Profitability With No Boundaries

Profi tability with No Boundaries

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ASQ Quality PressMilwaukee, Wisconsin

Profi tability with No Boundaries

Optimizing TOC, Lean, Six Sigma Results

FocusReduce Waste

Contain Variability

Reza (Russ) M. Pirasteh

Robert E. Fox

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American Society for Quality, Quality Press, Milwaukee 53203© 2011 American Society for QualityAll rights reserved. Published 2010Printed 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– Profi tability 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 2010658.4�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. TonyAcquisitions Editor: Matt MeinholzProject Editor: Paul O’MaraProduction Administrator: Randall Benson

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v

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

Part I: Leadership Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Chapter 1: Productivity, Growth, and Prosperity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

HENRY FORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3ALFRED SLOAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5TAIICHI OHNO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12TLS—A FOURTH WAVE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Chapter 2: Productivity Improvement—A Checkered History . . . . . . . . . . . . . . . . . . . 25

Chapter 3: What Is an Improvement? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

HOW TO MEASURE AN IMPROVEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31WHY IMPROVEMENTS EFFORTS OFTEN FAIL . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Chapter 4: Current CPI Favorites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

THEORY OF CONSTRAINTS (TOC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39The Five Focusing Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Thinking Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Critical Chain Project Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

LEAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42SIX SIGMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Six Sigma—A Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Six Sigma—A Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Step 1—Defi ne. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Step 2—Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Step 3—Analyze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Step 4—Improve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Step 5—Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Design For Six Sigma (DFSS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Six Sigma—A Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Summary/Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Chapter 5: The TLS Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

WHAT IS iTLS™®? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53How Does iTLS™® Work? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

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vi Profi tability with No Boundaries

WHAT IS UNIQUE ABOUT ITLS™®? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55HOW SHOULD ITLS™® BE APPLIED? WHAT IS THE

SEQUENCE OF EVENTS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Chapter 6: Results: Has a More Global Focus Worked? . . . . . . . . . . . . . . . . . . . . . . . . . 61

INITIAL CONDITIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61WHICH METHODOLOGY WAS MOST EFFECTIVE?. . . . . . . . . . . . . . . . . . . . . 62

Chapter 7: Throughput Operating Strategies (TOS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

A NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67V NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69T NETWORKS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70I NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Chapter 8: Management Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

FOCUSING IMPROVEMENT EFFORTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76MOTIVATING THE DESIRED BEHAVIORS AND BUILDING TRUST. . . . . . . . . . 77THE MISSING LINK. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Chapter 9: Closing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Part II: Practitioner’s Guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Chapter 1: Productivity Growth and Prosperity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

FORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89SLOAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Product Pricing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Investment Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Make vs. Buy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

OHNO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96A FOURTH WAVE—iTLS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Chapter 2: The Productivity Improvement Dilemma . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

IMPROVEMENT CHALLENGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Chapter 3: CPI Favorites: TOC, Lean, Six Sigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

THEORY OF CONSTRAINTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123LEAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Principle 1: Specify the Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130Principle 2: Defi ne the Value Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132Principle 3: Make Value Flow without Any Interruptions . . . . . . . . . . . . . . . . . . . . . 134Principle 4: Make Customer Pull from Supplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Kanban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144Principle 5: Perfection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147Principle 6: Agility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

SIX SIGMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150Six Sigma—A Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Six Sigma—A Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Step 1—Defi ne. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158Step 2—Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

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Contents vii

Step 3—Analyze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Step 4—Improve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160Step 5—Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

DESIGN FOR SIX SIGMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161Phase 1—Identify. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162Phase 2—Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162Phase 3—Optimize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163Phase 4—Validate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166WHERE WE WENT WRONG. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

Case I: Direct Labor Reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169Case II: Increase Throughput. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169Case III: Labor and Throughput Improvements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

Chapter 4: What Is an Improvement? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

HISTORY OF “IMPROVEMENTS” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185HOW TO MEASURE AN IMPROVEMENT? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187WHY DO IMPROVEMENT EFFORTS OFTEN FAIL?. . . . . . . . . . . . . . . . . . . . . . . . 191

Chapter 5: The iTLS Model and How It Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

WHAT IS THIS iTLS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195Step 1—Mobilize and Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203Step 2—Decide How to Exploit the Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208Step 3—Eliminate Sources of Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211Step 4—Control Process Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221Step 5—Control Supporting Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227Step 6—Remove the Constraint and Stabilize. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230Step 7—Reevaluate the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

Chapter 6: iTLS Study and Results Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257

HAS A MORE GLOBAL FOCUS WORKED?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Initial Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258Which Method Is More Effective?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262

Chapter 7: River System Optimization with TOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

A NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268V NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273T NETWORKS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279I NETWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281BETTER UNDERSTAND YOUR PROCESSES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284

