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Operations Management An Integrated Approach

5th EDIT ION

R. Dan Reid Nada R. Sanders

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Library of Congress Cataloging-in-Publication Data

Reid, R. Dan (Robert Dan), 1949- Operations management [electronic resource] / R. Dan Reid, Nada R.

Sanders.—5th ed. 1 online resource.

Includes bibliographical references and index. Description based on print version record and CIP data provided by publisher;

resource not viewed. ISBN 978-1-118-47589-8 (pdf)—ISBN 978-1-118-12267-9 (cloth : alk. paper)

(print) 1. Production management. I. Sanders, Nada R. II. Title. TS155 658.5—dc23 2012030805

ISBN-13 978-1-118-12267-9 (Main book) ISBN-10 1-118-12267-4 ISBN-13 978-1-118-34851-2 (Binder Ready Version)

Printed in the United States of America

10 9 8 7 6 5 4 3 2 1

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iii

P R E F A C E

Today, companies are competing in a very different environment than they were only a few years ago. Rapid changes such as global competition, e-business, the Internet, and advances in technology have required businesses to adapt their standard practices. Operations management (OM) is the critical function through which companies can succeed in this competitive landscape.

Operations management concepts are not confined to one department. Rather, they are far-reaching, affecting every functional aspect of the organization. Whether studying accounting, finance, human resources, information technology, manage- ment, marketing, or purchasing, students need to understand the critical impact operations management has on any business.

We each have more than 25 years of teaching experience and understand the chal- lenges inherent in teaching and taking the introductory OM course. The vast majority of students taking this course are not majoring in operations management. Rather, classes are typically composed of students from various business disciplines or students who are undecided about their major and have little knowledge of operations management. The challenge is not only to teach the foundation of the field, but also to help students understand the impact operations management has on the business as a whole and the close relationship of operations management with other business functions.

We were motivated to write this book to help students understand operations management and to make it easier for faculty to teach the introductory operations management course. We continue to have three major goals for this book.

GOALS OF THE BOOK 1. Provide a Solid Foundation of Operations Management Our book provides a solid foundation of OM concepts and techniques, but also covers the latest on emerging topics such as e-business, supply chain management, enterprise resource planning (ERP), and information technology. We give equal time to strategic and tactical decisions and provide coverage of both service and manu- facturing organizations. We look closely at some of the unique challenges faced by service operations.

2. Provide an Integrated Approach to Operations Management While several excellent textbooks provide appropriate foundation coverage, we believe that few provide sufficient motivation for students. We are aware that a major teaching challenge in OM is that students aren’t motivated to study OM because they don’t understand its relevance to their majors. We think the course textbook can greatly support the professor in this area; therefore, a chief goal of this book is to integrate coverage of why and how OM is integral to all organiza- tions. Interfunctional coordination and decision making have become the norm in today’s business environment. Throughout each chapter we discuss information flow between business functions and the role of each function in the organization. On the opening page of each chapter we ask the reader “What’s in OM for Me?” and the chapter ends with a section called “OM across the Organization” to review the answers to that question.

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iv • PREFACE

The text also illustrates the linkages and integration between the various OM topics. Our end-of-chapter feature entitled “Within OM: How It All Fits Together” describes how the chapter topic is related to other OM decisions. It addresses the issue that OM topics are linked and interdependent, not independent of one another.

As supply chain management (SCM) has taken on an increasingly important role, we have added an end-of-chapter section titled “The Supply Chain Link” which explains the relationships between the specific chapter topic covered and supply chain management.

3. Help Students to Understand the Concepts This course remains challenging for students to take and professors to teach. Students often have no prior exposure to operations concepts and little real business experi- ence. They have a broad spectrum of quantitative sophistication and often find the math in the course extremely challenging. Therefore, a chief goal of the text and sup- plement package is to help students with these concepts. We begin each chapter with an example from everyday life, often a consumer or personal example, to help students intuitively understand what the chapter will be about. Then we explain each concept clearly and carefully, with enough depth for non-majors. Sustainability in operations is highlighted at the end of each chapter.

The new edition is focused on helping students by offering problem-solving hints and tips as part of the solution to most examples and solved problems throughout the entire text. Two unique supplements support student comprehension. A “Quantitative Survival Guide,” available as an optional supplement packaged with the text, provides “help with the math” for all chapters. WileyPLUS (available on-line via a password in an optional package with the book) provides plenty of homework practice, feed- back for students, an e-book, and much more. In addition, algorithmic homework problems have been designed for each chapter in order to provide unlimited practice opportunity.

ORGANIZATION AND CONTENT OF THE BOOK We have arranged the topics in the book in progressive order from strategic to tactical. Early in the book we cover operations topics that require a strategic perspective and a cultural change within the organization, such as supply chain management, total quality management, and just-in-time systems. Progressively we move to more tactical issues, such as work management, inventory management, and scheduling concerns. We recognize that most faculty will select the chapters relevant to their needs. To make it easier for students and faculty, each chapter can stand alone. Any specific knowledge needed for a chapter is summarized at the beginning of each chapter, with specific topic and page references for easy review.

Balanced Coverage of Quantitative and Qualitative Topics We have tried to find a balance between the quantitative and qualitative treatment and coverage of OM topics. To meet students’ needs, this text presents the applica- tion of OM concepts through the extensive use of practical and relevant business examples. We eliminated from the printed book coverage of topics less frequently covered at the introductory level. However, complete supplementary chapters on spreadsheet modeling, optimization, master production scheduling, rough-cut capac- ity planning, and waiting-line models are available on the book’s Web site (www. wiley.com/college/reid).

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PREFACE • v

Integrated Technology Perspective Advances in e-commerce and the Internet are transforming the business environ- ment, and we integrate these concepts in every chapter. We discuss a range of topics from enterprise resource planning (ERP) and electronic data interchange (EDI) to quality issues of buying goods on-line.

Changes to this Edition We made a number of changes to this edition in order to make the text as cur- rent, user-friendly, and relevant as possible. We updated all the chapters in order to incorporate the latest available information, increased the emphasis on service operations, increased emphasis on e-business and information technology, updated business examples, expanded the number of cases, and increased the number of problem-solving hints. As in the previous editions, we continue to emphasize inter- functional coordination and decision making, and have added or updated a number of features.

Before You Begin. In order to help students when solving quantitative problems, we have added a feature called Before You Begin, placed immediately prior to the solution of most in-chapter example problems and end-of-chapter solved problems. Emphasizing our focus on strong pedagogy, this feature provides problem-solving tips and hints that the student should consider before proceeding to solve the problem.

Supply Chain Link. To emphasize the increasingly important role of supply chain management, we have added a new section on supply chain management and expanded coverage of supply chain and services in every chapter.

Sustainability Link. In order to address the latest challenges facing business, we have added a new feature to each chapter called The Sustainability Link. This feature discusses how the subject of the chapter directly ties to today’s sustainability concerns and challenges, providing specific business examples that illustrate the issues.

Problem Solving. While our goal is to provide balanced coverage of quantitative and qualitative topics, the new edition further emphasizes and integrates problem solving to help students experience the course more successfully. We provide algorithmic homework problems for every chapter of the text (via WileyPLUS) for unlimited practice opportunities, include problem-solving help in the book (Before You Begin) and on-line via WileyPLUS, and provide step-by-step solved problems in the book and on-line. We also provide “help with math” as needed via WileyPLUS. We believe that these changes to the new edition greatly enhance student learning.

FEATURES OF THE BOOK We have developed our pedagogical features to implement and reinforce the goals discussed previously and address the many challenges in this course.

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vi • PREFACE

Pedagogy that Provides an Integrated Approach Chapter Opening Vignettes and Within OM: How It All Fits Together To help students intuitively under- stand the topic, each chapter begins with a description of a personal prob- lem that can be solved using the con- cepts discussed in the chapter. Our objective is to attract the attention of students by starting with a personal example to which they can relate. We demonstrate that OM is not just about operating a plant or a busi- ness, but that it is relevant in every- thing that we do. An end-of-chapter section titled “Within OM: How It All Fits Together” describes how the chapter topic is related to other OM decisions. It emphasizes the point

that OM decisions are not made independent of one another, but that they are linked together and are dependent on one another.

Links to Practice Other OM texts have many boxes and sidebars, which make it diffi- cult for students to understand what they need to know. Furthermore, the many examples frequently interrupt the flow of the text and make a chap- ter difficult to read and assimilate. We recognize the importance of includ- ing “real-world” examples, but believe they should be integrated into the stream of the text instead of interrupt- ing the text. Therefore, we have devel- oped embedded boxes titled Links

to Practice, which provide brief examples from actual companies in every chapter. Embedded by both content and design into the general text discussion, each provides a concise and relevant example without interrupting the flow of the text.

Current textbooks typically do not use business examples to which students can relate. The typical examples provided are from large corporations such as General Motors, IBM, or Xerox. Primarily using these types of examples creates the impression for students that this is a field that is either beyond their reach or irrelevant to their needs. We have found that students understand the concepts better when these concepts are also presented in a context that is smaller in scale. The examples chosen range from large multinational organizations to small local businesses.

The Bama Companies is an inno- vator of wholesome bakery products and caters to the needs of many well- known restaurant chains. The company’s core product lines are hand-held pies, biscuits, buns, pie shells, and pizza crust. Bama supplies innovative culinary and product development services and cus- tom-made oven-ready products to cus- tomers in more than 20 countries. Bama is a past Malcolm Baldrige National

Quality Award (discussed in Chapter 5) winner. The key to Bama’s success has been focus- ing on quality relationships and products.

The old adage, treat others how you want to be treated, is the basis for the rela- tionships Bama builds with its suppliers. Bama reduced the number of suppliers used and focused on developing long-term relationships. Now the company only deals with 50–60 key suppliers and most of these have been partners with Bama for at least 15 years. The company prefers to deal with a single supplier for an ingredient rather than multiple sources, thereby giving as much business as possible to that single supplier. This creates supplier loyalty and a commitment to the success of Bama.

LINKS TO PRACTICE The Bama Companies

www.bama.com

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Proper layout planning cuts costs by eliminating unnecessary steps and increasing efficiency. However, a good layout plan can do much more for a company by improving worker attitude and creat- ing a positive organizational climate. Consider the SAS Institute, a software company known for having its facilities arranged for comfort and enjoyment of their employees. The company has on-site child care facilities, a cafeteria with a pianist, a gym with a swimming pool, horseback riding, and a health clinic. The facility layout was designed to be aesthetically pleasing to the employees. The consequences have been high pro- ductivity and very low turnover. For this reason, SAS is regularly on Fortune maga- zine’s list of top companies to work for.

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PREFACE • vii

OM across the Organization and Cross-Functional Icons Unique to this book is an end-of-chap- ter summary titled “OM across the Organization” that highlights the rela- tionship between OM and key business functions, such as accounting, finance, human resources, information tech- nology, management, marketing, and purchasing. This section is designed to help students understand the close relationship of operations manage- ment with other business functions and appreciate the critical impact OM has on other business functions. In addition, a cross-functional icon is used throughout the text to highlight sections in the text where the relationships between OM and other key business functions are discussed.

Interactive Cases There are two Web-based cases for this edi- tion. The first case features an Internet site for a simulated cruise company that has hired a student intern to help solve operations prob- lems. The second case features an Internet site for a simulated consulting company that works in the medical industry that has hired a student to help solve operations problems. In both cases, the students are given assign- ments that require them to use information provided at the book Web site to develop solutions. These exercises offer students hands-on experience in the areas of supply chain management, statistical quality control, forecasting, just-in-time, aggregate planning, inventory management, scheduling, and project manage- ment, and help tie all the topics in the book together in a service environment.

Cases Each chapter ends with four cases that reinforce the issues and topics discussed in the chapter. The first two cases are within the text, while the other two are online cases. The cases can provide the basis for group discussion or can be assigned as individual exer- cises for students. Many cases conclude with a list of questions for students to answer.

In addition, each chapter offers a unique interactive learning exercise titled Internet Challenge where students are provided with a short case and given specific Internet assignments.

Pedagogy to Help Students Master the Course Learning Objectives At the beginning of each chapter, students are provided with a short statement of what they need to either know or review from previous chapters, referring students to specific topic and page information. This enables students to review previous material necessary to understand the topic being covered.

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Introduction to Cruise International, Inc. You will be an intern for Cruise International, Inc. (CII). The company competes in the cruise industry in the small-ship, medium-ship, and large-ship markets. Your internship begins in a few weeks. Bob Bristol, your immediate boss, has asked you to become familiar with the cruise industry and its basic markets prior to beginning any of your assignments. This assignment will enhance your knowledge of the material in Chapter 1 of your textbook while preparing your future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments

OM ACROSS THE ORGANIZATION Now that we know the role of the operations management function and the decisions that operations managers make, let’s look at the relationship between operations and other business functions. As mentioned previously, most businesses are supported by three main functions: operations, marketing, and finance. Although these functions involve different activities, they must interact to achieve the goals of the organization. They must also follow the strategic direction developed at the top level of the organiza- tion. Figure 1-10 shows the flow of information from the top to each business func- tion, as well as the flow between functions.

Many of the decisions made by operations managers are dependent on informa- tion from the other functions. At the same time, other functions cannot be carried out properly without information from operations. Figure 1-11 shows these relationships.

Marketing is not fully capable of meeting customer needs if marketing managers do not understand what operations can produce, what due dates it can and cannot meet, and what types of customization operations can deliver. The marketing depart- ment can develop an exciting marketing campaign, but if operations cannot produce the desired product, sales will not be made. In turn, operations managers need infor- mation about customer wants and expectations. It is up to them to design products with characteristics that customers find desirable, and they cannot do this without

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L E A R N I N G O B J E C T I V E S

After completing this chapter you should be able to

1 Defi ne operations management. 2 Explain the role of operations management in business. 3 Describe decisions that operations managers make. 4 Describe the diff erences between service and manufacturing operations. 5 Identify major historical developments in operations management. 6 Identify current trends in operations management. 7 Describe the fl ow of information between operations management and other business functions.

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viii • PREFACE

Before You Go On Sections strate- gically placed within every chapter summarize key material the student should know before continuing. Often the material in chapters can be over- whelming. We felt that breaking up the chapter with a brief summary of key material is highly beneficial in aiding learning and comprehension.

Key Terms and Definitions Key terms and concepts are highlighted in boldface when they are first explained in the text, are defined in the margin next to their discus- sion in the text, and are listed at the end of the chapter with page references.

Before You Begin Most example problems within the chapters, and end-of- chapter solved problems, have an added feature called Before You Begin. The feature provides the student with problem-solving tips and hints they need to consider before solving the problem. The purpose is to help students with their problem- solving ability.

Solved Problems Numerous solved prob- lems are provided, complete with step- by-step explanations to ensure students understand the process and why the prob- lem is solved in a particular way. Where appropriate, we provide a series of steps for problem solving and offer Problem-Solving Tips.

On-line Resources Using the Text and the Web Site We have created a number of interactive learning activities for students, which will help them learn the material in a dynamic and interesting way. At the end of each chapter, there is a list of activities avail- able on the Web site. Students can work on these activities on their own, and instruc- tors have the flexibility to assign material for individual or group study. The activities include the Interactive Case, interactive simulations, interactive spreadsheets, com- pany tours, Internet Challenge, and addi- tional Web resources.

Before You Go On

You should understand that operations management (OM) is the business function responsible for planning, coor- dinating, and controlling the resources needed to produce a company’s goods and services. OM is directly respon- sible for managing the transformation of a company’s inputs (e.g., materials, technology, and information) into finished products and services. OM requires a wide range of strategic and tactical decisions. Strategic decisions are long-range and very broad in scope (e.g., unique features of the company’s product and process). They determine the direction of tactical decisions, which are more short term and narrow in scope (e.g., policy for ordering raw materials). All organizations can be separated into manufacturing and service operations, which differ based on product tangibility and degree of customer contact. Service and manufacturing organizations have very different operational requirements.

Solved Problems • Problem 1 Bluegill Furniture is a small furniture shop that focuses on mak- ing kitchen chairs. The weekly dollar value of its output, includ- ing finished goods and work in progress, is $14,280. The value of inputs, such as labor, materials, and capital, is approximately $16,528. Compute the total productivity measure for Bluegill Furniture.

• Before You Begin: In this problem you are being asked for the total productivity. Recall that it is simply the ratio of total output over input.

• Solution

Total productivity 5 output input

5 $14,280 $16,528

5 0.864

(See student companion site for Excel template.)

• Problem 2 Bluegill has just purchased a new sanding machine that pro- cesses 17 chairs in 8 hours. What is the productivity of the sand- ing machine?

• Before You Begin: In this problem you are being asked for machine productivity, which is a partial productivity measure.

• Solution

Machine productivity 5 number of chairs processing time

5 17 chairs 8 hours

5 2.125 chairs/hour

Online Resources Companion Website www.wiley.com/college/reid

Sensenich Propeller Manufacturing Company • Find links for Additional Web Resources for this chapter

Th e Association for Manufacturing Excellence, www.ame.org

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PREFACE • ix

Our supporting material has been designed to make learning OM easier for students and teaching OM easier for faculty.

Book Companion Site www.wiley.com/college/reid An extensive Web site has been developed in support of Operations Management. The site is available at www.wiley.com/college/reid, and offers a range of information for instructors and students. The Web site includes the following resources:

• Virtual Company Cases: Interactive cases for hands-on experience. • Company Tours: Web links to the plant tours of various companies are provided,

along with a brief description of the tour and discussion questions for students to consider aft er viewing the tour.

• Web Links: Direct links to related Web sites are provided.

For Instructors • Instructor’s Manual: Includes a suggested course outline, teaching tips and strat-

egies, war stories, answers to all end-of-chapter material, brief description of the additional resources referenced in the Interactive Learning box, additional in- class exercises, and tips on integrating the theory of constraints.

• Solutions Manual: A complete set of detailed solutions is provided for all problems. • Virtual Company Cases Instructor’s Materials: Include accompanying Instruc-

tor’s Manual with answers to exercises and Excel solutions. • Test Bank: A comprehensive Test Bank comprised of approximately 1700 ques-

tions that consist of multiple choice, true-false, essay questions, and open-ended problems for each chapter. Th e Test Bank is also available in a computerized ver- sion that allows instructors to customize their exams.

• PowerPoint Lecture Slides: PowerPoint Slides are available for use in class. Full- color slides highlight key fi gures from the text as well as many additional lecture outlines, concepts, and diagrams. Together, these provide a versatile opportunity to add high-quality visual support to lectures.

• Operations Management Video Series: Th e comprehensive video package, includ- ing clips from the Baldridge Video Series and Wiley’s own Student OM Videos, off ers video selections that tie directly to the theme of operations management and bring to life many of the examples used in the text. Videos can be viewed on the Companion Site or within WileyPLUS.

For Students • Supplemental Chapters: Th e supplement chapters include Supplement A:

Spreadsheet Modeling: An Introduction; Supplement B: Introduction to Optimi- zation; Supplement B; Waiting Line Models; Supplement D: Master Scheduling and Rough-cut Capacity Planning.

• Excel Spreadsheets: Templates are provided so that students can model and solve problems presented in the textbook. A spreadsheet icon appears next to those examples and problems in the textbook that have an accompanying Excel template available on the student Web site. Step-by-step directions are provided. Directions prompt students as they work through each spreadsheet. Expected outcomes and questions are also given.

TEACHING AND LEARNING RESOURCES

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x • PREFACE

• Quantitative Survival Guide. (978-0-471-67877-9) Th is provides chapter-by-chapter review of the math necessary to understand and solve the problems in each chapter and includes extra practice problems. Th is resource is also available within WileyPLUS.

WileyPLUS WileyPLUS is an innovative, research-based online environment for effective teaching and learning.

WileyPLUS builds students’ confidence because it takes the guesswork out of studying by providing students with a clear roadmap: what to do, how to do it, if they did it right. This interactive approach focuses on:

CONFIDENCE Research shows that students experience a great deal of anxiety over studying. That’s why we provide a structured learning environment that helps stu- dents focus on what to do, along with the support of immediate resources.

MOTIVATION To increase and sustain motivation throughout the semester, Wiley- PLUS helps students learn how to do it at a pace that’s right for them. Our integrated resources—available 24/7—function like a personal tutor, directly addressing each student’s demonstrated needs with specific problem-solving techniques.

SUCCESS WileyPLUS helps to assure that each study session has a positive outcome by putting students in control. Through instant feedback and study objective reports, students know if they did it right, and where to focus next, so they achieve the stron- gest results.

With WileyPLUS, our efficacy research shows that students improve their outcomes by as much as one letter grade. WileyPLUS helps students take more initiative, so you’ll have greater impact on their achievement in the classroom and beyond.

What do students receive with WileyPLUS?

• Th e complete digital textbook, saving students up to 60% off the cost of a printed text.

• Question assistance, including links to relevant sections in the online digital textbook.

• Immediate feedback and proof of progress, 24/7. • Integrated, multi-media resources—including virtual cases, videos, demonstra-

tion problems the Quantitative Survival Guide, and much more—that provide multiple study paths and encourage more active learning.

What do instructors receive with WileyPLUS?

• Reliable resources that reinforce course goals inside and outside of the classroom. • Th e ability to easily identify those students who are failing behind. • Media-rich course materials and assessment content including—Instructor’s

Manual, Test Bank, PowerPoint®Slides, Solutions Manual, Computerized Test Bank, Lecture Quizzes, Decision Sciences Weekly Updates, and much more.

www.wileyplus.com. Learn More.

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PREFACE • xi

ACKNOWLEDGMENTS The development of this fifth edition of Operations Management benefited greatly from the comments and suggestions of colleagues who teach operations management courses. We would like to acknowledge the contributions made by the following individuals:

Charles Foley, Columbus State Community College Nicholas C. Georgantzas, Professor, Management Systems and Director, System Dynamics Consultancy, Fordham University Business Schools Gregory A. Graman, Ph.D., Associate Professor of Operations and Supply Chain Management, Michigan Technological University Roger Dean lles, Instructor, Department of Marketing and Supply Chain Management, Fogelman

College of Business and Economics, The University of Memphis Tony R. Johns, Associate Professor, Administrative Science, Clarion University of Pennsylvania Anita Lee-Post, Associate Professor, Decision Science and Information Systems Area, Gatton College of Business and Economics, University of Kentucky Douglas Schneiderheinze, Instructor, Economics, Lewis and Clark Community College

Reviewers of the third edition include:

Dennis Agboh, Morgan State University; Karen Eboch, Bowling Green State University; Greg Graman, Michigan Tech University; GG Hegde University of Pittsburgh; Seung-Lae Kim, Drexel University;

John Kros, East Carolina University; Anita Lee-Post, University of Kentucky; David, Little High Point University; Robert Vokurka, Texas A&M University; John Wang, Montclair State University.

Reviewers of the fourth edition include:

Yossi Aviv, Washington University in St. Louis; Kevin Caskey, The State University of New York at New Paltz; Scott T. Crino, United States Military Academy; Phillip C. Fry, Boise State University; Thomas F. Gattiker, Boise State University; Christian Grandzol, Bloomsburg University; Samuel Hazen, Tarleton State University; James He, Fairfield University; John Jensen, University of Southern Maine; Mark Kesh, University of Texas

at El Paso; Anita Lee-Post, University of Kentucky; Winston T. Lin, The State University of New York at Buffalo; Jaideep Motwani, Grand Valley State University; Fariborz Y. Partovi, Drexel University; Tamara Reid, Seattle University; Dmitriy Shaltayev, Christopher Newport University; Marilyn Smith, Winthrop University; Robert J. Vokurka, Texas A&M University–Corpus Christi; Pamaela J. Zelbst, Sam Houston State University.

Ajay Aggarwal, Millsaps College; Nezih Altay, University of Richmond; Suad Alway, Chicago State University; Robert Amundsen, New York Institute of Technology; Gordon Bagot, California State University, Los Angeles; Cliff Barber, California Polytechnic State University, San Luis Obispo; Hooshang Beheshti, Radford University; Prashanth Bharadwaj, Indiana University of Pennsylvania; Joe Biggs, California Polytechnic State University; Debra

Bishop, Drake University; Vincent Calluzzo, Iona College; James Campbell, University of Missouri– St. Louis; Kevin Caskey, SUNY New Paltz; Sohail Chaudhry, Villanova University; Chin-Sheng Chen, Florida International University; Kathy Dhanda, University of Portland; Barb Downey, University of Missouri–Columbia; Joe Felan, University of Arkansas at Little Rock; Wade Ferguson, Western Kentucky University; Teresa Friel, Butler University;

Reviewers of the second edition include:

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xii • PREFACE

The first edition was reviewed by:

David Alexander, Angelo State University; Stephen L. Allen, Truman State University; Jerry Allison, University of Central Oklahoma; Suad Alwan, Chicago State University; Tony Arreola-Risa, Texas A&M University; Gordon F. Bagot, California State University–Los Angeles; Brent Bandy, University of Wisconsin–Oshkosh; Joseph R. Biggs, California Polytechnic State University at San Luis Obispo; Jean-Marie Bourjolly, Concordia University; Ken Boyer, DePaul University; Karen L. Brown, Southwest Missouri State University; Linda D. Brown, Middle Tennessee State University; James F. Campbell, University of Missouri–St. Louis; Cem Canel, University of North Carolina at Wilmington; Chin- Sheng Chen, Florida International University; Louis Chin, Bentley College; Sidhartha R. Das, George Mason University; Greg Dobson, University of Rochester; Ceasar Douglas, Grand Valley State University; Shad Dowlatshahi, University of Missouri–Kansas City; L. Paul Dreyfus, Athens State University; Lisa Ferguson, Hofstra University; Mark Gershon, Temple University; William Giauque, Brigham Young University; Greg Graman, Wright State University; Jatinder N.D. Gupta, Ball State University; Peter Haug, Western Washington University; Daniel Heiser, DePaul University; Ted Helmer, F. Theodore Helmer and Associates, Inc.; Lew Hofmann, The College of New Jersey; Lisa Houts,

California State University–Fresno; Tim C. Ireland, Oklahoma State University; Peter T. Ittig, University of Massachusetts–Boston; Jayanth Jayaram, University of Oregon; Robert E. Johnson, University of Connecticut; Mehdi Kaighobadi, Florida Atlantic University; Yunus Kathawala, Eastern Illinois University; Basheer Khumawala, University of Houston; Thomas A. Kratzer, Malone College; Ashok Kumar, Grand Valley State University; Cynthia Lawless, Baylor University; Raymond P. Lutz, University of Texas at Dallas; Satish Mehra, University of Memphis; Brad C. Meyer, Drake University; Abdel-Aziz M. Mohamed, California State University–Northridge; Charles L. Munson, Washington State University; Kenneth E. Murphy, Florida International University; Jay Nathan, St. Johns University; Harvey N. Nye, University of Central Oklahoma; Susan E. Pariseau, Merrimack College; Carl J. Poch, Northern Illinois University; Claudia H. Pragman, Minnesota State University; Willard Price, University of the Pacific; Feraidoon Raafat, San Diego State University; William D. Raffield, University of St. Thomas; Ranga Ramasesh, Texas Christian University; Paul H. Randolph, Texas Tech University; Robert M. Saltzman, San Francisco State University; George O. Schneller IV, Baruch College–City University of New York; A. Kimbrough Sherman, Loyola College in Maryland; William R. Sherrard, San Diego State

Daniel Heiser, DePaul University; Lewis Hofmann, The College of New Jersey; Lisa Houts, California State University, Fullerton; Tony Inman, Louisiana Tech University; Richard Insinga, SUNY Oneonta; Tim Ireland, Oklahoma State University; Mehdi Kaighobadi, Florida Atlantic University; Hale Kaynak, The University of Texas–Pan American; William Coty Keller, St. Josephs College; Robert Kenmore, Keller Graduate School of Management; Jennifer Kohn, Montclair State University; Dennis Krumwiede, Idaho State University; Kevin Lewis, University of Wyoming; Ardeshir Lohrasbi, University of Illinois at Springfield; Chris McDermott, Rensselaer Polytechnic Institute; John Miller, Mercer University; Ajay Mishra, SUNY Binghamton; Ken Murphy, Florida International University; Abraham Nahm, University of Wisconsin– Eau Claire; Len Nass, New Jersey City University; Joao Neves, The College of New Jersey; Susan Norman, Northern Arizona University; Muhammad Obeidat, Southern Polytechnic State University; Barbara Osyk,

The University of Akron; Taeho Park, San Jose State University; Eddy Patuwo, Kent State University; Carl Poch, Northern Illinois University; Leonard Presby, William Paterson University; Will Price, University of the Pacific; Randy Rosenberger, Juniata College; George Schneller, Baruch College-CUNY; Kaushik Sengupta, Hofstra University; LW Schell, Nicholls States University; William Sherrard, San Diego State University; Samia Siha, Kennesaw State University; Susan Slotnick, Cleveland State University; Ramesh Soni, Indiana University of Pennsylvania; Ted Stafford, University of Alabama in Huntsville; Peter Sutanto, Prairie View A&M University; Fataneh Taghaboni- Dutta, University of Michigan-Flint; Nabil Tamimi, University of Scranton; John Visich, Bryant College; Tom Wilder, California State University, Chico; Peter Zhang, Georgia State University; Faye X. Zhu, Rowan University.

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PREFACE • xiii

University; Chwen Sheu, Kansas State University; Sue Perrott Siferd, Arizona State University; Samia M. Siha, Kennesaw State University; Natalie Simpson, State University of New York–Buffalo; Barbara Smith; Niagara College; Victor E. Sower, Sam Houston State University; Linda L. Stanley, Our Lady of the Lake University; Donna H. Stewart, University of Wisconsin–Stout; Manouchehr Tabatabaei, University of Tampa; Nabil Tamimi, University of Scranton; Larry Taube, University of North Carolina–Greensboro; Giri K. Tayi, State University of New York at Albany;

Charles J. Teplitz, University of San Diego; Timothy L. Urban, The University of Tulsa; Michael L. Vineyard, Memphis State University; John Visich, University of Houston; Robert Vokurka, Texas A&M University; George Walker, Sam Houston State University; John Wang, Montclair State University; Theresa Wells, University of Wisconsin–Eau Claire; T.J. Wharton, Oakland University; Barbara Withers, University of San Diego; Steven A. Yourstone, University of New Mexico.

SPECIAL THANKS We would also like to personally thank and acknowledge the work of our supple- ments authors, who worked diligently to create a variety of support materials for both instructors and students.

We would also like to express our appreciation to Mark Sullivan, AIA, NCARB, of Mark Sullivan Architects, and Susan O’Hara, RN, MPH, of O’Hara HealthCare Consultants, who generously contributed a simulation showing the before and after designs of an ambulatory surgery unit. A working example of the Extend simulation they used to optimize the design of the renovated facility is available on the Web site. For more information on their work, please visit www.marksullivanarchitects.com or www.oharahealthcare.com.

We would like to offer special acknowledgment to the publishing team at Wiley for their creativity, talent, and hard work. Their great personalities and team spirit have made working on the book a pleasure. Special thanks go to Lisé Johnson, Executive Editor; Jennifer Manias, Content Editor; Kelly Simmons, Marketing Manager; and, Valerie Vargas, Senior Production Editor, for all their efforts. We could not have done it without you.

Other Wiley staff who contributed to the text and media include: Elena Santa Maria, Senior Media Specialist; Allison Morris, Senior Product Designer; Madeline Lesure, Senior Designer; Sheena Goldstein, Photo Editor; and, Melissa Solarz, Edi- torial Assistant. We would also like to thank David Krahl of Imagine That, Inc. for developing the Extend simulations.

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xiv • RUNNING HEAD

xiv

A B O U T T H E A U T H O R S

R. Dan Reid is Associate Pro- fessor Emeritus of Operations Management at the Whittemore School of Business and Eco- nomics at the University of New Hampshire. He holds a Ph.D. in Operations Management from The Ohio State University, an M.B.A. from Angelo State Uni- versity, and a B.A. in Business Management from the Univer- sity of Maryland. During the past twenty years, he has taught at The Ohio State Uni- versity, Ohio University, Bowling Green State Uni- versity, Otterbein College, and the University of New Hampshire.

Dr. Reid’s research publications have appeared in numerous journals such as the Production and Inven- tory Management Journal, Mid-American Journal of Business, Cornell Hotel and Restaurant Administra- tion Quarterly, Hospitality Research and Education Journal, Target, and the OM Review. His research interests include manufacturing planning and control systems, quality in services, purchasing, and supply chain management. He has worked for, or consulted with, organizations in the telecommunications, con- sumer electronics, defense, hospitality, and capital equipment industries. Dr. Reid has served as Program Chair and President of the Northeast Region of the Decision Sciences Institute (NEDSI) and as Associate Program Chair and Proceedings Editor of the First International DSI Conference, and held numerous positions within DSI. He has been the Program Chair and Chair of the Operations Management Division of the Academy of Management. Dr. Reid has also served as President of the Granite State Chapter of the American Production and Inventory Control Society. He has been a board member of the Operations Man- agement Association and the Manchester Manufac- turing Management Center. Dr. Reid is a past Editor of the OM Review.

Dr. Reid has designed and taught courses for under- graduates, graduates, and executives on topics such as resource management, manufacturing management, introduction to operations management, purchasing management, and manufacturing planning and control systems. In 2002 Dr. Reid received a University of New Hampshire Excellence in Teaching Award.

Nada R. Sanders is Professor and Iacocca Chair at Lehigh University. She holds a Ph.D. in Operations Management from The Ohio State University, an M.B.A. from The Ohio State University, and a B.S. degree in Mechanical Engineering from Franklin University. She has taught for more than twenty years at a variety of academic institutions including The Ohio State Univer- sity, Capital University, Wright

State University, and Texas Christian University, in addi- tion to lecturing to various industry groups. She has designed and taught classes for undergraduates, gradu- ates, and executives on topics such as operations manage- ment, operations strategy, forecasting, and supply chain management. She has received a number of teaching awards and is a Fellow of the Decision Sciences Institute.

Dr. Sanders has extensive research experience and has published in numerous journals such as Decisions Sciences, Journal of Operations Management, Sloan Management Review, Omega, Interfaces, Journal of Behavioral Decision Making, Journal of Applied Busi- ness Research, and Production & Inventory Manage- ment Journal. She has authored chapters in books and encyclopedias such as the Forecasting Principles Hand- book (Kluwer Academic Publishers), Encyclopedia of Production and Manufacturing Management (Kluwer Academic Publishers), and the Encyclopedia of Electri- cal and Electronics Engineering (John Wiley & Sons). Dr. Sanders has served as Vice President of Decision Sciences Institute (DSI), President of the Midwest Decision Sciences Institute, and has held numerous other positions within the Institute. In addition to DSI, Dr. Sanders is active in the Production Operations Management Society (POMS), APICS, INFORMS, Council of Supply Chain Management Professions (CSCMP), and the International Institute of Forecast- ers IIF). She has served on review boards and/or as a reviewer for numerous journals including Decision Sci- ences, Journal of Business Logistics, Production Opera- tions Management, International Journal of Production Research, Omega, and others. In addition, Dr. Sanders has worked and/or consulted for companies in the tele- communications, pharmaceutical, steel, automotive, warehousing, retail, and publishing industries, and is frequently called upon to serve as an expert witness.

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C H A P T E R 1 Introduction to Operations Management 1 WHAT IS OPERATIONS MANAGEMENT? 2

Links to Practice: The E-tailers 4 DIFFERENCES BETWEEN MANUFACTURING AND SERVICE

ORGANIZATIONS 5 Links to Practice: U.S. Postal Service 6

OPERATIONS MANAGEMENT DECISIONS 7 Links to Practice: Texas Instruments Incorporated 9

PLAN OF THIS BOOK 10 HISTORICAL DEVELOPMENT 11

Why OM? 11 Historical Milestones 11 Th e Industrial Revolution 12 Scientifi c Management 13 Th e Human Relations Movement 14 Management Science 14 Th e Computer Age 15 Just-in-Time 15 Total Quality Management 15 Business Process Reengineering 16 Flexibility 16 Time-Based Competition 16 Supply Chain Management 16 Links to Practice: The Publishing Industry 17 Global Marketplace 18 Sustainability and Green Operations 18 Electronic Commerce 18 Outsourcing and Flattening of the World 19

TODAY’S OM ENVIRONMENT 20 OPERATIONS MANAGEMENT IN PRACTICE 21 WITHIN OM: HOW IT ALL FITS TOGETHER 21 OM ACROSS THE ORGANIZATION 22 THE SUPPLY CHAIN LINK 24 THE SUSTAINABILITY LINK 24 Chapter Highlights 25 Key Terms 25 Discussion Questions 25 CASE: HIGHTONE ELECTRONICS, INC. 26 CASE: CREATURE CARE ANIMAL CLINIC (A) 26 INTERACTIVE CASE: VIRTUAL COMPANY 27 INTERNET CHALLENGE 28 ONLINE RESOURCES 28 Selected Bibliography 28

C O N T E N T S

C H A P T E R 2 Operations Strategy and Competitiveness 29 THE ROLE OF OPERATIONS STRATEGY 31

Th e Importance of Operations Strategy 31 DEVELOPING A BUSINESS STRATEGY 32

Mission 32 Environmental Scanning 33 Core Competencies 35 Putting It Together 36 Links to Practice: Dell Computer Corporation 36

DEVELOPING AN OPERATIONS STRATEGY 37 Competitive Priorities 37 Links to Practice: Southwest Airlines Company 38 Links to Practice: FedEx Corporation 40 Th e Need for Trade-Off s 40 Order Winners and Qualifi ers 41 Translating Competitive Priorities into Production

Requirements 42 STRATEGIC ROLE OF TECHNOLOGY 43

Types of Technologies 43 Technology as a Tool for Competitive Advantage 44

PRODUCTIVITY 44 Measuring Productivity 44 Interpreting Productivity Measures 47 Productivity and Competitiveness 47 Productivity and the Service Sector 48

OPERATIONS STRATEGY WITHIN OM: HOW IT ALL FITS TOGETHER 48

OPERATIONS STRATEGY ACROSS THE ORGANIZATION 49

THE SUPPLY CHAIN LINK 49 THE SUSTAINABILITY LINK 50 Chapter Highlights 50 Key Terms 51 Formula Review 51 Solved Problems 51 Discussion Questions 52 Problems 52 CASE: PRIME BANK OF MASSACHUSETTS 53 CASE: BOSEMAN OIL AND PETROLEUM (BOP) 54 INTERACTIVE CASE: VIRTUAL COMPANY 54 INTERNET CHALLENGE 55 ONLINE RESOURCES 55 Selected Bibliography 56

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xvi • CONTENTS

C H A P T E R 3 Product Design and Process Selection 57 PRODUCT DESIGN 59

Design of Services versus Goods 59 THE PRODUCT DESIGN PROCESS 60

Idea Development 60 Links to Practice: IBM Corporation 61 Product Screening 62 Preliminary Design and Testing 64 Final Design 64

FACTORS IMPACTING PRODUCT DESIGN 65 Design for Manufacture 65 Product Life Cycle 66 Concurrent Engineering 67 Remanufacturing 68

PROCESS SELECTION 68 Types of Processes 68

DESIGNING PROCESSES 72 PROCESS PERFORMANCE METRICS 74 LINKING PRODUCT DESIGN AND PROCESS

SELECTION 77 Product Design Decisions 78 Links to Practice: The Babcock & Wilcox Company 78 Competitive Priorities 79 Facility Layout 79 Product and Service Strategy 81 Degree of Vertical Integration 81

TECHNOLOGY DECISIONS 82 Information Technology 82 Links to Practice: Using GPS Technology in Product

Advertising 83 Automation 83 Links to Practice: Performing Robotic Surgery 85 e-Manufacturing 86

DESIGNING SERVICES 87 How Are Services Diff erent from Manufacturing? 87 How Are Services Classifi ed? 88 Th e Service Package 89 Diff ering Service Designs 90

PRODUCT DESIGN AND PROCESS SELECTION WITHIN OM: HOW IT ALL FITS TOGETHER 91

PRODUCT DESIGN AND PROCESS SELECTION ACROSS THE ORGANIZATION 92

THE SUPPLY CHAIN LINK 93 THE SUSTAINABILITY LINK 93 Chapter Highlights 94 Key Terms 94

Formula Review 95 Solved Problems 95 Discussion Questions 97 Problems 97 CASE: BIDDY’S BAKERY (BB) 99 CASE: CREATURE CARE ANIMAL CLINIC (B) 100 INTERACTIVE CASE: VIRTUAL COMPANY 100 INTERNET CHALLENGE 101 ONLINE RESOURCES 101 Selected Bibliography 101

C H A P T E R 4 Supply Chain Management 103 WHAT IS A SUPPLY CHAIN? 104 COMPONENTS OF A SUPPLY CHAIN FOR A

MANUFACTURER 106 External Suppliers 106 Internal Functions 108 External Distributors 108

THE BULLWHIP EFFECT 108 Causes of the Bullwhip Eff ect 109 Counteracting the Bullwhip Eff ect 110

SUPPLY CHAINS FOR SERVICE ORGANIZATIONS 110 Internal Operations 111 Th e External Distributors 112

MAJOR ISSUES AFFECTING SUPPLY CHAIN MANAGEMENT 112 Information Technology 112 E-Commerce 112 Business-to-Business (B2B) E-Commerce 113 Th e Evolution of B2B Commerce 113 Th e Benefi ts of B2B E-Commerce 114 Business-to-Consumer (B2C) E-Commerce 114 Consumer Expectations and Competition Resulting

from E-Commerce 115 Links to Practice: Lands’ End, Inc. 115 Globalization 116 Government Regulations and E-Commerce 118 Green Supply Chain Management 118 Infrastructure Issues 121 Product Proliferation 122

THE ROLE OF PURCHASING 122 Th e Traditional Purchasing Process 122 Th e E-purchasing Process 124

SOURCING ISSUES 127 Insourcing versus Outsourcing Decisions 128 Developing Supplier Relationships 130

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CONTENTS • xvii

How Many Suppliers? 130 Developing Partnerships 131 Links to Practice: The Bama Companies 132 Critical Factors in Successful Partnering 133 Links to Practice: Sweetheart Cup Company/

Georgia-Pacifi c Corporation 133 Benefi ts of Partnering 135 Ethics in Supply Management 136

SUPPLY CHAIN DISTRIBUTION 137 Th e Role of Warehouses 137 Links to Practice: Fingerhut Direct Marketing, Inc. 138 Crossdocking 139 Links to Practice: FedEx Freight 139 Radio Frequency Identifi cation Technology (RFID) 141 Th ird-Party Service Providers 142

IMPLEMENTING SUPPLY CHAIN MANAGEMENT 143 Strategies for Leveraging Supply Chain

Management 144 SUPPLY CHAIN PERFORMANCE METRICS 145 TRENDS IN SUPPLY CHAIN MANAGEMENT 147 SUPPLY CHAIN MANAGEMENT WIHTIN OM:

HOW IT ALL FITS TOGETHER 149 SCM ACROSS THE ORGANIZATION 149 THE SUPPLY CHAIN LINK 150 THE SUSTAINABILITY LINK 150 Chapter Highlights 151 Key Terms 152 Formula Review 152 Solved Problems 152 Discussion Questions 153 Problems 154 CASE: ELECTRONIC POCKET CALENDARS SUPPLY CHAIN

MANAGEMENT GAME 155 CASE: SUPPLY CHAIN MANAGEMENT AT DURHAM INTER-

NATIONAL MANUFACTURING COMPANY (DIMCO) 157 INTERACTIVE CASE: VIRTUAL COMPANY 158 INTERNET CHALLENGE 158 ONLINE RESOURCES 159 Selected Bibliography 159

C H A P T E R 5 Total Quality Management 161 DEFINING QUALITY 162

Diff erences between Manufacturing and Service Organizations 163

Links to Practice: General Electric Company/Motorola, Inc. 164

COST OF QUALITY 165 THE EVOLUTION OF TOTAL QUALITY MANAGEMENT

(TQM) 166 Quality Gurus 167

THE PHILOSOPHY OF TQM 171 Customer Focus 171 Continuous Improvement 171 Employee Empowerment 173 Links to Practice: The Walt Disney Company 174 Use of Quality Tools 174 Links to Practice: The Kroger Company/Meijer Stores

Limited Partnership 177 Product Design 177 Process Management 182 Managing Supplier Quality 182

QUALITY AWARDS AND STANDARDS 183 Th e Malcolm Baldrige National Quality Award

(MBNQA) 183 Th e Deming Prize 184 ISO 9000 Standards 184 ISO 14000 Standards 185

WHY TQM EFFORTS FAIL 186 TOTAL QUALITY MANAGEMENT (TQM) WITHIN OM:

HOW IT ALL FITS TOGETHER 186 TOTAL QUALITY MANAGEMENT (TQM) ACROSS THE

ORGANIZATION 187 THE SUPPLY CHAIN LINK 188 THE SUSTAINABILITY LINK 188 Chapter Highlights 189 Key Terms 189 Formula Review 190 Solved Problems 190 Discussion Questions 191 Problems 191 CASE: GOLD COAST ADVERTISING (GCA) 192 CASE: DELTA PLASTICS, INC. (A) 193 INTERACTIVE CASE: VIRTUAL COMPANY 194 INTERNET CHALLENGE 195 ONLINE RESOURCES 195 Selected Bibliography 196

C H A P T E R 6 Statistical Quality Control 197 WHAT IS STATISTICAL QUALITY CONTROL? 198

Links to Practice: Intel Corporation 199 SOURCES OF VARIATION: COMMON AND ASSIGNABLE

CAUSES 200

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xviii • CONTENTS

DESCRIPTIVE STATISTICS 200 Th e Mean 200 Th e Range and Standard Deviation 201 Distribution of Data 201

STATISTICAL PROCESS CONTROL METHODS 201 Developing Control Charts 202 Types of Control Charts 203

CONTROL CHARTS FOR VARIABLES 204 Mean (x-Bar) Charts 204 Range (R) Charts 208 Using Mean and Range Charts Together 209

CONTROL CHARTS FOR ATTRIBUTES 210 P-Charts 211 C-Charts 214

PROCESS CAPABILITY 216 Measuring Process Capability 216

SIX SIGMA QUALITY 221 Links to Practice: Motorola, Inc. 222

ACCEPTANCE SAMPLING 223 Sampling Plans 223 Operating Characteristic (OC) Curves 224 Developing OC Curves 226 Average Outgoing Quality 227

IMPLICATIONS FOR MANAGERS 229 How Much and How Oft en to Inspect 229 Where to Inspect 229 Which Tools to Use 230

STATISTICAL QUALITY CONTROL IN SERVICES 230 Links to Practice: The Ritz-Carlton Hotel Company, L.L.C./

Nordstrom, Inc. 231 Links to Practice: Marriott International, Inc. 231

STATISTICAL QUALITY CONTROL (SQC) WITHIN OM: HOW IT ALL FITS TOGETHER 232

STATISTICAL QUALITY CONTROL (SQC) ACROSS THE ORGANIZATION 232

THE SUPPLY CHAIN LINK 233 THE SUSTAINABILITY LINK 233 Chapter Highlights 234 Key Terms 234 Formula Review 235 Solved Problems 235 Discussion Questions 240 Problems 240 CASE: SCHARADIN HOTELS 243 CASE: DELTA PLASTICS, INC. (B) 244 INTERACTIVE CASE: VIRTUAL COMPANY 244 INTERNET CHALLENGE 245

ONLINE RESOURCES 245 Selected Bibliography 245

C H A P T E R 7 Just-in-Time and Lean Systems 246 THE PHILOSOPHY OF JIT 248

Eliminate Waste 249 A Broad View of Operations 249 Simplicity 249 Continuous Improvement 250 Visibility 250 Flexibility 250

ELEMENTS OF JIT 251 Just-in-Time Manufacturing 251 Total Quality Management (TQM) 253 Respect for People 253 Links to Practice: Texas Instruments 254

JUST-IN-TIME MANUFACTURING 254 Th e Pull System 254 Kanban Production 255 Variations of Kanban Production 258 Small Lot Sizes and Quick Setups 258 Uniform Plant Loading 259 Flexible Resources 260 Facility Layout 261 Links to Practice: Ryder Integrated Logistics 261

TOTAL QUALITY MANAGEMENT 262 Product versus Process 263 Quality at the Source 263 Preventive Maintenance 264 Work Environment 264

RESPECT FOR PEOPLE 265 Th e Role of Production Employees 265 Lifetime Employment 266 Th e Role of Management 267 Supplier Relationships 268

BENEFITS OF JIT 269 Links to Practice: Alcoa, Inc. 269

IMPLEMENTING JIT 270 JIT IN SERVICES 271

Improved Quality 271 Uniform Facility Loading 271 Use of Multifunction Workers 272 Reductions in Cycle Time 272 Minimizing Setup Times and Parallel Processing 272 Workplace Organization 272

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CONTENTS • xix

JIT AND LEAN SYSTEMS WITHIN OM: HOW IT ALL FITS TOGETHER 272

JIT AND LEAN SYSTEMS ACROSS THE ORGANIZATION 272

THE SUPPLY CHAIN LINK 273 THE SUSTAINABILITY LINK 274 Chapter Highlights 274 Key Terms 275 Formula Review 275 Solved Problems 275 Discussion Questions 275 Problems 276 CASE: KATZ CARPETING 276 CASE: DIXON AUDIO SYSTEMS 277 INTERACTIVE CASE: VIRTUAL COMPANY 278 INTERNET CHALLENGE 279 ONLINE RESOURCES 279 Selected Bibliography 279

C H A P T E R 8 Forecasting 280 PRINCIPLES OF FORECASTING 281 STEPS IN THE FORECASTING PROCESS 282

Links to Practice: Intel Corporation 283 TYPES OF FORECASTING METHODS 283

Links to Practice: Improving Sales Forecasting 284 Qualitative Methods 284 Quantitative Methods 286

TIME SERIES MODELS 288 Forecasting Level or Horizontal Pattern 289 Forecasting Trend 297 Forecasting Seasonality 300 Links to Practice: The Ski Industry Forecast 303

CAUSAL MODELS 303 Linear Regression 303 Correlation Coeffi cient 306 Multiple Regression 307

MEASURING FORECAST ACCURACY 307 Forecast Accuracy Measures 307 Tracking Signal 309

SELECTING THE RIGHT FORECASTING MODEL 310 FORECASTING SOFTWARE 311

Spreadsheets 311 Statistical Packages 311 Specialty Forecasting Packages 312 Guidelines for Selecting Forecasting Soft ware 312

FOCUS FORECASTING 313

COMBINING FORECASTS 313 Links to Practice: Combining Methods in Weather

Forecasting 314 COLLABORATIVE PLANNING, FORECASTING,

AND REPLENISHMENT (CPFR) 314 FORECASTING WITHIN OM: HOW IT ALL FITS

TOGETHER 315 FORECASTING ACROSS THE ORGANIZATION 315 THE SUPPLY CHAIN LINK 316 THE SUSTAINABILITY LINK 316 Chapter Highlights 317 Key Terms 317 Formula Review 318 Solved Problems 319 Discussion Questions 323 Problems 323 CASE: BRAM-WEAR 327 CASE: THE EMERGENCY ROOM (ER) AT NORTHWEST

GENERAL (A) 328 INTERACTIVE CASE: VIRTUAL COMPANY 329 INTERNET CHALLENGE 329 ONLINE RESOURCES 330 Selected Bibliography 330

C H A P T E R 9 Capacity Planning and Facility Location 331 CAPACITY PLANNING 333

Why Is Capacity Planning Important? 333 Links to Practice: Capacity Planning in the ER 333 Measuring Capacity 334 Capacity Considerations 336 Links to Practice: Focus in the Retail Industry 339

MAKING CAPACITY PLANNING DECISIONS 339 Identify Capacity Requirements 340 Develop Capacity Alternatives 341 Evaluate Capacity Alternatives 341

DECISION TREES 341 LOCATION ANALYSIS 344

What Is Facility Location? 345 Factors Aff ecting Location Decisions 345 Links to Practice: Locating in Silicon Valley 346 Globalization 347

MAKING LOCATION DECISIONS 349 Procedure for Making Location Decisions 349 Procedures for Evaluating Location Alternatives 349

CAPACITY PLANNING AND FACILITY LOCATION WITHIN OM: HOW IT ALL FITS TOGETHER 360

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xx • CONTENTS

CAPACITY PLANNING AND FACILITY LOCATION ACROSS THE ORGANIZATION 360

THE SUPPLY CHAIN LINK 361 THE SUSTAINABILITY LINK 361 Chapter Highlights 361 Key Terms 362 Formula Review 362 Solved Problems 362 Discussion Questions 365 Problems 365 CASE: DATA TECH, INC. 368 CASE: THE EMERGENCY ROOM (ER) AT NORTHWEST

GENERAL (B) 369 INTERACTIVE CASE: VIRTUAL COMPANY 369 INTERNET CHALLENGE 370 ONLINE RESOURCES 370 Selected Bibliography 371

C H A P T E R 1 0 Facility Layout 372 WHAT IS LAYOUT PLANNING? 373 TYPES OF LAYOUTS 374

Process Layouts 374 Links to Practice: Wal-Mart Stores, Inc. 376 Product Layouts 376 Links to Practice: Toyota Motor Corporation 378 Hybrid Layouts 378 Fixed-Position Layouts 379

DESIGNING PROCESS LAYOUTS 379 Step 1: Gather Information 379 Step 2: Develop a Block Plan 382 Step 3: Develop a Detailed Layout 385

SPECIAL CASES OF PROCESS LAYOUT 385 Warehouse Layouts 385 Offi ce Layouts 388

DESIGNING PRODUCT LAYOUTS 389 Step 1: Identify Tasks and Th eir Immediate

Predecessors 389 Step 2: Determine Output Rate 391 Step 3: Determine Cycle Time 391 Step 4: Compute the Th eoretical Minimum Number of

Stations 393 Step 5: Assign Tasks to Workstations (Balance the

Line) 393 Step 6: Compute Effi ciency, Idle Time, and Balance

Delay 394 Other Considerations 395

GROUP TECHNOLOGY (CELL) LAYOUTS 396

FACILITY LAYOUT WITHIN OM: HOW IT ALL FITS TOGETHER 397

FACILITY LAYOUT ACROSS THE ORGANIZATION 397 THE SUPPLY CHAIN LINK 398 THE SUSTAINABILITY LINK 398 Chapter Highlights 399 Key Terms 399 Formula Review 399 Solved Problems 400 Discussion Questions 402 Problems 403 CASE: SAWHILL ATHLETIC CLUB (A) 407 CASE: SAWHILL ATHLETIC CLUB (B) 408 INTERACTIVE CASE: VIRTUAL COMPANY 409 INTERNET CHALLENGE 409 ONLINE RESOURCES 410 Selected Bibliography 410

C H A P T E R 1 1 Work System Design 411 DESIGNING A WORK SYSTEM 412 JOB DESIGN 413

Designing a Job 413 Links to Practice: Google 413 Machines or People? 414 Level of Labor Specialization 415 Eliminating Employee Boredom 416 Team Approaches to Job Design 416 Links to Practice: The SCO Group 417 Th e Alternative Workplace 417 Links to Practice: AT&T 418

METHODS ANALYSIS 419 THE WORK ENVIRONMENT 421 WORK MEASUREMENT 422

Costing 422 Performance 423 Planning 423

SETTING STANDARD TIMES 423 How to Do a Time Study 423 Elemental Time Data 429 Predetermined Time Data 429 Work Sampling 431 Links to Practice: Pace Productivity 433

COMPENSATION 433 Time-Based Systems 433 Output-Based Systems 433 Group Incentive Plans 434

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CONTENTS • xxi

Links to Practice: Accounatable Care Organizations (ACO) 434

Incentive Plan Trends 435 LEARNING CURVES 435 WORK SYSTEM DESIGN WITHIN OM: HOW IT ALL FITS

TOGETHER 438 WORK SYSTEM DESIGN ACROSS THE ORGANIZATION 438 THE SUPPLY CHAIN LINK 439 THE SUSTAINABILITY LINK 439 Chapter Highlights 440 Key Terms 440 Formula Review 441 Solved Problems 441 Discussion Questions 444 Problems 444 CASE: THE NAVIGATOR III 447 CASE: NORTHEAST STATE UNIVERSITY 447 INTERACTIVE CASE: VIRTUAL COMPANY 448 INTERNET CHALLENGE 449 ONLINE RESOURCES 450 Selected Bibliography 450

C H A P T E R 1 2 Inventory Management 451 TYPES OF INVENTORY 452 HOW COMPANIES USE THEIR INVENTORY 453 OBJECTIVES OF INVENTORY MANAGEMENT 455

Customer Service 455 Cost-Effi cient Operations 456 Minimum Inventory Investment 457

RELEVANT INVENTORY COSTS 459 Item Costs 459 Holding Costs 459 Ordering Costs 461 Shortage Costs 461

ABC INVENTORY CLASSIFICATION 461 Procedure for an ABC Inventory Analysis 463 Inventory Control Using ABC Classifi cation 463

INVENTORY RECORD ACCURACY 464 Links to Practice: Cisco Systems, Inc. 464

INVENTORY IN SERVICE ORGANIZATIONS 466 DETERMINING ORDER QUANTITIES 467 MATHEMATICAL MODELS FOR DETERMINING ORDER

QUANTITY 468 Economic Order Quantity (EOQ) 468 Calculating the EOQ 471 Economic Production Quantity (EPQ) 473 Quantity Discount Model 477

WHY COMPANIES DON’T ALWAYS USE THE OPTIMAL ORDER QUANTITY 481

JUSTIFYING SMALLER ORDER QUANTITIES 481 Links to Practice: Kenworth Trucks 481 Understanding the EPQ Factors 482

DETERMINING SAFETY STOCK LEVELS 483 How Much Safety Stock? 483

PERIODIC REVIEW SYSTEM 485 Comparison of Continuous Review Systems and

Periodic Review Systems 487 THE SINGLE-PERIOD INVENTORY MODEL 488 INVENTORY MANAGEMENT WITHIN OM: HOW IT ALL

FITS TOGETHER 490 INVENTORY MANAGEMENT ACROSS THE

ORGANIZATION 491 THE SUPPLY CHAIN LINK 491 THE SUSTAINABILITY LINK 492 Chapter Highlights 492 Key Terms 493 Formula Review 493 Solved Problems 494 Discussion Questions 497 Problems 498 CASE: FABQUAL LTD. 500 CASE: KAYAKS!INCORPORATED 501 INTERACTIVE CASE: VIRTUAL COMPANY 502 INTERNET CHALLENGE 503 ONLINE RESOURCES 503 Selected Bibliography 504

C H A P T E R 1 3 Aggregate Planning 505 THE ROLE OF AGGREGATE PLANNING 507

Marketing Plan 507 Aggregate or Production Plan 507 Financial and Engineering Plans 508 Master Production Schedule 508 Links to Practice: Coca-Cola Midi (CCM) 509

TYPES OF AGGREGATE PLANS 509 Level Aggregate Plan 509 Chase Aggregate Plan 510 Hybrid Aggregate Plan 511

AGGREGATE PLANNING OPTIONS 511 Demand-Based Options 512 Capacity-Based Options 512

EVALUATING THE CURRENT SITUATION 514 Links to Practice: UPS Hires Seasonal Workers 515

DEVELOPING THE AGGREGATE PLAN 515

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xxii • CONTENTS

AGGREGATE PLANS FOR COMPANIES WITH TANGIBLE PRODUCTS 517

AGGREGATE PLANS FOR SERVICE COMPANIES WITH NONTANGIBLE PRODUCTS 520

AGGREGATE PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER 524

AGGREGATE PLANNING ACROSS THE ORGANIZATION 525

THE SUPPLY CHAIN LINK 525 THE SUSTAINABILITY LINK 526 Chapter Highlights 526 Key Terms 526 Solved Problems 527 Discussion Questions 533 Problems 534 CASE: NEWMARKET INTERNATIONAL MANUFACTURING

COMPANY (A) 535 CASE: JPC, INC.: KITCHEN COUNTERTOPS

MANUFACTURER 536 INTERACTIVE CASE: VIRTUAL COMPANY 537 INTERNET CHALLENGE 537 ONLINE RESOURCES 538 Selected Bibliography 538

C H A P T E R 1 4 Resource Planning 539 ENTERPRISE RESOURCE PLANNING 541

ERP Modules 542 THE EVOLUTION OF ERP 542

First-Generation ERP 542 Second-Generation ERP 543 Links to Practice: Arapahoe County Government 544

THE BENEFITS OF ERP 545 Links to Practice: i2 Technologies 545 Links to Practice: SAP AG 546

THE COST OF ERP SYSTEMS 546 MATERIAL PLANNING SYSTEMS 547 AN OVERVIEW OF MRP 547 TYPES OF DEMAND 548 OBJECTIVES OF MRP 550 MRP INPUTS 550

Authorized MPS 550 Inventory Records 550 Bills of Material 552

THE MRP EXPLOSION PROCESS 555 ACTION NOTICES 559 COMPARISON OF LOT SIZE RULES 559

THE ROLE OF CAPACITY REQUIREMENTS PLANNING (CRP) 560

RESOURCE PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER 562

RESOURCE PLANNING ACROSS THE ORGANIZATION 563 THE SUPPLY CHAIN LINK 564 THE SUSTAINABILITY LINK 564 Chapter Highlights 565 Key Terms 565 Formula Review 566 Solved Problems 566 Discussion Questions 569 Problems 569 CASE: NEWMARKET INTERNATIONAL MANUFACTURING

COMPANY (B) 571 CASE: DESSERTS BY J.B. 573 INTERACTIVE CASE: VIRTUAL COMPANY 573 INTERNET CHALLENGE 574 ONLINE RESOURCES 574 Selected Bibliography 575

C H A P T E R 1 5 Scheduling 576 SCHEDULING OPERATIONS 578 HIGH-VOLUME OPERATIONS 578

Characteristics of Flow Operations 578 LOW-VOLUME OPERATIONS 579

Gantt Chart 579 SCHEDULING WORK 580

Infi nite Loading 580 Finite Loading 581 Forward Scheduling 581 Backward Scheduling 582 Monitoring Workfl ow 582

HOW TO SEQUENCE JOBS 584 Priority Rules 584 How to Use Priority Rules 585

MEASURING PERFORMANCE 586 Links to Practice: Airline Scheduling 586 Job Flow Time 586 Average Number of Jobs in the System 587 Makespan 587 Job Lateness and Tardiness 587

COMPARING PRIORITY RULES 588 Comparing SPT and S/RO 591

SEQUENCING JOBS THROUGH TWO WORK CENTERS 591

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CONTENTS • xxiii

SCHEDULING BOTTLENECKS 593 Links to Practice: 1-800-FLOWERS.com 595

THEORY OF CONSTRAINTS 596 SCHEDULING FOR SERVICE ORGANIZATIONS 596

Scheduling Services Demanded 596 Scheduling Employees 598

DEVELOPING A WORKFORCE SCHEDULE 599 SCHEDULING WITHIN OM: PUTTING IT ALL

TOGETHER 600 SCHEDULING ACROSS THE ORGANIZATION 601 THE SUPPLY CHAIN LINK 602 THE SUSTAINABILITY LINK 602 Chapter Highlights 602 Key Terms 603 Formula Review 603 Solved Problems 603 Discussion Questions 607 Problems 607 CASE: AIR TRAFFIC CONTROLLER

SCHOOL (ATCS) 610 CASE: SCHEDULING AT RED, WHITE AND BLUE

FIREWORKS COMPANY 610 INTERACTIVE CASE: VIRTUAL COMPANY 611 INTERNET CHALLENGE 611 ONLINE RESOURCES 612 Selected Bibliography 612

C H A P T E R 1 6 Project Management 613 PROJECT LIFE CYCLE 615

Conception 615 Feasibility Analysis or Study 615 Planning 616 Execution 616 Termination 616

NETWORK PLANNING TECHNIQUES 616 Links to Practice: PERT and the Polaris Missile 616 Step 1: Describe the Project 617 Step 2: Diagram the Network 618 Step 3: Estimate the Project’s Completion

Time 619 Step 3 (a): Deterministic Time Estimates 620 Step 3 (b): Probabilistic Time Estimates 622 Step 4: Monitor the Project’s Progression 628 Links to Practice: Managing the Olympic Games 628

ESTIMATING THE PROBABILITY OF COMPLETION DATES 629

REDUCING PROJECT COMPLETION TIME 631 Crashing Projects 631

THE CRITICAL CHAIN APPROACH 634 Adding Safety Time 634 Wasting Safety Time 634

PROJECT MANAGEMENT WITHIN OM: HOW IT ALL FITS TOGETHER 636

PROJECT MANAGEMENT OM ACROSS THE ORGANIZATION 636

THE SUPPLY CHAIN LINK 637 THE SUSTAINABILITY LINK 637 Chapter Highlights 637 Key Terms 638 Formula Review 638 Solved Problems 638 Discussion Questions 642 Problems 642 CASE: THE RESEARCH OFFICE MOVES 645 CASE: WRITING A TEXTBOOK 645 INTERACTIVE CASE: VIRTUAL COMPANY 646 INTERNET CHALLENGE 647 ONLINE RESOURCES 647 Selected Bibliography 648

A P P E N D I X A Solutions to Odd-Numbered Problems 649

A P P E N D I X B The Standard Normal Distribution 671

A P P E N D I X C P-Chart 672 N A M E I N D E X 675

S U B J E C T I N D E X 678

S U P P L E M E N T A Spreadsheet Modeling: An Introduction A1 WHAT ARE MODELS? A2 THE SPREADSHEET MODELING PROCESS A4 EVALUATING SPREADSHEET MODELS A5

Planning the Model A7 Implementing the Model in Excel A8 Assessing the Model A9 Using the Model for Analysis A11 Adding Data Tables A14 Graphing the Model Results A17 Planning the Model A18

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xxiv • CONTENTS

Constructing the Model in Excel A19 Reviewing Relative and Absolute

Cell Referencing A20 Entering Formulas in the Model A21

USEFUL SPREADSHEET TIPS A26 IMPORTANT EXCEL FORMULAS A27 SPREADSHEET MODELING WITHIN OM: HOW IT ALL

FITS TOGETHER A28 Supplement Highlights A29 Key Terms A29 Discussion Questions A29 Problems A30 CASE: DIET PLANNING A31 ONLINE RESOURCES A32 Selected Bibliography A32

S U P P L E M E N T B Introduction to Optimization B1 INTRODUCTION B2 ALGEBRAIC FORMULATION B4

Examining the Formulation B6 SPREADSHEET MODEL DEVELOPMENT B7

Testing the Model B8 SOLVER BASICS B9 SETTING UP AND RUNNING SOLVER B9

Solving the Problem B12 INTERPRETING THE SOLUTION B13 SOLVER SOLUTION REPORTS B14 RECAP B16 OUTCOMES OF LINEAR PROGRAMMING PROBLEMS B16 OPTIMIZATION WITHIN OM: HOW IT ALL FITS

TOGETHER B18 Supplement Highlights B18 Key Terms B19 Solved Problems B19 Discussion Questions B23 Problems B24 CASE: EXETER ENTERPRISES B25 ONLINE RESOURCES B26 Selected Bibliography B26

S U P P L E M E N T C Waiting Line Models C1 ELEMENTS OF WAITING LINES C2

Links to Practice: Waiting for Fast Food C3 Th e Customer Population C3 Th e Service System C4

Arrival and Service Patterns C6 Waiting Line Priority Rules C6

WAITING LINE PERFORMANCE MEASURES C7 SINGLE-SERVER WAITING LINE MODEL C7 MULTISERVER WAITING LINE MODEL C10 CHANGING OPERATIONAL CHARACTERISTICS C13 LARGER-SCALE WAITING LINE SYSTEMS C14 WAITING LINE MODELS WITHIN OM: HOW IT ALL FITS

TOGETHER C15 Supplement Highlights C15 Key Terms C16 Formula Review C16 Solved Problems C16 Discussion Questions C18 Problems C19 CASE: THE COPY CENTER HOLDUP C19 ONLINE RESOURCES C20 Selected Bibliography C20

S U P P L E M E N T D Master Scheduling and Rough-Cut

Capacity Planning D1 MASTER PRODUCTION SCHEDULING D2 MPS AS A BASIS OF COMMUNICATION D3 OBJECTIVES OF MASTER SCHEDULING D4 DEVELOPING AN MPS D4 ROUGH-CUT CAPACITY PLANNING D6 EVALUATING AND ACCEPTING THE MPS D9 USING THE MPS D10 USING THE ATP RECORDS D11 STABILIZING THE MPS D14 MASTER PRODUCTION SCHEDULING AND ROUGH-CUT

CAPACITY PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER D15

Supplement Highlights D16 Key Terms D16 Formula Review D16 Solved Problems D17 Discussion Questions D22 Problems D22 CASE: NEWMARKET INTERNATIONAL MANUFACTURING

COMPANY (C) D23 ONLINE RESOURCES D24 Selected Bibliography D24

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CHAPTER

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

L E A R N I N G O B J E C T I V E S

C H A P T E R O U T L I N E

What Is Operations Management? 2 Diff erences between Manufacturing and

Service Organizations 5 Operations Management Decisions 7 Plan of Th is Book 10

Historical Development 11 Today’s OM Environment 20 Operations Management in Practice 21 Within OM: How It All Fits Together 21 OM across the Organization 22

After completing this chapter you should be able to

Introduction to Operations Management

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2 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

WHAT IS OPERATIONS MANAGEMENT?

Many of you reading this book may think that you don’t know what operations manage- ment (OM) is or that it is not something you are interested in. However, after reading this chapter you will realize that you already know quite a bit about operations management. You may even be working in an operations management capacity and have used certain operations management techniques. You will also realize that operations management is probably the most critical business function today. If you want to be on the frontier of business competition, you want to be in operations management.

Today companies are competing in a very dif- ferent environment than they were only a few years ago. To survive, they must focus on quality, time-based competition, efficiency, international perspectives, and customer relationships. Global competition, e-business, the Internet, and advances in technol- ogy require flexibility and responsiveness. Increased financial pressures require lean and agile organizations that are free of waste. This new focus has placed operations management in the business limelight because it is the function through which com- panies can achieve this type of competitiveness.

Consider some of today’s most successful companies, such as Wal-Mart, Southwest Airlines, General Electric, Starbucks, Apple Computer, Toyota, FedEx, and Procter & Gamble. These companies have achieved world-class status in large part due to a strong focus on operations management. In this book you will learn specific tools and techniques of operations management that have helped these and other companies achieve their success.

The purpose of this book is to help prepare you to be successful in this new busi- ness environment. Operations management will give you an understanding of how to help your organization gain a competitive advantage in the marketplace. Regardless of whether your area of expertise is marketing, finance, MIS, or operations, the techniques and concepts in this book will help you in your business career. The material will teach you how your company can offer goods and services cheaper, better, and faster. You will also learn that operations management concepts are far-reaching, affecting every aspect of the organization and even everyday life.

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Every business is managed through three major functions: finance, marketing, and operations management. Figure 1-1 illustrates this by showing that the vice presidents of each of these functions report directly to the president or CEO of the company. Other business functions—such as accounting, purchasing, human resources, and

FIN MKT

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WHAT IS OPERATIONS MANAGEMENT? • 3

engineering—support these three major functions. Finance is the function responsible for managing cash flow, current assets, and capital investments. Marketing is respon- sible for sales, generating customer demand, and understanding customer wants and needs. Most of us have some idea of what finance and marketing are about, but what does operations management do?

Operations management (OM) is the business function that plans, organizes, coordinates, and controls the resources needed to produce a company’s goods and services. Operations management is a management function. It involves managing people, equipment, technology, information, and many other resources. Operations management is the central core function of every company. This is true whether the company is large or small, provides a physical good or a service, is for-profit or not- for-profit. Every company has an operations management function. Actually, all the other organizational functions are there primarily to support the operations function. Without operations, there would be no goods or services to sell. Consider a retailer such as The Gap, which sells casual apparel. The marketing function provides promo- tions for the merchandise, and the finance function provides the needed capital. It is the operations function, however, that plans and coordinates all the resources needed to design, produce, and deliver the merchandise to the various retail locations. With- out operations, there would be no goods or services to sell to customers.

The role of operations management is to transform a company’s inputs into the finished goods or services. Inputs include human resources (such as workers and managers), facilities and processes (such as buildings and equipment), as well as mate- rials, technology, and information. Outputs are the goods and services a company pro- duces. Figure 1-2 shows this transformation process. At a factory the transformation is the physical change of raw materials into products, such as transforming leather and rubber into sneakers, denim into jeans, or plastic into toys. At an airline it is the efficient movement of passengers and their luggage from one location to another. At a hospital it is organizing resources such as doctors, medical procedures, and medica- tions to transform sick people into healthy ones.

Operations management is responsible for orchestrating all the resources needed to produce the final product. This includes designing the product; deciding what resources are needed; arranging schedules, equipment, and facilities; managing inven- tory; controlling quality; designing the jobs to make the product; and designing work methods. Basically, operations management is responsible for all aspects of the process

� Operations management (OM) The business function responsible for planning, coordinating, and controlling the resources needed to produce a company’s goods and services.

� Role of operations management To transform organizational inputs into outputs

Marketing V.P. of Marketing

Finance V.P. of Finance

President or CEO

Operations V.P. of Operations

Manages: people, equipment, technology, materials, and information To produce: goods and/or services

Manages: customer demands Generates: sales for goods and services

Manages: cash flow, current assets, and capital investments

FIGURE 1-1

Organizational chart showing the three major business functions

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4 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

of transforming inputs into outputs. Customer feedback and performance information are used to continually adjust the inputs, the transformation process, and characteris- tics of the outputs. As shown in Figure 1-2, this transformation process is dynamic in order to adapt to changes in the environment.

Proper management of the operations function has led to success for many com- panies. For example, in 1994 Dell Computer Corporation was a second-rate com- puter maker that managed its operations similarly to others in the industry. Then Dell implemented a new business model that completely changed the role of its operations function. Dell developed new and innovative ways of managing the operations func- tion that have become one of today’s best practices. These changes enabled Dell to provide rapid product delivery of customized products to customers at a lower cost. Today Dell customized computers can be en route to the customer within 36 hours, at a price 10–15 percent lower than industry standard. Dell’s model is one many have tried to emulate and is the key to its being an industry leader.

Just as proper management of operations can lead to company success, improper management of operations can lead to failure. This is illustrated by Kozmo.com, a Web-based home delivery company founded in 1997. Kozmo’s mission was to deliver products to customers—everything from the latest video to ice cream—in less than an hour. Kozmo was technology enabled and rapidly became a huge success. However, the initial success gave rise to overly fast expansion. The company found it difficult to manage the operations needed in order to deliver the promises made on its Web site. The consequences were too much inventory, poor deliveries, and losses in profits. The company rapidly tried to change its operations, but it was too late. It had to cease operations in April 2001.

The Web-based age has created a highly competitive world of on- line shopping that poses special challenges for operations manage- ment. The Web can be used for on- line purchasing of everything from CDs, books, and groceries to pre- scription medications and auto- mobiles. The Internet has given consumers flexibility; it has also created one of the biggest chal- lenges for companies: delivering

Inputs • Human Resources • Facilities & Processes • Technologies • Materials

The Transformation

Process

Outputs

• Goods • Services

Performance Information

Customer Feedback FIGURE 1-2

The transformation process

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www.Amazon.com www.Barnesandnoble

.com

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DIFFERENCES BETWEEN MANUFACTURING AND SERVICE ORGANIZATIONS • 5

exactly what the customer ordered at the time promised. As we saw with the example of Kozmo.com, making promises on a Web site is one thing; delivering on those promises is yet another. Ensuring that orders are delivered from “mouse to house” is the job of operations and is much more complicated than it might seem. In the 1990s many dot- com companies discovered just how difficult this is. They were not able to generate a profit and went out of business. To ensure meeting promises, companies must forecast what customers want and maintain adequate inventories of goods, manage distribution centers and warehouses, operate fleets of trucks, and schedule deliveries while keeping costs low and customers satisfied. Many companies like Amazon.com manage almost all aspects of their operation. Other companies hire outside firms for certain functions, such as outsourcing the management of inventories and deliveries to UPS. Competition among e-tailers has become intense as customers demand increasingly shorter delivery times and highly customized products. Same-day service has become common in met- ropolitan areas. For example, Barnesandnoble.com provides same-day delivery in Man- hattan, Los Angeles, and San Francisco. Understanding and managing the operations function of an on-line business has become essential in order to remain competitive.

For operations management to be successful, it must add value during the trans- formation process. We use the term value added to describe the net increase between the final value of a product and the value of all the inputs. The greater the value added, the more productive a business is. An obvious way to add value is to reduce the cost of activities in the transformation process. Activities that do not add value are consid- ered a waste; these include certain jobs, equipment, and processes. In addition to value added, operations must be efficient. Efficiency means being able to perform activities well and at the lowest possible cost. An important role of operations is to analyze all activities, eliminate those that do not add value, and restructure processes and jobs to achieve greater efficiency. Because today’s business environment is more competitive than ever, the role of operations management has become the focal point of efforts to increase competitiveness by improving value added and efficiency.

� Value added The net increase created during the transformation of inputs into final outputs.

� Efficiency Performing activities at the lowest possible cost.

DIFFERENCES BETWEEN MANUFACTURING AND SERVICE ORGANIZATIONS Organizations can be divided into two broad categories: manufacturing organiza- tions and service organizations, each posing unique challenges for the operations function. There are two primary distinctions between these categories. First, manufac- turing organizations produce physical, tangible goods that can be stored in inventory before they are needed. By contrast, service organizations produce intangible prod- ucts that cannot be produced ahead of time. Second, in manufacturing organizations most customers have no direct contact with the operation. Customer contact occurs through distributors and retailers. For example, a customer buying a car at a car deal- ership never comes into contact with the automobile factory. However, in service organizations the customers are typically present during the creation of the service. Hospitals, colleges, theaters, and barber shops are examples of service organizations in which the customer is present during the creation of the service.

The differences between manufacturing and service organizations are not as clear- cut as they might appear, and there is much overlap between them. Most manufac- turers provide services as part of their business, and many service firms manufacture physical goods that they deliver to their customers or consume during service delivery. For example, a manufacturer of furniture may also provide shipment of goods and assembly of furniture. A barber shop may sell its own line of hair care products. You might not know that General Motors’ greatest return on capital does not come from

� Manufacturing organizations Organizations that primarily produce a tangible product and typically have low customer contact.

� Service organizations Organizations that primarily produce an intangible product, such as ideas, assistance, or information, and typically have high customer contact.

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6 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

selling cars, but rather from postsales parts and service. Figure 1-3 shows the differ- ences between manufacturing and services, focusing on the dimensions of product tangibility and the degree of customer contact. It shows the extremes of pure manufac- turing and pure service, as well as the overlap between them.

Even in pure service companies some segments of the operation may have low cus- tomer contact while others have high customer contact. The former can be thought of as “back room” or “behind the scenes” segments. Think of a fast-food operation such as Wendy’s, for which customer service and customer contact are important parts of the business. However, the kitchen segment of Wendy’s operation has no direct customer contact and can be managed like a manufacturing operation. Similarly, a hospital is a high-contact service operation, but the patient is not present in certain segments, such as the lab where specimen analysis is done.

In addition to pure manufacturing and pure service, there are companies that have some characteristics of each type of organization. It is difficult to tell whether these companies are actually manufacturing or service organizations. Think of a post office, an automated warehouse, or a mail-order catalog business. They have low customer contact and are capital intensive, yet they provide a service. We call these companies quasi-manufacturing organizations.

The U.S. Postal Service is an example of a quasi-manufacturing type of company. It pro- vides a service: speedy, reliable delivery of letters, documents, and packages. Its output is intangi- ble and cannot be stored in inventory. Yet most operations management decisions made at the Postal Service are similar to those that occur in manufacturing. Customer contact is low, and at any one time there is a large amount of inventory. The Postal Service is capital intensive, having its own facilities and fleet of trucks and relying on

• Physical product

• Low customer contact

• Capital intensive

• Long response time

• Product can be inventoried

• Intangible product

• High customer contact

• Short response time

• Labor intensive

• Product cannot be inventoried

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LINKS TO PRACTICE U.S. Postal Service

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OPERATIONS MANAGEMENT DECISIONS • 7

scanners to sort packages and track customer orders. Scheduling enough workers at peak processing times is a major concern, as is planning delivery schedules. Note that although the output of the U.S. Postal Service is a service, inputs include labor, tech- nology, and equipment. The responsibility of OM is to manage the conversion of these inputs into the desired outputs. Proper management of the OM function is critical to the success of the U.S. Postal Service.

It is important to understand how to manage both service and manufacturing operations. However, managing service operations is of especially high importance. The reason is that the service sector constitutes a dominant segment of our economy. Since the 1960s, the percentage of jobs in the service-producing industries of the U.S. economy has increased from less than 50 to over 80 percent of total nonfarm jobs. The remaining 20 percent are in the manufacturing and goods-producing industries. Fig- ure 1-4 illustrates this large growth of the service sector.

Goods Producing (Manufacturing Construction)

Service Producing

Year

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U.S. employment by economic sector

OPERATIONS MANAGEMENT DECISIONS In this section we look at some of the specific decisions that operations managers have to make. The best way to do this is to think about decisions we would need to make if we started our own company—say, a company called Gourmet Wafers that produces praline– pecan cookies from an old family recipe. Think about the decisions that would have to be made to go from the initial idea to actual production of the product: that is opera- tions management. Table 1-1 breaks these down into the generic decisions that would be appropriate for almost any good or service, the specific decisions required for our exam- ple, and the formal terms for these decisions that are used in operations management.

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8 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

General Decisions to Be Made

Decision Specific for Cookie Production

Operations Management Term

What are the unique features of the business that will make it competitive?

The business offers freshly baked cookies “homemade” style, in a fast-food format.

Operations strategy

What are the unique features of the product?

The unique feature of the cookies is that they are loaded with extra-large and crunchy pecans and are fresh and moist.

Product design

What are the unique features of the process that give the product its unique characteristics?

A special convection oven is used to make the cookies in order to keep them fresh and moist. The dough is allowed to rise longer than usual to make the cookies extra light.

Process selection

What sources of supply should we use to ensure regular and timely receipt of the extract materials we need? How do we manage these sources of supply?

The key ingredients, pecans and syrup, will be purchased from only one supplier located in South Carolina because it offers the best products. A relationship is worked out in which the supplier sends the ingredients on the exact schedule that they are needed.

Supply chain management

How will managers ensure the quality of the product, measure quality and identify quality problems?

A quality check is made at each stage of cookie production. The dough is checked for texture; the pecans are checked for size and freshness; the syrup is checked for consistency.

Quality management

What is the expected demand for the product?

Expected sales for each day of the week have been determined; for example, it is expected that more cookies will be sold on weekdays and most during the lunch hours. Expected cookie sales for each month and for the year have also been determined.

Forecasting

Where will the facility be located? After looking at locations of customers and location costs, it is decided that the facility will be located in a shopping mall.

Location analysis

How large should the facility be? The business needs to be able to produce 200 cookies per hour, or up to 2000 cookies per day.

Capacity planning

How should the facility be laid out? Where should the kitchen and ovens be located? Should there be seating for customers?

Decisions are made about where the kitchen will be located and how the working area will be arranged for maximum efficiency. The business is competing on the basis of speed and quality; therefore, the facility should be arranged to promote these features. There will be a small seating area for customers and a large counter and display case for buying.

Facility layout

What jobs will be needed in the facility, who should do what task, and how will their performance be measured?

Two people will be needed in the kitchen during busy periods and one during slow periods. Their job duties are determined. One person will be needed for order taking at all times.

Job design and work measurement

How will the inventory of raw materials be monitored? When will orders be placed, and how much will be kept in stock?

A different policy is developed for common ingredients, such as flour and sugar. These ingredients will be ordered every two weeks for a two-week supply. A special purchasing arrangement is worked out with the supplier of specialty ingredients.

Inventory management

Who will work on what schedule? Two people will work the counter in split shifts. One kitchen employee will work a full shift, with a second employee working part-time.

Scheduling

TABLE 1-1

Operations Management Decisions for Gourmet Wafers

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OPERATIONS MANAGEMENT DECISIONS • 9

Note in the Gourmet Wafers example that the first decisions made were very broad in scope (e.g., the unique features of our product). We needed to do this before we could focus on more specific decisions (e.g., worker schedules). Although our example is simple, this decision-making process is followed by every company, including IBM, General Motors, Lands’ End, and your local floral shop. Also note in our example that before we can think about specific day-to-day decisions, we need to make decisions for the whole company that are long-term in nature. Long-term decisions that set the direction for the entire organization are called strategic decisions. They are broad in scope and set the tone for other, more specific decisions. They address questions such as: What are the unique features of our product? What market do we plan to compete in? What do we believe will be the demand for our product?

Short-term decisions that focus on specific departments and tasks are called tactical decisions. Tactical decisions focus on more specific day-to-day issues, such as the quan- tities and timing of specific resources. Strategic decisions are made first and determine the direction of tactical decisions, which are made more frequently and routinely. There- fore, we have to start with strategic decisions and then move on to tactical decisions. This relationship is shown in Figure 1-5. Tactical decisions must be aligned with strategic decisions because they are the key to the company’s effectiveness in the long run. Tacti- cal decisions provide feedback to strategic decisions, which can be modified accordingly.

You can see in the example of Gourmet Wafers how important OM decisions are. They are critical to all types of companies, large and small. In large companies these decisions are more complex because of the size and scope of the organization. Large companies typically produce a greater variety of products, have multiple location sites, and often use domestic and international suppliers. Managing OM decisions and coordinating efforts can be a complicated task, and the OM function is critical to the company’s success.

We can illustrate this point by looking at operations management decisions made by Texas Instruments (TI) in order to position itself for global collaboration with customers, distrib- utors, and suppliers. TI realized its business was growing exponentially, with more than 120,000 monthly orders received and processed elec- tronically. The coordination effort encompassed 56 factories, including subcontractors, and the management of over 45,000 products. To suc- ceed, the company needed to develop a system to generate better forecasts, coordinate manufacturing of products, manage orders, and track deliveries. Managing and coor- dinating global operations management functions was considered paramount to the

� Strategic decisions Decisions that set the direction for the entire company; they are broad in scope and long- term in nature.

� Tactical decisions Decisions that are specific and short-term in nature and are bound by strategic decisions.

STRATEGIC DECISIONS TACTICAL DECISIONS • Broad in scope • Long-term in nature • All-encompassing

• Narrow in scope • Short-term in nature • Concerning a small group of issuese.g., What are the unique features

of our product that make us competitive?

e.g., Who will work the 2nd shift tomorrow?

FIGURE 1-5

The relationship between strategic and tactical decisions

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LINKS TO PRACTICE Texas Instruments Incorporated www.ti.com

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10 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

company’s success. TI adopted a comprehensive software package called enterprise resource planning (ERP) that integrates information throughout the organization, manages forecasts, and coordinates factory operations. Designing and implementing the ERP system at TI required an understanding of all the strategic and tactical opera- tions decisions; otherwise, it would not be effective. The system has proven to be a suc- cess and a major achievement, enabling TI to consistently manage factory operations across the globe.

PLAN OF THIS BOOK The purpose here is to provide concepts and techniques that give you the ability to efficiently plan, order, and control the resources needed to produce a company’s goods and services. The topics progress from strategic to tactical, similar to the order of decisions used in the Gourmet Wafers example. Figure 1-6 shows the plan of the book. We begin with broad, overarching issues such as product design and process selection. Also early on we cover operations topics that require a strategic perspective and a cultural change within the organization, such as supply chain management, total quality management, and just-in-time systems. We progress to more tactical issues, such as work measurement, inventory management, and scheduling concerns.

We have designed the chapters to provide relevant operations management con- cepts and techniques that are important to business professionals regardless of their field of study. Throughout the chapters we show how the tools and concepts discussed relate to other functions in the organization and that operations management con- cepts are far-reaching, affecting every aspect of the organization.

Before You Go On

Type of Decision Operations Management Topic Chapter

Operations Strategy Product Design and Process Selection Supply Chain Management Total Quality Management Just-in-Time and Lean Systems Forecasting Capacity Planning and Location Analysis Facility Layout Work System Design Inventory and Resource Planning Scheduling Issues

Ch. 2 Ch. 3 Ch. 4 Ch. 5 and 6 Ch. 7 Ch. 8 Ch. 9 Ch. 10 Ch. 11 Ch. 12, 13, and 14 Ch. 15 and 16

Strategic

Tactical

FIGURE 1-6

Plan of the book

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HISTORICAL DEVELOPMENT • 11

HISTORICAL DEVELOPMENT

Why OM? Business did not always recognize the importance of operations management. In fact, following World War II the marketing and finance functions were predomi- nant in American corporations. The United States had just emerged from the war as the undisputed global manufacturing leader due in large part to efficient operations. At the same time, Japan and Europe were in ruins, their businesses and factories destroyed. U.S. companies had these markets to themselves, and so the post-World War II period of the 1950s and 1960s represented the golden era for U.S. business. The primary opportunities were in the areas of marketing, to develop the large potential markets for new products, and in finance, to support the growth. Since there were no significant competitors, the operations function became of second- ary importance, because companies could sell what they produced. Even the distin- guished economist John Kenneth Galbraith observed, “The production problem has been solved.”

Then in the 1970s and 1980s, things changed. American companies experienced large declines in productivity growth, and international competition began to be a challenge in many markets. In some markets such as the auto industry, American cor- porations were being pushed out. It appeared that U.S. firms had become lax due to the lack of competition in the 1950s and 1960s. They had forgotten about improving their methods and processes. In the meantime, foreign firms were rebuilding their facili- ties and designing new production methods. By the time foreign firms had recovered, many U.S. firms found themselves unable to compete. To regain their competitive- ness, companies turned to operations management, a function they had overlooked and almost forgotten about.

The new focus on operations and competitiveness has been responsible for the recovery of many corporations, and U.S. businesses experienced a resurgence in the 1980s and 1990s. Operations became the core function of organizational competitive- ness. Although U.S. firms have rebounded, they are fully aware of continued global competition, scarcity of resources, and increased financial pressure. Companies have learned that to achieve long-run success they must place much importance on their operations.

Historical Milestones When we think of what operations management does—namely, managing the trans- formation of inputs into goods and services—we can see that as a function it is as old as time. Think of any great organizational effort, such as organizing the first Olympic games, building the Great Wall of China, or erecting the Egyptian pyramids, and you will see operations management at work. Operations management did not emerge as a formal field of study, however, until the late 1950s and early 1960s, when scholars began to recognize that all production systems face a common set of problems and to stress the systems approach to viewing operations processes.

Many events helped shape operations management. We will describe some of the most significant of these historical milestones and explain their influence on the devel- opment of operations management. Later we will look at some current trends in oper- ations management. These historical milestones and current trends are summarized in Table 1-2.

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12 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

Concept Time Explanation

Industrial Revolution Late 1700s Brought in innovations that changed production by using machine power instead of human power.

Scientific management Early 1900s Brought the concepts of analysis and measurement of the technical aspects of work design and development of moving assembly lines and mass production.

Human relations movement 1930s to 1960s Focused on understanding human elements of job design, such as worker motivation and job satisfaction.

Management science 1940s to 1960s Focused on the development of quantitative techniques to solve operations problems.

Computer age 1960s Enabled processing of large amounts of data and allowed widespread use of quantitative procedures.

Environmental issues 1970s Considered waste reduction, the need for recycling, and product reuse.

Just-in-time systems (JIT) 1980s Designed to achieve high-volume production with minimal inventories.

Total quality management (TQM) 1980s Sought to eliminate causes of production defects.

Reengineering 1980s Required redesigning a company’s processes in order to provide greater efficiency and cost reduction.

Global competition 1980s Designed operations to compete in the global market.

Flexibility 1990s Offered customization on a mass scale.

Time-based competition 1990s Based on time, such as speed of delivery.

Supply chain management 1990s Focused on reducing the overall cost of the system that manages the flow of materials and information from suppliers to final customers.

Electronic commerce 2000s Uses the Internet and World Wide Web for conducting business activity.

Outsourcing and flattening of the world

2000s Convergence of technology has enabled outsourcing of virtually any job imaginable from anywhere around the globe, therefore “flattening” the world.

TABLE 1-2

Historical Development of Operations Management

The Industrial Revolution The Industrial Revolution had a significant impact on the way goods are produced today. Before this time, products were made by hand by skilled craftspeople in their shops or homes. Each product was unique, painstakingly made by one person. The Industrial Revolution changed all that. It started in the 1770s with the development of a number of inventions that relied on machine power instead of human power. The most important of these was the steam engine, which was invented by James Watt in 1764. The steam engine provided a new source of power that was used to replace human labor in textile mills, machine-making plants, and other facilities. The concept

� Industrial Revolution An industry movement that changed production by substituting machine power for labor power.

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HISTORICAL DEVELOPMENT • 13

of the factory was emerging. In addition, the steam engine led to advances in transpor- tation, such as railroads, that allowed for a wider distribution of goods.

About the same time, the concept of division of labor was introduced. First described by Adam Smith in 1776 in The Wealth of Nations, this concept would become one of the important ideas behind the development of the assembly line. Division of labor means that the production of a good is broken down into a series of small, elemental tasks, each of which is performed by a different worker. The repetition of the task allows the worker to become highly specialized in that task. Division of labor allowed higher volumes to be produced, which, coupled with the advances in transportation of steam-powered boats and railroads, opened up distant markets.

A few years later, in 1790, Eli Whitney introduced the concept of interchangeable parts. Prior to that time, every part used in a production process was unique. Inter- changeable parts are standardized so that every item in a batch of items fits equally. This concept meant that we could move from one-at-a-time production to volume production, for example, in the manufacture of watches, clocks, and similar items.

Scientific Management Scientific management was an approach to management promoted by Frederick W. Taylor at the turn of the twentieth century. Taylor was an engineer with an eye for efficiency. Through scientific management he sought to increase worker productiv- ity and organizational output. His concept had two key features. First, it assumed that workers are motivated only by money and are limited only by their physical ability. Taylor believed that worker productivity is governed by scientific laws and that it is up to management to discover these laws through measurement, analysis, and observation. Workers are to be paid in direct proportion to how much they produce. The second feature of this approach was the separation of the planning and doing functions in a company, which meant the separation of management and labor. Management is responsible for designing productive systems and determining acceptable worker output. Workers have no input into this process—they are permit- ted only to work.

Many people did not like the scientific management approach, especially workers, who thought that management used these methods to unfairly increase output with- out paying them accordingly. Still, many companies adopted the scientific manage- ment approach. Today many view scientific management as a major influence in the

� Scientific management An approach to management that focused on improving output by redesigning jobs and determining acceptable levels of worker output.

Robert Nicholas/Getty Images, Inc.Photo by Kean Collection/Getty Images, Inc.

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14 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

field of operations management. For example, piece-rate incentives, in which workers are paid in direct proportion to their output, came out of this movement. Also, Taylor introduced a widely used method of work measurement, stopwatch time studies. In stopwatch time studies, observations are made and recorded of a worker performing a task over many cycles. This information is then used to set a time standard for per- forming the particular task. This method is still used today to set a time standard for short, repetitive tasks.

The scientific management approach was popularized by Henry Ford, who used the techniques in his factories. Combining technology with scientific management, Ford introduced the moving assembly line to produce Ford cars. Ford also combined scientific management with the division of labor and interchangeable parts to develop the concept of mass production. These concepts and innovations helped him increase production and efficiency at his factories.

The Human Relations Movement The scientific management movement and its philosophy dominated in the early twentieth century. However, this changed with the publication of the results of the Hawthorne studies. The purpose of the Hawthorne studies, conducted at a Western Electric plant in Hawthorne, Illinois, in the 1930s, was to study the effects of environ- mental changes, such as changes in lighting and room temperature, on the productivity of assembly-line workers. The findings from the study were unexpected: the produc- tivity of the workers continued to increase regardless of the environmental changes made. Elton Mayo, a sociologist from Harvard, concluded that the workers were actu- ally motivated by the attention they were given. The idea of workers responding to the attention they are given came to be known as the Hawthorne effect.

The study of these findings by many sociologists and psychologists led to the human relations movement, an entirely new philosophy based on the recognition that factors other than money can contribute to worker productivity. The impact of this new philosophy on the development of operations management has been tremen- dous. Its influence can be seen in the implementation of a number of concepts that motivate workers by making their jobs more interesting and meaningful. For example, the Hawthorne studies showed that scientific management had made jobs too repeti- tive and boring. Job enlargement is an approach in which workers are given a larger portion of the total task to do. Another approach to giving more meaning to jobs is job enrichment, in which workers are given a greater role in planning.

Recent studies have shown that environmental factors in the workplace, such as adequate lighting and ventilation, can have a major impact on productivity. However, this does not contradict the principle that attention from management is a positive factor in motivation.

Management Science While some were focusing on the technical aspects of job design and others on the human aspects of operations management, a third approach, called management sci- ence, was developing that would make its own unique contribution. Management sci- ence focused on developing quantitative techniques for solving operations problems. The first mathematical model for inventory management was developed by F. W. Har- ris in 1913. Shortly thereafter, statistical sampling theory and quality control proce- dures were developed.

� Hawthorne studies The studies responsible for creating the human relations movement, which focused on giving more consideration to workers’ needs.

� Human relations movement A philosophy based on the recognition that factors other than money can contribute to worker productivity.

� Management science A field of study that focuses on the development of quantitative techniques to solve operations problems.

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HISTORICAL DEVELOPMENT • 15

World War II created an even greater need for the ability to quantitatively solve com- plex problems of logistics control, for weapons system design and deployment of missiles. Consequently, the techniques of management science grew more robust during the war and continued to develop after the war was over. Many quantitative tools emerged to solve problems in forecasting, inventory control, project management, and other areas. A mathematically oriented field, management science provides operations manage- ment with tools to assist in decision making. A popular example of such a tool is linear programming.

The Computer Age In the 1970s the use of computers in business became widespread. With computers, many of the quantitative models developed by management science could be employed on a larger scale. Data processing became easier, with important effects in areas such as forecasting, scheduling, and inventory management. A particularly important com- puterized system, material requirements planning (MRP), was developed for inven- tory control and scheduling. Material requirements planning was able to process huge amounts of data in order to compute inventory requirements and develop schedules for the production of thousands of items, processing that was impossible before the age of computers. Today the exponential growth in computing capability continues to impact operations management.

Just-in-Time Just-in-time (JIT) is a major operations management philosophy, developed in Japan in the 1980s, that is designed to achieve high-volume production using min- imal amounts of inventory. This is achieved through coordination of the flow of materials so that the right parts arrive at the right place in the right quantity; hence the term just-in-time. However, JIT is much more than the coordinated movement of goods. It is an all-inclusive organizational philosophy that employs teams of workers to achieve continuous improvement in processes and organizational effi- ciency by eliminating all organizational waste. Although JIT was first used in manu- facturing, it has been implemented in the service sector, for example, in the food service industry. JIT has had a profound impact on the way companies manage their operations. It is credited with helping turn many companies around and is used by companies such as Honda, Toyota, and General Motors. JIT promises to continue to transform businesses in the future.

Total Quality Management As customers demand ever higher quality in their products and services, companies have been forced to focus on improving quality in order to remain competitive. Total quality management (TQM) is a philosophy—promulgated by “quality gurus” such as W. Edwards Deming—that aggressively seeks to improve product quality by eliminat- ing causes of product defects and making quality an all-encompassing organizational philosophy. With TQM, everyone in the company is responsible for quality. Practiced by some companies in the 1980s, TQM became pervasive in the 1990s and is an area of operations management that no competitive company has been able to ignore. Its importance is demonstrated by the number of companies achieving ISO 9000 certi- fication. ISO 9000 is a set of quality standards developed for global manufacturers

� Just-in-time (JIT) A philosophy designed to achieve high-volume production through elimination of waste and continuous improvement.

� Total quality management (TQM) Philosophy that seeks to improve quality by eliminating causes of product defects and by making quality the responsibility of everyone in the organization.

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16 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

by the International Organization for Standardization (ISO) to control trade into the then-emerging European Economic Community (EEC). Today ISO 9000 is a global set of standards, with many companies requiring their suppliers to meet the standards as a condition for obtaining contracts.

Business Process Reengineering Business process reengineering means redesigning a company’s processes to increase efficiency, improve quality, and reduce costs. In many companies things are done in a certain way that has been passed down over the years. Often managers say, “Well, we’ve always done it this way.” Reengineering requires asking why things are done in a certain way, questioning assumptions, and then redesigning the processes. Operations management is a key player in a company’s reengineering efforts.

Flexibility Traditionally, companies competed by either mass-producing a standardized prod- uct or offering customized products in small volumes. One of the current competitive challenges for companies is the need to offer to customers a greater variety of product choices of a traditionally standardized product. This is the challenge of flexibility. For example, Procter and Gamble offers 13 different product designs in the Pampers line of diapers. Although diapers are a standardized product, the product designs are cus- tomized to the different needs of customers, such as the age, sex, and stage of develop- ment of the child using the diaper.

One example of flexibility is mass customization, which is the ability of a firm to produce highly customized goods and services and to do it at the high volumes of mass production. Mass customization requires designing flexible operations and using delayed product differentiation, also called postponement. This means keeping the product in generic form as long as possible and postponing completion of the product until specific customer preferences are known.

Time-Based Competition One of the most important trends within companies today is time-based competition— developing new products and services faster than the competition, reaching the market first, and meeting customer orders most quickly. For example, two companies may pro- duce the same product, but if one is able to deliver it to the customer in two days and the other in five days, the first company will make the sale and win over the custom- ers. Time-based competition requires specifically designing the operations function for speed.

Supply Chain Management Supply chain management (SCM) involves managing the flow of materials and infor- mation from suppliers and buyers of raw materials all the way to the final customer. The network of entities that is involved in producing and delivering a finished product to the final customer is called a supply chain. The objective is to have everyone in the chain work together to reduce overall cost and improve quality and service delivery. Supply chain management requires a team approach, with functions such as marketing,

� Reengineering Redesigning a company’s processes to make them more efficient.

� Flexibility An organizational strategy in which the company attempts to offer a greater variety of product choices to its customers.

� Mass customization The ability of a firm to highly customize its goods and services at high volumes.

� Time-based competition An organizational strategy focusing on efforts to develop new products and deliver them to customers faster than competitors.

� Supply chain management (SCM) Management of the flow of materials from suppliers to customers in order to reduce overall cost and increase responsiveness to customers.

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HISTORICAL DEVELOPMENT • 17

purchasing, operations, and engineering all working together. This approach has been shown to result in more satisfied customers, meaning that everyone in the chain prof- its. SCM has become possible with the development of information technology (IT) tools that enable collaborative planning and scheduling. The technologies allow syn- chronized supply chain execution and design collaboration, which enables companies to respond better and faster to changing market needs. Numerous companies, includ- ing Dell Computer, Wal-Mart, and Toyota, have achieved world-class status by effec- tively managing their supply chains.

SCM is as important in the service industry as it is in manufacturing, even in pure service industries such as the creative arts. Consider the publish- ing industry, which is responsible for delivering the creative art of literature to readers. The typical publishing sup- ply chain, shown in Figure 1-7, consists of the author, the publisher, and the bookstore retailer. In the traditional publishing supply chain, the publisher is typically responsible for all the functions involved in transforming the author’s liter- ary creation into a tangible product to be placed on a bookshelf. This includes editing, printing, distribution, inventory management, and marketing.

Many writers have seen the traditional publishing supply chain as a setback to maintaining control and innovation over their art. Large publishing houses maintain control of many critical functions of the supply chain, resulting in the commoditi- zation of the literary arts being sold in chain-type retailers. The net effect is often a homogenization of titles and writers across stores, creating a best-seller list that does not necessary reflect literary merit. This, in turn, creates a barrier for writers who have in fact created something highly personal.

Some writers have innovatively overcome the large supply chain barrier between author and bookshelf by forming their own publishing company. By doing this, they enable themselves to maintain control of all aspects of their art, including retaining artisitic editorial prerogatives, such as choosing to print their writing in unique styles,

LINKS TO PRACTICE The Publishing Industry

an a

S un

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an /A

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Publisher RetailerAuthor

Literary creation

Editing Printing Distribution Book sales

FIGURE 1-7

The traditional publishing supply chain

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18 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

rather than resorting to the same font and formatting dictated by a publishing house. This freedom is uniquely important to writers.

In addition to maintaining artistic freedom, this enables writers to retain full legal rights of their own work. This supply chain is shown in Figure 1-8.

Global Marketplace Today businesses must think in terms of a global marketplace in order to compete effec- tively. This includes the way they view their customers, competitors, and suppliers. Key issues are meeting customer needs and getting the right product to markets as diverse as the Far East, Europe, or Africa. Operations management is responsible for most of these decisions. OM decides whether to tailor products to different customer needs, where to locate facilities, how to manage suppliers, and how to meet local government standards. Also, global competition has forced companies to reach higher levels of excellence in the products and services they offer. Regional trading agreements, such as the North Ameri- can Free Trade Agreement (NAFTA), the European Union (EU), and the global World Trade Organization (WTO), guarantee continued competition on the international level.

Sustainability and Green Operations There is increasing emphasis on the need to reduce waste, recycle, and reuse products and parts. This is known as sustainability or green operations. Society has placed great pressure on business to focus on air and water quality, waste disposal, global warming, and other environmental issues. Operations management plays a key role in redesign- ing processes and products in order to meet and exceed environmental quality stan- dards. The importance of this issue is demonstrated by a set of standards termed ISO 14000. Developed by the International Organization for Standardization (ISO), these standards provide guidelines and a certification program documenting a company’s environmentally responsible actions.

Electronic Commerce Electronic commerce (e-commerce) is the use of the Internet for conducting busi- ness activities, such as communication, business transactions, and data transfer. The Internet, developed from a government network called ARPANET created in 1969

� Global marketplace A trend in business focusing on customers, suppliers, and competitors from a global perspective.

� Sustainability A trend in business to consciously reduce waste, recycle, and reuse products and parts.

FIGURE 1-8

Author-controlled publishing supply chain

Author Retailer

Literary creation

Editing Printing Distribution Book sales

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HISTORICAL DEVELOPMENT • 19

by the U.S. Defense Department, has become an essential business medium since the late 1990s, enabling efficient communication between manufacturers, suppliers, dis- tributors, and customers. It has allowed companies to reach more customers at a speed infinitely faster than ever before. It also has significantly cut costs, as it provides direct links between entities.

The electronic commerce that occurs between businesses, known as B2B (business- to-business) commerce, makes up the highest percentage of transactions. The most common B2B exchanges occur between companies and their suppliers, such as Gen- eral Electric’s Trading Process Network. A more familiar type of e-commerce occurs between businesses and their customers, known as B2C (business-to-customer) exchange, as engaged in by on-line retailers such as Amazon.com. E-commerce also occurs between customers, known as C2C (customer-to-customer) exchange, as on consumer auction sites such as eBay. E-commerce is creating virtual marketplaces that continue to change the way business functions.

Outsourcing and Flattening of the World Outsourcing is obtaining goods or services from an outside provider. This can range from outsourcing of one aspect of the operation, such as shipping, to outsourcing an entire part of the manufacturing process. The practice has rapidly grown in recent years, as you can see in Figure 1-9. It has helped companies be more efficient by focusing on what they do best. Outsourcing has been touted as the enabling factor that helps companies achieve the needed speed and flexibility to be competitive. Manage- ment guru Tom Peters has been quoted as saying, “Do what you do best and outsource the rest.”

� Business-to-business (B2B) Electronic commerce between businesses.

� Business-to-customers (B2C) Electronic commerce between businesses and their customers.

� Customer-to-customer (C2C) Electronic commerce between customers.

FIGURE 1-9

U.S. market for outsourcing services 1996–2010

Year

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$500

$400

$350

$250

$200

$300

$150

$100

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1999 2002 2005 2008 2010

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20 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

The convergence of technologies at the turn of this century has taken the concept of outsourcing to a new level. Massive investments in technology, such as worldwide broadband connectivity, the increasing availability and lower cost of computers, and the development of software such as e-mail, search engines, and other software, allow individuals to work together in real time from anywhere in the world. This has enabled countries like India, China, and many others to become part of the global supply chain for goods and services and has created a “flattening” of the world. Such “flattening,” or leveling of the playing field, has enabled workers anywhere in the world to com- pete globally for intellectual work. The result has been the outsourcing of virtually any job imaginable. Manufacturers have outsourced software development and prod- uct design to engineers in India; accounting firms have outsourced tax preparation to India; even some hospitals have outsourced the reading of CAT scans to doctors in India and Australia. The “flattening” of the world has created a whole new level of global competition that is more intense than ever before.

TODAY’S OM ENVIRONMENT Today’s OM environment is very different from what it was just a few years ago. Cus- tomers demand better quality, greater speed, and lower costs. In order to succeed, companies have to be masters of the basics of operations management. To achieve this ability, many companies are implementing a concept called lean systems. Lean sys- tems take a total system approach to creating an efficient operation and pull together best practice concepts, including just-in-time (JIT), total quality management (TQM), continuous improvement, resource planning, and supply chain management (SCM). The need for efficiency has also led many companies to implement large information systems called enterprise resource planning (ERP). ERP systems are large, sophisti- cated software programs for identifying and planning the enterprise-wide resources needed to coordinate all activities involved in producing and delivering products to customers.

Applying best practices to operations management is not enough to give a com- pany a competitive advantage. The reason is that in today’s information age best practices are quickly passed to competitors. To gain an advantage over their com- petitors, companies are continually looking for ways to better respond to customers. This requires them to have a deep knowledge of their customers and to be able to anticipate their demands. The development of customer relationship management (CRM) has made it possible for companies to have this detailed knowledge. CRM encompasses software solutions that enable the firm to collect customer-specific data, information that can help the firm identify profiles of its most loyal customers and provide customer-specific solutions. Also, CRM software can be integrated with ERP software to connect customer requirements to the entire resource network of the company.

Another characteristic of today’s OM environment is the increased use of cross- functional decision making, which requires coordinated interaction and decision making among the different business functions of the organization. Until recently, employees of a company made decisions in isolated departments, called “functional silos.” Today many companies bring together experts from different departments into cross-functional teams to solve company problems. Employees from each function

� Lean systems A concept that takes a total system approach to creating efficient operations.

� Enterprise resource planning (ERP) Large, sophisticated software systems used for identifying and planning the enterprise- wide resources needed to coordinate all activities involved in producing and delivering products.

� Customer relationship management (CRM) Software solutions that enable the firm to collect customer- specific data.

� Cross-functional decision making The coordinated interaction and decision making that occur among the different functions of the organization.

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WITHIN OM: HOW IT ALL FITS TOGETHER • 21

must interact and coordinate their decisions, which require employees to understand the roles of other business functions and the goals of the business as a whole, in addi- tion to their own expertise.

OPERATIONS MANAGEMENT IN PRACTICE Of all the business functions, operations is the most diverse in terms of the tasks per- formed. If you consider all the issues involved in managing a transformation process, you can see that operations managers are never bored. Who are operations managers and what do they do?

The head of the operations function in a company usually holds the title of vice president of operations, vice president of manufacturing, V.P., or director of supply chain operations and generally reports directly to the president or chief operating officer. Below the vice president level are midlevel managers: manufacturing man- ager, operations manager, quality control manager, plant manager, and others. Below these managers are a variety of positions, such as quality specialist, production ana- lyst, inventory analyst, and production supervisor. These people perform a variety of functions: analyzing production problems, developing forecasts, making plans for new products, measuring quality, monitoring inventory, and developing employee sched- ules. Thus, there are many job opportunities in operations management at all levels of the company. In addition, operations jobs tend to offer high salaries, interesting work, and excellent opportunities for advancement. Many corporate CEOs today have come through the ranks of operations. For example, the third president and CEO of Wal- Mart from January 2000 to January 2009, H. Lee Scott, came from a background in operations and logistics. The current CEO of Wal-Mart, Michael Duke, comes from a background of international operations. Also from the operations background are the former CEO of Home Depot, Bob Nardelli, and the former CEO of Lowe’s, Robert Tillman.

As you can see, all business functions need information from operations manage- ment in order to perform their tasks. At the same time, operations managers are highly dependent on input from other areas. This process of information sharing is dynamic, requiring that managers work in teams and understand each other’s roles.

WITHIN OM: HOW IT ALL FITS TOGETHER Just as OM decisions are linked with those of other business functions, decisions within the OM function need to be linked together. We learned that OM is responsible for a wide range of strategic and tactical decisions. These decisions directly impact each other and need to be carefully linked together, following the company’s strategic direction. In the Gourmet Wafers example we observed that decisions on product design are directly tied to process selection (Chapter 3). The reason is that a company’s process needs to be capable of producing the desired product (Chapter 6). Similarly, the forecast of expected demand (Chapter 8) directly impacts functions such as capacity planning (Chapter 9), inventory management (Chapter 12), and scheduling (Chapter 15). These are just a few examples of linkages within the OM function.

Throughout this book we will study different OM functions and will learn how each impacts the other. You will realize that OM decisions are not made in isolation. Rather, each decision is intertwined with other business functions and other OM decisions.

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22 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

Finance cannot realistically judge the need for capital investments, make-or- buy decisions, plant expansions, or relocation if finance managers do not understand operations concepts and needs. On the other hand, operations managers cannot make large financial expenditures without understanding financial constraints and meth- ods of evaluating financial investments. It is essential for these two functions to work together and understand each other’s constraints.

Information systems (IS) is a function that enables information to flow through- out the organization and allows OM to operate effectively. OM is highly dependent on information such as forecasts of demand, quality levels being achieved, inventory lev- els, supplier deliveries, and worker schedules. IS must understand the needs of OM in order to design an adequate information system. Usually, IS and OM work together to design an information network. This close relationship needs to be ongoing. IS must be capable of accommodating the needs of OM as they change in response to market

MKT

FIN

MIS

Sales and demand requirements

Organizational ability to meet

sales and demands

Financial status

Operations needs and financial requirements

Marketing V.P. of Marketing

Finance V.P. of Finance

President or CEO

Strategic Direction of Company

Operations V.P. of Operations

FIGURE 1-10

Organizational chart showing flow of information

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OM ACROSS THE ORGANIZATION • 23

demands. At the same time, it is up to IS to bring the latest capabilities in information technology to the organization to enhance the functioning of OM.

Human resource managers must understand job requirements and worker skills if they are to hire the right people for available jobs. To manage employees effectively, operations managers need to understand job market trends, hiring and layoff costs, and training costs.

Accounting needs to consider inventory management, capacity information, and labor standards in order to develop accurate cost data. In turn, operations managers must communicate billing information and process improvements to accounting, and they depend heavily on accounting data for cost management decisions.

Engineering and other disciplines that are not in the business field are also tied to operations. Operations management provides engineering with the operations capa- bilities and design requirements, and engineering, in turn, provides valuable input on technological trade-offs and product specifications. These are essential for the product design process.

The coordinated interaction and decision making between all these functions and OM are needed for success in today’s competitive environment. It is also important to extend this coordination to organizations that make up a supply chain, such as suppli- ers, manufacturers, and retailers. This is discussed in the following box.

HRM

ACC

Current operating capabilitiesCustomer demands

Technological capabilities

Information needs

Current performance measures

Operations capabilities

Design requirements

Product specs Labor requirements

Labor skills available

Capital investments planned Financial measures

Budgets

Stockholder requirements

Labor costsTechnological

trade-offs

Capital requirements

Inventory levels

Operations Management

Finance

Accounting Engineering

Marketing

Human Resources

Billing information

Process improvements

Output rates

Capacities

Customer feedback

Need for new products

FIGURE 1-11

Information flow between operations and other business functions

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24 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

Today’s companies understand that successfully man- aging their own OM functions is not enough to main- tain leadership in a highly competitive marketplace. The reason is that every company is dependent on other members of the supply chain to successfully deliver the right product to the final customer in a timely and cost-effective manner. For example, a com- pany is dependent on its suppliers for the delivery of raw materials and components in time to meet production needs. If these materials are delivered late or are of insufficient quality, production will be delayed. Similarly, a company depends on its distributors and retailers for the delivery of the product to the final cus- tomer. If these are not delivered on time, are damaged in the transportation process, or are poorly displayed

at the retail location, sales will suffer. Also, if the OM function of other members of the supply chain is not managed properly, excess costs will result, which will be passed down to other members of the supply chain in the form of higher prices. Therefore, each company

in the supply chain must successfully manage its OM function. Also, the companies that comprise a supply chain need to coordinate and link their OM functions so that the entire chain is operat- ing in a seamless and efficient manner. Just con-

sider the fact that most of the components Dell uses are warehoused within a 15-minute radius of its assem- bly plant and Dell is in constant communication with its suppliers. Dell considers this essential to its ability to produce and deliver components quickly.

THE SUPPLY CHAIN LINK

Environmental concerns, including climate change, energy use, environmental contamination, and resource depletion, are all part of the contemporary business landscape. Emerging economies, such as that of India and China, are growing at double-digit rates, and contin- ued growth of the world population has created short- ages of many resources we used to take for granted. Companies are increasingly aware that they must design their operations functions, and their entire sup- ply chains, for sustainability. This means designing their operations processes to better and more efficiently use their resources, to use environmentally friendly inputs, and create outputs that can be recycled and that do not contaminate the environment. It also means better use of human resources, from labor to suppliers.

The operations function, with its transformation role, is uniquely poised to help companies achieve their sustainability goals. One of the best examples of this is illustrated through the joint efforts of the National Resource Defense Council (NRDC) and Major League

Baseball (MLB) to change baseball operations to reduce the environmental impact. The “operation” of a base- ball game brings together numerous industries, such as the beverage and chemical industries, as well as auto- motive, plastics, steel, and agriculture. The NRDC and MLB have worked together to make MLB operations more sustainable by working with industries that serve as suppliers for baseball games, and placing “green” requirements on their goods and services. MLB has changed everything—from their purchasing and trans- portation decisions to the way they manage their sta- diums and arenas. For example, a “red carpet” event, planned for MLB, was designed to use a carpet made from only recycled fibers, in a factory that is LEED certified1 and wind and solar powered, emitting less global-warming pollutants. Simple changes such as this resulted in a more sustainable operation. This has also resulted in savings of thousands of dollars on energy, waste, and water bills.

THE SUSTAINABILITY LINK

1LEED certifi cation is the recognized standard in the United States and other countries for measuring building sustainability; we will learn about LEED certifi cation later in the text.

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DISCUSSION QUESTIONS • 25

Chapter Highlights 1 Operations management is the business function that is

responsible for managing and coordinating the resources needed to produce a company’s products and services. Without operations management there would be no products or services to sell.

2 Th e role of operations management is to transform organizational inputs—human resources, facilities, materials, technology, and information—into a company’s fi nished goods or services.

3 Operations management is responsible for a wide range of decisions, ranging from strategic decisions, such as designing the unique features of a product and process, to tactical decisions, such as planning worker schedules.

4 Organizations can be divided into manufacturing and service operations, which diff er in the tangibility of the product and the degree of customer contact. Manufacturing and service operations have very diff erent operational requirements.

5 A number of historical milestones have shaped operations management into what it is today. Some of the more signifi cant of these are the Industrial Revolution, scientifi c management, the human relations movement, management science, and the computer age.

6 OM is a highly important function in today’s dynamic business environment. Among the trends that have had a signifi cant impact on business are just-in-time, total quality management, reengineering, fl exibility, time-based competition, supply chain management, a global marketplace, and environmental issues.

7 Operations managers need to work closely with all other business functions in a team format. Marketing needs to provide information about customer expectations. Finance needs to provide information about budget constraints. In turn, OM must communicate its needs and capabilities to the other functions.

Key Terms operations management (OM) 3 role of operations management 3 value added 5 effi ciency 5 manufacturing organizations 5 service organizations 5 strategic decisions 9 tactical decisions 9 Industrial Revolution 12 scientifi c management 13

Hawthorne studies 14 human relations movement 14 management science 14 just-in-time (JIT) 15 total quality management (TQM) 15 reengineering 16 fl exibility 16 mass customization 16 time-based competition 16 supply chain management (SCM) 16

global marketplace 18 sustainability 18 business-to-business (B2B) 19 business-to-customers (B2C) 19 customer-to-customer (C2C) 19 lean systems 20 enterprise resource planning (ERP) 20 customer relationship management

(CRM) 20 cross-functional decision making 20

Discussion Questions 1. Define the term operations management. 2. Explain the decisions operations managers make and give

three examples. 3. Describe the transformation process of a business. Give

three examples. What constitutes the transformation process at an advertising agency, a bank, and a TV station?

4. What are the three major business functions, and how are they related to one another? Give specific examples.

5. What are the differences between strategic and tactical deci- sions, and how are they related to each other?

6. Find an article that relates to operations management in either the Wall Street Journal, Fortune, or Business Week. Come to class prepared to share with others what you learned in the article.

7. Examine the list of Fortune magazine’s top 100 companies. Do most of these companies have anything in common? Are there industries that are most represented?

8. Identify the two major differences between service and manufacturing organizations. Find an example of a service and manufacturing company and compare them.

9. What are the three historical milestones in operations man- agement? How have they influenced management?

10. Identify three current trends in operations management and describe them. How do you think they will change the future of OM?

11. Define the terms total quality management, just-in-time, and reengineering. What do these terms have in common?

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26 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

CASE: Hightone Electronics, Inc. George Gonzales, operations director of Hightone Electronics, Inc. (HEI), sat quietly at the conference table overlooking the lobby of the corporate headquarters office in Palo Alto, Califor- nia. He reflected on the board meeting that had just adjourned and the challenge that lay ahead for him. The board had just announced their decision to start an Internet-based division of HEI. Web-based purchasing in the electronics industry had been growing rapidly. The board felt that HEI needed to offer on- line purchasing to its customers in order to maintain its com- petitive position. The board looked to George to outline the key operations management decisions that needed to be addressed in creating a successful Internet-based business. The next board meeting was just a week away. He had his work cut out for him.

Hightone Electronics, Inc. was founded in Palo Alto, Califor- nia over 50 years ago. Originally, the company provided radio components to small repair shops. Products were offered for sale through a catalog that was mailed to prospective custom- ers every four months. The company built its reputation on high quality and service. As time passed, HEI began supplying more than just radio parts, adding items such as fuses, transformers, computers, and electrical testing equipment. The expansion of the product line had been coupled with an increase in the num- ber and type of customers the company served. Although the traditional repair shops still remained a part of the company’s market, technical schools, universities, and well-known corpora- tions in the Silicon Valley were added to the list of customers.

Today HEI operates the Palo Alto facility with the same dedi- cation to supplying quality products through catalog sales that it

had when it was first founded. Customer service remains the top priority. HEI stocks and sells over 22,000 different items. Most customers receive their orders within 48 hours, and all compo- nents are warranted for a full year.

Expanding HEI to include Web-based purchasing seems to be a natural extension of catalog sales that the company already does successfully. George Gonzales agrees that the company has no choice but to move in this competitive direction. However, George does not agree with the opinion of the board that this would be “business as usual.” He believes that there are many operations decisions that need to be identified and addressed. As he stated in the meeting, “Having a slick Web site is one thing, but making sure the right product is delivered to the right loca- tion is another. Operations is the key to making this happen.” His challenge for the next board meeting was to identify the key operations decision and persuade the board that these issues needed serious consideration.

Case Questions 1. Explain why operations management is critical to the

success of a business. Why would developing an Internet-based business require diff erent operations consideration for HEI? Is George Gonzales correct in his assessment that this would not be “business as usual”?

2. Recall that HEI wishes to continue its reputation of high quality and service. Identify key operations management decisions that need to be considered. How diff erent will these decisions be for the Internet business?

CASE: Creature Care Animal Clinic (A) It has been three years since Dr. Julia Barr opened Creature Care Animal Clinic, a suburban veterinary clinic. Dr. Barr thought that by now she would be enjoying having her own practice. She had spent many years in college and worked to save money in order to start a business. Instead, she felt overwhelmed with business problems that were facing the clinic. She thought to herself: “I don’t produce anything. I just provide a service doing something I enjoy. How can this be so complicated?”

Company Background Dr. Barr opened Creature Care Animal Clinic as a veterinary clinic specializing in the care of dogs and cats. The clinic was set to operate Monday through Friday during regular busi- ness hours, with half days on Saturday and extended hours on Wednesday evening. Dr. Barr hired another full-time veterinar- ian, Dr. Gene Yen, a staff of three nurses, an office manager, and an office assistant. Both doctors were to work during the week

12. Describe today’s OM environment. How different is it from that of a few years ago? Identify specific features you think characterize today’s OM environment.

13. Describe the impact of e-commerce on operations man- agement. Identify the challenges posed by e-commerce on opera- tions management.

14. Find a company you are familiar with and explain how it uses its operations management function. Identify what the company is doing correctly. Do you have any suggestions for improvement?

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INTERACTIVE CASE: VIRTUAL COMPANY • 27

and rotate the shift for Wednesday evenings and Saturdays. A similar schedule was set up for the nurses. The office manager worked during regular business hours, and the assistant worked on Wednesday evenings and Saturdays. Dr. Barr set up this schedule based on a clinic she had observed as a resident and thought it sounded reasonable.

Since the clinic was small, Dr. Barr did not have a formal sys- tem of inventory management. All physicians and nurses were allowed to place purchase orders based on need. Initially this system worked well, but after a few months problems started developing. Frequently, there was excess inventory of certain items, and in many cases there were multiple brands of the same product. Sometimes medications passed their expiration dates and had to be thrown away. At the same time, the clinic often unexpectedly ran out of stock of certain supplies and rush orders had to be placed. On one occasion, they ran so low on bandages that the assistant had to be sent to the local drug store.

Dr. Barr continued to rotate with Dr. Yen for coverage on Saturdays and Wednesday evenings. However, demand was

increasing so rapidly on Saturdays that one doctor was not enough to provide needed coverage. Also, the Friday afternoon schedule was usually so packed that the staff frequently had to stay late in the evening. At the same time, there was little demand on Wednesday evenings and Dr. Barr found herself working on paperwork on those evenings, while the nurse and office assis- tant performed menial office tasks.

Case Questions 1. Identify the operations management problems that

Dr. Barr is having at the clinic. 2. Th e schedule Dr. Barr set up worked well at the clinic

where she was a resident. What are some of the reasons why it might not be working here?

3. Identify some of the reasons why the clinic is having inventory problems.

4. What should Dr. Barr have done diff erently to avoid some of the problems she is currently experiencing?

5. What suggestions would you make to Dr. Barr now?

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments

On-line Case: Valley Memorial Hospital

Assignment: Welcome to Kaizen Consulting Bob Reilly, head of Kaizen Consulting, Inc. just called to let you know that you have been accepted as an intern for his company. Kaizen handles many different clients, but you will primarily be working with one of their healthcare clients, Valley Memorial Hospital. Your internship will start in a few weeks. Bob has advised you to get thoroughly familiar with the company and its operations before you start working on the specific tasks that will be assigned to you. This assignment will enable you to enhance your knowledge of the material in Chapter 1 while preparing you for a successful internship.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Valley Memorial Hospital • Click Kaizen Consulting, Inc. • Click Consulting Assignments • Click Welcome to Kaizen Consulting

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28 • CHAPTER 1 INTRODUCTION TO OPERATIONS MANAGEMENT

Visit the Web sites of at least one service and one manufacturing company. For each company, identify at least five characteristic OM decisions and show your results in a table. Which decisions are strategic and which are tactical? How do these decisions differ between the two companies? Here are some Web sites to consider. Select service company Web sites:

www.ritzcarlton.com (Ritz-Carlton Hotel) www.sprint.com (Sprint Corporation) www.yellowcorp.com (Yellow Corporation)

www.kmart.com (Kmart Corporation) www.yahoo.com (Yahoo!)

Select manufacturing company Web sites: www.saturn.com (Saturn Corporation) www.alcoa.com (Alcoa Inc.) www.milliken.com (Milliken & Company) www.Intel.com (Intel Corporation) www.ge.com (General Electric Company)

INTERNET CHALLENGE Demonstrating Your Knowledge of OM

Selected Bibliography Beach, R., A.P. Muhlemann, D.H.R. Price, A. Paterson, and J.A.

Sharp. “Manufacturing Operations and Strategic Flexibil- ity: Survey and Cases,” International Journal of Operations and Production Management, 20, 1, 2000, 7–30.

Carter, P.J., R.M. Monczka, and J. Mossconi. “Looking at the Future of Supply Management,” Supply Chain Manage- ment Review, December 2005, 27–29.

Chase. R.B., F.B. Jacob, and N.J. Aquilano. Operations Manage- ment for a Competitive Advantage, Eleventh Edition. New York: Irwin McGraw-Hill, 2006.

Dennis, Michael J., and Ajit Kambil. “Service Management: Building Profi ts aft er the Sale,” Supply Chain Management Review, January–February, 2003, 42–48.

Dischinger, J., D.J. Closs, E. McCulloch, C. Speier, W. Greno- ble, and D. Marshall. “Th e Emerging Supply Chain Man- agement Profession,” Supply Chain Management Review, January–February 2006, 62–68.

Friedman, T.L. Th e World Is Flat, New York: Farrar Straus and Giroux, 2005.

Fugate, B.S., and J.T. Mentzer. “Dell’s Suply Chain DNA,” Sup- ply Chain Management Review, 2004, 20–24.

Galbraith, J.K. Th e Affl uent Society. Boston: Houghton Miffl in, 1958.

Klassen, R.D., and D.C. Whybark. “Environmental Manage- ment in Operations: Th e Selection of Environmental Technologies,” Decision Sciences, 30, 3, 1999, 601–631.

Skinner, W. Manufacturing in the Corporate Strategy. New York: John Wiley & Sons, 1978.

Skinner, W. “Manufacturing Strategy on the ‘S’ Curve,” Pro- duction and Operations Management, Spring 1996, 3–14.

Wu, John C. “Anatomy of a Dot-Com,” Supply Chain Manage- ment Review, November–December, 2001, 42–51.

Online Resources Companion Website www.wiley.com/college/reid

• Take interactive practice quizzes to assess your knowledge and help you study in a dynamic way • Review PowerPoint slides or print slides for notetaking • Access the Virtual Company: Cruise International, Inc. • Find links for Additional Web Resources for this chapter • APICS, www.apics.org • Council of Logistics Management, www.clml.org

Additional Resources Available Only in WileyPLUS • Use the e-Book and launch directly to all interactive resources • Take the interactive Quick Test to check your understanding of the chapter material and get immediate feedback on your

responses • Check your understanding of the key vocabulary in the chapter with Interactive Flash Cards • Use the Animated Demo Problems to review key problem types • Practice for your test with additional problem sets

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L E A R N I N G O B J E C T I V E S

CHAPTER

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

C H A P T E R O U T L I N E

The Role of Operations Strategy 31 Developing a Business Strategy 32 Developing an Operations Strategy 37 Strategic Role of Technology 43

Productivity 44 Operations Strategy within OM: How It All Fits

Together 48 Operations Strategy across the Organization 49

Before studying this chapter you should know or, if necessary, review

1. The role of the OM function in organizations, Chapter 1, pp. 2–5. 2. Differences between strategic and tactical decisions, Chapter 1, pp. 7–10.

After studying this chapter you should be able to

1 Defi ne the role of business strategy. 2 Explain how a business strategy is developed. 3 Explain the role of operations strategy in the organization. 4 Explain the relationship between business strategy and operations strategy. 5 Describe how an operations strategy is developed. 6 Identify competitive priorities of the operations function. 7 Explain the strategic role of technology. 8 Defi ne productivity and identify productivity measures. 9 Compute productivity measures.

Operations Strategy and Competitiveness

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30 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

To maintain a competitive position in the mar-ketplace, a company must have a long-range plan. This plan needs to include the company’s long-term goals, an understanding of the mar- ketplace, and a way to differentiate the company from its competitors. All other decisions must support this long-range plan. Otherwise, each person in the company would pursue goals that he or she considered important, and the company would quickly fall apart.

The functioning of a football team on the field is similar to the functioning of a business and provides a good example of the importance of a plan or vision. Before the plays are made, the

team prepares a game strategy. Each player on the team must perform a particular role to support this strategy. The strategy is a “game plan” designed so that the team can win. Imagine what would occur if individual players decided to do plays that they thought were appropriate. Certainly the team’s chance of winning would not be very high. A successful football team is a unified group of players using their individual skills in support of a winning strategy. The same is true of a business.

The long-range plan of a business, designed to provide and sustain shareholder value, is called the business strategy. For a company to succeed, the business strategy must be supported by each of the individual business functions, such as operations, finance, and marketing. Operations strategy is a long-range plan for the operations function that specifies the design and use of resources to support the business strategy. Just as the players on a football team support the team’s strategy, the role of everyone in the company is to do his or her job in a way that supports the business strategy.

Let’s look at two companies operating in the same industry, but with very different business strategies. The first is Southwest Airlines, which has a strategy to compete on cost. Southwest offers low-cost services aimed at price-sensitive customers. To sup- port this strategy, every aspect of Southwest’s operation is focused on cutting costs out of the system. Later in this chapter we will look at specific operations decisions that Southwest has made to achieve this. The second company is Singapore Airlines, which has a strategy to compete on service. To support this strategy the airline offers free drinks, complimentary headsets, meals prepared by gourmet chefs, comfortable cabins, and even the biggest bed in business class, called the “spacebed.” Both airlines began as regional carriers, and each has grown to be a highly successful major air- line. Although they are in the same industry, their operations decisions are different because of their different business strategies.

p l k f s p h w

i p a

S p

or ts

Il lu

st ra

te d

/G et

ty Im

ag es

, I nc

� Business strategy A long-range plan for a business.

� Operations strategy A long-range plan for the operations function that specifies the design and use of resources to support the business strategy.

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THE ROLE OF OPERATIONS STRATEGY • 31

The role of operations strategy is to provide a plan for the operations function so that it can make the best use of its resources. Operations strategy specifies the policies and plans for using the organization’s resources to support its long-term competitive strat- egy. Figure 2-1 shows this relationship.

Remember that the operations function is responsible for managing the resources needed to produce the company’s goods and services. Operations strategy is the plan that specifies the design and use of resources to support the business strategy. This includes the location, size, and type of facilities available; worker skills and talents required; use of technology, special processes needed, special equipment; and quality control methods. The operations strategy must be aligned with the company’s busi- ness strategy and enable the company to achieve its long-term plan. For example, the business strategy of FedEx, the world’s largest provider of expedited delivery services, is to compete on time and dependability of deliveries. The operations strategy of FedEx developed a plan for resources to support its business strategy. To provide speed of delivery, FedEx acquired its own fleet of airplanes. To provide dependability of deliv- eries, FedEx invested in a sophisticated bar-code technology to track all packages.

The Importance of Operations Strategy Operations strategy did not come to the forefront until the 1970s. Up to that time, U.S. companies emphasized mass production of standard product designs. There were no serious international competitors, and U.S. companies could pretty much

HRM MIS

THE ROLE OF OPERATIONS STRATEGY

In today’s highly competitive, Internet-based, and global marketplace, it is impor- tant for companies to have a clear plan for achieving their goals. In this chapter we dis- cuss the role of operations strategy, its relationship to the business strategy, and ways in which the operations function can best support the business strategy. We conclude with a discussion of productivity, one measure of a company’s competitiveness.

FIGURE 2-1

Relationship between the business strategy and the functional strategies

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32 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

sell anything they produced. However, that changed in the 1970s and 1980s. Japanese companies began offering products of superior quality at lower cost, and U.S. compa- nies lost market share to their Japanese counterparts. In an attempt to survive, many U.S. companies copied Japanese approaches. Unfortunately, merely copying these approaches often proved unsuccessful; it took time to really understand the Japanese approaches. It became clear that Japanese companies were more competitive because of their operations strategy; that is, all their resources were specifically designed to directly support the company’s overall strategic plan.

Harvard Business School professor Michael Porter says that companies often do not understand the differences between operational efficiency and strategy. Opera- tional efficiency is performing operations tasks well, even better than competitors. Strategy, on the other hand, is a plan for competing in the marketplace. An analogy might be that of running a race efficiently, but the wrong race. Strategy is defining in what race you will win. Operational efficiency and strategy must be aligned; otherwise, you may be very efficiently performing the wrong task. The role of operations strategy is to make sure that all the tasks performed by the operations function are the right tasks. Consider a software company that recently invested millions of dollars in developing software with features not provided by competitors, only to discover that these were features customers did not particularly want.

Now that we know the meaning of business strategy and operations strategy and their importance, let’s look at how a company would go about developing a business strategy. Then we will see how an operations strategy would be developed to support the company’s business strategy.

DEVELOPING A BUSINESS STRATEGY

A company’s business strategy is developed after its managers have considered many factors and have made some strategic decisions. These include developing an under- standing of what business the company is in (the company’s mission), analyzing and developing an understanding of the market (environmental scanning), and identify- ing the company’s strengths (core competencies). These three factors are critical to the development of the company’s long-range plan, or business strategy. In this section we describe each of these elements in detail and show how they are combined to formu- late the business strategy.

Mission Every organization, from IBM to the Boy Scouts, has a mission. The mission is a state- ment that answers three overriding questions:

● What business will the company be in (“selling personal computers,” “operating an Italian restaurant”)?

● Who will the customers be, and what are the expected customer attributes (“home- owners,” “college graduates”)?

● How will the company’s basic beliefs defi ne the business (“gives the highest cus- tomer service,” “stresses family values”)?

� Mission A statement defining what business an organization is in, who its customers are, and how its core beliefs shape its business.

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DEVELOPING A BUSINESS STRATEGY • 33

Following is a list of some well-known companies and parts of their mission statements:

Dell Computer Corporation: “to be the most successful computer company in the world”

Delta Air Lines: “worldwide airlines choice” IBM: “translate advanced technologies into values for our customers as the world’s

largest information service company” Lowe’s: “helping customers build, improve and enjoy their homes” Ryder: “off ers a wide array of logistics services, such as distribution management,

domestically and globally”

The mission defines the company. In order to develop a long-term plan for a busi- ness, you must first know exactly what business you are in, what customers you are serving, and what your company’s values are. If a company does not have a well- defined mission, it may pursue business opportunities about which it has no real knowledge or that are in conflict with its current pursuits, or it may miss opportunities altogether.

For example, Dell Computer Corporation has become a leader in the computer industry in part by following its mission. If it did not follow its mission, Dell might decide to pursue other opportunities, such as producing mobile telephones similar to those manufactured by Motorola and Nokia. Although there is a huge market for mobile telephones, it is not consistent with Dell’s mission of focusing on computers.

Environmental Scanning A second factor to consider is the external environment of the business. This includes trends in the market, in the economic and political environment, and in society. These trends must be analyzed to determine business opportunities and threats. Environ- mental scanning is the process of monitoring the external environment. To remain competitive, companies have to continuously monitor their environment and be pre- pared to change their business strategy, or long-range plan, in light of environmental changes.

What Does Environmental Scanning Tell Us? Environmental scanning allows a company to identify opportunities and threats. For example, through environmental scanning we could see gaps in what customers need and what competitors are doing to meet those needs. A study of these gaps could reveal an opportunity for our company, and we could design a plan to take advantage of it. On the other hand, our company may currently be a leader in its industry, but environmental scanning could reveal competitors that are meeting customer needs better—for example, by offering a wider array of services. In this case, environmental scanning would reveal a threat and we would have to change our strategy so as not to be left behind. Just because a company is an industry leader today does not mean it will continue to be a leader in the future. In the 1970s Sears, Roebuck and Company was a retail leader, but it fell behind the pack in the 1990s.

What Are Trends in the Environment? The external business environment is always changing. To stay ahead of the competition, a company must constantly look out for trends or changing patterns in the environment, such as marketplace trends. These might include changes in customer wants and expectations and ways in which competitors are meeting those expectations. For example, in the computer indus- try customers are demanding speed of delivery, high quality, and low price. Dell has

� Environmental scanning Monitoring the external environment for changes and trends to determine business opportunities and threats.

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34 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

become a leader in the industry because of its speed of delivery and low price. Other computer giants, such as Compaq, have had to redesign their business and operations strategies to compete with Dell. Otherwise, they would be left behind. It is through environmental scanning that companies like Compaq can see trends in the market, analyze the competition, and recognize what they need to do to remain competitive.

There are many other types of trends in the marketplace. For example, we are see- ing changes in the use of technology, such as point-of-sale scanners, automation, computer-assisted processing, electronic purchasing, and electronic order tracking. One rapidly growing trend is e-commerce. For retailers like The Gap, Eddie Bauer, Fruit of the Loom, Inc., Barnes & Noble, and others, e-commerce has become a sig- nificant part of their business. Victoria’s Secret has even used the Internet to conduct a fashion show in order to boost sales. Some companies began using e-commerce early in their development. Others, like Sears, Roebuck, waited and then found themselves working hard to catch up to the competition.

In addition to market trends, environmental scanning looks at economic, political, and social trends that can affect the business. Economic trends include recession, infla- tion, interest rates, and general economic conditions. Suppose that a company is con- sidering obtaining a loan in order to purchase a new facility. Environmental scanning could show that interest rates are particularly favorable and that this may be a good time to go ahead with the purchase.

Political trends include changes in the political climate—local, national, and international—that could affect a company. For example, the creation of the European Union has had a significant impact on strategic planning for such global companies as IBM, Hewlett-Packard, and PepsiCo. Similarly, changes in trade relations with China have opened opportunities that were not available earlier. There has been a change in how companies view their environment, a shift from a national to a global perspective. Companies seek customers and suppliers all over the globe. Many have changed their strategies in order to take advantage of global opportunities, such as forming partner- ships with international firms, called strategic alliances. For example, companies like Motorola and Xerox want to take advantage of opportunities in China and are devel- oping strategic alliances to help them break into that market.

Finally, social trends are changes in society that can have an impact on a business. An example is the awareness of the dangers of smoking, which has made smoking less

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DEVELOPING A BUSINESS STRATEGY • 35

socially acceptable. This trend has had a huge impact on the tobacco industry. In order to survive, many of these companies have changed their strategy to focus on custom- ers overseas, where smoking is still socially acceptable, or have diversified into other product lines.

Core Competencies The third factor that helps define a business strategy is an understanding of the company’s strengths. These are called core competencies. In order to formulate a long-term plan, the company’s managers must know the competencies of their orga- nization. Core competencies could include special skills of workers, such as expertise in providing customized services or knowledge of information technology. Another example might be flexible facilities that can handle the production of a wide array of products. To be successful, a company must compete in markets where its core com- petencies will have value. Table 2-1 shows a list of some core competencies that com- panies may have.

Highly successful firms develop a business strategy that takes advantage of their core competencies or strengths. To see why it is important to use core competencies, think of a student developing plans for a successful professional career. Let’s say that this student is particularly good at mathematics but not as good in verbal communica- tion and persuasion. Taking advantage of core competencies would mean developing a career strategy in which the student’s strengths could provide an advantage, such as engineering or computer science. On the other hand, pursuing a career in market- ing would place the student at a disadvantage because of a relative lack of skills in persuasion.

Increased global competition has driven many companies to clearly identify their core competencies and outsource those activities considered noncore. Recall from Chapter 1 that outsourcing is obtaining goods or services from an outside provider. By outsourcing noncore activities, a company can focus on its core competencies.

� Core competencies The unique strengths of a business.

1. Workforce Highly trained Responsive in meeting customer needs Flexible in performing a variety of tasks Strong technical capability Creative in product design

2. Facilities Flexible in producing a variety of products Technologically advanced An efficient distribution system

3. Market Understanding Skilled in understanding customer wants and predicting market trends

4. Financial Know-how Skilled in attracting and raising capital

5. Technology Use of latest production technology Use of information technology Quality control techniques

TABLE 2-1

Organizational Core Competencies

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36 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

For example, Meijer, a grocery and general merchandise retailer, outsources the transportation of all its merchandise to a company called Total Logistics Con- trol (TLC). TLC is responsible for all deliveries, route scheduling, and all activi- ties involved in maintaining a fleet of trucks, allowing Meijer to focus on its core competencies.

Putting It Together Figure 2-2 shows how the mission, environmental scanning, and core competencies help in the formulation of the business strategy. This is an ongoing process that is con- stantly allowed to change. As environmental scanning reveals changes in the exter- nal environment, the company may need to change its business strategy to remain competitive while taking advantage of its core competencies and staying within its mission.

Let’s look at how Dell Computer Corporation combined its mission, environmental scanning, and core competencies to develop a highly successful business strategy. Dell’s mission is to “be the most suc- cessful computer company in the world at delivering the best cus- tomer experience in markets we serve. In doing so, Dell will meet customer expectations of: highest

quality, leading technology, competitive pricing, individual and company accountabil- ity, best-in-class service and support, flexible customization capability, superior cor- porate citizenship, and financial stability.” The mission defined what business Dell is in: highest quality, leading technology, computer company. It also defined Dell’s cus- tomers: focus on markets served. Finally, it defined how Dell would do this: through competitive pricing, best-in-class service and support, and flexible customization capa- bility. You can see how this mission defines Dell as a company.

FIGURE 2-2

Three inputs in developing a business strategy

LINKS TO PRACTICE Dell Computer

Corporation www.dell.com

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DEVELOPING AN OPERATIONS STRATEGY • 37

An environmental scan revealed that competing computer manufacturers, such as IBM and Compaq, used intermediate resellers to sell computers. This led to higher inventory, higher costs, and slower responsiveness to customer wants. Michael Dell’s idea was to sell directly to the customer and be able to put together exactly the system the customer wanted within a short time. Dell defined its core competencies as flex- ible manufacturing and the latest technological offering. Together, the mission, envi- ronmental scan, and core competencies were used to develop a competitive business strategy that provides customized computer solutions to customers within 36 hours at a highly competitive price.

Dell’s business strategy was to take advantage of an opportunity in the market. How- ever, to implement this strategy, the company needed to develop an operations strat- egy that arranged all the resources in ways that would support the business strategy. Operations strategy designs a plan for resources in order to take the business strategy from concept to reality. In the next section we look at how an operations strategy is developed.

Once a business strategy has been developed, an operations strategy must be formu- lated. This will provide a plan for the design and management of the operations func- tion in ways that support the business strategy. The operations strategy relates the business strategy to the operations function. The operations strategy focuses on spe- cific capabilities of the operation that give the company a competitive edge. These capabilities are called competitive priorities. By excelling in one of these capabilities, a company can become a winner in its market. These competitive priorities and their relationship to the design of the operations function are shown in Figure 2-3. Each part of this figure is discussed next.

Competitive Priorities Operations managers must work closely with marketing in order to understand the com- petitive situation in the company’s market before they can determine which competitive priorities are important. There are four broad categories of competitive priorities:

1. Cost Competing based on cost means offering a product at a low price relative to the prices of competing products. The need for this type of competition emerges from the business strategy. The role of the operations strategy is to develop a plan for the use of resources to support this type of competition. Note that a low-cost strategy can result in a higher profit margin, even at a competitive price. Also, low cost does not imply low quality. Let’s look at some specific characteristics of the operations function we might find in a company competing on cost.

� Competitive priorities Capabilities that the operations function can develop in order to give a company a competitive advantage in its market.

� Cost A competitive priority focusing on low cost.

DEVELOPING AN OPERATIONS STRATEGY

Before You Go On

Make sure that you understand the role of the business strategy in defining a company’s long-term plan. Without a business strategy the company would have no overriding plan. Such a plan acts like a compass, pointing the com- pany in the right direction. To be effective, a long-range plan must be supported by each of the business functions. The operations strategy looks at the business strategy and develops a long-range plan specifically for the operations function. In the next section we will see how the operations strategy is developed.

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38 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

To develop this competitive priority, the operations function must focus primarily on cutting costs in the system, such as costs of labor, materials, and facilities. Compa- nies that compete based on cost study their operations system carefully to eliminate all waste. They might offer extra training to employees to maximize their productivity and minimize scrap. Also, they might invest in automation in order to increase pro- ductivity. Generally, companies that compete based on cost offer a narrow range of products and product features, allow for little customization, and have an operations process that is designed to be as efficient as possible.

A company that successfully competes on cost is Southwest Air- lines. Southwest’s entire operations function is designed to support this strategy. Facilities are stream- lined: only one type of aircraft is used, and flight routes are generally short. This serves to minimize costs of scheduling crew changes, main- tenance, inventories of parts, and many administrative costs. Unnec- essary costs are completely elimi-

nated: there are no meals, printed boarding passes, or seat assignments. Employees are trained to perform many functions and use a team approach to maximize customer service. Because of this strategy, Southwest has been a model for the airline industry for a number of years.

FIGURE 2-3

Operations strategy and the design of the operations function

LINKS TO PRACTICE Southwest Airlines

Company www.southwest.com

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DEVELOPING AN OPERATIONS STRATEGY • 39

2. Quality Many companies claim that quality is their top priority, and many cus- tomers say that they look for quality in the products they buy. Yet quality has a sub- jective meaning; it depends on who is defining it. For example, to one person quality could mean that the product lasts a long time, such as with a Volvo, a car known for its longevity. To another person quality might mean high performance, such as a BMW. When companies focus on quality as a competitive priority, they are focusing on the dimensions of quality that are considered important by their customers.

Quality as a competitive priority has two dimensions. The first is high-performance design. This means that the operations function will be designed to focus on aspects of quality such as superior features, close tolerances, high durability, and excellent cus- tomer service. The second dimension is goods and services consistency, which mea- sures how often the goods or services meet the exact design specifications. A strong example of product consistency is McDonald’s, where we know we can get the same product every time at any location. Companies that compete on quality must deliver not only high-performance design but goods and services consistency as well.

A company that competes on this dimension needs to implement quality in every area of the organization. One of the first aspects that needs to be addressed is product design quality, which involves making sure the product meets the requirements of the customer. A second aspect is process quality, which deals with designing a process to produce error-free products. This includes focusing on equipment, workers, materials, and every other aspect of the operation to make sure it works the way it is supposed to. Companies that compete based on quality have to address both of these issues: the product must be designed to meet customer needs, and the process must produce the product exactly as it is designed.

To see why product and process quality are both important, let’s say that your favorite fast-food restaurant has designed a new sandwich called the “Big Yuck.” The restaurant could design a process that produces a perfect “Big Yuck” every single time. But if customers find the “Big Yuck” unappealing, they will not buy it. The same would be true if the restaurant designed a sandwich called the “Super Delicious” to meet the desires of its customers. Even if the “Super Delicious” were exactly what the cus- tomers wanted, if the process did not produce the sandwich the way it was designed, often making it soggy and cold instead, customers would not buy it. Remember that the product needs to be designed to meet customer wants and needs, and the pro- cess needs to be designed to produce the exact product that was intended, consistently without error.

3. Time Time or speed is one of the most important competitive priorities today. Companies in all industries are competing to deliver high-quality products in as short a time as possible. Companies like FedEx, LensCrafters, United Parcel Service (UPS), and Dell compete based on time. Today’s customers don’t want to wait, and compa- nies that can meet their need for fast service are becoming leaders in their industries.

Making time a competitive priority means competing based on all time-related issues, such as rapid delivery and on-time delivery. Rapid delivery refers to how quickly an order is received; on-time delivery refers to how often deliveries are made on time. Another time-competitive priority is development speed, which is the time needed to take an idea to the marketplace. This is especially critical in technology and computer software fields. When time is a competitive priority, the job of the operations function is to critically analyze the system and combine or eliminate processes in order to save time. Often companies use technology to speed up processes, rely on a flexible work- force to meet peak demand periods, and eliminate unnecessary steps in the production process.

� Quality A competitive priority focusing on the quality of goods and services.

� Time A competitive priority focusing on speed and on-time delivery.

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40 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

FedEx is an example of a com- pany that competes based on time. The company’s claim is to “abso- lutely, positively” deliver packages on time. To support this strategy, the operation function had to be designed to promote speed. Bar- code technology is used to speed up processing and handling, and the company uses its own fleet of airplanes. FedEx relies on a very flexible part-time workforce, such

as college students who are willing to work a few hours at night. FedEx can call on this part-time workforce at a moment’s notice, providing the company with a great deal of flexibility. This allows FedEx to cover workforce requirements during peak periods without having to schedule full-time workers.

4. Flexibility As a company’s environment changes rapidly, including customer needs and expectations, the ability to readily accommodate these changes can be a winning strategy. This is flexibility. There are two dimensions of flexibility. One is the ability to offer a wide variety of goods or services and customize them to the unique needs of clients. This is called product flexibility. A flexible system can quickly add new products that may be important to customers or easily drop a product that is not doing well. Another aspect of flexibility is the ability to rapidly increase or decrease the amount produced in order to accommodate changes in the demand. This is called volume flexibility.

You can see the meaning of flexibility when you compare ordering a suit from a custom tailor to buying it off the rack at a retailer. Another example would be going to a fine restaurant and asking to have a meal made just for you, versus going to a fast-food restaurant and being limited to items on the menu. The custom tailor and the fine restaurant are examples of companies that are flexible and will accommo- date customer wishes. Another example of flexibility is Empire West Inc., a company that makes a variety of products out of plastics, depending on what customers want. Empire West makes everything from plastic trays to body guards for cars.

Companies that compete based on flexibility often cannot compete based on speed because it generally requires more time to produce a customized product. Also, flexible companies typically do not compete based on cost because it may take more resources to customize the product. However, flexible companies often offer greater customer service and can meet unique customer requirements. To carry out this strategy, flex- ible companies tend to have more general-purpose equipment that can be used to make many different kinds of products. Also, workers in flexible companies tend to have higher skill levels and can often perform many different tasks in order to meet customer needs.

The Need for Trade-Offs You may be wondering why the operations function needs to give special focus to some priorities but not all. Aren’t all the priorities important? As more resources are dedicated to one priority, fewer resources are left for others. The operations function must place emphasis on those priorities that directly support the business

� Flexibility A competitive priority focusing on offering a wide variety of goods or services.

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DEVELOPING AN OPERATIONS STRATEGY • 41

strategy. Therefore, it needs to make trade-offs between the different priorities. For example, consider a company that competes on using the highest quality compo- nent parts in its products. Due to the high quality of parts, the company may not be able to offer the final product at the lowest price. In this case, the company has made a trade-off between quality and price. Similarly, a company that competes on making each product individually based on customer specifications will likely not be able to compete on speed. Here, the trade-off has been made between flexibility and speed.

It is important to know that every business must achieve a basic level of each of the priorities, even though its primary focus is only on some. For example, even though a company is not competing on low price, it still cannot offer its products at such a high price that customers would not want to pay for them. Similarly, even though a com- pany is not competing on time, it still has to produce its product within a reasonable amount of time; otherwise, customers will not be willing to wait for it.

One way that large facilities with multiple products can address the issue of trade- offs is using the concept of plant-within-a-plant (PWP), introduced by well-known Harvard professor Wickham Skinner. The PWP concept suggests that different areas of a facility be dedicated to different products with different competitive priorities. These areas should be physically separated from one another and should even have their own separate workforce. As the term suggests, there are multiple plants within one plant, allowing a company to produce different products that compete on differ- ent priorities. For example, hospitals use PWP to achieve specialization or focus in a particular area, such as the cardiac unit, oncology, radiology, surgery, or pharmacy. Similarly, department stores use PWP to isolate departments, such as the Sears auto service department versus its optometry center.

Order Winners and Qualifiers To help a company decide which competitive priorities to focus on, it is important to distinguish between order winners and order qualifiers, which are concepts developed by Terry Hill, a professor at Oxford University. Order qualifiers are those competitive priorities that a company has to meet if it wants to do business in a particular market. Order winners, on the other hand, are the competitive priorities that help a company win orders in the market. Consider a simple restaurant that makes and delivers piz- zas. Order qualifiers might be low price (say, less than $10.00) and quick delivery (say, under 15 minutes) because this is a standard that has been set by competing pizza res- taurants. The order winners may be “fresh ingredients” and “home-made taste.” These characteristics may differentiate the restaurant from all the other pizza restaurants. However, regardless of how good the pizza, the restaurant will not succeed if it does not meet the minimum standard for order qualifiers. Knowing the order winners and order qualifiers in a particular market is critical to focusing on the right competitive priorities.

It is important to understand that order winners and order qualifiers change over time. Often when one company in a market is successfully competing using a particular order winner, other companies follow suit over time. The result is that the order winner becomes an industry standard, or an order qualifier. To compete successfully, companies then have to change their order winners to differentiate themselves. An excellent example of this occurred in the auto industry. Prior to the 1970s, the order-winning criterion in the American auto industry was price. Then the Japanese automobile manufacturers entered the market competing on quality

� Trade-off The need to focus more on one competitive priority than on others.

� Order qualifiers Competitive priorities that must be met for a company to qualify as a competitor in the marketplace.

� Order winners Competitive priorities that win orders in the marketplace.

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42 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

at a reasonable price. The result was that quality became the new order winner and price became an order qualifier, or an expectation. Then by the 1980s American manufacturers were able to raise their level of quality to be competitive with the Japanese. Quality then became an order qualifier, as everyone had the same quality standard.

Translating Competitive Priorities into Production Requirements Operations strategy makes the needs of the business strategy specific to the operations function by focusing on the right competitive priorities. Once the competitive priori- ties have been identified, a plan is developed to support those priorities. The opera- tions strategy will specify the design and use of the organization’s resources; that is, it will set forth specific operations requirements. These can be broken down into two categories.

1. Structure—Operations decisions related to the design of the production process, such as characteristics of facilities used, selection of appropriate technology, and fl ow of goods and services through the facility.

2. Infrastructure—Operations decisions related to the planning and control systems of the operation, such as organization of the operations function, skills and pay of workers, and quality control approaches.

Together, the structure and infrastructure of the production process determine the nature of the company’s operations function.

The structure and infrastructure of the production process must be aligned to enable the company to pursue its long-term plan. Suppose we determined that time or speed of delivery is the order winner in the marketplace and the competitive prior- ity we need to focus on. We would then design the production process to promote speedy product delivery. This might mean having a system that does not necessarily produce the product at the absolutely lowest cost, possibly because we need costlier or extra equipment to help us focus on speed. The important thing is that every aspect of production of a product or delivery of a service needs to focus on supporting the competitive priority. However, we cannot neglect the other competitive priorities. A certain level of order qualifiers must be achieved just to remain in the market. The issue is not one of focusing on one priority to the exclusion of the others. Rather, it is a matter of degree.

Let’s return to the example of Dell Computer Corporation. Earlier we explained how Dell used its mission, environmental scanning, and core competencies to develop its business strategy. But to make this business plan a reality, the company needed to develop an operations strategy to create its structure and infrastructure. The focus was on customer service, cost, and speed. Dell set up a system in which customers could order computers directly from the company, without going through an inter- mediary, such as a retailer. An operations system was designed so that ordering of components and assembly of computers did not occur until an order was actually placed. This kept costs low because Dell did not have computers sitting in inventory. A warehousing system was designed so that when components were needed, sup- pliers would deliver them to the plant within 15 minutes; in contrast, competitors like IBM and Compaq must wait hours or even days to receive needed components. To further increase speed, Dell set up a shipping arrangement with United Parcel Service (UPS). With this structure and infrastructure, Dell was able to implement its business plan.

� Structure Operations decisions related to the design of the production process, such as facilities, technology, and flow of goods and services through the facility.

� Infrastructure Operations decisions related to the planning and control systems of the operation, such as organization of operations, skills and pay of workers, and quality measures.

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STRATEGIC ROLE OF TECHNOLOGY • 43

STRATEGIC ROLE OF TECHNOLOGY Over the last decade we have seen an unprecedented growth in technological capa- bility. Technology has enabled companies to share real-time information across the globe, to improve the speed and quality of their processes, and to design products in innovative ways. Companies can use technology to help them gain an advantage over their competitors. For this reason technology has become a critical factor for compa- nies in achieving a competitive advantage. In fact, studies have shown that companies that invest in new technologies tend to improve their financial position over those that do not. However, the technologies a company acquires should not be decided on randomly, such as following the latest fad or industry trend. Rather, the selected technology needs to support the organization’s competitive priorities, as we learned in the example of FedEx. Also, technology needs to be selected to enhance the company’s core competencies and add to its competitive advantage.

Types of Technologies There are three primary types of technologies. They are differentiated based on their application, but all three areas of technology are important to operations managers. The first type is product technology, which is any new technology developed by a firm. An example of this would include Teflon®, the material used in no-stick fry pans. Tef- lon became an emerging technology in the 1970s and is currently used in numerous applications. Other examples include CDs and flat-screened monitors. Product tech- nology is important as companies must regularly update their processes to produce the latest types of products.

A second type of technology is process technology. It is the technology used to improve the process of creating goods and services. Examples of this would include computer- aided design (CAD) and computer-aided manufacturing (CAM). These are technologies that use computers to assist engineers in the way they design and manufacture prod- ucts. Process technologies are important to companies, as they enable tasks to be accom- plished more efficiently. We will learn more about these technologies in Chapter 3.

The last type of technology is information technology, which enables communi- cation, processing, and storage of information. Information technology has grown rapidly over recent years and has had a profound impact on business. Just consider the changes that have occurred due to the Internet. The Internet has enabled elec- tronic commerce and the creation of the virtual marketplace and has linked custom- ers and buyers. Another example of information technology is enterprise resource planning (ERP), which functions via large software programs used for planning and coordinating all resources throughout the entire enterprise. ERP systems have enabled

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Before You Go On

By now you should have a clear understanding of how an operations strategy is developed and its role in helping the organization decide which competitive priorities to focus on. There are four categories of competitive priori- ties: cost, quality, time, and flexibility. A company must make trade-offs in deciding which priorities to focus on. The operations strategy and the competitive priorities dictate the design and plan for the operations function, which includes the structure and infrastructure of the operation. This is a dynamic process, and as the environment changes, the organization must be prepared to change accordingly. Operations strategy plays a key role in an orga- nization’s ability to compete. In the next section we discuss a way to measure a company’s competitive capability.

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44 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

companies to reduce costs and improve responsiveness but are highly expensive to purchase and implement. Consequently, as with any technology, investment in ERP needs to be a strategic decision.

Technology as a Tool for Competitive Advantage Technology can be acquired to improve processes and maintain up-to-date standards. Technology can also be used to gain a competitive advantage. For example, by acquir- ing technology a company can improve quality, reduce costs, and improve product delivery. This can provide an advantage over the competition and help gain market share. However, investing in technology can be costly and entails risks, such as over- estimating the benefits of the technology or incurring the risk of obsolescence due to rapid new inventions.

Technology should be acquired to support the company’s chosen competitive priori- ties, not just to follow the latest market fad. Also, technology may require the company to rethink its strategy. For example, when the Internet became available, it was generally assumed that it would replace traditional ways of doing business. This has not turned out to be the case. In fact, for many companies the Internet has enhanced traditional methods. Physical activities such as shipping, warehousing, transportation, and even physical contact must still be performed. For example, pharmacy chains such as Wal- greens and CVS have found that although customers place orders over the Internet, they prefer to pick them up in person. Similarly, the airlines have discovered that an easy-to- use Web site can increase airline bookings. However, successful use of a technology such as the Internet requires companies to develop strategies that integrate the technology. As you can see, acquiring technology is an important strategic decision for companies. Operations managers must consider many factors when making a purchase decision.

PRODUCTIVITY Sound business strategy and supporting operations strategy make an organization more competitive in the marketplace. But how does a company measure its competi- tiveness? One of the most common ways is by measuring productivity. In this section we will look at how to measure the productivity of each of a company’s resources as well as the entire organization.

Measuring Productivity Recall that operations management is responsible for managing the transformation of many inputs into outputs, such as goods or services. A measure of how efficiently inputs are being converted into outputs is called productivity. Productivity measures how well resources are used. It is computed as a ratio of outputs (goods and services) to inputs (e.g., labor and materials). The more efficiently a company uses its resources, the more productive it is:

Productivity 5 output input

(2-1)

This equation can be used to measure the productivity of one worker or many, as well as the productivity of a machine, a department, the whole firm, or even a nation. The possibilities are shown in Table 2-2.

When we compute productivity for all inputs combined, such as labor, machines, and capital, we are measuring total productivity. For example, let’s say that the weekly

� Productivity A measure of how efficiently an organization converts inputs into outputs.

� Total productivity Productivity computed as a ratio of output to all organizational inputs.

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PRODUCTIVITY • 45

dollar value of a company’s output, such as finished goods and work in progress, is $10,200 and that the value of its inputs, such as labor, materials, and capital, is $8600. The company’s total weekly productivity would be computed as follows:

Total productivity 5 output input

5 $10,200 $8600

5 1.186

Often it is much more useful to measure the productivity of one input variable at a time in order to identify how efficiently each is being used. When we compute pro- ductivity as the ratio of output relative to a single input, we obtain a measure of partial productivity, also called single-factor productivity. Following are two examples of the calculation of partial productivity: 1. A bakery oven produces 346 pastries in 4 hours. What is its productivity?

Machine productivity 5 number of pastries/oven time

5 346 pastries

4 hours 5 86.5 pastries/hour

2. Two workers paint tables in a furniture shop. If the workers paint 22 tables in 8 hours, what is their productivity?

Labor productivity 5 22 tables

2 workers 3 8 hours 5 1.375 tables/hour

Examples of select partial productivity measures are shown in Table 2-3. Sometimes we need to compute productivity as the ratio of output relative to a

group of inputs, such as labor and materials. This is a measure of multifactor produc- tivity. For example, let’s say that output is worth $382 and labor and materials costs are $168 and $98, respectively. A multifactor productivity measure of our use of labor and materials would be

Multifactor productivity 5 output

labor 1 materials

5 $382

$168 1 $98 5 1.436

� Partial productivity Productivity computed as a ratio of output to only one input (e.g., labor, materials, machines).

� Multifactor productivity Productivity computed as a ratio of output to several, but not all, inputs.

Total Productivity Measure Output produced All inputs used

Partial Productivity Measure Output Labor

or Output

Machines or

Output Materials

or Output Capital

Multifactor Productivity Measure Output Labor 1 machines

Output Labor 1 materials

Output Labor 1 capital 1 energy

TABLE 2-2

Productivity Measures

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46 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

Long Beach Bank employs three loan officers, each working eight hours per day. Each officer processes an average of five loans per day. The bank’s payroll cost for the officers is $820 per day, and there is a daily overhead expense of $500. The bank has just purchased new computer software that should enable each officer to process eight loans per day, although the overhead expense will increase to $550. Evaluate the change in labor and multifactor productivity before and after implementation of the new computer software.

• Before You Begin: When solving productivity problems, make sure that the value of outputs and inputs is computed over the same time period, such as day, week, month, or year. Also, when evaluating a change in productivity, compute the productivity before and after the expected change and calculate the percentage difference.

• Solution:

Labor productivity 1old 2 5 3 officers 3 5 loans/day 24 labor-hours

5 15 loans/day

24 labor-hours

5 0.625 loans per labor-hour

Labor productivity 1new 2 5 3 officers 3 8 loans/day 24 labor-hours

5 24 loans/day

24 labor-hours

5 1.00 loan per labor-hour

Multifactor productivity 1old 2 5 15 loans/day $820/day 1 $500/day

5 0.0113 loans/dollar

Multifactor productivity 1new 2 5 24 loans/day $820/day 1 $550/day

5 0.0175 loans/dollar

The change in labor productivity is from 0.625 to 1.00 loans per labor-hour. This results in an increase of 1.00/0.625 � 1.6, or an increase of 60 percent. The change in multifactor productivity is from 0.0113 to 0.0175 loans per dollar. This results in an increase of 0.0175/0.0113 � 1.55, or an increase of 55 percent.

EXAMPLE 2.1

Computing Productivity

Business Type Productivity Measure

Restaurant Customers served Labor-hour

or Customers served

Square foot

Hospital Patients Hospital bed

or Patients

Nurse-hour

Amusement park Visitors Square foot

or Visitors

Attraction

Cattle ranch Cattle Pound of feed

or Cattle

Acre of land

Garment manufacturer Sweaters Pound of yarn

or Sweaters

Machine-hour

TABLE 2-3

Examples of Partial Productivity Measures

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PRODUCTIVITY • 47

Interpreting Productivity Measures To interpret the meaning of a productivity measure, it must be compared with a similar productivity measure. For example, if one worker at a pizza shop produces 17 pizzas in two hours, the productivity of that worker is 8.5 pizzas per hour. This number by itself does not tell us very much. However, if we compare it to the productivity of two other workers, one who produces 7.2 pizzas per hour and another 6.8 pizzas per hour, it is much more meaningful. We can see that the first worker is much more productive than the other two workers. But how do we know whether the productivity of all three workers is reasonable? What we need is a standard. In Chapter 11 we will discuss ways to set stan- dards and how those standards can help in evaluating the performance of our workers.

It is also helpful to measure and compare productivity over time. Let’s say that we want to measure the total productivity of our three pizza makers (our “labor”) and we compute a labor productivity measure of 7.5 pizzas per hour. This number does not tell us much about the workers’ performance. However, if we compare weekly productivity measures over time, perhaps over the last four weeks, we get much more information:

Week 1 2 3 4

Productivity (pizzas/labor-hour) 5.4 6.8 7.1 7.5

Now we see that the workers’ productivity is improving over time. In fact, pro- ductivity changed from 5.4 to 7.5 pizzas per labor-hour, resulting in an increase of 7.5/5.4 = 1.39, or an increase of 39 percent. But what if we find out that our main competitor, a pizzeria down the street, has a productivity of 9.5 pizzas per labor-hour? This productivity rate is 26.7 percent (9.5/7.5 = 1.267) higher than our productivity in week 4. Suddenly we know that even though our productivity is going up, it should be higher. We may have to analyze our processes and increase our productivity in order to be competitive. By comparing our productivity over time and against similar opera- tions, we have a much better sense of how high our productivity really is.

When evaluating productivity and setting standards for performance, we also need to consider our strategy for competing in the marketplace—namely, our competitive priorities. A company that competes based on speed would probably measure produc- tivity in units produced over time. However, a company that competes based on cost might measure productivity in terms of costs of inputs such as labor, materials, and overhead. The important thing is that our productivity measure provides information on how we are doing relative to the competitive priority that is most important to us.

Productivity and Competitiveness Productivity is essentially a scorecard of how efficiently resources are used and a mea- sure of competitiveness. Productivity is measured on many levels and is of interest to a wide range of people. As we showed in earlier examples, productivity can be measured for individuals, departments, or organizations. It can track performance over time and help managers identify problems. Similarly, productivity can be measured for an entire industry and even a country.

The economic success of a nation and the quality of life of its citizens are related to its competitiveness in the global marketplace. Increases in productivity are directly related to increases in a nation’s standard of living. That is why business and government lead- ers continuously monitor the productivity at the national level and by industry sectors.

Productivity in the United States had been increasing for over 100 years. Then in the 1970s and 1980s productivity dropped, even lagging behind that of other industrial

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48 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

nations. Fortunately, productivity rebounded in the mid- and late 1990s. Today, com- panies understand the importance of competitiveness, and productivity in the United States continues to improve. Changes in U.S. productivity can be seen in Figure 2-4.

Productivity and the Service Sector Service sector companies have a unique challenge when trying to measure produc- tivity. The reason is that traditional productivity measures tend to focus on tangible outcomes, as seen with goods-producing activities. Services primarily produce intan- gible products, such as ideas and information, making it difficult to evaluate quality. Consequently, accurately measuring productivity improvements can be difficult. A good example of the difficulty in using traditional productivity measures in the service sector is the emergency room. Here inputs are the medical staff, yet outputs may not exist if no one needed treatment on that shift. In that case, by traditional measures, productivity would be zero! The real issue in this type of environment is the level of readiness, and the challenge is to adequately measure it.

As we discussed previously, employment in the service sector of the U.S. economy has grown rapidly over the past 30 years. Unfortunately, productivity gains in this sec- tor have been much lower than those of manufacturing. It is hoped that advancements in information technology will help standardize services and accelerate productivity in this sector.

Year

0 93 94 2000

A nn

ua l P

er ce

nt ag

e C

ha ng

2001 2002 2003 2004 2005 2006 2007 2008 2009 201095 96 97 98 99

Percentage change in U.S. business sector productivity (output per hour)

FIGURE 2-4

Source: Bureau of Labor Statistics

OPERATIONS STRATEGY WITHIN OM: HOW IT ALL FITS TOGETHER

We have learned that the strategic decisions of a firm drive its tactical decisions. Oper- ations strategy decisions are critical in this process because they serve as a linkage between the business strategy and all the other operations decisions. Recall that opera- tions strategy provides a plan for the OM function that supports the business strategy. In turn, decisions regarding operations strategy directly impact decisions on organi- zational structure and infrastructure of the company. This includes selection of the

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THE SUPPLY CHAIN LINK • 49

facilities (Chapter 10), type of process (Chapter 8), choice of technology (Chapter 3), quality control decisions (Chapters 5 and 6), skills and pay of workers (Chapter 11), and numerous other decisions. As in the example of Southwest Airlines, an operations strategy that focuses on cost competition would translate into specific operations deci- sions that eliminate all frills from the system.

In subsequent chapters of this book, we will study specific decisions that pertain to organizational structure and infrastructure. We will see that these decisions are gov- erned by the firm’s operations strategy. We will also learn how these specific decisions impact each other.

OPERATIONS STRATEGY ACROSS THE ORGANIZATION

The business strategy defines the long-range plan for the entire company and guides the actions of each of the company’s business functions. Those functions, in turn, develop plans to support the business strategy. However, in defining their individual strategies, it is important for the functions to work together and understand each oth- er’s needs.

Marketing identifies target markets, studies competition, and communicates with customers. In developing its own strategy, marketing needs to fully understand the capabilities of the operations function, the types of resources being used, and the way those resources are utilized. Otherwise, marketing’s strategy could entail making promises that operations cannot deliver. In turn, marketing needs to communicate to operations all its observed and anticipated market changes.

Finance develops financial plans to support the business strategy. However, since it is the operations function that manages all the organization’s resources, the financial plans in effect support operations activities. Before it can develop its own strategy, finance needs to communicate with operations in order to understand the financial requirements of planned resources. In turn, operations managers cannot fully develop a strategy until they have a clear understanding of financial capabilities.

The strategies of all the business functions need to support each other in achieving the goals set by the business strategy and are best developed through a team approach.

MKT

FIN

The operations strategy of a firm directly impacts deci- sions on its structure and infrastructure, including its supply chain. This includes the design of the supply chain, such as its length, and the relationships the firm has with its supply chain partners. Together, the operations strategy and the firm’s supply chain must support the business strategy of the firm. This can be illustrated by the competitive pri- orities of the firm, which directly impact the type of supply chain a company has in place. For example, a company that competes on cost must have a highly efficient supply chain with high integration of the OM function between supply chain partners. The reason is

that the supply chain plays a critical role in keeping both production and delivery costs down. Therefore, a firm competing on cost might structure its supply chain so that the least expensive suppliers are used rather than those with the highest quality supplies.

In contrast, a company that competes on quality will likely have a different supply chain. Compet- ing on quality means that a company’s products and services are known for their premium nature, such as product consistency and reliability. Many

aspects of the supply chain are altered when com- panies compete on quality versus another competitive priority, such as cost. The company will likely source its components from suppliers known for quality who have implemented total quality management throughout

THE SUPPLY CHAIN LINK

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50 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

their production process. The concept of quality will also be embedded in other aspects of the supply chain, such as transportation, delivery, and packaging.

An excellent example of aligning operations strategy with the supply chain is illustrated by Wal-Mart. Sam Walton, Wal-Mart’s founder, was a strategic visionary who developed the low-cost retail strategy that is sup- ported by its supply chain. Wal-Mart’s supply chain is designed to buy not from distributors but directly

from manufacturers in order to lower costs and offer a broad range of merchandise. In fact, Wal-Mart has had a legendary partnership with Procter & Gamble, where replenishment of inventories is done automati- cally. These supply chain actions were designed to help Wal-Mart meet its overall competitive strategy, which is to provide its customers with a wide product offering at a low price. This has helped Wal-Mart become the world’s largest retailer.

Governmental decision makers can require companies to adopt practices that are deemed environmentally safe and socially responsible, using laws and government regulations. Many companies, however, engage in vol- untary compliance in sustainable practices. These pro- vide benefits in the form of advertising—such as logos that appeal to the preferences of a growing number of environmentally conscious consumers—and group membership in certification programs, which provide directory listings and other perks. Like other operations decisions, the decision to embrace sustainability prac- tices, above and beyond those mandated by regulatory standards, must be driven by the business strategy.

The Brazilian company Aracruz Celulose offers a good example of sustainability decisions that are driven by the company’s business strategy. The company is responsible for 24% of the global supply of bleached eucalyptus pulp, used in the manufacture of paper. It has developed practices of sustainable operations and sup- ply chain management that support its goal of being a

recognized sustainability leader. For example, Aracruz ensures environmental sustainability by preserving the natural ecosystems surrounding the eucalyptus plan- tations. The company created a forestry management program to preserve native tree populations and prevent overharvesting, and has put in place strict environmental control technology to protect and monitor the impact of their operations on forests and rivers. In addition to the environment, Aracruz focuses on social sustainability by contributing to the communities near their operation sites. The company also gives back to the local commu- nities. In 2004, for example, Aracruz contributed $5 mil- lion to improve the education, health, and social welfare of local populations. All these efforts were put into place to support the company’s business strategy of sustain- ability leadership. This has resulted in financial benefits for the company that have come from ensuring a consis- tent supply of natural material and elimination of waste. Aracruz has also received brand recognition for its efforts. In 2008 Aracruz was highlighted as the company with the world’s best corporate sustainability practices by the Dow Jones Sustainability Index (DJSI World).

THE SUSTAINABILITY LINK

4 Th e role of business strategy is to serve as an overall guide for the development of the organization’s operations strategy.

5 Th e operations strategy focuses on developing specifi c capabilities called competitive priorities. In designing its operation, an organization is governed by the operations strategy and the specifi c competitive priorities it has chosen to develop.

Chapter Highlights 1 A business strategy is a long-range plan and vision for a

business. Each of the individual business functions needs to support the business strategy.

2 An organization develops its business strategy by doing environmental scanning and considering its mission and its core competencies.

3 Th e role of operations strategy is to provide a long-range plan for the use of the company’s resources in producing the company’s primary goods and services.

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SOLVED PROBLEMS • 51

Key Terms business strategy 30 operations strategy 30 mission 32 environmental scanning 33 core competencies 35 competitive priorities 37 cost 37

quality 39 time 39 fl exibility 40 trade-off 41 order qualifi ers 41 order winners 41 structure 42

infrastructure 42 productivity 44 total productivity 44 partial productivity 45 multifactor productivity 45

Formula Review

Productivity 5 output input

6 Th ere are four categories of competitive priorities: cost, quality, time, and fl exibility.

7 Technology can be used by companies to gain a competitive advantage and should be acquired to support the company’s chosen competitive priorities.

8 Productivity is a measure that indicates how effi ciently an organization is using its resources.

9 Productivity is computed as the ratio of organizational outputs divided by inputs.

• Before You Begin: In this problem you are being asked for the total productivity. Recall that it is simply the ratio of total output over input.

• Solution

Total productivity 5 output input

5 $14,280 $16,528

5 0.864

(See student companion site for Excel template.)

• Problem 2 Bluegill has just purchased a new sanding machine that pro- cesses 17 chairs in 8 hours. What is the productivity of the sand- ing machine?

• Before You Begin: In this problem you are being asked for machine productivity, which is a partial productivity measure.

• Solution

Machine productivity 5 number of chairs processing time

5 17 chairs 8 hours

5 2.125 chairs/hour

• Problem 3 Bluegill has hired two new workers to paint chairs. They have painted 10 chairs in 4 hours. What is their labor productivity?

• Before You Begin: Remember that you should compute the labor productivity of both workers combined.

• Solution

Labor productivity 5 10 chairs

2 workers 3 4 hours

5 1.25 chairs/hour

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52 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

• Problem 5 Last week employees at Bluegill produced 46 chairs after work- ing a total of 200 hours. Of the 46 chairs produced, 12 were damaged due to a problem with the new sanding machine. The damaged chairs can be discounted and sold for $25 each. The undamaged chairs are sold to a department store retail chain for $70 each. What was the labor productivity ratio for last week? If labor productivity was $15 in sales per hour the previous week, what was the change in labor productivity?

• Before You Begin: To compute productivity you must include the total value of the output. Notice that in this problem there are different quantities of products that have different values (damaged and good chairs). You must compute the value of each type of chair and add them together to obtain the total value before taking the ratio.

• Solution

Value to output 5 112 damaged chairs 3 $25/damaged chair 2 1 134 good chairs 3 $70/good chair 2

5 $2680

Labor-hours of input 5 200 hours

Labor productivity 5 value of output

labor-hours of input

Labor productivity 5 $2680

200 hours 5 $13.40 in sales per hour

The change in labor productivity was from $15 to $13.40 in sales per hour, or a reduction of 10.67 percent.

• Problem 4 On average, Bluegill produces 35 chairs per day. Labor costs average $480, material costs are typically $200, and overhead cost is $250. If Bluegill sells the chairs to a retailer for $70 each, determine the multifactor productivity.

• Before You Begin: When computing multifactor productivity, remember to com- pute the total value of the inputs before taking the ratio.

• Solution

Multifactor productivity

5 value of output

labor costs 1 material cost 1 overhead

5 35 chairs 3 $70/chair $480 1 $200 1 $250

5 $2450 $930

5 $2.63 of sales per dollar

Discussion Questions 1. Explain the importance of a business strategy. 2. Explain the role of operations strategy in a business. 3. Describe how a business strategy is developed. 4. Describe how an operations strategy is formulated from the

business strategy. 5. Explain what is meant by the term competitive priority and

describe the four categories of competitive priorities discussed in the chapter.

6. Find an example of a company that makes quality its com- petitive priority. Find another company that makes flexibility its competitive priority. Compare these strategies.

7. What is meant by the terms order qualifiers and order win- ners? Explain why they are important.

8. Describe the three types of technologies. Explain the strate- gic role of technology.

9. Describe the meaning of productivity. Why is it important? 10. Explain the three types of productivity measures.

Problems 1. Two workers have the job of placing plastic labels on

packages before the packages are shipped out. Th e fi rst worker can place 1000 labels in 30 minutes. Th e second worker can place 850 labels in 20 minutes. Which worker is more productive?

2. Last week a painter painted three houses in fi ve days. Th is week she painted two houses in four days. In which week was the painter more productive?

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CASE: PRIME BANK OF MASSACHUSETTS • 53

Work Crew Hours Homes Cleaned

Anna, Sue, and Tim 35 10 Jim, Jose, and Andy 45 15 Dan, Wendy, and Carry 56 18 Rosie, Chandra, and Seth 30 10 Sherry, Vicky, and Roger 42 18

Assuming the quality of cleaning was consistent between crews, which crew was most productive?

CASE: Prime Bank of Massachusetts Prime Bank of Massachusetts was started in 1964 with James Rogers as CEO, who is now chairman of the board. Prime Bank had been growing steadily since its beginning and has developed a loyal customer following. Today there are 45 bank locations throughout Massachusetts, with corporate headquarters in Newbury, Massachusetts. The bank offers a wide array of bank- ing services to commercial and noncommercial customers.

Prime Bank has considered itself to be a conservative, yet innovative, organization. Its locations are open Monday–Friday 9–4 and Saturday 9–12. Most of the facilities are located adja- cent to well-established shopping centers, with multiple ATM machines and at least three drive-through windows. However, Prime Bank’s growth has brought on certain problems. Having the right amount of tellers available in the bank as well as in the drive-through window has been a challenge. Some commercial

customers had recently expressed frustration due to long wait- ing time. Also, the parking lot has often become crowded during peak periods.

While Prime Bank was going through a growth period, the general banking industry had been experiencing tougher com- petition. Competitors were increasingly offering lower interest rates on loans and higher yields on savings accounts and cer- tificates of deposit. Also, Prime Bank was experiencing growing pains, and something needed to be done soon or it would begin losing customers to competition.

The board, headed by James Rogers, decided to develop a more aggressive strategy for Prime Bank. While many of its com- petitors were competing on cost, the board decided that Prime Bank should focus on customer service in order to differentiate itself from the competition. The bank had already begun moving

3. One type of bread-making machine can make six loaves of bread in fi ve hours. A new model of the machine can make four loaves in two hours. Which model is more productive?

4. A company that makes kitchen chairs wants to compare productivity at two of its facilities. At facility #1, six workers produced 240 chairs. At facility #2, four workers produced 210 chairs during the same time period. Which facility was more productive?

5. A painter is considering using a new high-tech paint roller. Yesterday he was able to paint three walls in 45 minutes using his old method. Today he painted two walls of the same size in 20 minutes. Is the painter more productive using the new paint roller?

6. Aztec Furnishings makes hand-craft ed furniture for sale in its retail stores. Th e furniture maker has recently installed a new assembly process, including a new sander and polisher. With this new system, production has increased to 90 pieces of furniture per day from the previous 60 pieces of furniture per day. Th e number of defective items produced has dropped from 10 pieces per day to 1 per day. Th e production facility operates strictly eight hours per day. Evaluate the change in productivity for Aztec using the new assembly process.

7. Howard Plastics produces plastic containers for use in the food packaging industry. Last year its average monthly production included 20,000 containers produced using one shift fi ve days a week with an eight-hour-a-day operation. Of the items produced 15 percent were deemed defective. Recently, Howard Plastics has implemented new production methods and a new quality improvement program. Its monthly production has increased to 25,000 containers with 9 percent defective.

(a) Compute productivity ratios for the old and new production system.

(b) Compare the changes in productivity between the two production systems.

8. Med-Tech labs is a facility that provides medical tests and evaluations for patients, ranging from analyzing blood samples to performing magnetic resonance imaging (MRI). Average cost to patients is $60 per patient. Labor costs average $15 per patient, materials costs are $20 per patient, and overhead costs are averaged at $20 per patient.

(a) What is the multifactor productivity ratio for Med-Tech? What does your fi nding mean?

(b) If the average lab worker spends three hours for each patient, what is the labor productivity ratio?

9. Handy-Maid Cleaning Service operates fi ve crews with three workers per crew. Diff erent crews clean a diff erent number of homes per week and spend a diff ering amount of hours. All the homes cleaned are about the same size. Th e manager of Handy- Maid is trying to evaluate the productivity of each of the crews. Th e following data have been collected over the past week.

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54 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

in that direction by offering a 24-hour customer service depart- ment to answer customers’ banking questions. Yet, there were difficulties with this effort, such as poor staffing and not enough telephone lines. James Rogers wanted Prime Bank to aggressively solve all customer service issues, such as staffing, layout, and facil- ities. He also wanted greater creativity in adding improvements in customer service, such as on-line banking, and special services for large customers. He believed that improving most aspects of the bank’s operation would give Prime Bank a competitive advantage.

The board presented their new strategy to Victoria Chen, vice president of operations. Victoria had recently been promoted to the V.P. level and understood the importance of operations management. She was asked to identify all changes that should be made in the operation function that would support this new strategy and present them at the next board meeting. Victoria had been hoping for an opportunity to prove herself since she began with the bank. This was her chance.

Case Questions 1. Why is the operations function important in implementing

the strategy of an organization? Explain why the changes put in place by Victoria Chen and her team could either hurt or help the bank.

2. Develop a list of changes for the operations function that should be considered by the bank. Begin by identifying operations management decisions that would be involved in operating a bank, for example, layout of facility, staff , drive- through service. Th en identify ways that they can be improved at Prime Bank in order to support the strategy focused on customer service.

3. Th ink of the improvements identifi ed in answering question 2. How diff erent would these improvements be if the bank had a strategy of cutting cost rather than supporting customer service?

CASE: Boseman Oil and Petroleum (BOP) Boseman Oil and Petroleum (BOP) is one of many oil companies operating offshore petroleum platforms in the Gulf of Mexico. The company identifies offshore sites for exploration drilling and con- structs drilling platforms. Once exploration activities are success- ful, the platforms are converted to a production platform to extract crude oil and natural gas. BOP operates multiple platforms and an onshore facility that serves as the primary interface between the platforms. Boats with specialized crews provide logistics services between the platforms and the onshore facility. The boats deliver fuel, water, equipment, and other needed supplies multiple times a day to the platforms. Accurate and timely delivery of materials is absolutely necessary for successful platform operations.

BOP had traditionally focused on exploration and produc- tion activities, paying little attention to operating costs. How- ever, operating costs had been increasing rapidly. A particularly significant cost was the operating of boats and crews needed to provide logistics services between platforms and the onshore facility. The boats are highly specialized, with built-in stor- age tanks and unique cargo space designs. The boat crews are

specially trained, and operating the boats and crews is highly expensive. Although BOP is dependent on the boat deliveries, it does not use the boats at full capacity and they are often idle.

Jeff Kessinger, director of offshore operations for BOP, is now faced with the decision of how to reduce operating costs. One option is to outsource the logistics service to a company special- izing in providing offshore logistics services. Logistics-Offshore Inc. is such a company, owning and maintaining its own fleet of boats and crews. Logistics-Offshore could be hired to perform this function. BOP could sell its boats and focus on oil explora- tion. Jeff is aware that outsourcing is an important strategic deci- sion and there is much to consider. He is not sure where to begin.

Case Questions 1. Identify the potential strategic advantages and disad-

vantages for BOP in outsourcing the boat logistics service to Logistics-Off shore. Explain the strategic implications of each.

2. Identify the type of information Jeff Kessinger needs to gather and evaluate in order to make his decision.

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Getting Acquainted with Cruise International, Inc. After a few minutes on hold, you hear Bob Bristol begin speaking on the phone. “Hello,” he begins. “I understand

that you wanted to let me know about your progress and needed some additional guidelines. My assistant, Shontelle, gave me the details of your work on the first assignment. I am pleased that you are beginning to learn about CII and the cruising industry. Since you still have some time before starting at CII, I think it would be a good idea for you to

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ONLINE RESOURCES • 55

get some broad insights into the industry and its competitive environment.

“You must look at the big picture and try to understand the company’s vision and its mission. I want to identify the core vales that CII emphasizes and explain how a specific emphasis dictates its business practices.” This assignment will enhance your knowledge of the material in Chapter 2 of your textbook while preparing you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Getting Acquainted with Cruise International, Inc.

On-line Case: Operations Strategy at Valley Memorial Hospital

Assignment: Getting Acquainted with Valley Memorial Hospital With a few more weeks before you join Kaizen and start work- ing for its client Valley Memorial Hospital, it is essential for you to get some broad insights into the company and its operations. Bob Reilly has given you a few preliminary research projects to work on in order to familiarize yourself with VMH and the healthcare industry. This assignment will enable you to enhance

your knowledge of the material in Chapter 2 while continuing to prepare you for a successful internship.

To access the Web site:

Select two companies in the same industry, either in service or in manufacturing. You can select industries such as fast-food, banking, healthcare, computer manufacturing, or auto manufac- turing. Use the Internet to visit the selected companies’ Web sites and collect the following information: their mission statement, target market, and specifics of their product and service offer- ings. Explain the differences between the companies’ business strategies and target markets. How do their product and service

offerings differ relative to their target markets and their overall strategies? Finally, how does their operations function support their business strategies? Try to explain how operations utilizes specific organizational resources to support the business strategy.

Web sites to consider: www.Ihcargo.com (Lufthansa Cargo) www.ualcargo.com (United Airlines, United Cargo)

INTERNET CHALLENGE Understanding Strategic Differences

Online Resources Companion Website www.wiley.com/college/reid

Sensenich Propeller Manufacturing Company • Find links for Additional Web Resources for this chapter

Th e Association for Manufacturing Excellence, www.ame.org

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56 • CHAPTER 2 OPERATIONS STRATEGY AND COMPETITIVENESS

Selected Bibliography Ahlstrom, P., and R. Westbrook. “Implications of Mass Cus-

tomization for OM: An Exploratory Survey,” International Journal of Operations and Production Management, 19, 3, 1999, 262–274.

Fine, Charles H. Clock Speed, Winning Industry Control in the Age of Temporary Advantage. New York: Perseus Books, 1998.

Gordon, Benjamin H. “Th e Changing Face of Th ird Party Logistics,” Supply Chain Management Review, March/ April 2003, 50–57.

Grover, V., and M.K. Malhotra. “A Framework for Examining the Interface between Operations and Information Sys- tems: Implications for Research in the New Millennium,” Decision Sciences, 30, 4, 1999, 901–919.

Hayes, Robert H., and Steven C. Wheelwright. Restoring Our Competitive Edge: Competing through Manufacturing. New York: John Wiley & Sons, 1984.

Hayes, Robert H., Gary Pisano, D. Upton, and S.C. Wheel- wright. Operations Strategy and Technology: Pursuing the Competitive Edge. New York: John Wiley & Sons, 2005.

Hill, Terry. Manufacturing Strategy Text and Cases. New York: McGraw-Hill, 2000.

Kendall, Ken E. “Th e Signifi cance of Information Systems Research on Emerging Technologies,” Decision Sciences, 28, 4, 1997, 775–792.

Peters, T.J. In Search of Excellence: Lessons from America’s Best Run Companies. New York: Warner Books, 1984.

Porter, Michael E. “What Is Strategy?” Harvard Business Review 74 (November–December 1996), 61–78.

Robert, Michel. Strategy Pure and Simple II. New York: McGraw-Hill, 1998.

Rondeau, P.J., M.S. Vonderembse, and T.S. Raghunathan. “Exploring Work System Practices for Time-Based Manu- facturers: Th eir Impact on Competitive Capabilities,” Jour- nal of Operations Management, 18, 2000, 509–529.

Spring, M., and J.F. Dalrymple. “Product Customization and Manufacturing Strategy,” International Journal of Opera- tions and Production Management, 20, 4, 2000, 441–467.

Vickery, S., C. Droge, and R. Germain. “Th e Relationship between Product Customization and Organizational Culture,” Journal of Operations Management, 17, 1999, 277–301.

Vokurka, R.J., and S.W. O’Leary-Kelly. “A Review of Empirical Research on Manufacturing Flexibility,” Journal of Opera- tions Management, 18, 2000, 485–501.

Ward, P.T., and R. Duray. “Manufacturing Strategy in Context: Environment, Competitive Strategy, and Manufacturing Strategy,” Journal of Operations Management, 18, 2000, 123–138.

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L E A R N I N G O B J E C T I V E S

CHAPTER

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

C H A P T E R O U T L I N E

Product Design 59 Th e Product Design Process 60 Factors Impacting Product Design 65 Process Selection 68 Designing Processes 72 Process Performance Metrics 74 Linking Product Design and Process Selection 77

Technology Decisions 82 Designing Services 87 Product Design and Process Selection within

OM: How It All Fits Together 91 Product Design and Process Selection across the

Organization 92

Before studying this chapter you should know or, if necessary, review

1. Differences between manufacturing and service organizations, Chapter 1, pp. 5–7. 2. Differences between strategic and tactical decisions, Chapter 1, pp. 7–10. 3. Competitive priorities, Chapter 2, pp. 37–43.

After completing this chapter you should be able to

1 Defi ne product design and explain its strategic impact on the organization. 2 Describe the steps used to develop a product design. 3 Use break-even analysis as a tool in deciding between alternative products. 4 Identify diff erent types of processes and explain their characteristics. 5 Understand how to use a process fl owchart. 6 Understand how to use process performance metrics. 7 Understand current technological advancements and how they impact process and product design. 8 Understand issues of designing service operations.

Product Design and Process Selection

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58 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

Have you ever been with a group of friends and decided to order pizzas? One person wants pizza from Pizza Hut because he likes the taste of stuffed-crust pizza made with cheese in the crust. Someone else wants Donatos pizza because she likes the unique crispy-thin crust. A third wants pizza from Spagio’s because of the wood-grilled oven taste. Even a simple product like a pizza can have different features unique to its producer. Different customers have different tastes, preferences, and product needs. The variety of product designs on the market appeals to the preferences of a particu- lar customer group. Also, the different product designs have different processing requirements. This is what product design and process selec- tion are all about.

We can all relate to the product design of a pizza just from everyday life. Now consider the complexities involved in designing more sophisticated products. For example, Palm, Inc. (www.palm.com) is a leading provider

of handheld computers whose slogan is “different people, different needs, different handhelds.” The company designs different products with differing capabilities, such as personal information management, wireless Internet access, and games, intended for different types of customers. The company also has to decide on the best process to produce the different types of handhelds.

The challenge of product design can also be illustrated by an example from the Alza Corporation. Alza is a leader in designing new ways that pharmaceutical drugs can be administered to different types of patients. One of their product designs is an under- the-skin implant for pharmaceutical drugs that previously could only be administered by injection. The product design had to include time release of the drug, as well as the best material and shape for the implant. In addition to the product design, a process had to be designed to produce the unique product.

These examples illustrate that a product design that meets customer needs, although challenging, can have a large impact on a company’s success. In fact, prod- uct design is so important that leading-edge companies routinely invest in product designs well into the future. For example, in 2009 Sony released cameras that include intelligent auto mode that give users picture-perfect shots without fumbling with set- tings. This type of innovative product design can give a company a significant com- petitive advantage.

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PRODUCT DESIGN • 59

In this chapter we will learn about product design, which is the process of decid- ing on the unique characteristics and features of the company’s product. We will also learn about process selection, which is the development of the process necessary to produce the designed product. Product design and process selection decisions are typically made together. A company can have a highly innovative design for its prod- uct, but if it has not determined how to make the product in a cost-effective way, the product will stay a design forever.

Product design and process selection affect product quality, product cost, and customer satisfaction. If the product is not well designed or if the manufacturing process is not true to the product design, the quality of the product may suffer. Furthermore, the product has to be manufactured using materials, equipment, and labor skills that are efficient and affordable; otherwise, its cost will be too high for the market. We call this the product’s manufacturability—the ease with which the product can be made. Finally, if a product is to achieve customer satisfaction, it must have the combined characteristics of good design, competitive pricing, and the ability to fill a market need. This is true whether the product is pizzas or cars.

� Manufacturability The ease with which a product can be made.

PRODUCT DESIGN Most of us might think that the design of a product is not that interesting. After all, it probably involves materials, measurements, dimensions, and blueprints. When we think of design, we usually think of car design or computer design and envision engineers working on diagrams. However, product design is much more than that. Product design brings together marketing analysts, art directors, sales forecasters, engineers, finance experts, and other members of a company to think and plan strategically. It is exciting and creative, and it can spell success or disaster for a company.

Product design is the process of defining all the features and characteristics of just about anything you can think of, from Starbucks’ cafe latte or Jimmy Dean’s sausage to GM’s Saturn or HP’s DeskJet printer. Product design also includes the design of services, such as those provided by Salazar’s Beauty Salon, La Petite Academy Day Care Center, or FedEx. Consumers respond to a product’s appearance, color, texture, and performance. All of its features, summed up, are the product’s design. Someone came up with the idea of what this product will look like, taste like, or feel like so that it will appeal to you. This is the purpose of product design. Product design defines a product’s characteristics, such as its appearance, the materials it is made of, its dimensions and tolerances, and its performance standards.

Design of Services versus Goods The design elements discussed are typical of industries such as manufacturing and retail in which the product is tangible. For service industries, where the product is intangible, the design elements are equally important, but they have an added dimension.

Service design is unique in that both the service and the entire service concept are being designed. As with a tangible product, the service concept is based on meeting customer needs. The service design, however, adds the aesthetic and psychological benefits of the product. These are the service elements of the operation, such as promptness and friendliness. They also include the ambiance, image, and “feel-good” elements of the service. Consider the differences in service design of a company

FINMKT

� Product design The process of defining all of the product’s characteristics.

Product design of cell phones combines portability, features, and aesthetics.

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60 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

like Canyon Ranch, which provides a pampering retreat for health-conscious but overworked professionals, versus Gold’s Gym, which caters to young athletes. As with a tangible product, the preference for a service is based on its product design. Service design defines the characteristics of a service, such as its physical elements, and the aesthetic and psychological benefits it provides.

� Service design The process of establishing all the characteristics of the service, including physical, sensual, and psychological benefits.

THE PRODUCT DESIGN PROCESS Certain steps are common to the development of most product designs: idea generation, product screening, preliminary design and testing, and final design. These steps are shown in Figure 3-1. Notice that the arrows show a circular process. Product designs are never finished, but are always updated with new ideas. Let’s look at these steps in more detail.

Idea Development All product designs begin with an idea. The idea might come from a product manager who spends time with customers and has a sense of what customers want, from an engineer with a flare for inventions, or from anyone else in the company. To remain competitive, companies must be innovative and bring out new products regularly. In some industries, the cycle of new product development is predictable. We see this in the auto industry, where new car models come out every year, or the retail industry, where new fashion is designed for every season.

In other industries, new product releases are less predictable but just as important. The Body Shop, retailer of plant-based skin care products, periodically comes up with new ideas for its product lines. The timing often has to do with the market for a product and whether sales are declining or continuing to grow.

Ideas from Customers, Competitors, and Suppliers The first source of ideas is customers, the driving force in the design of goods and services. Marketing is a vital link between customers and product design. Market researchers collect customer

FIGURE 3-1

Steps in the product design process

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THE PRODUCT DESIGN PROCESS • 61

information by studying customer buying patterns and using tools such as customer surveys and focus groups. Management may love an idea, but if market analysis shows that customers do not like it, the idea is not viable. Analyzing customer preferences is an ongoing process; customer preferences next year may be quite different from what they are today. For this reason, the related process of forecasting future consumer preferences is important, though difficult.

Competitors are another source of ideas. A company learns by observing its competitors’ products and their success rate. This includes looking at product design, pricing strategy, and other aspects of the operation. Studying the practices of companies considered “best-in-class” and comparing the performance of one’s own company against theirs is called benchmarking. We can benchmark against a company in a completely different line of business and still learn from some aspect of that company’s operation. For example, Lands’ End is well known for its successful catalog business, and companies considering catalog sales often benchmark against Lands’ End. Similarly, American Express is a company known for its success at resolving complaints, and it, too, is used for benchmarking.

The importance of benchmarking can be seen by IBM’s efforts to improve its distribution system. In 1997, IBM found its distribution costs increas- ing while customers were expecting decreasing times from factory to deliv- ery. It appeared that IBM’s supply chain practices were not keeping up with those of its competitors. To evaluate and solve this problem, IBM hired Mercer Management Consultants, who performed a large benchmarking study. IBM’s practices were compared to those of market leaders in the personal computer (PC) industry, as well as to the best logistics practices outside the technology area. The objective was to evaluate IBM’s current performance, that of companies considered best-in-class, and identify the gaps. Through the study, IBM discovered which specific costs exceeded industry bench- marks and which parts of the cycle time were excessively long. It also uncovered ways to simplify and reorganize its processes to gain efficiency. Based on findings from the benchmarking effort, IBM made changes in its operations. The results were reduced costs, improved delivery, and improved relationships with suppliers. IBM found benchmarking so beneficial that it plans to perform similar types of studies on an ongoing basis in the future.

Reverse Engineering Another way of using competitors’ ideas is to buy a competi- tor’s new product and study its design features. Using a process called reverse engi- neering, a company’s engineers carefully disassemble the product and analyze its parts and features. Ford Motor Company used this approach to design its Taurus model. Ford engineers disassembled and studied many other car models, such as BMW and Toyota, and adapted and combined their best features. Product design ideas are also generated by a company’s R & D (research and development) department, whose role is to develop product and process innovation.

Suppliers are another source of product design ideas. To remain competitive, more companies are developing partnering relationships with their suppliers to jointly

MKT

� Benchmarking The process of studying the practices of companies considered “best-in-class” and comparing your company’s performance against theirs.

� Reverse engineering The process of disassembling a product to analyze its design features.

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62 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

satisfy the end customer. Suppliers participate in a program called early supplier involvement (ESI), which involves them in the early stages of product design.

Product Screening After a product idea has been developed, it is evaluated to determine its likelihood of success. This is called product screening. The company’s product screening team evaluates the product design idea according to the needs of the major business functions. In their evaluation, executives from each function area may explore issues such as the following:

● Operations What are the production needs of the proposed new product, and how do they match our existing resources? Will we need new facilities and equip- ment? Do we have the labor skills to make the product? Can the material for pro- duction be readily obtained?

● Marketing What is the potential size of the market for the proposed new product? How much eff ort will be needed to develop a market for the product, and what is the long-term product potential?

● Finance Th e production of a new product is a fi nancial investment like any other. What is the proposed new product’s fi nancial potential, cost, and return on investment?

Unfortunately, there is no magic formula for deciding whether or not to pursue a particular product idea. Managerial skill and experience, however, are key. Companies generate new product ideas all the time, whether for a new brand of cereal or a new design for a car door. Approximately 80 percent of ideas do not make it past the screening stage. Management analyzes operations, marketing, and financial factors and then makes the final decision. Fortunately, we have decision-making tools to help us evaluate new product ideas. A popular one is break-even analysis, which we look at next.

Break-Even Analysis: A Tool for Product Screening Break-even analysis is a technique that can be useful when evaluating a new product. It computes the quan- tity of goods a company needs to sell just to cover its costs, or break even, called the “break-even” point. When evaluating an idea for a new product, it is helpful to com- pute its break-even quantity. An assessment can then be made as to how difficult or easy it will be to cover costs and make a profit. A product with a break-even quantity that is hard to attain might not be a good product choice to pursue. Next we look at how to compute the break-even quantity.

The total cost of producing a product or service is the sum of its fixed and variable costs. A company incurs fixed costs regardless of how much it produces. Fixed costs include overhead, taxes, and insurance. For example, a company must pay for overhead even if it produces nothing. Variable costs, on the other hand, are costs that vary directly with the amount of units produced and include items such as direct materials and labor. Together, fixed and variable costs add up to total cost:

Total cost 5 F 1 1VC 2Q where F � fi xed cost VC � variable cost per unit Q � number of units sold

� Early supplier involvement (ESI) Involving suppliers in the early stages of product design.

MKT FIN

� Break-even analysis A technique used to compute the amount of goods a company would need to sell to cover its costs.

� Fixed costs Costs a company incurs regardless of how much it produces.

� Variable costs Costs that vary directly with the amount of units produced.

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THE PRODUCT DESIGN PROCESS • 63

Figure 3-2 shows a graphical representation of these costs as well as the break- even quantity. Fixed cost is represented by a horizontal line as this cost is the same regardless of how much is produced. Adding variable cost to fixed cost creates total cost, represented by the diagonal line above fixed cost. When Q � 0, total cost is only equal to fixed cost. As Q increases, total cost increases through the variable cost com- ponent. The blue diagonal in the figure is revenue, the amount of money brought in from sales:

Revenue 5 1SP 2Q where SP � selling price per unit

When Q � 0, revenue is zero. As sales increase, so does revenue. Remember, however, that to cover all costs we have to sell the break-even amount. This is the quantity QBE, where revenue equals total cost. If we sell below the break-even point, we incur a loss, since costs exceed revenue. To make a profit, we have to sell above the break-even point. Since revenue equals total cost at the break-even point, we can use the previous equations to compute the value of the break-even quantity:

Total cost 5 total revenue

F 1 1VC 2Q 5 1SP 2Q Solving for Q, we get the following equation:

QBE 5 F

SP 2 VC Note that we could also find the break-even point by drawing the graph and finding where the total cost and revenue lines cross.

Break-even analysis is useful for more than just deciding between different prod- ucts. It can be used to make other decisions, such as evaluating different processes or deciding whether the company should make or buy a product.

FIGURE 3-2

Graphical approach to break-even analysis

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64 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

Fred Boulder, owner of Sports Feet Manufacturing, is considering whether to produce a new line of footwear. Fred has considered the processing needs for the new product as well as the market potential. He has also estimated that the variable cost for each product manufactured and sold is $9 and the fixed cost per year is $52,000.

(a) If Fred off ers the footwear at a selling price of $25, how many pairs must he sell to break even? (b) If Fred sells 4000 pairs at the $25 price, what will be the contribution to profi t?

• Solution: (a) To compute the break-even quantity:

Q 5 F

SP 2 VC

5 $52,000

$25 2 $9 5 3250 pairs

The break-even quantity is 3250 pairs. This is how much Fred would have to sell to cover costs. (b) To compute the contribution to profi t with sales of 4000 pairs, we can go back to the

relationship between cost and revenue:

Profit 5 total revenue 2 total cost

5 1SP 2Q 2[F 1 1VC 2Q] Profit 5 $25 14000 22[$52,000 1 $9 14000 2 ]

5 $12,000

The contribution to profit is $12,000 if Fred can sell 4000 pairs from his new line of footwear.

EXAMPLE 3.1

Computing the Break-even

Quantity

Preliminary Design and Testing Once a product idea has passed the screening stage, it is time to begin preliminary design and testing. At this stage design engineers translate general performance specifications into technical specifications. Prototypes are built and tested. Changes are made based on test results, and the process of revising, rebuilding a prototype, and testing continues. For service companies this may entail testing the offering on a small scale and working with customers to refine the service offering. Fast-food restaurants are known for this type of testing, where a new menu item may be tested in only one particular geographic area. Product refinement can be time-consuming, and the company may want to hurry through this phase to rush the product to market. However, rushing creates the risk that all the “bugs” have not been worked out, which can prove very costly.

Final Design Following extensive design testing, the product moves to the final design stage. This is where final product specifications are drawn up. The final specifications are then translated into specific processing instructions to manufacture the product, which include selecting equipment, outlining jobs that need to be performed, identifying specific materials needed and suppliers that will be used, and all the other aspects of organizing the process of product production.

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FACTORS IMPACTING PRODUCT DESIGN • 65

DFM Guidelines

1. Minimize parts.

2. Design parts for different products.

3. Use modular design.

4. Avoid tools.

5. Simplify operations.

TABLE 3-1

Guidelines for DFM

FIGURE 3-3

Progressive design of a toolbox using DFM

FACTORS IMPACTING PRODUCT DESIGN Here are some additional factors that need to be considered during the product design stage.

Design for Manufacture When we think of product design, we generally first think of how to please the cus- tomer. However, we also need to consider how easy or difficult it is to manufacture the product. Otherwise, we might have a great idea that is difficult or too costly to manufacture. Design for manufacture (DFM) is a series of guidelines that we should follow to produce a product easily and profitably. DFM guidelines focus on two issues:

1. Design simplifi cation means reducing the number of parts and features of the product whenever possible. A simpler product is easier to make, costs less, and gives higher quality.

2. Design standardization refers to the use of common and interchangeable parts. By using interchangeable parts, we can make a greater variety of products with less inventory and signifi cantly lower cost and provide greater fl exibility. Table 3-1 shows guidelines for DFM.

An example of the benefits of applying these rules is seen in Figure 3-3. We can see the progression in the design of a toolbox using the DFM approach. All of the

� Design for manufacture (DFM) A series of guidelines to follow in order to produce a product easily and profitably.

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66 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

pictures show a toolbox. However, the first design shown requires 20 parts. Through simplification and use of modular design, the number of parts required has been reduced to 2. It would certainly be much easier to make the product with 2 parts versus 20 parts. This means fewer chances for error, better quality, and lower costs due to shorter assembly time.

Product Life Cycle Another factor in product design is the stage of the life cycle of the product. Most products go through a series of stages of changing product demand called the product life cycle. There are typically four stages of the product life cycle: introduction, growth, maturity, and decline. These are shown in Figure 3-4.

Products in the introductory stage are not well defined, and neither is their market. Often all the “bugs” have not been worked out, and customers are uncertain about the product. In the growth stage, the product takes hold and both product and market continue to be refined. The third stage is that of maturity, where demand levels off and there are usually no design changes: the product is predictable at this stage and so is its market. Many products, such as toothpaste, can stay in this stage for many years. Finally, there is a decline in demand because of new technology, better product design, or market saturation.

The first two stages of the life cycle can collectively be called the early stages because the product is still being improved and refined and the market is still in the process of being developed. The last two stages of the life cycle can be referred to as the later stages because here both the product and market are well defined.

Understanding the stages of the product life cycle is important for product design purposes, such as knowing at which stage to focus on design changes. Also, when considering a new product, the expected length of the life cycle is critical in order to estimate future profitability relative to the initial investment. The product life cycle can be quite short for certain products, as seen in the computer industry. For other products it can be extremely long, as in the aircraft industry. A few products, such as paper, pencils, nails, milk, sugar, and flour, do not go through a life cycle. However, almost all products do, and some may spend a long time in one stage.

� Product life cycle A series of stages that products pass through in their lifetime, characterized by changing product demands over time.

FIGURE 3-4

Stages of the product life cycle

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FACTORS IMPACTING PRODUCT DESIGN • 67

Concurrent Engineering Concurrent engineering is an approach that brings many people together in the early phase of product design in order to simultaneously design the product and the process. This type of approach has been found to achieve a smooth transition from the design stage to actual production in a shorter amount of development time with improved quality results.

The old approach to product and process design was to first have the designers of the idea come up with the exact product characteristics. Once their design was complete they would pass it on to operations, who would then design the production process needed to produce the product. This was called the “over-the-wall” approach because the designers would throw their design “over-the-wall” to operations, who then had to decide how to produce the product.

There are many problems with the old approach. First, it is very inefficient and costly. For example, there may be certain aspects of the product that are not critical for product success but are costly or difficult to manufacture, such as a dye color that is difficult to achieve. Since manufacturing does not understand which features are not critical, it may develop an unnecessarily costly production process with costs passed down to the customers. Because the designers do not know the cost of the added fea- ture, they may not have the opportunity to change their design or may do so much later in the process, incurring additional costs. Concurrent engineering allows every- one to work together so these problems do not occur. Figure 3-5 shows the difference between the “over-the-wall” approach and concurrent engineering.

� Concurrent engineering An approach that brings together multifunction teams in the early phase of product design in order to simultaneously design the product and the process.

FIGURE 3-5

The first illustration shows sequential design with walls between functional areas. The second illustration shows concurrent design with walls broken down.

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68 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

A second problem is that the “over-the-wall” approach takes a longer amount of time than when product and process design are performed concurrently. As you can see in Figure 3-5, when product and process design are done together, much of the work is done in parallel rather than in sequence. In today’s markets, new product introductions are expected to occur faster than ever. Companies do not have the luxury of enough time to follow a sequential approach and then work the “bugs” out. They may eventually get a great product, but by then the market may not be there!

The third problem is that the old approach does not create a team atmosphere, which is important in today’s work environment. Rather, it creates an atmosphere where each function views its role separately in a type of “us versus them” mentality. With the old approach, when the designers were finished with the designs, they con- sidered their job done. If there were problems, each group blamed the other. With concurrent engineering, the team is responsible for designing and getting the product to market. Team members continue working together to resolve problems with the product and improve the process.

Remanufacturing Remanufacturing is a concept that has been gaining increasing importance as our society becomes more environmentally conscious and focuses on recycling and elimi- nating waste. Remanufacturing uses components of old products in the production of new ones. In addition to the environmental benefits, there are significant cost benefits because remanufactured products can be half the price of their new counterparts. Remanufacturing has been quite popular in the production of computers, televisions, and automobiles.

MKT

� Remanufacturing The concept of using components of old products in the production of new ones.

PROCESS SELECTION So far we have discussed issues involved in product design. Though product design is important for a company, it cannot be considered separately from the selection of the process. In this section we will look at issues involved in process design. Then we will show how product design and process selection issues are linked together.

Types of Processes When you look at different types of companies, ranging from a small coffee shop to IBM, it may seem like there are hundreds of different types of processes. Some locations are small, like your local Starbucks, and some are very large, like a Ford Motor Company plant. Some produce standardized “off-the-shelf” products, like Pepperidge Farm’s frozen chocolate cake, and some work with customers to customize their product, like cakes made to order by a gourmet bakery. Though there seem to be large differences between the processes of companies, many have certain processing characteristics in common. In this section we will divide these processes into groups with similar characteristics, allowing us to understand problems inherent with each type of process.

All processes can be grouped into two broad categories: intermittent operations and repetitive operations. These two categories differ in almost every way. Once we understand these differences, we can easily identify organizations based on the category of process they use.

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PROCESS SELECTION • 69

Intermittent Operations Intermittent operations are used to produce a variety of products with different processing requirements in lower volumes. Examples are an auto body shop, a tool and die shop, or a healthcare facility. Because different prod- ucts have different processing needs, there is no standard route that all products take through the facility. Instead, resources are grouped by function and the product is routed to each resource as needed. Think about a healthcare facility. Each patient, “the product,” is routed to different departments as needed. One patient may need to get an X-ray, go to the lab for blood work, and then go to the examining room. Another patient may need to go to the examining room and then to physical therapy.

To be able to produce products with different processing requirements, intermittent operations tend to be labor intensive rather than capital intensive. Workers need to be able to perform different tasks, depending on the processing needs of the products produced. Often we see skilled and semiskilled workers in this environment, with a fair amount of worker discretion in performing their jobs. Workers need to be flexible and able to perform different tasks as needed for the different products. Equipment in this type of environment is more general-purpose to satisfy different processing requirements. Automation tends to be less common because automation is typically product-specific. Given that many products are being produced with different processing requirements, it is usually not cost efficient to invest in automation for only one product type. Finally, the volume of goods produced is directly tied to the number of customer orders.

Repetitive Operations Repetitive operations are used to produce one or a few standardized products in high volume. Examples are a typical assembly line, cafeteria, or automatic car wash. Resources are organized in a line flow to efficiently accommo- date production of the product. Note that in this environment it is possible to arrange resources in a line because there is only one type of product. This is directly the oppo- site of what we find with intermittent operations.

To efficiently produce a large volume of one type of product, these operations tend to be capital intensive rather than labor intensive. An example is “mass-production” operations, which usually have much invested in their facilities and equipment to provide a high degree of product consistency. Often these facilities rely on automation

� Intermittent operations Processes used to produce a variety of products with different processing requirements in lower volumes.

� Repetitive operations Processes used to produce one or a few standardized products in high volume.

Felicia Martinez Photography/PhotoEdit

Designing a custom-made cake is an example of an intermittent operation.

Alistair Berg/Getty Images, Inc.

An assembly line is an example of a repetitive operation.

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70 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

and technology to improve efficiency and increase output rather than on labor skill. The volume produced is usually based on a forecast of future demands rather than on direct customer orders.

The most common differences between intermittent and repetitive operations relate to two dimensions: (1) the amount of product volume produced, and (2) the degree of product standardization. Product volume can range from making a unique product one at a time to producing a large number of products at the same time. Product standardization refers to a lack of variety in a particular product. Examples of standardized products are white undershirts, calculators, toasters, and television sets. The type of operation used, including equipment and labor, is quite different if a company produces one product at a time to customer specifications instead of mass production of one standardized product. Specific differences between intermittent and repetitive operations are shown in Table 3-2.

The Continuum of Process Types Dividing processes into two fundamental categories of operations is helpful in our understanding of their general characteristics. To be more detailed, we can further divide each category according to product volume and degree of product standardization, as follows. Intermittent operations can be divided into project processes and batch processes. Repetitive operations can be divided into line processes and continuous processes. Figure 3-6 shows a continuum of process types. Next we look at what makes these processes different from each other.

Decision Intermittent Operations

Repetitive Operations

Product variety Great Small

Degree of standardization Low High

Organization of resources Grouped by function Line flow to accommodate processing needs

Path of products through facility

In a varied pattern, depending on product needs

Line flow

Factor driving production Customer orders Forecast of future demands

Critical resource Labor-intensive operation (worker skills important)

Capital-intensive operation (equipment automation, technology important)

Type of equipment General-purpose Specialized

Degree of automation Low High

Throughput time Longer Shorter

Work-in-process inventory More Less

TABLE 3-2

Differences between Intermittent and Repetitive Operations

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PROCESS SELECTION • 71

● Project processes are used to make one-of-a-kind products exactly to customer specifi cations. Th ese processes are used when there is high customization and low product volume, because each product is diff erent. Examples can be seen in con- struction, shipbuilding, medical procedures, creation of artwork, custom tailoring, and interior design. With project processes the customer is usually involved in deciding on the design of the product. Th e artistic baker you hired to bake a wed- ding cake to your specifi cations uses a project process.

● Batch processes are used to produce small quantities of products in groups or batches based on customer orders or product specifi cations. Th ey are also known as job shops. Th e volumes of each product produced are still small, and there can still be a high degree of customization. Examples can be seen in bakeries, educa- tion, and printing shops. Th e classes you are taking at the university use a batch process.

● Line processes are designed to produce a large volume of a standardized product for mass production. Th ey are also known as fl ow shops, fl ow lines, or assembly lines. With line processes the product that is produced is made in high volume with little or no customization. Th ink of a typical assembly line that produces everything from cars, computers, television sets, shoes, candy bars, even food items.

● Continuous processes operate continually to produce a very high volume of a fully standardized product. Examples include oil refi neries, water treatment plants, and certain paint facilities. Th e products produced by continuous processes are usually in continual rather than discrete units, such as liquid or gas. Th ey usually have a single input and a limited number of outputs. Also, these facilities are usu- ally highly capital intensive and automated.

� Project process A type of process used to make a one-at-a-time product exactly to customer specifications.

� Batch process A type of process used to produce a small quantity of products in groups or batches based on customer orders or specifications.

� Line process A type of process used to produce a large volume of a standardized product.

� Continuous process A type of process that operates continually to produce a high volume of a fully standardized product.

FIGURE 3-6

Types of processes based on product volume and product standardization Source: Adapted from Robert H. Hayes and Steven C. Wheelwright, “Link Manufacturing Process and Product Life Cycles,” Harvard Business Review, January–February, 1979, 133–140.

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72 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

Note that both project and batch processes have low product volumes and offer customization. The difference is in the volume and degree of customization. Project processes are more extreme cases of intermittent operations compared to batch processes. Also, note that both line and continuous processes primarily produce large volumes of standardized products. Again, the difference is in the volume and degree of standardization. Continuous processes are more extreme cases of high volume and product standardization than are line processes.

Figure 3-6 positions these four process types along the diagonal to show the best process strategies relative to product volume and product customization. Companies whose process strategies do not fall along this diagonal may not have made the best process decisions. Bear in mind, however, that not all companies fit into only one of these categories: a company may use both batch and project processing to good advantage. For example, a bakery that produces breads, cakes, and pastries in batches may also bake and decorate cakes to order.

DESIGNING PROCESSES Now that we know about different types of processes, let’s look at a technique that can help with process design.

Process flow analysis is a technique used for evaluating a process in terms of the sequence of steps from inputs to outputs with the goal of improving its design. One of the most important tools in process flow analysis is a process flowchart. A process flowchart is used for viewing the sequence of steps involved in producing the product and the flow of the product through the process. It is useful for seeing the totality of the operation and for identifying potential problem areas.

There is no exact format for designing a flowchart. It can be very simple or highly detailed. The typical symbols used are arrows to represent flows, triangles to repre- sent decision points, inverted triangles to represent storage of goods, and rectangles as tasks. Let’s begin by looking at some elements used in developing a flowchart, as shown in Figure 3-7. Shown first, in Figure 3-7(a), are flows between stages in a simple multistage process, which is a process with multiple activities (“stages”). You can see that the arrows indicate a simple flow of materials between the different stages.

Often, multiple stages have storage areas or “buffers” between them for placement of either partially completed (work-in-process) or fully completed (finished goods) inventory, shown in Figure 3-7(b). This enables the two stages to operate indepen- dently of each other. Otherwise, the first stage would have to produce a product at the same exact rate as the second stage. For example, let’s say that the first stage of a multistage process produces one product in 40 seconds and the second stage in 60 seconds. That means that for every unit produced the first stage would have to stop and wait 20 seconds for the second stage to finish its work. Because the capacity of the second stage is holding up the speed of the process, it is called a bottleneck. Now let’s see what happens if the first stage takes 60 seconds to produce a product and the second stage 40 seconds. In this case the first stage becomes the bottleneck, and the second stage has to wait 20 seconds to receive a product. Obviously, the best is for both stages to produce at the same rate, though this is often not possible. Inventory is then placed between the stages to even out differences in production capacity.

Often stages in the production process can be performed in parallel, as shown in Figure 3-7(c) and (d). The two stages can produce different products (c) or the same

� Process flow analysis A technique used for evaluating a process in terms of the sequence of steps from inputs to outputs with the goal of improving its design.

� Process flowchart A chart showing the sequence of steps in producing the product or service.

� Bottleneck Longest task in the process.

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DESIGNING PROCESSES • 73

product (d). Notice that in the latter case this would mean that the capacity of the stage performed in parallel has effectively been doubled.

Now let’s look at an illustration of a flowchart using Antonio’s Pizzeria as an example. Let’s say that Antonio produces three different styles of pizzas to satisfy different types of customers. First are cheese pizzas made with standard ingredients and a standard crust. They are the most popular items, and Antonio makes them ahead of time to ensure that they are always available upon demand. This is called a make-to-stock strategy. Second are pizzas that use a standard crust prepared ahead of time but are assembled based on specific customer requests. This is called an assemble-to-order strategy. Lastly are pizzas made to order based on specific cus- tomer requirements, allowing choices of different types of crusts and toppings. This is called a make-to-order strategy. We will look at these product strategies more closely later in this chapter. For now, let’s look at the flowcharts for the three processes in Figure 3-8. Notice that although the flowcharts are similar, they show customer inter- action at different points in the process.

� Make-to-stock strategy Produces standard products and services for immediate sale or delivery.

� Assemble-to-order strategy Produces standard components that can be combined to customer specifications.

� Make-to-order strategy Produces products to customer specifications after an order has been received.

FIGURE 3-7

Elements of flowchart development

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74 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

Process flowcharts can also be used to map the flow of the customer through the process and to identify potential problem areas. Figure 3-9 shows a flowchart for Antonio’s Pizzeria that includes the steps involved in placing and processing a cus- tomer order. The points in the process for potential problems are indicated. Manage- ment can then monitor these problem areas. The chart could be even more detailed, including information such as frequency of errors or approximate time to complete a task. As you can see, process flowcharts are very useful tools when designing and evaluating processes.

Make Dough

Prepare Crust

Assemble Pizza

Bake

Assemble Pizzas

Bake

Delivery

Customer Order

Finished goods

inventory “pizza”

(a) Make-to-stock strategy

Make Dough

Prepare Crust

Bake

Customer Order

Work-in-progress inventory

of “crust”

(b) Assemble-to-order strategy

Delivery

Assemble Pizza

Make Dough

Prepare Crust

Customer Order

Ingredients

PROCESS PERFORMANCE METRICS An important way of ensuring that a process is functioning properly is to regularly measure its performance. Process performance metrics are measurements of different process characteristics that tell us how a process is performing. Just as accountants and finance managers use financial metrics, operations managers use process performance metrics to determine how a process is performing and how it is changing over time. There are many process performance metrics that focus on different aspects of the process. In this section we will look at some common metrics used by operations managers. These are summarized in Table 3-3 (see page 76).

� Process performance metrics Measurements of different process characteristics that tell how a process is performing.

Flowcharts for different product strategies at Antonio’s Pizzeria

FIGURE 3-8

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PROCESS PERFORMANCE METRICS • 75

A basic process performance metric is throughput time, which is the average amount of time it takes a product to move through the system. This includes the time someone is working on the product as well as the waiting time. A lower throughput time means that more products can move through the system. One goal of process improvement is to reduce throughput time. For example, think about the time spent at your last doctor’s appointment. The total amount of time you spent at the facility, regardless of whether you were waiting, talking with the physician, or having lab work performed, is throughput time.

Quite possibly much of the time at your last doctor’s appointment was spent wait- ing. An important metric that measures how much wasted time exists in a process is

� Throughput time Average amount of time it takes a product to move through the system.

FIGURE 3-9

Process flowchart of customer flow at Antonio’s Pizzeria

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76 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

process velocity. Process velocity is computed as a ratio of throughput time to value- added time:

Process velocity 5 throughput time

value-added time where value-added time is the time spent actually working on the product. Notice that the closer this ratio is to 1.00, the lower the amount of time the product spends on non-value-adding activities (e.g., waiting). Again recall your last doctor’s appointment. What was the value-added time? What was the throughput time? Can you estimate the process velocity?

Another important metric is productivity, which is the ratio of outputs over inputs. Productivity measures how well a company converts its inputs to outputs. Productiv- ity was discussed in detail in Chapter 2, so we will not repeat its computation here. Also important is utilization, which is the ratio of the time a resource is actually used versus the time it is available for use. Unlike productivity, which tends to focus on financial measures (e.g., dollars of output), utilization measures the actual time that a resource (e.g., equipment or labor) is being used. Last, efficiency is a metric that measures actual output relative to some standard of output. It tells us whether we are performing at, above, or below standard.

� Process velocity Ratio of throughput time to value-added time.

� Productivity Ratio of outputs over inputs.

� Utilization Ratio of time a resource is used to time it is available for use.

� Efficiency Ratio of actual output to standard output.

Measure Definition

1. Throughput time Average amount of time product takes to move through the system

2. Process velocity 5 throughput time value-added time

A measure of wasted time in the system

3. Productivity 5 output input

A measure of how well a company uses its resources

4. Utilization 5 time a resource used

time a resource available The proportion of time a resource is actually used

5. Efficiency 5 actual output

standard output Measures performance relative to a standard

TABLE 3-3

Process Performance Metrics

Frantz Title Company is analyzing its operation in an effort to improve performance. The following data have been collected:

It takes an average of 4 hours to process and close a title, with value-added time estimated at 30 minutes per title.

Each title offi cer is on payroll for 8 hours per day, though working 6 hours per day on average, accounting for lunches and breaks. Industry standard for labor utilization is 80 percent.

Th e company closes on 8 titles per day, with an industry standard of 10 titles per day for a comparable facility.

Determine process velocity, labor utilization, and efficiency for the company. Can you draw any conclusions?

EXAMPLE 3.2

Measuring Process Performance

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LINKING PRODUCT DESIGN AND PROCESS SELECTION • 77

• Before You Begin: When computing process performance metrics, be careful to make sure you use consistent units in the numerator and denominator of the equation you are using.

• Solution: Process velocity 5

throughput time value-added time

5 4 hours/title 1 2 hour/title

5 8

Labor utilization 5 6 hours/day 8 hours/day

5 0.75 or 75%

Efficiency 5 8 titles/day

10 titles/day 5 0.80 or 80%

A process velocity of 8 indicates that the amount of time spent on non-value-added activities is 8 times that of value-added activities. Also, labor utilization and efficiency are both below standard.

LINKING PRODUCT DESIGN AND PROCESS SELECTION Decisions concerning product design and process selection are directly linked and can- not be made independently of one another. The type of product a company produces defines the type of operation needed. The type of operation needed, in turn, defines many other aspects of the organization. This includes how a company competes in the marketplace (competitive priorities), the type or equipment and its arrangement in the facility, the type of organizational structure, and future types of products that can be produced by the facility. Table 3-4 summarizes some key decisions and how

MKT

Decision Intermittent Operations Repetitive Operations

Product design Early stage of product life cycle Later stage of product life cycle

Competitive priorities

Delivery, flexibility, and quality Cost and quality

Facility layout Resources grouped by function Resource arranged in a line

Product strategy Make-to-order/assemble-to-order Make-to-stock

Vertical integration Low High

TABLE 3-4

Differences in Key Organizational Decisions for Different Types of Operations

Before You Go On

Make sure that you understand the key issues in product design. Be familiar with the different stages of the product life cycle. Recall that products in the early stages of the life cycle are still being refined based on the needs of the market. This includes product characteristics and features. At this stage the market for the product has not yet been fully developed, and product volumes have not reached their peak. By contrast, products in the later stages of their life cycle have well-developed characteristics, and demand volumes for them are fairly stable.

Review the different types of processes and their characteristics. Recall that intermittent processes are designed to produce products with different processing requirements in smaller volumes. Repetitive operations, on the other hand, are designed for one or a few types of products produced in high volumes.

Next we discuss how product design and process selection decisions are interrelated.

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78 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

they differ for intermittent and repetitive types of operations. Next we look at each of these decision areas.

Product Design Decisions Intermittent and repetitive operations typically focus on producing products in differ- ent stages of the product life cycle. Intermittent operations focus on products in the early stage of the life cycle because facilities are general-purpose and can be adapted to the needs of the product. Because products in the early stage of the life cycle are still being refined, intermittent operations are ideally suited to them. Also, demand vol- umes for these products are still uncertain, and intermittent operations are designed to focus on producing lower volumes of products with differing characteristics.

Once a product reaches the later stages of the life cycle, both its product features and its demand volume are predictable. As volumes are typically larger at this stage, a facility that is dedicated to producing a large volume of one type of product is best from both efficiency and cost perspectives. This is what a repetitive operation pro- vides. Recall that repetitive operations are capital intensive, with much automation dedicated to the efficient production of one type of product. It would not be a good decision to invest such a large amount of resources for a product that is uncertain relative to its features or market. However, once a product is well defined with a siz- able market, repetitive types of operations are a better business alternative. This is why repetitive operations tend to focus on products in the later stages of their life cycle.

The product focus of both types of operations has significant implications for a company’s future product choices. Once a company has an intermittent operation in place, designed to produce a variety of products in low volumes, it is a poor strategic decision to pursue production of a highly standardized product in the same facility. The same holds true for attempting to produce a newly introduced product in a repeti- tive operation.

The differences between the two types of operations are great, including the way they are managed. Not understanding their differences is a mistake often made by companies. A company may be very successful at managing a repetitive operation that produces a standardized product. Management may then see an opportunity involv- ing products in the early stage of the life cycle. Not understanding the differences in the operational requirements, management may decide to produce this new product by applying their “know-how.” The results can prove disastrous.

The problems that can arise when a company does not understand the differences between intermittent and repetitive operations are illustrated by the experience of The Babcock & Wil- cox Company in the late 1960s. B & W was very successful at producing fos- sil-fuel boilers, a standardized product made via repetitive operation. Then the company decided to pursue production of nuclear pressure vessels, a new prod-

uct in the early stages of its life cycle that required an intermittent operation. B & W saw the nuclear pressure vessels as a wave of the future. Because they were successful at producing boilers, they believed they could apply those same skills to production of the new product. They began managing the production of nuclear pressure vessels— an intermittent operation—as if it were a repetitive operation. They focused primarily

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LINKING PRODUCT DESIGN AND PROCESS SELECTION • 79

on cost rather than delivery, did not give enough time for product refinement, and did not invest in labor skills necessary for a new product. Consequently, the venture failed, and the company almost went out of business. It was saved by its success in the production of boilers, to which it was able to return.

Competitive Priorities The decision of how a company will compete in the marketplace—its competitive priorities—is largely affected by the type of operation it has in place. Intermittent operations are typically less competitive on cost than repetitive operations. The reason is that repetitive operations mass-produce a large volume of one product. The cost of the product is spread over a large volume, allowing the company to offer that product at a comparatively lower price.

Think about the cost difference you would incur if you decided to buy a business suit “off the rack” from your local department store (produced by a repetitive opera- tion) versus having it custom made by a tailor (an intermittent operation). Certainly a custom-made suit would cost considerably more. The same product produced by a repetitive operation typically costs less than one made by an intermittent operation. However, intermittent operations have their own advantages. Having a custom-made suit allows you to choose precisely what you want in style, color, texture, and fit. Also, if you were not satisfied, you could easily return it for adjustments and alterations. Intermittent operations compete more on flexibility and delivery compared to con- tinuous operations.

Today all organizations understand the importance of quality. However, the ele- ments of quality that a company focuses on may be different depending on the type of operation used. Repetitive operations provide greater consistency among products. The first and last products made in the day are almost identical. Intermittent opera- tions, on the other hand, offer greater variety of features and workmanship not avail- able with mass production.

It is important that companies understand the competitive priorities best suited for the type of process that they use. It would not be a good strategic decision for an intermittent operation to try to compete primarily on cost, as it would not be very successful. Similarly, the primary competitive priority for a repetitive operation should not be variety of features because this would take away from the efficiency of the process design.

Facility Layout Facility layout, covered in Chapter 10, is concerned with the arrangement of resources in a facility to enhance the production process. If resources are not arranged prop- erly, a company will have inefficiency and waste. The type of process a company uses directly affects the facility layout and the inherent problems encountered.

Intermittent operations resources are grouped based on similar processes or func- tions. There is no one typical product that is produced; rather, a large variety of items are produced in low volumes, each with its own unique processing needs. Since no one product justifies the dedication of an entire facility, resources are grouped based on their function. Products are then moved from resource to resource, based on their processing needs. The challenge with intermittent operations is to arrange the loca- tion of resources to maximize efficiency and minimize waste of movement. If the intermittent operation has not been designed properly, many products will be moved long distances. This type of movement adds nothing to the value of the product and

MKT

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80 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

contributes to waste. Any two work centers that have much movement between them should be placed close to one another. However, this often means that another work center will have to be moved out of the way. This can make the problem fairly challenging.

Intermittent operations are less efficient and have longer production times due to the nature of the layout. Material handling costs tend to be high and resource schedul- ing is a challenge. Intermittent operations are common in practice. Examples include a doctor’s office or a hospital. Departments are grouped based on their function, with examining rooms in one area, lab in another, and X-rays in a third. Patients are moved from one department to another based on their needs. Another example is a bakery that makes custom cakes and pastries. The work centers are set up to perform different functions, such as making different types of dough, different types of fillings, and dif- ferent types of icing and decorations. The product is routed to different workstations depending on the product requirements. Some cakes have the filling in the center (e.g., Boston cream pie), others only on top (e.g., sheet cake), and some have no filling at all (e.g., pound cake).

Repetitive operations have resources arranged in sequence to allow for efficient production of a standardized product. Since only one product or a few highly similar products are being produced, all resources are arranged to efficiently meet produc- tion needs. Examples are seen on an assembly line, in a cafeteria, or even a car wash. Numerous products, from breakfast cereals to computers, are made using repetitive operations.

Though repetitive operations have faster processing rates, lower material handling costs, and greater efficiency than intermittent operations, they also have their short- comings. Resources are highly specialized and the operation is inflexible relative to the market. This type of operation cannot respond rapidly to changes in market needs for the products or to changes in demand volume. The challenge is to arrange worksta- tions in sequence and designate the jobs that will be performed by each to produce the product in the most efficient way possible. Figure 3-10 illustrates the differences in facility layout between intermittent and repetitive operations.

FIGURE 3-10

Facility layouts for intermittent versus repetitive operations

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LINKING PRODUCT DESIGN AND PROCESS SELECTION • 81

Product and Service Strategy The type of operation a company has in place is directly related to its product and service strategy. As we learned earlier in this chapter in the example of Antonio’s Pizzeria, product and service strategies can be classified as make-to-stock, assemble- to-order, and make-to-order. These strategies differ by the length of their delivery lead time, which is the amount of time from when the order is received to when the product is delivered. These strategies also differ by the degree of product customization. Figure 3-11 illustrates these differences.

Make-to-stock is a strategy that produces finished products for immediate sale or delivery, in anticipation of demand. Companies using this strategy produce a standardized product in larger volumes. Typically, this strategy is seen in repetitive operations. Delivery lead time is the shortest, but the customer has no involvement in product design. Examples include off-the-shelf retail apparel, soft drinks, standard automotive parts, or airline flights. A hamburger patty at a fast-food restaurant such as McDonald’s or Burger King is made-to-stock, as is a taco at Taco Bell. As a customer you gain speed of delivery but lose the ability to customize the product.

Assemble-to-order strategy, also known as build-to-order, produces standard com- ponents that can be combined to customer specifications. Delivery time is longer than in the make-to-stock strategy but allows for some customization. Examples include computer systems, prefabricated furniture with choices of fabric colors, or vacation packages with standard options.

Make-to-order is a strategy used to produce products to customer specifications after an order has been received. The delivery time is longest, and product volumes are low. Examples are custom-made clothing, custom-built homes, and customized professional services. Ordering a hamburger to your liking in a sit-down restaurant is another example of this strategy. This strategy is best for an intermittent operation.

Degree of Vertical Integration The larger the number of processes performed by a company in the chain from raw materials to product delivery, the higher the vertical integration. Vertical integration is a strategic decision that should support the future growth direction of the company.

FIGURE 3-11

Product and service strategy options

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82 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

Vertical integration is a good strategic option when there are high volumes of a small variety of input materials, as is the case with repetitive operations. The reason is that the high volume and narrow variety of input material allow task specialization and cost justification. An example is Dole Food Company, which owns and controls most of its canned pineapple production from pineapple farms to the processing plant. The company has chosen to be vertically integrated so as to have greater control of costs and product quality.

It is typically not a good strategic decision to vertically integrate into specialized processes that provide inputs in small volumes. This would be the case for intermit- tent operations. For example, let’s consider a bakery that makes a variety of different types of cakes and pies. Maybe the bakery purchases different fillings from different sources, such as apple pie filling from one company, chocolate filling from another, and cream filling from a third. If the company were to purchase production of the apple filling, it would not gain much strategically because it still relies on other sup- pliers. In this case, outsourcing may be a better choice. However, if the bakery shifted its production to making only apple pies, then the vertical integration might be a good choice.

In summary, vertical integration is typically a better strategic decision for repetitive operations. For intermittent operations it is generally a poor strategic choice.

TECHNOLOGY DECISIONS Advancements in technology have had the greatest impact on process design decisions. Technological advances have enabled companies to produce products faster, with better quality, at a lower cost. Many processes that were not imaginable only a few years ago have been made possible through technology. In this section we look at some of the greatest impacts technology has had on process design.

Information Technology Information technology (IT) is technology that enables storage, processing, and communication of information within and between firms. It is also used to orga- nize information to help managers with decision making. One type of information technology we are all familiar with is the Internet, which has had the greatest impact on the way companies conduct business. The Internet has linked trading partners— customers, buyers, and suppliers—and has created electronic commerce and the virtual marketplace.

Enterprise software is another powerful information technology, such as enterprise resource planning (ERP). These are large software programs used for planning and coordinating all resources throughout the entire enterprise. They allow data sharing and communication within and outside of the firm, enabling collaborative decision making. We will learn more about ERP in Chapter 14.

Other examples of IT include wireless communication technologies. We are all familiar with cellular phones and pagers in our own lives. These technologies can also significantly improve business operations. For example, wireless homing devices and wearable computers are being used in warehouses to quickly guide workers to locations of goods. Wireless technologies enhanced by satellite transmission can rapidly transmit information from one source to another. For example, Wal-Mart uses company-owned satellites to automatically transmit point-of-sale data to computers at replenishment warehouses.

� Information technology (IT) Technology that enables storage, processing, and communication of information within and between firms.

� Enterprise resource planning (ERP) Large software programs used for planning and coordinating all resources throughout the entire enterprise.

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TECHNOLOGY DECISIONS • 83

Global positioning systems (GPS) comprise another type of wireless technology that uses satellite transmission to communicate exact locations. GPS was originally developed by the Department of Defense in 1978 in order to help coordinate U.S. military operations. Today GPS has numerous business and individual applications. Large trucking companies use GPS technology to identify the exact locations of their vehicles. Farmers use GPS while riding on tractors to identify their exact location and apply the proper mix of nutrients to the correct plot of land. GPS capability is also available for personal use in handheld computers, such as the Palm Garmin iQue, that can identify the person’s location and plot a route to a destination.

