Certified Black Belt Handbook Chapter

Tni Cik¡iiiin Six Sicva8iack 8ii¡ Haxnnook Sicoxn Fni¡iox H1325_Kubiak_BOOK.indb i 11/20/08 6:14:12 PM Also available from ASQ Quality Press: The Certified Six Sigma Green Belt Handbook Roderick A. Munro, Matthew J. Maio, Mohamed B. Nawaz, Govindarajan Ramu, and Daniel J. Zrymiak Six Sigma for the New Millennium: A CSSBB Guidebook, Second Edition Kim H. Pries 5S for Service Organizations and Offices: A Lean Look at Improvements Debashis Sarkar The Executive Guide to Understanding and Implementing Lean Six Sigma: The Financial Impact Robert M. Meisel, Steven J. Babb, Steven F. Marsh, and James P. Schlichting Applied Statistics for the Six Sigma Green Belt Bhisham C. Gupta and H. Fred Walker Statistical Quality Control for the Six Sigma Green Belt Bhisham C. Gupta and H. Fred Walker Six Sigma for the Office: A Pocket Guide Roderick A. Munro Lean-Six Sigma for Healthcare: A Senior Leader Guide to Improving Cost and Throughput, Second Edition Chip Caldwell , Greg Butler, and Nancy Poston. Defining and Analyzing a Business Process: A Six Sigma Pocket Guide Jeffrey N. Lowenthal Six Sigma for the Shop Floor: A Pocket Guide Roderick A. Munro Six Sigma Project Management: A Pocket Guide Jeffrey N. Lowenthal Transactional Six Sigma for Green Belts: Maximizing Service and Manufacturing Processes Samuel E. Windsor Lean Kaizen: A Simplified Approach to Process Improvements George Alukal and Anthony Manos A Lean Guide to Transforming Healthcare: How to Implement Lean Principles in Hospitals, Medical Offices, Clinics, and Other Healthcare Organizations Thomas G. Zidel To request a complimentary catalog of ASQ Quality Press publications, call 800-248-1946, or visit our Web site at http://www.asq.org/quality-press. H1325_Kubiak_BOOK.indb ii 11/20/08 6:14:12 PM Tni Cik¡iiiin Six Sicva 8iack 8ii¡ Haxnnook Sicoxn Fni¡iox T. M. Kubiak Donald W. Benbow ASQ Quality Press Milwaukee, Wisconsin H1325_Kubiak_BOOK.indb iii 11/20/08 6:14:12 PM American Society for Quality, Quality Press, Milwaukee 53203 © 2009 by American Society for Quality All rights reserved. Published 2009 Printed in the United States of America 14 13 12 11 10 09 5 4 3 2 1 Library of Congress Cataloging-in-Publication Data Kubiak, T.M. The certified six sigma black belt handbook / T.M. Kubiak and Donald W. Benbow.—2nd ed. p. cm. ISBN 978-0-87389-732-7 (alk. paper) 1. Quality control—Statistical methods—Handbooks, manuals, etc. I. Benbow, Donald W., 1936– II. Title. TS156.B4653 2008 658.4’013--dc22 2008042611 No part of this book may be reproduced in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Publisher: William A. Tony Acquisitions Editor: Matt Meinholz Project Editor: Paul O’Mara Production Administrator: Randall Benson ASQ Mission: The American Society for Quality advances individual, organizational, and community excellence worldwide through learning, quality improvement, and knowledge exchange. Attention Bookstores, Wholesalers, Schools, and Corporations: ASQ Quality Press books, videotapes, audiotapes, and software are available at quantity discounts with bulk purchases for business, educational, or instructional use. For information, please contact ASQ Quality Press at 800-248-1946, or write to ASQ Quality Press, P.O. Box 3005, Milwaukee, WI 53201-3005. To place orders or to request a free copy of the ASQ Quality Press Publications Catalog, including ASQ membership information, call 800-248-1946. Visit our Web site at www.asq.org or www.asq.org/quality-press. Portions of the input and output contained in this publication/book are printed with permission of Minitab Inc. All material remains the exclusive property and copyright of Minitab Inc. All rights reserved. Printed on acid-free paper H1325_Kubiak_BOOK.indb iv 11/20/08 6:14:12 PM For Jaycob, my grandson: This world is changing with each passing day—sometimes for the better, sometimes not. I will strive to carry your burdens until you are able to do so for yourself. May you always be blessed with the best that life has to offer and always strive to improve not just your life but the lives of others. On life’s journey you will confront challenges that may seem impossible, but always know my strength and support will forever be with you. There will be many twists and turns, but always be faithful to your own values and convictions. Know that if you live life fully, you will surely achieve your dreams. I will always be there to help you find your way, but only you have the strength to spread your wings, soar high, and find your yellow brick road. When you follow your own path, there will be no limits to what you can accomplish. —T. M. Kubiak For my grandchildren Sarah, Emily, Dana, Josiah, Regan, Alec, Marah, and Liam. —Donald W. Benbow H1325_Kubiak_BOOK.indb v 11/20/08 6:14:13 PM (This page intentionally left blank) vii Table of Contents List of Figures and Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Preface to the Second Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiii Preface to the First Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxv Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxvii Part I Enterprise-Wide Deployment . . . . . . . . . . . . . . . . . . . . . . . . . 1 Chapter 1 Enterprise-Wide View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 History of Continuous Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Value and Foundations of Six Sigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Value and Foundations of Lean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Integration of Lean and Six Sigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Business Processes and Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Six Sigma and Lean Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 2 Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Enterprise Leadership Responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Organizational Roadblocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Change Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Six Sigma Projects and Kaizen Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Six Sigma Roles and Responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Part II Organizational Process Management and Measures . . . . 21 Chapter 3 Impact on Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Impact on Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Chapter 4 Critical to x (CTx) Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Critical to x (CTx) Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Chapter 5 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Chapter 6 Business Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Business Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 H1325_Kubiak_BOOK.indb vii 11/20/08 6:14:13 PM viii Table of Contents Chapter 7 Financial Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Common Financial Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Part III Team Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Chapter 8 Team Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Team Types and Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Team Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Team Member Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Launching Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Chapter 9 Team Facilitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Team Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Team Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Team Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Chapter 10 Team Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Team Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Chapter 11 Time Management for Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Time Management for Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Chapter 12 Team Decision- Making Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Team Decision- Making Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Chapter 13 Management and Planning Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Management and Planning Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Chapter 14 Team Performance Evaluation and Reward . . . . . . . . . . . . . . . . . . . 58 Team Performance Evaluation and Reward . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Part IV Define . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Chapter 15 Voice of the Customer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Customer Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Customer Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Customer Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Chapter 16 Project Charter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Problem Statement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Project Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Goals and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Project Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Chapter 17 Project Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Project Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 H1325_Kubiak_BOOK.indb viii 11/20/08 6:14:13 PM Tanii oi Cox¡ix¡s ix Part V Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Chapter 18 Process Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Input and Output Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Process Flow Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Process Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Chapter 19 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Types of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Measurement Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Sampling Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Collecting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Chapter 20 Measurement Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Measurement Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Measurement Systems Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Measurement Systems in the Enterprise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Metrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Chapter 21 Basic Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Basic Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Central Limit Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Graphical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Valid Statistical Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Chapter 22 Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Commonly Used Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 Other Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Chapter 23 Process Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Process Capability Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Process Performance Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Short- Term and Long- Term Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Process Capability for Non- Normal Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Process Capability for Attributes Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Process Capability Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Process Performance vs. Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Part VI Analyze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Chapter 24 Measuring and Modeling Relationships between Variables . . . . 184 Correlation Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Multivariate Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Multi- Vari Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 Attributes Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 H1325_Kubiak_BOOK.indb ix 11/20/08 6:14:13 PM x Table of Contents Chapter 25 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 Statistical vs. Practical Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Sample Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Point and Interval Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Tests for Means, Variances, and Proportions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 Analysis of Variance (ANOVA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Goodness- of-Fit (Chi Square) Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Contingency Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Non- Parametric Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Chapter 26 Failure Mode and Effects Analysis (FMEA) . . . . . . . . . . . . . . . . . . . 278 Failure Mode and Effects Analysis (FMEA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 Chapter 27 Additional Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Gap Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Root Cause Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 Waste Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Part VII Improve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Chapter 28 Design of Experiments (DOE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 Design Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Planning Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 One- Factor Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Two-Level Fractional Factorial Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Full Factorial Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Chapter 29 Waste Elimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 Waste Elimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 Chapter 30 Cycle-Time Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Cycle- Time Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Chapter 31 Kaizen and Kaizen Blitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Kaizen and Kaizen Blitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Chapter 32 Theory of Constraints (TOC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 Theory of Constraints (TOC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 Chapter 33 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Chapter 34 Risk Analysis and Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Risk Analysis and Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 H1325_Kubiak_BOOK.indb x 11/20/08 6:14:13 PM Tanii oi Cox¡ix¡s xi Part VIII Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Chapter 35 Statistical Process Control (SPC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358 Selection of Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 Rational Subgrouping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 Control Chart Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Control Chart Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Chapter 36 Other Control Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Total Productive Maintenance (TPM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Visual Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Chapter 37 Maintain Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Measurement System Re- analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Control Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406 Chapter 38 Sustain Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 Lessons Learned . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 Training Plan Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 Ongoing Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Part IX Design for Six Sigma (DFSS) Frameworks and Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Chapter 39 Common DFSS Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 DMADV (Define, Measure, Analyze, Design, and Validate) . . . . . . . . . . . . . . 414 DMADOV (Define, Measure, Analyze, Design, Optimize, and Validate) . . . 415 Chapter 40 Design for X (DFX) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 Design for X (DFX) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 Chapter 41 Robust Design and Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418 Robust Design and Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418 Chapter 42 Special Design Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Strategic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Tactical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Part X Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Appendix 1 ASQ Code of Ethics (May 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 Appendix 2A ASQ Six Sigma Black Belt Certification Body of Knowledge (2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 Appendix 2B ASQ Six Sigma Black Belt Certification Body of Knowledge (2001) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 H1325_Kubiak_BOOK.indb xi 11/20/08 6:14:13 PM xii Table of Contents Appendix 3 Control Chart Combinations for Measurement Data . . . . . . . . . . 460 Appendix 4 Control Chart Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Appendix 5 Constants for A 7 , B 7 , and B 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 Appendix 6 Factors for Estimating σ X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470 Appendix 7 Control Charts Count Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 Appendix 8 Binomial Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472 Appendix 9 Cumulative Binomial Distribution Table . . . . . . . . . . . . . . . . . . . . 476 Appendix 10 Poisson Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Appendix 11 Cumulative Poisson Distribution Table . . . . . . . . . . . . . . . . . . . . 489 Appendix 12 Standard Normal Distribution Table . . . . . . . . . . . . . . . . . . . . . . . 496 Appendix 13 Cumulative Standard Normal Distribution Table . . . . . . . . . . . 499 Appendix 14 t Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 Appendix 15 Chi-Square Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504 Appendix 16 F(0.99) Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 Appendix 17 F(0.975) Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Appendix 18 F(0.95) Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Appendix 19 F(0.90) Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Appendix 20 F(0.10) Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Appendix 21 F(0.05) Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Appendix 22 F(0.025) Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 Appendix 23 F(0.01) Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 Appendix 24 Median Ranks Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 Appendix 25 Normal Scores Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Appendix 26 Factors for One-Sided Tolerance Limits . . . . . . . . . . . . . . . . . . . . 546 Appendix 27 Factors for Two-Sided Tolerance Limits . . . . . . . . . . . . . . . . . . . . 550 Appendix 28 Equivalent Sigma Levels, Percent Defective, and PPM . . . . . . . 554 Appendix 29 Critical Values for the Mann-Whitney Test Table (One-Tail, Alpha = 0.05) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556 Appendix 30 Critical Values for the Mann-Whitney Test Table (One-Tail, Alpha = 0.01) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 H1325_Kubiak_BOOK.indb xii 11/20/08 6:14:13 PM Tanii oi Cox¡ix¡s xiii Appendix 31 Critical Values for the Mann-Whitney Test Table (Two-Tail, Alpha = 0.025) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 Appendix 32 Critical Values for the Mann-Whitney Test Table (Two-Tail, Alpha = 0.005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 Appendix 33 Critical Values for the Wilcoxon Signed-Rank Test . . . . . . . . . . 560 Appendix 34 Glossary of Six Sigma and Related Terms . . . . . . . . . . . . . . . . . . 561 Appendix 35 Glossary of Japanese Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 CD-ROM Contents Sample Examination Questions for Parts I–IX Certified Six Sigma Black Belt—Simulated Exam H1325_Kubiak_BOOK.indb xiii 11/20/08 6:14:13 PM (This page intentionally left blank) xv List of Figures and Tables Part I Table 1.1 Some approaches to quality over the years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Figure 1.1 Example of a process flowchart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Figure 1.2 Relationship among systems, processes, subprocesses, and steps. . . . . . . . . . 11 Part II Figure 4.1 Example of a CTQ tree diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Figure 7.1 Traditional quality cost curves. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Figure 7.2 Modern quality cost curves. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Part III Figure 9.1 Team stages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Figure 10.1 Team obstacles and solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Figure 12.1 Example of a force field analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Figure 13.1 Example of an affinity diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Figure 13.2 Example of an interrelationship digraph. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Figure 13.3 Example of a tree diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Figure 13.4 Example of a prioritization matrix—first step. . . . . . . . . . . . . . . . . . . . . . . . . . 55 Figure 13.5 Example of a prioritization matrix—second step. . . . . . . . . . . . . . . . . . . . . . . . 55 Figure 13.6 Example of a matrix diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Figure 13.7 Example of a PDPC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Figure 13.8 Example of an AND. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Part IV Figure 15.1 CTQ flow- down. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Figure 15.2 Example of a CTQ flow- down. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Figure 15.3 Example of a QFD matrix for an animal trap. . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Figure 15.4 Map of the entries for the QFD matrix illustrated in Figure 15.3. . . . . . . . . . . 68 Figure 15.5 Kano model for customer satisfaction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Figure 17.1 Project network diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Figure 17.2 Example of a Gantt chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 H1325_Kubiak_BOOK.indb xv 11/20/08 6:14:13 PM xvi List of Figures and Tables Part V Figure 18.1 Process diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Figure 18.2 Example of a SIPOC form. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Figure 18.3 Generic process flowchart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Figure 18.4 Process flowchart and process map example. . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Figure 18.5 Example of written procedures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Figure 18.6 Example of work instructions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Figure 18.7 Example of the symbology used to develop a value stream map. . . . . . . . . . 87 Figure 18.8 Example of a value stream map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Figure 18.9 Example of a spaghetti diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Figure 18.10 Example of a circle diagram.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Figure 20.1 Accuracy versus precision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Figure 20.2 Blank GR&R data collection sheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 101 Figure 20.3 GR&R data collection sheet with data entered and calculations completed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Figure 20.4 Blank GR&R report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Figure 20.5 GR&R report with calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Figure 20.6 Gage R&R Study—ANOVA method: source tables. . . . . . . . . . . . . . . . . . . . . . 108 Figure 20.7 Gage R&R study—ANOVA method: components of variation. . . . . . . . . . . . 109 Figure 20.8 Minitab session window output of the R&R study—X – /R method: source tables for Example 20.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 Figure 20.9 Graphical results of the GR&R study— – X/R method: – X and R control charts by operators (appraisers) for Example 20.3. . . . . . . . . . . . . . . . 110 Table 20.1 Attribute agreement analysis—data for Example 20.4. . . . . . . . . . . . . . . . . . . 111 Figure 20.10 Minitab session window output for Example 20.4. . . . . . . . . . . . . . . . . . . . . . . 113 Figure 20.11 Graphical results of the attribute agreement analysis for Example 20.4. . . . . 115 Table 20.2 Attribute gage study—data for Example 20.5. . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Figure 20.12 Graphical results of the attribute gage analysis for Example 20.5. . . . . . . . . . 117 Table 21.1 Commonly used symbols. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Figure 21.1 Dot plot for a simple population of three numbers. . . . . . . . . . . . . . . . . . . . . . 123 Table 21.2 Sampling distribution of the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Figure 21.2 Dot plot of the sample means from Table 21.2. . . . . . . . . . . . . . . . . . . . . . . . . . 124 Figure 21.3 Example of a histogram from a large non- normal looking population. . . . . . 124 Figure 21.4 Examples of the impact of the CLT when sampling from various populations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Figure 21.5 Example of a data set as illustrated by a frequency distribution, a dot plot, and a histogram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Table 21.3 Summary of descriptive measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Figure 21.6 Example of a cumulative frequency distribution in table and graph form. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Table 21.4 A comparison of various graphical methods. . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Figure 21.7 Stem-and-leaf diagrams. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Figure 21.8 Box plot with key points labeled. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 H1325_Kubiak_BOOK.indb xvi 11/20/08 6:14:13 PM lis¡ oi Ficukis axn Taniis xvii Figure 21.9 Examples of box plots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Figure 21.10 Example of a multiple box plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Figure 21.11 Example of a run chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Table 21.5 Data for scatter diagrams shown in Figure 21.12. . . . . . . . . . . . . . . . . . . . . . . . 134 Figure 21.12 Examples of scatter diagrams. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Figure 21.13 Example of the use of normal probability graph paper. . . . . . . . . . . . . . . . . . . 136 Figure 21.14 Example of a normal probability plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Figure 22.1 Venn diagram illustrating the probability of event A. . . . . . . . . . . . . . . . . . . . 139 Figure 22.2 Venn diagram illustrating the complementary rule of probability. . . . . . . . . 