Isbn 978 0 323 51080 6

30 Powerpoint Slides About IPM Operations

Prepare a 5 Slide Power Point Presentation that shows how what you have learned thus far (in Chapters 1, 2, 3, 4, 5,15 & 19) was used by your award winning company to conduct its operations

Acknowledgments I want to thank the many contributors to this edition. Review- ers and adopters of the text have provided a “continuously improving” wealth of ideas and suggestions. It is encourag- ing to me as an author. I hope all reviewers and readers will know their suggestions were valuable, were carefully consid- ered, and are sincerely appreciated. The list includes post- publication reviewers.

Robert Aboolian, California State University— San Marcos

Pamela Barnes, Kansas State University

Greg Bier, University of Missouri

Gary Black, University of Southern Indiana

Jeff Brand, Marquette University

Cenk Caliskan, Utah Valley University

Cem Canel, University of North Carolina—Wilmington

Jen-Yi Chen, Cleveland State University

Robert Clark, Stony Brook University

Dinesh Dave, Appalachian State University

Abdelghani Elimam, San Francisco State

Kurt Engemann, Iona College

Michael Fathi, Georgia Southwestern State

Warren Fisher, Stephen F. Austin State University

Gene Fliedner, Oakland University

Theodore Glickman, George Washington University

Haresh Gurnani, University of Miami

Johnny Ho, Columbus State University

Ron Hoffman, Greenville Technical College

Preface

The material in this book is intended as an introduction to the field of operations management. The topics covered include both strategic issues and practical applications. Among the topics are forecasting, product and service design, capacity planning, management of quality and quality control, inven- tory management, scheduling, supply chain management, and project management.

My purpose in revising this book continues to be to pro- vide a clear presentation of the concepts, tools, and appli- cations of the field of operations management. Operations management is evolving and growing, and I have found updating and integrating new material to be both reward- ing and challenging, particularly due to the plethora of new developments in the field, while facing the practical limits on the length of the book.

This text offers a comprehensive and flexible amount of content that can be selected as appropriate for different courses and formats, including undergraduate, graduate, and executive education.

This allows instructors to select the chapters, or portions of chapters, that are most relevant for their purposes. That flexibility also extends to the choice of relative weighting of the qualitative or quantitative aspects of the material and the order in which chapters are covered because chapters do not depend on sequence. For example, some instructors cover project management early, others cover quality or lean early, etc.

As in previous editions, there are major pedagogical fea- tures designed to help students learn and understand the material. This section describes the key features of the book, the chapter elements, the supplements that are available for teaching the course, highlights of the eleventh edition, and suggested applications for classroom instruction. By pro- viding this support, it is our hope that instructors and stu- dents will have the tools to make this learning experience a rewarding one.

What’s New in This Edition This edition has been revised to incorporate and integrate changes in the field of Operations Management, and the many suggestions for improvement received from instructors around the world who are using the text. The following are key among the revisions:

• New examples, discussion questions, and problems have been incorporated throughout.

• Some content has been rewritten or added to include current information, improve clarity and help understanding.

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viii Preface

Lisa Houts, California State University—Fresno

Stella Hua, Western Washington University

Neil Hunt, Suffolk University

Faizul Huq, Ohio University

Richard Jerz, St. Ambrose University

George Kenyon, Lamar University

Casey Kleindienst, California State University—Fullerton

John Kros, East Carolina University

Anita Lee-Post, University of Kentucky

Nancy Levenburg, Grand Valley State University

F. Edward Ziegler, Kent State University

Other contributors include accuracy checkers: Gary Black, University of Southern Indiana, Michael Godfrey, University of Wisconsin at Oshkosh, and Richard White, University of North Texas; Test Bank: Alan Cannon, University of Texas at Arlington; PowerPoints: David Cook, Old Dominion Univer- sity; Data Sets: Mehdi Kaighobadi, Florida Atlantic Univer- sity; Excel Templates and ScreenCam tutorials: Lee Tangedahl, University of Montana; Instructors Manual: Michael Godfrey.

