan is also thinking about hiring his old marketing professor to con- duct a marketing research study to see if there is a market for his services. The results of such a study could be either favorable or unfavorable. Develop a decision tree for Jonatan. PX
• • A.26 F. J. Brewerton Retailers, Inc., must decide whether to build a small or a large facility at a new location in Omaha. Demand at the location will either be low or high, with probabili- ties 0.4 and 0.6, respectively. If Brewerton builds a small facility and demand proves to be high, he then has the option of expand- ing the facility. If a small facility is built and demand proves to be high, and then the retailer expands the facility, the payoff is $270,000. If a small facility is built and demand proves to be high, but Brewerton then decides not to expand the facility, the payoff is $223,000.
If a small facility is built and demand proves to be low, then there is no option to expand and the payoff is $200,000. If a large facility is built and demand proves to be low, Brewerton then has the option of stimulating demand through local advertising. If he does not exercise this option, then the payoff is $40,000. If he does exercise the advertising option, then the response to advertising will either be modest or sizable, with probabilities of 0.3 and 0.7, respectively. If the response is modest, the payoff is $20,000. If it is sizable, the payoff is $220,000. Finally, if a large facility is built and demand proves to be high, then no advertising is needed and the payoff is $800,000. a) What should Brewerton do to maximize his expected payoff ? b) What is the value of this expected payoff ?
• • • A.27 Philip Musa can build either a large electronics sec- tion or a small one in his Birmingham drugstore. He can also gather additional information or simply do nothing. If he gath- ers additional information, the results could suggest either a favorable or an unfavorable market, but it would cost him $3,000 to gather the information. Musa believes that there is a 50–50 chance that the information will be favorable. If the market is favorable, Musa will earn $15,000 with a large section or $5,000 with a small one. With an unfavorable electronics market, how- ever, Musa could lose $20,000 with a large section or $10,000 with a small section. Without gathering additional information, Musa estimates that the probability of a favorable market is .7. A favorable report from the study would increase the prob- ability of a favorable market to .9. Furthermore, an unfavora- ble report from the additional information would decrease
the probability of a favorable market to .4. Of course, Musa could ignore these numbers and do nothing. What is your advice to Musa?
• • • • A.28 Jeff Kaufmann’s machine shop sells a variety of machines for job shops. A customer wants to purchase a model XPO2 drilling machine from Jeff’s store. The model XPO2 sells for $180,000, but Jeff is out of XPO2s. The customer says he will wait for Jeff to get a model XPO2 in stock. Jeff knows that there is a wholesale market for XPO2s from which he can purchase an XPO2. Jeff can buy an XPO2 today for $150,000, or he can wait a day and buy an XPO2 (if one is available) tomorrow for $125,000. If at least one XPO2 is still available tomorrow, Jeff can wait until the day after tomorrow and buy an XPO2 (if one is still available) for $110,000.
There is a 0.40 probability that there will be no model XPO2s available tomorrow. If there are model XPO2s available tomor- row, there is a 0.70 probability that by the day after tomorrow, there will be no model XPO2s available in the wholesale mar- ket. Three days from now, it is certain that no model XPO2s will be available on the wholesale market. What is the maximum expected profit that Jeff can achieve? What should Jeff do?
• • • • A.29 Louisiana is busy designing new lottery scratch- off games. In the latest game, Bayou Boondoggle, the player is instructed to scratch off one spot: A, B, or C. A can reveal “Loser,” “Win $1,” or “Win $50.” B can reveal “Loser” or “Take a Second Chance.” C can reveal “Loser” or “Win $500.” On the second chance, the player is instructed to scratch off D or E. D can reveal “Loser” or “Win $1.” E can reveal “Loser” or “Win $10.” The probabilities at A are .9, .09, and .01. The probabilities at B are .8 and .2. The probabilities at C are .999 and .001. The probabilities at D are .5 and .5. Finally, the probabilities at E are .95 and .05. Draw the decision tree that represents this scenario. Use proper symbols and label all branches clearly. Calculate the expected value of this game.
• • • • A.30 On the opening page of Module A and in Example A8, we follow the poker decision made by Paul Phillips against vet- eran T.J. Cloutier. Create a decision tree that corresponds with the decision made by Phillips. PX
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Additional problems A.31–A.32 are available in MyOMLab.
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696 P A R T 4 | B U S I N E S S A N A LY T I C S M O D U L E S
The requirement of opening all windows is particularly wor- risome to CCI because it means turning off the alarm system, leaving the warehouse vulnerable to burglary for 48 hours—the required amount of time for the poisonous gas to do its job. Therefore, Alex Ferrari, the warehouse manager, is thinking about hiring a security company to monitor the facility during that period. CCI’s property insurance deductible is $25,000, and Alex is assuming that if thieves are willing to enter a building full of poisonous gas to steal something, they would certainly take more than $25,000 worth of parts—or even an entire car!
After making a few phone calls, Alex gets in touch with ProGuard, a trustworthy local security company that charges $150 per hour to have a security guard stationed outside their warehouse. The city of Miami police records indicate that about 30% of businesses that left their facilities unattended during tent- ing reported stolen property in the past 3 years. Although Alex thinks ProGuard’s prices are reasonable, and having a guard out- side the warehouse would certainly help, he is still not sure whether it is worth spending the extra money. After all, ProGuard’s con- tract does not guarantee the protection it provides is infallible. In fact, an analysis of the company’s records indicates that 3% of their clients were burglarized over the past 3 years. (Despite this figure, ProGuard is still the best security company in the area.)
Discussion Questions
1. Create a decision tree analysis to help decide whether Alex Ferrari should hire ProGuard’s services.
2. Come up with a simple rule of thumb that can be applied to decisions of this nature, given any deductible amount d , extra surveillance cost c , and burglary probabilities p 1 (without sur- veillance) and p 2 (with surveillance).
3. Does your decision based on the tree guarantee success? Why or why not?
Source: Professor Tallys Yunes, University of Miami. Reprinted with permission.
CASE STUDY Warehouse Tenting at the Port of Miami
The Collector’s Choice Inc. (CCI), a luxury car import company, has an old warehouse at the Port of Miami, Florida, where it tem- porarily stores expensive sports cars and automotive parts that arrive from Europe. This summer, CCI has noticed that the ter- mite infestation in the warehouse has escalated to a point where tenting cannot be postponed anymore. (Tenting is the process of wrapping a building inside a huge tent that is subsequently filled with a poisonous gas capable of killing most forms of life inside, including insects, plants, pets, and human beings.) CCI’s pest control company’s contract specifies a to-do list of pre-tenting tasks, which include: ◆ Turning off all air-conditioning units and opening all windows
of the warehouse ◆ Turning off all internal and external lights, including those
operating on a timer ◆ Pruning all outdoor vegetation at least 18" away from the
warehouse ◆ Soaking the soil around the warehouse on the first day of tenting
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Endnote
• Additional Case Studies: Visit MyOMLab for these additional free case studies: Arctic, Inc.: A refrigeration company has several major options with regard to capacity and expansion. Ski Right Corp.: Which of four manufacturers should be selected to manufacture ski helmets? Tom Tucker’s Liver Transplant: An executive must decide whether or not to opt for a dangerous surgery.
1. To review these other statistical terms, refer to Tutorial 1, “Statistical Review for Managers,” in MyOMLab.
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Main Heading Review Material MyOMLab THE DECISION PROCESS IN OPERATIONS (pp. 678 – 679 )
To achieve the goals of their organizations, managers must understand how decisions are made and know which decision-making tools to use. Overcoming uncertainty is a manager’s mission. Decision tables and decision trees are used in a wide number of OM situations. j Big data —The huge amount of economic, production, and consumer data now
being collected in digital form.
Concept Questions: 1.1–1.4
FUNDAMENTALS OF DECISION MAKING (pp. 679 – 680 )
Alternative —A course of action or strategy that may be chosen by a decision maker. State of nature —An occurrence or a situation over which a decision maker has little or no control. Symbols used in a decision tree: 1. —A decision node from which one of several alternatives may be selected. 2. —A state-of-nature node out of which one state of nature will occur. When constructing a decision tree, we must be sure that all alternatives and states of nature are in their correct and logical places and that we include all possible alternatives and states of nature, usually including the “do nothing” option.
Concept Questions: 2.1–2.4
DECISION TABLES (p. 680 )
j Decision table —A tabular means of analyzing decision alternatives and states of nature.
A decision table is sometimes called a payoff table. For any alternative and a particular state of nature, there is a consequence , or an outcome , which is usually expressed as a monetary value; this is called the conditional value .
Concept Questions: 3.1–3.4
TYPES OF DECISION-MAKING ENVIRONMENTS (pp. 681 – 684 )
There are three decision-making environments: (1) decision making under uncer- tainty, (2) decision making under risk, and (3) decision making under certainty. When there is complete uncertainty about which state of nature in a decision environment may occur (i.e., when we cannot even assess probabilities for each possible outcome), we rely on three decision methods: (1) maximax, (2) maximin, and (3) equally likely. j Maximax —A criterion that finds an alternative that maximizes the maximum
outcome. j Maximin —A criterion that finds an alternative that maximizes the minimum
outcome. j Equally likely —A criterion that assigns equal probability to each state of nature. Maximax is also called an “optimistic” decision criterion, while maximin is also called a “pessimistic” decision criterion. Maximax and maximin present best case/worst case planning scenarios. Decision making under risk relies on probabilities. The states of nature must be mutually exclusive and collectively exhaustive, and their probabilities must sum to 1. j Expected monetary value (EMV) —The expected payout or value of a variable
that has different possible states of nature, each with an associated probability. The EMV represents the expected value or mean return for each alternative if we could repeat this decision (or similar types of decisions) a large number of times . The EMV for an alternative is the sum of all possible payoffs from the alternative, each weighted by the probability of that payoff occurring:
EMV (Alternative i) = (Payoff of 1st state of nature) * (Probability of 1st of state of nature) + (Payoff of 2nd state of nature) * (Probability of 2nd state of nature) + . . . + (Payoff of last state of nature) * (Probability of last state of nature) j Expected value of perfect information (EVPI) —The difference between the payoff
under perfect information and the payoff under risk. j Expected value with perfect information (EVwPI) —The expected (average) return
if perfect information is available. EVPI represents an upper bound on what you would be willing to spend on state-of-nature information:
EVPI = EVwPI - Maximum EMV EVwPI = (Best outcome for 1st state of nature) × (Probability of 1st state of nature) + (Best outcome for 2nd state of nature) × (Probability of 2nd state of nature) + . . . + (Best outcome for last state of nature) × (Probability of last state of nature)
Concept Questions: 4.1–4.4
Problems: A.1–A.20
Virtual Office Hours for Solved Problems: A.1, A.2
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Main Heading Review Material MyOMLab DECISION TREES (pp. 684 – 688 )
When there are two or more sequential decisions, and later decisions are based on the outcome of prior ones, the decision tree (as opposed to decision table) approach becomes appropriate. j Decision tree— A graphical means of analyzing decision alternatives and states of
nature. Analyzing problems with decision trees involves five steps: 1. Define the problem. 2. Structure or draw the decision tree. 3. Assign probabilities to the states of nature. 4. Estimate payoffs for each possible combination of decision alternatives and
states of nature. 5. Solve the problem by computing the expected monetary values (EMV) for each
state-of-nature node. This is done by working backward —that is, by starting at the right of the tree and working back to decision nodes on the left:
-$6,000 $20,000
$4,000
$6,000
$2,000
$10,000
$4,400
$4,800
$5,200 (.4)
(.4)
(.4)
(.6)
(.6)
(.6)
$5,200
Decision trees force managers to examine all possible outcomes, including unfavorable ones. A manager is also forced to make decisions in a logical, sequential manner. Short parallel lines on a decision tree mean “prune” that branch, as it is less favorable than another available option and may be dropped.
Concept Questions: 5.1–5.4
Problems: A.21–A.32
Virtual Office Hours for Solved Problem: A.3
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Module A Rapid Review continued
LO A.1 On a decision tree, at each state-of-nature node: a) the alternative with the greatest EMV is selected. b) an EMV is calculated. c) all probabilities are added together. d) the branch with the highest probability is selected. LO A.2 In decision table terminology, a course of action or a strategy
that may be chosen by a decision maker is called a(n): a) payoff. b) alternative. c) state of nature. d) all of the above. LO A.3 If probabilities are available to the decision maker, then the
decision-making environment is called: a) certainty. b) uncertainty. c) risk. d) none of the above. LO A.4 What is the EMV for Alternative 1 in the following decision
table?
STATE OF NATURE
Alternative S 1 S 2 A1 $15,000 $20,000 A2 $10,000 $30,000
Probability 0.30 0.70
a) $15,000 b) $17,000 c) $17,500 d) $18,500 e) $20,000
LO A.5 The most that a person should pay for perfect information is: a) the EVPI. b) the maximum EMV minus the minimum EMV. c) the minimum EMV. d) the maximum EMV. LO A.6 On a decision tree, once the tree has been drawn and the
payoffs and probabilities have been placed on the tree, the analysis (computing EMVs and selecting the best alternative):
a) is done by working backward (starting on the right and moving to the left).
b) is done by working forward (starting on the left and moving to the right).
c) is done by starting at the top of the tree and moving down.
d) is done by starting at the bottom of the tree and moving up.
LO A.7 A decision tree is preferable to a decision table when: a) a number of sequential decisions are to be made. b) probabilities are available. c) the maximax criterion is used. d) the objective is to maximize regret.
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the module and the key terms listed at the end of the module.
Answers: LO A.1. b; LO A.2. b; LO A.3. c; LO A.4. d; LO A.5. a; LO A.6. a; LO A.7. a.
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699
M O D U L E O U T L I N E
◆
Why Use Linear Programming? 700
◆
Requirements of a Linear Programming Problem 701
◆
Formulating Linear Programming Problems 701
◆
Graphical Solution to a Linear Programming Problem 702
◆
Sensitivity Analysis 705
◆
Solving Minimization Problems 708
◆
Linear Programming Applications 710
◆
The Simplex Method of LP 713
◆
Integer and Binary Variables 713
Linear Programming
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700
L E A R N I N G OBJEC TI V ES
LO B.1 Formulate linear programming models, including an objective function and constraints 702
LO B.2 Graphically solve an LP problem with the iso-profi t line method 704
LO B.3 Graphically solve an LP problem with the corner-point method 705
LO B.4 Interpret sensitivity analysis and shadow prices 706
LO B.5 Construct and solve a minimization problem 709
LO B.6 Formulate production-mix, diet, and labor scheduling problems 710
Why Use Linear Programming? Many operations management decisions involve trying to make the most effective use of an organization’s resources. Resources typically include machinery (such as planes, in the case of an airline), labor (such as pilots), money, time, and raw materials (such as jet fuel). These resources may be used to produce products (such as machines, furniture, food, or clothing) or services (such as airline schedules, advertising policies, or investment decisions). Linear programming (LP) is a widely used mathematical technique designed to help operations managers plan and make the decisions necessary to allocate resources.
A few examples of problems in which LP has been successfully applied in operations management are:
1. Scheduling school buses to minimize the total distance traveled when carrying students 2. Allocating police patrol units to high crime areas to minimize response time to 911 calls 3. Scheduling tellers at banks so that needs are met during each hour of the day while
minimizing the total cost of labor 4. Selecting the product mix in a factory to make best use of machine- and labor-hours
available while maximizing the firm’s profit 5. Picking blends of raw materials in feed mills to produce finished feed combinations at
minimum cost 6. Determining the distribution system that will minimize total shipping cost from several
warehouses to various market locations 7. Developing a production schedule that will satisfy future demands for a firm’s product
and at the same time minimize total production and inventory costs 8. Allocating space for a tenant mix in a new shopping mall to maximize revenues to the
leasing company
The storm front closed in quickly on Boston’s
Logan Airport, shutting it down without warning.
The heavy snowstorms and poor visibility sent
airline passengers and ground crew scurrying.
Because airlines use linear programming (LP)
to schedule flights, hotels, crews, and refueling,
LP has a direct impact on profitability. If an
airline gets a major weather disruption at one
of its hubs, a lot of flights may get canceled,
which means a lot of crews and airplanes in the
wrong places. LP is the tool that helps airlines
unsnarl and cope with this weather mess.
P a u l It a lia
n o /A
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y Linear programming (LP)
A mathematical technique
designed to help operations man-
agers plan and make decisions
necessary to allocate resources.
VIDEO B.1 Scheduling Challenges at Alaska
Airlines
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M O D U L E B | L I N E A R P R O G R A M M I N G 701
Requirements of a Linear Programming Problem All LP problems have four requirements: an objective, constraints, alternatives, and linearity: 1. LP problems seek to maximize or minimize some quantity (usually profit or cost). We
refer to this property as the objective function of an LP problem. The major objective of a typical firm is to maximize dollar profits in the long run. In the case of a trucking or air- line distribution system, the objective might be to minimize shipping costs.
2. The presence of restrictions, or constraints , limits the degree to which we can pursue our objec- tive. For example, deciding how many units of each product in a firm’s product line to man- ufacture is restricted by available labor and machinery. We want, therefore, to maximize or minimize a quantity (the objective function) subject to limited resources (the constraints).
3. There must be alternative courses of action to choose from. For example, if a company produces three different products, management may use LP to decide how to allocate among them its limited production resources (of labor, machinery, and so on). If there were no alternatives to select from, we would not need LP.
4. The objective and constraints in linear programming problems must be expressed in terms of linear equations or inequalities. Linearity implies proportionality and additivity. If x 1 and x 2 are decision variables, there can be no products (e.g., x 1 x 2 ) or powers (e.g., x 1
3 ) in the objective or constraints. For example, the expression 5 x 1 + 8 x 2 # 250 is linear; however, the expression 5 x 1 + 8 x 2 2 2 x 1 x 2 # 300 is not linear.
Formulating Linear Programming Problems One of the most common linear programming applications is the product-mix problem . Two or more products are usually produced using limited resources. The company would like to determine how many units of each product it should produce to maximize overall profit given its limited resources. Let’s look at an example.
Glickman Electronics Example The Glickman Electronics Company in Washington, DC, produces two products: (1) the Glickman x-pod and (2) the Glickman BlueBerry. The production process for each product is similar in that both require a certain number of hours of electronic work and a certain number of labor-hours in the assembly department. Each x-pod takes 4 hours of electronic work and 2 hours in the assembly shop. Each BlueBerry requires 3 hours in electronics and 1 hour in assembly. During the current production period, 240 hours of electronic time are available, and 100 hours of assembly department time are available. Each x-pod sold yields a profit of $7; each BlueBerry produced may be sold for a $5 profit.
Glickman’s problem is to determine the best possible combination of x-pods and BlueBerrys to manufacture to reach the maximum profit. This product-mix situation can be formulated as a linear programming problem.
We begin by summarizing the information needed to formulate and solve this problem (see Table B.1 ). Further, let’s introduce some simple notation for use in the objective function and constraints. Let:
X1 = number of x@pods to be produced X2 = number of BlueBerrys to be produced
STUDENT TIP Here we set up an LP example
that we will follow for most of this
module.
Objective function
A mathematical expression
in linear programming that
maximizes or minimizes some
quantity (often profit or cost, but
any goal may be used).
Constraints
Restrictions that limit the degree
to which a manager can pursue an
objective.
ACTIVE MODEL B.1 This example is further illustrated in
Active Model B.1 in MyOMLab.
TABLE B.1 Glickman Electronics Company Problem Data
HOURS REQUIRED TO PRODUCE ONE UNIT
DEPARTMENT X-PODS (X 1 ) BLUEBERRYS (X 2 ) AVAILABLE HOURS THIS WEEK
Electronic 4 3 240
Assembly 2 1 100
Profi t per unit $7 $5
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702 P A R T 4 | B U S I N E S S A N A LY T I C S M O D U L E S
LO B.1 Formulate linear programming models,
including an objective
function and constraints
Now we can create the LP objective function in terms of X1 and X2:
Maximize profit = +7X1 + +5X2
Our next step is to develop mathematical relationships to describe the two constraints in this problem. One general relationship is that the amount of a resource used is to be less than or equal to ( … ) the amount of resource available .
First constraint: Electronic time used is … Electronic time available. 4X1 + 3X2 … 240 (hours of electronic time)
Second constraint: Assembly time used is … Assembly time available.
2X1 + 1X2 … 100 (hours of assembly time)
Both these constraints represent production capacity restrictions and, of course, affect the total profit. For example, Glickman Electronics cannot produce 70 x-pods during the produc- tion period because if X1 = 70, both constraints will be violated. It also cannot make X1 = 50 x-pods and X2 = 10 BlueBerrys. This constraint brings out another important aspect of linear programming; that is, certain interactions will exist between variables. The more units of one product that a firm produces, the fewer it can make of other products.
Graphical Solution to a Linear Programming Problem The easiest way to solve a small LP problem such as that of the Glickman Electronics Company is the graphical solution approach . The graphical procedure can be used only when there are two decision variables (such as number of x-pods to produce, X1, and number of BlueBerrys to produce, X2 ). When there are more than two variables, it is not possible to plot the solution on a two-dimensional graph; we then must turn to more complex approaches described later in this module.
Graphical Representation of Constraints To find the optimal solution to a linear programming problem, we must first identify a set, or region, of feasible solutions. The first step in doing so is to plot the problem’s constraints on a graph.
The variable X1 (x-pods, in our example) is usually plotted as the horizontal axis of the graph, and the variable X2 (BlueBerrys) is plotted as the vertical axis. The complete problem may be restated as:
Maximize profit = +7X1 + +5X2 Subject to the constraints:
4X1 + 3X2 … 240 (electronics constraint) 2X1 + 1X2 … 100 (assembly constraint)
X1 Ú 0 (number of x@pods produced is greater than or equal to 0) X2 Ú 0 (number of BlueBerrys produced is greater than or equal to 0)
(These last two constraints are also called nonnegativity constraints .) The first step in graphing the constraints of the problem is to convert the constraint in-
equalities into equalities (or equations): Constraint A: 4X1 + 3X2 = 240 Constraint B: 2X1 + 1X2 = 100
The equation for constraint A is plotted in Figure B.1 and for constraint B in Figure B.2 . To plot the line in Figure B.1 , all we need to do is to find the points at which the line
4X1 + 3X2 = 240 intersects the X1 and X2 axes. When X1 = 0 (the location where the line touches the X2 axis), it implies that 3X2 = 240 and that X2 = 80. Likewise, when X2 = 0, we see that 4X1 = 240 and that X1 = 60. Thus, constraint A is bounded by the line running from (X1 = 0, X2 = 80) to (X1 = 60, X2 = 0). The shaded area represents all points that satisfy the original inequality .
Constraint B is illustrated similarly in Figure B.2 . When X1 = 0, then X2 = 100; and when X2 = 0, then X1 = 50. Constraint B, then, is bounded by the line between
Graphical solution approach
A means of plotting a solution to a
two-variable problem on a graph.
Decision variables
Choices available to a decision
maker.
STUDENT TIP We named the decision variables
X 1 and X
2 here, but any notations
(e.g., x-p and B or X and Y ) would
do as well.
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(X1 = 0, X2 = 100) and (X1 = 50, X2 = 0). The shaded area represents the original inequality.
Figure B.3 shows both constraints together (along with the nonnegativity constraints). The shaded region is the part that satisfies all restrictions. The shaded region in Figure B.3 is called the area of feasible solutions , or simply the feasible region . This region must satisfy all conditions specified by the program’s constraints and is thus the region where all constraints overlap. Any point in the region would be a feasible solution to the Glickman Electronics Company problem. Any point outside the shaded area would represent an infeasible solution . Hence, it would be feasible to manufacture 30 x-pods and 20 BlueBerrys (X1 = 30, X2 = 20), but it would violate the constraints to pro- duce 70 x-pods and 40 BlueBerrys. This can be seen by plotting these points on the graph of Figure B.3 .
Iso-Profit Line Solution Method Now that the feasible region has been graphed, we can proceed to find the optimal solution to the problem. The optimal solution is the point lying in the feasible region that produces the highest profit.
Once the feasible region has been established, several approaches can be taken in solving for the optimal solution. The speediest one to apply is called the iso-profit line method . 1
We start by letting profits equal some arbitrary but small dollar amount. For the Glickman Electronics problem, we may choose a profit of $210. This is a profit level that can easily be obtained without violating either of the two constraints. The ob- jective function can be written as +210 = 7X1 + 5X2.
This expression is just the equation of a line; we call it an iso-profit line . It represents all combinations (of X1, X2 ) that will yield a total profit of $210. To plot the profit line, we proceed exactly as we did to plot a constraint line. First, let X1 = 0 and solve for the point at which the line crosses the X2 axis:
+210 = +7(0) + +5X2 X2 = 42 BlueBerrys
Then let X2 = 0 and solve for X1:
+210 = +7X1 + +5(0) X1 = 30 x@pods
N u m
b e r
o f B
lu e B
e rr
ys
0
Number of x-pods
X1
X2
20
40
60
80
100
Constraint A
(X1 = 0, X2 = 80)
(X1 = 60, X2 = 0)
20 40 60 80 100
Figure B.1
Constraint A
N u m
b e r
o f B
lu e B
e rr
ys
0
Number of x-pods
X1
X2
20
40
60
80
100
Constraint B
(X1 = 0, X2 = 100)
(X1 = 50, X2 = 0)
20 40 60 80 100
Figure B.2
Constraint B
Feasible region
The set of all feasible
combinations of decision variables.
20 40 100 Number of x-pods
N u m
b e r
o f B
lu e B
e rr
ys
0 X 1
X 2
40
60
80
100
Electronics (constraint A)
Assembly (constraint B)
60 80
Feasible region
20
Figure B.3
Feasible Solution Region for the Glickman Electronics Company
Problem
Iso-profit line method
An approach to solving a
linear programming maximization
problem graphically.
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We can now connect these two points with a straight line. This profit line is illustrated in Figure B.4 . All points on the line represent feasible solutions that produce a profit of $210.
We see, however, that the iso-profit line for $210 does not produce the highest possible profit to the firm. In Figure B.5 , we try graphing three more lines, each yielding a higher profit. The middle equation, +280 = +7X1 + +5X2, was plotted in the same fashion as the lower line. When X1 = 0:
+280 = +7(0) + $5X2 X2 = 56 BlueBerrys
When X2 = 0:
+280 = +7X1 + +5(0) X1 = 40 x@pods
Again, any combination of x-pods (X1) and BlueBerrys (X2) on this iso-profit line will produce a total profit of $280.
Note that the third line generates a profit of $350, even more of an improvement. The farther we move from the 0 origin, the higher our profit will be. Another important point to note is that
these iso-profit lines are parallel. We now have two clues how to find the optimal solution to the original problem. We can draw a series of parallel profit lines (by carefully moving our ruler in a plane parallel to the first profit line). The highest profit line that still touches some point of the feasible region will pinpoint the optimal solution. Notice that the fourth line ($420) is too high to count because it does not touch the feasible region.
The highest possible iso-profit line is illustrated in Figure B.6 . It touches the tip of the feasible region at the point where the two resource constraints intersect. To find its coordinates accurately , we will have to solve for the intersection of the two constraint lines. As you may recall from algebra, we can apply the method of simultaneous equations to the two constraint equations:
4X1 + 3X2 = 240 (electronics time) 2X1 + 1X2 = 100 (assembly time)
To solve these equations simultaneously, we multiply the second equation by 22:
- 2(2X1 + 1X2 = 100) = - 4X1 - 2X2 = - 200
LO B.2 Graphically solve an LP problem with
the iso-profit line method
20 40 100 Number of x-pods
N u
m b
e r
o f
B lu
e B
e rr
ys
0 X 1
X 2
40
60
80
100
(30, 0)
$210 = $7
60 80
(0, 42)
20
X1 + $5X2
Figure B.4
A Profit Line of $210 Plotted for the Glickman Electronics Company
20 40 100 Number of x-pods
N u
m b
e r
o f
B lu
e B
e rr
ys
0 X1
X 2
40
60
80
100
60 80
20
$350 = $7X1+ $5X2
$280 = $7X1 + $5X2
$210 = $7X1 + $5X2
$420 = $7X1 + $5X2
Figure B.5
Four Iso-Profit Lines Plotted for the Glickman Electronics Company
20 40 100 Number of x-pods
N u m
b e r
o f B
lu e B
e rr
ys
0 X1
X 2
40
60
80
100
60 80
20
Maximum profit line
$410 = $7X1 + $5X2
Optimal solution point (X1 = 30, X2 = 40)
Figure B.6
Optimal Solution for the Glickman Electronics Problem
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and then add it to the first equation: + 4X1 + 3X2 = 240 - 4X1 - 2X2 = - 200 + 1X2 = 40
or: X2 = 40
Doing this has enabled us to eliminate one variable, X1, and to solve for X2. We can now sub- stitute 40 for X2 in either of the original constraint equations and solve for X1. Let us use the first equation. When X2 = 40, then:
4X1 + 3(40) = 240 4X1 + 120 = 240 4X1 = 120 X1 = 30
Thus, the optimal solution has the coordinates ( X1 = 30, X2 = 40 ). The profit at this point is $7(30) 1 $5(40) 5 $410 .
Corner-Point Solution Method A second approach to solving linear programming problems employs the corner-point method . This technique is simpler in concept than the iso-profit line approach, but it involves looking at the profit at every corner point of the feasible region.
The mathematical theory behind linear programming states that an optimal solution to any problem (that is, the values of X1, X2 that yield the maximum profit) will lie at a corner point , or extreme point , of the feasible region. Hence, it is necessary to find only the values of the variables at each corner; the maximum profit or optimal solution will lie at one (or more) of them.
Once again we can see (in Figure B.7 ) that the feasible region for the Glickman Electronics Company problem is a four-sided polygon with four corner, or extreme, points. These points are la- beled , , , and on the graph. To find the (X1, X2) values producing the maximum profit, we find out what the coordinates of each corner point are, then determine and compare their profit levels. (We showed how to find the coordinates for point ➂ in the previous section describing the iso-profit line solution method.)
Point : (X1 = 0, X2 = 0) Profit +7(0) + +5(0) = +0 Point : (X1 = 0, X2 = 80) Profit +7(0) + +5(80) = +400 Point : (X1 = 30, X2 = 40) Profit +7(30) + +5(40) = +410 Point : (X1 = 50, X2 = 0) Profit +7(50) + +5(0) = +350
Because point produces the highest profit of any corner point, the product mix of X1 = 30 x-pods and X2 = 40 BlueBerrys is the optimal solution to the Glickman Electronics problem. This solution will yield a profit of $410 per production period; it is the same solution we obtained using the iso-profit line method.
Sensitivity Analysis Operations managers are usually interested in more than the optimal solution to an LP prob- lem. In addition to knowing the value of each decision variable (the Xis ) and the value of the objective function, they want to know how sensitive these answers are to input parameter changes. For example, what happens if the coefficients of the objective function are not exact, or if they change by 10% or 15%? What happens if the right-hand-side values of the constraints
Corner-point method
A method for solving graphical
linear programming problems.
20 40 100
Number of x-pods
N u m
b e r
o f B
lu e B
e rr
ys
0 X 1
X 2
40
60
80
100
60 80
20
1
4
3
2
Figure B.7
The Four Corner Points of the Feasible Region
LO B.3 Graphically solve an LP problem with
the corner-point method
Parameter
Numerical value that is given in
a model.
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change? Because solutions are based on the assumption that input parameters are constant, the subject of sensitivity analysis comes into play. Sensitivity analysis , or postoptimality analysis, is the study of how sensitive solutions are to parameter changes.
There are two approaches to determining just how sensitive an optimal solution is to changes. The first is simply a trial-and-error approach. This approach usually involves resolv- ing the entire problem, preferably by computer, each time one input data item or parameter is changed. It can take a long time to test a series of possible changes in this way.
The approach we prefer is the analytic postoptimality method. After an LP problem has been solved, we determine a range of changes in problem parameters that will not affect the optimal solution or change the variables in the solution. This is done without resolving the whole problem. LP software, such as Excel’s Solver or POM for Windows, has this capability. Let us examine several scenarios relating to the Glickman Electronics example.
Program B.1 is part of the Excel Solver computer-generated output available to help a deci- sion maker know whether a solution is relatively insensitive to reasonable changes in one or more of the parameters of the problem. (The complete computer run for these data, including input and full output, is illustrated in Programs B.3 and B.4 later in this module.)
Sensitivity Report The Excel Sensitivity Report for the Glickman Electronics example in Program B.1 has two distinct components: (1) a table titled Variable Cells and (2) a table titled Constraints. These tables permit us to answer several what-if questions regarding the problem solution.
It is important to note that while using the information in the sensitivity report to answer what-if questions, we assume that we are considering a change to only a single input data value at a time. That is, the sensitivity information does not always apply to simultaneous changes in several input data values.
The Variable Cells table presents information regarding the impact of changes to the ob- jective function coefficients (i.e., the unit profits of $7 and $5) on the optimal solution. The Constraints table presents information related to the impact of changes in constraint right- hand-side (RHS) values (i.e., the 240 hours and 100 hours) on the optimal solution. Although different LP software packages may format and present these tables differently, the programs all provide essentially the same information.
Changes in the Resources or Right-Hand-Side Values The right-hand-side values of the constraints often represent resources available to the firm. The resources could be labor-hours or machine time or perhaps money or production materi- als available. In the Glickman Electronics example, the two resources are hours available of
Sensitivity analysis
An analysis that projects how
much a solution may change if
there are changes in the variables
or input data.
STUDENT TIP Here we look at the sensitivity of the
final answers to changing inputs.
Microsoft Excel 15.0 Sensitivity Report Report Created: 9:22:18 AM
Variable Cells Final Reduced Objective Allowable Allowable
Cell Name Value Cost Coefficient Increase Decrease $B$5 Variable Values x-pods 30 0 7 3 0.333333333 $C$5 Variable Values BlueBerrys 40 0 5 0.25 1.5
Constraints Final Shadow Constraint Allowable Allowable
Cell Name Value Price R.H. Side Increase Decrease $D$8 Electronic Time Available 240 1.5 240 60 40 $D$9 Assembly Time Available 100 0.5 100 20 20
The solution values for the variables appear. We should make 30 x-pods and 40 BlueBerrys.
If we use 1 more Electronics hour, our profit will increase by $1.50. This is true for up to 60 more hours. The profit will fall by $1.50 for each Electronics hour less than 240 hours, down to as low as 200 hours.
We will use 240 hours and 100 hours of Electronics and Assembly time, respectively.
Program B.1
Sensitivity Analysis for
Glickman Electronics, Using
Excel’s Solver
LO B.4 Interpret sensitivity analysis and
shadow prices
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electronics time and hours of assembly time. If additional hours were available, a higher total profit could be realized. How much should the company be willing to pay for additional hours? Is it profitable to have some additional electronics hours? Should we be willing to pay for more assembly time? Sensitivity analysis about these resources will help us answer these questions.
If the right-hand side of a constraint is changed, the feasible region will change (unless the constraint is redundant), and often the optimal solution will change. In the Glickman example, there were 100 hours of assembly time available each week and the maximum possible profit was $410. If the available assembly hours are increased to 110 hours, the new optimal solution seen in Figure B.8 (a) is (45,20) and the profit is $415. Thus, the extra 10 hours of time resulted in an increase in profit of $5 or $0.50 per hour. If the hours are decreased to 90 hours as shown in Figure B.8 (b), the new optimal solution is (15,60) and the profit is $405. Thus, reducing the hours by 10 results in a decrease in profit of $5 or $0.50 per hour. This $0.50 per hour change in profit that resulted from a change in the hours available is called the shadow price, or dual value . The shadow price for a constraint is the improvement in the objective function value that results from a one-unit increase in the right-hand side of the constraint. Validity Range for the Shadow Price Given that Glickman Electronics’ profit increases by $0.50 for each additional hour of assembly time, does it mean that Glickman can do this indefi- nitely, essentially earning infinite profit? Clearly, this is illogical. How far can Glickman increase its assembly time availability and still earn an extra $0.50 profit per hour? That is, for what level of increase in the RHS value of the assembly time constraint is the shadow price of $0.50 valid?
The shadow price of $0.50 is valid as long as the available assembly time stays in a range within which all current corner points continue to exist. The information to compute the upper and lower limits of this range is given by the entries labeled Allowable Increase and Allowable Decrease in the Sensitivity Report in Program B.1. In Glickman’s case, these values show that the shadow price of $0.50 for assembly time availability is valid for an increase of up to 20 hours from the current value and a decrease of up to 20 hours. That is, the available assembly time can range from a low of 80 ( = 100 - 20) to a high of 120 ( = 100 + 20) for the shadow price of $0.50 to be valid. Note that the allowable decrease implies that for each hour of assembly time that Glickman loses (up to 20 hours), its profit decreases by $0.50.
Changes in the Objective Function Coefficient Let us now focus on the information provided in Program B.1 titled Variable Cells . Each row in the Variable Cells table contains information regarding a decision variable (i.e., x-pods or BlueBerrys) in the LP model.
Shadow price (or dual value)
The value of one additional unit of
a scarce resource in LP.
20 40 60 80 100
100
(a)
80
60
40
20
0
Changed assembly constraint from 2X1 + 1X2 = 100 to 2X1 + 1X2 = 110
Electronics constraint is unchanged3
4
2
1
Corner point 3 is still optimal, but values at this point are now X1 = 45, X2 = 20, with a profit = $415.
X1
X2
100
20 40 60 80 100
80
60
40
20
0
Changed assembly constraint from 2X1 + 1X2 = 100 to 2X1 + 1X2 = 90
Electronics constraint is unchanged
Corner point 3 is still optimal, but values at this point are now X1 = 15, X2 = 60, with a profit = $405.3
4
2
1 X1
X2 (b)
Figure B.8
Glickman Electronics Sensitivity Analysis on the Right-Hand Side (RHS) of the Assembly Resource Constraint
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Allowable Ranges for Objective Function Coefficients As the unit profit con- tribution of either product changes, the slope of the iso-profit lines we saw earlier in Figure B.5 changes. The size of the feasible region, however, remains the same. That is, the locations of the corner points do not change.
The limits to which the profit coefficient of x-pods or BlueBerrys can be changed without affecting the optimality of the current solution is revealed by the values in the Allowable Increase and Allowable Decrease columns of the Sensitivity Report in Program B.1. The allow- able increase in the objective function coefficient for BlueBerrys is only $0.25. In contrast, the allowable decrease is $1.50. Hence, if the unit profit of BlueBerrys drops to $4 (i.e., a decrease of $1 from the current value of $5), it is still optimal to produce 30 x-pods and 40 BlueBerrys. The total profit will drop to $370 (from $410) because each BlueBerry now yields less profit (of $1 per unit). However, if the unit profit drops below $3.50 per BlueBerry (i.e., a decrease of more than $1.50 from the current $5 profit), the current solution is no longer optimal. The LP problem will then have to be resolved using Solver, or other software, to find the new optimal corner point.
Solving Minimization Problems Many linear programming problems involve minimizing an objective such as cost instead of maximizing a profit function. A restaurant, for example, may wish to develop a work schedule to meet staffing needs while minimizing the total number of employees. Also, a manufacturer may seek to distribute its products from several factories to its many regional warehouses in a way that minimizes total shipping costs.
Minimization problems can be solved graphically by first setting up the feasible solution region and then using either the corner-point method or an iso-cost line approach (which is analogous to the iso-profit approach in maximization problems) to find the values of X1 and X2 that yield the minimum cost.
Example B1 shows how to solve a minimization problem.
STUDENT TIP LP problems can be structured
to minimize costs as well as
maximize profits.
Iso-cost
An approach to solving a
linear programming minimization
problem graphically.
Example B1 A MINIMIZATION PROBLEM WITH TWO VARIABLES Cohen Chemicals, Inc., produces two types of photo-developing fluids. The first, a black-and-white picture chemical, costs Cohen $2,500 per ton to produce. The second, a color photo chemical, costs $3,000 per ton.
Based on an analysis of current inventory levels and outstanding orders, Cohen’s production man- ager has specified that at least 30 tons of the black-and-white chemical and at least 20 tons of the color chemical must be produced during the next month. In addition, the manager notes that an existing inventory of a highly perishable raw material needed in both chemicals must be used within 30 days. To avoid wasting the expensive raw material, Cohen must produce a total of at least 60 tons of the photo chemicals in the next month.
APPROACH c Formulate this information as a minimization LP problem.
Let: X1 = number of tons of black@and@white photo chemical produced X2 = number of tons of color photo chemical produced
Objective: Minimize cost = +2,500X1 + +3,000X2
Subject to: X1 Ú 30 tons of black@and@white chemical X2 Ú 20 tons of color chemical X1 + X2 Ú 60 tons total X1, X2 Ú 0 nonnegativity requirements
SOLUTION c To solve the Cohen Chemicals problem graphically, we construct the problem’s feasible region, shown in Figure B.9 .
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0 X 1
X2
Feasible region
X1 = 30 X2 = 20
10
10
20
30
40
50
20 30 40 50 60
60 X1 + X2 = 60
b
a
Figure B.9
Cohen Chemicals’ Feasible
Region
Minimization problems are often unbounded outward (that is, on the right side and on the top), but this characteristic causes no problem in solving them. As long as they are bounded inward (on the left side and the bottom), we can establish corner points. The optimal solution will lie at one of the corners.
In this case, there are only two corner points, a and b , in Figure B.9 . It is easy to determine that at point a , X1 = 40 and X2 = 20, and that at point b , X1 = 30 and X2 = 30. The optimal solution is found at the point yielding the lowest total cost. Thus:
Total cost at a = 2,500X1 + 3,000X2 = 2,500(40) + 3,000(20) = +160,000 Total cost at b = 2,500X1 + 3,000X2 = 2,500(30) + 3,000(30) = +165,000
The lowest cost to Cohen Chemicals is at point a . Hence the operations manager should produce 40 tons of the black-and-white chemical and 20 tons of the color chemical.
INSIGHT c The area is either not bounded to the right or above in a minimization problem (as it is in a maximization problem).
LEARNING EXERCISE c Cohen’s second constraint is recomputed and should be X2 Ú 15. Does anything change in the answer? [Answer: Now X1 = 45, X2 = 15, and total cost = +157,500. ]
RELATED PROBLEMS c B.25–B.31 (B.32, B.33 are available in MyOMLab)
EXCEL OM Data File ModBExB1.xls can be found in MyOMLab.
LO B.5 Construct and solve a minimization
problem
OM in Action LP at UPS On an average day, the $58.2 billion shipping giant UPS delivers 18 million
packages to 8.2 million customers in 220 countries. On a really busy day,
say a few days before Christmas, it handles almost twice that number, or
300 packages per second. It does all this with a fleet of 6001 owned and
chartered planes, making it one of the largest airline operators in the world.
When UPS decided it should use linear programming to map its entire
operation—every pickup and delivery center and every sorting facility (now nearly
2,000 locations)—to find the best routes to move the millions of packages, it
invested close to a decade in developing VOLCANO. This LP-based optimization sys-
tem (which stands for Volume, Location, and Aircraft Network Optimization ) is used
to determine the least-cost set of routes, fleet assignments, and package flows.
Constraints include the number of planes, airport restrictions, and plane
aircraft speed, capacity, and range.
The VOLCANO system is credited with saving UPS hundreds of millions
of dollars. But that’s just the start. UPS is investing $600 million more to
optimize the whole supply chain to include drivers—the employees closest to
the customer—so they will be able to update schedules, priorities, and time
conflicts on the fly.
The UPS “airline” is not alone. Southwest runs its massive LP model (called
ILOG Optimizer ) every day to schedule its thousands of flight legs. The program
has 90,000 constraints and 2 million variables. United’s LP program is called
OptSolver, and Delta’s is called Coldstart . Airlines, like many other firms, man-
age their millions of daily decisions with LP.
Sources: ups.com (June 2015); Aviation Daily (February 9, 2004); and Interfaces (January–February 2004).
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Linear Programming Applications The foregoing examples each contained just two variables ( X1 and X2 ). Most real-world prob- lems (as we saw in the UPS OM in Action box) contain many more variables, however. Let’s use the principles already developed to formulate a few more-complex problems. The practice you will get by “paraphrasing” the following LP situations should help develop your skills for applying linear programming to other common operations situations.
Production-Mix Example Example B2 involves another production-mix decision. Limited resources must be allocated among various products that a firm produces. The firm’s overall objective is to manufacture the selected products in such quantities as to maximize total profits.
STUDENT TIP Now we look at three larger
problems—ones that have more
than two decision variables each and
therefore are not graphed.
LO B.6 Formulate production-mix, diet,
and labor scheduling
problems
Example B2 A PRODUCTION-MIX PROBLEM Failsafe Electronics Corporation primarily manufactures four highly technical products, which it supplies to aerospace firms that hold NASA contracts. Each of the products must pass through the following depart- ments before they are shipped: wiring, drilling, assembly, and inspection. The time requirements in each department (in hours) for each unit produced and its corresponding profit value are summarized in this table:
DEPARTMENT
PRODUCT WIRING DRILLING ASSEMBLY INSPECTION UNIT PROFIT
XJ201 .5 3 2 .5 $ 9
XM897 1.5 1 4 1.0 $12
TR29 1.5 2 1 .5 $15
BR788 1.0 3 2 .5 $11
The production time available in each department each month and the minimum monthly production requirement to fulfill contracts are as follows:
DEPARTMENT CAPACITY (HOURS) PRODUCT MINIMUM PRODUCTION LEVEL
Wiring 1,500 XJ201 150
Drilling 2,350 XM897 100
Assembly 2,600 TR29 200
Inspection 1,200 BR788 400
APPROACH c Formulate this production-mix situation as an LP problem. The production manager first specifies production levels for each product for the coming month. He lets:
X1 = number of units of XJ201 produced X2 = number of units of XM897 produced X3 = number of units of TR29 produced X4 = number of units of BR788 produced
SOLUTION c The LP formulation is: Objective: Maximize profit = 9X1 + 12X2 + 15X3 + 11X4 subject to: .5X1 + 1.5X2 + 1.5X3 + 1X4 … 1,500 hours of wiring available
3X1 + 1X2 + 2X3 + 3X4 … 2,350 hours of drilling available 2X1 + 4X2 + 1X3 + 2X4 … 2,600 hours of assembly available .5X1 + 1X2 + .5X3 + .5X4 … 1,200 hours of inspection X1 Ú 150 units of XJ201 X2 Ú 100 units of XM897 X3 Ú 200 units of TR29 X4 Ú 400 units of BR788 X1, X2, X3, X4 Ú 0
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M O D U L E B | L I N E A R P R O G R A M M I N G 711
INSIGHT c There can be numerous constraints in an LP problem. The constraint right-hand sides may be in different units, but the objective function uses one common unit—dollars of profit, in this case. Because there are more than two decision variables, this problem is not solved graphically.
LEARNING EXERCISE c Solve this LP problem as formulated. What is the solution? [Answer: X1 = 150, X2 = 300, X3 = 200, X4 = 400. ]
RELATED PROBLEMS c B.5–B.8, B.10–B.14, B.37 (B.15, B.17, B.19, B.21, B.24 are available in MyOMLab)
Diet Problem Example Example B3 illustrates the diet problem , which was originally used by hospitals to determine the most economical diet for patients. Known in agricultural applications as the feed-mix problem , the diet problem involves specifying a food or feed ingredient combination that will satisfy stated nutritional requirements at a minimum cost level.
Example B3 A DIET PROBLEM The Feed ’N Ship feedlot fattens cattle for local farmers and ships them to meat markets in Kansas City and Omaha. The owners of the feedlot seek to determine the amounts of cattle feed to buy to satisfy minimum nutritional standards and, at the same time, minimize total feed costs.
Each grain stock contains different amounts of four nutritional ingredients: A, B, C, and D. Here are the ingredient contents of each grain, in ounces per pound of grain :
FEED
INGREDIENT STOCK X STOCK Y STOCK Z
A 3 oz 2 oz 4 oz
B 2 oz 3 oz 1 oz
C 1 oz 0 oz 2 oz
D 6 oz 8 oz 4 oz
The cost per pound of grains X, Y, and Z is $0.02, $0.04, and $0.025, respectively. The minimum require- ment per cow per month is 64 ounces of ingredient A, 80 ounces of ingredient B, 16 ounces of ingredient C, and 128 ounces of ingredient D.
The feedlot faces one additional restriction—it can obtain only 500 pounds of stock Z per month from the feed supplier, regardless of its need. Because there are usually 100 cows at the Feed ’N Ship feedlot at any given time, this constraint limits the amount of stock Z for use in the feed of each cow to no more than 5 pounds, or 80 ounces, per month.
APPROACH c Formulate this as a minimization LP problem. Let: X1 = number of pounds of stock X purchased per cow each month X2 = number of pounds of stock Y purchased per cow each month X3 = number of pounds of stock Z purchased per cow each month
SOLUTION c Objective: Minimize cost = .02X1 + .04X2 + .025X3
subject to: Ingredient A requirement: 3X1 + 2X2 + 4X3 Ú 64 Ingredient B requirement: 2X1 + 3X2 + 1X3 Ú 80 Ingredient C requirement: 1X1 + 0X2 + 2X3 Ú 16 Ingredient D requirement: 6X1 + 8X2 + 4X3 Ú 128 Stock Z limitation: X3 … 5 X1, X2, X3 Ú 0
The cheapest solution is to purchase 40 pounds of grain X1, at a cost of $0.80 per cow.
INSIGHT c Because the cost per pound of stock X is so low, the optimal solution excludes grains Y and Z.
LEARNING EXERCISE c The cost of a pound of stock X just increased by 50%. Does this affect the solution? [Answer: Yes, when the cost per pound of grain X is $0.03, X1 = 16 pounds, X2 = 16 pounds, X3 = 0, and cost = +1.12 per cow.]
RELATED PROBLEMS c B.27, B.28, B.40 (B.33 is available in MyOMLab)
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Example B4 SCHEDULING BANK TELLERS Mexico City Bank of Commerce and Industry is a busy bank that has requirements for between 10 and 18 tellers depending on the time of day. Lunchtime, from noon to 2 p.m., is usually heaviest. The table below indicates the workers needed at various hours that the bank is open.
TIME PERIOD NUMBER OF TELLERS REQUIRED TIME PERIOD NUMBER OF TELLERS REQUIRED
9 A.M.–10 A.M. 10 1 P.M.–2 P.M. 18
10 A.M.–11 A.M. 12 2 P.M.–3 P.M. 17
11 A.M.–Noon 14 3 P.M.–4 P.M. 15
Noon–1 P.M. 16 4 P.M.–5 P.M. 10
The bank now employs 12 full-time tellers, but many people are on its roster of available part-time employees. A part-time employee must put in exactly 4 hours per day but can start anytime between 9 a.m. and 1 p.m. Part-timers are a fairly inexpensive labor pool because no retirement or lunch benefits are provided them. Full-timers, on the other hand, work from 9 a.m. to 5 p.m. but are allowed 1 hour for lunch. (Half the full-timers eat at 11 a.m., the other half at noon.) Full-timers thus provide 35 hours per week of productive labor time.
By corporate policy, the bank limits part-time hours to a maximum of 50% of the day’s total requirement. Part-timers earn $6 per hour (or $24 per day) on average, whereas full-timers earn $75 per day in
salary and benefits on average.
APPROACH c The bank would like to set a schedule, using LP, that would minimize its total man- power costs. It will release 1 or more of its full-time tellers if it is profitable to do so.
We can let: F = full-time tellers
P1 = part-timers starting at 9 a.m. (leaving at 1 p.m.) P2 = part-timers starting at 10 a.m. (leaving at 2 p.m.) P3 = part-timers starting at 11 a.m. (leaving at 3 p.m.) P4 = part-timers starting at noon (leaving at 4 p.m.) P5 = part-timers starting at 1 p.m. (leaving at 5 p.m.)
SOLUTION c Objective function: Minimize total daily manpower cost = +75F + +24(P1 + P2 + P3 + P4 + P5)
Constraints: For each hour, the available labor-hours must be at least equal to the required labor-hours:
F + P1 Ú 10 (9 A.M. to 10 A.M. needs) F + P1 + P2 Ú 12 (10 A.M. to 11 A.M. needs) 12 F + P1 + P2 + P3 Ú 14 (11 A.M. to noon needs) 12 F + P1 + P2 + P3 + P4 Ú 16 (noon to 1 P.M. needs) F + P2 + P3 + P4 + P5 Ú 18 (1 P.M. to 2 P.M. needs) F + P3 + P4 + P5 Ú 17 (2 P.M. to 3 P.M. needs) F + P4 + P5 Ú 15 (3 P.M. to 4 P.M. needs) F + P5 Ú 10 (4 P.M. to 5 P.M. needs)
Only 12 full-time tellers are available, so: F … 12
Part-time worker-hours cannot exceed 50% of total hours required each day, which is the sum of the tellers needed each hour:
4(P1 + P2 + P3 + P4 + P5) … .50(10 + 12 + 14 + 16 + 18 + 17 + 15 + 10)
Labor Scheduling Example Labor scheduling problems address staffing needs over a specific time period. They are espe- cially useful when managers have some flexibility in assigning workers to jobs that require overlapping or interchangeable talents. Large banks and hospitals frequently use LP to tackle their labor scheduling. Example B4 describes how one bank uses LP to schedule tellers.
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M O D U L E B | L I N E A R P R O G R A M M I N G 713
Simplex method
An algorithm for solving linear
programming problems of all
sizes.
or:
4P1 + 4P2 + 4P3 + 4P4 + 4P5 … 0.50(112) F, P 1, P 2, P 3, P 4, P 5 Ú 0
There are two alternative optimal schedules that Mexico City Bank can follow. The first is to employ only 10 full-time tellers ( F = 10 ) and to start 7 part-timers at 10 a.m. ( P2 = 7 ), 2 part-timers at 11 a.m. and noon ( P3 = 2 and P4 = 2 ), and 3 part-timers at 1 p.m. ( P5 = 3 ). No part-timers would begin at 9 a.m.
The second solution also employs 10 full-time tellers, but starts 6 part-timers at 9 a.m. ( P1 = 6 ), 1 part-timer at 10 a.m. ( P2 = 1 ), 2 part-timers at 11 a.m. and noon ( P3 = 2 and P4 = 2 ), and 3 part- timers at 1 p.m. ( P5 = 3 ). The cost of either of these two policies is $1,086 per day.
INSIGHT c It is not unusual for multiple optimal solutions to exist in large LP problems. In this case, it gives management the option of selecting, at the same cost, between schedules. To find an alternate optimal solution, you may have to enter the constraints in a different sequence.
LEARNING EXERCISE c The bank decides to give part-time employees a raise to $7 per hour. Does the solution change? [Answer: Yes, cost = +1,142, F = 10, P1 = 6, P2 = 1, P3 = 2, P4 = 5, P5 = 0. ]
RELATED PROBLEMS c B.36
The Simplex Method of LP Most real-world linear programming problems have more than two variables and thus are too complex for graphical solution. A procedure called the simplex method may be used to find the optimal solution to such problems. The simplex method is actually an algorithm (or a set of instructions) with which we examine corner points in a methodical fashion until we arrive at the best solution—highest profit or lowest cost. Computer programs (such as Excel OM and POM for Windows) and Excel spreadsheets are available to solve linear programming prob- lems via the simplex method.
For details regarding the algebraic steps of the simplex algorithm, see Tutorial 3 at our text student download site or in MyOMLab, or refer to a management science textbook. 2
Integer and Binary Variables All the examples we have seen in this module so far have produced integer solutions. But it is very common to see LP solutions where the decision variables are not whole numbers. Computer software provides a simple way to guarantee only integer solutions. In addition, computers allow us to create special decision variables called binary variables that can only take on the values of 0 or 1. Binary variables allow us to introduce “yes-or-no” decisions into our linear programs and to introduce special logical conditions.
Creating Integer and Binary Variables If we wish to ensure that decision variable values are integers rather than fractions, it is gener- ally not good practice to simply round the solutions to the nearest integer values. The rounded solutions may not be optimal and, in fact, may not even be feasible. Fortunately, all LP soft- ware programs have simple ways to add constraints that enforce some or all of the decision variables to be either integer or binary. The main disadvantage of introducing such constraints is that larger programs may take longer to solve. The same LP that may take 3 seconds to solve on a computer could take several hours or more to solve if many of its variables are forced to be integer or binary. For relatively small programs, though, the difference may be unnoticeable.
Using Excel’s Solver (see Using Software to Solve LP Problems later in this module), integer and binary constraints can be added by clicking A dd from the main Solver dialog box. Using the Add Constraint dialog box (see Program B.2), highlight the decision variables themselves under Cell Reference:. Then select int or bin to ensure that those variables are integer or binary, respectively, in the optimal solution.
Binary variables
Decision variables that can only
take on the value of 0 or 1.
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Linear Programming Applications with Binary Variables In the written formulation of a linear program, binary variables are usually defined using the following form:
Y = e 1 if some condition holds 0 otherwise
Sometimes we designate decision variables as binary if we are making a yes-or-no decision; for example, “Should we undertake this particular project?” “Should we buy that machine?” or “Should we locate a facility in Arkansas?” Other times, we create binary variables to introduce additional logic into our programs.
Limiting the Number of Alternatives Selected One common use of 0-1 variables involves limiting the number of projects or items that are selected from a group. Suppose a firm is required to select no more than two of three potential projects. This could be modeled with the following constraint:
Y 1 1 Y 2 1 Y 3 # 2
If we wished to force the selection of exactly two of the three projects for funding, the following constraint should be used:
Y1 + Y2 + Y3 = 2
This forces exactly two of the variables to have values of 1, whereas the other variable must have a value of 0.
Dependent Selections At times the selection of one project depends in some way on the selection of another project. This situation can be modeled with the use of 0-1 variables. Suppose G.E.’s new catalytic converter could be purchased ( Y1 5 1) only if the software was also purchased ( Y2 51). The following constraint would force this to occur:
Y1 … Y2
or, equivalently,
Y1 - Y2 … 0
Thus, if the software is not purchased, the value of Y2 is 0, and the value of Y1 must also be 0 because of this constraint. However, if the software is purchased ( Y2 5 1), then it is possible that the catalytic converter could also be purchased ( Y1 5 1), although this is not required.
If we wished for the catalytic converter and the software projects to either both be selected or both not be selected, we should use the following constraint:
Y1 = Y2
or, equivalently,
Y1 - Y2 = 0
Thus, if either of these variables is equal to 0, the other must also be 0. If either of these is equal to 1, the other must also be 1.
Program B.2
Excel’s Solver Dialog Box
to Add Integer or Binary
Constraints on Variables
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A Fixed-Charge Integer Programming Problem Often businesses are faced with decisions involving a fixed charge that will affect the cost of future operations. Building a new factory or entering into a long-term lease on an existing facility would involve a fixed cost that might vary depending on the size of the facility and the location. Once a factory is built, the variable production costs will be affected by the labor cost in the particular city where it is located. Example B5 provides an illustration.
Example B5 A FIXED-CHARGE PROBLEM USING BINARY VARIABLES Sitka Manufacturing is planning to build at least one new plant, and three cities are being considered: Baytown, Texas; Lake Charles, Louisiana; and Mobile, Alabama. Once the plant or plants have been constructed, the company wishes to have sufficient capacity to produce at least 38,000 units each year. The costs associated with the possible locations are given in the following table.
SITE ANNUAL FIXED COST VARIABLE COST PER UNIT ANNUAL CAPACITY
Baytown, TX $340,000 $32 21,000
Lake Charles, LA $270,000 $33 20,000
Mobile, AL $290,000 $30 19,000
APPROACH c In modeling this as an integer program, the objective function is to minimize the total of the fixed costs and the variable costs. The constraints are: (1) total production capacity is at least 38,000; (2) the number of units produced at the Baytown plant is 0 if the plant is not built, and it is no more than 21,000 if the plant is built; (3) the number of units produced at the Lake Charles plant is 0 if the plant is not built, and it is no more than 20,000 if the plant is built; and (4) the number of units produced at the Mobile plant is 0 if the plant is not built, and it is no more than 19,000 if the plant is built.
Then, we define the decision variables as
Y1 = e 1 if factory is built in Baytown 0 otherwise
Y2 = e 1 if factory is built in Lake Charles 0 otherwise
Y3 = e 1 if factory is built in Mobile 0 otherwise
X1 5 number of units produced at the Baytown plant X2 5 number of units produced at the Lake Charles plant X3 5 number of units produced at the Mobile plant
SOLUTION c The integer programming problem formulation becomes
Objective: Minimize cost 5 340,000 Y1 1 270,000 Y2 1 290,000 Y3 1 32 X1 1 33 X2 1 30 X3 subject to: X1 + X2 + X3 Ú 38,000
X1 … 21,000Y1 X2 … 20,000Y2 X3 … 19,000Y3 X1, X2, X3 Ú 0 and integer Y1, Y2, Y3 = 0 or 1
INSIGHT c Examining the second constraint, the objective function will try to set the binary variable Y1 equal to 0 because it wants to minimize cost. However, if Y1 5 0, then the constraint will force X1 to equal 0, in which case no units will be produced, and the plant will not be opened. Alternatively, if the rest of the program deems it worthwhile or necessary to produce some units of X1 , then Y1 will have to equal 1 for the constraint to hold. And when Y1 5 1, the firm will be charged the fixed cost of $340,000, and production will be limited to the capacity of 21,000 units. The same logic applies for constraints 3 and 4.
LEARNING EXERCISE c Solve this integer program as formulated. What is the solution? [Answer: Y1 5 0, Y2 5 1, Y3 5 1, X1 5 0, X2 5 19,000, X3 5 19,000; Total Cost 5 $1,757,000.]
RELATED PROBLEMS c B.41, B.42
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Summary This module introduces a special kind of model, linear programming. LP has proven to be especially useful when trying to make the most effective use of an organization’s resources.
The first step in dealing with LP models is problem for- mulation, which involves identifying and creating an objec- tive function and constraints. The second step is to solve
the problem. If there are only two decision variables, the problem can be solved graphically, using the corner-point method or the iso-profit/iso-cost line method. With either approach, we first identify the feasible region, then find the corner point yielding the greatest profit or least cost. LP is used in a wide variety of business applications, as the examples and homework problems in this module reveal.
Key Terms
Linear programming (LP) (p. 700 ) Objective function (p. 701 ) Constraints (p. 701 ) Graphical solution approach (p. 702 ) Decision variables (p. 702 )
Feasible region (p. 703 ) Iso-profit line method (p. 703 ) Corner-point method (p. 705 ) Parameter (p. 705 ) Sensitivity analysis (p. 706 )
Shadow price (or dual value) (p. 707 ) Iso-cost (p. 708 ) Simplex method (p. 713 ) Binary variables (p. 713 )
Discussion Questions
1. List at least four applications of linear programming problems. 2. What is a “corner point”? Explain why solutions to linear
programming problems focus on corner points. 3. Define the feasible region of a graphical LP problem. What is
a feasible solution? 4. Each linear programming problem that has a feasible region
has an infinite number of solutions. Explain. 5. Under what circumstances is the objective function more
important than the constraints in a linear programming model? 6. Under what circumstances are the constraints more important
than the objective function in a linear programming model? 7. Why is the diet problem, in practice, applicable for animals
but not particularly for people? 8. How many feasible solutions are there in a linear program?
Which ones do we need to examine to find the optimal solution?
9. Define shadow price (or dual value). 10. Explain how to use the iso-cost line in a graphical minimiza-
tion problem. 11. Compare how the corner-point and iso-profit line methods
work for solving graphical problems. 12. Where a constraint crosses the vertical or horizontal axis, the
quantity is fairly obvious. How does one go about finding the quantity coordinates where two constraints cross, not at an axis?
13. Suppose a linear programming (maximation) problem has been solved and that the optimal value of the objective func- tion is $300. Suppose an additional constraint is added to this problem. Explain how this might affect each of the following:
a) The feasible region. b) The optimal value of the objective function.
Using Software to Solve LP Problems
All LP problems can be solved with the simplex method, using software such as Excel, Excel OM, or POM for Windows. X CREATING YOUR OWN EXCEL SPREADSHEETS
Excel offers the ability to analyze linear programming problems using built-in problem-solving tools. Excel’s tool is named Solver . We use Excel to set up the Glickman Electronics problem in Program B.3. The objective and constraints are repeated here:
Objective function: Maximize profit = +7(No. of x@pods) + +5(No. of BlueBerrys) Subject to: 4(x@pods) + 3(BlueBerrys) … 240 2(x@pods) + 1(BlueBerry) … 100
The decisions (the number of units to produce) go here.
The objective function value (profit) goes here.
These are simply labels.
=B6*$B$5+C6*$C$5
Action Copy D6 to D8:D9
Program B.3
Using Excel to Formulate the
Glickman Electronics Problem
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M O D U L E B | L I N E A R P R O G R A M M I N G 717
Program B.5
Excel Solution to Glickman
Electronics LP Problem
Be sure to set the solution method to Simplex LP.
Click Add to open the “Add Constraint”dialog box.
Check this box if all variables are non-negative.
Program B.4
Solver Dialog Boxes for the
Glickman Electronics Problem
To ensure that Solver always loads when Excel is loaded, click on FILE , then Options , then Add-Ins . Next to Manage : at the bottom, make sure that Excel Add-Ins is selected, and click on the Go... button. Check Solver Add-In , and click OK . Once in Excel, the Solver dialog box will appear by clicking on: Data , then Analysis: Solver . (Or if using Excel for Mac, select Tools , Solver .) Program B.4 shows how to use Solver to find the optimal (very best) solution to the Glickman Electronics problem. Click on Solve , and the solution will automatically appear in the spreadsheet in the green and blue cells.
The Excel screen in Program B.5 shows Solver’s solution to the Glickman Electronics Company problem. Note that the optimal solution is now shown in cells B5 and C5, which serve as the variables. The Reports selections perform more extensive analysis of the solution and its environment. Excel’s sensitivity analysis capability was illustrated earlier in Program B.1.
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Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM B.1 Smith’s, a Niagara, New York, clothing manufacturer that produces men’s shirts and pajamas, has two primary resources available: sewing-machine time (in the sewing department) and cutting-machine time (in the cutting department). Over the next month, owner Barbara Smith can schedule up to 280 hours of work on sewing machines and up to 450 hours of work on cutting machines. Each shirt produced requires
1.00 hour of sewing time and 1.50 hours of cutting time. Producing each pair of pajamas requires .75 hours of sewing time and 2 hours of cutting time.
To express the LP constraints for this problem mathemati- cally, we let:
X1 = number of shirts produced X2 = number of pajamas produced
SOLUTION
First constraint: 1X1 + .75X2 … 280 hours of sewing-machine time available—our first scarce resource
Second constraint: 1.5X1 + 2X2 … 450 hours of cutting-machine time available—our second scarce resource
Note: This means that each pair of pajamas takes 2 hours of the cutting resource. Smith’s accounting department analyzes cost and sales figures and states that each shirt produced will yield a $4 contribution to profit and that each pair of pajamas will yield a $3 contribution to profit.
This information can be used to create the LP objective function for this problem:
Objective function: Maximize total contribution to profit = +4X1 + +3X2
SOLVED PROBLEM B.2 We want to solve the following LP problem for Kevin Caskey Wholesale Inc. using the corner-point method:
Objective: Maximize profit = +9X1 + +7X2 Constraints: 2X1 + 1X2 … 40 X1 + 3X2 … 30 X1, X2 Ú 0
SOLUTION Figure B.10 illustrates these constraints: Corner@point a: (X1 = 0, X2 = 0) Profit = 0 Corner@point b: (X1 = 0, X2 = 10) Profit = 9(0) + 7(10) = +70 Corner@point d: (X1 = 20, X2 = 0) Profit = 9(20) + 7(0) = +180
Corner-point c is obtained by solving equations 2X1 + 1X2 = 40 and X1 + 3X2 = 30 simultaneously. Multiply the second equation by - 2 and add it to the first.
2X1 + 1X2 = 40 - 2X1 - 6X2 = - 60 - 5X2 = - 20
Thus X2 = 4
And X1 + 3(4) = 30 or X1 + 12 = 30 or X1 = 18 Corner-point c : (X1 = 18, X2 = 4) Profit = 9(18) + 7(4) = +190 Hence the optimal solution is: (x1 = 18, x2 = 4) Profit = +190
PX USING EXCEL OM AND POM FOR WINDOWS Excel OM and POM for Windows can handle relatively large LP problems. As output, the software provides optimal values for the variables, optimal profit or cost, and sensitivity analysis. In addition, POM for Windows provides graphical output for problems with only two variables.
0 X 1
X 2
10
10
20
30
40
20 30 40
dc
a
b
Figure B.10
K. Caskey Wholesale Inc.’s Feasible Region
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SOLVED PROBLEM B.3 Holiday Meal Turkey Ranch is considering buying two different types of turkey feed. Each feed contains, in varying proportions, some or all of the three nutritional ingredients essential for fattening turkeys. Brand Y feed costs the ranch $.02 per pound. Brand Z costs $.03 per pound. The rancher would like to determine the lowest-cost diet that meets the minimum monthly intake requirement for each nutritional ingredient.
The following table contains relevant information about the composition of brand Y and brand Z feeds, as well as the minimum monthly requirement for each nutritional ingredient per turkey.
