giáo trình Operations management 13th williams stevenson 1 Operations management 13th williams stevenson 1 Operations management 13th williams stevenson 1 Operations management 13th williams stevenson 1 Operations management 13th williams stevenson 1 Operations management 13th williams stevenson 1 Operations management 13th williams stevenson 1 Operations management 13th williams stevenson 1
Operations Management Operations Management THIRTEENTH EDITION William J Stevenson Saunders College of Business Rochester Institute of Technology This book is dedicated to you OPERATIONS MANAGEMENT, THIRTEENTH EDITION Published by McGraw-Hill Education, Penn Plaza, New York, NY 10121 Copyright © 2018 by McGraw-Hill Education All rights reserved Printed in the United States of America Previous editions © 2015, 2012, and 2009 No part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written consent of McGraw-Hill Education including, but not limited to, in any network or other electronic storage or transmission, or broadcast for distance learning Some ancillaries, including electronic and print components, may not be available to customers outside the United States This book is printed on acid-free paper LWI 21 20 19 18 17 ISBN 978-1-259-66747-3 MHID 1-259-66747-2 Chief Product Officer, SVP Products & Markets: G Scott Virkler Vice President, General Manager, Products & Markets: Marty Lange Vice President, Content Design & Deliver: Betsy Whalen Managing Director: Tim Vertovec Senior Brand Manager: Charles Synovec Director, Product Development: Rose Koos Lead Product Developer: Michele Janicek Product Developer: Christina Holt / Ryan McAndrews Marketing Manager: Trina Maurer Senior Director of Digital Content: Douglas Ruby Digital Product Analyst: Kevin Shanahan Director, Content Design & Delivery: Linda Avenarius Program Manager: Mark Christianson Content Project Managers: Harvey Yep (Core) / Kristin Bradley (Assessment) Buyer: Sandy Ludovissy Design: Matt Diamond Content Licensing Specialists: Shawntel Schmitt (Image) / Beth Thole (Text) Typeface: 10/12 STIX Mathjax Main Compositor: SPi Global Printer: LSC Communications – Willard Cover images: © Andrew Bret Wallis/Getty Images; © Peopleimages.com/Getty Images; © Echo/Getty Images; © Jorg Greuel/Getty Images; © Monty Rakusen/Getty Images Library of Congress Cataloging-in-Publication Data Names: Stevenson, William J., author Title: Operations management / William J Stevenson, Saunders College of Business, Rochester Institute of Technology Description: Thirteenth edition | New York, NY : McGraw-Hill Education, [2018] | Series: The McGraw-Hill series in operations and decision sciences Identifiers: LCCN 2016052871| ISBN 9781259667473 (alk paper) | ISBN 1259667472 (alk paper) Subjects: LCSH: Production management Classification: LCC TS155 S7824 2018 | DDC 658.5 dc23 LC record available at https://lccn.loc.gov/2016052871 All credits appearing on page or at the end of the book are considered to be an extension of the copyright page The Internet addresses listed in the text were accurate at the time of publication The inclusion of a website does not indicate an endorsement by the authors or McGraw-Hill Education, and McGraw-Hill Education does not guarantee the accuracy of the information presented at these sites mheducation.com/highered The McGraw-Hill Series in Operations and Decision Sciences Operations Management Beckman and Rosenfield, Operations, Strategy: Competing in the 21st Century, First Edition Benton, Purchasing and Supply Chain Management, Second Edition Bowersox, Closs, Cooper, and Bowersox, Supply Chain Logistics Management, Fourth Edition Brown and Hyer, Managing Projects: A Team-Based Approach, First Edition Burt, Petcavage, and Pinkerton, Supply Management, Eighth Edition Cachon and Terwiesch, Operations Management, First Edition Cachon and Terwiesch, Matching Supply with Demand: An Introduction to Operations Management, Third Edition Cooper and Schindler, Business Research Methods, Twelfth Edition Finch, Interactive Models for Operations and Supply Chain Management, First Edition Fitzsimmons, Fitzsimmons, and Bordoloi, Service Management: Operations, Strategy, Information Technology, Eighth Edition Gehrlein, Operations Management Cases, First Edition Harrison and Samson, Technology Management, First Edition Hayen, SAP R/3 Enterprise Software: An Introduction, First Edition Hill, Manufacturing Strategy: Text & Cases, Third Edition Hopp, Supply Chain Science, First Edition Jacobs, Berry, Whybark, and Vollmann, Manufacturing Planning & Control for Supply Chain Management, Sixth Edition Jacobs and Chase, Operations and Supply Management: The Core, Fourth Edition Jacobs and Chase, Operations and Supply Management, Fifteenth Edition Jacobs and Whybark, Why ERP? First Edition Larson and Gray, Project Management: The Managerial Process, Seventh Edition Leenders, Johnson, and Flynn, Purchasing and Supply Management, Fifteenth Edition Olson, Introduction to Information Systems Project Management, Second Edition Schroeder, Goldstein, Rungtusanatham, Operations Management: Contemporary Concepts and Cases, Seventh Edition Seppanen, Kumar, and Chandra, Process Analysis and Improvement, First Edition Simchi-Levi, Kaminsky, and Simchi-Levi, Designing and Managing the Supply Chain: Concepts, Strategies, Case Studies, Third Edition Sterman, Business Dynamics: Systems Thinking and Modeling for Complex World, First Edition Stevenson, Operations Management, Thirteenth Edition Swink, Melnyk, Cooper, and Hartley, Managing Operations Across the Supply Chain, Third Edition Thomke, Managing Product and Service Development: Text and Cases, First Edition Ulrich and Eppinger, Product Design and Development, Fourth Edition Zipkin, Foundations of Inventory Management, First Edition Quantitative Methods and Management Science Hillier and Hillier, Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, Fifth Edition Stevenson and Ozgur, Introduction to Management Science with Spreadsheets, First Edition v 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, inventory management, scheduling, supply chain management, and project management My purpose in