Special thanks goes out to Larry White, Eastern Illinois University, who helped revise, design, and develop interactive content in Connect ® Operations Management for this edition:

Finally I would like to thank all the people at McGraw- Hill/Irwin for their efforts and support. It is always a plea- sure to work with such a professional and competent group of people. Special thanks go to Thomas Hayward, Senior Brand Manager; Wanda Zeman, Senior Development Editor; Kristin Bradley, Project Manager; Debra Sylvester, Buyer; Heather Kazakoff, Senior Marketing Manager; Srdjan Savanovic, Designer; Rachel Townsend, Content Project Manager; Keri Johnson, Senior Photo Research Coordinator, and many others who worked “behind the scenes.”

I would also like to thank the many reviewers of previ- ous editions for their contributions. Vikas Agrawal, Fay- etteville State University; Bahram Alidaee, University of Mississippi; Ardavan Asef-Faziri, California State Uni- versity at Northridge; Prabir Bagchi, George Washington State University; Gordon F. Bagot, California State Uni- versity at Los Angeles; Ravi Behara, Florida Atlantic Uni- versity; Michael Bendixen, Nova Southeastern; Ednilson Bernardes, Georgia Southern University; Prashanth N. Bharadwaj, Indiana University of Pennsylvania; Greg Bier, University of Missouri at Columbia; Joseph Biggs, Cal Poly State University; Kimball Bullington, Middle Ten- nessee State University; Alan Cannon, University of Texas at Arlington; Injazz Chen, Cleveland State University; Alan Chow, University of Southern Alabama at Mobile; Chrwan-Jyh, Oklahoma State University; Chen Chung, University of Kentucky; Robert Clark, Stony Brook Uni- versity; Loretta Cochran, Arkansas Tech University; Lewis Coopersmith, Rider University; Richard Crandall, Appalachian State University; Dinesh Dave, Appalachian

State University; Scott Dellana, East Carolina University; Kathy Dhanda, DePaul University; Xin Ding, University of Utah; Ellen Dumond, California State University at Ful- lerton; Richard Ehrhardt, University of North Carolina at Greensboro; Kurt Engemann, Iona College; Diane Ervin, DeVry University; Farzaneh Fazel, Illinois State Univer- sity; Wanda Fennell, University of Mississippi at Hatties- burg; Joy Field, Boston College; Warren Fisher, Stephen F. Austin State University; Lillian Fok, University of New Orleans; Charles Foley, Columbus State Community Col- lege; Matthew W. Ford, Northern Kentucky University; Phillip C. Fry, Boise State University; Charles A. Gates Jr., Aurora University; Tom Gattiker, Boise State University; Damodar Golhar, Western Michigan University; Robert Graham, Jacksonville State University; Angappa Gunas- ekaran, University of Massachusetts at Dartmouth; Haresh Gurnani, University of Miami; Terry Harrison, Penn State University; Vishwanath Hegde, California State Univer- sity at East Bay; Craig Hill, Georgia State University; Jim Ho, University of Illinois at Chicago; Seong Hyun Nam, University of North Dakota; Jonatan Jelen, Mercy Col- lege; Prafulla Joglekar, LaSalle University; Vijay Kannan, Utah State University; Sunder Kekre, Carnegie-Mellon University; Jim Keyes, University of Wisconsin at Stout; Seung-Lae Kim, Drexel University; Beate Klingenberg, Marist College; John Kros, East Carolina University; Vinod Lall, Minnesota State University at Moorhead; Ken- neth Lawrence, New Jersey Institute of Technology; Jooh Lee, Rowan University; Anita Lee-Post, University of Kentucky; Karen Lewis, University of Mississippi; Bing- guang Li, Albany State University; Cheng Li, California State University at Los Angeles; Maureen P. Lojo, Califor- nia State University at Sacramento; F. Victor Lu, St. John’s University; Janet Lyons, Utah State University; James Maddox, Friends University; Gita Mathur, San Jose State University; Mark McComb, Mississippi College; George Mechling, Western Carolina University; Scott Metlen, Uni- versity of Idaho; Douglas Micklich, Illinois State Univer- sity; Ajay Mishra, SUNY at Binghamton; Scott S. Morris, Southern Nazarene University; Philip F. Musa, University of Alabama at Birmingham; Roy Nersesian, Monmouth University; Jeffrey Ohlmann, University of Iowa at Iowa City; John Olson, University of St. Thomas; Ozgur Ozluk, San Francisco State University; Kenneth Paetsch, Cleve- land State University; Taeho Park, San Jose State Univer- sity; Allison Pearson, Mississippi State University; Patrick Penfield, Syracuse University; Steve Peng, California State University at Hayward; Richard Peschke, Minne- sota State University at Moorhead; Andru Peters, San Jose State University; Charles Phillips, Mississippi State Uni- versity; Frank Pianki, Anderson University; Sharma Pil- lutla, Towson University; Zinovy Radovilsky, California State University at Hayward; Stephen A. Raper, Univer- sity of Missouri at Rolla; Pedro Reyes, Baylor University; Buddhadev Roychoudhury, Minnesota State University

