kim woods

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Prentice Hall Boston Columbus Indianapolis New York San Francisco Upper Saddle River

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Jesse H. Jones Professor of Business Administration Texas Lutheran University

Charles Harwood Professor of Operations Management Crummer Graduate School of Business

Rollins College

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Library of congress cataloging-in-publication data

ISBN 10: 0-13-216392-6 ISBN 13: 978-0-13-216392-7

Heizer, Jay H. Operations management / Jay Heizer, Barry Render. — 10th ed., Flexible version.

p. cm. Includes bibliographical references and index. ISBN-13: 978-0-13-216392-7 (alk. paper) ISBN-10: 0-13-216392-6 (alk. paper)

1. Production management. I. Render, Barry. II. Title. TS155.H3726 2010 658.5—dc22

2010039417

To: Tristan, Sebastian, Max, Kate, Owen, Clara; the next generation is in good hands.

—JH

To: Suzan, Samantha, Dara, and Joey —BR

Jay Heizer 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 visit- ing positions at Boston University, George Mason University, the Czech Management Center, and the Otto-Von-Guericka 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, including 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 articles 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 manage- ment courses in undergraduate, graduate, and executive programs.

Barry Render 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 Science Department. Dr. Render has also worked in the aerospace industry, for General Electric, McDonnell Douglas, and NASA.

Professor Render has co-authored 10 textbooks for Prentice Hall, 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 11th 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. From 1984 to 1993, Dr. Render was President of Management Service Associates of Virginia, Inc., whose technology clients included the FBI; the U.S. Navy; Fairfax County, Virginia; and C&P Telephone.

Dr. Render has taught operations management courses in Rollins College’s MBA and Executive MBA programs. He has received that school’s Welsh Award as leading Professor and was selected by Roosevelt University as the 1996 recipient of the St. Claire Drake Award for Outstanding Scholarship. In 2005, Dr. Render received the Rollins College MBA Student Award for Best Overall Course, and in 2009 was named Professor of the Year by full-time MBA students.

vi

About the Authors

PART ONE Introduction to Operations Management 1

1. Operations and Productivity 1 2. Operations Strategy in a Global

Environment 23

3. Project Management 47 4. Forecasting 83

PART TWO Designing Operations 123

5. Design of Goods and Services 123 6. Managing Quality 153

Supplement 6: Statistical Process Control 177

7. Process Strategy and Sustainability 201 Supplement 7: Capacity and Constraint Management 227

8. Location Strategies 251 9. Layout Strategies 273

10. Human Resources, Job Design, and Work Measurement 303

PART THREE Managing Operations 333 11. Supply-Chain Management 333

Supplement 11: Outsourcing as a Supply- Chain Strategy 359

12. Inventory Management 371 13. Aggregate Planning 407 14. Material Requirements Planning (MRP)

and ERP 433

15. Short-Term Scheduling 465 16. JIT and Lean Operations 495 17. Maintenance and Reliability 517

PART FOUR Quantitative Modules 531 A. Decision-Making Tools 531 B. Linear Programming 547 C. Transportation Models 567 D. Waiting-Line Models 583 E. Learning Curves 605 F. Simulation 615

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

vii

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ix

About the Authors vi Preface xvii

PART ONE Introduction to Operations Management 1

1. Operations and Productivity 1 Global Company Profile: Hard Rock Cafe 2

What Is Operations Management? 4

Organizing to Produce Goods and Services 4

Why Study OM? 6

What Operations Managers Do 7

The Heritage of Operations Management 8

Operations in the Service Sector 10

Differences Between Goods and Services 10

Growth of Services 11

Service Pay 11

Exciting New Trends in Operations Management 12

The Productivity Challenge 13

Productivity Measurement 14

Productivity Variables 16

Productivity and the Service Sector 18

Ethics and Social Responsibility 19

Chapter Summary 19 • Key Terms 20 • Solved Problems 20 • Bibliography 21

2. Operations Strategy in a Global Environment 23 Global Company Profile: Boeing 24

A Global View of Operations 26

Cultural and Ethical Issues 29

Developing Missions and Strategies 30

Mission 30

Strategy 30

Achieving Competitive Advantage Through Operations 31

Competing on Differentiation 31

Competing on Cost 32

Competing on Response 32

Ten Strategic OM Decisions 35

Issues in Operations Strategy 36

Strategy Development and Implementation 39

Key Success Factors and Core Competencies 39

Build and Staff the Organization 41

Integrate OM with Other Activities 41

Global Operations Strategy Options 42

International Strategy 42

Multidomestic Strategy 43

Global Strategy 43

Transnational Strategy 43

Chapter Summary 44 • Key Terms 44 • Solved Problem 44 • Bibliography 45

3. Project Management 47 Global Company Profile: Bechtel Group 48

The Importance of Project Management 50

Project Planning 50

The Project Manager 51

Work Breakdown Structure 52

Project Scheduling 53

Project Controlling 54

Project Management Techniques: PERT and CPM 55

The Framework of PERT and CPM 55

Network Diagrams and Approaches 55

Activity-on-Node Example 57

Activity-on-Arrow Example 60

Determining the Project Schedule 60

Forward Pass 61

Backward Pass 63

Calculating Slack Time and Identifying the Critical Path(s) 64

Variability in Activity Times 65

Three Time Estimates in PERT 66

Probability of Project Completion 68

Cost–Time Trade-Offs and Project Crashing 71

A Critique of PERT and CPM 73

Using Microsoft Project to Manage Projects 74

Chapter Summary 77 • Key Terms 77 • Using Software to Solve Project Management Problems 77 • Solved Problems 78 • Bibliography 81

Table of Contents

4. Forecasting 83 Global Company Profile: Walt Disney Parks & Resorts 84

What Is Forecasting? 86

Forecasting Time Horizons 86

The Influence of Product Life Cycle 87

Types of Forecasts 87

The Strategic Importance of Forecasting 87

Human Resources 87

Capacity 87

Supply-Chain Management 88

Seven Steps in the Forecasting System 88

Forecasting Approaches 89

Overview of Qualitative Methods 89

Overview of Quantitative Methods 89

Time-Series Forecasting 90

Decomposition of a Time Series 90

Naive Approach 90

Moving Averages 91

Exponential Smoothing 94

Measuring Forecast Error 95

Exponential Smoothing with Trend Adjustment 98

Trend Projections 101

Seasonal Variations in Data 103

Cyclical Variations in Data 108

Associative Forecasting Methods: Regression and Correlation Analysis 108

Using Regression Analysis for Forecasting 108

Standard Error of the Estimate 110

Correlation Coefficients for Regression Lines 111

Multiple-Regression Analysis 113

Monitoring and Controlling Forecasts 113

Adaptive Smoothing 115

Focus Forecasting 115

Forecasting in the Service Sector 116

Chapter Summary 117 • Key Terms 117 • Using Software in Forecasting 118 • Solved Problems 119 • Bibliography 121

PART TWO Designing Operations 123

5. Design of Goods and Services 123 Global Company Profile: Regal Marine 124

Goods and Services Selection 126

Product Strategy Options Support Competitive Advantage 126

Product Life Cycles 127

Life Cycle and Strategy 128

Product-by-Value Analysis 128 Generating New Products 129

New Product Opportunities 129

Importance of New Products 129 Product Development 130

Product Development System 130

Quality Function Deployment (QFD) 131

Organizing for Product Development 133

Manufacturability and Value Engineering 134

Issues for Product Design 135

Robust Design 135

Modular Design 135

Computer-Aided Design (CAD) 136

Computer-Aided Manufacturing (CAM) 137

Virtual Reality Technology 137

Value Analysis 137

Ethics, Environmentally-Friendly Designs, and Sustainability 138

Systems and Life Cycle Perspectives 138

Time-Based Competition 140

Purchasing Technology by Acquiring a Firm 141

Joint Ventures 142

Alliances 142

Defining a Product 142

Make-or-Buy Decisions 143

Group Technology 144

Documents for Production 144

Product Life-Cycle Management (PLM) 145

Service Design 146

Documents for Services 147

Application of Decision Trees to Product Design 149

Transition to Production 150

Chapter Summary 151 • Key Terms 151 • Solved Problem 151 • Bibliography 152

6. Managing Quality 153 Global Company Profile: Arnold Palmer Hospital 154

Quality and Strategy 156

Defining Quality 156

Implications of Quality 157

Malcolm Baldrige National Quality Award 158

Cost of Quality (COQ) 158

Ethics and Quality Management 158

International Quality Standards 159

ISO 9000 159

ISO 14000 159

Total Quality Management 160

Continuous Improvement 161

Six Sigma 161

x Table of Contents

Employee Empowerment 162

Benchmarking 163

Just-in-Time (JIT) 164

Taguchi Concepts 165

Knowledge of TQM Tools 166

Tools of TQM 166

Check Sheets 166

Scatter Diagrams 167

Cause-and-Effect Diagrams 167

Pareto Charts 167

Flowcharts 168

Histograms 169

Statistical Process Control (SPC) 169

The Role of Inspection 170

When and Where to Inspect 170

Source Inspection 171

Service Industry Inspection 171

Inspection of Attributes versus Variables 171

TQM in Services 172

Chapter Summary 175 • Key Terms 175 • Bibliography 175

Supplement 6: Statistical Process Control 177 Statistical Process Control (SPC) 178

Control Charts for Variables 180

The Central Limit Theorem 180

Setting Mean Chart Limits ( -Charts) 181

Setting Range Chart Limits (R-Charts) 185

Using Mean and Range Charts 185

Control Charts for Attributes 186

Managerial Issues and Control Charts 190

Process Capability 191

Process Capability Ratio (Cp) 191

Process Capability Index 192

Acceptance Sampling 193

Operating Characteristic Curve 194

Average Outgoing Quality 195

Supplement Summary 196 • Key Terms 196 • Using Software for SPC 196 • Solved Problems 197 • Bibliography 199

7 Process Strategy and Sustainability 201 Global Company Profile: Harley-Davidson 202

Four Process Strategies 204

Process Focus 204

Repetitive Focus 205

Product Focus 206

Mass Customization Focus 206

Comparison of Process Choices 208

Process Analysis and Design 211

Flowchart 211

Time-Function Mapping 211

Value-Stream Mapping 212

Process Charts 213

Service Blueprinting 214

Special Considerations for Service Process Design 214

Customer Interaction and Process Design 215

More Opportunities to Improve Service Processes 217

Selection of Equipment and Technology 217

Production Technology 218

Machine Technology 218

Automatic Identification Systems (AISs) and RFID 218

Process Control 219

Vision Systems 219

Robots 220

Automated Storage and Retrieval Systems (ASRSs) 220

Automated Guided Vehicles (AGVs) 220

Flexible Manufacturing Systems (FMSs) 220

Computer-Integrated Manufacturing (CIM) 220

Technology in Services 221

Process Redesign 223

Sustainability 223

Resources 223

Recycle 223

Regulations 224

Reputation 224

Chapter Summary 225 • Key Terms 225 • Solved Problem 225 • Bibliography 226

Supplement 7: Capacity and Constraint Management 227 Capacity 228

Design and Effective Capacity 228

Capacity and Strategy 230

Capacity Considerations 231

Managing Demand 231

Demand and Capacity Management in the Service Sector 233

Bottleneck Analysis and the Theory of Constraints 234

Process Times for Stations, Systems, and Cycles 235

Theory of Constraints 237

Bottleneck Management 237

Break-Even Analysis 238

Single-Product Case 240

1Cpk2

x

Table of Contents xi

xii Table of Contents

Multiproduct Case 240

Reducing Risk with Incremental Changes 242

Applying Expected Monetary Value (EMV) to Capacity Decisions 243

Applying Investment Analysis to Strategy-Driven Investments 244

Investment, Variable Cost, and Cash Flow 244

Net Present Value 244

Supplement Summary 247 • Key Terms 247 • Using Software for Break-Even Analysis 247 • Solved Problems 247 • Bibliography 250

8. Location Strategies 251 Global Company Profile: FedEx 252

The Strategic Importance of Location 254

Factors That Affect Location Decisions 255

Labor Productivity 256

Exchange Rates and Currency Risk 256

Costs 257

Political Risk, Values, and Culture 258

Proximity to Markets 258

Proximity to Suppliers 258

Proximity to Competitors (Clustering) 258

Methods of Evaluating Location Alternatives 259

The Factor-Rating Method 259

Locational Break-Even Analysis 260

Center-of-Gravity Method 262

Transportation Model 263

Service Location Strategy 264

How Hotel Chains Select Sites 265

The Call Center Industry 266

Geographic Information Systems 267

Chapter Summary 268 • Key Terms 268 • Using Software to Solve Location Problems 269 • Solved Problems 270 • Bibliography 271

9. Layout Strategies 273 Global Company Profile: McDonald’s 274

The Strategic Importance of Layout Decisions 276

Types of Layout 276

Office Layout 278

Retail Layout 279

Servicescapes 280

Warehousing and Storage Layouts 281

Cross-Docking 282

Random Stocking 282

Customizing 282

Fixed-Position Layout 282

Process-Oriented Layout 283

Computer Software for Process-Oriented Layouts 287

Work Cells 288

Requirements of Work Cells 288

Staffing and Balancing Work Cells 289

The Focused Work Center and the Focused Factory 291

Repetitive and Product-Oriented Layout 292

Assembly-Line Balancing 293

Chapter Summary 298 • Key Terms 298 • Using Software to Solve Layout Problems 298 • Solved Problems 299 • Bibliography 302

10. Human Resources, Job Design, and Work Measurement 303 Global Company Profile: Rusty Wallace’s NASCAR Racing Team 304

Human Resource Strategy for Competitive Advantage 306

Constraints on Human Resource Strategy 306

Labor Planning 307

Employment-Stability Policies 307

Work Schedules 307

Job Classifications and Work Rules 308

Job Design 308

Labor Specialization 308

Job Expansion 308

Psychological Components of Job Design 309

Self-Directed Teams 310

Motivation and Incentive Systems 311

Ergonomics and the Work Environment 311

Methods Analysis 314

The Visual Workplace 315

Labor Standards 317

Historical Experience 317

Time Studies 317

Predetermined Time Standards 322

Work Sampling 325

Ethics 328

Chapter Summary 328 • Key Terms 328 • Solved Problems 329 • Bibliography 331

PART THREE Managing Operations 333 11. Supply-Chain Management 333

Global Company Profile: Darden Restaurants 334

The Supply Chain’s Strategic Importance 336

Supply-Chain Risk 337

Ethics and Sustainability 339

Table of Contents xiii

Supply-Chain Economics 340

Make-or-Buy Decisions 341

Outsourcing 341

Supply-Chain Strategies 341

Many Suppliers 341

Few Suppliers 341

Vertical Integration 342

Joint Ventures 343

Keiretsu Networks 343

Virtual Companies 343

Managing the Supply Chain 343

Issues in an Integrated Supply Chain 344

Opportunities in an Integrated Supply Chain 345

E-Procurement 347

Online Catalogs 347

Auctions 348

RFQs 348

Real-Time Inventory Tracking 348

Vendor Selection 349

Vendor Evaluation 349

Vendor Development 350

Negotiations 350

Logistics Management 350

Distribution Systems 351

Third-Party Logistics 351

Cost of Shipping Alternatives 352

Security and JIT 353

Measuring Supply-Chain Performance 354

The SCOR Model 356

Chapter Summary 357 • Key Terms 357 • Solved Problems 357 • Bibliography 358

Supplement 11: Outsourcing as a Supply-Chain Strategy 359 What Is Outsourcing? 360

Strategic Planning and Core Competencies 361

The Theory of Comparative Advantage 362

Risks of Outsourcing 363

Evaluating Outsourcing Risk with Factor Rating 365

Rating International Risk Factors 365

Rating Outsource Providers 366

Advantages and Disadvantages of Outsourcing 367

Advantages of Outsourcing 367

Disadvantages of Outsourcing 367

Audits and Metrics to Evaluate Performance 368

Ethical Issues in Outsourcing 368

Supplement Summary 369 • Key Terms 369 • Using Software to Solve Outsourcing Problems 369 • Bibliography 369

12. Inventory Management 371 Global Company Profile: Amazon.com 372

The Importance of Inventory 374

Functions of Inventory 374

Types of Inventory 375

Managing Inventory 375

ABC Analysis 375

Record Accuracy 377

Cycle Counting 377

Control of Service Inventories 379

Inventory Models 380

Independent vs. Dependent Demands 380

Holding, Ordering, and Setup Costs 380

Inventory Models for Independent Demand 380

The Basic Economic Order Quantity (EOQ) Model 381

Minimizing Costs 381

Reorder Points 386

Production Order Quantity Model 387

Quantity Discount Models 390

Probabilistic Models and Safety Stock 393

Other Probabilistic Models 396

Single-Period Model 398

Fixed-Period (P) Systems 399

Chapter Summary 400 • Key Terms 401 • Using Software to Solve Inventory Problems 401 • Solved Problems 402 • Bibliography 405

13. Aggregate Planning 407 Global Company Profile: Frito-Lay 408

The Planning Process 410

Planning Horizons 410

The Nature of Aggregate Planning 411

Aggregate Planning Strategies 412

Capacity Options 413

Demand Options 414

Mixing Options to Develop a Plan 414

Methods for Aggregate Planning 415

Graphical Methods 415

Mathematical Approaches 420

Comparison of Aggregate Planning Methods 422

Aggregate Planning in Services 422

Restaurants 424

Hospitals 424

National Chains of Small Service Firms 424

Miscellaneous Services 424

Airline Industry 425

Yield Management 425

xiv Table of Contents

Chapter Summary 428 • Key Terms 429 • Using Software for Aggregate Planning 429 • Solved Problems 429 • Bibliography 431

14. Material Requirements Planning (MRP) and ERP 433 Global Company Profile: Wheeled Coach 434

Dependent Demand 436

Dependent Inventory Model Requirements 436

Master Production Schedule 436

Bills of Material 438

Accurate Inventory Records 441

Purchase Orders Outstanding 441

Lead Times for Components 441

MRP Structure 441

MRP Management 446

MRP Dynamics 446

MRP and JIT 446

Lot-Sizing Techniques 447

Extensions of MRP 451

Material Requirements Planning II (MRP II) 451

Closed-Loop MRP 452

Capacity Planning 453

MRP in Services 454

Distribution Resource Planning (DRP) 454

Enterprise Resource Planning (ERP) 455

Advantages and Disadvantages of ERP Systems 458

ERP in the Service Sector 458

Chapter Summary 458 • Key Terms 458 • Using Software to Solve MRP Problems 459 • Solved Problems 460 • Bibliography 463

15. Short-Term Scheduling 465 Global Company Profile: Delta Air Lines 466

The Importance of Short-Term Scheduling 468

Scheduling Issues 468

Forward and Backward Scheduling 470

Scheduling Criteria 470

Scheduling Process-Focused Facilities 471

Loading Jobs 472

Input–Output Control 472

Gantt Charts 474

Assignment Method 475

Sequencing Jobs 478

Priority Rules for Dispatching Jobs 478

Critical Ratio 481

Sequencing N Jobs on Two Machines: Johnson’s Rule 482

Limitations of Rule-Based Dispatching Systems 483

Finite Capacity Scheduling (FCS) 484

Scheduling Repetitive Facilities 485

Scheduling Services 486

Scheduling Service Employees with Cyclical Scheduling 488

Chapter Summary 489 • Key Terms 489 • Using Software for Short-Term Scheduling 489 • Solved Problems 492 • Bibliography 494

16. JIT and Lean Operations 495 Global Company Profile: Toyota Motor Corporation 496

Just-in-Time, the Toyota Production System, and Lean Operations 498

Eliminate Waste 498

Remove Variability 499

Improve Throughput 500

Just-in-Time (JIT) 500

JIT Partnerships 501

Concerns of Suppliers 502

JIT Layout 503

Distance Reduction 503

Increased Flexibility 503

Impact on Employees 503

Reduced Space and Inventory 503

JIT Inventory 504

Reduce Inventory and Variability 504

Reduce Lot Sizes 504

Reduce Setup Costs 506

JIT Scheduling 506

Level Schedules 507

Kanban 508

JIT Quality 510

Toyota Production System 511

Continuous Improvement 511

Respect for People 511

Standard Work Practice 511

Lean Operations 512

Building a Lean Organization 512

Lean Operations in Services 513

Chapter Summary 515 • Key Terms 515 • Solved Problems 515 • Bibliography 515

17. Maintenance and Reliability 517 Global Company Profile: Orlando Utilities Commission 518

The Strategic Importance of Maintenance and Reliability 520

Reliability 521

Improving Individual Components 521

Providing Redundancy 523

Table of Contents xv

Maintenance 524

Implementing Preventive Maintenance 524

Increasing Repair Capabilities 528

Autonomous Maintenance 528

Total Productive Maintenance 528

Techniques for Enhancing Maintenance 529

Chapter Summary 529 • Key Terms 529 • Using Software to Solve Reliability Problems 530 • Solved Problems 530 • Bibliography 530

PART FOUR Quantitative Modules 531 A. Decision-Making Tools 531

The Decision Process in Operations 532

Fundamentals of Decision Making 533

Decision Tables 534

Types of Decision-Making Environments 534

Decision Making under Uncertainty 535

Decision Making under Risk 536

Decision Making under Certainty 537

Expected Value of Perfect Information (EVPI) 537

Decision Trees 538

A More Complex Decision Tree 539

Using Decision Trees in Ethical Decision Making 541

The Poker Decision Process 542

Module Summary 543 • Key Terms 543 • Using Software for Decision Models 543 • Solved Problems 545 • Bibliography 546

B. Linear Programming 547 Why Use Linear Programming? 548

Requirements of a Linear Programming Problem 549

Formulating Linear Programming Problems 549

Shader Electronics Example 549

Graphical Solution to a Linear Programming Problem 550

Graphical Representation of Constraints 550

Iso-Profit Line Solution Method 551

Corner-Point Solution Method 553

Sensitivity Analysis 555

Sensitivity Report 556

Changes in the Resources or Right-Hand-Side Values 556

Changes in the Objective Function Coefficient 557

Solving Minimization Problems 557

Linear Programming Applications 559

Production-Mix Example 559

Diet Problem Example 560

Labor Scheduling Example 561

The Simplex Method of LP 562

Module Summary 563 • Key Terms 563 • Using Software to Solve LP Problems 563 • Solved Problems 564 • Bibliography 566

C. Transportation Models 567 Transportation Modeling 568

Developing an Initial Solution 570

The Northwest-Corner Rule 570

The Intuitive Lowest-Cost Method 571

The Stepping-Stone Method 572

Special Issues in Modeling 575

Demand Not Equal to Supply 575

Degeneracy 576

Module Summary 577 • Key Terms 577 • Using Software to Solve Transportation Problems 578 • Solved Problems 579 • Bibliography 581

D. Waiting-Line Models 583 Queuing Theory 584

Characteristics of a Waiting-Line System 585

Arrival Characteristics 585

Waiting-Line Characteristics 586

Service Characteristics 587

Measuring a Queue’s Performance 588

Queuing Costs 589

The Variety of Queuing Models 590

Model A (M/M/1): Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times 590

Model B (M/M/S): Multiple-Channel Queuing Model 593

Model C (M/D/1): Constant-Service-Time Model 597

Little’s Law 598

Model D: Limited-Population Model 599

Other Queuing Approaches 601

Module Summary 602 • Key Terms 602 • Using Software to Solve Queuing Problems 602 • Solved Problems 603 • Bibliography 604

E. Learning Curves 605 What Is a Learning Curve? 606

Learning Curves in Services and Manufacturing 607

Applying the Learning Curve 608

Arithmetic Approach 608

Logarithmic Approach 609

Learning-Curve Coefficient Approach 609

xvi Table of Contents

Strategic Implications of Learning Curves 611

Limitations of Learning Curves 612

Module Summary 613 • Key Terms 613 • Using Software for Learning Curves 613 • Solved Problems 614 • Bibliography 614

F. Simulation 615 What Is Simulation? 616

Advantages and Disadvantages of Simulation 617

Monte Carlo Simulation 618

Simulation of a Queuing Problem 621

Simulation and Inventory Analysis 623

Module Summary 626 • Key Terms 626 • Using Software in Simulation 626 • Solved Problems 628 • Bibliography 629

Appendices A1 Indices I1 Photo Credits P1

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

Artificial 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

Welcome to your operations management (OM) course. In this book, we present a state-of-the- art view of the activities of the operations function. Operations is an exciting area of manage- ment that has a profound effect on the productivity of both manufacturing and services. 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. OM includes a blend of topics from accounting, industrial engineering, management, manage- ment science, and statistics. Even if you are not planning on a career in the operations area, you will likely be working with people who are. Therefore, having a solid understanding of the role of operations in an organization is of substantial benefit to you. This book will also help you understand how OM affects society and your life. Certainly, you will better under- stand what goes on behind the scenes when you purchase a bag of Frito-Lay potato chips; buy a meal at an Olive Garden, a Red Lobster, or a Hard Rock Cafe; place an order through Amazon.com; buy a customized Dell computer over the Internet; or enter Arnold Palmer Hospital for medical care.

Although many of our readers are not OM majors, we know that marketing, finance, account- ing, and MIS students across the globe will find the material both interesting and useful as we develop a fundamental working knowledge of the operations side of the firm. More than 600,000 readers of our earlier editions seem to have endorsed this premise. We welcome comments by email from our North American readers, from students using the EU edition, the Indian edition, and our editions in Portuguese, Spanish, Turkish, Indonesian and Chinese. Our goal is to make this material useful and interesting to each of you.

NEW TO THIS EDITION Manufacturing Integration with Video Case Studies on Frito-Lay In this edi- tion, we take you behind the scenes at one of the most exciting manufacturers in North America, Frito-Lay, a subsidiary of PepsiCo. We provide five new Video Case Studies, photos, examples, problems, and a Global Company Profile (Chapter 13). This multi-billion-dollar snack food pro- ducer opened its doors so we could examine its use of statistical quality control (Supplement 6), green manufacturing and sustainability (Chapter 7), inventory management (Chapter 12), and maintenance (Chapter 17), as well as its overall OM strategy (Chapter 1) in a series of 8- to 14-minute videos.

Our prior editions focused on Darden Restaurants (Olive Garden/Red Lobster), Hard Rock Cafe, Arnold Palmer Hospital, Wheeled Coach Ambulances, and Regal Marine. These videos and cases appear in this edition as well, along with the five new ones for Frito-Lay.

xvii

Preface

Rapid Reviews In our never-ending quest to make this the most student-friendly text in our field, we now include a two-page Rapid Review in the Lecture Guide & Activities Manual. This detailed yet concise summary of the main points and equations in the chapter helps students pre- pare for homework, exams, and lectures by capturing the essence of the material. Each Rapid Review also includes a self-test, with questions linked to the learning objectives in that chapter. Key terms introduced in the chapter are part of the Rapid Review.

xviii Preface

myomlab and the Learning Process This powerful tool ties together all elements in our book into an innovative learning tool, an exam tool, a homework tool, and an assessment center. myomlab’s new version 2.0 accompanies this edition of the text. By using myomlab, instructors can assign thousands of problems from the text and/or problems/questions from the Test Item File for their students to take online, in any time frame determined by the instructor. With many options for randomizing the sequence, timing, and scoring, myomlab makes giving and grading homework and exams easy. Most problems have also been converted to an algorithmic form, meaning that there are numerous versions of each problem, with different data for each student. Solutions to each problem and its data set are available, if instructors wish, to students immediately after they complete each assignment. The program records grades into the instructor’s grade book. For help, students can click directly to the relevant text page, watch the text authors solve a similar problem, walk through other sample problems, or seek other useful forms of help. This new and innovative feature is truly a wonderful teaching and learning aid. Visit www.myomlab.com for more information.

Integration of Ethics Throughout the Book With this revision, we provide broad cov- erage of ethics as an OM consideration. The topic is addressed in most chapters and in the Lecture Guide & Activities Manual, we present an Ethical Dilemma that can be used for classroom discussion or homework.

Expanded Treatment of the Theory of Constraints Supplement 7 now contains expanded treatment on the theory of constraints, including the material previously covered in Chapter 15. Theory of constraints and bottleneck analysis coverage includes examples, a solved problem, and seven new homework problems.

Sustainability as an OM Responsibility Sustainability is now highlighted in several chapters, especially Chapters 5 (“Design of Goods and Services”) and 7 (“Process Strategy and Sustainability”). Chapter 7 also has two new case studies on the topic as it relates to Frito-Lay and to Walmart.

Author Comments You will notice a new feature throughout every chapter that we call Author Comments. Here we point out why a section, a figure, or a table is so important. The com- ments are intended to be motivational to students, as well as educational.

Preface xix

Additional Homework Problems Our text already contains more homework problems than any other text in the discipline. We have also added hundreds more problems to our Web site for instructors who seek even more variety and freshness. These problems are available at www.myomlab.com. Solutions to these additional problems appear along with regular text prob- lems in our Instructor’s Solution Manual, which was created and proofed by the authors.

CHAPTER-BY-CHAPTER CHANGES To highlight the extent of the revision from the ninth edition, here are a few of the changes, on a chapter-by-chapter basis. We have added new material on the subject of the theory of constraints (to Supplement 7); combined Chapter 10 and Supplement 10 into a new Chapter 10, called “Human Resources, Job Design, and Work Measurement”; and have added extensive new mater- ial on supply chains in Chapter 11 and Supplement 11. The Rapid Review section in the Lecture Guide & Activities Manual is new to each chapter. Active Models, which were illustrated with screen captures in most chapters, now appear in myomlab and the Companion Web site, www. pearsonhighered.com/heizer.

Chapter 1. Operations and Productivity We include a heavily revised section to help motivate students, called “Exciting New Trends in OM,” with a new emphasis on the environment and ethics that runs throughout the book. Four homework problems have been expanded. The Lecture Guide & Activities Manual includes the new Video Case Study “Frito-Lay: Operations Management in Manufacturing.” This new case introduces the company that we refer to throughout this edition and makes an excellent teaching comparison to the other Chapter 1 Video Case Study, “Hard Rock Cafe: Operations Management in Services.” The Zychol Chemical Corporation case has been moved to myomlab and the Companion Web site, www.pearsonhighered.com/heizer.

Chapter 2. Operations Strategy in a Global Environment This chapter has a new figure (Figure 2.4) that relates OM to strategy and a new section called “Issues in Operations Strategy,” which includes Porter’s value chain analysis and five forces model.

Chapter 3. Project Management We have revised our treatment of work breakdown struc- ture with a more visual approach (Figure 3.3), added a new OM in Action box, “Prepping for the Miami Heat Game,” and shortened coverage of Microsoft Project (from seven screen captures down to three).

Chapter 4. Forecasting We have revised the formula for tracking signals [see equation (4.18) and Example 16] and moved the review of forecasting formulas from Table 4.1 into the Rapid Review section in the Lecture Guide & Activities Manual.

Chapter 5. Design of Goods and Services We have added a manufacturer, Cisco, to our discussion of new product yields (Figure 5.6) and created a major new section titled “Ethics, the Environment, and Sustainability.”

Chapter 6. Managing Quality This chapter contains expanded coverage of ISO 14000, including a new OM in Action box, “Subaru’s Clean, Green Set of Wheels with ISO 14001,” and a discussion of ISO 24700. We have also added “A Hospital Benchmarks Against the Ferrari Racing Team” as another OM in Action box and have illustrated checklists as a way to improve quality. A new homework problem uses data from The Economist’s poll of air travel dislikes.

Supplement 6. Statistical Process Control Frito-Lay’s use of SPC is featured in both photographs and a new Video Case Study, “Frito-Lay’s Quality-Controlled Potato Chips.” This video is not only an exploration of the firm’s quality program but a tutorial on how a real firm creates control charts from scratch. There are seven expanded homework problems and one new one, which is based on the former Alabama Air case study.

Chapter 7. Process Strategy and Sustainability The chapter begins with a new Global Company Profile featuring Harley-Davidson’s repetitive manufacturing. We have exten- sively revised our treatment of the four process strategies. It is shortened and concisely illustrated in Figure 7.2. There is a new OM in Action box, “Mass Customization for Straight Teeth.” We have added a major new section on sustainability, where we introduce the four Rs of sustainability. In the Lecture Guide & Activities Manual, there are two new cases: “Environmental Sustainability at Walmart” and the Video Case Study, “Green Manufacturing and Sustainability at Frito-Lay.”

Supplement 7. Capacity and Constraint Management This supplement has been retitled and extensively revised to include coverage of constraint management and the theory of con- straints. Material on the theory of constraints, formerly in Chapter 15, has been melded into this new treatment. A new section, “Bottleneck Analysis and Theory of Constraints,” includes two examples (S3 and S4), a solved problem (S7.5), an OM in Action box on banking and the theory of constraints, and seven new homework problems (S7.9–S7.15). So as not to lengthen Supplement 7, we shortened our coverage of multi-product break-even analysis, replaced decision trees with an EMV approach to capacity decisions, and reduced our treatment of net present value analysis. Finally, the Video Case Study, “Capacity Planning at Arnold Palmer Hospital” has been updated.

Chapter 8. Location Strategies The homework problem selection has been expanded, and 11 problems have been revised to make them more challenging.

Chapter 9. Layout Strategies The main addition to this chapter is the new OM in Action box “Work Cells Increase Productivity at Canon.”

Chapter 10. Human Resources, Job Design, and Work Measurement With this edition, we have merged Supplement 10 (“Work Measurement”) into Chapter 10. This helps make the coverage of this material more concise, while bringing more quantitative material into the main chapter. Coverage of labor planning, job design, ergonomics, and the visual workplace has been edited for brevity. Examples S1–S6 from Supplement 10 remain, now as Examples 1–6. Solved Problems S10.1–S10.5 are now Solved Problems 10.2–10.6. There is a new OM in Action box, “Saving Seconds at Retail Boosts Productivity.” The case study “The Fleet That Wanders” has been moved to myomlab and the Companion Web site.

Chapter 11. Supply-Chain Management In keeping with the growing importance of supply chains as an OM topic, we have rewritten this chapter (and Supplement 11) to keep readers current in this dynamic field. There is new treatment of supply-chain risks, a new section on ethics and sustainability, more coverage of joint ventures, a new section on CPFR, a revision of the mate- rial on e-procurement, and new material explaining the SCOR model.

Supplement 11. Outsourcing as a Supply-Chain Strategy The material here has been heavily edited to keep current with this important topic, including a new Table S11.1 on ranking of outsourcing countries, NASA’s outsourcing shipments to the Space Station, and back- sourcing. We have deleted the section on break-even analysis, which is a model discussed in other chapters, and revised four of the homework problems to make them more challenging.

Chapter 12. Inventory Management The explanation of how to graph costs as a function of order quantity has been expanded to help students better understand the concept

xx Preface

(Figure 12.4). We now cover the single-period model in Example 15 and in three new home- work problems (12.36–12.38). Seven other homework problems have been revised and expanded. Finally, a new Video Case Study is called “Managing Inventory at Frito-Lay.”

Chapter 13. Aggregate Planning The chapter begins with a new Global Company Profile illustrating aggregate planning at Frito-Lay. We have also revised Examples 2–4 to make them more current.

Chapter 14. Material Requirements Planning (MRP) and ERP MRP II and its example in Table 14.4 have been rewritten, the order splitting discussion (and its Example 7) has been revised, the ERP section has been shortened, and a new case study, “Hill’s Automotive, Inc.” replaces the Ikon case, which now appears on myomlab and the Companion Web site, www. pearsonhighered.com/heizer.

Chapter 15. Short-Term Scheduling We have expanded and moved the treatment of the theory of constraints to Supplement 7 and rewritten Problem 15.17.

Chapter 16. Just-in-Time and Lean Operations The Global Company Profile on Toyota has been revised; Figure 16.3, explaining hidden problems, is new; there is a revised kan- ban figure (Figure 16.9); and kaizan is expanded both in the text and in the new OM in Action box “Kaizen at Ducati.” There is also a new OM in Action box on TPS at the Los Angeles Police Department, and there is a new case study, “JIT After a Catastrophe.”

Chapter 17. Maintenance and Reliability There are three new elements: a section covering autonomous maintenance, an Ethical Dilemma regarding the Space Shuttle, and a Video Case Study called “Maintenance Drives Profits at Frito-Lay.”

Module A. Decision-Making Tools This module now begins with an interesting dilemma that involved a famous poker player in a Legends of Poker Tournament. We return to the topic later in the module, with a section called “The Poker Decision Process,” Example 9, and Problem A.24.

Modules B and C. Linear Programming and Transportation Models There are no major changes in these two modules.

Module D. Waiting Line Models A new OM in Action box describes zero wait times at a Michigan Hospital’s ER, and we have added examples of queuing at Costco and Alaska Airlines, created a new section covering Little’s law, and expanded two of the homework problems.

Module E. Learning Curves We have updated Table E.1 and revamped the discussions on the consequences and application of the learning curve.

Module F. Simulation We have added a new OM in Action box, “Simulation Software Takes the Kinks Out of Starbucks’s Lines.”

STUDENT RESOURCES To liven up the course and help students learn the content material, we have made available the following resources:

• Student Study Guide (ISBN: 0-13-510725-3) created by Michael Donovan of Cedar Crest College. Study Guide is designed to help students understand the concepts and quantitative methods of operations management. Each chapter in the study guide consists nine basic com- ponents: Summary; Learning Objectives; Skills to Develop; Annotated Outline; Hints and Tips; Key Terms; Formulas; Self-test Questions; and Supplementary Materials.

• Thirty-one exciting video cases (Located on the Operations Management DVD Library, ISBN: 0-13-611981-6, and at www.myomlab.com.) These Video Case Studies feature real companies (Frito-Lay, Darden Restaurants, Regal Marine, Hard Rock Cafe, Ritz-Carlton, Wheeled Coach, and Arnold Palmer Hospital) and 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 specifically supplement the book’s content.

• DVD video clips (Located on the Operations Management DVD Library, ISBN: 0-13- 611981-6, and at www.myomlab.com.) We have provided 37 one- to two-minute video clips

Preface xxi

to illustrate chapter-related topics with videos at Frito-Lay, Harley-Davidson, Ritz-Carlton, Hard Rock Cafe, Olive Garden, and other firms.

• Virtual tours (Located on the Companion Web site, at www.pearsonhighered.com/heizer). These company tours provide direct links to companies—ranging from a hospital to an auto manufacturer—that practice key concepts. After touring each Web site, students are asked questions directly related to the concepts discussed in the chapter.

• Self-study quizzes (Located on the Companion Web site, www.pearsonhighered.com/ heizer.) These quizzes allow students to test their understanding of each topic. These exten- sive quizzes contain a broad assortment of questions, 20–25 per chapter, including multiple- choice, true/false, and Internet essay questions. The quiz questions are graded and can be transmitted to the instructor for extra credit or serve as practice exams.

• Active Models The 28 Active Models appear in files at www.myomlab.com and the Companion Web site, www.pearsonhighered.com/heizer.

• Excel OM data files Examples in the text that can be solved with Excel OM appear on data files on myomlab and the Companion Web site, www.pearsonhighered.com/heizer. They are identified at the end of each example.

• POM for Windows software (Located at www.myomlab.com and the Companion Web site, 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 www.myomlab.com and the Companion Web site, 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 fill in the data, and then Excel will display and graph (where appropriate) 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’s com- patible with Microsoft Excel 2007 as well as earlier versions of Excel.

