Profitability with No Boundaries
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Also available from ASQ Quality Press: The Logical Thinking Process: A Systems Approach to Complex Problem Solving H. William Dettmer 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 The Certified Six Sigma Black Belt Handbook, Second Edition T. M. Kubiak and Donald W. Benbow Six Sigma for the New Millennium: A CSSBB Guidebook, Second Edition Kim H. Pries The Certified Six Sigma Green Belt Handbook Roderick A. Munro, Matthew J. Maio, Mohamed B. Nawaz, Govindarajan Ramu, and Daniel J. Zrymiak 5S for Service Organizations and Offices: A Lean Look at Improvements Debashis Sarkar Lean Kaizen: A Simplified Approach to Process Improvements George Alukal and Anthony Manos Lean for Service Organizations and Offices: A Holistic Approach for Achieving Operational Excellence and Improvements Debashis Sarkar Lean ISO 9001: Adding Spark to your ISO 9001 QMS and Sustainability to Your Lean Efforts Mike Micklewright Root Cause Analysis: Simplified Tools and Techniques, Second Edition Bjørn Andersen and Tom Fagerhaug The Certified Manager of Quality/Organizational Excellence Handbook, Third Edition Russell T. Westcott, editor Enabling Excellence: The Seven Elements Essential to Achieving Competitive Advantage Timothy A. Pine 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.
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Profitability with No Boundaries Optimizing TOC, Lean, Six Sigma Results
Focus Reduce Waste Contain Variability
Reza (Russ) M. Pirasteh Robert E. Fox
ASQ Quality Press Milwaukee, Wisconsin
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American Society for Quality, Quality Press, Milwaukee 53203 © 2011 American Society for Quality All rights reserved. Published 2010 Printed in the United States of America 14 13 12 11 10
5 4 3 2 1
Library of Congress Cataloging-in-Publication Data Pirasteh, Reza M., 1956– Profitability with no boundaries : optimizing toc and lean-six sigma / Reza M. Pirasteh. p. cm. Includes bibliographical references and index. ISBN 978-0-87389-795-2 (alk. paper) 1. Theory of constraints (Management) 2. Six sigma (Quality control standard) 3. Industrial productivity. I. Title. HD69.T46.P57 2010 658.4⬘013—dc22 2010021466 ISBN-13: 978–0-87389-795-2
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 http://qualitypress.asq.org. Printed on acid-free paper
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Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Part I: Leadership Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
Chapter 1: Productivity, Growth, and Prosperity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HENRY FORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ALFRED SLOAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TAIICHI OHNO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TLS—A FOURTH WAVE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 2: Productivity Improvement—A Checkered History . . . . . . . . . . . . . . . . . . .
3 3 5 12 17 25
Chapter 3: What Is an Improvement? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HOW TO MEASURE AN IMPROVEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WHY IMPROVEMENTS EFFORTS OFTEN FAIL . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 4: Current CPI Favorites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THEORY OF CONSTRAINTS (TOC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Five Focusing Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinking Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critical Chain Project Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LEAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SIX SIGMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Six Sigma—A Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Six Sigma—A Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 1—Define. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 2—Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 3—Analyze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 4—Improve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 5—Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design For Six Sigma (DFSS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Six Sigma—A Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary/Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 5: The TLS Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WHAT IS iTLS™®? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How Does iTLS™® Work? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
31 31 35 39 39 39 41 42 42 45 45 46 46 47 47 48 48 49 49 50 53 53 53
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WHAT IS UNIQUE ABOUT ITLS™®? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HOW SHOULD ITLS™® BE APPLIED? WHAT IS THE SEQUENCE OF EVENTS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 6: Results: Has a More Global Focus Worked? . . . . . . . . . . . . . . . . . . . . . . . . . INITIAL CONDITIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WHICH METHODOLOGY WAS MOST EFFECTIVE?. . . . . . . . . . . . . . . . . . . . . Chapter 7: Throughput Operating Strategies (TOS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . A NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T NETWORKS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 8: Management Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FOCUSING IMPROVEMENT EFFORTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MOTIVATING THE DESIRED BEHAVIORS AND BUILDING TRUST. . . . . . . . . . THE MISSING LINK. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 9: Closing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
56 61 61 62 67 67 69 70 71 72 75 76 77 78 83
Part II: Practitioner’s Guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Chapter 1: Productivity Growth and Prosperity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SLOAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product Pricing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Investment Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Make vs. Buy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OHNO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A FOURTH WAVE—iTLS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 2: The Productivity Improvement Dilemma . . . . . . . . . . . . . . . . . . . . . . . . . . . IMPROVEMENT CHALLENGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 3: CPI Favorites: TOC, Lean, Six Sigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THEORY OF CONSTRAINTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LEAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle 1: Specify the Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle 2: Define the Value Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle 3: Make Value Flow without Any Interruptions . . . . . . . . . . . . . . . . . . . . . Principle 4: Make Customer Pull from Supplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle 5: Perfection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle 6: Agility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SIX SIGMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Six Sigma—A Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Six Sigma—A Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 1—Define. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 2—Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89 89 91 91 92 94 96 106 111 118 123 123 130 130 132 134 143 144 147 148 150 151 157 158 159
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Step 3—Analyze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 4—Improve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 5—Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DESIGN FOR SIX SIGMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phase 1—Identify. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phase 2—Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phase 3—Optimize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phase 4—Validate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WHERE WE WENT WRONG. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case I: Direct Labor Reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case II: Increase Throughput. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case III: Labor and Throughput Improvements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 4: What Is an Improvement? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HISTORY OF “IMPROVEMENTS” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HOW TO MEASURE AN IMPROVEMENT? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WHY DO IMPROVEMENT EFFORTS OFTEN FAIL?. . . . . . . . . . . . . . . . . . . . . . . . Chapter 5: The iTLS Model and How It Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WHAT IS THIS iTLS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 1—Mobilize and Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 2—Decide How to Exploit the Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 3—Eliminate Sources of Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 4—Control Process Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 5—Control Supporting Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 6—Remove the Constraint and Stabilize. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Step 7—Reevaluate the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 6: iTLS Study and Results Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HAS A MORE GLOBAL FOCUS WORKED? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Initial Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Which Method Is More Effective?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 7: River System Optimization with TOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T NETWORKS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I NETWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . BETTER UNDERSTAND YOUR PROCESSES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 8: “MOST” TLS Project Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LEADERSHIP RESPONSIBILITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobilize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Organize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Speed-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tie Loose Ends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Real-World Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . User Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159 160 160 161 162 162 163 165 166 166 169 169 170 185 185 187 191 195 195 203 208 211 221 227 230 236 257 257 258 259 262 267 268 273 279 281 284 285 285 286 287 288 289 289 291
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Chapter 9: Real-World Application of iTLS Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 293 STUDY—INVENTORY MANAGEMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 iTLS-O Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 Examples of Real-Life Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 CASE STUDY ELECTRONIC MANUFACTURING COMPANY . . . . . . . . . . . . . . . 296 Initial Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 iTLS in Action and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Step 1—Mobilize and Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Steps 2 and 3—Decide How to Exploit the Constraint and Eliminate Sources of Waste in the Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 Step 4—Control Process Variability and Error in the Constraint . . . . . . . . . . . . . . . 303 Step 5—Control Supporting Activities to the Constraint. . . . . . . . . . . . . . . . . . . . . . 303 Step 6—Remove the Constraint and Stabilize. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Step 7—Reevaluate System Performance and Go after the Next Constraint . . . . . . 305 Going Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 CASE STUDY—VALVE ASSEMBLY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Initial Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Step 1—Mobilize and Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Step 2—Decide How to Exploit the Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Step 3—Eliminate Sources of Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 Step 4—Control Process Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 Step 5—Control Supporting Activities to the Constraint. . . . . . . . . . . . . . . . . . . . . . 313 Step 6—Remove the Constraint and Stabilize the Process . . . . . . . . . . . . . . . . . . . . 313 Step 7—Reevaluate the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 CASE STUDY—CELSO CALIA OF BRAZIL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Our Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 The Votorantim Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 Initial Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 Step by Step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Step 1—Mobilize and Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Step 2—Identify and Exploit the Constraint (Drum). . . . . . . . . . . . . . . . . . . . . . . . . 320 Step 3—Eliminate Sources of Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Step 4—Control Process Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 Step 5—Control Supporting Activities to the Drum . . . . . . . . . . . . . . . . . . . . . . . . . 322 Step 6—Control Supporting Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 Step 7—Reevaluate the System and Go After the Next Drum . . . . . . . . . . . . . . . . . 323 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 Chapter 10: Closing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 About the Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
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Figures and Tables
PART 1 Figure 3.1
OE, I relationship. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
Figure 5.1
iTLS™® model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
54
Figure 5.2
iTLS™® seven-step process.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
56
Figure 5.3
iTLS™® seven-step flow, tools, and techniques. . . . . . . . . . . . . . . . . . . . . .
58
Figure 6.1
Lean and Six Sigma benefits/project.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
Figure 6.2
Lean–Six Sigma and TLS benefits/project. . . . . . . . . . . . . . . . . . . . . . . . . . .
63
Figure 6.3
Lean, Six Sigma, and iTLS™® financial return/project. . . . . . . . . . . . . . . .
64
Table 6.1
Lean, Six Sigma, and iTLS™® comparison . . . . . . . . . . . . . . . . . . . . . . . . .
65
Figure 6.4
Financial contribution by methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
Figure 7.1
A–river flow operation network—laptop computer manufacturing. . . . . . . .
68
Figure 7.2
V–river flow operation network—a pick-pack-ship warehouse. . . . . . . . . . .
69
Figure 7.3
T–river flow operation network—automobile assembly.. . . . . . . . . . . . . . . .
71
Figure 7.4
I–network river—airline meal tray assembly operations. . . . . . . . . . . . . . . .
72
Table 1.1
Product costs when most costs are variable . . . . . . . . . . . . . . . . . . . . . . . . . .
92
Table 1.2
Product costs when most costs are not variable. . . . . . . . . . . . . . . . . . . . . . .
92
Table 1.3
Investment decisions when most costs are variable . . . . . . . . . . . . . . . . . . . .
93
Table 1.4
Investment decisions when most costs are not variable. . . . . . . . . . . . . . . . .