139 Figure 22.3 Venn diagram illustrating the addition rule of probability with independent events A and B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Figure 22.4 Venn diagram illustrating the general version of the addition rule of probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Table 22.1 Example of a contingency table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Table 22.2 Contingency table for Examples 22.4–22.11. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Table 22.3 Summary of the rules of probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Table 22.4 Summary of formulas, means, and variances of commonly used distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Figure 22.5 Standard normal distribution for Example 22.14. . . . . . . . . . . . . . . . . . . . . . . . 150 Figure 22.6 Standard normal distribution for Example 22.15. . . . . . . . . . . . . . . . . . . . . . . . 151 Figure 22.7 Poisson distribution with mean λ = 4.2 for Example 22.16. . . . . . . . . . . . . . . . 153 Figure 22.8 Binomial distribution with n = 6 and p = 0.1428 for Example 22.17. . . . . . . . 155 Figure 22.9 Example of a chi- square distribution with various degrees of freedom. . . . . 156 Figure 22.10 Example of a t distribution with various degrees of freedom. . . . . . . . . . . . . 157 Figure 22.11 Example of an F distribution with various degrees of freedom. . . . . . . . . . . . 158 Table 22.5 Summary of formulas, means, and variances of other distributions. . . . . . . . 159 Figure 22.12 Hypergeometric distribution for Example 22.18. . . . . . . . . . . . . . . . . . . . . . . . 161 Figure 22.13 Exponential distribution for Example 22.19. . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Figure 22.14 Lognormal distribution for Example 22.20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Figure 22.15 Example of a Weibull function for various values of the shape parameter β. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Table 23.1 Cable diameter data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Figure 23.1 Example of a process capability analysis using the data given in Table 23.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Table 23.2 Methods of determining the standard deviation for use in process capability indices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Table 23.3 Binomial probabilities for Example 23.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Part VI Figure 24.1 Examples of different types of correlations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Table 24.1 Data for Example 24.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Figure 24.2 Graphical depiction of regression concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 Figure 24.3 Scatter diagram developed from the data given in Table 24.1. . . . . . . . . . . . . 191 H1325_Kubiak_BOOK.indb xvii 11/20/08 6:14:13 PM xviii List of Figures and Tables Figure 24.4 Scatter diagram from Figure 24.3 with two proposed lines. . . . . . . . . . . . . . . 191 Table 24.2 Computed values for the proposed lines in Figure 24.4. . . . . . . . . . . . . . . . . . 192 Table 24.3 Computed values for the proposed lines given in Figure 24.4 with residual values added. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Table 24.4 Residual values for the least squares regression line from Example 24.6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 Table 24.5 Census data for Examples 24.10 and 24.11. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 Figure 24.5 Example of a principal components analysis using the data given in Table 24.5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Figure 24.6 Scree plot for Examples 24.10 and 24.11. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Figure 24.7 Example of a factor analysis using the data given in Table 24.5. . . . . . . . . . . . 200 Table 24.6 Salmon data for Example 24.12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Figure 24.8 Example of a discriminant analysis using the data given in Table 24.6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Table 24.7 Plastic film data for Example 24.13. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Figure 24.9 Example of MANOVA using the data given in Table 24.7. . . . . . . . . . . . . . . . 205 Figure 24.10 Stainless steel casting with critical ID. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Figure 24.11 Data collection sheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 Table 24.8 Casting data for Example 24.14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Figure 24.12 Multi-vari chart of data from Table 24.8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 Figure 24.13 Multi-vari chart of data from Table 24.8 with the means of each factor connected by lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 Table 24.9 Casting data for Example 24.14 with precision parts. . . . . . . . . . . . . . . . . . . . 213 Figure 24.14 Multi-vari chart of data from Table 24.9 with the means of each factor connected by lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 Figure 24.15 Multi-vari chart of data from Table 24.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Table 24.10 Casting data for Example 24.14 after pressure wash. . . . . . . . . . . . . . . . . . . . . 216 Figure 24.16 Multi-vari chart of data from Table 24.10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Table 24.11 Resting pulse data for Example 24.15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Figure 24.17 Minitab session window output for the binary logistic regression based on data given in Table 24.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Figure 24.18 Delta chi- square versus probability analysis for Example 24.15. . . . . . . . . . . 222 Figure 24.19 Delta chi- square versus leverage analysis for Example 24.15. . . . . . . . . . . . . . 223 Table 24.12 Favorite subject data for Example 24.16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 Figure 24.20 Minitab session window output for the nominal logistic regression based on data given in Table 24.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Table 24.13 Toxicity data for Example 24.17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Figure 24.21 Minitab session window output for the ordinal logistic regression based on data given in Table 24.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Figure 25.1 Four outcomes associated with statistical hypotheses. . . . . . . . . . . . . . . . . . . . 231 Table 25.1 Sample size formulas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 Table 25.2 Confidence intervals for means. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 Table 25.3 Confidence intervals for variances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 Table 25.4 Confidence intervals for proportions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 H1325_Kubiak_BOOK.indb xviii 11/20/08 6:14:14 PM lis¡ oi Ficukis axn Taniis xix Table 25.5 Hypothesis tests for means. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Table 25.6 Hypothesis tests for variances or ratios of variances. . . . . . . . . . . . . . . . . . . . . 249 Table 25.7 Hypothesis tests for proportions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Figure 25.2 Hypothesis test flowchart (part 1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Figure 25.3 Hypothesis test flowchart (part 2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Figure 25.4 Hypothesis test flowchart (part 3). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Table 25.8 Example of a one-way ANOVA source table. . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Table 25.9 Moisture content data for Example 25.12.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Table 25.10 Completed one-way ANOVA source table for the data given in Table 25.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 Table 25.11 Example of a two-way ANOVA source table. . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Table 25.12 Historical data of defect types along with current data from a randomly selected week for Example 25.13. . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 Table 25.13 Goodness-of-fit table for Example 25.13. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Table 25.14 The general form of a two-way contingency table. . . . . . . . . . . . . . . . . . . . . . . 262 Table 25.15 Observed frequencies of defectives for Example 25.14. . . . . . . . . . . . . . . . . . . 262 Table 25.16 Computation of the expected frequencies for Example 25.14. . . . . . . . . . . . . . 263 Table 25.17 Comparison of parametric and non-parametric hypothesis tests. . . . . . . . . . 264 Table 25.18 Common non-parametric hypothesis tests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Table 25.19 Data for Mood’s Median test in Example 25.15. . . . . . . . . . . . . . . . . . . . . . . . . 268 Table 25.20 Computation of the expected frequencies for Example 25.15. . . . . . . . . . . . . . 268 Table 25.21 Data for Levene’s test for Example 25.16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Table 25.22 Levene’s test for Example 25.16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Table 25.23 Levene’s test for Example 25.16 (continued). . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Table 25.24 Levene’s test for Example 25.16 (continued). . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 Table 25.25 Levene’s test for Example 25.16 (continued). . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 Table 25.26 Data for Kruskal-Wallis test for Example 25.17. . . . . . . . . . . . . . . . . . . . . . . . . . 273 Table 25.27 Determining ranks for Example 25.17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 Table 25.28 Kruskal-Wallis test for Example 25.17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Table 25.29 Data for Mann-Whitney test for Example 25.18. . . . . . . . . . . . . . . . . . . . . . . . . 276 Table 25.30 Determining ranks for Example 25.18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Table 25.31 Mann-Whitney test for Example 25.18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Figure 26.1 Example of a PFMEA form. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Figure 26.2 Example of a DFMEA form. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 Figure 27.1 Example of a blank cause- and-effect diagram. . . . . . . . . . . . . . . . . . . . . . . . . . 285 Figure 27.2 Example of a cause- and-effect diagram after a few brainstorming steps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 Figure 27.3 Example of a Pareto chart for defects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 Table 27.1 Cost to correct each defect type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Figure 27.4 Example of a Pareto chart for defects weighted by the cost to correct. . . . . . 288 Figure 27.5 Basic FTA symbols. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Figure 27.6 Example of stoppage of agitation in a tank. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 H1325_Kubiak_BOOK.indb xix 11/20/08 6:14:14 PM xx List of Figures and Tables Part VII Table 28.1 A 2 3 full factorial data collection sheet for Example 28.1. . . . . . . . . . . . . . . . . . 296 Table 28.2 A 2 3 full factorial data collection sheet with data entered for Example 28.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 Table 28.3 A 2 3 full factorial data collection sheet with run averages. . . . . . . . . . . . . . . . 300 Figure 28.1 Graph of the main effects for the data given in Table 28.3. . . . . . . . . . . . . . . . 302 Table 28.4 A 2 3 full factorial design using the + and – format. . . . . . . . . . . . . . . . . . . . . . 303 Table 28.5 A 2 3 full factorial design showing interaction columns. . . . . . . . . . . . . . . . . . . 304 Figure 28.2 Graph of the interaction effects for the data given in Table 28.3. . . . . . . . . . . 305 Table 28.6 Half fraction of 2 3 (also called a 2 3–1 design). . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Table 28.7 Half fraction of 2 3 with completed interaction columns. . . . . . . . . . . . . . . . . . 306 Table 28.8 A 2 4 full factorial design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 Table 28.9 A 2 4–1 fractional factorial design with interactions. . . . . . . . . . . . . . . . . . . . . . . 308 Table 28.10 Statistical models for common experimental designs. . . . . . . . . . . . . . . . . . . . 312 Table 28.11 Examples of source tables for the models given in Table 28.10. . . . . . . . . . . . 314 Table 28.12 Sums of squares for the models given in Table 28.10. . . . . . . . . . . . . . . . . . . . . 315 Table 28.13 Examples of Latin squares from each main class up to order 5. . . . . . . . . . . . 316 Table 28.14 Latin square analysis for Example 28.8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Table 28.15 Completed Latin square source table for Example 28.8. . . . . . . . . . . . . . . . . . . 319 Table 28.16 A 2 4–1 fractional factorial for Example 28.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Table 28.17 Session window from Minitab for the data given in Table 28.16. . . . . . . . . . . 321 Table 28.18 Minitab main effects plot for the analysis given in Table 28.17. . . . . . . . . . . . 323 Table 28.19 Minitab interaction effects plot for the analysis given in Table 28.17. . . . . . . 324 Table 28.20 Minitab analysis of residuals for the data given in Table 28.16. . . . . . . . . . . . 325 Table 28.21 Relevant tables for two-way full factorial design. . . . . . . . . . . . . . . . . . . . . . . . 326 Table 28.22 Data for a 2 2 full factorial experiment with three replicates. . . . . . . . . . . . . . . 329 Table 28.23 Session window results for the data given in Table 28.22. . . . . . . . . . . . . . . . . 329 Table 28.24 Main effects plot for the data given in Table 28.22. . . . . . . . . . . . . . . . . . . . . . . 330 Table 28.25 Interaction plot for the data given in Table 28.22. . . . . . . . . . . . . . . . . . . . . . . . 330 Table 28.26 Residual plots for the data given in Table 28.22. . . . . . . . . . . . . . . . . . . . . . . . . 331 Figure 32.1 The Drum- Buffer-Rope subordinate step analogy—no rope. . . . . . . . . . . . . . 345 Figure 32.2 The Drum- Buffer-Rope subordinate step analogy—with rope. . . . . . . . . . . . 345 Figure 32.3 The interdependence of throughput, inventory, and operating expense measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 Figure 33.1 Example of a ranking matrix with criteria weights shown. . . . . . . . . . . . . . . . 347 Table 34.1 Data for Example 34.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Table 34.2 Data for Example 34.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Figure 34.1 SWOT analysis for Example 34.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Figure 34.2 PEST analysis for Example 34.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 Part VIII Figure 35.1 Function of SPC tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Figure 35.2 Conveyor belt in chocolate- making process. . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 H1325_Kubiak_BOOK.indb xx 11/20/08 6:14:14 PM lis¡ oi Ficukis axn Taniis xxi Figure 35.3 Conveyor belt in chocolate- making process with rational subgroup choice. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Table 35.1 Data for Examples 35.1 and 35.2—X – – R and X – – s charts, respectively. . . . . 363 Figure 35.4 X – – R chart for data given in Table 35.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 Figure 35.5 X – – s chart for data given in Table 35.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Table 35.2 Data for Example 35.3—individual and moving range chart. . . . . . . . . . . . . . 367 Figure 35.6 Individual and moving range chart for data given in Table 35.2. . . . . . . . . . . 367 Table 35.3 Data for Example 35.4—p chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Figure 35.7 p chart for data given in Table 35.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Table 35.4 Data for Example 35.5—np chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Figure 35.8 np chart for data given in Table 35.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372 Table 35.5 Data for Example 35.6—c chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Figure 35.9 c chart for data given in Table 35.5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 Table 35.6 Data for Example 35.7—u chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Figure 35.10 u chart for data given in Table 35.6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 Figure 35.11 Short-run SPC decision flowchart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Table 35.7 Summary of formulas for short-run SPC charts. . . . . . . . . . . . . . . . . . . . . . . . . 378 Table 35.8 Short-run chart data for Example 35.8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 Table 35.9 MAMR data for Example 35.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 Figure 35.12 Moving average chart of length three from Example 35.9. . . . . . . . . . . . . . . . 388 Figure 35.13 Moving average range chart of length three from Example 35.9. . . . . . . . . . . 388 Table 35.10 Interpreting control chart out-of-control conditions used by Minitab. . . . . . 391 Figure 35.14 Example of out- of-control condition #1 from Minitab. . . . . . . . . . . . . . . . . . . . 392 Figure 35.15 Example of out- of-control condition #2 from Minitab. . . . . . . . . . . . . . . . . . . . 392 Figure 35.16 Example of out- of-control condition #3 from Minitab. . . . . . . . . . . . . . . . . . . . 393 Figure 35.17 Example of out- of-control condition #4 from Minitab. . . . . . . . . . . . . . . . . . . . 393 Figure 35.18 Example of out- of-control condition #5 from Minitab. . . . . . . . . . . . . . . . . . . . 394 Figure 35.19 Example of out- of-control condition #6 from Minitab. . . . . . . . . . . . . . . . . . . . 394 Figure 35.20 Example of out- of-control condition #7 from Minitab. . . . . . . . . . . . . . . . . . . . 395 Figure 35.21 Example of out- of-control condition #8 from Minitab. . . . . . . . . . . . . . . . . . . . 395 Figure 35.22 Example of out- of-control condition #1 from AIAG.. . . . . . . . . . . . . . . . . . . . . 396 Figure 35.23 Example of out- of-control condition #2 from AIAG.. . . . . . . . . . . . . . . . . . . . . 396 Figure 35.24 Example of out- of-control condition #3 from AIAG.. . . . . . . . . . . . . . . . . . . . . 397 Figure 35.25 Example of out- of-control condition #4 from AIAG.. . . . . . . . . . . . . . . . . . . . . 397 Figure 35.26 Example of out- of-control condition #5 from AIAG.. . . . . . . . . . . . . . . . . . . . . 398 Figure 35.27 Example of out- of-control condition #6 from AIAG.. . . . . . . . . . . . . . . . . . . . . 398 Figure 37.1 Example of an acceptable level of variation due to the measurement system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Figure 37.2 Example of an unacceptable level of variation due to the measurement system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Figure 37.3 Example of two different formats for control plans. . . . . . . . . . . . . . . . . . . . . . 406 H1325_Kubiak_BOOK.indb xxi 11/20/08 6:14:14 PM xxii List of Figures and Tables Part IX Figure 41.1 Nonlinear response curve with input noise. . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Figure 41.2 Nonlinear response curve showing the impact on Q of input noise at P 1 .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Figure 41.3 Nonlinear response curve showing the impact on Q of input noise at P 1 , P 2 , and P 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420 Figure 41.4 Using a response curve to determine tolerance. . . . . . . . . . . . . . . . . . . . . . . . . 421 Figure 41.5 Conventional stack tolerance dimensioning. . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Figure 42.1 Example of a product family matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Figure 42.2 First step in forming a Pugh matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Figure 42.3 Second step in forming a Pugh matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Figure 42.4 Third step in forming a Pugh matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 Figure 42.5 Final step in forming a Pugh matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 H1325_Kubiak_BOOK.indb xxii 11/20/08 6:14:14 PM xxiii Preface to the Second Edition I n the spirit of customer- supplier relationships, we are pleased to provide our readers with the second edition of The Certified Six Sigma Black Belt Handbook. The handbook has been updated to reflect the most recent Six Sigma Black Belt Body of Knowledge, released in 2007. As with all ASQ certification–based handbooks, the primary audience for this work is the individual who plans to prepare to sit for the Six Sigma Black Belt certification examination. Therefore, the book assumes the individual has the nec- essary background and experience in quality and Six Sigma. Concepts are dealt with briefly but facilitated with practical examples. We have intentionally avoided theoretical discussion unless such a discussion was necessary to communicate a concept. As always, readers are encouraged to use additional sources when seek- ing much deeper levels of discussion. Most of the citations provided in the refer- ences will be helpful in this regard. A secondary audience for the handbook is the quality and Six Sigma profes- sional who would like a relevant Six Sigma reference book. With this audience in mind, we have greatly expanded the appendices section: Although the Body of Knowledge was updated in 2007, we have elected • to keep the 2001 Body of Knowledge so that readers can compare changes and perhaps offer recommendations for future Bodies of Knowledge. All tables were developed using a combination of Microsoft Excel • and Minitab 15. Thus, the reader may find some differences between our tables and those published in other sources. Appendices 29–33 are examples of where such differences might occur. Note that years ago many statistical tables were produced either by hand or by using rudimentary calculators. These tables have been handed down from author to author and have remained largely unchanged. Our approach was to revert to the formulas and algorithms that produced the tables and then redevelop them using statistical software. The table for control constants has been expanded to now include • virtually all control constants. To the best of our knowledge, this handbook is probably the only reference source that includes this information. H1325_Kubiak_BOOK.indb xxiii 11/20/08 6:14:14 PM xxiv Preface to the Second Edition Tables for both cumulative and noncumulative forms of the most useful • distributions are now present—for example, binomial, Poisson, and normal. Additional alpha values in tables have been included. For example, • large alpha values for the left side of the F distribution now exist. Thus, it will no longer be necessary to use the well- known conversion property of the distribution to obtain critical F values associated with higher alpha values. Though the conversion formula is straightforward, everyone seems to get it wrong. We expect our readers will appreciate this. The glossary has grown significantly. Most notable is the inclusion of • more terms relating to Lean. A second glossary has been added as well. This short glossary is limited • to the most common Japanese terms used by quality and Six Sigma professionals. We are confident that readers will find the above additions useful. As you might expect, chapter and section numbering follows the same method used in the Six Sigma Black Belt Body of Knowledge. This has made for some awk- ward placement of discussions (for example, the normal distribution is referred to several times before it is defined), and in some cases, redundancy of discussion exists. However, where possible, we have tried to reference the main content in the handbook and refer the reader there for the primary discussion. After the first edition was published, we received several comments from read- ers who stated that their answers did not completely agree with those given in the examples. In many instances, we found that discrepancies could be attributed to the following: use of computers with different bits, the number of significant dig- its accounted for by the software used, the sequence in which the arithmetic was performed, and the propagation of errors due to rounding or truncation. There- fore, we urge the reader to carefully consider the above points as the examples are worked. However, we do recognize that errors occasionally occur and thus have established a SharePoint site that will permit readers to recommend suggestions, additions, corrections, or deletions, as well as to seek out any corrections that may have been found and published. The SharePoint site address is http://asqgroups. asq.org/cssbbhandbook/. Finally, the enclosed CD contains supplementary problems covering each chapter and a simulated exam that has problems distributed among chapters according to the scheme published in the Body of Knowledge. It is suggested that the reader study a particular chapter, repeating any calculations independently, and then do the supplementary problems for that chapter. After attaining success with all chapters, the reader may complete the simulated exam to confirm mastery of the entire Six Sigma Black Belt Body of Knowledge. —The Authors H1325_Kubiak_BOOK.indb xxiv 11/20/08 6:14:14 PM xxv W e decided to number chapters and sections by the same method used in the Body of Knowledge (BOK) specified for the Certified Six Sigma Black Belt examination. This made for some awkward placement (the normal distribution is referred to several times before it is defined), and in some cases, redundancy. We thought the ease of access for readers, who might be strug- gling with some particular point in the BOK, would more than balance these disadvantages. The enclosed CD contains supplementary problems covering each chapter and a simulated exam that has problems distributed among chapters according to the scheme published in the Body of Knowledge. It is suggested that the reader study a particular chapter, repeating any calculations independently, and then do the supplementary problems for that chapter. After attaining success with all chapters, the reader may complete the simulated exam to confirm mastery of the entire Six Sigma Black Belt Body of Knowledge. —The Authors Preface to the First Edition H1325_Kubiak_BOOK.indb xxv 11/20/08 6:14:14 PM (This page intentionally left blank) xxvii W e would like to express our deepest appreciation to Minitab Inc., for pro- viding us with the use of Minitab 15 and Quality Companion 2 software and for permission to use several examples from Minitab 15 and forms from Quality Companion 2. This software was instrumental in creating and verify- ing examples used throughout the book. In addition we would like to thank the ASQ management and Quality Press staffs for their outstanding support and exceptional patience while we prepared this second edition. Finally, we would like to thank the staff of Kinetic Publishing Services, LLC, for applying their finely tuned project management, copyediting, and typesetting skills to this project. Their support has allowed us to produce a final product suit- able for the ASQ Quality Press family of publications. —The Authors Acknowledgments H1325_Kubiak_BOOK.indb xxvii 11/20/08 6:14:14 PM (This page intentionally left blank) 1 P a r t I Part I Enterprise-Wide Deployment Chapter 1 Enterprise-Wide View Chapter 2 Leadership H1325_Kubiak_BOOK.indb 1 11/20/08 6:14:33 PM 2 P a r t I . A . 