• Online Tutorial Chapters (Located on myomlab and the Companion Web site, www. pearsonhighered.com/heizer.) Statistical Tools for Managers, Acceptance Sampling, The Simplex Method of Linear Programming, The MODI and VAM Methods of Solving Transportation Problems, and Vehicle Routing and Scheduling are provided as additional material.

• Virtual office hours (Located at www.myomlab.com.) Professors Heizer and Render appear on myomlab, walking students through 72 Solved Problems.

• Additional practice problems (Located at www.myomlab.com.) These problems provide problem-solving experience. They supplement the examples and solved problems found in each chapter.

• Additional case studies (Located at www.myomlab.com.) These additional case studies supplement the ones in the text. Detailed solutions appear in the Solutions Manual.

• Microsoft Project 2007 (ISBN: 0-13-145421-8.) Microsoft Project, the most popular and powerful project management package, is now available on an additional student CD-ROM. This full version, documented in Chapter 3, is activated to work for 60 days.

INSTRUCTOR RESOURCES Register, Redeem, Log in At www.pearsonhighered.com/irc instructors can register and access a variety of print, media, and presentation resources that are available with this text in downloadable digital format. For most texts, resources are also available for course management platforms such as Blackboard, WebCT, and Course Compass.

It Gets Better Once you register, you will not have additional forms to fill out or multiple usernames and passwords to remember to access new titles and/or editions. As a registered faculty member, you can log in directly to download resource files and receive immediate access and instructions for installing course management content to your campus server.

Need Help? Our dedicated technical support team is ready to answer instructors’ questions about the media supplements that accompany this text. Visit http://247.prenhall.com for answers to frequently asked questions and toll-free user support phone numbers. The supple- ments are available to adopting instructors. Detailed descriptions are provided at the Instructor’s Resource Center.

Instructor’s Resource Manual The Instructor’s Resource Manual, extensively updated by Professor Charles Munson of Washington State University, contains many useful resources for

xxii Preface

instructors—course outlines, video notes, learning techniques, Internet exercises and sample answers, case analysis ideas, additional teaching resources, and faculty notes. It also provides a snapshot of the PowerPoint lecture slides. Instructors can download the Instructor’s Resource Manual from the Instructor’s Resource Center, at www.pearsonhighered.com/heizer.

Instructor’s Solutions Manual The Instructor’s Solutions Manual, written by the authors (and extensively proofed by Professor Annie Puciloski), contains the answers to all of the discussion questions, Ethical Dilemmas, Active Models, and cases in the text, as well as worked- out solutions to all the end-of-chapter problems, Internet problems, and Internet cases. Instructors can download the Instructor’s Solutions Manual from the Instructor’s Resource Center, at www. pearsonhighered.com/heizer.

PowerPoint Presentations An extensive set of PowerPoint presentations, created by Professor Jeff Heyl of Lincoln University, is available for each chapter. Comprising well over 2,000 slides, this set has excellent color and clarity. These slides can also be downloaded from the Instructor’s Resource Center, at www.pearsonhighered.com/heizer.

Test Item File The test item file, updated by Professor Greg Bier of University of Missouri–Columbia, contains a variety of true/false, multiple-choice, fill-in-the-blank, short- answer, and problem- and topic-integrating questions for each chapter. Instructors can download the test item file from the Instructor’s Resource Center, at www.pearsonhighered.com/heizer.

TestGen The computerized TestGen package allows instructors to customize, save, and gener- ate classroom tests. The test program permits instructors to edit, add, and delete questions from the test bank; edit existing graphics and create new graphics; analyze test results; and organize a database of test and student results. This software allows for extensive flexibility and ease of use. It provides many options for organizing and displaying tests, along with search and sort features. The software and the test banks can be downloaded from the Instructor’s Resource Center, at www.pearsonhighered.com/heizer.

myomlab This powerful tool, noted on the inside front cover, ties together all elements in this book into an innovative learning tool, an exam tool, a homework tool, and an assessment center. By using myomlab, instructors can assign thousands of problems from the text and/or problems/questions from the test item file for their students to take online at any time, as deter- mined by the instructor. Visit www.myomlab.com for more information.

Video Package Designed and created by the authors specifically for their Heizer/Render texts, the video package contains the following 31 videos:

Preface xxiii

• Frito-Lay: Operations Management in Manufacturing (Ch. 1)

• Operations Management at Hard Rock (Ch. 1)

• Regal Marine: Operations Strategy (Ch. 2) • Hard Rock Cafe’s Global Strategy (Ch. 2) • Project Management at Arnold Palmer

Hospital (Ch. 3) • Managing Hard Rock’s Rockfest (Ch. 3) • Forecasting at Hard Rock Cafe (Ch. 4) • Regal Marine: Product Design (Ch. 5) • The Culture of Quality at Arnold Palmer

Hospital (Ch. 6) • Ritz-Carlton: Quality (Ch. 6) • Frito-Lay’s Quality-Controlled Potato

Chips (Supp. 6) • SPC and Quality at Darden Restaurants

(Supp. 6) • Green Manufacturing and Sustainability at

Frito-Lay (Ch. 7) • Wheeled Coach: Process Strategy (Ch. 7) • Process Analysis at Arnold Palmer Hospital

(Ch. 7)

• Capacity Planning at Arnold Palmer Hospital (Supp. 7)

• Locating the Next Red Lobster (Ch. 8) • Where to Place the Hard Rock Cafe (Ch. 8) • Wheeled Coach: Facility Layout (Ch. 9) • Laying Out Arnold Palmer Hospital’s New

Facility (Ch. 9) • Hard Rock Cafe’s Human Resource

Strategy (Ch. 10) • Darden’s Global Supply Chains (Ch. 11) • Regal Marine: Supply-Chain Management

(Ch. 11) • Arnold Palmer Hospital’s Supply Chain

(Ch. 11) • Darden’s Global Outsourcing (Supp. 11) • Managing Inventory at Frito-Lay (Ch. 12) • Wheeled Coach: Inventory Control (Ch. 12) • Wheeled Coach: Materials Requirements

Planning (Ch. 14) • Scheduling at Hard Rock Cafe (Ch. 15) • JIT at Arnold Palmer Hospital (Ch. 16) • Maintenance Drives Profits at Frito-Lay

(Ch. 17)

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:

xxiv Preface

ALABAMA Philip F. Musa University of Alabama at Birmingham

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

CALIFORNIA Jean-Pierre Amor University of San Diego

Moshen Attaran California State University–Bakersfield

Ali Behnezhad California State University–Northridge

Joe Biggs California Polytechnic State University

Lesley Buehler Ohlone College

Richard Martin California State University–Long Beach

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

CONNECTICUT David Cadden Quinnipiac University

Larry A. Flick Norwalk Community Technical College

FLORIDA Rita Gibson Embry-Riddle Aeronautical University

Jim Gilbert Rollins College

Donald Hammond University of South Florida

Ronald K. Satterfield University of South Florida

Theresa A. Shotwell Florida A&M University

GEORGIA John H. Blackstone University of Georgia

Johnny Ho Columbus State University

John Hoft Columbus State University

John Miller Mercer University

Spyros Reveliotis Georgia Institute of Technology

ILLINOIS Suad Alwan Chicago State University

Lori Cook DePaul University

Zafar Malik Governors State University

INDIANA Barbara Flynn Indiana University

B.P. Lingeraj Indiana University

Frank Pianki Anderson University

Stan Stockton Indiana University

Jianghua Wu Purdue University

Xin Zhai Purdue University

IOWA 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

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 Northeastern University

Peter Rourke Wentworth Institute of Technology

Daniel Shimshak University of Massachusetts–Boston

Ernest Silver Curry College

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

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

NEW JERSEY Leon Bazil Stevens Institute of Technology

Mark Berenson Montclair State University

Joao Neves The College of New Jersey

Leonard Presby William Paterson University

NEW MEXICO William Kime University of New Mexico

NEW YORK Theodore Boreki Hofstra University

John Drabouski DeVry University

Richard E. Dulski Daemen 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 Ray Walters Fayetteville Technical Community College

OHIO Victor Berardi Kent State University

OKLAHOMA Wen-Chyuan Chiang University of Tulsa

OREGON Anne Deidrich Warner Pacific College

Gordon Miller Portland State University

PENNSYLVANIA Henry Crouch Pittsburgh State University

Prafulla Oglekar LaSalle University

David Pentico Duquesne University

Stanford Rosenberg LaRoche College

Edward Rosenthal Temple University

Susan Sherer Lehigh University

RHODE ISLAND Laurie E. Macdonald Bryant College

John Swearingen Bryant College

Susan Sweeney Providence College

SOUTH CAROLINA Larry LaForge Clemson University

Emma Jane Riddle Winthrop University

TENNESSEE Hugh Daniel Lipscomb University

TEXAS Warren W. Fisher Stephen F. Austin State University

Garland Hunnicutt Texas State University

Gregg Lattier Lee College

Preface xxv

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

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

Chuck Munson Washington State University

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. Safford Milwaukee Area Technical College

Rao J. Taikonda University of Wisconsin–Oshkosh

WYOMING Cliff Asay University of Wyoming

INTERNATIONAL Ronald Lau Hong Kong University of Science and Technology

xxvi Preface

In addition, we appreciate the wonderful people at Prentice Hall who provided both help and advice: Eric Svendsen, our editor-in-chief; Chuck Synovec, our dynamic decision sciences edi- tor; Anne Fahlgren, our marketing manager; Jason Calcano, our editorial assistant; Allison Longley, our media project development manager; Courtney Kamauf for her dedicated work on myomlab; Judy Leale, our senior managing editor; Becca Richter, our production project man- ager; Mary Kate Murray, our editorial project manager, and Andrea Stefanowicz, our production editor at PreMediaGlobal, Inc. Reva Shader developed the exemplary subject indexes for this text. Donna Render and Kay Heizer provided the accurate typing and proofing so critical in a rigorous textbook. We are truly blessed to have such a fantastic team of experts directing, guid- ing, and assisting us.

In this edition we were thrilled to be able to include one of the country’s premiere manufac- turers, Frito-Lay, in our ongoing video case series. This was possible because of the wonderful efforts of Tom Rao, VP-Florida Operations and his superb management team, including Todd Ehinger, Jim Wentzel, Angela McCormack, and Rod Hof. We are also particularly grateful to Aurora Gonzalez in the Public Relations Department at Frito-Lay headquarters in Plano, Texas.

We also appreciate the efforts of colleagues who have helped to shape the entire learning package that accompanies this text. Professor Howard Weiss (Temple University) developed the Active Models, Excel OM, and POM for Windows microcomputer software; Professor Jeff Heyl (Lincoln University) created the PowerPoints. Professor Chuck Munson (Washington State University) created the Instructor’s Resource Manual and helped with the major rewrite of Supplement 7; Professor Gregory L. Bier (University of Missouri–Columbia) prepared the Test Bank; Professor Geoff Willis (University of Central Oklahoma) created the online study guide and online virtual tours; Professor Michael Donovan (Cedar Crest College) prepared the study guide; Beverly Amer (Northern Arizona University) produced and directed the videos and DVD Video Case Study series; Professors Keith Willoughby (Bucknell University) and Ken Klassen (Brock University) contributed the two Excel-based simulation games; Professor Gary LaPoint (Syracuse

University) developed the Microsoft Project crashing exercise and the dice game for SPC. Finally, thanks to our accuracy checkers, Annie Puciloski and Vijay Gupta, for their attention to detail. We have been fortunate to have been able to work with all these people.

We wish you a pleasant and productive introduction to operations management.

BARRY RENDER GRADUATE SCHOOL OF BUSINESS ROLLINS COLLEGE WINTER PARK, FL 32789 EMAIL: [email protected]

Preface xxvii

JAY HEIZER TEXAS LUTHERAN UNIVERSITY 1000 W. COURT STREET SEGUIN, TX 78155 EMAIL: [email protected]

THREE VERSIONS ARE AVAILABLE This text is available in three versions: Operations Management, tenth edition, a hardcover; Principles of Operations Management, eighth edition, a paperback; and Operations Management, Flexible Version, a package of a paperback text and the unique Lecture Guide & Activities Manual. All three books include the identical core Chapters 1–17. However, Operations Management, tenth edition, and the Flexible Version, also include six quantitative modules in Part IV.

OPERATIONS MANAGEMENT, TENTH EDITION ISBN: 0-13-611941-7 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 6. Managing Quality

S6. Statistical Process Control 7. Process Strategy and Sustainability

S7. Capacity and Constraint Management 8. Location Strategies 9. Layout Strategies

10. Human Resources, Job Design, and Work Measurement

PRINCIPLES OF OPERATIONS MANAGEMENT, EIGHTH EDITION ISBN: 0-13-611446-6 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 6. Managing Quality

S6. Statistical Process Control 7. Process Strategy and Sustainability

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. Outsourcing as a Supply-Chain Strategy 12. Inventory Management 13. Aggregate Planning 14. Material Requirements Planning (MRP) and

ERP

15. Short-Term Scheduling 16. Just-in-Time and Lean Operations 17. Maintenance and Reliability

PART IV QUANTITATIVE 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

xxviii Preface

PART III MANAGING OPERATIONS 11. Supply-Chain Management

S11. Outsourcing as a Supply-Chain Strategy 12. Inventory Management 13. Aggregate Planning 14. Material Requirements Planning (MRP) and

ERP

15. Short-Term Scheduling 16. Just-in-Time and 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

This page intentionally left blank

Operations and Productivity

Chapter Outline

GLOBAL COMPANY PROFILE: HARD ROCK CAFE

What Is Operations Management? 4

Organizing to Produce Goods and Services 4

Why Study OM? 6

What Operations Managers Do 7

The Heritage of Operations Management 8

Operations in the Service Sector 10

Exciting New Trends in Operations Management 12

The Productivity Challenge 13

Ethics and Social Responsibility 19 � Design of Goods and Services

� Managing Quality

� Process Strategy

� Location Strategies

� Layout Strategies

� Human Resources

� Supply-Chain Management

� Inventory Management

� Scheduling

� Maintenance

PART ONE Introduction to Operations Management (Chapters 1–4)

1

GLOBAL COMPANY PROFILE: HARD ROCK CAFE

OPERATIONS MANAGEMENT AT HARD ROCK CAFE

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 customer 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,

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.

Operations managers are interested in the attractiveness of the layout, but they must be sure that the facility contributes to the efficient movement of people and material with the necessary controls to ensure that proper portions are served.

whether at Whirlpool, Dreamworks, Disney, or Hard

Rock, is demanding.

Orlando-based Hard Rock Cafe opened its first

restaurant in London in 1971, making it over 39 years

old and the granddaddy of theme restaurants.

Although other theme restaurants have come and

gone, Hard Rock is still going strong, with 129

restaurants in more than 40 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 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

Universal 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 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 managers

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.

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 wait staff, such as the one shown here, is doing its job.

3

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.

HARD ROCK CAFE �

4 PART 1 Introduction to Operations Management

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 hospi- tal, a restaurant, a department store, or a factory—the production of goods and services 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 a changing global economy. An array of informative examples, charts, text discussions, and pictures illus- trates concepts and provides information. 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 effi- cient production system.

Production is the creation of goods and services. Operations management (OM) is the set of activities 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 function 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 sur- vival. They are:

1. Marketing, which generates the demand, or at least takes the order for a product or service (nothing happens until there is a sale).

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.

Universities, churches or synagogues, and businesses all perform these functions. Even a volun- teer group such as the Boy Scouts of America is organized to perform these three basic functions. Figure 1.1 shows how a bank, an airline, and a manufacturing firm organize themselves to per- form these functions. The blue-shaded areas of Figure 1.1 show the operations functions in these firms.

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.

LO1: Define operations management

Chapter 1 Learning Objectives LO1: Define operations management 4

LO2: Explain the distinction between goods and services 10

LO3: Explain the difference between production and productivity 14

LO4: Compute single-factor productivity 14

LO5: Compute multifactor productivity 15

LO6: Identify the critical variables in enhancing productivity 16

VIDEO 1.1 Operations Management

at Hard Rock

AUTHOR COMMENT Let’s begin by defining what

this course is about.

AUTHOR COMMENT Operations is one of the three

functions that every organization performs.

VIDEO 1.2 Operations Management

at Frito-Lay

Chapter 1 Operations and Productivity 5

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)

Commercial Bank

Operations

Teller scheduling Check clearing Collection Transaction processing Facilities design/layout Vault operations Maintenance Security

Finance

Investments

Real estate Securities

Accounting

Loans Commercial Industrial Financial Personal Mortgage

Trust department

(A)

Auditing

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

Marketing

Traffic administration Reservations Schedules Tariffs (pricing)

Advertising

Sales

(B)

� 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.

AUTHOR COMMENT The areas in blue indicate the significant role that OM plays

in both manufacturing and service firms.

6 PART 1 Introduction to Operations Management

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.

APPROACH � Table 1.1 shows a simple profit-and-loss statement and three strategic options (marketing, finance/accounting, and operations) for the firm. The first option is a marketing option, where good marketing management may increase sales by 50%. By increasing sales by 50%, contribu- tion will in turn increase 71%. But increasing sales 50% may be difficult; it may even be impossible.

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 operation managers do. In addi- tion, understanding OM will help you explore the numerous and lucrative career opportuni- ties 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 opportu- nity for an organization to improve its profitability and enhance its service to society. Exam- ple 1 considers how a firm might increase its profitability via the production function.

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 � Given the conditions of our brief example, Fisher Technologies has increased con- tribution from $10,500 to $22,500. It may now have a bank willing to lend it additional funds.

� TABLE 1.1 Options for Increasing Contribution

AUTHOR COMMENT Good OM managers are scarce and, as a result,

career opportunities and pay are excellent.

Marketing Optiona

Finance/ Accounting

Optionb OM

Optionc

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 Contributiond $ 10,500 $ 18,000 $ 12,750 $ 22,500 aIncreasing sales 50% increases contribution by $7,500, or 71% (7,500/10,500). bReducing finance costs 50% increases contribution by $2,250, or 21% (2,250/10,500). cReducing production costs 20% increases contribution by $12,000, or 114% (12,000/10,500). dContribution to fixed cost (excluding finance costs) and profit.

Chapter 1 Operations and Productivity 7

Example 1 underscores the importance of an effective operations activity of a firm. Development 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 managers apply this management process to the decisions they make in the OM function. The 10 major decisions of OM are shown in Table 1.2. Successfully addressing each of these decisions requires planning, organizing, staffing, leading, and controlling. Typical issues relevant to these decisions and the chapter where each is discussed are also shown.

Where Are the OM Jobs? How does one get started on a career in operations? The 10 OM decisions identified in Table 1.2 are made by individuals who work in the disciplines shown in the blue areas of Figure 1.1. Competent business students who know their accounting, 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

INSIGHT � 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 virtually impossible. Reducing operations cost by 20% may be difficult but feasible.

LEARNING EXERCISE � What is the impact of only a 15% decrease in costs in the OM option? [Answer: A $19,500 contribution; an 86% increase.]

Management process The application of planning, organizing, staffing, leading, and controlling to the achievement of objectives.

AUTHOR COMMENT An operations manager must

successfully address the 10 decisions around which

this text is organized.

AUTHOR COMMENT Current OM emphasis on

quality and supply chain has increased job opportunities

in these 10 areas.

Ten Decision Areas Issues Chapter(s)

1. Design of goods and services What good or service should we offer? 5 How should we design these products?

2. Managing quality How do we define the quality? 6, Supplement 6 Who is responsible for quality?

3. Process and capacity design What process and what capacity will these products require?

7, Supplement 7

What equipment and technology is necessary for these processes?

4. Location strategy Where should we put the facility? 8 On what criteria should we base the location

decision?

5. Layout strategy How should we arrange the facility? 9 How large must the facility be to meet our plan?

6. Human resources and job design

How do we provide a reasonable work environment?

10

How much can we expect our employees to produce?

7. Supply-chain management Should we make or buy this component? 11, Supplement 11 Who should be our suppliers and how can we

integrate them into our strategy?

8. Inventory, material requirements planning, and JIT (just-in-time)

How much inventory of each item should we have?

12, 14, 16

When do we reorder?

9. Intermediate and short-term scheduling

Are we better off keeping people on the payroll during slowdowns?

13, 15

Which job do we perform next?

10. Maintenance How do we build reliability into our processes? Who is responsible for maintenance?

17

� TABLE 1.2 Ten Critical Decisions of Operations Management

8 PART 1 Introduction to Operations Management

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 1.2 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 individuals. We now introduce a few of these people, and we provide a summary of significant events in opera- tions management in Figure 1.3.

1/15 Plant Manager Division of Fortune 1000 company seeks plant manager for plant located in the upper Hudson Valley area. This plant manufacturers 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 excel- lent 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.

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. Knowledge of Oracle business systems I is a plus. Effective verbal and written communication skills are essential.

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) process audits to identify areas for improvement and (3) management of implementation of changes. Positions involve night hours and weekends. Send resume.

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.

� FIGURE 1.2 Many Opportunities Exist for Operations Managers

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 1995–2015 Globalization Internet/E-Commerce Enterprise Resource Planning International Quality Standards (ISO) Finite Scheduling Supply-Chain Management Mass Customization Build-to-Order Sustainability

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

Chapter 1 Operations and Productivity 9

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 per- sonnel selection, planning and scheduling, motion study, and the now popular field of ergonom- ics. 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.

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 revo- lutionary 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 pro- vided the foundations for statistical sampling in quality control. W. Edwards Deming (1950)

� FIGURE 1.3 Significant Events in Operations Management

10 PART 1 Introduction to Operations Management

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 with contributions from other disciplines, including industrial engineering and management science. These disciplines, along with statis- tics, management, and economics, contribute to improved models and decision making.

Innovations from the physical sciences (biology, anatomy, chemistry, physics) have also con- tributed to advances in OM. These innovations include new adhesives, faster integrated circuits, gamma rays to sanitize food products, and higher-quality glass for LCD 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 management science, 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.

OPERATIONS IN THE SERVICE SECTOR 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 ser- vice. Even the U.S. government has trouble generating a consistent definition. Because defini- tions vary, much of the data and statistics generated about the service sector are inconsistent. However, we define services as including repair and maintenance, government, food and lodg- ing, transportation, insurance, trade, financial, real estate, education, legal, medical, entertain- ment, and other professional occupations.1

Differences Between Goods and Services Let’s examine some of the differences between goods and services:

• Services are usually intangible (for example, your purchase of a ride in an empty airline seat between two cities) as opposed to a tangible good.

• Services are often produced and consumed simultaneously; there is no stored inventory. For instance, the beauty salon produces a haircut that is “consumed” simultaneously, or the doctor produces an operation that is “consumed” as it is produced. We have not yet figured out how to inventory haircuts or appendectomies.

• Services are often unique. Your mix of financial coverage, such as investments and insurance policies, may not be the same as anyone else’s, just as the medical procedure or a haircut pro- duced for you is not exactly like anyone else’s.

• Services have high customer interaction. Services are often difficult to standardize, automate, and make as efficient as we would like because customer interaction demands uniqueness. In fact, in many cases this uniqueness is what the customer is paying for; therefore, the opera- tions manager must ensure that the product is designed (i.e., customized) so that it can be delivered in the required unique manner.

• Services have inconsistent product definition. Product definition may be rigorous, as in the case of an auto insurance policy, but inconsistent because policyholders change cars and mature.

• Services are often knowledge based, as in the case of educational, medical, and legal services, and therefore hard to automate.

• Services are frequently dispersed. Dispersion occurs because services are frequently brought to the client/customer via a local office, a retail outlet, or even a house call.

The activities of the operations function are often very similar for both goods and services. For instance, both goods and services must have quality standards established, and both must be designed and processed on a schedule in a facility where human resources are employed.

Having made the distinction between goods and services, we should point out that in many cases, the distinction 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 tan- gible report. Similarly, the sale of most goods includes a service. For instance, many products

Services Economic activities that typically produce an intangible product (such as education, entertainment, lodging, government, financial, and health services).

LO2: Explain the distinction between goods and services

1This definition is similar to the categories used by the U.S. Bureau of Labor Statistics.

AUTHOR COMMENT Services are especially

important because almost 80% of all jobs are in

service firms.

Chapter 1 Operations and Productivity 11

� FIGURE 1.4 Development of the Service Economy and Manufacturing Productivity

Sources: U.S. Bureau of Labor Statistics; Federal Reserve Board, Industrial Production and Capacity Utilization (2009); and Statistical Abstract of the United States (2008).

Manufacturing employment

(left scale)

0

E m

p lo

ym e n t

(m ill

io n s)

30

10

20

0

125

150

100

75

50

25

E m

p lo

ym e

n t

(m ill

io n

s )

In d

e x : 1 9 9 7 =

1 0 0

1960 1980 2000 1950 1970 1990 2010 (est.)

Industrial production

(right scale)

Manufacturing

Services

1960 1980 2000 1950 1970 1990 2010 (est.)

40

United States

Canada

France

Italy

Britain

Japan

W. Germany

1970 2010 (est.) Percent

40 50 60 70 80

(a) U.S. manufacturing and service employment

(b) Number of people employed in U.S. manufacturing has decreased, but production continues to increase. (c) Services as percentage of GDP

20

40

60

80

100

120

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” activ- ities 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 manufac- turing 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, manufacturing employment has decreased in the past 30 years. The changes in manufacturing and service employment, in mil- lions, are shown in Figure 1.4(a). Interestingly, as Figure 1.4(b) indicates, the number of people employed in manufacturing has decreased since 1950, but each person is now producing almost 20 times more than in 1950. Services became the dominant 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, as shown in Figure 1.4(c). Consequently, much of the world can now enjoy the plea- sures 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 1.3. Table 1.3 also provides employment percentages for the nonservice sectors of manufactur- ing, 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 financial 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 Commerce categories of the 33 ser- vice 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 sec- tor, the average wage of all service workers is about 96% of the average of all private industries.

Service sector The segment of the economy that includes trade, financial, lodging, education, legal, medical, and other professional occupations.

12 PART 1 Introduction to Operations Management

AUTHOR COMMENT One of the reasons OM is

such an exciting discipline is that an operations manager

is confronted with ever- changing issues, from

technology to sustainability.

EXCITING NEW TRENDS IN OPERATIONS MANAGEMENT OM managers operate 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, prod- ucts, and money at electronic speeds. The direction now being taken by OM—where it has been and where it is going—is shown in Figure 1.5. Let’s look at some of these challenges:

• Ethics: Operations managers’ roles of buying from suppliers, transforming resources into finished goods, and delivering to customers places them at critical junctures where they must frequently make ethical decisions.

• Global focus: The rapid decline in communication and transportation costs has made mar- kets global. Similarly, resources in the form of capital, materials, talent, and labor are now also global. As a result, countries throughout the world are contributing to globalization as they vie for economic growth. Operations managers are rapidly responding with creative designs, efficient production, and quality goods.

• Rapid product development: Rapid international communication of news, entertainment, and lifestyles is dramatically chopping away at the life span of products. Operations managers are responding with management structures, technology, and alliances (partnerships) that are more responsive and effective.

• Environmentally sensitive production: Operation managers’ continuing battle to improve productivity is increasingly concerned with designing products and processes that are ecolog- ically sustainable. That means designing products and packaging that minimize resource use, are biodegradable, can be recycled, and are generally environmentally friendly.

• Mass customization: Once managers recognize the world as the marketplace, the cultural and individual differences become quite obvious. In a world where consumers are increasingly aware of innovation and options, substantial pressure is placed on firms to respond. And oper- ations managers are responding with creative product designs and flexible production processes that cater to the individual whims of consumers. The goal is to produce customized products, whenever and wherever needed.

• Empowered employees: The knowledge explosion and more technical workplace have com- bined to require more competence in the workplace. Operations managers are responding by moving more decision making to individual workers.

• Supply-chain partnering: Shorter product life cycles, demanding customers, and fast changes in technology, material, and processes require supply-chain partners to be more in tune with the needs of end users. And because suppliers can contribute unique expertise, oper- ations managers are outsourcing and building long-term partnerships with critical players in the supply chain.

• Just-in-time performance: Inventory requires financial resources and impedes response to rapid changes in the marketplace. These forces push operations managers to viciously cut inventories at every level, from raw materials to finished goods.

These trends are part of the exciting OM challenges that are discussed in this text.

AUTHOR COMMENT Service jobs with their

operations component are growing as a percentage of

all jobs.

Sector Example Percent of All Jobs

Service Sector Education, Legal, Medical, Other San Diego Zoo, Arnold Palmer Hospital 25.8 Trade (retail, wholesale) Walgreen’s, Wal-Mart, Nordstrom 14.4 Utilities, Transportation Pacific Gas & Electric, American Airlines 5.2 Professional and Business

Services Snelling and Snelling, Waste Management,

Inc. 10.7

78.8

Finance, Information, Real Estate Citicorp, American Express, Prudential, Aetna 9.6 Food, Lodging, Entertainment Olive Garden, Motel 6, Walt Disney 8.5 Public Administration U.S., State of Alabama, Cook County 4.6

Manufacturing Sector General Electric, Ford, U.S. Steel, Intel 11.2 Construction Sector Bechtel, McDermott 8.1 Agriculture King Ranch 1.4 Mining Sector Homestake Mining .5 Grand Total 100.0

� TABLE 1.3 Examples of Organizations in Each Sector

Sources: Statistical Abstract of the United States (2008), Table 600, and Bureau of Labor Statistics, 2008. x

Chapter 1 Operations and Productivity 13

Traditional Approach Reasons for Change Current Challenges

Ethics and regulation not at the forefront

Local, regional, national focus

Lengthy product development

Low-cost production, with little concern for environ- ment; free resources (air, water) ignored

Low-cost standard products

Emphasis on specialized, often manual tasks

Public concern over pollution, corruption, child labor, etc.

Growth of reliable, low-cost communication and transportation

Shorter life cycles; growth of global communication; CAD; Internet

Public sensitivity to environment; ISO 14000 standard; increasing disposal costs

Rise of consumerism; increased affluence; individualism

Recognizing the importance of the employee’s total contribution; knowledge society

High ethical and social responsibility; increased legal and professional standards (all chapters)

Global focus; international collaboration (Chapters 2, 11)

Rapid product development; design collaboration (Chapter 5)

Environmentally sensitive production; green manufacturing; sustainability (Chapters 5, 7)

Mass customization (Chapters 5, 7)

Empowered employees; enriched jobs (Chapter 10)

“In-house” production; low-bid purchasing

Rapid technology change; increasing competitive forces

Supply-chain partnering; joint ventures; alliances (Chapter 11, Supplement 11)

Large lot production Shorter product life; increasing need to reduce inventory

Just-in-time performance; lean; continuous improvement (Chapter 16)

� FIGURE 1.5 Changing Challenges for the Operations Manager

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.2

AUTHOR COMMENT Why is productivity

important? Because it determines our standard

of living.

� 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.

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

Productivity The ratio of outputs (goods and services) divided by one or more inputs (such as labor, capital, or management).

2Efficiency means doing the job well—with a minimum of resources and waste. Note the distinction between being efficient, which implies doing the job well, and effective, which means doing the right thing. A job well done—say, by applying the 10 decisions of operations management—helps us be efficient; developing and using the correct strategy helps us be effective.

14 PART 1 Introduction to Operations Management

“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’s 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 motion and cut 14 seconds off the average time of one 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 improvements at Starbucks outlets have increased the average yearly volume by nearly $200,000, to about $940,000 in the past 6 years. This is a 27% improvement in productivity—about 4.5% per year. In the service industry, a 4.5% per year increase is very tasty.

Sources: The Wall Street Journal (August 4, 2009): A1, A10 and (April 12, 2005): B2:B7; Industrial Engineer (January 2006): 66; and www.finfacts. com, October 6, 2005.

OM in Action � Improving Productivity at Starbucks

This improvement can be achieved in two ways: reducing inputs while keeping output con- stant or increasing output while keeping inputs constant. Both represent an improvement in pro- ductivity. 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 con- version 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 manage- ment 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 productivity 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 recently seen annual productivity increases exceeding 4%, and the service sector, with increases of almost 1%, has also shown some improvement. The combination has moved U.S. annual productivity growth in this early part of the 21st century slightly above the 2.5% range for the economy as a whole.3

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.

Productivity Measurement The measurement of productivity can be quite direct. Such is the case when productivity is mea- sured 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 (kilo- watts of electricity) can be used.4 An example of this can be summarized in the following equation:

(1-1)Productivity = Units produced

Input used

LO4: Compute single- factor productivity

4The quality and time period are assumed to remain constant.

3U.S. Dept. of Labor, July 2009: www.bls.gov/ipc/prodybar.html

LO3: Explain the difference between production and productivity

Chapter 1 Operations and Productivity 15

For example, if units and labor-hours used is 250, then:

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 pro- ductivity is also known as total factor productivity. Multifactor productivity is calculated by combining the input units as shown here:

(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.

Productivity = Output

Labor + Material + Energy + Capital + Miscellaneous

Productivity = Units produced

Labor-hours used =

1,000

250 = 4 units per labor-hour

produced = 1,000 Single-factor productivity Indicates the ratio of one resource (input) to the goods and services produced (outputs).

Multifactor productivity Indicates the ratio of many or all resources (inputs) to the goods and services produced (outputs).

LO5: Compute multifactor productivity

� EXAMPLE 2 Computing single- factor and multifactor gains in productivity

Collins Title wants to evaluate its labor and multifactor productivity with a new computerized 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 � Collins uses Equation (1-1) to compute labor productivity and Equation (1-2) to compute multifactor productivity.

SOLUTION �

Labor productivity has increased from .25 to .4375. The change is or a 75% increase in labor productivity. Multifactor productivity has increased from .0077 to .0097. This change is or a 26% increase in multifactor productivity.

INSIGHT � 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 � If the overhead goes to $960 (rather than $800), what is the multi- factor productivity? [Answer: .00875.]

RELATED PROBLEMS � 1.1, 1.2, 1.5, 1.6, 1.7, 1.8, 1.9, 1.11, 1.12, 1.14, 1.15

(.0097 - .0077)>.0077 = 0.26,

(.4375 - .25)>.25 = 0.75,

Multifactor productivity with the new system: 14 titles per day

$640 + 800 = .0097 titles per dollar

Multifactor productivity with the old system: 8 titles per day $640 + 400

= .0077 titles per dollar

Labor productivity with the new system: 14 titles per day 32 labor-hours = .4375 titles per labor-hour

Labor productivity with the old system: 8 titles per day 32 labor-hours = .25 titles per labor-hour

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 factors, but sub- stantial 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 peo- ple 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 system under study may not be directly responsible. A more reliable electric power service may

16 PART 1 Introduction to Operations Management

Productivity variables The three factors critical to productivity improvement— labor, capital, and the art and science of management.

LO6: Identify the critical variables in enhancing productivity

5“Can’t Read, Can’t Count,” Scientific American (October 2001): 24; and “Economic Time Bomb: U.S. Teens Are among Worst at Math,” The Wall Street Journal (December 7, 2004): B1.

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

FIGURE 1.7 � About Half of the 17-Year-Olds in the U.S. Cannot Correctly Answer Questions of This Type

greatly improve production, thereby improving the firm’s productivity because of this sup- port 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 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 outputs, 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.

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 oppor- tunity 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, more than 38% of American job applicants tested for basic skills were deficient in reading, writing, or math.5

AUTHOR COMMENT Perhaps as many as 25% of U.S. workers lack the basic skills needed for

their current job.

Chapter 1 Operations and Productivity 17

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 recession 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 expensive. 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, 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.

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 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.

Knowledge society A society in which much of the labor force has migrated from manual work to work based on knowledge.

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.

18 PART 1 Introduction to Operations Management

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 pri- marily on goods-producing activities. Consequently, most published economic data relate to goods production. But the data do indicate that, as our contemporary service economy has increased in size, we have had slower growth in productivity.

Productivity of the service sector has proven difficult to improve because service-sector work is:

1. Typically labor intensive (for example, counseling, teaching). 2. Frequently focused on unique individual attributes or desires (for example, investment

advice). 3. Often an intellectual task performed by professionals (for example, medical diagnosis). 4. Often difficult to mechanize and automate (for example, a haircut). 5. Often difficult to evaluate for quality (for example, performance of a law firm).

The more intellectual and personal the task, the more difficult it is to achieve increases in produc- tivity. 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 ser- vice. 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, in spite of 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 pro- ductivity. Retailing is twice as productive in the U.S. as in Japan, where laws protect shopkeep- ers 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 oligop- olies. 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.”)

Siemens, the 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.

Chapter 1 Operations and Productivity 19

ETHICS AND SOCIAL RESPONSIBILITY Operations managers are subjected to constant changes and challenges. The systems they build to convert resources into goods and services are complex. The physical and social environment changes, as do laws and values. These changes present a variety of challenges that come from the conflicting perspectives of stakeholders such as customers, distributors, suppliers, owners, lenders, and employees. These stakeholders, as well as government agencies at various levels, require constant monitoring and thoughtful responses.

Identifying ethical and socially responsible responses while building productive systems is not always clear-cut. Among the many ethical challenges facing operations managers are:

• Efficiently developing and producing safe, quality products. • Maintaining a sustainable environment. • Providing a safe workplace. • Honoring stakeholder commitments.

Managers must do all of this in an ethical and socially responsible way while meeting the demands of the marketplace. If operations managers have a moral awareness and focus on increasing productivity in a system where all stakeholders have a voice, then many of the ethical challenges will be successfully addressed. The organization 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 (see the Lecture Guide & Activities Manual).

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-thru lines by one 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 believe they 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 handle twice the volume with half the labor.

In 2010, Taco Bell will have completed the rollout of its new Grill-to-Order kitchens by installing water- and energy- savings grills that conserve 300 million gallons of water and 200 million KwH of electricity each year. This “green”- inspired cooking method also saves the company’s 5,600 restaurants $17 million per year.

Effective operations management has resulted in productivity increases that support Taco Bell’s low-cost strategy. Taco Bell is now the fast-food low-cost leader with a 73% share of the Mexican fast-food market.

Sources: Energy Business Journal (May 12, 2008): 111; Harvard Business Review (July/August 2008): 118; and J. Hueter and W. Swart, Interfaces (January–February 1998): 75–91.

OM in Action � Taco Bell Improves Productivity and Goes Green to Lower Costs

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 operations management has been made in the twentieth century, but since the beginning of time, humankind has been attempting to improve its material well-being. Oper- ations 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 three-quarters of the workforce is employed in the service sector. Produc- tivity improvements are difficult to achieve, but operations managers are the primary vehicle for making improvements.

CHAPTER SUMMARY

AUTHOR COMMENT Ethics must drive all of a manager’s

decisions.

20 PART 1 Introduction to Operations Management

Key Terms

Production (p. 4) Operations management (OM) (p. 4) Management process (p. 7) Services (p. 10)

Service sector (p. 11) Productivity (p. 13) Single-factor productivity (p. 15) Multifactor productivity (p. 15)

Productivity variables (p. 16) Knowledge society (p. 17)

Solved Problems Virtual Office Hours help is available at www.myomlab.com

� 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 equipment, to produce 240 crates per 100 logs. He currently purchases 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)

= .8 crates per labor-hour =

240

300

Current labor productivity = 240 crates

100 logs * 3 hours>log

(b)

Using current productivity (.80 from [a]) as a base, the increase will be 5.5% (.844/.8 = 1.055, or a 5.5% increase).