revising this book continues to be to provide a clear presentation of the concepts, tools, and applications of the field of operations management Operations management is evolving and growing, and I have found updating and integrating new material to be both rewarding 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 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 features 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 providing this support, it is our hope that instructors and students will have the tools to make this learning experience a rewarding one What’s New in This Edition Class preparation exercises are now available for all chapters and chapter supplements The purpose of these exercises is to introduce students to the subject matter before class in order to enhance classroom learning These exercises are available in the Instructor’s Resource Manual Special thanks to Linda Brooks for her help in developing the exercises Some content has been rewritten or added to improve clarity, shorten wording, or update information New material has been added on supply chains (including a different, more realistic, way to conceptualize supply chains), as well as on product life-cycle management, 3-D printing, drones, locations, and other topics New critical thinking exercises have been added The explanation of learning curve time reduction has been simplified with a new diagram Some older readings have been deleted, and new readings added on such topics as fracking, mass customization of fast foods, and self-driving vehicles Acknowledgments I want to thank the many contributors to this edition Reviewers and adopters of the text have provided a “continuously improving” wealth of ideas and suggestions It is encouraging to me as an author I hope all reviewers and readers will know their suggestions were valuable, were carefully considered, and are sincerely appreciated The list includes postpublication 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 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 vii viii Preface 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 University; Data Sets: Mehdi Kaighobadi, Florida Atlantic University; 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 McGrawHill/Irwin for their efforts and support It is always a pleasure to work with such a professional and competent group of people Special thanks go to Dolly Womack, Senior Brand Manager; Michele Janicek, Lead Product Developer; Christina Holt and Ryan McAndrews, Product Developers; Harvey Yep and Kristin Bradley, Content Project Managers; Sandy Ludovissy, Buyer; Matt Diamond, Designer; Shawntel Schmitt and Beth Thole, Content Licensing Specialists; and many others who worked behind the scenes I would also like to thank the many reviewers of previous editions for their contributions Vikas Agrawal, Fayetteville State University; Bahram Alidaee, University of Mississippi; Ardavan Asef-Faziri, California State University at Northridge; Prabir Bagchi, George Washington State University; Gordon F Bagot, California State University at Los Angeles; Ravi Behara, Florida Atlantic University; 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 Tennessee 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 University; 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 Fullerton; Richard Ehrhardt, University of North Carolina at Greensboro; Kurt Engemann, Iona College; Diane Ervin, DeVry University; Farzaneh Fazel, Illinois State University; Wanda Fennell, University of Mississippi at Hattiesburg; Joy Field, Boston College; Warren Fisher, Stephen F Austin State University; Lillian Fok, University of New Orleans; Charles Foley, Columbus State Community College; 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 Gunasekaran, University of Massachusetts at Dartmouth; Haresh Gurnani, University of Miami; Terry Harrison, Penn State University; Vishwanath Hegde, California State University 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 College; 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; Kenneth Lawrence, New Jersey Institute of Technology; Jooh Lee, Rowan University; Anita Lee-Post, University of Kentucky; Karen Lewis, University of Mississippi; Bingguang Li, Albany State University; Cheng Li, California State University at Los Angeles; Maureen P Lojo, California 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, University of Idaho; Douglas Micklich, Illinois State University; 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, Cleveland State University; Taeho Park, San Jose State University; Allison Pearson, Mississippi State University; Patrick Penfield, Syracuse University; Steve Peng, California State University at Hayward; Richard Peschke, Minnesota State University at Moorhead; Andru Peters, San Jose State University; Charles Phillips, Mississippi State University; Frank Pianki, Anderson University; Sharma Pillutla, T owson University; Zinovy Radovilsky, California State University at Hayward; Stephen A Raper, University of Missouri at Rolla; Pedro Reyes, Baylor University; Buddhadev Roychoudhury, Minnesota State University at Mankato; Narendra Rustagi, Howard University; Herb Schiller, Stony Brook University; Dean T Scott, DeVry University; Scott J Seipel, Middle Tennessee State University; Raj Selladurai, Indiana University; Kaushic Sengupta, 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; Kenneth Solheim, DeVry University; Young Son, Preface ix Bernard M Baruch College; Victor Sower, Sam H ouston State University; 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 Wisconsin 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, Montclair State University; Tekle Wanorie, Northwest Missouri State University; Jerry Wei, University of Notre Dame; Michael Whittenberg, University of Texas; Geoff Willis, 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 www.downloadslide.net Chapter Nine Management of Quality 413 (concluded) June 29 checkout line too long out of Dove soap out of Bisquick eggs cracked store not clean store too cold cashier too slow out of skim milk charged wrong price July restroom not clean couldn’t find sponges