GPS has even found its use in adver- tising. For example, Nielsen Media Research, the firm known for rating television shows, is using GPS to test billboard advertising. The company has recruited a sample of adults with known demographic characteristics and is using GPS to monitor their minute-by-minute movements. This information will then be used to deter- mine the best placement for particular billboard advertisements targeted to the particular demographic group.

Radio frequency identification (RFID) is another wireless technology that prom- ises to dramatically change business operations. RFID uses memory chips equipped with tiny radio antennas that can be attached to objects to transmit streams of data about the object. For example, RFID can be used to identify any product movement, reveal a missing product’s location, or have a shipment of products “announce” their arrival. Empty store shelves can signal that it is time for replenishment using RFID, or low inventories can signal the vendor that it is time to ship more products. RFID can also be used in the service environment, enabling innovative applications in locating and tracking people and assets. In fact, RFID has the potential to become the backbone of an infrastructure that can identify and track billions of individual objects all over the world, in real time.

A big adopter of RFID is Wal-Mart, which is investing heavily in RFID tags for its warehouses. Wal-Mart went live with RFID in January 2005 after pilot testing them at distribution centers in Dallas. The company has already seen a return on its investment. For example, out-of-stock items that are RFID tagged are replenished three times faster than before. The company is also experimenting with adding sen- sor tags to perishable items. This way, for example, it can track how long a crate of bananas has been in transit and how fresh it is. In 2010 Wal-Mart added removable RFID tags on individual garments from jeans to underwear in order to tightly moni- tor inventory.

Automation An important decision in designing processes is whether the firm should automate, to what degree, and the type of automation that should be used. Automation is the use of machinery able to perform work without human operators and can involve a single machine or an entire factory. Although there are tremendous advantages to automation, there are also disadvantages. Companies need to consider these carefully before making the final decision.

� Global positioning systems (GPS) A type of wireless technology that uses satellite transmission to communicate exact locations.

� Radio frequency identification (RFID) A wireless technology that uses memory chips equipped with radio antennas attached to objects used to transmit streams of data.

� Automation Using machinery to perform work without human operators.

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Automation has the advantage of product consistency and ability to efficiently produce large volumes of product. With automated equipment, the last part made in the day will be exactly like the first one made. Because automation brings consistency, quality tends to be higher and easier to monitor. Production can flow uninterrupted throughout the day, without breaks for lunch, and there is no fatigue factor.

However, automation does have its disadvantages. First, automation is typically very costly. These costs can be justified only by a high volume of production. Second, automation is typically not flexible in accommodating product and process changes. Therefore, automation would probably not be good for products in the early stages of their life cycle or for products with short life cycles. Automation needs to be viewed as another capital investment decision: financial payback is critical. For all these reasons automation is typically less present in intermittent than in repetitive operations.

Automated Material Handling In the past, the primary method of moving products was the conveyor in the form of belts or chains. Today’s material handling devices can read bar codes that tell them which location to go to and which are capable of moving in many directions. One such device is an automated guided vehicle (AGV), a small battery-driven truck that moves materials from one location to the other. The AGV is not operated by a human and takes its directions from either an onboard or central computer. Even AGVs have become more sophisticated over time. The older models followed a cable that was installed under the floor. The newer models follow optical paths and can go anywhere there is aisle space, even avoiding piles of inventory in their way. One of the biggest advantages of AGVs is that they can pretty much go anywhere, as compared to traditional conveyor belts. Managers can use them to move materials wherever they are needed.

Another type of automated material handling includes automated storage and retrieval systems (AS/RSs), which are basically automated warehouses. AS/RSs use AGVs to move material and also computer-controlled racks and storage bins. The storage bins can typically rotate like a carousel, so that the desired storage bin is avail- able for either storage or retrieval. All this is controlled by a computer that keeps track of the exact location and quantity of each item and controls how much will be stored or retrieved in a particular area. AS/RSs can have great advantages over traditional warehouses. Though they are much more costly to operate, they are also much more efficient and accurate.

Flexible Manufacturing Systems (FMS) A flexible manufacturing system (FMS) is a type of automation system that combines the flexibility of intermittent operations with the efficiency of repetitive operations. As you can see by the definition, this is a system of automated machines, not just a single machine. An FMS consists of groups of computer-controlled machines and/or robots, automated handling devices for moving, loading, and unloading, and a computer-control center.

Based on the instructions from the computer-control center, parts and materials are automatically moved to appropriate machines or robots. The machines perform their tasks and then the parts are moved to the next set of machines, where the parts automatically are loaded and unloaded. The routes taken by each product are deter- mined with the goal of maximizing the efficiency of the operation. Also, the FMS “knows” when one machine is down due to maintenance or if there is a backlog of work on a machine, and it will automatically route the materials to an available machine.

� Flexible manufacturing system (FMS) A type of automated system that combines the flexibility of intermittent operations with the efficiency of continuous operations.

Handheld scanner reading a bar code.

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TECHNOLOGY DECISIONS • 85

Flexible manufacturing systems are still fairly limited in the variety of products that they handle. Usually they can only produce similar products from the same family. For this reason, and because of their high cost, flexible manufacturing systems are not very widespread. A decision to use an FMS needs to be long-term and strategic, requiring a sizable financial outlay.

Robotics A robot in manufacturing is usually nothing more than a mechanical arm with a power supply and a computer-control mechanism that controls the movements of the arm. The arm can be used for many tasks, such as painting, welding, assembly, and loading and unloading of machines. Robots are excellent for physically dangerous jobs such as working with radioactive or toxic materials. Also, robots can work 24 hours a day to produce a highly consistent product.

Robots vary in their degree of sophistication. Some robots are fairly simple and follow a repetitive set of instructions. Other robots follow complex instructions, and some can be programmed to recognize objects and even make simple decisions. One type of automation similar to simple robotics is the numerically controlled (NC) machine. NC machines are controlled by a computer and can do a variety of tasks such as drilling, boring, or turning parts of different sizes and shapes. Factories of the future will most likely be composed of a number of robots and NC machines working together.

The use of robots has not been very widespread in U.S. firms. However, this is an area that can provide a competitive advantage for a company. Cost justification should consider not only reduction in labor costs but also the increased flexibility of opera- tion and improvement in quality. The cost of robots can vary greatly and depends on the robots’ size and capabilities. Generally, it is best for a company to consider pur- chasing multiple robots or forms of automation to spread the costs of maintenance and software support. Also, the decision to purchase automation such as robotics needs to be a long-term strategic one that considers the totality of the production pro- cess. Otherwise, the company may have one robot working 24 hours a day and piling up inventory while it waits for the other processes to catch up.

Robots can be used to improve operations of almost any business—even literal “operations.” Increasingly, robots have been used to per- form certain medical surgeries. For example, at New York Uni- versity doctors use minimally invasive robotic surgery to repair human heart valves. To perform the surgery, doctors use a robot arm to cut a 6-cm incision between the ribs and to place an endoscope that allows the surgeons to see what they are doing. The robot arm is controlled through a complex robotic surgical system. The doctors, seated at a workstation, manipulate conventional surgical instruments while the robotic surgical system mirrors these movements on an ultra-fine scale. The advantage of robots is that they can perform delicately fine, small, motor movements, have consistent finger dexterity, and require only tiny incisions. The prediction is that robots will become involved in performing many surgeries, such as eye surgery, neurosurgery, and cos- metic surgery.

� Numerically controlled (NC) machine A machine controlled by a computer that can perform a variety of tasks.

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e-Manufacturing Today’s Web-based environment has created numerous opportunities for business collaboration. This includes collaboration in product and process design, where customers, buyers, and designers can share information and jointly make decisions in real time. Let’s look at some of the computer systems that can aid e-manufacturing.

Computer-Aided Design (CAD) Computer-aided design (CAD) is a system that uses computer graphics to design new products. Gone are the days of drafting designs by hand. Today’s powerful desktop computers combined with graphics software allow the designer to create drawings on the computer screen and then manipulate them geometrically to be viewed from any angle. With CAD the designer can rotate the object, split it to view the inside, and magnify certain sections for closer view.

CAD can also perform other functions. Engineering design calculations can be performed to test the reactions of the design to stress and to evaluate strength of materials. This is called computer-aided engineering (CAE). For example, the designer can test how different dimensions, tolerances, and materials respond to different conditions such as rough handling or high temperatures. The designer can use the computer to compare alternative designs and determine the best design for a given set of conditions. The designer can also perform cost analysis on the design, evaluating the advantages of different types of materials.

Another advantage of CAD is that it can be linked to manufacturing. We have already discussed the importance of linking product design to process selection. Through CAD this integration is made easy. Computer-aided manufacturing (CAM) is the process of controlling manufacturing through computers. Since the product designs are stored in the computer database, the equipment and tools needed can easily be simulated to match up with the processing needs. Efficiencies of various machine choices and different process alternatives can be computed.

CAD can dramatically increase the speed and flexibility of the design process. Designs can be made on the computer screen and printed out when desired. Electronic versions can be shared by many members of the organization for their input. Also, electronic versions can be archived and compared to future versions. The designer can catalog features based on their characteristics—a very valuable feature. As future product designs are being considered, the designer can quickly retrieve certain fea- tures from past designs and test them for inclusion in the design being currently devel- oped. Also, by using collaborative product commerce (CPC) software, sharing designs with suppliers is possible.

Computer-Integrated Manufacturing Computer-integrated manufacturing (CIM) is a term used to describe the integration of product design, process plan- ning, and manufacturing using an integrated computer system. Computer-integrated manufacturing systems vary greatly in their complexity. Simple systems might inte- grate computer-aided design (CAD) with some numerically controlled machines (NC machines). A complex system, on the other hand, might integrate purchasing, sched- uling, inventory control, and distribution, in addition to the other areas of product design.

The key element of CIM is the integration of different parts of the operation process to achieve greater responsiveness and flexibility. The purpose of CIM is to improve how quickly the company can respond to customer needs in terms of product design and availability, as well as quality and productivity, and to improve overall efficiency.

� Computer-aided design (CAD) A system that uses computer graphics to design new products.

� Computer-integrated manufacturing (CIM) A term used to describe the integration of product design, process planning, and manufacturing using an integrated computer system.

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DESIGNING SERVICES • 87

Most of the issues discussed in this chapter are as applicable to service organizations as they are to manufacturing. However, there are issues unique to services that pose special challenges for service design.

Most of us think we know what is needed to run a good service organization. After all, we encounter services almost every day, at banks, fast-food restaurants, doctor’s offices, barber shops, grocery stores, and even the university. We have all experienced poor service quality and would gladly offer advice as to how we think it could be bet- ter. However, there are some very important features of services you may not have thought about. Let’s see what they are.

How Are Services Different from Manufacturing? In Chapter 1 we learned about two basic features that make service organizations dif- ferent from manufacturing. These are the intangibility of the product produced and the high degree of customer contact. Next we briefly review these and see how they impact service design.

Intangible Product Service organizations produce an intangible product, which can- not be touched or seen. It cannot be stored in inventory for later use or traded in for another model. The service produced is experienced by the customer. The design of the service needs to specify exactly what the customer is supposed to experience. For example, it may be relaxation, comfort, and pampering, such as offered by Canyon Ranch Spa. It may be efficiency and speed, such as offered by FedEx. Defining the customer experience is part of the service design. It requires identifying precisely what the customer is going to feel and think and consequently how he or she is going to behave. This is not always as easy as it might seem.

The experience of the customer is directly related to customer expectations. For services to be successful, the customer experience needs to meet or even exceed these expectations. However, customer expectations can greatly vary depending on the type of customer and customer demographic, including customer age, gen- der, background, and knowledge. The expectation is developed through product marketing to a particular market segment. It is highly important in designing the service to identify the target market the service is geared to and to create the correct expectation.

High Degree of Customer Contact Service organizations typically have a high degree of customer contact. The customer is often present while the service is being delivered, such as at a theater, restaurant, or bank. Also, the contact between the customer and service provider is often the service itself, such as what you experi- ence at a doctor’s office. For a service to be successful, this contact needs to be a positive experience for the customer, and this depends greatly on the service provider.

Unfortunately, since services often have multiple service providers, there can be great variation in the type of service delivered. We have all had experiences where the service of one organization varied greatly depending on the skills of the service provider. This could be a hairdresser at a hair salon, a food server at a restaurant, or a teller at a bank. We have all heard people say something similar to “I often have dinner

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at Aussie Steak Grill and I insist that Jenny be my server.” Similarly, someone might say, “I go to Olentangy Family Physicians, but I won’t see Dr. Jekyl because he is rude and unfriendly.” For a service to be successful, the service experience must be consis- tent at all times. This requires close quality management to ensure high consistency and reliability. Many of the procedures used in manufacturing to ensure high quality, such as standardization and simplification, are used in services as well. Fast-food res- taurants such as McDonald’s and Wendy’s are known for their consistency. The same is true of hotel chains such as Holiday Inn and Embassy Suites.

To ensure that the service contact is a positive experience for the customer, employees of the service need to have training that encompasses a great array of skills that include courtesy, friendliness, and overall disposition. The service company also needs to structure the proper incentive system to motivate employees.

How Are Services Classified? We can classify service organizations based on similar characteristics in order to understand them better. A common way to classify services is based on the degree of customer contact. This is illustrated in Figure 3-12.

Services with low customer contact are called “quasi-manufacturing.” These firms have a high degree of service standardization, have higher sales volumes, and are typically less labor intensive. These firms have almost no face-to-face contact with customers and are in many ways similar to manufacturing operations. Examples include warehouses, distribution centers, environmental testing laboratories, and back-office operations.

Services with high customer contact are called “pure services.” These firms have high face-to-face contact and are highly labor intensive. There is low product standardization,

FIGURE 3-12

Classification of service operations

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DESIGNING SERVICES • 89

as each customer has unique requirements, and sales volumes tend to be low. Pure service firms have an environment of lowest system efficiency compared to other service firms. The reason is that the service is typically customized. As each customer has unique requirements, there is less predictability in managing the operating environment. Examples include hospitals, restaurants, barber shops, and beauty salons.

Services that combine elements of both of these extremes are called “mixed services.” Some parts of their operation have face-to-face customer contact, though others do not. They include offices, banks, and insurance firms.

It is important to understand that companies with different levels of customer contact need to be managed differently. These differences also apply to high-contact and low-contact areas of firms. For example, companies should specifically hire people-oriented workers for high-contact areas, whereas technical skills are more important in low-contact areas. Also, noncontact activities should be partitioned from the customer to avoid disruptions in the flow of work. Noncontact areas can be managed borrowing tools from manufacturing, whereas high-contact areas need to focus on accommodating the customer.

The Service Package The really successful service organizations do not happen spontaneously. They are carefully thought out and planned, down to every employee action. To design a successful service, we must first start with a service concept or idea, which needs to be very comprehensive. We have learned that when purchasing a service, customers actually buy a service package or service bundle. The service package is a grouping of features that are purchased together as part of the service. There are three elements of the service package: (1) the physical goods, (2) the sensual benefits, and (3) the psychological benefits. The physical goods of the service are the tangible aspects of the service that we receive, or are in contact with, during service delivery. In a fine-dining restaurant the physical goods are the food consumed, as well as facilities such as comfortable tables and chairs, tablecloths, and fine china. The sensual benefits are the sights, smell, and sounds of the experience— all the items we experience through our senses. Finally, the psychological benefits include the status, comfort, and well-being provided by the experience.

It is highly important that the design of the service specifically identify every aspect of the service package. When designing the service, we should not focus only on the tangible aspects; it is often the sensual and psychological benefits that are the deciding factors in the success of the service. The service package needs to be designed to precisely meet the expectations of the target customer group.

Once the service package is identified, it can then be translated into a design using a process that is not too different from the one used in manufacturing. Details of the service, such as quality standards and employee training, can later be defined in keeping with the service concept. The service providers—the individuals who come in direct contact with the customers—must be trained and motivated to precisely understand and satisfy customer expectations.

Imagine going to a fast-food restaurant and having the server take his time asking you how you want your hamburger cooked and precisely what condiments you would like to accompany it, then waiting a long time to receive your food. Similarly, imagine going to an expensive hair salon and having the staff rush you through the process. In both cases, you as the customer would not be satisfied because the service delivery did not meet your expectations. Next time you might choose to go somewhere else. These examples illustrate what happens when there is a mismatch between the service concept and the service delivery.

� Service package A grouping of physical, sensual, and psychological benefits that are purchased together as part of the service.

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Differing Service Designs There is no one model of successful service design. The design selected should support the company’s service concept and provide the features of the service package that the target customers want. Different service designs have proved successful in different environments. In this section we look at three very different service designs that have worked well for the companies that adopted them.

Substitute Technology for People Substituting technology for people is an approach to service design that was advocated some years ago by Theodore Levitt.1 Levitt argued that one way to reduce the uncertainty of service delivery is to use tech- nology to develop a production-line approach to services. One of the most success- ful companies to use this approach is McDonald’s. Technology has been substituted wherever possible to provide product consistency and take the guesswork away from employees. Some examples of the use of technology include the following:

● Buzzers and lights are used to signal cooking time for frying perfect french fries. ● Th e size of the french fryer is designed to produce the correct amount of fries. ● Th e french fry scoop is the perfect size to fi ll an order. ● “Raw materials” are received in usable form (e.g., hamburger patties are premade;

pickles and tomatoes are presliced; french fries are precut). ● Th ere are 49 steps for producing perfect french fries. ● Steps for producing the perfect hamburger are detailed and specifi c. ● Products have diff erent-colored wrappings for easy identifi cation.

In addition to the use of technology in the production of the product, there is consis- tency in facilities and a painstaking focus on cleanliness. For example, the production process at McDonald’s is not left to the discretion of the workers. Rather, their job is to follow the technology and preset processes.

Today we are all accustomed to the product consistency, speed of delivery, and pre- dictability that are a feature of most fast-food restaurants. However, this concept was very new in the early 1970s. It is this approach to services that has enabled McDonald’s to establish its global reputation.

Substituting technology for people is an approach we have seen over the years in many service industries. For example, almost all gas stations have reduced the num- ber of cashiers and attendants with the advent of credit cards at self-serve pumps. Also, many hospitals are using technology to monitor patient heart rate and blood pressure without relying exclusively on nurses. As technologies develop in different service industries, we will continue to see an ever-increasing reliance on its use and an increase in the elimination of workers.

Get the Customer Involved A different approach to service design was proposed by C. H. Lovelock and R. F. Young.2 Their idea was to take advantage of the customer’s presence during the delivery of the service and have him or her become an active par- ticipant. This is different from traditional service designs where the customer passively

1Th eodore Levitt, “Production Line Approach to Services,” Harvard Business Review, 50, 5 (September–October 1972), 41–52. 2C.H. Lovelock and R.F. Young. “Look to Customers to Increase Productivity,” Harvard Business Review, 57, 2, 168–178.

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PRODUCT DESIGN AND PROCESS SELECTION WITHIN OM: HOW IT ALL FITS TOGETHER • 91

waits for service employees to deliver the service. Lovelock and Young proposed that since the customers are already there, “get them involved.”

We have all seen a large increase in the self-serve areas of many service firms. Traditional salad bars have led to self-serve food buffets of every type. Many fast- food restaurants no longer fill customer drink orders, but have the customers serve themselves. Grocery stores allow customers to select and package baked goods on their own. Many hotels provide in-room coffee makers and prepackaged coffee, allowing customers to make coffee at their convenience.

This type of approach has a number of advantages. First, it takes a large burden away from the service provider. The delivery of the service is made faster, and costs are reduced due to lowered staffing requirements. Second, this approach empowers customers and gives them a greater sense of control in terms of getting what they want, which provides a great deal of customer convenience and increases satisfaction. How- ever, since different types of customers have different preferences, many facilities are finding that it is best to offer full-service and self-service options. For example, many breakfast bars still allow a request for eggs cooked and served to order, and most gas stations still offer some full-service pumps.

High Customer Attention Approach A third approach to service design is providing a high level of customer attention. This is in direct contrast to the first two approaches. The first approach discussed automates the service and makes it more like manufactur- ing. The second approach requires greater participation and responsibility from the cus- tomer. The third approach is different from the first two in that it does not standardize the service and does not get the customer involved. Rather, it is based on customizing the service to the needs unique to each customer and having the customer be the passive and pampered recipient of the service. This approach relies on developing a personal relationship with each customer and giving the customer precisely what he or she wants.

There are a number of examples of this type of approach. Nordstrom, Inc. depart- ment stores is recognized in the retail industry for its attention to customer service. Salespeople typically know their customers by name and keep a record of their pref- erences. Returns are handled without question, and the customer is always right. Another example of this is a midwestern grocer called Dorothy Lane Market. Dorothy Lane prides itself on its ability to provide unique cuts of specialty meats precisely to customer order. As at Nordstrom, a list is kept of primary customers and their pref- erences. Customers are notified of special purchases, such as unique wines, specialty chocolates, and special cuts of meat.

Whereas the first two approaches to service design result in lowered service costs, this third approach is geared toward customers who are prepared to pay a higher amount for the services they receive. As you can see, different approaches are meant to serve different types of customers. The design chosen needs to support the specific service concept of the company.

PRODUCT DESIGN AND PROCESS SELECTION WITHIN OM: HOW IT ALL FITS TOGETHER

Product design decisions are strategic in nature. The features and characteristics of a product need to support the overall strategic direction of the company. In turn, product design decisions directly dictate the type of process selected. They determine the types of facilities that will be needed to produce the product, types of machines,

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worker skills, degree of automation, and other decisions. Most companies continually design new products. The design of these new products has to take into account the type of processes the company has; otherwise facilities may not be available to produce the new product design. Therefore, product design and process selection decisions are directly tied to each other.

Product design and process selection decisions are further linked to all other areas of operations management. They are linked to decisions such as the level of capacity needed (Chapter 9), degree of quality (Chapters 5 and 6), layout (Chapter 10) and location of facilities (Chapter 9), types of workers (Chapter 11), and many others. As we go through this book, we will see how product design and process selection specifically impact other operations decisions.

PRODUCT DESIGN AND PROCESS SELECTION ACROSS THE ORGANIZATION The strategic and financial impact of product design and process selection mandates that operations work closely with other organizational functions to make these deci- sions. Operations is an integral part of these decisions because it understands issues of production, ease of fabrication, productivity, and quality. Now let’s see how the other organizational functions are involved with product design and process selection.

Marketing is impacted by product design issues because they determine the types of products that will be produced and affect marketing’s ability to sell them. Marketing’s input is critical at this stage because marketing is the function that interfaces with customers and understands the types of product characteristics customers want. It is marketing that can provide operations with information on customer preferences, competition, and future trends.

Process selection decisions impact marketing as well. They typically require large capital outlays, and once made, they are typically difficult to change and are in place for a long time. Process decisions affect the types of future products that the company can produce. Because of this, marketing needs to be closely involved in ensuring that the process can meet market demands for many years to come.

Finance plays an integral role in product design and process selection issues because these decisions require large financial outlays. Finance needs to be a part of these decisions to evaluate the financial impact on the company. Process selection decisions should be viewed as any other financial investment, with risks and rewards. Finance must ensure that the trade-off between the risks and rewards is acceptable. Also, it is up to finance to provide the capital needed for this investment and to balance that against future capital requirements.

Information systems needs to be part of the process selection decisions. Operations decisions, such as forecasting, purchasing, scheduling, and inventory control, differ based on the type of operation the company has. Information systems will be quite different for intermittent versus continuous operations. Therefore, the information system has to be developed to match the needs of the production process being planned.

Human resources provides important input to process selection decisions because it is the function directly responsible for hiring employees. If special labor skills are needed in the process of production, human resources needs to be able to provide information on the available labor pool. The two types of operations discussed, intermittent and continuous, typically require very different labor skills. Intermittent operations usually require higher-skilled labor than continuous operations. Human resources needs to understand the specific skills that are needed.

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THE SUSTAINABILITY LINK • 93

Purchasing works closely with suppliers to get the needed parts and raw materials at a favorable price. It is aware of product and material availability, scarcity, and price. Often certain materials or components can use less expensive substitutes if they are designed properly. For this reason it is important to have purchasing involved in product design issues from the very beginning.

Engineering needs to be an integral part of the product design and process selection decisions because this is the function that understands product measurement, toler- ances, strength of materials, and specific equipment needs. There can be many product design ideas, but it is up to engineering to evaluate their manufacturability.

As you can see, product design and process selection issues involve many functions and aff ect the entire organization. For this reason, product design and process selection decisions need to be made using a team eff ort, with all these functions working closely together to come up with a product plan that is best for the company.

Changes in product design can significantly improve environmental and social sustainability performance. These changes can include the use of materials, sourc- ing, and disposal, as well as process modifications that can reduce pollution. Sometimes these changes can be highly innovative. Other times they can be simple changes that the company simply overlooked. A good example is Nike Air basketball sneakers with their signature air bubble in the heel. The company only recently became aware that the pocket of the sneakers contained a gas known as sulphur hexafluoride, or SF6,

which is actually a greenhouse gas. As part of Nike’s sustainability initiative, Nike replaced SF6 with nitro- gen, which breaks up more readily upon release and is not a greenhouse gas. This small change did not alter consumer perception of the product, nor cost the com- pany money, but it offered a significant environmental impact.

Companies can also work on product design changes that can be highly innovative and bring sustainability leadership to the firm. An excellent example is offered by PepsiCo, the world’s second largest food and beverage company. PepsiCo has worked to lower the environmen- tal impact associated with petroleum-derived beverage

THE SUSTAINABILITY LINK

In today’s competitive environment, companies typi- cally have a very short window of opportunity to enter the market with a new product design. Most companies are aware that they must get to the market early with an innovative product before their competitors. This requires the support of the entire sup- ply chain, where suppliers must be involved in the product design process. We have already learned about the time-saving advantages of con- current engineering and early supplier involvement. These require a carefully integrated supply chain that allows collaboration and simultaneous product design between suppliers and manufacturers.

Another important supply chain link relates to the technology decisions the firm makes. As companies acquire new technologies, they must consider how these technologies will be aligned with the technologies used by their supply chain partners. When an entire supply

chain uses technologies that are compatible, great strides can be made in the efficiency of production and movement of goods. Consider that Wal- Mart has mandated that its top 300 suppliers must put RFID tags on all their shipping crates

and pallets. Although RFID tags are expensive, this move has already incurred huge savings by increasing efficiency, better tracking of products, and a reduction in inventory.

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94 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

Chapter Highlights 1 Product design is the process of deciding on the unique

characteristics and features of a company’s product. Process selection, on the other hand, is the development of the process necessary to produce the product being designed. Product design is a big strategic decision for a company, because the design of the product defi nes who the company’s customers will be, as well as the company’s image, its competition, and its overall future growth.

2 Steps in product design include idea generation, product screening, preliminary design and testing, and fi nal design. A useful tool at the product-screening stage is break-even analysis.

3 Break-even analysis is a technique used to compute the amount of goods that have to be sold just to cover costs.

4 Production processes can be divided into two broad categories: intermittent and repetitive operations. Intermittent operations are used when products with diff erent characteristics are being produced in smaller volumes. Th ese types of operations tend to organize their resources by grouping similar processes together and having the products routed through the facility based on their needs. Repetitive operations are used when one or a few similar products are produced in high volume. Th ese operations arrange resources in sequence to allow for an effi cient buildup of the product. Both intermittent and repetitive operations have their advantages and disadvantages. Intermittent operations provide great fl exibility but have high material handling costs and

challenge scheduling resources. Repetitive operations are highly effi cient but infl exible.

5 Product design and process selection decisions are linked. Th e type of operation a company has in place is defi ned by the product the company produces. Th e type of operation then aff ects other organizational decisions, such as competitive priorities, facility layout, and degree of vertical integration.

6 A process fl owchart is used for viewing the fl ow of the processes involved in producing the product. It is a very useful tool for seeing the totality of the operation and for identifying potential problem areas. Th ere is no exact format for designing the chart. Th e fl owchart can be very simple or very detailed.

7 Diff erent types of technologies can signifi cantly enhance product and process design. Th ese include automation, automated material handling devices, computer-aided design (CAD), numerically controlled (NC) equipment, fl exible manufacturing systems (FMS), and computer- integrated manufacturing (CIM).

8 Designing services have more complexities than manufacturing because services produce an intangible product and typically have a high degree of customer contact. Diff erent service designs include substituting technology for people, getting the customer involved, and paying great attention to the customer.

bottles, which are composed of nonrenewable fossil fuels and carry huge environmental costs. In fact, it is these plas- tic bottles that litter public spaces and contribute the float- ing “garbage patches” that plague the world’s oceans. First, the company lowered the amount of plastic used in its Aquafina water bottles. It then developed a 100% recycled bottle for its Naked Juice line in 2009. Now, the company’s R&D team is working on designing and mass-producing recyclable bottles completely made from renewable mate- rials. The research team is working on developing bottles using wastes from its food businesses, including orange peels, potato peels, and oat hulls. They have already

designed the material and have considered how it would match the current process design of PepsiCo, as changes in process design would require major capital investments. The plant based bottles can be blown, filled, and labeled the same way as traditional plastic ones, and would feel the same in the hands of the consumer. The company is continuing testing before mass-producing the bottles on a large scale and tracking the environmental impacts of the bottles to account for the environmental savings. Such a breakthrough innovation would also position PepsiCo as the leader in the entire plastic packaging industry and the premier environmentally responsible brand.

Key Terms manufacturability 59 product design 59 service design 60

benchmarking 61 reverse engineering 61 early supplier involvement (ESI) 62

break-even analysis 62 fi xed costs 62 variable costs 62

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SOLVED PROBLEMS • 95

design for manufacture (DFM) 65 product life cycle 66 concurrent engineering 67 remanufacturing 68 intermittent operations 69 repetitive operations 69 project process 71 batch process 71 line process 71 continuous process 71 process fl ow analysis 72 process fl owchart 72

bottleneck 72 make-to-stock strategy 73 assemble-to-order strategy 73 make-to-order strategy 73 process performance metrics 74 throughput time 75 process velocity 76 productivity 76 utilization 76 effi ciency 76 information technology (IT) 82 enterprise resource planning (ERP) 82

global positioning systems (GPS) 83 radio frequency identifi cation

(RFID) 83 automation 83 fl exible manufacturing system

(FMS) 84 numerically controlled (NC)

machine 85 computer-aided design (CAD) 86 computer-integrated

manufacturing (CIM) 86 service package 89

Solved Problems

Formula Review 1. Total cost � fi xed cost � variable cost

2. Revenue � (SP)Q

3. F � (VC)Q � (SP)Q

4. QBE 5 F

SP 2 VC

5. Process velocity 5 throughput time

value-added time

6. Utilization 5 time a resource used

time a resource available

7. Effi ciency 5 actual output

standard output

• Problem 1 Joe Jenkins, owner of Jenkins Manufacturing, is considering whether to produce a new product. He has considered the operations requirements for the product as well as the market potential. Joe estimates the fixed costs per year to be $40,000 and variable costs for each unit produced to be $50. (a) If Joe sells the product at a price of $70, how many units

of product does he have to sell in order to break even? Use both the algebraic and graphical approaches.

(b) If Joe sells 3000 units at the product price of $70, what will be his contribution to profit?

• Before You Begin To solve this problem you must first use the break-even for- mula. Then to compute the contribution to profit, recall that profit is computed as

Profit 5 total revenue 2 total cost

• Solution (a) To compute the break-even quantity, we follow the equa-

tion and substitute the appropriate numerical values:

Q 5 F

SP 2 VC 5

$40,000 $70 2 $50

5 2000 units

Th e break-even quantity is 2000 units. Th is is how much Joe would have to sell in order to cover costs.

Graphically, we can obtain the same result. Th is is shown in the fi gure.

(See student companion site for Excel template.)Solved Problems

(b) To compute the contribution to profit with sales of 3000 units: Profit 5 total revenue 2 total cost

5 1SP 2Q 2 [F 1 1VC 2Q] Now we can substitute numerical values: Profit 5 $70 13000 2 2 [$40,000 1 $50 13000 2 ]

5 $20,000 Th e contribution to profi t is $20,000 if Joe can sell 3000 units of product.

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96 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

• Problem 2 Joe Jenkins, owner of Jenkins Manufacturing, has decided to produce the new product discussed in Problem 1. The product can be produced with the current equipment in place. However, Joe is considering the purchase of new equipment that would produce the product more efficiently. Joe’s fixed cost would be raised to $60,000 per year, but the variable cost would be reduced to $25 per unit. Joe still plans to sell the product at $70 per unit.

Should Joe produce the new product with the new or current equipment described in Problem 1? Specify the volume of demand for which you would choose each process.

• Solution As we mentioned in the chapter, break-even analysis can also be used to evaluate different processes. Here we show how this can be done. To decide which process to use, we first need to compute the point of indifference between the two processes. The point of indifference is where the cost of the two processes is equal. If we label the current equipment A and the new equip- ment B, the point of indifference occurs when the costs for each process are equal. This is shown as

Total costEquipment A 5 total costEquipment B

Again, total cost is the sum of fixed and variable costs:

$40,000 1 $50 Q 5 $60,000 1 $25 Q

$25 Q 5 20,000

Q 5 800 units produced

Q 5 800 units is the point of indifference, that is, the point where the cost of either equipment is the same. If demand is expected to be less than 800 units, equipment A should be used given that it has a lower fixed cost. If demand is expected to be greater than 800 units, equipment B should be used given that it has a lower variable cost. This is shown graphically.

• Problem 3 Zelle’s Dry Cleaners has collected the following data for its processing of dress shirts:

It takes an average of 312 hours to dry clean and press a dress shirt, with value-added time estimated at 110 minutes per shirt.

Workers are paid for a 7-hour workday and work 512 hours per day on average, accounting for breaks and lunch; labor utilization is 75 percent in the industry.

Th e dry cleaner completes 25 shirts per day, with an industry standard of 28 shirts per day for a comparable facility.

Determine process velocity, labor utilization, and efficiency for the company.

• Before You Begin When solving this problem, remember to keep the units of measure consistent in the numerator and denominator of each equation.

• Solution

Process velocity 5 throughput time

value-added time

5 210 minutes/shirt 110 minutes/shirt

5 1.90

Labor utilization 5 512 hours/day 7 hours/day

5 0.786 or 78.6%

Efficiency 5 25 shirts/day 28 shifts/day

5 0.89 or 89%

Process velocity shows room for process improvement, as throughput time is almost twice that of value-added time. Labor utilization is just above the industry standard, though overall efficiency is below.

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PROBLEMS • 97

Discussion Questions 1. Define product design and explain its relationship to busi-

ness strategy. 2. What are the differences between product and service design? 3. Explain the meanings of benchmarking and reverse engineering. 4. Explain the meaning of design for manufacture (DFM) and

give some examples. 5. Describe the stages of the product life cycle. What are

demand characteristics at each stage? 6. Explain the term concurrent engineering. Why is it

important? 7. Identify the two general types of operations. What are their

characteristics?

8. What is meant by the term vertical integration? What types of companies are more likely to become vertically integrated?

9. What is a process flowchart, and what is it used for? 10. Give some examples of automation. How has automation

changed the production process? 11. Discuss the benefits of computer-aided design (CAD). 12. What is meant by the term service package? 13. Name three service companies and describe their service

package. 14. Give examples of services that have a good match between

customer expectations and service delivery. Give examples of services that do not have a good match.

Problems 1. See-Clear Optics is considering producing a new line of

eyewear. Aft er considering the costs of raw materials and the cost of some new equipment, the company estimates fi xed costs to be $40,000 with a variable cost of $45 per unit produced.

(a) If the selling price of each new product is set at $100, how many units need to be produced and sold to break even? Use both the graphical and algebraic approaches.

(b) If the selling price of the product is set at $80 per unit, See-Clear expects to sell 2000 units. What would be the total contribution to profi t from this product at this price?

(c) See-Clear estimates that if it off ers the product at the original target price of $100 per unit, the company will sell about 1500 units. Will the pricing strategy of $100 per unit or $80 per unit yield a higher contribution to profi t?

2. Med-First is a medical facility that off ers outpatient medical services. Th e facility is considering off ering an additional service, mammography screening tests, on-site. Th e facility estimates the annual fi xed cost of the equipment and skills necessary for the service to be $120,000. Variable costs for each patient processed are estimated at $35 per patient. If the clinic plans to charge $55 for each screening test, how many patients must it process a year in order to break even?

3. Tasty Ice Cream is a year-round take-out ice cream restaurant that is considering off ering an additional product, hot chocolate. Considering the additional machine it would need plus cups and ingredients, it estimates fi xed costs to be $200 per year and the variable cost to be $0.20. If it charges $1.00 for each hot chocolate, how many hot chocolates does it need to sell in order to break even?

4. Slick Pads is a company that manufactures laptop notebook computers. Th e company is considering adding its own line of computer printers as well. It has considered the implications

from the marketing and fi nancial perspectives and estimates fi xed costs to be $500,000. Variable costs are estimated at $200 per unit produced and sold.

(a) If the company plans to off er the new printers at a price of $350, how many printers does it have to sell to break even?

(b) Describe the types of operations considerations that the company needs to consider before making the fi nal decision.

5. Perfect Furniture is a manufacturer of kitchen tables and chairs. Th e company is currently deciding between two new methods for making kitchen tables. Th e fi rst process is estimated to have a fi xed cost of $80,000 and a variable cost of $75 per unit. Th e second process is estimated to have a fi xed cost of $100,000 and a variable cost of $60 per unit.

(a) Graphically plot the total costs for both methods. Identify which ranges of product volume are best for each method.

(b) If the company produces 500 tables a year, which method provides a lower total cost?

6. Harrison Hotels is considering adding a spa to its current facility in order to improve its list of amenities. Operating the spa would require a fi xed cost of $25,000 a year. Variable cost is estimated at $35 per customer. Th e hotel wants to break even if 12,000 customers use the spa facility. What should be the price of the spa services?

7. Kaizer Plastics produces a variety of plastic items for packaging and distribution. One item, container #145, has had a low contribution to profi ts. Last year, 20,000 units of container #145 were produced and sold. Th e selling price of the container was $20 per unit, with a variable cost of $18 per unit and a fi xed cost of $70,000 per year.

(a) What is the break-even quantity for this product? Use both graphic and algebraic methods to get your answer.

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98 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

(b) Th e company is currently considering ways to improve profi tability by either stimulating sales volumes or reducing variable costs. Management believes that sales can be increased by 35 percent of their current level or that variable cost can be reduced to 90 percent of their current level. Assuming all other costs equal, identify which alternative would lead to a higher profi t contribution.

8. George Fine, owner of Fine Manufacturing, is considering the introduction of a new product line. George has considered factors such as costs of raw materials, new equipment, and requirements of a new production process. He estimates that the variable costs of each unit produced would be $8 and fi xed costs would be $70,000.

(a) If the selling price is set at $20 each, how many units have to be produced and sold for Fine Manufacturing to break even? Use both graphical and algebraic approaches.

(b) If the selling price of the product is set at $18 per unit, Fine Manufacturing expects to sell 15,000 units. What would be the total contribution to profi t from this product at this price?

(c) Fine Manufacturing estimates that if it off ers the product at the original target price of $20 per unit, the company will sell about 12,000 units. Which pricing strategy—$18 per unit or $20 per unit—will yield a higher contribution to profi t?

(d) Identify additional factors that George Fine should consider in deciding whether to produce and sell the new product.

9. Handy-Maid Cleaning Service is considering off ering an additional line of services to include professional offi ce cleaning. Annual fi xed costs for this additional service are estimated to be $9000. Variable costs are estimated at $50 per unit of service. If the price of the new service is set at $80 per unit of service, how many units of service are needed for Handy-Maid to break even?

10. Easy-Tech Soft ware Corporation is evaluating the production of a new soft ware product to compete with the popular word processing soft ware currently available. Annual fi xed costs of producing the item are estimated at $150,000, and the variable cost is $10 per unit. Th e current selling price of the item is $35 per unit, and the annual sales volume is estimated at 50,000 units.

(a) Easy-Tech is considering adding new equipment that would improve soft ware quality. Th e negative aspect of this new equipment would be an increase in both fi xed and variable costs. Annual fi xed costs would increase by $50,000 and variable costs by $3. However, marketing expects the better-quality product to increase demand to 70,000 units. Should Easy-Tech purchase this new equipment and keep the price of their product the same? Explain your reasoning.

(b) Another option being considered by Easy-Tech is the increase in the selling price to $40 per unit to off set the additional equipment costs. However, this increase would result in a decrease in demand to 40,000 units. Should Easy-Tech increase its selling price if it purchases the new equipment? Explain your reasoning.

11. Zodiac Furniture is considering the production of a new line of metal offi ce chairs. Th e chairs can be produced in-house using either process A or process B. Th e chairs can also be purchased from an outside supplier. Specify the levels of demand for each processing alternative given the costs in the table.

12. Mop and Broom Manufacturing is evaluating whether to produce a new type of mop. Th e company is considering the operations requirements for the mop as well as the market potential. Estimates of fi xed costs per year are $40,000, and the variable cost for each mop produced is $20.

(a) If the company sells the product at a price of $25, how many units of product have to be sold in order to break even? Use both the algebraic and graphical approaches.

(b) If the company sells 10,000 mops at the product price of $25, what will be the contribution to profi t?

13. Mop and Broom Manufacturing, from Problem 12, has decided to produce a new type of mop. Th e mop can be made with the current equipment in place. However, the company is considering the purchase of new equipment that would produce the mop more effi ciently. Th e fi xed cost would be raised to $50,000 per year, but the variable cost would be reduced to $15 per unit. Th e company still plans to sell the mops at $25 per unit. Should Mop and Broom produce the mop with the new or current equipment described in Problem 12? Specify the volume of demand for which you would choose each process.

14. Jacob’s Baby Food Company must go through the following steps to make mashed carrots: (1) unload carrots from truck; (2) inspect carrots; (3) weigh carrots; (4) move to storage; (5) wait until needed; (6) move to washer; (7) boil in water; (8) mash carrots; (9) inspect. Draw a process fl ow diagram for these steps.

15. Draw a process fl ow diagram of your last doctor’s offi ce visit. Identify bottlenecks. Did any activities occur in parallel?

16. Oakwood Outpatient Clinic is analyzing its operation in an eff ort to improve performance. Th e clinic estimates that a patient spends on average 312 hours at the facility. Th e amount of time the patient is in contact with staff (i.e., physicians, nurses, offi ce staff , lab technicians) is estimated at 40 minutes. On average the facility sees 42 patients per day. Th eir standard has been 40

Fixed Cost Variable Cost

Process A $20,000 $30 Process B $30,000 $50 Outside Supplier $0 $50

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CASE: BIDDY’S BAKERY (BB) • 99

CASE: Biddy’s Bakery (BB) Biddy’s Bakery was founded by Elizabeth McDoogle in 1984. Nicknamed “Biddy,” Elizabeth started the home-style bakery in Cincinnati, Ohio, as an alternative to commercially available baked goods. The mission of Biddy’s Bakery was to produce a variety of baked goods with old-fashioned style and taste. The goods produced included a variety of pies and cakes and were sold to the general public and local restaurants.

The operation was initially started as a hobby by Eliza- beth and a group of her friends. Many of the recipes they used had been passed down for generations in their families. The small production and sales facility was housed in a mixed commercial and residential area on the first floor of “Biddy’s” home. Elizabeth (“Biddy”) and three of her friends worked in the facility from 6 a.m. to 2 p.m. making and sell- ing the pies. The operation was arranged as a job shop with workstations set up to perform a variety of tasks as needed. Most of the customers placed advanced orders, and Biddy’s Bakery took pride in accepting special requests. The bakery’s specialty was the McDoogle pie, a rich chocolate confection in a cookie crust.

Meeting Capacity Needs Initially sales were slow, and there were periods when the busi- ness operated at a loss. However, after a few years Biddy’s Bakery began to attract a loyal customer following. Sales continued to grow slowly but steadily. In 1994 a first floor storage area was expanded to accommodate the growing business. However, Biddy’s Bakery quickly outgrew its current capacity. In May of 2000 Elizabeth decided to purchase the adjacent building and move the entire operation into the much larger facility. The new facility had considerably more capacity than needed, but the expectation was that business would continue to grow. Unfortunately, by the end of 2000 Elizabeth found that her sales expectations had not been met, and she was paying for a facility with unused space.

Getting Management Advice Elizabeth knew that her operations methods, though traditional, were sound. A few years ago she had called upon a team of

business students from a local university for advice as part of their course project. They had offered some suggestions but were most impressed with the efficient manner with which she ran her operation. Recalling this experience, she decided to con- tact the same university for another team of business students to help her with her predicament.

After considerable analysis the team of business students came up with their plan: Biddy’s Bakery should primarily focus on production of the McDoogle pie in large volumes, with major sales to go to a local grocery store. The team of business students discussed this option with a local grocery store chain that was pleased with the prospect. Under the agreement Biddy’s Bakery would focus its production on the McDoogle pie, which would be delivered in set quantities to one store location twice a week. The volume of pies required would use up all of the current excess capacity and take away most of the capacity from produc- tion of other pies.

Elizabeth was confused. The alternative being offered would solve her capacity problems, but it seemed that the business would be completely different, though she did not understand how or why. For the first time in managing her business she did not know what to do.

Case Questions 1. Explain the challenge Elizabeth faced in meeting her

capacity needs. What should she have considered before moving into the larger facility?

2. What is wrong with the proposal made by the team of business students? Why?

3. What type of operation does Biddy’s Bakery currently have in place? What type of operation is needed to meet the proposal made by the team of business students? Explain the diff erences between these two operations.

4. Elizabeth senses that the business would be diff erent if she accepts the proposal but does not know how and why. Explain how it would be diff erent.

5. What would you advise Elizabeth?

patients per day. Determine process velocity and effi ciency for the clinic.

17. Oakwood Outpatient Clinic rents a magnetic resonance imaging (MRI) machine for 30 hours a month for use on its patients. Last month the machine was used 28 hours out of the month. What was machine utilization?

18. Mop and Broom Manufacturing estimates that it takes 412 hours for each broom to be produced, from raw materials to fi nal product. An evaluation of the process reveals that the amount of time spent working on the product is 3 hours. Determine process velocity.

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100 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Service Package and Processes at Cruise Interna- tional, Inc. Now that you have learned something about the big picture at CII, you believe it is time to learn some specifics. You call Bob Bristol to report your progress and ask for the details of your next assignment.

“I am pleased with your progress in familiarizing yourself with CII and its operations. Now it is time for you to tackle a specific assignment under the direction of Leila Jensen, the hotel manager aboard the MS Friendly Dreams I. Specifi- cally, you must develop a good understanding of the service package currently offered by CII to its customers and the service delivery process it uses. At the strategic level, the

service package must be consistent with the company’s mis- sion statement and the competitive priorities that it wants to emphasize.” This assignment will enhance your knowledge of the material in Chapter 3 of your textbook while preparing you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Service Package and Processes at CII

CASE: Creature Care Animal Clinic (B) Company Background Creature Care Animal Clinic is a suburban veterinary clinic specializing in the medical care of dogs and cats. Dr. Julia Barr opened the clinic three years ago, hiring another full-time veterinarian, a staff of three nurses, an office manager, and an office assistant. The clinic operates Monday through Friday during regular business hours, with half days on Saturdays and extended hours on Wednesday evenings. Both doctors work during the week and take turns covering Wednesday evenings and Saturdays.

Dr. Barr opened the clinic with the intent of providing out- patient animal care. Overnight services are provided for surgical patients only. No other specialized services are offered. The facility for the clinic was designed for this type of service, with a spacious waiting and reception area. The examining and surgical rooms are in the rear, just large enough to accommodate their initial purpose.

As time has passed, however, the number of patients requesting specialized services has increased. Initially the requests were few, so Dr. Barr tried to accommodate them. As one of the nurses was also trained in grooming services, she began to alternate between her regular duties and pet groom- ing. Pet grooming was performed in the rear of the reception area, as it was spacious and there was no other room for this job. At first this was not a problem. However, as the number of pets being groomed increased, the flow of work began to be interrupted. Customers waiting with their pets would com- ment to the groomer in the rear, who had difficulty focusing

on the work. The receptionist was also distracted, as were the animals.

The number of customers requesting grooming services was growing rapidly. Customers wanted to drop off their pets for a “package” of examining, grooming, and even minor surgical procedures requiring overnight stays. The space for grooming and overnight services was rapidly taking over room for other tasks. Also, most of the staff was not trained in providing the type of service customers were now requiring.

The Dilemma Dr. Barr sat at her desk wondering how to handle the operations dilemma she was faced with. She started her business as a medi- cal clinic but found that she was no longer sure what business she was in. She didn’t understand why it was so complicated given that she was only providing a service. She was not sure what to do.

Case Questions 1. Identify the operations management problems that Dr. Barr

is having at the clinic. 2. How would you defi ne the “service bundle” currently being

off ered? How is this diff erent from the initial purpose of the clinic?

3. Identify the high-contact and low-contact segments of the operation. How should each be managed?

4. What should Dr. Barr have done diff erently to avoid the prob- lems she is currently experiencing? What should Dr. Barr do now?

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SELECTED BIBLIOGRAPHY • 101

On-line Case: Product Design and Process Selection at Valley Memorial Hospital

Assignment: Service Package and Processes at Valley Memo- rial Hospital With just a couple of weeks left before you start working at Kaizen for its client Valley Memorial Hospital, it is essential for you to get some specific insights into the company’s operations. This assignment will enable you to enhance your knowledge of the material in Chapter 3 while continuing to prepare you for a successful internship. Bob Reilly suggests you learn more about the service package at VMH.

To access the Web site:

Hospital

Online Resources Companion Website www.wiley.com/college/reid

• Take interactive practice quizzes to assess your knowledge and help you study in a dynamic way • Review PowerPoint slides or print slides for notetaking • Download Excel Templates to use for problem solving • Access the Virtual Company: Cruise International, Inc. • Find links to Company Tours for this chapter

Ercol Furniture Ltd. • Find links for Additional Web Resources for this chapter

Institute for Supply Chain Management, www.ism.ws

You have just taken a position with Country Comfort Furniture, a furniture manufacturer known for its custom-designed coun- try furniture. The primary focus of the company has been on kitchen and dining room furniture in the upper portion of the high-price range. Due to competitive pressures and changes in the market, Country Comfort is now considering production of prefabricated kitchen and dining room furniture in the medium- price range.

You have been asked to help Country Comfort evaluate the new product design it is considering. Perform an Internet search to identify at least two major competitors that Country Comfort would have if it chooses to pursue the new product line. Next, identify key product design features of each competitor’s products, their target market, and price range. Based on your search, what are your recommendations to Country Comfort on product design and current competition?

INTERNET CHALLENGE Country Comfort Furniture

Selected Bibliography Boyer, K.K. “Evolutionary Patterns of Flexible Automation and

Performance: A Longitudinal Study,” Management Science, 45, 6, 1999, 824–842.

Dennis, M.J., and A. Kambil. “Service Management: Building Profi ts aft er the Sale,” Supply Chain Management Review, January–February 2003, 42–49.

D’Souza, D.E., and F.P. Williams. “Toward a Taxonomy of Man- ufacturing Flexibility Dimensions,” Journal of Operations Management, 18, 2000, 577–593.

Fitzsimmons, J.A., and M.J. Fitzsimmons. Service Management: Operations, Strategy, and Information Technology, Fift h Edi- tion. New York: Irwin McGraw-Hill, 2005.

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102 • CHAPTER 3 PRODUCT DESIGN AND PROCESS SELECTION

Flynn, B.B., R.G. Schroeder, and E.J. Flynn. “WCM: An Inves- tigation of Hayes and Wheelwright’s Foundation,” Journal of Operations Management, 17, 1999, 249–269.

Hayes, R.H., and S.C. Wheelwright. Restoring Our Competitive Edge: Competing through Manufacturing. New York: John Wiley & Sons Inc., 1984.

Hayes, R.H., and S.C. Wheelwright. “Link Manufacturing Pro- cess and Product Life Cycles,” Harvard Business Review, 57, January–February 1979, 133–140.

Hayes, R.H., G. Pisano, D. Upton, and S.C. Wheelwright. Oper- ations Strategy and Technology: Pursuing the Competitive Edge. New York: John Wiley & Sons, 2005.

Hill, Terry. Manufacturing Strategy: Text and Cases, Th ird Edi- tion. New York: McGraw-Hill, 2000.

Klassen, R.D., and D.C. Whybark. “Environmental Manage- ment in Operations: Th e Selection of Environmental Technologies,” Decision Sciences, 30, 3, 1999, 601–631.

Pannirselvam, G.P., L.A. Ferguso, R.C. Ash, and S.P. Sifered. “Operations Management Research: An Update for the 1990’s,” Journal of Operations Management, 18, 1999, 95–112.

Rondeau, P.J., M.A. Vonderembse, and T.S. Raghunathan. “Exploring Work System Practices for Time-Base Manu- facturers: Th eir Impact on Competitive Capabilities,” Jour- nal of Operations Management, 18, 2000, 509–529.

Ward, P.C., T.K. McCreery, L.P. Ritzman, and D. Sharma. “Competitive Priorities in Operations Management,” Deci- sion Science, 29, 4, 1998, 1035–1046.

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L E A R N I N G O B J E C T I V E S

CHAPTER

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

103

C H A P T E R O U T L I N E

What Is a Supply Chain? 104 Components of a Supply Chain for a

Manufacturer 106 The Bullwhip Effect 108 Supply Chains for Service Organizations 110 Major Issues Affecting Supply Chain

Management 112 The Role of Purchasing 122

Sourcing Issues 127 Supply Chain Distribution 137 Implementing Supply Chain Management 143 Supply Chain Performance Metrics 145 Trends in Supply Chain Management 147 Supply Chain Management within OM: How It All

Fits Together 149 SCM across the Organization 149

Before studying this chapter you should know or, if necessary, review

1. The implications of competitive priorities, Chapter 2, pp. 37–40. 2. Product design considerations, Chapter 3, pp. 60–68. 3. Process selection considerations, Chapter 3, pp. 65–72.

After studying this chapter you should be able to

Supply Chain Management

1 Describe the structure of supply chains. 2 Describe the bullwhip effect. 3 Describe supply chains for service organizations. 4 Describe major issues affecting supply chains. 5 Describe electronic commerce. 6 Describe global issues affecting supply chains. 7 Describe government regulation issues affecting

supply chains. 8 Describe green supply chain management.

9 Describe the role of purchasing in supply chain management.

10 Describe sourcing issues. 11 Describe strategic purchasing partnerships. 12 Describe ethics in supplier management. 13 Describe supply chain distribution. 14 Describe how to implement supply chain management. 15 Describe supply chain management metrics. 16 Describe trends in supply chain management.

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104 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

Buying a product used to mean browsing through mail-order catalogs or getting dressed, leaving home, and shopping at stores or malls until you found what you wanted. Today, most of us can go on-line anytime during the day, seven days a week, and buy just about anything over the Inter- net. You can shop while sitting at your computer and never need to leave home. You can order food from a supermarket or a restaurant on-line or buy clothing and household goods. You can buy books, videos, CDs, or more expensive products like dia- monds and cars, or even book your vacation—the Internet has revolutionized the way we do business by allowing us access to numerous suppliers around the world.

The Internet also has allowed companies to change the way they find the mate- rials and supplies that are needed for their operations. Business-to-business (B2B) transactions are conducted between companies and their suppliers, distributors, and customers. Even though direct sales to the general public are more familiar, B2B transactions make up the majority of Internet transactions.

One of the most publicized examples is Covisint, a global business-to-business automotive supplier exchange site, begun in 2000 as an initiative by the U.S. auto- makers Ford Motor Company, General Motors, DaimlerChrysler, Nissan/Renault, and PSA Peugeot Citroën. Covisint became the largest industry-sponsored net mar- ketplace. Covisint has over 300,000 users, representing 45,000 different companies, located in 96 different countries.

While Covisint began as the electronic marketplace for the auto industry, in 2004 the company was bought by Compuware Company. Since that time Covisint has expanded into healthcare, oil and gas, and other industries. Covisint, a pioneer in cloud computing, developed applications that help organizations connect, communi- cate, and collaborate with members of their supply chain. Using AppCloud, a secure third-party application marketplace, Covisint enables access to software-as-a-service applications.

WHAT IS A SUPPLY CHAIN? A supply chain is the network of activities that delivers a finished product or service to the customer. These include sourcing raw materials and parts, manufacturing and assembling the products, warehousing, order entry and tracking, distribution through the channels, and delivery to the customer. An organization’s supply chain is facili- tated by an information system that allows relevant information such as sales data,

� Supply chain A network of all the activities involved in delivering a finished product or service to the customer.

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WHAT IS A SUPPLY CHAIN? • 105

sales forecasts, and promotions to be shared among members of the supply chain. Figure 4-1 shows a basic supply chain structure for a manufacturer.

At the beginning of the chain are the external suppliers who supply and transport raw materials and components to the manufacturers. Manufacturers transform these materials into finished products that are shipped either to the manufacturer’s own distribution centers or to wholesalers. Next, the product is shipped to retailers who sell the product to the customer. Goods flow from the beginning of the chain through the manufacturing process to the customer. Relevant information flows back and forth among members of the supply chain.

Supply chain management is the vital business function that coordinates and manages all the activities of the supply chain linking suppliers, transporters, internal departments, third-party companies, and information systems. Supply chain manage- ment for manufacturers entails

● Coordinating the movement of goods through the supply chain from suppliers to manufacturers to distributors to the final customers

● Sharing relevant information such as sales forecasts, sales data, and promotional campaigns among members of the chain

A prime example of operations management (OM), supply chain management pro- vides the company with a sustainable, competitive advantage, such as quick response time, low cost, state-of-the-art quality design, or operational flexibility.

MKT

� Supply chain management Coordinates and manages all the activities of the supply chain.

FIGURE 4-1

Basic supply chain

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106 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

Dell Computer Corporation is a good example of a company using its supply chain to achieve a sustainable competitive advantage. Quick delivery of customized com- puters at prices 10–15 percent lower than the industry standard is Dell’s competitive advantage. A customized Dell computer can be en route to the customer within 36 hours. This quick response allows Dell to reduce its inventory level to approximately 13 days of supply compared to Compaq’s 25 days of supply. Dell achieves this in part through its warehousing plan. Most of the components Dell uses are warehoused within 15 minutes travel time to an assembly plant. Dell does not order components at its Austin, Texas, facility; instead, suppliers restock warehouses as needed, and Dell is billed for items only after they are shipped. The result is better value for the customer.

COMPONENTS OF A SUPPLY CHAIN FOR A MANUFACTURER A company’s supply chain structure has three components: external suppliers, internal functions of the company, and external distributors. Figure 4-2 shows a simplified supply chain for packaged dairy products.

External suppliers include the dairy farmer, cardboard container manufacturer, label company, plastic container manufacturer, paper mill, chemical processing plant, lumber company, and chemical extraction plant. Internal functions include the pro- cessing of the raw milk into consumer dairy products and packaging and labeling dairy products for distribution to retail grocery outlets. The external distributors transport finished products from the manufacturer to retail grocers, where the products are sold to the customer. The supply chain includes every activity from collecting the raw milk, producing the consumer dairy products, packaging the dairy products, distributing the packaged dairy products to retail grocers, to selling the finished dairy products to the customer.

Let’s look at each component of the supply chain in detail.

External Suppliers Dairy products manufacturing involves several companies, as shown in Figure 4-2. The dairy products are packaged either in cardboard or plastic containers made by tier one suppliers. Note that any supplier that provides materials directly to the processing facility is designated as a tier one supplier (in this case, the dairy farm, the cardboard container manufacturer, the label company, and the plastic container manufacturer).

The paper mill and the chemical processing plant are tier two suppliers because they directly supply tier one suppliers but do not directly supply the packaging opera- tion. The lumber company that provides wood to the paper mill is a tier three sup- plier, as is the chemical extraction plant that supplies raw materials to the chemical processing plant.

Companies put substantial effort into developing the external supplier portion of the supply chain because the cost of materials might represent 50–60 percent or even more of the cost of goods sold. A company is typically involved in a number of sup- ply chains and often in different roles. In the supply chain for the plastics container manufacturer shown in Figure 4-2, for example, the chemical plant is now a tier one supplier and the chemical extraction facility is a tier two supplier. Even though the plastics container manufacturer was a tier one supplier to the milk processing facility, the plastic container manufacturer still has its own unique supply chain. Now consider

� Tier one supplier Supplies materials or services directly to the processing facility.

� Tier two supplier Directly supplies materials or services to a tier one supplier in the supply chain.

� Tier three supplier Directly supplies materials or services to a tier two supplier in the supply chain.

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COMPONENTS OF A SUPPLY CHAIN FOR A MANUFACTURER • 107

the supply chain for a retail grocer: the tier one suppliers are providers of packaged consumer products, and the grocer has no external distributors because the customers buy directly from the store. As you can see, supply chains come in all shapes and sizes.

Remember that tier one suppliers (the cardboard container manufacturer, dairy farm, label company, and plastics container manufacturer in Figure 4-2) directly sup- ply the consumer product manufacturer (packaged dairy products), whereas tier two suppliers (paper mill and chemical processing plant) directly supply tier one suppliers. To summarize: supply chains are a series of linked suppliers and customers in which each customer is a supplier to another part of the chain until the product is delivered to the final customer.

FIGURE 4-2

Dairy products supply chain

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108 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

Internal Functions Internal functions in, for example, a dairy products supply chain are as follows:

● Processing, which converts raw milk into dairy products and packages these products for distribution to retail grocery outlets.

● Purchasing, which selects appropriate suppliers, ensures that suppliers perform up to expectations, administers contracts, and develops and maintains good supplier relationships.

● Production planning and control, which schedules the processing of raw milk into dairy products.

● Quality assurance, which oversees the quality of the dairy products. ● Shipping, which selects external carriers and/or a private fleet to transport the

product from the manufacturing facility to its destination.

External Distributors External distributors transport finished products to the appropriate locations for even- tual sale to customers. Logistics managers are responsible for managing the movement of products between locations. Logistics includes traffic management and distribution management. Traffic management is the selection and monitoring of external carriers (trucking companies, airlines, railroads, shipping companies, and couriers) or internal fleets of carriers. Distribution management is the packaging, storing, and handling of products at receiving docks, warehouses, and retail outlets.

Next, we will look at a common challenge to supply chain managers called the bull- whip effect.

MKT

� Logistics Activities involved in obtaining, producing, and distributing materials and products in the proper place and in proper quantities.

� Traffic management Responsible for arranging the method of shipment for both incoming and outgoing products or materials.

THE BULLWHIP EFFECT Sharing product demand information between members of a supply chain is criti- cal. However, inaccurate or distorted information can travel through the chain like a bullwhip uncoiling. The bullwhip effect, as this is called, causes erratic replen- ishment orders placed on different levels in the supply chain that have no appar- ent link to final product demand. The results are excessive inventory investment, poor customer service levels, ineffective transportation use, misused manufacturing capacity, and lost revenues. We will discuss the causes of the bullwhip effect, and how they send inaccurate or distorted information down the supply chain. First, however, let’s look at the traditional supply chain, shown in Figure 4-3, and follow the product demand information flow from the final seller back to the manufacturer of the product:

1. The final seller periodically places replenishment orders with the next level of the supply chain, which could be a local distributor. The timing and order quantity— for example, monthly orders in varying amounts—are determined by the final seller. The timing and quantity can be fixed or variable.

2. The local distributor has many customers (final sellers) placing replenishment orders. Each final seller uses it own product demand estimates and quantity rules. Based on these replenishment orders, the local distributor places replenishment orders with its supplier, which could be a regional distribution center (RDC).

� Bullwhip effect Inaccurate or distorted demand information created in the supply chain.

MKT

� Distribution management Responsible for movement of material from the manu- facturer to the customer.

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THE BULLWHIP EFFECT • 109

3. As before, the customers (the local distributors) determine the timing and quantity of orders placed with the RDC. Each RDC periodically places orders based on demand at the RDC by ordering from the manufacturer of the finished good.

4. In turn, the manufacturer develops plans and schedules production orders based on orders from the RDCs. The manufacturer does not know what the demand is for the finished good by the final customer but knows only what the RDCs order.

The greater the number of levels in the supply chain, the further away the manufac- turer is from final customer demand. Since suppliers in the chain do not know what customer demand is or when a replenishment order might arrive, suppliers stockpile inventory.

Causes of the Bullwhip Effect The causes of the bullwhip effect are demand forecast updating, order batching, price fluctuation, and rationing and gaming. Let’s look at each of these causes.

Each member in the supply chain, beginning with the retailers, does demand fore- cast updating with every inventory review. Based on actual demand, the retailers update their demand forecast. The retailers review their current inventory level and, based on their inventory policies, determine whether a replenishment order is needed. The wholesalers repeat the process. Note that the demand is from the retailers’ inven- tory replenishments and may not reflect actual customer demand at the retail level. The wholesalers update their demand forecast and place appropriate replenishment orders with the distribution centers. The distributors repeat the process, updating their demand forecasts based on demand from the wholesalers. The distributors review their inventory levels and place the appropriate orders with the manufacturer. These orders are determined by the inventory policies at the distributors. Orders placed with the manufacturer end up replenishing each level in the supply chain rather than being directly linked to end-customer demand.

FIGURE 4-3

Traditional supply chain information flow

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110 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

A company does order batching when, instead of placing replenishment orders right after each unit is sold, it waits some period of time, sums up the number of units sold, and then places the order. This changes constant product demand to lumpy demand—a situation where certain levels in the supply chain experience periods of no demand. Order batching policies amplify variability in order timing and size.

Price fluctuations cause companies to buy products before they need them. Price fluctuations follow special promotions like price discounts, quantity discounts, cou- pons, and rebates. Each of these price fluctuations affects the replenishment orders placed in the supply system. When prices are lower, members of the supply chain tend to buy in larger quantities. When prices increase, order quantities decrease. Price fluc- tuations create more demand variability within the supply chain.

Rationing and shortage gaming result when demand exceeds supply and prod- ucts are rationed to members of the supply chain. Knowing that the manufacturer will ration items, customers within the supply chain often exaggerate their needs. For example, if you know the company is supplying only 50 percent of the order quantity, you double the order size. If you really need 100 pieces, you order 200 so you are sure to get what you need. Such game-playing distorts true demand information in the system.

Counteracting the Bullwhip Effect Here are four ways of counteracting the bullwhip effect:

1. Change the way suppliers forecast product demand by making this information from the final-seller level available to all levels of the supply chain. This allows all levels to use the same product demand information when making replenishment decisions. Companies can do this by collecting point-of-sale (POS) information, a function available on most cash registers.

2. Eliminate order batching. Companies typically use large order batches because of the relatively high cost of placing an order. Supply chain partners can reduce ordering costs by using electronic data interchange (EDI) to transmit information. Lower ordering costs eliminate the need for batch orders.

3. Stabilize prices. Manufacturers can eliminate incentives for retail forward buying by creating a uniform wholesale pricing policy. In the grocery industry, for example, major manufacturers use an everyday low-price policy or a value- pricing strategy to discourage forward buying.

4. Eliminate gaming. Instead of filling an order based on a set percentage, man- ufacturers can allocate products in proportion to past sales records. Customers then have no incentive to order a larger quantity to get the quantity they need.

SUPPLY CHAINS FOR SERVICE ORGANIZATIONS Up till now, the discussion has focused on supply chains for manufacturing organiza- tions rather than supply organizations. However, service organizations can also bene- fit from supply chain management. A supply chain for a service organization is similar to that for manufacturing organizations since external suppliers, internal operations, and external distributors can be needed. In the case of an e-tailer, supply chain man- agement can be used to integrate external suppliers (usually providing better demand

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SUPPLY CHAINS FOR SERVICE ORGANIZATIONS • 111

forecast information to its suppliers, thus reducing uncertainty within the chain); it has internal operations with regard to order processing, order picking, and so on; and it can have external distribution done by a third-party provider (such as FedEx, UPS, or DHL).

Let’s look at an example of a supply chain for a full-service travel agency as shown in Figure 4-4. The agency arranges a wide variety of trips for its customers. The ser- vice can be as simple as making airline, train, hotel, cruise, car rental, or personal tour arrangements for the customer. A customer can require a single type of service or have a highly complicated travel request.

Internal Operations The internal operations at the travel agency are split into two parts: Travel Enablement and Travel Billing.

Travel Enablement begins with qualifying potential suppliers of travel services. The agency does not provide the actual travel but makes the arrangements. The agency must know the scope of services provided, as well as the quality of the services, the reliability of the provider, the safety of the service, the financial solvency, the lan- guage capability of the provider, and the like. As people travel throughout the world, it is critical to qualify any service providers used by the agency. The better the agency develops its external suppliers, the better it can match its customers with the right service providers.

The next step deals with the different forms of credit since no business transac- tion is completed without assuring the financing first. Providers in many develop- ing nations don’t accept credit cards for payment, so it may be necessary to arrange bank transfers (possibly in the local currency) to finalize the service commitment. The agency must also clarify refund and cancellation policies.

Pricing deals with negotiation of terms for delivery of the service and price dis- counts. Group discounts may be available. Discounts may be offered for services booked during traditionally low seasons. The agency can use integrated cost data, including available discounts and other incentives, to influence customer decisions.

External Suppliers

Travel Planning Travel Payment Actual Providers

Internal Operations External Distributors

Airlines

Cruise lines

Trains

Tour operators

Hotels

Car rentals

Private guides

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Shore Trips

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FIGURE 4-4

Supply chain for a travel agency

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The last process of Travel Enablement is assuring that any risk is minimized for the customer. This can be done by having integrated supplier performance files. The agency can evaluate past performance by the supplier and determine how to minimize any risk associated with using the supplier (a backup provider, just in case) or travel insurance.

Travel Enablement is about having a database of service providers that can be accessed instantly for use in arranging real-time travel for customers. Often the cus- tomer is sitting at the desk of a travel agent or is on the phone or Internet with the agent. The integrated database facilitates travel arrangements.

Travel Billing includes invoicing, dispute resolution, and payment to suppliers. The agency creates an integrated invoice for its customers. The agency can have both commercial and private accounts. Commercial clients can often receive a consolidated invoice monthly. Most private accounts are invoiced after the trip has been booked. It can include a payment required at time of booking, with the balance due prior to the actual travel. Providing a consolidated invoice reduces the number of transactions with the customer. Often systems for electronic payment are used.

The agency can be the focal point for all complaint resolution. This reduces the risk of miscommunications and provides a resolution process for both the customer and the service provider.

Payment of the invoice to the agency results in subsequent payment to the service providers. Ideally, payments are processed by data transfer, thus reducing the time to cash for the service providers.

The External Distributors For the travel agency, the external distributors deliver the actual service to the cus- tomer. The external distributor is the service provider of the transportation or tour (the airline, the cruise company, the tour guides, the car rental agency, etc.). Although the agency makes all of the arrangements, the service provider is not part of the agency.

Services deal with many of the same issues that face manufacturers. The intent is to integrate the internal operations (arranging for the travel, billing the customer, paying the service suppliers, and tracking the services provided to the customer).

MAJOR ISSUES AFFECTING SUPPLY CHAIN MANAGEMENT

Information Technology Information technology enablers for supply chain management include the Internet, the Web, EDI (electronic data interchange), intranets and extranets, bar code scan- ners, and point-of-sale demand information. We begin by looking at the use of the Internet and the Web as a way of doing business.

E-Commerce E-commerce and e-business are defined as the use of the Internet and the Web to transact business. E-business refers to transactions and processes within an organiza- tion, such as a company’s on-line inventory control system, that support supply chain management. E-commerce includes B2B (business-to-business) and B2C (business- to-consumer) transactions. Let’s take a closer look B2B e-commerce.

� E-commerce Using the Internet and Web to transact business.

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Business-to-Business (B2B) E-Commerce In business-to-business e-commerce, companies sell and buy products to and from other businesses. B2B represents the largest segment of e-commerce sales transac- tions. Prior to the Internet, B2B was relatively inefficient. It took time and resources for companies to search for products, to arrange for purchase and payment, to handle shipment, and finally to receive the items. Using the Internet allowed companies to automate at least parts of the procurement process. Significant dollars are saved by organizations due to effective electronic purchasing research and reduced transaction costs. Let’s look at how B2B commerce has developed.

The Evolution of B2B Commerce B2B commerce began in the 1970s with automated order entry systems that used telephone models to send digital orders to suppliers. One company, Baxter Health- care Corporation, placed telephone modems in a customer’s purchasing department to automate reordering supplies from Baxter’s computerized inventory database. This technology changed in the 1980s to personal computers and in the 1990s to Internet workstations that access on-line catalogs. Automated order entry systems are seller- side solutions. They are owned by the supplier and only offer the supplier’s product line. The primary benefits to the customers are reduced inventory replenishment costs and supplier-paid system costs.

In the late 1970s, electronic data interchange (EDI) emerged. EDI is a form of computer-to-computer communication standardized for sharing business documents such as invoices, purchase orders, shipping bills, and product stocking numbers. Most large firms have EDI systems, and most inventory groups have industry standards for defining the documents to be communicated. EDI systems are buyer-side solutions: they are designed to reduce the procurement costs for the buyer. EDI systems gener- ally serve a specific industry.

In the mid-1990s, electronic storefronts emerged. Electronic showplaces are on- line catalogs of products made available to the general public by a single supplier. These storefronts evolved from the automated order entry systems. They are far less expensive than their predecessors because (1) they use the Internet as the communi- cation medium, and (2) the storefronts tend to carry products that serve a number of different industries.

Net marketplaces emerged in the late 1990s. A net marketplace is designed to bring hundreds or thousands of suppliers (each with electronic catalogs) together with a significant number of purchasing firms in a single Internet-based environment to conduct trade. Covisint (www.covisint.com) is an example of a successful net market- place. Covisint was started in 1999 by a consortium of the following auto manufactur- ers: General Motors, Ford Motor Company, DaimlerChrysler, Nissan, and Renault. Its purpose was to address escalating costs and gross inefficiencies within their industry. Covisint leveraged the power and potential of the Internet to solve industry-specific business problems in real time. Its goal was to deliver a secure marketplace, portal, and application-sharing platform for the global automotive industry. Since being bought by Compuware in 2004, Covisint has expanded in the healthcare, aerospace, public sector, and financial services industries.

A net marketplace can also facilitate on-line auctions. Two types of auctions are for- ward auctions (where suppliers auction excess inventory and receive the market price

� Business-to-business e-commerce Businesses selling to and buying from other businesses.

� Automated order entry system A method using telephone models to send digital orders to suppliers.

� Electronic data interchange (EDI) A form of computer-to- computer communications that enables sharing business documents.

� Electronic storefronts On-line catalogs of products made available to the general public by a single supplier.

� Net marketplaces Suppliers and buyers conduct trade in a single Internet- based environment.

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for their surplus goods) and reverse auctions (where buyers post electronic requests for quotes (eRFQs) for goods and services and suppliers bid for business on-line).

A virtual private network (VPN) is a computer network in which some of the links between nodes are carried by open connections or virtual circuits on the Inter- net instead of by physical wires. An organization can have a VPN for use by its own employees as well as suppliers and customers. Through this network, buyers and suppliers can work together on product design and development, manage inventory replenishments, coordinate production schedules, and work as partners. Access to the VPN is typically password controlled. More than likely, your university has a VPN for your use.

The Benefits of B2B E-Commerce The potential benefits from Internet-based B2B commerce include:

● Lower procurement administrative costs. ● Low-cost access to global suppliers. ● Lower inventory investment due to price transparency and quicker response

times. ● Better product quality because of increased cooperation between buyers and sell-

ers, especially during product design and development.

Business-to-Consumer (B2C) E-Commerce In business-to-consumer e-commerce, on-line businesses try to reach individual consumers. Let’s examine the different models that on-line businesses use to gener- ate revenue. In the advertising revenue model, a Web site offers its users informa- tion on services and products, and provides an opportunity for providers to advertise. The company receives fees for the advertising. Yahoo.com derives its primary revenue from selling advertising such as banner ads.

In the subscription revenue model, a Web site that offers content and services charges a subscription fee for access to the site. One example is Consumer Reports Online (www.consumerreports.org), which provides access to its content only to subscribers at a rate of $3.95 per month. Companies using this model must offer content perceived to be of high value that is not readily available elsewhere on the Internet for free.

In the transaction fee model, a company receives a fee for executing a transaction. For example, Orbitz (www.orbitz.com) charges a small fee to the consumer when an airline reservation is made. Another example, E*Trade Financial Corporation, an on- line stockbroker (www.etrade.com), receives a transaction fee each time it executes a stock transaction.

In the sales revenue model, companies sell goods, information, or services directly to customers. Amazon.com, primarily a book and music seller, Travelocity.com, an airline and hotel reservations provider, and DoubleClick Inc. (www.doubleclick.net), a company that gathers information about on-line users and sells it to other companies, all use the sales revenue model.

In the affiliate revenue model, companies receive a referral fee for directing business to an “affiliate” or receive some percentage of the revenue resulting from a referred sale. For example, MyPoints.com receives money for connecting companies with potential customers by offering special deals. When members take advantage of the deal, they earn points that can later be redeemed for goods.

� Electronic request for quote (eRFQs) An electronic request for a quote on goods and services.

� Virtual private network (VPN) A private Internet- based communications environment that is used by the company, its suppliers, and its customers for day-to- day activities.

� Business-to-consumer e-commerce (B2C) On-line businesses sell to individual consumers.

� Advertising revenue model Provides users with information on services and products and provides an opportunity for suppliers to advertise.

� Subscription revenue model A Web site that charges a subscription fee for access to its contents and services.

� Transaction fee model A company receives a fee for executing a transaction.

� Sales revenue model A means of selling goods, information, or services directly to customers.

� Affiliate revenue model Companies receive a referral fee for directing business to an affiliate.

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MAJOR ISSUES AFFECTING SUPPLY CHAIN MANAGEMENT • 115

In addition to the Internet, companies use other technology to help manage sup- ply chains. For example, intranets are networks internal to an organization. Intranets allow a company to network groups of internal computers together to form more effective information systems. Typically, members of the organization communicate internally on the intranet. Organizations can link intranet systems of the Internet to form extranets. The extranet can be expanded to include both a company’s suppliers and customers. Typically, real-time inventory status is available on the extranet as well as production schedules. Extranets allow suppliers and customers to “see” within the organization. The primary difference between the Internet, intranets, and extranets is who has access to the system. The Internet is wide open, the intranet is open to mem- bers of an organization, and the extranet is open to members of the organization as well as to suppliers and customers.

Consumer Expectations and Competition Resulting from E-Commerce On-line retailing, or business-to-consumer e-commerce, has shifted the power from the suppliers to the consumers. This shift in power has occurred because the Internet greatly reduced search and transaction costs for the consumer. It was estimated as early as April 2001 that some 100 million people and over 80 percent of all individuals with Internet access had purchased something (either a product or a service) on-line. In addition, millions more customers researched products on-line and subsequently bought those items off-line. The capability to quickly search, evaluate, compare, and purchase products gives the consumer considerable power. Examples of successful B2C businesses are Amazon.com, eBay, BMG Music Service, Barnesandnoble.com, Columbia House, Half.com, and JCPenney. E-tailers have penetrated significantly the following markets: computer hardware and software, books, travel, music and videos, collectibles and antiques, and event tickets.

Since customers have access to so many suppliers, it is important for suppliers to differentiate themselves by providing customers with excellent value. Dell Computer Corporation, Gateway, Inc., L.L.Bean, Inc., Lands’ End, Inc., Amazon.com, UPS, and FedEx are good examples of companies that put a premium on values such as pre- ferred customer service, short lead times, and/or quality guarantees. Dell differentiates itself with short lead times. The company does this by warehousing most of the com- ponents used to assemble its computers within 15 minutes of the assembly facility and building customized computers in an assemble-to-order strategy.

Consider how Lands’ End uses technology in its business. Lands’ End went on-line in 1995. The company sold only $160 worth of gear the first month. Today, Lands’ End is one of the nation’s largest apparel retailers on-line. The company has a live chat room that allows customers to ask questions about merchan- dise. It also offers a “shopping with a friend” service that allows a customer, his or her friend or friends, and a customer service

� Intranets Networks that are internal to an organization.

� Extranets Intranets that are linked to the Internet so that suppliers and customers can be included in the system.

LINKS TO PRACTICE Lands’ End, Inc. www.landsend.com

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representative to be linked together. Lands’ End’s “virtual model” highlights how far technology has advanced. A few strokes on the keyboard and the shopper is able to pro- duce an on-screen model with his or her body measurements. Even though this virtual model is not perfect, over 1 million shoppers have built their own models at the Lands’ End site.

An additional issue here is how companies handle the return of unwanted mer- chandise and provide for product exchanges or refunds. The Boston Consulting Group (BCG) reported that the “absence of a good return mechanism” was the second-highest reason shoppers cited for not shopping on the Web. There are methods for handling returns. An on-line company often first requires authorization to return an item, then the customer must pack up the item, pay to ship it back to the company, insure the item, and then wait for a credit to be made. Once the item is returned, the original seller must unpack the item, inspect the item, check the paperwork, and try to resell the item. Typically, neither the buyer nor seller is happy with the process.

Another approach allows the customer to drop the returned items at collection sta- tions (sometimes the physical stores of the company, for example, Staples, Inc., Sears, OfficeMax, Inc., etc.). The returned items can then be sold from the receiving store or picked up in bulk and returned to the distribution point. Another approach is to com- pletely outsource returns. FedEx and UPS provide such services.

In addition to buying products on-line, consumers also buy on-line services. Finance, insurance, real estate services, business services, and health services are the largest on-line service industries. Business services include consulting, advertising and marketing, and information processing.

Service organizations are categorized either as those that do transaction brokering or those that provide a “hands-on” service. An example of transaction brokering is a company providing financial services that has stockbrokers acting as intermediaries between buyers and sellers of stock. An example of a “hands-on” service is a legal ser- vice that interacts directly with the consumer to create a legal document. In general, most service organizations are knowledge- and information-intense. To provide value to consumers, these service companies must process considerable information (legal services or medical services) and employ a highly educated and skilled workforce (law- yers and doctors).

Globalization As globalization continues to increase, supply chains cover greater geographical dis- tances, face greater uncertainty, and can end up being less efficient. Beginning in 2004, prices for steel, oil, copper, cement, and coal began rising at double-digit rates. These increases led buyers to look globally for lower-cost alternative suppliers. After pur- chasing items offshore, companies were challenged with moving the commodities over longer distances. This increase in volume resulted in a transportation capacity crunch and led to higher transportation rates. The lack of capacity slowed down sup- ply chains, causing inventory levels to rise. The U.S. Commerce Department reported an increase in the level of inventory by November 2004 to 1.24. This means that it took approximately $1.24 of inventory to support every dollar of sales. In 2008, transporta- tion rates went even higher due to the very high fuel costs. These higher costs have prompted companies to reexamine their offshore sourcing and to consider returning to onshore suppliers.

Ocean carriers have predicted that the volume of goods shipped from Asia to the United States will continue to have double-digit annual increases. Most of these inbound goods are shipped through U.S. West Coast ports. The higher trade volume

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MAJOR ISSUES AFFECTING SUPPLY CHAIN MANAGEMENT • 117

has caused port congestion in California and Washington State. In an effort to ease port congestion, ocean carriers introduced larger ships for use on the transpacific routes. While it was hoped that these larger ships would add needed capacity and reduce operating costs, only a few U.S. ports are capable of handling the larger ships. The ships take longer to unload and reload, tying up the port for five to seven days rather than the normal two days. The longer port time has reduced terminal efficiency. Pre- viously, importers were allowed more free time (the time cargo may occupy assigned space free of storage charges) for containers at U.S. port terminals. Container free time basically provided a cheap form of portable warehousing for importers. Because of the terminal inefficiency, container free time has been reduced and ports have increased storage capacity in an effort to turn equipment around faster.

As a result of West Coast port congestion, companies frequently use Mexican ports at Lazaro Cardenas, Manzanillo, Veracrus, and Altamira. A large portion of the goods received in Mexican ports are used domestically in Mexico. However, major retailers— including Wal-Mart, Home Depot, and Target—regularly use Mexican ports to expe- dite delivery of Asia-sourced goods. With more than $200 billion spent transporting container cargo through the ports of Los Angeles and Long Beach annually and vol- ume is expected to double in the next decade, the Mexican ports are promoting the benefits of using Mexican ports to avoid congestion. Mexican port officials emphasize the lower labor cost (about one-fourth of the wages in Los Angeles and Long Beach), a record of labor stability (over 85 years without a strike at its ports), and improved speed to market. In response to the increasing volume of container traffic, Mexico began construction on a new mega-port in Punta Colonet in 2011.

Another issue is border security. Since September 11, 2001, the U.S. Bureau of Customs and Border Protection (CBP) has implemented new requirements. Essen- tially, these programs result in additional processing time. The CBP has also increased inspections at ports of suspicious cargo, creating additional port delays.

The U.S.-Canadian border has more than $1.6 billion and 300,000 travelers cross- ing the border on a daily basis. Businesses in both countries are concerned over the “thickening” of the border. This thickening is a result of new or increased fees and inspections, uncertainty over wait times, more rules and regulations from different departments, more stringent requirements once compliance is achieved, and infra- structure impediments. Because of this thickening of the border coupled with an economic downturn, border inventories and transportation costs are growing, cross- border tourism is discouraged, and more than 10 million jobs that rely on the U.S.- Canadian partnership are threatened. In 2008, the Canadian Chamber of Commerce and the U.S. Chamber of Commerce, along with 43 business associations, produced a report with short-term recommendations for reducing border costs. The Chambers followed this in 2009, with three major political goals. First, they would make both countries more secure by increasing participation in trusted shipper and trusted trav- eler programs. This would allow border agencies to focus their limited resources on protecting against unlawful trade and travel. Second, they would improve wait time predictability at the border, reducing the current level of uncertainty. Third, they would facilitate the movement of low-risk goods and people across the border.

In 2011, the U.S. and Mexican governments signed a memorandum of understand- ing on a new cross-border trucking program. With this new agreement, shippers will face fewer retaliatory tariffs on hundreds of products now being exported to Mexico. Shippers will need to be especially vigilant when they choose third-party logistics part- ners when engaged in cross-border trade.

As supply chains expand globally, they share some common logistical charac- teristics. For example, with ocean shipping, goods tend to arrive at a warehouse in

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118 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

very large quantities. Because of bulk arrivals, greater break-bulk activity is required. Break-bulk entails sorting the bulk shipments into smaller customer shipments. These global supply chains typically have higher inventory levels. Managing global supply chains will be challenging in the near future.

Government Regulation and E-Commerce The issue of government regulation of the Internet is still unresolved. Although early Internet users claimed the Internet could not be controlled given its decentral- ized design and its ability to cross borders, it is clear that the Internet can be con- trolled. In China, Malaysia, and Singapore, access to the Internet is controlled from government-owned centralized routers. This allows these countries to block access to U.S. or European Web sites. Search engines operating in these countries self-censor their Asian content by using only government-approved news sources. In other countries, freedom of expression has limited restrictions on the Internet. In order for leading Internet company Google to access China’s fast-growing market, it agreed to censor its search services. E-mail, chat room, and blogging services will also not be available because of concerns the government could demand users’ personal informa- tion. Google indicated that it would notify users when access had been restricted on certain search terms.

Another issue of concern is taxation. The question is how, and if, remote sales should be taxed. Think of the advantage gained by e-tailers over local merchants when no taxes are charged to the consumer. In addition to sales taxes, the issue of customs and tariffs must be addressed. Taxation remains a very complicated issue for Internet sales.

Copyright infringement is also an issue. The U.S. copyright law protects original forms of expression such as writing (books, periodicals), art, drawings, photographs, music, motion pictures, performances, and computer programs from being copied by others for a minimum of 50 years. One exception to the U.S. copyright law is the doc- trine of fair use. This doctrine allows teachers and writers to use materials without permission under certain circumstances. In response to copyright issues, the U.S. gov- ernment enacted the Digital Millennium Copyright Act (DMCA) of 1998. The DMCA declares it illegal to make, distribute, or use devices that circumvent technology-based protections of copyrighted materials and attaches stiff fines and prison sentences for violations.

Another issue concerns public safety and welfare. In the United States, e- commerce issues of safety and welfare center around the protection of children, strong anti- pornography sentiments, strong antigambling positions, and protection of public health by restricting sales of drugs and cigarettes. It is clear that many issues concern- ing government regulation of the Internet are yet to be determined.

Green Supply Chain Management Recently, green supply chain management has focused on the role of the supply chain with regard to its impact on the present natural environment as well as to the genera- tion of any future environmental change. The intent is to be able to meet our pres- ent needs without compromising the ability of future generations to meet their own needs. So, how do we green supply chains?

� Green supply chain management Focuses on the role of the supply chain with regard to its impact on the environment.

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MAJOR ISSUES AFFECTING SUPPLY CHAIN MANAGEMENT • 119

A number of organizations have intro- duced “greening” requirements for both their suppliers and distributors. These requirements can be purchasing clauses, specified supply chain goals (carbon emis- sion footprint reduction, reduced energy consumption, reduced inventory levels, reduced transportation costs, etc.), prac- tices (the use of sustainable farming, agree- ing not to use specified materials, etc.), and use or nonuse of specific technologies. Some companies develop a corporate sus- tainability plan, which is then translated into a code of conduct. This code can be shared with both suppliers and distribu- tors. Members of the supply chain are monitored for compliance. If vio lations of the code are found, corrective action is taken with the intent of building a better sup- ply chain capability. In short, the organization is telling the members of its supply chain: “This is what you must do to be part of our supply chain. If you don’t, we fix it. If it doesn’t get fixed, we find a new member for our supply chain.” The debate cen- ters around how many organizations have that much leverage with members of their supply chain. Clearly, an organization such as Wal-Mart has considerable leverage. In fact, it uses a packaging scorecard to evaluate packaging compliance by members of its supply chain.

For supply chains doing business globally, it is important that they understand the packaging restrictions. For example, within the European Union cardboard boxes must be removed from consumption sites and recycled. As a result, U.S. automobile parts producers ship products in reusable containers rather than disposable contain- ers. Reusable packaging products are used to move, store, and distribute products within a single operation or an entire supply chain. Plastic reusable packaging, a form of reusable packaging, can be used to package anything from raw materials to finished goods. It can be used within reverse logistics to return empty containers or pallets for reuse or replenishment. Products include handheld containers, pal- lets, bulk containers, protective interiors, and custom-designed and engineered packaging.

Companies doing business globally must also focus on the final disposition of prod- ucts and packaging. The Herman Miller Company uses a design for the environment approach in new product design. To eliminate confusion as to what is a sustainable product, Herman Miller uses the McDonough Braungart Design Chemistry certifica- tion process, which is a “cradle-to-cradle” protocol for designing products in such a way that they are better for the environment. For Herman Miller, the issues centered on chemicals used and ease of disassembly of recyclable parts. It is clear that for global supply chains it will be important to be green, not only in terms of the environment but also in terms of eliminating waste and allowing the supply chain to be financially solvent.

Other organizations perform supply chain carbon footprint analysis. Consider the following simple supply chain example shown in Figure 4-5. When looking at the supply chain for potato chips, the chain begins with the farmer growing the potatoes,

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120 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

moves to the manufacturing of the chips, then their packaging, the transporting of the chips to the customer, and finally the disposal of the empty packaging. If you mapped out the carbon footprint for this supply chain, you would find that 44 percent of the carbon footprint occured at the farm, 30 percent in the manufacturing of the chips, 15 percent in the packaging process, 9 percent in transporting the chips to market, and only 2 percent in the disposal process. In this supply chain, it turns out that the farmer was overweighting the potatoes with water, since the manufacturer bought the potatoes based on weight. Because of how the potatoes were bought, they had too much water in them; this slowed down the manufacturing process, which needed to boil out excess water before processing the chips. When the manufacturer changed how potatoes were bought—based now on volume rather than on weight—the manu- facturer required only half as much time to process the chips. This saved consider- able energy and reduced the carbon footprint. Unfortunately, attention is often fixed on the disposal process, which can be a very small contributor to the overall carbon footprint. The gain in this example lies not only in reducing the footprint, but also in becoming a more efficient and effective supply chain by eliminating a waste-causing activity.

In 2007, S.C. Johnson found a “greener” way to load trucks that reduced greenhouse gases via better truckload utilization. The company created a system that combines multiple customer orders and multiple products to optimize truckload capacity usage. After the first year, S.C. Johnson reported using 2,098 fewer trucks, cut fuel usage by 168,000 gallons, and saved approximately $1.6 million. It also eliminated 1,882 tons of greenhouse gases. To achieve these results, the company focused on consistently hitting a trailer’s maximum weight. S.C. Johnson also maximized its use of “day cabs” (truck cabs with no sleeping compartments). Day cabs are 3,000 pounds lighter than a standard truck cab. This allows the company to increase the amount of product it loads into a trailer before reaching the vehicle’s maximum weight.

Potato farmer (44%)

Processing (30%)

Packaging (15%)

Transporting (9%)

Disposal (2%)

FIGURE 4-5

Carbon footprint of potato chip supply chain

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MAJOR ISSUES AFFECTING SUPPLY CHAIN MANAGEMENT • 121

In 2011, Cardinal Logistics Management was recognized by Inbound Logistics as one of the Top 75 Green Supply Chain Partners. Cardinal’s green initiatives includes developing technologies that reduce fuel consumption and emissions by reducing miles and monitoring its fleet for efficient performance; testing renewable fuels; train- ing employees in proper maintenance, driving habits, and mile reduction; and pro- viding backhaul support to customers. Cardinal Logistics, headquartered in Concord, North Carolina, is a leading transportation solution provider, working with clients to optimize their supply chains.

Infrastructure Issues Global supply chains with members in developing countries can face substantial infra- structure issues (such as inadequate transportation networks, limited telecommunica- tion capabilities, uncertain power continuity, low worker skill, poor supply availability and quality, etc.). Each of these issues increases uncertainty in supply and demand for the supply chain, which results in higher costs and poorer service.

Inadequate transportation networks increase distribution lead times. Roads may be inadequate to transport heavy loads (necessitating the creation of smaller loads), rail travel may not be available or very limited in terms of frequency (once or twice a week), air service may be limited in frequency (one or two flights per day or less), and ocean shipping may be limited by the capabilities of the port. It is not unusual for an item to change hands four to eight times before reaching the final customer.

Poor telephone service can restrict the timely availability of supply and demand information. Because of this, a more extensive information system to keep track of items is often required. An unstable power supply can significantly affect the output of a supplier, both in terms of when the product can be produced and the effect on product quality caused by unstable power.

A lack of specific worker skills can limit the technology a firm uses. For example, numerically controlled (NC) machines use more easily trained machine setters and programmers rather than more highly skilled machinists. The increased use of NC machines in South America is a result of an inadequate number of skilled machinists.

A lack of available local materials and competent suppliers can force a firm to rede- sign its process, or even its product, to minimize or eliminate the use of scarce mate- rials. Imported raw materials may be difficult to obtain due to import restrictions. In some cases, no local suppliers are available. A case in point is McDonald’s. When McDonald’s started operations in Russia, it had considerable problems develop- ing high-quality Russian suppliers. McDonald’s used a vertically integrated strategy, developing its own plant and distribution facility for processing meat patties, produc- ing french fries, preparing dairy products, and baking buns and apple pies. Initially, McDonald’s even grew its own potatoes.

The logistics research company Transport Intelligence reported on 10 countries that are emerging logistics markets in 2011 and could become leading economies in the next decade. Topping the list is India. The ease of doing business and a highly educated workforce have been attractive to many global companies. Next on the list is Brazil, benefitting from tax incentives and infrastructure investment. Brazil is currently the eighth largest economy in the world and has significant natural resources. It is a major exporter of textiles, aircraft, coffee, orange juice, and soy- beans. Third on the list is Indonesia. Indonesia’s relaxed government stance on for- eign investment is expected to stimulate economic growth. The remaining countries on the list are Mexico, Russia, Turkey, United Arab Emirates, Egypt, Saudi Arabia, and Malaysia.

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Product Proliferation Global competition forces firms to supply highly customized products and services to multiple national markets. Usually, a firm manufactures a basic product that is adapt- able to many markets. The basic product contains most of the features and compo- nents of the finished product along with some market-specific add-on components. For example, computer products have country-specific power supplies to accom- modate local voltage, frequency, and plug types. In addition, keyboards and manuals must match the local language. Such minor variations create a large number of unique finished-product configurations to be managed. Product proliferation further compli- cates accurate demand forecasting.

Before You Go On

You need to understand the structure of a supply chain, the bullwhip effect, and the issues affecting supply chains. A supply chain structure has external suppliers, internal functions of the firm, and external distributors. Informa- tion technology enables supply chain management. E-commerce provides an effective means for communications between an organization and its suppliers and customers. Demand data provided through point-of-sale data (POS) helps counteract the bullwhip effect and reduces distorted demand information throughout the supply chain. Ser- vice organizations also have supply chains, all of which face many major issues. Issues such as information tech- nology, the Internet, demand uncertainty, government regulations, the need for greener supply chains, inadequate infrastructures, and product proliferation can affect all supply chains.

THE ROLE OF PURCHASING A company’s purchasing department plays an important role in supply chain man- agement decisions. Purchasing is typically responsible for selecting suppliers, nego- tiating and administering long-term contracts, monitoring supplier performance, placing orders to suppliers, developing a responsive supplier base, and maintaining good supplier relations. Since material costs may represent at least 50–60 percent of the cost of goods sold, purchasing significantly affects profitability. Moreover, changes in product cost structure, with materials comprising the bulk of the cost of goods sold, have elevated the role of purchasing in many organizations.

Let’s look at how purchasing has been done traditionally and also look at e-purchasing.

The Traditional Purchasing Process Before companies introduced e-purchasing, purchases were made following a flow similar to that shown in Figure 4-6. Typically, the process began when a requisi- tion request was created, often by a production planner, an inventory planner, or an administrative staff member. A requisition request is simply a form used to inform purchasing that an item or a material needs to be purchased. Before any action is taken on the request, it may need to be authorized, by either a supervisor or a manager. The level of authority needed is often based on the dollar amount of the requisition. The higher the amount, the higher the level of authority needed to okay the purchase. After the requisition has been authorized, it goes to purchasing and is given to the spe- cific buyer responsible for making that type of purchase. The buyer contacts potential

� Requisition request Request indicating the need for an item.

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THE ROLE OF PURCHASING • 123

suppliers of the item to determine price and availability for the item being requested. After analyzing the information from the different suppliers, the buyer decides where to place the order. At this point it is time to release a purchase order confirming all the details of the purchase. A purchase order number is assigned to facilitate tracking the order through the system.

Then printed copies of the purchase order are distributed typically to the supplier, the initial requestor, accounts payable, the receiving dock, and the purchasing depart- ment. The purchase order is the company’s commitment to the supplier to buy the item. A copy goes to the initial requestor to let the person know the item has been ordered and to provide delivery date information. Accounts payable needs a copy to reconcile the actual bill received with the negotiated terms of the purchase order with regard to quantity and price. The receiving dock gets a copy so that it knows the item has been ordered and it is okay to accept delivery of the package. The purchasing department typically keeps a copy for its file on that specific supplier. This allows the department to rate the performance of the supplier in terms of on-time delivery per- formance, quality and quantity, and billing accuracy.

When the item arrives at the receiving dock, the copy of the purchase order is checked to confirm that the item was ordered by the company and should be accepted.

� Price and availability The current price of the item and whether the quantity is available when needed.

� Purchase order A legal document committing the company to buy the goods and providing details of the purchase.

Req. to buyer

Phone suppliers

PO number Details

Print PO & distribute

Copy to receiving

Receipt of goods

Copy to acct. pay.

Invoice match

Payment

Authorization

Paper requisition

FIGURE 4-6

Traditional purchasing process

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124 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

The goods then might move to incoming inspection for quality verification. From incoming inspection they are taken to the storeroom and put into inventory until they are needed.

When the company receives the invoice for the goods, before authorizing pay- ment of the invoice the accounts payable department verifies that the goods have been received and that the cost per unit matches the purchase order terms.

In the past, this process tended to be slow, was prone to errors, and clearly was in need of a better way. Many companies now do e-purchasing to reduce transaction costs and provide an effective purchasing system.

The E-purchasing Process E-purchasing is defined as the use of information and communications technology through electronic means to enhance external and internal purchasing and supply management processes. It is not just automating the purchasing function, but requires that an organization have a well-defined purchasing and supply management strategy. It requires that an appropriate sourcing strategy be developed.

Let’s begin by looking at the e-purchasing process shown in Figure 4-7. From first glance, it is evident that the process has been streamlined and that some activities have been eliminated. The e-purchasing process begins with the initial requestor going to the approved supplier’s Web site or the appropriate net marketplace to place the order. The company may have preauthorized spending limits set for each of its individual requestors. Any order exceeding that limit would be routed to the necessary approving authority before being processed further. Once the order is in process, all the members of the organization who need to know about the transaction learn in real time all the details associated with the order. This is the same information that they previously learned from their copy of the purchase order. When the goods are received, this fact is inputted into the system so that receipt information is provided to the interested parties. Instead of preparing an invoice for each individual shipment, the supplier consolidates invoices over a period of time and sends a single invoice to the company. This reduces supplier transaction costs. Accounts payable verifies the information on the consolidated invoice and authorizes payment. One check or funds transfer is made for all the orders placed with the supplier rather than a separate check or transfer for

� Incoming inspection Verifies the quality of incoming goods.

� Sourcing strategy A plan indicating suppliers to be used when making purchases.

Supplier Web site

Receipt of goods

Consolidate invoice Manage by exception

Payment

Authorization

Requester

FIGURE 4-7

The e-purchasing process

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THE ROLE OF PURCHASING • 125

each individual order. This reduces the accounting transaction costs for the purchas- ing company. Key features of typical e-purchasing systems are shown in Table 4-1.

A summary of the expected benefits to the buyers from e-purchasing is shown in Table 4-2. Basic benefits are cost savings, fewer human errors, and a more efficient purchasing process.

To successfully use e-purchasing, there must also be benefits for the suppliers. Table 4-3 shows some of the expected benefits for suppliers.

Many organizations engaged in e-purchasing establish a Web site that potential suppliers can visit to determine what current opportunities are available. Typically, suppliers must register to access the site, and subsequent entry is controlled by a pass- word. Shown in Table 4-4 is an example of a Web site used by a county government to interact with its suppliers. The Web site informs potential suppliers on how to register for the site and how to certify itself as a supplier, and it shows the current opportuni- ties available for which quotes can be submitted. An example of such opportunities is shown in Table 4-5. Access to the Web site is limited by a password. Although this example shows a government organization’s Web site, similar Web sites are used by manufacturing and service organizations.

Regardless of whether an organization engages in traditional purchasing, e-purchasing, or a combination of both, an organization must decide which sources are used. The issues of sourcing are discussed next.

• Web-based purchase order approval

• Automatic routing of orders requiring additional authorization to appropriate recipient

• Automatic routing of completed purchase order copies to additional recipients

• Notification of receipt to appropriate recipients

• Secure e-mail sending, using standard electronic signature

• Electronic archiving to facilitate supplier evaluation and analysis of purchasing trends

TABLE 4-1

Key Features of E-Purchasing Systems

• Reduced purchase order processing costs

• Reduced purchase order cycle times

• Less data entry, thus reduced order processing time

• Reduced paperwork

• More efficient distribution of information throughout the system

• Reduced buyers’ research time with electronic catalogs and net marketplaces

• Fewer order processing errors leading to incorrect shipments

• Consolidated data to facilitate analysis of purchasing patterns and supplier performance

• Share performance measurement data, which encourages improved supplier performance

• Reduced inventory because of better supplier performance

TABLE 4-2

Benefits to the Buyers from E-Purchasing Systems

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126 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

• Time savings since orders do not need to be reentered

• Fewer input errors

• Reduced transaction costs and reduced purchase order cycle times

• Less inventory as a result of more efficient communications with customers

• Better forecasting information from customers

• Better supplier performance since supplier measurement information is shared

• Faster payment

• Improved information flow

TABLE 4-3

Benefits for Suppliers Engaged in an E-Purchasing Environment

TABLE 4-4

Example of a Web Site Used by a Government Organization

Northeast County E-Purchasing E-Purchasing

Welcome to Northeast County’s E-Purchasing Web site!

The Purchasing Division is responsible for the management and coordination of the acquisition of goods and services, including requisition processing, commod- ity code tracking, and bid specifi cations.

HOW TO DO BUSINESS WITH NORTHEAST COUNTY Step 1 REGISTER WITH PURCHASING

Persons or concerns desiring to be registered on the County’s Bidder List for goods and/or services must complete and submit a completed Vendor Registration Form. Registration can be completed online or in person.

Step 2 CERTIFY WITH HUMAN RELATIONS FEP compliance is required for all contracts greater than $50,000 (supply/service) and $100,000 (construction),

Northeast County Certifi cation Categories: • Fair Employment Practice (FEP) • Northeast County Based Enterprise (CBE) • Small Business Enterprise (SBE) • Targeted Growth Community Enterprise (TGCE) • Expanding Business Enterprise (EBE) • MBE/WBE (Declaration) • Disadvantaged Business Enterprise (DBE)

Step 3 QUOTES OPPORTUNITIES Quotes are published daily on this Web site.

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SOURCING ISSUES • 127

INVITATION FOR BIDS

DUE DATE BID # COMMODITY/SERVICE ADDENDA BUYER

6/29/11 11-705 Fifth Street Utility Replacement and Streetscape Improvements: Phase II and IIA

Drawings: Phase II: Set A Set B Phase IIA

Addendum No. 1

Bid Schedule (EXCEL)

RDR

DATE EXTENDED

7/28/11

12-711 2011 MUNICIPAL PAVING CONTRACT (SUMMER BID)

PAVING SUMMER 2011 ADDENDUM

Addenda #2

Addenda #3

LMM

8/18/2011 12-715 Parkview Waterline Relocation

Drawings

LMM

8/09/2011 12-716 (14) Light Duty Vehicles— Replacement

Addendum 1—7/29/11

Addendum 2—8/1/11

FRH

INVITATION FOR PROPOSALS

DUE DATE RFP # COMMODITY/SERVICE ADDENDA BUYER

7/16/11 11-707 Airport Engineering Svcs for Airfield Pavement and Apron Rehabilitation Work at Regional Airport

LMM

7/28/11 11-709 Juvenile Services Group Home Addendum No. 1 SRT

8/09/2011 12-713 Zoning Ordinance Diagnostic Consultant

Addendum No. 1 SRT

8/12/2011 10-587 Riverside Park Renovation, Phase 1 Addendum No. 1 SRT

TABLE 4-5

Example of a Web Site Showing Current Opportunities for Suppliers

SOURCING ISSUES

Which products or services are provided in-house by the manufacturer, and which are provided to the manufacturer by other members of the supply chain? Vertical integration is a measure of how much of the supply chain is owned or operated by the manufacturer. Products or services provided by the manufacturer are insourced. Products or services not provided by the manufacturer are outsourced. Outsourcing means that the manufacturer pays suppliers or third-party companies for their products or services, a practice that is on the rise. A recent survey reported that 35 percent of more than 1000 large companies have increased their outsourc- ing. Another survey of large companies reported that 86 percent outsourced at least some materials or services. The activity most frequently outsourced was manu facturing.

� Insource Processes or activities that are completed in-house.

� Vertical integration A measure of how much of the supply chain is actually

owned or operated by the manufacturing company.

MKT

� Outsource Processes or activities that are completed by suppliers.

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128 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

Backward integration is a company’s acquisition or control of sources of raw materials and component parts: the company acquires, controls, or owns the sources that were previously external suppliers in the supply chain. Forward integration is a company’s acquisition or control of its channels of distribution—what used to be the external distributors in the supply chain.

A company bases its level of vertical integration on its objectives. The greater the vertical integration, the lower is the level of outsourcing. Conversely, the higher the level of outsourcing, the lower is the level of vertical integration. Some factors favor vertical integration. For example, companies needing a high volume of a product or service can sometimes achieve economies of scale by providing the product or service in-house. Companies with special skills may find that it is cheaper to provide cer- tain products or services in-house. Other factors encourage outsourcing. For example, companies with low volumes generally find it cheaper to outsource a product or ser- vice rather than provide it in-house. Sometimes a company can get a better-quality product or service from a supplier than it can provide itself.

Now let’s look at the financial calculations behind insourcing and outsourcing decisions.

Insourcing versus Outsourcing Decisions It may be easy to calculate the costs of insourcing versus outsourcing and make the right financial decision. But such decisions involve more than financial calculations. Is a particular product or service critical to your company’s success? Is the product or service one of your company’s core competencies? Is it something your company must do to survive? If the answer is yes to any of these questions, your company will provide the product or service in-house. If the product or service is not one of its core competencies, the company needs to decide whether it should make or buy the prod- uct or service. Other considerations are, for example, whether the products or services provided in-house are identical to those outsourced. Is product quality in-house com- parable to product quality in the marketplace? Is product functionality comparable, or does one product have an advantage in terms of quality or functionality? Finally, does the company have the capital needed for any up-front costs to provide the product or service in-house?

Now let’s look at how a company might make the financial calculations. To make a financial calculation, we look at the total costs involved in either producing the entire quantity in-house or buying the entire quantity from a supplier. The total cost of buy- ing the item is any fixed annual cost associated with buying the product plus a variable cost for each item bought during the year, or

TCBuy 5 FCBuy 1 1VCBuy 3 Q 2 where TCBuy

= total annual costs of buying the item from a supplier FCBuy = fixed annual costs associated with buying the item from the supplier VCBuy = variable costs per unit associated with buying the item from the

supplier Q � quantity of units bought

Similarly, we calculate the total cost of making the item in-house as

TCMake 5 FCMake 1 1VCMake 3 Q 2 where TCMake � total annual costs of making the item in-house

FCMake � fixed annual costs associated with making the item in-house VCMake � variable costs per unit associated with making the item in-house

Q � quantity of units made in-house

� Backward integration Owning or controlling sources of raw materials and components. � Forward integration Owning or controlling the channels of distribution.

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SOURCING ISSUES • 129

The first step in solving the make-or-buy decision is to determine at what quantity the total costs of the two alternatives are equal. To do this, we set the total annual cost of buying equal to the total annual cost of making:

FCBuy 1 1VCBuy 3 Q 2 5 FCMake 1 1VCMake 3 Q 2 Solving this tells us the indifference point—that is, how many units we must buy or produce when the total costs are equal. If we need this exact amount, we would be indifferent to whether we bought the item or produced it in-house. If we need less than this quantity, we choose the alternative with the lower fixed cost and the higher vari- able cost. If we need more than this quantity, we choose the alternative with the lower variable costs.

Let’s look at a numerical example. Remember that when the quantity needed exceeds the indifference point, use the alternative with the lower variable cost. If the usage quantity is below the indifference point, then choose the alternative with the lower fixed cost.

Two recent college graduates, Mary and Sue, have decided to open a bagel shop. Their first decision is whether they should make the bagels on-site or buy the bagels from a local bakery. They do some check- ing and learn the following:

• If they buy from the local bakery, they will need new airtight containers in which to store the bagels delivered from the bakery. The fixed cost for buying and maintaining these containers is $1000 annually.

• The bakery has agreed to sell the bagels to Mary and Sue for $0.40 each. • If they make the bagels in-house, they will need a small kitchen with a fixed cost of $15,000

annually and a variable cost per bagel of $0.15. • They believe they will sell 60,000 bagels in the first year of operation.

(a) Should Mary and Sue make or buy the bagels? (b) If Mary and Sue are uncertain as to the demand for bagels next year, what is the indifference

point between making or buying the bagels?

• Before You Begin: To make their decision when demand is known, Mary and Sue need to calculate the total cost of making or buying 60,000 bagels. Determine the relevant data for these calculations. To find the total annual cost of buying 60,000 bagels, you need both the annual fixed costs ($1000) and the variable cost per bagel ($0.40). To calculate the total annual cost of mak- ing the 60,000 bagels, you need the annual fixed costs ($15,000) and the variable cost per bagel ($0.15).

• Solution: (a) The total cost for buying 60,000 bagels is $25,000. That is $1000 in annual fixed cost plus

60,000 bagels multiplied by the $0.40 unit variable cost. The total cost for making 60,000 bagels is $24,000. That is $15,000 in annual fixed costs plus the 60,000 bagels multiplied by the $0.15 unit variable cost.

(b) If Mary and Sue don’t know the demand for bagels, they can find the indifference point by setting the total annual costs of each option equal to the other, as shown below. Although in this problem we are comparing the options of making or buying, we could just as easily compare two different suppliers or two different internal processes.

Set the total annual costs equal to each other using the formula.

EXAMPLE 4.1

MS Bagel Shop: A Make-or-Buy Decision

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130 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

Outsourcing requires decisions about which supplier to contract with for products or services. These decisions in turn depend on the criticality and frequency of the product or service, and they determine the relationship the company forms with the supplier. For example, if the purchase is one-time only, the company does not need to develop a relationship with the supplier. However, if the company wants a reliable supplier for a critical product or service, it needs to develop a long-term relationship with the supplier.

Developing Supplier Relationships A strong supplier base is essential to the success of many organizations. Choosing a supplier is like choosing where to shop for something you want to buy. The first thing you decide is which merchants have the product or service you want. Adequate quality for the product or service is usually a prerequisite for even considering a merchant. What else is important to you when choosing a merchant? Availability, perhaps size and color for clothing, freshness and appearance for produce, and physical proximity so you can see the product or try it on. Quick response time, such as overnight ship- ping or rapid alterations; price, of course; ease of doing business; reputation; and war- ranty or service agreements are all considerations.

What is important to you as an individual when choosing a merchant is also impor- tant for your company when choosing a supplier. In general, we want merchants or suppliers who give us good value. Several studies report that the top three criteria for selecting suppliers are price, quality, and on-time delivery. Even more important, however, is that the choice of suppliers be consistent with a company’s mission. For example, if your company is competing on the basis of quick response time, your sup- pliers must offer minimal lead times and be able to respond quickly.

How Many Suppliers? Once your company has chosen its suppliers, the next question is: Should you give a single supplier all your business for a particular product or service? Or should you use multiple suppliers? Table 4-6 lists arguments in favor of a single supplier and multiple suppliers.

For some operations, like make-to-order products, it is easier to deal with a single supplier. This is especially true for scheduling deliveries, resolving problems, mini- mizing the cost of dies or tools, developing computer links, and so forth. In addition, using a single supplier can improve the quality of your finished product by ensuring the consistency of the input materials.

On the other hand, multiple suppliers reduce the risk of a disrupted supply—that is, if one supplier suffers a disaster, other suppliers can pick up the slack. Further, mul- tiple suppliers can more easily support changing quantity requirements. For example,

FCBuy 1 1VCBuy 3 Q 2 5 FCMake 1 1VCMake 3 Q 2 or

$1000 1 1$0.40 3 Q 2 5 $15,000 1 1$0.15 3 Q 2 Solving for Q, we have ($0.25Q) � $14,000, or Q � 56,000 bagels. Since the costs are equal at 56,000 bagels and Mary and Sue expect to use 60,000 bagels, they should make the bagels in-house rather than buy them from the local bakery. By making the bagels, the cost for each additional bagel above 56,000 is $0.15 instead of the $0.40 they would pay the local bakery for each bagel.

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SOURCING ISSUES • 131

Pros of One Supplier Pros of Multiple Suppliers

• The supplier may be the exclusive owner of essential patents and/or processes and thus be the only possible source.

• By using one supplier, quantity discounts may be achieved.

• The supplier will be more responsive if it has all of your business for the item.

• Contractual agreements may prohibit the splitting of an order.

• The supplier is so outstanding that no other supplier is a serious contender.

• Single sourcing is a prerequisite for partnering.

• The order is too small to split between suppliers.

• When the purchase involves a die, tool, mold, or expensive setup, the cost of duplicating may be prohibitive.

• Deliveries can be scheduled more easily.

• Supports just-in-time manufacturing and EDI.

• Allows for better supplier relations.

• The just-in-time philosophy can be better utilized.

• Competition among suppliers may provide better service and price.

• Probability of assured supply is better. Multiple suppliers spreads the risks.

• Eliminates a supplier’s dependence on the purchaser.

• Provides a greater flexibility of volume.

• No single supplier may have sufficient capacity.

• Allows for testing of new suppliers without jeopardizing the flow of materials.

• Government regulations may require multiple sources.

TABLE 4-6

Arguments in Favor of One Supplier and of Multiple Suppliers

if you need a larger quantity than a single supplier can supply, the order can be split among multiple suppliers. This is referred to as flexibility of volume. Finally, govern- ment regulations may require the use of multiple suppliers for some projects.

The answer to how many suppliers depends on your supply chain structure. If your company wants to integrate its supply chain, then partnering or using a single supplier makes sense.

Developing Partnerships One compelling argument in support of using single suppliers is that it is often a pre- requisite for developing a partnering relationship. Partnering with a supplier requires a commitment from both the company and the supplier. The goal is to establish an ongoing relationship in which both parties benefit from the arrangement—what is called a “win-win situation.”

The two kinds of partnerships are basic and expanded. A basic partnership is built on mutual respect, honesty, trust, open and frequent communications, and a shared understanding of each partner’s role in helping the supply chain achieve its objectives. Expanded partnerships are reserved for a few key suppliers. These are long-term rela- tionships built on mutual strategic goals. Expanded partners must be committed to helping each other succeed. They must place a high priority on maintaining the rela- tionship and on sharing information, risks, opportunities, and technologies.

� Partnering A process of developing a long-term relationship with a supplier based on mutual trust, shared vision, shared information, and shared risks.

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132 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

Quality Award (discussed in Chapter 5) winner. The key to Bama’s success has been focus- ing on quality relationships and products.

The company’s founder, Peter Marshall, built the bakery business on handshake deals. While today’s suppliers go through a more formal process, the company still maintains the handshake mentality to strengthen supplier relationships. Ingredient suppliers undergo frequent audits from Bama to ensure ingredient integrity and man- ufacturing practices. While the company expects a lot from its supplier partnerships, it also rewards its suppliers for exceptional performance. Bama determines award win- ners on 10 factors: innovation, continuous improvement, quality of products, service, fair pricing, cost containment, technical support, customer service support, shared philosophies and values, and the ability to bring new business to The Bama Compa- nies. In 2005, Bama implemented its Six Sigma quality program (discussed in Chap- ter 5) into its supply chain. As a result, one of its flour suppliers, in a joint project with Bama, addressed suboptimal truck weights. As a result, the supplier increased average weight per truckload by about 2,000 pounds and decreased truckload weight variation by 50%.

In recent years when sugar availability was limited because of bad hurricane sea- sons, Bama’s relationship with its sugar supplier has proven quite beneficial. Bama’s supplier maintained a consistent sugar supply during that period as well as honoring the original contract price.

A company evaluating potential partners looks at the following aspects of the potential partner’s business:

● History, sales volume, product lines, market share, number of employees, major customers, and major suppliers.

● Current management team in terms of past performance, stability, and strategic vision. ● Labor force in terms of skill, experience, commitment to quality, and relations

with the supplier. ● Internal cost structure, process and technology capabilities, financial stability,

information system compatibility, supplier sourcing strategies, and long-term relationship potential.

LINKS TO PRACTICE The Bama Companies

www.bama.com

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SOURCING ISSUES • 133

The company reduces the selection pool to a few potential partners, identifies a single partner, and commits to the partnership. At this point, both parties agree on how to measure the performance of the partnership. The partners set time frames for the frequency and methods of performance assessment and decide how problems will be resolved. When they reach agreement on these issues, the partners develop supply chain operating procedures and put the partnership into motion. Table 4-7 shows pos- sible benefits from partnering.

Critical Factors in Successful Partnering Impact, intimacy, and vision are critical factors in successful partnering. Impact means attaining levels of productivity and competitiveness that are not possible through normal supplier relationships. Intimacy means the working relationship between partners. Vision means the mission or objectives of the partnership. Let’s look at each of these factors.

Impact comes through mutual change. The supplier and the customer must be will- ing to make changes. Studies suggest that the three sources of impact are reduction of duplication and waste, leveraging core competence, and creating new opportunities.

Duplication can involve any activity done by both the supplier and the customer. For example, suppliers count items before shipping, and customers count the same items after receipt. What value is added by having both parties count the same items?

Waste reduction means eliminating any activity that does not add value. For exam- ple, moving items into and out of storage adds no value. It makes sense to have items delivered to and stored where they are used.

Duplication can also be eliminated in paperwork and administration. Sweet- heart, a manufacturer of paper drink- ing cups, faced price-cutting demands from a major customer. Sweetheart was told prices needed to be reduced 10 per- cent or the customer would use a dif- ferent supplier. To meet this challenge, Sweetheart partnered with paperboard producer Georgia-Pacific. A shared elec- tronic data interface reduced paperwork

Manufacturer’s Benefits Supplier’s Benefits

• Reduce costs

• Reduce duplication of effort

• Improve quality

• Reduce lead time

• Implement cost reduction program

• Involve suppliers earlier

• Reduce time to market

• Reduce inventory

• Increase sales volume

• Increase customer loyalty

• Reduce costs

• Improve demand data

• Improve profitability

• Reduce inventory

TABLE 4-7

Possible Benefits from Partnering

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LINKS TO PRACTICE Sweetheart Cup Company www.sweetheart.com Georgia-Pacific Corporation www.gp.com

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134 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

and administration and cut expensive inventory. Joint planning optimized production plans, giving Sweetheart a more consistent product from Georgia-Pacific at a better price. Sweetheart can satisfy its high-volume customers, and Georgia-Pacific benefits through more business.

Leveraging core competence is about sharing knowledge. Different companies have different strengths or competencies. Instead of making the supplier or the customer reinvent the wheel, all partners can benefit from shared expertise. Following is an example.

Hillenbrand Industries, a manufacturer of hospital room equipment products, has six geographically dispersed manufacturing facilities. The company uses its own 400- truck fleet and both domestic and international carriers. Hillenbrand decided to part- ner with UPS to improve the cost, quality, and responsiveness of Hillenbrand’s overall logistics. For UPS, fleet management is a core competence; for Hillenbrand, fleet man- agement is an expensive, noncore requirement. Hillenbrand does not want to incur the expense of building a world-class core competence in fleet management. By part- nering with UPS, Hillenbrand can leverage and benefit from its partner’s expertise. Hillenbrand was able to create $1.5 million in cost improvements during the first year of the partnership. UPS revenues with Hillenbrand have grown by almost $2 million. Both the supplier and the customer have benefited.

Creating new opportunities means partners working together to produce something that neither could have achieved alone. Let’s look at an example involving a tier one supplier to an automobile manufacturer.

This supplier used to make daily truckload deliveries of a major subassembly to the auto assembly plant approximately 1200 miles away. Every day, four trucks left the supplier filled with subassemblies, and every day four of the supplier’s empty trucks left the auto assembly plant. The supplier was wasting significant transport capacity, with empty trucks returning to its facility. To remedy the problem, the supplier worked with the automobile manufacturer to develop a new truck trailer that carried subas- semblies to the automaker and also hauled new autos back to a major metropolitan area. Thus, the auto manufacturer could send new cars to market and eliminate wasted transport capacity. Because the supplier transported the cars to market in an enclosed truck, protected from weather and road hazards, the cars arrived customer-ready. By using the empty trucks to haul the new autos to this market, the supplier eliminated the wasted transport capacity, provided a valuable service to the auto manufacturer, and generated cost savings for both partners. The partnership created a win-win situ- ation that neither could have created alone.

Intimacy comes from the working relationship between partners. Because part- ners share confidential information, trust between them is critical. Intimacy means eliminating surprises: sharing daily information with partners prevents surprises. For example, a tier one supplier to the automotive industry needs to know how many autos are produced daily, any planned changes in production rates, and the current number of days of finished goods inventory. This information shows the supplier near-term demand and facilitates better customer service to the auto producer.

Intimacy is a result of sharing information. This information includes sales data collected at the point of sale; order change notices such as additional orders or cancel- lations; global inventory management, both quantity and location; and global sourc- ing opportunities so that supply chain members can improve purchase leveraging and component standardization.

MKT

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SOURCING ISSUES • 135

Supermarkets use bar code scanners for obtaining point-of-sale information.

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• Have a long-term orientation

• Are strategic in nature

• Share information

• Share risks and opportunities

• Share a common vision

• Share short- and long-term plans

• Are driven by end-customer expectations

TABLE 4-8

Characteristics of Partnership Relations

The benefits of information sharing can be significant. When Osram GmbH bought GTE’s Sylvania lighting division, it initiated a supply chain integration program. Within six months, fill rates were at 95 percent and climbing; individual stock keeping unit (SKU) forecast accuracy had improved by 16 percent; obsolete inventory was down 10 percent; and the company had saved more than $300,000 on transportation costs.

Vision is the mission or objective of the partnership. The partners must articulate and share their vision. This shared vision provides the structure for the partnership and the role each partner plays in achieving success for the supply chain.

Successful partnering needs a substantial commitment by both partners. Many com- panies try to reduce the number of their suppliers and develop a smaller, highly focused supplier base. The emphasis is on finding viable suppliers and developing long-term part- ner relationships. Table 4-8 summarizes the different aspects of partner relationships.

Benefits of Partnering Early supplier involvement (ESI) is a natural result of partnering relationships and is one way to create impact. Critical suppliers become part of a cross-functional, new- product design team. These suppliers provide technical expertise in the initial phases of product design. Early involvement by suppliers often shortens new-product develop- ment time, improves competitiveness, and reduces costs. One example of early supplier involvement is Whirlpool Corporation’s partnership with Eaton, a supplier of gas valves

� Early supplier involvement (ESI) Involvement of critical suppliers in new product design.

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136 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

and regulators. Whirlpool used Eaton’s design expertise to bring a new gas range to mar- ket several months sooner than it could have using Whirlpool’s in-house design skills.

Ethics in Supply Management A constant concern within purchasing departments is the issue of ethics in manag- ing suppliers. Sales representatives from suppliers often offer buyers free lunches, free tickets to sporting or entertainment events, free weekend getaways, or valuable gifts. While suppliers may view these merely as promotional activities, at some point buyers need to consider how much is too much. Because buyers are in a position to influence or determine which supplier is awarded business, buyers must make certain that they avoid any appearance of unethical behavior or a conflict of interest.

Many companies have specific policies outlining what constitutes an acceptable gift or promotion. In some companies, buyers are not allowed to accept anything from a supplier, not even a pen. In other companies, there are dollar limits on what may be accepted. To guide purchasing employees, the Institute for Supply Man- agement (ISM) has approved a set of principles and standards, which are shown in Table 4-9.

TABLE 4-9

Principles and Standards of Ethical Supply Management Conduct Integrity in Your Decisions and Actions

Value for Your Employer Loyalty to Your Profession

From these principles are derived the ISM (Institute for Supply Management) global standards of supply management conduct.

1. Impropriety. Prevent the intent and appearance of unethical or compromising conduct in relationships, actions, and communications.

2. Conflicts of Interest. Ensure that any personal, business, or other activity does not conflict with the lawful interests of your employer.

3. Influence. Avoid behaviors or actions that may negatively influence, or appear to influence, supply management decisions.

4. Responsibilities to Your Employer. Uphold fiduciary and other responsibilities using reasonable care and granted authority to deliver value to your employer.

5. Promote positive supplier and customer relationships.

6. Sustainability and Social Responsibility. Champion social responsibility and sustainability practices in supply management.

7. Confidential and Proprietary Information. Protect confidential and proprietary information.

8. Reciprocity. Avoid improper reciprocal agreements. 9. Applicable Laws, Regulations, and Trade Agreements. Know and obey the

letter and spirit of laws, regulations, and trade agreements applicable to supply management.

10. Professional Competence. Develop skills, expand knowledge, and conduct business that demonstrates competence and promotes the supply management profession.

ISM Principles and Standards of Ethical Supply Management Conduct Reprinted with permission from the publisher, the Institute for Supply Management™, Principles and Standards of Ethical Supply Management Conduct, adopted January 2012.

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SUPPLY CHAIN DISTRIBUTION • 137

A report by business ethicist Professor Kirk O. Hanson identifies the best ethi- cal standards for the group purchasing organizations (GPOs) that serve most of the nation’s hospitals. New best practices recommended in the report are:

● Require a standard administrative fee (a percentage of total purchases under the contract) for each category of product or service;

● Limit equity investments in contracting companies by GPO employees; ● Prohibit more strictly gifts and perks from seller companies; ● Adopt as standard for financial disclosure the one used by not-for-profit

organizations.

At the center of the recommendations is concern for the ethical conflict of interest. These conflicts include the quality and the cost of goods used in medical care; unit price and cost-in-use; the benefits of standardization; the need to adopt superior tech- nologies; multiyear contracting; and spot-market purchasing. The GPO’s first obliga- tion is to serve its members, including the goals of good medical outcomes and cost containment. All types of organizations (manufacturing, healthcare, education, gov- ernment, etc.) have a code of ethics concerning the fair treatment of suppliers. Most of these are available on the organization’s web site.

Given these many codes of ethics, purchasing is still questioned by its internal employees about the ethics of the purchasing department. Consider the follow- ing circumstances that might suggest that members of the purchasing department behaved unethically. First, a purchasing member accepts a gift (even a small gift such as a pen) from a supplier. Second, a purchasing member has a personal or financial relationship with a supplier or an employee of a supplier (think of favorit- ism). Third, a purchasing team member owns its supplier’s stock. Fourth, a pur- chasing member mixes business and entertainment with a supplier (think about doing business on the golf course). Fifth, in a competitive bidding situation, infor- mation is provided to one supplier that was supplied to other suppliers. Sixth, it is not transparent as to why a particular supplier was chosen. And seventh, selec- tion criteria used was not consistent with criteria noted in the request for proposal. Any of the above situations can be done ethically, however, it is important for the purchasing department to understand how each of these activities could suggest a potential conflict of interest.

SUPPLY CHAIN DISTRIBUTION

The Role of Warehouses Warehouses include plant, regional, and local warehouses. They can be owned or operated by the supplier or wholesaler, or they can be public warehouses. A further classification is general warehouse or distribution warehouse.

A general warehouse is used for storing goods for long periods with minimal han- dling. A distribution warehouse is used for moving and mixing goods. Within the supply chain, warehouses have three roles: transportation consolidation, product mix- ing or blending, and service. The business of a general warehouse is storage. The busi- ness of a distribution warehouse is movement and handling; therefore, the size of the facility is less important than its throughput. At a distribution warehouse, goods are received in large-volume lots and broken down into small individual orders.

� General warehouse Used for long-term storage.

� Distribution warehouse Used for short-term storage, consolidation, and product mixing.

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138 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

A good example of the impor- tance of distribution warehous- ing in support of e- commerce is Fingerhut’s warehouse in St. Cloud, Minnesota. Employees rush through the warehouse on forklifts and cargo haulers fill- ing orders for on-line retailers. Every item is encoded to speed packing. Red lasers scan each package as it rushes down the conveyor, verifying the actual package weight against expected package weight. Packages that do not match are pushed aside

for further inspection. The crew at this warehouse can process as many as 30,000 items per hour.

Transportation Consolidation occurs when warehouses consolidate less-than-truckload (LTL) quantities into truckload (TL) quantities. This consolidation can be both in supplier shipments to the manufacturer and in finished goods shipped to distant warehouses. The goal is to use TL shipments for as much of the distance as possible because TL shipments are cheaper than LTL shipments.

For inbound supplier shipments, a manufacturer can have small LTL deliveries from several suppliers consolidated at a convenient warehouse and then shipped to the man- ufacturer in TL shipments. For outbound shipments, the manufacturer can send TL deliveries to distant warehouses that break down the shipment for LTL delivery to local markets. Transportation consolidation is usually done to reduce transportation costs.

Product Mixing is a value-added service for customers. With product mixing, the customer places an order to the warehouse for a variety of products. The warehouse groups the items together and ships the mixture of items directly to the customer. Without product mixing, the customer would have to place individual orders for each item and pay shipping for each item. Instead, product mixing enables quicker cus- tomer service and reduces transportation costs.

Services offered by the warehouses can improve customer service by moving goods closer to the customer and thus reducing replenishment time. For example, a tier one automotive supplier can use a warehouse located near an automotive assembly plant to store instrument panels. The producer requires the supplier to provide these instru- ment panels in VIN (vehicle identification number) order sequence within an hour. That means the warehouse must load and deliver the parts in the correct sequence to match the production as it occurs on the line, that is, right color, right style, right identification number, and so on.

Warehouses can also be used to finish custom products. For example, when manu- facturers use postponement in their product design process, almost-finished prod- ucts are delivered to the warehouse. When actual customer orders are received, the warehouse finishes the product according to the specifications of the customer. For example, in the furniture industry products can be left unstained until the customer order is received. The stain is then applied and the customer receives a custom product

MKT

� Postponement A strategy that shifts production differentiation closer to the consumer by postponing final configuration.

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LINKS TO PRACTICE Fingerhut Direct Marketing, Inc.

www.fingerhut.com

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SUPPLY CHAIN DISTRIBUTION • 139

in minimal time. In the electronic industry, a unit may need to have the appropri- ate power supply and cord attached based on the location of the customer. Using the warehouse in this manner allows a manufacturer to maintain flexibility with almost- finished products and also to quickly provide a custom product for the customer.

Crossdocking Crossdocking eliminates the storage and order-picking functions of a distribution ware- house while still performing its receiving and shipping functions. Trucks arrive at a cross- dock with goods to be sorted, consolidated with other products, and loaded onto outbound trucks. Those trucks may be headed to a manufacturer, a retailer, or another crossdock. Shipments are transferred directly from inbound trailers to outbound trailers without any storage in between. Shipments should spend less than 24 hours in a crossdock.

What is the big difference between crossdocking and traditional distribution ware- housing? In a traditional setting, the warehouse holds stock until a customer places an order; then the item is picked, packed, and shipped. The customer typically is not known before the items arrive at the warehouse. With crossdocking, the customer is known before the items arrive at the warehouse and there is no reason to move the items into storage.

Crossdocking has two major advantages. First, the retailer reduces inventory hold- ing costs by replacing inventory with information and coordination. Second, cross- docking can consolidate shipments to achieve truckload quantities and significantly reduce a company’s inbound transportation costs.

Types of Crossdocking Manufacturing crossdocking is the receiving and consoli- dating of inbound supplies to support just-in-time manufacturing. In this case, the warehouse might be near the manufacturing facility and used to prep subassemblies or consolidate kits of parts. Distributor crossdocking is the receiving and consolidat- ing of inbound products from different vendors into a multistock-keeping unit pallet that is delivered once the last product is received. Transportation crossdocking is the consolidating of shipments from LTL and small-package industries to gain economies of scale. Retail crossdocking is sorting product from multiple vendors onto outbound trucks headed for specific retail stores.

Home Depot, Inc., Wal-Mart, Costco Wholesale Corporation, and FedEx Freight are examples of companies using crossdock- ing. At FedEx Freight, pickup and delivery drivers are busy during the day picking up freight that must be delivered that night and making deliveries. Each evening, drivers return to the crossdock. Freight is unloaded, sorted, and placed onto outbound trucks. The trucks travel through the night to their destinations, where the freight is unloaded and sorted onto local delivery trucks. FedEx Freight has achieved economies of scale that allow cost-effective transportation to areas with relatively little freight traffic.

� Crossdocking Eliminates the storage and order-picking functions of a distribution warehouse.

� Manufacturing crossdocking The receiving and consolidating of inbound supplies and materials to support just-in-time manufacturing.

� Distributor crossdocking The receiving and consolidating of inbound products from different vendors into a multi-SKU pallet.

� Transportation crossdocking Consolidation of LTL shipments to gain economies of scale.

� Retail crossdocking Sorting product from multiple vendors onto outbound trucks headed for specific stores.

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Crossdocking Innovations Recent developments in information systems and soft- ware solutions have created more opportunities for crossdocking. Better supply chain visibility and improved data sharing between members of the supply chain have resulted. Older crossdocking systems were able to do pure crossdocking. The soft- ware worked only if it was a perfect match going directly to an outbound truck. Now software allows orders and order lines to be split. The ability to split orders and ship partial orders immediately can avoid shortages on store shelves.

By using Web-based portals, suppliers can create and transmit Advance Shipment Notification (ASN) information to their retail customers. ASN is an electronic file transmitted from suppliers to retail customers providing advance notice of what prod- ucts are being shipped to the retailer’s distribution center and their estimated time of arrival. Advance notice allows the retailer to allocate the product while it is still in transit. Basically, ASNs provide better visibility into the supply chain. In recent years, software providers also have been developing their products onto a single supply chain process platform. This reduces the problem of software compatibility.

In recent history equipment innovations have centered on conveyor sorting systems, print and apply mechanism, and automatic identification technologies. The sliding shoe sorter pushes cartons off the conveyor to the appropriate conveyor lane. Parallel sorting enables smaller gaps between cartons. More densely packed conveyors allow the crossdock opera- tors to reduce the speed of the conveyor and still maintain the same throughput as before. Slower conveyor speeds cause less wear and tear, less energy usage, and less maintenance.

Another issue is printing and applying labels to cartons. Print and apply systems allow crossdock operators the ability to deal with inadequately labeled cartons so that proper labels can be printed and applied on the cartons as they move along convey- ors. This allows the item to be crossdocked. By attaching an RFID (discussed in more detail in the next section) tag on a pallet, the simple act of moving the pallet from a tractor trailer through a receiving portal acknowledges receipt of the item and down- loads critical information so that automatic crossdock allocations can be made.

Crossdocking Implementation In concept, crossdocking looks simple. The inbound product arrives on one side of the dock and is transferred to waiting trucks on the other side of the dock. Crossdocking systems can include a number of distribution activities. One approach is simply in one door and directly out the other. However, crossdockers can also add value in the interval between receiving and shipping (print- ing and applying labels, releasing ASNs, etc.). An organization can expect the follow- ing from a successful crossdocking implementation:

● Reduction in capital investment in facilities and equipment (less space required) ● Reduction in inventory ● Reduction in personnel requirements ● Reduction in order cycle time ● Reduction in product damage (reduced touches) ● Reduction in freight costs

The primary reasons why implementations fail are: ● Poor facility layout ● Internal information systems are not integrated ● Low level of supply chain integration and collaboration

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SUPPLY CHAIN DISTRIBUTION • 141

● Noncompliance by all supply chain members ● The wrong products are selected for crossdocking ● Unreliable suppliers in terms of accuracy and on time deliveries ● Insufficient volume of activity ● Not understanding peak workload variations

Successful crossdock implementation requires visibility into what you will be receiv- ing before receiving it. ASNs are the source of that information. Let’s look at how RFID technology is improving the collection of data needed to support successful crossdocking.

Radio Frequency Identification Technology (RFID) Radio frequency identification (RFID) is an automated data collection technology. It uses radio frequency waves to transfer data between a reader and an RFID tag. The information is transmitted automatically so no one needs to unpack or scan individual bar code labels, yet it provides accurate data transmittal.

The RFID tags contain encoded information that identifies items at the case, pallet, or container level. Rolls-Royce now uses RFID technology to track components used in military transport and combat aircraft and helicopters. In states with toll highways, RFID technology is used to collect tolls automatically. As a car with an RFID tag slowly passes through a collection lane, the RFID reader records the relevant vehicle data, and the appropriate toll charge is processed.

In a survey concerning RFID implementation, nearly half of consumer goods makers, a third of food and beverage makers, and a quarter of textile and apparel manufacturers indicated that they were implementing RFID technology because of a mandate from Wal-Mart. The survey further indicated that 59 percent of companies in the automotive industry would deploy RFID technology during the coming year. Worldwide, RFID is forecasted to surpass $2.5 billion by 2010. There is some poten- tial controversy regarding personal privacy in some possible future uses of RFID technology.

New RFID applications have been developed for several industries. For example, in the hospitality industry, some hotels are now using RFID electronic locks. Instead of sliding a key through a card reader on the door, the guest merely holds the key card near the door lock and the RFID signal unlocks the door. The RFID electronic locks are more secure and not prone to cloning. Hotels also use RFID technology to track towels and linens. A washable RFID chip sewn into towels, robes, and bed sheets, allows hotels to track their linens. A hotel in Honolulu, using these RFID chips, reduced theft of its pool towels from 4,000 a month to just 750, a savings of more than $16,000 a month.

A jewelry retailer in Texas uses passive RFID tags in its stores. As an item is received into inventory, an employee attaches a tag encoded with the product’s item number. Each morning after stocking the display cases, a clerk moves a handheld RFID reader over each tray. The RFID reader transmits the captured data into the company’s infor- mation system providing a master inventory list.

Hospitals spend nearly 30% of their total operational budgets on securing and managing consumable medical supplies. Since these items play a crucial role in

� Radio frequency identification (RFID) Unpowered microchips are used to wirelessly transmit encoded information through antennae.

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patient care, medical facilities tend to overstock nursing stations. Combining the kanban philosophy (covered in Chapter 7) and passive RFID tags, a kanban-based resupply system was implemented. The RFID tag was embedded in each of the two baskets containing a specific item. For example, sutures may be contained in two baskets, each with 25 sets of sutures. The first container is placed in front of the second container. When a nurse removes the last set of sutures from the first con- tainer, it is removed from the shelf along with its RFID tag. The RFID tag is then clipped to a board designed for collecting the tags. At that point an RFID reader mounted behind the board collects the unique ID number encoded to the prod- uct’s tag and transports the data into the supply database. The software then auto- matically generates a resupply request. In 2011, the cardiovascular laboratory at a hospital in Kansas City deployed passive RFID tags and readers that have enabled the hospital to better manage its stock of pacemakers, coronary stents, and defibril- lators. Doing this, the hospital was able to reduce its coronary device inventory by $500,000.

Many manufacturers also use RFID technology to manage the replenishment of items used during the manufacturing process. The John Deere Planter factory reported a 40% increase in efficiency in welding due to improvements in material replenish- ment and fewer material-related delays.

RFID technology for tracking individual participants is being used at the New York City marathon. RFID data combined with GPS data allows runners to view their own progress during the race and also enable friends, family members, and others to deter- mine a particular runner’s location at any given time as well as where that participant is relative to spectators.

As you can see, RFID applications are numerous and expected to increase in the future as more companies understand how RFID technology can help better manage inventory and resupply processes.

Third-Party Service Providers The ease of developing an electronic storefront has allowed the discovery of suppli- ers from around the world. A good example is the success of artisans in Kenya, who by marketing over the Internet increased annual export earnings to $2 million from only $10,000. While smaller companies have benefited from these electronic store- fronts, they often are not prepared to handle the logistics aspect. Many of these B2C companies outsource the delivery and return of products to companies such as FedEx and UPS. This works especially well when the consumer is paying for delivery.

A good example is Bike World, a company known for its high-quality bicycles, expert advice, and personalized service. After beginning Internet operations in 1996, Bike World found itself overwhelmed processing orders, manually shipping packages, and responding to customer inquiries regarding order status. Because of this, Bike World outsourced its order fulfillment to FedEx. FedEx offered reasonably priced delivery that exceeded customers’ expectations.

Even larger companies often outsource their logistics. Consider when over 5400 CVS stores unveiled Gillette’s Fusion, a new shaving product, all on the same day. Eight to ten weeks of planning was needed for this one-day event. The product was shipped to five of the CVS distribution centers. At the distribution centers, CVS coordinated pick-and-pack activities and readied the product for shipping to the stores. CVS used

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IMPLEMENTING SUPPLY CHAIN MANAGEMENT • 143

IMPLEMENTING SUPPLY CHAIN MANAGEMENT

Implementing a strategic, integrated supply chain requires considerable effort on the part of the initiating company. This change often is a result of external pressures faced by the company, such as increased global competitors, an industry consolidation reducing the number of surviving companies in the industry, a switch to e-commerce (including e-purchasing and e-sourcing), or major technological changes within the industry.

Typically, a company begins by analyzing its current supply chain. A small cross- functional team leads the effort, examining all facets of the system to determine where improvements are possible and necessary. Most companies begin by looking at the parts of the supply chain, the internal dimension, in which they have the most con- trol: manufacturing or service processes, distribution processes, and/or retail capacity and the time and costs of sourcing, producing, and distributing products or services. Improving performance in these areas has been the priority of many supply chain management initiatives. For a typical manufacturer, this means investing in automa- tion and sales and operations planning technologies. For distributors and retailers, the priority has focused on supplier relationships, warehouse management, and transpor- tation management solutions.

Some companies also include product design and supply chain design in the internal dimension. Supply chain design includes determining the best locations for manufacturing and warehousing facilities, as well as for retail outlets, and how to design the store itself. In global supply chains, companies also must decide whether or not to adopt offshore production. This decision now considers the traditional cost factors, such as production costs, distribution, and inventory, but also takes into account the greater risk of product damage and delay resulting from offshore pro- duction. Product design includes quality function deployment (QFD), ease of manu- facturing, design for the environment (DFE), and ease of distribution throughout the supply chain.

The external dimension is that part of the supply chain that the company does not control. Predictable external factors include government regulations, mandates from trading partners, and environmental requirements. Often there is some advance notice of changes resulting from these factors. For example, a manufacturer can pre- pare itself to meet health, safety, or regulatory requirements such as the U.S. Food and Drug Administration (FDA) might apply to supply chains for food, drugs, and cosmetics. An example of a common trading partner mandate in the auto industry is the use of electronic data interchange (EDI) and membership in the net marketplace Covisint. If you want to be a supplier to an auto industry manufacturer, you must comply with these mandates. When integrating the external supplier base, many com- panies develop supplier relationships.

The requirement to use radio frequency identification device (RFID) technology is occurring in consumer goods and retail supply chains. Environmental considerations

expedited shipping through a third-party logistics provider, delivering razors to all stores by noon that day. It was the first chain to have the new product available, provid- ing it with increased market share.

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include restrictions on the discharge of solid, liquid, and gaseous waste. The supply chain can accommodate these predictable or expected external factors.

The external dimension also includes actions that are very difficult to predict and can devastate an organization. A sudden spike in fuel prices, worker strikes, delays in shipping due to customs noncompliance, port congestion, and major weather-related disasters can put considerable stress on the supply chain. Any problems that occur inside the supplier’s or the distributor’s supply chain also can lead to delays in sourc- ing, production, and movement of finished products, which threaten supply continu- ity and the quality of service to the final customer. To meet such challenges, companies need to improve their supply chain visibility so that real-time responses can be made to minimize the chance of disruption.

The customer dimension is where your company may have some influence but does not have total control. Ways to influence the customer include offering customers the fea- tures and functions they want, when they want them, and at an attractive price. Companies also can use promotional strategies to shape demand to better meet their business goals. Supply chains can typically provide better customer service when better product demand information is available to all members of the chain. This can be done using point-of-sale (POS) technology. Let’s look at some strategies to leverage your supply chain.

Strategies for Leveraging Supply Chain Management Although implementing supply chain management requires a set of actions that are unique to every company, the end goal is the same. Companies want a supply chain that makes it possible to manage and adapt to all of the business dynamics affecting the company. This includes improving insight and control over your own internal operations; catering to, influencing, and responding faster to events beyond your con- trol; and focusing on the customer and demand signals that affect your supply chain. You should consider each of the following actions.

1. You should regularly assess your supply chain network to make sure it is still suited for your business. Make sure that you are not making adjustments to a supply chain that is no longer suitable for meeting your current and future business needs. Many supply chains merely evolve over time or through acquisitions. They are not designed but rather worked around. Take time to make sure your network fits your needs. Long-term profitability depends on having the right capacity and the right location of your physical assets.

2. You should have a global view of demand. You need to anticipate demand and take opportunities to influence it to reach your financial objectives. Using a collaborative demand planning solution (discussed in Chapter 8) is a good way to develop a global view of demand. You need visibility into customer buying patterns so that you can determine appropriate inventory, production, sourcing, and distribution plans.

3. You should decide how to get products to your customers. You decide what to make or buy; whether to build products in America, Europe, or elsewhere; which distribution center to provide the product to the customer; and how much inventory to hold and where.

4. You should improve asset productivity. For manufacturers, better demand visibility allows for effective planning and scheduling to maximize returns on critical assets, such as equipment, materials, and people. For logistics providers

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SUPPLY CHAIN PERFORMANCE METRICS • 145

and retailers, the reduced uncertainty allows more effective use of warehousing and transportation assets.

5. You should expand your visibility. In global supply chains, product travels more miles, passes through more hands, and crosses more systems on its way to the final customer. There are numerous points where a disruption can occur. The use of RFID technology can provide real-time visibility into inventory status regardless of where it is and improve your ability to predict and plan for its arrival.

6. You need to know what happens, when it happens. For you to minimize the effect of an external event or to maximize the opportunity of new customer demand, you need to know about it as early as possible so that you can take the appropriate action. For example, if you learn immediately when a manufacturer has a major production problem, you can possibly allocate some production to another factory within the supply chain. If you learn immediately of increased customer demand in a specific region, you may be able to meet this demand by rerouting product as it moves through the supply chain.

7. You need to design to deliver. A holistic view of the supply chain must include the design of the product itself. Focus on ways to reduce development time and time to market. Consider whether it makes sense to use a third-party logistics provider for last-minute product customization. The postponement approach provides flexibility, as well as removing costs from manufacturing. It often allows for delivery of the final configured product within 24 hours to the customer. If a company wants to use a postponement approach, it must be considered during the product design.

8. You must track performance to allow for continuous improvement.

Implementing these strategies should result in benefits for members of the sup- ply chain. You should reduce operations expenses. Having a clearer picture of end - customer demand allows companies to better plan for the appropriate mix of inventory, production, and transportation strategies. The companies should expect improved profitability as internal operations become more efficient and cost effective. Customer service should be improved since the companies have better visibility into final cus- tomer demand. Competitiveness and faster growth should improve as the company gains better insight into the internal, external, and customer dimension of its business.

SUPPLY CHAIN PERFORMANCE METRICS A company can use traditional financial measures such as return on investment (ROI), profitability, market share, and revenue growth, as well as traditional inventory per- formance measures such as customer service levels, inventory turns, weeks of supply, and inventory obsolescence (all discussed in Chapter 12).

Let’s look at how a large state university used several metrics to analyze how well its supply chain achieved the following initiatives. The first initiative was a spend analy- sis. This was a strategic review of spend data across all purchase and payment methods to increase compliance and identify new organization-specific contracting and cost containment opportunities. The university also evaluated its level of progress in stra- tegic sourcing. The purpose of the supply base management initiatives was to leverage

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the university’s buying power and maximize the value of its strategic supplier busi- ness relationships. A third initiative concerned contract management. This initiative concerned the development and implementation of a university-wide best-in-class contract pricing agreement that would assure the university the “least total cost” for products and services required from external suppliers. The fourth initiative evalu- ated the use of collaborative buying. This examined how well the university had used local, regional, and national collaborative buying business partners to further lever- age buying power. The last initiative was concerned with compliance. It was designed to promote faculty and staff compliance with procurement and disbursement finan- cial policies and to determine the level of compliance to university-authorized buy- ing methods and use of contract suppliers. To summarize, the university analyzed the frequency with which members of its supply chain were used, while simultaneously checking to see if new suppliers should be added to its supply chain.

The university also used a number of key purchasing performance metrics. The financial metric measured ROI, the total cost containment versus the operating bud- get, the combination of cost savings and supply chain revenue, the negotiated product and service cost savings, and purchasing revenue. The contracting metric looked at the number of new preferred discount contracts, the level of spending done with con- tract suppliers, the amount of collaborative buying, the number of electronic competi- tive bidding events, and the number of strategic sources deactivated. The compliance metric also considered the level of purchase order buys and the level of buys through managed supplier relationships, the level of purchase activity at the university’s net marketplace and the use of marketplace suppliers, and the amount of green product and service purchases.

An analysis of the e-marketing summarized the number of unique visitors to the purchasing services Web site and to the supplier showcase. Operations were analyzed based on the percentage of customers very satisfied in a biannual survey. Economic inclusion measured the amount of buys placed with local community-based suppliers. It also analyzed the amount of purchases made with all diversity-owned suppliers, as well as all African American-owned suppliers. The number of diversity suppliers in the university marketplace was also monitored. The university used purchasing cards (p-cards) for staff to place small purchases without the need for a purchase order. The level of use of p-cards was tracked to determine the level of noncompliance with com- modity or supplier restrictions. Travel expenses were also monitored to determine compliance with negotiated travel service providers. And finally, the university mea- sured accounts payable activities, such as the percentage of electronic purchase order invoices, the effectiveness of the electronic invoice business process (EDI), the number of data errors causing rejections, the percentage of supplier invoices paid within terms, and the use of electronic fund transfers (EFT). Clearly, the number of specific metrics used by the university is quite significant. Other activities to analyze can include qual- ity and on-time delivery of products and services.

Organizations also often measure the quality of their products or services. An orga- nization can measure product or service quality by looking at warranty costs, products returned, and markdowns given to the customer due to poor quality or service. In Chapters 5 and 6, you will learn about different aspects of quality and methods for measuring quality levels. These techniques can easily track supply chain quality.

In Chapter 15, you will learn about scheduling performance measures that could be used to evaluate the company’s response time as well as to evaluate how well the com- pany is using its capacity. Excess capacity may enable much quicker response times, but the company must assess the cost of low capacity utilization.

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TRENDS IN SUPPLY CHAIN MANAGEMENT • 147

The company needs to determine what customer satisfaction means to its custom- ers. Does it mean filling the entire order? Does it mean how quickly it can respond to customer requests? Or is it more important to have the product always arrive on time? Answering these questions identifies the activities that support the supply chain’s objectives. These are the activities that must be measured.

An example of manufacturing performance metrics was reported in a study of U.S.– Mexican maquiladora operations. Performance measurements included cycle-time reduction (response time), routing and scheduling performance (on-time delivery, response time, and capacity utilization), and outbound cross-border transportation. The bottom line is that companies must measure performance of the supply chain and the measurements must support behavior that is consistent with the supply chain objectives.

The Supply Chain Operations Reference (SCOR) model is an effort to standardize measurement of supply chain performance. The SCOR model examines four differ- ent operational perspectives: reliability, flexibility, expenses, and assets/utilization. From a reliability perspective, the supply chain is measured on on-time delivery, order fulfillment lead time, and fill rate (the fraction of demand met from stock). In terms of flexibility, supply chain response time and production flexibility are measured. For expenses, supply chain management cost, warranty cost as a percentage of revenue, and value added per employee are examined. In terms of assets/utilization, total inven- tory days of supply, cash-to-cash cycle time, and net asset turns can be measured.

TRENDS IN SUPPLY CHAIN MANAGEMENT The past few years have seen increased use of electronic marketplaces that bring thou- sands of suppliers together with thousands of buyers. The objectives of these net mar- ketplaces are to have suppliers compete on price, to encourage automated low-cost transactions, and to reduce the price of industrial supplies.

E-distributors are the most common form of net marketplace. E-distributors provide electronic catalogs representing the products of thousands of suppliers. E-distributors are independently owned intermediaries that provide a single source for customers to make spot purchases. About 40 percent of a company’s items are pur- chased on a spot basis. E-distributors typically have fixed prices but do offer quantity discounts to large customers (see Chapter 12). The primary benefits of e-distribution to the manufacturing company are lower product search costs, lower transaction costs, a wide selection of suppliers, rapid product delivery, and low prices.

E-purchasing companies connect on-line suppliers offering maintenance, repair parts, and operating supplies (MRO) to businesses that pay fees to join the market. E-procurement companies are typically used for long-term contractual purchasing and offer value chain management services to both buyers and sellers. Value chain management (VCM) automates a firm’s purchasing or selling processes. VCM auto- mates purchase orders, requisitions, product sourcing, invoicing, and payment. For suppliers, VCM automates order status, order tracking, invoicing, shipping, and order corrections.

On-line exchanges connect hundreds of suppliers to unlimited buyers. Exchanges create a marketplace focusing on spot requirements of large firms in a single industry, such as the automotive industry or electronics industry. Examples of exchanges include ProcureSteel.com (a market for steel products), e-Greenbiz.com (spot market for nursery supplies), and Smarterwork.com (professional services from Web design to legal advice).

� E-distributors Independently owned net marketplaces having catalogs representing thousands of suppliers and designed for spot purchases.

� E-purchasing Companies that connect on-line MRO suppliers to businesses that pay fees to join the market, usually for long-term contractual purchasing.

� Value chain management (VCM) Automation of a firm’s purchasing or selling processes.

� Exchanges A marketplace that focuses on spot requirements of large firms in a single industry.

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The last type of net marketplace is an industry consortium. Industry consortia are industry-owned markets that enable buyers to purchase direct inputs from a limited set of invited participants. The objective of an industry consortium is the unification of supply chains within entire industries through a common network and computing platform. Examples of industry consortia include Covisint.com (automotive industry), Avendra.com (hospitality industry), and ForestExpress.com (paper and forest prod- ucts). It is clear that net marketplaces will be a dominant factor in effective supply chain management now and in the future. Technology continues to bring suppliers, buyers, and distributors closer together so that supply chains can be managed effectively.

As supply chains cover greater distances, they experience greater uncertainty and gen- erally are less efficient. It is likely that supply chain velocity in these global chains will decrease. Practitioners have identified the following characteristics associated with lower- velocity supply chains: lumpy supply/demand (goods tend to arrive at the warehouse in large quantities and sometimes without notice), more break-bulk activity because of the larger quantities, slower long-distance moves as companies source offshore, and higher inventory levels in the pipeline due to longer transit times. These lower-velocity supply chains raise additional questions, such as distribution center location or a better under- standing of the economics of break-bulk and more emphasis on pipeline design.

While numerous improvements in supply chain management have occurred in the past decade, even more areas of concern exist for the future of supply chain management. In 2010, PRTM published the results of its sixth annual survey of global supply chain trends with 300 international participants. This survey focused on the following areas of concern:

1. How to design and configure future supply chain networks, given increased globalization and outsourcing

2. How to maintain product quality and safety among global supply chain members 3. How to set priorities to ensure supply chain flexibility and to determine the

requirements for high-performance supply chains

According to the participants in the survey, the major trends driving innovative sup- ply chain design and configuration across all industries are:

1. Globalization is accelerating. Supply chain volatility and uncertainty have permanently increased. Market transparency and greater price sensitivity have reduced customer loyalty. It is much harder now to differentiate one’s product. Future globalization will target product and technology development in addition to manufacturing and assembly.

2. Achieving growth requires truly global customer and supplier networks. Future market growth is expected to come from international customers and custom- ized products. Reducing costs and penetrating new local markets are two key drivers of accelerated globalization. Despite average cost reductions of 17% per globalization initiative, many companies have not realized these savings. The gap between planned and actual benefits is caused by internal barriers that pre- vent full support of globalization efforts and external network partners failing to achieve expected performance.

3. Major barriers to globalization include limited supply chain flexibility and the lack of internal competency to manage partners.

4. Product quality and safety and supply chain delivery and security are the most critical concerns when expanding the supply chain globally.

5. China and India are major targets for globalization. Eastern Europe is catching up as a top offshoring option. Market dynamics require regional, cost-optimized sup- ply chain configurations. Investments are designed to secure access to local markets.

� Industry consortia Industry-owned markets that enable buyers to purchase direct inputs from a limited set of invited suppliers.

� Supply chain velocity The speed at which product moves through a pipeline from the manufacturer to the customer.

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SCM ACROSS THE ORGANIZATION • 149

6. Environmental sustainability is a major concern in developing future supply chain strategies. Currently, sustainability is mainly driven by regulatory compli- ance. It is not considered a strategic differentiator.

7. Existing supply chain organizations are not truly integrated and empowered. The supply chain must be treated as a single integrated organization.

The future of supply chain management seems focused on the development of sustain- able, global supply chains.

SUPPLY CHAIN MANAGEMENT WITHIN OM: HOW IT ALL FITS TOGETHER Supply chain management (SCM) is directly linked to many OM activities. The degree of supply chain management is a strategic decision for the organization (Chapter 2) and determines the level of vertical integration in the organization. SCM is concerned with external suppliers, internal operations, and external distributors.

Effective SCM requires supplier partnerships. Using the ISM Principles and Stan- dards of Ethical Supply Management Conduct, purchasing develops partnerships with suppliers to assure a continuous supply of materials at a reasonable cost. Purchasing also works with suppliers to improve communications, to develop flexibility in meet- ing changes of demand, to improve quality of materials, and to assure on-time deliv- ery. This assured, continuous supply of materials allows the production planners to effectively schedule jobs (Chapter 14) and to use equipment and personnel efficiently.

SCM also provides for streamlined communications between suppliers and the company, thus reducing purchasing lead time. As we will learn when studying inven- tory management in Chapter 12, reduced lead time results in lower inventory levels. The improved communications within the supply chain improve demand forecasting accuracy. This reduces demand uncertainty, allowing lower safety stock investment while still maintaining customer service levels. Improved demand forecast accuracy also contributes to the development of better staffing plans (Chapter 13), which can lead to lower personnel costs, lower inventory costs, and improved customer service.

SCM affects product and process design (Chapter 3) by specifying which items are done in-house and which items are outsourced. Good supply chain management pro- vides timely, accurate information that is critical to successful operations management.

SCM ACROSS THE ORGANIZATION Supply chain management changes the way companies do business. Consider how supply chain management affects different functional areas within the organization.

Accounting shares some of the benefits and responsibilities of supply chain man- agement. As inventory levels decrease, customer service levels increase. Accounting is exposed to the risks of information sharing and involved in developing long-term partner- ships. With information sharing comes the need for increased confidentiality and trust.

Marketing benefits by improved customer service levels achieved by POS data col- lection. A shared database provides marketing with current demand trends and elimi- nates demand filtering between levels of the supply chain. POS data also facilitate quicker customer response time. Global supply chains facilitate access into local markets.

Information systems are critical for supply chain management. Information systems provide the means for collecting relevant demand data, developing a common database, and providing a means for transmitting order information. Information systems enable information sharing through POS data, EDI, RFID, the Internet, intranets, and extranets.

MKT

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This chapter provides the framework for understand- ing supply chain management. Supply chains consist of external suppliers, internal processes, and exter- nal distributors. Internal processes (purchasing, processing, production planning and control, quality assurance, and shipping) are integrated first since the manufacturer has direct control over these activities. Integrating external suppliers into the supply chain begins with sourcing decisions and subsequent strategic partnership development by

the purchasing function. Electronic net marketplaces facilitate sourcing by bringing together thousands of suppliers and buyers to a common site. Distributors are usually the last segment of the chain to be integrated.

Warehouses can be used for consolidation, product mixing, or finalizing products. Crossdocking can reduce transit time as well as reduce handling of goods. RFID helps track materials as they flow through the supply chain. POS demand data are

provided to all members of the supply chain to reduce uncertainty and to assure that all members of the supply chain work with common data.

THE SUPPLY CHAIN LINK

Supply chain management has tremendous opportunity to impact sustainability as it cuts across all enterprises involved in product creation and delivery. A sustain- ability change in the operations function of one mem- ber of the supply chain does not solve the problem if the other members of the supply chain continue to violate sustainability goals. A company, for example, can develop production processes at its manufacturing facility with reduced emissions. However, not much has been accomplished if the materials it sources are pro- duced or delivered in ways that pollute the environ- ment. For changes to have impact, they must be made across the entire supply chain.

Companies must ensure that sustainable changes are not made to just one operation in the supply chain,

but to the entire system of sourcing, production, trans- portation, delivery, and all “operations” involved. For example, one important aspect of supply chain sustain- ability is the movement and storage activities of the sup- ply chain. These activities are among the most energy intensive. Carbon dioxide emissions from the transpor- tation sector alone account for 33% of the United States’ total CO2 emissions. Judicious consideration of modes of transportation and sourcing can dramatically impact sustainability. Think about the difference between energy consumed to ship a New Zealand lamb chop to a restaurant in New York City, versus procuring a lamb chop from a farm in upstate New York. The savings in product price or the benefits of product quality may be outweighed by the environmental costs of logistics across large distances.

THE SUSTAINABILITY LINK

Purchasing has an important role in supply chain management. Purchasing is responsible for sourcing materials and developing a strong global supplier base through long-term partnering agreements, using e-sourcing and e-purchasing concepts.

Operations uses timely demand information to more effectively plan produc- tion schedules and use manufacturing capacity. Operations responds more quickly to changing customer demand data, thus providing improved customer service levels.

Who is responsible for supply chain management within an organization? In a manufacturing company, it is often the materials manager, since he or she is more familiar with external suppliers, internal functions, and external distributors. The per- son who does supply chain management must see the “big picture” so that local priori- ties do not overshadow global priorities. In a service organization, the operations or office manager may be responsible for supply chain management.

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CHAPTER HIGHLIGHTS • 151

10 Sourcing is critical in establishing a solid, responsive supplier base. The use of net mareketplaces has allowed companies to reduce supplier search costs and facilitated the use of e-sourcing. Companies make insourcing and outsourcing decisions. These make-or-buy decisions are based on financial and strategic criteria. Companies typically do not outsource activities that are part of their core competencies.

11 Partnerships require sharing information, risks, technologies, and opportunities. Impact, intimacy, and vision are critical to successful partnering. Impact means attaining higher levels of productivity and competitiveness that are not possible through normal supplier relationships. Intimacy requires trust as companies share confidential information. Vision means having common objectives.

12 Ethics in supply management is an ongoing concern. Since buyers are in a position to influence or award business, it is imperative that they avoid any appearance of unethical behavior or conflict of interest. The Institute for Supply Management has established a set of principles and standards to guide buyers.

13 Supply chain distribution requires effective warehousing operations. The warehouses provide transportation consolidation, product mixing, and service. Warehouses consolidate (LTL) quantities into (TL) quantities. Product mixing adds value for the customer because the warehouse groups the items and ships them directly to the customer. Warehouses can improve customer service by placing goods closer to the customer to reduce response time. Crossdocking is an effective method for reducing transportation costs. RFID is gaining use as a means of accurately tracking shipments. Much of the distribution function has been outsourced to third-party logistics providers.

14 Implementing supply chain management usually begins with the manufacturer integrating internal processes first. Then, the company tries to integrate the external suppliers. The last step is integrating the external distributors.

15 A company needs to evaluate the performance of its supply chain. Regular performance metrics (ROI, profitability, market share, customer service levels, etc.) and other measures that reflect the objectives of the supply chain are used. In addition, the company can use the Supply Chain Operations Reference (SCOR) model to standardize its supply chain performance.

16 The emergence of net marketplaces has significantly affected supply chain management. As supply chains become longer, it is likely that supply chain velocity will decrease. It is possible that a more strategic and integrated approach is needed to advance supply chain management to the next level. Such an approach will combine world-class supply chain management, process leadership in lean manufacturing techniques, and “on-demand” information technology.

Chapter Highlights 1 Every organization is part of a supply chain, either as a

customer or as a supplier. Supply chains include all the processes needed to make a finished product, from the extraction of raw materials through to the sale to the end user. Supply chain management is the integration and coordination of all these activities.

2 The bullwhip effect distorts product demand information passed between levels of the supply chain. The more levels that exist, the more distortion that is possible. Variability results from updating demand estimates at each level, order batching, price fluctuations, and rationing.

3 Supply chains for service organizations can also have external suppliers, internal processes, and external distributors. The supply chain for a travel agency requires the agent to plan, arrange, schedule, and invoice travel by using potential travel service providers and then subsequently allowing those service providers to perform the actual travel-related service.

4 Many issues affect supply chain management. The Internet, the Web, EDI, intranets, extranets, bar code scanners, and POS data are supply chain management enablers. Technology advancements facilitate information sharing among supply chain members. Intranets and extranets improve communication flow within organizations and supply chains. Through the use of electronic net marketplaces, the Internet greatly reduces the cost of researching suppliers. Customer expectations are high as they demand better service, better product quality, quicker response times, reasonable prices, and an easy way to return merchandise.

5 B2B and B2C electronic commerce enable supply chain management. Net marketplaces bring together thousands of suppliers and customers, allowing for efficient sourcing and lower transaction costs.

6 Global supply chains often face greater uncertainty, potentially slowing the supply chain velocity and increasing supply chain inventory levels. Inadequate infrastructure can also complicate global supply chains.

7 Government regulations affecting supply chain management center on intellectual property rights and conformance with copyright laws. The Internet structure makes it difficult for governments to enact many regulations at this time.

8 Green supply chain management focuses on the environment and how anything done in the supply chain affects the environment. Supply chains try to reduce their carbon footprint and become more environmentally friendly.

9 Purchasing has a major role in supply chain management. Purchasing is involved in sourcing decisions and developing strategic long-term partnerships. Purchasing develops and maintains a supplier base capable of assuring material availability, timely delivery performance, high-quality products, and reasonable prices.

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152 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

Key Terms supply chain 104 supply chain management 105 tier one supplier 106 tier two supplier 106 tier three supplier 106 logistics 108 traffic management 108 distribution management 108 bullwhip effect 108 e-commerce 112 business-to-business e-commerce

(B2B) 113 automated order entry system 113 electronic data interchange (EDI) 113 electronic storefronts 113 net marketplaces 113 electronic request for quote

(eRFQ) 114 virtual private network (VPN) 114

business-to-consumer e-commerce (B2C) 114

advertising revenue model 114 subscription revenue model 114 transaction fee model 114 sales revenue model 114 affiliate revenue model 114 intranets 115 extranets 115 green supply chain management 118 requisition request 122 price and availability 123 purchase order 123 incoming inspection 124 sourcing strategy 124 vertical integration 127 insource 127 outsource 127 backward integration 128

forward integration 128 partnering 131 early supplier involvement (ESI) 135 general warehouse 137 distribution warehouse 137 postponement 138 crossdocking 139 manufacturing crossdocking 139 distributor crossdocking 139 transportation crossdocking 139 retail crossdocking 139 radio frequency identification

(RFID) 141 e-distributors 147 e-purchasing 147 value chain management (VCM) 147 exchanges 147 industry consortia 148 supply chain velocity 148

Formula Review For insourcing or outsourcing:

FCBuy 1 1VCBuy 3 Q 2 5 FCMake 1 1VCMake 3 Q 2 Solved Problems

• Problem 1 Jack Smith, owner of Jack’s Auto Sales, is deciding whether his company should process its own auto loan applications or outsource the process to Loans Etc. If Jack processes the auto loan applications internally, he faces an annual fixed cost of $2500 for membership fees, allowing him access to the Top- Notch credit company, and a variable cost of $25 each time he processes a loan application. Loans Etc. will process the loans for $35 per application, but Jack must lease equipment from Loans Etc. at a fixed annual cost of $1000. Jack estimates pro- cessing 125 loan applications per year. What do you think Jack should do?

(a) Should Jack process the loans internally or outsource the loans if demand is expected to be 125 loan applications?

(b) Is Jack indifferent to internal processing and outsourcing at one level of loan applications?

• Before You Begin To make his decision when demand is known, Jack needs to calculate the total cost of processing the auto loans in-house and compare it to the total cost of outsourcing the loan process- ing. You need to identify the relevant costs. If Jack processes the loans internally, he has an annual fixed cost of $2500 plus a per loan variable cost of $25. If the loan processing is outsourced, Jack has a fixed annual cost of $1000 and a per loan variable cost of $35.

• Solution (a) The total cost for processing 125 loan applications inter-

nally is $5625. That is $2500 in annual fixed costs plus 125 loan applications multiplied by the $25 per application vari- able cost. The total cost for outsourcing the applications is

(See student companion site for Excel template.)

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DISCUSSION QUESTIONS • 153

• Solution (a) If BSU does its housekeeping with its current staff, the cost

is $14,066,800.

Cost per housekeeper 1$23,000 1 39% fringe benefits 2 5 $31,970

Cost for 400 housekeepers 1400 3 $31,970 25 $12,788,800 Annual fixed costs � ˚1,278,000 Total annual costs $14,066,800

(b) If BSU has EEC do the housekeeping, the cost is $13,900,000.

Cost for 320 housekeepers 1320 3 20,000 2 5 6,400,000 Annual fixed costs � ˚7,500,000 Total annual costs $13,900,000

(c) The indifference point is found by setting the two cost functions equal to each other. Since EEC only needs 80% as many employees as BSU, we need to adjust the cost functions.

$1,278,800 1 $31,970 1Q 2 5 $7,500,000 1 10.8Q 2 1$20,000 2 Q � 389.55, or 390 employees. Therefore, if the school needs fewer than 390 in-house housekeepers, it should do the housekeeping rather than outsource it. If BSU needs more than 390 housekeepers, it should outsource with EEC.

$5375. That is $1000 in annual fixed costs plus 125 loan applications multiplied by the $35 per application variable cost. At 125 loan applications, it is cheaper for Jack to out- source the loan application processing.

(b) When demand is not known, set the total costs of each alternative equal to each other, or $1000 � ($35 * Q) �

$2500 � ($25 * Q). Solving for Q, we have 10Q � $1500, or Q � 150 loan applications. Since the costs are equal at 150 loan applications and Jack expects to need 125 applications processed, he is better off outsourcing the loan applications to Loans Etc.

• Problem 2 Big State University (BSU) is considering whether or not it should outsource its housekeeping service. Currently, BSU employs 400 housekeepers at an average annual wage of $23,000 plus another 39 percent for fringe benefits. Annual fixed costs associated with housekeeping are $1,278,800.

Eric’s Efficient Cleaners (EEC) will provide similar housekeeping for a fixed annual cost of $7,500,000 plus a variable cost of $20,000 per housekeeper required. Because Eric uses state-of-the-art equipment and well-trained employees, his company would need only 80 percent of the current BSU housekeeper staff (or 320 housekeepers).

(a) Calculate the annual cost of BSU using its current housekeeping staff.

(b) Calculate the annual cost if BSU lets EEC do the housekeeping.

• Before You Begin: Identify the relevant costs. You need to know the cost to BSU for using its own housekeeping staff. The average annual sal- ary per housekeeper is $23,000 plus fringe benefits (39 percent of the average annual salary). The annual fixed cost associated with housekeeping is $1,278,800. If housekeeping is outsourced, BSU doesn’t need to pay fringe benefits. EEC will provide simi- lar service for a fixed annual cost of $7,500,000 plus a variable cost of $20,000 per housekeeper. Remember that he only needs 320 housekeepers for the job.

Discussion Questions 1. Discuss the different types of e-commerce. 2. Explain the different revenue models used in e-commerce. 3. Give two examples from the Internet for each of the differ-

ent revenue models used in e-commerce. 4. Describe the evolution of business-to-business (B2B)

e-commerce. 5. For the next item you buy, determine its supply chain.

6. How do supply chains for service organizations differ from supply chains for manufacturing organizations?

7. How can companies satisfy increasing customer expectations?

8. Describe the additional factors that affect global supply chains. 9. Think of your last major purchase. What criteria did you

use to select the supplier?

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154 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

10. Explain the concept of partnering, including advantages and disadvantages.

11. Explain the benefits of using a single supplier as opposed to multiple suppliers.

12. Describe the kinds of information that are necessary in a supply chain.

13. Describe the role of warehouses in a supply chain. 14. Describe radio frequency identification (RFID) and how it

could be used by an organization. 15. Describe the current trends in e-commerce and how they

affect supply chain management.

Problems 1. Gabriela Manufacturing must decide whether to insource

or outsource a new toxic-free miracle carpet cleaner that works with its Miracle Carpet Cleaning Machine. If it decides to insource the product, the process would incur $300,000 of annual fixed costs and $1.50 per unit of variable costs. If it is outsourced, a supplier has offered to make it for an annual fixed cost of $120,000 and a variable cost of $2.25 per unit in variable costs.

(a) Given these two alternatives, determine the indifference point (where total costs are equal).

(b) If the expected demand for the new miracle cleaner is 300,000 units, what would you recommend that Gabriela Manufacturing do?

2. Gabriela Manufacturing was able to find a new supplier that would provide the item for $1.80 per unit with an annual fixed cost of $200,000. Should Gabriela Manufacturing insource or outsource the item?

3. Downhill Boards (DB), a producer of snow boards, is evaluating a new process for applying the finish to its snow boards. Durable Finish Company (DFC) has offered to apply the finish for $170,000 in fixed costs and a unit variable cost of $0.65. Downhill Boards currently incurs a fixed annual cost of $125,000 and has a variable cost of $0.90 per unit. Annual demand for the snow boards is 160,000.

(a) Calculate the annual cost of the current process used at Downhill Boards.

(b) Calculate the annual cost if Durable Finish Company applies the finish.

outsourcing the process? 4. Fast Finish, Inc. (FFI) has made a technological

breakthrough in snow board finish application. FFI will apply the finish for $0.23 per unit in variable costs plus a fixed annual cost of $230,000. Use the cost and demand information given in Problem 3 for Downhill Boards to evaluate this proposal.

(a) What will it cost Downhill Boards to outsource the finishing process?

(b) At what demand level does it make sense economically to outsource the finishing process?

5. Henri of Henri’s French Cuisine (HFC), a chain of 12 restaurants, is trying to decide if it makes sense to outsource the purchasing function. Currently, Henri employs two buyers at an annual fixed cost of $85,000. Henri estimates that the variable cost of each purchase order placed is $15. Value-Buy (VB), a group of purchasing specialists, will perform the purchasing function for a fixed annual fee of $100,000 plus $5 for each purchase order placed. Last year, HFC placed 1450 purchase orders.

(a) What was the cost last year to HFC when doing the purchasing in-house?

(b) What would the cost have been last year had HFC used Value-Buy?

(c) What is the indifference point for the two alternatives? (d) If HFC estimates it will place 1600 purchase orders next

year, should it use VB? (e) What additional factors should be considered by HFC? 6. Cal’s Carpentry is considering outsourcing its accounts

receivable function. Currently, Cal employs two full-time clerks and one part-time clerk to manage accounts receivable. Each full- time clerk has an annual salary of $36,000 plus fringe benefits costing 30 percent of their salary. The part-time clerk makes $18,000 per year but has no fringe benefits. Total salary plus fringe cost is $111,600. Cal estimates that each account receivable incurs a $10 variable cost. The Small Business Accounts Receivables Group (SBARG) specializes in handling accounts receivable for small- to medium-size companies. Doris Roberts from SBARG has offered to do the accounts receivable for Cal’s Carpentry at a fixed cost of $75,000 per year plus $30 per account receivable. Next year, Cal expects to have 2000 accounts receivable.

(a) Calculate the cost for Cal’s Carpentry to continue doing accounts receivable in-house.

(b) Calculate the cost for Cal’s Carpentry to use SBARG to handle the accounts receivable.

(c) If the fixed annual cost offered by SBARG is nonnegotiable but it is willing to negotiate the variable cost, what variable cost from SBARG would make Cal indifferent to the two options?

(d) What other alternatives might Cal consider in terms of his current staffing for accounts receivable?

(e) What additional criteria should Cal consider before outsourcing the accounts receivable?

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CASE: ELECTRONIC POCKET CALENDARS SUPPLY CHAIN MANAGEMENT GAME • 155

CASE: Electronic Pocket Calendars Supply Chain Management Game

In this supply chain game, retailers sell electronic pocket calen- dars to their customers and place replenishment orders to their wholesaler. The wholesaler sells the pocket calendars to the retailers and orders the calendars from a distributor. The dis- tributor sells the pocket calendars to the wholesalers and orders calendars directly from the factory. The distribution system is shown in the figure. For each period the game is played, partici- pants must follow the same sequence:

1. Receive any shipments into inventory. 2. Ship calendars to satisfy both new customer demand and

any back orders, as long as sufficient product is available. 3. Determine the ending inventory (a negative value indicates

back orders exist). 4. Determine the inventory position (ending inventory plus

any quantity already ordered). 5. Place replenishment orders. For this game, inventory holding costs will be $10 per case

per week and back order costs will be $15 per case per week. Each person must keep track of his or her own costs. The

weekly demand at the retailers will be provided by your profes- sor. Once the demand is known by the retailers, the retailers place the appropriate replenishment orders with the wholesalers. The wholesalers update their inventory records and place the nec- essary orders with the distributor. At this point, the distributor updates its inventory records and places the appropriate replen- ishment order with the factory. Lead time throughout the sup- ply chain is two weeks. For example, once the factory releases an order to be manufactured, it is two weeks before it is available; when the distributor orders pocket calendars from the factory, it is two weeks before they arrive.

A number of participants are needed in this game (see the figure). One person manages the factory (1). There are three dis- tribution centers, each needing a manager (3). Each distribution center supplies two different wholesalers (6), and each whole- saler supplies two unique retailers (12). In some cases, a location may have co-managers to speed up the transactions. The accom- panying table provides information regarding each location in the supply chain.

For each period of the game, retailers follow these procedures: 1. The retailer accepts into stock any orders due to arrive

during the current period. The beginning inventory plus the arriving order determine how much inventory the location has available to satisfy demand during that period.

2. Next, your professor provides each retailer with actual demand data for that period. The demand is given to the retailer on a paper order form. The data are not shown to other members of the supply chain but are treated as confidential information.

3. Retailers fill orders as long as sufficient inventory (calculated in Step 1) is available.

4. Retailers calculate their ending inventory level. If sufficient inventory is available, ending inventory is beginning inventory minus that period’s actual demand. If there is not sufficient inventory, then back orders occur. When a back order occurs, your ending inventory value is negative. For example, if you only have 30 units available and demand is 32 units, your inventory balance is �2 units.

5. Retailers calculate their inventory position. Inventory position is the ending inventory plus any quantity already ordered that has not yet arrived. For example, if your ending inventory is �2 but you have placed an order for 90 additional cases, your inventory position is 88 cases (�2 � 90).

6. If the retailer’s inventory position is at or below its reorder point, the retailer places an order with its wholesaler. Retailers A11 and A12 order from wholesaler A1, retailers A21 and A22 order from wholesaler A2, and so on. These orders are made in writing and delivered to the appropriate wholesaler. No other communication is permitted.

For the wholesalers, the procedure each period is the following:

1. The wholesaler accepts into stock any orders due to arrive during the current period. The beginning inventory plus the arriving order determine how much inventory the location has available to satisfy demand during that period.

Electronic pocket calendar supply chain

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156 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

Individual Location Information

Replenishment Order Quantity

(cases) Recorder point

(cases) Beginning

Inventory (cases) Average Weekly Demand (cases)

Factory 350 190 277 175 Distributor A 120 125 185 60 Distributor B 180 190 280 90 Distributor C 100 52 77 25 Wholesaler A1 90 95 140 45 Wholesaler A2 60 31 46 15 Wholesaler B1 105 110 163 52.5 Wholesaler B2 75 80 118 37.5 Wholesaler C1 60 31 46 15 Wholesaler C2 40 21 31 10 Retailer A11 60 62 92 30 Retailer A12 30 31 46 15 Retailer A21 45 24 35 11.25 Retailer A22 15 8 13 3.75 Retailer B11 75 78 116 37.5 Retailer B12 60 31 46 15 Retailer B21 40 42 62 20 Retailer B22 35 37 55 17.5 Retailer C11 40 21 31 10 Retailer C12 20 11 16 5 Retailer C21 20 12 18 5.5 Retailer C22 20 10 15 4.5

2. Next, the wholesalers look at the replenishment orders from the retailers for that period. These data are not shown to other members of the supply chain but are treated as confidential information.

3. Wholesalers fill orders as long as sufficient inventory (calculated in Step 1) is available.

4. Wholesalers calculate their ending inventory level. If sufficient inventory is available, ending inventory is beginning inventory minus that period’s actual demand. If there is not sufficient inventory, then back orders occur. When a back order occurs, your ending inventory value is negative.

5. Wholesalers calculate their inventory position. Inventory position is the ending inventory plus any quantity already ordered that has not yet arrived.

6. If the wholesaler’s inventory position is at or below its reorder point, the wholesaler places an order with its distributor. Wholesalers A1 and A2 order from distributor A, wholesalers B1 and B2 order from distributor B, and so on. These orders are in

writing and delivered to the appropriate distributor. No other communication is permitted.

For the distributors, the procedure followed each period is the following:

1. The distributor accepts into stock any orders due to arrive during the current period. The beginning inventory plus the arriving order determine how much inventory the location has available to satisfy demand during that period.

2. Next, the distributor looks at the replenishment orders from its wholesalers for that period. These data are not shown to other members of the supply chain but are treated as confidential information.

3. Distributors fill orders as long as sufficient inventory (calculated in Step 1) is available.

4. Distributors calculate their ending inventory level. If sufficient inventory is available, ending inventory is beginning inventory minus that period’s actual demand. If there is not sufficient inventory then back orders occur.

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CASE: SUPPLY CHAIN MANAGEMENT AT DURHAM INTERNATIONAL MANUFACTURING COMPANY (DIMCO) • 157

5. Distributors calculate their inventory position. Inventory position is the ending inventory plus any quantity already ordered that has not yet arrived.

6. If the distributor’s inventory position is at or below its reorder point, the distributor places an order with the factory. These orders are in writing and delivered to the appropriate distributor. No other communication is permitted.

The factory follows these procedures each period: 1. The factory accepts into stock any manufacturing orders

completed for the current period. The beginning inventory plus the arriving order determine how much inventory the location has available to satisfy demand during that period.

2. Next, the factory looks at the replenishment orders from the distributors for that period.

3. The factory fills orders as long as sufficient inventory (calculated in Step 1) is available.

4. The factory calculates its ending inventory level. If sufficient inventory is available, ending inventory is beginning inventory minus that period’s actual demand. If there is not sufficient inventory, then back orders occur. When a back order occurs, your ending inventory value is negative.

5. The factory calculates its inventory position. Inventory position is the ending inventory plus any quantity already ordered that has not yet arrived.

6. If the factory’s inventory position is at or below its reorder point, the factory releases an order to manufacturing.

Procedures for all locations include the following: 1. At the end of each period, record the amount of actual

inventory you have left, the actual number of back orders, the cost of holding the inventory, the cost of the back orders, and the total cost.

2. Update your total statistics, that is, keep a running total of the cases of inventory, the number of back orders, and the cumulative holding costs, cumulative back order costs, and total costs.

End of Game Discussion Questions 1. How well does the distribution system seem to work?

Talk about it in terms of customer service, costs, effective use of inventory, and information flows.

2. Given the amount of inventory in the system, why did back orders occur?

3. In this distribution chain, what happened to customer demand data?

4. How should customer demand data be communicated through the system?

5. What would you recommend be done differently?

CASE: Supply Chain Management at Durham International Manufacturing Company (DIMCO)

Lucille Jenkins, the CEO for the Durham International Manu- facturing Company (DIMCO), believes that the company can significantly increase its operating profit by implementing sup- ply chain management. DIMCO manufactures a variety of con- sumer electronic products, from hair dryers to humidifiers to massagers, for the world market.

Lucille believes that DIMCO has already integrated its internal processes and is ready to proceed with external inte- gration. However, she is uncertain as to which direction to take. Should the company work on integrating the suppliers or the distributors first? Currently, DIMCO uses approximately 1350 different components and/or raw materials in manufac- turing its product line. Those components and raw materi- als are purchased from approximately 375 different suppliers around the world. In terms of distribution, DIMCO currently sends its finished products to a central warehouse that supplies

10 regional distribution centers (RDC); 6 are domestic and 4 are located outside of the United States. Each RDC supplies an average of 12 local distributors that each supply an average of 35 retailers.

Lucille is looking for some advice.

1. Briefly describe DIMCO’s supply chain. 2. What are the advantages that DIMCO can gain by

implementing supply chain management? 3. What would you recommend DIMCO attempt next?

Should it work on integrating the suppliers or the distributors first? Or should it work on both simultaneously?

4. What are your recommendations with regard to the external suppliers?

5. What are your recommendations with regard to the external distributors?

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158 • CHAPTER 4 SUPPLY CHAIN MANAGEMENT

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Supply Chain Management at Cruise International, Inc. Bob Bristol, your boss at CII, just called to tell you that he was impressed with your progress thus far in familiarizing your- self with operations at CII—both the strategic details pertaining to its mission, competitive priorities, and so on, and the specific details concerning its services and processes. He tells you that with all the buzz about supply chain management (SCM) that you hear these days, CII is actively interested in exploring how different SCM concepts and techniques could be used in their operations. Providing an adequate, assured supply of a variety of mechanical equipment, entertainment equipment, retail mer- chandise, food products, and supplies for maintaining the ship is critical to CII. Meghan Willoughby, Chief Purser aboard the

Friendly Seas I, has a couple of specific assignments that you will work on later. But for now, Meghan would like a concise research report for the top management team addressing SCM issues relevant to CII. This assignment will enhance your knowl- edge of the material in Chapter 4 of your textbook while prepar- ing you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Supply Chain Management at CII

On-line Case: Supply Chain Management at Valley Memorial Hospital

Bob Reilly, head of Kaizen, just called you to say that he was impressed with your progress in familiarizing yourself with the operations at VMH—both the strategic details pertaining to its mission and competitive priorities and the specific details con- cerning its products and processes. He tells you that with all the buzz about supply chain management (SCM) that you hear these days, VMH is actively interested in exploring how SCM concepts and techniques could be adopted in their operations. Maintaining an adequate and timely supply of a variety of labo- ratory equipment, surgical instruments, and supplies is critical to VMH, Meg Willoughby, the head of materials management at VMH, has a couple of specific assignments that you will work

on later. For now, Meg has suggested that you prepare a concise research report for top management addressing SCM issues rel- evant to VMH. She has also put together a few specific questions for you to address. This assignment will enable you to enhance your knowledge of the material covered in Chapter 4.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site. • Click Virtual Company/Valley Memorial Hospital • Click Kaizen Consulting, Inc. • Click Consulting Assignments • Click Supply Chain Management at Valley Memorial

Hospital

Since the Internet provides access to products around the world, your challenge involves some global shopping. This year you have been given a budget of $10,000 to furnish and decorate your off-campus apartment. You have chosen a global theme. Your job is to find items from as many different parts of the world as you can to use in your apartment. You can spend up to $10,000 but you cannot exceed your budget. Do not forget that shipping must be included in your budget. You can choose more than a single item from any country.

(a) Visit the Internet to find products for your apartment. You need to furnish a one-bedroom apartment. You do

not need to worry about major appliances (computer, television, stereo, oven, refrigerator, dishwasher, etc.), but you do need everything else. Since you plan to host a major party in your new apartment, everything you buy must be delivered within six weeks.

(b) Provide a list of all of the items you would buy, the cost of each item, and the total money spent. Organize your list by the room the item is intended for. Be sure to identify the country of origin for each item. Have fun shopping!

INTERNET CHALLENGE Global Shopping

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SELECTED BIBLIOGRAPHY • 159

Online Resources

Companion Website www.wiley.com/college/reid • Take interactive practice quizzes to assess your knowledge and help you study in a dynamic way • Review PowerPoint slides or print slides for notetaking • Access the Virtual Company: Cruise International, Inc. • Find links to Company Tours for this chapter

Broad Run Cheese House, Curtains & Lace BMW Manufacturing Corporation Toyota Motor Corporation

• Find links for Additional Web Resources for this chapter Nummi (New United Motor Manufacturing, Inc.), www.nummi.org IBM, www.ibm.com/us Institute for Supply Management, www.ISM.ws www.manufacturingiscool.com

Selected Bibliography Atkinson, William “The Big Trends in Sourcing and Procure-

ment,” Supply Chain Management Review, May 1, 2008. Burnson, Patrick. “Mexico is once again more attractive

sourcing partner for U.S. manufacturers.” Strategic Sourc- ing Case Studies, May 4, 2011. http://www.scmr.com/ view/mexico_is_once_again_more_attractive_sourcing partner_for_U.S._manufacturers.

Burnson, Patrick. “Supply Chain Management: Ariba study looks into procurement’s crystal ball.” Strategic Sourcing Case Studies, July 11, 2011.

“Champion of Green: An interview with Drew Schramm,” Supply Chain Management Review, July 1, 2008

“City of Lynchburg: Current Solicitations”. http://www .lynchburgva.gov/lndex.aspx?page=4353. July 2011.

“City of Lynchburg: Purchasing Code and Ethics.” http:// www.lynchburgva.gov/lndex.aspx?page=979. April 2011.

“Finding the Balance: Shared Border of the Future.” http:// www.uschamber.com/reports/finding-balance-shared- border-future. July 2009.

“Frederick Community College: Purchasing and Ethics.” http://www.frederick.edu/purchasing/ethics.aspx.

Gould, Larry. “Where’s the Money in E-business?,” Automo- tive Design and Production, December 1, 2007.

“Healthcare Technology and News: Covisint Announces More than 50 Third-Party Applications on AppCloud.” http://www.healthtechzone.com/topics/healthcare/ articles/147351. February 22, 2011

LaLonde, Bud. “Crunch Time in the Supply Chain—Huge Domestic and Global Developments Could Radically Change the Way We Think about Supply Chain Manage- ment,” Supply Chain Management Review, March 1, 2005.

LaLonde, Bud. “As the World Goes Global, Supply Chain Velocity Will Actually Decrease in Many Instances. The Key Is How You Respond,” Supply Chain Management Review, January 1, 2006.

Norek, Christopher D., and Monica Isabell. “The Infrastruc- ture Squeeze on Global Supply Chains,” Supply Chain Management Review, October 1, 2005.

O’Neill, Jeff. “CVS Rolls Out Gillette’s Fusion before Super Bowl,” Modern Materials Handling, February 1, 2006.

Sberna, Robert. “Demand Calls, Mexico Responds.” Inboundlogistics.com. http:www.inboundlogistics.com. January 2007.

Schlegel, Gregory L., and Richard C. Smith. “The Next Stage of Supply Chain Excellence,” Supply Chain Management Review, March 1, 2006.

Simpson, Dayna, and Danny Samson. “Developing Strategies for Green Supply Chain Management,” Decision Line, July 2008.

Smith, Jim. “The Right Supply Chain Stuff—Speed and Flex- ibility Can Mean the Difference for Maximum Efficiency,” Industrial Distribution, February 1, 2005.

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Sutherland, Joel, and Bob Bennett. “The Seven Deadly Supply Chain Wastes,” Supply Chain Management Review, July 1, 2008

“Tackling the Logjam.” Portstrategy. http://www.portstrategy. com/features101. February 27, 2008.

Turban, Efraim, Ephraim McLean, and James Wetherbe. Information Technology for Management, Fourth Edition. New York: John Wiley & Sons, 2004.

Tyndall, Gene. “Strategic Trends and Issues in Supply Chain Management.” Supply Chain Digest, April 4, 2006.

“Unlock the Power of Cloud Computing.” http://www .information-age.com/article-archive/2688691/covisint- new-identity.thtml. February 2006.

“Worldwide RFID Spending to Reach 2.5bn in 2010 Report.” Logistics Management, February 15, 2006.

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CHAPTER

161

C H A P T E R O U T L I N E

L E A R N I N G O B J E C T I V E S

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

Defining Quality 162 Cost of Quality 165 The Evolution of Total Quality Management

(TQM) 166 The Philosophy of TQM 171 Quality Awards and Standards 183

Why TQM Efforts Fail 186 Total Quality Management (TQM) within OM:

How It All Fits Together 186 Total Quality Management (TQM) across the

Organization 187

Before studying this chapter you should know or, if necessary, review

1. Trends in total quality management (TQM), Chapter 1, p. 15–16. 2. Quality as a competitive priority, Chapter 2, p. 39.

After studying this chapter you should be able to

1 Explain the meaning of total quality management (TQM). 2 Identify costs of quality. 3 Describe the evolution of TQM. 4 Identify key leaders in the fi eld of quality and their contributions. 5 Identify features of the TQM philosophy. 6 Describe tools for identifying and solving quality problems. 7 Describe quality awards and quality certifi cations.

Total Quality Management

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162 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

DEFINING QUALITY

Everyone has had experiences of poor qual-ity when dealing with business organiza- tions. These experiences might involve an airline that has lost a passenger’s luggage, a dry cleaner that has left clothes wrinkled or stained, poor course offerings and scheduling at your college, a purchased product that is damaged or broken, or a pizza delivery ser- vice that is often late or delivers the wrong order. The experience of poor quality is exac- erbated when employees of the company either are not empowered to correct quality inadequacies or do not seem willing to do so. We have all encountered service employees

who do not seem to care. The consequences of such an attitude are lost customers and opportunities for competitors to take advantage of the market need.

Successful companies understand the powerful impact customer-defined quality can have on business. For this reason, many competitive firms continually increase their quality standards. For example, Ford Motor Company’s focus on quality improve- ments has led to new levels of customer satisfaction with vehicle quality. Ford focused on tightening already strict standards in their production process and implementing a quality program called Six Sigma. This enabled Ford to be tied with Toyota as the industry leader in 2009.

In this chapter you will learn that making quality a priority means putting cus- tomer needs first. It means meeting and exceeding customer expectations by involving everyone in the organization through an integrated effort. Total quality management (TQM) is an integrated organizational effort designed to improve quality at every level. In this chapter you will learn about the philosophy of TQM, its impact on orga- nizations, and its impact on your life. You will learn that TQM is about meeting qual- ity expectations as defined by the customer; this is called customer-defined quality. However, defining quality is not as easy as it may seem, because different people have different ideas of what constitutes high quality. Let’s begin by looking at different ways in which quality can be defined.

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The definition of quality depends on the point of view of the people defining it. Most consumers have a difficult time defining quality, but they know it when they see it. For example, although you probably have an opinion as to which manufacturer of ath- letic shoes provides the highest quality, it would probably be difficult for you to define your quality standard in precise terms. Also, your friends may have different opinions regarding which athletic shoes are of highest quality. The difficulty in defining quality exists regardless of product, and this is true for both manufacturing and service orga- nizations. Think about how difficult it may be to define quality for products such as

� Total quality management (TQM) An integrated effort designed to improve quality performance at every level of the organization.

� Customer-defined quality The meaning of quality as defined by the customer.

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DEFINING QUALITY • 163

airline services, child day-care facilities, college classes, or even OM textbooks. Further complicating the issue is that the meaning of quality has changed over time.

Today, there is no single, universal definition of quality. Some people view quality as “performance to standards.” Others view it as “meeting the customer’s needs” or “sat- isfying the customer.” Let’s look at some of the more common definitions of quality.

● Conformance to specifi cations measures how well the product or service meets the targets and tolerances determined by its designers. For example, the dimen- sions of a machine part may be specifi ed by its design engineers as 3 � 0.05 inches. Th is would mean that the target dimension is 3 inches, but the dimensions can vary between 2.95 and 3.05 inches. Similarly, the wait for hotel room service may be specifi ed as 20 minutes, but there may be an acceptable delay of an additional 10 minutes. Also, consider the amount of light delivered by a 60-watt light bulb. If the bulb delivers 50 watts, it does not conform to specifi cations. As these examples illustrate, conformance to specifi cation is directly measurable, though it may not be directly related to the consumer’s idea of quality.

● Fitness for use focuses on how well the product performs its intended function or use. For example, a Mercedes-Benz and a Jeep Cherokee both meet a fi tness for use defi nition if one considers transportation as the intended function. However, if the defi nition becomes more specifi c and assumes that the intended use is for transportation on mountain roads and carrying fi shing gear, the Jeep Cherokee has a greater fi tness for use. You can also see that fi tness for use is a user-based defi nition in that it is intended to meet the needs of a specifi c user group.

● Value for price paid is a defi nition of quality that consumers oft en use for prod- uct or service usefulness. Th is is the only defi nition that combines economics with consumer criteria; it assumes that the defi nition of quality is price sensitive. For example, suppose that you wish to sign up for a personal fi nance seminar and discover that the same class is being taught at two diff erent colleges at signifi cantly diff erent tuition rates. If you take the less expensive seminar, you will feel that you have received greater value for the price.

● Support services provided are oft en how the quality of a product or service is judged. Quality does not apply only to the product or service itself; it also applies to the people, processes, and organizational environment associated with it. For example, the quality of a university is judged not only by the quality of staff and course off erings but also by the effi ciency and accuracy of processing paperwork.

● Psychological criteria is a subjective defi nition that focuses on the judgmental evaluation of what constitutes product or service quality. Diff erent factors con- tribute to the evaluation, such as the atmosphere of the environment or the per- ceived prestige of the product. For example, a hospital patient may receive average healthcare, but a very friendly staff may leave the impression of high quality. Simi- larly, we commonly associate certain products with excellence because of their reputation; Rolex watches and Mercedes-Benz automobiles are examples.

Differences between Manufacturing and Service Organizations Defining quality in manufacturing organizations is often different than it is for ser- vice organizations. Manufacturing organizations produce a tangible product that can be seen, touched, and directly measured. Examples include cars, CD players, clothes, computers, and food items. Therefore, quality definitions in manufacturing usually focus on tangible product features.

� Conformance to specifications How well a product or service meets the targets and tolerances determined by its designers.

� Fitness for use A definition of quality that evaluates how well the product performs for its intended use.

� Value for price paid Quality defined in terms of product or service usefulness for the price paid.

� Support services Quality defined in terms of the support provided after the product or service is purchased.

� Psychological criteria A way of defining quality that focuses on judgmental evaluations of what constitutes product or service excellence.

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164 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

The most common quality definition in manufacturing is conformance, which is the degree to which a product characteristic meets preset standards. Other common definitions of quality in manufacturing include performance, such as acceleration of a vehicle; reliability, meaning that the product will function as expected without failure; features, the extras that are included beyond the basic characteristics; durability, the expected operational life of the product; and serviceability, how readily a product can be repaired. The relative importance of these definitions is based on the preferences of each individual customer. It is easy to see how different customers can have different definitions in mind when they speak of high product quality.

In contrast to manufacturing, service organizations produce a product that is intan- gible. Usually, the complete product cannot be seen or touched. Rather, it is experi- enced. Examples include delivery of healthcare, the experience of staying at a vacation resort, and learning at a university. The intangible nature of the product makes defin- ing quality difficult. Also, since a service is experienced, perceptions can be highly sub- jective. In addition to tangible factors, quality of services is often defined by perceptual factors. These include responsiveness to customer needs, courtesy and friendliness of staff, promptness in resolving complaints, and atmosphere. Other definitions of quality in services include time, the amount of time a customer has to wait for the service; and consistency, the degree to which the service is the same each time. For these reasons, defining quality in services can be especially challenging. Dimensions of quality for manufacturing versus service organizations are shown in Table 5-1.

Today’s customers demand and expect high quality. Com- panies that do not make qual- ity a priority risk long-run survival. World-class organi- zations such as General Elec- tric and Motorola attribute their success to having one of the best quality manage- ment programs in the world. These companies were some of the first to implement a qual- ity program called Six Sigma,

where the level of defects is reduced to approximately 3.4 parts per million. To achieve this level, everyone in the company is trained in quality. For example, individuals highly trained in quality improvement principles and techniques receive a designa- tion called “Black Belt.” The full-time job of Black Belts is to identify and solve qual- ity problems. In fact, Motorola was one of the first companies to win the prestigious

Manufacturing Organizations Service Organizations

Conformance to specifications Intangible factors

Performance Consistency

Reliability Responsiveness to customer needs

Features Courtesy/friendliness

Durability Timeliness/promptness

Serviceability Atmosphere

TABLE 5-1

Dimensions of Quality for Manufacturing versus Service Organizations

LINKS TO PRACTICE General Electric

Company www.ge.com

Motorola, Inc. www.motorola.com

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COST OF QUALITY • 165

Malcolm Baldrige National Quality Award in 1988 due to its high focus on quality. Both GE and Motorola have had a primary goal of achieving total customer satisfac- tion. To this end, the efforts of these organizations have included eliminating almost all defects from products, processes, and transactions. Both companies consider qual- ity to be the critical factor that has resulted in significant increases in sales and market share, as well as cost savings in the range of millions of dollars.

The reason quality has gained such prominence is that organizations have gained an understanding of the high cost of poor quality. Quality affects all aspects of the orga- nization and has dramatic cost implications. The most obvious consequence occurs when poor quality creates dissatisfied customers and eventually leads to loss of busi- ness. However, quality has many other costs, which can be divided into two catego- ries. The first category consists of costs necessary for achieving high quality, which are called quality control costs. These are of two types: prevention costs and appraisal costs. The second category consists of the cost consequences of poor quality, which are called quality failure costs. These include external failure costs and internal failure costs. These costs of quality are shown in Figure 5-1. The first two costs are incurred in the hope of preventing the second two.

Prevention costs are all costs incurred in the process of preventing poor quality from occurring. They include quality planning costs, such as the costs of developing and implementing a quality plan. Also included are the costs of product and process design, from collecting customer information to designing processes that achieve con- formance to specifications. Employee training in quality measurement is included as part of this cost, as well as the costs of maintaining records of information and data related to quality.

Appraisal costs are incurred in the process of uncov- ering defects. They include the cost of quality inspec- tions, product testing, and performing audits to make sure that quality standards are being met. Also included in this category are the costs of worker time spent mea- suring quality and the cost of equipment used for qual- ity appraisal.

Internal failure costs are associated with discover- ing poor product quality before the product reaches the customer site. One type of internal failure cost is rework, which is the cost of correcting the defective item. Some- times the item is so defective that it cannot be corrected and must be thrown away. This is called scrap, and its costs include all the material, labor, and machine cost spent in producing the defective product. Other types of internal failure costs include the cost of machine downtime due to failures in the process and the costs of discounting defective items for salvage value.

External failure costs are associated with quality problems that occur at the cus- tomer site. These costs can be particularly damaging because customer faith and loyalty can be difficult to regain. They include everything from customer complaints, product returns, and repairs to warranty claims, recalls, and even litigation costs resulting from product liability issues. A final component of this cost is lost sales and lost custom- ers. For example, manufacturers of lunch meats and hot dogs whose products have been recalled due to bacterial contamination have had to struggle to regain consumer confidence. Other examples include auto manufacturers whose products have been

� Prevention costs Costs incurred in the process of preventing poor quality from occurring.

� Appraisal costs Costs incurred in the process of uncovering defects.

� Internal failure costs Costs associated with discovering poor product quality before the product reaches the customer.

� External failure costs Costs associated with quality problems that occur at the customer site.

COST OF QUALITY

Prevention costs. Costs of preparing and implementing a quality plan.

Appraisal costs. Costs of testing, evaluating, and inspecting quality.

Internal failure costs. Costs of scrap, rework, and material losses.

External failure costs. Costs of failure at customer site, including returns, repairs, and recalls.

FIGURE 5-1

Cost of quality

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166 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

recalled due to major malfunctions such as problematic braking systems and airlines that have experienced a crash with many fatalities. External failure can sometimes put a company out of business almost overnight.

Companies that consider quality important invest heavily in prevention and appraisal costs in order to prevent internal and external failure costs. The earlier defects are found, the less costly they are to correct. For example, detecting and correcting defects during product design and product production is considerably less expensive than when the defects are found at the customer site. This is shown in Figure 5-2.

External failure costs tend to be particularly high for service organizations. The reason is that with a service the customer spends much time in the service delivery system, and there are fewer opportunities to correct defects than there are in manu- facturing. Examples of external failure in services include overbooking airline flights, long delays in airline service, and lost luggage.

Location of Defect

C os

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Product Production

Customer Site

FIGURE 5-2

Cost of defects

THE EVOLUTION OF TOTAL QUALITY MANAGEMENT (TQM) The concept of quality has existed for many years, though its meaning has changed and evolved over time. In the early twentieth century, quality management meant inspecting products to ensure that they met specifications. In the 1940s, during World War II, quality became more statistical in nature. Statistical sampling techniques were used to evaluate quality, and quality control charts were used to monitor the produc- tion process. In the 1960s, with the help of so-called quality gurus, the concept took on a broader meaning. Quality began to be viewed as something that encompassed the entire organization, not only the production process. Since all functions were respon- sible for product quality and all shared the costs of poor quality, quality was seen as a concept that affected the entire organization.

The meaning of quality for businesses changed dramatically in the late 1970s. Before then quality was still viewed as something that needed to be inspected and cor- rected. However, in the 1970s and 1980s, many U.S. industries lost market share to foreign competition. In the auto industry, manufacturers such as Toyota and Honda became major players. In the consumer goods market, companies such as Toshiba and

MKT FIN ACC

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THE EVOLUTION OF TOTAL QUALITY MANAGEMENT (TQM) • 167

Sony led the way. These foreign competitors were producing lower-priced products with considerably higher quality.

To survive, companies had to make major changes in their quality programs. Many hired consultants and instituted quality training programs for their employees. A new concept of quality was emerging. One result was that quality began to have a strategic meaning. Today, successful companies understand that quality provides a competi- tive advantage. They put the customer first and define quality as meeting or exceeding customer expectations.

Since the 1970s, competition based on quality has grown in importance and has generated tremendous interest, concern, and enthusiasm. Companies in every line of business are focusing on improving quality in order to be more competitive. In many industries quality excellence has become a standard for doing business. Companies that do not meet this standard simply will not survive. As you will see later in the chap- ter, the importance of quality is demonstrated by national quality awards and quality certifications that are coveted by businesses.

The term used for today’s new concept of quality is total quality management or TQM. Figure 5-3 presents a time line of the old and new concepts of quality. You can see that the old concept is reactive, designed to correct quality problems after they occur. The new concept is proactive, designed to build quality into the product and process design. Next, we look at the individuals who have shaped our understanding of quality.

Quality Gurus To fully understand the TQM movement, we need to look at the philosophies of nota- ble individuals who have shaped the evolution of TQM. Their philosophies and teach- ings have contributed to our knowledge and understanding of quality today. Table 5-2 summarizes their individual contributions.

Walter A. Shewhart Walter A. Shewhart was a statistician at Bell Labs during the 1920s and 1930s. Shewhart studied randomness and recognized that variability existed in all manufacturing processes. He developed quality control charts that are used to identify whether the variability in the process is random or due to an assignable cause, such as poor workers or miscalibrated machinery. He stressed that eliminating vari- ability improves quality. His work created the foundation for today’s statistical process control, and he is often referred to as the “grandfather of quality control.”

W. Edwards Deming W. Edwards Deming is often referred to as the “father of qual- ity control.” He was a statistics professor at New York University in the 1940s. After World War II, he assisted many Japanese companies in improving quality. The Japa- nese regarded him so highly that in 1951 they established the Deming Prize, an annual award given to firms that demonstrate outstanding quality. It was almost 30 years before American businesses began adopting Deming’s philosophy.

TIME: Early 1900s 1940s 1960s 1980s and Beyond

FOCUS: Inspection Statistical sampling

Organizational quality focus

Customer-driven quality

Old Concept of Quality: Inspect for quality after production.

New Concept of Quality: Build quality into the process. Identify and correct causes of

quality problems.

FIGURE 5-3

Time line showing the differences between old and new concepts of quality

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168 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

A number of elements of Deming’s philosophy depart from traditional notions of quality. The first is the role management should play in a company’s quality improve- ment effort. Historically, poor quality was blamed on workers—on their lack of pro- ductivity, laziness, or carelessness. However, Deming pointed out that only 15 percent of quality problems are actually due to worker error. The remaining 85 percent are caused by processes and systems, including poor management. Deming said that it is up to management to correct system problems and create an environment that pro- motes quality and enables workers to achieve their full potential. He believed that managers should drive out any fear employees have of identifying quality problems and that numerical quotas should be eliminated. Proper methods should be taught, and detecting and eliminating poor quality should be everyone’s responsibility.

Deming outlined his philosophy on quality in his famous “14 Points.” These points are principles that help guide companies in achieving quality improvement. The prin- ciples are founded on the idea that upper management must develop a commitment to quality and provide a system to support this commitment that involves all employees and suppliers. Deming stressed that quality improvements cannot happen without the organizational change that comes from upper management.

Joseph M. Juran After W. Edwards Deming, Dr. Joseph M. Juran is considered to have had the greatest impact on quality management. Juran originally worked in the quality program at Western Electric, a former equipment division of AT&T. He became better known in 1951 after the publication of his book Quality Control Hand- book. In 1954, he went to Japan to work with manufacturers and teach classes on qual- ity. Though his philosophy is similar to Deming’s, there are some differences. Whereas Deming stressed the need for an organizational “transformation,” Juran believed that implementing quality initiatives should not require such a dramatic change and that quality management should be embedded in the organization.

One of Juran’s significant contributions was his focus on the definition of qual- ity and the cost of quality. Juran is credited with defining quality as fitness for use

TABLE 5-2

Quality Gurus and Their Contributions

Quality Guru Main Contribution

Walter A. Shewhart –Contributed to understanding of process variability. –Developed concept of statistical control charts.

W. Edwards Deming –Stressed management’s responsibility for quality. –Developed “14 Points” to guide companies in quality improvement.

Joseph M. Juran –Defined quality as “fitness for use.” –Developed concept of cost of quality.

Armand V. Feigenbaum –Introduced concept of total quality control.

Philip B. Crosby –Coined phrase “quality is free.” –Introduced concept of zero defects.

Kaoru Ishikawa –Developed cause-and-effect diagrams. –Identified concept of “internal customer.”

Genichi Taguchi –Focused on product design quality. –Developed Taguchi loss function.

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THE EVOLUTION OF TOTAL QUALITY MANAGEMENT (TQM) • 169

rather than simply conformance to specifications. As we have learned in this chapter, defining quality as fitness for use takes into account customer intentions for use of the product, instead of focusing only on technical specifications. Juran is also credited with developing the concept of cost of quality, which allows us to measure quality in dollar terms rather than on the basis of subjective evaluations.

Juran is well known for originating the idea of the quality trilogy: quality planning, quality control, and quality improvement. The first part of the trilogy, quality plan- ning, is necessary so that companies identify their customers, product requirements, and overriding business goals. Processes should be set up to ensure that the quality standards can be met. The second part of the trilogy, quality control, stresses the regu- lar use of statistical control methods to ensure that quality standards are met and to identify variations from the standards. The third part of the quality trilogy is quality improvement. According to Juran, quality improvements should not be just break- throughs, but continuous as well. Together with Deming, Juran stressed that to imple- ment continuous improvement, workers need to have training in proper methods on a regular basis.

Armand V. Feigenbaum Another quality leader is Armand V. Feigenbaum, who introduced the concept of total quality control. In his 1961 book Total Quality Control, he outlined his quality principles in 40 steps. Feigenbaum took a total system approach to quality. He promoted the idea of a work environment where quality developments are integrated throughout the entire organization, where management and employees have a total commitment to improve quality, and where people learn from each other’s successes. This philosophy was adapted by the Japanese and termed “company-wide quality control.”

Philip B. Crosby Philip B. Crosby is another recognized guru of TQM. He worked in the area of quality for many years, first at Martin Marietta and then, in the 1970s, as the vice president for quality at ITT. He developed the phrase “Do it right the first time” and the notion of zero defects, arguing that no amount of defects should be con- sidered acceptable. He scorned the idea that a small number of defects is a normal part of the operating process because systems and workers are imperfect. Instead, he stressed the idea of prevention.

To promote his concepts, Crosby wrote a book titled Quality Is Free, which was published in 1979. He became famous for coining the phrase “quality is free” and for pointing out the many costs of quality, which include not only the costs of wasted labor, equipment time, scrap, rework, and lost sales but also organizational costs that are hard to quantify. Crosby stressed that efforts to improve quality more than pay for themselves because these costs are prevented. Therefore, quality is free. Like Deming and Juran, Crosby stressed the role of management in the quality improvement effort and the use of statistical control tools in measuring and monitoring quality.

Kaoru Ishikawa Kaoru Ishikawa is best known for the development of quality tools called cause-and-effect diagrams, also called fishbone or Ishikawa diagrams. These diagrams are used for quality problem solving, and we will look at them in detail later in the chapter. He was the first quality guru to emphasize the importance of the “internal customer,” the next person in the production process. He was also one of the first to stress the importance of total company quality control, rather than just focusing on products and services.

Dr. Ishikawa believed that everyone in the company needed to be united with a shared vision and a common goal. He stressed that quality initiatives should be

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170 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

pursued at every level of the organization and that all employees should be involved. Dr. Ishikawa was a proponent of implementation of quality circles, which are small teams of employees who volunteer to solve quality problems.

Genichi Taguchi Dr. Genichi Taguchi is a Japanese quality expert known for his work in the area of product design. He estimates that as much as 80 percent of all defec- tive items are caused by poor product design. Taguchi stresses that companies should focus their quality efforts on the design stage, as it is much cheaper and easier to make changes during the product design stage than later during the production process.

Taguchi is known for applying a concept called design of experiment to product design. This method is an engineering approach based on developing robust design, a design that results in products that can perform over a wide range of conditions. The idea is that it is easier to design a product that can perform over a wide range of envi- ronmental conditions than it is to control the environmental conditions.

Taguchi has also had a large impact on today’s view of the costs of quality. He pointed out that the traditional view of costs of conformance to specifications is incorrect and proposed a different way to look at these costs. Let’s briefly look at Dr. Taguchi’s view of quality costs.

Recall that conformance to specification specifies a target value for the product with specified tolerances, say 5.00 � 0.20. According to the traditional view of conformance to specifications, losses in terms of cost occur if the product dimensions fall outside of the specified limits. This is shown in Figure 5-4. However, Dr. Taguchi noted that from the customer’s view there is little difference whether a product falls just outside or just inside the control limits. He pointed out that there is a much greater difference in the quality of the product between making the target and being near the control limit. He also stated that the smaller the variation around the target, the better the quality. Based on this, he proposed the following: as conformance values move away from the target, loss increases as a quadratic function. The Taguchi loss function is shown in Figure 5-5. According to the function, smaller differences from the target result in smaller costs: the larger the differences, the larger the cost. The Taguchi loss function has had a significant impact on changing views of quality cost.

� Robust design A design that results in a product that can perform over a wide range of conditions.

� Taguchi loss function Costs of quality increase as a quadratic function as conformance values move away from the target.

Target 5.00

tolerances

Cost

0.480 0.520

Loss LossNo Loss

Target 5.00

tolerances

0.480 0.520

Loss Loss

FIGURE 5-4

Traditional view of the cost of nonconformance

FIGURE 5-5

Taguchi view of the cost of nonconformance— the Taguchi loss function

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THE PHILOSOPHY OF TQM • 171

THE PHILOSOPHY OF TQM What characterizes TQM is the focus on identifying root causes of quality problems and correcting them at the source, as opposed to inspecting the product after it has been made. Not only does TQM encompass the entire organization but it stresses that quality is customer driven. TQM attempts to embed quality in every aspect of the organization. It is concerned with the technical aspects of quality as well as the involvement of people in quality, such as customers, company employees, and suppli- ers. Here we look at the specific concepts that make up the philosophy of TQM. These concepts and their main ideas are summarized in Table 5-3.

Customer Focus The first, and overriding, feature of TQM is the company’s focus on its customers. Quality is defined as meeting or exceeding customer expectations. The goal is to first identify and then meet customer needs. TQM recognizes that a perfectly produced product has little value if it is not what the customer wants. Therefore, we can say that quality is customer driven. However, it is not always easy to determine what the cus- tomer wants because tastes and preferences change. Also, customer expectations often vary from one customer to the next. For example, in the auto industry trends change relatively quickly, from small cars to sports utility vehicles and back to small cars. The same is true in the retail industry, where styles and fashion are short-lived. Companies need to continually gather information by means of focus groups, market surveys, and customer interviews in order to stay in tune with what customers want. They must always remember that they would not be in business if it were not for their customers.

Continuous Improvement Another concept of the TQM philosophy is the focus on continuous improvement. Traditional systems operated on the assumption that once a company achieved a cer- tain level of quality, it was successful and needed to make no further improvements.

MKT HRM

� Continuous improvement A philosophy of never- ending improvement.

Concept Main Idea

Customer focus Goal is to identify and meet customer needs.

Continuous improvement A philosophy of never-ending improvement.

Employee empowerment Employees are expected to seek out, identify, and correct quality problems.

Use of quality tools Ongoing employee training in the use of quality tools.

Product design Products need to be designed to meet customer expectations.

Process management Quality should be built into the process; sources of quality problems should be identified and corrected.

Managing supplier quality Quality concepts must extend to a company’s suppliers.

TABLE 5-3

Concepts of the TQM Philosophy

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172 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

We tend to think of improvement in terms of plateaus that are to be achieved, such as passing a certification test or reducing the number of defects to a certain level. Tra- ditionally, for American managers change involves large magnitudes, such as major organizational restructuring. The Japanese, on the other hand, believe that the best and most lasting changes come from gradual improvements. To use an analogy, they believe that it is better to take frequent small doses of medicine than to take one large dose. Continuous improvement, called kaizen by the Japanese, requires that the com- pany continually strive to be better through learning and problem solving. Because we can never achieve perfection, we must always evaluate our performance and take measures to improve it.

Now let’s look at two approaches that can help companies with continuous improve- ment: the plan–do–study–act (PDSA) cycle and benchmarking.

The Plan–Do–Study–Act Cycle The plan–do–study–act (PDSA) cycle describes the activities a company needs to perform in order to incorporate continuous improve- ment in its operation. This cycle, shown in Figure 5-6, is also referred to as the Shewhart cycle or the Deming wheel. The circular nature of this cycle shows that con- tinuous improvement is a never-ending process. Let’s look at the specific steps in the cycle.

● Plan Th e fi rst step in the PDSA cycle is to plan. Managers must evaluate the cur- rent process and make plans based on any problems they fi nd. Th ey need to docu- ment all current procedures, collect data, and identify problems. Th is information should then be studied and used to develop a plan for improvement as well as specifi c measures to evaluate performance.

● Do Th e next step in the cycle is implementing the plan (do). During the imple- mentation process managers should document all changes made and collect data for evaluation.

● Study Th e third step is to study the data collected in the previous phase. Th e data are evaluated to see whether the plan is achieving the goals established in the plan phase.

● Act Th e last phase of the cycle is to act on the basis of the results of the fi rst three phases. Th e best way to accomplish this is to communicate the results to other members of the company and then implement the new procedure if it has been successful. Note that this is a cycle; the next step is to plan again. Aft er we have acted, we need to continue evaluating the process, planning, and repeating the cycle again.

� Kaizen A Japanese term that describes the notion of a company continually striving to be better through learning and problem solving.

� Plan–do–study–act (PDSA) cycle A diagram that describes the activities that need to be performed to incorporate continuous improvement into the operation.

Plan

Study

Act Do

FIGURE 5-6

The plan–do–study–act cycle

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THE PHILOSOPHY OF TQM • 173

Benchmarking Another way companies implement continuous improvement is by studying business practices of companies considered “best in class.” This is called benchmarking. The ability to learn and study how others do things is an important part of continuous improvement. The benchmark company does not have to be in the same business as long as it excels at something that the company doing the study wishes to emulate. For example, many companies have used Lands’ End to benchmark catalog distribution and order filling because Lands’ End is considered a leader in this area. Similarly, many companies have used American Express to benchmark conflict resolution.

Employee Empowerment Part of the TQM philosophy is to empower all employees to seek out quality prob- lems and correct them. Under the old concept of quality, employees were afraid to identify problems for fear that they would be reprimanded. Often, poor quality was passed on to someone else in order to make it “someone else’s problem.” The new con- cept of quality, TQM, provides incentives for employees to identify quality problems. Employees are rewarded for uncovering quality problems, not punished.

In TQM, the role of employees is very different from what it was in traditional sys- tems. Workers are empowered to make decisions relative to quality in the production process. They are considered a vital element of the effort to achieve high quality. Their contributions are highly valued, and their suggestions are implemented. In order to perform this function, employees are given continual and extensive training in quality measurement tools.

To further stress the role of employees in quality, TQM differentiates between exter- nal and internal customers. External customers are those that purchase the company’s goods and services. Internal customers are employees of the organization who receive goods or services from others in the company. For example, the packaging department of an organization is an internal customer of the assembly department. Just as a defec- tive item would not be passed to an external customer, a defective item should not be passed to an internal customer.

Team Approach TQM stresses that quality is an organizational effort. To facilitate the solving of quality problems, it places great emphasis on teamwork. The use of teams is based on the old adage that “two heads are better than one.” Using techniques such as brainstorming, discussion, and quality control tools, teams work regularly to cor- rect problems. The contributions of teams are considered vital to the success of the company. For this reason, companies set aside time in the workday for team meetings.

Teams vary in their degree of structure and formality, and different types of teams solve different types of problems. One of the most common types of teams is the quality circle, a team of volunteer production employees and their supervisors whose purpose is to solve quality problems. The circle is usually composed of eight to ten members, and decisions are made through group consensus. The teams usu- ally meet weekly during work hours in a place designated for this purpose. They fol- low a preset process for analyzing and solving quality problems. Open discussion is promoted, and criticism is not allowed. Although the functioning of quality circles is friendly and casual, it is serious business. Quality circles are not mere “gab sessions.” Rather, they do important work for the company and have been very successful in many firms.

� Benchmarking Studying the business practices of other companies for purposes of comparison.

� Quality circle A team of volunteer production employees and their supervisors who meet regularly to solve quality problems.

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174 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

The importance of excep- tional quality is demonstrated by The Walt Disney Company in the operation of its theme parks. The focus of the parks is customer satisfaction. This is accomplished through meticu- lous attention to every detail, with particular focus on the role of employees in service delivery. Employees are viewed as the most important orga- nizational resource, and great

care is taken in employee hiring and training. All employees are called “cast members,” regardless of whether they are janitors or performers. They are extensively trained in customer service, communication, and quality awareness. Continual monitoring of quality is considered important, and employees meet regularly in teams to evaluate their effectiveness. All employees are shown how the quality of their individual jobs contributes to the success of the park.

Use of Quality Tools You can see that TQM places a great deal of responsibility on all workers. If employees are to identify and correct quality problems, they need proper training. They need to understand how to assess quality by using a variety of quality control tools, how to interpret findings, and how to correct problems. In this section we look at seven dif- ferent quality tools, often called the seven tools of quality control (Figure 5-7). They are easy to understand, yet extremely useful in identifying and analyzing quality prob- lems. Sometimes workers use only one tool at a time, but often a combination of tools is most helpful.

Cause-and-Effect Diagrams Cause-and-effect diagrams identify potential causes of particular quality problems. They are often called fishbone diagrams because they look like the bones of a fish (Figure 5-8). The “head” of the fish is the quality prob- lem, such as damaged zippers on a garment or broken valves on a tire. The diagram is drawn so that the “spine” of the fish connects the “head” to the possible cause of the problem. These causes could be related to the machines, workers, measurement, suppliers, materials, and many other aspects of the production process. Each of these possible causes can then have smaller “bones” addressing specific issues that relate to each cause. For example, a problem with machines could be due to a need for adjust- ment, old equipment, or tooling problems. Similarly, a problem with workers could be related to lack of training, poor supervision, or fatigue.

Cause-and-effect diagrams are problem-solving tools commonly used by quality control teams. Specific causes of problems can be explored through brainstorming. The development of a cause-and-effect diagram requires the team to think through all the possible causes of poor quality.

Flowcharts A flowchart is a schematic diagram of the sequence of steps involved in an operation or process. It provides a visual tool that is easy to use and understand. By seeing the steps involved in an operation or process, everyone develops a clear picture of how the operation works and where problems could arise.

� Cause-and-effect diagram A chart that identifies potential causes of particular quality problems.

� Flowchart A schematic of the sequence of steps involved in an operation or process.

LINKS TO PRACTICE The Walt Disney

Company www.disney.com

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THE PHILOSOPHY OF TQM • 175

Checklists A checklist is a list of common defects and the number of observed occur- rences of these defects. It is a simple yet effective fact-finding tool that allows the worker to collect specific information regarding the defects observed. The checklist in Figure 5-7 shows four defects and the number of times they have been observed. It is clear that the biggest problem is ripped material. This means that the plant needs to focus on this specific problem—for example, by going to the source of supply or seeing whether the material rips during a particular production process. A checklist can also be used to focus on other dimensions, such as location or time. For example, if a defect is being observed frequently, a checklist can be developed that measures the number of occurrences per shift, per machine, or per operator. In this fashion we can isolate the location of the particular defect and then focus on correcting the problem.

� Checklist A list of common defects and the number of observed occurrences of these defects.

Suppliers

Environment

Workers

Process

Machines

Material

Quality Problems

1. Cause-and-Effect Diagram 4. Control Chart

5. Scatter Diagram

6. Pareto Chart

2. Flowchart

3. Checklist

Defect Type

Broken zipper Ripped material Missing buttons Faded color

3 7 3 2

No. of Defects Total

UCL

LCL

A B C D E

7. Histogram

A B C D E

Frequency

F re

qu en

FIGURE 5-7

The seven tools of quality control

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176 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

Control Charts Control charts are a very important quality control tool. We will study the use of control charts at great length in the next chapter. These charts are used to evaluate whether a process is operating within expectations relative to some measured value such as weight, width, or volume. For example, we could measure the weight of a sack of flour, the width of a tire, or the volume of a bottle of soft drink. When the production process is operating within expectations, we say that it is “in control.”

To evaluate whether or not a process is in control, we regularly measure the vari- able of interest and plot it on a control chart. The chart has a line down the center representing the average value of the variable we are measuring. Above and below the center line are two lines, called the upper control limit (UCL) and the lower control limit (LCL). As long as the observed values fall within the upper and lower control limits, the process is in control and there is no problem with quality. When a mea- sured observation falls outside of these limits, there is a problem.

Scatter Diagrams Scatter diagrams are graphs that show how two variables are related to one another. They are particularly useful in detecting the amount of cor- relation, or the degree of linear relationship, between two variables. For example, increased production speed and number of defects could be correlated positively; as production speed increases, so does the number of defects. Two variables could also be correlated negatively, so that an increase in one of the variables is associated with a decrease in the other. For example, increased worker training might be associated with a decrease in the number of defects observed.

The greater the degree of correlation, the more linear are the observations in the scatter diagram. On the other hand, the more scattered the observations in the dia- gram, the less correlation exists between the variables. Of course, other types of rela- tionships can also be observed on a scatter diagram, such as an inverted �. This may be the case when one is observing the relationship between two variables such as oven

� Control charts Charts used to evaluate whether a process is operating within set expectations.

� Scatter diagrams Graphs that show how two variables are related to each other.

Environment Processes Materials

Suppliers

late deliveries

Workers Machines

Quality Problem

training

ability

supervision

experience

maintenance

calibration

type

age

temperature process design

material grade

type

poor quality management

out of specification

poor product design dust

lighting

ventilation

out of spec material

defects

FIGURE 5-8

A general cause-and- effect (fishbone) diagram

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THE PHILOSOPHY OF TQM • 177

temperature and number of defects, since temperatures below and above the ideal could lead to defects.

Pareto Analysis Pareto analysis is a technique used to identify quality problems based on their degree of importance. The logic behind Pareto analysis is that only a few quality problems are important, whereas many others are not critical. The tech- nique was named after Vilfredo Pareto, a nineteenth-century Italian economist who determined that only a small percentage of people controlled most of the wealth. This concept has often been called the 80–20 rule and has been extended to many areas. In quality management the logic behind Pareto’s principle is that most quality problems are a result of only a few causes. The trick is to identify these causes.

One way to use Pareto analysis is to develop a chart that ranks the causes of poor quality in decreasing order based on the percentage of defects each has caused. For example, a tally can be made of the number of defects that result from different causes, such as operator error, defective parts, or inaccurate machine calibrations. Percentages of defects can be computed from the tally and placed in a chart like the one shown in Figure 5-7. We generally tend to find that a few causes account for most of the defects.

Histograms A histogram is a chart that shows the frequency distribution of observed values of a variable. We can see from the plot what type of distribution a particular variable displays, such as whether it has a normal distribution and whether the distri- bution is symmetrical.

In the food service industry the use of quality control tools is important in identifying quality problems. Grocery store chains, such as Kroger and Mei- jer, must record and monitor the quality of incoming produce, such as tomatoes and lettuce. Quality tools can be used to evaluate the acceptability of product quality and to monitor product qual- ity from individual suppliers. They can also be used to evaluate causes of quality problems, such as long transit time or poor refrigeration. Similarly, restaurants use quality control tools to evaluate and monitor the quality of delivered goods, such as meats, produce, or baked goods.

Product Design Quality Function Deployment A critical aspect of building quality into a product is to ensure that the product design meets customer expectations. This typically is not as easy as it seems. Customers often speak in everyday language. For example, a product can be described as “attractive,” “strong,” or “safe.” However, these terms can have very different meaning to different customers. What one person considers to be strong, another may not. To produce a product that customers want, we need to trans- late customers’ everyday language into specific technical requirements. However, this can often be difficult. A useful tool for translating the voice of the customer into spe- cific technical requirements is quality function deployment (QFD). Quality function deployment is also useful in enhancing communication between different functions, such as marketing, operations, and engineering.

� Pareto analysis A technique used to identify quality problems based on their degree of importance.

� Histogram A chart that shows the frequency distribution of observed values of a variable.

� Quality function deployment (QFD) A tool used to translate the preferences of the customer into specific technical requirements.

LINKS TO PRACTICE The Kroger Company www.kroger.com Meijer Stores Limited Partnership www.meijer.com

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178 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

QFD enables us to view the relationships among the variables involved in the design of a product, such as technical versus customer requirements. This can help us analyze the big picture—for example, by running tests to see how changes in certain technical requirements of the product affect customer requirements. An example is an automo- bile manufacturer evaluating how changes in materials affect customer safety require- ments. This type of analysis can be very beneficial in developing a product design that meets customer needs, yet does not create unnecessary technical requirements for production.

QFD begins by identifying important customer requirements, which typically come from the marketing department. These requirements are numerically scored based on their importance, and scores are translated into specific product characteristics. Evalu- ations are then made of how the product compares with its main competitors relative to the identified characteristics. Finally, specific goals are set to address the identified problems. The resulting matrix looks like a picture of a house and is often called the house of quality.

We will consider the example of manufacturing a backpack to show how we would use QFD. We will start with a relationship matrix that ties customer requirements to product characteristics, shown in Figure 5-9.

● Customer Requirements Remember that our goal is to make a product that the customer wants. Th erefore, the fi rst thing we need to do is survey our custom- ers to fi nd out specifi cally what they would be looking for in a product—in this

MKT

Customer Requirements

Durable

Lightweight

Roomy

Looks Nice

Low Cost

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XX X

X X

Product Characteristics

Competitive Evaluation

B A

B AUS

US/A

A US/B

1 2 3 4 5

B AUS 1 2 3 4 5

R el

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Relationship

Strong Positive

Positive

Negative

Strong NegativeX

US = Our Backpack A = Competitor A B = Competitor B

TOTAL 100

FIGURE 5-9

Relationship matrix

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THE PHILOSOPHY OF TQM • 179

case, a backpack for students. To fi nd out precisely what features students would like in a backpack, the marketing department might send representatives to talk to students on campus, conduct telephone interviews, and maybe conduct focus groups. Let’s say that students have identifi ed fi ve desirable features: the backpack should be durable, lightweight, and roomy; look nice; and not cost very much (Figure 5-10). Th e importance customers attach to each of these requirements is also determined and shown in Figure 5-10. Th is part of the fi gure looks like the chimney of the “house.” You can see that durability and roominess are given the greatest importance.

● Competitive Evaluation On the far right of our relationship matrix is an evalu- ation of how our product compares to those of competitors. In this example there are two competitors, A and B. Th e evaluation scale is from 1 to 5—the higher the rating, the better. Th e important thing here is to identify which customer require- ments we should pursue and how we fare relative to our competitors. For example, you can see that our product excels in durability relative to competitors, yet it does not look as nice. Th is means that we could gain a competitive advantage by focus- ing our design eff orts on a more appealing product.

● Product Characteristics Specifi c product characteristics are on top of the rela- tionship matrix. Th ese are technical measures. In our example they include the number of zippers and compartments, the weight of the backpack, the strength of the backpack, the grade of the dye color, and the cost of materials.

● Th e Relationship Matrix Th e strength of the relationship between customer requirements and product characteristics is shown in the relationship matrix. For example, you can see that the number of zippers and compartments is nega- tively related to the weight of the backpack. A negative relationship means that as we increase the desirability of one variable, we decrease the desirability of the other. At the same time, roominess is positively related to the number of zip- pers and compartments, as is appearance. A positive relationship means that an increase in desirability of one variable is related to an increase in the desirability of another. Th is type of information is very important in coordinating the product design.

● Th e Trade-off Matrix You can see how the relationship matrix is beginning to look like a house. Figure 5-10 shows the complete house of quality. Th e next step in our building process is to put the “roof” on the house. Th is is done through a trade-off matrix, which shows how each product characteristic is related to the others and thus allows us to see what trade-off s we need to make. For example, the number of zippers is negatively related to the weight of the backpack.

● Setting Targets Th e last step in constructing the house of quality is to evaluate competitors’ products relative to the specifi c product characteristics and to set targets for our own product. Th e bottom row of the house is the output of quality function deployment. Th ese are specifi c, measurable product characteristics that have been formulated from general customer requirements.

The house of quality has been very useful. You can see how it translates everyday terms like “lightweight,” “roominess,” and “nice looking” into specific product char- acteristics that can be used in manufacturing the product. Note also how the house of quality can help in the communication between marketing, operations, and design engineering.

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180 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

Reliability An important dimension of product design is that the product functions as expected. This is called reliability, the probability that a product, service, or part will perform as intended for a specified period of time under normal conditions. We are all familiar with product reliability in the form of product warranties. We also know that no product is guaranteed with 100 percent certainty to function properly. However, companies know that high reliability is an important part of customer- oriented qual- ity and try to build this into their product design.

Reliability is a probability, a likelihood, or a chance. For example, a product with a 90 percent reliability has a 90 percent chance of functioning as intended. Another way to look at it is that the probability the product will fail is 1 � 0.90 � 0.10, or 10 percent. This also means that 1 out of 10 products will not function as expected.

The reliability of a product is a direct function of the reliability of its component parts. If all the parts in a product must work for the product to function, then the

� Reliability The probability that a product, service, or part will perform as intended.

Customer Requirements

Durable

Lightweight

Roomy

Looks Nice

Low Cost

TOTAL

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X X

Relationship

Strong Positive

Positive

Negative

Strong Negative

Competitive Evaluation

B A

B AUS

US/A

A US/B

1 2 3 4 5

B AUS 1 2 3 4 5

R el

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po rt

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100

US = Our Backpack A = Competitor A B = Competitor B

A GradeB

Competitive Evaluation

OUR TARGETS

$10

1.2 lbs.

14 lbs.

Grade A

0.8 lbs.

10 lbs.

Grade A

0.5 lbs.

16 lbs.

Product Characteristics

FIGURE 5-10

House of quality

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THE PHILOSOPHY OF TQM • 181

reliability of the system is computed as the product of the reliabilities of the individual components:

Rs 5 1R1 2 1R2 2 1R3 2 . . . 1Rn 2 where Rs � reliability of the product or system

R1...n � reliability of components 1 through n

Assume that a product has two parts, both of which must work for the product to function. Part 1 has a reliability of 80 percent and part 2 has a reliability of 90 percent. Compute the reliability of the product.

0.80 0.90

Part 1 Part 2

• Before You Begin: Remember that the reliability of the system is simply the product of the individual reliabilities.

• Solution: The reliability of the product is

R 5 10.80 2 10.90 2 5 0.72

EXAMPLE 5.1

Computing Product Reliability

Notice in the example that the reliability of the “system” is lower than that of indi- vidual components. The reason is that all the components in a series, as in the exam- ple, must function for the product to work. If only one component doesn’t work, the entire product doesn’t work. The more components a product has, the lower its reli- ability. For example, a system with five components in series, each with a reliability of 0.90, has a reliability of only (0.90)(0.90)(0.90)(0.90)(0.90) � (0.90)5 � 0.59.

The failure of certain products can be very critical. One way to increase product reliability is to build redundancy into the product design in the form of backup parts. Consider the blackout during the summer of 2003, when most of the northeastern part of the United States was out of power for days. Critical facilities, such as hospitals, immediately switched to backup power generators that are available when the main systems fail. Consider other critical systems, such as the navigation system of an air- craft, systems that operate nuclear power plants, the space shuttle, or even the braking system of your car. What gives these systems such high reliability is the redundancy built into the product design that serves to increase reliability.

Redundancy is built into the system by placing components in parallel so that when one component fails the other component takes over. In this case, the reliability of the system is computed by adding the reliability of the first component to the reliability of the second (backup) component, multiplied by the probability of needing the backup. The equation is as follows:

Rs � �Reliabilityof 1stcomponent�� Reliabilityof 2ndcomponent�� Probabilityof needing2nd component�� Notice that if the reliability of the first component is 0.90, the probability of needing a second component is equal to the first component failing, which is (1 � 0.90) � 0.10. Now let’s look at an example.

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182 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

Two power generators, the main and backup, provide electricity to a facility. The main generator has a reliability of 0.95 and the backup a reliability of 0.90. What is the reliability of the system?

• Before You Begin: Notice in this problem that redundancy has been added to the system in the form of a backup component. Remember to draw the backup in parallel to the original component and compute the total reliability accordingly.

• Solution: The system can be represented in the following way

The reliability of the system is

Rs 5 0.95 1 [ 10.90 2 3 11 2 0.95 2 ] 5 0.995

EXAMPLE 5.2

Computing Product Reliability with Redundancy

Process Management According to TQM, a quality product comes from a quality process. This means that quality should be built into the process. Quality at the source is the belief that it is far better to uncover the source of quality problems and correct it than to discard defective items after production. If the source of the problem is not corrected, the problem will continue. For example, if you are baking cookies you might find that some of the cook- ies are burned. Simply throwing away the burned cookies will not correct the prob- lem. You will continue to have burned cookies and will lose money when you throw them away. It will be far more effective to see where the problem is and correct it. For example, the temperature setting may be too high; the pan may be curved, placing some cookies closer to the heating element; or the oven may not be distributing heat evenly.

Quality at the source exemplifies the difference between the old and new concepts of quality. The old concept focused on inspecting goods after they were produced or after a particular stage of production. If an inspection revealed defects, the defective products were either discarded or sent back for reworking. All this cost the company money, and these costs were passed on to the customer. The new concept of quality focuses on identifying quality problems at the source and correcting them.

In Chapter 6 we will learn how to monitor process quality using quality tools, such as control charts.

Managing Supplier Quality TQM extends the concept of quality to a company’s suppliers. Traditionally, companies tended to have numerous suppliers who engaged in competitive price bidding. When materials arrived, they were inspected for quality. TQM views this practice as contrib- uting to poor quality and wasted time and cost. The philosophy of TQM extends the concept of quality to suppliers and ensures that they engage in the same quality practices. If suppliers meet preset quality standards, materials do not have to be inspected upon arrival. Today, many companies have a representative residing at their supplier’s location, thereby involving the supplier in every stage from product design to final production.

� Quality at the source The belief that it is best to uncover the source of quality problems and eliminate it.