= .844 crates per labor-hour =

260

308

Labor productivity with buyer

= 260 crates

(100 logs * 3 hours>log) + 8 hours

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: crates/dollar= 240 crates>4,500 = .0533

� SOLVED PROBLEM 1.2 Art Binley has decided to look at his productivity from a multifac- tor (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 denomi- nator. His total labor-hours are now 300 per day and will increase

System with Professional Buyer

308 hrs. @10 = $3,080 1,000

350 150

$4,580

Multifactor productivity of proposed system: crates/dollar= 260 crates>4,580 = .0568

to 308 per day. His capital and energy costs will remain constant at $350 and $150 per day, respectively. 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

Using current productivity (.0533) as a base, the increase will be .066. That is, or a 6.6% increase..0568>.0533 = 1.066,

� Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study: Zychol Chemicals Corp.: The production manager must prepare a productivity report, which includes multifactor analysis.

Chapter 1 Operations and Productivity 21

Bibliography

Broedner, P., S. Kinkel, and G. Lay. “Productivity Effects of Outsourcing.” International Journal of Operations and Production Management 29, no. 2 (2009): 127.

Hounshell, D. A. From the American System to Mass Production 1800–1932: The Development of Manufacturing. Baltimore: Johns Hopkins University Press, 1985.

Lewis, William W. The Power of Productivity. Chicago: University of Chicago Press, 2004.

Maroto, A., and L. Rubalcaba. “Services Productivity Revisited.” The Service Industries Journal 28, no. 3 (April 2008): 337.

Sahay, B. S. “Multi-factor Productivity Measurement Model for Service Organization.” International Journal of Productivity and Performance Management 54, no. 1–2 (2005): 7–23.

San, G., T. Huang, and L. Huang. “Does Labor Quality Matter on Productivity Growth?” Total Quality Management and Business Excellence 19, no. 10 (October 2008): 1043.

Sprague, Linda G. “Evolution of the Field of Operations Management,” Journal of Operations Management 25, no. 2 (March 2007): 219–238.

Tangen, S. “Demystifying Productivity and Performance.” International Journal of Productivity and Performance Measurement 54, no. 1–2 (2005): 34–47.

Taylor, F. W. The Principles of Scientific Management. New York: Harper & Brothers, 1911.

van Biema, Michael, and Bruce Greenwald. “Managing Our Way to Higher Service-Sector Productivity.” Harvard Business Review 75, no. 4 (July–August 1997): 87–95.

Wren, Daniel A. The Evolution of Management Thought, New York: Wiley, 1994.

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Operations Strategy in a Global Environment

Chapter Outline

GLOBAL COMPANY PROFILE: BOEING

A Global View of Operations 26

Developing Missions and Strategies 30

Achieving Competitive Advantage Through Operations 31

Ten Strategic OM Decisions 35

Issues in Operations Strategy 36

Strategy Development and Implementation 39

Global Operations Strategy Options 42

23

� Design of Goods and Services

� Managing Quality

� Process Strategy

� Location Strategies

� Layout Strategies

� Human Resources

� Supply-Chain Management

� Inventory Management

� Scheduling

� Maintenance

This state-of-the-art Boeing 787 is also global. Led

by Boeing at its Everett, Washington, facility, an

international team of aerospace companies developed

the airplane. New technologies, new design, new

manufacturing processes, and committed international

suppliers are helping Boeing and its partners achieve

unprecedented levels of performance in design,

manufacture, and operation.

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 risk of over $5 billion,

Boeing needed partners. The global nature of both

technology and the aircraft market meant finding

exceptional developers and suppliers, wherever they

might be. It also meant finding firms willing to step up

to the risk associated with a very expensive new

product. These partners not only spread the risk but

also bring commitment to the table. Countries that

have a stake in the 787 are more likely to buy from

Boeing than from the European competitor Airbus

Industries.

Boeing teamed with more than 20 international

systems suppliers to develop technologies and design

concepts for the 787. Boeing found its 787 partners in

over a dozen countries; a few of them are shown in the

table on the left.

With the 787’s state-of-the-art design, more spacious interior, and global suppliers, Boeing has garnered record sales worldwide.

GLOBAL COMPANY PROFILE: BOEING

BOEING’S GLOBAL STRATEGY YIELDS COMPETITIVE ADVANTAGE

B oeing’s strategy for its 787 Dreamliner is

unique from both an engineering and global

perspective.

The Dreamliner incorporates the latest in a

wide range of aerospace technologies, from airframe

and engine design to superlightweight titanium graphite

laminate, carbon fiber and epoxy, and composites.

Another innovation is the electronic monitoring system

that allows the airplane to report maintenance

requirements to ground-based computer systems.

Boeing has also worked with General Electric and

Rolls-Royce to develop more efficient engines. 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.

Some of the International Suppliers of Boeing 787 Components

Latecoere France Passenger doors Labinel France Wiring Dassault France Design and PLM software Messier-Bugatti France Electric brakes Thales France Electrical power conversion

system and integrated standby flight display

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 and

horizontal stabilizer Toray Industries Japan Carbon fiber for wing and

tail units Fuji Heavy

Industries Japan Center wing box

Kawasaki Heavy Industries

Japan Forward fuselage, fixed sections of wing, landing gear wheel well

Teijin Seiki Japan Hydraulic actuators Mitsubishi Heavy

Industries Japan Wing box

Chengdu Aircraft Group

China Rudder

Hafei Aviation China Parts Korean Airlines South Korea Wingtips Saab Sweden Cargo and access doors

24

Boeing’s collaborative technol- ogy enables a “virtual work- space” that allows engineers on the 787, including partners in Australia, Japan, Italy, Canada and across the United States, to make concurrent design changes to the airplane in real time. Designing, building, and testing the 787 digitally before production reduced design errors and improved production efficiencies.

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 an assembly line in Everett, Washington. Although components come from throughout the world, about 35% of the 787 structure comes from Japanese companies.

The Japanese companies Toray, Teijin Seiki, Fuji,

Kawasaki, and Mitsubishi are producing over 35% of

the project, providing whole composite fuselage

sections. Italy’s Alenia Aeronautica is building an

additional 10% of the plane.

Many U.S. companies, including Crane Aerospace,

Fairchild Controls, Goodrich, General Dynamics,

Hamilton Sundstrand, Honeywell, Moog, Parker

Hannifin, Rockwell Collins, and Triumph Group are

also suppliers. Boeing has 70% to 80% of the

Dreamliner built by other companies. And even some

of the portion built by Boeing is produced at Boeing

facilities outside the United States, in Australia and

Canada.

The global Dreamliner is efficient, has a global

range, and is made from components produced

around the world. The result: a state-of-the-art airplane

reflecting the global nature of business in the 21st

century and one of the fastest-selling commercial

jets in history.

25

BOEING �

26 PART 1 Introduction to Operations Management

A GLOBAL VIEW OF OPERATIONS Today’s operations manager must have a global view of operations strategy. Since the early 1990s, nearly 3 billion people in developing countries have overcome the cultural, religious, eth- nic, and political barriers that constrain productivity and are now players on the global economic stage. As these barriers disappear, simultaneous advances are being made in technology, reliable shipping, and cheap communication. 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 operations globally with innovative strategies. For instance:

• Boeing is competitive because both its sales and production are worldwide. • Italy’s Benetton moves inventory to stores around the world faster than its competition by

building flexibility into design, production, and distribution. • Sony purchases components from suppliers in Thailand, Malaysia, and elsewhere around the

world for assembly in its electronic products. • Volvo, considered a Swedish company, recently controlled by a U.S. company, Ford. But the

current Volvo S40 is built in Belgium on a platform shared with the Mazda 3 (built in Japan) and the Ford Focus (built and sold 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.

Globalization means that domestic production and exporting may no longer be a viable busi- ness model; local production and exporting no longer guarantee success or even survival. There are new standards of global competitiveness that impact quality, variety, customization, conve- nience, timeliness, and cost. The globalization of strategy contributes efficiency and adds value to products and services, but it also complicates the operations manager’s job. Complexity, risk and competition are intensified; companies must carefully account for them.

LO1: Define mission and strategy 30

LO2: Identify and explain three strategic approaches to competitive advantage 30

LO3: Identify and define the 10 decisions of operations management 35

Chapter 2 Learning Objectives LO4: Understand the significance

of key success factors and core competencies 39

LO5: Identify and explain four global operations strategy options 42

0

5

10

15

20

30

P e rc

e n t

1960

25

35

1970 1975 1980 1985 1990 1995 2000 20051965 Year

Collapse of the Berlin Wall

2010 (est.*)

� FIGURE 2.1

Growth of World Trade (world trade as a percentage of world GDP)

AUTHOR COMMENT As Prof. Thomas Sewell

observed, “No great civilization has developed

in isolation.”

* Author estimate for 2010.

Source: Based on a speech by Mark A. Wynne, Federal Reserve Bank of Dallas, June 2009.

Chapter 2 Operations Strategy in a Global Environment 27

Maquiladoras Mexican factories located along the U.S.–Mexico border that receive preferential tariff treatment.

1The 27 members of the European Union (EU) as of 2010 were Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom. Not all have adopted the euro. In addition, Croatia, Macedonia, and Turkey are candidates for entry into the European Union.

We have identified six reasons why domestic business operations decide to change to some form of international operation. They are:

1. Reduce costs (labor, taxes, tariffs, etc.). 2. Improve the supply chain. 3. Provide better goods and services. 4. Understand markets. 5. Learn to improve operations. 6. Attract and retain global talent.

Let us examine, in turn, each of the six reasons.

Reduce Costs Many international operations seek to take advantage of the tangible opportuni- ties to reduce their costs. Foreign locations with lower wages can help lower both direct and indi- rect costs. (See the OM in Action box “U.S. Cartoon Production at Home in Manila.”) Less stringent government regulations on a wide variety of operation practices (e.g., environmental con- trol, health and safety, etc.) reduce costs. Opportunities to cut the cost of taxes and tariffs also encourage foreign operations. In Mexico, the creation of maquiladoras (free trade zones) allows manufacturers to cut their costs of taxation by paying only on the value added by Mexican workers. If a U.S. manufacturer, such as GM, brings a $500 engine to a maquiladora operation for assembly work costing $25, tariff duties will be charged only on the $25 of work performed in Mexico.

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 loca- tion 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. The impact of this approach is shown in the OM in Action box “Going Global to Compete” on the next page.

Trade agreements have also helped reduce tariffs and thereby reduce the cost of operating facilities 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 bar- riers 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 Korea, 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

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 understand 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. 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.

Sources: Journal of Global Information Technology Management (2007): 1–6; The New York Times (February 26, 2004): A29; and The Wall Street Journal (August 9, 2005): D8.

OM in Action � U.S. Cartoon Production at Home in Manila

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 27 member states.

currency, the euro. However, this major U.S. trading partner, with almost 500 million people, is also placing some of the world’s most restrictive conditions on products sold in the EU. Everything from recycling standards to automobile bumpers to hormone-free farm products must meet EU standards, complicating international trade.

Improve the Supply Chain The supply chain can often be improved by locating facilities in countries where unique resources are available. These resources may be expertise, labor, or raw material. For example, auto-styling studios from throughout the world are migrating to the auto mecca of southern California to ensure the necessary expertise 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 essence manufacturer wants a presence in Grasse, France, where much of the world’s perfume essences are prepared from the flowers of the Mediterranean.

Provide Better Goods and Services Although the characteristics of goods and services can be objective and measurable (e.g., number of on-time deliveries), they can also be subjective and less measurable (e.g., sensitivity to culture). We need an ever better understanding of differ- ences in culture and of the way business is handled in different countries. Improved understand- ing as the result of a local presence permits firms to customize products and services to meet unique cultural needs in foreign markets.

Another reason to have international operations is to reduce response time to meet customers’ 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 foreign cus- tomers, 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 now the Japanese lead with the latest cell phone fads. Knowledge of these 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 prod- ucts in less developed countries. For example, the U.S. market for personal computers could be characterized as “mature” but as in the “introductory” stage in many developing countries, such as Albania, Vietnam, and Myanmar (Burma).

28 PART 1 Introduction to Operations Management

Wachovia Corp, the giant subsidiary of Wells Fargo, has inked a $1.1 billion deal with India’s Genpact to outsource finance and accounting jobs. Wachovia has also handed over administration of its human resources programs to Illinois-based Hewitt Associates. This is “what we need to do to become a great customer-relationship company,” says Wachovia executive P. J. Sidebottom. The expected cost savings of $600 million to $1 billion over the next three years will be invested in the U.S. to boost the core banking business. These investments will be made in new ATMs, branches, and personnel.

Similarly, Dana Corp. of Toledo, Ohio, is also taking a global approach. Dana established a joint venture with Cardanes S.A. to produce truck transmissions in Queretaro, Mexico. Then Dana switched 288 U.S.

employees in its Jonesboro, Arkansas, plant from producing truck transmissions at breakeven to axle production at a profit. Productivity is up in Jonesboro, and the Mexican joint venture is making money. Employees in both Jonesboro and Queretaro, as well as stockholders, came out ahead on the move. Dana is also moving operations to China, India, Eastern Europe, and South America.

Resourceful organizations like Wachovia and Dana use a global perspective to become more efficient, which allows them to develop new products, retrain employees, and invest in new plant and equipment.

Sources: Business Week (January 30, 2006): 50–64; Forbes (May 8, 2006): 58; and www.dana.com/news/.

OM in Action � Going Global to Compete

Chapter 2 Operations Strategy in a Global Environment 29

Learn to Improve Operations Learning does not take place in isolation. Firms serve them- selves and their customers well when they remain open to the free flow of ideas. For example, GM found that it could improve operations by jointly building and running, with the Japanese, an auto assembly plant in San Jose, California. This strategy allowed GM to contribute its capital and knowledge of U.S. labor and environmental laws while the Japanese contributed production and inventory ideas. Similarly, operations managers have improved equipment and layout by learning from the ergonomic competence of the Scandinavians.

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 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 of the possible opportunities, from tangible to intangible, that international oper- ations 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 ranging 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 example, 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 uniformity is slowly being accepted by most nations.

In spite of 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 sec- tor, the telecommunications sector, and the logistics infrastructure of the world are healthy insti- tutions that foster efficient and effective use of capital, information, and goods. Globalization, with all its opportunities and risks, is here and will continue. It must be embraced as managers develop their missions and strategies.

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.

AUTHOR COMMENT As the owner of a Guatemala plant said, “The ethics of the world markets is very clear:

Manufacturers will move wherever it is cheapest or most convenient to

their interests.”

30 PART 1 Introduction to Operations Management

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.

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 concept 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 determines 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 devel- oped 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 sections, 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 managers trans- late these strategic concepts into tangible tasks to be accomplished. Any one or combination of these three strategic concepts can generate a system that has a unique advantage over competi- tors. For example, Hunter Fan has differentiated itself as a premier maker of quality ceiling fans that lower heating and cooling costs for its customers. Nucor Steel, on the other hand, satisfies customers by being the lowest-cost steel producer in the world. And Dell achieves rapid response by building personal computers with each customer’s requested software in a matter of hours.

Clearly, strategies differ. And each strategy puts different demands on operations manage- ment. Hunter Fan’s strategy is one of differentiating itself via quality from others in the industry. Nucor focuses on value at low cost, and Dell’s dominant strategy is quick, reliable response.

LO1: Define mission and strategy

Mission The purpose or rationale for an organization’s existence.

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.

Hard Rock Cafe

Our Mission: To spread the spirit of Rock ’n’ Roll by delivering an exceptional entertainment and dining experience. We are committed to being an important, contributing member of our community and offering the Hard Rock family a fun, healthy, and nurturing work environment while ensuring our long-term success.

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

Sources: Annual reports: courtesy of Merck, Hard Rock Cafe: Employee Handbook, Arnold Palmer Childrens’ Care Team.

VIDEO 2.1 Operations Strategy at

Regal Marine

Strategy How an organization expects to achieve its missions and goals.

AUTHOR COMMENT Getting an education and managing an organization

both require a mission and strategy.

LO2: Identify and explain three strategic approaches to competitive advantage

Chapter 2 Operations Strategy in a Global Environment 31

� FIGURE 2.3 Sample Missions for a Company, the Operations Function, and Major OM Departments

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 produce the production process and equipment that will be compatible with low-cost product, high quality, and a good quality of work life at economical cost.

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.

Product design To design and produce products and services with outstanding

Quality management To attain the exceptional value that is consistent with our company mission and marketing objectives by close attention to design, procurement, production, and field service opportunities.

quality and inherent customer value.

ACHIEVING COMPETITIVE ADVANTAGE THROUGH OPERATIONS Each of the three strategies provides an opportunity for operations managers to achieve compet- itive 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 sustainable way. Pure forms of these strategies may exist, but operations managers will more likely be called on to implement some combination of them. Let us briefly look at how managers achieve com- petitive 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 reactions about which doctors were complaining. When other glove makers caught up, Safeskin developed

Competitive advantage The creation of a unique advantage over competitors.

AUTHOR COMMENT For many organizations, the operations function provides the competitive advantage.

32 PART 1 Introduction to Operations Management

Differentiation Distinguishing the offerings of an organization in a way that the customer perceives as adding value.

hypoallergenic gloves. Then it added texture to its gloves. Then it developed a synthetic dispos- able glove for those allergic to latex—always staying ahead of the competition. Safeskin’s strat- egy 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 every- thing the firm does. Moreover, because most products include some service, and most services 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 defining 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 prod- uct. Such services can manifest themselves through convenience (location of distribution centers, stores, or branches), training, product delivery and installation, or repair and maintenance services.

In the service sector, one option for extending product differentiation is through an experience. 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 a dynamic visual and sound experience that complements the physical ride. Some rides further engage the customer by having them steer the ride or shoot targets or villains.

Theme restaurants, such as Hard Rock Cafe, likewise differentiate themselves by providing 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 avail- able 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.

Additionally, and less obviously, Southwest has very effectively matched capacity to demand and effectively utilized this capacity. It has done this by designing a route structure 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 others with low-cost strategies understand this and utilize resources effectively. Identifying the optimum size (and investment) allows firms to spread overhead costs, providing a cost advantage. For instance, Wal-Mart continues to pursue its low-cost strategy with superstores, open 24 hours a day. For 20 years, it has successfully grabbed market share. Wal-Mart 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. Franz Colruyt, as discussed in the OM in Action box, is also winning with a low-cost strategy.

Low-cost leadership entails achieving maximum value as defined by your customer. It requires examining each of the 10 OM decisions in a relentless effort to drive down costs while meeting cus- tomer expectations of value. A low-cost strategy does not imply low value or low quality.

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 scheduling and flexible performance.

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.

Response A set of values related to rapid, flexible, and reliable performance.

Chapter 2 Operations Strategy in a Global Environment 33

Belgian discount food retailer Franz Colruyt NV is so obsessed with cutting costs that there are no shopping bags at its checkout counters, the lighting at its stores is dimmed to save money on electricity, and employees clock out when they go on 5-minute coffee breaks. And to keep costs down at the company’s spartan headquarters on the outskirts of Brussels, employees don’t have voice mail on their phones. Instead, two receptionists take messages for nearly 1,000 staffers. The messages are bellowed out every few minutes from loudspeakers peppered throughout the building.

This same approach is evident at all 160 of Colruyt’s shopping outlets, which are converted factory warehouses, movie theaters, or garages, with black concrete floors, exposed electrical wires, metal shelves, and discarded boxes strewn about. There is no background music (estimated annual cost saving: € 2 million, or $2.5 million), nor are there bags for packing groceries (estimated annual cost saving: € 5 million). And all the store’s freezers have doors, so the company can save about € 3 million a year on electricity for refrigeration.

The company also employs a team of 30 “work simplifiers”—in Colruyt jargon—whose job is to come up with new ways to improve productivity. One recently discovered that 5 seconds could be shaved from every minute it takes customers to check out if they paid at a separate station from where groceries are scanned, so that when one customer steps away from the scanner, another can step up right away.

Chief Executive Rene De Wit says Colruyt’s strategy is simple: cut costs at every turn and undersell your competitors. In an industry where margins of 1% to 2% are typical, Colruyt’s cost cutting is so effective that a profit margin of 6.5% dwarfs those of rivals.

A low-cost strategy places significant demands on operations management, but Franz Colruyt, like Wal-Mart, makes it work.

Sources: The Wall Street Journal (January 5, 2005): 1 and (September 22, 2003): R3, R7.

OM in Action � Low-Cost Strategy Wins at Franz Colruyt

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 dra- matic. 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 sustainable competitive advantage.

The second aspect of response is the reliability of scheduling. One way the German machine 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 machine firms have meaningful schedules—and they perform to these schedules. Moreover, the results of

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.

34 PART 1 Introduction to Operations Management

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, discussed in the OM in Action box, competes on speed—speed in design, production, and delivery. Whether it is a production system at Johnson Electric, a pizza delivered in 5 minutes by Pizza Hut, or customized phone products delivered in three days from Motorola, 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 (see Figure 2.4). Proper implementation of the following deci- sions by operations managers will allow these advantages to be achieved.

Patrick Wang, managing director of Johnson Electric Holdings, Ltd., walks through his Hong Kong headquarters with a micromotor in his hand. This tiny motor, about twice the size of his thumb, powers a Dodge Viper power door lock. Although most people have never heard of Johnson Electric, we all have several of its micromotors nearby. This is because Johnson is the world’s leading producer of micromotors for cordless tools, household appliances (such as coffee grinders and food processors), personal care items (such as hair dryers and electric shavers), and cars. A luxury Mercedes, with its headlight wipers, power windows, power seat adjustments, and power side mirrors, may use 50 Johnson micromotors.

Like all truly global businesses, Johnson spends liberally on communications to tie together its global network of factories, R&D facilities, and design centers. For example, Johnson Electric installed a $20 million videoconferencing system that allows engineers in

Cleveland, Ohio, and Stuttgart, Germany, to monitor trial production of their micromotors in China.

Johnson’s first strength is speed in product development, speed in production, and speed in delivering—13 million motors a month, mostly assembled in China but delivered throughout the world. Its second strength is the ability to stay close to its customers. Johnson has design and technical centers scattered across the United States, Europe, and Japan. “The physical limitations of the past are gone” when it comes to deciding where to locate a new center, says Patrick Wang. “Customers talk to us where they feel most comfortable, but products are made where they are most competitive.”

Sources: Hoover’s Company Records (January 1, 2006): 58682; Far Eastern Economic Review (May 16, 2002): 44–45; and Just Auto (November 2008): 18–19.

OM in Action � Response Strategy at Hong Kong’s Johnson Electric

10 Operations Decisions Approach Example

Competitive Advantage

Product

Quality

Process

Location

Layout

Human resource

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’s high aircraft utilization Inventory management . . . . . . . . . . Wal-Mart’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

AUTHOR COMMENT These 10 decisions are used

to implement a specific strategy and yield a

competitive advantage.

Chapter 2 Operations Strategy in a Global Environment 35

TEN STRATEGIC OM DECISIONS Differentiation, low cost, and response can be achieved when managers make effective decisions in 10 areas of OM. These are collectively known as operations decisions. The 10 decisions of OM that support missions and implement strategies are:

1. Goods and service design: Designing goods and services defines much of the transfor- mation process. Costs, quality, and human resource decisions are often determined by design decisions. Designs usually determine the lower limits of cost and the upper limits of quality.

2. Quality: The customer’s quality expectations must be determined and policies and proce- dures established to identify and achieve that quality.

3. Process and capacity design: Process options are available for products and services. Process decisions commit management to specific technology, quality, human resource use, and maintenance. These expenses and capital commitments determine much of the firm’s basic cost structure.

4. Location selection: Facility location decisions for both manufacturing and service organi- zations may determine the firm’s ultimate success. Errors made at this juncture may over- whelm other efficiencies.

5. Layout design: Material flows, capacity needs, personnel levels, technology decisions, and inventory requirements influence layout.

6. Human resources and job design: People are an integral and expensive part of the total sys- tem design. Therefore, the quality of work life provided, the talent and skills required, and their costs must be determined.

7. Supply-chain management: These decisions determine what is to be made and what is to be purchased. Consideration is also given to quality, delivery, and innovation, all at a satisfactory price. Mutual trust between buyer and supplier is necessary for effective purchasing.

8. Inventory: Inventory decisions can be optimized only when customer satisfaction, suppli- ers, production schedules, and human resource planning are considered.

9. Scheduling: Feasible and efficient schedules of production must be developed; the demands on human resources and facilities must be determined and controlled.

10. Maintenance: Decisions must be made regarding desired levels of reliability and stability, and systems must be established to maintain that reliability and stability.

Operations managers implement these 10 decisions by identifying key tasks and the staffing needed to achieve them. However, the implementation of decisions is influenced by a variety of issues, including a product’s proportion of goods and services (see Table 2.1 on page 37). Few products are either all goods or all services. Although the 10 decisions remain the same for both goods and services, their relative importance and method of implementation depend on this ratio of goods and services. Throughout this text, we discuss how strategy is selected and imple- mented for both goods and services through these 10 operations management decisions.

Let’s look at an example of strategy development through one of the 10 decisions.

Pierre Alexander has just completed culinary school and is ready to open his own restaurant. After examining both the external environment and his prospective strengths and weaknesses, he makes a decision on the mission for his restaurant, which he defines as “To provide outstanding French fine dining for the people of Chicago.”

APPROACH � Alexander’s supporting operations strategy is to ignore the options of cost leader- ship and quick response and focus on differentiation. Consequently, his operations strategy requires him to evaluate product designs (menus and meals) and selection of process, layout, and location. He must also evaluate the human resources, suppliers, inventory, scheduling, and maintenance that will support his mission and a differentiation strategy.

SOLUTION � Examining just one of these 10 decisions, process design, requires that Alexander consider the issues presented in the following figure.

(Continued)

� EXAMPLE 1 Strategy development

Operations decisions The strategic decisions of OM are goods and service design, quality, process design, location selection, layout design, human resources and job design, supply-chain management, inventory, scheduling, and maintenance.

LO3: Identify and define the 10 decisions of operations management

AUTHOR COMMENT This text is structured

around these 10 decisions.

36 PART 1 Introduction to Operations Management

The 10 decisions of operations management are implemented in ways that provide competi- tive advantage, not just for fine-dining restaurants, but for all the goods and services that enrich our lives. How this might be done for two drug companies, one seeking a competitive advantage via differentiation, and the other via low cost, is shown in Table 2.2.

ISSUES IN OPERATIONS STRATEGY Whether the OM strategy is differentiation, cost, or response (as shown earlier in Figure 2.4), OM is a critical player. Therefore, prior to establishing and attempting to implement a strategy, some alter- nate perspectives may be helpful. One perspective is to take a resources view. This means think- ing in terms of the financial, physical, human, and technological resources available and ensuring

The first option is to operate in the lower right corner of the preceding figure, where he could produce high volumes of food with a limited variety, much as in an institutional kitchen. Such a process could produce large volumes of standard items such as baked goods and mashed potatoes prepared with state- of-the-art automated equipment. Alexander concludes that this is not an acceptable process option.

Alternatively, he can move to the middle of the figure, where he could produce more variety and lower volumes. Here he would have less automation and use prepared modular components for meals, much as a fast-food restaurant does. Again, he deems such process designs inappropriate for his mission.

Another option is to move to the upper right corner and produce a high volume of customized meals, but neither Alexander nor anyone else knows how to do this with gourmet meals.

Finally, Alexander can design a process that operates in the upper left corner of the figure, which requires little automation but lends itself to high variety. This process option suggests that he build an extremely flexible kitchen suitable for a wide variety of custom meals catering to the whims of each customer. With little automation, such a process would be suitable for a huge variety. This process strategy will support his mission and desired product differentiation. Only with a process such as this can he provide the fine French-style gourmet dining that he has in mind.

INSIGHT � By considering the options inherent in each of the 10 OM decisions, managers— Alexander, in this case—can make decisions that support the mission.

LEARNING EXERCISE � If Alexander’s mission were to offer less expensive meals and reduce the variety offered but still do so with a French flair, what might his process strategy be? [Answer: Alexander might try a repetitive (modular) strategy and mimic the La Madeleine cafeteria- style restaurants.]

V a ri

e ty

o f

p ro

d u

c ts

Moderate

Moderate

Volume

Low

Low

High

High

(Print shop, emergency room, machine shop,

fine-dining restaurant)

JOB SHOPS

Process focused

(Cars, appliances, TVs, fast-food restaurants)

ASSEMBLY LINE

Repetitive (modular) focus

(Steel, beer, paper, bread, institutional kitchen)

CONTINUOUS

Product focused

Customization at High Volume

(Dell Computer’s PC, cafeteria)

Mass Customization

Resources view A method managers use to evaluate the resources at their disposal and manage or alter them to achieve competitive advantage.

AUTHOR COMMENT An effective strategy finds

the optimum fit for the firm’s resources in the dynamic

environment.

� TABLE 2.1 The Differences between Goods and Services Influence How the 10 Operations Management Decisions Are Applied

Operations Decisions Goods Services

Goods and service design Product is usually tangible (a computer). Product is not tangible. A new range of product attributes (a smile).

Quality Many objective quality standards (battery life). Many subjective quality standards (nice color).

Process and capacity design

Customer is not involved in most of the process (auto assembly).

Customer may be directly involved in the process (a haircut).

Capacity must match demand to avoid lost sales (customers often avoid waiting).

Location selection May need to be near raw materials or labor force (steel plant near ore).

May need to be near customer (car rental).

Layout design Layout can enhance production efficiency (assembly line).

Can enhance product as well as production (layout of a classroom or a fine-dining restaurant).

Human resources and job design

Workforce focused on technical skills (stone mason). Labor standards can be consistent (assembly line employee). Output-based wage system possible (garment sewing).

Direct workforce usually needs to be able to interact well with customer (bank teller); labor standards vary depending on customer requirements (legal cases).

Supply-chain management

Supply chain relationships critical to final product.

Supply chain relationships important but may not be critical

Inventory Raw materials, work-in-process, and finished goods may be inventoried (beer).

Most services cannot be stored; so other ways must be found to accommodate fluctuations in demand (can’t store haircuts, but even the barber shop has an inventory of supplies).

Scheduling Ability to inventory may allow leveling of production rates (lawn mowers).

Often concerned with meeting the customer’s immediate schedule with human resources.

Maintenance Maintenance is often preventive and takes place at the production site.

Maintenance is often “repair” and takes place at the customer’s site.

� TABLE 2.2 Operations Strategies of Two Drug Companies

Brand Name Drugs, Inc. Generic Drug Corp.

Competitive Advantage Product Differentiation Low Cost

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 provide direction; other personnel paid below industry average

Supply Chain Long-term supplier relationships Tends to purchase competitively to find bargains

Inventory Maintains high finished goods inventory primarily to ensure all demands are met

Process focus drives up work-in-process inventory; finished 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

AUTHOR COMMENT The production of both goods and services requires execution of the

10 OM decisions.

AUTHOR COMMENT Notice how the 10 decisions are

altered to build two distinct strategies in the same industry.

that the potential strategy is compatible with those resources. Another perspective 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 competitive advantage. These are areas where the firm adds its unique value through product research, design, human resources, supply-chain management, process innovation, or quality management. 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 polit- ical, to legal, to cultural. They influence strategy development and execution and require con- stant scanning of the environment.

The firm itself is also undergoing constant change. Everything from resources, to technology, 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

38 PART 1 Introduction to Operations Management

Value-chain analysis A way to identify those elements in the product/service chain that uniquely add value.

Five forces analysis A method of analyzing the five forces in the competitive environment.

2M. E. Porter, Competitive Advantage: Creating and Sustaining Superior Performance. New York: The Free Press, 1985. 3Michael E. Porter, Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: The Free Press, 1980, 1998.

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

Sales

Drive-thru restaurantsInternet search engines

Analog TVs

CD-ROMs

Avatars

Boeing 787

Twitter

LCD & plasma TVs iPods

Xbox 360

� FIGURE 2.5 Strategy and Issues During a Product’s Life

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 envi- ronment. Faster processors, new computer languages, changing customer preferences, increased security issues, the Internet, and Google have all driven changes at Microsoft. These forces have moved Microsoft’s product strategy from operating systems to office products, to Internet ser- vice provider, and now to integrator of computers, cell phones, games, and television.

The more thorough the analysis and understanding of both the external and internal factors, the more likely that a firm can find the optimum use of its resources. Once a firm understands itself and the environment, a SWOT analysis, which we discuss next, is in order.

STRATEGY DEVELOPMENT AND IMPLEMENTATION A SWOT analysis is a formal review of the internal Strengths and Weakness and the external Opportunity 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 manufac- turing 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 mini- mizing 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 critical 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 that a firm must get them right to survive in the industry. A KSF for McDonald’s, for example, is layout. Without a play area, an effective drive-thru, and an efficient kitchen, McDonald’s cannot be suc- cessful. 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 advantage. 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’s core competence is gas- powered engines—engines for automobiles, motorcycles, 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 mission. A core competence may be a subset of 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 will help the OM function provide a competitive advantage?” • “Which elements contain the highest likelihood of failure, and which require additional com-

mitment of managerial, monetary, technological, and human resources?”

Chapter 2 Operations Strategy in a Global Environment 39

Key success factors (KSFs) Activities or factors that are key to achieving competitive advantage.

Core competencies A set of skills, talents, and activities in which a firm is particularly strong.

SWOT analysis A method of determining internal strengths and weaknesses and external opportunities and threats.

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 the strengths, weaknesses, opportunities, and threats. Understand the environment, customers, industry, and competitors.

� FIGURE 2.6 Strategy Development Process

AUTHOR COMMENT A SWOT analysis provides

an excellent model for evaluating a strategy.

LO4: Understand the significance of key success factors and core competencies

Only by identifying and strengthening key success factors and core competencies can an organi- zation achieve sustainable competitive advantage.

In this text we focus on the 10 OM decisions that typically include the KSFs. Potential KSFs for marketing, finance, and operations are shown in Figure 2.7. The 10 OM decisions we develop in this text provide an excellent initial checklist for determining KSFs and identifying core competencies within the operations function. For instance, the 10 decisions, related KSFs, and core competencies can allow a firm to differentiate its product or service. That differentiation may be via a core compe- tence of innovation and new products, where the KSFs are product design and speed to market, as is the case for 3M and Rubbermaid. Similarly, differentiation may be via quality, where the core com- petence is institutionalizing quality, as at Toyota. Differentiation may also be via maintenance, where the KSFs are product reliability and after-sale service, as is the case at IBM and Canon.

40 PART 1 Introduction to Operations Management

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 resource Supply chain Inventory Schedule Maintenance

Marketing Finance/Accounting Operations

Decisions Sample Options Chapter

Customized or standardized Define customer expectations and how to achieve them Facility design, capacity 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

6,S6 7,S7

8 9 10

11, S11 12,14,16

13,15 17

Support a Core Competence 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 Competences

Activity map A graphical link of competitive advantage, KSFs, and supporting activities.

Generators Automobiles 4-Wheel Scooters Water Pumps

Marine Motors Race Cars Motorcycles Snow Blowers

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.

Whatever the KSFs and core competences, they must be supported by the related activities. One approach to identifying the activities is an activity map, which links competitive advantage, KSFs, and supporting activities. For example, Figure 2.8 shows how Southwest Airlines, whose core competence is operations, built a set of integrated activities to support its low-cost compet- itive advantage. Notice how the KSFs support operations and in turn are supported by other activities. The activities fit together and reinforce each other. And the better they fit and reinforce each other, the more sustainable the competitive advantage. By focusing on enhancing its core

competence and KSFs with a supporting set of activities, Southwest Airlines has become one of the great airline success stories.

Build and Staff the Organization The operations manager’s job is a three-step process. Once a strategy and key success factors have been identified, the second step is to group the necessary activities into an organizational structure. The third step is to staff it 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 ways. The organization charts shown in Chapter 1 (Figure 1.1) indicate the way some firms have organized to perform the required activities.

Integrate OM with Other Activities The organization of the operations function and its relationship to other parts of the organization vary with the OM mission. Moreover, the operations function is most likely to be successful when the operations strategy is integrated with other functional areas of the firm, such as market- ing, finance, information technology, and human resources. In this way, all of the areas support the company’s objectives. For example, short-term scheduling in the airline industry is domi- nated by volatile customer travel patterns. Day-of-week preference, holidays, seasonality, col- lege schedules, and so on, all play a role in changing flight schedules. Consequently, airline scheduling, although an OM activity, can be a part of marketing. Effective scheduling in the trucking industry is reflected in the amount of time trucks travel loaded. However, scheduling of trucks requires information from delivery and pickup points, drivers, and other parts of the orga- nization. When the OM function results in effective scheduling in the air passenger and commer- cial trucking industries, a competitive advantage can exist.

The operations manager transforms inputs into outputs. The transformations may be in terms of storage, transportation, manufacturing, dissemination of information, and utility of the prod- uct or service. The operations manager’s job is to implement an OM strategy, provide competi- tive advantage, and increase productivity.

Chapter 2 Operations Strategy in a Global Environment 41

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

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 has 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 (advertising, HR, etc.)

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 (peanuts)

� FIGURE 2.8 Activity Mapping of Southwest Airlines’s 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, a low-cost advantage is highly dependent on a very well run operations function.

GLOBAL OPERATIONS STRATEGY OPTIONS As we suggested early in this chapter, many operations strategies now require an international dimension. We tend to call a firm with an international dimension an international business or a multinational corporation. An international business is any firm that engages in international trade or investment. This is a very broad category and is the opposite of a domestic, or local, firm.

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 computers 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 opportunities with one of four operations 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 differentia- tion 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.

International Strategy An international strategy uses exports and licenses to penetrate the global arena. As Figure 2.9 suggests, the international strategy is the least advantageous, with little local responsiveness and little cost advantage. There is little responsiveness because we are exporting or licensing goods from the home country. And the cost advantages may be few because we are using the existing production process at some distance from the new market. However, an international strategy is often the easiest, as exports can require little change in existing operations, and licensing agree- ments often leave much of the risk to the licensee.

42 PART 1 Introduction to Operations Management

International business A firm that engages in cross- border transactions.

Multinational corporation (MNC) A firm that has extensive involvement in international business, owning or controlling facilities in more than one country.

International strategy A strategy in which global markets are penetrated using exports and licenses.

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

Examples: Heinz McDonald’s The Body Shop Hard Rock Cafe

Multidomestic strategy

• Import/export or license existing product

Examples: U.S. Steel Harley-Davidson

International strategy

Global strategy

• Standardized product • Economies of scale • Cross-cultural learning

Examples: Texas Instruments Caterpillar Otis Elevator

Transnational strategy

Move material, people, or ideas across national boundaries Economies of scale Cross-cultural learning

Examples: Coca-Cola Nestlé

•

• •

� FIGURE 2.9 Four International Operations Strategies

Sources: See a similar presentation in M. Hitt, R. D. Ireland, and R. E. Hoskisson, Strategic Management, Competitiveness and Globalization, 7th ed. (Cincinnati: Southwestern College Publishing, 2009).

AUTHOR COMMENT Firms that ignore the global economy will not survive.

