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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Robust Optimization is a method to improve robustness using low-cost variations of a single, conceptual design. The benefits of Robust Optimization include faster product development cycles; faster launch cycles; fewer manufacturing problems; fewer field problems; lower-cost, higher performing products and processes; and lower warranty costs. All these benefits can be realized if engineering and product development leadership of automotive and manufacturing organizations leverage the power of using Robust Optimization as a competitive weapon. Written by world renowned authors, Robust Optimization: World's Best Practices for Developing Winning Vehicles, is a ground breaking book whichintroduces the technical management strategy of Robust Optimization. The authors discuss what the strategy entails, 8 steps for Robust Optimization and Robust Assessment, and how to lead it in a technical organization with an implementation strategy. Robust Optimization is defined and it is demonstrated how the techniques can be applied to manufacturing organizations, especially those with automotive industry applications, so that Robust Optimization creates the flexibility that minimizes product development cost, reduces product time-to-market, and increases overall productivity. Key features: * Presents best practices from around the globe on Robust Optimization that can be applied in any manufacturing and automotive organization in the world * Includes 19 successfully implemented best case studies from automotive original equipment manufacturers and suppliers * Provides manufacturing industries with proven techniques to become more competitive in the global market * Provides clarity concerning the common misinterpretations on Robust Optimization Robust Optimization: World's Best Practices for Developing Winning Vehicles is a must-have book for engineers and managers who are working on design, product, manufacturing, mechanical, electrical, process, quality area; all levels of management especially in product development area, research and development personnel and consultants. It also serves as an excellent reference for students and teachers in engineering.
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CONTENTS
Cover
Title Page
Copyright
Dedication
Preface
Acknowledgments
About the Authors
Chapter 1: Introduction to Robust Optimization
1.1 What Is Quality as Loss?
1.2 What Is Robustness?
1.3 What Is Robust Assessment?
1.4 What Is Robust Optimization?
Chapter 2: Eight Steps for Robust Optimization and Robust Assessment
2.1 Before Eight Steps: Select Project Area
2.2 Eight Steps for Robust Optimization
2.3 Eight Steps for Robust Assessment
2.4 As You Go through Case Studies in This Book
Chapter 3: Implementation of Robust Optimization
3.1 Introduction
3.2 Robust Optimization Implementation
Part One: Vehicle Level Optimization
Chapter 4: Optimization of Vehicle Offset Crashworthy Design Using a Simplified Analysis Model
4.1 Executive Summary
4.2 Introduction
4.3 Stepwise Implementation of DFSS Optimization for Vehicle Offset Impact
4.4 Conclusion
References
Chapter 5: Optimization of the Component Characteristics for Improving Collision Safety by Simulation
5.1 Executive Summary
5.2 Introduction
5.3 Simulation Models
5.4 Concept of Standardized S/N Ratios with Respect to Survival Space
5.5 Results and Consideration
5.6 Conclusion
Reference
Part Two: Subsystems Level Optimization by Original Equipment Manufacturers (OEMs)
Chapter 6: Optimization of Small DC Motors Using Functionality for Evaluation
6.1 Executive Summary
6.2 Introduction
6.3 Functionality for Evaluation in Case of DC Motors
6.4 Experiment Method and Measurement Data
6.5 Factors and Levels
6.6 Data Analysis
6.7 Analysis Results
6.8 Selection of Optimal Design and Confirmation
6.9 Benefits Gained
6.10 Consideration of Analysis for Audible Noise
6.11 Conclusion
Chapter 7: Optimal Design for a Double-Lift Window Regulator System Used in Automobiles
7.1 Executive Summary
7.2 Introduction
7.3 Schematic Figure of Double-Lift Window Regulator System
7.4 Ideal Function
7.5 Noise Factors
7.6 Control Factors
7.7 Conventional Data Analysis and Results
7.8 Selection of Optimal Condition and Confirmation Test Results
7.9 Evaluation of Quality Characteristics
7.10 Concept of Analysis Based on Standardized S/N Ratio
7.11 Analysis Results Based on Standardized S/N Ratio