93
Table 1.5
Make vs. buy decisions when most costs are variable . . . . . . . . . . . . . . . . . .
94
Table 1.6
Make vs. buy decisions when most costs are not variable. . . . . . . . . . . . . . .
94
Table 1.7
Standard costs of clutches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
95
Figure 1.1
Kanban system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
PART 2
ix
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Figure 1.2
Drum buffer rope system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Table 2.1
Job losses—major metropolitan areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Table 2.2
Comparison of improvement programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Table 2.3
Distribution of time a company has been using Six Sigma. . . . . . . . . . . . . . 114
Table 2.4
Support of Six Sigma implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Figure 2.1
Four major waves—the evolution of continuous improvement. . . . . . . . . . . 120
Table 3.1
Types of constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
Table 3.2
Constraint management steps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Table 3.3
Types of value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Figure 3.1
Hidden factory.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
Figure 3.2
Value characterization decision flowchart.. . . . . . . . . . . . . . . . . . . . . . . . . . . 132
Table 3.4
Seven sins of production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Figure 3.3
Order fulfillment value stream. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Figure 3.4
Takt time and balancing workload. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Figure 3.5
Takt time—unbalanced work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Figure 3.6
Process flow matrix.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Figure 3.7
Workflow transportation logistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
Figure 3.8
Example of a traditional layout.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Figure 3.9
Example of a work cell layout. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Figure 3.10
5S CANDO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Table 3.5
Components of 5S.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Figure 3.11
Poka-yoke, error-proofing.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Figure 3.12
Kanban system mechanics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Figure 3.13
Inventory hides problems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
Figure 3.14
Six Sigma evolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Figure 3.15
Normal distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Figure 3.16
Process control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Figure 3.17
1.5 sigma shift assumption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Figure 3.18
Area under normal curve assuming 1.5 sigma shift. . . . . . . . . . . . . . . . . . . . 154
Figure 3.19
Relationship of sigma levels and PPM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Figure 3.20
FPY in a two-step process.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Table 3.6
Relationship between Cp, PPM, and sigma level. . . . . . . . . . . . . . . . . . . . . . 156
Table 3.7
Sigma capability and DPM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
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Figures and Tables
xi
Table 3.8
DMAIC process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
Table 3.9
Process overall yield vs. Sigma.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Table 3.10
DMADV process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
Table 3.11
Benefits of reducing direct labor costs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Table 3.12
Benefits of increasing throughput. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Table 3.13
Benefits of increasing throughput and labor savings. . . . . . . . . . . . . . . . . . . 171
Table 3.14
GM story. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Figure 3.21
Sustainable operational ecosystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Figure 4.1
T, OE, I relationship. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
Figure 5.1
iTLS model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
Figure 5.2
iTLS model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Figure 5.3
Integration of TOC, Lean, Six Sigma iTLS. . . . . . . . . . . . . . . . . . . . . . . . . . 199
Figure 5.4
iTLS model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
Figure 5.5
iTLS seven-step process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Figure 5.6
iTLS seven-step flow and tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
Figure 5.7
Application of TOC tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Figure 5.8
Problem statement development checklist. . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Figure 5.9
Step 1, mobilize and focus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Figure 5.10
Exploit the constraint. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Figure 5.11
Application of Lean tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
Figure 5.12
Ishikawa cause and effect diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Table 5.1
CNX definition for factor screening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Figure 5.13
Five whys. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Table 5.2
FMEA objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Figure 5.14
PFMEA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Table 5.3
SEV scoring guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
Table 5.4
OCC scoring guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Table 5.5
DET scoring guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
Figure 5.15
Eliminate sources of waste. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
Figure 5.16
Application of Six Sigma tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
Figure 5.17
Comparisons of two processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Figure 5.18
Measurement errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
Figure 5.19
Control process variability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
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Figure 5.20
iTLS rapid problem-solving worksheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
Figure 5.21
Control supporting activities.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Figure 5.22
What and how for iTLS monitoring model.. . . . . . . . . . . . . . . . . . . . . . . . . . 231
Figure 5.23
Generic iTLS takt board layout.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Figure 5.24
Generic iTLS performance tally sheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
Figure 5.25
Remove the constraint and stabilize. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
Figure 5.26
Reevaluate the system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
Figure 5.27
Base-line assessment.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
Figure 5.28
iTLS Implementation road map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
Figure 5.29
High-level model for undesirable outcome of strategic planning.. . . . . . . . . 242
Figure 5.30
High-level generic model for collaborative strategic planning.. . . . . . . . . . . 243
Figure 5.31
Conversion of firm’s core values to balanced scorecard. . . . . . . . . . . . . . . . . 243
Figure 5.32
iTLS generic layout for balanced scorecard. . . . . . . . . . . . . . . . . . . . . . . . . . 247
Figure 5.33
Organization’s responsibilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
Figure 5.34
Different operating strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
Table 6.1
One-way ANOVA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
Figure 6.1
Lean and Six Sigma benefits.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
Figure 6.2
Lean, Six Sigma, and iTLS benefits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262
Figure 6.3
Lean, Six Sigma, and iTLS financial returns. . . . . . . . . . . . . . . . . . . . . . . . . 263
Table 6.2
Lean, Six Sigma, and iTLS Comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
Figure 6.4
Contribution percentage by method applied. . . . . . . . . . . . . . . . . . . . . . . . . . 265
Figure 7.1
A river flow operation network—laptop computer manufacturing.. . . . . . . . 269
Figure 7.2
V river flow operation network—a pick-pack-ship warehouse. . . . . . . . . . . 275
Figure 7.3
Relationship of increased uncertainty with the levels of two-headed forks.
Figure 7.4
T river flow operation network—automobile assembly. . . . . . . . . . . . . . . . . 279
Figure 7.5
I network—airline meal tray assembly operation.. . . . . . . . . . . . . . . . . . . . . 282
Figure 8.1
High-level process of developing a balanced scorecard. . . . . . . . . . . . . . . . . 286
Figure 8.2
Continuous improvement steering team configuration model. . . . . . . . . . . . 288
Figure 9.1
Inventory optimization model using TLS. . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
Figure 9.2
Inventory initial analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
Figure 9.3
iTLS seven-step process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299
Figure 9.4
Inventory analysis discovering step function. . . . . . . . . . . . . . . . . . . . . . . . . 300
Figure 9.5
Using a cause and effect diagram to identify key factors. . . . . . . . . . . . . . . . 301
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Figures and Tables
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Figure 9.6
Box plot of inventory before and after iTLS implementation.. . . . . . . . . . . . 304
Table 9.1
ANOVA indicating reduction significance. . . . . . . . . . . . . . . . . . . . . . . . . . . 304
Figure 9.7
Inventory position after implementation of the iTLS. . . . . . . . . . . . . . . . . . . 305
Figure 9.8
Goals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308
Figure 9.9
Spaghetti flow of the current layout. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
Figure 9.10
Cause-and-effect analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
Figure 9.11
FMEA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
Figure 9.12
New process flow after implementation of improvements. . . . . . . . . . . . . . . 312
Figure 9.13
Process time reduction monitoring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
Figure 9.14
iTLS model applied.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
Figure 9.15
The DBR model → D 5 drum, B 5 buffer, R 5 rope.. . . . . . . . . . . . . . . . . . . 317
Figure 9.16
Thinking tool applied for cause-and-effect determination. . . . . . . . . . . . . . . 320
Figure 9.17
Mobilize work teams.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
Figure 9.18
Buffer management.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
Figure 9.19
Application of SPC, Lean, and Six Sigma tools and techniques. . . . . . . . . . 322
Figure 9.20
Buffer performance and status dashboard in real time. . . . . . . . . . . . . . . . . . 323
Figure 9.21
Metallurgical plant expansion.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
Figure 9.22a, b, c, d
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Examples of four plants’ performances applying iTLS. . . . . . . . . . 324
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Preface This book is a result of a serendipitous meeting in 2007. Despite different career paths and experiences, we had each arrived at the conclusion that American industry was encountering a continuing, but unnecessary, decline in competitiveness. Over several decades many efforts to improve productivity had come and gone, both because they were ineffective and because in some cases they were counterproductive. We also believed that the situation was worsening because practitioners of the continuous productivity improvement (CPI) methodologies of choice—Lean, Six Sigma, and the Theory of Constraints (TOC)—often operated in a divisive rather than collaborative fashion. Each group seemed convinced that their methodology was the true religion. While they battled over who had the best solution, the competitiveness of many American industries continued to decline. We believe we know the core problem that caused previous improvement approaches to fail and are concerned that this obstacle will impair current efforts. We also know that when TOC, Lean, and Six Sigma (iTLS) are combined in a unique fashion results improve dramatically. A scientifically conducted study in the United States showed that iTLS projects produced more than four times the benefits of either Lean or Six Sigma projects. Subsequent experiences in other countries have produced even greater benefits. We have had an opportunity to implement iTLS in 15 countries in a variety of operations, including discrete and transactional environments, with phenomenal success. The resulting benefits include significant improvements in quality, productivity, and profitability. We also have developed an understanding of how these internal improvements can be further leveraged to increase sales, market share, and profits. The book is divided into two parts. The first is geared to senior decision makers—those who decide “if ” their company should adopt an iTLS approach. The second deals with the details of “how” and is directed at those responsible for implementing iTLS. Readers who would like more depth on any section of Part I can go directly to the matching chapter in Part II. iTLS concepts and principals are given relevant coverage in Part I and Part II with various depths. A small
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portion of the material in Part I has been repeated in Part II to provide continuity in the flow of ideas. Throughout the book we often use both “we” and “I” in describing our views and experiences. “I” is used when describing an experience unique to one of us, although we don’t normally distinguish which one. “We” is used when referring to our collective beliefs. If your intention is to learn how to systematically improve quality, process reliability, and throughput while creating a waste-less enterprise, then you should read on. This book is for you!