Original 0.95

Backup 0.90

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QUALITY AWARDS AND STANDARDS • 183

Before You Go On

Today’s concept of quality, called total quality management (TQM), focuses on building quality into the process, as opposed to simply inspecting for poor quality after production. TQM is customer driven and encompasses the entire company. Before you go on, you should know the four categories of quality costs. These are prevention and appraisal costs, which are costs that are incurred to prevent poor quality, and internal and external failure costs, which are costs that the company hopes to prevent. You should understand the evolution of TQM and the notable individuals who have shaped our knowledge of quality. Last, you should know the seven concepts of the TQM philosophy: customer focus, continuous improvement, employee empowerment, use of quality tools, product design, process management, and managing supplier quality.

QUALITY AWARDS AND STANDARDS

The Malcolm Baldrige National Quality Award (MBNQA) The Malcolm Baldrige National Quality Award was established in 1987 when Con- gress passed the Malcolm Baldrige National Quality Improvement Act. The award is named after the former Secretary of Commerce Malcolm Baldrige, and is intended to reward and stimulate quality initiatives. It is designed to recognize companies that establish and demonstrate high-quality standards and is given to no more than two companies in each of three categories: manufacturing, service, and small business. Past winners include Motorola Corporation, Xerox, FedEx, 3M, IBM, and the Ritz-Carlton.

To compete for the Baldrige Award, companies must submit a lengthy application, which is followed by an initial screening. Companies that pass this screening move to the next step, in which they undergo a rigorous evaluation process conducted by cer- tified Baldrige examiners. The examiners conduct site visits and examine numerous company documents. They base their evaluation on seven categories, which are shown in Figure 5-11. Let’s look at each category in more detail.

The first category is leadership. Examiners consider commitment by top management, their effort to create an organizational climate devoted to quality, and their active involve- ment in promoting quality. They also consider the firm’s orientation toward meeting customer needs and desires, as well as those of the community and society as a whole.

� Malcolm Baldrige National Quality Award An award given annually to companies that demonstrate quality excellence and establish best practice standards in industry.

1 Leadership

2 Strategic Planning

3 Customer and Market Focus

4 Information and Analysis

5 Human Resource Focus

6 Process Management

7 Business Results

TOTAL POINTS

Categories

120

450

1000

Points

FIGURE 5-11

Malcolm Baldrige National Quality Award criteria

The Ritz-Carlton is one of the past winners of the Malcolm Baldrige National Quality Award.

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184 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

The second category is strategic planning. The examiners look for a strategic plan that has high-quality goals and specific methods for implementation. The next cat- egory, customer and market focus, addresses how the company collects market and customer information. Successful companies should use a variety of tools toward this end, such as market surveys and focus groups. The company then needs to demon- strate how it acts on this information.

The fourth category is information and analysis. Examiners evaluate how the com- pany obtains data and how it acts on the information. The company needs to demon- strate how the information is shared within the company as well as with other parties, such as suppliers and customers.

The fifth and sixth categories deal with management of human resources and man- agement of processes, respectively. These two categories together address the issues of people and process. Human resource focus addresses issues of employee involvement. This entails continuous improvement programs, employee training, and functioning of teams. Employee involvement is considered a critical element of quality. Similarly, process management involves documentation of processes; use of tools for quality improvement, such as statistical process control; and the degree of process integration within the organization.

The last Baldrige category receives the highest points and deals with business results. Numerous measures of performance are considered, from percentage of defective items to financial and marketing measures. Companies need to demonstrate progres- sive improvement in these measures over time, not just a one-time improvement.

The Baldrige criteria have evolved from simple award criteria to a general frame- work for quality evaluation. Many companies use these criteria to evaluate their own performance and set quality targets even if they are not planning to formally compete for the award.

The Deming Prize The Deming Prize is a Japanese award given to companies to recognize their efforts in quality improvement. The award is named after W. Edwards Deming, who visited Japan after World War II upon the request of Japanese industrial leaders and engi- neers. While there, he gave a series of lectures on quality. The Japanese considered him such an important quality guru that they named the quality award after him.

The award has been given by the Union of Japanese Scientists and Engineers (JUSE) since 1951. Competition for the Deming Prize was opened to foreign com- panies in 1984. In 1989, Florida Power & Light was the first U.S. company to receive the award.

ISO 9000 Standards Increases in international trade during the 1980s led to the development of universal standards of quality. Universal standards were seen as necessary in order for compa- nies to be able to objectively document their quality practices around the world. Then in 1987 the International Organization for Standardization published its first set of standards for quality management, called ISO 9000. The purpose of the International Organization for Standardization (ISO) is to establish agreement on international quality standards. It currently has members from 91 countries, including the United States. It created ISO 9000 to develop and promote international quality standards. ISO 9000 consists of a set of standards and a certification process for companies. ISO 9000 certification demonstrates that companies have met the standards. The standards are applicable to all types of companies and have gained global acceptance. In many

HRM

� Deming Prize A Japanese award given to companies to recognize efforts in quality improvement.

� ISO 9000 A set of international quality standards and a certification demonstrating that companies have met all the standards specified.

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QUALITY AWARDS AND STANDARDS • 185

industries ISO certification has become a requirement for doing business. Also, ISO 9000 standards have been adopted by the European Community as a standard for companies doing business in Europe.

In December 2000 the first major changes to ISO 9000 were made, introducing the following three new standards:

● ISO 9000:2000, Quality Management Systems—Fundamentals and Standards: Pro- vides the terminology and defi nitions used in the standards. It is the starting point for understanding the system of standards.

● ISO 9001:2000, Quality Management Systems—Requirements: Th is is the standard for the certifi cation of a fi rm’s quality management system. It is used to demonstrate the conformity of quality management systems to meet customer requirements.

● ISO 9004:2000, Quality Management Systems—Guidelines for Performance: Pro- vides guidelines for establishing a quality management system. It focuses not only on meeting customer requirements but also on improving performance.

These three standards are the most widely used and apply to the majority of compa- nies. However, ten more published standards and guidelines exist as part of the ISO 9000 family of standards.

To receive ISO certification, a company must provide extensive documentation of its quality processes. This includes methods used to monitor quality, methods and frequency of worker training, job descriptions, inspection programs, and statistical process control tools used. High-quality documentation of all processes is critical. The company is then audited by an ISO 9000 registrar, who visits the facility to make sure the company has a well-documented quality management system and that the process meets the standards. If the registrar finds that all is in order, certification is received. Once a company is certified, it is registered in an ISO directory that lists certified companies. The entire process can take 18 to 24 months and can cost anywhere from $10,000 to $30,000. Companies have to be recertified by ISO every three years.

One of the shortcomings of ISO certification is that it focuses only on the process used and conformance to specifications. In contrast to the Baldrige criteria, ISO cer- tification does not address questions about the product itself and whether it meets customer and market requirements. Today there are over 40,000 companies that are ISO certified. In fact, certification has become a requirement for conducting business in many industries.

ISO 14000 Standards The need for standardization of quality created an impetus for the development of other standards. In 1996, the International Standards Organization introduced stan- dards for evaluating a company’s environmental responsibility. These standards, termed ISO 14000, focus on three major areas:

● Management systems standards measure systems development and integration of environmental responsibility into the overall business.

● Operations standards include the measurement of consumption of natural resources and energy.

● Environmental systems standards measure emissions, effl uents, and other waste systems.

With greater interest in green manufacturing and more awareness of environmen- tal concerns, ISO 14000 may become an important set of standards for promoting environmental responsibility.

� ISO 14000 A set of international standards and a certification focusing on a company’s environmental responsibility.

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186 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

WHY TQM EFFORTS FAIL

In this chapter we have discussed the meaning of TQM and the great benefits that can be attained through its implementation. Yet there are still many companies that attempt a variety of quality improvement efforts and find that they have not achieved any or most of the expected outcomes. The most important factor in the success or failure of TQM efforts is the genuineness of the organization’s commitment. Often, companies look at TQM as another business change that must be implemented due to market pressure without really changing the values of their organization. Recall that TQM is a complete philosophy that has to be embraced with true belief, not mere lip service. Looking at TQM as a short-term financial investment is a sure recipe for failure.

Another mistake is the view that the responsibility for quality and elimination of waste lies with employees other than top management. It is a “let the workers do it” mentality. A third common mistake is over- or underreliance on statistical process control (SPC) methods. SPC is not a substitute for continuous improvement, team- work, and a change in the organization’s belief system. However, SPC is a necessary tool for identifying quality problems. Some common causes for TQM failure are

● Lack of a genuine quality culture ● Lack of top management support and commitment ● Over- and underreliance on statistical process control (SPC) methods

Companies that have attained the benefits of TQM have created a quality culture. These companies have developed processes for identifying customer-defined quality. In addition, they have a systematic method for listening to their customers, collecting and analyzing data pertaining to customer problems, and making changes based on customer feedback. You can see that in these companies there is a systematic process for prioritizing customer needs that encompasses the entire organization.

TOTAL QUALITY MANAGEMENT (TQM) WITHIN OM: HOW IT ALL FITS TOGETHER

Implementing total quality management requires broad and sweeping changes throughout a company. It also affects all other decisions within operations manage- ment. The decision to implement total quality management concepts throughout the company is strategic in nature. It sets the direction for the firm and the level of com- mitment. For example, some companies may choose to directly compete on quality, whereas others may just want to be as good as the competition. It is operations strat- egy that then dictates how all other areas of operations management will support this commitment.

The decision to implement TQM affects areas such as product design (Chapter 3), which needs to incorporate customer-defined quality. Processes are then redesigned in order to produce products with higher quality standards. Job design (Chapter 11) is affected, as workers need to be trained in quality tools and become responsible for rooting out quality problems. Also, supply chain management (Chapter 4) is affected as the commitment to quality translates into partnering with suppliers. As you can see, virtually every aspect of the operations function must change to support the commit- ment to total quality management.

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TOTAL QUALITY MANAGEMENT (TQM) ACROSS THE ORGANIZATION • 187

TOTAL QUALITY MANAGEMENT (TQM) ACROSS THE ORGANIZATION As we have seen, total quality management impacts every aspect of the organization. Every person and every function is responsible for quality and is affected by poor qual- ity. For example, recall that Motorola implemented its Six Sigma concept not only in the production process but also in the accounting, finance, and administrative areas. Similarly, ISO 9000 standards do not apply only to the production process—they apply equally to all departments of the company. A company cannot achieve high quality if its accounting is inaccurate or the marketing department is not working closely with customers. TQM requires the close cooperation of different functions in order to be successful. In this section we look at the involvement of these other functions in TQM.

Marketing plays a critical role in the TQM process by providing key inputs that make TQM a success. Recall that the goal of TQM is to satisfy customer needs by pro- ducing the exact product that customers want. Marketing’s role is to understand the changing needs and wants of customers by working closely with them. This requires a solid identification of target markets and an understanding of whom the product is intended for. Sometimes, apparently small differences in product features can result in large differences in customer appeal. Marketing needs to accurately pass customer information along to operations, and operations needs to include marketing in any planned product changes.

Finance is another major participant in the TQM process because of the great cost consequences of poor quality. General definitions of quality need to be trans- lated into specific dollar terms. This serves as a baseline for monitoring the finan- cial impact of quality efforts and can be a great motivator. Recall the four costs of quality discussed earlier. The first two costs, prevention and appraisal, are preventive costs; they are intended to prevent internal and external failure costs. Not investing enough in preventive costs can result in failure costs, which can hurt the company. On the other hand, investing too much in preventive costs may not yield added benefits. Financial analysis of these costs is critical. You can see that finance plays a large role in evaluating and monitoring the financial impact of managing the quality process. This includes costs related to preventing and eliminating defects, training employees, reviewing new products, and all other quality efforts.

Accounting is important in the TQM process because of the need for exact cost- ing. TQM efforts cannot be accurately monitored and their financial contribution assessed if the company does not have accurate costing methods.

Engineering efforts are critical in TQM because of the need to properly translate customer requirements into specific engineering terms. Recall the process we fol- lowed in developing quality function deployment (QFD). It was not easy to translate a customer requirement such as “a good-looking backpack” into specific terms such as materials, weight, color grade, size, and number of zippers. We depend on engineering to use general customer requirements in developing technical specifications, identi- fying specific parts and materials needed, and identifying equipment that should be used.

Purchasing is another important part of the TQM process. Whereas marketing is busy identifying what the customers want and engineering is busy translating that information into technical specifications, purchasing is responsible for acquiring the materials needed to make the product. Purchasing must locate sources of supply, ensure that the parts and materials needed are of sufficiently high quality, and negoti- ate a purchase price that meets the company’s budget as identified by finance.

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188 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

Human resources is critical to the effort to hire employees with the skills neces- sary to work in a TQM environment. That environment includes a high degree of teamwork, cooperation, dedication, and customer commitment. Human resources is also faced with challenges relating to reward and incentive systems. In TQM, rewards and incentives are different from those found in traditional environments that focus on rewarding individuals rather than teams.

Information systems (IS) is highly important in TQM because of the increased need for information accessible to teams throughout the organization. IS should work closely with a company’s TQM development program in order to understand exactly the type of information system best suited for the firm, including the form of the data, the summary statistics available, and the frequency of updating.

HRM

MIS

The ultimate goal of TQM is to produce and deliver a good or service that provides value to the final cus- tomer. This can only be achieved if the concepts of TQM are adopted by all members of the supply chain. The reason is that the supply chain is a sys- tem of organizations that are linked together and that are dependent on each other’s performance. If just one member of the chain produces poor qual- ity, the entire chain will suffer, as the defective product will be passed down the chain until the defect is finally

discovered. This will result in higher cost for all chain members and possibly the loss of customers. That is why one of the main ideas of TQM is to extend qual- ity concepts to a company’s suppliers. In the chapter we

discussed the differences between the external and internal customer, stressing that no one in the company should pass a defective item on to the internal customer. Just as all the internal func-

tions of an organization are dependent on one another, so are the members of a supply chain. For

this reason, the concepts of TQM must be extended to everyone in the chain.

THE SUPPLY CHAIN LINK

Just as TQM must be extended to everyone in the sup- ply chain, so must sustainability standards for them to have a meaningful impact. Today companies are under increasing pressure from consumers and governmental agencies to be sustainable. As a result they are making numerous claims about their performance. So, how can we as consumers know that these claims are true? Being able to objectively verify these claims is critical. The ISO 14000 voluntary standards help with this veri- fication. Recall from the chapter that ISO 14000 is a set of international standards and a certification focusing on a company’s environmental responsibility. As with other quality measures, setting measurable standards

for sustainability is critical. ISO 14000 is designed to provide customers with reasonable assurance that the environmental performance claims of a company are correct. It is important to note, however, that the ISO 14000 standards do not themselves specify environ- mental performance goals. ISO 14000 is similar to ISO 9000 as both pertain to the process of how the product is produced, rather than the characteristics of the product itself.

When it comes to standards for product character- istics, it is up to the customers and other stakeholders to hold companies accountable. For that reason, there have been numerous other standards that have been developed by the government and various nonprofit agencies. For example, in the area of materials three

THE SUSTAINABILITY LINK

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KEY TERMS • 189

sustainability standards have been developed. The first is Global Organic Textile Standards (GOTS), the leading process standard for organic fiber textiles that provides textile processors and manufacturers with one organic textile certification. Another is the Green Seal certifica- tion that certifies office paper, paper towels, cleaning products, food packaging, and household products. There is also Cradle to Cradle that certifies paper and packaging, personal care products, textiles and fabrics, and many other products. Certifications have also been developed in the areas of building, construction, and

energy. The U.S. Green Building Council’s LEED cer- tification is the most widely recognized green building standard in the United States, providing certification for multiple building types. Similarly, Energy Star is the EPA’s certification for new and upgraded energy-effi- cient homes and office buildings. These certifications are important as they provide measurement, a baseline of performance, and validation of company claims. As sustainability continues to evolve we will continue to see more standards emerge that will help guide consum- ers and corporations.

Chapter Highlights 1 Total quality management (TQM) is diff erent from the old

concept of quality because its focus is on serving customers, identifying the causes of quality problems, and building quality into the production process.

2 Th ere are four categories of quality costs. Th e fi rst two are prevention and appraisal costs, which are incurred by a company in attempting to improve quality. Th e last two costs are internal and external failure costs, which are the costs of quality failures that the company wishes to prevent.

3 Th e seven most notable individuals who shaped today’s concept of quality are Walter A. Shewhart, W. Edwards Deming, Joseph M. Juran, Armand V. Feigenbaum, Philip B. Crosby, Kaoru Ishikawa, and Genichi Taguchi.

4 Seven features of TQM combine to create the TQM philosophy: customer focus, continuous improvement, employee empowerment, use of quality tools, product design, process management, and managing supplier quality.

5 Quality function deployment (QFD) is a tool used to translate customer needs into specifi c engineering requirements. Seven problem-solving tools are used in managing quality. Oft en called the seven tools of quality control, they are cause-and- eff ect diagrams, fl owcharts, checklists, scatter diagrams, Pareto analysis, control charts, and histograms.

6 Reliability is the probability that the product will function as expected. Th e reliability of a product is computed as the product of the reliabilities of the individual components.

7 Th e Malcolm Baldrige Award is given to companies to recognize excellence in quality management. Companies are evaluated in seven areas, including quality leadership and performance results. Th ese criteria have become a standard for many companies that seek to improve quality. ISO 9000 is a certifi cation based on a set of quality standards established by the International Organization for Standardization. Its goal is to ensure that quality is built into production processes. ISO 9000 focuses mainly on quality of conformance.

W. Edwards Deming Armand V. Feigenbaum

Malcolm Baldridge

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Key Terms total quality management (TQM) 162 customer-defi ned quality 162 conformance to specifi cations 163 fi tness for use 163

value for price paid 163 support services 163 psychological criteria 163 prevention costs 165

appraisal costs 165 internal failure costs 165 external failure costs 165 Walter A. Shewhart 167

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190 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

Formula Review 1. Reliability of parts in series:

Rs 5 1R1 2 1R2 2 . . . . 1Rn 2 2. Reliability of parts with redundancy (in parallel): Rs � �Reliabilityof 1stcomponent�� Reliabilityof 2ndcomponent�� Probabilityof needing2nd component��

W. Edwards Deming 167 Joseph M. Juran 168 Armand V. Feigenbaum 169 Philip B. Crosby 169 Kaoru Ishikawa 169 Genichi Taguchi 170 robust design 170 Taguchi loss function 170 continuous improvement 171 kaizen 172

plan–do–study–act (PDSA) cycle 172 benchmarking 173 quality circle 173 cause-and-eff ect diagram 174 fl owchart 174 checklist 175 control charts 176 scatter diagrams 176 Pareto analysis 177 histogram 177

quality function deployment (QFD) 177

reliability 180 quality at the source 182 Malcolm Baldrige National Quality

Award 183 Deming Prize 184 ISO 9000 184 ISO 14000 185

Solved Problems • Problem 1 An office security system at Delco, Inc. has two component parts, both of which must work for the system to function. Part 1 has a reliability of 80 percent, and part 2 has a reliability of 98 percent. Compute the reliability of the system.

• Before You Begin: Before you begin solving reliability problems, it is best to first draw a diagram of the components. Remember that the system of components is drawn in series, except when there is redun- dancy built into the system through a backup component. In that case, the backup component is drawn in parallel. Always read the problem carefully to determine whether redundancy is built into the system.

• Solution

The reliability of the system is

Rs 5 R1 3 R2

Rs 5 10.80 2 10.98 2 5 0.784

(See student companion site for Excel template.)

Part 1 Part 2

R1 � 0.80 R2 � 0.98

• Problem 2 Delco, Inc., from Problem 1, is not happy with the reliability of its security system and has decided to improve it. The company will add a backup component to part 1 of its security system. The backup component will also have a reliability of 0.80. What is the reliability of the improved security system?

• Before You Begin: Notice that in this problem there is redundancy in the form of a backup component for part 1. This means that when drawing the diagram you should place the two components for part 1 in parallel. Proceed by computing the reliability for part 1 and then the entire system.

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PROBLEMS • 191

Discussion Questions 1. Define quality for the following products: a university, an

exercise facility, spaghetti sauce, and toothpaste. Compare your definitions with those of others in your class.

2. Describe the TQM philosophy and identify its major characteristics.

3. Explain how TQM is different from the traditional notions of quality. Also, explain the differences between traditional orga- nizations and those that have implemented TQM.

4. Find three local companies that you believe exhibit high quality. Next, find three national or international companies that are recognized for their quality achievements.

5. Describe the four dimensions of quality. Which do you think is most important?

6. Describe each of the four costs of quality: prevention, appraisal, internal failure, and external failure. Next, describe how each type of cost would change (increase, decrease, or remain the same) if we designed a higher-quality product that was easier to manufacture.

7. Think again about the four costs of quality. Describe how each would change if we hired more inspectors without changing any other aspects of quality.

8. Explain the meaning of the plan–do–act–study cycle. Why is it described as a cycle?

9. Describe the use of quality function deployment (QFD). Can you find examples in which the voice of the customer was not translated properly into technical requirements?

10. Describe the seven tools of quality control. Are some more important than others? Would you use these tools separately or together? Give some examples of tools that could be used together.

11. What is the Malcolm Baldrige National Quality Award? Why is this award important, and what companies have received it in the past?

12. What are ISO 9000 standards? Who were they set by and why? Can you describe other certifications based on the ISO 9000 certification?

13. Who are the seven “gurus” of quality? Name at least one contribution made by at least three of them.

Problems 1. A CD player has fi ve components that all must function for

the player to work. Th e average reliability of each component is 0.90. What is the reliability of the CD player?

2. A jet engine has 10 components in series. Th e average reliability of each component is 0.998. What is the reliability of the engine?

3. An offi ce copier has four main components in a series with the following reliabilities: 0.89, 0.95, 0.90, and 0.90. Determine the reliability of the copier.

4. An engine system consists of three main components in a series, all having the same reliability. Determine the level of

• Solution The system for Delco, Inc. now looks like this:

Part 1 Part 2

0.80 0.98

0.80

This system can then be reduced to the following two components:

Part 1 Part 2

R1 � 0.96 R2 � 0.98

R1 5 0.80 1 0.80 11 2 0.80 2 5 0.96 R2 5 0.98 1 from Solved Problem 1 2 Rs 5 R1 3 R2

The reliability of the improved system is:

Rs 5 0.96 3 0.98 5 0.94

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192 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

CASE: Gold Coast Advertising (GCA) George Stein sat in his large office overlooking Chicago’s Michi- gan Avenue. As CEO of Gold Coast Advertising, he seemed to always be confronted with one problem or another. Today was no exception. George had just come out of a long meeting with Jim Gerard, head of the board for the small advertising agency. Jim was concerned about a growing problem with lowered sales expectations and a decreasing customer base. Jim warned George that something had to be done quickly or Jim would have to go to the board for action. George acknowledged that sales were down but attributed this to general economic conditions. He assured Jim that the problems would be addressed immediately.

As George pondered his next course of action, he admitted to himself that the customer base of GCA was slowly decreasing. The agency did not quite understand the reason for this decrease. Many regular customers were not coming back, and the rate of new customers seemed to be slowly declining. GCA’s competitors seemed to be doing well. George did not understand the problem.

What Do Customers Want? GCA was a Chicago-based advertising agency that developed campaigns and promotions for small and medium-sized firms.

Its expertise was in the retail area, but it worked with a wide range of firms from the food service industry to the medical field. GCA competed on price and speed of product develop- ment. Advertising in the retail area was competitive, and price had always been important. Also, since retail fashions change rapidly, speed in advertising development was thought to be critical.

George reminded himself that price and speed had always been what customers wanted. Now he felt confused that he really didn’t know his customers. This was just another crisis that would pass, he told himself. But he needed to deal with it immediately.

Case Questions 1. What is wrong with how Gold Coast Advertising measures

its quality? Explain why Gold Coast should ask its customers about how they defi ne quality.

2. Off er suggestions to George Stein on ways of identifying quality dimensions GCA’s customers consider important.

3. Develop a short questionnaire to be fi lled out by GCA’s customers that evaluates how customers defi ne quality.

reliability required for each of the components if the engine is to have a reliability of 0.998.

5. A bank loan processing system has three components with individual reliabilities as shown:

R1 � 0.90 R2 � 0.89 R3 � 0.95

What is the reliability of the bank loan processing system? 6. What would be the reliability of the bank system above if

each of the three components had a backup with a reliability of 0.80? How would the total reliability be diff erent?

7. An LCD projector in an offi ce has a main light bulb with a reliability of 0.90 and a backup bulb, the reliability of which is 0.80. Th e system looks as follows:

0.80

0.90

What is the reliability of the system? 8. A university Web server has fi ve main components, each with

the same reliability. All fi ve components must work for the server to function as intended. If the university wants to have a 95 percent reliability, what must be the reliability of each of the components?

9. BioTech Research Center is working to develop a new vaccine for the West Nile Virus. Th e project is so important that the fi rm has created three teams of experts to work on the project from diff erent perspectives. Team 1 has a 90 percent chance of success, team 2 an 85 percent chance of success, and team 3 a 70 percent chance. What is the probability that BioTech will develop the vaccine?

10. Th e following system of components has been proposed for a new product. Determine the reliability of the system.

R � 0.85 R � 0.90

R � 0.90 R � 0.85 R � 0.90 R � 0.95

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CASE: DELTA PLASTICS, INC. (A) • 193

CASE: Delta Plastics, Inc. (A) Company Background Delta Plastics, Inc. specialized in the design and manufacture of plastic containers, primarily for kitchen and household use. Its products were sold through merchandise retailers and were recognized for high quality. Delta also had an active R&D group that was continuously working to develop new plastic materials and new product designs. Delta was a recognized industry leader and was aggressively looking to increase brand recognition and market share.

Delta’s R&D group had recently developed a new plastic material that tolerates rapid changes in temperature, from heat- ing to deep cooling. This material could be used to make con- tainers for kitchen use that could immediately be moved from the oven to the refrigerator. Unlike glass containers with this capability, the plastic containers would not break or chip. Delta’s marketing group was eager to promote sales of containers made with the new material. Marketing believed the new material could revolutionize the industry, so it pushed for rapid produc- tion, arguing that the sooner the new products were available to customers, the sooner the company could corner the market.

The Decision The decision whether to initiate production or continue with material testing was made during a heated meeting on April 28. Isabelle Harrison, director of R&D, stated that more product test- ing was needed in order to fine-tune the characteristics of the

new material. Although there was no question regarding product safety, she wanted to refine the material to make sure that no unex- pected defects occurred during production. Jose De Costa, direc- tor of manufacturing, supported this position, stating that the new material might be susceptible to cracking. However, George Chad- wick, director of marketing, countered that millions of dollars had already been spent on design and testing. He argued that produc- tion needed to be as rapid as possible before a competitor came out with a similar design. At one point George looked at Isabelle and asked: “Are you certain that the product is safe?” She replied that it was. “Then” he said, “conducting more testing is unnecessary.”

The final decision came from Jonathan Fine, Delta’s CEO. He agreed with George. “If product safety is guaranteed, small problems in production should not be a big deal. Let’s initiate production as soon as possible.”

The Problem On June 15, exactly one month after production began, Jose De Costa sat at his desk looking at the latest production quality report. The report showed weekly defects for products made with the new material (dubbed by marketing as “super plastic”) versus the standard material. Jose knew he needed to conduct a better analysis of the data to see whether there were indeed differences in defects between the two materials. Jose was nervous. Even if there were differences in quality, he was not sure what actions to take.

Quality Report I Standard Material

Week 1 Week 2

Defect Type M T W Th F M T W Th F 1. Uneven edges 1 2 2 3 2 3 1 1 2 0 2. Cracks 2 3 2 3 0 3 2 2 1 3 3. Scratches 3 1 2 3 4 2 1 0 2 3 4. Air bubbles 4 2 2 3 4 4 3 2 4 3 5. Thickness variation 1 0 4 0 2 0 1 1 2 0

Week 3 Week 4

Defect Type M T W Th F M T W Th F 1. Uneven edges 2 2 1 3 2 3 1 1 2 2 2. Cracks 3 2 2 0 2 1 2 2 3 3 3. Scratches 3 1 0 1 3 3 1 0 1 3 4. Air bubbles 3 1 2 2 4 2 3 2 4 1 5. Thickness variation 1 1 3 0 2 2 1 1 2 0

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194 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Total Quality Management (TQM) at Cruising International, Inc. For this assignment, you will work again with Meghan Willoughby, Chief Purser aboard the Friendly Seas I. You know the assignment has something to do with quality, but you aren’t quite sure what. You meet Meghan aboard the ship. She greets you and says, “Let me tell you a bit about what you’ll be doing for us. We’ve been working on quality measures for several years, and now must focus on quality even more as our industry becomes more competitive. We need to make sure that our guests receive quality service from beginning to end. We need your help in bringing ideas together on how to measure

quality in a service organization.” This assignment will enhance your knowledge of the material in Chapter 5 of your textbook while preparing you for your future assignments.

To access the Web site:

II Super Plastic (New Material) Week I Week 2

Defect Type M T W Th F M T W Th F 1. Uneven edges 2 2 3 2 0 3 1 1 2 3 2. Cracks 6 6 4 3 7 4 4 4 3 3 3. Scratches 0 1 0 1 0 0 1 0 2 2 4. Air bubbles 2 0 2 1 3 4 3 2 4 3 5. Thickness variation 1 0 2 1 2 0 1 2 2 0

Week 3 Week 4

Defect Type M T W Th F M T W Th F 1. Uneven edges 1 2 2 2 3 3 2 1 2 0 2. Cracks 4 6 4 4 3 5 7 6 3 7 3. Scratches 0 1 2 1 0 1 1 0 2 3 4. Air bubbles 4 5 5 5 3 6 5 4 6 5 5. Thickness variation 1 0 4 0 1 0 1 1 2 0

Case Questions 1. Identify the diff erent costs of quality described in the

case. Explain the trade-off s between the costs of quality that Delta made in its decision. Was George Chadwick correct that conducting more tests was unnecessary?

2. Use one of the quality tools described in the chapter to analyze the defects in the case. How do the quality dimensions diff er between the two materials? Are there more defects associated with the super plastic versus the standard material?

3. Given your fi ndings, what should Jose do?

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ONLINE RESOURCES • 195

Online Resources Companion Website www.wiley.com/college/reid

Stickley, Inc., www.Stickley.com • Find links for Additional Web Resources for this chapter

American Society for Quality Control, www.asqc.org National Institute of Standards and Technology (NIST) quality program—Baldrige Award www.quality.nist.gov

On-line Case: Product Design and Process Selection at Valley Memorial Hospital

Assignment: Total Quality Management. For this assignment, you’ll be working with Jane Starr of Valley Memorial Hospital’s Risk Management Department. You know the assignment has something to do with quality, but you’re wondering how quality applies to health care. At the hospital, you find Jane Starr’s office. She greets you and says, “Let me tell you a bit about what you’ll be doing for us. We’ve been working on quality measures for sev- eral years, and now we have to focus on quality even more. The Joint Commission for Accreditation of Healthcare Organizations

is currently looking hard at quality when it visits hospitals and- decides whether to accredit them.We need your help in bringing ideas together on how to measure quality in a service organization.”

To access the Website:

You have recently taken a position with Snyder Bakeries, a producer of a variety of different types of baked goods that are packaged and sold directly to grocery chains. Snyder Bakeries has been in business since 1978. It is a small company with 95 employees, earning roughly $2.5 million annually. Competition in the baked goods market has been increasing steadily, and Sny- der Bakeries is being forced to look at its operations. In addition, turnover and dissatisfaction among Snyder employees have been high. Mr. Lowell Snyder, President of Snyder Bakeries, is looking to you for help in redesigning the company’s quality program. He would like you to focus on helping Snyder Bakeries develop a

team approach among its employees as part of the implementing principles of total quality management.

To help Mr. Snyder, use the Internet as a source of informa- tion. Perform an Internet search to identify at least two compa- nies that Snyder Bakeries can use as a benchmark for developing a team approach among employees. Explain how each of these competitors uses teams, how the teams are developed, how incentives are provided, and how employees are motivated. Also identify the benefits these companies have gained from using the team approach. Finally, outline a plan for Mr. Snyder based on the information you have gathered.

INTERNET CHALLENGE Synder Bakeries

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196 • CHAPTER 5 TOTAL QUALITY MANAGEMENT

Selected Bibliography Clark, Bill, John Chang, and Marcus Chao. “Helping Suppliers

to Focus on Quality,” Supply Chain Management Review, January–February 2006, 54–61.

Crosby, Philip B. Quality Is Free. New York: New American Library, 1979.

Crosby, Philip. Quality without Tears: Th e Art of Hassle-Free Management. New York: McGraw-Hill, 1984.

Deming, W. Edwards. Out of Crisis. Cambridge, Mass.: MIT Center for Advanced Engineering Study, 1986.

Evans, James R., and William M. Lindsay. Th e Management and Control of Quality, Fourth Edition. Cincinnati: South- Western, 1999.

Garvin, David A. “Competing on the Eight Dimensions of Quality,” Harvard Business Review, November–December 1987, 101–110.

Garvin, David A. Managing Quality. New York: Free Press, 1988. Goetsch, David L., and Stanley Davis. Implementing Total

Quality. Upper Saddle River, N.J.: Prentice-Hall, 1995. Hall, Robert. Attaining Manufacturing Excellence. Burr Ridge, Ill.:

Dow-Jones Irwin, 1987.

Juran, Joseph M. “Th e Quality Trilogy,” Quality Progress, 10, 8, 1986, 19–24.

Juran, Joseph M. Quality Control Handbook, Fourth Edition. New York: McGraw-Hill, 1988.

Juran, Joseph M. Juran on Planning for Quality. New York: Free Press, 1988.

Kitazawa, S., and J. Sarkis. “Th e Relationship between ISO 14001 and Continuous Source Reduction Programs,” Inter- national Journal of Operations and Production Manage- ment, 20, 2, 2000, 225–248.

Medori, D., and D. Steeple. “A Framework for Auditing and Enhancing Performance Measurement Systems,” Interna- tional Journal of Operations and Production Management, 20, 5, 2000, 520–533.

Rosenberg, Jarrett. “Five Myths about Customer Satisfaction,” Quality Progress 29, 12, December 1996, 57–60.

Zimmerman, R.E., L. Steinmann, and V. Schueler. “Designing Customer Surveys Th at Work,” Quality Progress, October 1996, 22–28.

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CHAPTER

197

L E A R N I N G O B J E C T I V E S

C H A P T E R O U T L I N E

What Is Statistical Quality Control? 198 Sources of Variation: Common and Assignable

Causes 200 Descriptive Statistics 200 Statistical Process Control Methods 201 Control Charts for Variables 204 Control Charts for Attributes 210 Process Capability 216

Six Sigma Quality 221 Acceptance Sampling 223 Implications for Managers 229 Statistical Quality Control in Services 230 Statistical Quality Control (SQC) within OM:

How It All Fits Together 232 Statistical Quality Control (SQC) across the

Organization 232

Before studying this chapter you should know or, if necessary, review

1. Quality as a competitive priority, Chapter 2, p. 39. 2. Total quality management (TQM) concepts, Chapter 5, pp. 166–189.

After studying this chapter you should be able to

1 Describe categories of statistical quality control (SQC). 2 Explain the use of descriptive statistics in measuring quality characteristics. 3 Identify and describe causes of variation. 4 Describe the use of control charts. 5 Identify the diff erences between x-bar, R-, p-, and c-charts. 6 Explain the meaning of process capability and the process capability index. 7 Explain the concept Six Sigma. 8 Explain the process of acceptance sampling and describe the use of operating characteristic (OC)

curves. 9 Describe the challenges inherent in measuring quality in service organizations.

Statistical Quality Control

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

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198 • CHAPTER 6 STATISTICAL QUALITY CONTROL

In Chapter 5 we learned that total quality management (TQM) addresses organi- zational quality from managerial and philosophical viewpoints. TQM focuses on customer-driven quality standards, managerial leadership, continuous improvement, quality built into product and process design, identifying quality problems at the source, and making quality everyone’s responsibility. However, talking about solving quality problems is not enough. We need specific tools that can help us make the right quality decisions. These tools come from the field of statistics and are used to help identify quality problems in the production process as well as in the product itself. Statistical quality control is the subject of this chapter.

Statistica1 quality control (SQC) is the term used to describe the set of statistical tools used by quality professionals. Statistical quality control can be divided into three broad categories:

1. Descriptive statistics are used to describe quality characteristics and relation- ships. Included are statistics such as the mean, the standard deviation, the range, and a measure of the distribution of data.

MKT

� Statistica1 quality control (SQC) The general category of statistical tools used to evaluate organizational quality.

� Descriptive statistics Statistics used to describe quality characteristics and relationships.

We have all had the experience of purchasing a prod-uct only to discover that it is defective in some way or does not function the way it was designed to. This could be a new backpack with a broken zipper or an “out of the box” malfunctioning computer printer. Many of us have struggled to assemble a product the manufacturer has indicated would need only “minor” assembly, only to find that a piece of the product is missing or defective. As con- sumers, we expect the products we purchase to function as intended. However, producers of products know that it is not always possible to inspect every product and every aspect of the production process at all times. The chal-

lenge is to design ways to maximize the ability to monitor the quality of products being produced and eliminate defects.

One way to ensure a quality product is to build quality into the process. Consider Steinway & Sons, the maker of premier pianos used in concert halls all over the world. Steinway has been making pianos since the 1880s. Since that time, the company’s manufacturing process has not changed significantly. It takes the company nine months to a year to produce a piano by fashioning some 12,000 hand-crafted parts, carefully measuring and monitoring every part of the process. Although many of Steinway’s competitors have moved to mass production, where pianos can be assem- bled in 20 days, Steinway has maintained a strategy of quality defined by skill and craftsmanship. Steinway’s production process is focused on meticulous process preci- sion and extremely high product consistency. This has contributed to making its name synonymous with top quality.

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WHAT IS STATISTICAL QUALITY CONTROL?

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WHAT IS STATISTICAL QUALITY CONTROL? • 199

2. Statistical process control (SPC) involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range. SPC answers the question of whether or not the process is functioning properly.

3. Acceptance sampling is the process of randomly inspecting a sample of goods and deciding whether to accept the entire lot based on the results. Acceptance sampling determines whether a batch of goods should be accepted or rejected.

The tools in each of these categories provide different types of information for use in analyzing quality. Descriptive statistics are used to describe certain quality char- acteristics, such as the central tendency and variability of observed data. Although descriptions of certain characteristics are helpful, they are not enough to help us evalu- ate whether there is a problem with quality. Acceptance sampling can help us do this. It helps us decide whether desirable quality has been achieved for a batch of products and whether to accept or reject the items produced. Although this information is help- ful in making the quality acceptance decision after the product has been produced, it does not help us identify and catch a quality problem during the production process. For this we need tools in the statistical process control (SPC) category.

All three of these statistical quality control categories are helpful in measuring and evaluating the quality of products or services. However, statistical process control (SPC) tools are used most frequently because they identify quality problems during the production process. For this reason, we will devote most of the chapter to this cat- egory of tools. The quality control tools we will be learning about do not only measure the value of a quality characteristic; they also help us identify a change or variation in some quality characteristic of the product or process. We will first see what types of variation we can observe when measuring quality. Then we will be able to identify the specific tools to use for measuring this variation.

Variation in the production process leads to quality defects and lack of product consis- tency. The Intel Corporation, the world’s largest and most prof- itable manufacturer of micro- processors, understands this. Therefore, Intel has implemented a program it calls “copy-exactly” at all its manufacturing facili- ties. The idea is that regardless of whether the chips are made in Arizona, New Mexico, Ireland, or any of its other plants, they are made in exactly the same way. This means using the same equipment, the same exact materials, and performing the same tasks in the exact same order. The level of detail to which the “copy-exactly” concept goes is meticulous. For example, when a chip- making machine was found to be a few feet longer at one facility than another, Intel made them match. When water quality was found to be different at one facility, Intel instituted a purification system to eliminate any differences. Even when a worker was found polishing equipment in one direction, he was asked to do it in the approved cir- cular pattern. Why such attention to exactness of detail? The reason is to minimize all variation. Now let’s look at the different types of variation that exist.

� Statistical process control (SPC) A statistical tool that involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range.

� Acceptance sampling The process of randomly inspecting a sample of goods and deciding whether to accept the entire lot based on the results.

LINKS TO PRACTICE Intel Corporation www.intel.com

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200 • CHAPTER 6 STATISTICAL QUALITY CONTROL

If you look at bottles of a soft drink in a grocery store, you will notice that no two bottles are filled to exactly the same level. Some are filled slightly higher and some slightly lower. Similarly, if you look at blueberry muffins in a bakery, you will notice that some are slightly larger than others and some have more blueberries than others. These types of differences are completely normal. No two products are exactly alike because of slight differences in materials, workers, machines, tools, and other factors. These are called common, or random, causes of variation. Common causes of varia- tion are based on random causes that we cannot identify. These types of variation are unavoidable and are due to slight differences in processing.

An important task in quality control is to find out the range of natural random vari- ation in a process. For example, if the average bottle of a soft drink called Cocoa Fizz contains 16 ounces of liquid, we may determine that the amount of natural variation is between 15.8 and 16.2 ounces. If this were the case, we would monitor the production process to make sure that the amount stays within this range. If production goes out of this range—bottles are found to contain on average 15.6 ounces—this would lead us to believe that there is a problem with the process because the variation is greater than the natural random variation.

The second type of variation that can be observed involves those where the causes can be precisely identified and eliminated. These are called assignable causes of variation. Examples of this type of variation are poor quality in raw materials, an employee who needs more training, or a machine in need of repair. In each of these examples, the problem can be identified and corrected. If the variation is allowed to persist, it will continue to create a problem in the quality of the product. In the example of the soft-drink bottling operation, bottles filled with 15.6 ounces of liquid would signal a problem. The machine may need to be readjusted, an assignable cause of variation. We can assign the variation to a particular cause (machine needs to be readjusted) and we can correct the problem (readjust the machine).

� Common causes of variation Random causes that cannot be identified.

� Assignable causes of variation Causes that can be identified and eliminated.

SOURCES OF VARIATION: COMMON AND ASSIGNABLE CAUSES

DESCRIPTIVE STATISTICS Descriptive statistics can be helpful in describing certain characteristics of a product and a process. The most important descriptive statistics are measures of central ten- dency such as the mean, measures of variability such as the standard deviation and range, and measures of the distribution of data. We first review these descriptive statistics and then see how we use them to measure changes in product and process characteristics.

The Mean In the soft-drink bottling example, we stated that the average bottle is filled with 16 ounces of liquid. The arithmetic average, or the mean, is a statistic that measures the central tendency of a set of data. Knowing the central point of a set of data is highly important. Just think how important that number is when you receive test scores!

To compute the mean, we simply sum all the observations and divide by the total number of observations. The equation for computing the mean is

x 5 a

i51 xi

� Mean (average) A statistic that measures the central tendency of a set of data.

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STATISTICAL PROCESS CONTROL METHODS • 201

where x � the mean xi � observation i, i � 1, . . . , n n � number of observations

The Range and Standard Deviation In the bottling example, we also stated that the amount of natural variation in the bottling process is between 15.8 and 16.2 ounces. This information provides us with the amount of variability of the data. It tells us how spread out the data are around the mean. There are two measures that can be used to determine the amount of varia- tion in the data. The first measure is the range, which is the difference between the largest and smallest observations. In our example, the range for natural variation is 0.4 ounces.

Another measure of variation is the standard deviation. The equation for comput- ing the standard deviation is

s 5ãa n

i51 1xi 2 x 2 2 n 2 1

where s � standard deviation of a sample x � the mean xi � observation i, i � 1, . . . , n n � the number of observations in the sample

Small values of the range and standard deviation mean that the observations are closely clustered around the mean. Large values of the range and standard deviation mean that the observations are spread out around the mean. Figure 6-1 illustrates the differ- ences between a small and a large standard deviation for our bottling operation. You can see that the figure shows two distributions, both with a mean of 16 ounces. How- ever, in the first distribution the standard deviation is large and the data are spread out far around the mean. In the second distribution the standard deviation is small and the data are clustered close to the mean.

Distribution of Data A third descriptive statistic used to measure quality characteristics is the shape of the distribution of the observed data. When a distribution is symmetric, there are the same number of observations below and above the mean. This is what we commonly find when only normal variation is present in the data. When a disproportionate number of observations are either above or below the mean, we say that the data have a skewed distribution. Figure 6-2 shows symmetric and skewed distributions for the bottling operation.

� Range The difference between the largest and smallest observations in a set of data.

� Standard deviation A statistic that measures the amount of data dispersion around the mean.

STATISTICAL PROCESS CONTROL METHODS Statistical process control methods employ descriptive statistics to monitor the quality of the product and process. As we have learned so far, there are common and assign- able causes of variation in the production of every product. Using statistical process control, we want to determine the amount of variation that is common or normal. Then we monitor the production process to make sure production stays within this

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202 • CHAPTER 6 STATISTICAL QUALITY CONTROL

normal range. That is, we want to make sure the process is in a state of control. The most commonly used tool for monitoring the production process is a control chart. Different types of control charts are used to monitor different aspects of the produc- tion process. In this section we will learn how to develop and use control charts.

Developing Control Charts A control chart (also called process chart or quality control chart) is a graph that shows whether a sample of data falls within the common or normal range of varia- tion. A control chart has upper and lower control limits that separate common from assignable causes of variation. The common range of variation is defined by the use of control chart limits. We say that a process is out of control when a plot of data reveals that one or more samples fall outside the control limits.

Figure 6-3 shows a control chart for the Cocoa Fizz bottling operation. The x axis represents samples (#1, #2, #3, etc.) taken from the process over time. The y axis rep- resents the quality characteristic that is being monitored (ounces of liquid). The center line (CL) of the control chart is the mean, or average, of the quality characteristic that is being measured. In Figure 6-3 the mean is 16 ounces. The upper control limit (UCL) is the maximum acceptable variation from the mean for a process that is in a state of control. Similarly, the lower control limit (LCL) is the minimum acceptable variation from the mean for a process that is in a state of control. In our example, the upper and

� Control chart A graph that shows whether a sample of data falls within the common or normal range of variation.

� Out of control The situation in which a plot of data falls outside preset control limits.

Normal distributions with varying standard deviations

FIGURE 6-1

Differences between symmetric and skewed distributions

FIGURE 6-2

FIGURE 6-3

Quality control chart for Cocoa Fizz

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STATISTICAL PROCESS CONTROL METHODS • 203

lower control limits are 16.2 and 15.8 ounces, respectively. You can see that if a sample of observations falls outside the control limits, we need to look for assignable causes.

The upper and lower control limits on a control chart are usually set at �3 standard deviations from the mean. If we assume that the data exhibit a normal distribution, these control limits will capture 99.74 percent of the normal variation. Control limits can be set at �2 standard deviations from the mean. In that case, control limits would capture 95.44 percent of the values. Figure 6-4 shows the percentage of values that fall within a particular range of standard deviation.

Looking at Figure 6-4, we can conclude that observations falling outside the set range represent assignable causes of variation. However, there is a small probability that a value that falls outside the limits is still due to normal variation. This is called Type I error, with the error being the chance of concluding that there are assignable causes of variation when only normal variation exists. Another name for this is alpha (a) risk, where alpha refers to the sum of the probabilities in both tails of the distribu- tion that fall outside the confidence limits. The chance of this happening is given by the percentage or probability represented by the shaded areas of Figure 6-5. For limits of �3 standard deviations from the mean, the probability of a Type I error is 0.26 per- cent (100% � 99.74%), whereas for limits of �2 standard deviations it is 4.56 percent (100% � 95.44%).

Types of Control Charts Control charts are one of the most commonly used tools in statistical process control. They can be used to measure any characteristic of a product, such as the weight of a cereal box, the number of chocolates in a box, or the volume of bottled water. The different characteristics that can be measured by control charts can be divided into two groups: variables and attributes. A control chart for variables is used to monitor characteristics that can be measured and have a continuum of values, such as height, weight, or volume. A soft-drink bottling operation is an example of a variable measure, since the amount of liquid in the bottles can be measured and can take on a number of different values. Other examples are the weight of a bag of sugar, the temperature of a baking oven, or the diameter of plastic tubing.

� Variable A product characteristic that can be measured and has a continuum of values (e.g., height, weight, or volume).

� Attribute A product characteristic that has a discrete value and can be counted.

FIGURE 6-4

Chance of Type I error for �3s (sigma-standard deviations)

FIGURE 6-5

Percentage of values captured by different ranges of standard deviation

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204 • CHAPTER 6 STATISTICAL QUALITY CONTROL

A control chart for attributes, on the other hand, is used to monitor characteristics that have discrete values and can be counted. Often they can be evaluated with a simple yes-or-no decision. Examples include color, taste, or smell. The monitoring of attri- butes usually takes less time than for variables because a variable needs to be measured (e.g., the bottle of soft drink contains 15.9 ounces of liquid). An attribute requires only a single decision, such as yes or no, good or bad, acceptable or unacceptable (e.g., the apple is good or rotten, the meat is good or stale, the shoes have a defect or do not have a defect, the light bulb works or it does not work), or counting the number of defects (e.g., the number of broken cookies in the box, the number of dents in the car, the number of barnacles on the bottom of a boat).

Statistical process control is used to monitor many different types of variables and attributes. In the next two sections we look at how to develop control charts for vari- ables and control charts for attributes.

CONTROL CHARTS FOR VARIABLES Control charts for variables monitor characteristics that can be measured and have a continuous scale, such as height, weight, volume, or width. When an item is inspected, the variable being monitored is measured and recorded. For example, if we were produc- ing candles, height might be an important variable, so we could take samples of candles and measure their heights. Two of the most commonly used control charts for variables monitor both the central tendency of the data (the mean) and the variability of the data (either the standard deviation or the range). Note that each chart monitors a different type of information. When observed values go outside the control limits, the process is assumed not to be in control. Production is stopped, and employees attempt to identify the cause of the problem and correct it. Next we look at how these charts are developed.

Mean (x-Bar) Charts A mean control chart is often referred to as an x-bar chart. It is used to monitor changes in the mean of a process. To construct a mean chart, we first need to construct the center line of the chart. To do this we take multiple samples and compute their means. Usually these samples are small, with about four or five observations. Each sample has its own mean, x. The center line of the chart is then computed as the mean of all k sample means, where k is the number of samples:

x 5 x1 1 x 2 1c1 xk

k To construct the upper and lower control limits of the chart, we use the following formulas:

Upper control limit (UCL) 5 x 1 zsx

Lower control limit (LCL) 5 x 2 zsx where x � the average of the sample means z � standard normal variable (2 for 95.44% confi dence, 3 for 99.74%

confi dence) sx � standard deviation of the distribution of sample means, computed as s/"n s � population (process) standard deviation n � sample size (number of observations per sample)

Example 6.1 shows the construction of a mean (x-bar) chart.

� x-bar chart A control chart used to monitor changes in the mean value of a process.

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CONTROL CHARTS FOR VARIABLES • 205

A quality control inspector at the Cocoa Fizz soft-drink company has taken 25 samples with 4 observa- tions each of the volume of bottles filled. The data and the computed means are shown in the table. If the standard deviation of the bottling operation is 0.14 ounces, use this information to develop control limits of 3 standard deviations for the bottling operation.

Sample Number

Observations (bottle volume in ounces) Average Range

1 2 3 4 X –

1 15.85 16.02 15.83 15.93 15.91 0.19 2 16.12 16.00 15.85 16.01 15.99 0.27 3 16.00 15.91 15.94 15.83 15.92 0.17 4 16.20 15.85 15.74 15.93 15.93 0.46 5 15.74 15.86 16.21 16.10 15.98 0.47 6 15.94 16.01 16.14 16.03 16.03 0.20 7 15.75 16.21 16.01 15.86 15.96 0.46 8 15.82 15.94 16.02 15.94 15.93 0.20 9 16.04 15.98 15.83 15.98 15.96 0.21

10 15.64 15.86 15.94 15.89 15.83 0.30 11 16.11 16.00 16.01 15.82 15.99 0.29 12 15.72 15.85 16.12 16.15 15.96 0.43 13 15.85 15.76 15.74 15.98 15.83 0.24 14 15.73 15.84 15.96 16.10 15.91 0.37 15 16.20 16.01 16.10 15.89 16.05 0.31 16 16.12 16.08 15.83 15.94 15.99 0.29 17 16.01 15.93 15.81 15.68 15.86 0.33 18 15.78 16.04 16.11 16.12 16.01 0.34 19 15.84 15.92 16.05 16.12 15.98 0.28 20 15.92 16.09 16.12 15.93 16.02 0.20 21 16.11 16.02 16.00 15.88 16.00 0.23 22 15.98 15.82 15.89 15.89 15.90 0.16 23 16.05 15.73 15.73 15.93 15.86 0.32 24 16.01 16.01 15.89 15.86 15.94 0.15 25 16.08 15.78 15.92 15.98 15.94 0.30

Total 398.75 7.17

• Before You Begin: Before developing control limits and constructing the chart, calculate the mean of all 25 samples. This will be the center line of the control data. Then compute the upper and lower control limits. To complete the control chart, notice that you actually plot sample means, not individual samples.

• Solution: The center line of the control data is the average of the samples:

x 5 398.75

25 x 5 15.95

The control limits are

UCL � x � zsx � 15.95 � 3 a0.14"4 b � 16.16 LCL � x � zsx � 15.95 � 3 a0.14"4 b � 15.74

EXAMPLE 6.1

Constructing a Mean (x-Bar) Chart

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206 • CHAPTER 6 STATISTICAL QUALITY CONTROL

The resulting control chart is

This can also be computed using a spreadsheet, as shown here.

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CONTROL CHARTS FOR VARIABLES • 207

Another way to construct the control limits is to use the sample range as an esti- mate of the variability of the process. Remember that the range is simply the difference between the largest and smallest values in the sample. The spread of the range can tell us about the variability of the data. In this case, control limits would be constructed as follows:

Upper control limit 1UCL 2 5 x 1 A2R Lower control limit 1LCL 2 5 x 2 A2R

where x � average of the sample means R � average range of the samples A2 � factor obtained from Table 6-1

Notice that A2 is a factor that includes 3 standard deviations of ranges and is depen- dent on the sample size being considered.

A quality control inspector at Cocoa Fizz is using the data from Example 6.1 to develop control limits. If the average range 1R 2 for the 25 samples is 0.29 ounces (computed as 7.1725 ) and the average mean 1x 2 of the observations is 15.95 ounces, develop 3-sigma control limits for the bottling operation.

• Before You Begin: To compute control limits from the sample range, remember that you need to look up the value of factor A2 from Table 6-1.

• Solution: x 5 15.95 ounces R 5 0.29

The value of A2 is obtained from Table 6-1. For n � 4, A2 � 0.73. This leads to the following limits:

The center of the control chart 5 CL 5 15.95 ounces

UCL 5 x 1 A2R 5 15.95 1 10.73 2 10.29 2 5 16.16 LCL 5 x 2 A2R 5 15.95 2 10.73 2 10.29 2 5 15.74

EXAMPLE 6.2

Constructing a Mean (x-Bar) Chart from the Sample Range

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208 • CHAPTER 6 STATISTICAL QUALITY CONTROL

Range (R) Charts Range (R) charts are another type of control chart for variables. Whereas x-bar charts measure a shift in the central tendency of the process, range charts monitor the disper- sion or variability of the process. The method for developing and using R-charts is the same as that for x-bar charts. The center line of the control chart is the average range, and the upper and lower control limits are computed as follows:

CD 5 R

UCL 5 D4R

LCL 5 D3R

where values for D4 and D3 are obtained from Table 6-1.

� Range (R) chart A control chart that monitors changes in the dispersion or variability of a process.

TABLE 6-1

Factors for 3-sigma control limits of x- and R-charts Source: Factors adapted from the ASTM Manual on Quality Control of Materials.

Sample Size n

Factor for x–-Chart Factor for R-Chart

A2 D3 D4 2 1.88 0 3.27

3 1.02 0 2.57

4 0.73 0 2.28

5 0.58 0 2.11

6 0.48 0 2.00

7 0.42 0.08 1.92

8 0.37 0.14 1.86

9 0.34 0.18 1.82

10 0.31 0.22 1.78

11 0.29 0.26 1.74

12 0.27 0.28 1.72

13 0.25 0.31 1.69

14 0.24 0.33 1.67

15 0.22 0.35 1.65

16 0.21 0.36 1.64

17 0.20 0.38 1.62

18 0.19 0.39 1.61

19 0.19 0.40 1.60

20 0.18 0.41 1.59

21 0.17 0.43 1.58

22 0.17 0.43 1.57

23 0.16 0.44 1.56

24 0.16 0.45 1.55

25 0.15 0.46 1.54

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CONTROL CHARTS FOR VARIABLES • 209

Using Mean and Range Charts Together You can see that mean and range charts are used to monitor different variables. The mean or x-bar chart measures the central tendency of the process, whereas the range chart measures the dispersion or variance of the process. Since both variables are important, it makes sense to monitor a process using both mean and range charts. It is possible to have a shift in the mean of the product but not a change in the dispersion.

The quality control inspector at Cocoa Fizz would like to develop a range (R) chart in order to monitor volume dispersion in the bottling process. Use the data from Example 6.1 to develop control limits for the sample range.

• Before You Begin: To develop control limits for the sample range, first compute the aver- age range of all 25 samples. Then use Table 6-1 to develop upper and lower control limits. To complete the control chart, you plot the sample ranges.

• Solution: From the data in Example 6.1 you can see that the average sample range is

R 5 7.17 25

R 5 0.29 n 5 4

From Table 6-1 for n � 4:

D4 5 2.28

D3 5 0

UCL 5 D4R 5 2.28 10.29 2 5 0.6612 LCL 5 D3R 5 0 10.29 2 5 0

The resulting control chart is

EXAMPLE 6.3

Constructing a Range (R) Chart

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210 • CHAPTER 6 STATISTICAL QUALITY CONTROL

For example, at the Cocoa Fizz bottling plant the machine setting can shift so that the average bottle filled contains not 16.0 ounces but 15.9 ounces of liquid. The disper- sion could be the same, and this shift would be detected by an x-bar chart but not by a range chart. This is shown in part (a) of Figure 6-6. On the other hand, there could be a shift in the dispersion of the product without a change in the mean. Cocoa Fizz may still be producing bottles with an average fill of 16.0 ounces. However, the dispersion of the product may have increased, as shown in part (b) of Figure 6-6. This condition would be detected by a range chart but not by an x-bar chart. Because a shift in either the mean or the range means that the process is out of control, it is important to use both charts to monitor the process.

FIGURE 6-6

Process shifts captured by x-charts and R-charts

CONTROL CHARTS FOR ATTRIBUTES Control charts for attributes are used for quality characteristics that are counted rather than measured. Attributes are discrete in nature and entail simple yes-or-no decisions, for example, the number of nonfunctioning light bulbs, the proportion of broken eggs in a carton, the number of rotten apples, the number of scratches on a tile, or the number of complaints received. Two of the most common types of control charts for attributes are p-charts and c-charts.

P-charts are used to measure the proportion of items in a sample that are defective. Examples are the proportion of broken cookies in a batch and the proportion of cars produced with a misaligned fender. P-charts are appropriate when both the number of

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CONTROL CHARTS FOR ATTRIBUTES • 211

defectives measured and the size of the total sample can be counted. A proportion can then be computed and used as the statistic of measurement.

C-charts count the actual number of defects. For example, we can count the num- ber of complaints from customers in a month, the number of bacteria in a petri dish, or the number of barnacles on the bottom of a boat. However, we cannot compute the proportion of complaints from customers, the proportion of bacteria in a petri dish, or the proportion of barnacles on the bottom of a boat. To summarize:

P-charts: Used when observations are placed in either of two groups. Examples:

● Defective or not defective ● Good or bad ● Broken or not broken

C-charts: Used when defects can be counted per unit of measure. Examples:

● Number of dents per item ● Number of complaints per unit of time (e.g., hour, month, year) ● Number of tears per unit of area (e.g., square foot, square meter)

Problem-Solving Tip The primary difference between using a p-chart and a c-chart is as follows. A p-chart is used when both the total sample size and the number of defects can be computed. A c-chart is used when we can compute only the number of defects but cannot compute the proportion that is defective.

P-Charts P-charts are used to measure the proportion that is defective in a sample. The computa- tion of the center line as well as the upper and lower control limits is similar to the com- putation for the other kinds of control charts. The center line is computed as the average proportion defective in the population, p. This is obtained by taking a number of sam- ple observations at random and computing the average value of p across all samples.

To construct the upper and lower control limits for a p-chart, we use the following formulas:

UCL 5 p 1 zsp LCL 5 p 2 zsp

where z � standard normal variable p � the sample proportion defective sp � the standard deviation of the average proportion defective

As with the other charts, z is selected to be either 2 or 3 standard deviations, depending on the amount of data we wish to capture in our control limits. Usually, however, the deviations are set at 3.

The sample standard deviation is computed as follows:

sp 5 Åp 11 2 p 2n where n is the sample size.

� p-chart A control chart that monitors the proportion of defects in a sample.

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212 • CHAPTER 6 STATISTICAL QUALITY CONTROL

A production manager at a tire manufacturing plant has inspected the number of defective tires in 20 random samples with 20 observations each. Following are the number of defective tires found in each sample:

Sample Number

Number of Defective

Tires

Number of Observations

Sampled Fraction Defective

1 3 20 0.15 2 2 20 0.10 3 1 20 0.05 4 2 20 0.10 5 1 20 0.05 6 3 20 0.15 7 3 20 0.15 8 2 20 0.10 9 1 20 0.05

10 2 20 0.10 11 3 20 0.15 12 2 20 0.10 13 2 20 0.10 14 1 20 0.05 15 1 20 0.05 16 2 20 0.10 17 4 20 0.20 18 3 20 0.15 19 1 20 0.05 20 1 20 0.05

Total 40 400

Construct a 3-sigma control chart (z = 3) with this information.

• Before You Begin: To solve this problem, you should use a p-chart because both the total sample size and the number of defects are provided.

• Solution: The center line of the chart is

CL 5 p 5 total number of defective tires total number of observations

5 40

400 5 0.10

sp 5 Åp 11 2 p 2n 5 Å 10.10 2 10.90 220 5 0.067 UCL 5 p 1 z 1sp 2 5 0.10 1 3 10.067 2 5 0.301 LCL 5 p 2 z 1sp 2 5 0.10 2 3 10.067 2 5 20.101 S 0

In this example the lower control limit is negative, which sometimes occurs because the computation is an approximation of the binomial distribution. When this occurs, the LCL is rounded up to zero because we cannot have a negative control limit.

EXAMPLE 6.4

Constructing a p-Chart

Gustau Nacarino/Reuters/ Landov LLC

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CONTROL CHARTS FOR ATTRIBUTES • 213

The resulting control chart is as follows:

This can also be computed using a spreadsheet, as shown here.

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214 • CHAPTER 6 STATISTICAL QUALITY CONTROL

C-Charts C-charts are used to monitor the number of defects per unit. Examples are the number of returned meals in a restaurant, the number of trucks that exceed their weight limit in a month, the number of discolorations on a square foot of carpet, and the number of bacteria in a milliliter of water. Note that the types of units of measurement we are considering are a period of time, a surface area, or a volume of liquid.

The average number of defects, c, is the center line of the control chart. The upper and lower control limits are computed as follows:

UCL 5 c 1 z"c LCL 5 c 2 z"c

� c-chart A control chart used to monitor the number of defects per unit.

The number of weekly customer complaints are monitored at a large hotel using a c-chart. Com- plaints have been recorded over the past 20 weeks. Develop 3-sigma control limits using the following data:

Total

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 No. of Complaints 3 2 3 1 3 3 2 1 3 1 3 4 2 1 1 1 3 2 2 3 44

• Before You Begin: To solve this problem, you should use a c-chart because only the num- ber of defects (complaints) is provided and you cannot compute the proportion defective.

• Solution: The average number of complaints per week is

44 20

5 2.2. Therefore, c = 2.2.

UCL 5 c 1 z"c 5 2.2 1 3"2.2 5 6.65 LCL 5 c 2 z"c 5 2.2 2 3"2.2 5 22.25 S 0

As in the previous example, the LCL is negative and should be rounded up to zero. Following is the control chart for this example:

LCL CL UCL p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Week

C om

pl ai

nt s

pe r

W ee

EXAMPLE 6.5

Computing a c-Chart

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CONTROL CHARTS FOR ATTRIBUTES • 215

This can also be computed using a spreadsheet, as shown here.

Before You Go On

We have discussed several types of statistical quality control (SQC) techniques. One category of SQC techniques consists of descriptive statistics such as the mean, range, and standard deviation. These tools are used to describe quality characteristics and relationships. Another category of SQC techniques consists of statistical process control (SPC) methods that are used to monitor changes in the production process. To understand SPC methods you must understand the differences between common and assignable causes of variation. Common causes of variation are

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216 • CHAPTER 6 STATISTICAL QUALITY CONTROL

So far, we have discussed ways of monitoring the production process to ensure that it is in a state of control and that there are no assignable causes of variation. A critical aspect of statistical quality control is evaluating the ability of a production process to meet or exceed preset specifications. This is called process capability. To understand exactly what this means, let’s look more closely at the term specification. Product specifications, often called tolerances, are preset ranges of acceptable quality charac- teristics, such as product dimensions. For a product to be considered acceptable, its characteristics must fall within this preset range. Otherwise, the product is not accept- able. Product specifications, or tolerance limits, are usually established by design engi- neers or product design specialists.

For example, the specifications for the width of a machine part may be specified as 15 inches �0.3. This means that the width of the part should be 15 inches, though it is acceptable if it falls within the limits of 14.7 inches and 15.3 inches. Similarly, for Cocoa Fizz, the average bottle fill may be 16 ounces with tolerances of �0.2 ounces. Although the bottles should be filled with 16 ounces of liquid, the amount can be as low as 15.8 or as high as 16.2 ounces.

Specifications for a product are preset on the basis of how the product is going to be used or what customer expectations are. As we have learned, any production process has a certain amount of natural variation associated with it. To be capable of producing an acceptable product, the process variation cannot exceed the preset specifications. Process capability thus involves evaluating process variability relative to preset product specifications in order to determine whether the process is capable of producing an acceptable product. In this section we will learn how to measure process capability.

Measuring Process Capability Simply setting up control charts to monitor whether a process is in control does not guarantee process capability. To produce an acceptable product, the process must be capable and in control before production begins. Let’s look at three exam- ples of process variation relative to design specifications for the Cocoa Fizz soft- drink company. Let’s say that the specification for the acceptable volume of liquid is preset at 16 ounces �0.2 ounces, which is 15.8 and 16.2 ounces. In part (a) of Figure 6-7 the process produces 99.74 percent (3 sigma) of the product with vol- umes between 15.8 and 16.2 ounces. You can see that the process variability closely matches the preset specifications. Almost all the output falls within the preset spec- ification range.

� Process capability The ability of a production process to meet or exceed preset specifications.

� Product specifications Preset ranges of acceptable quality characteristics.

random causes that cannot be identified. A certain amount of common or normal variation occurs in every process due to differences in materials, workers, machines, and other factors. Assignable causes of variation, on the other hand, are variations that can be identified and eliminated. An important part of statistical process control (SPC) is monitoring the production process to make sure that the only variations in the process are those due to common or normal causes. Under these conditions, we say that a production process is in a state of control.

You should also understand the different types of quality control charts that are used to monitor the produc- tion process: x-bar charts, R (range) charts, p-charts, and c-charts.

PROCESS CAPABILITY

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PROCESS CAPABILITY • 217

In part (b) of Figure 6-7, however, the process produces 99.74 percent (3 sigma) of the product with volumes between 15.7 and 16.3 ounces. The process variability is out- side the preset specifications, and a large percentage of the product will fall outside the specified limits. This means that the process is not capable of producing the product within the preset specifications.

Part (c) of Figure 6-7 shows that the production process produces 99.74 percent (3 sigma) of the product with volumes between 15.9 and 16.1 ounces. In this case, the process variability is within specifications and the process exceeds the minimum capability.

Process capability is measured by the process capability index, Cp, which is com- puted as the ratio of the specification width to the width of the process variability:

Cp 5 specification width

process width 5

USL 2 LSL 6s

where the specification width is the difference between the upper specification limit (USL) and the lower specification limit (LSL) of the process. The process width is computed as 6 standard deviations (6s) of the process being monitored. The reason

� Process capability index An index used to measure process capability.

FIGURE 6-7

Relationship between process variability and specification width

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218 • CHAPTER 6 STATISTICAL QUALITY CONTROL

we use 6s is that most of the process measurement (99.74 percent) falls within �3 standard deviations, which is a total of 6 standard deviations.

There are three possible ranges of values for Cp that also help us interpret its value:

Cp � 1: A value of Cp equal to 1 means that the process variability just meets speci- fi cations, as in Figure 6-7(a). We would then say that the process is minimally capable.

Cp � 1: A value of Cp below 1 means that the process variability is outside the range of specifi cation, as in Figure 6-7(b). Th is means that the process is not capable of producing within specifi cation and must be improved.

Cp � 1: A value of Cp above 1 means that the process variability is tighter than speci- fi cations and the process exceeds minimal capability, as in Figure 6-7(c).

A Cp value of 1 means that 99.74 percent of the products produced will fall within the specification limits. This also means that 0.26 percent (100% � 99.74%) of the products will not be acceptable. Although this percentage sounds very small, when we think of it in terms of parts per million (ppm), we can see that it can still result in a lot of defects. The number 0.26 percent corresponds to 2600 parts per million (ppm) defective (0.0026 � 1,000,000). That number can seem very high if we think of it in terms of 2600 wrong prescriptions out of a million, or 2600 incorrect medical proce- dures out of a million, or even 2600 malfunctioning aircraft out of a million. You can see that this number of defects is still high. The way to reduce the ppm defective is to increase process capability.

Three bottling machines at Cocoa Fizz are being evaluated for their capability:

Bottling Machine Standard Deviation A 0.05 B 0.1 C 0.2

If specifications are set between 15.8 and 16.2 ounces, determine which of the machines are capable of producing within specifications.

• Before You Begin: To solve this problem, you need to compute the process capability index, Cp, for each machine. The machine with a Cp value at or above 1 is capable of producing within specifications.

• Solution: To determine the capability of each machine, we need to divide the specification width (USL � LSL � 16.2 � 15.8 � 0.4) by 6s for each machine:

Bottling Machine S USL � LSL 6S Cp 5

USL 2 LSL 6s

A 0.05 0.4 0.3 1.33 B 0.1 0.4 0.6 0.67 C 0.2 0.4 1.2 0.33

Looking at the Cp values, only machine A is capable of filling bottles within specifications because it is the only machine that has a Cp value at or above 1.

EXAMPLE 6.6

Computing the CP Value at Cocoa Fizz

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PROCESS CAPABILITY • 219

Cp is valuable in measuring process capability. However, it has one shortcoming: it assumes that process variability is centered on the specification range. Unfortunately, this is not always the case. Figure 6-8 shows data from the Cocoa Fizz example. In the figure the specification limits are set between 15.8 and 16.2 ounces, with a mean of 16.0 ounces. However, the process variation is not centered; it has a mean of 15.9 ounces. Because of this, a certain proportion of products will fall outside the specifica- tion range.

The problem illustrated in Figure 6-8 is not uncommon, and it can lead to mistakes in the computation of the Cp measure. Because of this, another measure for process capability is used more frequently:

Cpk 5 minaUSL 2 m3s , m 2 LSL3s b where m � the mean of the process s � the standard deviation of the process

This measure of process capability helps us address a possible lack of centering of the process over the specification range. To use this measure, the process capability of each half of the normal distribution is computed and the minimum of the two is used.

Looking at Figure 6-8, we can see that the computed Cp is 1:

Process mean m � 15.9

Process standard deviation s � 0.067

LSL � 15.8

USL � 16.2

Cp 5 0.4

6 10.067 2 5 1 The Cp value of 1.00 leads us to conclude that the process is capable. However, from

the graph you can see that the process is not centered on the specification range and is producing out-of-spec products. Using only the Cp measure would lead to an incorrect

FIGURE 6-8

Process variability not centered across specification width

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220 • CHAPTER 6 STATISTICAL QUALITY CONTROL

conclusion in this case. Computing Cpk gives us a different answer and leads us to a different conclusion:

Cpk 5 minaUSL 2 m3s , m 2 LSL3s b Cpk 5 mina16.2 2 15.93 10.067 2 , 15.9 2 15.83 10.067 2 b Cpk 5 min 11.49, .45 2 Cpk 5 .45

The computed Cpk value is less than 1, revealing that the process is not capable.

Compute the Cpk measure of process capability for the following machine and interpret the findings. What value would you have obtained with the Cp measure?

Machine data: USL 5 110

LSL 5 50

Process s 5 10

Process m 5 60

• Before You Begin: To solve this problem, you should compute both the Cpk and Cp measures and compare the findings. Remember that each measure needs to be at or above 1 for the process to be considered capable.

• Solution: Compute the Cpk measure of process capability:

Cpk 5 minaUSL 2 m3s , m 2 LSL3s b 5 mina110 2 60

3 110 2 , 60 2 503 110 2 b � min(1.67, 0.33)

� 0.33

This means that the process is not capable. The Cp measure of process capability gives us the following measure:

Cp 5 60

6 110 2 5 1 leading us to believe that the process is capable. The reason for the difference in the measures is that the process is not centered on the specification range, as shown in Figure 6-9.

EXAMPLE 6.7

Computing the Cpk Value

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SIX SIGMA QUALITY • 221

Process capability of machines is a critical element of statistical process control.

M ic

ha el

R os

en fe

ld /G

et ty

Im ag

es , I

nc .

FIGURE 6-9

Process variability not centered across specification width for Example 6.7

The term Six Sigma® was coined by the Motorola Corporation in the 1980s to describe the high level of quality the company was striving to achieve. Sigma (s) stands for the number of standard deviations of the process. Recall that �3 sigma (s) means that 2600 ppm are defective. The level of defects associated with Six Sigma is approximately 3.4 ppm. Figure 6-10 shows a process distribution with quality levels of �3 sigma (s) and �6 sigma (s). You can see the difference in the number of defects produced.

� Six Sigma quality A high level of quality associated with approximately 3.4 defective parts per million.

SIX SIGMA QUALITY

FIGURE 6-10

PPM defective for �3s versus �6s quality (not to scale)

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222 • CHAPTER 6 STATISTICAL QUALITY CONTROL

To achieve the goal of Six Sigma, Motorola has instituted a quality focus in every aspect of its organization. Before a product is designed, market- ing ensures that product characteris- tics are exactly what customers want. Operations ensures that exact prod- uct characteristics can be achieved through product design, the manu- facturing process, and the materials used. The Six Sigma concept is an inte- gral part of other functions as well. It

is used in the finance and accounting departments to reduce costing errors and the time required to close the books at the end of the month. Numerous other compa- nies, such as General Electric, Lockheed Martin, Allied Signal, American Express, and Texas Instruments, have followed Motorola’s leadership and have also instituted the Six Sigma concept. In fact, the Six Sigma quality standard has become a benchmark in many industries.

There are two aspects to implementing the Six Sigma concept. The first is the use of technical tools to identify and eliminate causes of quality problems. In fact, Six Sigma relies heavily on quantitative and data-driven technical tools. These technical tools include the statistical quality control tools discussed in this chapter and also the problem-solving tools discussed in Chapter 5, such as cause-and-effect diagrams, flow charts, and Pareto analysis. In Six Sigma programs, the use of these technical tools is integrated throughout the entire organizational system.

The second aspect of Six Sigma implementation is people involvement. In Six Sigma, all employees have the training to use technical tools and are responsible for rooting out quality problems. Employees are given martial arts titles that reflect their skills in the Six Sigma process. Black belts and master black belts are individuals who have extensive training in the use of technical tools and are responsible for carrying out the implementation of Six Sigma. They are experienced individuals who oversee the measuring, analyzing, process controlling, and improving. They achieve this by acting as coaches, team leaders, and facilitators of the process of continuous improve- ment. Green belts are individuals who have sufficient training in technical tools to serve on teams or on small, individual projects.

The Six Sigma approach is organized around a five-step plan known as DMAIC, which stands for Define, Measure, Analyze, Improve, and Control:

Step 1: Defi ne the quality problem of the process. Step 2: Measure the current performance of the process. Step 3: Analyze the process to identify the root cause of the quality problem. Step 4: Improve the process by eliminating the root causes of the problem. Step 5: Control the process to ensure the improvements continue.

The first three steps provide a study of the existing process, whereas the last two steps are involved in process change. All steps extensively utililze quantitative tools, such as mea- suring the current performace and analyzing the process for root causes of problems. You can also see that like the PDSA cycle described in Chapter 5, this is a circular process that is ongoing and never ends. Part of Six Sigma is to continuously search for quality problems and improve upon them. In organizations, this effort is led by the black belts.

LINKS TO PRACTICE Motorola, Inc.

www.motorola.com

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ACCEPTANCE SAMPLING • 223

Successful Six Sigma implementation requires commitment from top company leaders. These individuals must promote the process, eliminate barriers to implemen- tation, and ensure that proper resources are available. A key individual is a champion of Six Sigma. This is a person who comes from the top ranks of the organization and is responsible for providing direction and overseeing all aspects of the process.

ACCEPTANCE SAMPLING Acceptance sampling, the third branch of statistical quality control, refers to the pro- cess of randomly inspecting a certain number of items from a lot or batch in order to decide whether to accept or reject the entire batch. What makes acceptance sampling different from statistical process control is that acceptance sampling is performed either before or after the process, rather than during the process. Acceptance sam- pling before the process involves sampling materials received from a supplier, such as randomly inspecting crates of fruit that will be used in a restaurant, boxes of glass dishes that will be sold in a department store, or metal castings that will be used in a machine shop. Sampling after the process involves sampling finished items that are to be shipped either to a customer or to a distribution center. Examples include ran- domly testing a certain number of computers from a batch to make sure they meet operational requirements and randomly inspecting snow boards to make sure that they are not defective.

You may be wondering why we would inspect only some items in the lot and not the entire lot. Acceptance sampling is used when inspecting every item is not physi- cally possible or would be overly expensive or when inspecting a large number of items would lead to errors due to worker fatigue. This last concern is especially important when a large number of items are processed in a short period of time. Another exam- ple of when acceptance sampling would be used is in destructive testing, such as test- ing eggs for salmonella or crash-testing vehicles. Obviously, in these cases it would not be helpful to test every item! However, 100 percent inspection does make sense if the cost of inspecting an item is less than the cost of passing on a defective item.

As you will see in this section, the goal of acceptance sampling is to determine the criteria for acceptance or rejection based on the size of the lot, the size of the sample, and the level of confidence we wish to attain. Acceptance sampling can be used in both attribute and variable measures, though it is most commonly used for attri butes. In this section we will look at the different types of sampling plans and at ways to evaluate how well sampling plans discriminate between good and bad lots.

Sampling Plans A sampling plan is a plan for acceptance sampling that precisely specifies the param- eters of the sampling process and the acceptance/rejection criteria. The variables to be specified include the size of the lot (N), the size of the sample inspected from the lot (n), the number of defects above which a lot is rejected (c), and the number of samples that will be taken.

There are different types of sampling plans. Some call for single sampling, in which a random sample is drawn from every lot. Each item in the sample is examined and is labeled as either “good” or “bad.” Depending on the number of defects or “bad” items found, the entire lot is either accepted or rejected. For example, a lot size of 50 cookies is evaluated for acceptance by randomly inspecting 10 cookies from the lot. The cook- ies may be inspected to make sure they are not broken or burned. If 4 or more of the 10 cookies inspected are bad, the entire lot is rejected. In this example, the lot size is

� Sampling plan A plan for acceptance sampling that precisely specifies the parameters of the sampling process and the acceptance/rejection criteria.

Sampling involves randomly inspecting items from a lot.

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224 • CHAPTER 6 STATISTICAL QUALITY CONTROL

N � 50, the sample size is n � 10, and the maximum number of defects at which a lot is accepted is c � 4. These parameters define the acceptance sampling plan.

Another type of acceptance sampling is called double sampling. This provides an opportunity to sample the lot a second time if the results of the first sample are inconclusive. In double sampling we first sample a lot of goods according to preset criteria for definite acceptance or rejection. However, if the results fall in the middle range, they are considered inconclusive and a second sample is taken. For example, a water treatment plant may sample the quality of the water ten times in random inter- vals throughout the day. Criteria may be set for acceptable or unacceptable water qual- ity, such as 0.05 percent chlorine and 0.1 percent chlorine. However, a sample of water containing between .05 percent and 0.1 percent chlorine is inconclusive and calls for a second sample of water.

In addition to single- and double-sampling plans, there are multiple-sampling plans. Multiple-sampling plans are similar to double-sampling plans except that cri- teria are set for more than two samples. The decision as to which sampling plan to select has a great deal to do with the cost involved in sampling, the time consumed by sampling, and the cost of passing on a defective item. In general, if the cost of col- lecting a sample is relatively high, single sampling is preferred. An extreme example is collecting a biopsy from a hospital patient. Because the actual cost of getting the sample is high, we want to get a large sample and sample only once. The opposite is true when the cost of collecting the sample is low but the actual cost of testing is high. This may be the case at a water treatment plant, where collecting the water is inexpensive but the chemical analysis is costly. In this section we focus primarily on single-sampling plans.

Operating Characteristic (OC) Curves As we have seen, different sampling plans have different capabilities for discriminat- ing between good and bad lots. At one extreme is 100 percent inspection, which has perfect discriminating power. However, as the size of the sample inspected decreases, so does the chance of accepting a defective lot. We can show the discriminating power of a sampling plan on a graph by means of an operating characteristic (OC) curve. This curve shows the probability or chance of accepting a lot given various propor- tions of defects in the lot.

Figure 6-11 shows a typical OC curve. The x axis shows the percentage of items that are defective in a lot. This is called “lot quality.” The y axis shows the probability or chance of accepting a lot. You can see that if we use 100 percent inspection, we are certain of accepting only lots with zero defects. However, as the proportion of defects in the lot increases, our chance of accepting the lot decreases. For example, we have a 90 percent probability of accepting a lot with 5 percent defects and an 80 percent prob- ability of accepting a lot with 8 percent defects.

Regardless of which sampling plan we have selected, the plan is not perfect. That is, there is still a chance of accepting lots that are “bad” and rejecting “good” lots. The steeper the OC curve, the better our sampling plan is for discriminating between “good” and “bad.” Figure 6-12 shows three different OC curves, A, B, and C. Curve A is the most discriminating and curve C the least. You can see that the steeper the slope of the curve, the more discriminating is the sampling plan. When 100 percent inspec- tion is not possible, there is a certain amount of risk for consumers in accepting defec- tive lots and a certain amount of risk for producers in rejecting good lots.

There is a small percentage of defects that consumers are willing to accept. This is called the acceptable quality level (AQL) and is generally on the order of 1–2

� Operating characteristic (OC) curve A graph that shows the probability or chance of accepting a lot given various proportions of defects in the lot.

� Acceptable quality level (AQL) The small percentage of defects that consumers are willing to accept.

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ACCEPTANCE SAMPLING • 225

percent. However, sometimes the percentage of defects that passes through is higher than the AQL. Consumers will usually tolerate a few more defects, but at some point the number of defects reaches a threshold level beyond which consumers will not tol- erate them. This threshold level is called the lot tolerance percent defective (LTPD). The LTPD is the upper limit of the percentage of defective items consumers are will- ing to tolerate.

Consumer’s risk is the chance or probability that a lot will be accepted that con- tains a greater number of defects than the LTPD limit. This is the probability of mak- ing a Type II error—that is, accepting a lot that is truly “bad.” Consumer’s risk or Type II error is generally denoted by beta (b). The relationships among AQL, LTPD, and b are shown in Figure 6-13. Producer’s risk is the chance or probability that a lot con- taining an acceptable quality level will be rejected. This is the probability of making a Type I error—that is, rejecting a lot that is “good.” It is generally denoted by alpha (a). Producer’s risk is also shown in Figure 6-13.

We can determine from an OC curve what the consumer’s and producer’s risks are. However, these values should not be left to chance. Rather, sampling plans are usually designed to meet specific levels of consumer’s and producer’s risk. For example, one common combination is to have a consumer’s risk (b) of 10 percent and a producer’s risk (a) of 5 percent, though many other combinations are possible.

� Lot tolerance percent defective (LTPD) The upper limit of the percentage of defective items consumers are willing to tolerate. � Consumer’s risk The chance of accepting a lot that contains a greater number of defects than the LTPD limit. � Producer’s risk The chance that a lot containing an acceptable quality level will be rejected.

FIGURE 6-11

Example of an operating characteristic (OC) curve

FIGURE 6-12

OC curves with different steepness levels and different levels of discrimination

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226 • CHAPTER 6 STATISTICAL QUALITY CONTROL

Developing OC Curves An OC curve graphically depicts the discriminating power of a sampling plan. To draw an OC curve, we typically use a cumulative binomial distribution to obtain prob- abilities of accepting a lot given varying levels of lot defects.1 The cumulative binomial table is found in Appendix C, and a small part is reproduced in Table 6-2. The top of the table shows values of p, which represents the proportion of defective items in a lot (5 percent, 10 percent, 20 percent, etc.). The left-hand column shows values of n, which represents the sample size being considered, and x represents the cumulative number of defects found. Let’s use an example to illustrate how to develop an OC curve for a specific sampling plan using the information from Table 6-2.

FIGURE 6-13

An OC curve showing producer’s risk (a) and consumer’s risk (b)

TABLE 6-2

Partial Cumulative Binomial Probability Table

Proportion of Items Defective (p)

.05 .10 .15 .20 .25 .30 .35 .40 .45 .50

n x

5 0 .7738 .5905 .4437 .3277 .2373 .1681 .1160 .0778 .0503 .0313 1 .9974 .9185 .8352 .7373 .6328 .5282 .4284 .3370 .2562 .1875

2 .9988 .9914 .9734 .9421 .8965 .8369 .7648 .6826 .5931 .5000

1For n 20 and p 0.05, a Poisson distribution is generally used.

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ACCEPTANCE SAMPLING • 227

Average Outgoing Quality As we observed with the OC curves, the higher the quality of the lot, the higher the chance that it will be accepted. Conversely, the lower the quality of the lot, the greater the chance that it will be rejected. Given that some lots are accepted and some rejected, it is useful to compute the average outgoing quality (AOQ) of lots to get a sense of the overall outgoing quality of the product. Assuming that all lots have the

� Average outgoing quality (AOQ) The expected proportion of defective items that will be passed to the customer under the sampling plan.

Let’s say that we want to develop an OC curve for a sampling plan in which a sample of n � 5 items is drawn from lots of N � 1000 items. The accept/reject criteria are set up in such a way that we accept a lot if no more than one defect (c � 1) is found.

• Solution: Let’s look at the partial binomial distribution in Table 6-2. Since our criteria require us to sample n � 5, we will go to the row where n equals 5 in the left-hand column. The “x” column tells us the cumulative number of defects found at which we reject the lot. Since we are not allowing more than one defect, we look for an x value that corresponds to 1. The row corresponding to n � 5 and x � 1 tells us our chance or probability of accepting lots with various proportions of defects using this sampling plan. For example, with this sampling plan we have a 99.74 percent chance of accepting a lot with 5 percent defects. If we move down the row, we can see that we have a 91.85 percent chance of accepting a lot with 10 percent defects, a 83.52 percent chance of accepting a lot with 15 percent defects, and a 73.73 percent chance of accepting a lot with 20 percent defects. Using these values and those remaining in the row, we can construct an OC chart for n � 5 and c � 1. This is shown in Figure 6-14.

EXAMPLE 6.8

Constructing an OC Curve

FIGURE 6-14

OC curve with n � 5 and c � 1

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228 • CHAPTER 6 STATISTICAL QUALITY CONTROL

same proportion of defective items, the average outgoing quality can be computed as follows:

AOQ 5 1Pac 2paN 2 nN b where Pac � probability of accepting a given lot p � proportion of defective items in a lot N � the size of the lot n � the sample size chosen for inspection

Usually, we assume the fraction in the previous equation to equal 1 and simplify the equation to the following form:

AOQ 5 1Pac 2 We can then use the information from Figure 6-14 to construct an AOQ curve for dif- ferent levels of probabilities of acceptance and different proportions of defects in a lot. As we will see, an AOQ curve is similar to an OC curve.

Let’s go back to our initial example, in which we sampled five items (n � 5) from a lot of 1000 (N � 1000) with an acceptance range of no more than 1 (c � 1) defect. Here we will construct an AOQ curve for this sampling plan and interpret its meaning.

• Solution: For the parameters N � 1000, n � 5, and c � 1, we can read the probabilities of Pac from Figure 6-14. Then we can compute the value of AOQ as AOQ � (Pac)p.

p .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 Pac .9974 .9185 .8352 .7373 .6328 .5282 .4284 .3370 .2562 .1875 AOQ .0499 .0919 .1253 .1475 .1582 .1585 .1499 .1348 .1153 .0938

Figure 6–15 shows a graphical representation of the AOQ values. The AOQ varies, depending on the proportion of defective items in the lot. The largest value of AOQ, called the average outgoing qual- ity limit (AOQL), is around 15.85 percent. You can see from Figure 6-15 that the average outgoing quality will be high for lots that are either very good or very bad. For lots that have close to 30 percent of defective items, the AOQ is the highest. Managers can use this information to compute the worst possible value of their average outgoing quality given the proportion of defective items (p). Then this information can be used to develop a sampling plan with appropriate levels of discrimination.

EXAMPLE 6.9

Constructing an AOQ Curve

FIGURE 6-15

The AOQ for n � 5 and c � 1

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IMPLICATIONS FOR MANAGERS • 229

In this chapter we have learned about a variety of different statistical quality control (SQC) tools that help managers make decisions about product and process quality. However, to use these tools properly managers must make a number of decisions. In this section we discuss some of the most important decisions that must be made when implementing SPC.

How Much and How Often to Inspect Consider Product Cost and Product Volume As you know, 100 percent inspection is rarely possible. The question then becomes one of how often to inspect in order to minimize the chances of passing on defects and still keep inspection costs manageable. This decision should be related to the product cost and product volume of what is being produced. At one extreme are high-volume, low-cost items, such as paper, pencils, nuts and bolts, for which the cost of 100 percent inspection would not be justified. Also, with such a large volume, 100 percent inspection would not be possible because worker fatigue sets in and defects are often passed on. At the other extreme are low- volume, high-cost items, such as parts that will go into a space shuttle or be used in a medical procedure, that require 100 percent inspection.

Most items fall somewhere between the two extremes just described. For these items, frequency of inspection should be designed to consider the trade-off between the cost of inspection and the cost of passing on a defective item. Historically, inspec- tions were set up to minimize these two costs. Today, it is believed that defects of any type should not be tolerated and that eliminating them helps reduce organizational costs. Still, the inspection process should be set up to consider issues of product cost and volume. For example, one company will probably have different frequencies of inspection for different products.

Consider Process Stability Another issue to consider when deciding how much to inspect is the stability of the process. Stable processes that do not change frequently do not need to be inspected often. On the other hand, processes that are unstable and change often should be inspected frequently. For example, if it has been observed that a particular type of drilling machine in a machine shop often goes out of tolerance, that machine should be inspected frequently. Obviously, such decisions cannot be made without historical data on process stability.

Consider Lot Size The size of the lot or batch being produced is another factor to consider in determining the amount of inspection. A company that produces a small number of large lots will have a smaller number of inspections than a company that produces a large number of small lots. The reason is that every lot should have some inspection, and when lots are large, there are fewer lots to inspect.

Where to Inspect Since we cannot inspect every aspect of a process all the time, another important deci- sion is where to inspect. Some areas are less critical than others. Following are some points that are typically considered most important for inspection.

IMPLICATIONS FOR MANAGERS

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230 • CHAPTER 6 STATISTICAL QUALITY CONTROL

Inbound Materials Materials that are coming into a facility from a supplier or dis- tribution center should be inspected before they enter the production process. It is important to check the quality of materials before labor is added to it. For example, it would be wasteful for a seafood restaurant not to inspect the quality of incoming lobsters only to later discover that its lobster bisque is bad. Another reason for check- ing inbound materials is to check the quality of sources of supply. Consistently poor quality in materials from a particular supplier indicates a problem that needs to be addressed.

Finished Products Products that have been completed and are ready for shipment to customers should also be inspected. This is the last point at which the product is in the production facility. The quality of the product represents the company’s overall qual- ity. The final quality level is what will be experienced by the customer, and an inspec- tion at this point is necessary to ensure high quality in such aspects as fitness for use, packaging, and presentation.

Prior to Costly Processing During the production process it makes sense to check quality before performing a costly process on the product. If quality is poor at that point and the product will ultimately be discarded, adding a costly process will simply lead to waste. For example, in the production of leather armchairs in a furniture fac- tory, chair frames should be inspected for cracks before the leather covering is added. Otherwise, if the frame is defective, the cost of the leather upholstery and workman- ship may be wasted.

Which Tools to Use In addition to where and how much to inspect, managers must decide which tools to use in the process of inspection. As we have seen, tools such as control charts are best used at various points in the production process. Acceptance sampling is best used for inbound and outbound materials. It is also the easiest method to use for attribute measures, whereas control charts are easier to use for variable measures. Surveys of industry practices show that most companies use control charts, especially x-bar and R-charts, because they require less data collection than p-charts.

STATISTICAL QUALITY CONTROL IN SERVICES Statistical quality control (SQC) tools have been widely used in manufacturing organi- zations for quite some time. Manufacturers such as Motorola, General Electric, Toy- ota, and others have shown leadership in SQC for many years. Unfortunately, service organizations have lagged behind manufacturing firms in their use of SQC. The pri- mary reason is that statistical quality control requires measurement, and it is difficult to measure the quality of a service. Remember that services often provide an intangible product and that perceptions of quality are often highly subjective. For example, the quality of a service is often judged by such factors as friendliness and courtesy of the staff and promptness in resolving complaints.

A way to measure the quality of services is to devise quantifiable measurements of the important dimensions of a particular service. For example, the number of com- plaints received per month, the number of telephone rings after which a response is

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STATISTICAL QUALITY CONTROL IN SERVICES • 231

received, or customer waiting time can be quantified. These types of measurements are not subjective or subject to interpretation. Rather, they can be measured and recorded. As in manufacturing, acceptable control limits should be developed and the variable in question should be measured periodically.

Another issue that complicates quality control in service organizations is that the service is often consumed during the production process. The customer is often pres- ent during service delivery, and there is little time to improve quality. The workforce that interfaces with customers is part of the service delivery. The way to manage this issue is to provide a high level of workforce training and to empower workers to make decisions that will satisfy customers.

One service organization that has demonstrated quality leadership is The Ritz-Carlton Hotel Company. This luxury hotel chain caters to travelers who seek high levels of cus- tomer service. The goal of the chain is to be recognized for outstanding service quality. To this end, com- puter records are kept of regular cli- ents’ preferences. To keep customers happy, employees are empowered to spend up to $2000 on the spot to correct any customer complaint. Consequently, The Ritz-Carlton has received a number of quality awards, including winning the Malcolm Baldrige National Quality Award twice. It is the only company in the service category to do so.

Another leader in service quality that uses the strategy of high levels of employee training and empowerment is Nordstrom Department Stores. Outstanding customer service is the goal of this department store chain. Its organizational chart places the customer at the head of the organization. Records are kept of regular clients’ prefer- ences, and employees are empowered to make decisions on the spot to satisfy cus- tomer wants. The customer is considered to always be right.

Service organizations must also use statistical tools to measure their processes and monitor performance. For example, the Marriott is known for regularly collect- ing data in the form of guest surveys. The company randomly surveys as many as a million guests each year. The collected data are stored in a large database and continu- ally examined for patterns, such as trends and changes in customer preferences. Statistical techniques are used to analyze the data and provide important informa- tion, such as identifying areas that have the highest impact on performance and those areas that need improvement. This information allows Marriott to provide a superior level of customer service, antici- pate customer demands, and allocate resources to service features most important to customers.

LINKS TO PRACTICE The Ritz-Carlton Hotel Company, L.L.C. www.ritzcarlton.com Nordstrom, Inc. www.nordstrom.com

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232 • CHAPTER 6 STATISTICAL QUALITY CONTROL

The decision to increase the level of quality and reduce the number of product defects requires support from every function within operations management. Two areas of operations management that are particularly affected are product and process design (Chapter 3). Process design needs to be modified to incorporate customer-defined quality and simplification of design. Processes need to be continuously monitored and changed to build quality into the process and reduce variation. Other areas that are affected are job design (Chapter 11), as the role of employees is expanded to become responsible for monitoring quality levels and to use statistical quality control tools. Supply chain management and inventory control (Chapter 12) are also affected as quality standard requirements from suppliers are increased and changes are made on the materials used. All areas of operations management are involved when increasing the quality standard of a firm.

STATISTICAL QUALITY CONTROL (SQC) WITHIN OM: HOW IT ALL FITS TOGETHER

STATISTICAL QUALITY CONTROL (SQC) ACROSS THE ORGANIZATION It is easy to see how operations managers can use the tools of SQC to monitor product and process quality. However, you may not readily see how these statistical techniques affect other functions of the organization. In fact, SQC tools require input from other functions, influence their success, and are actually used in designing and evaluating their tasks.

Marketing plays a critical role in setting up product and service quality standards. It is up to marketing to provide information on current and future quality standards required by customers and those being offered by competitors. Operations managers can incorporate this information into product and process design. Consultation with marketing managers is essential to ensure that quality standards are being met. At the same time, meeting quality standards is essential to the marketing department, since sales of products are dependent on the standards being met.

Finance is an integral part of the statistical quality control process because it is responsible for placing financial values on SQC efforts. For example, the finance department evaluates the dollar costs of defects, measures financial improvements that result from tightening of quality standards, and is actively involved in approving investments in quality improvement efforts.

Human resources becomes even more important with the implementation of TQM and SQC methods, as the role of workers changes. To understand and utilize SQC tools, workers need ongoing training and the ability to work in teams, take pride in their work, and assume higher levels of responsibility. The human resources depart- ment is responsible for hiring workers with the right skills and setting proper compen- sation levels.

Information systems is a function that makes much of the information needed for SQC accessible to all who need it. Information systems managers need to work closely with other functions during the implementation of SQC so that they under- stand exactly what types of information are needed and in what form. As we have seen, SQC tools are dependent on information, and it is up to information systems manag- ers to make that information available. As a company develops ways of using TQM and SQC tools, information systems managers must be part of this ongoing evolution to ensure that the company’s information needs are being met.

All functions need to work closely together in the implementation of statistical process control. Everyone benefits from this collaborative relationship: operations is

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THE SUSTAINABILITY LINK • 233

able to produce the right product efficiently; marketing has the exact product custom- ers are looking for; and finance can boast of an improved financial picture for the organization.

SQC also affects various organizational functions through its direct application in evaluating quality performance in all areas of the organization. SQC tools are not used only to monitor the production process and ensure that the product being produced is within specifications. As we have seen in the Motorola Six Sigma example, these tools can be used to monitor both quality levels and defects in accounting procedures, financial record keeping, sales and marketing, office administration, and other func- tions. Having high quality standards in operations does not guarantee high quality in the organization as a whole. The same stringent standards and quality evaluation procedures should be used in setting standards and evaluating the performance of all organizational functions.

The theme of this chapter has been measuring varia- tion of key quality characteristics of the product or process in order to ensure a high level of quality. We learned that variation in the production process leads to quality defects and contributes to prod- uct inconsistency. We also learned how lead- ing companies, such as Intel, make minimizing product variation a priority. For this reason, Intel has implemented the “copy-exactly” pro- gram, where the production process must be the same at every facility. This concept of minimizing product variation also has to extend to other mem- bers of the supply chain who are responsible for sup- plying component parts or delivering the product to the final customer. A product is only as good as

the quality of its component parts. Just as variation between production facilities contributes to prod- uct inconsistency, so does variation between suppli- ers, retailers, or distributors. To minimize product variation, precise standards of quality control must be implemented and SQC tools used by others in the

supply chain. These standards must be consistent across the supply chain. To help achieve this, many com panies today are using ISO 9000 and ISO 14000 certification as part of the supplier

selection process. This helps to determine pro- cess capability up front and avoid long-term accep-

tance samp ling needs. These standards are especially important in today’s environment, where companies find sources of supply all around the globe and need a way to ensure supplier quality. Otherwise, the conse- quences will be unreliable product quality.

THE SUPPLY CHAIN LINK

Setting standards of performance is a key aspect of reducing and measuring variation. Just as we need to measure quality performance and compare it against a standard we need to measure sustainability perfor- mance. We also need standards against which to base performance. Standards and metrics are essential to managing environmental outcomes and verifying claims of performance. Otherwise, claims can be seen as just unsubstantiated advertising. The same processes of statistical control can be applied here. However, instead

of a quality metric, we would use a sustainability met- ric. The important issue is developing these metrics and many can be highly industry specific.

For sustainability metrics to be effective, they often need to be industry specific. Industry-specific scor- ing and measurement systems are being developed in a wide range of industries. For example, in the truck- ing industry companies are working with the newly expanded Safety Management System from the Federal Motor Carrier Safety Administration. A detailed scor- ing system shows how trucking firms compare along metrics such as emission controls, vehicle maintenance,

THE SUSTAINABILITY LINK

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234 • CHAPTER 6 STATISTICAL QUALITY CONTROL

driver fitness, and crash history. Companies are using this scoring system to compare track records, assess who is really performing in terms of specific metrics, and using that information to choose logistics part- ners. High scorers such as Ryder Logistics, a company that has consistently ranked in the top 10% in safety

categories, has found its business growing as customers read the comparison chart and choose to partner with the high scorers. This system has provided standards of performance and objective comparison against stan- dards. We will be seeing more of these types of scoring systems in a wide range of industries.

Chapter Highlights 1 Statistical quality control (SQC) refers to statistical tools that

can be used by quality professionals. Statistical quality control can be divided into three broad categories: descriptive statistics, acceptance sampling, and statistical process control (SPC).

2 Descriptive statistics are used to describe quality characteristics, such as the mean, range, and variance. Acceptance sampling is the process of randomly inspecting a sample of goods and deciding whether to accept or reject the entire lot. Statistical process control (SPC) involves inspecting a random sample of output from a process and deciding whether the process is producing products with characteristics that fall within preset specifi cations.

3 Th ere are two causes of variation in the quality of a product or process: common causes and assignable causes. Common causes of variation are random causes that we cannot identify. Assignable causes of variation are those that can be identifi ed and eliminated.

4 A control chart is a graph used in statistical process control that shows whether a sample of data falls within the normal range of variation. A control chart has upper and lower control limits that separate common from assignable causes of variation. Control charts for variables monitor characteristics that can be measured and have a continuum of values, such as height, weight, or volume. Control charts

for attributes are used to monitor characteristics that have discrete values and can be counted.

5 Control charts for variables include x-bar charts and R-charts. X-bar charts monitor the mean or average value of a product characteristic. R-charts monitor the range or dispersion of the values of a product characteristic. Control charts for attributes include p-charts and c-charts. P-charts are used to monitor the proportion of defects in a sample. C-charts are used to monitor the actual number of defects in a sample.

6 Process capability is the ability of the production process to meet or exceed preset specifi cations. It is measured by the process capability index, Cp, which is computed as the ratio of the specifi cation width to the width of the process variability.

7 Th e term Six Sigma indicates a level of quality in which the number of defects is no more than 3.4 parts per million.

8 Th e goal of acceptance sampling is to determine criteria for acceptance or rejection based on lot size, sample size, and the desired level of confi dence. Operating characteristic (OC) curves are graphs that show the discriminating power of a sampling plan.

9 It is more diffi cult to measure quality in services than in manufacturing. Th e key is to devise quantifi able measurements for important service dimensions.

Key Terms statistical quality control (SQC) 198 descriptive statistics 198 statistical process control (SPC) 199 acceptance sampling 199 common causes of variation 200 assignable causes of variation 200 mean (average) 200 range 201 standard deviation 201 control chart 202

out of control 202 variable 203 attribute 203 x-bar chart 204 range (R) chart 208 p-chart 211 c-chart 214 process capability 216 product specifi cations 216 process capability index 217

Six Sigma quality 221 sampling plan 223 operating characteristic (OC)

curve 224 acceptable quality level (AQL) 224 lot tolerance percent

defective (LTPD) 225 consumer’s risk 225 producer’s risk 225 average outgoing quality (AOQ) 227

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SOLVED PROBLEMS • 235

Formula Review

1. Mean: x 5 a

i51 xi

2. Standard deviation: s 5ãa n

i51 1xi 2 x 2 2 n 2 1

3. Control limits for x-bar charts: Upper control limit

(ULC) � x � zsx Lower control limit

(LCL) � x � zsx

sx 5 s"n

4. Control limits for x-bar charts using sample range as an estimate of variability:

Upper control limit

(UCL) � x 1 A2R

Lower control limit

(LCL) � x 2 A2R

5. Control limits for R-charts: UCL � D4R LCL � D3R

6. Control limits for p-charts: UCL � p 1 z 1sp 2 LCL � p 2 z 1sp 2 7. Control limits for c-charts: UCL � c 1 z"c LCL � c 1 z"c 8. Measures for process capability:

Cp 5 specification width

process width 5

USL 2 LSL 6s

Cpk 5 minaUSL 2 m3s , m 2 LSL3s b 9. Average outgoing quality: AOQ � (Pac)p

Solved Problems • Problem 1 A quality control inspector at the Crunchy Potato Chip Com- pany has taken ten samples with four observations each of the volume of bags filled. The data and the computed means are shown in the following table:

If the standard deviation of the bagging operation is 0.2 ounces, use the information in the table to develop control limits of 3 standard deviations for the bagging operation.

• Before You Begin: To compute the control limits, you must first calculate the mean of the four samples and then use the UCL and LCL formulas.

• Solution The center line of the control data is the average of the samples:

x 5 125.0

10 5 12.5 ounces

The control limits are:

UCL 5 x 1 zsx � 12.5 1 3a 0.2"4b 5 12.80 LCL 5 x 2 zsx � 12.5 2 3a 0.2"4b 5 12.20

(See student companion site for Excel template.)

Sample of Potato Chip Bag Volume in Ounces

Sample Number

Observations Average x−1 2 3 4

1 12.5 12.3 12.6 12.7 12.525 2 12.8 12.4 12.4 12.8 12.6 3 12.1 12.6 12.5 12.4 12.4 4 12.2 12.6 12.5 12.3 12.4 5 12.4 12.5 12.5 12.5 12.475 6 12.3 12.4 12.6 12.6 12.475 7 12.6 12.7 12.5 12.8 12.65 8 12.4 12.3 12.6 12.5 12.45 9 12.6 12.5 12.3 12.6 12.5

10 12.1 12.7 12.5 12.8 12.525

Total 125.0

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236 • CHAPTER 6 STATISTICAL QUALITY CONTROL

Following is the associated control chart:

The problem can also be solved using a spreadsheet.

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SOLVED PROBLEMS • 237

• Problem 2 Use of the sample range to estimate variability can also be applied to the Crunchy Potato Chip operation. A quality con- trol inspector has taken four samples with five observations each, measuring the volume of chips per bag. If the average range for the four samples is 0.2 ounces and the average mean of the observations is 12.5 ounces, develop 3-sigma control lim- its for the bagging operation.

• Before You Begin: Recall that to compute control limits from the sample range you need to look up the value of factor A2 from Table 6-1 for the appropriate value of n. In this problem n � 5.

• Solution

x 5 12.5 ounces R 5 0.2

The value of A2 is obtained from Table 6-1. For n � 5, A2 � 0.58. This leads to the following limits:

Th e center of the control chart is CL � 12.5 ounces

UCL 5 x 1 A2R 5 12.5 1 10.58 2 10.2 2 5 12.62 LCL 5 x 2 A2R 5 12.5 2 10.58 2 10.2 2 5 12.38

• Problem 3 Ten samples with five observations each have been taken from the Crunchy Potato Chip Company plant in order to test for volume dispersion in the bagging process. The average sample range was found to be 0.3 ounces. Develop control limits for the sample range.

• Before You Begin: To compute the control limits for the sample range, remem- ber to look up the values for D4 and D3 from Table 6-1 for the appropriate value of n. In this problem n � 5.

• Solution R 5 0.3 ounces n 5 5

From Table 6-1 for n � 5: D4 5 2.11 D3 5 0

Therefore, UCL 5 D4R 5 2.11 10.3 2 5 0.633 LCL 5 D3R 5 0 10.3 2 5 0

• Problem 4 A production manager at a light bulb plant has inspected the number of defective light bulbs in 10 random samples with 30 observations each. Following are the numbers of defective light bulbs found:

• Before You Begin: To solve this problem, you should use a p-chart because both the total sample size and the number of defects are provided.

• Solution The center line of the chart is:

CL 5 p 5 number defective

number of observations 5

17 300

5 0.057

sp 5 Åp 11 2 p 2n 5 Å 10.057 2 10.943 230 5 0.042 UCL 5 p 1 z 1sp 2 5 0.057 1 3 10.042 2 5 0.183 LCL 5 p 2 z 1sp 2 5 0.057 2 3 10.042 2 5 20.069 S 0

Sample Number

Number of Observation in Sample

1 1 30 2 3 30 3 3 30 4 1 30 5 0 30 6 5 30 7 1 30 8 1 30 9 1 30

10 1 30

Total 17 300

Construct a 3-sigma control chart (z � 3) with this information.

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238 • CHAPTER 6 STATISTICAL QUALITY CONTROL

This is also solved using a spreadsheet.

• Problem 5 Kinder Land Child Care uses a c-chart to monitor the num- ber of customer complaints per week. Complaints have been recorded over the past 20 weeks. Develop a control chart with 3-sigma control limits using the following data:

• Before You Begin Notice that in this problem only the number of defects (com- plaints) has been collected over time. This means that you can- not compute the proportion that is defective and therefore you should use a c-chart.

• Solution

The average weekly number of complaints is 30 20

� 1.5. Therefore,

UCL 5 c 1 z"c 5 1.5 1 3"1.5 5 5.17 LCL 5 c 2 z"c 5 1.5 2 3"1.5 5 2 2.17 S 0

Week Number of Complaints Week

Number of Complaints

1 0 11 4 2 3 12 3 3 4 13 1 4 1 14 1 5 0 15 1 6 0 16 0 7 3 17 2 8 1 18 1 9 1 19 2

10 0 20 2

Total 30

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SOLVED PROBLEMS • 239

The resulting control chart is shown below.

LCL CL UCL p

Week

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

C om

pl ai

nt s

pe r

W ee

• Problem 6 Three bagging machines at the Crunchy Potato Chip Company are being evaluated for their capability. The following data are recorded:

Looking at the Cp values, only machine C is capable of bagging the potato chips within specifications because it is the only machine that has a Cp value at or above 1.

Bagging Machine Standard Deviation

A 0.2 B 0.3 C 0.05

If specifications are set between 12.35 and 12.65 ounces, deter- mine which of the machines are capable of producing within specification.

• Before You Begin: To solve this problem, you need to compute the process capa- bility index, Cp, for each of the three bagging machines. The

Bagging Machine S USL � LSL 6S

Cp 5 USL 2 LSL

A 0.2 0.3 1.2 0.25 B 0.3 0.3 1.8 0.17 C 0.05 0.3 0.3 1.00

machine that has a Cp 1 is capable of producing within specification.

• Solution To determine the capability of each machine, we need to divide the specification width (USL � LSL � 12.65 � 12.35 � 0.3) by 6s for each machine:

• Problem 7 Compute the Cpk measure of process capability for the following machine and interpret the findings. What value would you have obtained with the Cp measure?

Machine data: USL 5 80 LSL 5 50 Process s 5 5 Process m 5 60

• Before You Begin: In this problem you should compute both the Cp and Cpk mea- sures following the formulas from the text. For each measure, a numerical value at or above 1 indicates that the process is capable.

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240 • CHAPTER 6 STATISTICAL QUALITY CONTROL

• Solution To compute the Cpk measure of process capability:

Cpk 5 minaUSL 2 m3s , m 2 LSL3s b 5 mina80 2 60

3 15 2 , 60 2 503 15 2 b

5 min 11.33, 0.67 2 5 0.67

This means that the process is not capable. The Cp measure of process capability gives us the following measure:

Cp 5 30

6 15 2 5 1.0 which leads us to believe that the process is capable.

Discussion Questions 1. Explain the three categories of statistical quality control

(SQC). How are they different, what different information do they provide, and how can they be used together?

2. Describe three recent situations in which you were directly affected by poor product or service quality.

3. Discuss the key differences between common and assign- able causes of variation. Give examples.

4. Describe a quality control chart and how it can be used. What are upper and lower control limits? What does it mean if an observation falls outside the control limits?

5. Explain the differences between x-bar and R-charts. How can they be used together and why would it be important to use them together?

6. Explain the use of p-charts and c-charts. When would you use one rather than the other? Give examples of measurements for both p-charts and c-charts.

7. Explain what is meant by process capability. Why is it important? What does it tell us? How can it be measured?

8. Describe the process of acceptance sampling. What types of sampling plans are there? What is acceptance sampling used for?

9. Describe the concept of Six Sigma quality. Why is such a high quality level important?

Problems 1. A quality control manager at a manufacturing facility has taken

four samples with four observations each of the diameter of a part. (a) Compute the mean of each sample. (b) Compute an estimate of the mean and standard deviation

of the sampling distribution. (c) Develop control limits for 3 standard deviations of the

product diameter.

If the standard deviation of the shampoo bottle-fi lling operation is 0.2 ounces, use the information in the table to develop control limits of 3 standard deviations for the operation.

3. A quality control inspector has taken four samples with fi ve observations each at the Beautiful Shampoo Company, measuring the volume of shampoo per bottle. If the average range for the four samples is 0.4 ounces and the average mean of the observations is 19.8 ounces, develop 3-sigma control limits for the bottling operation.

4. A production manager at Ultra Clean Dishwashing Company is monitoring the quality of the company’s production process. Th ere has been concern relative to the quality of the operation in accurately fi lling the 16 ounces of dishwashing liquid. Th e product is designed for a fi ll level of 16.00 � 0.30. Th e company collected the following sample data on the production process:

Samples of Part Diameter in Inches

1 2 3 4

5.8 6.2 6.1 6.0 5.9 6.0 5.9 5.9 6.0 5.9 6.0 5.9 6.1 5.9 5.8 6.1

2. A quality control inspector at the Beautiful Shampoo Company has taken three samples with four observations each of the volume of shampoo bottles fi lled. Th e data collected by the inspector and the computed means are shown here:

Samples of Shampoo Bottle Volume in Ounces

Observation 1 2 3

1 19.7 19.7 19.7 2 20.6 20.2 18.7 3 18.9 18.9 21.6 4 20.8 20.7 20.0

Mean 20.0 19.875 20.0

Observations

Sample 1 2 3 4

1 16.40 16.11 15.90 15.78 2 15.97 16.10 16.20 15.81 3 15.91 16.00 16.04 15.92 4 16.20 16.21 15.93 15.95 5 15.87 16.21 16.34 16.43 6 15.43 15.49 15.55 15.92 7 16.43 16.21 15.99 16.00 8 15.50 15.92 16.12 16.02 9 16.13 16.21 16.05 16.01

10 15.68 16.43 16.20 15.97

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PROBLEMS • 241

(a) Are the process mean and range in statistical control? (b) Do you think this process is capable of meeting the design

standard? 5. Ten samples with fi ve observations each have been taken

from the Beautiful Shampoo Company plant in order to test for volume dispersion in the shampoo bottle-fi lling process. Th e average sample range was found to be 0.3 ounces. Develop control limits for the sample range.

6. Th e Awake Coff ee Company produces gourmet instant coff ee. Th e company wants to be sure that the average fi ll of coff ee containers is 12.0 ounces. To make sure the process is in control, a worker periodically selects at random a box of six containers of coff ee and measures their weight. When the process is in control, the range of the weight of coff ee samples averages 0.6 ounces.

(a) Develop an R-chart and an x-chart for this process. (b) Th e measurements of weight from the last fi ve samples

taken of the six containers follow:

(a) Develop a p-chart with a z � 3. (b) Suppose that the next four samples selected had 6, 3, 3,

and 4 defects. What conclusion can you make? 9. U-learn University uses a c-chart to monitor student

complaints per week. Complaints have been recorded over the past ten weeks. Develop 3-sigma control limits using the following data:

Sample x− R

1 12.1 0.7 2 11.8 0.4 3 12.3 0.6 4 11.5 0.4 5 11.6 0.9

Is the process in control? Explain your answer. 7. A production manager at a Contour Manufacturing plant

has inspected the number of defective plastic molds in 5 random samples of 20 observations each. Following are the number of defective molds found in each sample:

Sample Number of

Defects

Number of Observations

in Sample

1 1 20 2 2 20 3 2 20 4 1 20 5 0 20

Total 6 100

Construct a 3-sigma control chart (z � 3) with this information. 8. A tire manufacturer has been concerned about the number

of defective tires found recently. In order to evaluate the true magnitude of the problem, a production manager selected 10 random samples of 20 units each for inspection. Th e number of defective tires found in each sample are as follows:

Sample Number Defective

1 1 2 3 3 2 4 1 5 4 6 1 7 2 8 0 9 3

10 1

Week Number of Complaints

1 0 2 3 3 1 4 1 5 0 6 0 7 3 8 1 9 1

10 2

10. University Hospital has been concerned with the number of errors found in its billing statements to patients. An audit of 100 bills per week over the past 12 weeks revealed the following number of errors:

Week Number of Errors

1 4 2 5 3 6 4 6 5 3 6 2 7 6 8 7 9 3

10 4 11 3 12 4

(a) Develop control charts with z � 3. (b) Is the process in control?

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242 • CHAPTER 6 STATISTICAL QUALITY CONTROL

11. Th ree ice-cream packing machines at the Creamy Treat Company are being evaluated for their capability. Th e following data are recorded:

If the computer manufacturer decides to sample 2000 of the microchips received in each shipment, what acceptance number, c, would it want?

18. Joshua Simms has recently been placed in charge of purchasing at the Med-Tech Labs, a medical testing laboratory. His job is to purchase testing equipment and supplies. Med-Tech currently has a contract with a reputable supplier in the industry. Joshua’s job is to design an appropriate acceptance sampling plan for Med-Tech. Th e contract with the supplier states that the acceptable quality level is 1 percent defective. Also, the lot tolerance proportion defective is 4 percent, the producer’s risk is 5 percent, and the consumer’s risk is 10 percent.

(a) Develop an acceptance sampling plan for Joshua that meets the stated criteria.

(b) Draw the OC curve for the plan you developed. (c) What is the AOQL of your plan, assuming a lot size of

1000? 19. Breeze Toothpaste Company makes tubes of toothpaste.

Th e product is produced and then pumped into tubes and capped. Th e production manager is concerned whether the fi lling process for the tubes of toothpaste is in statistical control. Th e process should be centered on 6 ounces per tube. Six samples of fi ve tubes were taken and each tube was weighed. Th e weights are:

Packing Machine Standard Deviation

A 0.2 B 0.3 C 0.05

If specifi cations are set between 15.8 and 16.2 ounces, determine which of the machines are capable of producing within specifi cations.

12. Compute the Cpk measure of process capability for the following machine and interpret the fi ndings. What value would you have obtained with the Cp measure?

Machine data: USL 5 100

LSL 5 70

Process s 5 5

Process m 5 80

13. Develop an OC curve for a sampling plan in which a sample of n � 5 items is drawn from lots of N � 1000 items. Th e accept/reject criteria are set up in such a way that we accept a lot if no more than one defect (c � 1) is found.

14. Quality Style manufactures self-assembling furniture. To reduce the cost of returned orders, the manager of its quality control department inspects the fi nal packages each day using randomly selected samples. Th e defects include wrong parts, missing connection parts, parts with apparent painting problems, and parts with rough surfaces. Th e average defect rate is three per day.

(a) Which type of control chart should be used? Construct a control chart with 3-sigma control limits.

(b) Today the manager discovered nine defects. What does this mean?

15. Develop an OC curve for a sampling plan in which a sample of n � 10 items is drawn from lots of N � 1000. Th e accept/reject criteria are set up in such a way that we accept a lot if no more than one defect (c � 1) is found.

16. Th e Fresh Pie Company purchases apples from a local farm to be used in preparing the fi lling for its apple pies. Sometimes the apples are fresh and ripe. Other times they can be spoiled or not ripe enough. Th e company has decided that it needs an acceptance sampling plan for the purchased apples. Fresh Pie has decided that the acceptable quality level is 2 defective apples per 100, and the lot tolerance proportion defective is 5 percent. Producer’s risk should be no more than 5 percent and consumer’s risk 10 percent or less.

(a) Develop a plan that satisfi es the above requirements. (b) Determine the AOQL for your plan, assuming that the lot

size is 1000 apples. 17. A computer manufacturer purchases microchips from a

world-class supplier. Th e buyer has a lot tolerance proportion defective of 10 parts in 5000, with a consumer’s risk of 15 percent.

Ounces of Toothpaste per Tube

Sample 1 2 3 4 5

1 5.78 6.34 6.24 5.23 6.12 2 5.89 5.87 6.12 6.21 5.99 3 6.22 5.78 5.76 6.02 6.10 4 6.02 5.56 6.21 6.23 6.00 5 5.77 5.76 5.87 5.78 6.03 6 6.00 5.89 6.02 5.98 5.78

(a) Develop a control chart for the mean and range for the available toothpaste data.

(b) Plot the observations on the control chart and comment on your fi ndings.

20. Breeze Toothpaste Company has been having a problem with some of the tubes of toothpaste leaking. Th e tubes are packed in containers with 100 tubes each. Ten containers of toothpaste have been sampled. Th e following number of toothpaste tubes were found to have leaks:

Sample Number of

Leaky Tubes Sample Number of

Leaky Tubes

1 4 6 6 2 8 7 10 3 12 8 9 4 11 9 5 5 12 10 8

Total 85

Develop a p-chart with 3-sigma control limits and evaluate whether the process is in statistical control.

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CASE: SCHARADIN HOTELS • 243

21. Th e Crunchy Potato Chip Company packages potato chips in a process designed for 10.0 ounces of chips with an upper specifi cation limit of 10.5 ounces and a lower specifi cation limit of 9.5 ounces. Th e packaging process results in bags with an average net weight of 9.8 ounces and a standard deviation of 0.12 ounces. Th e company wants to determine if the process is capable of meeting design specifi cations.

22. Th e Crunchy Potato Chip Company sells chips in boxes with a net weight of 30 ounces per box (850 grams). Each box

contains ten individual 3-ounce packets of chips. Product design specifi cations call for the packet-fi lling process average to be set at 86.0 grams so that the average net weight per box will be 860 grams. Specifi cation width is set for the box to weigh 850 � 12 grams. Th e standard deviation of the packet-fi lling process is 8.0 grams per box. Th e target process capability ratio is 1:33. Th e production manager has just learned that the packet-fi lling process average weight has dropped down to 85.0 grams. Is the packaging process capable? Is an adjustment needed?

CASE: Scharadin Hotels Scharadin Hotels is a national hotel chain started in 1957 by Milo Scharadin. What started as one upscale hotel in New York City turned into a highly reputable national hotel chain. Today, Schar- adin Hotels serves over 100 1ocations and is recognized for its cus- tomer service and quality. Scharadin Hotels are typically located in large metropolitan areas close to convention centers and centers of commerce. They cater to both business and nonbusiness custom- ers and offer a wide array of services. Maintaining high customer service has been considered a priority for the hotel chain.

A Problem with Quality The Scharadin Hotel in San Antonio, Texas, had recently been experiencing a large number of guest complaints due to billing errors. The complaints seemed to center around guests disput- ing charges on their final hotel bill. Guest complaints ranged from extra charges, such as meals or services that were not pur- chased, to confusion for not being charged at all. Most hotel guests use express checkout on their day of departure. With express checkout, the hotel bill is left under the guest’s door in the early morning hours and, if all is in order, does not require any additional action on the guest’s part. Express checkout is a service welcomed by busy travelers who are free to depart the hotel at their convenience. However, the increased number of billing errors began creating unnecessary delays and frustration for the guests who unexpectedly needed to settle their bill with the front desk. The hotel staff often had to calm frustrated guests who were rushing to the airport and were aggravated that they were getting charged for items they had not purchased.

Identifying the Source of the Problem Larraine Scharadin, Milo Scharadin’s niece, had recently been appointed to run the San Antonio hotel. A recent business school graduate, Larraine had grown up in the hotel business. She was poised and confident and understood the importance of high quality for the hotel. When she became aware of the billing problem, she immedi- ately called a staff meeting to uncover the source of the problem.

During the staff meeting, discussion quickly turned to prob- lems with the new computer system and software that had been put in place. Tim Coleman, head of MIS, defended the system, stating that it was sound and the problems were exaggerated. Tim claimed that a few hotel guests made an issue of a few ran- dom problems. Scott Schultz, head of operations, was not so sure.

Scott said that he noticed that the number of complaints seemed to have significantly increased since the new system was installed. He said that he had asked his team to perform an audit of 50 random bills per day over the past 30 days. Scott showed the fol- lowing numbers to Larraine, Tim, and the other staff members.

Everyone looked at the data that had been presented. Then Tim exclaimed, “Notice that the number of errors increases in the last third of the month. The computer system had been in place for the entire month, so that can’t be the problem. Scott, it is probably the new employees you have on staff that are not entering the data properly.” Scott quickly retaliated, “The employees are trained properly! Everyone knows the problem is the computer system!”

The argument between Tim and Scott become heated, and Larraine decided to step in. She said, “Scott, I think it is best if you perform some statistical analysis of that data and send us your findings. You know that we want a high quality standard. We can’t be Motorola with Six Sigma quality, but let’s try for 3 sigma. Would you develop some control charts with the data and let us know if you think the process is in control?”

Case Questions 1. Set up 3-sigma control limits with the given data. 2. Is the process in control? Why? 3. Based on your analysis, do you think the problem is the

new computer system or something else? 4. What advice would you give to Larraine based on the

information that you have?

Day

Number of Incorrect

Bills Day

Number of Incorrect

Bills Day

Number of Incorrect

Bills

1 2 11 1 21 3 2 2 12 2 22 3 3 1 13 3 23 3 4 2 14 3 24 4 5 2 15 2 25 5 6 3 16 3 26 5 7 2 17 2 27 6 8 2 18 2 28 5 9 1 19 1 29 5

10 2 20 3 30 5

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244 • CHAPTER 6 STATISTICAL QUALITY CONTROL

CASE: Delta Plastics, Inc. (B) Jose De Costa, director of manufacturing at Delta Plastics, sat at his desk looking at the latest production quality report, showing the number and type of product defects per week (see the quality report in Delta Plastics, Inc. Case A, Chapter 5). He was faced with the task of evaluating production quality for products made with two different materials. One of the materials was new and called “super plastic” due to its ability to sustain large tempera- ture changes. The other material was the standard plastic that had been successfully used by Delta for many years.

The company had started producing products with the new “super plastic” material only a month earlier. Jose suspected that the new material could result in more defects during the produc- tion process than the standard material they had been using. Jose

had been opposed to starting production until R&D had fully completed testing and refining the new material. However, the CEO of Delta had ordered production despite objections from manufacturing and R&D. Jose carefully looked at the report in front of him and prepared to analyze the results.

Case Questions 1. Prepare a 3-sigma control chart for both production

processes, using the new and standard material (use the quality report in Delta Plastics, Inc. Case A, Chapter 5). Are both processes in control? What can you conclude?

2. Are both materials equally subject to the defects? 3. Given your fi ndings, what advice would you give Jose?

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Statistical Process Control at Cruise International, Inc. For this assignment, you will work again with Meghan Wil- loughby, Chief Purser. This assignment involves analyzing some of the data collected through our Quality Improvement Teams. While CII is concerned about quality in all aspects of its opera- tions, there are six areas that seem to be critical in terms of cus- tomer satisfaction. Customers quickly become dissatisfied as a result of (1) mistakes made in their onboard accounts, (2) house- keeping issues (3) quality problems in the restaurants and bars, (4) problems associated with shore excursions, (5) problems with

the entertainment, and (6) problems with the onboard shops. You will be using statistical process control techniques to analyze some data collected by the Quality Improvement Teams. This assignment will enhance your knowledge of the material in Chap- ter 6 of your textbook while preparing you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Statistical Process Control at CII

On-line Case: Statistical Quality Control at Valley Memorial Hospital Assignment: Statistical Quality Control This assign-

ment involves controlling nursing hours at Valley Memorial Hospital. Lee Jordan, director of the hospital’s Medical/Surgi- cal Nursing Unit, has already told you that VMH employs more than 500 nurses, with an annual nursing budget of $5,000,000. “We’re trying for a 5 percent reduction in nursing FTEs—full- time equivalents,” he says. “I’ve been personally recording the nursing hours per patient per day for over three months in Med/

Surg. I would like you to look at the numbers and see if you can tell me how to meet our goals.”

To access the Web site:

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SELECTED BIBLIOGRAPHY • 245

To gain business experience, you have volunteered to work at Safe-Air, a nonprofit agency that monitors airline safety records and customer service. Your first assignment is to compare three airlines based on their on-time arrivals and departures. Your manager has asked you to get your information from the Inter- net. Select any three airlines. For an entire week check the daily arrival and departure schedules of the three airlines from your city or closest airport. Remember that it is important to compare the arrivals and departures from the same location and during the same time period to account for factors such as the weather. Record the data that you collect for each airline. Then decide

which types of statistical quality control tools you are going to use to evaluate the airlines’ performances. Based on your find- ings, draw a conclusion regarding the on-time arrivals and departures of each of the airlines. Which is best and which is worst? Are there large differences in performance among the airlines? Also describe the statistical quality control tolls you have decided to use to monitor performance. If you have cho- sen to use more than one tool, are you finding the tools equally useful or is one better at capturing differences in performance? Finally, based on what you have learned so far, how would you perform this analysis differently in the future?

INTERNET CHALLENGE Safe-Air

On-line Resources Companion Website www.wiley.com/college/reid

The Peanut Roaster Welded Tubes, Inc.

• Find links for Additional Web Resources for this chapter American Society for Quality Control, www.asqc.org Australian Quality Council, www.aqc.org.au

Selected Bibliography Brue, G. Six Sigma for Managers. New York: McGraw-Hill, 2002. Duncan, A.J. Quality Control and Industrial Statistics, Fift h

Edition. Homewood, Ill.: Irwin, 1986. Evans, James R., and William M. Lindsay. Th e Management

and Control of Quality, Fourth Edition. Cincinnati: South- Western, 1999.

Feigenbaum, A.V. Total Quality Control. New York: McGraw- Hill, 1991.

Grant, E.L., and R.S. Leavenworth. Statistical Quality Control, Sixth Edition. New York: McGraw-Hill, 1998.

Hoyer, R.W., and C.E. Wayne. “A Graphical Exploration of SPC, Part 1,” Quality Progress, 29, 5, May 1996, 65–73.

Juran, J.M., and F.M. Gryna. Quality Planning and Analysis, Second Edition. New York: McGraw-Hill, 1980.

Spector, Robert E. “How Constraint Management Enhances Lean and Six Sigma,” Supply Chain Management Review, January–February 2006, 42–47.

Wadsworth, H.M., K.S. Stephens, and A.B. Godfrey. Modern Methods for Quality Control and Improvement. New York: John Wiley & Sons, 1986.

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L E A R N I N G O B J E C T I V E S

CHAPTER

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISHRM

246

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C H A P T E R O U T L I N E

The Philosophy of JIT 248 Elements of JIT 251 Just-in-Time Manufacturing 254 Total Quality Management 262 Respect for People 265 Benefits of JIT 269 Implementing JIT 270

JIT in Services 271 JIT and Lean Systems within OM: How It All

Fits Together 272 JIT and Lean Systems across the

Organization 272

Before studying this chapter you should know or, if necessary, review

1. JIT as a trend in OM, Chapter 1, p. 15. 2. Time as a competitive priority, Chapter 2, p. 39. 3. Total quality management concepts, Chapter 5, pp. 166–189.

After studying this chapter you should be able to

1 Explain the core beliefs of the just-in-time (JIT) philosophy. 2 Describe the meaning of waste in JIT. 3 Explain the diff erences between “push” and “pull” production systems. 4 Explain the key elements of JIT manufacturing. 5 Explain the elements of total quality management (TQM) and their role in JIT. 6 Describe the role of people in JIT and why respect for people is so important. 7 Understand the impact of JIT on service and manufacturing organizations. 8 Understand the impact of JIT on all functional areas of the company.

Just-in-Time and Lean Systems

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CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS • 247

The just-in-time (JIT) philosophy in the simplest form means getting the right quantity of goods at the right place and at the right time. The goods arrive just-in- time, which is where the term JIT comes from. Although many people think that JIT is an inventory reduction program or another type of manufacturing process, it is far more than that. JIT is an all-encompassing philosophy that is founded on the concept of eliminating waste. The word waste might make you think of gar- bage, or paper, or inventory. But JIT considers waste anything that does not add value—anything.

The broad view of JIT is now often termed lean production or lean systems. Its implementation has contributed to the success of many organizations and is used by companies worldwide. The benefits that can be obtained through JIT are so impressive that JIT has become a standard of operations in many industries, including the auto and computer industries. However, JIT is applicable to service organizations as well as to manufacturing and can even be used in your everyday life. JIT is not about any one factor, such as quality or inventory or efficiency. It is an entirely different way of

� Just-in-time (JIT) philosophy Getting the right quantity of goods at the right place at the right time.

� Waste Anything that does not add value. � A broad view of JIT A philosophy that encompasses the entire organization.

How many times have you looked frantically for a school paper or notebook, only to find it much later in the most unexpected place? Have you ever wasted time looking for a personal item—say, a par- ticular shirt or shoes or maybe a bill you needed to pay—and wondered how much easier life would be if everything was in its place? Have you ever purchased extra amounts of an item, maybe paper towels or laun- dry detergent, and then found that they were taking up space and getting in the way? Wouldn’t life be much simpler if you could somehow receive the items that you need exactly when you need them, without having to keep extra quantities in storage?

We have all experienced these situations. They illustrate the problem of waste: wasted time looking for things we misplaced, wasted space and cost of keeping extra items, and wasted energy because of the frustration of not finding things when we need them. These are the types of problems that just-in-time systems (JIT) seek to eliminate.

Waste has a large negative impact on the functioning of a business, resulting in high cost and lost customers. To eliminate these problems, many companies have turned to JIT. In fact, today the entire auto industry uses JIT as a standard of operations. Ford Motor Company’s plants use JIT principles to keep low levels of inventories of raw materials and parts. Toyota relies on JIT principles to keep a uniform flow of parts from its suppliers. BMW is using JIT principles to create an easy vehicle-ordering system for customers and a flexible operation that can rapidly respond to customer demands. However, JIT principles are not just used in manufacturing. They are equally applicable in services and are seen in companies such as McDonald’s, Wendy’s, Pizza Hut, and FedEx.

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248 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

THE PHILOSOPHY OF JIT

looking at things. As we will see, JIT is a philosophy that overrides all aspects of the organization, from administrative issues to manufacturing, worker management, sup- plier management, and even housekeeping. It has contributed to the success of com- panies like Toyota (called the Toyota Production System or “TPS”) and Honda (called “the Honda Way”), and it can even contribute to success in your own life.

The philosophy of JIT originated in Japan. After World War II, the Japanese set them- selves the goal of strengthening their industrial base, which included full employment and a healthy trade balance. Just-in-time (JIT) developed out of the nation’s need to survive after the devastation caused by the war. Although many authors say that the origins of JIT can be traced back to the early 1900s, no one can argue that the philoso- phy gained worldwide prominence in the 1970s. It was developed at the Toyota Motor Company, and the person most often credited with its development is Taiichi Ohno, a vice president of the company. JIT helped propel Toyota into a leadership position in the areas of quality and delivery. Since then, JIT has been widely adopted in all types of industries and has been credited with impressive benefits, including significant reduc- tions in operating costs, improved quality, and increased customer responsiveness. Companies such as Honda, GE, Ford, Boeing, Lockheed Martin, Hewlett- Packard, and IBM are among those that have made JIT part of their operations. Even the retailer Zara relies on JIT.

The central belief of the JIT philosophy is elimination of waste, but there are other beliefs that help define JIT philosophy. These include a broad view of operations, simplicity, continuous improvement, visibility, and flexibility. Next we look more closely at each of these beliefs.

� Defining beliefs of JIT Broad view of operations, simplicity, continuous improvement, visibility, and flexibility.

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THE PHILOSOPHY OF JIT • 249

Eliminate Waste The underlying premise of JIT is that all waste must be eliminated. Many think that the roots of the philosophy can be traced to the Japanese environment, which lacks space and natural resources. As a result, the Japanese have been forced to learn to use all their resources very efficiently, and waste of any kind is not tolerated. In JIT waste is anything that does not add value. Types of waste can include material, such as excess inventory to protect against uncertain deliveries by suppliers or poor qual- ity. Waste can be equipment that is used as a backup because regular equipment is not maintained properly. Other types of waste include time, energy, space, or human activity that does not contribute to the value of the product or service being produced.

The concept of waste addresses every aspect of the organization and has a far- reaching impact. For example, waste can be found in the production process itself, and JIT requires perfect synchronization in order to eliminate waiting and excess stock. Waste is also found in improper layout that necessitates the transportation of goods from one part of the facility to another. JIT requires a streamlined layout design so that resources are in close proximity to one another and material handling is kept to a minimum. Also, JIT requires compact layouts and increased visibility so that everyone can see what everyone else is doing. Waste can also take the form of poor quality, because scrap and rework cost money and add no value. Total quality management (TQM) programs thus are an integral part of JIT. Waste is also found in unnecessary motion, and JIT requires studying processes to eliminate unneces- sary steps.

A Broad View of Operations Part of the philosophy of JIT is that everyone in the organization should have a broad view of the organization and work toward the same goal, which is serving the cus- tomer. In traditional organizations, it is very easy for employees to focus exclusively on their own jobs and have a narrow view of the organization that includes only their assigned tasks. Companies whose employees have a narrow view become production- oriented, forgetting that individual tasks and procedures are important only if they meet the overall goals of the company. One example is an employee who will not help a customer with a problem, saying, “It’s not my job.” This might occur at a grocery store when a customer asks for the location of an item from an employee who is “only responsible for stocking shelves.” A broad view of operations involves understanding that all employees are ultimately responsible for serving the customer.

Simplicity JIT is built on simplicity—the simpler the better. JIT encourages employees to think about problems and come up with simple solutions. Although this may seem easy and crude, it is actually quite difficult. It is often tempting to solve an organizational problem using a complex and perhaps expensive method. It is far more difficult to think of a simple solution that goes directly to the root of the problem. The value of simple solutions is demonstrated by a company whose delivery truck was lodged in a passageway because it was too high to pass through. Many costly and complex solutions were being considered, such as getting a smaller truck or expanding the height of the doorway. After a bit of thought, an employee came up with a simple

� Types of waste Material, energy, time, and space.

� Broad view of the organization Tasks and procedures are important only if they meet the company’s overall goals.

� Simplicity The simpler a solution, the better it is.

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250 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

solution: reduce the air in the tires to bring down the height of the truck. The solu- tion worked.

Continuous Improvement A major aspect of the JIT philosophy is an emphasis on quality. Continuous improve- ment, called kaizen by the Japanese, in every aspect of the operation is a cornerstone of this philosophy. Continuous improvement applies to everything from reducing costs to improving quality to eliminating waste.

To understand the full impact of continuous improvement, try answering this ques- tion: When has JIT been implemented fully? The answer: Never. The reason is that an organization is never perfect and can always be improved in some way.

A number of companies are utilizing a powerful JIT approach called the “kaizen blitz.” This is an improvement tool that utilizes cross-functional teams to plan and deliver improvements to specific processes during two- or three-day marathon ses- sions. This process allows a small group of people to concentrate on a bite-size chunk of the problem for a short period of time. Companies find that a kaizen blitz can quickly deliver dramatic and low-cost improvements to processes.

Visibility Part of the JIT philosophy is to make all waste visible. Waste can be eliminated only when it is seen and identified. Also, if we see waste we can come up with simple solutions to eliminate it. When waste is hidden we forget about it, which creates problems.

Think about the closets in your home. Because the closet doors are closed, we often forget the clutter and junk we have inside. Now imagine that the closet doors were open and the inside was visible to us and everyone else. Certainly it would remind us that we need to eliminate the clutter.

JIT facilities are open and clean, with plenty of floor space. There is no clutter, and everyone can see what everyone else is doing. No one can hide extra inventory in a corner of his or her office or take a short nap in the afternoon. Also, part of the JIT philosophy is that a cluttered environment creates confusion and disrespect toward the workplace. By contrast, a clean and orderly environment creates calm and clear thoughts. Just because space is available, it should not automatically be filled. Visibility allows us to readily see waste. We can then eliminate it.

Flexibility JIT was based on the need for survival, and survival means being flexible in order to adapt to changes in the environment. A company can be flexible in many ways. First, flexibility can mean being able to make changes in the volume of a product produced. JIT accomplishes this by keeping the costs of facilities, equipment, and operations at such a low level that breaking even typically is not a problem.

A second way in which a company can be flexible is by being able to produce a wide variety of products. Although this is difficult to achieve, JIT systems are designed with the ability to produce different product models with different features through a manufacturing process that can easily switch from one product type to another by flexible workers who can perform many different tasks. Part of the JIT philosophy is to design operations that are highly efficient but flexible in order to accommodate chang- ing customer demands.

� Continuous improvement (kaizen) A philosophy of never- ending improvement.

� Visibility Problems must be visible to be identified and solved.

� Flexibility A company can quickly adapt to the changing needs of its customers.

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ELEMENTS OF JIT • 251

ELEMENTS OF JIT Now that you understand the core beliefs that define the philosophy of JIT, let’s look at the major elements that make up a JIT system. Three basic elements work together to complete a JIT system: just-in-time manufacturing, total quality management, and respect for people. These are shown in Figure 7-1 as overlapping circles. Often, it is assumed that JIT refers only to just-in-time manufacturing. However, this is only one element of JIT. Each of the three elements is dependent on the others to create a true JIT system.

Just-in-Time Manufacturing JIT is a philosophy based on elimination of waste. Another way to view JIT is to think of it as a philosophy of value-added manufacturing. By focusing on value-added processes, JIT is able to achieve high-volume production of high-quality, low-cost products while meeting precise customer needs. Just-in-time manufacturing is the element of JIT that focuses directly on the production system to make this possible. Many aspects of JIT manufacturing combine to provide a performance advantage. Later in the chapter we will look at some aspects of JIT manufacturing in more detail. First, let’s take an overall view.

The manufacturing process in JIT starts with the final assembly schedule, often called the master production schedule, which is a statement of which products and quantities will be made in specific time periods. The master production schedule is usually fixed for a few months into the future to allow all work centers and suppliers to plan their schedules. For the current month, the schedule is “leveled,” or developed so that the same amount of each product is produced in the same order every day. Note that with this arrangement there is repetition in the schedule from day to day, which places a constant demand on suppliers and work centers. Also, some quantity of every item is produced every day in accordance with what is needed. This is very different from traditional operations, which typically produce a large quantity of one product on one day. Since this quantity is usually more than what is immediately needed, the goods are stored in inventory. On a second day a large quantity of another product is produced, and it, too, is stored in inventory, resulting in high inventory costs.

JIT relies on a coordination system that withdraws parts from a previous work cen- ter and moves them to the next. The system typically relies on cards, called kanban, to pull the needed products through the production system. For this reason, JIT is often

� JIT system The three elements are just- in-time manufacturing, total quality management, and respect for people.

� Just-in-time manufacturing The element of JIT that focuses on the production system to achieve value- added manufacturing.

FIGURE 7-1

The three elements of JIT

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252 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

referred to as a pull system. The kanban specifies what is needed. There is no excess production because the only products and quantities produced are those specified by the kanban. Traditional manufacturing systems, in contrast, are push systems: they push products through the production system by producing an amount that has been set by a forecast of future demand. This type of production results in a higher level of inventory, which is stored for future consumption. Later in the chapter we will look in detail at how the kanban system works.

The reason traditional systems produce large quantities of one type of product before switching to production of another is high setup cost. This is the cost incurred when equipment is set up for a new production run. Setup includes activities such as recalibrating and cleaning equipment, changing blades, and readjusting equipment settings. Because setup costs are high in traditional systems, the objective is to produce as many units of a product as possible before having to incur the setup cost again. Of course, that means incurring a high inventory cost because of the extra goods that are kept in storage. JIT systems have been very efficient at reducing setup costs, which is a key to the success of JIT manufacturing. Setup times have been reduced from hours to mere seconds, and the goal is to reduce them to zero. Low setup times mean that small lot sizes of products can be produced as needed and that production lead times will be shorter. The ultimate goal of JIT is to produce products in a lot size of 1.

A major aspect of JIT manufacturing is its view of inventory. JIT manufacturing views inventory as a waste that needs to be eliminated. According to JIT, inventory is carried to cover up a wide variety of problems, such as poor quality, slow delivery, inefficiency, lack of coordination, and demand uncertainty. Inventory costs money and provides no value. Inventory also hurts the organization in another way: it does not allow us to see problems. According to JIT, by eliminating inventory we can clearly identify problems and work to eliminate them. An analogy that is often used to describe JIT’s view of inventory is that of a stream, as shown in Figure 7-2. The rocks in the stream represent problems. When the water in the stream covers the rocks, we cannot see what they are. By reducing the amount of water in the stream (by reducing inventory), we can finally identify the problems. However, identifying the problems is not enough—we have to solve them.

� Setup cost Cost incurred when setting up equipment for a production run.

FIGURE 7-2

Inventory hides problems

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ELEMENTS OF JIT • 253

In sum, JIT manufacturing is an efficiently coordinated production system that makes it possible to deliver the right quantities of products to the place they are needed just in time.

Total Quality Management (TQM) The second major element of JIT is total quality management (TQM), which is integrated into all functions and levels of the organization. The foundation of JIT is to produce the exact product that the customer wants. Quality is defined by the customer, and an effort is made by the whole company to meet the customer’s expectations.

Quality is an integral part of the organization; it permeates every activity and func- tion. The benefits of JIT cannot occur if the company is not working toward eliminat- ing scrap and rework. Traditional quality control systems use the concept of acceptable quality level (AQL) to indicate the acceptable number of defective parts. In JIT there is no such measure—no level of defects other than zero is acceptable.

Poor quality is considered a waste in JIT. Quality defects lead to scrap, rework, ser- vicing returned parts, and customer dissatisfaction. Quality defects cost money and can lead to lost customers. In JIT the entire organization is responsible for quality. Rather than hide poor quality or blame it on others, it is everyone’s goal to uncover and correct quality problems.

The concept of quality at the source is part of JIT. The objective is not only to iden- tify a quality problem but to uncover its root cause. Simply identifying and removing a defective product does not solve the problem. If the cause of the problem is not identi- fied, the problem will keep repeating itself. For example, a quality control check in a bakery might reveal that pies are overcooked and burned. Quality at the source tells us to identify the cause of the problem, such as an incorrect temperature setting or too long a baking time. Simply removing the burned pies does not eliminate the cause of the problem; overcooking will continue to occur.

The concept of continuous improvement is embedded in quality, which means that the company must continuously and actively work to improve. In JIT continuous improvement governs everything, from reducing the number of defects to lowering setup costs and lot sizes. For example, when implementing a JIT program, we cannot expect to eliminate inventories immediately. Continuous improvement tells us that we must do it gradually, slowly identifying and solving problems and then reducing inventory appropriately. However, continuous improvement goes beyond JIT manu- facturing. It includes improvement of worker skills, supplier quality and relationships, and even the performance of management.

Respect for People The third element of JIT is respect for people. Often, the study of JIT focuses exclu- sively on JIT manufacturing. However, the involvement of workers is central to the JIT philosophy. None of the improvements developed by JIT could be possible with- out respect for people. JIT requires total organizational reform and participation by everyone in the company. Everyone is equally important and equally involved. In a JIT system all functions of the company must work together to meet customer needs. Managers are not isolated in an administrative wing but spend time on the production floor.

Employees in JIT organizations are expected to be active participants in meeting customer needs, from developing improvements in the production process to making

� Total quality management (TQM) An integrated effort designed to improve quality performance at every level of the organization.

� Quality at the source Uncovering the root cause of a quality problem.

� Respect for people An element of JIT that considers human resources as an essential part of the JIT philosophy.

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254 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

Before You Go On

You should know that JIT is an all-encompassing philosophy that affects every level and function of the organization. The beliefs that make up the JIT philosophy include the following: (1) elimination of waste, (2) a broad view of operations, (3) simplicity, (4) continuous improvement, (5) visibility, and (6) flexibility. The philosophy of JIT is founded on these beliefs, and they govern all aspects of the organization. These beliefs are embodied in three specific elements: (1) JIT manufacturing, (2) total quality man- agement, and (3) respect for people. In the next section we look at specific features of each of these elements.

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sure quality standards are met at every level. JIT also relies on workers to perform multiple tasks and to work in teams, including management, labor, staff, and even suppliers.

JIT considers people to be a company’s most precious resource. The JIT philosophy believes in treating all employees with respect, providing job security, and offering sig- nificant rewards for well-performed tasks. Respect for people extends to suppliers. JIT believes in developing long-term relationships with suppliers in a partnership format.

Numerous companies, such as Texas Instru ments, Boeing, AT&T, and the Saturn Corporation provide excellent examples of the success that can be achieved by respect- ing and empowering people. These companies have also become models for successful use of self-managed teams. Self-managed teams are groups of workers that have no supervisors, inspectors, time clocks, or union stewards. Each team is responsible for every aspect of its busi- ness, such as productivity, quality, cost, pro- duction, and people. The crux of self- managed teams is respect for employees and their ability to be in charge of their own work. People are empowered to make decisions, and everyone is involved in the decision-making process. Work- ers are motivated through a system that directly rewards them for achieving their goals. The

workforce is viewed as a long-term asset, providing ongoing training and encouraging a sense of security and organizational belonging. These companies demonstrate the success that can be attained when an organization respects its people.

LINKS TO PRACTICE Texas Instruments

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JUST-IN-TIME MANUFACTURING The Pull System Traditional manufacturing operations are push-type systems. They are based on the assumption that it is better to anticipate future production requirements and plan for them. Traditional systems produce goods in advance in order to have products in

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JUST-IN-TIME MANUFACTURING • 255

place when demand occurs. Products are pushed through the system and are stored in anticipation of demand, which often results in overproduction because anticipated demand may not materialize. Also, there are costs associated with having inventories of products sitting in storage and waiting for consumption.

As noted earlier, JIT uses a pull system rather than a push system to move products through the facility. Communication in JIT starts either with the last workstation in the production line or with the customer and works backward through the system. Each station requests the precise amount of products that is needed from the previous workstation. If products are not requested, they are not produced. In this manner, no excess inventory is generated.

To see the difference between a push and a pull system, suppose that you have decided to have a backyard cookout for your friends. You have invited 20 people and are anticipating that each one will eat at least one hamburger and one hot dog. When your friends arrive, you decide to cook all the meat as quickly as you can process it on your grill, given your anticipation of demand for food. Your goal is to make it available for your guests. At the end of the party, however, you find that you are left with some hamburgers and quite a few hot dogs. Some people didn’t want both a hamburger and a hot dog, some people didn’t like one or the other, and some were vegetarian and didn’t want either one. As a result, after the party you are left with some cold, dried- out meat. This is the problem with a push system that produces large quantities in anticipation of demand that may or may not materialize.

Another way you could handle the cookout would be to grill a smaller quantity of meat—say, three hamburgers and three hot dogs, an amount that will fit on a serving tray. When the serving tray becomes empty, you could fill it with the meat on the grill, again enough to fill the tray. When the meat that was on the grill is removed, you can put fresh meat on the grill, again in a small quantity. No additional meat is placed on the grill until the cooked meat on the grill is removed. In this example, consumption of the food is pulling the meat through the system in small quantities. By “producing” the food in this manner, you will not end up with large amounts of “inventory” at the end of the cookout.

Kanban Production You can see that for the pull system to work, there must be good communication between the work centers. This communication is made possible by the use of a device called a kanban card; kanban means “signal” or “card” in Japanese. Most often a kan- ban card has such information on it as the product name, the part number, and the quantity that needs to be produced. The kanban is attached to a container. When workers need products from a preceding workstation, they pass the kanban and the empty container to that station. The kanban authorizes the worker at the preceding station to produce the amount of goods specified on the kanban. In effect, the kanban is a production authorization record. In our cookout example, the tray size served the purpose of the container. Now imagine that you had a card attached to the tray that specified three hamburgers and three hot dogs and that you could not produce any more or less than that amount. This procedure is similar to the way a kanban card works.

To make the system work smoothly and control the movement of empty and full containers, there are actually two types of kanban cards: production cards that authorize production and withdrawal cards that authorize withdrawal of materials. Figure 7-3 shows a diagram of how a pull system with two kanban cards works.

� Pull system JIT is based on a “pull” system rather than a “push” system.

� Kanban card A card that specifies the exact quantity of product that needs to be produced.

� Production card A kanban card that authorizes production of material.

� Withdrawal card A kanban card that authorizes withdrawal of material.

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256 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

Let’s look at the steps involved in this process.

Step 1. Th e worker at workstation B received an empty container to which a production kanban is attached. Th is means that the worker must produce enough of the requested material to fi ll the empty container. Step 2. To fi ll the requirement of the production kanban, the worker at workstation B takes a full container of material from its input area. Step 3. To replenish the material just taken, the worker at workstation B generates a request for more input from workstation A by sending a withdrawal kanban to the output area of process A. Step 4. Notice that workstation A already has some parts available in its output area. Th e worker at workstation A attaches the withdrawal kanban to the full container and sends it immediately to workstation B. Step 5. Th e worker at workstation B takes the production kanban that was originally attached to the full container and places it on an empty container, generating production at workstation A. Step 6. Th e worker at workstation A removes a container of materials from its input area. Th e same sequence of steps is then repeated between all the workstations.

When this process is used, the amount produced at any one time is the amount in one container. Production cannot take place unless a container is empty and a produc- tion card has authorized production. A full container cannot be withdrawn unless a withdrawal kanban authorizes it. It is the kanban cards that coordinate the pull pro- duction system. Without kanbans, the withdrawal and production of materials cannot take place. Another advantage of the kanban is that it is visual. Kanban cards and con- tainers are all placed in clearly visible areas for everyone to see.

There are as many kanban cards in the system as there are containers. If there are too many kanbans in the production system, there may be too much production and too much inventory. On the other hand, if there are not enough kanbans, the sys- tem may not be producing quickly enough. Sometimes the production manager may

FIGURE 7-3

The pull system with two kanban cards

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JUST-IN-TIME MANUFACTURING • 257

decide to add to or subtract from the number of kanbans to bring the system into balance. Remember, however, that the goal is to continually improve the efficiency of the system. This means striving to reduce the number of kanbans and the amount of inventory in the system.

The number of kanbans and, therefore, the number of containers in the system is a very important decision. The formula to compute the number of kanbans needed to control the production of a particular product is as follows:

N 5 DT 1 S

C where N � total number of kanbans or containers (one card per container) D � demand rate at a using workstation T � the time it takes to receive an order from the previous workstation (also

called the lead time) C � size of container S � safety stock to protect against variability or uncertainty in the system

(usually given as a percentage of demand during lead time)

Problem-Solving Tip Th e demand (D) and lead time (T) have to be in the same time units.

You can see from this equation that the number of containers needed at a workstation is dependent on four things: the demand rate, the size of the container, the lead time, and the safety stock level. To control the amount of inventory, the size of containers used is typically much smaller than the demand. For example, the containers generally do not hold more than 10 percent of the daily demand. The number of kanbans in the system can be reduced as efficiency improves. Let’s look at an example to see how this would work.

Jordan Tucker works for a production facility that makes aspirin. His job is to fill the bottles of aspirin, and he is expected to process 200 bottles of aspirin an hour. The facility where Jordan works uses a kan- ban production system in which each container holds 25 bottles. It takes 30 minutes for Jordan to receive the bottles he needs from the previous workstation. The factory sets safety stock at 10 percent of demand during lead time. How many kanbans are needed for the filling process?

• Solution: D 5 200 bottles per hour

T 5 30 minutes 5 1 2

hour

C 5 25 bottles per container

S 5 0.10 a200 3 1 2 b 5 10 bottles

N 5 DT 1 S

5 1200 bottles/hour 2 ¢ 1

2 hour≤ 1 10

25 bottles 5 4.4 kanbans and containers

The number of containers can be rounded up or down. Notice that rounding down to four containers would force us to make improvements in the operation. Rounding up to five would provide additional slack.

EXAMPLE 7.1

Computing the Number of Kanbans

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Variations of Kanban Production In many facilities the kanban system has been modified so that actual cards do not exist but some other type of signal is used to pull the goods through. This may be as simple as an empty place on the floor that identifies where the material should be stored. This is called a kanban square and is shown in Figure 7-4. An empty square indicates that it is time for the supplying operation to produce more goods. A full square indicates that no parts are needed.

Another type of signal might be some type of flag, as shown in Figure 7-5, that is used to indicate it is time to produce the next container of goods. This is called a signal kanban and is often used when inventory between workstations is necessary. When the inventory level is reduced to the point of reaching the signal, the signal is removed and placed on an order post, indicating that it is time for production.

The system of kanbans can also be used to coordinate delivery of goods by suppli- ers. These are called supplier kanbans. The suppliers bring the filled containers to the point of usage in the factory and at the same time pick up an empty container with a kanban to be filled later. Since a manufacturer may have multiple suppliers, “mail- boxes” can be set up at the factory for each supplier. The suppliers can check their “mailboxes” to pick up their orders. Kanbans are usually made of plastic or metal, but there are also bar-coded kanbans and electronic kanbans that further ease communi- cation with suppliers.

Small Lot Sizes and Quick Setups A principal way of eliminating inventory and excess processing while increasing flex- ibility is through small-lot production, which means that the amount of products produced at any one time is small—say, 10 versus 1000. This allows the manufacturer to produce many lots of different types of products. It also shortens the manufactur- ing lead time, the actual time it takes to produce a product, since it takes less time to produce 10 units than to produce 1000. Shorter lead time means that customers

� Small-lot production The ability to produce small quantities of products.

FIGURE 7-5

A signal kanban

FIGURE 7-4

A kanban square

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JUST-IN-TIME MANUFACTURING • 259

receive the specific products they want faster. The ultimate goal of JIT is to be able to economically produce one item at a time as the customer wants it.

Small-lot production gives a company a tremendous amount of flexibility and allows it to respond to customer demands more quickly. However, to be able to achieve small-lot production, companies have to reduce setup time. Recall that setup time is the time it takes to set up equipment for a production run. This includes cleaning and recalibrating equipment, changing blades and other tools, and all other activities nec- essary to switch production from one product to another.

To see the impact of setup time, let’s pretend that we are a producer of ice cream and that we make two different flavors—chocolate and vanilla—on the same pro- duction line. The system works by first producing a certain amount of chocolate ice cream. The equipment is then cleaned and the machines are reset for the proper ingredients (this is setup time) in order to switch production to vanilla ice cream. A traditional manufacturing approach would be to produce as much chocolate ice cream as possible, since everything is already set up for this product. Then we would set up the machines for production of vanilla ice cream and make as much of it as possible before we have to clean the equipment again. The problem with this approach is that we end up producing extra amounts of ice cream. The extra ice cream is inventory. It costs money and requires storage space, and some of it will probably go to waste.

A more effective approach would be to lower the time it takes to change from pro- duction of one flavor of ice cream to another. Then we can produce only what we need and no more. We would not have the cost of extra inventory. This approach would also allow us to respond quickly to changes in demand. For example, if a cus- tomer needed extra chocolate ice cream, producing it would not be a problem. This has been the approach used by JIT. Many large manufacturers have been able to reduce setup times from many hours to only a few minutes, which has resulted in tremendous flexibility.

To produce economically in small-lot sizes, JIT has found ways to reduce setup times. The goal is to achieve single setups, or setup times in single digits of minutes. There are a number of ways to achieve these low setup times. One approach is to sepa- rate setup into two components: internal setups and external setups. Internal setups require the machine to be stopped for the setup to be performed. External setups can be performed while the machine is still running. Almost all setups in traditional manu- facturing systems are internal. With JIT, much of the setup process has been converted to external setups. This requires engineering ingenuity and cleverly designed fixtures and tools. In a number of companies, the workers even practice the setup process and try to increase their speed.

Uniform Plant Loading Demand for a product can show sudden increases or decreases, which can mean disruptive changes in production schedules. These demand changes are typically magnified throughout the production line and the supply chain. They contribute to inefficiency and create waste. The JIT philosophy is to eliminate the problem by mak- ing adjustments as small as possible and setting a production plan that is frozen for the month. This is called uniform plant loading or “leveling” the production sched- ule. The term leveling comes from the fact that the schedule is uniform or constant throughout the planning horizon.

To meet demand and keep inventories low, a “level” schedule is developed so that the same mix of products is made every day in small quantities. This is in contrast to

� Internal setup Requires the machine to be stopped in order to be performed. � External setup Can be performed while the machine is still running.

� Uniform plant loading A constant production plan for a facility with a given planning horizon.

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260 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

traditional systems, which produce large quantities of one product on one day and of another product on the next day, causing large buildups of inventory. Table 7-1 shows how a level production system works in contrast to a traditional production system. In the table, a company produces five products: A, B, C, D, and E. The weekly production requirements for all products are met with both types of system. However, with the JIT system there is day-to-day repetition in the schedule, which prevents the company from having to carry large amounts of inventory and places predictable demands on all work centers and suppliers.

Flexible Resources A key element of JIT is having flexible resources in order to meet customer demands and produce small lots. One aspect of flexibility is relying on general-purpose equipment capable of performing a number of different functions. For example, a general-purpose drilling machine may be able to drill holes in an engine block and also perform some milling and threading operations. This is very different from hav- ing specialized equipment that can perform only one task. General-purpose equip- ment provides flexibility of operations and eliminates waste of space, movement from one machine to another, and setup of other machines. You can see how this concept works in your own life. Isn’t it easier to have one machine that is a printer, copier, and fax machine all in one, rather than have three different machines? With the press of a button you can print a copy and then fax it, rather than walk from one machine to the other, setting up each machine, not to mention solving the space requirements of three machines.

Another element of flexibility is the use of multifunction workers who can per- form more than one job—an essential aspect of JIT. To meet changing production requirements, workers in JIT are trained to operate and set up different machines. This provides flexibility in the schedule because workers can be moved around as needed. Also, workers in JIT are responsible for performing simple maintenance on

� Multifunction workers Capable of performing more than one job.

Weekly Production Requirements by Product

A: 10 units/week

B: 20 units/week

C: 5 units/week

D: 5 units/week

E: 10 units/week

Monday Tuesday Wednesday Thursday Friday

A A A A A

B B B B B

D D D D D

C C C C C

E E E E E

Monday Tuesday Wednesday Thursday Friday

A A B B B B

C D E E

A A B B B B

C D E E

A A B B B B

C D E E

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C D E E

Time

TABLE 7-1

Contrasting Level versus Traditional Production

Traditional Production Plan

JIT Production with Level Scheduling

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JUST-IN-TIME MANUFACTURING • 261

their machines and are trained to perform quality control procedures. As we will see later in the chapter, workers in JIT have considerable responsibility and perform many duties. Their many abilities give a tremendous amount of flexibility to JIT.

The flexibilities of workers and machines combine to produce great advantages. Note that the operating time of a machine is usually different from that of a worker because there is a period of time while the machine is running and the worker has nothing to do. A multifunction worker can operate more than one machine at a time.

Facility Layout Proper arrangement and layout of work centers and equipment is critical to JIT manu- facturing, a topic that is covered in detail in Chapter 10. Physical proximity and easy access contribute to the efficiency of the production process. Streamlined production is an important part of JIT; it relies heavily on assembly lines, dedicated to the produc- tion of a family of products.

JIT also relies on cell manufacturing, the placement of dissimilar machines and equipment together in order to produce a family of products with similar processing requirements. These machines create a small assembly line, and their grouping is usually called a cell. The machines in one grouping can be those needed to manufacture a set of parts belonging to the same family of products. The equipment in a work cell is usually arranged in a U shape, with the worker placed in the center of the U. This arrangement has a number of advantages. First, the use of cells provides production efficiency with the flexibility to produce a variety of different products. Second, the U shape allows workers to have easy reach and flexibility. No special material handling is needed because everything is within reach. Finally, worker satisfaction is higher because of the ability to per- form a variety of tasks.

Each cell produces similar items, so setup times within cells are low and lot sizes and inventories can be kept small. Figure 7-6 compares traditional production with cell manufacturing.

Companies such as Whirl- pool, Xerox, and Target Stores have adopted JIT as a way of doing business. To achieve their goals, these companies need just-in-time deliveries. For this they use Ryder Integrated Logistics, a global and domes- tic provider of transportation and distribution management services that offers just-in-time pickup and delivery of goods from suppliers. Ryder ensures that materials flow smoothly into assembly or manufacturing plants with properly sequenced deliveries. It uses a dedicated fleet of vehicles, onboard computers, and satellite/cellular communications as well as a sophisticated distribution system that enables it to support multiple suppliers. Also, to reduce cycle time, Ryder uses bar- code technology, EDI, and special software to enable the consolidation and distribu- tion of goods in and out of facilities. All this adds up to an organization dedicated to meeting the challenge of JIT.

� Cell manufacturing Placement of dissimilar machines and equipment together to produce a family of products with similar processing requirements.

LINKS TO PRACTICE Ryder Integrated Logistics www.ryder.com

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262 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

TOTAL QUALITY MANAGEMENT

Quality is a difficult term to define because it means different things to different people. A narrow viewpoint, typically used by traditional manufacturers, is to define quality as meeting specified target quality standards. This would mean producing a product within specified tolerances set by engineers and not exceeding the specified acceptable defect rate. However, today’s definition of quality takes a much broader view: quality is defined as meeting or exceeding customer expectations.

As customer needs and standards drive the production system, the company must define quality as it is seen by users of the product. The customer’s definition of qual- ity then must be interpreted by engineers and production managers. This process is not always easy; customers are not always sure what they want or may not be able to articulate their needs effectively. However, once quality has been defined in measur- able terms, it needs to be monitored on an ongoing basis. Targets for improvement need to be set and systematic methods for improvement developed. These methods include continual training of workers so they can identify and correct quality prob- lems. Together, this process outlines a strategy for quality improvement in JIT, as shown in Table 7-2. Note that the steps in Table 7-2 show an ongoing, dynamic pro- cess. Customers’ quality definitions must be monitored continuously as customers’ expectations and needs change over time.

FIGURE 7-6

Traditional versus cell manufacturing

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TOTAL QUALITY MANAGEMENT • 263

Product versus Process The costs of poor quality can be quite high when one includes product redesign, rework, scrap, servicing returned products, or even losing customers. All this rep- resents waste. Phil Crosby, a leading quality “guru,” pointed out that “quality is free.” It is poor quality that is costly. For these reasons, JIT does not tolerate poor quality.

In JIT the quality of the product is distinguished from the quality of the process used to produce the product. The idea is that a faulty product is a result of a faulty pro- cess. We may be able to repair a faulty product, but if we do not correct the process, we are not addressing the root cause of the problem and will continue to produce faulty products. Quality in JIT is centered on building quality into the process. A production process that is well within the set quality control limits should not produce a defective product.

Quality at the Source The notion of quality at the source means that the root cause of quality problems needs to be identified. This could be a problem with the design, suppliers, the process, or any other area. We know that it is much easier and less costly to build quality into a pro- cess than to try to correct problems after they occur.

Quality problems can come from many sources. Some examples of sources of qual- ity problems are the following:

Product design. In the design process, customer needs may be misunderstood and not incorporated into the product design.

Process design. Management and equipment problems may stem from the design of the production process. Operator error actually contributes to only about 15 percent of quality problems.

Suppliers. Quality problems caused by suppliers include low-quality materials and are oft en due to misunderstandings between manufacturer and supplier.

Monitoring quality is the responsibility of everyone in the organization. Workers are given the authority to stop the production line if quality problems are encoun- tered; this is called jidoka. To perform jidoka, each worker can use a switch above his or her workstation to turn on a call light or stop production. A green light means that production is flowing normally; a yellow light is a signal for help; and a red light means that the line is stopped. When a red light goes on, all personnel rush to the troubled spot to determine what the problem is. In JIT environments, stopping the line is not only allowed but expected. At JIT facilities, if a certain amount of time has passed without a line stoppage, personnel become concerned that quality problems are passing undetected.

� Jidoka Authority given to workers to stop the production line if a quality problem is detected.

Step 1: Define quality as seen by the customer.

Step 2: Translate customer needs into measurable terms.

Step 3: Measure quality on an ongoing basis.

Step 4: Set improvement targets and deadlines.

Step 5: Develop a systematic method for improvement.

TABLE 7-2

Strategy for Quality Improvement

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264 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

You can see that workers have much responsibility in a JIT system. Analyzing production problems is considered a serious business and is performed as part of the regular workday, not in one’s spare time. For this reason, JIT systems usually operate with seven hours of production and one hour of problem solving and working with teams. Called undercapacity scheduling, it is necessary in order to leave ample time for problem-solving activities.

To help workers identify quality problems, JIT relies on visual signals. One such signal is kanban control. Others include color coding, bulletin boards, lights, process control charts, and other visual displays. For example, color-coding tools and bins helps workers know which tools belong in which bins. Color-coding different sections of the work area helps workers identify stocking points and different processing sec- tions. Material handling routes are clearly marked in different colors. Instructional photographs located near equipment provide visual explanations of machine usage. Another type of visual signal is poka-yoke. The term means “foolproof” and refers to a device or mechanism that prevents defects from occurring. The device could be a clamp that can be placed only in a certain way or a lid that can be turned in only one direction.

Preventive Maintenance An important aspect of quality management in JIT is preventive maintenance. Not only do machines rarely break down at convenient times but breakdowns are costly in terms of lost production, unmet deadlines, disruption of work schedules, and unhappy customers—all considered wastes in JIT. To avoid unexpected machine stoppages, a company invests in preventive maintenance, which is regular inspections and maintenance designed to keep machines operational. Although preventive main- tenance is costly, the costs are significantly smaller than the cost of an unexpected machine breakdown. You know from your own experience how important preven- tive maintenance is, such as taking your car for a tune-up and oil change or going to the dentist for regular cleaning and checkups. Neither is fun and both are costly, but we do these things because we know that the alternatives could be much costlier.

According to JIT, workers should perform routine preventive maintenance activi- ties, including cleaning, lubricating, recalibrating, and making other adjustments to equipment. These duties are viewed as part of the worker’s job. JIT also places a great deal of importance on care of equipment and in training workers to operate and main- tain machines properly. Included are designing products so they can be easily pro- duced on current machines that can be easily operated and maintained.

Work Environment Another important element of quality management is the overall work environment. Order and simplicity are considered highly important. According to JIT, an orderly environment creates a calm, clear mind, whereas a disorganized environment cre- ates disorganized thoughts. Also, an orderly environment encourages respect for the workplace. It is much easier to hide waste in a cluttered room. When there is plenty of empty space and everything is in its place, it is easy to see if something is out of order. When entering a JIT facility, the first thing one notices is that it is very clean and orderly, with ample space and no clutter. Keeping the facility clean is the work- ers’ responsibility. Every worker is responsible for cleaning equipment and tools after using them and putting them back in their place. Everyone is responsible, so no

� Poka-yoke Foolproof devices or mechanisms that prevent defects from occurring.

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RESPECT FOR PEOPLE • 265

one can blame anyone else if something is misplaced. All this creates a positive work environment, which is considered essential to the quality of work life and contributes to employee satisfaction.

RESPECT FOR PEOPLE

Respect for people is considered central to the JIT philosophy. Of all the issues dis- cussed in this chapter, none departs more from traditional systems than the role of employees in a firm. According to JIT, genuine and meaningful respect for employ- ees must exist for a company to get the best from its workers. Employees perform a great many functions in JIT, and for true JIT to exist they must be genuinely respected and appreciated. Their inputs must be valued, and they must feel secure. The key words here are genuine and meaningful. Achieving this state is sometimes difficult in environments with a history of adversarial relationships, particularly between labor and management. Managers cannot mandate genuine and meaningful respect. They cannot send out a memo on a Friday saying, “On Monday there will be genuine and meaningful respect for people!” This is something that requires a complete change in organizational culture. Often it takes much effort and time.

JIT organizations rely on all employees to work together, including management and labor. The organizational hierarchy is generally flatter in JIT than in traditional organizations, and organizational layers are not strictly defined. Great responsibil- ity and autonomy are given to ordinary workers. All levels of employees often work in teams, and in many JIT organizations all dress the same way regardless of level, which helps break down traditional barriers and makes it easier for people to work together. In this section we look at some specific issues that relate to respect for people in JIT.

The Role of Production Employees In traditional systems, production employees often perform their jobs in an automatic fashion. In JIT, the role of production employees is just the opposite: workers are actively engaged in pursuing the goals of the company. JIT relies on cross-functional worker skills, meaning the ability of workers to perform many different tasks on many different machines. Part of workers’ duties is to be actively engaged in improving the production process, monitoring quality, and correcting quality problems. Continuous improvement relies heavily on the knowledge and skills of the workers closest to the operation. They are the ones best suited to make improvements in their jobs.

Production workers are required to continually check and monitor the quality of the production process. This includes inspecting their own work as well as the mate- rials received from previous operations. This is necessary in order to detect quality problems before a defective part can proceed to additional processing. For this system to succeed, workers need to have a very different attitude toward poor quality than in traditional systems. In JIT, discovering quality problems is a goal, not something that should be covered up or blamed on someone else. As we have learned, quality at the source means that all employees are responsible for getting to the root cause of quality problems.

Another part of a worker’s responsibility is recording data, such as the number of setups completed, the number of units produced, the number of defects and scrap, quality process control data, equipment malfunctions, and hours worked. It is up to the worker to understand how to use the data. One way to motivate workers is to

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266 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

use visible displays of data, such as performance measures, on a flip chart or chalk board near each workstation. Information such as quality problems or stoppages can be recorded on the chart for everyone to see.

However, merely recording data is not enough. Record keeping and posting results also serve to remind workers that they need to act on the information. The real task of production employees is to search for causes of problems in quality and produc- tion. Time needs to be set aside at the end of a shift for data analysis. Once data have been analyzed, problem-solving activities usually take place, using the team approach in group meetings. When workers become used to their new level of responsibility and respect, they develop the initiative to solve many problems on their own. The key in problem solving is to give workers the authority and incentive to solve problems rather than view problem solving as someone else’s responsibility.

Participation by all employees is vital to the success of JIT. For this reason, JIT uses a style of management called bottom-round management, which means consensus management by committees or teams. When a decision needs to be made, it is dis- cussed at all levels, starting at the bottom, so that everyone in the company contributes to the decision. This decision-making process is very slow, but it achieves consensus among all involved. In JIT, top management is usually concerned with strategic issues and leaves other decisions to employees.

Because everyone needs to work together, teams are an integral part of JIT. One of the most popular types of team is the quality circle. Quality circles are groups of about five to twelve employees who volunteer to solve quality problems in their area. Although participation is usually voluntary, the meetings take place during regular work hours. Quality circles usually meet weekly and attempt to develop solutions to problems and share them with management. Usually these work groups are led by a supervisor or a production employee and are made up of employees from the particu- lar areas involved.

You can see that in JIT the role of production employees is very different from their role in traditional organizations. Employees have much more responsibility and autonomy. Table 7-3 summarizes some of the key elements of the role of production employees in JIT.

Lifetime Employment Japanese companies have traditionally provided lifetime employment for most of their permanent employees. Employees must feel secure if they are to work in teams, feel free to say what they think, and act on their ideas. Today, lifetime employment covers a relatively small percentage of the total workforce. Even though lifetime employment

� Bottom-round management Consensus management by committees or teams.

� Quality circles Small teams of employees that volunteer to solve quality problems.

• Workers have cross-functional skills.

• Workers are actively engaged in solving production and quality problems.

• Workers are empowered to make production and quality decisions.

• Quality is everyone’s responsibility.

• Workers are responsible for recording and visually displaying performance data.

• Workers work in teams to solve problems.

• Decisions are made through bottom-round management.

• Workers are responsible for preventive maintenance.

TABLE 7-3

Role of Production Employees in JIT

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RESPECT FOR PEOPLE • 267

is rarely possible, a company must do certain things to reduce employee insecurity and encourage trust and openness. One answer is to commit to a policy of making no layoffs as a result of productivity improvements. This helps alleviate fears that produc- tivity improvements made by employees will result in job loss.

Most JIT facilities have company unions that work to build cooperative relation- ships between management and labor. It is understood that if the company performs well, the workers will share in the rewards through bonuses. This policy encourages workers to work harder.

The Role of Management Just as the role of production employees is different under JIT, so is the role of man- agement. Actually, it can often be difficult for management to truly accept the new role of production employees as being responsible for duties that traditionally were per- formed exclusively by management. However, in successful JIT environments manag- ers realize that all employees are on the same team and that a higher level of worker responsibility means more success for the firm as a whole.

The role of management is to create the cultural change necessary for JIT to suc- ceed. This is one of the most difficult tasks of JIT. It involves creating an organizational culture that provides an atmosphere of close cooperation and mutual trust. Remember that JIT relies on ordinary workers to independently solve production problems and take on many tasks. To be able to do this, employees must be problem solvers and be empowered to take action based on their ideas. Workers must feel secure in their jobs and know that they will not be reprimanded or lose their jobs for being proac- tive. They must also feel comfortable enough to discuss their ideas openly. It is up to management to develop an incentive system for employees that rewards this type of behavior.

In the JIT environment, the role of managers becomes more of a supporting func- tion. Managers are seen as facilitators and coaches rather than “bosses.” Their job is to help develop the capabilities of employees, to teach, make corrections, help individuals develop their skills, and serve as motivators. They assist with teamwork and prob- lem solving. Managers are also responsible for providing motivation and necessary recognition to employees. Their job also includes sharing information such as profit- ability and performance results, as well as making sure ample time is scheduled for all the activities employees must perform. Remember that the additional activities, such as quality control charting, maintenance, and working in teams, are not done during “free time” but during regular work hours. The role of management, summarized in Table 7-4, is highly important for JIT to succeed.

A team of employees working to solve a problem

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• Be responsible for creating a JIT culture.

• Serve as coaches and facilitators, not “bosses”.

• Develop an incentive system that rewards workers for their efforts.

• Develop employee skills necessary to function in a JIT environment.

• Ensure that workers receive multifunctional training.

• Facilitate teamwork.

TABLE 7-4

Role of Management in JIT

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268 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

Supplier Relationships JIT’s respect for people also extends to suppliers. With JIT, a company respects suppliers and focuses on building long-term supplier relationships. The tradi- tional approach of competitive bidding and buying parts from the cheapest sup- plier runs counter to the JIT philosophy. JIT companies understand that they are in a partnership with their suppliers, who are viewed as the external factory. The number of suppliers is typically much smaller than in traditional systems, and the goal is to shift to single-source suppliers that provide an entire family of parts for one manufacturer.

The benefits of a long-term relationship with a small number of suppliers are many. Together the supplier and manufacturer focus on improving process quality controls. There are fewer contacts by buyers, and there is a focused effort on developing a per- sonal relationship. There is also greater accountability for quality, delivery, or service problems. Having few suppliers makes it easier to develop stable and repetitive deliv- ery schedules and eliminate paperwork.

With a long-term relationship, a supplier can act as a service provider rather than a one-time seller. Part of such a relationship is cost and information sharing. The manufacturer shares information about forecasts and production schedules, allowing the supplier to “see” what is going to be ordered. The supplier, in turn, shares cost information and cost-cutting efforts with the manufacturer. Both parties help each other and together reap the benefits. Also, long-term relationships provide greater incentive for continuous quality improvement. Finally, with a long-term relationship suppliers are better able to plan capacity and production mix requirements, resulting in lower costs.

To provide JIT service to manufacturers, suppliers often locate near their custom- ers. Good examples are the Nissan plant in the Tennessee Valley and the Honda plant in Marysville, Ohio. These plants are surrounded by their suppliers. If close proximity is not possible, many suppliers have small warehouses near the manu- facturing plant, which can be used for housing frequently delivered items. Because JIT suppliers are extensions of the manufacturing facility, the “pull system” concept applies to them as well. JIT suppliers use standardized containers and make deliver- ies according to a preset schedule. As companies advance in JIT, they expect progres- sively shorter delivery cycles from their suppliers and will often fine them for not meeting the schedule. Often, a few suppliers will join together to help each other make small deliveries.

Many suppliers have become JIT certified, which means that they have received one or more designations that indicate they meet certain high-quality standards. Once a supplier has been certified, fewer quality checks are needed since quality standards are built into the certification process. A certified JIT supplier with a long- term agreement also has the advantage of receiving payment at regular intervals rather than on delivery of goods. Paperwork is eliminated, and electronic linkages can be set up between manufacturer and supplier. These result in direct savings for both the supplier and the manufacturer. For example, suppliers to Otis Elevator of North America provide over 40 percent of the total material or component require- ments directly to the production line. Replenishment is triggered by a visual kanban system and by a direct link from the Otis receiving dock to the supplier’s computer system.

As you can see, supplier relationships in JIT are another fundamental departure from traditional systems. Companies have learned much from JIT, and the new way of

� Single-source suppliers Suppliers that supply an entire family of parts for one manufacturer.

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BENEFITS OF JIT • 269

BENEFITS OF JIT

• Suppliers viewed as external factory.

• Use of single-source suppliers.

• Long-term supplier relationships developed.

• Suppliers locate near customer.

• Stable delivery schedules.

• Cost and information sharing.

TABLE 7-5

Key Elements of JIT Suppier Relationships

The benefits of JIT are very impressive. For this reason, many companies rush to adopt JIT without realizing all that is involved. Many of these companies do not reap the benefits because they do not take the time to implement the culture necessary for JIT to succeed. A recent study of JIT benefits has found that over a five-year period com- panies using JIT have experienced an 80–90 percent reduction in inventory invest- ment, an 80–90 percent reduction in lead time, a 75 percent reduction in rework and setup, a 50 percent reduction in space requirements, and a 50 percent reduction in material handling equipment.

The first implementation of JIT took place at the Toyota Motor Company in Japan in the early 1970s. Thus, much of what we have learned about JIT comes from Toy- ota’s experience. Since then, hundreds of companies have successfully implemented JIT, including Ford, General Electric, IBM, 3M, Nissan/Renault, and many others. Even for companies that do not achieve the dramatic benefits of a full JIT implementa- tion, JIT provides many benefits. Table 7-6 lists key benefits of JIT.

One of the greatest benefits of JIT is that it has changed the attitude of many firms toward eliminating waste, improving responsiveness, and competing based on time. Time-based competition is one of the primary ways in which companies operate today, and JIT is what makes it possible. Even companies that have not implemented JIT have had to make some changes in order to compete in a world that has left behind many traditional ways of doing business.

The large benefits JIT can bring to a company are demonstrated by the success achieved by Alcoa, a leader in the aluminum industry. Alcoa’s accomplishment included reducing inventories by more than a quarter of a billion dollars in 1999 while increasing sales by almost $1 billion. This was a direct benefit of implementing Toyota’s JIT system just a year earlier. In 1998, Alcoa found itself ill-prepared to meet customer needs. It was piling up inventory, yet not providing what the customer wanted. Alcoa turned to a full JIT “pull” manufacturing system. Benefits quickly began to appear at facilities all over the country. For example, an extrusion plant in Mississippi lost money in 1998 but within a year was capable of delivering customer orders in two days.

LINKS TO PRACTICE Alcoa, Inc. www.alcoa.com

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dealing with suppliers is the wave of the future, even for firms that do not fully imple- ment JIT. Table 7-5 shows some of the key aspects of JIT supplier relationships.

• Reduction in inventory

• Improved quality

• Reduced space requirements

• Shorter lead times

• Lower production costs

• Increased productivity

• Increased machine utilization

• Greater flexibility

TABLE 7-6

Benefits of JIT

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270 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

IMPLEMENTING JIT

We have seen that JIT affects every aspect of the organization. Therefore, the imple- mentation of a true JIT system requires a complete cultural change for the organiza- tion. To implement JIT successfully, a company does not need sophisticated systems. What is needed are correct attitude, employee involvement, and continuous improve- ment. A change of such profound magnitude needs to be driven by top management. JIT implementation cannot succeed if it is done only by middle or lower management.

Implementation needs to start with a shared vision of where the company is and where it wants to go. This vision needs to consider everyone who has a stake in the company, including customers, employees, suppliers, stockholders, and even the com- munity in which the company is located.

Once the vision has been developed, it is up to top management to create the right atmosphere. Managers need to involve workers in a meaningful way and not merely give lip service to the concept. Part of the change in atmosphere should consist of breaking down the barriers between departments and instilling “we” thinking in place of an “us-versus-them” attitude. Reward systems should be put in place to reward ideas and team cooperation.

A “champion” for JIT implementation must be designated, whether it is a plant man- ager, the CEO, or a steering committee. The purpose is to have a person or group over- see all the steps necessary in implementing such a large change. This person or group will be responsible for reviewing progress, addressing any problems that may develop, making sure ample resources are available, and ensuring that a proper reward system is in place. Another job of the JIT champion is sharing results with everyone in the com- pany. Such information is not shared with production workers in traditional systems. However, in JIT, sharing of this type of information with everyone in the company is considered a key to success and is done frequently and regularly. Financial information cannot be kept secret if everyone is to work together and share in the benefits.

In making specific changes in JIT manufacturing, some changes need to be imple- mented before others. Not all things can or should be changed at once. Following is a sequence of steps that should be followed in the implementation process:

1. Make quality improvements. Usually it is best to start the implementation process by improving quality. Th e reason is that quality is pervasive and all the JIT objectives are dependent on quality improvement.

2. Reorganize workplace. Reorganizing the workplace is the next step. Th is means proper facility layout, cleaning and organizing the work environment, designating storage spaces for everything, and removing clutter.

3. Reduce setup times. Th e next step is to focus on reducing setup times, which will involve manufacturing and industrial engineering. It will require analysis of current setup procedures, elimination of unneeded steps, and streamlining of motions. Workers will need to be trained in the proper setup procedures.

4. Reduce lot sizes and lead times. Once setup times have been reduced, the focus is on reducing lot sizes and lead times. Th is in turn will reduce the inventory between workstations and free up space. Th e empty space will contribute to visibility.

5. Implement layout changes. Th e next step is to arrange equipment and workstations in close proximity to one another and to form work cells.

6. Switch to pull production. Aft er the preceding changes have been implemented, it is time to switch to pull production. Changing from a push system to a pull

HRM

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JIT IN SERVICES • 271

system, including worker training, needs to be planned very carefully. However, the change needs to be made at once because a production facility cannot use a push and a pull system at the same time.

7. Develop relationship with suppliers. Changes in relationships with suppliers should be among the last steps implemented. Demands for smaller and more frequent deliveries should be instituted gradually.

By now you should understand that JIT is made up of many ideas that define its philosophy. Because of that, implementation of JIT is complicated. Most companies are so eager to receive the benefits of JIT that they jump in and begin making changes without thinking them through. Often, company executives will learn that for JIT implementation to succeed inventory needs to be eliminated, so they begin ordering reductions in inventory. This unplanned approach can have disastrous effects. Inven- tory is there to cover up problems. Unless the problems are solved first, simply reduc- ing inventory can completely halt production.

Finally, when it comes to implementation, remember that the concept of continu- ous improvement is an integral part of JIT. This means that the implementation pro- cess will not start and end in definite time periods. Rather, it will be a gradual process. Reductions in inventory have to be preceded by improvements in quality, changes in layout, reductions in setup times, and worker training. As improvements are made, inventory can be reduced. As new problems become visible, they must be solved before further reductions in inventory are made. This is an ongoing, gradual process. Imple- mentation is never complete because improving performance is a never-ending task.

JIT IN SERVICES People who think of JIT as applying only to manufacturing may not see how JIT could be applicable to service organizations. However, we have seen in this chapter that JIT is an all-encompassing philosophy that includes eliminating waste, improving qual- ity, continuous improvement, increased responsiveness to customers, and increased speed of delivery. That philosophy is equally applicable to any organization, service or manufacturing.

Following are examples of JIT concepts seen in service firms.

Improved Quality Service quality is often measured by intangible factors such as timeliness, service consistency, and courtesy. Building quality into the process of service delivery and implementing concepts such as quality at the source can significantly improve service quality dimensions. For example, McDonald’s has become famous by building quality into the process and standardizing the service delivery system. Regardless of location, McDonald’s customers receive the same product and service consistency.

Uniform Facility Loading The challenge for service operations is synchronizing their production with demand. Many service firms have developed unique ways to level customer demand in order to provide better service responsiveness. For example, hotels and restaurants use reserva- tion systems. Differential pricing systems can also be used to even out demand, such as airlines requiring a Saturday night stay for lower fares or the post office charging more for next-day delivery.

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272 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

Use of Multifunction Workers The use of multifunction workers in service organizations helps improve quality and customer responsiveness. An example of this is seen in department stores, where workers make sales, clean sales areas, and arrange displays.

Reductions in Cycle Time Competition based on speed is common in services, as can be seen in such companies as McDonald’s, Wendy’s, FedEx, and LensCrafters. These companies have used JIT concepts to reduce their cycle time and, consequently, increase their speed. One strat- egy is to eliminate unnecessary activities. For example, any processing step that does not add value is eliminated. Processing steps that add some value are reanalyzed and reengineered to improve their efficiency and reduce processing time.

Minimizing Setup Times and Parallel Processing The concept of setup time minimization and parallel processing can be seen in clean- ing companies. Merry Maids is a cleaning company that uses teams of workers to clean homes. Each member of the team is designated to carry out a specific category of clean- ing tasks. For example, one worker may be responsible for all the dusting, another for all the bathrooms, and another for the vacuuming. This minimizes setup time, and parallel processing reduces the cycle time.

Workplace Organization Improved housekeeping has become a priority for many service organizations, particu- larly since the customer is present during part of the production process. Service com- panies like Disney and McDonald’s pride themselves on the cleanliness of their facilities.

JIT AND LEAN SYSTEMS WITHIN OM: HOW IT ALL FITS TOGETHER As you have seen, JIT is about eliminating waste of every kind in order to be more effi- cient. As JIT is an overriding philosophy, it affects all other operations decisions. For example, JIT is directly linked to quality improvements within the operation (Chap- ters 5 and 6), partnering with suppliers as in supply chain management (Chapter 4), and changing job designs (Chapter 11) of production employees and management. JIT decisions also impact facility layout (Chapter 10) since they require rearrangements of the flow of materials, changes in the production process (Chapter 3) to a pull sys- tem with small lot sizes and uniform facility loading, and changes in inventory levels (Chapter 12). Virtually all operations decisions are linked to the implementation of JIT and lean systems.

JIT AND LEAN SYSTEMS ACROSS THE ORGANIZATION Implementing a philosophy such as JIT will inevitably have consequences for every aspect of the organization. The entire organization is affected by JIT, primarily because organizational barriers are eliminated. Functions that have not had much communi- cation with each other in the past must now work together. Included are functions such as marketing, manufacturing, and engineering, which in traditional systems have separate agendas but now need to work together to achieve the goals of the organiza- tion as a whole. Let’s see how some of these functions are affected.

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THE SUPPLY CHAIN LINK • 273

Accounting is strongly affected by JIT. Traditional accounting systems generally allocate overhead on the basis of direct labor hours. The problem with this method is that it does not accurately describe the actual use of overhead by different jobs. For example, jobs that are labor intensive in nature may be assigned a disproportionately high share of overhead. These numbers may lead management to make inappropriate decisions. JIT relies on activity-based costing to allocate overhead. In activity-based costing, specific costs are identified and then assigned to various types of activities, such as inspection, movement of goods, and machine processing. Overhead costs are then assigned to jobs depending on how many activities a particular job takes up.

Marketing plays a large role in JIT, as the interface with customers becomes more important. JIT focuses on customer-driven quality, not quality as defined by the pro- ducer. Marketing managers must understand customer needs and ensure that this information is passed on to operations managers for proper design, production, and delivery of the product or service.

Finance is responsible for approving and evaluating financial investments. Switching to a JIT system proves financially beneficial in the long run but generally requires an investment in resources. Included are hiring consultants, training work- ers, purchasing or modifying equipment, more record keeping, and rearrangement of facilities. Finance must evaluate these investments and measure their performance, which requires an understanding of JIT.

Engineering plays a major role in JIT. As we have seen in this chapter, reduction of setup time is critical to the success of JIT. It is up to engineering to design machines so as to reduce setup time and to design poka-yoke, or foolproof devices, that prevent defects from occurring. Engineering is largely responsible for designing the mechanisms that enable JIT to function as desired. Without engineering, true JIT could not exist.

Information systems (IS) create the network of information necessary for JIT to function. JIT is based on the assumption that information about quality, inventory levels, order status, and product returns is available to everyone in the organization. This type of information needs to be readily available and up-to-date. Otherwise, a JIT system would come to a halt. Communication with suppliers is another prerequisite of JIT that requires a high-level information system. JIT cannot function without the ongoing involvement of IS. In turn, IS needs to understand JIT functioning and information requirements.

ACC

MKT

FIN

MIS

The concept of JIT naturally extends itself to the entire supply chain. The philosophy of JIT teaches us that waste anywhere in the system hinders efficiency, doesn’t provide value to the customer, and ultimately increases cost. Every organization is just one ele- ment of an entire supply chain system. As such, waste anywhere in the supply chain is ultimately passed down to other members of the chain and the final customer. Also recall that JIT views a company’s suppliers as the external factory, focuses on building long-term relationships with suppliers, and promotes sharing data along the supply chain. In fact, a company’s pull system cannot work properly unless its suppliers are also using it. Otherwise, the JIT system of the company would not be able to function properly,

as there would be no guarantee of stable deliveries. Therefore, the principles of JIT need to be adopted by all members of a supply chain in order to have a full impact. This is often referred to as a lean supply chain.

Dell provides a good example of the impact JIT can have when it is implemented along the supply chain.

The company has a build-to-order model that pro- duces computers only when there is actual cus- tomer demand. Dell has implemented a JIT system throughout its supply chain and shares demand

information with its suppliers. As a result, Dell is able to introduce new technologies in its computers much quicker than competitors because they are seamlessly available in the supply chain. Dell also works closely with its suppliers to reduce inventories, align processes, and eliminate waste across the supply chain. The result has been high responsiveness at a competitive price.

THE SUPPLY CHAIN LINK

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274 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

JIT and Lean are about eliminating waste—a philosophy that is fully compatible with environmentalism and sus- tainability. Squeezed by high costs, companies have zero tolerance for unnecessary waste, and consumers and regulators are demanding eco-friendly practices every- where, from forest to factory to display case. The pres- sure has increased to drive fewer miles, use low-emission and natural-gas-powered trucks, have well-engineered warehouses, design lower-emission factories, and maxi- mize efficiency. In fact, concerns about water usage and disposal practices are just two of the factors that led Coca-Cola to name a Vice President for Sustainability this year. The sustainability pressure for companies is to figure out ways to do more with less, which is the basis of JIT and Lean, and relies on lessons discussed in this chapter. Minimizing waste is an essential part of sustain- ability and it requires expanding the notion of waste to elements of the operation that impact the environment.

Companies are increasingly applying JIT and Lean principles to move beyond recycling and set their sights on a higher standard, called the “zero waste” business model. This model builds on the traditional concepts of JIT and Lean by calling on companies to reduce as

much waste as possible during production, then reuse and recycle what can’t be avoided until waste is elimi- nated altogether. The “zero waste” model addresses every aspect of waste, including wasted water and energy, in order to maximize production efficiency as well as eliminate pollution and toxins. It is application of the principle of continuous improvement, knowing that zero waste may never be achieved but continuing to work toward that goal through constant innovation.

A successful example of applying principles of zero waste is offered by The Taylor Companies, a furniture manufacturer from Ohio. The company saved over $20,000 a year at its Bedford facility by identifying ways to successfully use its “waste.” For example, the com- pany needed to dispose of large amounts of sawdust, and found out that local horse farms needed sawdust as a raw material for composting. They connected with local farmers, who not only hauled away the company’s “waste,” but also paid them for it. Through continuous improvement efforts the company then began reusing scrap materials and partnered with a leather goods com- pany willing to pay $900 a year for leather scraps. As a result, the zero waste effort has not only eliminated waste for the company but has made them more sustain- able and has created new ways of generating revenue.

THE SUSTAINABILITY LINK

Chapter Highlights 1 JIT is a philosophy that was developed by the Toyota Motor

Company in the mid-1970s. It has since become the standard of operation for many industries. It focuses on simplicity, eliminating waste, taking a broad view of operations, visibility, and fl exibility. Th ree key elements of this philosophy are JIT manufacturing, total quality management, and respect for people.

2 JIT views waste as anything that does not add value, such as unnecessary space, energy, time, or motion.

3 Traditional manufacturing systems use “push” production, whereas JIT uses “pull” production. Push systems anticipate future demand and produce in advance in order to have products in place when demand occurs. Th is system usually results in excess inventory. Pull systems work backwards. Th e last workstation in the production line (or the customer) requests the precise amounts of materials required.

4 JIT manufacturing is a coordinated production system that enables the right quantities of parts to arrive when they are needed precisely where they are needed. Key elements of JIT manufacturing are the pull system and kanban production, small lot sizes and quick setups, uniform plant loading, fl exible resources, and streamlined layout.

5 Total quality management (TQM) creates an organizational culture that defi nes quality as seen by the customer. Th e concepts of continuous improvement and quality at the source are integral to allowing for continual growth and the goal of identifying the causes of quality problems.

6 JIT considers people to be the organization’s most important resource. All employees are highly valued members of the organization. Workers are empowered to make decisions and are rewarded for their eff orts. Team eff orts make possible cross-functional and multilayer coordination.

7 JIT is equally applicable in service organizations, particularly with the push toward time-based competition and the need to cut costs.

8 JIT success is dependent on interfunctional coordination and eff ort. Marketing must work closely with customers to defi ne customer-driven quality. IS must design a powerful information system. Engineering must develop equipment with low setup time and design jobs with foolproof devices. Finance must monitor fi nancial improvements with realistic expectations. Accounting must develop appropriate costing mechanisms.

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DISCUSSION QUESTIONS • 275

Key Terms just-in-time (JIT) philosophy 247 waste 247 a broad view of JIT 247 defi ning beliefs of JIT 248 types of waste 249 broad view of the organization 249 simplicity 249 continuous improvement (kaizen) 250 visibility 250 fl exibility 250 JIT system 251

just-in-time manufacturing 251 setup cost 252 total quality management (TQM) 253 quality at the source 253 respect for people 253 pull system 255 kanban card 255 production card 255 withdrawal card 255 small-lot production 258 internal setup 259

external setup 259 uniform plant loading 259 multifunction workers 260 cell manufacturing 261 jidoka 263 poka-yoke 264 bottom-round management 266 quality circles 266 single-source suppliers 268

Formula Review Determining the number of kanbans:

N 5 DT 1 S

Solved Problems • Problem 1 Suzie Sizewick works for a manufacturer of ballpoint pens, which come in packages of five pens each. Her job is to fill the packages with pens, and she is expected to process 100 packages an hour. The facility where Suzie works uses a kanban produc- tion system in which each container holds ten pen packages. It takes 15 minutes to receive the packages she needs from the previous workstation. The facility uses a safety stock of 12 per- cent. How many kanbans are needed for the filling process?

• Before You Begin: Remember to always check your units and make sure that demand (D) and lead time (T) are over the same time period.

• Solution D 5 100 packages per hour

T 5 15 minutes 5 1 4

hour

(See student companion site for Excel template.)

C 5 10 packages per container

S 5 0.12a100 3 1 4 b 5 3 packages

Step 1

N 5 DT 1 S

C Step 2

N 5 1100 packages/hour 2 1 14 hour 2 1 3

10 packages

5 2.8 kanbans and containers

Discussion Questions 1. Describe the core beliefs of the JIT philosophy. 2. Identify the three major elements of JIT. 3. Explain how JIT manufacturing works and its key elements. 4. Find an example of successful JIT manufacturing. 5. Explain the importance of total quality management in JIT. 6. Find an example of successful TQM implementation. 7. Explain the importance of respect for people in JIT.

8. Find an example of a company that has high respect for people. 9. Describe the JIT implementation process. Why should

some things be changed before others? 10. Find examples of JIT in services. Which aspects of JIT are

easiest to apply in services? 11. Explain how you could use JIT to make your life more

efficient.

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276 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

Problems 1. Jason Carter works for a producer of soaps that come in

packages of 6 each. His job is to fi ll the packages with soap, and he is expected to process 30 packages an hour. Th e facility where Jason works uses a kanban production system in which each container holds 5 packages of soap. It takes 20 minutes to receive the packages he needs from the previous workstation. How many kanbans are needed for the fi lling process?

2. A manufacturer of thermostats uses a kanban system to control the fl ow of materials. Th e packaging center processes 10 thermostats an hour and receives completed thermostats every 30 minutes. Containers hold 5 thermostats each.

(a) How many kanbans are needed for the packaging center? (b) If management decides to keep two thermostats as safety

stock, how many kanbans will be needed? 3. A production cell at Canderberry Candle facility operates

5 hours per day and uses a pull method to supply wicks to the assembly line. Th e wicks are used at a rate of 300 per day. Each container holds 20 wicks and usually waits 20 minutes in the production cell. Management wants a safety stock of 10 percent. How many containers should be used at the Canderberry Candle facility for purposes of pull production?

4. Carlos Gonzales is production manager at an assembly plant that manufactures cordless telephones. Th e company is planning to install a pull system. Th e process is being planned to have a usage rate of 50 pieces per hour. Each container is designed to hold 10 pieces. It takes an average of 30 minutes to complete a cycle.

(a) How many containers will be needed? (b) How will the number of needed containers change as the

system improves?

5. A dye cell at the Acme Clothing Factory uses 500 pounds of dye each day. Th e dye is moved in vats at a rate of approximately one per hour. Each vat holds 10 pounds of dye. Management has set safety stock at 10 percent. Th e facility operates 8 hours per day. How many vats should be used?

6. Anna works on an assembly line where it takes her 30 minutes to produce 20 units of a product needed to fi ll a container. It takes her an additional 5 minutes to transport the container to Josh, who works at the next station. Th e company uses a safety stock of 20 percent. Th e current assembly line uses fi ve kanbans between Anna’s and Josh’s stations. Compute the demand for the product.

7. Robert produces 300 units of a product per hour and 30 units are needed to fi ll a container. It takes 15 minutes to receive the materials needed from the previous workstation. Th e company currently uses a safely stock of 10 percent. Determine the number of kanbans needed between Robert’s station and the previous process.

8. Consider the information from Problem 7. Determine how the number of kanbans and the inventory level will be aff ected if the time required for Robert to receive the material increases to 30 minutes.

9. Consider the information from Problem 7. Determine how the number of kanbans and the inventory level will be aff ected if:

(a) Th e container size is decreased to 15 units. (b) Th e container size is increased to 40 units. (c) Th e safety stock is increased to 20 percent.

CASE: Katz Carpeting Josh Wallace, president of Katz Carpeting, had much on his mind. The end-of-year performance numbers for the carpet man- ufacturer were below expectations. Inventories of carpets were high, yet they had frequently been out of stock of items custom- ers wanted. It seemed that the plant was producing a lot of what they already had, yet not enough of what was needed. Quality was also becoming a problem, with customers frequently returning carpeting for rips or incorrect dye color. It seemed to Josh that operations was not doing its job. Something had to be done.

Background Katz Carpeting is a manufacturer of high-end commercial and residential carpeting. Katz produces two product lines of carpet- ing. The first line, a group of standardized products called “stan- dards,” is sold through catalogs and samples available at retail sites. The second line is “specials,” carpet products made to cus- tomer specifications of color and pattern. Currently, the volume of business is approximately evenly divided between standards and specials.

At Katz, standards and specials are made using a line opera- tion and sharing the same facilities (see Figure 7-7). Production of standards is made in a predictable and easily timed manner. The process begins with making the dye in large vats and dying the yarn. The yarn is then rolled, bonded, and added to a back- ing. The product is then cut and sent to shipping.

Production of specials is not as simple. The dying and weav- ing processes of specials are considerably more difficult due to the time necessary to ensure the dyes are correct. Colors of stan- dards are well established. However, colors of specials require an initial trial dying before full dying can begin. Also, patterns are frequently requested in special orders, and each pattern is typically unique. Because of the customized nature of producing specials, the time required for production is much longer, as is the cost involved.

At Katz, the marketing department is responsible for gener- ating forecasts, taking orders, and establishing due dates. This information is passed on to operations on a weekly basis, and a production schedule is made. Information on special “rush”

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CASE: DIXON AUDIO SYSTEMS • 277

orders is passed on daily, and requests are frequent. Operations tries to meet all the orders and produce extra inventories of stan- dards to be prepared for unexpected demand.

Considering JIT Josh Wallace called a meeting with Evelyn Jones, newly hired head of operations. He explained the problems Katz was fac- ing. Evelyn agreed that there were problems with inventory and customer service but noted that these were just symptoms of a problem. “One big problem is the setup and changeover time between the two product lines,” she explained. “The changeover from one standard product to another is approximately 15 min- utes, enough time for the new dye color to be loaded onto the machine. However, the changeover from one standard product to a special can be as much as hours. As both products are made on the same line, production of the specials holds up production of the standards. Also, operations frequently stops planned pro- duction to meet special rush orders.”

Evelyn then explained that the facility needed to move toward just-in-time production. “Yes, I have heard of that. That is a manufacturing process based on zero inventory,” said Josh.

“I have even heard that workers are paid to sit around and dis- cuss quality problems. Well, not here. Here they need to get rid of that inventory!”

Under the circumstances, Evelyn suggested that a consultant be brought in to guide Katz through the process of switching to JIT production. Josh reluctantly agreed.

Case Questions 1. What suggestions do you have for implementing JIT at

Katz? Should specials and standards be produced on the same line? (Hint: Do they require the same type of operation?)

2. If production of standards and specials is separated, how diff erent will JIT implementation be for production of the diff erent products? Explain what would be needed in JIT implementation for both products.

3. What suggestions do you have for improving the way the production schedule is currently made?

4. How would you characterize Josh’s view of JIT? What challenges do you think a consultant will face in implementing JIT at Katz? If you were a consultant, how would you approach these problems?

FIGURE 7-7

Production process at Katz Carpeting

CASE: Dixon Audio Systems Dixon Audio Systems had developed a reputation as a leading producer of speakers, audio systems, and car stereos. Dixon accomplished this through creating strict organizational

quality standards and demanding these same standards of its suppliers. Meeting quality standards was critical, particu- larly of plastic component parts. These parts were sourced

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278 • CHAPTER 7 JUST-IN-TIME AND LEAN SYSTEMS

from a number of vendors and required considerable experi- ence and skill to make. However, as Dixon’s quality standards increased, the number of defective components returned to vendors increased too. This was increasingly holding up pro- duction and costing the company in excess inventory and unmet orders.

Dixon’s director of purchasing proposed a new approach to dealing with the vendor quality problem. She proposed that they develop a new arrangement with their top vendor of plas- tic components, D&S Plastics. Under the relationship, D&S Plastics would become Dixon’s JIT supplier. The arrangement would require D&S to station a full-time representative at Dix- on’s headquarters. The representative would be paid by D&S but would work as a plastics buyer for Dixon, placing orders to D&S. The representative would also monitor material require- ments on plastic components that D&S supplied to Dixon and become involved in manufacturing planning at Dixon. The plan would provide the D&S representative full access to Dixon’s facilities, personnel, and computer systems. D&S

Plastics would be a sole supplier to Dixon and was guaran- teed business, provided it maintained the quality and delivery requirements. Dixon was assured a reliable supply of plastic component parts.

The proposed arrangement would completely change the way in which the two companies worked together and had a number of risks. Dixon’s managers worried that the company would lose control of its procurement process. Although Dixon was one of D&S’s biggest accounts, D&S worried that it would lose control of its operations. Many factors had to be considered before embarking on this type of arrangement.

Case Questions 1. Identify the pros and cons of a JIT relationship from a

supplier’s point of view. 2. Identify the pros and cons of a JIT relationship from a

buyer’s point of view. 3. What factors should Dixon and D&S consider before

making a decision on this relationship?

INTERACTIVE CASE Virtual Company

On-line Case: Virtual Company Assignment: Just-in-Time at Cruise international, Inc. Bob Bristol just called to congratulate you on

your supply chain management report. It was very well received by the CII management team. Meghan Willoughby, Chief Purser aboard the Friendly Seas I, was especially impressed with your ideas on supplier partnering. She would like you to pre- pare a similar report addressing just-in-time issues relevant for the Freiendly Seas I. While the management team has heard a great deal about JIT, many wonder just how JIT concepts would apply. Bob suggested you meet with Meghan for more details.

This assignment will enhance your knowledge of the material in Chapter 7 of your textbook while preparing you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Just-In-Time at CII

www.wiley.com/college/reid

On-line Case: JIT and Lean Systems at Valley Memorial Hospital Assignment: Just-in-Time at VMH Bob Reilly just

called to congratulate you on your report on supply chain man- agement. It was very well received by the managers at VMH. Meg Willoughby, head of materials management at VMH, was especially impressed with your ideas on partnering with sup- pliers and purchasing supplies and equipment. She has now suggested that you prepare a concise research report for top management addressing just-in-time (JIT) issues relevant to VMH. Managers at VMH have heard a great deal about JIT, but many wonder how JIT concepts could be applied at VMH. They

have put together a few specific questions for you to address in your report. This assignment will enable you to enhance your knowledge of the material in Chapter 7.

To access the Web site:

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SELECTED BIBLIOGRAPHY • 279

Truck-Fleet, Inc. is a small company that offers domestic logis- tics services such as distribution management and transporta- tion management. Truck-Fleet owns a small fleet of trucks and offers movement, tracking, and handling of inventory for cli- ents. Although Truck-Fleet is relatively small, with revenues of $500 million annually, it is growing rapidly as companies place greater emphasis on fast transportation. Truck-Fleet utilizes technology such as bar coding to facilitate consolidation and dis- tribution of products. Its strength lies in its dedicated employees, such as drivers with excellent driving and safety records and a good management staff.

Truck-Fleet needs to become a JIT service provider to remain competitive. You have just been hired to help direct its growth. Use the Internet to identify Truck-Fleet’s main competitors and their capabilities. Identify specific companies that offer just-in- time pickup and delivery against which Truck-Fleet can bench- mark. Then, identify the characteristics of main competitors that enable them to provide just-in-time services. Finally, establish specific guidelines for Truck-Fleet to follow in becoming a JIT service provider. Problem-Solving Tip: One potential competi- tor is Ryder (www.ryder.com).

INTERNET CHALLENGE Truck-Fleet, Inc.

Online Resources Companion Website www.wiley.com/college/reid

• Take interactive practice quizzes to assess your knowledge and help you study in a dynamic way • Review PowerPoint slides or print slides for notetaking • Access the Virtual Company: Cruise International, Inc. • Find links to Company Tours for this chapter • Toyota Motor Corporation • Find links for Additional Web Resources for this chapter • Th e Association for Manufacturing Excellence, www.ame.org • APICS—Th e Educational Society for Resource Management, www.apics.org

Selected Bibliography Garg, D., O.N. Kaul, and S.G. Deshmukh. “JIT Implementa-

tion: A Case Study,” Production and Inventory Manage- ment Journal, Th ird Quarter, 1998, 26–31.

Hall, R.W. Attaining Manufacturing Excellence. Burr Ridge, Ill.: Irwin Professional Publishing, 1987.

Hanna, M.D., W.R. Newman, and P. Johnson. “Link- ing Operational and Environmental Improvement through Employee Involvement,” International Journal of Operations and Production Management, 20, 2, 2000, 148–165.

Koste, L.L., and M.K. Malhotra. “Trade-off s among the Ele- ments of Flexibility: A Comparison from the Automotive Industry,” Omega, 28, 2000, 693–710.

Monden, Yasuchiro. Th e Toyota Management System: Linking the Seven Key Functional Areas. Cambridge, Mass.: Pro- ductivity Press, 1993.

Ohno, T. Toyota Production System: Beyond Large-Scale Pro- duction. Cambridge, Mass.: Productivity Press, 1988. (Original Japanese version published 1978.)

Pun, K., and K.H. Wong. “Implementing JIT/MRP in a PCB Manufacturer,” Production and Inventory Management Journal, First Quarter, 1998, 10–16.

Shingo, Shigeo. Modern Approaches to Manufacturing Improvement. Cambridge, Mass.: Productivity Press, 1990.

Vergin, R.C. “An Examination of Inventory Turnover in the Fortune 500 Industrial Companies,” Production and Inven- tory Management Journal, First Quarter, 1998, 51–56.

Vitasek, K., K.B. Manrodt, and J. Abbott. “What Makes a Lean Supply Chain?” Supply Chain Management Review, Octo- ber 2005, 39–45.

Womack, J.P., D.T. Jones, and D. Roos. Th e Machine Th at Changed the World. New York: Macmillan, 1990.

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CHAPTER

280

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

L E A R N I N G O B J E C T I V E S

C H A P T E R O U T L I N E

Principles of Forecasting 281 Steps in the Forecasting Process 282 Types of Forecasting Methods 283 Time Series Models 288 Causal Models 303 Measuring Forecast Accuracy 307 Selecting the Right Forecasting Model 310 Forecasting Software 311

Focus Forecasting 313 Combining Forecasts 313 Collaborative Planning, Forecasting, and

Replenishment (CPFR) 314 Forecasting within OM: How It All Fits

Together 315 Forecasting across the Organization 315

Before studying this chapter you should know or, if necessary, review

The role of forecasting in operations management decisions, Chapter 1, p. 8.

After studying this chapter you should be able to

1 Identify principles of forecasting. 2 Explain the steps involved in the forecasting process. 3 Identify types of forecasting methods and their characteristics. 4 Describe time series models and causal models. 5 Generate forecasts for data with diff erent patterns, such as level, trend, and seasonality and cycles. 6 Describe causal modeling using linear regression. 7 Compute forecast accuracy. 8 Explain the factors that should be considered when selecting a forecasting model.

Forecasting

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PRINCIPLES OF FORECASTING • 281

Have you ever gone to a restaurant and been told that they are sold out of their “specials,” or gone to the university bookstore and found that the texts for your course are on backorder? Have you ever had a party at your home only to realize that you don’t have enough food for everyone invited? Just like getting caught unprepared in the rain, these situations show the consequences of poor forecasting. Planning for any event requires a forecast of the future. Whether in business or in our own lives, we make forecasts of future events. Based on those forecasts, we make plans and take action.

Forecasting is one of the most important business functions because all other business decisions are based on a forecast of the future. Deci- sions such as which markets to pursue, which products to produce, how much inventory to carry, and how many people to hire all require a fore- cast. Poor forecasting results in incorrect business decisions and leaves the company unprepared to meet future demands. The consequences can be very costly in terms of lost sales and can even force a company out of business.

Forecasts are so important that companies are investing billions of dollars in technologies that can help them better plan for the future. For example, the ice-cream giant Ben & Jerry’s has invested in business intelligence soft- ware that tracks the life of each pint of ice cream, from ingredients to sale. Each pint is stamped with a tracking number that is stored in an Oracle database. Then the com- pany uses the information to track trends, problems, and new business opportunities. They can track such things as seeing if the ice-cream flavor Chocolate Chip Cookie Dough is gaining on Cherry Garcia for the top sales spot, product sales by location, and rates of change. This information is then used to more accurately forecast product sales. Numerous other companies, such as Procter & Gamble, General Electric, Lands’ End, Sears, and Red Robin Gourmet Burgers, are investing in the same type of soft- ware in order to improve forecast accuracy.

In this chapter you will learn about forecasting, the different types of forecasting methods available, and how to select and use the proper techniques. You will also learn about the latest available software that can help managers analyze and process data to generate forecasts.

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� Forecasting Predicting future events.

PRINCIPLES OF FORECASTING There are many types of forecasting models. They differ in their degree of complexity, the amount of data they use, and the way they generate the forecast. However, some features are common to all forecasting models. They include the following:

1. Forecasts are rarely perfect. Forecasting the future involves uncertainty. Th erefore, it is almost impossible to make a perfect prediction. Forecasters know that they

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282 • CHAPTER 8 FORECASTING

Regardless of what forecasting method is used, there are some basic steps that should be followed when making a forecast:

1. Decide what to forecast. Remember that forecasts are made in order to plan for the future. To do so, we have to decide what forecasts are actually needed. Th is is not as simple as it sounds. For example, do we need to forecast sales or demand? Th ese are two diff erent things, and sales do not necessarily equal the total amount of demand for the product. Both pieces of information are usually valuable.

An important part of this decision is the level of detail required for the forecast (e.g., by product or product group), the units of the forecast (e.g., product units, boxes, or dollars), and the time horizon (e.g., monthly or quarterly).

2. Evaluate and analyze appropriate data. Th is step involves identifying what data are needed and what data are available. Th is will have a big impact on the selection of a forecasting model. For example, if you are predicting sales for a new product, you may not have historical sales information, which would limit your use of forecasting models that require quantitative data.

We will also see in this chapter that diff erent types of patterns can be observed in the data. It is important to identify these patterns in order to select the correct forecasting model. For example, if a company was experiencing a high increase in product sales for the past year, it would be important to identify this growth in order to forecast correctly.

3. Select and test the forecasting model. Once the data have been evaluated, the next step is to select an appropriate forecasting model. As we will see, there are many models to choose from. Usually we consider factors like cost and ease of use in selecting a model. Another very important factor is accuracy. A common procedure is to narrow the choices to two or three diff erent models and then test them on historical data to see which one is most accurate.

have to live with a certain amount of error, which is the diff erence between what is forecast and what actually happens. Th e goal of forecasting is to generate good forecasts on the average over time and to keep forecast errors as low as possible.

2. Forecasts are more accurate for groups or families of items rather than for individual items. When items are grouped together, their individual high and low values can cancel each other out. Th e data for a group of items can be stable even when individual items in the group are very unstable. Consequently, one can obtain a higher degree of accuracy when forecasting for a group of items rather than for individual items. For example, you cannot expect the same degree of accuracy if you are forecasting sales of long-sleeved hunter green polo shirts that you can expect when forecasting sales of all polo shirts.

3. Forecasts are more accurate for shorter than longer time horizons. Th e shorter the time horizon of the forecast, the lower the degree of uncertainty. Data do not change very much in the short run. As the time horizon increases, however, there is a much greater likelihood that changes in established patterns and relationships will occur. Because of that, forecasters cannot expect the same degree of forecast accuracy for a long-range forecast as for a short-range forecast. For example, it is much harder to predict sales of a product two years from now than to predict sales two weeks from now.

STEPS IN THE FORECASTING PROCESS

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TYPES OF FORECASTING METHODS • 283

4. Generate the forecast. Once we have selected a model, we use it to generate the forecast. But we are not fi nished, as you will see in the next step.

5. Monitor forecast accuracy. Forecasting is an ongoing process. Aft er we have made a forecast, we should record what actually happened. We can then use that information to monitor our forecast accuracy. Th is process should be carried out continuously because environments and conditions oft en change. What was a good forecasting model in the past might not provide good results for the future. We have to constantly be prepared to revise our forecasting model as our data change.

The rapid growth of information technology (IT) has created a forecasting challenge for manufactur- ers of industry components such as microchips and semiconductors. Companies like Intel have had dif- ficulty in forecasting demand for information tech- nology used in internal applications. Forecasts are critical in order to plan production and have enough product to meet demand. However, overforecasting means having too much of an expensive product that will quickly become obsolete. The exponential growth in requirements and a short product life cycle have added much uncertainty to the forecasting process. Intel has had to consider many factors when generating its forecasts, such as key technology trends that are driving the information revolution and future directions in the use of IT.

LINKS TO PRACTICE Intel Corporation www.intel.com

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TYPES OF FORECASTING METHODS Forecasting methods can be classified into two groups: qualitative and quantitative. Table 8-1 shows these two categories and their characteristics.

Qualitative forecasting methods, often called judgmental methods, are methods in which the forecast is made subjectively by the forecaster. They are educated guesses

� Qualitative forecasting methods Forecast is made subjectively by the forecaster.

TABLE 8-1

Types of Forecasting Methods

Qualitative Methods Quantitative Methods

1. Characteristics Based on human judgment, opinions; subjective and nonmathematical.

Based on mathematics; quantitative in nature.

2. Strengths Can incorporate latest changes in the environment and “inside information.”

Consistent and objective; able to consider much information and data at one time.

3. Weaknesses Can bias the forecast and reduce forecast accuracy.

Often quantifiable data are not available. Only as good as the data on which they are based.

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284 • CHAPTER 8 FORECASTING

by forecasters or experts based on intuition, knowledge, and experience. When you decide, based on your intuition, that a particular team is going to win a baseball game, you are making a qualitative forecast. Because qualitative methods are made by peo- ple, they are often biased. These biases can be related to personal motivation (“They are going to set my budget based on my forecast, so I’d better predict high.”), mood (“I feel lucky today!”), or conviction (“That pitcher can strike anybody out!”).

Quantitative forecasting methods, on the other hand, are based on mathemati- cal modeling. Because they are mathematical, these methods are consistent. The same model will generate the exact same forecast from the same set of data every time. These methods are also objective. They do not suffer from the biases found in qualita- tive forecasting. Finally, these methods can consider a lot of information at one time. Because people have limited information-processing abilities and can easily experi- ence information overload, they cannot compete with mathematically generated fore- casts in this area.

Both qualitative and quantitative forecasting methods have strengths and weak- nesses. Although quantitative methods are objective and consistent, they require data in quantifiable form in order to generate a forecast. Often, we do not have such data, for example, if we are making a strategic forecast or if we are forecasting sales of a new product. Also, quantitative methods are only as good as the data on which they are based. Qualitative methods, on the other hand, have the advantage of being able to incorporate last-minute “inside information” in the forecast, such as an advertising campaign by a competitor, a snowstorm delaying a shipment, or a heat wave increas- ing sales of ice cream. Each method has its place, and a good forecaster learns to rely on both.

Inaccurate forecasts can cost com- panies billions of dollars in missed sales or excess inventory. One factor that can significantly impact sales is the weather. In the past, there was little companies could do to plan for weather problems. However, new businesses have sprung up to help companies use weather data to predict consumer behavior and manage weather risk. It could be as simple as predicting a hot

summer, a cold winter, or an early spring. This type of information can help companies move the right inventories to areas where consumers will be more likely to buy them.

Planalytics Inc. is a company that helps businesses use weather data to make their business plans. Its clients include Gillette’s Duracell® Batteries, Home Depot, and Wal- Mart. In one example, Planalytics helped Duracell move a large number of batteries to areas expecting to be hit by hurricanes during the hurricane season. Although using weather data does not replace traditional forecasting methods, it is one additional tool that can help companies improve their forecasting and planning.

Qualitative Methods There are many types of qualitative forecasting methods, some informal and some structured. Regardless of how structured the process is, however, remember that these models are based on subjective opinion and are not mathematical in nature. Some common qualitative methods are shown in Table 8-2 and are described in this section.

� Quantitative forecasting methods Forecast is based on mathematical modeling.

LINKS TO PRACTICE Improving Sales

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TYPES OF FORECASTING METHODS • 285

Executive Opinion Executive opinion is a forecasting method in which a group of managers meet and collectively develop a forecast. This method is often used for stra- tegic forecasting or forecasting the success of a new product or service. Sometimes it can be used to change an existing forecast to account for unusual events, such as an unusual business cycle or unexpected competition.

Although managers can bring good insights to the forecast, this method has a num- ber of disadvantages. Often the opinion of one person can dominate the forecast if that person has more power than the other members of the group or is very domineering. Think about times when you were part of a group for a course or for your job. Chances are that you experienced situations in which one person’s views dominated.

Market Research Market research is an approach that uses surveys and interviews to determine customer likes, dislikes, and preferences and to identify new-product ideas. Usually, the company hires an outside marketing firm to conduct a market research study. There is a good chance that you were a participant in such a study if someone called you and asked about your product preferences.

Market research can be a good determinant of customer preferences. However, it has a number of shortcomings. One of the most common has to do with how the survey questions are designed. For example, a market research firm may call and ask you to identify which of the following is your favorite hobby: gardening, working on cars, cooking, or playing sports. But maybe none of these is your favorite because you prefer playing the piano or fishing, and these options are not included. This question is poorly designed because it forces you to pick a category that you really don’t fit in, which can lead to misinterpretation of the survey results.

The Delphi Method The Delphi method is a forecasting method in which the objec- tive is to reach a consensus among a group of experts while maintaining their ano- nymity. The researcher puts together a panel of experts in the chosen field. These experts do not have to be in the same facility or even in the same country. They do not know who the other panelists are. The process involves sending questionnaires to the panelists, then summarizing the findings and sending them an updated questionnaire incorporating the findings. This process continues until a consensus is reached.

The idea behind the Delphi method is that a panel of experts in a particular field might not agree on certain things, but what they do agree on will probably happen.

� Executive opinion Forecasting method in which a group of managers collectively develop a forecast.

� Market research Approach to forecasting that relies on surveys and interviews to determine customer preferences.

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� Delphi method Approach to forecasting in which a forecast is the product of a consensus among a group of experts.

TABLE 8-2

Qualitative Forecasting Methods

Type Characteristics Strengths Weaknesses

Executive opinion

A group of managers meet and come up with a forecast.

Good for strategic or new- product forecasting.

One person’s opinion can dominate the forecast.

Market research

Uses surveys and interviews to identify customer preferences.

Good determinant of customer preferences.

It can be difficult to develop a good questionnaire.

Delphi method

Seeks to develop a consensus among a group of experts.

Excellent for forecasting long-term product demand, technological changes, and scientific advances.

Time-consuming to develop.

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286 • CHAPTER 8 FORECASTING

The researcher’s job is to identify what the experts agree on and use that as the fore- cast. This method has the advantage of not allowing anyone to dominate the con- sensus, and it has been shown to work very well. Although it takes a large amount of time, it has been shown to be an excellent method for forecasting long-range product demand, technological change, and scientific advances in medicine. For example, if you wished to predict the timing for an AIDS vaccine or a cure for cancer, you would probably use this technique.

Quantitative Methods Quantitative methods are different from qualitative ones because they are based on mathematics. Quantitative methods can also be divided into two categories: time series models and causal models. Although both are mathematical, the two categories differ in their assumptions and in the manner in which a forecast is generated. In this section we will study some common quantitative models, which are summarized in Table 8-3.

Time series models assume that all the information needed to generate a forecast is contained in the time series of data. A time series is a series of observations taken at regular intervals over a specified period of time. For example, if you were forecasting quarterly corporate sales and had collected five years of quarterly sales data, you would have a time series. Time series analysis assumes that we can generate a forecast based on patterns in the data. As a forecaster, you would look for patterns such as trend, sea- sonality, and cycle and use that information to generate a forecast.

Causal models, sometimes called associative models, use a very different logic to generate a forecast. They assume that the variable we wish to forecast is somehow related to other variables in the environment. The forecaster’s job is to discover how these variables are related in mathematical terms and use that information to forecast the future. For example, we might decide that sales are related to advertising dollars and GNP. From historical data we would build a model that explains the relationship of these variables and use it to forecast corporate sales.

� Time series models Based on the assumption that a forecast can be generated from the information contained in a time series of data.

� Time series A series of observations taken over time.

� Causal models Based on the assumption that the variable being forecast is related to other variables in the environment.

Market research being conducted. © clu/iStockphoto

Computers have made the use of quantitative models much easier.

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TYPES OF FORECASTING METHODS • 287

Time series models are generally easier to use than causal models. Causal models can be very complex, especially if they consider relationships among many variables. However, time series models can often be just as accurate and have the advantage of simplicity. They are easy to use and can generate a forecast more quickly than causal models, which require model building. Each of these models is used for forcasting in operations management and will be described in the next section.

TABLE 8-3

Quantitative Forecasting Models

Type Description Strengths Weaknesses

Time Series Models Naïve Uses last period’s actual value as

a forecast. Simple and easy to use. Only good if data change

little from period to period. Simple Mean Uses an average of past data as

a forecast. Good for level pattern. Requires carrying a lot of

data. Simple Moving Average

A forecasting method in which only n of the most recent observations are averaged.

Only good for level pattern. Important to select the proper moving average.

Weighted Moving Average

A forecasting method where n of the most recent observations are averaged and past observations may have different weights.

Good for level pattern; allows placing different weights on past demands.

Selection of weights requires good judgment.

Exponential Smoothing

A weighted average procedure with weights declining exponentially as data become older.

Provides excellent forecast results for short- to medium-length forecasts.

Choice of alpha is critical.

Trend-Adjusted Exponential Smoothing

An exponential smoothing model with separate equations for forecasting the level and trend.

Provides good results for trend data.

Should only be used for data with trend.

Linear Trend Line

Technique uses the least-squares method to fit a straight line to past data over time.

Easy to use and understand. Data should display a clear trend over time.

Seasonal Indexes

Computes the percentage amount by which data for each season are above or below the mean.

Simple and logical procedure for computing seasonality.

Make sure seasonality is actually present.

Causal (Associative) Models

Linear Regression

Uses the least-squares method to model a linear relationship between two variables.

Easy to understand; provides good forecast accuracy.

Make sure a linear relationship is present.

Multiple Regression

Similar to linear regression, but models the relationship of multiple variables with the variable being forecast.

A powerful tool in forecasting when multiple variables are being considered.

Significantly increases data and computational requirements.

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288 • CHAPTER 8 FORECASTING

TIME SERIES MODELS Remember that time series analysis assumes that all the information needed to generate a forecast is contained in the time series of the data. The forecaster looks for patterns in the data and tries to obtain a forecast by projecting that pattern into the future. The easiest way to identify patterns is to plot the data and examine the resulting graphs. If we did that, what could we observe? There are four basic patterns, which are shown in Figure 8-1. Any of these patterns, or a combination of them, can be present in a time series of data:

1. Level or horizontal. A level or horizontal pattern exists when data values fl uctuate around a constant mean. Th is is the simplest pattern and the easiest to predict. An example is sales of a product that do not increase or decrease over time. Th is type of pattern is common for products in the mature stage of their life cycle, in which demand is steady and predictable.

2. Trend. When data exhibit an increasing or decreasing pattern over time, we say that they exhibit a trend. Th e trend can be upward or downward. Th e simplest type of trend is a straight line, or linear trend.

3. Seasonality. A seasonal pattern is any pattern that regularly repeats itself and is of a constant length. Such seasonality exists when the variable we are trying to forecast is infl uenced by seasonal factors such as the quarter or month of the year or day of the week. Examples are a retail operation with high sales during November and December or a restaurant with peak sales on Fridays and Saturdays.

� Level or horizontal pattern Pattern in which data values fluctuate around a constant mean.

� Trend Pattern in which data exhibit increasing or decreasing values over time.

� Seasonality Any pattern that regularly repeats itself and is constant in length.

Quantity Quantity

TimeTime

Time (quarters)

(a) Level or Horizontal Pattern: Data follow a horizontal pattern around the mean

(b) Trend Pattern: Data are progressively increasing (shown) or decreasing

(d) Cycle: Data increase or decrease over time

QuantityQuantity

1 2 3 4 1 2 3 4 1 2 3 4 Time (quarters)

1 2 3 4 1 2 3 4 1 2 3 4

FIGURE 8-1

Types of data patterns

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TIME SERIES MODELS • 289

4. Cycles. Patterns that are created by economic fl uctuations such as those associated with the business cycle are called cycles. Th ese could be recessions, infl ation, or even the life cycle of a product. Th e major distinction between a seasonal pattern and a cyclical pattern is that a cyclical pattern varies in length and magnitude and therefore is much more diffi cult to forecast than other patterns.

Random variation is unexplained variation that cannot be predicted. So if we look at any time series, we can see that it is composed of the following:

Data �

level � trend � seasonality � cycles

pattern

� random variation

The first four components of the data are part of a pattern that we try to forecast. Random variation cannot be predicted. Some data have a lot of random variation and some have little. The more random variation a data set has, the harder it is to forecast accurately. As we will see, many forecasting models try to eliminate as much of the random variation as possible.

Forecasting Level or Horizontal Pattern The simplest pattern is the level or horizontal pattern. In this section we look at some forecasting models that can be used to forecast the level of a time series.

The Naïve Method The naïve method is one of the simplest forecasting models. It assumes that the next period’s forecast is equal to the current period’s actual. For example, if your sales were 500 units in January, the naïve method would forecast 500 units for February. It is assumed that there is little change from period to period. Mathematically, we could put this in the following form:

Ft11 5 At where Ft�1 � forecast for next period, t � 1 At � actual value for current period, t t � current time period

� Cycles Data patterns created by economic fluctuations.

� Random variation Unexplained variation that cannot be predicted.

� Naïve method Forecasting method that assumes next period’s forecast is equal to the current period’s actual value.

A restaurant is forecasting sales of chicken dinners for the month of April. Total sales of chicken dinners for March were 320. If management uses the naïve method to forecast, what is their forecast of chicken dinners for the month of April?

• Before You Begin: Remember that with the naïve method the forecast for next period (April) is equal to the current period’s actual value, which is 320 dinners for the month of March.

• Solution: Our equation is

Ft11 5 At

Adding the appropriate time period:

FApril 5 AMarch FApril 5 320 dinners

EXAMPLE 8.1

Forecasting with the Naïve Method

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290 • CHAPTER 8 FORECASTING

The naïve method can be modified to take trend into account. If we see that our trend is increasing by 10 percent and the current period’s sales are 100 units, a naïve method with trend would give us current period’s sales plus 10 percent, which is a forecast of 110 units for the next period. The naïve method can also be used for sea- sonal data. For example, suppose that we have monthly seasonality and know that sales for last January were 230 units. Using the naïve method, we would forecast sales of 230 units for next January.

One advantage of the naïve method is that it is very simple. It works well when there is little variation from one period to the next. Most of the time we use this method to evaluate the forecast performance of other, more complicated forecasting models. Because the naïve method is simple and effortless, we expect the forecasting model that we are using to perform better than naïve.

Simple Mean or Average One of the simplest averaging models is the simple mean or average. Here the forecast is made by simply taking an average of all data:

Ft11 5 SAt

n 5 At 1 At21 1 c1 At2n

where Ft�1 � forecast of demand for next period, t � 1 At � actual value for current period, t n � number of periods or data points to be averaged

� Simple mean or average The average of a set of data.

New Tools Corporation is forecasting sales for its classic product, Handy-Wrench. Handy-Wrench sales have been steady, and the company uses a simple mean to forecast. Weekly sales over the past five weeks are available. Use the mean to make a forecast for week 6.

Time Period (in weeks) Actual Sales Forecast

1 51 2 53 3 48 4 52 5 50 6 — 50.8

• Before You Begin: Remember that using the mean requires the averaging of all the avail- able data.

• Solution: The basic equation for the mean is

Ft11 5 SAt

F6 5 51 1 53 1 48 1 52 1 50

F6 5 50.8

EXAMPLE 8.2

Forecasting with the Mean

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TIME SERIES MODELS • 291

This model is only good for a level data pattern. As the average becomes based on a larger and larger data set, the random variation and the forecasts become more stable. One of the advantages of this model is that only two historical pieces of information need to be carried: the mean itself and the number of observations on which the mean was based.

Simple Moving Average The simple moving average (SMA) is similar to the simple average except that we are not taking an average of all the data, but are including only n of the most recent periods in the average. As new data become available, the oldest are dropped; the number of observations used to compute the average is kept constant. In this manner, the simple moving average “moves” through time. Like the simple mean, this model is good only for forecasting level data. The formula is as follows:

Ft11 5 SAt

n 5 At 1 At21 1 c1 At2n

where Ft�1 � forecast of demand for the next period, t � 1 At � actual value for current period, t n � number of periods or data points used in the moving average

The formula for the moving average is the same as that for the simple average, except that we use only a small portion of the data to compute the average. For exam- ple, if we used a moving average of n � 3, we would be averaging only the latest three periods. If we were using a moving average of n � 5, we would be averaging only the latest five periods.

� Simple moving average (SMA) A forecasting method in which only n of the most recent observations are averaged.

Sales forecasts for a product are made using a three-period moving average. Given the following sales figures for January, February, and March, make a forecast for April.

• Before You Begin: Remember that to use a three-period moving average, you have to compute the average of the latest three observations. As new data become available, you drop off the oldest data, always averaging the latest three observations.

• Solution: To find the forecast for April, we take an average of the last three observations:

Ft11 5 SAt

FApril 5 AJanuary 1 AFebruary 1 AMarch

3 5

200 1 300 1 200 3

5 233.3

If the actual sales for April turn out to be 300, let’s make a forecast for May. Using a three-period moving average, we take an average of the latest three observations. Since we are now able to include actual sales for April, we drop the sales for January:

FMay 5 AFebruary 1 AMarch 1 AApril

3 5

300 1 200 1 300 3

5 266.9

EXAMPLE 8.3

Forecasting with the Simple Moving Average (Three-Period MA)

Month Actual Sales January 200 February 300 March 200

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Just like the mean, the moving average is good only for a level pattern. You can see this in Example 8.3. The data shown in the example are level in the first four periods. However, after the fourth period the data begin to show a trend. You can see that the forecasts made with the moving average also begin to show an upward trend. Do you see a problem with the forecasts?

The problem is that the forecasts are trailing behind the actual data. We say that they are “lagging” the data. This is what happens when you apply a model that is good only for a level pattern to data that have a trend. You will not obtain a good forecast.

Similarly, if the actual sales for May turn out to be 400, we can make a forecast for June:

FJune 5 AMarch 1 AApril 1 AMay

3 5

200 1 300 1 400 3

5 300.0

Then, if the actual sales for June turn out to be 500, the forecast for July is computed as

FJuly 5 AApril 1 AMay 1 AJune

3 5

300 1 400 1 500 3

5 400.0

The other forecasts follow in a similar fashion. If actual sales for July and August turn out to be 600 and 650, respectively, then the respective forecasts for August and September are

FAugust 5 AMay 1 AJune 1 AJuly

3 5

400 1 500 1 600 3

5 500.0

FSeptember 5 AJune 1 AJuly 1 AAugust

3 5

500 1 600 1 650 3

5 583.3

Here is a summary of the forecasts we have made and the actual sales values:

Month Actual Sales Forecast Three-Period

Moving Average January 200 — February 300 — March 200 — April 300 233.3 May 400 266.9 June 500 300.0 July 600 400.0 August 650 500.0 September — 583.3

Using data from Example 8.3, make forecasts for the months of June, July, August, and September using a five-period moving average.

• Before You Begin: In this problem we are going to compute the average of the last five available observations. As we move through time and new data become available, we will drop the oldest data and add the most recent, always averaging the latest five observations.

• Solution: Notice that we are now going to average the last five available observations. Using a five-period moving average, the forecasts for June, July, August, and September are computed as follows:

EXAMPLE 8.4

Forecasts with the Simple

Moving Average (Five-Period MA)

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TIME SERIES MODELS • 293

FJune 5 AJanuary 1 AFebruary 1 AMarch 1 AApril 1 AMay

5 5

200 1 300 1 200 1 300 1 400 5

FJune 5 280

FJuly 5 AFebruary 1 AMarch 1 AApril 1 AMay 1 AJune

5 5

300 1 200 1 300 1 400 1 500 5

FJuly 5 340

FAugust 5 AMarch 1 AApril 1 AMay 1 AJune 1 AJuly

5 5

200 1 300 1 400 1 500 1 600 5

FAugust 5 400

FSeptember 5 AApril 1 AMay 1 AJune 1 AJuly 1 AAugust

5 5

300 1 400 1 500 1 600 1 650 5

FSeptember 5 490

Following is a summary of the forecasts and the actual sales values:

Month Actual Sales Forecast Three-Period

Moving Average

January 200 February 300 March 200 April 300 May 400 June 500 280 July 600 340 August 650 400 September — 490

Example 8.3, 8.4 Forecasting with Simple Moving Averages

100

200

300

400

500

600

700

800

Ja nu

ar y

Fe br

ua ry

M ar

ch Ap

ril M

ay Ju

ne Ju ly

Au gu

Se pt

em be

S al

es V

ol um

Actual 3-Period MA 5-Period MA

5-Period MA

3-Period MA

Actual Values

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294 • CHAPTER 8 FORECASTING

Comparing the three-period and five-period moving average forecasts, we can see that the three-period moving average forecasts are more responsive to the period-to- period changes in the actual data—they follow the data more closely. The reason is that the smaller the number of observations in the moving average, the more responsive the forecast is to changes in demand. However, the forecast is also more subject to the ran- dom changes in the data. If the data contain a lot of randomness, high responsiveness could lead to greater errors. On the other hand, the larger the number of observations in the moving average, the less responsive the forecast is to changes in the demand, but also to the randomness. These forecasts are more stable. One is not better than the other. Selection of the number of observations in the moving average should be based on the characteristics of the data.

Weighted Moving Average In the simple moving average each observation is weighted equally. For example, in a three-period moving average each observation is weighted one-third. In a five-period moving average each observation is weighted one-fifth. Sometimes a manager wants to use a moving average but gives higher or lower weights to some observations based on knowledge of the industry. This is called a weighted moving average. In a weighted moving average, each observation can be weighted differently provided that all the weights add up to 1.

Ft11 5 SCt At 5 C1A1 1 C2A2 1 c1 Ct At where Ft�1 � next period’s forecast

Ct � weight placed on the actual value in period t At � actual value in period t

� Weighted moving average A forecasting method in which n of the most recent observations are averaged and past observations may be weighted differently.

A manager at Fit Well department store wants to forecast sales of swimsuits for August using a three- period weighted moving average. Sales for May, June, and July are as follows:

The manager has decided to weight May (0.25), June (0.25), and July (0.50).

• Before You Begin: Remember that to compute a weighted moving average you need to multiply each observation by its corresponding weight. These values are then summed in order to get a weighted average.