7.12 Comparison between Analysis Based on Standardized S/N Ratio and Analysis Based on Conventional S/N Ratio
7.13 Conclusion
Further Reading
Chapter 8: Optimization of Next-Generation Steering System Using Computer Simulation
8.1 Executive Summary
8.2 Introduction
8.3 System Description
8.4 Measurement Data
8.5 Ideal Function
8.6 Factors and Levels
8.7 Pre-analysis for Compounding the Noise Factors
8.8 Calculation of Standardized S/N Ratio
8.9 Analysis Results
8.10 Determination of Optimal Design and Confirmation
8.11 Tuning to the Targeted Value
8.12 Conclusion
Chapter 9: Future Truck Steering Effort Robustness
9.1 Executive Summary
9.2 Background
9.3 Parameter Design
9.4 Acknowledgments
References
Chapter 10: Optimal Design of Engine Mounting System Based on Quality Engineering
10.1 Executive Summary
10.2 Background
10.3 Design Object
10.4 Application of Standard S/N Ratio Taguchi Method
10.5 Iterative Application of Standard S/N Ratio Taguchi Method
10.6 Influence of Interval of Factor Level
10.7 Calculation Program
10.8 Conclusions
References
Chapter 11: Optimization of a Front-Wheel-Drive Transmission for Improved Efficiency and Robustness
11.1 Executive Summary
11.2 Introduction
11.3 Experimental
11.4 Signal Strategy
11.5 Noise Strategy
11.6 Control Factor Selection
11.7 Orthogonal Array Selection
11.8 Results and Discussion
11.9 Conclusion
References
Chapter 12: Fuel Delivery System Robustness
12.1 Executive Summary
12.2 Introduction
12.3 Experiment Description
12.4 Noise Factors
12.5 Experiment Test Results
12.6 Sensitivity (β) Analysis
12.7 Confirmation Test Results
12.8 Conclusion
Chapter 13: Improving Coupling Factor in Vehicle Theft Deterrent Systems Using Design for Six Sigma (DFSS)
13.1 Executive Summary
13.2 Introduction
13.3 Objectives
13.4 The Voice of the Customer
13.5 Experimental Strategy
13.6 The System
13.7 The Experimental Results
13.8 Conclusions
Part Three: Subsystems Level Optimization by Suppliers
Chapter 14: Magnetic Sensing System Optimization
14.1 Executive Summary
14.2 Improvement of Design Technique
14.3 System Design Technique
14.4 Effect by Shortening of Development Period
14.5 Conclusion
References
Chapter 15: Direct Injection Diesel Injector Optimization
15.1 Executive Summary
15.2 Introduction
15.3 Simulation Model Robustness
15.4 Parameter Design
15.5 Tolerance Design
15.6 Conclusions
Reference and Further Reading
Chapter 16: General Purpose Actuator Robust Assessment and Benchmark Study
16.1 Executive Summary
16.2 Introduction
16.3 Objectives
16.4 Robust Assessment
16.5 Conclusion
Further Reading
Chapter 17: Optimization of a Discrete Floating MOS Gate Driver
17.1 Executive Summary
17.2 Background
17.3 Introduction
17.4 Developing the “Ideal” Function
17.5 Noise Strategy
17.6 Control Factors and Levels
17.7 Experiment Strategy and Measurement System
17.8 Parameter Design Experiment Layout
17.9 Results
17.10 Response Charts
17.11 Two-Step Optimization
17.12 Confirmation
17.13 Conclusions
Chapter 18: Reformer Washcoat Adhesion on Metallic Substrates
18.1 Executive Summary
18.2 Introduction
18.3 Experimental Setup
18.4 Control Factor Levels
18.5 Noise Factors
18.6 Description of Experiment
18.7 Two Step Optimization and Prediction
18.8 Confirmation
18.9 Measurement System Evaluation
18.10 Conclusion
18.11 Supplemental Background Information
18.12 Acknowledgment
Reference and Further Reading
Chapter 19: Making Better Decisions Faster: Sequential Application of Robust Engineering to Math-Models, CAE Simulations, and Accelerated Testing
19.1 Executive Summary
19.2 Introduction
19.3 Objective
19.4 Robust Optimization
19.5 Conclusions
Futher Reading
Chapter 20: Pressure Switch Module Normally Open Feasibility Investigation and Supplier Competition
20.1 Executive Summary
20.2 Introduction
20.3 Objective
20.4 Robust Assessment
20.5 Summary and Conclusions
Part Four: Manufacturing Process Optimization
Chapter 21: Robust Optimization of a Lead-Free Reflow Soldering Process
21.1 Executive Summary
21.2 Introduction
21.3 Experimental
21.4 Results and Discussion
21.5 Conclusion
References
Chapter 22: Catalyst Slurry Coating Process Optimization for Diesel Catalyzed Particulate Traps
22.1 Executive Summary
22.2 Introduction
22.3 Project Description
22.4 Process Map
22.5 First Parameter Design Experiment
22.6 Follow-up Parameter Design Experiment
22.7 Transfer to Florange
22.8 Conclusion
Index
End User License Agreement
List of Tables
Table 2.1
Table 2.2
Table 2.3
Table 2.4
Table 2.5
Table 2.6
Table 2.7
Table 2.8
Table 2.9
Table 2.10
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Table 2.12