Chapter 8: “MOST” TLS Project Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285

LEADERSHIP RESPONSIBILITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285Mobilize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286Organize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Speed-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288Tie Loose Ends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289Real-World Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289User Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291

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Chapter 9: Real-World Application of iTLS Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 293

STUDY—INVENTORY MANAGEMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293iTLS-O Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294Examples of Real-Life Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296

CASE STUDY ELECTRONIC MANUFACTURING COMPANY . . . . . . . . . . . . . . . 296Initial Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296iTLS in Action and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297Step 1—Mobilize and Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297Steps 2 and 3—Decide How to Exploit the Constraint and Eliminate Sources of Waste

in the Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302Step 4—Control Process Variability and Error in the Constraint . . . . . . . . . . . . . . . 303Step 5—Control Supporting Activities to the Constraint. . . . . . . . . . . . . . . . . . . . . . 303Step 6—Remove the Constraint and Stabilize. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303Step 7—Reevaluate System Performance and Go after the Next Constraint . . . . . . 305Going Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306

CASE STUDY—VALVE ASSEMBLY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306Initial Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306Step 1—Mobilize and Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Step 2—Decide How to Exploit the Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Step 3—Eliminate Sources of Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308Step 4—Control Process Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308Step 5—Control Supporting Activities to the Constraint. . . . . . . . . . . . . . . . . . . . . . 313Step 6—Remove the Constraint and Stabilize the Process . . . . . . . . . . . . . . . . . . . . 313Step 7—Reevaluate the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314

CASE STUDY—CELSO CALIA OF BRAZIL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314Our Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315The Votorantim Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318Initial Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318Step by Step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Step 1—Mobilize and Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Step 2—Identify and Exploit the Constraint (Drum). . . . . . . . . . . . . . . . . . . . . . . . . 320Step 3—Eliminate Sources of Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320Step 4—Control Process Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322Step 5—Control Supporting Activities to the Drum . . . . . . . . . . . . . . . . . . . . . . . . . 322Step 6—Control Supporting Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322Step 7—Reevaluate the System and Go After the Next Drum . . . . . . . . . . . . . . . . . 323Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324

Chapter 10: Closing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347

About the Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353

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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 fl ow, tools, and techniques. . . . . . . . . . . . . . . . . . . . . . 58

Figure 6.1 Lean and Six Sigma benefi ts/project.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Figure 6.2 Lean–Six Sigma and TLS benefi ts/project. . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Figure 6.3 Lean, Six Sigma, and iTLS™® fi nancial 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 fl ow operation network—laptop computer manufacturing. . . . . . . . 68

Figure 7.2 V–river fl ow operation network—a pick-pack-ship warehouse. . . . . . . . . . . 69

Figure 7.3 T–river fl ow operation network—automobile assembly.. . . . . . . . . . . . . . . . 71

Figure 7.4 I–network river—airline meal tray assembly operations. . . . . . . . . . . . . . . . 72

PART 2

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

Figures and Tables

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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 fl owchart.. . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Table 3.4 Seven sins of production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Figure 3.3 Order fulfi llment value stream. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Figure 3.4 Takt time and balancing workload. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Figure 3.5 Takt time—unbalanced work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Figure 3.6 Process fl ow matrix.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Figure 3.7 Workfl ow 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-proofi ng.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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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Table 3.8 DMAIC process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Table 3.9 Process overall yield vs. Sigma.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

Table 3.10 DMADV process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

Table 3.11 Benefi ts of reducing direct labor costs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Table 3.12 Benefi ts of increasing throughput. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

Table 3.13 Benefi ts 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 5.3 Integration of TOC, Lean, Six Sigma iTLS. . . . . . . . . . . . . . . . . . . . . . . . . . 199


Figure 5.5 iTLS seven-step process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

Figure 5.6 iTLS seven-step fl ow 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 defi nition 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 fi rm’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 benefi ts.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

Figure 6.2 Lean, Six Sigma, and iTLS benefi ts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262

Figure 6.3 Lean, Six Sigma, and iTLS fi nancial 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 fl ow operation network—laptop computer manufacturing.. . . . . . . . 269

Figure 7.2 V river fl ow operation network—a pick-pack-ship warehouse. . . . . . . . . . . 275

Figure 7.3 Relationship of increased uncertainty with the levels of two-headed forks. 276

Figure 7.4 T river fl ow 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 confi guration 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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Figure 9.6 Box plot of inventory before and after iTLS implementation.. . . . . . . . . . . . 304

Table 9.1 ANOVA indicating reduction signifi cance. . . . . . . . . . . . . . . . . . . . . . . . . . . 304

Figure 9.7 Inventory position after implementation of the iTLS. . . . . . . . . . . . . . . . . . . 305