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Part I Leadership Summary
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1 Productivity, Growth, and Prosperity
During the last century, three waves washed over the shores of developed and developing countries, creating both wealth and freedom. These waves were breakthroughs in how companies managed their businesses, and all have deep roots in the automobile industry. Despite beginning in a single industry, these management systems eventually influenced many industries and countries. The first system was initiated early in the last century by Henry Ford at Ford Motor Company, the second was led by Alfred Sloan at General Motors, and the third resulted from the efforts of Taichi Ohno at Toyota Motor Company. Although these breakthroughs resulted in enormous increases in productivity, growth, and prosperity, they were not the result of improvements in technology, at least not how technology is commonly viewed. They were changes in how these three automobile companies managed their businesses. Today we stand on the verge of a fourth system of management, which promises similar benefits in productivity, growth, and prosperity.
HENRY FORD So what did Henry Ford do that was so earthshaking? We often view him as the inventor of the assembly line, a very efficient production process with negative overtones of subjecting people to mind-numbing repetitive tasks. Although there is truth in both viewpoints, they miss the magnitude of Ford’s accomplishment. Ford’s goal was extraordinarily ambitious, to say the least. He wanted to produce a reliable, dependable automobile that the common man, including those who produced it, could afford. In the early 1900s, only the wealthy could afford an automobile. Such a purchase was far beyond the reach of the great majority, causing most people to live their entire lives within a few miles of their birthplaces. Ford’s management system changed all that. Between 1909 and 1927, he produced and sold 17 million Model Ts while driving the price down from $970 to $290, and that was without taking into account inflation. Ford claimed that every time he reduced the price of the Model T by $1, he created another 1000 buyers. In addition to developing a more efficient method of producing automobiles,
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Ford devised a way to greatly increase demand for his product. At one point, he more than doubled the going wage of his workforce to $5 a day while reducing the workday from nine to eight hours. This wage increase allowed his workers to purchase the product they produced by simply saving this wage increase for about a year. This action not only made his workers customers, but it also forced many other companies to pay a competitive wage in order to retain their best workers, again increasing the number of customers for Ford’s Model T. The effect in the United States was profound and quickly spread to Europe. Economic activity exploded, and the ensuing wealth was much more widely distributed. In rural areas, which comprised most of the United States, families were no longer tethered to the plow and the beasts that pulled it, which sometimes were the farmers themselves. We know a great deal more about the results of Ford’s management system than we do about the system itself. We are going to call it a “river system.” The outputs of his system were Model Ts being purchased by customers, often as they rolled off his assembly lines. The inputs were raw materials like iron ore for metal parts, silicates for glass, and textiles for fabrics. The flow eventually became so seamless from raw materials to finished product that it represented a smooth, fast-flowing river system. The content or materials flowed smoothly, steadily, and directly throughout the entire system, like small creeks feeding larger streams and then a larger river—a river system in which there were no meandering flows, dams, or rapids. Ford focused on expanding the breadth of his river system, shortening the length of the various tributaries and making it flow faster and more smoothly. Ford poured the company’s earnings into expanding and improving his river system. At the onset, he basically produced engines and assembled cars. Using the profits generated, he began to produce more of the components needed in his cars. He eventually integrated his supply lines to the point that instead of purchasing and assembling components he was buying basic raw materials such as iron ore, sand, and textiles and converting them into components. He was so single-minded in the pursuit of reinvesting in his river system that other investors sued him in order to force distribution of some of the profits. The rate at which Ford produced cars was dictated by the number and speed of his assembly lines; they controlled the flow rate, because for nearly 18 years demand always exceeded supply. It made for a very efficient management system. Everyone from Henry Ford on down knew exactly how many of each part needed to be received, produced, and shipped each day. The financial system was equally straightforward. Productivity was simple to measure—the number of cars produced divided by the number of employees times $5 (the daily wage). While expanding the breadth and increasing the flow rate of his river system, he simultaneously reduced its length. In his River Rouge plant, it took only 28 hours for iron ore to be converted into steel for engines, body panels, and other parts and roll off the assembly line as a finished car.
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Ford’s system relied on relentless execution. Workers needed to produce a required number of parts day in and day out. There was an intense focus on reducing disruptions caused by defects, absenteeism, equipment breakdowns, and the like. As these disruptions were reduced, his river system flowed faster and more smoothly. When volumes grew, the pressure on the workforce increased. Workers were prized for their ability to execute more and more of the assigned tasks. This philosophy, despite the increased wages, eventually led to widespread dissatisfaction in the workforce, unionization efforts, and bitter strikes. Some have minimized Ford’s achievement by pointing out that, “You could get a Model T in any color you wanted, as long as it was black.” The color actually was midnight blue, but the point was valid. Ford built his river system to produce one product in one color—no variations or options were available. Product improvements were phased in so as not to disturb the flow. As his river system become more and more efficient, he lowered the price, causing demand for this unchanging product to grow. His river system was so powerful that demand for the Model Ts exceeded supply for over 18 years, despite a huge expansion in output. In essence, this was his management system—constantly expanding his river system, increasing its speed, shortening its length, and smoothing the flow. In the process, the expense needed to make a Model T continually declined, and his goal of providing reliable, affordable transportation for the average man was achieved. Despite the enormous success of Ford’s river system, it fell victim to the very success it created. The economic benefits that it helped produce created a more affluent society. Many people could now afford to purchase more expensive automobiles, ones that were more comfortable and distinctive. Competitors began to produce “closed sedans,” which greatly increased comfort. They also added improved features and increased the available styles—from roadsters to town cars—to more closely match market needs and desire for status and performance. The demand for a basic, reliable, and economical automobile began to decline. As a result, the market shifted from Ford’s black Model T to the greater variety and improved features of competitors’ automobiles. Ford’s river system was so successful that it changed the world it was serving and in the process caused its own demise. It was replaced by a new system more suited to managing increasingly complex organizations producing a variety of products.
ALFRED SLOAN General Motors’s strategy was totally different from Ford’s. It had 10 car lines, while Ford had only two. Ford owned the low end of the market, the Model T, and had a strong foothold in the high end with the Lincoln. GM’s 10 lines covered all the market segments. Its strategy provided the greater variety of body styles, features, and prices that the market was now demanding. However, managing this diversity required a totally different management system.
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A single-product river system like Ford’s could be effectively managed in a centralized fashion. The General Motors assembled by William Durant operated initially in a highly decentralized fashion. General Motors had purchased a number of other companies; some assembled cars and others produced components. Most of these companies were managed by independent-minded founders, many of whom continued to run their companies after acquisition by GM. The 10 car lines often competed against one another in an uncoordinated, overlapping fashion. The individual car companies purchased components from other GM divisions, which, in turn, often served competitors of GM, like Chrysler and Nash. GM’s various divisions, unlike Ford’s, were neither physically integrated nor coordinated by a centralized management system run by a dominating personality. Despite being in an excellent position to capitalize on the fact that the constraint was shifting from production to the marketplace, the lack of an effective system to manage this complexity nearly brought GM to its knees on two occasions. The first occurred in 1920, when uncontrolled expenditures for expansion and inventory created a cash crisis, resulting in Durant’s resignation and the eventual elevation of Alfred Sloan as GM’s leader. Sloan reined in many of the excesses of this extreme decentralization and resolved the 1920 crisis by somewhat arbitrarily controlling how capital was allocated. However, in 1924, following a boom year, optimism again prevailed in all the operating divisions and they all ramped up production. At that time, GM had little information on the level of retail sales or the amount of inventories in the field—a problem that Ford evaded for nearly 18 years. The magnitude of the problem was uncovered during a famous field trip that Alfred Sloan and Donaldson Brown (GM’s treasurer) made in May 1924. They visited major car dealerships and literally counted the cars on their lots. To their dismay, they discovered huge numbers of unsold cars and realized that their factories were producing at a rate far in excess of retail sales. Sloan later recounted that these findings led him to issue one of his few direct orders to the operating divisions. He ordered them, with a couple of exceptions, to stop producing cars and buying materials. GM’s management system was out of sync with the realities of the marketplace. These two experiences greatly influenced Sloan’s thoughts on how to manage a decentralized organization while maintaining corporate control of the entire enterprise. Sloan’s thinking had already progressed to the point that he understood that enterprises should be largely judged based on the return on investment (ROI) they generated. However, in a loosely integrated organization like a 1924 General Motors, how do you decide prices for materials transferred between divisions? A high price would benefit the producing divisions and penalize the receiving ones, and vise versa. Differences in prices impacted the ROIs of the various divisions and determined which ones received capital for expansion and which didn’t. If
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the divisions were truly independent entities, actual market prices would exist. Unfortunately, because they were part of the same corporation, prices for the internal transfer of products could be established only by management, not the marketplace. An answer was needed that made sense to both the corporation and its entities. It was a formidable challenge. GM was fortunate to have gained the services of Donaldson Brown as a top financial manager. DuPont had purchased a large portion of GM’s shares and had dispatched Brown, a valued executive, to ensure a good return on its investment. Brown was instrumental in developing decision-making tools that became the heart of a new management system, one that enabled GM, and eventually other companies, to effectively manage decentralized systems producing a variety of products. It allowed GM to make good economic decisions in this new world of organizational complexity and product diversity. Brown devised a way to calculate the cost of each product, which became the core of what we now call “cost accounting,” an approach that soon became the basis for management decisions at General Motors and throughout much of the industrial world. Today managers often act as if cost accounting were one of the original Ten Commandments, because decisions based on its use are often considered to be holy and beyond challenge. The key to cost-accounting management was the assignment of the major cost components—material, labor, and overhead—to individual products. Brown believed that the “cost of a product” could be calculated by adding up these three components. The costs of material and labor were easily attributed to products, because at that time these costs varied directly with the volume of production. When production increased, more material was purchased and more labor employed. Material and labor costs increased in direct proportion to the increased production. When production volumes declined, the opposite happened. So despite changes in volumes, the cost of the material and labor in a product changed only in proportion to changes in material and labor costs. Even in Sloan’s day overhead expenses did not directly vary with volume, but because they represented a very small part of the overall cost of a product they could safely be allocated to a product without introducing significant error. During the early years of GM’s rise, material and labor accounted for 85–90% of the cost of a product, whereas overhead expenses were only 10–15%. Although it’s easy to see why material costs would vary directly with production volumes, today it isn’t so obvious why labor expense should. One must remember that in the early part of the last century most factory workers were paid on piecework, not on the number of hours they worked. In addition, companies could hire and fire workers at will, which they did, often on a daily basis, in order to keep production volumes and labor costs tightly linked. Knowing the cost of a product enabled companies to decentralize decision making and deal with an increasing variety of products when establishing
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external selling prices and internal transfer prices. A division could establish prices that yielded acceptable returns on the required investment. If sales fell far short of expectations on this product, the return on investment would be much less than expected. The division then needed to either reduce the costs of making the product, find a way to increase sales, or discontinue the product. If not, additional capital would be deployed to products and divisions that provided more attractive returns. This approach worked for both automobiles and components such as engines, transmissions, and the like. Once the cost of a product was known, divisions could make a host of sound management decisions without specific direction from the corporation. If they could reduce the cost of making their products, profits and ROI would increase, and more capital would be available to grow the business. Investing in more efficient equipment had the potential of reducing both labor and material costs. The existing material and labor costs for a particular operation or set of operations could be compared with the expected material and labor costs from using a new piece of equipment. The resulting savings could be used to calculate a return on the investment needed for the new equipment. If the return was above a certain threshold the investment made sense; if not, then other alternatives should be considered. As a result, investment decision making could be pushed lower and lower in organizations. Corporate managers usually retained final signoff on major investments, but their role shifted to checking the assumptions behind the various requests and allocating capital to the most attractive opportunities. A second avenue for increasing profits was to reduce material and labor costs by “in-sourcing” production. A “make vs. buy” analysis could be done to determine if costs would be reduced by producing items internally instead of purchasing them. This analysis simply compared the vendor’s price to the cost of internal production (material, labor, and overhead). If the savings were sufficient to justify the required change, companies should make the item instead of buying it. Many “make-buy” decisions could be made at a local level without corporate authorization or awareness. A third way to increase profits and ROI was to increase the efficiency of the workforce so that more products could be produced by the same number of people. Time study and industrial engineering techniques were widely employed to determine the most efficient methods and time standards for each operation. Workers were measured and held accountable for meeting these standards. More efficient methods and tighter labor standards resulted in lower costs, an admirable goal. Unfortunately, this process created divisiveness between management and workers. Workers were naturally reluctant to find or divulge ways to improve an activity, because the result would be tighter standards for measuring their performance without any direct benefit to them. In essence, the ability to calculate product cost gave rise to a series of procedures for establishing transfer and market prices, evaluating investment opportunities, deciding whether to make or buy items, and driving down labor costs.