COMPOSITION OF EACH POUND OF FEED
INGREDIENT BRAND Y FEED
BRAND Z FEED
MINIMUM MONTHLY REQUIREMENT
A 5 oz 10 oz 90 oz
B 4 oz 3 oz 48 oz
C .5 oz 0 1.5 oz
Cost/lb $.02 $.03
SOLUTION If we let:
X1 = number of pounds of brand Y feed purchased X2 = number of pounds of brand Z feed purchased
then we may proceed to formulate this linear programming problem as follows:
Objective: Minimize cost (in cents) = 2X1 + 3X2
subject to these constraints:
5X1 + 10X2 Ú 90 oz (ingredient A constraint) 4X1 + 3X2 Ú 48 oz (ingredient B constraint) 12X1 Ú 1
1 2 oz (ingredient C constraint)
Figure B.11 illustrates these constraints.
The iso-cost line approach may be used to solve LP mini- mization problems such as that of the Holiday Meal Turkey Ranch. As with iso-profit lines, we need not compute the cost at each corner point, but instead draw a series of parallel cost lines. The last cost point to touch the feasible region provides us with the optimal solution corner.
For example, we start in Figure B.12 by drawing a 54¢ cost line, namely, 54 = 2X1 + 3X2. Obviously, there are many points in the feasible region that would yield a lower total cost. We pro- ceed to move our iso-cost line toward the lower left, in a plane parallel to the 54¢ solution line. The last point we touch while still in contact with the feasible region is the same as corner point b of Figure B.11 . It has the coordinates ( X1 = 8.4, X2 = 4.8 ) and an associated cost of 31.2 cents.
Pounds of brand 0
X1
X2
5 10 15 20 Y
P o u n d s
o f b ra
n d
Z
5
10
15
20
b
Feasible region
Ingredient C constraint
Ingredient B constraint
Ingredient A constraint
c
a
Figure B.11
Feasible Region for the Holiday Meal Turkey
Ranch Problem
Pounds of brand 0
X 1
X2
5 10 15 20 25 Y
P o u n d s
o f b ra
n d
Z
5
10
15
20
Feasible region (shaded area)
(X1 = 8.4, X2 = 4.8)
X 1
31.2¢ = 2 + 3X
2
54¢ = 2X 1 + 3X
2 iso-cost line
Direction of decreasing cost
Figure B.12
Graphical Solution to the Holiday Meal Turkey Ranch Problem Using
the Iso-Cost Line
STUDENT TIP Note that the last line parallel to the
54¢ iso-cost line that touches the
feasible region indicates the optimal
corner point.
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has available a total of 25,000 lb of steel and 6,000 lb of zinc. Each model A gate requires a mixture of 125 lb of steel and 20 lb of zinc, and each yields a profit of $90. Each model B gate requires 100 lb of steel and 30 lb of zinc and can be sold for a profit of $70.
Find by graphical linear programming the best production mix of yard gates. PX
• • B.7 Green Vehicle Inc. manufactures electric cars and small delivery trucks. It has just opened a new factory where the C1 car and the T1 truck can both be manufactured. To make either vehicle, processing in the assembly shop and in the paint shop are required. It takes 1/40 of a day and 1/60 of a day to paint a truck of type T1 and a car of type C1 in the paint shop, respectively. It takes 1/50 of a day to assemble either type of vehicle in the assembly shop.
A T1 truck and a C1 car yield profits of $300 and $220, respectively, per vehicle sold. a) Define the objective function and constraint equations. b) Graph the feasible region. c) What is a maximum-profit daily production plan at the new
factory? d) How much profit will such a plan yield, assuming whatever
is produced is sold? PX
• B.8 The Lifang Wu Corporation manufactures two models of industrial robots, the Alpha 1 and the Beta 2. The firm employs 5 technicians, working 160 hours each per month, on its assembly line. Management insists that full employment (that is, all 160 hours of time) be maintained for each worker during next month’s operations. It requires 20 labor-hours to assemble each Alpha 1 robot and 25 labor-hours to assemble each Beta 2 model. Wu wants to see at least 10 Alpha 1s and at least 15 Beta 2s produced during the production period. Alpha 1s generate a $1,200 profit per unit, and Beta 2s yield $1,800 each.
Determine the most profitable number of each model of robot to produce during the coming month. PX
• • B.9 Consider the following LP problem developed at Zafar Malik’s Carbondale, Illinois, optical scanning firm:
Maximize profit = +1X1 + +1X2 Subject to: 2X1 + 1X2 … 100
1X1 + 2X2 … 100
a) What is the optimal solution to this problem? Solve it graphically.
b) If a technical breakthrough occurred that raised the profit per unit of X1 to $3, would this affect the optimal solution?
c) Instead of an increase in the profit coefficient X1 to $3, suppose that profit was overestimated and should only have been $1.25. Does this change the optimal solution? PX
• B.10 A craftsman named William Barnes builds two kinds of birdhouses, one for wrens and a second for bluebirds. Each wren birdhouse takes 4 hours of labor and 4 units of lum- ber. Each bluebird house requires 2 hours of labor and 12 units of lumber. The craftsman has available 60 hours of labor and 120 units of lumber. Wren houses yield a profit of $6 each, and bluebird houses yield a profit of $15 each. a) Write out the objective and constraints. b) Solve graphically. PX
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problem B.1 relates to Requirements of a Linear Programming Problem
• B.1 The LP relationships that follow were formulated by Richard Martin at the Long Beach Chemical Company. Which ones are invalid for use in a linear programming problem, and why?
Maximize = 6X1 + 1 2X1X2 + 5X3
Subject to: 4X1X2 + 2X3 … 70 7.9X1 - 4X2 Ú 15.6
3X1 + 3X2 + 3X3 Ú 21 19X2 -
1 3X3 = 17
9X1 - X2 + 4X3 = 5
4X1 + 2X2 + 32X3 … 80
Problems B.2–B.21 relate to Graphical Solution to a Linear Programming Problem
• B.2 Solve the following linear programming problem graphically:
Maximize profit = 4X + 6Y Subject to: X + 2Y … 8 5X + 4Y … 20 X, Y Ú 0 PX
• B.3 Solve the following linear programming problem graphically:
Maximize profit = X + 10Y Subject to: 4X + 3Y … 36 2X + 4Y … 40 Y Ú 3 X, Y Ú 0 PX • • B.4 Consider the following linear programming problem:
Maximize profit = 30X1 + 10X2 Subject to: 3X1 + X2 … 300 X1 + X2 … 200 X1 … 100 X2 Ú 50 X1 - X2 … 0 X1, X2 Ú 0 a) Solve the problem graphically. b) Is there more than one optimal solution? Explain. PX
• B.5 The Attaran Corporation manufactures two electrical products: portable air conditioners and portable heaters. The assembly process for each is similar in that both require a certain amount of wiring and drilling. Each air conditioner takes 3 hours of wiring and 2 hours of drilling. Each heater must go through 2 hours of wiring and 1 hour of drilling. During the next production period, 240 hours of wiring time are available and up to 140 hours of drilling time may be used. Each air conditioner sold yields a profit of $25. Each heater assembled may be sold for a $15 profit.
Formulate and solve this LP production-mix situation, and find the best combination of air conditioners and heaters that yields the highest profit. PX
• B.6 The Chris Beehner Company manufactures two lines of designer yard gates, called model A and model B. Every gate requires blending a certain amount of steel and zinc; the company
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administration has decided to make a 90-bed addition on a portion of adjacent land currently used for staff parking. The administrators feel that the labs, operating rooms, and X-ray department are not being fully utilized at present and do not need to be expanded to handle additional patients. The addition of 90 beds, however, involves deciding how many beds should be allo- cated to the medical staff (for medical patients) and how many to the surgical staff (for surgical patients).
The hospital’s accounting and medical records departments have provided the following pertinent information: The aver- age hospital stay for a medical patient is 8 days, and the average medical patient generates $2,280 in revenues. The average surgical patient is in the hospital 5 days and generates $1,515 in revenues. The laboratory is capable of handling 15,000 tests per year more than it was handling. The average medical patient requires 3.1 lab tests, the average surgical patient 2.6 lab tests. Furthermore, the average medical patient uses 1 X-ray, the average surgical patient 2 X-rays. If the hospital were expanded by 90 beds, the X-ray department could handle up to 7,000 X-rays without significant additional cost. Finally, the administration estimates that up to 2,800 additional operations could be performed in existing operat- ing-room facilities. Medical patients, of course, require no surgery, whereas each surgical patient generally has one surgery performed.
Formulate this problem so as to determine how many medical beds and how many surgical beds should be added to maximize revenues. Assume that the hospital is open 365 days per year. PX
Additional problems B.15–B.21 are available in MyOMLab.
Problems B.22–B.24 relate to Sensitivity Analysis
• • B.22 Kalyan Singhal Corp. makes three products, and it has three machines available as resources as given in the following LP problem:
Maximize contribution = 4X1 + 4X2 + 7X3 Subject to: 1X1 + 7X2 + 4X3 … 100 (hours on machine 1) 2X1 + 1X2 + 7X3 … 110 (hours on machine 2) 8X1 + 4X2 + 1X3 … 100 (hours on machine 3)
a) Determine the optimal solution using LP software. b) Is there unused time available on any of the machines with the
optimal solution? c) What would it be worth to the firm to make an additional
hour of time available on the third machine? d) How much would the firm’s profit increase if an extra 10 hours
of time were made available on the second machine at no extra cost? PX
• • • • B.23 A fertilizer manufacturer has to fulfill supply con- tracts to its two main customers (650 tons to Customer A and 800 tons to Customer B). It can meet this demand by shipping existing inventory from any of its three warehouses. Warehouse 1 (W1) has 400 tons of inventory on hand, Warehouse 2 (W2) has 500 tons, and Warehouse 3 (W3) has 600 tons. The company would like to arrange the shipping for the lowest cost possible, where the per-ton transit costs are as follows:
W1 W2 W3
Customer A $7.50 $6.25 $6.50
Customer B $6.75 $7.00 $8.00
• • B.11 Each coffee table produced by Kevin Watson Designers nets the firm a profit of $9. Each bookcase yields a $12 profit. Watson’s firm is small and its resources limited. During any given production period (of 1 week), 10 gallons of varnish and 12 lengths of high-quality redwood are available. Each coffee table requires approximately 1 gallon of varnish and 1 length of redwood. Each bookcase takes 1 gallon of varnish and 2 lengths of wood.
Formulate Watson’s production-mix decision as a linear programming problem, and solve. How many tables and book- cases should be produced each week? What will the maximum profit be? PX
• B.12 Par, Inc., produces a standard golf bag and a deluxe golf bag on a weekly basis. Each golf bag requires time for cutting and dyeing and time for sewing and finishing, as shown in the fol- lowing table:
HOURS REQUIRED PER BAG
PRODUCT CUTTING AND DYEING SEWING AND FINISHING
Standard bag 1/2 1
Deluxe bag 1 2/3
The profits per bag and weekly hours available for cutting and dyeing and for sewing and finishing are as follows:
PRODUCT PROFIT PER UNIT ($)
Standard bag 10
Deluxe bag 8
ACTIVITY WEEKLY HOURS AVAILABLE
Cutting and dyeing 300
Sewing and fi nishing 360
Par, Inc., will sell whatever quantities it produces of these two products. a) Find the mix of standard and deluxe golf bags to produce per
week that maximizes weekly profit from these activities. b) What is the value of the profit? PX
• • B.13 The Denver advertising agency promoting the new Breem dishwashing detergent wants to get the best exposure pos- sible for the product within the $100,000 advertising budget ceil- ing placed on it. To do so, the agency needs to decide how much of the budget to spend on each of its two most effective media: (1) television spots during the afternoon hours and (2) large ads in the city’s Sunday newspaper. Each television spot costs $3,000; each Sunday newspaper ad costs $1,250. The expected exposure, based on industry ratings, is 35,000 viewers for each TV commer- cial and 20,000 readers for each newspaper advertisement. The agency director, Deborah Kellogg, knows from experience that it is important to use both media in order to reach the broadest spectrum of potential Breem customers. She decides that at least 5 but no more than 25 television spots should be ordered, and that at least 10 newspaper ads should be contracted. How many times should each of the two media be used to obtain maximum expo- sure while staying within the budget? Use the graphical method to solve. PX
• • • • B.14 Baton Rouge’s Mt. Cedar Hospital is a large, private, 600-bed facility complete with laboratories, operating rooms, and X-ray equipment. In seeking to increase revenues, Mt. Cedar’s
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a) Explain what each of the six decision variables (V) is: ( Hint: Look at the Solver report below.) V A1: V A2: V A3: V B1: V B2: V B3:
b) Write out the objective function in terms of the variables (V A1, V A2, etc.) and the objective coefficients. c) Aside from nonnegativity of the variables, what are the five constraints? Write a short description for each constraint, and write out
the formula (and circle the type of equality/inequality).
Description Variables and Coeffi cients What Type? RHS
C1: _________________ Formula: _________________ (5 . | 5 | 5 ,) _________________ C2: _________________ Formula: _________________ (5 . | 5 | 5 ,) _________________ C3: _________________ Formula: _________________ (5 . | 5 | 5 ,) _________________ C4: _________________ Formula: _________________ (5 . | 5 | 5 ,) _________________ C5: _________________ Formula: _________________ (5 . | 5 | 5 ,) _________________
After you formulate and enter the linear program for Problem B.23 in Excel, the Solver gives you the following sensitivity report: Variable Cells
CELL NAME FINAL VALUE REDUCED COST OBJECTIVE COEFFICIENT ALLOWABLE INCREASE ALLOWABLE DECREASE
$B$6 V A1 0 1.5 7.5 1E130 1.5
$C$6 V A2 100 0 6.25 0.25 0.75
$D$6 V A3 550 0 6.5 0.75 0.25
$E$6 V B1 400 0 6.75 0.5 1E130
$F$6 V B2 400 0 7 0.75 0.5
$G$6 V B3 0 0.75 8 1E130 0.75
Constraints CELL NAME FINAL VALUE SHADOW PRICE CONSTRAINT R.H. SIDE ALLOWABLE INCREASE ALLOWABLE DECREASE
$H$7 C1 650 6.5 650 50 550
$H$8 C2 800 7.25 800 50 400
$H$9 C3 400 20.5 400 400 50
$H$10 C4 500 20.25 500 550 50
$H$11 C5 550 0 600 1E130 50
d) How many of the constraints are binding? e) What is the range of optimality on variable V A3? f) If we could ship 10 tons less to Customer A, how much money might we be able to save? If we could choose to short either
Customer A or Customer B by 10 tons, which would we prefer to short? Why? PX
Additional problem B.24 is available in MyOMLab.
Problems B.25–B.33 relate to Solving Minimization Problems
• B.25 Solve the following linear program graphically: Minimize cost = X1 + X2 8X1 + 16X2 Ú 64 X1 Ú 0 X2 Ú 92 PX ( Note: X 2 values can be negative in this problem.)
• B.26 Solve the following LP problem graphically: Minimize cost = 24X + 15Y Subject to: 7X + 11Y Ú 77 16X + 4Y Ú 80 X, Y Ú 0
• • B.27 Doug Turner Food Processors wishes to introduce a new brand of dog biscuits composed of chicken- and liver-flavored biscuits that meet certain nutritional requirements. The liver-flavored biscuits contain 1 unit of nutrient A and 2 units of nutrient B; the chicken-flavored biscuits contain 1 unit of nutrient A and 4 units of nutrient B. According to federal requirements, there must be at
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least 40 units of nutrient A and 60 units of nutrient B in a pack- age of the new mix. In addition, the company has decided that there can be no more than 15 liver-flavored biscuits in a package. If it costs 1¢ to make 1 liver-flavored biscuit and 2¢ to make 1 chicken-flavored, what is the optimal product mix for a package of the biscuits to minimize the firm’s cost? a) Formulate this as a linear programming problem. b) Solve this problem graphically, giving the optimal values of all
variables. c) What is the total cost of a package of dog biscuits using the
optimal mix? PX
• B.28 The Sweet Smell Fertilizer Company markets bags of manure labeled “not less than 60 lb dry weight.” The pack- aged manure is a combination of compost and sewage wastes. To provide good-quality fertilizer, each bag should contain at least 30 lb of compost but no more than 40 lb of sewage. Each pound of compost costs Sweet Smell 5¢ and each pound of sewage costs 4¢. Use a graphical LP method to determine the least-cost blend of compost and sewage in each bag. PX
• B.29 Consider Paul Jordan’s following linear programming formulation:
Minimize cost = +1X1 + +2X2 Subject to: X1 + 3X2 Ú 90 8X1 + 2X2 Ú 160 3X1 + 2X2 Ú 120 X2 … 70 a) Graphically illustrate the feasible region to indicate to Jordan
which corner point produces the optimal solution. b) What is the cost of this solution? PX
• B.30 Solve the following linear programming problem graphically:
Minimize cost = 4X1 + 5X2 Subject to: X1 + 2X2 Ú 80 3X1 + X2 Ú 75 X1, X2 Ú 0 PX
• • B.31 How many corner points are there in the feasible region of the following problem? Minimize cost = X - Y Subject to: X … 4 - X … 2 X + 2Y … 6 - X + 2Y … 8 Y Ú 0 ( Note: X values can be negative in this problem.)
Additional problems B.32–B.33 are available in MyOMLab.
Problems B.34–B.40 relate to Linear Programming Applications
• • • B.34 The Hills County, Michigan, superintendent of edu- cation is responsible for assigning students to the three high schools in his county. He recognizes the need to bus a certain number of students, because several sectors, A–E, of the county are beyond walking distance to a school. The superintendent partitions the county into five geographic sectors as he attempts to establish a plan that will minimize the total number of student miles traveled by bus. He also recognizes that if a student hap- pens to live in a certain sector and is assigned to the high school in that sector, there is no need to bus him because he can walk to school. The three schools are located in sectors B, C, and E.
The accompanying table reflects the number of high-school- age students living in each sector and the distance in miles from each sector to each school:
DISTANCE TO SCHOOL
SECTOR SCHOOL IN SECTOR B
SCHOOL IN SECTOR C
SCHOOL IN SECTOR E
NUMBER OF STUDENTS
A 5 8 6 700
B 0 4 12 500
C 4 0 7 100
D 7 2 5 800
E 12 7 0 400
2,500
Each high school has a capacity of 900 students. H
a n s
M a g e ls
se n /S
h u tt
e rs
to ck
a) Set up the objective function and constraints of this problem using linear programming so that the total number of student miles traveled by bus is minimized.
b) Solve the problem. PX
•• B.35 The Rio Credit Union has $250,000 available to invest in a 12-month commitment. The money can be placed in Brazilian treasury notes yielding an 8% return or in riskier high-yield bonds at an average rate of return of 9%. Credit union regulations require diversification to the extent that at least 50% of the invest- ment be placed in Treasury notes. It is also decided that no more than 40% of the investment be placed in bonds. How much should the Rio Credit Union invest in each security so as to maximize its return on investment? PX
• • B.36 Wichita’s famous Sethi Restaurant is open 24 hours a day. Servers report for duty at 3 a.m., 7 a.m., 11 a.m., 3 p.m., 7 p.m., or 11 p.m., and each works an 8-hour shift. The following table shows the minimum number of workers needed during the 6 periods into which the day is divided:
PERIOD TIME NUMBER OF SERVERS REQUIRED
1 3 A.M.–7 A.M. 3
2 7 A.M.–11 A.M. 12
3 11 A.M.–3 P.M. 16
4 3 P.M.–7 P.M. 9
5 7 P.M.–11 P.M. 11
6 11 P.M.–3 A.M. 4
Owner Avanti Sethi’s scheduling problem is to determine how many servers should report for work at the start of each time period in order to minimize the total staff required for one day’s operation. ( Hint: Let X i equal the number of servers beginning work in time period i , where i 5 1, 2, 3, 4, 5, 6.) PX
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• • B.37 Leach Distributors packages and distributes industrial supplies. A standard shipment can be packaged in a class A container, a class K container, or a class T container. A single class A container yields a profit of $9; a class K container, a profit of $7; and a class T con- tainer, a profit of $15. Each shipment prepared requires a certain amount of packing material and a certain amount of time.
RESOURCES NEEDED PER STANDARD SHIPMENT
CLASS OF CONTAINER
PACKING MATERIAL (POUNDS)
PACKING TIME (HOURS)
A 2 2
K 1 6
T 3 4
Total resource available each week:
130 pounds 240 hours
Hugh Leach, head of the firm, must decide the optimal number of each class of container to pack each week. He is bound by the previously men- tioned resource restrictions but also decides that he must keep his 6 full-time packers employed all 240 hours (6 workers 3 40 hours) each week.
Formulate and solve this problem using LP software. PX
••• B.38 Tri-State Manufacturing has three factories (1, 2, and 3) and three warehouses (A, B, and C). The following table shows the shipping costs between each factory and warehouse, the factory manufacturing capabilities (in thousands), and the warehouse capacities (in thousands). Management would like to keep the warehouses filled to capacity in order to generate demand.
TO FROM
WAREHOUSE A
WAREHOUSE B
WAREHOUSE C
PRODUCTION CAPABILITY
Factory 1 $ 6 $ 5 $ 3 6
Factory 2 $ 8 $10 $ 8 8
Factory 3 $11 $14 $18 10
Capacity 7 12 5
a) Write the objective function and the constraint equations. Let X1A = 1,000s of units shipped from factory 1 to warehouse A, and so on. b) Solve by computer. PX
•••• B.39 Bowman Builders manufactures steel storage sheds for commercial use. Joe Bowman, president of Bowman Builders, is con- templating producing sheds for home use. The activities necessary to build an experimental model and related data are given in Table B.2 . a) What is the project normal time completion date? (See Chapter 3 for a review of project management.) b) Formulate an LP problem to crash this project to 10 weeks.
•••• B.40 You have just been hired as a planner for the municipal school system, and your first assignment is to redesign the subsidized lunch program. In particular, you are to formulate the least expensive lunch menu that will still meet all state and federal nutritional guidelines.
The guidelines are as follows: A meal must be between 500 and 800 calories. It must contain at least 200 calories of protein, at least 200 calories of carbohydrates, and no more than 400 calories of fat. It also needs to have at least 200 calories of a food classified as a fruit or vegetable.
Table B.3 provides a list of the foods you can consider as possible menu items, with contract-determined prices and nutritional infor- mation. Note that all percentages sum to 100% per food—as all calories are protein, carbohydrate, or fat calories. For example, a serving of applesauce has 100 calories, all of which are carbohydrates, and it counts as a fruit/veg food. You are allowed to use fractional serv- ings, such as 2.25 servings of turkey breast and a 0.33 portion of salad. Costs and nutritional attributes scale likewise: e.g., a 0.33 portion of salad costs $.30 and has 33 calories.
TABLE B.2 Data for Problem B.39
ACTIVITY NORMAL TIME CRASH TIME NORMAL COST ($) CRASH COST ($) IMMEDIATE PREDECESSORS
A 3 2 1,000 1,600 —
B 2 1 2,000 2,700 —
C 1 1 300 300 —
D 7 3 1,300 1,600 A
E 6 3 850 1,000 B
F 2 1 4,000 5,000 C
G 4 2 1,500 2,000 D, E
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TABLE B.3 Data for Problem B.40
FOOD COST/SERVING CALORIES/SERVING % PROTEIN % CARBS % FAT FRUIT/VEG
Applesauce $0.30 100 0% 100% 0% Y
Canned corn $0.40 150 20% 80% 0% Y
Fried chicken $0.90 250 55% 5% 40% N
French fries $0.20 400 5% 35% 60% N
Mac and cheese $0.50 430 20% 30% 50% N
Turkey breast $1.50 300 67% 0% 33% N
Garden salad $0.90 100 15% 40% 45% Y
Formulate and solve as a linear problem. Print out your formula- tion in Excel showing the objective function coefficients and con- straint matrix in standard form.
◆ Display, on a separate page, the full Answer Report as generated by Excel Solver.
◆ Highlight and label as Z the objective value for the optimal solution on the Answer Report.
◆ Highlight the nonzero decision variables for the optimal solution on the Answer Report.
◆ Display, on a separate page, the full Sensitivity Report as generated by Excel Solver. PX
Problems B.41–B.42 relate to Integer and Binary Variables
• • B.41 Rollins Publishing needs to decide what textbooks from the following table to publish.
TEXT- BOOK DEMAND FIXED COST
VARIABLE COST
SELLING PRICE
Book 1 9,000 $12,000 $19 $40
Book 2 8,000 $21,000 $28 $60
Book 3 5,000 $15,000 $30 $52
Book 4 6,000 $10,000 $20 $34
Book 5 7,000 $18,000 $20 $45
For each book, the maximum demand, fixed cost of publishing, variable cost, and selling price are provided. Rollins has the capacity to publish a total of 20,000 books.
a) Formulate this problem to determine which books should be selected and how many of each should be published to maxi- mize profit.
b) Solve using computer software. PX
• • B.42 Porter Investments needs to develop an investment portfolio for Mrs. Singh from the following list of possible investments:
INVESTMENT COST EXPECTED RETURN
A $10,000 $ 700
B $12,000 $1,000
C $ 3,500 $ 390
D $ 5,000 $ 500
E $ 8,500 $ 750
F $ 8,000 $ 640
G $ 4,000 $ 300
Mrs. Singh has a total of $60,000 to invest. The following condi- tions must be met: (1) If investment F is chosen, then investment G must also be part of the portfolio, (2) at least four investments should be chosen, and (3) of investments A and B, exactly one must be included. Formulate and solve this problem using LP software to determine which stocks should be included in Mrs. Singh’s portfolio. PX
CASE STUDIES Quain Lawn and Garden, Inc.
a matter of months, they asked their attorney to file incorporation documents and formed the firm Quain Lawn and Garden, Inc.
Early in the new business’s existence, Bill Quain recognized the need for a high-quality commercial fertilizer that he could blend himself, both for sale and for his own nursery. His goal was to keep his costs to a minimum while producing a top-notch product that was especially suited to the New Jersey climate.
Working with chemists at Rutgers University, Quain blended “Quain-Grow.” It consists of four chemical compounds, C-30, C-92, D-21, and E-11. The cost per pound for each compound is indicated in the table on the next page:
Bill and Jeanne Quain spent a career as a husband-and-wife real estate investment partnership in Atlantic City, New Jersey. When they finally retired to a 25-acre farm in nearby Cape May County, they became ardent amateur gardeners. Bill planted shrubs and fruit trees, and Jeanne spent her hours potting all sizes of plants. When the volume of shrubs and plants reached the point that the Quains began to think of their hobby in a serious vein, they built a greenhouse adja- cent to their home and installed heating and watering systems.
By 2012, the Quains realized their retirement from real estate had really only led to a second career—in the plant and shrub business—and they filed for a New Jersey business license. Within
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Video Case Scheduling Challenges at Alaska Airlines Good airline scheduling is essential to delivering outstanding cus- tomer service with high plane utilization rates. Airlines must sched- ule pilots, flight attendants, aircraft, baggage handlers, customer service agents, and ramp crews. At Alaska Airlines, it all begins with seasonal flight schedules that are developed 330 days in advance.
Revenue and marketing goals drive the potential routing decisions, but thousands of constraints impact these schedules. Using SABRE scheduling optimizer software, Alaska considers the number of planes available, seat capacity, ranges, crew availability, union contracts that dictate hours that crews can fly, and maintenance regulations that reg- ularly take planes out of service, just to name a few. Alaska’s schedul- ing department sends preliminary schedules to the human resources, maintenance, operations, customer service, marketing, and other departments for feedback before finalizing flight schedules.
Alaska Airlines’ historic mission is to serve its extremely loyal customer base in the remote and unreachable small towns in Alaska. Serving many airports in Alaska is especially complex because the airline requires its pilots to have special skills to deal with extremely adverse weather, tight mountain passes, and short runways. Some airports lack full-time TSA agents or strong ground support and may not even be open 24 hours per day. In some cases, runways are not plowed because the village plow is busy clearing the roads for school buses. Navigational technology developed by Alaska Airlines has significantly reduced weather-related cancelled flights as Alaska can now land where many other carriers cannot.
After the SABRE optimizer schedules thousands of flights, scheduling activity turns to the next step: crew optimizing. The crew optimizer (Alaska uses Jeppersen software developed by Boeing and based on linear programming) attempts to eliminate unnecessary layovers and crew idle time while adhering to FAA and union restrictions. Alaska leads the industry in pilot “hard time” (i.e., the amount of time a pilot is being paid when passen- gers are actually being moved). After the crew requirements for every flight are determined, the 3,000 flight attendants and 1,500 pilots rank their preferred routings on a monthly basis. Personnel are assigned to each flight using seniority and feasibility.
Interestingly, not every pilot or flight attendant always bids on the Hawaii routes (about 20% of all flights), the long-haul East Coast routes, or the Mexico flights. Some prefer the flying challenge of the “milk run” flights to Ketchikan, Sitka, Juneau, Fairbanks, Anchorage, and back to Seattle, which are in keeping with the culture and contact with local residents.
As an airline that accentuates risk taking and empowers employees to think “out of the box,” Alaska recently decided to experiment with a schedule change on its Seattle-to-Chicago route. Given crew restrictions on flying hours per day, the flight had previously included a crew layover in Chicago. When a com- pany analyst documented the feasibility of running the same crew on the two 4-hour legs of the round trip (which implied an extremely tight turnaround schedule in Chicago), his data indi- cated that on 98.7% of the round trip flights, the crew would not “time out.” His boss gave the go-ahead.
Discussion Questions *
1. Why is scheduling for Alaska more complex than for other airlines?
2. What operational considerations may prohibit Alaska from adding flights and more cities to its network?
3. What were the risks of keeping the same crew on the Seattle— Chicago—Seattle route?
4. Estimate the direct costs to the airline should the crew “time out” and not be able to fly its Boeing 737 back to Seattle from Chicago on the same day. These direct variable costs should include moving and parking the plane overnight along with hotel and meal costs for the crew and passengers. Do you think this is more advantageous than keeping a spare crew in Chicago?
• Additional Case Studies: Visit MyOMLab for these free case studies: Chase Manhattan Bank: This scheduling case involves fi nding the optimal number of full-time versus part-time employees at a bank. Coastal States Chemical: The company must prepare for a shortage of natural gas.
Endnotes
1. Iso means “equal” or “similar.” Thus, an iso-profit line repre- sents a line with all profits the same, in this case $210.
2. See, for example, Barry Render, Ralph M. Stair, Michael Hanna, and T. Hale, Quantitative Analysis for Management , 12th ed. (Pearson Education, Inc., Upper Saddle River, NJ, 2013):
Chapters 7 – 9 ; or Raju Balakrishnan, Barry Render, and Ralph M. Stair, Managerial Decision Modeling with Spreadsheets , 3rd ed. (Pearson Education, Inc., Upper Saddle River, NJ, 2012): Chapters 2 – 4 .
CHEMICAL COMPOUND COST PER POUND
C-30 $.12
C-92 .09
D-21 .11
E-11 .04
The specifications for Quain-Grow are established as: a) Chemical E-11 must constitute at least 15% of the blend. b) C-92 and C-30 must together constitute at least 45% of the blend.
c) D-21 and C-92 can together constitute no more than 30% of the blend.
d) Quain-Grow is packaged and sold in 50-lb bags.
Discussion Questions
1. Formulate an LP problem to determine what blend of the four chemicals will allow Quain to minimize the cost of a 50-lb bag of the fertilizer.
2. Solve to find the best solution.
*You may wish to view the video that accompanies this case before addressing these questions.
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B Main Heading Review Material MyOMLab WHY USE LINEAR PROGRAMMING? (p. 700)
j Linear programming (LP) —A mathematical technique designed to help operations managers plan and make decisions relative to allocation of resources.
Concept Questions: 1.1–1.4 VIDEO B.1 Scheduling Challenges at Alaska Airlines
REQUIREMENTS OF A LINEAR PROGRAMMING PROBLEM (p. 701)
j Objective function —A mathematical expression in linear programming that maximizes or minimizes some quantity (often profit or cost, but any goal may be used).
j Constraints —Restrictions that limit the degree to which a manager can pursue an objective.
All LP problems have four properties in common: 1. LP problems seek to maximize or minimize some quantity. We refer to this
property as the objective function of an LP problem. 2. The presence of restrictions, or constraints , limits the degree to which we can
pursue our objective. We want, therefore, to maximize or minimize a quantity (the objective function) subject to limited resources (the constraints).
3. There must be alternative courses of action to choose from. 4. The objective and constraints in linear programming problems must be
expressed in terms of linear equations or inequalities.
Concept Questions: 2.1–2.4 Problem: B.1
FORMULATING LINEAR PROGRAMMING PROBLEMS (pp. 701–702)
One of the most common linear programming applications is the product-mix problem . Two or more products are usually produced using limited resources. For example, a company might like to determine how many units of each product it should produce to maximize overall profit, given its limited resources. An important aspect of linear programming is that certain interactions will exist between variables. The more units of one product that a firm produces, the fewer it can make of other products.
Concept Questions: 3.1–3.4 Virtual Office Hours for Solved Problem: B.1 ACTIVE MODEL B.1
GRAPHICAL SOLUTION TO A LINEAR PROGRAMMING PROBLEM (pp. 702–705)
j Graphical solution approach —A means of plotting a solution to a two-variable problem on a graph.
j Decision variables —Choices available to a decision maker. Constraints of the form X Ú 0 are called nonnegativity constraints . j Feasible region —The set of all feasible combinations of decision variables. Any
point inside the feasible region represents a feasible solution , while any point outside the feasible region represents an infeasible solution .
j Iso-profit line method —An approach to identifying the optimum point in a graphic linear programming problem. The line that touches a particular point of the feasible region will pinpoint the optimal solution.
j Corner-point method —Another method for solving graphical linear program- ming problems.
The mathematical theory behind linear programming states that an optimal solution to any problem will lie at a corner point , or an extreme point , of the feasible region. Hence, it is necessary to find only the values of the variables at each corner; the optimal solution will lie at one (or more) of them. This is the corner-point method.
Concept Questions: 4.1–4.4 Problems: B.2–B.21 Virtual Office Hours for Solved Problem: B.2
SENSITIVITY ANALYSIS (pp. 705–708)
j Parameter —A numerical value that is given in a model. j Sensitivity analysis —An analysis that projects how much a solution may change
if there are changes in the variables or input data. Sensitivity analysis is also called postoptimality analysis. There are two approaches to determining just how sensitive an optimal solution is to changes: (1) a trial-and-error approach and (2) the analytic postoptimality method.
Concept Questions: 5.1–5.4 Problems: B.22–B.24
Module B Rapid Review
3
2
1
4 X 1
X 2
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Module B Rapid Review continuedB Main Heading Review Material MyOMLab
To use the analytic postoptimality method, after an LP problem has been solved, we determine a range of changes in problem parameters that will not affect the optimal solution or change the variables in the solution. LP software has this capability. While using the information in a sensitivity report to answer what-if questions, we assume that we are considering a change to only a single input data value at a time. That is, the sensitivity information does not generally apply to simultaneous changes in several input data values. j Shadow price (or dual value) —The value of one additional unit of a scarce
resource in LP. The shadow price is valid as long as the right-hand side of the constraint stays in a range within which all current corner points continue to exist. The information to compute the upper and lower limits of this range is given by the entries labeled Allowable Increase and Allowable Decrease in the sensitivity report.
SOLVING MINIMIZATION PROBLEMS (pp. 708–709)
j Iso-cost —An approach to solving a linear programming minimization problem graphically.
The iso-cost line approach to solving minimization problems is analogous to the iso-profit approach for maximization problems, but successive iso-cost lines are drawn inward instead of outward.
Concept Questions: 6.1–6.3 Problems: B.25–B.33 Virtual Office Hours for Solved Problem: B.3
LINEAR PROGRAM- MING APPLICATIONS (pp. 710–713)
The diet problem , known in agricultural applications as the feed-mix problem , involves specifying a food or feed ingredient combination that will satisfy stated nutritional requirements at a minimum cost level. Labor scheduling problems address staffing needs over a specific time period. They are especially useful when managers have some flexibility in assigning workers to jobs that require overlapping or interchangeable talents.
Concept Questions: 7.1–7.3 Problems: B.34–B.40
THE SIMPLEX METHOD OF LP (p. 713)
j Simplex method —An algorithm for solving linear programming problems of all sizes.
The simplex method is actually a set of instructions with which we examine corner points in a methodical fashion until we arrive at the best solution—highest profit or lowest cost. Computer programs (such as Excel OM and POM for Windows) and Excel’s Solver add-in are available to solve linear programming problems via the simplex method.
Concept Questions: 8.1–8.2 Virtual Office Hours for Solved Problem: C.1 (note that this Module C video is an LP application of the transportation problem)
INTEGER AND BINARY VARIABLES (pp. 713–715)
j Binary variables—Decision variables that can only take on the value of 0 or 1. Using computer software, decision variables for linear programs can be forced to be integer or even binary. Binary variables extend the flexibility of linear programs to include such options as mutually exclusive alternatives, either-or constraints, contingent decisions, fixed-charge problems, and threshold levels.
Concept Questions: 9.1–9.4 Problems: B.41–B.42
j Before taking the self-test, refer to the learning objectives listed at the beginning of the module and the key terms listed at the end of the module.
LO B.1 Which of the following is not a valid LP constraint formulation? a) 3X + 4Y … 12 b) 2X * 2Y … 12 c) 3Y + 2Z = 18 d) 100 Ú X + Y e) 2.5X + 1.5Z = 30.6 LO B.2 Using a graphical solution procedure to solve a maximization
problem requires that we: a) move the iso-profit line up until it no longer intersects
with any constraint equation. b) move the iso-profit line down until it no longer intersects
with any constraint equation. c) apply the method of simultaneous equations to solve for
the intersections of constraints. d) find the value of the objective function at the origin. LO B.3 Consider the following linear programming problem: Maximize 4X + 10Y
Subject to: 3X + 4Y … 480 4X + 2Y … 360 X, Y Ú 0 The feasible corner points are (48,84), (0,120), (0,0), and (90,0). What is the maximum possible value for the objective function? a) 1,032 b) 1,200 c) 360 d) 1,600 e) 840
LO B.4 A zero shadow price for a resource ordinarily means that: a) the resource is scarce. b) the resource constraint was redundant. c) the resource has not been used up. d) something is wrong with the problem formulation. e) none of the above. LO B.5 For these two constraints, which point is in the feasible region
of this minimization problem? 14x + 6y Ú 42 and x + y Ú 3
a) x = - 1, y = 1 b) x = 0, y = 4 c) x = 2, y = 1 d) x = 5, y = 1 e) x = 2, y = 0 LO B.6 When applying LP to diet problems, the objective function is
usually designed to: a) maximize profits from blends of nutrients. b) maximize ingredient blends. c) minimize production losses. d) maximize the number of products to be produced. e) minimize the costs of nutrient blends.
Self Test
Answers: LO B.1. b; LO B.2. a; LO B.3. b; LO B.4. c; LO B.5. d; LO B.6. e.
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729
M O D U L E O U T L I N E
C ◆
Transportation Modeling 730
◆
Developing an Initial Solution 732
◆
The Stepping-Stone Method 734
◆
Special Issues in Modeling 737
M O
D U
L E
Transportation Models
A la
sk a A
ir lin
e s
A la
sk a A
ir lin
e s
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Transportation Modeling Because location of a new factory, warehouse, or distribution center is a strategic issue with substantial cost implications, most companies consider and evaluate several locations. With a wide variety of objective and subjective factors to be considered, rational decisions are aided by a number of techniques. One of those techniques is transportation modeling.
The transportation models described in this module prove useful when considering alterna- tive facility locations within the framework of an existing distribution system . Each new poten- tial plant, warehouse, or distribution center will require a different allocation of shipments, depending on its own production and shipping costs and the costs of each existing facility. The choice of a new location depends on which will yield the minimum cost for the entire system .
Transportation modeling finds the least-cost means of shipping supplies from several origins to several destinations. Origin points (or sources ) can be factories, warehouses, car rental agencies like Avis, or any other points from which goods are shipped. Destinations are any points that receive goods. To use the transportation model, we need to know the following:
1. The origin points and the capacity or supply per period at each. 2. The destination points and the demand per period at each. 3. The cost of shipping one unit from each origin to each destination.
The transportation model is one form of the linear programming models discussed in Business Analytics Module B. Software is available to solve both transportation problems and the more general class of linear programming problems. To fully use such programs, though, you need to understand the assumptions that underlie the model. To illustrate the transportation prob- lem, we now look at a company called Arizona Plumbing, which makes, among other products,
L E A R N I N G OBJEC TI V ES
LO C.1 Develop an initial solution to a transportation model with the northwest-corner and intuitive lowest-cost methods 732
LO C.2 Solve a problem with the stepping-stone method 734
LO C.3 Balance a transportation problem 737
LO C.4 Deal with a problem that has degeneracy 737
The problem facing rental companies like
Avis, Hertz, and National is cross-country
travel. Lots of it. Cars rented in New York
end up in Chicago, cars from L.A. come to
Philadelphia, and cars from Boston come to
Miami. The scene is repeated in over 100
cities around the U.S. As a result, there are
too many cars in some cities and too few in
others. Operations managers have to decide
how many of these rentals should be trucked
(by costly auto carriers) from each city with
excess capacity to each city that needs more
rentals. The process requires quick action for
the most economical routing, so rental car
companies turn to transportation modeling.
V ib
ra n t
Im a g e S
tu d io
/S h u tt
e rs
to ck
Transportation modeling
An iterative procedure for solving
problems that involves minimizing
the cost of shipping products from
a series of sources to a series of
destinations.
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a full line of bathtubs. In our example, the firm must decide which of its factories should supply which of its warehouses. Relevant data for Arizona Plumbing are presented in Table C.1 and Figure C.1 . Table C.1 shows, for example, that it costs Arizona Plumbing $5 to ship one bathtub from its Des Moines factory to its Albuquerque warehouse, $4 to Boston, and $3 to Cleveland.
TABLE C.1 Transportation Costs Per Bathtub for Arizona Plumbing
TO
FROM ALBUQUERQUE BOSTON CLEVELAND
Des Moines $5 $4 $3 Evansville $8 $4 $3 Fort Lauderdale $9 $7 $5
Albuquerque (300 units required)
Des Moines (100 units capacity)
Evansville (300 units capacity)
Cleveland (200 units required)
Boston (200 units required)
Fort Lauderdale (300 units capacity)
Figure C.1
Transportation Problem
Likewise, we see in Figure C.1 that the 300 units required by Arizona Plumbing’s Albuquer- que warehouse may be shipped in various combinations from its Des Moines, Evansville, and Fort Lauderdale factories.
The first step in the modeling process is to set up a transportation matrix . Its purpose is to summarize all relevant data and to keep track of algorithm computations. Using the informa- tion displayed in Figure C.1 and Table C.1 , we can construct a transportation matrix as shown in Figure C.2 .
From To
Des Moines
$5
Albuquerque
Evansville
$8
Fort Lauderdale
$9
$4
Boston
$4
$7
$3
Cleveland
$3
$5
Factory capacity
Warehouse requirement 300 200 200 700
300
300
100
Cleveland warehouse demand
Cost of shipping 1 unit from Fort Lauderdale factory to Boston warehouse
Total demand and total supply
Cell representing a possible source-to- destination shipping assignment (Evansville to Cleveland)
Des Moines capacity constraint
Figure C.2
Transportation Matrix for
Arizona Plumbing
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Developing an Initial Solution Once the data are arranged in tabular form, we must establish an initial feasible solution to the problem. A number of different methods have been developed for this step. We now discuss two of them, the northwest-corner rule and the intuitive lowest-cost method.
The Northwest-Corner Rule The northwest-corner rule requires that we start in the upper-left-hand cell (or northwest corner) of the table and allocate units to shipping routes as follows:
1. Exhaust the supply (factory capacity) of each row (e.g., Des Moines: 100) before moving down to the next row.
2. Exhaust the (warehouse) requirements of each column (e.g., Albuquerque: 300) before moving to the next column on the right.
3. Check to ensure that all supplies and demands are met.
Example C1 applies the northwest-corner rule to our Arizona Plumbing problem.
LO C.1 Develop an initial solution to a
transportation model with
the northwest-corner
and intuitive lowest-cost
methods
Northwest-corner rule
A procedure in the transportation
model where one starts at the
upper-left-hand cell of a table (the
northwest corner) and systemati-
cally allocates units to shipping
routes.
Example C1 THE NORTHWEST-CORNER RULE Arizona Plumbing wants to use the northwest-corner rule to find an initial solution to its problem.
APPROACH c Follow the three steps listed above. See Figure C.3 . SOLUTION c To make the initial solution, these five assignments are made: 1. Assign 100 tubs from Des Moines to Albuquerque (exhausting Des Moines’s supply). 2. Assign 200 tubs from Evansville to Albuquerque (exhausting Albuquerque’s demand). 3. Assign 100 tubs from Evansville to Boston (exhausting Evansville’s supply). 4. Assign 100 tubs from Fort Lauderdale to Boston (exhausting Boston’s demand). 5. Assign 200 tubs from Fort Lauderdale to Cleveland (exhausting Cleveland’s demand and Fort
Lauderdale’s supply).
From To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
$9
$4 $3
(A) Albuquerque
(B) Boston
(C) Cleveland
$3
Factory capacity
Warehouse requirement 300 200 200 700
300
300
100
200
100
100
100
200
Means that the firm is shipping 100 bathtubs from Fort Lauderdale to Boston
$5
$8 $4
$7 $5
Figure C.3
Northwest-Corner Solution to
Arizona Plumbing Problem
The total cost of this shipping assignment is $4,200 (see Table C.2 ).
TABLE C.2 Computed Shipping Cost
ROUTE
FROM TO TUBS SHIPPED COST PER UNIT TOTAL COST
D A 100 $5 $ 500 E A 200 8 1,600 E B 100 4 400 F B 100 7 700 F C 200 5 $1,000
Total: $4,200
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INSIGHTS c The solution given is feasible because it satisfies all demand and supply constraints. The northwest-corner rule is easy to use, but it totally ignores costs, and therefore should only be considered as a starting position.
LEARNING EXERCISE c Does the shipping assignment change if the cost from Des Moines to Albuquerque increases from $5 per unit to $10 per unit? Does the total cost change? [Answer: The initial assignment is the same, but cost = $4,700.]
RELATED PROBLEMS c C.1a, C.3a, C.15
The Intuitive Lowest-Cost Method The intuitive method makes initial allocations based on lowest cost. This straightforward approach uses the following steps:
1. Identify the cell with the lowest cost. Break any ties for the lowest cost arbitrarily. 2. Allocate as many units as possible to that cell without exceeding the supply or demand.
Then cross out that row or column (or both) that is exhausted by this assignment. 3. Find the cell with the lowest cost from the remaining (not crossed out) cells. 4. Repeat Steps 2 and 3 until all units have been allocated.
Intuitive method
A cost-based approach to finding
an initial solution to a transporta-
tion problem.
Although the likelihood of a minimum-cost solution does improve with the intuitive method, we would have been fortunate if the intuitive solution yielded the minimum cost. In this case, as in the northwest-corner solution, it did not. Because the northwest-corner and the intuitive
Example C2 THE INTUITIVE LOWEST-COST APPROACH Arizona Plumbing now wants to apply the intuitive lowest-cost approach.
APPROACH c Apply the 4 steps listed above to the data in Figure C.2 .
SOLUTION c When the firm uses the intuitive approach on the data (rather than the northwest-corner rule) for its starting position, it obtains the solution seen in Figure C.4 .
The total cost of this approach = +3(100) + +3(100) + +4(200) + +9(300) = +4,100. (D to C) (E to C) (E to B) (F to A)
From To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
Warehouse requirement
(A) Albuquerque
(B) Boston
(C) Cleveland
300 200 200 700
300
300
100
100
100
200
$9
$8
$5 $4
$4
$7
$3
$3
$5
300
Factory capacity
Second, cross out column C after entering 100 units in this $3 cell because column C is satisfied.
First, cross out top row (D) after entering 100 units in $3 cell because row D is satisfied.
Finally, enter 300 units in the only remaining cell to complete the allocations.
Third, cross out row E and column B after entering 200 units in this $4 cell because a total of 300 units satisfies row E and column B.
Figure C.4
Intuitive Lowest-Cost Solution to Arizona Plumbing Problem
INSIGHT c This method’s name is appropriate as most people find it intuitively correct to include costs when making an initial assignment.
LEARNING EXERCISE c If the cost per unit from Des Moines to Cleveland is not $3, but rather $6, does this initial solution change? [Answer: Yes, now D 2 B = 100, D 2 C = 0, E 2 B = 100, E 2 C = 200, F 2 A = 300. Others unchanged at zero. Total cost stays the same.]
RELATED PROBLEMS c C.1b, C.2, C.3b
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lowest-cost approaches are meant only to provide us with a starting point, we often will have to employ an additional procedure to reach an optimal solution.
The Stepping-Stone Method The stepping-stone method will help us move from an initial feasible solution to an optimal solution. It is used to evaluate the cost effectiveness of shipping goods via transportation routes not cur- rently in the solution. When applying it, we test each unused cell, or square, in the transportation table by asking: What would happen to total shipping costs if one unit of the product (for exam- ple, one bathtub) was tentatively shipped on an unused route? We conduct the test as follows: 1. Select any unused square to evaluate. 2. Beginning at this square, trace a closed path back to the original square via squares that
are currently being used (only horizontal and vertical moves are permissible). You may, however, step over either an empty or an occupied square.
3. Beginning with a plus (+) sign at the unused square, place alternating minus signs and plus signs on each corner square of the closed path just traced.
4. Calculate an improvement index by first adding the unit-cost figures found in each square containing a plus sign and then by subtracting the unit costs in each square containing a minus sign.
5. Repeat Steps 1 through 4 until you have calculated an improvement index for all unused squares. If all indices computed are greater than or equal to zero , you have reached an optimal solution. If not, the current solution can be improved further to decrease total shipping costs.
Example C3 illustrates how to use the stepping-stone method to move toward an optimal solution. We begin with the northwest-corner initial solution developed in Example C1.
Stepping-stone method
An iterative technique for moving
from an initial feasible solution to
an optimal solution in the trans-
portation method.
LO C.2 Solve a problem with the stepping-stone
method
Example C3 CHECKING UNUSED ROUTES WITH THE STEPPING-STONE METHOD Arizona Plumbing wants to evaluate unused shipping routes.
APPROACH c Start with Example C1’s Figure C.3 and follow the 5 steps listed above. As you can see, the four currently unassigned routes are Des Moines to Boston, Des Moines to Cleveland, Evansville to Cleveland, and Fort Lauderdale to Albuquerque.
SOLUTION c Steps 1 and 2. Beginning with the Des Moines–Boston route, trace a closed path using only currently occupied squares (see Figure C.5 ). Place alternating plus and minus signs in the corners of this path. In the upper-left square, for example, we place a minus sign because we have subtracted 1 unit from the original 100. Note that we can use only squares currently used for shipping to turn the corners of the route we are tracing. Hence, the path Des Moines–Boston to Des Moines–Albuquerque to Fort Lauderdale–Albuquerque to Fort Lauderdale–Boston to Des Moines–Boston would not be acceptable because the Fort Lauderdale–Albuquerque square is empty. It turns out that only one closed route exists for each empty square . Once this one closed path is identified, we can begin assigning plus and minus signs to these squares in the path.
Step 3. How do we decide which squares get plus signs and which squares get minus signs? The answer is simple. Because we are testing the cost-effectiveness of the Des Moines–Boston shipping route, we try shipping 1 bathtub from Des Moines to Boston. This is 1 more unit than we were sending between the two cities, so place a plus sign in the box. However, if we ship 1 more unit than before from Des Moines to Boston, we end up sending 101 bathtubs out of the Des Moines factory. Because the Des Moines factory’s capacity is only 100 units, we must ship 1 bathtub less from Des Moines to Albuquerque. This change prevents us from violating the capacity constraint.
To indicate that we have reduced the Des Moines–Albuquerque shipment, place a minus sign in its box. As you continue along the closed path, notice that we are no longer meeting our Albuquerque warehouse requirement for 300 units. In fact, if we reduce the Des Moines–Albuquerque shipment to 99 units, we must increase the Evansville–Albuquerque load by 1 unit, to 201 bathtubs. Therefore, place a plus sign in that box to indicate the increase. You may also observe that those squares in which we turn a corner (and only those squares) will have plus or minus signs.
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Result of proposed shift in allocation = 1 $4 – 1 $5 + 1 $8 – 1 $4 = + $3
Evaluation of Des Moines to Boston square
From To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
$9
$3
(A) Albuquerque
(B) Boston
(C) Cleveland
$3
Factory capacity
Warehouse requirement 300 200 200 700
300
300
100
200
100
100
100
200
$5
$8 $4
$7 $5
$4Start
201 200
$8 99 $4
99 $5 1 $4100
100
* * **
Figure C.5
Stepping-Stone Evaluation of
Alternative Routes for Arizona
Plumbing
Finally, note that if we assign 201 bathtubs to the Evansville–Albuquerque route, then we must reduce the Evansville–Boston route by 1 unit, to 99 bathtubs, to maintain the Evansville factory’s capacity constraint of 300 units. To account for this reduction, we thus insert a minus sign in the Evansville–Boston box. By so doing, we have balanced supply limitations among all four routes on the closed path.
Step 4. Compute an improvement index for the Des Moines–Boston route by adding unit costs in squares with plus signs and subtracting costs in squares with minus signs.
Des Moines9Boston index = +4 - +5 + +8 - +4 = + +3
This means that for every bathtub shipped via the Des Moines–Boston route, total transportation costs will increase by $3 over their current level.
Let us now examine the unused Des Moines–Cleveland route, which is slightly more difficult to trace with a closed path (see Figure C.6 ). Again, notice that we turn each corner along the path only at squares on the existing route. Our path, for example, can go through the Evansville–Cleveland box but cannot turn a corner; thus we cannot place a plus or minus sign there. We may use occupied squares only as stepping-stones:
Des Moines9Cleveland index = +3 - +5 + +8 - +4 + +7 - +5 = + +4
From To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
$9
(A) Albuquerque
(B) Boston
(C) Cleveland
$3
Warehouse requirement 300 200 200 700
300
300
100
200
100
100
100
200
$5
$7 $5
$4 Start $3
$8 $4
Factory capacity
Figure C.6
Testing Des Moines to
Cleveland
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Again, opening this route fails to lower our total shipping costs. Two other routes can be evaluated in a similar fashion:
Evansville9Cleveland index = +3 - +4 + +7 - +5 = + +1 (Closed path = EC - EB + FB - FC)
Fort Lauderdale9Albuquerque index = +9 - +7 + +4 - +8 = 9+2 (Closed path = FA - FB + EB - EA)
INSIGHT c Because this last index is negative, we can realize cost savings by using the (currently unused) Fort Lauderdale–Albuquerque route.
LEARNING EXERCISE c What would happen to total cost if Arizona used the shipping route from Des Moines to Cleveland? [Answer: Total cost of the current solution would increase by $400.]
RELATED PROBLEMS c C.1c, C.3c, C.4–C.11 (C.12, C.13 are available in MyOMLab)
EXCEL OM Data File ModCExC3.xls can be found in MyOMLab.
In Example C3, we see that a better solution is indeed possible because we can calculate a negative improvement index on one of our unused routes. Each negative index represents the amount by which total transportation costs could be decreased if one unit was shipped by the source–destination combination . The next step, then, is to choose that route (unused square) with the largest negative improvement index. We can then ship the maximum allowable num- ber of units on that route and reduce the total cost accordingly.
What is the maximum quantity that can be shipped on our new money-saving route? That quantity is found by referring to the closed path of plus signs and minus signs drawn for the route and then selecting the smallest number found in the squares containing minus signs . To obtain a new solution, we add this number to all squares on the closed path with plus signs and subtract it from all squares on the path to which we have assigned minus signs.
One iteration of the stepping-stone method is now complete. Again, of course, we must test to see if the solution is optimal or whether we can make any further improvements. We do this by evaluating each unused square, as previously described. Example C4 continues our effort to help Arizona Plumbing arrive at a final solution.
Example C4 IMPROVEMENT INDICES Arizona Plumbing wants to continue solving the problem.
APPROACH c Use the improvement indices calculated in Example C3. We found in Example C3 that the largest (and only) negative index is on the Fort Lauderdale–Albuquerque route (which is the route depicted in Figure C.7 ).
SOLUTION c The maximum quantity that may be shipped on the newly opened route, Fort Lauderdale– Albuquerque (FA), is the smallest number found in squares containing minus signs—in this case, 100 units. Why 100 units? Because the total cost decreases by $2 per unit shipped, we know we would like to ship the maximum possible number of units. Previous stepping-stone calculations indicate that each unit shipped over the FA route results in an increase of 1 unit shipped from Evansville (E) to Boston (B) and a decrease of 1 unit in amounts shipped both from F to B (now 100 units) and from E to A (now 200 units). Hence, the maximum we can ship over the FA route is 100 units. This solution results in zero units being shipped from F to B. Now we take the following four steps:
1. Add 100 units (to the zero currently being shipped) on route FA. 2. Subtract 100 from route FB, leaving zero in that square (though still balancing the row total for F). 3. Add 100 to route EB, yielding 200. 4. Finally, subtract 100 from route EA, leaving 100 units shipped.
Note that the new numbers still produce the correct row and column totals as required. The new solution is shown in Figure C.8 .
Total shipping cost has been reduced by (100 units) × ($2 saved per unit) = $200 and is now $4,000. This cost figure, of course, can also be derived by multiplying the cost of
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From To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
Warehouse demand
(A) Albuquerque
(B) Boston
(C) Cleveland
$3
300 200 200 700
300
300
100
200
100
100
200
$5
$7 $5
$4 $3
$8 $4
$9
100
Factory capacity
Figure C.7
Transportation Table:
Route FA
From To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
Warehouse demand
(A) Albuquerque
(B) Boston
(C) Cleveland
$3
300 200 200 700
300
300
100100
100
100
200
200
$5
$7 $5
$4 $3
$8 $4
$9
Factory capacity
Figure C.8
Solution at Next Iteration
(Still Not Optimal)
shipping each unit by the number of units transported on its respective route, namely: 100($5) + 100($8) + 200($4) + 100($9) + 200($5) = $4,000.
INSIGHT c Looking carefully at Figure C.8 , however, you can see that it, too, is not yet optimal. Route EC (Evansville–Cleveland) has a negative cost improvement index of –$1. Closed path = EC 2 EA + FA 2 FC.
LEARNING EXERCISE c Find the final solution for this route on your own. [Answer: Programs C.1 and C.2, at the end of this module, provide an Excel OM solution.]
RELATED PROBLEMS c C.4–C.11 (C.12–C.13 are available in MyOMLab)
STUDENT TIP FA has a negative index:
FA (+9) to FB (27) to EB (+4) to EA (28) = 2$2
Special Issues in Modeling Demand Not Equal to Supply A common situation in real-world problems is the case in which total demand is not equal to total supply. We can easily handle these so-called unbalanced problems with the solution procedures that we have just discussed by introducing dummy sources or dummy destinations . If total supply is greater than total demand, we make demand exactly equal the surplus by creating a dummy destination. Conversely, if total demand is greater than total supply, we introduce a dummy source (factory) with a supply equal to the excess of demand. Because these units will not in fact be shipped, we assign cost coefficients of zero to each square on the dummy location. In each case, then, the cost is zero.
Degeneracy To apply the stepping-stone method to a transportation problem, we must observe a rule about the number of shipping routes being used: The number of occupied squares in any solu- tion (initial or later) must be equal to the number of rows in the table plus the number of columns minus 1. Solutions that do not satisfy this rule are called degenerate .
LO C.3 Balance a transportation problem
LO C.4 Deal with a problem that has
degeneracy
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Degeneracy occurs when too few squares or shipping routes are being used. As a result, it becomes impossible to trace a closed path for one or more unused squares. The Arizona Plumbing problem we just examined was not degenerate, as it had 5 assigned routes (3 rows or factories + 3 columns or warehouses − 1).
To handle degenerate problems, we must artificially create an occupied cell: That is, we place a zero or a very small amount (representing a fake shipment) in one of the unused squares and then treat that square as if it were occupied. The chosen square must be in such a position as to allow all stepping-stone paths to be closed.
Degeneracy
An occurrence in transportation
models in which too few squares
or shipping routes are being used,
so that tracing a closed path for
each unused square becomes
impossible.
Summary The transportation model, a form of linear programming, is used to help find the least-cost solutions to system- wide shipping problems. The northwest-corner method (which begins in the upper-left corner of the transpor- tation table) or the intuitive lowest-cost method may be used for finding an initial feasible solution. The stepping- stone algorithm is then used for finding optimal solu- tions. Unbalanced problems are those in which the total
demand and total supply are not equal. Degeneracy refers to the case in which the number of rows + the number of columns −1 is not equal to the number of occupied squares. The transportation model approach is one of the four location models described earlier in Chapter 8 and is one of the two aggregate planning models discussed in Chapter 13 . Additional solution techniques are presented in Tutorial 4 in MyOMLab.
Key Terms
Transportation modeling (p. 730 ) Northwest-corner rule (p. 732 )
Intuitive method (p. 733 ) Stepping-stone method (p. 734 )
Degeneracy (p. 738 )
Discussion Questions
1. What are the three information needs of the transportation model?
2. What are the steps in the intuitive lowest-cost method? 3. Identify the three “steps” in the northwest-corner rule. 4. How do you know when an optimal solution has been
reached? 5. Which starting technique generally gives a better initial solu-
tion, and why? 6. The more sources and destinations there are for a transporta-
tion problem, the smaller the percentage of all cells that will be used in the optimal solution. Explain.
7. All of the transportation examples appear to apply to long distances. Is it possible for the transportation model to apply
on a much smaller scale, for example, within the departments of a store or the offices of a building? Discuss.
8. Develop a northeast -corner rule and explain how it would work. Set up an initial solution for the Arizona Plumbing problem analyzed in Example C1.
9. What is meant by an unbalanced transportation problem, and how would you balance it?
10. How many occupied cells must all solutions use? 11. Explain the significance of a negative improvement index in a
transportation-minimizing problem. 12. How can the transportation method address production costs
in addition to transportation costs? 13. Explain what is meant by the term degeneracy within the
context of transportation modeling.
Using Software to Solve Transportation Problems
Excel, Excel OM, and POM for Windows may all be used to solve transportation problems. Excel uses Solver, which requires that you enter your own constraints. Excel OM also uses Solver but is prestructured so that you need enter only the actual data. POM for Windows similarly requires that only demand data, supply data, and shipping costs be entered.
X USING EXCEL OM Excel OM’s Transportation module uses Excel’s built-in Solver routine to find optimal solutions to transportation prob- lems. Program C.1 illustrates the input data (from Arizona Plumbing) and total-cost formulas. In Excel 2007, 2010, and 2013 Solver is in the Analysis section of the Data tab. Be certain that the solving method is “Simplex LP.” The output appears in Program C.2.
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P USING POM FOR WINDOWS The POM for Windows Transportation module can solve both maximization and minimization problems by a variety of methods. Input data are the demand data, supply data, and unit shipping costs. See Appendix IV for further details .
Program C.2
Output from Excel OM with Optimal Solution to Arizona Plumbing Problem
Enter the origin and destination names, the shipping costs, and the total supply and demand figures.
Our target cell is the total cost cell (B21), which we wish to minimize by changing the shipment cells (B16 through D18). The constraints ensure that the number shipped is equal to the number demanded and that we don’t ship more units than we have on hand.
The total shipments to and from each location are calculated here.
These are the cells in which Solver will place the shipments.
In Excel 2007, 2010, and 2013, Solver is in the Analysis section of the Data tab. In the prior Excel version or on a Mac with Excel 2011, Solver is on the Tools menu. If Solver is not available, please visit www.pearsonhighered.com/weiss.
Nonnegativity constraints have been added through the Options button.
The total cost is created here by multiplying the data table by the shipment table using the SUMPRODUCT function.
Program C.1
Excel OM Input Screen and Formulas, Using Arizona Plumbing Data
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Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM C.1 Williams Auto Top Carriers currently maintains plants in Atlanta and Tulsa to supply auto top carriers to distribution centers in Los Angeles and New York. Because of expanding demand, Williams has decided to open a third plant and has
narrowed the choice to one of two cities—New Orleans and Houston. Table C.3 provides pertinent production and distribu- tion costs as well as plant capacities and distribution demands.
Which of the new locations, in combination with the existing plants and distribution centers, yields a lower cost for the firm?
TABLE C.3 Production Costs, Distribution Costs, Plant Capabilities, and Market Demands for Williams Auto Top Carriers
TO DISTRIBUTION CENTERS
FROM PLANTS LOS
ANGELES NEW YORK
NORMAL PRODUCTION
UNIT PRODUCTION COST
Existing plants Atlanta $8 $5 600 $6 Tulsa $4 $7 900 $5
Proposed locations New Orleans $5 $6 500 $4 (anticipated) Houston $4 $6 a 500 $3 (anticipated)
Forecast demand 800 1,200 2,000
a Indicates distribution cost (shipping, handling, storage) will be $6 per carrier between Houston and New York.
SOLUTION To answer this question, we must solve two transportation problems, one for each combination. We will recommend the location that yields a lower total cost of distribution and pro- duction in combination with the existing system.
We begin by setting up a transportation table that represents the opening of a third plant in New Orleans (see Figure C.9 ). Then we use the northwest-corner method to find an initial solution. The total cost of this first solution is $23,600. Note that the cost of each individual “plant-to-distribution-center” route is found by adding the distribution costs (in the body of Table C.3 ) to the respective unit production costs (in the right-hand column of Table C.3 ). Thus, the total production- plus-shipping cost of one auto top carrier from Atlanta to Los Angeles is $14 ($8 for shipping plus $6 for production).
Total cost = (600 units * +14) + (200 units * +9) + (700 units * +12) + (500 units * +10) = +8,400 + +1,800 + +8,400 + +5,000 = +23,600
From To
Atlanta
Tulsa
New Orleans
Demand
Los Angeles New York Production capacity
800 1,200 2,000
500
900
600
200 700
$14
500
$9
$9
$11
$12
$10
600
Figure C.9
Initial Williams Transportation Table for New Orleans
Is this initial solution (in Figure C.9 ) optimal? We can use the stepping-stone method to test it and compute improvement indices for unused routes:
Improvement index for Atlanta–New York route:
= + +11 (Atlanta9New York) - +14 (Atlanta9Los Angeles) + +9 (Tulsa9Los Angeles) - +12 (Tulsa9New York) = - +6
Improvement index for New Orleans–Los Angeles route:
= + +9 (New Orleans9Los Angeles) - +10 (New Orleans9New York) + +12 (Tulsa9New York) - +9 (Tulsa9Los Angeles) = +2
Because the firm can save $6 for every unit shipped from Atlanta to New York, it will want to improve the initial solu- tion and send as many units as possible (600, in this case) on this currently unused route (see Figure C.10 ). You may also
From To
Atlanta
Tulsa
New Orleans
Demand
Los Angeles New York
800 1,200 2,000
500
900
600
800 100
$14
500
600
$9
$9
$11
$12
$10
Production capacity
Figure C.10
Improved Transportation Table for Williams
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want to confirm that the total cost is now $20,000, a savings of $3,600 over the initial solution.
Next, we must test the two unused routes to see if their improvement indices are also negative numbers:
Index for Atlanta–Los Angeles:
= +14 - +11 + +12 - +9 = +6
Index for New Orleans–Los Angeles:
= +9 - +10 + +12 - +9 = +2
Because both indices are greater than zero, we have already reached our optimal solution for the New Orleans location. If Williams elects to open the New Orleans plant, the firm’s total production and distribution cost will be $20,000.
This analysis, however, provides only half the answer to Williams’s problem. The same procedure must still be followed to determine the minimum cost if the new plant is built in Houston. Determining this cost is left as a homework problem. You can help provide complete information and recommend a solution by solving Problem C.7 (on p. 742 ).
SOLVED PROBLEM C.2 In Solved Problem C.1, we examined the Williams Auto Top Carriers problem by using a transportation table. An alternative approach is to structure the same decision analysis using linear programming (LP), which we explained in detail in Business Analytics Module B .
SOLUTION Using the data in Figure C.9 (p. 740 ), we write the objective function and constraints as follows:
Minimize total cost = +14XAtl,LA + +11XAtl,NY + +9XTul,LA + +12XTul,NY + +9XNO,LA + +10XNO,NY Subject to: XAtl,LA + XAtl,NY … 600 (production capacity at Atlanta) XTul,LA + XTul,NY … 900 (production capacity at Tulsa) XNO,LA + XNO,NY … 500 (production capacity at New Orleans) XAtl,LA + XTul,LA + XNO,LA Ú 800 (Los Angeles demand constraint) XAtl,NY + XTul,NY + XNO,NY Ú 1200 (New York demand constraint)
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems C.1–C.3 relate to Developing an Initial Solution
• C.1 Find an initial solution to the following transporta- tion problem.
TO
FROM LOS ANGELES CALGARY PANAMA CITY SUPPLY
Mexico City $ 6 $18 $ 8 100 Detroit $17 $13 $19 60 Ottawa $20 $10 $24 40 Demand 50 80 70
a) Use the northwest-corner method. What is its total cost? b) Use the intuitive lowest-cost approach. What is its total
cost? c) Using the stepping-stone method, find the optimal solution.
Compute the total cost. PX
• C.2 Consider the transportation table at right. Unit costs for each shipping route are in dollars. What is the total cost of the basic feasible solution that the intuitive lowest-cost method would find for this problem? PX
A B C D E Supply
Destination
Source
Demand 6 8 12 4 2
12 8 5 10 4 18
14
1
2 6 11 3 7 9
• C.3 Refer to the table that follows. a) Use the northwest-corner method to find an initial feasible
solution. What must you do before beginning the solution steps?
b) Use the intuitive lowest-cost approach to find an initial feasi- ble solution. Is this approach better than the northwest-corner method?
c) Find the optimal solution using the stepping-stone method.