1 HISTORY OF CONTINUOUS IMPROVEMENT Describe the origins of continuous improvement and its impact on other improvement models. (Remember) Body of Knowledge I.A.1 Most of the techniques found in the Six Sigma toolbox have been available for some time, thanks to the groundbreaking work of many professionals in the qual- ity sciences. Walter A. Shewhart worked at the Hawthorne plant of Western Electric, where he developed and used control charts. He is sometimes referred to as the father of statistical quality control (SQC) because he brought together the disciplines of statistics, engineering, and economics. He describes the basic principles of SQC in his book Economic Control of Quality of Manufactured Product (1931). He was the first honorary member of the American Society for Quality (ASQ). W. Edwards Deming developed a list of 14 points in which he emphasized the need for change in management structure and attitudes. As stated in his book Out of the Crisis (1986), these 14 points are as follows: 1. Create constancy of purpose for improvement of product and service. 2. Adopt a new philosophy. 3. Cease dependence on inspection to achieve quality. 4. End the practice of awarding business on the basis of price tag alone. Instead, minimize total cost by working with a single supplier. 5. Improve constantly and forever every process for planning, production, and service. 6. Institute training on the job. 7. Adopt and institute leadership. 8. Drive out fear. 9. Break down barriers between staff areas. Chapter 1 Enterprise-Wide View H1325_Kubiak_BOOK.indb 2 11/20/08 6:14:33 PM Cnar¡ik ¡: Fx¡ikrkisiWini \iiw 3 P a r t I . A . 1 10. Eliminate slogans, exhortations, and targets for the workforce. 11. Eliminate numerical quotas for the workforce and numerical goals for management. 12. Remove barriers that rob people of pride of workmanship. Eliminate the annual rating or merit system. 13. Institute a vigorous program of education and self- improvement for everyone. 14. Put everybody in the company to work to accomplish the transformation. Joseph M. Juran pursued a varied career in management beginning in 1924 as an engineer, executive, government administrator, university professor, labor arbitra- tor, corporate director, and consultant. He developed the Juran trilogy, three mana- gerial processes—quality planning, quality control, and quality improvement—for use in managing for quality. Juran wrote hundreds of papers and 12 books, includ- ing Juran’s Quality Control Handbook (1999), Juran’s Quality Planning & Analysis for Enterprise Quality (with F. M. Gryna; 2007), and Juran on Leadership for Quality (2003). His approach to quality improvement includes the following points: Create awareness of the need and opportunity for improvement • Mandate quality improvement; make it a part of every job description • Create the infrastructure: Establish a quality council; select projects for • improvement; appoint teams; provide facilitators Provide training in how to improve quality • Review progress regularly • Give recognition to the winning teams • Propagandize the results • Revise the reward system to enforce the rate of improvement • Maintain momentum by enlarging the business plan to include goals for • quality improvement Deming and Juran worked in both the United States and Japan to help businesses understand the importance of continuous process improvement. Philip B. Crosby, who originated the zero defects concept, was an ASQ honorary member and past president. He wrote many books, including Quality Is Free (1979), Quality without Tears (1984), Let’s Talk Quality (1990), and Leading: The Art of Becoming an Executive (1990). Crosby’s 14 steps to quality improvement are as follows: 1. Make it clear that management is committed to quality 2. Form quality improvement teams with representatives from each department 3. Determine how to measure where current and potential quality problems lie H1325_Kubiak_BOOK.indb 3 11/20/08 6:14:33 PM 4 Part I: Enterprise-Wide Deployment P a r t I . A . 1 4. Evaluate the cost of quality and explain its use as a management tool 5. Raise the quality awareness and personal concern of all employees 6. Take formal actions to correct problems identified through previous steps 7. Establish a committee for the zero defects program 8. Train all employees to actively carry out their part of the quality improvement program 9. Hold a “zero defects day” to let all employees realize that there has been a change 10. Encourage individuals to establish improvement goals for themselves and their groups 11. Encourage employees to communicate to management the obstacles they face in attaining their improvement goals 12. Recognize and appreciate those who participate 13. Establish quality councils to communicate on a regular basis 14. Do it all over again to emphasize that the quality improvement program never ends Armand V. Feigenbaum originated the concept of total quality control in his book Total Quality Control (1991), first published in 1951. The book has been translated into many languages, including Japanese, Chinese, French, and Spanish. Feigen- baum is an ASQ honorary member and served as ASQ president for two consecu- tive terms. He lists three steps to quality: 1. Quality leadership 2. Modern quality technology 3. Organizational commitment Kaoru Ishikawa (1985) developed the cause- and-effect diagram. He worked with Deming through the Union of Japanese Scientists and Engineers (JUSE). The fol- lowing points summarize Ishikawa’s philosophy: Quality first—not short- term profit first. • Consumer orientation—not producer orientation. Think from the • standpoint of the other party. The next process is your customer—breaking down the barrier of • sectionalism. Using facts and data to make presentations—utilization of statistical • methods. Respect for humanity as a management philosophy—full participatory • management. Cross-function management. • H1325_Kubiak_BOOK.indb 4 11/20/08 6:14:33 PM Cnar¡ik ¡: Fx¡ikrkisiWini \iiw 5 P a r t I . A . 1 Genichi Taguchi taught that any departure from the nominal or target value for a characteristic represents a loss to society. He also popularized the use of fractional factorial experiments and stressed the concept of robustness. In addition to these noted individuals, Toyota Motor Company has been rec- ognized as the leader in developing the concept of lean manufacturing systems. Various approaches to quality have been in vogue over the years, as shown in Table 1.1. Table 1.1 Some approaches to quality over the years. Quality approach Approximate time frame Short description Quality circles 1979–1981 Quality improvement or self-improvement study groups composed of a small number of employees (10 or fewer) and their supervisor. Quality circles originated in Japan, where they are called quality control circles. Statistical process control (SPC) Mid-1980s The application of statistical techniques to control a process. Also called “statistical quality control.” ISO 9000 1987–present A set of international standards on quality management and quality assurance developed to help companies effectively document the quality system elements to be implemented to maintain an efficient quality system. The standards, initially published in 1987, are not specific to any particular industry, product, or service. The standards were developed by the International Organization for Standardization (ISO), a specialized international agency for standardization composed of the national standards bodies of 91 countries. The standards underwent major revision in 2000 and now include ISO 9000:2005 (definitions), ISO 9001:2008 (requirements), and ISO 9004:2000 (continuous improvement). Reengineering 1996–1997 A breakthrough approach involving the restructuring of an entire organization and its processes. Benchmarking 1988–1996 An improvement process in which a company measures its performance against that of best-in-class companies, determines how those companies achieved their performance levels, and uses the information to improve its own performance. The subjects that can be benchmarked include strategies, operations, processes, and procedures. Balanced Scorecard 1990s–present A management concept that helps managers at all levels monitor their results in their key areas. Continued H1325_Kubiak_BOOK.indb 5 11/20/08 6:14:33 PM 6 Part I: Enterprise-Wide Deployment P a r t I . A . 2 VALUE AND FOUNDATIONS OF SIX SIGMA Describe the value of Six Sigma, its philosophy, history, and goals. (Understand) Body of Knowledge I.A.2 A wide range of companies have found that when the Six Sigma philosophy is fully embraced, the enterprise thrives. What is this Six Sigma philosophy? Several definitions have been proposed, with the following common threads: Use of teams that are assigned well- defined projects that have direct • impact on the organization’s bottom line. Training in statistical thinking at all levels and providing key people • with extensive training in advanced statistics and project management. These key people are designated “Black Belts.” Emphasis on the DMAIC approach to problem solving: define, measure, • analyze, improve, and control. A management environment that supports these initiatives as a business • strategy. The literature is replete with examples of projects that have returned high dollar amounts to the organizations involved. Black Belts are often required to manage Table 1.1 Some approaches to quality over the years. Continued Quality approach Approximate time frame Short description Baldrige Award Criteria 1987–present An award established by the U.S. Congress in 1987 to raise awareness of quality management and recognize U.S. companies that have implemented successful quality management systems. Two awards may be given annually in each of six categories: manufacturing company, service company, small business, education, health care, and nonprofit. The award is named after the late secretary of commerce Malcolm Baldrige, a proponent of quality management. The U.S. Commerce Department’s National Institute of Standards and Technology manages the award, and ASQ administers it. Six Sigma 1995–present As described in Chapter 1. Lean manufacturing 2000–present As described in Chapter 1. Lean-Six Sigma 2002–present This approach combines the individual concepts of Lean and Six Sigma and recognizes that both are necessary to effectively drive sustained improvement. H1325_Kubiak_BOOK.indb 6 11/20/08 6:14:33 PM Cnar¡ik ¡: Fx¡ikrkisiWini \iiw 7 P a r t I . A . 3 four projects per year for a total of $500,000–$5,000,000 in contributions to the com- pany’s bottom line. Opinions on the definition of Six Sigma differ: Philosophy—The philosophical perspective views all work as processes • that can be defined, measured, analyzed, improved, and controlled (DMAIC). Processes require inputs and produce outputs. If you control the inputs, you will control the outputs. This is generally expressed as the y = f(x) concept. Set of tools—Six Sigma as a set of tools includes all the qualitative and • quantitative techniques used by the Six Sigma expert to drive process improvement. A few such tools include statistical process control (SPC), control charts, failure mode and effects analysis, and process mapping. Six Sigma professionals do not totally agree as to exactly which tools constitute the set. Methodology—The methodological view of Six Sigma recognizes the • underlying and rigorous approach known as DMAIC. DMAIC defines the steps a Six Sigma practitioner is expected to follow, starting with identifying the problem and ending with implementing long- lasting solutions. While DMAIC is not the only Six Sigma methodology in use, it is certainly the most widely adopted and recognized. Metrics—In simple terms, Six Sigma quality performance means 3.4 • defects per million opportunities (accounting for a 1.5-sigma shift in the mean). In the first edition of this book, we used the following to define Six Sigma: Six Sigma is a fact- based, data- driven philosophy of improvement that values defect prevention over defect detection. It drives customer satisfaction and bottom- line results by reducing variation and waste, thereby promoting a competitive advan- tage. It applies anywhere variation and waste exist, and every employee should be involved. However, going forward, we combined the definitions of Lean and Six Sigma and proffer a definition for Lean- Six Sigma. This is discussed in detail in Section I.A.4. VALUE AND FOUNDATIONS OF LEAN Describe the value of Lean, its philosophy, history, and goals. (Understand) Body of Knowledge I.A.3 The term “lean thinking” refers to the use of ideas originally employed in lean manufacturing to improve functions in all departments of an enterprise. The National Institute of Standards and Technology (NIST), through its Manu- facturing Extension Partnership, defines Lean as follows: H1325_Kubiak_BOOK.indb 7 11/20/08 6:14:33 PM 8 Part I: Enterprise-Wide Deployment P a r t I . A . 4 A systematic approach to identifying and eliminating waste (non-value-added activities) through continuous improvement by flowing the product at the pull of the customer in pursuit of perfection. ASQ defines the phrase “non-value-added” as follows: A term that describes a process step or function that is not required for the direct achievement of process output. This step or function is identified and examined for potential elimination. This represents a shift in focus for manufacturing engineering, which has tradition- ally studied ways to improve value- added functions and activities (for example, how can this process run faster and more precisely). Lean thinking doesn’t ignore the valued- added activities, but it does shine the spotlight on waste. A discus- sion of various categories of wastes is provided in the waste analysis section of Chapter 27. Lean manufacturing seeks to eliminate or reduce these wastes by use of the following: Teamwork • with well- informed cross- trained employees who participate in the decisions that impact their function Clean, • organized, and well- marked work spaces Flow systems • instead of batch and queue (that is, reduce batch size toward its ultimate ideal, one) Pull systems • instead of push systems (that is, replenish what the customer has consumed) Reduced lead times • through more efficient processing, setups, and scheduling The history of lean thinking may be traced to Eli Whitney, who is credited with spreading the concept of part interchangeability. Henry Ford, who went to great lengths to reduce cycle times, furthered the idea of lean thinking, and later, the Toyota Production System (TPS) packaged most of the tools and concepts now known as lean manufacturing. INTEGRATION OF LEAN AND SIX SIGMA Describe the relationship between Lean and Six Sigma. (Understand) Body of Knowledge I.A.4 After reading the description in the last few paragraphs of Section I.A.2, Six Sigma purists will be quick to say, “You’re not just talking about Six Sigma; you’re talking about Lean too.” The demarcation between Six Sigma and Lean has blurred. We are hearing about terms such as “Lean-Six Sigma” with greater frequency because pro- cess improvement requires aspects of both approaches to attain positive results. H1325_Kubiak_BOOK.indb 8 11/20/08 6:14:33 PM Cnar¡ik ¡: Fx¡ikrkisiWini \iiw 9 P a r t I . A . 5 Six Sigma focuses on reducing process variation and enhancing process control, whereas Lean—also known as lean manufacturing—drives out waste (non-value-added) and promotes work standardization and flow. Six Sigma prac- titioners should be well versed in both. More details of what is sometimes referred to as lean thinking are given in Chapters 29–33. Lean and Six Sigma have the same general purpose of providing the customer with the best possible quality, cost, delivery, and a newer attribute, nimbleness. There is a great deal of overlap, and disciples of both disagree as to which techniques belong where. Six Sigma Black Belts need to know a lot about Lean (witness the appearance of lean topics in the Body of Knowledge for Black Belt certification). The two initiatives approach their common purpose from slightly different angles: Lean focuses on waste reduction, whereas Six Sigma emphasizes • variation reduction Lean achieves its goals by using less technical tools such as kaizen, • workplace organization, and visual controls, whereas Six Sigma tends to use statistical data analysis, design of experiments, and hypothesis tests The most successful users of implementations have begun with the lean approach, making the workplace as efficient and effective as possible, reducing the (now) eight wastes, and using value stream maps to improve understanding and throughput. When process problems remain, the more technical Six Sigma statistical tools may be applied. One thing they have in common is that both require strong manage- ment support to make them the standard way of doing business. Some organizations have responded to this dichotomy of approaches by form- ing a Lean- Six Sigma problem- solving team with specialists in the various aspects of each discipline but with each member cognizant of others’ fields. Task forces from this team are formed and reshaped depending on the problem at hand. Given the earlier discussion, we believe a combined definition is required and proffer the following: Lean-Six Sigma is a fact- based, data- driven philosophy of improvement that val- ues defect prevention over defect detection. It drives customer satisfaction and bottom- line results by reducing variation, waste, and cycle time, while promoting the use of work standardization and flow, thereby creating a competitive advan- tage. It applies anywhere variation and waste exist, and every employee should be involved. BUSINESS PROCESSES AND SYSTEMS Describe the relationship among various business processes (design, production, purchasing, accounting, sales, etc.) and the impact these relationships can have on business systems. (Understand) Body of Knowledge I.A.5 H1325_Kubiak_BOOK.indb 9 11/20/08 6:14:34 PM 10 Part I: Enterprise-Wide Deployment P a r t I . A . 6 Processes A process is a series of steps designed to produce products and/or services. A pro- cess is often diagrammed with a flowchart depicting inputs, the path that material or information follows, and outputs. An example of a process flowchart is shown in Figure 1.1. Understanding and improving processes is a key part of every Six Sigma project. The basic strategy of Six Sigma is contained in DMAIC. These steps consti- tute the cycle Six Sigma practitioners use to manage problem- solving projects. The individual parts of the DMAIC cycle are explained in Chapters 15–38. Business Systems A business system is designed to implement a process or, more commonly, a set of processes. Business systems make certain that process inputs are in the right place at the right time so that each step of the process has the resources it needs. Perhaps most importantly, a business system must have as its goal the continual improve- ment of its processes, products, and services. To this end, the business system is responsible for collecting and analyzing data from the process and other sources that will help in the continual incremental improvement of process outputs. Fig- ure 1.2 illustrates relationships among systems, processes, subprocesses, and steps. Note that each part of a system can be broken into a series of processes, each of which may have subprocesses. The subprocesses may be further broken into steps. SIX SIGMA AND LEAN APPLICATIONS Describe how these tools are applied to processes in all types of enterprises: manufacturing, service, transactional, product and process design, inno- vation, etc. (Understand) Body of Knowledge I.A.6 Yes No Number of hours Hourly rate Calculate gross pay Over $100? Deduct tax Deduct Social Security Print check Figure 1.1 Fxample ol a process llowchart. H1325_Kubiak_BOOK.indb 10 11/20/08 6:14:34 PM Cnar¡ik ¡: Fx¡ikrkisiWini \iiw 11 P a r t I . A . 6 The most successful implementations of Lean and Six Sigma have an oversight group with top management representation and support. This group defines and prioritizes problems and establishes teams to solve them. The oversight group is responsible for maintaining a systemic approach. It also provides the training, support, recognition, and rewards for teams. The following are examples of problems that would be assigned to teams: A number of customers of an accounting firm have complained about • the amount of time the firm takes to perform an audit. The oversight group forms a team consisting of three auditors (one of them a lead auditor), two cost accountants, and two representatives from the firm’s top customers. The oversight group asks the team to determine if the lead time is indeed inordinate and to propose measures that will reduce it. The team begins by benchmarking (see Chapter 5) a customer’s internal audit process. After allowing for differences between internal and external audits, the team concludes that the lead time should be shortened. The team next uses the material discussed in Chapter 18 to construct a value stream map, which displays work in progress, cycle times, and communication channels. A careful study of the map data shows several areas where lead time can be decreased. A team has been formed to reduce cycle times on an appliance assembly • line. The team consists of the 12 workers on the line (six from each of the two shifts) as well as the 2 shift coaches and the line supervisor. Although this makes a large team, it helps ensure that everyone’s creative energy is tapped. The team decides to start a job rotation process in which each assembler will work one station for a month and then move on to the next station. After three months the workers universally dislike this procedure, but they agree to continue through at Systems Processes Subprocesses Steps Figure 1.2 kelationship among systems, processes, subprocesses, and steps. H1325_Kubiak_BOOK.indb 11 11/20/08 6:14:34 PM 12 Part I: Enterprise-Wide Deployment P a r t I . A . 6 least one complete rotation. At the end of nine months, or one and a half rotations, the team acknowledges that the rotation system has helped improve standard work (see Chapter 29) because each person better understands what the next person needs. They are also better equipped to accommodate absences and the training of new people. The resulting reduction in cycle times surprises everyone. A team has been charged with improving the operation of a shuttle • brazer. Automotive radiators are loaded on this machine and shuttled through a series of gas- fired torches to braze the connections. The operator can adjust the shuttle speed, wait time, gas pressure, torch angle, and torch height. There is a tendency to adjust one or more of these settings to produce leak- free joints, but no one seems to know the best settings. The team decides to conduct a full factorial 2 5 designed experiment with four replications (see Chapter 28) during a planned plant shutdown. A company is plagued with failure to meet deadlines for software • projects. A team is formed to study and improve the design/code/test process. The team splits into three subteams, one for each phase. The design subteam discovers that this crucial phase endures excess variation in the form of customer needs. This occurs because customers change the requirements and because sometimes the software package is designed to serve multiple customers whose needs aren’t known until late in the design phase. The subteam helps the designers develop a generic Gantt chart (see Chapter 17) for the design phase itself. It also establishes a better process for determining potential customer needs (see Chapter 15). The design group decides to develop configurable software packages that permit the user to specify the functions needed. The coding subteam finds that those responsible for writing the actual code are often involved with multiple projects, leading to tension between project managers. This results in spurts of activity and concentration being spent on several projects with the resulting inefficiencies. The subteam collaborates with the project manager to establish a format for prioritization matrices (see Chapter 13), which provide better guidance for coders. The testing subteam determines that there is poor communication between designers and testers regarding critical functions, especially those that appeared late in the design phase. After discussions with those involved, it is decided that for each project a representative of the testing group should be an ex officio member of the design group. References Crosby, P. B. 1979. Quality Is Free. New York: McGraw- Hill. ———. 1984. Quality without Tears: The Art of Hassle- Free Management. New York: New American Library. ———. 1990. Leading: The Art of Becoming an Executive. New York: McGraw- Hill. H1325_Kubiak_BOOK.indb 12 11/20/08 6:14:34 PM Cnar¡ik ¡: Fx¡ikrkisiWini \iiw 13 P a r t I . A . 6 Deming, W. Edwards. 1986. Out of the Crisis. Cambridge, MA: MIT Press. Feigenbaum, A. V. 1991. Total Quality Control. 3rd ed. New York: McGraw- Hill. Gryna, Frank M., Richard C. H. Chua, and Joseph A. DeFeo. 2007. Juran’s Quality Planning & Analysis for Enterprise Quality. 5th ed. New York: McGraw- Hill. Ishikawa, K. 1985. What Is Total Quality Control? Englewood Cliffs, NJ: Prentice Hall. Juran, Joseph M., and A. Blanton Godfrey. 1999. Juran’s Quality Control Handbook. 