checkout lines slow out of 18 oz Tide out of Campbell’s turkey soup out of pepperoni sticks checkout lines too long meat not fresh overcharged on melon July 13 wrong price on spaghetti water on floor store looked messy store too warm checkout lines too long cashier not friendly out of Cheese Doodles triple charged out of Saran Wrap out of Dove Bars out of Tip Top lunch bags out of vanilla soy milk store too warm price not as advertised need to open more checkouts shopping carts hard to steer debris in aisles out of Drano out of Chinese cabbage store too warm floors dirty and sticky out of Diamond chopped walnuts July 20 undercharged out of brown rice out of mushrooms overcharged checkout wait too long shopping cart broken couldn’t find aspirin out of Tip Top lunch bags out of Tip Top straws July 27 out of bananas reported accident in parking lot wrong price on cranapple juice out of carrots out of fresh figs out of Tip Top napkins out of Tip Top straws windows dirty out of iceberg lettuce dislike store decorations out of straws out of bird food overcharged on butter out of masking tape stockboy was not helpful lost child meat looked bad overcharged on butter out of Swiss chard too many people in store out of bubble bath out of Dial soap wanted to know who won the lottery store too warm oatmeal spilled in bulk section telephone out of order out of Tip Top tissues water on floor out of Tip Top paper towels out of Tip Top toilet paper spaghetti sauce on floor out of Peter Pan crunchy peanut butter out of cucumbers checkout lines too slow found keys in parking lot lost keys wrong price on sale item overcharged on corn wrong price on baby food out of 18 oz Tide out of Tip Top tissues checkout lines too long out of romaine lettuce out of Tip Top toilet paper out of red peppers out of Tip Top napkins out of apricots telephone out of order out of cocktail sauce water on floor out of onions out of squash out of iceberg lettuce out of Tip Top paper towels www.downloadslide.net 414 SELECTED BIBLIOGRAPHY AND FURTHER READINGS Chapter Nine Management of Quality Besterfield, Dale H., Carol Besterfield-Micha, Glen Besterfield, and Mary Besterfield-Sacre Total Quality Management, 3rd ed Upper Saddle River, NJ: Prentice Hall, 2011 Brassard, Michael, and Diane Ritter The Memory Jogger II: A Pocket Guide of Tools for Continuous Improvement and Effective Planning Methuen, MA: Goal/QPC, 1994 Butman, John Juran: A Lifetime of Influence New York: John Wiley & Sons, 1997 El-Haik, Basem, and David M Roy Service Design for Six Sigma: A Roadmap for Excellence Hoboken, NJ: John Wiley and Sons, 2005 Garvin, David A Managing Quality New York: Free Press, 1988 Goetsch, David L., and Stanley B Davis Quality Management for Organizational Excellence: Introduction to Total Quality Management, 6th ed Upper Saddle River, NJ: Prentice Hall, 2010 Gygi, Craig, Neil DeCarlo, and Bruce Williams Six Sigma for Dummies, 2nd ed Hoboken, NJ: John Wiley and Sons, 2012 Scherkenbach, W W The Deming Route to Quality and Productivity: Roadmaps and Roadblocks Rockville, MD: Mercury Press/Fairchild Publications, 1990 Snee, Ronald D., and Roger W Hoerl Six Sigma beyond the Factory Floor: Deployment Strategies for Financial Services, Health Care, and the Rest of the Real Economy Upper Saddle River, NJ: Pearson/Prentice Hall, 2005 Stevenson, William J “Supercharging Your Pareto Analysis.” Quality Progress October 2000, pp 51–55 Summers, Donna Quality, 5th ed Upper Saddle River, NJ: Prentice Hall, 2010 Trusko, Brett, Carolyn Pexton, Jim Harrington, and Praveen Gupta Improving Healthcare Quality and Cost with Six Sigma FT Press, 2007 www.downloadslide.net 10 www.downloadslide.net Quality Control LEARNING OBJECTIVES After completing this chapter, you should be able to: LO10.1 Explain the need for quality control LO10.2 Discuss the basic issues of inspection LO10.3 List and briefly explain the elements of the control process LO10.4 Explain how control charts are used to monitor a process and the concepts that underlie their use LO10.5 Use and interpret control charts LO10.6 Perform run tests to check for nonrandomness in process output LO10.7 Assess process capability C H A P T E R O U T L I N E 10.1 Introduction 417 10.2 Inspection 418 How Much to Inspect and How Often 419 Where to Inspect in the Process 420 Centralized versus On-Site Inspection 422 10.3 Statistical Process Control 423 Process Variability 423 Sampling and Sampling Distributions 424 416 The Control Process 425 Control Charts: The Voice of the Process 426 Control Charts for Variables 428 Control Charts for Attributes 433 Managerial Considerations Concerning Control Charts 436 Run Tests 437 Using Control Charts and Run Tests Together 441 What Happens When a Process Exhibits Possible Nonrandom Variation? 441 10.4 Process Capability 441 Capability Analysis 442 C p 443 C pk 445 Improving Process Capability 445 Taguchi Loss Function 446 Limitations of Capability Indexes 446 10.5 Operations Strategy 446 Cases: Toys, Inc. 460 Tiger Tools 460 FP O www.downloadslide.net © Gabriela Maj/Bloomberg via Getty This chapter covers quality control The purpose of quality control is to assure that processes are performing in an acceptable manner Companies accomplish this by monitoring process output using statistical techniques Quality control is a process that measures output relative to a standard and takes corrective action when output does not meet standards If the results are acceptable, no further action is required; unacceptable results call for corrective action Every process generates output that exhibits random variability That is natural and cannot be corrected However, if there are nonrandom variations in process output, that can be corrected Quality control tools are used to decide when corrective action is needed 10.1 INTRODUCTION Quality assurance that relies primarily on inspection of lots (batches) of previously produced items is referred to as acceptance sampling It is described in the chapter supplement Quality control efforts that occur during production are referred to as statistical process control, and these we examine in the following sections The best