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TO
FROM HOSP. 1 HOSP. 2 HOSP. 3 HOSP. 4 SUPPLY
Bank 1 $ 8 $ 9 $11 $16 50 Bank 2 $12 $ 7 $ 5 $ 8 80 Bank 3 $14 $10 $ 6 $ 7 120 Demand 90 70 40 50 250
• • C.7 In Solved Problem C.1 (page 740 ), Williams Auto Top Carriers proposed opening a new plant in either New Orleans or Houston. Management found that the total system cost (of production plus distribution) would be $20,000 for the New Orleans site. What would be the total cost if Williams opened a plant in Houston? At which of the two proposed loca- tions (New Orleans or Houston) should Williams open the new facility? PX
• • C.8 The Donna Mosier Clothing Group owns factories in three towns (W, Y, and Z), which distribute to three retail dress shops in three other cities (A, B, and C). The following table sum- marizes factory availabilities, projected store demands, and unit shipping costs:
From To
Factory W
Factory Y
$3
Dress Shop A
Dress Shop B
Dress Shop C
Factory availability
Store demand 30 65 135
50
50
35
$6 $7
$5
$6
Factory Z
$3
$2
$4
$8
40
a) Complete the analysis, determining the optimal solution for shipping at the Mosier Clothing Group.
b) How do you know whether it is optimal or not? PX
• • • C.9 Captain Borders Corp. manufacturers fishing equip- ment. Currently, the company has a plant in Los Angeles and a plant in New Orleans. William Borders, the firm’s owner, is decid- ing where to build a new plant—Philadelphia or Seattle. Use the
TO
FROM A B C SUPPLY
X $10 $18 $12 100 Y $17 $13 $ 9 50 Z $20 $18 $14 75 Demand 50 80 70
Problems C.4–C.13 relate to The Stepping-Stone Method
• C.4 Consider the transportation table below. The solu- tion displayed was obtained by performing some iterations of the transportation method on this problem. What is the total cost of the shipping plan that would be obtained by performing one more iteration of the stepping-stone method on this problem?
Denver Yuma Miami SupplySource
Chicago
10 10
10 20
Demand 20 20 20
$2 $8 $1
1010
20
30
Houston
St. Louis $4 $5 $6
$6 $3 $2
Destination
• • C.5 The following table is the result of one or more iterations.
From To
Demand
A
B
C
1 2 3
40
Capacity
50
30
75
1555560
10
20
30
10
30
30
45
1040 30
10
10 25
5
a) Complete the next iteration using the stepping-stone method. b) Calculate the “total cost” incurred if your results were to be
accepted as the final solution. PX
• • C.6 The three blood banks in Seminole County, Florida, are coordinated through a central office that facilitates blood delivery to four hospitals in the region. The cost to ship a standard container of blood from each bank to each hospital is shown in the table below. Also given are the biweekly number of containers available at each bank and the biweekly number of containers of blood needed at each hospital. How many ship- ments should be made biweekly from each blood bank to each hospital so that total shipment costs are minimized?
M in
er va
S tu
d io
/F ot
ol ia
PX
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LOCATION PRODUCTION COSTS ($)
Decatur $50 Minneapolis 60 Carbondale 70 East St. Louis 40 St. Louis 50
Additional problems C.12–C.13 are available in MyOMLab.
Problems C.14–C.18 relate to Special Issues in Modeling
• • C.14 Allen Air Conditioning manufactures room air condi- tioners at plants in Houston, Phoenix, and Memphis. These are sent to regional distributors in Dallas, Atlanta, and Denver. The shipping costs vary, and the company would like to find the least-cost way to meet the demands at each of the distribution centers. Dallas needs to receive 800 air conditioners per month, Atlanta needs 600, and Denver needs 200. Houston has 850 air conditioners available each month, Phoenix has 650, and Memphis has 300. The shipping cost per unit from Houston to Dallas is $8, to Atlanta $12, and to Denver $10. The cost per unit from Phoenix to Dallas is $10, to Atlanta $14, and to Denver $9. The cost per unit from Memphis to Dallas is $11, to Atlanta $8, and to Denver $12. How many units should owner Stephen Allen ship from each plant to each regional distribution center? What is the total transportation cost? (Note that a “dummy” destination is needed to balance the problem.) PX
• • C.15 For the following Gregory Bier Corp. data, find the starting solution and initial cost using the northwest-corner method. What must you do to balance this problem?
TO
FROM W X Y Z SUPPLY
A $132 $116 $250 $110 220 B $220 $230 $180 $178 300 C $152 $173 $196 $164 435 Demand 160 120 200 230
Additional problems C.16–C.18 are available in MyOMLab.
following table to find the total shipping costs for each potential site. Which should Borders select?
WAREHOUSE
PLANT PITTSBURGH ST. LOUIS DENVER CAPACITY
Los Angeles $100 $75 $50 150 New Orleans $ 80 $60 $90 225 Philadelphia $ 40 $50 $90 350 Seattle $110 $70 $30 350 Demand 200 100 400
• • C.10 Dana Johnson Corp. is considering adding a fourth plant to its three existing facilities in Decatur, Minneapolis, and Carbondale. Both St. Louis and East St. Louis are being consid- ered. Evaluating only the transportation costs per unit as shown in the table, decide which site is best.
FROM EXISTING PLANTS
TO DECATUR MINNEAPOLIS CARBONDALE DEMAND
Blue Earth $20 $17 $21 250 Ciro $25 $27 $20 200 Des Moines $22 $25 $22 350 Capacity 300 200 150
FROM PROPOSED PLANTS
TO EAST ST. LOUIS ST. LOUIS
Blue Earth $29 $27 Ciro $30 $28 Des Moines $30 $31 Capacity 150 150
• • C.11 Using the data from Problem C.10 and the unit pro- duction costs in the following table, show which locations yield the lowest cost.
CASE STUDY Custom Vans, Inc.
Custom Vans, Inc., specializes in converting standard vans into campers. Depending on the amount of work and customizing to be done, the customizing can cost from less than $1,000 to more than $5,000. In less than 4 years, Tony Rizzo was able to expand his small operation in Gary, Indiana, to other major outlets in Chicago, Milwaukee, Minneapolis, and Detroit.
Innovation was the major factor in Tony’s success in convert- ing a small van shop into one of the largest and most profitable custom van operations in the Midwest. Tony seemed to have a special ability to design and develop unique features and devices that were always in high demand by van owners. An example was Shower-Rific, which was developed by Tony only 6 months after Custom Vans, Inc., was started. These small showers were completely self-contained, and they could be placed in almost any type of van and in a number of different locations within a van. Shower-Rific was made of fiberglass, and contained towel racks,
built-in soap and shampoo holders, and a unique plastic door. Each Shower-Rific took 2 gallons of fiberglass and 3 hours of labor to manufacture.
Most of the Shower-Rifics were manufactured in Gary in the same warehouse where Custom Vans, Inc., was founded. The manufacturing plant in Gary could produce 300 Shower- Rifics in a month, but this capacity never seemed to be enough. Custom Van shops in all locations were complaining about not getting enough Shower-Rifics, and because Minneapolis was far- ther away from Gary than the other locations, Tony was always inclined to ship Shower-Rifics to the other locations before Minneapolis. This infuriated the manager of Custom Vans at Minneapolis, and after many heated discussions, Tony decided to start another manufacturing plant for Shower-Rifics at Fort Wayne, Indiana. The manufacturing plant at Fort Wayne could produce 150 Shower-Rifics per month.
PX
PX
PX
PX
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The manufacturing plant at Fort Wayne was still not able to meet current demand for Shower-Rifics, and Tony knew that the demand for his unique camper shower would grow rapidly in the next year. After consulting with his lawyer and banker, Tony concluded that he should open two new manufacturing plants as soon as possible. Each plant would have the same capacity as the Fort Wayne manufacturing plant. An initial investigation into possible manufacturing locations was made, and Tony decided that the two new plants should be located in Detroit, Michigan; Rockford, Illinois; or Madison, Wisconsin. Tony knew that selecting the best location for the two new manufacturing plants would be difficult. Transportation costs and demands for the var- ious locations would be important considerations.
The Chicago shop was managed by Bill Burch. This shop was one of the first established by Tony, and it continued to out- perform the other locations. The manufacturing plant at Gary was supplying 200 Shower-Rifics each month, although Bill knew that the demand for the showers in Chicago was 300 units. The transportation cost per unit from Gary was $10, and although the transportation cost from Fort Wayne was double that amount, Bill was always pleading with Tony to get an additional 50 units from the Fort Wayne manufacturer. The two additional manu- facturing plants would certainly be able to supply Bill with the additional 100 showers he needed. The transportation costs would, of course, vary, depending on which two locations Tony picked. The transportation cost per shower would be $30 from Detroit, $5 from Rockford, and $10 from Madison.
Wilma Jackson, manager of the Custom Van shop in Milwaukee, was the most upset about not getting an adequate supply of showers. She had a demand for 100 units, and at the present time, she was only getting half of this demand from the Fort Wayne manufacturing plant. She could not understand why Tony didn’t ship her all 100 units from Gary. The transportation cost per unit from Gary was only $20, while the transportation cost from Fort Wayne was $30. Wilma was hoping that Tony would select Madison for one of the manufacturing locations. She would be able to get all the showers needed, and the transportation cost per unit would only be $5. If not in Madison, a new plant in Rockford would be able to supply her total needs, but the trans- portation cost per unit would be twice as much as it would be from Madison. Because the transportation cost per unit from Detroit would be $40, Wilma speculated that even if Detroit became one of the new plants, she would not be getting any units from Detroit.
Custom Vans, Inc., of Minneapolis was managed by Tom Poanski. He was getting 100 showers from the Gary plant. Demand was 150 units. Tom faced the highest transportation costs of all locations. The transportation cost from Gary was $40 per unit. It would cost $10 more if showers were sent from the Fort Wayne location. Tom was hoping that Detroit would not be one of the new plants, as the transportation cost would be $60 per unit. Rockford and Madison would have a cost of $30 and $25, respectively, to ship one shower to Minneapolis.
The Detroit shop’s position was similar to Milwaukee’s—only getting half of the demand each month. The 100 units that Detroit did receive came directly from the Fort Wayne plant. The trans- portation cost was only $15 per unit from Fort Wayne, while it was
$25 from Gary. Dick Lopez, manager of Custom Vans, Inc., of Detroit, placed the probability of having one of the new plants in Detroit fairly high. The factory would be located across town, and the transportation cost would be only $5 per unit. He could get 150 showers from the new plant in Detroit and the other 50 showers from Fort Wayne. Even if Detroit was not selected, the other two locations were not intolerable. Rockford had a transportation cost per unit of $35, and Madison had a transportation cost of $40.
Tony pondered the dilemma of locating the two new plants for several weeks before deciding to call a meeting of all the managers of the van shops. The decision was complicated, but the objective was clear—to minimize total costs. The meeting was held in Gary, and everyone was present except Wilma.
Tony: Thank you for coming. As you know, I have decided to open two new plants at Rockford, Madison, or Detroit. The two locations, of course, will change our shipping practices, and I sincerely hope that they will supply you with the Shower-Rifics that you have been wanting. I know you could have sold more units, and I want you to know that I am sorry for this situation.
Dick: Tony, I have given this situation a lot of considera- tion, and I feel strongly that at least one of the new plants should be located in Detroit. As you know, I am now only getting half of the showers that I need. My brother, Leon, is very interested in running the plant, and I know he would do a good job.
Tom: Dick, I am sure that Leon could do a good job, and I know how difficult it has been since the recent lay- offs by the auto industry. Nevertheless, we should be considering total costs and not personalities. I believe that the new plants should be located in Madison and Rockford. I am farther away from the other plants than any other shop, and these locations would significantly reduce transportation costs.
Dick: That may be true, but there are other factors. Detroit has one of the largest suppliers of fiberglass, and I have checked prices. A new plant in Detroit would be able to purchase fiberglass for $2 per gallon less than any of the other existing or proposed plants.
Tom: At Madison, we have an excellent labor force. This is due primarily to the large number of students attend- ing the University of Wisconsin. These students are hard workers, and they will work for $1 less per hour than the other locations that we are considering.
Bill: Calm down, you two. It is obvious that we will not be able to satisfy everyone in locating the new plants. Therefore, I would like to suggest that we vote on the two best locations.
Tony: I don’t think that voting would be a good idea. Wilma was not able to attend, and we should be looking at all of these factors together in some type of logical fashion.
Discussion Question
Where would you locate the two new plants? Why?
Source: From Quantitative Analysis for Management , B. Render, R. M. Stair, M. Hanna, and T. Hale. 12th ed. Copyright © 2015. Reprinted by permission of Pearson Publishing, Upper Saddle River, NJ.
• Additional Case Study: Visit MyOMLab for this free case study: Consolidated Bottling (B): This case involves determining where to add bottling capacity.
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Module C Rapid Review C
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Main Heading Review Material MyOMLab TRANSPORTATION MODELING (pp. 730 – 731 )
The transportation models described in this module prove useful when considering alternative facility locations within the framework of an existing distribution system. The choice of a new location depends on which will yield the minimum cost for the entire system. j Transportation modeling —An iterative procedure for solving problems that
involves minimizing the cost of shipping products from a series of sources to a series of destinations.
Origin points (or sources ) can be factories, warehouses, car rental agencies, or any other points from which goods are shipped. Destinations are any points that receive goods. To use the transportation model, we need to know the following: 1. The origin points and the capacity or supply per period at each. 2. The destination points and the demand per period at each. 3. The cost of shipping one unit from each origin to each destination. The transportation model is a type of linear programming model. A transportation matrix summarizes all relevant data and keeps track of algorithm computations. Shipping costs from each origin to each destination are contained in the appropriate cross-referenced box.
TO FROM
DESTINATION 1 DESTINATION 2 DESTINATION 3 CAPACITY
Source A Source B Source C Demand
Concept Questions: 1.1–1.4
DEVELOPING AN INITIAL SOLUTION (pp. 732 – 734 )
Two methods for establishing an initial feasible solution to the problem are the northwest-corner rule and the intuitive lowest-cost method. j Northwest-corner rule —A procedure in the transportation model where one
starts at the upper-left-hand cell of a table (the northwest corner) and systemati- cally allocates units to shipping routes.
The northwest-corner rule requires that we: 1. Exhaust the supply (origin capacity) of each row before moving down to the
next row. 2. Exhaust the demand requirements of each column before moving to the next
column to the right. 3. Check to ensure that all supplies and demands are met. The northwest-corner rule is easy to use and generates a feasible solution, but it totally ignores costs and therefore should be considered only as a starting position. j Intuitive method —A cost-based approach to finding an initial solution to a trans-
portation problem. The intuitive method uses the following steps: 1. Identify the cell with the lowest cost. Break any ties for the lowest cost arbitrarily. 2. Allocate as many units as possible to that cell, without exceeding the supply or de mand.
Then cross out that row or column (or both) that is exhausted by this assignment. 3. Find the cell with the lowest cost from the remaining (not crossed out) cells. 4. Repeat Steps 2 and 3 until all units have been allocated.
Concept Questions: 2.1–2.4 Problems: C.1–C.3, C.15
THE STEPPING- STONE METHOD (pp. 734 – 737 )
j Stepping-stone method —An iterative technique for moving from an initial feasible solution to an optimal solution in the transportation method.
The stepping-stone method is used to evaluate the cost-effectiveness of shipping goods via transportation routes not currently in the solution. When applying it, we test each unused cell, or square, in the transportation table by asking: What would happen to total shipping costs if one unit of the product were tentatively shipped on an unused route? We conduct the test as follows: 1. Select any unused square to evaluate. 2. Beginning at this square, trace a closed path back to the original square via
squares that are currently being used (only horizontal and vertical moves are permissible). You may, however, step over either an empty or an occupied square.
Concept Questions: 3.1–3.4 Problems: C.4–C.13
Virtual Office Hours for Solved Problem: C.1
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Main Heading Review Material MyOMLab 3. Beginning with a plus (+) sign at the unused square, place alternative minus
signs and plus signs on each corner square of the closed path just traced. 4. Calculate an improvement index by first adding the unit-cost figures found in
each square containing a plus sign and then subtracting the unit costs in each square containing a minus sign.
5. Repeat Steps 1 through 4 until you have calculated an improvement index for all unused squares. If all indices computed are greater than or equal to zero , you have reached an optimal solution. If not, the current solution can be improved further to decrease total shipping costs.
Each negative index represents the amount by which total transportation costs could be decreased if one unit was shipped by the source–destination combination. The next step, then, is to choose that route (unused square) with the largest negative improvement index. We can then ship the maximum allowable number of units on that route and reduce the total cost accordingly. That maximum quantity is found by referring to the closed path of plus signs and minus signs drawn for the route and then selecting the smallest number found in the squares containing minus signs . To obtain a new solution, we add this number to all squares on the closed path with plus signs and subtract it from all squares on the path to which we have assigned minus signs. From this new solution, a new test of unused squares needs to be conducted to see if the new solution is optimal or whether we can make further improvements.
SPECIAL ISSUES IN MODELING (pp. 737 – 738 )
Dummy sources —Artificial shipping source points created when total demand is greater than total supply to effect a supply equal to the excess of demand over supply. Dummy destinations —Artificial destination points created when the total supply is greater than the total demand; they serve to equalize the total demand and supply. Because units from dummy sources or to dummy destinations will not in fact be shipped, we assign cost coefficients of zero to each square on the dummy location. If you are solving a transportation problem by hand, be careful to decide first whether a dummy source (row) or a dummy destination (column) is needed. When applying the stepping-stone method, the number of occupied squares in any solution (initial or later) must be equal to the number of rows in the table plus the number of columns minus 1. Solutions that do not satisfy this rule are called degenerate . j Degeneracy —An occurrence in transportation models in which too few squares
or shipping routes are being used, so that tracing a closed path for each unused square becomes impossible.
To handle degenerate problems, we must artificially create an occupied cell: That is, we place a zero (representing a fake shipment) into one of the unused squares and then treat that square as if it were occupied. Remember that the chosen square must be in such a position as to allow all stepping-stone paths to be closed.
Concept Questions: 4.1–4.4 Problems: C.14–C.18
Virtual Office Hours for Solved Problem: C.2
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Module C Rapid Review continued
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the module and the key terms listed at the end of the module.
LO C.1 With the transportation technique, the initial solution can be generated in any fashion one chooses. The only restriction(s) is that:
a) the solution be optimal. b) one use the northwest-corner method. c) the edge constraints for supply and demand be satisfied. d) the solution not be degenerate. e) all of the above. LO C.2 The purpose of the stepping-stone method is to: a) develop the initial solution to a transportation problem. b) identify the relevant costs in a transportation problem. c) determine whether a given solution is feasible. d) assist one in moving from an initial feasible solution to the
optimal solution. e) overcome the problem of degeneracy.
LO C.3 The purpose of a dummy source or a dummy destination in a transportation problem is to:
a) provide a means of representing a dummy problem. b) obtain a balance between total supply and total demand. c) prevent the solution from becoming degenerate. d) make certain that the total cost does not exceed some
specified figure. e) change a problem from maximization to minimization. LO C.4 If a solution to a transportation problem is degenerate, then: a) it will be impossible to evaluate all empty cells without
removing the degeneracy. b) a dummy row or column must be added. c) there will be more than one optimal solution. d) the problem has no feasible solution. e) increase the cost of each cell by 1.
Answers: LO C.1. c; LO C.2. d; LO C.3. b; LO C.4. a.
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747
M O D U L E O U T L I N E
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Queuing Theory 748
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Characteristics of a Waiting-Line System 749 ◆
Queuing Costs 753
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The Variety of Queuing Models 754
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Other Queuing Approaches 765
Waiting-Line Models D
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748
L E A R N I N G OBJEC TI V ES
LO D.1 Describe the characteristics of arrivals, waiting lines, and service systems 749
LO D.2 Apply the single-server queuing model equations 754
LO D.3 Conduct a cost analysis for a waiting line 757
LO D.4 Apply the multiple-server queuing model formulas 757
LO D.5 Apply the constant-service-time model equations 762
LO D.6 Perform a fi nite-population model analysis 763
Queuing Theory The body of knowledge about waiting lines, often called queuing theory , is an important part of operations and a valuable tool for the operations manager. Waiting lines are a common situation—they may, for example, take the form of cars waiting for repair at a Midas Muffler Shop, copying jobs waiting to be completed at a FedEx office, or vacationers waiting to enter a ride at Disney. Table D.1 lists just a few OM uses of waiting-line models.
Waiting-line models are useful in both manufacturing and service areas. Analysis of queues in terms of waiting-line length, average waiting time, and other factors helps us to understand service systems (such as bank teller stations), maintenance activities (that might repair broken machinery), and shop-floor control activities. Indeed, patients waiting in a doctor’s office and broken drill presses waiting in a repair facility have a lot in common from an OM perspective.
Paris’s EuroDisney, Tokyo’s Disney Japan, and the U.S.’s Disney
World and Disneyland all have one feature in common—long
lines and seemingly endless waits. However, Disney is one of the
world’s leading companies in the scientific analysis of queuing
theory. It analyzes queuing behaviors and can predict which rides
will draw what length crowds. To keep visitors happy, Disney
makes lines appear to be constantly moving forward, entertains
people while they wait, and posts signs telling visitors how many
minutes until they reach each ride.
Jo sh
u a S
u d o ck
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is
Queuing theory
A body of knowledge about
waiting lines.
Waiting line (queue)
Items or people in a line
awaiting service.
TABLE D.1 Common Queuing Situations
SITUATION ARRIVALS IN QUEUE SERVICE PROCESS
Supermarket Grocery shoppers Checkout clerks at cash register
Highway toll booth Automobiles Collection of tolls at booth
Doctor’s offi ce Patients Treatment by doctors and nurses
Computer system Programs to be run Computer processes jobs
Telephone company Callers Switching equipment forwards calls
Bank Customers Transactions handled by teller
Machine maintenance Broken machines Repair people fi x machines
Harbor Ships and barges Dock workers load and unload
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M O D U L E D | WA I T I N G - L I N E M O D E L S 749
Both use human and equipment resources to restore valuable production assets (people and machines) to good condition.
Characteristics of a Waiting-Line System In this section, we take a look at the three parts of a waiting-line, or queuing, system (as shown in Figure D.1 ):
1. Arrivals or inputs to the system: These have characteristics such as population size, behav- ior, and a statistical distribution.
2. Queue discipline, or the waiting line itself: Characteristics of the queue include whether it is limited or unlimited in length and the discipline of people or items in it.
3. The service facility: Its characteristics include its design and the statistical distribution of service times.
We now examine each of these three parts.
Arrival Characteristics The input source that generates arrivals or customers for a service system has three major characteristics:
1. Size of the arrival population 2. Behavior of arrivals 3. Pattern of arrivals (statistical distribution)
Size of the Arrival (Source) Population Population sizes are considered either unlim- ited (essentially infinite) or limited (finite). When the number of customers or arrivals on hand at any given moment is just a small portion of all potential arrivals, the arrival population is considered unlimited , or infinite . Examples of unlimited populations include cars arriving at a big- city car wash, shoppers arriving at a supermarket, and students arriving to register for classes at a large university. Most queuing models assume such an infinite arrival population. An example of a limited , or finite , population is found in a copying shop that has, say, eight copying machines. Each of the copiers is a potential “customer” that may break down and require service.
Pattern of Arrivals at the System Customers arrive at a service facility either ac- cording to some known schedule (for example, one patient every 15 minutes or one student every half hour) or else they arrive randomly . Arrivals are considered random when they are
Service facility
Arrivals to the system
Dave's Car Wash
Enter
In the system Exit the system
Exit
1st St.
3rd St.
2nd St.
1st St.
3rd St.
2nd St.
Queue (waiting line)Arrivals from the
general population . . .
Population of dirty cars
Ave. A
Ave. B
Ave. C
Ave. A
Ave. B
Ave. D
SW St.
SE St.
NW St.
NE St.
Exit the system
Waiting-Line CharacteristicsArrival Characteristics Size of arrival population Behavior of arrivals Statistical distribution of arrivals
Service Characteristics Service design Statistical distribution of service
Limited vs. unlimited Queue discipline
Figure D.1
Three Parts of a Waiting Line, or Queuing System, at Dave’s Car Wash
LO D.1 Describe the characteristics of arrivals,
waiting lines, and service
systems
Unlimited, or infinite, population
A queue in which a virtually un-
limited number of people or items
could request the services, or in
which the number of customers
or arrivals on hand at any given
moment is a very small portion of
potential arrivals.
Limited, or finite, population
A queue in which there are only a
limited number of potential users
of the service.
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750 P A R T 4 | B U S I N E S S A N A LY T I C S M O D U L E S
independent of one another and their occurrence cannot be predicted exactly. Frequently in queuing problems, the number of arrivals per unit of time can be estimated by a probability distribution known as the Poisson distribution . 1 For any given arrival time (such as 2 customers per hour or 4 trucks per minute), a discrete Poisson distribution can be established by using the formula:
P(x) = e9llx
x! for x = 0, 1, 2, 3, 4, . . . (D-1)
where P(x) 5 probability of x arrivals x 5 number of arrivals per unit of time λ 5 average arrival rate e 5 2.7183 (which is the base of the natural logarithms)
With the help of the table in Appendix II , which gives the value of e 2l for use in the Poisson distribution, these values are easy to compute. Figure D.2 illustrates the Poisson distribution for l 5 2 and l 5 4. This means that if the average arrival rate is l 5 2 customers per hour, the prob- ability of 0 customers arriving in any random hour is about 0.13 (13%), probability of 1 customer is about 27%, 2 customers about 27%, 3 customers about 18%, 4 customers about 9%, and so on. The chances that 9 or more will arrive are virtually nil. Arrivals, of course, are not always Poisson distributed (they may follow some other distribution). Patterns, therefore, should be examined to make certain that they are well approximated by Poisson before that distribution is applied.
Behavior of Arrivals
Most queuing models assume that an arriving customer is a patient customer. Patient cus- tomers are people or machines that wait in the queue until they are served and do not switch between lines. Unfortunately, life is complicated by the fact that people have been known to balk or to renege. Customers who balk refuse to join the waiting line because it is too long to suit their needs or interests. Reneging customers are those who enter the queue but then become impatient and leave without completing their transaction. Actually, both of these situ- ations just serve to highlight the need for queuing theory and waiting-line analysis.
Waiting-Line Characteristics The waiting line itself is the second component of a queuing system. The length of a line can be either limited or unlimited. A queue is limited when it cannot, either by law or because of physical restrictions, increase to an infinite length. A small barbershop, for example, will have only a limited number of waiting chairs. Queuing models are treated in this module under an assumption of unlimited queue length. A queue is unlimited when its size is unrestricted, as in the case of the toll booth serving arriving automobiles.
A second waiting-line characteristic deals with queue discipline . This refers to the rule by which customers in the line are to receive service. Most systems use a queue discipline known
Poisson distribution
A discrete probability distribution
that often describes the arrival rate
in queuing theory.
0
Distribution for
0.05
P ro
b a b ili
ty
0.10
0.15
0.20
0.25
2 3 4 5 6 7 8 9
n = 2
PProbability = (x ) = e n x !
-l x
X X0
Distribution for
0.05
P ro
b a b ili
ty
0.10
0.15
0.20
0.25
2 3 4 5 6 7 8 9
n = 4 10 1111
Figure D.2
Two Examples of the Poisson
Distribution for Arrival Times
STUDENT TIP Notice that even though the
mean arrival rate might be
λ 5 2 per hour, there is still a
small chance that as many as
9 customers arrive in an hour.
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M O D U L E D | WA I T I N G - L I N E M O D E L S 751
as the first-in, first-out (FIFO) rule . In a hospital emergency room or an express checkout line at a supermarket, however, various assigned priorities may preempt FIFO. Patients who are criti- cally injured will move ahead in treatment priority over patients with broken fingers or noses. Shoppers with fewer than 10 items may be allowed to enter the express checkout queue (but are then treated as first-come, first-served). Computer-programming runs also operate under priority scheduling. In most large companies, when computer-produced paychecks are due on a specific date, the payroll program gets highest priority. 2
Service Characteristics The third part of any queuing system is the service characteristics. Two basic properties are important: (1) design of the service system and (2) the distribution of service times.
Basic Queuing System Designs Service systems are usually classified in terms of their number of servers (number of channels) and number of phases (number of service stops that must be made). See Figure D.3 . A single-server (or single-channel) queuing system , with one server, is typified by the drive-in bank with only one open teller. If, on the other hand, the
First-in, first-out (FIFO) rule
A queue discipline in which the
first customers in line receive the
first service.
Single-server queuing system
A service system with one line and
one server.
Multiple-server, multiphase system
Departures after service
Queue
Single-server, single-phase system
Departures after service
Single-server, multiphase system
Queue
Queue
Multiple-server, single-phase system
Departures after service
Queue
Arrivals
Arrivals
Arrivals
Arrivals
Some college cafeterias
Most bank and post office service windows
A family dentist's office
Example
A McDonald's dual-window drive-through
Phase 1 service facility
Channel 1
Phase 1 service facility
Channel 2
Phase 2 service facility
Channel 1
Phase 2 service facility
Channel 2
Service facility
Departures after service
Service facility
Channel 3
Service facility
Channel 2
Service facility
Channel 1
Phase 1 service facility
Phase 2 service facility
Figure D.3
Basic Queuing System Designs
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bank has several tellers on duty, with each customer waiting in one common line for the first available teller, then we would have a multiple-server (or multiple channel) queuing system . Most banks today are multiple-server systems, as are most large barbershops, airline ticket counters, and post offices.
In a single-phase system , the customer receives service from only one station and then exits the system. A fast-food restaurant in which the person who takes your order also brings your food and takes your money is a single-phase system. So is a driver’s license agency in which the person taking your application also grades your test and collects your license fee. However, say the restaurant requires you to place your order at one station, pay at a second, and pick up your food at a third. In this case, it is a multiphase system . Likewise, if the driver’s license agency is large or busy, you will probably have to wait in one line to complete your application (the first service stop), queue again to have your test graded, and finally go to a third counter to pay your fee. To help you relate the concepts of servers and phases, Figure D.3 presents these four possible configurations.
Service Time Distribution Service patterns are like arrival patterns in that they may be either constant or random. If service time is constant, it takes the same amount of time to take care of each customer. This is the case in a machine-performed service operation such as an automatic car wash. More often, service times are randomly distributed. In many cases, we can assume that random service times are described by the negative exponential probability distribution.
Figure D.4 shows that if service times follow a negative exponential distribution, the probability of any very long service time is low. For example, when an average service time is 20 minutes (or three customers per hour), seldom if ever will a customer require more than 1.5 hours in the service facility. If the mean service time is 1 hour, the probability of spending more than 3 hours in service is quite low.
Measuring a Queue’s Performance Queuing models help managers make decisions that balance service costs with waiting-line costs. Queuing analysis can obtain many measures of a waiting-line system’s performance, including the following:
1. Average time that each customer or object spends in the queue 2. Average queue length 3. Average time that each customer spends in the system (waiting time plus service time) 4. Average number of customers in the system 5. Probability that the service facility will be idle 6. Utilization factor for the system 7. Probability of a specific number of customers in the system
Multiple-server queuing system
A service system with one waiting
line but with several servers.
Multiphase system
A system in which the customer
receives services from several
stations before exiting the system.
Single-phase system
A system in which the customer
receives service from only one
station and then exits the system.
Negative exponential probability distribution
A continuous probability distribu-
tion often used to describe the
service time in a queuing system.
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00
0.1
P ro
b a b ili
ty t h a t se
rv ic
e t im
e »
t
Time t (hours)
Probability that service time is greater than t = e–ot for t » 0
Average service rate, or average number served per time unit (o) = 3 customers per hour 1 Average service time = 20 minutes (or 1/3 hour) per customer
o = Average service rate (average number served per time unit) e = 2.7183 (the base of natural logarithms)
Average service rate (o) = 1 customer per hour
Figure D.4
Two Examples of the Negative
Exponential Distribution for
Service Times
STUDENT TIP Although Poisson and
exponential distributions are
commonly used to describe
arrival rates and service times,
other probability distributions
are valid in some cases.
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Queuing Costs As described in the OM in Action box “Zero Wait Time Guarantee at This Michigan Hospital’s ER,” operations managers must recognize the trade-off that takes place between two costs: the cost of providing good service and the cost of customer or machine waiting time. Managers want queues that are short enough so that customers do not become unhappy and either leave without buying, or buy, but never return. However, managers may be willing to allow some waiting if it is balanced by a significant savings in service costs.
One means of evaluating a service facility is to look at total expected cost. Total cost is the sum of expected service costs plus expected waiting costs.
As you can see in Figure D.5 , service costs increase as a firm attempts to raise its level of service. Managers in some service centers can vary capacity by having standby personnel and machines that they can assign to specific service stations to prevent or shorten exces- sively long lines. In grocery stores, for example, managers and stock clerks can open extra checkout counters. In banks and airport check-in points, part-time workers may be called in to help. As the level of service improves (that is, speeds up), however, the cost of time spent
OM in Action Other hospitals smirked a few years ago when Michigan’s Oakwood
Healthcare chain rolled out an emergency room (ER) guarantee that promised
a written apology and movie tickets to patients not seen by a doctor within
30 minutes. Even employees cringed at what sounded like a cheap
marketing ploy.
But if you have visited an ER lately and watched some patients wait for
hours on end—the official average wait is 47 minutes—you can understand
why Oakwood’s patient satisfaction levels have soared. The 30-minute
guarantee was such a huge success that fewer than 1% of the 191,000 ER
patients asked for free tickets. The following year, Oakwood upped the stakes
again, offering a 15-minute guarantee. Then Oakwood started its Zero Wait
Program in the ERs. Patients who enter any Oakwood emergency department
are immediately cared for by a healthcare professional.
Oakwood’s CEO even extended the ER guarantee to on-time surgery,
45-minute meal service orders, and other custom room services. “Medicine
is a service business,” says Larry Alexander, the head of an ER in Sanford,
Florida. “And people are in the mindset of the fast-food industry.”
Zero Wait Time Guarantee at This Michigan Hospital’s ER
Ric F
e ld
/A P I m
a g e s
How did Oakwood make
good on its promise to eliminate
the ER queue? It first studied
queuing theory, then reengi-
neered its billing, records, and
lab operations to drive down
service time. Then, to improve
service capability, Oakwood
upgraded its technical staff.
Finally, it replaced its ER physi-
cians with a crew willing to work
longer hours.
Sources: Wall Street Journal
(October 19, 2010); Time
(January 26, 2011); and
unitiv.com (February 27, 2014).
STUDENT TIP The two costs we consider here are
cost of servers and cost of lost time
waiting.
Minimum total cost
High level of service
Optimal service level
Cost of waiting time
Cost of providing service
Total expected cost
Cost
Low level of service
Figure D.5
The Trade-off Between Waiting
Costs and Service Costs
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waiting in lines decreases. (Refer to Figure D.5 .) Waiting cost may reflect lost productivity of workers while tools or machines await repairs or may simply be an estimate of the cost of customers lost because of poor service and long queues. In some service systems (for ex- ample, an emergency ambulance service), the cost of long waiting lines may be intolerably high.
The Variety of Queuing Models A wide variety of queuing models may be applied in operations management. We will intro- duce you to four of the most widely used models. These are outlined in Table D.2 , and exam- ples of each follow in the next few sections. More complex models are described in queuing theory textbooks or can be developed through the use of simulation (the topic of Module F ). Note that all four queuing models listed in Table D.2 have three characteristics in common. They all assume:
1. Poisson distribution arrivals 2. FIFO discipline 3. A single-service phase
In addition, they all describe service systems that operate under steady, ongoing conditions. This means that arrival and service rates remain stable during the analysis.
Model A (M/M/1): Single-Server Queuing Model with Poisson Arrivals and Exponential Service Times The most common case of queuing problems involves the single-server (also called single- channel) waiting line. In this situation, arrivals form a single line to be serviced by a single station (see Figure D.3 on p. 751 ). We assume that the following conditions exist in this type of system:
1. Arrivals are served on a first-in, first-out (FIFO) basis, and every arrival waits to be served, regardless of the length of the line or queue.
2. Arrivals are independent of preceding arrivals, but the average number of arrivals ( arrival rate ) does not change over time.
3. Arrivals are described by a Poisson probability distribution and come from an infinite (or very, very large) population.
STUDENT TIP This is the main section of
Module D . We illustrate four
important queuing models.
LO D.2 Apply the single-server queuing
model equations
TABLE D.2 Queuing Models Described in This Chapter
MODEL
NAME (TECHNICAL NAME IN PARENTHESES) EXAMPLE
NUMBER OF SERVERS (CHANNELS)
NUMBER OF
PHASES
ARRIVAL RATE
PATTERN
SERVICE TIME
PATTERN POPULATION
SIZE QUEUE
DISCIPLINE
A Single-server system (M/M/1)
Information counter at department store
Single Single Poisson Negative exponential
Unlimited FIFO
B Multiple-server (M/M/S)
Airline ticket counter Multi-server Single Poisson Negative exponential
Unlimited FIFO
C Constant service (M/D/1)
Automated car wash
Single Single Poisson Constant Unlimited FIFO
D Finite population (M/M/1 with fi nite source)
Shop with only a dozen machines that might break
Single Single Poisson Negative exponential
Limited FIFO
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4. Service times vary from one customer to the next and are independent of one another, but their average rate is known and follows the negative exponential distribution.
5. Service times occur according to the negative exponential probability distribution. 6. The service rate is faster than the arrival rate.
When these conditions are met, the series of equations shown in Table D.3 can be developed. Examples D1 and D2 illustrate how Model A (which in technical journals is known as the M/M/1 model) may be used. 3
The giant Moscow McDonald’s
boasts 900 seats, 800 workers,
and $80 million in annual sales
(vs. less than $2 million in a U.S.
outlet). Americans would balk at the
average waiting time of 45 minutes,
but Russians are used to such long
lines. McDonald’s represents good
service in Moscow. Some people have
even had their wedding receptions
there.
R o y/
E xp
lo re
r/ S ci
e n ce
S o u rc
e
TABLE D.3 Queuing Formulas for Model A: Single-Server System, also Called M/M/1
l 5 average number of arrivals per time period
m 5 average number of people or items served per time period (average service rate)
L s 5 average number of units (customers) in the system (waiting and being served)
= l
m 9 l
W s 5 average time a unit spends in the system (waiting time plus service time)
= 1
m 9 l
L q 5 average number of units waiting in the queue
= l2
m(m 9 l)
W q 5 average time a unit spends waiting in the queue
= l
m(m 9 l) =
Lq l
r 5 utilization factor for the system
= l
m
P 0 5 probability of 0 units in the system (that is, the service unit is idle)
= 1 - l
m
P n > k 5 probability of more than k units in the system, where n is the number of units in the system
= ¢ l m ≤k + 1
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Once we have computed the operating characteristics of a queuing system, it is often impor- tant to do an economic analysis of their impact. Although the waiting-line model just described
Example D1 A SINGLE-SERVER QUEUE Tom Jones, the mechanic at Golden Muffler Shop, is able to install new mufflers at an average rate of 3 per hour (or about 1 every 20 minutes), according to a negative exponential distribution. Customers seeking this service arrive at the shop on the average of 2 per hour, following a Poisson distribution. They are served on a first-in, first-out basis and come from a very large (almost infinite) population of possible buyers.
We would like to obtain the operating characteristics of Golden Muffler’s queuing system.
APPROACH c This is a single-server (M/M/1) system, and we apply the formulas in Table D.3 .
SOLUTION c l = 2 cars arriving per hour m = 3 cars serviced per hour
Ls = l
m - l =
2 3 - 2
= 2 1
= 2 cars in the system, on average
Ws = 1
m - l =
1 3 - 2
= 1
= 1@hour average time in the system
Lq = l2
m(m - l) =
22
3(3 - 2) =
4 3(1)
= 4 3
= 1.33 cars waiting in line, on average
Wq = l
m(m - l) =
2 3(3 - 2)
= 2 3
hour
= 40@minute average waiting time per car
r = l
m =
2 3
= 66.6, of time mechanic is busy
P0 = 1 - l
m = 1 -
2 3
= .33 probability there are 0 cars in the system
Probability of More Than K Cars in The system
K P n > k 5 (2/3) k+1
0 .667 ← Note that this is equal to 1 − P 0 5 1 − .33 5 .667. 1 .444
2 .296
3 .198 ← Implies that there is a 19.8% chance that more than 3 cars are in the system. 4 .132
5 .088
6 .058
7 .039
INSIGHT c Recognize that arrival and service times are converted to the same rate. For example, a service time of 20 minutes is stated as an average rate of 3 mufflers per hour . It’s also important to dif- ferentiate between time in the queue and time in the system .
LEARNING EXERCISE c If m 5 4 cars/hour instead of the current 3 arrivals, what are the new values of L s , W s , L q , W q , and P 0 ? [Answer: 1 car, 30 min., .5 cars, 15 min., 50%, .50.]
RELATED PROBLEMS c D.1–D.4, D.6–D.8, D.9a–e, D.10, D.11a–c, D.12a–d (D.31–D.33, D.34a–e, D.35a–e, D.36, D.38, D.39 are available in MyOMLab)
EXCEL OM Data File ModDExD1.xls can be found in MyOMLab.
ACTIVE MODEL D.1 This example is further illustrated in Active Model D.1 in MyOMLab.
m
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is valuable in predicting potential waiting times, queue lengths, idle times, and so on, it does not identify optimal decisions or consider cost factors. As we saw earlier, the solution to a queuing problem may require management to make a trade-off between the increased cost of providing better service and the decreased waiting costs derived from providing that service.
Example D2 examines the costs involved in Example D1 .
Example D2 ECONOMIC ANALYSIS OF EXAMPLE D1 Golden Muffler Shop’s owner is interested in cost factors as well as the queuing parameters computed in Example D1 . He estimates that the cost of customer waiting time, in terms of customer dissatisfaction and lost goodwill, is $15 per hour spent waiting in line. Jones, the mechanic, is paid $11 per hour.
APPROACH c First compute the average daily customer waiting time, then the daily salary for Jones, and finally the total expected cost.
SOLUTION c Because the average car has a 23@ hour wait (W q ) and because there are approximately 16 cars serviced per day (2 arrivals per hour times 8 working hours per day), the total number of hours that customers spend waiting each day for mufflers to be installed is:
2 3
(16) = 32 3
= 10 2 3
hour
Hence, in this case:
Customer waiting@time cost = +15a10 2 3 b = +160 per day
The only other major cost that Golden’s owner can identify in the queuing situation is the salary of Jones, the mechanic, who earns $11 per hour, or $88 per day. Thus:
Total expected costs = +160 + +88 = +248 per day
This approach will be useful in Solved Problem D.2 on page 767 .
INSIGHT c L q and W q are the two most important queuing parameters when it comes to cost analysis. Calculating customer wait times, we note, is based on average time waiting in the queue ( W q ) times the number of arrivals per hour (l) times the number of hours per day. This is because this example is set on a daily basis. This is the same as using L q because L q 5 W q l.
LEARNING EXERCISE c If the customer waiting time is actually $20 per hour and Jones gets a salary increase to $15 per hour, what are the total daily expected costs? [Answer: $333.33.]
RELATED PROBLEMS c D.12e–f, D.13, D.22, D.23, D.24 (D.37 is available in MyOMLab)
LO D.3 Conduct a cost analysis for a waiting line
Model B (M/M/S): Multiple-Server Queuing Model Now let’s turn to a multiple-server (multiple-channel) queuing system in which two or more servers are available to handle arriving customers. We still assume that customers awaiting service form one single line and then proceed to the first available server. Multiple-server, single-phase waiting lines are found in many banks today: a common line is formed, and the customer at the head of the line proceeds to the first free teller. (Refer to Figure D.3 on p. 751 for a typical multiple-server configuration.)
The multiple-server system presented in Example D3 again assumes that arrivals follow a Poisson probability distribution and that service times are negative exponentially distributed. Service is first-come, first-served, and all servers are assumed to perform at the same rate. Other assumptions listed earlier for the single-server model also apply.
The queuing equations for Model B (which also has the technical name M/M/S) are shown in Table D.4 . These equations are obviously more complex than those used in the single-server model, yet they are used in exactly the same fashion and provide the same type of information as the simpler model. ( Note: The POM for Windows and Excel OM software described later in this chapter can prove very useful in solving multiple-server and other queuing problems.)
LO D.4 Apply the multiple-server queuing
model formulas
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To shorten lines (or wait times),
each Costco register is staffed with
two employees. This approach has
improved efficiency by 20–30%.
P h o to
c o u rt
e sy
o f
C o st
co W
h o le
sa le
, 2 0 1 2
TABLE D.4 Queuing Formulas for Model B: Multiple-Server System, also Called M/M/S
M 5 number of servers (channels) open l 5 average number of arrivals per time period (average arrival rate) m 5 average service rate at each server (channel) The probability that there are zero people or units in the system is:
P0 = 1
c a M - 1
n = 0 1 n!
a l
m b
n
d + 1
M! a l
m b
M Mm
Mm - l
for Mm > l
The average number of people or units in the system is:
Ls = lm(l/m)M
(M - 1)!(Mm - l)2 P0 +
l
m
The average time a unit spends in the waiting line and being serviced (namely, in the system) is:
Ws = m(l/m)M
(M - 1)!(Mm - l)2 P0 +
1 m
= Ls l
The average number of people or units in line waiting for service is:
Lq = Ls - l
m
The average time a person or unit spends in the queue waiting for service is:
Wq = Ws - 1 m
= Lq l
Example D3 A MULTIPLE-SERVER QUEUE The Golden Muffler Shop has decided to open a second garage bay and hire a second mechanic to handle installations. Customers, who arrive at the rate of about l 5 2 per hour, will wait in a single line until 1 of the 2 mechanics is free. Each mechanic installs mufflers at the rate of about m 5 3 per hour.
The company wants to find out how this system compares with the old single-server waiting-line system.
APPROACH c Compute several operating characteristics for the M 5 2 server system, using the equations in Table D.4 , and compare the results with those found in Example D1 .
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We can summarize the characteristics of the two-server model in Example D3 and compare them to those of the single-server model in Example D1 as follows:
MEASURE SINGLE SERVER TWO SERVERS (CHANNELS)
Probability of 0 units in the system P 0 .33 .5
Number of units in the system L s 2 cars .75 car
Average time in the system W s 60 minutes 22.5 minutes
Average number in the queue L q 1.33 cars .083 car
Average time in the queue W q 40 minutes 2.5 minutes
The increased service has a dramatic effect on almost all characteristics. For instance, note that the time spent waiting in line drops from 40 minutes to only 2.5 minutes.
Use of Waiting-Line Tables Imagine the work a manager would face in dealing with M 5 3-, 4-, or 5- server waiting-line models if a computer was not readily available. The arithmetic becomes increasingly troublesome. Fortunately, much of the burden of manually examining multiple-server queues can be avoided by using Table D.5 . This table, the result of hundreds of computations, represents the relationship between three things: (1) a ratio, l/m, (2) number of servers open, and (3) the average number of customers in the queue, L q (which is what we’d like to find). For any combination of the ratio l/m and M 5 1, 2, 3, 4, or 5 servers, you can quickly look in the body of the table to read off the appropriate value for L q .
SOLUTION c
P0 = 1
c a 1
n = 0 1 n!
a 2 3 b
n d +
1 2!
a 2 3 b
2 2132 2132 - 2
= 1
1 + 2 3
+ 1 2 a
4 9 ba
6 6 9 2 b
= 1
1 + 2 3
+ 1 3
= 1 2
= .5 probability of zero cars in the system Then:
Ls = (2)(3)(2>3)2
1!32(3) - 242 a
1 2 b +
2 3
= 8>3 16 a
1 2 b +
2 3
= 3 4
= .75 average number of cars in the system
Ws = Ls l
= 3>4
2 =
3 8
hour
= 22.5 minutes average time a car spends in the system
Lq = Ls - l
m =
3 4
- 2 3
= 9 12
- 8 12
= 1 12
= .083 average number of cars in the queue (waiting)
Wq = Lq l
= .083
2 = .0415 hour
= 2.5 minutes average time a car spends in the queue (waiting)
INSIGHT c It is very interesting to see the big differences in service performance when an additional server is added.
LEARNING EXERCISE c If m 5 4 per hour, instead of m 5 3, what are the new values for P 0 , L s , W s , L q , and W q ? [Answers: 0.6, .53 cars, 16 min, .033 cars, 1 min.]
RELATED PROBLEMS c D.7h, D.9f, D.11d, D.15, D.20 (D.35f, D.36 are available in MyOMLab)
EXCEL OM Data File ModDEx.xls can be found in MyOMLab.
ACTIVE MODEL D.2 This example is further illustrated in Active Model D.2 in MyOMLab.
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TABLE D.5 Values of L q for M 5 1−5 Servers (channels) and Selected Values of L/M
POISSON ARRIVALS, EXPONENTIAL SERVICE TIMES
NUMBER OF SERVERS (CHANNELS), M
L/M 1 2 3 4 5
.10 .0111
.15 .0264 .0008
.20 .0500 .0020
.25 .0833 .0039
.30 .1285 .0069
.35 .1884 .0110
.40 .2666 .0166
.45 .3681 .0239 .0019
.50 .5000 .0333 .0030
.55 .6722 .0449 .0043
.60 .9000 .0593 .0061
.65 1.2071 .0767 .0084
.70 1.6333 .0976 .0112
.75 2.2500 .1227 .0147
.80 3.2000 .1523 .0189
.85 4.8166 .1873 .0239 .0031
.90 8.1000 .2285 .0300 .0041
.95 18.0500 .2767 .0371 .0053
1.0 .3333 .0454 .0067
1.2 .6748 .0904 .0158
1.4 1.3449 .1778 .0324 .0059
1.6 2.8444 .3128 .0604 .0121
1.8 7.6734 .5320 .1051 .0227
2.0 .8888 .1739 .0398
2.2 1.4907 .2770 .0659
2.4 2.1261 .4305 .1047
2.6 4.9322 .6581 .1609
2.8 12.2724 1.0000 .2411
3.0 1.5282 .3541
3.2 2.3856 .5128
3.4 3.9060 .7365
3.6 7.0893 1.0550
3.8 16.9366 1.5184
4.0 2.2164
4.2 3.3269
4.4 5.2675
4.6 9.2885
4.8 21.6384
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Example D4 illustrates the use of Table D.5 .
Example D4 USE OF WAITING-LINE TABLES Alaska National Bank is trying to decide how many drive-in teller windows to open on a busy Saturday. CEO Ted Eschenbach estimates that customers arrive at a rate of about l 5 18 per hour, and that each teller can service about m 5 20 customers per hour.
APPROACH c Ted decides to use Table D.5 to compute L q and W q .
SOLUTION c The ratio is l>m = 1820 = .90. Turning to the table, under l/m 5 .90, Ted sees that if only M 5 1 service window is open, the average number of customers in line will be 8.1. If two windows are open, L q drops to .2285 customers, to .03 for M 5 3 tellers, and to .0041 for M 5 4 tellers. Adding more open windows at this point will result in an average queue length of 0.
It is also a simple matter to compute the average waiting time in the queue, W q , since W q 5 L q/ l. When one service window is open, W q 5 8.1 customers/(18 customers per hour) 5 .45 hours 5 27 minutes waiting time; when two tellers are open, W q 5 .2285 customers/(18 customers per hour) 5 .0127 hours ≅ 34 minute; and so on.
INSIGHT c If a computer is not readily available, Table D.5 makes it easy to find L q and to then com- pute W q . Table D.5 is especially handy to compare L q for different numbers of servers ( M ).
LEARNING EXERCISE c The number of customers arriving on a Thursday afternoon at Alaska National is 15/hour. The service rate is still 20 customers/hour. How many people are in the queue if there are 1, 2, or 3 servers? [Answer: 2.25, .1227, .0147.]
RELATED PROBLEM c D.5
You might also wish to check the calculations in Example D3 against tabled values just to practice the use of Table D.5 . You may need to interpolate if your exact value is not found in the first column. Other common operating characteristics besides L q are published in tabular form in queuing theory textbooks.
S te
p h e n B
ra sh
e a r/
A P
I m
a g e s
E le
n a th
e w
is e /F
o to
lia
Long check-in lines (left photo) such as at Los Angeles International (LAX) are a common airport sight. This is an M/M/S model—passengers wait in a single queue for one
of several agents. But at Anchorage International Airport (right photo), Alaska Air has jettisoned the traditional wall of ticket counters. Instead, 1.2 million passengers per
year use self-service check-in machines and staffed “bag drop” stations. Looking nothing like a typical airport, the new system doubled the airline’s check-in capacity and
cut staff needs in half, all while speeding travelers through in less than 15 minutes, even during peak hours.
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Model C (M/D/1): Constant-Service-Time Model Some service systems have constant, instead of exponentially distributed, service times. When cus- tomers or equipment are processed according to a fixed cycle, as in the case of an automatic car wash or an amusement park ride, constant service times are appropriate. Because constant rates are certain, the values for L q , W q , L s , and W s are always less than they would be in Model A, which has variable service rates. As a matter of fact, both the average queue length and the average waiting time in the queue are halved with Model C. Constant-service-model formulas are given in Table D.6 . Model C also has the technical name M/D/1 in the literature of queuing theory.
LO D.5 Apply the constant-service-time
model equations
TABLE D.6 Queuing Formulas for Model C: Constant Service, also Called M/D/1
Average length of queue: Lq = l2
2m(m 9 l)
Average waiting time in queue: Wq = l
2m(m 9 l)
Average number of customers in system: Ls = Lq + l
m
Average time in system: Ws = Wq + 1 m
Example D5 gives a constant-service-time analysis.
Example D5 A CONSTANT-SERVICE-TIME MODEL Inman Recycling, Inc., collects and compacts aluminum cans and glass bottles in Reston, Louisiana. Its truck drivers currently wait an average of 15 minutes before emptying their loads for recycling. The cost of driver and truck time while they are in queues is valued at $60 per hour. A new automated compac- tor can be purchased to process truckloads at a constant rate of 12 trucks per hour (that is, 5 minutes per truck). Trucks arrive according to a Poisson distribution at an average rate of 8 per hour. If the new compactor is put in use, the cost will be amortized at a rate of $3 per truck unloaded.
APPROACH c CEO Tony Inman hires a summer college intern to conduct an analysis to evaluate the costs versus benefits of the purchase. The intern uses the equation for W q in Table D.6 .
SOLUTION c
Current waiting cost>trip = (1>4 hr waiting now)(+60>hr cost) = +15>trip
New system: l = 8 trucks/hr arriving m = 12 trucks>hr served
Average waiting time in queue = Wq = l
2m(m - l) =
8 2(12)(12 - 8)
= 1 12
hr
Waiting cost/trip with new compactor = (1>12 hr wait)(+60>hr cost) = +5>trip
Savings with new equipment = +15(current system) 9 +5(new system) = +10>trip
Cost of new equipment amortized: = + 3>trip
Net savings = + 7>trip
INSIGHT c Constant service times, usually attained through automation, help control the variability inherent in service systems. This can lower average queue length and average waiting time. Note the 2 in the denominator of the equations for L q and W q in Table D.6 .
LEARNING EXERCISE c With the new constant-service-time system, what are the average waiting time in the queue, average number of trucks in the system, and average waiting time in the system? [Answer: 0.0833 hours, 1.33 trucks, 0.1667 hours.]
RELATED PROBLEMS c D.14, D.16, D.21 (D.34f is found in MyOMLab)
EXCEL OM Data File ModDExD5.xls can be found in MyOMLab.
ACTIVE MODEL D.3 This example is further illustrated in Active Model D.3 in MyOMLab.
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Little’s Law A practical and useful relationship in queuing for any system in a steady state is called Little’s Law. A steady state exists when a queuing system is in its normal operating condition (e.g., after customers waiting at the door when a business opens in the morning are taken care of). Little’s Law can be written as either:
Ls = lWs (which is the same as Ws = Ls>l) (D-2)
or:
Lq = lWq (which is the same as Wq = Lq>l) (D-3)
The advantage of these formulas is that once two of the parameters are known, the other one can easily be found. This is important because in certain waiting-line situations, one of these might be easier to determine than the other.
Example D6 LITTLE’S LAW Customers walk into the local U.S. Post Office at an average rate of 20 per hour. On average, there are 5 people waiting in line to be served. The probability distributions that describe arrival and service times are unknown. The manager, Vicky Luo, wishes to determine how long customers are waiting in line.
APPROACH c Even though the probability distributions and even the number of servers are unknown, Vicky can use Little’s Law to quickly determine the average waiting time.
SOLUTION c l 5 20 customers per hour
L q 5 5 customers
Using Equation (D-3) , W q 5 L q > l
5 5>20 5 0.25 hours
And (0.25 hours)(60 minutes/hour) 5 15 minutes
INSIGHT c It can be relatively easy to count the number of arriving customers per hour, and the aver- age number of customers in line can be estimated by counting the line length throughout the day and taking the average of those lengths. However, it would take more effort to keep track of the time that the customers enter the facility and the time that they begin being served. Little’s Law eliminates the need to track actual waiting times.
LEARNING EXERCISE c Vicky believes that 15 minutes is an unreasonable waiting time. She adds a server to help during busy times, and the average number of customers in line reduces to 1.2 customers. Now how long do customers wait? [Answer: 3.6 minutes.]
RELATED PROBLEMS c D.25–D.30
Little’s Law is also important because it makes no assumptions about the probability distri- butions for arrivals and service times, the number of servers, or service priority rules. The law applies to all the queuing systems discussed in this module, except the finite-population model, which we discuss next.
Model D (M/M/1 with Finite Source): Finite-Population Model When there is a limited (or finite) population of potential customers for a service facility, we must consider a different queuing model. This model would be used, for example, if we were considering equipment repairs in a factory that has 5 machines, if we were in charge of mainte- nance for a fleet of 10 commuter airplanes, or if we ran a hospital ward that has 20 beds. The finite-population model allows any number of repair people (servers) to be considered.
This model differs from the three earlier queuing models because there is now a dependent relationship between the length of the queue and the arrival rate. Let’s illustrate the extreme situation: If your factory had five machines and all were broken and awaiting repair, the arrival rate would drop to zero. In general, then, as the waiting line becomes longer in the finite popula- tion model, the arrival rate of customers or machines drops.
LO D.6 Perform a finite-population model
analysis
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Example D7 illustrates Model D.
TABLE D.7
Queuing Formulas for Model D: Finite-Population, also called M/M/1 with Finite Source
l 5 average arrival rate m 5 average service rate N 5 size of the population
Average waiting time in the queue:
Wq = Lq
(N - Ls)l
Probability that the system is empty:
P0 = 1
a N
n = 0
N! (N - n)!
a l
m b
n
Average time in the system:
Ws = Wq + 1 m
Average length of the queue:
Lq = N - a l + m
l b(1 - P0)
Probability of n units in the system:
Pn = N!
(N - n)! a l
m b
n
P0 for n 5 0, 1, …, N
Average number of customers (units) in the system: Ls = Lq + (1 - P0)
Example D7 A FINITE-POPULATION MODEL Past records indicate that each of the 5 massive laser computer printers at the U.S. Department of Energy (DOE), in Washington, DC, needs repair after about 20 hours of use. Breakdowns have been determined to be Poisson distributed. The one technician on duty can service a printer in an average of 2 hours, follow- ing an exponential distribution. Printer downtime costs $120 per hour. The technician is paid $25 per hour.
APPROACH c To compute the system’s operation characteristics we note that the mean arrival rate is l 5 1>20 5 0.05 printers/hour. The mean service rate is m 5 1>2 5 0.50 printers/hour.
SOLUTION c
1. P0 = 1
a 5
n = 0
5! (5 - n)!
a 0.05 0.5 b
n = 0.564 (we leave these calculations for you to confirm)
2. Lq = 5 - a 0.05 + 0.5
0.05 b(1 - P0) 5 5 2 (11)(1 2 0.564) 5 5 2 4.8 5 0.2 printers
3. L s 5 0.2 1 (1 2 0.564) 5 0.64 printers
4. Wq = 0.2
(5 - 0.64)(0.05) =
0.2 0.22
= 0.91 hours
5. Ws = 0.91 + 1
0.50 = 2.91 hours
We can also compute the total cost per hour:
Total hourly cost 5 (Average number of printers down) (Cost per downtime hour) 1 Cost per technician hour 5 (0.64)($120) 1 $25 5 $76.80 1 $25.00 5 $101.80
INSIGHT c Management can now determine whether these costs and wait times are acceptable. Perhaps it is time to add a second technician.
The finite calling population model has the following assumptions:
1. There is only one server. 2. The population of units seeking service is finite. 4 3. Arrivals follow a Poisson distribution, and service times are negative exponentially distributed. 4. Customers are served on a first-come, first-served basis.
Table D.7 displays the queuing formulas for the finite-population model.
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Other Queuing Approaches Many practical waiting-line problems that occur in service systems have characteristics like those of the four mathematical models already described. Often, however, variations of these specific cases are present in an analysis. Service times in an automobile repair shop, for exam- ple, tend to follow the normal probability distribution instead of the exponential. A college registration system in which seniors have first choice of courses and hours over other students is an example of a first-come, first-served model with a preemptive priority queue discipline. A physical examination for military recruits is an example of a multiphase system, one that differs from the single-phase models discussed earlier in this module. A recruit first lines up to have blood drawn at one station, then waits for an eye exam at the next station, talks to a psychiatrist at the third, and is examined by a doctor for medical problems at the fourth. At each phase, the recruit must enter another queue and wait his or her turn. Many models, some very complex, have been developed to deal with situations such as these.
STUDENT TIP When the assumptions of the
4 models we just introduced do
not hold true, there are other
approaches still available to us.
Summary Queues are an important part of the world of operations management. In this module, we describe several common queuing systems and present mathematical models for ana- lyzing them.
The most widely used queuing models include Model A (M/M/1), the basic single-server, single-phase system with Poisson arrivals and negative exponential service times; Model B (M/M/S), the multiple-server equivalent of Model A; Model C (M/D/1), a constant-service-rate model; and Model D, a finite-population system (M/M/1 with finite
source). All four models allow for Poisson arrivals; first-in, first-out service; and a single-service phase. Typical operat- ing characteristics we examine include average time spent waiting in the queue and system, average number of custom- ers in the queue and system, idle time, and utilization rate.
A variety of queuing models exists for which all the assump- tions of the traditional models need not be met. In these cases, we use more complex mathematical models or turn to a tech- nique called simulation . The application of simulation to problems of queuing systems is addressed in Module F .
Key Terms
Queuing theory (p. 748 ) Waiting line (queue) (p. 748 ) Unlimited, or infinite, population (p. 749 ) Limited, or finite, population (p. 749 )
Poisson distribution (p. 750 ) First-in, first-out (FIFO) rule (p. 751 ) Single-server queuing system (p. 751 ) Multiple-server queuing system (p. 752 )
Single-phase system (p. 752 ) Multiphase system (p. 752 ) Negative exponential probability
distribution (p. 752 )
LEARNING EXERCISE c DOE has just replaced its printers with a new model that seems to break down after about 18 hours of use. Recompute the total hourly cost. [Answer: L s 5 .25, so cost 5 (.25)($120) 1 $25 5 $30 1 $25 5 $55.]
RELATED PROBLEMS c D.17, D.18, D.19
EXCEL OM Data File ModDExD7.xls can be found in MyOMLab.
Discussion Questions
1. Name the three parts of a typical queuing system. 2. When designing a waiting line system, what “qualitative”
concerns need to be considered? 3. Name the three factors that govern the structure of “arrivals”
in a queuing system. 4. State the seven common measures of queuing system performance. 5. State the assumptions of the “basic” single-server queuing
model (Model A, or M/M/1). 6. Is it good or bad to operate a supermarket bakery system on
a strict first-come, first-served basis? Why?
7. Describe what is meant by the waiting-line terms balk and renege . Provide an example of each.
8. Which is larger, W s or W q ? Explain. 9. Briefly describe three situations in which the first-in, first-out
(FIFO) discipline rule is not applicable in queuing analysis. 10. Describe the behavior of a waiting line where l . m. Use
both analysis and intuition. 11. Discuss the likely outcome of a waiting line system where
m . l but only by a tiny amount (e.g., m 5 4.1, l 5 4).
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Solved Problems Virtual Office Hours help is available in MyOMLab.
Using Software to Solve Queuing Problems g g
Both Excel OM and POM for Windows may be used to analyze all but the last two homework problems in this module.
X USING EXCEL OM Excel OM’s Waiting-Line program handles all four of the models developed in this module. Program D.1 illustrates our first model, the M/M/1 system, using the data from Example D1 .
Sample Calculations
Probability =1–B7/B8 =B16*B$7/B$8 =B17*B$7/B$8
Cumulative Probability =1–B7/B8 =C16+B17 =C17+B18
Enter the arrival rate and service rate in column B. Be sure that you enter rates rather than times.
Calculating Parameters =B7/B8 =B7^2/(B8*(B8–B7)) =B7(B8–B7) =B7/(B8*(B8–B7)) =1/(B8–B7) =1 – E7
Program D.1
Using Excel OM for Queuing
Example D1 ’s (Golden Muffler Shop) data are illustrated in the M/M/1 model.
P USING POM FOR WINDOWS There are several POM for Windows queuing models from which to select in that program’s Waiting-Line module. The program can include an economic analysis of cost data, and, as an option, you may display probabilities of various numbers of people/items in the system. See Appendix IV for further details .
12. Provide examples of four situations in which there is a lim- ited, or finite, waiting line.
13. What are the components of the following queuing systems? Draw and explain the configuration of each. a) Barbershop b) Car wash c) Laundromat d) Small grocery store
14. Do doctors’ offices generally have random arrival rates for patients? Are service times random? Under what circum- stances might service times be constant?
15. What happens if two single-server systems have the same mean arrival and service rates, but the service time is constant in one and exponential in the other?
16. What dollar value do you place on yourself per hour that you spend waiting in lines? What value do your classmates place on themselves? Why do the values differ?
17. Why is Little’s Law a useful queuing concept?
SOLVED PROBLEM D.1 Sid Das Brick Distributors in Jamaica currently employs 1 worker whose job is to load bricks on outgoing company trucks. An average of 24 trucks per day, or 3 per hour, arrive at the loading platform, according to a Poisson distribution. The worker loads them at a rate of 4 trucks per hour, following approximately the exponential distribution in his service times.
Das believes that adding an additional brick loader will sub- stantially improve the firm’s productivity. He estimates that a 2-person crew loading each truck will double the loading rate (m) from 4 trucks per hour to 8 trucks per hour. Analyze the effect on the queue of such a change, and compare the results to those achieved with one worker. What is the probability that there will be more than 3 trucks either being loaded or waiting?
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NUMBER OF BRICK LOADERS
1 2
Truck arrival rate (l) 3/hr 3/hr
Loading rate (m) 4/hr 8/hr
Average number in system ( L s ) 3 trucks .6 truck
Average time in system ( W s ) 1 hr .2 hr
Average number in queue ( L q ) 2.25 trucks .225 truck
Average time in queue ( W q ) .75 hr .075 hr
Utilization rate (r) .75 .375
Probability system empty (P 0 ) .25 .625
Probability of More than k Trucks in System
PROBABILITY N > K
K 1 LOADER 2 LOADERS
0 .75 .375
1 .56 .141
2 .42 .053
3 .32 .020
These results indicate that when only one loader is employed, the average truck must wait three-quarters of an hour before it is loaded. Furthermore, there is an average of 2.25 trucks wait- ing in line to be loaded. This situation may be unacceptable to management. Note also the decline in queue size after the addi- tion of a second loader.
SOLUTION
SOLVED PROBLEM D.2 Truck drivers working for Sid Das (see Solved Problem D.1) earn an average of $10 per hour. Brick loaders receive about $6 per hour. Truck drivers waiting in the queue or at the load- ing platform are drawing a salary but are productively idle and unable to generate revenue during that time. What would be the hourly cost savings to the firm if it employed 2 loaders instead of 1?
Referring to the data in Solved Problem D.1, we note that the average number of trucks in the system is 3 when there is only 1 loader and .6 when there are 2 loaders.
NUMBER OF LOADERS
1 2
Truck driver idle time costs [(Average number of trucks) 3 (Hourly rate)] 5 (3)($10) 5
$30 $ 6 5 (.6)($10)
Loading costs 6 12 5 (2)($6)
Total expected cost per hour $36 $18
SOLUTION
The firm will save $18 per hour by adding another loader.
SOLVED PROBLEM D.3
Sid Das is considering building a second platform or gate to speed the process of loading trucks. This system, he thinks, will be even more efficient than simply hiring another loader to help out on the first platform (as in Solved Problem D.1).
Assume that the worker at each platform will be able to load 4 trucks per hour each and that trucks will continue to arrive at the rate of 3 per hour. Then apply the appropriate equations to find the waiting line’s new operating conditions. Is this new approach indeed speedier than hiring a second loader, as Das has considered in the Solved Problems above?
SOLUTION
P0 = 1
c a 1
n = 0 1 n!
a 3 4 b
n d +
1 2!
a 3 4 b
2 2142 2142 - 3
= 1
1 + 3 4
+ 1 2
a 3 4 b
2 a
8 8 - 3
b
= .4545
Ls = 3(4)(3>4)2
(1)!(8 - 3)2 (.4545) +
3 4
= .873
Ws = .873
3 = .291 hr
Lq = .873 - 3>4 = .123
Wq = .123
3 = .041 hr
Looking back at Solved Problem D.1, we see that although length of the queue and average time in the queue are lowest when a second platform is open, the average number of trucks in the system and average time spent waiting in the system are smallest when two workers are employed at a single platform. Thus, we would probably recommend not building a second platform.