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Preface ix

at Mankato; Narendra Rustagi, Howard University; Herb Schiller, Stony Brook University; Dean T. Scott, DeVry University; Scott J. Seipel, Middle Tennessee State Uni- versity; Raj Selladurai, Indiana University; Kaushic Sen- gupta, Hofstra University; Kenneth Shaw, Oregon State University; Dooyoung Shin, Minnesota State University at Mankato; Michael Shurden, Lander University; Raymond E. Simko, Myers University; John Simon, Governors State University; Jake Simons, Georgia Southern University; Charles Smith, Virginia Commonwealth University; Ken- neth Solheim, DeVry University; Young Son, Bernard M. Baruch College; Victor Sower, Sam Houston State Uni- versity; Jeremy Stafford, University of North Alabama; Donna Stewart, University of Wisconsin at Stout; Dothang Truong, Fayetteville State University; Mike Umble, Baylor University; Javad Varzandeh, California State University

at San Bernardino; Timothy Vaughan, University of Wis- consin at Eau Claire; Emre Veral, Baruch College; Mark Vroblefski, University of Arizona; Gustavo Vulcano, New York University; Walter Wallace, Georgia State University; James Walters, Ball State University; John Wang, Mont- clair State University; Tekle Wanorie, Northwest Missouri State University; Jerry Wei, University of Notre Dame; Michael Whittenberg, University of Texas; Geoff Wil- lis, University of Central Oklahoma; Pamela Zelbst, Sam Houston State University; Jiawei Zhang, NYU; Zhenying Zhao, University of Maryland; Yong-Pin Zhou, University of Washington.

William J. Stevenson

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x

MAJOR STUDY AND LEARNING FEATURES

A number of key features in this text have been specifically designed to help introductory students learn, understand, and apply Operations concepts and problem-solving techniques.

Walkthrough

Sales of new houses and three-month lagged unemployment are shown in the following table. Determine if unemployment levels can be used to predict demand for new houses and, if so, derive a predictive equation.

Period ................................ 1 2 3 4 5 6 7 8 9 10 11

Units sold .......................... 20 41 17 35 25 31 38 50 15 19 14

Unemployment % (three-month lag) ..... 7.2 4.0 7.3 5.5 6.8 6.0 5.4 3.6 8.4 7.0 9.0

E X A M P L E 1 0 e celx

mhhe.com/stevenson12e

S O L U T I O N 1. Plot the data to see if a linear model seems reasonable. In this case, a linear model seems appropriate for the range of the data.

50

40

30

20

10

0 2 4 6 8 10

Level of unemployment (%), x

U n

it s

so ld

, y

2. Check the correlation coefficient to confirm that it is not close to zero using the Web site template, and then obtain the regression equation:

.966r � �

This is a fairly high negative correlation. The regression equation is

71.85 6.91y x� �

Note that the equation pertains only to unemployment levels in the range 3.6 to 9.0, because sample observations covered only that range.

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Examples with Solutions Throughout the text, wherever a quantitative or analytic technique is introduced, an example is included to illustrate the application of that tech- nique. These are designed to be easy to follow.