LO5: Identify and explain four global operations strategy options

Multidomestic Strategy The multidomestic strategy has decentralized authority with substantial autonomy at each business. Organizationally these are typically subsidiaries, franchises, or joint ventures with substantial inde- pendence. The advantage of this strategy is maximizing a competitive response for the local market; however, the strategy has little or no cost advantage. Many food producers, such as Heinz, use a mul- tidomestic strategy to accommodate local tastes because global integration of the production process is not critical. The concept is one of “we were successful in the home market; let’s export the manage- ment talent and processes, not necessarily the product, to accommodate another market.” McDonald’s is operating primarily as a multidomestic, which gives it the local responsiveness needed to modify its menu country by country. McDonald’s can then serve beer in Germany, wine in France, McHuevo (poached egg hamburger) in Uruguay, and hamburgers without beef in India. With over 2,000 restau- rants in Japan and a presence for more than a generation, the average Japanese family thinks Japan invented McDonald’s. Interestingly, McDonald’s prefers to call itself multilocal.4

Global Strategy A global strategy has a high degree of centralization, with headquarters coordinating the organi- zation 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 rec- ommend 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 strategies. 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, which allows Caterpillar to have individual factories focus on a limited line of products to be shipped worldwide. This results in economies of scale and learning within each facility. A global strategy also allows Texas Instruments to build optimum-size plants with similar processes and to then maximize learning by aggressive communication between plants. The result is an effective cost reduction advantage for Texas Instruments.

Transnational Strategy 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” country 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 pur- sue all three operations strategies (i.e., differentiation, low cost, and response). Such firms can be

Chapter 2 Operations Strategy in a Global Environment 43

Multidomestic strategy A strategy in which operating decisions are decentralized to each country to enhance local responsiveness.

4James L. Watson, ed., Golden Arches East: McDonald’s in East Asia (Stanford University Press, 1997): 12. Note: McDonald’s also operates with some of the advantages of a global organization. By using very similar product lines throughout the world, McDonald’s obtains some of the standardization advantages of a global strategy. However, it manages to retain the advantages of a multidomestic strategy.

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 dictate.

Global strategy A strategy in which operating decisions are centralized and headquarters coordinates the standardization and learning between facilities.

Transnational strategy A strategy that combines the benefits of global-scale efficiencies with the benefits of local responsiveness.

44 PART 1 Introduction to Operations Management

Global operations provide an increase in both the challenges and opportunities for operations managers. Although the task is challenging, operations managers can and do improve pro- ductivity. They can build and manage OM functions that con- tribute in a significant way to competitiveness. Organizations identify their strengths and weaknesses. They then develop effective missions and strategies that account for these strengths and weaknesses and complement the opportunities and threats in the environment. If this procedure is performed well, the organization can have competitive advantage through some combination of product differentiation, low

cost, and response. This competitive advantage is often achieved via a move to international, multidomestic, global, or transnational strategies.

Effective use of resources, whether domestic or international, is the respon- sibility 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 substantial.

Key Terms

Maquiladoras (p. 27) World Trade Organization (WTO) (p. 27) North American Free Trade Agreement

(NAFTA) (p. 27) European Union (EU) (p. 27) Mission (p. 30) Strategy (p. 30) Competitive advantage (p. 31) Differentiation (p. 32)

Experience differentiation (p. 32) Low-cost leadership (p. 32) Response (p. 32) Operations decisions (p. 35) Resources view (p. 36) Value-chain analysis (p. 38) Five forces analysis (p. 38) SWOT analysis (p. 39) Key success factors (KSFs) (p. 39)

Core competencies (p. 39) Activity map (p. 40) International business (p. 42) Multinational corporation

(MNC) (p. 42) International strategy (p. 42) Multidomestic strategy (p. 43) Global strategy (p. 43) Transnational strategy (p. 43)

thought of as “world companies” whose country identity is not as important as its interdependent network of worldwide operations. Key activities in a transnational company are neither centralized in the parent company nor decentralized so that each subsidiary can carry out its own tasks on a local basis. Instead, the resources and activities are dispersed, but specialized, so as to be both effi- cient and flexible in an interdependent network. 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. Similarly, service firms such as Asea Brown Boveri (an engineering firm that is Swedish but headquartered in Switzerland), Reuters (a news agency), Bertelsmann (a publisher), and Citicorp (a banking corporation) can be viewed as transna- tionals. We can expect the national identities of these transnationals to continue to fade.

Solved Problem Virtual Office Hours help is available at www.myomlab.com

� 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 approach- ing 60%. And the German tire maker Continental AG has strength- ened its position as fourth in the world, with a dominant presence in Germany. Against this formidable array, the old-line Italian tire company Pirelli SpA found it difficult to respond effectively. Although Pirelli still had 5% of the market, it was losing millions a year while the competition was getting stronger. Tires are a tough, competitive business that rewards companies having strong market shares and long production runs. Pirelli has some strengths: an out- standing reputation for excellent high-performance tires and an innovative manufacturing function.

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, utilize the internal marketing strength represented by Pirelli’s strong brand name and history of winning World Rally Championships. Third, maximize the internal innovative capabili- ties of the operations function.

To achieve these goals, Pirelli made a strategic shift out of low-margin standard tires and into higher-margin performance tires. Pirelli established deals with luxury brands Jaguar, BMW, Maserati, Ferrari, Bentley, and Lotus Elise and established itself as a provider of a large share of tires on new Porsches, S-class Mercedes, and Saabs. As a result, more than 70% of the com- pany’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 man- ufacture that allows much faster switching between models. This modular system, combined with investments in new manufacturing

CHAPTER SUMMARY

Chapter 2 Operations Strategy in a Global Environment 45

Bibliography Beckman, S. L., and D. B. Rosenfield. Operations Strategy:

Competing in the 21st Century. New York: McGraw-Hill, 2008.

Crotts, J. C., D. R. Dickson, and R. C. Ford. “Aligning Organizational Processes with Mission.” Academy of Management Executive 19, no. 3 (August 2005): 54–68.

Flynn, B. B., R. G. Schroeder, and E. J. Flynn. “World Class Manufacturing.” Journal of Operations Management 17, no. 3 (March 1999): 249–269.

Friedman, Thomas. The World Is Flat: A Brief History of the Twenty-first Century. New York: Farrar, Straus, and Giroux, 2005.

Greenwald, Bruce, and Judd Kahn. “All Strategy Is Local.” Harvard Business Review, 83, no. 9 (September 2005): 94–104.

Kaplan, Robert S., and David P. Norton. Strategy Maps. Boston: Harvard Business School Publishing, 2003.

Kathuria, R., M. P. Joshi, and S. Dellande. “International Growth Strategies of Service and Manufacturing Firms.” International

Journal of Operations and Production Management 28, no. 10 (2008): 968.

Porter, Michael, and Nicolaj Siggelkow. “Contextuality within Activity Systems and Sustainability of Competitive Advantage.” Academy of Management Perspectives 22, no. 2 (May 2008): 34–36.

Rudberg, Martin, and B. M. West. “Global Operations Strategy.” Omega 36, no. 1 (February 2008): 91.

Skinner, Wickham. “Manufacturing Strategy: The Story of Its Evolution.” Journal of Operations Management 25, no. 2 (March 2007): 328–334.

Slack, Nigel, and Mike Lewis. Operation Strategy, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2008.

Wolf, Martin. Why Globalization Works. London: Yale University Press, 2004.

Zakaria, Fareed. The Post American World. New York: W.W. Norton, 2008.

�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study: Motorola’s Global Strategy: Focuses on Motorola’s international strategy.

flexibility, has driven batch sizes down to as small as 150 to 200, making small-lot performance tires economically feasible. Manu- facturing innovations at Pirelli have streamlined the production process, moving it from a 14-step process to a 3-step process. A threat from the big three going after the performance market remains, but Pirelli has bypassed its weakness of having a small

market share. The firm now has 24 plants in 12 countries and a pres- ence in more than 160 countries, with sales exceeding $4.5 billion.

Sources: Just Auto (February 2009): 14–15 and (December 2008): 14–15; Hoover’s Company Records (October 15, 2005): 41369; and www.pirelli. com/web/investors.

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Project Management

Chapter Outline

GLOBAL COMPANY PROFILE: BECHTEL GROUP

The Importance of Project Management 50

Project Planning 50

Project Scheduling 53

Project Controlling 54

Project Management Techniques: PERT and CPM 55

Determining the Project Schedule 60

Variability in Activity Times 65

Cost–Time Trade-Offs and Project Crashing 71

A Critique of PERT and CPM 73

Using Microsoft Project to Manage Projects 74

47

GLOBAL COMPANY PROFILE: BECHTEL GROUP

PROJECT MANAGEMENT PROVIDES A COMPETITIVE ADVANTAGE FOR BECHTEL

N ow in its 112th year, 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, and rebuilding

of Kuwait’s oil and gas infrastructure after the invasion

by Iraq in 1990.

Conditions weren’t what Bechtel expected when it

won a series of billion-dollar contracts from the U.S.

government to help reconstruct Iraq in 2003–2006.

48

Workers wrestle with a 1,500-ton boring machine, measuring 25 feet in diameter, that was used to dig the Eurotunnel between England and France in the early 1990s. With over- runs that boosted the cost of the project to $13 billion, a Bechtel Group VP was brought in to head operations.

Saddam Hussein’s defeat by Allied forces hadn’t caused

much war damage. Instead, what Bechtel found was a

country that had been crumbling for years. None of the

sewage plants in Baghdad worked. Power flicked on and

off. Towns and cities in the anti-Hussein south had been

left to decay as punishment. And to complicate matters

even more, 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, was reopened when Bechtel dredged the

water and repaired the grain elevators. Electrical

generation was back to prewar levels in 10 months.

Bechtel refurbished more than 1,200 schools.

With a global procurement program, Bechtel easily

tapped the company’s network of suppliers and buyers

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.

49

worldwide to help rebuild Iraq’s infrastructure. Other

interesting recent Bechtel projects include:

• Building 26 massive distribution centers, in just 2 years, for the Internet company Webvan Group ($1 billion).

• 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 liquefied natural gas plants in Trinidad, West Indies ($1 billion).

• Building a new subway for Athens, Greece ($2.6 billion).

• Constructing a natural gas pipeline in Thailand ($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

demonstrated its competitive advantage.

Managing massive con- struction projects such as this is the strength of Bechtel. With large penal- ties for late completion and incentives for early comple- tion, a good project man- ager is worth his or her weight in gold.

Reconstructed terminal at Baghdad International Airport

Bechtel was the construction contractor for the Hoover Dam. This dam, on the Colorado River, is the highest in the Western Hemisphere.

BECHTEL GROUP �

50 PART 1 Introduction to Operations Management

Chapter 3 Learning Objectives LO1: Use a Gantt chart for scheduling 53

LO2: Draw AOA and AON networks 57

LO3: Complete forward and backward passes for a project 61

LO4: Determine a critical path 65

LO5: Calculate the variance of activity times 67

LO6: Crash a project 71

THE IMPORTANCE OF PROJECT MANAGEMENT When Bechtel, the subject of the opening Global Company Profile, entered Iraq after the 2003 war, it quickly had to mobilize an international force of manual workers, construction profes- sionals, cooks, medical personnel, and security forces. Its project management team had to access millions of tons of supplies to rebuild ports, roads, schools, and electrical systems. Similarly, when Hard Rock Cafe sponsors Rockfest, hosting 100,000 plus fans at its annual con- cert, the project manager begins planning some 9 months earlier. Using the software package Microsoft Project, described in this chapter, each of the hundreds of details can be monitored and controlled. When a band can’t reach the Rockfest site by bus because of massive traffic jams, Hard Rock’s project manager is ready with a helicopter backup.

Bechtel and Hard Rock are just two examples of firms that face 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 improve- ment. 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 is a difficult challenge for operations managers. The stakes in project 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 pro- duction 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 organization is an effective way of assigning the people and physical resources needed. It is a temporary orga- nization structure designed to achieve results by using specialists from throughout the firm. NASA and many other organizations use the project approach. You may recall Project Gemini and Project Apollo. These terms were used to describe teams that NASA organized to reach space exploration objectives.

The project organization works best when:

1. Work 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. 4. The project is temporary but critical to the organization. 5. The project cuts across organizational lines.

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.

AUTHOR COMMENT Wherever your career takes you, one of the most useful

tools you can have, as a manager, is the ability to

manage a project.

Chapter 3 Project Management 51

Planning the Project

Scheduling the Project

Controlling the 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

Before project

Start of project During project

Timeline

� FIGURE 3.1 Project Planning, Scheduling, and Controlling

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

The Project Manager An example of a project organization is shown in Figure 3.2. Project team members are temporar- ily assigned to a project and report to the project manager. The manager heading the project coor- dinates 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,

AUTHOR COMMENT Managers must “make

the plan and then work the plan.” Project planning helps

you do that.

AUTHOR COMMENT Project organizations can be

temporary or permanent. A permanent organization is

usually called a “matrix organization.”

� FIGURE 3.2 A Sample Project Organization

52 PART 1 Introduction to Operations Management

and information needed to do their jobs. This means that project managers should be good coaches and communicators, and be able to organize activities from a variety of disciplines.

Ethical Issues Faced in Project Management Project managers not only have high visi- bility 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 bonus or penalty schedules.

Using the Project Management Institute’s (www.pmi.org) ethical codes is one means of try- ing to establish standards. Research has shown that without good leadership and a strong organi- zational culture, most people follow their own set of ethical standards and values.1

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 managing the proj- ect and to scheduling success. Gross requirements for people, supplies, and equipment are also estimated in this planning phase.

The work breakdown structure typically decreases in size from top to bottom and is indented 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 sys- tem Windows 7. 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), project management (1.2), and system testing (1.3). Two major subtasks for 1.1 are development of graphical user interfaces (GUIs) (1.1.1) and creating compatibility with previous versions of Windows (1.1.2). The major subtasks for 1.1.2 are level-4 activities,

Work breakdown structure (WBS) A hierarchical description of a project into more and more detailed components.

1See Hilder Helgadottir, “The Ethical Dimension of Project Management,” International Journal of Project Management 26, no. 7 (October 2008): 743.

Level 2

Level 3

Level 4

Level 1 Develop Windows 7 Operating System

Software Design

Project Management

System Testing

Develop GUIs

Planning

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

Cost/Schedule Management

Compatible with Windows ME(Work packages)

Compatible with Windows Vista

Compatible with Windows XP

� FIGURE 3.3 Work Breakdown Structure

Chapter 3 Project Management 53

such as creating a team to handle compatibility with Windows ME (1.1.2.1), 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 how many people and materials will be needed at each stage of production. Managers also chart separate schedules for personnel needs by type of skill (management, engineering, or pouring concrete, for example). Charts also can be developed for scheduling materials.

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 docu- mented, (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. (A second illustration of a Gantt chart is also provided in Chapter 15, Figure 15.4.)

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 com- plex 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 bot-

tlenecks in the project.

Gantt charts Planning charts used to schedule resources and allocate time.

LO1: 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 hopes to save $50 million a year with this turnaround time, which is a reduction from its traditional 60-minute routine.

AUTHOR COMMENT Gantt charts are simple and visual, making them

widely used.

54 PART 1 Introduction to Operations Management

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: passengers, 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 departure 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 International Airport, views the turnaround operation like a pit boss awaiting a race car. Trained crews

are in place for Flight 574 with baggage carts 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 proportions 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.

Sources: Knight Ridder Tribune Business News (July 16, 2005): 1 and (November 21, 2002): 1.

OM in Action � Delta’s Ground Crew Orchestrates a Smooth Takeoff

PROJECT CONTROLLING The control of projects, like the control of any management system, involves close monitoring 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 on personal computers. Some of the more popular of these programs are Primavera (by Primavera Systems, Inc.), MacProject (by Apple Computer Corp.), Pertmaster (by Westminster Software, Inc.), VisiSchedule (by Paladin Software Corp.), Time Line (by Symantec Corp.), 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 for each task, (2) total program labor curves, (3) cost distribution 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.

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 in the Lecture Guide & Activities Manual.

VIDEO 3.2 Project Management at Arnold

Palmer Hospital

AUTHOR COMMENT Software has revolutionized

project control.

Chapter 3 Project Management 55

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, in 1957, as a tool developed by J. E. Kelly of Remington Rand and M. R. Walker of duPont to assist in the building and maintenance of chemical plants at duPont. Independently, PERT was developed in 1958 by Booz, Allen, and Hamilton 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 crit- ical 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 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 activity. 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 following 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 accomplish

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 activities in accordance with the work breakdown structure. There are two approaches for drawing a proj- ect network: activity-on-node (AON) and activity-on-arrow (AOA). Under the AON conven- tion, 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 finishing times of an activity and are also called events. So nodes in AOA consume neither time nor resources.

Figure 3.5 illustrates both conventions for a small portion of the airline turnaround Gantt chart (in Figure 3.4). The examples provide some background for understanding six common activity

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.

Activity-on-node (AON) A network diagram in which nodes designate activities.

Activity-on-arrow (AOA) A network diagram in which arrows designate activities.

AUTHOR COMMENT To use project management software, you first need to understand the next two sections in this chapter.

56 PART 1 Introduction to Operations Management

B C

A

B

C A

B

C

A

B

C

B

C

A B CA

B DA

C

CA

DB

A

B D

A C

B

C

DA

CA

DB

CA

DB

A comes before B, which comes before C.

A and B must both be completed before C can start.

B and C cannot begin until A is completed.

C and D cannot begin until both A and B are completed.

C cannot begin until both A and B are completed; D cannot begin until B is completed. A dummy activity is introduced in AOA.

B and C cannot begin until A is completed. D cannot begin until both B and C are completed. A dummy activity is again introduced in AOA.

Dummy activity

Dummy activity

Activity-on-Node (AON) Activity Meaning Activity-on-Arrow (AOA)

(a)

(b)

(c)

(d)

(e)

(f)

� FIGURE 3.5 A Comparison of AON and AOA Network Conventions

relationships in networks. In Figure 3.5(a), activity A must be finished before activity B is started, and B must, in turn, be completed before C begins. Activity A might represent “deplan- ing passengers,” while B is “cabin cleaning,” and C is “boarding new passengers.”

Figures 3.5(e) and 3.5(f) illustrate that the AOA approach sometimes needs the addition of a dummy activity to clarify relationships. A dummy activity consumes no time or resources, but is required when a network has two activities with identical starting and ending events, or when two or more follow some, but not all, “preceding” activities. The use of dummy activities is also important when computer software is employed to determine project completion time. A dummy activity has a completion time of zero and is shown graphically with a dashed line.

Although both AON and AOA are popular in practice, many of the project management soft- ware 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 subsequent discussions in this chapter.

Dummy activity An activity having no time that is inserted into a network to maintain the logic of the network.

Chapter 3 Project Management 57

Activity-on-Node Example

� EXAMPLE 1 Activity-on-node for EPA problem at Milwaukee Paper

Milwaukee Paper Manufacturing, Inc., located near downtown Milwaukee, has long been delaying the expense of installing air pollution control equipment in its facility. The Environmental Protection Agency (EPA) has recently given the manufacturer 16 weeks to install a complex air filter system. Milwaukee Paper has been warned that it may be forced to close the facility unless the device is installed in the allotted time. Joni Steinberg, the plant manager, wants to make sure that installation of the filtering system progresses smoothly and on time.

Given the following information, develop a table showing activity precedence relationships.

APPROACH � 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 the internal components are completed. Pouring the concrete floor and installation of the frame (activity 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-temperature 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 � Activities and precedence relationships may seem rather confusing when they are presented 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 activ- ity C. Likewise, both activities D and E must be performed prior to starting activity G.

INSIGHT � To complete a network, all predecessors must be clearly defined.

LEARNING EXERCISE � What is the impact on the sequence of activities if 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.]

LO2: Draw AOA and AON networks

Activity Description Immediate

Predecessors

A Build internal components — B Modify roof and floor — 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

� TABLE 3.1 Milwaukee Paper Manufacturing’s Activities and Predecessors

Note that in Example 1, it is enough to list just the immediate predecessors for each activity. For instance, in Table 3.1, since 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, using a project network, is con- venient and useful. A project network is a diagram of all the activities and the precedence rela- tionships that exist between these activities in a project. Example 2 illustrates how to construct a project network for Milwaukee Paper Manufacturing.

58 PART 1 Introduction to Operations Management

Monday. It is time for John Nicely to make a grocery list. He’s serving dinner on Saturday, so he’ll need a few things . . . 150 pounds of steak and chicken, ingredients for 48 gallons of shrimp bisque, 400 sushi rolls, and 25 pounds of jambalaya. Plus a couple hundred pizzas and a couple thousand hot dogs—just enough to feed the Miami Heat basketball players and the 19,600 guests expected. You see, Nicely is the executive chef at American Airlines Arena in Miami, and on Saturday the Heat are hosting the L.A. Lakers.

How do you feed huge crowds good food in a short time? It takes good project management, combined with creativity and improvisation. With 250 facilities serving food and beverage, “The Arena,” Nicely says, “is its own beast.”

Tuesday. Shopping day.

Wednesday–Friday. The staff prepares whatever it can, chopping vegetables, marinating meats, mixing salad dressings—everything but cooking the food. Nicely also begins his shopping lists for next Monday’s game against Toronto and for a Queen concert three days later.

Saturday. 3:55 P.M. Clutch time. Suddenly the kitchen is a joke-free zone. In five minutes, Nicely’s first clients, 200 elite season ticket holders, expect their meals—from a unique menu created for each game.

5:00 P.M. As the Heat and Lakers start warming up, the chefs move their operation in a brisk procession of hot boxes and cold-food racks to the satellite kitchens.

6:00 P.M. Nicely and team face surprises at concession stands: a shortage of cashiers and a broken cash register.

Halftime. There is a run on roasted potatoes in the

Flagship restaurant. But Nicely has thought ahead and anticipated. The backup potatoes arrive before customers even notice.

For John Nicely, successful project management means happy guests as the result of a thousand details that have been identified, planned, and executed. Just another night of delivering restaurant-quality meals and top-grade fast food to a sold-out stadium crowd in a span of a couple hours.

Sources: Fast Company (May, 2006): 52–57; and Knight Ridder Tribune Business News (March 9, 2006): 1.

OM in Action � Prepping for the Miami Heat Game

EXAMPLE 2 � AON graph for Milwaukee Paper

Draw the AON network for Milwaukee Paper, using the data in Example 1.

APPROACH � In the AON approach, we denote each activity by a node. The lines, or arrows, rep- resent the precedence relationships between the activities.

SOLUTION � In this example, there are two activities (A and B) that do not have any predeces- sors. We draw separate nodes for each of these activities, as shown in Figure 3.6. 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 3.6. 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 serves as the unique starting activity for the entire project.

A

B

Activity A (Build Internal Components)

Activity B (Modify Roof and Floor)

Start

Start Activity

� FIGURE 3.6 Beginning AON Network for Milwaukee Paper

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. Since activity A precedes activity C, we draw an arrow from node A to node C (see Figure 3.7). Likewise, we first draw a node to represent activity D. Then, since activities A and B both precede activity D, we draw arrows from A to D and from B to D (see Figure 3.7).

Chapter 3 Project Management 59

D

C

Activity A Precedes Activity C

Activities A and B Precede Activity D

Start

B

A

� FIGURE 3.7 Intermediate AON Network for Milwaukee Paper

D G

H

Arrows Show Precedence

Relationships

Start

B

A

F

E

C

� FIGURE 3.8 Complete 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 3.8.

INSIGHT � 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 � 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 to reflect the additional activity.]

RELATED PROBLEMS � 3.3, 3.6, 3.7, 3.9a, 3.10, 3.12, 3.15a

When we first draw a project network, it is not unusual that we place our nodes (activities) in the network in such a fashion that the arrows (precedence relationships) are not straight lines. That is, the lines could be intersecting each other, and even facing in opposite directions. For example, if we had switched the location of the nodes for activities E and F in Figure 3.8, the lines from F to H and E to G would have intersected. Although such a project network is per- fectly valid, it is good practice to have a well-drawn network. One rule that we especially recom- mend is to place the nodes in such a fashion that all arrows point in the same direction. To achieve this, we suggest that you first draw a rough draft of the network, making sure all the rela- tionships are shown. Then you can redraw the network to make appropriate changes in the loca- tion of the nodes.

As with the unique starting node, it is convenient to have the project network finish with a unique ending node. In the Milwaukee Paper example, it turns out that a unique activity, H, is the last activity in the project. We therefore automatically have a unique ending node.

In situations in which a project has multiple ending activities, we include a “dummy” ending activity. This dummy activity has all the multiple ending activities in the project as immediate predecessors. We illustrate this type of situation in Solved Problem 3.2 at the end of this chapter.

60 PART 1 Introduction to Operations Management

Activity-on-Arrow Example We saw earlier that in an AOA project network we can represent activities by arrows. A node rep- resents an event, which marks the start or completion time of an activity. We usually identify an event (node) by a number.

EXAMPLE 3 � Activity-on-arrow for Milwaukee Paper

Draw the complete AOA project network for Milwaukee Paper’s problem.

APPROACH � Using the data from Table 3.1 in Example 1, draw one activity at a time, starting with A.

SOLUTION � 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 difficulty, in such cases, we may need to add a dummy line (activity) to enforce the precedence relation- ship. The dummy activity, shown in Figure 3.9 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.

DETERMINING THE PROJECT SCHEDULE Look back at Figure 3.8 (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 activities is 25 weeks. However, since 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.

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 3.9 Complete AOA Network (with Dummy Activity) for Milwaukee Paper

Critical path analysis A process that helps determine a project schedule.

INSIGHT � Dummy activities are common in AOA networks. They do not really exist in the proj- ect and take zero time.

LEARNING EXERCISE � A new activity, EPA Approval, follows activity H. Add it to Figure 3.9. [Answer: Insert an arrowed line from node 7, which ends at a new node 8, and is labeled I (EPA Approval).]

RELATED PROBLEMS � 3.4, 3.5, 3.9b

AUTHOR COMMENT 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.

AUTHOR COMMENT We now add times to

complete each activity. This lets us find the critical path.

0 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

0 0

0 2 LS

LF = Min(2,4) = 2

2 4

10 13

LF = Min(LS of E, LS of F) = Min(4,10) = 4

4 8

1 4 4 8

LS = LF – 4

8 13

LF = EF of Project

13 15

Overlay 1: Latest Start and Finish Times Are Now Added

Overlay 2: Slack Times Are Now Computed and Added

0 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

0 0

0 2 2 4

10 13

4 8

1 4 4 8 8 13

13 15

Slack = 0

Slack = 0

Slack = 6

Slack = 1 Slack = 1

Slack = 0Slack = 0

Slack = 0

0 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

0 0

0 2 2 4

10 13

4 8

1 4 4 8 8 13

13 15

Overlay 3: The Critical Path Is Now Shown in Five Thick Blue Lines

Chapter 3 Project Management 61

Activity Description Time (weeks)

A Build internal components 2 B Modify roof and floor 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

� TABLE 3.2 Time Estimates for Milwaukee Paper Manufacturing

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:

We use a two-pass process, consisting of a forward pass and a backward pass, to determine 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 3.10. The ES of an activity is shown in the top left corner of the node denoting that activ- ity. 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.

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,

(3-1)

Earliest Finish Rule The earliest finish time (EF) of an activity is the sum of its earliest start time (ES) and its activity time. That is,

(3-2)EF = ES + Activity time

ES = Max {EF of all immediate predecessors}

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 Latest start (LS) = latest time at which an activity can start so as to not delay

Earliest finish (EF) = earliest time at which an activity can be finished predecessors have been completed

Earliest start (ES) = earliest time at which an activity can start, assuming all

Forward pass A process that identifies all the early times.

LO3: Complete forward and backward passes for a project

ES

Earliest Start

Earliest Finish

Activity Name or Symbol

Activity Duration

Latest Start

Latest Finish

A

2

LS

EF

LF

� FIGURE 3.10 Notation Used in Nodes for Forward and Backward Pass

AUTHOR COMMENT 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.

AUTHOR COMMENT All predecessor activities

must be completed before an acitivity can begin.

62 PART 1 Introduction to Operations Management

0 A

ES of A

ES EF = 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 3.11 Earliest Start and Earliest Finish Times for Milwaukee Paper

Calculate the earliest start and finish times for the activities in the Milwaukee Paper Manufacturing project.

APPROACH � 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 � With the help of Figure 3.11, we describe how these values are calculated. Since 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 B. 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:

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).

ES of D = Max(EF of A, EF of B) = Max(2, 3) = 3

EXAMPLE 4 � Computing earliest start and finish times for Milwaukee Paper

Chapter 3 Project Management 63

Backward pass An activity that finds all the late start and late finish times.

Finally, we come to activity H. Since 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). Since H is the last activity in the project, this also implies that the ear- liest time in which the entire project can be completed is 15 weeks.

INSIGHT � The ES of an activity that has only one predecessor is simply the EF of that predeces- sor. For an activity with more than one predecessor, we must carefully examine the EFs of all immedi- ate predecessors and choose the largest one.

LEARNING EXERCISE � 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 � 3.11, 3.14c

EXCEL OM Data File Ch03Ex4.xls can be found at www.pearsonhighered.com/heizer.

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.

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:

(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:

(3-4)LS = LF - Activity time

LF = Min{LS of all immediate following activities}

� EXAMPLE 5 Computing latest start and finish times for Milwaukee Paper

Calculate the latest start and finish times for each activity in Milwaukee Paper’s pollution project.

APPROACH � Use Figure 3.11 as a beginning point. Overlay 1 of Figure 3.11 shows the com- plete project network for Milwaukee Paper, along with LS and LF values for all activities. In what fol- lows, we see how these values were calculated.

SOLUTION � 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).

Since 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:

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).

LF of C = Min(LS of E, LS of F) = Min(4, 10) = 4

64 PART 1 Introduction to Operations Management

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 time2 that each activity has. Slack is the length of time an activity can be delayed without delaying the entire project. Mathematically:

(3-5)Slack = LS - ES or Slack = LF - EF

Slack time Free time for an activity.

EXAMPLE 6 � Calculating slack times for Milwaukee Paper

Activity

Earliest Start ES

Earliest Finish

EF

Latest Start LS

Latest Finish

LF Slack

LS – ES

On Critical

Path

A 0 2 0 2 0 Yes B 0 3 1 4 1 No C 2 4 2 4 0 Yes D 3 7 4 8 1 No E 4 8 4 8 0 Yes F 4 7 10 13 6 No G 8 13 8 13 0 Yes H 13 15 13 15 0 Yes

� TABLE 3.3 Milwaukee Paper’s Schedule and Slack Times

Calculate the slack for the activities in the Milwaukee Paper project.

APPROACH � Start with the data in Overlay 1 of Figure 3.11 in Example 5 and develop Table 3.3 one line at a time.

SOLUTION � Table 3.3 summarizes the ES, EF, LS, LF, and slack time for all of the firm’s activi- ties. Activity B, for example, has 1 week of slack time since 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 proj- ect can still be finished in 15 weeks.

2Slack time may also be referred to as free time, free float, or free slack.

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 � The LF of an activity that is the predecessor of only one activity is just the LS of that following 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 � 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 � 3.11, 3.14c.

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 Joni Steinberg wants to reduce the total project times, she will have to reduce the length of one of these activities.

Overlay 2 of Figure 3.11 shows the slack computed for each activity.

INSIGHT � Slack may be computed from either early/late starts or early/late finishes. The key is to find which activities have zero slack.

LEARNING EXERCISE � 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 � 3.6, 3.11, 3.27

ACTIVE MODEL 3.1 This example is further illustrated in Active Model 3.1 at www.pearsonhighered.com/heizer.

Chapter 3 Project Management 65

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).

LO4: Determine a critical path

� EXAMPLE 7 Showing critical path with blue arrows

Show Milwaukee Paper’s critical path and find the project completion time.

APPROACH � We use Table 3.3 and Overlay 3 of Figure 3.11. Overlay 3 of Figure 3.11 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 � The critical path follows the activities with slack = 0. This is considered the longest path through the network.

LEARNING EXERCISE � 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 � 3.3, 3.4, 3.5, 3.6, 3.7, 3.12, 3.14b, 3.15, 3.17, 3.20a, 3.22a, 3.23, 3.26

Total Slack Time Look again at the project network in Overlay 3 of Figure 3.11. Consider activities B and D, which have slack of 1 week each. Does it mean that we can delay each activ- ity 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 non- critical activities appear successively in a path, they share total slack.

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 com- pletion times vary depending on various factors.

Total slack Time shared among more than one activity.

To plan, monitor, and control the huge number of details involved in sponsoring a rock festival attended by more than 100,000 fans, Hard Rock Cafe uses Microsoft Project and the tools discussed in this chapter. The Video Case Study “Managing Hard Rock’s Rockfest,” in the Lecture Guide & Activities Manual, provides more details of the management task.

AUTHOR COMMENT 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.

66 PART 1 Introduction to Operations Management

For example, building internal components (activity A) for Milwaukee Paper Manufacturing is estimated to finish in 2 weeks. Clearly, factors such as late arrival of raw materials, absence of key personnel, and so on, could delay this activity. Suppose activity A actually ends up taking 3 weeks. Since 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 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 com- pletion 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 probability (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 dis- tribution (see Figure 3.12). This continuous distribution is often appropriate for determining the expected value and variance for activity completion times.

To find the expected activity time, t, the beta distribution weights the three time estimates as follows:

(3-6)

That is, the most likely time (m) is given four times the weight as the optimistic time (a) and pes- simistic 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 formula3:

(3-7)Variance = [(b - a)>6]2

t = (a + 4m + b)>6

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 3.12 Beta Probability Distribution with Three Time Estimates

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.

3This formula is based on the statistical concept that from one end of the beta distribution to the other is 6 standard deviations (±3 standard deviations from the mean). Since (b - a) is 6 standard deviations, the variance is [(b - a)/6]2.

Chapter 3 Project Management 67

� EXAMPLE 8 Expected times and variances for Milwaukee Paper

LO5: Calculate the variance of activity times

Activity Optimistic

a

Most Likely

m Pessimistic

b Expected Time

t = (a + 4m + b)/6 Variance

[(b � a)/6]2

A 1 2 3 2 [(3 - 1)/6]2 = 4/36 = .11 B 2 3 4 3 [(4 - 2)/6]2 = 4/36 = .11 C 1 2 3 2 [(3 - 1)/6]2 = 4/36 = .11 D 2 4 6 4 [(6 - 2)/6]2 = 16/36 = .44 E 1 4 7 4 [(7 - 1)/6]2 = 36/36 = 1.00 F 1 2 9 3 [(9 - 1)/6]2 = 64/36 = 1.78 G 3 4 11 5 [(11 - 3)/6]2 = 64/36 = 1.78 H 1 2 3 2 [(3 - 1)/6]2 = 4/36 = .11

� TABLE 3.4 Time Estimates (in weeks) for Milwaukee Paper’s Project

RELATED PROBLEMS � 3.13, 3.14a, 3.17a,b, 3.21a

EXCEL OM Data File Ch03Ex8.xls can be found at www.pearsonhighered.com/heizer.

Joni Steinberg and the project management team at Milwaukee Paper want an expected time and vari- ance for Activity F (Installing the Pollution Control System) where:

APPROACH � Use Equations (3-6) and (3-7) to compute the expected time and variance for F.

SOLUTION � The expected time for Activity F is:

The variance for Activity F is:

INSIGHT � Steinberg 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 � Review the expected times and variances for all of the other activi- ties in the project. These are shown in Table 3.4.

Variance = c (b - a)

6 d

2

= c (9 - 1)

6 d

2

= a 8

6 b

2

= 64

36 = 1.78

t = a + 4m + b

6 =

1 + 4(2) + 9 6

= 18

6 = 3 weeks

a = 1 week, m = 2 weeks, b = 9 weeks

AUTHOR COMMENT Can you see why the variance

is higher in some activities than in others? Note the

spread between the optimistic and pessimistic times.

We see here 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 or installing a new production line.

68 PART 1 Introduction to Operations Management

Probability of Project Completion The critical path analysis helped us determine that Milwaukee Paper’s expected project comple- tion time is 15 weeks. Joni Steinberg 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 overall project. Project variance is computed by summing variances of critical activities:

(3-8)s2p = Project variance = ©(variances of activities on critical path)

EXAMPLE 9 � Computing project variance and standard deviation for Milwaukee Paper

Milwaukee Paper’s managers now wish to know the project’s variance and standard deviation.

APPROACH � 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 � 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:

which implies:

INSIGHT � Management now has an estimate not only of expected completion time for the proj- ect but also of the standard deviation of that estimate.

LEARNING EXERCISE � 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 PROBLEM � 3.17e

Project standard deviation (sp) = 2Project variance = 23.11 = 1.76 weeks

Project variance (s2p) = 0.11 + 0.11 + 1.00 + 1.78 + 0.11 = 3.11

How can this information be used to help answer questions regarding the probability of finishing 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 independent. With these assumptions, the bell-shaped normal curve shown in Figure 3.13 can be used to represent project completion dates. This normal curve implies that there is a 50% chance that the manufac- turer’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 3.13 Probability Distribution for Project Completion Times at Milwaukee Paper

Chapter 3 Project Management 69

� EXAMPLE 10 Probability of completing a project on time

Joni Steinberg would like to find the probability that her project will be finished on or before the 16- week EPA deadline.

APPROACH � 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 � The standard normal equation can be applied as follows:

(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, we find a Z value of 0.57 to the right of the mean indi- cates 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 3.14.

= (16 weeks - 15 weeks)>1.76 weeks = 0.57 Z = (Due date - Expected date of completion)>sp

15 Weeks

16 Weeks

0.57 Standard Deviations

Time

Probability (T ≤ 16 Weeks) is 71.57%

� FIGURE 3.14 Probability That Milwaukee Paper will Meet the 16-Week Deadline

Determining Project Completion Time for a Given Confidence Level Let’s say Joni Steinberg 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 environ- mental group for more time. However, before she approaches the group, 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 group to agree to this extended deadline.

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 Milwaukee Paper’s project completion time follows a normal probability distribution with a mean of 15 weeks and a standard deviation of 1.76 weeks.

� EXAMPLE 11 Computing probability for any completion date

INSIGHT � 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 � What is the probability that the project will be completed on or before the 17th week? [Answer: About 87.2%.]

RELATED PROBLEMS � 3.14d, 3.17f, 3.21d,e, 3.22b, 3.24

Joni Steinberg wants to find the due date that gives her company’s project a 99% chance of on-time completion.

APPROACH � She first needs to compute the Z-value corresponding to 99%, as shown in Figure 3.15. Mathematically, this is similar to Example 10, except the unknown is now Z rather than the due date.

AUTHOR COMMENT Here is a chance to review

your statistical skills and use of a normal distribution table

(Appendix I).

70 PART 1 Introduction to Operations Management

SOLUTION � Referring again to the Normal Table in Appendix I, we identify a Z-value of 2.33 as being closest to the probability of 0.99. That is, Joni Steinberg’s due date should be 2.33 standard devi- ations above the mean project completion time. Starting with the standard normal equation (see Equa- tion [3-9]), we can solve for the due date and rewrite the equation as:

(3-10)

INSIGHT � If Steinberg can get the environmental group to agree to give her a new deadline of 19.1 weeks (or more), she can be 99% sure of finishing the project on time.