SOLVED PROBLEM D.4 Mount Sinai Hospital’s orthopedic care unit has 5 beds, which are virtually always occupied by patients who have just under- gone orthopedic surgery. One registered nurse is on duty in the unit in each of the three 8-hour shifts. About every 2 hours (following a Poisson distribution), one of the patients requires
a nurse’s attention. The nurse will then spend an average of 30 minutes (negative exponentially distributed) assisting the patient and updating medical records regarding the problem and care provided.
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Because immediate service is critical to the 5 patients, two important questions are: What is the average number of patients either waiting for or being attended by the nurse? What is the average time that a patient spends waiting for the nurse to arrive?
SOLUTION
l 5 .5 arrivals/hour m 5 2 served/hour N 5 5 patients
P0 = 1
a 5
n = 0
5! (5 - n)!
a .5 2 b
n = 0.20
Lq = 5 - a .5 + 2
.5 b(1 - 0.20) = 1 patient
L s 5 1 1 (1 2 0.20) 5 1.8 patients
Wq = 1
(5 - 1.8)(.5) = .62 hours 5 37.28 min.
Ws = .62 + 1 2
5 1.12 hours 5 67.28 min
So the average number of patients in the system 5 1.8 Average wait time in the queue 5 .62 hours 5 37.28 minutes
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
Problems D.1–D.39 relate to The Variety of Queuing Models
• D.1 Customers arrive at Rich Dunn’s Styling Shop at a rate of 3 per hour, distributed in a Poisson fashion. Rich’s service times follow a negative exponential distribution, and Rich can complete an average of 5 haircuts per hour. a) Find the average number of customers waiting for haircuts. b) Find the average number of customers in the shop. c) Find the average time a customer waits until it is his or her turn. d) Find the average time a customer spends in the shop. e) Find the percentage of time that Rich is busy. PX
• D.2 There is only one copying machine in the student lounge of the business school. Students arrive at the rate of l 5 40 per hour (according to a Poisson distribution). Copying takes an average of 40 seconds, or m 5 90 per hour (according to a negative exponential distribution). Compute the following: a) The percentage of time that the machine is used. b) The average length of the queue. c) The average number of students in the system. d) The average time spent waiting in the queue. e) The average time in the system. PX
• D.3 Paul Fenster owns and manages a chili-dog and soft- drink stand near the Kean U. campus. While Paul can service 30 customers per hour on the average (m), he gets only 20 custom- ers per hour (l). Because Paul could wait on 50% more customers than actually visit his stand, it doesn’t make sense to him that he should have any waiting lines.
Paul hires you to examine the situation and to determine some characteristics of his queue. After looking into the problem, you find it follows the six conditions for a single-server waiting line (as seen in Model A). What are your findings? PX
• D.4 Dr. Tarun Gupta, a Michigan vet, is running a rabies vaccination clinic for dogs at the local grade school. Tarun can “shoot” a dog every 3 minutes. It is estimated that the dogs will arrive independently and randomly throughout the day at a rate of one dog every 6 minutes according to a Poisson distribution. Also assume that Tarun’s shooting times are negative exponen- tially distributed. Compute the following: a) The probability that Tarun is idle. b) The proportion of the time that Tarun is busy. c) The average number of dogs being vaccinated and waiting to
be vaccinated. d) The average number of dogs waiting to be vaccinated. e) The average time a dog waits before getting vaccinated.
f) The average amount of time a dog spends waiting in line and being vaccinated. PX
• • D.5 The pharmacist at Arnold Palmer Hospital, Wende Huehn-Brown, receives 12 requests for prescriptions each hour, Poisson distributed. It takes her a mean time of 4 minutes to fill each, following a negative exponential distribution. Use the waiting- line table, Table D.5 , and W q 5 L q >l , to answer these questions. a) What is the average number of prescriptions in the queue? b) How long will the average prescription spend in the queue? c) Wende decides to hire a second pharmacist, Ajay Aggerwal, with
whom she went to school and who operates at the same speed in filling prescriptions. How will the answers to parts (a) and (b) change? PX
• D.6 Calls arrive at Lynn Ann Fish’s hotel switchboard at a rate of 2 per minute. The average time to handle each is 20 sec- onds. There is only one switchboard operator at the current time. The Poisson and negative exponential distributions appear to be relevant in this situation. a) What is the probability that the operator is busy? b) What is the average time that a customer must wait before
reaching the operator? c) What is the average number of calls waiting to be answered? PX
• • D.7 Automobiles arrive at the drive-through window at the downtown Baton Rouge, Louisiana, post office at the rate of 4 every 10 minutes. The average service time is 2 minutes. The Poisson distribution is appropriate for the arrival rate and service times are negative exponentially distributed. a) What is the average time a car is in the system? b) What is the average number of cars in the system? c) What is the average number of cars waiting to receive service? d) What is the average time a car is in the queue? e) What is the probability that there are no cars at the window? f) What percentage of the time is the postal clerk busy? g) What is the probability that there are exactly 2 cars in the system? h) By how much would your answer to part (a) be reduced if a sec-
ond drive-through window, with its own server, were added? PX
• D.8 Virginia’s Ron McPherson Electronics Corporation retains a service crew to repair machine breakdowns that occur on average l 5 3 per 8-hour workday (approximately Poisson in nature). The crew can service an average of m 5 8 machines per workday, with a repair time distribution that resembles the nega- tive exponential distribution. a) What is the utilization rate of this service system? b) What is the average downtime for a broken machine?
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c) How many machines are waiting to be serviced at any given time? d) What is the probability that more than 1 machine is in the sys-
tem? The probability that more than 2 are broken and waiting to be repaired or being serviced? More than 3? More than 4? PX
• • D.9 Neve Commercial Bank is the only bank in the town of York, Pennsylvania. On a typical Friday, an average of 10 cus- tomers per hour arrive at the bank to transact business. There is currently one teller at the bank, and the average time required to transact business is 4 minutes. It is assumed that service times may be described by the negative exponential distribution. If a single teller is used, find: a) The average time in the line. b) The average number in the line. c) The average time in the system. d) The average number in the system. e) The probability that the bank is empty. f) CEO Benjamin Neve is considering adding a second teller (who
would work at the same rate as the first) to reduce the waiting time for customers. A single line would be used, and the customer at the front of the line would go to the first available bank teller. He assumes that this will cut the waiting time in half. If a second teller is added, find the new answers to parts (a) to (e). PX
• • D.10 Beate Klingenberg manages a Poughkeepsie, New York, movie theater complex called Cinema 8. Each of the eight auditoriums plays a different film; the schedule staggers start- ing times to avoid the large crowds that would occur if all four movies started at the same time. The theater has a single ticket booth and a cashier who can maintain an average service rate of 280 patrons per hour. Service times are assumed to follow a nega- tive exponential distribution. Arrivals on a normally active day are Poisson distributed and average 210 per hour.
To determine the efficiency of the current ticket operation, Beate wishes to examine several queue-operating characteristics. a) Find the average number of moviegoers waiting in line to pur-
chase a ticket. b) What percentage of the time is the cashier busy? c) What is the average time that a customer spends in the system? d) What is the average time spent waiting in line to get to the
ticket window? e) What is the probability that there are more than two people in
the system? More than three people? More than four? PX
• • D.11 Bill Youngdahl has been collecting data at the TU stu- dent grill. He has found that, between 5:00 p.m. and 7:00 p.m., students arrive at the grill at a rate of 25 per hour (Poisson distributed) and service time takes an average of 2 minutes (negative exponential distri- bution). There is only 1 server, who can work on only 1 order at a time. a) What is the average number of students in line? b) What is the average time a student is in the grill area? c) Suppose that a second server can be added to team up with the
first (and, in effect, act as 1 faster server). This would reduce the average service time to 90 seconds. How would this affect the average time a student is in the grill area?
d) Suppose a second server is added and the 2 servers act inde- pendently, with each taking an average of 2 minutes. What would be the average time a student is in the system?
• • • D.12 The wheat harvesting season in the American Midwest is short, and farmers deliver their truckloads of wheat to a giant central storage bin within a 2-week span. Because of this, wheat- filled trucks waiting to unload and return to the fields have been known to back up for a block at the receiving bin. The central bin is owned cooperatively, and it is to every farmer’s benefit to make the
unloading/storage process as efficient as possible. The cost of grain deterioration caused by unloading delays and the cost of truck rental and idle driver time are significant concerns to the cooperative mem- bers. Although farmers have difficulty quantifying crop damage, it is easy to assign a waiting and unloading cost for truck and driver of $18 per hour. During the 2-week harvest season, the storage bin is open and operated 16 hours per day, 7 days per week, and can unload 35 trucks per hour according to a negative exponential dis- tribution. Full trucks arrive all day long (during the hours the bin is open) at a rate of about 30 per hour, following a Poisson pattern.
To help the cooperative get a handle on the problem of lost time while trucks are waiting in line or unloading at the bin, find the following: a) The average number of trucks in the unloading system b) The average time per truck in the system c) The utilization rate for the bin area d) The probability that there are more than three trucks in the
system at any given time e) The total daily cost to the farmers of having their trucks tied
up in the unloading process f) As mentioned, the cooperative uses the storage bin heavily
only 2 weeks per year. Farmers estimate that enlarging the bin would cut unloading costs by 50% next year. It will cost $9,000 to do so during the off-season. Would it be worth the expense to enlarge the storage area? PX
• • • D.13 Janson’s Department Store in Stark, Ohio, maintains a successful catalog sales department in which a clerk takes orders by telephone. If the clerk is occupied on one line, incoming phone calls to the catalog department are answered automatically by a recording machine and asked to wait. As soon as the clerk is free, the party who has waited the longest is transferred and serviced first. Calls come in at a rate of about 12 per hour. The clerk can take an order in an average of 4 minutes. Calls tend to follow a Poisson dis- tribution, and service times tend to be negative exponential.
The cost of the clerk is $10 per hour, but because of lost good- will and sales, Janson’s loses about $25 per hour of customer time spent waiting for the clerk to take an order. a) What is the average time that catalog customers must wait
before their calls are transferred to the order clerk? b) What is the average number of customers waiting to place an order? c) Pamela Janson is considering adding a second clerk to take
calls. The store’s cost would be the same $10 per hour. Should she hire another clerk? Explain your decision. PX
• D.14 Altug’s Coffee Shop decides to install an automatic coffee vending machine outside one of its stores to reduce the number of people standing in line inside. Mehmet Altug charges $3.50 per cup. However, it takes too long for people to make change. The service time is a constant 3 minutes, and the arrival rate is 15 per hour (Poisson distributed). a) What is the average wait in line? b) What is the average number of people in line? c) Mehmet raises the price to $5 per cup and takes 60 seconds
off the service time. However, because the coffee is now so expensive, the arrival rate drops to 10 per hour. Now what are the average wait time and the average number of people in the queue (waiting)? PX
• • • D.15 The typical subway station in Washington, DC, has six turnstiles, each of which can be controlled by the station man- ager to be used for either entrance or exit control—but never for both. The manager must decide at different times of the day how many turnstiles to use for entering passengers and how many to use for exiting passengers.
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At the George Washington University (GWU) Station, passen- gers enter the station at a rate of about 84 per minute between the hours of 7 a.m. and 9 a.m. Passengers exiting trains at the stop reach the exit turnstile area at a rate of about 48 per minute during the same morning rush hours. Each turnstile can allow an average of 30 passengers per minute to enter or exit. Arrival and service times have been thought to follow Poisson and negative exponential distri- butions, respectively. Assume riders form a common queue at both entry and exit turnstile areas and proceed to the first empty turnstile.
a) On the average, how many machines are waiting for service? b) On the average, what is the waiting time to be serviced? PX
• • • D.20 Ted Glickman, the administrator at D.C. General Hospital emergency room, faces the problem of providing treat- ment for patients who arrive at different rates during the day. There are four doctors available to treat patients when needed. If not needed, they can be assigned other responsibilities (such as doing lab tests, reports, X-ray diagnoses) or else rescheduled to work at other hours.
It is important to provide quick and responsive treatment, and Ted thinks that, on the average, patients should not have to sit in the waiting area for more than 5 minutes before being seen by a doctor. Patients are treated on a first-come, first-served basis and see the first available doctor after waiting in the queue. The arrival pattern for a typical day is as follows:
TIME ARRIVAL RATE
9 A.M.–3 P.M. 6 patients/hour
3 P.M.–8 P.M. 4 patients/hour
8 P.M.–midnight 12 patients/hour
Arrivals follow a Poisson distribution, and treatment times, 12 minutes on the average, follow the negative exponential pattern. a) How many doctors should be on duty during each period to
maintain the level of patient care expected? b) What condition would exist if only one doctor were on duty
between 9 a.m. and 3 p.m.? PX
• • • D.21 The Pontchartrain Bridge is a 16-mile toll bridge that crosses Lake Pontchartrain in New Orleans. Currently, there are 7 toll booths, each staffed by an employee. Since Hurricane Katrina, the Port Authority has been considering replacing the employees with machines. Many factors must be considered because the employees are unionized. However, one of the Port Authority’s concerns is the effect that replacing the employees with machines will have on the times that drivers spend in the system. Customers arrive to any one toll booth at a rate of 10 per minute. In the exact change lanes with employees, the ser- vice time is essentially constant at 5 seconds for each driver. With machines, the average service time would still be 5 seconds, but it would be negative exponential rather than constant, because it takes time for the coins to rattle around in the machine. Contrast the two systems for a single lane. PX
• • • D.22 The registration area has just opened at a large con- vention of dentists in Tallahassee, Florida. There are 200 people arriving per hour (Poisson distributed), and the cost of their wait- ing time in the queue is valued at $100 per person per hour. The Tallahassee Convention Center provides servers to register guests at a fee of $15 per person per hour. It takes about one minute to register an attendee (negative exponentially distributed). A single waiting line, with multiple servers, is set up. a) What is the minimum number of servers for this system? b) What is the optimal number of servers for this system? c) What is the cost for the system, per hour, at the optimum
number of servers? d) What is the server utilization rate with the minimum number
of servers? PX
• • D.23 Refer to Problem D.22. A new registration manager, Dwayne Cole, is hired who initiates a program to entertain the people in line with a juggler whom he pays $15/hour. This reduces the waiting costs to $50 per hour. a) What is the optimal number of servers? b) What is the cost for the system, per hour, at the optimal service level?
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The GWU station manager, Gerald Aase, does not want the average passenger at his station to have to wait in a turnstile line for more than 6 seconds, nor does he want more than 8 people in any queue at any average time. a) How many turnstiles should be opened in each direction every
morning? b) Discuss the assumptions underlying the solution of this prob-
lem using queuing theory. PX
• • D.16 Renuka Jain’s Car Wash takes a constant time of 4.5 minutes in its automated car wash cycle. Autos arrive following a Poisson distribution at the rate of 10 per hour. Renuka wants to know: a) The average waiting time in line. b) The average length of the line.
• • • D.17 Debra Bishop’s cabinet-making shop, in Des Moines, has five tools that automate the drilling of holes for the installation of hinges. Each machine needs an average of 3 “resettings” every 8-hour day, following the Poisson distribution. There is a single technician for setting these machines. Her service times are negative exponential, averaging 2 hours each. a) What is the probability this system is empty? b) What is the average number of machines in the system
(i.e., being reset or waiting to be reset)? c) What is the average waiting time in the queue? PX
• • • D.18 A technician monitors a group of 5 computers that run an automated manufacturing facility. It takes an average of 15 minutes (negative exponentially distributed) to adjust a com- puter that develops a problem. On average, one of the computers requires adjustment every 85 minutes. Determine the following: a) The average number of computers waiting for adjustment (i.e.,
in the queue) b) The average number in the system c) The probability no computer needs adjustment PX
• • • D.19 One mechanic services 5 drilling machines for a steel plate manufacturer. Machines break down on an average of once every 6 working days, and breakdowns tend to follow a Poisson distribution. The mechanic can handle an average of one repair job per day. Repairs follow a negative exponential distribution.
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• • • • D.24 The Chattanooga Furniture store gets an average of 50 customers per shift. Marilyn Helms, the manager, wants to calculate whether she should hire 1, 2, 3, or 4 salespeople. She has determined that average waiting times will be 7 min- utes with one salesperson, 4 minutes with two salespeople, 3 minutes with three salespeople, and 2 minutes with four sales- people. She has estimated the cost per minute that customers wait at $1. The cost per salesperson per shift (including fringe benefits) is $70.
How many salespeople should be hired?
• • D.25 During the afternoon peak hours the First Bank of Dubuque has an average of 40 customers arriving every hour. There is also an average of 8 customers at First Bank at any time. The probability of the arrival distribution is unknown. How long does the average customer spend in the bank?
• • D.26 An average of 9 cars can be seen in the system (both the drive-through line and the drive-through window) at Burger Universe. Approximately every 20 seconds, a car attempts to enter the drive-through line; however, 40% of cars simply leave the restaurant because they’re discouraged by the length of the line. On average, how long does a car spend going through the drive-through at Burger Universe?
• • D.27 Lobster World stores approximately 1,000 pounds of fish on average. In a typical day, the busy restaurant cooks and sells 360 (raw) pounds of fish. How long do the fish stay in stor- age on average?
• • D.28 Gamma Bank processes a typical loan application in 2.4 weeks. Customers fill out 30 loan applications per week. On average how many loan applications are being processed some- where in the system at Gamma Bank?
• • D.29 Fisher’s Furniture Store sells $800,000 worth of furni- ture to customers on credit each month. The Accounts Receivable balance in the accounting books averages $2 million. On average, how long are customers taking to pay their bills?
• • D.30 Vacation Inns, a chain of hotels operating in the southeastern region of the U.S., uses a toll-free telephone number to take reservations for all of its hotels. An average of 12 calls are received per hour. The probability distribution that describes the arrivals is unknown. Over a period of time, it is determined that the average caller spends 6 minutes on hold waiting for service. Find the average number of callers in the queue by using Little’s Law.
Additional problems D.31–D.39 are available in MyOMLab.
molds are moved into the cleaning, grinding, and preparation room, where they are dumped into large vibrators that shake most of the sand from the casting. The rough castings are then subjected to both sandblasting to remove the rest of the sand and grinding to finish some of their surfaces. Castings are then painted with a special heat-resistant paint, assembled into work- able stoves, and inspected for manufacturing defects that may have gone undetected. Finally, finished stoves are moved to stor- age and shipping, where they are packaged and transported to the appropriate locations.
At present, the pattern shop and the maintenance department are located in the same room. One large counter is used by both maintenance personnel, who store tools and parts (which are mainly used by the casting department), and sand molders, who need various patterns for the molding operation. Pete Nawler and Bob Dillman, who work behind the counter, can service a total of 10 people per hour (about 5 per hour each). On average, 4 people from casting and 3 from molding arrive at the counter each hour. People from molding and casting departments arrive randomly, and to be served, they form a single line.
Pete and Bob have always had a policy of first come, first served. Because of the location of the pattern shop and mainte- nance department, it takes an average of 3 minutes for an indi- vidual from the casting department to walk to the pattern and maintenance room, and it takes about 1 minute for an individual to walk from the molding department to the pattern and mainte- nance room.
After observing the operation of the pattern shop and main- tenance room for several weeks, George decided to make some changes to the factory layout. An overview of these changes appears in Figure D.7 .
Separating the maintenance shop from the pattern shop would have a number of advantages. It would take people from the casting department only 1 minute instead of 3 to get to the
CASE STUDIES New England Foundry
For more than 75 years, New England Foundry, Inc. (NEFI), has manufactured wood stoves for home use. In recent years, with increasing energy prices, president George Mathison has seen sales triple. This dramatic increase has made it difficult for George to maintain quality in all his wood stoves and related products.
Unlike other companies manufacturing wood stoves, NEFI is in the business of making only stoves and stove-related products. Its major products are the Warmglo I, the Warmglo II, the Warmglo III, and the Warmglo IV. The Warmglo I is the smallest wood stove, with a heat output of 30,000 BTUs, and the Warmglo IV is the largest, with a heat output of 60,000 BTUs.
The Warmglo III outsold all other models by a wide margin. Its heat output and available accessories were ideal for the typical home. The Warmglo III also had a number of other outstanding features that made it one of the most attractive and heat-efficient stoves on the market. These features, along with the accessories, resulted in expanding sales and prompted George to build a new factory to manufacture the Warmglo III model. An overview dia- gram of the factory is shown in Figure D.6 .
The new foundry used the latest equipment, including a new Disamatic that helped in manufacturing stove parts. Regardless of new equipment or procedures, casting operations have remained basically unchanged for hundreds of years. To begin with, a wooden pattern is made for every cast-iron piece in the stove. The wooden pattern is an exact duplicate of the cast-iron piece that is to be manufactured. All NEFI patterns are made by Precision Patterns, Inc. and are stored in the pattern shop and maintenance room. Next, a specially formulated sand is molded around the wooden pattern. There can be two or more sand molds for each pattern. The sand is mixed and the molds are made in the molding room. When the wooden pattern is removed, the resulting sand molds form a negative image of the desired casting. Next, molds are transported to the casting room, where molten iron is poured into them and allowed to cool. When the iron has solidified,
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new maintenance room. The time from molding to the pattern shop would be unchanged. Using motion and time studies, George was also able to determine that improving the layout of the maintenance room would allow Bob to serve 6 people from the casting depart- ment per hour; improving the layout of the pattern department would allow Pete to serve 7 people from the molding shop per hour.
Discussion Questions
1. How much time would the new layout save? 2. If casting personnel were paid $9.50 per hour and molding per-
sonnel were paid $11.75 per hour, how much could be saved per hour with the new factory layout?
3. Should George have made the change in layout?
The Winter Park Hotel
Lori Cook, manager of the Winter Park Hotel, is considering how to restructure the front desk to reach an optimum level of staff efficiency and guest service. At present, the hotel has five clerks on duty, each with a separate waiting line, during peak check- in time of 3:00 p.m. to 5:00 p.m. Observation of arrivals during this period shows that an average of 90 guests arrive each hour (although there is no upward limit on the number that could arrive at any given time). It takes an average of 3 minutes for the front-desk clerk to register each guest.
Ms. Cook is considering three plans for improving guest service by reducing the length of time that guests spend wait- ing in line. The first proposal would designate one employee as a quick-service clerk for guests registering under corporate accounts, a market segment that fills about 30% of all occu- pied rooms. Because corporate guests are preregistered, their registration takes just 2 minutes. With these guests separated from the rest of the clientele, the average time for registering a typical guest would climb to 3.4 minutes. Under this plan, noncorporate guests would choose any of the remaining four lines.
The second plan is to implement a single-line system. All guests could form a single waiting line to be served by whichever of five clerks became available. This option would require suffi- cient lobby space for what could be a substantial queue.
The use of an automatic teller machine (ATM) for check-ins is the basis of the third proposal. This ATM would provide about the same service rate as would a clerk. Because initial use of this technol- ogy might be minimal, Cook estimates that 20% of customers, pri- marily frequent guests, would be willing to use the machines. (This might be a conservative estimate if guests perceive direct benefits from using the ATM, as bank customers do. Citibank reports that some 95% of its Manhattan customers use its ATMs.) Ms. Cook would set up a single queue for customers who prefer human check- in clerks. This line would be served by the five clerks, although Cook is hopeful that the ATM will allow a reduction to four.
Discussion Questions
1. Determine the average amount of time that a guest spends check- ing in. How would this change under each of the stated options?
2. Which option do you recommend?
• Additional Case Study: Visit MyOMLab for this additional free case study: Pantry Shopper: The case requires the redesign of a checkout system for a supermarket.
4. Although there is no definite number that we can use to divide finite from infinite populations, the general rule of thumb is this: If the number in the queue is a significant proportion of the calling population, use a finite queuing model.
3. In queuing notation, the first letter refers to the arrivals (where M stands for Poisson distribution); the second letter refers to service (where M is again a Poisson distribution, which is the same as an exponential rate for service—and D is a constant service rate); the third symbol refers to the number of servers. So an M/D/1 system (our Model C) has Poisson arrivals, con- stant service, and one server.
1. When the arrival rates follow a Poisson process with mean arrival rate, l, the time between arrivals follows a negative exponential distribution with mean time between arrivals of 1/l. The negative exponential distribution, then, is also repre- sentative of a Poisson process but describes the time between arrivals and specifies that these time intervals are completely random.
2. The term FIFS (first-in, first-served) is often used in place of FIFO. Another discipline, LIFS (last-in, first-served), also called last-in, first-out (LIFO), is common when material is stacked or piled so that the items on top are used first.
Endnotes
Cleaning, grinding,
and preparation
Casting
Storage and
shipping
Molding
Sand
Pattern shop and
maintenance
Cleaning, grinding,
and preparation
Casting
Storage and
shipping
Molding
Maintenance
Sand
Pattern shop
Figure D.6
Overview of
Factory
Figure D.7
Overview of
Factory after
Changes
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Module D Rapid Review D
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Main Heading Review Material MyOMLab QUEUING THEORY (pp. 748 – 749 )
j Queuing theory —A body of knowledge about waiting lines. j Waiting line (queue) —Items or people in a line awaiting service.
Concept Questions: 1.1–1.3
CHARACTERISTICS OF A WAITING-LINE SYSTEM (pp. 749 – 752 )
The three parts of a waiting-line, or queuing, system are: Arrivals or inputs to the system; queue discipline, or the waiting line itself; and the service facility. j Unlimited, or infinite, population —A queue in which a virtually unlimited
number of people or items could request the services, or in which the number of customers or arrivals on hand at any given moment is a very small portion of potential arrivals.
j Limited, or finite, population —A queue in which there are only a limited number of potential users of the service.
j Poisson distribution —A discrete probability distribution that often describes the arrival rate in queuing theory:
P(x) = e-llx
x! for x = 0, 1, 2, 3, 4, c (D-1)
A queue is limited when it cannot, either by law or because of physical restrictions, increase to an infinite length. A queue is unlimited when its size is unrestricted. Queue discipline refers to the rule by which customers in the line are to receive service: j First-in, first-out (FIFO) rule —A queue discipline in which the first customers in
line receive the first service. j Single-server (single-channel) queuing system —A service system with one line and
one server. j Multiple-server (multiple-channel) queuing system —A service system with one
waiting line but with more than one server (channel). j Single-phase system —A system in which the customer receives service from only
one station and then exits the system. j Multiphase system —A system in which the customer receives services from
several stations before exiting the system. j Negative exponential probability distribution —A continuous probability distribu-
tion often used to describe the service time in a queuing system.
Concept Questions: 2.1–2.4
QUEUING COSTS (pp. 753 – 754 )
Operations managers must recognize the trade-off that takes place between two costs: the cost of providing good service and the cost of customer or machine waiting time.
Concept Questions: 3.1–3.4
THE VARIETY OF QUEUING MODELS (pp. 754 – 765 )
Model A: Single-Server System (M/M/1): Queuing Formulas: l 5 mean number of arrivals per time period m 5 mean number of people or items served per time period L s 5 average number of units in the system 5 l/(m 2 l) W s 5 average time a unit spends in the system 5 1/(m 2 l) L q 5 average number of units waiting in the queue 5 l 2 /[m(m 2 l)] W q 5 average time a unit spends waiting in the queue 5 l/[m(m 2 l)] 5 L q /l r 5 utilization factor for the system 5 l/m P 0 5 probability of 0 units in the system (i.e., the service unit is idle) 5 1 2 (l/m) P n.k 5 probability of . k units in the system 5 (l/m)
k 11
Model B: Multiple-Server System (M/M/S):
P0 = 1
c a M - 1
n = 0 1 n!
a l
m b
n d +
1 M!
a l
m b
M Mm Mm - l
for Mm > l
Ls = lm(l/m)M
(M - 1)!(Mm - l)2 P0 +
l
m
Ws = Ls /l Lq = Ls - (l/m) Wq = Lq/l Model C: Constant Service (M/D/1): Lq = l2/32m(m - l)4 Wq = l/32m(m - l)4
Ls = Lq + (l/m) Ws = Wq + (1/m)
Concept Questions: 4.1–4.4 Problems: D.1–D.14, D.16–D.21, D.24–D.39 Virtual Office Hours for Solved Problems: D.1–D.4 ACTIVE MODELS D.1, D.2, D.3
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OTHER QUEUING APPROACHES (p. 765 )
Often, variations of the four basic queuing models are present in an analysis. Many models, some very complex, have been developed to deal with such variations.
Concept Question: 5.1
Little’s Law A useful relationship in queuing for any system in a steady state is called Little’s Law:
Ls = lWs (which is the same as Ws = Ls /l) (D-2) Lq = lWq (which is the same as Wq = Lq /l) (D-3)
Model D: Finite Population (M/M/1 with finite source) With a limited, or finite, population, there is a dependent relationship between the length of the queue and the arrival rate. As the waiting line becomes longer, the arrival rate drops. N 5 size of the popuation
P0 = 1
a N
n = 0
N! (N - n)!
a l
m b
n
Lq = N - a l + m
l b(1 - P0)
Ls = Lq + (1 - P0)
Wq = Lq
(N - Ls)l
Ws = Wq + 1 m
Pn = N!
(N - n)! a l
m b
n P0 for n 5 0, 1,…, N
Module D Rapid Review continuedD R
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Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the module and the key terms listed at the end of the module.
LO D.1 Which of the following is not a key operating characteristic for a queuing system?
a) Utilization rate b) Percent idle time c) Average time spent waiting in the system and in the queue d) Average number of customers in the system and in the queue e) Average number of customers who renege LO D.2 Customers enter the waiting line at a cafeteria’s only cash
register on a first-come, first-served basis. The arrival rate follows a Poisson distribution, while service times follow an exponential distribution. If the average number of arrivals is 6 per minute and the average service rate of a single server is 10 per minute, what is the average number of customers in the system?
a) 0.6 b) 0.9 c) 1.5 d) 0.25 e) 1.0 LO D.3 In performing a cost analysis of a queuing system, the wait-
ing time cost is sometimes based on the time in the queue and sometimes based on the time in the system. The waiting cost should be based on time in the system for which of the fol- lowing situations?
a) Waiting in line to ride an amusement park ride b) Waiting to discuss a medical problem with a doctor c) Waiting for a picture and an autograph from a rock star d) Waiting for a computer to be fixed so it can be placed
back in service
LO D.4 Which of the following is not an assumption in a multiple- server queuing model?
a) Arrivals come from an infinite, or very large, population. b) Arrivals are Poisson distributed. c) Arrivals are treated on a first-in, first-out basis and do not
balk or renege. d) Service times follow the exponential distribution. e) Servers each perform at their own individual speeds. LO D.5 If everything else remains the same, including the mean
arrival rate and service rate, except that the service time becomes constant instead of exponential:
a) the average queue length will be halved. b) the average waiting time will be doubled. c) the average queue length will increase. d) we cannot tell from the information provided. LO D.6 A company has one computer technician who is responsible
for repairs on the company’s 20 computers. As a computer breaks, the technician is called to make the repair. If the repairperson is busy, the machine must wait to be repaired. This is an example of:
a) a multiple-server system. b) a finite population system. c) a constant service rate system. d) a multiphase system. e) all of the above.
Answers: LO D.1. e; LO D.2. c; LO D.3. d; LO D.4. e; LO D.5. a; LO D.6. b.
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M O D U L E O U T L I N E
E ◆
What Is a Learning Curve? 776
◆
Learning Curves in Services and Manufacturing 777
◆ Applying the Learning Curve 778
◆
Strategic Implications of Learning Curves 782
◆
Limitations of Learning Curves 783
M O
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Learning Curves
Al a sk
a A
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e s
A la
sk a A
ir lin
e s
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What Is a Learning Curve? Most organizations learn and improve over time. As firms and employees perform a task over and over, they learn how to perform more efficiently. This means that task times and costs decrease.
Learning curves are based on the premise that people and organizations become better at their tasks as the tasks are repeated. A learning curve graph (illustrated in Figure E.1 ) displays cost (or time) per unit versus the cumulative number of units produced. From it we see that the time needed to produce a unit decreases, usually following a negative exponential curve (part a), as the person or company produces more units. In other words, it takes less time to complete each additional unit a firm produces . However, we also see in Figure E.1 that the time savings in completing each subsequent unit decreases . These are the major attributes of the learning curve.
Learning curves were first applied to industry in a report by T. P. Wright of Curtis-Wright Corp. in 1936. 1 Wright described how direct labor costs of making a particular airplane de- creased with learning, a theory since confirmed by other aircraft manufacturers. Regardless of the time needed to produce the first plane, learning curves are found to apply to various categories of air frames (e.g., jet fighters versus passenger planes versus bombers). Learn- ing curves have since been applied not only to labor but also to a wide variety of other costs, including material and purchased components. The power of the learning curve is so signifi- cant that it plays a major role in many strategic decisions related to employment levels, costs, capacity, and pricing.
L E A R N I N G OBJEC TI V ES
LO E.1 Defi ne learning curve 776
LO E.2 Use the doubling concept to estimate times 778
LO E.3 Compute learning-curve eff ects with the formula and learning-curve table approaches 779
LO E.4 Describe the strategic implications of learning curves 782
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Medical procedures such as
heart surgery follow a learning
curve. Research indicates that the
death rate from heart transplants
drops at a 79% learning curve,
a learning rate not unlike that in
many industrial settings. It appears
that as doctors and medical teams
improve with experience, so do
your odds as a patient. If the
death rate is halved every three
operations, practice may indeed
make perfect.
Learning curves
The premise that people and
organizations get better at their
tasks as the tasks are repeated;
sometimes called experience
curves.
LO E.1 Define learning curve
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M O D U L E E | L E A R N I N G C U RV E S 777
The learning curve is based on a doubling of production: That is, when production doubles, the decrease in time per unit affects the rate of the learning curve. So, if the learning curve is an 80% rate, the second unit takes 80% of the time of the first unit, the fourth unit takes 80% of the time of the second unit, the eighth unit takes 80% of the time of the fourth unit, and so forth. This principle is shown as:
T × L n = Time required for the n th unit (E-1)
where T = unit cost or unit time of the first unit L = learning curve rate n = number of times T is doubled
If the first unit of a particular product took 10 labor-hours, and if a 70% learning curve is present, the hours the fourth unit will take require doubling twice—from 1 to 2 to 4. Therefore, the formula is:
Hours required for unit 4 = 10 × (.7) 2 = 4.9 hours
Learning Curves in Services and Manufacturing Different organizations—indeed, different products—have different learning curves. The rate of learning varies depending on the quality of management and the potential of the process and product. Any change in process, product, or personnel disrupts the learning curve. Therefore, caution should be exercised in assuming that a learning curve is continuing and permanent.
As you can see in Table E.1 , industry learning curves vary widely. The lower the number (say, 70% compared to 90%), the steeper the slope and the faster the drop in costs. By tradition, learning curves are defined in terms of the complements of their improvement rates. For ex- ample, a 70% learning curve implies a 30% decrease in time each time the number of repetitions is doubled. A 90% curve means there is a corresponding 10% rate of improvement.
Stable, standardized products and processes tend to have costs that decline more steeply than others. Between 1920 and 1955, for instance, the steel industry was able to reduce labor- hours per unit to 79% each time cumulative production doubled.
Learning curves have application in services as well as industry. As was noted in the cap- tion for the opening photograph, 1-year death rates of heart transplant patients at Temple
C o st
o r
tim e p
e r
re p e tit
io n
0 100 Cumulative repetitions (volume)
755025
100
(a) Exponential graph of learning
50
100
50
40
30
20
10 20 30 40
Cumulative repetitions (volume)
50 100
C o st
o r
tim e p
e r
re p e tit
io n
(b) Log-log graph of learning
Figure E.1
The Learning-Curve Effect States That Time per Repetition Decreases as the Number of Repetitions Increases
Both curves show that the labor-hours to build an airplane decline by 20% each time the production volume doubles. The left graph
(a) shows the exponential decline. The log-log graph (b) yields a straight line that is easier to extrapolate.
STUDENT TIP Learning is a universal concept, but
rates of learning differ widely.
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University Hospital follow a 79% learning curve. The results of that hospital’s 3-year study of 62 patients receiving transplants found that every three operations resulted in a halving of the 1-year death rate. As more hospitals face pressure from both insurance companies and the government to enter fixed-price negotiations for their services, their ability to learn from ex- perience becomes increasingly critical. In addition to having applications in both services and industry, learning curves are useful for a variety of purposes. These include:
1. Internal: Labor forecasting, scheduling, establishing costs and budgets. 2. External: Supply-chain negotiations (see the SMT case study at the end of this module). 3. Strategic: Evaluation of company and industry performance, including costs and
pricing.
The consequences of learning curves can be far-reaching. For instance, for Boeing’s 787 (the world’s fastest-selling commercial jet) to reach break-even at 1,000 planes, the unit cost must drop to $113 million, down from the $184 million it cost to make the 45th unit. This can be accomplished only with a very aggressive learning curve rate of 76%. If Boeing follows the 84% learning curve seen for its jumbo 777 model, losses will be in the billions. In addition, there may be major problems in scheduling if the learning improvement is not considered: labor and plants may sit idle a portion of the time. Firms may also refuse more work because they ignore their own efficiency improvements.
Applying the Learning Curve A mathematical relationship enables us to express the time required to produce a certain unit. This relationship is a function of how many units have been produced before the unit in ques- tion and how long it took to produce them. To gain a mastery of this relationship, we will work through learning curve scenarios using three different methods: the doubling approach, formula approach, and learning curve table approach.
Doubling Approach The doubling approach is the simplest approach to learning-curve problems. As noted ear- lier, each time production doubles, labor per unit declines by a constant factor, known as the learning curve rate. So, if we know that the learning curve rate is 80% and that the first unit
TABLE E.1 Examples of Learning-Curve Effects
EXAMPLE IMPROVING PARAMETER CUMULATIVE PARAMETER
LEARNING-CURVE SLOPE (%)
1. Model-T Ford production Price Units produced 86
2. Aircraft assembly Direct labor-hours per unit Units produced 80
3. Equipment maintenance at GE
Average time to replace a group of parts
Number of replacements
76
4. Steel production Production worker labor-hours per unit produced
Units produced 79
5. Integrated circuits Average price per unit Units produced 72 a
6. Handheld calculator Average factory selling price Units produced 74
7. Disk memory drives Average price per bit Number of bits 76
8. Heart transplants 1-year death rates Transplants completed 79
9. Cesarean section baby deliveries
Average operation time Number of surgeries 93
a Constant dollars.
STUDENT TIP Here are the three ways
of solving learning curve
problems.
LO E.2 Use the doubling concept
to estimate times
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produced took 100 hours, the hours required to produce the 2nd, 4th, 8th, and 16th units are as follows:
N TH UNIT PRODUCED HOURS FOR N TH UNIT
1 100.0
2 80.0 = (.8 × 100)
4 64.0 = (.8 × 80)
8 51.2 = (.8 × 64)
16 41.0 = (.8 × 51.2)
As long as we wish to find the hours required to produce N units and N is one of the doubled values, then this approach works. The doubling approach does not tell us how many hours will be needed to produce other units. For this flexibility, we turn to the formula approach.
Formula Approach The formula approach allows us to determine labor for any unit, TN , by the formula:
T N = T 1 ( N b ) (E-2)
where T N = time for the N th unit T 1 = time to produce the first unit
b = (log of the learning rate)>(log 2) = slope of the learning curve
Some of the values for b are presented in Table E.2 . Example E1 shows how this formula works.
LEARNING RATE (%) b
70 2.515 75 2.415 80 2.322 85 2.234 90 2.152
Learning-Curve Values of b
TABLE E.2
The learning-curve rate for a typical CPA to conduct a dental practice audit is 80%. Greg Lattier, a new graduate of Lee College, completed his first audit in 100 hours. If the dental offices he audits are about the same, how long should he take to finish his third job?
APPROACH c We will use the formula approach in Equation (E-2) .
SOLUTION c T N = T 1 ( N b )
T 3 = (100 hours)(3 b )
= (100)(3 log .8/log 2 ) = (100)(3 −.322 ) = 70.2 labor-hours
INSIGHT c Greg improved quickly from his first to his third audit. An 80% learning-curve rate means that from just the first to second jobs, his time decreased by 20%.
LEARNING EXERCISE c If Greg’s learning-curve rate were only 90%, how long would the third audit take? [Answer: 84.621 hours.]
RELATED PROBLEMS c E.1, E.2, E.9, E.10, E.11, E.16
EXCEL OM Data File ModEExE1.xls can be found in MyOMLab.
Example E1 USING LOGS TO COMPUTE LEARNING CURVES
The formula approach allows us to determine the hours required for any unit produced, but there is a simpler method.
Learning-Curve Table Approach The learning-curve table technique uses Table E.3 (to provide the coefficient C ) and the following equation:
T N = T 1 C (E-3)
where T N = number of labor-hours required to produce the N th unit T 1 = number of labor-hours required to produce the first unit C = learning-curve coefficient found in Table E.3
LO E.3 Compute learning-curve effects
with the formula and
learning-curve table
approaches
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The learning-curve coefficient, C , depends on both the learning curve rate (70%, 75%, 80%, and so on) and the unit number of interest.
Example E2 uses the preceding equation and Table E.3 to calculate learning-curve effects.
It took a Korean shipyard 125,000 labor-hours to produce the first of several tugboats that you expect to purchase for your shipping company, Great Lakes, Inc. Boats 2 and 3 have been produced by the Koreans with a learning factor of 85%. At $40 per hour, what should you, as purchasing agent, expect to pay for the fourth unit?
APPROACH c First, search Table E.3 for the fourth unit and a learning-curve rate of 85%. The learning-curve coefficient, C , is .723.
SOLUTION c To produce the fourth unit, then, takes:
T N = T 1 C T 4 = (125,000 hours)(.723) = 90,375 hours
To find the cost, multiply by $40:
90,375 hours × $40 per hour = $3,615,000
INSIGHT c The learning-curve table approach is very easy to apply. If we had not factored learning into our cost estimates, the price would have been 125,000 hours × $40 per hour (same as the first boat) = $6,000,000.
LEARNING EXERCISE c If the learning factor improved to 80%, how would the cost change? [Answer: It would drop to $3,200,000.]
RELATED PROBLEMS c E.1, E.2, E.3a, E.5a,c, E.6a,b, E.9, E.10, E.11, E.14, E.16, E.22 (E.26, E.27, E.28, E.30, E.31 are available in MyOMLab)
EXCEL OM Data File ModEExE2.xls can be found in MyOMLab.
ACTIVE MODEL E.1 This example is further illustrated in Active Model E.1 in MyOMLab.
Example E2 USING LEARNING-CURVE COEFFICIENTS
Table E.3 also shows cumulative values . These allow us to compute the total number of hours needed to complete a specified number of units. Again, the computation is straightforward. Just multiply the table coefficient value by the time required for the first unit. Example E3 illustrates this concept.
Example E2 computed the time to complete the fourth tugboat that Great Lakes plans to buy. How long will all four boats require?
APPROACH c We look at the “Total Time Coefficient” column in Table E.3 and find that the cumu- lative coefficient for 4 boats with an 85% learning-curve factor is 3.345.
SOLUTION c The time required is: T N = T 1 C
T 4 = (125,000)(3.345) = 418,125 hours in total for all 4 boats
INSIGHT c For an illustration of how Excel OM can be used to solve Examples E2 and E3, see Pro- gram E.1 at the end of this module.
LEARNING EXERCISE c What is the value of T4 if the learning-curve factor is 80% instead of 85%? [Answer: 392,750 hours.]
RELATED PROBLEMS c E.3b, E.4, E.5b,c, E.6c, E.7, E.15, E.19, E.20a
Example E3 USING CUMULATIVE COEFFICIENTS
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Using Table E.3 requires that we know how long it takes to complete the first unit. Yet, what happens if our most recent or most reliable information available pertains to some other unit? The answer is that we must use these data to find a revised estimate for the first unit and then apply the table coefficient to that number. Example E4 illustrates this concept.
TABLE E.3 Learning-Curve Coeffi cients, Where Coeffi cient C 5 N (LOG OF LEARNING RATE/LOG 2)
70% 75% 80% 85% 90% UNIT
NUMBER ( N )
UNIT TIME CO- EFFICIENT
TOTAL TIME CO- EFFICIENT
UNIT TIME CO- EFFICIENT
TOTAL TIME CO- EFFICIENT
UNIT TIME CO- EFFICIENT
TOTAL TIME CO- EFFICIENT
UNIT TIME CO- EFFICIENT
TOTAL TIME CO- EFFICIENT
UNIT TIME CO- EFFICIENT
TOTAL TIME CO- EFFICIENT
1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2 .700 1.700 .750 1.750 .800 1.800 .850 1.850 .900 1.900 3 .568 2.268 .634 2.384 .702 2.502 .773 2.623 .846 2.746 4 .490 2.758 .562 2.946 .640 3.142 .723 3.345 .810 3.556 5 .437 3.195 .513 3.459 .596 3.738 .686 4.031 .783 4.339 6 .398 3.593 .475 3.934 .562 4.299 .657 4.688 .762 5.101 7 .367 3.960 .446 4.380 .534 4.834 .634 5.322 .744 5.845 8 .343 4.303 .422 4.802 .512 5.346 .614 5.936 .729 6.574 9 .323 4.626 .402 5.204 .493 5.839 .597 6.533 .716 7.290 10 .306 4.932 .385 5.589 .477 6.315 .583 7.116 .705 7.994 11 .291 5.223 .370 5.958 .462 6.777 .570 7.686 .695 8.689 12 .278 5.501 .357 6.315 .449 7.227 .558 8.244 .685 9.374 13 .267 5.769 .345 6.660 .438 7.665 .548 8.792 .677 10.052 14 .257 6.026 .334 6.994 .428 8.092 .539 9.331 .670 10.721 15 .248 6.274 .325 7.319 .418 8.511 .530 9.861 .663 11.384 16 .240 6.514 .316 7.635 .410 8.920 .522 10.383 .656 12.040 17 .233 6.747 .309 7.944 .402 9.322 .515 10.898 .650 12.690 18 .226 6.973 .301 8.245 .394 9.716 .508 11.405 .644 13.334 19 .220 7.192 .295 8.540 .388 10.104 .501 11.907 .639 13.974 20 .214 7.407 .288 8.828 .381 10.485 .495 12.402 .634 14.608 25 .191 8.404 .263 10.191 .355 12.309 .470 14.801 .613 17.713 30 .174 9.305 .244 11.446 .335 14.020 .450 17.091 .596 20.727 35 .160 10.133 .229 12.618 .318 15.643 .434 19.294 .583 23.666 40 .150 10.902 .216 13.723 .305 17.193 .421 21.425 .571 26.543 45 .141 11.625 .206 14.773 .294 18.684 .410 23.500 .561 29.366 50 .134 12.307 .197 15.776 .284 20.122 .400 25.513 .552 32.142
Great Lakes, Inc., believes that unusual circumstances in producing the first boat (see Example E2) imply that the time estimate of 125,000 hours is not as valid a base as the time required to produce the third boat. Boat number 3 was completed in 100,000 hours. It wants to solve for the revised estimate for boat number 1. APPROACH c We return to Table E.3 , with a unit value of N = 3 and a learning-curve coefficient of C = .773 in the 85% column. SOLUTION c To find the revised estimate, divide the actual time for boat number 3, 100,000 hours, by C = .773:
100,000
.773 = 129,366 hours
So 129,366 hours is the new (revised) estimate for boat 1.
INSIGHT c Any change in product, process, or personnel will change the learning curve. The new estimate for boat 1 suggests that related cost and volume estimates need to be revised.
LEARNING EXERCISE c Boat 4 was just completed in 90,000 hours. Great Lakes thinks the 85% learning-curve rate is valid but isn’t sure about the 125,000 hours for the first boat. Find a revised esti- mate for boat 1. [Answer: 124,481, suggesting that boat 1’s time was fairly accurate after all.]
RELATED PROBLEMS c E.8, E.12, E.13, E.17, E.18, E.20b, E.21, E.23
EXCEL OM Data File ModEExE4.xls can be found in MyOMLab.
Example E4 REVISING LEARNING-CURVE ESTIMATES
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Examples E1 through E4 all assume that the learning curve rate is known. For a new product, this can be a major assumption. If a firm has observed the cost or time of any two products already produced, it’s easy to work backward from Equation (E-3) and Table E.3 to impute the actual learning curve rate . Example E5 illustrates this concept.
In 2012, Boeing completed production on its forty-fifth 787 airliner, at a cost of $184 million. The first plane off the assembly line, in 2010, cost $448 million. What is Boeing’s learning-curve rate for this model?
APPROACH c We use Equation (E-3) , with costs for T 1 and T 45 known, and then find the learning- curve coefficient ( C ) in Table E.3 .
SOLUTION c Equation (E-3) is T N = T 1 C . We solve for C = TN T1
.
C = 184 448
= .41
In Table E.3 , we follow the “Unit Number” row for N = 45, and we see that .41 falls under the 85% learning-curve rate for unit times (or costs, in this case).
INSIGHT c Boeing’s goal is to reach a 76% learning-curve rate, so OM must begin to lower costs dra- matically. Progress should be checked with each plane from this point on.
LEARNING EXERCISE c Let’s say Boeing’s fifth 787 cost $350 million. What was the learning-curve rate at that time relative to plane number 1? [Answer: C = $350 million/$448 million = .78. This suggests a 90% learning-curve rate, so Boeing’s performance has deteriorated.]
RELATED PROBLEMS c E.20, E.26 (E.29, E.32 are available in MyOMLab)
Example E5 COMPUTING THE LEARNING-CURVE RATE FROM OBSERVED PRODUCTION
Strategic Implications of Learning Curves So far, we have shown how operations managers can forecast labor-hour requirements for a product. We have also shown how purchasing agents can determine a supplier’s cost, knowl- edge that can help in price negotiations. Another important application of learning curves concerns strategic planning.
An example of a company cost line and industry price line are so labeled in Figure E.2 . These learning curves are straight because both scales are log scales. When the rate of change is constant, a log-log graph yields a straight line. If an organization believes its cost line to be the “company cost” line, and the industry price is indicated by the dashed horizontal line, then the company must have costs at the points below the dashed line (for example, point a or b ) or else operate at a loss (point c ).
LO E.4 Describe the strategic implications
of learning curves
STUDENT TIP Both the vertical and horizontal
axes of this figure are log scales
in this log-log graph.
Accumulated volume (log scale)
Gross profit margin
Selling price
Learning curve for industry price Loss
P ri ce
p e r
u n it
(l o g s
ca le
)
Learning curve for
com pany cost
(c)
(b)
(a)
Figure E.2
Industry Learning Curve for
Price Compared with Company
Learning Curve for Cost
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Lower costs are not automatic; they must be managed down. When a firm’s strategy is to pursue a learning curve steeper than the industry average (the company cost line in Figure E.2 ), it does this by:
1. Following an aggressive pricing policy 2. Focusing on continuing cost reduction and productivity improvement 3. Building on shared experience 4. Keeping capacity growing ahead of demand
Costs may drop as a firm pursues the learning curve, but volume must increase for the learn- ing curve to exist. Moreover, managers must understand competitors before embarking on a learning-curve strategy. Weak competitors are undercapitalized, stuck with high costs, or do not understand the logic of learning curves. However, strong and dangerous competitors con- trol their costs, have solid financial positions for the large investments needed, and have a track record of using an aggressive learning-curve strategy. Taking on such a competitor in a price war may help only the consumer.
Limitations of Learning Curves Before using learning curves, some cautions are in order:
◆ Because learning curves differ from company to company, as well as industry to industry, estimates for each organization should be developed rather than applying someone else’s.
◆ Learning curves are often based on the time necessary to complete the early units; there- fore, those times must be accurate. As current information becomes available, reevaluation is appropriate.
◆ Any changes in personnel, design, or procedure can be expected to alter the learning curve, causing the curve to spike up for a short time, even if it is going to drop in the long run.
◆ While workers and processes may improve, the same learning curves do not always apply to indirect labor and material.
◆ The culture of the workplace, as well as resource availability and changes in the process, may alter the learning curve. For instance, as a project nears its end, worker interest and effort may drop, curtailing progress down the curve.
STUDENT TIP Determining accurate rates
of learning requires careful
analysis.
The learning curve is a powerful tool for an operations manager. This tool can assist operations managers in deter- mining future cost standards for items produced as well as purchased. In addition, the learning curve can provide
understanding about company and industry performance. We saw three approaches to learning curves: the doubling approach, formula approach, and learning-curve table approach. Software can also help analyze learning curves.
Key Term
Learning curves (p. 776 )
1. What are some of the limitations of learning curves? 2. Identify three applications of the learning curve. 3. What are the approaches to solving learning-curve problems? 4. Refer to Example E2. What are the implications for Great
Lakes, Inc., if the engineering department wants to change the engine in the third and subsequent tugboats that the firm purchases?
5. Why isn’t the learning-curve concept as applicable in a high-volume assembly line as it is in most other human activities?
6. What are the elements that can disrupt the learning curve? 7. Explain the concept of the doubling effect in learning curves. 8. What techniques can a firm use to move to a steeper learning
curve?
Discussion Questions
Summary
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Using Software for Learning Curves
Excel, Excel OM, and POM for Windows may all be used in analyzing learning curves. You can use the ideas in the following section on Excel OM to build your own Excel spreadsheet if you wish.
X USING EXCEL OM Program E.1 shows how Excel OM develops a spreadsheet for learning-curve calculations. The input data come from Examples E2 and E3. In cell B7, we enter the unit number for the base unit (which does not have to be 1), and in B8 we enter the time for this unit. Learning-curve rates can also be developed from observed times or costs, as illustrated in Example E5.
=SUM($B$16:B16)
These are used for computations. Do not touch these cells. In cell B11, the time for the first unit is computed, allowing us to use initial units other than unit 1. In cell B12, the power to be raised to is computed, making the formulas in the rest of column B much simpler.
=$B$11*POWER(1,$B$12)
Program E.1
Excel OM’s Learning Curve Module, Using Data from Examples E2 and E3
P USING POM FOR WINDOWS The POM for Windows Learning Curve module computes the length of time that future units will take, given the time required for the base unit and the learning rate (expressed as a number between 0 and 1). As an option, if the times required for the first and N th units are already known, the learning rate can be computed. See Appendix IV for further details.
SOLVED PROBLEM E.1 Digicomp produces a new telephone system with built-in TV screens. Its learning-curve rate is 80%.
a) If the first one took 56 hours, how long will it take Digicomp to make the eleventh system?
b) How long will the first 11 systems take in total? c) As a purchasing agent, you expect to buy units 12
through 15 of the new phone system. What would be your expected cost for the units if Digicomp charges $30 for each labor-hour?
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLUTION
from Table E.3 , coefficient for 80% unit time a) T N = T 1 C T 11 = (56 hours)(.462) = 25.9 hours b) Total time for the first 11 units = (56 hours)(6.777) = 379.5 hours
from Table E.3 , coefficient for 80% total time
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c) To find the time for units 12 through 15, we take the total cumulative time for units 1 to 15 and subtract the total time for units 1 to 11, which was computed in part (b). Total time for the first 15 units = (56 hours)(8.511) = 476.6 hours. So the time for units 12 through 15 is 476.6 − 379.5 = 97.1 hours. (This figure could also be confirmed by computing the times for units 12, 13, 14, and 15 separately using the unit-time coefficient column and then adding them.) Expected cost for units 12 through 15 = (97.1 hours)($30 per hour) = $2,913.
SOLVED PROBLEM E.2 If the first time you performed a job took 60 minutes, how long will the eighth job take if you are on an 80% learning curve?
SOLUTION Three doublings from 1 to 2 to 4 to 8 implies .83. Therefore, we have:
60 × (.8) 3 = 60 × .512 = 30.72 minutes
or, using Table E.3 , we have C 5 .512. Therefore:
60 × .512 = 30.72 minutes
Problems E.1–E.32 relate to Applying the Learning Curve
• E.1 Susan Sherer, an IRS auditor, took 45 minutes to process her first tax return. The IRS uses an 85% learning curve. How long will the: a) 2nd return take? b) 4th return take? c) 8th return take? PX
• E.2 Temple Trucking Co. just hired Ed Rosenthal to verify daily invoices and accounts payable. He took 9 hours and 23 minutes to complete his task on the first day. Prior employees in this job have tended to follow a 90% learning curve. How long will the task take at the end of: a) the 2nd day? b) the 4th day? c) the 8th day? d) the 16th day? PX
• E.3 If Professor Laurie Macdonald takes 15 minutes to grade the first exam and follows an 80% learning curve, how long will it take her: a) to grade the 25th exam? b) to grade the first 10 exams? PX
• E.4 If it took 563 minutes to complete a hospital’s first cornea transplant, and the hospital uses a 90% learning rate, what is the cumulative time to complete: a) the first 3 transplants? b) the first 6 transplants? c) the first 8 transplants? d) the first 16 transplants? PX
• • E.5 Beth Zion Hospital has received initial certification from the state of California to become a center for liver trans- plants. The hospital, however, must complete its first 18 trans- plants under great scrutiny and at no cost to the patients. The very first transplant, just completed, required 30 hours. On the basis of research at the hospital, Beth Zion estimates that it will have an 80% learning curve. Estimate the time it will take to complete: a) the 5th liver transplant. b) all of the first 5 transplants. c) the 18th transplant. d) all 18 transplants. PX
• • E.6 Refer to Problem E.5. Beth Zion Hospital has just been informed that only the first 10 transplants must be per- formed at the hospital’s expense. The cost per hour of surgery is estimated to be $5,000. Again, the learning rate is 80% and the first surgery took 30 hours. a) How long will the 10th surgery take? b) How much will the 10th surgery cost? c) How much will all 10 cost the hospital? PX
• E.7 Manceville Air has just produced the first unit of a large industrial compressor that incorporated new technology in the control circuits and a new internal venting system. The first unit took 112 hours of labor to manufacture. The company knows from past experience that this labor content will decrease significantly as more units are produced. In reviewing past pro- duction data, it appears that the company has experienced a 90% learning curve when producing similar designs. The company is interested in estimating the total time to complete the next 7 units. Your job as the production cost estimator is to prepare the estimate. PX
• E.8 Elizabeth Perry, a student at SUNY, bought 6 book- cases for her dorm room. Each required unpacking of parts and assembly, which included some nailing and bolting.
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM.
d o b le
.d /F
o to
lia
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Elizabeth completed the first bookcase in 5 hours and the sec- ond in 4 hours. a) What is her learning rate? b) Assuming that the same rate continues, how long will the 3rd
bookcase take? c) The 4th, 5th, and 6th cases? d) All 6 cases? PX • • E.9 Professor Mary Beth Marrs took 6 hours to prepare the first lecture in a new course. Traditionally, she has experi- enced a 90% learning curve. How much time should it take her to prepare the 15th lecture? PX
• E.10 The first vending machine that William Kine, Inc., assembled took 80 labor-hours. Estimate how long the fourth machine will require for each of the following learning rates: a) 95% b) 87% c) 72% PX
• E.11 D. Shimshak Systems is installing networks for Advantage Insurance. The first installation took 46 labor-hours to complete. Estimate how long the 4th and the 8th installations will take for each of the following learning rates: a) 92% b) 84% c) 77% PX
• • • E.12 Providence Assessment Center screens and trains employees for a computer assembly firm in Boston. The progress of all trainees is tracked, and those not showing the proper pro- gress are moved to less demanding programs. By the tenth repeti- tion trainees must be able to complete the assembly task in 1 hour or less. Susan Sweaney has just spent 5 hours on the fourth unit and 4 hours completing her eighth unit, while another trainee, Julie Burgmeier, took 4 hours on the third and 3 hours on the sixth unit. Should you encourage either or both of the trainees to continue? Why? PX
• • E.13 The better students at Providence Assessment Center (see Problem E.12) have an 80% learning curve and can do a task in 20 minutes after just six times. You would like to weed out the weak students sooner and decide to evaluate them after the third unit. How long should the third unit take? PX
• • E.14 Suad Alwan, the purchasing agent for Dubai Airlines, is interested in determining what he can expect to pay for airplane number 4 if the third plane took 20,000 hours to produce. What would Alwan expect to pay for plane number 5? Number 6? Use an 85% learning curve and a $40-per-hour labor charge. PX
• • E.15 Using the data from Problem E.14, how long will it take to complete the 12th plane? The 15th plane? How long will it take to complete planes 12 through 15 inclusive? At $40 per hour, what can Alwan, as purchasing agent, expect to pay for planes 12 through 15? PX
• • E.16 Central Electronics Corp. produces semiconductors and has a learning curve of .7. The price per bit is 100 millicents when the volume is .7 × 10 12 bits. What is the expected price at 1.4 × 10 12 bits? What is the expected price at 89.6 × 10 12 bits? PX
• • E.17 Regional Power owns 25 small power generat- ing plants. It has contracted with Genco Services to overhaul the power turbines of each of the plants. The number of hours that Genco billed Regional to complete the third turbine was 460. Regional pays Genco $60 per hour for its services. As the maintenance manager for Regional, you are trying to estimate the cost of overhauling the fourth turbine. How much would you expect to pay for the overhaul of number 5 and number 6? All the turbines are similar, and an 80% learning curve is appropriate. PX
• • E.18 If it took Boeing 28,718 hours to produce the eighth 787 jet and the learning-curve factor is 80%, how long did it take to produce the tenth 787? PX
• • E.19 Richard Dulski’s firm is about to bid on a new radar system. Although the product uses new technology, Dulski believes that a learning rate of 75% is appropriate. The first unit is expected to take 700 hours, and the contract is for 40 units. PX a) What is the total amount of hours to build the 40 units? b) What is the average time to build each of the 40 units? c) Assume that a worker works 2,080 hours per year. How many
workers should be assigned to this contract to complete it in a year? PX
• • • E.20 As the estimator for Rajendra Tibrewala Enterprises, your job is to prepare an estimate for a potential customer ser- vice contract. The contract is for the service of diesel locomotive cylinder heads. The shop has done some of these in the past on a sporadic basis. The time required to service the first cylinder head in each job has been exactly 4 hours, and similar work has been accomplished at an 85% learning curve. The customer wants you to quote the total time in batches of 12 and 20. a) Prepare the quote. b) After preparing the quote, you find a labor ticket for this cus-
tomer for five locomotive cylinder heads. From the notations on the labor ticket, you conclude that the fifth unit took 2.5 hours. What do you conclude about the learning curve and your quote? PX
• • E.21 Girish Shambu and William Reisel are teammates at a discount store; their new job is assembling bicycles for custom- ers. Assembly of a bike has a learning rate of 90%. They forgot to time their effort on the first bike, but spent 4 hours on the second set. They have 6 more bikes to do. Determine approximately how much time will be (was) required for: a) the 1st unit b) the 8th unit c) all 8 units PX
• • E.22 Kelly-Lambing, Inc., a builder of government- contracted small ships, has a steady work force of 10 very skilled craftspeople. These workers can supply 2,500 labor-hours each per year. Kelly-Lambing is about to undertake a new contract, building a new style of boat. The first boat is expected to take 6,000 hours to complete. The firm thinks that 90% is the expected learning rate. a) What is the firm’s “capacity” to make these boats—that is,
how many units can the firm make in 1 year? b) If the operations manager can increase the learning rate
to 85% instead of 90%, how many units can the firm make?
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• • • E.23 The service times for a new data entry clerk have been measured and sequentially recorded as shown below:
REPORT TIME (MINUTES)
1 66
2 56
3 53
4 48
5 47
6 45
7 44
8 41
a) What is the learning curve rate, based on this information? b) Using an 85% learning curve rate and the above times,
estimate the length of time the clerk will take to complete the 48th report. PX
• • E.24 If the first unit of a production run takes 1 hour and the firm is on an 80% learning curve, how long will unit 100 take? ( Hint: Apply the coefficient in Table E.3 twice.) PX
• • • E.25 Boeing spent $270 million to make the eleventh 787 in its production line. The first 787 cost $448 million. What was the learning curve rate at this point?
Problem E.33 relates to Strategic Implications of Learning Curves
• • • • E.33 Using the accompanying log-log graph, answer the following questions:
Additional problems E.26–E.32 are available in MyOMLab.
500 400 300
200
Optimum Actual
L a
b o
r- h
o u
rs p
e r
u n
it
Total units produced
100 80 60
40
20
10 1 10 100 200 300 400
a) What are the implications for management if it has forecast its cost on the optimum line?
b) What could be causing the fluctuations above the optimum line?
c) If management forecasted the 10th unit on the optimum line, what was that forecast in hours?
d) If management built the 10th unit as indicated by the actual line, how many hours did it take?
control; and 9% for purchasing burden. Then, using an 85% learn- ing curve, he backed up his costs to get an estimate for the first unit. He next checked the data on hours and materials for the 25, 15, and 38 units already made and found that his estimate for the first unit was within 4% of actual cost. His check, however, had indicated a 90% learning-curve effect on hours per unit.
In the negotiations, SMT was represented by one of the two owners of the business, two engineers, and one cost estimator. The sessions opened with a discussion of learning curves. The IBM cost estimator demonstrated that SMT had in fact been operating on a 90% learning curve. But, he argued, it should be possible to move to an 85% curve, given the longer runs, reduced setup time, and increased continuity of workers on the job that would be possible with an order for 80 units. The owner agreed with this analysis and was willing to reduce his price by 4%.
However, as each operation in the manufacturing process was discussed, it became clear that some IBM cost estimates were too low because certain crating and shipping expenses had been overlooked. These oversights were minor, however, and in the following discussions, the two parties arrived at a common under- standing of specifications and reached agreements on the costs of each manufacturing operation.
CASE STUDY SMT’s Negotiation with IBM
IBM asked SMT and one other, much larger company to bid on 80 more units of a particular computer product. The RFQ (request for quote) asked that the overall bid be broken down to show the hourly rate, the parts and materials component in the price, and any charges for subcontracted services. SMT quoted $1.62 mil- lion and supplied the cost breakdown as requested. The second company submitted only one total figure, $5 million, with no cost breakdown. The decision was made to negotiate with SMT.
The IBM negotiating team included two purchasing managers and two cost engineers. One cost engineer had developed manu- facturing cost estimates for every component, working from engi- neering drawings and cost-data books that he had built up from previous experience and that contained time factors, both setup and run times, for a large variety of operations. He estimated material costs by working both from data supplied by the IBM corporate purchasing staff and from purchasing journals. He vis- ited SMT facilities to see the tooling available so that he would know what processes were being used. He assumed that there would be perfect conditions and trained operators, and he devel- oped cost estimates for the 158th unit (previous orders were for 25, 15, and 38 units). He added 5% for scrap-and-flow loss; 2% for the use of temporary tools, jigs, and fixtures; 5% for quality
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not understand why SMT had quoted such a low figure. He wanted to be sure that SMT was using the correct manufacturing process. In any case, if SMT estimators had made a mistake, it should be noted. It was IBM’s policy to seek a fair price both for itself and for its suppliers. IBM procurement managers believed that if a vendor was losing money on a job, there would be a ten- dency to cut corners. In addition, the IBM negotiator felt that by pointing out the error, he generated some goodwill that would help in future sessions.
Discussion Questions
1. What are the advantages and disadvantages to IBM and SMT from this approach?
2. How does SMT’s proposed learning rate compare with that of other industries?
3. What are the limitations of the learning curve in this case?
Source: Based on E. Raymond Corey, Procurement Management: Strategy, Organization, and Decision Making (New York: Van Nostrand Reinhold).
At this point, SMT representatives expressed great concern about the possibility of inflation in material costs. The IBM nego- tiators volunteered to include a form of price escalation in the contract, as previously agreed among themselves. IBM represent- atives suggested that if overall material costs changed by more than 10%, the price could be adjusted accordingly. However, if one party took the initiative to have the price revised, the other could require an analysis of all parts and materials invoices in arriving at the new price.
Another concern of the SMT representatives was that a large amount of overtime and subcontracting would be required to meet IBM’s specified delivery schedule. IBM negotiators thought that a relaxation in the delivery schedule might be possible if a price concession could be obtained. In response, the SMT team offered a 5% discount, and this was accepted. As a result of these negotiations, the SMT price was reduced almost 20% below its original bid price.
In a subsequent meeting called to negotiate the prices of certain pipes to be used in the system, it became apparent to an IBM cost estimator that SMT representatives had seriously underestimated their costs. He pointed out this apparent error because he could
Endnote
1. T. P. Wright, “Factors Affecting the Cost of Airplanes,” Journal of the Aeronautical Sciences (February 1936).
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E
R ap
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w Module E Rapid Review
Main Heading Review Material MyOMLab WHAT IS A LEARNING CURVE? (pp. 776 – 777 )
j Learning curves— The premise that people and organizations get better at their tasks as the tasks are repeated; sometimes called experience curves.
Learning usually follows a negative exponential curve. It takes less time to complete each additional unit a firm produces; however, the time savings in completing each subsequent unit decreases . Learning curves were first applied to industry in a report by T. P. Wright of Curtis-Wright Corp. in 1936. Wright described how direct labor costs of making a particular airplane decreased with learning. Learning curves have been applied not only to labor but also to a wide variety of other costs, including material and purchased components. The power of the learning curve is so significant that it plays a major role in many strategic decisions related to employment levels, costs, capacity, and pricing. The learning curve is based on a doubling of production: That is, when produc- tion doubles, the decrease in time per unit affects the rate of the learning curve.