Figure 9.8 Goals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308

Figure 9.9 Spaghetti fl ow of the current layout. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309

Figure 9.10 Cause-and-effect analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310

Figure 9.11 FMEA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311

Figure 9.12 New process fl ow 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 Examples of four plants’ performances applying iTLS. . . . . . . . . . 324

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xv

This book is a result of a serendipitous meeting in 2007. Despite different ca-reer paths and experiences, we had each arrived at the conclusion that American industry was encountering a continuing, but unnecessary, decline in competitive-ness. 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 oper-ated 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 ap-proaches 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 scientifi cally conducted study in the United States showed that iTLS projects produced more than four times the benefi ts of either Lean or Six Sigma projects. Subsequent experiences in other countries have produced even greater benefi ts. 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 benefi ts include signifi cant improvements in quality, productivity, and profi tability. We also have developed an understanding of how these internal improvements can be further leveraged to increase sales, market share, and profi ts.

The book is divided into two parts. The fi rst is geared to senior decision mak-ers—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

Preface

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xvi Profi tability with No Boundaries

portion of the material in Part I has been repeated in Part II to provide continuity in the fl ow 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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3

During the last century, three waves washed over the shores of developed and developing countries, creating both wealth and freedom. These waves were break-throughs 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 infl uenced many industries and countries. The fi rst 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 benefi ts 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 effi cient 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 pro-duce 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 infl ation. Ford claimed that ev-ery time he reduced the price of the Model T by $1, he created another 1000 buy-ers. In addition to developing a more effi cient method of producing automobiles,

1

Productivity, Growth, and Prosperity


4 Part I: Leadership Summary

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 dis-tributed. 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 fl ow eventually became so seamless from raw materials to fi nished product that it represented a smooth, fast-fl owing river system. The content or materials fl owed 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 fl ows, dams, or rapids. Ford focused on expanding the breadth of his river system, shortening the length of the various tributaries and making it fl ow 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 profi ts 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 profi ts.

The rate at which Ford produced cars was dictated by the number and speed of his assembly lines; they controlled the fl ow rate, because for nearly 18 years demand always exceeded supply. It made for a very effi cient management sys-tem. Everyone from Henry Ford on down knew exactly how many of each part needed to be received, produced, and shipped each day. The fi nancial 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 fl ow 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 fi nished car.


Chapter 1: Productivity, Growth, and Prosperity 5

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 re-ducing disruptions caused by defects, absenteeism, equipment breakdowns, and the like. As these disruptions were reduced, his river system fl owed 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 improve-ments were phased in so as not to disturb the fl ow. As his river system become more and more effi cient, 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 fl ow. 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 benefi ts that it helped produce created a more affl uent society. Many people could now afford to purchase more expensive au-tomobiles, 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 de-cline. 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, man-aging this diversity required a totally different management system.



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 num-ber 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 divi-sions, which, in turn, often served competitors of GM, like Chrysler and Nash. GM’s various divisions, unlike Ford’s, were neither physically integrated nor co-ordinated 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 fi rst 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 al-located. 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 fi eld—a problem that Ford evaded for nearly 18 years. The magnitude of the problem was uncovered during a famous fi eld trip that Alfred Sloan and Donald-son Brown (GM’s treasurer) made in May 1924. They visited major car dealer-ships 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 fi ndings 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 infl uenced Sloan’s thoughts on how to manage a decentralized organization while maintaining corporate control of the entire en-terprise. 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 benefi t 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



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 fi nancial 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 em-ployed. Material and labor costs increased in direct proportion to the increased production. When production volumes declined, the opposite happened. So de-spite 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 signifi cant 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 pro-duction 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 fi re 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 deci-sion making and deal with an increasing variety of products when establishing



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, fi nd a way to increase sales, or discontinue the product. If not, ad-ditional 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 specifi c direction from the corporation. If they could reduce the cost of making their products, profi ts and ROI would increase, and more capital would be available to grow the business. Investing in more effi cient equipment had the potential of reducing both labor and material costs. The existing material and labor costs for a particular operation or set of opera-tions 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 fi nal 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 profi ts 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 suffi cient 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 profi ts and ROI was to increase the effi ciency 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 effi cient methods and time standards for each operation. Workers were measured and held accountable for meeting these standards. More effi cient methods and tighter labor standards resulted in lower costs, an admi-rable goal. Unfortunately, this process created divisiveness between management and workers. Workers were naturally reluctant to fi nd or divulge ways to improve an activity, because the result would be tighter standards for measuring their performance without any direct benefi t to them.

In essence, the ability to calculate product cost gave rise to a series of proce-dures for establishing transfer and market prices, evaluating investment oppor-tunities, deciding whether to make or buy items, and driving down labor costs.