5th ed. New York: McGraw- Hill. H1325_Kubiak_BOOK.indb 13 11/20/08 6:14:34 PM (This page intentionally left blank) 609 Index Page numbers followed by f or t refer to figures or tables, respectively. A absolute zero, 91 accuracy components of, 97 defined, 97 precision vs., 98f activity network diagrams (ANDs), 57, 57f addition rule of probability, 139–141 adjusted coefficient of determination, 186 affinity diagrams, 52, 53f, 72 agenda committees, 49 air gages, 96 aliasing, 298 Altshuller, Genrich, 427 American Society for Quality (ASQ), 2 Code of Ethics, 433 Six Sigma Black Belt Certification Body of Knowledge (2001), 447–459 Six Sigma Black Belt Certification Body of Knowledge (2007), 434–446 analysis of variance (ANOVA) method, 107–109, 255 one-way, 256–258 two-way, 258–259 ANDs (activity network diagrams), 57, 57f ANOVA (analysis of variance) method. See analysis of variance (ANOVA) method appraisal costs, 34 appraiser variation (AV), 98 ASQ. See American Society for Quality (ASQ) assembly, design for, 417 attractive requirements, 70 attribute agreement analysis, 111–116 attribute charts, 368 c chart, 372–374 np chart, 370–375 p chart, 368–370 u chart, 374–376 attribute gage study—analytic method, 116–118 attributes data, 95 process capability for, 175–176 attributes data analysis, 217 binary logistic regression, 218–223 nominal logistic regression, 218, 224–226 ordinal logistic regression, 218, 226–229 attributes method, of measurement systems analysis, 111–118 attribute agreement analysis, 111–116 attribute gage study—analytic method, 116–118 authorizing entity, duties of, 39 Automotive Industry Action Group (AIAG), out-of-control rules of, 390 Automotive Industry Action Group (AIAG) method, 100–107 AV (appraiser variation), 98 average variation between systems, 99 axiomatic design, 428 B balanced design, 297, 306 balanced scorecards, 5t KPIs in, 29–30 perspectives of, 28–29 Baldrige Award Criteria, 6t benchmarking, 5t, 26–27 collaborative, 27 competitive, 27 functional, 27 internal, 27 steps in, 27 H1325_Kubiak_BOOK.indb 609 11/20/08 6:19:32 PM 610 Index between-conditions variation, 99 bias, defined, 97 binomial distributions, 149t cumulative table, 476–479 table, 472–475 bivariate distributions, 159t bivariate normal distributions, 159t, 162 Black Belts (BBs), 6–7, 17–18 black box engineering, 418 Blazey, Mark, 88 blocking, 297, 299–300 planning experiments and, 310 Bloom’s Taxonomy, 445–446, 458–459 boundaries, project, 72 box-and-whisker charts, 129t, 130 box plots, 130, 131f multiple, 131–132, 132f Brinnell method, 97 business processes, 10 business systems, 10 C calipers, 96 called yield, 179, 180 capability, tolerance and, 423 capability indices assumptions for, 174 long-term, 173–174 short-term, 173–174 causality, correlations vs., 186–187 cause-and-effect diagrams, 4, 72, 285, 286f, 385f causes common, 359 special, 359 c chart, 372–374 central limit theorem (CLT), 123–125 central tendency, measures of, 128 CFM (continuous flow manufacturing), 339 Champions, 15 change management, 15–16 changeover time, 83, 340 reducing, 340–341 check sheets, 93–94 chi square (goodness-of-fit) tests, 259–261 chi-square distributions, 149t, 155–156 table, 504–505 circle diagrams, 88–89, 89f CLT (central limit theorem), 123–125 CMMs (coordinate measuring machines), 96–97 coaches, duties of, 40 Code of Ethics, ASQ, 433 coefficient of determination, 186 adjusted, 186 cognition, levels of, based on Bloom’s Taxonomy, 445–446, 458–459 collaborative benchmarking, 27 common causes, 359 competitive benchmarking, 27 complementary rule of probability, 139 completeness of the system, law of, 427 conditional probability, 143–144 confidence intervals, 125 for correlations coefficient, 187–188 for means, 237–240, 238–239t point estimates and, 237 for proportions, 241–243, 242t for regression line, 194–195 for variances, 240–241 confounding, 297, 298 planning experiments and, 310 constraints. See theory of constraints (TOC) contingency tables, 141–143, 261–264 continuous data, 90, 95 continuous flow manufacturing (CFM), 339 control chart method, 109–111 control charts analyzing, 389–399 attribute charts, 368–376 c chart, 372–374 combinations for measurements, 460–461 constants, 462–464 constants for A 7 , B 7 , and B 8 , 465–469 control limits for, 362 count data, 471 individual and moving range chart, 366–367 moving average and moving range (MAMR), 383–389 np chart, 370–375 p chart, 368–370 purpose of, 359 short-run, 376–382 triggers for updating, 407 u chart, 374–376 variables, 361–362 variables selection for, 360 Xbar – R chart, 362–364 Xbar – s chart, 364–365 control limits, 362 formulas for, 389 control plans, 406–407 conversion/diversion, 51 H1325_Kubiak_BOOK.indb 610 11/20/08 6:19:32 PM I 611 coordinate measuring machines (CMMs), 96–97 correlation coefficient, 184–188 confidence interval for, 187–188 hypothesis test for, 187 correlations, causality vs., 186–187 cost curves, 35 modern quality, 35f traditional quality, 34f cost of quality, 34–35 costs appraisal, 34 defined, 33 external failure, 34 internal failure, 34 prevention, 34 quality, 34–35 CPM (Critical Path Method), 76 crashing projects, 76 critical parameter management, 428–429 critical path, defined, 76 Critical Path Method (CPM), 76 critical path time, defined, 76 critical-to-cost (CTC), 24 critical-to-delivery (CTD), 25 critical-to-process (CTP), 25 critical-to-quality (CTQ) flow-down tool, 64–65, 65f, 66f critical-to-quality (CTQs), 24 critical-to-safety (CTS), 25 critical to x (CTx) requirements, 24–25 Crosby, Philip B., 3–4 CTC (critical-to-cost), 25 CTD (critical-to-delivery), 25 CTQ (critical-to-quality) flow-down tool, 64–65, 65f, 66f customer loyalty, 31 customer perspective, 28 customers determining and meeting needs of, 64–70 external, 22 feedback from, 63 internal, 22 loyal, 31 profitable, 31 tolerant, 31 unprofitable, 31 customer segmentation, 31, 62 cycle time, 82–83 defined, 337 cycle-time reduction, 337–341 continuous flow manufacturing, 339 reducing changeover time, 340–341 cycle variation, 208 D data attribute, 95 collecting, 93–94 continuous, 95 discrete, 95 errors, 92–93 process capability for non-normal data, 174–175 quantitative, 90–91 variables, 95 decision-making tools, for teams conversion/diversion, 51 force field analysis, 50–51, 50f multivoting, 51 nominal group technique, 50 decision matrix, 429–430 defects per million opportunities (DPMO), 179, 180 defects per unit (DPU), 179, 180 define, measure, analyze, design, and validate (DMADV), 414–415 define, measure, analyze, design, optimize, and validate (DMADOV), 415 define, measure, analyze, improve, and control (DMAIC), 7, 10 Deming, W. Edwards, 2–3 dependent events, 144–145 descriptive statistics, 126–128 descriptive studies, 137 design FMEA (DFMEA), 278 design for assembly, 417 design for maintainability, 417 design for manufacturing, 417 design for producibility, 417 design for robustness, 417 functional requirements for, 418 noise factors for, 418–420 statistical tolerances for, 420–423 tolerance design and, 420 design for test, 417 design for X (DFX), 416–417 design of experiments (DOE) guidelines for conducting, 310–311 planning, 309–311 principles, 297–308 terminology for, 294–297 design space, defined, 295 Design-to-Cost (DTC), 416 DFX (Design for X), 416–417 discrete data, 90–91, 95 discriminant analysis, 198, 201–204 H1325_Kubiak_BOOK.indb 611 11/20/08 6:19:32 PM 612 Index discrimination, 99 distributions binomial, 153–155 bivariate, 159t bivariate normal, 159t, 162 chi-square, 155–156 exponential, 160t, 162-163 F, 157–158 frequency, 129t hypergeometric, 158–162, 159t lognormal, 160t, 164–165 normal, 148–151 Poisson, 152–153, 159t summary of, 149t t (Student’s t), 156–157 Weibull, 160t, 165–166 diversion/conversion, 51 dividing heads, 96 DMADOV (define, measure, analyze, design, optimize, and validate), 415 DMADV (define, measure, analyze, design, and validate), 414–415 DMAIC (define, measure, analyze, improve, and control), 7, 10 documentation, 410–411 DOE. See design of experiments (DOE) dot plots, 123, 123f, 124f DPMO (defects per million opportunities), 179, 180 DPU (defects per unit), 179, 180 driver, 54 Drum-Buffer-Rope subordinate step analogy, 344f, 345–346 E effect, defined, 294 effects interaction, 297, 303–305 main, 297, 300–303 efficient estimators, 235 energy transfer in the system, law of, 427 equipment variation (EV), 98 equivalent sigma levels, 554–555t errors in data, 92 experimental, 295 minimizing, 92–93 EV (equipment variation), 98 evaluation, ongoing, 411–412 events dependent, 144–145 independent, 144–145 mutually exclusive, 145 executives, 18 experimental errors, 188–189, 295 experimental plan, 310 experimental run, defined, 295 experiments. See also design of experiments (DOE) full factorial, 325–331 one-factor, 311–319 two-level fractional factorial, 319–325 exponential distributions, 160t, 162–163 external activities, 340 external customers, 22 external failure costs, 34 external suppliers, 22 F facilitators, duties of, 39 factor, defined, 294 factor analysis, 197, 200–201 factorial designs, defined, 319 failure mode and effects analyses (FMEAs), 278–282 design, 278 process, 278 fault tree analysis (FTA), 288–290 basic symbols, 289f fault trees, 54 F distribution, 149t, 157–158 F(0.01) distribution table, 535–537 F(0.025) distribution table, 531–533 F(0.05) distribution table, 527–529 F(0.10) distribution table, 523–525 F(0.90) distribution table, 519–521 F(0.95) distribution table, 515–517 F(0.975) distribution table, 511–513 F(0.99) distribution table, 507–509 feasibility studies, 351 feedback, from customers, 63 focus groups for, 63 in-person interviews for, 63 interviews for, 63 Feigenbaum, Armand V., 4 financial measures margin, 32 market share, 32 net present value, 33–34 return on investment (ROI), 32–33 revenue growth, 32 financial perspective, 28 fishbone diagrams, 285, 285f, 286f Fisher transformation, 235 five forces, Porter’s, 425 5S system, 333–334 H1325_Kubiak_BOOK.indb 612 11/20/08 6:19:32 PM I 613 5 whys technique, 285–286 flowcharts, 10, 10f, 84, 84f generic, 82f flows, metrics for evaluating process, 81–83, 82f focus groups, for customer feedback, 63 force field analysis, 50–51, 50f Ford, Henry, 8 forecasts, 339 formal, 38 formal teams, 38 Fourteen Points, Deming’s, 2–3 4:1 ratio (25%) rule, 404 fractional factorial experiments, 5 frequency distributions, 129t full factorial experiments, 325–331 source table for, 237t statistical model for, 326t sums of squares for, 328t functional benchmarking, 27 functional gages, 96 functional requirements, 418 G gage blocks, 95–96 gage repeatability and reproducibility (GR&R) study, 99, 403–404 example, 100–107 Gantt charts, 76, 77f for time management of teams, 49 gap analysis, 283 general stakeholders, 22–23 goals SMART statements for, 73 statement of, for teams, 44 goodness-of-fit (chi square) tests, 259–261 graphical methods, 129 gray box design, 418 Green Belts (GBs), 18 growth and learning perspective, 29 GR&R (gage repeatability and reproducibility) study, 99 example, 100–107 H harmonization, law, 427 height gages, 96 histograms, 124, 124f, 358–359 hoshin planning, 16, 425–426 hypergeometric distributions, 158–162, 159t hypothesis tests contingency tables, 261–264 for correlation coefficient, 187 goodness-of-fit (chi square) tests, 259–261 for means, 244–248, 245–247t non-parametric tests, 264–277 process for conducting, 244 for proportions, 250–255, 251t for regression coefficient, 196 for variances, 248–250, 249t I ideality, law of increasing, 427 Imai, Masaaki, 342 implementation, 347–350 framework for, 349–350 income, defined, 33 independent events, 144–145 individual and moving range chart, 366–367 inferential studies, 137 informal teams, 38 in-person interviews, for customer feedback, 63 interaction effects, 297, 303–305 internal activities, 340 internal benchmarking, 27 internal customers, 22 internal failure costs, 34 internal perspective, 29 internal suppliers, 22 interrelationship digraphs, 52–54, 53f interval scales, 91 interviews, for customer feedback, 63 Ishikawa, Kaoru, 4 Ishikawa diagrams, 285, 285f, 286f ISO 9000, 5t J Juran, Joseph M., 3 Juran trilogy, 3 K kaizen, 336, 337, 338 defined, 342–343 kaizen blitz, 337 defined, 342–343 kanban systems, 332–333 Kano model, 69–70, 69f Kaplan, Robert S., 28 H1325_Kubiak_BOOK.indb 613 11/20/08 6:19:32 PM 614 Index key performance indicators (KPIs) in balanced scorecards, 29–30 defined, 29 KPIs. See key performance indicators (KPIs) Kruskal-Wallis test, 266t, 272–275 kurtosis, 127 L Latin square designs, 313t, 315–319 source tables for, 314t sums of squares for, 315t leadership, 14–19 change management and, 15–16 organizational roadblocks to, 14–15 Lean, defined, 7–8 lean manufacturing, 6t, 8 Lean-Six Sigma, 6t, 8 defined, 9 implementations of, 11–12 lean thinking, 7–8, 340 integrating Six Sigma and, 8–9 learning and growth perspective, 29 level, defined, 295 Levene’s test, 265t, 269–272 limits natural process, 178 specification, 178–179 linearity, defined, 97 linear regression multiple, 196–197 simple, 189–192 linear regression coefficients, 189 linear regression equation, 189 link functions, 218 lognormal distributions, 160t, 164–165 long-term capability, 173–174 loyal customers, 31 M main effects, 297, 300–303 maintainability, design for, 417 MAMR (moving average and moving range) control charts, 383–389 considerations when using, 383–384 constructing, 384–385 Mann-Whitney test, 266t, 275–277 one-tail critical values for, 556–557 two-tail critical values, 558–559 MANOVA (multiple analysis of variance), 198, 204–208 manufacturing, design for, 417 margin, 32 market share, 32 Master Black Belts (MBBs), 18 matrix diagram, 56, 56f mean(s), 127–128, 128t, 358–359 commonly used symbol for, 122t confidence intervals for, 237–240, 238–239t hypothesis tests for, 244–248, 245–247t measurement error, causes of, 120–121 measurement scales, 91 interval, 91 nominal, 91 ordinal, 91 ratio, 91 measurement systems components of, 99 in enterprises, 118–119 re-analysis of, 403–405 measurement systems analysis, 97–99 attributes, 111–118 variables, 99–111 measurement tools, 95–97 examples of, 96–97 measures of central tendency, 128 median, 127–128, 128t median ranks table, 539–541 method of least squares, 188–189 metrology, 119–121 micrometers, 96 Minitab, 107–109 rules for out-of-control conditions, 390 mode, 127–128, 128t models, 348 Mood’s median test, 264–268, 265t moving average and moving range (MAMR) control charts, 383–389 considerations when using, 383–384 constructing, 384–385 moving range charts. See individual and moving range chart multiple analysis of variance (MANOVA), 198, 204–208 multiple linear regression, 196–197 multivariate analysis, 197 discriminant analysis, 198, 201–204 factor analysis, 197, 200–201 multiple analysis of variance (MANOVA), 198, 204–208 principal components analysis, 197, 198–200 multi-vari studies, 208–217 multivoting, 51, 55 H1325_Kubiak_BOOK.indb 614 11/20/08 6:19:32 PM I 615 must-be requirements, 70 mutually exclusive events, 145 N n, defined, 295 natural process limits, 178 net present value (NPV), 33–34 neutral characteristics, 70 NGT (nominal group technique), 50 Nippondenso, 400 noise factors defined, 295 planning experiments and, 310 for robust design, 418–420 nominal group technique (NGT), 50 nominal logistics regression, 218, 224–226 nominal scales, 91 non-normal data, process capability for, 174–175 non-parametric tests, 264, 265–266t Kruskal-Wallis test, 266t, 272–275 Levene’s test, 269–272 Mann-Whitney test, 266t, 275–277 Mood’s Median test, 264–268 non-value-added, defined, 8 normal distributions, 148–151, 149t cumulative standard table, 499–501 standard table, 496–498 normal probability plots, 135–137, 136 normal scores table, 543–545 norms, team, 44–45 Norton, David P., 28 np chart, 370–375 NPV. See net present value (NPV) O objectives, statement of, for teams, 44 observed value, defined, 294 Ohno, Taiichi, 332 one-dimensional requirements, 70 one-factor experiments, 311–319 completely randomized, 311 Latin square designs, 315–319 randomized complete block design (RCBD), 311 one-sided tolerance limits, factors for, 546–549 one-way ANOVA designs, 256–258, 311, 312t source tables for, 314t sums of squares for, 315t ongoing evaluation, 411–412 optical comparators, 96 order, 297, 298 run, 299 standard, 298 ordinal logistic regression, 218, 226–229 ordinal scales, 91 organizational memory, 408–409 out-of-control rules, 389 of Automotive Industry Action Group (AIAG), 390, 396–399 Minitab, 390, 391–396 P Pareto charts, 72, 285–288, 286f Pareto principle, 131–132 parts per million (PPM), 179, 180–181 payback period, 33 p chart, 368–370 PDPC (process decision program chart), 56–57 percent agreement, 99 percent defective, equivalent sigma levels and, 554–555 perspectives customer, 28 financial, 28 internal, 29 learning and growth, 29 PERT (Project Evaluation and Review Technique), 76 PEST (political, economic, social, and technological) analysis, 354 phone interviews, for customer feedback, 63 pilot runs, 348 Plackett-Burman designs, 310 planning, strategic, 424–426 tactical, 426–430 point estimates, 237 Poisson distributions, 149t, 152–153 cumulative table, 489–495 table, 481–487 poka-yoke, 335–336 population, 122 population parameters, 122 Porter, Michael, 425 Porter’s five forces, 425 portfolio architecting, 425 positional variation, 208 power defined, 230 power, sample size and, 297–298 PPM (parts per million), 179, 180–181 H1325_Kubiak_BOOK.indb 615 11/20/08 6:19:32 PM 616 Index practical significance, statistical significance vs., 231 precision components of, 98–99 defined, 98 precision protractors, 96 precision-to-tolerance ratio (PTR), 99 prediction intervals, 195, 235–236 prevention costs, 34 principal components analysis, 197, 198–200 prioritization matrix, 54–56, 55f probability addition rule of, 139–141 classic definition, 138 complementary rule of, 139 conditional, 143–144 multiplication rule of, 145–147 relative-frequency definition, 138 rules of, 147t problem statements, 71 procedures, written, 84–85, 85f, 86 process analysis tools, 83–89 flowcharts, 84, 84f process maps, 84, 84f spaghetti diagrams, 88, 88f value stream maps, 85–88, 87f written procedures, 84–85, 85f, 86f process capability for attributes data, 175–176 defined, 167 for non-normal data, 174–175 process capability indices, 167–171 process capability studies, 176–177 conducting, 177 process decision program chart (PDPC), 56–57 processes defined, 80 metrics for evaluating flow in, 81–83, 82f SIPOC tool for, 80–81, 81f processes, business, 10 process flowcharts, 10, 10f process flow metrics, 81–83 process FMEA (PFMEA), 278 process improvement teams, 38 process logs, 389 process maps, 72, 84, 84f process owners, 18–19 process performance defined, 171 specification vs., 178–181 process performance indices, 171–173 process performance metrics, 179–181 defects per million opportunities (DPMO), 179, 180 defects per unit (DPU), 179, 180 parts per million (PPM), 179, 180–181 rolled throughput yield (RTY), 179, 181 throughput yield, 179, 180 process-related training plans, developing, 410 process stakeholders, 22–23 process variation, sources of, 359 producibility, design for, 417 profitable customers, 31 project charters defined, 71 goals and objectives for, 73 performance measures for, 74 problem statements, 71 project scope, 72–73 Project Evaluation and Review Technique (PERT), 76 project tracking, 73–77 proportions confidence intervals for, 241–243, 242t hypothesis tests for, 250–255, 251t prototypes, 348 PTR (precision-to-tolerance ratio), 99 Pugh analysis, 429–430 pull systems, 333–334 push systems, 339 p-value defined, 230 Q quality circles, 5t quality costs, 34 quality function deployment (QFD), 66–69, 67f, 68f quality improvement, history of, 2–6 quartiles, 130 R randomization, 297, 299 planning experiments and, 310 randomized complete block design (RCBD), 311, 312t source tables for, 314t sums of squares for, 315t random sampling, 93 range, 127, 128t rapid continuous improvement (RCI), 337 H1325_Kubiak_BOOK.indb 616 11/20/08 6:19:32 PM I 617 rapid exchange of tooling and dies (RETAD), 340 rational subgroups, choosing, 360–361 ratio scales, 91 RCBD (randomized complete block design), 340 RCI (rapid continuous improvement), 337 re-analysis, of measurement systems, 403–405 recognition, as team motivation technique, 42 recorders duties of, 40 reengineering, 5t regression binary logistic, 218–223 nominal logistic, 218, 224–226 ordinal logistic, 218, 226–229 regression analysis, 188–197 confidence intervals for, 194–195 hypothesis tests for, 196 method of least squares, 192–194 multiple linear regression, 196–197 prediction intervals for, 195 simple linear regression, 189–192 relationships within teams, as motivation technique, 43 repeatability, 98 repeated measures, 297, 298 repetition, 298 replication, 297, 298 reproducibility, 98–99 requirements attractive, 70 must-be, 70 one-dimensional, 70 residuals, 188–189 resolution, 99, 297, 307–308 planning experiments and, 310 response variable, defined, 294 RETAD (rapid exchange of tooling and dies), 340 return on investment (ROI), 32–33 revenue growth, 32 reversal characteristics, 70 rewards, as team motivation technique, 42 ring gages, 96 risk analysis, 351–352 roadblocks, organizational, 14–15 robustness, 5 design for, 417 Rockwell method, 97 ROI (return on investment), 32–33 rolled throughput yield (RTY), 179, 180 root cause analysis, 284 cause-and-effect diagrams, 285, 285f, 286f fault tree analysis, 288–290, 289f 5 whys technique, 284–285 Pareto charts, 285–288, 286f RTY (rolled throughput yield), 179, 180 run charts, 130t, 132–133, 133f run order, 299 S sample homogeneity, 93 sample size, 231–234 commonly used symbol for, 122t formulas for, 232t power and, 297–298 sample standard deviation, 127, 128t sampling methods, 92–93 scales interval, 91 nominal, 91 ordinal, 91 ratio, 91 scatter diagrams, 130t, 133–135, 134t scope, defining, 72–73 screening designs, 310 scribes duties of, 40 self-directed teams, 38 setup time, 83 6Ms, 120 7Ms, 120–121 Shewhart, Walter A., 2 Shingo, Shigeo, 340 Shingo methodology, 340 short-run control charts, 376–382, 377f constructing, 380–381 rules for, 380 summary of formulas for, 378–379t short-term capability, 173–174 sigma levels, equivalent, 554–555 significance statistical vs. practical, 231 simple linear regression, 189–192 simulations, 348 sine bars, 96 single minute exchange of dies (SMED), 340 SIPOC (suppliers, inputs, process, outputs, customers) tool, 80–81, 81f Six Sigma, 6t defined, 6–7 integrating Lean and, 8–9 H1325_Kubiak_BOOK.indb 617 11/20/08 6:19:32 PM 618 Index projects, 16–17 responsibilities, 17–19 roles, 17–19 Six Sigma Black Belt Certification Body of Knowledge (2001), 447–459 Six Sigma Black Belt Certification Body of Knowledge (2007), 434–446 Six Sigma projects effective, 23 impact on stakeholders and, 23 storyboards for, 77 teams and, 23 skewness, 127 SMART (specific, measurable, achievable, relevant, timely) goal statements, 73 SMED (single minute exchange of dies), 340 source tables for full factorial experiments, 327t spaghetti diagrams, 88, 88f SPC (statistical process control), 5t objectives of tools for, 358–359 special causes, 359 specification limits, 178–179 process performance vs., 178–181 sponsor entity duties of, 39 SQC (statistical quality control), 2 stability, of measurement system, defined, 97 stakeholders, 22–23 general, 22 impact of Six Sigma projects on, 23 process, 22–23 standard deviation, 358–359 commonly used symbol for, 122t sample, 127, 128t standard error of the estimate, 194 standard operating procedures (SOPs), documenting, 411 standard order, 298 standard work, 334 statement of goals and objectives, for teams, 44 statistical conclusions descriptive, 137 inferential, 137 statistical control, state of, 167 statistical process control (SPC), 5t objectives of tools for, 358–359 statistical quality control (SQC), 2 statistical significance, practical significance vs., 231 statistics, 122 commonly used symbols, 122t stem-and-leaf diagrams, 129t, 130 storyboards, 77 for Six Sigma projects, 77 strategic planning, 424 hoshin planning, 425–426 Porter’s five forces model, 425 portfolio architecting model, 425 stratified sampling, 93 Student’s t distribution, 149t, 156–157 subgroups, choosing rational, 360–361 substance-field involvement, law of, 428 sums of squares for full factorial experiments, 328t for Latin square designs, 315t one-way ANOVA designs, 315t for randomized complete block design, 315t suppliers external, 22 internal, 22 surveys for customer feedback, 63 SWOT (strengths, weaknesses, opportunities, and threats) analysis, 353 symbols, statistical, commonly used, 122t systematic design, 428 systems, business, 10 T tactical planning, 426–430 axiomatic design, 428 critical parameter management, 428–429 Pugh analysis, 429–430 systematic design, 428 TRIZ, 427–428 Taguchi, Genichi, 5 takt time, 82, 83 defined, 338 tallies, 129t t distribution (Student’s t distribution), 149t, 156–157 table, 502–503 team leaders, duties of, 39 team members duties of, 40 selecting, 40 team motivation, techniques for, 42–43 team roles, 39–40 H1325_Kubiak_BOOK.indb 618 11/20/08 6:19:32 PM I 619 teams, 44f common obstacles and solutions for, 47–48f communication and, 44–45 decision-making tools for, 50–51 dynamics of, 46 growth stages of, 43 informal, 38 launching, 41 norms for, 44–45 performance criteria for, 58 process improvement, 38 rewards for, 58–59 selecting members for, 40 self-directed, 38 statement of objectives for, 44 time management for, 49 virtual, 38 work group, 38 temporal variation, 208 10:1 ratio rule, 404 test, design for, 417 theory of constraints (TOC), 344–346 impact of, 346 thread snap gages, 96 throughput, 83 throughput yield, 179 time management, for teams, 49 TOC. See theory of constraints (TOC) tolerance design, 420 tolerance intervals, 236–237 tolerance limits one-sided, factors for, 546–549 two-sided, factors for, 550–553 tolerances, statistical capability and, 423 conventional, 420, 421–422 statistical, 420, 422–423 tolerant customers, 31 total productive maintenance (TPM), 400–401 total quality control, 4 touch time, 82 Toyota Production System (TPS), 8 TPM (total productive maintenance), 400–401 tracking, project, 73–77 training initial, 409 recurring, 409 training plans considerations, 410 developing process-related, 410 transfer devices, 96 transition from macro to micro, law of, 428 transition to super system, law of, 428 treatment, defined, 295 tree diagrams, 54, 54f CTQ, 25f TRIZ (Teorija Rezbenija Izobretaltelshih Zadach), 427–428 Tukey, John, 130 two-level fractional factorial experiments, 319–325 two-sided tolerance limits, factors for, 550–553 two-way ANOVA, 258–259 Type I error, 231 defined, 230 Type II error, 231 defined, 230 U u chart, 374–376 unbiased estimators, 234–235 uneven development of parts, law of, 427 unprofitable customers, 31 V value-added, 332 value-added time, 83 value stream maps, 85–88, 87f variables control charts, 361–362, 389 variables data, 95 variables method, of measurement systems analysis, 99–111 ANOVA method, 107–109 control chart method, 109–111 GR&R study, 100–107 variables selection, for control charts, 360 variances confidence intervals for, 240–241 hypothesis tests for, 248–250 virtual teams, 38 visual controls, 402 visual factory, 401–402 voice of customer (VOC), 62, 66–67 W waste analysis, sources of, 290–291 waste elimination, 332–336 5S system for, 333–334 kaizen for, 336 H1325_Kubiak_BOOK.indb 619 11/20/08 6:19:32 PM 620 Index kanban systems for, 332–333 poka-yoke for, 335–336 pull systems for, 333–334 standard work for, 334 Weibull distributions, 160t, 165–166 Whitney, Eli, 8 Wilcoxon signed-rank test, critical values for, 560 within-system variation, 98 work group teams, 38 work in progress (WIP), 82 work in queue (WIQ), 82 written procedures, 84–85, 85f, 86f X Xbar – R chart, 362–364 Xbar – s chart, 364–365 Z zero defects concept, 3 H1325_Kubiak_BOOK.indb 620 11/20/08 6:19:32 PM Also available from ASQ Quality Press: The Certified Six Sigma Green Belt Handbook Roderick A. Munro, Matthew J. Maio, Mohamed B. Nawaz, Govindarajan Ramu, and Daniel J. Zrymiak Six Sigma for the New Millennium: A CSSBB Guidebook, Second Edition Kim H. Pries 5S for Service Organizations and Offices: A Lean Look at Improvements Debashis Sarkar The Executive Guide to Understanding and Implementing Lean Six Sigma: The Financial Impact Robert M. Meisel, Steven J. Babb, Steven F. Marsh, and James P. Schlichting Applied Statistics for the Six Sigma Green Belt Bhisham C. Gupta and H. Fred Walker Statistical Quality Control for the Six Sigma Green Belt Bhisham C. Gupta and H. Fred Walker Six Sigma for the Office: A Pocket Guide Roderick A. Munro Lean-Six Sigma for Healthcare: A Senior Leader Guide to Improving Cost and Throughput, Second Edition Chip Caldwell , Greg Butler, and Nancy Poston. Defining and Analyzing a Business Process: A Six Sigma Pocket Guide Jeffrey N. Lowenthal Six Sigma for the Shop Floor: A Pocket Guide Roderick A. Munro Six Sigma Project Management: A Pocket Guide Jeffrey N. Lowenthal Transactional Six Sigma for Green Belts: Maximizing Service and Manufacturing Processes Samuel E. Windsor Lean Kaizen: A Simplified Approach to Process Improvements George Alukal and Anthony Manos A Lean Guide to Transforming Healthcare: How to Implement Lean Principles in Hospitals, Medical Offices, Clinics, and Other Healthcare Organizations Thomas G. Zidel To request a complimentary catalog of ASQ Quality Press publications, call 800-248-1946, or visit our Web site at http://www.asq.org/quality-press. T C B B S H E S S T. M. Kubiak Donald W. Benbow ASQ Quality Press Milwaukee, Wisconsin American Society for Quality, Quality Press, Milwaukee 53203 © 2009 by American Society for Quality All rights reserved. Published 2009 Printed in the United States of America 14 13 12 11 10 09 5 4 3 2 1 Library of Congress Cataloging-in-Publication Data Kubiak, T.M. The certified six sigma black belt handbook / T.M. Kubiak and Donald W. Benbow.