companies emphasize designing quality into the process, thereby greatly reducing the need for inspection or control efforts As you might expect, different business organizations are in different stages of this evolutionary process: Some rely heavily on inspection However, inspection alone is generally not sufficient to achieve a reasonable level of quality Many occupy a middle ground that involves some inspection and a great deal of process control Figure 10.1 illustrates these phases of quality assurance Quality control A process that evaluates output relative to a standard and takes corrective action when output doesn’t meet standards LO10.1 Explain the need for quality control 417 www.downloadslide.net 418 Chapter Ten Quality Control FIGURE 10.1 Approaches to quality assurance Inspection Inspection alone Inspection and corrective action during production Quality built into the process Process control Continuous improvement The least progressive The most progressive 10.2 INSPECTION Inspection Appraisal of goods or services LO10.2 Discuss the basic issues of inspection Inspection is an appraisal activity that compares goods or services to a standard Inspection is a vital but often unappreciated aspect of quality control Although for well-designed processes little inspection is necessary, inspection cannot be completely eliminated And with increased outsourcing of products and services, inspection has taken on a new level of significance In lean organizations, inspection is less of an issue than it is for other organizations because lean organizations place extra emphasis on quality in the design of both products and processes Moreover, in lean operations, workers have responsibility for quality (quality at the source) However, many organizations not operate in a lean mode, so inspection is important for them This is particularly true of service operations, where quality continues to be a challenge for management Inspection can occur at three points: before production, during production, and after production The logic of checking conformance before production is to make sure that inputs are acceptable The logic of checking conformance during production is to make sure that the conversion of inputs into outputs is proceeding in an acceptable manner The logic of checking conformance of output is to make a final verification of conformance before passing goods on to customers Inspection before and after production often involves acceptance sampling procedures; monitoring during the production process is referred to as process control Figure 10.2 gives an overview of where these two procedures are applied in the production process To determine whether a process is functioning as intended or to verify that a batch or lot of raw materials or final products does not contain more than a specified percentage of defective goods, it is necessary to physically examine at least some of the items in question The purpose of inspection is to provide information on the degree to which items conform to a standard The basic issues are: 3 4 How much to inspect and how often At what points in the process inspection should occur Whether to inspect in a centralized or on-site location Whether to inspect attributes (i.e., count the number of times something occurs) or variables (i.e., measure the value of a characteristic) Consider, for example, inspection at an intermediate step in the manufacture of personal computers Because inspection costs are often significant, questions naturally arise on whether one needs to inspect every computer or whether a small sample of computers will suffice FIGURE 10.2 Acceptance sampling and process control Inputs Transformation Outputs Acceptance sampling Process control Acceptance sampling www.downloadslide.net Chapter Ten Quality Control 419 A Toyota technician prepares to remove the accelerator assembly in a recalled Toyota Avalon © Tim Boyle/Bloomberg via Getty Moreover, although inspections could be made at numerous points in the production process, it is not generally cost-effective to make inspections at every point Hence, the question comes up of which points should be designated for inspections Once these points have been identified, a manager must decide whether to remove the computers from the line and take them to a lab, where specialized equipment might be available to perform certain tests, or to test them where they are being made We will examine these points in the following sections How Much to Inspect and How Often The amount of inspection can range from no inspection whatsoever to inspection of each item numerous times Low-cost, high-volume items such as paper clips, roofing nails, and wooden pencils often require little inspection because (1) the cost associated with passing defective items is quite low and (2) the processes that produce these items are usually highly reliable, so defects are rare Conversely, high-cost, low-volume items that have large costs associated with passing defective products often require more intensive inspections Thus, critical components of a manned-flight space vehicle are closely scrutinized because of the risk to human safety and the high cost of mission failure In high-volume systems, automated inspection is one option that may be employed The majority of quality control applications lie somewhere between the two extremes Most require some inspection, but it is neither possible nor economically feasible to critically examine every part of a product or every aspect of a service for control purposes The cost of inspection, resulting interruptions of a process or delays caused by inspection, and the manner of testing typically outweigh the benefits of 100 percent inspection Note that for manual inspection, even 100 percent inspection does not guarantee that all defects will be found and removed Inspection is a process, and hence, subject to variation Boredom and fatigue are factors that cause inspection mistakes Moreover, when destructive testing is involved (items are destroyed by testing), that must be taken into account However, the cost of letting undetected defects slip through is sufficiently high that