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Cost-accounting thinking was essentially a way to decompose an organization into smaller and smaller activities and then focus efforts on reducing the cost of these activities. This approach was in marked contrast to Ford’s more holistic way of improving his river system by surfacing and resolving obstacles to faster and smoother flow. Another impact of cost-accounting thinking that has had a lasting impact is how managers tried to balance the cost of carrying inventory (producing in advance of need) and the costs of changing over equipment and assembly lines. The basic mechanism for resolving this conflict was to calculate an economic order quantity (EOQ). A formula was devised to allow users to do this calculation with their own information. Unfortunately, this formula considered only local costs and not the impact of the EOQ quantity on the entire system. It often resulted in large-lot production for components, causing work to move through the system like rabbits through a python. In the automotive industry, it encouraged the building of assembly lines dedicated to producing only one model because it was more efficient for the assembly line to run continuously than periodically change it over to produce a second model. General Motors, thanks largely to Brown, was the industry leader in developing and using these tools to manage in a world of increasing variety and decentralization. With these tools, complex, integrated organizations could be dissected and managed more effectively. This decision-making approach helped propel GM into a position of world leadership in the automotive industry. It wasn’t long before GM’s suppliers and many other American and European companies adopted similar systems for managing their companies. The rise of cost-accounting management systems fostered initial increases in productivity, growth, and prosperity. They enabled companies to make sound business decisions that both reduced the cost of their products and increased their availability. We believe that cost accounting should be given a special place in a hall of fame commemorating industrial growth and prosperity. Over the ensuing years, a number of changes occurred that began to erode the effectiveness of these local-cost-based approaches. Several factors caused labor costs to become less and less variable with production volumes. Among these were increases in unionization, changes in laws and regulations, and evolving societal norms. Workers were increasingly paid based on the hours they worked, not on the number of products they produced. Managements were less able to hire and fire workers based on short-term production needs. As a result of losing flexibility in such decisions, managements became increasingly focused on achieving local efficiencies, which encouraged them to build inventories well in advance of actual sales. As a result of increasing inventories, their river systems lengthened and become more removed from actual customer demand. In addition, increased mechanization and improved efficiencies significantly reduced labor cost as a proportion of a product’s cost. This trend became so pronounced that the U.S. automobile industry eventually signed a labor agreement
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that essentially made labor a fixed rather than variable cost. Workers not needed for the current level of production were allowed to be idle or participate in makework programs (like painting the fire hydrants in their community) rather than being furloughed. During these times, they were paid approximately 90% of wages they would have earned if they were producing products. Success in using cost-accounting techniques also caused material and labor costs to decline as a percentage of a product’s cost. Investment in more efficient equipment and making, rather than buying, more products reduced the proportion of material and labor in a product’s cost. At the same time, overhead became an increasingly larger portion of the cost of a product. The automobile industry had shifted from selling a Model T, whose design barely changed for 18 years, to annual product redesigns. The engineering, financial, and marketing staffs needed to develop, support, and sell this increased variety of products grew at a much faster pace than material and labor costs. Fixed costs, which were a mere 10–15% of General Motors’s product costs in the late 1920s, skyrocketed to 50–60% near the end of the century. At that point, instead of 85–90% of a product’s cost varying with volume, it was only 30–40%. This shift greatly undermined the soundness of the techniques used for pricing, investing, and deciding whether to purchase or make components, yet these cost-accounting techniques continued to be widely used and began to produce results that had negative, rather than positive, economic consequences. For specific examples, see Chapter 1, Part II. In addition, the use of EOQ thinking often resulted in large inventories that needed to be scrapped or sold at reduced prices when overproduction and model changes created obsolete parts and excess quantities of the prior years’ models. The negative effects of dedicating assembly lines to single models highlighted this problem. When actual demand exceeded the capacity of an assembly line, there would be a shortage of cars consumers wanted to purchase, resulting in lost sales. An even more common problem occurred when an assembly line overproduced models that were not selling. Because labor efficiency was a very important measure and workers could not be furloughed to save costs, plants often produced cars well in advance of actual sales. Unfortunately, in order to make room for the annual new model introduction, many of these cars needed to be sold at a discount. Over time, consumers noticed this pattern, causing many of them to defer purchasing a new car until dealers began offering yearend discounts, further magnifying the problem. The final and possibly most devastating impact of cost-accounting thinking was how it valued inventories. It employed a value-added concept that assumed that as raw materials are converted into finished products, the labor and overhead associated with these activities should be added to the raw material cost to obtain the cost of the product. These product costs were then used to value inventories. When the level of finished goods increases, a portion of the increase occurs
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because of the labor and overhead in the product cost. During that reporting period, the labor and overhead costs that increase finished goods inventory are excluded from the calculation of profits for that period. A company’s profits can actually increase simply because the level of finished goods increases. The opposite happens when the inventory is reduced. The labor and overhead that had previously been capitalized are now counted as additional expense, lowering the net profit for that period. When managers are rewarded based on net profits, the temptation to inflate profits by increasing inventories is often unavoidable. This distortion was not significant when directly variable costs were a large portion of a product’s cost. However, when they became a much smaller part, it created a serious problem. The most devastating example I am aware of occurred as the result of a highly successful experiment. This particular automobile company tested a different way of distributing cars to dealers. In one state, it shipped a portion of the cars that dealers ordered to a central distribution point rather than directly to the dealers. The distribution center had some capability to modify cars to fit the specific needs of the buyer, such as changing the seats, audio systems, and the like. As a result, the dealers maintained smaller inventories on their lots, but with enough variety that consumers could see and drive the various models. If the consumer wanted to purchase a car with specific features not currently available on a dealer’s lot, the dealer would check the central inventory to see whether the exact car the customer wanted could be made available in a day or two. If so, a sale was made. The experiment was a great success; inventories and shipping costs were significantly reduced. Most important, sales rose because more consumers could quickly get the exact car they wanted. Despite the success, however, the car company decided not to expand it to other models. So why didn’t the car company make it standard practice for all models? The simple reason was that such a system would significantly reduce the amount of cars in the field, which meant a one-time drop in sales to dealers, even though sales to consumers would increase. This inventory reduction would have caused a temporary, but significant, drop in the company’s profits. Fearful of Wall Street’s reaction, management decided not to implement the system company wide. Unfortunately, the inertia of long accepted and once successful way of making decisions tended to overwhelm “facts”—old habits die slowly. A marvelous example of this inertia occurred in the English Navy when it dominated the seas. Scurvy was a debilitating disease and a common problem among sailors, pirates, and others who spent extended periods at sea. In 1536, a French explorer, Jacques Cartier, learned from natives along the St. Lawrence River that he could save the lives of men dying from scurvy by using a citrus tonic. In 1753, the British Royal Navy finally approved a lime-based treatment, which incidentally is why British sailors are still referred to as “Limeys.” The pain of this inertia to change extended over 200 years and resulted in an enormous amount of misery.
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The change in the elements that comprised a product’s cost eventually became so widespread that they made the use of cost-accounting management not only obsolete, but actually destructive. The good economic decisions that resulted when most of a product’s cost varied with volume became bad economic decisions when much less of the cost varied with volume. In addition, the focus on optimizing local decisions resulted in negative effects on the total system. Ford’s idea of a smooth, fast-flowing river system dissipated as supply chains became more clogged with inventories and the results of local-cost thinking. Inertia has blocked change to a decision-making system more suitable to the current environment and has devastated many companies. It’s similar to the rise and fall of Ford’s river system. The seeds of its downfall grew as a result of its success, and they still infect decision making in many companies today.