• Solution: The forecast for August is computed as follows:

Ft11 5 SCt At

FAugust 5 10.25 2AMay 1 10.25 2AJune 1 10.50 2AJuly 5 10.25 2400 1 10.25 2500 1 10.50 2600 5 525

EXAMPLE 8.5

Forecasting with a Weighted Moving

Average

Month Actual Sales Forecast May 400 June 500 July 600

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TIME SERIES MODELS • 295

Exponential Smoothing The exponential smoothing model is a forecasting model that uses a sophisticated weighted average procedure to obtain a forecast. Even though it is sophisticated in the way it works, it is easy to use and understand. To make a fore- cast for the next time period, you need three pieces of information:

1. Th e current period’s forecast, 2. Th e current period’s actual value 3. Th e value of a smoothing coeffi cient, �, which varies between 0 and 1

The equation for the forecast is quite simple:

Next period’s forecast � a (current period’s actual) � (1 � a) (current period’s forecast)

In mathematical terms:

Ft11 5 aAt 1 11 2 a 2Ft where Ft�1 � forecast of demand for next period, t � 1

At � actual value for current period, t Ft � forecast for current period, t a � smoothing coefficient

Exponential smoothing models are the most frequently used forecasting techniques and are available on almost all computerized forecasting software. These models are widely used, particularly in operations management. They have been shown to produce accurate forecasts under many conditions, yet are relatively easy to use and understand.

� Exponential smoothing model Uses a sophisticated weighted average procedure to generate a forecast.

The Hot Tamale Mexican restaurant uses exponential smoothing to forecast monthly usage of tabasco sauce. Its forecast for September was 200 bottles, whereas actual usage in September was 300 bottles. If the restaurant’s managers use an � of 0.70, what is their forecast for October?

• Before You Begin: In this problem you are to use the exponential smoothing equation to get a forecast. You have been given the three pieces of information you need: the current period’s forecast (200 bottles), the current period’s actual value (300 bottles), and the value for the smooth- ing coefficient �, 0.70.

• Solution: The general equation for exponential smoothing is

Ft11 5 aAt 1 11 2 a 2Ft FOctober 5 aASeptember 1 11 2 a 2FSeptember

5 10.70 2 1300 2 1 10.30 2200 5 270 bottles

EXAMPLE 8.6

Forecasting with Exponential Smoothing

Selecting � Note that depending on which value you select for �, you can place more weight on either the current period’s actual or the current period’s forecast. In this manner the forecast can depend more heavily either on what happened most recently or on the current period’s forecast. Values of � that are low—say, 0.1 or 0.2—generate forecasts that are very stable because the model does not place much weight on the cur- rent period’s actual demand. Values of � that are high, such as 0.7 or 0.8, place a lot of weight on the current period’s actual demand and can be influenced by random varia- tions in the data. Thus, how � is selected is very important in getting a good forecast.

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296 • CHAPTER 8 FORECASTING

Starting the Forecasting Process with Exponential Smoothing One thing you may notice with exponential smoothing is that you need the current period’s actual and current period’s forecast to make a forecast for the next period. However, what if you are just starting the forecasting process and do not have a value for the current period’s forecast? There are many ways to handle this problem, but the most common is to use the naïve method to generate an initial forecast. Another option is to average the last few periods—say, the last three or four—just to get a starting point.

To illustrate the differences between different values of �, let’s consider two series of forecasts for a data set. One set of forecasts was developed using exponential smoothing with a � 0.10, another with an a � 0.60.

Exponential Smoothing Forecasts

Time Period (t) Actual Demand � = 0.10 � = 0.60

1 50 — — 2 46 50 50 3 52 49.60 47.60 4 51 49.84 50.24 5 48 49.96 50.70 6 45 49.77 49.08 7 52 49.29 46.63 8 46 49.56 49.85 9 51 49.20 47.54

10 48 49.38 49.62

• Before You Begin: When using the exponential smoothing equation, always make sure you have the three pieces of information needed: the current period’s forecast, the current period’s actual value, and a value for the smoothing coefficient, a. This problem illustrates how you can begin the exponential smoothing process when you do not have initial forecast values.

• Solution: Notice that we used the naïve method to derive initial values of forecasts for period 2. Then to obtain forecasts for period 3, we used the exponential smoothing equation with different values of a. For an a � 0.10, the forecast for period 3 is computed as

F3 5 10.10 2 146 2 1 10.90 2 150 2 5 49.6

EXAMPLE 8.7

Comparing Forecasts with

Different Values of �

1 2 3 4 5 6 7 8 9 10 Time Period

S al

es V

ol um

Actual Forecast with Alpha = 0.10 Forecast with Alpha = 0.60

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TIME SERIES MODELS • 297

For an a � 0.60, the forecast for period 3 is computed as:

F3 5 10.60 2 146 2 1 10.40 2 150 2 5 47.6 The remaining forecasts are computed in the same manner. We can see from the graph that the forecasts with a lower value of a (0.10) have less variation than those with the higher value of a (0.60), indicating that they are more stable.

Before You Go On

We have discussed the principles of forecasting, how to forecast, and different types of qualitative and quantitative forecast- ing models. We have also learned about different types of patterns present in the data. You should understand that to obtain a good forecast the forecasting model should be matched to the patterns in the available data. Our example of the moving average shows what happens when the data show a trend but the model selected is useful only for forecasting a level pattern.

All the quantitative models discussed so far are meant only for level data patterns. In the next section we turn to quantitative models that can be used for other data patterns, such as trend and seasonality. However, remember that the models already discussed are the foundation of forecasting.

Forecasting Trend There are many ways to forecast trend patterns in data. Most of the models used for forecasting trend are the same models used to forecast the level patterns, with an addi- tional feature added to compensate for the lagging that would otherwise occur. Here we will look at two of the most common trend models.

Trend-Adjusted Exponential Smoothing Trend-adjusted exponential smoothing uses three equations. The first smooths out the level of the series, the second smooths out the trend, and the third generates a forecast by adding up the findings from the first two equations. Because we are using a second exponential smoothing equation to compute trend, we have two smoothing coefficients. In addition to �, which is used to smooth out the level of the series, we have a second coefficient, b, which is used to smooth out the trend of the series. Like a, b can theoretically vary between 0 and 1, though we tend to keep the value conservatively low, around 0.1 or 0.2.

Three steps must be followed to generate a forecast with trend:

Step 1 Smoothing the Level of the Series

St 5 aAt 1 11 2 a 2 1St21 1 Tt21 2 Step 2 Smoothing the Trend

Tt 5 b 1St 2 St21 2 1 11 2 b 2Tt21 Step 3 Forecast Including Trend

FITt11 5 St 1 Tt where FITt�1 � forecast including trend for next period, t � 1

St � exponentially smoothed average of the time series in period t Tt � exponentially smoothed trend of the time series in period t a � smoothing coefficient of the level b � smoothing coefficient of the trend

Note that the last step simply adds up the findings from the first two steps. Next we will look at an example of how this works.

� Trend-adjusted exponential smoothing Exponential smoothing model that is suited to data that exhibit a trend.

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298 • CHAPTER 8 FORECASTING

Linear Trend Line Linear trend line is a time series technique that computes a fore- cast with trend by drawing a straight line through a set of data. This approach is a ver- sion of the linear regression technique, covered later in this chapter, and is useful for computing a forecast when data display a clear trend over time. The method is simple, easy to use, and easy to understand.

Green Grow is a lawn care company that uses exponential smoothing with trend to forecast monthly usage of its lawn care products. At the end of July the company wishes to forecast sales for August. The trend through June has been 15 additional gallons of product sold per month. Average sales have been 57 gallons per month. The demand for July was 62 gallons. The company uses a � 0.20 and b � 0.10. Make a forecast including trend for the month of August.

• Before You Begin: When solving this type of problem, always begin by identifying the information that is given in the problem.

The information we have is SJune 5 57 gallons/month

TJune 5 15 gallons/month

AJuly 5 62 gallons

a 5 0.20

b 5 0.10

Next, use the three equations needed to generate a forecast including trend. For each equation we will substitute the appropriate values.

• Solution: Step 1 Smoothing the Level of the Series

St 5 aAt 1 11 2 a 2 1St 2 1 1 Tt 2 1 2 SJuly 5 aAJuly 1 11 2 a 2 1SJune 1 TJune 2

5 10.20 2 162 2 1 10.80 2 157 1 15 2 5 70

Step 2 Smoothing the Trend

Tt 5 b 1St 2 St 2 1 2 1 11 2 b 2Tt 2 1 TJuly 5 b 1SJuly 2 SJune 2 1 11 2 b 2TJune

5 10.1 2 170 2 57 2 1 10.90 215 5 14.8

Step 3 Forecast Including Trend

FITt11 5 St 1 Tt

FITAugust 5 SJuly 1 TJuly

5 70 1 14.8

84.8 gallons

EXAMPLE 8.8

Forecasting with Trend-Adjusted

Exponential Smoothing

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TIME SERIES MODELS • 299

A linear trend line uses the following equation to generate a forecast:

Y 5 a 1 bX

where Y � forecast for period X X � the number of time periods from X � 0 a � value of Y at X � 0 (Y intercept) b � slope of the line

The coefficients a and b are computed using the least-squares method, which mini- mizes the sum of the squared errors. Developing the equations for a and b can be com- plicated, so we will only provide the equations needed for computation. The steps for computing the forecast using a linear trend line are as follows:

Step 1 Compute parameter b:

b 5 SXY 2 nXY SX 2 2 nX2

Step 2 Compute parameter a:

a 5 Y 2 bX

Step 3 Generate the linear trend line:

Y 5 a 1 bX

Step 4 Generate a forecast (Y) for the appropriate value of time (X)

A manufacturer has plotted product sales over the past four weeks. Use a linear trend line to generate a forecast for week 5.

• Before You Begin: Remember to follow the four steps given in the text for generating a forecast using a linear trend line.

• Solution:

Y 5 2375 X 5 2.5

Step 1 Compute parameter b:

b 5 SXY 2 nXY SX 2 2 nX 2

5 24,000 2 4 12.5 2 12375 2

30 2 4 12.5 2 2 5 2505 5 50 Step 2 Compute parameter a:

a 5 Y 2 bX 5 2375 2 150 2 12.5 2 5 2250

EXAMPLE 8.9

Forecasting with a Linear Trend Line

Weeks Sales X Y X2 XY 1 2,300 1 2,300 2 2,400 4 4,800 3 2,300 9 6,900 4 2,500 16 10,000

Totals 10 9,500 30 24,000

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300 • CHAPTER 8 FORECASTING

Forecasting Seasonality Recall that any regularly repeating pattern is a seasonal pattern. We are all familiar with quarterly and monthly seasonal patterns. Whether your university is on a quarter or semester plan, you can see that enrollment varies between quarters or semesters in a fairly predictable way. For example, enrollment is usually much higher in the fall than in the summer. Other examples of seasonality include sales of turkeys before Thanks- giving or ham before Easter, sales of greeting cards, hotel registrations, and sales of gardening tools.

The amount of seasonality is the extent to which actual values deviate from the average or mean of the data. Here we will consider only multiplicative seasonality, in which the seasonality is expressed as a percentage of the average. The percentage by which the value for each season is above or below the mean is a seasonal index. For example, if enrollment for the fall semester at your university is 1.30 of the mean, the fall enrollment is 30 percent above the average. Similarly, if enrollment for the summer semester is 0.70 of the mean, then summer enrollment is 70 percent of the average.

� Seasonal index Percentage amount by which data for each season are above or below the mean.

Step 3 Compute the linear trend line:

Y 5 a 1 bX

5 2250 1 50X

Step 4 For the fifth week, the value of sales would be

Y 5 2250 1 50 15 2 5 2500 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

A B C D E F G H Weeks (X) Sales (Y)

1 2300 2 2400 3 2300 4 2500

Slope Coefficient (b) 50.00 Intercept Coefficient (a) 2250.00

Example 8.9 Linear Regression with Time as an Independent Variable

y = 50x + 2250

2200

2250

2300

2350

2400

2450

2500

2550

1 2 3 4 Week

S al

es (

$ th

ou sa

nd s)

Actual Sales Linear Trend Line

B10: =SLOPE(B5:B8,A5:A8)

B11: =INTERCEPT(B5:B8,A5:A8)

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TIME SERIES MODELS • 301

Here we will show only the procedure for computing quarterly seasonality that lasts a year, though the same procedure can be used for any other type of seasonality. The procedure consists of the following steps:

Step 1 Calculate the Average Demand for Each Quarter or “Season.” Th is is done by dividing the total annual demand by 4 (the number of seasons per year). Step 2 Compute a Seasonal Index for Every Season of Every Year for Which You Have Data. Th is is done by dividing the actual demand for each season by the average demand per season (computed in Step 1). Step 3 Calculate the Average Seasonal Index for Each Season. For each season, compute the average seasonal index by adding up the seasonal index values for that season and dividing by the number of years. Step 4 Calculate the Average Demand per Season for Next Year. Th is could be done by using any of the methods used to compute annual demand. Th en we would divide that by the number of seasons to determine the average demand per season for next year. Step 5 Multiply Next Year’s Average Seasonal Demand by Each Seasonal Index. Th is will produce a forecast for each season of next year.

U-R-Smart University wants to develop forecasts for next year’s quarterly enrollment. It has collected quarterly enrollments for the past two years. It has also forecast total annual enrollment for next year to be 90,000 students. What is the forecast for each quarter of next year?

• Before You Begin: You can see that the data exhibit a seasonal pattern, with each quarter representing a “season.” To compute the forecast for each quarter of next year, follow the five steps given in the text on forecasting with seasonality.

• Solution: Step 1 Calculate the Average Demand for Each Quarter or “Season.” We do this by dividing the total annual demand for each year by 4:

Year 1: 80/4 5 20

Year 2: 84/4 5 21

Step 2 Compute a Seasonal Index for Every Season of Every Year for Which You Have Data. To do this we divide the actual demand for each season by the average demand per season.

EXAMPLE 8.10

Forecasting SeasonalityEnrollment (in thousands)

Quarter Year 1 Year 2 Fall 24 26 Winter 23 22 Spring 19 19 Summer 14 17 Total 80 84

Enrollment (in thousands)

Quarter Year 1 Year 2

Fall 24/20 = 1.20 26/21 = 1.238 Winter 23/20 = 1.15 22/21 = 1.048 Spring 19/20 = 0.95 19/21 = 0.905 Summer 14/20 = 0.70 17/21 = 0.810

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302 • CHAPTER 8 FORECASTING

Step 3 Calculate the Average Seasonal Index for Each Season. You can see that the seasonal indexes vary from year to year for the same season. The simplest way to handle this is to compute an average index, as follows:

Step 4 Calculate the Average Demand per Season for Next Year. We are told that the university forecast annual enrollment for the next year to be 90,000 students. The average demand per season, or quarter, is

90,000/4 5 22,500

Step 5 Multiply Next Year’s Average Seasonal Demand by Each Seasonal Index. This last step will give us the forecast for each quarter of next year:

This can also be computed using a spreadsheet, as shown below. Notice slight differences in final num- bers due to rounding.

Quarter Average Seasonal Index Fall (1.2 + 1.238)/2 = 1.219 Winter (1.15 + 1.048)/2 = 1.099 Spring (0.95 + 0.905)/2 = 0.928 Summer (0.70 + 0.810)/2 = 0.755

Quarter Forecast (Students)

Fall 22,500(1.219) = 27,428 Winter 22,500(1.099) = 24,728 Spring 22,500(0.928) = 20,880 Summer 22,500(0.755) = 16,988

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

A B C D E F

Quarter Year 1 Year 2 Fall 24 26

Winter 23 22 Spring 19 19

Summer 14 17 Total Demand 80 84

Average Demand per Quarter 20 21

Calculate Seasonal Indices

Quarter Year 1 Year 2 Average Fall 1.200 1.238 1.219

Winter 1.150 1.048 1.099 Spring 0.950 0.905 0.927

Summer 0.700 0.810 0.755

Calculate Forecast for Next Year Estimated annual enrollment 90000 Average per Quarter 22500

Expected Quarterly Enrollment, Based on Historical Seasonal Indices Quarter Forecast

Fall 27429 Winter 24723 Spring 20866

Summer 16982

Enrollment (thousands)

Individual

B16: =B6/B$11 (copied down)

C16: =C6/C$11 (copied down)

D16: =(B16+C16)/2 (copied down)

C10: =SUM(C6:C9)

C11: =C10/4

B23: =B22/4

B27: =B$23*D16 (copied down)

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CAUSAL MODELS • 303

Forecasting demand at ski resorts such as Snowshoe, Holiday Valley, and Seven Springs can be very challenging because data are highly seasonal. Multiple seasonal factors need to be considered, including the month of the year, day of the week, and ho1idays and long weekends, in addition to consider- ing the weather forecast. Historical data are used to develop the indexes for these seasons. In addition, the ski industry has been experi- encing an upward trend over the past years, particularly with the growth of snow boarding. A simple way to make forecasts in this industry is to forecast the trend and then make adjustments based on developed sea- sonal indexes.

LINKS TO PRACTICE The Ski Industry Forecast

M ar

c M

ue nc

h/ ©

C or

b is

CAUSAL MODELS Recall that causal, or associative, models assume that the variable we are trying to forecast is somehow related to other variables in the environment. The forecasting challenge is to discover the relationships between the variable of interest and these other variables. These relationships, which can be very complex, take the form of a mathematical model, which is used to forecast future values of the variable of interest. Some of the best-known causal models are regression models. In this section we look at linear and multiple regression and how they are used in forecasting.

Linear Regression In linear regression the variable being forecast, called the dependent variable, is related to some other variable, called the independent variable, in a linear (or straight- line) way. Figure 8-2 shows how a linear regression line relates to the data. You can

� Linear regression Procedure that models a straight-line relationship between two variables.

}

Linear Regression Equation: Y = a + bX

Actual Values

Error (difference between actual value and regression equation)

D ep

en de

nt V

ar ia

bl e

Independent Variable X

FIGURE 8-2

Linear regression line fit to historical data

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304 • CHAPTER 8 FORECASTING

see that the dependent variable is linearly related to the independent variable. The relationship between two variables is the equation of a straight line:

Y 5 a 1 bX where Y � dependent variable

X � independent variable a � Y intercept of the line b � slope of the line Many straight lines could be drawn through the data. Linear regression selects

parameters a and b, which define a straight line that minimizes the sum of the squared errors, or deviations from the line. This is called the least-squares straight line. Devel- oping the values for a and b can be complicated, so we simply give their computation here. You can assume that computing a and b in this way will produce a straight line through the data that minimizes the sum of the squared errors. The steps in comput- ing the linear regression equation are as follows:

Step 1 Compute parameter b:

b 5 SXY 2 nXY SX 2 2 nX2

where Y � average of the Y values X � average of the X values n � number of data points We compute parameter b first because that calculation is needed to compute para- meter a. Step 2 Compute parameter a:

a 5 Y 2 bX Step 3 Substitute these values to obtain the linear regression equation:

Y 5 a 1 bX Step 4 To make a forecast for the dependent variable (Y), substitute the appropri- ate value for the independent variable (X).

A maker of personalized golf shirts has been tracking the relationship between sales and advertising dol- lars over the past four years. The results are as follows:

Use linear regression to find out what sales would be if the company invested $53,000 in advertising for next year.

• Before You Begin: When using linear regression, always identify the independent and dependent variables. Remember that the dependent variable is the one you are trying to forecast.

• Solution: In this example sales are the dependent variable (Y) and advertising dollars are the independent variable (X). We assume that there is a relationship between these two variables. To compute the linear regres- sion equation, we set up the following table of information:

EXAMPLE 8.11

Forecasting with Linear Regression

Sales Dollars (in thousands)

Advertising Dollars (in thousands)

130 32 151 52 150 50 158 55

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CAUSAL MODELS • 305

X 5 47.25 Y 5 147.25 Now let’s follow the steps necessary for computing a linear regression equation:

Step 1 Compute parameter b:

b 5 SXY 2 nXY SX 2 2 nX 2

5 28,202 2 4 147.25 2 1147.25 2

9,253 2 4 147.25 2 2 5 371.75322.75 5 1.15 Step 2 Compute parameter a:

a 5 Y 2 bX 5 147.25 2 11.15 2 147.25 2 5 92.83 Step 3 Compute the linear regression equation:

Y 5 a 1 bX

5 92.83 1 1.15X

Step 4 Now that we have the equation, we can compute the value of Y for any value of X. To com- pute the value of sales when advertising dollars are $53,000, we can substitute that number for X:

Y 5 92.83 1 1.15 153 2 5 $153.87 1 in thousands 2 This can also be computed using a spreadsheet, as shown here.

Y X XY X2 Y2

130 32 4160 1024 16,900 151 52 7852 2704 22,801 150 50 7500 2500 22,500 158 55 8690 3025 24,964

Total 589 189 28,202 9,253 87,165

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

A B C D E F G H

Year

Advertising Dollars

(thousands) Sales

(thousands) 1 32 130 2 52 151 3 50 150 4 55 158

Average 47.25 147.25

Slope Coefficient (b) 1.15 Intercept Coefficient (a) 92.83

Example 8.11 Sales (thousands) vs Advertising (thousands)

y = 1.15x + 92.83

100

120

140

160

180

30 40 50 60

Advertising Dollars ($ thousands)

S al

es (

$ th

ou sa

nd s)

C9: =AVERAGE(C5:C8)

C11: =SLOPE(C5:C8,B5:B8)

C12: =INTERCEPT(C5:C8,B5:B8)

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306 • CHAPTER 8 FORECASTING

Correlation Coefficient When performing linear regression, it is helpful to compute the correlation coefficient, which measures the direction and strength of the linear relationship between the inde- pendent and dependent variables. The correlation coefficient is computed using the fol- lowing equation:

r 5 n 1SXY 2 2 1SX 2 1SY 2"[n 1SX2 2 2 1SX 2 2 ] #"[n 1SY 2 2 2 1SY 2 2 ]

Although the equation seems complicated, it is easy to compute and the values of r can be easily interpreted. Values of r range between �1 and �1 and have the following meanings:

r � �1: Th ere is a perfect positive linear relationship between the two variables. For every 1-unit increase in the independent variable, there is a 1-unit increase in the dependent variable.

r � �1: Th ere is a perfect negative linear relationship between the two variables. Just because the relationship is negative does not mean that there is no relationship. It is still a linear relationship except that it is negative; the two variables move in opposite directions. A unit increase in the independent variable is accompanied by a unit decrease in the dependent variable.

r � 0: Th ere is no linear relationship between the variables.

Obviously, the closer the value of r is to 1.00 or �1.00, the stronger is the linear relationship between the two variables. If we square the correlation coefficient, r2, we can determine how well the independent variable explains changes in the dependent variable. This statistic shows how well the regression line “fits” the data. The higher the r, the better. A high r2—say, 0.80 or higher—would indicate that the independent variable can be used effectively as a predictor of the dependent variable.

� Correlation coefficient Statistic that measures the direction and strength of the linear relationship between two variables.

Using the information from Example 8.11, compute the correlation coefficient and evaluate the strength of the linear relationship between sales and advertising dollars.

• Before You Begin: To solve this problem, compute the correlation coefficient using the formula from the text. Remember that the closer the computed value is to 1, the stronger the rela- tionship between the two variables.

• Solution: Given our information, we can compute the correlation coefficient as follows:

r 5 n 1SXY 2 2 1SX 2 1SY 2"[n 1SX 2 2 2 1SX 2 2 ] #"[n 1SY 2 2 2 1SY 2 2 ]

5 4 128,202 2 2 1189 2 1589 2"[4 19,253 2 2 1189 2 2 ] #"[4 187,165 2 2 1589 2 2 ] 5 0.992

The computed correlation coefficient is close to 1, which means that there is a strong positive linear rela- tionship between the two variables. Also, if we compute r 2, we get 0.984, which means that 98.4 percent of the variability in sales is explained by advertising dollars.

EXAMPLE 8.12

Computing the Correlation Coefficient

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MEASURING FORECAST ACCURACY • 307

Multiple Regression Multiple regression is an extension of linear regression. However, unlike in linear regression where the dependent variable is related to one independent variable, mul- tiple regression develops a relationship between a dependent variable and multiple independent variables. The general formula for multiple regression is as follows:

Y 5 B0 1 B1X1 1 B2X2 1 c1 BKXK where Y � dependent variable

B0 � the Y intercept B1 . . . BK � coefficients that represent the influence of the independent variables on

the dependent variable X1 . . . XK � independent variables

For example, the dependent variable might be sales and the independent variables might be number of sales representatives, number of store locations, area population, and per capita income.

Multiple regression is a powerful tool for forecasting and should be used when mul- tiple factors influence the variable that is being forecast. However, multiple regression does significantly increase data and computational requirements needed for forecast- ing. Fortunately, most standard statistical software programs have multiple regression capabilities.

MEASURING FORECAST ACCURACY One of the basic principles of forecasting is that forecasts are rarely perfect. How- ever, how does a manager know how much a forecast can be off the mark and still be reasonable? One of the most important criteria for choosing a forecasting model is its accuracy. Also, data can change over time, and a model that once provided good results may no longer be adequate. The model’s accuracy can be assessed only if fore- cast performance is measured over time. For all these reasons it is important to track model performance over time, which involves monitoring forecast errors.

Forecast Accuracy Measures Forecast error is the difference between the forecast and actual value for a given period, or

Et 5 At 2 Ft where Et � forecast error for period t

At � actual value for period t Ft � forecast for period t

However, error for one time period does not tell us very much. We need to measure forecast accuracy over time. Two of the most commonly used error measures are the mean absolute deviation (MAD) and the mean squared error (MSE). MAD is the average of the sum of the absolute errors:

MAD S|actual 2 forecast|

n MSE is the average of the squared error:

MSE 5 S 1actual 2 forecast 2 2

� Forecast error Difference between forecast and actual value for a given period.

� Mean absolute deviation (MAD) Measure of forecast error that computes error as the average of the sum of the absolute errors.

� Mean squared error (MSE) Measure of forecast error that computes error as the average of the squared error.

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308 • CHAPTER 8 FORECASTING

One of the advantages of MAD is that it is based on absolute values. Consequently, the errors of opposite signs do not cancel each other out when they are added. We sum the errors regardless of sign and obtain a measure of the average error. If we are comparing different forecasting methods, we can then select the method with the lowest MAD.

MSE has an additional advantage: due to the squaring of the error term, large errors tend to be magnified. Consequently, MSE places a higher penalty on large errors. This can be a useful error measure in environments in which large errors are particularly destructive. For example, a blood bank forecasts the demand for blood. Because fore- casts are rarely perfect, there will be errors. However, in this environment a large error could be very damaging. Using MSE as an error measure would highlight any large errors in the blood bank’s forecast. As with MAD, when comparing the forecast per- formance of different methods we would select the method with the lowest MSE.

To evaluate forecast performance, you need to use only one forecast error measure. However, a good forecaster learns to rely on multiple methods to evaluate forecast performance. Example 8.13 illustrates the use of MAD and MSE.

Standard Parts Corporation is comparing the accuracy of two methods that it has used to forecast sales of its popular valve. Forecasts using method A and method B are shown against the actual values for January through May. Which method provided better forecast accuracy?

• Before You Begin: In this problem you are to compare the forecast accuracy of two fore- casting methods. Since a good forecaster relies on multiple error measures, compute both the MAD and MSE using the formulas given in the text. The method that gives the lowest MAD and MSE provides the better accuracy.

• Solution:

Accuracy for method A:

MAD 5 S|actual 2 forecast|

n 5

5 5

5 1

MSE 5 S 1actual 2 forecast 2 2

n 5

7 5

5 1.4

Accuracy for method B:

MAD 5 S|actual 2 forecast|

n 5

10 5

5 2

MSE 5 S 1actual 2 forecast 2 2

n 5

30 5

5 6

Of the two methods, method A produced a lower MAD and a lower MSE, which means that it provides better forecast accuracy. Note that the magnitude of difference in values is greater for MSE than

EXAMPLE 8.13

Measuring Forecast Accuracy

Actual Method A Method B Month Sales Forecast Error Error Error2 Forecast Error Error Error2 January 30 28 2 2 4 30 0 0 0 February 26 25 1 1 1 28 –2 2 4 March 32 32 0 0 0 36 –4 4 16 April 29 30 –1 1 1 30 –1 1 1 May 31 30 1 1 1 28 3 3 9 Total 3 5 7 –4 10 30

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MEASURING FORECAST ACCURACY • 309

Tracking Signal When there is a difference between forecast and actual values, one problem is to iden- tify whether the difference is caused by random variation or is due to a bias in the forecast. Forecast bias is a persistent tendency for a forecast to be over or under the actual value of the data. We cannot do anything about random variation, but bias can be corrected.

One way to control for forecast bias is to use a tracking signal. A tracking signal is a tool used to monitor the quality of the forecast. It is computed as the ratio of the algebraic sum of the forecast errors divided by MAD:

Tracking signal 5 algebraic sum of forecast errors

MAD or

Tracking signal 5 S 1actual 2 forecast 2

MAD

As the forecast errors are summed over time, they can indicate whether there is a bias in the forecast. To monitor forecast accuracy, the values of the tracking signal are compared against predetermined limits. These limits are usually based on judgment and experience and can range from �3 to �8. In this chapter we will use the limits of �4, which compare to limits of 3 standard deviations. If errors fall outside these limits, the forecast should be reviewed.

� Forecast bias A persistent tendency for a forecast to be over or under the actual value of the data.

� Tracking signal Tool used to monitor the quality of a forecast.

for MAD. Recall that MSE magnifies large errors through the squaring process. For the month of March, method B had a magnitude of error that was much larger than for other periods, causing MSE to be high. This can also be computed using a spreadsheet, as shown next.

5 6 7 8 9 10 11 12 13 14 23 24 25 26 27 28 29

A B C D E F G H I J

Month Actual Sales Forecast Error

Absolute Error Error2 Forecast Error

Absolute Error Error2

January 30 28 2 2 4 30 0 0 0 February 26 25 1 1 1 28 –2 2 4 March 32 32 0 0 0 36 –4 4 16 April 29 30 –1 1 1 30 –1 1 1 May 31 30 1 1 1 28 3 3 9 Totals 3 5 7 –4 10 30

MAD = 1.0 MAD = 2.0 MSE = 1.4 MSE = 6.0

Key Formulas (similar formulas for method B) D6 =$B6-C6 (copied down) E6 =ABS(D6) (copied down) F6 =D6^2 (copied down) E13 =AVERAGE(E6:E10) E14 =AVERAGE(F6:F10)

Method A Method B

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310 • CHAPTER 8 FORECASTING

A number of factors influence the selection of a forecasting model. They include the following:

1. Amount and type of available data. Quantitative forecasting models require certain types of data. If there are not enough data in quantifi able form, it may be necessary to use a qualitative forecasting model. Also, diff erent quantitative models require diff erent amounts of data. Exponential smoothing requires a small amount of historical data, whereas linear regression requires considerably more. Th e amount and type of data available play a large role in the type of model that can be considered.

2. Degree of accuracy required. Th e type of model selected is related to the degree of accuracy required. Some situations require only rough forecast estimates, whereas others require precise accuracy. Oft en, the greater the degree of accuracy required, the higher is the cost of the forecasting process. Th is is because increasing accuracy means increasing the costs of collecting and processing data, as well as the cost of the computer soft ware required. A simpler and less costly

SELECTING THE RIGHT FORECASTING MODEL

A company uses a tracking signal with limits of �4 to decide whether a forecast should be reviewed. Compute the tracking signal given the following historical information and decide when the forecast should be reviewed. The MAD for this item was computed as 2.

• Before You Begin: In this problem you have to compute the tracking signal for weeks 1 through 4 and determine if it exceeds the set limit of �4. Remember that the tracking signal is the sum of the forecast errors (the cumulative deviation) divided by the MAD. In this problem MAD has been computed as 2. You need to compute the cumulative deviation for each period and divide by the MAD.

• Solution:

The forecast should be reviewed in week 4 because the tracking signal has exceeded �4.

EXAMPLE 8.14

Developing a Tracking Signal

Weeks Actual Forecast Deviation Cumulative Deviation

Tracking Signal

4 2 1 8 10 2 11 10 3 12 10 4 14 10

Weeks Actual Forecast Deviation Cumulative Deviation

Tracking Signal

4 2 1 8 10 –2 2 1 2 11 10 1 3 1.5 3 12 10 2 5 2.5 4 14 10 4 9 4.5

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FORECASTING SOFTWARE • 311

forecasting model may be better overall than one that is very sophisticated but expensive.

3. Length of forecast horizon. Some forecasting models are better suited to short forecast horizons, whereas others are better for long horizons. It is very important to select the correct model for the forecast horizon being used. For example, a manufacturer that wishes to forecast sales of a product for the next three months will use a very diff erent forecasting model than an electric utility that wishes to forecast demand for electricity over the next twenty-fi ve years.

4. Data patterns present. It is very important to identify the patterns in the data and select the appropriate model. For example, lagging can occur when a forecasting model meant for a level pattern is applied to data with a trend.

FORECASTING SOFTWARE Today much commercial forecasting is performed using computer software. Many software packages can be used for forecasting. Some can handle thousands of variables and manipulate huge databases. Others specialize in one forecasting model. Conse- quently, it may be difficult to select the right forecasting software. Most forecasting software packages fall into one of three categories: (1) spreadsheets, (2) statistics pack- ages, and (3) specialty forecasting packages. In this section we look at the differences among these categories. Then we present some guidelines for selecting a software package for forecasting.

Spreadsheets Spreadsheets, such as Microsoft Excel®, Quattro Pro®, and Lotus 1-2-3®, are preva-lent in business, and most people are familiar with at least one of them. These pack- ages provide basic forecast capability, such as simple exponential smoothing and regression. Also, simple forecasting programs can be written very quickly for most spreadsheet programs. However, the disadvantage of using spreadsheets for forecast- ing is that they do not have the capability for statistical analysis of forecast data. As we have seen, proper forecasting requires much data analysis. This involves analyz- ing the data for patterns, studying relationships among variables, monitoring forecast errors, and evaluating the performance of different forecasting models. Unfortunately, spreadsheets do not offer this capability as readily as packages designed specifically for forecasting.

Statistical Packages Statistical software includes packages designed primarily for statistical analysis, such as SPSS, SAS, NCSS, and Minitab. Almost all of these packages also offer forecasting capabilities, as well as extensive data analysis capability. There are large differences among statistical packages, particularly between those versions for mainframe versus those for microcomputers. Overall, these packages offer large capability and a variety of options. However, their many features can be overwhelming for someone interested only in forecasting. Statistical software packages are best for a user who seeks many statistical and graphical capabilities in addition to forecasting features.

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312 • CHAPTER 8 FORECASTING

Specialty Forecasting Packages Specialized forecasting software is specifically intended for forecasting use. These packages often provide an extensive range of forecasting capability, though they may not offer large statistical analysis capability. Popular packages include Forecast Master, Forecast Pro, SIBYL/Runner, Autobox, and SCA. Some of these packages offer a wide range of forecasting models, whereas others specialize in a particular model category. Forecasters who need extensive statistical analysis capability may need to use a statisti- cal package in addition to the forecasting package.

Guidelines for Selecting Forecasting Software There are many forecasting software packages to choose from, and the process can be overwhelming. Following are some guidelines for selecting the right package:1

1. Does the package have the facilities you want? Th e fi rst question to ask is whether the forecasting methods you are considering using are available in the package. Other issues to consider are the soft ware’s graphics capabilities, data management, and reporting facilities. You need to consider how important these are given the purpose of the forecasts you will be generating.

2. What platform is the package available for? You obviously need to make sure that the soft ware is available for the platform you are using. Also, it may be necessary to consider the availability for multiple platforms, depending on who will use the soft ware and whether there will be transferring of fi les.

3. How easy is the package to learn and use? Some packages off er many capabilities but may be hard to use. Generally, the more comprehensive the array of capabilities, the more diffi cult the package is to use. Make sure that you can master the soft ware. Also check the ease of importing and exporting data.

4. Is it possible to implement new methods? Oft en forecasters prefer to modify existing methods to fi t their particular needs. Many forecasting packages allow the addition of new models or the modifi cation of existing ones through a programming language.

5. Do you require interactive or repetitive forecasting? In many operations management situations we need to make forecasts for hundreds of items on a regular basis, such as monthly or quarterly. For these situations it is very useful to have a “batch” forecasting capability. Th is is not necessary, however, for forecasts that are generated interactively with the forecaster.

6. Do you have very large data sets? Almost all packages have a limit on how many variables and how many observations can be processed. Sometimes very powerful forecasting packages can handle only relatively small data sets. Make sure the package you purchase is capable of processing the data you need.

7. Is there any local support? Make sure there is ample documentation and good technical support, and check for any other local support that may be available. Remember that all packages can encounter glitches. A number of forecasting vendors off er seminars, and there are oft en courses that can be taken for the more popular methods, such as SAS and SPSS.

1S. Makridakis, S. Wheelwright, and R. Hyndman, Forecasting Methods and Applications, 3rd ed. (New York: John Wiley, 1998).

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COMBINING FORECASTS • 313

8. Does the package give the right answers? Most people assume that a computer package will generate correct results. However, this is not always the case. Th ere are small diff erences in output between diff erent packages due to diff erences in the algorithms used for computing. Some diff erences can result from actual errors in the programs, especially when large data sets are used. One recommendation is to compare output from the soft ware against published results or against output from another package.

Another factor to consider is the cost of the package relative to the importance of its use. Some packages are very expensive and comprehensive, while others are less expensive. Evaluate the use of the forecasts generated and their importance in the managerial situation before purchasing a highly expensive package. Finally, you need to consider compatibility with existing software, especially for other operations man- agement applications such as scheduling and inventory control. The output from fore- casting usually feeds into these systems, so you need to make sure these systems can communicate with one another.

FOCUS FORECASTING Focus forecasting is a forecasting approach that has gained some popularity in busi- ness. It was developed by Bernie Smith,2 who argues that statistical methods do not work well for forecasting. He believes simple rules that have worked well in the past are best used to forecast the future. The idea behind focus forecasting is to test these rules on past data and evaluate how they perform. New rules can be added at any time, and old ones that have not performed well can be eliminated.

Focus forecasting uses a computer simulation program that evaluates the forecast performance of a number of rules on past data. The program keeps track of the rules and evaluates how well they perform. Following are some examples of rules:

1. We will sell over the next three months what we sold over the last three months. 2. What we sold in a three-month period last year, we will sell in the same three-

month period this year. 3. We will sell over the next three months 5 percent of what we sold over the last

three months. 4. We will sell over the next three months 15 percent of what we sold over the same

three-month period last year.

You can see that these rules use commonsense concepts. In focus forecasting, manag- ers can come up with any new rules that they believe reflect accurate forecasts in their business and then test their value on historical data.

Smith claims to have achieved great success with focus forecasting. He states that he has compared its accuracy to that of conventional methods, such as exponential smoothing, and that focus forecasting consistently provides superior results.

COMBINING FORECASTS One approach to forecasting that has been shown to result in improved forecast accu- racy is to combine forecasts from two or more different forecasting methods. Studies have shown that combining forecasts can lead to forecast accuracy that is better than

2Bernard T. Smith, Focus Forecasting: Computer Techniques for Inventory Control (Boston: CBI, 1984).

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314 • CHAPTER 8 FORECASTING

that of the individual forecasts. The forecasting methods that are combined should be different and can even be based on different information or data.

One of the simplest ways to combine is to use a simple average of the individual forecasts. Even though there are more sophisticated ways of combining, a simple aver- age has been shown to be very effective in improving forecast accuracy.

The idea of relying on different types of forecasting methods and combining their results to get a final forecast has even been used by weather forecasters. Weather forecasting can be challenging, and many factors need to be considered, such as long-range trends and current weather fronts. Weather forecasters have been able to improve their forecast accuracy by combining the results of forecasts

made at different time intervals. For example, a weather forecast for the upcoming weekend may be formulated by combining computer-generated forecasts made on the preceding Monday, Tuesday, and Wednesday. This method is called “ensemble fore- casting” and has proven to be very successful.

COLLABORATIVE PLANNING, FORECASTING, AND REPLENISHMENT (CPFR) Collaborative Planning, Forecasting, and Replenishment (CPFR) is a collaborative process between two trading partners that establishes formal guidelines for joint forecasting and planning. The premise behind CPFR is that companies can be more successful if they join forces to bring value to their customers, share risks of the mar- ketplace, and improve their performances.

In previous chapters we learned about the benefits that can be attained by sharing information with suppliers and developing long-term relationships. CPFR is a formal way of achieving this. By implementing CPFR, trading partners jointly set forecasts, plan production, replenish inventories, and evaluate their success in the marketplace. The most complete form of CPFR utilizes a nine-step process:

1. Establish collaborative relationships. Buyers and sellers formally establish their relationship, including expectations and performance measures. Th is is usually reevaluated annually.

2. Create a joint business plan. Buyers and sellers develop a joint business plan. 3. Create a sales forecast. Sales forecasts are generated based on available data. Th is

is usually done monthly or weekly. 4. Identify exceptions for sales forecasts. Items that are exceptions to the sales forecast

are identifi ed. 5. Resolve/collaborate on exceptions to sales forecasts. Buyers and sellers jointly

investigate exceptions by analyzing shared data. 6. Create order forecast. An order forecast is generated that supports the shared

sales forecast and joint business plan. 7. Identify exceptions for order forecast. Buyers and sellers jointly identify which

items are exceptions to the order forecast.

LINKS TO PRACTICE Combining Methods in

Weather Forecasting

R al

p h

W et

m or

e/ G

et ty

ag es

, I nc

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FORECASTING ACROSS THE ORGANIZATION • 315

8. Resolve/collaborate on exceptions to order forecast. Exceptions are identifi ed and resolved by analyzing shared data.

9. Generate order. Usually performed weekly or daily.

Most of the outlined steps are performed on a weekly or monthly basis, and the agreement between parties is evaluated annually. You can see that a large amount of time is spent jointly identifying and reconciling exceptions, with the focus on sup- porting the jointly set business plan. Also, note that CPFR is an iterative process. That means that it is done over and over again.

CPFR has contributed to the success of many companies, including Wal-Mart, Tar- get, Black & Decker, and Ace Hardware. The German-based manufacturer of house- hold cleaners and home care products, Henkel KgaA, was able to significantly improve sales forecasts and reduce error rates in just six months by implementing CPFR.

FORECASTING WITHIN OM: HOW IT ALL FITS TOGETHER Forecasts impact not only other business functions but all other operations deci- sions. Operations managers make many forecasts, such as the expected demand for a company’s products. These forecasts are then used to determine product designs that are expected to sell (Chapter 2), the quantity of product to produce (Chapters 5 and 6), and the amount of supplies and materials that are needed (Chapter 12). Also, a company uses forecasts to determine future space requirements (Chapter 10), capacity and location needs (Chapter 9), and the amount of labor needed (Chap- ter 11). Forecasts drive strategic operations decisions, such as choice of competitive priorities, changes in processes, and large technology purchases (Chapter 3). Fore- cast decisions also serve as the basis for tactical planning, such as developing worker schedules (Chapter 11). Virtually all operations management decisions are based on a forecast of the future.

FORECASTING ACROSS THE ORGANIZATION Forecasting is an excellent example of an activity that is critical to the management of all functional areas within a company. In business organizations, forecasts are made in virtually every function and at every organizational level. Budgets are set, resources allocated, and schedules made based on forecasts. Without a forecast of the future, a company would not be able to make any plans, including day-to-day and long-range plans. In this section we look at how forecasting affects some of the other functions of an organization.

Marketing relies heavily on forecasting tools to generate forecasts of demand and future sales. However, the marketing department also needs to forecast sizes of mar- kets, new competition, future trends, and changes in consumer preferences. Most of the forecasting methods discussed in this chapter are used by marketing. Marketing often works in conjunction with operations to assess future demands.

Finance uses the tools of forecasting to predict stock prices, financial perfor- mance, capital investment needs, and investment portfolio returns. The accuracy of demand forecasts, in turn, affects the ability of finance to plan future cash flow and financial needs.

MKT

FIN

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316 • CHAPTER 8 FORECASTING

Information systems play an important role in the forecasting process. Today’s forecasting requires sharing of information and databases not only within a business but also between business entities. Often companies share their forecasts or demand information with their suppliers. These capabilities would not be possible without an up-to-date information system.

Human resources relies on forecasting to determine future hiring requirements. In addition, forecasts are made of the job market, labor skill availability, future wages and compensation, hiring and layoff costs, and training costs. In order to recruit proper talent, it is necessary to forecast labor needs and availability.

Economics relies on forecasting to predict the duration of business cycles, economic turning points, and general economic conditions that affect business. Whenever a plan of action is required, that plan is based on some anticipation of the future—a forecast. Whether in business, industry, government, or in other fields such as medicine, engi- neering, and science, proper planning for the future starts with a good forecast.

MIS

HRM

All entities of a supply chain are working to fulfill final customer demands. The forecast of demand is critical, as it affects all the plans made by each company in the supply chain. When entities of the supply chain make their forecasts independent of one another, they each have their own separate forecast of demand. The consequences of this are a mismatch between supply and demand because each company is working to fulfill a different level of demand. Consider that Dell starts its planning process with a forecast of future demand to determine the amount of components

it needs to order. At the same time, Intel, which sup- plies Dell with microprocessors, needs to determine its production and inventory schedules. If Dell and Intel made their forecasts separately, their forecasts would be

different and Intel would not be able to supply the exact amounts Dell needs. In contrast, when there is collaboration between suppliers and manufac- turers in generating the forecast, all entities are responding to the same level tof demand. A good

example of this is the implementation of Collabora- tive Planning, Forecasting, and Replenishment (CPFR), which we discussed earlier in the chapter.

THE SUPPLY CHAIN LINK

Planning for any event requires a forecast of the future. In sustainability, this includes forecasting future avail- ability of scarce resources, pollution trends and future levels of pollutants, catastrophic global events, inno- vations that will impact sustainability practices, even issues of climate change. For example, numerous fore- casting techniques are used to scientifically predict the effects of climate change. These forecasts impact the plans that are made by policymakers and organiza- tions, and how money and resources are allocated to mitigate risks. These forecasts can have an enormous impact on preparedness in case these events material- ize. When these events do occur, such as the oil spill in the Gulf of Mexico, forecasts are needed to estimate the resources needed to be brought to the site to effectively

address the problem, the duration of the problem, and the impact on the affected region. These plans are all based on forecasts and they require decisions regarding how best to manage resources in their respective supply chains.

Consider breakthrough innovations in sustainability that can change the way we do business. An example might be PepsiCo’s development of a plastic bottle made exclusively from recycled material. We may want to forecast the likelihood of success for this new product and the timing of it reaching the market, as this would significantly alter sustainability practices. Companies in the plastics industry would be interested in that forecast to prepare for such a change.

Similarly, consider pending legislation that may change emission standards in an industry. Forecast- ing the new standard and the likelihood of it passing

THE SUSTAINABILITY LINK

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KEY TERMS • 317

Chapter Highlights 1 Th ree basic principles of forecasting are: forecasts are rarely

perfect; forecasts are more accurate for groups or families of items rather than for individual items; and forecasts are more accurate for shorter than longer time horizons.

2 Th e forecasting process involves fi ve steps: decide what to forecast; evaluate and analyze appropriate data; select and test a forecasting model; generate the forecast; and monitor forecast accuracy.

3 Forecasting methods can be classifi ed into two groups: qualitative and quantitative. Qualitative forecasting methods generate a forecast based on the subjective opinion of the forecaster. Some examples of qualitative methods include executive opinion, market research, and the Delphi method. Quantitative forecasting methods are based on mathematical modeling. Th ey can be divided into two categories: time series models and causal models.

4 Time series models are based on the assumption that all the information needed for forecasting is contained in the time series of data. Causal models assume that the variable being forecast is related to other variables in the environment.

5 Th ere are four basic patterns of data: level or horizontal, trend, seasonality, and cycles. In addition, data usually

would be critical for members in that industry, as it would enable them to prepare and design new products accordingly.

As you can see, forecasting is critical to sustainability, as it lets us peek into the future. It is especially important in predicting energy trends, availability of resources,

technological innovation, population growth and the economy, and identifying driving forces that will shape change. Forecasting these issues of sustainability, using the very techniques we discuss in this chapter, enables companies to be better prepared for the future.

contain random variation. Some forecasting models that can be used to forecast the level of a time series are naïve, simple mean, simple moving average, weighted moving average, and exponential smoothing. Separate models are used to forecast trend, such as trend-adjusted exponential smoothing. Forecasting seasonality requires a procedure in which we compute a seasonal index, the percentage by which each season is above or below the mean.

6 A simple causal model is linear regression, in which a straight-line relationship is modeled between the variable we are forecasting and another variable in the environment. Th e correlation coeffi cient is used to measure the strength of the linear relationship between these two variables.

7 Th ree useful measures of forecast accuracy are mean absolute deviation (MAD), mean square error (MSE), and a tracking signal.

8 Th ere are four factors to consider when selecting a forecasting model: the amount and type of data available, the degree of accuracy required, the length of forecast horizon, and patterns present in the data.

Key Terms forecasting 281 qualitative forecasting methods 283 quantitative forecasting

methods 284 executive opinion 285 market research 285 Delphi method 285 time series models 286 time series 286 causal models 286

level or horizontal pattern 288 trend 288 seasonality 288 cycles 289 random variation 289 naïve method 289 simple mean or average 290 simple moving average (SMA) 291 weighted moving average 294 exponential smoothing model 295

trend-adjusted exponential smoothing 297

seasonal index 300 linear regression 303 correlation coeffi cient 306 forecast error 307 mean absolute deviation (MAD) 307 mean squared error (MSE) 307 forecast bias 309 tracking signal 309

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318 • CHAPTER 8 FORECASTING

Formula Review Name Formula

1. Naïve Ft11 5 At 2. Simple mean

Ft11 5 SAt

n 3. Moving average

Ft11 5 SAt

n 4. Weighted moving average Ft11 5 SCt At 5. Exponential smoothing Ft11 5 aAt 1 11 2 a 2Ft 6. Trend-adjusted exponential smoothing Step 1: Smoothing the level of the series:

St 5 aAt 1 11 2 a 2 1St21 1 Tt21 2 Step 2: Smoothing the trend: Tt 5 b 1St 2 St21 2 1 11 2 b 2Tt21 Step 3: Forecast including trend: FITt11 5 St 1 Tt

7. Seasonality Step 1: Calculate the average demand for each season.

Step 2: Compute a seasonal index for every season of every year for which you have data.

8. Linear trend line/linear regression Step 1: Compute parameter b:

b 5 SXY 2 nXY SX 2 2 nX 2

Step 2: Compute parameter a:

a 5 Y 2 bX Step 3: Obtain equation:

Y 5 a 1 bX

9. Correlation coeffi cient r 5

n 1SXY 2 2 1SX 2 3 1SY 2"[n 1SX 2 2 2 1SX 2 2 ] #"[n 1SY 2 2 2 1SY 2 2 ] 10. Forecast error Et 5 At 2 Ft 11. Mean absolute deviation

MAD 5 S|actual 2 forecast|

n 12. Mean squared error

MSE 5 S 1actual 2 forecast 2 2

n 13. Tracking signal

Tracking signal 5 S 1actual 2 forecast 2

MAD

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SOLVED PROBLEMS • 319

Solved Problems • Problem 1 Given the following data, calculate forecasts for months 4, 5, 6, and 7 using a three-month moving average and an exponential smoothing forecast with an alpha of 0.3. Assume a forecast of 61 for month 3:

To compute the moving average forecasts:

Ft11 5 SAt

F4 5 A1 1 A2 1 A3

3 5

56 1 76 1 58 3

5 63.33

F5 5 A2 1 A3 1 A4

3 5

76 1 58 1 67 3

5 67.00

F6 5 A3 1 A4 1 A5

3 5

58 1 67 1 75 3

5 66.66

F7 5 A4 1 A5 1 A6

3 5

67 1 75 1 76 3

5 72.66

To compute the exponential smoothing forecasts:

Ft11 5 aAt 1 11 2 a 2Ft F4 5 aA3 1 11 2 a 2F3 F4 5 10.30 2 158 2 1 10.70 261 5 60.1 F5 5 10.30 2 167 2 1 10.70 260.1 5 62.17 F6 5 10.30 2 175 2 1 10.70 262.17 5 66.02 F7 5 10.30 2 176 2 1 10.70 266.02 5 69.01

(See student companion site for Excel template.)

Month Actual Sales

Forecast 3-Month Moving Average

Forecast Exponential Smoothing

1 56 2 76 3 58 4 67 5 75 6 76 7

• Before You Begin: To use a three-period moving average, remember that you always have to compute the average of the latest three observa- tions. As new data become available, drop off the oldest data. For the exponential smoothing part of this problem, before you begin make sure that you have the three pieces of information you need: the current period’s forecast (61 for month 3), the current period’s actual value (58), and a value for the smoothing coefficient (a � 0.3).

• Solution

Month Actual Sales

Forecast 3-Month Moving Average

Forecast Exponential Smoothing

1 56 2 76 3 58 4 67 63.33 60.1 5 75 67.00 62.17 6 76 66.66 66.02 7 72.66 69.01

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320 • CHAPTER 8 FORECASTING

• Problem 2 True Beauty is a cosmetics company that uses exponential smoothing with trend to forecast monthly sales of its special face cream. At the end of November, the company wants to forecast sales for December. The trend through October has been 10 additional boxes sold per month. Average sales have been 60 boxes per month. The demand for November was 68 boxes. The company uses a � 0.20 and b � 0.10. Make a fore- cast including trend for the month of December.

• Before You Begin: Before you begin solving this type of problem, first identify the information given in the problem.

The information we have is:

SOct 5 60 boxes/month

TOct 5 10 boxes/month

ANov 5 68 boxes

a 5 0.20

b 5 0.10

Now we can follow the three steps in the chapter for trend- adjusted exponential smoothing.

4 5 6

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

A B C D E F G H I alpha = 0.30

Month Actual Sales

3-month Moving Avg

Exponential Smoothing

1 56 2 76 3 58 61 61 4 67 63.33 60.10 5 75 67.00 62.17 6 76 66.67 66.02 7 72.67 69.01

Forecasts

C11: =AVERAGE(B8:B10) (copied down)

D11: =D$4*B10+(1-D$4)*D10 (copied down)

Solved Problem 8.1 Moving Average and Exponential Smoothing Forecasts

1 2 3 4 5 6 7

Month

S al

Actual Sales 3-month Moving Avg Exponential Smoothing

This can also be computed using a spreadsheet, as shown here.

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SOLVED PROBLEMS • 321

• Solution We need to use three equations to generate a forecast including trend. For each equation we substitute the appropriate values:

Step 1 Smoothing the level of the series:

St 5 aAt 1 11 2 a 2 1St21 1 Tt21 2 SNov 5 aANov 1 11 2 a 2 1SOct 1 TOct 2

5 10.20 2 168 2 1 10.80 2 160 1 10 2 5 69.6

Step 2 Smoothing the trend:

Tt 5 b 1St 2 St21 2 1 11 2 b 2Tt21 TNov 5 b 1SNov 1 SOct 2 1 11 2 b 2TOct

5 10.1 2 168 2 1 169 2 60 2 1 10.90 210 5 9.9

Step 3 Forecast including trend:

FITt11 5 St 1 Tt

FITDec 5 SNov 1 TNov

5 69 1 9.9

5 78.9 boxes

Cactus Trees Sold Season Year 1 Year 2 Fall 100 110 Winter 82 95 Spring 180 173 Summer 110 110 Total 472 488

Cactus Trees Sold Season Year 1 Year 2 Fall 100/118 = 0.847 110/122 = 0.902 Winter 82/118 = 0.695 95/122 = 0.778 Spring 180/118 = 1.53 173/122 = 1.42 Summer 110/118 = 0.932 110/122 = 0.902

Season Average Seasonal Index Fall (0.847 + 0.902)/2 = 0.875 Winter (0.695 + 0.778)/2 = 0.737 Spring (1.53 + 1.42)/2 = 1.48 Summer (0.932 + 0.902)/2 = 0.917

Season Forecast (Cactus Trees) Fall 125(0.875) = 109.4 Winter 125(0.737) = 92.13 Spring 125(1.48) = 185.0 Summer 125(0.917) = 114.4

• Problem 3 A gardener wants to develop a forecast for next year’s quarterly sales of cactus trees. He has collected quarterly sales for the past two years and expects total sales for next year to be 500 cactus trees. The data clearly exhibit seasonality. How much can he expect to sell during each quarter of next year accounting for seasonality?

Step 3 Calculate the average seasonal index for each season.

• Before You Begin: To solve this problem, follow the five steps given in the chapter for forecasting seasonality.

• Solution We follow the steps used in developing seasonal indexes:

Step 1 Calculate the average demand for each season:

Year 1: 472/4 5 118

Year 2: 488/4 5 122

Step 2 Compute a seasonal index for every season of every year for which there are data.

Step 4 Calculate the average demand per season for next year.

We are told that the sales forecast for next year is 500 cactus trees. The average demand per season, or quarter, is

500/4 5 125

Step 5 Multiply next year’s average seasonal demand by each seasonal index.

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322 • CHAPTER 8 FORECASTING

Sales (in 000s of $)

Disposable Household

Income (in 000s of $)

Y X XY X2 Y2

29.8 16.8 500.6 282.2 888.0 35.9 18.4 660.6 338.6 1,228.8 38.8 20.4 791.5 416.2 1,505.4 43.6 22.9 998.4 524.4 1,900.9 46.8 25.7 1,202.8 660.5 2,190.2 49.5 27.3 1,351.4 745.3 2,450.3 52.3 32.1 1,678.8 1,030.4 2,735.3 55.2 35.2 1,943.0 1,239.0 3,047.0 57.2 36.3 2,076.4 1,317.7 3,271.8 58.6 38.2 2,238.5 1,459.2 3,433.9

Total 467.7 273.3 13,442.0 8,013.5 22,711.6

• Problem 4 A sneaker manufacturer has plotted sales of its most popular brand of sneakers over the past four months. Use a linear trend line to compute sales of sneakers for month 5.

• Before You Begin: To solve this problem, follow the four steps given in the chapter for finding a linear trend line.

• Solution

Step 1 Compute parameter b:

b 5 1SXY 2 2 nXY

SX 2 2 nX 2

5 1194 2 4 12.5 2 1115.75 2

30 2 4 12.5 2 2 5 36.55 5 7.3 Step 2 Compute parameter a:

a 5 Y 2 bX 5 115.75 2 17.3 2 12.5 2 5 97.5 Step 3 Compute the linear trend line.

Y 5 a 1 bX Y 5 97.5 1 7.3X

Step 4 For the fifth month, the value of sales would be

Y 5 97.5 1 7.3 15 2 5 134 sneakers

Month Sales X Y X2 XY 1 100 1 100 2 120 4 240 3 118 9 354 4 125 16 500

Total 10 463 30 1194 Y 5 115.75 X 5 2.5

• Problem 5 A retailer of household appliances has collected data on the relationship between the company’s sales and disposable house- hold income. For the presented data: (a) Obtain a linear regression equation for the data. (b) Compute a correlation coefficient and determine the

strength of the linear relationship. (c) Use the linear regression equation to develop a forecast of

sales if disposable household income is $37,800.

• Before You Begin: To solve this problem follow the four steps given in the chapter for generating a forecast using linear regression. Make sure to clearly identify the independent and dependent variables. As you proceed, remember that you first compute parameter b, then parameter a, then develop the regression equation, and finally generate the forecast.

• Solution

(a) Step 1 Compute parameter b:

b 5 1SXY 2 2 nXY

SX 2 2 nX 2 5

13,442.0 2 110 2 127.3 2 146.8 2 8,013.5 2 110 2 127.3 2 2

5 1.19

Step 2 Computer parameter a:

a 5 Y 2 bX 5 46.8 2 11.19 2 127.3 2 5 14.31 Step 3 Compute the linear regression line:

Y 5 a 1 bX

Y 5 14.31 1 1.19X

(b) Computing the correlation coefficient:

r 5 n 1SXY 2 2 1SX 2 1SY 2"n 1SX 2 2 2 1SX 2 2 # "n 1SY 2 2 2 1SY 2 2

r 5 10 113,442 2 2 1273.3 2 1467.7 2"10 18,013.5 2 2 1273.3 2 2 # "10 122,711.6 2 2 467.7 2 2

r 5 0.977

The correlation coefficient is close to 1.00, indicating a strong positive linear relationship.

This means that if disposable household income is $37,800, the company’s sales are expected to be $59,290.

X 5 27.3 Y 5 46.8

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PROBLEMS • 323

• Problem 6 A company is comparing the accuracy of two different fore- casting methods. Use MAD to compare the accuracies of these methods for the past five weeks of sales. Which method pro- vides greater forecast accuracy?

• Before You Begin: Remember that MAD is the average of the sum of the absolute errors and that a lower MAD indicates better forecast accuracy.

• Solution Actual Sales

Method A Method B Week Forecast Error Error Forecast Error Error

1 25 30 –5 5 30 –5 5 2 18 20 –2 2 16 2 2 3 26 23 3 3 25 1 1 4 28 29 –1 1 30 –2 2 5 30 25 5 5 28 2 2

Total 0 16 –2 12

Accuracy for method A:

MAD 5 S|actual 2 forecast|

n 5

16 5

5 3.2

Accuracy for method B:

MAD 5 S|actual 2 forecast|

n 5

12 5

5 2.4

Of the two methods, method B produced a lower MAD, which means that it provides greater forecast accuracy. Note, however, that the sum of the errors was actually 0 for method A, which shows how this error measure can be misleading.

Discussion Questions 1. Give three examples showing why a business needs to

forecast. 2. Give three examples from your life in which you may fore-

cast the future. 3. Describe the steps involved in forecasting. 4. Identify the key differences between qualitative and quan-

titative forecasting methods. Which is better in your opinion and why?

5. What are the main types of data patterns? Give examples of each type.

6. Describe the different assumptions of time series and causal models.

7. What are the differences among models that forecast the level, trend, and seasonality?

8. Explain why it is important to monitor forecast errors. 9. Explain some of the factors to be considered in selecting a

forecasting model.

Problems 1. Sales for a product for the past three months have been 200,

350, and 287. Use a three-month moving average to calculate a forecast for the fourth month. If the actual demand for month 4 turns out to be 300, calculate the forecast for month 5.

2. Lauren’s Beauty Boutique has experienced the following weekly sales:

Forecast sales for week 6 using the naïve method, a simple average, and a three-period moving average.

3. Hospitality Hotels forecasts monthly labor needs. (a) Given the following monthly labor fi gures, make a forecast

for June using a three-period moving average and a fi ve- period moving average.

Week Sales 1 432 2 396 3 415 4 458 5 460

Month Actual Values January 32 February 41 March 38 April 39 May 43

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324 • CHAPTER 8 FORECASTING

(b) What would be the forecast for June using the naïve method?

(c) If the actual labor fi gure for June turns out to be 41, what would be the forecast for July using each of these models?

(d) Compare the accuracy of these models using the mean absolute deviation (MAD).

(e) Compare the accuracy of these models using the mean squared error (MSE).

4. Th e following data are monthly sales of jeans at a local department store. Th e buyer would like to forecast sales of jeans for the next month, July.

(a) Forecast sales of jeans for March through June using the naïve method, a two-period moving average, and exponential smoothing with an a � 0.2. (Hint: Use naïve to start the exponential smoothing process.)

(b) Compare the forecasts using MAD and decide which is best.

(b) Is your answer the same as in Problem 5? Why or why not?

Month Sales January 45 February 30 March 40 April 50 May 55 June 47

Week Demand

(lab requirements)

1 330 2 350 3 320 4 370 5 368 6 343

Week Demand 1 20 2 31 3 36 4 38 5 42 6 40

Week Demand

(in patients serviced)

1 430 2 289 3 367 4 470 5 468 6 365

5. Th e manager of a small health clinic would like to use exponential smoothing to forecast demand for laboratory services in the facility. However, she is not sure whether to use a high or low value of �. To make her decision, she would like to compare the forecast accuracy of a high and low a on historical data. She has decided to use an a � 0.7 for the high value and a � 0.1 for the low value. Given the following historical data, which do you think would be better to use?

6. Th e manager of the health clinic in Problem 5 would also like to use exponential smoothing to forecast demand for emergency services in the facility. As in Problem 5, she is not sure whether to use a high or low value of a. To make her decision, she would like to compare the forecast accuracy of a high and low a on historical data. Again, she has decided to use an a � 0.7 for the high value and a � 0.1 for the low value.

(a) Given the following historical data, which value of a do you think would be better to use?

7. Th e following historical data have been collected representing sales of a product. Compare forecasts using a three- period moving average, exponential smoothing with a a � 0.2, and linear regression. Using MAD and MSE, which forecasting model is best? Are your results the same using the two error measures?

8. A manufacturer of printed circuit boards uses exponential smoothing with trend to forecast monthly demand of its product. At the end of December, the company wishes to forecast sales for January. Th e estimate of trend through November has been 200 additional boards sold per month. Average sales have been around 1000 units per month. Th e demand for December was 1100 units. Th e company uses a � 0.20 and b � 0.10. Make a forecast including trend for the month of January.

9. Demand at Nature Trails Ski Resort has a seasonal pattern. Demand is highest during the winter, as this is the peak ski season. However, there is some ski demand in the spring and even fall months. Th e summer months can also be busy as visitors oft en come for summer vacation to go hiking on the mountain trails. Th e owner of Nature Trails would like to make a forecast for each season of the next year. Total annual demand has been estimated at 4000 visitors. Given the last two years of historical data, what is the forecast for each season of the next year?

Visitors Season Year 1 Year 2 Fall 200 230 Winter 1400 1600 Spring 520 580 Summer 720 831

10. Rosa’s Italian restaurant wants to develop forecasts of daily demand for the next week. Th e restaurant is closed on Mondays and experiences a seasonal pattern for the other six days of the

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PROBLEMS • 325

Number of Customers Day Week 1 Week 2 Tuesday 52 48 Wednesday 36 32 Thursday 35 30 Friday 89 97 Saturday 98 99 Sunday 65 69

Sales (in thousands)

Training Hours

25 10 40 12 36 12 50 15 11 6

week. Mario, the manager, has collected information on the number of customers served each day for the past two weeks. If Mario expects total demand for next week to be around 350, what is the forecast for each day of next week?

11. Th e president of a company was interested in determining whether there is a correlation between sales made by diff erent sales teams and hours spent on employee training. Th ese fi gures are shown.

(a) Compute the correlation coeffi cient for the data. What is your interpretation of this value?

(b) Using the data, what would you expect sales to be if training was increased to 18 hours?

12. Th e number of students enrolled at Spring Valley Elementary has been steadily increasing over the past fi ve years. Th e school board would like to forecast enrollment for years 6 and 7 in order to better plan capacity. Use a linear trend line to forecast enrollment for years 6 and 7.

13. Happy Lodge Ski Resorts tries to forecast monthly attendance. Th e management has noticed a direct relationship between the average monthly temperature and attendance.

(a) Given fi ve months of average monthly temperatures and corresponding monthly attendance, compute a linear regression equation of the relationship between the two. If next month’s average temperature is forecast to be 45 degrees, use your linear regression equation to develop a forecast.

Year Enrollment 1 220 2 245 3 256 4 289 5 310

Month Average

Temperature Resort Attendance

(in thousands) 1 24 43 2 41 31 3 32 39 4 30 38 5 38 35

Park Attendance (in thousands) Quarter Year 1 Year 2 Fall 352 391 Winter 156 212 Spring 489 518 Summer 314 352

Week Cheeseburger

Sales 1 354 2 345 3 367 4 322 5 356

(b) Compute a correlation coeffi cient for the data and determine the strength of the linear relationship between average temperature and attendance. How good a predictor is temperature for attendance?

14. Small Wonder, an amusement park, experiences seasonal attendance. It has collected two years of quarterly attendance data and made a forecast of annual attendance for the coming year. Compute the seasonal indexes for the four quarters and generate quarterly forecasts for the coming year, assuming annual attendance for the coming year to be 1525.

15. Burger Lover Restaurant forecasts weekly sales of cheeseburgers. Based on historical observations over the past fi ve weeks, make a forecast for the next period using the following methods: simple average, three-period moving average, and exponential smoothing with a � 0.3, given a forecast of 328 cheeseburgers for the fi rst week.

If actual sales for week 6 turn out to be 368, compare the three forecasts using MAD. Which method performed best?

16. A company uses exponential smoothing with trend to forecast monthly sales of its product, which show a trend pattern. At the end of week 5, the company wants to forecast sales for week 6. Th e trend through week 4 has been 20 additional cases sold per week. Average sales have been 85 cases per week. Th e demand for week 5 was 90 cases. Th e company uses a � 0.20 and b � 0.10. Make a forecast including trend for week 6.

17. Th e number of patients coming to the Healthy Start maternity clinic has been increasing steadily over the past eight months. Given the following data, use a linear trend line to forecast attendance for months 9 and 10.

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326 • CHAPTER 8 FORECASTING

Month Clinic Attendance (in thousands)

1 3.4 2 3.9 3 4.5 4 5.0 5 5.8 6 5.9 7 6.5 8 6.7

Period Actual Forecast 1 15 17 2 18 3 14 4 16 5 13 6 16

Period Actual Method

A Method

B Method

C 1 10 10 9 8 2 8 11 10 11 3 12 12 8 10 4 11 13 12 11 5 12 14 11 12

Period Actual

Demand Forecast 1 Forecast 2 1 90 78 87 2 87 85 88 3 92 84 90 4 95 92 97 5 98 100 102 6 98 102 101

Weeks Actual Sales Forecast Deviation

Cumulative Deviation

Tracking Signal

6 3 1 12 11 2 14 13 3 14 14 4 16 14

Month Sales Month Sales Month Sales 1 239 9 310 17 369 2 248 10 335 18 378 3 256 11 348 19 367 4 260 12 353 20 383 5 271 13 355 21 394 6 280 14 368 22 393 7 295 15 379 23 405 8 305 16 358 24 412

18. Given the following data, use exponential smoothing with a � 0.2 and a � 0.5 to generate forecasts for periods 2 through 6. Use MAD and MSE to decide which of the two models produced a better forecast.

19. Pumpkin Pies Galore is trying to forecast sales of pies for the month of December. Demand for pies in September, October, and November has been 230, 304, and 415, respectively. Edith, the company’s owner, uses a three-period weighted moving average to forecast sales. Based on her experience, she chooses to weight September as 0.1, October as 0.3, and November as 0.6.

(a) What would Edith’s forecast for December be? (b) What would her forecast be using the naïve method? (c) If actual sales for December turned out to be 420 pies,

which method was better (use MAD)? 20. A company has used three diff erent methods to forecast

sales for the past fi ve months. Use MAD and MSE to evaluate the performance of the three methods.

(a) Which forecasting method performed best? Do MAD and MSE give the same results?

(b) Which of these is actually the naïve method? 21. Two diff erent forecasting models were used to forecast

sales of a popular soda on a college campus. Actual demand and the two sets of forecasts are shown. Use MAD to explain which method provided a better forecast.

22. A producer of picture frames uses a tracking signal with limits of �4 to decide whether a forecast should be reviewed. Given historical information for the past four weeks, compute the tracking signal and decide whether the forecast should be reviewed. Th e MAD for this item was computed as 2.

23. Mop and Broom Manufacturing has tracked the number of units sold of their most popular mop over the past 24 months. Th is is shown.

(a) Develop a linear trend line for the data. (b) Compute a correlation coeffi cient for the data and

evaluate the strength of the linear relationship. (c) Using the linear trend line equation, develop a forecast for

the next period, month 25. 24. Given the sales data from Problem 23, generate forecasts

for months 7–24 using a six-period and a three-period moving average. Use MAD to compare the forecasts. Which forecast is more stable? Which is more responsive and why?

25. Th e following data were collected on the study of the relationship between a company’s retail sales and advertising dollars:

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CASE: BRAM-WEAR • 327

Retails Sales ($) Advertising ($) 29,789 16,893 35,434 18,398 38,732 20,376 43,585 22,982 46,821 25,732 49,283 27,281 52,271 32,182 55,289 35,298 57,298 36,281 58,293 38,178

(a) Obtain a linear regression line for the data. (b) Compute a correlation coeffi cient and determine the

strength of the linear relationship. (c) Using the linear regression equation, develop a forecast of

retail sales for advertising dollars of $40,000.

CASE: Bram-Wear Lenny Bram, owner and manager of Bram-Wear, was analyzing performance data for the men’s clothing retailer. He was con- cerned that inventories were high for certain clothing items, meaning that the company would potentially incur losses due to the need for significant markdowns. At the same time, it had run out of stock for other items early in the season. Some customers appeared frustrated by not finding the items they were looking for and needed to go elsewhere. Lenny knew that the problem, though not yet serious, needed to be addressed immediately.

Background Bram-Wear was a retailer that sold clothing catering to young, urban, professional men. It primarily carried upscale, casual attire, as well as a small quantity of outerwear and footwear. Its success did not come from carrying a large product variety, but from a very focused style with an abundance of sizes and colors.

Bram-Wear had extremely good financial performance over the past five years. Lenny had attributed the company’s success to a group of excellent buyers. The buyers seemed able to accurately target the style preferences of their customers and correctly forecast product quantities. One challenge was keeping up with customer buying patterns and trends.

The Data To determine the source of the problem, Lenny had requested forecast and sales data by product category. Looking at the sheets of data, it appeared that the problem was not with the specific styles or items carried in stock; rather, the problem appeared

to be with the quantities ordered by the buyers. Specifically, the problem centered on two items: an athletic shoe called Urban Run and the five-pocket cargo jeans.

Urban Run was a popular athletic shoe that had been car- ried by Bram-Wear for the past four years. Quarterly data for the past four years are shown in the table. The company seemed to always be out of stock of this athletic shoe. The model used by buyers to forecast sales for this item had been seasonal exponen- tial smoothing. Looking at the data, Lenny wondered if this was the best method to use. It seemed to work well in the beginning, but now he was not so sure.

The data for the five-pocket cargo jean seemed also to point to a forecasting problem. When the product was introduced last year, it was expected to have a large upward trend. The buy- ers believed the trend would continue and used an exponential smoothing model with trend to forecast sales. However, they seemed to have too much inventory of this product. As with the Urban Run athletic shoe, Lenny wondered if the right forecasting model was being applied to the data. It seemed he would have to dig out his old operations management text to solve this problem.

Demand for Urban Run Athletic Shoe

Quarter Year 1

Demand Year 2

Demand Year 3

Demand Year 4

Demand I 10 14 20 30 II 29 31 26 31 III 26 29 28 33 IV 15 18 30 35

Demand for 5-Pocket Cargo Jeans

Month Year 1

Demand Year 2

Demand January 36 98 February 42 101 March 56 97 April 75 99 May 85 100 June 94 95 July 101 107 August 108 104 September 105 98 October 114 104 November 111 100 December 110 102

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328 • CHAPTER 8 FORECASTING

Case Questions 1. Is seasonal exponential smoothing the best model for

forecasting Urban Run athletic wear? Why? 2. Explain what has happened to the data for Urban Run.

What are the consequences of continuing to use seasonal expo- nential smoothing? What model would you use? Generate a fore- cast for the four quarters of the fourth year using your model. Determine your forecast error and the inventory consequences.

3. Is exponential smoothing with trend the best model for forecasting five-pocket cargo jeans? Why?

4. What method would you use to forecast monthly cargo jean demand for the second year given the previous year’s monthly demand? Explain why you selected your approach. Generate the forecasts for each month of the second year with your method. Determine your forecast error and the inventory consequences.

CASE: The Emergency Room (ER) at Northwest General (A) Jenn Kostich is the new department director for emergency ser- vices at Northwest General Hospital. One of her responsibilities is to ensure proper staffing in the emergency room (ER) by schedul- ing nurses to appropriate shifts. This has historically been a prob- lem for the ER. The former director did not base nurse schedules on forecasts, but used the same fixed schedule week after week.

Jenn had recently received her degree in operations man- agement. She knew that schedules needed to be based on fore- casts of demand. She needed to start by analyzing historical data in order to determine the best forecasting method to use. Jenn’s assistant provided her with information on patient arrivals in the ER by hour and day of the week for the previous month, October. October was considered a typical month for the ER, and Jenn thought it was a good starting point. Jenn reviewed

the information (shown in the chart) that she had requested and wondered where to begin.

Case Questions 1. What is your opinion of the level at which the data are

being collected? What are some of the advantages of collecting data at this level?

2. Aggregate the original data for October as you see appro- priate (e.g., sum up by day of week, time of day, week of the month, etc.). This will give you a new data set to work with. Ana- lyze your data for patterns. Can you find any?

3. Use at least two different forecasting models on the new data set you developed in question 2 by aggregating the original data. Compare their forecast performance and provide an evaluation.

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INTERNET CHALLENGE: ON-LINE DATA ACCESS • 329

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Forecasting at Cruising Inernational, Inc. In this assignment you will focus on forecasting demand for cruises in different geographical regions. CII is trying to determine which regions have faster-growing demand so that they can be sure to have sufficient cruises available in each specific region. You are to meet with Jean Burkette, Director of Demand Management, at CII corporate. Your meeting ends with Jean saying, “I need you to develop a forecast for the upcoming cruising season … using any method that you believe provides a reliable forecast.”

This assignment will enhance your knowledge of the material in Chapter 8 of your textbook while preparing you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Forecasting at CII

On-line Case: Forecasting at Valley Memorial Hospital

Assignment: Forecasting Today, your assignment has taken you out to City Central Airfield. You’ll be meeting with Jean Burger, director of the Valley Memorial Helicopter Service (VMHS), the air transportation arm of the medical center. She’s asked you to meet her at the VMHS hangar. As you drive up, she comes out to greet you. “We have a forecasting problem for you. For the past five years or so, we’ve had three helicopters. As they age, they need more maintenance, which means more downtime and

more missed flights. We probably need to add another helicopter or, at a minimum, replace the oldest one—or maybe even both.”

To access the Web site:

You have been hired by a government agency to collect and ana- lyze economic data and generate economic forecasts. Since you do not have much experience in this area, your manager, Ms. Her- nandez, has decided to give you a chance to practice your skills. Ms. Hernandez believes that it would be a good idea for you to use the Internet to collect and monitor a sample of economic data. She has given you a list of Web sites to access. Your first assignment is to collect a sample of local, national, or international economic data from one of these sites. Next, try to analyze the data you have collected and identify any patterns. Then, using one of the tech- niques discussed in the chapter, generate a forecast for the future. Finally, as new data are posted, evaluate your performance using

the error measures discussed in the chapter. How did you do, and what have you learned about the data you collected? Web sites:

1. Census Bureau—provides economic and demographic information from the U.S. economy (http://www.census.gov/)

2. Penn World Tables—provides international economic data (http://www.hbs.edu/units/bgie/internet/penn.html)

3. Regional Economic Information System—provides regional, state, and local data (http://www.ciesin.org/datasets/ reis/reis-home.html)

4. Resources for Economists on the Internet—provides busi- ness and economic data (http://www.rfe.org)

INTERNET CHALLENGE On-line Data Access

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330 • CHAPTER 8 FORECASTING

Online Resources

Companion Website www.wiley.com/college/reid • Take interactive practice quizzes to assess your knowledge and help you study in a dynamic way • Review PowerPoint slides or print slides for notetaking • Download Excel Templates to use for problem solving • Access the Virtual Company: Cruise International, Inc. • Find links to Company Tours for this chapter

Artesyn Communication Products, LLC Ercol Furniture

• Find links for Additional Web Resources for this chapter Forecasting Principles, www.forecastingprinciples.org Institute of Business Forecasters, www.ibf.org International Institute of Forecasting, www.ms.ic.ac.uk/iif/index.htm

Selected Bibliography Andraski, J.C., and J. Haedicke. “CPFR: Time for the Break-

through?” Supply Chain Management Review, May–June 2003, 54–60.

Armstrong, J. Scott. “Evaluating and Selecting Forecasting Methods.” In J. Scott Armstrong (ed.), Principles of Fore- casting: A Handbook for Researchers and Practitioners. Nor- well, Mass.: Kluwer Academic Publishers, 2001.

Armstrong, J. Scott. Long-Range Forecasting from Crystal Ball to Computer, Second Edition. New York: John Wiley & Sons, 1985.

Cattani, K., and W. Hauseman. “Why Are Forecast Updates Oft en Disappointing?” Manufacturing & Service Opera- tions Management, 2, 2, spring 2000, 119–127.

Clements, M.P., and D.F. Hendry. Forecasting Economic Time Series. Cambridge, England: Cambridge University Press, 1998.

Fischer, I., and N. Harvey. “Combining Forecasts: What Infor- mation Do Judges Need to Outperform the Simple Aver- age?” International Journal of Forecasting, 15, 3, 1999, 227–246.

Lawrence, M., and M. O’Connor. “Sales Forecasting Updates: How Good Are Th ey in Practice?” International Journal of Forecasting, 16, 3, 2000, 369–383.

Makridakis, S., S. Wheelwright, and R. Hyndman. Forecasting Methods and Applications, Th ird Edition. New York: John Wiley & Sons, 1998.

McCullough, B.D. “Is It Safe to Assume Th at Soft ware Is Accu- rate?” International Journal of Forecasting, 16, 3, 2000, 349–358.

Pearson, R. “Increasing the Credibility of Your Forecasts: 7 Suggestions,” FORESIGHT, 3, February 2006, 27–32.

Smith, B. Focus Forecasting: Computer Techniques for Inventory Control. Boston: CBI, 1984.

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L E A R N I N G O B J E C T I V E S

CHAPTER

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

331

C H A P T E R O U T L I N E

Capacity Planning 333 Making Capacity Planning Decisions 339 Decision Trees 341 Location Analysis 344 Making Location Decisions 349

Capacity Planning and Facility Location within OM: How It All Fits Together 360

Capacity Planning and Facility Location across the Organization 360

Before studying this chapter you should know or, if necessary, review

1. Globalization, Chapter 1, p. 18. 2. Differences between strategic and tactical decisions, Chapter 1, p. 9. 3. Break-even analysis, Chapter 3, pp. 62–63. 4. Qualitative forecasting methods, Chapter 8, pp. 284–286.

After studying this chapter you should be able to

1 Defi ne capacity planning. 2 Defi ne location analysis. 3 Describe the relationship between capacity planning and location and their importance

to the organization. 4 Explain the steps involved in capacity planning and location analysis. 5 Describe the decision support tools used in capacity planning. 6 Identify key factors in location analysis. 7 Describe the decision support tools used in location analysis.

Capacity Planning and Facility Location

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332 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

Have you ever signed up for a course at your college or university only to find out that it is closed? Have you ever attended a class that was held in a remote location and found that the room was overcrowded? Most of us have had these experiences as students. These examples illustrate problems of poor capacity planning and location—problems that can greatly affect the success of a business. Students have been known to drop out of a course that is uncomfortable to sit in, difficult to get to, or even to leave a program in which courses are frequently closed. Similarly, businesses can lose customers by not being able to produce enough goods or by being in an inconvenient location.

Matching the capacity of a business with customer demand can be a challenge. Having too much capacity is just as problematic as not having enough capacity. The first leads to excess cost from having idle facilities, workers, and equipment. The second leads to lost sales as the company cannot satisfy customer demands.

After the terrorist attacks of September 11, 2001, many firms in the hospitality industry found them- selves with excess capacity. This included such busi- nesses as hotels, airlines, cruise ships, and amusement parks. Many of these companies, such as the Marriott

Corporation, Walt Disney Company, and Carnival Cruises, offered promotional incentives to increase customer demand. Similarly, after the SARS epidemic in 2003 many international airlines offered large discounts on fares as they found themselves with excess capacity in the form of idle aircraft.

Capacity planning and location analysis are actually two separate decisions. Capac- ity planning deals with the maximum output rate that a facility can have, determined by the size of facilities and equipment. Location analysis, on the other hand, deals with the best location for a facility. You can probably see why these two decisions are usually made simultaneously. When a company decides to open a new facility, it must also decide on both the size of the facility and its location. The size of the facility may also be affected by the location.

In this chapter we will learn about both capacity planning and location analysis. We will see how companies make both kinds of decisions. We will also see how both of these issues can affect not only the success of a company but your everyday life as well.

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CAPACITY PLANNING • 333

Capacity can be defined as the maximum output rate that can be achieved by a facility. The facility may be an entire organization, a division, or only one machine. Planning for capacity in a company is usually performed at two levels, each cor- responding to either strategic or tactical decisions, as discussed in Chapter 1. The first level of capacity decisions is strategic and long-term in nature. This is where a company decides what investments in new facilities and equipment it should make. Because these decisions are strategic in nature, the company will have to live with them for a long time. Also, they require large capital expenditures and will have a great impact on the company’s ability to conduct business. The second level of capac- ity decisions is more tactical in nature, focusing on short-term issues that include planning of workforce, inventories, and day-to-day use of machines. In this chapter we focus on long-term, strategic capacity decisions. Short-term capacity decisions are discussed in Chapter 14.

Why Is Capacity Planning Important? Capacity planning is the process of establishing the output rate that can be achieved by a facility. If a company does not plan its capacity correctly, it may find that it either does not have enough output capability to meet customer demands or has too much capacity sitting idle. In our university example, that would mean either not being able to offer enough courses to accommodate all students or hav- ing too few students in the classrooms. Both cases are costly to the university. Another example is a bakery. Not having enough capacity would mean not being able to produce enough baked goods to meet sales. The bakery would often run out of stock, and customers might start going somewhere else. Also, the bakery would not be able to take advantage of the true demand available. On the other hand, if there is too much capacity, the bakery would incur the cost of an unnecessarily large facility that is not being used, as well as much higher operating costs than necessary.

A hospital emergency room (ER) exemplifies the challenges of capacity planning. The prob- lems of over- or undercapacity we just discussed also occur in the ER, only with poten- tially dire consequences. A number of factors contribute to the capacity of the ER. One is the number of beds and the amount of space available. If there are not enough beds, patients may have to wait long periods of time to be examined. Too many empty beds, on the other hand, result in wasted space.

Another factor affecting the ER’s capacity is the number of nurses and doctors scheduled to work on a shift. If not enough staff is available, patients may not have anyone to treat them. The consequences of not having enough capacity can be grave. However, scheduling more staff than needed results in excess capacity in the form of highly paid professionals not having anything to do.

� Capacity The maximum output rate that can be achieved by a facility.

FIN

� Capacity planning The process of establishing the output rate that can be achieved by a facility.

CAPACITY PLANNING

LINKS TO PRACTICE Capacity Planning in the ER

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Capacity planning problems are notorious in the ER, partly due to high fluctuations in demand and the high costs of insufficient capacity. In fact, the American College of Emergency Physicians estimates that 62 percent of U.S. emergency rooms are at or above capacity. Particularly troubling are long patient waiting times that average over 47 minutes before a doctor is seen but can be as long as many hours. Many ERs are looking at ways to address this problem. One alternative being implemented is to immediately screen patients and identify those with minor ailments. These patients are then put into a “fast-track” category to be quickly treated and released. This tech- nique serves to free up capacity for those patients that need it.

Planning for capacity is important if a company wants to grow and take full advantage of demand. At the same time, capacity decisions are complicated because they require long-term commitments of expensive resources, such as large facili- ties. Once these commitments have been made, it is costly to change them. Think about a business that purchases a larger facility in anticipation of an increase in demand, only to find that the demand increase does not occur. It is then left with a huge expense, no return on its investment, and the need to decide how to use a partially empty facility. Recall from Chapter 8 that forecasting future demands entails a great deal of uncertainty and risk; this makes long-term facility purchases inherently risky.

Another issue that complicates capacity planning is the fact that capacity is usually purchased in “chunks” rather than in smooth increments. Facilities, such as buildings and equipment, are acquired in large sizes, and it is virtually impos- sible to achieve an exact match between current needs and needs based on future demand. You can see this in the classroom example. If a university anticipates a large demand for a particular course, it may offer multiple sections. Each addi- tional section adds capacity in chunks equal to one class size. If one class can hold a maximum of 45 students, opening up another class means adding capacity for up to an additional 45 students. The university must consider its forecast of the additional demand for the course. If the forecast for additional demand is only 4 additional students, the university will probably not open up another section. The reason is that the cost for each section takes the form of chunks that include the room, the instructor, and utilities. This cost is the same whether 1 student or 45 students attend.

Because of the uncertainty of future demand, the overriding capacity planning decision becomes one of whether to purchase a larger facility in anticipation of greater demand or to expand in slightly smaller but less efficient increments. Each strategy has its advantages and disadvantages. Think about a young married couple who want to purchase a home. They can purchase a very small home that would be more afford- able, knowing that if they have children they eventually will need to face the disrup- tion and cost of moving. On the other hand, if they purchase a larger home now they will be better prepared for the future but will be paying for additional space that they currently do not need.

Measuring Capacity Although our definition of capacity seems simple, there is no one way to measure it. Different people have different interpretations of what capacity means, and the units of measurement are often very different. Table 9-1 shows some examples of how capacity might be measured by different organizations.

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CAPACITY PLANNING • 335

Note that each business can measure capacity in different ways and that capac- ity can be measured using either inputs or outputs. Output measures, such as the number of cars per shift, are easier to understand. However, they do not work well when a company produces many different kinds of products. For example, if we operate a bakery that bakes only pumpkin pies, then a measure such as pies per day would work well. However, if we made many different kinds of pies and varied the combination from one day to the next, then simply using pies per day as our measure would not work as well, especially if some pies took longer to make than others. Suppose that pecan pies take twice as long to make as pumpkin pies. If one day we made 20 pumpkin pies and the next day we made 10 pecan pies, using pies per day as our measure would make it seem as if our capacity was underutilized on the second day, even though it was equally utilized on both days. When a company produces many different kinds of products, input mea- sures work better.

When discussing the capacity of a facility, we need two types of information. The first is the amount of available capacity, which will help us understand how much capacity our facility has. The second is effectiveness of capacity use, which will tell us how effectively we are using our available capacity. Next we look at how to quantify and interpret this information.

Measuring Available Capacity Let’s return to our bakery example for a moment. Suppose that on the average we can make 20 pies per day. However, if we are really pushed, such as during holidays, maybe we can make 30 pies per day. Which of these is our true capacity? We can make 30 pies per day at a maximum, but we cannot keep up that pace for long. Saying that 30 per day is our capacity would be misleading. On the other hand, saying that 20 pies per day is our capacity does not reflect the fact that we can, if necessary, push our production to 30 pies.

Through this example you can see that different measures of capacity are useful because they provide different kinds of information. Following are two of the most common measures of capacity:

Design capacity is the maximum output rate that can be achieved by a facility under ideal conditions. In our example, this is 30 pies per day. Design capacity can be sustained only for a relatively short period of time. A company achieves this output rate by using many temporary measures, such as overtime, overstaffi ng, maximum use of equipment, and subcontracting.

� Design capacity The maximum output rate that can be achieved by a facility under ideal conditions.

Type of Business Input Measures of Capacity

Output Measures of Capacity

Car manufacturer Labor hours Cars per shift

Hospital Available beds per month Number of patients per month

Pizza parlor Worker hours per day Number of pizzas per day Ice-cream manufacturer Operational hours per day Gallons of ice cream

per day

Retail store Floor space in square feet Revenues per day

TABLE 9-1

Examples of Different Capacity Measures

Overcrowding is a sign of insufficient capacity.

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336 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

Eff ective capacity is the maximum output rate that can be sustained under normal conditions. Th ese conditions include realistic work schedules and breaks, regular staff levels, scheduled machine maintenance, and none of the temporary measures that are used to achieve design capacity. Note that eff ective capacity is usually lower than design capacity. In our example, eff ective capacity is 20 pies per day.

Measuring Effectiveness of Capacity Use Regardless of how much capacity we have, we also need to measure how well we are utilizing it. Capacity utilization simply tells us how much of our capacity we are actually using. Certainly there would be a big difference if we were using 50 percent of our capacity, meaning our facilities, space, labor, and equipment, rather than 90 percent. Capacity utilization can simply be computed as the ratio of actual output over capacity:

Utilization 5 actual output rate

capacity 1100% 2

However, since we have two capacity measures, we can measure utilization relative to either design or effective capacity:

Utilizationeffective 5 actual output

effective capacity 1100% 2

Utilizationdesign 5 actual output

design capacity 1100% 2

� Effective capacity The maximum output rate that can be sustained under normal conditions.

� Capacity utilization Percentage measure of how well available capacity is being used.

In the bakery example, we have established that design capacity is 30 pies per day and effective capacity is 20 pies per day. Currently, the bakery is producing 27 pies per day. What is the bakery’s capacity utilization relative to both design and effective capacity?

• Before You Begin: To compute capacity utilization, you need to calculate the ratio of actual output (27 pies per day) over capacity. The difference between the two capacity measures is that one uses effective capacity (20 pies per day) and the other uses design capacity (30 pies per day).

• Solution:

Utilizationeffective 5 actual output

effective capacity 1100% 2 5 27

20 1100% 2 5 135%

Utilizationdesign 5 actual output

design capacity 1100% 2 5 27

30 1100% 2 5 90%

The utilization rates show that the bakery’s current output is only slightly below its design capacity and output is considerably higher than its effective capacity. The bakery can probably operate at this level for only a short time.

EXAMPLE 9.1

Computing Capacity

Utilization

Capacity Considerations We have seen that changing capacity is not as simple as acquiring the right amount of capacity to exactly match our needs. The reason is that capacity is purchased in dis- crete chunks. Also, capacity decisions are long term and strategic in nature. Acquiring

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CAPACITY PLANNING • 337

anticipated capacity ahead of time can save cost and disruption in the long run. Later, when demand increases, output can be increased without incurring additional fixed cost. Extra capacity can also serve to intimidate and preempt competitors from enter- ing the market. Important implications of capacity that a company needs to consider when changing its capacity are discussed in this section.

Economies of Scale Every production facility has a volume of output that results in the lowest average unit cost. This is called the facility’s best operating level. Figure 9-1 illustrates how the average unit cost of output is affected by the volume produced. You can see that as the number of units produced is increased, the average cost per unit drops. The reason is that when a large amount of goods is produced, the costs of production are spread over that large volume. These costs include the fixed costs of buildings and facilities, the costs of materials, and processing costs. The more units are produced, the larger the number of units over which costs can be spread—that is, the greater the economies of scale. The concept of economies of scale is very well known. It basically states that the average cost of a unit produced is reduced when the amount of output is increased.

You use the concept of economies of scale in your daily life, whether or not you are aware of it. Suppose you decide to make cookies in your kitchen. Think about the cost per cookie if you make only five cookies. There would be a great deal of effort—getting the ingredients, mixing the dough, shaping the cookies—all for only five cookies. If you had everything set up, making five additional cookies would not cost much more. Perhaps making even ten more cookies would cost only slightly more because you had already set up all the materials. This lower cost is due to economies of scale.

Diseconomies of Scale What if you continued to increase the number of cookies you chose to produce? For a while, making a few more cookies would not require much additional effort. However, after a certain point there would be so much material that the kitchen would become congested. You might have to get someone to help because there was more work than one person could handle. You might have to make cookies longer than expected, and the cleanup job might be much more difficult. You would be experiencing diseconomies of scale. Diseconomies of scale occur at a point beyond the best operating level, when the cost of each additional unit made increases. Diseconomies of scale are also illustrated in Figure 9-1.

Operating a facility close to its best operating level is clearly important because of the impact on costs. However, we have to keep in mind that different facility sizes have different best operating levels. In our cookie example, we can see that the number of

� Best operating level The volume of output that results in the lowest average unit cost.

� Economies of scale A condition in which the average cost of a unit produced is reduced as the amount of output is increased.

� Diseconomies of scale A condition in which the cost of each additional unit made increases.

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338 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

cookies comfortably produced by one person in a small kitchen would be much lower than the number produced by three friends in a large kitchen. Figure 9-2 shows how best operating level varies between facilities of different sizes.

You can see that each facility experiences both economies and diseconomies of scale. However, their best operating levels are different. This is a very important con- sideration when changing capacity levels. The capacity of a business can be changed by either expanding or reducing the amount of capacity. Although both decisions are important, expansion is typically a costlier and more critical event.

When expanding capacity, management has to choose between one of the follow- ing two alternatives:

Alternative 1: Purchase one large facility, requiring one large initial investment. Alternative 2: Add capacity incrementally in smaller chunks as needed.

The first alternative means that we would have a large amount of excess capacity in the beginning and that our initial costs would be high. We would also run the risk that demand might not materialize and we would be left with unused overcapacity. On the other hand, this alternative allows us to be prepared for higher demand in the future. Our best operating level is much higher with this alternative, enabling us to operate more efficiently when meeting higher demand. Our costs would be lower in the long run, since one large construction project typically costs more than many smaller construction projects due to startup costs. Thus, alternative 1 provides greater rewards but is more risky. Alternative 2 is less risky but does not offer the same opportunities and flexibility. It is up to management to weigh the risks versus the rewards in selecting an alternative.

Focused Factories Facilities can respond more efficiently to demand if they are small, specialized, and focused on a narrow set of objectives; this concept is referred to as focused factories. We encountered this concept in Chapter 7 when we studied just- in-time (JIT) systems. Focused factories are only one of many factors that contribute to the success of JIT, but the concept is applicable to any facility.

The idea that large facilities are necessary for success because they bring economies of scale is rather dated. Today’s facilities must succeed in a business environment that has short product and technology life cycles and in which flexibility is more important than ever before. Large facilities tend to be less flexible because they generally contain larger machines or process technology that is costly to change in order to make other goods and services. Many companies have realized that to be more agile they need to

� Focused factories Facilities that are small, specialized, and focused on a narrow set of objectives.

Small Facility

Medium Facility

Large Facility

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MAKING CAPACITY PLANNING DECISIONS • 339

be focused. A smaller, specialized facility can be more efficient because it can focus on a smaller number of tasks and fewer goals.

Even a large facility can benefit from the concept of the focused factory by creating what is known as a plant within a plant, or PWP. A PWP is a large facility divided into smaller, more specialized facilities that have separate operations, competitive priorities, technology, and workforce. They can be physically separated with a wall or barrier and kept independent from one another. In this manner, unnecessary layers of bureaucracy are eliminated, and each “plant” is free to focus on its own objectives. PWP was discussed in detail in Chapter 3.

Recent trends in the retail industry provide an excellent example of factory focus. In the 1980s, retail sales were dominated by large department stores such as Sears, JC Penney’s, and Federated Department Stores. However, in the 1990s, gains in sales were made by specialty stores such as the Gap, The Limited, and Ann Taylor, while large depart- ment stores faltered. The reason is that consumer preferences change very rapidly, and each small specialty store can focus precisely on the needs of its customer group. Specialty stores are able to focus on a specific set of customers and respond to their unique needs. The Limited and the Gap are excellent examples of factory focus, with specialty stores such as Limited Too, Baby Gap, and Gap Kids.

Subcontractor Networks Another alternative to having a large production facility is to develop a large network of subcontractors and suppliers who perform a number of tasks. This is one of the fastest-growing trends today. Companies are realizing that to be successful in today’s market, they need to focus on their core capabilities—for example, by hiring third parties or subcontractors to take over tasks that the company does not need to perform itself. Companies such as American Airlines and Procter & Gamble have hired outside firms to manage noncritical inventories. Also, many companies are contracting with suppliers to perform tasks that they used to perform themselves. A good example is in the area of quality management. Historically, companies performed quality checks on goods received from suppliers. Today, suppliers and manufacturers work together to achieve the same quality standards, and much of the quality checking of incoming materials is performed at the supplier’s site. Another example can be seen in the auto industry, where manufacturers are placing more responsibility on suppliers to perform tasks such as design of packaging and transportation of goods. By placing more responsibility on subcontractors and suppliers, a manufacturer can focus on tasks that are critical to its success, such as product development and design.

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LINKS TO PRACTICE Focus in the Retail Industry

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MAKING CAPACITY PLANNING DECISIONS The three-step procedure for making capacity planning decisions is as follows:

Step 1 Identify Capacity Requirements Th e fi rst step is to identify the levels of capacity needed by the company now, as well as in the future. A company cannot decide whether to purchase a new facility without knowing exactly how much capacity it will need in the future. It also needs to identify the gap between available capacity and future requirements.

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Step 2 Develop Capacity Alternatives Once capacity requirements have been identifi ed, the company needs to develop a set of alternatives that would enable it to meet future capacity needs.

Step 3 Evaluate Capacity Alternatives Th e last step in the procedure is to evaluate the capacity alternatives and select the one alternative that will best meet the company’s requirements.

Let’s look at these steps in a little more detail.

Identify Capacity Requirements Long-term capacity requirements are identified on the basis of forecasts of future demand. Certainly, companies look for long-term patterns such as trends when mak- ing forecasts. However, long-term patterns are not enough at this stage. Planning, building, and starting up a new facility can take well over five years. Much can happen during that time. When the facilities are operational, they are expected to be utilized for many years into the future. During this time frame numerous changes can occur in the economy, consumer base, competition, technology, and demographic factors, as well as in government regulation and political events.

Forecasting Capacity Capacity requirements are identified on the basis of forecasts of future demand. Forecasting at this level is performed using qualitative forecasting methods, some of which are discussed in Chapter 8. Qualitative forecasting methods, such as executive opinion and the Delphi method, use subjective opinions of experts. These experts may consider inputs from quantitative forecasting models that can numerically compute patterns such as trends. However, because so many variables can influence demand at this level, the experts use their judgment to validate the quantitative forecast or modify it based on their own knowledge.

One way to proceed with long-range demand forecasting at this stage is to first forecast overall market demand. For example, experts might forecast the total market for overnight delivery to be $30 billion in five years. Then the company can estimate its market share as a percentage of the total. For example, our market share may be 15 percent. From that we can compute an estimate of demand for our company in five years by multiplying the overall market demand with the percentage held by our company (0.15 � $30 billion � $4.5 billion). That forecast of demand can then be translated into specific facility requirements.

Capacity Cushions Companies often add capacity cushions to their regular capacity requirements. A capacity cushion is an amount of capacity added to the needed capacity in order to provide greater flexibility. Capacity cushions can be helpful if demand is greater than expected. Also, cushions can help the ability of a business to respond to customer needs for different products or different volumes. Finally, businesses that operate too close to their maximum capacity experience many costs due to diseconomies of scale and may also experience deteriorating quality.

Strategic Implications Finally, a company needs to consider how much capacity its competitors are likely to have. Capacity is a strategic decision, and the position of a company in the market relative to its competitors is very much determined by its capacity. At the same time, plans by all major competitors to increase capacity may signal the potential for overcapacity in the industry. Therefore, the decision as to how much capacity to add should be made carefully.

MKT

� Capacity cushion Additional capacity added to regular capacity requirements to provide greater flexibility.

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DECISION TREES • 341

Develop Capacity Alternatives Once a company has identified its capacity requirements for the future, the next step is to develop alternative ways to modify its capacity. One alternative is to do nothing and reevaluate the situation in the future. With this alternative, the company would not be able to meet any demands that exceed current capacity levels. Choosing this alternative and the time to reevaluate the company’s needs is a strategic decision. The other alternatives require deciding whether to purchase one large facility now or add capacity incrementally, as discussed earlier in the chapter.

Capacity Alternatives: 1. Do nothing 2. Expand large now 3. Expand small now, with option to add later

Evaluate Capacity Alternatives There are a number of tools that we can use to evaluate our capacity alternatives. Recall that these tools are only decision-support aids. Ultimately, managers have to use many different inputs, as well as their judgment, in making the final decision. One of the most popular of these tools is the decision tree. In the next section we look more closely at how decision trees can be helpful to managers at this stage.

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Large expansion alternatives often involve construction of new facilities.

DECISION TREES

Decision trees are useful whenever we have to evaluate interdependent decisions that must be made in sequence and when there is uncertainty about events. For that rea- son, they are especially useful for evaluating capacity expansion alternatives given that future demand is uncertain. Remember that our main decision is whether to purchase a large facility or a small one with the possibility of expansion later. You can see that the decision to expand later is dependent on choosing a small facility now. Which

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342 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

alternative ends up being best will depend on whether demand turns out to be high or low. Unfortunately, we can only forecast future demand and have to incur some risks.

A decision tree is a diagram that models the alternatives being considered and the possible outcomes. Decision trees help by giving structure to a series of decisions and providing an objective way of evaluating alternatives. Decision trees contain the fol- lowing information:

● Decision points. Th ese are the points in time when decisions, such as whether or not to expand, are made. Th ey are represented by squares, called “nodes.”

● Decision alternatives. Buying a large facility and buying a small facility are two deci- sion alternatives. Th ey are represented by “branches” or arrows leaving a decision point.

● Chance events. Th ese are events that could aff ect the value of a decision. For exam- ple, demand could be high or low. Each chance event has a probability or likeli- hood of occurring. For example, there may be a 60 percent chance of high demand and a 40 percent chance of low demand. Remember that the sum of the probabili- ties of all chances must add up to 100 percent. Chance events are “branches” or arrows leaving circular nodes.

● Outcomes. For each possible alternative an outcome is listed. In our example, that may be expected profi t for each alternative (expand now or later) given each chance event (high demand or low demand).

These diagrams are called decision trees because the diagram of the decisions resem- bles a tree. Simple decision trees are not hard to understand. Next we look at an exam- ple to see how a decision tree might be used to solve a capacity alternative problem.

� Decision tree Modeling tool used to evaluate independent decisions that must be made in sequence.

Anna, the owner of Anna’s Greek Restaurant, has determined that she needs to expand her facility. The decision is whether to expand now with a large facility, incurring additional costs and taking the risk that demand will not materialize, or expand now on a smaller scale, knowing that she will have to consider expanding again in three years. She has estimated the following chances for demand:

• Th e likelihood of demand being high is 0.70. • Th e likelihood of demand being low is 0.30. She has also estimated profits for each alternative:

• Large expansion has an estimated profi tability of either $300,000 or $50,000, depending on whether demand turns out to be high or low.

• Small expansion has a profi tability of $80,000, assuming that demand is low. • Small expansion with an occurrence of high demand would require considering whether to

expand further. If she expands at that point, her profi tability is expected to be $200,000. If she does not expand further, profi tability is expected to be $150,000.

Next we develop a decision tree to solve Anna’s problem.

• Before You Begin: Remember that before you begin a decision tree problem, you should first draw a decision tree diagram. Then add the given information to the diagram and proceed to evaluate it.

• Solution: To solve this problem we first need to draw the decision tree. Table 9-2 shows steps in drawing a decision tree.

EXAMPLE 9.2

Using Decision Trees

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DECISION TREES • 343

A decision tree is shown in Figure 9-3. We read the diagram from left to right, with node 1 representing the first decision point. The two alternatives at that decision point are presented as branches. They are labeled with the two alternatives “Expand Small” and “Expand Large.” Regardless of which alternative is followed, some chance events will take place. In our example the chance events are the occurrence of either high or low demand. The circular node represents the chance events, with the branches providing the label and the probability of the event. For example, the chance of high demand is 0.70 and the chance of low demand is 0.30.

If we start with a small expansion and high demand occurs, we will have to decide whether or not to expand further. This second decision point is represented by node 2. The dollar amounts at the end of each alternative are the estimated profits. Now that we have drawn the decision tree, let’s see how we can solve it. The procedure for solving a decision tree is outlined in Table 9-3.

We drew the decision tree from left to right. To evaluate it, we work backward, from right to left, to determine the expected value. The expected value (EV) is a weighted average of the chance events, where each chance event is given a probability of occurrence. We start with the profitability of each alternative, working backward and selecting the most profitable alternative. For example, at node 2 we should decide to expand further, because the profits from that decision are higher ($200,000 versus $150,000). If we come to that point, that is the decision we should make. The expected

� Expected value (EV) A weighted average of chance events, where each chance event is given a probability of occurrence.

TABLE 9-2

Procedure for Drawing a Decision Tree

1. Draw a decision tree from left to right. Use squares to indicate decisions and circles to indicate chance events.

2. Write the probability of each chance event in parentheses.

3. Write out the outcome for each alternative in the right margin.

Expand $200,000

Don’t expand $150,000

Low demand (0.30) $80,000

Low demand (0.30) $50,000

High demand (0.70) $300,000

Ex pa

nd sm

all

Expand large

High demand (0.70) ($200,000)

($164,000)

($225,000)

FIGURE 9-3

Decision tree for Anna’s restaurant

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344 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

value (EV) of profits at that point is written below node 2. This is the expected value if we decide on a small expansion and high demand occurs.

To compute the expected value (EV) of the small expansion, we evaluate it as a weighted average of estimated profits given the probability of occurrence of each chance event:

EVsmall expansion 5 0.30 1$80,000 2 1 0.70 1$200,000 2 5 $164,000 EVlarge expansion 5 0.30 1$50,000 2 1 0.70 1$300,000 2 5 $225,000

The large expansion gives the higher expected value. This means that Anna should pursue a large expansion now.

TABLE 9-3

Procedure for Solving a Decision Tree

1. To solve a decision tree, work from right to left. At each circle representing chance events, compute the expected value (EV).

2. Write the EVs below each circle.

3. Select the alternative with the highest EV.

Before You Go On

Up to this point we have focused exclusively on capacity planning. By now you should understand that capacity is the maximum output rate of a facility. Capacity is defined in different ways, depending on the nature of the business. You should understand the basic trade-off made in choosing between capacity planning alternatives and the procedure used to evaluate alternatives. Finally, make sure you understand the relationship between capacity planning and location analysis. In the next section we discuss location analysis, which is another decision area for operations managers. Note, however, that location analysis is usually made in conjunction with capacity planning. Because the size of a facility is typically tied to its location, these decisions are made together. Make sure you under- stand the relationship between these decisions and their strategic implications for the firm.

LOCATION ANALYSIS You might have heard the old real estate adage: the three most important factors in the value of a property are location, location, location. Have you ever left a service provider that you liked—say, a doctor, barber, or tailor—because they were in a loca- tion that was difficult to get to or too far away? Look at the business locations in your own neighborhood. We have all seen facilities in certain locations that have a high turnover of businesses and owners. The types of businesses and owners may be com- pletely different, yet something about the location does not make it successful. Why do most fast-food restaurants locate near one another? In order to draw customers to one location. Why are the large automakers centered in Michigan? To draw suppliers to one area. Why do many medical facilities locate near hospitals? To be accessible to patients. Why do retail stores typically locate near each other? To attract a higher volume of customers.

These examples illustrate the strategic importance of location decisions. All other aspects of a business can be designed efficiently, but if the location is selected poorly, the business will have a harder time being successful. Different types of businesses emphasize different factors when making location decisions. Service organizations such

� Location analysis Techniques for determining location decisions.

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LOCATION ANALYSIS • 345

as restaurants, movie theaters, and banks focus on locating near their customers. Manu- facturing organizations seek to be close to sources of transportation, suppliers, and abundant resources such as labor. However, many other factors need to be considered.

What Is Facility Location? Facility location is determining the best geographic location for a company’s facil- ity. Facility location decisions are particularly important for two reasons. First, they require long-term commitments in buildings and facilities, which means that mistakes can be difficult to correct. Second, these decisions require sizable financial investment and can have a large impact on operating costs and revenues. Poor location can result in high transportation costs, inadequate supplies of raw materials and labor, loss of competitive advantage, and financial loss. Businesses therefore have to think long and hard about where to locate a new facility.

In most cases, there is no one best location for a facility. Rather, there are a number of acceptable locations. One location may satisfy some factors whereas another loca- tion may be better for others. If a new location is being considered in order to provide more capacity, the company needs to consider options such as expanding the current facility if the current location is satisfactory. Another option might be to add a new facility but also keep the current one. As you can see, there is a lot to consider.

Factors Affecting Location Decisions Many factors can affect location decisions, including proximity to customers, trans- portation, source of labor, community attitude, proximity to suppliers, and many other factors. The nature of the firm’s business will determine which factors should dominate the location decision. As already mentioned, service and manufacturing firms will focus on different factors. Profit-making and nonprofit organizations will also focus on different factors. Profit-making firms tend to locate near the markets they serve, whereas nonprofit organizations generally focus on other criteria.

It is important to identify factors that have a critical impact on the company’s strategic goals. For example, even though proximity to customers is typically a critical factor for service firms, if the firm provides an in-home service (say, carpet cleaning), this may not be a critical issue. Also, while profit-making firms might locate near the markets they serve, nonprofit firms might choose to be near their major benefactors. Managers should also eliminate factors that are satisfied by every location alternative. Next we look more closely at some factors that affect location decisions.

Proximity to Sources of Supply Many firms need to locate close to sources of supply. The reasons for this can vary. In some cases, the firm has no choice, such as in farming, forestry, or mining operations, where proximity to natural resources is necessary. In other cases, the location may be determined by the perishable nature of goods, such as in preparing and processing perishable food items. Dole Pineapple has its pineapple farm and plant in Hawaii for both these purposes. Similarly, Tropicana has its processing plant in Florida, near the orange-growing orchards.

Another reason to locate close to sources of supply is to avoid high transportation costs—for example, if a firm’s raw materials are much bulkier and costlier to move than the finished product. Transporting the finished product outbound is less costly than transporting the raw materials inbound, and the firm should locate closer to the source of supply. A paper mill is an example. Transporting lumber would be much more costly than transporting the paper produced.

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346 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

The importance of location decisions can be seen in the case of Internet companies dur- ing the boom of the dot-coms. Locating in Silicon Valley or San Francisco had become a major priority in the 1990s for close proximity to highly skilled talent. Dot-coms were seeking over 4 million square feet of space in San Francisco, where only 1 million square feet was available. Consequently, the

cost of locating there, including rent and leasing requirements, had become increas- ingly expensive. Tenants were paying $60 per square foot, an increase from the $40 per square foot paid just six months earlier. Landlords had also become picky about their tenants, and some were making unusual demands. Many were requiring as much as two years’ rent in advance. Others were even asking for equity in the company.

Then came the fall of the dot-coms. Space became abundant as many companies went out of business, and the cost of space dropped. Other companies, in an effort to remain financially viable, sought less costly locations. Almost overnight the impor- tance of locating in Silicon Valley diminished.

Proximity to Customers Locating near the market they serve is often critical for many organizations, particularly service firms. To capture their share of the business, service firms need to be accessible to their customers. For this reason, service firms typically locate in high-population areas that offer convenient access. Examples include retail stores, fast-food restaurants, gas stations, grocery stores, dry cleaners, and flower shops. Large retail firms often locate in a central area of the market they serve. Smaller service firms usually follow the larger retailers because of the large number of the customers they attract. The smaller firms can usually count on getting some of the business.

Other reasons for locating close to customers may include the perishable nature of the company’s products or high costs of transportation to the customer site. Food items such as groceries and baked goods, fresh flowers, and medications are perish- able and need to be offered close to the market. Also, items such as heavy metal sheets, pipes, and cement need to be produced close to the market because the costs of trans- porting these materials are high.

Proximity to Source of Labor Proximity to an ample supply of qualified labor is important in many businesses, especially those that are labor intensive. The company needs to consider the availability of a particular type of labor and whether special skills are required. Some companies, such as those looking for assembly-line workers, want to be near a supply of blue-collar labor. Other companies may be looking for computer or technical skills and should consider locating in areas with a concentration of those types of workers.

Other factors that should be considered are local wage rates, the presence of local unions, and attitudes of local workers. Work ethics and attitudes toward work can vary greatly in different parts of the country and between urban and rural workers. Attitudes toward factors such as absenteeism, tardiness, and turnover can greatly affect a company’s productivity.

LINKS TO PRACTICE Locating in Silicon

Valley

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LOCATION ANALYSIS • 347

Community Considerations The success of a company at a particular location can be affected by the extent to which it is accepted by the local community. Many communities welcome new businesses, viewing them as providing sources of tax revenues and opportunities for jobs, and as contributing to the overall well-being of the community. However, communities do not want businesses that bring pollution, noise, and traffic and that lower the quality of life. Extreme examples are a nuclear facility, a trash dump site, and an airport. Less extreme examples are companies like Wal-Mart, which often are not accepted by smaller communities, which may view such large merchants as a threat to their way of life and thus actively work to discourage them from locating there.

Site Considerations Site considerations for a particular location include factors such as utility costs, taxes, zoning restrictions, soil conditions, and even climate. These factors are not too different from those one would consider when purchasing a home or a lot to build on. Just as most homeowners consider their purchase to be an investment, so does a business. Inspectors should be hired to perform a thorough evaluation of the grounds, such as checking for adequate drainage. Site-related factors can also limit access roads for trucks and make it difficult for customers to reach the site.

Quality-of-Life Issues Another important factor in location decisions is the quality of life a particular location offers the company’s employees. This factor can also become important in the future when the business is recruiting high-caliber employees. Quality of life includes factors such as climate, a desirable lifestyle, good schools, and a low crime rate. Certainly, quality of life would not be considered the most critical factor in selecting a location. However, when other factors do not differ much from one location to another, quality of life can be the decisive factor.

Other Considerations In addition to the factors discussed so far, there are others that companies need to consider. They include room for customer parking, visibility, customer and transportation access, as well as room for expansion. Room for expansion may be particularly important if the company has decided to expand now and possibly expand further at a later date. Other factors include construction costs, insurance, local competition, local traffic and road congestion, and local ordinances.

Globalization In addition to considering the specific factors affecting site location in the United States, companies need to consider how they will be affected by a major trend in busi- ness today: globalization. Globalization is the process of locating facilities around the world. Over the past decade it has become not only a trend but a matter-of-fact way of conducting business. Technology such as faxes, e-mails, video conferencing, and overnight delivery have made distance less relevant than ever before. Markets and competition are increasingly global. To compete effectively based on cost, many companies have had to expand their operations to include global sources of sup- ply. Factors other than mere distance have become critical in selecting a geographic location.

Deciding to expand an operation globally is not a simple decision. There are many things to consider, and the problems must be weighed along with the benefits. In this section we look at both advantages and disadvantages of global operations. We also look at some additional implications of global operations that managers need to consider.

� Globalization The process of locating facilities around the world.

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348 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

Advantages of Globalization There are many reasons why companies choose to expand their operations globally. The main one, however, is to take advantage of foreign markets. The demand for imported goods has grown tremendously, and these markets offer a new arena for competition. Also, locating production facilities in foreign countries reduces the stigma associated with buying imports. This concept works not only for U.S. companies abroad but for foreign companies in the United States as well. For example, Japanese automobile manufacturers have located in the United States and employed American workers, which has gone a long way toward eliminating negative attitudes about buying Japanese cars. Being in the United States has also reduced their exposure to currency variations between the dollar and yen.

Another advantage of global locations is reduction of trade barriers. By producing goods in the country where customers are located, a company can avoid import quo- tas. Trade barriers have also been reduced through the creation of trading blocs such as the European Union and trade agreements such as NAFTA (North American Free Trade Agreement). We discussed the contribution of these agreements to globaliza- tion in Chapter 1.

Cheap labor in countries such as Korea, Taiwan, and China has also attracted firms to locate there. Often it is cheaper to send raw materials to these countries for fabri- cation and assembly and then ship them elsewhere for final consumption than it is to keep the process in this country. The cost of labor can be so low that it more than offsets the additional transportation costs.

An area that has further encouraged globalization is the growth of just-in-time manufacturing, which encourages suppliers and manufacturers to be in close proxim- ity to one another. Many suppliers have moved closer to the manufacturers they sup- ply, and some manufacturers have moved closer to their suppliers.

Disadvantages of Globalization Although there are advantages to globalization, there are also a number of disadvantages that companies should consider. Political risks can be large, particularly in countries with unstable governments. For example, during a period of political unrest a company may have its technology confiscated. Foreign governments may also impose restrictions, tariffs on particular industries, and local ordinances that must be obeyed.

Using offshore suppliers might mean that a company may need to share some of its proprietary technology. Today’s age of total quality management encourages the sharing of this type of information between manufacturers and suppliers to the advantage of both parties. A manufacturer may want to think carefully before shar- ing, however.

Another issue is whether to use local employees. Companies are often attracted to cheap foreign labor. However, the company might find that worker attitudes toward tardiness and absenteeism are different. Also, worker skills and productivity may be considerably lower, offsetting the benefits of lower wages.

The local infrastructure is another important issue. Many foreign countries do not have the developed infrastructure necessary for companies to operate in the manner that they do in their home country. Infrastructure includes everything from roads to utilities as well as other support services.

Issues to Consider in Locating Globally Firms are attracted to foreign locations in order to take advantage of foreign markets; cheaper suppliers or labor; and natural resources such as copper, aluminum, and timber. However, there are many issues

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MAKING LOCATION DECISIONS • 349

to consider when locating globally. One such issue is the effect of a different culture. Each culture has a different set of values, norms, ethics, and standards. For example, in France it is considered polite to be slightly late for an appointment, and such lateness is quite customary. The British, on the other hand, consider punctuality highly important and tardiness very rude. You can see how misunderstandings can develop even through simple differences like this one.

Language barriers are another potential problem. Employees need to be able to communicate easily in their work environment. Engaging in discussions, follow- ing instructions, and understanding exactly what is being said can become difficult when employees speak different languages. Even when one language is translated into another, the translation may have lost very essential parts of the meaning, resulting in damaging misunderstanding.

Different laws and regulations—including everything from pollution regulations to labor laws—may require changes in business practices. Also, what is acceptable in one culture may be completely unacceptable or even illegal in another. For example, in some countries offering a bribe may be an acceptable part of doing business, whereas in others it may land a person in jail.

Although it is important to know the factors affecting facility location, it is not enough for making good location decisions. In the next section we look at specific tools that can help managers with facility location decisions.

MAKING LOCATION DECISIONS Procedure for Making Location Decisions As with capacity planning, managers need to follow a three-step procedure when mak- ing facility location decisions. These steps are as follows:

Step 1 Identify Dominant Location Factors. In this step managers identify the location factors that are dominant for the business. Th is requires managerial judg- ment and knowledge.

Step 2 Develop Location Alternatives. Once managers know what factors are dominant, they can identify location alternatives that satisfy the selected factors.

Step 3 Evaluate Location Alternatives. Aft er a set of location alternatives have been identifi ed, managers evaluate them and make a fi nal selection. Th is is not easy because one location may be preferred based on one set of factors, whereas another may be better based on a second set of factors.

Procedures for Evaluating Location Alternatives A number of procedures can help in evaluating location alternatives. These are decision-support tools that help structure the decision-making process. Some of them help with qualitative factors that are subjective, such as quality of life. Others help with quantitative factors that can be measured, such as distance. A manager may choose to use multiple procedures to evaluate alternatives and come up with a final decision. Remember that the location decision is one that a company will have to live with for a long time. It is highly important that managers make the right decision.

Factor Rating You have seen by now that many of the factors that managers need to consider when evaluating location alternatives are qualitative in nature. Their importance is also highly subjective, based on the opinion of who is evaluating them.

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350 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

An excellent procedure that can be used to give structure to this process is called factor rating. Factor rating can be used to evaluate multiple alternatives based on a number of selected factors. It is valuable because it helps decision makers structure their opinions relative to the factors identified as important. The following steps are used to develop a factor rating:

Step 1 Identify dominant factors (e.g., proximity to market, access, competition, quality of life).

Step 2 Assign weights to factors refl ecting the importance of each factor relative to the other factors. Th e sum of these weights must be 100.

Step 3 Select a scale by which to evaluate each location relative to each factor. A commonly used scale is a fi ve-point scale, with 1 being poor and 5 excellent.

Step 4 Evaluate each alternative relative to each factor, using the scale selected in Step 3. For example, if you chose to use a fi ve-point scale, a location that was excel- lent based on quality of life might get a 5 for that factor.

Step 5 For each factor and each location, multiply the weight of the factor by the score for that factor and sum the results for each alternative. Th is will give you a score for each alternative based on how you have rated the factors and how you have weighted each of the factors at each location.

Step 6 Select the alternative with the highest score.

Let’s look at an example to see how this procedure is used.

� Factor rating A procedure that can be used to evaluate multiple alternative locations based on a number of selected factors.

Antonio is evaluating three different locations for his new Italian restaurant. Costs are comparable at all three locations. He has identified seven factors that he considers important and has decided to use factor rating to evaluate his three location alternatives based on a five- point scale, with 1 being poor and 5 excellent. Table 9-4 shows Antonio’s factors, the weights he has assigned to each factor, as well as the factor score for each factor at each location.

From Table 9-4 it is clear that Antonio considers facility appearance and proximity to market the two most important factors, because he has rated each of these with a 20. Other factors are slightly less important. Note that Antonio selected the factors first. Then he decided to weight them based on his perception of their importance. He then computed a factor score for each factor at each location. Looking at the factor scores he selected, it appears that location 1 is excellent based on appearance, parking, and access, but poor based on closeness to the market. Location 3 is just the opposite, being excellent based on closeness to the market but poor based on facility appearance. Location 2

EXAMPLE 9.3 Using Factor Rating

Factor Factor Weight

Factor Score at Each Location

Weighted Score for Each Location

(Factor Weight � Factor Score) Location 1 Location 2 Location 3 Location 1 Location 2 Location 3

Appearance 20 5 3 2 100 60 40 Ease of expansion 10 4 4 2 40 40 20 Proximity to market 20 2 3 5 40 60 100 Customer parking 15 5 3 3 75 45 45 Access 15 5 2 3 75 30 45 Competition 10 2 4 5 20 40 50 Labor supply 10 3 3 4 30 30 40 Total 100 380 305 340

Table 9-4 Factor Rating for Antonio’s Italian Restaurant

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MAKING LOCATION DECISIONS • 351

The Load–Distance Model The load–distance model is a procedure for evaluating location alternatives based on distance. The distance to be measured could be proximity to markets, proximity to suppliers or other resources, or proximity to any other facility that is considered important. The objective of the model is to select a location that minimizes the total amount of loads moved weighted by the distance traveled. What is a load? A load represents the goods moved in or out of a facility or the number of movements between facilities. For example, if 200 boxes of Kellogg’s cereal are shipped between the local warehouse and a grocery store, that is the load between the warehouse and grocery store. The idea is to reduce the amount of distance between facilities that have a high load between them.

� Load–distance model A procedure for evaluating location alternatives based on distance.

appears to be somewhere in the middle. To evaluate which location alternative is best, Antonio needed to multiply the factor weight by the factor score for each factor at each location and then sum them. The best location alternative is that with the highest factor rating score. In Antonio’s case, it is location 1. This problem can also be solved with a spreadsheet as shown:

2 3 4

5 6 7 8 9 10 11 12 13 14 15 16

17 18 19 20 21 22 23 24 25 26 27 28

A B C D E F G

Factor Rating for Antonio's Italian Restaurant

Factor Location

1 Location

2 Location

3 Factor Weight

Appearance 5 3 2 20 Ease of expansion 4 4 2 10 Proximity to market 2 3 5 20 Customer parking 5 3 3 15 Access 5 2 3 15 Competition 2 4 5 10 Labor supply 3 3 4 10

Total 100

Compute Weighted Factor Scores and Overall Scores for Each Location

Factor Location

1 Location

2 Location

3 Appearance 100 60 40 Ease of expansion 40 40 20 Proximity to market 40 60 100 Customer parking 75 45 45 Access 75 30 45 Competition 20 40 50 Labor supply 30 30 40 Totals 380 305 340

Best Total Score 380 Best Location Location 1

Factor Scores (1-5 scale)

Weighted Factor Scores

E13: =SUM(E6:E12)

B18: =B6*$E6 (copied to B18:D24)

B25: =SUM(B18:B24) (copied right)

B27: =MAX(B25:D25)

B28: =INDEX(B17:D17,MATCH(B27,B25:D25,0))

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352 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

The model is shown in Table 9-5. Relative locations are compared by computing the load–distance, or ld, score for each location. The ld score for a particular location is obtained by multiplying the load (denoted by l) for each location by the distance traveled (denoted by d) and then summing over all the locations. This score is a sur- rogate measure for movement of goods, material handling, or even communication. Our goal is to make the ld score as low as possible by reducing the distance the large loads have to travel.

Next we look at the steps in developing the load–distance model.

Step 1 Identify Distances. Th e fi rst step is to identify the distances between loca- tion sites. It is certainly possible to use the actual mileage between locations. How- ever, it is much quicker, and just as eff ective, to use simpler measures of distance. A frequently used measure of distance is rectilinear distance, the shortest distance between two points measured by using only north–south and east–west movements. To measure rectilinear distance, we place grid coordinates on a map and use them to measure the distance between two locations. Figure 9-4 presents an example of how the distance between locations A and B could be measured using rectilinear distance.

Th e rectilinear distance between two locations, A and B, is computed by sum- ming the absolute diff erences between the x coordinates and the absolute diff erences between the y coordinates. Th e equation is as follows:

dAB 5 |xA 2 xB| 1 |yA 2 yB|

In our example, the coordinates for location A are (30, 40). Th e coordinates for loca- tion B are (10, 15). Th erefore, the rectilinear distance between these two points is

dAB 5 |30 2 10| 1 |40 2 15| 5 45 miles

Step 2 Identify Loads. Th e next step is to identify the loads between diff erent locations. Th e notation lij is used to indicate the load between locations i and j.

� Rectilinear distance The shortest distance between two points measured by using only north–south and east–west movements.

TABLE 9-5

The Load–Distance Model

ld score for a location � S lijdij where lij � load between locations i and j

dij � distance between locations i and j

y (miles)

x (miles)20 30 4010

A(30, 40)

B(10, 15)

FIGURE 9-4

Rectilinear distance between points A and B

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MAKING LOCATION DECISIONS • 353

Step 3 Calculate the Load–Distance Score for Each Location. Next we calculate the load–distance score for each location by multiplying the load, lij, by the distance, dij. We compute the sum of lijdij to get the ld score. Finally, we select the site with the lowest load–distance score.

Next we look at an example to see how to use the model.

Matrix Manufacturing Corporation is considering where to locate its warehouse in order to service its four stores located in four Ohio cities: Cleveland, Columbus, Cincinnati, and Dayton. Two possible sites for the warehouse are being considered. One is in Mansfield, Ohio, and the other is in Springfield, Ohio. Let’s follow the steps of the load–distance model to select the best location for the warehouse.

• Before You Begin: To solve this problem, follow the three steps given in the text for select- ing a location with the load–distance model.

• Solution: Step 1 Identify Distances. The distances between the locations can be seen in Figure 9-5, which shows a map of the cities with grid coordinates. The coordinates allow us to compute the distances between the cities. To compute the specific distances, we use the rectilinear distance measure.

From Figure 9-5 we can compute the distances between the four cities and the Springfield site as follows:

City Distance to Springfield

Cleveland |11 � 6| � |22 � 6.5| � 20.5 Columbus |10 � 6| � |7 � 6.5| � 4.5 Cincinnati |4 � 6| � |1 � 6.5| � 7.5 Dayton |3 � 6| � |6 � 6.5| � 3.5

EXAMPLE 9.4

Using the Load–Distance Model

FIGURE 9-5

Location map for Matrix Manufacturing

1 2 3 4 5 6 7 8 9 10 11

y (miles)

x (miles)

Cleveland (11, 22)

Mansfield (11, 14)

Springfield (6, 6.5)Dayton (3, 6)

Cincinnati (4, 1)

Columbus (10, 7)

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354 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

Similarly, we can compute the distance between the four cities and the Mansfield site as follows:

City Distance to Mansfield

Cleveland |11 � 11| � |22 � 14| � 8 Columbus |10 � 11| � |7 � 14| � 8 Cincinnati |4 � 11| � |1 � 14| � 20 Dayton |3 � 11| � |6 � 14| � 16

Step 2 Identify Loads. The next step is to identify the loads between the four cities and the ware- house. Remember that these loads will be the same regardless of where the warehouse is located. For this reason, we want to locate the warehouse at a place that will minimize the amount of distance large loads will have to travel.

City Load between City

and Warehouse

Cleveland 15 Columbus 10 Cincinnati 12 Dayton 4

Step 3 Calculate the Load–Distance Score for Each Location. The final step is to calculate the load–distance score for each location. The computation for Springfield is shown in Table 9-6.

Table 9-6 Computing the Load–Distance Score for Springfield

City Load (lij) Distance (dij) lij dij Cleveland 15 20.5 307.5 Columbus 10 4.5 45 Cincinnati 12 7.5 90 Dayton 4 3.5 14

Total Load–Distance Score: (456.5)

The load–distance score computed for Springfield does not tell us very much by itself. This number is useful only when comparing relative locations—that is, when we compare it to another load–distance score. The load–distance score for Mansfield is shown in Table 9-7.

Table 9-7 Computing the Load–Distance Score for Mansfield

City Load (lij) Distance (dij) lijdij Cleveland 15 8 120 Columbus 10 8 80 Cincinnati 12 20 240 Dayton 4 16 64

Total Load–Distance Score: (504)

The load–distance score for Mansfield is higher than the score for Springfield. Therefore, Matrix Manufacturing should locate its warehouse in Springfield. Note in the computation for the load– distance score for Mansfield that the load between the city and the warehouse did not change. What changed was the distance. Through the load–distance model we select a location that will minimize the distance large loads travel. This can also be computed using a spreadsheet, as shown.

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MAKING LOCATION DECISIONS • 355

The Center of Gravity Approach When we used the load–distance model, we compared only two location alternatives. The load–distance was lower for Springfield than for Mansfield. However, we can also use the model to find other locations that may give an even lower load–distance score than Springfield. An easy way to do this is to start by testing the location at the center of gravity of the target area. The X and Y coordinates that give us the center of gravity for a particular area are computed in the following way:

Xc.g. 5 S lixi S li

Yc.g. 5 S liyi S li

c.g. 5 center of gravity

The X coordinate for the center of gravity is computed by taking the X coordinate for each point and multiplying it by its load. These are then summed and divided by the sum of the loads. The same procedure is used to compute the Y coordinate.

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

A B C D E F G H I

Load-Distance Model for Matrix Manufacturing

Coordinates of Stores (miles) X Y

Cincinnati 4 1 Cleveland 11 22 Columbus 10 7 Dayton 3 6

Coordinates of Warehouses (miles) Mansfield 11 14 Springfield 6 6.5

Distances (rectilinear, in miles) Cincinnati Cleveland Columbus Dayton

Mansfield 20 8 8 16 Springfield 7.5 20.5 4.5 3.5

Load between Warehouse and Store Cincinnati Cleveland Columbus Dayton

Load 12 15 10 4

Load-Distance Calculations Cincinnati Cleveland Columbus Dayton Total

Mansfield 240 120 80 64 504 Springfield 90 307.5 45 14 456.5

Minimum Load-Dis 456.5 Best Location: Springfield

Locations of Cities and Possible Warehouses

Cleveland (11,22)

Columbus (10,7)

Cincinnati (4,1)

Dayton (3,6)

Mansfield (11,14)

Springfield (6,6.5)

0 2 4 6 8 10 12 X (miles)

Y (

m ile

B18: =ABS($B12-$B$6)+ABS($C12-$C$6) (copied to B18:E19)

B27: =B$23*B18 (copied down and right)

F27: =SUM(B27:E27) (copied down)

C30: =MIN(F27:F28)

C31: =INDEX(A27:A28,MATCH(C30,F27:F28,0))

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356 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

The location identified with the center of gravity puts a larger penalty on long dis- tances. This can have practical value given that longer distances impose more costs on the organization. However, the location identified may not be a feasible site because of geographic restrictions. For example, the center of gravity might turn out to be in the middle of Lake Michigan. However, the center of gravity provides an excellent start- ing point. We can use it to test the load–distance score of other locations in the area.

Find the center of gravity for the Matrix Manufacturing problem.

• Before You Begin: To solve this problem, use the center of gravity equations. Remember that you need to find both the X and Y coordinates.

• Solution:

Location Coordinates (X, Y) Load (li) lixi liyi Cleveland (11, 22) 15 165 330 Columbus (10, 7) 10 100 70 Cincinnati (4, 1) 12 48 12 Dayton (3, 6) 4 12 24 Total 41 325 436

Now we need to find the coordinates for the center of gravity:

Xc.g. 5 S li xi S li

5 325 41

5 7.9

Yc.g. 5 S li yi S li

5 436 41

5 10.6

EXAMPLE 9.5

Computing the Center of Gravity

Break-Even Analysis Break-even analysis is a technique used to compute the amount of goods that must be sold just to cover costs. The break-even point is precisely the quantity of goods a company needs to sell to break even. Whatever is sold above that point will bring a profit. Below that point the company will incur a loss. We discussed break-even analysis in Chapter 3 as a technique for evaluating the success of different products. In this chapter we use break-even analysis to evaluate different location alter- natives. Remember that break-even analysis works with costs, such as fixed and vari- able costs. It can be an excellent technique when the factors under consideration can be expressed in terms of costs. Let’s briefly review the basic break-even equations:

Total cost 5 F 1 cQ

Total revenue 5 pQ

where F � fi xed cost c � variable cost per unit Q � number of units sold p � price per unit

� Break-even analysis Technique used to compute the amount of goods that must be sold just to cover costs.

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MAKING LOCATION DECISIONS • 357

At the break-even point, total cost and total revenue are equal. We can use those equations to solve for Q, which is the break-even quantity:

Q 5 F

p 2 c

As we saw in Chapter 3, these quantities can be obtained graphically. Now let’s look at the basic steps in using break-even analysis for location selection.

Step 1: For Each Location, Determine Fixed and Variable Costs. Recall from Chapter 3 that fi xed costs are incurred regardless of how many units are produced and include items such as overhead, taxes, and insurance. Variable costs are costs that vary directly with the number of units produced and include items such as materials and labor. Total cost is the sum of fi xed and variable costs.

Step 2: Plot the Total Costs for Each Location on One Graph. To plot any straight line we need two points. One point is Q � 0, which is the y intercept. Another point can be selected arbitrarily, but it is best to use the expected volume of sales in the future.

Step 3: Identify Ranges of Output for Which Each Location Has the Lowest Total Cost.

Step 4: Solve Algebraically for the Break-Even Points over the Identifi ed Ranges. Select the location that gives the lowest cost for the range of output required by the new facility.

Clean-Clothes Cleaners is a dry cleaning business that is considering four possible sites for its new operation. The annual fixed and variable costs for each site have been estimated as follows:

Location Fixed Costs Variable Costs

A $350,000 $ 5/Unit B $170,000 $25/Unit C $100,000 $40/Unit D $250,000 $20/Unit

(a) Plot the total cost curves for each location on the same graph and identify the range of output for which each location provides the lowest total cost.

(b) If demand is expected to be 10,000 units per year, which is the best location?

• Before You Begin: To solve this problem, follow the four steps given in the text for using break-even analysis in location selection.

• Solution: (a) Step 1 in the break-even procedure has already been completed; that is, we have identifi ed the fi xed and variable costs. Th e next

step is to plot the total costs of each location on a graph. For each line that we have to plot, we need two points. Th e fi rst point can be Q � 0. We can compute the second point using expected demand, which is Q � 10,000 units. For Q � 10,000 units we compute the following total costs for each location:

EXAMPLE 9.6Using Break-Even Analysis

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358 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

Location Fixed Cost Variable Cost Total Cost

A $350,000 $ 5 (10,000) $400,000 B $170,000 $25 (10,000) $420,000 C $100,000 $40 (10,000) $500,000 D $250,000 $20 (10,000) $450,000

The plots of these graphs are shown in Figure 9-6. You can see that depending on the range of output, locations C, B, and A are best. Location D is never a best option.

(b) We can see the approximate ranges for each location in Figure 9-6. We can compute the exact ranges for each output location by fi nding the exact output level for which locations C and B are equal and for which locations B and A are equal. We can do this by computing the output levels at which the total cost equations for these locations are equal:

Total cost equation for C 5 Total cost equation for B

100,000 1 $40 Q 5 170,000 1 $25 Q

Q 5 4666.7 units

Thus, the point between C and B is 4666.7, or roughly 4667 units.

Total cost equation for B 5 Total cost equation for A

170,000 1 $25 Q 5 350,000 1 $5 Q

Q 5 9000 units

The breaking point between B and A is 9000 units, which means that location A would provide the lowest cost if we produce 9000 units or more. If we plan to meet a demand of 10,000 units, we should select location A. This problem can also be solved using a spreadsheet, as shown.

B Best

C Best

A Best

Quantity (000’s of units) 2 4 6 8 10

800

600

400

200

Total Cost (000’s of $)

Break-even graph for Clean-Clothes Cleaners

FIGURE 9-6

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MAKING LOCATION DECISIONS • 359

2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

A B C D E F G H I J K

Break-Even Point Analysis for Clean Clothes Cleaners

Location Fixed Cost Variable Cost A $350,000 $5 B $170,000 $25 C $100,000 $40 D $250,000 $20

Calculation of Exact Break-Even Points Quantity

Between B and C 4,666.67 Between A and B 9,000.00

Enter any quantity The Total Costs are computed, showing the best location for that quantity.

Quantity 4000 Location 0 Minimum Cost $260,000 A $370,000 Best Location C B $270,000

C $260,000 D $330,000

Data Table to Compare Locations (highlighting shows best choice for each quantity) Quantity A B C D Best Best Loc

$370,000 $270,000 $260,000 $330,000 $260,000 C 0 $350,000 $170,000 $100,000 $250,000 $100,000 C

1000 $355,000 $195,000 $140,000 $270,000 $140,000 C 2000 $360,000 $220,000 $180,000 $290,000 $180,000 C 3000 $365,000 $245,000 $220,000 $310,000 $220,000 C 4000 $370,000 $270,000 $260,000 $330,000 $260,000 C 5000 $375,000 $295,000 $300,000 $350,000 $295,000 B 6000 $380,000 $320,000 $340,000 $370,000 $320,000 B 7000 $385,000 $345,000 $380,000 $390,000 $345,000 B 8000 $390,000 $370,000 $420,000 $410,000 $370,000 B 9000 $395,000 $395,000 $460,000 $430,000 $395,000 A

10000 $400,000 $420,000 $500,000 $450,000 $400,000 A 11000 $405,000 $445,000 $540,000 $470,000 $405,000 A 12000 $410,000 $470,000 $580,000 $490,000 $410,000 A 13000 $415,000 $495,000 $620,000 $510,000 $415,000 A 14000 $420,000 $520,000 $660,000 $530,000 $420,000 A 15000 $425,000 $545,000 $700,000 $550,000 $425,000 A

Example 9.6 Break-Even Analysis for Alternate Locations (black line shows best cost and location)

AAAAAAA B

B B

B C

C C

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

$800,000

0 2000 4000 6000 8000 10000 12000 14000 16000

Quantity (units)

A B C D Best

B12: =(B6-B7)/(C7-C6)

B13: =(B5-B6)/(C6-C5)

B18: =MIN(E18:E21)

B19: =INDEX(A5:A8,MATCH(B18,E18:E21,0))

E18: =B5+C5*B$17 (copied down)

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360 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

The Transportation Method The transportation method of linear programming is a useful technique for solving specific location problems; it is discussed in detail in the supplement for this text. The method relies on a specific algorithm to evaluate the cost impact of adding potential location sites to the network of existing facilities. For example, an existing network of facilities may consist of multiple sending and receiv- ing sites. Our task might be to evaluate adding a new location site to this network, either a receiving site or a sending site. We might also wish to evaluate adding multiple new sites or completely redesigning the network. The transportation method can effi- ciently analyze all these situations and provide the lowest cost for each configuration considered.

CAPACITY PLANNING AND FACILITY LOCATION WITHIN OM: HOW IT ALL FITS TOGETHER

Decisions about capacity and location are highly dependent on forecasts of demand (Chapter 8). Forecasts determine the size of current and future capacity needs. Incorrect forecasts, where capacity is either over- or underestimated, can have a devastating effect on the capacity decision. Capacity is also affected by operations strategy ( Chapter 2), as size of capacity is a key element of organizational structure. For example, the decision whether to expand now or later can be an important strategic choice. The former can preempt competition by enabling the company to be ready to meet demand. The lat- ter can provide flexibility. Other operations decisions that are affected by capacity and location are issues of job design and labor skills (Chapter 11), choice on the mix of labor and technology, as well as choices on technology and automation (Chapter 3).

CAPACITY PLANNING AND FACILITY LOCATION ACROSS THE ORGANIZATION By now it should be clear how capacity planning and location analysis affect opera- tions management. However, these decisions are also important to many other func- tions in the company. In particular, finance and marketing have a great stake in capacity planning and location decisions.

Finance must be actively involved in the organization’s capacity planning decisions. At the same time, operations managers need input from finance in order to finalize their capacity decisions. The reason should be clear. Capacity planning requires large finan- cial expenditures. Building a large facility now would mean that funds would be tied up in excess capacity from which no financial return would be obtained for several years. At the same time, expanding capacity in increments could prove to be a greater financial drain due to poor planning. Location analysis, which is tied to the capacity planning decision, is basically a financial investment. Certain locations may be cheaper but may prove to be a poorer business investment. Finance needs to be an active participant in both the capacity planning and location analysis decisions.

Marketing is another function that is highly affected by capacity planning and location decisions. The amount of current and future capacity restricts the ability to meet demand. Building a large facility that enables the company to capture future demand and position itself in the marketplace could be advantageous from a mar- keting perspective. On the other hand, given future demands and competition this may not be a critical issue. Marketing is the function that has this information. Also, locating near customers can be critical for certain businesses, particularly service orga- nizations. Marketing managers are in the best position to understand which location factors are most important to customers.

FIN

MKT

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CHAPTER HIGHLIGHTS • 361

Capacity planning and location analysis are excellent examples of decisions that must be made by operations, finance, and marketing working together. As you can see, each of these functional areas has its domain of expertise and provides informa- tion that the others do not have. Together, they must arrive at capacity and location decisions that are best for the company in the long run.

Think of a supply chain as a pipeline that supplies a certain level of customer demand. In order for the pipeline to satisfy this demand, the pipeline must flow smoothly without disruption. This can only happen if capacity is uniform throughout the entire supply chain and is matched between entities. For example, a manufacturer must make sure that the capacity of its suppliers is sufficient to meet its own capac- ity needs and that there is no gap in product delivery.

The link to supply chains also ties to the location decision. Many firms locate close to their source of

supply or require their suppliers to locate in close proximity to them. Recall that Dell requires its suppliers to be located within a 15-minute radius of its production facility. Without close proxim- ity and a match in capacity between supply chain

entities, smooth flow throughout the supply chain would not be possible.

THE SUPPLY CHAIN LINK

Capacity planning and location decisions have a criti- cal impact on sustainability. The most obvious is increased travel distance to suppliers and customers, which not only increases transportation costs but also increases measures of environmental impact, such as the company’s carbon footprint. However, poorly made decisions in this area have other consequences. Just consider the negative consequences involved with facilities being located on natural habitats (ecosys- tems) and the resulting habitat destruction that could potentially occur. Also consider the negative effect of poor location on humans and animals, from increased noise pollution and energy consumption to the poten- tial contamination of air and water. Capacity planning and location decisions made with sustainability in mind would involve minimizing total material and personnel travel distances to and from the facility.

They would avoid runoff from construction activity, abate noise pollution, and consider air pollution effects on the community.

To provide a simple illustration of what a sustain- ability analysis would include, consider the decision of determining a location for a chemical processing facil- ity. Initially, the choice of location would be determined by considering economic factors, such as real estate cost or transportation, as discussed in the chapter. However, sustainability requires an analysis of environmental and social factors that go well beyond these basic economic considerations. A chemical factory may affect nearby plant and animal populations as well as local human health by introducing air, water, and noise pollution. Furthermore, the choice of location may implicate job availability and commuter behavior, while requiring decisions about wages for local employees. In consider- ing sustainability, a company would need to consider these broader factors in their decision.

THE SUSTAINABILITY LINK

Chapter Highlights 1 Capacity planning is deciding on the maximum output rate

of a facility.

2 Location analysis is deciding on the best location for a facility.

3 Capacity planning and location analysis decisions are oft en made simultaneously because the location of a facility is usually related to its capacity. When a business decides to expand, it usually also addresses the issue of

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362 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

where to locate. Th ese decisions are very important because they require long-term investments in buildings and facilities, as well as a sizable fi nancial outlay. Also, if capacity planning and location analysis are not done properly, a business will not be able to meet customer demands or may fi nd that it is losing customers due to lack of proximity to the market.

4 In both capacity planning and location analysis, managers must follow a three-step process to make a good decision. Th e steps are assessing needs, developing alternatives, and evaluating alternatives.

5 To choose between capacity planning alternatives managers may use decision trees, which are a modeling tool for evaluating independent decisions that must be made in sequence.

6 Key factors in location analysis include proximity to customers, transportation, source of labor, community attitude, and proximity to supplies. Service and manufacturing fi rms focus on diff erent factors. Profi t- making and nonprofi t organizations also focus on diff erent factors.

7 Several tools can be used to facilitate location analysis. Factor rating is a tool that helps managers evaluate qualitative factors. Th e load–distance model and center of gravity approach evaluate the location decision based on distance. Break-even analysis is used to evaluate location decisions based on cost values. Th e transportation method is an excellent tool for evaluating the cost impact of adding sites to the network of current facilities.

Key Terms capacity 333 capacity planning 333 design capacity 335 eff ective capacity 336 capacity utilization 336 best operating level 337

economies of scale 337 diseconomies of scale 337 focused factories 338 capacity cushion 340 decision tree 342 expected value (EV) 343

location analysis 344 globalization 347 factor rating 350 load–distance model 351 rectilinear distance 352 break-even analysis 356

Formula Review 1. ld 5 S lijdij

2. Utilizationeffective 5 actual output

effective capacity 1100% 2

3. Utilizationdesign 5 actual output

design capacity 1100% 2

4. Xc.g. 5 S lixi S li

Yc.g. 5 S liyi S li

5. Q 5 F

p 2 c

Solved Problems • Problem 1 A manufacturer of ballet shoes has determined that its produc- tion facility has a design capacity of 300 shoes per week. The effective capacity, however, is 230 shoes per week. What is the manufacturer’s capacity utilization relative to both design and effective capacity if output is 200 shoes per week?

• Before You Begin: Remember that utilization is computed as the ratio of actual output over capacity. The difference between the two capacity measures is that one uses effective capacity and the other uses design capacity.

• Solution

Utilizationeffective 5 actual output

effective capacity 1100% 2

5 200 230

1100% 2 5 86.9% Utilizationdesign 5

actual output design capacity

1100% 2 5

200 300

1100% 2 5 66.7% The utilization rates computed show that the facility’s cur- rent output is comfortably below its design capacity. It is also slightly below effective utilization, which means that the manu- facturer is not using capacity to its fullest extent.

(See student companion site for Excel template.)

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SOLVED PROBLEMS • 363

• Problem 2 EKG Software Development Corporation has determined that it needs to expand its current capacity. The decision has come down to whether to expand now with a large facility, incurring additional costs and taking the risk that the demand will not materialize, or to undertake a small expansion, knowing that the decision will have to be reconsidered in five years. Manage- ment has estimated the following chances for demand:

• The likelihood of demand being high is 0.60. • The likelihood of demand being low is 0.40.

Profits for each alternative have been estimated:

• Large expansion has an estimated profitability of either $1,000,000 or $600,000, depending on whether demand turns out to be high or low.

• Small expansion has a profitability of $500,000, assuming that demand is low.

• Small expansion with an occurrence of high demand would require considering whether to expand further. If the com- pany expands at that point, the profitability is expected to be $700,000. If it does not expand further, the profitability is expected to be $500,000.

• Before You Begin: Always begin a decision tree problem by drawing a decision tree diagram and adding the information that you are given. Then you can proceed to evaluate it.

• Solution To solve this problem we need to draw the decision tree and evaluate it. A decision tree for this problem is shown in Figure 9-7. We read the diagram from left to right, with node 1 repre- senting the first decision point: expanding with a large facility or expanding small. Following each decision are chance events, which are the occurrence of either high or low demand. The probabilities for each event are shown on each branch. Notice that decision point 2 is where we may have to make our second decision, but only if we expand small now and demand turns out to be high. Then in five years we would decide whether to expand further. The estimated profits are shown in the right margins. We can see that at node 2 we should decide to expand further because the profits from that decision are higher ($700,000 versus $500,000). The expected value (EV) of profits at that point is written below node 2. The dollar amounts at the end of each alternative are the estimated profits.

Now that we have drawn the decision tree, let’s see how we can solve it. We do this by computing the expected value (EV) of the small and large expansions:

EVsmall expansion 5 0.60 1$700,000 2 1 0.40 1$500,000 2 5 $620,000

EVlarge expansion 5 0.60 1$1,000,000 2 1 0.40 1$600,000 2 5 $840,000

A large expansion now gives us a higher expected value.

Expand $700,000

Do not expand $500,000

Low demand (0.40) $500,000

Low demand (0.40) $600,000

High demand (0.60) $1,000,000

Sm all

ex pa

ns ion

Large expansion

($840,000)

($620,000)

($700,000)High demand (0.60)

FIGURE 9-7

Decision tree for EKG Corporation

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364 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

• Problem 3 As a recent business school graduate, you are considering two job opportunities that both require relocation. The two jobs are identical and have the same career potential. Therefore, your decision will be based on an evaluation of the two locations. You have decided to use factor rating to make your decision and have identified the most important factors. You have also placed a weight on each factor that reflects its importance and have developed a factor score for each location based on a five- point scale. This information is shown in the table. Using the procedure for factor rating, complete the table.

• Before You Begin: To solve this problem, for each location multiply the weight of the factor by the score for that factor and sum the results for each alternative. Then select the alternative with the highest score.

• Solution The completed factor rating spreadsheet is shown on the next page.

Factor Factor Weight

Factor Score at Each Location

Location 1 Location 2 Cost of living 10 5 2 Proximity to family 20 4 2 Climate 30 2 5 Transportation system 10 5 3 Quality of life 30 3 5

2 3 4

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

A B C D E F

Job Opportunity Location Analysis

Factor Location 1 Location 2 Factor Weight

Cost of Living 5 2 10 Proximity to Family 4 2 20 Climate 2 5 30 Transportation System 5 3 10 Quality of Life 3 5 30

Total 100

Compute Weighted Factor Scores and Overall Scores for Each Location

Factor Location 1 Location 2 Cost of Living 50 20 Proximity to Family 80 40 Climate 60 150 Transportation System 50 30 Quality of Life 90 150 Totals 330 390

Best Total Score 390 Best Location Location 2

Factor Scores (1-5 scale)

Weighted Factor Scores

D11: =SUM(D6:D10)

B16: =B6*$D6 (copied to B16:C20)

B21: =SUM(B16:B20) (copy right)

B23: =MAX(B21:C21)

B24: =INDEX(B15:C15,MATCH(B23,B21:C21,0))

Based on these results, you should move to location 2.

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PROBLEMS • 365

• Problem 4 Shoeless Joe is a specialty retailer that is deciding where to locate its new facility. The annual fixed and variable costs for each site under consideration have been estimated as follows:

Location Fixed Costs Variable Costs A $70,000 $1/unit B $34,000 $5/unit C $20,000 $8/unit D $50,000 $4/unit

If demand is expected to be 2000 units, which location is best?

• Before You Begin: To solve this problem, you must first determine fixed and vari- able costs and add them to compute total cost. Then select the location with the lowest total cost.

• Solution For Q � 2000 units, we compute the following total costs for each location:

Location Fixed Cost Variable Cost Total Cost

A $70,000 $1 (2000) $72,000 B $34,000 $5 (2000) $44,000 C $20,000 $8 (2000) $36,000 D $50,000 $4 (2000) $58,000

Shoeless Joe should locate at location C because it provides the lowest total cost for the expected demand of 2000 units.

Discussion Questions 1. Explain why capacity planning is important to a business. 2. Explain the differences between design capacity and effec-

tive capacity. 3. How is capacity utilization computed, and what does it tell us? 4. What are the steps in capacity planning? 5. What are decision trees, and how do they help us make

better decisions? 6. Find and discuss business examples of overcapacity and

undercapacity. 7. Explain the consequences of poor location decisions for a

business.

8. Find examples of good and bad location decisions. 9. Describe three advantages and three disadvantages of

globalization. 10. Describe the steps used to make location decisions. 11. Describe five factors that should be considered in the loca-

tion decision. 12. Explain the differences among factor rating, the load–

distance model, and break-even analysis. What criteria does each method use to make the location decision?

Problems 1. Joe’s Tasty Burger has determined that its production facility

has a design capacity of 400 hamburgers per day. Th e eff ective capacity, however, is 250 hamburgers per day. Lately Joe has noticed that output has been 300 hamburgers per day. Compute both design and eff ective capacity utilization measures. What can you conclude?

2. A manufacturer of printed circuit boards has a design capacity of 1000 boards per day. Th e eff ective capacity, however, is 700 boards per day. Recently, the production facility has been producing 950 boards per day. Compute the design and eff ective capacity utilization measures. What do they tell you?

3. Beth’s Bakery can comfortably produce 60 brownies in one day. If Beth takes some unusual measures, such as hiring her two aunts to help in the kitchen and work overtime, she can produce up to 100 brownies in one day.

(a) What are the design and eff ective capacities for Beth’s Bakery?

(b) If Beth is currently producing 64 brownies, compute the capacity utilization for both measures. What can you conclude?

4. Th e town barber shop can accommodate 35 customers per day. Th e manager has determined that if two additional barbers are hired, the shop can accommodate 80 customers per day. What are the design and eff ective capacities for the barber shop?

5. Th e design and eff ective capacities for a local paper manufacturer are 1000 and 600 pounds of paper per day, respectively. At present, the manufacturer is producing 500 pounds per day. Compute capacity utilization for both measures. What can you conclude?

6. Th e design and eff ective capacities for a local emergency facility are 300 and 260 patients per day, respectively. Currently, the emergency room processes 250 patients per day. What can you conclude from these fi gures?

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366 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

7. Th e Steiner-Wallace Corporation has determined that it needs to expand in order to accommodate growing demand for its laptop computers. Th e decision has come down to either expanding now with a large facility, incurring additional costs and taking the risk that the demand will not materialize, or expanding small, knowing that in three years management will need to reconsider the question.

Management has estimated the following chances for demand:

• Th e likelihood of demand being high is 0.60. • Th e likelihood of demand being low is 0.40.

Profi ts for each alternative have been estimated as follows:

• Large expansion has an estimated profi tability of either $100,000 or $60,000, depending on whether demand turns out to be high or low.

• Small expansion has a profi tability of $50,000, assuming that demand is low.

• Small expansion with an occurrence of high demand would require considering whether to expand further. If the company expands at that point, the profi tability is expected to be $70,000. If it does not expand further, the profi tability is expected to be $45,000.

(a) Draw a decision tree showing the decisions, chance events, and their probabilities, as well as the profi tability of outcomes.

(b) Solve the decision tree and decide what Steiner-Wallace should do.

8. Th e owners of Sweet-Tooth Bakery have determined that they need to expand their facility in order to meet their increased demand for baked goods. Th e decision is whether to expand now with a large facility or expand small with the possibility of having to expand again in fi ve years.

Th e owners have estimated the following chances for demand:

• Th e likelihood of demand being high is 0.70. • Th e likelihood of demand being low is 0.30.

Profi ts for each alternative have been estimated as follows:

• Large expansion has an estimated profi tability of either $80,000 or $50,000, depending on whether demand turns out to be high or low.

• Small expansion has a profi tability of $40,000, assuming demand is low.

• Small expansion with an occurrence of high demand would require considering whether to expand further. If the bakery expands at this point, the profi tability is to be $50,000.

(a) Draw a decision tree showing the decisions, chance events, and their probabilities, as well as the profi tability of outcomes.

(b) Solve the decision tree and decide what the bakery should do.

9. Demand has grown at Dairy May Farms, and it is consid- ering expanding. One option is to expand by purchasing a very

large farm that will be able to meet expected future demand. Another option is to expand the current facility by a small amount now and take a wait-and-see attitude, with the possibility of a larger expansion in two years.

Management has estimated the following chances for demand:

• Th e likelihood of demand being high is 0.70. • Th e likelihood of demand being low is 0.30.

Profi ts for each alternative have been estimated as follows:

• Large expansion has an estimated profi tability of either $40,000 or $20,000, depending on whether demand turns out to be high or low.

• Small expansion has a profi tability of $15,000, assuming that demand is low.

• Small expansion with an occurrence of high demand would require considering whether to expand further. If the company expands at that point, the profi tability is expected to be $35,000. If it does not expand further, the profi tability is expected to be $12,000.

(a) Draw a decision tree diagram for Dairy May Farms. (b) Solve the decision tree you developed. What should Dairy

May Farms do? 10. Spectrum Hair Salon is considering expanding its

business, as it is experiencing a large growth. Th e question is whether it should expand with a bigger facility than needed, hoping that demand will catch up, or with a small facility, knowing that it will need to reconsider expanding in three years.

Th e management at Spectrum has estimated the following chances for demand:

• Th e likelihood of demand being high is 0.70. • Th e likelihood of demand being low is 0.30.

Estimated profi ts for each alternative are as follows:

• Large expansion has an estimated profi tability of either $100,000 or $70,000, depending on whether demand turns out to be high or low.

• Small expansion has a profi tability of $50,000, assuming that demand is low.

• Small expansion with an occurrence of high demand would require considering whether to expand further. If the business expands at this point, the profi tability is expected to be $90,000. If it does not expand further, the profi tability is expected to be $60,000.

Draw a decision tree and solve the problem. What should Spectrum do?

11. Jody of Jody’s Custom Tailoring is considering expanding her growing business. Th e question is whether to expand with a bigger facility than she needs or with a small facility, knowing that she will have to reconsider expanding in three years.

Jody has estimated the following chances for demand:

• Th e likelihood of demand being high is 0.50. • Th e likelihood of demand being low is 0.50.

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PROBLEMS • 367

She has also estimated profi ts for each alternative:

• Large expansion has an estimated profi tability of either $200,000 or $100,000, depending on whether demand turns out to be high or low.

• Small expansion has a profi tability of $80,000, assuming that demand is low.

• Small expansion with an occurrence of high demand would require considering whether to expand further. If the business expands at that point, the profi tability is expected to be $120,000. If it does not expand further, the profi tability is expected to be $70,000.

Draw a decision tree and solve it. What should Jody’s Custom Tailoring do?

12. Th e owners of Speedy Logistics, a company that provides overnight delivery of documents, are considering where to locate their new facility in the Midwest. Th ey have narrowed their search down to two locations and have decided to use factor rating to make their decision. Th ey have listed the factors they consider important and assigned a factor score to each location based on a fi ve-point scale. Th e information is shown here. Using the procedure for factor rating, decide on the better location.

Factor Factor Weight

Factor Score at Each Location

Location 1 Location 2

Proximity to airport

40 5 3

Proximity to road access

30 4 1

Proximity to labor source

10 3 5

Size of facility 20 2 4

13. Sue and Joe are a young married couple who are considering purchasing a new home. Th eir search has been reduced to two homes that they both like at diff erent locations. Th ey have decided to use factor rating to help them make their decision. Th ey have listed the factors they consider important and assigned a factor score to each location based on a fi ve-point scale. Th e information is shown here. Using the procedure for factor rating, complete the table and help Sue and Joe make their decision.

Factor Factor Weight

Factor Score at Each Location

Location 1 Location 2

Proximity to work 10 5 2 Proximity to family 20 4 2 Size of home 30 2 5 Transportation system 10 5 3 Neighborhood 30 3 5

14. Th e Bakers Dozen Restaurant is considering opening a new location. It has considered many factors and identifi ed the ones that are most important. Two locations are being evaluated based on these factors, using factor rating. Each location has been evaluated relative to the factors on a fi ve-point scale. Th ese numbers are shown here. Use factor rating to help the restaurant decide on the better location.

Factor Factor Weight

Factor Score at Each Location

Location 1 Location 2

Proximity to customers 30 5 2 Proximity to competition

10 4 2

Proximity to labor supply

30 2 5

Transportation system 20 5 3 Quality of life 10 3 5

15. Joe’s Sports Supplies Corporation is considering where to locate its warehouse in order to service its four stores in four towns: A, B, C, and D. Two possible sites for the warehouse are being considered, one in Jasper and the other in Longboat. Th e following table shows the distances between the two locations being considered and the four store locations. Also shown are the loads between the warehouse and the four stores. Use the load– distance model to determine whether the warehouse should be located in Jasper or in Longboat.

Town Distance to Jasper

Distance to Longboat

Load between City and

Warehouse

A 30 12 15 B 6 12 10 C 10.5 30 12 D 4.5 24 8

16. Given here are the coordinates for each of the four towns to be serviced by the warehouse in Problem 15. Use the information from Problem 15 and the center of gravity method to determine coordinates for the warehouse.

Town Coordinates (X, Y)

A (4, 18) B (12, 2) C (10, 8) D (8, 15)

17. Shoeless Joe is a specialty retailer that is deciding where to locate a new facility. Th e annual fi xed and variable costs for each possible site have been estimated as follows:

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368 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

Location Fixed Costs Variable Costs

A $70,000 $1/unit B $34,000 $5/unit C $20,000 $8/unit D $50,000 $4/unit

If demand is expected to be 2000 units, which location is best? 18. Th e Quick Copy center for document copying is deciding

where to locate a new facility. Th e annual fi xed and variable costs for each site it is considering have been estimated as follows:

Location Fixed Costs Variable Costs

A $85,000 $2/unit B $49,000 $7/unit C $35,000 $10/unit D $65,000 $6/unit

If demand is expected to be 3000 units, which location is best?

CASE: Data Tech, Inc. Data Tech, Inc. is a small but growing company started by Jeff Styles. Data Tech is a business that transfers hard copies of documents, such as invoices, bills, or mailing lists, onto CDs. As more companies move to a paperless environment, placing data on CDs is the wave of the future. Jeff had started the company in his two-car garage three years earlier by purchasing the necessary software and signing two large corporations as his first customers. Now he was about to sign on two additional corporate customers. Suddenly what was a small garage operation was turning into a major business.

The Business The operations function of Data Tech seems deceptively simple. Every day Data Tech receives packages of mail from corporate customers containing documents they want transferred to disc. Data Tech usually receives anywhere from 10,000 to 30,000 pieces of mail per day that need to be processed. The first step requires workers to unpack and sort the mail received. Next, workers scan each item through one of two scanning machines that transfer content to disc. An accuracy check is then made to ensure that information was transferred correctly. This stage is particularly important as many of the documents contain important private information. Finally, the discs and the docu- ments are packaged and sent back to the customer, with Data Tech keeping a backup disc for its records.

The Need for Capacity and Relocation Running a full-time business out of his two-car garage is a chal- lenge for Jeff Styles. Jeff has spent a great deal of time ensuring that the operation of Data Tech runs smoothly without any bot- tlenecks. He has been successful, and his two original customers have just signed long-term contracts with him. In addition, he has acquired two additional customers. This means that Data Tech needs to move to a larger facility that could accommodate the larger size of the business.

Jeff has narrowed his search to three potential locations. He has identified the factors that are important to him and rated each location considering a number of criteria. Some factors are especially important, such as proximity to the postal service that

delivers the daily packages. Another is closeness to the airport, as Jeff frequently travels to customer locations.

A factor that is particularly troubling for Jeff is the issue of capacity. Two of the locations he is considering are larger than he currently needs and offer excess growth capacity. The third loca- tion would meet current capacity needs but would not offer ample room for expansion. He doesn’t know which is a better strategy. In his list of factor weights Jeff has made spaces for both capacity options, giving himself some time to think about the issues.

The information that Jeff has compiled is shown in the table.

Factor Factor Weight

Factor Score at Each Location

#1 #2 #3

Proximity to airport

20 3 4 4

Proximity to postal service

30 4 2 5

Facility with excess capacity

? 4 5 0

Facility with potential for expansion

? 0 1 5

Close to business community

10 5 4 4

Pleasant environment

10 3 4 4

To Expand Large or Small Jeff is not sure how to evaluate whether he should focus on moving into a larger facility now or moving into a smaller facil- ity with potential for expansion. He has estimated the following chances for demand:

• Th e likelihood of demand being high is 0.70. • Th e likelihood of demand being low is 0.30.

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INTERACTIVE CASE: VIRTUAL COMPANY • 369

He also estimated profitability for each alternative:

• Moving into a large facility has a profi tability of either $1,000,000 or $600,000, depending on whether demand turns out to be high or low.

• Moving into a small facility has a profi tability of $500,000, assuming that demand is low.

• Moving into a small facility would require considering expanding if demand turned high. If Data Tech decided to expand at that point, profi tability would be $800,000. If it did not expand further, the profi tability would be $500,000.

Case Questions 1. Help Jeff decide whether he should give greater priority to a

smaller facility with possibility for expansion or move into a larger facility immediately. Decide on which is the best alternative and choose weights for the two capacity factors based on your fi ndings.

2. Once you have selected the factors for the two capacity alternatives, use factor rating to select a new location for Data Tech.

3. How would your factor analysis be diff erent if you had selected a diff erent capacity alternative?

CASE: The Emergency Room (ER) at Northwest General (B) Jenn Kostich, director of emergency services at Northwest Gen- eral Hospital, is faced with a decision on how to respond to a recent memo. Her response could affect the entire ER operation, and she wants to make sure it is prepared correctly.

The Problem Jenn has just learned that the board of Northwest General has approved plans for a large remodeling and expansion proj- ect. All department directors of the hospital have been asked to provide an assessment of their capacity needs if they were requesting an increase in their departmental space. The directors were told to specify the amount of increase they required and provide justification for the request. They were also directed to base their requests on the average of their departments’ demand requirements.

The ER desperately needs more space, and Jenn is easily able to provide the needed documentation. However, she is not sure whether it is reasonable to base capacity requirements for the ER on average demand.

Background Northwest General is the only major hospital in the area between Seattle and Vancouver. Its ER is always busy, since it is the only hospital servicing the local population and visitors during the long tourist season.

The area has been stable in population growth over the past 10 years. The area is also a significant tourist destination for campers, hikers, and nature lovers. During the tourist season— consisting of summer months (June, July, and August), winter holidays (December), and spring break (March and April)—the population swells by as much as 30 percent.

The ER has been able to meet demand adequately during the nontourist season. However, it does not have sufficient capac- ity to meet demand when tourists arrive. These peak periods, amounting to 6 out of 12 months, have been extremely difficult for the ER staff. The ER does not have enough space capacity for the large number of patients during these periods. Frequently, they have to resort to using hallways and closets for patient space. The staff feel that this is unacceptable, not to mention unsafe.

The capacity problems occur only during the busy tourist season. Computing the average of the capacity requirements does not reveal this problem, as the peak demands are averaged with the lower demands during the nontourist season.

Case Questions 1. Discuss the pros and cons of using average demand to

assess capacity requirements. Is this a reasonable approach for the ER?

2. Make a recommendation for Jenn as to what she should do and the information that she should provide in her request.

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Capacity Analysis at Cruise International, Inc. Bob Bristol just called to congratulate you on your excellent

work on the various assignments at CII. He now wants you to do some capacity analysis for Meghan Willoughby, the Chief Purser. Meghan is concerned about the capacity needed for the embarkation process. If there is too much capacity, it is an unnecessary expense. However, if there is insufficient capacity,

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370 • CHAPTER 9 CAPACITY PLANNING AND FACILITY LOCATION

passengers are forced to wait in line for too long and their vaca- tion starts with a negative experience. The amount of capacity is flexible as Meghan negotiates arrangements with the owners of the pier used, in terms of both square footage and the amount of time. This assignment will enhance your knowledge of the material in Chapter 9 of your textbook while preparing you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Capacity Analysis at CII

On-line Case: Capacity Planning at Valley Memorial Hospital Assignment: Capacity Analysis at Valley Memorial

Hospital Bob Reilly just called to congratulate you on your excellent work on the various assignments at VMH. He now wants you to do some capacity analysis for Lee Jordan, director of the Medical/Surgical Nursing Unit. Lee has been concerned about the capacity of the beds in the maternity ward to meet the projected patient demand next year and wants some quick analysis done prior to exploring options for capacity expansion. The maternity ward currently has 80 beds and expects to admit about 9125 patients next year. On average, a patient stays for

three days. Lee wants you to address a few specific questions. This assignment will enable you to enhance your knowledge of the material in this chapter.

To access the Web site:

EDS is a national distributor of office supplies that delivers goods to department and specialty stores. It is planning to build a large distribution center in your state and is analyz- ing different location sites. You have been assigned the task of selecting the major city in your state that you think should be the site of the new distribution center. Here are some facts to consider. At present EDS has no other distribution center in your state. The goal is to locate in a major city that has easy

access to major roadways; this will enable EDS to reach other destinations in the state. Although your decision will be subjec- tive, be prepared to justify it. Go to the Internet to find a map of your state. Analyze roadways, distances, and access to other locations. Then use the Internet to get other information, such as traffic patterns, populations, and other geographic factors. Decide on the best location for the EDS distribution center and explain your decision.

INTERNET CHALLENGE EDS Office Supplies, Inc.

Online Resources Companion Website www.wiley.com/college/reid

Northeast Knitting Mills Coppley Apparel Group

• Find links for Additional Web Resources for this chapter Th e Association for Manufacturing Excellence, www.ame.org APICS—Th e Educational Society of Resource Management, www.apics.org

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SELECTED BIBLIOGRAPHY • 371

Selected Bibliography Berry, W.L., and T. Hill. “Linking Systems to Strategy,” Interna-

tional Journal of Operations and Production Management, 12, 10, 1992, 3–15.

Florida, R. “Lean and Green: Th e Move to Environmen- tally Conscious Manufacturing,” California Management Review, 39, 1, 1996, 80–105.

Francis, R.L., J.A. White, and L. McGinniss. Facility Layout and Location: An Analytical Approach, Second Edition. Englewood Cliff s, N.J.: Prentice Hall, 1991.

Meijboom, B., and B. Vos. “International Manufacturing and Location Decisions: Balancing Confi guration and Coor- dination Aspects,” International Journal of Operations and Production Management, 17, 8, 1997, 790–805.

Pagell, M., and D.R. Krause. “A Multiple Method Study of Environmental Uncertainty and Manufacturing Flexibil- ity,” Journal of Operations Management, 17, 1999, 307–325.

Swink, M., and W.J. Hegarty. “Core Manufacturing Capabili- ties and Th eir Link to Product Diff erentiation,” Interna- tional Journal of Operations and Production Management, 18, 4, 1998, 374–396.

Upton, D.M. “Flexibility as Process Mobility: Th e Manage- ment of Plant Capabilites for Quick Response Manu- facturing,” Journal of Operations Management, 12, 1995, 205–224.

Ward, P.T., R. Duray, G.K. Leong, and C.C. Sum. “Business Environment, Operations Strategy and Performance: An Empirical Study of Singapore Manufacturers,” Journal of Operations Management, 13, 2, 1995, 99–115.

Wysocki, Bernard, Jr. “Hospitals Cut ER Waits,” Wall Street Journal, July 3, 2002.

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CHAPTER

372

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

L E A R N I N G O B J E C T I V E S

What Is Layout Planning? 373 Types of Layouts 374 Designing Process Layouts 379 Special Cases of Process Layout 385 Designing Product Layouts 389

Group Technology (Cell) Layouts 396 Facility Layout within OM: How It All Fits

Together 397 Facility Layout across the Organization 397

Before studying this chapter you should know or, if necessary, review

1. The Hawthorne studies and human relations movement, Chapter 1, p. 14. 2. Types of operations and their characteristics, Chapter 3, pp. 69–72. 3. The load–distance model for location planning, Chapter 9, pp. 351–355. 4. Measuring rectilinear distance, Chapter 9, p. 352.

Facility Layout

After studying this chapter you should be able to

1 Define layout planning and explain its importance. 2 Identify and describe different types of layouts. 3 Compare process layouts and product layouts. 4 Describe the steps involved in designing a process layout. 5 Describe the steps involved in designing a product layout. 6 Explain the advantages of hybrid layouts. 7 Explain the meaning of group technology (cell) layouts.

C H A P T E R O U T L I N E

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WHAT IS LAYOUT PLANNING? • 373

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WHAT IS LAYOUT PLANNING? Layout planning is deciding on the best physical arrangement of all resources that consume space within a facility. These resources might include a desk, a work cen- ter, a cabinet, a person, an entire office, or even a department. Decisions about the arrangement of resources in a business are not made only when a new facility is being designed; they are made any time there is a change in the arrangement of resources, such as a new worker being added, a machine being moved, or a change in procedure being implemented. Also, layout planning is performed any time there is an expansion in the facility or a space reduction.

The arrangement of resources in a facility can significantly affect the productivity of a business. As you saw in the opening examples, a lot of wasted time, energy, and con- fusion can result from a poor layout. There are also other reasons layout planning is

� Layout planning Deciding on the best physical arrangement of all resources that consume space within a facility.

In this chapter you will learn why layout planning is important. You will also learn about different types of layouts and how to design them so as to maximize efficiency.

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374 • CHAPTER 10 FACILITY LAYOUT

important. In many work environments, such as office settings, face-to-face interaction between workers is important. Proper layout planning can be critical in building good working relationships, increasing the flow of information, and improving communi- cation. Similarly, in retail organizations layout can affect sales by promoting visibility of key items and contributing to customer satisfaction and convenience. As you can see, layout planning affects many areas of a business, and its importance should not be underestimated.

In Chapter 3 we learned about different types of operations based on degree of product standardization and volume of output. We learned that there are two broad categories of operations: intermittent and repetitive processing systems. Intermit- tent processing systems are seen in organizations that produce a large variety of different products, each in low volume. An example is a typical job shop. On the other hand, repetitive processing systems are used to produce a small variety of standardized products in high volume. An example is an assembly line. As we will see in this chapter, the nature of a company’s operations is directly related to the type of layout it uses.

MKT

� Intermittent processing systems Systems used to produce low volumes of many different products. � Repetitive processing systems Systems used to produce high volumes of a few standardized products.

TYPES OF LAYOUTS There are four basic layout types: process, product, hybrid, and fixed position. In this section we look at the basic characteristics of each of these types. Then we examine the details of designing some of the main types.

Process Layouts Process layouts are layouts that group resources based on similar processes or func- tions. This type of layout is seen in companies with intermittent processing systems. You would see a process layout in environments in which a large variety of items are produced in a low volume. Since many different items are produced, each with unique processing requirements, it is not possible to dedicate an entire facility to each item. It is more efficient to group resources based on their function. The products are then moved from one resource to another, based on their unique needs.

The challenge in process layouts is to arrange resources to maximize efficiency and minimize waste of movement. If the process layout has not been designed properly, many products will have to be moved long distances, often on a daily basis. This type of movement adds nothing to the value of the product and contributes to waste. Any pair of work centers that have a large number of goods moved between them should be placed in close proximity to each other. However, this often means that some other work center will have to be moved out of the way. The process layout problem thus can become quite complex, since we are not only looking at the relationship of two resources at one time but at all our resources simultaneously.

Process layouts are very common. A hospital is an example of process layout. Departments are grouped based on their function, such as cardiology, radiology, laboratory, oncology, and pediatrics. The patient, the product in this case, is moved between departments based on his or her individual needs. A university is another example. Colleges and departments are grouped based on their function. You, the student, move between departments based on the unique program you have chosen. Another example is a metalworking shop, where resources such as drills, welding, grinding, and painting are each grouped based on the function they perform. Other examples include a printing facility that prints books, magazines, and newspapers, or a bakery that makes many different baked goods.

� Process layouts Layouts that group resources based on similar processes or functions.

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Process layouts arrange items by type as seen in this grocery store.

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TYPES OF LAYOUTS • 375

Recall that process layouts are designed to produce many different items, often to customer specifications. To achieve this goal they have certain unique characteristics:

1. Resources used are general purpose. Th e resources in a process layout need to be capable of producing many diff erent products.

2. Facilities are less capital intensive. Process layouts have less automation, which is typically devoted to the production of one product.

3. Facilities are more labor intensive. Process layouts typically rely on higher-skilled workers who can perform diff erent functions.

4. Resources have greater fl exibility. Process layouts need to have the ability to easily add or delete products from their existing product line, depending on market demands.

5. Processing rates are slower. Process layouts produce many diff erent products, and there is greater movement between workstations. Consequently, it takes longer to produce a product.

6. Material handling costs are higher. It costs more to move goods from one process to another.

7. Scheduling resources is more challenging. Scheduling equipment and machines is particularly important in this environment. If it is not done properly, long waiting lines can form in front of some work centers while others remain idle.

8. Space requirements are higher. Th is type of layout needs more space due to higher inventory storage needs.

Improper design of process layouts can result in costly inefficiencies, such as high material handling costs. A good design can help bring order to an environment that might otherwise be very chaotic.

A tool and die manufacturing plant is an example of a process layout.Ro

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376 • CHAPTER 10 FACILITY LAYOUT

The importance of a good process layout is illustrated by Wal-Mart, a company that has revolutionized retailing. A great deal of thought and analysis went into designing the layout of the Wal-Mart facilities. Most Wal-Mart locations have the same layout to provide predict- ability and comfort to custom- ers. As in most retail operations, the merchandise is grouped by category. For example, all shoes

are grouped in one location, as are clothing items, stationery, and snack items. How- ever, Wal-Mart layouts provide for maximum use of floor space. For example, the lay- outs are designed with multiple narrow aisles as opposed to a smaller number of wide aisles. The reason is to maximize customer exposure to merchandise. Also, Wal-Mart makes maximum use of height to store inventory and give product visibility to custom- ers. These are some of the reasons Wal-Mart is the world’s largest retailer today.

Product Layouts Product layouts are layouts that arrange resources in a straight-line fashion to pro- mote efficient production. They are called product layouts because all resources are arranged to meet the production needs of the product. This type of layout is used by companies that have repetitive processing systems and produce one or a few standard- ized products in large volume.

Examples of product layouts are seen on assembly lines, in cafeterias, or even at a car wash. In product layouts the material moves continuously and uniformly through a series of workstations until the product is completed. The challenge in designing product layouts is to arrange workstations in sequence and designate the jobs that will be performed by each station in order to produce the product in the most efficient way possible. Operations managers must decide exactly what tasks will be performed by every workstation in the sequence. They need to consider the logical order in which jobs should be done. For example, at a car wash you cannot perform drying before you have performed washing. Managers also need to consider how fast production occurs and how many units can be processed through the sys- tem. The faster production occurs, the more units that can be processed through the system.

Remember that product layouts are designed to produce one type or just a few types of products in high volume. Product layouts have the following characteristics:

1. Resources are specialized. Product layouts use specialized resources designed to produce large quantities of a product.

2. Facilities are capital intensive. Product layouts make heavy use of automation, which is specifi cally designed to increase production.

3. Processing rates are faster. Processing rates are fast, as all resources are arranged in sequence for effi cient production.

4. Material handling costs are lower. Due to the arrangement of work centers in close proximity to one another, material handling costs are signifi cantly lower than for process layouts.

� Product layouts Layouts that arrange resources in sequence to allow for an efficient buildup of the product.

LINKS TO PRACTICE

Wal-Mart Stores, Inc. www.walmart.com

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TYPES OF LAYOUTS • 377

5. Space requirements for inventory storage are lower. Product layouts have much faster processing rates and less need for inventory storage.

6. Flexibility is low relative to the market. Because all facilities and resources are specialized, product layouts are locked into producing one type of product. Th ey cannot easily add or delete products from the existing product line.

The characteristic differences between process and product layouts are shown in Table 10-1.

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Product layouts arrange resources in a line fashion to promote efficiency. Here workers at See’s Candies plant in San Francisco prepare 4-pound boxes of chocolates for Valentine’s Day.

A cafeteria line is a good example of a product layout.

Process Layouts Product Layouts

Able to produce a large number of different products.

Resources used are general purpose.

Facilities are more labor intensive.

Greater flexibility relative to the market.

Slower processing rates.

High material handling costs.

Higher space requirements.

Able to produce a small number of products efficiently.

Resources used are specialized.

Facilities are more capital intensive.

Low flexibility relative to the market.

Processing rates are faster.

Lower material handling costs.

Lower space requirements.

TABLE 10-1

Characteristics of Process and Product Layouts

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378 • CHAPTER 10 FACILITY LAYOUT

The importance of an efficient product layout can be seen at the Toyota Motor Corporation, the leader of just-in-time production. Toyota had pioneered the pull pro- duction system in the 1970s, which has been widely used in practice. The work centers are arranged in a line fashion and are in close proximity to one another, allowing easy transfer of work between stations. On the pro- duction line, a worker with any prob-

lem (e.g., a product defect or a malfunctioning machine) can pull a cord that summons a team leader to address the problem. The line has been designed so that workers can easily communicate their needs to one another. Upstream workers can respond to “pull” signals from workers downstream who require orders of goods. Also, the layout of the facility is designed so that workers can see each other, as visibility of the opera- tion is considered highly important. Toyota’s system is focused on eliminating waste from every aspect of the operation and is the factor that has contributed to Toyota’s large success.

Hybrid Layouts Hybrid layouts combine aspects of both process and product layouts. This is the case in facilities where part of the operation is performed using an intermittent process- ing system and another part is performed using a continuous processing system. For example, Winnebago, which makes mobile campers, manufactures the vehicle itself as well as the curtains and bedspreads that go into the camper. The vehicles are produced on a typical assembly line, whereas the curtains and bedspreads are made in a fabrica- tion shop that uses a process layout. Hybrid layouts are very common. Often, some elements of the operation call for the production of standardized parts, which can be produced more efficiently in a product layout, whereas other parts need to be made individually in a process layout.

Hybrid layouts are often created in an attempt to bring the efficiencies of a product layout to a process layout environment. To develop a hybrid layout, we can try to iden- tify parts of the process layout operation that can be standardized and produce them in a product layout format. One example of this is called group technology (GT) or cell layouts. First, families of products that are similar in their processing characteris- tics and resource requirements are identified. Managers can then create cells, or small product layouts, that are dedicated to the production of these families of products. This approach brings greater efficiency to the process layout environment. Later in the chapter we will learn more about group technology.

Other examples of hybrid layouts can be seen in everyday life. For example, retail stores and grocery stores use hybrid layouts. In these environments, goods such as dairy items, meat, or produce are stored based on their function. From that standpoint these are process layouts. However, the layout is also designed to con- sider a path or sequence of purchases in a straight-line fashion, making it similar to a product layout. For example, pasta is stored immediately following spaghetti sauce.

� Hybrid layouts Layouts that combine characteristics of process and product layouts.

� Group technology (GT) or cell layouts Hybrid layouts that create groups of products based on similar processing requirements.

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LINKS TO PRACTICE Toyota Motor Corporation

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DESIGNING PROCESS LAYOUTS • 379

Fixed-Position Layouts A fixed-position layout is used when the product is large and cannot be moved due to its size. All the resources for producing the product—including equipment, labor, tools, and all other resources—have to be brought to the site where the product is located. Examples of fixed-position layouts include building construction, dam or bridge construction, shipbuilding, or large aircraft manufacture. The challenge with a fixed-position layout is scheduling different work crews and jobs and managing the project. Project management is discussed more fully in Chapter 16.

� Fixed-position layout A layout in which the product cannot be moved due to its size and all the resources have to come to the production site.

DESIGNING PROCESS LAYOUTS We have mentioned that the objective in designing process layouts is to place resources close together based on the need for proximity. This need could stem from the number of trips that are made between these resources or from other factors, such as sharing of information and communication.

There are three steps in designing process layouts:

Step 1 Gather information. Step 2 Develop a block plan or schematic of the layout. Step 3 Develop a detailed layout.

Next we look at how each of these steps is performed.

Step 1: Gather Information The first step is to collect information that will be used to design an initial layout. Sev- eral kinds of information are needed.

Identify Space Needed The first piece of information to be collected is the amount of space needed for each of the organization’s key resources. At this stage, managers generally focus on larger resources, such as departments and work centers. Operations managers must identify the space requirements of each department relative to their capacity needs, such as size of equipment and number of employees, as well as circulation room, such as aisles.

Recovery First Sports Medicine Clinic is an outpatient medical facility that provides a variety of medi- cal services to patients suffering from sports injuries. The services include exams and X-rays, physical therapy, and outpatient surgery. The departments housed in the medical facility and their exact space requirements in square feet are shown here:

EXAMPLE 10.1

Recovery First Sports Medicine Clinic: Developing a Block Plan

Department Area Needed (Square feet) A. Radiology 400 B. Laboratory 300 C. Lobby and waiting area 300 D. Examining rooms 800 E. Surgery and recovery 900 F. Physical therapy 1050 Total 3750

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380 • CHAPTER 10 FACILITY LAYOUT

Identify Available Space The available space of a facility is best seen by using a block plan, a schematic that shows the placement of departments in a facility. Using a block plan, we can visualize the available space and evaluate whether we can meet space needs. The current block plan for Recovery First is shown in Figure 10-1. The facility is 75 feet long by 50 feet wide, meaning that there are 3750 square feet of available space. The available space meets our total space requirements, but we will have to allocate more space to some departments. The first step in designing a process layout is to determine the best location of departments relative to one another. The easiest way to do this is to divide the available space into equal sizes to determine the departments’ relative location. Much later, in the detail design stage, we can give more or less space to individual departments based on need.

Identify Closeness Measures Recall that the main criterion in deciding the location of departments relative to one another is the importance of proximity between them. At this stage we need a measure of the importance of having any pair of departments in close proximity to one another. There are two simple tools that can be used for this purpose: a from–to matrix and a REL chart. Both provide measures of the importance of having any pair of resources, such as work centers or departments, close together. This information can be used to design a good layout.

A from–to matrix is a table that shows the number of trips or units of product moved between any pair of departments. Table 10-2 shows the from–to matrix for Recovery First, with daily trips made between each pair of departments. The number of trips between departments can be obtained in many ways–for example, from rout- ing slips or order forms, by performing statistical sampling to determine frequencies, or by interviewing management. Note in Table 10-2 that all entries are above the diag- onal of the matrix. Remember that we are interested in the total amount of movement

� Block plan Schematic showing the placement of resources in a facility.

� From–to matrix Table that gives the number of trips or units of product moved between any pair of departments.

FIGURE 10-1

Block plan for Recovery First

Trips between Departments

Department A B C D E F

A. Radiology — — — 45 12 25

B. Laboratory — — 45 14 5

C. Lobby and waiting area — 50 20 43

D. Examining rooms — — 12

E. Surgery and recovery —

F. Physical therapy —

TABLE 10-2

From–To Matrix for Recovery First

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DESIGNING PROCESS LAYOUTS • 381

between any two departments, regardless of direction. Therefore, the matrix has con- solidated movements from both directions. For example, the total number of trips between departments A and D is 45. This could mean that 20 trips are being made from A to D and 25 trips from D to A. However, for our purpose here we are not con- cerned with the direction of the trips, but only with the total number of trips in order to measure the importance of having these departments close together.

Another tool that can be used to provide information about the importance of prox- imity is a REL chart, short for relationship chart. A REL chart is a tool that reflects opinions of managers with regard to the importance of having any two departments close together. It is a good tool to use when we need to consider the judgments of managers in deciding where to locate departments. This would be the case when other factors need to be considered in making a location decision, such as communication in an office setting, face-to-face contact, or customer access as in retail businesses. In these environments it is often impossible to obtain numerical values of product flow. Using a relationship chart to develop acceptable layouts is part of a classic layout tech- nique called systematic layout planning (SLP). A REL chart can be used in much the same way as a from–to matrix.

A REL chart for Recovery First is shown in Table 10-3. The importance of having departments close together is calculated using a predetermined scale, which is shown with the table. Values in the chart can be obtained by interviewing management and staff.

Finally, in addition to considering closeness information, a company needs to take into account other information when making layout decisions. It is very common not to be able to move certain departments due to physical constraints. For example, Recovery First has decided not to move department C, the lobby and waiting area, because it is closest to the parking lot.

Now that we have collected all the needed information, let’s move to the next step in designing a process layout.

� REL chart Table that reflects opinions of managers with regard to the importance of having any two departments close together.

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TABLE 10-3Closeness Rating between Departments

Department A B C D E F

A. Radiology — U U 0 A(2) 0

B. Laboratory — U 0 I(3) U

C. Lobby and waiting area — E(1) X(4) I(1)

D. Examining rooms — 0 I(1)

E. Surgery and recovery 0

F. Physical therapy —

REL Chart for Recovery First

Explanation of Rating Codes

Rating Definition Code Meaning

A Absolutely necessary 1 Patient convenience

E Especially important 2 Sharing of medical staff

I Important 3 Access to equipment

O Ordinary closeness 4 Patient privacy

U Unimportant

X Undesirable

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382 • CHAPTER 10 FACILITY LAYOUT

Step 2: Develop a Block Plan The next step in the layout planning process is to develop a new block plan or a bet- ter block plan than the one already in existence. A block plan can be developed either by trial and error or by choosing from a variety of decision-support tools. We will first use trial and error to develop a better block plan for Recovery First. When the layout problem is small in scope, trial and error can work well. However, when the layout problem is large, it may be necessary to rely on available software. Regardless of whether you choose to use software to make your layout decisions, it is important to understand the logic behind trial and error, because decision-support tools are based on heuristics that use logic similar to that used in trial and error. To understand how the decision-support tools work, you need to understand the trial-and-error process.

Using Trial and Error Recall that the goal is to develop a layout that places departments close together that have been identified as needing close proximity by either the from–to matrix or the REL chart. Recovery First has decided to develop a layout that minimizes the number of trips made in order to improve its efficiency. We will use information in the from–to matrix in Table 10-2 to identify critical pairings of departments.

Looking at the from–to matrix, we begin by identifying pairs of departments that need to be located close together. We look for pairs of departments with a high num- ber of trips between them. From Table 10-2 we can identify the following pairs of departments:

Departments C and D, which have 50 trips between them Departments A and D, which have 45 trips between them Departments B and D, which have 45 trips between them Departments C and F, which have 43 trips between them

These departments have a much higher number of trips between them compared to the other department pairs. However, note that this is an arbitrary decision, one that uses judgment. If the trips are close in numerical value, the operations manager can use information from the REL chart to decide on critical pairings of departments.

Based on these criteria, we can propose the block plan shown in Figure 10-2. This plan appears to meet set criteria, but how do we know whether it is indeed better than the current layout? We need a way to measure its effectiveness quantitatively. We can do this by using the load–distance model that was discussed in Chapter 9. The model is shown in Table 10-4. Recall that relative locations can be compared by computing the ld score, which is obtained by multiplying the load for each department by the dis- tance traveled and then summing over all the departments. The resulting score is a surrogate measure for material handling, movement, or communication. Our goal is to make the ld score as low as possible by reducing the distance large loads have to travel.

� Load–distance model Model used to compare the relative effectiveness of different layouts.

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FIGURE 10-2

Proposed block plan for Recovery First

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DESIGNING PROCESS LAYOUTS • 383

The load is the number obtained from the from–to matrix; it shows the number of trips between departments. But how do we determine the distance? We can obtain the distance from the block plan. Because the size of each block is the same, we do not need to measure the distance in feet. Rather, to keep it simple we can use one block as a measure of distance. To measure the distance between departments, we typically use rectilinear distance, which we studied in Chapter 9. Remember that the rectilinear distance between any two locations is the shortest distance using only north–south and east–west movements. Therefore, from our proposed block plan we can see that the distance between departments A and D is one block unit. Between A and C the dis- tance is two block units, and between A and F it is three block units. Using this logic, let’s compute the load–distance score for the current and proposed layouts and decide which layout is better.

Table 10-5 shows computations of ld scores for both the current and proposed lay- outs for Recovery First. We can see that the proposed layout is better than the current one, as it has a lower ld score. In fact, the proposed layout is an almost 30 percent improvement over the current layout. To get the actual distance in feet, we could have

ld score for a layout 5 a lijdij where lij 5 load between departments i and j,

obtained from either the from–to matrix or the REL chart

dij 5 distance between departments i and j, obtained from a block plan

TABLE 10-4

The Load–Distance Model

Current Layout Proposed Layout

Departments

Number of Trips (obtained from

from–to matrix) l

Distance (obtained

from current block plan) d

Load– Distance Score ld

Distance (obtained from proposed block

plan) d

Load– Distance Score ld

A and D 45 1 45 1 45

A and E 12 2 24 1 12

A and F 25 3 75 3 75

B and D 45 2 90 1 45

B and E 14 1 14 1 14

B and F 5 2 10 1 5

C and D 50 3 150 1 50

C and E 20 2 40 3 60

C and F 43 1 43 1 43

D and F 12 2 24 2 24

Total 515 373

TABLE 10-5

ld Score Computations for Current and Proposed Layouts for Recovery First

� Rectilinear distance The shortest distance between two locations using north–south and east–west movements.

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384 • CHAPTER 10 FACILITY LAYOUT

multiplied the distance in the figure by 25, as each block is 25 feet long. However, mul- tiplying both sides by 25 would not change their relative relationship.

The solution to the layout problem for Recovery First can also be solved using a spreadsheet, as shown.

Using Decision-Support Tools Using trial and error to develop a layout plan can often lead to satisfactory results. If we continued with trial and error in our example, we could find a solution that lowered the ld score even further. However, when dealing with layout problems of a more realistic size, we need to use decision-support tools. The reason is that the layout problem is a combinatorial problem. For a block plan of 6 departments, there are actually 6! different solutions, or 720 possible solutions. You can imagine how many layout alternatives there would be for a facility with 50 different departments.

A number of computer software packages can be used as decision-support tools in making the layout decision. Two of the most popular are ALDEP (automated lay- out design program) and CRAFT (computerized relative allocation of facilities tech- nique). They are called decision-support tools because they use different heuristics to develop a solution. They do not give an optimal solution, and they consider only one criterion at a time in designing a layout. The best way to use these software packages is to consider the software solution as a starting point in developing a final layout.

ALDEP works from a REL chart. It constructs a layout within the boundaries of the facility by trying to link together departments that have either an A or an E rating in the REL chart. Remember that an A rating stands for absolutely necessary and an E rat- ing for especially important. ALDEP uses this logic to link these departments together. The first department is selected randomly. To evaluate a layout, the computer program computes a score that is similar to the ld score we computed using trial and error. Depending on the starting point selected, many different layouts can be obtained.

� ALDEP and CRAFT Computer software packages for designing process layouts.

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SPECIAL CASES OF PROCESS LAYOUT • 385

CRAFT works differently from ALDEP. It is also a heuristic, but it uses a different logic to find a solution. CRAFT uses a from–to matrix and an existing layout as a start- ing point. It proceeds by making paired exchanges of departments that lead to a reduc- tion of the ld score and continues in this manner until there are no more exchanges that can reduce the ld score. The solution with the lowest ld score is the final solution.

Many other sophisticated computer software packages for layout planning can be used to design office buildings, warehouses, and other large facilities. They are capable of designing layouts for multiple floors, and they can consider height for assigning storage locations, as in retail or warehousing. For example, SPACECRAFT is a modi- fied version of CRAFT developed for designing multistory layouts. These software programs, including ALDEP and CRAFT, can work with a large number of depart- ments of different sizes and shapes.

Step 3: Develop a Detailed Layout The last step in designing a process layout is the development of a detailed layout design. At this stage the block plan is translated into a more realistic schematic. We begin to consider exact sizes and shapes of departments and work centers. We also focus on specific work elements, such as desks, cabinets, and machines, as well as aisles, stair- ways, and corridors. Operations managers can use a variety of tools in this final stage; they include drawings, three-dimensional models, and computer graphics software.

SPECIAL CASES OF PROCESS LAYOUT A number of unique cases of process layout require special attention. In this section we look at two special cases: warehouse layouts and office layouts.

Warehouse Layouts Warehouse layouts have the key characteristics of process layouts: products are stored based on their function, and there is movement of goods. The main difference is that movement within a warehouse is primarily between the loading/unloading dock and the areas where goods are stored. Typically, there is no movement between the stor- age areas themselves; the primary function of a warehouse is to provide storage space, so the only movement is inbound or outbound. Think about a warehouse that stores computer equipment and supplies. Printers might be stored in one area, keyboards in another, and ink cartridges in a third. Certainly there would be no movement between the keyboard storage area and the area where ink cartridges are stored. The movement would consist of bringing items either in or out of the warehouse.

Storage Areas of Equal Sizes The primary decision in designing warehouse layouts is to decide where to locate individual departments relative to the dock. Using the same logic we used for process layouts in general, the goal is to assign departments to locations in order to minimize the number of trips to the dock. As before, we need a from–to matrix that shows the number of trips. Since the movements are only between the departments and the dock, we simply locate the departments with the highest number of trips closest to the dock. Next, we locate the department with the second-highest number of trips in the next available space closest to the dock. We proceed in this manner until all the departments have been assigned. Example 10.2 illustrates a simple example.

In a warehouse distribution center, items are stored based on their function.

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386 • CHAPTER 10 FACILITY LAYOUT

Green Grocer stores its dry goods in a nearby warehouse. The different categories of foods are stored in departments that each take up the same amount of space, shown in Figure 10-3. Given the available warehouse space and the number of trips made for each category of foods, Green Grocer needs to decide where to locate each department.

• Before You Begin: Remember that when solving warehouse layouts the objective is to place the departments with the highest number of trips closest to the dock.

• Solution: To assign departments to specific storage areas, we progressively assign departments with the highest number of trips closest to the dock. Department 2 is placed closest because it has the highest number of trips. Next comes department 6, and so forth. Using this logic, we develop the block plan shown in Figure 10-4.

EXAMPLE 10.2

Green Grocer Makes Location

Assignments FIGURE 10-3

Warehouse storage areas for Green Grocer

AisleDock

2 4 5

6 1 3

Block plan for Green Grocer warehouse

FIGURE 10-4

Department Food Category Trips to and from Dock

1 Canned goods 50 2 Cereals 63 3 Condiments 35 4 Diapers and baby products 55 5 Cookies and candies 48 6 Fruit and vegetable juices 60

Storage Areas of Unequal Size In Example 10.2 all the departments required equal- sized storage areas. What would happen if the storage areas required were of different sizes? It is common for some departments to need more room than others based on the size of the product or volumes needed. Number of trips is not a good measure because it can be misleading. For example, if department A takes up 4 storage areas

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SPECIAL CASES OF PROCESS LAYOUT • 387

Looking Good Clothes is a clothing retailer for teenagers and young adults. The company is in the pro- cess of assigning storage areas in its warehouse in order to minimize the number of trips made to retrieve items needed. Following are the departments that need to be located, the number of trips made per week for each department, and the area needed by each department:

The warehouse block plan is shown in Figure 10-5.

• Before You Begin: To solve this problem, follow the two-step process given for making location assignments when storage areas are of unequal size.

• Solution: Step 1: Take the ratio of trips to the number of areas taken up by the department.

EXAMPLE 10.3

Looking Good Clothes Assigns Storage Areas

and makes 20 trips to the dock, it actually has fewer trips per area than department B, which takes up 1 storage area and makes 15 trips. The reason is that when trips per area are considered, department A only has 5 trips whereas B has 15.

To make location assignments when departments take up storage areas of unequal size, we need to follow these steps:

Step 1 Take the ratio of the number of trips relative to the storage area required. Step 2 Use the ratios from Step 1 to make assignments. Assign the department with the highest ratio closest to the dock. Next, assign the department with the second-highest ratio second closest to the dock. Continue in this manner until all departments have been assigned.

Example 10.3 is another example of how this would work.

Department Trips to and from Dock Area Needed

1. Backpacks 160 2 2. Hiking boots 150 3 3. Jeans 100 1 4. T-shirts 120 1 5. Bomber jackets 270 3

FIGURE 10-5

Warehouse storage areas for Looking Good Clothes

Department Trips to and from Dock Area Needed

Ratio of Trips to Area Needed

1. Backpacks 160 2 80 2. Hiking boots 150 3 50 3. Jeans 100 1 100 4. T-shirts 120 1 120 5. Bomber jackets 270 3 90

Total Area 10

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388 • CHAPTER 10 FACILITY LAYOUT

Step 2: Use the ratios from Step 1 to assign departments to storage areas. These are shown in the block plan in Figure 10-6.

FIGURE 10-6

Block plan for Looking Good warehouse

Office Layouts Office layouts are another special case of process layouts. Merely looking at the num- ber of trips between departments or the movement of goods is not sufficient to design a good office layout because human interaction and communication are the primary factors that need to be considered when designing office layouts. Recall from Chapter 1 that an important lesson learned from the Hawthorne studies in the 1930s was that workers respond greatly to their physical environment and have many psychological needs. This information, coupled with the fact that almost half of the workforce in the United States works in an office environment, makes office layouts very important.

Proximity versus Privacy One of the key trade-offs that has to be made in an office layout is between proximity and privacy. The ability of workers to communicate and interact with one another is highly important in an office environment. As companies increasingly embrace team approaches, open office environments are valued because they provide visibility and allow workers to interact easily. Studies have shown that

A properly designed office layout can significantly improve productivity.

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DESIGNING PRODUCT LAYOUTS • 389

workers who are in close proximity to one another have greater understanding, tolerance, and trust for one another.

However, office layouts that enhance team interactions do not allow privacy. Often, employees need privacy to think and work quietly without being interrupted. Also, it may be difficult to have confidential conversations with coworkers and clients in an open office environment. When designing an office layout, these considerations must be addressed in order to enhance productivity.

Other Factors in Designing Office Environments One important consideration in designing any layout is flexibility. Flexible layouts remain desirable many years into the future or can be easily modified to meet changing demands. Traditional load- bearing walls provide privacy but do not provide flexibility. Partitions, on the other hand, are very flexible but do not offer privacy.

Companies are becoming more creative in meeting the needs of their employees, enhancing productivity, and designing flexible layouts. One option is to use what is commonly called office landscaping. This entails using plants, decor, and indoor land- scaping to provide natural-looking partitions and sections that allow for privacy and flexibility but still have the feel of an open office environment. In addition, the natural look of office landscaping provides a pleasant working environment.

� Flexible layouts Layouts that remain desirable many years into the future or can be easily modified to meet changing demand.

Before You Go On

We have described several different types of layouts. By now you should know how to design process layouts and understand the unique characteristics of warehouse and office layouts. We will now learn how to design product layouts. Since process and product layouts are very different, make sure you review these differences. Before pro- ceeding further, also review the characteristics of product layouts.

DESIGNING PRODUCT LAYOUTS Recall that product layouts arrange resources in sequence so that the product can be made as efficiently as possible. This type of layout is used in repetitive processing sys- tems that produce a large volume of one standardized product.

Product layouts are completely different from process layouts. In product layouts the material moves continuously and uniformly through a sequence of operations until the work is completed. The sequence of operations allows for the simultaneous performance of work. When designing product layouts, our objective is to decide on the sequence of tasks to be performed by each workstation. To accomplish this we need to consider the logical order of the tasks to be performed and the time required to perform each task. Also, we need to consider the speed of the production process, which will tell us how much time there is at each workstation to perform the assigned tasks. This entire process is called line balancing. Next we will go through the steps that must be followed in designing product layouts.

Step 1: Identify Tasks and Their Immediate Predecessors The first step in designing product layouts is to identify the tasks or work elements that must be performed in order to produce the product. We also need to determine how long each task takes to perform and which tasks must be performed in sequence. The task or tasks that must be performed immediately before another task can be done is called the task’s immediate predecessor. We use an example to illustrate this point.

� Line balancing The process of assigning tasks to workstations in a product layout in order to achieve a desired output and balance the workload among stations.

� Imme diate predecessor A task that must be performed immediately before another task.

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390 • CHAPTER 10 FACILITY LAYOUT

Vicki’s Pizzeria is planning to make boxed take-out versions of its famous pepperoni, sausage, and mushroom pizza. The pizzas will be made on a small assembly line. Vicki has identified the tasks that need to be performed, the time required for each task, and each task’s immediate predecessor. This information is shown here:

Often it is helpful to have a visual representation of the precedence relationships between the tasks that need to be performed. This is called a precedence diagram. Figure 10-7 illustrates the precedence diagram for assembling Vicki’s pizzas. The diagram is read from left to right. The circles, or nodes, represent the tasks, and the arrows, or arcs, show the connections between them. Together, they show how the tasks are connected. To find a task’s immediate predecessor, follow the arrows backward from your task. In the diagram you can see that the first task that must be performed is task A. After A has been completed, B should be done. Next comes task C and then D. After task D, however, we can do either E, F, or G. However, to be able to complete task H, we must have completed all the predecessors of H—namely, E, F, and G. Finally, after all the other tasks have been completed, task I can be performed.

� Precedence diagram A visual representation of the precedence relationships between tasks.

EXAMPLE 10.4

Vicki’s Pizzeria and the

Precedence Diagram

Work Element Task Description Immediate Predecessor

Task Time (in seconds)

A Roll dough None 50 B Place on cardboard backing A 5 C Spread sauce B 25 D Sprinkle cheese C 15 E Add pepperoni D 12 F Add sausage D 10 G Add mushrooms D 15 H Shrinkwrap pizza E, F, G 18 I Pack in box H 15

Total task time 165

FIGURE 10-7

Precedence diagram for Vicki’s Pizzeria

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DESIGNING PRODUCT LAYOUTS • 391

Step 2: Determine Output Rate The next step is to determine how many units of product we wish to produce over a period of time, called the output rate. Then we can design a product layout that pro- duces the desired number of units with as few work centers as possible and balance the workload at each workstation. In our example, Vicki has decided that she wishes to produce 60 pizzas per hour in order to meet her growing demand.

Notice that the total task time to produce 1 pizza is 165 seconds. If Vicki wants to perform all nine work elements herself, her maximum output in 1 hour would be:

Maximum output 5 3600 seconds/hour 165 seconds/unit

5 21.8 pizzas/hour

If Vicki wants to produce 60 pizzas in an hour, she will have to divide the work among a number of people working simultaneously at workstations to achieve her desired output rate. Let’s see what Vicki has to consider.

Step 3: Determine Cycle Time Cycle time is the maximum amount of time each workstation has to complete its assigned tasks. Cycle time also tells us how frequently a product is completed. Recall that in product layouts work is being performed on many workstations that are arranged in sequence. At the beginning of the line, workers are carrying out the initial stages of putting the product together. At the end of the line, the last steps of produc- tion are being completed. If you were to stop the process at any one point in time, you would find products at all stages of production, from raw materials through work-in- process, as well as completed products.

Cycle time is directly related to the volume that can be produced. The faster the cycle time (the lower its numerical value), the greater the output. A cycle time of 50 seconds means that each workstation has 50 seconds to perform its assigned tasks and that one unit is completed every 50 seconds. By contrast, a cycle time of 100 seconds would give more time to each workstation. It also means that a product would be com- pleted every 100 seconds. You can see that by producing a unit every 50 seconds we will produce more units at the end of the day, as opposed to producing a unit every 100 seconds. Therefore, cycle time is directly related to output.

Output 5 available time

cycle time

General Cycle Time Equation Cycle time can be computed from the preceding equation as follows:

Cycle time 5 C 5 available time

output

Computing Cycle Time When Output Is in Units per Hour Cycle time is generally computed in seconds per unit. Note that available time and output are measured over a period of time, such as per hour or per day. Remember that these need to be over the same time period for the computation to work. For example,

Cycle time 1seconds/unit 2 5 C 5 available time 1seconds/hour 2 output 1units/hour 2

Note that the “per hour” in the numerator cancels out the “per hour” in the denomina- tor and the final measure is “seconds per unit.”

� Output rate The number of units we wish to produce over a specific period of time.

� Cycle time The maximum amount of time each workstation has to complete its assigned tasks.

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392 • CHAPTER 10 FACILITY LAYOUT

Computing Cycle Time When Output Is in Units per Day The same type of com- putation would be performed if we were given the desired output in units per day:

Cycle time 1seconds/unit 2 5 C 5 available time 1seconds/day 2 output 1units/day 2

As before, the “per day” in the numerator cancels out the “per day” in the denomina- tor and the final measure is “seconds per unit.” The important thing is to use the same units in the denominator as in the numerator.

Now let’s compute the cycle time for Vicki’s assembly line. Vicki said that she wanted to produce 60 pizzas per hour as her desired output. We start with the general equation for cycle time. Then we substitute the specific numerical values and perform the computations:

Cycle time 1seconds/unit 2 5 C 5 available time 1seconds/hour 2 desired output 1units/hour 2

5 60 minutes/hour 3 60 seconds/minute

60 units/hour

5 3600 seconds/hour

60 units/hour

5 60 seconds/unit

Vicki needs to have a cycle time of 60 seconds per unit to produce 60 pizzas in an hour. This means that each workstation has 60 seconds to perform its task. Also, this means that 1 completed pizza will be finished every 60 seconds. After 1 hour, Vicki will have 60 pizzas.

Relationship between Minimum Cycle Time (Bottleneck) and Maximum Output What if Vicki changed her mind and wanted to produce more than 60 pizzas per hour? This would mean that her cycle time would have to be faster (its numerical value would be lower), and pizzas would be produced more frequently than every 60 seconds. Perhaps she could lower the cycle time to 55 seconds or even 50 seconds. But what is the lowest possible value for the cycle time?

Note that if Vicki lowered the cycle time below 50 seconds, there would not be enough time to do task A, which requires 50 seconds. Therefore, given the current task times, 50 seconds is the lowest cycle time Vicki’s assembly line could have. Task A is the longest task and thus acts as a constraint. This is called the bottleneck. The bottleneck constrains the production process and determines the lowest or minimum cycle time.

Sometimes it is possible to reduce the bottleneck by splitting the task into smaller ones that can be done separately. For example, maybe our bottleneck task, which is rolling dough, can be divided into smaller tasks, such as placing dough on a floured board and rolling it out. However, there will always be a bottleneck. Once we eliminate one bottleneck, the next-longest task becomes the bottleneck.

The bottleneck is important because it provides the lowest limit on the cycle time. Cycle time is related to the amount of output; therefore, this minimum cycle time determines the maximum output that can be achieved given current tasks. The rela- tionship can be derived as follows:

Maximum output 5 available time

minimum cycle time 1bottleneck 2

� Bottleneck task The longest task in a process.

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DESIGNING PRODUCT LAYOUTS • 393

Using this equation, we can compute the maximum output Vicki can have on her assembly line given that task A (the bottleneck) takes 50 seconds:

Maximum output 5 3600 seconds/hour

50 seconds/unit 5 72 units/hour, or 72 pizzas per hour

The maximum that Vicki can produce on her assembly line is 72 pizzas per hour.

Maximum versus Minimum Cycle Time We learned that the minimum cycle time is equal to the bottleneck, or longest, task. In our example, the minimum cycle time was 50 units per second, resulting in an output of 72 pizzas per hour. This would require that the work be spread out over multiple workstations working simultaneously. The maximum cycle time is equal to the sum of the task times, or 165 seconds. As we saw earlier, this would result in the production of 21.8 pizzas per hour and would require that all tasks be performed at a single workstation.

The minimum and maximum cycle times are important because they establish the range of output for the production line. In our case, the range of output is between 21.8 and 72 pizzas per hour and is dependent on the cycle time. Vicki’s desired output, 60 pizzas per hour, falls within this range.

Step 4: Compute the Theoretical Minimum Number of Stations Before we decide to assign specific tasks to workstations, it is usually helpful to com- pute the theoretical minimum number of stations, or TM. The theoretical minimum number of stations is the number of workstations that would be needed if the line was 100 percent efficient. Rarely do we achieve 100 percent efficiency, and often we will have more stations than the theoretical minimum. However, computing this number gives us a baseline for the number of stations we should have. The computation for the theoretical minimum number of stations is as follows:

TM 5 St C

where �t � sum of the task times needed to complete a unit C � cycle time

For Vicki’s assembly line, the theoretical minimum number of stations (TM) is

TM 5 165 seconds 60 seconds

5 2.75, or 3 stations

Theoretical minimum numbers of stations that end with a fraction are always rounded up because there can be no partial workstations. Notice that the theoretical minimum number of stations results in the production of daily requirements when no ineffi- ciency exists.

Step 5: Assign Tasks to Workstations (Balance the Line) Given the tasks we have to perform and their precedence relationships as well as the cycle time, we can now proceed to assign tasks to workstations. To do this, a number of rules can be used at this stage. We will use the longest task time rule, which basically states that when selecting from a group of tasks we should pick the task that takes the longest time. However, in practice a number of other rules can be used. Following are the basic steps in this process:

� Theoretical minimum number of stations The number of workstations needed on a line to achieve 100 percent efficiency.

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394 • CHAPTER 10 FACILITY LAYOUT

Let’s see how these steps apply to Vicki’s Pizzeria. A convenient method is to make a table with columns labeled Workstation, Eligible Task, Task Selected, Task Time, and Idle Time. We can then fill in the table by following the steps we have outlined and keeping a cycle time of 60 seconds. This is shown in Table 10-6.

Step 6: Compute Efficiency, Idle Time, and Balance Delay After tasks have been assigned to workstations, we should compute the efficiency of the arrangement. Efficiency is the ratio of total productive time divided by total time, given as a percentage:

Efficiency 1% 2 5 St NC 1100 2

where �t � sum of the task times N � number of workstations

C � cycle time Note that in this equation the numerator is the actual work time, whereas the denomi- nator is the time allocated for performing tasks. To improve efficiency, we try to assign as much work to the lowest number of workstations needed to produce the volume of product desired while keeping the workloads balanced.

Often it is helpful to compute the amount by which the efficiency of the line falls short of 100 percent. Called the balance delay, it is computed as follows:

Balance delay 1% 2 5 100 2 efficiency

� Efficiency The ratio of total productive time divided by total time, given as a percentage.

� Balance delay The amount by which the line efficiency falls short of 100 percent.

TABLE 10-6

Assignments of Tasks to Workstations for Vicki’s Pizzeria

Workstation Eligible Task Task Selected Task Time Idle Time

1 A A 50 10

B B 5 5

2 C C 25 35

D D 15 20

E, F, G G 15 5

3 E, F E 12 48

F F 10 38

H H 18 20

I I 15 5

Cycle time = 60 seconds

Steps Procedure for Assigning Tasks to Workstations A Start with the first station; make a list of eligible tasks to be performed, following

precedence relationships. B Select from the eligible task list by picking the task that takes the longest time (longest

task time rule). If only one task is eligible, we do not need to use the rule. C When the cycle time has been used up at one station or no tasks can be assigned to the

remaining time, start a new station.

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DESIGNING PRODUCT LAYOUTS • 395

Other Considerations In designing process layouts we went from a crude block plan to the design of a detailed layout. Similarly, many details of product layout design need to be addressed in addi- tion to the ones we have discussed.

Shape of the Line We know that product layouts arrange work centers in sequence to allow for efficient production. Even though this sequence is linear, the actual shape of the product layout usually is not one long, straight line. If it were, we would need an unusually long, straight building. Also, having a long, straight line may not be best from a productivity standpoint. Arranging the shape of the line so that workers can see and communicate with one another can improve productivity and worker satisfaction. The actual shape of the line can be an S shape, a U shape, an O, or an L. Much thought should go into the choice of an appropriate shape. For example, shapes such as U and O can store frequently used resources in the center, where they are accessible to everyone.

Paced versus Unpaced Lines Another issue to decide on is whether to have a paced or an unpaced line. On paced lines the product being worked on is physically attached to the line and automatically moved from one station to the next when cycle time elapses. The amount of time workers have to perform their tasks is identical to cycle time. Unpaced lines, on the other hand, allow the product to be physically removed from the line to be worked on. Workers can then vary the amount of time they spend working on the product. Storage areas for inventory are often placed between workstations, to be used when there is a delay in production at a station.

The work environment has a significant impact on worker satisfaction and produc- tivity. Some studies have found that unpaced lines lead to greater productivity when they are coupled with a good incentive program. This situation provides more auton- omy and freedom for workers. However, in some environments a paced line is the only option—for example, when the product is very large and cannot be moved. This would be the case when assembling large and heavy items such as a large refrigerator or an automobile.

Number of Product Models Produced Another consideration is whether to have a single-model or a mixed-model line. A single-model line is designed to produce only one version of a product. In contrast, a mixed-model line is designed to produce many versions. For example, a single line might produce only Jeep Wranglers, whereas a mixed-model line might produce two types of Jeeps, such as the Wrangler and the Cherokee. A mixed model is more flexible, but there may be more complications with regard to scheduling and changing production from one model to the other.

� Paced line A system in which the product being worked on is physically attached to the line and automatically moved to the next station when the cycle time has elapsed.

� Single-model line A line designed to produce only one version of a product. � Mixed-model line A line designed to produce many versions of a product.

For Vicki’s assembly line, we can compute the efficiency and balance delay:

Efficiency 1% 2 5 165 seconds 3 stations 3 60 seconds

1100 2 5 165 180

5 91.66%

Balance delay 1% 2 5 100 2 91.66 5 8.34%

EXAMPLE 10.5

Computing Efficiency and Balance Delay

On paced lines the product being worked on automatically moves from one station to the next.

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396 • CHAPTER 10 FACILITY LAYOUT

Before You Go On

Both process and product layouts have their strengths and weaknesses. Process layouts are flexible and can produce many different kinds of products. Process layouts are less efficient than product layouts because material handling costs can create much inefficiency. Product layouts, on the other hand, are less flexible because all their resources are devoted to the production of one type of product. However, they are very efficient and create little waste. Make sure you understand these differences, because in the next section we will look at ways of combining some of the strengths of process and product layouts.

GROUP TECHNOLOGY (CELL) LAYOUTS Hybrid layouts combine characteristics of both process and product layouts. They are created whenever possible in order to combine the strengths of each type of layout. One of the most popular types of hybrid layouts is group technology (GT) or cell layouts. Group technology has the advantage of bringing the efficiencies of a product layout to a process layout environment.

If we look at a company that produces many different products, we may find that some products are similar to each other in the way they are made and the resources they require. For example, a company may produce 500 different products. However, if we analyze how each of the products is manufactured, we may be able to create groups of products—say, one group of 150, another group of 100, and so on—that are very similar in the way they are produced. To be efficient, we could place all the resources needed for each group in a separate area, called a cell. The production of a group, or family, of items would be done very efficiently because all the resources required would be in close proximity. This is the goal of group technology.

Group technology is the process of creating groupings of products based on similar processing requirements. For example, Figure 10-8 shows parts that all belong to the same family. These parts are all different, but they are very similar in the way they are made. Group technology essentially creates small product layouts dedicated to the production of a group of items. Figure 10-9 shows process flows before and after the use of GT cells. The first picture is a process layout with different product routes for the many products a company produces. The second picture shows the implementa- tion of group technology in that environment. Cells have been created for each family, and there is a much more orderly flow through the facility.

� Group technology (GT) Brings the efficiencies of a product layout to a process layout.

FIGURE 10-8

Unorganized and organized parts

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FACILITY LAYOUT ACROSS THE ORGANIZATION • 397

FIGURE 10-9

Process flows before and after the use of GT cells

FACILITY LAYOUT WITHIN OM: HOW IT ALL FITS TOGETHER As you have learned in this chapter, layout decisions are directly related to issues of prod- uct design and process selection (Chapter 3). The company’s process dictates the type of layout the company will have. In turn, facility layout decisions are linked with a number of other operations decisions. One such issue is that of job design, as process layouts tend to require greater worker skills than do product layouts (Chapter 11). Another issue is the degree of automation, as product layouts tend to be more capital intensive and use more automation compared to process layouts (Chapter 3). Layout decisions are also affected by implementation of just-in-time (JIT) systems, which dictate a line flow and the use of group technology (GT) cells (Chapter 7). As layout decisions specify the flow of goods through the facility, they impact all other aspects of operations management.

FACILITY LAYOUT ACROSS THE ORGANIZATION We have seen in this chapter why layout planning is important for operations manage- ment. The production process could not run efficiently, and there would be much waste, if we did not design a proper layout for a facility. Layout planning is also impor- tant for other functions in the organization.

Marketing is highly affected by layout planning, particularly in environments where customers and clients come to the site. This is especially true in retail environ- ments. The location and placement of goods in the facility, their visibility, and ease of access can greatly influence sales. In these types of environments the marketing of the product takes place, in part, through the layout of a facility. For example, attractive displays and product positioning in a grocery store can promote advertising and sales. Thus, it is very important for marketing to work with operations in designing the layout.

Human resources knows how the management of people can be affected by lay- out planning. Studies have shown that people who work and interact together on a regular basis have better working relationships. Managers need to give much thought

MKT

HRM

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398 • CHAPTER 10 FACILITY LAYOUT

to which groups of people need to work closely together and place them in close prox- imity to one another to facilitate teamwork and cooperation.

Finance is involved whenever large financial outlays and cost considerations are at stake. This is certainly true in layout planning. Redesign of layouts can be very costly, particularly if they are large scale, as when switching to an open office environment or a cell layout. Finance needs to measure the value of these investments and work with operations to create budgets. They must also understand the long-range implications of a good layout for the entire organization in order to evaluate them properly.

Everyone in the company is affected by the design of a facility’s layout. Whatever organization you work at, you are somehow affected by its layout design, including the location of your office and department, the appearance of your office, and the degree of privacy you have. Also included are proximity to coworkers and other people you must interact with, whether you must travel long distances to get supplies or clerical assistance, and the aesthetics of the layout. Regardless of what business function you are in, the degree of satisfaction with your work environment is greatly dependent on the layout.

FIN

Entities that make up a supply chain must be linked efficiently, with product flowing smoothly through- out the chain. This includes efficient shipments and deliveries between entities. Facility layout plays an important role in making sure this takes place. Arranging layouts for efficient delivery of materials to move directly to the production line is important. In locating the shipping and receiving docks, consideration must be given to the layout of the pro- duction facility and the shape of the production line to

enable smooth flow between inbound and outbound shipments and the production facility. Similarly, in the retail environment delivery of products must be done in

a manner that enhances sales. For example, suppli- ers may deliver products on display-ready pallets to directly and efficiently move the product into the retail flow, eliminating the need for unload-

ing and stocking shelves. Arranging the facility layout to be linked to inbound and outbound ship-

ments can greatly enhance the smooth flow of products throughout the supply chain.

THE SUPPLY CHAIN LINK

In addition to promoting organizational efficiency, facility layout can play a significant role in improving organizational sustainability. Office spaces use huge amounts of energy for lighting, heating, and air condi- tioning, and performing operations. The facility design and layout—such as office space designs with ample natural light, the use of cutting-edge insulation material, and the use of innovative recycled building material— can go a long way in reducing energy consumption and creating models of energy efficiency.

An excellent example of savings that can be achieved through environmental layout design is seen in the offices of the National Resource Defense Council (NRDC). The NRDC has been a pioneer in the area of sustainability and wanted to showcase environmental principles through the design of their own offices, which incorporate green

design features. For example, their New York office space was on the top three floors of a 12-story Art Deco build- ing in a neighborhood with few skyscrapers, selected for its abundance of natural light. The combination of this “free” light and energy using energy-efficient technolo- gies has cut energy consumption by 70% compared to conventional offices. Similar savings have been seen in their Washington office, which showcases not only similar energy-efficiency measures, but also the use of innovative building materials as compressed straw wall panels, countertops made of soybeans and recycled news- papers, and ceramic tile containing 70% recycled glass from windshields and plate glass. The result of these “green offices” has been an annual operating cost sav- ings of $65,000 and energy savings of more than $1 per square foot. This illustrates that layout design with an eye toward sustainability not only helps the environment but also has a direct positive impact on the bottom line.

THE SUSTAINABILITY LINK

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FORMULA REVIEW • 399

Chapter Highlights 1 Layout planning is deciding on the best physical

arrangement of all resources that consume space within a facility. Proper layout planning is highly important for the effi cient running of a business. Otherwise, there can be much wasted time and energy, as well as confusion.

2 Th ere are four basic types of layouts: process, product, hybrid, and fi xed position. Process layouts group resources based on similar processes or functions, as in a hospital or a machine job shop. Product layouts arrange resources in straight-line fashion, as on an assembly line. Hybrid layouts combine elements of both process and product layouts in their operation. Finally, fi xed-position layouts occur when the product is large and cannot be moved.

3 Process layouts provide much fl exibility and allow for the production of many products with diff ering characteristics. Product layouts, on the other hand, provide great effi ciency when producing one type of product.

4 Th e steps in designing a process layout are (1) gathering information about space needs, space availability, and

closeness requirements of departments; (2) developing a block plan or schematic of the layout; and (3) developing a detailed layout.

5 The steps in designing a product layout are (1) identifying tasks that need to be performed and their immediate predecessors; (2) determining output rate; (3) determining cycle time; (4) computing the theoretical minimum number of stations; (5) assigning tasks to workstations; and (6) computing efficiency, idle time, and balance delay.

6 Hybrid layouts have advantages over other layout types because they combine elements of both process and product layouts to increase effi ciency.

7 An example of hybrid layouts is group technology or cell layouts. Group technology is the process of creating groupings of products based on similar processing requirements. Cells are created for each grouping of products, resulting in a more orderly fl ow of products through the facility.

Key Terms layout planning 373 intermittent processing systems 374 repetitive processing systems 374 process layouts 374 product layouts 376 hybrid layouts 378 group technology (GT) or cell

layouts 378 fi xed-position layout 379 block plan 380

from–to matrix 380 REL chart 381 load–distance model 382 rectilinear distance 383 ALDEP and CRAFT 384 fl exible layouts 389 line balancing 389 immediate predecessor 389 precedence diagram 390 output rate 391

cycle time 391 bottleneck task 392 theoretical minimum number of

stations 393 effi ciency 394 balance delay 394 paced line 395 single-model line 395 mixed-model line 395 group technology (GT) 396

Formula Review 1. ld score 5 Slijdij

2. Cycle time 5 C 5 available time

output

3. Output 5 available time

cycle time

4. Maximum output 5 available time

minimum cycle time 1bottleneck 2 5. TM 5

St C

6. Efficiency 1% 2 5 St NC

1100 2 7. Balance delay 1% 2 5 100 2 efficiency

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400 • CHAPTER 10 FACILITY LAYOUT

Solved Problems • Problem 1 Jeff-Co Industries is a metalworking shop that is redesigning its layout. The from–to matrix of the numbers of trips between departments is shown in Table 10-7.

The current layout is shown in Figure 10-10. Find an improved layout using trial and error. Which departments should you locate close together?

• Before You Begin: To solve this problem, begin by identifying the departments with the highest number of trips between them from the from– to matrix in Table 10-7. Then redesign the initial layout by bringing the identified departments in close proximity to one another. Compute the ld scores for the initial and proposed lay- outs and compare.

• Solution The following departments have the highest numbers of trips between them and should be located close together:

A and F, which have 50 trips between them C and D, which have 45 trips between them

Using this as our criterion, we can construct the proposed layout shown in Figure 10-11.

(See student companion site for Excel template.)

Table 10-7 From–To Matrix for Jeff-Co Industries Trips between Departments

Department A B C D E F A — 10 15 — — 50 B — — 20 10 20 C — 45 — 10 D — — 20 E — F —

We can now compute the ld scores for both the current and proposed layouts in order to make an evaluation. See Table 10-8 on the next page.

Based on the ld score, the proposed layout is an improve- ment of 43 percent over the current layout. This problem can also be solved using a spreadsheet. This is shown in the spread- sheet below.

FIGURE 10-10

Current layout for Jeff-Co Industries

FIGURE 10-11

Proposed layout for Jeff-Co Industries

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SOLVED PROBLEMS • 401

Table 10-8 ld Score Computations for Current and Proposed Layouts for Jeff-Co Industries Current Layout Proposed Layout

Departments

Number of Trips

(obtained from from–to matrix)

Distance (obtained

from current block plan)

Load– Distance

Score ld

Distance (obtained from proposed block

plan) d

Load– Distance

Score ld

A and B 10 1 10 2 20 A and C 15 2 30 2 30 A and F 50 3 150 1 50 B and D 20 2 40 1 20 B and E 10 1 10 1 10 B and F 20 2 40 1 20 C and D 45 3 135 1 45 C and F 10 1 10 3 30 D and F 20 2 40 2 40

Total 465 265

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402 • CHAPTER 10 FACILITY LAYOUT

• Problem 2 Parachutes By Dave is a parachute production facility that assembles and packages parachutes. Table 10-9 shows the tasks required to perform the job, the times required by each task, and their immediate predecessors.

If Dave wants to produce 50 parachutes per hour, compute the following:

(a) The appropriate cycle time (b) The theoretical minimum number of stations (c) Which tasks should be assigned to which workstations

(using the longest task time rule) (d) The efficiency and balance delay of your solution

• Before You Begin: To solve this problem, use the steps given for designing product layouts. Notice that the sum of the task time is 164 seconds.

• Solution

(a) Cycle time 5 C 5 available time per hour

output per hour

5 3600 seconds/hour

50 units/hour 5 72 seconds/unit

(b) Th eoretical minimum number of stations:

TM 5 St C

5 164 seconds

72 seconds/unit

5 2.28 stations, or 3 stations

(c) Assigning tasks to workstations with a cycle time of 72 sec- onds and using the longest task time rule, we obtain the fol- lowing solution:

Cycle time 5 72 seconds

(d) Efficiency 1% 2 5 St N 3 C

1100 2 5 164 3 3 72

1100 2 5 75.93%

Balance delay 1% 2 5 24.07%

Table 10-9 Task Information for Parachutes by Dave Work Task Immediate Task Time

Element Description Predecessor (sec) A Cast lines None 45 B Attach harness A 15 C Sew rings A 27 D Attach lines to chute B 52 E Perform safety check C, D 7 F Pack chute E 18

Total 164 seconds

Workstation Eligible

Task Task

Selected Task Time

Idle Time

1 A A 45 27 B, C C 27 0

2 B B 15 57 D D 52 5

3 E E 7 65 F F 18 47

Discussion Questions 1. Explain the importance of layout planning for a business.

What are the consequences of a poor layout? 2. Explain the importance of layout planning for everyday life.

How has poor layout planning affected your life? 3. Identify the four types of layouts and their characteristics. 4. Identify the steps in designing a process layout. 5. Find examples of a process layout in local businesses. Draw

a picture of the locations of departments.

6. Identify the steps in designing a product layout. 7. Find examples of a product layout in local businesses.

Draw a picture to show the workstations and the tasks performed. 8. Explain the concept of cycle time and how it affects out-

put. Give an example. 9. Define group technology. Why is it important? 10. Give an example of a poor layout. Find a better solution

for that layout problem.

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PROBLEMS • 403

Problems 1. Fresh Foods Grocery is considering redoing its facility

layout. Th e from–to matrix showing daily customer trips between departments is shown in Table 10-10, and their current layout is shown in Figure 10-12. Fresh Foods is considering exchanging the locations of the dry groceries department (A) and the health and beauty aids department (F). Compute the ld score for Fresh Foods’ current and proposed layouts. Which is better?

4. Use trial and error to fi nd a better layout for Mason Machine Tools in Problem 3. Compute the ld score and compare it to Mason’s current and proposed layouts in Problem 3.

5. Gator Offi ce Systems is comparing two layouts for the design of its offi ce building. It has interviewed managers in order to develop the from–to matrix shown in Table 10-12. Th e two layouts considered are shown in Figure 10-14. Which layout do you think is better for Gator Offi ce Systems, using the load– distance model?

Table 10-10 From–To Matrix for Fresh Foods Trips between Departments

Department A B C D E F A. Dry groceries — 15 45 25 10 50 B. Bread — 30 16 25 25 C. Frozen foods — 34 15 20 D. Meats — 40 10 E. Vegetables 20 F. Health and

beauty aids —

2. Use trial and error to fi nd a better layout for Fresh Foods Grocery in Problem 1. Compute the ld score and compare it to the ld scores computed for Fresh Foods’ current and proposed layouts. Which is best?

3. Mason Machine Tools is reevaluating its facility layout. Th e current layout is shown in Figure 10-13 and the from–to matrix is in Table 10-11. Mason has to leave department C in its current location because relocation costs are too high. It is considering exchanging departments B and D. Evaluate the proposal by computing the ld score for both layouts.

Table 10-11 From–To Matrix for Mason Machine Tools Trips between Departments

Department A B C D E F A — 5 20 5 — 8 B — — 30 10 10 C — 20 15 5 D — — — E 17 F —

Table 10-12 From–To Matrix for Gator Office Supplies Trips between Departments

Department A B C D E F A — 30 — 34 50 25 B — — 55 10 10 C — — 15 5 D — — — E 30 F —

FIGURE 10-13

Current layout for Mason Machine Tools

FIGURE 10-12

Current layout for Fresh Foods

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404 • CHAPTER 10 FACILITY LAYOUT

6. Use trial and error to develop a better layout for Gator Offi ce Supplies. Which departments do you think need to be in close proximity to one another?

7. T-Shirts Unlimited is a retailer that sells every kind of T-shirt imaginable. Th e diff erent types of T-shirts are stored in departments that all take up the same amount of space. Given the available warehouse space (Figure 10-15) and a from–to matrix showing the number of trips to and from each department (Table 10-13), help T-Shirts Unlimited decide where to store each type of T-shirt.

FIGURE 10-14

Current and proposed layouts for Gator Office Supplies

Table 10-13 From–To Matrix for T-Shirts Unlimited

Department Category Trips to and from Dock

1 Sports T-shirts 50 2 Men’s T-shirts 63 3 Women’s T-shirts 35 4 Children’s T-shirts 55 5 Fashion T-shirts 48 6 Undershirts 60

Table 10-14 Department Information for David’s Sport Supplies

Department Trips to and from Dock

Area Needed

1. Baseball equipment 160 2 2. Football gear 100 1 3. Hockey equipment 150 3 4. Basketball equipment 120 1 5. Sports clothes 270 3

FIGURE 10-16

Warehouse storage areas for David’s Sport Supplies

FIGURE 10-15

Warehouse storage areas for T-Shirts Unlimited

8. David’s Sport Supplies is a store that sells sports equipment and gear for teenagers and young adults. David’s is in the process of assigning the location of storage areas in its warehouse (Figure 10-16) to minimize the number of trips made to retrieve needed items. Given here in Table 10-14 are the departments that need to be located, the number of trips made per week for each department, and the area needed by each department.

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PROBLEMS • 405

9. MMS Associates is a telecommunications service provider. Th e company is currently redesigning its main offi ce to accommodate six newly hired salespeople. Some of the salespeople are expected to work in teams, so offi ce assignments are very important. Table 10-15 presents the from–to matrix showing the expected frequency of contacts between members of the new sales staff . Th e block plan in Figure 10-17 shows the assigned offi ce locations for the six sales members. Assume equal-sized offi ces and rectilinear distances. How would you evaluate the developed layout? What is the ld score for MMS Associates?

10. Michael Marc, the President of MMS Associates, is considering an alternative plan for the sales staff situation described in Problem 9. His alternative plan is shown in Figure 10-18. What is the ld score for this plan? How does it compare to the original plan considered in Problem 9?

FIGURE 10-17

Assigned office locations for sales staff at MMS Associates

Table 10-15 Number of Contacts between Sales Staff Sales Person A B C D E F

A — 6 12 18 1 1 B — 4 19 3 0 C — 5 0 0 D — 7 19 E — 0 F —

Table 10-16 Task Information for Problem 12 Task Immediate Predecessor Task Time(sec)

A None 35 B A 50 C A 21 D B 38 E C 25 F D, E 58 G F 15

FIGURE 10-18

Alternative office locations for sales staff at MMS Associates

11. Use trial and error to fi nd a better layout for MMS Associates. Which salespeople will be your priority to keep together?

12. A manufacturing company is designing an assembly line to produce its main product. Th e line should be able to produce 60 units per hour. Th e following data in Table 10-16 give the necessary information.

(a) Which task is the bottleneck? (b) Draw a precedence diagram for the above information. (c) Compute the cycle time with a desired output of 60 units

per hour. 13. An assembly line must be designed to produce 50 packages

per hour. Th e following data in Table 10-17 give the necessary information.

Table 10-17 Task Information for Problem 13 Task Immediate Predecessor Task Time (sec)

A None 25 B A 60 C B 35 D B 45 E B 10 F C, D, E 50

(a) Draw a precedence diagram. (b) Compute the cycle time (in seconds) to achieve the

desired output rate. (c) What is the theoretical minimum number of stations? (d) Which work element should be assigned to which

workstation? (e) What are the resulting effi ciency and balance delay

percentages? 14. An assembly line must be designed to produce 40

containers per hour. Th e following data in Table 10-18 give the necessary information.

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406 • CHAPTER 10 FACILITY LAYOUT

Table 10-19 Task Information for ABC Corporation Task Immediate Predecessor Task Time (sec)

A None 55 B A 30 C A 22 D B 35 E B, C 50 F C 15 G F 5 H G 10

Table 10-20 Task Information for Kiko Teddy Bear Work

Element Task Description

Immediate Predecessor

Task Time (sec)

A Cut teddy bear pattern None 90 B Sew teddy bear cloth A 75 C Stuff teddy bear B 50 D Glue on eyes C 20 E Glue on nose C 15 F Sew on mouth C 35 G Attach manufacturer’s

label B 15

H Inspect and pack teddy bear

D, E, F, G 40

Table 10-18 Task Information for Problem 14 Task Immediate Predecessor Task Time (sec)

A None 60 B A 12 C B 35 D A 55 E D 10 F E 50 G F, C 5

(a) Draw a precedence diagram. (b) Compute the cycle time (in seconds) to achieve the

desired output rate. (c) What is the theoretical minimum number of stations? (d) Which work element should be assigned to which

workstation? (e) What are the resulting effi ciency and balance delay

percentages? 15. Th e ABC Corporation is designing its new assembly line.

Th e line will produce 50 units per hour. Th e tasks, their times, and their immediate predecessors are shown in Table 10-19.

(a) Which task is the bottleneck? (b) Compute the cycle time with a desired output of 50 units

per hour. (c) Use a cycle time of 72 seconds/unit to assign tasks to

workstations. (d) Compute the theoretical minimum number of stations.

Did you end up using more stations than the theoretical minimum?

(e) Compute the effi ciency and balance delay of the line. 16. Kiko Teddy Bear is a manufacturer of stuff ed teddy bears.

Kiko would like to be able to produce 40 teddy bears per hour on its assembly line. Use the information provided in Table 10-20 to answer the questions that follow.

(a) Draw a precedence diagram. (b) What is the cycle time? (c) What is the theoretical minimum number of stations? (d) Assign tasks to specifi c workstations using the cycle time

you computed in part (b). (e) What are the effi ciency and balance delay of the line? (f) Which task is the bottleneck? (g) Compute the maximum output. 17. Use the longest task time rule to balance the assembly line

described in Table 10-21; the line can produce 30 units per hour.

Table 10-21 Assembly-Line Task Information

Work Element Immediate Predecessor

Task Time (sec)

A None 25 B A 30 C A 15 D A 30 E C, D 40 F D 20 G B 10 H G 15 I E, F, H 35 J I 25 K J 25

(a) What is the cycle time? (b) What is the theoretical minimum number of stations? (c) Which work elements are assigned to which workstations? (d) What are the resulting effi ciency and balance delay

percentages?

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CASE: SAWHILL ATHLETIC CLUB (A) • 407

Table 10-22 Dress-Making Task Information

Work Element Immediate Predecessor

Task Time (sec)

A. Cut dress body None 30 B. Cut sleeves None 40 C. Cut collar None 20 D. Sew dress body A 100 E. Sew sleeves to dress B, D 25 F. Sew collar to dress C, D 50 G. Hem dress D, E, F 50 H. Package dress G 90

18. A dress-making operation is being designed as an assembly line. Table 10-22 shows the tasks that need to be performed, their task times, and preceding tasks. If the goal is to produce 30 dresses per hour, answer the questions that follow the table.