inspection cannot be completely ignored The amount of inspection needed is governed by the costs of inspection and the expected costs of passing defective items As illustrated in Figure 10.3, if inspection activities increase, inspection costs increase, but the costs of undetected defects decrease The traditional goal www.downloadslide.net 420 Chapter Ten Quality Control FIGURE 10.3 Cost Traditional view: The amount of inspection is optimal when the sum of the costs of inspection and passing defectives is minimized Total cost Cost of inspection Cost of passing defectives Optimal Amount of inspection was to minimize the sum of these two costs In other words, it may not pay to attempt to catch every defect, particularly if the cost of inspection exceeds the penalties associated with letting some defects get through Current thinking is that every reduction in defective output reduces costs, although not primarily by inspection As a rule, operations with a high proportion of human involvement necessitate more inspection effort than mechanical operations, which tend to be more reliable The frequency of inspection depends largely on the rate at which a process may go out of control or on the number of lots being inspected A stable process will require only infrequent checks, whereas an unstable one or one that has recently given trouble will require more frequent checks Likewise, many small lots will require more samples than a few large lots because it is important to obtain sample data from each lot For high-volume, repetitive operations, computerized automatic inspections at critical points in a process are cost effective Where to Inspect in the Process Many operations have numerous possible inspection points Because each inspection adds to the cost of the product or service, it is important to restrict inspection efforts to the points where they can the most good In manufacturing, some of the typical inspection points are: 4 Raw materials and purchased parts There is little sense in paying for goods that not meet quality standards and in expending time and effort on material that is bad to begin with Supplier certification programs can reduce or eliminate the need for inspection Finished products Customer satisfaction and the firm’s image are at stake here, and repairing or replacing products in the field is usually much more costly than doing it at the factory Likewise, the seller is usually responsible for shipping costs on returns, and payments for goods or service may be held up pending delivery of satisfactory goods or remedial service Well-designed processes, products and services, quality at the source, and process monitoring can reduce or eliminate the need for inspection Before a costly operation The point is to not waste costly labor or machine time on items that are already defective Before an irreversible process In many cases, items can be reworked up to a certain point; beyond that point they cannot For example, pottery can be reworked prior to firing After that, defective pottery must be discarded or sold as seconds at a lower price Before a covering process Painting, plating, and assemblies often mask defects www.downloadslide.net Chapter Ten Quality Control 421 Inspection can be used as part of an effort to improve process yield One measure of process yield is the ratio of output of good product to the total output Inspection at key points can help guide process improvement efforts to reduce the scrap rate and improve the overall process yield, and reduce or eliminate the need for inspection In the service sector, inspection points are incoming purchased materials and supplies, personnel, service interfaces (e.g., service counter), and outgoing completed work (e.g., repaired appliances) Table 10.1 illustrates a number of examples Type of Business Inspection Points Characteristics Fast food Cashier Accuracy Counter area Appearance, productivity Eating area Cleanliness, no loitering Building and grounds Appearance, safety hazards Kitchen Cleanliness, purity of food, food storage, health regulations Parking lot Safety, good lighting Accounting/billing Accuracy, timeliness Building and grounds Appearance and safety Main desk Appearance, waiting times, accuracy of bills Maid service Completeness, productivity Personnel Appearance, manners, productivity Reservations/occupancy Over/underbooking, percent occupancy Restaurants Kitchen, menus, meals, bills Room service Waiting time, quality of food Supplies Ordering, receiving, inventories Cashiers Accuracy, courtesy, productivity Deliveries Quality, quantity Produce Freshness, ample stock Aisles and stockrooms Uncluttered layout Inventory control Stock-outs Shelf stock Ample supply, rotation of perishables Shelf displays Appearance Checkouts Waiting time Shopping carts Good working condition, ample supply, theft/vandalism Parking lot Safety, good lighting Personnel Appearance, productivity Waiting room Appearance, comfortable Examination room Clean, temperature controlled Doctor Neat, friendly, concerned, skillful, knowledgeable Doctor’s assistant Neat, friendly, concerned, skillful Patient records Accurate, up-to-date Billing Accurate Other Waiting time minimal, adequate time with doctor Hotel/motel Supermarket Doctor’s office TABLE 10.1 Examples of inspection points in service organizations www.downloadslide.net READING MAKING POTATO CHIPS A potato chip is a delicate thing Fragile A pound of pressure will crush it So when you’re making potato chips, you need to have a system If you aren’t careful, instead of potato chips, you’ll end up with potato chip crumbs The Jays company in Chicago was a producer of a variety of snack products, one of which was potato chips The company is now owned by Snyders Nonetheless, there is much to be learned from a description of Jay’s operations To avoid the tendency of potato chips to crush into crumbs, Jays used a system of conveyor belts, radial filling chutes and gently vibrating slides, where masses of chips, a yard deep, were gently moved through the process The process started with the arrival of semi-trailers full of potatoes; usually about a dozen a day The potatoes were