Introduction to engineering statistics & six sigma

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  • 1. Introduction to Engineering Statistics andSix Sigma
  • 2. Theodore T. AllenIntroduction toEngineering Statisticsand Six SigmaStatistical Quality Control and Design ofExperiments and SystemsWith 114 Figures123
  • 3. Theodore T. Allen, PhDDepartment of Industrial Welding and Systems EngineeringThe Ohio State University210 Baker Systems1971 Neil AvenueColombus, OH 43210-1271USABritish Library Cataloguing in Publication DataAllen, Theodore T. Introduction to engineering statistics and six sigma: statistical quality control and design of experiments and systems 1. Engineering - Statistical methods 2. Six sigma (Quality control standard) I. Title 620.0072ISBN-10: 1852339551Library of Congress Control Number: 2005934591ISBN-10: 1-85233-955-1 e-ISBN 1-84628-200-4 Printed on acid-free paperISBN-13: 978-1-85233-955-5 Springer-Verlag London Limited 2006Apart from any fair dealing for the purposes of research or private study, or criticism or review, aspermitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced,stored or transmitted, in any form or by any means, with the prior permission in writing of thepublishers, or in the case of reprographic reproduction in accordance with the terms of licences issuedby the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should besent to the publishers.The use of registered names, trademarks, etc. in this publication does not imply, even in the absence ofa specic statement, that such names are exempt from the relevant laws and regulations and thereforefree for general use.The publisher makes no representation, express or implied, with regard to the accuracy of the infor-mation contained in this book and cannot accept any legal responsibility or liability for any errors oromissions that may be made.Printed in Germany987654321Springer Science+Business Mediaspringer.com
  • 4. Dedicated to my wife and to my parents
  • 5. PrefaceThere are four main reasons why I wrote this book. First, six sigma consultantshave taught us that people do not need to be statistical experts to gain benefits fromapplying methods under such headings as statistical quality control (SQC) anddesign of experiments (DOE). Some college-level books intertwine the methodsand the theory, potentially giving the mistaken impression that all the theory has tobe understood to use the methods. As far as possible, I have attempted to separatethe information necessary for competent application from the theory needed tounderstand and evaluate the methods. Second, many books teach methods without sufficiently clarifying the contextin which the method could help to solve a real-world problem. Six sigma, statisticsand operations-research experts have little trouble making the connections withpractice. However, many other people do have this difficulty. Therefore, I wantedto clarify better the roles of the methods in solving problems. To this end, I havere-organized the presentation of the techniques and included several complete casestudies conducted by myself and former students. Third, I feel that much of the theory in standard textbooks is rarely presentedin a manner to answer directly the most pertinent questions, such as: Should I use this specific method or an alternative method? How do I use the results when making a decision? How much can I trust the results? Admittedly, standard theory (e.g., analysis of variance decomposition,confidence intervals, and defining relations) does have a bearing on thesequestions. Yet the widely accepted view that the choice to apply a method isequivalent to purchasing a risky stock investment has not been sufficientlyclarified. The theory in this book is mainly used to evaluate in advance the risksassociated with specific methods and to address these three questions. Fourth, there is an increasing emphasis on service sector and bioengineeringapplications of quality technology, which is not fully reflected in some of thealternative books. Therefore, this book constitutes an attempt to include moreexamples pertinent to service-sector jobs in accounting, education, call centers,health care, and software companies. In addition, this book can be viewed as attempt to build on and refocus materialin other books and research articles, including: Harry and Schroeder (1999) andPande et al. which comprehensively cover six sigma; Montgomery (2001) andBesterfield (2001), which focus on statistical quality control; Box and Draper
  • 6. viii Preface(1987), Dean and Voss (1999), Fedorov and Hackl (1997), Montgomery (2000),Myers and Montgomery (2001), Taguchi (1993), and Wu and Hamada (2000),which focus on design of experiments. At least 50 books per year are written related to the six sigma movementwhich (among other things) encourage people to use SQC and DOE techniques.Most of these books are intended for a general business audience; few provideadvanced readers the tools to understand modern statistical method development.Equally rare are precise descriptions of the many methods related to six sigma aswell as detailed examples of applications that yielded large-scale returns to thebusinesses that employed them. Unlike many popular books on six sigma methods, this material is aimed atthe college- or graduate-level student rather than at the casual reader, and includesmore derivations and analysis of the related methods. As such, an importantmotivation of this text is to fill a need for an integrated, principled, technicaldescription of six sigma techniques and concepts that can provide a practical guideboth in making choices among available methods and applying them to real-worldproblems. Professionals who have earned black belt and master black belttitles may find material more complete and intensive here than in other sources. Rather than teaching methods as correct and fixed, later chapters build theoptimization and simulation skills needed for the advanced reader to develop newmethods with sophistication, drawing on modern computing power. Design ofexperiments (DOE) methods provide a particularly useful area for the developmentof new methods. DOE is sometimes called the most powerful six sigma tool.However, the relationship between the mathematical properties of the associatedmatrices and bottom-line profits has been only partially explored. As a result, usersof these methods too often must base their decisions and associated investments onfaith. An intended unique contribution of this book is to teach DOE in a new way,as a set of fallible methods with understandable properties that can be improved,while providing new information to support decisions about using these methods. Two recent trends assist in the development of statistical methods. First,dramatic improvements have occurred in the ability to solve hard simulation andoptimization problems, largely because of advances in computing speeds. It is nowfar easier to simulate the application of a chosen method to test likely outcomesof its application to a particular problem. Second, an increased interest in six sigmamethods and other formal approaches to making businesses more competitive hasincreased the time and resources invested in developing and applying newstatistical methods. This latter development can be credited to consultants such as Harry andSchroeder (1999), Pande et al. (2000), and Taguchi (1993), visionary businessleaders such as General Electrics Jack Welch, as well as to statistical software thatpermits non-experts to make use of the related technologies. In addition, there is apush towards closer integration of optimization, marketing, and statistical methodsinto improvement systems that structure product-design projects from beginningto end. Statistical methods are relevant to virtually everyone. Calculus and linearalgebra are helpful, but not necessary, for their use. The approach taken here is tominimize explanations requiring knowledge of these subjects, as far as possible.