LEARNING EXERCISE � What due date gives the project a 95% chance of on-time comple- tion? [Answer: About 17.9 weeks.]

RELATED PROBLEMS � 3.22c, 3.24e

= 15 + (2.33 * 1.76) = 19.1 weeks Due date = Expected completion time + (Z * sp)

0 2.33 Z2.33 Standard

Deviations

Probability of 0.99

Probability of 0.01

� FIGURE 3.15 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 since these activities are, by definition, the more important activities in a proj- ect 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 3.11 (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 com- pletion times. We observe, however, that D has a variance of 0.44 (see Table 3.4 in 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 necessary for us to not focus only on the critical path(s). Indeed, some research has suggested that expend- ing project resources to reduce the variability of activities not on the critical path can be an effec- tive element in project management.4 We may need also to compute these probabilities for noncritical paths, especially those that have relatively large variances. It is possible for a noncrit- ical 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 techniques have thus far been able to provide Joni Steinberg 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 Steinberg is interested in.

4F. M. Pokladnik, T. F. Anthony, R. R. Hill, G. Ulrich, “A Fresh Look at Estimated Project Duration: Noncritical Path Activity Contribution to Project Variance in PERT/CPM,” Proceedings of the 2003 Southwest Decision Science Conference, Houston.

Chapter 3 Project Management 71

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 Steinberg 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 scheduled proj- ect 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 pos- sible 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 activity. 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 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:

(3-11)

STEP 2: Using the current activity times, find the critical path(s) in the project network. Identify 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, return to Step 2.

We illustrate project crashing in Example 12.

Crash cost per period = (Crash cost - Normal cost) (Normal time - Crash time)

Crashing Shortening activity time in a network to reduce time on the critical path so total completion time is reduced.

LO6: Crash a project

AUTHOR COMMENT When a project needs to

be shortened, we want to find the most economical way

of “crashing” it.

72 PART 1 Introduction to Operations Management

EXAMPLE 12 � Project crashing to meet a deadline at Milwaukee Paper

Suppose that Milwaukee Paper Manufacturing has been given only 13 weeks (instead of 16 weeks) to install the new pollution control equipment or face a court-ordered shutdown. As you recall, the length of Joni Steinberg’s critical path was 15 weeks, but she must now complete the project in 13 weeks.

APPROACH � Steinberg needs to determine which activities to crash, and by how much, to meet this 13-week due date. Naturally, Steinberg is interested in speeding up the project by 2 weeks, at the least additional cost.

SOLUTION � 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).

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

� TABLE 3.5 Normal and Crash Data for Milwaukee Paper Manufacturing

This calculation for Activity B is shown in Figure 3.16. Crash costs for all other activities can be computed in a similar fashion.

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 3.16 Crash and Normal Times and Costs for Activity B

Steps 2, 3, and 4 can now be applied to reduce Milwaukee Paper’s project completion time at a min- imum cost. We show the project network for Milwaukee Paper again in Figure 3.17.

Chapter 3 Project Management 73

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 3.17 Critical Path and Slack Times for Milwaukee Paper

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. Joni Steinberg 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 sim- ply 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).

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, since the total crashing cost will now be only $1,500 (compared with the $2,000 if we crash C and D).

INSIGHT � To crash the project down to 13 weeks, Steinberg 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 � 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 � 3.16, 3.18, 3.19, 3.20, 3.25

EXCEL OM Data File Ch03Ex12.xls can be found at www.pearsonhighered.com/heizer.

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.

AUTHOR COMMENT Every technique has shortfalls

as well as strengths. It is important to know both.

74 PART 1 Introduction to Operations Management

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 activities 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 activ- ity (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 dura- tions, 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.

On September 11, 2001, American Airlines Flight 77 slammed into the Pentagon. The world was shocked by this and the other terrorist attacks on the Twin Towers in New York City. One hundred and twenty-five people died when a large portion of the Pentagon was severely damaged. Among the first to react were construction workers renovating another portion of the Pentagon. Their heroism saved lives and eased suffering. Within hours of the disaster, heavy equipment began arriving on the site, accompanied by hundreds of volunteer construction workers driven by patriotism and pride.

Just four days after the attack, Walker Evey, named program manager for “Project Phoenix,” promised to rebuild the damaged portions of the Pentagon “faster than anyone has a right to expect . . . and to have people back in the damaged portion of the building, right where the plane hit, by September 11, 2002.”

Preliminary construction reports estimated it would take 3 to 4 years and $3/4 billion to rebuild. By directing the project with teamwork, handshake contracts, creativity, and ingenuity—not to mention emotional 20-hour days 6 to 7 days a week—Evey’s Project Phoenix met its psychological and physical goal. In less than 11 months, and for only $501 million, workers demolished and rebuilt

the damaged sections— 400,000 square feet of structure, 2 million square feet of offices, 50,000 tons of debris—using 1,000 construction workers from 80 companies. By September 9, 2002, over 600 military and civilian personnel were sitting at their desks in rebuilt Pentagon offices.

Outside, the blackened gash is long gone. Instead, some 4,000 pieces of limestone—mined from the same Indiana vein that the Pentagon’s original stone came from 65 years ago—have been placed on the building’s façade. For this impressive accomplishment, the Pentagon and Walker Evey were nominated for the Project Management Institute’s 2003 Project of the Year Award.

Sources: Knight-Ridder Tribune Business News (February 1, 2004): 1; ENR (September 2, 2002): 6; U.S. News & World Report (September 16, 2002): 35.

Milwaukee Paper Co. Activities

Time Prede- Activity (wks) 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

AUTHOR COMMENT 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.

OM in Action � Rebuilding the Pentagon after 9/11

Chapter 3 Project Management 75

Project will finish on Friday, 10/14.

View has been zoomed out to show weeks.

Click here to select different views.

Gantt chart view.

� PROGRAM 3.1 Gantt Chart in Microsoft Project for Milwaukee Paper Manufacturing

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 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 proj- ect 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.

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

76 PART 1 Introduction to Operations Management

Using PERT/CPM, Taco Bell built and opened this fast-food restaurant in Compton, California, in just 2 days! Typically, 2 months are needed to accomplish such a task. Good project management means a faster revenue stream instead of money tied up in construction.

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 man- agement 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 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 per- cent 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 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.

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

Chapter 3 Project Management 77

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 process.

� PROGRAM 3.3 Tracking Project Progress in Microsoft Project

PERT, CPM, and other scheduling techniques have proven to be valuable tools in controlling large and complex projects. With these tools, managers understand the status of each activ- ity and know which activities are critical and which have slack; in addition, they know where crashing makes the most sense. Projects are segmented into discrete activities, and specific resources are identified. This allows project managers to respond aggressively to global competition. Effective project management also allows firms to create products and services for global markets. As with Microsoft Project illustrated in this

chapter, a wide variety of software pack- ages are available to help managers han- dle network modeling problems.

PERT and CPM do not, however, solve all the project scheduling and man- agement problems. Good management prac- tices, clear responsibilities for tasks, and straightforward and timely reporting systems are also needed. It is important to remember that the models we described in this chapter are only tools to help managers make better decisions.

CHAPTER SUMMARY

Key Terms Project organization (p. 50) Work breakdown structure (WBS) (p. 52) Gantt charts (p. 53) Program evaluation and review technique

(PERT) (p. 55) Critical path method (CPM) (p. 55) Critical path (p. 55)

Activity-on-node (AON) (p. 55) Activity-on-arrow (AOA) (p. 55) Dummy activity (p. 56) Critical path analysis (p. 60) Forward pass (p. 61) Backward pass (p. 63) Slack time (p. 64)

Total slack (p. 65) Optimistic time (p. 66) Pessimistic time (p. 66) Most likely time (p. 66) Crashing (p. 71)

Using Software to Solve Project Management Problems In addition to the Microsoft Project software just illustrated, 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.

78 PART 1 Introduction to Operations Management

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.

� PROGRAM 3.4 Excel OM’s Use of Milwaukee Paper Manufacturing’s Data from Examples 4 and 5

Solved Problems Virtual Office Hours help is available at www.myomlab.com

� SOLVED PROBLEM 3.1 Construct an AON network based on the following:

Immediate Activity Predecessor(s)

A — B — C — D A, B E C

� SOLUTION

End

D

E

B

A

C

Start

� SOLVED PROBLEM 3.2 Insert a dummy activity and event to correct the following AOA network:

� SOLUTION Since 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

3 days

5 days

2

3

4

51

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 network with either one or three time estimates. POM for Windows also performs project crashing. For further details refer to Appendix IV.

Chapter 3 Project Management 79

� SOLVED PROBLEM 3.3 Calculate the critical path, project completion time T, and project variance based on the following AON network information:

Activity Time Variance ES EF LS LF Slack

A 2 0 2 0 2 0

B 3 0 3 1 4 1

C 2 2 4 2 4 0

D 4 3 7 4 8 1

E 4 4 8 4 8 0

F 3 4 7 10 13 6

G 5 8 13 8 13 0 1 6

1 6

2 6

4 6

4 6

2 6

2 6

s2p,

� SOLUTION We conclude that the critical path is Start-A-C-E-G-End:

and

Variances on the critical path = 2

6 +

4

6 +

2

6 +

1

6 =

9

6 = 1.5s2p = ©

Total project time = T = 2 + 2 + 4 + 5 = 13

Start End E G

A

B

FC

D

Expected Time Activity (in weeks) Variance

A 2

B 3

C 5

D 9

E 5 1

F 5

G 2 1

9

1

9

1

9

1

9

1

9

1

9

� 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 vari- ance for each activity:

Immediate Activity a m b 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 66 in this chapter. The results are summarized in the following table:

80 PART 1 Introduction to Operations Management

� 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 addi- tion, he would like to determine the earliest and latest start and fin- ish times for all activities.

� SOLUTION The AON network for Gilbert’s project is shown in Figure 3.18. 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 3.18 shows the earliest and latest times for all activi- ties. 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.

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 3.18 Critical Path for Solved Problem 3.5

T = 62Due date = 44

� SOLVED PROBLEM 3.6 The following information has been computed from a project:

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 com- pletion date, 62 weeks. The desired completion date is 44 (or 62 – 18) weeks:

The normal curve appears as follows:

= 44 - 62

9 =

- 18 9

= - 2.0

Z = Due date - Expected completion date

sp

sp = 2Project variance

Project variance (s2p) = 81 Expected total project time = T = 62 weeks

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 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%.

Z = + 2.0,

Chapter 3 Project Management 81

� 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

Start

B

C

D E

End

A

Normal Crash Time Time Normal Crash

Activity (months) (months) Cost 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

Normal Crash Cost – Crash Time – Normal Cost/ Critical

Activity Crash Time Cost Month 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 completion 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 activi- ties. This solution is summarized in the following table:

Activity Months Reduced Cost

E 1 $200 B 2 500

Total: $700

The critical path consists of activities B, D, and E. Next, crash cost/month must be computed for each activity:

�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study: Shale Oil Company: This oil refinery must shut down for maintenance of a major piece of equipment.

Bibliography

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Cleland, D. L., and L. R. Ireland. Project Management, 5th ed. New York: McGraw-Hill/Irwin (2007).

Gray, C. L., and E. W. Larson. Project Management with MS Project. New York: McGraw-Hill/Irwin (2008).

Helgadottir, Hilder. “The Ethical Dimension of Project Management.” International Journal of Project Management 26, no. 7 (October 2008): 743.

Karlos, A., et al. “Foundations of Project Management.” International Journal of Project Management 27, no. 1 (January 2009): 1.

Kerzner, H. Project Management Case Studies, 3rd ed. New York: Wiley (2009).

Kumar, P. P. “Effective Use of Gantt Chart for Managing Large-Scale Projects.” Cost Engineering 47, no. 7 (July 2005): 14–21.

Ling, F. Y. Y., et al. “Key Project Management Practices Affecting Singaporean Firms’ Project Performance in China.” International Journal of Project Management 27, no. 1 (January 2009): 59.

Matta, N. F., and R. N. Ashkenas. “Why Good Projects Fail Anyways.” Harvard Business Review (September 2003): 109–114.

Maylor, Harvey. Project Management, 4th ed. Upper Saddle River, NJ: Prentice Hall (2008).

Meredith, J. R., and S. Mantel. Project Management, 7th ed. New York: Wiley (2008).

Oates, David. “Understanding and Solving the Causes of Project Failure.” Knowledge Management Review 9, no. 5 (May–June 2006): 5.

Render, B., R. M. Stair, and M. Hanna. Quantitative Analysis for Management, 10th ed. Upper Saddle River, NJ: Prentice Hall (2009).

Verzuh, Eric. The Fast Forward MBA in Project Management. New York: Wiley (2008).

Wysocki, R. K. Effective Project Management, 5th ed. New York: Wiley (2009).

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Forecasting

83

Chapter Outline

GLOBAL COMPANY PROFILE: WALT DISNEY PARKS & RESORTS

What Is Forecasting? 86

The Strategic Importance of Forecasting 87

Seven Steps in the Forecasting System 88

Forecasting Approaches 89

Time-Series Forecasting 90

Associative Forecasting Methods: Regression and Correlation Analysis 108

Monitoring and Controlling Forecasts 113

Forecasting in the Service Sector 116

GLOBAL COMPANY PROFILE: WALT DISNEY PARKS & RESORTS

FORECASTING PROVIDES A COMPETITIVE ADVANTAGE FOR DISNEY

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 Hong Kong

Disneyland (opened 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 $43 billion

corporation, which is ranked 54th in the Fortune 500

and 79th in the 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 near Orlando, the report contains only two numbers:

the forecast of yesterday’s attendance at the parks

(Magic Kingdom, Epcot, Disney’s Animal Kingdom,

Disney-MGM Studios, Typhoon Lagoon, and Blizzard

Beach) and the actual attendance. An error close to zero

is expected. Iger takes his forecasts very seriously.

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 20 hotels on the massive 47-square-mile property (about the size of San Francisco and twice the size of Manhattan).

Mickey and Minnie Mouse, and other Disney characters, with Cinderella Castle in the background, provide the public image of Disney to the world. Forecasts drive the work schedules of 58,000 cast members working at Walt Disney World Resort near Orlando.

84

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.

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 behavior at each ride (e.g., how long

people will wait, how many times they will ride). Inputs to

the monthly forecasting 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 parks, with the “FAST

PASS” reservation system, and by shifting crowds from

rides to more street parades.

At Disney, forecasting is a key driver in the

company’s success and competitive advantage.

WALT DISNEY PARKS & RESORTS �

� Disney uses characters such as Minnie Mouse to entertain guests when lines are forecast to be long. On slow days, Disney calls fewer cast members to work.

� 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.

� 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.

85

86 PART 1 Introduction to Operations Management

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 equipment 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 happen in the future in the face of uncertainty. Making good estimates is the main pur- pose 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 pro- vide 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 manufac- turing operations.

Forecasting is the art and science of predicting future events. Forecasting may involve taking historical data and projecting them into the future with some sort of mathematical model. It may be a subjective or intuitive prediction. Or it may involve a combination of these—that is, a math- ematical model adjusted by a manager’s good judgment.

As we introduce different forecasting techniques in this chapter, you will see that there is sel- dom one superior method. What works best in one firm under one set of conditions may be a complete disaster in another organization, or even in a different department of the same firm. In addition, you will see that there are limits as to what can be expected from forecasts. They are seldom, if ever, perfect. They are also costly and time-consuming to prepare and monitor.

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 assign- ments, 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.

Medium and long-range forecasts are distinguished from short-range forecasts by three features:

1. First, intermediate and long-run forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants, and processes. Implement- ing 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,

LO1: Understand the three time horizons and which models apply for each 86

LO2: Explain when to use each of the four qualitative models 89

LO3: Apply the naive, moving-average, exponential smoothing, and trend methods 92

Chapter 4 Learning Objectives LO4: Compute three measures of forecast

accuracy 95

LO5: Develop seasonal indices 104

LO6: Conduct a regression and correlation analysis 108

LO7: Use a tracking signal 114

Forecasting The art and science of predicting future events.

LO1: Understand the three time horizons and which models apply for each

AUTHOR COMMENT An increasingly complex world economy makes forecasting challenging.

Chapter 4 Forecasting 87

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 company’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 horizon 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.

The Influence of Product Life Cycle Another factor to consider when developing sales forecasts, especially longer ones, is product life cycle. Products, and even services, do not sell at a constant level throughout their lives. Most successful products pass through four stages: (1) introduction, (2) growth, (3) maturity, and (4) decline.

Products in the first two stages of the life cycle (such as virtual reality and the Boeing 787 Dreamliner) need longer forecasts than those in the maturity and decline stages (such as large SUVs and skateboards). Forecasts that reflect life cycle are useful in projecting dif- ferent staffing levels, inventory levels, and factory capacity as the product passes from the first to the last stage. The challenge of introducing new products is treated in more detail in Chapter 5.

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 sup- plies, 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. These forecasts, also called sales forecasts, drive a company’s production, capacity, and schedul- ing systems and serve as inputs to financial, marketing, and personnel planning.

Economic and technological forecasting are specialized techniques that may fall outside the role of the operations manager. The emphasis in this book 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 esti- mate of demand until actual demand becomes known. Forecasts of demand therefore drive deci- sions in many areas. Let’s look at the impact of product demand forecast on three activities: (1) human resources, (2) capacity, and (3) supply-chain management.

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 breakdown 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 low-fat Snackwell Devil’s Food Cookies. Even with production lines working overtime, Nabisco could not keep up with demand, and it lost customers. As the photo on the next page shows, Amazon made the same error with its Kindle. On the other hand, when excess capacity exists, costs can skyrocket.

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 planning horizon.

VIDEO 4.1 Forecasting at Hard Rock Cafe

88 PART 1 Introduction to Operations Management

Supply-Chain Management Good supplier relations and the ensuing price advantages for materials and parts depend on accu- rate forecasts. In the global marketplace, where expensive components for Boeing 787 jets are manufactured in dozens of countries, coordination driven by forecasts is critical. Scheduling transportation to Seattle for final assembly at the lowest possible cost means no last-minute sur- prises that can harm already-low profit margins.

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 develops daily, weekly, monthly, annual, and 5-year forecasts.

4. Select the forecasting model(s): Disney uses a variety of statistical models that we shall dis- cuss, 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 analysts 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.

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 forecast- ing 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:

• Forecasts are seldom perfect. This means that outside factors that we cannot predict or control often impact the forecast. Companies need to allow for this reality.

• 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 fore- casts. Disney, for example, aggregates daily attendance forecasts by park. This approach helps balance the over- and underpredictions of each of the six attractions.

Even vaunted Amazon can make a major forecasting error, as it did in the case of its much-hyped Kindle e-book reader. With the holiday shopping season at hand, Amazon’s Web page announced “Due to heavy customer demand, Kindle is sold out . . . ships in 11 to 13 weeks.” Underforecasting demand for the product was the culprit, according to the Taiwanese manufacturer Prime View, which has since ramped up production.

Chapter 4 Forecasting 89

FORECASTING APPROACHES There are two general approaches to forecasting, just as there are two ways to tackle all decision modeling. One is a quantitative analysis; the other is a qualitative approach. Quantitative fore- casts 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 fore- cast. 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 makers 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 judg- ments 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 eco- nomic forecast. An amazing 90% of the state’s budget is derived from 1.5 million barrels of oil pumped daily through a pipeline at Prudhoe Bay. The large Delphi panel of experts had to rep- resent all groups and opinions in the state and all geographic areas. Delphi was the perfect forecasting tool because panelist travel could be avoided. It also meant that leading Alaskans could participate because their schedules were not affected by meetings and distances.

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. Consumer market survey: This method solicits input from customers or potential cus- tomers regarding future purchasing plans. It can help not only in preparing a forecast but also in improving 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. The 2001 crash of the telecommunication indus- try was the result of overexpansion to meet “explosive customer demand.” Where did these data come from? Oplink Communications, a Nortel Networks supplier, says its “company forecasts over the last few years were based mainly on informal conversations with customers.”1

Overview of Quantitative Methods Five quantitative forecasting methods, all of which use historical data, are described in this chap- ter. 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

Quantitative forecasts Forecasts that employ mathematical modeling to forecast demand.

Qualitative forecasts Forecasts that incorporate such factors as the decision maker’s intuition, emotions, personal experiences, 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.

1“Lousy Sales Forecasts Helped Fuel the Telecom Mess,” The Wall Street Journal (July 9, 2001): B1–B4.

LO2: Explain when to use each of the four qualitative models

Delphi method A forecasting technique using a group process that allows experts to make forecasts.

Sales force composite A forecasting technique based on salespersons’ estimates of expected sales.

Consumer market survey A forecasting method that solicits input from customers or potential customers regarding future purchasing plans.

AUTHOR COMMENT Forecasting is part science

and part art.

90 PART 1 Introduction to Operations Management

Time-Series Models Time-series models predict on the assumption that the future is a func- tion 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 variables 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 competi- tors’ 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 vari- ables, 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 quar- ters. There are six common seasonality patterns:

Time series A forecasting technique that uses a series of past data points to make a forecast.

Naive approach A forecasting technique which assumes that demand in the next period is equal to demand in the most recent period.

Number of Period of Pattern “Season” Length “Seasons” in Pattern

Week Day 7 Month Week 4–4 Month Day 28–31 Year Quarter 4 Year Month 12 Year Week 52

1 2

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 and Red Lobster.” Beer distributors forecast yearly patterns, with monthly seasons. Three “seasons”—May, July, and September—each contain a big beer-drinking holiday.

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 planning. 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 com- ponents, 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. Does this make any sense? It turns out that for some product lines, this naive approach is the most cost-effective

AUTHOR COMMENT Here is the meat of this

chapter. We now show you a wide variety of models that

use time-series data.

AUTHOR COMMENT The peak “seasons” for sales

of Frito-Lay chips are the Super Bowl, Memorial Day, Labor Day, and the Fourth

of July.

Chapter 4 Forecasting 91

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

(4-1)

where n is the number of periods in the moving average—for example, 4, 5, or 6 months, respec- tively, for a 4-, 5-, or 6-period moving average.

Moving average = © demand in previous n periods

n

Moving averages A forecasting method that uses an average of the n most recent periods of data to forecast the next period.

Seasonal peaks

Random variation

Actual demand line

Average demand over 4 years

Trend component

1

D e m

a n

d f

o r

p ro

d u

c t

o r

s e rv

ic e

Time (years) 2 3 4

� FIGURE 4.1 Demand Charted over 4 Years with a Growth Trend and Seasonality Indicated

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, Red Lobster, Seasons 52, and Bahama Breeze for value and consistency—and they get it.

Every night, Darden’s computers crank out forecasts that tell store managers what demand to anticipate the next day. The forecasting software generates 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 customers are going to order.

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 providing 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.

Source: Interviews with Darden executives.

OM in Action � Forecasting at Olive Garden and Red Lobster

AUTHOR COMMENT Forecasting is easy when

demand is stable. But with trend, seasonality, and cycles

considered, the job is a lot more interesting.

EXAMPLE 1 � Determining the moving average

Donna’s Garden Supply wants a 3-month moving-average forecast, including a forecast for next January, for shed sales.

APPROACH � Storage shed sales are shown in the middle column of the table below. A 3-month moving average appears on the right.

92 PART 1 Introduction to Operations Management

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:

(4-2)

Example 2 shows how to calculate a weighted moving average.

Weighted moving average = © (Weight for period n)(Demand in period n)

© Weights

SOLUTION � The forecast for December is . To project the demand for sheds in the coming January, we sum the October, November, and December sales and divide by 3: January forecast

INSIGHT � Management now has a forecast that averages sales for the last 3 months. It is easy to use and understand.

LEARNING EXERCISE � If actual sales in December were 18 (rather than 14), what is the new January forecast? [Answer: ]

RELATED PROBLEMS � 4.1a, 4.2b, 4.5a, 4.6, 4.8a,b, 4.10a, 4.13b, 4.15, 4.47

EXCEL OM Data File Ch04Ex1.xls can be found at www.pearsonhighered.com/heizer.

ACTIVE MODEL 4.1 This example is further illustrated in Active Model 4.1 at www.pearsonhighered.com/heizer.

1713.

= (18 + 16 + 14)/3 = 16.

20 23

Example 1 shows how moving averages are calculated.

Month Actual Shed Sales 3-Month Moving Average

January 10

February 12

March 13

April 16 (10 + 12 + 13)/3 =

May 19 (12 + 13 + 16)/3 =

June 23 (13 + 16 + 19)/3 = 16

July 26 (16 + 19 + 23)/3 =

August 30 (19 + 23 + 26)/3 =

September 28 (23 + 26 + 30)/3 =

October 18 (26 + 30 + 28)/3 = 28

November 16 (30 + 28 + 18)/3 =

December 14 (28 + 18 + 16)/3 = 2023

2513

2613

2223

1913

1323

1123

LO3: Apply the naive, moving-average, exponential smoothing, and trend methods

Chapter 4 Forecasting 93

� 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 � Assign more weight to recent data, as follows:

SOLUTION � The results of this weighted-average forecast are as follows:

INSIGHT � In this particular forecasting situation, you can see that more heavily weighting the lat- est month provides a much more accurate projection.

LEARNING EXERCISE � 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 weights = 1 now, so there is no need for a denominator. When the weights sum to 1, calculations tend to be simpler.

RELATED PROBLEMS � 4.1b, 4.2c, 4.5c, 4.6, 4.7, 4.10b

EXCEL OM Data File Ch04Ex2.xls can be found at www.pearsonhighered.com/heizer.

©

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 bet- ter, but it makes the method less sensitive to real 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

Weights Applied Period

3 Last month 2 Two months ago 1 Three months ago 6 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

3-Month Weighted Month Actual Shed Sales Moving Average

January 10

February 12

March 13

April 16 [(3 × 13) + (2 × 12) + (10)]/6 = May 19 [(3 × 16) + (2 × 13) + (12)]/6 = June 23 [(3 × 19) + (2 × 16) + (13)]/6 = 17 July 26 [(3 × 23) + (2 × 19) + (16)]/6 = August 30 [(3 × 26) + (2 × 23) + (19)]/6 = September 28 [(3 × 30) + (2 × 26) + (23)]/6 = October 18 [(3 × 28) + (2 × 30) + (26)]/6 = November 16 [(3 × 18) + (2 × 28) + (30)]/6 = December 14 [(3 × 16) + (2 × 18) + (28)]/6 = 1823

2313

2813

2712

2356

20 12

1413

1216

94 PART 1 Introduction to Operations Management

changes. Even in periods of downturn (see November and December), it more closely tracks the demand.

Exponential Smoothing Exponential smoothing is a sophisticated weighted-moving-average forecasting method that is still fairly easy to use. It involves very little record keeping of past data. The basic exponential smoothing formula can be shown as follows:

(4-3)

where α is a weight, or smoothing constant, chosen by the forecaster, that has a value between 0 and 1. Equation (4-3) can also be written mathematically as:

(4-4)

where Ft � new forecast Ft–1 � previous period’s forecast

α � smoothing (or weighting) constant (0 ≤ α ≤ 1) At–1 � previous period’s actual demand

The concept is not complex. The latest estimate of demand is equal to the old estimate adjusted by a fraction of the difference between the last period’s actual demand and the old estimate. Example 3 shows how to use exponential smoothing to derive a forecast.

F t = F t –1 + a(At–1 - F t–1)

+ � (Last period’s actual demand - Last period’s forecast) New forecast = Last period’s forecast

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 between 0 and 1.

EXAMPLE 3 � Determining a forecast via exponential smoothing

Weighted moving average

Actual sales

Moving average

Jan. Feb. Mar. Apr. May June

Month July Aug. Sept. Oct. Nov. Dec.

20

S a le

s d

e m

a n

d 15

10

5

25

30

� FIGURE 4.2 Actual Demand vs. Moving- Average and Weighted- Moving-Average Methods for Donna’s Garden Supply

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 α = .20, the dealer wants to fore- cast March demand using the exponential smoothing model.

APPROACH � The exponential smoothing model in Equations (4-3) and (4-4) can be applied.

SOLUTION � Substituting the sample data into the formula, we obtain:

Thus, the March demand forecast for Ford Mustangs is rounded to 144.

= 144.2 New forecast (for Marche demand) = 142 + .2(153 - 142) = 142 + 2.2

AUTHOR COMMENT Moving average methods

always lag behind when there is a trend present, as shown by the blue line (actual sales) for January through August.

Chapter 4 Forecasting 95

INSIGHT � Using just two pieces of data, the forecast and the actual demand, plus a smoothing constant, we developed a forecast of 144 Ford Mustangs for March.

LEARNING EXERCISE � If the smoothing constant is changed to .30, what is the new fore- cast? [Answer: 145.3]

RELATED PROBLEMS � 4.1c, 4.3, 4.4, 4.5d, 4.6, 4.9d, 4.11, 4.12, 4.13a, 4.17, 4.18, 4.37, 4.43, 4.47, 4.49

LO4: Compute three measures of forecast accuracy

The smoothing constant, α, is generally in the range from .05 to .50 for business applications. It can be changed to give more weight to recent data (when α is high) or more weight to past data (when α is low). When α reaches the extreme of 1.0, then in Equation (4-4), Ft = 1.0At–1. 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 α = .5, we can see that the new forecast is based almost entirely on demand in the last three or four periods. When α = .1, the forecast places little weight on recent demand and takes many periods (about 19) of historical values into account.

Weight Assigned to

Most 2nd Most 3rd Most 4th Most 5th Most Recent Recent Recent Recent Recent

Smoothing Period Period Period Period Period Constant (α) α(1 − α) α(1 − α)2 α(1 − α)3 α(1 − α)4

α = .1 .1 .09 .081 .073 .066 α = .5 .5 .25 .125 .063 .031

Selecting the Smoothing Constant The exponential smoothing approach is easy to use, and it has been successfully applied in virtually every type of business. However, the appropriate value of the smoothing constant, α, can make the difference between an accurate forecast and an inaccurate forecast. High values of α are chosen when the underlying average is likely to change. Low values of α 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 values. If Ft denotes the forecast in period t, and At denotes the actual demand in period t, the forecast error (or deviation) is defined as:

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):

(4-5)MAD = © ƒ Actual - Forecast ƒ

n

= At - Ft Forecast error = Actual demand - Forecast value

Mean absolute deviation (MAD) A measure of the overall forecast error for a model.

AUTHOR COMMENT The forecast error tells us how well the model

performed against itself using past data.

96 PART 1 Introduction to Operations Management

Example 4 applies MAD, as a measure of overall forecast error, by testing two values of α.

EXAMPLE 4 � Determining the mean absolute deviation (MAD)

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 quar- ter was 175 tons. Two values of α are to be examined: α = .10 and α = .50.

APPROACH � Compare the actual data with the data we forecast (using each of the two α values) and then find the absolute deviation and MADs.

SOLUTION � The following table shows the detailed calculations for α = .10 only:

To evaluate the accuracy of each smoothing constant, we can compute forecast errors in terms of absolute deviations and MADs:

INSIGHT � On the basis of this comparison of the two MADs, a smoothing constant of α = .10 is preferred to α = .50 because its MAD is smaller.

LEARNING EXERCISE � If the smoothing constant is changed from α = .10 to α = .20, what is the new MAD? [Answer: 10.21.]

RELATED PROBLEMS � 4.5b, 4.8c, 4.9c, 4.14, 4.23, 4.37a

EXCEL OM Data File Ch04Ex4a.xls and Ch04Ex4b.xls can be found at www.pearsonhighered.com/heizer.

ACTIVE MODEL 4.2 This example is further illustrated in Active Model 4.2 at www.pearsonhighered.com/heizer.

Most computerized forecasting software includes a feature that automatically finds the smooth- ing constant with the lowest forecast error. Some software modifies the α value if errors become larger than acceptable.

Actual Tonnage Forecast Forecast

Quarter Unloaded with α = .10 with α = .50 1 180 175 175 2 168 175.50 = 175.00 + .10(180 − 175) 177.50 3 159 172.75 4 175 165.88 5 190 170.44 6 205 180.22 7 180 192.61 8 182 186.30 9 ? 184.15178.59 = 178.22 + .10(182 - 178.22)

178.22 = 178.02 + .10(180 - 178.02) 178.02 = 175.02 + .10(205 - 175.02) 175.02 = 173.36 + .10(190 - 173.36) 173.36 = 173.18 + .10(175 - 173.18) 173.18 = 174.75 + .10(159 - 174.75) 174.75 = 175.50 + .10(168 - 175.50)

Absolute Absolute Actual Forecast Deviation Forecast Deviation

Tonnage with for with for Quarter Unloaded α = .10 α = .10 α = .50 α = .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

10.31 12.33 MAD = ©|Deviations|

n

Chapter 4 Forecasting 97

Mean Squared Error The mean squared error (MSE) is a second way of measuring over- all forecast error. MSE is the average of the squared differences between the forecasted and observed values. Its formula is:

(4-6)

Example 5 finds the MSE for the Port of Baltimore introduced in Example 4.

MSE = ©(Forecast errors)2

n

� EXAMPLE 5 Determining the mean squared error (MSE)

The operations manager for the Port of Baltimore now wants to compute MSE for α = .10.

APPROACH � Use the same forecast data for α = .10 from Example 4, then compute the MSE using Equation (4-6).

SOLUTION �

INSIGHT � 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 � Find the MSE for α = .50. [Answer: MSE = 195.24. The result indi- cates that α = .10 is a better choice because we seek a lower MSE. Coincidentally, this is the same con- clusion we reached using MAD in Example 4.]

RELATED PROBLEMS � 4.8d, 4.14, 4.20

MSE = ©(Forecast errors)2

n = 1,526.54>8 = 190.8

A drawback of using the MSE is that it 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 mea- sure 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:

(4-7)

Example 6 illustrates the calculations using the data from Examples 4 and 5.

MAPE = a n

i= 1 100 ƒ Actuali - Forecasti ƒ>Actuali

n

Mean squared error (MSE) The average of the squared differences between the forecasted and observed values.

Mean absolute percent error (MAPE) The average of the absolute differences between the forecast and actual values, expressed as a percent of actual values.

Actual Tonnage Forecast for Quarter Unloaded α = .10 (Error)2

1 180 175 2 168 175.50 3 159 174.75 4 175 173.18 5 190 173.36 6 205 175.02 7 180 178.02 8 182 178.22

Sum of errors squared � 1,526.46

(3.78)2 = 14.31 (1.98)2 = 3.92

(29.98)2 = 898.70 (16.64)2 = 276.89 (1.82)2 = 3.33

( - 15.75)2 = 248.06 (—7.5)2 = 56.25

52 = 25

98 PART 1 Introduction to Operations Management

EXAMPLE 6 � Determining the mean absolute percent error (MAPE)

The Port of Baltimore wants to now calculate the MAPE when α = .10.

APPROACH � Equation (4-7) is applied to the forecast data computed in Example 4.

SOLUTION �

INSIGHT � MAPE expresses the error as a percent of the actual values, undistorted by a single large value.

LEARNING EXERCISE � What is MAPE when α is .50? [Answer: MAPE = 6.75%. As was the case with MAD and MSE, the α = .1 was preferable for this series of data.]

RELATED PROBLEMS � 4.8e, 4.33c

MAPE = © absolute percent errors

n =

44.75%

8 = 5.59%

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.

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.

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 α = 0.4 in our exponential smoothing model. The following table shows a severe lag in the 2nd, 3rd, 4th, and 5th months, even when our initial estimate for month 1 is perfect:

Month Actual Demand Forecast for Month T(FT)

1 100 2 200 3 300 4 400 5 500 F5 = F4 + a (A4 - F4) = 204 + .4(400 - 204) = 282

F4 = F3 + a (A3 - F3) = 140 + .4(300 - 140) = 204 F3 = F2 + a (A2 - F2) = 100 + .4(200 - 100) = 140 F2 = F1 + a (A1 - F1) = 100 + .4(100 - 100) = 100 F1 = 100 (given)

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:

(4-8)+ Exponentially smoothed trend(Tt) Forecast including trend(FITt) = Exponentially smoothed forecast(Ft)

Actual Tonnage Forecast for Absolute Percent Error Quarter Unloaded α = .10 100 (|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%

Chapter 4 Forecasting 99

With trend-adjusted exponential smoothing, estimates for both the average and the trend are smoothed. This procedure requires two smoothing constants: α for the average and β for the trend. We then compute the average and trend each period:

Ft = α(Actual demand last period) + (1 − α)(Forecast last period + Trend estimate last period)

or:

(4-9)

or:

(4-10)

where Ft � exponentially smoothed forecast of the data series in period t Tt � exponentially smoothed trend in period t At � actual demand in period t α � smoothing constant for the average (0 ≤ α ≤ 1) β � smoothing constant for the trend (0 ≤ β ≤ 1)

So the three steps to compute a trend-adjusted forecast are:

Step 1: Compute Ft, the exponentially smoothed forecast for period t, using Equation (4-9). Step 2: Compute the smoothed trend, Tt, using Equation (4-10). Step 3: Calculate the forecast including trend, FITt, by the formula FITt = Ft + Tt (from

Equation [4-8]).

Example 7 shows how to use trend-adjusted exponential smoothing.

Tt = �(Ft - Ft - 1) + (1 - �)Tt - 1

Tt = b(Forecast this period - Forecast last period) + (1 - b)(Trend estimate last period)

Ft = a(At - 1) + (1 - a)(Ft - 1 + Tt - 1)

� EXAMPLE 7 Computing a trend-adjusted exponential smoothing forecast

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:

Smoothing constants are assigned the values of α = .2 and β = .4. The firm assumes the initial forecast for month 1 (F1) was 11 units and the trend over that period (T1) was 2 units.

APPROACH � A trend-adjusted exponential smoothing model, using Equations (4-9), (4-10), and (4-8) and the three steps above, is employed.

SOLUTION �

Step 1: Forecast for month 2:

Step 2: Compute the trend in period 2:

Step 3: Compute the forecast including trend (FITt):

= 14.72 units = 12.8 + 1.92

FIT2 = F2 + T2

= (.4)(1.8) + (.6)(2) = .72 + 1.2 = 1.92 = .4(12.8 - 11) + (1 - .4)(2)

T2 = b(F2 - F1) + (1 - b)T1

= 2.4 + (.8)(13) = 2.4 + 10.4 = 12.8 units F2 = (.2)(12) + (1 - .2)(11 + 2) F2 = aA1 + (1 - a)(F1 + T1)

Month (t) Actual Demand (At) Month (t) Actual Demand (At)

1 12 6 21 2 17 7 31 3 20 8 28 4 19 9 36 5 24 10 ?

100 PART 1 Introduction to Operations Management

We will also do the same calculations for the third month:

Step 1:

Step 2:

Step 3:

Table 4.1 completes the forecasts for the 10-month period.