separated into big and small sizes; big potatoes for big chips that go into large bags; and small potatoes for small chips for lunch-size bags Computers keep track of everything, shunting potatoes to 15,000-pound holding bins Each bin feeds into a pipe containing a turning screw—a version of the ancient Archimedes screw used to pump water—that moves the potatoes from the bin to conveyor belts, to where they are washed and skinned—the skin scrubbed off by metal bristle brushes No machine can detect if a potato is rotten inside So a pair of human inspectors gave the potatoes a quick squeeze as they moved along a conveyor, and removed those likely to have rot The cleaned potatoes were sent into high-speed chippers— spinning brass rings, each with eight blades inside, straight blades for straight chips, ripple blades for ripple chips The blades cut the potatoes, but cutting dulled the blades, so every three hours the line had to be stopped so that the blades could be replaced. The raw chips spent three minutes cooking in hot corn oil, which was constantly circulated and filtered Then they were salted, and any flavorings such as barbecue were added After the chips were fried, there was another quality check, in which workers removed burned and deformed chips out of the masses passing by © Jay Reeves/AP Images The chips also were laser-inspected Chips with dark spots or holes were removed by a puff of air that knocked them off the line, into a discard bin The discards—about percent of production—were gathered up and used: Starch was drawn out and sold to cornstarch makers; the rest went to hog feed. Getting the chips in the bags was another challenge: You can’t just fill up bags and seal them; the chips would be smashed Rather, a conveyor poured chips—gently—onto the central hub of a large, wheel-like device, where the chips scattered into 15 buckets that were essentially scales A computer monitored the weight of each bucket to assure there would be just the right amount to fill a 14-ounce bag The bags were packed into boxes that read: “HANDLE LIKE EGGS.” While not exactly perishable, potato chips have a shelf life of about eight weeks, only one day of which is spent at the plant Questions What characteristics of potato chips concern Jays in terms of quality? Do you feel that Jays is overdoing it with its concern for quality? Explain Source: Adapted from Neil Steinberg, “In the Chips,” Chicago Sun-Times, December 26, 1997 Copyright © 2003 Chicago Sun-Times Reprinted with special permission from the Chicago Sun-Times, Inc Centralized versus On-Site Inspection Some situations require that inspections be performed on site For example, inspecting the hull of a ship for cracks requires inspectors to visit the ship At other times, specialized tests can best be performed in a lab (e.g., performing medical tests, analyzing food samples, testing metals for hardness, running viscosity tests on lubricants) The central issue in the decision concerning on-site or lab inspections is whether the advantages of specialized lab tests are worth the time and interruption needed to obtain the results Reasons favoring on-site inspection include quicker decisions and avoidance of introduction of extraneous factors (e.g., damage or other alteration of samples during transportation to the lab) 422 www.downloadslide.net Chapter Ten Quality Control 423 A Mattel technician in China does a pulling test with a Dora the Explorer doll in the name of product safety Mattel has 10 labs in six countries and has set up strict requirements for vendors because of safety recalls © Chang W Lee/The New York Times/Redux On the other hand, specialized equipment and a more favorable test environment (less noise and confusion, lack of vibrations, absence of dust, and no workers “helping” with inspections) offer strong arguments for using a lab Some companies rely on self-inspections by operators if errors can be traced back to specific operators This places responsibility for errors at their source (quality at the source) 10.3 STATISTICAL PROCESS CONTROL Quality control is concerned with the quality of conformance of a process: Does the output of a process conform to the intent of design? Variations in characteristics of process output provide the rationale for process control Statistical process control (SPC) is used to evaluate process output to decide if a process is “in control” or if corrective action is needed Process Variability Quality of conformance A product or service conforms to specifications Statistical process control (SPC) Statistical evaluation of the output of a process All processes generate output that exhibits some degree of variability The issue is whether the output variations are within an acceptable range The issue is addressed by answering two basic questions about the process variations: Are the variations random? If nonrandom variations are present, the process is considered to be unstable Corrective action will need to be taken to improve the process by eliminating the causes of nonrandomness to achieve a stable process Given a stable process, is the inherent variability of process output within a range that conforms to performance criteria? This involves assessment of a process’s capability to meet standards If a process is not capable, that situation will need to be addressed The natural or inherent process variations in process output are referred to as chance or random variations Such variations are due to the combined influences of countless minor factors, each one so unimportant that even if it could be eliminated, the impact on process variations would be negligible In Deming’s terms, this is referred to as common variability The amount of inherent variability differs from process to process For instance, older Random variation Natural variation in the output of a process, created by countless minor factors www.downloadslide.net 424 Chapter Ten Quality Control Assignable variation In process output, a variation whose cause can be identified A nonrandom variation machines generally exhibit a higher degree of natural variability than newer machines, partly because of worn parts and partly