Table 4.1
Table 4.2
Table 4.3
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Table 5.6
Table 5.7
Table 5.8
Table 5.9
Table 6.1
Table 6.2
Table 6.3
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Table 7.1
Table 7.2
Table 7.3
Table 7.4
Table 7.5
Table 7.6
Table 7.7
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Table 8.1
Table 8.2
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Table 9.1
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Table 10.1
Table 11.1
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Table 11.7
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Table 12.1
Table 12.2
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Table 12.6
Table 12.7
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Table 13.1
Table 14.1
Table 14.2
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Table 15.1
Table 15.2
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Table 15.7
Table 15.8
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Table 16.1
Table 16.2
Table 16.3
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Table 17.1
Table 17.2
Table 17.3
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Table 18.1
Table 18.2
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Table 19.1
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Table 20.1
Table 20.2
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Table 20.5
Table 20.6
Table 20.7
Table 20.8
Table 20.9
Table 21.1
Table 21.2
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Table 21.5
Table 22.1
Table 22.2
Table 22.3
Table 22.4
Table 22.5
Table 22.6
Table 22.7
Table 22.8
Table 22.9
Table 22.10
List of Illustrations
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5
Figure 2.6
Figure 2.7
Figure 2.8
Figure 2.9
Figure 2.10
Figure 2.11
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Figure 2.21
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Figure 2.24
Figure 2.25
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Figure 2.30
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Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
Figure 4.7
Figure 4.8
Figure 4.9
Figure 4.10
Figure 4.11
Figure 4.12A
Figure 4.12B
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
Figure 5.7
Figure 5.8
Figure 5.9
Figure 6.1
Figure 6.2
Figure 6.3
Figure 6.4
Figure 6.5
Figure 6.6
Figure 6.7
Figure 7.1
Figure 7.2
Figure 7.3
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Figure 7.5
Figure 7.6
Figure 7.7
Figure 7.8
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Figure 7.10
Figure 7.11
Figure 7.12
Figure 7.13
Figure 7.14
Figure 8.1
Figure 8.2
Figure 8.3
Figure 8.4
Figure 8.5
Figure 8.6
Figure 8.7
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Figure 8.10
Figure 8.11
Figure 9.1
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Figure 9.6
Figure 9.7
Figure 9.8
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Figure 9.10
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Figure 9.14
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Figure 10.1
Figure 10.2
Figure 10.3
Figure 10.4
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Figure 10.6
Figure 10.7
Figure 10.8
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Figure 10.12
Figure 10.13
Figure 10.14
Figure 10.15
Figure 11.1
Figure 11.2
Figure 11.3
Figure 11.4
Figure 11.5
Figure 11.6
Figure 11.7
Figure 12.1
Figure 12.2
Figure 12.3
Figure 12.4
Figure 12.5
Figure 13.1
Figure 13.2
Figure 13.3
Figure 13.4
Figure 13.5
Figure 13.6
Figure 13.7
Figure 13.8
Figure 13.9
Figure 13.10
Figure 14.1
Figure 14.2
Figure 14.3
Figure 14.4
Figure 14.5
Figure 14.6
Figure 14.7
Figure 14.8
Figure 14.9
Figure 15.1
Figure 15.2
Figure 15.3
Figure 15.4
Figure 15.5
Figure 15.6
Figure 15.7
Figure 15.8
Figure 15.9
Figure 15.10
Figure 15.11
Figure 15.12
Figure 15.13
Figure 15.14
Figure 15.15
Figure 15.16
Figure 15.17
Figure 15.18
Figure 15.19
Figure 15.20
Figure 15.21
Figure 16.1
Figure 16.2
Figure 16.3
Figure 16.4
Figure 16.5
Figure 16.6
Figure 16.7
Figure 16.8
Figure 16.9
Figure 16.10
Figure 16.11
Figure 17.1
Figure 17.2
Figure 17.3
Figure 17.4
Figure 17.5
Figure 17.6
Figure 17.7
Figure 17.8
Figure 17.9
Figure 18.1
Figure 18.2
Figure 18.3
Figure 18.4
Figure 18.5
Figure 18.6
Figure 18.7
Figure 18.8
Figure 18.9
Figure 18.10
Figure 18.11
Figure 19.1
Figure 19.2
Figure 19.3
Figure 19.4
Figure 19.5
Figure 19.6
Figure 19.7
Figure 19.8
Figure 19.9
Figure 19.10
Figure 19.11
Figure 19.12
Figure 19.13
Figure 19.14
Figure 19.15
Figure 19.16
Figure 20.1
Figure 20.2
Figure 20.3
Figure 20.4