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SOLVED PROBLEMS

The tasks shown in the following precedence diagram are to be assigned to workstations with the intent of minimizing idle time. Management has designed an output rate of 275 units per day. Assume 440 minutes are available per day.

a. Determine the appropriate cycle time.

b. What is the minimum number of stations possible?

c. Assign tasks using the “positional weight” rule: Assign tasks with highest following times (including a task’s own time) first. Break ties using greatest number of following tasks.

d. Compute efficiency.

Problem 1

a c e

b d f

g h i

0.3 minute 0.2 minute 0.1 minute 0.5 minute 0.3 minute0.4 minute

0.6 minute 0.6 minute1.2 minutes

a. Cycle time Operating time

Desired output

440 minutes per day

275 units per day 1.6 minutes� � � Solution per unit

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xi

Solved Problems At the end of chapters and chapter supplements, “solved problems” are provided to illustrate problem solving and the core concepts in the chapter. These have been carefully prepared to help students understand the steps involved in solving different types of problems. The Excel logo indicates that a spreadsheet is available on the text’s Web site, to help solve the problem.

TABLE 16.5 Excel solution for Example 2a

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Excel Spreadsheet Solutions Where applicable, the examples and solved prob- lems include screen shots of a spreadsheet solution. Many of these were taken from the Excel templates, which are on the text’s website. Templates are programmed to be fully functional in Excel 2013 and earlier.

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xii

C H A P T E R

4 CHAPTER OUTLINE

4.1 Introduction, 136 What Does Product and Service Design Do? 136

Key Questions, 136

Reasons for Product or Service Design or Redesign, 137

4.2 Idea Generation, 139

4.3 Legal and Ethical Considerations, 141

4.4 Human Factors, 142

4.5 Cultural Factors, 143

4.6 Global Product and Service Design, 143

4.7 Environmental Factors: Sustainability, 144 Cradle-to-Grave Assessment, 144

End-of-Life Programs, 144

The Three Rs: Reduce, Reuse, Recycle, 144

Reduce: Value Analysis, 145

Reuse: Remanufacturing, 145

Recycle, 146

4.8 Other Design Considerations 149 Strategies for Product or Service Life Stages, 149

Degree of Standardization, 151

Designing for Mass Customization, 151

Reliability, 153

Robust Design, 154

Degree of Newness, 155

Quality Function Deployment, 155

The Kano Model, 158

4.9 Phases in Product Design and Development, 159

4.10 Designing for Production, 160 Concurrent Engineering, 160

Computer-Aided Design, 160

Production Requirements, 161

Component Commonality, 162

4.11 Service Design, 162 Overview of Service Design, 163

Differences between Service Design and Product Design, 163

Phases in the Service Design Process, 164

Service Blueprinting, 164

Characteristics of Well-Designed Service Systems, 165

Challenges of Service Design, 166

Guidelines for Successful Service Design, 166

4.12 Operations Strategy, 167

Operations Tour: High Acres Landfill, 170

Chapter Supplement: Reliability, 171

Product and Service Design

After completing this chapter, you should be able to:

LO4.1 Explain the strategic importance of product and service design.

LO4.2 Describe what product and service design does.

LO4.3 Name the key questions of product and service design.

LO4.4 Identify some reasons for design or redesign.

LO4.5 List some of the main sources of design ideas.

LO4.6 Discuss the importance of legal, ethical, and sustainability considerations in product and service design.

LO4.7 Explain the purpose and goal of life cycle assessment.

LO4.8 Explain the phrase “the 3 Rs.”

LO4.9 Briefly describe the phases in product design and development.

LO4.10 Discuss several key issues in product or service design.

LO4.11 Discuss the two key issues in service design.

LO4.12 List the characteristics of well-designed service systems.

LO4.13 List some guidelines for successful service design.

LEARNING OBJECTIVES

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The essence of a business organization is the products and services it offers, and every aspect of the organization and its supply chain are structured around those products and services. Organizations that have well-designed products or services are more likely to realize their goals than those with poorly designed products or services. Hence, organizations have a stra- tegic interest in product and service design. Product or service design should be closely tied to an organization’s strategy. It is a major factor in cost, quality, time-to-market, customer satisfaction, and competitive advantage. Consequently, marketing, finance, operations, accounting, IT, and HR need to be involved. Demand forecasts and projected costs are important, as is the expected impact on the supply chain. It is significant to note that an important cause of operations failures can be traced to faulty design. Designs that have not been well thought out, or incorrectly implemented, or instructions for assembly or usage that are wrong or unclear, can be the cause of product and service failures, leading to lawsuits, injuries and deaths, product recalls, and damaged reputations.