= 15.18 + 2.10 = 17.28. FIT3 = F3 + T3

= (.4)(2.38) + (.6)(1.92) = .952 + 1.152 = 2.10 T3 = b(F3 - F2) + (1 - b)T2 = (.4)(15.18 - 12.8) + (1 - .4)(1.92)

= 3.4 + (.8)(14.72) = 3.4 + 11.78 = 15.18 F3 = aA2 + (1 - a)(F2 + T2) = (.2)(17) + (1 - .2)(12.8 + 1.92)

Forecast Actual Smoothed Smoothed Including Trend,

Month Demand Forecast, Ft Trend, Tt FITt

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

�TABLE 4.1 Forecast with = .2 and = .4BA

40

35

30

25

20

15

10

5

0

1 2 3 4 5 Time (months)

P ro

d u

c t

d e m

a n

d

6 7 8 9

Actual demand (At )

Forecast including trend (FITt ) with α = .2 and β = .4

� FIGURE 4.3 Exponential Smoothing with Trend-Adjustment Forecasts Compared to Actual Demand Data

INSIGHT � Figure 4.3 compares actual demand (At) to an exponential smoothing forecast that includes trend (FITt). 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 � Using the data for actual demand for the 9 months, compute the exponentially smoothed forecast without trend (using Equation [4-4] as we did earlier in Examples 3 and 4). Apply α = .2 and assume an initial forecast for month 1 of 11 units. Then plot the months 2–10 fore- cast 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 � 4.19, 4.20, 4.21, 4.22, 4.44

EXCEL OM Data File Ch04Ex7.xls can be found at www.pearsonhighered.com/heizer.

ACTIVE MODEL 4.3 This example is further illustrated in Active Model 4.3 at www.pearsonhighered.com/heizer.

The value of the trend-smoothing constant, β, resembles the α constant because a high β is more responsive to recent changes in trend. A low β gives less weight to the most recent trends and tends to smooth out the present trend. Values of β 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, or double smoothing. Other advanced exponential- smoothing models are also used, including seasonal-adjusted and triple smoothing, but these are beyond the scope of this book.2

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 line into the future for medium to long-range forecasts. Several mathematical trend equations can be developed (for example, expo- nential 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:

(4-11)

where (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:

(4-12)b = ©xy - nxy ©x2 - nx2

yN

yN = a + bx

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

2For more details, see D. Groebner, P. Shannon, P. Fry, and K. Smith, Business Statistics, 8th ed. (Upper Saddle River, NJ: Prentice Hall, 2011).

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.

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102 PART 1 Introduction to Operations Management

where b = slope of the regression line Σ = summation sign x = known values of the independent variable y = known values of the dependent variable

= average of the x-values = average of the y-values

n = number of data points or observations

We can compute the y-intercept a as follows:

(4-13)

Example 8 shows how to apply these concepts.

a = y - bx

y x

EXAMPLE 8 � Forecasting with least squares

The demand for electric power at N.Y. Edison over the period 2003 to 2009 is shown in the following table, in megawatts. The firm wants to forecast 2010 demand by fitting a straight-line trend to these data.

Electrical Electrical Year Power Demand Year Power Demand

2003 74 2007 105 2004 79 2008 142 2005 80 2009 122 2006 90

APPROACH � With a series of data over time, we can minimize the computations by transform- ing the values of x (time) to simpler numbers. Thus, in this case, we can designate 2003 as year 1, 2004 as year 2, and so on. Then Equations (4-12) and (4-13) can be used to create the trend projection model.

SOLUTION �

Time Electric Power Year Period (x) Demand (y) x2 xy

2003 1 74 1 74 2004 2 79 4 158 2005 3 80 9 240 2006 4 90 16 360 2007 5 105 25 525 2008 6 142 36 852 2009 7 122 49 854

©xy = 3,063©x2 = 140©y = 692©x = 28

Thus, the least squares trend equation is To project demand in 2010, we first denote the year 2010 in our new coding system as x = 8:

INSIGHT � 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 2008 to 2009 swing in demand.

= 141.02, or 141 megawatts Demand in 2010 = 56.70 + 10.54(8)

yN = 56.70 + 10.54x.

a = y - bx = 98.86 - 10.54(4) = 56.70

b = ©xy - nxy ©x2 - nx2

= 3,063 - (7)(4)(98.86)

140 - (7)(42) =

295

28 = 10.54

x = ©x n

= 28

7 = 4 y = ©y

n =

692

7 = 98.86

LEARNING EXERCISE � Estimate demand for 2011. [Answer: 151.56 or 152 megawatts.]

RELATED PROBLEMS � 4.6, 4.13c, 4.16, 4.25, 4.39, 4.49

EXCEL OM Data File Ch04Ex8.xls can be found at www.pearsonhighered.com/heizer.

ACTIVE MODEL 4.4 This example is further illustrated in Active Model 4.4 at www.pearsonhighered.com/heizer.

î

2003 2004 2005 2006 2007 2008 2009 2010 2011

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

� FIGURE 4.5 Electrical Power and the Computed Trend Line

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. They are 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 up-and-down 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 “ ” Us, The Christmas Store, and Hallmark Card Shops also exhibit seasonal variation in customer traffic and sales.

Similarly, understanding seasonal variations is important for capacity planning in organizations that handle peak loads. These include electric power companies during extreme cold and warm peri- ods, banks on Friday afternoons, and buses and subways during the morning and evening rush hours.

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 average 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

R

Seasonal variations Regular upward or downward movements in a time series that tie to recurring events.

AUTHOR COMMENT 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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104 PART 1 Introduction to Operations Management

Demand for many prod- ucts is seasonal. Yamaha, the manufacturer of these jet skis and snowmobiles, produces products with complementary demands to address seasonal fluctuations.

EXAMPLE 9 � Determining seasonal indices

A Des Moines distributor of Sony laptop computers wants to develop monthly indices for sales. Data from 2007–2009, by month, are available.

APPROACH � Follow the five steps listed above.

SOLUTION �

Demand Average Average

2007–2009 Monthly Seasonal Month 2007 2008 2009 Demand Demanda Indexb

Jan. 80 85 105 90 94 Feb. 70 85 85 80 94 Mar. 80 93 82 85 94 Apr. 90 95 115 100 94 May 113 125 131 123 94 June 110 115 120 115 94 July 100 102 113 105 94 Aug. 88 102 110 100 94 Sept. 85 90 95 90 94 Oct. 77 78 85 80 94 Nov. 75 82 83 80 94 Dec. 82 78 80 80 94

Total average annual demand = 1,128

aAverage monthly demand bSeasonal index = Average 2007- 2009 monthly demand

Average monthly demand .=

1,128

12 months = 94.

.851 (= 80>94)

.851 (= 80>94)

.851 (= 80>94)

.957 (= 90>94) 1.064 (= 100>94) 1.117 ( = 105>94) 1.223 (= 115>94) 1.309 (= 123>94) 1.064 (= 100>94) .904 ( = 85>94) .851 (= 80>94) .957 ( = 90>94)

trend has been removed from the data. Otherwise, the magnitude of the seasonal data will be dis- torted 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 available. 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 actual historical demand (from step 1) by the average demand over all months (from step 2). For example, if the aver- age historical January demand over the past 3 years is 8 units and the average 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 aver- age 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 that month. This provides the seasonal forecast.

Example 9 illustrates this procedure as it computes seasonal indices from historical data.

LO5: Develop seasonal indices

If we expected the 2010 annual demand for computers to be 1,200 units, we would use these seasonal indices to forecast the monthly demand as follows:

Month Demand Month Demand

Jan. July

Feb. Aug.

Mar. Sept.

Apr. Oct.

May Nov.

June Dec. 1,200

12 * .851 = 85

1,200

12 * 1.223 = 122

1,200

12 * .851 = 85

1,200

12 * 1.309 = 131

1,200

12 * .851 = 85

1,200

12 * 1.064 = 106

1,200

12 * .957 = 96

1,200

12 * .904 = 90

1,200

12 * 1.064 = 106

1,200

12 * .851 = 85

1,200

12 * 1.117 = 112

1,200

12 * .957 = 96

INSIGHT � Think of these indices as percentages of average sales. The average sales (without seasonality) would be 94, but with seasonality, sales fluctuate from 85% to 131% of average.

LEARNING EXERCISE � If 2010 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 � 4.27, 4.28

EXCEL OM Data File Ch04Ex9.xls can be found at www.pearsonhighered.com/heizer.

For simplicity, only 3 periods are used for each monthly index in the preceding example. Example 10 illustrates how indices that have already been prepared can be applied to adjust trend-line forecasts for seasonality.

� 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 � A trend line is created; then monthly seasonal indices are computed. Finally, a multiplicative seasonal model is used to forecast months 67 to 78.

SOLUTION � Using 66 months of adult inpatient hospital days, the following equation was computed:

where � 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)

yN

yN = 8,090 + 21.5x

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106 PART 1 Introduction to Operations Management

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

a y 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, Vol. 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 times the appropriate seasonal index did it obtain good forecasts. Thus, for period 67 (January):

The patient days for each month are:

Patient days = (Trend-adjusted forecast) (Monthly seasonal index) = (9,530)(1.04) = 9,911

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 9,911 9,265 9,764 9,691 9,520 9,542 9,949 10,068 9,411 9,724 9,355 9,572

with Trend & Seasonality

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.

INSIGHT � Notice that with trend only, the September forecast is 9,702, but with both trend and seasonal adjustments, the forecast is 9,411. By combining trend and seasonal data, the hospital was bet- ter able to forecast inpatient days and the related staffing and budgeting vital to effective operations.

LEARNING EXERCISE � 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 inpa- tient days? [Answer: 9,708.]

RELATED PROBLEMS � 4.26, 4.29

Example 11 further illustrates seasonality for quarterly data at a department store.

Jan. 67

Feb. 68

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

Mar. 69

Apr. 70

1.01

1.04

0.97

1.02

May 71

0.99

June 72

0.99

July 73

1.03

Aug. 74

Sept. 75

1.04

0.97

Oct. 76

1.00

Dec. 78

0.98

Nov. 77

0.96

� FIGURE 4.7 Seasonal Index for San Diego Hospital

A graph showing the forecast that combines both trend and seasonality appears in Figure 4.8.

Jan. 67

Feb. 68

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

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

� EXAMPLE 11 Adjusting trend data with seasonal indices

Management at Davis’s Department Store has used time-series regression to forecast retail 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 4 quarters have been found to be 1.30, .90, .70, and 1.10, respectively.

APPROACH � To compute a seasonalized or adjusted sales forecast, we just multiply each sea- sonal index by the appropriate trend forecast:

yN seasonal = Index * yN trend forecast

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108 PART 1 Introduction to Operations Management

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 intro- duction 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 fore- casting 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 vari- ables 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 powerful than the time-series methods that use only the historical values for the fore- casted 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 pro- motional 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 . But now the independent variable, x, need no longer be time. We use the equation:

where 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.

yN =

yN = a + bx

yN

Cycles Patterns in the data that occur every several years.

Linear-regression analysis A straight-line mathematical model to describe the functional relationships between independent and dependent variables.

LO6: Conduct a regression and correlation analysis

SOLUTION �

INSIGHT � The straight-line trend forecast is now adjusted to reflect the seasonal changes.

LEARNING EXERCISE � 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 � 4.26, 4.29

Quarter IV: yNIV = (1.10)($160,000) = $176,000 Quarter III: yNIII = (.70)($140,000) = $98,000 Quarter II: yNII = (.90)($120,000) = $108,000 Quarter I: yN I = (1.30)($100,000) = $130,000

AUTHOR COMMENT 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.

� EXAMPLE 12 Computing a linear regression equation

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 � Nodel’s VP of operations has prepared the following table, which lists company revenues and the amount of money earned by wage earners in West Bloomfield during the past 6 years:

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:

Area payroll (in $ billions)

0

1.0

2.0

3.0

4.0

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 vari- able (payroll) and the dependent variable (sales): As payroll increases, Nodel’s sales tend to be higher.

SOLUTION � We can find a mathematical equation by using the least-squares regression approach:

The estimated regression equation, therefore, is:

or:

Sales = 1.75 + .25 (payroll)

yN = 1.75 + .25x

a = y - bx = 2.5 - (.25)(3) = 1.75

b = ©xy - nx y ©x2 - nx 2

= 51.5 - (6)(3)(2.5)

80 - (6)(32) = .25

y = ©y 6

= 15

6 = 2.5

x = ©x 6

= 18

6 = 3

Nodel’s Sales Area Payroll Nodel’s Sales Area Payroll (in $ millions), y (in $ billions), x (in $ millions), y (in $ billions), x

2.0 1 2.0 2 3.0 3 2.0 1 2.5 4 3.5 7

Sales, y Payroll, x x2 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

Σy = 15.0 Σx = 18 Σx2 = 80 Σxy = 51.5

AUTHOR COMMENT A scatter diagram is a

powerful data analysis tool. It helps quickly size up the relationship between two

variables.

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110 PART 1 Introduction to Operations Management

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:

or:

INSIGHT � 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 a mil- lion dollars for every quarter billion dollars in the local area payroll. This is because b = .25.

LEARNING EXERCISE � What are Nodel’s sales when the local payroll is $8 billion? [Answer: $3.75 million.]

RELATED PROBLEMS � 4.24, 4.30, 4.31, 4.32, 4.33, 4.35, 4.38, 4.40, 4.41, 4.46, 4.48, 4.49

EXCEL OM Data File Ch04Ex12.xls can be found at www.pearsonhighered.com/heizer.

Sales = $3,250,000

= 1.75 + 1.50 = 3.25 Sales (in $ millions) = 1.75 + .25(6)

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 diffi- culty of determining future values of some common independent variables (such as unemploy- ment 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:

(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

Sy,x = A ©(y - yc)2

n - 2

Standard error of the estimate A measure of variability around the regression line—its standard deviation.

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

Glidden Paints’ assembly lines fill thousands of cans per hour. To pre- dict demand, the firm uses associa- tive forecasting methods such as linear regression, with independent variables such as disposable per- sonal income and GNP. Although housing starts would be a natural variable, Glidden found that it corre- lated poorly with past sales. It turns out that most Glidden paint is sold through retailers to customers who already own homes or businesses.

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 intervals around the point estimate3:

(4-15)

Example 13 shows how we would calculate the standard error of the estimate in Example 12.

Sy, x = A ©y2 - a©y - b©xy

n - 2

� EXAMPLE 13 Computing the standard error of the estimate

Nodel’s VP of operations now wants to know the error associated with the regression line computed in Example 12.

APPROACH � Compute the standard error of the estimate, Sy,x, using Equation (4-15).

SOLUTION � The only number we need that is not available to solve for Sy,x is Some quick addition reveals Therefore:

The standard error of the estimate is then $306,000 in sales.

INSIGHT � 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 � What is the probability sales will exceed $3,556,000? [Answer: About 16%.]

RELATED PROBLEMS � 4.41e, 4.48b

= 2.09375 = .306 (in $ millions)

= A

39.5 - 1.75(15.0) - .25(51.5) 6 - 2

Sy,x = A ©y2 - a©y - b©xy

n - 2

©y2 = 39.5. ©y2.

Correlation Coefficients for Regression Lines The regression equation is one way of expressing the nature of the relationship between two vari- ables. Regression lines are not “cause-and-effect” relationships. They merely describe the rela- tionships 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. Usually

Coefficient of correlation A measure of the strength of the relationship between two variables.

3When the sample size is large (n > 30), the prediction interval value of y can be computed using normal tables. When the number of observations is small, the t-distribution is appropriate. See D. Groebner et al., Business Statistics, 8th ed. (Upper Saddle River, NJ: Prentice Hall, 2011).

Chapter 4 Forecasting 111

112 PART 1 Introduction to Operations Management

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 regres- sion line. The rather lengthy equation for r is:

(4-16)

Example 14 shows how to calculate the coefficient of correlation for the data given in Examples 12 and 13.

r = n©xy - ©x©y

2[n©x2 - (©x)2][n©y2 - (©y)2]

EXAMPLE 14 � Determining the coefficient of correlation

(a) Perfect positive correlation:

r = +1

(b) Positive correlation: 0 < r < 1

(c) No correlation: r = 0

(d) Perfect negative correlation:

r = –1

x

y

x

y

x

y

x

y� FIGURE 4.10 Four 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 asso- ciation between area payroll and sales.

APPROACH � We compute the r value using Equation (4-16). We need to first add one more col- umn of calculations—for y2.

SOLUTION � The data, including the column for y2 and the calculations, are shown here:

INSIGHT � This r of .901 appears to be a significant correlation and helps confirm the closeness of the relationship between the two variables.

LEARNING EXERCISE � 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 � 4.24d, 4.35d, 4.38c, 4.41f, 4.48b

= 39

43.3 = .901

= 309 - 270 2(156)(12)

= 39

21,872

r = (6)(51.5) - (18)(15.0)

2[(6)(80) - (18)2][(6)(39.5) - (15.0)2]

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 The coefficient of determination0 … r 2 … 1.

Coefficient of determination A measure of the amount of variation in the dependent variable about its mean that is explained by the regression equation.

y x x2 xy y2

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

Σy = 15.0 Σx = 18 Σx2 = 80 Σ xy = 51.5 Σy2 = 39.5

is the percent of variation in the dependent variable (y) that is explained by the regression equa- tion. 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:

(4-17)

where � dependent variable, salesyN

yN = a + b1x1 + b2x2

Multiple regression An associative forecasting method with more than one independent variable.

� EXAMPLE 15 Using a multiple- regression equation

Nodel Construction wants to see the impact of a second independent variable, interest rates, on its sales.

APPROACH � The new multiple-regression line for Nodel Construction, calculated by computer software, is:

We also find that the new coefficient of correlation is .96, implying the inclusion of the variable x2, interest rates, adds even more strength to the linear relationship.

SOLUTION � 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:

or:

INSIGHT � 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 � If interest rates were only 6%, what would be the sales forecast? [Answer: or $3,300,000.]

RELATED PROBLEMS � 4.34, 4.36

1.8 + 1.8 - 5.0(.06) = 3.3,

Sales = $3,000,000

= 3.00 = 1.8 + 1.8 - .6

Sales($ millions) = 1.80 + .30(6) - 5.0(.12)

yN = 1.80 + .30x1 - 5.0x2

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 projected. 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, how- ever, about money managers who are consistently off by 25% in their stock market forecasts.

AUTHOR COMMENT Using a tracking signal is a good way to make sure the forecasting system is continuing to do a

good job.

a � a constant, the y intercept x1 and x2 � values of the two independent variables, area payroll and interest rates,

respectively b1 and b2 = 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, b1, and b2 to statistics textbooks. However, Example 15 shows how to interpret Equation (4-17) in forecasting Nodel’s sales.

Chapter 4 Forecasting 113

114 PART 1 Introduction to Operations Management

Tracking signal A measurement of how well a forecast is predicting actual values.

One way to monitor forecasts to ensure that they are performing well is to use a tracking sig- nal. 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):

(4-18)

where

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 sea- sonal 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 forecast- ing 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 deviation, ±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.4

Example 16 shows how the tracking signal and cumulative error can be computed.

(MAD) = © ƒ Actual - Forecast ƒ

n

= ©(Actual demand in period i - Forecast demand in period i)

MAD

(Tracking signal) = Cumulative error

MAD

+

–

0 MADs

Upper control limit

Lower control limit

Time

Signal exceeded limit

Tracking signal

Acceptable range

*

� FIGURE 4.11 A Plot of Tracking Signals

4To 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.

Bias A forecast that is consistently higher or consistently lower than actual values of a time series.

LO7: Use a tracking signal

� EXAMPLE 16 Computing the tracking signal at Carlson’s Bakery

Carlson’s Bakery wants to evaluate performance of its croissant forecast.

APPROACH � Develop a tracking signal for the forecast and see if it stays within acceptable lim- its, which we define as ±4 MADs.

SOLUTION � Using the forecast and demand data for the past 6 quarters for croissant sales, we develop a tracking signal in the table below:

INSIGHT � 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 � 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 track- ing signal for period 6 would be 2 MADs.]

RELATED PROBLEMS � 4.37, 4.45

and Tracking signal = Cumulative error

MAD =

35

14.2 = 2.5 MADs

At the end of quarter 6, MAD = ©|Forecast errors|

n =

85

6 = 14.2

Cumulative Tracking Absolute Absolute Signal

Actual Forecast Cumulative Forecast Forecast (cumulative Quarter Demand Demand Error Error Error Error MAD error/MAD)

1 90 100 –10 –10 10 10 10.0 –10/10 = –1 2 95 100 –5 –15 5 15 7.5 –15/7.5 = –2 3 115 100 +15 0 15 30 10.0 0/10 = 0 4 100 110 –10 –10 10 40 10.0 –10/10 = –1 5 125 110 +15 +5 15 55 11.0 +5/11 = +0.5 6 140 110 +30 +35 30 85 14.2 +35/14.2 = +2.5

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 α and β 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 fore- casting 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.

Bernard Smith, inventory manager for American Hardware Supply, coined the term focus fore- casting. Smith’s job was to forecast quantities for 100,000 hardware products purchased by American’s 21 buyers.5 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 buyers 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 sub- tracted from the most recent actual demands, giving a simulated forecast error. The forecast

Adaptive smoothing An approach to exponential smoothing forecasting in which the smoothing constant is automatically 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.

5Bernard T. Smith, Focus Forecasting: Computer Techniques for Inventory Control (Boston: CBI Publishing, 1978).

Chapter 4 Forecasting 115

116 PART 1 Introduction to Operations Management

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 barber- shop catering to men expects peak flows on Fridays and Saturdays. Indeed, most barbershops 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. The OM in Action box “Forecasting at FedEx’s Customer Service Centers” provides an example of a major service-sector industry, the call center.

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 Valentine’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 deliv- ered 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 fore- casts 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 14.12(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 stores’ computers, the model makes weekly projections of customer

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

a y

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.

AUTHOR COMMENT Forecasting at McDonald’s, FedEx, and Walmart is as

important and complex as it is for manufacturers such as

Toyota and Dell.

The world’s largest express shipping company, FedEx, generates $38 billion in revenues, using 675 planes, 44,000 trucks, and a workforce of 145,000 in 220 countries. To support this global network, the company has 51 customer service call centers, whose service goal is to answer 90% of all calls within 20 seconds. With a half- million daily calls just in the U.S., FedEx makes extensive use of forecasting models for staffing decisions and to ensure that customer satisfaction levels stay the highest in the industry.

FedEx’s Forecasting & Modeling department makes several different forecasts. One-year and five-year models predict number of calls, average handle time, and staffing needs. They break forecasts into weekday, Saturday, and Sunday and then use the Delphi method and time-series analysis.

FedEx’s tactical forecasts are monthly and use 8 years of historical daily data. This time-series model addresses

month, day of week, and day of month to predict caller volume. Finally, the operational forecast uses a weighted moving average and 6 weeks of data to project the number of calls on a half-hourly basis.

FedEx’s forecasts are consistently accurate to within 1% to 2% of actual call volumes. This means

coverage needs are met, service levels are maintained, and costs are controlled.

Sources: Hoover’s Company Records (July 1, 2009): 10552; Baseline (January 2005): 54; and Journal of Business Forecasting (Winter 1999–2000): 7–11.

OM in Action � Forecasting at FedEx’s Customer Service Centers

Forecasts are a critical part of the operations manager’s func- tion. 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 histor- ical data and causal, or associative, relations to project future demands. The Rapid Review for this chapter (found in the Lecture Guide & Activities Manual) summarizes the

formulas we introduced in quantitative forecasting. Forecast calculations are seldom performed by hand. Most oper- ations managers turn to software pack- ages such as Forecast PRO, SAP, AFS, SAS, SPSS, or Excel.

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 challeng- ing, but rewarding, part of managing.

CHAPTER SUMMARY

6J. Hueter and W. Swart, “An Integrated Labor Management System for Taco Bell,” Interfaces 28, no. 1 (January–February 1998): 75–91.

transactions. These in turn are used by store managers 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 customer service while documenting more than $50 million in labor cost savings in 4 years of use.6

Key Terms

Forecasting (p. 86) Economic forecasts (p. 87) Technological forecasts (p. 87) Demand forecasts (p. 87) Quantitative forecasts (p. 89) Qualitative forecasts (p. 89) Jury of executive opinion (p. 89) Delphi method (p. 89) Sales force composite (p. 89) Consumer market survey (p. 89) Time series (p. 90)

Naive approach (p. 90) Moving averages (p. 91) Exponential smoothing (p. 94) Smoothing constant (p. 94) Mean absolute deviation (MAD) (p. 95) Mean squared error (MSE) (p. 97) Mean absolute percent error (MAPE)

(p. 97) Trend projection (p. 101) Seasonal variations (p. 103) Cycles (p. 108)

Linear-regression analysis (p. 108) Standard error of the estimate (p. 110) Coefficient of correlation (p. 111) Coefficient of determination (p. 112) Multiple regression (p. 113) Tracking signal (p. 114) Bias (p. 114) Adaptive smoothing (p. 115) Focus forecasting (p. 115)

Chapter 4 Forecasting 117

� PROGRAM 4.1 Using Excel to Develop Your Own Forecast, with Data from Example 8

As an alternative, you may want to experiment with Excel’s built-in regression analysis. To do so, under the Data menu bar selection choose Data Analysis, then Regression. Enter your Y and X data into two columns (say B and C). 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.

X Using Excel OM Excel OM’s forecasting module has five components: (1) moving averages, (2) weighted moving aver- ages, (3) exponential smoothing, (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.

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 α of 0, POM for Windows will find the α value that yields the minimum MAD.

Appendix IV provides further details.

Computations

Value Cell Excel Formula Action

Trend line column D4 =$B$16+$B$17*C4 Copy to D5:D14 (or =TREND($B$4:$B$10,$C$4:$C$10,C4))

Intercept B16 =INTERCEPT(B4:B10, C4:C10) Slope (trend) B17 =SLOPE(B4:B10, C4:C10) Standard error B19 =STEYX(B4:B10, C4:C10) Correlation B20 =CORREL(B4:B10, C4:C10)

118 PART 1 Introduction to Operations Management

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 and is found on our text web site. Third, POM for Windows is another program that is located on our web site at www.pearsonhighered.com/heizer.

Creating Your Own Excel Spreadsheets Excel spreadsheets (and spreadsheets in general) are frequently used in forecasting. Exponential smooth- ing, 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 2003–2009 data. Note that in cell D4 you can enter either = $B$16 + $B$17 * C4 or = TREND ($B$4: $B$10, $C$4: $C$10, C4).

Chapter 4 Forecasting 119

� PROGRAM 4.2 Analysis of Excel OM’s Weighted-Moving-Average Program, Using Data from Example 2 as Input

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)

Solved Problems Virtual Office Hours help is available at www.myomlab.com

� 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 in 2004 that 2005 sales would be 410 VWs. Using exponential smoothing with a weight of α = .30, develop forecasts for 2006 through 2010.

� SOLUTION

� SOLUTION

= 20.14 FIT4 = 17.82 + 2.32

= 2.32 = 1.056 + 1.26 = (.4)(2.64) + (.6)(2.10) = (.4)(17.82 - 15.18) + (1 - .4)(2.10)

T4 = b(F4 - F3) + (1 - b)T3

= 17.82 = 4.0 + 13.82 = 4.0 + (.8)(17.28) = (.2)(20) + (1 - .2)(15.18 + 2.10)

F4 = aA3 + (1 - a)(F3 + T3)

Year Sales Forecast

2005 450 410 2006 495 2007 518 2008 563 2009 584 2010 ?

Year Forecast

2005 410.0 2006 2007 2008 2009 2010 521.8 = 495.2 + .3 (584 - 495.2)

495.2 = 466.1 + .3 (563 - 466.1) 466.1 = 443.9 + .3 (518 - 443.9) 443.9 = 422 + .3 (495 - 422) 422.0 = 410 + .3 (450 - 410)

� SOLVED PROBLEM 4.2 In Example 7, we applied trend-adjusted exponential smoothing to forecast demand for a piece of pollution-control equipment for months 2 and 3 (out of 9 months of data provided). Let us now continue this process for month 4. We want to confirm the forecast for month 4 shown in Table 4.1 (p. 100) and Figure 4.3 (p. 100).

For month 4, A4 = 19, with α = .2, and β = .4.

� SOLVED PROBLEM 4.3 Room registrations in the Toronto Towers Plaza Hotel have been recorded for the past 9 years. To project future occupancy, man- agement would like to determine the mathematical 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 relating registrations to time (e.g., a trend equation). Then forecast 2011 registrations. Room registrations are in the thousands:

2001: 17 2002: 16 2003: 16 2004: 21 2005: 20 2006: 20 2007: 23 2008: 25 2009: 24

� SOLUTION

Transformed Registrants, y Year Year, x (in thousands) x2 xy

2001 1 17 1 17 2002 2 16 4 32 2003 3 16 9 48 2004 4 21 16 84 2005 5 20 25 100 2006 6 20 36 120 2007 7 23 49 161 2008 8 25 64 200 2009 9 24 81 216

Σx = 45 Σy = 182 Σx2 = 285 Σxy = 978

The projection of registrations in the year 2011 (which is x = 11 in the coding system used) is:

or 27,030 guests in 2011 yN = 14.545 + (1.135)(11) = 27.03

yN (registrations) = 14.545 + 1.135x a = y- bx = 20.22 - (1.135)(5) = 20.22 - 5.675 = 14.545

b = ©xy - nxy ©x2 - nx2

= 978 - (9)(5)(20.22)

285 - (9)(25) =

978 - 909.9 285 - 225

= 68.1

60 = 1.135

� SOLVED PROBLEM 4.4 Quarterly demand for Ford F150 pickups at a New York auto dealer is forecast with the equation:

where x = quarters, and:

Quarter I of 2008 = 0 Quarter II of 2008 = 1

Quarter III of 2008 = 2 Quarter IV of 2008 = 3

Quarter I of 2009 = 4 and so on

and:

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 2010. Then, seasonalize each forecast to adjust for quarterly variations.

yN = quarterly demand

yN = 10 + 3x

� SOLUTION Quarter II of 2009 is coded x = 5; Quarter III of 2009, x = 6; and Quarter IV of 2009, x = 7. Hence, Quarter I of 2010 is coded x = 8; Quarter II, x = 9; and so on.

Adjusted forecast = (.90)(43) = 38.7 Adjusted forecast = (1.30)(40) = 52 Adjusted forecast = (1.00)(37) = 37 Adjusted forecast = (.80)(34) = 27.2

yN (2010 Quarter IV) = 10 + 3(11) = 43 yN (2010 Quarter III) = 10 + 3(10) = 40 yN (2010 Quarter II) = 10 + 3(9) = 37 yN (2010 Quarter I) = 10 + 3(8) = 34

120 PART 1 Introduction to Operations Management

Chapter 4 Forecasting 121

�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study: North-South Airlines: Reflects the merger of two airlines and addresses their maintenance costs.

Bibliography

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Berenson, Mark, Tim Krehbiel, and David Levine. Basic Business Statistics, 11th ed. Upper Saddle River, NJ: Prentice Hall, 2009.

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Diebold, F. X. Elements of Forecasting, 5th ed. Cincinnati: South-Western College Publishing, 2010.

Fildes, Robert, and Paul Goodwin. “Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting.” Decision Sciences 37, no. 6 (November–December 2007): 570–576.

Georgoff, D. M., and R. G. Murdick. “Manager’s Guide to Forecasting.” Harvard Business Review 64 (January–February 1986): 110–120.

Gilliland, M., and M. Leonard. “Forecasting Software—The Past and the Future.” The Journal of Business Forecasting 25, no. 1 (Spring 2006): 33–36.

Hanke, J. E. and D. W. Wichern. Business Forecasting, 9th ed. Upper Saddle River, NJ: Prentice Hall, 2009.

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Design of Goods and Services

Chapter Outline

GLOBAL COMPANY PROFILE: REGAL MARINE

Goods and Services Selection 126

Generating New Products 129

Product Development 130

Issues for Product Design 135

Ethics, Environmentally-Friendly Designs, and Sustainability 138

Time-Based Competition 140

Defining a Product 142

Documents for Production 144

Service Design 146

Application of Decision Trees to Product Design 149

Transition to Production 150

� Design of Goods and Services

� Managing Quality

� Process Strategy

� Location Strategies

� Layout Strategies

� Human Resources

� Supply-Chain Management

� Inventory Management

� Scheduling

� Maintenance

PART TWO Designing Operations (Chapters 5–10)

123

T hirty years after its founding by potato farmer

Paul Kuck, Regal Marine has become a powerful

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 introducing 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 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 automotive technology. Regal’s naval

architects’ goal is to continue to reduce the time

from concept to prototype to production. The

sophisticated CAD system not only has reduced

GLOBAL COMPANY PROFILE: REGAL MARINE

PRODUCT STRATEGY PROVIDES COMPETITIVE ADVANTAGE AT REGAL MARINE

CAD/CAM is used to design the hull of a new product. This process results in faster and more efficient design and production.

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.

product development time but also has reduced

problems with tooling and production, resulting

in a superior product.

All of Regal’s products, from its $14,000 19-foot

boat to the $500,000 44-foot Commodore 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

124

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.

Larger boats, such as this luxurious Commodore 4260 Express, are water tested on a lake or ocean. Regal is one of the few boat builders in the world to earn the ISO 9001:2000 quality certification.

At the final stage, smaller boats, such as this one, are placed in this test tank, where a rain machine ensures watertight fits.

125

REGAL MARINE �

Here the deck, suspended from ceiling cranes, is being finished prior to being moved to join the hull.

126 PART 2 Designing Operations

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 excel- lent product strategy can be devastating to a firm. To maximize the potential for success, top companies focus on only a few products and then concentrate on those products. For instance, Honda’s focus is engines. Virtually all of Honda’s sales (autos, motorcycles, generators, lawn mowers) are based on its outstanding engine technology. Likewise, Intel’s focus is on micro- processors, 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. Good operations managers insist on strong communication among customer, product, processes, and suppliers that results in a high success rate for their new prod- ucts. 3M’s goal is to produce 30% of its profit from products introduced in the last 4 years. Benchmarks, of course, vary by industry; Regal introduces six new boats a year, and Rubbermaid introduces a new product each day!

One product strategy is to build particular competence in customizing an established family of goods or services. This approach allows the customer to choose product variations while rein- forcing the organization’s strength. Dell Computer, for example, has built a huge market by de- livering computers with the exact hardware and software desired by end users. And Dell does it fast—it understands that speed to market is imperative to gain a competitive edge.

Note that many service firms also refer to their offerings as products. For instance, when Allstate Insurance offers a new homeowner’s policy, it is referred to as a new “product.” Simi- larly, when Citicorp opens a mortgage department, it offers a number of new mortgage “prod- ucts.” Although the term products may often refer to tangible goods, it also refers to offerings by service organizations.

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 deci- sion 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 developing a competitive advantage via differentiation, low cost, rapid response, or a combina- tion 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 specialize in various types of patients and medical procedures. A hospital’s management may decide to oper- ate 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

Product decision The selection, definition, and design of products.

VIDEO 5.1 Product Strategy at Regal Marine

Chapter 5 Learning Objectives LO1: Define product life cycle 127

LO2: Describe a product development system 130

LO3: Build a house of quality 132

LO4: Describe how time-based competition is implemented by OM 140

LO5: Describe how products and services are defined by OM 142

LO6: Describe the documents needed for production 145

LO7: Describe customer participation in the design and production of services 146

LO8: Apply decision trees to product issues 149

AUTHOR COMMENT Product strategy is critical to achieving

competitive advantage.

Chapter 5 Design of Goods and Services 127

LO1: Define product life cycle

(b) Technology: Michelin’s latest technology: radical new tires that don’t go flat.

(c) Packaging: Sherwin Williams’s Dutch Boy has revolutionized the paint industry with its square Twist & Pour paint container.

Product Design Can Manifest Itself in Concepts, Technology, and Packaging. Whether it is a design focused on style at Nike (a), the application of technology at Michelin (b), or a new container at Sherwin-Williams (c), operations managers need to remind themselves that the creative process is ongoing with major implications for production.

(a) Concepts: Nike, in its creative way, has moved athletic shoes from utilitarian necessities into glamorous accessories and in the process is constantly reinventing all parts of the shoe, including the heel.

world-renowned specialization in hernia-repair service is so effective it allows patients to return to normal living in 8 days as opposed to the average 2 weeks—and with very few complications. The entire production system is designed for this one product. Local anesthetics are used; patients enter and leave the operating room on their own; rooms are spartan, and 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 has demonstrated, product selection affects the entire production system.

Taco Bell has developed and executed a low-cost strategy through product design. By design- ing 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 implications 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 col- umn has a simpler design, 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 hours (a newspaper), months (seasonal fashions and personal computers), years (video cassette tapes), or decades (Volkswagen Beetle). Regard- less of the length of the cycle, the task for the operations manager is the same: to design a sys- tem that helps introduce new products successfully. If the operations function cannot perform

128 PART 2 Designing Operations

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.

1Contribution is defined as the difference between direct cost and selling price. Direct costs are labor and material that go into the product.

effectively at this stage, the firm may be saddled with losers—products that cannot be produced efficiently and perhaps not at all.

Figure 5.1 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 pre- pared to develop strategies for new and existing products. Periodic examination of 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 expenditures for (1) research, (2) product development, (3) process modification and enhancement, and (4) supplier development. For example, when cellular phones were 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 effective fore- casting 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 par- ing 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 tal- ent. 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 (i.e., focus on the critical few, not the trivial many) 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 individual 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 particu- lar 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

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� FIGURE 5.1 Product Life Cycle, Sales, Cost, and Profit

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 manage- ment which product offerings should be eliminated and which fail to justify further investment in research and development or capital equipment. Product-by-value analysis focuses manage- ment’s attention on the strategic direction for each product.

GENERATING NEW PRODUCTS Because products die; because products must be weeded out and replaced; because firms gener- ate most of their revenue and profit from new products—product selection, definition, and de- sign take place on a continuing basis. Consider recent product changes: TV to HDTV, radio to satellite radio, coffee shops to Starbucks lifestyle coffee, traveling circuses to Cirque du Soleil, land lines to cell phones, cell phone to Blackberry, Walkman to iPod, mops to Swiffers—and the list goes on. Knowing how to successfully find and develop new products is a requirement.

New Product Opportunities Aggressive new product development requires that organizations build structures internally that have open communication with customers, innovative organizational cultures, aggressive R&D, strong leadership, formal incentives, and training. Only then can a firm profitably and energeti- cally 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, organiza- tions, 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 partic- ularly 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 cell phones to iPods to artificial hearts. 5. Political/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 prod- uct opportunities, the products themselves, product volume, and product mix.

Importance of New Products The importance of new products cannot be overestimated. As Figure 5.2(a) shows, leading com- panies generate a substantial portion of their sales from products less than 5 years old. Even Disney (Figure 5.2(b)) needs new theme parks to boost attendance. And giant Cisco Systems is expanding from its core business of making routers and switches into building its own computer servers (Figure 5.2(c)). The need for new products is why Gillette developed its multi-blade razors, in spite of continuing high sales of its phenomenally successful Sensor razor and why Disney innovates in spite of being the leading family entertainment company in the world.

Despite constant efforts to introduce viable new products, many new products do not succeed. Indeed, for General Mills to come up with a winner in the breakfast cereal market—defined as a cereal that gets a scant half of 1% of the market—isn’t easy. Among the top 10 brands of cereal, the youngest, Honey Nut Cheerios, was created in 1979. DuPont estimates that it takes 250 ideas to yield one marketable product.2

Chapter 5 Design of Goods and Services 129

AUTHOR COMMENT Societies reward those

who supply new products that reflect their needs.