—2nd ed. p. cm. ISBN 978-0-87389-732-7 (alk. paper) 1. Quality control—Statistical methods—Handbooks, manuals, etc. I. Benbow, Donald W., 1936– II. Title. TS156.B4653 2008 658.4’013--dc22 2008042611 No part of this book may be reproduced in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Publisher: William A. Tony Acquisitions Editor: Matt Meinholz Project Editor: Paul O’Mara Production Administrator: Randall Benson ASQ Mission: The American Society for Quality advances individual, organizational, and community excellence worldwide through learning, quality improvement, and knowledge exchange. Attention Bookstores, Wholesalers, Schools, and Corporations: ASQ Quality Press books, videotapes, audiotapes, and software are available at quantity discounts with bulk purchases for business, educational, or instructional use. For information, please contact ASQ Quality Press at 800-248-1946, or write to ASQ Quality Press, P.O. Box 3005, Milwaukee, WI 53201-3005. To place orders or to request a free copy of the ASQ Quality Press Publications Catalog, including ASQ membership information, call 800-248-1946. Visit our Web site at www.asq.org or www.asq.org/quality-press. Portions of the input and output contained in this publication/book are printed with permission of Minitab Inc. All material remains the exclusive property and copyright of Minitab Inc. All rights reserved. Printed on acid-free paper For Jaycob, my grandson: This world is changing with each passing day—sometimes for the better, sometimes not. I will strive to carry your burdens until you are able to do so for yourself. May you always be blessed with the best that life has to offer and always strive to improve not just your life but the lives of others. On life’s journey you will confront challenges that may seem impossible, but always know my strength and support will forever be with you. There will be many twists and turns, but always be faithful to your own values and convictions. Know that if you live life fully, you will surely achieve your dreams. I will always be there to help you find your way, but only you have the strength to spread your wings, soar high, and find your yellow brick road. When you follow your own path, there will be no limits to what you can accomplish. —T. M. Kubiak For my grandchildren Sarah, Emily, Dana, Josiah, Regan, Alec, Marah, and Liam. —Donald W. Benbow (This page intentionally left blank) . . . . . . . . . . . . . . . . . . History of Continuous Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Organizational Roadblocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact on Stakeholders . . . . . . . . . . . . . . . . . . . . . Benchmarking . . . . . . . Value and Foundations of Lean . . . Part II Organizational Process Management and Measures . . . Critical to x (CTx) Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Six Sigma and Lean Applications . . . . . Preface to the Second Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 5 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 4 Critical to x (CTx) Requirements . . . . . . . . . . . . Six Sigma Roles and Responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enterprise Leadership Responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integration of Lean and Six Sigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 6 Business Performance Measures . 1 2 2 6 7 8 9 10 14 14 14 15 16 17 Chapter 1 Enterprise-Wide View . . Business Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Value and Foundations of Six Sigma . . . . . . . . . . . . . . . . . . 21 22 22 24 24 26 26 28 28 Chapter 3 Impact on Stakeholders . . . . . . . . . . . . . . . . . . . . xv xxiii xxv xxvii Part I Enterprise-Wide Deployment . . . . . Six Sigma Projects and Kaizen Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Business Processes and Systems . . . . . . . . . . . . . . . . . Preface to the First Edition . . . . . . . . . .Table of Contents List of Figures and Tables . . . . . . . . . . . . . . Chapter 2 Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Change Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Team Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Team Stages . . . . . . . . . . . . . . . . . . . . . . . . Customer Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Goals and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 14 Team Performance Evaluation and Reward . . . . . . . . . . . . . . . . Customer Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 11 Time Management for Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Launching Teams . . . . . . . . . . . . . . . Chapter 9 Team Facilitation . . . . . . . . . . . . . . . . . . . . . 61 62 62 63 64 71 71 72 73 74 75 75 Chapter 15 Voice of the Customer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part IV Define . . . . . . Chapter 12 Team Decision-Making Tools . . . . . . . . . . . . . . Team Types and Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Team Member Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Project Performance Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Common Financial Measures . . . . . . . Time Management for Teams. . . . . . . . . . 37 38 38 39 40 41 42 42 43 44 46 46 49 49 50 50 52 52 58 58 Chapter 8 Team Formation . . . . Team Performance Evaluation and Reward . . . . . . . . Team Motivation . . . . . . Project Tracking . . . . . . Chapter 16 Project Charter . . . Chapter 10 Team Dynamics . . . . . . . . . . . . . Management and Planning Tools . . . . . . . . . . . . . . . . . . . . . . . Chapter 17 Project Tracking . . . . . . . Team Decision-Making Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Team Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 13 Management and Planning Tools. . . . . . . . . . . . . . . . Problem Statement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Team Dynamics . . . . . . . . . . . . .viii Table of Contents Chapter 7 Financial Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Project Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 32 Part III Team Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Customer Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 80 80 81 83 90 90 91 92 93 95 95 97 118 119 122 122 123 126 129 137 138 138 148 158 167 167 171 173 174 175 176 178 Chapter 18 Process Characteristics . . . . . . . . . . . . . . . . . . . . Multi-Vari Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Central Limit Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 21 Basic Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metrology . . . . . . . . . . . . . . . . . . . . . . . . Graphical Methods . . . . . . . . . . Correlation Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic Terms . Chapter 22 Probability . . . . . Process Capability for Non-Normal Data . . . . . . . . . . . . . . . Basic Concepts . . . . . . . . . . . . . . . . . . . . . . Collecting Data . . . . . . . . . . . . . . . . . . . . .T C ix Part V Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attributes Data Analysis. . . . . . . Commonly Used Distributions . . . . Process Performance Indices . . . . . . . . . . . . . . . Short-Term and Long-Term Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling Methods. . . . . . . . . . . . Process Flow Metrics . . . . . . . Part VI Analyze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process Capability Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement Methods . . Process Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . Process Performance vs. . . . . . . Process Capability for Attributes Data . . . . Types of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement Systems in the Enterprise . . . . . . . . . . . . . . . Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process Capability Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 184 184 188 197 208 217 Chapter 24 Measuring and Modeling Relationships between Variables . . . Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 23 Process Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multivariate Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 20 Measurement Systems . . . . . . . . . . . Input and Output Variables . . . . . . . . . . Measurement Systems Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Valid Statistical Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 19 Data Collection . . . . . . . . . . . . . . . Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Point and Interval Estimates . . . . . . . . . . . . . . . . . . . Full Factorial Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implementation . . . . . . . . . . . . . . . . Chapter 30 Cycle-Time Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cycle-Time Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of Variance (ANOVA) . Goodness-of-Fit (Chi Square) Tests . . . . . . and Proportions . . . . . . . . . . . . . . . . . . . . . . . . . . 230 230 231 231 234 244 255 259 261 264 278 278 283 283 284 290 Part VII Improve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Terminology . . . . . . . . . . . . . .x Table of Contents Chapter 25 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Statistical vs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Waste Elimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 34 Risk Analysis and Mitigation . . . . . . . . . . . . . . . . . Two-Level Fractional Factorial Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 32 Theory of Constraints (TOC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Practical Significance . . . . . . . . . . . . . . . . Chapter 29 Waste Elimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 31 Kaizen and Kaizen Blitz. . . . . . . . . . . . . . . . . . . . . . . . Theory of Constraints (TOC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gap Analysis . . Chapter 26 Failure Mode and Effects Analysis (FMEA). . . . . . . Variances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tests for Means. . . . . . . . . . . . . . . Design Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Terminology . . . . . . . . . . . . . . Risk Analysis and Mitigation . . . Planning Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Failure Mode and Effects Analysis (FMEA) . . . . . . . . . Sample Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 294 294 297 309 311 319 325 332 332 337 337 342 342 344 344 347 347 351 351 Chapter 28 Design of Experiments (DOE) . . . . . . . . . . Chapter 27 Additional Analysis Methods . . . . . . . . . . . . . . Root Cause Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaizen and Kaizen Blitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 33 Implementation . Waste Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contingency Tables . . . . . . . . . . . . . . . . . . . . . One-Factor Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Non-Parametric Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 37 Maintain Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 433 434 447 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement System Re-analysis . . . . . . . . . . . . . . . . . . . . . . . Tactical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DMADV (Define. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 36 Other Control Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Control Plan . . . . . . . . . . . . . . . . . . . . . . . . and Validate) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ongoing Evaluation . . . 413 414 414 415 416 416 418 418 424 424 426 Part X Appendices . . . . and Validate) . Chapter 42 Special Design Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 40 Design for X (DFX) . . . . . . Design for X (DFX) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 1 ASQ Code of Ethics (May 2005) . . . . . . . . . . . . . . . Visual Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DMADOV (Define. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analyze. . . . . . . . . . . . Selection of Variables. . . . . . . . . . . . . . . . . . . . . Chapter 39 Common DFSS Methodologies . . . . . . . . . . . . . . . . Lessons Learned. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design. . . . . . . . . . . . . . . . . . . . . . . . . Chapter 41 Robust Design and Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analyze. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rational Subgrouping . . . . . . . . . . . . . . . . . . . . . . . . . Robust Design and Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Control Chart Selection . . . . . . . . . . . 357 358 358 360 360 361 389 400 400 401 403 403 406 408 408 409 410 411 Chapter 35 Statistical Process Control (SPC) . . . . . Optimize. . . Strategic . . . . Part IX Design for Six Sigma (DFSS) Frameworks and Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Objectives . . . . . . . . . . . . . . . . . . . . . Measure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measure. . . . . . . . . . . . . . . . . . Control Chart Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Training Plan Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . .T C xi Part VIII Control . . . . . . . . . . . . . . Appendix 2B ASQ Six Sigma Black Belt Certification Body of Knowledge (2001) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design. . . Chapter 38 Sustain Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 2A ASQ Six Sigma Black Belt Certification Body of Knowledge (2007) . . . . . Documentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total Productive Maintenance (TPM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 22 F(0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alpha = 0. . . . Factors for Two-Sided Tolerance Limits . . . . . . . . . . . . . . Cumulative Poisson Distribution Table . . . . . . . . . Appendix 17 F(0. . . . . . . . . . . . . . Factors for Estimating σX . . . . .99) Distribution Table. Control Charts Count Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 30 Critical Values for the Mann-Whitney Test Table (One-Tail. . . . . . . . . . . . . . . . . Appendix 20 F(0. . . Percent Defective. . . . and PPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . and B8 . . . . . . Binomial Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cumulative Standard Normal Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 15 Chi-Square Distribution Table . . . . . Appendix 29 Critical Values for the Mann-Whitney Test Table (One-Tail. . . . . . . . . .01) Distribution Table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .01) . . . . . . . . . . . . . . .10) Distribution Table. . . . . . . . . .975) Distribution Table. . . . . . . . . Poisson Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standard Normal Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . Appendix 16 F(0. . . Factors for One-Sided Tolerance Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .05) Distribution Table. . . . Appendix 23 F(0. . . . . . . . . . . . . . . . . . . . . . Appendix 18 F(0. . . . . . . . . . . . . . . . . . . . . . . . . 460 462 465 470 471 472 476 481 489 496 499 502 504 507 511 515 519 523 527 531 535 539 543 546 550 554 556 557 Appendix 14 t Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95) Distribution Table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .90) Distribution Table. . . . . . Constants for A7. . . Cumulative Binomial Distribution Table . . . . . . . . . . . . . . . . . . . . . . . . B7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 19 F(0. . . . . . . . . . . . . . . . . . . . . . . . Alpha = 0. . . . . . . . . . . . . Appendix 24 Appendix 25 Appendix 26 Appendix 27 Appendix 28 Median Ranks Table . . . . . . . . . . Equivalent Sigma Levels. . . . . . . . . . . . . . . . . . . . . . . . . . .025) Distribution Table. . . . . . . . . . . . . . . .xii Table of Contents Appendix 3 Appendix 4 Appendix 5 Appendix 6 Appendix 7 Appendix 8 Appendix 9 Appendix 10 Appendix 11 Appendix 12 Appendix 13 Control Chart Combinations for Measurement Data . Control Chart Constants . . Normal Scores Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .05) . . . . . . Appendix 21 F(0. . . . . . . . . . . 558 559 560 561 600 603 609 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .025) . . . . . . . . . . . . Index . . . . . . . . Alpha = 0. . . . . . . . . . . . . . . . . .T C xiii Appendix 31 Critical Values for the Mann-Whitney Test Table (Two-Tail. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .005) . . . . . . . . . . . Appendix 32 Critical Values for the Mann-Whitney Test Table (Two-Tail. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alpha = 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . CD-ROM Contents Sample Examination Questions for Parts I–IX Certified Six Sigma Black Belt—Simulated Exam . . . . . . . Glossary of Six Sigma and Related Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glossary of Japanese Terms . . . . Appendix 33 Appendix 34 Appendix 35 Critical Values for the Wilcoxon Signed-Rank Test . . . . . . . . . . . . . . . . . . . . (This page intentionally left blank) . . . . . . . . . . . . . . . . . . . . . . . . . Example of a force field analysis. . . . . . . . . . . . . . . . . . . . . . . . .5 Figure 17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 Figure 13. . . . . . . . . . . Example of a matrix diagram. . . . . Example of a PDPC. . . . . . .. . . . . . . . . .1 Figure 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 Figure 15. .3 Figure 13. . Example of a prioritization matrix—second step. . . . . .2 CTQ flow-down. . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of a prioritization matrix—first step. . . . .1 Figure 7. . . . . . . .8 Team stages.4 Figure 15. . . . . . 5 10 11 Part II Figure 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of a Gantt chart. . . . . . . . . . . . Example of a QFD matrix for an animal trap. . . . . . . . . . . . . . . . . . . .1 Figure 15. . . . . . . . . . . . . . . . . . . . Example of an interrelationship digraph. . . . . . . . . . . . . . . . . . . . . . . . Example of an AND. . . . . . . .1 Figure 17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 34 35 Part III Figure 9. . . . . . . . . . . .2 Some approaches to quality over the years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .2 Figure 15. . . . . .1 Figure 1. . . . . . . . . . . . . . . . . . . . . . . . . . Example of an affinity diagram. . . . . . . . . . . . . . . . . . . . . . .5 Figure 13. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 Example of a CTQ tree diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 47 50 53 53 54 55 55 56 57 57 Part IV Figure 15. . . . . . 65 66 67 68 69 76 77 xv . . . . . . . . .1 Figure 10. . . . . . . . . . . . . . and steps. . . . . .List of Figures and Tables Part I Table 1. . . . . . . . . . . Example of a CTQ flow-down. . . . . . . . . . . .1 Figure 7. . . . . . . . . . Traditional quality cost curves. . . . . . . . . . . . Map of the entries for the QFD matrix illustrated in Figure 15. . . . . . . . . processes. . Team obstacles and solutions . . . . . . . . . . . . . Example of a tree diagram. . . . . . . . . . . . . . . . . . . . . . . . .3. . . . . . . .2 Figure 13. . . . . . . . . . . . . . . . . . . . . . . .6 Figure 13. . . . . . . . .1 Figure 13. . . Example of a process flowchart. . . . . . . . . . . . . Relationship among systems. . . . . . . . . . . . . . . . . . . . . . . . . . . Project network diagram. . . . . . . . . . . . . . . . . . . Kano model for customer satisfaction. . . . . .1 Figure 12. . . . . . . subprocesses. Modern quality cost curves. . . . . . . . . . . . . . .7 Figure 13.4 Figure 13. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3. . . . . . . . Blank GR&R data collection sheet. . . . .. . . . . . . . . . . . . . . . . .5 Figure 18. . Example of written procedures. . . . . . . . . . . . . . .7 Figure 20. . . . Attribute agreement analysis—data for Example 20. . . . . . . Accuracy versus precision. . . . . . . . . .9 Figure 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 Figure 20. . . . . . . . . . . . . . . . . . a dot plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of descriptive measures. . . . . . . . . . . .8 Figure 18. . . . . . . . . . . . . . .7 Figure 21. Gage R&R Study—ANOVA method: source tables. . .1 Figure 20. . . . .11 Graphical results of the attribute agreement analysis for Example 20. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .4 Figure 21. . . . . . . . . . . . . . . . . . . . . .3 Figure 21. . . . . . . . . . . . . . – – Graphical results of the GR&R study—X/R method: X and R control charts by operators (appraisers) for Example 20. . . . . . . . and a histogram. . . . . . . – Minitab session window output of the R&R study—X/R method: source tables for Example 20. . . . . . . . . . . . . . . . . . . . . .xvi List of Figures and Tables Part V Figure 18. . . . . . . . . . . . . . . . . . . . . . . . . Process flowchart and process map example. . . . . . . .8 Attribute gage study—data for Example 20. . . . . . . . . . . . . . . GR&R report with calculations. . . . . . . . . Sampling distribution of the mean. . . . . . . . 80 81 82 84 85 86 87 87 88 89 98 101 103 105 106 108 109 110 110 111 113 115 117 117 122 123 123 124 124 125 126 128 128 129 130 131 Figure 18. . . . . . . . . . . . . .2 Figure 20. . . . . . . . . . . . . . . . . . Example of a histogram from a large non-normal looking population. . . . . . . .2 Figure 18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9 Table 20. . . . Blank GR&R report. . . . . . . . . . . . . . . Figure 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of a value stream map. Dot plot of the sample means from Table 21. . . . . . . . . . . . . . GR&R data collection sheet with data entered and calculations completed. . . .7 Figure 18.5. . . . . . . . . . . . . . . . .4.2 Figure 21. . . . . .6 Table 21. . . . . . . . . . . . . . . . . . . . Figure 20. . . . . . . . . . . .5. . . . . . . . . . . . . . . . . . . . . . . Example of a cumulative frequency distribution in table and graph form. . . . . . . . . . . . . . . . . . . . . . . .1 Figure 21. . . . . . . . . . . . . . . . .. . . Example of a SIPOC form. . . . . . . . . . . . . . . . . . . . . . Example of a spaghetti diagram. . . . . . . . . . . . . . . . . .1 Table 21. . . . . . . . . . . . . . .1 Process diagram. . . . . . . . . Stem-and-leaf diagrams.3 Figure 20. . . . . . . . . . . . . . . .1 Figure 18. . .3. . . . . .. . . . . . . . . . . . Commonly used symbols. . . . . . . . . . . . . . . . . . . . .4. . . . . . . . . . . . . . . . . . . . . .12 Graphical results of the attribute gage analysis for Example 20. . Examples of the impact of the CLT when sampling from various populations. . . . . . . . . . . . . . . . . . . . . .8 Figure 20.5 Figure 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 Figure 18. . . Example of the symbology used to develop a value stream map. . . . . . . . . . . Gage R&R study—ANOVA method: components of variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2. . . . . . . . . . . . . . . . . . .4. Dot plot for a simple population of three numbers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generic process flowchart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 20.2 Table 21.3 Figure 21.4 Figure 18. . . . . . . . . . . Box plot with key points labeled. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A comparison of various graphical methods. . . . . . . . . .2 Figure 21.4 Figure 21. . . . .6 Figure 18. . . . . . . . . . . . . . . . . . . . . . . . . . Example of a data set as illustrated by a frequency distribution. . . .6 Figure 20. . . . . . . . . . . . . . . . . . Figure 20. . . . . . . . . . . . . . . . . . . . .10 Minitab session window output for Example 20. . . .5 Table 21. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10 Example of a circle diagram. . . . . . . . . . . . . . . . . . . . . . . . . . Example of work instructions. . . . . . . . . . . . . . . . . . . . . . . . . .2 Figure 24. . . . . . . . . . . . . . . .1. . . . . . . . . . . . . .13 Example of the use of normal probability graph paper. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Figure 22. . . . . . . . . . . . . . . . .9 Examples of box plots.12 Hypergeometric distribution for Example 22. . . Figure 21. . . . . . .5 Data for scatter diagrams shown in Figure 21. . . . . . . . . . . . .11 Example of a run chart. . . . .20. . . . . . . . . . .5 Summary of formulas. . . . . Figure 21. . . . .1 Table 22. . . Table 23. . . . . . . . . . . . .1 Table 23. . . . . 185 185 190 191 . . . . . . . . . . . . . . Part VI Figure 24. . . . . . . . . . . . . . . . . . . . . . Venn diagram illustrating the general version of the addition rule of probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of formulas. . . . . . . . . . . . . . . . . . . . . . . .2 for Example 22. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 Table 22. . . . . . . . . . . . .3 Cable diameter data. . . . . . . .2 Table 23. . . . . . . . . . . . . . . . . . . . Binomial distribution with n = 6 and p = 0. . . . . . Example of a chi-square distribution with various degrees of freedom. . . . . . . . . Poisson distribution with mean λ = 4. . Figure 21. . . . . . . . Venn diagram illustrating the complementary rule of probability. . . . . . .15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 21. . . . . . . . . . . . . . . Graphical depiction of regression concepts. . . . and variances of other distributions. . . . . .14 Lognormal distribution for Example 22. . . . . . . .1 Figure 24. . .10 Example of a t distribution with various degrees of freedom. . . . . . . . . . . . . . . . . . Figure 22. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of a contingency table. . . . . .11 Example of an F distribution with various degrees of freedom. . . Contingency table for Examples 22. . . . . . . . . .13 Exponential distribution for Example 22. .1. . . . .4 Table 22. . . . . . . . . . Binomial probabilities for Example 23. . . . .1 Figure 22. . . .3 Examples of different types of correlations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standard normal distribution for Example 22. . . . . . . . . Summary of the rules of probability. . . . . . . . . Scatter diagram developed from the data given in Table 24. . . . .12. . . . . . . . . . . . . . . . . . . . . . . .6 Figure 22. . . .17. . .1. . .15 Example of a Weibull function for various values of the shape parameter β. .4. .1 Figure 23. . . . .. . .. . . Venn diagram illustrating the addition rule of probability with independent events A and B. . Standard normal distribution for Example 22. . . . . . . . . . . .5 Figure 22. . . . . . . . . . . . . and variances of commonly used distributions. . . . . . . . . . . . .14 Example of a normal probability plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19. . . . . . . . . .11. . . . . . . .3 Figure 22. . . . . . . Table 22. . . . . . . . . . . . . . . . . . Figure 22. . . . . . . . . . . . . 131 132 133 134 134 136 136 139 139 140 141 141 142 147 149 150 151 153 155 156 157 158 159 161 163 164 166 170 171 174 176 Figure 21. . . . . . . . . . . . . . . . . . .9 Venn diagram illustrating the probability of event A. .10 Example of a multiple box plot. . . . . . . . . . . . . . . . .8 Figure 22. . . . . . . . . Figure 22. . . . . . . . . . . . . . . . .14. . . . . . . . . . . . . . . . Data for Example 24. . . . . . . . . . . . . . . . . . . Figure 22. . . . . . . . . . . . . . . .L F T xvii Figure 21. . . .. . . . . . . . .7 Figure 22. . . means. . . . Figure 22. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of a process capability analysis using the data given in Table 23. . . . . . . . . . . . . . . . . .3 Table 22. . . . . . . . . . . . . . . . . . . . . . .1 Table 24. . . . . . .2 Figure 22. . . . . . . .16. . . . . . . . . . . . Figure 22. . . . . . . Figure 21. . . . . . .4 Figure 22. . . . . . . . . . . . . . . . . means. . . . . . .1428 for Example 22. . . . . Methods of determining the standard deviation for use in process capability indices.12 Examples of scatter diagrams. . . . . . .4–22. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18. . . . . . . . . .. . . . .10 and 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 Figure 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . .8 Casting data for Example 24. . . . . . . . . . . . . . Figure 24.11 Casting data for Example 24. . . . . . . . .17 Minitab session window output for the binary logistic regression based on data given in Table 24. . . . . . . . . . .4 Table 24.13 Multi-vari chart of data from Table 24. . . . .9. . . . . . . . . . . . .10 Stainless steel casting with critical ID. .3 with two proposed lines. . . . .6 Figure 24. . . .2 Table 24. . . . . . .6 Figure 24. . . . . . . . . . . . . . . . .11 Data collection sheet. .20 Minitab session window output for the nominal logistic regression based on data given in Table 24. .10 and 24.11. . . Figure 24. . . . . . . . . . . . . . . . . . .9 with the means of each factor connected by lines. . . . . . . . . . . . . . . . . . . . . . Computed values for the proposed lines given in Figure 24. . . . . . . . . . . . . . . .4 with residual values added. . . .13. .13 Toxicity data for Example 24. . . . . . . .9 Casting data for Example 24. . . . . . . . . Example of a factor analysis using the data given in Table 24. . . . . . .9.4 Table 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6. . . . . . . . . . Salmon data for Example 24. . .5. . .5 Figure 24. . . . .8. . . . . . . . . . . . . . . . . . . . . . Resting pulse data for Example 24. . . . . . . . . . . . . . . . . . . . . Census data for Examples 24. .. . . . . . . . . . . . . . . . . . . . . . . . .16 Multi-vari chart of data from Table 24. . . . . .15. Example of a principal components analysis using the data given in Table 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8 Table 24. . .9. . . . . . . . Table 24. . . . . .11. . . . . . . .1 Table 25. . . . . . . . .3 Table 25. . . . . . . . . . . . . Table 24. . . Sample size formulas. .15. . . . . . . . . . . . Table 24.7 Table 24. . . . . . . . . .8 with the means of each factor connected by lines. .xviii List of Figures and Tables Figure 24. . . . . . . . . . . . . . . . . . .6. . . . . . . . . . . . . . . . . . Confidence intervals for means. . . . . .19 Delta chi-square versus leverage analysis for Example 24. . . . . . . . . . . . . . . . . . . . . . . .14 Multi-vari chart of data from Table 24. . . . . . .10. . . . . Table 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9. . . . . . . . . . . . . . . Figure 25. . . . . . . . . . . . . . . . . . . . . . . . . Computed values for the proposed lines in Figure 24. . . . . . Confidence intervals for variances. . . . . . Figure 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Figure 24.15 Multi-vari chart of data from Table 24. . . . . . . . . . . . . . . . . . . . . . . .7. . . . . Figure 24. . . .3 Table 24. . . . . . . . .15. . . . . . . Figure 24. . . . . . . . . . . . . .10 Table 24. . . . . . . . . . .. . .4 Four outcomes associated with statistical hypotheses. . . . . . . . . . . . . . . . . Figure 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 Favorite subject data for Example 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Figure 24. . . . . .12. . . . .4. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 Multi-vari chart of data from Table 24. . . . . . . . . . . . . . . . . . .5 Figure 24. . Confidence intervals for proportions. . . . . . . . . . . . . . . . . . . . . . . . . Residual values for the least squares regression line from Example 24. . . . . . . . . . . . . . . . . .5. .21 Minitab session window output for the ordinal logistic regression based on data given in Table 24. . . . . . Plastic film data for Example 24. . . . . . . . . . . .17. . . .. . . . . . . . . . . . . . Figure 24. . . .. .14. . .. . . . . . . . . Table 24. . . . . . . . . . . . . . . . . . . . .9 Scatter diagram from Figure 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18 Delta chi-square versus probability analysis for Example 24. . . . . . . . . . . . . . . . . 191 192 192 194 198 199 199 200 201 203 205 205 209 210 211 212 212 213 214 215 216 217 219 221 222 223 224 225 227 228 231 232 238 240 242 Figure 24. . .14 after pressure wash. . . . . . . . . . . . . . . . . .14 with precision parts. . . . . . . Figure 24. . . . . . . . . . . . . . Figure 24.16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 Table 25. . . . . . . .2 Table 25. . . . . Example of a discriminant analysis using the data given in Table 24. . Scree plot for Examples 24. . . . . . . . . . . . Example of MANOVA using the data given in Table 24. . . . . . . .31 Figure 26. . .8 Table 25. . . . . . .20 Table 25. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 Figure 25. .16 (continued).21 Table 25. . Hypothesis test flowchart (part 1). . . . . . . . . . . . . . . . . . . . . . . .15 Table 25. . 245 249 251 253 254 255 257 257 258 259 260 261 262 262 263 264 265 268 268 270 271 271 272 272 273 274 275 276 277 277 279 280 285 286 286 287 288 289 290 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of a DFMEA form. . . . . . . . . . .12 Table 25. . . . . . . . . . . .2 Figure 27. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16. . . . . .18. . . . . . .L F T xix Table 25. . . . . . .27 Table 25. . . . . . . . . . .15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 (continued).18. . . . . . . . . . . . . Mann-Whitney test for Example 25. . . . . . . . . . . . . . . . . . . . . . Example of a blank cause-and-effect diagram. . . .9.2 Figure 25. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Levene’s test for Example 25. . . . . . .. . . . Moisture content data for Example 25. . . Completed one-way ANOVA source table for the data given in Table 25. . . . . . . . . . . . . . . .26 Table 25. . . . . . . . . . . . . . . . . . . . . . . Example of a PFMEA form. .. . . . . . . . . . . . .4 Figure 27. . . . . Hypothesis tests for proportions. . . . . . . . . . . . . . .11 Table 25. . . . . . . . . . . . . . Historical data of defect types along with current data from a randomly selected week for Example 25. . . . . . . . . .14. . . . . . . . .13. . . . . . . . . . . . . . . . . . . . . . . . . . . . Observed frequencies of defectives for Example 25. . . . . . . . . . . . . . . . . . . . . Example of a Pareto chart for defects. .17 Table 25. . . . . . . . . . . . . . . . . Determining ranks for Example 25.18. . . . . . . . . . Comparison of parametric and non-parametric hypothesis tests. . . . . . . . .16. . . . . . . . . . . . . . . . Levene’s test for Example 25. . . . Hypothesis test flowchart (part 2). . . . . . . . . . . . . . . . . . . . . . . . . . . .2 Figure 27. . . . . . . . . . . . Goodness-of-fit table for Example 25. . . . . . . . . Example of a Pareto chart for defects weighted by the cost to correct. . . . . . . . . . . . . . . . . . . . .13 Table 25. . . . . . . . . . . . .3 Table 27. .14. . . . . . . .19 Table 25. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hypothesis tests for variances or ratios of variances. Computation of the expected frequencies for Example 25. . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . Basic FTA symbols. . . . . . . . Example of a cause-and-effect diagram after a few brainstorming steps. . . . . . . . . .23 Table 25. . . . Example of a one-way ANOVA source table. . . . . . . . . . . . . . . . . .9 Table 25. . . . . . . . . . . .12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13.14 Table 25. . . . . . . .22 Table 25. . . . . . . . . . . . . .1 Figure 27. . . . . . Example of a two-way ANOVA source table. . . . .6 Hypothesis tests for means. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15. . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . Common non-parametric hypothesis tests. . .1 Figure 27. . . . . . . . Computation of the expected frequencies for Example 25. . . . .16 Table 25. . . . . . . . . . . . . . . . . . . . . . . . . . .5 Figure 27. . .28 Table 25. . . . . . . . . . .10 Table 25. . . . . . . . . . . . . . . . . Hypothesis test flowchart (part 3). .17. . . The general form of a two-way contingency table. . . Levene’s test for Example 25. . . . . . . . . . . . . . Cost to correct each defect type. . . . . . .29 Table 25. . . .7 Figure 25. . . Levene’s test for Example 25. . . . . . . . Kruskal-Wallis test for Example 25. . . .1 Figure 26. . . . . . . . . . . . . . .24 Table 25.16 (continued).6 Table 25. . . . . . . .4 Table 25. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . Data for Mann-Whitney test for Example 25. . . . . . . . . . . . . . . . . . . . .5 Table 25. . . Data for Mood’s Median test in Example 25. Example of stoppage of agitation in a tank. . . . . . . . . . . . . .25 Table 25. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data for Levene’s test for Example 25. . . . . . . . . . . . . Determining ranks for Example 25. . . . .17. . . . .17. . . . . .18 Table 25. . . . . Data for Kruskal-Wallis test for Example 25. . . .30 Table 25. . . . . . . . . . . . . . . . Half fraction of 23 (also called a 23–1 design). . . . . . . . . . . .2 Table 28. . . . . . Minitab main effects plot for the analysis given in Table 28. . . . . . . . . . . . . . . . . . . . 3 3 296 296 300 302 303 304 305 305 306 308 308 312 314 315 316 317 319 320 321 323 324 325 326 329 329 330 330 331 345 345 346 347 352 352 353 354 A 23 full factorial design showing interaction columns. . . . . . 359 361 . . . . . . . . . . . . . . . . . . . . . . . . . . . A 24–1 fractional factorial design with interactions. . . . . . .16.22 Table 28. . . . The Drum-Buffer-Rope subordinate step analogy—with rope. . . . . . . . . . . . . . . . . . . Interaction plot for the data given in Table 28. . . . . . .5 Figure 28. . . .xx List of Figures and Tables Part VII Table 28. .1 Figure 32. . . . . . . . . . . . . . . . . . . . . . . Residual plots for the data given in Table 28.22. . . . . . . .9. . . . . . . . . . . . . Data for a 22 full factorial experiment with three replicates. Latin square analysis for Example 28.22. . . . Statistical models for common experimental designs. . . . . . . . . . . . . . . . . . . . . . . . . . . Main effects plot for the data given in Table 28. . . .20 Table 28. . . . . . . . . Session window from Minitab for the data given in Table 28. . . . .15 Table 28. . . . . . . . . .1 Table 34. PEST analysis for Example 34. . .2 Table 28.1 Figure 35.25 Table 28. . . . . . .22. . . . . . . . . . . . . . . . . A 2 full factorial design using the + and – format. . . . . . . . . . . . . . . . . . . . . . . . . . .7 Table 28. . .1 Table 28. .17. . . . . . . . . . . . . . . . . . . . . .8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 Table 28. . . . . . . . Graph of the interaction effects for the data given in Table 28. . . . . . . . . . . . . . .24 Table 28. . . . . . . . . . . Conveyor belt in chocolate-making process. . . .2. . . Data for Example 34. . . . .8. . . . . . .6 Table 28. . . . . . . . .9 Table 28. . . . . . . . . . . . The interdependence of throughput. . . . . . . . .26 Figure 32. . . . . .3 Figure 33. . . .2 Figure 34. . . . . . . . . . . . .3. . . . . . . . . . . Session window results for the data given in Table 28. . . . . . . . . . . . . . Sums of squares for the models given in Table 28. . . The Drum-Buffer-Rope subordinate step analogy—no rope. . .14 Table 28. . . . Half fraction of 23 with completed interaction columns. . . . . .8 Table 28. . . . . . . . . . . . . .17. . . . . . . . . . . . . A 24–1 fractional factorial for Example 28. . . . . A 24 full factorial design. . . . . . . . . . . .22. . . . . . . . . . . . . . . . . . . .18 Table 28.1 Figure 34. . . Part VIII Figure 35. . . . . . . . . . . . . . . . . . . . . . . . .16. . . . . . . . . . . . . . . . . . inventory. . . . . . . . . Examples of source tables for the models given in Table 28. . . . . . . . . . . . . . . . . Data for Example 34. . . . .1 Table 28. . . . .3. . . . . . . . . . . . . . . . . . . . . . .4. . . Graph of the main effects for the data given in Table 28. . . . . . . . . . . . . . . .1. . . . . . . . . . . . . . . . . . . . . . Completed Latin square source table for Example 28. . . . . . . . . . .1. . . . . . . . . . . . Minitab interaction effects plot for the analysis given in Table 28. . . . . . . . . . .23 Table 28. . . . . . . . . . . . . . Minitab analysis of residuals for the data given in Table 28. .2 Function of SPC tools. . . . . . . . Examples of Latin squares from each main class up to order 5. .10 Table 28. . .12 Table 28.10. . . . . . . . . . . . . . . . . . . . . . . .11 Table 28. A 2 full factorial data collection sheet with run averages. . . . . . . . .16 Table 28. . . . . . SWOT analysis for Example 34. . . . Relevant tables for two-way full factorial design. . . . . . . . . . . . . . . . . . . . . . . Example of a ranking matrix with criteria weights shown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A 23 full factorial data collection sheet with data entered for Example 28. .3. . and operating expense measures. . . . . . .10.2 A 23 full factorial data collection sheet for Example 28. . . .13 Table 28. . . . . . . . . . . . . . . . . . .2 Figure 32. . . . . . . . .1 Table 34. . .. . . .1. . . . . . . . . . . . .3 Figure 28. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 Table 28. . .19 Table 28. . . . . .21 Table 28. . . . . . . . . . . . . . . . . . . . . . Example of two different formats for control plans. . . . . . . .3. . . . . . . . Figure 35. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Figure 35. . . . . . . . . . . . . . . . . . . .6—c chart. . . . .7 Table 35. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 363 364 365 367 367 369 370 371 372 373 374 375 376 377 378 382 386 388 388 391 392 392 393 393 394 394 395 395 396 396 397 397 398 398 404 405 406 Figure 35. . Figure 35. Data for Example 35. . . . . . . . . . . . . . . . . . . . .21 Example of out-of-control condition #8 from Minitab. . . . . . . .14 Example of out-of-control condition #1 from Minitab. . . . . . . . . . . . . . . . . .9 Summary of formulas for short-run SPC charts. . . . . . . . . . . .6. . . . . . . . . . .17 Example of out-of-control condition #4 from Minitab. . . . . . . . . . . . . . . . . . . . .. . . . . . . Data for Example 35. . . . .9 Table 35. . . . . .26 Example of out-of-control condition #5 from AIAG. . .12 Moving average chart of length three from Example 35. . . .1. . . . . Figure 35.3 Figure 35. . . . . . . . . . . . . . . . . . . . . . . . . . . .8. . . . . . . .. . . . . . . . . . . Figure 35. . . .3—individual and moving range chart. . . . . . . . . . . . . . . . . . . . . . .3 Example of an acceptable level of variation due to the measurement system. . . . . . . . . . . . . . . . . . . Figure 37. . . . . . . . . Figure 35. . . . . – – Data for Examples 35. c chart for data given in Table 35.9. . . . . . . . . . . . . . . . . . . . respectively. . . . . . . . . . . . . . . . . . .5 Figure 35. . . . . . . . . . . . . . . . . . Example of an unacceptable level of variation due to the measurement system. . . . . . . . . . . . . . . . . . . . . . Short-run chart data for Example 35. . . .15 Example of out-of-control condition #2 from Minitab. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 Figure 35. . . . . . . . . . . . . . . .2. . . . . . . . . . .2 Figure 35. . np chart for data given in Table 35. . . . . . . . . . . . . . . . . . . . . .25 Example of out-of-control condition #4 from AIAG. . . . . . . . . . . . . . .. . . .4—p chart. . . . . . Figure 35. . . . . . . . . Figure 35. . . . .2—X – R and X – s charts. . . . . .L F T xxi Figure 35. . . . . . . . . . . . . . . . . . . . Individual and moving range chart for data given in Table 35. . . . . . . . . . Figure 35. . .8 Table 35. . . . . . . . . . . . . . . . . . Figure 35. . .10 u chart for data given in Table 35. . . . . . . . . . . . . . . . . . . . . .8 Table 35. . . . . . . . .4 Figure 35. . . . . . . . . . . .9. . . . . . . . . . . . . . . . . . . . . . . .7 Table 35. . . . . . . . . . . . . . . . . . . . .. . . .16 Example of out-of-control condition #3 from Minitab. . . . Figure 35. . . . . . . . Figure 35. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 Figure 35. . . . .11 Short-run SPC decision flowchart. . . . .1 Figure 37. . . . . . Figure 35. Figure 35. .20 Example of out-of-control condition #7 from Minitab. . . . . . . . . . . . . . . – X – R chart for data given in Table 35. . . . . . . . .6 Table 35. . . ..23 Example of out-of-control condition #2 from AIAG.7—u chart. . . .13 Moving average range chart of length three from Example 35. . . . . . . . .3 Table 35. .4. . . . . . . . . . . . . . . MAMR data for Example 35. . . . . .18 Example of out-of-control condition #5 from Minitab. . . . . . . . . . . . . . Table 35. . . . . . .22 Example of out-of-control condition #1 from AIAG. . . . . . . . . . . . .19 Example of out-of-control condition #6 from Minitab. . . . . . . . . . . . .1 and 35. . . . . . .1. . . . . . . . . . . . . . Figure 35. . . . . . . . . . . . . Table 35. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . p chart for data given in Table 35. . Figure 35. . . .6 Conveyor belt in chocolate-making process with rational subgroup choice. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27 Example of out-of-control condition #6 from AIAG. . . . . .9. .24 Example of out-of-control condition #3 from AIAG. . . . . . .10 Interpreting control chart out-of-control conditions used by Minitab.5. . . . . . . . . . . . . . . Figure 35. . .5 Table 35. . . Data for Example 35. . . .2 Figure 37. Data for Example 35. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data for Example 35. . . . . . .5—np chart. . . . . . . . – X – s chart for data given in Table 35. . . . . . . . . . . . . . . . . . . . . . . P2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Final step in forming a Pugh matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 419 420 421 421 426 429 429 430 430 . . . . . . . . . . . . . . . . . . . . .2 Figure 42. . . . .2 Figure 41. . . . . . . . . . . . .5 Nonlinear response curve with input noise. . . . . . . . . . Example of a product family matrix. . . . . . . . . . . and P3. . . . . . . . . . .. . . . . Nonlinear response curve showing the impact on Q of input noise at P1. . . Third step in forming a Pugh matrix. .xxii List of Figures and Tables Part IX Figure 41. . Using a response curve to determine tolerance. . . . . . .1 Figure 42. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nonlinear response curve showing the impact on Q of input noise at P1. . First step in forming a Pugh matrix. . . .3 Figure 42. Conventional stack tolerance dimensioning. . . . . . . . . . . . . . . . . . . . . . . .4 Figure 41. . . . . .4 Figure 42. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 Figure 41. . . . . . Second step in forming a Pugh matrix. . . . . . . . . . . . . . . . .1 Figure 41. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 Figure 42. . . . . . . . . . . . . . . . . . . . . . . Therefore. • The table for control constants has been expanded to now include virtually all control constants. As with all ASQ certification–based handbooks. Appendices 29–33 are examples of where such differences might occur. Most of the citations provided in the references will be helpful in this regard. this handbook is probably the only reference source that includes this information. readers are encouraged to use additional sources when seeking much deeper levels of discussion. released in 2007. The handbook has been updated to reflect the most recent Six Sigma Black Belt Body of Knowledge. Thus. Concepts are dealt with briefly but facilitated with practical examples. we have greatly expanded the appendices section: • Although the Body of Knowledge was updated in 2007. As always. the reader may find some differences between our tables and those published in other sources. the book assumes the individual has the necessary background and experience in quality and Six Sigma. we have elected to keep the 2001 Body of Knowledge so that readers can compare changes and perhaps offer recommendations for future Bodies of Knowledge. • All tables were developed using a combination of Microsoft Excel and Minitab 15.Preface to the Second Edition n the spirit of customer-supplier relationships. we are pleased to provide our readers with the second edition of The Certified Six Sigma Black Belt Handbook. With this audience in mind. These tables have been handed down from author to author and have remained largely unchanged. To the best of our knowledge. Our approach was to revert to the formulas and algorithms that produced the tables and then redevelop them using statistical software. We have intentionally avoided theoretical discussion unless such a discussion was necessary to communicate a concept. Note that years ago many statistical tables were produced either by hand or by using rudimentary calculators. the primary audience for this work is the individual who plans to prepare to sit for the Six Sigma Black Belt certification examination. A secondary audience for the handbook is the quality and Six Sigma professional who would like a relevant Six Sigma reference book. I xxiii . the reader may complete the simulated exam to confirm mastery of the entire Six Sigma Black Belt Body of Knowledge. • A second glossary has been added as well. However. Thus. • Additional alpha values in tables have been included. After attaining success with all chapters. Therefore. binomial. corrections. and in some cases. We are confident that readers will find the above additions useful. Though the conversion formula is straightforward. The SharePoint site address is http://asqgroups. asq. the enclosed CD contains supplementary problems covering each chapter and a simulated exam that has problems distributed among chapters according to the scheme published in the Body of Knowledge. the normal distribution is referred to several times before it is defined). repeating any calculations independently. chapter and section numbering follows the same method used in the Six Sigma Black Belt Body of Knowledge. This short glossary is limited to the most common Japanese terms used by quality and Six Sigma professionals.xxiv Preface to the Second Edition • Tables for both cumulative and noncumulative forms of the most useful distributions are now present—for example. In many instances. It is suggested that the reader study a particular chapter. the number of significant digits accounted for by the software used. After the first edition was published. large alpha values for the left side of the F distribution now exist. everyone seems to get it wrong. where possible. or deletions. as well as to seek out any corrections that may have been found and published. and the propagation of errors due to rounding or truncation. we do recognize that errors occasionally occur and thus have established a SharePoint site that will permit readers to recommend suggestions. For example. and normal. • The glossary has grown significantly. Finally.org/cssbbhandbook/. As you might expect. —The Authors . Most notable is the inclusion of more terms relating to Lean. We expect our readers will appreciate this. we urge the reader to carefully consider the above points as the examples are worked. we received several comments from readers who stated that their answers did not completely agree with those given in the examples. However. and then do the supplementary problems for that chapter. we found that discrepancies could be attributed to the following: use of computers with different bits. we have tried to reference the main content in the handbook and refer the reader there for the primary discussion. Poisson. additions. the sequence in which the arithmetic was performed. redundancy of discussion exists. it will no longer be necessary to use the well-known conversion property of the distribution to obtain critical F values associated with higher alpha values. This has made for some awkward placement of discussions (for example. the reader may complete the simulated exam to confirm mastery of the entire Six Sigma Black Belt Body of Knowledge. After attaining success with all chapters. would more than balance these disadvantages.Preface to the First Edition W e decided to number chapters and sections by the same method used in the Body of Knowledge (BOK) specified for the Certified Six Sigma Black Belt examination. redundancy. We thought the ease of access for readers. who might be struggling with some particular point in the BOK. and in some cases. and then do the supplementary problems for that chapter. —The Authors xxv . This made for some awkward placement (the normal distribution is referred to several times before it is defined). The enclosed CD contains supplementary problems covering each chapter and a simulated exam that has problems distributed among chapters according to the scheme published in the Body of Knowledge. repeating any calculations independently. It is suggested that the reader study a particular chapter. (This page intentionally left blank) . Their support has allowed us to produce a final product suitable for the ASQ Quality Press family of publications. —The Authors xxvii . for applying their finely tuned project management. In addition we would like to thank the ASQ management and Quality Press staffs for their outstanding support and exceptional patience while we prepared this second edition. for providing us with the use of Minitab 15 and Quality Companion 2 software and for permission to use several examples from Minitab 15 and forms from Quality Companion 2. Finally. we would like to thank the staff of Kinetic Publishing Services. copyediting. This software was instrumental in creating and verifying examples used throughout the book. and typesetting skills to this project. LLC..Acknowledgments W e would like to express our deepest appreciation to Minitab Inc. (This page intentionally left blank) . Part I Enterprise-Wide Deployment Chapter 1 Chapter 2 Enterprise-Wide View Leadership Part I 1 . Institute training on the job. He was the first honorary member of the American Society for Quality (ASQ). Adopt a new philosophy.A. As stated in his book Out of the Crisis (1986). 