The introduction of new products or services, or changes to product or service designs, can have impacts throughout the organization and the entire supply chain. Some processes may change very little, while others may have to change consider- ably in terms of what they do or how and when they do it. New processes may have to be added, and some current ones may be eliminated. New suppliers and distributors may need to be found and integrated into the system, and some current suppliers and distributors may no longer be an appropriate fit. Moreover, it is necessary to take into account projected impact on demand as well as financial, marketing, and distribution implications. Because of the potential for widespread effects, taking a “big picture” systems approach early and throughout the design or redesign process is imperative to reduce the chance of missing some implications and costs, and to understand the time it will take. Likewise, input from engineering, operations, marketing, finance, accounting, and supply chains is crucial.

In this chapter you will discover insights into the design process that apply to both product and service design.

LO4.1 Explain the strategic importance of product and service design.

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CHAPTER ELEMENTS

Within each chapter, you will find the following elements that are designed to facilitate study and learning. All of these have been carefully developed over many editions and have proven to be successful.

Chapter Outlines Every chapter and supplement includes an outline of the topics covered.

Learning Objectives Every chapter and supplement lists the learning objectives to achieve when studying the chapter material. The learning objectives are also included next to the specific material in the margins of the text.

Opening Vignettes Each chapter opens with an introduction to the important operations topics covered in the chapter. This enables students to see the relevance of operations management in order to actively engage in learning the material.

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Figures and Photos The text includes photographs and graphic illustrations to support student learning and provide interest and motivation. Approximately 100 carefully selected photos highlight the twelfth edition. The photos illustrate applications of operations and supply chain concepts in many successful companies. More than 400 graphic illustrations, more than any other text in the field, are included and all are color coded with pedagogical consistency to assist students in understanding concepts.

FIGURE 6.1 Process selection and capacity planning influence system design Forecasting

Product and service design

Technological change

Facilities and equipment

Layout

Work design

Capacity Planning

Process Selection

Inputs Outputs

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A major key to Apple’s continued success is its ability to keep pushing the boundaries of innovation. Apple has demonstrated how to create growth by dreaming up products so new and ingenious that they have upended one industry after another.

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Icons Icons are included in the text, to point out relevant applications in a discussion or concept. These include: Excel icons to point out Excel applications; and ScreenCam Tutorial icons to link to the tutorials on the text’s website.

e celx www.mhhe.com/stevenson11e

e celx mhhe.com/stevenson12e SCREENCAM TUTORIAL

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5.12 OPERATIONS STRATEGY The strategic implications of capacity decisions can be enormous, impacting all areas of the organization. From an operations management standpoint, capacity decisions establish a set of conditions within which operations will be required to function. Hence, it is extremely important to include input from operations management people in making capacity decisions.

Flexibility can be a key issue in capacity decisions, although flexibility is not always an option, particularly in capital-intensive industries. However, where possible, flexibility allows an organi- zation to be agile—that is, responsive to changes in the marketplace. Also, it reduces to a certain extent the dependence on long-range forecasts to accurately predict demand. And flexibility makes it easier for organizations to take advantage of technological and other innovations. Maintaining excess capacity (a capacity cushion) may provide a degree of flexibility, albeit at added cost.

Some organizations use a strategy of maintaining a capacity cushion for the purpose of blocking entry into the market by new competitors. The excess capacity enables them to pro- duce at costs lower than what new competitors can. However, such a strategy means higher- than-necessary unit costs, and it makes it more difficult to cut back if demand slows, or to shift to new product or service offerings.

Efficiency improvements and utilization improvements can provide capacity increases. Such improvements can be achieved by streamlining operations and reducing waste. The chapter on lean operations describes ways for achieving those improvements.

Bottleneck management can be a way to increase effective capacity, by scheduling non- bottleneck operations to achieve maximum utilization of bottleneck operations.