(a) Compute the cycle time. (b) Which task is the bottleneck? (c) What is the maximum output for this line? (d) Compute the theoretical minimum number of stations. (e) Assign work elements to stations, using the longest task

time rule. (f) Compute the effi ciency and balance delay of your

assignment.

(a) Calculate the cycle time needed to produce 480 doors in an eight-hour work day.

(b) What is the minimum number of workstations that can be used on the line and still achieve the desired production rate? Balance the line and calculate its effi ciency.

20. Use the data from Problem 19 to rebalance the line with a cycle time of 90 seconds. How does the number of workstations change? What happens to the output and the line’s effi ciency?

19. Table 10-23 shows the tasks required to assemble an aluminum storm door and the length of time needed to complete each task.

Table 10-23 Task Information for Problem 19 Task Immediate Predecessor Task Time (sec)

A None 32 B A 43 C A 12 D A 23 E B, C, D 15 F E 25 G None 20 H F, G 5

CASE: Sawhill Athletic Club (A) Sawhill Athletic Club was an athletic facility in suburban Scotts- dale, Arizona. It was designed to provide a wide range of athletic opportunities, including racquetball courts and exercise facili- ties. The facilities were modern, and the staff focused on provid- ing high customer service. To provide flexibility to its members, the club had a wide range of hours of operation. The members were primarily families and young professionals who lived in the area.

Membership at the club had been steady since it opened five years ago. To monitor the club’s quality, members were often asked to fill out satisfaction surveys. Most members liked the club’s attention to customer satisfaction, but many complained that the facilities did not have a good layout. They complained of having to walk long distances from one location to another, cit- ing this as a significant inconvenience. Another complaint was that all the departments were separated with high walls, creat- ing a “closed-in” feeling. A new athletic facility was going to be opening in the area in the near future. The owners of Sawhill

thought that they had better listen to the requests of their cus- tomers in order to remain competitive.

Improving the Layout Lauren Nicole was hired to manage Sawhill and to offer any recommendations for changing the layout of the facility. She was told to be creative and use her knowledge of facility layout design. She was even provided with a diagram of the facility

Lobby Racquetball Courts

Food Court Pro Shop

Showers/Locker Child Care Room G Facility F

Layout of Sawhill Athletic Club

Exercise and Weight Room

C D

A B

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408 • CHAPTER 10 FACILITY LAYOUT

and averages of daily trips made by clients between each of the departments in the facility.

Lauren began looking at the information she received, shown in the table. She noticed that all the departments were of approx- imately equal size except the racquetball courts and exercise facilities. These were approximately twice the size of the other departments and could not be split. All the departments were eligible to be moved. She would take that into consideration as she decided to study the information and redesign the facil- ity. Then she would think about which walls to lower between departments to create a more open environment. Decorating would come last.

Case Questions 1. Develop an ld score for the current layout. What problems

can you identify with the current layout? 2. Use trial and error to come up with a better layout that

lowers the ld score. Explain the departments you thought needed to be in close proximity to one another.

3. Imagine an athletic facility such as Sawhill. What strategies would you suggest for creating an open environment?

Number of Trips between Departments

Department A B C D E F G

A. Lobby — 15 34 32 14 54 76 B. Racquetball

courts — 2 2 34 0 72

C. Food court — 26 0 47 3 D. Pro shop — 9 1 4 E. Exercise &

weight room — 7 74

F. Child care facility

— 57

G. Showers & locker

—

CASE: Sawhill Athletic Club (B) Sawhill Athletic Club, an athletic facility in Scottsdale, Ari- zona, was known for providing a high level of customer service to its members. Member complaints were taken seriously and immediately addressed. Lauren Nicole, the new manager of Sawhill, was now faced with having to resolve another service problem.

Sawhill provided a clean towel to each member upon enter- ing the women’s or men’s locker room. However, the facility had been regularly running out of clean towels for some time. Over the past month, members have complained loudly that some- thing had to be done because they were tired of waiting for clean towels.

Currently the facility had one industrial-size washing machine and one dryer, each with a capacity to hold 20 towels. The washing machine took 20 minutes to complete a load, and the dryer took 60 minutes. Following the drying process, the towels were folded and made available to members. The folding process took approximately 1 hour for 60 towels. The washing, drying, and folding of towels was done on an almost continual basis with at least one full-time person assigned to the area. The

demand averaged 60 towels per hour. The process operated as follows:

“The solution is simple. We will purchase an additional washer and dryer. That will solve the problem.” Lauren said confidently.

Case Questions 1. What is the reason Sawhill is regularly running out of

towels? 2. What is the cycle time of the current washing-drying-

folding process? What should the cycle time be in order to meet towel demand?

3. Will purchasing an additional washer and dryer solve the problem? How will the cycle time change with an additional washer and dryer? Suggest a solution to the problem.

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INTERNET CHALLENGE: DJ AND ASSOCIATES, INC. • 409

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Layout Analysis at Cruise International, Inc. Your next assignment will be with the Cruise Director, Jacque- line Downs. She is concerned that passengers start their cruise vacation in a good frame of mind. Therefore, she is quite con- cerned about the embarkation process and wants to make sure that guests are processed as quickly and efficiently as possible. Jacqueline wants you to examine the current embarkation pro- cess. Determine where potential waiting occurs for the guests, 5 and determine whether there might be a better layout for the

embarkation process. This assignment will enhace your knowl- edge of the material in Chapter 10 of your textbook while pre- paring you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Layout Analysis at CII

On-line Case: Facility Layout Design at Valley Memorial Hospital

Assignment: Layout Design at Valley Memorial Hospital Bob Reilly just called to tell you about a new assignment for you at VMH. Based on the projected growth in demand and the chang- ing needs of the community VMH serves, the top management is considering setting up a larger and more modern out-patient clinic in a vacant lot adjacent to the main building. This new facility, housed in a building with 4000 square feet of usable floor space (100 ft. by 40 ft.) will serve walk-in and minor emergency patients. Carol Gardner, assistant to the chief administrator, has a preliminary block plan indicating the layout of the differ- ent departments, such as Lobby and Registration, Radiology, and Nurses’ Station, that has been prepared by the emergency department staff. She wants you to analyze the preliminary

layout and also examine an alternate layout based on some changes that she thinks might be worth considering. Data on the traffic (i.e., number of trips) between the departments have also been compiled. This assignment will enable you to enhance your knowledge of the material in this chapter.

To access the Web site:

The law firm of DJ and Associates has just moved into a new facility. The spacious reception space has room for three recep- tionists and a client waiting area. The law firm has hired you to help with the layout of the reception area. It has given you a bud- get of $15,000 and asked you to purchase furniture for the recep- tion area that will fit into the layout, given certain constraints.

The reception area is 50 feet long by 20 feet wide. The cli- ent waiting area is 400 square feet, leaving 600 square feet for the three receptionists, their desks, chairs, file cabinets, and aisle room. The furniture for the client waiting area has been pur- chased. Your job is to purchase furniture for the receptionists. For each receptionist you are to purchase a desk with two chairs

and two large file cabinets that will fit the constraints of the room and your budget. Since appearance is an important factor, the desks must be made of a high-grade wood. Also, there must be at least 5 feet of walking space between desks.

Use the Internet to carry out your assignment. Find Web sites for commercial office furniture sellers and browse sites for the specific furniture you need, checking dimensions and prices. Finally, come up with a plan of purchase and suggestions for a layout. It may be a good idea to read up on office layouts to get suggestions. The firm is considering hiring you for future work, so you want to develop a good plan that might include additional suggestions that were not asked for.

INTERNET CHALLENGE DJ and Associates, Inc.

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410 • CHAPTER 10 FACILITY LAYOUT

Online Resources Companion Website www.wiley.com/college/reid

Thomson-Shore, Inc. Konica Corporation

• Find links for Additional Web Resources for this chapter Association for Manufacturing Excellence, www.ame.org APICS—The Educational Society for Resource Management, www.apics.org

Selected Bibliography Binkley, C. “Sheraton Chain Gets a Makeover from Orange

Shag to Pin Stripes,” Th e Wall Street Journal, April 19, 2000. Goldstein, L. “Whatever Space Works for You,” Fortune, July 10,

2000, 269–270. Lee, L. “Nordstrom Cleans Out Its Closets,” Business Week, May

22, 2000, 105–108. Muther, R., and K. McPherson. “Four Approaches to Comput-

erized Layout Planning,” Industrial Engineering, 2, 1970, 39–42.

Umble, M.M., and M.L. Srikanth. Synchronous Manufacturing. Cincinnati, Ohio: South-Western Publishing, 1990.

Upton, D.M., “What Really Makes Factories Flexible,” Harvard Business Review, July–August 1995, 74–84.

Vokurka, R.J., S.W. O’Leary-Kelly, and B. Flores. “Approaches to Manufacturing: Use and Performance Implication,” Pro- duction and Inventory Management Journal, Second Quar- ter, 1998, 42–48.

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L E A R N I N G O B J E C T I V E S

CHAPTER

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISHRM

411

ACC

C H A P T E R O U T L I N E

Designing a Work System 412 Job Design 413 Methods Analysis 419 The Work Environment 421 Work Measurement 422 Setting Standard Times 423

Compensation 433 Learning Curves 435 Work System Design within OM: How

It All Fits Together 438 Work System Design across

the Organization 438

Before studying this chapter you should know or, if necessary, review

1. Competitive priorities, Chapter 2, pp. 37–43. 2. Process selection, Chapter 3, pp. 68–72. 3. Layout types, Chapter 10, pp. 374–379.

After studying this chapter you should be able to

1 Describe the elements of work system design and the objectives of each element. 2 Describe relevant job design issues. 3 Describe methods analysis. 4 Understand the importance of work measurement. 5 Describe how to do a time study. 6 Describe how to do work sampling. 7 Develop standard times. 8 Show how to use work standards. 9 Describe compensation plans. 10 Describe learning curves.

Work System Design

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412 • CHAPTER 11 WORK SYSTEM DESIGN

Have you ever been to a restaurant, a store, or any other place where one person seems to be doing several different jobs? For example, is that person the maitre d’, the wine steward, the wait staff, the chef, or the dish- washer? And does that person have to switch jobs at a moment’s notice?

Productive employees are employees who know what is expected of them and what to expect of themselves. They know what their role is in the company, and they understand the goals of their job.

Because of the seasonal nature of its busi- ness, UPS has spent considerable time design-

ing jobs, making sure to clearly define expectations. This is critical for UPS since it uses more than 95,000 part-time seasonal employees during the period from Thanks- giving to Christmas. UPS part-time employees typically work four to five hours daily. The different jobs include driver helpers, package handlers and sorters, and extra delivery drivers. The sheer size of this increase in the workforce demands that each person know his or her role.

Designing a work system that supports a company’s objectives is essential to the company’s success. Let’s look at how companies design their work systems.

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DESIGNING A WORK SYSTEM First, a company determines its objectives, and then it develops an operations strategy to achieve those objectives. Part of the operations strategy is designing a work system, which provides the structure for the productivity of the company. The work system includes job design, work measurement, and worker compensation. The company determines the purpose of each job, what the job consists of, and the cost of the employees to do the job. A job must add value and enable the company to achieve its objectives.

Suppose your company is an organization with an objective to operate a fancy, upscale restaurant. To achieve its objective, the restaurant must define a set of jobs, the tasks each job consists of, and a system for evaluating the employee’s performance in the job. The set of jobs at your restaurant would include a chef, a trained kitchen staff, a professional wait staff, a maitre d’, a wine steward, and so forth. The chef’s tasks would include developing the food motif and menu, for example. The performance measurement would be based on revenue.

Job design ensures that each employee’s duties and responsibilities are geared toward achieving the restaurant’s mission. Methods analysis eliminates unnecessary tasks and improves the process for completing tasks. Work measurement is a process for evaluating employee performance and comparing alternative processes. Let’s begin with job design.

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JOB DESIGN • 413

Designing a Job Job design specifies the work activities of an individual or a group in support of an organization’s objectives. You design a job by answering questions such as: What is your description of the job? What is the purpose of the job? Where is the job done? Who does the job? What background, training, or skills does an employee need to do the job? For example, if one of your company’s objectives is to establish itself as a leader in customer service, jobs must be designed to encourage and reward good customer service practices. In addition, performance measurements for each job must validate the behavior that supports the company’s objective. Let’s look at three additional fac- tors in job design: technical feasibility, economic feasibility, and behavioral feasibility.

Technical Feasibility The technical feasibility of a job is the degree to which an individual or group of individuals is physically and mentally able to do the job. The more demanding the job, the smaller is the applicant pool for that job. Suppose your company requires the candidate for a job to be capable of lifting up to 250 pounds. Few people will qualify for the job. But if the company can reduce the lifting require- ment to 50 pounds, many more people will qualify.

Good job design eliminates unreasonable requirements and ensures that any con- straining requirements are necessary to do the job. This in turn widens the applicant pool and gives your company a chance to hire the best candidates on the market.

Economic Feasibility The economic feasibility of a job is the degree to which the value a job adds and the cost of having the job done create profit for the company. If the job as it is designed costs more than the value it adds, then it is not economically feasible. Suppose your company can reduce a job’s lifting requirement from a maxi- mum 250 pounds to a maximum 50 pounds because the company can buy materials in smaller quantities and pack them in lighter boxes. If the materials are more expen- sive, however, the company has to weigh the higher material costs against the higher labor costs, choose the alternative that makes more sense economically, or even come up with a third alternative—for example, hire two workers to lift the heavy packages.

Behavioral Feasibility The behavioral feasibility of a job is the degree to which an employee derives intrinsic satisfaction from doing the job. The challenge is to design a job so the worker feels good about doing the job and adds value by doing it. This pres- ents two problems. First, what motivates one worker may not motivate another worker. Second, someone has to do the boring jobs. One solution is to provide an enjoyable work environment. Employees at companies like Google, The AES Corporation, The Men’s Wearhouse, SAS, and Southwest Airlines stay with their companies because work is fun. In this case, fun means working in a place where people can use their talents and skills and work with others in an atmosphere of mutual respect.

In 2007 and 2008, Google was rec- ognized as the top company to work for by Fortune magazine. For the years 2009–2011, Google had slipped slightly to fourth in the rankings. Companies

� Job design Specifies the contents of the job.

� Technical feasibility The job must be physically and mentally doable.

� Economic feasibility The cost of the job should be less than the value it adds.

� Behavioral feasibility Degree to which the job is intrinsically satisfying to the employee.

JOB DESIGN

LINKS TO PRACTICE Google http://money.cnn.com/ magazines/fortune/ bestcompanies/2011/ full-list

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414 • CHAPTER 11 WORK SYSTEM DESIGN

ranking higher than Google in 2011 were: SAS, Boston Consulting Group, and Wegmans Food Markets. What is it about Google that consistently makes it such a great workplace? At Google headquarters, there are 11 free gourmet cafeterias for the employees. In addition to the cafés, there are snack rooms that contain various cere- als, candies, nuts, yogurts, carrots, fresh fruits, and cappuccinos. It is unlikely that any worker is more than 150 feet away from food.

While many Silicon Valley companies provide shuttle bus transporation to work from area train stations, Google operates free, Wi-Fi–enabled coaches from five Bay area locations. For those employees deciding to drive themselves, Google offers on-site car washes and oil changes. For any employee planning to buy a hybrid automobile, Google gives the employee $5000 toward the purchase price.

Employees can also get haircuts on-site. If an employee refers a friend to Google for employment, the employee receives a $2000 reward for the referral. If an employee has a new baby, Google will reimburse that employee for up to $500 in takeout food during the first four weeks home. Of course, Google has a gym for its employees, so they can attend subsidized exercise classes, get a massage, study a foreign language, or use the personal concierge to make dinner reservations.

Google understands how pressed for time its employees are, so it helps with the household errands. For example, employees can do laundry for free in company washers and dryers (free detergent, too) or drop off dry cleaning. Google also has five on-site doctors available for employee checkups, free of charge.

Google also recognizes the contributions of its employees. The shuttle buses run- ning from the Bay area locations are a direct result of one employee’s efforts. The employee contacted a bus company, worked out routes, calculated costs, and then brought the idea forward to senior management. Instead of forming a committee to study the proposal, Google just did it.

Google allows dogs to come to work. Realizing that problems can arise from on-site pets, Google has established appropriate guidelines to accommodate both the pet owner and other employees. Google also recognizes the importance of keeping its workforce satisfied, offering a number of compensation incentives and founder awards that can be worth millions. No wonder Google was recognized as the top com- pany to work for in 2008.

A final concern in job design is whether or not the job can and should be auto- mated. Let’s discuss the pros and cons of machines versus people in job design.

Machines or People? When a company considers the technical, economic, and behavioral feasibility of job design, a central question is: Should the job or some part of it be automated? Obvi- ously, machines do some things better than people, whereas people do other things better than machines. For example, we use calculators to do arithmetic or when we need high levels of precision. We use a machine when a job might be dangerous. We also use machines to lift or move very heavy objects or very hot objects or to do simple, repetitive tasks. On the other hand, people are vastly superior to machines in a number of activities. Examples are personal interactions with others, creative think- ing, judgments involving multiple variables, expressions of compassion or empathy, complex operations that may not follow linear logic, and teaching.

Using machines versus people is both a tangible economic decision in job design and a decision based on intangibles, such as customer acceptance. For example, auto- mated voice messaging systems are the norm in offices today. But do these automated systems create a favorable impression on the customer? Is it worth the extra expense to provide a live receptionist so your customers can talk to a person rather than a

MKT

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JOB DESIGN • 415

machine? When your company makes a decision about machines versus people in job design, support of the company’s objectives is the deciding factor.

If a job is designed for people rather than machines, the next question is how spe- cialized an employee should be.

Level of Labor Specialization The higher the level of specialization, the narrower is the employee’s scope of exper- tise. The professions—medicine, law, academics—are highly specialized; however, some low-level assembly or service jobs are also specialized.

In the professions, worker satisfaction is one reason for specialization in a particu- lar area of expertise. A doctor who specializes in heart disease, a lawyer who specializes in international law, or a professor who specializes in operations management may do so because of intrinsic satisfaction in the job. Without question, other factors also influence people to enter into particular professional careers.

On the other hand, an assembly or service worker whose work is highly special- ized often has a monotonous job. These individuals may have narrowly focused jobs because their skill levels are limited. Yet specialized assembly and service workers contribute to organizational objectives because they yield high productivity and low unit costs. Consider the assembly worker who inserts and tightens four bolts into each product as it passes by on the assembly line. The work is repetitive, but the worker quickly becomes very proficient. A file clerk who spends eight hours each day filing documents and the data-entry person keying in data eight hours each day also have highly specialized, narrowly focused jobs.

Table 11-1 highlights some of the advantages and disadvantages of using special- ization in job design. The table shows that management benefits from specialization because the jobs are narrowly defined and easily learned, so less training is needed. Workers achieve high productivity, and wage costs are reasonable. The table also shows that a disadvantage of specialization is worker dissatisfaction, resulting in high turnover and absenteeism, a high number of grievances filed, higher scrap rates, and sabotage.

� Specialization The breadth of the job design.

Specialization from Management’s Perspective Advantages Disadvantages

Readily available labor Lack of flexibility Minimal training needed Worker dissatisfaction characterized by Reasonable wage cost • high absenteeism High productivity • high turnover rates

• high scrap rates • grievances filed

Specialization from the Employee’s Perspective Advantages Disadvantages

Minimal credentials needed Boredom Minimal responsibilities Little growth opportunity Minimal mental effort needed Little control over work Reasonable wages Little room for initiative

Little intrinsic satisfaction

The Advantages and Disadvantages of Specialization in Job Design

TABLE 11-1

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416 • CHAPTER 11 WORK SYSTEM DESIGN

From the worker’s point of view, this type of job design minimizes the education and skills required, requires little mental effort, has minimal job responsibility, and provides reasonably good wages given the skill requirements. The major disadvan- tages of such job designs are worker boredom, little room for growth or advancement, little control over the work, and minimal self-fulfillment. In an effort to reduce work- ers’ boredom, a number of behavioral approaches have been suggested. Discussion of these approaches follows.

Eliminating Employee Boredom Companies that choose highly specialized job design have several options for reducing worker boredom, including job enlargement, job enrichment, and job rotation.

Job enlargement is the horizontal expansion of a job. The job designer adds other related tasks to the job so the worker produces a portion of the final product that he or she can recognize. For example, an assembly worker gets to do additional tasks that complete a portion of the final product, which enables the worker to experience pride in the final product. The worker can then point to the final product and take pride in the portion that he or she was responsible for building. By reducing the level of spe- cialization, however, job enlargement may result in some lost productivity compared to what was specified in the original job design.

One example of job enlargement concerns employees proofreading telephone directories. Rather than proofreading randomly assigned pages, a proofreader is responsible for specific letters in the alphabet. The proofreader can identify those portions of the directory that he or she proofread. Job enlargement is used to instill worker pride in the final product and give the employee some task variety.

Job enrichment is the vertical expansion of a job. The job designer adds worker respon- sibility for work planning and/or inspection. This allows the worker some control over the workload in terms of scheduling—although not in terms of how much work to do—and instills a sense of pride in the worker. For example, workers know they must complete 100 model A’s, 50 model B’s, and 150 model C’s in one week, but the sequence is up to them. They can do all of the C’s first, then the B’s followed by the A’s, and so forth, as long as the work is finished in one week. Or, for example, workers do their own inspection before the part or product is passed to the next workstation. This procedure instills pride in the output and has the worker perform tasks usually done at a higher level in the organization.

Job rotation exposes a worker to other jobs in the work system. Rotation allows workers to see how the output from their previous assignment is used later in the production or service process. Workers see more of the big picture and have a better overall understanding of the work system. In addition, they acquire more skills that may increase their value to the company. Job rotation provides more flexibility for the company, as its workers have upgraded skills.

Team Approaches to Job Design Another option for job design is using teams rather than individuals for certain assign- ments. Problem-solving teams, special-purpose teams, and self-directed teams are three different kinds of employee teams.

Problem-solving teams are small groups of employees who meet to identify, ana- lyze, and solve operational problems. Employees typically volunteer to participate in problem-solving teams, and team members are trained in problem-solving techniques and data collection. A team may meet once a week, during normal working hours, for one to two hours. After the team has completed its initial training, it concen- trates on a particular operational problem. The team analyzes the problem, collects

� Job enlargement A horizontal expansion of the job through increasing the scope of the work assigned.

� Job enrichment A vertical expansion of the job through increased worker responsibility.

� Job rotation Workers shift to different jobs to increase understanding of the total process.

� Problem-solving teams Small groups of employees and supervisors trained in problem-solving techniques who meet to identify, analyze, and propose solutions to workplace problems.

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JOB DESIGN • 417

data, develops alternative solutions, and then presents a proposed solution to upper management. Management then decides whether or not to use the proposed solution.

The purpose of problem-solving teams is to use the employees’ knowledge of opera- tional procedures. Management cannot know as much about detailed operations as the employees who do the work daily. Problem-solving teams are useful for improving oper- ations and as a way to improve communications between employees and management.

Special-purpose teams address issues of major significance to the company. They are often short-term, special task forces with a focused agenda. Members of special- purpose teams typically represent several functional areas for an overall view of the problem. For example, a university might use a special-purpose team to hire a new, high-level administrator. The team has a specific task and a limited time frame to do that task. Since the new administrator will have many constituencies, the team may consist of representatives from each college, the operating staff, the professional/ administrative staff, trustees, alumni, and students. Including each constituency in the selection process ensures that their concerns are made known.

Self-directed or self-managed teams are designed to achieve a high level of employee involvement and an integrated team approach. A self-directed team is a group of people working together in their own ways toward a common goal that the team has defined. The team decides on compensations and disciplinary actions when team rules are broken and acts as a profit center. A self-managed team is a group of people working together in their own ways toward a common goal that is defined by a source outside of the team. The team does its own work scheduling and training and provides its own rewards and recognition, but does not set its overall objective.

A good example of self-directed work teams is The SCO Group. At The SCO Group Operation, the initial team consisted of twelve people, including five assemblers and a quality control person, for both the day and evening shifts. The team subsequently added two material handlers, a product spe- cialist, and a master scheduler. The team was responsible for building 65 computer operating system products on a single assembly line. Before the self-managing teams, the company had a problem with product consistency. Each shift did things its own way and blamed the other shift for any problems. The self-directed team approach greatly improved communication between shifts and allowed workers to resolve product-consistency problems. The team reduced person-minutes per unit by 22.4 percent, decreased defects to 1.5 or fewer per 1000 units built, cross-trained its members, and documented the procedures for what needed to be done and how it should be done. Although most workers continue to perform on-site at the company’s facilities, many employees now work in alternative workplaces.

The Alternative Workplace The alternative workplace is a combination of nontraditional work practices, settings, and locations that supplements traditional offices. The alternative workplace moves the work to the worker rather than the worker to the work.

Companies have begun using alternative workplaces for the benefits they afford in terms of cost reduction, productivity, and flexibility. The cost reduction is achieved through eliminating offices people do not need, consolidating others, and thereby

� Special-purpose teams Highly focused, short-term teams addressing issues important to management and labor.

� Self-directed teams Integrated teams empowered to control portions of their process.

� Alternative workplace Brings work to the worker rather than the worker to the workplace.

LINKS TO PRACTICE The SCO Group www.sco.com

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418 • CHAPTER 11 WORK SYSTEM DESIGN

reducing overhead expenses. Alternative workplaces can improve productivity because employees tend to devote more time to customers and/or meaningful work activities and less time to unproductive office routines like socializing, making up work to look busy, and attending unnecessary meetings. A survey of alternative work- place employees at IBM revealed that 87 percent believed their personal productivity and job effectiveness had increased significantly. The alternative workplace offers the flexibility some employees need to balance work and family, which helps companies build and keep a valuable workforce.

Let’s look at six different alternative workplace arrange- ments: (l) teleworking and telecommuting, (2) a telework center, (3) a virtual office or virtual workplace, (4) hoteling, (5) hot desking, and (6) desk sharing. Teleworking and tele- commuting are synonymous terms meaning the act of per- forming all or a portion of work functions at an alterna- tive worksite, such as working

at home or a telework center. The intent is to reduce or eliminate an employee’s com- mute. To be considered telework, it must occur at least one day per week on a regular and recurring basis. It does not include situational telework (unscheduled, project-oriented, nonrecurring or full-time mobile work arrangements). A telework center is a facility that provides workstations and other office facilities/services that an employee can use for a fee and is a geographically convenient alternative workplace for the employee.

A virtual office or virtual workplace is a work environment in which employees work cooperatively from different locations using a computer network. The actual physical locations of employees working in a virtual office can be temporary or per- manent and can be nearly anywhere (their homes, satellite offices, hotel rooms, cor- porate offices, airports, airplanes, or automobiles).With hoteling, employees work in one facility (home base) part of the time and at one or more alternative worksites the rest of the time. When working at the home base, the employees use nondedicated, nonpermanent workspaces assigned on a first-come, first-served basis.

Desk sharing is a work arrangement in which two or more employees share the same workstation in a prearranged manner allowing each employee to have sole access to the specified workstation on given days. The difficulty in desk sharing is arranging a schedule that works for all parties involved in terms of completing their work assign- ments. Compatible work habits are also important. Most companies use a combina- tion of alternative workplaces customized to their individual company needs.

AT&T, IBM and the U.S. Army save significant money in real estate and infra- structure costs by having workers work from home, even when the company must provide these employees computers, software, and tech support. Working from home can improve employee productivity. A study of one well-managed office reported that conversation and other normal office behavior distracted people from productive work an average of 70 minutes during an 8-hour day. Employees working from home are often more satisfied with their work, and that satisfaction can translate into better customer service. So why isn’t every organization letting their workers work at home?

To determine whether the alternative workplace is right for your organization, consider the following seven questions.

LINKS TO PRACTICE AT&T

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METHODS ANALYSIS • 419

1. Are you committed to new ways of doing things? Will you reward employees for results achieved in an alternative workplace?

2. Is your organization industrial or informational? If your structure is designed for making products or providing services that require face-to-face interaction, the potential for using alternative workplaces is likely to be limited.

3. Does your organization have an open culture and proactive managers? Your managers must be open to change and must support the migration to alternative workplaces for it to work.

4. Can you establish clear links among staff , functions, and time? You need to identify what must be done and when. Th ere can be no ambiguity in expectations. Schedules must be clear.

5. Are you prepared to “push-back” or loosen your control over your direct reports? Managers must respect their direct reports and trust that work will be completed when the worker is out of sight.

6. Are there known external barriers? Do your employees have adequate space to work from home? Will they be able to work at home, or will there be additional distractions?

7. Are you willing to invest in the tools and training needed to make the alternative workplace succeed? Make sure that managers are given guidance in monitoring remote employees, that employees know what is expected of them, and that the customers are fully informed.

Many companies use alternative workplaces. AT&T uses desk sharing, providing flexible workstations so workers can rotate in and out as needed. Cisco Systems uses hot desking, whereby the employees choose an available workstation as they arrive. Sun Microsystems gives many of its designers the option to work at home. Companies such as KPMG, Peat Marwick, Ernst & Young, and Andersen Consulting practice “hoteling,” using either hotel workspaces furnished, equipped, and supported with typical office services or corporate workspaces that are reserved by an employee via the company “concierge.” In both cases, the equipment and/or space can be booked by the hour, day, or week. Today, more than 30 million employees work in alternative workplaces.

METHODS ANALYSIS Methods analysis is the study of how a job is done. Whereas job design shows the structure of the job and names the tasks within the structure, methods analysis details the tasks and how to do them.

Methods analysis is used by companies when developing new products or services and for improving the efficiency of methods currently in use. Suppose your restau- rant has an accepted procedure for communicating a customer’s dinner choices to the kitchen without errors. Methods analysis documents this accepted procedure, including specific notations that identify customer preferences. The result is a stan- dard operating procedure your restaurant can use for training new employees and for evaluating the performance of existing employees.

Methods analysis consists of the following steps:

1. Identify the operation to be analyzed. 2. Gather all relevant information about the operation, including tools, materials,

and procedures. 3. Talk with employees who use the operation or have used similar operations. Th ey

may have suggestions for improving it. 4. Chart the operation, whether you are analyzing an existing operation or a new operation.

� Methods analysis Process concerned with the detailed process for doing a particular job.

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420 • CHAPTER 11 WORK SYSTEM DESIGN

5. Evaluate each step in the existing operation or proposed new operation. Does the step add value? Does it only add cost?

6. Revise the existing or new operation as needed. 7. Put the revised or new operation into eff ect, then follow up on the changes or new

operation. Do your changes to the existing operation improve it? Does your new operation add to the company’s overall operations?

Now let’s look at an example of methods analysis for an established operation.

Companies typically try to identify operations that are labor intensive; are done often; are dangerous, tedious, or fatiguing; and/or are designated as problem operations. In this example, the FEAT Company, a producer of electronic consumer goods, uses a particular trans- former (Figure 11-1) in several of its finished products. A transformer changes the current in the primary circuit to the current needed in the secondary circuit. You have the task of applying methods analysis to one of FEAT’s problem operations.

You decide to concentrate your methods analysis on the transformer wiring operation because it has been a source of quality problems in the past. After talking with the current operators, you develop a flowchart of the operation, as shown in Figure 11-2. The flowchart shows that the operator has to solder six individual wires onto six individual terminals of the transformer.

The operator follows these steps to solder the wires onto the terminal.

EXAMPLE 11.1 Methods Analysis at FEAT Company

A 12-terminal transformer

FIGURE 11-1

Chart of the wiring activity

FIGURE 11-2

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THE WORK ENVIRONMENT • 421

So far we have discussed job design and methods analysis in detail. We also need to understand the effect of working conditions on worker productivity, product quality, and worker safety.

Temperature, relative humidity, ventilation, lighting, and noise level are all fac- tors in work system design. People work well when the temperature is comfortable; typically, the more strenuous the work, the lower the temperature should be. Excess humidity is uncomfortable for most people and can be detrimental to some equip- ment. Too little humidity results in dry air and may create undesirable static charges. Exchanging or filtering the air can prevent the stale air caused by poor ventilation. Inadequate lighting can lead to production mistakes and/or physical discomfort such as headaches. Detailed work normally needs stronger light. Also, high noise levels can be distracting and can result in errors or accidents as well as impair hearing.

1. Picks up the appropriate wire with the left hand and moves the wire to the terminal to be soldered. 2. Simultaneously picks up the solder iron in the right hand and moves it to the terminal to be soldered. 3. Solders the wire to the terminal and replaces the solder iron in its holder. 4. Solders terminal number 1, then solders terminals 2 through 6, going from right to left .

The layout of the operator’s workstation is shown in Figure 11-3.

The FEAT Company is concerned about this operation—significant rework is needed because of poor solder joints and the operation is not efficient. You analyze the operation and realize that attaching the wires from right to left is problematic for a right-handed operator. You also note that when soldering the next terminal, the operator places the soldering iron directly above the recently soldered joint. This slows down the process because the operator now has to be careful not to disturb that joint.

Following your methods analysis, you make this recommendation to the FEAT Company to improve the operation: Reverse the order in which the wires are soldered to the terminals, beginning at terminal 6 and ending at terminal 1. Now the right- handed operator does not reach directly over the recently soldered joints and can improve both the quality and efficiency of the operation. The final step in your methods analysis is to follow up to make sure that the new operation resolves the quality problems the FEAT

Company was concerned about.

THE WORK ENVIRONMENT

Wiring the transformer

FIGURE 11-3

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422 • CHAPTER 11 WORK SYSTEM DESIGN

Concern for worker safety brought about the enactment in 1970 of the Occupa- tional Safety and Health Act (OSHA), which created the Occupational Safety and Health Administration. The law was designed to ensure that all workers have healthy and safe working conditions. It mandates specific safety conditions that are inspected randomly by OSHA inspectors. Violations can result in warnings, fines, and/or court-imposed shutdowns. The law requires the company to ensure a safe working environment for its employees. Therefore, worker safety is the primary concern in work system design. Workplace accidents are usually the result of worker carelessness or workplace hazards. Carelessness is defined as unsafe acts, such as failing to use protective equipment, overriding safety controls, disregarding safety procedures, or improperly using tools and equipment. Workplace hazards include conditions such as unprotected equipment, poor lighting, and poor ventilation.

WORK MEASUREMENT The third component in work system design, work measurement, is a way of deter- mining how long it should take to do a job. Work measurement techniques are used to set a standard time for a specific job. The standard time is the time it should take a qualified operator, working at a sustainable pace and using the appropriate tools and process, to do the job. The standard time is the sustainable time it takes to do either a whole job or a portion or element of a job. In our restaurant example, the time needed to take the customer’s order and communicate that information to the kitchen staff can be calculated as the standard time.

Why should a company set the standard time for a job? Companies use standard times for costing, evaluating, and planning.

Costing When costing a product, a company includes labor in the total cost estimate. Instead of timing the labor to build single units, companies typically use a standard labor cost. To do this, they multiply the standard labor time by a given hourly labor cost to determine the direct labor content of a product. For example, if a company invests three standard hours of labor in building a product and its labor cost is $22 per hour, then the standard labor content is $66 per unit. This does not mean each unit has exactly three hours of labor spent on it: some units will have slightly more labor content, and others will have slightly less. Thus the company estimates that it should take three hours of labor to build that product. For pricing purposes, the company charges for three labor hours of content in each unit.

Standard times allow companies to evaluate new-product proposals, the use of new materials and equipment, new processes or techniques for building a product, and individual operator proficiency. The standard time provides a benchmark for compa- nies to use when evaluating other alternatives.

� Work measurement Determines how long it should take to do a job.

� Standard time The length of time it should take a qualified worker using appropriate process and tools to complete a specific job, allowing time for personal fatigue and unavoidable delays.

Let’s return to the transformer operation in which six wires are soldered to the transformer. FEAT Company might consider buying transformers with individual wires already attached to each of the terminals, as shown in Figure 11-4. The transformer has wires attached to all twelve terminals, so the operator has to remove the six extra wires.

Suppose FEAT evaluates this change in material and process by determining the change in standard time and comparing that with the change in material costs. The result is a trade-off between reduced labor costs versus higher material costs. For example, what should FEAT do if the prewired transformer costs $0.40 more than the unwired transformer, but the standard time is reduced by 45 seconds per unit and the hourly labor cost is $22?

EXAMPLE 11.2

An Alternative Material for FEAT

Company

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SETTING STANDARD TIMES • 423

Performance If the standard time shows that a worker should produce 96 units per eight-hour shift, a supervisor can then track the worker’s performance and see whether that worker is matching the standard time. If a worker fails to match the standard time, the company should provide training to improve the worker’s performance.

Planning Standard times also help a company to plan. If you know how long it takes to do a job and how often that job is repeated, you can plan your workload from that job.

If an operation takes 0.30 standard hour of labor and you need 1000 of these opera- tions to complete a job, then you need 300 standard hours to complete the job (0.30 hour times 1000 operations). This information allows you to make workload forecasts, plan the labor needed, and schedule work. If you know how long a job should take, you know how long your resources will be busy with that job before you can start the next job. With this knowledge you can schedule jobs and promise viable delivery dates to your customers.

• Before You Begin: For this problem we are comparing the increased cost for the trans- former with the decreased labor cost. You need to determine the value of the labor saved on each transformer that is used. You do this by finding the hourly labor rate ($22.00) and multiplying by the 45 seconds (or 0.0125 hour) saved per unit. Compare this labor savings with the additional cost of $0.40 per transformer.

• Solution: The value of the labor saved is $0.275 per transformer. The additional material cost is $0.40 per unit. If FEAT changes to the prewired transformer, it will cost an additional $0.125 per transformer ($0.40 � $0.275), the difference between the increased material cost and the reduced labor cost. If FEAT is concerned only with the economics, it should not change to prewired transformers. However, if FEAT can improve quality, this benefit must be factored in to the decision.

We can do evaluations of this kind for any new idea. Moreover, we can use standard times to find out whether workers are producing at the expected level.

SETTING STANDARD TIMES Among the commonly used processes for setting standard times are the time study, elemental time data, predetermined time data, and work sampling. The time study dates back to Frederick Winslow Taylor in the late nineteenth century.

How to Do a Time Study The time study sets a standard time based on timed observations of one employee taken over a number of cycles. A cycle includes all the elements of the job. This

� Time study A technique for developing a standard time based on actual observations of the operator.

A prewired transformer

FIGURE 11-4

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424 • CHAPTER 11 WORK SYSTEM DESIGN

standard time is applied to all workers doing the job. Table 11-2 shows the steps in a time study.

Now let’s look at these steps in detail.

Step 1. As an individual, you set standards for yourself based on the tasks that are typical of your workday. Th e same is true for your company: you base standard times on the routine, labor-intensive jobs rather than one-of-a-kind jobs. Use this criterion for choosing your time-study job in Step 1. Step 2. It is also important to inform the employee in advance that you will be making a time study of the job. Be sensitive to how the employee will feel as you time performance. Step 3. To break a job down into easily recognizable elements, think about making a hand-tossed pizza:

1. Find the right-sized ball of dough (this depends on the pizza size). 2. Flatten out the dough. 3. Spin and toss the dough until it is the size you want. 4. Put the pizza on the working area. 5. Add sauce. 6. Add cheese. 7. Add additional toppings. 8. Put the pizza in the oven and bake.

Each of these elements has a clear starting and ending point, and you cannot break it down any further. Step 4. When making a time study, you need to know how many cycles, or how many times, you must observe the worker to ensure the results you want. Th e num- ber of cycles is a function of the variability of observed times, the desired level of accuracy or precision, and the desired level of confi dence for the estimated stan- dard times. We oft en express the desired accuracy level as a percentage of the mean observed times. For example, we might want an accuracy level so that the standard time is within 10 percent of the true mean of the time it takes to do the job.

The formula for determining the number of observations needed is

n $ c a zab a s x b d 2

where n � the number of observations of an element that are needed z � the number of normal standard deviations needed for desired confi dence s � the standard deviation of the sample a � the desired accuracy or precision x � the mean of the sample observations

Step 1. Choose the job for the time study. Step 2. Tell the worker whose job you will be studying. Step 3. Break the job into easily recognizable units.

Step 4. Calculate the number of cycles you must observe. n $ c azab a s xb d

Step 5. Time each element, record the times, and rate the worker’s performance. Step 6. Compute normal time. NT � (MOT) (PRF) (F) Step 7. Compute the standard tune. ST � (NT) (AF)

Procedure for a Time Study

TABLE 11-2

Hand-tossing dough

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SETTING STANDARD TIMES • 425

To compute the number of observations needed, we begin by making a small number of observations so that we can determine the sample mean and standard deviation. We also need to know the appropriate value of z to use because it determines our confi- dence level. Common values of z are shown in Table 11-3. Calculating the number of observations needed will be demonstrated by Example 11.3.

Desired Confidence (%) z Value

90 1.65 95 1.96

95.5 2.00

96 2.05

97 2.17

98 2.33

99 2.58

Common z Values

TABLE 11-3

To determine the number of observations needed, we take some initial observations of the job being studied. In this case, let’s observe how long it takes to prepare a large, hand-tossed pepperoni and cheese pizza.

We begin by taking ten observations of each of the seven elements. The elements, the standard deviation of the observed times for each element, and the mean observed time for each element are shown in Table 11-4. Determine the appropriate sample size if the standard time for any work ele- ment is to be within 5 percent of the true mean 95 percent of the time.

• Before You Begin: To solve this problem, use the formula shown in Step 4. The formula requires that you know the number of normal standard deviations needed for the desired confidence level (from Table 11-3 you can see that a 95 percent confidence level requires 1.96 normal standard deviations). You also need the desired accuracy or precision (5 percent, or 0.05, in this case). The sample standard deviation and the mean of the sample observations come from Table 11-4. After calculating the required number of observations for each element, we select the highest number of observations as the appropriate number for our time study.

EXAMPLE 11.3

Observing Pizza Preparation at Pat’s Pizza Place

Table 11-4 Mean Observed Times

Work Unit Standard Deviation

(minutes) Mean Observed Time

(minutes)

1. Get appropriate ball of dough. 0.010 0.12 2. Flatten dough. 0.030 0.25 3. Spin and toss dough. 0.040 0.50 4. Place dough on work counter. 0.005 0.12 5. Pour sauce on formed dough. 0.035 0.30 6. Place grated cheese on top of sauce. 0.025 0.25 7. Place pepperoni on formed dough. 0.030 0.24

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426 • CHAPTER 11 WORK SYSTEM DESIGN

Performance Rating Factor This section deals with setting a normalized standard time based on observed times. The observed work pace may be average, above aver- age, or below average. As the time-study analyst, you must make a judgment about the work pace of the observed worker in terms of how far from the average it is. This is a factor of 1.0 called the performance rating factor. If a factor of 1.0 represents an average work pace, any performance rating below 1.0 is a below-average work pace. A performance rating of above 1.0 is an above-average work pace. Performance rating is an attempt to counterbalance any unusual patterns noted in the worker’s perfor- mance. For any element in which the worker performs below average, the worker may need additional training to improve efficiency.

� Performance rating factor A subjective estimate of a worker’s pace relative to a normal work pace.

• Solution: The calculations are shown here. All noninteger solutions are rounded up.

Work element 1: n $ c a1.96 0.05

b a0.01 0.12

b d 25 11 observations

Work element 2: n $ c a1.96 0.05

b a0.03 0.25

b d 25 23 observations

Work element 3: n $ c a1.96 0.05

b a0.04 0.50

b d 2 5 10 observations

Work element 4: n $ c a1.96 0.05

b a0.005 0.12

b d 2 5 3 observations

Work element 5: n $ c a1.96 0.05

b a0.035 0.30

b d 2 5 21 observations

Work element 6: n $ c a1.96 0.05

b a0.025 0.25

b d 2 5 16 observations

Work element 7: n $ c a1.96 0.05

b a0.03 0.24

b d 2 5 25 observations Work element 7 requires the most observations (25) to ensure the accuracy and confidence

needed. Step 5. From the calculations completed above, you know that 15 additional cycles must be observed (25 needed less 10 previously completed). Table 11-5 shows the revised mean observed times.

From the revised mean observed times for each work element, we can determine the normal time for the elements. The normal time is different from the observed times because we multiply it by a performance rating factor and the frequency of occurrence.

� Mean observed time The average of the observation times for each of the work elements. Table 11-5 Revised Mean Observed Times

Work Element Mean Observed Time (minutes)

1 0.15 2 0.25 3 0.60 4 0.15 5 0.30 6 0.28 7 0.28

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SETTING STANDARD TIMES • 427

It is not uncommon for workers to work faster than normal when they are observed or, in some cases, to work more slowly than normal when they are observed. The perfor- mance rating is used to develop a standard that is fair to the worker and to the company.

Frequency of Occurrence One other factor to consider when calculating the time standard is the frequency of occurrence (F) for each work element. We expect most elements to be done every cycle, so they would have a frequency of occurrence equal to 1. However, some elements are not done each cycle, so we adjust the frequency accord- ingly. If an element is done every other cycle, it would have a frequency equal to 0.5, or, on average, half of it is done each cycle. If an element is done once every five cycles, F � 0.2. We calculate the frequency of occurrence by dividing 1 by the number of cycles between occurrences (1 divided by 10 cycles between occurrences means that F � 0.1).

Step 6. We compute the normal time for each work element by multiplying the mean observed time by the performance rating factor by the frequency of occurrence [NT � (MOT)(PRF)(F)]. The normal time calculations are shown in Table 11-6. Remember that the performance rating factor scores and the frequency of occurrence must be known for you to calculate the normal time. The normal time for preparing a large cheese and pepperoni pizza is 1.966 minutes. This normal time reflects how long it may take to produce a single large cheese and pepperoni pizza. However, it does not allow for personal time, fatigue, or unavoidable delays (PFD) during the typical workday. Therefore, we adjust the normal time with an allowance factor.

Allowance Factor Personal time, fatigue, and unavoidable delays affect how much an employee can produce during the day. There are two methods used to convert the job’s normal time into the job’s standard time. With the first method, the PFD allow- ance is based on individual jobs the worker does throughout the day. This is typically used when workers perform several different types of jobs, with some of the jobs more fatiguing than others and other jobs having longer unavoidable delays. In this case, the amount of output expected must take into account the actual jobs performed by the worker. The PFD allowance factor is computed as

AFJob 5 1 1 PFD

where PFD � percentage allowance adjustment based on the individual job. The other method is used when all of the different jobs an employee does are similar, having the same allowance factor. We compute this allowance factor as

AFTime worked 5 1

1 2 PFD

� Frequency of occurrence How often the work element must be done each cycle.

� Normal time The mean observed time multiplied by the performance rating factor by the frequency of occurrence.

� Allowance factor The amount of time the analyst allows for personal time, fatigue, and unavoidable delays.

Work Element

Mean Observed Time

(minutes)

Performance Rating Factor Frequency

Normal Time

(minutes)

1 0.15 0.90 1 0.135 2 0.25 1.00 1 0.250

3 0.60 0.85 1 0.510

4 0.15 1.10 1 0.165

5 0.30 1.20 1 0.360

6 0.28 1.00 1 0.280

7 0.28 0.95 1 0.266

Total 1.966

Calculated Normal Times

TABLE 11-6

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428 • CHAPTER 11 WORK SYSTEM DESIGN

Let’s look at a numerical comparison of the two methods for setting the allowance factor. If the allowance factor of PFD � 0.15, we compute allowance based on the job as

AFJob 5 1 1 0.15 5 1.15, or 115%

When computing the allowance based on time worked, the result is

AFTime worked 5 1

1 2 0.15 5 1.176, or 117.6%

Step 7. Now that we know how to calculate the allowance factor, let’s continue with our pizza-making example and determine a standard time. The standard time equals the normal time multiplied by the allowance factor.

To determine the standard time, we multiply the normal time by the allowance factor. For the large, hand-tossed cheese and pepperoni pizza, we use an allowance factor of 15 percent based on time worked because all the hand-tossed pizza preparations should have a similar allowance.

• Before You Begin: To compute the standard time for each element, you need to know the normal time (shown in Table 11-6) and the allowance factor (15 percent based on time worked). Multiply the normal time for the element by the allowance factor.

• Solution: We calculate the standard time for each work element as follows.

ST 5 1NT 2 1AF 2 where ST � standard time NT � normal time AF � allowance factor

Solving for work element 1 in our example, we have

STElement 1 5 10.135 2 a 11 2 0.15b 5 0.159 minute The standard times for each element using a 15 percent allowance based on work time are shown

in Table 11-7.

The standard time for preparing a large, hand-tossed cheese and pepperoni pizza is 2.313 minutes. This means that during an eight-hour day, our worker should be able to prepare 207 (480 minutes per eight-hour shift, divided by the standard time of 2.313 minutes) large, hand-tossed cheese and pepperoni pizzas.

EXAMPLE 11.4

Calculating Standard Time for

a Hand-Tossed Cheese and

Pepperoni Pizza

Table 11-7 Standard Times for Making Large Cheese and Pepperoni Pizza

Work Element Normal Time

(minutes) Standard Time

(minutes)

1. Get appropriate ball of dough. 0.135 0.159 2. Flatten dough. 0.250 0.294 3. Spin and toss dough. 0.510 0.600 4. Place dough on work counter. 0.165 0.194 5. Pour sauce on formed dough. 0.360 0.424 6. Place grated cheese on top of sauce. 0.280 0.329 7. Place pepperoni on top of cheese. 0.266 0.313

Total 1.966 2.313

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SETTING STANDARD TIMES • 429

Elemental Time Data After your company performs and validates time studies, it stores those accepted time studies in an elemental time database for possible future use. Many jobs consist of the same work elements. For example, in many jobs the operator has to reach for mate- rials, position an item, or insert and tighten something. Instead of recalculating the time it should take to do a particular work element, the time-study analyst checks the database for a valid time study. If the company has already done a time study for that work element, the analyst uses it in the standard time for the job. When a time-study analyst uses standard elemental times, the procedure is as follows:

1. Identify the standard elements of the job. 2. Check the database for time studies done on these elements. 3. If no valid studies exist for this or a similar work element, do a time study for the

new work element. 4. Adjust the database times if needed. Note that you can adjust database times if

the work element is slightly diff erent. Suppose the database has time studies on reaching 6 inches for a tool and reaching 12 inches for a tool, but for the job you are studying, the operator reaches 9 inches for the tool. Instead of developing a new time study, you can interpolate between the two values and derive a reasonable time for reaching 9 inches.

5. Add the element times to determine the normal time, then multiply by the allowance factor to determine the standard time.

The advantage of using standard elemental times is that you need fewer time stud- ies. The results of each time study go into the database and are available for setting future standard times. Also, using standard elemental times eliminates the workplace disruptions caused by making time studies. The disadvantage is that using standard elements may discourage new process development and improvements.

Predetermined Time Data Just as elemental time databases are useful for individual companies, predetermined time data are useful for the many companies that share similar work elements. Predetermined time data is a large database of valid work element times. One

� Elemental time data Establish standards based on previously completed time studies, stored in an organization’s database.

� Predetermined time data Published database of elemental time data used for establishing standard times.

Before You Go On

Before you go on, let’s make sure you understand how to do a time study. Remember, the first step is identifying the job you will study. Break the job into small, easily recognizable work elements, which allows you to analyze the different elements, look for ways to improve the existing operation, and identify areas where workers need additional training.

To determine the number of observations needed, you must consider the variability of observed times, the desired level of accuracy, and the desired level of confidence for the estimated standard time. Take some observa- tions so you can determine the variability of the observed times. The degree of accuracy and level of confidence factors are managerial decisions.

After determining the number of observations needed, perform any necessary additional observations. Using the observed times, calculate the mean observed time for each work element. Calculate the normal time by mul- tiplying the mean observed time by the performance rating factor and the frequency of occurrence for each work element. Adjust the normal time by multiplying it by the appropriate allowance factor.

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430 • CHAPTER 11 WORK SYSTEM DESIGN

commonly used system is methods-time measurement (MTM), which was developed in the 1940s. The tables used in MTM deal with basic elemental motions and associ- ated times. An example of an MTM table is shown in Table 11-8.

Let’s take a closer look at Table 11-8. The first column shows how far the operator must move the object in inches. Columns A through E explain the activity. Column A applies when the operator has to reach for an object in a fixed location or an object in the operator’s other hand. Column B applies when the operator has to reach for a single object in a location that may vary slightly from cycle to cycle. Columns C and D apply when the operator has to reach for an object jumbled with other objects in a group (search and select), when the operator has to reach for a very small object, or when the operator needs an accurate grasp. Column E applies when the operator has to reach for an indefinite location to position the hand for the next motion. The last two columns on the right-hand side (labeled A and B) represent the time measure- ment units (TMUs) if the hand is already in motion.

When using predetermined time data, you split the job into basic elements (reach, grasp, move, engage, insert, turn, disengage), measure the distances

Distance Moved

(inches)

Time (TMUs) Hand in Motion

Cases and DescriptionsA B C or D E A B

3/4 or less

2.0

2.5

4.0

5.3

2.0

2.5

4.0

5.3

2.0

3.6

5.9

7.3

2.0

2.4

3.8

5.3

1.6

2.3

3.5

4.5

1.6

2.3

2.7

3.6

A Reach to object in fixed location or to object in other hand or on which other hand rests

B Reach to single object in location, which may vary slightly from cycle to cycle

C Reach to object jumbled with other objects in a group so that search and select occur

D Reach to a very small object or where accurate grasp is required

E Reach to indefinite location to get hand in position for body balance or next motion or out of way

6.1

6.5

7.0

7.4

6.4

7.8

8.6

9.3

8.4

9.4

10.1

10.8

6.8

7.4

8.0

8.7

4.9

5.3

5.7

6.1

4.3

5.0

5.7

6.5

7.9

8.3

8.7

9.6

10.5

10.1

10.8

11.5

12.9

14.4

11.5

12.2

12.9

14.2

15.6

9.3

9.9

10.5

11.8

13.0

6.5

6.9

7.3

8.1

8.9

7.2

7.9

8.6

10.1

11.5

11.4

12.3

13.1

14.0

15.8

17.2

18.6

20.1

17.0

18.4

19.8

21.2

14.2

15.5

16.7

18.0

9.7

10.5

11.3

12.1

12.9

14.4

15.8

17.3 24

14.9

15.8

16.7

17.5

21.5

22.9

24.4

25.8

22.5

23.9

25.3

26.7

19.2

20.4

21.7

22.9

12.9

13.7

14.5

15.3

18.8

20.2

21.7

23.2

TABLE 11-8

MTM Reach Table Source: Copyright by the MTM Association for Standards and Research. Reprinted with permis- sion from the MTM Association, 1111 East Touhy Ave., Des Plaines, IL 60018.

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SETTING STANDARD TIMES • 431

involved (how far must the operator reach?), and rate the difficulty of the item (does the operator grasp a single piece of wire or one of many wires?). For each job, you find the time for the individual elements in the appropriate data table and sum the times to get the normal time for the job. You adjust the normal time by the PFD allowance factor to determine the standard time. Element times are typically in time measurement units (TMUs): there are 100,000 TMUs in one hour; one TMU equals 0.0006 minute.

To use predetermined time data, you must be skilled in the method of calculating a standard time and knowledgeable about the job being studied. You must understand how the job is done, the appropriate workplace layout, and the level of difficulty of dif- ferent work elements. The advantages of using predetermined time data are that you do not have to rate individual operator performance, you do not disrupt the workplace to determine the standard time, and you can calculate standard times before the job even begins. The disadvantages are the skill level needed and the variability among analysts in assessing the level of difficulty of different work elements.

Work Sampling Work sampling is a method used for estimating the proportion of time that an employee or machine spends on different work activities. Work sampling does not provide a standard time for an activity, but instead provides an estimate of what por- tion of the day a worker uses for that activity. For example, a secretary may spend the day managing files, generating letters, taking phone calls, greeting visitors, and maintaining an appointment schedule. Work sampling does not specify how long it should take the secretary to generate a particular letter, but it does say what proportion of the day is typically spent on generating letters. For a machine, the company may use work sampling to determine what proportion of the day the machine is used for rush jobs or is idle.

You do not time activities for work sampling. Instead, you make random observa- tions and note the kind of activity. For example, you walk into the secretary’s office and see that the secretary is composing, editing, and printing letters. You record your observations and use them to estimate the proportion of time the employee spends on different activities. Work sampling consists of the following procedures:

1. Identify the worker or machine to be sampled. 2. Defi ne the activities to be observed. 3. Estimate the sample size based on the desired level of accuracy and confi dence. 4. Develop the random observation schedule. Make your observations over a time

period that is representative of normal work conditions. 5. Make your observations and record the data. Check to see whether the estimated

sample size remains valid. 6. Estimate the proportion of time spent on the given activity.

In the following example, we use work sampling to estimate the proportion of time a secretary spends scheduling appointments. You can use work sampling to estimate the proportion of delays a worker experiences and also as input when cal- culating the PFD (personal, fatigue, and delay) allowance factor. For example, you can observe whether a worker is working or experiencing delays. After you make enough observations, you can estimate delay time and use it as part of the PFD allowance factor.

� Work sampling A technique for estimating the proportion of time a worker spends on a particular activity.

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432 • CHAPTER 11 WORK SYSTEM DESIGN

We use work sampling to estimate the proportion of time a secretary spends doing a given activity during a normal office day. The secretary’s normal activities are managing files, generating letters, taking phone calls, greeting visitors, maintaining an appointment schedule, and being idle. We want to know how much of the secretary’s time is used for scheduling appointments.

• Before You Begin: To use work sampling to estimate the proportion of time spent on a particular activity like scheduling appointments, you begin with an estimate of the time spent on the activity. When you have no initial estimate of the proportion, you do a preliminary sample size calculation setting the proportion equal to 0.50. You then take some initial observations, enough to represent a normal distribution (30), and determine the appropriate proportion to use in cal- culating the sample size actually needed.

• Solution: Determine how many observations to make. Work sampling is designed to produce a value, p̂, which estimates the true proportion, p, that a particular activity normally occurs, within some allowable error, e. To estimate the number of observations needed for a given level of error, we use the follow- ing formula.

n $ azeb 2

p̂ 11 2 p̂ 2

where n � the number of observations that are needed z � the number of normal standard deviations needed for desired confidence e � the allowable error level (given as a percentage)

Let’s assume that we want to estimate with 97 percent confidence (z � 2.17), the proportion of time the secretary spends on scheduling appointments. We want the resulting estimate to be within 5 percent of the true value. To solve the equation, we need a sample estimate. When we do not know the sample estimate of p̂, we calculate a preliminary estimate of the sample size by setting p̂ � .50. Since we do not have an estimate for the sample proportion, we compute the preliminary sample size for our work sampling example as follows:

n $ a2.17 0.05

b20.5 11 2 0.5 2 5 470.89 observations If we use a preliminary sample size, we verify that the sample size is correct after we make initial observations and compute the proportion sample. For example, we observed the secretary 30 times, and he was scheduling appointments during 6 of those observations. The proportion sample is 0.2 (6 times arranging meetings/30 observations). The new estimate of the sample size is

n $ a2.17 0.05

b20.2 11 2 0.2 2 5 301.37 observations Whenever the value of n is not a whole number, we round up to the next whole number. In our example, we may need to check our sample size once more after completing more observations just to be sure that we are using the right sample size. After completing the 302 observations, we see that the secretary was scheduling appointments on 60 separate occasions, or 19.9 percent (60/302) of the time. We examine the other activities to estimate the proportion of time the secretary spends on each. The company can use these estimates to describe the job to prospective employees or in performance reviews for the current secretary.

EXAMPLE 11.5

Determining the Proportion

of Time Used to Schedule

Appointments

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COMPENSATION • 433

Pace Productivity offers the TimeCorder for con- ducting work-sampling time studies. The device, developed in 1989 by Mark Ellwood, was completely redesigned and reintroduced in 2004. It allows employees to easily track time by pushing buttons associated with precoded activities, a system they can learn in a few seconds. The employee pushes a coded button and time begins recording on that activity, similar to a stopwatch. When a new button is pushed, time stops for the previous activity and starts for the new activity. A typical study takes six to eight weeks. The company identifies the objectives of the study, determines the activities for each job, and selects employees to participate. Participants use the TimeCorder for two weeks, the devices are returned to Pace Productivity, and the data are analyzed and a report provided to the company. The analysis focuses on opportu- nities for the company to improve productivity.

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LINKS TO PRACTICE Pace Productivity www.paceproductivity .com

COMPENSATION Worker compensation is the third part of work system design. Companies need to develop compensation systems that reinforce the behaviors needed to meet the com- pany’s objectives.

Compensation systems are typically based either on time spent working or on out- put generated. Time-based systems compensate the employee according to the num- ber of hours worked during the pay period. Compensation is not linked to employee performance but to employee presence at the workplace. Output-based or incentive systems link employee pay to performance. Employees are paid based on their output and not on the number of hours they work.

Time-Based Systems Time-based compensation systems are normally used when measuring output per employee is not applicable—say, for managers, administrative support staff, and some direct laborers. Consider an employee working in your company’s research and devel- opment group. This employee is doing creative work, which is not easily measured in terms of output. The advantage of the time-based system is its simplicity. For the company, wages are easily calculated. For the employees, the pay is steady, and they know what they will get in their regular paycheck.

Output-Based Systems Output-based (incentive) systems, or piece-rate systems, or commission systems can be linked to Frederick Taylor’s theory that humans are economically motivated. These systems reward workers for their output. The more the worker produces, the more the worker earns. The assumption is that some workers are motivated by money and produce more when pay is linked to performance.

These incentive systems can be designed to compensate either the individual or an entire group of employees. Individual incentive plans typically provide a base salary for the employee, plus a bonus for output achieved above the standard. For example, an employee has a base salary of $250 per week regardless of how much is produced. The standard for the employee is to produce 50 units per week. The employee earns $250 per

� Time-based compensation systems Pay based on the number of hours worked.

� Output-based (incentive) systems Pay based on the number of units completed.

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434 • CHAPTER 11 WORK SYSTEM DESIGN

week until the employee’s output exceeds the standard 50 units per week. For every unit above 50, the employee earns an additional $7. If the employee produces 55 units dur- ing the week, he or she earns $285 for that pay period—$250 base, plus a bonus of $35 (5 extra units at $7 each). An employee producing 60 units per week would earn $320.

Successful individual incentive plans are linked to quality as well as to quantity. Encouraging a worker to produce low-quality units faster does not make good busi- ness sense. The incentive plan must be clear on how output is counted.

Group Incentive Plans Group incentive plans are designed to reward employees when the company achieves certain performance objectives. Two methods are profit sharing and gain sharing. Profit sharing rewards employees when the company achieves certain profitability levels.

Profit Sharing One variation of profit sharing places half the profits in excess of the minimum return on investment into a bonus pool for employees. Individual bonuses can then be based on an employee’s base pay, on the percentage of time during the past year the employee worked for the company, or on similar arrangements. For example, a company may give a bonus equal to 15 percent of an employee’s base pay. If employee Heather Jones has a base salary of $34,000, her bonus would be $5100 ($34,000 times 0.15). This may seem like a substantial bonus, but if the company fails to meet its profitability levels, Heather’s bonus is zero.

Gain Sharing Gain sharing emphasizes the costs of output rather than profit levels. With this plan, employees share the benefits of quality and productivity improve- ments made during the year. The pool of money for the bonuses comes from the cost items under the control of employees. The individual bonuses should represent an appropriate share of the gains.

While we traditionally think of gain sharing as a way to reward individuals within an organization for reducing the costs of output, the concept is being expanded to healthcare provided through Medicare. The accountable care organiza- tion (ACO) is a new Medicare option for physicians, hospi- tals, and other healthcare pro- viders to share in cost savings. ACOs provide an opportunity

to transform healthcare delivery and provider alignment. The Medicare Gain-Sharing Program allows healthcare providers to form ACOs. Providers will be responsible for quality and overall care of their Medicare patients. Medicare will share with ACO pro- viders the savings from improved quality, fewer hospitalizations, and the elimination of unnecessary costs. Beginning in 2012, the ACO model will be a nationwide option in Medicare fee-for-service (FFS). In addition to shared savings, the ACO option includes freedom of choice for Medicare beneficiaries, national quality measures, evidence-based medicine, patient-centered care delivery, advanced care coordination, and information sharing. The ACO model is designed to break down barriers and help providers work together to improve care and reduce medical costs. Some potential legal obstacles must be resolved before the 2012 implementation. The Secretary of the Department of Health

LINKS TO PRACTICE Accounatable Care

Organizations (ACO) http://www.a dhbonline

.com/article/new- accountable-care-

organizations-and- medicare-gain-sharing-

program

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c11.indd 434c11.indd 434 27/07/12 11:54 PM27/07/12 11:54 PM

LEARNING CURVES • 435

and Human Services (HHS) will need to waive certain laws and rules that conflict with the gain-sharing and provider alignment in ACOs. Specifically, aspects of federal antikick- back and physician self-referral laws must be waived sufficiently to allow the ACO model to work but not enough to create loopholes. It is also possible that the ACO model may violate federal and state antitrust laws. The ACO model offers the potential for a genuine win-win situation for physicians, hospitals, beneficiaries, and taxpayers.

Incentive Plan Trends Despite a trend toward using individual incentive systems, there are a number of dis- advantages. Such systems have been shown to undermine teamwork and give employ- ees a short-term focus. A study of 20 Social Security Administration offices showed that an individual merit pay system had no effect on worker performance. This find- ing tells us that individual incentive plans need significantly more data collection by management than time-based systems.

Some evidence suggests that group incentive systems suffer from the “free-rider” problem. The free-rider is the person who does not do his or her fair share but is still rewarded by the work of the group. Still, the extent of free-riding is modest, most likely because of group pressure. Overall, companies using group incentive systems tend to outperform companies that do not use such systems.

FIGURE 11-5

Learning curve

LEARNING CURVES An important factor in calculating labor times is the learning effect. We all can recall a new task or job that took a long time to finish the first time we tried. However, each subsequent time we did the task, it took less time. This is the basis of learning curve theory. People learn from doing a task and get quicker each time they repeat that task. A learning curve is shown in Figure 11-5.

The major attributes of a learning curve are that it takes less time to complete the task each additional time it is done by the same employee, and the time savings decrease with each additional time that task is done by that employee. When the num- ber of times the task is completed doubles, the decrease in time per task affects the rate of the learning curve. For example, if a learning curve has an 85 percent learning rate, the second time the task is done will take 85 percent of the time it took the first

c11.indd 435c11.indd 435 27/07/12 11:54 PM27/07/12 11:54 PM

436 • CHAPTER 11 WORK SYSTEM DESIGN

time the task was done. The fourth time the task is done will take 85 percent of the time it took the second time. The eighth time will take 85 percent of the time it took the fourth time, and so on. The formula for calculating the time the task should take is

T 3 Ln 5 time required for nth time the task is done

where T � time needed to complete task the fi rst time L � learning curve rate n � number of times the task is doubled

If the first time the task was done, the employee took twelve labor hours, and the learn- ing rate is 85 percent, calculate how long the sixteenth time that task is done should take.

Hours needed for 16th task 5 12 3 10.85 2 4 5 6.26 hours The sixteenth time the task is done will be the fourth doubling of output—the second time, the fourth time, the eighth time, and finally the sixteenth time.

An early use of the learning curve

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You have been asked to develop a time estimate for an order of 24 conveyor bucket systems. Your best estimate is that the first system will take 120 hours of labor, and an 85 percent learning curve is expected.

(a) How many labor-hours should the twelft h conveyor bucket system require? (b) How many labor-hours will the twenty-fourth conveyor bucket system require? (c) How many total labor-hours will be needed to build all 24 systems?

• Solution: (a) To determine how many hours the twelft h system requires, we use Table 11-9, the Learning

Curve Coeffi cients table. Looking in the row for the twelft h unit and the column for an 85 percent learning curve, we fi nd the coeffi cient is 0.558. We multiply the number of labor hours for the fi rst unit by the coeffi cient: 120 hours � 0.558 � 66.96 hours.

(b) Th e coeffi cient for the twenty-fourth unit is 0.475, so the twenty-fourth unit should require 57 hours, or 120 hours � 0.475.

(c) Th e total time required to produce the 24 systems is calculated by fi nding the coeffi cient for total time in the row for the twenty-fourth unit and the column titled total time for the 85 percent learning curve. In this case, the coeffi cient is 14.331. Now multiply the hours for the fi rst unit times the coeffi cient. Total labor required to build the 24 systems is 1719.72 hours, or 120 hours � 14.331.

EXAMPLE 11.6

Using the Table of Learning Curve

Coefficients

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LEARNING CURVES • 437

Understanding learning curves is important. The amount of labor needed to finish the same task or build the same product a number of times can be calculated, which allows a company to better estimate the labor cost. The company can also better schedule its workload since it knows how much labor is needed to complete the task a specific number of times. Calculating the learning rate also is a means for evaluating productivity. The company can compare its learning rate with that of its competi- tors. The rate of learning is affected when there are changes in designs, personnel, or procedures. Changes mean that an employee must learn something new, which slows down the improvement rate.

An alternative method of calculating the time required to produce a specific unit is to use a table of learning curve coefficients, as shown in Table 11-9. This table allows

70% 75% 80% 85% 90% Unit

Number Unit Time

Total Time

Unit Time

Total Time

Unit Time

Total Time

Unit Time

Total Time

Unit Time

Total Time

1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

2 .700 1.700 .750 1.750 .800 1.800 .850 1.850 .900 1.900

3 .568 2.268 .634 2.384 .702 2.502 .773 2.623 .846 2.746 4 .490 2.758 .562 2.946 .640 3.142 .723 3.345 .810 3.556

5 .437 3.195 .513 3.459 .596 3.738 .686 4.031 .783 4.339

6 .398 3.593 .475 3.934 .562 4.299 .657 4.688 .762 5.101

7 .367 3.960 .446 4.380 .534 4.834 .634 5.322 .744 5.845 8 .343 4.303 .422 4.802 .512 5.346 .614 5.936 .729 6.574

9 .323 4.626 .402 5.204 .493 5.839 .597 6.533 .716 7.290

10 .306 4.932 .385 5.589 .477 6.315 .583 7.116 .705 7.994

11 .291 5.223 .370 5.958 .462 6.777 .570 7.686 .695 8.689 12 .278 5.501 .357 6.315 .449 7.227 .558 8.244 .685 9.374

13 .267 5.769 .345 6.660 .438 7.665 .548 8.792 .677 10.052

14 .257 6.026 .334 6.994 .428 8.092 .539 9.331 .670 10.721

15 .248 6.274 .325 7.319 .418 8.511 .530 9.861 .663 11.384

16 .240 6.514 .316 7.635 .410 8.920 .522 10.383 .656 12.040

17 .233 6.747 .309 7.944 .402 9.322 .515 10.898 .650 12.690

18 .226 6.973 .301 8.245 .394 9.716 .508 11.405 .644 13.334

19 .220 7.192 .295 8.540 .388 10.104 .501 11.907 .639 13.974 20 .214 7.407 .288 8.828 .381 10.485 .495 12.402 .634 14.608

21 .209 7.615 .283 9.111 .375 10.860 .490 12.892 .630 15.237

22 .204 7.819 .277 9.388 .370 11.230 .484 13.376 .625 15.862

23 .199 8.018 .272 9.660 .364 11.594 .479 13.856 .621 16.483

24 .195 8.213 .267 9.928 .359 11.954 .475 14.331 .617 17.100

25 .191 8.404 .263 10.191 .355 12.309 .470 14.801 .613 17.713

TABLE 11-9

Learning Curve Coefficients

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438 • CHAPTER 11 WORK SYSTEM DESIGN

you to calculate the time it should take to build the nth unit as well as to calculate the total production time required to build the entire production run when the nth unit is the last one completed.

WORK SYSTEM DESIGN WITHIN OM: HOW IT ALL FITS TOGETHER Work system design includes job design, methods analysis, and work measurement. Manufacturing or industrial engineers often do these activities. Job design determines exactly how the product or service will be done and is linked directly to product and process design. Based on the type of product (standard or custom) and its proposed process (mass-producing or producing one at a time), a company determines the skills set needed by its employees as well as the necessary equipment.

Method analysis provides a means for evaluating different processes and materials, thus allowing a company to focus on continuous improvement. This ties in directly with a company’s total quality management (TQM) focus.

Work measurement techniques allow a company to develop standards to use as a basis for evaluating the cost and effectiveness of different methods and materials for building a product or providing a service. These time standards provide a time estimate to use as a basis for establishing detailed work schedules and for determining long-term staffing levels. These time estimates can be used as a basis for making deliv- ery or completion-time promises to customers. Standard times are used to develop lead-time estimates, which are inputs for the MRP (material requirement planning) system as well as the MPS (master production schedule) process.

Work system design provides the means for setting standards against which to com- pare new methods, new materials, and new designs; assures that employees know how to do their job; and provides the information needed by the company to calculate its costs.

WORK SYSTEM DESIGN ACROSS THE ORGANIZATION Work system design affects functional areas throughout a company. Let’s look at why individual functional areas are concerned with work system design.

Accounting calculates the cost of products manufactured or services provided. Labor can be a significant portion of the cost of goods sold, especially in the service industries. Accounting measures variances between planned product cost and actual product cost. Accounting also typically measures operational efficiency, which is based on work standards. Work system design is an important resource for account- ing activities.

Marketing is concerned with work system design because it is the basis for deter- mining lead time. Accurate work projections enable marketing to make viable promise dates to customers.

Information systems uses estimates of job duration and resources in the software for scheduling and tracking operations.

Purchasing handles requests for materials based on a schedule projected from the work system design. Accurate scheduling enables cost-effective materials and labor purchasing decisions. Standard time provides a benchmark for evaluation of new materials and processes.

Manufacturing responds to effective job design, process analysis, and work mea- surement with high levels of performance and on-time delivery of finished goods.

Human resources uses work sampling to establish and validate hiring criteria.

OM ACC

MKT

MIS

HRM

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THE SUSTAINABILITY LINK • 439

You can see that work system design involves all aspects of an organization and has an impact on how well the organization performs. In many manufacturing companies, job design and process analysis are both done by a manufacturing or an industrial engineer. The engineer works with product blueprints and the workforce to develop job instructions. From these detailed job instructions, the company can develop time standards. Work measurement information is often provided by workers as they complete a job. Accounting may use this information to report the efficiency of manu- facturing operations. In a service organization, an operations manager or operations analyst may do the job design and process analysis.

Work system design helps companies understand the total costs of making a prod- uct or providing a service. It allows companies to evaluate their product or service line and view the bottom line for each product or service.

Work system design includes job design, methods analysis, work measurement, and compensation. Each of these is critical to successful supply chain management. Job design requires clearly specify- ing what has to be done, who has to do it, how it should be done, and how it should be measured. Every member of the supply chain needs to have his or her role similarly defined. Methods analysis is used to improve processes and eliminate non-value-adding

activities. Such analysis can reduce duplicate efforts among supply chain members. Work measurement is

used to develop standards for activities performed by members of the supply chain. Standards for

picking and packing materials at warehouses or standards for unloading and loading at cross- docks are critical for coordinating material flow

within the supply chain. Using standards to mea- sure job performance is critical to improving supply chain success.

THE SUPPLY CHAIN LINK

A significant aspect of sustainability is the design of jobs and work with an eye toward minimizing harm to the environment. This includes work and jobs within a company’s own operations, as well as the job require- ments it makes on its suppliers. Starbucks is a company that provides an excellent example of such work system design, being a leader in sustainability practices. These practices include careful selection of suppliers that involve spending 18 weeks visiting coffee growers in various nations, and providing exacting specifications for growing, harvesting, and delivery of coffee. They demand their growers protect biodiversity in their growing and harvesting practices, including specifying precisely how the coffee will be picked so as not to dam- age the surrounding habitat. Starbucks then specifies details of how coffee beans will be stored and handled, such as storing all coffee beans in recyclable burlap bags, and specifying when electronic seals will be added

to ensure product integrity. At the retail stores, jobs continue to be designed with the environment in mind, including a job for recycling coffee grounds and using it as nitrogen-rich garden compost. These are all specific job duties within the operation and supply chain that ensure sustainability practices are designed to support the overall strategy of sustainability. The benefits to Starbucks have been high product quality and consis- tency, as well as improved profitability.

Another important aspect of sustainability in work system design is addressing social responsibility, which involves employing and respecting a diverse workforce with competitive wages and ample time away from work. Social sustainability requires man- agement practices with respect to employees that should go above and beyond minimum requirements of health, safety, and nondiscrimination mandated by labor laws. Employment practices should maxi- mize the productivity and quality of employees by fostering a culture of mutual respect, appreciation,

THE SUSTAINABILITY LINK

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440 • CHAPTER 11 WORK SYSTEM DESIGN

and care. This involves redesigning difficult jobs and moving away from long hours and poor pay. Investing in employees—in the form of continu- ing education programs, leave time, childcare, and opportunities for advancement—should be seen as investments in the company itself. The result is hap- pier, healthier, more productive employees and an

enhanced reputation of the company. An excellent example of these benefits is the SAS Institute in North Carolina, a company consistently on Fortune maga- zine’s top companies to work for. The company pro- vides a superb work environment—from childcare to on-site massages—resulting in high productivity and almost no turnover.

Chapter Highlights 1 Work system design involves job design, methods or process

analysis, and work measurement. Job design specifi es the work activities of an individual or group in support of organizational objectives.

2 Relevant job design issues include design feasibility, the choice of human or machine, the use of teams, and the location where the work is to be done. Technical feasibility is the degree to which an individual or a group of individuals is physically and mentally able to do the job. Economic feasibility is the degree to which the value a job adds and the cost of having the job done are profi table for the company. Behavioral feasibility is the degree to which an employee derives intrinsic satisfaction from doing the job. Another job design issue concerns whether the job should be done by people or by machines. If a person does the job, you need to decide on the level of specialization. If jobs are extremely specialized, you will probably need a way to reduce boredom. Rather than individual job design, an organization can use team approaches. Th ese include problem-solving teams, special-purpose teams, and self-directed teams. An additional consideration is the location where the work is done. Many employees may work in alternative workplaces in the future.

3 Methods or process analysis is concerned with how the employee does the job. Methods analysis can also be used to improve the effi ciency of an operation.

4 Work measurement is used to determine standard times. A standard time is how long it should take a qualifi ed operator, using the appropriate process, material, and equipment, and working at a sustainable pace, to do a particular job. Standard times are used for product costing, for process and material evaluation, and for planning workloads and staffi ng. Standard times are usually based on time studies. Work sampling is used to estimate the proportion of time that should be spent on an activity.

5 To do a time study, you identify the job and break the job into work elements. Th en you determine the number of observations needed and perform the observations.

6 Work sampling involves random observations of a worker. Each time you observe the worker, you note what activity the worker is doing. Aft er numerous observations, you can project the expected proportion of time the worker should spend on diff erent activities.

7 Standard times are developed with either time studies, elemental time data, or predetermined time data. You learned how to develop standard times using time studies. Aft er conducting the time study, you compute the mean observed time for each work element. You compute the normal time for the work element by multiplying the mean observed time by the performance rating factor. You fi nd the standard time for each work element by multiplying the normal time by the allowance factor.

8 Standards are used to compare alternative processes, evaluate new materials or components, and evaluate individual worker performance. Standards also allow you to determine when a job should be completed or how many units can be done in a period of time.

9 Worker compensation systems are either time-based or output- based. Time-based systems pay the employee for the number of hours worked. Output-based systems pay the employee for the number of units completed. Compensation schemes can be based on either individual or group performance.

10 Learning curves show the rate of learning that occurs when an employee repeats the same task. Using learning curves, you can estimate how long a particular task will take. It allows the company to schedule better and calculate costs more accurately.

Key Terms job design 413 technical feasibility 413 economic feasibility 413

behavioral feasibility 413 specialization 415 job enlargement 416

job enrichment 416 job rotation 416 problem-solving teams 416

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SOLVED PROBLEMS • 441

special-purpose teams 417 self-directed teams 417 alternative workplace 417 methods analysis 419 work measurement 422 standard time 422

time study 423 mean observed time 426 performance rating factor 426 frequency of occurrence 427 normal time 427 allowance factor 427

elemental time data 429 predetermined time data 429 work sampling 431 time-based compensation systems 433 output-based (incentive) systems 433

Formula Review 1. Calculating the number of observations for an element in a

time study

n $ c a z a b a s

x b d 2

2. Calculating the allowance factor based on job time

AFJob 5 1 1 PFD

3. Calculating the allowance factor based on time worked

AFTime worked 5 1

1 2 PFD

4. Calculating the normal time

NT 5 1MOT 2 1PRF 2 1F 2

5. Calculating standard time ST 5 1NT 2 1AF 2

6. Calculating the number of observations for work sampling

n $ azeb 2

p̂ 11 2 p̂ 2

7. Calculating the time required for the nth time the task is done

T 3 Ln

Solved Problems • Problem 1 Frank’s BBQ Delight restaurant sells barbecued chicken sand- wiches. The owner wants to set a standard time for assembling each kind of sandwich he sells. You have been asked to set a standard time for the Super Chicken sandwich—8 ounces of barbecued chicken served on a bulkie roll.

(a) Determine the number of observations you need to make if the owner wants the standard time to be within 5 per- cent of the true value 90 percent of the time. Results from your first 10 observations are shown here.

(See student companion site for Excel template.)

(b) Determine the normal time for each of the activities. Determine the standard time if the owner decides to use a 10 percent allowance factor based on the time of the job.

(d) If the owner expects demand for the Super Chicken sand- wich to be 100 per hour, how many employees should be assigned to preparing the Super Chicken sandwiches?

• Before You Begin: To determine the number of observations needed for an ele- ment in a time study, use the formula: n $ 3 1z/a 2 1s/x 2 2 4. The owner has the desired confidence and accuracy. The standard deviation and the mean observed time for each element are provided for you in (a).

• Solution a With z � 1.65 and e � 0.05, determine the number of observa- tions needed for each work element.

Work element 1:

n $ c a1.65 0.05

b a0.20 2.40

b d 2 5 7.56 or 8 observations

Work Element Standard Deviation

Mean Observed

Time

1. Get bulkie rolls (box of 12). 0.200 2.40 2. Prepare bulkie roll, move to prep

area, open up roll. 0.005 0.08

3. Get barbecued chicken (4-lb. box). 0.550 4.00 4. Place 8 oz. chicken on roll. 0.015 0.25 5. Spread on special BBQ sauce. 0.010 0.10 6. Close bulkie roll, slice in half. 0.0125 0.10 7. Wrap sandwich, place in pickup

area. 0.010 0.15

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442 • CHAPTER 11 WORK SYSTEM DESIGN

• Before You Begin: To calculate normal time for an element, you multiply the mean observed time by the performance rating factor and then by the frequency. In this case, use the revised mean observed time, which includes all of the observations.

• Solution b The normal times are shown in the spreadsheet. To calculate the normal times, multiply the mean observed time by the per- formance factor and the frequency factor.

To determine standard time, we multiply the normal time by the allowance factor. Since the allowance is based on job time, we use the formula 1 � allowances. The company has decided on a 10 percent allowance.

The standard times are shown in the far right column in the spreadsheet.

Work element 2:

n $ c a1.65 0.05

b a0.005 0.08

b d 2 5 4.25 or 5 observations Work element 3:

n $ c a1.65 0.05

b a0.55 4.0

b d 2 5 20.59 or 21 observations Work element 4:

n $ c a1.65 0.05

b a0.015 0.25

b d 2 5 3.92 or 4 observations Work element 5:

n $ c a1.65 0.05

b a0.01 0.10

b d 2 5 10.89 or 11 observations Work element 6:

n $ c a1.65 0.05

b a0.0125 0.10

b d 2 5 17.02 or 18 observations Work element 7:

n $ c a1.65 0.05

b a0.01 0.15

b d 2 5 4.84 or 5 observations Calculate the number of observations needed for each of the work elements. Then determine that we need a total of 21 observations to be 90 percent confident that we will be within 5 percent of the true mean. After you make the additional 11 observations, the following data are available.

Work Unit

Mean Observed Time

(minutes) Performance

Rating Factory Frequency

1 2.20 1.10 0.083 2 0.10 0.90 1 3 4.25 0.95 0.125 4 0.20 1.25 1 5 0.10 1.00 1 6 0.12 0.90 1 7 0.12 1.25 1

The following spreadsheet shows how these data could be used in a spreadsheet:

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SOLVED PROBLEMS • 443

• Before You Begin: As long as the employee is working at the standard rate, you can determine how many sandwiches the employee can prepare per hour, by dividing the 60 minutes in an hour by the standard time per sandwich (1.5448 minutes).

• Solution c Since the standard time is 1.5448 minutes per cycle, a worker performing at the standard should be able to produce 38.84 sandwiches per hour (60 minutes/1.5448 minutes per sandwich).

• Before You Begin: When you know how many sandwiches need to be made each hour and you know how many can be made by an employee work- ing at standard, you determine the number of employees needed by dividing the quantity needed (100 per hour) by the number an employee working at standard can produce in one hour (38.84).

• Solution d If the owner plans to have staffing to prepare 100 Super Chicken sandwiches, he will need three employees (100 sandwiches per hour divided by 38.84 sandwiches per employee per hour).

• Problem 2 You make 20 observations of a business professor at State Uni- versity. The results of the observations are as follows:

• Before You Begin: To determine the number of observations needed to estimate the proportion of the professor’s time spent on classroom preparations, you use the formula n � 1z /e 2 2p̂ 11 2 p̂ 2 . Cal- culate the initial proportion estimate using the results of the initial observations.

• Solution Based on the preliminary observations, the estimate of the pro- portion of time the professor spends preparing for class is 0.15 (3 observed times/20 observations taken).

n 5 a1.96 0.05

b2 [0.15 11 2 0.15 2 ] 5 195.92 observations We need to take a total of 196 observations to satisfy our confi- dence and error constraints.

Activity Times Observed

Professor at class 5 Professor grading 2 Meeting with students 1 Preparing for class 3 Working on research 3 Idle 2 Speaking on phone 1 Not available 3

Based on this information, how many observations do you need to estimate the proportion of the professor’s time spent on classroom preparations? Assume a 95 percent confidence (z � 1.96) that the resulting estimate will be within 5 percent of the true value.

• Problem 3 You need to develop a cost estimate for a customer order of 12 custom commercial printing presses. It is estimated that the first press will require 750 labor-hours; a learning curve of 80 percent is expected.

(a) How many labor-hours are required for the sixth print- ing press?

(b) How many labor-hours are required for the twelfth print- ing press?

(d) If the average labor-hour cost is $24, what is the total labor cost for building the 12 printing presses?

(e) If your company typically prices products at 212 times the labor cost, what is the price quoted to the customer?

• Solution (a) Using Table 11-9, we see that the coefficient for the sixth

unit with an 80 percent learning curve is 0.562. The time for the sixth unit is 421.50 hours (or 750 hours � 0.562).

(b) The coefficient for the twelfth unit is 0.449, so the time to build the twelfth unit is 336.75 hours.

(d) Total labor cost is $130,086 (or $24 per hour � 5420.25 hours).

(e) The price quoted to the customer is $325,215 (or $130,086 � 2.5).

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444 • CHAPTER 11 WORK SYSTEM DESIGN

Discussion Questions 1. Describe the major components of work system design. 2. Visit a local business and describe the jobs to be done, the

workers needed for the jobs, and how the workers help achieve the objectives of the business.

3. Describe the objectives of job design. 4. Explain why it is hard to design jobs in a business setting. 5. Explain what we mean by technical feasibility, economic

feasibility, and behavioral feasibility. 6. Describe cases in which people are preferable to machines. 7. Describe cases in which machines are preferable to people. 8. Describe the advantages and disadvantages of using a high

level of job specialization. 9. Describe factors affecting the work environment that must

be considered in work systems design.

10. Describe the alternative workplace approach. 11. Create a process flowchart for an activity that you do

daily—for example, getting ready for school each day. 12. Analyze a daily activity to see whether you can improve

the process. 13. Compare and contrast the four work measurement

techniques. 14. Explain the difference between time-based and

output-based compensation plans. 15. Explain why it makes sense to use time-based compensa-

tion systems. 16. Explain why it makes sense to use an output-based com-

pensation system.

Problems 1. Given the following information, determine the sample size

needed if the standard time estimate is to be within 5 percent of the true mean 97 percent of the time.

of the true mean 99 percent of the time. Calculate the percentage increase in sample size for the higher precision.

Use the following information from the Arkade Company for Problems 5–10.

Work Element

Standard Deviation (minutes)

Mean Observed Time

(minutes)

l 0.20 1.10 2 0.10 0.80 3 0.15 0.90 4 0.10 1.00

2. Using the information in Problem 1, determine the sample size needed if the standard time estimate is to be within 5 percent of the true mean 99 percent of the time.

3. Using the following information, determine the sample size needed if the standard time estimate is to be within 5 percent of the true mean 95 percent of the time.

Work Element

Standard Deviation (minutes)

Mean Observed Time

(minutes)

1 0.60 2.40 2 0.20 1.50 3 1.10 3.85 4 0.85 2.55 5 0.40 1.60 6 0.50 2.50

4. Using the information in Problem 3, calculate the sample size needed if the standard time estimate is to be within 5 percent

Work Element

Mean Observed Time

(minutes) Performance Rating Factor

1 1.20 0.95 2 1.00 0.85 3 0.80 1.10 4 0.90 1.10

5. Calculate the normal time for each of the work elements. 6. Th e Arkade Company has decided to use a 15 percent

allowance factor based on job time. Calculate the standard time for each work element and for the total job.

7. Based on the standard time calculated in Problem 6, how many units should an employee operating at 100 percent of standard complete during an eight-hour workday?

8. Th e Arkade Company is considering switching to a 15 percent allowance based on time worked. Calculate the new standard time for each work element and for the total job.

9. Based on the standard time calculated in Problem 8, how many units should an employee operating at 100 percent of standard complete during an eight-hour workday?

10. Compare the two standards calculated in Problems 6 and 8. What other factors should be considered in selecting the method for determining the allowance factor?

11. Jake’s Jumbo Jacks has collected the following information to develop a standard time for building jumbo jacks.

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PROBLEMS • 445

Element (in minutes) Observations 1 2 3 4 5

Cycle 1 2.18 1.25 1.70 2.74 1.57 Cycle 2 2.22 1.23 1.75 2.66 1.55 Cycle 3 2.20 1.29 1.72 2.60 1.57 Cycle 4 2.18 1.30 1.80 2.56 1.57 Cycle 5 2.21 1.26 1.84 2.58 1.59 Cycle 6 2.22 1.22 1.79 2.58 1.61 Cycle 7 2.17 1.26 1.78 2.60 1.57 Cycle 8 2.21 1.26 1.75 2.58 1.61 Cycle 9 2.18 1.30 1.80 2.60 1.55 Cycle 10 2.17 1.28 1.78 2.58 1.57

Rating factor 0.90 0.80 1.10 1.05 0.95 Frequency 1 1 1 1 1

(a) Calculate the mean observed time for each element. (b) Calculate the normal time for each element. (c) Using an allowance factor of 20 percent of job time,

calculate the standard time for each element and for the entire job.

(d) How many units should be completed each hour if the worker performs at 100 percent of the standard?

(e) How many units should be completed each hour if the worker performs at 90 percent of the standard?

12. Frank’s Fabricators has collected the following information to develop a standard time for producing their high-volume Navigator III, a universal remote control. All of the times are in minutes.

Element Observations 1 2 3 4 5 6

Cycle 1 1.10 3.00 0.92 1.23 1.46 1.80 Cycle 2 1.08 0.88 1.30 1.64 1.78 Cycle 3 1.15 3.20 0.85 1.26 1.55 1.76 Cycle 4 1.16 0.88 1.33 1.52 1.80 Cycle 5 1.07 3.10 0.90 1.28 1.62 1.82 Cycle 6 1.10 0.94 1.30 1.60 1.82 Rating factor 0.95 0.90 1.05 1.0 0.85 1.10 Frequency 1.00 0.50 1.0 1.0 1.0 1.0

(a) Calculate the mean observed time for each element. (b) Calculate the normal time for each element. (c) Using an allowance factor of 15 percent of job time,

calculate the standard time for each element. (d) Calculate the standard time for completing one Navigator

III. (e) If an employee is able to produce at a rate equal to the

standard (100 percent effi ciency), how many units should she produce each hour?

(f) If an employee is working at 90 percent effi ciency, how many units should she complete in one hour?

(g) If a process improvement has changed the mean observed time for element 6 to 1.50 minutes, what is the new standard time for the Navigator III?

(h) If the company builds 20,000 Navigator IIIs each month, how much less time does it require using the new process?

13. Th e following information is provided to you for each of fi ve elements performed in building the Aviator model, a basic universal remote control.

Element

Mean Observed Time

(minutes)

Performance Rating Factor Frequency

1 0.96 0.96 1.0 2 1.45 1.10 1.0 3 3.33 1.00 0.33 4 1.24 0.90 1.0 5 1.18 1.05 1.0

(a) Calculate the normal time for each element. (b) If the company uses a 15 percent allowance factor based

on time worked, calculate the standard time for each element.

standard. 14. You have 25 observations of university policeman Sgt. Jack

B. Nimble during his normal workday. Th e results are shown here. Assume that the estimated proportion is to be within 5 percent of the true proportion 95 percent of the time.

Activity Observed Number of

Times Observed

Doing paperwork 9 On the phone 3 Eating doughnuts 3 Cleaning weapon 4 Idle 2 Not in sight 4

(a) Based on your preliminary observations, how many total observations do you need to estimate the proportion of time Sgt. Nimble spends doing paperwork?

(b) How many total observations do you need to estimate the proportion of time Sgt. Nimble spends on the phone?

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446 • CHAPTER 11 WORK SYSTEM DESIGN

Element (in minutes) Observations 1 2 3 4 5

Cycle 1 0.58 1.50 0.79 0.30 Cycle 2 0.61 0.75 0.35 Cycle 3 0.59 0.73 0.33 Cycle 4 0.54 0.72 0.35 Cycle 5 0.60 1.40 0.72 0.30 2.00 Cycle 6 0.57 0.71 0.32 Cycle 7 0.53 0.80 0.30 Cycle 8 0.59 0.78 0.28 Cycle 9 0.63 1.54 0.77 0.35 Cycle 10 0.58 0.79 0.33 2.20 Cycle 11 0.56 0.72 0.32 Cycle 12 0.55 0.79 0.34 Cycle 13 0.58 1.62 0.77 0.29 Cycle 14 0.60 0.80 0.33 Cycle 15 0.62 0.74 0.30 2.10

Rating factor 0.95 0.90 1.00 1.10 0.90

Frequency 1 0.25 1 1 0.20

(a) Develop the mean observed time for each element. (b) Calculate the normal time for each element. (c) Using an allowance factor of 15 percent of job time, calculate

the standard time for each element and for the entire job. (d) How many units should be completed each hour if the

worker performs at 100 percent of the standard? (e) How many units should be completed each hour if the

worker performs at 110 percent of the standard? 16. As a class project you have been asked to project the

proportion of time a professor spends on various activities. You have decided to use the work-sampling method. Your initial observations are shown.

(a) Calculate the sample size needed to estimate the propor- tion of time Dr. Knowitall spends away on emergencies.

(b) Calculate the sample size needed to estimate the proportion of time Dr. Knowitall spends reviewing test results.

18. You need to develop a labor time estimate for a customer order of 20 network installations. It is estimated that the fi rst installation will require 60 hours of labor, and a learning curve of 90 percent is expected.

(a) How many labor-hours are required for the fi ft eenth installation?

(b) How many labor-hours are required for the twentieth installation?

(d) If the average labor cost is $32, what is the total labor cost for installing the networks?

(b) Determine what proportion of time the professor spends teaching class.

(d) If the professor works approximately 54 hours per week, determine the amount of time that would normally be spent on each activity.

17. Your 20 observations of Dr. Knowitall reveal the following information. Assume that the estimate is to be within 5 percent of the true proportion 95 percent of the time.

You are instructed that your estimates are to be within 5 percent of the true value with 97 percent confi dence (z � 2.17).

(a) Based on your initial observations, how many total observations are needed to estimate the proportion of time the professor spends on each activity?

Activity Observed Number of

Times Observed

Grading 4 Administrative paperwork 6 Preparing for class 5 Teaching class 5 Meeting with student(s) 8 On the phone 2 Working on research 6 Unavailable 4

Total 40

Activity Observed Number of

Times Observed

Grading 30 Administrative paperwork 50 Preparing for class 30 Teaching class 30 Meeting with student(s) 66 On the phone 17 Working on research 45 Unavailable 34 Total 302

Activity Observed Number of

Times Observed

With patient 6 Reviewing test results 3 On phone 2 Idle 1 Away on emergency 4 Not available 4

15. You are given the following information. Aft er taking additional observations, the following data are available.

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CASE: NORTHEAST STATE UNIVERSITY • 447

Th e item is sold for $92. Th e learning rate is 80 percent. Overhead is assessed at a rate of 55 percent of unit labor cost.

(a) Determine the average unit cost for the 20 units if the fi rst unit takes four hours.

(b) Determine the minimum number of units that need to be made before the selling price meets or exceeds the average unit cost.

19. Students in an operations management class have been assigned six similar computer homework problems. Alexis needed 40 minutes to complete the fi rst problem. Assuming an 80 percent learning curve, how much total time will Alexis need to complete the assignment?

20. Your company has received an order for 20 units of a product. Th e labor cost to produce the item is $9.50 per hour. Th e setup cost for the item is $60 and material costs are $25 per unit.

CASE: The Navigator III Frank Jones, the owner of Frank’s Fabricators, has collected the following information to develop a standard time for producing the Navigator III, a universal remote control. All of the times are in minutes.

Frank has noticed that one of his senior operators, Sam, is able to consistently produce the Navigator III much quicker than other employees. However, Sam always produces exactly what the standard requires each day. Sam insists that he does every- thing according to the job directions. He offers no additional insight into how quickly he achieves the standard output levels.

During casual observations, Frank noticed that Sam appears to be doing element 6 much quicker than anyone else. Because

of this, he asks Susan, a time-study analyst, to observe how Sam is doing element 6. After observing Sam, she determined that his mean observed time for element 6 was 1.50 minutes, and she rated his performance at 100 percent. This suggests that his output is not being accomplished by unusual effort on his part. Since Sam was significantly quicker than the standard but was only working at 100 percent, Susan suggested that he had devel- oped a new method for doing element 6.

Questions 1. Based on Susan’s observations, determine how long it

actually takes Sam to produce a Navigator III. 2. If direct labor is assessed at $18 per hour, what would the

labor savings per Navigator III be if all the employees used the same method that Sam uses?

3. If Frank’s Fabricators produces 20,000 Navigator IIIs each month, what are the potential annual savings by using Sam’s method for element 6?

4. Why do you think Sam is reluctant to share his method improvement for element 6 with the company?

5. Do standard times inhibit process improvement? 6. How can you ensure that employees will share time- saving

improvements? 7. From an employee’s perspective, why wouldn’t you share a

process improvement with the company?

Elements

Observations 1 2 3 4 5 6

Cycle 1 1.10 3.00 0.92 1.23 1.46 1.80 Cycle 2 1.08 0.88 1.36 1.64 1.78 Cycle 3 1.15 3.20 0.85 1.26 1.55 1.76 Cycle 4 1.16 0.88 1.33 1.52 1.80 Cycle 5 1.07 3.10 0.90 1.28 1.62 1.82 Cycle 6 1.10 0.94 1.30 1.60 1.82

Rating factor 0.95 0.90 1.05 1.0 0.85 1.10

Frequency 1.00 0.50 1.0 1.0 1.0 1.0

CASE: Northeast State University Dr. Woodrow Bay, chairperson of the Decision Sciences Department in the College of Business Administration, sat at his desk pondering his latest predicament. For the last 45 minutes at the faculty meeting, faculty members complained about poor administrative support. Numerous examples of unavailable administrative assistants were given. As the profes- sors left, one muttered, “There are universities that provide plenty of administrative support; maybe it is time to update our resumes.”

Dr. Bay knew this to be true and that these professors could easily leave for another university given the current job market. He needed to determine whether their perceptions regarding inadequate administrative support were justified.

Background The Decision Sciences Department houses four functional areas: operations management, quantitative methods, statistics, and information systems. The faculty have national or international

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448 • CHAPTER 11 WORK SYSTEM DESIGN

reputations based on their excellent scholarship. Higher student enrollments have resulted in the department increasing its faculty from 12 to 20 full-time professors during the past three years. Unfortunately, there was no increase in the administrative support for the department.

As professors were added, the strain on the administrative staff increased. There were more classes, so more course mate- rials needed to be prepared (syllabi, handouts, and exams). In addition, since the faculty were expected to publish research, the administrative assistants spent more time working on manu- scripts. New professors required significantly more interaction with the support staff to explain what was wanted and when. Administrative assistants often ran errands for faculty mem- bers (placing items on reserve at the library, dealing with the book store, making photocopies, distributing mail, doing cor- respondence, providing supplies, making travel arrangements, arranging meetings, and placing meal orders). The administra- tive assistants seemed unable to complete all of their work in a normal eight-hour day.

The faculty, on the other hand, believed the primary work activity of the administrative assistants was keyboard entry: keying in manuscripts, course outlines, correspondence, and grant proposals. When faculty did not see the administrative assistants keying in information, the impression was that the staff was not doing its job. It seemed that too much time was wasted talking with faculty, students, or each other, and work was not being done. Dr. Bay did not believe this was the case. He needed to gather information about how the administrative assistants spent their time and compare that with data from the faculty concerning the administrative assistants. Both the faculty and the administrative assistants believed that the solution was hiring an additional administrative assistant. For Dr. Bay this was next to impossible, given the proposed budget cuts at the

university, so he decided to collect data to gain insight into the problem.

For two weeks data were collected. From the faculty, Dr. Bay received estimates as to what percentage of the administrative assistants’ time the faculty perceived was spent on different activities. He also did a work sample of the administrative assis- tants. The results are shown here.

Case Questions Your assignment is to analyze the data collected by Dr. Bay. In particular, you should

1. Describe how Dr. Bay can use the data. 2. Suggest ways in which the administrative assistants’ jobs

can be changed to make better use of their time and be more supportive to the faculty.

3. Suggest ways the faculty can change work habits to reduce the burden on the administrative assistants.

4. Consider other alternatives to reduce the strain on the administrative assistants.

Estimates of Administrative Assistants' Use of Time

Activity Faculty

Estimate (%) Work

Sample (%)

Working on computer 20 40 Talking on phone 25 7 Away from offi ce 20 10 Talking with faculty 2 8 Talking with others 15 10 Filing 3 5 Photocopying 5 15 Other 10 5

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Work System Design at Cruise International, Inc. You have been asked to work with Monita Terazzi, the head of Housekeeping Services. She is concerned about the high turn- over rate among cabin stewards/stewardesses within her depart- ment. Currently, each steward is responsible for 16 staterooms. Many of the departing employees have complained that there is too much work to do during the scheduled hours and they often need to work additional hours. Monita wants you to do a work sampling to determine how the 70 scheduled hours for cabin stewards/stewardesses are used each week. This assignment will

enable you to enhance your knowledge of the material in Chap- ter 11 of your textbook and prepare you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Work System Design at CII

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INTERNET CHALLENGE: E-COMMERCE JOB DESIGN • 449

You have been chosen to head up the development of an e-commerce direct retail site for your company. Since this is new to both you and your company, you need to gather some initial information. You will use this information to develop standard times for measuring your company Web site’s performance. Your company is planning to start by offering between 100 and 200 popular products on-line. Of particular concern to your company is the customer’s ease in using your site.

Begin by visiting at least three different Web sites and docu- menting your experience at the site with a process flowchart.

Show the sequential steps you as the customer must follow when visiting the site to gain product information, to place an order, to arrange payment, and to track the order. For one of the sites you visit, collect data on how long it takes a customer to com- plete a visit to the site. Break the visit into distinct elements such as finding product information, placing an order, choosing the shipping method, making payment, and confirming your order. For one of the three sites you visit, analyze the procedures used and propose changes that you think would improve the cus- tomer effectiveness of the site.

INTERNET CHALLENGE E-Commerce Job Design

On-Line Case: Work System Design at Valley Memorial Hospital.

Assignment: Work System Design at Valley Memorial Hospital You have been asked to work with Lee Jordan, director of the Medical/Surgical Nursing Unit. Lee is concerned about nursing services in the obstetric area. Recently, patients have been com- plaining about the timeliness of the response to their nursing needs. Recognizing that nursing is extremely important to the success of VMH, Lee has always tried to make sure that these areas are adequately staffed. Her staffing decisions have been based on a conservative assumption that nurses should be able to devote about 60 percent of their time to actual patient nurs- ing, with the remainder used for other non nursing activities. These include (a) paperwork or documentation pertaining to record keeping and insurance, (b) answering queries from doc- tors and patients either in person or on the telephone, and (c) other activities such as breaks and personal needs. Lee wants you

to design a study to estimate the percentage of the nurses time spent on actual nursing and non nursing activities and prepare a concise report addressing activities and prepare a concise report addressing a few specific questions. This assignment will enable you to enhance your knowledge of the material in this chapter.

To computer this assignment, go to www.wiley.com/college/ reid to get the details needed assignment questions are given at the site.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion site • Click Virtual Company/Valley Memorial Hospital • Click Kaizen Consulting, Inc. • Click Consulting Assignments • Click Work System Design at Valley Memorial Hospital

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450 • CHAPTER 11 WORK SYSTEM DESIGN

Online Resources Companion Website www.wiley.com/college/reid:

SuperbServ, Inc. 3M-AiT, Ltd.

• Find links for Additional Web Resources for this chapter The AES Corporation, www.aesc.org Southwest Airlines, www.southwest.org Irving Paper Mill, Irving Forest Discovery Network, www.ifdn.com/paper/paper.htm

Selected Bibliography Barnes, Ralph M. Motion and Time Study: Design and Mea-

surement of Work, Eighth Edition. New York: Wiley, 1980. Hatcher, Larry, and Timothy L. Ross. “From Individual Incen-

tives to an Organization-Wide Gainsharing Plan: Eff ects on Teamwork and Product Quality,” Journal of Organizational Behavior, May 1991, 169.

Ledford, Gerald E., Jr., Edward E. Lawlet III, and Susan A. Mohrman, “Reward Innovations in Fortune 1000 Compa- nies,” Compensation and Benefi ts Review, April 1995, 76.

Marwell, Gerald. “Altruism and the Problem of Collective Action.” In Cooperation and Helping Behavior: Th eories and Research. New York: Academic Press, 1982.

Niebel, Benjamin, and Andris Freivalds. Methods, Standards, and Work Design, Eleventh Edition. New York: McGraw- Hill Higher Education, 2003.

“Th e 100 Best Companies to Work for 2011.” http://money.cnn .com/magazines/Fortune/bestcompanies/2011/full_list

Pace, Randy. “Santa Cruz Operation’s Self Managing Work Groups, A Team Member’s Story,” Target, 8, 6, November/ December 1992, 7.

Patterson, Gregory A. “Distressed Shoppers, Disaff ected Work- ers Prompt Stores to Alter Sales Commissions,” Wall Street Journal, July 1, 1992, B1.

Pearce, Jone L., William B. Stevenson, and James L. Perry. “Managerial Compensation Based on Organizational Per- formance: A Time Series Analysis of the Eff ects of Merit Pay,” Academy of Management Journal, June 1985, 261.

Pfeff er, Jeff rey. “Six Dangerous Myths About Pay,” Harvard Business Review, 76, 3, May/June 1998, 109.

Piper, Kip. “Th e New Accountable Care Organizations and Medicare Gain-Sharing Program,” http://www.ahdbonline .com/new_accountable-care-organizations-and-medicare- gainsharing

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CHAPTER

451

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

L E A R N I N G O B J E C T I V E S

C H A P T E R O U T L I N E

Types of Inventory 452 How Companies Use Their Inventory 453 Objectives of Inventory Management 455 Relevant Inventory Costs 459 ABC Inventory Classification 461 Inventory Record Accuracy 464 Inventory in Service Organizations 466 Determining Order Quantities 467 Mathematical Models for Determining Order Quantity 468

Why Companies Don’t Always Use the Optimal Order Quantity 481

Justifying Smaller Order Quantities 481 Determining Safety Stock Levels 483 Periodic Review System 485 The Single-Period Inventory Model 488 Inventory Management within OM: How It All Fits

Together 490 Inventory Management across the Organization 491

Before studying this chapter, you should know or, if necessary, review

1. Competitive priorities, Chapter 2, pp. 37–43. 2. Internal and external customers, Chapter 4, p. 106. 3. Advantages of small lot sizes, Chapter 7, pp. 258–259. 4. Forecast error, Chapter 8, pp. 307–310.

After studying this chapter you should be able to

Inventory Management

1 Describe the diff erent types and uses of inventory. 2 Describe the objectives of inventory management. 3 Calculate inventory performance measures. 4 Understand the relevant costs associated with inventory. 5 Perform ABC inventory control and analysis. 6 Understand the role of cycle counting in inventory

record accuracy. 7 Understand the role of inventory in service

organizations.

8 Calculate order quantities. 9 Evaluate the total relevant costs of diff erent inven-

tory policies. 10 Understand why companies don’t always use the

optimal order quantity. 11 Understand how to justify smaller order sizes. 12 Calculate appropriate safety stock inventory policies. 13 Calculate order quantities for single-period

inventory.

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452 • CHAPTER 12 INVENTORY MANAGEMENT

TYPES OF INVENTORY Inventory comes in many shapes and sizes, as shown in Figure 12-1. Most manufac- turing firms have the following types of inventory. Raw materials are the purchased items or extracted materials that are transformed into components or products. For example, gold is a raw material that is transformed into jewelry. Components are parts or subassemblies used in building the final product. For example, a transformer is a component in an electronic product. Work-in-process (WIP) refers to all items in process throughout the plant. Since products are not manufactured instantaneously,

� Raw materials Purchased items or extracted materials transformed into components or products. � Components Parts or subassemblies used in the final product.

Have you ever been in a rush to get through the grocery checkout only to be stuck in line behind a person buying numerous varieties of the same general item? Perhaps a person buying 24 cans of pet food, with each can being a different fla- vor. You watch in dismay as the cashier scans each individual can, wondering why the cashier doesn’t just scan one can and enter a quantity of 24. Although it would be much easier to let the cash register do the work, it is critical that the cashier scan each individual can.

Many retailers, like Wal-Mart, Sears, Victoria’s Secret, Home Depot, and Kroger, use point-of-sale cash registers to collect data on each item sold. This information is then used to update their inventory records to determine when a replenishment order should be placed.

When the cashier scans only a single flavor and enters a quantity of 24, the register reports that 24 cans of that specific flavor have been bought by this customer and adjusts the inven- tory record for that item. In reality, the customer bought 1 can each of 24 different varieties. Failure to scan each item results

in all 24 inventory records becoming inaccurate. These inaccurate inventory records cause companies to replenish the wrong items and result in shortages on the shelves.

Information collected with point-of-sale registers is the basis for generating auto- matic replenishment orders. When making replenishment decisions, a business decides what, when, and how much should be purchased. When a company replen- ishes the wrong item because of inaccurate inventory records, the customer is often not satisfied. If a company replenishes items too soon because of inaccurate records, it has invested money in unnecessary inventory and risks item spoiling or deteriorating. It is also possible that the company might not have the necessary storage space because of ordering the wrong item.

Companies make replenishment decisions when managing inventory. In this chap- ter we look at different types of inventory and how companies use those inventories, the costs of different inventory policies, inventory management objectives and perfor- mance measures, and techniques for determining how much of an item to replenish.

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HOW COMPANIES USE THEIR INVENTORY • 453

there is always some WIP inventory flowing through the plant. After the product is completed, it becomes finished goods—the bicycles, stereos, CDs, and automobiles that the company sells to its customers. Distribution inventory consists of finished goods and spare parts at various points in the distribution system—for example, stored in warehouses or in transit between warehouses and consumers. Maintenance, repair, and operational (MRO) inventory are supplies that are used in manufacturing but do not become part of the finished product. Examples of MRO are hand tools, lubricants, and cleaning supplies.

� Work-in-process (WIP) Items in process throughout the plant.

� Finished goods Products sold to customers.

� Distribution inventory Finished goods in the distribution system.

HOW COMPANIES USE THEIR INVENTORY Companies have different kinds of inventory. They also use inventory for different purposes. Let’s look at six ways of using inventory.

1. Anticipation Inventory or Seasonal Inventory is built in anticipation of future demand, planned promotional programs, seasonal fluctuations, plant shutdowns, and vacations. Companies build anticipation inventory to maintain level production throughout the year. For example, the toy industry builds toys throughout the year in anticipation of high seasonal sales in December.

2. Fluctuation Inventory or Safety Stock is carried as a cushion to protect against possible demand variation, “just in case” of unexpected demand. For example, you might keep extra food in the freezer just in case unexpected company drops in. Fluc- tuation inventory or safety stock is also called buffer stock or reserve stock.

3. Lot-size Inventory or Cycle Stock results when a company buys or produces more than is immediately needed. The extra units of lot-size inventory are carried in inventory and depleted as customers place orders. Consider what happens when you buy a 24-can case of soda. You do not normally drink all 24 cans at once. Instead, what you do not need right away, you store for future consumption. You may buy more of an item than you need to take advantage of lower unit costs or quantity discounts. Cycle stock also occurs when making products and the process has a minimum greater than is needed.

4. Transportation or Pipeline Inventory is in transit between the manufacturing plant and the distribution warehouse. Transportation inventory are items that are not available for satisfying customer demand until they reach the distribution warehouse,

� Anticipation inventory Inventory built in anticipation of future demand.

� Fluctuation inventory Provides a cushion against unexpected demand.

� Lot-size inventory A result of the quantity ordered or produced.

� Transportation inventory Inventory in movement between locations.

FIGURE 12-1

Types of inventory

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454 • CHAPTER 12 INVENTORY MANAGEMENT

so the company needs to decide between using slower, inexpensive transportation or faster, more expensive transportation. To calculate the average amount of inventory in transit, we use the formula

ATI 5 tD 365

where ATI � average transportation inventory (in units) t � transit time (in days) D � annual demand (in units)

Suppose the Nadan Company, a producer of brass sculptures, needs to ship finished goods from its manufacturing facility to its distribution warehouse. Annual demand at Nadan is 1460 units. The company has a choice of sending the finished goods regular parcel service (three days transit time) or via public carrier, which takes eight days transit time. Calculate the average annual transportation inventory for each of the alternatives. Note that the average transportation inventory does not con- sider shipment quantity but only transit time and annual demand. To reduce transit inventory, you reduce transit time.

• Solution: When using the regular parcel service,

ATI 5 3 3 1460

365 5 12 units

When using the public carrier,

ATI 5 8 3 1460

365 5 32 units

EXAMPLE 12.1

Calculating Average Transportation

Inventory

5. Speculative or Hedge Inventory is a buildup to protect against some future event such as a strike at your supplier, a price increase, or the scarcity of a product that may or may not happen. A company typically builds speculative inventory to ensure a continuous supply of necessary items. Think about booking an airline flight three months in advance so you can take advantage of a reduced fare. You assume that the airfare will not be reduced further and that you will still need the ticket three months from now. It is a gamble.

6. Maintenance, Repair, and Operating (MRO) Inventory includes maintenance supplies, spare parts, lubricants, cleaning compounds, and daily operating supplies such as pens, pencils, and note pads. MRO items support general operations and maintenance but are not part of the product the company builds.

Inventory plays multiple roles in a company’s operations. For this reason, compa- nies develop inventory management objectives and performance measures to evaluate how well they are handling their inventory investment. The six functions of inventory are summarized in Table 12-1.

� Speculative inventory Used to protect against some future event.

� Maintenance, repair, and operating inventory (MRO) Items used in support of manufacturing and maintenance.

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OBJECTIVES OF INVENTORY MANAGEMENT • 455

The objectives of inventory management are to provide the desired level of customer service, to allow cost-efficient operations, and to minimize the inventory investment.

Customer Service What is customer service? Customer service is a company’s ability to satisfy the needs of its customers. When we talk about customer service in inventory management, we mean whether or not a product is available for the customer when the customer wants it. In this sense, customer service measures the effectiveness of the company’s inven- tory management. Customers can be either external or internal: any entity in the sup- ply chain is considered a customer.

Suppose your company, Kayaks!Incorporated, offers a line of kayaks and kayaking equipment through catalog sales and an accompanying Web site. As product manager, you need to know whether the inventory management system you introduced is effec- tive. One way to measure its effectiveness would be to measure the level of customer service: are customers getting the kayaking equipment they request, and are their orders shipped on time? To answer your questions, you can measure the percentage of orders shipped on schedule, the percentage of line items shipped on schedule, the percentage of dollar volume shipped on schedule, or manufacturing idle time due to inventory shortages.

Percentage of Orders Shipped on Schedule is a good measure for finished goods customer service, such as your kayaking equipment company, if all orders and cus- tomers have similar value and late deliveries are not excessively late. For a different kind of company, such as one that designs computer networks, some customers have much greater value. Obviously, this method does not adequately capture the value of those customers’ orders.

For example, if the book publishing company John Wiley & Sons, Inc. represents 50 percent of your demand but is only 1 out of 20 orders on the schedule, deliver- ing late to Wiley is certainly more harmful to your company than shipping a smaller order late. With this measure, however, all late orders are treated equally. If you have

MKT

� Customer service The ability to satisfy customer requirements. � Percentage of orders shipped on schedule A customer service measure appropriate for use when orders have similar value.

OBJECTIVES OF INVENTORY MANAGEMENT

Anticipation inventory Items built in anticipation of future demand. Allows company to maintain a level production strategy.

Fluctuation inventory Protects against unexpected demand variations. Assures customer service levels.

Lot-size inventory Results from the actual quantity purchased. Allows for lower unit costs.

Transportation inventory Items in movement between locations. Inventory moves from manufacturer to distribution facilities.

Speculative inventory Extra inventory built up or purchased to protect against some future events. Allows for continuous supply.

MRO Includes maintenance supplies, spare parts, lubricants, cleaning agents, and daily operating supplies. Facilitates day-to-day operations.

TABLE 12-1

Functions of Inventory

Good inventory management results in satisfied customers.

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456 • CHAPTER 12 INVENTORY MANAGEMENT

only one late shipment, the customer service level is 95 percent (19 of 20 shipped on schedule). But if the late order is to Wiley, you have met only 50 percent of your demand.

Percentage of Line Items Shipped on Schedule recognizes that not all orders are equal but fails to take into account the dollar value of orders. This measure needs more information—the number of line items instead of the number of orders—than the previous measure. Therefore, this measure is more expensive to use and is most appropriate for finished goods inventory.

As an example of the percentage of line items shipped on schedule, consider the following. Your sister company, White Water Rafts, Inc., determines that from the 20 orders scheduled for delivery this month, customers requested 250 different line items. White Water can ship 225 of these line items on schedule. Their customer service level is 90 percent (225 items shipped on time divided by 250 line items requested).

Percentage of Dollar Volume Shipped on Schedule recognizes the differences in orders in terms of both line items and dollar value. Instead of measuring line items to determine the customer service level, a company totals the value of the orders. For example, if the 20 orders for the Apple iPads had a total value of $400,000 and the company shipped on schedule Apple iPads valued at $380,000, the customer service level is 95 percent ($380,000 shipped, divided by $400,000 ordered).

Idle Time Due to Material and Component Shortages applies to internal cus- tomer service. This is an absolute measure of the manufacturing or service time lost because material or parts are not available to the workforce. Absolute measures make sense when a company has historical data to use in comparisons. For example, Kayaks!Incorporated’s supplier historically has lost no more than two manufacturing days per year because of material and component shortages. This year, however, it has lost four manufacturing days for this reason. Obviously, this year’s case is worse and needs management’s attention.

These are only a few of the measures companies use to evaluate customer service. The desired level of customer service should be consistent with the company’s overall strategy. If customer service is your company’s competitive advantage, the company must achieve a very high level of customer service. Even when customer service isn’t the primary focus, your company must still maintain an acceptable level of customer service.

Now let’s look at how inventory helps manufacturers operate efficiently.

Cost-Efficient Operations Companies can achieve cost-efficient operations by using inventory in the following ways. First, companies use work-in-process (WIP) inventory to buffer operations. Suppose one of the Hewlett-Packard (HP) printed circuit board (PCB) manufac- turing facilities runs two or more operations in a sequence at different rates of output. In this case, buffer inventories build up between the workstations to ensure that each of the operations runs efficiently. For example, PCBs flow from Ken’s workstation (tasks take 120 seconds) to Barbara’s workstation (tasks take only 90 seconds). If there are no PCBs between the two workstations, Barbara will be idle for 30 seconds out of every 120 seconds because she finishes her tasks 30 seconds before Ken finishes his. If the floor supervisor, Maria, ensures that there is buffer

� Percentage of line items shipped on schedule A customer service measure appropriate when customer orders vary in number of line items ordered.

� Percentage of dollar volume shipped on schedule A customer service measure appropriate when customer orders vary in value.

MKT

Customer service of Apple iPads can be measured as a percentage of dollar volume shipped on schedule.

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OBJECTIVES OF INVENTORY MANAGEMENT • 457

stock between the workstations, Barbara’s idle time will be eliminated so she can produce more PCBs.

Second, inventories allow manufacturing organizations to maintain a level work- force throughout the year despite seasonal demand for production. (Level production plans are discussed in Chapter 13.) A company can do this by building inventory in advance of seasonal demands. This in turn allows the company to maintain a level workforce throughout the year and to reduce the costs of overtime, hiring and firing, training, subcontracting, and additional capacity.

Third, by building inventory in long production runs, the setup cost is spread over a larger number of units, decreasing the per unit setup cost. Setup costs include the cost of scrap (wasted material and labor), calibration, and downtime to prepare the equipment and materials for the next product to be manufactured. Longer runs mean that the equipment does not need as many setups, so less machine time is lost prepar- ing for production.

Fourth, a company that is willing to acquire inventory can buy in larger quantities at a discount. These larger purchases decrease the ordering cost per unit. For example, the Rustic Garden Furniture Company needs 50,000 pieces of wrought iron annually. Rustic’s supplier has offered a unit price of $1.10 if Rustic buys the wrought iron in orders of 10,000 or more pieces at a time. If Rustic chooses to buy in smaller quanti- ties, the unit price is $1.29.

Now let’s look at ways to measure inventory investment.

Minimum Inventory Investment A company can measure its minimum inventory investment by its inventory turnover—that is, by the level of customer demand satisfied by the supply on hand. We calculate the inventory turnover measure as

Inventory turnover 5 annual cost of goods sold

average inventory in dollars

� Setup cost Costs such as scrap costs, calibration costs, and downtime costs associated with preparing the equipment for the next product being produced.

� Inventory turnover A measure of inventory policy effectiveness.

If the annual cost of goods sold at the Nadan Company is $5,200,000 and the average inventory in dollars is $1,040,000, what is the inventory turnover?

• Solution:

Inventory turnover 5 $5,200,000 $1,040,000

5 5 inventory turns

EXAMPLE 12.2

Computing Inventory Turns

The ratio at the Nadan Company should be compared with that achieved by other companies within the industry. Although there is no magic number for inventory turnover, the higher the number, the more effectively the company is using its inven- tory. One measure of the level of demand that can be satisfied by on-hand inventory is weeks of supply. Weeks of supply is calculated by dividing the average on-hand inven- tory by the average weekly demand.

Weeks of supply 5 average inventory on hand in dollars

average weekly usage in dollars

� Weeks of supply A measure of inventory policy effectiveness.

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458 • CHAPTER 12 INVENTORY MANAGEMENT

Note that there is a relationship between inventory turnover and weeks of supply. If you divide total weeks per year (52) by the weeks of supply (10.4), you see that the answer is the same as when you calculated inventory turnover. If you divide total number of weeks (52) by the inventory turnover rate (5), the answer is 10.4 weeks of supply. In some companies, inventory performance is measured in either days or hours of supply. To calculate days of supply, we use the formula

Days of supply 5 average inventory on hand in dollars

average daily usage in dollars

and hours of supply is calculated as

Hours of supply 5 average inventory on hand in dollars

average hourly usage in dollars

Let’s look at an example using both of these measures.

Suppose that the Nadan Company wants to calculate its weeks of supply. From the previous example, we know that annual cost of goods sold is $5,200,000.

• Solution: To determine the weekly cost of goods sold, we divide the annual cost of goods sold by 52 weeks ($5,200,000/52 � $100,000). Given that Nadan maintains an average inventory of $1,040,000, we calculate the weeks of supply as follows:

Weeks of supply 5 $1,040,000 $100,000

5 10.4 weeks of supply

EXAMPLE 12.3

Calculating Weeks of Supply

Suppose that the Jenny Company, a specialty gift organization, wants to calculate its days of supply. The annual cost of goods sold is $1,300,000, the average inventory is $15,600, and the company oper- ates 250 days per year.

• Solution: First, we calculate the average daily usage. We divide the annual cost of goods sold by the number of days the company operates ($1,300,000 divided by 250 days equals $5200). Second, using the for- mula, we divide the average inventory on hand by the average daily usage.

Days of supply 5 $15,600 $5200

5 3 days of supply

Suppose the Jenny Company uses a new process that reduces the average inventory held to $3250. To calculate its current hours of supply, we first calculate the average hourly usage. Using the data provided and assuming an eight-hour day, we divide the average daily usage ($5200) by eight hours. The average hourly usage is $650. Therefore, the hours of supply are

Hours of supply 5 $3250 $650

5 5 hours of supply

EXAMPLE 12.4

Calculating Inventory Supply

at the Jenny Company

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RELEVANT INVENTORY COSTS • 459

Inventory Objectives

Customer service Measured by any of the following: • Percentage of orders shipped on schedule • Percentage of line items shipped on schedule • Percentage of dollar volume shipped on

schedule • Idle time due to component and material

shortages Cost-efficient operations Inventories help achieve cost-effective

operations by • Using buffer stock to assure smooth

production flow • Maintaining a level workforce • Allowing longer production runs, which

spreads the cost of setups • Taking advantage of quantity discounts

Minimum inventory investment Measured by any of the following: • Inventory turnover • Weeks of supply • Days of supply

TABLE 12-2

Inventory Objectives

RELEVANT INVENTORY COSTS Inventory management policies have cost implications. Decisions about how much inventory to hold affect item costs, holding costs, ordering costs, and stockout (short- age) costs.

Item Costs The item costs of a purchased item include the price paid for the item and any other direct costs for getting the item to the plant, such as inbound transportation, insur- ance, duty, or taxes. For an item built by the manufacturing company, the item costs include direct labor, direct materials, and factory overhead.

Holding Costs Holding costs include the variable expenses incurred by the firm for the volume of inventory held. As inventory increases, so do the holding costs. We can determine unit holding costs by examining three cost components: capital costs, storage costs, and risk costs. Annual holding costs are typically stated in either dollars per unit ($3.50 per unit per year) or as a percentage of the item value (25 percent of the unit value).

Capital costs are the higher of either the cost of the capital or the opportunity cost for the company. The cost of the capital is the interest rate the company pays to borrow

� Item cost Includes price paid for the item plus other direct costs associated with the purchase.

� Holding costs Include the variable expenses incurred by the plant related to the volume of inventory held.

� Capital costs The higher of the cost of capital or the opportunity cost for the company.

Table 12-2 summarizes the inventory objectives we just discussed.

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460 • CHAPTER 12 INVENTORY MANAGEMENT

money to invest in inventory. The opportunity cost is the rate of return the company could have earned on the money if it were used for something other than investing in inventory. The opportunity cost is at least as much as the interest the company could get at the prevailing interest rate. It may be higher if more lucrative opportunities are available. Suppose you have a startup company and need to finance your inventory with a bank loan at 8 percent. Or the company can invest its capital in the stock market and generate a 20 percent return on the investment. For its capital cost, the company would use the 20 percent opportunity cost rather than the 8 percent cost of the loan. The capital cost is typically expressed as an annual interest rate.

Storage costs usually include the cost of space, workers, and equipment. For our purposes, however, we are concerned only with the additional out-of-pocket expenses resulting from the size of the inventory. For example, we include the cost of storage space if it is public warehousing and varies based on the amount of inventory held. If the company already owns the storage space and incurs no additional expense for storing the inventory, we do not include it in the holding cost. The same is true for employees. If an employee works overtime because of the level of inventory, this is an out-of-pocket expense and needs to be included. However, if the employee’s workload is merely higher during the normal day, the cost of the employee is not included.

Risk costs include obsolescence, damage or deterioration, theft, insurance, and taxes. These costs vary based on industry. Companies operating in a high-tech envi- ronment typically experience much greater obsolescence and theft. Companies that manufacture consumer products may find higher levels of theft.

In general, risk costs are associated with higher levels of inventory. The more inventory you have, the longer it lasts—therefore, the greater the chance of it becom- ing obsolete. The more inventory you have sitting around, the more likely it is to be damaged. Think of walking through an overloaded basement: you bump into some- thing; it falls and breaks. Theft also typically increases as inventory increases. When a company has few items in inventory, it is more noticeable when an item disappears. However, if the company has a lot of inventory, it is harder to notice when only one item disappears. Insurance costs are typically based on the value of the inventory, so larger inventories have higher insurance premiums. The same is true for taxes: the more valuable the inventory, the higher the tax.

While theft is often associated with employees, on some occasions theft comes from customers. In the hospitality industry, hotels often have considerable difficulty controlling the theft of towels, robes, and bed sheets. Linen Technology Tracking, a company based in Miami, Florida, has patented a washable RFID chip that can be sewn into towels, robes, and bed sheets. A hotel in Honolulu using the technology reported reduced theft of its pool towels from 4,000 a month to just 750. The savings were reported as $16,000 per month. Such savings will increase the opportunities to use technology to reduce theft and provide better accountability for the company.

� Storage costs Include the variable expenses for space, workers, and equipment related to the volume of inventory held.

� Risk costs Include obsolescence, damage or deterioration, theft, insurance, and taxes associated with the volume of inventory held.

The Nadan Company currently maintains an average inventory of $1,040,000. The company esti- mates its capital cost at 12 percent, its storage costs at 5 percent, and its risk costs at 8 percent. Calcu- late the annual holding costs for the Nadan Company.

• Solution: Annual holding cost per unit of inventory equals 25 percent (capital cost � storage costs � risk costs).

Annual cost of holding inventory 5 $1,040,000 3 0.25 5 $260,000

EXAMPLE 12.5

Calculating Annual Holding Costs

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ABC INVENTORY CLASSIFICATION • 461

Although many textbooks use an annual holding cost of between 20 percent and 30 percent, in real life it depends on the type of business. The risk costs can vary signifi- cantly. Let’s look at how annual holding costs are calculated.

Ordering Costs Ordering costs are fixed costs for either placing an order with a supplier for a pur- chased component or raw material or for placing an order to the manufacturing organization for a product built in-house. When you buy an item, the ordering costs include the cost of the clerical work to prepare, release, monitor, and receive orders and the physical handling of the goods. The ordering costs are considered constant regardless of the number of items or the quantities ordered. For example, if the cost to place an order is estimated at $100, every time you place an order with a supplier, the ordering cost is constant ($100).

When an order is released for manufacturing in-house, the ordering or setup costs are the clerical work to prepare the manufacturing order and the list of materials to be picked up and delivered to the manufacturing location, plus the cost to prepare the equipment for the job (calibration, appropriate jigs and fixtures, etc.). Like the ordering costs for purchased items, the ordering or setup costs for jobs done in-house are constant.

Shortage Costs Companies incur shortage costs when customer demand exceeds the available inven- tory for an item. Suppose a customer, Tom Martin, places an order through your kaya- king equipment Web site for a high-end kayak, but that kayak is out of stock. One of two things happens. Either Tom allows you to back-order the kayak—that is, Tom is willing to wait until the kayak is available—or Tom decides to buy the kayak from another company and the result for your company is a lost sale.

In both cases, your company incurs shortage costs. In the case of the back order, shortage costs result from the additional paperwork to track the order and the pos- sible added expense of overnight shipping rather than normal delivery. There is also the lost customer goodwill, an intangible cost. Although Tom accepted the delay this time, you have no guarantee that he will buy from your company again. In the case of the lost sale, the shortage costs typically include loss of the possible profit, plus loss of the contribution to overhead costs. Your company also faces the risk that Tom will not return with future orders. Shortage costs can also result from internal parts shortages, including the cost of downtime due to lack of materials, additional setups, premium transportation costs, and so forth.

� Ordering costs The fixed costs associated with either placing an order with a supplier or setup costs incurred for in-house production.

� Shortage costs Incurred when demand exceeds supply.

� Back order Delaying delivery to the customer until the item becomes available. � Lost sale Occurs when the customer is not willing to wait for delivery. MKT

ABC INVENTORY CLASSIFICATION All items in a company’s inventory are not equal and do not need the same level of con- trol. Fortunately, we can apply Pareto’s law to determine the level of control needed for individual items. Pareto’s law implies that roughly 10 to 20 percent of a compa- ny’s inventory items account for approximately 60 to 80 percent of its inventory costs. These relatively few high-dollar-volume items are classified as A items. Moderate-dol- lar-volume items, roughly 30 percent of the items, account for about 25 to 35 percent of the company’s inventory investment. These are classified as B items. Low-dollar- volume items, about 50 to 60 percent of the items, represent only 5 to 15 percent of the company’s inventory investment and are classified as C items. These percentages are not absolute and are used only as guidelines to determine an item’s ABC classification.

� Pareto’s law Implies that about 20 percent of the inventory items will account for about 80 percent of the inventory value.

� ABC classification A method for determining level of control and frequency of review of inventory items.

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462 • CHAPTER 12 INVENTORY MANAGEMENT

AAU is considering doing an ABC analysis of its entire inventory but has decided to test the tech- nique on a small sample of 15 of its stock-keeping units. The annual usage and unit cost for these items are shown in the table.

(a) Calculate the annual dollar volume for each item. (b) List the items in descending order based on annual dollar usage. (c) Calculate the cumulative annual dollar volume. (d) Group the items into classes.

• Before You Begin: To do an ABC analysis, you need to know the annual usage and the value of each item. That information is provided for you in the problem data. Multiply the unit value by the annual usage of the item to determine the annual dollar volume for each item. Now list the items in descending order based on annual dollar usage. You can now calculate the percentage of the total inventory value each part represents. This allows you to classify the items into groups.

• Solution: (a)

EXAMPLE 12.6

ABC Analysis at Auto Accessories Unlimited (AAU)

ABC Problem Data

Item Unit $ Value Annual Usage

(in units)

101 12.00 80 102 50.00 10 103 15.00 50 104 50.00 40 105 40.00 80 106 75.00 220 107 4.00 250 108 1.50 400 109 2.00 250 110 25.00 500 111 5.00 450 112 7.50 80 113 3.50 250 114 1.00 1200 115 15.00 300

ABC Annual Usage Values

Item Unit $ Value Annual Usage (in units) Annual Usage ($)

101 12.00 80 960 102 50.00 10 500 103 15.00 50 750 104 50.00 40 2,000 105 40.00 80 3,200 106 75.00 220 16,500 107 4.00 250 1,000 108 1.50 400 600 109 2.00 250 500 110 25.00 500 12,500 111 5.00 450 2,250 112 7.50 80 600 113 3.50 250 875 114 1.00 1200 1,200 115 15.00 300 4,500

Total $47,935

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ABC INVENTORY CLASSIFICATION • 463

Procedure for an ABC Inventory Analysis The first step for an ABC inventory analysis is to determine the annual usage for each item. We calculate the total annual dollar volume by multiplying the annual usage by the item cost. We then rank items in descending order based on total dollar volume and calculate the total inventory investment.

1. Calculate the annual dollar usage for each item. 2. List the items in descending order based on annual dollar usage. 3. Calculate the cumulative annual dollar volume. 4. Classify the items into groups.

Figure 12-2 graphically depicts the results of the ABC classification. The A items, 106 and 110, combine for 60.5 percent of the total dollar value in inventory and approximately 13.3 percent of the items in inventory. The B items, 115, 105, 111, and 104, account for 25 percent of the total dollar value and 26.7 percent of the items. The C items make up the last 14.5 percent of the total dollar value and 60 percent of the items.

Inventory Control Using ABC Classification After classifying inventory items into A, B, and C classes, we can determine the appro- priate level of inventory control. For our most important and expensive A items, we need very tight control, highly accurate inventory records, and frequent or continu- ous review. A continuous review system keeps track of an inventory item 24/7. It tracks every inventory transaction as it occurs, whether it is more material going into inventory or material being withdrawn from the stockroom. Consequently, the EOQ

� Continuous review system Updates inventory balances after each inventory transaction

(b, c and d)

ABC Solution

Item Annual Usage ($) Percentage of Total Dollars

Cumulative Percentage of Total Dollars

Item Classification

106 16,500 34.4 34.4 A 110 12,500 26.1 60.5 A 115 4,500 9.4 69.9 B 105 3,200 6.7 76.6 B 111 2,250 4.7 81.3 B 104 2,000 4.2 85.5 B 114 1,200 2.5 88.0 C 107 1,000 2.1 90.1 C 101 960 2.0 92.1 C 113 875 1.8 93.9 C 103 750 1.6 95.5 C 108 600 1.3 96.8 C 112 600 1.3 98.1 C 102 500 1.0 99.1 C 109 500 1.0 100.1* C

Total $47,935 *Total exceeds 100% due to rounding.

Remember that these are not absolute rules for classifying items. Your company wants to group their more valuable items together to make sure that they get the most control.

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464 • CHAPTER 12 INVENTORY MANAGEMENT

model (discussed later in this chapter) is often used. B items need normal control, moderately accurate inventory records, and a reasonable time period between reviews. For B items, a periodic review system (discussed later in this chapter) can be used. A periodic review system reviews the inventory level of the item at regular intervals (daily, weekly, monthly) to determine whether a replenishment order is needed. C items require the least amount of control. Possible options for C items are the two-bin system or an infrequent periodic review system. A two-bin system splits an incoming replenishment order into two separate bins. One bin is placed on the factory floor so workers can take what they need. The other bin is kept in the storeroom. This second bin should have enough items to cover normal demand during the replenishment lead time. Lead time is the amount of time it takes from order placement until the ordered item is received. When the bin on the floor is empty, workers go to the stockroom to request additional material. The bin in the stockroom is released to the workers on the floor and a replenishment order is placed.

� Periodic review system Requires regular periodic reviews of the on-hand quantity to determine the size of the replenishment order. � Two-bin system One bin with enough stock to satisfy demand during replenishment time is kept in the storeroom; the other bin is placed on the manufacturing floor. � Lead time Time from order placement to order receipt.

INVENTORY RECORD ACCURACY For effective inventory use, the inventory records must accurately reflect the quantity of materials available. Inaccurate inventory records can result in lost sales (finished good not available at time of sale), disrupted operations (not enough of a component or raw material to complete a job), poor customer service (late deliveries to custom- ers), lower productivity (additional setups to complete a job), poor material planning (the inventory records are critical in determining MRP quantities), and excessive expediting (trying to obtain necessary items in less than normal lead time).

One exceptionally productive approach to inventory management is the automated inventory tracking sys- tem used by the very successful Cisco Systems—a world leader in providing networking solutions for all types of businesses. This tracking system forms an intricate network of suppliers, manu- facturers, and customers and provides for real-time transactions. When a cus- tomer places an order via the Internet, ©A

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LINKS TO PRACTICE Cisco Systems, Inc.

www.cisco.com

FIGURE 12-2

ABC classification of materials

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INVENTORY RECORD ACCURACY • 465

suppliers can instantaneously see what parts are needed and can quickly respond by shipping the needed parts and then restocking. Such a system provides accurate, timely information, which helps both Cisco and suppliers to schedule, budget, and forecast. Since most of Cisco’s orders are transacted over the Web, Cisco is able to save millions of dollars annually.

Scannabar is an inventory control system used by managers of hotels, restaurants, and bars that provides a method for keeping an accurate measure of wine, beer, and liquor supplies. Scannabar can individually track and monitor every ounce of liquor purchased from the time it enters the bar or restaurant until its contents are depleted. The system facilitates the reordering of stocked items as well as eliminating the theft of supplies and overpouring by the staff. Scannabar is able to measure the contents of 120 bottles in roughly 15 minutes. Users have reported reducing shrinkage from the industry average of 25% down to 1–3%, and reducing beverages by 25–50%. Such sav- ings substantially impact a company’s bottom line.

Inventory record errors occur because of unauthorized withdrawals of material, unsecured stockrooms, inaccurate paperwork, and/or human errors. Since an accu- rate database is needed to successfully use the information systems, it is important to detect errors in the inventory records. Two methods are available for checking inven- tory record accuracy: periodically counting all of the items (typically annually) and cyclically counting specified items (typically daily).

Periodic counting satisfies auditors that the inventory records accurately reflect the value of the inventory on hand. For material planners, the physical in ventory is an opportunity to correct errors. The four steps in taking a physical inventory are

1. Count the quantity of the item and record the count on a ticket attached to the item. 2. Verify by recounting. 3. Aft er verifi cation, collect the tickets. 4. Reconcile inventory records with actual counts. For major discrepancies,

investigate further. For minor discrepancies, adjust the inventory records.

Taking physical inventories does not always improve inventory record accuracy. In many cases, companies close down manufacturing to take the physical inventory; the job is often rushed and is typically done by employees not trained for checking inven- tory. In some cases, inventory record errors are increased rather than reduced. The other alternative method is cycle counting.

Cycle counting is a method of counting inventory throughout the year. This is a series of mini-physical inventories done daily of some prespecified items. The fre- quency of counting a particular item depends on the importance and value of the item. Typically, A items are counted most frequently.

The advantages of cycle counting are

● Timely detection and correction of inventory record problems. ● Elimination of lost production time since the company does not need to shut

down operations. ● Th e use of employees dedicated to cycle counting.

Scheduling individual item counts can be done in several ways. An item can be counted just before a replenishment order is placed. At this time, the planner has an accurate count of the item on hand and can determine whether a replenishment order is needed. The quantity to be counted also is relatively low. A planner also can choose to count when new orders arrive. This way, the inventory is at its lowest level. Remember

� Periodic counting A physical inventory is taken periodically, usually annually.

� Cycle counting Prespecified items are counted daily.

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466 • CHAPTER 12 INVENTORY MANAGEMENT

that most replenishment orders arrive just as the on-hand inventory is running out. Another possibility is to schedule a count after a certain number of transactions have occurred. For example, a planner can request a physical count after every 20 transac- tions involving a particular item. Since errors typically occur during transactions, the greater the number of transactions, the more likely an error will be introduced. One other possibility is to do a count whenever an error is detected. This allows for correc- tive action to be taken immediately. Regardless of the method, the intent is to improve inventory record accuracy.

In some cases, companies have shifted the burden of inventory accuracy and replenishment decisions to their vendor. Vendor-managed inventory (VMI) requires the vendor to maintain an inventory of certain items at the customer’s facility. The supplier still owns the inventory until the customer actually withdraws it for use. At that time, the customer pays for the items. The customer does not have to order any of the inventory, as the supplier is responsible for maintaining an adequate supply. Companies use this approach most frequently with lower-level C items that have a relatively standard design.

� Vendor-managed inventory (VMI) The supplier maintains an inventory at the customer’s facility.

INVENTORY IN SERVICE ORGANIZATIONS When we compare service organizations with manufacturing organizations, a major difference is that manufacturers have tangible inventory while service providers typically do not. However, extensive tangible inventory is required in wholesale and retail services. How well this inventory is managed often determines whether a ser- vice provider is profitable. Consider the importance of inventory in the food service business, especially highly perishable food items. If a manager orders too much of an item, spoilage can occur; if not enough is ordered, customer orders can be lost. It is a constant struggle to order just the right amount of perishable items. Any inventory that perishes, is damaged, or is stolen prior to its actual sale is an inventory loss. In retailing, it is considered good performance when a company has an inventory loss of only 1 percent or less. Some companies face losses exceeding 3 percent of the value of their inventory. Since retailers deal with desirable consumer goods, it is critical for retailers to practice good inventory control and maintain accurate inventory records. To achieve good inventory control, retailers, wholesalers, and food service providers should do the following:

● Select, train, and discipline personnel. It is critical to select good, honest, reliable personnel because employees have direct access to desirable merchandise.

● Have tight control over incoming shipments. Many fi rms track incoming ship- ments through bar code scanning and radio frequency identifi cation systems (RFIDs). Shipments are read into the system, and quantities are reconciled with purchase orders. Each item must have a unique stock-keeping unit (SKU).

● Have tight control over items leaving the facility. Th is is oft en done with barcode scanners so that point-of-sale (POS) information can be fed into the system to main- tain inventory record accuracy. It is critical that stores train personnel in proper scanning techniques to make sure inventory records remain accurate. Attempts to defeat theft include antitheft magnetic strips or security fi xtures attached directly to the merchandise. Th ese are used to activate security alarms as a person exits the facility with unpaid merchandise. Other retailers have personnel stationed near the exits for direct observation of customers leaving the store. In some stores in high- loss areas, one-way mirrors can be used as well as direct video surveillance.

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DETERMINING ORDER QUANTITIES • 467

DETERMINING ORDER QUANTITIES The objectives of inventory management are to provide the desired level of customer service, enable cost-efficient operations, and minimize the inventory investment. To achieve these objectives, a company must first determine how much of an item to order at a time.

Inventory management and control are done at the level of the individual item or stock-keeping unit (SKU). An SKU is a specific item at a particular geographic loca- tion. For example, a pair of jeans, size 32 � 32, in inventory at the plant and also eight different warehouses, represents nine different SKUs. A pair of the same jeans held at the same locations but a different size (32 � 34) represents nine additional SKUs. The same style of jeans in a different color represents additional SKUs.

Let’s look at how a company determines how much of an SKU to order. We will consider some common approaches in this section, summarized in Table 12-4. In the next section we will look at mathematical models for determining order quantity.

� Stock-keeping unit (SKU) An item in a particular geographic location.

Successful wholesaling, retailing, or food service operations require very good inventory control with accurate records. An additional problem, other than theft, fac- ing many retailers is the inability to locate specific merchandise. It is not uncommon for customers to change their minds and simply place merchandise they no longer want in a convenient spot, not necessarily anywhere close to where it belongs. It is also common for clerks returning merchandise to the floor (either items from dressing rooms or items picked up that had been misplaced) to neglect to place the merchan- dise exactly where it belongs. Being unable to find items can lead to poorer customer service and unnecessary replenishment orders. The success of service organizations using a tangible product depends on practicing good inventory control.

Before You Go On

Before you continue further into the chapter, you need to be sure that you understand the relevant inventory costs. Item cost, holding cost, ordering cost, and shortage cost are summarized in Table 12-3. The next section of this chapter focuses on determining order quantities and uses inventory cost information.

TABLE 12-3 Relevant Inventory Costs Item cost Price paid per item plus any other direct costs associated with

getting the item to the plant Holding costs Capital, storage, and risk costs Ordering costs Fixed, constant dollar amount incurred for each order placed Shortage costs Loss of customer goodwill, back-order handling, and lost sales

Lot-for-lot Order exactly what is needed.

Fixed-order quantity Order a predetermined amount each time an order is placed.

Min-max system When on-hand inventory falls below a predetermined minimum level, order a quantity that will take the inventory back up to its predetermined maximum level.

Order n periods Order enough to satisfy demand for the next n periods.

TABLE 12-4

Common Ordering Approaches

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468 • CHAPTER 12 INVENTORY MANAGEMENT

Lot-for-lot is ordering exactly what you need. You adjust the ordering quantity to your ordering needs, which ensures that you will not have leftover inventory. You use lot-for-lot when demand is not constant and you have information about expected needs. Ordering sandwiches for a business lunch meeting is a good example of when to use lot-for-lot. The number of persons attending the meeting can vary based on the meeting topic. Since sandwiches are perishable, you do not want to have leftover inventory. This system is also commonly used in material requirements planning (MRP) systems, which we discuss in Chapter 14.

Fixed-order quantity specifies the number of units to order each time you place an order for a certain SKU or item. The quantity may be arbitrary (perhaps 100 units at a time), or it may be the result of how the item is packaged or prepared (such as 144 per box or a loaf of bread). The advantage of this system is that it is easily understood; the disadvantage is that it does not minimize inventory costs.

The min-max system involves placing an order when the on-hand inventory falls below a predetermined minimum level. The quantity ordered is the difference between the quantity available and the predetermined maximum inventory level. For example, if the minimum is set at 50 units, the maximum is set at 250 units, and the quantity available at the time of the order is 40 units, the order quantity is 210 units (250 − 40). With this system, both the time between orders and the quantity ordered can vary.

Order n periods means that you determine the order quantity by summing your com- pany’s requirements for the next n periods. Suppose you have to order enough each time you place an order to satisfy your company’s requirements for the next three periods. If these requirements for the next three weeks are 60, 45, and 100, your order is for 205 units. A concern with this system is determining the number of periods to include in the order.

� Lot-for-lot The company orders exactly what is needed.

� Fixed-order quantity Specifies the number of units to order whenever an order is placed.

� Min-max system Places a replenishment order when the on-hand inventory falls below the predetermined minimum level. An order is placed to bring the inventory back up to the maximum inventory level. � Order n periods The order quantity is determined by total demand for the item for the next n periods.

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SKUs at a retail store

MATHEMATICAL MODELS FOR DETERMINING ORDER QUANTITY Now let’s look at some mathematical models that determine order quantity and mini- mize inventory costs, beginning with the economic order quantity (EOQ) model.

Economic Order Quantity (EOQ) The economic order quantity model (EOQ) has been around since the early 1900s and remains useful for determining order quantities. EOQ is a continuous review system, used to keep track of the inventory on hand each time stock is added or withdrawn. If

� Economic order quantity model (EOQ) An optimizing method used for determining order quantity and reorder points.

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MATHEMATICAL MODELS FOR DETERMINING ORDER QUANTITY • 469

the withdrawal reduces the inventory level to the reorder point or below, you make a replenishment order.

Thus, EOQ tells you when to place a replenishment order and determines the order quantity that minimizes annual inventory cost. Suppose you decide that your kayak- ing equipment company needs to place a replenishment order whenever the inventory level of item K310 reaches 100 units. Right now you have 105 units of item K310 in inventory. You withdraw 5 K310s to satisfy a customer order, resulting in an updated inventory level of 100 units. Since the inventory level has reached the reorder point, it is time to place a replenishment order for K310. A key characteristic of the continuous review system is that it keeps track of inventory as it is withdrawn.

In the following section we look at some assumptions made by the basic EOQ model.

EOQ Assumptions The basic EOQ model makes these assumptions:

● Demand for the product is known and constant. Th is means that we know how much the demand is for every time period and that this amount never changes. For example, demand is 50 units per week every week or 10 units per day every day. Th is assumption is indicated by the straight line that shows the depletion of our inventory in Figure 12-3.

● Lead time is known and constant. It is the amount of time it takes from order placement until it arrives at the manufacturing company (for example, 10 working days between order placement and receipt of merchandise).

Because you know how long it takes for the replenishment order to arrive, you can determine when you need to place the order. By fi nding the reorder point (shown in Figure 12-3), you schedule the arrival of the replenishment quantity just as your company’s inventory level reaches zero. Th e minimum inventory level with the basic EOQ should be zero.

● Quantity discounts are not considered: the cost of all units is the same, regardless of the quantity ordered. (We discuss this in more detail later in the chapter.)

● Ordering and setup costs are fi xed and constant: the dollar amount to place an order is always the same, regardless of the size of the order.

FIGURE 12-3

The EOQ model

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470 • CHAPTER 12 INVENTORY MANAGEMENT

● Since the company knows demand with certainty, the assumption is that all demand is met. Th e basic model does not permit back orders, but more advanced models are less rigid.

● Th e quantity ordered arrives at once, as shown in Figure 12-3. Since the order is scheduled to arrive just as the company runs out of inventory, the maximum inventory level equals the economic order quantity.

Figure 12-3 shows the basic workings of the EOQ model. The inventory replenish- ment process begins when the inventory reaches the reorder point. This is the point at which you place an order for Q units, which are timed to arrive just as your com- pany’s inventory level reaches zero. The inventory goes from zero to Q and then is depleted at a constant rate. Once the inventory reaches the reorder point, the process begins again.

Since the basic model assumes certainty about demand and lead time, the reorder point is set equal to demand during lead time, or

R 5 dL

where R � reorder point d � average daily demand L � lead time in days

Problem-Solving Tip When solving for R, it is possible to use other than daily demand and lead time in days. Use whatever is convenient. If lead time is given in weeks, then use average weekly demand. If lead time is given in months, then use average monthly demand.

For example, if average daily demand is 40 units and lead time is five days, then the reorder point is 200 (40 units times five days). When the inventory reaches 200, it is time to place an order.

Calculating Inventory Policy Costs Since companies are interested in the costs asso- ciated with inventory policies, let’s calculate the annual ordering or setup costs and the annual holding costs associated with the basic EOQ model. We do not include short- age costs since all demand is satisfied with the basic EOQ model. We do not include the annual item cost either: no quantity discounts are considered in the basic EOQ model, so the annual item cost remains constant regardless of the quantity ordered each time. Given that, our total costs are

Total annual cost 5 annual ordering costs 1 annual holding costs

Problem-Solving Tip When calculating total annual costs, do not round off the number of orders to whole numbers. Although it is true that a partial order cannot be placed, for purposes of compari- son we leave the number of orders as a mixed number.

We calculate annual ordering costs by multiplying the number of orders placed per year by the cost to place an order. To find the number of orders placed per year, we divide the annual demand by the quantity ordered.

Suppose annual demand for motherboards at Palm Pilot, the handheld-computer company, is 10,000 units and it currently orders 500 motherboards each time. The number of orders placed per year is 20 (10,000/500). If the cost to place an order is $75, then the annual ordering cost is $1500 (20 orders � $75 ordering cost).

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MATHEMATICAL MODELS FOR DETERMINING ORDER QUANTITY • 471

Problem-Solving Tip When solving for Q, it is not necessary to always use annual demand and annual holding costs. If you have demand given in a different time frame (days, weeks, or months), you can use that as long as the holding costs are expressed in the same time frame, that is, daily demand and daily holding costs, or weekly demand and weekly holding costs.

We calculate annual holding costs by multiplying the average inventory level by the annual holding cost per unit. The average inventory is equal to the maximum inven- tory plus the minimum inventory divided by 2. In the EOQ model, the maximum inventory is Q and the minimum is zero. Therefore, the average inventory level is Q /2. For example, if the order quantity is 500 units, the holding cost is $6 per unit per year, and the annual holding cost is $1500 (500 units/2 � $6 per unit). Sometimes the hold- ing cost is given as a percentage, such as 20 percent of the item price. In this case, we multiply the item price by the percentage to determine the annual unit holding costs. For example, if the holding cost is 20 percent of the item price and the item price is $30, then the annual holding cost is $6 per unit ($30 item price � 20 percent holding cost).

The formula for calculating the total relevant annual costs for the basic EOQ model is

TC 5 aD Q

Sb 1 aQ 2

Hb where TC � total annual cost D � annual demand Q � quantity to be ordered H � annual holding cost S � ordering or setup cost For our example, the total cost is

TC 5 a10,000 500

$75b 1 a500 2

$6b or

TC 5 $1500 1 $1500 5 $3000

Note that the annual ordering costs equal the annual holding costs. This is true when we use the EOQ model without rounding. In addition, with the EOQ model, the mini- mum total cost always results when the annual ordering costs equal the annual hold- ing costs, as shown in Figure 12-4. Note, too, in Figure 12-4 that as order quantity increases so do holding costs and, at the same time, ordering costs decrease since fewer orders are placed. The total costs, however, are always higher when we use an order quantity other than the EOQ.

Calculating the EOQ We calculate the economic order quantity (Q) using the following formula:

Q 5 Å2DSH where Q � optimal order quantity D � annual demand S � ordering or setup cost H � holding cost

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472 • CHAPTER 12 INVENTORY MANAGEMENT

FIGURE 12-4

Holding costs equal ordering costs

Find the economic order quantity and the reorder point, given the following information:

Annual demand (D) � 10,000 units Ordering cost (S) � $75 per order Annual holding cost (H) � $6 per unit Lead time (L) � 5 days Th e company operates 250 days per year.

• Before You Begin: Identify the appropriate formula to use for calculating the economic order quantity (EOQ) and the reorder point. The formula for the EOQ is

Q 5 Å2DSH and the formula for finding the reorder point is R � dL. Remember to make sure that the holding cost is for the same time period as your demand. For example, if demand is annual, then the hold- ing cost must be an annual holding cost per unit. If demand is monthly, then use a monthly holding cost per unit. You also need to convert annual demand into daily demand to use the reorder point formula. Do this by dividing annual demand by the number of days the company operates per year.

• Solution:

Q 5 Å2 3 10,000 3 $75$6 5 500 units Daily demand is 40 units per day (10,000 units demanded annually, divided by 250 days of operation).

R 5 40 units 3 5 days 5 200 units

The inventory policy for this item is to place a replenishment order for 500 units (Q) when the inven- tory reaches 200 units (R). The replenishment order will arrive just as the current inventory reaches zero. On the previous page, we calculated total annual cost for this policy ($3000). The EOQ model always minimizes total annual costs.

EXAMPLE 12.7

Calculating the Economic Order

Quantity

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MATHEMATICAL MODELS FOR DETERMINING ORDER QUANTITY • 473

What Happens When a Non-EOQ Order Quantity Is Used? To illustrate what hap- pens to annual inventory costs when we use an order quantity other than the EOQ, let’s look at an example with a non-EOQ quantity. Determine the total annual costs for your company if you choose to order 1000 units each time a replenishment order is placed.

TC 5 a10,000 1000

$75b 1 a1000 2

$6b 5 $3750 The total annual cost for this non-EOQ inventory policy is $3750 compared to $3000

for the EOQ policy. Thus we can say that the difference between the EOQ policy and any other policy is a penalty cost incurred by your company for not using the EOQ policy.

Economic Production Quantity (EPQ) The basic EOQ model assumes that the entire replenishment order arrives at one time, but this is not always the case. For example, if we bake four batches each of one-dozen chocolate-chip cookies, our inventory will probably never reach four-dozen cookies. Why? Because we or our friends are sure to eat some of the cookies as soon as we bake them! This means that the maximum inventory level will always be less than the total quantity we produce. If out of every batch of one-dozen cookies, we eat 4 cookies immediately, we will end up with 32 cookies in inventory after baking the four one- dozen batches ((12 baked − 4 used) � 4 batches).

Figure 12-5 shows the economic production quantity (EPQ) model. The cycle begins when we start making the product. Each day, we use some of what we make to satisfy immediate demand; we put the remainder in inventory. We make the product until we have completed Q units. At that point, the inventory has reached its maximum level. From this point on, we satisfy demand from the on-hand inventory, depleting it daily. When we reach the reorder point, we order another batch. Our company starts producing the new batch just as we run out of the current inventory.

The EPQ model is appropriate when some of the product we make is used as soon as we make it. In manufacturing, this is typical when a single manufacturing facility produces the parts to build the end product. For example, HP builds deskjet printers using printed circuit boards (PCBs). HP’s manufacturing facility builds PCBs in batches; some of these PCBs are assembled into the end product immediately, and the rest are put into inventory.

� Economic production quantity (EPQ) A model that allows for incremental product delivery.

FIGURE 12-5

The EPQ model

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474 • CHAPTER 12 INVENTORY MANAGEMENT

The total cost formula for the EPQ model is

TC 5 aD Q

Sb 1 a IMax 2

Hb where TC � total annual cost D � annual demand Q � quantity to be ordered H � annual holding cost S � ordering or setup cost

IMax 5 Qa1 2 dpb where d � average daily demand rate p � daily production rate

If HP uses 6 PCBs per day, can produce 20 PCBs, and produces PCBs in batches of 200 units, deter- mine the maximum inventory level.

• Before You Begin: Remember that when calculating IMax, your answer will always be less than the economic production quantity (EPQ) since you are using some items as soon as they are completed. You really don’t need a formula to compute IMax. In the following example, you are pro- ducing a total of 200 PCBs, which takes a total of 10 days to complete (200 units required/20 units produced daily). Each of the 10 days you produce this PCB, you use 6 of the just completed units to satisfy immediate demand and the remaining 14 units go into inventory. Since we do this for 10 straight days, our maximum inventory is 140 units (14 units per day times 10 days). As shown here, you can also use the equation.

• Solution:

IMax 5 200a1 2 620b 5 140 units

EXAMPLE 12.8

Calculating the Maximum Inventory

Level

W ill

& D

en i M

cI nt

yr e/

S to

ne /G

et ty

Im ag

es , I

nc .

Making cookies!

The production rate must always be greater than the demand rate. Otherwise, a company could never produce enough to satisfy demand and no inventory would be generated. Using the chocolate-chip cookie scenario as an example: it is impos- sible to eat more than 12 cookies after the batch is baked because no matter how

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MATHEMATICAL MODELS FOR DETERMINING ORDER QUANTITY • 475

much we might want to eat more than 12, we must wait for the next batch to be completed.

Although the formula identifies d as daily demand and p as daily production, we can use other time frames for these variables. We can use hourly demand and hourly production, weekly demand and weekly production, monthly demand and monthly production, quarterly demand and quarterly production, or even annual demand and annual production. The important thing to remember is that the time frame must be the same for both demand and production. That way, the ratio always remains the same.

Calculate the ratio of d/p using daily, weekly, and annual demand. Annual demand is 10,000 units and annual production is 25,000 units. The company operates 50 weeks per year, 5 days per week.

• Before You Begin: This example is to show you that the most important issue in calcu- lating ratios of demand/production is to use the same time frame. The ratio remains constant whether we use daily demand/daily production, weekly demand/weekly production, or annual demand/annual production. Just make sure that both the demand and production rates are for the same time period.

• Solution: When using daily figures,

Average daily demand: d � 10,000 units/250 days � 40 units per day Daily production: p � 25,000 units/250 � 100 units per day Th erefore, the ratio d/p � 40/100 or 0.4.

When using weekly figures,

Average weekly demand: d � 10,000 units/50 weeks � 200 units per week Weekly production: p � 25,000 units/50 weeks � 500 units per week Th erefore, the ratio d/p � 200/500 or 0.4.

When using annual figures,

Average annual demand: d � 10,000 units Annual production: p � 25,000 units Th erefore, the ratio d/p � 10,000/25,000 or 0.4.

EXAMPLE 12.9

Calculating Ratios

Calculating EPQ The formula to calculate the economic production quantity is

Q 5ã 2DS

Ha1 2 d p b

where D � annual demand in units S � setup or ordering cost H � annual holding costs per unit d � average daily demand rate p � daily production rate

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476 • CHAPTER 12 INVENTORY MANAGEMENT

EXAMPLE 12.10 Ashlee’s Beach Chairs Company produces upscale beach chairs. Annual demand for the chairs is esti-mated at 18,000 units. The frames are made in batches before the final assembly process. Ashlee’s final assembly department needs frames at a rate of 1500 per month. Ashlee’s frame department can produce 2500 frames per month. The setup cost is $800, and the annual holding cost is $18 per unit. The com- pany operates 20 days per month. Lead time is 5 days. Determine the optimal order quantity, the total annual costs, and the reorder point.

• Before You Begin: To determine the optimal EPQ, use the formula

Q 5ã 2DS

Ha1 2 d p b

Remember that the demand and production rates used to calculate the ratio must be in the same time frame (daily, weekly, monthly, quarterly, or annually). To calculate the reorder point, use the formula R = dL. Don’t forget to transform monthly demand into daily demand to find the reorder point. Reorder points should be found using the easiest numbers possible. For example, if lead time is given as three weeks, then you should find average weekly demand and multiply by the three weeks. If lead time is given in months, use average monthly demand.

• Solution: To determine the total cost, you must calculate the maximum inventory level. To do this you must first calculate the economic production quantity:

Q 5ã 2 3 18,000 3 $800

$18a1 2 1500 2500

b 5 2000 units Therefore, IMax is

IMax 5 2000a1 2 15002500b 5 800 units and the total annual cost is

TC 5 a18,000 2000

$800b 1 a800 2

$18b 5 $7200 1 $7200

5 $14,400 Note that the ordering cost equals the annual holding cost. The reorder point is calculated as R � 75 units � 5 days � 375 units. Therefore, the inventory policy is to order a quantity of 2000 frames when the inven- tory reaches 375 units. The total annual cost (excluding item cost) associated with this policy is $14,400.

Calculating EPQ at Ashlee’s Beach

Chairs

Compare this policy to Ashlee’s current inventory policy of producing in quantities of 1500 units. First, determine the maximum inventory level.

IMax 5 1500a1 2 15002500b 5 600 units Therefore, total cost is

TC 5 a18,000 1500

$800b 1 a600 2

$18b 5 $15,000 The extra cost or penalty cost associated with Ashlee’s current policy is $600 ($15,000 − $14,400).

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MATHEMATICAL MODELS FOR DETERMINING ORDER QUANTITY • 477

When you use the EOQ and/or the EPQ model, you need to know when the inventory level reaches the reorder point. A perpetual inventory record provides an up-to-date inventory balance by recording all inventory transactions—items received into inventory or items disbursed from inventory—as they happen.

An alternative to using perpetual inventory records is the two-bin system. In a two-bin system, a quantity equal to demand during replenishment time is held back, often in a second bin. When stock available is depleted, the held-back quantity is made available for use and a replenishment order is placed. Deciding on the right quan- tity replenishment order is complicated when quantity discounts are available. Let’s extend the basic EOQ model to consider quantity discounts.

Quantity Discount Model The basic EOQ model assumes that no quantity discounts are available. In real life, however, quantity discounts are often available, so we need to modify the basic model for these situations. Quantity discounts are price incentives to encourage a company to buy in larger quantities. For example, a supplier charges your company $7.50 per pound if your company’s order is less than 500 pounds. If your order is for 500 to 999 pounds, the price per pound is $6.90. On orders of 1000 pounds or more, the supplier charges $6.20 per pound.

Whenever the price per unit is not fixed but varies based on the size of your order, the total annual cost formula for any inventory policy used must include the cost of material, as shown next.

TC 5 aD Q

Sb 1 aQ 2

Hb 1 CD where D � annual demand in units Q � order quantity in units S � ordering or setup cost H � annual holding cost C � unit price

� Perpetual inventory record Provides an up-to-date inventory balance.

� Quantity discount model Modifies the EOQ process to consider cases where quantity discounts are available.

Jeannette’s Steak House currently orders 200 pounds of single-portion filet mignons at a time (a two- week supply). The annual demand for the filets is 5200 pounds. The ordering cost is estimated at $50. The annual holding cost is 30 percent of the unit price. Jeannette pays $7.50 per pound for the steaks. Therefore, the annual holding cost rate is $2.25 ($7.50 � 0.30). What are the annual total costs?

• Before You Begin: In this problem we must include the cost of the steaks as we consider quantity discounts. It is never wrong to include the material costs in the total cost calculation, but we usually omit the material cost unless different replenishment policies result in different mate- rial costs. If the material cost is not affected by the policy, then it is a constant and does not need to be included.

• Solution:

TC 5 a5200 200

$50b 1 a200 2

$2.25b 1 1$7.50 3 5200 2 5 $40,525

EXAMPLE 12.11

Annual Total Costs at Jeannette’s Steak House

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478 • CHAPTER 12 INVENTORY MANAGEMENT

FIGURE 12-6

Quantity discount total cost curves

Jeannette’s supplier has offered the following price incentives. If Jeannette places an order for 500 or more pounds, the cost per pound is $6.90. For orders of 1000 pounds or more, the supplier will charge Jeannette $6.20 per pound. For orders of less than 500 pounds, Jeannette would continue to pay $7.50 per pound. Now there are three possible prices based on the size of the order. Let’s look at how Jeannette can deter- mine the best policy for her business.

Figure 12-6 shows the total annual cost curves for each of the three prices. You can see that the $7.50 price is only valid when the order quantity falls between 1 and 499 pounds; the $6.90 price per pound is only valid when the order quantity falls between 500 and 999 pounds; and the $6.20 price is valid for orders of 1000 or more pounds.

The Quantity Discount Procedure The first step is to calculate the order quantity using the basic EOQ model and the cheapest price available. In our example, Jean- nette’s cheapest price is $6.20 per pound. Therefore, the annual holding cost is $1.86 (that is, $6.20 � 0.30), and the EOQ is

Q 5 Å2 3 5200 3 $50$1.86 5 528.74 pounds Now determine whether the order quantity is feasible. If Jeannette orders this quan- tity, will she be charged the price used to calculate the EOQ? If Jeannette orders 528.74 pounds, the supplier will charge her $6.90 per pound rather than the $6.20 she used in calculating the order quantity. Therefore, this is an infeasible quantity. If it were feasible, we would be done calculating Jeannette’s optimal inventory policy. Since the order quantity is infeasible, we calculate the order quantity using the next higher price, $6.90 per pound.

Q 5 Å2 3 5200 3 $50$2.07 5 501.20 pounds

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MATHEMATICAL MODELS FOR DETERMINING ORDER QUANTITY • 479

If Jeannette orders 501 pounds, the supplier charges her $6.90 per pound, which is the same as the price we used in calculating the order quantity. Therefore, this is a fea- sible order quantity. Once Jeannette finds the feasible quantity, she calculates the total annual costs for this order quantity.

TC 5 a5200 501

$50b 1 a501 2

$2.07b 1 1$6.90 3 5200 2 5 $36,917.50 Jeannette compares the total annual cost of this feasible order quantity with the total annual cost of the minimum order quantities necessary to qualify for any prices lower than the price at which she found the feasible solution. For example, to qualify for a price of $6.20 per pound, Jeannette must order a minimum of 1000 pounds at a time. The total annual cost of ordering 1000 pounds at a time is

TC 5 a5200 1000

$50b 1 a1000 2

$1.86b 1 1$6.20 3 5200 2 5 $33,430.00 In this case, Jeannette’s annual cost is less if she orders 1000 pounds at a time rather than the EOQ quantity of 501 pounds at a time. The optimal inventory policy for Jean- nette is to order 1000 pounds at a time.

Note that this assumes Jeannette has adequate storage capacity and can accommo- date 1000 pounds at a time. The quantity discount procedure when holding costs are given as a percentage of the unit price is summarized in Table 12-5.

At times, the holding cost can remain constant regardless of the price paid for an item. When the holding cost is a constant dollar amount, there is a common Q. The Q calculated will only be feasible in one of the price ranges. If the Q is in the least expen- sive price range, that is the optimal order quantity. If the Q is in a higher price range, total costs must be calculated and compared to the total costs of all lower price breaks.

1. Calculate the order quantity using the basic EOQ model and the cheapest price possible.

2. Determine whether the order quantity is feasible. That is, if we order this quantity will the supplier charge us the price we used to determine our order quantity? If this is a feasible order quantity, you are done. Otherwise, go to Step 3.

3. If the EOQ quantity found in Step 1 was infeasible, calculate the EOQ for the next higher price.

4. Check again to determine if this quantity is feasible. If it is not feasible, repeat Step 3. If it is feasible, move on to Step 5.

5. Calculate the total annual costs associated with your feasible order quantity. You must include ordering, holding, and material costs.

6. Calculate the total annual costs associated with buying the minimum quantity required to qualify for any prices that are lower than the price at which the feasible solution was found.

7. Compare the total annual costs of buying these minimum quantities to receive the cheaper price against the cost of the feasible Q.

8. Recommend whichever order policy has the lowest total annual cost.

TABLE 12-5

Quantity Discount Procedure

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480 • CHAPTER 12 INVENTORY MANAGEMENT

VGHC operates its own laboratory on-site. The lab maintains an inventory of test kits for a variety of procedures. VGHC uses 780 A1C kits each year. Ordering costs are $15 and holding costs are $3 per kit per year. The new price list indicates that orders of fewer than 73 kits will cost $60 per kit, 73 through 144 kits will cost $56 per kit, and orders of more than 144 kits will cost $53 per kit. Determine the optimal order quantity and the total cost.

• Before You Begin: When you have constant holding costs, you only need to calculate a single Q value using the basic EOQ formula:

Q 5 Å2DSH Check to see what price you must pay per unit if this order quantity is used. If it is the cheapest possible price, this is your optimal replenishment order quantity. If cheaper prices are available, calculate the total annual cost if you buy just enough to qualify for the cheaper price. Do this for all prices cheaper than the price you qualified for with the EOQ. Select the policy that has the low- est total costs, making sure that material costs were included.

• Solution: The first step is to calculate the common Q.

Q 5 Å2 3 780 3 $15$3 5 88.3, or 89 kits This quantity qualifies for a price of $56 per kit. Since it is not the lowest possible price, we calculate the total cost at this price and compare it to the total cost at any lower price breaks. The total cost when ordering 89 kits is

TC 5 a780 89

$15b 1 a89 2

$3b 1 1$56 3 780 2 5 $43,944.96 Total cost when ordering 145 kits is

TC 5 a780 145

$15b 1 a145 2

$3b 1 1$53 3 780 2 5 $41,638.19 Therefore, the VGHC should order 145 kits at a time since it will save $2306.77 each year ($43,944.96 − $41,638.19). The total annual cost curves are shown in Figure 12-7.

EXAMPLE 12.12

Quantity Discounts with Constant

Holding Costs at Valley Grand Health

Clinic (VGHC)

FIGURE 12-7

Quantity discount total annual cost with constant holding cost

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JUSTIFYING SMALLER ORDER QUANTITIES • 481

WHY COMPANIES DON’T ALWAYS USE THE OPTIMAL ORDER QUANTITY Even though it can be shown mathematically that not using the optimal EOQ quantity results in additional costs for a company, it is not unusual for companies to order a quantity other than the EOQ.

Some companies do not have known uniform demand. In some cases, companies experience lumpy demand (that is, some periods with significant demand and other peri- ods with no demand). This violates one of the underlying assumptions of the EOQ model. In such cases, it is better to use a period-order quantity (discussed later in this chapter).

Some suppliers have a minimum order quantity that they will sell to a company. This minimum order quantity can be based on how the item is packaged. If the item comes in boxes of 1000, the minimum order for the item becomes 1000 pieces. If you need more than one box, you must order additional boxes. To obtain 4000 pieces, you would order four boxes. Some suppliers are willing to break boxes, but many are not. At other times, the minimum order quantity can be based on how the material is shipped. The minimum order quantity may be what is needed to qualify for a full truckload or full railcar load rate. There are also times when a company may not have sufficient storage capacity to accommodate a large order quantity. When that is the case, companies must order less than the EOQ.

Remember that the EOQ must be checked when quantity discounts are available. The basic model did not allow for discounts, so you must confirm what the optimal order policy should be.

The EOQ policy always provides a benchmark to compare against other policies. It is not wrong not to use the EOQ, but it should be more expensive. You need to justify the additional expenses incurred.

JUSTIFYING SMALLER ORDER QUANTITIES One of the principles of the just-in-time philosophy, discussed in Chapter 7, is to reduce order quantities ideally down to an order size of one unit. Smaller orders improve customer responsiveness, reduce cycle inventory, reduce work-in-process (WIP) inventory, and reduce inventories of raw materials and purchased components. Since many good things happen with smaller order quantities, we need to understand how companies economically reduce their order quantities.

Kenworth Trucks, a manufacturer of elite custom-built trucks, leads the industry in operations due largely to the just-in-time effect. Turning out over 35 trucks a day, Kenworth has been able to cut production time from the industry norm of six to eight weeks down to a mere three weeks. Such an outstand- ing feat is the result of the implementa- tion of several cutting-edge ideas. Most importantly, there is the use of elec- tronic transmission, which allows the plant to receive specifications as soon as a buyer has placed an order and which immediately involves parts suppliers in the details of the order. This synchronization results in supplies going almost directly to the assem- bly line. With such a fine-tuned operation, it is no wonder Kenworth Trucks is known as the premier of its industry!

Let’s use the economic production quantity model to illustrate how companies justify smaller lot sizes.

M .L

. S in

ib al

d i/©

C or

b is

LINKS TO PRACTICE Kenworth Trucks www.kenworth.com

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482 • CHAPTER 12 INVENTORY MANAGEMENT

Understanding the EPQ Factors Looking at the EPQ formulation, we can see that three variables influence the size of the optimal order quantity. The demand, setup cost, and holding cost are the variables used.

Q 5ã 2DS

Ha1 2 d p b

where D � annual demand S � setup cost H � annual holding cost per unit d � average daily demand p � average daily production

To decrease the optimal order quantity, we must reduce the product of the terms under the square root. We can reduce the numerator or increase the denominator. It doesn’t make sense for a company to want to increase its holding costs, so we elimi- nate the idea of increasing the denominator. To reduce the numerator, we can reduce either the annual demand or the setup cost. Most companies are not trying to reduce their annual demand, so we have only one variable left to use: setup cost.

Let’s look at an example to see what happens when setup cost is reduced. The Gamma Toy Company has an annual demand of 10,000 units for one of its toys. The daily demand is 50 units. The daily production rate is 75 units. Annual holding cost per unit per period is $6. Setup cost is estimated to be $100. When we use these values, the economic production quantity is 1000 units, as shown.

Q 5ã 2 110,000 2100 6a1 2 50

75 b 5 1000 units

Now let’s look at what happens if we reduce the setup cost from $100 down to $25. When we use the new setup cost, the economic production quantity is 500 units, as shown.

Q 5ã 2 110,000 225 Ha1 2 50

75 b 5 500 units

Let’s compare the total annual costs of these two different lot sizes. When Q � 1000 units, the total annual cost is

TC 5 10,000 1000

3 100 1 ± (1000)a1 2 5075b(6) 2

≤ 5 $2000 as opposed to a total cost of $1000 when using Q � 500 units.

TC 5 10,000

500 3 25 1 ± (500)a1 2 5075b(6)

2 ≤ 5 $1000

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DETERMINING SAFETY STOCK LEVELS • 483

You can see that reducing the setup cost allows us to economically decrease order quantity. If a company fails to reduce its setup costs and just starts producing in smaller order quantities, it will face higher total annual inventory costs.

DETERMINING SAFETY STOCK LEVELS Companies are vulnerable to shortages during replenishment lead times, so one func- tion of inventory is to provide safety stock as a cushion for satisfying unexpected cus- tomer demand. Remember that you typically place the replenishment order when the inventory level reaches the reorder point. Remember, too, that your company may experience a shortage between the time you place the replenishment order and the time you receive the items you ordered.

When we have no demand uncertainty, we set the reorder point to equal-to-average demand during lead time, or

R 5 dL where R � reorder point in units d � daily demand in units L � lead time in days

Therefore, if d � 20 units and L � 10 days, the reorder point is 200 units. Since we know demand and lead time with certainty, the replenishment order arrives just as the on-hand inventory is depleted.

Suppose your kayak suppliers cannot always keep a firm delivery date because of fluctuation in materials availability at their end. As a result, uncertainty is a condition of your kayaking equipment operation. To support your company’s customer service objectives, your policy is to carry safety stock. You add the amount of safety stock car- ried to the reorder point, and the reorder point becomes

R 5 dL 1 SS where SS � safety stock in units

For example, if d � 20 units, L � 10 days, and SS � 50 units, the reorder point is 250 units. When your company carries safety stock, it increases the reorder point, as shown in Figure 12-8. The replenishment order is now expected to arrive when the inventory on hand equals the safety stock level rather than zero. If demand is greater than expected, then your customers are satisfied from the safety stock. If demand is less than expected, the replacement inventory arrives before the on-hand inventory reaches the safety stock level. Figure 12-9 shows when the replenishment order will arrive.

How Much Safety Stock? As safety stock increases, so does the customer service level, thus decreasing the chance of shortage. At the same time, however, holding safety stock requires additional inventory investment. Thus it is important to limit the amount of safety stock your company holds.

Order-cycle service level is the probability that demand during lead time does not exceed on-hand inventory—that on-hand stock is adequate to meet demand. A ser- vice level of 95 percent implies that demand does not exceed supply 95 percent of the time. If your company places 20 orders annually, a 95 percent service level implies that demand will not exceed the on-hand quantity in 19 of the 20 replenishment lead times. We calculate the stockout risk as (1 � the order-cycle service level), or 5 percent in the preceding example.

MKT

� Order-cycle service level The probability that demand during lead time will not exceed on-hand inventory.

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484 • CHAPTER 12 INVENTORY MANAGEMENT

FIGURE 12-8

How safety stock changes the reorder point

FIGURE 12-9

Demand uncertainty

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PERIODIC REVIEW SYSTEM • 485

The amount of safety stock to hold depends on the variability of demand and lead time and the desired order-cycle service level. The safety stock needed to achieve a par- ticular order-cycle service level increases as demand and lead time variability increase. The greater the uncertainty, the more safety stock is needed.

Let’s look at a case in which an estimate of demand during lead time and its stan- dard deviation are known. In this case, the formula for calculating safety stock is

SS 5 zsdL where SS � safety stock in units z � number of standard deviations σdL � standard deviation of demand during lead time in units

Suppose that the owner of the campus bar, Nick’s, has determined that demand for beer during lead time averages 5000 bottles. Nick, the owner, believes the demand during lead time can be described by a normal distribution with a mean of 5000 bottles and a standard deviation of 300 bottles. Nick is willing to accept a stockout risk of approximately 4 percent. Determine the appropriate z value to use. Calculate how much safety stock Nick should hold. Also determine the reorder point.

• Before You Begin: To determine how much safety stock, should be held, use the formula SS � zσdL. You also need to use Appendix B to determine the appropriate z value. To determine the reor- der point, use the formula R � dL � SS. Note that safety stock always results in a higher reorder point.

• Solution: Go to Appendix B. To find the appropriate z value associated with the order-cycle service level (1 � 0.04 � 0.9600), you must understand that the appendix shows only positive z values. A z value of 0 represents 0.5000. You need to find the z value that is the difference between the desired service level and a z value of 0 (0.9600 � 0.5000 � 0.4600). Look for the entry closest to 0.4600. If you look at the entry associated with a z value of 1.75, you should see 0.4599, which is as close to 0.4600 as we can get. Therefore, the appropriate z value is 1.75. To determine the appropriate amount of safety stock, do the following calculation:

SS 5 1.75 3 300 bottles 5 525 bottles of safety stock

The reorder point would now be

R 5 5000 1 525 5 5525 bottles

EXAMPLE 12.13

Nick’s Safety Stock

With the periodic review system, you determine the quantity of an item your company has on hand at specified, fixed-time intervals (such as every Friday or the last day of every month). You place an order for an amount (Q) equal to the target inventory level (TI), minus the quantity on hand (OH), similar to the min-max system. The dif- ference is that with the periodic review system, the time between orders is constant (such as every hour, every day, every week, or every month) with varying quantities ordered. The min-max system varies both the time between orders and the quantities ordered.

� Target inventory level (TI) Used in determining order quantity in the periodic review system. Target inventory less on-hand inventory equals order quantity.

PERIODIC REVIEW SYSTEM

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486 • CHAPTER 12 INVENTORY MANAGEMENT

An advantage of the periodic review system is that inventory is counted only at specific time intervals. You do not need to monitor the inventory level between review periods. This system also makes sense when you order several different items from a supplier. For example, if your company buys 10 different items from the same sup- plier, you can place one order for all 10 items rather than 10 individual orders, one for each item.

Potential disadvantages include the varying replenishment levels. First, since you must have sufficient space to store the largest possible order quantity, often you will have excess space when the replenishment orders are smaller. Second, because of varying quantities, you may not be able to qualify for specific quantity discounts.

One result from using the periodic review system is a larger average inventory level. Your company must carry enough inventory to protect against stockout for the replenishment lead time plus the review period. The two major decisions to be made when using the periodic review system concern the time between orders and the target inventory level.

The time between orders (TBO) may be selected for convenience reasons. That is, it may be easier for you to review your inventory at the end of each week and prepare your replenishment order then. An alternative is to base your TBO on the economic order quantity calculation. For example, if you determine that the EOQ � 75 units and that weekly demand is 25 units, it makes sense to place orders every three weeks. You simply divide the EOQ by the average weekly demand.

The target inventory (TI) level is calculated as:

TI 5 d 1RP 1 L 2 1 SS where TI � target inventory level in units d � average period demand in units (period can be day, week, month, etc.) RP � review period (in days, weeks, or months) L � lead time (in days, weeks, or months) SS � safety stock in units

The safety stock is calculated as SS 5 zsRP1L

where z � number of standard deviations σRP�L � standard deviation of demand during review period and lead time and is

calculated as

sRP1L 5 st"RP 1 L where σt � standard deviation of demand during interval t RP � review period L � lead time

To calculate the replenishment order quantity, use the following formula:

Q 5 TI 2 OH

where Q � replenishment order quantity TI � target inventory level OH � on-hand quantity

Note that when the lead time is greater than the review period, the on-hand quantity must include any on-order amounts.

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PERIODIC REVIEW SYSTEM • 487

Gray’s Pharmacy uses a periodic review inventory system. Every Friday, the pharmacist reviews her inventory and determines the size of the replenishment order. For example, she knows that demand for 500-mg metformin tablets, a drug for diabetics, is normally distributed with a mean of 6000 tablets each week with a standard deviation of 500 tablets per week. Lead time is three weeks. The desired cycle-service level is 95 percent. There are currently no outstanding orders.

(a) Calculate the required safety stock. (b) Calculate the target inventory level. (c) If, when she reviews her inventory of metformin, the pharmacist fi nds that she currently has

19,000 tablets, calculate the appropriate replenishment order quantity.

• Before You Begin: For this problem, you must determine the target inventory level and make a decision as to the replenishment quantity to order. To calculate the target inventory, find the appropriate safety stock level. Use the formula SS � zσRP�L. Calculate the target inventory as TI � d(RP � L � SS). After determining the target inventory, calculate the appropriate order size as Q � TI � OH.

• Solution: (a) Go to Appendix B, the area under the standardized normal curve, and look for the z value

that equates to 95 percent of the area under the curve. Since the appendix only uses positive z values, and they start at 0.50, we need to look for a z value that matches the diff erence between the desired cycle-service level (0.95) and the starting point of 0.50. So we are looking for a value close to 0.4500. In the appendix we can see that z � 1.64 has a value of 0.4495 while z � 1.65 has a value of 0.4505. By interpolation, a z � 1.645 has a value of exactly 0.4500. Th erefore, our desired z value is 1.645.

SS 5 1.645 1st"RP 1 L 2 SS 5 1.645 1500"1 1 3 2 5 1645 tablets

(b) Th e target inventory level is

TI 5 6000 11 1 3 2 1 1645 5 25,645 units (c) If the current inventory of metformin is 19,000 tablets, the pharmacist should order 6645

tablets, or Q � 25,645 � 19,000 tablets.

EXAMPLE 12.14

Using the Periodic Review System

Comparison of Continuous Review Systems and Periodic Review Systems The advantages of continuous review systems (CRS) are the disadvantages of peri- odic review systems (PRS). For instance, a CRS has no set review periods. This lack of specified review periods means that less inventory is needed to protect against stockouts.

With a PRS, enough inventory must be carried to cover both the lead time and the review period. Since a CRS has no review period, it has a smaller average inventory investment. On the other hand, a CRS means significantly more work because the inventory balances are updated after each transaction rather than periodically. A PRS means less work because inventory balances are only reviewed and updated periodi- cally. So a PRS makes it easier to consolidate orders from a single supplier because you

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488 • CHAPTER 12 INVENTORY MANAGEMENT

can review all of those items at the same time interval, whereas the CRS is designed to handle items individually.

In general, companies use CRS for items that are expensive and/or critical to the company because CRS more closely monitors these items and reduces inventory investment. Companies typically use both systems depending on the value and criti- cality of the items to be monitored.

THE SINGLE-PERIOD INVENTORY MODEL Some finished goods inventories have very short selling seasons. Items such as holiday decorations, Christmas trees, long-stemmed red roses, newspapers, and magazines are good examples. These products typically have a high value for a relatively short period; then the value diminishes dramatically to either zero or some minimum salvage value. For example, week-old newspapers are inexpensive compared to newspapers offering fresh news. The question is how many of these products you should order to maximize your expected profit.

The single-period model is designed for products that share the following characteristics:

● Th ey are sold at their regular price only during a single time period. ● Demand for these products is highly variable but follows a known probability

distribution. ● Salvage value of these products is less than their original cost, so you lose money

when they are sold for their salvage value.

The objective is to balance the gross profit generated by the sale of a unit with the cost incurred for each unit that is not sold until after the primary selling period has elapsed. When demand follows a discrete probability distribution, we can solve the problem using an expected value matrix.

� Single-period model Designed for use with products that are highly perishable.

MKT

Christmas trees for sale.

P/ W

id e W

or ld

P ho

to s

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THE SINGLE-PERIOD INVENTORY MODEL • 489

Rick Jones is chairman of this year’s Walk for Diabetes event. Each year, the organizers of the event typically have commemorative T-shirts available for purchase by the entrants in the walk. Rick needs to order the shirts well in advance of the actual event. He must place his order in multiples of 10 (60, 70, 80, etc.). Based on past walks, the organizers have determined that the probability of selling differ- ent quantities of T-shirts in a given year is as follows:

Rick plans to sell the T-shirts for $20 each. He pays his supplier $8 for each shirt and can sell any unsold shirts for rags at $2 each. Determine how many T-shirts Rick should order to maximize his expected profits.

• Before You Begin: In this problem, you need to determine how many T-shirts to order for the event. If you order too many, you will have leftover shirts with little value. If you don’t order enough, you forgo achieving the profit associated with each shirt plus creating some customer ill will. The easiest way to approach this decision is to develop a payoff table to calculate expected profit with each possible order quantity.

• Solution: Based on the information provided, develop a payoff table to determine expected profit with each possible order quantity. Calculate net profit for each combination of order quantity and demand as shown next.

The numbers in the payoff table are calculated based on what happened. The three possible out- comes are: (1) the number of shirts ordered equals the number of shirts demanded, (2) the number of shirts ordered is greater than the number of shirts demanded, and (3) the number of shirts ordered is less than the number of shirts demanded. To find the payoff when supply equals demand,

Payoff 5 demand 1selling price 2 unit cost 2 In our example, look at what happens when 100 T-shirts are bought and 100 T-shirts are sold.

Payoff 5 100 1$20 2 $8 2 5 $1200

EXAMPLE 12.15

Walk for Diabetes

Payoff Table

Probability of occurrence 0.20 0.25 0.30 0.15 0.10

Customer demand (shirts) 80 90 100 110 120

Number of Shirts Ordered

Expected Profit

80 $960 $ 960 $ 960 $ 960 $ 960 $ 960 90 $900 $1080 $1080 $1080 $1080 $1044

100 $840 $1020 $1200 $1200 $1200 $1083 110 $780 $ 960 $1140 $1320 $1320 $1068 120 $720 $ 900 $1080 $1260 $1440 $1026

Demand (shirts) Probability

80 0.20 90 0.25

100 0.30 110 0.15 120 0.10

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490 • CHAPTER 12 INVENTORY MANAGEMENT

When the number of shirts ordered is less than demand, the payoff is calculated as

Payoff � (number of items demanded) � (selling price � item cost) – ((items ordered � items demanded) � (item cost � item salvage value))

If 100 T-shirts are ordered and demand is for only 80 shirts, the payoff is

Payoff � 80($20 � $8) � ((100 � 80) � ($8 � $2)) � $840

When the number of shirts ordered is less than demand, the payoff is calculated as

Payoff � number of items ordered � (selling price � item cost)

Returning to the example, determine the payoff when 100 T-shirts are ordered but 120 shirts are demanded.

Payoff 5 100 1$20 2 $8 2 5 $1200 After we calculate the payoffs for each combination, we can determine the expected profit for each order quantity. We do this by multiplying the payoff for an order quantity by the prob- ability for each level of demand. For example, we calculate the payoff for ordering 100 shirts, $1083, as

1$840 3 0.20 2 1 1$1020 3 0.25 2 1 1$1200 3 0.30 2 1 1$1200 3 0.15 2 1 1$1200 3 0.10 2 Once we generate the expected profit for each of the possible order quantities, we select the order quantity with the highest expected profit. In our case, Rick should order 100 shirts since doing so has an expected profit of $1083.

INVENTORY MANAGEMENT WITHIN OM: HOW IT ALL FITS TOGETHER Inventory management provides the materials and supplies needed to support actual manufacturing or service operations. A product cannot be built unless the required material is available. Inventory replenishment policies guide the master production scheduler when determining which jobs and what quantity should be scheduled (Supplement D). The master production schedule inserted into the mate- rial requirements planning (MRP) system generates the replenishment orders. This output is used to guide purchasing in terms of the frequency and size of orders. Too much inventory is costly to the organization, yet too little can create major inefficiencies.

Inventory record accuracy is especially critical for MRP users. MRP relies on inven- tory records to process material requirements, so inaccurate records make the MRP output worthless. This, in turn, can cause manufacturing to shut down and/or miss a deadline.

Inventory management policies also affect the layout of the facility. A policy of small lot sizes and frequent shipments reduces the space needed to store materials (Chapter 7). Point-of-delivery placement of inventory affects the size of work centers. Inventory management also affects throughput time. As a facil- ity increases its work-in-process, throughput times increase. Longer throughput times reduce an organization’s ability to respond quickly to changing customer demands (Chapter 4).

Good inventory management assures continuous supply and minimizes inventory investment while achieving customer service objectives.

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THE SUPPLY CHAIN LINK • 491

INVENTORY MANAGEMENT ACROSS THE ORGANIZATION Inventory management policies affect functional areas throughout a company. Let’s con- sider why individual functional areas are concerned with inventory management policies.

Accounting is concerned because of the cost implications of inventory, such as the holding costs incurred, the capital needed to invest in inventory, and projected cash flow budgets. Accounting is concerned with all types of inventory.

Marketing is concerned because stocking decisions affect the level of customer service provided. Marketing’s primary focus is finished goods inventory, where the goods are held within the distribution system, the response time to satisfy customers, and safety stock levels.

Information systems is involved because a system to track and control invento- ries is needed, especially when perpetual inventory records are used. Given the large number of SKUs and a high volume of inventory transactions, manual processing is impractical for most companies, so a computerized information system is essential.

Purchasing’s workload is directly affected by inventory policies. Policies regarding order frequency, order volume, acceptable suppliers, and inventory investment deter- mine the number of purchases made. Purchasing is concerned primarily with buying raw materials, components, and subassemblies.

Manufacturing’s cost efficiency can be affected by inventory decisions. If insufficient material is available, either because items are not ordered on time or not ordered in the right quantities, manufacturing efficiency decreases and unit costs increase. Unit costs can also increase when too much material is ordered or when it is ordered too soon.

As you can see, inventory decisions affect many functional areas in a company and may involve input from management in these areas. In addition, inventory decisions have a significant impact on the company’s profitability.

Who makes aggregate inventory decisions? Typically, it is the materials manager. This person is evaluated based on customer service levels achieved and inventory turnover. For individual finished goods products, the master scheduler makes decisions about how much of a particular item to produce and how much to keep in inventory. A master scheduler is evaluated based on customer service levels and manufacturing efficiency.

For raw materials, components, and subassemblies, inventory planners, material planners, or controllers make decisions about when to place replenishment orders, either for in-house manufacturing or for external purchasing. Planners and con- trollers are typically evaluated according to customer service levels and inventory investment.

OMACC

MKT

MIS

Inventory management deals with economically based item-replenishment policies, safety stock levels, and the appropriate review system for use within a supply chain. Inventory flows from the suppliers to the manufacturers to the distributors. Inventory management provides an understanding of the total costs of inventory as well as the customer ser- vice ramifications of specific policies. By themselves, uncoordinated replenishment policies can cause the

bullwhip effect (discussed in Chapter 4) in the supply chain. Vendor-managed inventory is one approach committed to improving service levels while reduc- ing inventory investment in the supply chain. A

policy of making demand information available (point-of-sale information) to all members of the supply chain reduces demand uncertainty and allows a company to achieve its desired customer

service levels with a smaller inventory investment. Inventory management is a key component of effective supply chain performance.

THE SUPPLY CHAIN LINK

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492 • CHAPTER 12 INVENTORY MANAGEMENT

Inventory management, order policies, and storage and movement of goods are all areas where significant gains in sustainability can be made for companies. Consider that modes of transportation used to move materials have significant effects on energy consumption, traf- fic congestion, and pollution, including noise pollution. Decisions such as sourcing locally, reducing the number of shipments, and selecting transportation modes wisely can go a long way toward meeting sustainability goals. Some companies are also measuring their cost of goods as total cost of ownership (TCO), which includes usage and disposal costs. For example, the cost of a car battery may significantly change if we include its end-of-life cost, as improper disposal can pose significant negative envi- ronmental effects. This may result in replenishment poli- cies that are modified and reflect these additional costs.

Another area of inventory management that impacts sustainability is decisions regarding specific products sourced with a consideration of their environmental impact. Even seemingly small changes in items pur- chased can have a sizable impact. For example, some- thing as simple as the purchase of a wine-bottle stopper can have substantial sustainability impact. Production of petroleum-based plastic wine bottle stoppers causes 50% more global-warming pollution than does the man- ufacture of natural cork stoppers. Similarly, production of metal screw caps for wine bottles produces any- where from three to five times as much global-warming pollution. Of the three inventory item choices—plastic wine bottle stopper, natural cork stopper, or metal screw cap—the natural cork stopper is a far more sustainable choice. Inventory management needs to consider these differences in deciding on items sourced, in addition to traditional costs.

THE SUSTAINABILITY LINK

Chapter Highlights 1 Raw materials, purchased components, work-in-process

(WIP), fi nished goods, distribution inventory and maintenance, repair and operating supplies are all types of inventory. Inventories have several uses: anticipation inventory is built before it is needed; fl uctuation stock provides a cushion against uncertain demand; cycle stock is a result of the company’s ordering quantity; transportation inventory includes items in transit; speculative inventory is a buildup to protect against some future event; and MRO inventory supports daily operations.

2 Th e objectives of inventory management are to provide the desired level of customer service, to allow cost-effi cient operations, and to minimize inventory investment. Customer service can be measured in several ways, including as a percentage of orders shipped on schedule, a percentage of line items shipped on schedule, a percentage of dollar volume shipped on schedule, or idle time due to material and component shortages. Cost-effi cient operations are achieved by using inventory as buff er stocks, allowing a stable year-round workforce, and spreading the setup cost over a larger number of units.

3 Inventory investment is measured in inventory turnover and/or level of supply. Inventory performance is calculated as inventory turnover or weeks, days, or hours of supply.

4 Relevant inventory costs include item costs, holding costs, ordering costs, and shortage costs. Holding costs include capital costs, storage costs, and risk costs. Ordering costs

are fi xed costs for placing an order or performing a setup. Shortage costs include costs related to additional paperwork, additional shipping expense, and the intangible cost of lost customer goodwill.

5 Th e ABC classifi cation system allows a company to assign the appropriate level of control and frequency of review of an item based on its annual dollar volume.

6 Cycle counting is a method for maintaining accurate inventory records. Determining what and when to count are the major decisions.

7 Retailers, wholesalers, and food service organizations use tangible inventory even though they are service organizations. Proper inventory control and management for these organizations oft en is the diff erence between a profi t and loss. Since the items are oft en desirable, organizations must strive to reduce the amount of theft by customers and employees. Magnetic strips, security devices, and surveillance systems are all means of reducing inventory loss.

8 Lot-for-lot, fi xed-order quantity, min-max systems, order n periods, periodic review systems, EOQ models, quantity discount models, and single-period models can be used to determine order quantities.

9 Ordering decisions can be improved by analyzing total costs of an inventory policy. Total costs include ordering cost, holding cost, and material cost.

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FORMULA REVIEW • 493

10 Practical considerations can cause a company to not use the optimal order quantity, that is, minimum order requirements.

11 Smaller lot sizes give a company fl exibility and shorter response times. Th e key to reducing order quantities is to reduce ordering or setup costs.

12 Calculating the appropriate safety stock policy enables companies to satisfy their customer service objectives at

minimum cost. Th e desired customer service level determines the appropriate z value.

13 Inventory decisions about perishable products (like newspapers) can be made using the single-period inventory model. Th e expected payoff is calculated to assist the quantity decision.

Key Terms raw materials 452 components 452 work-in-process (WIP) 453 fi nished goods 453 distribution inventory 453 anticipation inventory 453 fl uctuation inventory 453 lot-size inventory 453 transportation inventory 453 speculative inventory 454 maintenance, repair, and operating

inventory (MRO) 454 customer service 455 percentage of orders shipped on

schedule 455 percentage of line items shipped on

schedule 456 percentage of dollar volume shipped on

schedule 456

setup cost 457 inventory turnover 457 weeks of supply 457 item cost 459 holding costs 459 capital costs 459 storage costs 460 risk costs 460 ordering costs 461 shortage costs 461 back order 461 lost sale 461 Pareto’s law 461 ABC classifi cation 461 continuous review system 463 periodic review system 464 two-bin system 464 lead time 464 periodic counting 465

cycle counting 465 vendor-managed inventory (VMI) 466 stock-keeping unit (SKU) 467 lot-for-lot 468 fi xed-order quantity 468 min-max system 468 order n periods 468 economic order quantity (EOQ) 468 economic production quantity

(EPQ) 473 perpetual inventory record 477 quantity discount model 477 order-cycle service level 483 target inventory level (TI) 485 single-period model 488

Formula Review 1. Calculating average transportation inventory (ATI):

ATI 5 tD 365

where t � transit time in days and D � annual demand in units.

2. Calculating inventory turnover and periods of supply:

Inventory turnover 5 annual cost of goods sold

average inventory in dollars

Weeks of supply 5 average inventory on hand in dollars

average weekly usage in dollars

Days of supply 5 average inventory on hand in dollars

average daily usage in dollars

3. Calculating target inventory (TI):

TI 5 d 1RP 1 L 2 1 SS where d � average daily demand, RP � review period in days, and SS � safety stock.

4. Calculating safety stock in a periodic review model:

SS 5 zsRP1L

Standard deviation of demand during review period and lead time:

sRP1L 5 st"RP 1 L 5. Calculating reorder point without safety stock:

R 5 dL

where d � average daily demand and L � lead time in days.

6. Calculating the economic order quantity (EOQ):

Q 5 Å2DSH where D � annual demand, S � ordering cost, and H � hold- ing cost.

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494 • CHAPTER 12 INVENTORY MANAGEMENT

7. Calculating total costs:

TC 5 aD Q

Sb 1 aQ 2

Hb 8. Calculating the economic production quantity (EPQ):

Q 5ã 2DS

Ha1 2 d p b

9. Calculating total costs:

TC 5 aD Q

Sb 1 aIMax 2

Hb where IMax is the maximum inventory level.

10. Calculating IMax:

IMax 5 Q a1 2 dpb where d � daily demand and p � daily production rate.

11. Calculating total costs for quantity discount comparisons:

TC 5 aD Q

Sb 1 aQ 2

Hb 1 CD where C � price per unit.

12. Calculating amount of safety stock:

SS 5 zs sdL

where SS � safety stock, z � number of standard deviations, and σdL � standard deviation of demand during lead time in units.

Solved Problems • Problem 1 Tacky Souvenirs sells lovely handmade tablecloths at its island store. These tablecloths cost Tacky $15 each. Customers want to buy the tablecloths at a rate of 240 per week. The company operates 52 weeks per year. Tacky, the owner, estimates his ordering cost at $50. Annual holding costs are 20 percent of the unit cost. Lead time is 2 weeks. Using the information given, (a) Calculate the economic order quantity. (b) Calculate the total annual costs using the EOQ. (c) Determine the reorder point.

• Before You Begin: To calculate the economic order quantity, you use the formula

Q 5 Å2DSH Remember that the demand information and the holding cost must be for the same time frame. That is, if you use annual demand, you must use an annual holding cost. Once you have cal- culated the EOQ, you calculate total annual costs with the formula

TC 5 ¢D Q

S≤ 1 ¢Q 2

H≤ To find the reorder point, use the formula: R � dL. Remember that demand must be in the same time frame as is given for lead time. For example, if lead time is given as three weeks, then use weekly demand. If lead time is given in days, use daily demand.

• Solution (a) First, calculate the annual demand and the annual holding

cost.

Annual demand 5 152 weeks 3 240 units per week 2 5 12,480 units

Annual holding cost 5 10.20 3 $15 2 5 $3.00 per unit per year

Now calculate the economic order quantity as shown in the spreadsheet.

Q 5 Å2 3 12480 3 $50$3 5 644.98, or 645 tablecloths Examine the spreadsheet to see how you can solve EOQ prob- lems using a spreadsheet. Note that you can use weekly demand since the lead time is given in weekly increments. Just make sure that the average demand time frame matches the time frame used with lead time. (b) The total costs are

TC 5 ¢ 12480 645

$50≤ 1 ¢ 645 2

$3≤ 5 $1934.94 (c) The reorder point is

R 5 240 units 3 2 weeks 5 480 units

(See student companion site for Excel template.)

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SOLVED PROBLEMS • 495

• Problem 2 Jack’s Packs manufactures backpacks made from microfabrics. The cutting department prepares the material for use by the backpack stitching department. The cutting department can cut enough material to make 200 backpacks per day. The backpack stitching department produces 90 backpacks per day. Annual demand for the product is 22,500 units. The company operates 250 days per year. Estimated setup cost is $60. Annual holding cost is $6 per backpack.

(a) Calculate the economic production quantity for the cutting department.

(b) Calculate the total annual costs for the EPQ.

• Before You Begin: For this problem, calculate the EPQ. We use a modified ver- sion of the EOQ formula since we have relaxed the assumption regarding all of the items being delivered at one time. With the EPQ model, units are produced daily. Some are used immedi- ately to satisfy demand, while the other units are put into inven- tory. The appropriate formula is

Q 5ã 2DS

H¢1 2 dp≤ Remember that the ratio d/p does not have to be daily demand divided by daily production. You need only use figures for the same time frame. The ratio of annual demand divided by annual production is equivalent to the daily demand divided by daily production. You also should check to be sure that the demand rate is smaller than the production rate. Otherwise, you can never produce enough to satisfy demand. When calcu- lating total costs, make sure that you determine the maximum inventory level when assessing holding costs.

• Solution (a) First, calculate the EPQ as follows:

Q 5ã 2 3 22,500 3 $60

$6¢12 90 200

≤ 5 904.53, or 905 backpacks

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496 • CHAPTER 12 INVENTORY MANAGEMENT

(b) To calculate total costs, determine the maximum inventory level as follows:

IMax 5 905¢1 2 90200 ≤ 5 497.75, or 498 backpacks

Now that you have determined the maximum inventory level, calculate total costs:

TC 5 ¢ 22,500 905

$60≤ 1 ¢ 498 2

$6≤ 5 $2985.71

• Problem 3 Ye Olde Shoe Repaire has customers requesting leather soles throughout the year. The owner, Warren, buys these soles from The Leather Company (TLC) at a price of $8 per pair. In an effort to improve profitability by selling in greater quantities, the sales rep for TLC has made the following offer to Ye Olde Shoe Repaire: If Warren orders from 1 to 50 pairs at a time, the cost per pair is $8.00. If the order is between 51 and 100 pairs at a time, the cost is $7.60. On orders for more than 100 pairs at a time, the cost per pair is $7.40. The owner estimates annual demand to be 625 pairs of soles. Holding costs are 20 percent of unit price. The cost to place an order is $10. Determine the most cost-effective ordering policy for Ye Olde Shoe Repaire.

• Before You Begin: This is a quantity discount problem with proportional holding costs. You begin by calculating the EOQ for the least expensive unit price. Check to see if this quantity is feasible. Feasibility occurs when you can order the EOQ quantity and pay the unit price that was used in your calculation. For example, if the EOQ turns out to be 92 pairs of leather soles and you used a unit price of $7.40 per pair, you need to check to see whether or not you will be charged $7.40 per pair if you place an order for 92 pairs. If the initial price assumption does not match what you would actually pay, then the quantity is infeasible. Once you find a feasible quantity, calculate the total annual costs for that policy, including the annual material costs. You must also calculate the total costs associated with ordering just enough units to qualify for any cheaper prices available. For example, if the feasible quantity occurs with a cost of $7.60 per pair and you know that if you buy 100 pairs at a time you qualify for a unit price of $7.40, you calculate the total annual cost assuming that you would order just enough (100 pairs) to qualify for the lower unit price. You must do this for all prices lower than the price of the feasible EOQ. Your best policy is based on the total annual costs.

• Solution (a) First, we need to calculate the EOQ at the lowest price

offered. The annual holding cost is 20 percent of the unit cost, or $1.48—that is, $7.40 times 20 percent.

Q 5 Å2 3 625 3 $10$1.48 5 91.9, or 92 pairs Since this order quantity does not match the unit price used to calculate the EOQ, this answer is infeasible. This means if we place an order for 92 pairs, we are charged $7.60 per pair rather than the $7.40 we used in calculating the EOQ.

(b) Since the first Q is infeasible, we calculate the EOQ for the next higher price. Make sure to calculate the new annual holding cost, 20 percent of $7.60, or $1.52.

Q 5 Å2 3 625 3 $10$1.52 5 90.68, or 91 pairs If we place an order for 91 pairs, we will be charged $7.60 per pair, which is the price we used to calculate this EOQ. Therefore, this is a feasible order quantity. We are ready to calculate the total annual cost for this policy:

TC 5 ¢ 625 91

$10≤ 1 ¢ 91 2

$1.52≤ 1 1$7.60 3 625 2 5 $4887.84

Since the feasible solution was not at the lowest price, we must now compute the total cost of any cheaper price, assuming that we order just enough to qualify for the cheaper price. This means we need to order 101 pairs to qualify for the $7.40 price. The total cost of this policy is

TC 5 ¢ 625 101

$10≤ 1 ¢ 101 2

$1.48≤ 1 1$7.40 3 625 2 5 $4761.62

Since the total annual cost of ordering 101 pairs at a time is less expensive, Ye Olde Shoe Repaire should order 101 pairs each time leather soles are needed.

• Problem 4 Frank’s Ribs knows that the demand during lead time for his world- famous ribs is described by a normal distribution with a mean of 1000 pounds and a standard deviation of 100 pounds. Frank is willing to accept a stockout risk of approximately 2 percent.

(a) Determine the appropriate z value. (b) Calculate how much safety stock Frank should hold.

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DISCUSSION QUESTIONS • 497

Discussion Questions 1. Visit a local business and identify the different types of

inventory used. 2. After visiting a local business, explain the different func-

tions of its inventory. 3. Explain the objectives of inventory management at the local

business. 4. Describe how the objectives of inventory management can

be measured. 5. Explain the different methods for measuring customer

service. 6. Compare the two techniques, inventory turnover and weeks

of supply. 7. Describe the relevant costs associated with inventory

policies. 8. Explain what is included in the annual holding cost. 9. Describe what is included in ordering or setup costs.

10. Describe what is included in shortage costs. 11. Explain the assumptions of the EOQ model. 12. Describe techniques for determining order quantities other

than the EOQ or EPQ. 13. Describe how changes in the demand, ordering cost,

or holding cost affect the EOQ. 14. Explain how a company can justify smaller order quantities. 15. Explain what safety stock is for. 16. Explain how safety stock affects the reorder point. 17. Describe the type of products that require a single-period

model. 18. Explain the basic concept of ABC analysis. 19. Explain the concept of perpetual review. 20. Explain how two-bin systems work.

• Before You Begin: In this problem, you need to find out how much safety stock should be held. First, use Appendix B to determine the z value for the desired safety stock level. Then, using the formula SS � zσdL, calculate the required safety stock.

• Solution (a) Go to Appendix B. You need to find the z value associated

with 0.4800, which is the difference between the desired

service level, 0.9800, and the z value of 0, 0.5000. Looking at the entry for z � 2.05, you should see 0.4798, which is as close to 0.4800 as we can get. Therefore, the appropriate z value is 2.05.

(b) To determine the amount of safety stock Frank should hold, multiply the z value by the standard deviation:

SS 5 2.05 3 100 pounds 5 205 pounds

Frank should hold 205 pounds of ribs in safety stock.

• Problem 5 Peter sells programs at State University’s home football games. Peter must buy the programs before the game in multiples of 100 (2000, 2100, 2200, etc.). Peter has determined that the probability of selling different quantities of programs at a given game is as follows:

Peter plans to sell the programs for $4 each. He pays $2.50 for each program and there is no salvage value. Determine how many programs Peter should buy to maximize his profit.

• Before You Begin: For this problem, we are only able to make a single purchase. Determine which order quantity has the highest expected pay- off. Develop a payoff table to show the expected value from each order quantity.

• Solution Based on the information given, we developed a payoff table to determine the expected profit for each possible order quantity. Net profit for each combination or order quantity and demand are calculated as shown. The order quantity with the highest expected profit is 2200 programs. Peter should order 2200 programs.

Demand for Programs Probability of Demand

2000 0.10 2100 0.20 2200 0.40 2300 0.20 2400 0.10

Probability of Occurrence

0.10 0.20 0.40 0.20 0.10

Actual customer demand (programs) 2000 2100 2200 2300 2400

Number of Programs Ordered

Expected Profit

2000 $3000 $3000 $3000 $3000 $3000 $3000 2100 $2750 $3150 $3150 $3150 $3150 $3110 2200 $2500 $2900 $3300 $3300 $3300 $3140 2300 $2250 $2650 $3050 $3450 $3450 $3010 2400 $2000 $2400 $2800 $3200 $3600 $2800

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498 • CHAPTER 12 INVENTORY MANAGEMENT

Problems 1. Elyssa’s Elegant Eveningwear (EEE) needs to ship fi nished

goods from its manufacturing facility to its distribution warehouse. Annual demand for EEE is 2400 gowns. EEE can ship the gowns via regular parcel service (3 days transit time), premium parcel service (1 day transit time), or via public carrier (7 days transit time). Calculate the average annual transportation inventory for each alternative.

2. Yasuko’s Art Emporium (YAE) ships art from its studio located in the Far East to its distribution center located on the West Coast of the United States. YAE can send the art either via transoceanic ship freight service (15 days transit) or by air freight (2 days transit time). YAE ships 18,000 pieces of art annually.

(a) Calculate the average annual transportation inventory when sending the art via transoceanic ship freight service.

(b) Calculate the average annual transportation inventory when sending the art via air freight.

3. Joe, the owner of Genuine Reproductions (GR), a company that manufactures reproduction furniture, is interested in measuring inventory eff ectiveness. Last year the cost of goods sold at GR was $3,000,000. Th e average inventory in dollars was $250,000.

(a) Calculate the inventory turnover for GR. (b) Calculate the weeks of supply. Assume 52 weeks per year. (c) Calculate the days of supply. Assume that GR operates

5 days per week. 4. Genuine Reproductions (GR) plans on increasing next

year’s sales by 20 percent while maintaining its same average inventory in dollars of $250,000.

(a) Calculate the expected inventory turnover for next year. (b) Calculate the expected weeks of supply. 5. What is the inventory turnover for Genuine Reproductions

from Problems 3 and 4 if sales actually increase 20 percent but the average inventory rises to $325,000?

6. Frederick’s Farm Factory (FFF) currently maintains an average inventory valued at $3,400,000. Th e company estimates its capital cost at 10 percent, its storage cost at 4.5 percent, and its risk cost at 6 percent.

(a) Calculate the annual holding cost rate for FFF. (b) Calculate the total annual holding costs for FFF. 7. Th e Federal Reserve Board has just increased the interest

rate. FFF in Problem 6 now has to pay 12 percent for its capital. Calculate the impact on total annual holding costs for FFF.

8. A technology problem has rendered some of the inventory at FFF (Problem 6) obsolete. FFF estimates that the risk cost of its inventory is now 10 percent.

(a) Calculate the new annual holding cost rate. (b) Calculate the new total annual holding costs for FFF. 9. Custom Computers, Inc. assembles custom home computer

systems. Th e heat sinks needed are bought for $12 each and are ordered in quantities of 1300 units. Annual demand is 5200 heat sinks, the annual inventory holding cost rate is $3 per unit, and

the cost to place an order is estimated to be $50. Calculate the following:

(a) Average inventory level (b) Th e number of orders placed per year (c) Th e total annual inventory holding cost (d) Th e total annual ordering cost (e) Th e total annual cost 10. Custom Computers, Inc. from Problem 9 is considering a

new ordering policy. Th e new order quantity would be 650 heat sinks. Recalculate Problem 9, parts (a) through (e), and compare results.

11. Bill Maze, recently hired by Custom Computers, Inc., has suggested using the economic order quantity for the heat sinks. Using the information in Problem 9, calculate the following:

(a) Economic order quantity (b) Average inventory level (c) Th e number of orders placed per year (d) Th e total annual ordering cost (e) Th e total annual holding cost (f) Th e total annual cost Compare these results with the costs calculated in Problems

9 and 10. 12. A local nursery, Greens, uses 1560 bags of plant food

annually. Greens works 52 weeks per year. It costs $10 to place an order for plant food. Th e annual holding cost rate is $5 per bag. Lead time is one week.

(a) Calculate the economic order quantity. (b) Calculate the total annual costs. (c) Determine the reorder point. 13. Rapid Grower, the supplier of plant food for Greens in

Problem 12, has off ered the following quantity discounts. If the nursery places orders of 50 bags or less, the cost per bag is $20. For orders greater than 50 bags but less than 100 bags, the cost per bag is $19. For orders of 100 bags or more, the cost is $18 per bag. Greens estimates its holding cost to be 25 percent of the unit price. Determine the most cost-eff ective ordering policy for Greens.

14. In an eff ort to reduce its inventory, Rapid Grower is off ering Greens, a local nursery (Problems 12 and 13), two additional price breaks to consider. If the nursery orders a three-month supply, the cost per bag is $16. If Greens orders a six-month supply, the cost per bag is $14.50. Should Greens change its order quantity calculated in Problem 13?

15. In a further attempt to liquidate its inventory, Rapid Grower has off ered Greens, the local nursery, an option to buy the entire year’s supply at one time. Th e cost per bag would be $12. Should Greens take advantage of this off er?

16. Sam’s Auto Shop services and repairs a particular brand of foreign automobile. Sam uses oil fi lters throughout the year. Th e shop operates 52 weeks per year, and weekly demand is 150 fi lters. Sam estimates that it costs $20 to place an order and his annual holding cost rate is $3 per oil fi lter. Currently, Sam orders in quantities of 650 fi lters. Calculate the total annual costs associated with Sam’s current ordering policy.

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PROBLEMS • 499

17. Using the information in Problem 16, calculate the following:

(a) Th e economic order quantity (b) Th e total annual costs using the EOQ ordering policy (c) Th e penalty costs Sam is incurring by using his current

policy 18. Th e local Offi ce of Tourism sells souvenir calendars. Sue,

the head of the offi ce, needs to order these calendars in advance of the main tourist season. Based on past seasons, Sue has determined the probability of selling diff erent quantities of the calendars for a particular tourist season.

Th e Offi ce of Tourism sells the calendars for $12.95 each. Th e calendars cost Sue $5 each. Th e salvage value is estimated to be $0.50 per unsold calendar. Determine how many calendars Sue should order to maximize expected profi ts.

19. Th e Offi ce of Tourism (Problem 18) has decided to heavily promote local events this year and anticipates more tourists this season. Sue has changed the probability of selling diff erent quantities of calendars as shown. Given the new probabilities, determine how many calendars Sue should order to maximize expected profi ts.

20. Given the following list of items, (a) Calculate the annual usage cost of each item. (b) Classify the items as A, B, or C.

21. Using the information provided in Problem 20, (a) Calculate the economic order quantity for each item.

(Round to the nearest whole number.) (b) Calculate the company’s maximum inventory investment

throughout the year. (c) Calculate the company’s average inventory level. 22. Tax Preparers, Inc. works 250 days per year. Th e company

uses adding machine tape at a rate of eight rolls per day. Usage is believed to be normally distributed with a standard deviation of three rolls during lead time. Th e cost of ordering the tape is $10, and holding costs are $0.30 per roll per year. Lead time is two days.

(a) Calculate the economic order quantity. (b) What reorder point will provide an order-cycle service

level of 97 percent? (c) How much safety stock must the company hold to have a

97 percent order-cycle service level? (d) What reorder point is needed to provide an order-cycle

service level of 99 percent? (e) How much safety stock must the company hold to have a

99 percent order-cycle service level? 23. Healthy Plants Ltd. (HP) produces its premium plant

food in 50-pound bags. Demand for the product is 100,000 pounds per week. HP operates 50 weeks per year and can produce 250,000 pounds per week. The setup cost is $200 and the annual holding cost rate is $0.55 per bag. Currently, HP produces its premium plant food in batches of 1,000,000 pounds.

(a) Calculate the maximum inventory level for HP. (b) Calculate the total annual costs of this operating policy. 24. Using the data provided in Problem 23, determine what

will happen if HP uses the economic production quantity model to establish the quantity produced each cycle.

(a) Calculate the economic production quantity (EPQ). (b) Calculate the maximum inventory level using the EPQ. (c) Calculate the total annual cost of using the EPQ. (d) Calculate the penalty cost HP is incurring with its current

policy. 25. Greener Pastures Incorporated (GPI) produces a high-

quality organic lawn food and weed eliminator called Super Green (SG). Super Green is sold in 50-pound bags. Monthly demand for Super Green is 75,000 pounds. Greener Pastures has capacity to produce 24,000 50-pound bags per year. Th e setup cost to produce Super Green is $300. Annual holding cost is estimated to be $3 per 50-pound bag. Currently, GP is producing in batches of 2500 bags.

(a) Calculate the total annual costs of the current operating policy at GPI.

(b) Calculate the economic production quantity (EPQ). (c) Calculate the total annual costs of using the EPQ. (d) Calculate the penalty cost incurred with the present

policy. 26. Lissette Jones, the materials manager for an upscale retailer,

wants to measure her customer service level. She has collected the following representative data.

Demand for Calendars Probability of Demand

75,000 0.15 80,000 0.25 85,000 0.30 90,000 0.20 95,000 0.10

Demand for Calendars Probability of Demand

75,000 0.05 80,000 0.20 85,000 0.25 90,000 0.30 95,000 0.20

Item Annual Demand

Ordering Cost ($)

Holding Cost (%)

Unit Price ($)

101 500 10 20 0.50 102 1500 10 30 0.20 103 5000 25 30 1.00 104 250 15 25 4.50 105 1500 35 35 1.20 201 10000 25 15 0.75 202 1000 10 20 1.35 203 1500 20 25 0.20 204 500 40 25 0.80 205 100 10 15 2.50

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500 • CHAPTER 12 INVENTORY MANAGEMENT

CASE: FabQual Ltd. FabQual Ltd. manufactures parts and subassemblies for a num- ber of small-volume manufacturers of specialized construction equipment, including bulldozers, graders, and cement mix- ers. FabQual also manufactures and distributes spare parts.

The company has made a specialty of providing spare parts for equipment no longer in production; this includes wear parts that are no longer in production for any OEM.

Assuming that orders 1–6 and 8–10 shipped on schedule: (a) Calculate the customer service level using the percentage

of orders that shipped on schedule. (b) Calculate the customer service level using the percentage

of line orders that shipped on schedule. (c) Calculate the customer service level using the percentage

of dollar volume that shipped on schedule. (d) Which of these measures would you recommend to

Lissette? 27. Your new company has decided to use a periodic review

system. You have learned that average weekly demand is 48 units per week with a standard deviation of 8 units. You believe that your cycle-service level should be 94 percent. Lead time is two weeks. Initially, you believe that you should do a review every Friday. Determine the required safety stock and the target inventory level.

(a) How would this procedure change if the cycle-service level needed to be 98 percent?

(b) What is the impact of changing the review period from every Friday to every other Friday, assuming that the cycle-service level is 94 percent?

28. Michael’s Offi ce Supply (MOS) sells offi ce furniture, equipment, and supplies. Th is week the company has received 50 customer orders. Each order has an average of fi ve line items. Th e average dollar amount of each order is $1200. MOS was able to ship 47 of the 50 orders on schedule.

(a) Using the percentage of orders shipped on time, calculate the customer service level.

(b) If Michael’s calculates customer service level by using the percentage of line items shipped on schedule, how many

line items must be shipped to achieve the same customer service level calculated in part (a)?

(c) If Michael’s calculates customer service level by the percentage of dollar volume shipped, how many dollars of product must be shipped to achieve the same customer service level calculated in part (a)?

(d) What factors determine the customer service level measure that MOS should use?

29. My Kitchen Delights (MKD), a regional producer of gourmet jams and jellies, uses approximately 24,000 glass jars each month during its production. Because of space limitations, MKD orders 5000 jars at a time. Monthly holding cost is $0.08 per jar, and the ordering cost is $60 per order. Th e company operates 20 days per month.

(a) What penalty cost is the company incurring by its present replenishment policy?

(b) MKD would prefer to order eight times each month but needs to justify any change in order size. How much would ordering cost need to be reduced to justify a lot size of 3000 jars?

30. My Kitchen Delights (MKD) is considering two new suppliers for the jars used in the production process. Th e quality at both suppliers is equal. Assume that the annual holding cost is 30 percent of the unit price. Monthly demand averages 20,000 jars. Ordering cost with these two suppliers is $30 per order. Th e price lists for the suppliers are as follows:

(a) Determine the optimal order quantity when using Supplier A.

(b) Determine the optimal order quantity when using Supplier B.

Supplier A

Quantity Unit Price 1–2499 $3.00 2500–3499 2.90 3500–4999 2.80 5000 or more 2.70

Supplier B

Quantity Unit Price 1–1999 $3.50 2000–2999 3.15 3000–3999 2.85 4000–4999 2.75 5000 or more 2.60

Order Number

Number of Line Items

Dollar Value of Order

1 4 1000 2 8 1440 3 2 1600 4 6 920 5 10 1800 6 8 1200 7 8 2700 8 4 1560 9 5 1780

10 5 1000

Totals 60 $15,000

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CASE: KAYAKS!INCORPORATED • 501

The Materials Management Group (MMG) orders parts— both for delivery to a customer’s production line and for spares—from the Fabrication Department. Spares are stocked in a finished goods store. FabQual’s part number 650810/ss/R9/o is a wear part made only for spares demand. It has had demand averaging 300 units per week for more than a year, and this level of demand is expected to persist for at least four more years. The standard deviation of weekly demand is 50 units.

The MMG has been ordering 1300 units monthly of part number 650810/ss/R9/o from the Fabrication Department to meet the forecast annual demand of 15,600 units. The order is placed in the first week of each month. In order to provide Fab- rication with scheduling flexibility, as well as to help with plan- ning raw material requirements, a three-week manufacturing lead time is allowed for parts.

In the Fabrication Department, two hours is now allowed for each setup for a run of part number 650810/ss/R9/o. This time includes strip-down of the previous setup; delivery of raw materi- als, drawings, tools and fixtures, and the like; and buildup of the new setup. The two-hour setup time is a recent improvement over the previous four hours, as the result of setup reduction activi- ties in the Fabrication Department. The Fabrication Department charges £20 per hour for setups. (If you prefer to work in dollars, you can find the current exchange rate in the Wall Street Journal.) Part number 650810/ss/R9/o enters the finished goods stores at a full manufacturing cost of £55. The Financial Office requires a 25 percent per item per year cost for inventory planning and control. (This is your annual holding cost rate.)

Case Questions 1. What is the total annual cost of the present ordering policy

for part number 650810/ss/R9/o? 2. What would be the lot size for part number 650810/ss/

R9/o if FabQual were to use an economic order quantity (EOQ)? 3. What would be the total annual cost of using an economic

order quantity for part number 650810/ss/R9/o? 4. What would be the reorder point for part number 650810/

ss/R9/o if FabQual wanted a delivery performance of 95 percent? What would it be if the company wanted a delivery performance of 99 percent?

5. Under the present scheme—ordering 1300 units each month in the fi rst week of each month—there are typically 700 to 800 units on hand when the new batch of 1300 units arrives toward the end of each month. What would be the impact on the overall inventory level of part number 650810/ss/R9/o of a change from the present order policy to an EOQ-based policy?

6. What are other implications of a change from the present scheme to one based on the economic order quantity? If this part is representative of a great many spare parts, what would be the overall impact?

CASE: Kayaks!Incorporated Kayaks!Incorporated manufactures a line of sea kayaks and accessories in a make-to-stock environment. These products are sold to boat dealers and major department stores through- out North America, which then sell these products to the final customer. Customers expect immediate receipt of the goods, so it is critical to have sufficient inventory held by the dealers and department stores. Aeesha Grant, the materials manager at Kayaks! wants to make sure that the customer service level is being correctly calculated before she considers any changes to manufacturing. She has collected the following information for you to analyze and prepare a report on the customer service level being provided by Kayaks!Incorporated to the boat dealers and department stores.

Case Questions 1. Kayaks!Incorporated has always measured customer

service as the number of complete orders that ship on schedule. Using this measure, calculate the customer service level provided by Kayaks!Incorporated.

2. Does this method of calculating the customer service level make sense for Kayaks!Incorporated?

3. What other methods might be useful in measuring Kayaks! customer service level? How would these aff ect your analysis of customer service?

4. What is your report to Aeesha Grant with regard to the customer service being provided by Kayaks!Incorporated?

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502 • CHAPTER 12 INVENTORY MANAGEMENT

Customer Line

Items Dollar Value

Line Items Shipped

on Schedule

Dollar Value

on Schedule

1 2 2,000 2 2,000 2 17 40,000 16 37,500 3 9 16,000 9 16,000 4 7 9,500 6 9,000 5 24 68,000 22 64,000 6 4 6,000 4 6,000 7 7 14,000 7 14,000 8 3 14,000 3 14,000 9 9 6,000 7 4,800

10 12 18,500 11 18,000 11 7 16,000 7 16,000 12 12 14,000 11 11,000 13 11 19,500 9 15,000

Customer Line

Items Dollar Value

Line Items Shipped

on Schedule

Dollar Value

on Schedule

14 5 8,000 5 8,000 15 5 6,000 5 6,000 16 7 12,000 6 11,500 17 16 28,000 15 24,500 18 11 12,000 11 12,000 19 9 17,500 9 17,500 20 3 7,500 3 7,500 21 4 11,000 4 11,000 22 8 12,000 8 12,000 23 20 48,000 19 44,000 24 1 2,500 1 2,500 25 12 9,000 12 9,000

Totals 225 417,000 212 392,800

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Innventory Management at Cruise International, Inc. In this assignment you will work with Andrew Jaworski, the cruise ship’s Medical Officer. He has a special offer from a supplier for a disposable syringe filled with a premeasured dos- age of medicine to alleviate motion sickness, which needs to be evaluated. He has provided you with all the necessary informa- tion needed to analyze the quantity discount offered by the sup- plier. One additional concern comes from Peggy Johnson, the Corporate Medical Officer. The Food and Drug Administration (FDA) is currently testing a new motion sickness medicine that would make the other medicine obsolete. She believes there is a 10 percent chance of the new medicine receiving FDA approval

and that we will know in approximately nine months. Your job is to make a recommendation regarding this quantity discount offer. This assignment will enable you to enhance your knowl- edge of the material in Chapter 12 of your textbook and prepare you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Inventory Management at CII

On-line Case: Inventory Management at Valley Memorial Hospital Assignment: Independent Demand Inventory Man-

agement “This assignment just came up yesterday,” says Meg Willoughby, head of Material management at VMH. “We’ve been purchasing 600 cholesterol-testing kits for the lab here every three months. Yesterday, our supplier called and offered us a discount on the price per kit if we purchase in greater bulk. One complication is that Peggy Dundee in the lab says that a new cholesterol kit being tested by the Food and Drug Admin- istration might make the current kits obsolete. So I’m not sure whether to take the deal or not and could use your help. Of course, you’ll want precise information about costs and such, so

let’s sit down in the conference room and I’ll show you every- thing you need to know.”

To complete this assignment, go to www.wiley.com/ college/reid to get the details needed. Assignment questions are given at the site.

To access the Web site:

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ONLINE RESOURCES • 503

Online Resources

Companion Website www.wiley.com/college/reid: • Take interactive practice quizzes to assess your knowledge and help you study in a dynamic way • Review PowerPoint slides or print slides for notetaking • Download Excel Templates to use for problem solving • Access the Virtual Company: Cruise International, Inc. • Access the Virtual Company: Valley Memorial Hospital • Find links to Company Tours for this chapter

Coffman Stairs—Division of Visador Company Canadian Springs Water Company

• Find links for Additional Web Resources for this chapter Coffman Stairs, www.coffmanstairs.com/about.htm Folbot, www.folbot.com/plant.tour.html Universal Screenprinting, www.simon.ca/simonfr.htm

Your nonprofit club holds a major fund-raiser for two weeks each year to support community improvement projects. The club sells packages of cookies throughout the community and donates the proceeds. The goal of the event is to raise at least $40,000 for the community. This year you are in charge of the fund-raising event. Your first step is to search the Internet and identify at least three potential suppliers of the cookies to be sold this year. At least one of the suppliers should be in the immedi- ate vicinity of your town or city.

From past fund-raisers, the club believes that an acceptable price of the cookies to the customers does not allow for more than a $1 markup over the regular cost per package. However, if quantity discounts can be obtained, then the profit per package can exceed $1. It is believed that regardless of the cookies sold, demand will be 40,000 packages. If you decide to buy more than 40,000 packages, any leftover cookies will be donated to local

shelters. Since you are a nonprofit organization, no tax advan- tage is gained.

For each of the potential suppliers, you need to identify the total cost associated with buying the packages of cookies. Be sure to consider transportation costs as well as any quan- tity discounts. Remember that your objective is to raise at least $40,000 for the community. It is also important to consider the logistics of your plan. Will all of the cookies arrive at one time or will deliveries be spread over the two-week fund-raiser? Find out how far in advance you need to place your order and when payment for the cookies is due. Explain how you can be sure the cookies will arrive on time. You need to put together a report for your next meeting comparing your three suppliers and make a recommendation as to which supplier should be used, the quan- tity of cookies to purchase, the expected profit to be donated, and the logistics for the fund-raiser.

INTERNET CHALLENGE Community Fund-Raiser (A)

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504 • CHAPTER 12 INVENTORY MANAGEMENT

Selected Bibliography “An Inventory Control System Th at’s Fast, Accurate, Reliable,

and Simple to Use”. http://en.scannabar.com/ Arnold, J.R. Tony, Stephen N. Chapman, and Lloyd M. Clive.

Introduction to Materials Management, Seventh Edition. Upper Saddle River, N.J.: Pearson Education Limited, 2012.

Buff a, Elwood S., and Jeff rey G. Miller. Production-Inventory Systems: Planning and Control, Th ird Edition. Homewood, Ill.: Irwin, 1979.

Cox, James F., III, John H. Blackstone, and Michael S. Spencer, eds. APICS Dictionary, Eleventh Edition. Falls Church, Va.: American Production and Inventory Control Society, Inc., 2005.

Fogarty, Donald W., John H. Blackstone, and Th omas R. Hoff - man. Production and Inventory Management, Second Edi- tion. Cincinnati, Ohio: South-Western Publishing, 1991.

Inventory Management Reprints. Falls Church, Va.: American Production and Inventory Control Society, 1993.

Love, Stephen F. Inventory Control. New York: McGraw-Hill, 1979.

“RFID Chips in Hotel Towels? What’s Next?” http://www. securitymagazine.com/articles/82047-security-blog-2011- 05-15-rfi d-chips-in-hotel-towels.

Vollmann, Th omas E., William L. Berry, D. Clay Whybark, and F. Robert Jacobs. Manufacturing Planning and Control Sys- tems, Fift h Edition. Burr Ridge, Ill.: McGraw-Hill/Irwin, 2005.

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CHAPTER

MKT

W H A T ’ S I N O M F O R M E ?

FIN OM MISACC HRM

505

L E A R N I N G O B J E C T I V E S

C H A P T E R O U T L I N E

The Role of Aggregate Planning 507 Types of Aggregate Plans 509 Aggregate Planning Options 511 Evaluating the Current Situation 514 Developing the Aggregate Plan 515 Aggregate Plans for Companies with

Tangible Products 517

Aggregate Plans for Service Companies with Nontangible Products 520

Aggregate Planning within OM: How It All Fits Together 524

Aggregate Planning across the Organization 525

Before studying this chapter, you should know or, if necessary, review

1. Competitive priorities, Chapter 2, pp. 37–43. 2. Capacity management concepts, Chapter 9, pp. 333–339. 3. Work standards, Chapter 11, pp. 422–428. 4. Relevant inventory costs, Chapter 12, pp. 459–461. 5. Order quantity models, Chapter 12, pp. 467–480.

After studying this chapter you should be able to

1 Explain business planning. 2 Explain sales and operations planning. 3 Identify diff erent aggregate planning strategies and options for changing demand and/or capacity

in aggregate plans. 4 Develop aggregate plans, calculate associated costs, and evaluate the plan in terms of operations,

marketing, fi nance, and human resources. 5 Describe the diff erences between aggregate plans for service organizations and manufacturing

companies.

Aggregate Planning

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506 • CHAPTER 13 AGGREGATE PLANNING

As a student, you have a limited amount of hours available for you to study. You prob- ably prepare for an exam in one of two ways. You wait until the night before the exam to cram three to five weeks of studying into one night. Or you regularly review your notes, maybe two or three times a week, and just do your normal review the night before the exam. While cram- ming might work if you have only a single exam to prepare for, sometimes that is not the case. Changing demands on your fixed amount of time (capacity) can create problems in achieving your objectives.

Companies also often have limited capacity to handle changing demands and typically take one of these two approaches or a com- bination of the two approaches to satisfy demand fluctuations. Companies providing perishable products or services are often forced to wait until the very last possible minute in order to assure fresh products or timely services. For example, consider how G’s Naturally Fresh, a salad and vegetable-growing company based in the United Kingdom, must handle both seasonal demand and seasonal production.

Lettuce is a perishable item and must be kept refrigerated prior to shipment and during transport. Even then, the product stays fresh for only one week. In northern Europe, demand for lettuce occurs year-round, decreasing during the winter to about half the summer demand. However, lettuce cannot be grown outdoors during the winter months, and greenhouse cultivation is considered to be too expensive.

G’s Naturally Fresh responded to the problem by buying a farm and packaging facility located in southeastern Spain to provide the lettuce needed during the win- ter. The lettuce is transported daily to the United Kingdom by a fleet of refrigerated trucks. When demand is higher than expected, the picking rigs and their crews pick into the middle of the night using floodlights. Staffing is a problem for G’s Naturally Fresh. The UK operation maintains a permanent full-time staff while the Spanish workforce is primarily temporary, with very few employees working through the summer season.

Companies typically use a planning approach that tries to level the workload, or they try to change capacity to meet demand fluctuations. Companies trying to level the workload carry inventory, use back orders, and try to level demand. Companies changing capacity to meet demand fluctuations use overtime, undertime, hiring and firing, and/or subcontracting.

The level and timing of resources for production are detailed in a company’s aggregate plan. The master production schedule determines how those resources are to be used. Let’s look at the role of aggregate planning in your company’s strategic business plan.

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THE ROLE OF AGGREGATE PLANNING • 507

THE ROLE OF AGGREGATE PLANNING Aggregate planning is an integral part of the business planning process. This process begins when your company’s top management gathers input from finance, marketing, operations, and engineering to develop a strategic business plan. The strategic business plan, with its long-term focus, provides your company’s direction and objectives for the next 2 to 10 years. The strategic business plan is normally updated and reevaluated annu- ally. The strategic business plan is also the starting point for sales and operations planning. It states the company’s objectives for profitability, growth rate, and return on investment.

Sales and operations planning integrates the medium-range functional plans developed by marketing, operations, engineering, and finance. Sales and operations planning begins with the marketing plan developed by the marketing group based on information shared with operations, finance, and engineering.

Marketing Plan The marketing plan is intended to meet the objectives of the strategic business plan. The marketing plan identifies the sales needed to achieve the profitability level, the growth rate, and the return on investment stated in the strategic business plan. Detailed in the marketing plan are the targeted market segments; necessary market share; competitive focus such as price, quality, flexibility, or time; expected profit margins; and any new products needed.

If the marketing plan does not meet the strategic business objectives, top manage- ment and marketing management revise either the objectives or the marketing plan itself until it fully supports the strategic objectives.

Aggregate or Production Plan The aggregate plan, also called the production plan, identifies the resources needed by the operations group during the next 6 to 18 months to support the marketing plan. The aggregate plan details the aggregate production rate and the size of the workforce, which enables planners to determine the amount of inventory to be held; the amount of overtime or undertime authorized; any authorized subcontracting, hiring, or firing of employees; and back-ordering of customer orders. The aggregate plan is usually updated and reevaluated monthly by the operations group.

Your company normally develops the aggregate plan based on a composite product that represents the expected product mix (to minimize the level of detail, individual products are not represented in the aggregate plan). Companies may group products into major product families to facilitate aggregate planning. For example, if your com- pany produces several varieties of stereo equipment, you might have product families based on kinds of stereos. Product families could include home theater stereos, por- table stereos, or automobile stereo systems. Each family can include different items as long as each item has similar processing needs.

Regardless of the method, the goal is to reduce the number of calculations to develop the aggregate plan. Using a composite product, or product families, reduces the level of detail but still provides the information needed for decision making at this stage. Common terms of output used in the aggregate plan are units, gallons, pounds, standard hours, and dollars.

To summarize, the purpose of the aggregate plan is to develop production rates and authorize resources that accommodate the marketing plan and allow your company to meet the objectives of the strategic business plan. Figure 13-1 summarizes the business planning flow.

� Strategic business plan A statement of long-range strategy and revenue, cost, and profit objectives.

� Sales and operations planning The process that brings together all the functional business plans (marketing, operations, engineering, and finance) into one integrated plan.

� Marketing plan Identifies the markets to be served, desired levels of customer service, product competitive advantage, profit margins, and the market share needed to achieve the objectives of the strategic business plan.

� Aggregate plan Includes the budgeted levels of finished products, inventory, backlogs, workforce size, and aggregate production rate needed to support the marketing plan.

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508 • CHAPTER 13 AGGREGATE PLANNING

Financial and Engineering Plans The financial plan indicates the sources and uses of funds, expected cash flows, antici- pated profits, and projected budgets. The engineering plan supports the research and development of new products introduced in the marketing plan and subsequently planned for in the aggregate plan.

The sales and operations planning process evaluates the company’s performance regularly throughout the year. The process begins in sales and marketing with comparisons of real demand against forecasted demand. The forecast is updated and the market reevaluated. Based on the updated forecast, marketing communi- cates to the operations, finance, and engineering groups the proposed changes to the marketing plan and makes the changes all three groups agree on. The other groups adjust their plans accordingly. If operations, finance, or engineering cannot support the proposed changes to the marketing plan, marketing again revises the marketing plan.