because new machines may incorporate design improvements that lessen the variability in their output A second kind of variability in process output is called assignable variation, or nonrandom variation In Deming’s terms, this is referred to as special variation Unlike natural variation, the main sources of assignable variation can usually be identified (assigned to a specific cause) and eliminated Tool wear, equipment that needs adjustment, defective materials, human factors (carelessness, fatigue, noise and other distractions, failure to follow correct procedures, and so on) and problems with measuring devices are typical sources of assignable variation Sampling and Sampling Distributions Sampling distribution A theoretical distribution of sample statistics Central limit theorem The distribution of sample averages tends to be normal regardless of the shape of the process distribution In statistical process control, periodic samples of process output are taken and sample statistics, such as sample means or the number of occurrences of a certain type of outcome, are determined The sample statistics can be used to judge randomness of process variations The sample statistics exhibit variation, just as processes The variability of sample statistics can be described by its sampling distribution, a theoretical distribution that describes the random variability of sample statistics For a variety of reasons, the most frequently used distribution is the normal distribution Figure 10.4A illustrates a sampling distribution and a process distribution (i.e., the distribution of process variations) Note three important things in Figure 10.4A: (1) both distributions have the same mean; (2) the variability of the sampling distribution is less than the variability of the process; and (3) the sampling distribution is normal This is true even if the process distribution is not normal In the case of sample means, the central limit theorem states that as the sample size increases, the distribution of sample averages approaches a normal distribution regardless of the shape of the sampled population This tends to be the case even for fairly small sample sizes For other sample statistics, the normal distribution serves as a reasonable approximation to the shape of the actual sampling distribution Figure 10.4B illustrates what happens to the shape of the sampling distribution relative to the sample size The larger the sample size, the narrower the sampling distribution This means that the likelihood that a sample statistic is close to the true value in the population is higher for large samples than for small samples A sampling distribution serves as the theoretical basis for distinguishing between random and nonrandom values of a sampling statistic Very simply, limits are selected within which FIGURE 10.4A The sampling distribution of means is normal, and it has less variability than the process distribution, which might not be normal Distribution of probable sample statistics (e.g., means) that could come from the process FIGURE 10.4B The larger the sample size, the narrower the sampling distribution n = 100 n = 60 Distribution of process output n = 40 n = 20 Process and sampling distribution mean Sampling distribution means www.downloadslide.net Chapter Ten Quality Control 425 FIGURE 10.5 Percentage of values within given ranges in a normal distribution σ = Standard deviation -3σ -2σ Mean +2σ +3σ 95.44% 99.74% most values of a sample statistic should fall if its variations are random The limits are stated in terms of number of standard deviations from the distribution mean Typical limits are ± standard deviations or ± standard deviations Figure 10.5 illustrates these possible limits and the probability that a sample statistic would fall within those limits if only random variations are present Conversely, if the value of a sample statistic falls outside those limits, there is only a small probability (1 − 99.74 = 0026 for ± limits, and − 95.44 = 0456 for ± limits) that the value reflects randomness Instead, such a value would suggest nonrandomness The Control Process Sampling and corrective action are only a part of the control process Effective control requires the following steps: Define The first step is to define in sufficient detail what is to be controlled It is not enough, for example, to simply refer to a painted surface The paint can have a number of important characteristics such as its thickness, hardness, and resistance to fading or chipping Different characteristics may require different approaches for control purposes LO10.3 List and briefly explain the elements of the control process Food and beverage companies use Omron Electronics’ fiber optic sensors to monitor processes and to perform quality inspections such as checking beverage content and caps Omron Electronics LLC www.downloadslide.net 426 Chapter Ten Quality Control Measure Only those characteristics that can be counted or measured are candidates for control Thus, it is important to consider how measurement will be accomplished Compare There must be a standard of comparison that can be used to evaluate the measurements This will relate to the level of quality being sought Evaluate Management must establish a definition of out of control Even a process that is functioning as it should will not yield output that conforms exactly to a standard, simply because of the natural (i.e., random) variations inherent in all processes, manual or mechanical—a certain amount of variation is inevitable The main task of quality control is to distinguish random from nonrandom variability, because nonrandom variability means that a process is out of control Correct When a process is judged to be out of control, corrective action must be taken This involves uncovering the cause of nonrandom variability (e.g., worn equipment, incorrect methods, failure to follow specified procedures) and correcting it Monitor results To ensure that corrective action is effective, the output of a process must be monitored for a sufficient period of time to verify that the problem has been eliminated In sum, control is achieved by checking a portion of the