2Rosabeth Kanter, John Kao, and Fred Wiersema, Innovation Breakthrough Thinking at 3M, DuPont, GE, Pfizer, and Rubbermaid (New York: HarperBusiness, 1997).

130 PART 2 Designing Operations

As one can see, product selection, definition, and design occur frequently—perhaps hundreds of times for each financially successful product. Operations managers and their organizations must be able to accept risk and tolerate failure. They must accommodate a high volume of new product ideas while maintaining the activities to which they are already committed.

PRODUCT DEVELOPMENT Product Development System An effective product strategy links product decisions with cash flow, market dynamics, product life cycle, and the organization’s capabilities. A firm must have the cash for product develop- ment, understand the changes constantly taking place in the marketplace, and have the necessary talents and resources available. The product development 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.

The screening process extends to the operations function. Optimum product development de- pends 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

LO2: Describe a product development system

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Much of Cisco’s growth has come from new non-networking products.

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� FIGURE 5.2 Innovation and New Products Yield Results for Both Manufacturing and Services

AUTHOR COMMENT Motorola went through 3,000

working models before it developed its first pocket

cell phone.

Source: The Orlando Sentinel and trade magazines.

Source: Cisco Systems, Inc.

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Overlay 1. First, through market research, Great Cameras, Inc., determined what the customer wants. Those wants are shown on the left of the house of quality. Second, the product development team determined how the organization is going to translate those customer wants into product design and process attribute targets. These hows are entered across the top portion of the house of quality.

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Low relationship (1) Overlay 2. Third, the team evaluated each of the customer wants against the hows. In the relationship matrix of the house, the team evaluated how well its design meets customer needs. Fourth, the “roof” of the house, indicates the relationship between the attributes. Fifth, the team developed importance ratings for its design attributes on the bottom row of the table. This was done by assigning values (5 for high, 3 for medium, and 1 for low) to each entry in the relationship matrix, and then multiplying each of these values by the customer’s importance rating. The values in |the “Our importance ratings” row provide a ranking of how to proceed with product and process design, with the highest values being the most critical to a successful product.

Overlay 3. Sixth, the house of quality is also used for the evaluation of competitors. The two columns on the right indicate how market research thinks competitors, A and B, satisfy customer wants (Good, Fair, or Poor). Products from other firms and even the proposed product can be added next to company B.

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Overlay 4. Seventh, the team identifies the technical attributes and evaluates how well Great Cameras, Inc., and its competitors address these attributes. Here the team decided on the noted technical attributes.

Chapter 5 Design of Goods and Services 131

likely to capture market share, be cost effective, and 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 cus- tomer 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 informa- tion 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. The house of quality is a graphic technique for defining the relationship between customer desires and product (or service). Only by defining this relationship in a rigorous way can operations managers design products and processes with features desired by customers. 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 prospective customers want in this product?) 2. Identify how the good/service will satisfy customer wants. (Identify specific product charac-

teristics, 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 a paint pallet because they all require electricity. This relationship is shown in the “roof” of the house in Example 1.)

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 cus- tomer “wants” to “how” the firm is going to meet those “wants.”

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

Does firm have ability to carry out idea?

Ideas from many sources

Product specifications: How the product will be made

Test market: Does product meet customer expectations?

Introduction to market

Evaluation (success?)

� FIGURE 5.3 Product Development Stages Product concepts are developed from a variety of sources, both external and internal to the firm. 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.

132 PART 2 Designing Operations

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 per- formance against these attributes. (This is done at the bottom of the figure in Example 1).

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 � Use QFD’s house of quality.

SOLUTION � 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

LO3: Build a house of quality

Chapter 5 Design of Goods and Services 133

INSIGHT � QFD provides an analytical tool that structures design features and technical issues, as well as providing importance rankings and competitor comparison.

LEARNING EXERCISE � If the market research for another country indicates that “light- weight” has the most important customer ranking (5), and reliability a 3, what is the new total impor- tance ranking for low electricity requirements, aluminum components, and ergonomic design? [Answer: 18, 15, 27, respectively.]

RELATED PROBLEMS � 5.1, 5.2, 5.3, 5.4

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 processes. 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.

Much of the QFD effort is devoted to meeting customer requirements with design characteris- tics (House 1 in Figure 5.4), and its importance is not to be underestimated. However, the sequence of houses is a very effective way of identifying, communicating, and allocating resources throughout the system. The series of houses helps operations managers determine where to deploy quality resources. In this way we meet customer requirements, produce quality products, 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 department 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 disadvantage is lack of forward thinking: How will downstream departments in the process deal with the concepts, ideas, and designs pre- sented to them, and ultimately what will the customer think of the product?

A second and popular approach is to assign a product manager to “champion” the product through the product development system and related organizations. However, a third, and per- haps the best, product development approach used in the U.S. seems to be the use of teams. Such teams are known variously as product development teams, design for manufacturability teams, and value engineering teams.

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� FIGURE 5.4 House of Quality Sequence Indicates How to Deploy Resources to Achieve Customer Requirements

134 PART 2 Designing Operations

The Japanese use a fourth approach. They bypass the team issue by not subdividing organiza- tions 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 conven- tional wisdom, is to use teams.

Product development teams are charged with the responsibility of moving from market requirements for a product to achieving a product success (refer to Figure 5.3 on page 131). Such teams often include representatives from marketing, manufacturing, purchasing, quality 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.

Use of such teams is also called concurrent engineering and implies a team representing all affected areas (known as a cross-functional team). Concurrent engineering also implies speedier product development through simultaneous performance of various aspects of product develop- ment.3 The team approach is the dominant structure for product development by leading organi- zations in the U.S.

Manufacturability and Value Engineering Manufacturability and value engineering activities are concerned with improvement of design and specifications at the research, development, design, and production stages of product devel- opment. (See the OM in Action box “Design Challenges with Trident’s Splash.”) In addition to immediate, obvious cost reduction, design for manufacturability and value engineering may pro- duce 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.

Cadbury Schweppes PLC sells a lot of gum—Dentyne, Bubbaloo, and Trident—some $4.2 billion of a $15.4 billion market that is growing about 6% per year. However, Cadbury perceived a niche for a new gum that would be a low calorie substitute for unhealthy snacks. Cadbury wanted the new product to compete with the creamy or crunchy mouth experience one gets from snacks other than gum.

The R&D team eventually designed a unique three-layer pellet with a candy shell over sugarless gum with a liquid center. For the liquid center Cadbury scientists evaluated scores of long lasting flavors before settling on two unusual blends: strawberry-lime and peppermint-vanilla.

Development wasn’t easy; neither was designing a product that could be produced. Although Cadbury acquired the liquid center technology from Pfizer, some of the flavors were too water-soluble—making the gum soft.

Early formulations leaked during production. Others survived production only to fail when subjected to the punishment of transportation.

Adding to production problems was the lack of sugar in the gum. Sugar traditionally adds strength and bulk to aid the production process, but with artificial sweeteners, the centers were not strong enough for the application of the candy coating. The machinery crushed the weakened pellets and the liquid flavors oozed out. This in turn contributed to some messy production equipment.

It took two years and millions of dollars, but Cadbury’s biggest ever new-product development effort, Trident Splash, is now on the market.

Sources: The Wall Street Journal (January 12, 2006): A1, A8; Fortune (April 3, 2006): 33

OM in Action �Design Challenges with Trident’s Splash

Product development teams Teams charged with moving from market requirements for a product to achieving product success.

Concurrent engineering Use of participating teams in design and engineering activities.

Manufacturability and value engineering Activities that help improve a product’s design, production, maintainability, and use.

3Firms that have high technological or product change in their competitive environment tend to use more concurrent engineering practices. See X. Koufteros, M. Vonderembse, and W. Doll, “Concurrent Engineering and Its Consequences,” Journal of Operations Management 19, no. 1 (January 2001): 97–115.

Chapter 5 Design of Goods and Services 135

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 achieving the functional specifications necessary to meet customer requirements in an optimal way. Value engineering programs, when effectively managed, typically reduce costs between 15% and 70% without reducing quality. Some studies have indicated that for every dollar spent on value engi- neering, $10 to $25 in savings can be realized.

Product design affects virtually all aspects of operating expense and sustainability. Consequently, the development process needs to ensure a thorough evaluation of design prior to a commitment to produce. The cost reduction achieved for a specific bracket via value engineer- ing is shown in Figure 5.5

ISSUES FOR PRODUCT DESIGN In addition to developing an effective system and organization structure for product develop- ment, several techniques 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 (CAD), (4) computer- aided manufacturing (CAM), (5) virtual reality technology, and (6) value analysis.

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 cir- cuit that could be used in many products to amplify voice signals. As originally designed, the cir- cuit 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 cir- cuit was reduced—a minor change with no associated costs—the circuit would be far less sensi- tive 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 modularity helpful because it makes product development, production, and subsequent changes easier. Moreover, 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 variety of motor- cycles. 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 mod- ules 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.

1 3

$3.50 $2.00

2

$.80

� FIGURE 5.5 Cost Reduction of a Bracket via Value Engineering

AUTHOR COMMENT Each time the bracket

is redesigned and simplified, we are able to

produce it for less.

Robust design A design that can be produced to requirements even with unfavorable 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) Computer-aided design (CAD) is the use of computers to interactively design products and pre- pare engineering documentation. Use and variety of CAD software is extensive and is rapidly expanding. CAD software allows designers to use three-dimensional drawings to save time and money by shortening development cycles for virtually all products (see the 3-D design photos below). 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, accurate flow of information to other departments—all contribute to a tremendous 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. It allows designers to examine the integration of product designs before the product is manufactured. For instance, DFMA allows automobile designers to examine how a transmission will be placed in a car on the production line, even while both the transmission and the car are still in the design stage.

A second CAD extension is 3-D object modeling. The technology is particularly useful for small prototype development (as shown in the photo on page 137). 3-D object modeling rapidly builds up a model in very thin layers of synthetic materials for evaluation. This technology speeds development by avoiding a more lengthy and formal manufacturing process. 3-D printers, costing as little as $5,000, are also now available. Shoemaker Timberland, Inc., uses theirs to allow footwear designers to see their constructions overnight rather than waiting a week for model-mak- ers to carve them.

Some CAD systems have moved to the Internet through e-commerce, where they link com- puterized design with purchasing, outsourcing, manufacturing, and long-term maintenance. This move supports rapid product change and the growing trend toward “mass customization.” With CAD on the Internet, customers can enter a supplier’s design libraries and make design changes. The supplier’s software can then automatically generate the drawings, update the bill of material, and prepare instructions for the supplier’s production process. The result is customized products produced faster and at less expense.

As product life cycles shorten and design becomes more complex, collaboration among depart- ments, facilities and suppliers throughout the world becomes critical. The potential of such collab- oration has proven so important that a standard for its exchange has been developed, known as the standard for the exchange of product data (STEP). STEP permits manufacturers to express

136 PART 2 Designing Operations

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.

3-D object modeling An extension of CAD that builds small prototypes.

Standard for the exchange of product data (STEP) A standard that provides a format allowing the electronic transmittal of three-dimensional data.

The increasing sophistication of CAD software provides (a) 3D solid design, (b) integrated assembly, and (c) analysis of stress, pressure, and thermal issues, which improves design, speeds the design process, and provides computer code for CAM equipment while reducing costs.

(a)

(b)

(c)

Chapter 5 Design of Goods and Services 137

3-D product information in a standard format so it can be exchanged internationally, allowing geo- graphically dispersed manufacturers to integrate design, manufacture, and support processes.4

Computer-Aided Manufacturing (CAM) Computer-aided manufacturing (CAM) refers to the use of specialized computer programs to direct and control manufacturing equipment. When computer-aided design (CAD) information is translated into instructions for computer-aided manufacturing (CAM), the result of these two technologies is CAD/CAM.

The benefits of CAD and CAM include:

1. Product quality: CAD permits the designer to investigate more alternatives, potential prob- lems, and dangers.

2. Shorter design time: A shorter design phase lowers cost and allows a more rapid response to the market.

3. Production cost reductions: Reduced inventory, more efficient use of personnel through improved scheduling, and faster implementation of design changes lower costs.

4. Database availability: Provides information for other manufacturing software and accurate prod- uct data so everyone is operating from the same information, resulting in dramatic cost reductions.

5. New range of capabilities: For instance, the abilities to rotate and depict objects in three- dimensional form, to check clearances, to relate parts and attachments, and to improve the use of numerically controlled machine tools—all provide new capability for manufacturing. CAD/CAM removes substantial detail work, allowing designers to concentrate on the con- ceptual and imaginative aspects of their task.

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 computer-aided design. 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 restau- rants to amusement parks. Changes to mechanical design, restaurant layouts, or amusement park rides are much less expensive at the design stage than later.

Value Analysis Although value engineering (discussed on page 134) focuses on preproduction design improve- ment, 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.

Computer-aided manufacturing (CAM) The use of information technology to control machinery.

Virtual reality A visual form of communication in which images substitute for reality and typically allow the user to respond interactively.

Value analysis A review of successful products that takes place during the production process.

4The STEP format is documented in the European Community’s standard ISO 10303.

This prototype wheel for a tire (at the left of the photo) is being built using 3-D System’s Stereolithography technology, a 3-D object modeling system. This technology uses data from CAD and builds structures layer by layer in .001-inch increments. The technique reduces the time it takes to create a sample from weeks to hours while also reducing costs. The technique is also known as rapid prototyping.

ETHICS, ENVIRONMENTALLY-FRIENDLY DESIGNS, AND SUSTAINABILITY An operations manager’s task is to enhance productivity while delivering desired goods and ser- vices in an ethical, environmentally sound, and sustainable way. In an OM context, sustainability means ecological stability. This means operating a production system in a way that supports conservation and renewal of resources. The entire product life cycle—from design, to production, to final destruction or recycling—provides an opportunity to preserve resources. Planet Earth is finite; managers who squeeze more out of its resources are its heroes. The good news is that operations managers have tools that can drive down costs or improve margins while preserving resources. Here are examples of how firms do so:

• At the design stage: DuPont developed a polyester film stronger and thinner so it uses less material and costs less to make. Also, because the film performs better, customers are willing to pay more for it. Similarly, Nike’s new Air Jordan shoe contains very little chemical-based glue and an outsole made of recycled material, yielding lower manufacturing cost and less impact on the environment.

• At the production stage: Bristol-Myers Squibb established an environmental and pollution prevention program designed to address environmental, health, and safety issues at all stages of the product life cycle. Ban Roll-On was one of the first products studied and an early suc- cess. Repackaging Ban in smaller cartons resulted in a reduction of 600 tons of recycled paperboard. The product then required 55% less shelf space for display. As a result, not only is pollution prevented but store operating costs are also reduced.

• At the destruction stage: The automobile industry has been very successful: The industry now recycles more than 84% of the material by weight of 13 million cars scrapped each year. Much of this success results from care at the design stage. For instance, BMW, with environ- mentally friendly designs, recycles most of a car, including many plastic components (see the photo).

These efforts are consistent with the environmental issues raised by the ISO 14000 standard, a topic we address in Chapter 6.

Systems and Life Cycle Perspectives One way to accomplish programs like those at DuPont, Bristol-Myers Squibb, and BMW is to add an ethical and environmental charge to the job of operations managers and their value engi- neering/analysis teams. Team members from different functional areas working together can pre- sent a wide range of environmental perspectives and approaches. Managers and teams should consider two issues.

First, they need to view products from a “systems” perspective—that is, view a product in terms of its impact on sustainability—ecological stability. This means taking a comprehensive look at the inputs to the firm, the processes, and the outputs, recognizing that some of the resources, long considered free, are in fact not free. Particulates and sulfur in the air are pollution for someone else; similarly, bacteria and phosphates in the water going downstream become

138 PART 2 Designing Operations

Sustainability A production system that supports conservation and renewal of resources.

AUTHOR COMMENT OM can do a lot to save

our planet. Saving the planet is good business and

good ethics.

BMW uses parts made of recycled plastics (blue) and parts that can be recycled (green). “Green manufacturing” means companies can reuse, refurbish, or dispose of a product’s components safely and reduce total life cycle product costs.

Chapter 5 Design of Goods and Services 139

someone else’s problem. In the case of the battle between styrofoam and paper containers, which one is really “better,” and by what criteria? We may know which is more economical for the firm, but is that one also most economical for society?

Second, operations managers must consider the life cycle of the product, that is, from design, through production, to final disposition. This can be done via value engineering, as noted earlier, or as a part of a life cycle assessment (LCA) initiative. LCA is part of the ISO 14000 environ- mental management standard. The goal is to reduce the environmental impact of a product throughout its life—a challenging task.

The likelihood that ethical decisions will be made is enhanced when managers maintain these two perspectives and maintain an open dialogue among all stakeholders.

Goals Consistent with the two issues above, goals for ethical, environment-friendly designs are:

1. Develop safe and more environmentally sound products. 2. Minimize waste of raw materials and energy. 3. Reduce environmental liabilities. 4. Increase cost-effectiveness of complying with environmental regulations. 5. Be recognized as a good corporate citizen.

Guidelines The following six guidelines may help operations managers achieve ethical and environmentally-friendly designs:

1. Make products recyclable: Many firms are doing this on their own, but the U.S. and the EU now have take-back laws that affect a variety of products, from automobiles and tires to computers. Not only is most of a car recycled but so are over half the aluminum cans and a large portion of paper, plastic, and glass. In some cases, as with tires, the manufacturer is responsible for 100% disposal.

2. Use recycled materials: Scotch-Brite soap pads at 3M are designed to use recycled plastics, as are the park benches and other products made by Plastic Recycling Corporation. Recycled plastics and old clothing are making their way into seat upholstery for the Ford Escape hybrid sport-utility. This application has added benefits: it’s waterproof and it will save 600,000 gallons of water, 1.8 million pounds of carbon doxide, and more than 7 million kilowatt hours of electricity per year.5

3. Use less harmful ingredients: Standard Register, like most of the printing industry, has replaced environmentally dangerous inks with soy-based inks that reduce air and water pollution.

4. Use lighter components: The auto and truck industries continue to expand the use of alu- minum and plastic components to reduce weight. Mercedes is even building car exteriors from a banana plant fiber that is both biodegradable and lightweight. Similarly, Boeing is using carbon fiber, epoxy composites, and titanium graphite laminate to reduce weight in its new 787 Dreamliner. These changes can be expensive, but they make autos, trucks, and air- craft more environmentally friendly by improving payload and fuel efficiency.

5. Use less energy: While the auto, truck, and airframe industries are redesigning to improve mileage, General Electric is designing a new generation of refrigerators that requires sub- stantially less electricity during their lifetime. DuPont is so good at energy efficiency that it has turned its expertise into a consulting business.

6. Use less material: Organizations fight to drive down material use—in the plant and in the packaging. An employee team at a Sony semiconductor plant achieved a 50% reduction in the amount of chemicals used in the silicon wafer etching process. And Frito-Lay’s U.S. plants have driven down water consumption over 31% in the past 10 years, with a goal of 75% reduction by 2017. These and similar successes reduce both production costs and envi- ronmental concerns. To conserve packaging, Boston’s Park Plaza Hotel eliminated bars of soap and bottles of shampoo by installing pump dispensers in its bathrooms, saving the need for a million plastic containers a year.

Laws and Industry Standards Laws and industry standards can help operations managers make ethical and socially responsible decisions. In the last 100 years we have seen development of legal and industry standards to guide managers in product design, manufacture/assembly, and disassembly/disposal.

Life cycle assessment (LCA) Part of ISO 14000; assesses the environmental impact of a product, from material and energy inputs to disposal and environmental releases.

5“Vehicles That Use Recycled Material,” The Wall Street Journal (January 25, 2007): D6.

Design: On the legal side, U.S. laws and regulations such as those promulgated by the Food and Drug Administration, Consumer Product Safety Commission, National Highway Safety Administration, and Children’s Product Safety Act provide guidance, if not explicit law, to aid deci- sion making. Guidance is also provided by phrases in case law like “design for foreseeable misuse” and in regard to children’s toys, “The concept of a prudent child is . . . a grotesque combination.”

Manufacture/assembly: The manufacture and assembly of products has standards and guide- lines from the Occupational Safety and Health Administration (OSHA), Environmental Protection Agency (EPA), professional ergonomic standards, and a wide range of state and fed- eral laws that deal with employment standards, disabilities, discrimination, and the like.

Disassembly/disposal: Product disassembly and disposal in the U.S., Canada, and the EU are governed by increasingly rigid laws. In the U.S., the Vehicle Recycling Partnership, supported by the auto industry, provides Design for Disassembly Standards for auto disassembly and disposal. However, in the fragmented electronics industry, safe disposal of TVs, computers, and cell phones is much more difficult and dangerous (see the photos above).

Ethical, socially responsible decisions can be difficult and complex—often with no easy answers—but such decisions are appreciated by the public, and they can save money, material, and the environment. These are the types of win–win situations that operations managers seek.

TIME-BASED COMPETITION As product life cycles shorten, the need for faster product development increases. Additionally, as technological sophistication of new products increases, so do the expense and risk. For instance, drug firms invest an average of 12 to 15 years and $600 million before receiving regulatory approval of each new drug. And even then, only 1 of 5 will actually be a success. Those operations managers who master this art of product development continually gain on slower product develop- ers. 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. For example, when Kodak first introduced its Ektar film, it sold for 10% to 15% more than conventional film and Apple’s innovative iPod and new versions have a 75% market share even after 5 years.

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, or features, such as are taking place in cellular phones (see OM in Action box “Chasing Fads in the Cell Phone Industry”), 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

140 PART 2 Designing Operations

With increasing restrictions on disposal of TVs, cell phones, computers, and other electronic waste, much of such waste (left) ends its life in Guangdong province on China’s southern coast (right). Here, under less- than-ideal conditions, Chinese women strip old circuit boards to salvage the chips.

AUTHOR COMMENT 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.

LO4: Describe how time-based competition is implemented by OM

Chapter 5 Design of Goods and Services 141

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. Black & Decker has used its “platform” expertise in hand-powered tools to build a leading position in that market. Similarly, Hewlett-Packard has done the same in the printer business. Enhancements and migra- tions are a way of building on existing expertise and extending a product’s life cycle.

The product development strategies on the lower left of Figure 5.6 are internal development strategies, while the three approaches we now introduce can be thought of as external devel- opment 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 organization, its

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

� FIGURE 5.6 Product Development Continuum

In the shrinking world marketplace, innovations that appeal to customers in one region rapidly become global trends. The process shakes up the structure of one industry after another, from computers to automobiles to consumer electronics.

Nowhere has this impact been greater in recent years than in the cell phone industry. The industry sells about 1.3 billion phones each year, but product life cycle is short. Competition is intense. Higher margins go to the innovator— and manufacturers that jump on an emerging trend early can reap substantial rewards. The swiftest Chinese manufacturers, such as Ningbo Bird and TCL, now replace some phone models after just 6 months. In the past, Motorola, Nokia, and other industry veterans enjoyed what are now considered long life cycles—2 years. New styles and technological advances in cell phones constantly appear somewhere in the world. Wired, well-traveled consumers seek the latest innovation; local retailers rush to offer it; and telecommunication providers order it.

Contemporary cell phones may be a curvy, boxy, or a clamshell fashion item; have a tiny keyboard for quick and

easy typing or a more limited number pad for a phone; have a built-in radio or a digital music player; have a camera, Internet access, or TV clips; function on cellular or wireless (Wi-Fi) networks; or have games or personal organizers. Mattel and Nokia even have Barbie phones for preteen girls, complete with prepaid minutes, customized ringtones, and faceplates. The rapid changes in features and demand are forcing manufacturers into a frenzied race to keep up or simply to pull out.

“We got out of the handset business because we couldn’t keep up with the cycle times,” says Jeffrey Belk, Marketing V.P. for Qualcomm Inc., the San Diego company that now focuses on making handset chips.

Developing new products is always a challenge, but in the dynamic global market place of cell phones, product development takes on new technology and new markets at breakneck speed.

Sources: Supply Chain Management Review (October, 2007): 28; The Wall Street Journal (October 30, 2003): A1 and (Sept. 8, 2004): D5; and International Business Times (March 3,2009).

OM in Action � Chasing Fads in the Cell Phone Industry

AUTHOR COMMENT Managers seek a variety of approaches to obtain speed to market. The president of

one U.S. firm says: “If I miss one product cycle, I’m dead.”

142 PART 2 Designing Operations

technology, its product lines, and its culture into the buying firm, rather than a product develop- ment issue.

Joint Ventures Joint ventures are 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. For instance, GM and Toyota formed a joint venture to pro- duce the GM Prism and the Toyota Corolla. Both companies saw a learning opportunity as well as a product they both needed in the North American market. Toyota wanted to learn about building and managing a plant in North America, and GM wanted to learn about manufacturing a small car with Toyota’s manufacturing techniques. The risks were well understood, as were the respective commitments. Similarly, Fuji-Xerox, a manufacturer and marketer of photo- copiers, is a joint venture of Xerox, the U.S. maker of photocopiers, and Fuji, Japan’s largest manufacturer of film.

Alliances Alliances are cooperative agreements that allow firms to remain independent but use comple- menting strengths to pursue strategies consistent with their individual missions. When new prod- ucts are central to the mission, but substantial resources are required and sizable risk is present, then alliances may be a good strategy for product development. Alliances are particularly benefi- cial when the products to be developed also have technologies that are in ferment. For example, Microsoft is pursuing a number of alliances with a variety of companies to deal with the conver- gence of computing, the Internet, and television broadcasting. Alliances in this case are appropri- ate 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. However, alliances are much more difficult to achieve and maintain than joint ventures because of the ambiguities associated with them. It may be help- ful 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 product 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 typically has a vari- ety 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 location of buttons may make substantial differences in ease of manufacture, quality, and market acceptance.

Rigorous specifications of a product are necessary to assure efficient production. Equipment, layout, and human resources cannot be determined until the product is defined, designed, 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 standard 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 Department 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 components, their description, and the quantity of each required to make one unit of a product.

Joint ventures Firms establishing joint ownership to pursue new products or markets.

Alliances Cooperative agreements that allow firms to remain independent, but pursue strategies consistent with their individual missions.

LO5: Describe how products and services are defined by OM

AUTHOR COMMENT Before anything can be produced, a product’s

functions and attributes must be defined.

Engineering drawing A drawing that shows the dimensions, tolerances, materials, and finishes of a component.

Bill of material (BOM) A list of the components, their description, and the quantity of each required to make one unit of a product.

Chapter 5 Design of Goods and Services 143

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.

In the food-service industry, bills of material manifest themselves in portion-control standards. 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 mate- rial 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 delivery sched- ules, 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 on the bill of material shown in Figure 5.9(a), for which there will be SAE (Society of Automotive Engineers) specifications. Therefore, there typically is no need for the firm to duplicate this specification in another document. We discuss the make-or-buy decision in more detail in Chapter 11.

§ 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.

� FIGURE 5.7 Monterey Jack A portion of the general requirements for the U.S. grades of Monterey cheese is shown here.

.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 FULL

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 � FIGURE 5.8 Engineering Drawings Such as This One Show Dimensions, Tolerances, Materials, and Finishes

Make-or-buy decision The choice between producing a component or a service and purchasing it from an outside source.

144 PART 2 Designing Operations

Group Technology Engineering drawings may also include codes to facilitate group technology. Group technology requires that components be identified by a coding scheme that specifies the type of processing (such as drilling) and the parameters of the processing (such as size). This facilitates standardiza- tion of materials, components, and processes as well as the identification of families of parts. As families of parts are identified, activities and machines can be grouped to minimize setups, rout- ings, and material handling. An example of how families of parts may be grouped is shown in Figure 5.10. Group technology provides a systematic way to review a family of components to see if an existing component might suffice on a new project. Using existing or standard compo- nents eliminates all the costs connected with the design and development of the new part, which is a major cost reduction. For these reasons, successful implementation of group technology leads to the following advantages:

1. Improved design (because more design time can be devoted to fewer components). 2. Reduced raw material and purchases. 3. Simplified production planning and control. 4. Improved layout, routing, and machine loading. 5. Reduced tooling setup time, and work-in-process and production time.

The application of group technology helps the entire organization, as many costs are reduced.

DOCUMENTS FOR PRODUCTION Once a product is selected, designed, and ready for production, production is assisted by a vari- ety 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 com- ponents 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 com- ponents, 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 operation 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

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

� FIGURE 5.9 Bills of Material Take Different Forms in a (a) Manufacturing Plant and a (b) Restaurant, but in Both Cases, the Product Must Be Defined

Group technology A product and component coding system that specifies the type of processing and the parameters of the processing; it allows similar products to be grouped.

AUTHOR COMMENT Production personnel

need clear, specific documents to help them

make the product.

Assembly drawing An exploded view of the product.

Assembly chart A graphic means of identifying how components flow into subassemblies and final products.

Route sheet A listing of the operations necessary 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.

AUTHOR COMMENT Hard Rock’s recipe here

serves the same purpose as a bill of material in a factory:

It defines the product for production.

Chapter 5 Design of Goods and Services 145

various pharmaceuticals or items from inventory, to perform various functions, and to assign per- sonnel to perform those functions.

Engineering change notices (ECNs) change some aspect of the product’s definition or doc- umentation, 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 the tech- niques 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

LO6: Describe the documents needed for production

(a) Ungrouped Parts (b) Grouped Cylindrical Parts (families of parts)

Grooved Slotted Threaded Drilled Machined

� FIGURE 5.10 A Variety of Group Technology Coding Schemes Move Manufactured Components from (a) Ungrouped to (b) Grouped (families of parts)

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

� FIGURE 5.11 Assembly Drawing and Assembly Chart

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 components are accurately identified.

Product life-cycle management (PLM) Software programs that tie together many phases of product design and manufacture.

146 PART 2 Designing Operations

routing, materials, layout, assembly, maintenance and even environmental issues.6 Integration of these tasks makes sense because many of these decisions 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 assembly 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 about mate- rials and manufacturing processes, and more environmental issues all make PLM an appealing tool for operations managers.

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 industries include banking, finance, insurance, transportation, and communications. The products 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 movie.

Designing services is challenging because they often have unique characteristics. One reason productivity improvements in services are so low is because both the design and delivery of ser- vice products include customer interaction. When the customer participates in the design process, the service supplier may have a menu of services from which the customer selects options (see Figure 5.12a). At this point, the customer may even participate in the design of the service. Design specifications may take the form of a contract or a narrative description with photos (such as for cosmetic surgery or a hairstyle). Similarly, the customer may be involved in the delivery of a service (see Figure 5.12b) or in both design and delivery, a situation that maxi- mizes the product design challenge (see Figure 5.12c).

However, as with goods, a large part of cost and quality of a service is defined at the design stage. Also as with goods, a number of techniques can both reduce costs and enhance the product. One technique is to design the product so that customization is delayed as late in the process as pos- sible. This is the way a hair salon operates: Although shampoo and rinse are done in a standard way with lower-cost labor, the tint and styling (customizing) are done last. It is also the way most restau- rants operate: How would you like that cooked? Which dressing would you prefer with your salad?

The second approach is to modularize the product so that customization takes the form of changing modules. This strategy allows modules to be designed as “fixed,” standard entities. The modular approach to product design has applications in both manufacturing and service. Just as modular design allows you to buy a Harley-Davidson motorcycle or a high-fidelity sound system

Each year the JR Simplot potato- processing facility in Caldwell, Idaho, produces billions of french fries for McDonald’s (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).

AUTHOR COMMENT Services also need

to be defined and documented.

LO7: Describe customer participation in the design and production of services

6Some PLM vendors include supply chain elements such as sourcing, material management, and vendor evaluation in their packages, but in most instances, these are considered part of the ERP systems discussed along with MRP in Chapter 14. See, for instance, SAP PLM (www.mySAP.com), Parametric Technology Corp. (www.ptc.com), UGS Corp. (www.ugs.com), and Proplanner (www.proplanner.com).

Chapter 5 Design of Goods and Services 147

with just the features you want, modular flexibility also lets you buy meals, clothes, and insurance on a mix-and-match (modular) basis. Similarly, investment portfolios are put together on a mod- ular basis, as are college curricula. Both are examples of how the modular approach can be used to customize a service.

A third approach to the design of services is to divide the service into small parts and identify those parts that lend themselves to automation or reduced customer interaction. For instance, by isolating check-cashing activity via ATM machines, banks have been very effective at designing a product that both increases customer service and reduces costs. Similarly, airlines are moving to ticketless service. Because airlines spend $15 to $30 to produce a single ticket (including labor, printing, and travel agent’s commission), ticketless systems save the industry a billion dollars a year. Reducing both costs and lines at airports—and thereby increasing customer satisfaction— provides a win–win “product” design.

Because of the high customer interaction in many service industries, a fourth technique is to focus design on the so-called moment of truth. Jan Carlzon, former president of Scandinavian Airways, believes 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 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. Figure 5.13 shows a moment-of-truth analysis for a computer company’s cus- tomer service hotline. 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 prod- uct to production are different from those used in goods-producing operations. The documenta- tion for a service will often take the form of explicit job instructions that specify what is to happen at the moment of truth. For instance, regardless of how good a bank’s products may be in

State College

Registration

Customer

Delivery

(a) Customer participation in design

(b) Customer participation in delivery

(c) Customer participation in design and delivery

Design

Customer

Delivery

Design

Customer

Delivery

(prearranged funeral services or cosmetic surgery)

(stress test for cardiac exam or delivery of a baby)

(counseling, college education, financial management of personal affairs, or interior decorating)

Design

� FIGURE 5.12 Customer Participation in the Design of Services

148 PART 2 Designing Operations

terms of checking, savings, trusts, loans, mortgages, and so forth, if the moment of truth is not done well, the product may be poorly received. Example 2 shows the kind of documentation a bank may use to move a product (drive-up window banking) to “production.” In a telemarketing service, the product design is communicated to production personnel in the form of telephone script, while a storyboard is used for movie and TV production.

EXAMPLE 2 � Service documentation for production

Experience Enhancers

Standard Expectations

Experience Detractors

Better

Best I had to call more than once to get through.

A recording spoke to me rather than a person.

While on hold, I get silence, and I wonder if I am disconnected.

The technician sounded like he was reading a form of routine questions.

The technician sounded uninterested.

The technician rushed me.

Only one local number needs to be dialed.

I never get a busy signal.

I get a human being to answer my call quickly, and he or she is pleasant and responsive to my problem.

A timely resolution to my problem is offered.

The technician is able to explain to me what I can expect to happen next.

The technician was sin- cerely concerned and apologetic about my problem.

The technician asked intelligent questions that allowed me to feel confident in his abilities.

The technician offered various times to have work done to suit my schedule. Ways to avoid future problems were suggested.

� FIGURE 5.13 Moment of Truth: Customer Contacts at a Computer Company’s Service Hotline Improve As We Move from Left to Right.

First Bank Corp. wants to ensure effective delivery of service to its drive-up customers.

APPROACH � Develop a “production” document for the tellers at the drive-up window that pro- vides the information necessary to do an effective job.

SOLUTION �

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 communi- cation 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 � By providing documentation in the form of a script/guideline for tellers, the likeli- hood of effective communication and a good product/service is improved.

LEARNING EXERCISE � 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 � 5.7

Chapter 5 Design of Goods and Services 149

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 man- agement problems. They are particularly helpful 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 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 pay- off of achieving this branch.

3. The objective is to determine the expected value 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.

� EXAMPLE 3 Decision tree applied to product design

Silicon, Inc., a semiconductor manufacturer, is investigating the possibility of producing and marketing 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.

⎧ ⎪ ⎨ ⎪ ⎩

$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

� FIGURE 5.14 Decision Tree for Development of a New Product

LO8: Apply decision trees to product issues

AUTHOR COMMENT The manager’s options are to

purchase CAD, hire/train engineers, or do nothing. Purchasing CAD has the

highest EMV.

AUTHOR COMMENT A decision tree is a great tool for thinking through

a problem.

150 PART 2 Designing Operations

APPROACH � Use of a decision tree seems appropriate as Silicon, Inc., has the basic ingredients: a choice of decisions, probabilities, and payoffs.

SOLUTION � In Figure 5.14 we draw a decision tree with a branch for each of the three deci- sions, assign the respective probabilities 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:

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:

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 � Use of the decision tree provides both objectivity and structure to our analysis of the Silicon, Inc., decision.

LEARNING EXERCISE � 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 � 5.10, 5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.18

ACTIVE MODEL 5.1 This example is further illustrated in Active Model 5.1 at www.pearsonhighered.com/heizer.

= $365,000

EMV 1Hire>train engineers2 = 1.421$875,0002 + 1.621$25,0002

= $388,000

EMV 1purchase CAD syatem2 = 1.421$1,000,0002 + 1.621- $20,0002

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 termination of the product idea. One of the arts of modern management is knowing when to move a prod- uct 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.

Some companies appoint a project manager; others use product development teams to ensure that the transition from development to production is successful. Both approaches allow a wide range of resources and talents to be brought to bear to ensure satisfactory production of a prod- uct that is still in flux. A third approach is integration of the product development and manufac- turing 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.

AUTHOR COMMENT One of the arts of

management is knowing when a product should

move from development to production.

Chapter 5 Design of Goods and Services 151

Effective product strategy requires selecting, designing, and defining a product and then transitioning that product to pro- duction. Only when this strategy is carried out effectively can the production function contribute 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 advan- tage for the firm. As products move through their life cycle (introduction, growth, maturity, and decline), the options that the operations manager should pursue change. Both man- ufactured and service products have a variety of techniques

available to aid in performing this activity efficiently.

Written specifications, bills of mate- rial, and engineering drawings aid in defining products. Similarly, assembly 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 analysis is appropriate to ensure maximum product value. Engineering change notices and configuration management provide prod- uct documentation.

CHAPTER SUMMARY

Key Terms

Product decision (p. 126) Product-by-value analysis (p. 128) Quality function deployment (QFD) (p. 131) House of quality (p. 131) Product development teams (p. 134) Concurrent engineering (p. 134) Manufacturability and value engineering

(p. 134) Robust design (p. 135) Modular design (p. 135) Computer-aided design (CAD) (p. 136) Design for manufacture and assembly

(DFMA) (p. 136)

3-D object modeling (p. 136) Standard for the Exchange of Product

Data (STEP) (p. 136) Computer-aided manufacturing

(CAM) (p. 137) Virtual reality (p. 137) Value analysis (p. 137) Sustainability (p. 138) Life cycle assessment (LCA) (p. 139) Time-based competition (p. 140) Joint ventures (p. 142) Alliances (p. 142) Engineering drawing (p. 142)

Bill of material (BOM) (p. 142) Make-or-buy decision (p. 143) Group technology (p. 144) Assembly drawing (p. 144) Assembly chart (p. 144) Route sheet (p. 144) Work order (p. 144) Engineering change notice (ECN)

(p. 145) Configuration management (p. 145) Product life-cycle management (PLM)

(p. 145)

Solved Problem Virtual Office Hours help is available at www.myomlab.com

� 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 5.15.

For design A:

For design B:

The highest payoff is design option B, at $600,000.