Master Production Schedule The sales and operations plan is evaluated and updated monthly. The master pro- duction schedule and the detailed sales plan are reviewed weekly or even daily. The master production schedule is an anticipated production schedule and is typically stated as specific finished goods. It details how operations will use available resources and which units or models will be built in each time frame. This allows marketing to make informed commitments to customers. Master production scheduling and cus- tomer commitments are discussed in Supplement D.

� Financial plan Identifies the sources and uses of funds; projects cash flows, profits, return on investment; and provides budgets in support of the strategic business plan.

� Engineering plan Identifies new products or modifications to existing products that are needed to support the marketing plan.

� Master production schedule The anticipated production schedule for the company expressed in specific configurations, quantities, and dates.

FIGURE 13-1

The business planning hierarchy

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TYPES OF AGGREGATE PLANS • 509

A study done in 2010 by the Aberdeen Group (www.aberdeen.com) surveyed 196 companies with regard to sales and operations planning-related initiatives. The study revealed the following successes by companies considered best-in-class. These compa- nies reported an average forecasting accuracy at the product family level of 82%; perfect orders delivered to customers (complete and on-time) at 97%; cash-to-cash cycle time reduced by 0.3% year over year; and gross profit margin increased by 48.1%. Companies considered to be in the bottom 30% of the aggregate performance scorers were classified as laggards. When compared to the best-in-class companies, laggards performed poorly. For the laggards, average forecast accuracy was 51%, only 69.1% of orders were delivered to customers complete and on time, the cash-to-cash cycle actually increased by 2.4% year over year, and the gross profit margin improved at 25.4%. The improvement in gross profit margin for the laggards was likely a result of the economic rebound from the 2008–2009 recession.

Companies reported the key pressures for improving sales and operations plan- ning were to improve top-line revenue; reduce supply chain operation costs; man- age increasing demand volatility; customer mandates for faster, more accurate, and more unique fulfillment; and the need to use manufacturing assets with maximum efficiency. Let’s look at how sales and operations has been used by Coca-Cola Midi.

Coca-Cola Midi (CCM), a regional manu- facturing division of Coca-Cola located in France, produces soft-dink concentrates and juice beverage bases for Europe, Asia, and Africa. CCM manages more than 700 SKUs, creating more than 79,000 tons of product valued at hundreds of millions of sales dollars.

At CCM, 72 percent of its product volume is produced by third-party juice processors. These third-party providers deliver juice directly to Coca-Cola bottling operations, third-party packers, and other customer locations.

CCM has used both enterprise resources planning (ERP, discussed in Chapter 14) and sales and operations planning since opening in 1991. Sales and operations planning is the basis for all planning, manufacturing, and supply chain activities. CCM begins working on its monthly sales and operations planning update 10 days before the end of the month, when preliminary demand data are updated. These data are adjusted over the next two weeks, with the new sales and operations plan completed four days into the new month.

Sales and operations planning enables disciplined and formalized communica- tions across the company and among suppliers, partners, and customers. Continuous improvements in customer service, inventory management, obsolete products, and freight costs have resulted from CCM’s use of sales and operations planning.

Now let’s look at different kinds of aggregate plans so we can see how the aggregate plan fits into the business planning hierarchy. We use the terms aggregate plan and production plan interchangeably.

LINKS TO PRACTICE Coca-Cola Midi (CCM) www.thecoca- colacompany.com

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TYPES OF AGGREGATE PLANS Level Aggregate Plan We categorize aggregate plans as level, chase, or hybrid plans. A level aggregate plan maintains a constant workforce and produces the same amount of product in each time period of the plan. Example 13.1 shows how to calculate the number of employ- ees needed to produce a specified output.

One advantage of a level production plan is workforce stability. Your company sets labor and equipment capacity equal to average demand, rather than hire excess labor

� Level aggregate plan A planning approach that produces the same quantity each time period. Inventory and back orders are used to absorb demand fluctuations.

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510 • CHAPTER 13 AGGREGATE PLANNING

Wavetop, Inc. currently has 10 employees, each producing 5 complete units per day, for a total of 50 units every workday. Calculate the number of employees needed in the company’s level aggregate plan if the company has an average weekly demand of 500 units and plans on satisfying all of its demand.

• Solution: If average weekly demand is 500 units and employees work five days per week, we need to produce 100 units per day. Thus, the workforce should be 20 employees (100 units needed each day divided by 5 units completed per employee per day). If we use this method, inventory accumulates when demand is below average and depletes when demand exceeds the average level. If we do not have enough inventory on hand to satisfy demand, then back orders result.

EXAMPLE 13.1

Calculating the Number of

Employees

or buy additional tools and equipment just to meet peak demand. In addition, the labor force is not subjected to varying work levels during the year, such as periods of layoff or undertime followed by periods of hiring and/or overtime.

The disadvantages of the level plan are the buildup of inventory and/or possible poor customer service from extensive use of back orders. The level plan is often used with make-to-stock products such as stereos, kitchen appliances, and hardware. Example 13.2 calculates the level workforce needed when demand varies throughout the planning horizon.

Calculating Average Monthly Net Demand for Wavetop, Inc.

Wavetop, Inc., a producer of water ski equipment, anticipates the following demand for its water skis. Demand for January is 12,000 units; for February, 9000; March, 12,000; April, 15,000; May, 18,000; and June, 24,000. The company has 6000 units in beginning inventory. Calculate the average monthly net demand for the company.

• Solution Summing the monthly demands, the company needs a total of 90,000 units during the next six months. Since Wavetop, Inc. already has 6000 units in inventory, net demand is 84,000 units. The company has six months to satisfy demand, so it must build 14,000 units monthly (84,000 units divided by six months � 14,000 units needed per month).

By calculating the amount of production needed each month, the company can plan the appro- priately sized workforce. If each employee can build 25 units per normal workday and the company operates 20 days per month, then each employee builds 500 units per month. To calculate the num- ber of employees needed, divide the number of units needed per month by the monthly output per employee (14,000 units divided by 500 units per employee � 28 employees needed).

EXAMPLE 13.2

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Chase Aggregate Plan A chase aggregate plan produces exactly what is needed to satisfy demand dur- ing each period. The production rate changes in response to demand fluctuations. Whereas the level aggregate plan sets capacity to accommodate average demand, the chase aggregate plan sets labor and equipment capacity to satisfy demand each period.

The advantage of the chase plan is that it minimizes finished goods holding costs. This may be a better option when a company produces make-to-order products such as custom cabinets, special-purpose equipment, one-of-a-kind items, or highly perish- able products. The disadvantages are constantly changing capacity needs and the need for enough equipment to meet peak demand. The additional equipment needed to

� Chase aggregate plan A planning approach that varies production to meet demand each period.

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AGGREGATE PLANNING OPTIONS • 511

meet peak demand creates excess capacity in nonpeak demand periods. Many options for short-term capacity changes are expensive. Example 13.3 shows how the workforce size fluctuates for Wavetop when a chase plan is used.

Hybrid Aggregate Plan A hybrid aggregate plan typically uses a combination of options. With this plan, your company might maintain a stable workforce supplemented by an inventory buildup and some overtime production to meet demand. Or the company may back-order a portion of its demand. Any combination of options is possible. Because of the number of options you can combine in a hybrid plan, you need to evaluate your company’s current situation and limit the options you choose from.

Next, let’s look at the demand-based and capacity-based options used in aggregate plans.

� Hybrid aggregate plan A planning approach that uses a combination of level and chase approaches while developing the aggregate plan.

AGGREGATE PLANNING OPTIONS Companies can choose from two groups of options when formulating an aggregate plan. The first group, demand-based options, includes two reactive options and one proactive option. These are

● Reactive options, in which the operations department uses inventories and back orders to react to demand fl uctuations.

● Th e proactive option, in which marketing tries to shift the demand patterns to minimize demand fl uctuations.

An example for the proactive option is the early-bird dinners offered by some res- taurants. The reduced price for a specific time period encourages customers to dine earlier and spreads the demand out over a longer period of time.

The second group, capacity-based options, changes output capacity to meet demand through the use of overtime, undertime, subcontracting, hires, fires, and part-timers or temps. These options are required when current capacity isn’t equal to current demand. Each of these offers relief for fluctuating demand, but each has cost and operational implications for the company. Let’s look at each option individually.

� Demand-based options A group of options that respond to demand fluctuations through the use of inventory or back orders, or by shifting the demand pattern.

MKT

� Capacity-based options A group of options that allow the firm to change its current operating capacity.

Let’s look at what would happen if Wavetop, Inc. decides to adjust its capacity by hiring and firing employees each month. We calculate the number of employees needed during each period based on the net demand.

• Solution: For example, January demand is 12,000 units, but since we have 6000 units in inventory, the net demand is only 6000 units. Each employee builds 500 units per month, so Wavetop, Inc. needs 12 employees (6000 units divided by 500 units per employee per month) in January. The company needs 18 employees in February, 24 employees in March, 30 employees in April, 36 employees in May, and 48 employees in June. How does this affect the space needed, the number of workstations, the sets of tools, and so forth?

When Wavetop, Inc. used a level aggregate plan, it needed space and equipment to accommodate 28 employees. With the chase aggregate plan, however, the company needs space and equipment for 48 people.

EXAMPLE 13.3

Chase Aggregate Plan at Wavetop, Inc.

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512 • CHAPTER 13 AGGREGATE PLANNING

Demand-Based Options Using finished goods inventory to absorb demand fluctuations allows your company to develop a stable work environment. The company produces at average demand levels throughout the year rather than change capacity from one period to the next. When demand is less than average, the extra units go into inventory. When demand exceeds production, the extra units come out of inventory. Producing at average demand levels allows your company to invest less in capacity.

As we saw earlier, when Wavetop, Inc. used a level aggregate plan using inventory, the company needed space and equipment for only 28 employees. With the chase aggregate plan using hires and fires, the company needed space and equipment for 48 employees. However, the stable working environment isn’t free. The company does have increased inventory holding costs because inventory is built and warehoused in anticipation of future demand. Inventory holding costs range from 15 to 35 percent of the cost of the inventory. Unfortunately, some companies with highly seasonal sales may have no choice but to use this option. For example, companies that make holiday products often experience 60 to 90 percent of their demand over a short period of time (one or two months out of the year). Producing the majority of its annual demand at the last minute could mean enormous machine and labor capacity, which would be underused or even idle the rest of the year.

Back orders result when your company does not have enough production and/or inventory on hand to cover current demand, so it promises to deliver the product to the customer at a later date. Customers may wait or they may take their business else- where. When a customer is unwilling to wait, the back order turns into a lost sale. Your company must understand its customers and its marketplace to judge whether or not back orders are a viable option. If alternative products or sources are readily available, customers probably will not wait. They are more likely to wait for unique products.

Unique has different meanings. Perhaps your company is the sole producer of a certain product, the reputation of your company merits waiting, or the price is much lower than for any substitute product. Whatever your reason for choosing back orders as an option, it must be a good one if you expect a customer to wait for your product.

In addition to possible lost sales, your company may have extra administrative costs because of the back order, such as higher shipping costs for overnight delivery. (These costs are discussed in Chapter 12.) In general, the back order should be used sparingly.

Shifting demand is a proactive marketing approach to leveling demand in which your company tries to change consumer buying patterns by offering incentives. Prime examples of this are movie matinees, early-bird dinners, and preseason or off-season discounts. In most cases, there is no out-of-pocket cost to the company—the profit is simply less per sale. This strategy makes sense when the company has high fixed costs and low variable costs. For example, a movie theater has a high fixed cost (the building and the projection equipment) but is empty during most of a 24-hour day. The variable cost for showing the movie is quite low. A matinee allows the theater to make better use of its capital investment.

Another way to level demand is offering preseason or off-season discounts. Shifting some of the company’s demand means that less inventory is needed to satisfy demand during the prime season. Thus the inventory investment is lower, less floor space is needed, and some customers are pleased by the discount.

Capacity-Based Options Overtime is the most common method for increasing output capacity. It is an expen- sive option, however, and should only be used short-term. Using overtime to increase output typically means your company pays a 50 percent wage premium to its workers.

� Finished goods inventory Products available for shipment to the customer.

� Back orders Unfilled customer orders

MKT

� Shifting demand A marketing strategy that attempts to shift demand from peak periods to nonpeak periods to smooth out the demand pattern.

� Overtime Work beyond normal established operation hours that usually requires a premium be paid to the workers.

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AGGREGATE PLANNING OPTIONS • 513

Unfortunately, when people work overtime, their productivity does not increase pro- portionately, so the cost of labor per unit increases. Workers typically do not produce more during overtime.

Worker productivity—and the quality of the work—may even decrease. In fact, the more overtime a company uses, the more likely it will experience reduced productiv- ity and quality. Reduced productivity and quality tend to increase costs even more. Therefore, overtime is at best a short-term option for increasing production capacity.

Undertime results when a company does not need an employee to produce at 100 percent of his or her capability. Undertime is normally the result of reduced demand and a desire not to build up inventory levels. Undertime does not cost a company a wage premium, but it does increase the labor cost per unit, because fewer units are built but regular-time wages stay the same.

Why would a company keep employees around if they are not needed? The answer is straightforward: economics. It may be cost-effective to carry valuable workers for a short period of time if the company expects demand to return to previous levels. If the company releases employees immediately, it may incur high replacement costs when it eventually hires new employees. The problem with undertime is that employee morale may suffer when people realize that there is not enough work to keep everyone busy. Thus undertime is also a short-term option.

Subcontracting means letting another company do some of the work for you. Subcontracting provides additional output capacity during periods of high demand. Unlike strategic outsourcing decisions that have components, subassemblies, or final products previously done in-house instead produced by another company, subcon- tracting is a tactical decision as to how to increase output in periods of high demand. For example, a publishing company may choose to outsource technical information development (a task requiring technical writing expertise) and have no in-house capa- bility. Or a publishing company may need to subcontract additional technical infor- mation development because the in-house group has more work than it can handle. Outsourcing decisions identify the core business that the company is in. Subcontract- ing decisions provide extra capacity for the company.

The advantages of subcontracting are additional output without investing in addi- tional tools, equipment, and labor. The disadvantage of subcontracting is the cost, which is substantial. The first step is to find a qualified subcontractor for the job. Then you have the additional cost of shipping parts to the subcontractor and having finished subassemblies or products shipped back to you. In addition, subcontracting means your company loses some degree of control. By contrast, work done in-house is always under your control: you know exactly where it is and how it is progressing. Since finding a good subcontractor takes time, subcontracting is a medium- to long- term option.

Hiring and firing changes the size of the workforce. Both hiring and firing can mean high costs for your company. Hiring requires the administrative work of identi- fying the position, communicating with potential applicants, evaluating the materials submitted by applicants, interviewing, verifying employment and references, running background checks, making decisions, verifying physical condition, making offers, and completing negotiations. After the employee is hired, your company sets up pay- roll, health insurance, security ID and badge, computer log-in, phone extension, and so forth. During employee training, output is normally lower and mistakes are typi- cally higher. Thus hiring a permanent employee is a long-term option.

Firing employees is also expensive. Excessive firing can lead to increased unem- ployment compensation premiums. (Unemployment compensation is like any insurance policy: when you have a lot of claims, your rates increase.) Add to that the

� Undertime A condition occurring when there are more people on the payroll than are needed to produce the planned output.

� Subcontracting Sending production work outside to another manufacturer or service provider.

ACC

� Hiring and firing Long-term option for increasing or decreasing capacity.

ACC

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514 • CHAPTER 13 AGGREGATE PLANNING

severance pay that companies typically pay to permanently laid-off employees, and the cost mounts. Significant, too, is the expense in lost knowledge when you termi- nate employees. Employees may leave with individual know-how that the remaining workforce will have to learn for themselves. For example, your employee may not have documented a minor change to the job instructions that increases productivity or improves product quality. The next employee on the job will have to learn this secret of improved output.

Finally, you have the cost in morale, which may affect productivity. Deciding who will stay and who will go is not a pleasant exercise. Some companies base the decision on seniority rather than what makes sense from an operational standpoint. When senior employees remain and junior employees are forced out, the remaining employ- ees may return to jobs that have experienced major technological change. Thus respect for seniority rewards company loyalty but often at the expense of lost productivity.

For all of these reasons, hiring and firing employees is in the category of long-term options. Table 13-1 summarizes aggregate planning options.

Demand-Based Options Capacity-Based Options

• Inventory • Overtime/undertime

• Back orders • Subcontracting

• Shifting demand • Hiring and firing

TABLE 13-1

Summary of Aggregate Planning Options

Before You Go On

Make sure you understand the two kinds of options used in aggregate planning: demand-based options and capacity-based options.

Demand-based options stabilize capacity and react to demand fluctuations through the use of inventories and back orders, which may enable you to shift some demand to make the demand pattern smoother. Still, the longer you use either inventories or back orders, the riskier they become. Consumer preferences change over time, and demand for your product may erode. Demand-based options are used primarily with level aggregate plans.

Capacity-based options are used to change capacity levels. Some capacity-based options are short-term, such as overtime and undertime; some are medium-term, such as subcontracting or using temporaries; and some are long-term, such as hiring or firing employees.

Your company should choose the option that satisfies the time frame needed (short, medium, or long) for the capacity change. Capacity-based options are used primarily with chase aggregate plans; all options can be used with hybrid aggregate plans.

Now that you understand the options and the kinds of aggregate plans, let’s consider some additional factors in developing aggregate plans.

EVALUATING THE CURRENT SITUATION When you are considering the different options, it is important to evaluate your com- pany’s current situation in terms of point of departure, magnitude of the change, and duration of the change.

The point of departure is the percentage of normal capacity your company is currently operating at. For example, if you are operating at 100 percent of normal capacity and need to increase capacity by 10 percent, you might use a relatively simple option such as overtime to achieve that 10 percent extra capacity. If you are already

� Point of departure The percentage of normal capacity the company is currently using.

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DEVELOPING THE AGGREGATE PLAN • 515

using overtime—say, you are operating at 125 percent of normal capacity—you might look for a different way to increase capacity. At this point, subcontracting or hiring temporary workers might be more economical. If you need to increase capacity even more, maybe it is time to hire some new permanent workers. The same is true when you need to reduce capacity. If you are making a small reduction—perhaps down to 90 percent of normal capacity—you might decide to use undertime. For even greater reduction, you might cut back hours or furlough employees. If the need to reduce is still greater, you might choose permanent layoffs. Thus point of departure affords your company perspective on the best options.

Magnitude of the change is the size of change needed. Smaller changes may be easier to implement—as we saw when Wavetop, Inc. used the chase aggregate plan and increased its workforce from 12 to 48 employees in a six-month period. Larger capacity changes need more drastic measures, such as hiring or firing a shift, and the effects on productivity are greater.

Duration of the change is the length of time you expect to need the different level of capacity. If the duration is a brief seasonal surge, then hiring temporary or seasonal workers makes sense. For example, many retail stores hire additional clerks during holiday seasons. Some of these employees work for several years at the same store. When you expect the increased need for capacity to be permanent, a long-term solu- tion like subcontracting or hiring new employees is more appropriate.

Evaluating the point of departure, magnitude of change, and duration of change allows your company to reduce the number of viable options for its aggregate plan.

When companies face highly seasonal demand, an alternative to hiring full-time permanent employees is the use of seasonal employees. UPS experiences highly seasonal demand each year, delivering approximately 300 mil- lion packages globally during the four weeks between Thanksgiv- ing and Christmas. To meet this high demand, UPS hires around 90,000 part-time seasonal employ- ees. That number includes 50,000 package handlers, who load and unload packages; 34,000 driver helpers; 2400 seasonal drivers for the delivery vans; and 850 tractor-trailer drivers. UPS hires and trains these employees each year. These trained, seasonal employees are often considered for permanent part-time jobs with UPS after the holidays. UPS has suc- cessfully implemented the use of seasonal workers to achieve its corporate strategy. As a result, UPS was recognized by Your Money magazine as providing the best part-time jobs in the nation.

� Magnitude of the change The relative size of the change needed.

� Duration of the change The expected length of time the different capacity level is needed.

LINKS TO PRACTICE UPS Hires Seasonal Workers www.ups.com

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DEVELOPING THE AGGREGATE PLAN Here are the steps in developing an aggregate plan:

Step 1 Identify the aggregate plan that matches your company’s objectives: level, chase, or hybrid.

Step 2 Based on the aggregate plan, determine the aggregate production rate.

● If you use the level plan with inventories and back orders, the aggregate produc- tion rate is set equal to average demand. In addition, if you allow no back orders, the size of the workforce is changed initially so that all demand is met on time.

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516 • CHAPTER 13 AGGREGATE PLANNING

● If you use the chase aggregate plan, calculate how much output capacity you need each period. Calculate how many units will be produced on regular time and over- time and how many units will be subcontracted.

Step 3 Calculate the size of the workforce.

● If you use the level aggregate plan, calculate how many workers you need to achieve the average production rate needed.

● If you change capacity each period with hires and fi res, calculate how many work- ers you need each period and make the necessary change in the workforce.

● If you change capacity through a variety of options, calculate how much of a par- ticular option you need each period.

Step 4 Test the aggregate plan.

● Using the production rate and initial workforce size, calculate your inventory lev- els (excesses and shortages), any shortages you face, expected number of employ- ees hired and fi red, and when you will need overtime.

● Calculate the total costs for your plan.

Step 5 Evaluate the plan’s performance in terms of cost, customer service, human resources, and operations.

After you develop a plan, it is critical to evaluate it in terms of cost, customer service, operations, and human resources. Cost comparisons are simple if you are comparing similar ending positions—that is, plans with the same ending inventory level or pro- ducing the same number of units.

The comparisons are less clear when plans produce different quantities and leave different ending inventories. In this case, you can use a per unit cost comparison. To do this for customer service, measure how many back orders were placed during each period and throughout the duration of the plan. Decide whether this is an acceptable level of customer service to satisfy marketing’s objectives. Assess the plan first in terms of operations, then in terms of human resources. Are the workers putting in excessive overtime one month and doing little the next? How does this plan affect the work- force? Does it lower morale or does it provide stability for the workers?

When you evaluate the plan from several perspectives, you can decide how it can best satisfy your company’s objectives. Table 13-2 shows you how to do this. Table 13-3 summarizes the steps to develop an aggregate plan.

Perspective Measurements

Cost Total cost Unit cost Inventory levels

Customer service level Number of back orders

Operations Stability of schedule Equipment utilization Labor use

Human resources Effect on workforce Employment stability

TABLE 13-2

Evaluation Perspectives and Measurements

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AGGREGATE PLANS FOR COMPANIES WITH TANGIBLE PRODUCTS • 517

Now let’s develop a couple of aggregate plans so we can evaluate their effectiveness. We use the problem data in Table 13-4.

Before You Go On

Review the five steps to develop an aggregate plan:

TABLE 13-3 Steps to Develop an Aggregate Plan

Step 1: Identify the type of aggregate plan: level, chase, or hybrid. Step 2: Calculate the aggregate production rate. Step 3: Calculate the size of the workforce. Step 4: Test the plan and calculate costs. Step 5: Evaluate the plan in terms of cost, customer service, operations, and human resources.

AGGREGATE PLANS FOR COMPANIES WITH TANGIBLE PRODUCTS

TABLE 13-4

Data for Sophisticated Skates

4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

A B Cost Data

Regular-time labor cost per hour $15.00 Overtime labor cost per hour $22.00

Hiring cost per employee $500.00 Firing cost per employee $750.00

Inventory holding cost per unit per period $5.00 Shortage cost per unit per period $7.50

Material cost per unit $30.00

Capacity Data Beginning workforce (employees) 18

Beginning inventory (units) 2500 Production standard per unit (hours) 0.64

Regular-time available per period (hours) 160 Overtime available per period (hours) 20

Demand Data (units) Nov 3000 Dec 6000 Jan 2000 Feb 8500 Mar 4000 Apr 5500

May 1500

Total Number of Periods 7

Sophisticated Skates produces a variety of inline roller skates. Management wants you to develop an aggregate plan that covers the next seven months. Develop an aggregate plan using a level production strategy (stable workforce throughout the plan, inventories, and back orders).

• Before You Begin: Be sure that you understand the data provided in Table 13-4. The labor costs are given as the hourly wage cost of one employee. If you are working regular time, you are paid $15.00 per hour. For overtime, you receive $22.50 per hour. Employees are hired or fired at the beginning of the plan. For level plans you can adjust your workforce at the beginning of the plan,

EXAMPLE 13.4Plan A: Level Aggregate Plan Using Inventories and Back Orders

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518 • CHAPTER 13 AGGREGATE PLANNING

and then it remains constant throughout the plan. The hiring and firing costs are per person. The inventory holding cost is assessed to the ending inventory for each period. In this case, there is a $5 holding cost per unit per period. The shortage or back-order cost is given as $7.50 per unit per period. The material cost used to build each unit is $30.00. In terms of capacity, the company currently has 18 employees. There is a beginning inventory of 2500 units. Every unit produced takes 0.64 labor-hours, and each period of the plan has 160 regular-time hours available for production from each employee. Each employee can work up to 20 hours of overtime each period. The demand data, given in units, indicates total demand for the plan of 30,500 units. We are concerned with the net cumulative demand, so we subtract the beginning inventory of 2500 units, leaving us with a net cumulative demand of 28,000 units.

Since the type of aggregate plan has been identified (level), you need to understand how that affects your options. A level plan has the same output every period, whether you define your period as a month, quarter, week, or day. You must first determine what the aggregate production rate must be for each period. Then you will check to see how many employees are required to produce that number of units each period. At this point you are ready to try out your plan. Then you show what happens each period. Next you cal- culate the costs of your plan. And finally, you evaluate your plan in terms of customer service, costs, operations, and human resources.

• Solution: Step 1 Identify the type of aggregate plan. Th is is given as a level production strategy, using a constant workforce, inventories, and back orders to satisfy demand. Step 2 Calculate the aggregate production rate. You calculate the aggregate production rate by dividing the net cumulative demand by the number of periods in the plan. Net cumulative demand is the total demand for the plan less any beginning inventory. In this case, the aggregate production rate is 4000 units (28,000 units demanded divided by 7 periods). Step 3 Calculate the workforce. Th e workforce is the aggregate production rate (4000 units) divided by the number of units per employee per period produced on regular time. Each employee works 160 regular-time hours per period; each unit requires 0.64 hours of labor time. Th erefore, each employee produces 250 units per period on regular time. You need 16 employees (4000 units divided by 250 units per employee). Since the current workforce has 18 employees, you need to fi re 2 employees. Step 4 Test and cost the plan. Th e completed plan is shown in a spreadsheet in Table 13-5. Note that demand for November has been reduced from 3000 units to 500 units. Th is is because the beginning inventory is netted out in the fi rst period of the plan.

3 4

1 2

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

36 35

E F G H I J K L M

Solution for Level Aggregate Plan Using Inventories and Back Orders

Compute Level Production Rate Total Demand 30500

Less: Beginning Inventory 2500 Total Net Demand 28000

Average Demand per Period 4000

Compute Workforce Needed Units per Worker per Period 250

Worker Needed 16 Number to Hire 0 Number to Fire 2

Detailed Plan Computations Nov Dec Jan Feb Mar Apr May

Demand (units) net of beginning inventory 500 Cumulative demand (units) 500

Production per period (units) 4000 28000 Cumulative production (units) 4000

Cum. Dem. Minus Cum. Prod. (units) -3500 Ending inventory (units) 3500 8500

Back orders (units) 0

6000 6500 4000 8000 -1500 1500

2000 8500 4000

12000 -3500 3500

8500 17000 4000

16000 1000

0 1000

4000 21000 4000

20000 1000

0 1000

5500 26500 4000

24000 2500

0 2500

1500 28000 4000

28000 0 0 0 4500

Cost Calculations for Plan Regular-time labor cost $268,800

Materials cost $840,000 Inventory holding cost $42,500

Back-order cost $33,750 Hiring cost $0 Firing cost $1,500 Total cost $1,186,550

Period

TABLE 13-5

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AGGREGATE PLANS FOR COMPANIES WITH TANGIBLE PRODUCTS • 519

In the spreadsheet, notice the “Cum. Demand Minus Cum. Prod.” row. Th is is shown merely to illustrate whether or not there is excess inventory or whether there is a shortage (back order). For example, in November, net demand was 500 units and the company produced 4000 units, leaving 23500 units in cell F24. A negative number here means that you have produced more than is currently demanded, or you have excess inventory (as shown in cell F25). If the number in row 24 is positive (cell I24), it means that you do not have enough inventory to meet demand and you must back-order units.

The bottom section of the spreadsheet calculates the costs of your plan. In this case, the regular-time labor cost is regular-time hourly rate times number of regular hours per period times number of periods in plan times number of employees ($15.00 � 160 hours � 7 periods � 16 employees). Alternatively, since all of the regular-time hours were used productively, you could multiply the number of units built (28,000) times the cost to build 1 unit ($15.00 � 0.64 hour required to build 1 unit), or 28,000 � $15.00 � 0.64 hour. The total material cost is calculated by multiplying the 28,000 units built by the material cost per unit. Inventory cost is calculated by multiplying the ending inventory in units for each period by the period holding cost. The same is true for any back orders. Step 5 Evaluate the plan in terms of customer service, costs, operations, and human resources. We won’t compare costs yet since this is the only plan. We can look at customer service, noting that we experience back orders in February, March, and April. Th e fi ll rate (the number of customers satisfi ed out of the total demand) for this plan is 83.9 percent (23,500 units satisfi ed when demanded divided by a total demand of 28,000 units). Th is is likely too low a fi ll rate. Other than that, the inventory levels seem to be okay, assuming there is room to store up to 3500 units. From a human resources perspective, we fi red two employees initially, but the number remained stable for the rest of the plan.

A chase strategy is illustrated in Example 13.5.

Using the same problem data as Example 13.4, develop a chase aggregate plan using hires and fires, but no overtime production.

• Before You Begin: Since we are using a chase strategy that only allows hiring and firing, we need to determine how many employees are needed to satisfy the net demand for each period. We can do that by dividing net period demand by the number of units one employee can make per period on regular time. Once we determine the number of employees needed, we either hire or fire based on our requirements.

• Solution: Step 1 Identify the type of aggregate plan. Th is is given as a chase aggregate plan. Remember that a chase plan has no ending inventory for any period. Th is problem limits you to hiring and fi ring employees as a means of adjusting your production output. Step 2 Calculate the aggregate production rate. In a chase plan, the aggregate production rate is equal to each period’s net requirements. Remember that a pure chase plan allows no ending inventory. However, in this example there is some beginning inventory that must be netted out. Aft er netting out the initial inventory, no other period will have a beginning inventory. Th e production rate for each period is shown in Table 13-6.

EXAMPLE 13.5Plan B: Chase Aggregate Plan Using Hiring and Firing

38 39

40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

E F G H I J K L M

Solution for Chase Aggregate Plan Using Hiring and Firing (no overtime)

Beginning number of employees 18 Units per Worker per Period 250

Detailed Plan Computations Nov Dec Jan Feb Mar Apr May

Demand (units) net of beginning inventory 500 500Production per period (units)

2 28000

Employees needed in period 0

6000 6000

24 22 0

2000 2000

8 0

8500 8500

34 26 0

4000 4000

16 0

5500 5500

22 6 0

1500 1500

6 112 0

16 Number to hire Number to fire

54 66

Cost Calculations for Plan Regular-time labor cost $268,800

Materials cost $840,000 $27,000Hiring cost

Firing cost $49,500 Total Cost $1,185,300

Period

TABLE 13-6

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520 • CHAPTER 13 AGGREGATE PLANNING

Step 3 Calculate the workforce needed each period. Th e number of workers needed equals the period production rate divided by the number of units produced per employee per period. Confi rm the calculations shown in Table 13-6. Step 4 Test and cost the plan. Th e completed plan and costs are shown in Table 13-6. Step 5 Evaluate the plan. On the basis of cost, this plan is slightly less expensive than the level plan. However, you should be concerned about whether you have captured all the costs associated with the chase plan. For example, in the chase plan, the number of employees required per period ranges from 2 in November up to 34 in February, which means you need space and equipment for 34 employees. However, space and equipment are underutilized in every other month of the plan. Even in December, the next busiest period, you need only roughly 70.6 percent of capacity (24/34). In other months, utilization is even lower. Chase plans with extreme ranges of output oft en waste capacity. Th e impact of this chase plan on morale would be signifi cant. Employees would never be certain of their job. And this plan could not be used if the employees needed any signifi cant level of skills.

Additional aggregate plans for companies with tangible products can be found in the Solved Problems at the end of the chapter. These plans provide a chance to con- sider additional aggregate planning alternatives.

AGGREGATE PLANS FOR SERVICE COMPANIES WITH NONTANGIBLE PRODUCTS In the previous examples, your company used inventory buildup as a way of leveling the aggregate plan. When your company’s product is nontangible—for example, if your company offers a service as do banks, healthcare providers, and hair stylists— inventory is no longer a viable option. We use the problem data shown in Table 13-7 to develop a level aggregate plan.

4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

26 27

A B Cost Data

Regular-time labor cost per hour $8.00 Overtime labor cost per hour $12.00

Subcontracting cost per unit (labor only) $60.00 Hiring cost per employee $250.00 Firing cost per employee $150.00

Capacity Data Beginning workforce (employees) 60 Service standard per call (hours) 4

Regular-time available per period (hours) 160 Overtime available per period (hours) 24

Demand Data (calls) Period 1 2400 Period 2 1560 Period 3 1200 Period 4 2040 Period 5 2760 Period 6 1680 Period 7 1320 Period 8 2400

Total Number of Periods 8

TABLE 13-7

Data for Plans C, D, and E

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AGGREGATE PLANS FOR SERVICE COMPANIES WITH NONTANGIBLE PRODUCTS • 521

With this plan, your company maintains a level workforce with no back orders. Any demand not satisfied is lost to a competing service provider, so the company must meet all demand.

• Before You Begin: Remember that level aggregate plans use inventories and back orders to handle demand fluctuations. In this problem, you do not have a tangible product, so you cannot use inventories. The problem also stipulates that no back orders are allowed to occur. You need to determine the workforce size needed to satisfy demand during the peak period. Since this is a level plan, you must maintain this size workforce throughout the entire plan. Level aggregate plans for companies without a tangible product that require 100 percent customer service will always set the staff level to meet peak demand.

• Solution: Step 1 Choose the kind of aggregate plan. In this example, we use a level plan. Step 2 Calculate the production rate. Since the company is not going to back-order, you staff to accommodate peak demand. Th e aggregate production rate is set equal to the highest demand in any period during the plan. Period 5 has 2760 service calls, which means 11,040 hours of regular-time labor must be available. Step 3 Calculate the size of the workforce. You need 69 employees (11,040 hours divided by 160 hours per employee per period). Each period, you have 11,040 hours of regular-time labor available. Step 4 Test and cost the plan. Table 13-8 shows Plan C using 69 employees and meeting demand each period. As you can see, this plan creates excess labor: only period 5 uses the total workforce. Period 3 uses a little over 43 percent of capacity (4800/11,040). Over the life of the plan, your company uses just under 70 percent of its available regular-time workforce (61,440/88,320). Table 13-8 shows the costs of the plan. An additional calculation not shown in the table is the cost per service call, which is $46.15 ($708,810 divided by 15,360 service calls).

Step 5 Evaluate the plan. We have no other plans for nontangible products for comparison yet, but it is likely that underuse of the regular-time workforce will make Plan C cost-prohibitive. It is also likely that the high undertime will lower employee morale. From an operational perspective, it might be better to keep the present workforce and supplement with overtime. When we use overtime, we reduce undertime. In fact, when we use the maximum amount of overtime permitted, we minimize undertime.

HRM

EXAMPLE 13.6 Plan C: Level Aggregate Plan with No Back Orders, No Tangible Product

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

D E F G H I J K L M N O P

Plan C: Level Aggregate Plan with No Backorders, No Tangible Product

Compute Workforce Needed Key Formulas (some are copied) Maximum Demand 2760 <-- Need to staff to meet the maximum number of calls $E$6 =MAX($B$18:$B$25)

Calls per Worker per Period 40 $E$7 =$B$14/$B$13 Workers Needed 69 $E$8 =E6/E7

Number to Hire 9 $E$9 =MAX(E8-$B$12,0) Number to Fire 0 $E$10 =MAX(B12-$E$8,0)

Detailed Plan Computations 1 2 3 4 5 6 7 8 Total

Demand (calls) 2400 1560 1200 2040 2760 1680 1320 2400 15360 $E$14 =TRANSPOSE($B$18:$B$25) Service hours needed 9600 6240 4800 8160 11040 6720 5280 9600 61440 $E$15 =E14*$B$13

Regular-time hours available 11040 11040 11040 11040 11040 11040 11040 11040 $E$16 =$E$8*$B$14 Undertime hours 1440 4800 6240 2880 0 4320 5760 1440 26880 $E$17 =E16-E15

Cost Calculations for Plan E Regular-time labor cost $706,560 $E$20 =E8*$B$14*$B$5*$B$27

Hiring cost $2,250 $E$21 =E9*$B$8 Firing cost $0 $E$22 =E10*$B$9 Total Cost $708,810 $E$23 =SUM(E20:E22)

Period

TABLE 13-8

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522 • CHAPTER 13 AGGREGATE PLANNING

Let’s look at a plan that uses some overtime.

Now we will develop a hybrid aggregate plan using a workforce of 60 employees working 160 regular-time hours per period. We use over- time when regular-time capacity is inadequate.

• Before You Begin: We have a workforce of 60 employees who provide 9600 regular-time hours each period. Calculate when overtime is needed and how much overtime should be authorized. Any period that requires less than 9600 hours to satisfy demand will not need overtime. In this case, the only time you need any overtime hours is in period 5. The amount of overtime is the differ- ence between the total time needed less the regular time available. When such a plan is used, you might expect to see considerable amounts of undertime.

• Solution: Step 1 Choose the kind of aggregate plan. We use a hybrid plan. Step 2 Calculate the production rate. In this plan, our regular-time aggregate production rate is the same as for Plan C because we are keeping the initial workforce of 60 employees. Th us we have 9600 regular-time hours available each period (60 employees � 160 hours per employee per period). Th e overtime needed in a period depends on the number of service calls expected. For each period, we calculate the number of hours needed to satisfy the service calls. For example, in period 4, we need 8160 hours to meet demand (2040 calls � 4 hours per call). Step 3 Calculate the size of the workforce. We know that the workforce is 60 employees working 160 hours of regular time each period. Step 4 Test and cost the plan. Table 13-9 shows the completed plan. We need overtime only in period 5; the initial workforce has more than enough capacity during the other periods. We calculate the overtime by subtracting the available regular-time hours (9600

hours) from the service hours needed in period 5 (11,040), which yields 1440 hours of overtime. Table 13-9 shows the total costs of regular-time labor and overtime labor. Step 5 Evaluate the plan. Th is plan reduces regular-time capacity from 88,320 hours in Plan C to 76,800 hours. Th us we increase regular-time labor use to 80 percent (61,440/76,800). In addition, the cost per call drops to $41.15. Th is is a major improvement over Plan C in terms of cost and customer service. However, it is still problematic in terms of the amount of undertime.

ACC

EXAMPLE 13.7 Plan D: Hybrid Aggregate Plan Using Initial Workforce and Overtime as Needed

26 27 28 29 30 31 32 33 34 35 36 37 38 39

D E F G H I J K L M N O P

Plan D: Hybrid Aggregate Plan Using Initial Workforce and Overtime as Needed

Detailed Plan Computations 1 2 3 4 5 6 7 8 Total

Demand (calls) 2400 1560 1200 2040 2760 1680 1320 2400 15360 $E$30 =TRANSPOSE($B$18:$B$25) Service hours needed 9600 6240 4800 8160 11040 6720 5280 9600 61440 $E$31 =E30*$B$13

Regular-time hours of capacity 9600 9600 9600 9600 9600 9600 9600 9600 76800 $E$32 =$B$12*$B$14 Overtime hours needed 0 0 0 0 1440 0 0 0 1440 $E$33 =MAX(E31-E32,0)

Undertime hours 0 3360 4800 1440 0 2880 4320 0 16800 $E$34 =MAX(E32-E31,0)

Cost Calculations for Plan F Regular-time labor cost $614,400 $E$37 =$B$12*$B$14*$B$5*$B$27

Overtime labor cost $17,280 $E$38 =M33*B6 Total Cost $631,680 $E$39 =SUM(E37:E38)

Period

TABLE 13-9

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AGGREGATE PLANS FOR SERVICE COMPANIES WITH NONTANGIBLE PRODUCTS • 523

Next, let’s see what happens when we develop a plan that eliminates undertime.

With this plan, your company reduces its undertime costs using hiring and firing.

• Before You Begin: This is a pure chase aggregate plan. Calculate exactly how many workers you need to satisfy demand each period. Once you have calculated the number of employees needed, either hire or fire as required.

• Solution: Step 1 Choose the kind of aggregate plan. In this example, we use a chase plan. Step 2 Calculate the production rate. We calculate the production rate based on the number of service calls each period, multiplied by the productivity rate of four hours per service call. Table 13-10 shows the production hours needed for each period. Step 3 Calculate the number of employees needed for each period. To do this, we multiply the number of service calls for each period by the time per service call (for example, in period 2, 1560 calls � 4 hours each call � 6240 hours needed). Divide the number of hours needed by the number of hours per employee per period (160 hours per employee) and determine that 39 employees are needed in period 2. Table 13-10 shows the appropriate workforce for each period. Step 4 Test and cost the plan. Table 13-10 shows the completed plan. We calculate the number of hires or fi res by comparing the number of employees needed in the current period with the number of employees used in the previous period. For example, in period 1, we used 60 employees, and in period 2 we need 39 employees. Th us we need to fi re 21 employees at the start of period 2. In period 3, we need only 30 employees, so we must fi re an additional 9 employees.

In this plan, your company experiences fluctuations in the workforce with a low of 30 employees and a high of 69. The minimum change in workforce for any given period is 9 workers, which represents a substantial portion of the workforce. We can easily calculate the regular-time wage costs and the cost of hiring and firing, but we cannot capture the intangible cost for such a widely fluctuating workforce. Table 13-10 evaluates the plan costs. Step 5 Evaluate the plan. At $33.72, the cost per call is a good deal lower and regular-time labor utilization is 100 percent. Still, this plan has potential problems. Since employees interface with the customer, it is important to maintain performance level. Your company will have to train and retrain the changing workforce. Th is plan also needs an investment in enough space and equipment for up to 69 employees. In periods of less than high demand, this extra capacity will be severely underused.

When we compare Plans C, D, and E, it is obvious that we have not yet found the best solution. Try working with the problem data further: maybe a smaller permanent workforce and additional overtime would be a better alternative.

MKT ACC HRM

EXAMPLE 13.8Plan E: Chase Aggregate Plan for Nontangible Products Using Hiring and Firing

42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58

D E F G H I J K L M N O P

Plan E: Chase Aggregate Plan Using Hiring and Firing

Beginning Number of Employees 60

Detailed Plan Computations 1 2 3 4 5 6 7 8 Total

Demand (calls) 2400 1560 1200 2040 2760 1680 1320 2400 15360 $F$48 =TRANSPOSE($B$18:$B$25) Service hours needed 9600 6240 4800 8160 11040 6720 5280 9600 61440 $F$49 =F48*$B$13

Number of employees needed 60 39 30 51 69 42 33 60 384 $F$50 =F49/$B$14 Number of hires 0 0 0 21 18 0 0 27 66 $F$51 =MAX(F50-E50,0) Number of fires 0 21 9 0 0 27 9 0 66 $F$52 =MAX(E50-F50,0)

Cost Calculations for Plan G Regular-time labor cost $491,520

Hiring cost $16,500 Firing cost $9,900 Total Cost $517,920

Period

TABLE 13-10

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524 • CHAPTER 13 AGGREGATE PLANNING

As you can see, when examining aggregate plans for services, the labor cost is often very critical. Different methods of controlling the cost of labor for service firms include (1) accurately scheduling labor-hours to assure quick response to customer demand (using prearranged appointments); (2) having an on-call labor pool that can be added or deleted to meet unexpected demand; (3) having multiskilled workers who can be reassigned in response to unexpected demand; and (4) having flexible work hours to meet changing demand.

Think about services that use these different methods. Emergency response orga- nizations often have the right to call in off-duty workers in the case of a major emer- gency. When business is slow at a restaurant, bar, or retail store, companies often send workers home early. In cases when workers must stay, additional work is done during customer lags. In supermarkets, we often see a rush of personnel to the front of the store when a rush of business occurs. These are multiskilled workers being temporar- ily reallocated to meet the customer demand. Since it is clear that service organizations must manage labor costs well, another approach to consider is manipulating demand to reduce the high demand variability often encountered in service organizations. One such approach is the use of yield management.

Yield management is the process of allocating scarce resources to customers at prices that maximize the yield to the company. Although we normally assume that companies charge all customers the same price for a product or service, sometimes that is not the case. Think of the airlines as one such organization. All passengers reach the destination together, but the ticket prices for the flight vary considerably. The underlying concept of yield management is to match demand to supply by charging based on a customer’s willingness to pay. Management identifies possible differences in the service and prices them accordingly. In the airlines, this includes nonstop ser- vice, convenient schedules, type of aircraft, and routing choices.

Yield management dates back to the 1980s when American Airlines’ reservations system allowed the airline to alter ticket prices in real time. When expensive seats were not in demand, more discounted seats would be offered. If demand for full-fare seats was high, then the number of reduced-fare seats was lowered. Similar logic applies readily to hotel rooms, rental cars, and cruise line cabins. When there is a perishable resource, such as a seat in an airplane, it yields no revenue if it is empty when the plan takes off. It is better to fill the seat at a reduced rate (as long as it covers the variable cost) rather than let it go empty. The major providers of hotel rooms, car rentals, airline transportation, and cruise lines use yield management in an effort to fully utilize their perishable services.

� Yield management Allocates scarce resources to maximize yield.

AGGREGATE PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER Aggregate planning determines the resources available to operations to support the overall business plan. It is critical that accurate demand forecasts be available (Chap- ter 8) so that a reasonable production plan can be developed. A company needs to determine the aggregate production rate output required to determine the appropriate size of the workforce. After these determinations have been made, the company can calculate its inventory levels, back-order levels, capacity requirements, and customer service levels. If the plan requires seven-day-a-week operations, appropriate staff schedules need to be developed (Chapter 15).

The aggregate plan specifies the number of employees needed. This allows the company to determine how much equipment and workspace are needed, as well as to provide the input needed for developing a workplace layout (Chapter 9) within the operations area. Aggregate planning provides the resources needed by operations to achieve the company’s strategic objective.

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THE SUPPLY CHAIN LINK • 525

Aggregate planning, master production scheduling, and rough-cut capacity affect functional areas throughout an organization. The MPS is a basis for communication among those functional areas. Let’s look at how each area is affected.

Accounting is aff ected because the aggregate plan details the resources needed by operations during the next 12 to 18 months. Accounting uses this information to proj- ect cash fl ows, calculate the capital needed to support operations, and project earnings. It also sets a benchmark for measuring the eff ectiveness of operations.

Marketing is involved because the aggregate plan supports the marketing plan. Marketing must know whether operations can provide the necessary output at a price that allows marketing to achieve its targeted profi t margins. Furthermore, the aggregate plan gives marketing insight into operations’ goals and activities for the year.

Information systems (IS) maintains the databases that support demand forecasts and other such information used to develop the aggregate plan.

Purchasing calculates long-term needs based on the aggregate plan. Th e aggregate plan gives purchasing information about the future that facilitates long-term relation- ships and contracts with suppliers. Th is information also allows purchasing to evaluate quantity discounts.

Manufacturing learns from the aggregate plan what resources are available to achieve its goals: how many workers, how much work can be subcontracted, how much inven- tory can be held, and so forth.

In most companies, the operations manager develops the aggregate plan. Th e opera- tions manager submits an annual operations budget request based on the resources identifi ed in the aggregate plan, which justifi es new hiring or expected fi ring during the year and planned or budgeted levels of overtime, subcontracting, and inventory.

When the plan is implemented, the operations manager measures operations’ per- formance against the plan, checking progress at least monthly. Th e operations man- ager also evaluates performance against the authorized operations budget. Variances need to be explained at all levels and the plan updated. At that point, the past month is dropped from the aggregate plan and replaced by an additional month at the end of the aggregate plan. Th e monthly review and update allow the company to monitor its performance, maintain its medium-term plan, and correct for changes since the plan was developed.

ACC

MKT

MIS

AGGREGATE PLANNING ACROSS THE ORGANIZATION

Aggregate planning assures the necessary resources to satisfy an organization’s objectives (profit, cus- tomer service, inventory investment, etc.). It determines both the output rate and the work- force size. These resources are based on product demand and reflect the customer service objectives of the company. The master production schedule is shared with all members of the supply chain so that

everyone knows expected completion dates and avail- ability of different products. Members of the chain also

know when demand differs from the plan as well as any problems encountered by operations that affect the availability quantity or date for differ- ent products. This allows members of the chain to respond to potential problems and not make

unsustainable delivery commitments. The master production schedule is the primary means of commu- nication within the supply chain.

THE SUPPLY CHAIN LINK

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526 • CHAPTER 13 AGGREGATE PLANNING

Until recently buyers of goods were content as long as well-priced goods arrived on time, in the specified order quantities, and intact. The aggregate plan drove these commitments. How the goods actually got to the buyer was of little concern. Today, however, with the pres- sure of high fuel costs and consumers and regulators demanding eco-friendly practices, companies are under great pressure to ensure that the delivery of inventories of goods they have ordered abide by best practices. One strategy companies are exploring is to minimize total material movement to and from the facility through delivery consolidation and accepting the carrying of larger inventory quantities. Some companies are even

changing order cycles and delivery commitments with customers to improve transportation efficiency.

Sustainable practices may require modifying tradi- tional ordering policies to consolidate orders to reduce transportation emissions and a company’s carbon foot- print. It may require modifying traditional order com- putations to ensure that a moving truck is always full—rather than partially full—and that orders are coordinated so that a truck is never returning empty. This may also require modifying orders to minimize waste of packaging and consolidating orders to satisfy requirements that minimize waste of packaged material. It is these small changes in inventory management that can play a significant difference in ensuring sustain- ability practices.

THE SUSTAINABILITY LINK

Chapter Highlights 1 Planning begins with the development of the strategic

business plan that provides your company’s direction and objectives for the next 2 to 10 years. Marketing develops a plan that enables the company to satisfy the goals of the strategic business plan. Th e aggregate plan identifi es the resources needed by operations to support the marketing plan.

2 Sales and operations planning integrates plans from the other functional areas and regularly evaluates the company’s performance. Th e process begins with a comparison of actual demand and sales against planned demand. Forecasts are updated and the market is reevaluated, and any changes in the marketing plan are reviewed by operations, engineering, and fi nance.

3 Th e level aggregate plan maintains the same size workforce and produces the same output each period. Inventories and back orders absorb fl uctuations in demand. Th e chase aggregate plan changes the capacity each period to match demand.

4 Demand patterns can be smoothed through pricing incentives, reduced prices for out-of-season purchases, or nonprime service times. Capacity can be changed by using overtime or undertime, hiring or fi ring employees, subcontracting, using temporary employees, and so forth. You develop the aggregate plan by identifying the options you want to use; calculating the production rate; calculating the size of the workforce needed to reach the production rate; calculating the cost of the plan; and evaluating the plan in terms of cost, customer service, operations, and human resources.

5 Th e diff erence in aggregate planning for companies that do not provide a tangible product is that inventory is no longer an option. In many service organizations, it is critical to control labor costs. Hotels, airlines, car rentals, and cruise lines use yield management techniques to match demand to supply.

Key Terms strategic business plan 507 sales and operations planning 507 marketing plan 507 aggregate plan 507 fi nancial plan 508 engineering plan 508 master production schedule 508 level aggregate plan 509

chase aggregate plan 510 hybrid aggregate plan 511 demand-based options 511 capacity-based options 511 fi nished goods inventory 512 back orders 512 shift ing demand 512 overtime 512

undertime 513 subcontracting 513 hiring and fi ring 513 point of departure 514 magnitude of the change 515 duration of the change 515 yield management 524

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SOLVED PROBLEMS • 527

Solved Problems Use the problem data in Table 13-11 for Solved Problems 1–4. • Problem 1: Plan 1: Level Aggregate Plan

Using Inventories and Back Orders Develop a level aggregate plan using inventories and back orders. Test and evaluate the plan.

• Before You Begin: Remember that a level strategy can use inventories and back orders to satisfy fluctuations in demand. You need to determine the aggregate production rate needed and then the size of the workforce needed to produce that rate. Follow the steps below.

• Solution

Step 1 Choose the kind of aggregate plan. In this case we use a level aggregate plan. Step 2 Calculate the aggregate production rate. We use the level option to calculate the average period demand rate. First, we add up the period demands and divide by the number of periods (14,400 units/8 � 1800 units per period); then we find the average production rate needed. If we have a beginning inventory, we subtract it from the total demand figure. If we have a desired ending inventory, we add that to the total demand fig- ure. Otherwise, the 1800 units we calculated earlier is our average demand and subsequently our aggregate production rate. Step 3 Calculate the number of workers to produce 1800 units per period. Since each employee works 160 hours per period and each unit takes 8 hours of labor to produce, each employee can produce 20 units per period. We need 1800 units produced, so 1800 units divided by 20 units per employee means that we need a workforce of 90. Since we have 90 workers, we do not need to hire or fire any workers.

Now let’s look at a level aggregate plan that would improve customer service with inventories but not allow back orders. All demand must be met each period.

(See student companion site for Excel template.)

4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

A B Cost Data

Regular-time labor cost per hour $12.50 Overtime labor cost per hour $18.75

Subcontracting cost per unit (labor only) $125.00 Back-order cost per unit per period $25.00

Inventory holding cost per unit per period $10.00 Hiring cost per employee $800.00 Firing cost per employee $500.00

Capacity Data Beginning workforce (employees) 90

Beginning inventory (units) 0 Production standard per unit (hours) 8

Regular-time available per period (hours) 160 Overtime available per period (hours) 40

Demand Data (units) Period 1 1920 Period 2 2160 Period 3 1440 Period 4 1200 Period 5 2040 Period 6 2400 Period 7 1740 Period 8 1500

Total Number of Periods 8

TABLE 13-11

Data for Solved Problems 1–4

• Problem 2: Plan 2: Level Aggregate Plan Using Inventories but No Back Orders Develop a level aggregate plan using inventories but no back orders. Test and evaluate the plan. Compare to Plan 1.

• Before You Begin: In this problem, you are constrained to a level strategy and are not allowed to have any back orders. Determine a production rate that does not allow for any back orders. To do this, use the cumulative demand divided by the number of periods up to that point. An example is shown in Step 2. Do this calcula- tion for each period. The largest value represents the aggregate production rate needed to avoid back orders. Once the rate is determined, calculate the size of the workforce needed.

• Solution

Step 1 Choose the kind of aggregate plan. In this case we use a level plan. Step 2 Calculate the aggregate production rate. When we do not allow back orders, we do not use the average demand rate to calculate the workforce, as shown in Table 13-12. A new row (row 44), labeled “cumulative demand/number of periods,” divides the cumulative demand to each point by the number of periods in which to produce that quantity. For example, by the end of period 2, we need a total of 4080 units. Th us we plan to build 2040 units each month to satisfy all demand for the fi rst two periods with the level aggregate plan. We calculate this value for each period. Th e period with

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528 • CHAPTER 13 AGGREGATE PLANNING

the highest needed production rate determines the aggregate production rate we use in the plan. For our plan, 2040 units is the highest production rate needed, so it becomes our planned aggregate production rate. Step 3 Calculate how many employees we need to produce 2040 units per period. Since each employee can produce 20 units per period during regular time, we need 102 employees for this plan. We must hire 12 employees. Step 4 Test and cost the plan using a production rate per period of 2040 units and a workforce of 102 employees. Table 13-12 shows the results of the plan.

Th is plan creates high inventory, especially aft er period 2. Th e inventory ranges from 0 to 1920 units, or from 0 weeks of supply to more than 4 weeks of supply.

We can calculate most of the costs of the plan. However, we cannot capture the true holding costs because we do not know when the ending inventory of 1920 units will be consumed. We need to remember this when we compare our plans. Table 13-12 shows the costs for this plan. Step 5 Evaluate the plan. Compared with Plan 1, Plan 2 costs at least an additional $239,700 and possibly more given the ending inventory level. Th is is more than a 16 percent increase in total cost. However, we need to make sure our comparisons are fair. In Plan 1, we build 14,400 units,

whereas in Plan 2, we build 16,320 units. We should expect total costs of Plan 2 to be higher since we need more labor to build the additional units. Note that when plans require building diff erent quantities, it may be easier to use the unit cost per plan for our comparisons. Th e unit cost for Plan 1 is $102.90 ($1,481,700 divided by 14,400 units). Th e unit cost for Plan 2 is $105.48 ($1,721,400 divided by 16,320 units). Plan 2 costs $2.58 more per unit, or roughly 2.5 percent more ($2.58 divided by $102.90).

What are we getting for this extra cost? Improved cus- tomer service. Plan 2 provides 100 percent customer service because no products are back-ordered. Now your company must decide whether 100 percent customer service is worth at least an additional 2.5 percent in cost or if another approach might be more cost-effective.

Remember, we have under- stated our holding costs. From an operations standpoint, this is a relatively easy plan to implement. We increase our workforce by 12 employees, or just over 13 percent. Our out- put is still level and we do not need overtime or undertime. Morale should be fine.

38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

D E F G H I J K L M N O P

Plan 2: Level Aggregate Plan, Using Inventories but No Back Orders

Detailed Plan Computations 1 2 3 4 5 6 7 8 Total

Demand (units) (net of beg. Inventory) 1920 2160 1440 1200 2040 2400 1740 1500 $E$42 =$B$21-$B$15 Cumulative demand (units) 1920 4080 5520 6720 8760 11160 12900 14400 $E$43 =SUM($E42:E42)

Cumulative demand/periods 1920 2040 1840 1680 1752 1860 1843 1800 $E$44 =E$43/E$41 Period production (units) 2040 2040 2040 2040 2040 2040 2040 2040 16320 $E$45 =$E$52

Cumulative production (units) 2040 4080 6120 8160 10200 12240 14280 16320 $E$46 =SUM($E45:E45) Cum.Dem. Minus Cum.Prod. -120 0 -600 -1440 -1440 -1080 -1380 -1920 $E$47 =E43-E46

Ending Inventory (units) 120 0 600 1440 1440 1080 1380 1920 7980 $E$48 =IF(E47<0,-E47,0) Backorders (units) 0 0 0 0 0 0 0 0 0 $E$49 =IF(E47>0,E47,0)

Compute Level Production Rate and Workforce Needed Production Rate (units) 2040 $E$52 =MAX(E44:L44)

Units per Employee per Period 20 $E$53 =B$17/B$16 Employees Needed 102 $E$54 =E52/E53

Number to Hire 12 $E$55 =MAX(E54-B$14,0) Number to Fire 0 $E$56 =MAX(B$14-E54,0)

Cost Calculations for Plan B Regular-time labor cost $1,632,000 $E$59 =E54*B$17*B$5*B$30

Overtime labor cost $0 $E$60 0 Inventory holding cost $79,800 $E$61 =M48*B$9

Back-order cost $0 $E$62 =M49*B$8 Hiring cost $9,600 $E$63 =E55*B$10 Firing cost $0 $E$64 =E56*B$11 Total Cost $1,721,400 $E$65 =SUM(E59:E64)

Period

TABLE 13-12

HRMACCMKT

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SOLVED PROBLEMS • 529

• Problem 3: Plan 3: Chase Aggregate Plan Using Hiring and Firing Develop a chase aggregate plan using hiring and firing. Test and evaluate the plan. Compare with Plans 1 and 2.

• Before You Begin: When using a pure chase strategy with hires and fires, calcu- late the number of employees needed to satisfy each period’s demand. Once you have established the number of employees needed, either hire or fire as required.

• Solution

Step 1 Choose the kind of aggregate plan. In this example we use a chase plan. Step 2 Calculate the production rate. For example, in period 1, we need to build 1920 units. Since the com- pany builds exactly what is needed to satisfy each period’s demand, the production rate for each period is set equal to the demand rate. Table 13-13 shows the level of output needed in each period. Step 3 Calculate the size of the workforce. The initial workforce is 90, so we hire 6 employees for period 1. The number of employees we need to hire or fire depends on our ending workforce level in the previous period. We can see from Table 13-13 that we need 96 employees in period 1. We calculate this by dividing the number of units needed by the number of units produced by each employee per period (1920 units divided by 20 units per employee). Since we need 108 employees in period 2 and we ended period 1 with 96 employees, we must hire an additional 12 employees for period 2. We will have no ending inventories or back orders.

Step 4 Test and cost the plan. Table 13-13 shows the period-to-period testing of the plan. Note that this plan cre- ates fluctuation in the size of the workforce, ranging from a high of 120 employees to a low of 60 employees. This means that the company must have enough space, tools, and equip- ment for up to 120 employees working simultaneously dur- ing a given time. It also means that half that space can be idle during other periods. The number of changes in the size of the workforce and the magnitude of some of these changes could cause problems. Suppose we fire a total of 48 workers during periods 3 and 4, only to hire 42 new employees in period 5. Your workforce has increased by 70 percent (42 divided by 60). Imagine the mass confusion and the loss of productivity at the beginning! Step 5 Evaluate Plan 3. The costs of Plan 3 are the high- est of the three plans. The unit cost for Plan 3 is $107.56 ($1,548,900 divided by 14,400 units), an increase of more than $2 per unit from Plan 2, which also provided excellent customer service.

From a customer service standpoint, Plan 3 is fine because no products are back-ordered. From an operations standpoint, however, this plan is not easy to implement. We need space, tools, and equipment for up to 120 individuals in period 6, whereas we will have only 60 employees in period 4. We need to consider the feasibil- ity of major changes like these. Employee morale might be low because of the lack of job secu- rity. How many employees can afford to take a month or two off?

HRMACCMKT

68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88

D E F G H I J K L M N O P

Plan 3: Chase Aggregate Plan, Using Hiring and Firing (no overtime)

Beginning Number of Employees 90 $E$70 =$B$14 Units per Worker per Period 20 (used to compute workforce size requirement each period) $E$71 =B17/B16

Detailed Plan Computations 1 2 3 4 5 6 7 8 Total

Demand (units) (net of beg. Inventory) 1920 2160 1440 1200 2040 2400 1740 1500 $E$75 =$B$21-$B$15 Production per period (units) 1920 2160 1440 1200 2040 2400 1740 1500 14400 $E$76 =E75 Employees needed in period 96 108 72 60 102 120 87 75 720 $E$77 =E76/$E$71

Number to hire 6 12 0 0 42 18 0 0 78 $E$78 =MAX(E77-E70,0) Number to fire 0 0 36 12 0 0 33 12 93 $E$79 =MAX(E70-E77,0)

Cost Calculations for Plan C Regular-time labor cost $1,440,000 $E$82 =M77*B$17*B$5

Overtime labor cost $0 $E$83 0 Inventory holding cost $0 $E$84 0

Back-order cost $0 $E$85 0 Hiring cost $62,400 $E$86 =M78*B$10 Firing cost $46,500 $E$87 =M79*B$11 Total Cost $1,548,900 $E$88 =SUM(E82:E87)

Period

TABLE 13-13

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530 • CHAPTER 13 AGGREGATE PLANNING

• Problem 4: Plan 4: Hybrid Aggregate Plan Using Initial Workforce and Overtime as Needed Develop a hybrid aggregate plan using the initial workforce of 90 employees supplemented with overtime when demand exceeds regular-time production. Test and evaluate the plan. Compare with Plans 1, 2, and 3.

• Before You Begin: In this problem, the size of the workforce is given. You are to supplement the regular-time output of this workforce with overtime when needed. Determine when overtime is needed. The key to remember is that in periods when the demand is lower than the production rate, inventory is generated. The effect of ending inventory in any period is that it reduces the amount of production needed the following period. Be sure to carefully read Step 2 as you look at the results in Table 13-14.

• Solution

Step 1 Choose the kind of aggregate plan. In this example we use a hybrid plan. Step 2 Calculate the production rate. We already know the regular-time production rate of 1800 units per period because we are using the initial workforce. This plan increases capacity when period demand exceeds the product available through that period’s production and the begin- ning inventory for that period. For example, in period 1, we

need 1920 units to satisfy demand. The hybrid aggregate plan in Table 13-14 shows that we build 1800 units during regular-time production and produce the remaining 120 units using overtime. We do not need overtime after period 2 because we can build up enough inventory to handle demand fluctuations.

We can see that the demand figures for periods 4, 5, 6, 7, and 8 have changed. These changes reflect the net demand for the period after subtracting the ending inventory of the previous period. For example, in period 4, demand is 1200 units. However, since we have 360 units in ending inventory in period 3, we subtract those out of period 4’s demand to arrive at a net demand of 840 units.

Step 3 Calculate the workforce size. We know that the workforce size for this plan is 90 employees.

Step 4 Test and cost the plan. We test the plan using 90 employees producing 1800 units per period during regular time and using overtime for production when regular-time production is not adequate to satisfy demand. Table 13-14 shows the plan.

Plan 4 uses a stable workforce of 90 employees and uses overtime in periods 1 and 2. All other demand fluctuations are handled through inventory. Total production for this plan is 14,880 units (14,400 during regular time and 480 dur- ing overtime). Ending inventories range from 0 to 960 units. Table 13-14 shows the total costs for Plan 4.

91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113

D E F G H I J K L M Plan 4: Hybrid Aggregate Plan Using Initial Workforce and Overtime as Needed

Compute Regular-Time Production Rate Number of Employees 90

Units per Employee per Period 20 Regular-Time Production per Period 1800

Detailed Plan Computations 1 2 3 4 5 6 7 8 Total

Total Demand in Period 1920 2160 1440 1200 2040 2400 1740 1500 Net Demand After Inventory Considered 1920 2160 1440 840 1080 1680 1620 1320

Regular-Time Production 1800 1800 1800 1800 1800 1800 1800 1800 14400 Overtime Production Needed 120 360 0 0 0 0 0 0 480

Ending Inventory 0 0 360 960 720 120 180 480 2820

Cost Calculations for Plan D Regular-time labor cost $1,440,000

Overtime labor cost $72,000 Inventory holding cost $28,200

Back-order cost $0 Hiring cost $0 Firing cost $0 Total Cost $1,540,200

Period

TABLE 13-14

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SOLVED PROBLEMS • 531

Step 5 Evaluate Plan 4. The per unit cost in Plan 4 is $103.51, which is only 61 cents higher than for our Plan 1 level aggregate plan using inventories and back orders. However, Plan 4 provides 100 percent customer service, and the per unit cost differential is only about 0.6 percent. Plan 4 achieves excellent customer service at the lowest cost so far, which should satisfy both finance and marketing. In terms of operations and human resources, we are using overtime in the short term for periods 1 and 2. We do not ask workers to put in more than 20 percent overtime in period 2 (360

units on overtime in period 2, while producing 1,800 units on regular time). Overtime in period 1 is limited to 7 percent of the regular-time production. Thus we are not overusing the workers, and we keep overtime to a minimum. In addition, Plan 4 should not be hard to implement from an operations standpoint.

HRMACCMKT

• Problem 5 Psychics of the World, Inc. wants an aggregate plan for its orga- nization. Given the nature of the business, Psychics has decided that back orders are not acceptable. If a caller cannot be handled immediately, the call is a lost sale.

Psychics has predicted the following number of calls: May— 8000 calls, June—5000 calls, July—6000 calls, August—7000 calls, September—6000 calls, October—8000 calls, Novem- ber—10,000 calls, and December—12,000 calls.

Psychics of the World pays each of its 48 employees $4000 per month. Each psychic works 160 regular-time hours per month or 40 regular-time hours per week. The regular-time labor cost of a call is $20, and the overtime labor cost per call is $30. Each psychic is expected to serve 200 callers per month. The management has limited overtime to 50 calls per month per psychic. It costs $3000 to hire a new psychic and $2000 to fire a psychic.

(a) Using the data in Table 13-15, develop a level aggregate plan without inventory, without back orders, and without overtime.

(b) Using the same problem data, develop an aggregate plan using a level workforce supplemented by overtime. Mini- mize the wasted capacity. No back orders are permitted.

• Before You Begin: This requires an aggregate plan for a company without a tan- gible product, so inventory is not an option. The plan also does not allow for back orders or overtime. Therefore, you need to staff for peak demand and maintain that workforce throughout the plan.

TABLE 13-15

Data for Psychics of the World

4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

A B Cost Data

Regular-time labor cost per month $4,000.00 Regular-time labor cost per hour $25.00

Overtime labor cost per hour $37.50 Hiring cost per employee $3,000.00 Firing cost per employee $2,000.00

Capacity Data Beginning workforce (employees) 48 Service standard per call (hours) 0.8

Regular-time available per period (hours) 160 Overtime available per period (hours) 40

Demand Data (calls) May 8000 June 5000 July 6000 Aug 7000

Sept 6000 Oct 8000 Nov 10000 Dec 12000

Total Number of Periods 8

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532 • CHAPTER 13 AGGREGATE PLANNING

• Solution a

Step 1: Choose the kind of aggregate plan. We are using a level plan. Step 2: Calculate the aggregate production rate. Since inventory and back orders are not permitted, find the period with the highest demand (December has demand of 12,000 calls). This is the aggregate production rate. Step 3: Calculate the workforce size given the aggregate production rate (12,000 calls divided by 200 calls per psychic per month). The workforce should have 60 psychics, so we need to hire 12 more psychics. Step 4: Test and cost the plan. The plan is shown in Table 13-16.

We calculate wasted capacity by subtracting the capac- ity used (calls to be serviced) from the available regular-time capacity. We calculate the costs in Step 5.

Step 5: Evaluate the plan. We have no other plan to com- pare the cost with, but this plan appears to waste substantial capacity (34,000 more calls could have been handled). Total calls demanded were 62,000, whereas we had capacity for 96,000. Since management is concerned with wasting valu- able psychic time, let’s develop a plan that minimizes the amount of wasted capacity (minimizes undertime).

• Before You Begin: In this scenario, you want to use a level workforce supple- mented by overtime. No back orders are permitted, and you are to minimize wasted capacity. To minimize wasted capacity,

calculate how large a workforce is needed if each worker not only works the 160 regular-time hours per period but also works the available 40 hours of overtime. Therefore, when cal- culating how many employees are needed, divide by 200 total hours per employee per period (the sum of regular time and overtime). This reduces the size of your workforce, thus mini- mizing wasted capacity.

• Solution b

Step 1: Choose the kind of aggregate plan. Here, we use a level workforce. Step 2: Calculate the aggregate production rate. This is the same as for the previous plan (12,000 calls). Step 3: Calculate the appropriate workforce. This time we divide the aggregate production rate by the maximum num- ber of calls per psychic per period (regular time plus over- time). Each psychic can provide up to 250 calls per period. This reduces the workforce from 60 psychics in the previous plan to 48 psychics in this plan (12,000 calls divided by 250 calls per psychic). No hires or fires are needed. Step 4: Test and cost the plan. The plan is shown in Table 13-17. Step 5: Evaluate the plan. This plan reduces the wasted regular-time capacity by 16,400 calls (34,000 � 17,600). The total cost is $336,000 less than the previous plan. Overtime is needed only in two periods, so it should not create morale problems. This plan is an improvement, but better plans are possible.

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E F G H I J K L M N O P Q

Solution a: Level Aggregate Plan, No Inventory, No Back Orders, No Overtime

Compute Workforce Needed Key Formulas (some are copied) Maximum Demand 12000 <-- Need to staff to meet the maximum number of calls $F$6 =MAX($B$18:$B$25)

Calls per Worker per Period (Reg Time) 200 $F$7 =$B$14/$B$13 Workers Needed 60 $F$8 =F6/F7

Number to Hire 12 $F$9 =MAX(F8-$B$12,0) Number to Fire 0 $F$10 =MAX(B12-$F$8,0)

Detailed Plan Computations May June July Aug Sept Oct Nov Dec Total

Demand (calls) 8000 5000 6000 7000 6000 8000 10000 12000 62000 $F$14 =TRANSPOSE($B$18:$B$25) Service hours needed 6400 4000 4800 5600 4800 6400 8000 9600 49600 $F$15 =F14*$B$13

Employees Needed 32 20 24 28 24 32 40 48 $F$16 =F15/$F$7 Regular-time hours of capacity 9600 9600 9600 9600 9600 9600 9600 9600 $F$17 =$F$8*$B$14

Wasted Capacity Hours 3200 5600 4800 4000 4800 3200 1600 0 27200 $F$18 =F17-F15

Cost Calculations for Part a Regular-time labor cost $1,920,000 $F$21 =F8*$B$14*$B$6*$B$27

Hiring cost $36,000 $F$22 =F9*$B$8 Firing cost $0 $F$23 =F10*$B$9 Total Cost $1,956,000 $F$24 =SUM(F21:F23)

Period

TABLE 13-16

Solution for Psychics, Part a

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DISCUSSION QUESTIONS • 533

38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

E F G H I J K L M N O P Q

Solution b: Level Aggregate Plan with Overtime, No Inventory, No Back Orders

Compute Workforce Needed Key Formulas (some are copied) Maximum Demand 12000 <-- Need to staff to meet the maximum number of calls $F$41 =MAX($B$18:$B$25)

Calls per Worker per Period (Reg Time) 200 $F$42 =$B$14/$B$13 Calls per Worker per Period (Overtime) 50 $F$44 =F41/(F42+F43)

Workers Needed 48 $F$45 =MAX(F44-$B$12,0) Number to Hire 0 $F$46 =MAX(B47-$F$8,0) Number to Fire 0

Detailed Plan Computations May June July Aug Sept Oct Nov Dec Total

Demand (calls) 8000 5000 6000 7000 6000 8000 10000 12000 62000 $F$50 =TRANSPOSE($B$18:$B$25) Service hours needed 6400 4000 4800 5600 4800 6400 8000 9600 49600 $F$51 =F50*$B$13

Regular-time hours of capacity 7680 7680 7680 7680 7680 7680 7680 7680 61440 $F$52 =$F$44*$B$14 Overtime hours needed 0 0 0 0 0 0 320 1920 2240 $F$53 =MAX(F51-F52,0)

Wasted regular time capacity hours 1280 3680 2880 2080 2880 1280 0 0 14080 $F$54 =MAX(F52-F51,0)

Cost Calculations for Part b Regular time labor cost $1,536,000 $F$57 =F44*$B$14*$B$6*$B$27

Overtime labor cost $84,000 $F$58 =N53*B7 Hiring cost $0 $F$59 =F45*$B8 Firing cost $0 $F$60 =F46*$B9 Total Cost $1,620,000 $F$61 =SUM(F57:F60)

Period

TABLE 13-17

Solution for Psychics, Part b

Discussion Questions 1. Explain the importance of the strategic business plan. 2. Describe sales and operations planning in terms of its pur-

pose, components, and frequency. 3. Define the aggregate plan. 4. Explain why we use an aggregate or a composite product

when developing the aggregate plan. 5. Compare and contrast the level and the chase aggregate

plans. 6. Describe the different demand-based options used in aggre-

gate planning and their implications for a company. 7. Describe the different capacity-based options used in

aggregate planning and their implications for a company. 8. Explain what the hybrid aggregate plan is and why it is

used. 9. Explain the procedure for developing an aggregate plan.

10. Describe the factors to consider before developing an aggregate plan.

11. Explain how aggregate planning is different when the company does not provide a tangible product.

12. Visit a local manufacturer and determine how it uses aggregate planning.

13. Visit a local service provider and determine how it uses aggregate planning.

14. What two items must you calculate first when developing an aggregate plan?

15. Visit a local business and learn how it calculates its resources.

16. Describe the inputs needed to do master production scheduling.

17. Describe the different sources of demand.

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534 • CHAPTER 13 AGGREGATE PLANNING

Problems Use the following data to solve the first seven problems. 4. Now that you have calculated the number of workers needed

each period in Problem 3, Jill wants to see how the plan would actually work. You need to:

(a) Show what would happen if this plan were implemented. (b) Calculate the costs associated with this plan. (c) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 5. Jill Nicholas is concerned about BackPack’s corporate image

and has decided against using hires and fi res. Instead, she has asked you to consider a chase aggregate plan using the current workforce supplemented by either overtime or undertime to change capacity to match demand exactly. She has asked for the following information from you:

(a) How many production hours would be required each period to produce the exact quantity needed?

(b) How many regular-time production hours are available each period?

(d) Show what would happen if this plan were implemented. (e) Calculate the costs associated with this plan. (f) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 6. Jill Nicholas believes there must be a better aggregate plan.

She has suggested a hybrid plan, using a permanent workforce of 195 employees and subcontracting as needed. Once again, Jill has requested that you provide the following information:

(a) Calculate the regular-time production possible each period given a workforce of 195.

(b) Show what would happen if this plan were implemented. (c) Calculate the costs associated with this plan. (d) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 7. Jill wants you to consider a hybrid aggregate plan, using

up to the maximum overtime per employee for any period where demand cannot be satisfi ed with the current regular-time production and the available inventory. Back orders can occur.

(a) Show what would happen if this plan were implemented. (b) Calculate the costs associated with this plan. (c) Evaluate the plan in terms of cost, customer service,

operations, and human resources. Use the information shown here for Problems 8 through 13. The Draper Tax Company provides tax services to local busi- nesses. Draper chooses to meet all demand as it occurs because customers are unwilling to accept back orders. The company has provided the following cost, capacity, and demand information.

Problem Data

Cost data Regular-time labor cost per hour $10.00 Overtime labor cost per hour $15.00 Subcontracting cost per unit (labor only) $84.00 Holding cost per unit per period $10.00 Back-order cost per unit per period $20.00 Hiring cost per employee $600.00 Firing cost per employee $450.00

Capacity data Beginning workforce 210 employees Beginning inventory 400 units Labor standard per unit 6 hours Regular time available per period 160 hours Overtime available per period 32 hours Subcontracting maximum per period 1000 units Subcontracting minimum per period 500 units

Demand data Period 1 6000 units Period 2 4800 units Period 3 7840 units Period 4 5200 units Period 5 6560 units Period 6 3600 units

1. Th e BackPack Company produces a line of backpacks. Th e manager, Jill Nicholas, is interested in using a level aggregate plan. Inventories and back orders will be used to handle demand fl uctuations. She has asked you to develop such a plan.

(a) Calculate the aggregate production rate. (b) Calculate the appropriate workforce given the aggregate

production rate. (c) Show what would happen if this plan were implemented. (d) Calculate the costs of this plan. (e) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 2. Jill has decided that the BackPack Company must have

very good customer service. She has asked you to develop a level aggregate plan using inventories but not back orders. All demand must be met each period. You must:

(a) Calculate the aggregate production rate. (b) Calculate the appropriate workforce given the aggregate

production rate. (c) Show what would happen if this plan were implemented. (d) Calculate the costs of this plan. (e) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 3. Although the BackPack Company has always used a level

aggregate plan, Jill is interested in evaluating chase aggregate plans also. She has asked you to calculate how many hires and fi res would be necessary to adjust capacity to meet demand exactly each period. If necessary, incur some undertime. Calculate the number of workers needed each period.

Draper Tax Company Problem Data

Cost data Regular-time labor cost per hour $25.00 Overtime labor cost per hour $37.50 Temporary worker cost per hour $40.00 Hiring cost per permanent worker $2000.00 Firing cost per permanent worker $1200.00 Backorder cost $500.00

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CASE: NEWMARKET INTERNATIONAL MANUFACTURING COMPANY (A) • 535

Capacity data Beginning workforce 12 employees Labor standard per service 12 hours Regular-time hours per period 40 hours Overtime hours per period 8 hours

Demand Data Week 1 48 clients Week 4 40 clients Week 2 36 clients Week 5 38 clients Week 3 50 clients Week 6 48 clients

employees with temporary workers. Any temporary worker must work the entire week. Th ere is no hiring or fi ring cost associated with temporary workers. Develop this aggregate plan for Draper.

(a) Show what would happen if this plan were implemented. (b) Calculate the costs associated with this plan. (c) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 14. W. C. Sanders, owner of Fort Engines, a producer of heavy-

duty snow blower engines, needs to develop an aggregate plan for the coming year. Th e company currently uses 20 individuals working 160 regular-time hours each month. Each worker is capable of producing 10 heavy-duty snow blowers per month. Employees are paid $12 per hour. Overtime is limited to a maximum of 40 hours per month per employee. Holding costs are $5 per unit per period. Back-order cost is $10 per unit per period. Th e beginning inventory is 40 units. Monthly demand projections are:

8. Calculate the size of the workforce needed for the company to meet average weekly demand.

9. Develop a level aggregate plan for the Draper Tax Company if back orders are permitted.

(a) Show what would happen if this plan were implemented. (b) Calculate the costs associated with this plan. (c) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 10. Develop a level aggregate plan for the Draper Company if

no back orders are permitted. (a) Show what would happen if this plan were implemented. (b) Calculate the costs associated with this plan. (c) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 11. Develop a chase aggregate plan using hires and fi res to adjust

the capacity for Draper. All demand must be met each period. (a) Show what would happen if this plan were implemented. (b) Calculate the costs associated with this plan. (c) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 12. Develop a chase aggregate plan for Draper using a

permanent workforce of 12 employees supplemented by overtime. All demand must be met each period.

(a) Show what would happen if this plan were implemented. (b) Calculate the costs associated with this plan. (c) Evaluate the plan in terms of cost, customer service,

operations, and human resources. 13. Concerned about the welfare of its workers, Draper has

decided to try a strategy without any overtime. Instead of overtime, Draper has decided to supplement the permanent workforce of 12

Month Demand (units) Month Demand (units)

January 250 July 220 February 230 August 220 March 190 September 260 April 170 October 260 May 200 November 240 June 220 December 220

(a) Develop a hybrid aggregate plan using the initial workforce supplemented by overtime. If demand in any period exceeds regular-time production plus overtime production plus any beginning inventory, the company will use back orders. Calculate the cost of this plan.

(b) Another alternative is to try a level plan that uses inventory and back orders to absorb fl uctuation. Calculate the cost of this plan.

(c) A third alternative being considered is to use a hybrid plan but also to close down the facility for the entire month of July. Overtime, inventory, and back orders can be used. Calculate the cost of this plan.

(d) Compare the three plans in terms of cost, customer service, operations, and human resources.

CASE: Newmarket International Manufacturing Company (A) Marcia Blakely, plant manager at the Newmarket Interna- tional Manufacturing Company (NIMCO), was preparing for a meeting with her management team. Joining her would be Jack Novak, the company controller; Amy Granger, regional marketing manager; and Joe Barnes, the production manager. The goal of the meeting was to develop a staffing plan for the second quarter. A quick performance review covering the past two quarters proved disappointing. Customer service was poor in spite of higher component inventory levels. Stockouts of some components were a problem and caused production inefficien- cies. Nothing seemed to be working smoothly.

Company History

NIMCO was founded by Marcia Blakely when she was only two years out of graduate school. Marcia’s knowledge of mass customization has been the driving force behind NIMCO. The company produces three major custom products. Volume on the products is quite high even though each item is customized specifically for the customer. Each of the products is processed through up to four different work centers. Although each item is unique, the processing time at each work center is constant due to the sophisticated equipment used.

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536 • CHAPTER 13 AGGREGATE PLANNING

NIMCO currently has 75 full-time employees working in manufacturing. Each employee is scheduled to work 40 hours per week. Because mass customization is used, NIMCO carries no finished goods inventory. The company policy is to meet all demand each period; no back orders or stockouts are permitted.

Joe Barnes, the production manager, received the following information in advance of the meeting: demand forecasts for products A, B, and C for each week of the second quarter as shown in the table, and standard labor time estimates for each product. The standard labor times are: product A—0.24 hour, product B—0.38 hour, and product C—0.29 hour.

Joe knew that it would be useful to have you, his assistant, generate additional information for this meeting. You are to convert the individual product forecasts into the total number of labor hours needed each week. For example, in week 14, there are 3600 product A’s multiplied by 0.24 hour, 4000 product B’s

times 0.38 hour, and 2000 product C’s times 0.29 hour. The total standard labor time associated with products demanded in period 14 is 2964 hours. After determining the required labor hours each period, Joe wants you to develop three possible staff- ing plans. The first plan uses a level workforce and does not allow back orders in any period. The second plan uses the origi- nal full-time workforce (75 employees) supplemented by the use of overtime to avoid back orders. The third plan adjusts the workforce each period to satisfy all demand by hiring and firing employees. To develop these plans and calculate their associated costs, you need to know that the regular-time wage rate is $14 per hour, overtime is $21 per hour, hiring costs are $500 per employee, and firing costs are $750 per employee.

Your job is to provide analyses of these three plans for Joe to use at the meeting. Make sure to include your recommendation after considering cost, customer service, and operations.

Quarter 2 Demand Forecasts

Week Demand for Product A

Demand for Product B

Demand for Product C Week

Demand for Product A

Demand for Product B

Demand for Product C

14 3600 4000 2000 21 4300 3600 3000 15 4000 4000 2500 22 4000 3600 3000 16 4300 4000 2800 23 4000 3800 2800 17 4400 3800 3100 24 3600 3800 2800 18 4500 3800 3200 25 3200 3800 2600 19 4500 3800 3200 26 3000 4000 2600 20 4400 3600 3200

CASE: JPC, Inc.: Kitchen Countertops Manufacturer JPC, Inc. is a major producer of solid-surface kitchen coun- tertops. The countertops are made from a durable nonporous acrylic polymer and are available in a wide variety of colors. The tops resist stains, scratches, fading from sunlight, and heat. They also do not promote the growth of mildew or bacteria, thus providing an easy-care, sanitary countertop. Its products are distributed throughout North America and until recently have experienced growing demand. The products are used primarily in new house construction. The company currently operates three different manufacturing plants.

During the recent housing slowdown, demand for the coun- tertops has dropped significantly (from 2,300,000 tops last year to an expected 1,700,000 tops), and JPC is considering closing one of its three plants. The annual regular-time production capacity at Plant 1 is 960,000 tops; Plant 2 can produce 480,000 tops per year; and Plant 3 can produce 720,000 tops per year. Each plant is allowed to use up to an additional 20 percent overtime for top production. This means that, theoretically, JPC could produce as many as 2,592,000 tops with its current facilities.

If JPC closes down a plant, the weekly costs associated with the closed, nonoperating plant will decrease. Closing Plant 1

would reduce annual fixed costs to $312,000. If Plant 1 remains operational, the fixed costs are $728,000. If Plant 2 is closed, annual fixed costs drop to $260,000 rather than the normal $600,000. If Plant 3 is closed, annual fixed costs drop to $390,000 rather than the normal $780,000.

The variable production costs are different at each plant. At Plant 1, the variable cost to produce one countertop during regular time is $420; the variable cost to produce during over- time is $525 per top. At Plant 2, the variable cost to produce one countertop during regular time is $390; the variable cost to produce during overtime is $500. At Plant 3, the variable cost to produce one countertop during regular time is $410; the variable cost to produce during overtime is $510 per top.

Your job is to analyze the data and make a recommendation as to whether or not JPC should close one of its manufacturing facilities. You should also indicate how much capacity and the type of capacity needed at the different operational manufactur- ers. In addition, you should note whether other factors should be considered in making this decision.

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INTERNET CHALLENGE: CRUISING • 537

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc. Assignment: Staff Planning at Cruise Interna- tional, Inc. You are meeting today with Monita

Terazzi, the Head of Housekeeping. She wants you to help her develop an 18-month staffing plan for the floor supervisors, head room supervisors, cabin stewards/stewardesses, and assis- tant cabin stewards/stewardesses. Currently, new employees (with 18-month contracts) are added to the ship every 3 months. Consider how the current method is working and make any rec- ommendations for improvement that you can. Also explain how the use of contract employees changes the aggregate planning

process. Completion of this assignment will enable you to enhance your knowledge of the material covered in Chapter 13 of the text. It will also better prepare you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Staff Planning at CII

On-line Case: Aggregate Planning at Valley Memorial Hospital You’re meeting today with Carol Gardner, assistant to

the chief administrator at VMH. She tells you she’s been saving a big project for you: “My job is scheduling all hospital staff for every shift, but I want you to focus on the nursing staff, so I can plan the nursing schedule for next year. After you finish, I’ve also got a smaller job for you. Let’s go back in my office and I’ll tell you what you need to know.”

To complete this assignment, go to www.wiley.com/college/ reid to get the details needed. Assignment questions are given at the site.

To access the Web site:

Aggregate planning identifies the resources needed by opera- tions during a specified time period. Once these resources have been authorized, operations must make do or justify any devia- tions from the plan. With this in mind, identify the resources if you and three of your peers take a cruise of 10 to 14 days during a period when school is not in session.

On the Internet, investigate at least three different cruise lines. Develop a set of criteria for selecting which cruise line you will use. Select a cruise that you would like to take. The destina- tion of the cruise is up to you. In preparation for the cruise, calculate how much and what kinds of clothing you’ll take, as well as what accessories (cameras, binoculars, snorkeling gear, golf clubs, etc.) and how much cash. Then check to see which items you own and which items you need to buy.

Remember to check the site to see how many formal nights, informal nights, casual nights, and theme nights there will be

during your cruise. Also check the shore excursions so you can calculate adequate funding. Don’t forget to check your itinerary so you have proper clothes for any ports you’ll be visiting (some countries frown on tank tops and shorts). Your professor should indicate any budgetary restrictions that you have.

You can put many of your expenses on a credit card, but you’ll also need cash for gratuities, incidental expenses at the different ports, and money for the casinos or bingo. You need to plan for transportation to and from the airport or for parking at the airport if one of you drives. You also need to be sure to take care of stopping your mail and taking care of any other respon- sibilities while you are away.

Develop an aggregate plan detailing the resources you will need for this cruise. Then disaggregate your aggregate plan by detailing what resources will be spent each day of your trip. Be ready to justify your expenditures.

INTERNET CHALLENGE Cruising

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538 • CHAPTER 13 AGGREGATE PLANNING

Online Resources Companion Website www.wiley.com/college/reid:

Motorola, Inc. Ferrara Fire Apparatus, Inc.

• Find links for Additional Web Resources for this chapter H&R Block, www.hrblock.com United Parcel Service of America, Inc., www.ups.com APICS—Educational Society for Resource Management, www.apics.org

Selected Bibliography Arnold, J.R. Tony, Stephen N. Chapman, and Lloyd M. Clive.

Introduction to Materials Management, Seventh Edition. Upper Saddle River, N.J.: Pearson Education Limited, 2012.

Blackstone, John H. Jr. Capacity Management. Cincinnati, Ohio: South-Western, 1989.

Boyer, John E., Jr. “Sales and Operations Planning Blueprint,” APICS Magazine, March/April 2008, 27–30.

Cox, James F., III, John H. Blackstone, and Michael S. Spencer, eds. APICS Dictionary, Eleventh Edition. Falls Church, Va.: American Production and Inventory Control Society, Inc., 2005.

Dougherty, John R. “Lessons from the Pros,” APICS Magazine, November/December 2007, 31–33.

Gessner, Robert A. Master Production Schedule Planning. New York: John Wiley & Sons, 1986.

Narasimhan, Sim, Dennis W. McLeavey, and Peter Billington. Production Planning and Inventory Control, Second Edi- tion. Englewood Cliff s, N.J.: Prentice-Hall, 1995.

Plossl, George W. Production and Inventory Control: Principles and Techniques, Second Edition. Englewood Cliff s, N.J.: Prentice-Hall, 1985.

Slack, Nigel, Stuart Chambers, and Robert Johnston. Opera- tions Management, Th ird Edition. Upper Saddle River, N.J.: Pearson Education Limited, 2001.

Vollmann, Th omas E., William L. Berry, D. Clay Whybark, and F. Robert Jacobs. Manufacturing Planning and Control Sys- tems, Fift h Edition. Burr Ridge, Ill.: McGraw-Hill/Irwin, 2005.

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CHAPTER

539

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

C H A P T E R O U T L I N E

Enterprise Resource Planning 541 Th e Evolution of ERP 542 Th e Benefi ts of ERP 545 Th e Cost of ERP Systems 546 Material Planning Systems 547 An Overview of MRP 547 Types of Demand 548 Objectives of MRP 550

MRP Inputs 550 Th e MRP Explosion Process 555 Action Notices 559 Comparison of Lot Size Rules 559 Th e Role of Capacity Requirements Planning (CRP) 560 Resource Planning within OM: How It All Fits

Together 562 Resource Planning across the Organization 563

Before studying this chapter you should know or, if necessary, review

1. E-commerce, Chapter 4, pp. 112–122. 2. Calculating available capacity, Chapter 9, pp. 334–339. 3. Calculating order quantities, Chapter 12, pp. 467–480. 4. Inventory record accuracy, Chapter 12, pp. 464–466. 5. Developing the MPS, Supplement D, pp. D4–D5.

After studying this chapter you should be able to

Resource Planning

1 Describe enterprise resource management. 2 Describe the evolution of ERP systems. 3 Describe the benefi ts and costs of ERP systems. 4 Provide an overview of MRP. 5 Explain the diff erent types of demand. 6 Describe the objectives of MRP. 7 Describe the inputs needed for MRP.

8 Explain MRP operating logic. 9 Describe action notices. 10 Use diff erent lot size rules with MRP. 11 Describe the role of capacity requirements planning

(CRP). 12 Calculate the workloads at critical work centers

using CRP.

L E A R N I N G O B J E C T I V E S

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540 • CHAPTER 14 RESOURCE PLANNING

Do you remember the first time you invited your fiancée and par-ents for a very special dinner at your place? You decided to serve salad, grilled steaks, corn-on-the-cob, baked potatoes, and apple pie à la mode. You decided to special order the steaks two days ahead of time. You needed to make the pie the night before, so you made a special trip to get fresh apples and the other necessary ingredients. You bought the ice cream, corn, salad mix, some tomatoes, cucumbers, croutons, and salad dressing the day of the dinner as well as picked up the steaks. You also managed to pick up a special bottle of wine to go with dinner.

The night before the dinner, you baked the apple pie. The day of the dinner, you started the baked potatoes about an hour before dinner- time. You then mixed your salad. You seasoned the steaks and started the grill about 30 minutes before dinner. At the same time, you put a pot of water on to boil to cook the corn. You put the steaks on the grill to cook and the corn in the boiling water. You set the table while you were waiting. Just as you had planned, the dinner was ready on

time. The steaks were done just right, the baked potatoes perfect, and the corn hot and juicy.

Your dinner was a huge success because you had used the basic concepts of mate- rial requirements planning: a master production schedule, bills of material, inventory records, and backward scheduling. The master production schedule was your planned menu for the dinner. To determine the materials needed, you had to know what you wanted to prepare. After setting the menu, the next step was to look at the bill of material (recipe) for each item. This allowed you to determine how much of each component or material was needed. Once you had the list assembled as to what was needed to make the dinner, you checked your inventory to see what you already had and what still had to be purchased.

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To use backward scheduling, we take a desired completion time or due date, consider all the activities that must be completed, and schedule the activities so that everything is ready at the appropriate time. Not all activities need the same amount of time to be completed, so we schedule when different activities must begin.

Your dinner didn’t take too long to prepare, but building products typically takes some time. Consider a company like Dell Computer, well known for its rapidly built- to-order computers. Dell begins assembly of a customer’s order almost immediately after receiving the order. To do this, Dell must carefully manage its component inventories, knowing the availability of needed components to complete the assembly. Information regarding the order is sent to members of its supply chain to assure on- time delivery of the finished computer. Dell uses this approach to support the concept of mass customization.

In this chapter, we will examine enterprise resource planning (ERP), as well as learn the basic mechanics of material requirements planning (MRP) and the role of capacity requirements planning (CRP).

� Backward scheduling Starts with the due date for an order and works backward to determine the start date for each activity.

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ENTERPRISE RESOURCE PLANNING • 541

Enterprise resource planning (ERP) is software designed for organizing and man- aging business processes (core and administrative) by sharing information across functional areas. Core processes include production planning and control, inventory management, purchasing, and distribution; administrative processes include account- ing (cost control, accounts payable and receivable, etc.) and human resource manage- ment. Figure 14-1 shows an overview of enterprise resource planning.

The number of finished goods sold to final customers is a good example of the type of useful information shared throughout the supply chain. Knowing actual sales figures allows improved decision making by members of the supply chain and can help eliminate the bullwhip effect (discussed in Chapter 4). For example, using the information about actual sales to the customer, manufacturing can determine more accurately the quantity and timing for product replenishments. Warehouse manage- ment then can plan for the receipt and subsequent distribution of the replenishments. Suppliers can determine the materials and components needed by manufacturing to meet the manufacturing schedule. All members of the supply chain are aware of what is happening and can plan accordingly. The primary objective of ERP is to integrate all departments and functions, internal and external, into a single computer system to serve the enterprise’s needs.

The availability of information can increase productivity as well as customer satis- faction. For example, after Master Product Company began using ERP, sales increased by 20 percent while inventory investment decreased 30 percent. Owens Corning reported saving $65 million by using ERP to coordinate customer orders, financial reporting, and global procurement. Currently, ERP systems are used in thousands of medium and large companies globally. These systems typically consist of modules that can be used either alone or in various configurations. Let’s look at the typical ERP modules.

� Enterprise resource planning (ERP) An information system designed to integrate internal and external members of the supply chain.

ENTERPRISE RESOURCE PLANNING

FIGURE 14-1

Enterprise resource management

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542 • CHAPTER 14 RESOURCE PLANNING

ERP Modules All modules are fully integrated, using a common database and support processes that go across functional areas. A transaction in any module is immediately available to all other modules and to all relevant parties. There are four basic categories of ERP mod- ules: finance and accounting, sales and marketing, production and materials manage- ment, and human resources.

The finance and accounting module can include the following capabilities: financial report generation, investment management, cost control analysis, asset management, capital management, debt management, and so on. It defines cost and profit centers, uses activity-based costing, facilitates capital budgeting and profitability analysis, and tracks enterprise performance measures. A company can see the financial implication of every transaction.

The sales and marketing function handles customer-related activities. A customer can check for pricing, availability, and shipping options, as well as special promo- tions. The sales module can do a profitability analysis using different pricing options, discount structures, and rebates. The module also allows more accurate delivery date projections by providing insight into a company’s finished goods and work-in- process inventories, as well as access to master scheduling information. Distribution requirements (documentation, packaging, etc.), transportation management (mode of transport), and shipping schedules are included. This module also handles billing, invoicing, rebate processing, product registrations, and customer complaints.

The production and materials module processes planning, bill of material gen- eration, and product costing. The module implements engineer change orders, plans material requirements (MRP), allocates resources, and schedules and monitors pro- duction. It links manufacturing, sales, and finance together in real time. In terms of materials, it generates purchasing needs, manages inventory and warehouse functions, and supports supplier evaluations and invoice verification.

The human resources module includes workforce planning, employee scheduling, training and development, payroll and benefits, expense reimbursement, job descrip- tions, organizational charts, and workflow analysis.

These four modules can be implemented either individually or as a fully integrated system. ERP uses a common database to ensure the same information is used through- out the company to improve decision making across functional areas. Let’s look at the evolution of ERP systems.

THE EVOLUTION OF ERP First-Generation ERP An ERP system provides a single interface for managing all routine activities per- formed in manufacturing—from order entry to after-sales customer service. In the later 1990s, ERP systems were extended to external members of the supply chain (suppliers and customers). These extensions provide customer interaction and sup- plier management modules. Using a single interface can provide significant savings. Large companies—for example, ExxonMobil—consolidated 300 different information systems into one ERP system by implementing SAP R/3 (a leading ERP system) in its U.S. petrochemical operations. Initially, ERP was designed to handle business transac- tions and not to support supply chains. The second generation of ERP was designed to overcome this deficiency.

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THE EVOLUTION OF ERP • 543

Second-Generation ERP First-generation ERP was designed to automate routine business transactions and did it very well. Merrill Lynch reported that almost 40 percent of U.S. companies with greater than $1 billion in annual revenues had implemented ERP. Most companies had received the major benefits of ERP systems by the late 1990s. The development of second-generation ERP systems has begun. Its objectives are to leverage existing systems to increase efficiency in handling transactions, improve decision making, and support e-commerce.

While first-generation ERP systems gave planners plenty of statistics about what happened in the company, in terms of costs and financial performance, the reports were merely snapshots of the business at a single point in time. These reports did not support the continuous planning needed in supply chain management. This defi- ciency led to the development of planning systems focused on decision making. These new systems are referred to as SCM (supply chain management) software.

SCM software is designed to improve decision making in the supply chain. It helps answer such questions as: (1) What is the best way to ship a product to a specific cus- tomer? (2) What is the optimal production plan? (3) How much product should ship to specific intermediaries? (4) How can outbound and inbound transportation costs be minimized? SCM software typically includes decision-support modules, such as linear programming and simulation, to help answer these questions.

Let’s consider how ERP and SCM software can work together. Think about the task of order processing. With SCM software, the question is, “Should I take your order?” while the ERP approach is, “How can I best take or fulfill your order?” Both are merely infor- mation systems. SCM systems complement ERP systems, providing intelligent decision support. The SCM system is designed to overlay existing systems and extract data from every part of the supply chain. This way, the company has a clear picture of where it is heading rather than simply having automated processes. Supply chain intelligence (SCI) is the capability of collecting business intelligence along the supply chain. This intelli- gence enables strategic decision making by analyzing data along the entire supply chain.

An example of a successful SCM system implementation is IBM. IBM restructured its global supply chain to achieve quick responsiveness to customers while holding minimal inventory. To do this, IBM developed an extended-enterprise supply chain analysis tool called the Asset Management Tool (AMT). AMT allows for quantitative analysis of interenterprise supply chains. IBM used AMT to analyze inventory bud- gets, inventory turnover objectives, customer service target levels, and new-product introductions. AMT benefits have included the saving of over $750 million in material costs and price-protection expenses each year.

Another example of an ERP application with an SCM module is Colgate- Palmolive (C-P). C-P produces oral-care products (mouthwashes, toothpaste, and toothbrushes), personal-care products (baby care, deodorants, shampoos, and soaps), and pet food. Foreign sales account for about 70 percent of C-P’s total revenues. An important fac- tor for C-P was whether it could use ERP software across the entire spectrum of its business. The company needed the ability to coordinate globally, yet act locally. C-P’s U.S. division opted to use SAP R/3 for this effort.

Another option for businesses wanting ERP functions is to lease applications rather than to build systems. In leased applications, the ERP vendor takes care of the func- tionalities and internal integration problems. The ERP vendor is typically referred to as an application service provider (ASP). The ASP sets up the system and runs it for the company. This approach often works well for small to medium companies. The software is usually delivered via the Internet.

� SCM software Designed to improve decision making in the supply chain.

� Supply chain intelligence (SCI) Enables strategic decision making along the supply chain.

� Application service provider (ASP) Sets up and runs ERP systems.

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544 • CHAPTER 14 RESOURCE PLANNING

Since many companies involved in e-commerce have ERP systems, and since e-commerce needs to interface with the ERP systems, integration is necessary, primar- ily for order fulfillment and collaboration with business partners.

Let’s look at how a nonmanufacturing com- pany might use an ERP system. The Arapahoe County government serves a population of more than 537,000, nine school districts, 14 incorpo- rated communities (including the county seat of Littleton), and 163 local improvement and ser- vice districts. It has an annual operating budget of approximately $300 million and a staff of 1800. Currently, the county has one of the lowest prop- erty tax rates in the Denver metropolitan area.

The key challenges facing the county govern- ment included multiple, separate, stand-alone financial systems; limited visibility into financial operations; outdated information (six to eight weeks old); difficulty in complying with new government reporting regulations; an inefficient procure-to-payment process; and multiple rec- onciliations needed to keep information in sync. No vendor payment discounts were taken.

Even though Arapahoe County only has 1800 employees, it has multiple financial systems. Numerous Microsoft Excel spreadsheets and Microsoft Access databases had been developed by individuals over the years. These disparate systems could not communicate with each other, so simple inquiries could take weeks to answer. Ven- dor payments were often late, resulting in project delays. Monthly reports took six to eight weeks to produce and were out of date before they were completed. Officials had trouble managing the constrained budgets because of the lack of visibility into the future.

After implementing an ERP system, the following strategic and financial benefits were noted: elimination of redundant financial systems; improved timeliness and accuracy of key reports; improved vendor relations and increased negotiating power; improved visibility; compliance with the latest government reporting requirements; more staff time for value-added tasks; and increased vendor payment discounts. The following operational benefits occurred: purchase order cycle time was reduced 80 percent; the age of information in monthly reports was reduced from six to eight weeks to real time; full-time staff equivalents in accounts payable and purchasing were reduced 50 percent; and the financial closing process time (done annually) was reduced by 150 hours.

The use of an ERP system by Arapahoe County illustrates that such systems are useful in nonmanufacturing organizations as well as in traditional manufacturing companies. You should understand that ERP systems deal more often with back-office applications (accounting, inventory, scheduling, etc.), while e-commerce applications deal more with front-office applications such as sales, order entry, customer service, and customer relationship management. You also should understand that order or service fulfillment problems occur since so many customers are served. Companies operating on-line must find the products or information that are needed, package them in an appropriate manner, arrange for delivery to the customer, collect money from the customers if appropriate, and handle the return of unwanted or defective products or services.

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THE BENEFITS OF ERP • 545

One benefit of ERP is that it integrates the complete range of an organization’s opera- tions in order to present a holistic view of the business functions from a single informa- tion and IT architecture. This single information source improves the organizational information flow. Because of improved information flow, an organization increases its ability to incorporate best practices that facilitate better managerial control, speedier decision making, and cost reductions throughout the organization. The basic archi- tecture of an ERP system builds upon a single database, one application, and a unified interface across the entire enterprise, thus allowing an integrated approach.

A study by Benchmarking Partners for the Deloitte & Touche consulting corpora- tion classifies companies’ motivations for implementing ERP systems into two groups: technological and operational. Technological motivation relates to the replacement of disparate systems; improved quality and visibility of information; integration of busi- ness processes and systems; replacement of older, obsolete systems; and the acquisi- tion of systems that can support future business growth. For example, ExxonMobil used ERP to replace 300 different systems.

Operational motivation is related to improving inadequate business performance, reducing high-cost structures, improving customer responsiveness, simplifying complex processes, supporting global expansion, and standardizing best practices throughout the enterprise. Cybex International is a good example of an organization using ERP to improve customer responsiveness.

ERP provides both tangible and intangible benefits. Tangible benefits refer to reductions in inventory and staffing, increased productivity, improved order manage- ment, quicker closing of financial cycles, reduced IT and purchasing costs, improved cash flow management, increased revenue and profits, reduced transportation and logistics costs, and improved on-time delivery performance. Intangible benefits refer to the improved visibility of corporate data, improved customer responsiveness, bet- ter integration between systems, standardization of computing platforms, improved flexibility, global sharing of information, and better visibility into the supply chain management process.

A study of ERP implementations reported that it took companies eight months until after the new system was established to see any benefits. The median annual sav- ings from a new ERP system was $1.6 million.

One company that has devel- oped various ERP software pack- ages to enhance manufacturers’ overall productivity is i2 Tech- nologies. By using this software, manufacturers can now improve supply chain activities by moni- toring, managing, and optimiz- ing their internal and external activities. For example, manufacturers can connect immediately with suppliers and shippers in real time and can examine the supply chain. In addition, manufacturers can obtain reports that discuss efficiency and forecast potential problems. i2 Tech- nologies’ Transportation Solutions helps manufacturers optimize delivery schedules. Also, i2 Technologies’ Softgoods Matrix.com helps softgoods retailers, manufacturers, and suppliers coordinate on-line business, improve response to changes in consumer trends, and attract potential customers. Some of the world’s largest manufacturing firms have adopted software developed by i2 Technologies.

THE BENEFITS OF ERP

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546 • CHAPTER 14 RESOURCE PLANNING

SAP AG, one of the leading developers of enterprise solutions software, provides companies with mySAP.com, a software platform for open systems. Open systems allow users to communicate with another user without being constrained by a par- ticular organization’s solution. This software includes functionality for material requirements planning, including manufacturing and financial applications, materials management, product design management, sales and distribution, human resources, production planning, quality assurance, and plant maintenance. SAP also provides functionality that promotes the ability to do collaborative planning on the Web via collaborative exchanges and public marketplaces.

Although enterprise software (formerly known as ERP) is often associated with manufacturing operations, it also has applications in the service sector. SAP Public Sector and Education SAP Public Services, Inc. has announced a plan to offer an offender management system that may revolutionize the corrections industry. The initial implementation will modernize the Commonwealth of Virginia’s Department of Corrections. The SAP offender management system will provide Web-based case management. The new system will enable the Virginia Department of Corrections to enter into the e-government world.

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THE COST OF ERP SYSTEMS SAP AG, Peoplesoft, Oracle, and Infor Global Solutions are major suppliers of ERP systems. The cost of an ERP system ranges from hundreds of thousands of dollars to several million dollars. In addition to the software cost is the cost of outside consultants used in the selection, configuration, and implementation of the ERP system. An IT research firm, Gartner Group, reports that companies can expect to spend up to three times as much money for consultants as they do for the ERP system. Additional costs include the human resources needed to work on the implementation of the system, new hardware to run the program, and the development of a new, integrated database.

A review of successful ERP implementations indicates that the most critical factors are leadership and top management commitment. Top management must clearly set the vision and direction for the business, as well as establish a culture that enables the business to benefit by using the technological capabilities of an ERP system. Champi- ons are needed to effectively implement change programs and promote best practices. Let’s look at how some companies have used ERP.

When selecting the ERP system to use, a study by the Aberdeen Group (www .aberdeen.com) reports that functionality, ease of use, and total cost of ownership (TCO) are the top three selection criteria. Total cost of ownership is the software cost, services cost, and three years of maintenance cost.

Service costs are for external professional services. These might include implemen- tation, training, customization, or consulting, but do not include company employees. Maintenance costs are based on average maintenance fees paid in aggregate and on a per user basis. Maintenance costs include technical support and bug fixes as well as new product innovations.

Total cost of ownership varied based on size of the company. For companies with less than $50 million in annual sales, the average total was $366,583. Companies between $50 and $100 million had an average total cost of $892,765. For companies with sales between $500 million and $1 billion, average total costs were $3,483,776. And for companies with sales exceeding $5 billion, the average total cost was $7,148,750. The average number of users in these companies went from a low of 35 up to a high of 3274.

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AN OVERVIEW OF MRP • 547

The study also reported the business benefits achieved through ERP. Companies expect to reduce inventory investment, manufacturing operational costs, and admin- istrative costs, improve complete and on-time shipments, and enhance manufacturing schedule compliance. Companies implementing ERP generally reported improve- ments in the 10 to 20 percent range.

Now that you have a basic understanding of ERP systems, let’s examine the manu- facturing planning systems that were the basis for today’s production and materials modules.

MATERIAL PLANNING SYSTEMS In the 1960s, manufacturing planning systems focused primarily on traditional inven- tory control issues (when to order, how much to order, etc.). This led to the develop- ment of material requirements planning (MRP) systems. These systems translated the approved master production schedule of final products into time-phased net require- ments for subassemblies and final assemblies for manufacturing and components and raw materials for purchasing. The initial MRP systems evolved into closed-loop MRP.

Closed-loop MRP is an MRP system that includes sales and operations planning, master production scheduling, and capacity requirements planning (discussed in Chapter 13). After realistic and attainable plans are developed, manufacturing exe- cutes the plan. This involves input–output capacity measurement, detailed scheduling and dispatching (we will discuss these in Chapter 15), anticipated delay reports from the manufacturing facility and the suppliers, as well as scheduling deliveries from sup- pliers. Closed-loop means that each function is included in the overall system and that feedback mechanisms are in place to make sure that the plan remains valid.

In the mid-1970s, manufacturing resource planning (MRP II), the next gen- eration of manufacturing planning systems, was developed. MRP II has three major components: management planning, operations planning, and operations execution. The company’s strategy is translated into business objectives for the current year. These objectives drive the development of the marketing plan that in turn drives the development of the production plan. The production plan identifies the resources available to manufacturing to achieve the output needed by marketing. Then the master production schedule shows how the resources from the production plan are to be used. Operations planning is the MRP function. One of the primary inputs to the MRP system is the master production schedule. The output from the system is the order release schedule. Operations execution brings the plan to life. Raw materials and components are purchased, subassemblies and final assemblies scheduled, quality assured, labor managed, and production completed. Problems encountered in pro- duction are fed back to the MRP component. Ongoing performance evaluation pro- vides feedback—that is, additional resource requirements, changing market demands, and so on—to business planning for any necessary corrective actions. Shortcomings in MRP II in managing a production facility’s orders, production plans, and inven- tories, along with the need to integrate external functions, led to the development of ERP systems.

� Closed-loop MRP An MRP system that includes production planning, master production scheduling, and capacity requirements planning

� Manufacturing resource planning (MRP II) A method for the effective planning and integration of all internal resources.

AN OVERVIEW OF MRP Material requirements planning (MRP) is an information system that uses the con- cept of backward scheduling. MRP enables companies that produce items in batches to have the right materials in the right amounts available at the right time. While having the material is critical, the company also needs the capacity to process the materials

� Material requirements planning (MRP) A system that uses the MRP, inventory record data, and BOM to calculate material requirements.

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548 • CHAPTER 14 RESOURCE PLANNING

on time. Companies use capacity requirements planning (CRP) to check that enough work is scheduled for operations and that the amount of work is feasible. CRP reveals potential problems, which gives operations a chance to prevent problems from occur- ring. For example, if you know that you need 250 hours of test equipment time four weeks from now and you only have 200 hours of test equipment time available during that week, you can do something about it now. You can change the master schedule so that some of the items needing testing are scheduled for a different time period, or you can authorize additional workers in the test area, or authorize overtime for that work center. You don’t wait until four weeks from now and then figure out what to do.

When having your dinner party we described at the beginning of this chapter, you had to do several activities before you served the dinner. First, you planned the menu. Second, you determined the number of servings needed. Third, you reviewed the recipes for each item on the menu to determine the materials needed. Fourth, you checked your cupboards, refrigerator, and freezer to see if you had any of the materials on hand. Fifth, you purchased any materials that you still needed. Sixth, you prepared the dinner.

Planning the menu and calculating the number of servings is equivalent to creating an authorized master production schedule (MPS). Reviewing the recipes to determine the materials needed is equivalent to checking the bill of material (BOM) file to determine the materials needed to build a product. The BOM file lists all the subas- semblies, component parts, and raw materials that go into the end item and shows the usage quantity of each. Using the list of components and materials needed, MRP checks the inventory records to determine if sufficient quantities of those materials are available or if the purchasing department needs to procure these materials. MRP systems are designed to handle dependent demand. Let’s look at different types of demand.

� Capacity requirements planning (CRP) Determines the labor and machine resources needed to fill the open and planned orders generated by the MRP.

� Bill of material (BOM) Lists all the subassemblies, component parts, and raw materials that go into an end item and shows the usage quantity of each required.

TYPES OF DEMAND The two types of demand are independent and dependent. Independent demand is the demand for finished products; it does not depend on the demand for other prod- ucts. Finished products include any item sold directly to a consumer. For example, if a company builds and sells CD cabinets, the demand for the CD cabinet is not depen- dent on anything else. The company could also sell decorative replacement hinges or handles as independent products. Figure 14-2 is a drawing of the CD cabinet. Although you can’t see inside the cabinet, it does have four shelves.

Dependent demand is derived from finished products. For example, when a company makes CD cabinets (see Figure 14-2), it needs tops, bottoms, feet, doors, door magnets, door hinges, door handles, screws, left sides, right sides, door catches,

� Independent demand The demand for an item is unrelated to the demand for other items.

� Dependent demand Demand for component parts is based on the number of end items being produced.

FIGURE 14-2

A CD Cabinet

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TYPES OF DEMAND • 549

cabinet shelves, and shelf holders. The company can determine how many of each of these items is needed based on how many CD cabinets the company plans to build. If the company builds 100 CD cabinets, operations needs 100 tops, 100 bottoms, 100 doors, 100 left sides, 100 right sides, 200 door hinges (2 are needed to build one fin- ished product), 400 feet and 400 cabinet shelves (4 are needed to build one finished product), 1000 screws (8 are needed for the door to attach the hinges, magnet, and handle; and 2 are needed for the right side to attach the door catch), and 1600 shelf holders (16 for each finished product). The company does not forecast dependent demand but, rather, calculates the material needs based on the final products to be produced. MRP computerized information systems are designed to manage depen- dent demand inventory and to schedule necessary replenishment orders. Let’s look at a typical MRP system.

Figure 14-3 is an overview of an MRP system. The authorized MPS is the primary input to the MRP system. The MPS details the company’s planned products, quantity, and the schedule used by marketing when promising deliveries. The product due dates are critical to the MRP system since they set the completion dates used to backward- schedule production. Part of the MRP system is developing a time-phased schedule that shows future demand, supply, and inventories by time period. The time-phased schedule shows the production planner when in the production process parts and materials must be available. Not all parts and materials have to be available at the start of production, but they must be available at the stage of production in which they are needed. For example, when you are building a furniture cabinet, you do not need the stain before you start building the cabinet; you need it when you are ready to apply the finish. On the other hand, you must have the wood before you can begin building the cabinet.

The MRP system checks the BOM file to determine the materials needed, how much, and when. The system generates the gross requirements of each part and mate- rial needed to accomplish the MPS. The system inserts the gross requirements into the individual inventory records and computes the projected available quantity for each item so that you know if there’s enough inventory or if you need a replenishment order. If you need a replenishment order, the MRP system tells you when to place the order, either to a supplier or to the manufacturing floor, to ensure that the parts or

� Time-phased Expressing future demand, supply, and inventories by time period.

� Gross requirements The total-period demand for an item.

FIGURE 14-3

Overview of the MRP process

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550 • CHAPTER 14 RESOURCE PLANNING

material are available when needed. The MRP system generates planned replenish- ment order release schedules and can generate additional reports, which we discuss later in the chapter. Now let’s consider the objectives of an MRP system.

OBJECTIVES OF MRP The objectives of an MRP system are to determine the quantity and timing of material requirements and to keep priorities updated and valid.

● Determine the quantity and timing of material requirements. Your company uses MRP to determine what to order (it checks the BOM), how much to order (it uses the lot size rule for the specifi c item), when to place the order (it looks at when the material is needed and backward-schedules to account for lead time), and when to schedule delivery (it schedules the material to arrive just as it is needed).

● Maintain priorities. Your company also uses MRP to keep priorities updated and valid. Requirements change. Customers change order quantities and/or timing. Suppliers deliver late and/or the wrong quantities. Unexpected scrap results from manufacturing. Equipment breaks down and production is delayed. In an ever- changing environment, you use an MRP system to respond to changes in the daily environment, to reorganize priorities, and to keep plans current and viable.

Next, we discuss the inputs needed to run an MRP system.

MRP INPUTS The three inputs to an MRP system are the authorized MPS, the BOM, and the indi- vidual item inventory records. Using the CD cabinet from Figure 14-2 as our end item, let’s look at each of these inputs.

Authorized MPS The authorized MPS is a statement of what and when your company expects to build. Table 14-1 shows the first MPS record for the CD cabinet. From the MPS record, we calculate when we need to have replenishment orders of CD cabinets. We calculate the timing of MPS orders by the projected available quantity. When we do not have enough inventory to satisfy the forecast for a particular period, we need an MPS order. The quantity of the replenishment order is based on the lot sizing rule used. Table 14-2 shows the completed MPS record.

Inventory Records To determine whether enough inventory is available or whether a replenishment order is needed, the MRP system checks the inventory records of all items listed in the

Item: CD Cabinet Lead time: 1 week

Lot size rule: FOQ 5 100 Beginning inventory: 80 1 2 3 4 5 6 7 8 9 10 11 12

Gross Requirements: 25 25 25 25 30 30 30 30 35 35 35 35

Projected Available: 55 30 5 220 MPS

TABLE 14-1

Initial MPS Record for CD Cabinet

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MRP INPUTS • 551

BOM. Table 14-3 shows the CD cabinet’s inventory record. Let’s look at the informa- tion in the record.

The top part of the record contains product or part identification information— typically either a part number, part name, or description. In our example, the part name is the CD cabinet. The top portion also contains planning factors. These can include the lot size rule, lead time, safety stock requirements, and so forth.

In our example, the lot size rule is lot-for-lot (L4L), and the planned lead time is one week. This information remains relatively constant and is needed by the system to determine how much to order and when to place the replenishment order. Addi- tional information in the records changes with each inventory transaction. These transactions include releasing new orders, receiving previously ordered materials, withdrawing inventory, canceling orders, correcting inventory record errors, and adjusting for rejected shipments. The record shows how much inventory of an item is available, projects future needs, and shows the projected inventory level in different time periods.

One problem with an MRP system is inventory record accuracy. Because the system checks the inventory record to see whether it has to generate a replenishment order, an inaccuracy in the record can cause an error in replenishment ordering. Cycle count- ing, discussed in Chapter 12, is a technique for improving inventory record accuracy. Let’s look at the inventory record shown in Table 14-3.

The item is a CD cabinet and the lot size rule is lot-for-lot. The lead time is one week. Thus if we want 100 CD cabinets to be available in week 4, we have to begin the final assembly of the CD cabinets in week 3. For our purposes, gross requirements are due at the beginning of the period (Monday morning), and planned orders are started at the beginning of a time period. Final assembly is done during week 3 so we can have 100 CD cabinets at the beginning of week 4.

Gross requirements for finished products are taken from the authorized MPS. Scheduled receipts are replenishment orders that have been placed but not yet received. For example, if we placed an order last week and we know it will arrive in period 1, it would be in the scheduled receipts row.

� Planning factors Factors include the lot size rule, replenishment lead times, and safety stock requirements. � Lead time The span of time needed to perform an activity or series of activities.

� Scheduled receipt An open order that has an assigned due date.

Item: CD Cabinet Lead time: 1 week

Lot size rule: FOQ � 100 Beginning inventory: 80 1 2 3 4 5 6 7 8 9 10 11 12

Gross Requirements: 25 25 25 25 30 30 30 30 35 35 35 35

Projected Available: 55 30 5 80 50 20 90 60 25 90 55 20

MPS 100 100 100

TABLE 14-2

Updated MPS Record for CD Cabinet

Item: CD Cabinet Lead time: 1 week

Lot size rule: L4L Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 0 100 0 0 100 0 0 100 0 0 Scheduled Receipts: Projected Available: 0 0 0 –100 Planned Orders

TABLE 14-3

First Inventory Record for CD Cabinet

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552 • CHAPTER 14 RESOURCE PLANNING

The projected available quantity is a period-by-period projection of how much inventory should be available. The projected available quantity equals the beginning inventory, plus any replenishment order due, less the gross requirements for that period. For example, in period 4, we have no beginning inventory and we have 0 units scheduled to arrive, less our gross requirements of 100 units in period 4. Thus our projected available at the end of period 4 is –100, as shown in Table 14-3. The begin- ning inventory for any time period is equal to the projected available quantity at the end of the previous period.

Planned orders result when we do not have enough inventory to cover the gross requirements for a period. For example, unless we plan an order to arrive in period 4, we will be short 100 CD cabinets. When we need a replenishment order, we calculate the quantity by the lot size rule and we calculate the timing by the lead time. For example, we need an order to arrive in period 4, the lot size rule L4L dictates that we order just enough to cover our requirement (100 units), and the lead time of one week means that we must place the order one week before we need it (so we have a planned order of 100 units in period 3). Table 14-4 shows the updated inventory record for the CD cabinet.

Bills of Material A bill of material (BOM) lists the subassemblies, intermediate assemblies, component parts, raw materials, and quantities of each needed to produce one final product. It is exactly like a recipe for baking a cake. As we would follow the recipe for the cake, the manufacturer is expected to follow the BOM precisely. No extra parts are added. No substitutions are made without appropriate paperwork. Companies that use MRP systems must have a disciplined workforce that uses only the materials authorized by the BOM. The BOMs used as input to the MRP system are indented bills of materials. Table 14-5 shows an indented bill of material for the CD cabinet. In an indented BOM, the highest-level item (“parent”) is closest to the left margin, with components (“chil- dren”) going into that item indented to the right. In our example, the CD cabinet is the highest-level item and all the components are indented. The components for the cabinet door are indented even farther to the right since these components go directly into the door assembly rather than the CD cabinet.

A product structure tree visually represents the BOM for a product. Although product trees are seldom used in the workplace, for our purposes they make it easier to explain the MRP process. Figure 14-4 is a product structure tree for the CD cabinet with the name of the item, the usage quantity per parent item, and the replenishment lead time.

At the top of the product structure tree is the end item, the product sold to the customer. In this case, the end item is the CD cabinet, but the end item could also be a repair part such as decorative hinges or door handle.

� Projected available The inventory balance projected into the future.

� Planned orders Suggested order quantities, release dates, and due dates created by an MRP system.

� Indented bill of material Shows the highest-level “parents” closest to the left margin and the “children” indented toward the right. Subsequent levels are indented farther to the right.

� Product structure tree The visual representation of the BOM, clearly defining the parent–child

� End item A product sold as a completed item or repair part.

Item: CD Cabinet Lead time: 1 week

Lot size rule: L4L Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 0 100 0 0 100 0 0 100 0 0 Scheduled Receipts: Projected Available: 0 0 0 0 0 0 0 0 0 0 0 0 Planned Orders 100 100 100

TABLE 14-4

Updated Inventory Record for CD Cabinet

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MRP INPUTS • 553

Part Number Description Quantity Required

CD1001-01 CD Cabinet 1

CD1001T-01 Cabinet top 1

CD1001B-01 Cabinet bottom 1

CD1001F-01 Feet 4

CD1001D-01 Cabinet door 2

CD1001DM-01 Door magnet 1

CD1001DH-01 Door hinges 2

CD1001DK-01 Door handle 1

CD1001DS-01 Screws 8

CD1001S-01 Cabinet sides 2

CD1001SC-01 Door catch 1

CD1001DS-01 Screws 2

CD1001SH-01 Cabinet shelf 4

CD1001SS-01 Shelf holder 16

TABLE 14-5

Indented BOM

In the MRP system, a parent item is any end item made from one or more components. In our example, the CD cabinet is made from these components: a cabinet top, a cabinet bottom, cabinet door, cabinet left side, cabinet right side, 4 cabinet shelves, and 16 shelf holders. To simplify MRP processing logic, we call the end item the “parent” and its components the “children,” and we show each item’s parents or children in each of the inventory records. Table 14-6 shows the updated inventory record for the CD cabinet with this additional information. Since the CD cabinet is the end item, it has no parents. The immediate components of the CD cabinet are its children.

� Parent item An item produced from one or more children (components).

� Components Raw materials, purchased items, or subassemblies that are part of a larger assembly.

FIGURE 14-4

Product structure tree

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554 • CHAPTER 14 RESOURCE PLANNING

Item: CD Cabinet Parent: none

Lot size rule: L4L Children: Top, bottom, doors, sides,

Lead time: 1 week shelves, shelf supports

Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12

Gross Requirements: 0 0 0 100 0 0 100 0 0 100 0 0

Scheduled Receipts:

Projected Available: 0 0 0 0 0 0 0 0 0 0 0 0

Planned Orders 100 100 100

TABLE 14-6

Updated Inventory Record for CD Cabinet

When your company has inventory on hand, the lead time can be less than the cumulative lead time. Suppose all the feet were already in inventory. The lead time of the CD cabinet is reduced to eight weeks, the next-longest path through the product structure tree since the feet to cabinet bottom to CD cabinet path would only need five weeks now, since the feet are already in stock. Thus, having inventory on hand allows you to respond more quickly because you can reduce lead times.

We need to calculate the cumulative lead time for the end item, a CD cabinet.

• Before You Begin: Determine how long it takes to build a CD cabinet if none of the activi- ties has been completed. You need to order every component part, build every subassembly, and complete the final assembly. Start by looking at the product structure tree. Determine the total time required for each connected pathway or route from the bottom to the top. For example, look at the component hinges in Figure 14-4. The lead time for the hinges is four weeks. The parent of the hinges is the cabinet door. Lead time for the cabinet door is three weeks. Continue on to the parent for the cabinet door, the final assembly of the CD cabinet. Its lead time is one week. The total lead time for this connected pathway or route is eight weeks (4 � 3 � 1). Cumulative lead time for this product is the largest value associated with any individual connected path from the lowest level to the final assembly level. Beginning inventories can reduce the amount of time it takes to complete an order. For example, if sufficient hinges were already available, four weeks of lead time is subtracted from the lead time of this connected path.

• Solution: We can use the product structure tree to calculate the cumulative lead time for the end item, a CD cabinet. We do this by summing the individual lead times for each route from the lowest level to the end item. The first possible route includes only the cabinet top and final assembly of the CD cabinet. Thus the total lead time for this route is four weeks (three weeks for the cabinet top and one week for the CD cabinet final assembly). The next path includes the feet, the cabinet bottom, and the final assembly of the CD cabinet for a total of nine weeks, which is the longest path through the product structure tree. Table 14-7 shows all the paths through the product structure tree.

EXAMPLE 14.1

Calculating Cumulative Lead

Time for a CD Cabinet

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THE MRP EXPLOSION PROCESS • 555

Table 14-7 Paths through the Product Structure Tree

Path from Bottom to Top Cumulative Lead

Time (weeks) Cabinet top to CD cabinet 4 Feet to cabinet bottom to CD cabinet 9 Magnet to cabinet door to CD cabinet 6 Hinge to cabinet door to CD cabinet 8 Handle to cabinet door to CD cabinet 7 Screws to cabinet door to CD cabinet 5 Cabinet side to CD cabinet 4 Door catch to side to CD cabinet 5 Screws to side to CD cabinet 5 Shelf to CD cabinet 3 Shelf holder to CD cabinet 2

THE MRP EXPLOSION PROCESS

� Explosion process Calculates the demand for the children of a parent by multiplying the parent requirements by the children’s usage as specified in the BOM.

Complete the MRP records for each of the items in the bill of material for the CD cabinet.

• Before You Begin: In this problem, determine the timing of planned orders for each item used in the construction of the CD cabinet. Using input from the master production schedule, deter- mine the timing of the finished CD cabinet (shown in Table 14-6). Process the MRP records, level by level. Complete all of the level-one items before beginning the level-two items, and so on. The end result of this problem should be completed MRP records for every item used in the CD cabinet.

• Solution: This example illustrates the MRP explosion process. Using Table 14-6, we begin the MRP explosion process. MRP calculates the materials needed to meet the authorized MPS. The gross requirements for end items are always dictated by the authorized MPS. When we input these quantities into the proper time frame, MRP calculates the gross requirements for components. The MRP program begins by pro- cessing the inventory records of each component of the end item.

We will work through this example starting with the cabinet top. Table 14-8a shows the appropriate inventory record. Let’s look at the differences in the inventory record. First, the lot size rule is a fixed- order quantity of 144 units, which means the order quantity is always 144 units. If an order of 144 units is not enough to cover the gross requirements, we can place a double order (288 units) or triple order (432 units). The lead time is three weeks, so we must place the replenishment order three weeks before it is needed. Gross requirements for a component, or child, are determined by the planned orders of its parent or parents.

EXAMPLE 14.2

The MRP System at Storage Solutions by Elyssa, Inc.

Before You Go On

Be sure that you understand the logic behind MRP. The system checks the gross requirements for each period, compares that with the inventory available (the beginning inventory for that period, plus any replenishment orders due). If the gross requirements exceed the inventory available, an order must be scheduled to arrive in that period. The system calculates the timing of the replenishment order by subtracting the lead time (in weeks) from the period the material is needed to satisfy the gross requirements. The system calculates the quantity of the replenishment order by the lot size rule for that item.

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556 • CHAPTER 14 RESOURCE PLANNING

The planned orders of the parent item determine the timing of the gross requirements of the child. In our case, the parent item (the CD cabinet) has planned orders in periods 3, 6, and 9, and its children (the top, bottom, door, sides, shelves, and shelf supports) will all have gross requirements in periods 3, 6, and 9. The quantity of the gross requirement for the child is determined by the usage quantity. Since each CD cabinet needs one cabinet top, the gross requirement for the cabinet top is 100 pieces. This is the planned order quantity of the parent multiplied by the usage rate of the child (100 3 1). The begin- ning inventory of the cabinet tops is 120 units.

We can see from the inventory record in Table 14-8a that we need replenishment orders (each for a quantity of 144 units) in periods 3 and 6. If no replenishment order is placed in period 3, we will not have enough cabinet tops to satisfy our gross requirement in period 6.

Table 14-8 (a–f) has the inventory records for all the children of the CD cabinet. Note that all of the CD cabinet children have gross requirements in periods 3, 6, and 9. This is because the timing of gross requirements for a child is derived from the planned orders of its parent or parents. After the system sets the gross requirements, it projects the available inventory and back-schedules replenish- ment orders using the lead time needed for the order to arrive in the appropriate period. For example, the 144 cabinet tops ordered in period 3 will arrive in period 6 to help satisfy the gross requirement in that period. The next order for cabinet tops will be placed in period 6 to arrive in period 9. Quantities shown as scheduled receipts have already been ordered. See Table 14-8b, period 3.

Table 14-8 Inventory Records for CD Cabinet Components Table 14-8a Inventory Record for Cabinet Top Item: Cabinet Top Parent: CD Cabinet Lot size rule: FOQ 5 144 Children: none Lead time: 3 weeks Beginning inventory: 120

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 100 0 0 100 0 0 100 0 0 0 Scheduled Receipts: Projected Available: 120 120 20 20 20 64 64 64 108 108 108 108 Planned Orders: 144 144

Table 14-8b Inventory Record for Cabinet Bottom Item: Cabinet Bottom Parent: CD Cabinet Lot size rule: FOQ 5 144 Children: Feet Lead time: 3 weeks Beginning inventory: 20

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 100 0 0 100 0 0 100 0 0 0 Scheduled Receipts: 144 Projected Available: 20 20 64 64 64 108 108 108 8 8 8 8 Planned Orders: 144

Table 14-8c Inventory Record for Cabinet Door Item: Cabinet Door Parent: CD Cabinet Lot size rule: FOQ 5 216 Children: Magnet, hinge, handle, screws Lead time: 4 weeks Beginning inventory: 120

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 200 0 0 200 0 0 200 0 0 0 Scheduled Receipts: 216 Projected Available: 120 120 136 136 136 152 152 152 168 168 168 168 Planned Orders: 216 216

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THE MRP EXPLOSION PROCESS • 557

Table 14-8d Inventory Record for Cabinet Sides Item: Side Parent: CD Cabinet Lot size rule: FOQ 5 216 Children: none Lead time: 3 weeks Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 200 0 0 200 0 0 200 0 0 0 Scheduled Receipts: 216 Projected Available: 0 0 16 16 16 32 32 32 48 48 48 48 Planned Orders: 216 216

Table 14-8e Inventory Record for Cabinet Shelves Item: Shelf Parent: CD Cabinet Lot size rule: L4L Children: none Lead time: 2 weeks Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 400 0 0 400 0 0 400 0 0 0 Scheduled Receipts: Projected Available: 0 0 0 0 0 0 0 0 0 0 0 0 Planned Orders: 400 400 400

Table 14-8f Inventory Record for Shelf Supports Item: Shelf Supports Parent: CD Cabinet Lot size rule: FOQ 5 2500 Children: none Lead time: 1 week Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 1600 0 0 1600 0 0 1600 0 0 0 Scheduled Receipts: Projected Available: 0 0 900 900 900 1800 1800 1800 200 200 200 200 Planned Orders: 2500 2500

After MRP reviews and updates the children of the CD cabinet, it drops to the next lower level in the BOM and processes the inventory records at that level. In our example, those records are for the door magnet, door hinge, handle, screws, door catch, and feet. The cabinet door is the parent item for the mag- net, hinge, handle, and screws. The cabinet right side is the parent of the door catch as well as a second parent for the screws. The cabinet bottom is the parent of the feet. Table 14-9 shows the inventory records for these remaining components. The process is the same as for the children of the CD cabinet. You look to the planned order releases of the parent item to determine the gross requirements of the components. For example, look at Table 14-9c (the inventory record for the handle); its parent (cabinet door) has planned orders in periods 3 and 6. Therefore, the handle must have gross requirements in periods 3 and 6. Next, let’s look at how MRP provides information to the production and inventory control planners.

Table 14-9 Inventory Records for Remaining Components Table 14-9a Inventory Record for Door Magnet Item: Door Magnet Parent: Cabinet Door Lot size rule: FOQ 5 250 Children: none Lead time: 2 weeks Beginning inventory: 12

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 216 0 0 216 0 0 0 0 0 0 Scheduled Receipts: Projected Available: 12 12 46 46 46 80 80 80 80 80 80 80 Planned Orders: 250 250

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558 • CHAPTER 14 RESOURCE PLANNING

Table 14-9b Inventory Record for Door Hinge Item: Door Hinge Parent: Cabinet Door Lot size rule: FOQ 5 932 Children: none Lead time: 4 weeks Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 432 0 0 432 0 0 0 0 0 0 Scheduled Receipts: 932 Projected Available: 0 0 500 500 500 68 68 68 68 68 68 68 Planned Orders:

Table 14-9c Inventory Record for Door Handle Item: Handle Parent: Cabinet Door Lot size rule: FOQ 5 200 Children: none Lead time: 3 weeks Beginning inventory: 50

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 216 0 0 216 0 0 0 0 0 0 Scheduled Receipts: 200 Projected Available: 50 50 34 34 34 18 18 18 18 18 18 18 Planned Orders: 200

Table 14-9d Inventory Record for Door Screws Item: Screw Parent: Cabinet Door, Cabinet Side Lot size rule: FOQ 5 2000 Children: none Lead time: 1 week Beginning inventory: 500

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 2160 0 0 2160 0 0 0 0 0 0 Scheduled Receipts: Projected Available: 500 500 340 340 340 180 180 180 180 180 180 180 Planned Orders: 2000 2000

Table 14-9e Inventory Record for Door Catches Item: Door Catch Parent: Cabinet Sides Lot size rule: FOQ 5 252 Children: none Lead time: 1 week Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 216 0 0 216 0 0 0 0 0 0 Scheduled Receipts: Projected Available: 0 0 36 36 36 72 72 72 72 72 72 72 Planned Orders: 252 252

Table 14-9f Inventory Record for Feet Item: Foot Parent: Cabinet Bottom Lot size rule: L4L Children: none Lead time: 4 weeks Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 Gross Requirements: 0 0 576 0 0 0 0 0 0 0 0 0 Scheduled Receipts: 576 Projected Available: 0 0 0 0 0 0 0 0 0 0 0 0 Planned Orders:

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COMPARISON OF LOT SIZE RULES • 559

ACTION NOTICES MRP systems typically provide inventory planners with action notices, which indi- cate the items that need the planner’s attention. An action notice is created when a planned order needs to be released, when due dates of orders need to be adjusted, or when there is insufficient lead time for a planned replenishment order. Let’s look at the different kinds of action notices.

A positive quantity in the current period’s planned order row means that an order must be released. We call the current period the action bucket because that is the period in which we take actions such as releasing, rescheduling, or canceling orders.

Production and inventory control planners release orders to either an external supplier or to the shop floor. An order released to a supplier authorizes the shipment of the material so that it arrives as needed. An order released to the shop authorizes withdrawal of the needed materials and the start of production. Action notices are generated only for actions taken in the current period. Production and inventory con- trol planners adjust the due dates of orders (both opened and planned) to make sure the material does not arrive too soon or too late but just as it is needed. If an order is scheduled to arrive before it is needed (for example, because the gross requirements changed), the planner delays receipt of the replenishment order until it is needed. If the order is not scheduled to arrive in time, the planner tries to rush or expedite the order. Action notices indicate that a decision must be made or an action taken. The production and inventory control planner uses the available information and makes the decision.

� Action notices Output from an MRP system that identifies the need for an action to be taken.

� Action bucket The current time period.

� Expedite To rush orders that are needed in less than the normal lead time.

COMPARISON OF LOT SIZE RULES Different lot size rules can be used with MRP systems, such as least unit cost, least total costs, and parts period balancing. In this book, we cover the fixed-order quantity (FOQ), lot-for-lot (L4L), and period-order quantity (POQ). These lot size rules are discussed in Chapter 12. Different lot size rules change the frequency of replenishment orders and determine the quantity of the order. Let’s look at an example comparing FOQ, L4L, and POQ.

Given the following gross requirements, let’s calculate the planned replenishment orders needed, then calculate the inventory and ordering costs for the next 13 weeks. The CD cabinet has gross requirements of 25 in periods 2 and 3; 40 in periods 4 and 5; and 60 in periods 7, 8, 9, 11, 12, and 13. The first lot size to try is FOQ 5 144, then use L4L, and finally use a POQ 5 4 periods. The cost to place an order is $25, and the holding cost per unit per period is $0.10.

• Before You Begin: Companies using MRP often use different lot-sizing techniques. Dif- ferent techniques determine the timing of replenishment orders, the amount of inventory carried, and the frequency of setups. In this problem, you compare three different lot size rules. Calcu- late the costs associated with each ordering policy and determine which lot size rule makes the most sense. Remember that the fixed-order quantity (FOQ) rule requires you to order the same quantity each time, while lot-for-lot (L4L) means you order just enough for the next period, and period-order quantity (POQ) means that you order enough to satisfy your requirements for the next n periods.

EXAMPLE 14.3

Comparing Different Lot Size Rules at Storage Solutions by Elyssa, Inc.

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560 • CHAPTER 14 RESOURCE PLANNING

• Solution: Tables 14-10a–c show the completed inventory records. As you can see, the planned replenishment orders vary in frequency and in quantity. Note the different levels of inventory held because of the lot size rule. Lot-for-lot always minimizes a company’s inventory investment because it orders only what is needed for one period. However, L4L also maximizes a company’s ordering costs.

Let’s calculate the costs for each of these different lot size rules for this 13-week situation. The FOQ lot size rule has ending inventory in all but the first period. In total, 825 units are held for a holding cost of $82.50 (825 units � $0.10 per unit per period). The ordering cost is $100 (4 orders � $25 per order). Total holding and ordering cost using the FOQ is $182.50. The L4L lot size rule has no ending inventory during the 13 weeks. However, it does need a total of ten replenishment orders. The total holding and ordering cost for L4L is $250. The POQ � 4 periods lot size rule has ending inventory in periods 2, 3, 4, 7, 8, 11, and 12. Total units held are 585, for holding costs of $58.50. POQ requires three replenishment orders (ordering cost equals $75). Total costs for POQ are $133.50. In this case, the POQ lot size rule has the lowest total holding and ordering costs. To ensure that costs are minimized, we have to do the cost comparisons.

Table 14-10 Inventory Records Comparing Lot Size Rules Table 14-10a Inventory Record Using Fixed-Order Quantity Item: CD Cabinet Lead Time: 1 week Lot size rule: FOQ 5 144 Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 13 Gross Requirements: 0 25 25 40 40 0 60 60 60 0 60 60 60 Scheduled Receipts: Projected Available: 0 119 94 54 14 14 98 38 122 122 62 2 86 Planned Orders: 144 144 144 144

Table 14-10b Inventory Record Using Lot-for-Lot Item: CD Cabinet Lead Time: 1 week Lot size rule: L4L Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 13 Gross Requirements: 0 25 25 40 40 0 60 60 60 0 60 60 60 Scheduled Receipts: Projected Available: 0 0 0 0 0 0 0 0 0 0 0 0 0 Planned Orders: 25 25 40 40 60 60 60 60 60 60

Table 14-10c Inventory Record Using Period-Order Quantity Item: CD Cabinet Lead Time: 1 week Lot size rule: FOQ 5 4 periods Beginning inventory: 0

1 2 3 4 5 6 7 8 9 10 11 12 13 Gross Requirements: 0 25 25 40 40 0 60 60 60 0 60 60 60 Scheduled Receipts: Projected Available: 0 105 80 40 0 0 120 60 0 0 120 60 0 Planned Orders: 130 180 180

THE ROLE OF CAPACITY REQUIREMENTS PLANNING (CRP) A company uses a rough-cut capacity planning technique to determine whether a proposed MPS is feasible. In Supplement D, we see how to evaluate the feasibility of a proposed MPS with capacity planning using overall planning factors (CPOPF). Rough-cut capacity planning techniques use data from the proposed MPS. Capacity

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THE ROLE OF CAPACITY REQUIREMENTS PLANNING (CRP) • 561

requirements planning (CRP) uses data from MRP. We calculate workloads for criti- cal work centers based on open shop orders and planned shop orders. Work begins on open shop orders while planned shop orders are scheduled to be done. We trans- late these orders into hours of work by work center and by time period.

� Open shop orders Released manufacturing orders.

Table 14-11 shows items scheduled for Work Center 101. These items are either taken directly from MRP’s planned orders or they are already open shop orders. We want to calculate workloads for Work Center 101.

• Before You Begin: Capacity requirements planning (CRP) uses the planned order releases from the MRP output to calculate the workload for specific work centers. The workload associ- ated with a planned order has two parts: the setup to do the job and the processing time for the job. The primary difference between rough-cut capacity planning (RCCP) and CRP is that CRP uses the actual planned orders instead of the quantity needed just to complete the final product assembly. For example, using RCCP, if we want to estimate the time needed to build 100 CD cabi- nets, we assume that we make just enough pieces of everything to build 100 units. We don’t take into account beginning inventories. However, with CRP, we take into account only the items that have a planned order scheduled. This also indicates the quantity to be built of the item. If a lot size rule other than L4L (lot-for-lot) is used, more capacity must be used to complete the planned order for the item. CRP provides a better estimate of the total capacity needed.

• Solution: We calculate the total item time by summing the setup time and the total run time for the item.

Total item time 5 setup time 1 1quantity 3 run time per unit 2 The setup time is incurred each time the machine is prepared to produce the desired quantity of an item. We calculate the total run time by multiplying the quantity to be produced by the run time per unit. In our example, for item DN100, we plan to produce 250 units, with each unit needing 0.20 hour of run time. The total run time is 50.0 hours (250 units � 0.20 hour per unit). Total workload placed on the work center by item DN100 is 53 hours: 3 hours to set up the machine and 50 hours to run the quantity. We make similar calculations for each of the other items. When we have calculated the workloads, we compare them to the available capacity for the work center in those time period.

EXAMPLE 14.4

Calculating Workloads

Table 14-11 Workload for Work Center 101

Period Item

Number Quantity Setup Time

(hours)

Run Time per Unit in Standard

Hours Total Item

Time (hours)

Weekly Workload

(hours) 4 DN100 250 3.0 0.20 53.0

DP100 250 5.0 0.18 50.0 DS119 150 2.5 0.30 47.5 DT136 400 3.5 0.27 111.5 262.0

5 EQ555 1000 8.0 0.08 88.0 ER616 500 4.0 0.22 114.0 ES871 100 2.0 0.35 37.0 239.0

6 FA314 250 3.0 0.30 78.0 FF369 100 1.5 0.12 13.5 FR766 50 0.5 0.15 8.0 FS119 200 3.0 0.35 73.0 FY486 500 6.0 0.27 141.0 313.5

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562 • CHAPTER 14 RESOURCE PLANNING

We calculate available capacity (discussed in Chapter 9) by multiplying the number of machines available 3 number of shifts used 3 number of hours per shift 3 number of days per week 3 usage 3 efficiency.

Available capacity 5

number of machines available

3 number of shifts used 3

number of hours per shift

3 number of days per week

3 utiliza- tion 3 efficiency

In our case, we have four machines and we use two ten-hour shifts for five days per week, so our usage is 85 percent and our efficiency is 95 percent.

Available capacity

5 4 machines 3 2 shifts 3 10 hours per shift

3 5 days per week

3 0.85 utilization 3 0.95 efficiency

The available capacity per week is 323 standard hours. Figure 14-5 shows the workload compared to available capacity.

If the available capacity is not adequate, the company has a number of options. The easiest and quickest way to increase available capacity may be to authorize overtime at the work center. Another approach is to reduce the capacity needed by doing some of the work at an alternate work center. If the gap between available and needed capacity is significant, the company can hire a subcontractor for temporary extra capacity.

FIGURE 14-5

Workload for Work Center 101

RESOURCE PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER Enterprise resource planning provides a common database for use by an organiza- tion, its suppliers, and its customers. Second-generation ERP systems are designed to support supply chain management and e-commerce. These systems automate routine transactions and provide real-time information to all members of the enterprise. ERP systems typically have a production and materials module (MRP) to determine what is needed, how much is needed, and when it is needed.

CRP enables a company to evaluate both the feasibility of the MRP system and how well the company is using its critical work centers.

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RESOURCE PLANNING ACROSS THE ORGANIZATION • 563

MRP reports are used by the production and inventory planners to (1) generate purchasing requisitions and (2) develop schedules of different activities to be done on the manufacturing floor. Techniques for sequencing activities are discussed in Chapter 15. The authorized MPS, the bill of material (BOM) file, and the inventory records are inputs to the MRP system. It is critical that the MPS be feasible and that the BOM file and the inventory records be accurate. This implies that the time stan- dards (Chapter 11) are valid and that cycle counting (Chapter 12) is used to maintain inventory record accuracy. If not, material is not ordered at the appropriate time in the right quantity. The master scheduler is responsible for the feasibility of the MPS, and a manufacturing engineer is probably responsible for the BOM file. The production and inventory planners are often held accountable for the accuracy of the inventory records.

Even though rough-cut capacity planning (RCCP) using MPS data was done to check for feasibility, it is still important to do capacity requirements planning (CRP) for any critical work centers (bottlenecks or potential bottlenecks). CRP operates at a greater level of detail than does RCCP, using information generated by the MRP system. Production planners do this to make sure the detailed schedule of production is feasible.

Resource planning is designed to ensure that the right materials, in the right quanti- ties, are available at the right time. And to ensure that the right job is being done on the right equipment.

RESOURCE PLANNING ACROSS THE ORGANIZATION

Since MRP determines the quantity and timing of materials needed, it affects several functional areas in the company. Let’s first look at how each functional area is affected by MRP and then consider the effects of ERP.

Accounting calculates future material commitments based on MRP output. Accounting then develops cash flow budgets and the inventory investment to support the current MPS. With a common database, accounting should be able to determine the exact status of outstanding orders, including cost, quantity, and delivery date. Since there is a common database, discrepancies between supplier and manufacturer should be reduced.

Marketing is primarily concerned with the MPS, which identifies when finished goods will be completed. MRP reveals potential material shortages that directly affect marketing since the shortages may delay product completion. Marketing can also use MRP for allocating scarce materials to maximize customer service. One major advantage of ERP is that marketing can track actual sales at the final product level (using POS) to determine what actions, if any, need to be taken to maximize customer service.

Information systems maintains MRP, which is a large database that includes the BOM, the inventory records, and the MPS. Minimizing errors in the database is essen- tial to producing useful reports. ERP will help IS by using a single integrated database for both internal and external members of the supply chain.

Purchasing uses the planned orders generated by MRP to evaluate the feasibility of long-term or blanket contracts and to determine delivery need. The lead times that are input into MRP often come directly from purchasing. ERP will facilitate supplier- managed inventory approaches and reduce transaction costs for purchasing.

Manufacturing uses the output generated by MRP to develop daily manufacturing schedules. MRP ensures that the right materials in the right quantity are available to support the MPS. Manufacturing also uses MRP to allocate scarce materials. ERP will provide manufacturing with improved insight into actual customer demand. It should

ACC

MKT

MIS

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564 • CHAPTER 14 RESOURCE PLANNING

increase the probability that manufacturing is working on products actually needed to satisfy customer demand.

In most manufacturing operations, production or inventory control planners are responsible for working with MRP. Planners are typically responsible for certain inventory items, including end items, subassemblies, and components. Planners check the MRP output for action notices related to the items for which they are responsible. Planners schedule, reschedule, and expedite materials to support the MPS. A planning position is often an entry-level job in the materials field.

As companies continue to move toward ERP, all functional areas will work from a central database. The database gives all areas in the company access to the same infor- mation simultaneously and improves organizational effectiveness.

Enterprise resource planning provides the structure for common databases across the organization, its suppliers, and its customers. Suppliers can access the master production schedule (MPS) to see projected build dates for products that use materials supplied by them. The current trend is to integrate e-commerce and ERP systems. Tangible

benefits of an ERP system include reduced inventory levels, reduced staffing, improved order launching,

reduced IT and purchasing costs, improved cash flow, and increased profits. Intangible benefits include improved visibility of system demand, improved customer responsiveness, and improved flexibil- ity. Enterprise resource planning systems provide

the structure needed for effective supply chain management.

THE SUPPLY CHAIN LINK

ERP systems are now a major part of life for most businesses and their supply chains. When first imple- mented, the role of ERP was to manage a company’s data and processes from a single system. Today, ERP systems have become increasingly important in manag- ing sustainability, as companies are under tremendous pressure to meet and document their sustainability initiatives. As regulatory and environmental pressures grow, companies are now turning to ERP systems for help in executing and tracking the success of their sustainability initiatives. Remember that ERP systems are about managing resources and measures of sustain- ability to show how well a company uses these resources toward an eye toward “greening.” As a result, ERP sys- tems are increasingly becoming platforms for this type of analysis, providing accurate reports on items such as carbon footprints, carbon trades, and information to support product-level carbon labeling.

To meet this growing demand, ERP software vendors, such as SAP and Oracle, are enhancing their software offering in the areas of sustainability reporting, plan- ning, and management, adding modules such as carbon management. Using ERP systems to link resources to sustainability metrics is so huge that even small software vendors are developing these systems. These solutions involve expanding current ERP capability to include gathering and analyzing enormous amounts of data, as sustainability initiatives run the full length of a company’s supply chain. This involves collecting product and SKU- level waste data from manufacturing sites, gathering emissions figures from transport providers, and obtain- ing “real-time” energy consumption information from relevant utilities. So to really track and report success of corporate sustainability initiatives, these systems require complex streams of data. As the need for sustainable use of resources grows, ERP systems will increasingly add capabilities in this area.

THE SUSTAINABILITY LINK

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KEY TERMS • 565

Chapter Highlights 1 Enterprise resource planning (ERP) is soft ware designed

for organizing and managing business processes by sharing information across functional areas using a common database and a single computer system. ERP systems typically have modules for fi nance and accounting, sales and marketing, production and materials management, and human resources.

2 First-generation ERP systems provide a single interface for managing routine activities performed in manufacturing. Second-generation ERP systems or SCM soft ware is designed to improve decision making in the supply chain. Th e current trend is integrating e-commerce and ERP systems.

3 Tangible benefi ts from ERP systems include reductions in inventory and staffi ng, increased productivity, improved order management, quicker closing of fi nancial cycles, reduced IT and purchasing costs, improved cash fl ow management, and increased revenue and profi ts. Intangible benefi ts include improved visibility of corporate data, improved customer responsiveness, and improved fl exibility. Th e cost of ERP systems ranges from hundreds of thousands of dollars to several million dollars.

4 Material requirements planning (MRP) systems are designed to calculate material requirements for items with dependent demand. MRP systems use backward scheduling to determine when each activity starts so that the fi nished product or service is completed on time.

5 Independent demand is the demand for fi nished products, whereas dependent demand is demand that is derived from fi nished products. MRP systems use dependent demand.

6 Th e objectives of MRP are to determine the quantity and timing of material requirements and to keep schedule priorities updated and valid. MRP determines what to order,

how much to order, when to place the order, and when to schedule the order’s arrival. It maintains priorities by recognizing changes in the operations environment and making the necessary adjustments.

7 MRP needs three inputs: the authorized MPS, the BOM fi le, and the inventory records fi le. Th e MPS is the planned build schedule, the BOM fi le shows the materials needed to build an item, and the inventory records fi le shows the inventory on hand.

8 Once the MPS has been input, MRP checks the inventory records to determine if enough end-item inventory is available. If suffi cient end item inventory is not on hand, MRP checks the end item’s BOM fi le to determine what materials are needed and in what quantities. Th e MRP system then generates planned replenishment orders.

9 Action notices show when to release planned orders, reschedule orders, or adjust due dates. Th ey allow the planner to use the MRP output information eff ectively.

10 Diff erent lot size rules are used with MRP systems to generate diff erent order quantities and order frequencies. Th e lot-for-lot (L4L) rule always minimizes inventory investment but maximizes ordering costs. A cost comparison shows the eff ect of using diff erent lot sizing rules.

11 Planned orders generated by MRP, plus any open shop orders, are inputs to capacity requirements planning (CRP). CRP checks to see if available capacity is suffi cient to complete the orders scheduled in a particular work center during a specifi c time period.

12 CRP calculates the workloads at critical work centers by using the planned orders generated by the MRP system. Th ese planned orders are multiplied by the standard times to calculate individual work center loads.

Key Terms backward scheduling 540 enterprise resource planning (ERP) 541 SCM soft ware 543 supply chain intelligence (SCI) 543 application service provider (ASP) 543 closed-loop MRP 547 manufacturing resource planning

(MRP II) 547 material requirements planning

(MRP) 547 capacity requirements planning

(CRP) 548

bill of material (BOM) 548 independent demand 548 dependent demand 548 time-phased 549 gross requirements 549 planning factors 551 lead time 551 scheduled receipt 551 projected available 552 planned orders 552 indented bill of material 552 product structure tree 552

end item 552 parent item 553 components 553 explosion process 555 action notices 559 action bucket 559 expedite 559 open shop orders 561

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566 • CHAPTER 14 RESOURCE PLANNING

Formula Review 1. To calculate total item time:

Total item time � setup time � (quantity � run time per unit)

2. To calculate available capacity:

Available capacity � number of machines available � number of shift s used � number of hours per shift � utilization � effi ciency

Solved Problems • Problem 1 Using the product tree shown in Figure 14-6, calculate the cumulative lead time for Item 100 if you have no inventory.

How long is the lead time if you have enough inventory for Parts 102, 104, 201, and 203?

• Before You Begin: Determine the minimum amount of time needed to build Item 100. To find the cumulative lead time, calculate the total time it takes for each connected pathway from the lowest level up to the finished product. If there is no inventory in stock, this represents the quickest way you can respond to an order for this item. Then, consider the effect of having some inventory on hand. Inventory of component parts and/or subassemblies can reduce the amount of time it takes to respond to an order.

• Solution Check all the paths through the product structure tree to find the longest path.

The path from Part 201 to Part 101 to Item 100 is the longest (7 weeks), so it is the cumulative lead time.

When we have enough inventory for some parts, we can eliminate that segment of the path and all levels below that inventory. For example, if we have enough of Part 101, we do not need any more of its component parts (201). Th e new paths when we have suffi cient inventory for Parts 102, 104, 201, and 203 are shown here.

(See student companion site for Excel template.)

FIGURE 14-6

Product structure tree

Path through the Product Structure

Total Lead Time (weeks)

Part 201 to Part 101 to Item 100 7 Part 102 to Item 100 6 Part 202 to Part 103 to Item 100 5 Part 203 to Part 103 to Item 100 6 Part 104 to Item 100 6

Path through the Product Structure

Total Lead Time (weeks)

Part 101 to Item 100 4 Part 202 to Part 103 to Item 100 5

Given that we have enough inventory, we are concerned with only two paths through the product tree. In this situation, the minimum time to produce this item is 5 weeks.

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SOLVED PROBLEMS • 567

• Problem 2 Complete the inventory record for Item 500 and do an MRP explosion of its component parts. Figure 14-7 shows the prod- uct structure tree for Item 500.

• Before You Begin: This problem requires an MRP explosion for Item 500. Begin the explosion process with the finished good, and then work downward level by level through the product structure tree. Remember that the timing and quantity of the gross require- ments for children are determined by the planned orders of the parents. After the explosion, you will have the planned orders necessary to produce the units listed in the master production schedule.

Now that we have a completed inventory record for the end item, we can do the MRP explosions for its children. Remember that each of the children will have gross requirements in the periods that the parent has a planned order (weeks 2, 4, 6, and 8). Th e completed records for the four children follow.

Since the usage rate for Item 501 is (2) per parent item, the gross requirement is double the planned order quantity of the parent, or 250 � 2 � 500 units. Th e lot size rule is a fi xed-order quantity of 1000 pieces. Each time an order is placed, it is for 1000 pieces. Th us, Item 501 has two planned orders, one in period 2 and one in period 6.

Item: 500 Parent: none Lot Size Rule: L4L Children: 501, 502, 503, 504 Lead Time: 2 weeks Beginning inventory: 0

1 2 3 4 5 Gross Requirements: 0 0 0 250 0 Scheduled Receipts: Projected Available: Planned Orders:

6 7 8 9 10 Gross Requirements: 250 0 250 0 250 Scheduled Receipts: Projected Available: Planned Orders:

Item: 500 Parent: none Lot Size Rule: L4L Children: 501, 502, 503, 504 Lead Time: 2 weeks Beginning inventory: 0

1 2 3 4 5 Gross Requirements: 0 0 0 250 0 Scheduled Receipts: Projected Available: 0 0 0 0 0 Planned Orders: 0 250 0 250 0

6 7 8 9 10 Gross Requirements: 250 0 250 0 250 Scheduled Receipts: Projected Available: 0 0 0 0 0 Planned Orders: 250 0 250 0 0

• Solution Given the gross requirements, we will need planned orders for Item 500 in weeks 2, 4, 6, and 8. Each of the planned orders is for 250 units, exactly the quantity needed to satisfy the gross require- ments. The completed record is shown here.

FIGURE 14-7

Product structure tree

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568 • CHAPTER 14 RESOURCE PLANNING

Using lot-for-lot as our lot size rule, we need to place four orders for Item 502. We have planned orders in periods 1, 3, 5, and 7.

Once again, we calculate the gross requirements by multiply- ing the parent’s planned order quantity by the usage factor (3) shown in the product structure tree. This results in the gross requirement for Item 503, which is triple the order quantity of the parent’s planned order.

Item: 502 Parent: none Usage: 1 Lot Size Rule: L4L Children: 812, 813 Lead Time: 1 week Beginning inventory: 0

1 2 3 4 5 Gross Requirements: 0 250 0 250 0 Scheduled Receipts: Projected Available: 0 0 0 0 0 Planned Orders: 250 0 250 0 250

6 7 8 9 10 Gross Requirements: 250 0 250 0 0 Scheduled Receipts: Projected Available: 0 0 0 0 0 Planned Orders: 0 250 0 0 0

Given the lot size rule for this item, we need only one planned order.

Now let’s look at the children of Item 502. Th e gross requirements for Item 812 are double the quantity of its parent’s planned orders. Th e lot size rule, POQ 5 4 periods, means that the planned order quantity should be enough to cover the requirements in the period it is scheduled to arrive plus the next three periods. For example, we need an order to arrive in period 3. Th is planned order must be large enough to cover the gross requirements in periods 3, 4, 5, and 6. Th e inventory records for both 812 and 813 follow.

Item: 504 Parent: 500 Usage: 2 Lot Size Rule: FOQ 5 2000 Children: none Lead Time: 2 weeks Beginning inventory: 600

1 2 3 4 5 Gross Requirements: 0 500 0 500 0 Scheduled Receipts: Projected Available: 600 100 100 1600 1600 Planned Orders: 0 2000 0 0 0

6 7 8 9 10 Gross Requirements: 500 0 500 0 0 Scheduled Receipts: Projected Available: 1100 1100 600 600 600 Planned Orders: 0 0 0 0 0

Item: 503 Parent: 500 Usage: 3 Lot Size Rule: FOQ 5 1500 Children: 812, 813 Lead Time: 3 weeks Beginning inventory: 800

1 2 3 4 5 Gross Requirements: 0 750 0 750 0 Scheduled Receipts: Projected Available: 800 50 50 800 800 Planned Orders: 1500 0 0 0 1500

6 7 8 9 10 Gross Requirements: 750 0 750 0 0 Scheduled Receipts: Projected Available: 50 50 800 800 800 Planned Orders: 0 0 0 0 0

Item: 501 Parent: 500 Usage: 2 Lot Size Rule: FOQ 5 1000 Children: none Lead Time: 2 weeks Beginning inventory: 600

1 2 3 4 5 Gross Requirements: 0 500 0 500 0 Scheduled Receipts: Projected Available: 600 100 100 600 600 Planned Orders: 0 1000 0 0 0

6 7 8 9 10 Gross Requirements: 500 0 500 0 0 Scheduled Receipts: Projected Available: 100 100 600 600 600 Planned Orders: 1000 0 0 0 0

Item: 812 Parent: 502 Usage: 2 Lot Size Rule: POQ 5 4 periods Children: none Lead Time: 2 weeks Beginning inventory: 500

1 2 3 4 5 Gross Requirements: 500 0 500 0 500 Scheduled Receipts: Projected Available: 0 0 500 500 0 Planned Orders: 1000 0 0 0 500

6 7 8 9 10 Gross Requirements: 0 500 0 0 0 Scheduled Receipts: Projected Available: 0 0 0 0 0 Planned Orders: 0 0 0 0 0

Item: 813 Parent: 502 Usage: 3 Lot Size Rule: FOQ 5 1500 Children: none Lead Time: 3 weeks Beginning inventory: 1500

1 2 3 4 5 Gross Requirements: 750 0 750 0 750 Scheduled Receipts: Projected Available: 750 750 0 0 750 Planned Orders: 0 1500 0 0 0

6 7 8 9 10 Gross Requirements: 0 750 0 0 0 Scheduled Receipts: Projected Available: 750 0 0 0 0 Planned Orders: 0 0 0 0 0

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PROBLEMS • 569

• Problem 3 EJ Fabricators operates six machines, three eight-hour shifts, five days per week. EJ’s usage rate is 82 percent and its efficiency rate is 90 percent. Calculate the available capacity. Calculate EJ’s workload in periods 7 and 8 and determine whether there is a capacity problem.

• Before You Begin: In this problem, determine if adequate capacity is available. Calculate available capacity by multiplying the number of machines available for use by the number of shifts operated by the number of days per week by the utilization level by the efficiency level. This, in effect, reduces the output expected by factoring in utilization and efficiency. This allows more realistic expectations from manufacturing.

• Solution We calculate the available capacity by multiplying the number of machines by the number of shifts by the number of hours per shift by the number of days per week by the utilization rate by the efficiency rate:

6 machines 3 3 shift s 3 8 hours per shift 3 5 days per week 3 0.82 utilization 3 0.90 effi ciency

which equals 531.36 hours of available capacity. To calculate the workload, we need information about the jobs scheduled in each period. We have shown you how to calculate the capacity available. Now go to Spreadsheet 14.3 on your CD to calculate the workload for each period.

Discussion Questions 1. Describe enterprise resource planning and its role in an

organization. 2. Describe the basic modules of an ERP system. 3. Describe the evolution of ERP systems. 4. Describe the role of SCM software and give examples of

how it differs from first-generation ERP. 5. Explain what independent demand is and give examples of

products with independent demand. 6. Explain what dependent demand is and give examples of

how you can use dependent demand in your personal life. 7. Explain the concept of backward scheduling and give exam-

ples of how you use backward scheduling in your personal life.

8. What are the objectives of MRP? 9. Describe how MRP works. 10. Describe the inputs needed for MRP. 11. For each input needed, describe problems that might arise

when you run MRP. 12. Explain what happens when you use different lot size rules

in MRP. 13. Explain why companies do capacity requirements planning. 14. Describe the inputs needed for capacity requirements

planning. 15. Describe how MRP II differs from MRP. 16. Describe enterprise resource planning.

Problems Use the information given here for the next fi ve problems.

1. Will’s Welded Widgets (WWW) makes its Q Model from components R, S, and T. Component R is made from two units of component X and one unit of component Y. Component T is made

from one unit of component V and three units of component Z. Draw the product structure tree for the Q Model.

2. Using the given information, calculate the replenishment lead time for the Q Model assuming that you have no beginning inventories.

3. Using the given information, calculate the gross requirements for each of the components if the company plans to build 100 of its Q Models. Assume that there are no beginning inventories.

4. Using the given information, calculate the gross requirements for each of the components when the company plans to build 100 of its Q Models if you have these inventories: 150 units of component T and 200 units of component R.

5. Using the given information and the beginning inventories from Problem 4, calculate the minimum replenishment time for the 100 Q Models.

Item Usage per

Parent Load Time (weeks)

Q — 2 R 2 3 S 1 4 T 3 2 X 2 3 Y 1 2 V 1 3 Z 3 2

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570 • CHAPTER 14 RESOURCE PLANNING

Use the following information for Problems 6 through 10.

Component Immediate

Parent

Usage per

Parent

Lead Time

(weeks) Beginning Inventory

A none — 1 0 B A 2 2 250 C A 1 6 500 D A 3 3 750 E A 2 2 750 F B 4 2 3000 G B 2 4 1000 H D 3 2 5000 I D 2 4 5000 J E 1 8 1000 K E 5 1 5000 L E 2 4 2500 M F 3 3 250 N F 6 3 2560 O H 2 4 0 P K 1 2 500 Q K 2 3 1000

Item: AB500 Lot Size Rule: L4L Lead Time: 2 weeks

Parent: None Children: AB501, AB511, AB521

1 2 3 4 5 Gross Requirements: 150 250 150 Scheduled Receipts: Projected Available: Planned Orders:

6 7 8 9 10 Gross Requirements: 250 150 250 150 250 Scheduled Receipts: Projected Available: Planned Orders:

Component Lot Size

Rule Time

(weeks)

Lead Usage Factor

Beginning Inventory

AB501 L4L 2 2 1100 AB511 FOQ = 350 3 1 650 AB521 POQ = 3 2 3 1650

16. Using the planned orders generated in Problem 15, complete the inventory record for components AB501, AB511, and AB521. Use the lot size rule, lead time, and usage information given in Problem 12. Indicate any problems that occur.

17. Fill in the partially completed inventory record shown here.

Item: AB500 Lot Size Rule: FOQ = 200 Lead Time: 2 weeks

Parent: None Children: AB501, AB511, AB521

1 2 3 4 5 Gross Requirements: 150 250 150 Scheduled Receipts: Projected Available: Planned Orders:

6 7 8 9 10 Gross Requirements: 250 150 250 150 250 Scheduled Receipts: Projected Available: Planned Orders:

Item: AB500 Lot Size Rule: POQ = 3 Lead Time: 2 weeks

Parent: None Children: AB501, AB511, AB521

1 2 3 4 5 Gross Requirements: 150 250 150 Scheduled Receipts: Projected Available: Planned Orders:

6 7 8 9 10 Gross Requirements: 250 150 250 150 250 Scheduled Receipts: Projected Available: Planned Orders:

6. Flora’s Fabulous Fountains’ (FFF) top product is its Model A. Using the information given, draw the product structure tree for the Model A.

7. Using the information given, calculate the replenishment time when no beginning inventory exists.

8. Flora is preparing for her busy season and is building 2500 Model A fountains. Calculate the gross requirements for each component assuming that there is no beginning inventory.

9. Using the information given and assuming that 2500 Model A fountains are scheduled for completion, calculate the gross requirements of each component. Use the beginning inventories given.

10. Calculate the minimum replenishment time for the Model A fountains given the beginning inventories.

11. Fill in the partially completed inventory record shown here.

13. Using the inventory records completed in Problem 12, calculate the average inventory level of AB501, AB511, and AB521.

14. Use the planned orders generated in Problem 11. Calculate the average inventory records if the company decides to switch the lot size rule for AB511 and AB521 to lot-for-lot. Compare the number of replenishment orders using the new lot size rules.

15. Using the information given, fi ll in the partially completed inventory record shown here.

12. Using the planned orders generated in Problem 11, complete inventory records for components AB501, AB511, and AB521. Th e lot size rule, lead time, and usage information are shown here.

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CASE: NEWMARKET INTERNATIONAL MANUFACTURING COMPANY (B) • 571

20. Th e Yankee Machine Shop currently has three machines working in Work Center 111, eight hours per day, fi ve days per week, a utilization rate of 90 percent, and an effi ciency rate of 90 pecent.

(a) Calculate the available capacity. (b) Is the available capacity enough to complete the orders

given in Problem 19 that are already scheduled in Work Center 111? If not, how much additional capacity is needed?

21. Th e Yankee Machine Shop has decided to schedule its workforce to work ten hours per day, fi ve days per week. Does this new policy provide enough capacity to complete the orders shown in Problem 19?

22. Unfortunately, aft er extending the work day from eight hours to ten hours, the Yankee Machine Shop has noted that effi ciency has decreased to 80 percent. Given this new piece of information, is there enough capacity to complete the orders given in Problem 19?

23. In week 13, the Yankee Machine Shop has the following orders scheduled for Work Center 111. Calculate the capacity needed.

Orders Quantity Setup Time

(hours) Run Time per Piece (hours)

LL110 10 2.0 1.2 LL118 25 4.0 0.4 LL131 100 6.0 0.6 LL140 50 4.0 0.2

Item On

Hand Scheduled Receipts

Lot Size Rule MPS

Lead Time

GB 0 0 L4L 150, period 8 1 GD 0 0 L4L 100, period 6 2 A 250 200, period 4 FOQ = 200 4 B 25 0 FOQ = 300 2 C 0 0 L4L 3

26. Using the information in Problem 25, determine the minimum lead time to satisfy a new order for the Gamma Blaster. Determine the minimum lead time required to satisfy a new order for the Gamma Disaster.

Orders Quantity Setup Time

(hours) Run Time per Piece (hours)

MM078 100 4.0 0.3 MM118 250 6.0 0.1 MM213 100 3.0 0.3 MM240 500 8.0 0.1

18. Using the planned orders generated in Problem 17, complete inventory records for components AB501, AB511, and AB521. Use the lot size rule, lead time, and usage information given in Problem 12.

19. Th e Yankee Machine Shop has the following orders scheduled in Work Center 111 for week 12. Calculate the capacity needed.

24. In an eff ort to increase capacity in Work Center 111 for week 13, Yankee Machine Shop has authorized overtime. Th e work center will be staff ed 12 hours per day for six days. Because of the additional stress on the three machines, it is expected that the utilization rate will drop to 85 percent. Th e effi ciency rate is expected to fall to 80 percent.

(a) Calculate the capacity available in Work Center 111 for week 13.

(b) Will this plan provide suffi cient capacity to complete the orders given in Problem 23? If not, what do you recommend be done?

25. Th e Gamma Ray Company produces two products, the Gamma Blaster (GB) and the Gamma Disaster (GD). Each product is made from three components: A, B, and C. Th e Gamma Blaster is made from the following components: A (2), B (3), and C (4). Th e Gamma Disaster is made from A (3), B (2), and C (1). All other relevant information is provided. Complete the appropriate inventory records.

CASE: Newmarket International Manufacturing Company (B) The Newmarket International Manufacturing Company (NIMCO) was started by Marcia Blakely just two years after she finished graduate school. Her knowledge of mass customiza- tion has been the driving force behind NIMCO. The company produces three major custom products. Volume on the prod- ucts is high even though each item is customized specifically for the customer. The products are processed through up to four different work centers. Even though each item is unique,

the processing time at each work center is constant due to the sophisticated equipment used.

Developing a Material Requirements Plan Joe Barnes, the production manager, reviewed your rough-cut capacity planning report and developed a new MPS that bet- ter uses capacity at each work center. Joe has given you an authorized MPS and has asked you to generate the schedule

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572 • CHAPTER 14 RESOURCE PLANNING

of material requirements. The authorized master production schedule is shown in Table 14-12.

(a) Generate the material requirements. You need a BOM for each of the three products (A, B, and C), beginning inventory levels, and scheduled receipts. Th e BOMs are shown in Figure 14-8. All items use lot-for-lot as the lot size rule. No beginning inventories exist. Lead time is two weeks for all items except items D and F, which have a lead time of three weeks. All other information is provided for you in Table 14-13.

(b) Aft er completing the material requirements plan, develop a load profi le for each work center for each week of the second quarter. Use the planned order releases and calculate the workload at each work center for weeks 14 through 26. Th e standard times are shown in the table. Use the load profi les to identify potential problems. Th e eff ective capacity at each work center is 960 hours each period.

FIGURE 14-8

Product structure tree

TABLE 14-12 Authorized MPS

Period 14 15 16 17 18 19 20

A 7600 8700 9000 8700 B 4000 4000 4000 3800 3800 3800 3600 C 5300 6300 6400

Period 21 22 23 24 25 26

A 8000 6800 3000 B 3600 3600 3800 3800 3800 4000 C 6000 5600 5200

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INTERACTIVE CASE: VIRTUAL COMPANY • 573

TABLE 14-13 Additional Information

Item Scheduled Receipts

Work Done at

Work Center

Standard Hours per

Piece

A 0 4 0.04 B 0 4 0.10 C 0 4 0.06 D 22,800 in Period 12 1 0.02

26,100 in Period 14 E 15,200 in Period 12 3 0.02 F 7,600 in Period 12 3 0.02

8,700 in Period 14 G 8,000 in Period 12 2 0.02 H 16,000 in Period 12 1 0.0375 I 34,800 in Period 12 3 0.02 J 48,000 in Period 12 2 0.015

22,800 in Period 13 K 15,900 in Period 12 3 0.03

18,900 in Period 13 R none scheduled 2 0.04 X none scheduled 3 0.04

CASE: Desserts by J.B. Jay Brown (J.B. to his friends) is a student at the Northwest Culi- nary Institute and specializes in preparing elaborate desserts. After graduation, J.B. wants to open up a bakery. The bakery, Desserts by J.B., would offer elaborate, European-style desserts. As J.B. prepares his business plan, the issues of material and capacity planning arise. At the Institute, J.B. never worried about such issues. Someone else was responsible for assuring material was available and for scheduling the equipment.

Since J.B. knows that you are studying business, he has asked for your help. He needs some guidance on material planning and capacity management. In order to assist your analysis, J.B. has asked you to compile a list of necessary information. Once you

have adequate information, J.B. needs to know how to determine his material requirements and how to determine his capacity needs.

(a) Develop a list of the information you will need before you can help J.B.

(b) Using at least fi ve recipes for elaborate European-styled desserts, demonstrate how you would plan for materials.

(d) Explain to J.B. how he will be able to use an MRP approach in his bakery. Be sure to explain issues such as planned orders, projected available quantities, lot sizing rules, BOMs, and inventory records.

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: ERP systems at Cruise International, Inc. Bob Bristol wants you to examine the possible benefits from implementing an ERP system. Since planning and coordina- tion of a wide range of resources is critical to CII, Bob believes that an ERP system will be useful. He wants a concise research report for top management at CII addressing ERP issues relevant to CII. Completion of this assignment will enable you to enhance your knowledge of the material covered in

Chapter 14 of the text. It will also better prepare you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click ERP Systems at CII

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574 • CHAPTER 14 RESOURCE PLANNING

On-line Case: An ERP System for Valley Memorial Hospital Bob Reilly, head of Kaizen, just called you to say that

he was impressed with your work on the various consulting assignments for VMH. As you are approaching the comple- tion of your internship, he recognizes that you have a thorough understanding of the operations at VMH and wants you to examine a broader issue, which has implications across the entire organization. He tells you that, with the current buzz about applications of information technology, administrators and managers at VMH have heard a great deal about enterprise resource planning (ERP) systems. Although they are not sure if ERP would work well for VMH, they are interested in exploring whether an ERP system could be beneficial. Clearly, effective management (i.e., planning and coordination) of a wide range of resources is critical to VMH. Bob wants you to prepare a concise

research report for the top management at VMH addressing relevant ERP issues. He has provided a few specific questions for you to consider. This assignment will enable you to enhance your knowledge of the material in this chapter.

To complete this assignment, go to www.wiley.com/college/ reid to get more details. Assignment questions are given at the site.

To access the Web site:

Your university’s Department of Hospitality Management hosts several gourmet dinners throughout the year. To show how OM concepts are useful in the service industry, the department has asked you to help manage the next gourmet dinner from the standpoint of materials planning.

The dinner is typically a five-course meal: appetizer, soup, salad, entrée, and dessert. Your Internet challenge is to develop the menu using the many cooking Web sites available and then to calculate the kinds and quantities of raw material you will need. Assume that the facility where the dinner is hosted will take care of the beverages and that the kitchen and staff have enough capacity for your menu selections. The gourmet dinner

will have 300 attendees. If any menu items need more than 12 hours of preparation (remember you are planning for 300 guests), be sure the items arrive in time. Based on your menu, make a list of the raw materials you will need. Specify delivery dates for each item. Calculate how long each item on the menu will take to prepare for 300 guests. Decide what time the staff needs to start preparation for the dinner to be served beginning at 8:00 p.m. Calculate the time the staff needs to start preparing each item, assuming that the appetizers will be served at 8:00, the soup at 8:20, the salad at 8:35, the entrée at 8:50, and the dessert at 9:15. Bon appetit!

INTERNET CHALLENGE The Gourmet Dinner

Online Resources Companion Website www.wiley.com/college/reid:

The Ashford Group Marrs Printing, Inc

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SELECTED BIBLIOGRAPHY • 575

Selected Bibliography Al-Mashari, Majed, Abdullah Al-Mudimigh, and Mohamed

Zairi. “Enterprise Resource Planning: A Taxonomy of Criti- cal Factors,” European Journal of Operational Research, 146 (2003), 352–364.

Arnold, J.R. Tony, Stephen N. Chapman, and Lloyd M. Clive. Introduction to Materials Management, Seventh Edition. Upper Saddle River, N.J.: Pearson Education Limited, 2012.

Blackstone, John H. Capacity Management. Cincinnati, Ohio: South-Western, 1989.

Blackstone, John H., and James F. Cox III. APICS Dictionary, Twelft h Edition. Falls Church, Va.: American Production and Inventory Control Society, 2008.

Cox, James F., III, John H. Blackstone, and Michael S. Spencer, eds. APICS Dictionary, Eleventh Edition. Falls Church, Va.: American Production and Inventory Control Society, Inc., 2005.

Nelson, Emily, and Evan Ramstad. “Hershey’s Biggest Dud Has Turned Out to Be New Computer System,” Wall Street Jour- nal October 29, 1999, 1.

Orlicky, J. Material Requirements Planning. New York: McGraw- Hill, 1975.

“SAP Off ers Supply Chain Optimization to Help Industry Meet Global Challenge,” Chemical Market Reporter, 254, 15, October 12, 1998.

Stefanac, Rosalind. “As the Picture Gets Bigger, the Focus Becomes Sharper,” Computing Canada, 24, 45, November 30, 1998.

Stein, Tom. “ERP’s Future Linked to E-Supply Chain,” Informa- tion Week, October 19, 1998.

Turban, Efraim, Dorothy Leidner, Ephraim McLean, and James Wetherbe. Information Technology for Management: Trans- forming Organizations in the Digital Economy, Sixth Edi- tion. Hoboken, NJ: John Wiley & Sons, 2008.

Vollmann, Th omas E., William L. Berry, D. Clay Whybark, and F. Robert Jacobs. Manufacturing Planning and Control Sys- tems, Fift h Edition. Burr Ridge, Ill.: McGraw-Hill/Irwin, 2005.

Wight, Oliver W. Manufacturing Resource Planning: MRP II. Essex Junction, Vt.: Oliver Wight, 1984.

• Find links for Additional Web Resources for this chapter IBM, http://houns54.clearlake.ibm.com Hershey Foods Corporation, www.hersheys.com/tour/index.html Mars, Inc., www.m-ms.com/factory/tour

immediate feedback on your responses. • Check your understanding of the key vocabulary in the chapter with Interactive Flash Cards • Use the Animated Demo Problems to review key problem types • Practice for your tests with additional problem sets

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CHAPTER

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

576

C H A P T E R O U T L I N E

Scheduling Operations 578 High-Volume Operations 578 Low-Volume Operations 579 Scheduling Work 580 How to Sequence Jobs 584 Measuring Performance 586 Comparing Priority Rules 588

Sequencing Jobs through Two Work Centers 591 Scheduling Bottlenecks 593 Theory of Constraints 596 Scheduling for Service Organizations 596 Developing a Workforce Schedule 599 Scheduling within OM: Putting It All Together 600 Scheduling across the Organization 601

Before studying this chapter you should know or, if necessary, review

1. Operational impact of competitive priorities, Chapter 2, pp. 37–40. 2. The differences between high-volume and low-volume operations, Chapter 10, pp. 374–379. 3. Line balancing, Chapter 10, p. 389. 4. Techniques for reducing employee boredom, Chapter 11, pp. 416–417. 5. Order promising, Supplement D, pp. D10–D14. 6. Order planning, Chapter 14, pp. 550–555.

Scheduling

After studying this chapter you should be able to

L E A R N I N G O B J E C T I V E S

1 Explain the diff erent kinds of scheduling operations. 2 Describe diff erent shop loading methods. 3 Develop a schedule using priority rules. 4 Calculate scheduling performance measures. 5 Develop a schedule for multiple workstations.

6 Describe the theory of constraints. 7 Describe scheduling techniques for service

applications. 8 Develop a workforce schedule in which each

employee has two consecutive days off .

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SCHEDULING • 577

Are you a list maker? Many of us are. For some people, the To Do list is a way of life. In fact, many people create electronic lists on their PDAs. We often make lists of errands, business meetings, and social events. The list might include picking up the dry cleaning, washing the dog, buying a new remote for the TV at the mall, going to the bank, paying bills, cleaning out the garage, meeting a friend for lunch, calling Mom, preparing dinner, and so forth. Organizing the list effectively into an operational schedule is more difficult.

Suppose that you needed to schedule all the activities listed above. Some of the tasks are errands, some are household chores, and some are social/family obligations. You could get the dry clean- ing first since it is right near your home, but you need money to pay for the cleaning and the bank is in the other direction. So you head to the bank first. Then you pick up the dry cleaning and head home. You wash the dog, getting completely soaked in the process. You change clothes and go to the mall to pick up a new TV remote. You return home and clean the garage and your clothes get dirty. You shower and change clothes in time to meet your friend for lunch. Then it is back home to do the laundry. You still need to call Mom, pay your bills, and prepare dinner before your guests arrive. There must be a better way to do these tasks!

When developing an operational schedule, there are numerous things to consider. For example, are there some activities that can be done in parallel (simultaneously)? That is, can you pay your bills on-line while dinner is baking in the oven and a load of clothes is being washed? Can you arrange your schedule to avoid bottlenecks? For example, go to the bank early in the morning before lines develop. Can you let someone else do some of the tasks for you? Maybe a neighbor can pick up the dry cleaning, or maybe you can pay one of the neighborhood teens to clean out the garage. Are there some activities that can be postponed to another time? Put the garage cleaning off for a few days. Do you create additional tasks because of the sequence you choose to follow? For example, do you need to look nice when shopping at the mall or going to the bank? Will you look nice if you have just finished cleaning the garage or washing the dog? Sometimes the length of time needed to complete your scheduled jobs depends on whether certain jobs require more time because of the preceding task. For example, since you washed the dog before going to the mall, now you need to change clothes before your next task.

To schedule effectively, you must know what has to be done, how long it should take to do, who must do it, and what its priority is. Planning your schedule is the last step before actually completing the tasks on the schedule.

Businesses also make schedules to show how labor, materials, and equipment should be used. Consider an automobile maintenance and repair facility. The sched- ule can identify the start and finish time for each activity, and the resources to be used—such as auto mechanic Alex using repair bay number two to do a tune-up on a PT Cruiser, starting at 9:00 and finishing by 9:45. Then Alex will be scheduled to do a

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578 • CHAPTER 15 SCHEDULING

A company’s overall strategy provides the framework for making decisions in many operational areas. Companies differentiate themselves based on product volume and product variety. This differentiation affects how the company organizes its operations. A company providing a high-volume, standardized, consistent-quality, lower- margin product or service such as a commercial bakery or a fast-food restaurant focuses on product and layout. This type of operation needs dedicated equipment, less-skilled employees, and a continuous or repetitive process flow. Companies providing low-volume, customized, higher-margin products or services, such as a custom furniture maker or an upscale restaurant, focus on process. They need general-purpose equipment, more highly skilled employees, and flexible process flows. Each kind of operation needs a different scheduling technique. Let’s look at high-volume operations first.

brake job on a Lincoln Navigator (starting at 9:45 and finishing at 11:00, once again in repair bay number two). Well-defined schedules allow a company to make promises to customers concerning the completion time of the service provided.

In this chapter, we learn how the schedule affects on-time delivery to customers. We examine high-volume and low-volume scheduling operations. We look at differ- ent ways of scheduling jobs as well as ways to measure schedule effectiveness. We also consider the theory of constraints and scheduling for service operations. Let’s begin with scheduling operations.

SCHEDULING OPERATIONS

HIGH-VOLUME OPERATIONS High-volume operations, also called flow operations, can be repetitive operations for discrete products like automobiles, appliances, or bread, or services like license renew- als at the Division of Motor Vehicles. Or they can be continuous operations for goods produced in a continuous flow as in a product like gasoline or a service like waste treatment. High-volume standard items, either discrete or continuous, have smaller profit margins, so cost efficiency is important. Companies achieve cost efficiency in a high-volume operation through high levels of labor and equipment utilization. Design of the work environment ensures a smooth flow of products or customers through the system. One design is line balancing, which we covered in Chapter 10. Flow operations have the following characteristics.

Characteristics of Flow Operations Flow operations use fixed routings—the product or service is always done the same way in the same sequence with the same workstations. The workstations are arranged sequentially according to the routing. Similar processing times are needed at each workstation to achieve a balanced line. Workstations are dedicated to a single prod- uct or a limited family of products. They use special-purpose equipment and tooling. In a service operation, individuals performing a specific but limited activity are the

� Flow operations Processes designed to handle high-volume, standard products.

� Routing Provides information about the operations to be performed, their sequence, the work centers, and the time standards.

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LOW-VOLUME OPERATIONS • 579

equivalent of special-purpose equipment. For example, when you attend the theater, you go through a number of processing points. First you buy the tickets at the box office. Then you hand the ticket to the ticket taker. Next you are escorted to your seat by an usher. Each person attending the performance goes through these same process- ing points.

Material flows between workstations may be automated. A well-designed sys- tem minimizes work-in-process inventory and reduces the throughput time for the product or service. The design of the production line dictates the capacity of the flow system. The workstation or processing point that needs the greatest amount of time is the system’s bottleneck, which determines how many products or services the sys- tem can complete. Thus the goal is to sequence the operations so they need the least control possible.

A major concern with flow operations is employee boredom with repetitive tasks. Companies use techniques like job enrichment, job enlargement, and job rotation (discussed in Chapter 11) to reduce boredom and maximize line output. At the other extreme in scheduling environments is the low-volume operation, discussed next.

� Bottleneck A facility, department, or resource whose capacity is less than the demand placed on it.

LOW-VOLUME OPERATIONS

Low-volume or job-shop operations are used for high-quality, customized products such as custom stereo systems or custom automobile paint jobs, or for services such as personal fitness, with higher profit margins. Companies with low-volume operations use highly skilled employees, general-purpose equipment, and a process layout. The objective is flexibility, both in product variation and product volume. Equipment is not dedicated to particular jobs but is available for all jobs. In low-volume operations, products are made to order. Each product or service can have its own routing through a unique sequence of workstations, processes, materials, or setups. As a result, sched- uling is complex. The workload must be distributed among the work centers or service personnel. A useful tool for viewing the schedule and workload is a Gantt chart. Let’s look at how a Gantt chart is used.

Gantt Chart Gantt charts are named after Henry Gantt, who developed these charts in the early 1900s. A Gantt chart is a visual representation of a schedule over time. Two kinds of Gantt charts are the load chart and the progress chart.

Load Chart The load chart shows the planned workload and idle times for a group of machines or individual employees or for a department. Figure 15-1 is an example of a load chart showing the jobs assigned to each mechanic and each mechanic’s lunch break. In this example, Bob and J.J. are at lunch from 12:00 to 1:00, and Alex and Sam are at lunch from 12:30 to 1:30 p.m. All employees work from 8:00 a.m. to 5:00 p.m.

� Gantt chart Planning and control chart designed to graphically show workloads or to monitor job progress.

� Load chart A chart that visually shows the workload relative to the capacity at a resource.

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Sample load chart

Mechanic 8–9 9–10 10–11 11–12 12–1 1–2 2–3 3–4 4–5

Bob

Sam

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J.J.

JOB A

JOB B

JOB E

JOB D JOB F JOB MJOB L

JOB C

JOB G JOB I

JOB JJOB H JOB N

JOB OJOB K

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580 • CHAPTER 15 SCHEDULING

Progress Chart The progress chart monitors job progress by showing the relation- ship between planned performance and actual performance. In the progress chart in Figure 15-2, the brackets indicate when the activity is scheduled to be finished, and the shaded area shows the progress of the activity. Note that the first activity, “Com- plete design specs,” begins on time and is finished as scheduled. The second and third operations start on time. Materials sourcing is finished on time, but process design is not finished until late April. Because of the delay in the process design, the pilot run does not start as scheduled and the feedback activity has not yet begun. Both of these operations are behind schedule. The transition to manufacturing will probably also be behind schedule. Gantt charts provide a visual image of the progress of jobs through the system. Now we need to learn how to schedule work.

� Progress chart A chart that visually shows the planned schedule compared to actual performance.

FIGURE 15-2

Sample progress chart

Activity Jan Feb Mar April May June July Complete design specs

Source materials

Design process

Pilot run

Feedback

Transition to manufacturing

Current date

[ ]

[ ] = planned activity progress

[ ]

= actual activity progress

Two kinds of work scheduling or work loading are infinite loading and finite load- ing. Infinite loading schedules work without regard to capacity limits. They let you know how much capacity you need to meet a schedule.

Infinite Loading Manufacturing companies can use infinite loading according to a proposed master production schedule (MPS). A service organization like a law firm can use infinite loading to identify the resources needed to complete the proposed case load. Infi- nite loading identifies uneven workloads and bottlenecks. Figure 15-3 is an example of infinite loading. We can see from the chart that the shop has enough capacity in

� Infinite loading Scheduling that calculates the capacity needed at work centers in the time period needed without regard to the capacity available to do the work.

� Finite loading Scheduling that loads work centers up to a predetermined amount of capacity.

SCHEDULING WORK

FIGURE 15-3

Infinite loading Availablecapacity

Period 3 4 5 6 7 8

Under

Over

Under

Over

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SCHEDULING WORK • 581

periods 4 and 7 but not enough capacity in periods 5 and 8. In this way, we identify time periods when capacity is either poorly used or inadequate and change the sched- ule to level the resource requirements.

Finite Loading Finite loading is an operational schedule with start and finish times for each activity. It does not allow you to load more work than can be done with the available capac- ity. The finite loading schedule shows how a company plans to use available capacity at each work center. In a manufacturing company, the schedule shows the jobs to be done at a particular work center if the work center uses a set number of production hours each day. For example, if the work center can build 50 wire assemblies per hour and the company needs 1000 wire assemblies, the job will take 20 hours of capacity at that work center. In a service organization, a doctor’s office is a good example. To spend ten minutes with each patient, the doctor can have six patients scheduled per hour.

Figure 15-4 is an example of finite loading. Note that no work center is assigned more work than it is able to handle. The disadvantage of finite loading is that it tends to break down over the long term: problems arise and the schedule slips, causing jobs to be rescheduled. Finite loading is why you may have to wait at the doctor’s office.

Companies benefit from both infinite and finite loading. Infinite loading identi- fies resource bottlenecks for a proposed schedule so that planners can find solutions proactively, such as changing the schedule and increasing the resource capacity. Finite loading develops the operational schedule that uses the available capacity. Finite and infinite loading assign work to specific work centers based on a proposed schedule. Both techniques use either a schedule (infinite loading) or a prioritized list of jobs to be done (finite loading). Two additional techniques are forward scheduling and backward scheduling.

Forward Scheduling With forward scheduling, processing starts immediately when a job is received, regardless of its due date. Each job activity is scheduled for completion as soon as possible, which allows you to determine the job’s earliest possible completion date. Figure 15-5 shows an example of forward scheduling. The job is due at the end of week 10, but it can be finished as early as the end of week 7. With forward scheduling, it is not unusual for jobs to be finished before their due date. The disadvantage to finish- ing a job early is that it causes an inventory buildup if items are not delivered before the due date.

� Forward scheduling Schedule that determines the earliest possible completion date for a job.

� Due date Time when the job is sup- posed to be finished.

FIGURE 15-4

Finite loadingAvailablecapacity

Period 3 4 5 6 7 8

Under

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582 • CHAPTER 15 SCHEDULING

Backward Scheduling With backward scheduling, you begin scheduling the job’s last activity so that the job is finished right on the due date. To do this, you start with the due date and work back- ward, calculating when to start the last activity, when to start the next-to-last activity, and so forth. Figure 15-5 gives an example of backward scheduling. Backward sched- uling shows you how late the job can be started and still be finished on time. When you are using backward scheduling and forward scheduling together, a difference between the start time of the first activity indicates slack in the schedule. Slack means that you can start a job immediately but you do not have to do so. You can start it any time up to the start time in your backward schedule and still meet the due date.

Monitoring Workflow Input/output control is a capacity-control technique used to monitor workflow at individual work centers. Input/output control monitors the planned inputs and outputs at a work center against the actual inputs and outputs. Planned inputs are based on the operational schedule, whereas planned outputs use capacity-planning techniques. Actual input is compared with planned input to ensure that enough work enters the measured work center. A work center cannot process items that have not yet arrived. Actual output is used to identify possible problems in the work center, such as an equipment problem or unexpected absences.

� Backward scheduling Scheduling method that determines when the job must be started to be done on the due date.

� Slack The amount of time a job can be delayed and still be finished by its due date.

� Input/output control A technique for monitoring the flow of jobs between work centers.

FIGURE 15-5

Forward and backward scheduling

Forward scheduling

Backward scheduling

Materials

ordered

First

operation

Second

operation

Third

operation

Final

operation

1 2 3 4 5 6 7 8 9 10

Materials

ordered

First

operation

Second

operation

Third

operation

Final

operation

Order received

Due date

JT produces custom wine storage units. JT is very concerned about the performance of Work Cen- ter 101, the bottleneck in the manufacturing process. You have the input/output report shown in Figure 15-6 and you need to explain the numbers.

• Before You Begin: Explain the numbers in the input/output report. The purpose of this report is to monitor the flow of work between work centers and backlog size. First, examine the input report to determine if the supplying work center is actually supplying the amount of work expected. Then examine the output report to see how Work Center 101 is performing. A key indi- cator is the size of its backlog. If the backlog is increasing, then Work Center 101 is unable to keep up with its input. If the backlog is decreasing, then Work Center 101 is outputting more product than is being inputted. If this continues, the backlog will disappear entirely.

EXAMPLE 15.1

Input/Output Control at JT’s

Custom Storage Units

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SCHEDULING WORK • 583

• Solution: The input/output report for Work Center 101 shows any deviations in input or output at the work cen- ter. The input deviation is calculated by subtracting the planned input from the actual input. The output deviation is calculated as planned output subtracted from actual output. For example, based on the input information in period 4, the planned input was 800 standard hours of work, but the actual input was only 750 standard hours. Remember that standard hours of work are the amount of time needed to complete the work if the employee works at 100 percent efficiency. Subtracting the planned input from the actual input (750 standard hours minus 800 standard hours) results in a deviation of 50 standard hours. In period 5, the actual input exceeded the planned input, resulting in a positive deviation (780 standard hours minus 750 standard hours � 30 standard hours deviation).

The cumulative deviation is a running sum of the deviations. For example, in period 4, the deviation is �50 hours; the cumulative deviation is equal to this period’s deviation plus the previous cumulative deviation total. In our case, the previous cumulative deviation is zero, so the cumulative deviation at the end of period 4 is �50 hours. After period 5, the cumulative deviation is �20 hours [a 130 hour devia- tion in period 5 plus the previous cumulative deviation (�50 hours)]. We calculate the deviations and cumulative deviations in the same way for both input and output. Management uses cumulative devia- tion values to indicate possible input or output problems. If planned input is consistently below actual input, the feeding work center may not have enough capacity to meet the planned input. The same is true when the work center’s actual output is consistently less than the planned output. The work center is not producing with the efficiency expected.

The backlog row is the amount of work waiting to be finished at the work center. In our example, Work Center 101 has 100 standard hours of work waiting. The only time the size of the backlog changes is when actual input does not equal actual output. When a work center receives more work than it finishes, the backlog increases. When a work center produces more output than the input received, the backlog decreases. In period 4, Work Center 101 receives 750 standard hours of new work. Work Center 101 finishes 800 standard hours of work during period 4; this is possible only if Work Center 101 finishes some of the 100 hours of backlog from the beginning of period 4. The new input plus the backlog equals the maximum amount of work that can be finished at Work Center 101 (850 standard hours). Since the work center finishes 800 standard hours of work, the backlog decreases to 50 standard hours of work. In

Planned output 800 hours 800 hours 800 hours 800 hours 800 hours

4 5 6 7 8

Actual output 800 hours 750 hours 780 hours 850 hours 825 hours

Deviation 0 hours –50 hours –20 hours +50 hours +25 hours

Cumulative 0 hours –50 hours –70 hours –20 hours +5 hours deviation

Backlog 50 hours 80 hours 80 hours 40 hours 25 hours 100 hours

Planned input 800 hours 750 hours 800 hours 820 hours 800 hours

4 5 6 7 8

Actual input 750 hours 780 hours 780 hours 810 hours 810 hours

Deviation –50 hours +30 hours –20 hours –10 hours +10 hours

Cumulative –50 hours –20 hours –40 hours –50 hours –40 hours deviation

Output Information

Input information

Period

Period FIGURE 15-6

Input/output report for Work Center 101

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584 • CHAPTER 15 SCHEDULING

period 5, the backlog increases because Work Center 101 receives 780 standard hours of work but only produces 750 standard hours of work. The difference between the actual input and the actual output is 30 standard hours; therefore, the backlog increases by 30 standard hours. The input/output report allows a planner to monitor how well the available capacity is used at individual work centers and provides insight into process problems.

HOW TO SEQUENCE JOBS When several jobs need to be done, how do you decide which one to do first? Do you work on the job that you have to finish first? The job you enjoy doing? The job you can finish the fastest? The job that has the biggest payoff? When you decide which job to do first, you are sequencing them.

The eleventh edition of the APICS Dictionary defines operation sequencing or job sequencing as a technique for short-term planning of actual jobs to be run in each work center based on capacity and priorities. We expect a work center to have several jobs waiting to be processed, so we decide on the sequence for processing the jobs. Operation sequencing sets projected start and finish times and expected queues. A job’s priority is its position in the sequence.

Priority Rules Job priority is often set by a priority rule. (See Table 15-1 for explanations of some commonly used priority rules.) Priority rules are typically classified as local or global. A local priority rule sets priority based only on the jobs waiting at that individual work center. For example, the highest priority might be given to the job that arrives first or the job that can be done the fastest. Global priority rules, like critical ratio or slack over remaining operations, set priority according to factors such as the sched- uled workload at the remaining workstations that the job must be processed through.

� Operation sequencing A short-term plan of actual jobs to be run in each work center based on available capacity and priorities.

� Queue Waiting line. � Priority rule Determines the priority of jobs at a work center.

� Local priority rule Makes a priority decision based on jobs currently at that work center.

� Global priority rule Makes a priority decision based on information that includes the remaining work centers a job must pass through.

First come, first served (FCFS): Jobs are processed in the order in which they arrive at a machine or work center.

Last come, first served (LCFS): The last job into the work center or at the top of the stack is processed first.

Earliest due date (EDD): The job due the earliest has the highest priority.

Shortest processing time (SPT): The job that requires the least processing time has the highest priority

Longest processing time (LPT): The job that requires the longest processing time has the highest priority.

Critical ratio (CR): The job with the smallest ratio of time remaining until due date to its processing time remaining has the highest priority.

Slack per remaining operations (S/RO): The job with the least slack per remaining operations is given the highest priority. Calculate by dividing slack by remaining operations.

TABLE 15-1

Commonly Used Priority Rules

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HOW TO SEQUENCE JOBS • 585

A work center needs priority rules when multiple jobs await processing (but not if only a single job needs processing). Priority rules assume that there is no variability in either the setup time or the run time of the job. Let’s look at how to use priority rules.

How to Use Priority Rules Using priority rules is straightforward. Just follow these steps.

Step 1 Decide Which Priority Rule to Use. Diff erent priority rules achieve diff er- ent results. We will discuss this when we look at performance measurements. Step 2 List All the Jobs Waiting to Be Processed at the Work Center and Their Job Time. Job time includes setup and processing time. Step 3 Using Your Priority Rule, Determine Which Job Has the Highest Priority and Should Be Worked on First, Second, Third, and So On. To illustrate the use of priority rules, let’s use SPT to sequence a group of jobs waiting at Work Center 102, Jill’s Machine Shop.

Using SPT as a priority rule, determine the sequence for the following jobs waiting at Work Center 102 at Jill’s Machine Shop. The job information follows.

• Before You Begin: In this problem, the objective is to develop a sequence to perform the batch of jobs. The priority rule is used when one or more jobs are available to be put in the sequence. SPT means that the job with the shortest processing time is put first in the sequence, and the job with the second shortest time is placed second. When there is a tie between available jobs, it does not matter which job is placed first in the sequence.

EXAMPLE 15.2

Using SPT at Jill’s Machine Shop

Job Number Job Time at Work Center 102 (includes setup and run time)

AZK111 3 days BRU872 2 days CUF373 5 days DBR664 4 days EZE101 1 day FID448 4 days

Position in Sequence Job Number

First EZE101 Second BRU872 Third AZK111 Fourth DBR664 Fifth FID448 Sixth CUF373

Step 1 Choose the priority rule. You must use SPT. Step 2 List the jobs waiting for processing at Work Center 102 and their job times. Th is information is given in the table. Step 3 Using the priority rule, determine the sequence of jobs.

The highest priority goes to job EZE101 (one day) since it takes the least amount of time. The second job is BRU872 (two days). The third job is AZK111 (three days). Job DBR664 and job FID448 are tied for fourth place because both take four days. Since we have no additional information, it does not matter which of these is done fourth and which one is done fifth. We will do DBR664 fourth and FID448 fifth. The last job is CUF373. Our completed sequence is

How well a priority rule works depends on the performance measurement the company uses. In the next section, we will cover commonly used performance measurements.

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586 • CHAPTER 15 SCHEDULING

MEASURING PERFORMANCE Companies measure scheduling effectiveness according to their competitive priori- ties. For example, if your company is concerned with customer response time, you measure scheduling effectiveness in terms of response time. Mean job flow time and the mean number of jobs in the system each measure a company’s responsiveness. On the other hand, if your company competes on cost, it is concerned with efficiency. If on-time delivery is of primary concern, the company measures on-time delivery per- formance. Makespan measures efficiency; mean job lateness and mean job tardiness measure due-date performance. After looking at airline scheduling, we discuss differ- ent performance measures.

Consider scheduling in the airline industry. Cheaper fares, competition factors, weather patterns, equipment and expansion difficulties, and poor scheduling are only some of the reasons why scheduling problems occur. How- ever, various remedies can help alleviate these scheduling problems, including charging peak travel fares, requiring the FAA and weather service to work more closely for more accurate and frequent

weather forecasts, “technologizing” (i.e., utilizing scheduling technology, modern- izing the air traffic control system, and automating ticketing and boarding), building new runways and using abandoned military airfields, and, most important, designing realistic schedules (i.e., cutting back the number of flights, moving leisure flights to off-peak times, extending the operation day, and spreading out arrival and departure times). As long as the number of air travelers continues to boom, optimal scheduling will be an important issue to the airline industry.

Job Flow Time Job flow time measures response time—the time a job spends in the shop, from the time it is ready to be worked on until it is finished. It includes waiting time, setup time, process time, and possible delays. We calculate job flow time as

Job flow time 5 time of completion 2 time job was first available for processing

� Makespan The amount of time it takes to finish a batch of jobs.

� Job flow time Measurement of the time a job spends in the shop before it is finished.

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LINKS TO PRACTICE Airline Scheduling

Calculate the mean flow time for the following sequence of jobs.

• Before You Begin: In this problem, sum the individual job flow times and divide by the number of jobs in the sequence to calculate the mean job flow time. Flow time is the amount of time a job spends in the shop before being completed.

• Solution: To calculate mean job flow time, we sum the individual job flow times for each job and divide by the number of jobs. Viewing Figure 15-7, we can see that the job flow time for job A is 10 days, 13 days for job B, 17 days for job C, and 20 days for job D. Adding these job flow times together (10 1 13 1 17 1 20) gives us a total job flow time of 60 days. We divide this by the number of jobs to determine mean job flow time—that is, 60 days divided by 4 jobs equals a mean job flow time of 15 days.

EXAMPLE 15.3

Calculating Mean Flow Time

Job A finishes on day 10 Job B finishes on day 13

Job C finishes on day 17

Job D ends on day 20

FIGURE 15-7

Job schedule

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MEASURING PERFORMANCE • 587

Average Number of Jobs in the System The average number of jobs in the system measures the work-in-process inventory and also affects response time. The greater the number of jobs in the system, the longer the queues and subsequently the longer the job flow times. If quick customer response is critical to your company, the number of jobs waiting in the system should be relatively low.

� Average number of jobs in the system Measures work-in-process inventory.

Calculate the average number of jobs in the system.

• Before You Begin: To calculate the average number of jobs in the system, sum the indi- vidual job flow times and divide that by the makespan (the amount of time it takes to finish the entire batch of jobs). This is the average number of jobs in the shop. If this number is too low, it is likely that some work centers will be starved for work. If the number is too high, it means that work-in-process inventories will be higher.

• Solution: To calculate the average number of jobs in the system, we need to know the individual job flow times for each job. Once again, look at Figure 15-7 to determine job flow times. The total job flow time (10 1 13 1 17 1 20 5 60 days) is divided by the total number of days it takes to complete the whole batch of jobs (20 days). Therefore, the average number of jobs in the system is 3. The higher the average number of jobs in the system, the longer the waiting time.

EXAMPLE 15.4

Calculating the Average Number of Jobs in the System

Makespan Makespan measures efficiency by telling us how long it takes to finish a batch of jobs. To calculate makespan, we subtract the starting time of the first job from the comple- tion time of the last job in the group. Using the data from Example 15.4, a calendar showing the progress of the jobs would look like Figure 15-7. In this case, the makes- pan for this group of jobs is 20 days. Note that makespan has no link to customer due dates: you can have an efficient schedule in terms of finishing a batch of jobs but still have relatively poor customer service.

Job Lateness and Tardiness Job lateness, a measure of customer service, is the difference between the time a job is finished and the time it is supposed to be finished (its due date). When a job is finished ahead of schedule, it has negative lateness. For example, if job X is due on day 15 and it is finished on day 12, it has a lateness value of negative 3 days. If job X is finished on day 15, its lateness value is zero. If job X is done on day 17, its lateness value is a positive 2 days. Positive job lateness values are typically described as job tardiness. Tardiness indicates how many days pass after the due date before the job is completed.

� Job lateness Measures whether the job is done ahead of, on, or behind schedule.

� Job tardiness Measures how long after the due date the job is completed.

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588 • CHAPTER 15 SCHEDULING

Let’s compare two priority rules.

Calculate mean job lateness using the following data.

• Before You Begin: Lateness measures how closely to the due date different jobs are completed. A negative lateness value means that the job is completed before it is due. A positive lateness value means the job is completed after it was due. A value of zero means that the job was completed on its due date. Tardiness is used when companies do not include jobs completed early in their calculation.

EXAMPLE 15.5

Calculating Job Lateness

Job Completion

Date Due Date Lateness A 10 15 –5 B 13 15 –2 C 17 10 7 D 20 20 0

• Solution: Job A is finished five days ahead of its due date, and job B is finished two days earlier than its due date. Job C is finished seven days tardy, and job D is finished on its due date. The performance measure we use to evaluate the schedule is mean job lateness, which sums all the individual lateness values and divides by the number of jobs processed. In our case, it is 0 divided by 4 jobs equals 0 days job lateness. On average, the jobs are finished on their due dates.

Some companies do not include negative values of lateness in the calculation because there is no perceived benefit to finishing the job early. In this case, we substitute zeroes for the negative numbers, and we sum the lateness values and then divide by the number of jobs to get the average tardiness of the jobs. The updated information using zeroes instead of negative lateness values is as shown in the table.

In this case, average tardiness is 1.75 days ((0 1 0 1 7 1 0)/4 jobs). If customer service is important to your company, average tardiness is probably a more relevant measurement than job lateness.

Job Completion

Date Due Date Tardiness A 10 15 0 B 13 15 0 C 17 10 7 D 20 20 0

Before You Go On

Be sure you know how to use priority rules and how to measure a schedule’s effectiveness. Different priority rules measure different aspects of performance, depending on your company’s competitive priorities. SPT (shortest pro- cessing time) always minimizes mean job flow time, mean job lateness, and average number of jobs in the system. FCFS (first come, first served) is considered a fair rule because everyone is treated equally. EDD (earliest due date) and S/RO (slack per remaining operations) tend to perform well in terms of minimizing mean job tardiness.

COMPARING PRIORITY RULES Now that you know how to use priority rules and how to measure schedule effective- ness, compare the effectiveness of different priority rules.

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COMPARING PRIORITY RULES • 589

Using the job information shown in Table 15-2, compare the shortest processing time priority rule (SPT) to the slack per remaining operations priority rule (S/RO). Determine the job sequence and calculate mean job flow time, average number of jobs in the system, mean job lateness, and mean job tardiness.

• Before You Begin: First, develop the sequence of jobs using the appropriate priority rule. Begin with SPT and calculate the performance measurements. Repeat using S/RO.

The first priority rule is SPT and the available jobs are listed, so we need to calculate only the sequence. Using SPT, we base the sequence on doing the job that needs the least amount of time at the work center first (in our case, job E). We do job E first, and then we look for the next shortest job time (job A takes three days) to be second in our sequence. The complete sequence for SPT is job E, A, D, F, C, and then B, shown graphically here.

Using this information, we calculate the mean job flow time. The flow time for job E is 2 days; for job A it is 5 days; for job D it is 9 days; for job F it is 14 days; for job C it is 20 days; and for job B it is 27 days. To find the mean job flow time, we add up these individual job flow times and divide by the number of jobs: (2 1 5 1 9 1 14 1 20 1 27)/6 jobs � 12.83 days. We calculate the average number of jobs in the system by dividing total job flow time by the makespan, which is 27 days. Total job flow time is 77 days (2 1 5 1 9 1 14 1 20 1 27). The average number of jobs in the system is 2.85 jobs. To calculate the mean lateness and mean tardiness of the jobs processed, we need to know when each job leaves Work Center 301. Table 15-3 shows those results.

Problem-Solving Tip Remember that job fl ow time is the amount of time the job is in the system (its completion date minus when the job was fi rst available). Since all the jobs were available at the same time, their fl ow time is the same as their completion time.

To find mean job lateness, we add up the individual lateness values (�10 1 7 � 10 � 11 � 20 � 6), which equals �50 days. Dividing by the number of jobs (6), the mean job lateness is �8.33 days. On average, jobs take 8.33 fewer days to get through the shop than we expected. How is this information useful? Your company can correlate job flow time with lead times quoted to customers. If the job flow time is less than expected, marketing can consider using reduced lead times as a competitive advantage.

MKT

EXAMPLE 15.6

Using SPT

Table 15-2 Job Data

Job

Job Time at Work Center

301 (days) Due Date

(days from now)

Remaining Job Time at

Other Work Centers (days)

Remaining Number of Operations

A 3 15 6 2 B 7 20 8 4 C 6 30 5 3 D 4 20 3 2 E 2 22 7 3 F 5 20 5 3

Table 15-3 Work Center 301 Completion Data Using SPT

Job Completion

Date Due Date Lateness

(days) Tardiness

(days) A 5 15 –10 0 B 27 20 7 7 C 20 30 –10 0 D 9 20 –11 0 E 2 22 –20 0 F 14 20 –6 0

E done at end of day 2

A done at end of day 5

D done at end of day 9

F done at end of day 14

C done at end of day 20

D done at end of day 27

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590 • CHAPTER 15 SCHEDULING

Problem-Solving Tip Negative lateness means the job is fi nished ahead of its due date. Zero late- ness means the job fi nished on its due date. Positive lateness means the job fi nished aft er its due date.

Tardiness applies only to jobs finished after the due date. We treat all negative lateness values as zeroes. To find mean job tardiness, we sum the individual job tardiness values and divide by the number of jobs. In this example, only one job is tardy and the sum of the tardiness values is 7. The mean job tardiness is 1.17 days, or 7/6 jobs. The maximum tardiness is 7 days. This information is also important for marketing because it implies the level of customer service provided. Now let’s look at what happens when we use the S/RO priority rule to develop our sequence.

• Solution: We calculate the S/RO by finding the amount of slack each job has and then dividing that slack by the remaining number of operations (including the current operation). We calculate slack by subtracting the total work remaining (current operation plus all other undone operations times) from the amount of time left until the due date. We calculate the values in the slack time column by adding the job time at Work Center 301 plus the remaining job time at other work centers (for job A, 3 1 6 � 9 days of remaining work) and subtracting that total from the number of days till the due date (for job A, 15 days). The difference is the slack (for job A, 6 days) as shown in Table 15-4.

We calculate the S/RO values by dividing the slack for the job (6 days for job A) by the number of remaining operations, including the current operation (two plus the current one, or three remaining operations), which equals an S/RO of 2 for job A. Table 15-4 shows the S/RO value for each job. The lower the value of the S/RO, the higher is its priority. For our problem, job B should be done first, fol- lowed by A, F, E, D, and then C. Graphically, the sequence appears as follows.

Table 15-4 Job Data with Slack Calculations

Job

Job Time at Work Center

301 (days)

Remaining Job Time at Other Work

Center (days) Due Date

(days from now)

Slack Time (days)

Remaining Number of

Operations after Work Center 301 S/RO

A 3 6 15 6 2 2 B 7 8 20 5 4 1 C 6 5 30 19 3 4.75 D 4 3 20 13 2 4.33 E 2 7 22 13 3 3.25 F 5 5 20 10 3 2.5

Table 15-5 shows the completion dates of the jobs when we use S/RO.

Table 15-5 Work Center 301 Completion Data Using S/RO

Job Completion

Date Due Date Lateness

(days) Tardiness

(days) A 10 15 –5 0 B 7 20 –13 0 C 27 30 –3 0 D 21 20 1 1 E 17 22 –5 0 F 15 20 –5 0

B done at end of day 7

A done at end of day 10

F done at end of day 15

E done at end of day 17

D done at end of day 21

C done at end of day 27

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SEQUENCING JOBS THROUGH TWO WORK CENTERS • 591

Comparing SPT and S/RO Mean job lateness using S/RO is 25 days, compared to 28.33 days using SPT. The SPT rule always minimizes mean job lateness. The mean tardiness using S/RO is 0.167 days, compared to 1.17 days using SPT. The maximum tardiness is less using S/RO (1 day), compared to SPT (7 days). Priority rules based on due date are better at reduc- ing the maximum tardiness. Mean job flow time using S/RO is 16.17 days, compared to only 12.83 days with SPT. Note that the SPT priority rule always minimizes mean job flow time. Average number of jobs in the system using S/RO is 3.59 jobs, compared to 2.85 jobs using SPT. Since SPT sets priority on getting several jobs done as quickly as possible, we can expect less work-in-process or fewer average jobs in the system.

Now that we have used two different priority rules to develop a sequence for a single machine or work center, let’s look at a technique for developing the sequence when two different work centers are involved.

SEQUENCING JOBS THROUGH TWO WORK CENTERS At times, all jobs must be processed through the same two work centers sequentially. For example, when you do laundry, clothes go through the washer before the dryer. Different kinds of clothing need different wash cycles and different drying times, but the sequence is the same. To shorten the time it takes to do your laundry, you can use Johnson’s rule. Johnson’s rule is a scheduling technique for developing a sequence when jobs are processed through two successive operations. The operations can be at machine centers, departments, or different geographical locations. The job flow must be unidirectional: the first activity for every job is the same, and you must finish it before you can begin the second activity (wash the clothes before you dry the clothes). Johnson’s rule is an optimizing technique and always minimizes makespan. To use Johnson’s rule, follow this procedure.

Step 1 List the jobs and the processing time for each activity. Step 2 Find the shortest activity processing time among all the jobs not yet sched- uled. If the shortest activity processing time is a fi rst activity, put the job needing that activity in the earliest available position in the job sequence. If the shortest activity processing time is a second activity, put the job needing that activity in the last avail- able position in the job sequence. When you schedule a job, eliminate it from further consideration. Step 3 Repeat Step 2 until you have put all the activities for the job in the schedule.

� Johnson’s rule A technique for minimizing makespan in a two-stage, unidirectional process.

Vicki’s Office Cleaners does the annual major cleaning of university buildings. The job requires mop- ping and waxing the floors in five buildings at Mideast University. Each building must have the floors mopped and stripped (first activity), and then waxed and buffed (second activity). Vicki wants to mini- mize the time it takes her crews to finish cleaning the five buildings. Use Johnson’s method to develop the sequence Vicki should follow.

• Before You Begin: In this problem, find the best sequence for mopping and waxing the different university buildings. Whenever the scheduling problem has a unidirectional flow and two different activities to be done, use Johnson’s rule. This sequence, to be followed by the mop- ping and waxing crews, minimizes makespan.

EXAMPLE 15.6

Vicki’s Office Cleaners

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592 • CHAPTER 15 SCHEDULING

• Solution: Step 1 List the Jobs and the Processing Time.

Step 2 Find the Shortest Activity Processing Time among the Jobs. Th e shortest activity processing time is one day for mopping Adams Hall. Since the shortest activity time is a fi rst activity, we put mopping Adams Hall in the top available position in the sequence. Th e fi rst job in our sequence is mopping Adams Hall. We eliminate Adams Hall since it has a spot in our sequence. We also eliminate the fi rst position in our sequence. Let’s look at the remaining jobs and repeat Step 2.

Step 2 Find the Shortest Activity Processing Time among the Remaining Jobs. Th ere are two activities tied this time: waxing the Evans Center and mopping the Chemistry Building—each takes two days. When a tie occurs and the shortest processing time is for the same activity, either building can be selected. In a case like this where the shortest processing times are for diff erent activities, we schedule both. Since the shortest time for the Evans Center is the second activity, it takes the lowest available spot in our sequence. Th e Evans Center will be done fi fth. Th e shortest processing time for the Chemistry Building is for its fi rst activity, so it takes the highest available spot in our sequence. Th e Chemistry Building will be mopped second. We update our remaining jobs, removing the Evans Center and the Chemistry Building and the fi ft h and second positions. Now repeat Step 2 again.

Step 2 Find the Shortest Activity Processing Time among the Remaining Jobs. Th e shortest activity processing time is three days for mopping the Bryce Building. Since this is the fi rst activity, we put mopping the Bryce Building in the earliest available spot in the sequence, which is third. Since only one job is left , Drake Union, we put it in the only remaining spot in the sequence, fourth.

The sequence Vicki should use is shown here. When we have a tie for the shortest activity, it does not matter which job we put first.

Activity 1 Mopping (days)

Activity 2 Waxing (days)

Adams Hall 1 2 Bryce Building 3 5 Chemistry Building 2 4 Drake Union 5 4 Evans Center 4 2

Activity 1 Mopping (days)

Activity 2 Waxing (days)

Bryce Building 3 5 Chemistry Building 2 4 Drake Union 5 4 Evans Center 4 2

Activity 1 Mopping (days)

Activity 2 Waxing (days)

Bryce Building 3 5 Drake Union 5 4

Sequence Position Job

First Adams Hall (A) Second Chemistry Bldg (C) Third Bryce Building (B) Fourth Drake Union (D) Fifth Evans Center (E)

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SCHEDULING BOTTLENECKS • 593

Now let’s look at the sequence graphically.

Vicki’s Cleaners begins mopping Adams Hall on day 1. No waxing is done because none of the floors has been mopped yet. At the end of day 1, Vicki’s Cleaners has finished mopping Adams Hall. On day 2, the mopping crew starts mopping the Chemistry Building while the waxing crew begins at Adams Hall. At the end of day 3, the mopping crew finishes the Chemistry Building and moves on to the Bryce Building to start day 4. The waxing crew finishes Adams Hall and starts the Chemistry Building on day 4. Note that this sequence has a makespan of 18 days. We can find no other sequence for these jobs that can take less time because Johnson’s rule always minimizes makespan.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Mopping A C C B B B D D D D D E E E E Waxing A A C C C C B B B B B D D D D E E

SCHEDULING BOTTLENECKS When companies schedule a job shop, bottlenecks are common. A bottleneck is any resource whose capacity is less than the demand placed on it. For example, let’s consider Akito’s Flowers, a retail florist. When a customer orders flowers, three steps follow. First, the clerk takes the order and processes payment. Second, the clerk gives the order to the flower arrangers, who gather the appropriate materials and do the flower arrangement. Third, the drivers deliver the flowers. At Akito’s Flowers, the clerk can process 30 telephone orders per hour. Each of the three flower arrangers can make 7 arrangements per hour, and each of the three drivers can make ten deliv- eries per hour. The flower arrangers are the bottleneck in this process. Regardless of the number of orders processed by the clerk, the arrangers can do a maximum of 21 arrangements per hour, and Akito’s can deliver no more than 21 floral arrangements per hour. Thus the output of the process is reduced to the capacity of the bottleneck. Bottlenecks typically result when one operation in a job takes longer than the other operations.

Techniques for scheduling bottleneck systems emerged in the late 1970s with the introduction of optimized production technology (OPT) by Eli Goldratt. OPT classifies resources as either bottlenecks or nonbottlenecks, and makes bottlenecks the basis for scheduling and capacity planning. According to OPT, companies should schedule bottleneck resources to full capacity and schedule nonbottleneck resources to support the bottleneck resources. In our example, the bottleneck resource is the flower arrangers; the nonbottleneck resources are the clerks processing orders and the delivery drivers. Nonbottleneck resources can be idle and still not affect the output of the system because output is determined by the bottleneck resource, not by capacity at the nonbottleneck resources. OPT also introduces the concept of capacity-constrained resources, which are resources that have become bottlenecks because of inefficient usage. In our florist shop example, the delivery drivers could become a capacity-constrained resource if Aki- to’s does not attend to consolidating shipments and using the drivers efficiently. Table 15-6 lists OPT principles.

Let’s look at each of these principles.

Balance the process rather than the flow. Traditionally, managers try to make the same amount of capacity available in each department or work center. Th is

� Optimized production technology (OPT) A technique used to schedule bottleneck systems.

� Nonbottleneck A work center with more capacity than demand.

� Capacity-constrained resource Bottleneck caused by inefficient usage.

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594 • CHAPTER 15 SCHEDULING

means every resource is a bottleneck. At Akito’s Flowers, balanced capacity means processing 21 orders per hour or needing only 0.7 order clerks and 2.1 drivers. Although in theory this approach provides capacity for 21 fl oral arrangements per hour, in real life we cannot use partial employees or machines, so we have some excess capacity. If we have no excess capacity, we guarantee that fewer than 21 orders will be processed each hour, because real life has statistical fl uctuations and dependent events. At Akito’s Flowers, processing does not begin until the clerk receives an order. What happens when no orders are received for the fi rst 20 minutes of the day? Th at 20 minutes is lost capacity not only for the clerk but also for the fl oral arrangers and the delivery drivers. If we have some excess capacity at the nonbottleneck resources, we can operate the bottleneck at full capacity.

Nonbottleneck usage is determined by some other constraint in the system. At the fl oral shop, the order processing and the delivery service are nonbottle- necks. Th eir level of usage is determined by the fl ower arrangers (the bottleneck resource).

Usage and activation of a resource are not the same. Activation of a resource means the resource is used to process materials or products. Usage means that the resource activated is contributing positively to the company’s performance. Th us usage means the resource is performing a needed activity.

An hour lost at a bottleneck resource is an hour lost forever. If an organization’s goal is to maximize throughput, the bottleneck must be fully used. Suppose our fl oral arrangers cannot produce arrangements for an hour because the delivery of fl owers to the shop is delayed. Th us the shop can only produce a maximum of 147 arrangements that day instead of 168 arrangements (7 hours � 3 arrangers � 7 arrangements per hour instead of 8 hours � 3 arrangers � 7 arrangements per hour).

An hour lost at a nonbottleneck is a mirage. Time lost at a nonbottleneck resource is critical only if the lost time causes the resource to become a bottleneck. For example, if one of the fl orist’s drivers leaves work an hour early, it may or may not aff ect the output for the day. It aff ects the output only if more than 14 deliveries must be made during that last hour. Otherwise, there is no decrease in output. If

� Throughput The quantity of finished goods that can be sold.

• Balance the process rather than the flow.

• Use of a nonbottleneck resource is determined by some other constraint within the system.

• Use and activation of a resource are not the same.

• An hour lost at a bottleneck resource is an hour lost forever.

• An hour lost at a nonbottleneck resource is just a mirage.

• Bottlenecks determine throughput and inventory in the system.

• The transfer batch does not need to be equal to the process batch.

• The process batch should be variable.

• Schedules should be established by considering all constraints simultaneously. Lead times are the result of the schedule and are not predetermined.

TABLE 15-6

OPT Principles

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SCHEDULING BOTTLENECKS • 595

more than 14 deliveries are needed, the delivery service has become a bottleneck resource.

Bottlenecks determine throughput and system inventory. A bottleneck resource determines the throughput for the system. It also determines how much inventory is needed in the system to ensure the continuous operation of the bottleneck resource. For example, the florist can process 168 orders per day. Therefore, the flower inventory must be sufficient to produce 168 arrangements.

The transfer batch does not have to equal the process batch. Th e transfer batch is the quantity of items moved at the same time from one resource to the next. At Akito’s Flowers, that is the number of orders the clerk processes before forward- ing the orders to the fl oral arrangers. If the clerk forwards orders only once per hour, the fl oral arrangers’ output is directly aff ected. If the clerk forwards each order as it arrives, the number of orders the clerk can process per hour is prob- ably aff ected.

Th e process batch is the quantity of an item processed at a resource before that resource is changed to produce a diff erent product. If one of the fl oral arrang- ers specializes in preparing business fl oral arrangements and typically produces these arrangements in batches of 14, the process batch is 14 units. Th e arranger could transfer these arrangements immediately to the delivery area aft er each one is produced. In this case, the transfer batch quantity is 1. Th e delivery service could begin immediately to prepare the arrangement for delivery rather than waiting until all 14 arrangements are ready.

The process batch should be variable. We do not always have to produce the same quantity, but instead we should produce what is needed. At Akito’s Flowers, one of the fl oral arrangers produced a batch of 14 business fl oral arrangements at a time, but this does not mean that the arrangers always have to produce 14. Maybe two of the business customers close for summer vacation. In this case, the arranger should make only 12 arrangements and not 14, because the addi- tional 2 will not be sold. Th us, the process batch quantity should be linked to demand.

Schedules should be established by considering all constraints simultaneously. Lead times are the result of the schedule and are not predetermined. You should develop the schedule considering all your constraints. If you do not know what your workload is, you cannot tell a customer how long it will take to do a job. Once you know how much work you need to do, you can determine how long it should take.

Instead of losing capacity because of order- processing delays, a florist can improve the order entry procedure. An approach by 1-800-FLOWERS.com, a nationwide network of 1500 independent florists, allows customers to enter a Web site and private chat room to discuss their order with a customer service representative in real time. With its on-line propri- etary order-processing system designed to handle a high volume of transactions, 1-800-FLOWERS.com is positioned to provide highly personalized real-time customer service. The company has been operating on the Web since 1992.

� Transfer batch The quantity of items moved at the same time from one resource to the next.

� Process batch The quantity produced at a resource before the resource is switched over to produce another product.

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596 • CHAPTER 15 SCHEDULING

The theory of constraints (TOC) is an extension of OPT. According to the TOC, a system’s output is determined by three kinds of constraints: internal resource con- straint, market constraint, and policy constraint. An internal resource constraint is the classic bottleneck discussed in the previous section. A market constraint results when market demand is less than production capacity. Since companies do not want excess inventory buildup, the market determines the rate of production. Policy con- straint means that a specific policy dictates the rate of production (for example, a policy of no overtime).

TOC tries to improve system performance by focusing on constraints. Improve- ment is measured financially and operationally. Financial measurements are net profit, return on investment, and cash flow. Operational measurements include throughput, inventory, and operating expenses. Throughput is the rate at which money is gener- ated by the system through sales. Unsold product is not throughput. Inventory is the money the system has invested in buying materials to produce items it intends to sell and does not include labor or overhead. Operating expense is the money spent to convert inventory into throughput, including all labor, overhead, and other expenses.

The procedure for using TOC consists of the following steps. Step 1 Identify the System’s Bottleneck(s). At Akito’s Flowers, identify the fl oral arrangers as the bottleneck. Step 2 Exploit the Bottleneck(s). For the fl oral shop, take orders ahead of time to make sure there is always a buff er of orders for the arrangers to work on. Th is pre- vents idle time at the bottleneck resource. Step 3 Subordinate All Other Decisions to Step 2. Schedule nonbottleneck resources to support the maximum use of the bottleneck. For Akito’s Flowers, have the clerk transfer orders to the arrangers every ten minutes at the start of the day to make sure the bottleneck is fully used. You may have to arrive early or stay late to be sure the orders are processed and waiting for the arrangers to arrive fi rst thing each day. Step 4 Elevate the Bottleneck(s). If aft er Steps 1 through 3 the bottleneck is still a constraint, then consider increasing the capacity of the bottleneck. At Akito’s Flow- ers, add another fl oral arranger. Step 5 Do Not Let Inertia Set In. Although the fl oral arrangers may improve their throughput, check to see whether new constraints have developed. If so, work on increasing throughput.

� Theory of constraints (TOC) A management philosophy that extends the concepts of OPT. � Internal resource constraint A regular bottleneck.

� Policy constraint The condition that results when a specific policy dictates the rate of production.

� Market constraint The condition that results when market demand is less than production capacity.

SCHEDULING FOR SERVICE ORGANIZATIONS In many service organizations, scheduling is complicated because service demand— quantity, type of service, and timing—is often variable and hard to forecast. In addition, inventories may not be possible and capacity is limited. For example, a movie theater cannot show the movie before the customers arrive and hope to satisfy demand. The theater is also limited as to how many people can occupy the theater at any given time. Because of these constraints, some additional techniques are available for scheduling services. These include scheduling the services demanded and scheduling the workforce.

Scheduling Services Demanded Techniques for scheduling services demanded range from setting appointments, requiring reservations, using a public schedule, and delaying or back-ordering the service. Let’s look at each of these individually.

THEORY OF CONSTRAINTS

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SCHEDULING FOR SERVICE ORGANIZATIONS • 597

Appointments Appointment systems set a time for the customer to use the service. For example, students make appointments with professors to discuss class work. Appoint- ments minimize customer waiting time and make good use of the service provider’s capacity. Appointment systems are used by physicians, lawyers, auto repair or service shops, and hair salons. The shared component of each of these services is that no tan- gible inventory is usually possible. Disadvantages of an appointment system include the problem of “no-shows”—people who miss appointments—and insufficient time sched- uled for customers. In the case of “no-shows,” the service provider may be idle until the next scheduled appointment and incur a loss of revenue. In the case of insufficient time, the service provider often falls behind schedule and keeps customers waiting.

Research is being done on scheduling outpatients in a dynamic, multiperiod environment. The objectives are to minimize the delay between the time a patient requests an appointment and when the patient is actually seen and to determine where unscheduled appointment slots should be maintained during the day. These unscheduled slots are for urgent-care patients needing to be seen as soon as possible by the physician. For complete details of the study, see Klassen and Rohleder (2004).

Reservations A reservation system enables the customer to take control or temporary possession of an item—for example, a hotel room, an automobile, or a banquet hall. A reservation system provides advance notice of when the item is needed and for how long. Deposits usually reduce the problem of last-minute cancellations or “no-shows.”

Posted Schedules Many service providers post a schedule indicating when a service is available. Movie theaters, universities, airlines, trains, buses, retail stores, museums, concerts, and sporting events are all examples of services that post schedules. The posted schedule tells the customer the event’s date and time.

Delayed Services or Backlogs Another method used to schedule customer demand is delayed services or backlogs. Restaurants that do not take reservations are one example. The restaurant puts customers on a waiting list until a table becomes available. Other examples are banks, grocery stores, retail stores, repair services, and barber shops. In most of these organizations, customers are served in the order in which they arrive. These methods are aimed at better managing the service organization’s capacity. An alternative method for managing capacity involves the way the workforce is scheduled.

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598 • CHAPTER 15 SCHEDULING

Scheduling Employees Since organizations may not always be able to schedule demand, the alternative is to manage capacity in the way they schedule employees. Organizations can staff for peak demand, use floating personnel, have employees on call, or use temporary employees, seasonal employees, part-time employees, or any combination of the above.

Staffing for Peak Demand With this procedure, the organization has enough ser- vice providers to accommodate the maximum level of customer demand. The obvi- ous problem with this is cost. The workforce is fully used only during peak demand. Otherwise, a portion of the workforce is idle. Organizations typically staff for peak demand when the service providers have significant skills and the size of the work- force cannot be changed quickly. An example is your local fire or police department.

Floating Employees When customer demand for services can change daily, organi- zations use floating employees to their advantage. Floating employees perform a num- ber of services and are assigned where they are needed each day. Hospitals use floating employees because the number of patients and degree of care needed can change daily. A disadvantage of this approach for some employees is the uncertainty of their work location and the transient nature of short-term assignments.

Employees on Call Some organizations use on-call employees during specific peri- ods of the week. Physicians at a hospital may be on call in case of emergencies, though not physically present unless needed. Maintenance employees may also be on call in case of emergencies. Although being on call restricts an employee’s normal free time, it also means the employee does not have to be physically present in the workplace during the specified period.

Temporary Employees Using temporary employees is another way for an organiza- tion to adjust workforce level. An organization can hire temporary employees with almost any skill set through a temp agency. The agency provides the employees and bills the organization.

Seasonal Employees Service organizations such as retailers with seasonal customer demand hire seasonal employees. These are short-term hires who expect their job to be terminated at the end of the season. Growers, for example, use seasonal employees to process items as they ripen. These organizations need an expanded workforce for a short period of time and cannot justify hiring additional permanent employees.

Part-Time Employees For some organizations, customer demand is higher dur- ing certain times of the day and lower at other times. At a fast-food restaurant, for example, demand is high at meal times—breakfast, lunch, and dinner—and lower during the rest of the day. Instead of hiring employees for a full eight-hour shift, the organization uses its capacity more effectively by hiring part-time employees.

Organizations can combine any of these techniques to manage their service capac- ity effectively. Although these techniques provide the means for managing capacity, organizations still need to develop employee schedules that comply with legal require- ments and contractual obligations. Legal requirements may dictate the minimum number of employees physically present and on duty at a fire station, for example. Contractual obligations are determined by labor agreements. These obligations may limit overtime and concern the number of consecutive days off each week.

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DEVELOPING A WORKFORCE SCHEDULE • 599

Now let’s look at a technique for developing a workforce schedule when each employee works full-time and needs to have two consecutive days off each week.

DEVELOPING A WORKFORCE SCHEDULE Tibrewala, Phillippe, and Brown developed a technique in 1972 that enables a com- pany to operate seven days a week and give each of its full-time employees two consec- utive days off. The purpose is to find the two consecutive days off for each employee, satisfy staffing needs, and minimize excess capacity. For example, a swimming pool or beach needs lifeguards seven days a week. This scheduling technique can develop the schedules to be used so that each lifeguard has two consecutive days off during the week. Since most companies define the starting day and ending day of their pay week, we will use Monday as the first day of the week. Thus an employee cannot be off on Sunday and Monday since those are not two consecutive days during the same pay week. We use Tibrewala, Phillippe, and Brown’s technique for developing the schedule by following these steps:

Step 1 Find out the Minimum Number of Employees Needed for Each Day of the Week.

Step 2 Given the Minimum Number of Employees Needed Each Day, Calculate the Number of Employees Needed for Each Pair of Consecutive Days during the Pay Week. For example, a total of nine employees are needed on Monday and Tuesday. Th e sum for each pair of days is as shown in the table.

Step 3 Find the Pair of Days with the Lowest Total Needed. Th ese are the two consecutive days off for one employee, who will work the other fi ve days of the week. In our case, the lowest total is for the Saturday and Sunday pair of days. Employee number 1 works Monday, Tuesday, Wednesday, Th ursday, Friday, and is off on Sat- urday and Sunday. When there is a tie, we can choose any of the tied pairs. We base our decision on an existing labor contract or established company procedures, or we can break the tie arbitrarily. Step 4 Update the Number of Employees You Still Need to Schedule for Each Day. We decrease our employee needs for Monday through Friday by 1 because employee number one is scheduled to work those days. Th e number of employees needed for Saturday and Sunday has not changed because no one has been sched- uled yet to work those days. Th e updated staffi ng needs are shown here.

ACC HRM

Day of the Week M T W Th F Sa Su Number of staff needed 4 5 5 3 5 2 3

Pair of Consecutive Days Total of Staff Needed

Monday and Tuesday 9 employees Tuesday and Wednesday 10 employees Wednesday and Thursday 8 employees Thursday and Friday 8 employees Friday and Saturday 7 employees Saturday and Sunday 5 employees

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600 • CHAPTER 15 SCHEDULING

Step 5 Using the Updated Staffi ng Needs, Repeat Steps 2 Through 4 Until You Have Satisfi ed All Needs.

Now we repeat Step 2.

Step 2 The New Total Number of Staff for Each Pair of Days Is as Follows:

Step 3 The Days Off for Employee Number 2 Are Also Saturday and Sunday.

This employee will work Monday through Friday.

Step 4 Update the Staffi ng Needs.

Since there are still unsatisfied needs, we return to Step 2. We continue doing this until there are no unsatisfied needs. The final schedule is shown next.

This technique gives the manager work schedules for each employee to satisfy mini- mum daily staffing requirements. Although it is not a unique solution, the schedule gives each full-time employee two consecutive days off. The next step is to replace employee numbers with employee names. The manager can give the senior employee first choice of schedules and proceed until all the employees have been assigned a schedule.

Day of the Week M T W Th F Sa Su

Number of staff needed 3 4 4 2 4 2 3

Pair of Consecutive Days Total of Staff Needed

Monday and Tuesday 7 employees Tuesday and Wednesday 8 employees Wednesday and Thursday 6 employees Thursday and Friday 6 employees Friday and Saturday 6 employees Saturday and Sunday 5 employees

Day of the Week M T W Th F Sa Su

Number of staff needed 2 3 3 1 3 2 3

Employee M T W Th F Sa Su

1 X X X X X off off 2 X X X X X off off 3 X X off off X X X 4 X X X X X off off 5 off off X X X X X 6 X X X X off off X

SCHEDULING WITHIN OM: PUTTING IT ALL TOGETHER Scheduling is the final planning that occurs before the actual execution of the plan. A job’s position in the schedule is determined by its priority status. Production planners track the performance of operations in meeting the planned schedule. This is critical

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SCHEDULING ACROSS THE ORGANIZATION • 601

because the master scheduler (Chapter 13) evaluates production planners on the level of customer service achieved for their product responsibilities.