TAIICHI OHNO Sloan’s management system, like Ford’s, was developed in the U.S. automotive industry and eventually spread to other industries and countries, greatly benefiting those who adopted it. The next wave of improvement, however, originated in Japan’s automobile industry. Taiichi Ohno’s management system, often referred to as the Toyota Production System (TPS), had a huge impact there and reverberated throughout other countries. It’s acceptance in the United States and Europe, however, has not been universally positive, both because of inertia and a lack of understanding as to why the Toyota Production System was so effective. This misunderstanding was in part due to Ohno’s successful efforts to mislead and confuse non-Japanese companies. After the Second World War, the bicycle was the standard mode of transportation in Japan and only the wealthy few could afford an automobile. Ohno’s goal was not so different from Ford’s. He wanted to help Japan become a modern industrial nation by producing an automobile that could be purchased even by the workers who produced it. Why was Ohno’s Toyota Production System a leap forward from Ford’s? Essentially they were both highly efficient “river systems” with the same types of inputs and outputs. However, Ohno built a river system that worked for multiple products with uncertain demand. Unlike Ford, Toyota produced several models in a variety of colors and with a number of options. Creating an efficient river system for such an environment was much more difficult. Ford had developed a highly integrated supply system with dedicated manufacturing processes that produced the same quantity of each item day in and day out. Volume increased only when new assembly lines were added or when the productivity of the existing lines increased. The assembly lines and most
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machines were dedicated to make the same item, and machine changeovers were rare, which greatly simplified synchronizing the flow of materials. Ohno had no such luxury. While the demand for the Model T exceeded Ford’s capacity for 18 years, demand for the various Toyota models was uncertain and subject to frequent change. Forecasting sales of the various Toyota models was compounded by the annual introduction of new models by competitors. In addition, Ohno had to produce different models that were assembled from a variety of parts. Since he couldn’t dedicate equipment, he had to frequently changeover equipment in order to produce the needed parts. Ohno faced another obstacle Ford did not have to deal with: the widespread acceptance of cost accounting for managing complex production organizations. Ohno told me that cost-accounting thinking was the biggest obstacle he had to overcome in developing his system. When he eventually surmounted this obstacle, he gained a competitive edge, because many of his competitors still have not yet overcome this hurdle. Ohno could have dealt with these obstacles in the same way as other car companies—by producing large batches of the various parts and models to avoid changeovers and obtain local production efficiencies. The result would have been dams (piles of inventories), rapids (shortages that required expediting), and meandering flows in his river system. It also would have required maintaining a large supply of cars in the showrooms to buffer the assembly lines from changing consumer tastes. It was clear from my discussions with Ohno and from his writings that he rejected these options. He spoke clearly and forcefully about developing a smoothflowing river system that closely linked actual sales to the assembly of cars, production, and receipt of components. He believed it was the only way that Toyota and Japan could compete with entrenched, well-financed competitors in the United States and Europe. He knew that Japan initially had some advantage due to low labor costs, but that this competitive edge would eventually disappear as Japan became a modern industrial nation. He believed he had to devise a more efficient management system than his competitors in order to reach his goal. He spent over 40 years developing and refining such a system. The results of his efforts speak for themselves. In addition to the three-headed hurdle of uncertain demand, machine changeovers, and cost-accounting thinking, Ohno had to rely on vendors for many components. As a result, he had little direct control over a large part of his river system. In order to have a fast, smooth-flowing river system like Ford’s, the assembly of cars, production of components, and receipt of materials needed to be tightly linked to actual sales. This required flexible rather than dedicated assembly lines—lines that could produce several models. It also meant that the machine shops, which produced the major components such as engines and transmissions,
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had to be equally flexible, necessitating very quick changeovers. The production of large batches of parts was totally incompatible with his river system, although it was in line with cost-accounting thinking. Finally, he needed vendors who were able to synchronize deliveries with Toyota’s needs. Two factors greatly influenced Ohno’s efforts. A layoff of workers in June 1950, during a postwar period of great hardship, resulted in a bloody strike, causing Toyota management to vow to avoid such layoffs in the future. The second was the sincere belief that workers were the “local experts” and should be treated accordingly. The first influence convinced Ohno that Toyota couldn’t afford the boom and bust cycles caused by building more cars than consumers were buying; this practice caused additional hiring and increased production followed by severe cutbacks and layoffs or unacceptable financial losses. His system had to be much more closely linked to actual sales in order to avoid these fluctuations. Second, he wanted to develop a system that used not just the physical strength of his workforce, but also its knowledge and skills. Ohno pursued the development of his river system sequentially. He started at the assembly line and figured our how to intersperse different models. By producing a variety of models each day he was able to more closely synchronize production with sales. This capability allowed him to greatly reduce Toyota’s reliance on forecasts and avoid both shortages of cars that were selling and excess inventory of those that were not. This approach was met with considerable resistance by the workforce because the workers believed it was more efficient to assemble a large quantity of one model and then switch over and assemble a large quantity of a different model. Ohno knew such an approach would make assembly more efficient but the company less efficient. Another example of his drive to link production closely with sales was the development of models that could be either a left-hand or right-hand drive. These cars were shipped to a distribution center in Europe where the drives were installed, depending on actual sales in order to more closely link production and sales. After a number of years of development, the mixed-model assembly process was working well. Ohno then moved to the machine shop, where he met even greater resistance. Workers were accustomed to producing parts in large lots in order save the cost of extra setups. The equipment used to produce parts was the same that was being used by Western companies and had been designed for efficient large-batch production. He told me that this thinking was deeply imbedded in the minds of his people. Workers believed it was inefficient to frequently change over their machines. He stressed to them that what was important was the efficiency of the company, not the efficiency of a particular operation. When I asked how he persuaded them to do it his way, he smiled and said, “I tried logic and persuasion, but neither worked, so I used a gun! I threatened that if they didn’t do it my way I would literally shoot them. It took several years, but they finally did it my way.”
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He claimed that the resistance to small-batch production was strictly psychological. As an example, he talked about large stamping presses that previously required more than eight hours of a skilled technician to do a changeover. By closely analyzing these activities, he was able to change the press over in less than five minutes using only semiskilled workers. He overcame order-quantity thinking by pointing out that if the time for performing a setup were greatly reduced then the formula would suggest a small rather than a large batch. After several years of effort, the machine shop was able to produce parts in small batches and synchronize its production with assembly line needs. His next step was to align vendors with his internal river system. He began by inviting all vendors to a meeting at Toyota to both explain and demonstrate what Toyota had accomplished. He told them that Toyota would assist them in changing their systems, but he did not insist that they do so. Again, over a period of several years the vendors drew on Toyota’s experience and made the changes necessary to synchronize their deliveries to Toyota’s schedules. Ohno’s approach created the structure for a river system, but in order to make it flow more quickly and smoothly he needed a way to systematically eliminate the myriad factors that disrupt the flow of work. Once the major disruptors, like long setup times, were eliminated, he developed a system to both control the flow of work and to surface problems that disrupted flow. The system, called kanban, allowed for inventories of predetermined size between operations or groups of operations. These inventories were used to decide when an operation should produce and when it should stop producing. If the inventory immediately following an operation was at its preset level, then the preceding operation should stop producing. If the inventory was less than the preset level, then the preceding operation should continue to produce. The purpose of the inventory was twofold. First, it served as a buffer to protect the flow of work from all but the worst disruptions, such as defects, machine problems, and worker performance. Second, it used disruptions as a reason to temporarily halt production and focus on fixing the cause of the disruption. The workers’ knowledge of the process often played an important role in resolving the cause of the disruption. While Ohno’s system, like Ford’s, drove for relentless improvement, it relied on both the physical capabilities and the knowledge of the workforce. Once the disruption was resolved, production resumed. When work was again flowing more smoothly. Ohno would further reduce the amount of inventory in the system so that variations would again cause the system to periodically stop. Each stoppage was used to identify the cause so that improvement efforts could be initiated to remedy the problem. When enough variations were eliminated so that smooth flow resumed, more inventory was removed so that stoppages once again occurred. This trial-and-error process was repeated over and over again to identify and eliminate the causes of disruptions and enable Ohno’s system to flow more rapidly and more smoothly.
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Ohno’s process of identifying problems came at a steep price. Whenever a line was stopped it meant that throughput was lost, which negatively impacted profits and ROI. Once his kanban system was in place, Toyota and its vendors continued to refine the system by reducing the variability that caused such stoppages. They systematically reduced the amount of inventory in the system so that smaller and smaller disruptions affected the flow, which resulted in more efforts to reduce these disruptions. After repeated efforts over many years, he finally had created a fast, smooth-flowing system from his vendor base to the purchasers of Toyota’s automobiles. While it took nearly 40 years to convert his idea of a smooth, fast-flowing river system into reality, in the end his efforts yielded great results. In the early 1990s, Eli Goldratt and I met with Taiichi Ohno to better understand his approach. See Part II of this chapter for a detailed description of this fascinating session. We learned two really important lessons. First, all of Ohno’s efforts to improve his Toyota Production System were geared primarily toward one objective—selling more cars. Improving quality did reduce the cost of production and helped increase profits. However, Ohno stressed, the greatest leverage from his quality efforts came from the increased sales that resulted from the superior quality and reliability of their cars. Following this quality emphasis, they focused on implementing a “just-in-time” philosophy to reduce inventories. Ohno again stressed that the larger benefit came not because of the reduction in investment but because lower inventories allowed them to become more closely connected with consumer demand. As Ohno’s river system became more and more effective, other Japanese companies, including his competitors, came to learn the source of Toyota’s success. He openly shared how his system worked and why it was so effective. A number of companies adopted his approach, which contributed greatly to Japan’s rise in the industrial world. He also described to us how he went to great lengths to confuse Western visitors as to why his system worked so well for fear they would copy it before Japan could fully compete with Western companies. At the time of our meeting, he had become comfortable with the strength and capability of Japanese industries and was now willing to share the source of their success. Although it had taken Ohno nearly 40 years to develop and refine his system, during our meeting we provided an insight that made him reevaluate how he could have developed his river system much more quickly. This incident is described in detail in Chapter 1, Part II. Essentially, it involved refocusing his improvement efforts. In his drive to speed and smooth the flow of his river system he would halt production for anything that disrupted flow, even though it resulted in lost throughput. Our simple example illustrated how Ohno could have developed his system much more quickly by first placing most of his inventory in a buffer prior to the operation that was most constricting flow (bottleneck) rather than scattering inventory throughout the system. In this manner, very few disruptions would ever starve the bottleneck, avoiding lost throughput. The disruptions
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that most deeply or most frequently threatened depletion of the buffer could be traced to their causes. The sources of these disruptions would be the prime candidates for improvement. Essentially, the degree of buffer penetration would provide a Pareto list of improvement opportunities. Using this approach, throughput is increased, less inventory is required, and the causes of disruptions are prioritized. This laser-like approach enables a company to much more rapidly develop a smooth, fast-flowing river system without the loss of valuable throughput. We knew that Ohno agreed when he exclaimed, “If I had thought about it that way I could have developed my system in less than half the time.” He said that he didn’t have a way to know which disruptions were most important, so he worked on eliminating each disruption as it appeared. The obvious question is whether there is a seed of destruction in Ohno’s system that will cause its demise. We think the short answer is no. However, we do believe that by building on the lessons of Ford, Sloan, and Ohno, a much superior and more widely applicable management system can be constructed. The major drawbacks to Ohno’s system are twofold. First, it takes a long time to embed it in the DNA of a company and achieve a true competitive edge. Second, it has been used primarily in higher volume, stable production environments. Unfortunately, many companies do not have this ideal environment and certainly few can wait many years to achieve similar benefits.