T × L n = Time required for the n th unit (E-1)
where T = unit cost or time of the first unit L = learning curve rate n = number of times T is doubled
Concept Questions: 1.1–1.4
LEARNING CURVES IN SERVICES AND MANUFACTURING (pp. 777 – 778 )
Different organizations—indeed, different products—have different learning curves. The rate of learning varies, depending on the quality of management and the potential of the process and product. Any change in process, product, or personnel disrupts the learning curve. Therefore, caution should be exercised in assuming that a learning curve is continuing and permanent. The steeper the slope of the learning curve, the faster the drop in costs. By tradition, learning curves are defined in terms of the complements of their improvement rates (i.e., a 75% learning rate is better than an 85% learning rate). Stable, standardized products and processes tend to have costs that decline more steeply than others. Learning curves are useful for a variety of purposes, including: 1. Internal: Labor forecasting, scheduling, establishing costs and budgets 2. External: Supply-chain negotiations 3. Strategic: Evaluation of company and industry performance, including costs
and pricing
Concept Questions: 2.1–2.4
APPLYING THE LEARNING CURVE (pp. 778 – 782 )
If learning curve improvement is ignored, potential problems could arise, such as scheduling mismatches, leading to idle labor and productive facilities, refusal to accept new orders because capacity is assumed to be full, or missing an opportunity to negotiate with suppliers for lower purchase prices as a result of large orders. Three ways to approach the mathematics of learning curves are (1) doubling approach, (2) formula approach, and (3) learning-curve table approach. The doubling approach uses the production doubling Equation (E-1) . The formula approach allows us to determine labor for any unit, T N , by the formula:
T N = T 1 ( N b ) (E-2)
where T N = time for the N th unit T 1 = time to produce the first unit b = (log of the learning rate)/(log 2) = slope of the learning curve The learning-curve table approach makes use of Table E.3 and uses the formula:
T N = T 1 C (E-3)
where T N = number of labor-hours required to produce the N th unit T 1 = number of labor-hours required to produce the first unit C = learning-curve coefficient found in Table E.3 The learning-curve coefficient, C , depends on both the learning rate and the unit number of interest.
Concept Questions: 3.1–3.4
Problems: E.1–E.32
Virtual Office Hours for Solved Problems: E.1, E.2
ACTIVE MODEL E.1
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Main Heading Review Material MyOMLab Formula (E-3) can also use the “Total Time Coefficient” columns of Table E.3 to provide the total cumulative number of hours needed to complete the specified number of units. If the most recent or most reliable information available pertains to some unit other than the first, these data should be used to find a revised estimate for the first unit, and then the applicable formulas should be applied to that revised number.
STRATEGIC IMPLICATIONS OF LEARNING CURVES (pp. 782 – 783 )
When a firm’s strategy is to pursue a learning cost curve steeper than the industry average, it can do this by: 1. Following an aggressive pricing policy 2. Focusing on continuing cost reduction and productivity improvement 3. Building on shared experience 4. Keeping capacity growing ahead of demand Managers must understand competitors before embarking on a learning-curve strategy. For example, taking on a strong competitor in a price war may help only the consumer.
Concept Questions: 4.1–4.3
Problem: E.33
LIMITATIONS OF LEARNING CURVES (p. 783 )
Before using learning curves, some cautions are in order: j Because learning curves differ from company to company, as well as industry
to industry, estimates for each organization should be developed rather than applying someone else’s.
j Learning curves are often based on the time necessary to complete the early units; therefore, those times must be accurate. As current information becomes available, reevaluation is appropriate.
j Any changes in personnel, design, or procedure can be expected to alter the learning curve, causing the curve to spike up for a short time, even if it is going to drop in the long run.
j While workers and process may improve, the same learning curves do not always apply to indirect labor and material.
j The culture of the workplace, as well as resource availability and changes in the process, may alter the learning curve. For instance, as a project nears its end, worker interest and effort may drop, curtailing progress down the curve.
Concept Questions: 5.1–5.4
E R
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Module E Rapid Review continued
LO E.1 A learning curve describes: a) the rate at which an organization acquires new data. b) the amount of production time per unit as the total
number of units produced increases. c) the increase in production time per unit as the total
number of units produced increases. d) the increase in number of units produced per unit time as
the total number of units produced increases.
LO E.2 A surgical procedure with a 90% learning curve required 20 hours for the initial patient. The fourth patient should require approximately how many hours?
a) 18 b) 16.2 c) 28 d) 30 e) 54.2
LO E.3 The first transmission took 50 hours to rebuild at Bob’s Auto Repair, and the learning rate is 80%. How long will it take to rebuild the third unit? (Use at least three decimals in the exponent if you use the formula approach.)
a) under 30 hours b) about 32 hours c) about 35 hours d) about 60 hours e) about 45 hours
LO E.4 Which one of the following courses of action would not be taken by a firm wanting to pursue a learning curve steeper than the industry average?
a) Following an aggressive pricing policy b) Focusing on continuing cost reduction c) Keeping capacity equal to demand to control costs d) Focusing on productivity improvement e) Building on shared experience
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the module and the key term listed at the end of the module.
Answers: LO E.1. b; LO E.2. b; LO E.3. c; LO E.4. c.
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791
M O D U L E O U T L I N E
F ◆
What Is Simulation? 792
◆
Advantages and Disadvantages of Simulation 793
◆
Monte Carlo Simulation 794
◆
Simulation with Two Decision Variables: An Inventory Example 797
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792
What Is Simulation? Simulation models abound in our world. The city of Atlanta, for example, uses them to control traffic. Europe’s Airbus Industries uses them to test the aerodynamics of proposed jets. The U.S. Army simulates war games on computers. Business students use management gaming to simulate realistic business competition. And thousands of organizations like Bay Medical Center develop simulation models to help make operations decisions.
Most of the large companies in the world use simulation models. Table F.1 lists just a few areas in which simulation is now being applied.
Simulation is the attempt to duplicate the features, appearance, and characteristics of a real system. In this module, we will show how to simulate part of an operations management sys- tem by building a mathematical model that comes as close as possible to representing the reality
L E A R N I N G OBJEC TI V ES
LO F.1 List the advantages and disadvantages of modeling with simulation 793
LO F.2 Perform the fi ve steps in a Monte Carlo simulation 794
LO F.3 Simulate an inventory problem 798
LO F.4 Use Excel spreadsheets to create a simulation 800
When Bay Medical Center faced severe overcrowding at its outpatient clinic, it turned to computer simulation to try to reduce bottlenecks and improve patient flow.
A simulation language called Micro Saint analyzed current data relating to patient service times between clinic rooms. By simulating different numbers of doctors and
staff, simulating the use of another clinic for overflow, and simulating a redesign of the existing clinic, Bay Medical Center was able to make decisions based on an
understanding of both costs and benefits. This resulted in better patient service at lower cost.
Source: Micro Analysis and Design Simulation Software, Inc., Boulder, CO.
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Simulation
The attempt to duplicate the
features, appearance, and
characteristics of a real system,
usually via a computerized model.
TABLE F.1 Some Applications of Simulation
Ambulance location and dispatching Bus scheduling Assembly-line balancing Design of library operations Parking lot and harbor design Taxi, truck, and railroad dispatching Distribution system design Production facility scheduling Scheduling aircraft Plant layout Labor-hiring decisions Capital investments Personnel scheduling Production scheduling Traffi c-light timing Sales forecasting Voting pattern prediction Inventory planning and control
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M O D U L E F | S I M U L AT I O N 793
of the system. The model will then be used to estimate the effects of various actions. The idea behind simulation is threefold:
1. To imitate a real-world situation mathematically 2. Then to study its properties and operating characteristics 3. Finally, to draw conclusions and make action decisions based on the results of the simulation
In this way, a real-life system need not be touched until the advantages and disadvantages of a major policy decision are first measured on the model.
To use simulation, an OM manager should:
1. Define the problem. 2. Introduce the important variables associated with the problem. 3. Construct a numerical model. 4. Set up possible courses of action for testing by specifying values of variables. 5. Run the experiment. 6. Consider the results (possibly modifying the model or changing data inputs). 7. Decide what course of action to take.
These steps are illustrated in Figure F.1 . The problems tackled by simulation may range from very simple to extremely complex,
from bank-teller lines to an analysis of the U.S. economy. Although small simulations can be conducted by hand, effective use of the technique requires a computer. Large-scale models, simulating perhaps years of business decisions, are virtually all handled by computer.
In this module, we examine the basic principles of simulation and then tackle some prob- lems in the areas of waiting-line analysis and inventory control. Why do we use simulation in these areas when mathematical models described in other chapters can solve similar problems? The answer is that simulation provides an alternative approach for problems that are very com- plex mathematically. It can handle, for example, inventory problems in which demand or lead time is not constant.
Advantages and Disadvantages of Simulation Simulation is a tool that has become widely accepted by managers for several reasons. The main advantages of simulation are as follows:
1. It can be used to analyze large and complex real-world situations that cannot be solved by conventional operations management models.
2. Real-world complications can be included that most OM models cannot permit. For example, simulation can use any probability distribution the user defines; it does not require standard distributions.
3. “Time compression” is possible. The effects of OM policies over many months or years can be obtained by computer simulation in a short time.
4. Simulation allows “what-if ?” types of questions. Managers like to know in advance what options will be most attractive. With a computerized model, a manager can try out several policy decisions within a matter of minutes.
5. Simulations do not interfere with real-world systems. It may be too disruptive, for example, to experiment physically with new policies or ideas in a hospital or manufacturing plant.
The main disadvantages of simulation are as follows:
1. Good simulation models can take a long time to develop. 2. It is a repetitive approach that may produce different solutions in repeated runs. It does
not generate optimal solutions to problems (as does linear programming). 3. Managers must generate all of the conditions and constraints for solutions that they want
to examine. The simulation model does not produce answers without adequate, realistic input.
4. Each simulation model is unique. Its solutions and inferences are not usually transferable to other problems.
Define problem
Introduce variables
Construct model
Conduct simulation
Examine results
Select best course
Specify values of variables
Figure F.1
The Process of Simulation
STUDENT TIP There are many reasons it’s
better to simulate a real-world
system than to experiment
with it.
LO F.1 List the advantages and
disadvantages of
modeling with simulation
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Monte Carlo Simulation When a system contains elements that exhibit chance in their behavior, the Monte Carlo method of simulation may be applied. The basis of Monte Carlo simulation is experimentation on chance (or probabilistic ) elements by means of random sampling.
The technique breaks down into five simple steps: 1. Setting up a probability distribution for important variables. 2. Building a cumulative probability distribution for each variable. 3. Establishing an interval of random numbers for each variable. 4. Generating random numbers. 5. Actually simulating a series of trials. Let’s examine these steps in turn.
Step 1. Establishing Probability Distributions. The basic idea in the Monte Carlo simulation is to generate values for the variables making up the model under study. In real-world systems, a lot of variables are probabilistic in nature. To name just a few: inventory demand; lead time for orders to arrive; times between machine breakdowns; times between customer arrivals at a service facility; service times; times required to complete project activities; and number of employees absent from work each day.
One common way to establish a probability distribution for a given variable is to examine histori- cal outcomes. We can find the probability, or relative frequency, for each possible outcome of a vari- able by dividing the frequency of observation by the total number of observations. Here’s an example.
The daily demand for radial tires at Barry’s Auto Tire over the past 200 days is shown in columns 1 and 2 of Table F.2 . Assuming that past arrival rates will hold in the future, we can convert this demand to a probability distribution by dividing each demand frequency by the total demand, 200. The results are shown in column 3.
Step 2. Building a Cumulative Probability Distribution for Each Variable. The conversion from a regular probability distribution, such as in column 3 of Table F.2 , to a cumulative probability distribution is an easy job. In column 4, we see that the cumulative probability for each level of demand is the sum of the number in the probability column (column 3) added to the previous cumulative probability.
Computer simulation models have been developed to address a variety of productivity issues at
fast-food restaurants such as Burger King. In one, the ideal distance between the drive-through
order station and the pickup window was simulated. For example, because a longer distance
reduced waiting time, 12 to 13 additional customers could be served per hour—a benefit
of about $20,000 in extra sales per restaurant per year. In another simulation, a second
drive-through window was considered. This model predicted a sales increase of 15%.
D o n n a S
h a d e r
Monte Carlo method
A simulation technique that uses
random elements when chance
exists in their behavior.
LO F.2 Perform the five steps in a Monte Carlo
simulation
STUDENT TIP To establish a probability
distribution for tires, we assume
that historical demand is a good
indicator of future demand.
Cumulative probability distribution
The accumulation of individual
probabilities of a distribution.
(1) DEMAND FOR TIRES
(2) FREQUENCY
(3) PROBABILITY OF
OCCURRENCE
(4) CUMULATIVE PROBABILITY
0 10 10>200 = .05 .05
1 20 20>200 = .10 .15
2 40 40>200 = .20 .35
3 60 60>200 = .30 .65
4 40 40>200 = .20 .85
5 30 30>200 = .15 1.00
200 days 200>200 = 1.00
TABLE F.2 Demand for Barry’s Auto Tire
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Step 3. Setting Random-Number Intervals. Once we have established a cumulative probabil- ity distribution for each variable in the simulation, we must assign a set of numbers to repre- sent each possible value or outcome. These are referred to as random-number intervals. Basically, a random number is a series of digits (say, two digits from 01, 02, . . . , 98, 99, 00) that have been selected by a totally random process—a process in which each random number has an equal chance of being selected.
If, for example, there is a 5% chance that demand for Barry’s radial tires will be 0 units per day, then we will want 5% of the random numbers available to correspond to a demand of 0 units. If a total of 100 two-digit numbers is used in the simulation, we could assign a demand of 0 units to the first 5 random numbers: 01, 02, 03, 04, and 05. 1 Then a simulated demand for 0 units would be created every time one of the numbers 01 to 05 was drawn. If there is also a 10% chance that demand for the same product will be 1 unit per day, we could let the next 10 random numbers (06, 07, 08, 09, 10, 11, 12, 13, 14, and 15) represent that demand—and so on for other demand levels.
Similarly, we can see in Table F.3 that the length of each interval on the right corresponds to the probability of 1 of each of the possible daily demands. Thus, in assigning random numbers
STUDENT TIP You may start random-number
intervals at either 01 or 00,
but the text starts at 01 so that
the top of each range is the
cumulative probability.
Random-number intervals
A set of numbers to represent
each possible value or outcome in
a computer simulation.
Random number
A series of digits that have been
selected by a totally random
process.
TABLE F.3 The Assignment of Random-Number Intervals for Barry’s Auto Tire
DAILY DEMAND PROBABILITY CUMULATIVE PROBABILITY
INTERVAL OF RANDOM NUMBERS
0 .05 .05 01 through 05 1 .10 .15 06 through 15 2 .20 .35 16 through 35 3 .30 .65 36 through 65 4 .20 .85 66 through 85 5 .15 1.00 86 through 00
TABLE F.4 Table of 2-Digit Random Numbers
52 06 50 88 53 30 10 47 99 37 66 91 35 32 00 84 57 07 37 63 28 02 74 35 24 03 29 60 74 85 90 73 59 55 17 60 82 57 68 28 05 94 03 11 27 79 90 87 92 41 09 25 36 77 69 02 36 49 71 99 32 10 75 21 95 90 94 38 97 71 72 49 98 94 90 36 06 78 23 67 89 85 29 21 25 73 69 34 85 76 96 52 62 87 49 56 59 23 78 71 72 90 57 01 98 57 31 95 33 69 27 21 11 60 95 89 68 48 17 89 34 09 93 50 44 51 50 33 50 95 13 44 34 62 64 39 55 29 30 64 49 44 30 16 88 32 18 50 62 57 34 56 62 31 15 40 90 34 51 95 26 14 90 30 36 24 69 82 51 74 30 35 36 85 01 55 92 64 09 85 50 48 61 18 85 23 08 54 17 12 80 69 24 84 92 16 49 59 27 88 21 62 69 64 48 31 12 73 02 68 00 16 16 46 13 85 45 14 46 32 13 49 66 62 74 41 86 98 92 98 84 54 33 40 81 02 01 78 82 74 97 37 45 31 94 99 42 49 27 64 89 42 66 83 14 74 27 76 03 33 11 97 59 81 72 00 64 61 13 52 74 05 81 82 93 09 96 33 52 78 13 06 28 30 94 23 37 39 30 34 87 01 74 11 46 82 59 94 25 34 32 23 17 01 58 73 59 55 72 33 62 13 74 68 22 44 42 09 32 46 71 79 45 89 67 09 80 98 99 25 77 50 03 32 36 63 65 75 94 19 95 88 60 77 46 63 71 69 44 22 03 85 14 48 69 13 30 50 33 24 60 08 19 29 36 72 30 27 50 64 85 72 75 29 87 05 75 01 80 45 86 99 02 34 87 08 86 84 49 76 24 08 01 86 29 11 53 84 49 63 26 65 72 84 85 63 26 02 75 26 92 62 40 67 69 84 12 94 51 36 17 02 15 29 16 52 56 43 26 22 08 62 37 77 13 10 02 18 31 19 32 85 31 94 81 43 31 58 33 51
Source: Adapted from A Million Random Digits with 100,000 Normal Deviates . New York: The Free Press, 1995. Used by permission.
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to the daily demand for 3 radial tires, the range of the random-number interval (36 through 65) corresponds exactly to the probability (or proportion) of that outcome. A daily demand for 3 radial tires occurs 30% of the time. All of the 30 random numbers greater than 35 up to and including 65 are assigned to that event.
Step 4. Generating Random Numbers. Random numbers may be generated for simulation problems in two ways. If the problem is large and the process under study involves many simulation trials, computer programs are available to generate the needed random numbers. If the simulation is being done by hand, the numbers may be selected from a table of random digits.
Step 5. Simulating the Experiment. We may simulate outcomes of an experiment by simply selecting random numbers from Table F.4 . Beginning anywhere in the table, we note the interval in Table F.3 into which each number falls. For example, if the random number chosen is 81 and the interval 66 through 85 represents a daily demand for 4 tires, then we select a demand of 4 tires. Example F1 carries the simulation further.
Example F1 SIMULATING DEMAND Barry’s Auto Tire wants to simulate 10 days of demand for radial tires.
APPROACH c Earlier, we went through Steps 1 and 2 in the Monte Carlo method (in Table F.2 ) and Step 3 (in Table F.3 ). Now we need to generate random numbers (Step 4) and simulate demand (Step 5).
SOLUTION c We select the random numbers needed from Table F.4 , starting in the upper-left-hand corner and continuing down the first column, and record the corresponding daily demand:
DAY NUMBER RANDOM NUMBER SIMULATED DAILY DEMAND
1 52 3 2 37 3 3 82 4 4 69 4 5 98 5 6 96 5 7 33 2 8 50 3 9 88 5
10 90 5 39 Total 10-day demand 39/10 = 3.9 = tires average daily demand
INSIGHT c It is interesting to note that the average demand of 3.9 tires in this 10-day simulation differs substantially from the expected daily demand, which we may calculate from the data in Table F.3 :
Expected demand = a 5
i = 0 (probability of i units) * (demand of i units)
= (.05)(0) + (.10)(1) + (.20)(2) + (.30)(3) + (.20)(4) + (.15)(5) = 0 + .1 + .4 + .9 + .8 + .75 = 2.95 tires
However, if this simulation was repeated hundreds or thousands of times, the average simulated demand would be nearly the same as the expected demand.
LEARNING EXERCISE c Resimulate the 10 days, this time with random numbers from column 2 of Table F.4 . What is the average daily demand? [Answer: 2.5.]
RELATED PROBLEMS c F.1–F.12 (F.13–F.15 are available in MyOMLab)
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Naturally, it would be risky to draw any hard and fast conclusions about the operation of a firm from only a short simulation like Example F1 . Seldom would anyone actually want to go to the effort of simulating such a simple model containing only one variable. Simulating by hand does, however, demonstrate the important principles involved and may be useful in small-scale studies.
Simulation with Two Decision Variables: An Inventory Example Often, there is more than one variable to be simulated. In Example F1 it was demand for tires. But many OM decisions have multiple variables. In a queuing situation, it may be arrival times and service times. In an inventory problem, as we saw in Chapter 12 , both demand and lead time might be variable (not constant).
In this section, we present an inventory problem with two decision variables and two proba- bilistic components. The owner of the hardware store in Example F2 would like to establish order quantity and reorder point decisions for a particular product that has probabilistic (uncer- tain) daily demand and reorder lead time. He wants to make a series of simulation runs, trying out various order quantities and reorder points, to minimize his total inventory cost for the item. Inventory costs in this case will include ordering, holding, and stockout costs.
OM in Action Simulation Takes the Kinks out of Starbucks’ Lines The animation on the computer screen is not encouraging. Starbucks is
running a digital simulation of customers ordering new warm sandwiches and
pastries at a “virtual” store.
At first, things seem to go well, as animated workers rush around, prepar-
ing orders. But then they can’t keep up. Soon the customers are stacking up
in line, and the goal of serving each person in less than 3 minutes is blown.
The line quickly reaches the point at which customers decide the snack or
drink isn’t worth the wait—called the “balking point” in queuing theory.
Fortunately for Starbucks, the customers departing without their Frap-
puccinos and decaf slim lattes are digital. The simulation helps operations
managers find out what caused the backup before the scene repeats itself in
the real world.
Simulation software is also
used to find the point where
capital expenditures will pay
off. In large chains such as
Starbucks, adding even a
minor piece of equipment can
add up. A $200 blender in
each of Starbucks’ more than
22,000 stores in 65 countries
can cost the firm millons.
Sources: www.news.starbucks.com/news ; The Wall Street Journal (August 4, 2009); and slideshare.net (April 25, 2015).
R o sa
Ir e n e B
e ta
n co
u rt
3 /A
la m
y
STUDENT TIP Most real-world inventory
systems have probabilistic
events and benefit from a
simulation approach.
Example F2 AN INVENTORY SIMULATION WITH TWO VARIABLES Simkin’s Hardware Store, in Reno, sells the Ace model electric drill. Daily demand for this particular product is relatively low but subject to some variability. Lead times tend to be variable as well. Mark Simkin wants to develop a simulation to test an inventory policy of ordering 10 drills, with a reorder point of 5. In other words, every time the on-hand inventory level at the end of the day is 5 or less, Simkin will call his supplier that evening and place an order for 10 more drills. Simkin notes that if the lead time is 1 day, the order will not arrive the next morning but rather at the beginning of the following workday. Stockouts become lost sales, not backorders.
APPROACH c Simkin wants to follow the 5 steps in the Monte Carlo simulation process.
SOLUTION c Over the past 300 days, Simkin has observed the sales shown in column 2 of Table F.5 . He converts this historical frequency into a probability distribution for the variable daily demand (column 3). A cumulative probability distribution is formed in column 4 of Table F.5 . Finally, Simkin establishes an interval of random numbers to represent each possible daily demand (column 5).
When Simkin places an order to replenish his inventory of drills, there is a delivery lag of from 1 to 3 days. This means that lead time may also be considered a probabilistic variable. The number of days
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that it took to receive the past 50 orders is presented in Table F.6 . In a fashion similar to the creation of the demand variable, Simkin establishes a probability distribution for the lead time variable (column 3 of Table F.6 ), computes the cumulative distribution (column 4), and assigns random-number intervals for each possible time (column 5).
TABLE F.5 Probabilities and Random-Number Intervals for Daily Ace Drill Demand
(1) DEMAND FOR
ACE DRILL (2)
FREQUENCY (3)
PROBABILITY
(4) CUMULATIVE PROBABILITY
(5) INTERVAL OF
RANDOM NUMBERS
0 15 .05 .05 01 through 05 1 30 .10 .15 06 through 15 2 60 .20 .35 16 through 35 3 120 .40 .75 36 through 75 4 45 .15 .90 76 through 90 5 30 .10 1.00 91 through 00
300 days 1.00
LO F.3 Simulate an inventory problem
TABLE F.6 Probabilities and Random-Number Intervals for Reorder Lead Time
(1) LEAD TIME (DAYS)
(2) FREQUENCY
(3) PROBABILITY
(4) CUMULATIVE PROBABILITY
(5) RANDOM-NUMBER
INTERVAL
1 10 .20 .20 01 through 20 2 25 .50 .70 21 through 70 3 15 .30 1.00 71 through 00
50 orders 1.00
The entire process is simulated in Table F.7 for a 10-day period. We assume that beginning inventory (column 3) is 10 units on day 1. We took the random numbers (column 4) from column 2 of Table F.4 .
TABLE F.7 Simkin Hardware’s First Inventory Simulation. Order Quantity = 10 Units; Reorder Point = 5 Units
(1) DAY
(2) UNITS
RECEIVED
(3) BEGINNING INVENTORY
(4) RANDOM NUMBER
(5) DEMAND
(6) ENDING
INVENTORY
(7) LOST SALES
(8) ORDER?
(9) RANDOM NUMBER
(10) LEAD TIME
1 10 06 1 9 0 No 2 0 9 63 3 6 0 No 3 0 6 57 3 3 a 0 Yes 02 b 1 4 0 3 94 c 5 0 2 No d 5 10 e 10 52 3 7 0 No 6 0 7 69 3 4 0 Yes 33 2 7 0 4 32 2 2 0 No 8 0 2 30 2 0 0 No 9 10 f 10 48 3 7 0 No
10 0 7 88 4 3 0 Yes 14 1 Totals: 41 2
a This is the first time inventory dropped to the reorder point of five drills. Because no prior order was outstanding, an order is placed.
b The random number 02 is generated to represent the first lead time. It was drawn from column 2 of Table F.4 as the next number in the list being
used. A separate column could have been used from which to draw lead-time random numbers if we had wanted to do so, but in this example, we did
not do so.
c Again, notice that the random digits 02 were used for lead time (see footnote b ). So the next number in the column is 94.
d No order is placed on day 4 because there is an order outstanding from the previous day that has not yet arrived.
e The lead time for the first order placed is 1 day, but as noted in the text, an order does not arrive the next morning but rather the beginning of the
following day. Thus, the first order arrives at the start of day 5.
f This is the arrival of the order placed at the close of business on day 6. Fortunately for Simkin, no lost sales occurred during the 2-day lead time
before the order arrived.
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Table F.7 was filled in by proceeding 1 day (or line) at a time, working from left to right. It is a four- step process:
1. Begin each simulated day by checking to see whether any ordered inventory has just arrived. If it has, increase current inventory by the quantity ordered (10 units, in this case).
2. Generate a daily demand from the demand probability distribution for the selected random number. 3. Compute: Ending inventory = Beginning inventory minus Demand. If on-hand inventory is insuffi -
cient to meet the day’s demand, satisfy as much demand as possible and note the number of lost sales. 4. Determine whether the day’s ending inventory has reached the reorder point (5 units). If it has, and
if there are no outstanding orders, place an order. Lead time for a new order is simulated for the selected random number corresponding to the distribution in Table F.6 .
INSIGHTS c Simkin’s inventory simulation yields some interesting results. The average daily ending inventory is:
Average ending inventory = 41 total units
10 days = 4.1 units>day
We also note the average lost sales and number of orders placed per day:
Average lost sales = 2 sales lost
10 days = .2 units>day
Average number of orders placed = 3 orders 10 days
= .3 orders>day
LEARNING EXERCISE c How would these 3 averages change if the random numbers for day 10 were 04 and 93 instead of 88 and 14? [Answer: 4.4, .2 (no change), and .2.]
RELATED PROBLEMS c F.16–F.21 (F.22–F.25 are available in MyOMLab)
Now that we have worked through Example F2 we want to emphasize something very important: This simulation should be extended many more days before we draw any conclu- sions as to the cost of the order policy being tested. If a hand simulation is being conducted, 100 days would provide a better representation. If a computer is doing the calculations, 1,000 days would be helpful in reaching accurate cost estimates. (Moreover, remember that even with a 1,000-day simulation, the generated distribution should be compared with the desired distribution to ensure valid results.)
Summary Simulation involves building mathematical models that attempt to act like real operating systems. In this way, a real-world situation can be studied without imposing on the actual system. Although simulation models can be devel- oped manually, simulation by computer is generally more
desirable. The Monte Carlo approach uses random num- bers to represent variables, such as inventory demand or people waiting in line, which are then simulated in a series of trials. Simulation is widely used as an operations tool because its advantages usually outweigh its disadvantages.
Key Terms
Simulation (p. 792 ) Monte Carlo method (p. 794 )
Cumulative probability distribution (p. 794 ) Random-number intervals (p. 795 )
Random number (p. 795 )
Discussion Questions
1. State the seven steps, beginning with “Defining the Problem,” that an operations manager should perform when using sim- ulation to analyze a problem.
2. List the advantages of simulation. 3. List the disadvantages of simulation. 4. Explain the difference between simulated average demand
and expected average demand.
5. What is the role of random numbers in a Monte Carlo simulation?
6. Why might the results of a simulation differ each time you make a run?
7. What is Monte Carlo simulation? What principles underlie its use, and what steps are followed in applying it?
8. List six ways that simulation can be used in business.
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9. Why is simulation such a widely used technique? 10. What are the advantages of special-purpose simulation
languages (see below)? 11. In the simulation of an order policy for drills at Simkin’s
hardware ( Example F2 , pp. 797 – 799 ), would the results (of Table F.7 ) change significantly if a longer period were simulated? Why is the 10-day simulation valid or invalid?
12. Why is a computer necessary in conducting a real-world simulation?
13. Why might a manager be forced to use simulation instead of an analytical model in dealing with a problem of: a) inventory order policy? b) ships docking in a port to unload? c) bank-teller service windows? d) the U.S. economy?
Using Software in Simulation
Computers are critical in simulating complex tasks. They can gener- ate random numbers, simulate thousands of time periods in a mat- ter of seconds or minutes, and provide management with reports that improve decision making. A computer approach is almost a necessity in order to draw valid conclusions from a simulation.
Computer programming languages can help the simulation process. General-purpose languages , such as BASIC or C++, constitute one approach. Special-purpose simulation languages , such as GPSS and SIMSCRIPT, have a few advantages: (1) they require less programming time for large simulations, (2) they are usually more efficient and easier to check for errors, and (3) random-number generators are already built in as subroutines.
Commercial, easy-to-use prewritten simulation programs are also available. Some are generalized to handle a wide variety of situations ranging from queuing to inventory. These include programs such as Extend, Modsim, Witness, MAP/1, Enterprise Dynamics, Simfactory, ProModel, Micro Saint, and ARENA.
Spreadsheet software such as Excel can also be used to develop simulations quickly and easily. Such packages have
built-in random-number generators and develop outputs through “data-fill” table commands.
X USING EXCEL SPREADSHEETS The ability to generate random numbers and then “look up” these numbers in a table to associate them with a spe- cific event makes spreadsheets excellent tools for conducting simulations. Program F.1 illustrates an Excel simulation for Example F1 .
Notice that the cumulative probabilities are calculated in column E of Program F.1. This procedure reduces the chance of error and is useful in larger simulations involving more levels of demand.
The VLOOKUP function in column I looks up the random number (generated in column H) in the leftmost column of the defined lookup table. The VLOOKUP function moves down- ward through this column until it finds a cell that is bigger than the random number. It then goes to the previous row and gets the value from column B of the table.
From historical data, enter demand in B7:B12 and the frequency that each demand occured in C7:C12.
Use the Excel array function FREQUENCY here. First select B21:B26, then enter: =FREQUENCY(I7:I16,A21:A26) Then instead of pressing <Enter>, press: <Ctrl><Shift><Enter>
=C7/$C$13 =D7 =E7+D8
=SUM(C7:C12)
=SUMPRODUCT(B7:B12,D7:D12)
=E7
=B7
=C21
=B21/$B$27 =AVERAGE(I7:I16)
=SUM(B21:B26) =D21+C22
=RAND()
=VLOOKUP(H7,$A$7:$B$12,2)
Actions Copy A8 to A9:A12 Copy D7 to D8:D12 Copy E8 to E9:E12 Copy H7 to H8:H16 Copy I7 to I8:I16 Copy A21 to A22:A26 Copy C21 to C22:C26 Copy D22 to D23:D26 To simulate, press <F9>
LO F.4 Use Excel spreadsheets to
create a simulation
Program F.1
Using Excel to Simulate
Tire Demand for Barry’s
Auto Tire Shop
The output shows a simulated
average of 3.2 tires per day
(in cell I17).
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In column H, for example, the first random number shown is .716. Excel looked down the left-hand column of the lookup table ($A$7:$B$12) of Program F.1 until it found .85. From the previous row it retrieved the value in column B which is 4. Pressing the F9 function key recalculates the random numbers and the simulation.
PX USING POM FOR WINDOWS AND EXCEL OM POM for Windows and Excel OM are capable of handling any simulation that contains only one random variable, such as Example F1 . For further details, please refer to Appendix IV .
Solved Problems Virtual Office Hours help is available in MyOMLab.
SOLVED PROBLEM F.1 Higgins Plumbing and Heating maintains a stock of 30-gallon water heaters that it sells to homeowners and installs for them. Owner Jim Higgins likes the idea of having a large supply on hand to meet any customer demand. However, he also recog- nizes that it is expensive to do so. He examines water heater sales over the past 50 weeks and notes the following:
WATER HEATER SALES PER WEEK
NUMBER OF WEEKS THIS NUMBER WAS SOLD
4 6 5 5 6 9 7 12 8 8 9 7
10 3 50 weeks total data
a) If Higgins maintains a constant supply of 8 water heaters in any given week, how many times will he stockout during a 20-week simulation? We use random numbers from the 7th column of Table F.4 (on p. 795 ), beginning with the random digit 10.
b) What is the average number of sales per week over the 20-week period?
c) Using an analytic nonsimulation technique, determine the expected number of sales per week. How does this compare with the answer in part (b)?
SOLUTION
HEATER SALES PROBABILITY CUMULATIVE PROBABILITY RANDOM-NUMBER INTERVALS
4 .12 .12 01 through 12 5 .10 .22 13 through 22 6 .18 .40 23 through 40 7 .24 .64 41 through 64 8 .16 .80 65 through 80 9 .14 .94 81 through 94
10 .06 1.00 95 through 00 1.00
a)
WEEK RANDOM NUMBER SIMULATED SALES WEEK RANDOM NUMBER SIMULATED SALES
1 10 4 11 08 4 2 24 6 12 48 7 3 03 4 13 66 8 4 32 6 14 97 10 5 23 6 15 03 4 6 59 7 16 96 10 7 95 10 17 46 7 8 34 6 18 74 8 9 34 6 19 77 8
10 51 7 20 44 7
With a supply of 8 heaters, Higgins will stock out three times during the 20-week period (in weeks 7, 14, and 16).
b) Average sales by simulation = total sales>20 weeks = 135>20 = 6.75 per week c) Using expected values, we obtain:
E (sales) = .12(4 heaters) + .10(5) + .18(6) + .24(7) + .16(8) + .14(9) + .06(10) = 6.88 heaters With a longer simulation, these two approaches will lead to even closer values.
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SOLVED PROBLEM F.2 Random numbers may be used to simulate continuous distribu- tions. As a simple example, assume that fixed cost equals $300, profit contribution equals $10 per item sold, and you expect an equally likely chance of 0 to 99 units to be sold. That is, profit equals −$300 + $10 X , where X is the number sold. The mean amount you expect to sell is 49.5 units.
a) Calculate the expected value. b) Simulate the sale of 5 items, using the following double-digit
randomly-selected numbers of items sold: 37 77 13 10 85
c) Calculate the expected value of (b) and compare with the results of (a).
SOLUTION a) Expected value = − 300 + 10(49.5) = $195
b) - 300 + +10(37) = +70 - 300 + +10(77) = +470 - 300 + +10(13) = - +170 - 300 + +10(10) = - +200 - 300 + +10(85) = +550 c) The mean of these simulated sales is $144. If the sample size
were larger, we would expect the two values to be closer.
Problems Note: PX means the problem may be solved with POM for Windows and/or Excel OM or Excel.
The problems that follow involve simulations that can be done by hand. However, to obtain accurate and meaningful results, long periods must be simulated. This task is usually handled by a com- puter. If you are able to program some of the problems in Excel or a computer language with which you are familiar, we suggest you try to do so. If not, the hand simulations will still help you understand the simulation process.
Problems F.1–F.15 relate to Monte Carlo Simulation
• F.1 The daily demand for tuna sandwiches at an Ohio University cafeteria vending machine is 8, 9, 10, or 11, with probabil- ities 0.4, 0.3, 0.2, or 0.1, respectively. Assume the following random numbers have been generated: 09, 55, 73, 67, 53, 59, 04, 23, 88, and 84. Using these numbers, generate daily sandwich sales for 10 days. PX
• F.2 The number of machine breakdowns per day at Yuwen Chen’s factory is 0, 1, or 2, with probabilities 0.5, 0.3, or 0.2, respectively. The following random numbers have been gener- ated: 13, 14, 02, 18, 31, 19, 32, 85, 31, and 94. Use these numbers to generate the number of breakdowns for 10 consecutive days. What proportion of these days had at least one breakdown? PX
• F.3 The table below shows the partial results of a Monte Carlo simulation. Assume that the simulation began at 8:00 a.m., and there is only one server.
CUSTOMER NUMBER ARRIVAL TIME SERVICE TIME
1 8:01 6 2 8:06 7 3 8:09 8 4 8:15 6 5 8:20 6
a) When does service begin for customer number 3? b) When will customer number 5 leave? c) What is the average waiting time in line? d) What is the average time in the system?
• F.4 Barbara Flynn sells papers at a newspaper stand for $.35. The papers cost her $.25, giving her a $.10 profit on each one she sells. From past experience Barbara knows that: a) 20% of the time she sells 100 papers. b) 20% of the time she sells 150 papers. c) 30% of the time she sells 200 papers. d) 30% of the time she sells 250 papers.
Assuming that Barbara believes the cost of a lost sale to be $.05 and any unsold papers cost her $.25, simulate her profit outlook over 5 days if she orders 200 papers for each of the 5 days. Use the following random numbers: 52, 06, 50, 88, and 53. PX
• • F.5 Arnold Palmer Hospital is studying the number of emergency surgery kits that it uses on weekends. Over the past 40 weekends, the number of kits used was as follows:
NUMBER OF KITS FREQUENCY
4 4 5 6 6 10 7 12 8 8
The following random numbers have been generated: 11, 52, 59, 22, 03, 03, 50, 86, 85, 15, 32, 47. Simulate 12 weekends of emer- gency kit usage. What is the average number of kits used during these 12 weekends? PX
• F.6 Susan Sherer’s grocery store has noted the following figures with regard to the number of people who arrive at the store’s three checkout stands and the time it takes to check them out:
ARRIVALS/MINUTE FREQUENCY
0 .3 1 .5 2 .2
SERVICE TIME/MINUTE FREQUENCY
1 .1 2 .3 3 .4 4 .2
Simulate the utilization of the three checkout stands over 5 minutes, using the following random numbers: 07, 60, 77, 49, 76, 95, 51, 16, and 14. Record the results at the end of the 5-minute period. Start at time = 0. PX
• F.7 A warehouse manager at Mary Beth Marrs Corp. needs to simulate the demand placed on a product that does not fit standard models. The concept being measured is “demand during lead time,” where both lead time and daily demand are variable. The historical record for this product, along with the cumulative
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distribution, appear in the table. Random numbers have been gen- erated to simulate the next 5 order cycles; they are 91, 45, 37, 65, and 51. What are the five demand values? What is their average?
DEMAND DURING LEAD TIME PROBABILITY
CUMULATIVE PROBABILITY
100 .01 .01 120 .15 .16 140 .30 .46 160 .15 .61 180 .04 .65 200 .10 .75 220 .25 1.00
• F.8 Phantom Controls monitors and repairs control circuit boxes on elevators installed in multistory buildings in downtown Chicago. The company has the contract for 108 buildings. When a box malfunctions, Phantom installs a new one and rebuilds the failed unit in its repair facility in Gary, Indiana. The data for failed boxes over the last 2 years is shown in the following table:
NUMBER OF FAILED BOXES PER MONTH PROBABILITY
0 .10 1 .14 2 .26 3 .20 4 .18 5 .12
Simulate 2 years (24 months) of operation for Phantom and deter- mine the average number of failed boxes per month from the simu- lation. Was it common to have fewer than 7 failures over 3 months of operation? (Start your simulation at the top of the 10th column of Table F.4 on page 795 , RN = 37, and go down in the table.) PX
• F.9 The number of cars arriving at Patti Miles’s Car Wash, in Orono, Maine, during the last 200 hours of operation is observed to be the following:
NUMBER OF CARS ARRIVING FREQUENCY
3 or fewer 0 4 20 5 30 6 50 7 60 8 40
9 or more 0 200
a) Set up a probability and cumulative-probability distribution for the variable of car arrivals.
b) Establish random-number intervals for the variable. c) Simulate 15 hours of car arrivals and compute the average num-
ber of arrivals per hour. Select the random numbers needed from column 1, Table F.4 , beginning with the digits 52. PX
• • F.10 Leonard Presby’s newsstand uses naive forecasting to order tomorrow’s papers. The number of newspapers ordered corresponds to the previous day’s demands. Today’s demand for papers was 22. Presby buys the newspapers for $.20 and sells them for $.50. Whenever there is unsatisfied demand, Presby estimates the lost goodwill cost at $.10. Complete the accompanying table, and answer the questions that follow.
DEMAND PROBABILITY
21 .25 22 .15 23 .10 24 .20 25 .30
DAY PAPERS
ORDERED RANDOM NUMBER DEMAND REVENUE COST
GOODWILL COST
NET PROFIT
1 22 37 2 19 3 52 4 8 5 22 6 61
a) What is the demand on day 3? b) What is the total net profit at the end of the 6 days? c) What is the lost goodwill on day 6? d) What is the net profit on day 2? e) How many papers has Presby ordered for day 5? PX • • F.11 Every home football game for the past 8 years at Southwestern University has been sold out. The revenues from ticket sales are significant, but the sale of food, beverages, and souvenirs has contributed greatly to the overall profitability of the football program. One particular souvenir is the football program for each game. The number of programs sold at each game is described by the probability distribution given in the following table.
NUMBERS OF PROGRAMS SOLD PROBABILITY
2,300 0.15 2,400 0.22 2,500 0.24 2,600 0.21 2,700 0.18
Each program costs $.80 to produce and sells for $2.00. Any pro- grams that are not sold are donated to a recycling center and do not produce any revenue. a) Simulate the sales of programs at 10 football games. Use the
last column in the random-number table ( Table F.4 on p. 795 ) and begin at the top of the column.
b) If the university decided to print 2,500 programs for each game, what would the average profits be for the 10 games that were simulated?
c) If the university decided to print 2,600 programs for each game, what would the average profits be for the 10 games that were simulated? PX
• F.12 Refer to the data in Solved Problem F.1, on page 801 , which deals with Higgins Plumbing and Heating. Higgins has now collected 100 weeks of data and finds the following distribu- tion for sales:
WATER HEATER SALES PER WEEK
NUMBER OF WEEKS THIS
NUMBER WAS SOLD
WATER HEATER SALES PER WEEK
NUMBER OF WEEKS THIS
NUMBER WAS SOLD
3 2 8 12 4 9 9 12 5 10 10 10 6 15 11 5 7 25 100
PX
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804 P A R T 4 | B U S I N E S S A N A LY T I C S M O D U L E S
a) Simulate the trail followed by 10 emergency room patients. Proceed, one patient at a time, from each one’s entry at the initial exam station until he or she leaves through outprocessing. You should be aware that a patient can enter the same depart- ment more than once.
b) Using your simulation data, determine the chances that a patient enters the X-ray department twice.
• • • F.18 Connecticut Tanning has two tanning beds. One bed serves the company’s regular members exclusively. The second bed serves strictly walk-in customers (those without appointments) on a first-come, first-served basis. Orv Karan, the store manager, has noticed on several occasions during the busy 5 hours of the day (2:00 p.m. until 7:00 p.m.) that potential walk-in customers will most often walk away from the store if they see one person already waiting for the second bed. He wonders if capturing this lost demand would justify adding a third bed. Leasing and maintaining a tanning bed costs Connecticut Tanning $600 per month. The price paid per customer varies according to the time in the bed, but Orv has calculated the average net income for every 10 minutes of tanning time to be $2. A study of the pattern of arrivals dur- ing the busy hours and the time spent tanning has revealed the following:
TIME BETWEEN ARRIVALS (MINUTES) PROBABILITY
TIME IN TANNING BED
(MINUTES) PROBABILITY
5 0.30 10 0.20 10 0.25 15 0.30 15 0.20 20 0.40 20 0.15 25 0.10 25 0.10
a) Simulate 4 hours of operation (arrivals over 4 hours). Use the 14th column of Table F.4 (p. 795 ) for arrival times and the 8th column for tanning times. Assume there is one person who has just entered the bed at 2:00 p.m. for a 20-minute tan. Indicate which customers balk at wait- ing for the bed to become available. How many custom- ers were lost over the 4 hours (the simulation ends at 6:00 p.m.)?
b) If the store is open an average of 24 days a month, will captur- ing all lost sales justify adding a new tanning bed?
• • • F.19 Kathryn Marley owns and operates the largest Mercedes-Benz auto dealership in Pittsburgh. In the past 36 months, her sales have ranged from a low of 6 new cars to a high of 12 new cars, as reflected in the following table:
SALES OF NEW CARS/MONTH FREQUENCY
6 3 7 4 8 6 9 12
10 9 11 1 12 1
36 months
Marley believes that sales will continue during the next 24 months at about the same historical rates, and that delivery times will
a) Assuming that Higgins maintains a constant supply of 8 heat- ers, simulate the number of stockouts incurred over a 20-week period (using the seventh column of Table F.4 ).
b) Conduct this 20-week simulation two more times and compare your answers with those in (a). Did they change significantly? Why or why not?
c) What is the new expected number of sales per week? PX
Additional problems F.13–F.15 are available in MyOMLab.
Problems F.16–F.25 relate to Simulation with Two Decision Variables: An Inventory Example
• • F.16 The time between arrivals at the drive-through win- dow of Barry Harmon’s fast-food restaurant follows the distribu- tion given in the table. The service-time distribution is also given. Use the random numbers provided to simulate the activity of the first 4 arrivals. Assume that the window opens at 11:00 a.m. and that the first arrival occurs afterward, based on the first interar- rival time generated.
TIME BETWEEN ARRIVALS PROBABILITY SERVICE TIME PROBABILITY
1 .2 1 .3 2 .3 2 .5 3 .3 3 .2 4 .2
Random numbers for arrivals: 14, 74, 27, 03 Random numbers for service times: 88, 32, 36, 24 At what time does the fourth customer leave the system? PX
• • • F.17 Central Hospital in York, Pennsylvania, has an emer- gency room that is divided into six departments: (1) an initial exam station to treat minor problems or to make a diagnosis; (2) an X-ray department; (3) an operating room; (4) a cast-fitting room; (5) an observation room (for recovery and general observa- tion before final diagnosis or release); and (6) an outprocessing department (where clerks check out patients and arrange for pay- ment or insurance forms).
The probabilities that a patient will go from one department to another are presented in the following table:
FROM TO PROBABILITY
Initial exam at X-ray department .45 emergency Operating room .15 room entrance Observation room .10
Outprocessing clerk .30 X-ray department Operating room .10
Cast-fi tting room .25 Observation room .35 Outprocessing clerk .30
Operating room Cast-fi tting room .25 Observation room .70 Outprocessing clerk .05
Cast-fi tting room Observation room .55 X-ray department .05 Outprocessing clerk .40
Observation room Operating room .15 X-ray department .15 Outprocessing clerk .70
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M O D U L E F | S I M U L AT I O N 805
also continue to follow the following pace (stated in probability form):
DELIVERY TIME (MONTHS)* PROBABILITY
1 .44 2 .33 3 .16 4 .07
1.00
* With cars arriving at the end of the month (i.e., orders placed at the end of the 1st
month, with a lead time of 2 months, will arrive at the end of the 3rd month, too late
for sales in the 3rd month).
Marley’s current policy is to order 14 cars at a time (two full truckloads, with 7 autos on each truck), and to place a new order whenever the stock on hand reaches 12 autos. a) What are the results of this policy when simulated over the
next 2 years? b) Marley establishes the following relevant costs: (1) carrying
cost per Mercedes per month is $600; (2) cost of a lost sale averages $4,350; and (3) cost of placing an order is $570. What is the total inventory cost of this policy?
• • F.20 Dumoor Appliance Center sells and services several brands of major appliances. Past sales for a particular model of refrigerator have resulted in the following probability distribution for demand:
Demand per week 0 1 2 3 4 Probability 0.20 0.40 0.20 0.15 0.05
The lead-time in weeks is described by the following distribution:
Lead time (weeks) 1 2 3 Probability 0.15 0.35 0.50
Based on cost considerations as well as storage space, the company has decided to order 10 of these each time an order is placed. The holding cost is $1 per week for each unit that is left in inventory at the end of the week. The stockout cost has been set at $40 per stockout. The company has decided to place an order whenever there are only two refrigerators left at the end of the week. Simulate 10 weeks of operation for Dumoor Appliance, assuming that there are currently 5 units in inven- tory. Determine what the weekly stockout cost and weekly hold- ing cost would be for the problem. Use the random numbers in the first column of Table F.4 for demand and the second column for lead time.
• • F.21 Repeat the simulation in Problem F.20, assuming that the reorder point is 4 units rather than 2. Compare the costs for these two situations. Use the same random numbers as in Problem F.20.
Additional problems F.22–F.25 are available in MyOMLab.
CASE STUDY Alabama Airlines’ Call Center
Alabama Airlines opened its doors in December 2015 as a commuter service with its headquarters and hub located in Birmingham. The airline was started and managed by two for- mer pilots, David Douglas and George Devenney. It acquired a fleet of 12 used prop-jet planes and the airport gates vacated by Delta Air Lines in 2014.
TABLE F.8 Incoming Call Distribution
TIME BETWEEN CALLS (MINUTES) PROBABILITY
1 .11 2 .21 3 .22 4 .20 5 .16 6 .10
With business growing quickly, Douglas turned his attention to Alabama Air’s “800” reservations system. Between midnight and 6:00 a.m., only one telephone reservations agent had been on duty. The time between incoming calls during this period is dis- tributed as shown in Table F.8 . Carefully observing and timing the agent, Douglas estimated that the time required to process passenger inquiries is distributed as shown in Table F.9 .
TABLE F.9 Service-Time Distribution
TIME TO PROCESS CUSTOMER INQUIRIES (MINUTES) PROBABILITY
1 .20 2 .19 3 .18 4 .17 5 .13 6 .10 7 .03
All customers calling Alabama Air go “on hold” and are served in the order of the calls received unless the reservations agent is available for immediate service. Douglas is decid- ing whether a second agent should be on duty to cope with customer demand. To maintain customer satisfaction, Alabama Air wants a customer to be “on hold” for no more than 3 to 4 minutes; it also wants to maintain a “high” operator utilization.
Furthermore, the airline is planning a new TV advertising campaign. As a result, it expects an increase in “800” line phone inquiries. Based on similar campaigns in the past, the incoming call distribution from midnight to 6:00 a.m. is expected to be as shown in Table F.10 . (The same service-time distribution will apply.)
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806 P A R T 4 | B U S I N E S S A N A LY T I C S M O D U L E S
Discussion Questions
1. Given the original call distribution, what would you advise Alabama Air to do for the current reservation system? Create a simulation model to investigate the scenario. Describe the model carefully, and justify the duration of the simulation, assumptions, and measures of performance.
2. What are your recommendations regarding operator utiliza- tion and customer satisfaction if the airline proceeds with the advertising campaign?
Source: Professor Zbigniew H. Przasnyski, Loyola Marymount University. Reprinted by permission.
• Additional Case Study: Visit MyOMLab for this free case study: Saigon Transport: This Vietnamese shipping company is trying to determine the ideal truck fl eet size.
1. Alternatively, we could have assigned the random numbers 00, 01, 02, 03, and 04 to represent a demand of 0 units. The 2 digits 00 can be thought of as either 0 or 100. As long as 5 numbers out of 100 are assigned to the 0 demand, it does not make any difference which 5 they are.
Endnote
TABLE F.10 Incoming Call Distribution
TIME BETWEEN CALLS (MINUTES) PROBABILITY
1 .22 2 .25 3 .19 4 .15 5 .12 6 .07
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Main Heading Review Material MyOMLab WHAT IS SIMULATION? (pp. 792 – 793 )
Most of the large companies in the world use simulation models. j Simulation —The attempt to duplicate the features, appearance, and characteris-
tics of a real system, usually via a computerized model. The idea behind simulation is threefold: 1. To imitate a real-world situation mathematically 2. Then to study its properties and operating characteristics 3. Finally, to draw conclusions and make action decisions based on the results of
the simulation In this way, a real-life system need not be touched until the advantages and dis- advantages of a major policy decision are first measured on the model. To use simulation, an OM manager should: 1. Define the problem. 2. Introduce the important variables associated with the problem. 3. Construct a numerical model. 4. Set up possible courses of action for testing by specifying values of variables. 5. Run the experiment. 6. Consider the results (possibly modifying the model or changing data inputs). 7. Decide what course of action to take.
Concept Questions: 1.1–1.4
ADVANTAGES AND DISADVANTAGES OF SIMULATION (p. 793 )
The main advantages of simulation are: 1. It can be used to analyze large and complex real-world situations that cannot be
solved using conventional operations management models. 2. Real-world complications can be included that most OM models cannot permit.
For example, simulation can use any probability distribution the user defines; it does not require standard distributions.
3. “Time compression” is possible. The effects of OM policies over many months or years can be obtained by computer simulation in a short time.
4. Simulation allows “what-if ?” types of questions. Managers like to know in advance what options will be most attractive. With a computerized model, a manager can try out several policy decisions within a matter of minutes.
5. Simulations do not interfere with real-world systems. It may be too disruptive, for example, to experiment physically with new policies or ideas.
The main disadvantages of simulation are: 1. Good simulation models can be very expensive; they may take many months to
develop. 2. It is a repetitive approach that may produce different solutions in repeated runs.
It does not generate optimal solutions to problems.
Concept Questions: 2.1–2.4
F
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Module F Rapid Review
Define problem
Introduce variables
Construct model
Conduct simulation
Examine results
Select best course
Specify values of variables
Figure F.1
The Process of Simulation
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F R
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Module F Rapid Review continued
Main Heading Review Material MyOMLab 3. Managers must generate all of the conditions and constraints for solutions that
they want to examine. The simulation model does not produce answers without adequate, realistic input.
4. Each simulation model is unique. Its solutions and inferences are not usually transferable to other problems.
MONTE CARLO SIMULATION (pp. 794 – 797 )
j Monte Carlo method —A simulation technique that selects random numbers assigned to a distribution.
The Monte Carlo method breaks down into five simple steps: 1. Setting up a probability distribution for important variables 2. Building a cumulative probability distribution for each variable 3. Establishing an interval of random numbers for each variable 4. Generating random numbers 5. Actually simulating a series of trials One common way to establish a probability distribution for a given variable is to examine historical outcomes. We can find the probability, or relative frequency, for each possible outcome of a variable by dividing the frequency of observation by the total number of observations. j Cumulative probability distribution —The accumulation (summary) of probabili-
ties of a distribution. j Random-number intervals —A set of numbers to represent each possible value or
outcome in a computer simulation. j Random number —A series of digits that have been selected using a totally
random process. Random numbers may be generated for simulation problems in two ways: (1) If the problem is large and the process under study involves many simulation trials, computer programs are available to generate the needed random numbers; or (2) if the simulation is being done by hand, the numbers may be selected from a table of random digits.
Concept Questions: 3.1–3.4 Problems: F.1–F.15 Virtual Office Hours for Solved Problems: F.1, F.2
SIMULATION WITH TWO DECISION VARIABLES: AN INVENTORY EXAMPLE (pp. 797 – 799 )
The commonly used EOQ models are based on the assumption that both product demand and reorder lead time are known, constant values. In most real-world inventory situations, though, demand and lead time are variables, so accurate analysis becomes extremely difficult to handle by any means other than simulation.
Concept Questions: 4.1–4.4 Problems: F.16–F.25
Self Test j Before taking the self-test, refer to the learning objectives listed at the beginning of the module and the key terms listed at the end of the module.
LO F.1 Which of the following is not an advantage of simulation? a) Simulation is relatively straightforward and flexible. b) Good simulation models are usually inexpensive to develop. c) “Time compression” is possible. d) Simulation can study the interactive effects of individual
variables. e) Simulations do not interfere with real-world systems. LO F.2 The five steps required to implement the Monte Carlo simulation
technique are _______ , _______ , _______ , _______ , and _______ . LO F.3 Two particularly good candidates to be probabilistic compo-
nents in the simulation of an inventory problem are: a) order quantity and reorder point. b) setup cost and holding cost.
c) daily demand and reorder lead time. d) order quantity and reorder lead time. e) reorder point and reorder lead time. LO F.4 One important reason that spreadsheets are excellent tools
for conducting simulations is that they can: a) generate random numbers. b) easily provide animation of the simulation. c) provide more security than manual simulations. d) prohibit “time compression” from corrupting the results. e) be easily programmed.
Answers: LO F.1. b; LO F.2. set up a probability distribution for each of the important variables, build a cumulative probability distribution for each of the important variables, establish an interval of random numbers for each variable, generate sets of random numbers, actually simulate a set of trials; LO F.3. c; LO F.4. a.
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APPENDIX I Normal Curve Areas
APPENDIX II Values of e −l for Use in the Poisson Distribution
APPENDIX III Table of Random Numbers
APPENDIX IV Using Excel OM and POM for Windows
APPENDIX V Solutions to Even-Numbered Problems
Appendixes
A1
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A2 A P P E N D I X I
APPENDIX I N O R M A L C U R V E A R E A S
1.55
1.55
Standard deviations
0 Mean Z
Area is .93943
To find the area under the normal curve, you can apply either Table I.1 or Table I.2 . In Table I.1 , you must know how many stan-
dard deviations that point is to the right of the mean. Then, the area under the normal curve can be read directly from the normal
table. For example, the total area under the normal curve for a point that is 1.55 standard deviations to the right of the mean
is .93943.
Table I.1 Z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09
.0 .50000 .50399 .50798 .51197 .51595 .51994 .52392 .52790 .53188 .53586 .1 .53983 .54380 .54776 .55172 .55567 .55962 .56356 .56749 .57142 .57535 .2 .57926 .58317 .58706 .59095 .59483 .59871 .60257 .60642 .61026 .61409 .3 .61791 .62172 .62552 .62930 .63307 .63683 .64058 .64431 .64803 .65173 .4 .65542 .65910 .66276 .66640 .67003 .67364 .67724 .68082 .68439 .68793 .5 .69146 .69497 .69847 .70194 .70540 .70884 .71226 .71566 .71904 .72240 .6 .72575 .72907 .73237 .73565 .73891 .74215 .74537 .74857 .75175 .75490 .7 .75804 .76115 .76424 .76730 .77035 .77337 .77637 .77935 .78230 .78524 .8 .78814 .79103 .79389 .79673 .79955 .80234 .80511 .80785 .81057 .81327 .9 .81594 .81859 .82121 .82381 .82639 .82894 .83147 .83398 .83646 .83891 1.0 .84134 .84375 .84614 .84849 .85083 .85314 .85543 .85769 .85993 .86214 1.1 .86433 .86650 .86864 .87076 .87286 .87493 .87698 .87900 .88100 .88298 1.2 .88493 .88686 .88877 .89065 .89251 .89435 .89617 .89796 .89973 .90147 1.3 .90320 .90490 .90658 .90824 .90988 .91149 .91309 .91466 .91621 .91774 1.4 .91924 .92073 .92220 .92364 .92507 .92647 .92785 .92922 .93056 .93189 1.5 .93319 .93448 .93574 .93699 .93822 .93943 .94062 .94179 .94295 .94408 1.6 .94520 .94630 .94738 .94845 .94950 .95053 .95154 .95254 .95352 .95449 1.7 .95543 .95637 .95728 .95818 .95907 .95994 .96080 .96164 .96246 .96327 1.8 .96407 .96485 .96562 .96638 .96712 .96784 .96856 .96926 .96995 .97062 1.9 .97128 .97193 .97257 .97320 .97381 .97441 .97500 .97558 .97615 .97670 2.0 .97725 .97784 .97831 .97882 .97932 .97982 .98030 .98077 .98124 .98169 2.1 .98214 .98257 .98300 .98341 .98382 .98422 .98461 .98500 .98537 .98574 2.2 .98610 .98645 .98679 .98713 .98745 .98778 .98809 .98840 .98870 .98899 2.3 .98928 .98956 .98983 .99010 .99036 .99061 .99086 .99111 .99134 .99158 2.4 .99180 .99202 .99224 .99245 .99266 .99286 .99305 .99324 .99343 .99361 2.5 .99379 .99396 .99413 .99430 .99446 .99461 .99477 .99492 .99506 .99520 2.6 .99534 .99547 .99560 .99573 .99585 .99598 .99609 .99621 .99632 .99643 2.7 .99653 .99664 .99674 .99683 .99693 .99702 .99711 .99720 .99728 .99736 2.8 .99744 .99752 .99760 .99767 .99774 .99781 .99788 .99795 .99801 .99807 2.9 .99813 .99819 .99825 .99831 .99836 .99841 .99846 .99851 .99856 .99861 3.0 .99865 .99869 .99874 .99878 .99882 .99886 .99889 .99893 .99896 .99900 3.1 .99903 .99906 .99910 .99913 .99916 .99918 .99921 .99924 .99926 .99929 3.2 .99931 .99934 .99936 .99938 .99940 .99942 .99944 .99946 .99948 .99950 3.3 .99952 .99953 .99955 .99957 .99958 .99960 .99961 .99962 .99964 .99965 3.4 .99966 .99968 .99969 .99970 .99971 .99972 .99973 .99974 .99975 .99976 3.5 .99977 .99978 .99978 .99979 .99980 .99981 .99981 .99982 .99983 .99983 3.6 .99984 .99985 .99985 .99986 .99986 .99987 .99987 .99988 .99988 .99989 3.7 .99989 .99990 .99990 .99990 .99991 .99991 .99992 .99992 .99992 .99992 3.8 .99993 .99993 .99993 .99994 .99994 .99994 .99994 .99995 .99995 .99995 3.9 .99995 .99995 .99996 .99996 .99996 .99996 .99996 .99996 .99997 .99997
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A P P E N D I X I A3
0 1.55
Mean Z
Area shaded is .43943
1.55 Standard deviations
As an alternative to Table I.1 , the numbers in Table I.2 represent the proportion of the total area away from the mean, μ, to one side. For example, the area between the mean and a point that is 1.55 standard deviations to its right is .43943.
Table I.2 Z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09
0.0 .00000 .00399 .00798 .01197 .01595 .01994 .02392 .02790 .03188 .03586 0.1 .03983 .04380 .04776 .05172 .05567 .05962 .06356 .06749 .07142 .07535 0.2 .07926 .08317 .08706 .09095 .09483 .09871 .10257 .10642 .11026 .11409 0.3 .11791 .12172 .12552 .12930 .13307 .13683 .14058 .14431 .14803 .15173 0.4 .15542 .15910 .16276 .16640 .17003 .17364 .17724 .18082 .18439 .18793 0.5 .19146 .19497 .19847 .20194 .20540 .20884 .21226 .21566 .21904 .22240 0.6 .22575 .22907 .23237 .23565 .23891 .24215 .24537 .24857 .25175 .25490 0.7 .25804 .26115 .26424 .26730 .27035 .27337 .27637 .27935 .28230 .28524 0.8 .28814 .29103 .29389 .29673 .29955 .30234 .30511 .30785 .31057 .31327 0.9 .31594 .31859 .32121 .32381 .32639 .32894 .33147 .33398 .33646 .33891 1.0 .34134 .34375 .34614 .34850 .35083 .35314 .35543 .35769 .35993 .36214 1.1 .36433 .36650 .36864 .37076 .37286 .37493 .37698 .37900 .38100 .38298 1.2 .38493 .38686 .38877 .39065 .39251 .39435 .39617 .39796 .39973 .40147 1.3 .40320 .40490 .40658 .40824 .40988 .41149 .41309 .41466 .41621 .41174 1.4 .41924 .42073 .42220 .42364 .42507 .42647 .42786 .42922 .43056 .43189 1.5 .43319 .43448 .43574 .43699 .43822 .43943 .44062 .44179 .44295 .44408 1.6 .44520 .44630 .44738 .44845 .44950 .45053 .45154 .45254 .45352 .45449 1.7 .45543 .45637 .45728 .45818 .45907 .45994 .46080 .46164 .46246 .46327 1.8 .46407 .46485 .46562 .46638 .46712 .46784 .46856 .46926 .46995 .47062 1.9 .47128 .47193 .47257 .47320 .47381 .47441 .47500 .47558 .47615 .47670 2.0 .47725 .47778 .47831 .47882 .47932 .47982 .48030 .48077 .48124 .48169 2.1 .48214 .48257 .48300 .48341 .48382 .48422 .48461 .48500 .48537 .48574 2.2 .48610 .48645 .48679 .48713 .48745 .48778 .48809 .48840 .48870 .48899 2.3 .48928 .48956 .48983 .49010 .49036 .49061 .49086 .49111 .49134 .49158 2.4 .49180 .49202 .49224 .49245 .49266 .49286 .49305 .49324 .49343 .49361 2.5 .49379 .49396 .49413 .49430 .49446 .49461 .49477 .49492 .49506 .49520 2.6 .49534 .49547 .49560 .49573 .49585 .49598 .49609 .49621 .49632 .49643 2.7 .49653 .49664 .49674 .49683 .49693 .49702 .49711 .49720 .49728 .49736 2.8 .49744 .49752 .49760 .49767 .49774 .49781 .49788 .49795 .49801 .49807 2.9 .49813 .49819 .49825 .49831 .49836 .49841 .49846 .49851 .49856 .49861 3.0 .49865 .49869 .49874 .49878 .49882 .49886 .49889 .49893 .49897 .49900 3.1 .49903 .49906 .49910 .49913 .49916 .49918 .49921 .49924 .49926 .49929
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A4 A P P E N D I X I I
APPENDIX II V A L U E S O F e −l F O R U S E I N T H E P O I S S O N D I S T R I B U T I O N
Values of e −l
l e −l l e −l l e −l l e −l
.0 1.0000 1.6 .2019 3.1 .0450 4.6 .0101
.1 .9048 1.7 .1827 3.2 .0408 4.7 .0091
.2 .8187 1.8 .1653 3.3 .0369 4.8 .0082
.3 .7408 1.9 .1496 3.4 .0334 4.9 .0074
.4 .6703 2.0 .1353 3.5 .0302 5.0 .0067
.5 .6065 2.1 .1225 3.6 .0273 5.1 .0061
.6 .5488 2.2 .1108 3.7 .0247 5.2 .0055
.7 .4966 2.3 .1003 3.8 .0224 5.3 .0050
.8 .4493 2.4 .0907 3.9 .0202 5.4 .0045
.9 .4066 2.5 .0821 4.0 .0183 5.5 .0041
1.0 .3679 2.6 .0743 4.1 .0166 5.6 .0037
1.1 .3329 2.7 .0672 4.2 .0150 5.7 .0033
1.2 .3012 2.8 .0608 4.3 .0136 5.8 .0030
1.3 .2725 2.9 .0550 4.4 .0123 5.9 .0027
1.4 .2466 3.0 .0498 4.5 .0111 6.0 .0025
1.5 .2231
APPENDIX III T A B L E O F R A N D O M N U M B E R S
52 06 50 88 53 30 10 47 99 37 66 91 35 32 00 84 57 07
37 63 28 02 74 35 24 03 29 60 74 85 90 73 59 55 17 60
82 57 68 28 05 94 03 11 27 79 90 87 92 41 09 25 36 77
69 02 36 49 71 99 32 10 75 21 95 90 94 38 97 71 72 49
98 94 90 36 06 78 23 67 89 85 29 21 25 73 69 34 85 76
96 52 62 87 49 56 59 23 78 71 72 90 57 01 98 57 31 95
33 69 27 21 11 60 95 89 68 48 17 89 34 09 93 50 44 51
50 33 50 95 13 44 34 62 64 39 55 29 30 64 49 44 30 16
88 32 18 50 62 57 34 56 62 31 15 40 90 34 51 95 26 14
90 30 36 24 69 82 51 74 30 35 36 85 01 55 92 64 09 85
50 48 61 18 85 23 08 54 17 12 80 69 24 84 92 16 49 59
27 88 21 62 69 64 48 31 12 73 02 68 00 16 16 46 13 85
45 14 46 32 13 49 66 62 74 41 86 98 92 98 84 54 33 40
81 02 01 78 82 74 97 37 45 31 94 99 42 49 27 64 89 42
66 83 14 74 27 76 03 33 11 97 59 81 72 00 64 61 13 52
74 05 81 82 93 09 96 33 52 78 13 06 28 30 94 23 37 39
30 34 87 01 74 11 46 82 59 94 25 34 32 23 17 01 58 73
59 55 72 33 62 13 74 68 22 44 42 09 32 46 71 79 45 89
67 09 80 98 99 25 77 50 03 32 36 63 65 75 94 19 95 88
60 77 46 63 71 69 44 22 03 85 14 48 69 13 30 50 33 24
60 08 19 29 36 72 30 27 50 64 85 72 75 29 87 05 75 01
80 45 86 99 02 34 87 08 86 84 49 76 24 08 01 86 29 11
53 84 49 63 26 65 72 84 85 63 26 02 75 26 92 62 40 67
69 84 12 94 51 36 17 02 15 29 16 52 56 43 26 22 08 62
37 77 13 10 02 18 31 19 32 85 31 94 81 43 31 58 33 51
Source: Excerpted from A Million Random Digits with 100,000 Normal Deviates , The Free Press (1955): 7, with permission of the
RAND Corporation.