Cost-accounting thinking was essentially a way to decompose an organiza-tion 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 fl ow.

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 ad-vance of need) and the costs of changing over equipment and assembly lines. The basic mechanism for resolving this confl ict 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 sys-tem 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 effi cient 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 develop-ing and using these tools to manage in a world of increasing variety and decen-tralization. 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 fi re workers based on short-term production needs. As a result of los-ing fl exibility in such decisions, managements became increasingly focused on achieving local effi ciencies, 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 effi ciencies signifi cantly reduced labor cost as a proportion of a product’s cost. This trend became so pro-nounced that the U.S. automobile industry eventually signed a labor agreement



that essentially made labor a fi xed rather than variable cost. Workers not needed for the current level of production were allowed to be idle or participate in make-work programs (like painting the fi re 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 effi cient equipment and making, rather than buying, more products reduced the propor-tion 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, fi -nancial, 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 tech-niques 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 specifi c 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 effi ciency 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 year-end discounts, further magnifying the problem.

The fi nal 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 fi nished 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 invento-ries. When the level of fi nished goods increases, a portion of the increase occurs



because of the labor and overhead in the product cost. During that reporting period, the labor and overhead costs that increase fi nished goods inventory are excluded from the calculation of profi ts for that period.

A company’s profi ts can actually increase simply because the level of fi nished 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 profi t for that period. When managers are rewarded based on net profi ts, the temptation to infl ate profi ts by increasing inventories is often unavoidable. This distortion was not signifi cant 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 dif-ferent 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 fi t the spe-cifi c 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 con-sumer wanted to purchase a car with specifi c 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 signifi cantly reduced. Most important, sales rose because more consumers could quickly get the exact car they wanted. Despite the success, however, the car com-pany 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 signifi cantly reduce the amount of cars in the fi eld, 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 signifi cant, drop in the company’s profi ts. 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 mak-ing 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, pi-rates, 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 fi nally 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.



The change in the elements that comprised a product’s cost eventually be-came so widespread that they made the use of cost-accounting management not only obsolete, but actually destructive. The good economic decisions that re-sulted 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-fl owing 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. auto-motive industry and eventually spread to other industries and countries, greatly benefi ting those who adopted it. The next wave of improvement, however, origi-nated 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 transpor-tation 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? Es-sentially they were both highly effi cient “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 effi cient river system for such an environment was much more diffi cult. 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



machines were dedicated to make the same item, and machine changeovers were rare, which greatly simplifi ed synchronizing the fl ow 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 addi-tion, 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 ob-stacle, 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 effi ciencies. The result would have been dams (piles of inventories), rapids (shortages that required expediting), and me-andering fl ows 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 re-jected these options. He spoke clearly and forcefully about developing a smooth-fl owing 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-fi nanced 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 effi cient management system than his competitors in order to reach his goal. He spent over 40 years developing and refi ning such a system. The results of his ef-forts speak for themselves.

In addition to the three-headed hurdle of uncertain demand, machine change-overs, and cost-accounting thinking, Ohno had to rely on vendors for many com-ponents. As a result, he had little direct control over a large part of his river system. In order to have a fast, smooth-fl owing river system like Ford’s, the as-sembly of cars, production of components, and receipt of materials needed to be tightly linked to actual sales. This required fl exible rather than dedicated assem-bly lines—lines that could produce several models. It also meant that the machine shops, which produced the major components such as engines and transmissions,



had to be equally fl exible, 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 infl uenced Ohno’s efforts. A layoff of workers in June 1950, during a postwar period of great hardship, resulted in a bloody strike, caus-ing 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 fi rst infl uence convinced Ohno that Toyota couldn’t afford the boom and bust cycles caused by building more cars than consumers were buy-ing; this practice caused additional hiring and increased production followed by severe cutbacks and layoffs or unacceptable fi nancial losses. His system had to be much more closely linked to actual sales in order to avoid these fl uctuations. 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 fi gured 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 ex-cess inventory of those that were not. This approach was met with considerable resistance by the workforce because the workers believed it was more effi cient 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 effi cient but the company less effi cient.

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 effi cient large-batch production. He told me that this thinking was deeply imbed-ded in the minds of his people. Workers believed it was ineffi cient to frequently change over their machines. He stressed to them that what was important was the effi ciency of the company, not the effi ciency 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 fi nally did it my way.”