Schedules are essential to shop floor supervisors. The schedule details when a job is to be worked on, what resources to use, and how much time the job should need. Schedules ensure that manufacturing is working on the right job, using the right equipment, at the right time. Dispatching with the use of priority rules allows floor supervisors to determine which job should be done next.

The amount of time to complete a job is often determined by a time standard (Chapter 11). If the time standards are inaccurate (either too stringent or too loose), the worker’s morale may be affected. If standards are too stringent, it may be impos- sible to keep up with the schedule. If standards are too loose, there is no incentive to push and resources are often underutilized.

In service operations, the schedule is critical in terms of projecting when a job will be completed for the customer. Customers often need to know when the service will be provided (think of cable installers) so that the customer is available. Customers often link quality of service (Chapter 5) with adherence to the schedule (if the company delivers on time, everything is fine). If the company does not schedule adequate time for the service to be completed, the worker may either rush through the service or run late throughout the day. Think of the last time you waited in a doctor’s office. In either case, the perceived quality of the service can be affected.

SCHEDULING ACROSS THE ORGANIZATION Scheduling executes a company’s strategic business plan, so it affects functional areas throughout the company.

Accounting relies on schedule information and completion of customer orders to develop revenue projections, calculate actual job costs, and do cash flow analysis.

Marketing uses schedule effectiveness measurements to determine whether the company is using lead times for competitive advantage, whether flow time is corre- lated to estimated lead times, and whether deliveries are made on time. Knowing lead times allows marketing to make realistic delivery promises to customers.

Information systems maintains the scheduling database, which includes rout- ings and processing times. Information systems also provides the software to monitor product movement through the scheduling process.

Purchasing follows items through the process to determine whether components and/or raw materials need to be expedited when the job is ahead of schedule or de- expedited when jobs are behind schedule to ensure the items are available when needed.

Operations uses the schedule to maintain its priorities and to provide customer service by finishing jobs on time. The schedule reflects operations’ workload and is used to measure performance.

In manufacturing companies, production planners typically schedule individual jobs; in service organizations, the office manager or shift supervisor does the scheduling. Both planners and managers are evaluated on the customer service levels they achieve. The production planner is concerned with the sequencing of jobs through the factory. The office manager or shift supervisor is concerned with adequate staffing. Scheduling jobs and developing work schedules should reflect the company’s competitive strategy and serve as a tool to keep all the functional areas synchronized.

ACC

MKT

MIS

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602 • CHAPTER 15 SCHEDULING

Scheduling is the final planning step before a product is built or a service performed. The detailed sched- ule shows when work on a specific customer order is to begin and when it is to be completed. These two valuable pieces of information are shared with members of the supply chain, who can then track the progress of the order. Visibil- ity into actual production performance can improve communications among the supply chain members

and alert members when execution problems occur. Detailed scheduling often requires using the due-date

information for the different jobs to be scheduled. These due dates should reflect the objectives of the supply chain. Different priority rules can be tested to see which rule provides the best sched- ule performance when considering supply chain

objectives. The linkage of customer order prior- ity and supply chain objectives should provide more effective operations.

THE SUPPLY CHAIN LINK

Recall that scheduling techniques vary depending on the environment and that we use different priority rules to make scheduling decisions. With the increasing push toward sustainability from customers and other stake- holders, these traditional priority rules and scheduling techniques need to be modified to help develop sched- ules that meet sustainability criteria. Scheduling has always been about jobs, people, and materials. Now we have to expand these rules to include meeting sustain- ability criteria.

In addition to traditional priority rules, schedul- ing decisions should include developing schedules to

minimize carbon emission, minimize energy use, lower material chemical content and other environmental pollutants, and maximize the use of recycled mate- rial. Job and worker schedules can also be developed to ensure fair treatment of workers as part of social responsibility. This concern also extends to treatment, schedule development, and work demands of suppli- ers. Therefore, in developing schedules we may want to modify traditional rules—such as ensuring shortest processing time—to also include minimizing certain sustainability standards, such as emissions. These pri- ority rules are not in contradiction but sustainability just adds another criteria for us to consider when devel- oping schedules.

THE SUSTAINABILITY LINK

Chapter Highlights 1 Diff erent kinds of environments need diff erent scheduling

techniques. Scheduling in the high-volume environment is typically done through line design and balancing. Scheduling in a low-volume environment typically involves the use of priority rules. In this environment, Gantt charts are oft en used to view the workload and jobs in process.

2 Shop loading techniques include infi nite and fi nite loading. Infi nite loading schedules include jobs without capacity constraints. Finite loading loads jobs up to a predetermined capacity level. Loading can be done using forward or backward scheduling. Forward scheduling starts the job as soon as possible, whereas backward scheduling works back from the due date.

3 Priority rules are used to make scheduling decisions. SPT always minimizes mean job fl ow time, mean job lateness, and average number of jobs in the system. FCFS is considered one of the fairest priority rules. Rules related to due dates tend to minimize the maximum tardiness of the jobs. Priority rules need to support organizational objectives.

4 Performance measurements refl ect the priorities of the organization. Mean job fl ow time, mean job lateness, mean job tardiness, makespan, and the average number of jobs in the system measure the eff ectiveness of schedules.

5 Johnson’s rule is an eff ective technique for minimizing makespan when two successive workstations are needed to complete the process.

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SOLVED PROBLEMS • 603

Key Terms fl ow operations 578 routing 578 bottleneck 579 Gantt chart 579 load chart 579 progress chart 580 infi nite loading 580 fi nite loading 580 forward scheduling 581 due date 581 backward scheduling 582 slack 582

input/output control 582 operation sequencing 584 queue 584 priority rule 584 local priority rule 584 global priority rule 584 makespan 586 job fl ow time 586 average number of jobs in the

system 587 job lateness 587 job tardiness 587

Johnson’s rule 591 optimized production technology

(OPT) 593 nonbottleneck 593 capacity-constrained resource 593 throughput 594 transfer batch 595 process batch 595 theory of constraints (TOC) 596 internal resource constraint 596 market constraint 596 policy constraint 596

6 When scheduling bottleneck systems, the basic principles of OPT apply. Th e theory of constraints expands OPT into a managerial philosophy of continuous improvement.

7 Service organizations use diff erent scheduling techniques such as appointments, reservations, and posted schedules for eff ective use of service capacity.

8 A method developed by Tibrewala, Phillippe, and Brown constructs workforce schedules when a company uses full- time employees, operates seven days each week, and gives its employees two consecutive days off .

Formula Review 1. To calculate job fl ow time:

Job flow time 5 time of completion 2 time job was first available for processing

2. To calculate mean job fl ow time:

Mean job flow time 5 sum of individual flow times

number of jobs

3. To calculate average number of jobs in system:

Average number of jobs in system 5 total flow time

makespan

Solved Problems • Problem 1 The Fargoe Forge Company has collected the following data regarding the input and output of work into Work Center 222. Complete the partially filled in input/output chart.

• Before You Begin: This plan requires the completion of the input/output report. The planned input, actual input, planned output, and actual output are provided. Calculate the deviation by period (actual minus planned). Do this for both input and output and cal- culate the cumulative deviation for both. Finally, calculate the backlog. The backlog only changes when actual input is differ- ent from actual output. If actual input is greater than actual out- put, the backlog increases. When actual output is greater, the backlog is reduced.

• Solution Step 1 Calculate the period-by-period deviations. Sub- tract the planned input from the actual input. Subtract the planned output from the actual output. The results are shown in the spreadsheet.

Step 2 Calculate the cumulative deviation for input and output. Add the deviation for the current period to the cumulative deviation of the previous period. For example, in period 5, the cumulative deviation of the input is 75 hours of work (50 hours at the end of period 4, plus 25 hours in period 5). The cumulative deviations are shown in the spreadsheet.

(See student companion site for Excel template.)

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604 • CHAPTER 15 SCHEDULING

Step 3 Calculate the backlog at Work Center 222. The backlog changes only when the actual input is different from the actual output in a period. The beginning backlog is 125 hours of work. The backlog at the end of period 4 changes to 95 hours because the actual input is only 650 hours of work, whereas the actual output is 680 hours of work. Since

the work center completed 30 hours more than it actually received, the backlog has to be reduced by 30 hours, or a total of 95 hours. The backlog for period 5 remains at 95 hours. In period 6, the backlog drops to 45 hours. The backlog in period 7 is 20 hours and drops to 15 hours in period 8, as shown in the spreadsheet.

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

A B C D E F G

Fargoe Forge Company

Input Information (hours) 4 5 6 7 8

Planned Input 600 675 625 650 650 Actual Input 650 700 600 575 675

Deviation Cumulative Deviation

Output Information (hours) 4 5 6 7 8

Planned Output 680 680 680 680 680 Actual Output 680 700 650 600 680

Deviation Cumulative Deviation

Backlog 125

Period

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

A B C D E F G

Fargoe Forge Company

Input Information (hours) 4 5 6 7 8

Planned Input 600 675 625 650 650 Actual Input 650 700 600 575 675

Deviation 50 25 −25 −75 25 Cumulative Deviation 50 75 50 −25 0

Output Information (hours) 4 5 6 7 8

Planned Output 680 680 680 680 680 Actual Output 680 700 650 600 680

Deviation 0 20 −30 −80 0 Cumulative Deviation 0 20 −10 −90 −90

Backlog 125 95 95 45 20 15

Key Formulas C8 =C7-C6 copy to right C9 =C8 D9 =C9+D8 copy to right

C15 =C14-C13 copy to right C16 =C15 D16 =C16+D15copy to right C18 =B18+C7-Ccopy to right

Period

$C$8: =C7-C6 (copy right)

$C$9: =C8 $D$9: =C9+D8 (copy right)

$C$18: =B18+C7-C14 (copy right)

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SOLVED PROBLEMS • 605

• Problem 2 Custom Glass, Inc. produces custom storm windows. The company has the following jobs waiting to be processed at its glass-cutting work center.

Job Job Time (days)

Due Date (days from now)

A 8 20 B 4 15 C 6 30 D 7 24 E 9 10

(a) Using earliest due date as the priority rule, determine the sequence for these jobs.

(b) Using shortest processing time as the priority rule, determine the sequence for these jobs.

(c) Calculate the mean job fl ow time, average number of jobs in the system, mean job lateness, and mean job tardiness for the schedule using earliest due date.

(d) Calculate the mean job fl ow time, average number of jobs in the system, mean job lateness, and mean job tardiness for the schedule using the shortest processing time.

• Before You Begin: This problem compares two different priority rules and evalu- ates their effectiveness when scheduling a batch of jobs. First, develop the sequence for each rule. Then calculate the different performance measures. Compare the results.

• Solution (a) Th e sequence generated using earliest due date as the priority

rule is E, B, A, D, C. Job E has the highest priority since it is due in the least amount of time. Th e schedule is as follows:

• Job E is done after 9 days, • Job B is done after 13 days, • Job A is done after 21 days, • Job D is done after 28 days, • Job C is done after 34 days.

(b) Th e sequence generated using shortest processing time as the priority rule is B, C, D, A, E. Job B has the highest priority since it takes the least amount of time to complete. Th e SPT schedule is as follows:

• Job B is done after 4 days, • Job C is done after 10 days, • Job D is done after 17 days, • Job A is done after 25 days, • Job E is done after 34 days.

EDD Results

Job

Job Flow Time

(days)

Due Date (days

from now)

Job Lateness (days)

Job Tardiness

(days)

A 21 20 1 1 B 13 15 22 0 C 34 30 4 4 D 28 24 4 4 E 9 10 21 0

Totals 105 6 9

Mean job fl ow time is 21 days (105 days divided by 5 jobs). Remember that job fl ow time extends from the time the job is available to work on to its completion. Since all the jobs are available at the same time, the fl ow time for each job is equal to its completion time. Th e average number of jobs in the system is 3.1 jobs (105 days of total fl ow time divided by the makespan of 34 days). Total lateness is 6 days (1 2 2 1 4 1 4 2 1); therefore, mean job lateness is 1.2 days (6 days divided by 5 jobs). Total tardiness is 9 days; therefore, mean job tardiness is 1.8 days.

(d) Based on the schedule generated using the SPT rule, the results are as shown here.

SPT Results

Job

Job Flow Time

(days)

Due Date (days

from now)

Job Lateness (days)

Job Tardiness

(days) A 25 20 5 5 B 4 15 211 0 C 10 30 220 0 D 17 24 27 0 E 34 10 224 24

Totals 90 29 29

Mean job flow time is 18 days (90 days divided by 5 jobs). The average number of jobs in the system is 2.65 jobs (90 days of total flow time divided by the makespan of 34 days). Total lateness is negative 9 days (5 2 11 2 20 2 7 1 24); therefore, mean job lateness is 21.8 days (29 days divided by 5 jobs). Total tardiness is 29 days; therefore, mean job tardiness is 5.8 days.

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606 • CHAPTER 15 SCHEDULING

• Problem 3 Jack’s Machine Shop has the following batch of jobs that need to be scheduled so that the makespan is minimized. Each job is processed first at Machine Center 1 and then at Machine Center 2. The job information is as follows.

Step 2 Th e shortest individual activity processing time is from Job B at Machine Center 1. Th erefore, we put Job B in the highest available spot in the sequence, which is fi rst. Now we eliminate Job B from consideration and repeat Step 2.

Job

Machine Center 1 Processing Time

(days)

Machine Center 2 Processing Time

(days)

A 4 3 B 2 7 C 6 5 D 4 5 E 3 4 F 5 1

Using Johnson’s rule, develop a sequence for Jack’s Machine Shop that minimizes makespan.

• Before You Begin: The objective is to minimize makespan. When the scheduling problem has a unidirectional flow and two different activities to be done, use Johnson’s rule to minimize makespan.

• Solution The jobs and the processing times are listed so we can begin at Step 2.

Step 2 Th e shortest individual activity processing time is one day for Job F at Machine Center 2. Since this time is on the second activity, Job F takes the last available spot in the sequence, which is sixth. Step 3 Removing the job we just sequenced, we repeat Step 2 until we have sequenced all jobs. The remaining jobs are as follows.

Job

Machine Center 1 Processing Time

(days)

Machine Center 2 Processing Time

(days)

A 4 3 B 2 7 C 6 5 D 4 5 E 3 4

Job

Machine Center 1 Processing Time

(days)

Machine Center 2 Processing Time

(days)

A 4 3 C 6 5 D 4 5 E 3 4

Step 2 Th e shortest activity processing time is now in two places, from Job A for three days at Machine Center 2 and from Job E for three days at Machine Center 1. We can put both of these jobs into our sequence. Job A goes into the last available spot, fi ft h, and Job E goes into the highest available spot, second. We can now eliminate both jobs from consideration. Th e updated list of remaining jobs is shown next.

Job

Machine Center 1 Processing Time

(days)

Machine Center 2 Processing Time

(days)

C 6 5 D 4 5

Step 2 Th e shortest activity processing time is four days for Job D at Machine Center 1. Job D should be placed in the highest available slot, third. We put the remaining job in the only available position in the sequence, fourth.

The final sequence is B, E, D, C, A, F.

• Problem 4 The Sports Injury Clinic operates seven days a week. Based on historical data, the manager, Joan, has determined the follow- ing daily minimum staffing requirements. She believes that she needs three employees on Monday and Thursday, four employ- ees on Tuesday, two employees on Wednesday and Sunday, and five employees on Friday and Saturday. Joan wants a workforce

schedule that allows each employee two consecutive days off each week and minimizes excess staffing. Develop a schedule.

• Before You Begin: Many service operations conduct business seven days per week. When those companies schedule staff, they may do so with the objective of providing each staff member with two consecutive

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PROBLEMS • 607

days off during the week. The technique developed by Tibre- wala, Phillippe, and Brown determines how many employees are needed and what their schedules need to be.

• Solution Step 1 Find out the minimum number of employees needed for each day of the week.

Step 3 Find the pair of days that has the lowest total need. Wednesday and Th ursday have the lowest total need, so they become the days off for the fi rst employee. Step 4 Update the number of staff still needed. Reduce the staff needed by one employee for Monday, Tuesday, Friday, Saturday, and Sunday since the first employee will work on those days. Step 5 Using the updated requirements, repeat Steps 2 through 4 until all requirements have been met. If you continue to develop the workforce schedule, an alternative schedule is shown. Given the amount of slack, several other alternatives are also possible.

Day of the Week M T W Th F S Su

Number of staff needed 3 4 2 3 5 5 2

Step 2 Given the minimum staff needed each day, calculate the number of employees needed for each pair of consecutive days.

Pair of Consecutive Days Total of Staff Needed

Monday and Tuesday 7 Tuesday and Wednesday 6 Wednesday and Thursday 5 Thursday and Friday 8 Friday and Saturday 10 Saturday and Sunday 7

Employee M T W Th F S Su

1 X X off off X X X 2 X X X X X off off 3 off off X X X X X 4 X X off off X X X 5 off off X X X X X 6 X X off off X X X

Discussion Questions 1. Compare and contrast high-volume and low-volume

scheduling operations. 2. Describe a high-volume service operation and how sched-

uling should be done. 3. Describe a low-volume service operation and how schedul-

ing should be done. 4. Visit a local service operation and describe its scheduling

procedures. 5. Visit a local manufacturing operation and describe how it

sequences jobs through the shop. 6. Describe infinite loading.

7. Explain how the output from infinite loading is used. 8. Explain how finite loading is done. 9. Explain the benefits of finite loading. 10. Describe forward scheduling. 11. Describe backward scheduling. 12. Visit a local service or manufacturing operation and learn

how it measures schedule effectiveness. 13. Describe the principles of OPT. 14. Describe the theory of constraints. 15. Describe different methods that might be useful for

scheduling service operations.

Problems 1. Jack, the owner and manager of Jack’s Box Company, wants

to monitor usage at Work Center 3, which is a bottleneck in the system. He has collected data on the planned and actual input and output.

Input Information (in hours)

4 5 6 7 8

Planned input 40 50 50 60 60 Actual input 45 45 45 55 60 Deviation Cumulative deviation

Output Information (in hours)

4 5 6 7 8

Planned output 70 70 70 70 70 Actual output 60 60 60 60 60 Deviation Cumulative deviation Backlog 75 hours

(a) Complete the input/output record. (b) Describe your concerns based on the results of the

input/output analysis.

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608 • CHAPTER 15 SCHEDULING

2. Since Jack believes that Work Center 3 is his bottleneck, he has asked you to do the following:

(a) Calculate the percentage of planned output needed to complete the planned inputs (planned input 1 backlog/ planned output) at Work Center 3.

(b) Calculate the percentage of planned input that actually happened (actual input 1 backlog/planned input 1 backlog).

(c) Calculate the percentage of available output that was actually accomplished (actual output/planned output) at Work Center 3.

(d) Given your results, do you believe that Jack is correct in assuming that Work Center 3 is his bottleneck? Justify your answer.

3. Based on your concerns, Jack has gathered the following information for Work Center 2, the work center that directly feeds Work Center 3. Complete the input/output analysis of Work Center 2.

(a) Determine the sequence Henri should follow if he uses the SPT (shortest processing time) priority rule.

(b) Based on the sequence developed in part (a), calculate the following performance measures: makespan, mean job fl ow time, average number of jobs in the system, mean job lateness, mean job tardiness, and maximum tardiness.

6. Henri has decided to try a diff erent priority rule. (a) Using the data in Problem 5, determine the sequence

Henri should follow if he uses the EDD (earliest due date) priority rule.

7. Henri has heard of the LPT (longest processing time) priority rule and wonders how that would change the sequence of the jobs listed in Problem 5.

(a) Determine the sequence Henri should follow if he uses the LPT priority rule.

8. In an eff ort to be fair to his customers, Henri has decided to use the FCFS (fi rst come, fi rst served) priority rule. Using the job data from Problem 5, assume that the jobs arrive in order—that is, A fi rst, then B, then C.

(a) Using the data in Problem 5, determine the sequence Henri should follow if he uses the FCFS (fi rst come, fi rst served) priority rule.

9. Henri recently learned about global priority rules. He is interested in the slack over remaining operations (S/RO) rule.

(a) Using the data in Problem 5, determine the sequence Henri should follow if he uses the S/RO priority rule.

10. Joe’s Twenty-four Seven Laundromat has the following jobs waiting to be processed. Th e fi rst step of the process includes

Input Information (in hours)

4 5 6 7 8

Planned input 40 50 50 60 60 Actual input 25 35 35 40 40 Deviation Cumulative deviation

Output Information (in hours)

4 5 6 7 8

Planned output 40 50 50 60 60 Actual output 45 45 45 55 60 Deviation Cumulative deviation Backlog 75 hours

(a) Complete the input/output record. (b) Describe your concerns based on the results of the input/

output analysis. 4. Jack has asked you to calculate the following for Work

Center 2. (a) Calculate the percentage of planned output needed to

complete the planned inputs (planned input 1 backlog/ planned output) at Work Center 2.

(b) Calculate the percentage of planned input that actually happened (actual input 1 backlog/planned input 1 backlog) at Work Center 2.

(c) Calculate the percentage of available output that was actually accomplished (actual output/planned output) at Work Center 2.

(d) What insights can you off er Jack about Work Centers 2 and 3?

5. Henri’s Custom Gowns has six jobs waiting to be processed. Each gown is at the beading work center. Th e job information is shown here.

Job Job Time (in days)

Days until Due

Job Time at Other

Work Centers

Operations Remaining at Other

Work Centers

A 9 30 10 3 B 5 10 2 1 C 8 24 8 2 D 10 40 18 3 E 7 26 12 1 F 6 15 6 2

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PROBLEMS • 609

washing and drying the clothes; the second step is pressing the clothing. Joe wants to minimize the amount of time it takes to do all the jobs. Th e fi ve jobs waiting to be processed are shown here.

15. Raquel has asked you to develop a sequence that minimizes makespan for the sequence of jobs given in Problem 13. Compare the makespan, mean job fl ow time, and average number of jobs in the system to your results in Problems 13 and 14.

16. Barb’s Beach Bar operates seven days per week. Barb uses only full-time employees and wants each employee to have two consecutive days off each week. She believes that she needs a minimum of three employees on Monday, Tuesday, Wednesday, Th ursday, and Sunday. On Friday and Saturday, she believes that she needs six employees. Develop a workforce schedule for Barb.

17. Next week is a three-day weekend. Barb believes that she will need a minimum of six employees on Friday, Saturday, and Sunday. Th e other days will still need a minimum of three workers. Remember that Barb wants each employee to have two consecutive days off each week. Develop a workforce schedule for the holiday weekend.

18. Marvin’s Beach Cleaners is responsible for keeping the beach clean. Marvin estimates that he needs a minimum of two people on Monday; three people on Tuesday and Sunday; four people on Wednesday and Th ursday; and fi ve people on Friday and Saturday. Contractually, Marvin is required to give each employee two consecutive days off during the week. Develop a workforce schedule for Marvin to use.

19. Marvin is preparing for the upcoming three-day weekend. He estimates that he will need three additional workers on Sunday of that week. Using the requirements given in Problem 18 for the other days of the week, develop a workforce schedule for Marvin to use.

20. Cathy’s Coney Islands operates seven days per week. Demand is relatively constant during the week and tails off on the weekend. She estimates that she needs fi ve employees Monday through Friday, and two employees on Saturday and Sunday. She is committed to giving each employee two consecutive days off during the week. Develop a workforce schedule for Cathy.

21. Cathy’s Coney Islands has just purchased new equipment. Cathy believes the improved effi ciency will reduce the number of people needed Monday through Friday down to four. She does not believe she can ever have fewer than two persons working, so the weekend requirements remain the same. Develop a workforce schedule for Cathy.

22. Bill’s Bar & Grill is open seven nights a week. Business is busiest on Th ursday, when there is a concert in the park across the street. Bill wants a workforce schedule that allows each employee two consecutive days off each week. He believes that he needs four employees every day except on Th ursday, when he believes he needs six employees. Develop a workforce schedule for Bill.

23. During the winter, no concerts are given in the park. Bill believes that he needs a minimum of four employees every day of the week. Develop a workforce schedule for Bill that allows each employee two consecutive days off each week. Compare the number of workers he needs during the winter to the number of workers needed during the concert season.

24. Student Premier Painters (SPP) has six house painting jobs in a particular neighborhood. Th e houses vary in size, condition, and painting requirements, but each house must be prepped (cleaned, sanded, and primed) fi rst, before it is painted. Th e relevant information is given.

Job Wash and

Dry (hours) Press

(hours)

A 6 4 B 3 5 C 2 3 D 7 5 E 4 3

(a) Using FCFS, assume the jobs arrive in the order shown (A, then B, then C, etc.). Show the beginning and ending time for each job.

(b) Calculate the makespan, the mean job fl ow time, and the average number of jobs in the system.

11. Joe thinks that it is probably more effi cient to use SPT (shortest processing time) as his priority rule.

(a) Develop a sequence using SPT based on processing time for the wash and dry operation.

(b) Calculate the makespan, the mean job fl ow time, and the average number of jobs in the system.

12. Joe has asked you to develop a sequence that minimizes makespan for the sequence of jobs given in Problem 10. Compare the makespan, mean job fl ow time, and average number of jobs in the system to your results in Problems 10 and 11.

13. Raquel’s Landscaping Company has contracted for several landscaping jobs. Each job requires preparing the areas (identifying the locations and types of plants, preparing the soil, etc.) and then planting the trees, bushes, and shrubs. Th e expected time for each of the jobs is shown next.

Job Preparing the Area (days)

Planting (days)

R 3 2 S 1 3 T 4 5 U 8 5 V 6 4 W 4 3

(a) Using FCFS (fi rst come, fi rst served), assume the jobs arrive in the order shown (R, then S, then T, etc.). Show the beginning and ending time for each job.

(b) Calculate the makespan, the mean job fl ow time, and the average number of jobs in the system.

14. Raquel is concerned with effi ciency. She believes it is probably more effi cient to use SPT (shortest processing time) as her priority rule.

(a) Develop a sequence using SPT based on processing time for preparing the area.

(b) Calculate the makespan, the mean job fl ow time, and the average number of jobs in the system.

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610 • CHAPTER 15 SCHEDULING

Days

Houses Prep Paint

Arnold 4 3 Blake 3 6 Coffman 2 5 Downs 3 2 Eckels 6 4 Farber 5 7

(a) Sequence the painting jobs to minimize makespan. (b) Calculate the mean job fl ow time associated with this

sequence. (c) Calculate the average number of jobs in the system with

this sequence. 25. Using the job information in Problem 24, develop a

sequence based on the shortest processing time priority rule.

Prioritize based on prep time. In the case of ties, select the house that has the shortest paint time.

(a) Calculate the makespan. (b) Calculate the mean job fl ow time associated with this

sequence. (c) Calculate the average number of jobs in the system with

this sequence. 26. Using the information in Problem 24, develop a sequence

based on the longest processing time priority rule. Prioritize based on prep time. In the case of ties, select the house that has the longest paint time.

(a) Calculate the makespan. (b) Calculate the mean job fl ow time associated with this

sequence. (c) Calculate the average number of jobs in the system with

this sequence.

CASE: Air Traffic Controller School (ATCS) ATCS provides training for future air traffic controllers. One of the skills air traffic controllers need is the ability to sequence aircraft for landing purposes. The controller decides who lands immediately and who goes into a holding pattern. The following data are provided to you, the student, to develop an acceptable landing sequence. Any sequence that results in an aircraft not being scheduled to land before it runs out of remaining flying time is unacceptable. The following aircraft are currently await- ing your decision as to their landing sequence.

There are many ways to sequence this group of aircraft wait- ing to land. Since cost is an obvious factor, consider a sequence that minimizes total cost to land the aircraft. Multiply the cost per minute of flying time by the remaining number of minutes.

This gives you the maximum cost associated with an airplane circling in a holding pattern until the last possible moment.

(a) Develop a landing sequence that gives priority to those aircraft with the highest cost of slack time (excess fl ying time multiplied by cost per minute of fl ying time). For example, Flight 616’s 18.75 minutes of slack time (22 2 3.25) times $300 per minute means that if Flight 616 does not land until its time is all used up, it incurs an extra fl ying cost of $5,625. Make a Gantt chart showing the landing sequence and evaluate the sequence in terms of performance. Calculate mean fl ow time, mean lateness, and average number of planes in the system.

(b) Develop a sequence using SPT as a priority rule. Make a Gantt chart showing the landing sequence and evaluate the sequence in terms of performance. Calculate mean fl ow time, mean lateness, and average number of planes in the system.

(c) Develop a third sequence using EDD (earliest due date) as a priority rule. Th e plane with the least amount of fl ying time remaining has the highest priority. Make a Gantt chart showing the landing sequence and evaluate the sequence in terms of performance. Calculate mean fl ow time, mean lateness, and average number of planes in the system. Calculate the total cost associated with this sequence (fl ow time multiplied by cost per minute of fl ying time for each fl ight).

(d) Try to develop an alternative sequence that lands all of the aircraft safely and reduces the total cost.

Flight Number

Minutes on Runway

Remaining Flying Time (minutes)

Cost per Minute of

Flying Time ($)

101 2.00 10 100 118 3.00 15 150 217 2.75 8 125

8076 1.50 5 80 219 3.50 12 200 894 1.75 19 150 024 2.50 16 400 616 3.25 22 300

CASE: Scheduling at Red, White, and Blue Fireworks Company Joan Bennett, the production manager at the Red, White, and Blue Fireworks Company (RWBFC), has decided to change the work schedule for her manufacturing employees. Because of high product demand and an inability to expand at the current

facility, RWBFC needs to operate ten hours each day, seven days each week. In order to avoid the use of overtime and to keep her employees fresh, Joan decided to have employees work four ten- hour shifts each week. She insists that every employee must have

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INTERNET CHALLENGE • 611

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Scheduling at Cruise International, Inc. You are meeting with Wilhelm Schmidt, the Cruise Ship First Engineer, to help him with a maintenance scheduling problem. He needs help in assigning each of the six maintenance workers to specific tasks. You are also to meet with Sara Jones from the Business Office. William Stein, the Assistance Purser, would like you to review how Sara processes reports throughout the day, Her objective is to process the entire batch of reports as soon as possi- ble. Completion of these assignments will enable you to enhance

your knowledge of the material covered in Chapter 15 of your text. It will also better prepare you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Scheduling at CII

On-line Case: Scheduling at Valley Memorial Hospital Today’s assignment is with Carol Gardner, assistant to

the chief administrator at Valley Memorial Hospital. “Actually,” she says, “there are four different problems I’d like your help with. Two days ago, Tami Zarkin, the 2-East supervisor, called about a schedul- ing issue: how to assign the six nurses on the ward to the six patients there, given that some nurses are better than others with certain patients or at certain tasks. Then, Susie Berkman from the intensive care unit came by to ask for help scheduling the nurses on the 3 pm to 11 pm shift. Yesterday, Gail Johnson, our supervisor at the blood bank, said she needed help scheduling the volunteer staff there. And finally, this morning, Don Maltby, who manages the business office, asked me to help him figure out the most efficient way for his

assistant, Naomi Engel, to process cost reports and for the data entry staff to get the processed reports into our database. Let’s discuss how to get the information you’ll need from each of these people.”

To complete this assignment, go to www.wiley.com/college/ reid to get more details. Assignment questions are given at the site.

To access the Web site:

three consecutive days off. The work week begins on Monday and ends on Sunday. The manufacturing process requires a minimum of 20 employees. Joan has asked you to develop a staffing plan that will determine the exact number of full-time employees needed, as well as the schedule each employee will need to work. Remember, each employee must have three consecutive days off.

Joan also has asked you to determine which other factors you consider relevant in making the transition to the new schedule. In particular, she is very interested in a method for determining

which employees work which schedule and how to implement the new schedule with minimal dissatisfaction.

(a) Develop a staffi ng plan for RWBFC in accordance with the constraints stated above.

(b) Explain the method used in developing your plan. (c) Explain how employees should be assigned to the diff erent

schedules. (d) Discuss any concerns you have with the new staffi ng plan.

As a world-class fan of major league baseball, you have always wanted to watch a game in person at each of the ballparks across the country. Now that you are about to graduate, you have decided it is time to achieve this goal. To do so, you must first know the location of each ballpark, when ball games are sched- uled, and the driving distance between parks. The Internet can give you all this information.

Decide on a priority rule for building your schedule. You will drive between ballparks, so be sure to leave enough time to reach the next ballpark. You can average 60 miles per hour when trav- eling on the open road and cover up to 600 miles each day. You must visit each of the major league ballparks during the course of one season. Since you do not graduate until the end of May, you cannot start your adventure until June 1. Your objective is

INTERNET CHALLENGE Batter Up

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612 • CHAPTER 15 SCHEDULING

Online Resources Companion Website www.wiley.com/college/reid:

Hershey Foods Corporation Northeast Knitting Mills

• Find links for Additional Web Resources for this chapter Herman Goelitz Candy Co., Inc., www.jellybelly.com Merlin Metalworks, www.merlinbike.com/html/technology /technology/html Production-Scheduling.com, www.production-scheduling.com Suzy Systems, Inc., www.suzy.com Magi (Manufacturing Action Group, Inc.), www.magimfg.com PeopleSoft, www.peoplesoft.com SAP’s R/3, www.sap.com i2 Technologies, www.i2.com

Additional Resources Available Only in WileyPLUS: • Use the e-Book and launch directly to all interactive resources • Take the interactive Quick Test to check your understanding of the chapter material and get immediate feedback on your responses • Check your understanding of the key vocabulary in the chapter with Interactive Flash Cards • Use the Animated Demo Problems to review key problem types • Practice for your tests with additional problem sets

Selected Bibliography Abernathy, W., N. Baloff , and J. Hershey. “Th e Nurse Staffi ng

Problem: Issues and Prospects,” Sloan Management Review 13, 1, Fall 1971.

Blackstone, John H., Jr. Capacity Management. Cincinnati, Ohio: South-Western, 1989.

Blackstone, John H., Jr., and James F. Cox III, eds. APICS Dic- tionary, Twelft h Edition. Falls Church, Va.: American Pro- duction and Inventory Control Society, 2008.

Buff a, Elwood S., and Jeff rey G. Miller. Production-Inventory Systems: Planning and Control, Th ird Edition. Homewood, Ill.: Irwin, 1979.

Johnson, S.M. “Optimal Two Stage and Th ree Stage Produc- tion Schedules with Setup Times Included,” Naval Logistics Quarterly 1, 1, March 1954.

Klassen, Kenneth J., and Th omas R. Rohleder. “Outpa- tient Appointment Scheduling with Urgent Clients in a Dynamic, Multi-period Environment,” International Jour- nal of Service Industry Management, 15, 2, 2004, 167–186.

Sipper, Daniel, and Robert L. Buffi n, Jr. Production: Planning, Control and Integration. Burr Ridge, Ill.: McGraw-Hill, 1998.

Umble, M. Michael, and M.L. Srikanth. Synchronous Manufac- turing. Cincinnati, Ohio: South-Western, 1990.

Vollmann, Th omas E., William L. Berry, D. Clay Whybark, and F. Robert Jacobs. Manufacturing Planning and Control Sys- tems for Supply Chain Management, Fift h Edition. Home- wood, III.: McGraw-Hill/Irwin, 2005.

to minimize the total amount of time it takes to visit each ball- park and to minimize the number of miles you drive. You must include any time it takes to return home. You are constrained

to driving no more than 10 hours or 600 miles per day. Use the Internet to find the locations of each ballpark, the scheduled base- ball games, and the mileage between cities. Batter up!

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L E A R N I N G O B J E C T I V E S

CHAPTER

613

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

C H A P T E R O U T L I N E

Project Life Cycle 615 Network Planning Techniques 616 Estimating the Probability of Completion

Dates 629 Reducing Project Completion Time 631

The Critical Chain Approach 634 Project Management within OM: How It All Fits

Together 636 Project Management OM across the

Organization 636

Before studying this chapter you should know or, if necessary, review

1. The implications of competitive priorities, Chapter 2, pp. 37–43. 2. Time standards, Chapter 11, pp. 423–433. 3. Gantt charts, Chapter 15, pp. 579–580.

Project Management

After studying this chapter you should be able to

1 Describe project management applications. 2 Describe the project life cycle. 3 Diagram networks of project activities. 4 Estimate the completion time of a project. 5 Compute the probability of completing a project by a specifi c time. 6 Determine how to reduce the length of a project eff ectively. 7 Describe the critical chain approach to project management.

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614 • CHAPTER 16 PROJECT MANAGEMENT

Think about life’s major events and what it takes to make them successful. Consider what is involved in plan- ning a surprise 50th wedding anniversary party for your grandparents. Such an event is a complex project with many simultaneous and sequential activities leading up to the day of the party. Consider the following partial list of activities to prepare the surprise.

1. Decide on the actual date for the celebration. (Sometimes the exact anniversary date doesn’t work.)

2. Prepare a preliminary budget. (Most projects have some budgetary constraints.)

3. Develop the guest list of family and friends. 4. Determine the theme for the celebration (maybe a slide show of major events

in the past 50 years). 5. Evaluate the possible facilities for the party. ( Consider location, handicap

accessibility, and size.) 6. Evaluate food options (full sit-down dinner, a buffet, or just appetizers?). 7. Book the facility.

8. Select and order invitations. (Consider lead time and quantities.) 9. Select and order decorations. (Consider the limitations of the facility that you

booked.) 10. Evaluate party favors (maybe some personalized memento).

11. Select type of music (background or dance?). 12. Evaluate music options (recorded, live, or DJ?).

13. Finalize the music. 14. Send invitations. 15. Collect RSVPs to determine final headcount. 16. Help out-of-area guests arrange local accommodations. 17. Finalize food options. 18. Finalize party favors. 19. Arrange seating chart. 20. Hire a limo service to pick up your grandparents. 21. Decorate the facility.

These activities need to be done before the surprise anniversary party. Many of these can be done simultaneously, but you must do some activities sequentially. For example, you must know how many guests you are expecting before you can book the facility, order invitations, plan for food, or do a seating chart. Each activity in prepa- ration for the surprise celebration is related to other activities, and the order of the activities is clearly defined.

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PROJECT LIFE CYCLE • 615

While most of us dislike waiting in traffic due to road construction, think about all the activities that must be considered. Think about replacing a bridge on a country road. Once the local government begins to consider replacing the bridge, certain activi- ties must occur. Engineers and architects need to develop a plan for the new bridge, including materials needed, and a budget. After plan approval, the local government issues a request for bid to construction companies. Each company needs to submit a bid detailing the total cost of the project and the expected completion time. The local government then awards the contract. At that point the fun begins. The project manager coordinates the flow of equipment and materials to the site of the bridge and makes sure that the right workers are there at the right time (just imagine all the types of equipment and workers needed). The manager also maintains some flexibility in case of bad weather and makes sure that detours are set up or some type of traffic control is provided. Such projects easily take four to eight months for completion.

Project management techniques are useful when a project consists of several activi- ties, some simultaneous and others sequential. A project is a unique, one-time set of activities that is intended to achieve an objective in a given time period. The project is of some length (weeks, months, or even years) and uses resources (human, capital, materials, and equipment).

For the surprise 50th anniversary party for your grandparents, it can easily take three to six months of planning and cost thousands of dollars. In the business world, projects can be designing new products, installing new systems, constructing new facilities, designing an advertising campaign, designing information systems, and developing company Web sites. In politics, a project can be designing a political cam- paign. Projects consist of several tasks and take place in a given time period. Every project has a life cycle.

� Project Endeavor with a specific objective, multiple activities, and defined precedence relationships, to be completed in a specified time period.

PROJECT LIFE CYCLE Projects vary in terms of objectives, but each project has a common life cycle or sequence of activities. The life cycle begins with an initial concept, followed by a fea- sibility study, the planning of the project, the execution of the plan, and finally the termination of the project. Let’s look at each phase of a project life cycle.

Conception Identify the need for the project. In our anniversary example, the concept is the rec- ognition of two individuals’ lifelong commitment to each other. In the business world, the concept might be the company’s decision to launch a new product, implement a new information system, or become involved in e-commerce. In politics, the concept might be a candidate’s decision to run for office.

Feasibility Analysis or Study Evaluate expected costs, benefits, and risks of the project. For our anniversary example, a feasibility study might mean deciding whether the couple would be happier by being guests at the party or if they would rather take a getaway trip to visit some exotic part of the world. For the company launching a new product, a feasibility study means exam- ining the potential market, the market share, and profits for the new product compared to the costs. In politics, a feasibility study is a candidate’s assessment of the resources needed to run a successful political campaign and the benefits of elected office.

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616 • CHAPTER 16 PROJECT MANAGEMENT

Planning Analyze the work to be done and develop time estimates for completing each of the activities. In our wedding example, you plan what must be done, by whom, and when. When planning the party, a friend or another family member might do the initial screening of caterers, musicians, and so forth, allowing you to make the final decision. In business, planning consists of the activities needed to launch the new product. For example, the company must design the new product; source and order the materi- als, equipment, and tools; choose the process to use; design the layout; write the job instructions; do a pilot run; evaluate the process and the product design; and transi- tion the product to manufacturing. In politics, planning might include deciding how to raise funds, schedule personal appearances and debates, handle public relations, and adopt policy positions.

Execution Carry out the activities that make up the project. For our party, this means actually booking the facility, arranging the music, doing the seating arrangements, finalizing the menu, arranging the limo pickup, and so on. In business, the execution of the project entails completing the product design, obtaining the materials and equipment needed, setting up the process, writing job instructions, and making the product. For a political candidate, execution includes fund-raising, making public appearances, and showcasing the political message.

Termination End the project. After this date, resources can be used for different activities. For you, termination occurs after the party is over. In business, termination means product design engineers work on new products, purchasing agents can return to routine activities or a new project, and manufacturing engineers work on new projects. For a political candidate, termination means serving in an elected office or looking for a new job. See Table 16-1 for a summary of these project life-cycle phases.

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NETWORK PLANNING TECHNIQUES

Program evaluation and review technique (PERT) and critical path method (CPM) date back to the 1950s. PERT was originally developed to plan and monitor the Polaris missile, an extremely large project using over 3000 contractors and involving thousands of activities. PERT is credited with reducing the proj- ect duration by two years. Because of its success, most government contracts still require the use of PERT or a similar technique. CPM was initially developed to plan and coordinate maintenance projects in chemi- cal plants.

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Polaris Missile

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NETWORK PLANNING TECHNIQUES • 617

Concept: Identify the need for the project.

Feasibility analysis or study: Evaluate costs, benefits, and risks.

Planning: Decide who does what, how long it should take, and what you need to do it.

Execution: Do the project.

Termination: End the project.

TABLE 16-1

Project Life-Cycle Phases

The benefits of network planning techniques include the following: ● Graphical display of the project, including the relationships and sequence of

activities. ● Estimate of the expected project length. ● Method for determining which activities are critical to the timely completion of

the project and are therefore included in the critical path. ● Method for determining the amount of slack associated with individual project

activities.

Both PERT and CPM portray the project as a network diagram. The project activities and their precedence relationships are illustrated in the network diagram by nodes (circles) and arrows. Project activities are actions that consume resources and/or time. For example, making the drawings for a new product is an activity—it takes time and uses resources (human and equipment). Precedence relationships structure the sequencing of activities; that is, you have to finish one before you can start the next. These are like course prerequisites: before you can enroll in an operations manage- ment class, you have to take a basic statistics course. In business, before a company can source materials for a new product, the designers have to finish the product design. Project managers use these network planning techniques to:

Identify project activities and precedent relationships. Project managers use this technique to create a document refl ecting the activities, the responsible department, and the necessary resources (time, people, and equipment).

Calculate the expected completion time of the project. Project managers use this technique to plan additional projects and negotiate contracts with clients. For example, the customer may include a monetary penalty if the project is not completed by a certain date or a bonus if the project is completed ahead of schedule. Th e project manager can evaluate whether the probability risk of completing the project on schedule is acceptable or if additional resources are needed to assure timely completion.

Identify the activities critical to the timely completion of the project. Project managers use this technique to identify activities that can be delayed without aff ecting the project’s completion, to provide some fl exibility for the project manager.

Suppose you are a project manager using network planning techniques. You would typically describe the project, diagram the network, estimate the project’s completion time, and monitor the project’s progression. Let’s look at these four steps in detail.

Step 1: Describe the Project Describe the project in terms that everyone involved will understand. Include the project objective and the project end date. For example, the project may be the release

� Program evaluation and review technique (PERT) Network planning technique used to determine a project’s planned completion date and identify the project’s critical path.

� Critical path method (CPM) Network planning technique, with deterministic times, used to determine a project’s planned completion date and identify the project’s critical path.

� Project activities Specific tasks that must be completed and that require resources.

� Precedence relationships Establishes the sequencing of activities to ensure that all necessary activities are completed before a subsequent activity is begun.

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618 • CHAPTER 16 PROJECT MANAGEMENT

of a specific new product by September 15 or it may be ISO certification by the end of the calendar year. Given the project objective, define the project activities in terms of resource requirements (labor, equipment, and cash) and precedence relationships.

Step 2: Diagram the Network Diagram the project as a network, visually displaying the interrelationships between the activities. There are two kinds of network diagrams: activity-on-arrow (AOA) and activity-on-node (AON). We will focus on activity-on-node diagrams.

The activity-on-nodes (AON) diagram represents project activities by nodes and precedence relationships by arrows. Figure 16-1a shows the simple case in which we must do activity A before doing activity B, which we must do before activity C. In Figure 16-1b, the diagram indicates that we must finish activity A first before we can begin both activities B and C. In Figure 16-1c, we must finish both activities A and B before either activity C or D can begin. Figure 16-1d is an example in which we can begin two activities (B and C) when we finish activity A and we can begin activity D only after we finish both activities B and C.

We can use the network diagram to determine the project’s critical path. The critical path is the longest sequential path of interrelated activities in the network and shows the minimum completion time for the project. Any delay in an activity that is on the critical path will delay the whole project. Let’s diagram a sample project.

� Activity-on-node Network diagramming notation that places activities in the nodes and arrows to signify precedence relationships.

� Critical path The longest sequential path through the network diagram.

A B C

a. Activity A precedes activity B, which precedes activity C.

b. Activity A must be completed before activities B and C can begin.

c. Activities A and B must both be completed before activity C or D can begin.

d. Activities B and C can begin once activity A has been completed; activity D cannot begin until both B and C are completed.

FIGURE 16-1

Network notation

Two recent graduates, Michael and Elyssa, own Cables By Us, a company that produces cable assem- blies. Business has been good, so Michael and Elyssa have decided to expand their product line to include a new cable product. The new product will be manufactured in the current facility in an area not now in use. Michael and Elyssa have decided to use a project management approach to bring the new product on-line and have identified eleven activities and their precedence relationships, as shown in Table 16-2. Develop an AON diagram of the project.

EXAMPLE 16.1

Network Diagram of the New-Product

Project for Cables By Us

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NETWORK PLANNING TECHNIQUES • 619

Step 3: Estimate the Project’s Completion Time To estimate the completion time of the project, the project manager evaluates the con- nected paths through the diagram to determine which of the routes takes the longest time. The longest connected route through the diagram is the critical path. The sum of the lengths of time of each of the activities on the critical path determines the mini- mum completion time for the project. If an activity on the critical path is delayed, the completion of the project is delayed.

We estimate the project completion time based on the time estimates for the project activities. Activity time estimates can be either probabilistic or deterministic. We use probabilistic time estimates when we are unsure about the duration of project activi- ties—for example, because of technical problems, bad weather, delayed material delivery, and less-than-expected labor productivity. We use deterministic time estimates when we have done similar activities in the past and can make a reliable time estimate. Let’s look first at estimating the project’s completion date using deterministic time estimates.

� Probabilistic time estimate Process that uses optimistic, most likely, and pessimistic time estimates.

� Deterministic time estimate Assumption that the activity duration is known with certainty.

• Before You Begin: A network diagram visually represents the different project activities and their precedence relationships. For Michael and Elyssa, the first activity (A) is developing product specifications. After activity A is completed, both activities B and C can begin. The dia- gram needs to reflect the connected pathways through the project.

• Solution: In Figure 16-2, note that the project begins with activity A. Once activity A is completed, both activities B and C can begin. After completing activity B, activity D can be started. Following activity D’s comple- tion, activity E begins. When activity C is completed, we can start activity F. Activity G cannot be started until both activities E and F have been completed. Following the completion of activity G, we can begin working on both activities H and I. Activity J can begin once activities H and I are finished. The final activity, K, can be done after activity J is finished.

Table 16-2 Project Activities and Precedence Relationships

Activity Description Immediate

Predecessors A Develop product specifications none B Design manufacturing process A C Source and purchase materials A D Source and purchase tooling and equipment B E Receive and install tooling and equipment D F Receive materials C G Pilot production run E, F H Evaluate product design G I Evaluate process performance G J Write documentation report H, I K Transition to manufacturing J

FIGURE 16-2

Initial network diagram

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620 • CHAPTER 16 PROJECT MANAGEMENT

Step 3 (a): Deterministic Time Estimates With the deterministic time estimate, we make a single time estimate for each project activ- ity. Table 16-3 shows the time estimates for each of Michael and Elyssa’s project activities.

Given this information, we need to transfer the activity time requirement to the network diagram, as shown in Figure 16-3. We include the time estimate with the activity designator in the appropriate node. For example, activity A should take four weeks. We determine project completion time by calculating how long each of the paths through the network will take.

Activity Description Time Estimate

(weeks) A Develop product specifications. 4

B Design manufacturing process. 6

C Source and purchase materials. 3

D Source and purchase tooling and equipment. 6

E Receive and install tooling and equipment. 14

F Receive materials. 5

G Pilot production run. 2

H Evaluate product design. 2

I Evaluate process performance. 3

J Write documentation report. 4

K Transition to manufacturing. 2

TABLE 16-3

Deterministic Time Estimates

FIGURE 16-3

Identifying paths

Identify the four connected paths in the project, running from the beginning to the end, and calculate how long it takes to complete each path.

• Before You Begin: First, identify the connected pathways through the network diagram. Sum the total time to complete all the activities on each pathway. Compare the results. The path- way that takes the greatest amont of time to finish determines the minimum completion time for this project.

• Solution: For example, the first path includes activities A, B, D, E, G, H, J, and K. Add the time estimates for each of these activities together to find the length of time it takes to complete the path. Verify the results shown in Table 16-4.

EXAMPLE 16.2

Calculating the Path Completion

Times Using Deterministic

Time Estimates

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NETWORK PLANNING TECHNIQUES • 621

Since the critical path is the longest connected path through the network, the critical path includes activities A, B, D, E, G, I, J, and K. Activities C, F, and H are not included on the critical path.

When the project and number of pathways are larger, another technique can be used to determine the project’s completion time and the project’s critical path. In this case, ES (earliest start time), EF (earliest finish time), LS (latest start time), and LF (latest finish time) are used along with deterministic time estimates to identify any activity that has slack. Slack means that the start time for a specific activity can be delayed without delaying the project’s planned completion date. Figure 16-4 shows the network diagram and the calculated ESs and EFs. By convention, the project starts at time 0, which is the earliest time any activity can start. Since activity A must be done first, it has ES � 0. The earliest that activity A can be finished is calculated as its earliest start time plus the time it takes to do the activity. The general formula is EF � ES � activity time estimate. For activity A, the formula is EF � ES � the activity A time estimate. Or, EF � 0 � 4 weeks, or a total of 4 weeks. The earliest start time for both activities B or C is equal to the EF for activity A, the activity immediately preceding it.

� Slack The amount of time an activity can be delayed without affecting the project’s planned completion time.

TABLE 16-4 Completion Times for Each Path Activities on Path Completion Time (weeks) A, B, D, E, G, H, J, K 40 A, B, D, E, G, I, J, K 41 A, C, F, G, H, J, K 22 A, C, F, G, I, J, K 23

FIGURE 16-4

Earliest start, earliest finish network

Confirm the calculations made in Figure 16-4 and Figure 16-5.

• Before You Begin: When confirming the ES and EF calculations, pay particular attention to activities G and J. In this situation, these activities have multiple immediate predecessors. Both activities E and F must be finished before activity G can begin. Both activities H and I must be completed before activity J can begin. Remember that since both immediate predecessors must be completed before the next activity can start, the immediate predecessor with the larger EF becomes the ES for the following activity.

• Solution: By convention, the ES for activity A is 0. The earliest finish time is 4. Once activity A is completed, activi- ties B and C can be started. The ES for each is equal to the EF of its immediate predecessor, activity A. Continue moving from left to right through the diagram. The ES for activity G is 30, since both activities E and F must be completed before activity G can begin. The larger EF time for these predecessors is the ES for activity G. The estimated project duration is 41 weeks, the EF for the final project activity.

EXAMPLE 16.3

Using ES, EF, LS, and LF to Find Slack

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622 • CHAPTER 16 PROJECT MANAGEMENT

Step 3 (b): Probabilistic Time Estimates With probabilistic time estimates, we make three time estimates for each project activ- ity: the optimistic time, the most likely time, and the pessimistic time. The optimistic time, denoted as (o), is the shortest time in which the activity can be completed. The most likely time, denoted as (m), is the most reasonable time estimate. The pessimistic time, denoted as (p), is the longest time in which the activity can be completed. Using Michael and Elyssa’s plan for a new product, let’s add some time estimates so we can determine the project’s critical path. Table 16-5 shows the optimistic, most likely, and pessimistic time estimates for the project activities.

When we calculate the expected time for an activity, we treat each activity time estimate as a random variable derived from a beta probability distribution. The beta distribution can have various shapes typically found in project management activities and also has definite end points. These end points limit the possible completion times of the project between the optimistic and the pessimistic completion times. The most likely time completion date is the mode of the beta distribution. Figure 16-6 shows an example of a beta probability distribution.

� Optimistic time estimate The shortest time period in which the activity can be completed.

� Most likely time estimate The normal time that the activity is expected to take.

� Pessimistic time estimate The longest time period in which the activity will be completed.

� Beta probability distribution Typically represents project activities.

After moving from left to right through the network computing the ESs and EFs, the LSs and LFs are calculated working from right to left. Since the project duration has been established as 41 weeks, the LF for the final project activity (K) is set to 41 weeks. Beginning with the LF for the final activity, move from right to left through the network diagram. If activity K must be finished by time 41, then it must be started no later than time 39 (the LFK � the time to complete activity K). If activity K must be started no later than time 39, then its immediate predecessor (activity J) must be finished no later than time 39, so LFJ � 39. The latest activity J can be started and still be completed on schedule is time 35. At activity G, there are two activities that follow the completion of activity G. In this case when there are two or more activities (H and I) going back to a single activity (G), the smaller LS value (LSI � 32) is the LF for activity G. Continue through the network.

After the LSs and LFs are completed, determine which activities are on the critical path. Any activity with slack is not on the critical path. To determine whether slack exists for an activity, compare its ES value with the LS value. If these two values are equal, then that activity has no slack. Look at activity I for an example with no slack. Look at activity H for an example with slack. In this example, activities A, B, D, E, G, I, J, and K are on the critical path. Activities C, F, and H have slack.

FIGURE 16-5

Latest start, latest finish network

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NETWORK PLANNING TECHNIQUES • 623

We use three time estimates to compute an expected time for finishing each of the activities. The expected time for each activity is a weighted average, calculated using the formula

Expected time 5 optimistic time 1 4 1most likely time 2 1 the pessimistic time

6 For activity A, the expected time is 4 weeks.

Expected timeA 5 2 1 4 14 2 1 6

6 5

24 6

5 4 weeks

FIGURE 16-6

Beta distribution

Confirm the expected time for each of the activities in the Cables By Us project. Identify the different connected pathways through the network and calculate the expected completion time for each pathway.

• Before You Begin: To calculate the expected time for each activity, use the following formula:

Expected time 5 optimistic time 1 4 1most likely time 2 1 pessimistic time

Identify the connected pathways through the network and calculate the expected time for each. Remember that the pathway that takes the longest time to complete is the critical path for the project.

• Solution: Table 16-5 shows the expected time for each of the activities. Note that in some cases, the optimistic, most likely, and pessimistic times can be identical, as they are for activities G and K. For those activities, no uncertainty exists; we know for sure how long those activities will take.

EXAMPLE 16.4

Calculating the Expected Times

Table 16-5 Probabilistic Time Estimates

Activity Description

Optimistic Time (o) (weeks)

Most Likely Time (m) (weeks)

Pessimistic Time (p) (weeks)

Expected Time (ET)

(weeks) A Develop product

specifications 2 4 6 4 B Design manufacturing

process 3 7 10 6.83 C Source and purchase

materials 2 3 5 3.17 D Source and purchase

tooling and equipment 4 7 9 6.83 E Receive and install

tooling and equipment 12 16 20 16 (Continued)

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624 • CHAPTER 16 PROJECT MANAGEMENT

Table 16-5 (Continued)

Activity Description

Optimistic Time (o) (weeks)

Most Likely Time (m) (weeks)

Pessimistic Time (p) (weeks)

Expected Time (ET)

(weeks) F Receive materials 2 5 8 5 G Pilot production run 2 2 2 2 H Evaluate product design 2 3 4 3 I Evaluate process

performance 2 3 5 3.17 J Write documentation

report 2 4 6 4 K Transition to

manufacturing 2 2 2 2

The next step is to transfer the expected activity times to the network diagram, as shown in Figure 16-7. Now we determine the critical path through the project. We have two ways to find the critical path. The first, which is more practical for small network diagrams, is to calculate the expected time each path through the network takes to complete. In Figure 16-7, we can see four connected or sequential paths through the project. Table 16-6 shows these paths and the expected times to complete them.

FIGURE 16-7

Network diagram with expected activity times

The first connected path includes activities A, B, D, E, G, H, J, and K. The second path includes activities A, B, D, E, G, I, J, and K. The third path includes activities A, C, F, G, H, J, and K. The fourth path includes activities A, C, F, G, I, J, and K. There are no other connected paths through the project. To calculate the expected time of each path, we sum the expected times for the activities on the path. For example, we expect the first path to take 44.66 weeks (4 weeks for activity A � 6.83 weeks for activity B � 6.83 weeks for activity D � 16 weeks for activity E � 2 weeks for activity G � 3 weeks for activity H � 4 weeks for activity J and 2 weeks for activity K). The critical path is the longest connected path through the network. Therefore, the second path—A, B, D, E, G, I, J, K—is the critical path and deter- mines the expected completion time for the project.

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NETWORK PLANNING TECHNIQUES • 625

An alternative method for determining the expected project duration using proba- bilistic time estimates is to find which activities have slack time. These are the activities we can delay without affecting the project completion date.

Table 16-6 Paths through the Network

Path Number Activities on Path Expected Completion

Time (weeks) 1 A, B, D, E, G, H, J, K 44.66 2 A, B, D, E, G, I, J, K 44.83 3 A, C, F, G, H, J, K 23.17 4 A, C, F, G, I, J, K 23.34

Given Michael and Elyssa’s project, determine which activities have slack by calculating the ES, EF, LS, and LF for each activity.

• Before You Begin: The ES and LF calculations using probabilistic time estimates are done exactly as were the deterministic time estimates used in Example 16.3.

• Solution: By convention, we set the timing at 0 to begin the project. Therefore, the earliest we can begin activity A is at time 0, which means that the earliest we can finish activity A is at time 4. Both activities B and C can begin as early as time 4. Activity B can be finished as early as time 10.83 (the earliest start time plus the expected activity time). Activity C can be finished as early as time 7.17.

When we have to finish two or more activities before another activity can begin, such as activities E and F being finished before beginning activity G, we calculate the earliest finish time for each activity. Figure 16-8 shows that the earliest we can finish activity E is time 33.66, whereas we can finish activity F as early as time 17.17. Since both activities must be finished before we can begin activity G, the earliest we can start activity G is time 33.66. Note that when two or more activities must be done before the next activity can begin, the earliest start time of that activity is the larger of the incoming earliest finish times.

EXAMPLE 16.5

Calculating Earliest Start Times and Latest Start Times

FIGURE 16-8

Network diagram with early starts and early finishes

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626 • CHAPTER 16 PROJECT MANAGEMENT

The Gantt chart in Figure 16-9 shows the project with each activity finished at the earliest possible start date. Note that we cannot begin activity D until we finish both activities B and C. We can begin activity F as soon as we finish activity C.

Using Michael and Elyssa’s project, determine the latest start and latest finish times for each of the activities.

• Solution: In Figure 16-10, we have added the latest start times (LS) and the latest finish times (LF) to the network diagram. LS is the latest time we can start an activity without delaying the entire project. LF is the latest time we can finish an activity without delaying the entire project. By convention, we set the LF for the final activity equal to the EF for that activity (44.83 weeks in this case). To calculate the latest start time for activity K, we subtract the expected activity time (2 weeks) from the latest finish time (44.83 � 2.0 � 42.83). The latest finish time for activity J is the latest start time for activity K. When we have two activi- ties going back to a common activity, such as activities B and C going back to activity A, we calculate the LS for each and then we use the smaller of the latest start times (for example, the LS for activity B is 4, the LS for activity C is 25.49, therefore the LF for activity A is 4). The Gantt chart in Figure 16-11 shows the project schedule using the latest possible start times for each of the activities if the project is to be completed in 44.83 weeks.

When we have computed the ES and EF for each activity, we can determine which activities are on the critical path. All activities that have equal ES and EF are on the critical path. From Figure 16-10 we can see that activities A, B, D, E, G, I, J, and K are on the critical path. Activities C, F, and H are not on the critical path. Any activity not on the critical path has slack time, so we can delay the completion of that activity. To illustrate this, let’s look at our network diagram in Figure 16-10. We can see that activities C and F together need only an expected time of 8.17 weeks. The other activi- ties that we must finish before activity G—activities B, D, and E—are expected to take 29.66 weeks. The difference (21.49 weeks) represents how long we can delay activities C and F and still not affect the project’s completion date. As long as we finish C and F by time 33.66, we can finish the project as planned.

FIGURE 16-9

Earliest-start Gantt chart

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NETWORK PLANNING TECHNIQUES • 627

The Gantt chart in Figure 16-11 shows the project schedule using the latest possible start times for each of the activities if the project is to be completed in 44.83 weeks.

When we compare the earliest-start schedule with the latest-start schedule, we can see that three activities (C, F, and H) have different starting and finishing times. We can start activity C as early as week 4 or as late as week 25.49, so activity C has slack. The same is true for activity F: we can start it as early as week 7.17 and as late as week 28.66. Activity H has less slack. We can start it as early as week 35.66 and no later than week 35.83. Since each of these activities has slack, we do not include them in the project’s critical path.

FIGURE 16-10

Network diagram with late starts and late finishes

FIGURE 16-11

Latest-start Gantt chart

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628 • CHAPTER 16 PROJECT MANAGEMENT

Step 4: Monitor the Project’s Progression Even though you have carefully planned the project, things happen that can affect its progress. Parts or equipment may arrive later than expected, materials may not meet quality specifications and will need to be replaced, less labor than expected may not be available, and bad weather—all can delay the project’s completion. Planning the project is necessary, but monitoring its progress is even more important in meeting the sched- uled completion date. You focus initially on activities in the critical path since any delay in these activities delays the whole project. You are also aware of activities not on the critical path that have little slack because a relatively short delay in these activities can affect the project’s completion time.

One large-scale project to manage is the Olympic Games—a project that necessitates an exceptional amount of advance research and planning. The logistics and infrastructures of the city that hosts the Olympic Games and the Organizing Committee for the Games both have to oversee hundreds of smaller projects that culminate to ensure a suc- cessful run of the Olympics. From tasks such as shipping and receiving the tre- mendous amount of freight (for exam-

ple, broadcast equipment) to facilitating the stay of the athletes, project managers must pay close attention to details. Of course, with such a large-scale event comes a myriad of problems, such as enough timely transportation, leadership changes, and security con- cerns. Only with extensive preparation can such a large-scale event be successful.

MKT

LINKS TO PRACTICE Managing the

Olympic Games www.olympics.com

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ESTIMATING THE PROBABILITY OF COMPLETION DATES • 629

ESTIMATING THE PROBABILITY OF COMPLETION DATES An advantage of using probabilistic time estimates is the ability to predict the probabil- ity of project completion dates. We learned how to calculate the expected time for each activity with the three time estimates provided. Now we need to calculate the variance for each activity. The variance of the beta probability distribution for each activity is

s 2 5 ¢p 2 o 6

≤2

where p � pessimistic activity time estimate o � optimistic activity time estimate

Before You Go On

Be sure that you understand how to use network planning techniques. 1. Describe the project in terms of your objective, the project activities, and the precedence relationships. 2. Estimate the project activities. For probabilistic time estimates, make an optimistic, most likely, and pessimis-

tic estimate and compute the expected time for each project activity. For deterministic time estimates, make a single time estimate for each activity.

3. Diagram the project using either activity-on-arrow or activity-on-node notation to reflect the precedence relationships between activities.

Calculate the variance of each activity in the Cables By Us project. Then calculate the variance associated with each connected pathway through the project.

• Before You Begin: The variance is calculated using the formula

s2 5 ap 2 o 6

• Solution: Using activity A to illustrate the formula, the variance is 0.44:

s2A 5 a6 2 26 b 2

5 0.44

Confirm the variances for the remaining activities as shown in Table 16-7. Then sum the total variances associated with each pathway. Confirm the values shown in Table 16-8.

EXAMPLE 16.6

Calculating the Variance of Activities

Table 16-7 Project Activity Variance

Activity Optimistic

Time Most Likely

Time Pessimistic

Time Variance

A 2 4 6 0.44 B 3 6 10 1.36 C 2 3 5 0.25 D 4 7 9 0.69 E 12 16 20 1.78 F 2 5 8 1.00 G 2 2 2 0.00 H 2 3 4 0.11 I 2 3 5 0.25 J 2 4 6 0.44 K 2 2 2 0.00

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630 • CHAPTER 16 PROJECT MANAGEMENT

When you know the expected completion time of each path and its variance, you can determine the probability of specific completion dates. For example, you may want to know the probability of completing the project in 48 weeks. We can use the following formula to determine the probability of finishing each of the paths at a specified date:

z 5 specified time 2 path expected completion time

path standard deviation or

z 5 DT 2 EFPath "s2Path

where DT � the specifi ed completion date EFPath � the expected completion time of the path

s 2Path � variance of path

For a particular path through the project, the z value shows the path’s number of standard deviations that the specified time is past the expected path completion time. A negative z value shows that the specified time is earlier than the expected path completion time. After calculating the z value, you can look it up in Appendix B to determine the probability of finishing the path by the specified time. Note that the probability of finishing the path by the specified time equals the area under the normal curve to the left of z, as shown in Figure 16-12.

Using the variance for each activity, calculate the variance for paths through the network. To calculate the variance for a specific path through the network, add the variance for each of the activities on the path. For example, the variance for the critical path A, B, D, E, G, I, J, K is 4.96 weeks (0.44 � 1.36 � 0.69 � 1.78 � 0.00 � 0.25 � 0.44 � 0.00). The size of the variance reflects the degree of uncertainty for the path. The greater the variance, the greater is the uncertainty. The variance for the second-longest path through the network (A, B, D, E, G, H, J, K) is 4.82 weeks. Table 16-8 shows the four specific paths and their variances.

Table 16-8 Variances of Paths through the Network Path Number Activities on Path Path Variance (weeks)

1 A, B, D, E, G, H, J, K 4.82 2 A, B, D, E, G, I, J, K 4.96 3 A, C, F, G, H, J, K 2.24 4 A, C, F, G, I, J, K 2.38

FIGURE 16-12

Probability of path 1 finished in 48 days

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REDUCING PROJECT COMPLETION TIME • 631

In general, we give any activity with a z value of 2.50 or larger a 100 percent prob- ability of completion by the specified time. You may be more concerned with the probability of the critical path’s completion by the specified time. Still, it is a good idea to determine the probability of other paths with similar completion times. The prob- ability of finishing the critical path for this project in 48 weeks is 0.9222. It is true that a delay in any activity in the critical path will delay the project’s completion. This does not mean, however, that we ignore what happens in the other paths. These lengths of other paths through the project are sometimes almost the same as that of the critical path. Thus, an extended delay or delay in a combination of activities on an alternate path could ultimately delay the project’s completion.

Calculate the probability of finishing the project in 48 weeks.

• Solution: We compute the probability of path number 1 as follows:

z 5 48 weeks 2 44.66 weeks

!4.82 5 1.52 The z value of 1.52 in Appendix B shows that there is a 0.9357 probability of finishing the path in no lon- ger than 48 weeks. Conversely, there is only a 0.0643 probability of not finishing this path by 48 weeks. Table 16-9 shows the z value calculations and the probability of completion for the other three paths.

EXAMPLE 16.7

Calculating the Probability of Finishing the Project in 48 Weeks

Table 16-9 z-Value Calculations and Path Probabilities of Finishing in 48 Weeks

Path Number Activities on Path

Path Variance (weeks) z value

Probability of Completion

1 A, B, D, E, G, H, J, K 4.82 1.5216 .9357 2 A, B, D, E, G, I, J, K 4.96 1.4215 .9222 3 A, C, F, G, H, J, K 2.24 16.5898 1.000 4 A, B, F, G, I, J, K 2.38 15.9847 1.000

REDUCING PROJECT COMPLETION TIME You may need to reduce the time you spend finishing a particular project because of deadlines, promised completion dates, penalty clauses for late completion, or the need to put resources on a new project. When you plan a project, you make time estimates based on normal procedures and resources. However, you may be able to speed up a project by making additional resources available. For example, your company could have materials shipped via premium rather than normal transportation to get the materials faster and the activity finished sooner. You could authorize overtime to speed up an activity. Another possibility, as is done in highway construction, is to bring in lights so workers can work through the night and minimize traffic disrup- tions. Whatever the method, you can often reduce the time needed to finish an activity.

Crashing Projects At the same time that you shorten a project’s duration, you also need to minimize the additional expense. We call shortening a project crashing the project. To crash the project and minimize expense, you need additional information about your project

� Crashing Reducing the completion time of the project.

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632 • CHAPTER 16 PROJECT MANAGEMENT

activities. Let’s use Michael and Elyssa’s project with deterministic time estimates (shown in Figure 16-3). Table 16-10 shows the new crashing information.

In Table 16-10, normal time and normal cost refer to how long the activity typi- cally takes to complete and the cost for completing the activity in that amount of time. Crash time is the least possible amount of time in which the activity can be done. The crash cost is the amount of money you must pay to complete the activity in the minimum amount of time. If you choose to only partially speed up an activity, the additional cost must be calculated. For example, if we choose to complete activity E in 13 weeks, the additional cost is $6,000. The difference between the crash cost and the normal cost ($72,000 � $60,000 � $12,000) is divided by the difference between nor- mal time and crash time (14 weeks � 12 weeks � 2 weeks). The cost to crash activity E, then, is $12,000/2, which equals $6,000 per week.

Activity Normal Time

(weeks) Normal Cost

($) Crash Time (weeks)

Crash Cost ($)

Maximum Weeks of Reduction

Cost per Week to Reduce

($)

A 4 8,000 3 11,000 1 3,000

B 6 30,000 5 35,000 1 5,000

C 3 6,000 3 6,000 0 0

D 6 24,000 4 28,000 2 2,000

E 14 60,000 12 72,000 2 6,000

F 5 5,000 4 6,500 1 1,500

G 2 6,000 2 6,000 0 0

H 2 4,000 2 4,000 0 0

I 3 4,000 2 5,000 1 1,000

J 4 4,000 2 6,400 2 1,200

K 2 5,000 2 5,000 0 0

TABLE 16-10

Normal and Crash Cost Estimates

FIGURE 16-13

Crashed project diagram

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REDUCING PROJECT COMPLETION TIME • 633

Note that we cannot reduce activities C, G, H, or K, so for those activities the maxi- mum number of weeks of reduction is set to 0. Also note that reducing the lengths of different activities costs different amounts. We can reduce activity I by 1 week for a cost of $1,000, whereas we can reduce activity B by 1 week for a cost of $5,000. When you need to reduce the length of a project, you first consider reducing activities on the critical path. Activities not on the critical path have slack and typically do not need to be reduced. Figure 16-13 shows the revised network diagram.

Suppose you are the project manager for Cables By Us. Consider what activities you would crash if Michael and Elyssa want to finish their project in 36 weeks. A quick look at Table 16-4 shows you that the normal project completion time is 41 weeks and the critical path includes activities A, B, D, E, G, I, J, and K. You need to consider crashing these activities to reduce the overall project length from 41 to 36 weeks. Table 16-10 shows that activities G and K cannot be crashed, so you eliminate them. Table 16-11 shows the remaining activities on the critical path.

Michael and Elyssa want their project completed in 36 weeks instead of the 41 weeks shown in Table 16-4. They also want to minimize the extra cost incurred for crashing the project. Recommend which activities should be crashed so that the project takes only 36 weeks.

• Before You Begin: Table 16-11 has additional information showing the costs of complet- ing each of the activities on the critical path in the normal time allotted, as well as how quickly the activity can be completed and the cost per week for reducing the activity time. For example, activity A normally takes 4 weeks and costs $8,000 to complete. If needed, activity A can be done in only 3 weeks, but the cost of speeding up this activity raises the total cost of activity A up to $11,000. So to reduce activity A by 1 week costs an additional $3,000. Only activities on the criti- cal path are considered initially. A reduction in time to complete an activity that already has slack does not reduce the time to complete the project. Be aware that it is possible to change the critical path of the network if an activity is crashed too much.

• Solution: To minimize the cost to crash the project, look in the last column for the least expensive critical path activity to crash per week. Activity I costs $1,000 to crash per week and you can crash it by 1 week. If you crash activity I by 1 week, the project will take 40 weeks. You still need to cut the project by 4 more weeks. The next least expensive activity to crash is activity J. It costs $1,200 per week to crash and you can crash it for 2 weeks. This reduces the project’s length to 38 weeks. The next least expensive activity to crash is activity D at a cost of $2,000 per week and you can crash it for 2 weeks. This reduces the total project duration to 36 weeks at a cost of $7,400, as shown in Figure 16-13.

EXAMPLE 16.8

Crashing the Project

Crash activity I from 3 weeks to 2 weeks $1,000 Crash activity J from 4 weeks to 2 weeks $2,400 Crash activity D from 6 weeks to 4 weeks $4,000 Total Crash Cost $7,400

Table 16-11 Normal and Crash Cost Estimates for Remaining Critical Path Activities

Activity

Normal Time

(weeks)

Normal Cost ($)

Crash Time

(weeks)

Crash Cost ($)

Maximum Weeks

of Reduction

Cost per Week to Reduce

($)

A 4 8,000 3 11,000 1 3,000 B 6 30,000 5 35,000 1 5,000 D 6 24,000 4 28,000 2 2,000 E 14 60,000 12 72,000 2 6,000 I 3 4,000 2 5,000 1 1,000 J 4 4,000 2 6,400 2 1,200

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634 • CHAPTER 16 PROJECT MANAGEMENT

THE CRITICAL CHAIN APPROACH

Before You Go On

In summary, to reduce project length, you need this information:

1. Normal activity time estimate and normal activity cost. 2. Crash activity time estimate and crash activity cost. 3. The activities on the critical path.

When you have this information, you need to do the following:

1. Determine how much the project needs to be reduced. 2. Determine which activities on the critical path can be reduced. 3. Crash critical activities on the basis of increasing cost.

• Crash the least expensive activity first, the next least expensive second, and so on, until you have shortened the project to the desired length.

• Calculate the total costs associated with crashing the project and determine whether the cost is justified.

The critical chain approach is to get projects done faster and more consistently at or before the project due date. The focus is on the final due date rather than on individual activities or project milestones. The idea of the critical chain is that project activities are uncertain. Because of this uncertainty, we add safety time to project time estimates. In some cases, the safety time added exceeds 200 percent of the work time estimate.

Adding Safety Time We have three ways to add safety time. First, we base time estimates on a pessimistic experience. Most time estimates include enough safety time to ensure that the project activity is completed on time 80 to 90 percent of the time. From statistics, we know that to cover 80 to 90 percent of the area under the curve, we have to add a substantial safety factor. Second, the more management levels involved, the greater the safety factor. Since no manager wants to look bad, we add more safety factors (perhaps an extra 10 to 20 percent). When each management level adds this safety factor, the total safety is greatly increased. Third, top management may make global reductions in project length. If we know that the total project length is likely to be reduced by 20 to 25 percent, we inflate our time estimate by 20 to 25 percent.

Wasting Safety Time Just as we have three ways to add safety time, we have three ways to waste safety time. One is the student syndrome. If we have six weeks allotted for an activity that should take only two weeks, we do not start it until two weeks before it is due. Then if anything unexpected happens, we miss the due date because we wasted the safety cushion. A second way is multitasking, which uses a person or resource for more than one proj ect. Thus we have to decide which project to work on: assigning resources to a low-priority project wastes valuable safety time. On the other hand, the amount of safety time may be the reason for using it on a lower-priority project. Suppose we have two activities. Activity A is on schedule and has plenty of safety time, whereas activity B is behind schedule and has little safety time. Typically, we work on activity B first and waste activity A’s safety time. The third way to waste safety time comes

� Critical chain approach Focus on the final due date that is based on the theory of constraints.

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THE CRITICAL CHAIN APPROACH • 635

from dependencies between activities in which delays accumulate and advances are wasted. Let’s look at an example. Activity A is scheduled to take ten days. Activity B is scheduled to start on day 10 after activity A is finished. Think about what happens if we finish activity A in eight days. Do we start activity B earlier? Typically, no: we start activity B on day 10 as originally planned and we waste the safety time. What happens if we do not finish activity A until day 12? When do we start activity B? Day 12. The delays accumulate and they are passed on.

How does the critical chain approach solve the problem of safety time? The critical chain removes safety time from the individual activities and puts the total safety time at the end of the critical path, which creates a project buffer. Let’s compare the critical paths shown in Figure 16-14. The completion time is the same, but the original critical path has safety time added to each activity. The critical path with the project buffer eliminates the individual safety times. Activities not finished on time eat into the proj- ect buffer instead of wasting activity safety time.

Since the theory of constraints (discussed in Chapter 15), which is the basis of the critical chain, focuses on keeping the bottleneck busy, we can put time buffers before bottlenecks in the critical path, as shown in Figure 16-15. The feeding buffer protects the critical path from delays in noncritical paths. When the delay exceeds the feeding buffer, the project completion date is still protected by the project buffer. (For more information, see Critical Chain by Eliyahu M. Goldratt.)

� Project buffer Safety time placed at the end of the critical path.

FIGURE 16-14

Comparing critical paths

FIGURE 16-15

Example with feeder buffers

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636 • CHAPTER 16 PROJECT MANAGEMENT

PROJECT MANAGEMENT WITHIN OM: HOW IT ALL FITS TOGETHER Project management techniques provide a structure for the project manager to track the progress of different activities required to complete the project. Particular concern is given to critical path (the longest connected path through the project network) activities. Any delay to a critical path activity affects the project completion time. These techniques indicate the expected completion time and cost of a project. The project manager reviews this information to ensure that adequate resources exist and that the expected completion time is reasonable. If the expected completion date has a high probability of exceeding a contractual due date and incurring penalty costs, the proj ect manager evaluates different methods for crashing the project (reducing proj- ect completion time). This evaluation is used to economically justify the application of more resources or to acknowledge a high probability of facing penalty costs. The proj ect manager also looks at the resource load profile in an attempt to level resource requirements for the project.

After resolving the issue of resources, the project manager tracks the progress of the project. Once the earliest and latest start and finish times are determined, the pro- duction and material planners order materials and schedule required operations. The project manager interacts with the people responsible for different project activities and informs them of any proposed project schedule changes.

PROJECT MANAGEMENT OM ACROSS THE ORGANIZATION Since projects tend to be long term and consume a company’s resources, functional areas through the company work with expected completion dates, resource require- ments, and the consequences of activity delays. Let’s look at how the functional areas use project management information.

Accounting uses project management information to provide a time line for major expenditures associated with the project. Accounting measures actual cost perfor- mance against planned costs to calculate profits. Accounting also calculates the cost benefit of crashing a particular project.

Marketing uses project management information to monitor the progress of a project and to provide honest and realistic updates to the customer. The project sched- ule allows marketing to evaluate whether or not to crash a project.

Information systems develop and maintain the software that supports project management. Choosing, installing, and training users in the appropriate software is vital to successful project management.

Purchasing uses project management information to deal with project delays by de-expediting items and rescheduling these items for later delivery. This allows the company to keep a lower inventory investment. Purchasing can also suggest when to avoid late deliveries to keep the project on schedule and how to reduce delivery time to help put a project back on schedule.

Operations uses project management information to monitor the progress of activi- ties on and off the critical path and to manage resource requirements in terms of the quantity and time needed for operations. Within an organization, the project manager or an assistant may develop the project schedule, typically using software for projects with many activities. Project managers can be product managers, manufacturing engineers, operations analysts, or office managers. Project management is a function not only of manufacturing companies but of service organizations too. Suppose you are planning the worldwide tour of a major art exhibit. Project scheduling techniques will help you effec- tively manage the many activities in this and other similar projects for your organization.

ACC

MKT

MIS

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CHAPTER HIGHLIGHTS • 637

Project management provides a structure to track the progress of a project. Any delays in an activity on the critical path delay the completion date for the entire project. Projects, such as introducing new products, can be undertaken by a supply chain. Communication among members of the chain to ensure a timely completion of the project is criti- cal. Given that members of the supply chain share information, as well as a common database, all members should have real-time access to the project’s progress. Any delays should be communicated throughout the chain. The project management approach indicates the

timing and the quantity of resources needed. For manu- facturing, this input is needed by the master production scheduler and is used to develop a valid MPS. Subse- quently, this is input into the MRP system to develop

the planned orders necessary to support the project. Project tracking allows supply chain members to make changes as needed without surprising other members of the chain, thus increasing the

chances of completing the project on schedule. For service projects, the input is used by the

project manager to develop the project structure. The project manager ensures that members are aware of progress in the service project and the impact of any delays.

THE SUPPLY CHAIN LINK

Project management requires great efficiency and effec- tiveness from project managers in scheduling activities. In today’s business environment this also requires that managers pay more attention to environmental issues, specifically in the area of sustainability.

Project management, as in any other type of sched- uling, lends itself to the integration of sustainability principles. This means scheduling resources and plan- ning activities with an eye toward meeting sustainability metrics, such as minimization of the carbon footprint or emissions and pollutants, and minimization of waste of natural resources. This is true regardless of the proj- ect managed and involves scheduling activities of both materials and people.

Bringing sustainability into project management, however, poses special challenges, as each project is unique and a project is divided into distinct activities,

typically performed by different individuals or groups. One challenge is to ensure that there is a system view of the project and that everyone, regardless of the activity for which they are responsible, understands the sustain- ability goals of the project. The second challenge is to ensure that sustainability goals continue to be realized as one party “hands off” their activity to the next. As each project goes through five phases, it is best to begin integrating sustainability at the start of the project—in the concept phase—and determine how sustainability criteria will be included in all other phases of the proj- ect. Integrating sustainability in this phase can help project managers understand how to integrate sustain- ability into each activity of the project in a consistent manner. This can also result in innovation as to how best to incorporate sustainability throughout all activi- ties of the project, as everyone has a say, and can result in a shared purpose for the team and better overall project results.

THE SUSTAINABILITY LINK

Chapter Highlights 1 A project is a unique, one-time event of some duration

(weeks, months, or even years) that consumes resources (human, capital, materials, and equipment capacity) and is designed to achieve an objective in a given time period. In business, projects can be designing new products, installing new systems, constructing new facilities, mounting advertising campaigns, designing information systems, and developing company Web pages. In politics, a project can be designing a political campaign.

2 Each project goes through a fi ve-phase life cycle: concept, feasibility study, planning, execution, and termination. In the concept phase, we identify the need for the project. With the feasibility study, we evaluate expected costs, benefi ts, and risks. Planning consists of calculating the work and the time to do it; execution is doing the work; and termination is fi nishing the project.

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638 • CHAPTER 16 PROJECT MANAGEMENT

3 Two network planning techniques are PERT and CPM. PERT uses probabilistic time estimates. CPM uses deterministic time estimates.

4 PERT and CPM determine the critical path of the project and the estimated completion time. On smaller projects, we determine completion time by evaluating each connected path through the network. On larger projects, soft ware programs are available to identify the critical path.

5 PERT uses probabilistic time estimates to determine the probability that a project will be done by a specifi ed time. We calculate a z value and then determine the probability

that the critical path and other near-critical paths will be completed by a given date.

6 To reduce the length of a project, we need to know the critical path and the cost of reducing individual activity times. Crashing activities that are not on the critical path typically does not reduce project completion time.

7 Th e critical chain approach removes excess safety time from individual activities and creates a project buff er at the end of the critical path. Feeder buff ers are used on noncritical paths merged with the critical path.

Key Terms project 615 program evaluation and review tech-

nique (PERT) 617 critical path method (CPM) 617 project activities 617 precedence relationships 617

activity-on-node 618 critical path 618 probabilistic time estimate 619 deterministic time estimate 619 slack 621 optimistic time estimate 622

most likely time estimate 622 pessimistic time estimate 622 beta probability distribution 622 crashing 631 critical chain approach 634 project buff er 635

Formula Review 1. Expected time for each activity:

Expected time 5 optimistic time 1 4 1most likely time 2 1 the pessimistic time

6 2. Variance for each activity:

s 2 5 ap 2 o 6

3. Calculating the z value to estimate probability of completion:

z 5 specified time 2 path expected completion time

path standard deviation or z 5 aDT 2 EFPath!s2Path b

Solved Problems Problem 1 Use the following information to diagram the project network.

activity D begins. When C is done, activity E begins. Activity F begins after both D and E are finished. Figure 16-16 shows the diagrammed network.

(See student companion site for Excel template.)

Activity Immediate Predecessors

A none B A C A D B E C F D, E G F

FIGURE 16-16

Project network diagram

Diagram the network using AON notation.

• Solution The AON notation is straightforward for this project. Activity A is done first, then activities B and C can begin. When B is done,

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SOLVED PROBLEMS • 639

• Problem 2 Your boss gives you the following information about the new project you are leading. The information includes the activities, the three time estimates, and the precedence relationships. (a) Calculate the expected time for each of the activities. (b) Determine the expected completion time of the project. (c) Calculate the variance of each of the project activities. (d) Determine the probability that each of the connected paths through the project will be completed within 30 weeks.

Activity Immediate

Predecessor’s Time Optimistic (weeks)

Most Likely Time (weeks)

Pessimistic Time (weeks)

A none 6 10 14 B A 3 6 10 C A 4 8 10 D B 5 6 7 E C 4 6 12 F D, E 3 3 3 G F 3 4 5

• Before You Begin: This project has three estimates for each activity. First, calculate the expected time for each project:

Expected time of activity 5 o 1 14 3 m 2 1 p

Determine the expected completion time by summing the total expected time for each pathway through the network. Then calcu- late the variance of each activity:

Variance of activity 5 ap 2 o 6

Finally, determine the probability of completing each pathway in 30 weeks. To find the z value, use the following formula:

z 5 aDT 2 EFPath!s2Path b

• Solution (a) The formula for calculating the expected time for an activity is

Expected time 5 optimistic time 1 14 3 most likely time 2 1 pessimistic time

The expected times for each activity are shown in Table 16-12.

Table 16-12 Calculation of Activity Expected Times and Variances

Activity Optimistic

Time Most Likely

Time Pessimistic

Time Expected

Time Variance

A 6 10 14 10.00 1.78 B 3 6 10 6.17 1.36 C 4 8 10 7.67 1.00 D 5 6 7 6.00 0.11 E 4 6 12 6.67 1.78 F 3 3 3 3.00 0.00 G 3 4 5 4.00 0.11

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640 • CHAPTER 16 PROJECT MANAGEMENT

(b) Using Figure 16-17, we can identify the two connected paths running through the project. The first path includes activities A, B, C, F, and G and needs 29.17 weeks to complete. The second path includes activities A, C, E, F, and G and needs 31.34 weeks to complete. The early-start/early-finish and late-start/late-finish times are shown in Table 16-13.

FIGURE 16-17

Project diagram

Table 16-13 Calculation of Early-Start/Early-Finish Times and Late-Start/Late-Finish Times

Activity Expected

Time

First Immediate Predecessor

Second Immediate Predecessor

Early-Start Time

Early-Finish Time

Late-Start Time

Late-Finish Time Slack

A 10.00 none 0.00 10.00 0.00 10.00 0.00 B 6.17 A 10.00 16.17 12.17 18.33 2.17 C 7.67 A 10.00 17.67 10.00 17.67 0.00 D 6.00 B 16.17 22.17 18.33 24.33 2.17 E 6.67 C 17.67 24.33 17.67 24.33 0.00 F 3.00 D E 24.33 27.33 24.33 27.33 0.00 G 4.00 F 27.33 31.33 27.33 31.33 0.00

s2 5 ap 2 o 6

The variance for each activity is shown in Table 16-12. (d) To determine the probability of completing the project in

30 weeks, we need to calculate the variance for each path through the project. We do this by summing the individual variances of each activity included on the path. For the first path—A, B, D, F, and G—the variance is 3.36 weeks. The variance for the second path—A, C, E, F, and G—is 4.67 weeks. We use the following formula to determine the prob- ability of completion by a specified time:

z 5 aDT 2 EFPath"s2Path b

The probability that the first path (A, B, D, F, G) will be completed within 30 weeks is

z 5 a30 2 29.17"3.36 b 5 0.45 A z value of 0.45 equates to a probability of 0.6736, or a 67.36 percent chance of this path being completed in 30 weeks. The probability that the second path (A, C, E, F, G) will be completed within 30 weeks is

z 5 a30 2 31.34"4.67 b 5 20.62 A z value of �0.62 equates to a probability of 0.2676, or a 26.76 percent chance that this path will be completed in 30 weeks.

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SOLVED PROBLEMS • 641

• Problem 3 You are in charge of a new project that needs to be completed within 24 weeks. Figure 16-18 shows the network diagram and other relevant information.

• Before You Begin: First, determine the expected completion time for this project. Identify the connected pathways through the network and sum the total time to complete the activities on each connected path- way. The longest time is the expected project duration. Since the

desired project completion date is 20 weeks, calculate how many weeks the project must be crashed. Crash the project by crash- ing activities on the critical path. Find the cheapest such activity and crash it. Continue crashing until the expected project dura- tion is 20 weeks.

• Solution First we need to determine the completion time for the project. Using Figure 16-18, we can identify the four paths through the project and calculate the project completion time. The first

Activity Normal Time Normal Cost ($) Crash Time Crash Cost ($) Maximum Weeks

Crashed Crash Cost per Week ($)

A 4 4,000 3 4,500 1 500 B 6 9,000 6 9,000 0 0 C 5 1,500 3 2,000 2 250 D 3 6,000 2 9,000 1 3,000 E 4 8,000 2 16,000 2 4,000 F 6 3,000 5 3,500 1 500 G 8 4,000 6 6,000 2 1,000 H 3 3,600 2 4,800 1 1,200

FIGURE 16-18

Project diagram

path (A, B, D, E, F, H) takes 26 weeks (4 � 6 � 3 � 4 � 6 � 3). Each of the paths and its completion time are shown here.

Path Completion Time (weeks)

A, B, D, E, F, H 26 A, B, D, E, G, H 28 A, C, D, E, F, H 25 A, C, D, E, G, H 27

Given the crash costs, we need to reduce the completion time from 28 weeks to 24 weeks. To do this, we consider the activities on the critical path and their associated crash cost. We do not need to consider crashing activities C or F because they are not part of the critical path and we cannot crash activity B because it cannot be reduced. Of the remaining activities, the least expensive activity to crash is A, which we can crash 1 week at a cost of $500. Since we want to reduce the project by 4 weeks, we have to find additional reductions. The next least expensive activity to crash is G, which we can crash 2 weeks at a total cost of $2,000. We need only one more week of reduction. Activity H is the next least expensive activity to crash, at $1,200 per week. By crashing these three activi- ties, we can finish the project in 24 weeks. The additional cost for crashing the project is $3,700 ($500 for A, $2,000 for G, and $1,200 for H). Figure 16-19 shows the crashed project diagram.

We determine the time to finish this project by the connected path that takes the longest time to complete. In this case, the completion time is 28 weeks. The critical path of this project includes activities A, B, D, E, G, and H.

A(3) E(4)D(3)

B(6)

C(5)

H(2)

F(6)

G(6)

FIGURE 16-19

Crashed project diagram

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642 • CHAPTER 16 PROJECT MANAGEMENT

Discussion Questions 1. Identify some projects that are currently underway in your

community. Is there a new hospital being built, a new retail store being opened, highway construction being done? For at least one project, try to identify the major activities.

2. Visit a local organization to learn about the kinds of projects it is working on and how it manages these projects.

3. Identify a personal project that you have recently com- pleted or are in the process of completing—for example, writing a research paper or organizing a social event. Identify the major activities you had to complete.

4. Explain the advantage of using probabilistic time estimates.

5. Explain how we calculate the expected time value. 6. Explain the phases of a project’s life cycle. 7. Describe the life cycle of a project you have done. 8. Provide an example of precedence relationships from your

personal life. 9. Explain why determining the critical path is important in

project management.

Activity Activity Time

(weeks) Immediate

Predecessor(s)

A 3 none B 4 A C 2 B D 5 B E 4 C F 3 D G 2 E, F

Problems Use the following project information for Problems 1 and 2.

1. Construct a network diagram using AON notation. 2. Using the network diagram constructed in Problem 1, (a) Calculate the completion time for the project. (b) Determine which activities are included on the critical

path. 3. Jack’s Floating Banana Party Company is planning to add

a new party vessel for the upcoming season. Jack has identifi ed several activities that must be fi nished before the start of the season. Using the following information,

Activity Activity Time

(weeks) Immediate

Predecessor(s)

A 6 none B 5 none C 3 A D 3 B E 6 C, D F 9 D

(a) Draw the network diagram for this project. (b) Identify the critical path. (c) Calculate the expected project length.

Activity

Optimistic Time

Estimate (weeks)

Most Likely Time

Estimates (weeks)

Pessimistic Time

Estimates (weeks)

Immediate Predecessor (s)

A 3 6 9 none B 3 5 7 A C 4 7 12 A D 4 8 10 B E 5 10 16 C F 3 4 5 D, E G 3 6 8 D, E H 5 6 10 F I 5 8 11 G J 3 3 3 H, I

4. Using the information given, construct a network diagram using AON notation.

5. Using the information given, calculate the expected time for each of the project activities.

6. Using the information given, calculate the variance for each of the project activities.

7. Using your results from Problems 4 and 5, (a) Calculate the completion time for this project. (b) Identify the activities included on the critical path of this

project. 8. Using your results from Problem 6, (a) Calculate the probability that the project will be completed

in 38 weeks. (b) Calculate the probability that the project will be completed

in 42 weeks. Use the information provided in Table 16-14 and the network diagram in Figure 16-20 for the next four problems.

Use the following project information for Problems 4 through 8.

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PROBLEMS • 643

Activity Normal Time

(weeks) Normal Cost ($)

Crash Time (weeks)

Crash Cost ($)

Maximum weeks

Reduced Crash Cost per Week ($)

A 4 800 3 1,200 1 400

B 3 900 2 1,000 1 100

C 5 1,250 3 2,250 2 500

D 2 800 2 800 0 0

E 5 1,500 4 2,000 1 500

F 6 2,000 5 3,000 1 1,000

G 4 600 3 900 1 300

H 3 900 3 900 0 0

TABLE 16-14

(b) Calculate the variance for each of the project activities. (c) Evaluate the connected paths through the diagram to

determine the expected project completion time.

FIGURE 16-20

AON network diagram

9. Using the information given, (a) Calculate the completion time of the project. (b) Identify the activities on the critical path. 10. Using the information given and the project completion

time calculated in Problem 9(a), reduce the completion time of the project by 3 weeks in the most economical way.

11. Using the information given and the project completion time calculated in Problem 9(a), reduce the completion time of the project by 5 weeks in the most economical way.

12. Using the information given and the project completion time calculated in Problem 9(a), calculate the minimum time for completing the project possible.

Use Figure 16-21 and the following project data for the next two questions.

13. Using the information given, (a) Calculate the expected time for each of the project

activities.

Activity

Optimistic Time

(weeks)

Most Likely Time

(weeks) Pessimistic

Time (weeks)

A 8 10 12 B 4 10 16 C 4 5 6 D 6 8 10 E 4 7 12 F 6 7 9 G 4 8 12 H 3 3 3

14. Using the information given and expected project completion time from Problem 13(c),

(a) Calculate the probability of completing the project in 36 weeks.

(b) Calculate the probability of completing the project in 40 weeks.

15. Th e accounting department at Northeast University is off ering a combined fi ve-year B.S./M.S. in accounting. Th e senior accounting professor has identifi ed the project activities and any precedence relationships, as shown in Table 16-15. Th ree time estimates for each activity are included.

(a) Develop a network diagram for this project using AON notation.

(b) Calculate the expected time for each of the project activities.

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644 • CHAPTER 16 PROJECT MANAGEMENT

Activity Immediate

Predecessor(s)

Normal Time

(weeks) Normal Cost

Crash Time

(weeks) Crash Cost

A none 10 $16,000 8 $20,000 B none 4 6,000 3 9,000 C A, B 6 12,000 3 24,000 D A 7 7,000 5 10,000 E B 14 28,000 12 34,000 F C, D, E 3 4,500 3 4,500 G F 4 7,200 3 9,000 H F 2 5,000 2 5,000 I F 5 15,000 4 18,000 J H, I 2 7,000 2 7,000 K J 5 10,000 4 11,000 L K 10 24,000 10 24,000

16. Th e dean of the business school wants to start off ering this program starting 32 weeks from now. Using the information provided in Problem 15,

(a) Calculate the probability of the program starting on time.

(b) If the dean needs a 95 percent probability of being done on time, how long can the expected project duration be?

Use the following information for Problems 17 through 19.

17. Draw the network diagram. Determine the normal time it will take to complete this project. Determine the critical path for the project. Calculate the cost of completing the project in normal time.

18. Determine the absolute minimum time it will take to complete the project if the project is crashed as far as possi- ble. Calculate the cost associated with this approach to the project.

19. If the company wants to complete the project in 40 weeks, which activities should be crashed? Calculate the additional cost incurred to complete the project in 40 weeks.

FIGURE 16-21

Network diagram

Activity Description of Activity Immediate

Predecessor(s)

Optimistic Time

Estimates (weeks)

Most Likely Time Estimates (weeks)

Pessimistic Time

Estimates (weeks)

A Design general curriculum requirements none 4 8 16

B Develop program brochure A 2 3 6

C Identify prospective students none 3 6 9

D Develop advertising campaign B, C 4 7 10

E Design specifi c curriculum content A 8 16 20

F Send brochure and student application D 2 3 4

G Evaluate applications F 2 4 6

H Accept students, notify students G 1 2 3

I Schedule rooms for classes H 1 1 7

J Designate professors to teach courses H 1 2 3

K Select texts for courses J 3 5 7

L Order and receive texts K 6 8 17

TABLE 16-15

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CASE: WRITING A TEXTBOOK • 645

CASE: The Research Office Moves Jeannette, the senior administrative assistant, has just learned that she is in charge of the upcoming move of the research office at Southwest University. She has coordinated several such moves before and immediately begins organizing her thoughts. Determining what needs to be done, when it needs to be done, and who needs to do it are critical to a successful move. From past moves, Jeannette knows the first step is having the manage- ment team allocate the offices available to the different depart- ments. She knows that each department manager fights for the best office space. Because of the politics, Jeannette expects this activity to take three weeks.

After the management team finalizes departmental allo- cations, each department manager allocates office space to individuals within the department. This is also quite political and typically takes two weeks. Individuals often take the office space allocations personally, and each manager needs time to smooth any ruffled feathers. The allocation decisions are returned to Jeannette so that she can develop an overall layout for the move. She normally does this in about four weeks. Dur- ing the first week of this phase, Jeannette sends each individual a printout of the floor space they will have and requests that they determine how the furniture is to be arranged. Individu- als inform her of any additional or replacement office furni- ture needs. They indicate where phone jacks and computer hookups should be. Each individual requests the packing sup- plies needed to pack up their office items. These requests are returned in three weeks.

When Jeannette receives the individual requests, she consoli- dates the requests to form lists of packing supplies and furniture. She orders the supplies from the university-approved supplier, and the supplies arrive in two weeks. She chooses among three approved office furniture suppliers and selects and orders the office furniture, which is scheduled to arrive in six weeks. When

the packing supplies arrive, Jeannette distributes them to each individual so that packing can be done. It normally takes a week to sort and distribute supplies. Individuals pack their office items and tag their office furniture that is to be moved. They are expected to complete their packing in two weeks.

After ordering the furniture, Jeannette makes arrangements for the movers to move the items, the telecommunications office to move or install telephones, and computer services to provide Internet hookups. The movers require three weeks notice but move the items in a single day. The phone installers demand two weeks notice but complete the work in one day. The computer services technicians require four weeks notice and complete the hookups in one day. The final activity is moving day. All three of these groups—the movers, the phone installers, and the com- puter technicians—are there on the same date to minimize office disruption and minimize the time the office is unable to provide customer service.

In past moves, Jeannette has had trouble making sure that everything flows smoothly. She believes that there must be a method available to help her manage this office move.

(a) Why are offi ce allocations so diffi cult? What factors must be considered when planning an offi ce layout?

(b) Off er Jeannette a method for monitoring the offi ce move. Explain why this method or approach would be reasonable.

(c) How long should it take from the day the decision is made to move until the move is completed? Employees only work Monday through Friday. All of Jeannette’s activity time estimates assume a fi ve-day work week.

(d) What are the critical activities for the timely completion of this offi ce move?

(e) What recommendations could you make to Jeannette to make this easier in the future?

CASE: Writing a Textbook Two professors of operations management, Susan and Chris, have decided to write a new textbook for use in the undergradu- ate introductory operations management course. As they discuss the project, they put together the following list of activities that must be done

1. Write a prospectus. (Th is entails defi ning what is unique about the textbook and how it compares with other texts currently on the market.) Time estimate: 4 weeks.

2. Discuss the book concept with several publishers, provide publishers with prospectus. Time estimate: 1 week. (All major publishers attend annual professional academic meetings, so you

can meet with all of them during that week.) Th is activity cannot be done before the prospectus is fi nished.

3. Conduct focus groups of faculty to test the concept of the text. Time estimate: 1 week. (Th is can be done at the same conference as activity 2.)

4. Select a publisher for the text from those expressing interest. Time estimate: 2 weeks. (Th is cannot be done until aft er completing activity 2.)

5. Update and submit prospectus and a sample chapter. Time estimate: 4 weeks. (Th is cannot be done until activities 3 and 4 are completed.)

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646 • CHAPTER 16 PROJECT MANAGEMENT

6. Negotiate contract with publisher. Time estimate: 3 weeks. (Th is cannot be done until activity 5 is completed.)

7. Susan begins writing Chapters 1 through 8, at a rate of one per month. Susan works sequentially, doing Chapter 1, then Chapter 2, and so on. Th is activity begins aft er the contract has been signed.

8. Chris begins writing Chapters 9 through 16, at a rate of one per month. Chris also works sequentially. Th is activity begins aft er the contract has been signed.

9. Chapters are sent out for review. Each chapter undergoes external review aft er the publisher receives the chapter. Each review takes 4 weeks. Reviews can begin as soon as the publisher begins receiving chapters from the authors.

10. Susan reviews comments from external reviewers and revises chapters. Time estimate is 2 weeks per chapter. Susan can begin these as soon as the fi rst reviews are received by the publisher. Susan will do these at the same time as she is writing new chapters.

11. Chris reviews comments from external reviewers and revises chapters. Time estimate is 2 weeks per chapter. Chris can begin these as soon as the fi rst reviews are received by the publisher. Chris will do these at the same time as he is writing new chapters.

12. Develop list of photo and artwork requirements. Time estimate: 24 weeks. Th is can begin as soon as the revised fi rst chapters are completed. Requirements for each chapter are needed.

13. Select persons to write instructor’s manual, solutions manual, test bank, and PowerPoint presentation. Time estimate: 4 weeks. Th is can be done aft er activity 6 is completed.

14. Determine what will be placed on the student CD and determine a responsible individual to handle this process. Time estimate: 2 weeks. Th is activity can be done aft er activity 6 is completed.

15. Write instructor’s manual. Time estimate: 12 weeks. Th is cannot be started until at least half of the revised chapters have been received by the publisher.

16. Write test bank. Time estimate: 12 weeks. Th is cannot be started until at least half of the revised chapters have been received by the publisher.

17. Write solutions manual. Time estimate: 12 weeks. Th is cannot be started until at least half of the revised chapters have been received by the publisher.

18. Write PowerPoint presentation. Time estimate: 12 weeks. Th is cannot be started until at least half of the revised chapters have been received by the publisher.

19. Design text cover. Time estimate: 8 weeks. Th is activity can be done aft er activity 6 is completed.

20. Design marketing campaign. Time estimate: 10 weeks. Th is activity can be done aft er activity 6 is completed.

21. Publisher produces galley proofs. Time estimate: 6 weeks following receipt of fi nal revised chapters.

22. Proofreading galley proofs. Time estimate: 4 weeks aft er completion of galley proofs.

23. Produce student CD. Time estimate: 4 weeks. Th is activity can be done aft er activities 15, 17, and 18 have been fi nished.

24. Publisher prints text. Time estimate: 16 weeks. Th is activity can be done aft er activities 20 and 21 have been completed.

These activities represent a portion of the tasks involved in writing a textbook. The listed activities deal only with the actual production of the text and the ancillary materials. We have not considered the marketing activities or the logistics associated with a new text.

Susan and Chris are interested in establishing a project schedule for the new textbook.

(a) Add any additional activities that you think are necessary to the project.

(b) Draw the network diagram for this project. (c) Determine how long it should take to complete the

project. (d) Consider what external issues might interfere with the

timely completion of the project.

www.wiley.com/college/reidINTERACTIVE CASE Virtual Company

On-line Case: Cruise International, Inc.

Assignment: Project Management at Cruise International, Inc. CII wants to launch a new onboard telemedicine procedure, and the project is time-critical. You need to provide project management assistance to Susan Petersen in CII’s marketing department as well as to Dr. Janelle Black at Corporate Medical Services. You need to make sure that all the necessary activities have been identified and that all precedence relationships are correct. After that, you need to evaluate the cost trade-offs asso- ciated with speeding up specific project activities. Completion of

this assignment will enable you to enhance your knowledge of the material covered in Chapter 16 of the text. It will also better prepare you for future assignments.

To access the Web site:

• Go to www.wiley.com/college/reid • Click Student Companion Site • Click Virtual Company/Cruise International, Inc. • Click Consulting Assignments • Click Project Management at CII

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ONLINE RESOURCES • 647

On-line Case: Project Management at Valley Memorial Hospital For today’s assignment you’re meeting with Jim

Hernandez from Valley Memorial Hospital’s Marketing Depart- ment. “The project I have in mind for you,” he says, “involves a new telemedicine procedure that we’re ready to introduce at the clinic on the Native American reservation about 85 miles from here. The procedure requires the acquisition, installation, and marketing of a new telemedicine communications system. Dr. Henry Suver of our Emergency Department is responsible for implementing the system, and he can tell you all about it. Let’s go over to Emergency, and I’ll introduce you to him.”

To complete this assignment, go to www.wiley.com/college/ reid to get more details. Assignment questions are given at the site.

To access the Web site:

Your Internet challenge is to help plan the 50th wedding anniversary party described at the beginning of this chapter. Use the list of activi- ties that were identified for the party preparation and search the Internet for information on each activity. Complete the following:

1. Find time estimates for each activity. You can add to the list but you must include all the activities.

2. Develop a project schedule for the party. You can use either PERT or CPM. You can use PERT with single time estimates, but you will not be able to calculate the probability of on-time completion.

3. Determine the critical path for the party. 4. Based on the party date, determine when activities on the

critical path must be started and fi nished.

INTERNET CHALLENGE Creating Memories

Online Resources Companion Website www.wiley.com/college/reid:

Thomson-Shore, Inc. • Find links for Additional Web Resources for this chapter

Project Management Institute, www.pmi.org The Project Management Site, www.projectmanagement.com/main.htm The PM Forum, www.pmforum.org

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648 • CHAPTER 16 PROJECT MANAGEMENT

Selected Bibliography Denzler, David R. “A Review of CA-Super Project,” APICS—

Th e Performance Advantage, September 1991, 40–41. Goldratt, Eliyahu M. Critical Chain. Great Barrington, Mass.:

Th e North River Express, 1997. Kerzner, Harold. Advanced Project Management: Best Prac-

tices on Implementation, Th ird Edition. New York: John Wiley & Sons, 2004.

Kerzner, Harold. Project Management: A Systems Approach to Planning, Scheduling, and Controlling, Sixth Edition. New York: John Wiley & Sons, 1999.

Kerzner, Harold. Project Management for Executives. New York: Van Nostrand Reinhold, 1984.

Mantel, Samuel J., Jr., Jack R. Meredith, Scott M. Shafer, and Margaret M. Sutton. Project Management in Practice, Th ird Edition. New York: John Wiley & Sons, 2007.

Meredith, Jack R., and Samuel J. Mantel, Jr. Project Manage- ment, A Managerial Approach, Sixth Edition. New York: John Wiley & Sons, 2005.

Moder, J.E., E.W. Davis, and C. Phillips. Project Management with CPM and PERT. New York: Van Nostrand Reinhold, 1983.

Smith-Daniels, Dwight E., and Nicholas J. Aquilano. “Con- strained Resource Project Scheduling.” Journal of Opera- tions Management, 4, 4, 1984, 369–387.

c16.indd 648c16.indd 648 10/08/12 11:53 AM10/08/12 11:53 AM

649

A APPENDIX

Chapter 2 1. Productivity of worker 1 5 1000 labels/30 minutes 5

33.3 labels per minute Productivity of worker 2 5 850 labels/20 minutes 5

42.5 labels per minute Worker 2 is more productive. 3. Productivity of older model machine 5 6 loaves/

5 hours 5 1.2 loaves per hour Productivity of newer model machine 5 4 loaves/

2 hours 5 2.0 loaves per hour Th e newer machine is more productive. 5. Productivity using former method 5 3 walls/

45 minutes 5 0.07 walls per minute Productivity using new method 5 2 walls/

20 minutes 5 0.10 walls per minute Th e new method is more productive. 7. a. Using only the nondefective production,

productivity went from (20,000 3 0.85) 5 17,000 units/month to (25,000 3 0.91) 5 22,750 units/month

b. Change in productivity 5 (22,750 2 17,000)/ 17,000 3 100% 5 33.8% increase

9. Work crew Productivity Anna, Sue, and Tim 10 homes/35 hours 5

0.29 homes per hour Jim, Jose, and Andy 15 homes/45 hours 5

0.33 homes per hour Dan, Wendy, and Carry 18 homes/56 hours 5

0.32 homes per hour Rosie, Chandra, and Seth 10 homes/30 hours 5

0.33 homes per hour Sherry, Vicky, and Roger 18 homes/42 hours 5

0.43 homes per hour Sherry, Vicky, and Roger are the most productive group.

Chapter 3 1. a. Total Cost 5 $40,000 1 $45Q

Total Revenue 5 $100Q

Solutions to Odd-Numbered Problems

Break-even Quantity: Q 5 Fixed Cost/(Selling Price 2 Variable Cost) 5 40,000/(100 2 45)

Q 5 727.3, so break even is exceeded at 728 units.

Solutions to selected problems are available on the Student Web site. To access these solutions, go to www.wiley. com/college/reid. Select the fourth edition, then Student Resources; and then Selected Solutions.

b. Contribution to Profi t 5 Total Revenue 2 Total Cost

5 SP(Q) 2 [FC 1 VC(Q)] 5 80(2000) 2 [40,000 1 45(2000)] 5 $30,000 c. Contribution to Profi t 5 Total Revenue 2

Total Cost 5 SP(Q) 2 [FC 1 VC(Q)] 5 100(1500) 2 [40,000 1 45(1500)] 5 $42,500 3. Break-even Quantity 5 Fixed Cost/(Selling Price 2

Variable Cost) 5 200/(1 2 0.20) 5 250 hot chocolates

5. a.

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650 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

Process A is better than outsourcing when demand is over 1000 chairs

Indiff erence Quantity between Process B and Outsourcing

Total Cost B 5 Total Cost O 5 [FC 1 VC(Q)]B 5 [FC 1 VC(Q )]O

Q 5 30,000/(50 2 15) 5 857.14, or 858 chairs. Process B is better than outsourcing when demand

is over 857 chairs. In summation:

b. Process 1 is lower in total cost when the quantities are fewer than approximately 1300 tables. Process 2 becomes lower in total cost with quantities higher than 1300 tables. Th e actual break even may be calculated to be 1334.

7. a.

Break Even

T ot

al D

ol la

Quantity

800,000

700,000

600,000

500,000

400,000 20,000 30,000 35,000 40,000

Total Revenue Total Cost

Break-even Quantity 5 Fixed Cost/(Selling Price 2 Variable Cost) 5 70,000/(20 2 18) 5 35,000 units of product

b. Increase Sales: 20,000(1.35) 5 27,000 units Contribution to Profi t 5 Total Revenue 2

Total Cost 5 SP(Q) 2 [FC 1 VC(Q)] 5 27,000(20) 2 [70,000 1 18(27,000)] 5 2$16,000

Reduce Variable Costs: 0.90(18) 5 $16.20 Contribution to Profi t 5 Total Revenue 2 Total Cost 5 SP(Q) 2 [FC 1 VC(Q)] 5 20,000(20) 2 [70,000 1 16.20(20,000)] 5 $6000

Reducing the variable costs contributes more to profi ts.

9. Break-even Quantity 5 Fixed Cost/(Selling Price 2 Variable Cost) 5 9000/(80 2 50) 5 300 units of service

11. Indiff erence Quantity between Process A and Process B

Total Cost A 5 Total Cost B 5 [FC 1 VC(Q)]A 5 [FC 1 VC(Q)]B

Th e equation rearranges to: Q 5 (FCB 2 FCA)/ (VCA 2 VCB) 5 (30,000 2 20,000)/(30 2 15) 5 666.7, or 667 chairs.

Process A is better than Process B when demand is below 667 chairs.

Indiff erence Quantity between Process A and Outsourcing

Total Cost A 5 Total Cost O 5 [FC 1 VC(Q)]A 5 [FC 1 VC(Q)]O

Q 5 20,000/(50 2 30) 5 1000 chairs

Demand Ranges

Cheapest Production

0 to 857 Outsource 858 and more Process B

Process A is never the best alternative. 13. It is known from Problem 12, that money is lost

if demand is under 8,000 units. Given they will proceed:

Break-even Between Old and New Equipment: 5 FCo 1 VCo(Q) 5 FCn 1 VCn(Q)

Q 5 2000 Use the old equipment if demand is at most 2000 units. Use the new equipment if demand is over 2000 units.

15. Arrive at Offi ce S Wait in Reception Area S Brought to Examination Room S Wait for Doctor S Meet with Doctor S Return to Reception Desk S Depart

Bottlenecks occur during the waiting periods. Parallel activities such as lab results or X-rays may be analyzed during some of the waiting times.

17. Utility 5 used/available 5 (28/30) 3 100% 5 93.3%

Chapter 4 1. Indiff erence Point: Total Cost of Insourcing 5

Total Cost of Outsourcing Total Cost 5 FC 1 VC(Q)

a. 300,000 1 1.5(Q) 5 120,000 1 2.25(Q) Q 5 240,000 units

b. Since the demand is expected to be over the indiff erence point, insourcing is cheaper. Th e total cost for insourcing would be $750,000 and the total cost for outsourcing would be $795,000. Th e actual diff erence may be computed to be $45,000.

3. a. Total Cost 5 FC 1 VC(Q) 5 125,000 1 0.90(160,000) 5 $269,000

b. Total Cost from Durable 5 170,000 1 0.65(160,000) 5 $274,000

c. Indiff erence point: Total Cost of Insourcing 5 Total Cost of Outsourcing 125,000 1 0.90(Q) 5 170,000 1 0.65(Q) Q 5 180,000 snow boards

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 651

standard deviation s can be estimated from the

4 samples using the equation:

where n 5 16 and X 5 5.97; therefore the estimate is 0.1138.

The standard deviation of the sampling distribution of the sample means is equal to 0.0569, which is estimated using

"n, where σ 5 0.1138 and n 5 4 (i.e., the number of observations in each sample). c. Center Line (CL) 5 x 5 5.97

UCL 5 x 1 3 s

"n 5 5.97 1 3 10.0569 2 5 6.14 LCL 5 x 2 3

"n 5 5.97 2 3 10.0569 2 5 5.80 3. x 5 19.8 ounces, R 5 0.4 ounces, A2 5 0.58 for n 5 5 CL 5 x 5 19.8 UCL 5 x 1 A2R 5 19.8 1 0.58 10.4 2 5 20.03 LCL 5 x 2 A2R 5 19.8 2 0.58 10.4 2 5 19.57 5. R 5 0.3 ounces, D4 5 2.11, D3 5 0, n 5 5 CL 5 R 5 0.3 ounces UCL 5 D4R 5 2.11 10.3 2 5 0.633 LCL 5 D3R 5 0 10.3 2 5 0 7. CL 5 P 5

6 100

5 0.06

UCL 5 0.06 1 3Å 0.06 11 2 0.06 2

20 5 0.22

LCL 5 0.06 2 3Å 0.06 11 2 0.06 2

20 5 0

(rounded to zero since the LCL value is negative)

9. CL 5 C 5 12 10

5 1.20

UCL 5 1.20 1 3"1.20 5 4.49 LCL 5 1.20 2 3"1.20 5 0 (rounded to zero since the LCL value is negative)

11. CP 1Machine A 2 5 USL 2 LSL6s 5 16.2 2 15.8

6 10.2 2 5 0.33

CP 1Machine B 2 5 USL 2 LSL6s 5 16.2 2 15.8

6 10.3 2 5 0.22

CP 1Machine C 2 5 USL 2 LSL6s 5 16.2 2 15.8

6 10.05 2 5 1.33

Machine C is the only capable machine since its CP value is greater than 1.

d. Annual demand must exceed 180,000 snow boards to justify outsourcing as the cheaper process. Th at increase is 12.5%, or 20,000 units over the current demand.

5. a. Total Cost Last Year 5 FC 1 VC(Q) 5 85,000 1 15(1450) 5 $106,750

b. Total Cost from VB 5 100,000 1 5(1450) 5 $107,250

c. Indiff erence point: Total Cost of Insourcing 5 Total Cost of Outsourcing 85,000 1 15(Q) 5 100,000 1 5(Q) Q 5 1500 orders

d. Yes, VB is a cheaper alternative whenever demand exceeds 1500 orders.

e. Additional factors that should be considered include the economic stability of VB, the ability of VB to manage quality, the ability of VB to “partner,” the ability of VB to deliver on time, and the impact of outsourcing on remaining employees.

Chapter 5 1. Reliability of CD player 5 (0.90)5 5 0.5905 3. Reliability of copier 5 (0.89)(0.95)(0.90)(0.90) 5

0.6849 5. Reliability of bank system 5 (0.90)(0.89)(0.95) 5

0.7610 7. Probability of each component working 5 P(A) 1

P(B) 2 P(A)P(B); or consider that either the original component or the backup must work; then one can compute 1-probability that both fail for that step and compute P(1)P(2).

Reliability of LCD projector 5 1 2 {(1 2 0.90) (1 2 0.80)} 5 0.98

9. Probability of developing vaccine 5 1 2 {(1 2 0.90)(1 2 0.85)(1 2 0.70)} 5 0.9955

Chapter 6 1. a. Mean of sample 1 5 (5.8 1 5.9 1 6.0 1 6.1)/4 5

5.95 Mean of sample 2 5 (6.2 1 6.0 1 5.9 1 5.9)/4 5

6.0 Mean of sample 3 5 (6.1 1 5.9 1 6.0 1 5.8)/4 5

5.95 Mean of sample 4 5 (6.0 1 5.9 1 5.8 1 6.1)/4 5

5.975 b. Th e mean of the sampling distribution is the

average of the sample means x 5 Mean 5 (5.95 1 6 1 5.95 1 5.975)/4 5 5.97

Th e standard deviation of the sampling

distribution is computed as s

"n. Th e population

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652 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

b. 13.

15.

Percentage of Items Defective (p)

0.05 0.1 0.15 0.2 0.25 0.9138 0.7361 0.5443 0.3758 0.244

0.3 0.35 0.4 0.45 0.5 0.1493 0.086 0.0464 0.0233 0.0107

17. Assuming the actual proportion of defect is 0.0022 (11 defects in the 5000) and using n 5 2000, if c 5 1, the consumer’s risk is 0.066. Using c 5 2, the consumer’s risk is 0.1848.

19. a. Ranges in samples 1 through 6 are 1.11, 0.34, 0.46, 0.67, 0.27, 0.24, respectively. R 5 0.515.

x-bar chart: CL 5 5.95 UCL 5 5.95 1 (0.58)(0.515) 5 6.25 LCL 5 5.95 2 (0.58)(0.515) 5 5.65 R-chart: CL 5 0.515 UCL 5 (2.11)(0.515) 5 1.087 LCL 5 0

Th e mean is in control, but the range is not. (Sample 1 is above the UCL.)

21. Cp 5 1.39, which appears the process is in control. However, upon calculating the Cpk;

Cpk 5 Min e 10.50 2 9.803 10.12 2 , 9.80 2 9.50

3 10.12 2 f 5 Min51.94, 0.8336 5 0.833

Th e process is not capable.

Chapter 7 1. D 5 30 packages per hour T 5 20 minutes 5 1/3 hour C 5 5 packages per container

N 5 DT C

5 130 2 11/3 2

5 5 2 kanbans

3. D 5 (300 wicks/day)(1 day/5 hours)(1 hour/ 60 minutes) 5 1 wick/minute Plus 10% safety stock 5 1.1

T 5 20 minutes C 5 20 per container

N 5 DT/C 5 (1.1)(20)/20 5 1.1 containers

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 653

c. N 5 16.0 215

30 5 3.0 containers

Both the number of containers and inventory level will increase.

Chapter 8 1. F4 5 (A1 1 A2 1 A3)/3 5 (200 1 350 1 287)/3 5

279.0 F5 5 (A2 1 A3 1 A4)/3 5 (350 1 287 1 300)/3 5

312.33 3. a. 3-Period Moving Average: FJune 5 (AMarch 1 AApril

1 AMay)/3 5 (38 1 39 1 43)/3 5 40 5-Period Moving Average: FJune 5 (AJanuary 1

AFebruary 1 AMarch 1 AApril 1 AMay)/5 5 (32 1 41 1 38 1 39 1 43)/5 5 38.6

b. Naïve: FJune 5 AMay 5 43 c. 3-Period Moving Average: FJuly 5 (AApril 1 AMay 1

AJune)/3 5 (39 1 43 1 41)/3 5 41 5-Period Moving Average: FJuly 5 (AFebruary 1

AMarch 1 AApril 1 AMay 1 AJune)/5 5 (41 1 38 1 39 1 43 1 41)/5 5 40.4

Naïve: FJuly 5 AJune 5 41

5. D 5 (500 pounds/day)(l day/8 hours) 5 62.5 pounds/hour

Plus 10% safety stock 5 6.25 T 5 1 hour C 5 10 per container N 5 (62.5 1 6.25)(1)/10 5 6.875 containers 7. D 5 (300 units/hour)(1 hour/60 minutes) 5

5 units/minute plus 10% safety stock 5 5.5 units/minute

T 5 15 minutes C 5 30 units/container

N 5 15.5 2 115 2

30 5 2.75 containers

9. a. N 5 15.5 2 115 2

15 5 5.5 containers

Number of containers increases. Inventory level is the same.

b. N 5 15.5 2 115 2

40 5 2.06

Number of containers decreases. Inventory level is the same.

d. Assuming the June value is to be used as known:

Month Actual

3-Period Moving Average

Absolute Error

5-Period Moving Average

Absolute Error Natïve

Absolute Error

January 32 February 41 32 9 March 38 41 3 April 39 37 2 38 1 May 43 39.33 3.67 39 4 June 41 40 1 38.6 2.4 43 2

MAD (3-periodmoving average) 5 S|Actual 2 Forecast|

5 12 1 3.67 1 1 2/3 5 2.22 MAD (5-periodmoving average) 5

S|Actual 2 Forecast| n

5 2.4/1 5 2.4

MAD (Naïve) 5 S|Actual 2 Forecast|

n 5 19 1 3 1 1 1 4 1 2 2/5 5 3.8

Th e 3-period moving average provides the best historical fi t using the MAD criterion and would be better to use.

Month Actual

3-Period Moving Average

Squared Error

5-Period Moving Average

Squared Error Natïve

Squared Error

January 32 February 41 32 81 March 38 41 9 April 39 37 4 38 1 May 43 39.33 13.47 39 16 June 41 40 1 38.6 5.76 43 4

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654 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

MSE (3-period moving average) 5

S 1Actual 2 Forecast 2 2 n

5 14 1 13.47 1 1 2/3 5 6.1567 MSE (5-periodmoving average) 5

S 1Actual 2 Forecast 22 n : 5.76

MSE (Naïve) 5 S 1Actual 2 Forecast 2 2

n 5 181 1 9 1 1 1 16 1 4 2/5 5 111/5 5 22.20

Th e 5-period moving average provides the best historical fi t using the MSE criterion, but it only measures one error term.

5. Forecasts using α 5 0.1:

Week Demand Exponential Smoothing

Absolute Error

1 330 2 350 330 20 3 320 332 12 4 370 330.8 39.2 5 368 334.72 33.28 6 343 338.048 4.952

MAD: 21.89

Forecasts using α 5 0.7:

Week Demand Exponential Smoothing

Absolute Error

1 330 2 350 330 20 3 320 344 24 4 370 327.2 42.8 5 368 357.16 10.84 6 343 364.748 21.748

MAD: 23.88

Week Demand 3-Period

Moving Average Absolute

Error 1 20 2 31 3 36 4 38 29 9 5 42 35 7 6 40 38.67 1.33

MAD: 5.77 MSE: 43.92

Week Demand Exponential Smoothing

Absolute Error

1 20 2 31 20 11 3 36 22.2 13.8

Week Demand Exponential Smoothing

Absolute Error

4 38 24.96 13.04 5 42 27.568 14.432 6 40 30.4544 9.545

MAD: 12.36 MSE: 156.17

Regression model: Demand 5 21 1 3.857 Time Time (X) Demand (Y) X2 XY

1 20 1 20 2 31 4 62 3 36 9 108 4 38 16 152 5 42 25 210 6 40 36 240

Total: 21 207 91 792

X 5 21/6 5 3.5 Y 5 207/6 5 34.5

b 5 SXY 2 nXY SX 2 2 nX 2

5 792 2 16 2 13.5 2 134.5 2

91 2 6 13.5 2 2 5 3.857 a 5 Y 2 bX 5 34.5 2 3.857 13.5 2 5 21

Week Demand Regression

Line Absolute

Error 1 20 24.857 4.857 2 31 28.714 2.286 3 36 32.571 3.429 4 38 36.428 1.572 5 42 40.285 1.715 6 40 44.142 4.142

MAD: 3.00 MSE: 10.52

Season Year 1 Year 2 Fall 200/710 = 0.282 230/810.25 = 0.284 Winter 1400/710 = 1.972 1600/810.25 = 1.975 Spring 520/710 = 0.732 580/810.25 = 0.716 Summer 720/710 = 1.014 831/810.25 = 1.026

Fall 0.283 Winter 1.973 Spring 0.724 Summer 1.020

Using α 5 0.1 provides a better historical fi t based on the MAD criterion.

Th e linear regression model provides the best historical fi t using the MAD and the MSE criteria.

Th e sales data have a reasonably good fi t to a linear model.

9. Step 1: Average demand for each season: Year 1: 2840/4 5 710 Year 2: 3241/4 5 810.25

Step 2: Seasonal index for each season:

Step 3: Average seasonal index for each season:

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 655

F10 5 3.011 1 0.489(10) 5 7.901 attendees (in thousands)

19. a. Forecast using a weighted moving average 5 230(0.1) 1 304(0.3) 1 415(0.6) 5 363.2

b. Forecast using the naïve approach 5 415 c. Absolute deviation using a weighted moving

average 5 �420 2 363.2� 5 56.8 Absolute deviation using naïve method 5 �420 2 415� 5 5 Th e naïve approach is better.

21. MAD1 5 (12 1 2 1 8 1 3 1 2 1 4)/6 5 31/6 5 5.17 MAD2 5 (3 1 1 1 2 1 2 1 4 1 3)/6 5 15/6 5 2.5 Forecast 2 provides a better historical fi t using the MAD criterion.

23. a. Sales 5 243.07 1 7.424 (month) b. Correlation coeffi cient is 0.97.

It indicates a strong, positive linear relationship. c. Forecast for month 25 5 243.07 1 (7.424)25

5 428.68 or 429 units. Please see www.wiley.com/college/reid for solution to Problem 25.

Chapter 9

1. UtilizationEffective 5 Actual Output

Effective Capacity 1100% 2

5 300 250

1100% 2 5 120% UtilizationDesign 5

Actual Output Design Capacity

1100% 2 5

300 400

1100% 2 5 75% Th e utilization rates show that the facility’s current

output is below its design capacity and considerably higher than its eff ective capacity. If the eff ective capacity is realistically set, it is expected that the facility will operate over that level for a short time.

3. a. Eff ective capacity 5 60 brownies Design capacity 5 100 brownies

b. UtilizationEffective 5 Actual Output

Effective Capacity 1100% 2

5 64 60

1100% 2 5 106.67% UtilizationDesign 5

Actual Output Design Capacity

1100% 2 5

64 100

1100% 2 5 64% Th e utilization rates show that the facility’s current output is far below its design capacity and higher than its eff ective capacity. Th e design capacity occurs only with extra help.

Step 4: Average demand per season 5 4000/4 5 1000.

Step 5: Multiply next year’s average seasonal demand by each seasonal index.

11. Sxy 5 1978 Sx 2 5 649 Sy 2 5 6142 Sx 5 55 Sy 5 162

a. Th e correlation coeffi cient is 0.9887. Th is high correlation indicated that there is a strong positive linear association between sales and training hours.

b. Using a regression model: Sales 5 216.6 1 4.455 training hours If training hours 5 18: Sales 5 216.6 1 4.455 3 18 5 63.59

(in thousands) 13. Sxy 5 6021 Sx 2 5 5625 Sy 2 5 7000

Sx 5 165 Sy 5 186 a. Resort attendance 5 a 1 b (average temperature)

Using regression analysis, the estimated model is: Resort attendance 5 58.65 2 0.65 (average temperature) Resort attendance forecast when the average temperature is 45 degrees: 5 58.65 2 0.65(45) 5 29.4 thousand attendees

b. Th e correlation coeffi cient is 20.97. Since this value is very close to negative 1, it indicates that the average temperature is a strong predictor of resort attendance. Note that since the sign is negative, it indicates that an inverse or a negative relationship exists between the two variables.

15. Period 6 Forecasts: Simple average: F6 5 348.8 3-Period moving average: F6 5 348.33 Exponential smoothing: Using the fi ft h-period forecast of 328, F6 5 336.40 Looking at the error only in period 6, MAD (simple average) 5 �368 2 348.8� 5 19.2 MAD (3-period moving average) 5 �368 2 348.33� 5

19.67 MAD (exponential smoothing) 5 �368 2 336.40� 5

31.60 Simple average provides the lowest MAD. 17. Sxy 5 208.2 Sx 2 5 204 Sy 2 5 227.61

Sx 5 36 Sy 5 41.7 Regression model: Clinic attendance 5 3.011 1 0.489 month F9 5 3.011 1 0.489(9) 5 7.412 attendees

(in thousands)

Season Forecast Fall 283 Winter 1973 Spring 724 Summer 120

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656 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

b. EVsmall expansion 5 35,000 10.70 2 1 15,000 10.30 2 5 $29,000

EVlarge expansion 5 40,000 10.70 2 1 20,000 10.30 2 5 $34,000

Company should opt for the large expansion.

11. a.

5. UtilizationEffective 5 Actual Output

Effective Capacity 1100% 2

5 500 600

1100% 2 5 83.33% UtilizationDesign 5

Actual Output Design Capacity

1100% 2 5

500 1000

1100% 2 5 50% Th e computed utilization rates show that the

facility’s current output is below its design and eff ective capacities. Th is illustrates that the manufacturer is not using capacity to its fullest extent and there is room for improvement.

7. a.

b. EVsmall expansion 5 50,000 10.40 2 1 70,000 10.60 2 5 $62,000

EVlarge expansion 5 60,000 10.40 2 1 100,000 10.60 2 5 $84,000

Company should opt for the large expansion. 9. a.

b. EVsmall expansion 5 120,000 10.50 2 1 80,000 10.50 2 5 $100,000

EVlarge expansion 5 200,000 10.50 2 1 100,000 10.50 2 5 $150,000

Company should opt for the large expansion.

13.

Location 2 is the preferred one.

15. Load-Distance ScoreJasper 5 �lijdij 5 (30)(15) 1 (6)(10) 1 (10.5)(12) 1 (4.5)(8) 5 672

Load-Distance ScoreLongboat 5 �lijdij 5 (12)(15) 1 (12)(10) 1 (30)(12) 1 (24)(8) 5 852

Warehouse should be located in Jasper.

17. Total CostA 5 70,000 1 1(2000) 5 $72,000 Total CostB 5 34,000 1 5(2000) 5 $44,000 Total CostC 5 20,000 1 8(2000) 5 $36,000 Total CostD 5 50,000 1 4(2000) 5 $58,000 Location C is best.

Weighted Score

Location 1 Location 2 50 20 80 40 60 150 50 30 90 150

Total: 330 390

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 657

Chapter 10 1. Current Layout Proposed Layout

Departments Number

of Trips (l) Distance

(d) Load–Distance

Score (ld) Distance

(d) Load–Distance

Score (ld) AB 15 1 15 2 30 AC 45 2 90 1 45 AD 25 1 25 2 50 AE 10 2 20 1 10 AF 50 3 150 3 150 BC 30 1 30 1 30 BD 16 2 32 2 32 BE 25 1 25 1 25 BF 25 2 50 1 25 CD 34 3 102 3 102 CE 15 2 30 2 30 CF 20 1 20 2 40 DE 40 1 40 1 40 DF 10 2 20 1 10 EF 20 1 20 2 40

Total: 669 659

Current Layout Proposed Layout

Departments Number

of Trips (l) Distance

(d) Load–Distance

Score (ld) Distance

(d) Load–Distance

Score (ld)

AB 5 1 5 1 5 AC 20 2 40 2 40 AD 5 1 5 1 5 AF 8 2 16 2 16 BD 30 2 60 2 60 BE 10 2 20 2 20 BF 10 3 30 1 10 CD 20 3 60 1 20 CE 15 1 15 1 15 CF 5 2 10 2 10 EF 17 1 17 1 17

Total: 278 218

Th e proposed layout is the preferred one due to a slightly lower ld score. 3.

Current Layout Proposed Layout

Departments Number

of Trips (l) Distance

(d) Load–Distance

Score (ld) Distance

(d) Load–Distance

Score (ld)

AB 30 3 90 3 90 AD 34 1 34 1 34 AE 50 1 50 2 100 AF 25 2 50 1 25 BD 55 2 110 2 110 BE 10 2 20 1 10 BF 10 1 10 2 20 CE 15 1 15 2 30 CF 5 2 10 3 15 EF 30 1 30 1 30

Total: 419 464

Proposed location is better based on the ld score. 5.

Th e current location is better based on the ld score.

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658 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

7. To assign departments to specifi c storage areas, we progressively assign departments with the highest number of trips closest to the dock. Using this logic, we develop the following block plan:

2 4 5

Dock

6 1 3

9. It is reasonable to assume the layout can be improved. A starting point would have D directly next to F, B, and A.

Current Layout Current Layout

Departments Number of Trips (l)

Distance (d)

Load–Distance Score (ld) Departments

Number of Trips (l)

Distance (d)

Load–Distance Score (ld)

AB 6 1 6 BF 0 2 0 AC 12 2 24 CD 5 3 15 AD 18 1 18 CE 0 2 0 AE 1 2 2 CF 0 1 0 AF 1 3 3 DE 7 1 7 BC 4 1 4 DF 19 2 38 BD 19 2 38 EF 0 1 0 BE 3 1 3

Total: 158

11. Th e following departments have the highest number of trips between them and should be located close to each other.

B and D, which have 19 trips between them D and F, which have 19 trips between them A and D, which have 18 trips between them A and C, which have 12 trips between them

Proposed layout: B D A E F C

A slightly better layout exists. Proposed Layout

Departments Distance

(d) Load–Distance

Score (ld)

AB 2 12 AC 1 12 AD 1 18 AE 3 3 AF 2 2 BC 3 12 BD 1 19 BE 1 3 BF 2 0 CD 2 10 CE 2 0 CF 1 0 DE 2 14 DF 1 19 EF 1 0

Total: 124

13. a.

b. C 5 (3600 seconds/hour)/(50 units/hour) 5 72 seconds/unit

c. TM 5 St C

5 225 72

5 3.125 or 4 stations

d. Station Task 1 A 2 B, E 3 D 4 C 5 F

e. Efficiency 5 St NC

1100% 2 5 22515 2 172 2 1100% 2 5 62.5%

Balance delay (%) 5 37.5%

15. a. Task A is the bottleneck. b. C 5 (3600 seconds/hour)/(50 units/hour)

5 72 seconds/unit

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 659

Effi ciency 5 175/240 5 72.9% Th is is one possible solution. c. Maximum output would use the bottleneck

task: (3600 seconds/hour)/(43 seconds/unit) 5

83.7 units per hour Minimum output uses the total time in one workstation: 3600/175 5 20.57 units per hour

Chapter 11 1. With z 5 2.17 and e 5 0.05, the number of

observations needed for each work element is:

Work element 1: n $ c 2.17 0.05

3 0.20 1.10

d 2 5 63

Work element 2: n $ c 2.17 0.05

3 0.10 0.80

d 2 5 30

Work element 3: n $ c 2.17 0.05

3 0.15 0.90

d 2 5 53

Work element 4: n $ c 2.17 0.05

3 0.10 1.00

d 2 5 19 Sample size needed is 63.

3. Work element 1: n $ c 1.96 0.05

3 0.60 2.40

d 2 5 97

Work element 2: n $ c 1.96 0.05

3 0.20 1.50

d 2 5 28

Work element 3: n $ c 1.96 0.05

3 1.10 3.85

d 2 5 126

Work element 4: n $ c 1.96 0.05

3 0/85 2.55

d 2 5 171

Work element 5: n $ c 1.96 0.05

3 0.40 1.60

d 2 5 97

Work element 6: n $ c 1.96 0.05

3 0.50 2.50

d 2 5 62 Sample size needed is 171.

c. Work- station

Task Selected

Task Time

Idle Time

1 A 55 17

2 B 30 42 C 22 20 F 15 5

3 D 35 37

4 E 50 22 G 5 17 H 10 7

d. TM 5 St C

5 222 72

5 3.08 or 4 stations

Same number of stations as the theoretical minimum stations was used.

e. Efficiency 5 St NC

1100% 2 5 22214 2 172 2 1100% 2 5 77.08% Balance delay (%) 5 22.92%

17. a. C 5 13600 seconds/hour 2/ 130 units/hour 2 5 120 seconds/unit

b. TM 5 St C

5 270 120

5 2.25 or 3 stations

c. Work- station

Task Selected

1 A B D F C

2 E G H I

3 J K

Workstation 1 has no idle time, while work- stations 2 and 3 have 20 seconds and 70 seconds, respectively.

d. Efficiency 5 St NC

1100% 2 5

270 13 2 1120 2 1100% 2 5 75%

Balance delay 5 25% 19. a. C 5 480 doors/8 hours 5 60 doors/hour;

(3600 sec/hour/60 doors/hour) 5 60 sec/door. b. TM 5 175/60 5 2.9 or 3 workstations

Work- station

Task Selected

1 A

2 B

3 C, D, E

4 F, G, H

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660 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

c. 60 minutes/7.037 5 8.526 units per hour d. 8.526 3 0.90 5 7.6734 units per hour 15. a.

5. Work Element Normal Time (NT) 1 1.14 2 0.85 3 0.88 4 0.99

7. From Problem 6, the sum of the standard times is 4.439 minutes (1.311 1 0.9775 1 1.012 1 1.1385).

(1 unit/4.44 minutes) 3 (60 minutes/hour) 3 (8 hours/day) 5 108.1 units/day

9. From Problem 8, the sum of the standard times is 4.541 minutes (1.341 1 1.000 1 1.035 1 1.165).

(1 unit/4.541 minutes) 3 60 minutes/hour) 3 (8 hours/day) 5 105.7 units/day

11. a. Element Mean Observed Time 1 2.194 2 1.265 3 1.771 4 2.608 5 1.576

b. Normal time 5 (mean observed time) (performance rating factor)(frequency) Normal timeelement 1 5 12.194 2 10.90 2 11 2

5 1.975 minutes Normal timeelement 2 5 11.265 2 10.80 2 11 2

5 1.012 minutes Normal timeelement 3 5 11.771 2 11.10 2 11 2

5 1.948 minutes Normal timeelement 4 5 12.608 2 11.05 2 11 2

5 2.738 minutes Normal timeelement 5 5 11.576 2 10.95 2 11 2

5 1.497 minutes c. STelement 1 5 11.975 2 11.20 2 5 2.370 minutes

STelement 2 5 11.012 2 11.20 2 5 1.214 minutes STelement 3 5 11.948 2 11.20 2 5 2.338 minutes STelement 4 5 12.738 2 11.20 2 5 3.286 minutes STelement 5 5 11.497 2 11.20 2 5 1.796 minutes

Standard time for Job 5 (2.37 1 1.214 1 2.338 1 3.286 1 1.796) 5 11.004 minutes

d. (60 minutes/hour)(1 unit/11.004 minutes) 5 5.45 units/hour

e. (5.45 units/hour)(0.90) 5 4.9 units/hour 13. a. b.

Element Mean Time (minutes) Normal Time Standard Time

1 0.96 0.9216 1.084 2 1.45 1.595 1.876 3 3.33 1.110 1.306 4 1.24 1.116 1.313 5 1.18 1.239 1.458

Sum = 7.037 minutes

Element Mean Observed Time (minutes)

1 0.582 2 1.515 3 0.759 4 0.319 5 2.1

b. Normal timeelement 1 5 10.582 2 10.95 2 11 2 5 0.553 minutes

Normal timeelement 2 5 11.515 2 10.90 2 10.25 2 5 0.341 minutes

Normal timeelement 3 5 10.759 2 11 2 11 2 5 0.759 minutes

Normal timeelement 4 5 10.319 2 11.10 2 11 2 5 0.351 minutes

Normal timeelement 5 5 12.10 2 10.90 2 10.20 2 5 0.378 minutes

c. STelement 1 5 10.553 2 11.15 2 5 0.636 minutes STelement 2 5 10.341 2 11.15 2 5 0.392 minutes STelement 3 5 10.759 2 11.15 2 5 0.873 minutes STelement 4 5 10.351 2 11.15 2 5 0.404 minutes STelement 5 5 10.378 2 11.15 2 5 0.435 minutes

Standard time for job 5 (0.636 1 0.392 1 0.873 1 0.404 1 0.435) 5 2.740 minutes

d. 60/2.740 5 21.898 units per hour e. 21.898 3 1.1 5 24.088

17. a. n 5 a1.96 0.05

b2 10.2 2 11 2 0.2 2 5 246 observations

b. n 5 a1.96 0.05

b210.15 2 11 2 0.15 2 5 196 observations c. Th e largest observed frequency was 6

(with patient)

n 5 a1.96 0.05

b2 10.3 2 11 2 0.3 2 5 323 observations 19. T 5 40 minutes, n 5 6, total learning curve

coeffi cient 5 4.299. Total time 5 40(4.299) 5 171.96 minutes, or 2.87 hours.

Chapter 12

1. ATIregular service 5 13 2 12400 2

365 5 19.7 units

ATIpremium service 5 11 2 12400 2

365 5 6.6 units

ATIpublic carrier 5 17 2 12400 2

365 5 46.0 units

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 661

e. Annual inventory holding cost

5 Q 2

H 5 1208.2 2 13 2 5 $624.50

f. Total annual cost 5 $624.50 1 $624 5 $1249.50

g. Th e economic order quantity provides the lowest total annual costs.

13. EOQ at price of $18/bag 5 Å (2)(1560)(10)

(18)(0.25) 5

83.3 bags (infeasible quantity)

EOQ at price of $19/bag 5 Å (2)(1560)(10)

(19)(0.25) 5

81.0 bags (feasible quantity) Total cost at feasible EOQ amount 5 (81/2)(19 3

0.25) 1 (1560/81)(10) 1 (19)(1560) 5 $30024.97 Total cost at Q 5 100: (100/2)(18 3 0.25) 1

(1560/100)(10) 1 (18)(1560) 5 $28461 Optimal ordering policy is to order 100 bags at a

time. 15. EOQ at price break of $12/bag

5 Å (2)(1560)(10) (12.00)(0.25)

5 101.98

(not a feasible quantity at the given price break) Total cost at Q 5 1560 bags: 5 (1560/2)(12 3 0.25) 1 (1560/1560)(10)

1 (12)(1560) 5 $21,070 Greens should take advantage of this off er.

17. a. EOQ 5 Å 2 DS

5 Å (2)(150 3 52)(20)

3 5 322.5 units

b. Total annual costs 5 Q 2

H 1 D Q

5 1322.5 2

2 13 2 1 1150 3 52 2

322.5 120 2 5 $967.50

c. Lost annual savings 5 $1215 2 $967.5 5 $247.50

3. a. Inventory Turnover 5 $3,000,000 $250,000

5 12

b. Weeks of Supply 5 $250,000

$3,000,000 52

5 4.33 weeks

c. Days of Supply 5 $250,000

$3,000,000 260

5 21.7 days

5. Inventory Turnover 5 $3,600,000/$325,000 5 11.1 inventory turns

7. Annual holding cost 5 (0.225)($3,400,00) 5 $765,000

Annual holding cost increases $68,000 or 9.76%

9. a. Average inventory level 5 Q 2

5 1300

5 650 units b. Number of orders placed per year 1N 2

5 D Q

5 5200 1300

5 4 orders c. Annual inventory holding cost

5 Q 2

H 5 1650 2 13 2 5 $1950

d. Total annual ordering cost 5 (D/Q)(S) 5 (4)(50) 5 $200

e. Total annual cost 5 $1950 1 $200 5 $2150 (not counting the cost of the heat sinks, which is $62,400)

11. a. EOQ 5 Å 2DS H

5 Å (2)(5200)(50)

3 5 416.33 units

b. Average inventory 5 416.33

2 5 208.2 units

c. Number of orders placed per year 5 D Q

5 5200

416.33 5 12.5

d. Annual ordering cost 5 (12.5)(50) 5 $625

Demand (000’s) Expected Profit (000’s)75 80 85 90 95

75 596.25 596.25 596.25 596.25 596.25 596.250 Order 80 573.75 636 636 636 636 632.888

Amount 85 551.25 613.5 675.75 675.75 675.75 657.075 (000’s) 90 528.75 591 653.25 715.5 715.5 665.700

95 506.25 568.5 630.75 693.00 755.25 655.650

19.

Sue should order 90,000 calendars to maximize expected profi ts.

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662 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

b. EPQ 5

ã 2 3 18000 3 300

3a12 18000 24000

b 5 3794.73, or 3795

Imax 5 3795(1 2 .75) 5 948.75, or 949 Holding Cost 5 (949/2) 3 3 5 $1423.50 Setup Cost 5 18,000 3 300/3795 5 $1422.92 Total Cost 5 $2846.42 c. Penalty Cost5 $3097.50 2 $2846.42 5 $251.08 27. a. Th e z for 94% is found to be approximately 1.55

(the area above it is the risk of stockouts). Safety stock 5 1.55 [s"RP 1 L 4

5 21.48, or 22 units TI 5 d(RP 1 L) 1 SS 5 48(3) 1 22 5 166 units b. Z becomes 2.05, safety stock becomes 28.4 or 29,

and the target inventory becomes 173. c. Safety stock 5 1.55 [s"RP 1 L] 5 24.8, or 25 Target inventory becomes 192 1 25 5 217 units 29. a. Current Total Cost at Q 5 5000 5 4.8(60) 1

2500(0.08) 5 $488 per month EOQ 5 6000 per month: Total Cost 5 4(60) 1

3000(0.08) 5 $480 Th e penalty cost of the existing system is $8

per month. b. With Q 5 3000, holding costs are reduced to

$120. To keep the total cost at $480, the order costs would be $360. Solving 8(S) 5 $360, order costs would have to be reduced to $45 to stay at the same expense as the EOQ.

c. If S 5 $30, the EOQ becomes 4242.6 jars.

21. a. Item

Annual Demand

Ordering Cost

Holding Cost (%)

Unit Price EOQ

101 500 $ 10.00 20% $ 0.50 316.228 102 1500 $ 10.00 30% $ 0.20 707.1068 103 5000 $ 25.00 30% $ 1.00 912.8709 104 250 $ 15.00 25% $ 4.50 81.6497 105 1500 $ 35.00 35% $ 1.20 500 201 10000 $ 25.00 15% $ 0.75 2108.185 202 1000 $ 10.00 20% $ 1.35 272.1655 203 1500 $ 20.00 25% $ 0.20 1095.445 204 500 $ 40.00 25% $ 0.80 447.2136 205 100 $ 10.00 15% $ 2.50 73.0397

Rounded EOQ results.

Item 101 102 103 104 105 201 202 203 204 205 EOQ 316 707 913 82 500 2108 272 1095 447 73 Product Cost 158 141.48 913 369 600 1581 367.20 219 357.60 183.5

b. Using the EOQ number times the unit cost, the company’s maximum inventory investment throughout the year occurs for item 201 with an inventory level of 2108 units and value of $1581.14.

c. Item EOQ Average Inventory 101 316 158 102 707 353.5 103 913 456.5 104 82 41 105 500 250 201 2108 1054 202 272 136 203 1095 547.5 204 447 223.5 205 73 36.5

Average inventory in dollars 5 $2444.35 23. a. Imax 5 Q(1 2 d/p) 5 1,000,000(1 2 100,000/

250,000) 5 600,000 pounds

b. Total Cost 5 15,000,000 2 1200 2

1,000,000 1

1600,000 2 10.55/50 2

2 5 $4300

25. a. Setup Cost: Number of setups 5 Yearly demand/ 2500 bags per batch

5 (75,000 3 12/50)/2500 5 7.2 batches per year 7.2 3 $300 5 $2160

Imax 5 2500(1 2 0.75) 5 625 Average inventory 5 312.5 units Total cost of existing policy is $3097.50

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 663

b. (5600 units 3 6 hours each)/160 hours per employee 5 210 employees

Chapter 13

1. a. Total aggregate production 34,000 units Production rate per period 5 (34000 2 400)/6 5

5600 units

c. Period 1 2 3 4 5 6 Demand 6000 4800 7840 5200 6560 3600 Production 5600 5600 5600 5600 5600 5600 Ending Inventory 400 800 0 Back order 0 1440 1040 2000 0

d. Back-order costs: 4480 units 3 $20 5 $89,600 Holding costs: 800 units 3 $10 5 $8000 Regular-time labor cost: $2,016,000 Total cost 5 $2,113,600

e. Th e costs appear very high, considering the customer is served very poorly. However, the level aggregate plan is good for operations and human resources.

c. Using the hours required and the hours available from parts (a) and (b):

Period 1 2 3 4 5 6 Total

Employees Needed

210 180 294 195 246 135 1260

5. a. Th e amount of production hours needed each period:

Period 1 2 3 4 5 6

Hours Needed

33,600 28,800 47,040 31,200 39,360 21,600

b. Amount of regular-time production using a workforce of 210 employees: (160 hours/ employee/period)(210 employees) 5 33,600 hours/period

Period 1 2 3 4 5 6 Total

Overtime 0 0 13,440 0 5,760 0 19,200 Hours Needed Undertime 0 4800 2400 12,000 19,200

d. Period 1 2 3 4 5 6

Overtime Hours per Employee 64 27.42

Th e overtime available hours are exceeded by 100%. e. Overtime cost: 19,200 3 $15 5 $288,000

Regular labor cost: $2,016,000 Total cost: $2,304,000

f. Th e customer demand is being met, but costs are $81,000 over the costs of the hiring and fi ring plan in Problem 3. Once again, this chase plan is disruptive to operations and to human resources. Certainly, if this plan is implemented, arrangements must be made to increase available overtime.

7. a. Given the production rate, that is, 1/6 unit per hour (from 6 hours per unit), the production from

the maximum of 32 hours of overtime for each of the 210 employees is computed to be 1120 units.

Period 1 2 3 4 5 6 Total

Demand 6000 4800 7840 5200 6560 3600 34,000 Production 5600 5600 5600 5600 5600 3600 Overtime Units 0 0 1120 0 880 0 2000 Ending Inventory 400 0 800 0 80 0 0 880 Back Orders 0 0 320 0 0 0 320

5 a 6 i 5 1demandi 5

b. Overtime cost: 12,000 hours 3 $15 5 $180,000 Back-order costs: 320 3 $20 5 $6400 Holding costs: 880 3 $10 5 $8800 Regular labor cost: $2,016,000 Total cost 5 $2,211,200

c. Th is hybrid aggregate plan provides some back orders, but only in period 3. Overtime costs are still a signifi cant amount at $180,000, but this expense is far less than using only overtime to meet demand. Th is plan fi ts the existing available overtime hours and should therefore not be too disruptive to operations.

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664 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

c. Th is plan provides a uniform availability of weekly hours. However, in weeks 1, 3, 4, and 6

Week 1 2 3 4 5 6 Total

Number of Clients 48 36 50 40 38 48 260 Hours Required 576 432 600 480 456 576 Hours Available 480 480 480 480 480 480 Unmet Hours 96 0 120 0 0 96 Number of Temporary Employees 3 0 3 0 0 3 9

13. a. Hours Available 12 3 40 5 480

Period 1 2 3 4 5 6 Total

Number of Clients 48 40.67 50 46.67 41.34 48 274.68 Hours Required 576 488.04 600 560.04 496.08 576 3296.16 Number of Regular Time Hours Available 520 520 520 520 520 520 3120 Number of Hours Backordered 56 0 80 40.04 0 56 232.04 Number of Calls Backordered 4.67 6.67 3.34 4.67 19.34

9. a.

Note: Backordered calls are added to next period’s demand (4.67 1 36 5 40.67) b.

Regular Time Labor Cost $78,000 Hiring Cost $2,000 Backorder Cost $9,670 Total Cost $89,670

there are unmet client hours. If the clients are unwilling to wait for their services, this could result in potential revenue loss to the fi rm. Also, there are excess service hours in weeks 2 and 5, which cannot be inventoried.

11. a. Period 1 2 3 4 5 6 Total

Number of Clients 48 36 50 40 38 48 260 Hours Required 576 432 600 480 456 576 3120 Number of Employees Required 14.4 10.8 15 12 11.4 14.4 Actual Number of Employees 15 11 15 12 12 15 80 Number of Hires (beginning workforce 5 12) 3 4 3 10 Number of Fires 4 3 7

b. Regular Time Labor Cost $80,000 Hiring Cost $20,000 Firing Cost $8,400 Total Cost $108,400

c. Customer demand is met at a cost $108,400. Th e hiring and fi ring would be diffi cult for operations and aff ect employee morale.

b. 9 employees 5 360 hours @$40/hour 5 $14,400

12 permanent employees 5 480 hours 3 6 3 $25 5 $72,000

Total Cost 5 $86,400

c. Customer demand is met at a lower price than hiring and fi ring permanent employees. Th is plan is very good for the permanent employees, with minimal operational issues if the temporary employees are readily available.

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 665

Chapter 14 1.

Note: numbers inside parentheses represent usage per parent while numbers outside the boxes indicate lead time in weeks. 3. Product Q R S T X Y V Z

Gross Requirements

100 200 100 300 400 200 300 900

5. From Problem 4: Th e cumulative lead time is 7 weeks (VTQ).

7. From Problem 6: Th e cumulative lead time, the longest planned length of time to complete the end product, is 11 weeks (i.e., path J, E, A).

9. Component Gross Requirement

A 2500 B 4750 C 2000 D 6750 E 4250 F 16,000 G 8500 H 15,250 I 8500 J 3250 K 16,250 L 6000 M 47,750 N 95,750 O 30,500 P 15,750 Q 31,500

11. Product: AB500

Week 1 2 3 4 5 6 7 8 9 10

Gross Requirements 150 250 150 250 150 250 150 250 Scheduled Receipts 150 250 150 250 150 250 150 250 Projected Available: 0 0 0 0 0 0 0 0 0 0 0 Planned Orders 150 250 150 250 150 250 150 250

13. From the inventory records in Problem 12: Average Inventory for AB501 5 1100/10 5

110 units Average Inventory for AB511 5 1350/10 5

135 units (Assuming the remaining inventory

aft er the gross requirements are satisfi ed, is used in the next production cycle.)

Average Inventory for AB521 5 4350/10 5 435 units

15. Item: AB500 1 2 3 4 5 6 7 8 9 10 Gross Requirements 150 250 150 250 150 250 150 250 Scheduled Receipts 200 200 200 200 200 200 200 200 Projected Available 0 0 50 0 50 0 50 0 50 0 Planned Orders 200 200 200 200 200 200 200 200

Item: AB500 1 2 3 4 5 6 7 8 9 10

Gross Requirements 150 250 150 250 150 250 150 250 Scheduled Receipts 550 650 400 Projected Available: 0 0 0 400 150 0 400 250 0 250 0 Planned Orders 550 650 400

17.

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666 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

Total time: 108 hours Capacity needed: 108 hours 21. From the capacity requirement of 108 hours in

Problem 19 (and detail from Problem 20): Available weekly capacity 5 (3 machines)(10 hours/day)

19. (5 days/week)(0.90 utilization)(0.90 effi ciency) 5 121.5 hours

Th is new policy provides adequate capacity.

23.

Total time: 156 hours Capacity needed: 156 hours

25. Th e 150 Gamma Blasters need a planned order for period 7. Th e 100 Gamma Disasters need a planned order in period 4.

Chapter 15 1. a. Work Center 3

Component A:

b. If actual output is consistently below planned output, Work Center 3 may not have enough capacity to meet the planned input for Work Center 4. However, the backlog is consistently decreasing in Work Center 3, as their actual output exceeds the actual inputs in periods 4 through 7.

3. a. Work Center 2

b. Th e work center nearly has the capacity to meet planned input if backlogs did not exist. Actual output is consistently less than the planned output, especially in periods 5, 6, and 7, thus indicating that the work center is not producing with the effi ciency expected. Work Center 1 has to be studied regarding its output.

Orders Setup + Run Time (hours)

LL110 2.0 + 1.2(10) = 14 LL118 4.0 + 0.4(25) = 14 LL131 6.0 + 0.6(100) = 66 LL140 4.0 + 0.2(50) = 14

Orders Setup + Run Time (hours)

MM078 34 MM118 31 MM213 33 MM240 58

Period 1 2 3 4 5 6 7 8

Gross requirements 300 300 Scheduled Receipts 200 200 Projected Available 250 250 250 250 150 150 150 50 50 Planned Orders 200

Component B: Period 1 2 3 4 5 6 7 8

Gross requirements 200 450 Scheduled Receipts 300 600 Projected Available 25 25 25 25 125 125 125 275 275 Planned Orders 300 600

4 5 6 7 8

Planned Input 40 50 50 60 60 Actual Input 45 45 45 55 60 Deviation 5 25 25 25 0 Cumulative Deviation 5 0 25 210 210

4 5 6 7 8

Planned Output 70 70 70 70 70 Actual Output 60 60 60 60 60 Deviation 210 210 210 210 210 Cumulative Deviation 210 220 230 240 250 Backlog 75 hours 60 45 30 25 25

4 5 6 7 8

Planned Input 40 50 50 60 60 Actual Input 25 35 35 40 40 Deviation 215 215 215 220 220 Cumulative Deviation 215 230 245 265 285

4 5 6 7 8

Planned Output 40 50 50 60 60 Actual Output 45 45 45 55 60 Deviation 5 25 25 25 0 Cumulative Deviation 5 0 25 210 210 Backlog 75 hours 55 45 35 20 0

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 667

5. a. Job sequence: B, F, E, C, A, D b.

B done at end of day 5

F done at end of day 11

E done at end of day 18

C done at end of day 26

A done at end of day 35

D done at end of day 45

Makespan 5 45 days Mean job fl ow time 5 (5 1 11 1 18 1 26 1 35 1

45)/6 5 23.33 days Average number of jobs in the system 5 140/45 5

3.11 jobs

Job Completion Date Due Date Lateness (days) Tardiness

B 5 10 25 0 F 11 15 24 0 E 18 26 28 0 C 26 24 2 2 A 35 30 5 5 D 45 40 5 5

Total = 25 Total = 12

Mean job lateness 5 25/6 5 20.83 days Mean job tardiness 5 12/6 5 2.0 days Maximum tardiness 5 5 days

7. a. D done at end of day 10

A done at end of day 19

C done at end of day 27

E done at end of day 34

F done at end of day 40

B done at end of day 45

b. Makespan 5 45 days Mean job fl ow time 5 (10 1 19 1 27 1 34 1 40 1 45)/6 5 29.17 days Average number of jobs in the system 5 175/45 5

3.89 jobs

Job Completion Date Due Date Lateness (days) Tardiness

D 10 40 230 0 A 19 30 211 0 C 27 24 3 3 E 34 26 8 8 F 40 15 25 25 B 45 10 35 35

Total 5 30 Total 5 71

Mean job lateness 5 30/6 5 5 days Mean job tardiness 5 71/6 5 11.83 days Maximum tardiness 5 35 days

9. a.

Job Job Time Remaining Job

Time at Other W/C Due Date Slack Times Remaining Number of

Operations at Other W/C S/RO

A 9 10 30 11 3 2.75 B 5 2 10 3 1 1.5 C 8 8 24 8 2 2.67 D 10 18 40 12 3 3 E 7 12 26 7 1 3.5 F 6 6 15 3 2 1

F done at end of day 6

B done at end of day 11

C done at end of day 19

A done at end of day 28

D done at end of day 38

E done at end of day 45

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668 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

b. Makespan 5 45 days Mean job fl ow time 5 (6 1 11 1 19 1 28 1

38 1 45)/6 5 24.5 days Average number of jobs in the system 5 147/45 5

3.27 jobs

Job Completion Date Due Date Lateness (days) Tardiness

F 6 15 29 0 B 11 10 1 1 C 19 24 25 0 A 28 30 22 0 D 38 40 22 0 E 45 26 19 19

Total = 2 Total = 20

Mean job lateness 5 2/6 5 0.33 days Mean job tardiness 5 20/6 5 3.33 days Maximum tardiness 5 19 days

11. a.

b. Makespan 5 27 hours Mean job fl ow time 5 (5 1 10 1 13 1 19 1

27)/5 5 14.8 hours

Average number of jobs in the system 5 74/27 5 2.74 jobs

b. Makespan 5 29 days Mean job fl ow time 5 (5 1 8 1 13 1 21 1 26 1

29)/6 5 17 days

Average number of jobs in the system 5 102/29 5 3.52 jobs

13. a.

15. a.

b. Makespan 5 28 days Mean job fl ow time 5 (4 1 10 1 18 1 23 1 26 1

28)/6 5 18.17 days Average number of jobs in the system 5 109/28 5

3.89 jobs

Johnson’s rule has produced the minimum makespan. However, this has the highest mean job fl ow time and the highest average number of jobs in the system.

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APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS • 669

17. Employee Monday Tuesday Wednesday Thursday Friday Saturday Sunday 1 OFF OFF X X X X X 2 X X OFF OFF X X X 3 OFF OFF X X X X X 4 X X OFF OFF X X X 5 OFF OFF X X X X X 6 X X OFF OFF X X X

Various answers may result given the ties that occur at the start of the exercise.

19.

Various answers may result from the ties that occur aft er the fi rst four employees are assigned days off .

Employee Monday Tuesday Wednesday Thursday Friday Saturday Sunday

1 OFF OFF X X X X X 2 OFF OFF X X X X X 3 X X OFF OFF X X X 4 OFF OFF X X X X X 5 X X OFF OFF X X X 6 X X X X OFF OFF X

21. Employee Monday Tuesday Wednesday Thursday Friday Saturday Sunday 1 X X X X X OFF OFF 2 X X X X X OFF OFF 3 OFF OFF X X X X X 4 X X OFF OFF X X X 5 X X X X OFF OFF X

Various answers may result from the ties that occur aft er the fi rst two employees are assigned days off .

23. Employee Monday Tuesday Wednesday Thursday Friday Saturday Sunday 1 OFF OFF X X X X X 2 X X OFF OFF X X X 3 X X X X OFF OFF X 4 X X X X X OFF OFF 5 OFF OFF X X X X X 6 X X OFF OFF X X X

Various answers may result given the ties that occur at the start of the exercise. Th is winter schedule has one fewer employee, with 2 days out of 7 having 5 employees at work.

25. a.

b. Makespan 5 29 days c. Mean job fl ow time 5 (7 1 9 1 15 1 18 1 25 1

29)/6 5 17.17 days

d. Average number of jobs in the system 5 103/29 5 3.55 jobs

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670 • APPENDIX A SOLUTIONS TO ODD-NUMBERED PROBLEMS

Chapter 16 1.

3. a.

b. Critical path 5 BDF c. Path completion times: A C E 5 15 weeks B D E 5 14 weeks B D F 5 17 weeks Expected project length 5 17 weeks 5.

Activity A B C D E F G H I J

Expected Time 6 5 7.33 7.67 10.17 4 5.83 6.33 8 3

7. a. Path completion times: ABDFHJ 5 32 weeks ABDGIJ 5 35.5 weeks ACEFHJ 5 36.83 weeks ACEGIJ 5 40.33 Project completion time 5 40.33 weeks b. Critical activities: A C E G I J 9. a. Path completion times: ABEGH 5 19 weeks ACEGH 5 21 weeks ADFGH 5 19 weeks ACFGH 5 22 weeks Project completion time 5 22 weeks b. Critical activities: ACFGH 11. Reduce G by 1 week, then A by 1 week, then C by

2 weeks, and then F by 1 week. Total additional cost is $2700.

13. a.

Activity A B C D E F G H

Expected Time 10 10 5 8 7.33 7.17 8 3

c. ABDFH 5 38.17 ABDGH 5 39 ACEFH 5 32.5 ACEGH 5 33.33 Expected project completion time 5 39 weeks 15. a.

Activity A B C D E F G H

Variance 0.44 4 0.11 1.44 1.78 0.25 1.78 0

c. Path completion times: AE 5 24 weeks ABDFGHI 5 30 weeks ABDFGHJKL 5 44.17 CDFGHI 5 24 weeks CDFGHJKL 5 38.17 weeks Critical path: ABDFGHJKL

d. Estimated project completion time 5 44.17 weeks 17. a.

Activity A B C D E F G H I J K L

Expected Time 8.67 3.33 6 7 15.33 3 4 2 2 2 5 9.17

b. ADFIJKL 5 42 weeks BEFIJKL 5 43 weeks Normal time is 43 weeks (critical path). c. Th e cost of the normal times for the project is

$141,700. 19. a. Reduce K, I, and B by one week. b. Total additional cost is $7000 5 ($1000 1

$3000 1 $3000).

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671

B APPENDIX

This table gives the area under the standardized normal curve from 0 to z, as shown by the shaded portion of the following figure.

Examples: If z is the standard normal random variable, then Prob (0 # z # 1.32) 5 0.4066 Prob (z $ 1.32) 5 0.5000 2 0.4066 5 0.0934 Prob (z # 1.32) 5 Prob (z # 0) 1 Prob (0 # z # 1.32) 5 0.5000 1 0.4066 5 0.9066 Prob (z # 21.32) 5 Prob (z $ 1.32) 5 0.0934 (by symmetry)

The Standard Normal Distribution

Source: Adapted from Robert Markland, Topics in Management Science, 3rd ed. New York: John Wiley & Sons, 1989.

bapp02.indd 671bapp02.indd 671 27/07/12 12:52 AM27/07/12 12:52 AM

672

C A

PP EN

D IX

P 1 x#

c2 5 a

x 5

0 an xb

px 1 1 2

p 2n2

P- Ch

ar t

bapp03.indd 672bapp03.indd 672 27/07/12 12:53 AM27/07/12 12:53 AM

673

bapp03.indd 673bapp03.indd 673 27/07/12 12:53 AM27/07/12 12:53 AM

674

bapp03.indd 674bapp03.indd 674 27/07/12 12:53 AM27/07/12 12:53 AM

675

Consumer Reports Online, 114 Costco Wholesale Corporation, 139 Country Comfort Furniture, 101 Covisint, 104, 113, 143, 148 CVS, 44, 142–43 Cybex International, 545

D DaimlerChrysler, 104, 113 Dell Computer Corporation, 4, 17, 24, 28, 33–34, 36, 37, 39,

42, 106, 115, 273, 316, 361, 540 Deloitte &Touche, 545 Delta Air Lines, 33, 111 Department of Health and Human Services (HHS), 434–35 DHL, 111 Dole Food Company, 82 Donatos, 58 Dorothy Lane Market, 91 DoubleClick Inc., 114 Durham International Manufacturing Company

(DIMCO), 157

E Eaton, 135–36 eBay, 19, 115 Eddie Bauer, 34 e-Greenbiz.com, 147 Embassy Suites, 88 Empire West Inc., 40 EPA, 189 Ernst & Young, 419 E*Trade Financial Corporation, 114 ExxonMobil, 542, 545

F Federated Department Stores, 339 FedEx, 2, 31, 39, 40, 43, 59, 87, 111, 115, 116, 139, 142, 183,

247, 272 Fingerhut, 138 Florida Power & Light, 184 Ford Motor Company, 14, 61, 68, 104, 113, 162, 247, 248, 269 ForestExpress.com, 148 Fortune magazine, 373, 413, 440 Frantz Title Company, 76–77 Fruit of the Loom, Inc., 34

G Gamma Toy Company, 482 Gap, The, 3, 34, 339 Gartner Group, 546 Gateway, Inc., 115 General Electric (GE), 2, 19, 28, 164, 165, 222, 230,

248, 269, 281

A Aberdeen Group, 546 Ace Hardware, 315 AES Corporation, 413, 450 Akito’s Flowers, 593, 594, 595, 596 Alcoa Inc., 28, 269 Allied Signal, 222 Alza Corporation, 58 Amazon.com, 4, 5, 19, 114, 115 American Express, 61, 173, 222 Andersen Consulting, 419 Ann Taylor, 339 Antonio’s Pizzeria, 73–74, 75, 81 App Cloud, 104 Apple Computer, 2, 456 Aracruz Celulose, 50 Arapahoe County government, 544 AT&T, 168, 254, 418, 419 Aussie Steak Grill, 88 Avendra.com, 148 Avis, 111

B Babcock & Wilcox Company, 78 Barnes & Noble, 4, 5, 34, 115 Baxter Healthcare Corporation, 113 Bell Labs, 167 Benchmarking Partners, 545 Ben & Jerry’s, 281 Bike World, 142 BMG Music Service, 115 BMW, 39, 61, 159 Boeing, 55, 248, 254 Boseman Oil and Petroleum (BOP), 54 Boston Consulting Group (BCG), 116, 414 Boy Scouts, 32 Burger King, 81

C Canadian Chamber of Commerce, 117 Canyon Ranch Spa, 60, 87 Cardinal Logistics Management, 121 Carnival Cruises, 332 Celebrity Cruises, 111 Cisco Systems, 419, 464–65 Coca-Cola, 274, 509 Coca-Cola Midi (CCM), 509 Cocoa Fizz, 200, 202, 205, 209, 210, 216, 218–19 Colgate-Palmolive (C-P), 543 Columbia House, 115 Compaq, 34, 37, 42, 106 Compuware Company, 104, 113

N A M E I N D E X

bindex01.indd 675bindex01.indd 675 16/08/12 3:18 PM16/08/12 3:18 PM

676 • NAME INDEX

M Major League Baseball (MLB), 24 Marriott, 111, 231, 332 Martin Marietta, 169 Master Product Company, 541 McDonald’s, 39, 81, 88, 90, 121, 247, 271, 272 Meijer, 36, 177 Men’s Wearhouse, 413 Mercedes-Benz, 163 Mercer Management Consultants, 61 Merrill Lynch, 543 Merry Maids, 272 Microsoft, 311, 544 Milliken & Company, 28 Minitab, 311 Motorola, 33, 34, 164–65, 183, 187, 221–22, 230, 233, 243 MyPoints.com, 114

N Naked Juice, 94 National Resource Defense Council (NRDC), 24, 398 NCSS, 311 New York University, 85, 167 Nielsen Media Research, 83 Nike, 93 Nissan/Renault, 104, 113, 268, 269 Nokia, 33 Nordstrom, Inc., 91, 231

O Occupational Safety and Health Administration, 422 OfficeMax, Inc., 116 Olympic Games, 628 1-800-FLOWERS.com, 595 Oracle, 281, 546, 564 Orbitz, 114 Organizing Committee for the Olympic Games, 628 Orient Express, 111 Osram GmbH, 135 Otis Elevator, 268 Owens Corning, 541

P Pace Productivity, 433 Palm, Inc., 58 Palm Garmin iQue, 83 Palm Pilot, 470 Peat Marwick, 419 Peoplesoft, 546 Pepperidge Farm, 68 PepsiCo, 34, 93–94, 316 Pizza Hut, 58, 247 Procter & Gamble, 2, 16, 50, 281, 339 ProcureSteel.com, 147 PRTM, 148 PSA Peugeot Citroën, 104

General Motors (GM), 5–6, 9, 15, 59, 104, 113 Georgia-Pacific, 133–34 Gillette, 142–43, 284 Gold’s Gym, 60 Google, 118, 413–14 Gourmet Wafers, 7–9, 10, 21 G’s Naturally Fresh, 506 GTE, 135

H Half.com, 115 Henkel KgaA, 315 Herman Miller Company, 119 Hertz, 111 Hewlett-Packard (HP), 34, 59, 248, 456, 473–74 Hillenbrand Industries, 134 Holiday Inn, 88 Holiday Valley resort, 303 Home Depot, 21, 117, 139, 284, 452 Honda Motor Company, 15, 166, 248, 268 Hyatt, 111

I IBM, 9, 32–33, 34, 37, 42, 61, 68, 183, 248, 269, 418, 543 Inbound Logistics, 121 Infor Global Solutions, 546 Institute for Supply Management (ISM), 136 Intel Corporation, 28, 199, 233, 283, 316 ITT, 169 i2 Technologies, 545

J JC Penney’s, 115, 339 Jeep, 163, 395 Jenkins Manufacturing, 95–96 Jimmy Dean, 59 John Wiley & Sons, Inc., 455–56

K Kenworth Trucks, 481 Kmart Corporation, 28 Kozmo.com, 4, 5 KPMG, 419 Kroger Company, The, 177, 452

L Lands’ End, 9, 61, 115–16, 173, 281 La Petite Academy Day Care Center, 59 LensCrafters, 39, 272 Limited, The, 339 Linen Technology Tracking, 460 L.L.Bean, 115 Lockheed Martin, 222, 248 Logistics-Offshore Inc., 54 Lowe’s, 21, 33 Lufthansa Cargo, 55

bindex01.indd 676bindex01.indd 676 16/08/12 3:18 PM16/08/12 3:18 PM

NAME INDEX • 677

Toyota Motor Corporation, 2, 15, 17, 61, 162, 166, 230, 247, 248, 269, 274, 279, 378

Transport Intelligence, 121 Travelocity.com, 114

U United Airlines, 55, 111 United Cargo, 55 United Parcel Service (UPS), 5, 39, 42, 111, 115, 116, 134, 142,

412, 515 U.S. Army, 418 U.S. Bureau of Customs and Border Protection (CBP), 117 U.S. Commerce Department, 116, 117 U.S. Defense Department, 19 U.S. Department of Defense, 83 U.S. Food and Drug Administration (FDA), 143 U.S. Green Building Council, 189 U.S. Postal Service, 6–7

V Victoria’s Secret, 34, 452 Virginia Department of Corrections, 546 Volvo, 39

W Wal-Mart, 2, 17, 21, 50, 82, 83, 93, 117, 119, 139, 141, 284, 315,

347, 376, 452 Walt Disney Company, The, 174, 272, 332 Wegmans Food Markets, 414 Wendy’s, 6, 88, 247, 272 Western Electric, 14 Whirlpool Corporation, 135–36, 261 Winnebago, 378

X Xerox, 34, 183, 261

Y Yahoo!, 28, 114 Yellow Corporation, 2

Z Zara, 248

R Red Robin Gourmet Burgers, 281 Ritz-Carlton Hotel Company, 28, 183, 231 Rolex, 163 Rolls-Royce, 141 Ryder Integrated Logistics, 33, 234, 261, 279

S Salazar’s Beauty Salon, 59 SAP AG, 546 SAS Institute, 311, 312, 373, 413, 414, 440 Saturn Corporation, 28, 254 S.C. Johnson, 120 SCO Group, 417 Sears, Roebuck and Company, 33, 34, 41, 116, 281,

339, 452 Seven Springs resort, 303 Shore Trips, 111 Singapore Airlines, 30 Smarterwork.com, 147 Snowshoe resort, 303 Sony, 58, 167 Southwest Airlines, 2, 30, 38, 49, 413, 450 Southwest University, 645 Spagio’s, 58 Sports Feet Manufacturing, 64 Sprint Corporation, 28 SPSS, 311, 312 Staples, Inc., 116 Starbucks, 2, 59, 68, 439 Steinway & Sons, 198 Sun Microsystems, 419 Sweetheart, 133–34 Sylvania, 135

T Taco Bell, 81 Target, 117, 261, 315 Taylor Companies, The, 274 Texas Instruments (TI), 9–10, 222, 254 3M, 183, 269 Total Logistics Control (TLC), 36

bindex01.indd 677bindex01.indd 677 16/08/12 3:18 PM16/08/12 3:18 PM

S U B J E C T I N D E X

678

marketing plan in, 507 master production schedule in, 508–9 in OM, 524 role of, 507–9 in supply chain, 525 in sustainability, 526

ALDEP, 384–85 Allowance factor, 427–28 Alpha (a) risk, 203 Alternative workplace, 417–19 Anticipation inventory, 453, 455 Application service provider (ASP), 543 Appointments, scheduling, 597 Appraisal costs, 165 ARPANET, 18–19 Assemble-to-order strategy, 73, 74, 81 Assignable causes of variation, 200 Associative models. See Causal models Atmosphere in service organizations, 164 Attribute, defined, 203 Attributes, control charts for, 204, 210–15

c-charts, 211, 214–15 p-charts, 211–13

Automated guided vehicle (AGV), 84 Automated inventory tracking system, 464–65 Automated material handling, 84 Automated order entry system, 113 Automated storage and retrieval systems (AS/RSs), 84 Automation, 83–85

automated material handling, 84 defined, 83 flexible manufacturing system, 84–85 robotics, 85

Average number of jobs in the system, 587 Average outgoing quality (AOQ), 227–28

B Backlogs, scheduling, 597 Back orders, 461, 512 Backward integration, 128 Backward scheduling, 540, 582 Balance delay, 394–95 Basic partnership, 131 Batch process, 70, 71, 72 Behavioral feasibility, 413–14 Benchmarking, 173

defined, 61 in idea development, 61

Best operating level, 337 Best practices

in ERP, 545, 546 in ethical standards for GPOs, 137

A ABC classification, 461–64

ABC inventory analysis in, procedure for, 463 defined, 461 example of, 462–63 inventory control using, 463–64 Pareto’s law in, 461

Acceptable quality level (AQL), 224, 225, 253 Acceptance sampling, 223–28

average outgoing quality in, 227–28 defined, 199 operating characteristic curves in, 224–27 sampling plans in, 223–24

Accountable care organization (ACO), 434–35 Accounting

in aggregate planning, 525 in inventory management, 491 in JIT systems, 273 in OM, 23 in project management, 636 in resource planning, 563 scheduling in, 601 in SCM, 149 in TQM, 187 in work system design, 438

Accuracy in forecasting, measuring. See Forecast accuracy measures

Action bucket, 559 Action notices, 559 Activity-on-nodes (AON), 618 Advance Shipment Notification (ASN), 140, 141 Advertising revenue model, 114 Affiliate revenue model, 114 Aggregate plan, 509–11

chase aggregate plan, 510–11 for companies with tangible products, 517–20 defined, 507 developing, 515–17 hybrid aggregate plan, 511 level aggregate plan, 509–10 for service companies with nontangible

products, 520–24 types, 509–11

Aggregate planning, 505–38 across the organization, 525 aggregate plan in, 507 capacity-based options, 512–14 current situation and, evaluating, 514–15 demand-based options, 512, 514 engineering plan in, 508 financial plan in, 508 hierarchy, 508

bindex02.indd 678bindex02.indd 678 28/07/12 12:02 AM28/07/12 12:02 AM

SUBJECT INDEX • 679

C Capacity

alternatives, 340–41 available capacity, 335–36 best operating level in, 337 capacity utilization, 336 considerations, 336–39 cushion, 340 defined, 333 design capacity, 335 diseconomies of scale in, 337–38 economies of scale in, 337 effective capacity, 336 examples of, 335 focused factories in, 338–39 measuring, 334–36 requirements, 339, 340 subcontractor networks in, 339 utilization, 336

Capacity-based options, 512–14 Capacity-constrained resource, 593 Capacity planning, 333–44

across the organization, 360–61 decisions, 339–41 defined, 333 importance of, 333–34 in OM, 360 in supply chain, 361 in sustainability, 361

Capacity requirements planning (CRP), 560–62

in calculating workloads, 561–62 CPOPF, 560 defined, 548 role of, 560–62

Capital costs, 459–60 Carbon footprint analysis, 119–20 Causal models, 303–7

correlation coefficient in, 306 defined, 286 linear regression in, 303–5 multiple regression in, 307 in quantitative forecasting, 286–87 summarized, 287 vs. time series models, 287

Cause-and-effect diagrams, 169, 174, 175, 176 C-chart, 211, 214–15 Cell layouts, 396–97 Cell manufacturing, 261 Center of gravity approach, 355–56 Chase aggregate plan, 510–11 Checklists, 175 Closed-loop MRP, 547 Collaborative Planning, Forecasting, and Replenishment

(CPFR), 314–15

in inventory deliveries, 526 in OM, 4, 20

Beta probability distribution, 622 Bill of material (BOM)

defined, 548 indented, 552 in MRP inputs, 552–55

Black belts, 164–65, 222 Block plan

decision-support tools in, 384–85 defined, 380 develop, 382–85 load-distance model in, 382–83 rectilinear distance in, 383 trial and error in, 382–84

Bottlenecks defined, 72, 579 in design processes, 72 scheduling, 593–95

Bottleneck task, 392 Bottom-round management, 266 Boulder, Fred, 64 Break-bulk activity, 118 Break-even analysis

defined, 62, 356 in evaluating location alternatives, 356–59 in product screening, 62–64

Break-even point, 62 Broad view

of JIT, 247–48 of the organization, 249

B2B e-commerce (business-to-business), 19, 113–14

B2C e-commerce (business-to-consumer), 114–15 B2C e-commerce (business-to-customers), 19 Buffer stock. See Safety stock Build-to-order strategy, 73, 74, 81 Bullwhip effect, 108–10

causes of, 109–10 counteracting, 110 defined, 108 supply chain information flow and, 108–9

Business strategy combining strategies, 36–37 core competencies, 35–36 defined, 30 developing, 32–37 environmental scanning, 33–35 functional strategy and, relationship between, 31 mission, 32–33

Business-to-business e-commerce (B2B), 19, 113–14

Business-to-consumer e-commerce (B2C), 114–15

Business-to-customers e-commerce (B2C), 19

bindex02.indd 679bindex02.indd 679 28/07/12 12:02 AM28/07/12 12:02 AM

680 • SUBJECT INDEX

Core competencies defined, 35 in developing business strategy, 35–36 as source of impact in partnering, 134

Correlation coefficient, 306 Cost-efficient operations, 456–57 Costing, in work measurement, 422–23 Costs

in competitive priorities, 37–38 in ERP, 546–47 of nonconformance, 170 of quality, 165–66 in quantity discount model, 478 setup, 252, 457 in SQC, 299 total, 63

Courtesy in service organizations, 164 Cradle to Cradle certification, 189 Cradle-to-cradle protocol, 119 CRAFT, 384–85 Crashing projects, 631–33 Critical chain approach, 634–35 Critical path, 618 Critical path method (CPM), 616, 617 Critical ratio (CR), 584 Crosby, Philip B., 168, 169, 263 Crossdocking, 139–41

defined, 139 implementation, 140–41 innovations, 140 types of, 139

Cross-functional decision making, 20–21 Cross-functional worker skills, 265 C2C e-commerce (customer-to-customer), 19 Customer

attention, in service design, 91 external and internal, in TQM, 173 focus in TQM, 171 in idea development, 60–61 involvement, in service design, 90–91 in location decisions, 346–47

Customer contact in classifying services, 88–89 in service design, 87–88

Customer-defined quality, 162 Customer relationship management (CRM), 20 Customer service, 455–56

defined, 455 idle time due to material and component

shortages, 456 percentage of dollar volume shipped on schedule, 456 percentage of line items shipped on schedule, 456 percentage of orders shipped on schedule, 455–56

Customer-to-customer e-commerce (C2C), 19 Cycle counting, 465

Collaborative product commerce (CPC) software, 85

Common causes of variation, 200 Compensation systems, 433–35

group incentive plans, 434–35 individual incentive plans, 435 output-based, 433–34 time-based, 433

Competitiveness, productivity and, 47–48 Competitive priorities, 37–40

cost, 37–38 defined, 37 design of operations functions and, 37, 38 flexibility, 40 in operations strategy, production requirements translated

into, 42–43 in product design and process selection, 79 quality, 39 time, 39–40

Competitors, in idea development, 61 Completion

dates, estimating probability of, 629–31 time, reducing, 631–33

Components, 452, 553 Computer age, 12, 15 Computer-aided design (CAD), 43, 86 Computer-aided engineering (CAE), 86 Computer-aided manufacturing (CAM), 43, 86 Computer-integrated manufacturing (CIM),

86–87 Conception, in project life cycle, 615, 617 Concurrent engineering, 67–68 Conformance

in manufacturing, 164 to specifications, 163

Consistency in service organizations, 164 Consumer’s risk, 225 Continuous improvement (kaizen), 172, 250 Continuous improvement in TQM, 171–73

benchmarking in, 1723 defined, 171 kaizen, 172 PDSA cycle in, 172

Continuous processes, 70, 71, 72 Continuous review systems (PRS), 487–88

defined, 463–64 vs. periodic review systems, 487–88

Contracting metric, 146 Control charts, 175, 176, 202–4

for attributes, 204, 210–15 defined, 202 developing, 202–3 types of, 203–4 for variables, 203, 204–10

bindex02.indd 680bindex02.indd 680 28/07/12 12:02 AM28/07/12 12:02 AM

SUBJECT INDEX • 681

Design simplification, 65 Design standardization, 65 Desk sharing, 418 Deterministic time estimate, 620–22 Digital Millennium Copyright Act (DMCA), 118 Dilbert’s approach to project management, 628 Diseconomies of scale, 337–38 Distribution. See also Supply chain distribution

beta probability, 622–23 of data, 201 inventory, 453 management, 108 skewed, 201, 202

Distribution warehouse, 137–38 Distributor crossdocking, 139 Distributors in supply chain

e-distributors, 147 external, 108, 112 greening requirements for, 119

Division of labor, 13 DMAIC plan, 222 Double sampling, 224 Due date, 581 Duke, Michael, 21 Duplication as source of impact in partnering, 133 Durability in manufacturing, 164 Duration of the change, 515

E Earliest due date (EDD), 584 Earliest finish time (EF), 621–22 Earliest start time (ES), 621–22 Early supplier involvement (ESI), 62, 135–36 Ease of distribution, 143 Ease of manufacturing, 143 E-commerce, 112–16

business-to-business, 19, 113–14 business-to-consumer, 19, 114–15 business-to-customers, 19 consumer expectations/competition resulting

from, 115–16 customer-to-customer, 19 defined, 112 distribution warehousing in, 137–38 government regulation and, 118 historical development of, 12, 18–19

Economic feasibility, 413 Economic order quantity (EOQ) model, 468–73

assumptions made by, 469–70 calculating, 471–72 defined, 468 example of, 472 inventory policy costs calculated with, 470–71 non-EOQ order quantity and, results when using, 473 optimal EOQ quantity and, 481

Cycles, 289 Cycle stock, 453, 455 Cycle time, 391–93

computing, 391–92 defined, 391 equation, 391 minimum, vs. maximum output and, 392–93 minimum vs. maximum, 393 reductions in, 272

D Data, distribution of, 201 Decisions, organizational

competitive priorities, 79 differences in, 77 facility layout, 79–80 product and service strategy, 81 product design, 78–79 vertical integration, 81–82

Decision-support tools for block plan development, 382, 384–85 for evaluating location alternatives, 349–60

Decision trees, 341–44 defined, 342 drawing, 343, 344 expected value in, 343, 344 overview of, 341–42 solving, 344 using, 342

Defining beliefs of JIT, 248 Delayed product differentiation. See Postponement Delayed services, scheduling, 597 Dell, Michael, 40 Delphi method, 285–86 Demand-based options, 512, 514 Demand forecast updating, 109 Demand types

dependent, 548–50 gross requirements in, 549–50 independent, 548 time-phased, 549

Deming, W. Edwards, 15, 167–68 Deming Prize, 168, 184 Deming wheel, 172 Dependent demand, 548–50 Dependent variable, 303 Descriptive statistics, 198, 200–201

distribution of data in, 201 mean in, 200–201 range in, 201 standard deviation in, 201, 202

Design capacity, 335 Design for manufacture (DFM), 65–66 Design for the environment (DFE), 143 Design of experiment, 170

bindex02.indd 681bindex02.indd 681 28/07/12 12:02 AM28/07/12 12:02 AM

682 • SUBJECT INDEX

Environmental factors in development of OM, 12, 14, 18 sustainability and, 18, 24 in TQM, 264–65 in work system design, 421–22

Environmental scanning, 33–35 defined, 33 opportunities and threats identified by, 33 trends in, 33–35

Environmental systems standards, 185 EOQ model. See Economic order quantity

(EOQ) model EPQ model. See Economic production quantity

(EPQ) model E-purchasing, 124–27, 147 ERP. See Enterprise resource planning (ERP) Ethics in SCM, 136–37 European Economic Community (EEC), 16 European Union (EU), 18 Exchanges, 147 Execution, in project life cycle, 616, 617 Executive opinion, 285 Expanded partnership, 131 Expected value (EV), 343, 344 Expedite, 559 Explosion process, 555–58 Exponential smoothing model, 295–97

selecting α in, 295–97 starting forecasting process with, 296–97 trend-adjusted, 297–98

External distributors in supply chain, 108, 112 External failure costs, 165–66 External setup, 259 External supplies in supply chain, 106–7 Extranets, 115

F Facilities, as core competency, 35 Facility layout, 372–410

across the organization, 397–98 group technology layouts, 396–97 for intermittent operations, 79–80 in JIT manufacturing, 261–62 layout planning in, 373–74 in OM, 397 for repetitive operations, 80 in supply chain, 398 in sustainability, 398 types (See Layout types)

Facility loading, uniform, 271 Factor rating, 349–51 Factory, concept of, 12–13 Feasibility analysis or study, 615, 617 Features in manufacturing, 164 Feigenbaum, Armand V., 168, 169

Economic production quantity (EPQ) model, 473–77

defined, 473 example of, 476 maximum inventory level calculated with, 474 perpetual inventory record in, 477 ratios calculated with, 475 smaller order quantities and, 482–83

Economic trends, 34 Economies of scale, 337 E-distributors, 147 Effective capacity, 336 Efficiency

defined, 5 operational, vs. strategy, 32 in process performance metrics, 76 in product layout design, 394

80–20 rule, 177 Electronic commerce. See E-commerce Electronic data interchange (EDI), 110, 113, 143 Electronic fund transfers (EFT), 146 Electronic request for quote (eRFQ), 114 Electronic storefronts, 113 Elemental time data, 429 Elemental time database, 429 E-manufacturing, 86–87

computer-aided design, 86 computer-integrated manufacturing, 86–87 defined, 86

Employees boredom, eliminating, 416 empowerment in TQM, 173–74 production, in JIT, 265–66 scheduling, 598–99

Employees on call, scheduling, 598 End item, 552 Energy Star certification, 189 Engineering

computer-aided, 86 concurrent, 67–68 plan, 508 in product design and process selection, 93 reverse, 61–62

Ensemble forecasting, 314 Enterprise resource planning (ERP), 541–47

benefits of, 545–46 best practices in, 545, 546 cost of, 546–47 defined, 20, 82, 541 evolution of, 542–44 first-generation, 542 modules, 542 second-generation, 543–44 technology and, 43–44 in TI’s OM, 9–10

bindex02.indd 682bindex02.indd 682 28/07/12 12:02 AM28/07/12 12:02 AM

SUBJECT INDEX • 683

in OM, 315 principles of, 281–82 process, steps in, 282–83 in supply chain, 316 in sustainability, 316–17

Forecasting methods combining, 313–14 focus forecasting, 313 qualitative, 283–86 quantitative, 283, 284, 286–87 types of, 283–87

Forecasting models causal, 303–7 selecting, factors influencing, 310–11 time series, 288–303

Forecasting software, 311–13 selecting, guidelines for, 312–13 specialty forecasting packages, 312 spreadsheets, 311 statistical packages, 311

Forward integration, 128 Forward scheduling, 581–82 Free-rider, 435 Frequency of occurrence, 427 Friendliness in service organizations, 164 From–to matrix, 380–81 Functional strategies, business strategy and, 31

G Gain sharing, 434–35 Galbraith, John Kenneth, 11 Gantt, Henry, 579 Gantt chart

defined, 579 load charts, 579 progress charts, 580 in scheduling operations, 579–80

General warehouse, 137 Globalization

advantages of, 348 defined, 347 disadvantages of, 348 locating globally, issues in, 348–49 in location analysis, 347–49 in SCM, 116–18

Global marketplace, 12, 18 Global Organic Textile Standards (GOTS), 189 Global positioning systems (GPS), 83 Global priority rule, 584 Goldratt, Eli, 593, 635 Goods and services consistency, 39 Green belts, 222 Green operations. See Sustainability Green SCM, 118–21 Green Seal certification, 189

Finance in capacity planning and facility location, 360 facility layouts in, 398 forecasting in, 315 in JIT systems, 273 in OM, 3–4, 22 in operations strategy, 49 in product design and process selection, 92 in product screening, 62 in TQM, 187

Financial know-how, as core competency, 35 Financial metric, 146 Financial plan, 508 Finished goods, 453 Finished goods inventory, 512 Finite loading, 581 Firing, hiring and, 513–14 First come, first served (FCFS), 584 Fishbone diagrams. See Cause-and-effect diagrams Fitness for use, 163 Fixed costs, 62–63 Fixed-order quantity, 467, 468 Fixed-position layout, 379 Flexibility, 12, 16

in competitive priorities, 40 in JIT manufacturing, 260–61 in JIT philosophy, 250 product, 40 volume, 40 of volume, 131

Flexible layouts, 389 Flexible manufacturing system (FMS), 84–85 Floating employees, scheduling, 598 Flowcharts, 174, 175 Flow operations, 578–79

characteristics of, 578–79 defined, 578

Flow shops, 71 Fluctuation inventory, 453, 455 Focused factories, 338–39 Foolproof, 264 Ford, Henry, 14 Forecast accuracy measures, 307–10

forecast error, 307–9 tracking signal, 309–10

Forecast bias, 309 Forecast error, 307–9 Forecasting, 280–330

accuracy, measuring (See Forecast accuracy measures) across the organization, 315–16 bias in, 309 CPFR in, 314–15 defined, 281 errors in, 307–9 focus, 313

bindex02.indd 683bindex02.indd 683 28/07/12 12:02 AM28/07/12 12:02 AM

684 • SUBJECT INDEX

reverse engineering, 61–62 suppliers, 61–62

Idle time due to material/component shortages, 456 in product layout design, 394

Immediate predecessor, 389–90 Impact in partnering, 133–34

creating new opportunities as source of, 134 duplication as source of, 133 leveraging core competence as source of, 134 waste reduction as source of, 133–34

Implicative seasonality, 300 Inbound materials in SQC, 229–30 Incoming inspection, 124 Indented bill of material, 552 Independent demand, 548 Independent variable, 303 Individual incentive plans, 435 Industrial Revolution, 12–13 Industry consortia, 148 Infinite loading, 580–81 Information systems (IS)

in aggregate planning, 525 defined, 22 in forecasting, 316 in inventory management, 491 in JIT systems, 273 in OM, 22–23 in product design and process selection, 92 in project management, 636 in resource planning, 563–64 in scheduling, 601 in SCM, 149 in TQM, 188 in work system design, 438

Information technology (IT), 82–83. See also Technology defined, 43, 82 ERP, 82 global positioning systems, 83 radio frequency identification, 83 SCM issues and, 112 wireless communication technologies, 82

Infrastructure defined, 42 issues, 121 in operations decisions, 42

Input/output control, 582–84 Inputs, MRP, 550–55

authorized MPS, 550 bills of material, 552–55 inventory records, 550–52

Insource defined, 127 vs. outsource, 128–30

Interchangeable parts, 13

Gross requirements, 549–50 Group incentive plans, 434–35

gain sharing, 434–35 vs. individual incentive plans, 435 profit sharing, 434

Group purchasing organizations (GPOs), 137 Group technology (GT) or cell layouts, 396–97

H Hanson, Kirk O., 137 Harris, F. W., 14 Hawthorne effect, 14 Hawthorne studies, 14 High-performance design, 39 High-volume operations. See Flow operations Hill, Terry, 41 Hiring and firing, 513–14 Histograms, 175, 177 Historical developments in OM, 11–20

computer age, 15 electric commerce, 18–19 environmental factors, 12, 14, 18 global marketplace, 18 Hawthorne studies, 14 human relations movement, 14 Industrial Revolution, 12–13 Just-in-time (JIT), 15 management science, 14–15 outsourcing, 19–20 overview of, 11–12 reengineering, 16 scientific management, 13–14 SCM, 16–18 sustainability, 18 time-based competition, 16 TQM, 15–16

Holding costs, 459–61 Hot desking, 418 Hoteling, 418, 419 House of quality, 178–80 Human relations movement, 12, 14 Human resource managers, 23 Human resources

in facility layouts, 397–98 in forecasting, 316 in product design and process selection, 92 in TQM, 188 in work system design, 438–39

Hybrid aggregate plan, 511 Hybrid layout, 378

I Idea development, sources of, 60–62

benchmarking, 61 competitors, 61 customers, 60–61

bindex02.indd 684bindex02.indd 684 28/07/12 12:02 AM28/07/12 12:02 AM

SUBJECT INDEX • 685

periodic review system in, 485–88 safety stock levels, determining, 483–85 single-period inventory model, 488–90 summarized, 459 in supply chain, 491 in sustainability, 492

Invoice business process (EDI), 146 Ishikawa, Kaoru, 168, 169–70 Ishikawa diagrams. See Cause-and-effect diagrams ISO 9000, 15–16, 184–85 ISO 14000, 18, 185 Item costs, 459

J Jensen, Leila, 100 Jidoka, 263 Job design, 413–19

alternative workplace and, 417–19 behavioral feasibility in, 413–14 defined, 413 economic feasibility in, 413 employee boredom and, eliminating, 416 machines vs. people in, using, 414, 415 specialization in, 415–16 team approaches to, 416–17 technical feasibility in, 413

Job enlargement, 14, 416 Job enrichment, 14, 416 Job flow time, 586 Job lateness, 587–88 Job rotation, 416 Job sequencing, 584 Job shops, 71 Job tardiness, 587 Johnson, Peggy, 502 Johnson’s rule, 591–93 Judgmental methods. See Qualitative forecasting methods Juran, Joseph M., 168–69 Just-in-time (JIT) manufacturing, 251–53, 254–62

defined, 251 facility layout in, 261–62 flexible resources in, 260–61 kanban cards in, 251, 255–58 overview of, 251–53 pull system in, 251–52, 254–55 quick steps in, 259 small-lot production in, 258–59 uniform plant loading in, 259–60

Just-in-time (JIT) philosophy, 247–50 broad view of, 247–48 broad view of the organization in, 249 continuous improvement in, 250 defined, 247 defining beliefs of JIT, 248 flexibility in, 250

Intermittent operations batch processes in, 70, 71, 72 competitive priorities in, 79 defined, 69 facility layout for, 79–80 product design decisions in, 78–79 project processes in, 70, 71, 72 vs. repetitive operations, 70

Intermittent processing systems, 374 Internal failure costs, 165 Internal functions in supply chain, 108 Internal operations in service organizations, 111–12 Internal resource constraint, 596 Internal setup, 259 International Organization for Standardization (ISO), 184–85

ISO 9000 standards, 15–16, 184–85 ISO 14000 standards, 18, 185 sustainability and green operations and, 18 totla quality management and, 15–16

Internet government regulation and, 118 impact of, on conducting business, 82 OM and, challenges of, 4–5

Intimacy in partnering, 134–35 Intranets, 115 Inventory

anticipation inventory, 453 fluctuation inventory, 453 functions of, summarized, 455 lot-size inventory, 453 MRO inventory, 454 record accuracy, 464–66 records in MRP inputs, 550–52 in services organizations, 466–67 speculative inventory, 454 transportation inventory, 453–54 turnover, 457–58 types of, 452–53

Inventory costs capital costs, 459–60 holding costs, 459–61 item costs, 459 ordering costs, 461 policy costs, 470–71 risk costs, 460 shortage costs, 461 storage costs, 460

Inventory management, 451–503. See also Order quantities, determining

across the organization, 491 cost-efficient operations in, 456–57 customer service in, 455–56 minimum inventory investment in, 457–58 objectives of, 455–59 in OM, 490

bindex02.indd 685bindex02.indd 685 28/07/12 12:02 AM28/07/12 12:02 AM

686 • SUBJECT INDEX

simple moving average, 291–94 weighted moving average, 294

Levitt, Theodore, 90 Life cycle, project, 615–16 Lifetime employment, 266–67 Linear regression, 303–5 Line balancing, 389 Line process, 70, 71, 72 Lines in product layout design

balancing, 389 mixed-model, 395 paced vs.unpaced, 395 shape of, 395 single-model, 395

Load chart, 579 Load-distance model, 351–55, 382–83

in block plan, 382–83 defined, 351 in location alternatives, 351–55

Local priority rule, 584 Location alternatives, procedures for evaluating, 349–60

break-even analysis, 356–59 center of gravity approach, 355–56 factor rating, 349–51 load-distance model, 351–55 transportation method, 360

Location analysis, 344 across the organization, 360–61 decision-making process in (See Location decisions) defined, 344 facility location and, 345 globalization and, 347–49 in OM, 360 in supply chain, 361 in sustainability, 361

Location decisions, 345–60. See also Location alternatives, procedures for evaluating

community considerations, 347 customer, proximity to, 346 customer and transportation access, 347 customer parking, 347 location alternatives, procedures for evaluating, 349–60 quality-of-life issues, 347 room for expansion, 347 site considerations, 347 source of labor, proximity to, 346 sources of supply, proximity to, 345–46 visibility, 347

Logistics, 108 Longest processing time (LPT), 584 Longest task time rule, 393 Lost sale, 461 Lot-for-lot, 467, 468 Lot quality, 224 Lot size, in SQC, 229

Just-in-time (JIT) philosophy (cont.) simplicity in, 249–50 in supply chain, 273 in sustainability, 274 visibility in, 250 waste elimination in, 249

Just-in-time (JIT) system, 251–54 across the organization, 272–73 benefits of, 269 defined, 15, 251 elements of, 251–54 historical development of, 12 implementing, 270–71 JIT manufacturing in, 251–53, 254–62 in OM, 272 philosophy of, 247–50 respect for people in, 253–54, 265–69 in services, 271–72 in supply chain, 273 in sustainability, 274 TQM in, 253, 262–65

K Kaizen, 172, 250 Kaizen blitz, 250 Kanban cards, 251, 255–58 Kanban production, 258 Kanban square, 258 Kessinger, Jeff, 54

L Last come, first served (LCFS), 584 Latest finish time (LF), 621–22 Latest start time (LS), 621–22 Layout planning, 373–74 Layout types, 374–79

fixed-position, 379 hybrid, 378 process, 374–76 product, 376–78

Lead time, 464, 551, 554–55 Lean systems

across the organization, 272–73 defined, 20 in OM, 272

Learning curve coefficients, 436–37 Learning curves, 435–38 Learning curve theory, 435 Least-squares straight line, 304 Less-than-truckload (LTL) quantities, 138 Level aggregate plan, 509–10 Level or horizontal patterns, defined, 288 Level or horizontal patterns, forecasting, 289–97

exponential smoothing, 295–97 naïve method, 289–90 simple mean or average, 290–91

bindex02.indd 686bindex02.indd 686 28/07/12 12:02 AM28/07/12 12:02 AM

SUBJECT INDEX • 687

Market understanding, as core competency, 35 Mass customization, 16 Mass-production operations, 69, 70 Master black belts, 222 Master production schedule (MPS)

in aggregate planning, 508–9 authorized, 550 defined, 508 infinite loading and, 580 in JIT manufacturing, 251

Material requirements planning (MRP), 547–60 action notices and, 559 closed-loop, 547 defined, 547–48 demand in, types of, 548–50 development of, 15 explosion process, 555–58 inputs, 550–55 lot size rules and, comparison of, 559–60 objectives of, 550 overview of, 547–48 process, overview of, 549

Mathematical models for determining order quantities, 468–80

economic order quantity model, 468–73 economic production quantity model, 473–77 quantity discount model, 477–80

Mayo, Elton, 14 McDonough Braungart Design Chemistry certification

process, 119 Mean (average), 200–201 Mean absolute deviation (MAD), 307–8 Mean (x-bar) charts, 204–8 Mean observed time, 426 Mean squared error (MSE), 307–8 Measuring process performance. See Process performance

metrics Methods analysis, 419–21 Methods-time measurement (MTM), 430 Minimum inventory investment, 457–58 Min-max system, 467, 468 Mission, 32–33 Mixed-model line, 395 Mixed services, 89 Most likely time estimate, 622 Moving assembly line, 14 Multifactor productivity, 45 Multifunction workers, 260, 261, 272 Multiple regression, 307 Multiple-sampling plans, 224

N Naïve method, 289–90 Nardelli, Bob, 21 Net marketplaces, 113–14

Lot-size inventory, 453, 455 Lot tolerance percent defective (LTPD), 225 Lovelock, C. H., 90–91 Low-volume operations, 579–80

M Machines vs. people, in job design, 414, 415 Magnitude of the change, 515 Maintenance, repair, and operating (MRO)

defined, 454 in e-purchasing, 147 examples of, 453, 455

Makespan, 586, 587 Make-to-order strategy, 73, 74, 81 Make-to-stock strategy, 73, 74, 81 Malcolm Baldrige National Quality Award, 165, 183–84 Management functions. See Operations management (OM) Management in JIT, role of, 267 Management science, 12, 14–15 Management systems standards, 185 Manufacturability, 59 Manufacturing

components in supply chain, 106–8 crossdocking, 139 lead time, 258–60 technology in, vs. services, 87–88

Manufacturing organizations, 5 characteristics of, 6 defined, 5 quality in, 163–65 vs. service organizations, 5–7

Manufacturing resource planning (MRP II), 547 Market constraint, 596 Marketing

in aggregate planning, 525 in capacity planning and facility location, 360 in facility layouts, 397 in forecasting, 315 in inventory management, 491 in JIT systems, 273 in operations strategy, 49 in product design and process selection, 92 in product screening, 62 in project management, 636 in resource planning, 563 in scheduling, 601 in SCM, 149 in TQM, 187 in work system design, 438

Marketing management, 3–4, 22 Marketing plan, 507 Marketplace trends, 33–34 Market research

defined, 285 in qualitative forecasting, 285

bindex02.indd 687bindex02.indd 687 28/07/12 12:02 AM28/07/12 12:02 AM

688 • SUBJECT INDEX

proper, 4–5 role of, 3–4 service vs. manufacturing operations, 5–6 today’s OM environment, 20–21 transformation process in, 3, 4 value added and, 5 Web and, challenges of, 4–5

Operations managers decisions made by, 7–10 information sharing in teams and, 22–23 levels of, 21

Operations strategy, 29–55 across the organization, 49–50 competitive priorities in, 37–43 defined, 30 developing, 37–43 in OM, 48–49 operations function and, design of, 38 order winners and qualifiers in, 41–42 productivity and, 44–48 role of, 31–32 technology in, 43–44 trade-offs in, 40–41

Optimistic time estimate, 622 Optimized production technology (OPT), 593–95 Order batching, 110 Order-cycle service level, 483–85 Ordering costs, 461 Order n periods, 467, 468 Order qualifiers, 41–42 Order quantities, determining, 467–68

mathematical models for, 468–80 optimal, 481 ordering approaches, summarized, 467 smaller, justifying, 481–83

Order winners, 41–42 Out of control, 202 Output-based compensation systems, 433–34 Output rate

cycle time computed with, 391–93 defined, 391 maximum, vs. minimum cycle time, 392–93

Outsource defined, 127 historical development of, 12, 19–20 vs. insource, 128–30

Over-the-wall approach, 67–68 Overtime, 512–13

P Paced line, 395 Packaging restrictions, 119 Parallel processing, 272 Parent item, 553 Pareto, Vilfredo, 177

Network diagram, 618–19 Network planning techniques, 616–28

benefits of, 617 critical path method, 616, 617 diagram of network, 618–19 manager’s use of, 617 PERT, 616, 617 project description, 617, 618 project progression, monitoring, 628 time estimates, 619–27

Nonbottleneck, 593 Nonconformance costs, 170 Normal time, 427 North American Free Trade Agreement

(NAFTA), 18 Numerically controlled (NC) machines, 85, 86, 121

O Occupational Safety and Health Act (OSHA), 422 Office landscaping, 389 Office layouts, 388–89

flexibility in, 389 proximity vs. privacy, 388–89

OM. See Operations management (OM) On-time delivery, 39, 42 Open shop orders, 561 Operating characteristic (OC) curves, 224–27

acceptable quality level in, 224, 225 consumer’s risk in, 225 defined, 224 developing, 226–27 producer’s risk in, 225

Operations product screening in, 62 in project management, 636 scheduling in, 601 in SCM, 150 standards, 185

Operation sequencing, 584–85. See also Priority rules defined, 584 of jobs, 584 through two work centers, 591–93

Operations management (OM), 1–28. See also Historical developments in OM

within, 21 across the organization, 22–24 business functions in, 3–4 decisions, 7–10 defined, 3 efficiency and, 5 importance of, TI example of, 9–10 improper, 5 overview of, 2–3 plan of book, 10 in practice, 21

bindex02.indd 688bindex02.indd 688 28/07/12 12:02 AM28/07/12 12:02 AM

SUBJECT INDEX • 689

Policy constraint, 596 Political trends, 34 Porter, Michael, 32 Posted schedules, 597 Postponement, 16, 138–39 Precedence diagram, 390 Precedence relationships, 617 Predetermined time data, 429–31 Prevention costs, 165 Preventive maintenance in TQM, 264 Price and availability, 123 Price fluctuations, 110 Priority rules, 584–85

commonly used, 584 comparing, 588–91 defined, 584 using, methods of, 585

Probabilistic time estimate, 622–27, 629–31 beta probability distribution in, 622–23 of completion dates, 629–31 defined, 619 of earliest/latest start times, calculating, 625–27 of expected times, calculating, 623–25

Problem-solving teams, 416–17 Process batch, 595 Process capability, 216–21

defined, 216 measuring, 216–21

Process capability index, 217 Process charts. See Control charts Process design

defined, 59 issues involved in, 68–72 process flow analysis in, 72–74 process performance metrics in, 74, 75–77 quality problems in, 263 technology decisions in, 82–86

Process flow analysis, 72–74 Process flowchart, 72–74, 75 Process layout, defined, 374 Process layout design, 379–85. See also Block plan

characteristics of, 375, 377 closeness measures, identifying, 380–81 detailed, 385 information gathering in, 379–81 office layouts, 388–89 space available, identifying, 380 space needed, identifying, 379 special cases of, 385–89 warehouse layouts, 385–88

Process management in TQM, 182 Process performance metrics, 74, 75–77

defined, 74 efficiency, 76 process velocity, 75–76

Pareto analysis, 175, 177 Pareto’s law, 461 Partial productivity, 45, 46 Partnering, 131–36

basic and expanded partnerships in, 131 benefits of, 133, 135–36 characteristics of partnerships in, 136 defined, 131 developing partnerships in, 131–33 successful, critical factors in, 133–35

Part-time employees, scheduling, 598 p-chart

for attributes, 211–13 defined, 211 values chart, 672–73

Peak demand, scheduling staff for, 598 Percentage of dollar volume shipped on schedule, 456 Percentage of line items shipped on schedule, 456 Percentage of orders shipped on schedule, 455–56 Performance

in manufacturing, 164 metrics, in supply chain, 145–47 rating factor, 426–27 in work measurement, 423

Performance measures average number of jobs in the system, 587 job flow time, 586 job lateness, 587–88 job tardiness, 587 makespan, 587 scheduling and, 586–88

Periodic counting, 465 Periodic review system (PRS), 485–88

vs. continuous review systems, 487–88 defined, 464 in inventory management, 485–88 target inventory level and, 485–86 using, example of, 487

Perpetual inventory record, 477 Pessimistic time estimate, 622 Peters, Tom, 19 Piece-rate incentives, 14 Plan–do–study–act (PDSA) cycle, 172 Planned orders, 552 Planning

factors, 551 in project life cycle, 616, 617 in work measurement, 423

Plant-within-a-plant (PWP), 41, 339 Point of departure, 514–15 Point-of-sale (POS)

to counteract bullwhip effect, 110 in implementing SCM, 144 in inventory control in service organizations, 466

Poka-yoke, 264

bindex02.indd 689bindex02.indd 689 28/07/12 12:02 AM28/07/12 12:02 AM

690 • SUBJECT INDEX

multifactor, 45 in operations strategy, 44–48 partial, 45, 46 service sector and, 48 total, 44–45

Product layout, defined, 376 Product layout design, 389–95

balance delay in, computing, 394–95 characteristics of, 376–77 cycle time, determining, 391–93 efficiency in, computing, 394 idle time in, computing, 394 immediate predecessor in, 389–90 line balancing in, 389 line shape in, 395 output rate, determining, 391 paced vs.unpaced lines in, 395 product models produced in, number of, 395 theoretical minimum number of stations in, computing, 393 workstation tasks, assigning, 393–94

Product life cycle in intermittent and repetitive operations, 77, 78 in product design, 66 project management and, 615–16 stages of, 66

Product mixing, 138 Product screening, 62–64

break-even analysis in, 62–64 defined, 62 issues explored in, 62

Product specifications, 216 Product standardization, 70, 71, 72, 88–89 Product strategy decisions, 81 Product structure tree, 552, 553 Product technology, 43 Product volume, in SQC, 229 Profit sharing, 434 Program evaluation and review technique (PERT), 616, 617 Progress chart, 580 Project

activities, 617 buffer, 635 defined, 615 process, 70, 71, 72

Projected available, 552 Project life cycle, 615–16

conception, 615 execution, 616 feasibility analysis or study, 615 phases, summarized, 617 planning, 616 termination, 616

Project management, 613–47 across the organization, 636 critical chain approach to, 634–35

Process performance metrics (cont.) productivity, 76 throughput time, 75, 76 utilization, 76

Process quality, 39 Process selection. See also Process types

across the organization, 92–93 defined, 59 in OM, 91–92 product design and, 77–82

Process stability, in SQC, 229 Process technology, 43 Process types, 68–72

continuum of, 70–72 intermittent operations, 69 repetitive operations, 69–70

Process velocity, 75–76 Process vs. product in TQM, 263 Producer’s risk, 225 Product

cost, in SQC, 229 flexibility, 40 vs. process, in TQM, 263

Product design across the organization, 92–93 concurrent engineering in, 67–68 decisions, 78–79 defined, 59 design for manufacture in, 65–66 designing processes in, 72–74 factors impacting, 65–68 final design in, 64 idea development, 60–62 in OM, 91–92 overview of, 60 preliminary designing and testing in, 64 process, 60–64 process selection and, 77–82 product life cycle in, 66 product screening in, 62–64 quality, 39 quality problems in, 263 remanufacturing in, 68 services vs. goods in, design of, 59–60 supply chain in, 93 sustainability in, 93–94 in TQM, 177–82

Production cards, 255, 256 Production employees in JIT, role of, 265–66 Production plan. See Aggregate plan Productivity, 76

competitiveness and, 47–48 computing, 46 defined, 44 measures, 44–47

bindex02.indd 690bindex02.indd 690 28/07/12 12:02 AM28/07/12 12:02 AM

SUBJECT INDEX • 691

Quality Control Handbook (Juran), 168 Quality function deployment (QFD)

defined, 177 in implementing SCM, 143 in product design, 177–80

Quality gurus, 167–71 Crosby, Philip B., 168, 169, 263 Deming, W. Edwards, 15, 167–68 Feigenbaum, Armand V., 168, 169 Ishikawa, Kaoru, 168, 169–70 Juran, Joseph M., 168–69 overview of, 168 Shewhart, Walter A., 167, 168 Taguchi, Genichi, 168, 170

Quality Is Free (Crosby), 169 Quality tools in TQM, 174–77

cause-and-effect diagrams, 169, 174, 175, 176 checklists, 175 control charts, 175, 176 flowcharts, 174, 175 histograms, 175, 177 Pareto analysis, 175, 177 scatter diagrams, 175, 176–77

Quantitative forecasting methods, 283, 284, 286–87 causal models, 286–87 defined, 284 summarized, 287 time series models, 286, 287

Quantity discount defined, 477 procedure, 478–80

Quantity discount model, 477–80 cost curves in, 478 defined, 477 examples of, 477, 480

Quasi-manufacturing, 88 Quasi-manufacturing organizations, 6 Queue, 584

R Radio frequency identification (RFID)

in crossdocking, 140 defined, 83 in supply chain, 93 in supply chain distribution, 141–42

Random variation, 289 Range, 201 Range (R) charts, 208–9 Rapid delivery, 39, 42 Rationing and shortage gaming, 110 Raw materials, 452 Rectilinear distance, 383 Redundancy in product design, 181–82 Reengineering, 16 Relationship matrix, 178, 179

Dilbert’s approach to, 628 network planning techniques in, 616–28 in OM, 636 probability of completetion dates and,

estimating, 629–31 product life cycle and, 615–16 project completetion time, reducing, 631–33 in supply chain, 637 in sustainability, 637

Promptness in service organizations, 164 Prototypes, 64 Psychological criteria, 163 Public safety and welfare, 118 Pull system, 251–52, 254–55 Purchase order, 123 Purchasing

e-purchasing, 124–27 in product design and process selection, 93 requisition request in, 122–23 in SCM, 122–27, 150 traditional, 122–24

Purchasing cards (p-cards), 146 Pure services, 88–89 Push system, 252

Q Qualitative forecasting methods, 283–86

defined, 283 Delphi method, 285–86 executive opinion, 285 market research, 285 summarized, 285

Quality awards and standards, 183–85 in competitive priorities, 39 cost of, 165–66 customer-defined, 162 defined, 162–63 failure costs, 165 improvement, 169 improvement, strategy for, 263 in manufacturing and service organizations, 163–65 planning, 169 service, in JIT, 263 at the source, 182, 253, 263–64 supplier, managing, 182 trilogy, 169

Quality circles defined, 173 Ishikawa and, 170 in team approach, 173–74, 266

Quality control, 169 charts (See Control charts) costs, 165 Juran and, 169

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692 • SUBJECT INDEX

Scheduled receipt, 551 Scheduling, 576–612. See also Operation sequencing

across the organization, 601 bottlenecks, 593–95 employees, 598–99 Gantt charts in, 579–80 high-volume, 578–79 low-volume, 579–80 in OM, 600–601 operations, 578–80 performance measures and, 586–88 sequencing jobs, 584 for service organizations, 596–99 for services demanded, 596–97 in supply chain, 602 in sustainability, 602 theory of constraints and, 595 Tibrewala, Phillippe, and Brown technique of,

599–600 work, 580–84 workforce, developing, 599–600

Scientific management, 12, 13–14 SCM. See Supply chain management (SCM) SCM software, 543 Scott, H. Lee, 21 Scrap, 165 Seasonal employees, scheduling, 598 Seasonal index, 300 Seasonal inventory, 453, 455 Seasonality

defined, 288 forecasting, 300–303

Self-directed teams, 417 Serviceability in manufacturing, 164 Service design, 87–91

classification of service organizations in, 88–89 customer attention in, high level of, 91 customer contact in, 87–88 customer involvement in, 90–91 defined, 60 vs. goods, 59–60 intangible products in, 87 service packages in, 89 substituting technology for people in, 90 technology substituted for people, 90

Service organizations characteristics of, 6 defined, 5 external distributors in, 112 internal operations in, 111–12 vs. manufacturing organizations, 5–7 quality in, 163–65 scheduling for, 596–99 supply chains for, 110–12

Service package, 89

REL chart, 381 Reliability

defined, 180 in manufacturing, 164 in product design, 180–82

Remanufacturing Remanufacturing in product design, 68 Repetitive operations

competitive priorities in, 79 continuous processes in, 70, 71, 72 defined, 69 facility layout for, 80 vs. intermittent operations, 70 line processes in, 70, 71, 72 product design decisions in, 78–79

Repetitive processing systems, 374 Requisition request, 122–23 Reservations, scheduling, 597 Reserve stock. See Safety stock Resource planning, 539–75

across the organization, 563–64 capacity requirements planning, 560–62 ERP, 541–47 material requirements planning, 547–60 in OM, 562–63 in supply chain, 564 in sustainability, 564

Respect for people, 253–54, 265–69 defined, 253 lifetime employment and, 266–67 management and, role of, 267 overview of, 253–54 production employees and, role of, 265–66 supplier relationships and, 268–69

Retail crossdocking, 139 Reverse engineering, 61–62 Risk costs, 460 Robotics, 85 Robust design, 170 Role of OM, 3–4 Rough-cut capacity planning (RCCP), 563 Routing, 578–79

S Safety stock

defined, 453, 455 levels, determining, 483–85

Safety time adding, 634 wasting, 634–35

Sales and operations planning, 507 Sales revenue model, 114 Sampling plan, 223–24 Scannabar, 465 Scatter diagrams, 175, 176–77

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SUBJECT INDEX • 693

Specialization, 415–16 Special-purpose teams, 417 Specialty forecasting packages, 312 Specifications, 216 Speculative inventory, 454, 455 Spreadsheets, 311 SQC. See Statistical quality control (SQC) Standard deviation, 201, 202 Standard normal deviation, 671 Standard time, 423–33

defined, 422 elemental time database in, 429 predetermined time data in, 429–31 time study in, 423–29 work sampling in, 431–33

Statistical packages, 311 Statistical process control (SPC)

charts, 202–4 defined, 199 methods, 201–4 (See also Control charts) in TQM efforts, 186

Statistical quality control (SQC), 197–245 acceptance sampling in, 199, 223–28 across the organization, 232–33 attributes, control charts for, 204, 210–15 categories, 198–99 defined, 198 descriptive statistics in, 198, 200–201 inspection, 229–30 lot size in, 299 managers in, implications for, 229–30 in OM, 232 process capability in, 216–21 process stability in, 299 product cost and volume in, 299 in services, 230–31 Six Sigma quality in, 221–23 statistical process control in, 199 supply chain in, 233 sustainability in, 233–34 variables, control charts for, 204–10 variation in, common and assignable

causes of, 200 Statistical sampling theory, 14 Stock-keeping unit (SKU), 467–68 Stopwatch time studies, 14 Storage costs, 460 Strategic alliances, 34 Strategic business plan, 507 Strategic decisions, 9 Strategy Strategy, operational efficiency vs., 32 Structure, in operations decisions, 42 Subcontracting, 513 Subcontractor networks, 339

Service providers, third-party, 142–43 Services

customer contact in, degree of, 87–88 demanded, scheduling for, 596–97 intangible products in, 87 JIT systems in, 271–72 vs. manufacturing in technology, 87–88 SQC, 230–31 strategy decisions in, 81 in warehouses, 138–39

Service sector, productivity and, 48 Setup cost, 252, 457 Setup time, minimizing, 272 Seven tools of quality control. See Quality tools in TQM Shewart cycle, 172 Shewhart, Walter A., 167, 168 Shifting demand, 512 Shortage costs, 461 Shortest processing time (SPT)

defined, 584 S/RO and, comparing, 591 using, 589–90

Signal kanbans, 258 Simple mean or average, 290–91 Simple moving average (SMA), 291–94 Simplicity, in JIT philosophy, 249–50 Single-factor productivity, 45, 46 Single-model line, 395 Single-period inventory model, 488–90 Single sampling, 223–24 Single-source suppliers, 268 Six Sigma quality, 221–23 Skewed distribution, 201, 202 Skinner, Wickham, 41 Slack

in backward scheduling, 582 in calculating completion times, 621–22 defined, 582, 621

Slack per remaining operations (S/RO) defined, 584 SPT and, comparing, 591

Small-lot production, 258–59 Smith, Adam, 13 Social trends, 34–35 Solutions to odd-numbered problems, 649–70 Sourcing issues in SCM, 127–37

backward integration, 128 ethics, 136–37 forward integration, 128 insourcing vs. outsourcing, 127, 128–30 partnerships, 131–36 single vs. multiple suppliers, 130–31 supplier relationships, developing, 130 vertical integration, 127

Sourcing strategy, 124

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694 • SUBJECT INDEX

Supply chain management (SCM) issues, 112–22 e-commerce, 112–16 globalization, 116–18 green SCM, 118–21 information technology, 112 infrastructure, 121 product proliferation, 122

Supply Chain Operations Reference (SCOR), 147 Support services, 163 Sustainability

defined, 18 forecasting and, 316–17 JIT philosophy and, 274 JIT systems and, 274 location analysis and, 361 in OM, 24 operations strategy and, 50 product design and, 93–94 SCM and, 150 SQC and, 233–34 standards, in TQM, 188–89

Systematic layout planning (SLP), 381

T Tactical decisions, 9 Taguchi, Genichi, 168, 170 Taguchi loss function, 170 Target inventory level (TI), 485–87 Taxation, 118 Taylor, Frederick W., 13, 14, 423, 433 Team approach

to job design, 416–17 problem-solving teams in, 416–17 self-directed teams in, 417 special-purpose teams in, 417 in TQM, 173–74

Technical feasibility, 413 Technology. See also Information technology (IT)

automation, 83–85 in classifying services, 88–89 as core competency, 35 e-manufacturing, 86–87 outsourcing and, 20 in process design decisions, 82–86 in service designs, 90–91 service package, 89 in services vs. manufacturing, 87–88 strategic role of, 43–44 as tool for competitive advantage, 44 types of, 43–44 (See also Enterprise resource planning

(ERP)) Telework, 418 Temporary employees, scheduling, 598 Termination, in project life cycle, 616, 617 Testing, in product design process, 64 Theoretical minimum number of stations, 393

Subscription revenue model, 114 Supplier

early supplier involvement program, 62 e-purchasing systems and, 126 external, 106–7 greening requirements for, 119 in idea development, 61–62 kanbans, 258 quality, managing in TQM, 182 quality problems in, 263 relationships, JIT, 268–69 relationships with, developing, 130 tier one/two/three, 106–7

Supply chain bullwhip effect in, 108–10 carbon footprint analysis, 119–20 defined, 104–5 forecasting in, 316 JIT philosophy in, 273 JIT systems in, 273 location analysis in, 361 in operations strategy, 49–50 performance metrics, 145–47 in product design, 93 radio frequency identification in, 93 for service organizations, 110–12 in SQC, 233 TQM in, 188 velocity, 148

Supply chain components for manufacturer, 106–8 external distributors, 108 external supplies, 106–7 internal functions, 108

Supply chain distribution, 137–43 crossdocking in, 139–41 radio frequency identification in, 141–42 third-party service providers in, 142–43 warehouses in, 137–39

Supply chain intelligence (SCI), 543 Supply chain management (SCM), 16–18, 103–59

across the organization, 149–50 author-controlled supply chain, example of, 17, 18 defined, 16, 105 game, 155–57 historical development of, 12 implementing, 143–45 importance of, 17 leveraging, strategies for, 144–45 objective of, 16–17 in OM, 149 in operations management, 24 purchasing in, role of, 122–27 sourcing issues in, 127–37 sustainability and, 150 traditional publishing supply chain, example of, 17 trends in, 147–49

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SUBJECT INDEX • 695

process management in, 182 product design in, 177–82 product vs. process in, 263 quality at the source in, 253, 263–64 quality tools in, 174–77 supplier quality in, managing, 182 in supply chain, 188 in sustainability, 188–89 work environment in, 264–65

TQM. See Total quality management (TQM) Tracking signal, 309–10 Trade-off, 40–41 Trade-off matrix, 179 Traffic management, 108 Transaction fee model, 114 Transfer batch, 595 Transformation

defined, 3 process, in OM, 3, 4

Transportation consolidation, 138 crossdocking, 139 inventory, 453–54 method, 360

Trend, defined, 288 Trend, forecasting, 297–300

linear trend line, 298–300 trend-adjusted exponential smoothing, 297–98

Trend-adjusted exponential smoothing, 297–98 Truckload capacity usage, 120 Truckload (TL) quantities, 138 Two-bin system, 464 Type I error, 203 Types of waste, 249

U Undercapacity scheduling, 264 Undertime, 513 Uniform plant loading, 259–60 U.S. copyright law, 118 U.S.–Mexican maquiladora operations, 147 Utilization, 76

V Value added, 5 Value chain management (VCM), 147 Value for price paid, 163 Variable, defined, 203 Variable costs, 62–63 Variables, control charts for, 204–10

mean (x-bar) charts, 204–8 range (R) charts, 208–9 using mean and range charts together, 209–10

Vendor-managed inventory (VMI), 466 Vertical integration

decisions, 81–82 defined, 127

Theory of constraints (TOC), 595 Third-party service providers, 142–43 Throughput, 594 Throughput time, 75, 76 Tibrewala, Phillippe, and Brown scheduling

technique, 599–600 Tier one supplier, 106–7 Tier three supplier, 106–7 Tier two supplier, 106–7 Tillman, Robert, 21 Time, in competitive priorities, 39–40 Time-based compensation systems, 433 Time-based competition, 12, 16 Time estimates, 619–27

deterministic, 620–22 most likely, 622 optimistic, 622 probabilistic, 622–27, 629–31 for project completion, 619 for safety time, 634–35

Time in service organizations, 164 Time measurement units (TMUs), 430–31 Time-phased, 549 Time series, defined, 286 Time series models, 288–303

vs. causal models, 287 cycles in, 289 defined, 286 level or horizontal patterns in, 288, 289–97 in quantitative forecasting, 286, 287 seasonality in, 288, 300–303 summarized, 287 trends in, 288, 297–300

Time study, 423–29 allowance factor in, 427–28 defined, 423 frequency of occurrence in, 427 performance rating factor in, 426–27 procedure for, overview of, 424

Tolerances, 216 Total cost, 63 Total Logistics Control (TLC), 36 Total productivity, 44–45 Total Quality Control (Feigenbaum), 169 Total quality management (TQM), 12, 15–16, 161–95

across the organization, 187–88 continuous improvement, 171–73 customer focus in, 171 defined, 162, 253 employee empowerment in, 173–74 evolution of, 166–70 failures in, reasons for, 186 in JIT systems, 253, 262–65 in OM, 186 philosophy of, 171–83 preventive maintenance in, 264

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696 • SUBJECT INDEX

Work-in-process (WIP), 452–53, 456 Work measurement, 422–23

costing in, 422–23 defined, 422 performance in, 423 planning in, 423

Workplace organization, 272 Work sampling, 431–33 Work scheduling, 580–84

backward scheduling, 582 finite loading, 581 forward scheduling, 581–82 infinite loading, 580–81 input/output control, 582–84

Workstation tasks in product layout design, 393–94 Work system design, 411–50

across the organization, 438–39 compensation systems and, 433–35 job design and, 413–19 learning curves and, 435–38 methods analysis and, 419–21 in OM, 438 standard times and, setting, 423–33 in supply chain, 439 in sustainability, 439–40 work environment and, 421–22 work measurement and, 422–23

World Trade Organization (WTO), 18

X x-bar charts, 204–8

Y Yield management, 524 Young, R. F., 90–91

Z Zero defects, 169 Zero waste business model, 274

Virtual office or virtual workplace, 418 Virtual private network (VPN), 114 Visibility, in JIT philosophy, 250 Vision in partnering, 135 Volume flexibility, 40

W Walton, Sam, 50 Warehouse layouts, 385–88

storage areas of equal size, 385–86 storage areas of unequal size, 386–88

Warehouses in supply chain distribution, 137–39 distribution warehouse, 137–38 general warehouse, 137 product mixing in, 138 services in, 138–39 transportation consolidation in, 138

Waste defined, 247 elimination, in JIT philosophy, 249 reduction, in partnering, 133–34 types of, 249 zero waste business model, 274

Watt, James, 12–13 Wealth of Nations, The (Smith), 13 Web. See Internet Weeks of supply, 457–58 Weighted moving average, 294 Whitney, Eli, 13 Win-win situation, 131 Wireless communication technologies, 82 Withdrawal cards, 255, 256 Work environment

in TQM, 264–65 in work system design, 421–22

Workflow, monitoring, 582–84 Workforce, as core competency, 35 Workforce scheduling, 599–600

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SUPPLEMENT

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

What Are Models? A2 Th e Spreadsheet Modeling Process A4 Evaluating Spreadsheet Models A5 Useful Spreadsheet Tips A26

Important Excel Formulas A27 Spreadsheet Modeling within OM: How It All Fits

Together A28

Spreadsheet Modeling: An Introduction

Before studying this supplement you should know or, if necessary, review

1. Familiarity with spreadsheets. 2. Knowledge of basic Excel commands.

After completing this supplement you should be able to

1 Explain what models are and why they are used. 2 Identify the main types of models. 3 Describe the diff erent components of mathematical models. 4 Identify the recommended steps in the spreadsheet modeling process. 5 Explain the importance of model correctness, fl exibility, and documentation. 6 Construct spreadsheet models applying sound modeling principles. 7 Enter key Excel formulas and functions in models. 8 Use the Goal Seek and Data Table features of Excel to perform meaningful analysis. 9 Develop meaningful charts representing the results of analysis.

S U P P L E M E N T O U T L I N E

L E A R N I N G O B J E C T I V E S

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A2 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

Have you ever approached an intersection as the traffic light changed from green to yellow? What do you do? You need to make a quick decision, whether to stop or go. Further, you probably need to consider several factors before making this decision, such as your current speed or the presence of police cars. In an instant of time, you need to transform these factors into measures such as the odds of an accident if you stop or go, or being stopped by a police officer if you go through the light. How can you accomplish transforming these factors into a decision? Whether you realize it or not, you use a model.

In business, models are used every day to aid in the decision-making process. Mod- els can help executives make strategic decisions about acquisitions and expansions, for example. They can also help clarify tactical decisions such as production and employee scheduling, route planning for vehicles, and product mixes. Sometimes models are embedded in complex information systems, and other times they are simpler and implemented separately. Spreadsheets have become a common platform for the devel- opment and use of many business models because they provide the end-user with tre- mendous flexibility and analytical tools.

What is a model? It turns out that everyone uses models every day of their lives. Most models are very informal, and we use them without thinking about it, such as for the traffic-light decision. In this supplement, we focus on spreadsheet models as a decision-making tool. Note that although we use Microsoft Excel in the examples with this text, most of the software’s capabilities are available in competing spreadsheet packages. This is not an Excel tutorial; it assumes you already have some familiarity with spreadsheets. The main purpose is to help you learn to develop and use spread- sheet models more effectively and efficiently in addressing quantitative problems. A number of spreadsheet concepts and skills will be addressed, but if you need more of an introduction to the use of spreadsheets themselves, specific keystrokes, and menu choices, you should consult an Excel-specific reference.

WHAT ARE MODELS? A number of different types of models exist. The most common are mental models, which we “build” in our heads and use to make decisions. The traffic-light situation calls for a mental model. Visual models use graphics or diagrams to represent real objects or situations. For example, a road atlas represents a system of roads and other key land features. Physical models involve objects that represent other objects, such as an architect’s scale model of a new building. Mathematical models use equations and relationships among quantities to represent situations. Many of the concepts in this textbook are shown through the use of mathematical models. Spreadsheet models are a means of implementing mathematical models.

Although there are a number of different model types, commonalities exist among them. First, the use of models is motivated by a decision that needs to be made, for

� Mental model A decision-making process we conduct in our heads. � Visual model A model in which graphics or diagrams are used to convey real objects or situations. Examples are a map or a graph.

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WHAT ARE MODELS? • A3

example, whether to stop or go at a yellow light or how much of a product to order at one time. Second, all models rely on inputs. Inputs are quantities or factors that affect the situation. Inputs can be controllable or uncontrollable. Controllable inputs, also called decision variables, are quantities or factors that a decision maker can change (usually within limits) for the current situation. For example, in the traffic-light situ- ation, you obviously have control over whether to stop or go, how much pressure to apply to the brakes (if you decide to stop), or how fast to accelerate (if you decide to go). In an order-quantity situation, the decision maker has control over how much quantity to order. Uncontrollable inputs, sometimes called parameters, are quanti- ties or factors that are important to the situation but are outside the decision maker’s direct control. Obviously, the presence of a police officer is outside your control in the traffic-light situation, but it may affect your decision to stop or go. In an order- quantity situation, uncontrollable inputs might represent quantities such as the cost to hold inventory of the material, the cost to place an order for the material, and the demand or usage rate of the material.

Models also have outputs. An output is a quantity or a factor that depends on how the inputs are related to one another. In the traffic-light example, an output might include the probability of an accident. An output generally changes if one or more of the inputs change. For example, an accident is probably more likely under bad weather conditions than good weather conditions. In the order-quantity situation, the primary output would be the total cost of the ordering policy. Secondary outputs would be the total inventory holding cost and the total ordering cost. Some models have several outputs, but usually one or two are considered primary.

What models do, then, is to transform inputs into outputs. In the traffic-light example, you mentally process the inputs (including the stop-or-go decision variable), “calculate” the outputs, and then make a decision. In the order-quantity situation, we would use the mathematical relationships among the input quantities to calculate the total cost. Then we might consider different possible order quantities and choose the one that produces the least total cost.

A model, however, needs to do more than transform inputs to outputs; it must have a purpose. That is, we need to know how a model will help us make a decision. The mental model for the traffic-light situation represents, in a sense, a prediction of the future course of events and helps you to make the decision whether to stop or go. The model in the order-quantity situation represents our future orders, holding costs, and ordering costs. Models should focus on those factors most important to the situation, thereby ignoring other variables. In the order-quantity situation we did not directly consider whether demand was seasonal. This could certainly be included, but it would make the model more complicated. Whether or not we include a particular factor can be a difficult modeling decision. The benefits of including it must be weighed against the increased complexity of the model.

We can now more fully define a model. A model is a purposeful representation of the key factors in a situation and the relationships among them. It is an abstraction of the real situation, and should incorporate enough detail so the results meet the cur- rent needs, but omit unnecessary details. As Albert Einstein said, “Everything should be made as simple as possible, but not simpler.” This statement applies perfectly to modeling.

Figure A-1 shows a generic diagram of a mathematical model. Note the two input types: the “model” box, which is really a set of relationships among the inputs, and the outputs. Thinking back to this diagram will be useful as we begin to develop and use spreadsheet models.

� Physical model A model in which physical objects are used to represent the real objects or situation, usually on a smaller scale. Examples are model cars and buildings. � Mathematical model A model in which quantitative relationships are used to represent a real situation or phenomenon. An example is a weather- prediction model. � Spreadsheet model A mathematical model implemented in the form of a computer spreadsheet. � Inputs Quantities or factors that affect the decision-making situation. � Controllable inputs (decision variables) Quantities or factors that a decision maker can change for the current situation. An example is the order quantity in an inventory planning situation. � Uncontrollable inputs (parameters) Quantities or factors that a decision maker cannot control for the current situation. An example is the unit cost of a raw material that must be purchased to produce a product. � Output A quantity or factor that is calculated from the inputs of a model and is of interest to the decision maker.

� Model A purposeful representation of the key factors in a situation and the relationships among them.

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A4 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

You need to follow basic steps in order to develop an effective spreadsheet model.

1. Ironically, the fi rst step is to turn off the computer and instead draw a picture to better understand the situation. Identify the uncontrollable inputs, the decision variables, and the outputs. Defi ne the logic necessary to transform the inputs into the outputs.

2. On paper, sketch out an overall plan for the model. In general, group the inputs together. Determine where the inputs, intermediate calculations, and outputs will go. Plan to highlight the key inputs and outputs to make the model easier to use for what-if analysis. Determine the formulas relating the inputs to the intermediate calculations and outputs. Th is can be very simple for some models (i.e., Profi t 5 revenue 2 expenses), or it may be quite complicated. In general, the time spent planning a model in this step is normally much less than the time spent debugging an unplanned, completed model.

3. Develop the base case spreadsheet model. Group the inputs together logically. It usually helps to use a color-coding scheme so the user can quickly determine what are the inputs and outputs of the model. Break down the intermediate calculations so that each formula is relatively simple. You can then more easily spot and correct errors. Research has shown that most spreadsheet model developers believe their products to be error-free, but this assessment is usually wrong! Th us, you need to scrutinize formulas and results during and aft er the spreadsheet development eff ort. Use specifi c text labels, including units of measure, so that others reading the model can follow your thought process. Th e outputs should also be clearly labeled and color-coded. For large models (loosely speaking, those that do not fi t in a window screen), it is oft en very helpful to provide a summary of the outputs next to the inputs.

4. Test the spreadsheet model using trial values. Verify the results by hand, if possible. If you have broken down the intermediate calculations into relatively simple formulas, this step is much easier.