6. 5. Cease dependence on inspection to achieve quality.1 Enterprise-Wide View HISTORY OF CONTINUOUS IMPROVEMENT Describe the origins of continuous improvement and its impact on other improvement models. Walter A. He describes the basic principles of SQC in his book Economic Control of Quality of Manufactured Product (1931). minimize total cost by working with a single supplier. Create constancy of purpose for improvement of product and service. where he developed and used control charts. Instead. (Remember) Body of Knowledge I. Drive out fear.A. W. 2 . 3. Improve constantly and forever every process for planning. 2. He is sometimes referred to as the father of statistical quality control (SQC) because he brought together the disciplines of statistics. production. Shewhart worked at the Hawthorne plant of Western Electric. End the practice of awarding business on the basis of price tag alone.Chapter 1 Part I. Break down barriers between staff areas. 7.1 Most of the techniques found in the Six Sigma toolbox have been available for some time. thanks to the groundbreaking work of many professionals in the quality sciences. and service. 8. these 14 points are as follows: 1. 9. and economics. engineering. Edwards Deming developed a list of 14 points in which he emphasized the need for change in management structure and attitudes. Adopt and institute leadership. 4. 2007).1 12. 11. Eliminate the annual rating or merit system. and targets for the workforce. Form quality improvement teams with representatives from each department 3. executive. and consultant. appoint teams. who originated the zero defects concept. M. provide facilitators • Provide training in how to improve quality • Review progress regularly • Give recognition to the winning teams • Propagandize the results • Revise the reward system to enforce the rate of improvement • Maintain momentum by enlarging the business plan to include goals for quality improvement Deming and Juran worked in both the United States and Japan to help businesses understand the importance of continuous process improvement. Determine how to measure where current and potential quality problems lie . and quality improvement—for use in managing for quality. Juran’s Quality Planning & Analysis for Enterprise Quality (with F. Let’s Talk Quality (1990). quality control. Remove barriers that rob people of pride of workmanship. select projects for improvement.C 1: E -W V 3 10. Institute a vigorous program of education and self-improvement for everyone. Joseph M. make it a part of every job description • Create the infrastructure: Establish a quality council. labor arbitrator. government administrator. Juran wrote hundreds of papers and 12 books. Gryna. Juran pursued a varied career in management beginning in 1924 as an engineer. Eliminate slogans. Part I. Crosby. three managerial processes—quality planning. university professor. exhortations. He developed the Juran trilogy. and Leading: The Art of Becoming an Executive (1990). 14. Make it clear that management is committed to quality 2. including Quality Is Free (1979). His approach to quality improvement includes the following points: • Create awareness of the need and opportunity for improvement • Mandate quality improvement. was an ASQ honorary member and past president. Put everybody in the company to work to accomplish the transformation. 13.A. including Juran’s Quality Control Handbook (1999). He wrote many books. Quality without Tears (1984). Eliminate numerical quotas for the workforce and numerical goals for management. and Juran on Leadership for Quality (2003). Philip B. Crosby’s 14 steps to quality improvement are as follows: 1. corporate director. Do it all over again to emphasize that the quality improvement program never ends Armand V. Raise the quality awareness and personal concern of all employees Part I. Quality leadership 2. . The following points summarize Ishikawa’s philosophy: • Quality first—not short-term profit first. • Respect for humanity as a management philosophy—full participatory management. Encourage employees to communicate to management the obstacles they face in attaining their improvement goals 12. Organizational commitment Kaoru Ishikawa (1985) developed the cause-and-effect diagram. Chinese. Establish quality councils to communicate on a regular basis 14. Feigenbaum originated the concept of total quality control in his book Total Quality Control (1991). • The next process is your customer—breaking down the barrier of sectionalism. and Spanish. Hold a “zero defects day” to let all employees realize that there has been a change 10. Take formal actions to correct problems identified through previous steps 7. He lists three steps to quality: 1. including Japanese. • Using facts and data to make presentations—utilization of statistical methods. Encourage individuals to establish improvement goals for themselves and their groups 11. Train all employees to actively carry out their part of the quality improvement program 9. • Cross-function management.4 Part I: Enterprise-Wide Deployment 4. He worked with Deming through the Union of Japanese Scientists and Engineers (JUSE). Think from the standpoint of the other party. Feigenbaum is an ASQ honorary member and served as ASQ president for two consecutive terms.A. Recognize and appreciate those who participate 13. Evaluate the cost of quality and explain its use as a management tool 5.1 6. • Consumer orientation—not producer orientation. Modern quality technology 3. French. Establish a committee for the zero defects program 8. first published in 1951. The book has been translated into many languages. Part I. and uses the information to improve its own performance. and procedures. or service. ISO 9001:2008 (requirements).1.A. A management concept that helps managers at all levels monitor their results in their key areas. Quality circles originated in Japan. The standards were developed by the International Organization for Standardization (ISO). a specialized international agency for standardization composed of the national standards bodies of 91 countries. The subjects that can be benchmarked include strategies. An improvement process in which a company measures its performance against that of best-in-class companies. operations. as shown in Table 1. processes. A breakthrough approach involving the restructuring of an entire organization and its processes. Toyota Motor Company has been recognized as the leader in developing the concept of lean manufacturing systems. The standards underwent major revision in 2000 and now include ISO 9000:2005 (definitions).1 Table 1. Various approaches to quality have been in vogue over the years. The standards.” A set of international standards on quality management and quality assurance developed to help companies effectively document the quality system elements to be implemented to maintain an efficient quality system. Also called “statistical quality control. and ISO 9004:2000 (continuous improvement). product. are not specific to any particular industry. determines how those companies achieved their performance levels. In addition to these noted individuals.C 1: E -W V 5 Genichi Taguchi taught that any departure from the nominal or target value for a characteristic represents a loss to society. Continued Quality approach Quality circles Statistical process control (SPC) ISO 9000 Mid-1980s 1987–present Reengineering Benchmarking 1996–1997 1988–1996 Balanced Scorecard 1990s–present . Approximate time frame 1979–1981 Short description Quality improvement or self-improvement study groups composed of a small number of employees (10 or fewer) and their supervisor.1 Some approaches to quality over the years. where they are called quality control circles. The application of statistical techniques to control a process. initially published in 1987. He also popularized the use of fractional factorial experiments and stressed the concept of robustness. 1 Some approaches to quality over the years. with the following common threads: • Use of teams that are assigned well-defined projects that have direct impact on the organization’s bottom line. small business.A. Part I. the enterprise thrives. Two awards may be given annually in each of six categories: manufacturing company. Continued Approximate time frame 1987–present Short description An award established by the U. The U.6 Part I: Enterprise-Wide Deployment Table 1. companies that have implemented successful quality management systems. health care. Commerce Department’s National Institute of Standards and Technology manages the award. improve. its philosophy. history. The literature is replete with examples of projects that have returned high dollar amounts to the organizations involved.S. service company.” • Emphasis on the DMAIC approach to problem solving: define. • A management environment that supports these initiatives as a business strategy. These key people are designated “Black Belts.2 A wide range of companies have found that when the Six Sigma philosophy is fully embraced. As described in Chapter 1. (Understand) Body of Knowledge I. and control.A. What is this Six Sigma philosophy? Several definitions have been proposed. and nonprofit. and ASQ administers it. measure. Congress in 1987 to raise awareness of quality management and recognize U. As described in Chapter 1. analyze. Black Belts are often required to manage .S. This approach combines the individual concepts of Lean and Six Sigma and recognizes that both are necessary to effectively drive sustained improvement. a proponent of quality management. and goals. • Training in statistical thinking at all levels and providing key people with extensive training in advanced statistics and project management.S. education. The award is named after the late secretary of commerce Malcolm Baldrige.2 Quality approach Baldrige Award Criteria Six Sigma Lean manufacturing Lean-Six Sigma 1995–present 2000–present 2002–present VALUE AND FOUNDATIONS OF SIX SIGMA Describe the value of Six Sigma. 3 • Philosophy—The philosophical perspective views all work as processes that can be defined. analyzed. through its Manufacturing Extension Partnership. This is generally expressed as the y = f(x) concept. it is certainly the most widely adopted and recognized.3 The term “lean thinking” refers to the use of ideas originally employed in lean manufacturing to improve functions in all departments of an enterprise. The National Institute of Standards and Technology (NIST). starting with identifying the problem and ending with implementing long-lasting solutions. and process mapping. A few such tools include statistical process control (SPC).000. thereby promoting a competitive advantage. However. In the first edition of this book. Six Sigma professionals do not totally agree as to exactly which tools constitute the set. and every employee should be involved. and controlled (DMAIC). and goals. its philosophy. history. DMAIC defines the steps a Six Sigma practitioner is expected to follow.000 in contributions to the company’s bottom line. defines Lean as follows: . you will control the outputs.A. VALUE AND FOUNDATIONS OF LEAN Describe the value of Lean. While DMAIC is not the only Six Sigma methodology in use. going forward. • Set of tools—Six Sigma as a set of tools includes all the qualitative and quantitative techniques used by the Six Sigma expert to drive process improvement. measured. Processes require inputs and produce outputs. Opinions on the definition of Six Sigma differ: Part I. It drives customer satisfaction and bottom-line results by reducing variation and waste. we combined the definitions of Lean and Six Sigma and proffer a definition for Lean-Six Sigma. improved. (Understand) Body of Knowledge I. data-driven philosophy of improvement that values defect prevention over defect detection.A. control charts. It applies anywhere variation and waste exist.A. we used the following to define Six Sigma: Six Sigma is a fact-based. If you control the inputs. • Metrics—In simple terms.000–$5. Six Sigma quality performance means 3.4 defects per million opportunities (accounting for a 1. This is discussed in detail in Section I.C 1: E -W V 7 four projects per year for a total of $500. • Methodology—The methodological view of Six Sigma recognizes the underlying and rigorous approach known as DMAIC.5-sigma shift in the mean). failure mode and effects analysis.4. you’re talking about Lean too. and later. This step or function is identified and examined for potential elimination. but it does shine the spotlight on waste. Part I.A. who is credited with spreading the concept of part interchangeability. Lean thinking doesn’t ignore the valued-added activities.2. one) • Pull systems instead of push systems (that is. and scheduling The history of lean thinking may be traced to Eli Whitney. This represents a shift in focus for manufacturing engineering.” The demarcation between Six Sigma and Lean has blurred. A discussion of various categories of wastes is provided in the waste analysis section of Chapter 27. furthered the idea of lean thinking. reduce batch size toward its ultimate ideal. INTEGRATION OF LEAN AND SIX SIGMA Describe the relationship between Lean and Six Sigma. replenish what the customer has consumed) • Reduced lead times through more efficient processing. which has traditionally studied ways to improve value-added functions and activities (for example. (Understand) Body of Knowledge I. “You’re not just talking about Six Sigma. We are hearing about terms such as “Lean-Six Sigma” with greater frequency because process improvement requires aspects of both approaches to attain positive results. setups. Henry Ford. . Lean manufacturing seeks to eliminate or reduce these wastes by use of the following: • Teamwork with well-informed cross-trained employees who participate in the decisions that impact their function • Clean. the Toyota Production System (TPS) packaged most of the tools and concepts now known as lean manufacturing. who went to great lengths to reduce cycle times.8 Part I: Enterprise-Wide Deployment A systematic approach to identifying and eliminating waste (non-value-added activities) through continuous improvement by flowing the product at the pull of the customer in pursuit of perfection. how can this process run faster and more precisely). and well-marked work spaces • Flow systems instead of batch and queue (that is.4 ASQ defines the phrase “non-value-added” as follows: A term that describes a process step or function that is not required for the direct achievement of process output. Six Sigma purists will be quick to say.A.4 After reading the description in the last few paragraphs of Section I. organized.A. 5 . There is a great deal of overlap. making the workplace as efficient and effective as possible. It applies anywhere variation and waste exist. and every employee should be involved. cost. Task forces from this team are formed and reshaped depending on the problem at hand. etc.A. while promoting the use of work standardization and flow. delivery. Some organizations have responded to this dichotomy of approaches by forming a Lean-Six Sigma problem-solving team with specialists in the various aspects of each discipline but with each member cognizant of others’ fields. sales. and visual controls. and a newer attribute. Six Sigma Black Belts need to know a lot about Lean (witness the appearance of lean topics in the Body of Knowledge for Black Belt certification). and disciples of both disagree as to which techniques belong where. workplace organization. and using value stream maps to improve understanding and throughput. reducing the (now) eight wastes. When process problems remain. we believe a combined definition is required and proffer the following: Lean-Six Sigma is a fact-based.5 BUSINESS PROCESSES AND SYSTEMS Describe the relationship among various business processes (design. Six Sigma practitioners should be well versed in both. thereby creating a competitive advantage. purchasing. It drives customer satisfaction and bottom-line results by reducing variation. One thing they have in common is that both require strong management support to make them the standard way of doing business. and hypothesis tests The most successful users of implementations have begun with the lean approach. Lean and Six Sigma have the same general purpose of providing the customer with the best possible quality. whereas Six Sigma emphasizes variation reduction • Lean achieves its goals by using less technical tools such as kaizen. More details of what is sometimes referred to as lean thinking are given in Chapters 29–33. waste. nimbleness. the more technical Six Sigma statistical tools may be applied. production. (Understand) Body of Knowledge I. whereas Lean—also known as lean manufacturing—drives out waste (non-value-added) and promotes work standardization and flow. and cycle time. whereas Six Sigma tends to use statistical data analysis. Part I.) and the impact these relationships can have on business systems. data-driven philosophy of improvement that values defect prevention over defect detection.C 1: E -W V 9 Six Sigma focuses on reducing process variation and enhancing process control.A. accounting. Given the earlier discussion. The two initiatives approach their common purpose from slightly different angles: • Lean focuses on waste reduction. design of experiments. SIX SIGMA AND LEAN APPLICATIONS Describe how these tools are applied to processes in all types of enterprises: manufacturing. the business system is responsible for collecting and analyzing data from the process and other sources that will help in the continual incremental improvement of process outputs. The basic strategy of Six Sigma is contained in DMAIC. the path that material or information follows. and steps. Business Systems A business system is designed to implement a process or. These steps constitute the cycle Six Sigma practitioners use to manage problem-solving projects. processes.6 . products. and services.6 Number of hours Calculate gross pay Over $100? Yes Deduct tax Hourly rate No Deduct Social Security Print check Figure 1. An example of a process flowchart is shown in Figure 1. Note that each part of a system can be broken into a series of processes. transactional. Figure 1.2 illustrates relationships among systems. subprocesses.1. (Understand) Body of Knowledge I. etc. a business system must have as its goal the continual improvement of its processes. and outputs. innovation. each of which may have subprocesses.1 Example of a process flowchart. Business systems make certain that process inputs are in the right place at the right time so that each step of the process has the resources it needs. Processes A process is a series of steps designed to produce products and/or services.A.A. The subprocesses may be further broken into steps. product and process design. service. Understanding and improving processes is a key part of every Six Sigma project. To this end. more commonly. Perhaps most importantly. The individual parts of the DMAIC cycle are explained in Chapters 15–38.10 Part I: Enterprise-Wide Deployment Part I. a set of processes. A process is often diagrammed with a flowchart depicting inputs. and steps. support.6 Processes Subprocesses Steps Figure 1. After allowing for differences between internal and external audits. The following are examples of problems that would be assigned to teams: • A number of customers of an accounting firm have complained about the amount of time the firm takes to perform an audit. and two representatives from the firm’s top customers. The team next uses the material discussed in Chapter 18 to construct a value stream map.A. It also provides the training. The oversight group asks the team to determine if the lead time is indeed inordinate and to propose measures that will reduce it. A careful study of the map data shows several areas where lead time can be decreased. Although this makes a large team. recognition. which displays work in progress. it helps ensure that everyone’s creative energy is tapped. The team begins by benchmarking (see Chapter 5) a customer’s internal audit process. After three months the workers universally dislike this procedure. The team consists of the 12 workers on the line (six from each of the two shifts) as well as the 2 shift coaches and the line supervisor.C 1: E -W V 11 Systems Part I. • A team has been formed to reduce cycle times on an appliance assembly line. The most successful implementations of Lean and Six Sigma have an oversight group with top management representation and support. but they agree to continue through at . The oversight group forms a team consisting of three auditors (one of them a lead auditor). and rewards for teams. two cost accountants. cycle times. the team concludes that the lead time should be shortened.2 Relationship among systems. subprocesses. The team decides to start a job rotation process in which each assembler will work one station for a month and then move on to the next station. processes. This group defines and prioritizes problems and establishes teams to solve them. and communication channels. The oversight group is responsible for maintaining a systemic approach. P. wait time. ———. it is decided that for each project a representative of the testing group should be an ex officio member of the design group. but no one seems to know the best settings. 1984. The team splits into three subteams. which provide better guidance for coders. especially those that appeared late in the design phase. References Crosby. the team acknowledges that the rotation system has helped improve standard work (see Chapter 29) because each person better understands what the next person needs. ———. Leading: The Art of Becoming an Executive. The team decides to conduct a full factorial 25 designed experiment with four replications (see Chapter 28) during a planned plant shutdown. At the end of nine months. The design subteam discovers that this crucial phase endures excess variation in the form of customer needs. After discussions with those involved. It also establishes a better process for determining potential customer needs (see Chapter 15). The resulting reduction in cycle times surprises everyone. and torch height. • A company is plagued with failure to meet deadlines for software projects. The testing subteam determines that there is poor communication between designers and testers regarding critical functions. leading to tension between project managers. Quality without Tears: The Art of Hassle-Free Management. B. Automotive radiators are loaded on this machine and shuttled through a series of gas-fired torches to braze the connections. New York: New American Library.6 least one complete rotation. • A team has been charged with improving the operation of a shuttle brazer. The operator can adjust the shuttle speed. New York: McGraw-Hill. New York: McGraw-Hill. Quality Is Free. The subteam helps the designers develop a generic Gantt chart (see Chapter 17) for the design phase itself. This occurs because customers change the requirements and because sometimes the software package is designed to serve multiple customers whose needs aren’t known until late in the design phase. A team is formed to study and improve the design/code/test process. The subteam collaborates with the project manager to establish a format for prioritization matrices (see Chapter 13). The design group decides to develop configurable software packages that permit the user to specify the functions needed. 1979. or one and a half rotations. gas pressure. They are also better equipped to accommodate absences and the training of new people. . This results in spurts of activity and concentration being spent on several projects with the resulting inefficiencies. The coding subteam finds that those responsible for writing the actual code are often involved with multiple projects.A.12 Part I: Enterprise-Wide Deployment Part I. There is a tendency to adjust one or more of these settings to produce leak-free joints. 1990. torch angle. one for each phase. Richard C. Total Quality Control. 5th ed. 1999. NJ: Prentice Hall. Edwards. 2007.A. Out of the Crisis. 1986. 1991. MA: MIT Press. Chua.. W. Juran’s Quality Control Handbook. H. 3rd ed. Gryna.6 . A. Frank M. and A. Cambridge. Juran’s Quality Planning & Analysis for Enterprise Quality. V.C 1: E -W V 13 Deming. Juran. Joseph M. What Is Total Quality Control? Englewood Cliffs. New York: McGraw-Hill. K. Feigenbaum. Ishikawa. New York: McGraw-Hill. New York: McGraw-Hill. 1985.. DeFeo. 5th ed. and Joseph A. Part I. Blanton Godfrey. (This page intentionally left blank) . 28–29 Baldrige Award Criteria. 298 Altshuller. 258–259 ANDs (activity network diagrams). 111–116 attribute gage study—analytic method. 433 Six Sigma Black Belt Certification Body of Knowledge (2001). 372–374 np chart. 70 attribute agreement analysis. 100–107 AV (appraiser variation). 