He claimed that the resistance to small-batch production was strictly psycho-logical. 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 fi ve 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. Af-ter 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 fl ow more quickly and smoothly he needed a way to systematically eliminate the myriad factors that disrupt the fl ow of work. Once the major disruptors, like long setup times, were eliminated, he developed a system to both control the fl ow of work and to surface problems that disrupted fl ow. 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 pro-duce 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 fl ow of work from all but the worst dis-ruptions, such as defects, machine problems, and worker performance. Second, it used disruptions as a reason to temporarily halt production and focus on fi xing 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 capa-bilities and the knowledge of the workforce. Once the disruption was resolved, production resumed.

When work was again fl owing 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 fl ow 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 fl ow more rapidly and more smoothly.



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 profi ts and ROI. Once his kanban system was in place, Toyota and its vendors continued to refi ne 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 fl ow, which resulted in more efforts to reduce these disruptions. After repeated efforts over many years, he fi nally had created a fast, smooth-fl owing system from his vendor base to the purchasers of Toyota’s auto-mobiles. While it took nearly 40 years to convert his idea of a smooth, fast-fl owing 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 un-derstand 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 profi ts. 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 benefi t 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 suc-cess. 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 refi ne his sys-tem, 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 fl ow of his river system he would halt production for anything that disrupted fl ow, even though it resulted in lost throughput.

Our simple example illustrated how Ohno could have developed his sys-tem much more quickly by fi rst placing most of his inventory in a buffer prior to the operation that was most constricting fl ow (bottleneck) rather than scat-tering inventory throughout the system. In this manner, very few disruptions would ever starve the bottleneck, avoiding lost throughput. The disruptions



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 ap-proach, 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-fl owing 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 sys-tem 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 benefi ts.

iTLS—A FOURTH WAVE

What is this new wave that might produce another tsunami of economic ben-efi ts 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 method-ologies alone contains all the elements needed to create a fourth wave.

TOC’s strength lies in providing focus for improvement efforts. Its empha-sis 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 fl ow of work, improve quality, and reduce variability. TOC also does not take in account human resources values and signifi cance.



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 pro-cesses. 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 meth-odologies, 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 fi rst 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 profi tability. Every organization produces and delivers its products or services through a network of activities. When these networks are mapped so that the fl ows 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 simplifi es 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 fl ow of items converging to form a



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 refi neries, 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 fl ow, in which products neither converge nor diverge. A singular input is processed through a number of opera-tions 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.



The T structure is probably the least common of the four river systems. It’s essentially a network in which a number of items fl ow 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 automo-biles, 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 magnifi ed by local measurements, which often cause people to work on what is easy, or benefi cial to do, rather than what is necessary to do. These murky and changing priorities both slow and disrupt fl ow.

The control points provide the linkage between the procurement, production, distribution, and consumption of a company’s products. It is important to distin-guish 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 continu-ally speed and smooth the fl ow 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



are perceived bottlenecks to the fl ow. Even worse are bottlenecks that seem to fl oat or shift within the river system. Our experience is that very few, if any, orga-nizations 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 pro-duced 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 sustainabil-ity, 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 fl ow of work in our river systems.

At the fi rst Continuous Productivity Improvement (CPI) Conference in 2006, sponsored by Weber University, I presented the results of an extensive and rigor-ously 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 benefi ts than projects using either Lean or Six Sigma. Even more telling was the fact that iTLS projects were responsible for 80% of the fi nancial benefi ts even though they were used in less than 30% of the plants. More than 211 practitioners in 21 plants conducted 105 projects, which demon-strates 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 de-scribing the enormous effect on profi tability, 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 Ne-vada to introduce iTLS™® to TOC practitioners and report on the increased benefi ts 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.



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 Ko-rea, and Singapore. As our iTLS process has become refi ned and better under-stood, 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 pre-sentation that showed how they had successfully implemented iTLS™® and achieved very signifi cant results. He explained that using TOC had allowed them to make signifi cant positive changes, but variability in the processes was killing them, and they could not understand why. They seemed to be constantly chas-ing ghosts and fl oating 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 signifi cant 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, de-veloping 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-fl owing river system requires three ele-ments. 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 manag-ing the fl ows. 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 disrup-tions that impede rapid and smooth fl ow so that the time needed to turn materials into purchased products shrinks.

Third, we need a combination of courage and consensus to make the transi-tion 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



has experienced the hockey-stick impact of the end of the month (sometimes it’s the end of the quarter); during the fi rst portion of the period we focus on lo-cal performance measures (effi ciencies, 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 im-mediately revert to the old ways of operating. This oscillation of effort continues month in and month out, considerably disrupting the fl ow of our river systems.