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A P P E N D I X I V A5
APPENDIX IV U S I N G E X C E L O M A N D P O M F O R W I N D O W S
Two approaches to computer-aided decision making are provided with this text: Excel OM and POM (Production and Operations Management) for Windows . These are the two most user-friendly soft- ware packages available to help you learn and understand operations management. Both programs
can be used either to solve homework problems identified with an icon or to check answers you have
developed by hand. Both software packages use the standard Windows interface and run on any IBM-
compatible PC operating Windows XP or newer. Excel OM is also available for the Mac.
EXCEL OM Excel OM has been designed to help you to better learn and understand both OM and Excel. Even though the software contains 24 modules and more than 60 submodules, the screens for every module
are consistent and easy to use. The Chapter menu (Excel 2007 and later for PCs) or the Heizer menu
(Excel 2011 for Macs) lists the modules in chapter order, as illustrated for Excel 2007 and later for PCs in Program IV.1(a). The Alphabetical menu (Excel 2007 and later for PCs) or Excel OM menu (Excel
2011 for Macs) lists the modules in alphabetical order, as illustrated for Excel OM for Macs in Program
IV.1(b). The menu for Excel 2016 for Macs is slightly different and not displayed here. This software
is provided at no cost to users of MyOMLab or purchasers of a new copy of this textbook. The software
can also be purchased at www.pearsonhighered.com/heizer. Excel 2007 or newer must be on your PC
or Excel 2011 or greater must be on your Mac.
To install Excel OM, click on the Download Center on the left side of your MyOMLab course,
and follow the instructions. Default values have been assigned in the setup program, but you may
change them if you like. For Windows, the default folder into which a program will be installed is
▼ PROGRAM IV.1(a) Excel OM Modules Menu in Excel OM Tab in Excel 2007 and later for PCs
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A6 A P P E N D I X I V
named C:\Program Files(x86)\ExcelomQMv4. Generally speaking, it is simply necessary to click Next each time the installation asks a question. For Macs, simply unzip the zip file that is provided.
Starting the Program To start Excel OM using Windows, double-click on the Excel OM V4 shortcut
placed on the desktop during installation. The Excel OM menu will appear in an Excel OM tab that will
be added to the Excel ribbon, as displayed in Program IV.1(a). To start Excel OM on the Mac, open
the file excelomqmv4.xla. Be certain that you open the file from the folder that has the excelomqmv4
.lic file. On the Mac under Excel 2011, the Excel OM menu will appear in the main menu of Excel, as
displayed in Program IV.1(b).
If you have Excel 2007 or later and do not see an Excel OM tab on the Ribbon, then your Excel
security settings need to be revised to enable Excel OM V4. Please consult the Excel instructions at the
support site, www.pearsonhighered.com/weiss, or see the document Excel.Security.pdf that has been
installed in the default directory.
Excel OM serves two purposes in the learning process. First, it helps you solve problems. You enter
the appropriate data, and the program provides numerical solutions. POM for Windows operates on the
same principle. However, Excel OM allows for a second approach: the Excel formulas used to develop solutions can be modified to deal with a wider variety of problems. This “open” approach enables you
to observe, understand, and even change the formulas underlying the Excel calculations—conveying
Excel’s power as an OM analysis tool.
POM FOR WINDOWS POM for Windows is decision support software that is also offered free to students who use MyOMLab or who purchased this as a new text. It can also be purchased at our Web site www.pearsonhighered
.com/heizer. Program IV.2 shows a list of the 25 OM modules that can be accessed from the menu tree
on the left. The modules can also be accessed from the MODULE menu where they are in alphabeti-
cal order or the HEIZER menu where they are in chapter order. Once you follow the standard setup
instructions, a POM for Windows program icon will be added to your desktop, and a program group
will be added to your Start, All Programs menu. The program may be accessed by double-clicking on
the desktop icon.
▲ PROGRAM IV.1(b) Excel OM Modules Menu in OM for Macs 2011
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A P P E N D I X I V A7
▲ PROGRAM IV.2 POM for Windows Module List
USING EXCEL OM AND POM FOR WINDOWS WITH MYOMLAB Both Excel OM and POM for Windows have MyOMLab toolbars that make it very easy to copy from
or paste to MyOMLab and very easy to set the specific number of decimals for which MyOMLab asks
for any problem. To copy data from MyOMLab: after creating a model in Excel OM or POM, go
to MyOMLab and click on the Copy icon. Then, in POM or in Excel OM, click on the Paste from MyOMLab icon on the toolbar or in the right-click menu.
Updates to POM for Windows and Excel OM are available on the Internet through the Downloads
and Support links at www.pearsonhighered.com/weiss .
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A8 A P P E N D I X V
APPENDIX V S O L U T I O N S T O E V E N - N U M B E R E D P R O B L E M S
Chapter 1 1.2 (a) 2 valves/hr. (b) 2.25 valves/hr. (c) 12.5% 1.4 (a) 20 pkg/hour (b) 26.6 pkg/hour (c) 33.0% 1.6 .078 fewer resources (7.8% improvement) 1.8 (a) 2.5 tires/hour (b) 0.025 tires/dollar (c) 2.56% increase 1.10 .000375 autos per dollar of inputs 1.12 4 workers 1.14 1.6% 1.16 $57.00 per labor hour
Chapter 2 2.2 Venezuela, China, United States, Switzerland, Denmark 2.4 Differentiation is evident when comparing most restaurants or
restaurant chains.
2.6 (a) Focus more on standardization, make fewer product changes, find optimum capacity, and stabilize manufacturing process
are a few possibilities
(b) New human resource skills, added capital investment for new equipment/processes
(c) Same as (b) 2.8 (a) Canada, 1.7 (b) No change 2.10 (a) Worldwide, 81.5 weighted average, 815 weighted total (b) No change (c) Overnight Shipping now preferred, weighted total 5 880 2.12 Company C, 1.0 … w … 25.0
Chapter 3 3.2 Here are some detailed activities for the first two activities for
Day’s WBS:
1.1.1 Set initial goals for fundraising. 1.1.2 Set strategy, including identifying sources and solicitation. 1.1.3 Raise the funds. 1.2.1 Identify voters’ concerns. 1.2.2 Analyze competitor’s voting record. 1.2.3 Establish position on issues. 3.4 (a) AON network:
B Purchasing
D Sawing
E Placement
F Assembly
H Outfill
I Decoration
G Infill
A Planning
20
60 30
20 10
20
30
10 C
Excavation
100
(b) AOA network:
A Plan
B Purchase
D Saw
E Place
F Assemble
G Infill
Dummy
Dummy
1 2 3 5
4
6 7 9
8
10 Outfill
I Decorate
CExcavate
H
3.6
A 5
B 2
5
D 5
E
G 2
FC 4
H
I 5
35
A–C–F–G–I is critical path; 21 days.
This is an AOA network.
3.8 (a)
1
5
Start
C
6
F
2
A
3
E
B
10
D
8
G
(b) B–D–E–G (c) 26 days (d)
Activity Slack
3
1
1
A 1
B 0
C 1
D 0
E 0
F 1
G 0
3.10 (a)
A
A
9
B
7
D
6
H
5
E
9
C
3
F
4
G
6
I
3
(b) A–B–E–G–I is critical path. (c) 34 3.12
Start
End
A C
D
B E
F H
G
3.14 (a) List all courses you must take. (b) List all immediate predecessors. (c) Develop a network. Note the difficulty of scheduling electives.
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A P P E N D I X V A9
3.16 (a)
EB
G
D F
Q
K
I N O R S T U
P
H L M
J
C
A Start
(b) Critical path is A–B–J–K–L–M–R–S–T–U for 18 days. (c) i. No, transmissions and drivetrains are not on the critical
path.
ii. No, halving engine-building time will reduce the critical path by only 1 day.
iii. No, it is not on the critical path. (d) Reallocating workers not involved with critical-path activities
to activities along the critical path will reduce the critical path
length.
3.18 A: 15, 1.33 B: 32, 2.33
C: 18, 0
D: 13.17, 1.83
E: 18.17, 0.5
F: 19, 1.0
3.20 (a) A: 10, 0.11 B: 10, 4
C: 10, 0.11
D: 8, 1
(b) A–C: 20 weeks B–D: 18 weeks
(c) .22, 5 (d) 1.0 (e) 0.963 3.22 (a) 32 weeks, C-H-M-O (b) 50% (c) No impact 3.24 (a) A 5 5, B 5 6, C 5 7, D 5 6, E 5 3, Time 5 15 (b) 1, 1, 1, 4, 0; Project variance 5 2 3.26 (b) Expected times are: 10.0, 7.2, 3.2, 20.0, 7.0, 10.0, 7.3, 15.0,
11.2, 7.0, 6.7, 2.2
(c) Slack times are: 0, 22.8, 19.8, 0, 19.8, 0, 17.7, 0, 10.8, 0, 0, 11.5
(d) A–D–F–H–J–K; 68.7 days (e) 0.644 (f ) 0.99934 (g) 0.99999 3.28 Crash C to 3 weeks at $200 total for one week. Now both paths are
critical. Not worth it to crash further.
3.30 (a) Reduce A, cheapest path @ $600 per time period (b) Reduce B, best choice now @ $900 (c) Total cost 5 $1,500 3.32 (a) Slacks are: 0, 2, 11, 0, 2, 11, 0 (b) First, crash D by 2 weeks. Then crash D and E by 2 weeks
each.
(c) Minimum completion time 5 7. Crash cost 5 $1,550.
Chapter 4 4.2 (a) None obvious. (b) 7, 7.67, 9, 10, 11, 11, 11.33, 11, 9
(c) 6.4, 7.8, 11, 9.6, 10.9, 12.2, 10.5, 10.6, 8.4 (d) The 3-yr. moving average. 4.4 (a) 41.6 (b) 42.3 (c) Banking industry’s seasonality. 4.6 (b) Naive = 23; 3-mo. moving = 21.33; 6-mo. weighted = 20.6;
exponential smoothing = 20.62; trend = 20.67 (c) Trend projection 4.8 (a) 91.3 (b) 89 (c) MAD = 2.7 (d) MSE = 13.35 (e) MAPE = 2.99% 4.10 (a) 4.67, 5.00, 6.33, 7.67, 8.33, 8.00, 9.33, 11.67, 13.7 (b) 4.50, 5.00, 7.25, 7.75, 8.00, 8.25, 10.00, 12.25, 14.0 (c) Forecasts are about the same. 4.12 72 4.14 Method 1: MAD = .125; MSE = .021 Method 2: MAD = .1275; MSE = .018 4.16 (a) y = 421 + 33.6x. When x = 6, y = 622.8. (b) MAD = 5.6 (c) MSE = 32.88 4.18 alpha = .25; forecast = 49 4.20 a = .1, b = .8, August forecast = $71,303; MSE = 12.7 for
b = .8 vs. MSE = 18.87 for b = .2 in Problem 4.19. 4.22 Confirm that you match the numbers in Table 4.1. 4.24 y = 5 + 20x, y = 105 4.26 1,680 sailboats 4.28 96.344, 132.946, 169.806, 85.204 4.30 y = 29.76 + 3.28x Year 11 5 65.8, Year 12 5 69.1 (patients)
r2 5 0.853 4.32 Trend adjustment does not appear to give any significant improve-
ment based on MAD.
4.34 (a) Trend analysis: y = - 18.968 + 1.638 * year r 5 .846, MAD 5 10.587 4.36 7.86 4.38 (a) 13.67, MAD 5 2.20 (b) 13.17, MAD 5 2.72 4.40 150,000; 126,000; 120,000; 198,000 4.42 (a) 7,000 (b) 9,000 4.44 (a) 337 (b) 380 (c) 423 4.46 (a) y = 50 + 18x (b) $410 4.48 (a) 83,502 (d) 0.397 4.50 (a) y = - .158 + .1308x (b) 2.719 (c) r = .966; r2 = .934 4.52 (b) y = 0.511 + 0.159x (c) 2,101,000 riders (d) 511,000 people (e) .404 (rounded to .407 in POM software) (f) r2 5 0.840 4.54 (a) Sales (y) = - 9.349 + .1121 (contracts) (b) r = .8963; Sxy = 1.3408 4.56 (a) y = 1 + 1x (b) 0.45 (c) 3.65 4.58 Games lost 5 6.41 1 0.533 3 rainy days 4.60 MAD 5 10.875; Tracking signal 5 3.586
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Chapter 5 5.2 Possible strategies: Tablet (growth phase):
Increase capacity and improve balance of production system.
Attempt to make production facilities more efficient.
Smart watch (introductory phase): Increase R&D to better define required product characteristics.
Modify and improve production process.
Develop supplier and distribution systems.
Hand calculator (decline phase): Concentrate on production and distribution cost reduction.
A10 A P P E N D I X V
5.4 Shown below is a house of quality for a sports watch in the under $50 market.
5.6 Build a house of quality similar to the one shown in Example 1 in the text.
Consider customer requirements such as:
Effective Luring, Reliability, Kills Quickly, Finger Safe, etc.
Consider Manufacturing issues such as:
Luring Radius, Dead Mouse Ratio, Time to Kill, Cost, etc.
5.8 House of Quality Sequence for Ice Cream
Prepare a control process that will ensure that the
desired ingredients are indeed mixed at the correct ratio and temperature.
DESIGN MATRIX
OPERATING MATRIX
CONTROL MATRIX
Design characteristics
Design characteristics
Components/ features
Components/ features
Controls
Production process
Customer requirements
Production process
Design a process that will allocate and blend the
ratio of ingredients at the right
temperature.
Define the features that will fulfill the
desired characteristics such as percent of
butterfat and grams of sugar.
5.10 An assembly chart for the eyeglasses is shown below:
SA 4
RL101 Right Lens Frame Assembly
SFA101 A4
Left Temple
Assembly LTA101 A2
Right Temple
Assembly RTA101 A3
Case Assembly
CBL101 A1
2
LL101 Left Lens 3
SF101 Frame 1
SA 2
LTH101 Temple Hinge 5
LT101 Temple 6
LTE101 Temple Ear Pad 4
SA 3
RTH101 Temple Hinge 8
RT101 Temple 9
RTE101 Temple Ear Pad 7
SA 1
BC101 Clip 11
BB101 Back 12
BF101 Front 10
Q1 Poka-Yoke Inspection
Relationship
L a rg
e L
C D
D is
p la
ys
C le
a r
in st
ru ct
io n s
W e ig
h t o f w
a tc
h
E rg
o n
o m
ic d
e si
g n o
f cl
a sp
A ve
ra g e li
fe t o f a ilu
re
L u m
e n s
o f lig
h tin
g
L itt
le m
e ta
l c o n te
n t
IR O
N M
A N
G -S
H O
C K
M O
S S
IM O
High = 5
Medium = 3
Low = 1
Easy to program 3
4
5
2
5
1
40 15 20 3 10
G G = Good
F = Fair
P = Poor
F
F
G
G
G
F
F
P
G
G
F
F
G
G
P
F
G
25 12
Easy to read
Reliable
Digital readouts
Easy to fasten
Our importance ratings
Lightweight
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5.12 Assembly chart for a table lamp:
B1
C1
D1SD1
B3 Body Case
B4 Body Cap
P2 Washer
P1 Nut
4
5
6
B1 Base
P3 Feet
B2 Center Pipe
2
3
7
P4 Socket
P5 Line Cord
P6 Plug
8
9
10
P7 Shade
Test: Inspection
Poka Yoke Inspection
11
B1
1
5.14 Bill of material for a wooden pencil with eraser:
Description Quantity
Pencil 1
Wood half 2
Graphite rod 1
Band 1
Eraser 1
Yellow paint 1 gram
Glue 1 gram
5.16 The major assemblies in the bill of material for a computer mouse (GeniMouse):
Bill of Material for GeniMouse
Part Number Description Quantity
GM1001 GeniMouse 1
SC004 Phillips Head No. 12 0.5 inch. Screw 1
TA101 Top Mouse Assembly 1
BA101 Base Assembly 1
GML101 GeniMouse Label 1
5.18 (a) For computer repair service, customer interaction is a strategic choice.
Supplier’s Process Domain
Factory repair or remanufacturer
Local computer repair service
Repair at business or home
Computer components sold based on discussion with customer
Parts purchased online by end user
Independent processing
Independent processing
Surrogate interaction
Surrogate interaction
Direct interaction
Direct interaction
Consumer
Consumer’s Process Domain
Supplier
(b) Parts (b) and (c) should be prepared in a style similar to part (a).
A P P E N D I X V A11
5.20 Moving to the left is likely to be most efficient in terms of resourc- es used (economies of scale), but there may be shipping cost and
shipping time. Also, customization may be complicated.
Moving to the right may be faster and lend itself to more
customization, but it may be less efficient. It may also provide less
competence (less training, specialized skills, and testing).
5.22 The company should complete the value analysis, for an expected payoff of $55,025,000.
5.24 (a) Best decision, buy the semiconductors. Expected Payoff (cost) 5 $1,500,000.
(b) Expected monetary value, minimum cost. (c) Worst case: Ritz fails at mfg. and must buy. Cost of
$3,500,000.
Best case: They make the semiconductor and spend only $1,000,000.
5.26 For design A: EMV (Design A) 5 (.9)($500,000) 1 (.1)($1,250,000) 5 $450,000 1
$125,000 5 $575,000.
For design B:
EMV (Design B) 5 (.8)($500,000) 1 (.2)($500,000) 5 $400,000 1
$100,000 5 $500,000.
The highest payoff is design option A at $575,000.
EMV = $575,000 (.9)
(.1)
$9,000,000 –7,500,000 –1,000,000 –––––––––
Sales 60,000 at $150 Mfg. cost 100,000 at $75 Design cost
$500,000
Mean Yield 60
Mean Yield 65
EMV = $500,000
(.8)
(.2)
Mean Yield 64
Mean Yield 64
$9,750,000 –7,500,000 –1,000,000 –––––––––
Sales 65,000 at $150 Mfg. cost 100,000 at $75 Design cost
$1,250,000
$9,600,000 –7,500,000 –1,350,000
Sales 64,000 at $150 Mfg. cost 100,000 at $75 Design cost New process cost –250,000
––––––––– $500,000
$9,600,000 –7,500,000 –1,350,000Design cost
New process cost –250,000 –––––––––
$500,000
Sales 64,000 at $150 Mfg. cost 100,000 at $75
Design A
Design B
5.28 The EMV is maximized when using existing material; $8,910,000
Chapter 5 Supplement S5.2 Brew Master revenue retrieval = $5.31 provides higher opportunity. S5.4 $66,809 S5.6 3.57 years S5.8 3.48 years S5.10 66,667 miles S5.12 (a) $4.53 (b) $3.23 (c) GF Deluxe S5.14 (a) $4.53, GF Deluxe (b) $5.06, Premium Mate (c) Premium Mate model S5.16 (a) 45,455 miles (b) gas vehicle S5.18 42,105 miles
Chapter 6 6.2 Individual answer, in the style of Figure 6.6(b). 6.4 Individual answer, in the style of Figure 6.6(f).
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6.6 Partial flowchart for planning a party:
2.0 Find
location
1.0 Determine party size
3.0 Invite
guests
6.8 See figure below for a partial fish-bone. Individual answer in the style of Figure 6.7 in the chapter.
6.10 Individual answer, in the style of Figure 6.7 in the chapter. 6.12 Pareto chart, in the style of Example 1 with parking/drives most
frequent, pool second, etc.
6.14 See figure below. Issues: Materials: 4, 12, 14; Methods: 3, 7, 15, 16; Manpower: 1, 5,
6, 11; Machines: 2, 8, 9, 10, 13.
6.16 (a) A scatter diagram in the style of Figure 6.6(b) that shows a strong positive relationship between shipments and defects
(b) A scatter diagram in the style of Figure 6.6(b) that shows a mild relationship between shipments and turnover
(c) A Pareto chart in the style of Figure 6.6(d) that shows fre- quency of each type of defect
(d) A fishbone chart in the style of Figure 6.6(c) with the 4 Ms showing possible causes of increasing defects in shipments
6.18 Stitching Seams alignment
Buttons and buttonholes
Collar alignment
Hem
6.20 Factor I Illegible handwriting Factor II Failure to proofread Factor III Failure to use spell checker (Factors II and III are interchangeable.)
▼ Figure for Problem 6.8.
Material
Methods
Partial Fish-Bone Chart for Dissatisfied Airline Customer
Dissatisfied Customer
Overpriced food at airport Not enough parking
Tickets too expensive Not enough handicap access
Food cold Poor food
Security lines are awful Lost luggage
Plane was late Dirty bathroom
Poor connections
Machinery
Manpower
▼ Figure for Problem 6.14.
Manpower
Materials
Incorrect Formulation
Operator misreads display
Incorrect measurement Technician calculation off
Inadequate cleanup
Machines
Methods
Equipment in disrepair Inadequate flow controls
Variability
Temperature controls off Antiquated scales
Partial Fish-Bone for Incorrect Formulation
Chapter 6 Supplement S6.2 (a) UCLx = 52.31
LCLx = 47.69 (b) UCLx = 51.54
LCLx = 48.46 S6.4 (a) UCLx = 440 calories
LCLx = 400 calories (b) UCLx = 435 calories
LCLx = 405 calories
A12 A P P E N D I X V
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S6.6 UCLx = 3.728 LCLx = 2.236 UCLR = 2.336 LCLR = 0.0
The process is in control.
S6.8 (a) UCLx = 16.08 LCLx = 15.92 (b) UCLx = 16.12 LCLx = 15.88 S6.10 (a) sx = 0.61 (b) Using sx, UCLx = 11.83, and LCLx = 8.17. Using A2, UCLx = 11.90, and LCLx = 8.10. (c) UCLR = 6.98; LCLR = 0 (d) Yes S6.12 UCLR = 6.058; LCLR = 0.442
Averages are increasing.
S6.14
UCL LCL
.062 0
.099 0
.132 0
.161 0
.190 .01
S6.16 UCLp = .0313; LCLp = 0 S6.18 (a) UCLp = 0.077; LCLp = 0.003 S6.20 (a) UCLp = .0581 LCLp = 0 (b) in control (c) UCLp = .1154 LCLp = 0 S6.22 (a) c-chart (b) UCLc = 13.35 LCLc = 0 (c) in control (d) not in control S6.24 (a) UCLc = 26.063 LCLc = 3.137 (b) No point out of control. S6.26 (a) UCLx = 61.131, LCLx = 38.421, UCLR = 41.62, LCLR = 0 (b) Yes, the process is in control for both x- and R-charts. (c) They support West’s claim. But variance from the mean needs
to be reduced and controlled.
S6.28 UCLx = 76.85 LCLx = 73.15 S6.30 UCLx = 60.924 LCLx = 59.076
UCLR = 5.331 LCLR = 0.669 S6.32 (a) UCLx = 20.15 LCLx = 19.65 (b) UCLR = 0.78 LCLR = 0 S6.34 At least 29 holes that meet tolerance, but no more than 88 holes
before being replaced.
S6.36 UCL p 5 0.0209 LCL
p 5 0.0011
S6.38 UCL p 5 0.154 LCL
p 5 0.031
Sample 16 exceeds the UCL. S6.40 Cp = 1.0. The process is barely capable. S6.42 Cpk = 1.125. Process is centered and will produce within tolerance. S6.44 Cpk = .1667 S6.46 Cpk = 0.33 S6.48 Machine 1 has index of Cpk = 0.83 (not capable) Machine 2 has index of Cpk = 1.0 (capable) S6.50 Cp = 1.667 (very capable) S6.52 AOQ 5 0.02 S6.54 AOQ 5 0.0117
Chapter 7 7.2 GPE is best below 100,000.
FMS is best between 100,000 and 300,000.
DM is best over 300,000.
7.4 Optimal process will change at 100,000 and 300,000. 7.6 (a)
3,000
2,000
1,000
0
4,000
$5,000
1,0000 2,000 4,0003,000 Units
A
B
C
2,333 units
750 units
(b) Plan c (c) Plan b 7.8 Rent HP software since projected volume of 80 is above the
crossover point of 75.
7.10 (a) 82,000 units (b) Loss of $10,000 (c) Profit of $1,000 7.12 (a) 7,750 units (b) Proposal A 7.14
Present Method
SUBJECT CHARTED
DIST. IN
FEET
TIME IN
MINS.
CHART SYMBOLS PROCESS DESCRIPTION
DEPARTMENT
Totals
Proposed MethodPROCESS CHART DATE
CHART BY
SHEET NO. ___ OF ___
Clean/Brush Shoes
Obtain Polish
Open and Apply Polish
Buff
Inspect
Collect Payment
0.67
0.05
0.5
0.75
0.05
0.25
Shoe Shine X
J.C. 1 1
1.
2.27 4 111.
7.16 Under control of customer
Area of service provider and customer interaction
Actions performed away from customer
Clean/buff shoes
Line of visibility
Open polish
Apply and buff
Visual inspection
Customer arrival
Customer pays
Supply storage
Selection and purchase
of supplies
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Chapter 7 Supplement S7.2 69.2% S7.4 88.9% S7.6 Design = 88,920
Fabrication = 160,680 Finishing = 65,520
S7.8 5.17 (or 6) bays S7.10 15 min/unit S7.12 (a) Throughput time = 40 min (b) Bottleneck time = 12 min. (c) Station 2 (d) Weekly capacity = 240 units S7.14 (a) Work station C at 20 min/unit (b) 3 units/hr S7.16 (a) 2,000 units (b) $1,500 S7.18 (a) $150,000 (b) $160,000 S7.20 (a) BEPA = 1,667; BEPB = 2,353 (b, c) Oven A slightly more profitable (d) 13,333 pizzas S7.22 (a) $18,750 (b) 375,000 S7.24 Yes, purchase new equipment and raise price. Profit = $2,500 S7.26 BEP+ = +7,584.83 per mo
Daily meals = 9 S7.28 (a) 6,250 (b) 7,000 S7.30 10,000 S7.32 Option B; $74,000 S7.34 $4,590 S7.36 NPV = $1,764 S7.38 (a) Purchase two large ovens. (b) Equal quality, equal capacity. (c) Payments are made at end of each time period, and future
interest rates are known.
S7.40 Investment A; payoff = $24,234 S7.42 (11 percent) should not purchase; NPV = –$7,678 (4 percent) should purchase; NPV = $5,379 S7.44 Machine B; NPV = $85,983
Chapter 8 8.2 China, $1.44 8.4 India is $.05 less than elsewhere. 8.6 (a) Mobile = 53; Jackson = 60; select Jackson. (b) Jackson now = 66. 8.8 (a) Hyde Park, with 54.5 points. (b) Present location = 51 points. 8.10 (a) Location C, with a total weighted score of 1,530. (b) Location B = 1,360 (c) B can never be in first place. 8.12 (a) Great Britain, at 4.6. (b) Great Britain is now 3.6. 8.14 (a) Italy is highest. (b) Spain always lowest. 8.16 (a) Site 1 up to 125, site 2 from 125 to 233, site 3 above 233 (b) Site 2 8.18 (a) Above 10,000 cars, site C is lowest cost (b) Site A optimal from 0 to 10,000 cars. (c) Site B is never optimal. 8.20 (a) (5.15, 7.31) (b) (5.13, 7.67) 8.22 (a) (6.23, 6.08) (b) safety, etc. 8.24 (a) Site C is best, with a score of 374 (b) For all positive values of w7 such that w7 … 14
8.26 Downtown rating 5 2.24 Shopping mall rating 5 3.24 (best)
Coral Gables rating 5 2.42
When grade A 5 4, B 5 3, C 5 2, D 5 1
8.28 Site 1 5 78.125 Site 2 5 75.0
Site 3 5 86.56 (highest)
Site 4 5 80.94
8.30 (a) Atlanta TC 5 125,000 1 6x Burlington TC 5 75,000 1 5x Cleveland TC 5 100,000 1 4x Denver TC 5 50,000 1 12x (b) Denver best from 0 to 3,571 units (c) At 5,000 units, Burlington best 8.32 (7.97, 6.69) 8.34 England, 3.55
Chapter 9 9.2 (a) $23,400 (b) $20,600 (c) $22,000 (d) Plan B 9.4 Benders to area 1; Materials to 2; Welders to 3; Drills to 4;
Grinder to 5; and Lathes to 6; Trips * Distance = 13,000 ft. 9.6 Layout #1, distance = 600 with areas fixed
Layout #2, distance = 602 with areas fixed 9.8 Layout #4, distance = 609
Layout #5, distance = 478 9.10
Entrance (1)
Exam II (3)
Lab, EKG (5)
O.R. (6)
Exam I (2)
X-ray (4)
Casts (8)
R.R. (7)
Patient movement = 4,500 feet
9.12 (a) 20 seconds (b) 3 (c) Yes; Station 1 with A, C; Station 2 with B, D;
Station 3 with E
9.14 (a) 4 stations (b) Cannot be done with theoretical minimum; requires 5 stations (c) 80% for 5 stations 9.16 (b) Station 1–A, G, B, with .5 min. left Station 2–C, D, E with no time left
Station 3–F, H, I, J with .5 min left
(c) If stations 1 and 3 each had 0.5 min. work more to do, the line would be 100% efficient
(d) 3 9.18 (a) Station 1–A, C Station 2–E
Station 3–B, D
Station 4–F, H
Station 5–G, I
(b) 97.6% with cycle time 5 3.33 (theoretical efficiency) 87.6% is operating efficiency
(c) 4 (d) 2 min./boat 9.20 (b) 15 min. (c) 144 units/day (d) 5 stations (e) 83.33% (f) 10 min./cycle
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9.22 (b) 3.75 patients/hour (c) Medical exam station, 16 min. (d) Paramedics are idle 2 minutes, doctors 10 minutes for each
patient
(e) 5 patients/hour 9.24 (a) Algorithm No. Workstations Efficiency Fewest following tasks 13 84.56%
Longest operating time 12 84.61
Most following tasks 12 84.61
Ranked positional weight 12 84.61
Shortest operating time 13 78.10
(b) Algorithm No. Workstations Efficiency Fewest following tasks 13 77.56%
Longest operating time 12 84.02
Most following tasks 12 84.02
Ranked positional weight 11 91.66
Shortest operating time 12 84.02
9.26 (a)
A B
D F
G H
C E
(b) Multiple alternatives are possible (c) 92.5%
Chapter 10 10.2 The first 8 steps of the process chart are:
Process Chart Summary Operation
Transport
Inspect
Delay
Store
Vert. Dist.
Hor. Dist.
Time (min)
26
4
2
24
24.4
Charted by
Date: Sheet of
Problem:
Distance (ft)
Time (mins)
Chart Symbols
Process Description
Park auto
Set parking brake
Set gear shift to park
Turn off engine
Exit vehicle
Move to trunk of auto
Open trunk
Remove jack and spare tire
1.0
0.1
0.1
0.1
0.2
0.1
0.3
1.0
8
10.4 Individually prepared solution in the style of Figure 10.6 10.6 The first 8 steps of the process chart are:
Process Chart Summary Operation
Transport
Inspect
Delay
Store
Vert. Dist.
Hor. Dist.
Time (min)
14
4
1
68
24.7
Charted by
Date: Sheet of
Problem:
Tum computer off
Disconnect all cables
Move computer to table top
Remove screws from cover
Remove cover
Set cover on floor
Find board to be replaced
Bring box with new board to table
0.2
2.0
1.0
1.5
1.0
0.1
0.5
0.2
adding a memory board to
your computer
1 1
Distance (ft)
Time (mins)
Chart Symbols
Process Description
30
3
5
10.8
Charted by
Distance (feet)
Time (seconds)
Chart Symbols
Date
Process Chart Summary
Operation
Process Description
H. Molano
15
10
5 2.5 1.2 2.0 1.8 1.0 2.0 2.0 Move to right side of car
Move back over wall from left side
Raise car Wait for tire exchange to finish Move to left side of car Raise car Wait for tire exchange to finish
Problem Pit crew jack man Sheet of1 1
Transport
Inspect
Delay
Store
Vert. Dist.
Hor. Dist.
Time (seconds)
2 3
2
12.5
10.10 The first portion of the activity chart is shown below.
OPERATOR #1 OPERATOR #2
TIME % TIME %
WORK
IDLE
OPERATIONS:
EQUIPMENT:
OPERATOR:
STUDY NO.: ANALYST:
SUBJECT PRESENT PROPOSED DEPT.
SHEET OF
CHART BY
DATE
TIME TIME TIME
ACTIVITY CHART
11.75 84 11.75 84
Wash and Dry Dishes
Fill sink w/dishes
Fill sink w/soap/ water
Wash dishes (2 min.)
Dry dishes (3 min.)
Fill sink w/dishes (1 min.)
Operator #1 Operator #2
Sink, Drip Rack, Towels, Soap
HSM2.25 16 2.25 16
Rinse (1 min.)
Idle
Idle
Idle
1
1 1 HankHOUSECLEANING
10.12 The first portion of the process chart is shown below.
Proposed Method
SUBJECT CHARTED
DIST. IN
FEET
TIME IN
MINS.
CHART SYMBOLS PROCESS DESCRIPTION
DEPARTMENT Clerical
Present Method PROCESS CHART
DATE
CHART BY.
CHART NO.
SHEET NO. ___ OF ___
Click on Print Command
Move to Printer
Wait for Printer
Read Error Message
Move to Supply Room
Locate Correct Paper
0.25
0.25
0.50
0.10
0.50100
0.25
Printing and Copying Document X
HSM
1 1 1
50
10.14 NT = 7.65 sec; slower than normal 10.16 (a) 6.525 sec (b) 6.2 sec (c) 6.739 sec 10.18 (a) 12.6 min (b) 15 min 10.20 (a) 12.0 sec (b) 14.12 sec 10.22 10.12 min 10.24 (a) 3.24 min (b) 4.208 min 10.26 n = 14.06, or 15 observations 10.28 (a) 45.36, 13.75, 3.6, 15.09 (b) 91.53 min (c) 97 samples 10.30 (a) 47.6 min (b) 75 samples
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10.32 n = 348 10.34 73.8% 10.36 6.55 sec 10.38 (a) 240 min (b) 150 hr (c) Clean 8 rooms; refresh 16 rooms; 38 housekeepers (d) 50 employees
Chapter 11 11.2 (a) 25% decrease in material costs; $45,000 (b) 75% increase in sales; $175,000 11.4 Problems include communication, product valuation, selecting vir-
tual partners
11.6 (a) Weeks of supply = 3.85 (b) % of assets in inventory = 11.63% (c) Turnover = 13.5 (d) No, but note they are in different industries 11.8 (a) Last year = 10.4 (b) This year = 9.67 (c) Yes
Chapter 11 Supplement S11.2 Two suppliers best, $42,970 S11.4 (a) P(2) = 0.017463 (b) P(2) = 0.018866 (c) Option 1 (2 local suppliers) has lower risk. S11.6 (a) 2.5 (b) 1.2 (c) 1.25 (d) 1.8 (e) Retailer S11.8 (a) 1.20 (b) Bullwhip = 0 if order sizes all the same. S11.10 Donna Inc., 8.2; Kay Corp., 9.8 S11.12 Individual responses. Issues might include academics, location,
financial support, size, facilities, etc.
S11.14 (a) Alternative (1) (slower shipping) (b) Customer satisfaction and interest earned on earlier payments S11.16 (a) Alternative (2) (faster shipping) (b) Potential delay in the production process S11.18 (a) Item B (b) Item A S11.20
Dock
B B B B C E D D G
B B B B F
Aisle
E D D A
Chapter 12 12.2 (a) A items are G2 and F3; B items are A2, C7, and D1; all
others are C.
12.4 108 items 12.6 (D23) A (D27) C
(R02) C
(R19) C
(S123) C
(U11) $5,600; B
(U23) $1,500; C
(V75) $6,000
12.8 (a) 600 units (b) 424.26 units (c) 848.53 units 12.10 (a) 80 units (b) 73 units
12.12 (a) 2,100 units (b) 4,200 units (c) 1,050 units 12.14 (a) 189.74 units (b) 94.87 (c) 31.62 (d) 7.91 (e) $1,897.30 (f) $601,897 12.16 (a) Order quantity variations have limited impact on total cost. (b) EOQ = 50 12.18 (a) 671 units (b) 18.63 (c) 559 = max. inventory (d) 16.7% (e) $1,117.90 12.20 (a) 1,217 units (b) 1,095 = max. inventory (c) 8.22 production runs (e) $657.30 12.22 (a) EOQ = 200, total cost = $1,446,380 (b) EOQ = 200, total cost = $1,445,880 12.24 (a) 16,971 units (b) $530.33 (c) $530.33 (d) $56,250 (e) $57,310.66 12.26 (a) EOQ = 410 (b) Vendor Allen has slightly lower cost. (c) Optimal order quantity = 1,000 @ total cost of $128,920 12.28 (a) EOQ (1) = 336; EOQ (2) = 335 (b) Order 1,200 from Vendor 2. (c) At 1,200 lb., total cost = $161,275. (d) Storage space and perishability. 12.30 (a) 32 (b) 2 (c) 20; $2,400 (d) 336; 168 (e) $10,800 (f) 214; $1,820 12.32 $8 12.34 7,000 12.36 (a) $1,220 (b) $1,200 (c) 24 12.38 (without discount) $2,200 (with discount) $2,510
No, do not take the discount.
12.40 Order more than 50 sheets; cost 5 $1,901.22 12.42 (a) Z = 1.88 (b) Safety stock = Zs = 1.88(5) = 9.4 drives (c) ROP = 59.4 drives 12.44 100 kilos of safety stock 12.46 (a) 291 towels (b) 2,291 towels 12.48 (a) ROP = 1,718 cigars (b) 1,868 cigars (c) A higher service level means a lower probability of stocking
out.
12.50 55 12.52 28 cakes
Chapter 13 13.2 (a) $109,120 = total cost (b) $106,640 5 total cost (c) No, plan 2 is better at $105,152. 13.4 Cost 5 $244,000 for plan B
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13.6 (a) Plan D, $128,000 (b) Plan E is $140,000 13.8 Extra total cost 5 $2,960. 13.10 (a) Plan C, $104,000; (b) plan D, $93,800, assuming initial
inventory 5 0
13.12 (a) Plan A: Cost is $314,000. Plan B: Cost is $329,000. Plan C: Cost is $222,000 (lowest cost). (b) Plan C, with lowest cost and steady employment. 13.14 $1,186,810 13.16 $100,750 13.18 $90,850 13.20 $20,400 13.22 $308,125 13.24 (b) Cost using O.T. and Forrester 5 $195,625. (c) A case could be made for either position. 13.26 Current model 5 $9,200 in sales; proposed model yields $9,350,
which is only slightly better.
Chapter 14 14.2 The time-phased plan for the gift bags is:
P.M.A.M.
10 11 12 1 2 3 4 5
L K
J
M
Someone should start on item M by noon.
14.4 (a) Time-phased product structure for bracket with start times:
1 2 3 4 5
Week Activity is Scheduled to Start
6 7 8 9 101
Housing 1.032(2)
Bearing 1.033(2)
Base 1.011(1)
Shaft 1.043(1) Bracket
1.000
Casting 1.023(1)
Handle 1.013(1)
Spring 1.021(2)
Handle 1.013(4)
Casting 1.023(1)
Clamp 1.022(4)
Clamp 1.022(1)
(b) Castings need to start in week 4.
14.6 Gross material requirements plan:
Week Lead Time
Item 1 2 3 4 5 6 7 8 (wk)
S Gross req. 100
Order release 100 2
T Gross req. 100
Order release 100 1
U Gross req. 200
Order release 200 2
V Gross req. 100
Order release 100 2
W Gross req. 200
Order release 200 3
X Gross req. 100
Order release 100 1
Y Gross req. 400
Order release 400 2
Z Gross req. 600
Order release 600 1
14.8 Gross material requirements plan, modified to include the 20 units of U required for maintenance purposes:
Week Lead Time
Item 1 2 3 4 5 6 7 8 (wk)
S Gross req. 100
Order release 100 2
T Gross req. 100
Order release 100 1
U Gross req. 200 20
Order release 200 20 2
V Gross req. 100
Order release 100 2
W Gross req. 200
Order release 200 3
X Gross req. 100
Order release 100 1
Y Gross req. 400 40
Order release 400 40 2
Z Gross req. 600 60
Order release 600 60 1
14.10 (a) Gross material requirements plan for the first three items:
Week
Item 1 2 3 4 5 6 7 8 9 10 11 12
X1 Gross req. 50 20 100
Order release 50 20 100
B1 Gross req. 50 20 100
Order release 50 20 100
B2 Gross req. 100 40 200
Order release 100 40 200
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(b) The net materials requirement plan for the first two items:
Level: 0 Parent: Quantity: Item: X1 Lead Time: Lot Size: L4L
Week No. 1 2 3 4 5 6 7 8 9 10 11 12
Gross Requirement 50 20 100
Scheduled Receipt
On-hand Inventory 50 0 0
Net Requirement 0 20 100
Planned Order Receipt 20 100
Planned Order Release 20 100
Level: 1 Parent: X1 Quantity: 1X Item: B1 Lead Time: 2 Lot Size: L4L
Week No. 1 2 3 4 5 6 7 8 9 10 11 12
Gross Requirement 20 100
Scheduled Receipt
On-hand Inventory 20 0
Net Requirement 0 100
Planned Order Receipt 100
Planned Order Release 100
14.12 (a) Net material requirements schedule (only items A and H are shown):
Week
1 2 3 4 5 6 7 8 9 10 11 12
A Gross Required 100 50 150
On Hand 0 0 0
Net Required 100 50 150
Order Receipt 100 50 150
Order Release 100 50 150
H Gross Required 100 50
On Hand 0 0
Net Required 100 50
Order Receipt 100 50
Order Release 100 50
(b) Net material requirements schedule (only items B and C are shown; schedule for items A and H remains the same as in part a).
Week
1 2 3 4 5 6 7 8 9
B Gross Requirements 200
100
100
100 0
100 100
100 100
300
0
300
10 11 12 13
300
100100 300
Scheduled Receipts
Projected on Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
C Gross Requirements 300100100200
00
200
50
300100100200150
300100100200150
300100100200150
Scheduled Receipts
Projected on Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
50 0 0
14.14 (a)
Level Description Qty
0 A 1
1 B 1
2 C 1
2 D 1
3 1
1 F 1
2 G 1
2 H 1
3 1
3 1
E
E
C
Note: with low-level coding “C” would be a level-3 code
(b) Solution for Items A, B, F (on next page):
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14.14 (b) Net material requirements schedule (only items A, B, and F are shown).
14.16 (a) Only item G changes. (b) Component F and 4 units of A will be delayed one week. (c) Options include: delaying 4 units of A for 1 week; asking
supplier of G to expedite production.
14.18 (a)
D(3)
Alpha
F(2)
(b)
5 6
A D
F
4321 Week
(c)
Week 2 3 4 5 6
Required Date 10 A
Order Release 10
Required Date 30 D
Order Release 30
Required Date 20 F
Order Release 20
Low- Lot Lead On Safety Allo- Level Item Period (week) Size Time Hand Stock cated Code ID 1 2 3 4 5 6 7 8
Lot Gross Requirement 10
10
2 2 2 2 2 2 2 0
8
8
8
10
5 5 5 5 5 5 5 0
5
5
5
for Scheduled Receipt
Lot Projected on Hand 0
Net Requirement 10
Planned Receipt 10
Planned Release 10
Lot Gross Requirement
for Scheduled Receipt
Lot Projected on Hand
Net Requirement
Planned Receipt
Planned Release
Lot Gross Requirement
for Scheduled Receipt
Lot Projected on Hand
Net Requirement
Planned Receipt
Planned Release
1 0 — —
— —
— —
1 2
1 5
0 A
1 B
1 F
14.20 (a)
A
D
Treadmill
C(2)
D(2) E(3)
B(3)
F(2) E F(3)
(b) D
A
B Treadmill
F E
D
E
1 2 3 4 5 6 7 8
C
F
(c) Treadmill: release P.O. for 30 in week 6 release P.O. for 20 of part A in week 5
release P.O. for 60 of part B in week 4
release P.O. for 160 of part B in week 2 and 40 in week 3
14.22 Lot-for-lot: Total cost 5 7 orders 3 $150/order 1 20 units 3 $2.50/unit/period 5 $1,100.
14.24 POQ: Setup cost 5 5 3 $150 5 $750; Holding cost 5 2.50 3 $170 5 $425; Total $1,175
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14.26 (a) Lot-for-lot: Setup cost 5 $800; Holding cost 5 $0.0; Total cost 5 $800. (b) EOQ: EOQ 5 18, Setup cost 5 $800; Holding cost 5 $370; Total cost 5 $1,170. (c) POQ: EOQ 5 18, POQ 5 2, Setup cost 5 $800; Holding cost 5 $0.0; Total cost 5 $800. (d) Lot-for-lot and POQ have the same cost. 14.28 (a) EOQ 5 105; orders released now and in weeks 3 and 7; Actual cost 5 $307.50 (b) Lot-for-lot: orders released now and in weeks 1, 2, 4, 5, 6, 8, 9; Total cost 5 $400.
14.30 Selection for first 5 weeks:
Capacity Capacity Required Available Over/
Week Units (time) (time) (Under) Production Scheduler’s Action
3,900 2,250 1,650 Lot split. Move 300 minutes (4.3 units) to week 2 and 1,350 minutes to week 3.
2 1,950 2,250 (300)
3 650 2,250 (1,600)
4 2,600 2,250 350 Lot split. Move 250 minutes to week 3. Operations split. Move 100 minutes to
another machine, overtime, or subcontract.
5 4,550 2,250 2,300 Lot split. Move 1,600 minutes to week 6. Overlap operations to get product out door.
Operations split. Move 700 minutes to another machine, overtime, or subcontract.
1 60
30
10
40
70
14.32
Coffee Table Master Schedule Hrs Required Lead Time Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8
640 640 128 128
Table Assembly 2 1 1,280 1,280 256 256
Top Preparation 2 1 1,280 1,280 256 256
Assemble Base 1 1 640 640 128 128
Long Brace (2) 0.25 1 320 320 64 64
Short Brace (2) 0.25 1 320 320 64 64
Leg (4) 0.25 1 640 640 128 128
Total Hours 0 1,280 3,200 3,456 1,920 640 256
Employees needed @ 8 hrs. each 0 160 400 432 240 80 32
Chapter 15 15.2
Now
Job
D
E
F
G
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Day 8
Day 9
15.4 (a) 1–D, 2–A, 3–C, 4–B (b) 40 15.6 Chris–Finance, Steve–Marketing, Juana–H.R., Rebecca Operations,
$210
15.8 Ajay–Jackie, Jack–Barbara, Gray–Stella, Raul–Dona, 230 15.10
Period 1 2 3 4
Input deviation +5 +5 –15 –15
Output deviation –5 –5 –10 –10
Backlog 30 30 35 40
15.12 Divorce to Attorney 3 Felony to Attorney 1
Discrimination to Attorney 2
Total cost 5 $2,700
15.14 G99 to 1; E2 to 2; C81 to 3; D5 to 4; D44 to 5; C53 to 6;
E35 to 8; no component to 7.
Total cost 5 $1.18
15.16 Sequence 517, 103, 309, 205, 412 15.18 (b) SPT for best flow time (c) SPT for best utilization (d) EDD for best lateness (e) LPT scores poorly on all three criteria 15.20 Johnson’s Rule finishes in 21 days First-come, First-served finishes in 23 days
15.22 (a) V–Y–U–Z–X–W–T (c) 57 hours (d) 7 hours (e) unchanged 15.24 (a) 010, 020, 030, 040, 050 (b) 020, 010, 030, 040, 050 (030 could come before 010) (c) 030, 050, 020, 040, 010 (d) 010, 040, 020, 050, 030 Average no. jobs in system: FCFS 5 3.4; EDD 5 3.3; SPT 5 2.4;
LPT 5 3.6
15.26 Worker 1, M–F; Worker 2; M–F; Worker 3, M, Th, F, S, Su; Worker 4, M-F;
Worker 5, M, T, F, S, Su; Worker 6, W, Th, F, S, Su;
Worker 7, M, T, W, S (only 4 days)
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Chapter 16 16.2 Setup time 5 3.675 minutes 16.4 Number of kanbans needed 5 5 16.6 Production order quantity 5 50; 5 Kanbans 16.8 (a) EOQ 5 100 lamps (b) TC 5 $1,200 (c) 20 orders/year 16.10 EOQ size decreases; orders increase; inventory costs can be
expected to drop.
16.12 Kanban size 5 158; number of kanbans needed 5 10
Chapter 17 17.2 From Figure 17.2, about 13% overall reliability. 17.4 (a) 5.0% (b) .00001026 failures/unit-hr. (c) .08985 (d) 98.83 17.6 R
s 5 .9941
17.8 R p 5 .99925
17.10 R p 5 .984
17.12 R 5 .7918 17.14 System B is slightly higher, at .9397. 17.16 From Figure 17.2, about 82% 17.18 2.7 breakdowns 17.20 The figure suggests that there are likely to be at least three separate
modes of failure; one or more causes of infant mortality, and two
modes of failure which occur at later times.
17.22 1.5 breakdowns; $15 17.24 Current cost 5 $1,255/week; Contract cost 5 $1,395; therefore,
eliminate maintenance contract.
Business Analytics Module A A.2 (a)
Size of Good Fair Poor EV Under First Market Market Market Equally Station ($) ($) ($) Likely
Small 50,000 20,000 –10,000 20,000
Medium 80,000 30,000 –20,000 30,000
Large 100,000 30,000 – 40,000 30,000
Very large 300,000 25,000 –160,000 55,000
(b) Maximax: Build a very large station. (c) Maximin: Build a small station. (d) Equally likely: Build a very large station. (e)
Small $20,000
Medium $30,000
Good $50,000
$20,000
–$10,000
$80,000
$30,000
–$20,000
$100,000
$30,000
–$40,000
$300,000
$25,000
–$160,000
Fair
Poor
Good
Fair
Poor
Good
Fair
Poor
Good
Fair
Poor
Large $30,000
Very large $55,000
A.4 (a) Alternatives: N, M, L, D. States of nature: Fixed, Slight Increase, Major Increase
(b) Use maximin criterion. No floor space (N). A.6 Buying equipment at $733,333
A.8 (a) E(cost full-time) 5 $520 E(cost part-timers) 5 $475 A.10 Alternative B; 74 A.12 8 cases; EMV 5 $352.50 A.14 (b) EMV(Y) 5 4.2, which is best A.16 (a) EMV(Alt. 1) 5 110 5 max. EMV (b) EVPI 5 7 A.18 (a) Stock 11 cases; EMV 5 $385.00 (b) Stock 13 cases; EMV 5 $411.25 A.20 Alternative A, with an EMV 5 $1,400,000 A.22 (a)
Build large
Build small
Do not build
Favorable
Unfavorable
Favorable
Unfavorable
Favorable
Unfavorable $0
$0
–$10
$80
–$300
$400
K
K
K
K
(b) Small plant with EMV 5 $26,000 (c) EVPI 5 $134,000 A.24 (a) Resort has higher EMV than home (b) EMV (Resort) 5 $76 A.26 (a) Build large facility (b) $544,000 A.28 Maximum expected profit 5 $33,000 Michael should wait 1 day. Then if an XP02 is available, he should
buy it. Otherwise, he should stop pursuing an XP02 on the whole-
sale market.
A.30
Paul drops out –$39,000
T.J. folds (.8)
(.45) Paul winsPaul raises
T.J. calls (.2)
(.55) Paul loses
$79,200
$38,790
–$46,420
–$7,630
$71,570
A.32 (a) Minor renovation; EMV 5 $10,000
Business Analytics Module B B.2 X 5 1.33, Y 5 3.33; profit 5 $25.33 B.4 (b) Yes; P 5 $3,000 at (75, 75) and (50, 150) B.6 x
1 5 200, x
2 5 0, profit 5 $18,000
B.8 10 Alpha 1s, 24 Beta 2s, profit 5 $55,200 B.10 (a) Let X 5 wren houses, Y 5 bluebird houses Maximize profit 5 6X 1 15Y Subject to:
4X 1 2Y # 60 4X 1 12Y # 120 X, Y $ 0 (b) X 5 12, Y 5 6; profit 5 $162 B.12 (a) Standard 5 240, Deluxe 5 180 (b) $3,840 B.14 One approach results in 2,790 medical patients and 2,104 surgical
patients, with a revenue of $9,551,659 per year (which can change
slightly to $9,548,760 with rounding). This yields 61 integer medi-
cal beds and 29 integer surgical beds.
B.16 X 5 4, Y 5 8; profit 5 $52
A P P E N D I X V A21
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B.18 X 1 5 18.75, X
2 5 18.75; profit 5 $150
B.20 X 1 5 2, X
2 5 6; profit 5 $36
B.22 (a) x 1 5 7.95, x
2 5 5.95, x
3 5 12.59, P 5 $143.76
(b) No unused time (c) 26¢ (d) $7.86 B.24 (a) A158 (b) Nonbinding; increasing; reducing (c) 0 (d) should; $119.32 (e) Objective function; 17.91; 49.99; 24.00; 8.88; 77.01 (f) Constraints 7 through 11 become $0, and Constraint 12 is
still $20.
B.26 X 5 3.86, Y 5 4.54; cost 5 $160.86 B.28 Let X 5 number of pounds of compost in each bag Let Y 5 number of pounds of sewage waste in each bag X 5 30, Y 5 30; cost 5 $2.70 B.30 x
1 5 14, x
2 5 33, cost 5 221
B.32 Objective function: Minimize Z (in thousands) 10X 1 1 8X
2
Constraints: 0X 1 1 1X
2 $ 5
2X 1 1 1X
2 $ 20
Solution: 7.5 thousand round tables, 5 thousand square tables;
cost 5 $115 (in thousands)
B.34 (a) Let X ij 5 number of students bused from sector i to school j.
Objective: minimize total travel miles 5
5X AB
1 8X AC
1 6X AE
1 0X BB
1 4X BC
1 12X BE
1 4x CB
1 0X CC
1 7X CE
1 7X DB
1 2X DC
1 5x DE
1 12X EB
1 7X EC
1 0X EE
Subject to:
X AB
1 X AC
1 X AE
5 700 (number of students in sector A) X
BB 1 X
BC 1 X
BE 5 500 (number students in sector B)
X CB
1 X CC
1 X CE
5 100 (number students in sector C) X
DB 1 X
DC 1 X
DE 5 800 (number students in sector D)
X EB
1 X EC
1 X EE
5 400 (number of students in sector E) X
AB 1 X
BB 1 X
CB 1 X
DB 1 X
EB # 900 (school B capacity)
X AC
1 X BC
1 X CC
1 X DC
1 X EC
# 900 (school C capacity) X
AE 1 X
BE 1 X
CE 1 X
DE 1 X
EE # 900 (school E capacity)
(b) Solution: X AB
5 400
X AE
5 300
X BB
5 500
X CC
5 100
X DC
5 800
X EE
5 400
Distance 5 5,400 “student miles”
B.36 Hire 30 workers; three solutions are feasible; two of these are: 16 begin at 7 a.m.
9 begin at 3 p.m.
2 begin at 7 p.m.
3 begin at 11 p.m.
An alternate optimum is:
3 begin at 3 a.m.
9 begin at 7 a.m.
7 begin at 11 a.m.
2 begin at 3 p.m.
9 begin at 7 p.m.
0 begin at 11 p.m.
B.38 (a) Minimize 5 6X 1A
1 5X 1B
1 3X 1C
1 8X 2A
1 10X 2B
1 8X 2C
1
11X 3A
1 14X 3B
1 18X 3C
Subject to:
X 1A
1 X 2A
1 X 3A
5 7
X 1B
1 X 2B
1 X 3B
5 12
X 1C
1 X 2C
1 X 3C
5 5
X 1A
1 X 1B
1 X 1C
# 6
X 2A
1 X 2B
1 X 2C
# 8
X 3A
1 X 3B
1 X 3C
# 10
(b) Minimum cost 5 $219,000
B.40 Apple sauce 5 0, Canned corn 5 1.33, Fried chicken 5 0.46, French fries 5 0, Mac & Cheese 5 1.13, Turkey 5 0, Garden salad 5
0, Cost 5 $1.51.
B.42 Include all but investment A.; total expected return 5 $3,580
Business Analytics Module C C.2 $208 C.4 $170 C.6 $2,020 C.8 Total cost 5 $505 C.10 Optimal site is St. Louis, at $17,250 C.12 D–A, 100; E–B, 200; F–A, 200; E–C, 100; F–C, 100. $3,900 total
cost.
C.14 $14,700, Houston-Dallas, 800; Houston-Atlanta, 50; Phoenix-Atlanta, 250; Phoenix-Denver, 200; Phoenix-Dummy, 200; Memphis-Atlanta,
300.
C.16 A–1, 20; B–1, 20; B–2, 10; C–2, 50; C–3, 25; Dummy–3, 30 C.18 Dublin, $1,535,000 Fountainbleau, $1,530,000 (lowest cost)
Business Analytics Module D D.2 (a) 44% (b) .36 people (c) .8 people (d) .53 min (e) 1.2 min D.4 (a) .5 (b) .5 (c) 1 (d) .5 (e) .05 hr (f) .1 hr D.6 (a) .667 (b) .667 min (c) 1.33 D.8 (a) .375 (b) 1.6 hr (or .2 days) (c) .225 (d) 0.141, 0.053, 0.020, 0.007 D.10 (a) 2.25 (b) .75 (c) .857 min. (.014 hr) (d) .64 min. (.011 hr) (e) 42%, 32%, 24% D.12 (a) 6 trucks (b) 12 min (c) .857 (d) .54 (e) $1,728/day (f) Yes, save $3,096 in the first year. D.14 (a) .075 hrs (4.5 min) (b) 1.125 people (c) .0083 hrs (0.5 min), 0.083 people D.16 (a) .113 hr. 5 6.8 min (b) 1.13 cars D.18 (a) 0.577 (b) 1.24 (c) 33.6% D.20 (a) 3, 2, 4 MDs, respectively (b) Because l 7 m, an indefinite queue buildup can occur. D.22 (a) 4 servers (b) 6 servers (c) $109 (d) 83.33%
A22 A P P E N D I X V
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D.24 2 salespeople ($340) D.26 5 min. D.28 72 loans D.30 1.2 callers D.32 (a) 0.833 (b) 1.667 hours (c) 4.167 (d) 0.335 D.34 (a) 1.33 cars (b) 0.0667 hours (c) 0.10 hours (d) 0.667 (e) 0.33 (f) L
q 5 0.083, W
q 5 0.0042 hrs., W
s 5 0.0209 hrs., r 5 0.333,
P 0 5 0.667
D.36 (a) 0.0377 (b) 4.53 persons (c) 22.65 min. (d) 7.62 min. (or 0.127 hrs.) (e) 1.53 customers (f) 0.9533 D.38 (a) 4.167 cars (b) 0.4167 hrs. (c) 0.5 hrs. (d) 0.8333 (e) 0.1667
Business Analytics Module E E.2 (a) 507 min (b) 456 min (c) 410 min (d) 369 min E.4 (a) 1,546 min (b) 2,872 min (c) 3,701 min (d) 6,779 min E.6 (a) 14.31 hr (b) $71,550 (c) $947,250 E.8 (a) 80% (b) 3.51 (c) 3.2, 2.98, 2.81 (d) 21.5 E.10 (a) 72.2 hr (b) 60.55 hr (c) 41.47 hr E.12 Susan will take 3.67 hr and Julie 2.43 hr. Neither trainee will reach
1 hr by the 10th unit.
E.14 $748,240 for fourth, $709,960 for fifth, $679,960 for sixth E.16 (a) 70 millicents/bit (b) 8.2 millicents/bit E.18 26,755 hr E.20 (a) 32.98 hr, 49.61 hr (b) Initial quote is high. E.22 (a) Four boats can be completed. (b) Five boats can be completed. E.24 .227 hr E.26 (a) 476.3 (b) 429 (c) 410.4 (d) 369.3 E.28 1,106.4 days E.30 58,320 hours E.32 .877 or 88%
Business Analytics Module F F.2 0, 0, 0, 0, 0, 0, 0, 2, 0, 2 F.4 Profits 5 20, 215, 20, 17.50, 20; average equals $12.50. F.6 At the end of 5 min, two checkouts are still busy and one is
available.
F.8 Aver. no. of failures 5 2.88 units/month 7 units failed over each 3-month stretch
F.10 (a) 24 (b) $36.70 (c) $0.30 (d) $6.10 (e) 21 F.12 (a) 5 times (b) 6.95 times; yes (c) 7.16 heaters F.14 Average 5 7 F.16
Arrivals Arrival Time Service Time Departure Time
1 11:01 3 11:04
2 11:04 2 11:06
3 11:06 2 11:08
4 11:07 1 11:09
F.18 During 4 hours, 7 customers balked. Taboo missed 105 minutes 5 $21.00/day or
$504/month. Additional bed not justified.
F.20 Average weekly stockout cost 5 $20; Weekly holding cost 5 $2.30 F.22 First order, total demand during lead time 5 31. No stockout.
Second order, total demand during lead time 5 42.
One stockout.
F.24 Balance never drops below $400, she should be able to balance her account.
Online Tutorial 1 T1.2 5.45; 4.06 T1.4 (a) .2743 (b) 0.5 T1.6 .1587; .2347; .1587 T1.8 (a) .0548 (b) .6554 (c) .6554 (d) .2119
Online Tutorial 2 T2.2 (selected values)
Fraction Defective Mean of Poisson P(x 1)
.01 .05 .999
.05 .25 .974
.10 .50 .910
.30 1.5 558
.60 3.0
0 .
0 .199
1.00 5.00 .040
T2.4 The plan meets neither the producer’s nor the consumer’s requirement.
A P P E N D I X V A23
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Online Tutorial 3 T3.2 (a) Max 3x
1 1 9x
2
x 1 1 4x
2 1 s
1 5 24
x 1 1 2x
2 1 s
2 5 16
(b) See the steps in the tutorial. (c) Second tableau:
cj Mix x1 x2 s1 s2 Qty.
9 x 2
.25 1 .25 0 6
0 s 2
.50 0 .50 1 4
zj 2.25 9 2.25 0 54 cj zj .75 0 2.25 0
(d) x 1 5 8, x
2 5 4, Profit 5 $60
T3.4 Basis for 1st tableau: A
1 5 80
A 2 5 75
Basis for 2nd tableau:
A 1 5 55
X 1 5 25
Basis for 3rd tableau:
X 1 5 14
X 2 5 33
Cost 5 $221 at optimal solution
T3.6 (a) x 1
(b) A 1
Online Tutorial 4 T4.2 Cost 5 $980; 1–A 5 20; 1–B 5 50; 2–C 5 20; 2–Dummy 5 30;
3–A 5 20; 3–C 5 40
T4.4 Total 5 3,100 mi; Morgantown–Coaltown 5 35; Youngstown– Coal Valley 5 30; Youngstown–Coaltown 5 5; Youngstown–Coal
Junction 5 25; Pittsburgh–Coaltown 5 5; Pittsburgh–Coalsburg 5 20
T4.6 (a) Using VAM, cost 5 $635; A–Y 5 35; A–Z 5 20; B–W 5 10; B–X 5 20; B–Y 5 15; C–W 5 30
(b) Using MODI, cost is also $635 (i.e., initial solution was opti- mal). An alternative optimal solution is A–X 5 20; A–Y 5 15;
A–Z 5 20; B–W 5 10; B–Y 5 35; C–W 5 30
Online Tutorial 5 T5.2 (a) I
13 5 12
(b) I 35
5 7
(c) I 51
5 4
T5.4 (a) Tour: 1–2–4–5–7–6–8–3–1; 37.9 mi (b) Tour: 4–5–7–6–8–1–2–3–4; 30.2 mi T5.6 (a) Vehicle 1: Tour 1–2–4–3–5–1 5 $134 Vehicle 2: Tour 1–6–10–9–8–7–1 5 $188 T5.8 The cost matrix is shown below:
1 2
1 — 107.26 118.11 113.20 116.50 123.50 111.88 111.88
2 — 113.53 111.88 118.10 125.30 116.50 118.10
3 — 110.56 118.70 120.50 119.90 124.90
4 — 109.90 119.10 111.88 117.90
111.88 106.60 118.50
111.88 123.50
113.20
3 4 5 6 7 8
—5
—6
—7
—8
A24 A P P E N D I X V
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B1
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Diehl Gregory W., and A. J. Armstrong “Making MRP Work.” Industrial Engineer 43, no. 11 (November 2011): 35–40.
Kanet, J., and V. Sridharan. “The Value of Using Scheduling Information in Planning Material Requirements.” Decision Sciences 29, no. 2 (Spring 1998): 479–498.
Krupp, James A. G. “Integrating Kanban and MRP to Reduce Lead Time.” Production and Inventory Management Journal 43, no. 3–4 (3rd/4th quarter 2002): 78–82.
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Module A Balakrishnan, R., B. Render, and R. M. Stair Jr. Managerial Decision
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Keefer, Donald L. “Balancing Drug Safety and Efficacy for a Go/No-Go Decision.” Interfaces 34, no. 2 (March–April 2004): 113–116.
Miller, C. C., and R. D. Ireland. “Intuition in Strategic Decision Making.” Academy of Management Executive 19, no. 1 (February 2005): 19.
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Module B Anderson, Ross, et al. “Kidney Exchange and the Alliance for Paired
Donation: Operations Research Changes the Way Kidneys Are Transplanted.” Interfaces 45, no. 1 (January–February 2015), 26–42.
Balakrishman, R., B. Render, and R. M. Stair. Managerial Decision Modeling with Spreadsheets , 3rd ed. Upper Saddle River, NJ: Prentice Hall, 2012.
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Harrod, Steven. “A Spreadsheet-Based, Matrix Formulation Linear Programming Lesson.” Decision Sciences Journal of Innovative Education 7, no. 1 (January 2009): 249.
Martin, C. H. “Ohio University’s College of Business Uses Integer Programming to Schedule Classes.” Interfaces 34 (November–December 2004): 460–465.
Matthews, C. H. “Using Linear Programming to Minimize the Cost of Nurse Personnel.” Journal of Health Care Finance (September 2005).
Pasupathy, K., and A. Medina-Borja. “Integrating Excel, Access, and Visual Basic to Deploy Performance Measurement and Evaluation at the American Red Cross.” Interfaces 38, no. 4 (July–August 2008): 324–340.
Render, B., R. M. Stair, and Michael Hanna. Quantitative Analysis for Management , 11th ed. Upper Saddle River, NJ: Prentice Hall, 2012.
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Prabhu, N. U. Foundations of Queuing Theory. Dordecht, Netherlands: Kluwer Academic Publishers, 1997.
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Yan Kovic, N., and L. V. Green. “Identifying Good Nursing Levels: A Queuing Approach.” Operations Research 59, no. 4 (July/August 2011): 942–955.
Module E Boh,W. F., S. A. Slaughter, and J. A. Espinosa. “Learning from Experience
in Software Development.” Management Science 53, no. 8 (August 2007): 1315–1332.
Couto, J. P., and J. C. Teixeira. “Using a Linear Model for Learning Curve Effect on Highrise Floor Construction.” Construction Management & Economics 23 (May 2005): 355.
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Module F Balakrishnan, R., B. Render, and R. M. Stair. Managerial Decision
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Banks, Jerry, and Randall R. Gibson. “The ABC’s of Simulation Practice.” Analytics (Spring 2009): 16–23.
Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. Discrete-Event System Simulation , 5th ed. Upper Saddle River, NJ: Prentice Hall, 2010.
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Rossetti, Manuel D. Simulation Modeling and ARENA . New York: Wiley, 2009.
Saltzman, Robert M., and Vijay Mehrotra. “A Call Center Uses Simulation to Drive Strategic Change.” Interfaces 31, no. 3 (May–June 2001): 87–101.
Sud, V. P., et al. “Reducing Flight Delays Through Better Traffic Management.” Interfaces 39, no. 1 (January/February 2009): 35–51.
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Zhang, Aheng, and Xiaolan Xie. “Simulation-based Optimization for Surgery Appointment Scheduling of Multiple Operating Rooms.” IIE Transactions 47, no. 9 (September 2015): 998–1012.