iTLS—A FOURTH WAVE What is this new wave that might produce another tsunami of economic benefits and growth? If Ford needed 5 years, Sloan 15, and Ohno nearly 40 years to perfect their management systems, how long will it take for a fourth wave to have a real impact? We believe that, for the most part, the knowledge and techniques needed to create a fourth wave already exist and have been tested and proven in a variety of companies, industries, and countries. Much of the heavy lifting has already been done. It is now a matter of assembling the pieces in a coherent fashion so that they can be repetitively used. As is evident from the title, iTLS integrates the Theory of Constraints (TOC), Lean, and Six Sigma, in a unique and effective fashion. No one of these methodologies alone contains all the elements needed to create a fourth wave. TOC’s strength lies in providing focus for improvement efforts. Its emphasis on generating more throughput by breaking constraints to increase volume provides the needed direction for all improvement efforts. TOC, however, lacks many of the analytical tools and techniques needed to expand constraint capacity, eliminate disruptions in the flow of work, improve quality, and reduce variability. TOC also does not take in account human resources values and significance.
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Lean, in constrast, is replete with tools for reducing the seven wastes (muda) but lacks a global focusing mechanism for prioritizing when and where to attack these wastes. Effort to reduce some wastes can itself be a waste. Six Sigma’s strength lies in its statistical tools for reducing variations in processes. Like Lean, it is short on a focusing mechanism for prioritizing actions for reducing these variations and removing the most important wastes. Because of the similarities, overlaps, and shortcoming of these three methodologies, it has not been a simple process to develop an iTLS system capable of creating a fourth wave of prosperity, productivity, and growth. We believe that the unique combination we have developed and tested has the potential to be such a system. We call this unique combination iTLS™®. From this point forward, we use the terms TLS and iTLS™® interchangeably. So what is required to implement this new management system? It consists of three major elements. The first is the development of an overall strategy to manage our river systems. We refer to such a strategy as a Throughput Operating Strategy (TOS), because the primary emphasis should be on continually growing throughput (revenue) and profitability. Every organization produces and delivers its products or services through a network of activities. When these networks are mapped so that the flows of activities are displayed vertically, they take on one of four shapes, or a combination of these four shapes. The fact there appears to be only four shapes greatly simplifies the development of a Throughout Operating Strategy. These four shapes roughly resemble either an A, V, I, or T. In an A network, a variety of inputs, or materials, typically converge to form a single or small number of end items. Organizations that fabricate and assemble products are typically A structures. Ford, Sloan, and Ohno all dealt with A-shaped river systems, although they varied greatly in complexity.
A V-shaped river system may initially look like an upside down A, but it has entirely different characteristics. Instead of the flow of items converging to form a
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small number of end items, they diverge to form a large, sometimes a very large, number of end items. Examples of V structures include oil refineries, semiprocess industries (e.g., steel, aluminum, paper), and operations that convert animals into a wide variety of food products. Many distribution systems, reverse logistics, and repair operations also take on the shape of a V.
An I structure is a river system with a singular flow, in which products neither converge nor diverge. A singular input is processed through a number of operations and emerges as a single item. Wafer fabrication plants and assembly lines are examples of I structures. In a wafer plant, a wafer of silicon may be processed through more than 300 operations, yet it emerges as a single wafer, albeit with all types of circuitry etched on it.
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The T structure is probably the least common of the four river systems. It’s essentially a network in which a number of items flow to an assembly point where they can be combined in myriad ways to form a much larger number of end items. An excellent example of T networks are automotive companies, where a few thousand parts can be combined to produce millions of unique automobiles, especially when color, fabric, audio systems, and so forth are considered.
These four networks, or combinations of them, represent the river systems by which almost all products and services are produced and distributed. Once the shape of a network is known, the next step is to select a control point to synchronize production and sales and to control when materials are released into the system. In most organizations, work is released much earlier than necessary, which inevitably creates confusion about production priorities. This confusion is magnified by local measurements, which often cause people to work on what is easy, or beneficial to do, rather than what is necessary to do. These murky and changing priorities both slow and disrupt flow. The control points provide the linkage between the procurement, production, distribution, and consumption of a company’s products. It is important to distinguish between the sale of a company’s products and their consumption. Sloan and Donaldson discovered to their dismay that just because dealers were buying cars, it didn’t mean that they were being sold to consumers. The second element in our iTLS system is a robust methodology to continually speed and smooth the flow of the river system—a superior kanban system. It begins with a process for both exposing the root causes of the disruptions most impacting throughput and then providing tools for eliminating the sources of these disruptions. Finally, it contains a process for prioritizing disruptions so that our improvement efforts are focused on the areas that will have the most positive impact on the total system. Without such a process, we are doomed to using Ohno’s trial-and-error method. Among the most important of these disruptions
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are perceived bottlenecks to the flow. Even worse are bottlenecks that seem to float or shift within the river system. Our experience is that very few, if any, organizations have real production bottlenecks that cannot quickly be broken with the right focus and effort. We have found that a minimum of 25% more can be produced in every organization by applying proven tools to perceived bottlenecks. Archimedes, a famous Greek philosopher, supposedly claimed that “If I had a long enough lever I could move the whole world.” Expanding the capacity of our organizations by breaking bottlenecks usually means that the organization can produce additional products at essentially the cost of the purchased materials—a very, very long lever. It is estimated that these three methodologies (TOC, Lean, and Six Sigma) comprise more than 90% of current improvement efforts. Unfortunately, the practitioners of these methodologies often spend considerable time touting their methodologies and defending their approaches rather than trying to determine if and how they could be combined into a much superior system. The good news is that the environment is beginning to change. My research and practice concluded that a successful TLS methodology should use TOC to determine where to focus improvement efforts—to set the priorities. Based on these priorities, Lean, with its array of tools for reducing waste, is best used to identify and eliminate the causes of these wastes. Then, to stabilize the processes and achieve the desired statistical control for sustainability, we employ Six Sigma tools. The combination of Lean and Six Sigma focused by TOC, which I call iTLS, provides a proven methodology for smoothing and speeding the flow of work in our river systems. At the first Continuous Productivity Improvement (CPI) Conference in 2006, sponsored by Weber University, I presented the results of an extensive and rigorously conducted 2.5-year test of a unique comparison of the iTLS, Lean, and Six Sigma methodologies. The results of this experiment demonstrated that iTLS yielded four times more benefits than projects using either Lean or Six Sigma. Even more telling was the fact that iTLS projects were responsible for 80% of the financial benefits even though they were used in less than 30% of the plants. More than 211 practitioners in 21 plants conducted 105 projects, which demonstrates the validity of the results. I made a similar presentation to the American Production Inventory Control Society (APICS) during the same year and in articles in APICS’s magazine describing the enormous effect on profitability, agility, and quality. Frankly, I was overwhelmed by the interest and excitement created among the CPI practitioners. In 2007, I was a keynote speaker at Goldratt’s TOC-ICO conference in Nevada to introduce iTLS™® to TOC practitioners and report on the increased benefits that result from the interaction effects when TOC, Lean, and Six Sigma are combined in a logical sequence. iTLS™® was warmly embraced by the TOC practitioners, including Eli Goldratt, its developer.