The Fourth Wave Management System, iTLS, provides a consistent method for running organizations. Educating employees in the new TOS helps refi ne it and engenders both understanding and acceptance, providing a solid consen-sus for change. When measurements are closely aligned with a more global ap-proach, 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 refl ect 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 shut-downs, on top of the mammoth costs for the recall for the millions of vehicles, Toyota’s pristine consumer confi dence was severely bruised. Its stocks devalu-ated 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 technol-ogy, 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 Pro-duction System (TPS) protect this once-fi ne 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



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 Traffi c Safety Administration reported 492 recalls for the same year, 2010, in-volving 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’ profi tability, 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 indi-cate 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, signifi cant bottom-line ben-efi ts will appear within a couple of months and that within one to two years many companies will have more than doubled their profi ts. 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.


Index

5S-CANDO, 140–141

AA networks, 67–69, 268–273accounting, cost, 7–9, 92–96activities, control of, 227–230agility, 148–150analysis of variance, 333analyze, 159–160Anderson, M. J., 329Aquilano, N. J., 329Arai, K., 331assembly lines, 4assignable cause, 333

BBakerjian, R., 329balanced scorecard (BSC), 240–244

components of, 244–249Ballis, J. P., Sr., 329Ballis, John, 110Barnard, W. W., 329behaviors, motivating, 77–78Benbow, D., 329Benson, G. P., 330bias, 333Black Belts, 50blocking, 333Bluman, A. G., 329bottleneck, 16box plot, 333BPR. See business process reengineering

(BPR)Breyfogle, F. W., III, 329Brown, Donaldson, 6–7, 9, 83

347

Brue, G., 329Brussee, W., 329BSC. See balanced scorecard (BSC)business process reengineering (BPR),

26–27business value-add (BVA), 131BVA. See business value-add (BVA)

Ccalibration, 334capability, 334capability index, 334capability study, 334capable process, 334Carnell, M., 330, 332Cartier, Jacques, 11case studies

Celso Calia, 314–325electronics manufacturer, 296–306inventory, 293–296valve assembly, 306–314Vorantim, 318–325

cause-and-effect diagram, 334–335Cavanagh, R., 331CCPM. See critical chain project managementCelso Calia case study, 314–325centerpoint, 335champion, 253Champy, J., 330characteristic, 335characterization, 335Chase, R. B., 329checklist, 335Chowdhury, S., 331common cause, 335confi dence interval, 335

05_Index_pg347-352_Pirasteh_ASQ.indd 34705_Index_pg347-352_Pirasteh_ASQ.indd 347 8/31/10 4:00 PM8/31/10 4:00 PM

348 Index

constraint, 334exploitation, 208–211removing, 230–236

constraint management, 129continuous improvement. See continuous

productivity improvement (CPI)continuous productivity improvement (CPI), 108,

195control, 160–161, 335control chart, 335control factor, 335–336control group, 336control limits, 336control point, 72–73control process variability, 221–227COPQ, 336correlation, 336cost accounting, 7–9, 92–96, 100cost of poor quality. See COPQcost reduction programs, 25Cox, J., 330CPI. See continuous productivity improvement

(CPI)credibility, of measurements, 225Creveling, C. M., 329critical chain project management (CCPM), 42,

249–250critical characteristic, 336critical parameters, 336critical to quality, 336Crosby, P. B., 329Crowther, S., 329CRT. See current reality treeCupello, J. M., 32current reality tree (CRT), 41customers, 244–245, 336

Ddata-driven, 336De Feo, J. A., 329defects per million (DPM), 337defects per unit (DPU), 337defi ne, 158–159delivery performance, 245demand driven, 143–144Deming, E. W., 329Design for Six Sigma (DFSS), 49, 161–166design phase, 162–163Design, Measure, Analyze, Improve, Control

(DMAIC). See DMAICdesign, optimize, and verify (DOV), 161DET. See escaped detection, probability

Detriot Motor Company, 89DFSS. See Design for Six Sigma (DFSS)direct labor reductions, 169distribution, 336DMAIC, 46–49, 158–161dot plot, 337DOV. See design, optimize, and verify (DOV)DPM. See defects per million (DPM)DPU. See defects per unit (DPU)drum-buffer rope system, 104Durant, William, 6Dusharme, D., 329

Eeconomic order quantity (EOQ), 9–10effect, 337EI-Haik, B. S., 332electronic manufacturer case study, 296–306enterprise resource planning (ERP), 26, 186EOQ. See economic order quantity (EOQ)ERP. See enterprise resource planning (ERP)escaped detection, probability (DET), 216, 220evolutionary operation (EVOP), 337EVOP. See evolutionary operation (EVOP)expert, 253–254