  • 7. Preface ixThis book is organized into three parts. For a single introductory course, the firstfew chapters in Parts One and Two could be used. More advanced courses could bebuilt upon the remaining chapters. At The Ohio State University, I use each part fora different 11 week course.ReferencesBox GEP, Draper NR (1987) Empirical Model-Building and Response Surfaces. Wiley, New YorkBesterfield D (2001) Quality Control. Prentice Hall, Columbus, OHBreyfogle FW (2003) Implementing Six Sigma: Smarter Solutions Using Statistical Methods, 2nd edn. Wiley, New YorkDean A, Voss DT (1999) Design and Analysis of Experiments. Springer, Berlin Heidelberg New YorkFedorov V, Hackl P (1997) Model-Oriented Design of Experiments. Springer, Berlin Heidelberg New YorkHarry MJ, Schroeder R (1999) Six Sigma, The Breakthrough Management Strategy Revolutionizing The Worlds Top Corporations. Bantam Doubleday Dell, New YorkMontgomery DC (2000) Design and Analysis of Experiments, 5th edn. John Wiley & Sons, Inc., Hoboken, NJMontgomery DC (2001) Statistical Quality Control, 4th edn. John Wiley & Sons, Inc., Hoboken, NJMyers RH, Montgomery DA (2001) Response Surface Methodology, 5th edn. John Wiley & Sons, Inc., Hoboken, NJPande PS, Neuman RP, Cavanagh R (2000) The Six Sigma Way: How GE, Motorola, and Other Top Companies are Honing Their Performance. McGraw-Hill, New YorkTaguchi G (1993) Taguchi Methods: Research and Development. In Konishi S (ed.) Quality Engineering Series, vol 1. The American Supplier Institute, Livonia, MIWu CFJ, Hamada M (2000) Experiments: Planning, Analysis, and Parameter Design Optimization. Wiley, New York
  • 8. AcknowledgmentsI thank my wife, Emily, for being wonderful. I thank my son, Andrew, for beingextremely cute. I also thank my parents, George and Jodie, for being exceptionallygood parents. Both Emily and Jodie provided important editing and conceptualhelp. In addition, Sonya Humes and editors at Springer Verlag including KateBrown and Anthony Doyle provided valuable editing and comments. Gary Herrin, my advisor, provided valuable perspective and encouragement.Also, my former Ph.D. students deserve high praise for helping to develop theconceptual framework and components for this book. In particular, I thank LiyangYu for proving by direct test that modern computers are able to optimizeexperiments evaluated using simulation, which is relevant to the last four chaptersof this book, and for much hard work and clear thinking. Also, I thank MikhailBernshteyn for his many contributions, including deeply involving my researchgroup in simulation optimization, sharing in some potentially importantinnovations in multiple areas, and bringing technology in Part II of this book to themarketplace through Sagata Ltd., in which we are partners. I thank Charlie Ribardofor teaching me many things about engineering and helping to develop many of thewelding-related case studies in this book. Waraphorn Ittiwattana helped to developapproaches for optimization and robust engineering in Chapter 14. NavaraChantarat played an important role in the design of experiments discoveries inChapter 18. I thank Deng Huang for playing the leading role in our exploration ofvariable fidelity approaches to experimentation and optimization. I am grateful toJames Brady for developing many of the real case studies and for playing theleading role in our related writing and concept development associated with sixsigma, relevant throughout this book. Also, I would like to thank my former M.S. students, including ChaitanyaJoshi, for helping me to research the topic of six sigma. Chetan Chivate alsoassisted in the development of text on advanced modeling techniques (Chapter 16).Also, Gavin Richards and many other students at The Ohio State University playedkey roles in providing feedback, editing, refining, and developing the examples andproblems. In particular, Mike Fujka and Ryan McDorman provided the studentproject examples. In addition, I would like to thank all of the individuals who have supported thisresearch over the last several years. These have included first and foremost AllenMiller, who has been a good boss and mentor, and also Richard Richardson andDavid Farson who have made the welding world accessible; it has been a pleasure
  • 9. xii Acknowledgmentsto collaborate with them. Jose Castro, John Lippold, William Marras, Gary Maul,Clark Mount-Campbell, Philip Smith, David Woods, and many others contributedby believing that experimental planning is important and that I would some daymanage to contribute to its study. Also, I would like to thank Dennis Harwig, David Yapp, and Larry Brown bothfor contributing financially and for sharing their visions for related research.Multiple people from Visteon assisted, including John Barkley, Frank Fusco, PeterGilliam, and David Reese. Jane Fraser, Robert Gustafson, and the Industrial andSystems Engineering students at The Ohio State University helped me to improvethe book. Bruce Ankenman, Angela Dean, William Notz, Jason Hsu, and TomSantner all contributed. Also, editors and reviewers played an important role in the development of thisbook and publication of related research. First and foremost of these is AdrianBowman of the Journal of the Royal Statistical Society Series C: AppliedStatistics, who quickly recognized the value of the EIMSE optimal designs (seeChapter 13). Douglas Montgomery of Quality and Reliability EngineeringInternational and an expert on engineering statistics provided key encouragementin multiple instances. In addition, the anonymous reviewers of this book providedmuch direct and constructive assistance i...

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