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Since 2003, iTLS™® has been used by more than 4000 practitioners in more than 70 plants in the United States, Canada, United Kingdom, Germany, France, Finland, Israel, Mexico, Brazil, Ireland, Spain, Hungary, China, India, South Korea, and Singapore. As our iTLS process has become refined and better understood, the results have improved by more than 50% over the initial test. One day in the fall of 2009, I received a call from two Brazilian consultants, Celso Calia and Fabiano Almeida, partners with Goldratt Associados, de Brasil, who specialize in implementing continuous improvement processes in Brazil’s heavy industries. Historically, they had focused on using TOC. Celso had read my articles and other materials published by APICS and wanted to arrange a meeting. I agreed and at a meeting in Dallas, Texas, he gave a PowerPoint presentation that showed how they had successfully implemented iTLS™® and achieved very significant results. He explained that using TOC had allowed them to make significant positive changes, but variability in the processes was killing them, and they could not understand why. They seemed to be constantly chasing ghosts and floating bottlenecks. With considerable reservations they decided, at least temporarily, to shed their current paradigm and apply what they had learned from my articles and presentations. They were pleasantly surprised that they were able to not only achieve significant process improvements, but were also able to systematically control process variability from the onset. Now they wanted more . . . they wanted to learn more about the nuts and bolts of the iTLS process and its details, and asked if I could help them. They were very proud of their accomplishments and so was I . . . like a proud grandparent! Having agreement on a 40,000-foot view of how these three methodologies should be combined is a major step forward. However, given the scope of the various tools and techniques and the fact that in some cases they overlap, developing a ground-level working process was not a simple task. It was similar to trying to combine the best racing engine, the best transmission, and the best suspension system in order to produce a superior race car. In summary, creating a smooth, fast-flowing river system requires three elements. First, we need to understand the shape of the network(s) by which we deliver our products or services and develop an appropriate TOS for managing the flows. Ideally, the TOS will encompass procurement, production, and distribution so that we can more closely connect all these activities with the marketplace. Second, we need a robust process for prioritizing and removing the disruptions that impede rapid and smooth flow so that the time needed to turn materials into purchased products shrinks. Third, we need a combination of courage and consensus to make the transition from managing with a local focus to a more global one. The broader and deeper the management consensus, the less courage is needed and vice versa. Today, we oscillate between local and global actions. Almost everyone who has worked in an organization that produces and delivers products and services
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has experienced the hockey-stick impact of the end of the month (sometimes it’s the end of the quarter); during the first portion of the period we focus on local performance measures (efficiencies, limiting overtime, long production runs, etc.). As we near the end of the period, the emphasis turns sharply to the global focus of meeting shipping goals. We then take the opposite actions in order to get as much produced and shipped as possible. Once the new period starts, we immediately revert to the old ways of operating. This oscillation of effort continues month in and month out, considerably disrupting the flow of our river systems. The Fourth Wave Management System, iTLS, provides a consistent method for running organizations. Educating employees in the new TOS helps refine it and engenders both understanding and acceptance, providing a solid consensus for change. When measurements are closely aligned with a more global approach, they reinforce the desired behaviors. The timetable for adoption of a Fourth Wave Management System depends almost solely on overcoming inertia, because the changes needed are mostly in policies and mind-sets, not physical changes. Hopefully, companies will move much more quickly than the British did in using citrus drinks to eliminate scurvy. Great organizations with excellent performances still need to constantly stay on top of Voice Of the Customer (VOC) and the Voice Of the Processes (VOP), particularly when it comes to favoring costs savings ahead of safety and quality. The slightest slip in those dimensions can cause catastrophes that damage the organization’s reputation, good will, and market health. The organization’s past performance cannot necessarily guarantee present and future health without a commitment to constant improvements that strengthen the basic fundamentals that were causes of successes for the organization. Let’s take a moment and reflect on a devastating situation in 2010 with the Japanese automobile industry, causing Toyota to recall millions of its vehicles due to manufacturing defects. Toyota recalled over 8,000,000 of its vehicles. The recall included many models, such as the legendary Prius hybrid automobile. Aside from losing nearly one billion dollars a month in cost of production shutdowns, on top of the mammoth costs for the recall for the millions of vehicles, Toyota’s pristine consumer confidence was severely bruised. Its stocks devaluated by double digits in matter of only a few weeks as the automaker’s defects became public knowledge. Toyota, the icon of auto-making quality and technology, became the subject for the stand-up comedians! Toyota’s leading market position in the auto industry came under question and began sliding backwards. This became an opportunity for the other automakers to penetrate into Toyota’s market share, which previously was a protected fortress. What do you think happened to Toyota? Why didn’t the famed Toyota Production System (TPS) protect this once-fine organization? Did it not seem as though Toyota lost focus on what was important, allowing reliability variability to sneak into its multinational operations? The TPS has been the cornerstone of
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its impressive quality and reliability record. But as discussed earlier, the Lean system has its limitations. Auto industry failures were not limited to Toyota. The National Highway Traffic Safety Administration reported 492 recalls for the same year, 2010, involving more than 16.4 million vehicles. Among them were: GM with 1,300,000 vehicles, Nissan with 539,864 cars, Honda with 410,000 Odyssey minivans, and Ford with 18,000 Fusion and Mercury hybrids. We believe that our proposed application, iTLS™®, provides the needed long-term protection for organizations’ profitability, reliability, and agility. Frequently, well-performing organizations assume that the challenges for achieving excellence have been met. These organizations often don’t invest in the additional resources needed to sustain their competitive edge. That is why many companies have up-and-down performances. When performance is poor, they spend energy and resources to improve things, then, as the metrics indicate necessary improvements have been achieved, the organization relaxes and risks relinquishing their sustainability efforts. Over time, performance plummets again, and this vicious cycle repeats. We believe that by properly implementing iTLS, significant bottom-line benefits will appear within a couple of months and that within one to two years many companies will have more than doubled their profits. We know that making such claims is extremely dangerous, not because they are not possible, but because of the reaction of you, the reader. You may be inclined to immediately put down this book and dismiss us. It’s a totally natural reaction, because these claims are so far beyond most people’s personal experience and intuition that they assume there is no chance of their being valid. Smart, dedicated people in many companies have worked very hard and for a long time in order to squeeze out much smaller gains. If our claims are true, it suggests that we have been either really stupid or that some magic bullet has been invented. Of course, neither of these is true. We simply ask you to read on to understand what’s involved in this Fourth Wave Management System. If it makes sense to you, try it. If it works, extend it and share it with others to help prevent future economic and industrial disasters like the one we currently experiencing.
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Index
5S-CANDO, 140–141
Brue, G., 329 Brussee, W., 329 BSC. See balanced scorecard (BSC) business process reengineering (BPR), 26–27 business value-add (BVA), 131 BVA. See business value-add (BVA)
A A networks, 67–69, 268–273 accounting, cost, 7–9, 92–96 activities, control of, 227–230 agility, 148–150 analysis of variance, 333 analyze, 159–160 Anderson, M. J., 329 Aquilano, N. J., 329 Arai, K., 331 assembly lines, 4 assignable cause, 333
C calibration, 334 capability, 334 capability index, 334 capability study, 334 capable process, 334 Carnell, M., 330, 332 Cartier, Jacques, 11 case studies Celso Calia, 314–325 electronics manufacturer, 296–306 inventory, 293–296 valve assembly, 306–314 Vorantim, 318–325 cause-and-effect diagram, 334–335 Cavanagh, R., 331 CCPM. See critical chain project management Celso Calia case study, 314–325 centerpoint, 335 champion, 253 Champy, J., 330 characteristic, 335 characterization, 335 Chase, R. B., 329 checklist, 335 Chowdhury, S., 331 common cause, 335 confidence interval, 335
B Bakerjian, R., 329 balanced scorecard (BSC), 240–244 components of, 244–249 Ballis, J. P., Sr., 329 Ballis, John, 110 Barnard, W. W., 329 behaviors, motivating, 77–78 Benbow, D., 329 Benson, G. P., 330 bias, 333 Black Belts, 50 blocking, 333 Bluman, A. G., 329 bottleneck, 16 box plot, 333 BPR. See business process reengineering (BPR) Breyfogle, F. W., III, 329 Brown, Donaldson, 6–7, 9, 83
347
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348
Index
constraint, 334 exploitation, 208–211 removing, 230–236 constraint management, 129 continuous improvement. See continuous productivity improvement (CPI) continuous productivity improvement (CPI), 108, 195 control, 160–161, 335 control chart, 335 control factor, 335–336 control group, 336 control limits, 336 control point, 72–73 control process variability, 221–227 COPQ, 336 correlation, 336 cost accounting, 7–9, 92–96, 100 cost of poor quality. See COPQ cost reduction programs, 25 Cox, J., 330 CPI. See continuous productivity improvement (CPI) credibility, of measurements, 225 Creveling, C. M., 329 critical chain project management (CCPM), 42, 249–250 critical characteristic, 336 critical parameters, 336 critical to quality, 336 Crosby, P. B., 329 Crowther, S., 329 CRT. See current reality tree Cupello, J. M., 32 current reality tree (CRT), 41 customers, 244–245, 336
D data-driven, 336 De Feo, J. A., 329 defects per million (DPM), 337 defects per unit (DPU), 337 define, 158–159 delivery performance, 245 demand driven, 143–144 Deming, E. W., 329 Design for Six Sigma (DFSS), 49, 161–166 design phase, 162–163 Design, Measure, Analyze, Improve, Control (DMAIC). See DMAIC design, optimize, and verify (DOV), 161 DET. See escaped detection, probability