Ffactor, 337factor level, 337factor range, 337factorial experiment, 337–338failure mode effect analysis (FMEA), 215–217Farah, K., 331features, 246Fisher, R. A., 329fi ve focusing steps, 39–41fi ve whys, 214–215Florida Power and Light, 28fl ow rules, 73FMEA. See failure mode effect analysis (FMEA)focus, 205–208focus groups, 338Ford Motor Company, 3–5, 89–91Ford, H., 329Ford, Henry, 3–5, 17, 73, 83, 89–91Fourth Wave Management System, 23Fox, R. E., 330Fox, Robert, 110frequency of occurrence (OCC), 215–216, 219FRT. See future reality tree (FRT)future reality tree (FRT), 42


Index 349

GGeneral Motors, 3, 5–10, 83, 91–92George, M., 330George, M. L., 329global focus, 257–266Godfrey, B. A., 330Goldratt, E. M., 330Goldratt, Eli, 15, 21, 39, 96–106, 110Goldratt, Eliyahu, 123Gooch, J., 330goodness-of-fi t, 338Great Depression, 91Green Belts, 50growth, 246–249Gryna, F. M., 330

HHammer, M., 330Harrington, J., 330Hibino, S., 331histogram, 338Hobbs, D., 330Hoffherr, G. D., 330Horn, S., 331

II networks, 71–72, 281–284identify, 162Imai, M., 330improve, 160improvement, 25, 31

failing, 35–37, 191–193history, 185–187measuring, 31–35, 187–191missing link, 78–81

improvement challenges, 118–121improvement dilemma, 111–121improvement efforts

focusing, 76–77motivating, 77–78

improvement programs, comparisons, 113in control, 338Inamori, K., 330increase throughput, 169–170individuals chart, 338inspection, 339interaction, 339inventory case study, 293–296inventory management, case study, 293–296inventory optimization model, 294–295

investment decisions, 92–93iTLS, 19–22, 24, 53–60, 84, 106–110. See also

TLSapplication, 202, 204applying, 56–60benefi ts, 250case studies, 293–325competitive advantage, 252customer benefi ts, 250defi ned, 195–196effectiveness, 62–66, 257–266event sequence, 56–60examples, 293–325experiment, 61–66, 257–266features, 55–56function, 198–202growth strategy, 251–252initial implementation, 197–198inventory management, 294–296versus Lean, 259–266model, 198–202monitoring systems, 230–231river, 109–110roadmap, 238–240shareholder benefi ts, 250and Six Sigma, 196, 259–266starting, 253steps, 202–204successful implementation, 237–238sustain strategy, 251–252sustainable implementation, 250–251and TOC, 196, 259–266and Toyota Production System, 196training classifi cations, 253–254types of organizations, 250–251unique features, 202

JJacobs, R. F., 329JIT. See just-in-time (JIT)Jones, D. T., 332Juran, J. M., 330just-in-time (JIT), 61, 89, 257

KKaizen, 141–142kanban, 15–16, 20, 144–147, 339kanban system, 102–103KCIV. See key critical input variable (KCIV)key critical input variable (KCIV), 124key input variable (KIV), 124


350 Index

KIV. See key input variable (KIV)Kubiak, T. M., 329

Llabor, improvements, 170–183LaMarch, J., 330Launsby, R. G., 331leadership responsibilities, 285–291Lean, 17–18, 26, 37, 42–45, 106–110, 112–113,

130–150, 257application, 211–212versus iTLS, 259–266

learning, 246–249Levinson, W. A., 330linearity, 339

Mmake versus buy, 94–96Malcolm Baldrige Quality Award, 28management issues, 75–81Mann, D., 330markets, 244–245Master Black Belts, 50materials requirement planning (MRP), 26,

186McClaves, J. T., 330Meadows, B., 329measure, 159measurement

credibility, 225errors, 226reliability, 225system validity, 225–227

Mills, C., 330, 332missing link, 78–81mobilize, 204–208, 286–287mobilize, organize, speed up, tie up. See MOSTmodel, 339Model T, 3–5, 83, 89–91monitoring systems, iTLS, 230–231Montgomery, D., 330Moody, P. E., 331Moran, J. W., 330MOST, 176, 285–289

application, 289–290feedback, 291

motion, 133MRP. See materials requirement planning

(MRP)muda, 28, 131, 133, 339multitasking, 249

NNadler, G., 330, 331Nadler, Gerald, 110net profi t, 32, 188Neuman, R., 331noise factor, 339–340non-value add (NVA), 131normal distribution, 340NVA. See non-value add (NVA)

OOCC. See frequency of occurrence (OCC)Ohdahl, T. P., 331Ohno, T., 331Ohno, Taichi, 3, 12–17, 42, 74, 83, 96–106operating expenses, 33, 189optimize phase, 163–165order fulfi llment value stream, 135organize, 287–288orthogonal array, 340out of control, 340outliers, 340overproduction, 133