5. Use the model to perform the needed analysis. Th is may involve a relatively simple calculation, preparation of a chart, or more substantial analysis. Two common types of analysis are scenario analysis and sensitivity analysis. A scenario is a specifi c set of conditions that could occur in a real situation. A common practice is to look at the base-case, best-case, and worst-case scenarios. Scenario analysis helps a decision manager gain additional insight into a situation. Sensitivity analysis involves studying the changes to the output of the model (e.g., profi t) as one or more of the inputs (e.g., demand) change. Sensitivity analysis helps to identify the inputs that cause the most change in the output. Since the values for the inputs are oft en just estimates, it is important to understand this sensitivity.

� Base case The model containing the “default” or “given” values for the inputs. This is normally the starting point for the analysis.

FIGURE A-1

Basic mathematical model

Uncontrollable Inputs (Parameters)

Mathematical Model: set of relationships

(Spreadsheet Formulas) Controllable Inputs

(Decision Variables) Outputs

THE SPREADSHEET MODELING PROCESS

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EVALUATING SPREADSHEET MODELS • A5

Break-even analysis is one special case of sensitivity analysis. No matter how simple or complex the analysis, the time invested developing a high-quality base- case model will pay off when you need to do additional analysis. Spreadsheet programs contain a number of tools to assist the analyst; we will provide an overview of some of these later in this supplement.

6. Document the model so that others can easily understand it. Remember, others may not think of the problem in exactly the way you do, so descriptive labels and a logical layout are extremely important. Indeed, ideal spreadsheet models are almost “self-documenting” as a result of the way you organize and label them. Cell comments can also be used to document and explain key formulas.

Do not become discouraged if, after you follow these steps, you find that your model does not work or appear the way you intended. All modelers experience this. Realize that modeling is a learning process, and this learning process itself often leads to better decisions than you would make had you not developed a model. Sometimes the things you learn about the real situation during the modeling process are even more important than the numerical results of the model. In any event, models often need substantial revision before you’re pleased with the results.

EVALUATING SPREADSHEET MODELS After you develop a spreadsheet model, how do you evaluate it? One useful way to think about the quality of a spreadsheet is to assess it along three dimensions. Spe- cifically, a spreadsheet model should be correct, flexible, and documented. A spread- sheet model should produce the correct answer for the information given. Usually we think of this in the context of the base case: for the “given” values for the inputs, does the model calculate the correct results? However, a spreadsheet model needs to do more than simply calculate the correct answer.

A spreadsheet model must be flexible in producing accurate results even if the user changes any of the inputs (controllable or uncontrollable). To provide this flex- ibility, users should enter each input only once in the model. For example, consider a model that includes a unit cost as an input. This value, say $3.50, would be entered into a single cell, for instance, B8. Any other cells using unit cost in their calcula- tions would then reference cell B8 rather than having the $3.50 value “hard-coded” inside its formula. In this way, the user only needs to change the data item in a single cell to analyze a new problem. Flexibility is often ignored by people developing a spreadsheet they think will only be used once. Most models in the real world are used repeatedly, with different input data. Even if you think yours is a model that will be used only once, your model will be easier to explain to others if every input is shown explicitly and only once. It is vital to get into the habit of building flex- ible models from the start. The key is to keep all the inputs of the model separate from the formulas of the model. That is, never embed a numerical input inside a spreadsheet formula. If you do, and that problem input changes, then the user must (1) know how to edit formulas, and (2) remember to edit the formula in order for the solution to be correct.

Finally, a spreadsheet model should be well documented. To ensure that others understand the ability of a model, follow these guidelines:

● Include descriptive text labels for all numerical inputs and calculations. Include in the label the units of measure of the quantity (e.g., money, hours, pounds, square feet).

� Correct model A model is correct if it produces the numerically correct values for the outputs for the current values of the inputs. � Flexible model A model is flexible if it produces the numerically correct values for the outputs for any legitimate values of the inputs, without making any changes to the formulas in the model. � Documented model A model is documented if someone else generally familiar with the situation can understand the model without having the model developer explain it in detail. A documented model can be put away for months at a time, and when viewed again by the modeler, is readily understandable.

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A6 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

● Use numerical formatting (Format/Cells/Number) to display the numerical infor- mation in the model. Th e most common useful formats are number (fi xed num- ber of decimal places), currency, and percentage. Others are also useful in certain situations (e.g., date, time, custom formats).

● Apply appropriate cell formatting, such as fi ll colors, font colors, text attributes (e.g., bold, italic), and cell borders. As noted, color-code the inputs and the outputs of the model. A judicious use of coloring can make certain items in a model stand out for the user. Do not overformat, however, as too much is distracting. In the examples provided in this supplement, we use a standard formatting convention.

● Insert Cell Comments for key cells. To use Excel’s Comment feature, right-click any cell. From the submenu, click Insert Comment. You can type anything you want into the box that pops up. Th is box will remain “attached” to the cell and can even be printed with the model. Use comments to add explanatory information about a calculation or assumption that does not have to be in the model itself. Th ey can help remind you of the logic behind a calculation or the justifi cation for a particular value of an input. In many of the spreadsheet examples in this supple- ment, we used the Cell Comment feature to show the formula in a cell.

● Print a copy of the spreadsheet itself with row and column headings, gridlines, and a footer. Also, print a copy of the spreadsheet formulas.

Ideally, a spreadsheet model should be self-documenting. That is, there should be little work involved in documenting a spreadsheet model if you adopt a logical structure for the data and calculations, add descriptive labels that include the units of measure, use numerical and cell formatting appropriately, and provide additional comments to highlight or explain key, or possibly more difficult, aspects of the model.

Before You Go On

A model is a purposeful representation of a real situation, designed to address a particular situation. We use mental models every day to make many decisions. Visual and physical models help people to better understand a situ- ation. Mathematical models comprise a set of relationships linking inputs to outputs. Mathematical models are often implemented using spreadsheets. The spreadsheet modeling process consists of understanding the problem, planning the spreadsheet on paper, developing the base-case model, testing the model, using the model for analysis, and documenting the model. Scenario and sensitivity analysis are useful tools used to gain more insight into the problem. Spreadsheet models should be correct, flexible, and documented.

Sports Feet Manufacturing is considering whether to produce a new line of footwear. The company has considered both the processing needs for the new product and the market potential. The company also estimated that the variable cost for each product manufactured and sold is $9 and the fixed cost per year is $52,000. The selling price is $25 per pair.

• Before You Begin: This example demonstrates the spreadsheet model development pro- cess. Before beginning to develop the spreadsheet model, make sure that you have the necessary input data and that you understand the quantitative relationships. Pay particular attention to the instruc- tion provided on the use of Excel tools.

• Solution: Th e company is mainly interested in the break-even point—that is, how many pairs of shoes must be made and sold (assuming it can sell all that it makes) in order for the total revenue to equal the

EXAMPLE A.1

Modeling the Break-even Point

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EVALUATING SPREADSHEET MODELS • A7

Planning the Model Recall that the first step in developing a good spreadsheet model is to understand the problem and to determine the inputs and the outputs. Part of this step, then, is obvi- ously determining what main question the model will answer. Although we will even- tually want to find the break-even point, the model should answer a more fundamental question: “For some quantity made and sold, what profit (or loss) will be attained?” Thus, the primary output of the model (at least initially) will be the annual profit (loss). In order to compute the profit, we will need the annual revenue and annual total cost. Our inputs include the annual fixed cost ($52,000), the unit variable cost ($9), and the unit selling price ($25). In the context of the current situation, these are uncontrollable inputs. We also have as an input the quantity made and sold. The company can control this quantity (at least to a certain extent), so we classify this as a controllable input. Notice that sometimes the distinction between a controllable and an uncontrollable input is not always clear. Figure A-2 contains a “black box” diagram of the model, showing the inputs and the outputs.

Before starting Excel, we need to think about the organization of the spread- sheet, as well as the relationships (formulas) inside the “black box” of Figure A-2. Our spreadsheet should clearly identify the inputs and the outputs. We separate the inputs from the outputs, so the user knows exactly which values to change in order to perform what-if analysis. This model is small enough so that everything will fit onto one worksheet nicely. If it were a larger problem, the organizational question would be something we would need to consider carefully in order to make a useful, coherent model.

The mathematical relationships of the model essentially become the formulas in the spreadsheet. We will write them out first, however. When developing models, writing out some of the key formulas before committing them to the spreadsheet provides a helpful debugging tool.

In the relationships listed next, the yellow color-coding indicates an input value to the model and a green color indicates the primary output of the model.

total cost. Th is situation is simple enough that you can easily calculate this quantity. Th erefore, we will use this example to demonstrate some of the key spreadsheet modeling concepts and guidelines. Furthermore, there are other reasons a spreadsheet model is appropriate in this case:

• If we design the model to be fl exible, then we can solve any problem with this same basic structure very quickly using the spreadsheet model.

• Spreadsheets provide a good environment for what-if, sensitivity, and scenario analysis. • Spreadsheets provide the ability to create graphs, which can help to communicate the analysis

and result to others (e.g., management).

FIGURE A-2

Black box diagram for Sports Feet Manufacturing

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A8 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

Annual profi t 5 annual revenue 2 annual total cost

Annual revenue 5 unit selling price 3 quantity made and sold

Annual total cost 5 annual fi xed cost 1 annual variable cost

Annual variable cost 5 unit variable cost 3 quantity made and sold

Since we have written out four equations, our spreadsheet model will essentially contain four formulas in cells. Note that we try to keep each equation as simple as pos- sible, rather than combine many calculations in the same formula. For example, with some substitution, you could instead calculate the annual profit as follows:

Annual profi t 5 [unit selling price 3 quantity made and sold] 2 annual fi xed cost 2 [unit variable cost 3 quantity made and sold]

This formula is certainly correct from an algebraic standpoint, and if implemented correctly, the spreadsheet model would calculate the correct annual profit. However, you should break up larger calculations into smaller ones for the following reasons:

● To determine intermediate values. For example, management is oft en interested in both annual revenue and annual total cost, in addition to annual profi t. Th e spreadsheet should show these values explicitly.

● To avoid errors. Mistakes are commonly introduced when writing longer formu- las. Further, you can more easily spot any errors if you have broken down com- plex calculations into smaller pieces. Th us, it is better to write a larger number of simple formulas, rather than a smaller number of more complex formulas.

Implementing the Model in Excel We’ll now turn to the spreadsheet model construction itself. First, use a descriptive label for the worksheet (as opposed to saving it as “Sheet1”), and include a short description of the problem in the upper left of the sheet (see Figure A-3). We’ll walk through the logic of the model here, which follows directly from the identification of inputs, outputs, and relationships we’ve already done. The cells containing for- mulas have cell comments attached to them (Insert/Comment) to help document the model.

FIGURE A-3

Spreadsheet model for Sports Feet Manufacturing

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EVALUATING SPREADSHEET MODELS • A9

In the spreadsheet, we clearly separated the inputs from the outputs. The value inserted for the “Quantity Made and Sold” in cell B9 is just a test value at this point. Recall that this quantity is a controllable input (decision variable), so the decision maker can choose the value for this cell. By making this an input quantity, the user can easily use the model as a what-if tool. Based on the inputs, the calculations are performed. We have used four equations to represent the relationships of the model, which translate into the following five formulas in this spreadsheet:

● Cell B12: Enter the formula “5B5*B9” to calculate the annual revenue. ● Cell B13: Enter the formula “5B9*B7” to calculate the annual variable cost. ● Cell B14: Enter the formula “5B6” to “calculate” the annual fi xed cost. Note that

this is not a calculation, nor is this formula absolutely required, as the fi xed cost is already entered as an input. However, having the model repeat the value here helps to make the logical fl ow clear. When you do this, be sure to enter the formula “5B6”. Do not use Excel’s copy and paste feature to copy cell B6 to cell B14. If you use copy and paste, you will lose the link back to the input. Remember, we want to allow the user to change only the input cells.

● Cell B15: Enter the formula “5B131B14” to calculate the annual total costs. ● Cell B16: Enter the formula “5B122B15” to calculate the annual profi t.

Note the logical flow of the calculations. First we calculate revenue, then the compo- nents of cost, then the total cost, and then profit.

Assessing the Model At this point, we should now assess our spreadsheet model. Specifically, is the model correct, flexible, and documented? To verify correctness, carefully scrutinize the for- mulas. In addition, verify the calculations of the model by hand. For the input values we used:

Annual revenue 5 ($25/pair) 3 (2500 pairs) 5 $62,500

Annual variable cost 5 ($9/pair) 3 (2500 pairs) 5 $22,500

Annual total cost 5 $52,000 1 $22,500 5 $74,500

Annual profi t (loss) 5 $62,500 2 $74,500 5 $12,000

We have now verified the correctness of the model (for the current input quan- tities). But is the model flexible? Yes, because we have entered each input exactly once (cells B5:B7, and B9), and used cell references everywhere else to refer to them. Therefore, the model will correctly calculate revenue, cost, and profit for other input values.

We now ask our last assessment question, “Is the model relatively self- documenting?” Referring back to the previous discussion, our model should include appropriately formatted numerical values, descriptive and specific text labels, and clearly designated input and output cells. As Figure A-3 shows, our model meets these requirements. To format cells as dollars, the “Currency” format is recommended (Format/Cells/ Number). For “large” dollar amounts, show whole-dollar amounts. For smaller val- ues, such as the unit sales price and the unit variable cost, show the cents for greater precision. Note that when entering formulas (such as for revenue), Excel sometimes uses the formatting of one of the cells involved in the formula. If this formatting is not appropriate, change it to something that is!

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A10 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

As a further way to document our model, we should create a page footer containing important information about the file. The recommended footer includes the file name, the worksheet name, author, date, and time. As Figure A-4 shows, you can easily cre- ate a custom footer in the File/Page Setup/Header and Footer dialog box. Every time you print the worksheet, Excel will automatically update the footer information, so you will always know which copy you printed last.

Finally, print a copy of the spreadsheet and the spreadsheet formulas. First, set up the spreadsheet to print showing “Gridlines” and “Row and Column Headings.” The gridlines are the fine horizontal and vertical lines that separate the cells of the work- sheet on the screen, but these do not print by default. The row and column headings are the (1,2,3, . . .) and (A,B,C, . . .) identifiers, respectively. Without these, you will find evaluating a printed spreadsheet more difficult. To set gridlines and row and col- umn headings to print, go to File/Page Setup/Sheet, and then click the “Gridlines” and “Row and column headings” boxes. See Figure A-5.

To print the spreadsheet formulas, first make a copy of the worksheet. Right-click the worksheet tab, click “Move or Copy,” and then in the resulting box, be sure to check “Create a copy.” An exact copy of the current worksheet will be created. On

FIGURE A-4

Custom footer dialog box

FIGURE A-5

Page setup dialog box to set useful printing options

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EVALUATING SPREADSHEET MODELS • A11

the copy, go to Tools/Options/View, and check the “Formulas” box (see Figure A-6). Excel then doubles all column widths, so you may need to adjust the width of columns. Print this worksheet as a record of the formulas. Figure A-7 shows this printout.

Using the Model for Analysis Until now, we focused on the logic of the model, as well as ensuring that it is correct, flexible, and documented. All of this could have been done without knowing any of the specific numerical values for sales price, fixed cost, variable cost, and quantity. We now turn to using the model to help guide decision making.

We know the values for unit sales price ($25), unit variable cost ($9), and annual fixed cost ($52,000). We can now perform what-if analysis; that is, we can enter any value into the Quantity Made cell (cell B9), and the model will calculate the annual

FIGURE A-6

Options dialog box to show formulas

FIGURE A-7

Printout of formulas for Sports Feet Manufacturing

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A12 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

revenue, cost, and profit. For that matter, our what-if analysis can also explore changes to the uncontrollable inputs. Here we’ll vary the order quantity from 0 to 8000 pairs. Table A-1 shows the results provided by the model.

For every additional 1000 pairs that the company makes and sells, profit increases by $16,000. Where does this number originate? Note that from the original problem data, the selling price is $25 and the variable cost is $9. Therefore, the contribution to profit is $25 2 $9 5 $16 per pair, or $16,000 for 1000 pairs. Obviously this finding does not require a spreadsheet model. More complex situations that involve many factors can make it very difficult to find this type of “marginal” result from a direct analysis of the input data. However, if you create and use a flexible spreadsheet model, you can easily find the results.

What is the break-even point, that is, the sales level at which profit is equal to $0? From our what-if analysis, we can see that the break-even point lies somewhere between 3000 and 4000 pairs. One quick method of finding the exact value is to use Excel’s Goal Seek feature. We’ll then check the results using the algebraic approach.

Goal Seek Approach Excel’s Goal Seek feature does exactly what we need here. You access Goal Seek by going to Tools/Goal Seek. A dialog box appears as shown in Figure A-8. The basic idea is that you provide Goal Seek a “set cell,” a “value,” and “by changing cell.” The set cell is the cell in your model that you want to force to a certain value. In our case, we want to force the profit cell (B16) to the value zero. We want to do this by changing (more precisely, allowing Excel to change) the value of the Quantity Made and Sold (cell B9). In the Goal Seek dialog box, type “B16” into the Set Cell field (or click on cell B16), type “0” into the “To value” field, and type “B9” into the “By chang- ing cell” field (or click on cell B9). See Figure A-9. The dialog box here shows absolute cell references ($ signs in the references). Excel puts these in when you click on the cells in the worksheet rather than when you type them directly into the box. For the

� Goal Seek An analysis tool in Excel that allows one to search for the value of an input that results in an output becoming a specific value.

Quantity Profit

0 2$52,000 1000 2$36,000

2000 2$20,000

3000 2$4,000

4000 $12,000

5000 $28,000

6000 $44,000

7000 $60,000

8000 $76,000

TABLE A-1

What-If Analysis: Profit as a Function of Quantity Made and Sold

FIGURE A-8

Goal Seek dialog box (empty)

FIGURE A-9

Goal Seek dialog box (completed)

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EVALUATING SPREADSHEET MODELS • A13

Goal Seek feature, it doesn’t matter whether you use absolute or relative references. After clicking “OK,” you should notice that Excel changes the value in cell B9 to 3250. As a result of this, the profit (cell B16) is equal to $0. When Goal Seek finishes, a box similar to Figure A-10 appears. If you want to keep the results, click “OK”; if you want to revert to the previous values, click “Cancel.” In some cases, the results may not be exactly 3250 and $0, respectively, but will be very close.

Algebraic Approach As discussed in the textbook, the break-even point can be found using the formula Q 5 FSP 2 VC, where F 5 fixed cost, SP 5 unit selling price, and VC 5 unit variable cost. In this case, Q 5 $52,000$25 2 $9 5 3250 pairs. If Sports Feet Manufac- turing makes and sells more than this quantity, the company makes an overall profit; if less, it incurs a loss. We can certainly add this formula to our Excel model. The model not only allows a user to change the trial production quantity but it also calculates the break-even quantity for any values of the fixed cost, selling price, and variable cost. As Figure A-11 shows, the formula “5B6/(B52B7)” has been entered into cell B18 (note the parentheses in the formula; they are important).

Thus, we have two ways to find the break-even quantity. You may wonder why we would want to use the Goal Seek method. The algebraic approach when implemented

FIGURE A-10

Goal Seek completion message

FIGURE A-11

Expanded model with break-even point calculation

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A14 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

in Excel automatically calculates the break-even quantity. However, for many real, complex business situations, no formula can be easily found, but this poses no dif- ficulty for Goal Seek! Also, we can use Goal Seek for more than the simple break-even quantity found here. For example, suppose we want to find the quantity that will allow us to earn a profit of $10,000, not zero. While we could adjust the algebraic formula, an easier method is to change the “To value” field from “0” to “10000” in the Goal Seek dialog box. The result (which you can verify) is that we need to make and sell 3875 pairs in order to make a profit of $10,000. This required no additional algebraic work, just a simple running of Goal Seek.

Goal Seek provides other advantages. Suppose we are fairly confident we can sell 4000 pairs at a price of $25 each, but we are unsure about our estimate of the $9 per pair variable cost. This uncertainty is realistic, especially when dealing with new prod- ucts or costs subject to market uncertainty. Now the question is, “To what value can our unit variable cost rise and still allow us to make a profit?” To address this, we enter “4000” into cell B9 (Quantity). Then using Goal Seek, we enter “B16” and “0” into the “Set cell” and “To value” fields as before, but we enter “B7” (unit variable cost) into the “By changing cell” field. Clicking “OK,” we find that our unit variable cost can be as high as $12 before we start incurring a loss.

These are just a couple of ways we can use Goal Seek to perform additional interac- tive analysis with our flexible spreadsheet model. As you continue building spread- sheet models, you will find many imaginative uses for Goal Seek if you understand its capabilities.

Adding Data Tables Another extremely valuable feature of Excel is called the Data Table, which performs repetitive what-if analysis quickly and easily. For example, the what-if analysis shown in Table A-1 can be set up and run automatically with a Data Table. We will also cre- ate a graph, driven directly from the Data Table results. Best of all, when you change one or more of the model inputs, Excel automatically updates both the Data Table and graph.

Briefly, you provide a list of input values to the Data Table. It then inserts this range of input values one at a time into a cell you specify. Excel calculates the spreadsheet and remembers the results. Then the Data Table inserts the next input number in the range, repeating the process. The user completely controls the values.

The best way to understand Data Tables is to work through an example. We encour- age you to work through this example at the computer. It takes a couple of times to get used to the process of setting up and running Data Tables.

Let’s say we want to create a Data Table that varies the Quantity Made and Sold from 0 to 8000, in 500-unit increments (see Figure A-11). For each of these quanti- ties, we want to record the annual profit. First, we need to determine the location of this Data Table on our spreadsheet. Because we will end up with two columns, one for quantities and the other for profit, we will put the table in columns A and B, starting in row 22. Figure A-12 shows the final Data Table. Let’s review the steps of how to get there.

1. Enter the text labels into rows 22 and 23, as shown in Figure A-12. 2. In cells A25:A41, enter the quantities 0 to 8000, in 500-unit increments. Skip the

row between the label “Quantity” in cell A23 and the value 0 in cell A25. To enter these values, you can enter 0 in cell A25, enter the formula “5A251500” into cell

� Data Table An analysis tool in Excel that allows one to vary one (or two, in a two-way Data Table) input and automatically calculate the value of an output.

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EVALUATING SPREADSHEET MODELS • A15

A26, and copy this formula down. Alternatively, you can use Excel’s fi ll feature (Edit/Fill/Series).

3. In cell B24, enter the formula “5B16” (don’t worry about the cell comment shown in Figure A-12). Th is formula is simply a cell reference to the profi t. In other words, whatever profi t is calculated in B16 will automatically be shown in B24 as well. Th e formula in B24 is what the Data Table command uses to know what you want to keep track of as the “output.”

4. Be careful, this step can be tricky! Select the range from A24:B41. Include the top row, which contains an empty cell in cell A24, and the simple cell reference formula in cell B24. Also, include all rows for which you entered a quantity value. Leave this range selected as you move to Step 5. See Figure A-13.

5. With the A24:B41 range selected, click Data/Table from the Excel menu. Th e dialog box shown in Figure A-14 appears. Leave the “Row input cell” fi eld blank. Put the cursor in the “Column input cell” fi eld and type “B9” (or click on cell B9). Th at is, you’re entering the “Quantity Made and Sold” cell as the column input cell. See Figure A-15. As with Goal Seek, when you click on a cell to enter it into the dialog box, Excel shows it as an absolute reference; however, you can simply type the relative cell reference in the dialog box.

6. Click “OK” on the Data Table dialog box. Th e second column of the Data Table (column B) should fi ll in with diff erent values of profi t, as shown in Figure A-12. In this fi gure, the profi t values have been formatted with the Currency numerical format.

How does the Data Table automatically calculate the output values? Excel sub- stitutes each of the values in the “input” range (the left column, cells A24:A41) into the “Column input cell” (cell B9). Then, Excel calculates the worksheet and puts the “output” (the value in cell B24, which is the Profit value) into the corresponding right-hand column cell. For example, Excel inserts the first input quantity (0) into cell B9 and calculates the worksheet. The resulting profit (2$52,000) is placed in cell B25. Then the next input quantity (500) is inserted into cell B9, the worksheet is

FIGURE A-12

Completed Data Table showing profit as a function of quantity

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A16 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

FIGURE A-13

Range to select for Data Table

FIGURE A-14

Data Table dialog box (empty)

FIGURE A-15

Data Table dialog box (completed)

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EVALUATING SPREADSHEET MODELS • A17

calculated, and the resulting profit (2$44,000) is put into cell B26. Excel repeats this process for all input values in the left-hand column (based on the range you selected in Step 4).

Problem-Solving Tip: If your Data Table works incorrectly:

● Check that you entered a formula in cell B24. Many people just type a value in this cell or copy the formula from B16 to B24; these procedures are both wrong.

● Make sure you selected the entire data table range, A24:B41, in Step 4 (see Figure A-13); this must remain selected in Step 5 when the Data Table dialog box is showing.

● Th e default calculation mode of Excel may have been altered. Go to Tools/Options/Calculation. Set the calculation option to “Automatic.”

The Data Table is now complete. As you can see, it may involve more time, espe- cially initially, but once set up, you have added a very useful tool to your model. Not only have you just done a sensitivity analysis by varying the Quantity Made and Sold, the Data Table now recalculates itself automatically if you change one of the other input quantities. For example, if you want to see what effect a different sales price would have on profitability, simply change the Unit Sales Price value. The Data Table will automatically update with no further work.

Graphing the Model Results Using tables to list quantities and profits is useful, but a graph can sometimes convey information better. For example, Figure A-16 shows a graph of the Data Table results (the range A24:B41 was selected, and Excel’s Chart Wizard was used). Although we do not review the mechanics of Charting here (consult Excel’s on-line help or any standard Excel reference book), you do need to understand the difference between an XY (Scatter) graph (shown here) and a Line graph. For an XY graph, the X (horizon- tal) axis contains a quantitative variable (in our case, Quantity). In a Line graph, the horizontal axis serves as a category axis. For example, if you have sales data by region (e.g., east, west, north, south), use a Line graph to display the data. However, because we have a quantitative variable, we use an XY graph.

The graph (Figure A-16) shows how profit varies as a function of quantity. For a quantity of 0, the profit is 2$52,000 (equal to the fixed cost), and profit rises as a function of quantity. We can see that break even occurs between 3000 and 4000 pairs (3250, as was found earlier).

Before You Go On

With a relatively simple example, we have demonstrated a sound spreadsheet model development process. Using pencil and paper, we developed a picture of the model, identifying the inputs and outputs. Then we determined the relationships to transform the inputs to the outputs. Turning to the spreadsheet, we organized the inputs together, calculated the outputs in a logical sequence, and used formatting and color coding to help highlight key infor- mation. The model should be correct, flexible, and documented. Turning to the analysis, we used simple what-if analysis (to find the values in Table A-1). Then we demonstrated the Goal Seek, Data Table, and Charting capabili- ties of Excel. You should apply these basic modeling principles and analysis tools to other problems you encounter in your study of operations management and other topics.

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A18 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

Planning the Model As before, we first spend time thinking about the problem from a modeling standpoint before delving into the spreadsheet. What are the inputs to this problem? For this problem it is fairly clear: inputs consist of the factors, a weight (importance) for each factor, and scores for how well each location does on each factor. These inputs appear to be uncontrollable, but they are subjective in nature, rather than “hard data” such as selling prices.

FIGURE A-16

XY graph of Data Table results

Antonio’s Italian Restaurant is evaluating three options for a new location. Costs are comparable at all three locations. The company has identified seven factors considered important and has used factor rat- ing (another name for multiple-criteria decision making) to evaluate the three location alternatives based on a 5-point scale, with 1 being poor and 5 excellent. Table A-2 lists the factors, the weights (importance) assigned to each factor, and the score for each factor at each location.

• Before You Begin: This example uses Excel to model a location selection problem. Look at the different factors and their relative weights considered in this decision. Design your worksheet such that the spreadsheet model is flexible, well documented, and correct.

EXAMPLE A.2

Modeling Multiple-Criteria Decision Making

Factor Weight

Factor Score at Each Location (1−5 scale) Factor Location 1 Location 2 Location 3 Appearance 20 5 3 2 Ease of expansion 10 4 4 2 Proximity to market 20 2 3 5 Customer parking 15 5 3 3 Access 15 5 2 3 Competition 10 2 4 5 Labor supply 10 3 3 4

Table A-2 Factor Weights and Scores for Antonio’s Italian Restaurant

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EVALUATING SPREADSHEET MODELS • A19

What should be the output of this model? We need to determine which location is “best.” We will do this with a multiple-criteria scoring model, which takes the fac- tor weights times the factor scores for a location and adds up the results to obtain an overall score for each location. Therefore, the primary output of the model will be the overall scores for each location. We will have some intermediate calculations, and we will also use Excel to automatically extract the best location based on the results.

Constructing the Model in Excel The data presented are already in a tabular format, which is very compatible with Excel. Sometimes you will need to seriously consider the best way to organize the data of a problem. Experience will make this decision easier, but a primary guideline when developing spreadsheet models is that once you have determined a structure for the data to be stored in Excel, the formulas of the model should stay consistent with that structure. We will see that even though there are many Excel formulas, we need to write only a very small number of them. Instead, we will take advantage of relative and absolute cell referencing and the ability to copy formulas.

The first part of the model, containing the data, is shown in Figure A-17. The data colored yellow indicates input values. The SUM formula in cell E13 simply checks the factor weights, as they should total 100. After constructing the model, users will be able to change any of these values to see the effect on the results. For example, if we decide that Appearance is really worth 30 and Customer parking only 5, these changes need to be made only in this section of the model.

Now, the overall score for a given location will be the factor scores for that location, multiplied by the factor weights, and summed together. For example, the overall score for Location 1 will be 5*20 1 4*10 1 2*20 1    1 3*10. We will break down this calculation in the model, as shown in Figure A-18.

After entering the labels in cells A15 and B16, you can center the label in B16 across the columns B, C, and D. Select B16:D16, go to Format/Cells/Alignment, and click “Center across selection” from the Horizontal Alignment drop-down box. A little- known but very handy Excel shortcut is that you can copy text labels by using for- mulas. Look at cell A17, which contains the formula “5A5.” Cell A5 contains the text “Factor.” When this formula is put into cell A17, the contents of cell A5 are put into cell A17. If you later decide to change the word “Factor” to “Criteria,” you only need to

FIGURE A-17

Input section of model for Antonio’s Italian Restaurant

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A20 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

change it in cell A5. This formula in cell A17 was copied down to cell A24 and across to cell D17.

For the quantitative portion of the calculations, we have seven factors and three locations; therefore, 21 formulas need to be entered in the range B18:D24. Although we could certainly write each one individually, this would take time and might introduce errors. Fortunately, we need to write only a single formula in cell B18 and copy it to all the other cells. We do this through the use of relative and absolute cell referencing.

Reviewing Relative and Absolute Cell Referencing Relative and absolute cell referencing is probably the most important Excel concept if you want to become an efficient and effective modeler of business problems in the spreadsheet environment. Having a good understanding of cell referencing will save you time in model development and will make your models less error-prone. Mod- els often contain a repetitive structure that requires a similar formula be applied to a range of cells. Relative and absolute cell referencing allows you to write one for- mula and then copy it, rather than retyping a formula many times. As a result, we’ll spend some time here explaining relative and absolute referencing and the difference between them. However, you should review the information in the Excel on-line help, and experiment on your own with Excel until you have a firm grasp of this important concept.

You enter formulas in Excel by hitting the “5” key. Suppose for cell B18, we enter the formula “5B6*E6.” This formula takes the factor score for Appearance (Location 1), which is 5, and multiplies it by the Factor Weight for Appearance, which is 20, resulting in 100 “points.” This is correct for cell B18. Now, suppose we copy this for- mula down one cell, to cell B19. Will the formula remain the same, or will it change? In this case, it will change, because the original formula was entered with relative ref- erences. Relative references are indicated by the lack of a dollar-sign character (“$”) preceding either the column or the row reference. Relative references change when you copy them. Therefore, the formula in cell B19, after the copy, would be “5B7*E7” because we copied the formula one row down within the same column. If we copy the formula in cell B18 down three rows and over two columns, to cell D21, what would the formula in D21 be? Since we have relative references, the row numbers would increase by three, and column letters would “increase” by two. Therefore, cell D21 would contain the formula “5D9*G9.” This is the wrong formula for cell D21 in the model, but it shows how relative referencing works.

� Relative reference A cell reference that changes when it is copied.

FIGURE A-18

Model calculations for Antonio’s Italian Restaurant

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EVALUATING SPREADSHEET MODELS • A21

Suppose, instead, we enter the formula “5$B$6*$E$6” in cell B18 and then, as before, copy this formula down to cell B19. What will the formula in cell 19 be now? The formula will not change when we copy it. The reason is that the formula in cell B18 has absolute references. Absolute references are indicated by a dollar sign (“$”) preceding the column and row reference. Absolute references do not change when you copy them. In fact, we could have copied this formula to any other cell in the work- sheet, and it would still be “5$B$6*$E$6.”

This appears straightforward: if you want the cell references in a formula to change when you copy them, use relative references; if you want the cell references to stay the same, use absolute references. Unfortunately, it’s not quite that simple. Relative and absolute references can be applied to any cell reference within a formula, not solely to the entire formula. Therefore, part of a formula’s references can change while another part can stay the same. Furthermore, you can even make the column reference abso- lute and the row relative, or vice versa. For example, suppose a formula contains the reference “D$38.” If you copy this formula down (or up), within the same column, the reference will remain “D$38.” In fact, no matter where the formula is copied, the row reference will always be “$38” because the dollar sign makes the row refer- ence absolute. However, if this formula is copied to a different column, the column reference will change, because there is no dollar sign in front of the “D.” Suppose you copy the formula three cells to the right and five cells down. Then the reference in the destination will become “G$38” because column G is three columns to the right of column D. A similar example could be explored for a reference such as “$E44.” Since the column is absolute, no matter where the formula is copied, it will always refer to column E. However, the row is relative, so it will change as the formula is copied to different rows.

Entering Formulas in the Model Let us now return to the example and Figure A-18. The formula shown for cell B18 is “5B6*$E6.” Why did we enter the formula this way? The reason is that we always want to multiply a factor score (contained in the range B6:D12; see Figure A-17) by the Factor Weight, contained in the range E6:E12. The factor weights do not change for the different locations; hence, they must be “absolute.” By entering the formula as shown, when we copy the formula down within column B, the row numbers will change to 7,8,9, . . . , corresponding to the different factors, which is correct. When we copy the formula to the right (for the different locations), the reference to “B6” will change to “C6” for Location 2 and to “D6” for Location 3. However, the reference to “$E6” will remain at “$E6” because the column is absolute. Hence, as the formula is copied to the right, the factor scores for a given location will always be multiplied by the factor weights.

Problem-Solving Tip: To edit formulas in Excel, you can either double-click the cell or hit the F2 key. Excel also provides an easy way to toggle between the different possible absolute and relative ref- erencing options. When you are editing a formula, place the cursor somewhere in the cell reference you want to change and hit F4. Repeatedly hitting F4 will change the relative/absolute referencing of the cell reference. For example, suppose you have entered “5B6*E6” in cell B18. Hit F2 to edit this formula and place the cursor between the “E” and the “6.” Then hit F4. The “E6” reference will change to “5$E$6.” Repeatedly hitting F4 will result in “E$6,” “$E6,” and back to “E6.” The F4 key essentially toggles among the four possible relative/absolute referencing combinations for a particu- lar cell reference.

� Absolute reference A cell reference that stays the same when copied.

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A22 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

We are nearly done with the calculations for the example. We only need to add up the weighted scores for each location. We can do this using the SUM function in cell B25, with this formula copied over to D25. As you can see, Location 1 has the highest overall score (380). Considering these factors, factor weights, and scores, Location 1 is the best location for the new restaurant.

We decide to also create a chart, shown in Figure A-19, to provide a more visual impression of the three location alternatives. This figure, a Column Chart in Excel, provides a quick summary of the overall results. The range graphed is B25:D25. Loca- tion 1 is the best, with Location 3 next, and Location 2 the worst.

As the overall scores are really sums of individual weighted factor scores, we could also develop a chart that shows the contribution of each factor to each location’s overall score. Excel has a Stacked Column chart that we can use here. As Figure A-20 shows, the total height of the columns exactly matches those in Figure A-19, but now

FIGURE A-19

Column chart of overall scores

FIGURE A-20

Stacked column chart showing individual contributions from each factor

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EVALUATING SPREADSHEET MODELS • A23

the relative strengths and weaknesses of each location are observable. For example, Location 1 scores well in terms of appearance, customer parking, and access. Loca- tion 3 provides good proximity to market. Location 2, however, lacks any advantages. The range graphed for the Stacked Column chart is A17:D24, and “Series in rows” is selected in the Chart Wizard. Note that we do not include the “Total Score” row, but Excel automatically stacks the individual scores so the column heights represent the total score for each location. By including column A and row 17 in the selection, Excel automatically generates the x-axis labels and the labels for the legend.

An enhancement to the model will be demonstrated here. As you become more comfortable and familiar with modeling, you will want to make your models more user-friendly. This enhancement will automatically have the model extract the best total score from among the three locations and then identify the best location. You can easily accomplish the first part by using the Excel MAX function.

The MAX function takes a range of values as its input and returns the maximum value from that range (there is a corresponding MIN function that does the oppo- site). Therefore, in cell B27, we enter the formula “5MAX(B25:D25)” to return the maximum overall score, 380 in this case. That part was easy. Now, how do we have the model determine that the best overall score belongs to Location 1? Keep in mind that for different values of the Factor Weights and Factor Scores, the best location may not be Location 1. Therefore, we want a formula that will provide the best location even when the input values change.

In this case we use two “lookup” functions in Excel. The first is the MATCH func- tion. As Figure A-21 shows, MATCH takes three arguments. The first argument, Lookup_value, is the value that we want to test. In this case, it is 380, but more pre- cisely, it is the maximum value of the overall scores. This is what we just found in cell B27. Therefore, enter “B27” as the Lookup_value. The second argument is Lookup_array. Lookup array defines the range of values for which you want to look for a match. In our example, we want to look in the range B25:D25, because these cells contain the total overall scores, one of which is the maximum. The third argument, Match_type, should be set to zero to force the MATCH function to look for an exact match of Lookup_value in the Lookup_array. Figure A-22 shows a completed MATCH dialog box.

As shown in Figure A-22, the result of this particular MATCH function is the value 1. What does this mean? It means that the MATCH function found the Lookup_value in the first position of the Lookup_array. Indeed this is the case; 380 is the maximum score, and it is the first value in the range B25:D25 (which contains the overall scores).

FIGURE A-21

MATCH function dialog box (empty)

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A24 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

We are almost finished. We know that the maximum overall score is in the first position, but how can we get Excel to display, in a user-friendly way, that this corre- sponds to Location 1? The answer is to use the INDEX function. The INDEX function takes up to three arguments (although our example needs only two). The first argu- ment is an array (range), and the second is a column number. The function returns the entry from the range corresponding to the column number you specify. In this case, we will look up the first position in the array B17:D17, which contains the Loca- tion labels. Figure A-23 shows the completed Function Arguments dialog box for the INDEX function. For now, a “1” has just been entered in the Column_num field, to look up the first entry in the array.

Finally, Figure A-24 illustrates an updated section of the model containing the cal- culations (rows 27 and 28). Cell B27 finds the maximum value, and cell B28 uses the INDEX function (with a nested MATCH function) to determine that the 380 overall score is produced by Location 1.

In this model, we first multiplied the factor weights by the factor scores for each alternative and then added to get the total score for each alternative (Figure A-18). This required that we have a second matrix of weighted factor scores. There is a more direct way to do the same calculation, which is useful for larger models. The SUMPRODUCT function takes as inputs two separate ranges (each with the same number of cells). It multiplies each pair of numbers in the ranges (i.e., the first num- ber of the first range by the first number of the second range, and so on) and then

FIGURE A-22

MATCH function dialog box (completed)

FIGURE A-23

INDEX function dialog box (completed)

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EVALUATING SPREADSHEET MODELS • A25

adds up all these individual products. Think about it: this is exactly what the calcula- tions in Figure A-18 do, except they do it in two steps instead of one. Figure A-25 shows the model using SUMPRODUCT to perform the calculations. In cell B17, the formula “5SUMPRODUCT($E6:$E12,B6:B12)” is entered and is copied across to cells C17:D17. This formula is equivalent to the much longer formula “5$E6*B6 1 $E7*B7 1 $E8*B8 1 $E9*B9 1 $E10*B10 1 $E11*B11 1 $E12*B12.” Using the SUMPRODUCT function can make you a more efficient modeler. Use of it also tends to reduce errors with these long, repetitive calculations. The SUMPRODUCT function is very useful in models involving tables of values where you need to mul- tiply the values in one column (row) by the values in another column (row) and add the results. The optimization models in Supplement B make use of the SUMPROD- UCT function.

FIGURE A-24

Completed model with enhancement to automatically report best location

FIGURE A-25

Antonio’s Restaurant model using SUMPRODUCT function

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A26 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

This section provides a brief list of tips that will help you to be a more effective and efficient spreadsheet modeler. Some of these tips have been mentioned in the context of the previous examples.

1. Lay out the data and the model in a logical and consistent way. Th is allows you to take advantage of the ability to copy formulas. Many seemingly complex spreadsheet models really comprise only a handful of truly distinct formulas.

2. Name each worksheet of the workbook. Do not leave them as “Sheet1,” “Sheet2,” etc. Give each worksheet a descriptive name, and delete the unused ones.

3. Learn to use Relative and Absolute References for cells. Th is is vital to being productive when modeling.

4. Use Cut (Ctrl-X) and Paste (Ctrl-V) to move formulas, not Copy (Ctrl-C) and Paste. Cutting and Pasting will keep the cell references correct in the formula(s) you are moving. Copying and Pasting will consider the relative and absolute referencing of cells within the formula and adjust the references accordingly.

5. Using the Shift 1 Arrow keys will select cells in a range, just like the mouse Click 1 Drag. Using the keyboard is easier for many people when precision is needed.

6. Learn to use keyboard shortcuts to select cells and menu items. While the mouse is a great tool, keyboard shortcuts can greatly cut the time it takes to develop a model and perform routine operations. Find out more about keyboard shortcuts by searching in Excel’s help system for “keyboard shortcut.”

7. Set up printouts to print with “Row and column headings” and “Gridlines.” Set these options in the File/Page Setup/Sheet box. Also set Comments to print “As displayed on sheet.”

8. Use Print Preview, Page Setup, and Page Break Preview to get your model to print exactly the way you want. Unlike word processing documents, data moved to a separate page can sometimes dramatically change the usefulness of a printout. You can control all of this relatively easily with these features.

9. Create and use a standard footer for all your spreadsheet printouts. We recommend including the fi le name, the worksheet name, author, date, and time in the footer. Th is can be created as a “custom footer” in the File/Page Setup/ Header and Footer dialog box.

Before You Go On

The multiple-criteria decision model provides a useful framework for many personal and business-related deci- sions. The process of defining the alternatives and the criteria, along with assigning weights and scores, helps you to learn more about the situation and come to a more informed decision. From a technical modeling stand- point, this example shows the importance of being efficient in the spreadsheet model development process. We could have written 21 formulas for the main calculations of the model, but with wise use of relative and absolute cell referencing, we only needed to write one formula that was then copied to the other cells (Figure A-18). The Column and Stacked Column charts are good ways to show the comparison of a relatively small number of alternatives. Finally, we illustrated an alternative, more streamlined way to perform the calculations using the SUMPRODUCT function.

USEFUL SPREADSHEET TIPS

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IMPORTANT EXCEL FORMULAS • A27

10. Print a copy of the spreadsheet formulas in addition to the regular printout. First make a copy of the worksheet (right-click the worksheet tab). Th en go to Tools/ Options/View, and select Formulas (a keyboard shortcut is Ctrl 1 ‘). Adjust column widths as needed and print.

IMPORTANT EXCEL FORMULAS The examples in this supplement demonstrate sound spreadsheet modeling and analysis practices. Two features of Excel that are probably new to most of you were introduced: Goal Seek and Data Table. These two features are valuable when analyzing quantitative models with Excel. Another key feature that many people have not used extensively is Relative and Absolute Referencing.

Our examples used only a few Excel functions. You should get familiar with the Insert Function box as a way to learn about the many different functions (Insert/ Function from menu, or click the fx button on the toolbar). Table A-3 serves as a reference for some of the most often used Excel functions in quantitative modeling. You may be familiar with some of these already. One of the most important aspects of modeling with spreadsheets is to understand what is possible. You don’t have to memorize how to do everything, but if you know what is possible, you can figure the details out when needed.

Function Brief Description Example

SUM(range) Returns the sum of all the numbers in the range.

=SUM(B10:B17)

SUMPRODUCT (range1, range2)

Multiplies corresponding numbers in the two ranges and returns the sum of those products.

=SUMPRODUCT (B10:B17, C10:C17) (returns the value of B10*C10 + B11*C11 + . . . + B17*C17)

AVERAGE(range) Returns the average of all the numbers in the range.

=AVERAGE(B10:B17)

MIN(range) Returns the minimum value of all the numbers in the range.

=MIN(B10:B17)

MAX(range) Returns the maximum value of all the numbers in the range.

=MAX(B10:B17)

COUNT(range) Returns the number of cells containing numerical values.

=COUNT(B10:B17)

STDEV(range) Returns the sample standard deviation (n – 1 denominator) of the values in the range.

=STDEV(B10:B17)

SQRT(number) Returns the square root. =SQRT(E13)

ABS(number) Returns the absolute value. =ABS(E13)

Selected Important Excel Functions

TABLE A-3

(Continued)

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A28 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

Function Brief Description Example

IF(logical test, value if true, value if false)

Checks whether “logical test” is true or false. If true, the result of the function is the “value if true.” If false, the result is the “value if false.”

=IF(F22.=69.5,”Pass”,”Fail”)

MATCH(lookup_value, lookup_array, match type)

Returns the relative positive in the array that matches the lookup value.

=MATCH (B10, B15:B20, 0) (looks in B15:B20 for a value that exactly equals the value in B10, returns the relative positive in the array)

INDEX(range, position) Returns the value from the range in the position specified. Can also be used with two-dimensional ranges.

=INDEX (B10:B17, 3) (returns the contents of the third cell in the range, in this case, B12)

NORMSDIST(z) Returns the standard normal (mean of 0, standard deviation of 1) cumulative probability for the value of z.

=NORMSDIST(1.96) (result is 0.975)

NORMSINV(p) Returns the z value of the standard normal distribution corresponding to a cumulative probability of p.

=NORMSINV(0.975) (result is 1.96)

(Continued)TABLE A-3

SPREADSHEET MODELING WITHIN OM: HOW IT ALL FITS TOGETHER The models in this chapter have been fairly basic, but you should begin to see some of the powerful uses of models and quantitative analysis. Heery International is a con- struction firm with an office in Nashville, Tennessee. The average budget for a proj- ect is about $2,000,000, varying from $50,000 to $50,000,000. Heery has developed a spreadsheet-based model to help it better assign managers to construction projects. As a result of this model, the company reports that the project assignment process takes much less time than before, assignments are perceived as being more fair among the managers, and travel costs (from managers’ homes to job sites) have been reduced considerably, allowing for increased productivity.

A problem in the airline industry is the recovery from unexpected events, such as weather delays, mechanical problems, and crew unavailability. When such a disrup- tion occurs, significant costs to the airlines and inconveniences to passengers can result. For example, when a flight is canceled, that aircraft does not get to its destina- tion, which causes an aircraft shortage at that location. Continental Airlines recently developed, in association with CALEB Technologies, a system to help the airline recover from such events. While it is not spreadsheet-based, it demonstrates the power of modeling to help solve operational problems. CALEB Technologies and Continen- tal Airlines recently won the Franz Edelman Award for Achievement in Operations Research and the Management Sciences.

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DISCUSSION QUESTIONS • A29

Supplement Highlights 1 A model is a purposeful representation of the key factors in

a situation and of the relationships among them. It abstracts the real situation, incorporating those factors that are important to the decision it was designed to address.

2 Th e main types of models are mental models, visual models, physical models, and mathematical models. Spreadsheet models are essentially mathematical models and are the focus of this supplement.

3 Mathematical models translate inputs into outputs through a set of relationships. Inputs consist of uncontrollable inputs and controllable inputs, sometimes called decision variables. Th ere can be many outputs of mathematical models, but oft en we are interested in a relatively small number of primary outputs.

4 Th e recommended spreadsheet modeling process consists of understanding the problem, drawing a sketch of the model, developing a base-case spreadsheet, testing the spreadsheet, using the model to perform analysis, and documenting the model.

5 Models should be correct, fl exible, and documented. Correctness implies that the numerical calculations are correct for the current situation. Being fl exible implies that the user can change any of the input values and the results will be correctly calculated. A well-documented spreadsheet can be understood by someone else without a detailed explanation from the developer.

6 Th is supplement focused on the construction of models by applying sound modeling principles. You should invest time applying the principles to problems in this supplement as well as other problems in this text.

7 Key Excel formulas and functions were addressed in this supplement. A critical skill is the correct use of Relative and Absolute References. Mastery allows you to develop a model in a fraction of the time it would take otherwise. Several important functions were shown in Table A-3.

8 Two useful Excel analysis tools, Goal Seek and Data Table, were illustrated. Goal Seek allows you to fi nd the value of an input that causes an output to be equal to a value you specify. For example, it allows you to determine the quantity (an input) at which profi t (an output) will equal $0. A Data Table allows you to vary one (or two) inputs and automatically calculate the value of an output for each of the input values in the range. We covered Data Tables in which one input was varied, but you can also create two-way Data Tables, in which two input values are varied.

9 Several diff erent chart types were used to illustrate model results. Th ese were the XY chart, the Column chart, and the Stacked Column chart. Other useful chart types for presentation of model results are Pie charts, Line charts, and Bar charts. Excel has many other, more specialized chart types that can be useful in certain situations.

Key Terms mental model A2 visual model A2 physical model A3 mathematical model A3 spreadsheet model A3 inputs A3

controllable inputs (decision variables) A3 uncontrollable inputs (parameters) A3 output A3 model A3 base case A4 correct model A5

fl exible model A5 documented model A5 Goal Seek A12 Data Table A14 relative reference A20 absolute reference A21

Discussion Questions 1. Give an example (besides the ones noted in the text) of a men-

tal model, a visual model, and a mathematical or spreadsheet model. 2. Give an example of a decision-making situation in which

you used a mental model. 3. Give an example of a decision-making situation in which

you used a mathematical or spreadsheet model. 4. Discuss the importance of model flexibility. 5. Discuss the importance of a model being well documented. 6. Explain the steps of the spreadsheet modeling process.

7. Give an example (besides the one used in the text) of when using Goal Seek would be helpful.

8. Give an example (besides the one used in the text) of when using a Data Table would be helpful.

9. What are absolute and relative cell references? 10. Why is it important to use absolute and relative cell

references? 11. When are tables of numbers useful in the decision-making

process? When are graphs of those numbers useful?

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A30 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

Problems 1. Kaizer Plastics produces a variety of plastic items for

packaging and distribution. One item, container #145, has had a low contribution to profi ts. Last year, 20,000 units of container #145 were produced and sold. Th e selling price of the container was $20 per unit, with a variable cost of $18 per unit and a fi xed cost of $70,000 per year.

(a) Construct a correct, fl exible, and documented base-case spreadsheet model that allows the user to easily vary the inputs to the problem and see the resulting revenue, costs, and profi t. What is the profi t level for the base case?

(b) Using the model and Goal Seek, fi nd the break-even quantity.

(d) Th e company is considering ways to improve profi tability by either stimulating sales volume or reducing variable costs. Management believes sales can be increased by 35 percent of their current levels or that variable costs can be reduced to 90 percent of their current level. Assuming all other costs stay at the base-case levels, use the model to determine which alternative would lead to a higher profi t contribution.

(e) Suppose that the two alternatives in (d) are being considered, but that to increase sales by 35 percent, fi xed costs would rise by $5000 annually to cover additional promotional expenses. Similarly, to reduce variable costs to 90 percent of their base-case level would require $10,000 annually to cover costs of new equipment. Make any changes needed and repeat the comparison.

2. Harrison Hotels is considering adding a spa to its current facility in order to improve the list of amenities. Operating the spa would require a fi xed cost of $25,000 per year. Variable cost is estimated at $35 per customer. Th e hotel wants to break even if 12,000 customers use the spa facility.

(a) Construct a spreadsheet model, assuming that selling price is an input to be specifi ed by the user.

(b) Use Goal Seek to fi nd the selling price for which the hotel will break even.

3. Gabriela Manufacturing must decide whether to insource (make) or outsource (buy) a new toxic-free carpet cleaner that works with its Miracle Carpet Cleaning Machine. If it decides to insource the product, the process would incur $300,000 of annual fi xed costs and $1.50 per unit of variable costs. If it is outsourced, a supplier has off ered to make it for an annual fi xed cost of $120,000 and a variable cost of $2.25 per unit in variable costs.

(a) Construct a base-case spreadsheet model that shows both of these alternatives side-by-side. Use the same number of units (stored in a single cell) to drive the calculations.

(b) If the expected annual demand for the new cleaner is 300,000 units, what would you recommend that Gabriela Manufacturing do? Provide evidence to support your recommendation.

(c) Use Goal Seek to fi nd the indiff erence point (where total costs are equal) between these two alternatives. (Hint: Goal

Seek can work only with a single “Set cell.”) Create a cell in your model that computes the diff erence between the total costs of the two alternatives before running Goal Seek.

(d) Construct an XY chart of total cost versus quantity. Graph both alternatives on the same chart. Use a Data Table to generate the values for the chart. (Hint: Th e Data Table will need to track two output values: the total costs of the two alternatives. When setting up the Data Table, it will simply have an additional column, compared to the example presented in the text.)

4. Henri of Henri’s French Cuisine (HFC), a chain of twelve restaurants, is trying to decide if it makes sense to outsource the purchasing function. Currently, Henri employs two buyers at an annual fi xed cost of $85,000. Henri’s estimates that the variable cost of each placed purchase order is $15. Value-Buy (VB), a group of purchasing specialists, will perform the purchasing function for a fi xed annual fee of $100,000 plus $5 for each purchase order placed. Last year, HFC placed 1450 purchase orders.

(a) Construct a base-case spreadsheet model that shows both of these alternatives side-by-side. Use the same number of orders (stored in a single cell) to drive the calculations.

(b) Using last year’s quantity of purchase orders, which alternative would have been the better?

(c) Use Goal Seek to fi nd the indiff erence point (where total costs are equal) between these two alternatives. (Hint: Goal Seek can work only with a single “Set cell.” Create a cell in your model that computes the diff erence between the total costs of the two alternatives before running Goal Seek.)

5. Sue and Joe are a young married couple who are considering purchasing a new home. Th eir search has been reduced to three homes that they both like, at diff erent locations. Th ey have identifi ed the factors they consider important and assigned a factor score to each location based on a 5-point scale. Th e information is shown in the table.

Factor Score at Each Location

Factor Location

1 Location

2 Location

3 Factor Weight

Proximity to work 5 2 1 10 Proximity to family 4 2 5 25 Size of home 2 5 5 15 Transportation system 5 3 1 10 Neighborhood 3 5 2 20 Cost 1 2 4 20

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CASE: DIET PLANNING • A31

(a) Construct a spreadsheet model to help Sue and Joe make their decision. What is your recommendation?

(b) Construct a stacked column chart to illustrate the alternatives.

6. Th e manager of a small health clinic needs to forecast demand for laboratory services in the facility. Data from the last six weeks are available and are, respectively, 330, 350, 320, 370, 368, and 343 tests. Th e manager decides to use a forecasting technique known as the three-period moving average. For this technique, the most recent three weeks’ demands are averaged together to produce the forecast for the upcoming week. For example, weeks 1–3 are used to produce the forecast for week 4.

(a) Construct a spreadsheet model to help the manager forecast demand. Assume that this technique has been in place in the previous weeks as well, and generate the forecasted demand for week 7. You will not be able to generate forecasted demands for weeks 1–3.

(b) Expand the model to calculate the error in the forecast each week. Th e error should be calculated as the actual demand for the week minus the forecasted demand. Which week had the largest error? Which had the smallest error? What does it mean that some errors are positive and some are negative?

7. Burger Lover Restaurant forecasts weekly sales of cheeseburgers. Th e last fi ve weeks of actual sales and forecasted sales are provided in the table.

(d) Expand the model to calculate what is known as the root mean squared error, or RMSE, which is a common measure of forecast accuracy. RMSE is calculated as RMSE 5 "Se 2i /n, where ei is the forecast error for week i and n is the total number of forecast errors calculated. (Hint: To raise a value to a power in Excel, use the carat (“^”) symbol. For example, the formula “5B16^2” squares the value in cell B16. Also, you will need to use Excel’s SQRT function.)

(e) Expand the model to calculate what is known as the mean absolute percent error, or MAPE, which is a common measure of forecast accuracy. MAPE is essentially the average percentage forecast error and is calculated as

MAPE 5 S 1 |ei|/yi 2

n , where ei is the forecast error for week

i, yi is the actual value for week i, and n is the total number of forecast errors calculated.

8. Custom Computers, Inc. assembles custom home computer systems. Th e heat sinks for the central processing units are bought for $12 each and are ordered in quantities of 1300 units. Annual demand is 5200 heat sinks, the annual inventory holding cost is $3 per unit, and the cost to place an order is estimated to be $50. Assume that demand occurs continuously through the year and that a new order is placed in time so the order arrives at the same time as the previous inventory runs out.

(a) Construct a base-case spreadsheet model to help analyze diff erent order quantities. Th e primary output of interest is the total annual inventory-related cost, comprising total ordering cost and total holding cost. Holding cost can be applied to the average inventory level through the year. Given the assumption about continuously occurring demand and that orders arrive just in time, the average inventory level will be half of the order quantity.

(b) For the base-case order quantity of 1300 heat sinks, what is the average inventory level, the number of orders placed per year, the total annual inventory holding cost, the total annual ordering cost, and the total annual cost?

(d) Th e Economic Order Quantity (EOQ) is defi ned as the order quantity that minimizes total inventory-related costs. Construct a Data Table and an XY chart and fi nd the EOQ for this situation.

(e) How much would ordering the EOQ save Custom Computers on an annual basis, compared to its current order quantity?

Week Actual Sales Forecast Sales

1 354 320 2 345 340 3 367 380 4 322 366 5 356 330

(a) Construct a spreadsheet model to compute the forecast error for each week. Th e forecast error is defi ned to be the actual sales minus the forecasted sales and can be positive or negative.

(b) Explain the meaning of positive and negative forecast errors.

(c) Expand the model to calculate what is known as the mean absolute deviation, or MAD, which is a common measure of forecast accuracy. MAD is calculated as MAD 5 Sueiu/n, where ei is the forecast error for week i and n is the total number of forecast errors calculated. (Hint: You will need to use Excel’s ABS function to compute the absolute values of the errors.)

CASE: Diet Planning Given your busy schedule, you eat many meals on the go. Lately, you’ve started to get concerned about the nutritional value of all those burger and fries lunches. You decide to build a model to help you decide ahead of time on a reasonably nutritious diet for a single day at your favorite restaurant.

A number of restaurants (e.g., Burger King, McDonald’s, Wendy’s) have nutritional data for their menu items available on the Web. In some cases these are essentially electronic versions of their printed nutritional brochures; in other cases they are in a form that you can copy and paste into an Excel spreadsheet.

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A32 • SUPPLEMENT A SPREADSHEET MODELING: AN INTRODUCTION

Explore the Web sites of several different restaurants to find available data. Choose a single restaurant for this case. Alter- natively, you can use foods that you might prepare at home. The U.S. Department of Agriculture maintains a Web site con- taining documents that list the nutritional value of foods. See http://www.nal.usda.gov/fnic/foodcomp/.

(a) You want to construct a model so that you can enter the number of each menu item you would choose to consume in one day (e.g., 1 egg and sausage sandwich, 2 cheeseburgers, 3 chocolate shakes), and have the model automatically calculate the total nutritional statistics for that particular choice of items. You will then be able to use the model to adjust your food and drink choices to help plan a more nutritious diet.

(b) Choose a number of menu items as possible breakfast, lunch, and dinner items from the data available on-line. Enter data for these into a spreadsheet model. Enter the nutritional information (e.g., calories, fat, protein, vitamins, and minerals) as well. You should have at least 15 items to choose from.

(c) Build the logic of the model to allow the user to enter the number of each menu item to consume in a day and have the nutritional statistics calculated automatically. If the model is organized well, you will be able to use the SUMPRODUCT function to easily calculate the totals for each of the nutritional categories, given the user’s input of the number of each item to consume. Your model should also include goal or target values for the nutritional items. For example, you might want a diet that has between 1800 and 2200 calories, with no more than 30 percent of the calories from fat. Some facts you will need are that 1 gram of fat contains 9 calories, 1 gram of protein contains 4 calories, and 1 gram of carbohydrate contains 4 calories.

(d) Experiment with diff erent values of items to consume, and develop your planned diet for the day. Are you willing to implement that diet? Discuss advantages and disadvantages of the diet, considering nutritional, taste, and cost factors.

Online Resources 1. Spreadsheets for the two examples in this chapter are available on the CD. 2. Internet Challenge:Web-Based Excel Resources

Th ere are many Internet resources available to learn more about spreadsheets and spreadsheet modeling. Four sites are listed here, but you are not limited to these for this challenge. Th e Spreadsheet Page, by John Walkenbach: http://j-walk.com/ss/ Pearson Soft ware Consulting, LLC: http://www.cpearson.com/ excel.htm MrExcel.com: http://www.mrexcel.com/learnexcel.shtml Th e Spreadsheet Detective: http://www.uq.net.au/detective/ home.html

(a) Using the listed sites and any others you fi nd on your own, identify at least fi ve features of Excel that you were not aware of or familiar with. Briefl y explain the feature and how it might be useful in the modeling and analysis of business problems.

(b) Find at least one more Excel-related site (beside Microsoft ’s) that provides useful information for those doing business modeling. Briefl y describe what is available there and how it could be useful to modelers.

Selected Bibliography Albright, S. Christian, and Wayne Winston. Essentials of Prac-

tical Management Science. Mason, OH: Cengage, 2005. Beskeen, David, Carol Cram, Jennifer Duff y, and Lisa Fried-

richsen. Microsoft Offi ce 2007 Illustrated Introductory, Windows XP Edition. Boston: Course Technology, 2007.

Gross, Debra, Frank Akaiwa, and Kathleen Nordquist. Suc- ceeding in Business with Microsoft Offi ce Excel 2007: A Problem-Solving Approach, First Edition. Boston: Cours Technology, 2008.

Parsons June Jamrich, Dan Oha, Roy Ageloff , and Patrick Carey. New Perspective on Microsoft Excel 2007, Introduc- tory. Boston: Course Technology, 2008.

Ragsdale, Cliff . Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science, Revised, Fift h Edtion. Stamford, Conn.: Th omson, 2008.

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L E A R N I N G O B J E C T I V E S

S U P P L E M E N T O U T L I N E

Introduction B2 Algebraic Formulation B4 Spreadsheet Model Development B7 Solver Basics B9 Setting Up and Running Solver B9 Interpreting the Solution B13

Solver Solution Reports B14 Recap B16 Outcomes of Linear Programming Problems B16 Optimization within OM: How It All Fits

Together B18

After completing this supplement you should be able to

1 Recognize decision-making situations that may benefi t from an optimization modeling approach. 2 Formulate algebraic models for linear programming problems. 3 Develop spreadsheet models for linear programming problems. 4 Use Excel’s Solver add-in to solve linear programming problems. 5 Interpret the results of models and perform basic sensitivity analysis.

Introduction to OptimizationB

SUPPLEMENT

W H A T ’ S I N O M F O R M E ?

MISFIN MKT OMACC HRM

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B2 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

Everyone, almost daily, solves optimization problems in informal ways by using mental models. If you have ever looked at a map when planning a trip and decided upon a route to your destination to try to minimize either distance or time (or even to maximize scenic or recreational benefits), you have solved an optimization problem. If you have ever been faced with too much school work (i.e., studying for final exams and completing final projects) and a fixed amount of time, you have undoubtedly solved an optimization problem that seeks to allocate the time available so that you would, perhaps, maximize your grades in the various courses.

Whereas informal optimization is done nearly every day by individuals, organiza- tions carry out formal optimization to assist in such decisions as product mix, pricing, scheduling, routing and logistics, supply chain management, facility location analysis, and financial planning and asset management. Many of these topics are either directly related to operations management or have strong connections to it. Optimization as a decision support tool has applications across all functional areas of an organization.

INTRODUCTION This chapter provides an introduction to optimization models and solution approaches. Optimization is a major field within the discipline of management science. The emphasis is on developing appropriate mathematical models to describe situations, implementing these models in a spreadsheet, using a spreadsheet-based solver to solve the optimization problems, and using human intelligence and judgment to interpret the results. We emphasize a particular type of optimization problem, called linear pro- gramming problems (or linear optimization). In linear programming (LP) problems, all of the relationships among the variables are linear.

Think briefly about the allocation of study time mentioned in the opening para- graphs. In an optimization mindset, there is an objective you want to either maximize or minimize, and there may be constraints within which you need to operate. There are also specific quantities, called decision variables, over which you have control. Therefore, this is termed a constrained optimization problem. A verbal statement of the study time problem might be that you want to maximize your grade point aver- age. Constraints are a limited total amount of time to study and a desire to pass every course. Decision variables are the amounts of time allocated to each course. A more structured statement of the problem is

Maximize the objective: Grade point average Subject to the constraints: Stay within available study time Pass each course with a grade of at least C Decision variables: Amount of time to spend on each course

Our purpose is not to develop a formal mathematical model of the study time example. However, the actual process of thinking about a problem in a structured way often leads to insights you can use to make a better decision. You would prob- ably think about your current grade in each course, the remaining requirements of the course, and the amount of study time needed in order to at least pass each course. You might also think about how much time you would need to spend in order to get the best possible grade in each course. This structured thinking often leads to a better

� Objective The quantity to be maximized or minimized.

� Constraints Limitations or requirements that must be satisfied.

� Decision variables Quantities under the control of the decision maker.

� Constrained optimization problem A mathematical model in which one is trying to maximize or minimize some quantity, while satisfying a set of constraints.

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INTRODUCTION • B3

overall decision, even if a formal mathematical model is never developed. Even though this example is fairly simple, there are multiple factors to consider when ultimately making the decision. For example, if you spend the time required to get the best grade possible in a particular course, does that jeopardize your ability to pass another class?

The advantage of formal optimization modeling is that it can simultaneously con- sider the effects of alternative decisions to produce the best overall decision according to the objective. Humans are capable of considering a few factors simultaneously. As the size and complexity of a problem increase, humans cannot adequately keep in mind all of the effects of a decision. Large-scale optimization software can handle thousands or even millions of decision variables and constraints. Human intelligence is still needed in the problem definition, or formulation, stage of problem solving and in the interpretation of the results of the computer solution. Computer algorithms are best suited to taking a well-formulated problem definition and making the necessary computations to produce the best solution to that problem. In this chapter we focus on problem formulation, spreadsheet model development, solution with Solver, an Excel add-in, and interpretation of the results.

The steps involved in solving optimization problems are shown in Figure B-1. These steps should be looked at as a guide.

FIGURE B-1

Steps involved in solving optimization problems

DJJ Enterprises manufactures automotive parts. Two of these parts are camshafts and gears. Camshafts earn a profit of $25 per unit and gears earn $18 per unit. Three major resources are utilized in the production process: steel, labor, and machine time. It takes 5 pounds of steel to make a camshaft and 8 pounds to make a gear. Camshafts require 1 hour of labor; gears require 4 hours. It takes 3 hours of machine time per camshaft and 2 hours per gear. For the current planning period, 5000 pounds of steel, 1500 hours of labor, and 1000 hours of machine time are available. DJJ would like to maximize profit during the current planning period, within allowable resources.

• Before You Begin: Deciding on how much of each product to produce (the product mix) is the focus of this problem. In order to formulate this problem, identify the objective function and the profit associated with each product to be made and sold. Next, determine the constraints. The constraints limit resources. In this problem, there are limited amounts of steel, labor, and machine time. Put the objective function and constraints into a spreadsheet model and use Solver to find the maximum-profit product mix.

EXAMPLE B.1

Product Mix Decision

Steps Involved in Solving Optimization Problems

• Understand the problem, perhaps by drawing a diagram which represents the problem. • Write a problem formulation in words, including decision variables, objective function, and

constraints.

• Write the algebraic formulation of the problem. • Define the decision variables. • Write the objective function. • Write the constraints.

• Develop a spreadsheet model. • Set up the Solver settings and solve the problem. • Examine the results and make corrections to the model. • Analyze and interpret the results.

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B4 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

ALGEBRAIC FORMULATION The text description of the problem is fine for communicating a general understand- ing, but in order to address the problem using optimization we need to develop a formal algebraic description, called a formulation. A formulation contains explicit definitions of the decision variables, an algebraic expression of the objective function, and algebraic statements of the constraints. This is not as hard as it sounds, but it is a crucial step if you hope to develop a high-quality spreadsheet model that Solver can successfully solve.

A diagram of the situation can help to structure the problem so that the algebraic formulation can be written down more easily. A well-constructed diagram is also a valuable communication tool. In this situation, three resources are combined to pro- duce two products. Specific amounts of each resource are needed, and each product generates a known profit. See Figure B-2 for one way to show this. One of the impor- tant points is that the total amount of each resource available must be shared between the two products (assuming both are to be produced).

A diagram, coupled with a text-based formulation, often serves as a valuable step- ping stone to a formal algebraic formulation. In this situation, a text-based formula- tion can be stated as follows:

Text-Based Formulation

● Decision variables: Number of camshaft s to make, number of gears to make ● Objective function: Maximize profi t ● Constraints: Must not exceed our resource availability in steel, labor, and machine

hours

The text-based formulation helps to structure the problem. The next step is to write the algebraic formulation. The algebraic formulation states the decision variables, objective function, and constraints in algebraic terms. The decision variables corre- spond to the quantities of camshafts and gears to make. We do not know ahead of time the best values for these (that’s what we’re trying to determine), so we create a variable to represent each quantity, as follows:

� Formulation A formal, algebraic statement of a constrained optimization problem.

FIGURE B-2

DJJ Enterprises diagram

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ALGEBRAIC FORMULATION • B5

Decision Variables C � number of camshaft s to make G � number of gears to make

The actual names of the decision variables are not crucial (some people prefer X1 and X2, for example); however, it is helpful to name the variables so the names remind you of the quantities they represent.

The next step is to write the objective function in terms of the decision variables just defined. The objective function is simply an expression that determines how much profit will be earned if C camshafts are made and G gears are made. We also indicate whether we want to maximize (“Max”) or minimize (“Min”) the objective function. Since we know the unit profits of each, the objective function is

Objective Function

Max 25C 1 18G 1profit, $ 2 It is good practice to include the units and the quantity represented by the objective function, as shown in parentheses.

Finally, we need to write expressions for the constraints. In this example, we are limited by the amounts of the resources—steel, labor, and machine time—available. For steel, we know that 5000 pounds are available. We also know that each camshaft uses 5 pounds and each gear uses 8 pounds. Therefore, if we make C and G camshafts and gears, respectively, it will require 5C � 8G lb of steel. In order for this product mix to be possible, this combined total must not exceed 5000 pounds. We write this in mathematical terms as follows:

5C 1 8G # 5000 1steel, lb 2 Using similar logic, we write the constraints for labor and machine time. Convince yourself that these constraints should be written as

1C 1 4G # 1500 1 labor, hr 2 3C 1 2G # 1000 1machine time, hr 2

Each of these constraints is a less-than-or-equal-to constraint. A constraint can also be a greater-than-or-equal-to constraint or an equality constraint. A problem can have a mix of these constraint types. For example, it could be that we are required to produce at least a certain number of camshafts and gears combined (� constraint). Alternatively, perhaps we want to force our solution to use exactly 1500 labor hours (� constraint). Strict inequalities (�, �) are not used in optimization problems; the three constraint relationship types are �, �, and �.

There are two additional constraints that initially may not seem required. They force each of the decision variables to remain nonnegative, that is, greater than or equal to zero. We refer to these as the nonnegativity constraints. From a business sense we know that we cannot produce negative quantities of camshafts and gears. However, neither a mathematical model nor a spreadsheet model “knows” these seem- ingly obvious things. These constraints are written:

C $ 0

G $ 0

Most of the time, these would be written together as

C, G $ 0 1nonnegativity 2

� Less-than-or-equal-to constraint A constraint such as 3x1 1 5x2 # 22, often used to model a limitation on the amount of a resource that can be used.

� Greater-than-or-equal-to constraint A constraint such as 4x1 1 7x2 $ 50, often used to model a requirement that must be satisfied.

� Equality constraint A constraint such as 6x1 � 3x2 � 30, used to specify that a requirement must be met exactly.

� Nonnegativity constraints Constraints of the form x1 $ 0, which are nearly universal in linear programming problems. They are used to represent the fact that negative quantities of products cannot be made, shipped, etc.

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B6 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

Putting all the constraints together, we have

Constraints

5C 1 8G # 5000 1steel lb 2 1C 1 4G # 1500 1 labor, hr 2 3C 1 2G # 1000 1machine time, hr 2

C, G $ 0 1nonnegativity 2 Normally, we consolidate the entire formulation together into a standard format, as follows:

Complete Formulation

Decision variables:

C 5 number of camshafts to make G 5 number of gears to make

Max: 25C 1 18G 1profit, $ 2 Subject to (or, “s.t.”):

5C 1 8G # 5000 1steel, lb 2 1C 1 4G # 1500 1 labor, hr 2 3C 1 2G # 1000 1machine time, hr 2

C, G $ 0 1nonnegativity 2

Examining the Formulation The formulation is a concise mathematical description of the problem. It indicates that we want to find the values of C and G that produce the largest value of the objective function while satisfying all the constraints. In this formulation all of the relationships among the variables are linear. That is, all expressions involving the variables C and G consist of a constant multiplied by the variable itself. Combinations of variables can be added (or subtracted) to one another, but there are no nonlinear expressions involv- ing variables, such as C 2, G/C, or "C. Thus, this formulation is referred to as a linear program (LP), or an LP formulation. A linear program (LP) is an optimization prob- lem in which all of the relationships among the decision variables are linear. LPs are much easier to solve, in general, than problems involving nonlinear expressions. The solver built into Excel has the capability to solve both linear and nonlinear problems, but if your problem can be formulated as an LP, it is best to do so because its solution algorithm is faster and more reliable.

A feasible solution to an optimization problem in general, or an LP as in this case, is a particular combination of C and G that satisfies all of the constraints. An infeasible solution violates at least one of the constraints. The optimal solution is the feasible solution with the largest (for a “Max” problem) or smallest (for a “Min” problem) objective function value.

Consider the solution C � 75, G � 200. Is this solution feasible? To determine if it is, evaluate each of the constraints. The amount of steel required would be 5(75) � 8(200) � 1975 lb, which is less than the 5000 pounds available. Similarly, the solution requires 1(75) � 4(200) � 875 hr labor (less than the 1500 hours available), and

� Linear program (LP) A constrained optimization problem in which all the functions involving decision variables are linear.

� Feasible solution A specific combination of values of the decision variables such that all of the constraints are satisfied.

� Infeasible solution A specific combination of values of the decision variables such that at least one of the constraints is violated.

� Optimal solution The feasible solution with the largest (for a maximization problem) or smallest (for a minimization) objective value.

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SPREADSHEET MODEL DEVELOPMENT • B7

3(75) � 2(200) � 625 hr machine time. Both C and G are nonnegative, so this solu- tion is feasible. Now consider C � 300, G � 200. Verify that this solution requires 3100 pounds steel, 700 hours of labor, and 1300 hours of machine time. Is the solution feasible? It satisfies two of the constraints, but violates the machine time constraint. Therefore, this solution is infeasible. The point is that if a solution violates even a single constraint, the solution is infeasible.

Is either one of these solutions the optimal solution? The second solution, C � 300 and G � 200, cannot be optimal because it is not even feasible. The first solution, C � 75 and G � 200, is at least feasible. Actually, it is one of an infinite number of feasible solutions to this problem. However, it is not the optimal solution. You can try to find a better feasible solution. In fact, for this small problem, you could probably, using pencil, paper, and a calculator, find the optimal solution. If the problem were larger, perhaps with 15 products and 8 constraints, finding the optimal solution by trial and error would be nearly impossible.

An ingenious algorithm has been developed to solve LPs. It is called the Simplex Method and was developed by George Dantzig in 1947. Improvements have been made in solving large LPs since then, but the Simplex Method is still the most com- mon method for solving them. Learning the intricacies of the Simplex Method is a significant study in itself. Fortunately, Solver, part of Microsoft Excel, implements the Simplex Method. The user does not need to learn the details of the mathematical manipulations, but can focus on developing a high-quality spreadsheet model from an algebraic formulation before setting up the problem in Solver. Once Solver is able to find the optimal solution, the user can perform additional analysis to gain insight into the business problem.

� Simplex Method A mathematical algorithm developed by George Dantzig that, when implemented in software, can solve LPs very quickly.

SPREADSHEET MODEL DEVELOPMENT Now that an algebraic formulation has been developed, we will implement the formu- lation in the spreadsheet. There is no single way to set up an LP in a spreadsheet, but there are some guidelines that can be established:

● Develop a correct, fl exible, and documented model, as described in Supplement A. Th e user should be able to use the model in a what-if manner, changing any deci- sion variable or coeffi cient in the model without having to change any formulas.

● Divide the worksheet into three sections: one for the decision variables, one for the objective function, and one for the constraints.

● Use the algebraic formulation and the natural structure of the problem to guide the structure of the spreadsheet.

● Use one cell for each decision variable. ● Store the coeffi cients of the objective function in separate cells, and use another

cell to store a formula that calculates the value of the objective function (by refer- ring to the decision variable cells and the coeffi cient cells).

● Likewise, store the coeffi cients of the constraints in cells, and write formulas to compute the “left -hand-side” (LHS) value of each constraint. Th e LHS value is the value of the constraint expression to the left of the �, �, or � sign. Th en store the “right-hand-side” value (RHS value) of each constraint next to this formula for easy comparison.

� LHS value The value of the constraint expression to the left of the �, �, or � sign.

� RHS value The value of the constraint expression to the right of the �, $, or 5 sign.

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B8 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

A spreadsheet for this problem is shown in Figure B-3. It uses C � 75 and G � 200 as trial values. Note the structure of the model: column A is used for text labels, columns B and C are used to represent camshafts and gears, respectively, and columns D–F are used to compute formulas and show the resource limitations. There is a row (row 5) reserved to contain the decision variables, a row (row 8) to store the coefficients and compute the value of the objective function, and one row for each of the constraints (rows 11–13). As in Supplement A, values highlighted in yellow rep- resent data provided with the problem, and the light blue cell represents the primary output measure, the objective function. In addition, green cells represent the decision variables.

Cells B5:C5 contain values of the decision variables. The user can change these to see the effects of different solutions, visually checking to see if the constraints are satisfied. Cells B8:C8 contain the objective function coefficients, in this case, the unit profits. Cells B11:C13 contain the coefficients of the constraints. Refer to the algebraic formulation given previously. The first constraint is 5C � 8G � 5000. The coefficients are 5 and 8, meaning that 5 lb of steel are required for each camshaft and 8 lb of steel are required for each gear. In cell F11, the value 5000 is stored.

The formula in cell D8 is �B8*B$5�C8*C$5. This references the objective coeffi- cients (B8:C8) and the decision variables (B5:C5) to compute the total profit. If either a decision variable or a coefficient changes, the total profit will still calculate properly. Copy this formula to cells D11:D13 to compute the LHS values of the constraints, that is, the actual resources used by the solution. You could also write three additional formulas in cells D11:D13, but using absolute and relative references allows you to write one formula and copy it to other cells needing formulas. Cell D11 computes the amount of steel used by the current solution. A � sign is entered into cell E11 for readability, and the RHS value is entered in F11.

Testing the Model After the base-case model is developed, you should enter some trial values into the cells as decision variables in order to check the model’s calculations. The trial solution shown in Figure B-3 is the same solution we discussed when defining feasible and infea- sible solutions. Note that the spreadsheet calculates $8800 profit, 3300 pounds steel used, 1500 hours labor used, and 1000 hours machine time used. These values match those noted previously. We can quickly see from the model that this is a feasible solu- tion by observing that the values in cells D11:D13 are all � the values in cells F11:F13.

FIGURE B-3

Spreadsheet for DJJ problem

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SETTING UP AND RUNNING SOLVER • B9

If the spreadsheet model is larger, the model testing phase is more involved. Finding errors in spreadsheet models is generally not easy, which makes this step especially important. No matter how much a spreadsheet has been used, you should always think about the outputs generated by a model to see if they make sense given your knowledge of the situation. Excel has tools available to help diagnose problems in models. These are Error Checking (Tools/Error Checking) and Formula Auditing (Tools/Formula Auditing). These are not demonstrated in this supplement, but every spreadsheet modeler should be familiar with them.

SOLVER BASICS Given the spreadsheet model, you could use it to try out different solutions. As we saw previously, it is easy to tell whether a solution is feasible. Therefore, through some trial and error, you could most likely find the optimal solution to this problem. Since there are only two decision variables, this probably will not take you long. However, for a larger problem, finding the optimal solution using this approach would at best take much longer, and most likely you would never find the optimal solution.

Excel’s Solver add-in solves optimization problems. An add-in is an additional piece of software that enhances Excel’s core capabilities. By default, Solver does not load automatically when Excel starts. To load Solver, go to Tools/Add Ins and select Solver from the list that appears. If this is the first time you have used Solver on your machine, you may be prompted to insert your Microsoft Office CD so that the Solver software can be copied onto your hard disk. Once you have selected (and possibly installed) Solver from the Add Ins list, it will automatically be loaded every time Excel starts until you deselect it from the Add Ins list.

Problem-Solving Tip: Solver Is Not in the Add Ins List If you are using Excel 2000 or later, Solver should appear on the Add Ins list when you select Tools/Add Ins. Th e fi rst time you run Solver, you may be prompted to insert your Microsoft Offi ce CD. You will only need to do this once. If you are using Excel 97 or earlier, Solver does not necessarily appear in the Add Ins list. If it does not, you will need to fi rst quit Excel and run the Microsoft Offi ce Setup program from the Microsoft Offi ce CD. Follow the on-screen prompts to install Solver.

SETTING UP AND RUNNING SOLVER To set up Solver for the DJJ problem, choose Tools/Solver from the menu. A dialog box similar to the one shown in Figure B-4 appears. Setting up an optimization prob- lem with Solver consists of four steps:

1. Specify the Target Cell (corresponds to the objective function in the algebraic model).

2. Specify the Changing Cells (corresponds to the decision variables in the algebraic model).

3. Specify the Constraints. 4. Specify Solver Options.

These steps are easy if you have previously completed the algebraic formulation and spreadsheet model. They are very difficult, and perhaps impossible, if you haven’t. Most difficulties students have with Solver are not really with Solver, but arise because

� Target Cell In Solver, the cell to be maximized or minimized. This cell corresponds to the objective value.

� Changing Cells In Solver, the cells that can be changed during the optimization process. These cells correspond to the decision variables.

� Constraints In Solver, the limitations and requirements that must be met.

� Solver Options Solver provides many options for the solution process. For LPs, the two most commonly used are Assume Linear Model and Assume Non-Negative.

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B10 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

they either have not fully understood the problem or developed an algebraic formula- tion or have not developed a correct and flexible spreadsheet model.

The Target Cell is the cell that you want to maximize or minimize. That is, it rep- resents the objective value of the model. In this example, cell D8 calculates the profit obtained from some combination of camshafts and gears. The Target Cell must always contain a formula; otherwise Solver generates an error message. To specify D8 as the Target Cell, either click on cell D8 directly or type “D8” into the Target Cell field. Then make sure the radio button on the “Equal To” line is set to “Max.” There are three choices on this line, but we will only deal with maximizing and minimizing problems. Click in the “By Changing Cells” field. The Changing Cells represent the decision variables of the model—in our case, the numbers of camshafts and gears. These values are stored in cells B5 and C5. The easiest way to enter these into the By Changing Cells field is to simply select them directly in the spreadsheet using either the mouse to select both cells or by clicking on one cell, holding down the Shift key, and then using the keyboard arrow keys to select all of the pertinent cells. Alternatively, the range “B5:C5” can be typed directly in the dialog box. The Solver Parameters box to this point is shown in Figure B-5. Note that if you type in the cell references directly for the Target Cell and the Changing Cells, you do not have to type in the “$” characters. This is one feature where it doesn’t matter if you use absolute or relative cell referencing (see Supplement A for more information on absolute and relative references).

Entering the constraints is slightly more complicated, but not difficult if the spreadsheet model is designed well. To enter a constraint, click the “Add” button. A box appears, as shown in Figure B-6. A constraint in Solver consists of three pieces of

FIGURE B-4

Solver Parameters box (empty)

FIGURE B-5

Solver Parameters box (partially complete)

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SETTING UP AND RUNNING SOLVER • B11

information: a “Cell Reference,” which should refer to a spreadsheet cell containing a formula; a relationship operator, which can be �, �, or �; and a “Constraint,” which should refer to a spreadsheet cell containing a value. More specifically, a constraint is of the form

LHS Cell 5# ,5 , or $6 RHS Cell Consider our first constraint, dealing with steel. The algebraic form of this constraint is

5C 1 8G # 5000 1steel, lb 2 and is implemented in row 11 of the spreadsheet. Cells B11 and C11 store the coef- ficients, cell D11 contains the formula “�B11*B$5�C11*C$5,” cell E11 contains the � relationship, and cell F11 contains the value 5000. How do we fill in the Add Con- straint box in Figure B-6? Since cell D11 computes the amount of steel actually used by a given combination of camshafts and gears, D11 is the “LHS Cell” for the constraint. Similarly, since F11 contains the amount of steel available, it is the “RHS Cell” for the constraint. The completed Add Constraint box is shown in Figure B-7.

We have two other constraints to enter, so click “Add” on the Add Constraint box. If you have already clicked “OK,” then simply click “Add” on the Solver Parameters box. The other two constraints are entered in exactly the same way. They are both � constraints. Using the previous logic, these constraints should be entered as

D12 # F12

D13 # F13

Once you have entered the last constraint, click “OK” on the Add Constraint box. The completed Solver Parameters box is shown in Figure B-8.

Problem-Solving Tip: What if some of the constraints are � or � constraints? Most problems contain more than one type of constraint. For these, simply pull down the arrow at the center of the Add Constraint box and change the relationship to either � or �, as appropriate. Th ere are two additional choices, “int” and “bin” (not shown in the fi gures). Th ese choices are for problems with integer and binary variables, which are beyond the scope of this example. Th ey represent a special type of restriction on the decision variables.

FIGURE B-6

Add Constraint box (empty)

FIGURE B-7

Add Constraint box (complete)

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B12 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

We have completed the first three steps of specifying the problem to Solver. To complete the fourth step, click the “Options” button on the Solver Parameters box. On the resulting Solver Options dialog box, select “Assume Linear Model” and “Assume Non-Negative,” as shown in Figure B-9. Don’t worry about or change any of the other options. Once you have done this, click “OK” to return to the Solver Parameters box.

The Assume Non-Negative option adds the requirement that each of the Changing Cells be � 0. This specifies our requirement in the algebraic formulation that C � 0 and G � 0. The Assume Linear Model option tells Solver to use the Simplex Method, mentioned previously. Solver actually comprises two solution algorithms, the Simplex Method (for linear programming problems) and a more general solution algorithm that can solve both linear and nonlinear problems. For linear programming problems, the Simplex Method is much faster and more reliable than the more general algorithm. It also provides more sensitivity analysis information.

Solving the Problem The problem has now been completely specified. To actually solve it, click “OK” on the completed Solver Parameters box. Solver then applies the algorithm to the problem

FIGURE B-8

Solver Parameters box (complete)

FIGURE B-9

Solver Options box (complete)

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INTERPRETING THE SOLUTION • B13

and produces a completion message, as shown in Figure B-10. The values of the Changing Cells (and any other cells dependent on them) also will change in the model. It is very important to read the Solver Results box. This one says “Solver found a solu- tion. All constraints and optimality conditions are satisfied.” If you did not obtain the same message for this problem, you should go back and work through the problem again, making sure each step is followed carefully. This message essentially means that Solver was successful in finding the optimal solution, and the solution shown in the spreadsheet is guaranteed to be the optimal solution to the model. There are many possible messages for the Solver Results box. Later we discuss some of the more com- monly encountered ones and their meanings. You may notice that you can generate reports (Answer, Sensitivity, and Limits) from this dialog box as well. These reports can help you understand more about the solution and generate additional insights into the business problem. These are discussed later.

INTERPRETING THE SOLUTION As shown in Figure B-11, the optimal solution is to make 100 camshafts and 350 gears. Doing so will yield a profit of $8800 and will require 3300 pounds of steel, 1000 hours of labor, and 1000 hours of machine time. Notice that we don’t use all the steel that is available. In fact, there are 1700 pounds of steel unused. However, all of the labor and machine time is used. Having steel left over might seem nonintuitive, but making more of either camshafts or gears (to use up more steel) would also require more labor and machine time, of which we have no extra. Therefore, we are at a limit of sorts in how far we can “push” this solution. In general, if we have more constraints than deci- sion variables, there will some constraints that are not at their limits. Constraints at

FIGURE B-11

DJJ optimal solution

FIGURE B-10

Solver Results box

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B14 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

their limits when at the optimal solution, that is, with the LHS value equal to the RHS value, are called binding constraints. Those that are not at their limits are called non- binding constraints. Here the binding constraints are labor and machine time, and the nonbinding constraint is steel. As noted, we have 1700 pounds of steel remaining at this solution. This value is called the slack of the constraint and is simply the differ- ence between the RHS value and the LHS value (slack values for binding constraints are equal to 0). This is a fundamental insight gained from the solution. Before solving the problem, we didn’t know that labor and machine time would run out, in a sense, before steel. Putting this situation into practical business terms, if we want to do even better than this optimal solution, we will need to address the resource limitations of labor and/or machine time or find another use for the excess steel.

In specifying the model to Solver, we must be careful to retain the flexibility of the model. This is done in Solver by using only cell references, not specific values. Now we have a powerful tool for analysis of the problem. We can change any parameter value in the model (any value highlighted in yellow), go to Tools/Solver, and click “OK.” Solver remembers all the problem settings, so we don’t need to reenter them. For example, if we have more labor and/or machine time available, we can enter the new value(s) in cell F12 and/or F13, run Solver again, and immediately see the impact of the modified resource amount. More importantly, we should do this before we actu- ally commit to paying overtime for labor or committing more machine time; this way we can determine if, indeed, it is worthwhile. This ability to immediately reoptimize the problem is extremely powerful and can lead to additional insights to assist in deci- sion making.

� Binding constraints Constraints that are satisfied exactly at the optimal solution.

� Nonbinding constraints Constraints that are satisfied at the optimal solution, but as strict inequalities (� or �).

� Slack The difference between the left-hand side and the right- hand side of a constraint. Loosely speaking, the amount of a resource left over (for a # constraint) or the amount by which a requirement is satisfied (for a $ constraint).

SOLVER SOLUTION REPORTS Three reports are available when Solver has successfully found an optimal solution. These are the Answer Report, the Sensitivity Report, and the Limits Report. They are shown as choices when Solver completes its work and displays the Solver Results box (Figure B-10). To generate these reports, select them and then click “OK.” Solver inserts three new worksheets in the workbook. A detailed discussion of these reports is beyond the scope of this supplement, but an overview is provided of the Answer and Sensitivity Reports. Refer to one of the management science texts listed at the end of this supplement for more detailed information.

The Answer Report for this problem is shown in Figure B-12. It contains three sec- tions, one each for the Target Cell, Adjustable (Changing) Cells, and the Constraints. Shown in this report are the “original” and “final” values. The original values are the values that were in the cells when Solver started its calculations, and the final values are the optimal values. By looking at the Target Cell and Adjustable Cells sections, you can identify that the optimal solution contains 100 camshafts and 350 gears and yields $8800 in profit. The Constraint section contains the cell value (the LHS value) of the constraint, the actual constraint itself as specified to Solver (the Formula column in the report), whether the constraint is binding or nonbinding, and the slack value. Two constraints are shown as binding, as we have already discovered by analyzing the solution in the spreadsheet.

The Answer Report doesn’t provide any new information compared to what is available from an analysis of the final spreadsheet. However, it puts the information into a standard format. If two people develop spreadsheets for the same problem, they will likely look different. However, if the models are set up and solved correctly, the Answer Reports will look very similar. Another use of the Answer Report is as a

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SOLVER SOLUTION REPORTS • B15

debugging tool. Looking in all three sections, we find that the explicit cell references are given for the Target Cell, the Adjustable (Changing) Cells, and the Constraints. The Answer Report provides the settings that were actually set up in Solver, which sometimes differ from what modelers think they told Solver!

The Sensitivity Report, shown in Figure B-13, contains information that is not directly available in the spreadsheet model itself. There is one section for the Adjust- able (Changing) Cells and one for the Constraints. This report tells what will happen when certain changes are made to the model. We will not discuss all the different aspects of this report; however, two pieces of information from the Sensitivity Report can quickly provide insight into the solution.

In the Adjustable Cells section, the Allowable Increase and Allowable Decrease columns provide an indication as to how sensitive the optimal solution is to changes in the objective coefficients. In this example, the objective coefficients are $25 per cam- shaft and $18 per gear. From the report, the allowable increase of 2 for camshafts indi- cates that if the unit profit on camshafts were to increase by more than $2 per camshaft (i.e., to more than $27 per camshaft), holding all other data of the problem constant, the optimal solution (100 camshafts, 350 gears) would change. Conversely, the allow- able decrease of 20.5 means that the unit profit of camshafts can decline by $20.50 per camshaft (to $4.50), again holding all other data constant, before the optimal solution will change. Therefore, the optimal solution appears quite sensitive to increases in the unit profit for camshafts but insensitive to decreases in the unit profit. Similar (but opposite in direction) observations can be made for the unit profit of gears. In reality,

FIGURE B-12

Answer Report for DJJ Enterprises

FIGURE B-13

Sensitivity Report for DJJ Problem

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B16 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

objective coefficients are rarely known with certainty, so the allowable increase and decrease ranges provide a level of confidence in the optimal solution found by Solver.

Looking to the Constraint section, we focus on the Shadow Price column, as well as the Allowable Increase and Allowable Decrease. The shadow price for a constraint is the amount the optimal objective value will change if the RHS of the constraint is increased by 1 unit. For example, the shadow price of the labor constraint is shown as 0.4. This means that if the number of labor hours were increased by 1 (to 1501), the optimal level of profit would increase by $0.40. The correct units would be $0.40 per labor hour. This is commonly referred to as the marginal value of the resource. This rate of change in the objective function does not continue indefinitely; the Allowable Increase column tells us that if we increase the labor available by up to 500 hours (to 2000 total hours), each 1-hour increase will result in $0.40 of additional profit. Similar logic applies to the allowable decrease. By comparing shadow prices of constraints having the same units, it is easy to see which constraints will affect the objective value the most. Here, additional machine time is much more valuable than labor, since each additional machine hour can be used to generate an additional $8.20 in profit. Finally, the shadow price for the steel constraint is shown as 0, or $0 per pound of steel. Note that steel is a nonbinding constraint. Therefore, since only 3300 pounds of steel are required at the optimal solution, an additional pound of steel is worthless in terms of generating additional profit at this time. In general, shadow prices of nonbinding constraints are always zero.

� Shadow price The amount by which the optimal objective value will change if the right-hand side of a constraint is increased by one unit, assuming all other coefficients of the problem remain the same.

� Marginal value Another name for shadow price.

RECAP To address the DJJ Enterprises problem, we first developed a better understanding through a picture and a structured text formulation. Then we developed the formal algebraic formulation and created a spreadsheet model based on it. We provided the Target Cell, Changing Cells, and Constraints to Solver and set the appropriate options. After solving the problem, we generated solution reports and interpreted the results. Although the example in this chapter is simplified, this is the basic process you can use for addressing all linear programming problems.

OUTCOMES OF LINEAR PROGRAMMING PROBLEMS When an LP is solved, there are several possible outcomes. One outcome is an optimal solution, as we have seen for the example. Solver shows a Solver Results box (see Figure B-10) saying that “Solver found a solution. All constraints and optimality con- ditions are satisfied.” This is what we usually hope for when setting up and solving an LP. However, there are two additional possible outcomes when solving an LP. One occurs when Solver cannot find any feasible solution to the problem. Suppose we had these two constraints:

x1 1 x2 $ 10

x1 1 x2 # 3

There is no combination of values for x1 and x2 that satisfies both constraints. When an LP does not have a feasible solution, we say the problem is infeasible. When this is discovered by Solver, Solver displays the Solver Results box shown in Figure B-14. If you get this message, the values of the decision variables are not meaningful. Be careful to always read the Solver Results message.

� Infeasible problem An LP in which it is impossible for all the constraints to be satisfied simultaneously.

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OUTCOMES OF LINEAR PROGRAMMING PROBLEMS • B17

Some problems indeed are infeasible, but many times this box is obtained when the modeler has made an error in the model itself or in how the constraints were submitted to Solver. Even a small, simple error, such as entering a � constraint incor- rectly as a � constraint, can cause the model to be infeasible. Careful scrutiny of the model is needed in order to debug it.

Essentially the opposite of a problem being infeasible is the problem being unbounded. Being unbounded means that the objective value can increase (for a maximization problem) or decrease (for a minimization problem) without bound, that is, without ever running into a constraint. Consider the following problem:

Max x1 subject to

x1 1 x 2 $ 6

x1, x 2 $ 0

You can see from the constraints that as long as the sum of x1 and x2 is at least 6, and both x1 and x2 are nonnegative, the solution is feasible. Further, since the objective is to maximize x1, we simply set x2 to be some value (say, zero) and make x1 infinitely large. The objective value can grow without bound; this is the source of the term unbounded. The Solver Results box that appears when Solver encounters an unbounded model is shown in Figure B-15. Even though the language of this box is not as clear as it could be, it does make sense. The Set Cell (Target Cell) values do not converge on a specific, finite value. Therefore, the problem is unbounded.

Another Solver Results box you may encounter is shown in Figure B-16. This says, “The conditions for Assume Linear Model are not satisfied.” This box appears when the user checks “Assume Linear Model” in Solver Options but Solver does not recog- nize the model as being linear. Solver does its best to determine if the model is linear. If you are certain your problem is linear, make sure all spreadsheet formulas involving variables are linear. Solver’s check for a model being linear is not perfect, however,

� Unbounded problem An LP in which the objective value can increase (for a maximization problem) or decrease (for a minimization problem) without bound.

FIGURE B-15

Solver Results box indicating unbounded problem

FIGURE B-14

Solver Results box indicating infeasible problem

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B18 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

and sometimes simply solving the model again will cause a more normal completion message. Particular culprits for true nonlinearity are constraints involving percentages or ratios (usually these can be put into a linear form) or the use of functions such as IF, MAX, MIN, and other functions in the model. These functions are nonlinear and cannot be used to manipulate the decision variables in an LP.

FIGURE B-16

Solver Results box indicating model not detected as linear

OPTIMIZATION WITHIN OM: HOW IT ALL FITS TOGETHER As we discussed in the opening paragraphs of this chapter, everyone uses informal optimization thinking to make many routine decisions. However, formal optimiza- tion is used in all types of decisions in organizations. On a large scale, Continental Airlines recently developed an optimization-based system to better recover from unexpected events such as inclement weather, aircraft mechanical problems, and crew (pilots and flight attendants) availability. Disruptions in one part of the flight network cause ripple effects throughout, because planes and crews do not get where they need to go. CALEB Technologies developed a system called CrewSaver that has already helped Continental recover from major disruptions ranging from weather delays to the September 11, 2001 terrorist attacks. Since 2000, CALEB estimates it has saved Continental $40 million due to more effective recovery procedures.

On a smaller scale, but no less significant to the organization, Welch’s has devel- oped a spreadsheet-based optimization tool to help it develop the optimal recipes for fruit juices. Since raw ingredient supplies and prices vary and demand also changes, management must have a tool that will provide the optimal allocation of the raw mate- rials to the products. The tool is used each month to aid management in the logistics plan. The savings during the first year (1994) were between $130,000 and $170,000.

Supplement Highlights 1 People use informal “optimization” to make decisions almost

every day.

2 Organizations use formal optimization methods to address problems across the organization, from optimal pricing to locating a new facility.

3 Th e algebraic formulation of an LP comprises the defi nitions of the decision variables, an algebraic statement of the objective function, and algebraic statements of the constraints.

4 Th e spreadsheet model for an optimization problem should be guided by the algebraic formulation.

5 Solver, an Excel add-in, is able to solve both linear and nonlinear problems. Th is supplement focuses on solving linear problems.

6 Aft er solving an LP, you must interpret the results to see if they make sense, fi x problems with the model, and fi nd the insights useful for management.

7 Solver can generate the Answer and Sensitivity Reports. Th e Sensitivity Report provides additional information about what happens to the solution when certain coeffi cients of the problem are changed.

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SOLVED PROBLEMS • B19

Key Terms objective B2 constraints B2 decision variables B2 constrained optimization problem B2 formulation B4 less-than-or-equal-to constraint B5 greater-than-or-equal-to constraint B5 equality constraint B5 nonnegativity constraints B5

linear program (LP) B6 feasible solution B6 infeasible solution B6 optimal solution B6 Simplex Method B7 LHS value B7 RHS value B7 Target Cell B9 Changing Cells B9

Solved Problems • Problem 1 TowAlong makes trailers at plants in Kansas City, Denver, and Raleigh and ships these units to distribution centers in Birmingham, Milwaukee, Los Angeles, and Seattle. In plan- ning production for the next year, TowAlong estimates unit shipping cost between any plant and distribution center, plant capacities, and distribution center demands. These numbers are given in the table. TowAlong faces the problem of determining

how much to ship between each plant and distribution center to minimize the total transportation cost, while not exceeding capacity and while meeting demand.

(a) Formulate an LP to minimize the total shipping cost. (b) Set up and solve the problem on a spreadsheet. What is the

optimal solution?

(See student companion site for Excel template.)

• Before You Begin In this problem, formulate an LP model to minimize the total shipping cost. After formulating the problem, put it into a spreadsheet model and use Solver to determine the optimal solution. First, formulate the objective function. This objec- tive function has 12 decision variables. Each decision variable represents a specific route that could be used to satisfy demand. Each of the three plants can ship to each of the four distribution centers. Next, formulate the constraints. In this problem, there are plant capacity constraints and distribution center demand constraints. Next put the problem formulation into a spread- sheet model and solve.

• Solution (a) There are essentially 12 decisions to make when determining

how much to ship from each plant to each distribution center. These decisions can be represented by xij, where i represents the plant and j the distribution center. Therefore, we have the variables x11, x12, x13, x14, . . . , x34. Demand must be satisfied,

and capacity constraints must not be violated. The algebraic formulation can be stated as follows:

Minimize total cost 5 35x11 1 40x12 1 65x13 1 120x14

1 30x21 1 30x22 1 45x23 1 130x24

1 60x31 1 65x32 1 50x33 1 100x34 subject to

x11 1 x12 1 x13 1 x14 # 12,000 1Kansas City capacity 2 x21 1 x22 1 x23 1 x24 # 8000 1Denver capacity 2 x31 1 x32 1 x33 1 x34 # 5000 1Raleigh capacity 2 x11 1 x21 1 x31 $ 9000 1Birmingham demand 2 x12 1 x22 1 x32 $ 3000 1Milwaukee demand 2 x13 1 x23 1 x33 $ 9500 1Los Angeles demand 2 x14 1 x24 1 x34 $ 1500 1Seattle demand 2 xij $ 0 for all combinations 1nonnegativity 2

Constraints (in Solver) B9 Solver Options B9 binding constraints B14 nonbinding constraints B14 slack B14 shadow price B16 marginal value B16 infeasible problem B16 unbounded problem B17

Distribution Center

Plant Birmingham Milwaukee Los Angeles Seattle Capacity

Kansas City $35 $40 $60 $120 12,000 Denver $30 $30 $45 $130 8,000 Raleigh $60 $65 $50 $100 5,000 Demand 9,000 3,000 9,500 1,500

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B20 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

(b) The spreadsheet model can be structured in different ways, but it makes sense to arrange it in a way that will communicate the information clearly to management. The solved spreadsheet is shown. The minimum total cost is $1,010,000. To achieve this, ship 9000 from Kansas City to Birmingham, 1000 from Kansas City to Milwau- kee, 2000 from Denver to Milwaukee, 6000 from Denver to Los Angeles, 3500 from Raleigh to Los Angeles, and 1500 from Raleigh to Seattle.

Th e spreadsheet has three sections: unit costs, transportation plan, and transportation costs. Th e 12 decision variables are the 12 cells in the transportation plan section. Based on this plan and the unit costs, the cost for each plant/distribution center combination is

computed and then totaled to get the objective function. Each row of the transportation plan section represents the amounts shipped from each plant, summed in cells F15:F17, and each column represents the amounts received at each distribution center, summed in cells B18:E18. Th e Solver settings are as follows for this model:

Target Cell E30 (minimize) Changing Cells B15:E17 Constraints F15:F17 � F6:F8 (capacity constraints) B18:E18 � B9:E9 (demand constraints) Options Assume Linear Model Assume Non-Negative

• Problem 2 Chris is a student at the local college. Being on the go through the day, he sometimes does not pay as much attention to his eating habits and nutrition as he should. In an effort to improve nutrition, Chris wonders if there is a way to eat a reasonably

nutritious lunch and dinner diet at the local fast-food chain. Chris has collected some nutritional information about the menu items most often consumed. This isn’t necessarily a diet that Chris will eat every day. However, since Chris often

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SOLVED PROBLEMS • B21

studies for hours at a time at the restaurant, it is likely that both lunch and dinner will sometimes be eaten there. Chris has been advised by a doctor to be careful about high sodium intake.

Nutritional and cost data are provided in the table. Chris has decided to use linear programming to try to fi nd a reasonably nutritious diet that minimizes sodium intake. He has determined

that the nutrition requirements indicate that the diet must contain 2000–2500 calories, have no more than 85 grams of fat, and contain at least 100 percent of the “daily value” (DV) of vitamin C, vitamin A, calcium, and iron. Chris has $15 budgeted for the two meals combined and is willing to eat no more than two of each item from the menu.

• Before You Begin This is similar to Solved Problem 1, just a bit more complicated. In this case, the objective function is to minimize sodium intake subject to constraints on calories, fat, vitamin A, vitamin C, calcium, iron, and total cost, while limited to a maximum of two of any menu item. This is a product mix problem, but now we are determining the mix of inputs rather than the mix of final products. Write out the objective function first, then write out the constraints. Since we would like an integer solution for this problem, restrict Solver to an integer solution. Solve the problem using Excel.

• Solution This problem is actually quite similar to the example in this supplement, only larger. As such, the spreadsheet will be arranged in a similar way. A text-based formulation is given. Note the objective function here is not monetary, but rather to minimize sodium intake, subject to a set of constraints, one of which is a budget constraint.

Text-Based Formulation • Decision variables: Number of each menu item to purchase

and consume. • Objective function: Minimize sodium intake. • Constraints: Calories, fat, vitamin A, vitamin C, calcium,

iron, total cost, and the maximum of two of each menu item.

Th e other diff erence in this problem is that we cannot realistically order a fractional quantity of an item. Th erefore, we will use Solver’s capability to restrict the decision variables to integers. You can consult one of the management science books in the bibliography for more information about integer variables.

A solved spreadsheet is shown. While the spreadsheet is somewhat large, there is only one unique formula written, which is for the objective value (total sodium) in cell L8. Th is formula is then copied to cells L10:L17 to compute the LHS values of the constraints. Th e formula in cell L8 is �SUMPRODUCT (B8:K8,B$4:K$4). Th e SUMPRODUCT function was covered in Supplement A, but briefl y, it multiplies each corresponding pair of entries (e.g., B8*B$4, C8*C$4, etc.) and then adds up these individual products.

Somewhat surprisingly, the optimal solution is to eat 1 Small Burger, 1 Deluxe Salad, 2 Baked Potatoes, and 2 Shakes. Th e total Sodium is 2090 mg. Considering the nutritional and cost constraints, we see that they are all satisfi ed, with Iron being the only constraint that is actually binding. Th e cost of this diet is $9.44, signifi cantly less than our budget. Th e Solver settings are as follows for this model:

Target Cell L8 (minimize) Changing Cells B4:K4 Constraints L10 � N10 (calorie lower limit) L11:L12 � N11:12 (calorie & fat upper

limits) L13:L16 � N13:16 (vitamins A & C,

calcium, & iron lower limits) L17 � N17 (cost upper limit) B4:K4 � B5:K5 (item lower limit) B4:K4 � B6:K6 (item upper limit) B4:K4 integer (to restrict to only integer

values) Options Assume Linear Model Assume Non-Negative

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B22 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

The B4:K4 integer constraint is entered in the same way as the other constraints, using the Add Constraint dialog box. When pulling down the relationship selection menu, choose “int” rather than �, �, or �. Integer restrictions should not automatically be added to a problem, even if the variables need to be integer in the

real situation. The reason is that integer restrictions can dramatically increase the time required to solve a problem. It is usually best to first solve the problem without integer restrictions and add them only if necessary. See one of the references at the end of the chapter for more information.

• Problem 3 IMC, Inc. needs to schedule production of a certain item for the next four months. The unit cost is estimated to be $12 for the first two months and $14 for the last two months. From the company’s forecasting models, monthly demands are estimated to be 400, 750, 950, and 900 units, respectively. IMC can produce a maxi- mum of 850 units each month on regular time. Overtime can be scheduled during the second and third months, which increases monthly capacity by 200 units. However, units produced on over- time cost $4 more to produce. Excess production can be stored at a cost of $3 per unit per month, but a maximum of 50 units can be stored during any month. Assuming that beginning and ending inventory levels are zero, how should the production be scheduled so as to minimize total costs?

• Before You Begin This is also a product mix problem of sorts. The mix here is concerned with how much production is done on regular time and how much is done on overtime. The problem formulation must take into account inventory levels, holding costs, regular-time production costs, and overtime production costs. There are demand

constraints dictating how many customers must be satisfied each period. The end result should be a spreadsheet model indicating how much production must occur each period, how much overtime is needed, and how much inventory is held.

• Solution Similar to Solved Problem 1, we will let the structure of this problem drive the way the spreadsheet model is arranged. Essentially, we must allow the user (or Solver) to change the quantities associated with regular and overtime production, and the model must compute the inventories (if any) as well as all costs.

A completed and solved spreadsheet is shown. Input data is listed in the Costs and Capacities section. Th e production plan contains the primary logic of the model. Each month the same pattern occurs. We start with some beginning inventory, add to that through regular and overtime production, experience demand, and come out with an ending inventory. From this production plan and the resulting inventories, the costs can be calculated. Th e solution to this problem is to have regular production be 400 in month 1, 800 in month 2, and 850 in

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DISCUSSION QUESTIONS • B23

months 3 and 4. During month 3, we will also plan 100 units of overtime production. Th is plan results in the lowest possible total cost, $40,300.

Th e Solver settings are listed. Th e third constraint, requiring ending inventories to be �0, forces all demand to be met during the month in which it occurs (a negative inventory would indicate that some demand was not met).

Target Cell F27 (minimize) Changing Cells B18:E19

Constraints B18:E19 � B11:E12 (production capacity constraints)

B21:E21 � B13:E13 (inventory capacity constraints)

B21:E21 � 0 (inventory cannot fall below zero)

Options Assume Linear Model Assume Non-Negative

Discussion Questions 1. What are the three main elements of an optimization prob-

lem? Give an example of each. 2. Describe a recent decision you made that could be viewed

from an optimization perspective. In a few words, what were the “decision variables”? What was the objective? What were the constraints? Did you have more than one objective?

3. Think of a company you are familiar with. Describe at least three problems the company faces that could be addressed, at

least in part, through optimization. The problems can be either strategic or operational in nature.

4. When specifying the Target Cell, Changing Cells, and Con- straints to Solver, why is it important to use only cell references rather than fixed numbers?

5. Explain the meaning of binding and nonbinding constraints. 6. Why is it important to conduct sensitivity analysis after

solving an optimization problem?

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B24 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

Problems 1. DynaTune is trying to determine how to allocate its $145,000

advertising budget for a new product. Radio and television are the two media outlets selected for advertising. Th e following table shows the costs of advertising in these two media and the estimated number of new customers reached by increasing amounts of advertising.

For example, each of the fi rst 10 ads the company places on the radio will cost $1000 and is expected to reach 900 new customers. Each of the next 10 will cost $900 and is expected to reach 700 new customers. Assume the company will purchase no more than 30 radio ads and no more than 15 television ads.

(a) Formulate an LP to maximize the number of new customers reached.

(b) Set up and solve the problem on a spreadsheet. (c) What is the optimal solution? Explain the rationale for the

solution. (d) Suppose the number of new customers reached by 11–15

television ads is 7500 and the number reached by 6–10 ads is 5000. Resolve the problem. What is wrong with this solution? Is Solver incorrect, or is there some missing logic in the model?

2. Montana Pulp (MP) produces recycled paperboard by combining four grades of recycled paper stock. Each grade of stock has a diff erent strength, color, and texture. Th e strength, color, and texture of the fi nished paperboard are a weighted average of those characteristics of the paper stock inputs. Th e table provides the characteristics of the paper inputs and their costs per ton. An order for 500 tons of paperboard with a strength of at least 7, a color of at least 5, and a texture of at least 6 has been received. MP needs to fi nd the minimal-cost mix of inputs required to satisfy this order.

(a) Formulate an LP to minimize the cost of the order. (b) Set up and solve the problem on a spreadsheet. (c) What is the optimal solution? Explain the rationale for the

solution. 3. A company has three manufacturing plants (in Atlanta,

Tulsa, and Springfi eld) that produce a product that is then shipped

Paper Stock Strength Color Texture Cost/Ton

1 8 9 8 $150 2 6 7 5 $110 3 5 5 6 $90 4 3 4 5 $50

to one of four distribution centers. Th e three plants can produce 13, 18, and 12 truckloads of product each week, respectively. Each distribution center needs 10 truckloads of product each week. Th e shipping costs per truckload between the plants and distribution centers are given in the table. Th e company needs to determine how much to ship from each plant to each distribution center and would like to minimize total shipping costs.

(a) Formulate an LP to minimize the total shipping costs. (b) Set up and solve the problem on a spreadsheet. (c) What is the optimal solution? Explain the rationale for the

solution. 4. FiberTech makes newsprint for newspapers at three mills, A,

B, and C. Th e cost of producing newsprint is estimated to be $210 at mill A, $225 at B, and $220 at C. Five primary geographical markets are served from these mills. Th e monthly demand at each market, the shipping cost (per ton) between each mill and market, and the monthly production capacity of each mill are given in the table.

FiberTech would like to assign production responsibilities to the mills, and also specify how much should be shipped from each plant to each market, so as to minimize total production and distribution costs.

(a) Formulate an LP to minimize total production and distribution costs.

(b) Set up and solve the problem on a spreadsheet. (c) What is the optimal solution? Explain the rationale for the

solution. 5. Northwest Pipe (NP) makes water pipe. NP is planning

production for the next seven months, March through September. Th e forecast demands (in thousands of feet) are, respectively, 40, 60, 70, 80, 90, 100, and 80. NP can make 75,000 feet of pipe per month using regular-time production, at a cost of $1.25 per foot. Th ey can make up to an additional 15,000 feet using overtime production at a cost of $1.50 per foot. Any pipe made in one month and sold in a later month incurs an inventory holding cost of $0.15 per foot, per month. NP expects to end February with 5000 feet of pipe and would like to plan to end September with 10,000 feet in inventory. NP would like to plan their production schedule to minimize total cost during the next seven months.

Distribution Center

Plant A B C D

Atlanta $800 $1300 $400 $700 Tulsa $1100 $1400 $600 $1000 Springfi eld $600 $1200 $800 $900

Market Capacity (tons/month)Mill 1 2 3 4 5

Shipping A $20 $25 $30 $15 $35 1200 Cost/Ton B $30 $20 $32 $28 $19 1500

C $25 $18 $28 $23 $31 900 Monthly 600 100 500 800 500 Demand (tons)

Media Type & Number of Ads

Number New Customers Reached Cost per Ad

Radio 1–10 900 $1,000 Radio 11–20 700 $900 Radio 21–30 400 $800 Television 1–5 10,000 $12,000 Television 6–10 7,500 $10,000 Television 11–15 5,000 $8,000

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CASE: EXETER ENTERPRISES • B25

(a) Formulate an LP to minimize total costs. (b) Set up and solve the problem on a spreadsheet. (c) What is the optimal solution? Explain the rationale for the

solution. 6. A1 Credit provides credit information to its customers

throughout the country, 24 hours per day. Credit representatives answer customer calls and provide information. Based on demand patterns, the estimated number of representatives needed during four-hour time periods of each day are shown in the table.

Employees work shift s of eight consecutive hours, and shift s can start at the beginning of any of the six periods shown in the table. Assume this schedule will be repeated day aft er day, so that someone starting at 8 pm will contribute to the need for representatives in the midnight–4 am time period. Th e supervisor has been instructed to schedule employees so that all demands can be met with a minimum possible number of employees.

(a) Formulate an LP to minimize the number of representatives needed.

(b) Set up and solve the problem on a spreadsheet. (c) What is the optimal solution? Explain the rationale for the

solution.Time Period

Number Representatives

Needed

Midnight–4 am 3 4 am–8 am 6 8 am–noon 13 Noon–4 pm 15 4 pm–8 pm 12 8 pm–midnight 9

CASE: Exeter Enterprises Exeter Enterprises produces premium golf clubs. It is in need of a planning tool that will help it make decisions over the next eight quarters. The demand forecast for the upcoming eight- quarter period is developed every quarter, and the production manager must determine how many people to hire and lay off and also how much overtime and subcontracting to schedule.

Information relevant to the problem is shown. The demand for the upcoming eight quarters is for the following number of sets of clubs, respectively: 1920, 2160, 1440, 1200, 2040, 2400, 1740, and 1500. Clubs can be inventoried from one period to the

next, incurring the holding cost. Back orders are also allowed, although the company does not prefer to use them.

The production manager would ideally like a tool in which one could enter the decision values (number of sets to assign to regular time, overtime, and subcontracting; number of sets to back-order; and number of employees to hire and lay off each quarter). The manager could then compute a cost breakdown to compare different plans. Ultimately, the production manager would like the tool to be able to find the plan minimizing the total costs over the next eight quarters.

Cost Data

Regular-time labor cost per hour $12.50 Overtime labor cost per hour $18.75 Subcontracting cost per unit (labor only) $125.00 Back-order cost per unit per quarter $25.00 Inventory holding cost per unit per quarter $10.00 Hiring cost per employee $800.00 Firing cost per employee $500.00

Capacity Data

Beginning workforce 90 employees Beginning inventory 0 units Production standard per unit 8 hours of labor per unit Regular time available per period 160 hours per period per employee Overtime available per period 40 hours per period per employee

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B26 • SUPPLEMENT B INTRODUCTION TO OPTIMIZATION

Questions 1. Develop a spreadsheet decision tool that will allow the

production manager to experiment with diff erent plans. 2. Develop an optimization model using Solver to allow

the manager to fi nd the minimal-cost plan. Make what you think are reasonable assumptions about the maximum number of back orders allowed, as well as the maximum number of

employees that could be hired and laid off in any one quarter. Aft er solving the problem, you may decide to make additional assumptions.

3. Write a short summary of your fi ndings, comparing several plans entered manually as well as the optimal plan.

Online Resources

1. Spreadsheets for Example B.1 and Solved Problems 1, 2, and 3 are available on the CD. 2. Frontline Systems, Inc. developed Solver for Microsoft and continues to develop enhanced versions of Solver. Go to

http://www.solver.com and gather information about Front-line’s products and services. Prepare a one-page summary. 3. Lindo Systems, Inc. is a developer of several diff erent optimization tools, including What’s Best, which is a competitor to Solver.

Go to http://www.lindo.com/table/wbt.html and gather information about this product. How does it compare to Solver? 4. Go to http://www.solver.com/solutions.htm. Optimization examples from fi nance, investment, production, distribution,

purchasing, and scheduling are available. Choose two examples from two diff erent areas. Study the examples, and prepare a ten-minute presentation to explain the examples.

5. On Wendy’s Web site (http://www.wendys.com), you can build a meal and obtain all the nutritional information. Click on the “Nutritional Guide” to access this part of the Web site. Build a meal plan for a one-day diet, and assess the nutritional value. Compare and contrast this diet-planning tool with the optimization-based approach presented in Solved Problem 2.

Selected Bibliography Albright, S. Christian, and Wayne Winston. Essentials of Prac-

tical Management Science. Mason, OH: Cengage, 2005. Lawrence, John A., and Barry A. Pasternack. Applied Manage-

ment Science: A Computer-Integrated Approach for Deci- sion Making. Second Edition. New York: Wiley, 2002.

Ragsdale, Cliff T. Spreadsheet Modeling & Decision Analysis, Fift h Edition. Stamford, Conn.: Th omson, 2008.

Schuster, Edmund W., and Stuart J. Allen. “Raw Material Man- agement at Welch’s, Inc.,” Interfaces, 28, 5, 1998, 13–28.

Yu, Gang, Michael Arguello, Gao Song, Sandra McCowan, and Anna White. “A New Era for Crew Recovery at Continental Airlines,” Interfaces, 33, 1, 2003, 5–22.

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SUPPLEMENT

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

Elements of Waiting Lines C2 Waiting Line Performance Measures C7 Single-Server Waiting Line Model C7 Multiserver Waiting Line Model C10

Changing Operational Characteristics C13 Larger-Scale Waiting Line Systems C14 Waiting Line Models within OM: How It All Fits

Together C15

Waiting Line Models

Before studying this supplement you should know or, if necessary, review

1. Competitive advantages, Chapter 2. 2. Priority rules, Chapter 16.

After completing this supplement you should be able to

1 Describe the elements of a waiting line problem. 2 Use waiting line models to estimate system performance. 3 Use waiting line models to make managerial decisions.

S U P P L E M E N T O U T L I N E

L E A R N I N G O B J E C T I V E S

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C2 • SUPPLEMENT C WAITING LINE MODELS

Waiting in lines is part of everyday life. Some estimates state that Americans spend 37 billion hours per year waiting in lines. Whether it is waiting in line at a gro- cery store to buy deli items (by taking a number) or checking out at the cash registers (finding the quickest line), waiting in line at the bank for a teller, or waiting at an amusement park to go on the newest ride, we spend a lot of time waiting. We wait in lines at the movies, campus dining rooms, the registrar’s office for class registration, at the Division of Motor Vehicles, and even at the end of the school term to sell books back. Think about the lines you have waited in just during the past week. How long you wait in line depends on a number of factors. Your wait is a result of the number of people served before you, the number of servers working, and the amount of time it takes to serve each individual customer.

Wait time is affected by the design of the waiting line system. A waiting line system (or queuing system) is defined by two elements: the population source of its customers and the process or service system itself. In this supplement we examine the elements of waiting line systems and appropriate performance measures. Perfor- mance characteristics are calculated for different waiting line systems. We conclude with descriptions of managerial decisions related to waiting line system design and performance.

� Waiting line system Includes the customer population source as well as the process or service system.

� Queuing system Another name to define a waiting line.

ELEMENTS OF WAITING LINES Any time there is more customer demand for a service than can be provided, a wait- ing line occurs. Customers can be either humans or inanimate objects. Examples of objects that must wait in lines include a machine waiting for repair, a customer order waiting to be processed, subassemblies in a manufacturing plant (that is, work-in- process inventory), electronic messages on the Internet, and ships or railcars waiting for unloading.

In a waiting line system, managers must decide what level of service to offer. A low level of service may be inexpensive, at least in the short run, but may incur high costs of customer dissatisfaction, such as lost future business and actual processing costs of complaints. A high level of service will cost more to provide and will result in lower dissatisfaction costs. Because of this trade-off, manage- ment must consider what is the optimal level of service to provide. This is illus- trated in Figure C-1.

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ELEMENTS OF WAITING LINES • C3

Fast-food restaurants illus- trate the transient nature of waiting line systems. Waiting lines oc cur at a fast-food res- taurant drive-through dur- ing peak meal times each day. There is a temporary surge in demand that cannot be quickly handled with the available capacity. In an effort to speed up delivery, some restaurants use an extra window—the first window for paying and the second window for picking up the food. At other times of the day, the restaurant uses a single window and may have no waiting line at the drive- through window.

The challenge is designing service systems with adequate but not excessive amounts of capacity. A fast-food restaurant experiences variable demand and variable service times. The restaurant cannot be sure how much customer demand there will be, and it does not know exactly what each customer will order—each order can be unique and require a different service time. It is important to understand the different elements of a waiting line system. These elements include the customer population source, the service system, the arrival and service patterns, and the priorities used for controlling the line. Let’s first look at the primary input into the waiting line system: the customers.

The Customer Population The customer population can be considered to be finite or infinite. When potential new customers for the waiting line system are affected by the number of customers already in the system, the customer population is finite. For example, if you are in

� Finite customer population The number of potential new customers is affected by the number of customers already in the system.

K ris

tin S

la d

en /T

he Im

ag e

W or

LINKS TO PRACTICE Waiting for Fast Food

FIGURE C-1

Waiting cost and service level trade-off

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C4 • SUPPLEMENT C WAITING LINE MODELS

a class with nine other students, the total customer population for meeting with the professor during office hours is ten students. As the number of students waiting to meet with the professor increases, the population of possible new customers decreases. There is a finite limit as to how large the waiting line can ever be.

When the number of customers waiting in line does not significantly affect the rate at which the population generates new customers, the customer population is consid- ered infinite. For example, if you are taking a class with 500 other students (a rela- tively large population) and the probability of all the students trying to meet with the professor at the same time is very low, then the number of students in line does not significantly affect the population’s ability to generate new customers.

In addition to waiting, a customer has other possible actions. For example, a customer may balk, renege, or jockey. Balking occurs when the customer decides not to enter the waiting line. For example, you see that there are already 12 students waiting to meet with your professor, so you choose to come back later. Reneging occurs when the customer enters the waiting line but leaves before being serviced. For example, you enter the line waiting to meet with your professor, but after waiting 15 minutes and seeing little progress, you decide to leave. Jockeying occurs when a customer changes from one line to another, hoping to reduce the waiting time. A good example of this is picking a line at the grocery store and changing to another line in the hope of being served quicker.

The models used in this supplement assume that customers are patient; they do not balk, renege, or jockey; and the customers come from an infinite population. The mathematical formulas become more complex for systems in which customer popula- tion must be considered finite and when customers balk, renege, or jockey.

The Service System The service system is characterized by the number of waiting lines, the number of servers, the arrangement of the servers, the arrival and service patterns, and the service priority rules.

The Number of Waiting Lines Waiting line systems can have single or multiple lines. Banks often have a single line for customers. Customers wait in line until a teller is free and then proceed to that teller’s position. Other examples of single-line systems include airline counters, rental car counters, restaurants, amusement park attractions, and call centers. The advantage of using a single line when multiple servers are available is the customer’s perception of fairness in terms of equitable waits. That is, the customer is not penalized by picking the slow line but is served in a true first-come, first-served fashion. The single-line approach eliminates jockeying behavior. Finally, a single-line, multiple-server system has better performance in terms of waiting times than the same system with a line for each server.

The multiple-line configuration is appropriate when specialized servers are used or when space considerations make a single line inconvenient. For example, in a grocery store some registers are express lanes for customers with a small number of items. Using express lines reduces the waiting time for customers making smaller purchases. Examples of single- and multiple-line systems are shown in Figure C-2.

The Number of Servers System serving capacity is a function of the number of service facilities and server proficiency. In waiting line systems, the terms server and channel are used interchangeably. It is assumed that a server or channel can serve one

� Infinite customer population The number of potential new customers is not affected by the number of customers already in the system.

� Balking The customer decides not to enter the waiting line.

� Reneging The customer enters the line but decides to exit before being served.

� Jockeying The customer enters one line and then switches to a different line in an effort to reduce the waiting time.

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ELEMENTS OF WAITING LINES • C5

customer at a time. Waiting line systems are either single server (single channel) or multiserver (multichannel). Single-server examples include small retail stores with a single checkout counter, a theater with a single person selling tickets and controlling admission into the show, or a ballroom with a single person controlling admission. Multiserver systems have parallel service providers offering the same service. Multiserver examples include grocery stores (multiple cashiers), drive-through banks (multiple drive-through windows), and gas stations (multiple gas pumps).

The Arrangement of the Servers Services require a single activity or a series of activities and are identified by the term phase. Refer to Figure C-2. In a single-phase system, the service is completed all at once, such as with a bank transaction or a grocery store checkout. In a multiphase system, the service is completed in a series of steps, such as at a fast-food restaurant with ordering, pay, and pick-up windows or in many manufacturing processes.

In addition, some waiting line systems have a finite size of the waiting line. Some- times this happens in multiphase systems. For example, perhaps only two cars can physically fit between the ordering and pay window of a fast-food drive-through. Finite size limitations can also occur in single-phase systems and can be associated either with the physical system (for example, a call center has only a finite number of incoming phone lines) or with customer behavior (if a customer arrives when a certain number of people are already waiting, the customer chooses to not join the line).

FIGURE C-2

Examples of waiting line systems

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C6 • SUPPLEMENT C WAITING LINE MODELS

Arrival and Service Patterns Waiting line models require an arrival rate and a service rate. The arrival rate speci- fies the average number of customers per time period. For example, a system may have ten customers arrive on average each hour. The service rate specifies the average number of customers that can be serviced during a time period. The service rate is the capacity of the service system. If the number of customers you can serve per time period is less than the average number of customers arriving, the waiting line grows infinitely. You never catch up with the demand!

It is the variability in arrival and service patterns that causes waiting lines. Lines form when several customers request service at approximately the same time. This surge of customers temporarily overloads the service system and a line develops. Wait- ing line models that assess the performance of service systems usually assume that customers arrive according to a Poisson probability distribution, and service times are described by an exponential distribution. The Poisson distribution specifies the prob- ability that a certain number of customers will arrive in a given time period (such as per hour). The exponential distribution describes the service times as the probability that a particular service time will be less than or equal to a given amount of time.

Problem-Solving Tip: Make sure the arrival rate and service rate are for the same time period, that is, the number of customers per hour, or per day, or per week.

Waiting Line Priority Rules A waiting line priority rule determines which customer is served next. A frequently used priority rule is first-come, first-served. This priority rule selects customers based on who has been waiting the longest in line. Generally, customers consider first-come, first-served to be the fairest method for determining priority.

However, it is not the only priority rule used. Other rules include best customers first, highest-profit customer first, quickest-service requirement first, largest-service requirement first, emergencies first, and so on. Although each priority rule has merit,

it is important to use the priority rule that best supports the overall organizational strategy. For example, a first- come, first-served rule doesn’t make sense in a hospital emergency room and in fact could cause unnecessary deaths.

The priority rule used affects the performance of the waiting line system. As an example, first-come, first served is generally considered fair, yet it is biased against customers requiring short service times. When check- ing out at a store that is using first-come, first-served as a priority rule, a customer waiting behind another cus- tomer with a large number of items waits longer than a customer waiting behind a second customer with only a few items. Although processing is sequential, the wait times vary because of the preceding customer. Also, priority rules besides first-come, first-served may imply that some customers wait extremely long periods of time. For example, in a busy emergency room, some- one not critically sick or injured could wait a significant period of time.

� Arrival rate The average number of customers arriving per time period.

� Service rate The average number of customers that can be served per time period.

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SINGLE-SERVER WAITING LINE MODEL • C7

The models in this chapter assume a service system with a single waiting line, single or multiple servers, a single phase, and a first-come, first-served priority rule.

WAITING LINE PERFORMANCE MEASURES Performance measures are used to gain useful information about waiting line systems. These measures include:

1. Th e average number of customers waiting in line and in the system. Th e number of customers waiting in line can be interpreted in several ways. Short waiting lines can result from relatively constant customer arrivals (no major surges in demand) or from the organization’s having excess capacity (many cashiers open). On the other hand, long waiting lines can result from poor server effi ciency, inadequate system capacity, and/or signifi cant surges in demand.

2. Th e average time customers spend waiting, and the average time a customer spends in the system. Customers oft en link long waits to poor-quality service. When long waiting times occur, one option may be to change the demand pattern. Th at is, the company can off er discounts or better service at less busy times of the day or week. For example, a restaurant off ers early-bird diners a discount so that demand is more level. Th e discount moves some demand from prime-time dining hours to the less desired dining hours.

If too much time is spent in the system, customers might perceive the competency of the service provider as poor. For example, the amount of time customers spend in line and in the system at a retail checkout counter can be a result of a new employee not yet profi cient at handling the transactions.

3. Th e system utilization rate. Measuring capacity utilization shows the percentage of time the servers are busy. Management’s goal is to have enough servers to assure that waiting is within allowable limits but not so many servers as to be cost-ineffi cient.

We calculate these measures for two different waiting line models: the single-server model and the multiserver model.

SINGLE-SERVER WAITING LINE MODEL The easiest waiting line model involves a single-server, single-line, single-phase sys- tem. The following assumptions are made when we model this environment:

1. Th e customers are patient (no balking, reneging, or jockeying) and come from a population that can be considered infi nite.

2. Customer arrivals are described by a Poisson distribution with a mean arrival rate of l (lambda). Th is means that the time between successive customer arrivals follows an exponential distribution with an average of 1/l.

3. Th e customer service rate is described by a Poisson distribution with a mean service rate of m (mu). Th is means that the service time for one customer follows an exponential distribution with an average of 1/m.

4. Th e waiting line priority rule used is fi rst-come, fi rst-served.

Using these assumptions, we can calculate the operating characteristics of a waiting line system using the following formulas:

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C8 • SUPPLEMENT C WAITING LINE MODELS

l 5 mean arrival rate of customers 1average number of customers arriving per unit of time 2

m 5 mean service rate 1average number of customers that can be served per unit of time 2

p 5 l

m 5 the average utilization of the system

L 5 l

m 2 l 5 the average number of customers in the service system

LQ 5 pL 5 the average number of customers waiting in line

W 5 1

m 2 l 5 the average time spent waiting in the system, including service

WQ 5 pW 5 the average time spent waiting in line

Pn 5 11 2 p 2pn 5 the probability that n customers are in the service system at a given time

Note: The service rate must be greater than the arrival rate, that is, m � l. If m � l, the waiting line would eventually grow infinitely large. Before using the formulas, check to be sure that m � l.

The computer lab at State University has a help desk to assist students working on computer spread- sheet assignments. The students patiently form a single line in front of the desk to wait for help. Students are served based on a first-come, first-served priority rule. On average, 15 students per hour arrive at the help desk. Student arrivals are best described using a Poisson distribution. The help desk server can help an average of 20 students per hour, with the service rate being described by an expo- nential distribution. Calculate the following operating characteristics of the service system.

(a) Th e average utilization of the help desk server (b) Th e average number of students in the system (c) Th e average number of students waiting in line (d) Th e average time a student spends in the system (e) Th e average time a student spends waiting in line (f) Th e probability of having more than 4 students in the system

• Before You Begin: The key to solving queuing problems is to identify the mean arrival rate of customers and the mean service rate. In this case, on average, 15 customers arrive each hour. On average, the consultant can serve 20 customers per hour. Once you have established these values, you merely plug them into the appropriate formula.

• Solution: (a) Average utilization: p �

m �

15 20

� 0.75, or 75%.

(b) Average number of students in the system: L � l

m 2 l �

15 20 2 15

� 3 students

(d) Average time a student spent in the system: W � 1

m 2 l 5

1 20 2 15

� 0.2 hours, or 12 minutes

EXAMPLE C.1

Single-Server Operating

Characteristics at the Help Desk

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SINGLE-SERVER WAITING LINE MODEL • C9

(e) Average time a student spent waiting in line: WQ � pW � 0.75 � (0.2) � 0.15 hours, or 9 minutes

(f) Th e probability that there are more than four students in the system equals one minus the probability that there are four or fewer students in the system. We use the following formula.

Problem-Solving Tip: Any term raised to the zero power is equal to 1.

P 5 1 2 a 4

n50 Pn 5 1 2 a

n50 11 2 p 2pn

5 1 2 0.25 11 1 0.75 1 0.752 1 0.753 1 0.754 2 5 1 2 0.7626 5 0.2374

or a 0.2374 (23.74 percent) chance of having more than four students in the system.

Figure C-3 shows a spreadsheet solution of this problem. The spreadsheet formulas shown are a direct implementation of the single-server formulas for performance measures. Figure C-4 is a graph of the probabilities of certain numbers of customers in the system.

FIGURE C-3

Spreadsheet for single- server operating characteristics

Key Formulas

B9: � 1/B5 B10: � 1/B6 B13: � B5/B6 B14: � 1�B13 B15: � B5/(B6�B5) B16: � B13*B15 B17: � 1/(B6�B5) B18: � B13*B17 B22: � (1�B$13)*(B13^B21)

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C10 • SUPPLEMENT C WAITING LINE MODELS

FIGURE C-4

Single-server probabilities of customers in the system

MULTISERVER WAITING LINE MODEL In the single-line, multiserver, single-phase model, customers form a single line and are served by the first server available. The model assumes that there are s identical servers, the service time distribution for each server is exponential, and the mean ser- vice time is 1/m. Using these assumptions, we can describe the operating characteris- tics with the following formulas:

s 5 the number of servers in the system

p 5 l

sm 5 the average utilization of the system

P0 5 cas21 n50

1l/m 2n

n! 1 1l/m 2 s

s! a 1

1 2 p b d 21

LQ 5 Po 1l/m 2 sp s! 11 2 p 2 2 5 the average number of customers waiting in line

WQ 5 LQ l

5 the average time spent waiting in line

W 5 WQ 1 1 m

5 the average time spent in the system, including service

L 5 lW 5 the average number of customers in the service system

Pn5 μ (l/m)n

n! P0 for n # s

(l/m)n

s!sn2s P0 for n . s

5 the probability that n customers are in the system at a given time

5 the probability that no customers are in the system

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MULTISERVER WAITING LINE MODEL • C11

Note: The total service rate must be greater than the arrival rate, that is, sm � l. If sm � l, the waiting line would eventually grow infinitely large. Before using the for- mulas, check to be sure that sm � l.

State University has decided to increase the number of computer assignments in its curriculum and is concerned about the impact on the help desk. Instead of a single person working at the help desk, the university is considering a plan to have three identical service providers. It expects that students will arrive at a rate of 45 per hour, according to a Poisson distribution. The service rate for each of the three servers is 18 students per hour, with exponential service times. Calculate the following operating characteristics of the service system:

(a) Th e average utilization of the help desk (b) Th e probability that there are no students in the system (c) Th e average number of students waiting in line (d) Th e average time a student spends waiting in line (e) Th e average time a student spends in the system (f) Th e average number of students in the system

Problem-Solving Tip: By definition, zero factorial, 0!, equals 1.

• Solution:

(a) Average utilization: p 5 l

sm 5

4513 3 18 2 5 0.833, or 83.3% (b) Th e probability that there are no students in the system:

P0 5 cas21 n50

(l/m)n

n! 1

(l/m)s

s! a 1

1 2 p b d21

5 c 145/18 2 0 0!

1 145/18 2 1

1! 1 145/18 2 2

2! 1 a 145/18 2 3

3! a 1

1 2 0.833 bb d 21

5 1

22.215 5 0.045, or 4.5% of having no students in the system

LQ 5 P0 1l/m 2 sp s! 11 2 p 2 2 5 0.045 145/18 2 3 3 0.8333! 3 11 2 0.833 2 2 5 0.58570.1673 5 3.5 students

(d) Th e average time a student spends waiting in line: WQ 5 LQ l

5 3.5 45

5 0.078 hour, or 4.68 minutes

(e) Th e average time a student spends in the system:

W 5 WQ 1 1 m

5 0.078 1 1

18 5 0.134 hour, or 8.04 minutes

(f) Th e average number of students in the system:

L 5 lW 5 45 10.134 2 5 6.03 students Figure C-5 shows a spreadsheet solution of this problem. The spreadsheet formulas are a direct implementation of the multiple-server formulas for performance measures. Because of the complexity of the P0 calculation, the columns E–H break this computation down.

EXAMPLE C.2Multiserver Operating Characteristics at the Help Desk

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C12 • SUPPLEMENT C WAITING LINE MODELS

Then the formula in cell B16 looks up the value from column H corresponding to the number of servers. The spreadsheet shown here will work for up to a 100-server system. Key formulas are listed here:

Key Formulas

F10: 5 F$5^E10 (copied down) G10: 5 E10*G9 (copied down) H10: 5 H91(F10/G10) (copied down) F5: 5 B5/B6 F6: 5 INDEX(G9:G109, B711) B10: 5 1/B5 B11: 5 1/B6 B12: 5 B7*B6 B15: 5 B5/B12 B16: 5 (INDEX(H9:H109, B7)1(((F5^B7)/F6)*((1)/(12B15))))^(21) B17: 5 B5*B19 B18: 5 (B16*(F5^B7)*B15)/(INDEX(G9:G109, B711)*(12B15)^2) B19: 5 B201(1/B6) B20: 5 B18/B5 B24: 5 IF(B23 ,5B7, ((F5^B23)*B16)/INDEX(G9:G109, B2311),

((F5^B23)*B16)/(INDEX(G9:G109, B712)*(B7^(B232B7))))

FIGURE C-5

Spreadsheet for multiple-server operating characteristics

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CHANGING OPERATIONAL CHARACTERISTICS • C13

Figure C-6 is a graph of the probabilities of certain numbers of customers in the system.

FIGURE C-6

Multiple-server probabilities of customers in the system

CHANGING OPERATIONAL CHARACTERISTICS After calculating the operating characteristics for a waiting line system, sometimes you need to change the system to alter its performance. Let’s look at the type of changes you can make to the different elements of the waiting line system.

Customer arrival rates. You can try to change arrival rates in a number of ways. For example, you can provide discounts or run special promotions during the nonpeak hours to attract customers.

Number and type of service facilities. You can either increase or decrease the number of server facilities. For example, a grocery store can easily change the number of cashiers open for business (up to the number of registers available). Th e store increases the number of cashiers open when lines are too long.

Another approach is to dedicate specifi c servers for specifi c transactions. One example would be to limit the number of items that can be processed at a particular cashier (ten items or less) or to limit a cashier to cash-only transactions. Still another possibility is to install self-service checkout systems.

Changing the number of phases. You can use a multiphase system where servers specialize in a portion of the total service rather than needing to know the entire service provided. Since a server has fewer tasks to learn, the individual server profi ciency should improve. Th is goes back to the concept of division of labor.

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C14 • SUPPLEMENT C WAITING LINE MODELS

Server effi ciency. You can improve server effi ciency through process improvements or dedication of additional resources. For example, cashier accuracy and speed are improved through the use of scanners. Service speed can also be increased by dedicating additional resources. For example, if a grocery bagger is added at each cashier station, service speed will be improved and customers will fl ow through the system more quickly.

Changing the priority rule. Th e priority rule determines who should be served next. Th ere are priority rules other than fi rst-come, fi rst-served. If you want to change priority rules, consider the impact on those customers who will wait longer.

Changing the number of lines. Changing to a single-line model from a multiline model is most appropriate when the company is concerned about fairness for its customers. A single line ensures that customers do not jockey in an attempt to gain an advantage over another customer. Multiline models easily accommodate specialty servers (express lanes).

Once changes are suggested, evaluate their impact on the performance characteristics of the waiting line system. Changes in one area can require changes in other areas. For example, if you achieve a more constant customer arrival rate, you may be able to reduce the number of service facilities.

LARGER-SCALE WAITING LINE SYSTEMS This supplement provides an introduction to waiting line system analysis. There are many variations of the mathematical models that are not discussed here, such as finite queue length, finite customer population, and nonexponential arrival and service times. As waiting line systems become more complex, especially as they involve more than one phase, concise mathematical formulas in general do not exist for system per- formance measures. Therefore, for most larger-scale waiting line systems, discrete- event simulation is often used to analyze these systems. Discrete-event simulation products allow the user to define the system, arrival and service patterns, and other aspects of the system. Then the simulation is run to mimic the behavior of the sys- tem in reality, and the results are statistically analyzed to determine system perfor- mance. Figure C-7 shows a screen shot from a model completed in the ProcessModel™

FIGURE C-7

Customer service center simulation layout

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SUPPLEMENT HIGHLIGHTS • C15

simulation software. In this system, calls arrive at a customer service center and are answered by one operator (with a second operator as a backup server). Some calls are resolved quickly, others need further research, and still others don’t get answered in a timely fashion and the callers renege (hang up) before being served. Once the system is completely specified, including arrival and service rates, the simulation is run and system performance measures are automatically calculated.

WAITING LINE MODELS WITHIN OM: HOW IT ALL FITS TOGETHER Although it is unlikely that you calculate performance measures for the lines you wait in on a day-to-day basis, you should now be aware of the potential for math- ematical analysis of these systems. More importantly, management has a tool by which it can evaluate system performance and make decisions as to how to improve the performance while weighing performance against the costs to achieve that performance.

Waiting line models are important to a company because they directly affect customer service perception and the costs of providing a service. Several functional areas are affected by waiting line decisions. Accounting is concerned with the cost of the waiting line system used. If system average utilization is low, that suggests the waiting line design is inefficient and too expensive. Poor system design can result in overstaffing or unnecessary capital acquisitions in an effort to improve customer service. Marketing is concerned about response time for customers—how long cus- tomers must wait in line before being served and how long it takes to be served. Quick service or response can be a competitive advantage. Long waits suggest a lack of concern by the organization or can be linked to a perception of poor service quality. Purchasing must be sure to buy capital equipment capable of achieving the proposed service rate. Operations uses waiting line theory to estimate queues or waiting times at different processing points, to allow for a better estimate of lead time and improve due-date delivery promising. Operations is also affected by the system design. When single-phase systems are used, operators must have greater skills. The organization needs to hire employees with higher skill levels or provide training to upgrade the workforce.

Supplement Highlights 1 Th e elements of a waiting line system include the customer

population source, the patience of the customer, the service system, arrival and service distributions, waiting line priority rules, and system performance measures. Understanding these elements is critical when analyzing waiting line systems.

2 Waiting line models allow us to estimate system performance by predicting average system utilization, average number of customers in the service system, average number of customers waiting in line, average time a customer spends in the system, average time a customer

waits in line, and the probability of n customers in the service system.

3 Th e benefi t of calculating operational characteristics is to provide management with information as to whether system changes are needed. Management can change the operational performance of the waiting line system by altering any or all of the following: the customer arrival rates, the number of service facilities, the number of phases, server effi ciency, the priority rule, and the number of lines in the system. Based on proposed changes, management can then evaluate the expected performance of the system.

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C16 • SUPPLEMENT C WAITING LINE MODELS

Key Terms waiting line system C2 queuing system C2 fi nite customer population C3

infi nite customer population C4 balking C4 reneging C4

jockeying C4 arrival rate C6 service rate C6

Formula Review For Single-Server Waiting Line Models

p 5 l

m is the average utilization of the system

W 5 l

m 2 l is the average number of customers in the

service system

LQ 5 pL is the average number of customers waiting in line

W 5 1

m 2 l is the average time spent waiting in the system,

including service

WQ 5 pW is the average time spent waiting in line

Pn 5 (1 2 p)p n is the probability that n customers are in the

service system at a given time

For the Multiserver Waiting Line Model

p 5 l

sm is the average utilization of the system

P0 5 c as 2 1 n 5 0

1l/m 2n

n! 1 1l/m 2 s

s! a 1

1 2 p b d 2 1 is the probability

that no customers are in the system

LQ 5 Po 1l/m 2 sp s! 11 2 p 2 2 is the average number of customers waiting in line

WQ 5 LQ l

is the average time spent waiting in line

W 5 WQ 1 1 m

is the average time spent in the system, including

service

L 5 lW is the average number of customers in the service system

Pn5 μ (l/m)n

n! P0 for n # s

(l/m)n

s!sn2s P0 for n . s

the probability that n customers are in the system at a given time

Solved Problems • Problem 1 The local Division of Motor Vehicles (DMV) is concerned with its waiting line system. Currently, the DMV uses a single- server, single-line, single-phase system when processing license renewals. Based on historical evidence, the average number of customers arriving per hour is 9 and is described by a Poisson distribution. The service rate is 12 customers per hour with the service times following an exponential distribution. The cus- tomers are patient and come from an infinite population. The manager of the DMV would like you to calculate the opera- tional characteristics of the waiting line system.

(a) What is the average system utilization? (b) What is the average number of customers in the system? (c) What is the average number of customers waiting in line?

(d) What is the average time a customer spends in the system? (e) What is the average time a customer spends waiting in line?

• Before You Begin The key here is identifying the mean arrival rate and the mean service rate. The average number of customers per hour is given as 9. The server is able to service 12 customers per hour on aver- age. Plug these values into the appropriate formula.

• Solution (a) Average utilization is 0.75, or 75 percent.

p 5 l

m 5

9 12

5 0.75

(See student companion site for Excel template.)

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SOLVED PROBLEMS • C17

(b) Average number of customers in the system is 3.

L 5 l

m 2 l 5

9 12 2 9

5 3 customers

(d) Average time a customer spends in the system is 0.33 hours, or 20 minutes.

W 5 1

m 2 l 5

1 12 2 9

5 0.33 hours

(e) Average time a customer spends waiting in line is 0.25 hours, or 15 minutes.

WQ 5 pW 5 0.75 3 0.33 5 0.25 hours

Th ese operational characteristics can be calculated as shown in the spreadsheet. Using a spreadsheet allows the modeler to vary parameters quickly and see the resulting operational characteristics.

Note: Th is is the same spreadsheet model as introduced with Example C.1; only the input values have been changed.

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

A B C

Queuing Analysis: Single Server Solved Problem C.1 Inputs Time unit hour Arrival Rate (lambda) 9 customers/hour Service Rate (mu) 12 customers/hour

Intermediate Calculations Average time between arrivals 0.111111 hour Average service time 0.083333 hour

Performance Measures Rho (average server utilization) 0.75 P0 (probability the system is empty) 0.25 L (average number in the system) 3 customers Lq (average number waiting in the queue) 2.25 customers W (average time in the system) 0.333333 hour Wq (average time in the queue) 0.25 hour

Probability of a specific number of customers in the system Number 2 Probability 0.140625

• Problem 2 The county has decided to consolidate several of its DMV facili- ties into a larger, centrally located facility. The DMV manager wants you to calculate the operational characteristics of a mul- tiserver, single-phase waiting line system. The arrival rate is expected to be 72 customers per hour and follows a Poisson dis- tribution. The number of identical servers is seven. Each server will be able to serve an average of 12 customers per hour. The service times are described by an exponential distribution. Your job is to calculate the following:

(a) The average system utilization (b) The probability of no customers in the system (c) The average number of customers waiting in line (d) The average time a customer waits in line (e) The average time a customer spends in the system

• Before You Begin Once again, the key is identifying the mean arrival rate and the mean service rate. Here the arrival rate is given as 72 customers per

hour, while each of the seven servers is able to serve 12 customers per hour. Therefore, 84 customers can be served per hour.

• Solution (a) Average system utilization is 0.857, or 85.7 percent.

p 5 m

sl 5

72 7 3 12

5 0.8571

(b) The probability that no customers are in the system is 0.0016, or 0.2 percent.

P0 5 cas21 n50

1l/m 2n

n! 1 1l/m 2 s

s! a 1

1 2 p b d 21

5 c c 172/12 2 0 0!

1 172/12 2 1

1! 1 172/12 2 2

2! 1 172/12 2 3

3! 1 172/12 2 4

1 172/12 2 5

5! 1 172/12 2 6

6! d 1 c 172/12 2 7

7! a 1

1 2 p b d d 2 1

5 1

623.8 5 0.001579

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C18 • SUPPLEMENT C WAITING LINE MODELS

LQ 5 P0 1l/m 2 sp s! 11 2 p 2 2

5 0.001579 172/12 2 7 3 0.857

7! 11 2 0.857 2 2 5 3.683 customers (d) The average time spent waiting in line is 0.05115 hours,

or 3.07 minutes.

WQ 5 LQ l

5 3.683

72 5 0.05115 hours

(e) The average time a customer spends in the system is 0.1345 hours, or 8.07 minutes.

W 5 WQ 1 1 m

5 0.05115 1 1

12 5 0.1345 hours

The spreadsheet solution is shown. This model is the same as that introduced with Example C.2, with only the input values changed.

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

A B C

Queuing Analysis: Multiple Servers Solved Problem C.2 Inputs Time unit hour Arrival Rate (lambda) 72 customers/hour Service Rate per Server (mu) 12 customers/hour Number of Servers (s) 7 servers

Intermediate Calculations Average time between arrivals 0.013889 hour Average service time per server 0.083333 hour Combined service rate (s*mu) 84 customers/hour

Performance Measures Rho (average server utilization) 0.857143 P0 (probability the system is empty) 0.001579 L (average number in the system) 9.682981 customers Lq (average number waiting in the queue) 3.682981 customers W (average time in the system) 0.134486 hour Wq (average time in the queue) 0.051153 hour

Probability of a specific number of customers in the system Number 5 Probability 0.102305

Discussion Questions 1. Describe the elements of a waiting line system. 2. Provide examples of when a single-line, single-server,

single-phase waiting line system is appropriate. 3. Describe the operating performance characteristics calcu-

lated for evaluating waiting line systems. 4. Describe the implications for customer service and server

skills when using a single-line, single-server, single-phase waiting line system.

5. Describe the implications for customer service and server skills when using a single-line, multiserver, single-phase waiting line system.

6. Describe the implications for customer service and server skills when a multiserver, multistage waiting line system is used.

7. Describe a situation in your daily life that could be improved by waiting line analysis.

8. Explain how the design of a waiting system can negatively affect customers.

9. Visit your local bank and observe the waiting line system. Describe the system in terms of number of lines, number of facili- ties, and number of phases.

10. On your next trip to the Division of Motor Vehicles, eval- uate its waiting line system.

11. Describe any disadvantages of using waiting line models.

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CASE: THE COPY CENTER HOLDUP • C19

Problems 1. Melanie is the manager of the Clean Machine car wash

and has gathered the following information. Customers arrive at a rate of eight per hour according to a Poisson distribution. Th e car washer can service an average of ten cars per hour with service times described by an exponential distribution. Melanie is concerned about the number of customers waiting in line. She has asked you to calculate the following system characteristics:

(a) Average system utilization (b) Average number of customers in the system (c) Average number of customers waiting in line 2. Melanie realizes that how long the customer must wait is also

very important. She is also concerned about customers balking when the waiting line is too long. Using the arrival and service rates in Problem 1, she wants you to calculate the following system characteristics:

(a) Th e average time a customer spends in the system (b) Th e average time a customer spends waiting in line (c) Th e probability of having more than three customers in

the system (d) Th e probability of having more than four customers in the

system 3. If Melanie adds an additional server at Clean Machine car

wash, the service rate changes to an average of 16 cars per hour. Th e customer arrival rate is 10 cars per hour. Melanie has asked you to calculate the following system characteristics:

(a) Average system utilization (b) Average number of customers in the system (c) Average number of customers waiting in line 4. Melanie is curious to see the diff erence in waiting times for

customers caused by the additional server added in Problem 3. Calculate the following system characteristics for her:

(a) Th e average time a customer spends in the system (b) Th e average time a customer spends waiting in line (c) Th e probability of having more than three customers in

the system

(d) Th e probability of having more than four customers in the system

5. Aft er Melanie added another car washer at Clean Machine (service rate is an average of 16 customers per hour), business improved. Melanie now estimates that the arrival rate is 12 customers per hour. Given this new information, she wants you to calculate the following system characteristics:

(a) Average system utilization (b) Average number of customers in the system (c) Average number of customers waiting in line 6. As usual, Melanie then requested you to calculate system

characteristics concerning customer time spent in the system. (a) Calculate the average time a customer spends in the system. (b) Calculate the average time a customer spends waiting in line. (c) Calculate the probability of having more than four

customers in the system. 7. Business continues to grow at Clean Machine. Melanie has

decided to use a second car washing bay, staff ed with another identical two-person team. Clean Machine will now use a single- line, multiserver, single-phase waiting line system. Th e arrival rate is estimated to average 24 customers per hour according to a Poisson distribution. Each of the car wash teams can service an average of 16 customers per hour according to an exponential distribution. Calculate the following operational characteristics:

(a) Average system utilization (b) Average number of customers in the system (c) Average number of customers waiting in line (d) Average time a customer spends in the system (e) Average time a customer spends waiting in line (f) Probability of having more than four customers in the system. 8. Melanie is very concerned about the number of customers

waiting in line. Given the information in Problem 7, calculate how high the customer arrival rate can increase without the average number of customers waiting in line exceeding four.

CASE: The Copy Center Holdup Catherine Blake, the office manager for the College of Busi- ness Administration, has received numerous complaints lately from several department chairpersons. In the past few months, the chairpersons have insisted that something be done about the amount of time their administrative assistants waste waiting in line to make copies. Currently, the college has two photocopy centers dedicated to small copying jobs: copy center A on the third floor and copy center B on the fourth floor. Both centers are self-serve and have identical processing capabilities. The copying machines are not visible to the administrative assistants from their offices. When copying is required, the administrative assistant goes to the copy room and waits in line to make the necessary copies. Cath- erine’s assistant, Brian, was assigned to investigate the problem.

Brian reported that, on average, administrative assistants arrive at copy center A at the rate of 10 per hour and at copy center B at the rate of 14 per hour. Each of the copy centers can service 15 jobs per hour. The administrative assistants’ arrivals essentially follow a Pois- son distribution, and the service times are approximated by a nega- tive exponential distribution. Brian has proposed that the two copy centers be combined into a single copy center with either two or three identical copy machines. He estimates that the arrival rate would be 24 per hour. Each machine would still service 15 jobs per hour. Cur- rently, administrative assistants earn an average of $15 per hour. (a) Determine the utilization of each of the copy centers. (b) Determine the average waiting time at each of the copy

centers.

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C20 • SUPPLEMENT C WAITING LINE MODELS

(d) Determine the utilization of the combined copy center with two copiers.

(e) Determine the average waiting time at the combined copy center.

(f) What would be the annual cost of the administrative assistants’ average waiting time using the combined two- copier setup?

(g) What would be the utilization of the combined copy center with three copiers?

(h) What would be the annual cost of the administrative assistants’ average waiting time using the combined three-copier setup?

(i) What would you recommend to Catherine?

Online Resources Visit our dynamic Web site, www.wiley.com/college/reid, for more cases, Web links, and additional information.

1. Spreadsheets for Examples C.1 and C.2 and Solved Problems 1 and 2 are available on the CD. 2. Additional Web Resources

• Real Queuing Examples: http://www2.uwindsor.ca/�hlynka/qreal.html Th is site contains excerpts from news articles that deal with aspects of waiting lines.

• ClearQ: http://clearq.com/ Th is company produces “take-a-number” systems for service facilities (e.g., delis), but also provides performance information about the waiting line.

• Qmatic: http://us.q-matic.com/index.html Th is company produces informational displays and other products to keep customers informed about waiting times.

• “Queuing Presentation” by Richard Larson, given at the Institute for Operations Research and the Management Sciences: http://caes.mit.edu/people/larson/MontrealINFORMS1/sld001.htm.

• Th e Queuing Th eory Tutor: http://www.dcs.ed.ac.uk/home/jeh/Simjava/queueing/mm1_q/mm1_q.html. Th is site has two animated displays of waiting lines. Th e user can change arrival and service rates to see how performance is aff ected.

• Myron Hlynka’s Queuing Page: http:www2.uwindsor.ca/�hlynka/queue.html. Th is Web site contains information about waiting lines as well as links to other interesting sites.

• Queuing ToolPak: http://www.bus.ualberta.ca/aingolfsson/qtp/. Th e Queuing ToolPak is an Excel add-in that allows you to easily compute performance measures for a number of diff erent waiting line models.

Internet Challenge

Visit the Web site for the United Network for Organ Sharing (UNOS) �http://www.unos.org�. Th is is the United States organization coordinating the donation, assignment, and transplantation of human organs. Research the organ donation situation, and prepare a report summarizing your fi ndings from a waiting line standpoint. What are some of the important performance measures in this waiting line system? Describe the elements of the waiting line system. What is the priority rule used for selecting the next customer? Propose at least two other priority rules, and discuss what changes you think would occur in the operating characteristics. Besides the quantitative performance characteristics, what other considerations would need to be made in order to change the priority rule? Support your discussion with data and information obtainable at the UNOS Web site.

Selected Bibliography Albright, S. Christian, and Wayne Winston. Essentials of Prac-

tical Management Science. Mason, OH: Cengage, 2005. Hall, Randolph W. Queueing Methods for Services and Manu-

facturing. Englewood Cliff s, N.J.: Prentice-Hall, 1991.

Moore, P.M. Queues, Inventories and Maintenance. New York: Wiley, 1958.

Ragsdale, Cliff T. Spreadsheet Modeling & Decision Analysis, Fift h Edition. Stamford, Conn.: Th omson, 2008.

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SUPPLEMENT

W H A T ’ S I N O M F O R M E ?

FIN MKT OM MISACC HRM

Master Production Scheduling D2 MPS as a Basis of Communication D3 Objectives of Master Scheduling D4 Developing an MPS D4 Rough-Cut Capacity Planning D6 Evaluating and Accepting the MPS D9

Using the MPS D10 Using the ATP Records D11 Stabilizing the MPS D14 Master Production Scheduling and Rough-Cut

Capacity Planning within OM: How It All Fits Together D15

Master Scheduling and Rough-Cut Capacity Planning

Before studying this supplement you should know or, if necessary, review

1. Capacity management concepts, Chapter 9, pp. 333–339. 2. Work standards, Chapter 11, pp. 422–429. 3. Relevant inventory costs, Chapter 12, pp. 459–461. 4. Order quantity models, Chapter 12, pp. 467–480. 5. Aggregate Planning, Chapter 13, pp. 507–509.

After completing this supplement you should be able to

1 Explain the role of the master production schedule and describe the objectives of master production scheduling.

2 Develop a master production schedule and project the capacity needed using rough-cut capacity planning.

3 Calculate available-to-promise quantities. 4 Describe time fence policies.

S U P P L E M E N T O U T L I N E

L E A R N I N G O B J E C T I V E S

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D2 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

When planning your social and work-related activities for the upcoming semester, you use the same logic that is used in developing a master schedule and doing rough-cut capacity planning. You have a schedule of activities to do (the master pro- duction schedule), and you check to see whether you actually have the resources to do those activities (rough-cut capacity planning). Social activities could include sport- ing events, concerts, the movies, clubbing, dinners out, club meetings, shopping, fam- ily commitments, and/or just hanging out. You have work-related activities as well, such as classes, homework, an outside job, and/or group projects. Each of these activi- ties requires some of your resources. At the very least, you must reserve time to do the activity. In some cases, you must also set aside some cash. Unfortunately, almost everyone has limited resources. The same is true for companies. You develop a list of the activities that you plan to do, estimate the resources required, and then determine whether your proposed list is feasible. Companies do the same thing: they develop a master production schedule, and then rough-cut capacity planning determines whether the schedule is feasible.

MASTER PRODUCTION SCHEDULING Your company’s aggregate plan specifies the resources authorized for use by the com- pany’s operations group. These resources include the size of the workforce, level of inventory held, number of planned shortages, authorized level of overtime and under- time, aggregate number of units or services to be produced in-house, and number of units or services to be subcontracted.

The master production schedule (MPS) is often stated in product or service speci- fications rather than dollars. It shows how many products or services are planned for each time period, based on the resources authorized in the aggregate plan. In manu- facturing, the master scheduler develops the schedule based on the available capacity. In service companies, the office manager, department manager, or assistant manager might develop the schedule.

The MPS is the anticipated build schedule for manufacturing specific end products or providing specific services. The key distinction here is that the MPS is a statement of production or services and is not a statement of demand—a plan to satisfy cus- tomer demand while considering operational effectiveness and cost. Because of this, individual products can be finished ahead of time and held in inventory rather than finished as needed. The master scheduler or office manager balances customer service and capacity usage.

The aggregate plan shows how many products or services are planned for each time period. The MPS identifies the specific products or services planned for a given time period. Let’s look at an example of how an aggregate plan is linked to the MPS.

Although this example involves manufacturing, think about how an MPS might look in a service operation such as a law firm. The firm knows how many hours of staff time are available (resources) and its current case load (demands). It must now decide how best to use the attorneys and law clerks each period to satisfy the corporate objectives.

� Master production schedule (MPS) The anticipated build schedule.

� Master scheduler The person responsible for managing, developing, reviewing, and maintaining the master schedule.

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MPS AS A BASIS OF COMMUNICATION • D3

MPS AS A BASIS OF COMMUNICATION The MPS is a basis for communication between operations and other functional areas. It is stated in product or service specifications rather than dollars. Your company uses an effective MPS in making customer delivery promises, using company capacity wisely, achieving the company’s objectives, and making trade-off decisions between marketing and operations. Figure D-1 shows the connections between the master scheduling role and other parts of the planning process. Let’s examine these connec- tions, beginning with demand management.

Demand management includes a company’s forecasting, order-entry, order- promising, and physical distribution activities. Demand management captures all activities that use capacity. These demands can be customer orders for products or ser- vices, a forecast of demand for products and services, interplant requirements, service parts requirements, and/or distribution requirements. If a demand is excluded, it will not be scheduled for completion. Communications between the master scheduler and demand management are ongoing.

MKT

� Demand management The function of recognizing all demands for goods and services to support the marketplace.

Production plan

Rough-cut capacity planning

Demand management

Master production schedule

Material requirements

planning

FIGURE D-1

Master production schedule linkages

Amber’s Backpack Company (ABC) produces three models of backpacks: the basic backpack, the urban backpack, and the evening backpack. Each model needs the same amount of production time. ABC’s aggregate production rate is 400 backpacks per week. A possible MPS for ABC is shown here.

The aggregate plan states that ABC should produce 400 units each week. The MPS shows the models and quantities that constitute the 400 units to be produced each week of the schedule.

EXAMPLE D.1

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Week 1 2 3 4 5 6 7 8

Basic backpack 400 400 400 400 400 Urben backpack 250 250 250 Evening backpack 150 150 150 Total 400 400 400 400 400 400 400 400

Developing the MPS at Amber’s Backpack Company

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D4 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

The production or aggregate plan supports the marketing plan. The master sched- uler must work within the authorized resources of the plan. The process for develop- ing an MPS is as follows: 1. Th e master scheduler develops a proposed MPS. 2. Th e master scheduler uses a rough-cut capacity planning technique to calculate

whether the company has the capacity to meet the proposed MPS. Th is is done using a rough-cut capacity planning technique.

3. If the proposed MPS is feasible, it is evaluated by the master scheduler in terms of customer service, eff ective use of resources, and inventory investment.

4. If the proposed MPS is accepted, it becomes the authorized MPS. If capacity is insuffi cient, either the MPS is modifi ed or capacity is expanded.

The authorized MPS is a critical input into the material requirements planning (MRP) system. The MPS tells the MRP system what the company plans to build and when. The MRP system then calculates the materials needed to build the products in the schedule and plans for the necessary materials. (MRP systems are discussed in depth in Chapter 14.)

� Material requirements planning (MRP) A technique using the master production schedule, bill of material data, and inventory records to calculate requirements for materials

OBJECTIVES OF MASTER SCHEDULING The master scheduler considers the following objectives when developing the MPS: 1. Achieve the desired customer service level either by maintaining fi nished

goods inventory or by scheduling completion of the item or service to meet the customer’s delivery needs.

2. Make the best use of the company’s resources: material, labor, and equipment. 3. Ensure that the inventory investment is at the appropriate level. To meet these objectives, the master schedule must satisfy customer demand, not exceed operations’ capacity, and work within the constraints of the aggregate plan. Let’s look at how to develop an MPS.

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DEVELOPING AN MPS The master scheduler develops a proposed MPS, checks the schedule for feasibility in terms of available capacity, modifies as needed, and authorizes the MPS. The master scheduler starts by creating, revising, and finishing an MPS record for each product. The master scheduler uses the finished MPS records to develop a proposed master production schedule, which he or she then checks for feasibility with a rough-cut capacity planning technique.

Consider the following examples of an MPS record for a product built in a make-to- stock environment with inventory held. The product is built in a fixed-order quantity of 125 units, and there are 110 units in beginning inventory. Table D-1 is an initial MPS record showing the demand forecasts for the next 12 weeks.

Week 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 50 50 50 50 75 75 75 75 50 50 50 50

Projected available 60 10 −40

MPS 0 0 0 0 0 0 0 0 0 0 0 0

TABLE D-1

First MPS Record

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DEVELOPING AN MPS • D5

The top row of the record shows the time periods (weeks, in our example); the fore- cast row shows the forecasted demand for the product. The projected available quan- tity row keeps track of how many units are available at the end of each time period. The MPS row shows when replenishment shipments need to arrive. MPS shipments arrive at the beginning of the time period. To calculate the projected available quantity, we use the following formula:

Projected available 5 beginning inventory 1 MPS shipment 2 forecasted demand

Period 1 To calculate the projected available quantity at the end of period 1, we take the beginning inventory (110 units), add any MPS shipments arriving that period (0 units), and subtract the forecasted demand for the period (50 units). This leaves the projected available quantity at the end of period 1, or (110 � 0) � 50 � 60 units projected available quantity.

Period 2 The beginning inventory in period 2 is 60 units, no MPS shipment is scheduled, and the forecast is 50 units. Therefore, the projected available quantity at the end of period 2 is 10 units.

Period 3 In period 3, we have 10 units beginning inventory, no MPS quantity, a forecast of 50 units, and a projected available quantity of �40 units; that is, we do not have enough of this product available to satisfy the forecasted demand in period 3. Thus we need to plan an MPS shipment to arrive at the beginning of period 3. We calculate the size of the replenishment order by the order quantity rule—in our case, a fixed-order quantity of 125 units. Table D-2 shows the revised MPS record with an MPS quantity of 125 units scheduled to arrive in period 3.

We continue to calculate the projected available quantity until we find the next period in which the projected available quantity is negative, which is period 5. Therefore, we need to schedule another MPS replenishment for delivery at the start of period 5. At that point, we add another MPS shipment and continue forward. Table D-3 pres- ents the completed MPS record, which shows replenishment orders needed in periods 3, 5, 7, 8, and 11. Each MPS quantity is determined using the given order quantity rule (125 units).

Week 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 50 50 50 50 75 75 75 75 50 50 50 50

Projected available 60 10 85 35 −40

MPS 0 0 125 0 0 0 0 0 0 0 0 0

TABLE D-2

First Revised MPS Record

Week 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 50 50 50 50 75 75 75 75 50 50 50 50

Projected available 60 10 85 35 85 10 60 110 60 10 85 35

MPS 0 0 125 0 125 0 125 125 0 0 125 0

Completed MPS Record

TABLE D-3

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D6 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

ROUGH-CUT CAPACITY PLANNING Rough-cut capacity planning (RCCP) calculates a rough estimate of the workload placed on critical resources by the proposed MPS. This workload is compared against demonstrated capacity for each critical resource. This comparison enables the mas- ter scheduler to develop a feasible MPS. Among the several approaches to rough-cut capacity planning is capacity planning using overall planning factors (CPOPF).

Capacity planning using overall planning factors (CPOPF) is a simple, rough- cut capacity planning technique. CPOPF develops a planning factor for each critical resource based on historical data. A planning factor shows how much of a resource is needed for one completed unit. Table D-4 shows the procedure for using CPOPF. Next, we will work through an example using the procedure.

� Rough-cut capacity planning (RCCP) The process of converting the master production schedule into requirements for key resources such as direct labor and machine time.

� Demonstrated capacity Proven capacity calculated from actual performance data.

� Capacity planning using overall planning factors (CPOPF) A rough-cut capacity planning technique. MPS items are multiplied by historically determined planning factors for key resources.

1. Determine the appropriate planning factors using historical data.

2. Multiply the MPS quantities by the appropriate planning factor.

3. Sum capacity requirements for each resource by time period.

4. Allocate capacity requirements to individual work centers based on historical percentages.

5. Evaluate the workload at each resource to validate MPS feasibility.

TABLE D-4

Procedure for CPOPF

Heavenly Ballroom Shoes, Inc. (HBS) produces two models of ballroom dance shoes. One model is for men (Model M) and the other is for women (Model W). Charles, the master scheduler, has accumulated the following historical data. Dur- ing the past three years, HBS has produced 72,000 pairs of Model M, using 21,600 hours of direct labor and 5760 machine hours. During that same period, HBS produced 108,000 pairs of Model W, using 43,200 hours of direct labor and 12,960 hours of machine time.

• Before You Begin: To implement capacity planning using overall planning factors (CPOPF), planning factors are needed. In this case, we have two products, Model M and Model W. Each product requires both direct labor and machine time. We have historical data to determine, on average, how much of each resource has been used in the past to build these products. After developing the planning factors, determine how much capacity is needed to satisfy a specific pro- posed master production schedule. That is, if you know how many Model M’s you plan to produce and you know how much direct labor each Model M takes on average, you can calculate the total amount of direct labor needed to build the Model M’s for a particular period. You do the same thing for Model W’s. Then do this for all periods in your plan. Summing these requirements will give you the total amount of direct labor and machine hours you need to build the units in the proposed master production schedule. These data can be further broken down by individual work centers based on historical data.

EXAMPLE D.2

CPOPF at Heavenly Ballroom Shoes

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ROUGH-CUT CAPACITY PLANNING • D7

Step 1 Determine the planning factors. Charles uses two resources, direct labor and machine time, for each of the products. He needs four planning factors: direct labor for Model M, machine time for Model M, direct labor for Model W, and machine time for Model W. Using the historical data, Charles computes the planning factors as follows:

Planning factor for direct labor 5 total direct labor spent building model

number of units of model built For Model M, this translates to

Planning factor for direct labor 5 21,600 hours of direct labor

72,000 pairs The planning factor for direct labor for Model M is 0.30 hours. That means he allows for 0.30 hours

of direct labor for each pair of Model M shoes. To calculate the planning factor for machine time for Model M, Charles substitutes total machine hours spent building Model M into the numerator as shown.

Planning factor for machine time 5 total machine hours spent building model

number of units of model built The planning factor for the Model M machine time is 0.08 hours (5760 hours of machine time

divided by 72,000 pairs of Model M). Charles continues the process to calculate the planning factors for Model W. The direct labor planning factor for Model W is 0.40 hours and the machine time planning factor is 0.12 hours. Table D-5 shows the four planning factors.

Given a proposed MPS, how would Charles calculate the workload for the proposed MPS? Table D-6 shows the proposed quarterly MPS for HBS.

Step 2 Calculate the workload generated by this schedule. Charles multiplies the MPS quantity times the appropriate planning factor. For example, Charles begins in Quarter 1 and calculates how much labor is needed to build 6000 pairs of Model M and 10,000 pairs of Model W. He multiplies 6000 pairs of Model M by its labor planning factor of 0.30 hours to arrive at 1800 hours of direct labor needed. He continues for Model W, multiplying 10,000 pairs by its labor planning factor of 0.40 hours to arrive at 4000 hours of direct labor needed. The total number of direct hours needed in Quarter 1 is 5800 hours (1800 for Model M and 4000 for Model W). Charles continues to do this for each time period. Table D-7 shows all the labor needs.

To calculate the machine needs, Charles continues the process. He multiplies the Quarter 1 needs for Model M by its machine time planning factor (6000 Model M’s � 0.08 hours machine time � 480 hours needed). For Model W, 1680 hours of machine time are needed (10,000 Model W’s � 0.12 hours machine time � 1200 hours needed). Table D-8 shows the machine needs for each product in each quarter.

A B C

3 Planning Factors (hours per pair)

4 Direct Labor

Machine Time5

6 Model M 0.30 0.08 7 Model W 0.40 0.12

TABLE D-5

Planning Factors

A B C D E F

9 Quarterly Master Production Schedule (MPS) (pairs) 10 Q1 Q2 Q3 Q4 Totals

11 Model M 6000 5500 9500 6500 27500

12 Model W 10000 12000 7500 10100 39600

TABLE D-6

Proposed MPS

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D8 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

Step 3 Calculate the total capacity needs for each resource for each time period. Charles does this by summing up the individual capacity needs for each of the products. Table D-7 shows the total labor hours needed for each quarter. Table D-8 shows the same information for machine hours needed.

Step 4 Calculate individual work center capacity needs based on historical percentage alloca- tion. Charles calculates that 60 percent of HBS’s direct labor is used in work center 101 and 40 percent is used in work center 102. The same is true for its machine time. How would Charles calculate by quar- ters how much direct labor and machine time is needed at work centers 101 and 102? Table D-9 shows direct labor needs by work center. To calculate the labor hours needed in work center 101 in Quarter 1, Charles multiplies the total Quarter 1 labor needs by 60 percent (5800 hours of total labor needed by 60 percent equals 3480 hours of labor needed in work center 101 in Quarter 1). The labor in work center 102 in Quarter 1 is 2320 hours (5800 hours � 40 percent). Charles now has an estimate of the direct labor needs by quarter for each of the work centers. He can compare the direct labor hours needed with the available direct labor hours and make adjustments either to the available capacity or to the MPS.

Charles calculates machine hour needs in each department for each quarter in the same way as he calculates labor needs. Table D-10 shows the machine hours needed by each of the work centers for each quarter. Given this information, Charles can decide whether HBS needs additional equipment or whether it has adequate machine capacity in each of the work centers. Remember that a company can

21 22 23 24 25 26 27 28

A B C D E F

Machine Time (Hours) Required Q1 Q2 Q3 Q4 Totals

Model M 480 440 760 520 2200 Model W 1200 1440 900 1212 4752

Totals 1680 1880 1660 1732 6952

B24: =B11*$C6 (copied to B24:E25)

B26: =SUM(B24:B25) F24: =SUM(B24:E24)

TABLE D-8

Machine Time (Hours) Required

31 32 33 34 35 36

A B C D E F G Direct Labor Hours Required by Work Center

Historical Allocation Q1 Q2 Q3 Q4 Totals

Center 101 60% 3480 3870 3510 3594 14454 Center 102 40% 2320 2580 2340 2396 9636

Totals 5800 6450 5850 5990 24090

C32: =B$18*$B32 (copied to C32:F33)

C34: =SUM(C32:C33) G32: =SUM(C32:F32)

TABLE D-9

Direct Labor Needs by Work Center

13 14 15 16 17 18 19 20

A B C D E F

Direct Labor Hours Required Q1 Q2 Q3 Q4 Totals

Model M 1800 1650 2850 1950 8250 Model W 4000 4800 3000 4040 15840

Totals 5800 6450 5850 5990 24090

B16: =B11*$B6 (copied to B16:E17)

B18: =SUM(B16:B17) F16: =SUM(B16:E16)

TABLE D-7

Direct Labor Hours Required

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EVALUATING AND ACCEPTING THE MPS • D9

EVALUATING AND ACCEPTING THE MPS To evaluate the MPS in terms of customer service, the master scheduler checks that promised customer delivery dates are met. He or she also makes sure that the MPS provides enough flexibility to respond to new customer orders.

To evaluate the MPS for effective use of resources, the master scheduler checks that enough capacity is available to meet the schedule in each time period. This capacity includes short-term changes such as overtime, subcontracting, and temporary employees.

To evaluate the MPS in terms of cost, the master scheduler compares the MPS to the aggregate plan, which specifies available resources. If the MPS needs additional resources, the company may not achieve the objectives of the marketing plan and, consequently, the business plan.

When the master scheduler has evaluated and accepted the MPS, this authorized MPS is input into the MRP system, which is discussed further in Chapter 14.

MKT

Before You Go On

Be sure that you understand the sequential process of master scheduling:

1. The master scheduler uses a rough-cut capacity planning technique to calculate whether the company has the capacity to meet the proposed MPS.

2. If the proposed MPS is feasible, it is evaluated in terms of customer service, effective use of resources, and inventory investment.

3. If the proposed MPS is accepted, it becomes the authorized MPS. If capacity is insufficient, either the MPS is modified or capacity is expanded.

increase its capacity with overtime, temporary workers, subcontracting, or by using alternative manu- facturing processes. When Charles is certain that the proposed MPS is feasible, he evaluates the MPS in terms of customer service, effectiveness of resource usage, and cost.

39 40 41 42 43 44

A B C D E F G Machine Time Hours Required by Work Center

Historical Allocation Q1 Q2 Q3 Q4 Totals

Center 101 60% 1008 1128 996 1039.2 4171.2 Center 102 40% 672 752 664 692.8 2780.8

Totals 1680 1880 1660 1732 6952

C40: =B$26*$B40 (copied to C40:F41)

C42: =SUM(C40:C41) G40: =SUM(C40:F40)

TABLE D-10

Machine Hour Needs by Work Center

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D10 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

USING THE MPS One use of the authorized MPS is order promising. When a customer places an order for a product but does not expect immediate delivery, the delivery date is negotiated. The customer typically requests delivery at a future date and the company decides whether it can promise delivery on that date. This is called order promising. For example, your parents order a new car custom-built for you at the factory for delivery on graduation day. The company is order promising when it decides whether it can produce the car and deliver it on that date. By extending the MPS records we began using in Table D-1, we can do order promising. Let’s look at an extended MPS record in Table D-11.

The table now has two additional rows, one for customer orders and the other for available-to-promise quantity. The customer orders row has orders promised to cus- tomers for delivery in that time period. For example, the company has promised 35 units to be delivered in period 1, 25 units for delivery in period 2, and so forth.

The available-to-promise (ATP) row shows how many uncommitted units the company has available for delivery at a given time. Next, we look at an example of how to calculate the ATP quantity and the projected available balances.

� Order promising The process of making order- delivery commitments.

� Available-to-promise (ATP) The uncommitted portion of a company’s inventory and planned production, maintained in the MPS to support order promising.

Period 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 50 50 50 50 75 75 75 75 50 50 50 50

Customer orders 35 25 25 20 0 15 0 0 10 0 0 10

Projected available

ATP

MPS 0 0 125 0 125 0 125 125 0 0 125 0

Extended MPS Record

TABLE D-11

Using the data shown in Table D-11, let’s look at how we get the numbers. In this case, both the forecast numbers and the customer orders are already filled in. The first calculations we need to do are in the projected available row: we take the beginning inventory, add to it any MPS shipment, and subtract the greater of the forecast quantity or the customer orders. For example, in period 1, the beginning inven- tory is 110 units, there is no MPS shipment, and since the forecast of 50 is greater than the customer orders (35), we have 110 units � 0 units � 50 units � 60 units projected available at the end of period 1.

We calculate the projected available quantity as

Projected available 5 beginning inventory 1 MPS shipment 2 the greater of the period's forecast or the actual customer orders promised for delivery that period

Table D-12 shows the appropriate projected available quantities. We calculate the ATP quantity for the current period or, as it is called, the action bucket, and each time an MPS replenishment order is sched- uled. In our example, that means we compute the ATP quantity in periods 1, 3, 5, 7, 8, and 11.

� Action bucket The current period.

EXAMPLE D.3

Filling in the MPS Record

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USING THE ATP RECORDS • D11

We calculate the ATP quantity in the current period differently from how we calculate it in the future replenishment periods. In the current period (the action bucket), the ATP quantity equals the beginning inventory, plus any MPS quantity. These two represent the available inventory we have to work with. In period 1, this is 110 units plus 0, or 110 total units available. From this amount, we subtract the customer orders promised for delivery between now and the next replenishment order (period 3 in our case). The number of units already promised is 60 (35 units for delivery in period 1 and 25 units for delivery in period 2). After subtracting the 60 units promised from the 110 units available, we still have 50 ATP units for delivery either in period 1, period 2, or at some future date. We calculate ATP in the action bucket as

ATPAction Bucket 5 beginning inventory 1 MPS shipment 2 customer orders before next replenishment

As we said, we calculate the ATP differently at future replenishment periods. In these cases, the ATP equals the MPS replenishment quantity less any customer orders promised for delivery between the date of the replenishment received and the next replenishment scheduled. For example, a replenishment order of 125 units is arriving in period 3. The customer orders already promised for delivery before the next replenishment order (period 5) total 45 units (25 units in period 3 and 20 units in period 4). Of those 125 units arriving, we have already committed 45, leaving 80 units still available to promise. We calculate the ATP at periods other than the action bucket as

ATP 5 MPS shipment 2 customer orders between current MPS shipment and next scheduled replenishment

In periods other than the action bucket, we do not include the beginning inventory since it is not clear what the available amount will be. Remember that we subtracted the greater of the forecast or customer orders promised for delivery each period. Thus, if the forecast is greater than the customer orders, we end up with more units in inventory than are reflected on the MPS record. Table D-12 shows the completed ATP quantities.

USING THE ATP RECORDS The ATP records show how much inventory is available to satisfy customer demand, so your company bases its delivery promises to customers on these records. Using the completed ATP record in Table D-12, let’s look at whether or not your company can promise delivery of a new order.

Suppose that marketing has a customer willing to purchase 200 units if your company can deliver the units in period 5. Using the ATP record, we need to see whether that delivery is possible. One way to do this is to adjust the ATP record as if the order had already been accepted. Table D-13 shows a revised ATP record including the new order.

MKT

Period 1 2 3 4 5 6 7 8 9 10 11 12 Forecast 50 50 50 50 75 75 75 75 50 50 50 50 Customer orders 35 25 25 20 0 15 0 0 10 0 0 10 Projected available 110 60 10 85 35 85 10 60 110 60 10 85 35 Available-to-promise 50 80 110 125 115 115 MPS 125 125 125 125 125

TABLE D-12

Completed ATP MPS Record

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D12 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

In the customer orders row, 200 units are now scheduled for customer delivery in period 5. This changes the projected available quantities from period 5 onward and changes the ATP quantity in period 5. The two rules to remember here are the following.

1. A negative number in the projected available row is sometimes a problem. We calculate the projected available quantity by subtracting the greater of the forecast or the customer orders promised for delivery each period. If the forecast is subtracted because it is larger as in period 1 (50 units forecast compared to 35 units of customer orders), the company may not sell any more units for delivery in period 1. In that case, the ending inventory for period 1 would be 75 units (110 units to start with, less 35 units delivered).

2. A negative number in the available-to-promise row is always a problem. Th is means your company does not have enough inventory to cover the delivery. In our example, the company needs an additional 90 units to cover the new order scheduled for delivery in period 5.

One way to handle this problem is to look at earlier ATP quantities to see whether any inventory is available from earlier shipments. Period 1 has 50 uncommitted units and period 3 has an additional 80 available units. If we set 90 of these units aside for the period 5 order, we can agree to the new customer order. Look at the revised ATP record in Table D-14.

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Revised ATP MPS Record

TABLE D-13 Period 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 50 50 50 50 75 75 75 75 50 50 50 50

Customer orders 35 25 25 20 200 15 0 0 10 0 0 10

Projected available 110 60 10 85 35 240 2115 265 215 265 2115 240 290

Available-to- promise 50 80 290 125 115 115

MPS 125 125 125 125 125

1These quantities have changed in order to account for the additional 90 units needed to satisfy the order of 200 units for delivery in period 5. We commit 80 units from the period 3 replenishment order and 10 units from the ATP quantity in period 1.

Period 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 50 50 50 50 75 75 75 75 50 50 50 50

Customer orders 451 25 1051 20 1101 15 0 0 10 0 0 10

Projected available 110 60 10 30 220 25 280 230 20 230 280 25 255

Available-to- promise 40 0 0 125 115 115

MPS 125 125 125 125 125

Second Revised ATP Record

TABLE D-14

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USING THE ATP RECORDS • D13

Think of it this way: We set 10 units aside in period 1 and put the customer’s name on the boxes for delivery in period 5. In period 3, we put names on an additional 80 boxes of units. Then in period 5, we use the 90 boxes of units already set aside plus 110 of the new units to total the 200 units needed. In this way, we can promise delivery of 200 units in period 5.

After we agree to the order for 200 units for delivery in period 5, the next order arrives requesting an additional 50 units for period 4 delivery. We put this order into the record and see the results in Table D-15.

The ATP quantity in period 3 is now �50. Since a negative number in the ATP row is always a problem, we must check to see whether there is a way we can change that quantity to zero and accept the order. The only available inventory before delivery is requested is the 40 units available in period 1. Everything else has been promised. Therefore, we cannot accept this order for delivery in period 4. The earliest we can promise delivery is in period 7. Unless the customer is willing to agree with this deliv- ery date, we cannot accept the order. We delete the 50 units from the period 4 customer orders and recalculate the other quantities. The ATP record reverts back to the num- bers previously shown in Table D-14.

Let’s look at a couple of additional orders and calculate whether they can be accepted for delivery at the requested time. The first new order is for an additional 50 units to be delivered in period 8; the second order is for 30 additional units in period 12. When we put these orders into the customer order row, the ATP record is updated, as shown in Table D-16. Since there are no negative values in the ATP row, we can promise both of these orders for delivery.

1These quantities have changed to account for the additional 90 units needed to satisfy the order of 200 units for delivery in period 5. We commit 80 units from the period 3 replenishment order and 10 units from the ATP quantity in period 1.

Period 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 50 50 50 50 75 75 75 75 50 50 50 50

Customer orders 451 25 1051 70 1101 15 0 0 10 0 0 10

Projected available 110 60 10 30 240 225 2100 250 0 250 2100 225 275

Available-to- promise 40 250 0 125 115 115

MPS 125 125 125 125 125

Third Revised ATP Record

TABLE D-15

Period 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 50 50 50 50 75 75 75 75 50 50 50 50

Customer orders 451 25 1051 20 1101 15 0 50 10 0 0 40

Projected available 110 60 10 30 220 25 280 230 20 230 280 25 255

Available-to- promise 40 0 0 125 65 85

MPS 125 125 125 125 125

Final Revised ATP Record

TABLE D-16

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D14 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

The key requirement for using ATP is that manufacturing delivers the MPS replen- ishment on the scheduled date. For your company to meet these critical dates, opera- tions must have the capacity specified in the MPS, and therefore the MPS itself must be feasible.

STABILIZING THE MPS The master scheduler tries to minimize the number of changes made to an authorized MPS because each proposed change can affect the feasibility of the MPS. To deal with MPS changes, companies sometimes use time fence policies. Figure D-2 illustrates the MPS and time fences. The figure shows the demand time fence and the planning time fence, which split the MPS into three parts. The portion of the master schedule from the current time up to the demand time fence is frozen and any changes are kept to a minimum.

Companies typically require these changes to be authorized by a person other than the master scheduler because they may need additional resources. This portion of the MPS normally uses all available capacity to produce customer orders. Adding any orders without adding resources results in a delay of the currently scheduled jobs.

The third portion of the MPS starts at the planning time fence and extends into the future. This portion of the MPS is considered liquid because all orders requested during this portion are accepted as long as the resources authorized by the aggregate plan are adequate. The planning time fence is typically placed far enough into the future so that there is enough time to order any necessary materials and complete the normal manufac- turing process. Production planners, discussed in Chapter 14, generally place these orders.

� Time fence policies Partition the MPS into areas requiring different operating procedures.

� Demand time fence Establishes that point of time in the future inside of which changes to the MPS must be approved by a higher authority.

� Planning time fence Establishes a point of time in the future inside of which changes must be made by the master scheduler and changes outside of which can be changed by system planning logic.

Demand time fence

Planning time fence

FROZEN (Changes made

by plant manager or above)

SLUSHY (Product mix

changes made by master scheduler)

LIQUID (Regular processing

by planners)

Current time

Final assembly lead time

Cumulative manufacturing lead timeTime

FIGURE D-2

Time fences

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MASTER PRODUCTION SCHEDULING AND ROUGH-CUT CAPACITY PLANNING WITHIN OM • D15

Heavenly Ballroom Shoes (HBS) has frozen the first month of its MPS. Quarter 1 is shown in monthly time periods. The HBS marketing director, Marilee, has gotten an endorsement from this year’s national ballroom champions for HBS shoes. Because of this endorsement, marketing needs two new, more upscale shoe models (Model WC and Model MC) for use in major competitions. The new models must be available by the end of January or HBS will lose market share. Marilee has asked Charles, the master scheduler, to schedule production of 500 pairs of each of these new models in January. The endorsement represents a major business opportunity for HBS, but HBS does not have the capacity to accept the order for the new shoes. HBS can accept the order only by authorizing more resources for manufacturing or by delaying the shoes that are currently scheduled.

HBS has set up its MPS so that a decision to change the frozen portion has to be authorized by a man- ager who is also authorized to provide resources. In this way, HBS protects against jeopardizing existing orders to satisfy new ones.

The second portion of the MPS is the time between the demand time fence and the planning time fence. This is called the slushy portion of the MPS, during which it is the master scheduler’s responsibility to negotiate changes in the MPS. For example, if Marilee in HBS marketing needs a product other than what is scheduled, Charles, the HBS master scheduler, first calculates whether the materials are available to make the other product. Then Charles asks Marilee which items marketing would be willing to do without. This is necessary to free up capacity so that the other product can be made.

In this portion of the MPS, changes in the product mix are possible, but changes needing additional resources are not. Thus, HBS will produce 5333 pairs of shoes during a month, but the mix of the shoe models may vary. A revised MPS for February might look like the following. The product mix has changed but the total number of pairs produced remains constant.

MKT

EXAMPLE D.4

Time Fences at HBS

Model January (pairs)

February (pairs)

March (pairs)

Total Q-1 (pairs)

Model M 666 5,334 6,000 Model W 5,333 4,667 10,000 Total 5,333 5,333 5,334 16,000 Planned capacity available (pairs) 5,335 5,335 5,335 16,005

Model Quantity

Model M 166 Model MC 500 Model W 3667 Model WC 1000 Total pairs 5333

MASTER PRODUCTION SCHEDULING AND ROUGH-CUT CAPACITY PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER

In most companies, the operations manager develops the aggregate plan, which deter- mines the resources available to operations to execute the master production schedule. The plan will clearly indicate the size of the workforce and the aggregate production rate. The master production scheduler then develops the master production schedule. The only feasible plan that can be developed is one by the scheduler working with marketing, operations, demand management, and the customers. The master sched- uler’s job is to develop an MPS that achieves the company’s desired customer service level, uses capacity effectively, and minimizes the inventory investment. To make sure that the master production schedule is feasible, rough-cut capacity planning is done.

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D16 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

Key Terms master production schedule (MPS) D2 master scheduler D2 demand management D3 material requirements planning

(MRP) D4 rough-cut capacity planning

(RCCP) D6

demonstrated capacity D6 capacity planning using overall

planning factors (CPOPF) D6 order promising D10 available-to-promise (ATP) D10 action bucket D10

time fence policies D14 demand time fence D14 planning time fence D14

Supplement Highlights 1 Th e MPS shows how the resources authorized by the

aggregate plan will be used to satisfy the objectives of the organization. Th e MPS specifi es the products and quantities to be built in each time period. Th e MPS is a common organizational document used to facilitate communication between diff erent functional areas. Th e master scheduler develops a proposed MPS based on input received from the aggregate plan and demand management. Th is MPS is checked for feasibility using a rough-cut capacity planning technique.

2 Th e objectives of master scheduling are to satisfy customer service objectives, use resources eff ectively, and minimize costs. An MPS is developed by looking at individual MPS records and calculating when replenishment quantities are needed. Th e individual MPS records are summed together to show the total proposed workload.

3 Available-to-promise logic is used when promising order delivery dates to customers. ATP logic allows the company to make viable delivery promises.

4 Time fence policies stabilize the MPS. Th e demand time fence and the planning time fence divide the MPS into three portions: frozen, slushy, and liquid. Changes in the frozen portion are infrequent and must be authorized by a manager with authority to release additional resources to operations. Changes to the slushy portion are negotiated between the master scheduler and marketing. Changes in the product mix can occur but not changes in total volume. Changes in the liquid portion are made by planners since adequate lead time is available.

Formula Review 1. Calculating the projected available quantity:

Projected available 5 beginning inventory 1 MPS shipment 2 forecasted demand

2. Calculating a planning factor for direct labor:

Planning factor for direct labor

5 total direct labor spent building model

number of units of model built

3. Calculating a planning factor for machine time:

Planning factor for machine time

5 total machine time spent building model

number of units of model built

4. Calculating projected available quantity when using ATP MPS records: Projected available 5 beginning inventory 1 MPS shipment

2 the greater of the period’s forecast or the actual customer orders promised for delivery that period

5. Calculating ATP quantity in the action bucket: ATPAction Bucket 5 beginning inventory 1 MPS shipment

2 customer orders before next replenishment 6. Calculating ATP quantity at future replenishment periods: ATP 5 MPS shipment

2 customer orders between current MPS shipment and next scheduled replenishment

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SOLVED PROBLEMS • D17

Solved Problems • Problem 1 Complete the following MPS records. The beginning inventory is 20 units and the order quantity is an FOQ � 50 units in part (a) and a POQ � two periods in part (b).

• Before You Begin: This problem compares two different replenishment policies, an FOQ � 50 units with a POQ � two periods. Calculate the projected available quantity to determine when a replenish- ment order is needed. When the projected available quantity is negative, a replenishment order is needed in that period. Note that different policies can have orders due in different periods. The number of orders and the inventory levels can vary based on replenishment policy.

• Solution a Begin by calculating the projected available quantities. At the end of period 1, the projected available should be 0 (20 units of beginning inventory less 20 units forecasted demand). If no order is received, the projected available row is negative at the end of period 2. When this row turns negative, we need a replenishment order for that period. The replenishment order in this case is 50 units. The updated MPS record is shown in Table D-17.

When spreadsheeting this problem, you can include a con- ditional statement to check whether the projected available quantity is negative. When the projected available is negative, an MPS quantity is then entered into the appropriate cell. The program then recalculates the projected available quantity and continues on to the next negative result.

(See student companion site for Excel template.)

Period 1 2 3 4 5 6 7 Forecast 20 20 20 20 10 10 10 Projected available MPS

Period 8 9 10 11 12 13 Forecast 10 10 10 20 20 20 Projected available MPS

Period 1 2 3 4 5 6 7 Forecast 20 20 20 20 10 10 10 Projected available 0 30 10 MPS 50

Period 8 9 10 11 12 13 Forecast 10 10 10 20 20 20 Projected available MPS

2 3 4 5 6 7 8 9

10 11 12 13 14 15 16

A B C D E F G H I J K L M N

Master Production Scheduling

Beginning Inventory 20 Order Quantity 50

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 Forecast 20 20 20 20 10 10 10 10 10 10 20 20 20

Projected Available 0 30 10 40 30 20 10 0 40 30 10 40 20 MPS 0 50 0 50 0 0 0 0 50 0 0 50 0

B9: =B4+B10-B8 C9: =B9+C10-C8 (copy right)

B10: =IF($B$4-B8<0,$B$5,0)

C10: =IF(B9-C8<0,$B$5,0) (copy right)

Solution to Solved Problem 1a

TABLE D-17

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D18 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

When you are solving the problem manually, continue cal- culating the projected available quantities until the next nega- tive. At that point, we need another replenishment order. The completed MPS is shown here.

Once again, we need an MPS replenishment in period 2. To calculate the quantity, we sum the forecasted demand for the next two periods and subtract any beginning inventory. We need 20 units in both periods 2 and 3 for a total of 40 units; we have no beginning inventory, so we order 40 units. Look at the final MPS record that follows.

We need the next replenishment order in period 4, which should be for 30 units (20 demanded in period 4 and 10 units for period 5). The completed MPS record is shown above.

Note that replenishment orders are now scheduled for peri- ods 2, 4, 6, 8, 10, and 12.

Period 1 2 3 4 5 6 7 Forecast 20 20 20 20 10 10 10 Projected available 0 30 10 40 30 20 10 MPS 50 50

Period 8 9 10 11 12 13 Forecast 10 10 10 20 20 20 Projected available 0 40 30 10 40 20 MPS 50 50

Period 1 2 3 4 5 6 7 Forecast 20 20 20 20 10 10 10 Projected available 0 20 MPS 40

Period 8 9 10 11 12 13 Forecast 10 10 10 20 20 20 Projected available MPS

Period 1 2 3 4 5 6 7 Forecast 20 20 20 20 10 10 10 Projected available 0 20 0 10 0 10 0 MPS 40 30 20

Period 8 9 10 11 12 13 Forecast 10 10 10 20 20 20 Projected available 10 0 20 0 20 0 MPS 20 30 40

• Problem 2 Tim’s Wire Shop builds two different, complicated wiring assemblies. Production of the WA-1001 model has averaged 50,000 units annually. Historically, Tim’s Wire Shop has used 12,000 direct labor hours and 5000 hours of machine time annually to build the WA-1001 wiring harness. The other wiring assembly, WA-5005, has an average annual production of 40,000 units. Annual direct labor used on the WA-5005 is 26,000 hours and 2000 hours of machine time.

Develop the following four planning factors:

Planning factor for direct labor for WA-1001 Planning factor for direct labor for WA-5005 Planning factor for machine time for WA-1001 Planning factor for machine time for WA-5005

• Before You Begin: In this problem, you need to calculate planning factors. Use the historical data to determine on average how much direct labor

was used to make a model WA-1001 and a model WA-5005. Do the same for machine time.

• Solution To calculate the planning factor for direct labor for WA-1001, divide the annual direct labor hours by the annual number of units produced.

Planning factor for direct labor for WA-1001 � 12,000 direct labor hours/50,000 units, or 0.24 hour per unit

Planning factor for direct labor for WA-5005 � 26,000 direct labor hours/40,000 units, or 0.65 hour per unit

Planning factor for machine time for WA-1001 � 5000 hours of machine time/50,000 units, or 0.10 hour per unit

Planning factor for machine time for WA-5005 � 2000 hours of machine time/40,000 units, or 0.05 hour per unit

• Solution b

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SOLVED PROBLEMS • D19

• Problem 3 Given the proposed MPS shown here and the planning factors from Solved Problem 2, calculate the needed capacity for each period for direct labor and machine time.

• Before You Begin: Use the planning factors developed in Solved Problem 2. Calculate the amount of capacity needed to satisfy the master production schedule shown. Determine the quarterly requirements for both direct labor and machine time.

Use the same approch to calculate machine time needs.

• Solution To find the direct labor hours needed, multiply the MPS quantity for each item by its planning factor. Do this for each period.

Item Quarter 1 Quarter 2 Quarter 3 Quarter 4

WA-1001 10,000 15,000 15,000 13,000 WA-5005 15,000 10,000 9,000 8,000 Total 25,000 25,000 24,000 21,000

Model

Machine Time Factor

Q-1 MPS Qty

Q-1 Hours Req’d

Q-2 MPS Qty

Q-2 Hours Req’d

Q-3 MPS Qty

Q-3 Hours Req’d

Q-4 MPS Qty

Q-4 Hours Req’d

Total MPS Qty

Total Hours Req’d

WA-1001 0.10 10,000 1,000 15,000 1,500 15,000 1,500 13,000 1,300 53,000 5,300 WA-5005 0.05 15,000 750 10,000 500 9,000 450 8,000 400 42,000 2,100 Total 25,000 1,750 25,000 2,000 24,000 1,950 21,000 1,700 95,000 7,400

Model

Direct Labor Factor

Q-1 MPS Qty

Q-1 Hours Req’d

Q-2 MPS Qty

Q-2 Hours Req’d

Q-3 MPS Qty

Q-3 Hours Req’d

Q-4 MPS Qty

Q-4 Hours Req’d

Total MPS Qty

Total Hours Req’d

WA-1001 0.24 10,000 2,400 15,000 3,600 15,000 3,600 13,000 3,120 53,000 12,720 WA-5005 0.65 15,000 9,750 10,000 6,500 9,000 5,850 8,000 5,200 42,000 27,300 Total 25,000 12,150 25,000 10,100 24,000 9,450 21,000 8,320 95,000 40,020

• Problem 4 Tim’s Wire Shop uses three different departments to produce these wire assemblies. Each of these departments uses direct labor and machine time. Historically, Department 101 uses 25 percent of the direct labor time and machine time for these products, Department 102 uses 35 percent and Department 103 uses 40 percent. Calculate the direct labor and machine time needs for each department for each quarter.

• Before You Begin: In this problem, allocate the capacity requirements calculated in Problem 3 among three different departments. Use the given

historical averages to allocate the direct labor and machine hours needed each period by each department.

• Solution To calculate the direct labor hours needed for each department, take the total direct labor needed for the quarter and multi- ply it by the appropriate percentage for each work center. For example, in Quarter 1, the total direct labor needed is 12,150 hours. Multiply that by 25 percent to allocate the appropriate amount of labor to Department 101. The same approach is used for allocating machine time among departments. The com- pleted allocations are shown here.

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D20 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

Department

Historical Percentage Allocation

Labor Hours Required

Q-1

Labor Hours Required

Q-2

Labor Hours Required

Q-3

Labor Hours Required

Q-4

Total Labor

Required

Department 101 25% 3,037.5 2,525 2,362.5 2,080 10,005 Department 102 35% 4,252.5 3,535 3,307.5 2,912 14,007 Department 103 40% 4,860 4,040 3,780 3,328 16,008 Total 12,150 10,100 9,450 8,320 40,020

Labor Allocations

Department

Historical Percentage Allocation

Machine Hours

Required Q-1

Machine Hours

Required Q-2

Machine Hours

Required Q-3

Machine Hours

Required Q-4

Total Machine Labor

Required

Department 101 25% 437.5 500 487.5 425 1,850 Department 102 35% 612.5 700 682.5 595 2,590 Department 103 40% 700 800 780 680 2,960 Total 1,750 2,000 1,950 1,700 7,400

Machine Allocations

• Problem 5 Jeannette’s Cashmere Sweaters has authorized the following MPS for her exclusive line of cashmere sweaters. She wants to use the MPS record for promising future orders. Current order promises are included. The MPS order quantity is 60 units. Beginning inventory is 0. Complete the following MPS record.

• Before You Begin: Complete the MPS record. Calculate the projected avail- able quantity by subtracting the greater of the forecast or the actual customer orders from the sum of the previous period’s projected available quantity plus any MPS order being received this period. Remember that a negative value in this row does not necessarily mean that you have a shortage. Calculate the available-to-promise quantity in the action bucket (period 1) and each period when an MPS shipment is due to arrive.

Remember that only in the action bucket calculation is the beginning inventory included. In other periods, do not consider the beginning inventory since it is not clear what that quantity will be. A negative number in this row is always a problem.

• Solution Projected available quantity is calculated as

Projected available � beginning inventory � MPS shipment � the greater of the period’s forecast or the

customer orders promised for delivery

Therefore, the projected available quantity at the end of period 1 is 45 units (the beginning inventory of 0 plus the MPS ship- ment of 60, less the forecast of 15). The correct projected avail- able quantities are shown here.

Period 1 2 3 4 5 6 Forecast 15 15 15 15 20 20 Customer orders 12 10 8 5 0 0 Projected available 45 30 15 0 40 20 Available-to-promise 25 45 MPS 60 60

Period 7 8 9 10 11 12 Forecast 20 20 25 25 25 25 Customer orders 15 0 30 0 0 0 Projected available 0 40 10 45 20 55 Available-to-promise 30 60 60 MPS 60 60 60

Period 7 8 9 10 11 12 Forecast 20 20 25 25 25 25 Customer orders 15 0 30 0 0 0 Projected available Available-to-promise MPS 60 60 60

Period 1 2 3 4 5 6 Forecast 15 15 15 15 20 20 Customer orders 12 10 8 5 0 0 Projected available Available-to-promise MPS 60 60

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SOLVED PROBLEMS • D21

The ATP quantity in the action bucket is 25 units: a beginning inventory of 0 plus an MPS shipment of 60 units, less the cus- tomer orders of 35 units before the next replenishment (12 in period 1, 10 in period 2, 8 in period 3, and 5 in period 4). Use the formula for ATP at MPS replenishments to calculate the other ATP quantities.

ATP � MPS shipment � customer orders between current MPS shipment and next scheduled replenishment

Compare your answers with those shown.

• Problem 6 Jeannette has received several additional orders to consider. Using the ATP record calculated in Problem 5, calculate which of the new orders Jeannette should accept. The new orders are (1) 20 units for delivery in period 4, (2) 50 units for delivery in period 8, (3) 40 units for delivery in period 12.

• Before You Begin: In this problem, determine which orders can be promised for delivery. The key is to understand which units are not yet com- mitted and might be used to satisfy a future delivery. Place the new orders into your MPS record and see what problems occur. Then try to determine how to solve these problems.

• Solution The first step is to put the new orders into the MPS record and consider the implications. The updated MPS record is shown here.

Note that Jeannette can accept order 1 for delivery of 20 additional sweaters in period 4. She can also accept order 3 for 40 units delivered in period 12. However, Jeannette has a prob- lem accepting order 2. The ATP quantity in period 8 is �20, which means Jeannette must ensure that enough sweaters are available to satisfy that order. Since the ATP quantity in period 5 is 25 sweaters, Jeannette can set aside 20 of these sweaters so that she has enough to satisfy order 2. Therefore, she can accept all three orders.

On the revised MPS record, we see that the customer order in period 8 is reduced 20 units, which are transferred to period 5. The 20 units transferred to period 5 are the 20 units that were not available in period 8. Look at the changes in the updated MPS record.

Period 1 2 3 4 5 6 Forecast 15 15 15 15 20 20 Customer orders 12 10 8 25 20 0 Projected available 45 30 15 210 30 10 Available-to-promise 5 25 MPS 60 60

Period 7 8 9 10 11 12 Forecast 20 20 25 25 25 25 Customer orders 15 50 30 0 0 40 Projected available 210 0 230 5 220 0 Available-to-promise 220 60 20 MPS 60 60 60

Period 1 2 3 4 5 6 Forecast 15 15 15 15 20 20 Customer orders 12 10 8 25 40 0 Projected available 45 30 15 210 10 210 Available-to-promise 4 4 MPS 60 60

Period 7 8 9 10 11 12 Forecast 20 20 25 25 25 25 Customer orders 15 30 30 0 0 40 Projected available 230 0 230 5 220 0 Available-to-promise 0 60 20 MPS 60 60 60

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D22 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

Discussion Questions 1. Visit a local business and learn how it calculates its resources. 2. Describe the inputs needed to do master production

scheduling. 3. Describe the different sources of demand. 4. Discuss the objectives of the master scheduler and how they

influence master scheduling decisions. 5. Explain the process of developing an authorized MPS. 6. Explain the importance of rough-cut capacity planning in

the MPS process.

7. Describe the role of time fence policies. 8. Explain the changes you can make in the frozen portion of

the MPS and who must authorize such changes. 9. Explain the changes you can make in the slushy portion of

the MPS and who authorizes such changes. 10. Discuss how the MPS might be used in a service

organization.

Problems 1. David’s Delightful Kites Company (DDKC) manufactures

kites. Th e most popular is David’s Daredevil model. Demand management has prepared forecast estimates for the next six weeks. Beginning inventory is 15 David’s Daredevils. As the master scheduler for DDKC, you must prepare an MPS. Your MPS order quantity is 72 kites.

(a) Prepare the MPS using an order quantity of 72 kites. (b) Calculate the ending inventory for each period. (c) What is the maximum number of units held in inventory? (d) How many MPS orders are needed? 2. In an eff ort to reduce setup frequency, DDKC has decided

to use an MPS order quantity of 120 kites. (See Problem 1 for data.)

(a) Prepare the MPS using the new order quantity. (b) Calculate the ending inventory for each period. (c) Compare this MPS with the MPS developed in Problem 1. 3. DDKC has decided to change its replenishment policy.

Instead of producing 72 or 120 kites each order, DDKC has reduced its MPS order quantity to 48 kites.

(a) Develop an MPS using 48 kites as the order quantity. (b) Calculate the ending inventory for each period. (c) Compare this MPS with the schedules developed in

Problems 1 and 2. 4. Wine Accessories Inc. (WAI) produces two models of

corkscrews, the standard model and a deluxe model. WAI follows a level aggregate plan, producing 20,000 corkscrews per month, or 5000 corkscrews per week. Th e MPS is developed in weekly time periods. Th e forecasts for each model and the projected available are shown in the next two tables. Th e replenishment order quantity is 2000 units for the standard model and 1000 units for the deluxe model. Note that you can place multiple orders if a single order is insuffi cient to cover the forecast (you can produce 4000 units of the standard model if necessary, or 2000 or 3000 units of the deluxe model). Remember that total

weekly production is limited to 5000 corkscrews. Develop an MPS for each of the products.

5. WAI uses two resources for each product that it builds: direct labor and machine time. From historical records, WAI calculates the following planning factors:

Direct labor for standard corkscrew is 0.20 hour. Direct labor for deluxe corkscrew is 0.50 hour. Machine time for standard corkscrew is 0.10 hour. Machine time for deluxe corkscrew is 0.30 hour.

Calculate the capacity needed for the MPS developed in Problem 4. 6. WAI is evaluating a new machine that reduces the machine

time needed for the standard corkscrew to 0.08 hour, but increases the machine time for the deluxe corkscrew to 0.40 hour.

(a) Calculate the machine capacity needed for each period if WAI uses the new machine.

(b) Will the new machine prove benefi cial to WAI? 7. WAI knows from historical records that approximately 40

percent of its labor is used in Department 101 and 60 percent is used in Department 102. Th e reverse is true of machine time; 60 percent is used in Department 101 and 40 percent is used in Department 102.

(a) Calculate the labor and machine hours needed in Department 101 for each period of the MPS developed in Problem 4.

(b) Calculate the labor and machine hours needed in Department 102 for each period of the MPS developed in Problem 4.

8. WAI has developed the following MPS. Calculate the projected available and the available-to-promise quantities for WAI.

Week 1 2 3 4 5 6

Forecast 20 35 50 50 45 40 Proj. Available 15 MPS

Standard Corkscrew 1 2 3 4

Forecast 3000 3500 5000 4000 Proj. Available 2000 MPS

Deluxe Corkscrew 1 2 3 4

Forecast 2000 1500 1000 3000 Proj. Available 2000 MPS

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CASE: NEWMARKET INTERNATIONAL MANUFACTURING COMPANY (C) • D23

9. WAI, the company in Problem 8, has received the following additional customer orders shown here. You must determine which of the orders the company can accept. If the company must reject any orders, you must explain why.

10. Josh Randall, a sales representative for WAI, has convinced his customer (order #2 in Problem 9) to reschedule. Order #2 has agreed to accept delivery of 25 units in week 2 as long as WAI can also guarantee delivery of an additional 25 units in week 5. If WAI cannot meet both delivery requirements, the customer has threatened to withdraw its entire order. You have already promised orders 1, 3, and 4. Can you assure Josh that WAI will deliver on time? Show your calculations.

1 2 3 4 5 6 7 8

Forecast 30 30 30 40 40 40 45 45 Customer Order 35 15 20 18 12 0 15 0 Proj. Available 50 Available-to-promise MPS 100 100 100

Order Number Order Quantity Desired Week

1 10 3 2 25 1 3 40 5 4 20 6

CASE: Newmarket International Manufacturing Company (C) Newmarket International Manufacturing Company (NIMCO) was founded by Marcia Blakely only two years after leaving grad- uate school. Her knowledge of mass customization was the driv- ing force behind starting NIMCO. The company produces three major custom products. Volume on the products is high even though each item is customized specifically for the customer. The products are processed through up to four different work cen- ters. Even though each item is unique, the processing time at each work center is constant due to the sophisticated equipment used.

Today’s Opportunity Joe Barnes had just left the staffing meeting. The informa- tion you provided was quite helpful, and he believes he has

adequate resources to accomplish the required manufactur- ing for the second quarter. Effective capacity levels (including regular time and planned overtime) for each work center are shown here, as is the amount of time required for each product at each work center. The demand forecasts for each product are also shown.

Work Center 1

Work Center 2

Work Center 3

Work Center 4

920 hours 740 hours 920 hours 725 hours

Effective Weekly Capacity Levels

Product

Standard Hours at Work

Center 1

Standard Hours at Work

Center 2

Standard Hours at Work

Center 3

Standard Hours at Work

Center 4

Total Standard Time

(Hours)

A 0.06 0.14 0.04 0.24 B 0.15 0.13 0.10 0.38 C 0.03 0.08 0.12 0.06 0.29

Product Standard Time by Work Center

Quarter 2 Demand Forecasts

Week Demand for Product A

Demand for Product B

Demand for Product C Week

Demand for Product A

Demand for Product B

Demand for Product C

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D24 • SUPPLEMENT D MASTER SCHEDULING AND ROUGH-CUT CAPACITY PLANNING

Selected Bibliography Arnold, J.R. Tony, Stephen N. Chapman, and Lloyd M. Clive.

Introduction to Materials Management, Sixth Edition. Upper Saddle River, N.J.: Pearson Education Limited, 2008.

Blackstone, John H. Jr., Capacity Management. Cincinnati, Ohio: South-Western, 1989.

Cox, James F., III, John H. Blackstone, and Michael S. Spencer, eds. APICS Dictionary, Twelvth Edition. Falls Church, Va.: American Production and Inventory Control Society, Inc., 2005.

Gessner, Robert A. Master Production Schedule Planning. New York: John Wiley & Sons, 1986.

Narasimhan, Sim, Dennis W. McLeavey, and Peter Billington. Production Planning and Inventory Control, Second Edi- tion. Englewood Cliff s, N.J.: Prentice-Hall, 1995.

Plossl, George W. Production and Inventory Control: Principles and Techniques, Second Edition. Englewood Cliff s, N.J.: Prentice-Hall, 1985.

Slack, Nigel, Stuart Chambers, and Robert Johnston. Opera- tions Management, Th ird Edition. Upper Saddle River, N.J.: Pearson Education Limited, 2001.

Vollmann, Th omas E., William L. Berry, D. Clay Whybark, and F. Robert Jacobs. Manufacturing Planning and Control Sys- tems, Fift h Edition. Burr Ridge, Ill.: McGraw-Hill/Irwin, 2005.

1. Joe now needs a rough-cut capacity check to determine whether the capacity at each work center is adequate to support the expected demand. Using the forecasted demand as your proposed master schedule, calculate the load profi le for each work center for each week of the second quarter. Highlight any weeks in which problems might occur.

2. Joe knows from past experience that he has some fl exi- bility in his workforce. Th erefore, as long as the total capacity

needed does not exceed the total available, the master schedule should be feasible. He also knows that he can increase his capacity total an additional 2.5 percent through extra overtime and still be within his budget. Given this new information, what recommendations do you have for Joe? What weeks are likely to be problems, and how should he use the capacity in those weeks? How important to NIMCO is it to have a fl exible workforce?

Online Resources Companion Website www.wiley.com/college/reid:

Motorola, Inc. Ferrara Fire Apparatus, Inc.

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Cover Page
Title Page
Copyright Page
Preface
About the Authors
Contents
CHAPTER 1: Introduction to Operations Management

WHAT IS OPERATIONS MANAGEMENT?

Links to Practice: The E-tailers

DIFFERENCES BETWEEN MANUFACTURING AND SERVICE ORGANIZATIONS

Links to Practice: U.S. Postal Service

OPERATIONS MANAGEMENT DECISIONS

Links to Practice: Texas Instruments Incorporated

PLAN OF THIS BOOK
HISTORICAL DEVELOPMENT

Why OM?
Historical Milestones
The Industrial Revolution
Scientific Management
The Human Relations Movement
Management Science
The Computer Age
Just-in-Time
Total Quality Management
Business Process Reengineering
Flexibility
Time-Based Competition
Supply Chain Management
Links to Practice: The Publishing Industry
Global Marketplace
Sustainability and Green Operations
Electronic Commerce
Outsourcing and Flattening of the World

TODAY'S OM ENVIRONMENT
OPERATIONS MANAGEMENT IN PRACTICE
WITHIN OM: HOW IT ALL FITS TOGETHER
OM ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Discussion Questions

CASE: HIGHTONE ELECTRONICS, INC.
CASE: CREATURE CARE ANIMAL CLINIC (A)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 2: Operations Strategy and Competitiveness

THE ROLE OF OPERATIONS STRATEGY

The Importance of Operations Strategy

DEVELOPING A BUSINESS STRATEGY

Mission
Environmental Scanning
Core Competencies
Putting It Together
Links to Practice: Dell Computer Corporation

DEVELOPING AN OPERATIONS STRATEGY

Competitive Priorities
Links to Practice: Southwest Airlines Company
Links to Practice: FedEx Corporation
The Need for Trade-Offs
Order Winners and Qualifiers
Translating Competitive Priorities into Production Requirements

STRATEGIC ROLE OF TECHNOLOGY

Types of Technologies
Technology as a Tool for Competitive Advantage

PRODUCTIVITY

Measuring Productivity
Interpreting Productivity Measures
Productivity and Competitiveness
Productivity and the Service Sector

OPERATIONS STRATEGY WITHIN OM: HOW IT ALL FITS TOGETHER
OPERATIONS STRATEGY ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: PRIME BANK OF MASSACHUSETTS
CASE: BOSEMAN OIL AND PETROLEUM (BOP)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 3: Product Design and Process Selection

PRODUCT DESIGN

Design of Services versus Goods

THE PRODUCT DESIGN PROCESS

Idea Development
Links to Practice: IBM Corporation
Product Screening
Preliminary Design and Testing
Final Design

FACTORS IMPACTING PRODUCT DESIGN

Design for Manufacture
Product Life Cycle
Concurrent Engineering
Remanufacturing

PROCESS SELECTION

Types of Processes

DESIGNING PROCESSES
PROCESS PERFORMANCE METRICS
LINKING PRODUCT DESIGN AND PROCESS SELECTION

Product Design Decisions
Links to Practice: The Babcock & Wilcox Company
Competitive Priorities
Facility Layout
Product and Service Strategy
Degree of Vertical Integration

TECHNOLOGY DECISIONS

Information Technology
Links to Practice: Using GPS Technology in Product Advertising
Automation
Links to Practice: Performing Robotic Surgery
e-Manufacturing

DESIGNING SERVICES

How Are Services Different from Manufacturing?
How Are Services Classified?
The Service Package
Differing Service Designs

PRODUCT DESIGN AND PROCESS SELECTION WITHIN OM: HOW IT ALL FITS TOGETHER
PRODUCT DESIGN AND PROCESS SELECTION ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: BIDDY'S BAKERY (BB)
CASE: CREATURE CARE ANIMAL CLINIC (B)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 4: Supply Chain Management

WHAT IS A SUPPLY CHAIN?
COMPONENTS OF A SUPPLY CHAIN FOR A MANUFACTURER

External Suppliers
Internal Functions
External Distributors

THE BULLWHIP EFFECT

Causes of the Bullwhip Effect
Counteracting the Bullwhip Effect

SUPPLY CHAINS FOR SERVICE ORGANIZATIONS

Internal Operations
The External Distributors

MAJOR ISSUES AFFECTING SUPPLY CHAIN MANAGEMENT

Information Technology
E-Commerce
Business-to-Business (B2B) E-Commerce
The Evolution of B2B Commerce
The Benefits of B2B E-Commerce
Business-to-Consumer (B2C) E-Commerce
Consumer Expectations and Competition Resulting from E-Commerce
Links to Practice: Lands' End, Inc.
Globalization
Government Regulations and E-Commerce
Green Supply Chain Management
Infrastructure Issues
Product Proliferation

THE ROLE OF PURCHASING

The Traditional Purchasing Process
The E-purchasing Process

SOURCING ISSUES

Insourcing versus Outsourcing Decisions
Developing Supplier Relationships
How Many Suppliers?
Developing Partnerships
Links to Practice: The Bama Companies
Critical Factors in Successful Partnering
Links to Practice: Sweetheart Cup Company/Georgia-Pacific Corporation
Benefits of Partnering
Ethics in Supply Management

SUPPLY CHAIN DISTRIBUTION

The Role of Warehouses
Links to Practice: Fingerhut Direct Marketing, Inc.
Crossdocking
Links to Practice: FedEx Freight
Radio Frequency Identification Technology (RFID)
Third-Party Service Providers

IMPLEMENTING SUPPLY CHAIN MANAGEMENT

Strategies for Leveraging Supply Chain Management

SUPPLY CHAIN PERFORMANCE METRICS
TRENDS IN SUPPLY CHAIN MANAGEMENT
SUPPLY CHAIN MANAGEMENT WIHTIN OM: HOW IT ALL FITS TOGETHER
SCM ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: ELECTRONIC POCKET CALENDARS SUPPLY CHAIN MANAGEMENT GAME
CASE: SUPPLY CHAIN MANAGEMENT AT DURHAM INTERNATIONAL MANUFACTURING COMPANY (DIMCO)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 5: Total Quality Management

DEFINING QUALITY

Differences between Manufacturing and Service Organizations
Links to Practice: General Electric Company/Motorola, Inc.

COST OF QUALITY
THE EVOLUTION OF TOTAL QUALITY MANAGEMENT (TQM)

Quality Gurus

THE PHILOSOPHY OF TQM

Customer Focus
Continuous Improvement
Employee Empowerment
Links to Practice: The Walt Disney Company
Use of Quality Tools
Links to Practice: The Kroger Company/Meijer Stores Limited Partnership
Product Design
Process Management
Managing Supplier Quality

QUALITY AWARDS AND STANDARDS

The Malcolm Baldrige National Quality Award (MBNQA)
The Deming Prize
ISO 9000 Standards
ISO 14000 Standards

WHY TQM EFFORTS FAIL
TOTAL QUALITY MANAGEMENT (TQM) WITHIN OM: HOW IT ALL FITS TOGETHER
TOTAL QUALITY MANAGEMENT (TQM) ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: GOLD COAST ADVERTISING (GCA)
CASE: DELTA PLASTICS, INC. (A)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 6: Statistical Quality Control

WHAT IS STATISTICAL QUALITY CONTROL?

Links to Practice: Intel Corporation

SOURCES OF VARIATION: COMMON AND ASSIGNABLE CAUSES
DESCRIPTIVE STATISTICS

The Mean
The Range and Standard Deviation
Distribution of Data

STATISTICAL PROCESS CONTROL METHODS

Developing Control Charts
Types of Control Charts

CONTROL CHARTS FOR VARIABLES

Mean (x-Bar) Charts
Range (R) Charts
Using Mean and Range Charts Together

CONTROL CHARTS FOR ATTRIBUTES

P-Charts
C-Charts

PROCESS CAPABILITY

Measuring Process Capability

SIX SIGMA QUALITY

Links to Practice: Motorola, Inc.

ACCEPTANCE SAMPLING

Sampling Plans
Operating Characteristic (OC) Curves
Developing OC Curves
Average Outgoing Quality

IMPLICATIONS FOR MANAGERS

How Much and How Often to Inspect
Where to Inspect
Which Tools to Use

STATISTICAL QUALITY CONTROL IN SERVICES

Links to Practice: The Ritz-Carlton Hotel Company, L.L.C./ Nordstrom, Inc.
Links to Practice: Marriott International, Inc.

STATISTICAL QUALITY CONTROL (SQC) WITHIN OM: HOW IT ALL FITS TOGETHER
STATISTICAL QUALITY CONTROL (SQC) ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: SCHARADIN HOTELS
CASE: DELTA PLASTICS, INC. (B)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 7: Just-in-Time and Lean Systems

THE PHILOSOPHY OF JIT

Eliminate Waste
A Broad View of Operations
Simplicity
Continuous Improvement
Visibility
Flexibility

ELEMENTS OF JIT

Just-in-Time Manufacturing
Total Quality Management (TQM)
Respect for People
Links to Practice: Texas Instruments

JUST-IN-TIME MANUFACTURING

The Pull System
Kanban Production
Variations of Kanban Production
Small Lot Sizes and Quick Setups
Uniform Plant Loading
Flexible Resources
Facility Layout
Links to Practice: Ryder Integrated Logistics

TOTAL QUALITY MANAGEMENT

Product versus Process
Quality at the Source
Preventive Maintenance
Work Environment

RESPECT FOR PEOPLE

The Role of Production Employees
Lifetime Employment
The Role of Management
Supplier Relationships

BENEFITS OF JIT

Links to Practice: Alcoa, Inc.

IMPLEMENTING JIT
JIT IN SERVICES

Improved Quality
Uniform Facility Loading
Use of Multifunction Workers
Reductions in Cycle Time
Minimizing Setup Times and Parallel Processing
Workplace Organization

JIT AND LEAN SYSTEMS WITHIN OM: HOW IT ALL FITS TOGETHER
JIT AND LEAN SYSTEMS ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: KATZ CARPETING
CASE: DIXON AUDIO SYSTEMS
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 8: Forecasting

PRINCIPLES OF FORECASTING
STEPS IN THE FORECASTING PROCESS

Links to Practice: Intel Corporation

TYPES OF FORECASTING METHODS

Links to Practice: Improving Sales Forecasting
Qualitative Methods
Quantitative Methods

TIME SERIES MODELS

Forecasting Level or Horizontal Pattern
Forecasting Trend
Forecasting Seasonality
Links to Practice: The Ski Industry Forecast

CAUSAL MODELS

Linear Regression
Correlation Coefficient
Multiple Regression

MEASURING FORECAST ACCURACY

Forecast Accuracy Measures
Tracking Signal

SELECTING THE RIGHT FORECASTING MODEL
FORECASTING SOFTWARE

Spreadsheets
Statistical Packages
Specialty Forecasting Packages
Guidelines for Selecting Forecasting Software

FOCUS FORECASTING
COMBINING FORECASTS

Links to Practice: Combining Methods in Weather Forecasting

COLLABORATIVE PLANNING, FORECASTING, AND REPLENISHMENT (CPFR)
FORECASTING WITHIN OM: HOW IT ALL FITS TOGETHER
FORECASTING ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: BRAM-WEAR
CASE: THE EMERGENCY ROOM (ER) AT NORTHWEST GENERAL (A)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 9: Capacity Planning and Facility Location

CAPACITY PLANNING

Why Is Capacity Planning Important?
Links to Practice: Capacity Planning in the ER
Measuring Capacity
Capacity Considerations
Links to Practice: Focus in the Retail Industry

MAKING CAPACITY PLANNING DECISIONS

Identify Capacity Requirements
Develop Capacity Alternatives
Evaluate Capacity Alternatives

DECISION TREES
LOCATION ANALYSIS

What Is Facility Location?
Factors Affecting Location Decisions
Links to Practice: Locating in Silicon Valley
Globalization

MAKING LOCATION DECISIONS

Procedure for Making Location Decisions
Procedures for Evaluating Location Alternatives

CAPACITY PLANNING AND FACILITY LOCATION WITHIN OM: HOW IT ALL FITS TOGETHER
CAPACITY PLANNING AND FACILITY LOCATION ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: DATA TECH, INC.
CASE: THE EMERGENCY ROOM (ER) AT NORTHWEST GENERAL (B)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 10: Facility Layout

WHAT IS LAYOUT PLANNING?
TYPES OF LAYOUTS

Process Layouts
Links to Practice: Wal-Mart Stores, Inc.
Product Layouts
Links to Practice: Toyota Motor Corporation
Hybrid Layouts
Fixed-Position Layouts

DESIGNING PROCESS LAYOUTS

Step 1: Gather Information
Step 2: Develop a Block Plan
Step 3: Develop a Detailed Layout

SPECIAL CASES OF PROCESS LAYOUT

Warehouse Layouts
Office Layouts

DESIGNING PRODUCT LAYOUTS

Step 1: Identify Tasks and Their Immediate Predecessors
Step 2: Determine Output Rate
Step 3: Determine Cycle Time
Step 4: Compute the Theoretical Minimum Number of Stations
Step 5: Assign Tasks to Workstations (Balance the Line)
Step 6: Compute Efficiency, Idle Time, and Balance Delay
Other Considerations

GROUP TECHNOLOGY (CELL) LAYOUTS
FACILITY LAYOUT WITHIN OM: HOW IT ALL FITS TOGETHER
FACILITY LAYOUT ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: SAWHILL ATHLETIC CLUB (A)
CASE: SAWHILL ATHLETIC CLUB (B)
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 11: Work System Design

DESIGNING A WORK SYSTEM
JOB DESIGN

Designing a Job
Links to Practice: Google
Machines or People?
Level of Labor Specialization
Eliminating Employee Boredom
Team Approaches to Job Design
Links to Practice: The SCO Group
The Alternative Workplace
Links to Practice: AT&T

METHODS ANALYSIS
THE WORK ENVIRONMENT
WORK MEASUREMENT

Costing
Performance
Planning

SETTING STANDARD TIMES

How to Do a Time Study
Elemental Time Data
Predetermined Time Data
Work Sampling
Links to Practice: Pace Productivity

COMPENSATION

Time-Based Systems
Output-Based Systems
Group Incentive Plans
Links to Practice: Accounatable Care Organizations (ACO)
Incentive Plan Trends

LEARNING CURVES
WORK SYSTEM DESIGN WITHIN OM: HOW IT ALL FITS TOGETHER
WORK SYSTEM DESIGN ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: THE NAVIGATOR III
CASE: NORTHEAST STATE UNIVERSITY
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 12: Inventory Management

TYPES OF INVENTORY
HOW COMPANIES USE THEIR INVENTORY
OBJECTIVES OF INVENTORY MANAGEMENT

Customer Service
Cost-Efficient Operations
Minimum Inventory Investment

RELEVANT INVENTORY COSTS

Item Costs
Holding Costs
Ordering Costs
Shortage Costs

ABC INVENTORY CLASSIFICATION

Procedure for an ABC Inventory Analysis
Inventory Control Using ABC Classification

INVENTORY RECORD ACCURACY

Links to Practice: Cisco Systems, Inc.

INVENTORY IN SERVICE ORGANIZATIONS
DETERMINING ORDER QUANTITIES
MATHEMATICAL MODELS FOR DETERMINING ORDER QUANTITY

Economic Order Quantity (EOQ)
Calculating the EOQ
Economic Production Quantity (EPQ)
Quantity Discount Model

WHY COMPANIES DON'T ALWAYS USE THE OPTIMAL ORDER QUANTITY
JUSTIFYING SMALLER ORDER QUANTITIES

Links to Practice: Kenworth Trucks
Understanding the EPQ Factors

DETERMINING SAFETY STOCK LEVELS

How Much Safety Stock?

PERIODIC REVIEW SYSTEM

Comparison of Continuous Review Systems and Periodic Review Systems

THE SINGLE-PERIOD INVENTORY MODEL
INVENTORY MANAGEMENT WITHIN OM: HOW IT ALL FITS TOGETHER
INVENTORY MANAGEMENT ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: FABQUAL LTD.
CASE: KAYAKS!INCORPORATED
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 13: Aggregate Planning

THE ROLE OF AGGREGATE PLANNING

Marketing Plan
Aggregate or Production Plan
Financial and Engineering Plans
Master Production Schedule
Links to Practice: Coca-Cola Midi (CCM)

TYPES OF AGGREGATE PLANS

Level Aggregate Plan
Chase Aggregate Plan
Hybrid Aggregate Plan

AGGREGATE PLANNING OPTIONS

Demand-Based Options
Capacity-Based Options

EVALUATING THE CURRENT SITUATION

Links to Practice: UPS Hires Seasonal Workers

DEVELOPING THE AGGREGATE PLAN
AGGREGATE PLANS FOR COMPANIES WITH TANGIBLE PRODUCTS
AGGREGATE PLANS FOR SERVICE COMPANIES WITH NONTANGIBLE PRODUCTS
AGGREGATE PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER
AGGREGATE PLANNING ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Solved Problems
Discussion Questions
Problems

CASE: NEWMARKET INTERNATIONAL MANUFACTURING COMPANY (A)
CASE: JPC, INC.: KITCHEN COUNTERTOPS MANUFACTURER
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 14: Resource Planning

ENTERPRISE RESOURCE PLANNING

ERP Modules

THE EVOLUTION OF ERP

First-Generation ERP
Second-Generation ERP
Links to Practice: Arapahoe County Government

THE BENEFITS OF ERP

Links to Practice: i2 Technologies
Links to Practice: SAP AG

THE COST OF ERP SYSTEMS
MATERIAL PLANNING SYSTEMS
AN OVERVIEW OF MRP
TYPES OF DEMAND
OBJECTIVES OF MRP
MRP INPUTS

Authorized MPS
Inventory Records
Bills of Material

THE MRP EXPLOSION PROCESS
ACTION NOTICES
COMPARISON OF LOT SIZE RULES
THE ROLE OF CAPACITY REQUIREMENTS PLANNING (CRP)
RESOURCE PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER
RESOURCE PLANNING ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: NEWMARKET INTERNATIONAL MANUFACTURING COMPANY (B)
CASE: DESSERTS BY J.B.
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 15: Scheduling

SCHEDULING OPERATIONS
HIGH-VOLUME OPERATIONS

Characteristics of Flow Operations

LOW-VOLUME OPERATIONS

Gantt Chart

SCHEDULING WORK

Infinite Loading
Finite Loading
Forward Scheduling
Backward Scheduling
Monitoring Workflow

HOW TO SEQUENCE JOBS

Priority Rules
How to Use Priority Rules

MEASURING PERFORMANCE

Links to Practice: Airline Scheduling
Job Flow Time
Average Number of Jobs in the System
Makespan
Job Lateness and Tardiness

COMPARING PRIORITY RULES

Comparing SPT and S/RO

SEQUENCING JOBS THROUGH TWO WORK CENTERS
SCHEDULING BOTTLENECKS

Links to Practice: 1-800-FLOWERS.com

THEORY OF CONSTRAINTS
SCHEDULING FOR SERVICE ORGANIZATIONS

Scheduling Services Demanded
Scheduling Employees

DEVELOPING A WORKFORCE SCHEDULE
SCHEDULING WITHIN OM: PUTTING IT ALL TOGETHER
SCHEDULING ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: AIR TRAFFIC CONTROLLER MANUFACTURER
CASE: SCHEDULING AT RED, WHITE AND BLUE FIREWORKS COMPANY
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

CHAPTER 16: Project Management

PROJECT LIFE CYCLE

Conception
Feasibility Analysis or Study
Planning
Execution
Termination

NETWORK PLANNING TECHNIQUES

Links to Practice: PERT and the Polaris Missile
Step 1: Describe the Project
Step 2: Diagram the Network
Step 3: Estimate the Project's Completion Time
Step 3 (a): Deterministic Time Estimates
Step 3 (b): Probabilistic Time Estimates
Step 4: Monitor the Project's Progression
Links to Practice: Managing the Olympic Games

ESTIMATING THE PROBABILITY OF COMPLETION DATES
REDUCING PROJECT COMPLETION TIME

Crashing Projects

THE CRITICAL CHAIN APPROACH

Adding Safety Time
Wasting Safety Time

PROJECT MANAGEMENT WITHIN OM: HOW IT ALL FITS TOGETHER
PROJECT MANAGEMENT OM ACROSS THE ORGANIZATION
THE SUPPLY CHAIN LINK
THE SUSTAINABILITY LINK

Chapter Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: THE RESEARCH OFFICE MOVES
CASE: WRITING A TEXTBOOK
INTERACTIVE CASE: VIRTUAL COMPANY
INTERNET CHALLENGE
ONLINE RESOURCES

Selected Bibliography

APPENDIX A: Solutions to Odd-Numbered Problems
APPENDIX B: The Standard Normal Distribution
APPENDIX C: P-Chart
NAME INDEX
SUBJECT INDEX
SUPPLEMENT A: Spreadsheet Modeling: An Introduction

WHAT ARE MODELS?
THE SPREADSHEET MODELING PROCESS
EVALUATING SPREADSHEET MODELS

Planning the Model
Implementing the Model in Excel
Assessing the Model
Using the Model for Analysis
Adding Data Tables
Graphing the Model Results
Planning the Model
Constructing the Model in Excel
Reviewing Relative and Absolute Cell Referencing
Entering Formulas in the Model

USEFUL SPREADSHEET TIPS
IMPORTANT EXCEL FORMULAS
SPREADSHEET MODELING WITHIN OM: HOW IT ALL FITS TOGETHER

Supplement Highlights
Key Terms
Discussion Questions
Problems

CASE: DIET PLANNING
ONLINE RESOURCES

Selected Bibliography

SUPPLEMENT B: Introduction to Optimization

INTRODUCTION
ALGEBRAIC FORMULATION

Examining the Formulation

SPREADSHEET MODEL DEVELOPMENT

Testing the Model

SOLVER BASICS
SETTING UP AND RUNNING SOLVER

Solving the Problem

INTERPRETING THE SOLUTION
SOLVER SOLUTION REPORTS
RECAP
OUTCOMES OF LINEAR PROGRAMMING PROBLEMS
OPTIMIZATION WITHIN OM: HOW IT ALL FITS TOGETHER

Supplement Highlights
Key Terms
Solved Problems
Discussion Questions
Problems

CASE: EXETER ENTERPRISES
ONLINE RESOURCES

Selected Bibliography

SUPPLEMENT C: Waiting Line Models

ELEMENTS OF WAITING LINES

Links to Practice: Waiting for Fast Food
The Customer Population
The Service System
Arrival and Service Patterns
Waiting Line Priority Rules

WAITING LINE PERFORMANCE MEASURES
SINGLE-SERVER WAITING LINE MODEL
MULTISERVER WAITING LINE MODEL
CHANGING OPERATIONAL CHARACTERISTICS
LARGER-SCALE WAITING LINE SYSTEMS
WAITING LINE MODELS WITHIN OM: HOW IT ALL FITS TOGETHER

Supplement Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: THE COPY CENTER HOLDUP
ONLINE RESOURCES

Selected Bibliography

SUPPLEMENT D: Master Scheduling and Rough-Cut Capacity Planning

MASTER PRODUCTION SCHEDULING
MPS AS A BASIS OF COMMUNICATION
OBJECTIVES OF MASTER SCHEDULING
DEVELOPING AN MPS
ROUGH-CUT CAPACITY PLANNING
EVALUATING AND ACCEPTING THE MPS
USING THE MPS
USING THE ATP RECORDS
STABILIZING THE MPS
MASTER PRODUCTION SCHEDULING AND ROUGH-CUT CAPACITY PLANNING WITHIN OM: HOW IT ALL FITS TOGETHER

Supplement Highlights
Key Terms
Formula Review
Solved Problems
Discussion Questions
Problems

CASE: NEWMARKET INTERNATIONAL MANUFACTURING COMPANY (C)
ONLINE RESOURCES

Selected Bibliography

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