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Detriot Motor Company, 89 DFSS. See Design for Six Sigma (DFSS) direct labor reductions, 169 distribution, 336 DMAIC, 46–49, 158–161 dot plot, 337 DOV. See design, optimize, and verify (DOV) DPM. See defects per million (DPM) DPU. See defects per unit (DPU) drum-buffer rope system, 104 Durant, William, 6 Dusharme, D., 329
E economic order quantity (EOQ), 9–10 effect, 337 EI-Haik, B. S., 332 electronic manufacturer case study, 296–306 enterprise resource planning (ERP), 26, 186 EOQ. See economic order quantity (EOQ) ERP. See enterprise resource planning (ERP) escaped detection, probability (DET), 216, 220 evolutionary operation (EVOP), 337 EVOP. See evolutionary operation (EVOP) expert, 253–254
F factor, 337 factor level, 337 factor range, 337 factorial experiment, 337–338 failure mode effect analysis (FMEA), 215–217 Farah, K., 331 features, 246 Fisher, R. A., 329 five focusing steps, 39–41 five whys, 214–215 Florida Power and Light, 28 flow rules, 73 FMEA. See failure mode effect analysis (FMEA) focus, 205–208 focus groups, 338 Ford Motor Company, 3–5, 89–91 Ford, H., 329 Ford, Henry, 3–5, 17, 73, 83, 89–91 Fourth Wave Management System, 23 Fox, R. E., 330 Fox, Robert, 110 frequency of occurrence (OCC), 215–216, 219 FRT. See future reality tree (FRT) future reality tree (FRT), 42
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Index
G General Motors, 3, 5–10, 83, 91–92 George, M., 330 George, M. L., 329 global focus, 257–266 Godfrey, B. A., 330 Goldratt, E. M., 330 Goldratt, Eli, 15, 21, 39, 96–106, 110 Goldratt, Eliyahu, 123 Gooch, J., 330 goodness-of-fit, 338 Great Depression, 91 Green Belts, 50 growth, 246–249 Gryna, F. M., 330
H Hammer, M., 330 Harrington, J., 330 Hibino, S., 331 histogram, 338 Hobbs, D., 330 Hoffherr, G. D., 330 Horn, S., 331
I I networks, 71–72, 281–284 identify, 162 Imai, M., 330 improve, 160 improvement, 25, 31 failing, 35–37, 191–193 history, 185–187 measuring, 31–35, 187–191 missing link, 78–81 improvement challenges, 118–121 improvement dilemma, 111–121 improvement efforts focusing, 76–77 motivating, 77–78 improvement programs, comparisons, 113 in control, 338 Inamori, K., 330 increase throughput, 169–170 individuals chart, 338 inspection, 339 interaction, 339 inventory case study, 293–296 inventory management, case study, 293–296 inventory optimization model, 294–295
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investment decisions, 92–93 iTLS, 19–22, 24, 53–60, 84, 106–110. See also TLS application, 202, 204 applying, 56–60 benefits, 250 case studies, 293–325 competitive advantage, 252 customer benefits, 250 defined, 195–196 effectiveness, 62–66, 257–266 event sequence, 56–60 examples, 293–325 experiment, 61–66, 257–266 features, 55–56 function, 198–202 growth strategy, 251–252 initial implementation, 197–198 inventory management, 294–296 versus Lean, 259–266 model, 198–202 monitoring systems, 230–231 river, 109–110 roadmap, 238–240 shareholder benefits, 250 and Six Sigma, 196, 259–266 starting, 253 steps, 202–204 successful implementation, 237–238 sustain strategy, 251–252 sustainable implementation, 250–251 and TOC, 196, 259–266 and Toyota Production System, 196 training classifications, 253–254 types of organizations, 250–251 unique features, 202
J Jacobs, R. F., 329 JIT. See just-in-time (JIT) Jones, D. T., 332 Juran, J. M., 330 just-in-time (JIT), 61, 89, 257
K Kaizen, 141–142 kanban, 15–16, 20, 144–147, 339 kanban system, 102–103 KCIV. See key critical input variable (KCIV) key critical input variable (KCIV), 124 key input variable (KIV), 124
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KIV. See key input variable (KIV) Kubiak, T. M., 329
L labor, improvements, 170–183 LaMarch, J., 330 Launsby, R. G., 331 leadership responsibilities, 285–291 Lean, 17–18, 26, 37, 42–45, 106–110, 112–113, 130–150, 257 application, 211–212 versus iTLS, 259–266 learning, 246–249 Levinson, W. A., 330 linearity, 339
M make versus buy, 94–96 Malcolm Baldrige Quality Award, 28 management issues, 75–81 Mann, D., 330 markets, 244–245 Master Black Belts, 50 materials requirement planning (MRP), 26, 186 McClaves, J. T., 330 Meadows, B., 329 measure, 159 measurement credibility, 225 errors, 226 reliability, 225 system validity, 225–227 Mills, C., 330, 332 missing link, 78–81 mobilize, 204–208, 286–287 mobilize, organize, speed up, tie up. See MOST model, 339 Model T, 3–5, 83, 89–91 monitoring systems, iTLS, 230–231 Montgomery, D., 330 Moody, P. E., 331 Moran, J. W., 330 MOST, 176, 285–289 application, 289–290 feedback, 291 motion, 133 MRP. See materials requirement planning (MRP) muda, 28, 131, 133, 339 multitasking, 249
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N Nadler, G., 330, 331 Nadler, Gerald, 110 net profit, 32, 188 Neuman, R., 331 noise factor, 339–340 non-value add (NVA), 131 normal distribution, 340 NVA. See non-value add (NVA)
O OCC. See frequency of occurrence (OCC) Ohdahl, T. P., 331 Ohno, T., 331 Ohno, Taichi, 3, 12–17, 42, 74, 83, 96–106 operating expenses, 33, 189 optimize phase, 163–165 order fulfillment value stream, 135 organize, 287–288 orthogonal array, 340 out of control, 340 outliers, 340 overproduction, 133
P Pande, P., 331 Pareto analysis, 341 Parkinson’s Law, 249–250 parts per million, 340 Pearson, K., 331 perfection, 147–148 Pirasteh, R.M., 331 poka-yoke, 143–144 population, 341 precision, 341 prerequisite tree (PRT), 42 prevention, 341 price, 245 probability plot, 341 process, 341 capability index, 152 flow matrix, 137 process capability study, 341 process potential, 341 production volumes, 9 production, seven sins, 133 products, 246 project sponsor, 253 PRT. See prerequisite tree (PRT) Ptak, C., 330
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Index
Q quality, 245–246
R R chart, 342 R-squared, 343 randomization, 341–342 range, 342 rational subgroups, 342 reliability, of measurements, 225 repeatability, 342 reproducibility, 342 Rerick, R. A., 330 residual, 342 resolution, 342 response, 342 response surface model (RSM), 342 return on investment (ROI), 32, 93, 126, 188, 190 River Rouge Plant, 4, 89 river system, 4–5, 12–16, 20, 67, 90, 97–106, 109, 267–284 ROI. See return on investment (ROI) Roos, D., 332 rope, 73 RSM. See response suface model (RSM)
S S chart, 344 Salvendy, G., 331 sample, 343 sample size, 343 sampling error, 343 scatter plot, 343 Schmidt, S. R., 331 Schonoberger, R. J., 331 Schragenheim, E., 330 Sekine, K., 331 services feedback, 245 SEV. See severity (SEV) severity (SEV), 216, 218 Sharma, A., 331 sigma level, 344 Sincich, T., 330 Six Sigma, 17–18, 26–27, 37, 45–52, 106–110, 112, 113, 150–161, 257 application of tools, 222 methodology, 157–161 metric, 151–157 versus iTLs, 196, 259–266 Sloan, Alfred, 3, 5–8, 17, 73, 83, 91
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SPC. See statistical process control (SPC) special cause, 344 specialist, 253–254 specification limits, 344 speed-up, 288–289 Srikanth, M. L., 331 stability, 344 stabilizing, 230–236 stable process, 344 standard deviation, 344 standard error, 344 statistic, 344 statistical control, 344 statistical process control (SPC), 139–140, 344 stratification, 345 student syndrome, 249 supporting activities, control of, 227–230 survey, 345 sustainable operation, 179 system reevaluation, 236–244
T T networks, 70–71, 279–281 Taguchi, G., 331 takt, 136, 345 takt board, 232–236 target value, 345 The Goal, 39 theory of constraints (TOC), 17, 26, 37, 39–42, 106–110, 112, 113, 123–129, 345 application of tools, 205 and iTLS, 196 thinking processes, 41–42 throughput, 33, 189 improvements, 170–183 increase, 169–170 throughput operating strategy (TOS), 18, 41, 67–74, 267–284 tie loose ends, 289 time buffer, 73 TLS, 18. See also iTLS TOC. See theory of constraints (TOC) TOS. See throughput operating strategy (TOS) total quality management (TQM), 26–27, 126, 186 Toyota, 83 Toyota Motor Company, 3, 12, 15, 97–106 Toyota Production System (TPS), 12, 16, 42, 97–106 TPS. See Toyota Production System (TPS) TQM. See total quality management (TQM) transition tree (TT), 42
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transportation, 133 Triola, M. F., 331 trust, building, 77–78 TT. See transition tree two-level designs, 345
U UAW. See United Auto Workers (UAW) UDE. See undesirable effects (UDE) Umble, M. M., 331 undesirable effects (UDE), 41 United Auto Workers (UAW), 91
V V networks, 69–70, 273–278 validate phase, 165–166 validity, measurement system, 225–227 value, 345 value added, 33, 130 value flow, 134–142 value specification, 130–132 value stream, 132–134, 345 valve assembly case study, 306–314 variance components, 345–346 variation, 346 VOC. See voice of the customer (VOC) voice of the customer (VOC), 125 voice of the process (VOP), 125
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volumes, production, 9 VOP. See voice of the process (VOP) Vorantim case study, 318–325
W Wallace Company, 28 Wantuck, K. A., 332 waste, eliminating sources, 211–221 Way, M., 332 WCE. See work cycle efficiency (WCE) Welch, J., 332 Welch, S., 332 Wheat, B., 330, 332 Whitcomb, P. J., 329 Womack, J. P., 332 work cycle efficiency (WCE), 131 workflow transportation logistics, 138 Wu, Y., 331
X X chart, 346 X-bar-R chart, 346 X-bar-S chart, 346
Y Yang, K., 332 yield, 346
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About the Authors Dr. Reza (Russ) M. Pirasteh is founder of iTLS-ISO Group®. He has held executive, staff, and line positions and has 25 years of solid experience in implementation of continuous improvement systems in manufacturing and transactional environments. He has earned a Ph.D. in Engineering, an MBA in Industrial Management, a BS in Industrial Engineering, and a PMP (PMI). He is also a Certified Lean Six Sigma Master Black Belt and a Certified Lean Master. He has formulated iTLS™® to fill the gaps among CPI methodologies he has experienced. Reza has published numerous articles and conducted lectures for APICS, IIE, TOC-ICO, Weber State University, UTA, OSU, and IndustryWeek. He is a member of APICS, ASQ, IIE, and PMI.
Robert E. Fox is a founder of The Goldratt Institute, The TOC Center, Inc., and Viable Vision LLC. He earned an MS in Industrial Administration from Carnegie Mellon and a BS in Engineering from the University of Notre Dame. His has extensive industrial and consulting experience and has served as Vice President of Booz & Co. and President of Tyndale, Inc. He authored The Race and The Theory of Constraints Journal. In honor of his 50 years of contribution to organizational improvement, the Fox Award was established to honor organizations and individuals who have demonstrated excellence. Steven Covey and Peter Senge have been recipients of a lifetime Fox Award.
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Belong to the Quality Community! Established in 1946, ASQ is a global community of quality experts in all fields and industries. ASQ is dedicated to the promotion and advancement of quality tools, principles, and practices in the workplace and in the community.
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ASQ Membership Research shows that people who join associations experience increased job satisfaction, earn more, and are generally happier*. ASQ membership can help you achieve this while providing the tools you need to be successful in your industry and to distinguish yourself from your competition. So why wouldn’t you want to be a part of ASQ?
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ASQ Certification ASQ certification is formal recognition by ASQ that an individual has demonstrated a proficiency within, and comprehension of, a specified body of knowledge at a point in time. Nearly 150,000 certifications have been issued. ASQ has members in more than 100 countries, in all industries, and in all cultures. ASQ certification is internationally accepted and recognized.
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