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I1
Name Index
A Abernathy, Frederick H., B5 Adenso-Diaz, B., B5 Aft, Larry, B4 Aghazadeh, S. M., B3 Ait-Kadi, D., B6 Alexander, Larry, 753 Alexy, Oliver, B2 Ambec, Stefan, B2 Anand, Gopesh, B2 Anderson, Edward James, B3 Anderson, Ross, B6 Andrews, D. M., B4 Anthony, S. D., B2 Anupindi, Ravi, B3 Armstrong, A. J., B5 Arnold, J. R., B5 Ashkenas, R. N., B1 Autry, Chad W., B4
B Babai, M. Z., B5 Babbage. Charles, 412 Bakar, N. A., B4 Baker, Kenneth A., B6 Bakir, S. T., B2 Balakrishnan, R., 713 n, B1 , B6 , B7 Baldridge, Malcolm, 218 Ballou, Ronald H., B3 Bamford, James, B2 Banerjee, S. B., B2 Banks, Jerry, B7 Barba-Gutierrez, Y., B5 Barnes, R. M., B4 Baron, Opher, B5 Bartness, A. D., B3 Bauer, Eric, B6 Beatty, Richard W., B4 Becker, Brian E., B4 Beckman, S. L., B1 Bell, Glenn, 18 Bell, Steve, B5 Benton, W. C., B5 Berenson, Mark, B1 Berger, Paul D., B4 Berry, Leonard L., 233 n Berry, W. L., B3 , B5 Besterfi eld, Dale H., B2 Bilginer, Özlem, B5 Birchfi eld, J., B3 Birchfi eld, J. C., B3 Black, I. T., B6 Blackburn, Joseph, B4 Blackstone, John H., B3 Blank, Ronald, B6 Blecker, Thorsten, B4
Bockstette, V., 194 n Boh,W. F., B7 Bolander, Steven, B5 Bowers, John, B3 Boyd, L. H., B3 Boyer, Kenneth K., B4 Bradley, Milton, 493 Bravard, J., B1 Bridger, R. S., B4 Broedner, P., B1 Brown, Bruce, B2 Brown, Mark G., B2 Buboltz, W.C., 413 n Buchannan, Leigh, B6 Buckley, Peter J., B2 Burch, Bill, 744 Burt, D. N., B5 Burton, Jeff , 409 Busch, Kurt, 409 Buxey, G., B5
C Caiola, Gene, B5 Camevalli, J. A., B2 Campbell, Omar, B1 Canonaco, P., B7 Carson, J. S., B7 Cavanagh, R. R., B2 Cayirli, Tugba, B6 Chambers, Chester, B3 Chapman, S. N., B5 Chapman, Stephen, B5 , B6 Chen, Bintong, B4 Chen, Fangruo., B5 Chen, Ming, B5 Chen, Wen-Chih, B3 Chen, Zhi-Long, B5 Chien, C., B3 Chopra, S., B3 Chopra, Sunil, B4 , B5 Chu, K. F., B7 Chua, R. C. H., B2 Cleland, D. L., B1 Clive, L. M., B5 Cloutier, T. J., 678 Cochran, J. K., B7 Collins, Bob, 404 Combs, James G., B4 Coogan, Denise, 205 Cook, Lori, 772 Coupe, Henry, 402 Couto, J. P., B7 Crandall, Richard E., B4 , B5 Criscuelo, P., B2 Crook, T. Russell, B4 Crosby, Philip B., 219 , 219 n, B2
Crotts, J. C., B1
D Dahlgaard, J. J., B2 Datar, S. M., 242 n Davenport, T. H., B3 Davis, Leon, 630 Davis, Stanley B., B2 Dawande, M., B5 De Jong, A., B4 De Mathis J. J., B5 De Ruyter, K., B4 Debo, L. G., B3 DeFeo, J. A., B2 DeHoratius, N., 495 n Deimler, Mike, B2 Dellaert, N. P., B3 Dellande, S., B1 DeLong, George, 656 Deming, W. Edwards, 10 , 218 , 219 ,
220 , 246 n Denton, Brian T., B6 Derman, O., B5 Deshmukh, S., B3 Devenney, George, 805 Dibbern, J., B1 Dickson, D. R., B1 Diebold, F. X., B1 Diehl Gregory W., B5 Dietrich, Brenda, B6 Dillman, Bob, 771 Disney, Stephen M., B4 Doğru, Mustafa K., B5 Dolgui, A., B5 Donnelly, P., B6 Dorso, Chris, 560 Douglas, David, 805 Drezner, Z., B3 , B7 Duran, G., B6 Duray, R., B3
E Elg, M., B2 Eppinger, S., B2 Epstein, Marc J., B2 Erhun, Feryal, B5 Ernst, David, B2 Espinosa, J. A., B7 Evans, J. R., B2
F Farmer, Adam, B6 Fayurd, A.L., 372 n Feigenbaum, A. V., 219 Ferrari, Alex, 696 Fildes, Robert, B1 Finigen, Tim, B6
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I2 N A M E I N D E X
Fisher, Marshall L., 446 n Fitzsimmons, J., 351 n Fletcher, Allison, 656 Flinchbauh, Jamie, B6 Florida, R., B3 Flynn, B. B., B1 Flynn, E. J., B1 Ford, Henry, 10 Ford, R. C., B1 Fornell, C., 46 n Foster, G., 242 n Foster, Thomas, B2 Freivalds, Andris, B4
G Galt, J., B1 Gantt, Henry L., 9 , 607 Gardiner, Stanley C., B3 Gardner, Leslie, 239 Georgoff , D. M., B1 Gerstenfeld, Arthur, B4 Gerwin, Donald, B2 Gianipero, L. C., B4 Gibson, Randall R., B7 Gilbreth, Frank, 9 , 420 , 426 Gilbreth, Lillian, 9 , 420 Gilliland, M., B1 Gitlow, Howard S., B2 Goetsch, David L., B2 Goldratt, Eliyaha, 317 n, B3 Goldsby, Thomas J., B4 Gonul, M. S., B1 Gonzalez-Benito, J., B2 Gonzalez-Benito, O., B2 Goodale, John C., B3 Goodwin, Paul, B1 Gordon, Jeff , 408 – 409 Gordon, John Steel, B1 Graban, Mark, B6 Gray, John V., B2 Green, L. V., B7 Greenwald, Bruce, B1 Groebner, D., 134 n Gross, Donald, B7 Gross, E. E. Jr., 418 Gryna, F. M., B2 Guergachi, A., B7 Gupta, M. C., B3 Gupta, S. M., B5 Gwynne, Peter, B2
H Hackman, J. R., 413 , 413 n Hafeznezami, S., B3 Haines, Steven, B2 Hale, T., 713 n, 744 n Hall, Joseph M., B3 Hall, Robert W., B6 Hammond, J. S., B6 Handfi eld, R. B., B4 Handfi eld, Robert B., B4 Hanke, J. E., B1
Hanna, M., 713 n, 744 n, B1 , B3 , B5 , B6 , B7
Harmon, Larry, 189 Harris, Carl M., B7 Harris, Chris, B5 Harrod, Steven, B6 Haugh, Helen M., B2 Hegde, V. G., B3 Heinzl, A., B1 Helgadottir, Hilder, B1 Helms, A. S., 427 n Heragu, S. S., B4 Heyer, N., B4 Hirschheim, R., B1 Hoff man, Ken, 632 Holweg, M., B6 Hooper, Ana, 276 Horngren, C. T., 242 n Hounshell, D. A., B1 Hu, J., B4 Huang, H. C., B7 Huang, L., B1 Huang, T., B1 Hug, Z., B3 Hult, G., B4 Humphries, Jim, B6 Huselid, Mark A., B4 Hvolby, H., B5
I Iger, Robert, 106 Immonen, A., B2 Inderfurth, Karl, B3 Inoue, L., B6 Ireland, L. R., B1 Ireland, R. D., B6
J Jackson, Jonathan, B5 Jackson, Wilma, 744 Jacobs, F. R., B5 Jain, Chaman L., B1 Jarrett, Dale, 409 Jayaraman, V., B4 Jennings, C. L., B3 Johns, T., B1 Johnson, Alan, B4 Johnson, M. Eric, 285 n, B3 Johnson, Steven, B4 Jones, Daniel T., B6 Jonsson, Patrik, B3 Joshi, M. P., B1 Juran J. M., 219 , 219 n, 227
K Kahn, Judd, B1 Kanawaty, George, 422 n Kane, H., B6 Kanet, J., B5 Kaplan, Robert S., B1 Kara, S., B4 Karlos, A., B1 Kathuria, R., B1
Kayis, B., B4 Keating, B., B1 Keefer, Donald L., B6 Kellogg, Deborah L., B6 Kelton, W. D., B7 Kennedy, M., B3 Kenney, R. L., B6 Keren, Baruch, B5 Kersten, Wolfgang, B4 Kerzner, H., B1 Keyte, Beau, B6 Kimes, S., 351 n Kimes, S. E., B5 Kin, S., B5 Kinard, Jerry, 275 n King-Metters, K., B5 Kinkel, S., B1 Kirchmier, Bill, B6 Klamroth, K., B3 Klassen, K., B7 Klassen, R., B4 Knight, Jim, 438 Koberstein, A., B5 Kohnke, E. J., B4 Koksalan, M., B7 Konz, S., B4 Konz, Stephan, 422 n Kopezak, Laura Rock, 285 n Kouvelis, Panos, B7 Kramer, M. R., 194 n Krehbiel, Tim, B1 Kreipl, Stephan, B4 Krishnan, M.S., 46 n Krishnan, V., B2 Krupp, James A. G., B5 Kuo, C., 348 n
L Ladner, John, 655 Lambrecht, Marc R., B4 Lane, Randy, 630 Langella, I. M., B3 Lanoie, Paul, B2 Larson, E. W., B1 Larson, S., B4 Laszlo, Chris, B2 Laubacher, R J., B1 Lawrence, M., B1 Lay, G., B1 Lee, Chieh, B4 Lee, Chung-Yee, B1 Lee, Eva K., B3 Lee, H., B5 Lee, Hau L., B1 Lemmink, J., B4 Leno, Jay, 172 Leon, Steve, 194 Leonard, M., B1 Leone, Charlie, 631 – 632 Leong, G. K., B6 Leong, G. Keong, B4 Leppa, Carol J., B6
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N A M E I N D E X I3
Levine, David, B1 Lewis, Mike, B1 Lewis, William W., B1 Li, Shanling, B3 Lian, Z., B5 Liker, Jeff rey K., B6 Lin, H., B3 Lindner, C.A., 427 n Lindsay, W. M., B2 Lindsey, Todd, 155 Linton, J. D., B4 Liu, X., B5 Loch, C. H., B2 Locher, Drew, B6 Lopez, P., B6 Louly, Mohamed-Aly, B5
M MacLean, D., B2 Maier, Anja M., B3 Malone, T. W., B1 Malone, Tom, 199 Malykhina, E., 495 n Mantel, S., B1 Maroto, A., B1 Martin, C. H., B6 Martins, Alex, 208 – 209 Marucheck, Ann S., B4 Mathison, George, 771 – 772 Matta, N. F., B1 Matthes, N., B3 Matthews, C. H., B6 Mattsson, Stig-Arne, B3 May, Jerrold H., B3 Maylor, Harvey, B1 McAlister, V. C., B7 McCormack, Angela, 275 McDonald, A., B7 McDonald, Stan C., B5 McEwan, Dr. Angus, 224 McFadden, Kathleen, 437 McGinnis, L. F., B4 McLaren, T., B7 McLaughlin, Patrick, B1 Medina-Borja, A., B6 Mehrotra, Vijay, B7 Meindl, Peter, B4 Menge, J. B., B6 Mentzer, John T., B3 Meredith, J. R., B1 Metters, Richard, B5 Midler, Paul, B1 Miguel, P. A. C., B2 Miller, C. C., B6 Miller, Luke T., B6 Milligan, G. W., B3 Mincsovics, G. Z., B3 Minicucci, Ben, 240 – 241 , 655 Mitra, Amit, B2 , B3 Monczka, R. M., B4 Mondschein, S. V., B6 Montgomery, D. C., B3
Morgan, James M., B6 Morgan, R., B1 Morrice, D., B3 Morrison, J. Bradley, B7 Morton, D., B3 Morton, Thomas E., B6 Moultrie, James, B3 Mukhopadhyay, S., B5 Munday, Oliver, 363 – 364 Munson, Charles L., B4 , B5 Murat, A., B4 Murdick, R. G., B1 Muthusamy, S. K., B4
N Najjar, L., B3 Narayanan, Sriram, B4 Nawler, Pete, 771 Nelson, B. L., B7 Nelson-Peterson, Dana L, B6 Neuman, R. P., B2 Neumann, Patrick W., B4 Newton, Wayne, 241 , 303 Nicely, John, 595 – 596 , 604 Nicol, D. M., B7 Niebel, B. W., 422 n, B4 Nielsen, P., B5 Nielson, I., B5 Niemann, G., 426 n Norri, S., B7 Norris, G., B5 Norton, David P., B1
O O’ Sullivan, Jill, B5 O’Connell, Andrew, B6 Oates, David, B1 Oberwetter, R., 548 n Ohno, Taiichi, 636 , 638 , 649 Oldham, Greg R., 413 , 413 n Oliver, Alexy, B2 Olson, Paul R., 402 n Olsson, J., B2 Onkal, D., B1 Özen, Ulaş, B5 Ozer, O., B5
P Paine, Lincoln, B4 Paleologo, G. A., B6 Pande, P. S., B2 Parasuranam, A., 233 n Pareto, Vefredo, 227 , 491 Parker, Thomas, B5 Parks, Charles M., B6 Parlar, M., B7 Parmigiani, G., B6 Partovi, F. Y., B3 Pasupathy, K., B6 Patterson, J. L., B4 Pentico, David W., B6 Perez, Anthony, 154 , 560
Petcavage, S., B5 Peterson, A.P. G., 418 Peterson, R., B5 Peterson, Silva, 14 Petroski, Henry, B2 Pfeff er, Jeff rey, B4 Pfi tzer, M., 194 n Pfund, M. E., B3 Phillippi, Brian, B6 Phillips, D. T., B6 Phillips, Paul, 678 Phyper, J. D., B2 Pinedo, M., B6 Pinedo, Michael, B4 Ping, J., B7 Pinkerton, R., B5 Pisano, Gary P., B2 , B4 Plenert, Gerhard, B6 Poanski, Tom, 744 Porter, M. E., 194 n Porter, Michael, 40 , 40 n, B1 , B3 Prabhu, N. U., B7 Prahalad, C. K., B2 Pronovost, Dr. Peter, 232 Pullman, M., B5 Pullman, Madeleine E., B3 Pyke, D. F., B5
Q Qi, Y., B3 Quain, Bill, 725 – 726 Quain, Jeane, 725 – 726 Quillien, Jenny, B4
R Radnor, Zoe J., B6 Rahman, Anisur, B6 Raiff a, H., B6 Raman, A., 495 n Rangaswami, M. R., B2 Rao, Tom, 674 Reeves, Martin, B2 Reich, Robert, 416 Reiner, Rob, 362 – 363 Reinhardt, Gilles, B5 Render, Barry, 713 n, 744 n, B1 , B3 ,
B5 , B6 , B7 Renouf, E., B7 Richardson, D. J., 409 Rizzo, Tony, 743 – 744 Robidoux, L., B6 Roche, K., B7 Rolewicz, Jamie, 408 Roodbergen, Kees Jan, B4 Rosenfi eld, D. B., B1 Rossetti, Manuel D., B7 Roth, Aleda V., B2 Roth, H. P., B3 Rothfeder, Jeff ry, B1 Roubellat, F., B6 Roy, Anjan, B3 Rubalcaba, L., B1
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I4 N A M E I N D E X
Rubin, Paul A., B5 Rudberg, Martin, B1 Rugtusanatham, M. Johnny, B3
S Saaksvuori, A., B2 Sadowski, R. P., B7 Sahay, B. S., B1 Salter, A., B2 Saltzman, Robert M., B7 Salvador, Fabrizio, B3 Salvendy, G., B4 Samaddar, S., B5 Sampson, Scott, 179 – 180 , 180 n San, G., B1 Sasser, W. Earl, 402 n Sayre, Kim, B6 Schlaifer, R., B6 Schmeidler, Neil, B4 Schmenner, R. W., B7 Schonberger, Richard J., B6 Schrattenholzer, L., B7 Schroeder, R. G., B1 , B2 Scudder, Gary, B4 Segerstedt, A., B5 Seider, Warren D., B2 Sethi, Suresh P., B5 Shah, Piyush, B1 Shahrokh, Shahraki, B4 Sharafali, M., B7 Shaw, B. W., 427 n Sheen, G., B3 Shewart, Walter, 10 , 246 Shi, Chunming, B4 Shingo, Shiego, 644 Shortle, John F., B7 Siggelkow, Nicolaj, B1 Sillekens, Thomas, B5 Simmons, B. L., B4 Singhal, V. R., B4 Sinha, K. K., B4 Skinner, Wickham, B1 Slack, Nigel, B1 Slaughter, S. A., B7 Smith, Adam, 412 Smith, Bernard, 140 , 140 n Smith, Fred, 338 Smith, Jeff rey S., B6 Smunt, T. L., B7 Snir, Eli M., B3 Snyder, L. V., B3 Sodhi, M. S., B7 Sonne, Michael, B4 Sorensen, Charles, 10 Sower, V. E., B3 Spangler, W. E., B3 Sprague, Linda G., B1 Sridharan, V., B5 Sriskandarajah, C., B5 Stair, R. M., 713 n, 744 n, B1 , B3 , B5 ,
B6 , B7
Stair, R. M. Jr., B6 Stamp, M., 194 n Stanford, D. A., B7 Stanley, L. L., B4 Stanowy, A., B4 Stark, Cathy, 630 Steger-Jensen, Kenn, B5 Stein, Andrew C., B5 Stephens, M. P., B6 Stepney, Nigel, 224 Stern, Scott, B3 Stewart, D. M., B2 Stoner, J. A., 95 n Stoner, J. A. F., B2 Strange, Roger, B2 Strum, D. P., B3 Sud, V. P., B7 Suhl, L., B5 Summer, M., B5 Summers, Donna, B2 , B3 Sunny Yang, Shu-Jung, B3 Sural, H., B7 Surowski, Tom, 630 Sutera, Carla, 66 Swamidass, Paul M., B3 Swanson, Kathy, 240 , 304 , 333 , 402 Swets, N. B., B7 Syntetos, A. A., B5
T Taghavi, A., B4 Tallman, Stephen, B3 Talwar, A., B2 Tan, K., B4 Tang, Kwei, B2 Tangen, S., B1 Tanrisever, Fehmi, B3 Taylor, Bernard, B7 Taylor, Frederick W., 9 , 10 , 415 , 420 , 421 Taylor, G. Don Jr., B4 Taylor, S. J .E., B7 Taylor, Sam G., B5 Teixeira, J. C., B7 Terwiesch, C., B2 Teunter, Ruud H., B5 Thomas, A., 413 n Thompson, G. M., B5 Thompson, James M., B7 Tian, Feng, B5 Tian, Zhili, B7 Tibben-Lembke, Ronald S., B3 Tippet, L., 427 Toktay, L. B., B3 Tolo, B., B4 Tomas M., B4 Tompkins, James A., B4 Ton, Z., 495 n Toyoda, Eiji, 649 Trietsch, Dan, B6 Tseng, C. L., B4 Turbide, Dave, B5
U Ulrich, Karl T., B2 Upton, David, B4 Urs, Rajiv, B1
V Vaidyanathan, R., B6 van Biema, Michael, B1 van Veen-Dirks, Paula, B6 Van Wassenhove, L. N., B3 Vargas, L. G., B3 Veeraraghavan, S., B6 Veral, Emre, B6 Verganti, Roberto, B2 , B4 Verma, Rohit, B3 Verzuh, Eric, B1 Village, Judy, B4 Villalta, D. L., B4 Vis, I. F. A., B4 Vollmann, T. E., B5
W Wagner, H. M., B5 Walczak, Steven, B6 Wallace, Rusty, 408 Walsh, Ellen, B4 Walter, Janie, 189 Walton, S., B5 Wang, L., B3 Wang, Phanich P., B4 Wang, Q., B6 Wankel, C., B2 Ward, P. T., B3 Waring, J., B6 Watson, Kevin J., B3 Watts, C. A., B7 Weeks, J., 372 n Weil, Marty, B6 Weintraub, G. Y., B6 Welborn, Cliff , B3 Wells, John, 274 Wemmerlöv, U., B4 West, B. M., B1 Weston, M., B7 Whalen, Lynn, 596 Wheeler, J. V., B4 Whitaker, J., 46 n White, Catherine M., B4 White, G., B3 White, J. A., B4 White, R. E., 348 n Whitin, T. M., B5 Whitney, Eli, 9 Whybark, D. C., B5 Wichern, D. W., B1 Wilson, J. H., B1 Winkelspecht, C., 413 n Wisner, Joel, B4 Wisner, Joel D., 239 Witt, Clyde E., B5 Wolf, Martin, B1 Womack, James P., B6
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N A M E I N D E X I5
Wren, Daniel A., B1 Wright, George, 630 Wright, T. P., 776 , 776 n Wyckoff , D. Daryl, 402 n Wynter, L., B6 Wysocki, R. K., B1
X Xie, Xiaolan, B7 Xin Xu, Sean, B5
Y Yan Kovic, N., B7 Yan, Xinghao, B2 Yang, Shilei, B4 Yoo, S., B5 Yoogalingam, R., B7 Yurklewicz, Jack, B1
Z Zeithaml, Valarie A., 233 n Zeng, Amy Z., B4
Zhang, Aheng, B7 Zhang, M., B3 Zhang, X., B6 Zhao, Hui, B2 Zhao, T., B4 Zhao, X., B3 Zhao, Yong-Pin, 524 Zhexembayeva, N., B2 Zipkin, Paul, B3
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I7
General Index
A ABC analysis, 491 – 492 Acceptable quality level (AQL), 263 Acceptance sampling, 262 – 265 , T2 – 1
to T2 – 7 average outgoing quality (AOQ),
264 – 265 , T2 – 5 to T2 – 6 erroneous conclusions and, 263 n operating characteristic (OC) curve
and, 263 – 264 sampling plans, T2 – 2
Accurate inventory records, MRP and, 570
Accurate pull data, 452 Activity charts, job design and, 418 Activity map, 43 Activity times, variability in, 77 – 82 Activity-on-arrow (AOA), 68 , 71 Activity-on-node (AON), 68 , 69 – 70 ,
90 Adaptive smoothing, forecasting and,
139 Additive manufacturing, 295 Advanced shipping notice (ASN), 454 Aggregate planning, 529 – 562
capacity options, 535 – 536 chase strategy and, 537 comparison of planning methods
for, 537 demand options and, 536 – 537 disaggregation and, 535 ethical dilemma, 551 graphical methods and, 538 – 543 level strategy and, 538 master production schedule and,
535 mathematical approaches and,
543 – 545 methods for, 538 – 543 mixing options to develop a plan
and, 537 – 538 nature of, 534 – 535 planning process and, 532 – 533 revenue management and, 547 – 550 sales and operating planning
(S&OP) and, 533 – 534 scheduling issues and, 603 services and, 545 – 547 software for, 552 – 553 S&OP defi nition, 533 strategies for, 535 – 538 transportation method of linear
programming and, 543 – 545 yield management and, 547 – 550
Aggregate scheduling. See Aggregate planning
Agile project management, 67 Air Berlin, 257 Airbus, 31 Aircraft industry, 417 Airfreight, logistics management and,
457 Airline industry:
aggregate planning and, 547 capacity, matching to demand, 313 inventory, 652 organizational chart, 5 revenue management, 551 scheduling services in, 619 sustainability, 194
AirTran, 257 Alabama Airlines, 805 – 806 Alaska, long-range economic
forecasting, 111 Alaska Airlines:
baggage process strategy, 303 – 304 Global Company Profi le, 600 – 601 human resources, 437 – 438 inspection, 232 lean operations, 651 , 655 – 656 quality, 240 – 242 scheduling challenges, 726 short-term scheduling, 600 – 602 sustainability, 199
Alenia Aeronautica, 30–31 Algebraic approach, break-even
analysis and, 319 – 320 Align Technology, 285 Alliances, 175
time-based competition and, 175 Allowable ranges for objective
function coeffi cients, 708 Allstate Insurance, 163 All-units discount, 507 Amazon.com, 110
Global Company Profi le, 488 – 489 supply chain, 445 warehouse strategy, 376
Ambient conditions, 375 American Hardware Supply, 140 American National Can Company,
289 , 290 American Society for Quality (ASQ),
8 , 217 Amway Center, 208 – 209 , 631 – 632 Analysis and design, process strategy
and, 289 Andon, 637 , 642
Anheuser-Busch, 463 AOA (Activity-on-arrow), 68 , 71 AON (Activity-on-node), 68 , 69 – 70 APEC, 34 APICS (Association for Operations
Management), 8 APICS Supply Chain Council, 464 Apple, 41 , 45 , 109 – 110 , 162 , 195 Application of decision trees
evaluating disaster risk, 473–474 product design, 182–184
Appointment system, 314 Appraisal costs, quality and, 218 Approaches to forecasting, 111 – 112 Arby’s, use of GIS system, 353 ArcGIS, 353 Arcs, routing and scheduling vehicles
and, T5 – 3 Area under the normal curve, T1 – 4
to T1 – 5 Argentina, MERCOSUR and, 34 Arnold Palmer Hospital, 66
building construction, 66 capacity planning, 333 – 334 fl owchart, 229 Global Company Profi le, 214 – 215 hospital layout, 402 – 404 inspection, 232 JIT, 656 labor standards and, 428 managing quality, 214 – 215 mission statement, 36 process analysis, 304 process focus, 283 project management, 99 – 100 supply chain, 468
Arrival characteristics, waiting line systems and, 749 – 752
behavior of arrivals, 750 characteristics of, 749 – 750 pattern of arrivals and, 749 – 750 Poisson distribution, 737
Artifacts, servicescapes and, 375 Artifi cial variables, T3 – 8 ASRS, 296 Assembly chart, 178 Assembly drawing, 178 Assembly line
labor specialization, 412 product-oriented layout and, 386 regulations, 203 – 204 repetitive manufacturing, 280 – 281 scheduling, 605 sustainability, 203
Note: Page numbers beginning with a T refer to the Online Tutorial chapters that appear on our web site www.pearsonhighered.com/heizer.
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I8 G E N E R A L I N D E X
Assembly-line balancing, product- oriented layout and, 386
Assets committed to inventory, 461 – 463
Assignable variations, statistical process control and, 247
Assignment method, loading and, 608 – 610
Association for Operations Management (APICS), 8
Associative forecasting methods, 112 , 131 – 137
correlation coeffi cient for regression lines, 134 – 136
linear-regression analysis, 131–136 multiple regression analysis, 136 –
137 regression analysis, 131 – 137 standard deviation of the estimate,
133 – 134 standard error of the estimate,
133 – 134 Assumptions, break-even analysis
and, 318 – 319 Atlas GIS, 353 AT&T, 218 Attract and retain global talent,
global view of operations and, 34 – 35
Attribute(s): c -charts and, 257 – 259 control charts for, 256 – 259 p -charts and, 256 – 257 , 259 versus variables, inspection and,
233 Auctions, online, 456 Auctions, supply chain management,
and, 455 Audi, 46 Australia, SEATO and, 34 Automated Storage and Retrieval
Systems (ASRS), 296 , 297 Automatic Guided Vehicles (AGV),
296 Automatic identifi cation systems
(AIS), 295 , 377 Automation, service effi ciency and, 181 Automobile manufacturing. See also
Ford Motor Co. bullwhip eff ect in, 476 sustainability, 203 , 205
Autonomous maintenance, 670 Avendra, 456 Average observed time, 422 Average outgoing quality, 264 – 265 ,
T2 – 5 to T2 – 6 Avis car rental, 730
B Babbage, Charles, 412 Back ordering, 536 – 537 Backup redundancy, 665 – 666
Backward integration, 448 Backward pass, 74 Backward scheduling, 604 BAE Systems, 30 Balancing work cells, 384 – 386 Balking customers, 750 Ballard Power Systems, 175 Baltimore, Port of, 119 – 120 Bank of America, 643 Banks
organizational chart, 5 scheduling for services and, 618
Basic feasible solution, T3 – 3 Basic variables, T3 – 3 Baxter International, 642 Bay Medical Center, simulation and,
792 Bayfi eld Mud Company, SPC and,
274 – 275 Bechtel Group, Global Company
Profi le, 60 – 61 Beer, supply chain for, 444 Behavior of arrivals, 750 Bell Laboratories, 246 Benchmarking, 222 – 224
supply chain, 463 Benetton, 32 , 370 , 453 , 585 Best Buy, 375 Beta probability distribution, 77 – 78 Bias, forecasts and, 138 Big data, 679 Bills-of-material (BOM), 175 , 568 –
570 Binary variables, 713 – 715 Blanket orders, 453 Blue Cross, 46 BMW, 34 , 203 Boeing Aircraft, 173 , 388 – 389
Global Company Profi le, 30 – 31 operations strategy, 40 product reliability, 668 supply chain risks and tactics, 450 sustainable product design, 199
Bose Corp., 216 Boston Medical Center, 415 Bottleneck analysis and theory of
constraints, 314 – 318 management of, 317 – 318 time and, 315
Bottom line, triple, 195 – 198 BP, 204 Brazil, MERCOSUR and, 34 Break-bulk warehouse function,
457 Breakdown maintenance, 667 Break-even analysis, 318 – 322
assumptions and, 318 – 319 defi nition, 318 fi xed costs, 318 multiproduct case and, 320 – 322 revenue function, 318 single-product case and, 319 – 320
Bristol-Myers Squibb, 111 British Petroleum (BP), 204 Buckets, MRP and, 576 Buff er, in bottleneck management, 317 Building the supply base,
centralized purchasing, 455 – 456 Build-to-order (BTO), 285 Bullwhip eff ect, 452 , 474 – 476 Burger King, 386 , 794 Buybacks, 455
C CAD, 171 – 172 Cadillac, 218 CAFTA, 34 Calculating slack time, 75 – 76 California drought, 197 Call center industry, location
strategies and, 47 CAM, 172 Canada, NAFTA and, 34 Canon, 384 “Cap and trade” principle, 204 Capacity. See also Break-even
analysis aggregate planning, 535 – 536 analysis and, 314 applying expected monetary value
(EMV) to capacity decisions, 323
applying investment analysis to strategy-driven investments, 324 – 326
bottleneck analysis and theory of constraints, 314 – 318
break-even analysis, 318 – 322 capacity exceeds demand, 312 considerations and, 311 defi nition, 308 demand management and, 312 – 313 design and, 309 – 311 eff ective capacity, 309 – 311 forecasting and, 109, 110 managing demand, 312 – 313 net present value and, 324 – 326 reducing risks with incremental
changes, 322 – 323 service sector demand, 313 – 314 strategy and, 311 strategy-driven investments and,
324 – 326 theory of constraints, 317 using software, 327
Capacity analysis, 314 Capacity management, service sector
and, 314 Capacity planning, MRP and,
581 – 583 Capacity plans, scheduling issues
and, 602 – 603 Capital, as a productivity variable,
15 , 16
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G E N E R A L I N D E X I9
Car rental companies, 730 Carbon footprint, 197 Cartoon industry in Manila, 33 Carvel Ice Cream, use of GIS system,
352 Case Studies:
Alabama Airlines’ Call Center, 805 – 806
Alaska Airlines: human resources, 437 – 438 lean operations, 655 – 656 process strategy, 303–304 quality, 240 – 242 scheduling challenges, 726
Amway Center sustainability, 208 – 209
Andrew Carter, Inc. aggregate planning, 559 – 560
Arnold Palmer Hospital capacity planning, 333 – 334 hospital layout, 402 – 404 JIT, 656 process analysis, 304 project management, 99 – 100 quality, 240 supply chain, 468
Bayfi eld Mud Company, SPC and, 274 – 275
Custom Vans. Inc. transportation problem, 743 – 744
Darden Restaurants outsourcing off shore, 56 Red Lobster, location and
strategies, 362 – 363 statistical process control, 276 supply chain and, 467
De Mar’s Product Strategy, 189 Frito-Lay
inventory management, 525 – 526
maintenance, 674 operations management, 25 statistical process control, 275 sustainability, 209 – 210
Hard Rock Cafe forecasting, 155–156 global strategy, 55 – 56 human resource strategy, 438 location strategy, 363 – 364 operations management in
services, 25–26 project management, 77,
100–102 scheduling, 632
Jackson Manufacturing Co., work measurement, 437
New England Foundry, waiting- line models, 771 – 772
Old Oregon Wood Store, short- term scheduling and, 630
Orlando Magic forecasting, 154 – 155
MRP and, 595 – 596 revenue management, 560 short-term scheduling, 631 – 632 sustainability, 208–209
Parker Hi-Fi Systems, inventory management and, 525
Phlebotomists, routing and scheduling, T5 – 17 to T5 – 18
Port of Miami warehouse tenting, 696
Quain Lawn and Garden, Inc., LP problem, 725 – 726
Rapid-Lube, operations strategy in a global environment and, 55
Regal Marine global strategy, 55 product design, 189–190 supply chain management,
467 – 468 Ritz-Carlton Hotel company
quality, 242 Rochester Manufacturing Corp.,
302 SMT’s negotiation with IBM,
787 – 788 Southern Recreational Vehicle
Co., location strategies and, 362
Southwestern University: forecasting, 153 – 154 project management, 98 – 99 quality, 239 – 240
State automobile license renewals, 402
Uber Technologies, Inc., 24 Wheeled Coach
inventory control, 526 layout strategy, 404 MRP and, 596 process strategy, 302 – 303
Winter Park Hotel, waiting line models, 772
Zhou Bicycle Co., inventory management and, 524 – 525
Cash fl ow, investment analysis and, 324
Cash for Clunkers program, 476 Caterpillar, 49 , 50 , 203 , 474 Cause-and-eff ect diagrams, 226 , 227 c- charts, 257 – 259 Center-of-gravity method, location
strategies and, 348 – 349 , 348 n Central limit theorem, 248 – 249 Certainty, decision making under,
683 Certifi cation, supplier, 454 Cessna Aircraft Company, 642 Changes in objective function
coeffi cient, 707 – 708 Changes in resources or right-hand-
side values, LP and, 706 – 707
Channel assembly, supply chain management and, 457 – 458
Characteristics of goods and services, 11
Characteristics of vehicle routing and scheduling problems, T5 – 3 to T5 – 5
Charleston, port of, 451 Charts. See Control charts Chase strategy, aggregate scheduling
and, 537 Check sheets, TQM tools and,
226 – 227 Checklist, source inspection and,
231 Chengdu Aircraft, 30 Chile, SEATO and, 34 China
ethics within supply chain, 460 manufacturing in, 48 , 51
Chinese postman problem (CPP), T5 – 4
CIM, 297 – 298 Cisco Systems, 372 Clark and Wright Savings heuristic,
T5 – 5 , T5 – 7 to T5 – 8 Classifying routing and scheduling
vehicle problems, T5 – 3 to T5 – 4
Closed-loop material requirements planning, 581
Closed-loop supply chain, 203 , 461 Cluster fi rst, route second approach,
T5 – 10 to T5 – 11 Clustering, 344 Cobham, 30 Coca-Cola, 198 , 463 Coeffi cient approach, learning curve
and, 779 – 782 Coeffi cient of correlation, 134 – 136 Coeffi cient of determination, 136 Collaborative planning, forecasting,
and replenishment (CPFR), 110 , 453
Colruyt, Franz, 38 Commodities transport, 457 Common, resources in the, 195 Company reputation, quality and,
217 Comparative advantage, theory of
outsourcing, 46 Comparison of aggregate planning
methods, 537 Comparison of process choices,
286 – 288 Competing on cost
diff erentiation, operations and, 37 – 38
experience diff erentiation, 38 operations and, 38 product strategy options and, 164 response, operations, 39 – 40
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I10 G E N E R A L I N D E X
Competitive advantage, operations and, 36 – 39
Amazon.com, 488 – 489 Arnold Palmer Hospital and,
214 – 215 Bechtel and, 60 – 61 Boeing and, 30 – 31 cost and, 38 Darden Restaurants, 442 – 443 defi nition, 36 – 37 diff erentiation and, 37 – 38 Federal Express, 338 – 339 Frito-Lay, 530 – 531 human resources and, 410 – 411 lean operations and, 636 – 637 McDonalds, 368 – 369 Orlando Utilities Commission,
660 – 661 product strategy options and,
163 – 164 Regal Marine and, 160 – 161 Walt Disney World and, 106 – 107 Wheeled Coach, 564 – 565
Competitive bidding, 455 Competitors, location proximity to,
344 Components, lead time for, 570 – 571 Computer numerical control (CNC),
295 Computer software. See also
Excel, creating your own spreadsheets; Excel OM; POM for Windows
for process oriented layouts, 382 – 383 for simulations, 800
Computer-aided design (CAD), 161 , 171 , 297
Computer-aided manufacturing (CAM), 172 , 297
Computer-integrated manufacturing (CIM), 297 – 298
Concurrent engineering, 170 Concurrent scheduler approach,
T5 – 13 Confi guration management, 178 Consignment inventory, JIT and, 642 Constant work-in-process (ConWIP),
606 – 607 Constant-service-time model, 762 Constraints:
graphical representation of, LP problem and, 702 – 703
human resource strategy and, 410 linear programming and, 701
Consumer market survey, forecasting and, 111 – 112
Consumer Product Safety Commission, 203
Consumer’s risk, 263 , T2 – 3 to T2 – 4 Containers. See Kanban Continuous improvement
TPS and, 649
TQM and, 220 – 221 Continuous probability distributions,
statistical tools and, T1 – 5 to T1 – 8
Contracting, with suppliers, 455 Contribution, break-even analysis
and, 318 Contribution, defi ned, 165 n Control charts, 230 , 241
attributes, 256 – 258 building process, 247 – 248 c -charts, 257 – 259 defi ned, 246 managerial issues and, 259 – 260 patterns on, 259 p- charts, 256 – 257 , 259 R -charts, 248 , 253 – 254 steps to follow in using, 254 – 255 variables, 248 , 259 x-bar chart, 248 , 250 – 253 , 259
Control of service inventory, 494 – 495
Controlling, project management and, 66 – 67
Controlling forecasts, 138 – 140 ConWIP cards, 606 – 607 Coors, 112 Core competencies, 42 – 43 Core job characteristics, 413 – 414 Corner-point solution method, 705 Corporate social responsibility
(CSR), 194 Correlation analysis, associative
forecasting methods and, 131 – 137
Correlation coeffi cients for regression lines, 134 – 136
Cost of goods sold, 462 Cost of quality (COQ), 218 – 219 Cost-based price model, 455 Costco, 495 , 758 Cost(s)
breakdown, 668 – 669 competing on, 38 globalization, 33 intangible, 342 intuitive lowest-cost transportation
model, 733 – 734 location and, 340 – 341 queuing, 753 – 754 reduction through value
engineering, 170 tangible, 342
Cost-time trade-off s, project management and, 82 – 85
Council of Supply Chain Management Professionals, 8
C p ratio, 260 – 261 CPFR (collaborative planning,
forecasting, and replenishment), 110
C pk index, 261 – 262
CPM. See Critical path method (CPM)
Crashing, project management and, 82 – 85
Criteria, scheduling and, 604 – 605 Critical path, 67 , 91 – 92 Critical path analysis, 71 – 72 Critical path method (CPM)
activity-on-arrow example, 68 , 71 activity-on-node example, 69 – 70 calculating slack time, 75 – 76 critique of, 85 – 86 determining the project schedule,
71 – 76 framework of, 67 – 68 Gantt charts versus, 65 identifying the critical path, 72 network diagrams and approaches,
68 variability in activity time, 77 – 82
Critical ratio (CR), sequencing and, 614 – 615
Critique of PERT and CPM, 85 – 86 Crosby, Philip B., 219 Cross-docking, 376 Crossover charts, 286 – 287 Cross-sourcing, 450 CSR (corporate social responsibility),
194 Cultural issues, global view of
operations and, 35 Culture, location strategy and, 343 Cumulative probability distribution,
Monte Carlo Simulation and, 794
Currency risks, location strategies and, 342
Curtis-Wright Corp., 776 Custom Vans. Inc., 743 – 744 Customer interaction, process design
and, 293 – 294 Customer relationship management
(CRM) software, 584 Customer service, dissatisfaction
from outsourcing, 46 Customers, understanding
focus, 288 new products and, 166 , 167 sustainability, 195 – 196
Customizing. See also Mass customization
service effi ciency, increasing, 181 warehousing layout and, 377
Cycle counting, inventory management and, 493 – 494
Cycle time, focused work center and focused factory and, 389 , 389 n
Cycles, forecasting and, 113 , 131 Cyclical scheduling, 620 – 621 Cyclical variations in data,
forecasting and, 131
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G E N E R A L I N D E X I11
D Daimler-BMW, joint venture and, 448 Darden Restaurants. See also Olive
Garden Restaurant, Red Lobster Restaurant
Global Company Profi le, 442 – 443 outsourcing off shore, 56 statistical process control, 253 , 276 supply chain management, 442 –
443 , 460 , 467 supply chain risks and tactics, 450 use of GIS system, 352
Dassault, 30 Data, big, 679 De Mar, 189 Deadhead time, T5 – 12 Decibel (dB) levels, 418 Decision making:
under certainty, 681 – 682 , 683 expected value of perfect
information (EVPI), 683 – 684 expected value with perfect
information (EVwPI), 683 – 684 under risk, 682 – 683
Decision making tools, 677 – 698 . See also decision trees
under certainty, 681 – 682 , 683 decision tables, 680 decision trees, 684 – 688 expected value of perfect
information (EVPI), 683 – 684 expected value with perfect
information (EVwPI), 683 – 684 fundamentals of, 679 – 680 process in operations and, 678 – 679 under risk, 682 – 683
Decision tables, 680 Decision trees, 684 – 688
defi nition, 684 evaluating disaster risk, 473–474 more complex, 686 – 688 poker decision process, 688 product design and, 182 – 184 using software, 689 – 690
Decision variables, linear programming and, 702
Decline phase, product life cycle and, 165
Decomposition of a time series, 112 – 113
Defects, control charts for, 258 Defi ning a product, 175 – 177 Degeneracy, transportation modeling
and, 737 – 738 Delay
allowances, 422 waste, 289 n
Delay customization, adding service effi ciency and, 181
Dell computers inventory management, 463 mass customization and, 284 – 285
quality, 216 Deloitte & Touche, 372 Delphi method, forecasting and, 111 Delta Airlines, 66, 257
Gantt chart of service activities, 65 Demand exceeds capacity, 312 Demand forecasts, 109
exponential smoothing, 116 – 117 moving averages, 114 – 116 regression, 131–137 steady (naive approach), 113 – 114
Demand is variable and lead time is constant, probability models and, 511
Demand management in service sector, 313 – 314
Demand not equal to supply, transportation models and, 737
Demand options, aggregate strategies and, 536 – 537
Deming, W. Edwards, 218 , 219 , 246 n Deming Prize, 218 Deming’s 14 points, quality and, 220 Denim production, 197 Department of Agriculture
product defi nition, 175 – 176 Dependent demand, 495 , 566 Dependent inventory model
requirements, 566 – 571 accurate inventory records and, 570 bills-of-material and, 568 – 570 lead times for components and,
570 low level coding, 570 master production schedule and,
567 – 568 modular bills, 569 – 570 planning bill, 570 purchase orders outstanding and,
570 Dependent selections, 714 Depot node, routing and scheduling
vehicles and, T5 – 3 Design and production for
sustainability, 19 , 173 , 198 – 200 Design capacity, 309 Design for manufacture and assembly
(DFMA), 171 Design of goods and services,
159 – 192 . See also Product Development
adding service effi ciency and, 181 alliances and, 175 application of decision trees to
product design, 182 – 184 bill of material (BOM) and, 175 computer-aided design (CAD) and,
171 – 172 computer-aided manufacturing
(CAM) and, 172 defi ning the product, 175 – 177 documents for production, 178 – 179
documents for services, 181 – 182 generating new products, 165 – 166 goods and services selection,
162 – 165 group technology and, 177 issues for product design, 171 – 173 joint ventures and, 174 – 175 life cycle and strategy and, 164 –
165 life cycle assessment (LCA), 173 make-or-buy decisions and, 176 –
177 modular design and, 171 OM decisions and, 8 process-chain-network (PCN)
analysis, 179 – 181 product development, 166 – 170 product development continuum,
173 – 175 product life cycles and, 164 product life-cycle management
(PLC) and, 178 – 179 product strategy options support
competitive advantage and, 163 – 164
product-by-value analysis, 165 purchasing technology by
acquiring a fi rm and, 174 robust design and, 171 service design, 179 – 182 sustainability, 19 , 173 , 198 – 200 time-based competition, 173 – 175 transition to production, 184 value analysis and, 173 virtual reality technology and,
172 – 173 Determinants of service quality, 234 Determining project schedule
backward pass, 74 – 75 calculating slack time, 75 – 76 forward pass, 72 – 74 identifying critical paths, 75 – 76
Developing missions and strategies, 35 – 36
DFMA, 171 DHL, supply chain and, 457 , 458 Diehl, 30 Diet problem, LP and, 711 Diff erences between goods and
services, 11 Diff erentiation, competitive
advantage and, 37 – 38 , 163 – 164
Direct interaction, process chain and, 180
Disaggregation, aggregate planning and, 535
Disaster risk in supply chain, evaluating, 472 – 474
Discrete probability distributions, strategic tools and, T1 – 2 to T1 – 3
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I12 G E N E R A L I N D E X
Diseconomies of scale, 311 Disney Parks and Resorts. See Walt
Disney Parks and Resorts Disney World. See Walt Disney Parks
and Resorts Disneyland, 375 Dispatching jobs, priority rules and,
611 – 612 Distance reduction, JIT layout and,
643 Distribution management, supply
chain management and, 459 Distribution resource planning
(DRP), 584 Distribution systems, supply-chain
management, 459 Distributions, control chart, 247 DMAIC, TQM and, 221 Documents:
for production, 178 – 179 for services, 181 – 182
Domestic Port Radiation Initiative supply chain risks and tactics, 450
Dominican Republic, CAFTA and, 34
Double smoothing, 124 Doubling approach, learning curve,
778 – 779 Dow Chemical, 194 Drop shipping, 454 Drum, in bottleneck management,
317 Dual value, 707 Dubuque, Iowa, call center, 47 Dummy destinations, 737 Dummy sources, 737 DuPont, 46 , 163 , 222 Dutch auctions, 456 Dynamics, MRP and, 575
E Earliest due date (EDD), 611 , 614 Earliest fi nish time (EF), critical path
analysis and, 72 , 73 Earliest start time (ES), critical path
analysis and, 72 , 73 Earthquake damage, 472 Eco Index, 204 Economic change, generating new
products, 166 Economic forecasts, 109 Economic order quantity (EOQ)
models, 496 – 497 lot sizing and, 577 – 578 minimize costs, 497 production order quantity model,
502 – 504 quantity discount model, 505 – 507 reorder points, 501 – 502 robust model, 500 – 501
Economic sustainability, 197 Economies of scale, 311
Eff ective capacity, 309 Effi ciency
capacity and, 310 of line balance, 391
Electronic data interchange (EDI), 454
Electronic ordering and funds transfer, 453 – 454
Eliminate waste, lean operations and, 638 – 639
Elliot Health System, 415 Emergency room
process layout, 379 queuing, 753
Emirates, 257 Employee empowerment
job expansion and, 413 OM and, 18 TPS and, 649 TQM and, 222
Employees lean operations and JIT, 643 recruiting globally, 34 – 35
Employment stability policies, 411 EMV. See Expected monetary value End-of-life phase, 203 Energy Star rating, 204 Engineering change notice (ECN),
178 Engineering drawing, 175 Enterprise Resource Planning (ERP),
566 , 584 – 587 . See also Material requirements planning
Environment supply chain ethics and, 460 sustainability and, 196 – 197
Environmental Protection Agency (EPA), 204
Environmentally sensitive production, OM and, 18
EOQ (economic order quantity models), 496 – 497
E-procurement, 456 Equally likely, decision-making under
uncertainty, 681 Equipment, selecting for process
strategy, 288 Ergonomics, work environment and,
415 – 417 feedback to operators, 417 operators input to machines,
416 – 417 Erie Canal, 457 Errors, type 1 and type 2 , 263 Ethical dilemmas:
aggregate planning, 551 airline revenue management, 551 car battery recycling, 20 design of goods and services, 185 human resources, job design, work
measurement and, 431 inventory management, 515
layout strategy, 392 lean operations, 653 location strategies, 354 maintenance and reliability, 671 managing quality, 235 material requirements planning
(MRP) and ERP, 587 operation and productivity, 20 operation strategy in a global
environment, 51 process strategy, 300 project management, 64 , 89 short-term scheduling, 621 – 622 supply-chain management, 465 test scores and forecasting, 141
Ethical issues global view of operations and, 35
Ethics: human resources, job design and
work measurement, 430 project management and, 64 quality management and, 219 response identifi cation, 19 social responsibility and, 19 supply chain and, 460 , 465 sustainability and, 19
EU Emissions Trading System (EUETS), 204
European Union (EU), 34 , 34 n environmental regulations, 204 standard for the exchange of
product data (STEP), 172 Evaluating disaster risk in supply
chain, 472 – 474 Even-numbered problems, solutions
to, A7 – A20 EVPI (expected value of perfect
information), 683 – 684 EVwPI (expected value with perfect
information), 683 – 684 Excel OM:
accessing, 21 aggregate planning and, 553 break-even analysis and, 327 decision models and, 689 , 690 develop x-bar charts, p- charts,
c- charts, OC curves, acceptance sampling and process capability, 266
forecasting and, 143 inventory management and, 517 layout problems and, 393 learning curves and, 784 linear programming, 718 location problems and, 354 MRP and ERP and, 588 – 589 outsourcing problems, 51 project scheduling and, 89 , 90 queuing problems, 766 reliability and, 672 short-term scheduling and, 622 –
624
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G E N E R A L I N D E X I13
simulation and, 801 transportation problems, 738 – 739 ,
738 – 7397 using and, A5
Excel Solver, 552–553, 706 , 713 – 714
Excel spreadsheets, creating your own
aggregate planning, 552 – 553 break-even analysis, 327 control limits for c-chart, 266 creating your own spreadsheets,
21 decision models, 689 , 690 factor rating analysis, 52 forecasting, 142 – 143 inventory management, 516 linear programs, 716 – 717 location strategies, 354 outsourcing problems, 51 simulation, 800 – 801 transportation problems, 738
Exchange rates reducing risk through
globalization, 33 – 34 , 342 Expected monetary value (EMV),
682 – 683 capacity decisions, 323 decision tree analysis, 684 – 686
Expected value: of discrete probability distribution,
statistical tools and, T1 – 3 of perfect information (EVPI),
683 – 684 with perfect information, (EVwPI),
683 – 684 Experience diff erentiation, 38
Disney and, 38 Exponential smoothing
forecasting and, 116 – 117 smoothing constant, 116 – 117 trend adjustment and, 120 – 124
Extensions of MRP, 580 – 583 capacity planning, 581 – 583 closed loop, 581 material requirements planning II,
580 – 581 External failure costs, quality and,
218
F Fabrication line, production-oriented
layout and, 386 Factor weighting technique, 477 Factor-rating method
location strategies and, 345 – 346 outsource providers, 47
Factors aff ecting location decisions and, 341 – 344
costs, 342 currency risk, 342 exchange rates, 342
labor productivity, 342 political risks, 343 proximity to markets, 343 proximity to suppliers, 344
Faro Technologies, 264 Fast Track, 66 Fast-food restaurants
forecasting and, 140 – 141 repetitive process using modules,
283 Fatigue allowances, 422 Feasible region, 703 Feasible tour, T5 – 3 FedEx, 141 , 218
Global Company Profi le, 338 – 339 customized warehouses, 377 logistics, 458
Feedback to operators, 417 Feed-mix problem, LP and, 711 Feigenbaum, Armand V., 219 Ferrari racing team, 224 FIFS (fi rst in, fi rst served), 751 n Finance/accounting, OM and, 4 Finished-goods inventory, 491 Finite capacity scheduling (FCS),
597 , 617 – 618 Finite loading, 604 Finite-population waiting model,
749 , 763 – 765 First Simplex tableau, T3 – 2 to T3 – 4 First-come, fi rst-served (FCFS)
system, 314 , 611 , 614 First-in, fi rst-out (FIFO), 751 , 751 n First-in, fi rst-served (FIFS), 751 n First-order smoothing, 124 Fish-bone chart, 227 Five forces analysis, 40 5 Ss, lean operations and, 639 ,
639 n Fixed costs, break-even analysis and,
318 Fixed fees, 507 Fixed-charge integer programming
problem, 715 Fixed-period (P) inventory systems,
514 – 515 Fixed-position layout, 370 , 377 – 378 Fixed-quantity (Q) inventory system,
514 Flexibility, process strategy and,
288 Flexibility increased, JIT layout and,
643 Flexible manufacturing system
(FMS), 297 Flexible response, 39 Flexible workweek, 412 Flextronics, 45 Flow diagrams, 418 , 419 Flow time, 611 Flowcharts, 226 , 228 – 229 , 289 Flowers Bakery, 246
Focus forecasting, 139 – 140 Focused factory, 386 Focused processes, 287 – 288 Focused work center, 386 Food and Drug Administration, 203 Foot Locker, 586 Ford Motor Co., 175 , 198 , 447 , 646 Forecast error, measuring, 117 – 120 Forecasting, 105 – 166 . See also
Associative forecasting methods; Time series forecasting
adaptive smoothing and, 139 approaches to, 111 – 112 associative methods, regression
& correlation analysis and, 131 – 137
bias and, 138 capacity and, 110 coeffi cient of determination and,
136 correlation coeffi cients for
regression lines and, 134 – 136 defi ned, 108 Delphi method and, 111 demand forecast and, 109 economic forecasts and, 109 fast food restaurants and, 140 focus forecasting and, 139 – 140 human resources and, 110 jury of executive opinion and,
111 linear regression analysis and,
131–136 market survey and, 111 – 112 monitoring and controlling
forecasts and, 138 – 140 multiple regression analysis and,
136 – 137 qualitative methods and,
111 – 112 quantitative method and, 112 regression analysis and, 131 – 137 service sector and, 140 – 141 seven steps in, 110 – 111 software in, 142 – 144 specialty retail shops and, 140 standard error of the estimate and,
133 – 134 strategic importance of, 109 – 110 supply chain management and,
109 – 110 technological forcasts and, 109 time horizons and, 108 – 109 types of, 109 using software and, 142 – 144
Formula approach, learning curves and, 779
Formulating problems, LP and, 701 – 702
Forward integration, 448 Forward pass, 72 – 74 Forward scheduling, 603
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I14 G E N E R A L I N D E X
Four process strategies, 282 – 288 mass customization focus, 284 –
285 process focus, 282 – 283 product focus, 284 repetitive focus, 283
Framework of PERT and CPM, 67 – 68
Franz Colruyt, low-cost strategy and, 38
Free slack, 75 Freezing schedule, 647 Frito-Lay
aggregate planning and, 530 – 531 Global Company Profi le, 530–531 maintenance, 662 , 674 managing inventory, 525 – 526 operations management, 25 product focus, 284 statistical process control, 275 sustainability, 197 – 198 , 200 , 209 –
210 x-bar charts, 255
Fuji Heavy Industries, 30 Functional area, mission and, 36 Functionality, servicescapes and, 375 Functions of inventory, 490 Future time horizon, forecasting and,
108 – 109
G Gantt charts, 607 – 608
load chart, 607 Microsoft Project view, 86 – 87 project scheduling and, 65 schedule chart, 607 – 608
Gap, 421 , 460 Gemba or Gemba walk, 651 , 655 General Electric, 30 , 195 , 219 , 221 ,
246 General Motors (GM), 164 Generating new products, 165 – 166 Geographic information systems
(GISs), location strategies and, 351 – 353
GeoMedia, 353 Giant Manufacturing Company, 447 Gillette, 162 Glidden Paints, 134 Global Aquaculture Alliance, 460 Global Company Profi les:
Alaska Airlines, 600 – 601 Amazon.com, 488 – 489 Arnold Palmer Hospital, 214 – 215 Bechtel Group, 60 – 61 Boeing Aircraft, 30 – 31 Darden Restaurants, 442 – 443 FedEx, 338 – 339 Frito-Lay, 530 – 531 Hard Rock Cafe, 2 – 3 Harley-Davidson, 280 – 281 McDonald’s, 368 – 369
NASCAR Racing Team, 408 – 409 Orlando Utilities Commission,
660 – 661 Regal Marine, 160 – 161 Toyota Motor Corp., 636 – 637 Walt Disney Parks and Resorts,
106 – 107 Wheeled Coach, 564 – 565
Global implications impact of culture and ethics and,
35 quality and, 218
Global Insights, 110 Global operations. See Operations
strategy in a global environment
Global operations strategy options, 49 – 50
Global strategy, 49 Global view of operations, supply
chains and, 32 – 35 attract and retain global talent and,
34 – 35 improve products and, 34 improve supply chain and, 33 reduce costs, 33
The Goal: A Process of Ongoing Improvement (Goldratt and Cox), 317 n
“Going green.” See Sustainability GOL-Brazil, 257 Goods, diff erences from services, 11 Goods and services selection
design of, 162 – 165 importance of, 162 life cycle and strategy, 164 – 165 operations and, 11 – 13 product decision, 163 product life cycles, 164 product strategy options, 163 – 164 product-by-value analysis, 165
Graphic approach, break-even analysis and, 319 – 320
Graphical methods for aggregate scheduling, 538 – 543
Graphical representation of constraints, LP and, 702 – 705
Graphical solution to LP problem, 702 – 705
Graphical techniques, 538 – 543 Great Ormond Street Hospital, 223 ,
224 Greenhouse gas (GHG) emissions,
204 Greenlist classifi cation, 196 Gross material requirements plan,
MRP and, 571 – 572 Group technology, 177 Growth of services, OM and,
11 – 12 Growth phase, product life cycle and,
165
H Hafei Aviation, 30 Haier, globalization and, 32 Hallmark, 385 – 386 Hansel, 651 Hard Rock Cafe
Global Company Profi le, 2 – 3 experience diff erentiation, 38 forecasting, 155 – 156 global strategy, 55 – 56 human resource strategy, 415 , 438 layout strategy, 375 location strategy, 363 – 364 operations management in services,
25 – 26 Pareto charts and, 228 portion-control standard, 176 ,
177 project management, 77 , 100 – 102 scheduling, 632 supply chain, 450
Hard Rock Hotel, 232 Harley-Davidson, 289
Global Company Profi le, 280 – 281 Hawthorne studies, 413 Heart surgery learning curve, 776 ,
778 Heijunka, 647 , 651 Heinz, 49 Heritage of OM, 8 – 10 Hershey, 172 Hertz Car Rental, 730 Heuristic, assembly-line balancing
and, 389 Hewlett-Packard, 39 , 46 , 173
supply chain management, 453 Histogram, 226 Historical experience, labor standards
and, 421 Holding costs, 495 Home Depot
inventory management, 461 , 463 use of GIS system, 353
Honda, 42 , 172 supply chain risks and tactics,
450 Hong Kong, SEATO and, 34 Hospitals. See also Arnold Palmer
Hospital accident avoidance, 232 benchmarking handoff s, 224 diff erentiation, 163 – 164 emergency room process layout,
379 forecasting, 129 – 130 inventory management, 652 lean operations, 651 learning curve, 776 , 778 MRP and, 584 operating room traffi c jams, 415 process strategy, 284 quality management, 223 , 224
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G E N E R A L I N D E X I15
scheduling services and, 618 zero-wait ER guarantee, 766
Hotels MRP and, 584 site selection, location strategies
and, 351 technology changes, 298
House of quality, 166 – 169 HP Project, 66 Human resource, job design, work
management and, 407 – 440 . See also labor standards
competitive advantage for, 410 constraints on, 410 employment-stability policies and,
411 ergonomics and the work
environment, 415 – 417 ethics, the work measurement and,
430 – 431 forecasting, 110 job classifi cations, work rules and,
412 job design and, 412 – 415 labor planning and, 411 – 412 labor standards, 420 – 430 methods analysis, 417 – 419 motivation, incentive systems and,
415 objective of, 410 psychological components of job
design , 413 – 414 self-directed teams and, 414 visual work place, 420 work schedules and, 411 – 412
Human resources, forecasting and, 109
Humidity, in work area, 417 Hyatt, 456 Hyundai Shipyard, 79
I IBM, 46 , 49
closed-loop supply chain, 461 reshoring, 47 SMT’s negotiation with, 787 – 788
Idea Works, 257 Ikea, 377 Illumination, 417 – 418 Impact on employees, JIT layout and,
643 Implementing 10 strategic OM
decisions, 44 Importance of
inventory, 490 – 491 project management, 62
Improve operations, global view of operations and, 34
Improve products, global view of operations and, 34
Improve supply chain, global view of operations and, 33
Incentives: for individual or group
productivity, 415 job design and, 415 managing the supply chain, 452
Increased fl exibility, JIT layout and, 643 Independent demand, inventory
models and, 495 basic economic order quantity
(EOQ) model, 496 – 497 minimizing costs, 497 – 501 production order quantity model,
502 – 504 quantity discount models, 505 – 507 reorder points, 501 – 502
Independent processing region, 180 India, call centers in, 47 Indonesia, manufacturing in, 51 Industrial engineering, OM and, 10 Infant mortality, 667 Infi nite arrival population, 749 Infi nite loading, 604 Information technology, 10 Ingall Ship Building Corporation,
378 Initial failure rate, 667 Initial solution, transportation
models and, 732 – 734 Innovation, new products and, 163 Input-output control, loading jobs
and, 606 – 607 Inspection:
attributes versus variables, 233 defi nition, 230 quality role, 230 – 233 service industry and, 232 source and, 231 waste, 289 n when and where, 230 – 231
Institute for Supply Management (ISM), 8
Intangible costs, location strategies and, 342
Integer variables, 713 Integrated supply chain, managing
and, 451 – 454 issues in, 451 – 452 opportunities in, 452 – 454
Intermittent processes, 282 Internal benchmarking, 223 Internal failure costs, quality and, 218 International business, 49 International Organization for
Standardization (ISO), 204 – 205
International standards environmental, 204 – 205 quality, 218 , 454
International strategy, global operations and, 49
Introductory phase, product life cycle and, 165
Intuitive lowest-cost method, 733 – 734
Inventory accurate records, 570 assets committed to, 462 – 463 capacity options, 535 – 536 functions of, 490 lean operations, 643 – 646 , 652 maintenance/repair/operating
(MRO) inventory, 490 – 491 quantities and values, 462 n raw material inventory and, 490 types of, 490 – 491 work-in-process (WIP) and, 490
Inventory analysis, simulation and, 797 – 799
Inventory management, 487 – 528 . See also Independent demand
classifying items through ABC analysis, 491 – 492
control of service inventories, 494 – 495
cycle counting, 493 – 494 demand, independent vs.