PPande, P., 331Pareto analysis, 341Parkinson’s Law, 249–250parts per million, 340Pearson, K., 331perfection, 147–148Pirasteh, R.M., 331poka-yoke, 143–144population, 341precision, 341prerequisite tree (PRT), 42prevention, 341price, 245probability plot, 341process, 341

capability index, 152fl ow matrix, 137

process capability study, 341process potential, 341production volumes, 9production, seven sins, 133products, 246project sponsor, 253PRT. See prerequisite tree (PRT)Ptak, C., 330


Index 351

Qquality, 245–246

RR chart, 342R-squared, 343randomization, 341–342range, 342rational subgroups, 342reliability, of measurements, 225repeatability, 342reproducibility, 342Rerick, R. A., 330residual, 342resolution, 342response, 342response surface model (RSM), 342return on investment (ROI), 32, 93, 126, 188, 190River Rouge Plant, 4, 89river system, 4–5, 12–16, 20, 67, 90, 97–106,

109, 267–284ROI. See return on investment (ROI)Roos, D., 332rope, 73RSM. See response suface model (RSM)

SS chart, 344Salvendy, G., 331sample, 343sample size, 343sampling error, 343scatter plot, 343Schmidt, S. R., 331Schonoberger, R. J., 331Schragenheim, E., 330Sekine, K., 331services feedback, 245SEV. See severity (SEV)severity (SEV), 216, 218Sharma, A., 331sigma level, 344Sincich, T., 330Six Sigma, 17–18, 26–27, 37, 45–52, 106–110,

112, 113, 150–161, 257application of tools, 222methodology, 157–161metric, 151–157versus iTLs, 196, 259–266

Sloan, Alfred, 3, 5–8, 17, 73, 83, 91

SPC. See statistical process control (SPC)special cause, 344specialist, 253–254specifi cation limits, 344speed-up, 288–289Srikanth, M. L., 331stability, 344stabilizing, 230–236stable process, 344standard deviation, 344standard error, 344statistic, 344statistical control, 344statistical process control (SPC), 139–140, 344stratifi cation, 345student syndrome, 249supporting activities, control of, 227–230survey, 345sustainable operation, 179system reevaluation, 236–244

TT networks, 70–71, 279–281Taguchi, G., 331takt, 136, 345takt board, 232–236target value, 345The Goal, 39theory of constraints (TOC), 17, 26, 37, 39–42,

106–110, 112, 113, 123–129, 345application of tools, 205and iTLS, 196

thinking processes, 41–42throughput, 33, 189

improvements, 170–183increase, 169–170

throughput operating strategy (TOS), 18, 41, 67–74, 267–284

tie loose ends, 289time buffer, 73TLS, 18. See also iTLSTOC. See theory of constraints (TOC)TOS. See throughput operating strategy (TOS)total quality management (TQM), 26–27, 126,

186Toyota, 83Toyota Motor Company, 3, 12, 15, 97–106Toyota Production System (TPS), 12, 16, 42,

97–106TPS. See Toyota Production System (TPS)TQM. See total quality management (TQM)transition tree (TT), 42


352 Index

transportation, 133Triola, M. F., 331trust, building, 77–78TT. See transition treetwo-level designs, 345

UUAW. See United Auto Workers (UAW)UDE. See undesirable effects (UDE)Umble, M. M., 331undesirable effects (UDE), 41United Auto Workers (UAW), 91

VV networks, 69–70, 273–278validate phase, 165–166validity, measurement system, 225–227value, 345value added, 33, 130value fl ow, 134–142value specifi cation, 130–132value stream, 132–134, 345valve assembly case study, 306–314variance components, 345–346variation, 346VOC. See voice of the customer (VOC)voice of the customer (VOC), 125voice of the process (VOP), 125

volumes, production, 9VOP. See voice of the process (VOP)Vorantim case study, 318–325

WWallace Company, 28Wantuck, K. A., 332waste, eliminating sources, 211–221Way, M., 332WCE. See work cycle effi ciency (WCE)Welch, J., 332Welch, S., 332Wheat, B., 330, 332Whitcomb, P. J., 329Womack, J. P., 332work cycle effi ciency (WCE), 131workfl ow transportation logistics, 138Wu, Y., 331

XX chart, 346X-bar-R chart, 346X-bar-S chart, 346

YYang, K., 332yield, 346


353

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 imple-mentation of continuous improvement systems in manufacturing and transac-tional 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 Certifi ed Lean Six Sigma Master Black Belt and a Certifi ed Lean Master. He has formulated iTLS™® to fi ll the gaps among CPI methodologies he has experi-enced. 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 organi-zations and individuals who have demonstrated excellence. Steven Covey and Peter Senge have been recipients of a lifetime Fox Award.

About the Authors

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Profitability With No Boundaries