= $600,000 EMV1design B2 = 1.821$750,0002 + 1.221$02

= $425,000 EMV1design A2 = 1.921$350,0002 + 1.121$1,100,0002

� SOLVED PROBLEM 5.1 Sarah King, president of King Electronics, Inc., has two design options for her new line of high-resolution cathode-ray tubes (CRTs) for CAD workstations. The life cycle sales forecast for the CRT is 100,000 units.

Design option A has a .90 probability of yielding 59 good CRTs per 100 and a .10 probability of yielding 64 good CRTs 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 CRT will cost $75. Each good CRT will sell for $150. Bad CRTs are destroyed and have no salvage value. We ignore any disposal costs in this problem.

152 PART 2 Designing Operations

EMV = $425,000 (.9)

(.1)

$8,850,000 –7,500,000 –1,000,000 –––––––––

Sales 59,000 at $150 Mfg. cost 100,000 at $75 Design cost

$350,000

⎧ ⎪ ⎨ ⎪ ⎩

Yield 59

Yield 64

EMV = $600,000

(.8)

(.2)

Yield 64

Yield 59

$9,600,000 –7,500,000 –1,000,000 –––––––––

Sales 64,000 at $150 Mfg. cost 100,000 at $75 Design cost

$1,100,000

⎧ ⎪ ⎨ ⎪ ⎩

$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

FIGURE 5.15 � Decision Tree for

Solved Problem 5.1

Bibliography

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Managing Quality

Chapter Outline

GLOBAL COMPANY PROFILE: ARNOLD PALMER HOSPITAL

Quality and Strategy 156

Defining Quality 156

International Quality Standards 159

Total Quality Management 160

Tools of TQM 166

The Role of Inspection 170

TQM in Services 172

� Design of Goods and Services

� Managing Quality

� Process Strategy

� Location Strategies

� Layout Strategies

� Human Resources

� Supply-Chain Management

� Inventory Management

� Scheduling

� Maintenance

153

GLOBAL COMPANY PROFILE: ARNOLD PALMER HOSPITAL

MANAGING QUALITY PROVIDES A COMPETITIVE ADVANTAGE AT ARNOLD PALMER HOSPITAL

S ince 1989, the 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 16,000

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.

• 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 monitors standards in many areas and provides monthly feedback to the hospital.

• Just-in-time: Supplies are delivered to Arnold Palmer on a JIT basis. This keeps inventory costs low and keeps quality problems from hiding.

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.

The lobby of Arnold Palmer Hospital, with its 20-foot-high Genie, is clearly intended as a warm and friendly place for children.

154

The Mark Twain quote on the board reads “Always Do Right. This will gratify some people and astonish most.” 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.

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.

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 of the Lecture Guide & Activities Manual examines this layout in detail.

• Tools such as Pareto charts and flowcharts: 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.

ARNOLD PALMER HOSPITAL �

155

156 PART 2 Designing Operations

QUALITY AND STRATEGY As Arnold Palmer Hospital and many other organizations have found, quality is a wonderful 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 processes that produce consis- tent quality. And Dell Computers rapidly responds to customer orders because quality systems, with little rework, have allowed it to achieve rapid throughput in its plants. Indeed, quality may be the critical 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 qual- ity. 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 importantly, 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 qual- ity, 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.

DEFINING QUALITY An operations manager’s objective is to build a total quality management system that identifies 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, at 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.”

Quality The ability of a product or service to meet customer needs.

LO1: Define quality and TQM 157

LO2: Describe the ISO international quality standards 159

LO3: Explain what Six Sigma is 161

LO4: Explain how benchmarking is used in TQM 163

Chapter 6 Learning Objectives LO5: Explain quality robust products and

Taguchi concepts 165

LO6: Use the seven tools of TQM 167

AUTHOR COMMENT Quality is an issue that affects

an entire organization.

AUTHOR COMMENT To create a quality good or

service, operations managers need to know what the

customer expects.

AUTHOR COMMENT High-quality products and

services are the most profitable.

VIDEO 6.1 The Culture of Quality at Arnold Palmer Hospital

Improved Quality Increased Profits Reduced Costs via

Increased productivity Lower rework and scrap costs Lower warranty costs

Sales Gains via

Two Ways Quality Improves Profitability

Improved response Flexible pricing Improved reputation

� FIGURE 6.1 Ways Quality Improves Profitability

Chapter 6 Managing Quality 157

LO1: Define quality and TQM

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 peo- ple like this approach and so do customers. To them, higher quality means better performance, 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 ensure 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 quality products.

2. Product liability: The courts increasingly hold organizations that design, produce, or dis- tribute 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, night- gowns 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.

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 that Are Necessary to Achieve Total Quality Management

158 PART 2 Designing Operations

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.quality.nist.gov.)

The Japanese have a similar award, the Deming Prize, named after an American, Dr. W. Edwards Deming.

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 that result from production of defective parts or services before deliv- ery to customers (e.g., rework, scrap, downtime).

• External costs: costs that occur after delivery of defective parts or services (e.g., rework, returned goods, liabilities, lost goodwill, costs to society).

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 recently (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 aggres- sively 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 man- agement, including Deming, Feigenbaum, and Juran. Table 6.1 summarizes their philosophies and contributions.

Ethics and Quality Management For operations managers, one of the most important jobs is to deliver healthy, safe, and quality products and services to customers. The development of poor-quality products, because of inade- quate design and production processes, results not only 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 dictate 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 con- taminated. A manufacturer must accept responsibility for any poor-quality product released to the public. In recent years, Ford (the Explorer SUV maker) and Firestone (the radial tire maker) have been accused of failing to issue product recalls, of withholding damaging information, and of handling complaints on an individual basis.2

There are many stakeholders involved in the production and marketing of poor-quality prod- ucts, 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.

Cost of quality (COQ) The cost of doing things wrong—that is, the price of nonconformance.

Takumi is a Japanese character that symbolizes a

broader dimension than quality, a deeper process than education, and a more perfect

method than persistence.

1Philip 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 precision. 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.” 2For further reading, see O. Fisscher and A. Nijhof, “Implications of Business Ethics for Quality Management,” TQM Magazine 17 (2005): 150–161; and M. R. Nayebpour and D. Koehn, “The Ethics of Quality: Problems and Preconditions,” Journal of Business Ethics 44 (April, 2003): 37–48.

Chapter 6 Managing Quality 159

� TABLE 6.1 Leaders in the Field of Quality Management

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 defining quality as fitness for use, not necessarily the written specifications.

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 field that integrated the processes of a company. His work in how people learn from each other’s successes led to the field 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 first time. Crosby coined the term zero defects and stated, “There is absolutely no reason for having errors or defects in any product or service.”

INTERNATIONAL QUALITY STANDARDS ISO 9000 Quality is so important globally that the world is uniting around a single quality standard, ISO 9000. ISO 9000 is the only quality standard with international recognition. In 1987, 91 member nations (including the U.S.) published a series of quality assurance standards, known collectively as ISO 9000. The U.S., through the American National Standards Institute (ANSI), has adopted the ISO 9000 series as the ANSI/ASQ Q9000 series. The focus of the standards is to establish quality management procedures, through leadership, detailed documentation, work instructions, and recordkeeping. These procedures, we should note, say nothing about the actual quality of the product—they deal entirely with standards to be followed.

To become ISO 9000 certified, organizations go through a 9- to 18-month process that involves documenting quality procedures, an on-site assessment, and an ongoing series of audits of their products or services. To do business globally being listed in the ISO directory is critical. As of 2009, there were over 1 million certifications awarded to firms in 175 countries. About 40,000 U.S. firms are ISO 9000 certified. Over 200,000 Chinese firms have received certificates.

ISO upgraded its standards in 2008 into more of a quality management system, which is detailed in its ISO 9001: 2008 component. Leadership by top management and customer requirements and satisfaction play a much larger role, while documented procedures receive less emphasis under ISO 9001: 2008.

ISO 14000 The continuing internationalization of quality is evident with the development of ISO 14000. 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. The new standard could have several advantages:

• Positive public image and reduced exposure to liability. • Good systematic approach to pollution prevention through the minimization of ecological

impact of products and activities. • Compliance with regulatory requirements and opportunities for competitive advantage. • Reduction in need for multiple audits.

This standard is being accepted worldwide, with ISO 14001, which addresses environmental impacts of activities systematically, receiving great attention. The OM in Action box “Subaru’s Clean, Green Set of Wheels with ISO 14001” illustrates the growing application of the ISO 14000 series.

As a follow-on to ISO 14000, ISO 24700 reflects the business world’s current approach to reusing recovered components from many products. These components must be “qualified as

AUTHOR COMMENT International quality standards grow in

prominence every year. See www.iso.ch and www.asq.org to learn

more about them.

LO2: Describe the ISO international quality standards

ISO 14000 A series of environmental management standards established by the International Organization for Standardization (ISO).

ISO 9000 A set of quality standards developed by the International Organization for Standardization (ISO).

160 PART 2 Designing Operations

good as new” and meet all safety and environmental criteria. Xerox was one of the companies that helped write ISO 24700 and an early applicant for certification.

TOTAL QUALITY MANAGEMENT Total quality management (TQM) refers to a quality emphasis that encompasses the entire organization, from supplier to customer. TQM stresses a commitment by management to have a continuing companywide drive toward excellence in all aspects of products and services that are 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 expecta- tions 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 imple- mented TQM. We develop these into seven concepts for an effective TQM program: (1) continu- ous improvement, (2) Six Sigma, (3) employee empowerment, (4) benchmarking, (5) just-in-time (JIT), (6) Taguchi concepts, and (7) knowledge of TQM tools.

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.

AUTHOR COMMENT The 7 concepts that make

up TQM are part of the lexicon of business.

Going green had a humble beginning. 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 things 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 suggestions were a lot more dirty. “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 four 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. It thereby 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: The Wall Street Journal (March 23, 2009): R4; Industry Week (July 2008): 36–41; and Industrial Engineer (April 2006): 26–29.

OM in Action � Subaru’s Clean, Green Set of Wheels with ISO 14001

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.

� TABLE 6.2 Deming’s 14 Points for Implementing Quality Improvement

Chapter 6 Managing Quality 161

Continuous Improvement Total quality management requires a never-ending process of continuous improvement that cov- ers 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, developed 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.3 The PDCA cycle is shown in Figure 6.3 as a circle to stress the continuous nature of the improvement process.

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 meanings 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 bag- gage handling 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) Defines the project’s purpose, scope, and outputs and then identifies the required process information, keeping in mind the customer’s

PDCA A continuous improvement model of plan, do, check. act.

LO3: Explain what Six Sigma is

Six Sigma A program to save time, improve quality, and lower costs.

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.

Upper limits

Lower limits

2,700 defects/million

Mean

±3σ

±6σ

3.4 defects/million

� FIGURE 6.4 Defects per million for vs. —6S—3S

AUTHOR COMMENT Recall that provides

99.73% accuracy, while is 99.9997%.

; 6s ; 3s

3As a result, the Japanese refer to the PDCA cycle as a Deming circle, while others call it a Shewhart circle.

� FIGURE 6.3 PDCA cycle

162 PART 2 Designing Operations

definition of quality; (2) Measures the process and collects data; (3) Analyzes the data, ensur- ing repeatability (the results can be duplicated), and reproducibility (others get the same result); (4) Improves, by modifying or redesigning, existing processes and procedures; and (5) Controls 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 to stiff competition. The company first set a goal of reducing defects by 90%. Within 1 year it had achieved such impressive results—through benchmarking 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.

Implementing Six Sigma Implementing Six Sigma “is a big commitment,” says the head of that program at Praxair, a major industrial gas company. “We’re asking our executives to spend upward of 15% of their time on Six Sigma. If you don’t spend the time, you don’t get the results.” Indeed, successful Six Sigma programs in every firm, from GE to Motorola to DuPont to Texas Instruments 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.4

Employee Empowerment Employee empowerment means involving employees in every step of the production process. Consistently, business literature suggests that some 85% of quality problems have to do with materials and processes, not with employee performance. Therefore, the task is to design equip- ment 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 pro- grams that delegate responsibility for quality to shop-floor employees tend to be twice as likely to succeed as those implemented with “top-down” directives.5

When nonconformance occurs, the worker is seldom wrong. Either the product was designed wrong, the system 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 net- works that include employees; (2) developing open, supportive supervisors; (3) moving respon- sibility from both managers and staff to production employees; (4) building high-morale organizations; and (5) creating such formal organization structures as teams and quality 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 reg- ularly to solve work-related problems. The members receive training in group planning, 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 financially, 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.

4To 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. Master Black Belts are full-time teachers who have extensive training in statistics, quality tools, and leadership. They mentor Black Belts, who in turn are project team leaders, directing perhaps a half-dozen projects per year. Dow Chemical and DuPont have more than 1,000 Black Belts each in their global operations. DuPont also has 160 Master Black Belts and introduces over 2,000 Green Belts per year into its ranks. 5“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.)

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.

Chapter 6 Managing Quality 163

Benchmarking Benchmarking is another ingredient in an organization’s TQM program. Benchmarking involves selecting a demonstrated standard of products, services, costs, or practices that repre- sent 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 partic- ular 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 as fast as it could. After benchmarking, it 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 divisions. 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 on the next page.)

Internal Benchmarking When an organization is large enough to have many divisions or busi- ness 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.

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.

Benchmarking Selecting a demonstrated standard of performance that represents the very best performance for a process or an activity.

LO4: Explain how benchmarking is used in TQM

Best Practice Justification

Make it easy for clients to complain. Respond quickly to complaints. Resolve complaints on the first contact. Use computers to manage complaints. Recruit the best for customer service jobs.

It is free market research. It adds customers and loyalty. It reduces cost Discover trends, share them, and align your services. It should be part of formal training and career advancement.

Source: Canadian Government Guide on Complaint Mechanism.

� TABLE 6.3 Best Practices for Resolving Customer Complaints

164 PART 2 Designing Operations

6Note that benchmarking is good for evaluating how well you are doing the thing you are doing compared with the industry, but the more imaginative approach to process improvement is to ask, Should we be doing this at all? Comparing your warehousing operations to the marvelous job that L.L. Bean does is fine, but maybe you should be out- sourcing the warehousing function (see Supplement 11).

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.

In one of the most unlikely benchmarks in modern medicine, 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 42% to 49%, with a bonus of faster handoff time.

Sources: The Wall Street Journal (December 3, 2007): B11 and (November 14, 2006): A1, A8.

OM in Action � A Hospital Benchmarks against the Ferrari Racing Team?

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.6

Just-in-Time (JIT) The philosophy behind just-in-time (JIT) is one of continuing improvement and enforced prob- lem 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 immedi- ately 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.

Chapter 6 Managing Quality 165

• 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 qual- ity. 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, quality loss function, and target-oriented quality.7

Quality robust products are products that can be produced uniformly and consistently in ad- verse 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 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 quality loss function (QLF) identifies all costs connected with poor quality and shows how these costs increase as the product moves away from being exactly what the customer wants. These costs include not only customer dissatisfaction but also warranty and service costs; internal inspection, repair, and scrap costs; and costs that can best be described as costs to soci- ety. Notice that Figure 6.5(a) shows the quality loss function as a curve that increases at an increasing rate. It takes the general form of a simple quadratic formula:

All the losses to society due to poor performance are included in the loss function. The smaller the loss, the more desirable the product. The farther the product is from the target value, the more severe the loss.

Taguchi observed that traditional conformance-oriented specifications (i.e., the product is good as long as it falls within the tolerance limits) are too simplistic. As shown in Figure 6.5(b), conformance-oriented quality accepts all products that fall within the tolerance limits, producing more units farther from the target. Therefore, the loss (cost) is higher in terms of customer satis- faction and benefits to society. Target-oriented quality, on the other hand, strives to keep the prod- uct at the desired specification, producing more (and better) units near the target. Target-oriented quality is a philosophy of continuous improvement to bring the product exactly on target.

C = cost of the deviation at the specification limit D2 = square of the distance from the target value

where L = loss to society L = D2C

LO5: Explain quality robust products and Taguchi concepts

Quality robust Products that are consistently built to meet customer needs in spite of adverse conditions in the production process.

Quality loss function (QLF) A mathematical function that identifies all costs connected with poor quality and shows how these costs increase as product quality moves from what the customer wants.

Target-oriented quality brings products toward the target value.

Conformance-oriented quality keeps products within 3 standard deviations.

Distribution of Specifications for

Products Produced

(b)

Quality Loss Function

(a) 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) Quality Loss Function and (b) Distribution of Products Produced Taguchi aims for the target because products produced near the upper and lower acceptable specifications result in higher quality loss function.

7G. Taguchi, S. Chowdhury, and Y. Wu, Taguchi’s Quality Engineering Handbook (New York: Wiley, 2004).

Target-oriented quality A philosophy of continuous improvement to bring a product exactly on target.

166 PART 2 Designing Operations

Knowledge of TQM Tools To empower employees and implement TQM as a continuing effort, everyone in the organization must be trained in the techniques of TQM. In the following section, we focus on some of the di- verse and expanding tools that are used in the TQM crusade.

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 re- cording 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

AUTHOR COMMENT These 7 tools will prove useful in many of your

courses and throughout your career.

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

Chapter 6 Managing Quality 167

example 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.

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 repairperson 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 Ms 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.

LO6: Use the seven tools of TQM

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

Pareto charts Graphics that identify the few critical items as opposed to many less important ones.

Source: Adapted from MoreSteam.com, 2007.

� FIGURE 6.7 Fish-Bone Chart (or Cause-and-Effect Diagram) for Problems with Missed Free-throws

168 PART 2 Designing Operations

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 � A Pareto chart is an excellent choice for this analysis.

SOLUTION � 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.

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 ca- ble 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 engi- neers 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.

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 � This visual means of summarizing data is very helpful—particularly with large amounts of data, as in the Southwestern University case study in the Lecture Guide & Activities Manual. We can immediately spot the top problems and prepare a plan to address them.

LEARNING EXERCISE � Hard Rock’s bar manager decides to do a similar analysis on com- plaints 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 � 6.1, 6.3, 6.7b, 6.12, 6.13, 6.16c

ACTIVE MODEL 6.1 This example is further illustrated in Active Model 6.1 at www.pearsonhighered.com/heizer.

Flowcharts Block diagrams that graphically describe a process or system.

Chapter 6 Managing Quality 169

� 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 � 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 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 � Here is the flowchart:

1 112 3 4 5 6

9

8

10

7 80%

20%

INSIGHT � 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 � 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:

2 3 4

RELATED PROBLEMS � 6.6, 6.15

AUTHOR COMMENT Flowcharting any process

is an excellent way to understand and then try to

improve that process.

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 vari- ations 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 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.

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

Statistical process control (SPC) A process used to monitor standards, make measurements, and take corrective action as a product or service is being produced.

Control charts Graphic presentations of process data over time, with predetermined control limits.

170 PART 2 Designing Operations

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 up-per 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 the average percentages of samples in a control chart. When the average of the samples falls 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 control 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 at the expected quality level, control of the process is needed. The best processes have little variation from the standard expected. The operations man- ager’s task is to build such systems and to verify, often by inspection, that they are performing to standard. This inspection can involve measurement, tasting, touching, weighing, or testing of the product (sometimes even destroying it when doing so). Its goal is to detect a bad process imme- diately. 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 managers 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.

The seven tools of TQM discussed in the previous section aid in this “when and where to in- spect” 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.8 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. Additionally, inspectors are only human: They become bored, they become tired, and

1

Game number

0%

10%

20%

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

� FIGURE 6.8 Control Chart for Percentage of Free-throws Missed by the Chicago Bulls in Their First Nine Games of the New Season

AUTHOR COMMENT One of the themes of quality

is that “quality cannot be inspected into a product.”

Inspection A means of ensuring that an operation is producing at the quality level expected.

8Statistical Quality Control (Springfield, MA: Monsanto Chemical Company, n.d.): 19.

Chapter 6 Managing Quality 171

the inspection equipment itself has variability. Even with 100% inspection, inspectors cannot guarantee perfection. Therefore, 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 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, communications, 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 example 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 bag 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 another type of poka-yoke. The idea of source inspection and poka-yokes is to ensure that 100% good product or service is provided at each step in the process.

Service Industry Inspection In service-oriented organizations, inspection points can be assigned at a wide range of locations, as illustrated in Table 6.4. Again, the operations manager must decide where inspections are jus- tified and may find the seven tools of TQM useful when making these judgments.

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

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.

Source inspection Controlling or monitoring at the point of production or purchase—at the source.

Poka-yoke Literally translated, “foolproof”; it has come to mean a device or technique that ensures the production of a good unit every time.

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.

172 PART 2 Designing Operations

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 microme- ter 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 statistical 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 ser- vice 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 product com- parison differs little between goods and services. However, what is very different about the se- lection of services is the poor definition of the (1) intangible differences between products and (2) the intangible expectations customers have of those products. Indeed, the intangible attributes

AUTHOR COMMENT The personal component

of a service can make quality measurement

difficult.

Using checklists, as simple as they are, is a powerful way to improve quality. Everyone from airline pilots to physicians use them. Johns Hopkins Hospital uses checklists to monitor patients, and the Michigan Health and Hospital Association is using them with great success to reduce infections.

� TABLE 6.4 Examples of Inspection in Services

Organization What Is Inspected Standard

Jones Law Offices 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 1 minute Restaurant Busboy Clears all entrée 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

Chapter 6 Managing Quality 173

Hair can’t grow below shirt collar

Turn in sales leads

No smoking in front of customers

No beards

Use DIAD to log everything from driver’s miles per gallon to tracking data on parcels

Key ring held on the pinky finger

“All Good Kids Love Milk”: the five seeing habits of drivers: Aim high in steering, Get the big picture, Keep your eyes moving, Leave yourself an out, Make sure they see you

Sideburns can’t grow below the bottom of the ear

Undershirts must be either white or brown

Shirts can’t be unbuttoned below the first button

Load boxes neatly and evenly like a stack of bricks

Walk briskly. No running allowed

Sport clean uniform every day

Black or brown polishable shoes, nonslip soles

Toot horn when arriving at business or residence

Present parcels for five stops ahead

UPS drivers are taught 340 precise methods of how to correctly deliver a package. Regimented? Absolutely. But UPS credits its uniformity and efficiency with laying the foundation for its high- quality service.

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 (see the photo of the UPS driver).

The operations manager plays a significant role in addressing several major aspects of service 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 complete 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 design processes (service products) that have these attributes and can ensure their quality through the TQM techniques discussed in this chapter.

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 com- parison of their before-service expectations with their actual-service experience. 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

VIDEO 6.2 TQM at Ritz-Carlton Hotels

Service recovery Training and empowering frontline workers to solve a problem immediately.

174 PART 2 Designing Operations

drilled in the LEARN routine—Listen, Empathize, Apologize, React, Notify—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.

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.

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 spying. 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?

• Is the bathroom tub and shower spotlessly clean? • How many minutes does it take to get coffee 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: Hotel and Motel Management (August 2002): 128; The Wall Street Journal (May 12, 1999): B1, B12; and Forbes (October 5, 1998): 88–89.

OM in Action � Richey International’s Spies

� TABLE 6.5 Determinants of Service Quality

Reliability involves consistency of performance and dependability. It means that the firm performs the service right the first time and that the firm 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.

Source: 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 (Fall 1985): 44; Journal of Marketing, 58, no. 1 (January 1994): 111–125; Journal of Retailing 70 (Fall 1994): 201–230.

Chapter 6 Managing Quality 175

CHAPTER SUMMARY Quality is a term that means different things to different peo- ple. We define quality as “the totality of features and charac- teristics 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 concepts, and knowl- edge of TQM tools. The seven TQM tools introduced in this chapter are check sheets, scatter diagrams, cause-and- effect diagrams, Pareto charts, flowcharts, his- tograms, and statistical process control (SPC).

Key Terms

Quality (p. 156) Cost of quality (COQ) (p. 158) ISO 9000 (p. 159) ISO 14000 (p. 159) Total quality management (TQM) (p. 160) PDCA (p. 161) Six Sigma (p. 161) Employee empowerment (p. 162) Quality circle (p. 162)

Benchmarking (p. 163) Quality robust (p. 165) Quality loss function (QLF) (p. 165) Target-oriented quality (p. 165) Cause-and-effect diagram, Ishikawa

diagram, or fish-bone chart (p. 167) Pareto charts (p. 167) Flowcharts (p. 168) Statistical process control (SPC) (p. 169)

Control charts (p. 169) Inspection (p. 170) Source inspection (p. 171) Poka-yoke (p. 171) Attribute inspection (p. 171) Variable inspection (p. 171) Service recovery (p. 173)

�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study: Westover Electrical, Inc.: This electric motor manufacturer has a large log of defects in its wiring process.

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Mitra, Amit. Fundamentals of Quality Control and Improvement. New York: Wiley, 2009.

Pande, P. S., R. P. Neuman, R. R. Cavanagh. What Is Design for Six Sigma? New York: McGraw-Hill, 2005.

Schroeder, Roger G., et al. “Six Sigma: Definition and Underlying Theory.” Journal of Operations Management 26, no. 4 (2008): 536–554.

Soltani, E., P. Lai, and P. Phillips. “A New Look at Factors Influencing Total Quality Management Failure.” New Technology, Work, and Employment 23, no. 1–2 (March 2008): 125.

Stewart, D. M. “Piecing Together Service Quality: A Framework for Robust Service.” Production and Operations Management (Summer 2003): 246–265.

Summers, Donna. Quality Management, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2009.

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Statistical Process Control

Supplement Outline

Statistical Process Control (SPC) 178

Process Capability 191

Acceptance Sampling 193

177

SUPPLEMENTSUPPLEMENT

BetzDearborn, A Division of Hercules Incorporated, is headquartered in Trevose, Pennsylvania. It is a global supplier of specialty chemicals for the treatment of industrial water, wastewater, and process systems. The company uses statistical process control to monitor the performance of treatment programs in a wide variety of industries throughout the world. BetzDearborn’s quality assurance laboratory (shown here) also uses statistical sampling techniques to monitor manufacturing processes at all of the company’s production plants.

STATISTICAL PROCESS CONTROL (SPC) In this supplement, we address statistical process control—the same techniques used at BetzDearborn, at IBM, at GE, and at Motorola to achieve quality standards. We also introduce acceptance sampling. Statistical process control is the application of statistical techniques to the control of processes. Acceptance sampling is used to determine acceptance or rejection of material evaluated by a sample.

Statistical process control (SPC) is a statistical technique that is widely used 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 and special causes of variation. Many people now refer to these variations as natural and assignable causes. He developed a simple but powerful tool to separate the two— the control chart.

We use statistical process control to measure performance of a process. 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 cus- tomer expectations can be assessed. The objective of a process control system is to provide a sta- tistical signal when assignable causes of variation are present. Such a signal can quicken appropriate action to eliminate assignable causes.

LO1: Explain the purpose of a control chart 180

LO2: Explain the role of the central limit theorem in SPC 181

LO3: Build -charts and R-charts 181

LO4: List the five steps involved in building control charts 185

x

AUTHOR COMMENT In this supplement we

show you how to set up a control chart.

Supplement 6 Learning Objectives

LO5: Build p-charts and c-charts 186

LO6: Explain process capability and compute Cp and Cpk 191

LO7: Explain acceptance sampling 193

LO8: Compute the AOQ 195

Statistical process control (SPC) A process used to monitor standards by taking measurements and corrective action as a product or service is being produced.

Control chart A graphical presentation of process data over time.

1Removing assignable causes is work. Quality expert W. Edwards Deming observed that a state of statistical control is not a natural 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. See J. R. Thompson and J. Koronacki, Statistical Process Control, The Deming Paradigm and Beyond. Boca Raton, FL: Chapman and Hall, 2002.

178 PART 2 Designing Operations

Supplement 6 Statistical Process Control 179

Natural variations Variability that affects every production process to some degree and is to be expected; also known as common cause.

Natural Variations Natural variations affect almost every production process and are to be expected. Natural variations are the many sources of variation that occur within a process that is in statistical control. Natural variations behave like a constant system of chance causes. Although individual values are all different, as a group they form a pattern that can be described as a distribution. When these distributions are normal, they are characterized by two parameters:

• Mean, (the measure of central tendency—in this case, the average value) • Standard deviation, (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.

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 variation. The second is, of course, to identify and eliminate assignable variations so that the processes 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 variation. 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 dis- tributions formed in (b) and (c) will fall in a predictable pattern (d) if only natural variation 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).

s

m

Assignable variation Variation in a production process that can be traced to specific causes.

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

180 PART 2 Designing Operations

Control Charts The process of building control charts is based on the concepts presented 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 resulting 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 established control limits, (b) in control but the process is not capable of producing within established limits, or (c) out of control. We now look at ways to build control charts that help the operations 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, or x-bar, and the range, R, are used to monitor processes that have continuous dimen- sions. The -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 materi- als. 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 slop- piness on the part of a machine operator. The two types of charts go hand in hand when monitor- ing variables because they measure the two critical parameters: central tendency and dispersion.

The Central Limit Theorem The theoretical foundation for -charts is the central limit theorem. This theorem states that regardless of the distribution of the population, the distribution of s (each of which is a mean of a sample drawn from the population) will tend to follow a normal curve as the number of sam- ples increases. Fortunately, even if the 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 s (called ) will equal the mean of the overall population (called ); and (2) the standard deviation of the sampling distribution, , will be the population standard deviation, divided by the square root of the sample size, n. In other words:

(S6-1)x = m

sx

mxx

x x

x x

-chart A quality control chart for variables that indicates when changes occur in the central tendency of a production process.

x

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.

LO1: Explain the purpose of a control chart

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

2The standard deviation is easily calculated as s = Q

a n

i= 1 1xi - x22

n - 1 .

Central limit theorem The theoretical foundation for

-charts, which states that regardless of the distribution of the population of all parts or services, the distribution of s tends to follow a normal curve as the number of samples increases.

x

x

2

Supplement 6 Statistical Process Control 181

and

(S6-2)

Figure S6.3 shows three possible population distributions, each with its own mean, and stan- dard deviation, If a series of random samples ( 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 will appear as they do in Figure S6.3.

Moreover, the sampling distribution, as is shown in Figure S6.4, will have less variability 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 if the process has only natural variations.

• 99.73% of the time, the sample averages will fall within if the process has only natural variations.

If a point on the control chart falls outside of the control limits, then we are 99.73% sure the process has changed. This is the theory behind control charts.

Setting Mean Chart Limits ( -Charts) If we know, through past data, the standard deviation of the process population, we can set upper and lower control limits by using these formulas:

(S6-3)

(S6-4)

where � 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%)

� standard deviation of the sample means = s>1nsx

x

Lower control limit 1LCL2 = x - zsx

Upper control limit 1UCL2 = x + zsx

s,

x

; 3sx

; 3sx

; 2sx

xis

x1, x2, x3, x4,s. m,

sx = s

1n

LO2: Explain the role of the central limit theorem in SPC

LO3: Build -charts and R-charts

x

95.45% fall within ±2σx

99.73% of all x ´s fall within ±3σx

(mean)

Beta

Normal

Uniform

Population distributions

Distribution of sample means

+2σx +3σx+1σx–1σx–2σx–3σx x

Mean of sample means = x

Standard deviation of the sample means =σx

σ √n

=

� 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 and standard deviation the distribution of sample means always approaches a normal distribution.

1s2, 1m2

s � population (process) standard deviation n � sample size

Example S1 shows how to set control limits for sample means using standard deviations.

182 PART 2 Designing Operations

EXAMPLE S1 � Setting control limits using samples

(mean)

x = μ

Sampling distribution of means

Process distribution of means

� FIGURE S6.4 The Sampling Distribution of Means Is Normal and Has Less Variability Than the Process Distribution In this figure, the process distribution from which the sample was drawn was also normal, but it could have been any distribution.

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 � Randomly select and weigh nine 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:

1n = 92

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

SOLUTION �

Also, the population standard deviation 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:

1s2

= 16.1 ounces.

The average weight in the first sample = 17 + 13 + 16 + 18 + 17 + 16 + 15 + 17 + 16

9

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 of the 12 samples is calculated to be exactly 16 ounces. We therefore have ounces, ounce, and The control limits are:

The 12 samples are then plotted on the following control chart:

LCLx = x - zsx = 16 - 3¢ 1 19 ≤ = 16 - 3¢1

3 ≤ = 15 ounces UCLx = x + zsx = 16 + 3¢ 119≤ = 16 + 3¢13≤ = 17 ounces

z = 3.n = 9,� = 1 x = 16

AUTHOR COMMENT 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, Pepsi, or any other brand.

Notice that the liquids are not the same measurement.

Supplement 6 Statistical Process Control 183

Because process standard deviations are either not available or difficult to compute, 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 is defined as the differ- ence 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:

(S6-5)

and:

(S6-6)

average range of the samples

value found in Table S6.1

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.

x = A2 =

where R = LCLx = x - A2R

UCLx = x + A2R

Sample Size, n Mean Factor, A2 Upper Range, D4 Lower Range, D3 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.

INSIGHT � 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 � If Oat Flakes’s population standard deviation is 2 (instead of 1), what is your conclusion? [Answer: the process would be in control.]

RELATED PROBLEMS � S6.1, S6.2, S6.4, S6.8, S6.10a,b

EXCEL OM Data File Ch06SExS1.xls can be found at www.pearsonhighered.com/heizer.

LCL = 14, UCL = 18; 1s2

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 causes16 = Mean

15 = LCL

� TABLE S6.1 Factors for Computing Control Chart Limits (3 sigma)

184 PART 2 Designing Operations

EXAMPLE S2 � Setting mean limits using table values

Super Cola bottles soft drinks labeled “net weight 12 ounces.” Indeed, an overall process average of 12 ounces has been found by taking many samples, in which each sample contained 5 bottles. The average range of the process is .25 ounce. The OM team wants to determine the upper and lower control limits for averages in this process.

APPROACH � Super Cola applies Equations (S6-5) and (S6-6) and uses the column of Table S6.1.

SOLUTION � Looking in Table S6.1 for a sample size of 5 in the mean factor column, we find the value .577. Thus, the upper and lower control chart limits are:

INSIGHT � 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 � If the sample size was and the average range ounces, what are the revised and ? [Answer: 12.146, 11.854.]

RELATED PROBLEMS � S6.3a, S6.5, S6.6, S6.7, S6.9, S6.10b,c,d S6.11, S6.34

EXCEL OM Data File Ch06SExS2.xls can be found at www.pearsonhighered.com/heizer.

LCLxUCLx

= .20n = 4

= 11.856 ounces = 12 - .144

LCLx = x - A2R

= 12.144 ounces

= 12 + .144

= 12 + 1.57721.252

UCLx = x + A2R

A2

A2

VIDEO S6.1 Farm to Fork: Quality

of Darden Restaurants

11.5 UCL=11.524

UCL=0.6943

R=0.2125

LCL=10.394

LCL=0

USL

LSL 10 USL 12

Specifications LSL

x=10.959

1 3 5 7 9 11 13 15 17

10.5

11.0

x Bar Chart

S a m

p le

M e a n

0.8

1 3 5

10 .2

10 .5

10 .8

11 .1

11 .4

11 .7

12 .0

7 9 11 13 15 17 0.0

0.4

Range Chart

Capability Histogram

S a m

p le

R a n g e

Mean = 10.959 Std.dev = 1.88 Cp = 1.77 Cpk = 1.7

Capability

AUTHOR COMMENT Here the restaurant chain

uses weight (11 oz) as a measure of SPC for

salmon filets.

AUTHOR COMMENT The range here is the

difference between the heaviest and the lightest

salmon filets weighed in each sample. A range chart shows

changes in dispersion.

Salmon filets are monitored by Darden Restaurant’s SPC software, which includes and R-charts and a process capability histogram. The video case study “Farm to Fork,” in the Lecture Guide & Activities Manual, asks you to interpret these figures.

x-

Supplement 6 Statistical Process Control 185

Setting Range Chart Limits (R-Charts) In Examples S1 and S2, we determined the upper and lower control limits for the process average. 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 estab- lished that contain standard deviations of the distribution for the average range We can use the following equations to set the upper and lower control limits for ranges:

(S6-7)

(S6-8)

where upper control chart limit for the range lower control chart limit for the range values from Table S6.1

Example S3 shows how to set control limits for sample ranges using Table S6.1 and the average range.

D4 and D3 = LCLR = UCLR =

LCLR = D3R

UCLR = D4R

R.; 3

� EXAMPLE S3 Setting range limits using table values

The average range of a product at Clinton Manufacturing is 5.3 pounds. With a sample size of 5, owner Roy Clinton wants to determine the upper and lower control chart limits.

APPROACH � Looking in Table S6.1 for a sample size of 5, he finds that and

SOLUTION � The range control limits are:

INSIGHT � Computing ranges with Table S6.1 is straightforward and an easy way to evaluate dispersion.

LEARNING EXERCISE � Clinton decides to increase the sample size to What are the new and values? [Answer: 10.197, 0.403]

RELATED PROBLEMS � S6.3b, S6.5, S6.6, S6.7, S6.9, S6.10c, S6.11, S6.12, S6.34

LCLRUCLR

n = 7.

LCLR = D3R = 10215.3 pounds2 = 0

UCLR = D4R = 12.115215.3 pounds2 = 11.2 pounds

D3 = 0.D4 = 2.115

Using Mean and Range Charts The normal distribution is defined by two parameters, the mean and standard deviation. The (mean)-chart and the R-chart mimic these two parameters. The -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 -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 -chart in Figure S6.5(b) detect no shift (because none is present), but the R-chart does detect the shift in the dispersion. Both charts are required to track the process accurately.

Steps to Follow When Using Control Charts There are five steps that are generally followed in using and R-charts:

1. Collect 20 to 25 samples, often of or observations each, from a stable process and compute the mean and range of each.

2. Compute the overall means ( and ), set appropriate control limits, usually at the 99.73% level, and calculate the preliminary upper and lower control limits. Refer to

Rx

n = 5n = 4

x-

x

x

x x

LO4: List the five steps involved in building control charts

186 PART 2 Designing Operations

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 does not detect change in mean.)

(x-chart does not detect the increase in dispersion.)

(R-chart detects increase in dispersion.)

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

Table S6.2 for other control limits. If the process is not currently stable and in control, use the desired mean, instead of to calculate limits.

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.

Control Charts for Attributes Control charts for 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 calcu- late p-chart limits when sample sizes are large. The procedure resembles the -chart approach, which is also based on the central limit theorem.

The formulas for p-chart upper and lower control limits follow:

(S6-9)

(S6-10)LCLp = p - zspN

UCLp = p + zspN

x

x

xm,

LO5: Build p-charts and c-charts

p-chart A quality control chart that is used to control attributes.

AUTHOR COMMENT Mean ( ) 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.

x

� 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

Supplement 6 Statistical Process Control 187

VIDEO S6.2 Frito-Lay’s Quality-Controlled

Potato Chips

where = mean fraction defective in the samples z = number of standard deviations ( for 95.45% limits; for 99.73% limits) = standard deviation of the sampling distribution

is estimated by the formula:

(S6-11)

where = number of observations in each samples

Example S4 shows how to set control limits for p-charts for these standard deviations.

n

spN = A p11 - p2 n

spN

spN

z = 3z = 2 p

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 � Samples of the work of 20 clerks are gathered (and shown in the table). Mosier carefully 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.

� EXAMPLE S4 Setting control limits for percent defective

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

SOLUTION �

(Note: 100 is the size of each sample n.)=