goods or services, comparing the results to a predetermined standard, evaluating departures from the standard, taking corrective action when necessary, and following up to ensure that problems have been corrected Control Charts: The Voice of the Process Control chart A visual tool for monitoring forecast errors LO10.4 Explain how control charts are used to monitor a process and the concepts that underlie their use An important tool in statistical process control is the control chart, which was developed by Walter Shewhart A control chart is a time-ordered plot of sample statistics It is used to distinguish between random variability and nonrandom variability It has upper and lower limits, called control limits, that define the range of acceptable (i.e., random) variation for the sample statistic A control chart is illustrated in Figure 10.6 The purpose of a control chart is to monitor process output to see if it is random A necessary (but not sufficient) condition for a process to be deemed “in control,” or stable, is for all the data points to fall between the upper and lower control limits Conversely, a data point that falls outside of either limit would be taken as evidence that the process output may be nonrandom and, therefore, not “in control.” If that happens, the process would be halted to find and correct the cause of the nonrandom variation The essence of statistical process control is to assure that the output of a process is random so that future output will be random The basis for the control chart is the sampling distribution, which essentially describes random variability There is, however, one minor difficulty relating to the use of a normal sampling distribution The theoretical distribution extends in either direction to infinity Therefore, any value is theoretically possible, even one that is a considerable distance from the mean of the distribution However, as a practical matter, we know that, say, 99.7 percent of the values will be within ± standard deviations of the mean of the distribution FIGURE 10.6 Out of control Example of a control chart Abnormal variation due to assignable sources UCL Normal variation due to chance Mean LCL 10 11 12 Sample number Abnormal variation due to assignable sources www.downloadslide.net Chapter Ten Quality Control FIGURE 10.7 Sampling distribution (x, σx ) Process distribution (x, σx ) LCL Mean 427 Control limits are based on the sampling distribution UCL Therefore, we could decide to set the limit, so to speak, at values that represent ± standard deviations from the mean, and conclude that any value that was farther away than these limits was a nonrandom variation In effect, these limits are control limits: the dividing lines between what will be designated as random deviations from the mean of the distribution and what will be designated as nonrandom deviations from the mean of the distribution Figure 10.7 illustrates how control limits are based on the sampling distribution Control charts have two limits that separate random variation and nonrandom variation The larger value is the upper control limit (UCL), and the smaller value is the lower control limit (LCL) A sample statistic that falls between these two limits suggests (but does not prove) randomness, while a value outside or on either limit suggests (but does not prove) nonrandomness It is important to recognize that because any limits will leave some area in the tails of the distribution, there is a small probability that a value will fall outside the limits even though only random variations are present For example, if ± sigma (standard deviation) limits are used, they would include 95.5 percent of the values Consequently, the complement of that number (100 percent = 95.5 percent = 4.5 percent) would not be included That percentage (or probability ) is sometimes referred to as the probability of a Type I error, where the “error” is concluding that nonrandomness is present when only randomness is present It is also referred to as an alpha risk, where alpha (α) is the sum of the probabilities in the two tails Figure 10.8 illustrates this concept Using wider limits (e.g., ± sigma limits) reduces the probability of a Type I error because it decreases the area in the tails However, wider limits make it more difficult to detect nonrandom variations if they are present For example, the mean of the process might shift (an assignable cause of variation) enough to be detected by two-sigma limits, but not enough to be readily apparent using three-sigma limits That could lead to a second kind of error, known as a Type II error, which is concluding that a process is in control when it is really out of control (i.e., concluding nonrandom variations are not present, when they are) In theory, the costs of making each error should be balanced by their probabilities However, in practice, two-sigma limits and three-sigma limits are commonly used without specifically referring to the probability of a Type II error Table 10.2 illustrates how Type I and Type II errors occur Each sample is represented by a single value (e.g., the sample mean) on a control chart Moreover, each value is compared to the extremes of the sampling distribution (the control Control limits The dividing lines between random and nonrandom deviations from the mean of the distribution Type I error Concluding a process is not in control when it actually is Type II error Concluding a process is in control when it is not FIGURE 10.8 The probability of a Type I error α/2 α/2 LCL Mean α = Probability of a Type I error UCL ... Crystal Ball 11 3 Summary 11 4 Key Points 11 6 Key Terms 11 7 Solved Problems 11 7 Discussion and Review Questions 12 3 Taking Stock 12 4 Critical Thinking Exercises 12 4 Problems 12 4 Case M&L... Forecasting Techniques 10 1 Monitoring Forecast Error 10 6 Choosing a Forecasting Technique 11 0 Using Forecast Information 11 1 Computer Software in Forecasting 11 2 Operations Strategy 11 2 Reading: Gazing... Analysis 342 Management of Quality 372 10 Quality Control 416 11 Aggregate Planning and Master Scheduling 462 12 MRP and ERP 500 13 Inventory Management 550 14 JIT and Lean Operations 608