2 Code of Ethics. 226–229 attributes method. 374–376 attribute gage study—analytic method. 218. 49 air gages. duties of. 52. 370–375 p chart. 306 balanced scorecards. out-of-control rules of. 57f ANOVA (analysis of variance) method. 57. 224–226 ordinal logistic regression. See American Society for Quality (ASQ) assembly. 26–27 collaborative. of measurement systems analysis. 27 steps in. 95 process capability for. 417 attractive requirements. 107–109. 447–459 Six Sigma Black Belt Certification Body of Knowledge (2007). 5t KPIs in. 390 Automotive Industry Action Group (AIAG) method. 27 internal. Genrich. 255 one-way. A absolute zero. 98 average variation between systems. 96 aliasing. design for. 39 Automotive Industry Action Group (AIAG).. 111–118 attribute agreement analysis. 97 defined. 116–118 attributes data. 175–176 attributes data analysis. 297. 99 axiomatic design. 139–141 adjusted coefficient of determination. 427 American Society for Quality (ASQ). 218–223 nominal logistic regression. 111–116 attribute charts. 434–446 analysis of variance (ANOVA) method. 27 functional. 5t. 116–118 authorizing entity. 72 agenda committees. 57. 368 c chart. 98f activity network diagrams (ANDs). 91 accuracy components of. 29–30 perspectives of. 368–370 u chart. 217 binary logistic regression.Index Page numbers followed by f or t refer to figures or tables. respectively. 186 affinity diagrams. 98 ASQ. 6t benchmarking. 218. 97 precision vs. 34 appraiser variation (AV). 27 609 . 256–258 two-way. See analysis of variance (ANOVA) method appraisal costs. 57f addition rule of probability. 53f. 428 B balanced design. 27 competitive. 186 cognition. ASQ. 109–111 control charts analyzing. levels of. 242t for regression line. defined. 297. 361–362 variables selection for. 238–239t point estimates and. 368–370 purpose of. 374–376 variables. 237 for proportions. 298 planning experiments and. 310 constraints. 159t. 458–459 boundaries. 187–188 for means. 93–94 chi square (goodness-of-fit) tests. 186–187 cause-and-effect diagrams. 360 Xbar – R chart. 471 individual and moving range chart. 362 formulas for. 462–464 constants for A7. 143–144 confidence intervals. 418 Blazey. 385f causes common. 445–446. 368–376 c chart. 339 Champions. 149t. 389–399 attribute charts. 340–341 check sheets. 51 . 155–156 table. 149t cumulative table. 179. 240–241 confounding. 123–125 central tendency.610 Index between-conditions variation.. 423 capability indices assumptions for. correlations vs. 96 called yield. 132f Brinnell method. 406–407 conversion/diversion. 17–18 black box engineering. 362 count data. 241–243. 310 Bloom’s Taxonomy. 15 change management. duties of. 362–364 Xbar – s chart. 173–174 short-term. measures of. 72. 72 box-and-whisker charts. 372–374 central limit theorem (CLT). 10 business systems. based on Bloom’s Taxonomy. 141–143. 476–479 table. 339 control chart method. 389 control plans. 10 C calipers. 364–365 control limits. tolerance and. 130 box plots. 96–97 coaches. B7. 359 competitive benchmarking. 15–16 changeover time. 299–300 planning experiments and. 27 complementary rule of probability. 83. 359 c chart. 180 capability. 131f multiple. 99 bias. 261–264 continuous data. 376–382 triggers for updating. 97 business processes. 90. 139 completeness of the system. 89f CLT (central limit theorem). 159t bivariate normal distributions. 174 long-term. 6–7. 504–505 circle diagrams. and B8. 465–469 control limits for. 88–89. 359 short-run. Mark. 173–174 causality. 237–240. 95 continuous flow manufacturing (CFM). 445–446. 125 for correlations coefficient. 129t. 366–367 moving average and moving range (MAMR). 123–125 CMMs (coordinate measuring machines). 370–375 p chart. 194–195 for variances. 460–461 constants. 372–374 combinations for measurements. 286f. 128 CFM (continuous flow manufacturing). 162 Black Belts (BBs). 259–261 chi-square distributions. 97 binomial distributions. 383–389 np chart. 4. 27 common causes. 427 conditional probability. 407 u chart. 433 coefficient of determination. 186 adjusted. 131–132. law of. 340 reducing. See theory of constraints (TOC) contingency tables. 285. 88 blocking. 458–459 collaborative benchmarking. project. 40 Code of Ethics. 359 special. 297. 130. 472–475 bivariate distributions. design. 10 Deming. 51 nominal group technique. 92–93 process capability for non-normal data. 297–308 terminology for. 34 defined. 418 noise factors for. 428–429 critical path. 35 modern quality. 25 critical-to-quality (CTQ) flow-down tool. 22 feedback from. 310–311 planning. 95 collecting. measure. 418–420 statistical tolerances for. 31. 417 design for X (DFX). 90–91. 337 cycle-time reduction. 64–70 external. 50f multivoting. Philip B. 340–341 cycle variation. 76 critical parameter management. W. 416–417 discrete data. 31 customer perspective. and validate (DMADOV). 76 critical path time. 415 define. for teams conversion/diversion.. measure. 180 define. and control (DMAIC). 31 customer segmentation. optimize. 82–83 defined. 65f. 35f traditional quality. 25 CTQ (critical-to-quality) flow-down tool.I 611 coordinate measuring machines (CMMs). 90–91 variables. 295 Design-to-Cost (DTC). 95 discriminant analysis. analyze. 34–35 costs appraisal. 62 cycle time. 417 design for manufacturing. 96–97 correlation coefficient. 66f customer loyalty. defined. 51 force field analysis. 3–4 CTC (critical-to-cost). analyze. 208 D data attribute. Edwards. 179. 309–311 principles. 2–3 dependent events. 278 design for assembly. 24–25 Crosby. improve. 144–145 descriptive statistics. 198. defined. 414–415 define. 25 critical to x (CTx) requirements. 417 design for producibility. 187–188 hypothesis test for. 416 DFX (Design for X). 95 errors. 420–423 tolerance design and. 25 critical-to-process (CTP). design. 76 Critical Path Method (CPM). 31 unprofitable. 174–175 quantitative. 34–35 CPM (Critical Path Method). 24 critical-to-safety (CTS). 186–187 cost curves. 65f. 34 internal failure. 339 reducing changeover time. analyze. 180 defects per unit (DPU). 24 critical-to-delivery (CTD). 28 customers determining and meeting needs of. 76 critical-to-cost (CTC). 50 decision matrix. 416–417 design of experiments (DOE) guidelines for conducting. 25 CTD (critical-to-delivery). defined. 126–128 descriptive studies. 184–188 confidence interval for. 179. 187 correlations. 34 quality. 95 decision-making tools. 31 tolerant. 22 loyal. causality vs. 50–51. 76 crashing projects. 64–65.. 31 profitable. and validate (DMADV). 34f cost of quality. 420 design for test. measure. 417 design for maintainability. 137 design FMEA (DFMEA). 33 external failure. 93–94 continuous. 417 design for robustness. 66f critical-to-quality (CTQs). 34 prevention. 7. 429–430 defects per million opportunities (DPMO). 63 internal. 294–297 design space. 201–204 . 337–341 continuous flow manufacturing. 64–65. 417 functional requirements for. 95 discrete. 152–153. 28 fishbone diagrams. 180 driver. 92–93 EV (equipment variation). defined. 4 financial measures margin. measure. 162 chi-square. 159t.025) distribution table. 165–166 diversion/conversion. 179. 300–303 efficient estimators. 527–529 F(0. 92 experimental. 319 failure mode and effects analyses (FMEAs). design. and validate). 98 equivalent sigma levels. 414–415 DMAIC (define. 410–411 DOE. 344f. 159t lognormal. See design of experiments (DOE) dot plots. 22 external failure costs. 319–325 exponential distributions. 311–319 two-level fractional factorial. improve. and control). defined. 157–158 frequency. 159t summary of. ongoing. 149t. 297.01) distribution table. 144–145 mutually exclusive. 156–157 Weibull. 124f DPMO (defects per million opportunities). 96 DMADOV (define. 155–156 exponential. 519–521 F(0. 51 dividing heads. 511–513 F(0. 427 equipment variation (EV). 32 net present value. 153–155 bivariate. 351 feedback. 425 5S system. 285. and validate). 288–290 basic symbols. 285f. 554–555t errors in data. 180 DPU (defects per unit). 295 experimental plan. 22 F facilitators. defined. 200–201 factorial designs. 278–282 design. 123. optimize. analyze. 535–537 F(0. from customers. 325–331 one-factor. 145 . 160t. 34 external suppliers. 289f fault trees. law of. 164–165 normal.. 235 energy transfer in the system. 7. 33–34 return on investment (ROI). 160t. Porter’s. 54 F distribution. 32 market share. analyze.975) distribution table. 63 focus groups for. 39 factor. Armand V. 129t hypergeometric. 144–145 independent. 158–162. 197. 10 documentation. 278 process. 63 in-person interviews for. defined. 278 fault tree analysis (FTA).95) distribution table. 123f. 98 evaluation. See also design of experiments (DOE) full factorial. 310 experimental run. 160t.10) distribution table. 160t. 179. 515–517 F(0.612 Index discrimination. 99 distributions binomial. 18 experimental errors. 235 five forces. design. 411–412 events dependent. 162-163 F. 63 interviews for. measure. 188–189. 523–525 F(0.99) distribution table. 295 experiments. 286f Fisher transformation. 303–305 main.90) distribution table. 295 minimizing. 333–334 E effect. 294 effects interaction. 531–533 F(0. 162–163 external activities. 54 Drum-Buffer-Rope subordinate step analogy. 149t t (Student’s t). 340 external customers. 63 Feigenbaum. 297. 32–33 revenue growth. 159t bivariate normal.05) distribution table. measure. 32 financial perspective. 294 factor analysis. 345–346 executives. analyze. 148–151 Poisson. 507–509 feasibility studies. 415 DMADV (define. duties of. 157–158 F(0. defined. 285. 427 height gages. 340 internal benchmarking. Henry. for teams. 250–255. 82f flows. 53f interval scales. 29 GR&R (gage repeatability and reproducibility) study. 261–264 for correlation coefficient. 63 interaction effects. Deming’s. 196 for variances. Masaaki. 427 Imai. 283 general stakeholders. 29 internal suppliers. 418 hypothesis tests contingency tables. 5t G gage blocks. 96 functional requirements. 332–333 Kano model. 3 Juran trilogy. 342 implementation. 2–3 4:1 ratio (25%) rule.. 76. 27 internal customers. 124. 425–426 hypergeometric distributions. 16. 38 formal teams. 81–83. 50f Ford. 244–248. 245–247t non-parametric tests. 259–261 for means. 342–343 kanban systems. 69–70. 326t sums of squares for. 303–305 internal activities. 342–343 kaizen blitz. 366–367 inferential studies. 349–350 income. 100–107 Gantt charts. 259–261 graphical methods. 129t full factorial experiments. 18 growth and learning perspective. 137 informal teams. 84. 187 goodness-of-fit (chi square) tests. 264–277 process for conducting. 22 interrelationship digraphs. 124f. Kaoru. 129 gray box design. 251t for regression coefficient.I 613 5 whys technique. for customer feedback. 285f. 50–51. 22 internal failure costs. Robert S. 328t functional benchmarking. 325–331 source table for. 69f Kaplan. 337 defined. 82f focus groups. 99. 403–404 example. 144–145 individual and moving range chart. 297. Joseph M. 339 formal. 337. 63 Ishikawa. law. 38 in-person interviews. 358–359 hoshin planning. 10. 237t statistical model for. 33 independent events. 286f ISO 9000. 3 H harmonization. 34 internal perspective. 10f. 100–107 J Juran. 404 fractional factorial experiments. 77f for time management of teams. 73 statement of. 5 frequency distributions. 347–350 framework for. for customer feedback. 22–23 goals SMART statements for.. 52–54. law of increasing. 338 defined. 28 . for customer feedback. 38 Fourteen Points. 44 goodness-of-fit (chi square) tests. 95–96 gage repeatability and reproducibility (GR&R) study. 244 for proportions. 418 Green Belts (GBs). 158–162. 8 forecasts. 4 Ishikawa diagrams. 96 histograms. 336. 159t K kaizen. 249t I ideality. 84f generic. 91 interviews. 99 example. 248–250. 63 force field analysis. 27 functional gages. 285–286 flowcharts. 49 gap analysis. metrics for evaluating process. 14–15 Lean. 99–111 measurement tools. 208–217 multivoting. 127 L Latin square designs. 417 margin. 189–192 linear regression coefficients. 198. 197. 127–128. 384–385 Mann-Whitney test. 29 KPIs. See individual and moving range chart multiple analysis of variance (MANOVA). 315–319 source tables for. 91 interval. 51. 11–12 lean thinking. 197. 269–272 limits natural process. 8 Lean-Six Sigma. 204–208 principal components analysis. 340 integrating Six Sigma and. 200–201 multiple analysis of variance (MANOVA). 7–8 lean manufacturing. design for. 197 discriminant analysis. 198–200 multi-vari studies. 390 mode. 8–9 learning and growth perspective. 128t median ranks table. 188–189 metrology. 97 linear regression multiple. 417 MAMR (moving average and moving range) control charts. 384–385 moving range charts. 313t. 15–16 organizational roadblocks to. 383–389 considerations when using. 272–275 kurtosis. 32 market share. 91 measurement systems components of. 91 ordinal. 173–174 loyal customers. 6t. 244–248. 164–165 long-term capability. 264–268. 198. 128 median. 107–109 rules for out-of-control conditions. 96 Minitab. 315t leadership. 348 Mood’s median test. 7–8. 18 matrix diagram. 189 linear regression equation.614 Index key performance indicators (KPIs) in balanced scorecards. design for. 198. 55 . 31 M main effects. 539–541 method of least squares. 358–359 commonly used symbol for. causes of. defined. 314t sums of squares for. 265t moving average and moving range (MAMR) control charts. 265t. 160t. See key performance indicators (KPIs) Kruskal-Wallis test. 99 in enterprises. 128t models. 56. 14–19 change management and. 238–239t hypothesis tests for. 201–204 factor analysis. 196–197 multivariate analysis. 275–277 one-tail critical values for. 119–121 micrometers. 95–97 examples of. 128t. 29 level. 295 Levene’s test. 96–97 measures of central tendency. 383–389 considerations when using. 300–303 maintainability. 204–208 manufacturing. 266t. 556–557 two-tail critical values. 32 Master Black Belts (MBBs). 558–559 MANOVA (multiple analysis of variance). 237–240. 266t. 403–405 measurement systems analysis. 91 ratio. 245–247t measurement error. 383–384 constructing. defined. 111–118 variables. 178 specification. 204–208 multiple linear regression. 127–128. 122t confidence intervals for. 178–179 linearity. 189 link functions. 56f mean(s). 91 nominal. 6t. 118–119 re-analysis of. 8 defined. defined. 297. 9 implementations of. 120–121 measurement scales. 198. 97–99 attributes. 127–128. 196–197 simple. 383–384 constructing. 218 lognormal distributions. 29–30 defined. 70 mutually exclusive events. strategic. 179. 368–370 PDPC (process decision program chart). 294 Ohno. 91 organizational memory. 135–137. 489–495 table. 315t ongoing evaluation. equivalent sigma levels and. 122 population parameters. 275–277 Mood’s Median test. 178 net present value (NPV). economic. 70 NGT (nominal group technique). 28 np chart. process capability for. 295 natural process limits. 91 non-normal data. 264. Michael. 226–229 ordinal scales. 481–487 poka-yoke. sample size and. 148–151. 218. 295 planning experiments and. 299 standard. and technological) analysis. 50 nominal logistics regression. 310 planning. 554–555 perspectives customer. 311. 297. 230 power. 180–181 O objectives. defined. 400 noise factors defined. 63 pilot runs. 70 one-factor experiments. 499–501 standard table. 96 order. 332 one-dimensional requirements. 256–258. 426–430 point estimates. 8 normal distributions. 389 of Automotive Industry Action Group (AIAG). 237 Poisson distributions. 370–375 NPV. 424–426 tactical. 496–498 normal probability plots. 136 normal scores table. 218. 33–34 neutral characteristics. 269–272 Mann-Whitney test. 546–549 one-way ANOVA designs. 408–409 out-of-control rules. factors for. 56–57 percent agreement. 265–266t Kruskal-Wallis test. 29 PERT (Project Evaluation and Review Technique). 180–181 payback period. 131–132 parts per million (PPM). See net present value (NPV) optical comparators. 314t sums of squares for. 411–412 . 312t source tables for. for teams. team. 152–153 cumulative table. 543–545 norms. 266t. 310 for robust design.. 208 power defined. 390. 44 observed value. 285–288. statement of. David P. 354 phone interviews. 311 one-sided tolerance limits. 264–268 non-value-added. 145 N n. 224–226 nominal scales. 33 p chart. 391–396 P Pareto charts. 28 internal. 44–45 Norton. 348 Plackett-Burman designs. 272–275 Levene’s test. 311–319 completely randomized. 179. 286f Pareto principle. 72. defined. 425 portfolio architecting. 311 Latin square designs. Taiichi. social. 298 run. 76 PEST (political. 298 ordinal logistic regression. 122 Porter. 174–175 non-parametric tests. 396–399 Minitab. 99 percent defective. 28 financial. 425 Porter’s five forces. 149t. 29 learning and growth. 297–298 PPM (parts per million). 390. 418–420 nominal group technique (NGT).I 615 must-be requirements. for customer feedback. 315–319 randomized complete block design (RCBD). 425 positional variation. 335–336 population. defined. 149t cumulative standard table. 266t. 50 Nippondenso. 74 problem statements. 180 defects per unit (DPU). 180 parts per million (PPM). 84–85. 179. 195. 145–147 relative-frequency definition. 73 performance measures for. 171 specification vs. 22–23 process variation. 99 Pugh analysis. 68f quality improvement. 67f. 84. 86 process analysis tools. 85f. 84. 197. 72. 167–171 process capability studies. 297. business. 10 process flowcharts. 179.. 417 profitable customers. 81–83. 2–6 quartiles. 299 planning experiments and. 198–200 prioritization matrix. 55f probability addition rule of. 85f. 81–83 process FMEA (PFMEA). 10. 84f process maps. 389 process maps. 310 randomized complete block design (RCBD). 278 process improvement teams. 171–173 process performance metrics. 76 project tracking. 147t problem statements. 84–85. 312t source tables for. history of. 86f process capability for attributes data. design for. 250–255. 180 process-related training plans. 66–69. 80 metrics for evaluating flow in. 98–99 defined. 139–141 classic definition. 179–181 defects per million opportunities (DPMO). 242t hypothesis tests for. 230 Q quality circles. 251t prototypes. 71 procedures. 34 principal components analysis. 99 prediction intervals. 10f process flow metrics. 176–177 conducting. 128t rapid continuous improvement (RCI). 180–181 rolled throughput yield (RTY). 5t quality costs. 72–73 Project Evaluation and Review Technique (PERT). 84f process owners. 348 PTR (precision-to-tolerance ratio). 139 conditional.616 Index practical significance. 82f SIPOC tool for. 96 precision-to-tolerance ratio (PTR). 138 rules of. 314t sums of squares for. statistical significance vs.. 88f value stream maps. 231 precision components of. 54–56. 93 range. 130 R randomization. 410 process stakeholders. developing. 311. 179. 73–77 proportions confidence intervals for. 80–81. 71 project scope. 315t random sampling. 179. 143–144 multiplication rule of. 88. 18–19 process performance defined. 71 goals and objectives for. 138 complementary rule of. 235–236 prevention costs. 359 producibility. 181 throughput yield. 84f spaghetti diagrams. 177 process decision program chart (PDPC). 81f processes. 38 process logs. 85–88. 175–176 defined. sources of. 83–89 flowcharts. 174–175 process capability indices. 167 for non-normal data. 337 . 87f written procedures. 178–181 process performance indices. 333–334 push systems. 339 p-value defined. written. 56–57 processes defined. 84. 34 quality function deployment (QFD). 98 precision protractors. 127. 241–243. 31 project charters defined. 429–430 pull systems. 179. 133–135. 40 self-directed teams. 98–99 requirements attractive. 91 RCBD (randomized complete block design). 194–195 hypothesis tests for. 189–192 simulations. 286f RTY (rolled throughput yield). 96 single minute exchange of dies (SMED). equivalent. 284–285 Pareto charts. 554–555 significance statistical vs. 32 reversal characteristics. 310 response variable. 8–9 . 98 repeated measures. 298 replication. 38 setup time. 196 method of least squares. 232t power and. 340 rational subgroups. 42 recorders duties of. 351–352 roadblocks. 285–288. 298 reproducibility. 180 root cause analysis. 377f constructing. outputs. 297–298 sample standard deviation. 173–174 sigma levels. of measurement systems. 226–229 regression analysis. 224–226 ordinal logistic. choosing. 188–197 confidence intervals for. 70 rewards. 417 Rockwell method. 97 ROI (return on investment). 189–192 relationships within teams. organizational. 5t regression binary logistic. 218. 196–197 prediction intervals for. 2 Shingo. 218. 337 re-analysis. 360–361 ratio scales. 179. 92–93 scales interval. 99. 286f fault tree analysis. Shigeo. 5 design for. 43 repeatability. 298 repetition. 96 risk analysis. 120–121 Shewhart. 6–7 integrating Lean and. 340 SIPOC (suppliers.. defining. 134t scope. as team motivation technique. 128t sampling methods. 348 sine bars. defined. 289f 5 whys technique. 14–15 robustness. 42 ring gages. 284 cause-and-effect diagrams. 91 ordinal. 188–189 resolution. 32–33 rolled throughput yield (RTY). 192–194 multiple linear regression. 378–379t short-term capability. 91 scatter diagrams. 80–81. 130t. 132–133. 299 S sample homogeneity. 340 Shingo methodology. as team motivation technique. 380–381 rules for. 93 sample size. 179. 297. 285. 32–33 revenue growth. 122t formulas for. 340 RCI (rapid continuous improvement). 288–290.I 617 rapid exchange of tooling and dies (RETAD). 81f Six Sigma. 195 simple linear regression. 40 reengineering. 180 run charts. 297. 294 RETAD (rapid exchange of tooling and dies). as motivation technique. 231–234 commonly used symbol for. 6t defined. 307–308 planning experiments and. 120 7Ms. practical. 70 residuals. 376–382. 218–223 nominal logistic. 380 summary of formulas for. 70 must-be. customers) tool. Walter A. 340 short-run control charts. 285f. 133f run order. 70 one-dimensional. 297. 340 return on investment (ROI). 91 ratio. inputs. 83 6Ms. 91 nominal. 72–73 screening designs. process. 231 simple linear regression. 130t. 310 scribes duties of. 403–405 recognition. 127. 23 impact on stakeholders and. practical significance vs. 40 team motivation. 22–23 standard deviation. 129t t distribution (Student’s t distribution). 178–181 sponsor entity duties of. 2 stability. 40 selecting. 130 storyboards. 97 stakeholders.618 Index projects. 5t objectives of tools for. 77 strategic planning. of measurement system. 39 SQC (statistical quality control). 428 TRIZ. 426–430 axiomatic design. commonly used. 353 symbols. 429–430 systematic design. 93 Student’s t distribution. 298 standard work. 149t. 23 skewness. 358–359 special causes. 328t for Latin square designs. 340 source tables for full factorial experiments. measurable. 39 team members duties of. 359 specification limits. 360–361 substance-field involvement. 425 portfolio architecting model. 434–446 Six Sigma projects effective. 17–19 roles. 137 statistical control. 149t. law of. opportunities. and threats) analysis. 315t suppliers external. 22 surveys for customer feedback. 42–43 team roles. relevant. 167 statistical process control (SPC). state of. 129t. 502–503 team leaders. 10 T tactical planning. 122t stem-and-leaf diagrams. 127. 447–459 Six Sigma Black Belt Certification Body of Knowledge (2007). 427–428 Taguchi. 17–19 Six Sigma Black Belt Certification Body of Knowledge (2001). 63 SWOT (strengths. 327t spaghetti diagrams. 411 standard order. 39–40 . statistical. 137 inferential. 122t systematic design. 156–157 subgroups. choosing rational. timely) goal statements. 315t one-way ANOVA designs. 88f SPC (statistical process control). 358–359 statistical quality control (SQC). 425–426 Porter’s five forces model. 88. 73 SMED (single minute exchange of dies). 428–429 Pugh analysis. techniques for. 334 statement of goals and objectives. 424 hoshin planning. 425 stratified sampling. 428 sums of squares for full factorial experiments. 127 SMART (specific. 128t standard error of the estimate. 22–23 general. 338 tallies. 428 systems. 428 critical parameter management. 122 commonly used symbols. Genichi. 122t sample.. duties of. 83 defined. 2 statistical significance. for teams. 194 standard operating procedures (SOPs). 16–17 responsibilities. 82. 44 statistical conclusions descriptive. 23 process. 22 internal.. 178–179 process performance vs. achievable. business. 315t for randomized complete block design. 5 takt time. documenting. 23 storyboards for. defined. 22 impact of Six Sigma projects on. 77 teams and. 231 statistics. 358–359 commonly used symbol for. 77 for Six Sigma projects. 5t objectives of tools for. 156–157 table. weaknesses. 410 developing process-related. 404 test. 58 process improvement. 409 training plans considerations. law of. 49 TOC. 50–51 dynamics of. 38 launching. statistical capability and. 83 value stream maps. 44–45 performance criteria for. 236–237 tolerance limits one-sided. 550–553 tolerances. 41 norms for. 428 treatment. 130 two-level fractional factorial experiments. 420 tolerance intervals. 8 TPM (total productive maintenance). 389 variables data. 96 transition from macro to micro. 231 defined. 83 throughput yield. for control charts. 550–553 two-way ANOVA. 38 temporal variation. 107–109 control chart method. 319–325 two-sided tolerance limits. 234–235 uneven development of parts. 420. 44f common obstacles and solutions for. 230 U u chart. 400–401 total quality control. factors for. 49 virtual. 179 time management. 409 recurring. 31 V value-added. 73–77 training initial. 31 total productive maintenance (TPM). factors for. project. 40 self-directed. defined. 427 unprofitable customers. 290–291 waste elimination. 230 Type II error. 95 variables method. of measurement systems analysis. for teams. 44 time management for. 258–259 Type I error. 54f CTQ. 427–428 Tukey. 346 thread snap gages. 38 statement of objectives for. 99–111 ANOVA method. 295 tree diagrams. 82 Toyota Production System (TPS). 400–401 tracking. 100–107 variables selection. design for. 47–48f communication and. 25f TRIZ (Teorija Rezbenija Izobretaltelshih Zadach). 332–336 5S system for. 332 value-added time. 422–423 tolerant customers. 62. 402 visual factory. 44–45 decision-making tools for. 231 defined. 58–59 selecting members for. 43 informal. 38 rewards for. 374–376 unbiased estimators. law of. 66–67 W waste analysis. John. 96 throughput. 420. 336 . 428 transition to super system. 38 visual controls. 38 work group. 410 transfer devices. 361–362. 423 conventional.I 619 teams. 46 growth stages of. 240–241 hypothesis tests for. 333–334 kaizen for. 360 variances confidence intervals for. 208 10:1 ratio rule. law of. 248–250 virtual teams. 546–549 two-sided. 87f variables control charts. 401–402 voice of customer (VOC). 344–346 impact of. See theory of constraints (TOC) tolerance design. 85–88. 109–111 GR&R study. 54. 417 theory of constraints (TOC). factors for. sources of. 421–422 statistical. 4 touch time. critical values for. 98 work group teams. 86f X Xbar – R chart. 85f.620 Index kanban systems for. Eli. 8 Wilcoxon signed-rank test. 364–365 Z zero defects concept. 335–336 pull systems for. 84–85. 82 written procedures. 560 within-system variation. 160t. 334 Weibull distributions. 3 . 38 work in progress (WIP). 362–364 Xbar – s chart. 82 work in queue (WIQ). 332–333 poka-yoke for. 333–334 standard work for. 165–166 Whitney.