dependent, 495 ethical dilemma, 515 fi xed-period (P) systems and,
514 – 515 holding, ordering, and setup costs,
495 – 496 importance of, 490 – 491 independent demand, models for,
496 – 507 just-in-time, 643 – 646 kanban, 647 – 649 managing and, 491 – 495 mass customization, 285 models, 495 – 514 other probabilistic models, 511 – 513 probabilistic models and safety
stock, 508 – 513 record accuracy, 493 single-period model, 513 – 514 using software, 516 – 517
Inventory turnover, 462 Inventory types, 490 – 491 Investment analysis, capacity
planning and, 324 – 326 Invisalign Corp., 172 iPad menu, 289 Ishikawa diagrams, 227 ISO, 703 n ISO 9000 , 218 , 454 ISO 14000 , 204 – 205 , 454 ISO-cost line, 708 – 709 ISO-profi t line solutions method,
703 – 705 Issues in:
integrated supply chain, 451 – 452 operations strategy, 40 – 41 short-term scheduling, 602 – 605
Item aggregation, 507
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I16 G E N E R A L I N D E X
J J R Simplot, 179 Jackson Manufacturing Co., 437 Jagoda Wholesalers, 130 – 131 Japan/Japanese. See also Toyota
Production System collegial management structure, 170 Deming Prize, 218 earthquake damage, 472 5Ss, 639 Gemba or Gemba walk, 651 kanban, 647 – 649 keiretsu networks, 448 – 449 SEATO and, 34 Takumi, 219
JC Penney, 453 Jidoka, 650 , 651 JIT. See Just-in-time Jo-Ann Stores, use of GIS system,
353 Job characteristics, 413 – 414 Job classifi cations, 412 Job design, 412 – 415 . See also
Methods analysis core job characteristics and, 413 –
414 defi nition, 412 Hawthorne Studies and, 413 job expansion, 413 labor specialization, 412 limitations of job expansion,
414 motivation and incentive systems
and, 415 psychological components of,
413 – 414 self-directed teams, 414
Job enlargement, human resource strategy and, 413
Job enrichment, 413 Job expansion, 413
limitations, 414 Job lots, 379 Job rotation, 413 Job shops, facilities, 605
scheduling, 617 Job specialization, 412 John Deere, 536 Johns Hopkins Medical Center, 232 Johnson & Johnson, 46 , 219 Johnson Controls, 463 Johnson Electric Holdings, LTD.,
39 Johnson’s rule, sequencing and,
615 – 616 Johnsonville Sausage Co., 415 Johnstown Foundry, Inc., 431 Joint ventures
supply-chain management and, 448
time-based competition and, 174 – 175
Jones Law Offi ces inspection, 232
Juran, Joseph M., 219 Jury of executive opinion, 111 Just-in-time (JIT)
consignment inventory and, 642 defi nition of, 638 distance reduction and, 643 impact on employees and, 643 increased fl exibility and, 643 inventory and, 643 – 646 Kanban and, 647 – 649 layout and, 642 – 643 MRP and, 576 partnerships and, 640 – 642 quality and, 224 , 649 reduced space and inventory and,
643 remove variability and, 643 – 644 scheduling and, 646 – 649 security, 451 supply chain risk and, 451 TQM and, 224
Just-in-time (JIT) inventory reduce setup costs, 645 – 646
K Kaizen, lean production and, 649 ,
651 Kaizen event, 649 Kanban, JIT and, 420 , 647 – 649
advantages of, 648 – 649 defi nition, 647 number of cards or containers and,
648 Kawasaki Heavy Ind, 30 Keiretsu networks, 448 – 449 Key success factors (KSFs), 41 – 43 ,
341 Kindle, forecasting and, 110 Kits, BOMs and, 570 Kitted material, MRP and, 570 Knowledge society, 16 – 17 Knowledge-based pay systems, 415 Kodak, 646 Komatsu, 50 Korean Airlines, 30 Kroger, 375
L La Quinta hotel site selection, 351 Labinel, 30 Labor planning, human resources
and, 411 – 412 employment-stability policies and,
411 job classifi cations and, 412 work rules and, 412 work schedules and, 411 – 412
Labor productivity location strategies and, 342 as productivity variable, 15 , 16
Labor scheduling example, LP and, 712 – 713
Labor specialization, job design and, 412
Labor standards, 420 – 430 historical experience, 421 predetermined time standards,
425 – 427 time studies, 421 – 425 work sampling, 427 – 430
Large lots, integrated supply chain and, 452
Last-in, fi rst-out (LIFO), 751 n Last-in, fi rst-served (LIFS), 751 n Latecoere, 30 Latest fi nish time (LF), 72 , 74 Latest start time (LS), 72 , 74 Layout, types of, 370 – 371 Layout design, OM decisions and, 42 Layout strategies, 367 – 406
ethical dilemma, 392 fi xed-position layout, 370 , 377 – 378 lean operations, 642 – 643 , 652 lean operations and JIT, 642 – 643 offi ce layout and, 370 , 371 – 372 process-oriented layout and, 371 ,
378 – 383 product-oriented, 371 repetitive and product-oriented
layout and, 386 – 391 retail layout and, 370 , 372 – 375 servicescapes, 375 strategic importance of, 370 types of, 370 – 371 using software, 393 warehouse and storage layouts and,
370 , 375 – 377 work cells, 371 , 383 – 386
Lead time: additional probabilistic models,
511 – 513 inventory model and, 501 MRP and, 570 – 571 probabilistic model, inventory
management and, 511 – 513 Leaders in quality, 219 Lean operations, 19 , 635 – 658
defi nition, 638 eliminate waste and, 638 – 639 ethical dilemma, 653 5Ss and, 639 improve throughput and, 640 inventory and, 643 – 646 just-in-time, 640 – 649 kanban, 647 – 649 layout and, 642 – 643 lean organizations, 650 – 652 material requirements planning
and, 576 quality and, 649 removing variability, 639 – 640 scheduling and, 646 – 649
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G E N E R A L I N D E X I17
services, 652 seven wastes and, 638 supplier partnerships, 640 – 642 throughput, 640 Toyota production system and,
638 , 649 – 650 waste elimination, 638 – 639
Learn to improve operations, global view of operations and, 34
Learning curves, 775 – 790 applying, 778 – 782 coeffi cients and, 779 – 781 defi nition, 776 doubling approach and, 777 ,
778 – 779 formula approach and, 779 limitations of, 783 services, manufacturing and,
777 – 778 strategic implication of, 782 – 783 table approach and, 779 – 782
Least-squares method, trend projections and, 124 – 126
Legal change, product development and, 166
Legends of Poker, 678 , 688 Lekin ® fi nite capacity scheduling
software, 617 – 618 Level schedules, JIT and, 647 Level scheduling, 538 Level strategy, aggregate planning
and, 538 Levi, 197 Lexus, 111 Life cycle, strategy and, 34 , 164 – 165 Life cycle assessment, 173 , 198 Limitations of :
job expansion, 414 learning curves, 783 MRP, 575 – 576 rule-based dispatching systems,
616 – 617 Limited arrival population, 749 Linear programming (LP), 699 – 728
changes in the objective function coeffi cient and, 707 – 708
changes in the resources, 706 – 707 corner-point method and, 705 defi nition of, 700 diet problem and, 711 feasible region and, 703 feed-mix problem and, 711 fi xed-charge problem, 715 formulating problem and, 701 – 702 Glickman Electronics example,
701 – 702 graphical solution to, 702 – 705 integer and binary variables,
713 – 715 iso cost, 708 iso-profi t line solution method and,
703 – 705
labor scheduling and, 712 – 713 minimization problems and,
708 – 709 objective function and, 701 , 707 –
708 production-mix example and,
710 – 711 requirements of a programming
problem and, 701 right-hand-side values and, 706 –
707 sensitivity analysis, 705 – 708 sensitivity report, 706 simplex method of, 713 transportation method, 543 – 545 using software, 716 – 718 validity range for shadow prices,
707 why we use LP, 707
Linear regression analysis, 131 – 136 Lion King revenue management, 548 Little’s Law, 763 L.L. Bean, 223 Loading jobs, short term scheduling
and, 604 , 605 assignment method, 608 – 610 Gantt charts, 607 – 608 input-output control, 606 – 607
Local optimization, managing the supply chain and, 451 – 452
Location, costs and, 340 – 341 Location, importance of, 340 – 341 Location decisions, factors aff ecting,
341 – 344 Location strategies, 337 – 366
center-of-gravity method and, 348 – 349
costs of, 340 – 341 , 342 exchange rates and currency risk
and, 342 factors aff ecting location decisions,
341 – 344 geographic information systems
(GIS), 351 – 353 methods of evaluating location
alternatives, 344 – 350 objective of, 340 political risks, values and culture
and, 343 proximity to competitors and, 344 proximity to markets and, 343 –
344 proximity to suppliers and, 344 service location strategy, 350 – 351 strategic importance of, 340 – 341 tangible costs and, 342 transportation model, 349 – 350 using software, 354 – 355
Locational cost-volume analysis, 346 – 347
Lockheed Martin, 172 Logan Airport, 700
Logistics management, supply chain management, and, 456 – 459
reverse, 203 , 460 – 461 shipping systems and, 456 – 457 sustainability, 200 – 202 third-party logistics (3PL) and,
458 – 459 warehousing and, 457 – 458
Longest processing time (LPT), 611 Long-range forecast, 108 – 109 Los Angeles Airport, M/M/S model
and, 761 Lot size reduction
integrated supply chain and, 452 lean operations and JIT, 644 – 645
Lot sizing decision, 576 – 580 Lot sizing summary, 579 – 580 Lot sizing techniques, MRP and,
576 – 580 economic order quantity, 577 – 578 lot-for-lot, 576 periodic order quantity, 578 – 579
Lot tolerance percent defective (LTPD), 359
Lot-for-lot, 576 Low-cost leadership, 38 Low-level coding, MRP and, 570 Lufthansa, 257 , 313
M Machine technology, 294 – 295 Machines, operator input to, 416 – 417 MAD (mean absolute deviation),
118 – 119 , 121 Maintenance and reliability, 659 – 676
autonomous maintenance, 670 defi ned, 662 ethical dilemma, 671 increasing repair capabilities, 670 preventive maintenance, 667 – 670 reliability, 663 – 667 strategic importance of, 662 – 663 total productive maintenance, 671
Maintenance/repair/operating (MROs) inventories and, 490 – 491
Major league baseball umpires, 610 Make-or-buy decisions, 176 – 177 ,
446 – 447 Malcolm Baldrige National Quality
Awards, 45 , 218 Management, as productivity
variable, 15 , 16 – 17 Management, MRP and
dynamics of, 575 JIT and, 576
Management process, OM and, 7 Manager, project, 63 – 64 Managerial issues, control charts and,
259 – 260 Managing bullwhip eff ect, 474 – 476 Managing demand, capacity and,
312 – 313
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I18 G E N E R A L I N D E X
Managing inventory. See Inventory management
Managing quality, 213 – 244 . See also Total quality management
cost of, 218 – 219 defi ning, 217 – 219 ethics and, 219 implications of, 217 – 218 international quality standards, 218 role of inspection, 230 – 233 services and, 233 – 234 strategy and, 216 tools of TQM, 226 – 230 total quality management, 219 – 226
Manila, cartoon industry in, 33 Manufacturability, product
development and, 170 Manufacturing
learning curve and, 777 – 778 organizational chart, 5 regulations, 203 – 204 repetitive, 280 – 281
Manufacturing cycle time, 640 MAPE (Mean absolute percent
error), 120 , 121 MapInfo, 353 Maptitude, 353 Maquiladoras, 34 Market survey, 111 – 112 Market-based price model, 455 Marketing, OM and, 4 Markets
global view of operations and, 34 proximity to, 343 – 344
Marriott, 456 Mass customization
focus in, 284 – 285 , 286 OM and, 19
Mastek Corp., agile project management, 67
Master production schedule, 535 , 567 – 568
Master schedule, scheduling issues and, 575 , 603
Material fl ow cycle, 491 Material handling costs, 375 Material requirements planning
(MRP), 563 – 598 . See also Dependent inventory model requirements
buckets and, 576 capacity planning and, 581 – 583 closed loop, 581 defi ned, 566 dependent demand, 566 dependent inventory model
requirements and, 566 – 571 distribution resource planning
(DRP) and, 584 dynamics, 575 enterprise resource planning
(ERP), 584 – 587
ethical dilemma, 587 extensions of, 580 – 583 gross material requirements plan
and , 571 – 572 JIT and, 576 limitations and, 575 – 576 lot-sizing techniques and, 576 – 580 net requirements plan and, 572 –
575 planning bill, time-phased product
structure and, 570 safety stock and, 575 services and, 583 – 584 , 587 structure for, 571 – 575 using software, 588 – 589
Material requirements planning II (MRP II), 580 – 581
Mathematical approaches, aggregate planning and, 543 – 545
Matrix organization, 63 Mattel, 48, 204 Maturity phase, product life cycle
and, 165 Maximax, decision-making under
uncertainty and, 681 Maximin, decision-making under
uncertainty, 681 Maximization problems, linear
programming and, T3 – 7 McDonald’s Corp., 42 , 175 , 235 , 755
Global Company Profi le, 368 – 369 hamburger assembly line, 387 inventory management, 463 , 652 process analysis, 288 process strategy, 298 quality, 235 scheduling, 652 supply chain risks and tactics, 450
McKesson Corp., 494 , 642 Mean absolute deviation (MAD),
118 – 119 , 121 Mean absolute percent error
(MAPE), 120 , 121 Mean chart limits, setting of, 250 – 253 Mean squared error (MSE), 119 – 120 ,
121 Mean time between failures (MTBF),
664 – 665 Measurement, productivity and,
14 – 15 Measuring:
forecast error, 117 – 120 supply chain performance, 461 – 464
Medium-range forecast, 108 – 109 Meijer, 421 Mercedes-Benz, 46 , 172 , 175 , 223 ,
340 sustainability, 198 , 203
Merck mission statement, 36 MERCOSUR, 34 Mercury Marine, 445 Messier-Bugatti, 30
Messier-Dowty, 30 Methods analysis, 417 – 419 Methods for aggregate planning,
538 – 545 Methods of evaluating location
alternatives, center-of-gravity method, 348 – 349 factor-rating method, 344 – 350 locational cost-volume analysis,
346 – 347 transportation model, 349 – 350
Methods time measurement (MTM), 427
Methods Time Measurement Association, 427 n
Mexico, NAFTA and, 34 Micro Saint software, 792 MicroGreen Polymers, 199 Microsoft Corp., 40 , 64 – 65 , 112 , 172 ,
174 Microsoft Project, 66
entering data, 86 PERT analysis, 87 project management and, 86 – 88 tracking the time status of a
project, 87 – 88 viewing the project schedule, 86 – 87
Milliken, 218 Milton Bradley, 493 Milwaukee Paper
activity-on-arrow (AOA), 71 activity-on-node (AON), 68 – 70 , 71 completion of product on time
and, 79 computing project variance, 78 critical path, 76 earliest fi nish time (EFT), 73 earliest start time (EST), 73 expected times and, 78 latest fi nish time (LFT) and, 74 – 75 latest start time, (LST) and, 74 – 75 project crashing, 83 – 85 project network, 87 project schedule, 86 – 87 , 90 sample Gantt chart, 86 slack time and, 75 – 76 standard deviation and, 79 time estimates, 71 variances, 78 , 79
MindView, 66 Minimal-cost-fl ow problem, T5 – 13 Minimization problems, LP and,
708 – 709 , T3 – 7 to T3 – 8 Minimizing costs, independent
demand inventory and, 497 – 501
Minimum cost of insertion technique, T5 – 10
Miscellaneous services, aggregate planning and, 546 – 547
Missions, global view of operations and, 35 – 36 , 37
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G E N E R A L I N D E X I19
Mitigation tactics, supply chain risks, 450
Mitsubishi Heavy Ind, 30 Mixed strategy, aggregate planning
and, 538 Mixing options, aggregate scheduling
and, 537 – 538 MNC (Multinational corporation),
49 Models, independent demand and,
502 – 507 MODI method (modifi ed
distribution): how to use, T4 – 2 to T4 – 4 solving a problem, T4 – 2 to T4 – 4 transportation problems and, T4 – 2
to T4 – 4 Modular bills, MRP and, 569 – 570 Modular design, product
development and, 171 Modularization, service effi ciency
and, 181 Modules, repetitive focus and, 283 Moment-of-truth, service design and,
181 Monitoring forecasts, 138 – 139 Monte Carlo method, 794 Monte Carlo simulation, 794 – 797 Monterey Jack cheese, 175 – 176 Most likely time, PERT and, 77 Motivation, incentive systems, 415 Motivation systems, job design and,
415 Motorola, 218 , 221 Moving averages
quantitative forecasting and, 114 – 116
weighted, 115–116 MROs, 490 – 491 MRP., see Material requirements
planning Mrs. Field’s Cookies, 375 MSE (mean squared error), 119 – 120 ,
121 Muda, 651 Multidomestic strategy, global
operations and, 49 Multifactor productivity, 14 – 15 Multimodal shipping, logistics
management and, 457 Multinational corporation (MNC), 49 Multiphase system, 752 Multiple regression, 136 – 137 Multiple regression analysis, 136 – 137 Multiple server queuing model,
757 – 761 Multiple traveling salesmen problem
(MTSP), T5 – 4 , T5 – 8 Multiplicative seasonal model, 127 Multiproduct case, break-even
analysis and, 320 – 322 Muther Grid, 372
N Nabisco, 110 NAFTA (North American Free
Trade Agreement), 34 Naive approach, quantitative
forecasting and, 113 – 114 NASCAR Racing Team, Global
Company Profi le, 408 – 409 National car rental, 730 National chains, aggregate planning
and, 546 National Highway Safety
Administration, 203 Natural variations, statistical process
control and, 246 – 247 Nature of aggregate planning, 534 –
535 Nearest neighbor procedure, T5 – 5 to
T5 – 7 Negative exponential probability
distribution, 752 Negotiation strategies, vendor
selection and, 455 Nestlé, 50 , 455 Net material requirements plan,
MRP and, 572 – 574 Net present value, strategy-driven
investments and, 324 – 326 Net requirements plan, MRP and,
572 – 575 Networks, routing and scheduling
vehicles and, T5 – 3 New challenges in OM, 18 – 19 New England Foundry, 771 – 772 New Guinea, SEATO and, 34 New products, generating, 165 – 166 New York City potholes, regression
analysis of, 137 New Zealand, SEATO and, 34 Night Hawk, 314 Nike, 112 , 458 Nintendo, 110 Nissan
annual inventory turnover, 463 level strategy, 538 low-emission vehicles, 194 scheduling, 577 supply chain risks and tactics,
450 Nodel Construction Company,
132 – 137 Nodes, routing and scheduling
vehicles and, T5 – 3 Noise, in work area, 417 , 418 Non-basic variables, T3 – 3 Nordstrom Department Store
inspection, 232 Normal curve areas, A2 – A3 , T1 – 4 to
T1 – 7 Normal distribution, A2 – A3 Normal time, labor standards and,
422 , 423
North American Free Trade Agreement, 34
Northwest-corner rule, transportation models and, 732 – 733
Nucor, 216 Nurse shortage, 619 N.Y. Edison, 125 – 126
O Oakwood Hospital, 766 Objective function, LP problems and,
701 Objective function coeffi cients,
allowable ranges and, 707 – 708 Objectives of routing and scheduling
vehicle problems, T5 – 2 Occupational Safety and Health
Administration (OSHA), 203, 431 , 431 n
Offi ce Depot, 421 , 459 Offi ce layout, 370 , 371 – 372 Offi ce Max, 534 Offi ce relationships chart, 372 Offi cial Board Markets weekly
publication, 455 Old Oregon Wood Store, 630 Olive Garden Restaurant. See also
Darden Restaurants forecasting, 113 inspection, 232 JIT, 640
OM. See Operations management OM in Action
airline capacity, matching to demand, 313
Align Technology mass customization, 285
Amazon robot warehouse strategy, 376 Benetton, ERP software and, 585 blue jeans and sustainability, 197 car assembly lines, sustainability
in, 203 Cessna Aircraft Company lean
production, 642 Delta Airlines, project management
and, 66 Denmark’s meat cluster, 345 DHL, supply chain and, 458 Disney musical’s revenue
management, 548 frequent fl yer miles, booking seat
with, 257 hospital accident avoidance, 232 hospital benchmarks against
Ferrari Racing Team, 224 hotel industry, technology changes
and, 298 incentives to unsnarl traffi c jams in
the OR, 415 Iowa data center locations, 343 iPad menu, 289
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I20 G E N E R A L I N D E X
JC Penney, supply chain and, 453 La Quinta hotel site selection, 351 Lean Production at Cessna
Aircraft, 642 mass customization for straight
teeth, 285 Mastek Corp. agile project
management, 67 Milton Bradley inventory
management, 493 missing perfect chair, 416 New York City potholes and
regression analysis, 137 Oakwood Healthcare ER
guarantee, 753 Olive Garden, forecasting and, 113 operating room traffi c jams, 415 Orlando Magic short-term
scheduling, 604 Reshoring to Small-Town U.S.A., 47 retail services, saving time, 421 retail’s last 10 yards, 495 RFID tags help control bullwhip
eff ect, 475 Richey International’s spies, 235 roses, supply-chain management
and, 446 San Francisco General Hospital
lean operations, 651 Snapper, aggregate planning and,
535 Starbucks Coff ee
productivity and, 14 simulation, 797
Subaru’s clean, green set of wheels, 205
Taco Bell product development, 174 productivity and lower costs and,
18 3D printers, 172 Tour de France, 85 Toyota reworking production, 650 UPS
linear programming, 709 staffi ng, 426
U.S. cartoon production in Manila, global view of operations, 33
Zero wait time guarantee in Michigan’s ER, 753
One-sided window, T5 – 12 Online auctions, 456 Online catalogues, 456 Online exchanges, 456 On-time delivery, 488 Operating characteristics (OC)
curves, 263 – 264 , T2 – 2 to T2 – 3 Operations analyst, OM and, 9 Operations and productivity,
1 – 28 . See also operations management
Operations chart job design and, 418 method analysis and, 419
Operations layout strategy. See Layout strategies
Operations management decision process in, 678 – 679 defi nition, 4 ethics and social responsibility, 19 goods and services, 11 – 13 growth of services, 11 – 12 Hard Rock Café and, 2 – 3 heritage of, 8 – 10 integrating with other activities, 43 job opportunities in, 7 – 8 management process, 7 new challenges, 18 – 19 operations for goods and services,
11 – 13 organizing to produce goods and
services, 4 , 6 productivity, service sector and,
17 – 18 productivity challenge, 13 – 18 productivity measurement, 14 – 15 productivity variables, 15 – 17 reasons to study, 6 – 7 service growth, 11 – 12 service pay, 12 – 13 signifi cant events in OM, 10 supply chain and, 6 ten strategy decisions, 7 , 8 what operation managers do, 7 – 8 where OM jobs are, 7 – 8 why study?, 6 – 7
Operations managers jobs, 7 – 8 Operations strategy in a global
environment, 29 – 58 competitive advantage through
operations, 36 – 39 developing missions and strategies,
35 – 36 global view, 32 – 35 issues in, 40 – 41 outsourcing, 44 – 48 strategy development and
implementation, 41 – 44 strategy options, 49 – 50 ten strategic OM decisions, 39 , 43 ,
44 using software, 51 – 52
Operator input to machines, 416 – 417
Opportunities in an integrated supply chain, 452 – 454
Opportunity cost, assignment method and, 608 – 610 , 608 n
Opportunity loss, 610 Optimistic time in PERT, 77 Options, limiting for service
effi ciency, 181 Oracle Primavera, 66
Ordering cost, 495 – 496 Organization, building and staffi ng,
43 – 44 Organization charts, 5 Organizing for product development,
169 – 170 Organizing to produce goods and
services, 4 , 6 Origin points, transportation
modeling and, 730 Orlando Magic, 230 . See also Amway
Center aggregate planning, revenue
management, and, 560 control chart, 230 forecasting and, 154 – 155 MRP and, 595 – 596 short-term scheduling and, 604 ,
631 – 632 sustainability, 208–209
Orlando Utilities Commission, Global Company Profi le, 660 – 661
Otis Elevator, 342 Outsourcing
defi ned, 44 – 46 rating outsource providers, 47 – 48 risks of, 46 – 47 theory of comparative advantage,
46 types of, 447
P P system, 514 – 515 Pacifi c Cycle LLC, 447 Paddy-Hopkirk Factory, 419 Paraguay, MERCOSUR and, 34 Parallel path, redundancy and,
666 – 667 Parameter, sensitivity analysis and,
705 – 706 Parametric Technology Corp., 179 Pareto charts, 226 , 227 – 228 Pareto principle, 491 Park Plaza Hotel, 198 Parker Hi-Fi Systems, 525 Partial tour, T5 – 6 Partnering relationships, supply chain
strategies and, 19 Partnerships, JIT and, 640 – 642 Part-time employees, 412 Path, T5 – 6 Pattern of arrivals at the system,
749 – 750 Pay, service sector and, 12 – 13 p- charts, 256 – 257 , 259 Pegging, 575 People, sustainability issues and,
195 – 196 PepsiCo
mission statement, 36 supply chain management, 462 – 463
OM in Action (Continued)
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G E N E R A L I N D E X I21
sustainable production process, 200
Performance criteria, for sequencing jobs, 611
Periodic inventory systems, 493 Periodic order quantity (POQ),
578 – 579 Perpetual inventory system, 493 , 514 Perrier, 219 Personal ethics, 460 Personal time allowances, 422 PERT (program evaluation and
review technique) activity-on-arrow (AOA) example,
71 activity-on-node (AON) example,
69 – 70 critique, 85 – 86 framework, 67 – 68 Gantt charts versus, 65 Microsoft Project analysis, 87 network diagrams and approaches,
68 probability of project completion,
79 – 82 time estimates, 77 – 78
PERT analysis, Microsoft Project and, 87
Pessimistic time estimate, PERT and, 77
Phantom bills of material, MRP and, 570
Pharmaceutical companies, use of RFID tags, 295
Philippines, cartoon industry and, 33 Physical sciences, OM and, 10 Pig production, 300 Pilferage, 494 Ping Inc., 285 Pipelines, logistics management and,
457 Pirelli SpA, 52–53 Pivot column, T3 – 4 Pivot number, T3 – 4 Pivot row, T3 – 4 Pixar, 373 Plan-Do-Check-Act (PDCA), 220 –
221 Planned order receipt, MRP and, 574 Planned order release, MRP and, 574 Planning bills, MRP and, 570 Planning horizons, aggregate
planning and, 532 – 533 Planning process, aggregate planning
and, 532 – 533 Plant manager position, OM and, 9 Poisson distribution, 250 n, 750 Poisson table, A4 Poka-yoke
lean operations, 650 service blueprinting, 292 source inspection and, 231
Political change, generating new products and, 166
Political risk, location strategy and, 343
Polycon Industries, 646 POM for Windows, A6 – A7
accessing, 21 aggregate planning, 553 break-even analysis, 327 decision table and trees, 689 factor rating models, 51 forecasting, 144 inventory problems, 517 layout strategy, 393 learning curves, 784 linear programming, 718 location problems, 354 – 355 material requirements planning
(MRP), 588 project scheduling, 89 queuing problems, 766 reliability problems, 672 short-term scheduling, 624 simulation and, 801 SPC control charts, OC curves,
acceptance sampling and process capability, 267
transportation problems, 739 use of, A6 – A7
Porsche, 650 Port of Baltimore, 119 – 120 Port of Charleston, 451 Port of Miami warehouse tenting,
696 Portion-control standards, 176 Postponement, mass customization
and, 285 supply chain management and,
453 Potholes, New York City, 137 Pratt & Whitney, machine technology
and, 295 Precedence relationship, in assembly-
line balancing, 387 Predetermined time standards,
425 – 427 Preferred Hotels and Resorts
Worldwide, 235 Prevention costs, quality and, 218 Preventive maintenance, 667 – 670 Priority rules, sequencing jobs and,
611 – 614 Probabilistic inventory models and
safety stock, 508 – 510 other models, 511 – 513
Probability distribution, Monte Carlo simulation, 794
Process analysis, design and, 288 – 293 fl owchart, 289 job design and, 419 process chart, 289 – 290 service blueprinting, 292 – 293
time-function mapping, 289 value-stream mapping, 290 – 291
Process capability, SPC and, 260 – 262 defi nition, 260 index (C pk ) and, 261 – 262 ratio (C p ) and, 260 – 261
Process charts, 289 – 290 , 418 , 419 Process choices, comparison of,
286 – 288 Process comparison, 286 – 288 Process control, 295 – 296 Process design
mass customization, 285 OM and
customer interaction and, 293 – 294
Process focus process strategies and, 282 – 283 ,
286 scheduling, 605
Process improvement consultants, OM positions and, 9
Process mapping, 289 Process redesign, 298 – 299 Process strategy, 279 – 305
analysis and design, 288 – 293 comparison and, 286 – 288 defi ned, 282 equipment and technology
selection, 288 four process strategies, 282 – 288 mass customization focus and,
284 – 286 process focus and, 282 – 283 process redesign, 298 – 299 product focus, 284 production technology, 294 – 298 repetitive focus and, 283 service process design, special
considerations for, 293 – 294 technology in services, 298
Process time of a station, 314 Process-chain-network (PCN)
analysis, 179 – 181 Process-focused facilities, 605 Process-oriented layout, 371 , 378 – 383
computer software for, 382 – 383 focused work center and focused
factory, 386 work cells and, 371 , 383 – 386
Procter & Gamble, 172 – 173 , 195 , 198 , 474
Producer’s risk, 263 , T2 – 3 to T2 – 4 Product decision, 163 Product design issues, 171 – 173 . See
also Design of goods and services
application of decision trees and, 182 – 184
computer-aided design (CAD), 171 computer-aided manufacturing
(CAM), 172
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I22 G E N E R A L I N D E X
life cycle assessment (LCA), 173 mass customization, 285 modular design, 171 regulations, 203 robust design, 171 standard for the exchange of
product data (STEP), 172 sustainability, 173 , 198 – 200 3D printing and, 172 value analysis, 173 virtual reality technology, 172 – 173
Product development, 166 – 170 alliances and, 175 continuum, 173 – 175 development system, 166 global OM, 34 house of quality and, 166 – 169 issues for design and, 171 – 173 joint ventures and, 174 – 175 manufacturability and, 170 OM challenges, 19 organizing for, 169 – 170 purchasing technology by
acquiring a fi rm and, 174 quality function deployment
(QFD), 166 – 169 teams and, 170 3-D printing, 172 value engineering and, 170
Product development continuum, 173 – 175
Product failure rate (FR), reliability and, 664
Product focus, 284 , 286 , 288 Product generation, new
opportunities, 166 Product liability, quality and, 218 Product life cycle, 164
management and, 178 – 179 strategy and, 41 , 164 – 165
Product life-cycle management (PLM), 178 – 179
Product manager, 169 – 170 Product strategy options support
competitive advantage, 163 – 164
Product-by-value analysis, 165 Product-focused facilities, 605 Production
defi ned, 4 doubling along learning curve,
777 transition from design to, 184 varying capacity, 536
Production order quantity model, 502 – 504
Production technology, 294 – 298 automated guided vehicles (AGV),
296 automated storage and retrieval
system (ASRS), 296
automatic identifi cation system (AIS), 295
computer-integrated manufacturing (CIM), 297 – 298
fl exible manufacturing system (FMS), 297
machine technology, 294 – 295 process control, 295 – 296 radio frequency identifi cation
(RFID), 295 robots, 296 vision systems, 296
Production-mix example, LP and, 710 – 711
Production/operations, OM and, 4 Productivity
defi ned, 13 multifactor, 14 – 15 single-factor, 14
Productivity, Starbucks Coff ee and, 14
Productivity challenge and OM, 13 defi ned, 13 measurement of, 14 – 15 service sector and, 17 – 18 variables, 15 – 17
Productivity variables, 15 – 17 Product-oriented layout, 371 , 386 –
391 , 387 assembly line balancing and,
387 – 391 Profi t, sustainability and, 197 Program evaluation and review
technique (PERT). See PERT Project completion probability, 79 – 82 Project controlling, 66 – 67 Project crashing and cost-time trade-
off s, 82 – 85 Project management, 60 – 104
activity-on-arrow example, 71 activity-on-node example, 69 – 70 calculating slack time, 75 – 76 cost-time trade-off s, 82 – 85 critical path analysis, 71 – 72 critique of PERT and CPM, 85 – 86 determining the project schedule,
71 – 76 ethical dilemma, 89 ethical issues in, 64 framework of PERT and CPM,
67 – 68 identifying the critical path, 75 – 76 importance of, 62 Microsoft Project, 77 , 86 – 88 network diagrams and approaches,
68 non-critical paths, 81 – 82 PERT/CPM in, 67 – 68 probability of project completion,
79 – 82 project controlling, 66 – 67 project crashing, 82 – 85
project planning, 62 – 65 project scheduling and, 65 time estimates in, 77 – 78 using software, 89 – 90 variability in activity times, 77 – 82 work breakdown structure, 64 – 65
Project Management Institute (PMI), 8 , 64
Project manager, 63 – 64 Project network, 69 Project organization, 62 – 63 Project planning, 62 – 65 Project scheduling, 65
determining, 71 – 76 Microsoft Project view, 86 – 87
Proplanner, 179 , 383 Provide better goods and services,
global view of operations and, 34
Providing redundancy, reliability and, 665 – 667
Proximity to competitors, location strategies
and, 344 to markets, location strategies and,
343 – 344 to suppliers, location strategies
and, 344 in workspace, 372
Psychological components, job design and, 413 – 414
Pull data, 452 Pull system, 640
kanban, 647 – 649 Purchase orders outstanding, MRP
and, 570 Purchase technology by acquiring
fi rm, 174 Purchasing, centralized, 455 – 456 Purdue Pharma LP, 298 Push systems, 640
Q Q systems, 514 QFD. See Quality Function
Deployment (QFD) Quain Lawn and Garden, Inc.,
725 – 726 Qualitative forecasting methods, 111
consumer market survey, 111 – 112 Delphi method, 111 jury of executive opinion, 111 market survey, 111 – 112 sales force composite, 111
Quality, 213 – 244 . See also Total quality management (TQM)
cost of, 218 – 219 defi ning, 217 – 219 demand, capacity and, 311 – 287 ethics and, 219 house of, 166 – 167 implications of, 217 – 218
Product design issues (Continued)
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G E N E R A L I N D E X I23
international quality standards, 218 , 454
leaders in, 219 lean operations, 649 Malcolm Baldrige National Quality
Award, 218 productivity measurement, 15 role of inspection, 230 – 233 services and, 233 – 234 strategy and, 216 tools of TQM, 226 – 230 total quality management, 219 – 226
Quality circle, 222 Quality control, 10 Quality function deployment (QFD),
166 – 169 Quality loss function (QLF), 225 Quality manager, 9 Quality robust, 224 – 225 Quantitative forecasts, 111
associative models, 112 time series models, 112
Quantity discount models, inventory management and, 505 – 507
Quantity discounts contracting, 455
Queue costs, 753 – 754 Queuing models, variety of, 754 – 765
Little’s Law and, 763 Model A(M/M/l): single channel
with Poisson arrivals/ exponential service times, 754 – 757
Model B(M/M/S): multiple-channel queuing model, 757 – 761
Model C(M/D/l): constant-service- time model, 762
Model D: fi nite-population model, 763 – 765
use of waiting-line tables, 759 – 760
using software, 766 Queuing problems, simulation of, 797 Queuing theory, 748 – 749 Quik Lube, 260
R Rackspace, 534 Radio frequency identifi cation
(RFID), 295 , 452 , 475 Railroads, logistics management and,
457 Random number
table of, 795, A4 Random number intervals, Monte
Carlo simulation and, 795 Random stocking, warehouse layout
and, 377 Random variations, time series
forecasting and, 113 Range chart limits, setting of,
253 – 254
using of, 253 – 254 Rapid Reviews:
Chapter 1 Operations and Productivity, 27 – 28
Chapter 2 Operations Strategy in a Global Environment, 57 – 58
Chapter 3 Project Management, 103 – 104
Chapter 4 Forecasting, 165 – 166 Chapter 5 Design of Goods and
Services, 191 – 192 Chapter 6 Managing Quality,
243 – 244 Chapter 7 Process Strategy and
Sustainability, 305 – 306 Chapter 8 Location Strategies,
365 – 366 Chapter 9 Layout Strategies,
405 – 406 Chapter 10 Human Resources,
Job Design, and Work Measurement, 439 – 440
Chapter 11 Supply-Chain Management, 469 – 470
Chapter 12 Inventory Management, 527 – 528
Chapter 13 Aggregate Planning, 561 – 562
Chapter 14 Material Requirement Planning (MRP) and ERP, 597 – 598
Chapter 15 Short-Term Scheduling, 633 – 634
Chapter 16 Lean Operations, 657 – 658
Chapter 17 Maintenance and Reliability, 675 – 676
Module A. Decision Making Tools, 697 – 698
Module B. Linear Programming, 727 – 728
Module C. Transportation Model, 745 – 746
Module D. Waiting Line Models, 773 – 774
Module E. Learning Curves, 789 – 790
Module F. Simulation, 807 – 808 Supplement 5 Sustainability in the
Supply Chain, 211 – 212 Supplement 6 Statistical Process
Control, 277 – 278 Supplement 7 Capacity and
Constraint Management, 305 – 306
Supplement 11 Supply-Chain Management Analytics, 485 – 486
Rapid-Lube case study, 55 Rating outsource providers, 47 – 48 Raw material inventory, 490 R -chart, 248 , 253 – 254
Record accuracy, inventory management and, 493
Red Lobster Restaurants location studies, 362 – 363 time study, 423
Reduce costs, global view of operations and, 33 – 34
Reduce inventory, JIT and, 643 – 644 Reduce lot sizes, JIT and, 644 – 645 Reduce setup costs, JIT and,
645 – 646 Reduce variability, JIT inventory and,
643 – 644 Reduced space and inventory, JIT
and, 643 Redundancy, reliability and, 665 – 667 Regal Marine, 55 , 162
Global Company Profi le, 160 – 161 product design, 189–190 strategy at, 55 supply chain management,
467 – 468 Regression and correlation analysis,
forecasting and, 131 – 137 Relationship chart, 372 Reliability, 663 – 667 . See also
Maintenance and reliability defi ned, 662 providing redundancy and,
665 – 667 using software to improve, 672
Reneging customers, 750 Reorder point (ROP) inventory
management and, 501 – 502 Repair capabilities, increasing, 670 Repetitive and product-oriented
layout, 386 – 391 Repetitive facilities, scheduling and,
605 Repetitive focus, process strategy and,
283 , 286 Repetitive manufacturing, Harley
Davidson and, 280 – 281 Reputation, quality and, 217 “Request for quotation,” 447 Requirements of an LP problem, 701 Reservations systems, 314 Reshoring, 46 , 47 Resource Conservation and Recovery
Act, 204 Resources, linear programming and,
706 – 707 Resources view, operations strategy
and, 40 Respect for people, TPS and, 649 Response, competitive advantage
and, 39 – 40 Restaurants. See also Darden
restaurants; Fast-food restaurants aggregate planning and, 546 MRP and, 583 – 584
Retail layout, 370 , 372 – 375
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I24 G E N E R A L I N D E X
Retail stores. See also Specialty retail shops
inventory management, 495 job design, 421 scheduling services and, 618 – 619
Revenue function, break-even analysis and, 318
Revenue management, aggregate planning and, 547 – 550
Revenue sharing, 455 Reverse auctions, 456 Reverse logistics, 203 , 460 – 461 RFID, 295 , 452 , 475 RFQs (requests for quotes), 447 Richey International, 235 Ricoh Corp., 228 Right-hand/left-hand chart, 418 Right-hand-side values, LP and,
706 – 707 Risk
decision making under, 682 – 683 outsourcing, 46 – 47 reducing with incremental changes,
322 – 323 supply chain and, 449 – 451 , 472 –
474 Ritz-Carlton Hotels
quality, 218 , 242 Robert Bosch, 34 Robots, 296 , 297 , 376 Robust design, product development
and, 171 Robust model, inventory
management and, 500 – 501 Rochester Manufacturing Corp.,
302 Rolls-Royce, 30 Rope, in bottleneck management, 317 Route sheet, 178 Routing service vehicles, T5 – 5 to
T5 – 11 Routing vehicles, T5 – 4 Rubbermaid, 162 , 534 Run test, charts and, 260 Rusty Wallace’s NASCAR Racing
Team, Global Company Profi le, 408 – 409
S Saab, 30 Saber Roofi ng, use of GIS system,
352 – 353 SAE (Society of Automotive
Engineers), 176 – 177 Safe Drinking Water Act, 204 Safe Place Infant Security Solution,
295 Safeskin Corp., 37 Safety stock
inventory management and, 501 , 508 – 510
MRP and, 575
Sales and operations planning (S&OP), 533 – 534
Sales force composite, forecasting and, 111
Sales incentives, 452 Sample missions, 37 Samples, SPC and, 247 . See also
Acceptance sampling Sam’s Club, 495 Samsung, 34 , 163 San Diego Hospital, 129 – 130 San Francisco General Hospital, 651 SAP AG, 586 SAP PLM, 179 SAS, 257 SAS/GIS, 353 S.C. Johnson, 196 , 200 Scatter diagrams, TQM tools and,
226 , 227 Scheduling
criteria, 604 – 605 decisions, 532 forward and backward and, 603 –
604 just-in-time and, 646 – 649 lean operations, 646 – 649 , 652 linear programming example,
712 – 713 mass customization, 285 OM decisions and, 8 project, 65 , 71 – 76 service vehicles and, T5 – 11 to
T5 – 13 vehicles, T5 – 4
Schneider National, 457 Schwinn Bicycle Co., 447 SCOR model, 463 – 464 Seasonal demands, capacity and, 312
aggregate planning, 537 airline industry, 313
Seasonal variations in data, 126 – 131 Seasonality, time series and, 112 SEATO, 34 Second-order smoothing, 124 Security, JIT, supply chain
management and, 451 Selection of equipment and
technology, process strategy and, 294 – 298
Self-directed teams, 414 Sensitivity analysis, LP and, 705 –
708 Sensitivity Report, 706 Sequencing, jobs in work centers,
611 – 617 critical ratio and, 614 – 615 defi nition, 611 earliest due date, 611 , 614 fi rst come, fi rst served, 614 Johnson’s rule and, 615 – 616 limitations of rule-based
dispatching systems, 616 – 617
priority rules for dispatching jobs, 611 – 614
shortest processing time, 611 , 614 Sequential sampling, T2 – 2 Service blueprinting, 292 – 293 Service industry inspection, 232 Service level, probabilistic models
and, 508 Service pay, 12 – 13 Service recovery, 234 Service sector
defi ned, 12 demand and capacity management
in, 313 – 314 documents for, 181 – 182 effi ciency and, 181 forecasting and, 140 – 141 location strategy, 350 – 351 operations in, 11 – 13 organization chart, 5 productivity, boosting, 294 productivity and, 17 – 18 TQM in, 232 – 234
Service vehicle scheduling, T5 – 11 to T5 – 13
Service(s). See also Service Sector aggregate planning and, 545 – 547 defi ned, 11 design of, goods and, 179 – 182 diff erences from goods and, 11 documents for, 179 – 182 focus, 288 growth of, 11 – 12 inventory control, 494 – 495 lean operations in, 652 learning curves in, 777 – 778 MRP and, 583 – 584 pay in, 12 – 13 process design, 293 – 294 scheduling and, 618 – 621 service blueprinting, process
strategy and, 292 – 293 service characteristics, waiting line
system and, 751 – 752 service time distribution, waiting
line system and, 752 technology, 298 total quality management, services
and, 233 – 234 Servicescapes, 375 SERVQUAL, 234 Setup cost, 496
reducing in lean operations and JIT, 645 – 646
Setup time, 496 steps for reducing, 646
Seven steps in forecasting, 110 – 111 Seven tools of TQM, 226 – 230 Seven wastes, lean operations and,
638 Shader Electronics, LP problem
example, T3 – 1 to T3 – 7
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G E N E R A L I N D E X I25
Shadow price, 707 Shared value, 194 Shell Lubricants, 299 Sherwin Williams, 163 Shipping systems, 456 – 457 Shortest processing time (SPT), 611 ,
614 Short-range forecast, 108 Short-term scheduling, 599 – 634 . See
also Scheduling airlines, 619 cyclical scheduling, service
employees and, 620 – 621 ethical dilemma, 621 – 622 fi nite capacity (FCS) and, 617 – 618 fi nite loading, and, 604 importance of, 602 infi nite loading and, 604 issues and, 602 – 605 limitations of rule-based
dispatching systems, 616 – 617 loading jobs, 605 – 610 priority rules for sequencing jobs
and, 611 – 614 process-focused facilities and, 605 repetitive facilities and, 605 scheduling criteria, 604 – 605 sequencing, jobs in work centers,
611 – 617 services and, 618 – 621 using software, 622 – 624
Shouldice hospital, 163 – 164 , 284 Shrinkage, 494 Siemens, 179 , 382 Signifi cant events in OM, 10 Signs, symbols, artifacts, 375 Simplex method of LP, 713 , T3 – 1
to T3 – 10 . See also Linear programming
artifi cial and surplus variable, T3 – 7 converting constants to equations,
T3 – 2 defi nition, 713 , T3 – 2 setting up fi rst simplex table, T3 – 2
to T3 – 4 simplex solution procedures, T3 – 4
to T3 – 6 solving minimization problems,
T3 – 7 to T3 – 8 summary of simplex steps for
maximization problems, T3 – 6 Simulation, 791 – 808
advantages and disadvantages, 793 defi ned, 792 inventory example, 797 – 799 Monte Carlo, 794 – 797 queuing problem and, 797 using software, 800 – 801
Singapore Airlines, 257 Single channel queuing model/
Poisson arrivals/ exponential service times, 754 – 757
Single sampling, T2 – 2 Single-factor productivity, 14 Single-period inventory model,
513 – 514 Single-phase system, 752 Single-product case, break-even
analysis and, 319 – 320 Single-server queuing system, 751 ,
754 – 757 Single-stage control of
replenishment, 452 Six Sigma, 221 – 222 , 222 n, 261 Size of arrival population, 749 SKUs, 495 Slack time, 75 – 76 Slack variables, simplex method and,
T3 – 2 Slotting fees, 374 , 392 Smith, Adam, 412 Smiths Aerospace, 30 Smoothing constant, 116 – 117 SMT’s negotiation with IBM, 787 –
788 Snapper Lawn Mowers, 534 – 535 Social accounting, 197 Social responsibility, OM and, 19 Sociological and demographic
change, generating new products and, 166
Software. See Excel OM; Excel spreadsheets, creating your own; POM for Windows
Solutions to even-numbered problems, A8 – A19
Solved Problems aggregate planning, 554 – 555 capacity and constraint
management, 328 – 329 decision making, 691 – 692 forecasting, 144 – 146 human resources, job design, and
work measurement, 432 – 433 inventory management, 517 – 519 layout strategy, 394 – 396 lean operations, 653 – 654 learning curves, 784 – 785 linear programming, 718 – 719 location problems, 355 – 357 maintenance and reliability, 672 modem production, 53 MRP and ERP and, 589 – 592 process strategy, 300 product design, 186 project management, 90 – 93 quality, 236 queuing, 766 – 768 short-term scheduling, 624 – 626 simulation, 801 – 802 statistical process control, 267 – 268 supply chain management, 465 – 466 supply chain management
analytics, 480 – 481
sustainability, 206 – 207 tire industry globalization, 52 – 53 transportation modeling, 740 – 741
Solving routing and scheduling vehicle problems, T5 – 4
Sony, 32 , 46 , 225 Source inspection, 231 Sourcing issues: make-or-buy vs.
outsourcing, 446 – 447 South Korea
SEATO and, 31 South Korea, manufacturing in, 51 Southern Recreational Vehicle Co.,
362 Southwest Airlines, 38 , 257 , 313 , 414
activity mapping, 43 , 44 activity mapping of low-cost
competitive advantage, 44 Southwestern University
forecasting, 154 – 155 project management, 98 – 99 quality management, 239 – 240
Spatial layout, 375 Special considerations for service
process design, 293 – 294 Special packaging, 454 Specialty retail shops, forecasting
and, 140 St. John’s Health Center, 415 Staffi ng
capacity, 536 global talent, 34 – 35 organization, 43 – 44 work cells, 384 – 386
Stakeholders, 19 Standard deviation, calculation, 248 n Standard deviation of the regression,
133 Standard error of the estimate,
133 – 134 Standard for the exchange of product
data (STEP), 172 Standard normal distribution, T1 – 5
to T1 – 7 Standard normal table, A2 – A3 , T1 – 5
to T1 – 7 Standard time, labor standards and,
422 , 423 Standard work practice, TPS and,
650 Standardization, supply-chain
management and, 453 Starbucks Coff ee
productivity and, 14 scheduling software, 619 simulation and, 797
Statistical process control (SPC), 226 , 229 – 230 , 245 – 278
acceptance sampling, 262 – 265 assignable variations, 247 average outgoing quality (AOQ)
and, 264 – 265
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I26 G E N E R A L I N D E X
c- charts, 257 – 259 central limit theorem and, 248 – 249 control charts, 230 , 241 , 247 – 248 control charts for attributes, 256 –
259 control charts for variables, 248 defi nition, 246 managerial issues and control
charts, 259 – 260 mean chart limits, 250 – 253 natural variations, 246 – 247 operating characteristic curve and,
263 – 264 patterns on control charts, 259 p- charts, 256 – 257 , 259 process capability, 260 – 262 process capability index and,
261 – 262 process capability ratio and, 260 –
261 R -chart, 248, 253 – 254 samples, 247 setting mean and range charts,
254 – 256 setting range chart limits and,
253 – 254 using ranges and mean charts,
250 – 255 using software, 266 – 267 variables for, 248 which chart to use, 259 x-bar chart, 248 , 250 – 253 , 259
Statistical tools for managers, T1 – 1 to T1 – 8
continuous probability distributions, T1 – 4 to T1 – 7
discrete probability distribution, T1 – 2 to T1 – 4
expected value of a discrete probability distribution, T1 – 3
variance of a discrete probability distribution, T1 – 3 to T1 – 4
Status, viewing in Microsoft Project, 87 – 88
Steakhouses restaurant chain, 289 Stepping-stone method,
transportation model and, 734 – 737
Stock-keeping units (SKUs), 377 Stop & Shop, 376 Stopwatch studies, 421 – 424 Storage, 289 n, 478 – 479 Strategic importance of:
layout decisions, 370 learning curves, 782 – 783 location, 340 – 341 maintenance and reliability, 662 –
663 short-term scheduling, 602 supply-chain management, 444 –
446
Strategic OM decisions, 41 – 43 Strategy
aggregate planning, 535 – 538 capacity and, 311 competitive advantages and, 163 – 164 defi nition, 36 developing missions and, 35 – 36 driven investments, applying
investment analysis to, 324 – 326 global operation options and, 49 human resource, 410 international, 49 issues in operations and, 41 – 44 life cycle and, 164 – 165 multidomestic, 49 operations in a global environment,
29 – 58 quality and, 216 service locations and, 350 – 351 transnational, 50
Strategy, aggregate planning development and implementation
key success factors, 41 – 42 Strategy development and
implementation, 41 – 44 building and staffi ng the
organization, 43 – 44 core competencies and, 42 – 43 implementing 10 strategic OM
decisions and, 44 integrating OM and other
activities, 43 key success factors, 41 – 43
Structure for MRP, 571 – 575 Subaru, 205 Subcontracting, 536 Sub-Saharan Africa, 460 Subtours, T5 – 8 Super Fast Pizza, 40 Supplier selection analysis, 476 – 477 Suppliers
audits of, 195 development, 454 – 455 evaluation, 454 lean operations in services and, 652 location in proximity to, 344 partnerships in lean operations and
JIT, 640 – 642 Supply chain, 6
partnering, 19 risk, 449 – 451
Supply-chain management, 441 – 470 Supply chain management analytics,
471 – 486 evaluation techniques, 472 – 474 managing the bullwhip eff ect, 474 – 476 supplier selection analysis and,
476 – 477 Supply Chain Operations
Reference model (SCOR), 463 – 464
transportation mode analysis and, 477 – 478
warehouse storage, 478 – 479 Supply Chain Operations Reference
model (SCOR), 463 – 464 benchmarking and, 463 defi nition, 444 distribution management, 459 E-procurement and, 456 ethics and, 460 , 465 forecasting and, 109 – 110 globalization, 33 integrated and, 451 – 454 inventory assets, 461 – 463 JIT and, 451 joint ventures and, 448 keiretsu networks and, 448 – 449 logistics management, 456 – 459 Manager and Planner, 9 measuring performance and,
461 – 464 mitigation tactics and, 450 objective of, 444 risk, 449 – 451 software, 584 sourcing issues: make-or-buy vs.
outsourcing, 446 – 447 sourcing strategies and, 447 – 449 strategic importance of, 444 – 446 strategies and, 447 – 449 suppliers, few, many and, 447 supply base, building, 454 – 456 Supply Chain Operations
Reference (SCOR) model, 463 – 464
sustainability and, 460 – 461 vertical integration, 448 virtual companies, 449
Surplus variables, T3 – 7 Surrogate (substitute) interaction,
process, 180 Survey, market, 111 – 112 Sustainability
commons, 195 corporate social responsibility, 194 end-of-life phase, 203 lean operations, 652 logistics, 200 – 202 OM and, 19 product design, 198 – 200 product design and, 173 production process, 200 regulations and industry standards,
203 – 205 supply-chain management, and,
460 – 461 systems view, 195 triple bottom line, 195 – 198
Sustainability software, 584 SWOT analysis, 41 Symbols, servicescapes, and, 375 System nervousness, 575
Statistical process control (Continued)
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G E N E R A L I N D E X I27
System reliability, 663 – 665 Systems view of sustainability, 195
T Tables
control chart limits, 252 learning-curve approach, 779 – 782 Normal curve areas, A2–A3 Poisson distribution, A4 random numbers, 795, A4
TacoBell, 164 forecasting, 140 lowering costs with productivity
and energy savings, 18 product development, 174
Taguchi concepts, 224 – 225 Takt time, 384 , 384 n Takumi, 219 TAL Apparel Ltd., 452 , 453 Tangible costs, location strategies
and, 342 Target-oriented quality, 225 Teams, self-directed, 414 Techniques for evaluating supply
chain, 472 Technological change, generating new
products and, 166 Technological forecasts, 109 Technology. See also Production
technology acquiring by purchasing fi rm, 174 focus, 288 group, 177 human resource constraints, 410
Teijin Ltd, 174 – 175 Teijin Seiki, 30 Temperature, in work area, 417 Temple University Hospital, 776 , 778 Ten OM strategy decisions, 7 , 8 , 39 ,
43 , 44 Tesla, 194 Test scores, forecasting success by,
141 Texas Instruments, 49 , 218 , 222 Thales, 30 Theory of comparative advantage, 46 Theory of constraints (TOC),
capacity and constraint management, 317
Therbligs, 426 Third-party logistics (3PL), 458 – 459 Three Mile Island nuclear facility,
417 Three time estimates in PERT, 77 – 78 3M, 162 3-D printing, 172 Throughput, lean operations and,
640 Throughput time, 315 Time aggregation, 507 Time estimates, in PERT, 77 – 78 Time fences, 575
Time horizons, 108 – 109 Time Measurement Units (TMUs),
426 Time series forecasting, 112 – 131
cycles in, 113 cyclical variations in data, 131 decomposition of time series and,
112 – 113 exponential smoothing and,
116 – 117 exponential smoothing with trend
adjustment, 120 – 124 least-square methods, 124 – 126 mean absolute deviation, 118 – 119 measuring forecast error, 117 – 120 moving averages and, 114 – 116 naive approach to, 113 – 114 random variations and, 113 seasonal variations in data,
126 – 131 seasonality, 112 – 113 smoothing constant, 116 – 117 trend and, 112 trend projections and, 124 – 126
Time status, viewing in Microsoft Project, 87 – 88
Time studies, labor standards and, 421 – 425 , 430
Time-based competition, product development and, 173 – 175
alliances, 175 joint ventures, 174 – 175 purchasing technology by buying a
fi rm, 174 Time-function mapping, 289 Tools of total quality management,
225 – 230 . See also Control charts
cause and eff ect diagrams, 227 check sheets, 226 fl owcharts, 228 – 229 histogram, 229 knowledge of, 225 Pareto charts, 227 – 228 scatter diagrams, 227 statistical process control,
229 – 230 Toray Industries, 30 Toshiba, 497 Total productive maintenance
(TPM), 671 Total quality management (TQM),
219 – 230 benchmarking, 222 – 223 continuous improvement, 220 – 221 defi nition, 219 employee empowerment, 222 fl ow chart, 216 just-in-time, 224 plan-do-check-act (PDCA),
220 – 221 services, 232 – 234
Six Sigma, 221 – 222 Taguchi concepts, 224 – 225 tools of, 225 – 230 total productive maintenance
(TPM), 671 Total slack time, 76 Tour, T5 – 15 Tour de France, 85 Toy manufacturing in China, 48 Toyota Motor Corp., 34 , 110 , 164 ,
651 annual inventory turnover, 463 Global Company Profi le, 636 – 637 level strategy, 538 low-emission vehicles, 194 reworking production line, 650 supply chain risks and tactics,
450 Toyota Production System, 636 – 637 ,
649 – 650 continuous improvement and, 649 defi ned, 638 respect for people, 649 standard work practice, 650
Toys “R” Us, 421 TQM. See Total quality management
(TQM) Tracking signal, forecasts and, 138 Transition to production, 184 Transnational strategy, global
operations and, 50 Transportation
location strategies and, 349 – 350 waste, 289 n
Transportation matrix, 731 Transportation method of linear
programming, 543 – 545 Transportation mode analysis,
477 – 478 Transportation models, 729 – 746
defi ned, 730 degenergy and, 737 – 738 demand not equal to supply and,
737 initial solution and, 732 – 734 intuitive lowest-cost method and,
733 – 734 location and strategies and,
349 – 350 northwest-corner rule and, 732 – 733 special issues in, 737 – 738 stepping-stone method and,
734 – 737 using software, 738 – 739
Transportation problems, MODI and VAM methods and,
T4 – 1 to T4 – 10 MODI method, T4 – 2 to T4 – 4 VOGEL’s approximation method
(VAM), T4 – 4 to T4 – 7 Traveling salesman problem (TSP),
T5 – 4 , T5 – 5 to T5 – 8
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I28 G E N E R A L I N D E X
Trend projections, forecasting and, 112 , 124 – 126
Triple bottom line, 195 – 198 Trucking
logistics management and, 457 sustainability and, 201
Trust, managing the supply chain and, 453
TRW airbag plant, 222 Turkish airline, 257 24/7 operations, scheduling services
and, 619 Twin Falls, Idaho, call center, 50 Two-bin system, 493 Two-sided window, T5 – 12 Type I error, 263 Type II error, 263 Types of forecasts, 109 Tyson, 450
U Uber Technologies, Inc., 24 Umpires, major league baseball, 610 U.N. Framework Convention on Climate
Change (UNFCCC), 204 Understand markets, global view of
operations and, 34 Undirected arcs, routing and
scheduling vehicles, T5 – 3 Unifi , 445 United Airlines, 354 United States trade agreements, 34 Unlimited arrival population, 749 UPS (United Parcel Service), 416 ,
426 , 457 , 651 logistics, 201 , 458 maintenance and, 662 sustainable product design, 198
Uruguay, MERCOSUR and, 34 U.S., NAFTA and, 34 U.S. Army, 792 US Airways, 257 U.S. Navy, 67 Using ExcelOM, A5 Using POM for Windows, A6 – A7 Utilization, capacity and, 310
V Validity range for the shadow price,
LP and, 707 Value, shared, 194 Value analysis, 173 Value engineering, product
development and, 170 Value stream mapping (VSM), 290 – 291 Value-chain analysis, 40 Values, location strategy and, 343 Values for use in Poisson distribution,
A4 Variability, lean operations and,
639 – 640 , 643 – 644 Variability in activity times, project
management and, 77 – 82
probability of project completion, 79 – 82
three time estimates in PERT, 77 – 78
Variable costs, break-even analysis and, 318 , 324
Variable data, control charts for, 259 Variable inspection, 232 Variable(s), control charts for, 248 ,
259 Variance of a discrete probability
distribution, statistical tools and, T1 – 3 to T1 – 4
Vehicle routing and scheduling, T5 – 1 to T5 – 18
characteristics of problems, and, T5 – 3 to T5 – 5
introduction, T5 – 2 objectives of routing and
scheduling problems, T5 – 2 other problems, T5 – 13 to T5 – 14 routing service vehicles, T5 – 5 to
T5 – 11 scheduling service vehicles, T5 – 11
to T5 – 13 Vendor-managed inventory (VMI),
452 – 453 Vertical integration, supply-chain
management and, 448 Video Case Studies:
Alaska Airlines: human resources, 437 – 438 lean operations, 655 – 656 process strategy, 303 – 304 quality, 240 – 242 scheduling challenges, 726
Amway Center sustainability, 208 – 209
Arnold Palmer Hospital: capacity planning, 333 – 334 culture of quality, 240 hospital layout, 402 – 404 JIT, 656 process analysis, 304 project management, 99 – 100 supply chain and, 468
Darden Restaurants: location strategies, 362 – 363 outsourcing off shore, 56 statistical process control, 276 supply chain, 467
Frito-Lay: maintenance, 674 managing inventory, 525 – 526 operations management, 25 statistical process control, 275 sustainability, 209 – 210
Hard Rock Cafe: forecasting, 155 – 156 global strategy, 55 – 56 human resource strategy, 438 location strategy, 363 – 364
operations management in services, 25 – 26
project management, 77 , 100 – 102
scheduling, 632 Orlando Magic:
forecasting and, 154 – 155 MRP and, 595 – 596 revenue management, 560 short-term scheduling, 631 – 632 sustainability, 208–209
Red Lobster, location and strategies, 362 – 363
Regal Marine: product design, 189 – 190 strategy at, 55 supply-chain management at,
467 – 468 Ritz-Carlton Hotel company, 242 Wheeled Coach:
inventory control, 526 layout strategy, 404 MRP and, 596 process strategy, 302 – 303
Viewing the project schedule, Microsoft Project and, 86 – 87
Virgin Australia, 257 Virtual companies, sourcing strategies
and, 449 supply chain strategies and, 449
Virtual reality technology, 172 – 173 Vision systems, production
technology and, 296 Visual workplace, job design and, 420 Vizio, Inc., 449 Vogel’s approximation method
(VAM), transportation problems and, T4 – 4 to T4 – 7
Volkswagen, 173 , 349 – 350 Volvo, 32
W Waiting line models, 747 – 774
arrival characteristics and, 749 – 750 characteristics of waiting line
system, 749 – 752 measuring queue performance and, 752 multiphase system and, 752 multiple-server queuing system
and, 752 other queuing approaches, 765 queuing costs, 753 – 754 queuing models, varieties of,
754 – 765 queuing theory, 748 – 749 service characteristics and, 751 – 752 single-phase system and, 752 use of tables and, 759 – 761 using software, 766 waiting-line characteristics and,
750 – 751 Waiting lines, 748
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G E N E R A L I N D E X I29
Walmart, 375 competing on cost and, 38 cross-docking strategy, 376 retail layout and, 375 RFID, 475 RFID and supply chain, 452 , 475 scheduling, 652 supply chain management and,
110 , 445 , 460 , 463 supply chain risks and tactics, 450 supply-chain review, 196
Walmart Marketplace, 454 Walt Disney Co. See also Walt Disney
Parks and Resorts Lion King revenue management,
548 Walt Disney Parks and Resorts
experience diff erentiation and, 38 forecasting and, 110 – 111 Global Company Profi le, 106 – 107 maintenance, 662 new products, 162 waiting lines and, 748
Warehouse logistics management and, 457 –
458 storage, 478 – 479
Warehousing layout, 370 , 375 – 377 cross-docking, 376 customizing, 377 objective, 375 random stocking, 377
Waste, 289 n Waste elimination, lean production
and, 638 – 639
Waterfall approach to projects, 67 Waterways, logistics management
and, 457 WBS (work breakdown structure),
64 – 65 Weeks of supply, 462 – 463 Weighted moving average forecasts,
115–116 Western Electric Hawthorne plant,
413 What is a learning curve?, 776 – 777 Wheeled Coach
Global Company Profi le, 564–565 inventory control, 526 layout strategy, 404 MRP and, 564 – 565 , 568 , 596 process strategy, 302 – 303
Where are OM jobs?, 7 – 8 Whirlpool, 35 , 201 , 296 Why study OM, 6 – 7 Why use linear programming, 700 Wilheim Karmann, 46 Windows, Microsoft’s development
structure, 64 – 65 Winter Park Hotel, 772 Work balance chart, 385 Work breakdown structure (WBS),
project management and, 64 – 65
Work cells, layout and, 371 , 383 – 386
advantage of, 384 focused work center and focused
factory, 386 requirements of, 383 – 384
staffi ng and balancing, 384 – 386 Work environment
ergonomics and, 415 – 417 job design and, 417
Work measurement (labor standards) historical experience and, 421 predetermined time standards and,
425 – 427 time studies and, 421 – 425 work sampling and, 427 – 430
Work order, 178 Work rules, human resources and,
412 Work sampling, labor standards and,
427 – 429 Work schedules, labor planning and,
411 – 412 Work-in-process (WIP) inventory,
490 , 604 – 605 World Trade Organization (WTO), 34
ethical issues, 35
X x-bar chart, 248 , 250 – 253 , 259 Xerox, 218 , 223
Y Yield management, aggregate
planning and, 547 – 550
Z z values, A2 – A3
sample size for time study, 424 Zero opportunity costs, 609 Zhou Bicycle Co., 524 – 525
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ISBN-13: 978-0-13-413042-2 ISBN-10: 0-13-413042-1
9 780 13 4 1 30422
9 0 0 0 0
OPE R AT IONS M A NAGE M E N T Sustainability and Supply Chain Management
T W E L F T H E D I T I O N
O P
E R
A T
IO N
S M
A N
A G
E M
E N
T S u
stain ab
ility an d
S u
p p
ly C h
ain M
an agem
en t
T W E L F T H E D I T I O N
JAY HEIZER | BARRY RENDER | CHUCK MUNSON
HEIZER RENDER MUNSON
www.pearsonhighered.com
IMPROVING RESULTS A proven way to help individual students achieve
the goals that educators set for their course.
ENGAGING EXPERIENCES Dynamic, engaging experiences that personalize and
activate learning for each student.
AN EXPERIENCED PARTNER From Pearson, a long-term partner with a true grasp
of the subject, excellent content, and an eye on the future of education.
Pearson’s MyLab™
- Cover
- Title Page
- Copyright Page
- About the Authors
- Brief Table of Contents
- Table of Contents
- Preface
- Acknowledgments
- Part One: Introduction to Operations Management
- Chapter 1: Operations and Productivity
- Global Company Profile: Hard Rock Cafe
- What Is Operations Management?
- Organizing to Produce Goods and Services
- The Supply Chain
- Why Study OM?
- What Operations Managers Do
- The Heritage of Operations Management
- Operations for Goods and Services
- The Productivity Challenge
- Current Challenges in Operations Management
- Ethics, Social Responsibility, and Sustainability
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software for Productivity Analysis
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 2: Operations Strategy in a Global Environment
- Global Company Profile: Boeing
- A Global View of Operations and Supply Chains
- Developing Missions and Strategies
- Achieving Competitive Advantage Through Operations
- Issues in Operations Strategy
- Strategy Development and Implementation
- Strategic Planning, Core Competencies, and Outsourcing
- Global Operations Strategy Options
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software to Solve Outsourcing Problems
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 3: Project Management
- Global Company Profile: Bechtel Group
- The Importance of Project Management
- Project Planning
- Project Scheduling
- Project Controlling
- Project Management Techniques: PERT and CPM
- Determining the Project Schedule
- Variability in Activity Times
- Cost-Time Trade-Off s and Project Crashing
- A Critique of PERT and CPM
- Using Microsoft Project to Manage Projects
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software to Solve Project Management Problems
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 4: Forecasting
- Global Company Profile: Walt Disney Parks & Resorts
- What is Forecasting?
- The Strategic Importance of Forecasting
- Seven Steps in the Forecasting System
- Forecasting Approaches
- Time-Series Forecasting
- Associative Forecasting Methods: Regression and Correlation Analysis
- Monitoring and Controlling Forecasts
- Forecasting in the Service Sector
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software in Forecasting
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Part Two: Designing Operations
- Chapter 5: Design of Goods and Services
- Global Company Profile: Regal Marine
- Goods and Services Selection
- Generating New Products
- Product Development
- Issues for Product Design
- Product Development Continuum
- Defining a Product
- Documents for Production
- Service Design
- Application of Decision Trees to Product Design
- Transition to Production
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Solved Problem
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Supplement 5: Sustainability in the Supply Chain
- Corporate Social Responsibility
- Sustainability
- Design and Production for Sustainability
- Regulations and Industry Standards
- Summary
- Key Terms
- Discussion Questions
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 6: Managing Quality
- Global Company Profile: Arnold Palmer Hospital
- Quality and Strategy
- Defining Quality
- Total Quality Management
- Tools of TQM
- The Role of Inspection
- TQM in Services
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Supplement 6: Statistical Process Control
- Statistical Process Control (SPC)
- Process Capability
- Acceptance Sampling
- Summary
- Key Terms
- Discussion Questions
- Using Software for SPC
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 7: Process Strategy
- Global Company Profile: Harley-Davidson
- Four Process Strategies
- Selection of Equipment
- Process Analysis and Design
- Special Considerations for Service Process Design
- Production Technology
- Technology in Services
- Process Redesign
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Solved Problem
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Supplement 7: Capacity and Constraint Management
- Capacity
- Bottleneck Analysis and the Theory of Constraints
- Break-Even Analysis
- Reducing Risk with Incremental Changes
- Applying Expected Monetary Value (EMV) to Capacity Decisions
- Applying Investment Analysis to Strategy-Driven Investments
- Summary
- Key Terms
- Discussion Questions
- Using Software for Break-Even Analysis
- Solved Problems
- Problems
- Case Study
- Endnote
- Rapid Review
- Self Test
- Chapter 8: Location Strategies
- Global Company Profile: Fedex
- The Strategic Importance of Location
- Factors That Affect Location Decisions
- Methods of Evaluating Location Alternatives
- Service Location Strategy
- Geographic Information Systems
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software to Solve Location Problems
- Solved Problems
- Problems
- Case Studies
- Endnote
- Rapid Review
- Self Test
- Chapter 9: Layout Strategies
- Global Company Profile: Mcdonald’s
- The Strategic Importance of Layout Decisions
- Types of Layout
- Office Layout
- Retail Layout
- Warehouse and Storage Layouts
- Fixed-Position Layout
- Process-Oriented Layout
- Work Cells
- Repetitive and Product-Oriented Layout
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software to Solve Layout Problems
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 10: Human Resources, Job Design, and Work Measurement
- Global Company Profile: Rusty Wallace’s Nascar Racing Team
- Human Resource Strategy for Competitive Advantage
- Labor Planning
- Job Design
- Ergonomics and the Work Environment
- Methods Analysis
- The Visual Workplace
- Labor Standards
- Ethics
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Part Three: Managing Operations
- Chapter 11: Supply Chain Management
- Global Company Profile: Darden Restaurants
- The Supply Chain’s Strategic Importance
- Sourcing Issues: Make-or-Buy and Outsourcing
- Six Sourcing Strategies
- Supply Chain Risk
- Managing the Integrated Supply Chain
- Building the Supply Base
- Logistics Management
- Distribution Management
- Ethics and Sustainable Supply Chain Management
- Measuring Supply Chain Performance
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Solved Problems
- Problems
- Case Studies
- Endnote
- Rapid Review
- Self Test
- Supplement 11: Supply Chain Management Analytics
- Techniques for Evaluating Supply Chains
- Evaluating Disaster Risk in the Supply Chain
- Managing the Bullwhip Effect
- Supplier Selection Analysis
- Transportation Mode Analysis
- Warehouse Storage
- Summary
- Discussion Questions
- Solved Problems
- Problems
- Rapid Review
- Self Test
- Chapter 12: Inventory Management
- Global Company Profile: Amazon.Com
- The Importance of Inventory
- Managing Inventory
- Inventory Models
- Inventory Models for Independent Demand
- Probabilistic Models and Safety Stock
- Single-Period Model
- Fixed-Period (P) Systems
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software to Solve Inventory Problems
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 13: Aggregate Planning and S&OP
- Global Company Profile: Frito-Lay
- The Planning Process
- Sales and Operations Planning
- The Nature of Aggregate Planning
- Aggregate Planning Strategies
- Methods for Aggregate Planning
- Aggregate Planning in Services
- Revenue Management
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software for Aggregate Planning
- Solved Problems
- Problems
- Case Studies
- Endnote
- Rapid Review
- Self Test
- Chapter 14: Material Requirements Planning (MRP) and ERP
- Global Company Profile: Wheeled Coach
- Dependent Demand
- Dependent Inventory Model Requirements
- MRP Structure
- MRP Management
- Lot-Sizing Techniques
- Extensions of MRP
- MRP in Services
- Enterprise Resource Planning (ERP)
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software to Solve MRP Problems
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 15: Short-Term Scheduling
- Global Company Profile: Alaska Airlines
- The Importance of Short-Term Scheduling
- Scheduling Issues
- Scheduling Process-Focused Facilities
- Loading Jobs
- Sequencing Jobs
- Finite Capacity Scheduling (FCS)
- Scheduling Services
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software for Short-Term Scheduling
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Chapter 16: Lean Operations
- Global Company Profile: Toyota Motor Corporation
- Lean Operations
- Lean and Just-in-Time
- Lean and the Toyota Production System
- Lean Organizations
- Lean in Services
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Solved Problem
- Problems
- Case Studies
- Endnote
- Rapid Review
- Self Test
- Chapter 17: Maintenance and Reliability
- Global Company Profile: Orlando Utilities Commission
- The Strategic Importance of Maintenance and Reliability
- Reliability
- Maintenance
- Total Productive Maintenance
- Summary
- Key Terms
- Ethical Dilemma
- Discussion Questions
- Using Software to Solve Reliability Problems
- Solved Problems
- Problems
- Case Study
- Rapid Review
- Self Test
- Part Four: Business Analytics Modules
- Module A: Decision-Making Tools
- The Decision Process in Operations
- Fundamentals of Decision Making
- Decision Tables
- Types of Decision-Making Environments
- Decision Trees
- Summary
- Key Terms
- Discussion Questions
- Using Software for Decision Models
- Solved Problems
- Problems
- Case Study
- Endnote
- Rapid Review
- Self Test
- Module B: Linear Programming
- Why Use Linear Programming?
- Requirements of a Linear Programming Problem
- Formulating Linear Programming Problems
- Graphical Solution to a Linear Programming Problem
- Sensitivity Analysis
- Solving Minimization Problems
- Linear Programming Applications
- The Simplex Method of LP
- Integer and Binary Variables
- Summary
- Key Terms
- Discussion Questions
- Using Software to Solve LP Problems
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Module C: Transportation Models
- Transportation Modeling
- Developing an Initial Solution
- The Stepping-Stone Method
- Special Issues in Modeling
- Summary
- Key Terms
- Discussion Questions
- Using Software to Solve Transportation Problems
- Solved Problems
- Problems
- Case Study
- Rapid Review
- Self Test
- Module D: Waiting-Line Models
- Queuing Theory
- Characteristics of a Waiting-Line System
- Queuing Costs
- The Variety of Queuing Models
- Other Queuing Approaches
- Summary
- Key Terms
- Discussion Questions
- Using Software to Solve Queuing Problems
- Solved Problems
- Problems
- Case Studies
- Endnotes
- Rapid Review
- Self Test
- Module E: Learning Curves
- What Is a Learning Curve?
- Learning Curves in Services and Manufacturing
- Applying the Learning Curve
- Strategic Implications of Learning Curves
- Limitations of Learning Curves
- Summary
- Key Term
- Discussion Questions
- Using Software for Learning Curves
- Solved Problems
- Problems
- Case Study
- Endnote
- Rapid Review
- Self Test
- Module F Simulation
- What Is Simulation?
- Advantages and Disadvantages of Simulation
- Monte Carlo Simulation
- Simulation with Two Decision Variables: An Inventory Example
- Summary
- Key Terms
- Discussion Questions
- Using Software in Simulation
- Solved Problems
- Problems
- Case Study
- Endnote
- Rapid Review
- Self Test
- Appendix
- Bibliography
- Name Index
- General Index
- Back Cover
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- 2016-04-15T10:09:09+0000
- Preflight Ticket Signature