Figure 20.5
Figure 20.6
Figure 20.7
Figure 20.8
Figure 20.9
Figure 20.10
Figure 20.12
Figure 20.11
Figure 20.13
Figure 20.14
Figure 21.1
Figure 21.2
Figure 21.3
Figure 21.4
Figure 22.1
Figure 22.2
Figure 22.3
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Figure 22.5
Figure 22.6
Figure 22.7
Figure 22.8
Figure 22.9
Figure 22.10
Figure 22.11
Figure 22.12
Figure 22.13
Figure 22.14
Figure 22.15
Figure 22.16
Figure 22.17
Figure 22.18
Guide
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Robust Optimization:
World's Best Practices for Developing Winning Vehicles
Subir Chowdhury
Shin Taguchi
ASI Consulting Group, LLC, USA
This edition first published 2016© 2016 Subir Chowdhury, Shin Taguchi and ASI Consulting Group, LLC. All rights reserved.
Registered officeJohn Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom
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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.
Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book.
Limit of Liability/Disclaimer of Warranty: While the publisher and authors have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the authors shall be liable for damages arising herefrom. If professional advice or other expert assistance is required, the services of a competent professional should be sought
Library of Congress Cataloging-in-Publication Data
Names: Chowdhury, Subir, author. | Taguchi, Shin, author.
Title: Robust optimization : world's best practices for developing winning
vehicles / Subir Chowdhury, Shin Taguchi.
Description: Chichester, West Sussex, United Kingdom : John Wiley & Sons,
Inc., [2016] | Includes bibliographical references and index.
Identifiers: LCCN 2015035260 | ISBN 9781119212126 (cloth)
Subjects: LCSH: Motor vehicles–Design and construction. | Robust
optimization. | Manufacturing processes.
Classification: LCC TL240 .C435 2016 | DDC 629.2/31–dc23 LC record available at http://lccn.loc.gov/2015035260
A catalogue record for this book is available from the British Library.
ISBN: 9781119212126
Dedication
In memory of our teachers, colleagues, friends:Genichi TaguchiYuin Wu
Preface
What is Robust Optimization? Put simply, it's a method to improve robustness using low-cost variations of a single, conceptual design. Jim Pratt, a former Vice-President of ITT, once said that using Robust Optimization on manufacturing processes was like “picking gold up off the floor!” Robust Optimization uses Robust Assessment to estimate the robustness of low-cost combinations of design parameter (control factor) values with a single conceptual design in order to discover the most robust combination of design parameter (control factor) values. A design parameter is called a control factor in Robust Optimization. A control factor value is a quantitative or qualitative level of a variable contained within the selected conceptual design. In mechanical designs, dimensions, radii, and material properties are typical control factors. In electrical designs, resistance, impedance, and capacitance are frequent control factors. In chemical processes, temperatures, rate of temperature change, pressures, pressure rate, reagents, and catalysts are common control factors.
In product or process development, Robust Optimization occurs after system (conceptual) design is complete and before the conceptual design is adjusted to meet requirements. Robust Optimization is best conducted before the requirements are defined.
Robust Optimization as a first step discovers a robust, low-cost combination of control factor values. This combination may not meet or even be close to meeting the design requirements. However, Robust Optimization as a second step discovers how to adjust that low-cost combination of control factor values so that the product can meet requirements. That very stable combination of control factor values is adjusted or “tuned” so that the product or process can easily meet requirements or specifications.
Selection of design concept that is robust is critical. Robust Optimization and assessment allow us to evaluate how robust the new concept is quickly so that we can try many concepts. We don't want to pass poor concepts through design gates. We need to detect bad design early. If we are going to fail, it is better to fail early so we can move on to the next concept quickly.
The benefits of Robust Optimization include such things as faster product development cycles; faster launch cycles; fewer manufacturing problems; fewer field problems; lower-cost, higher performing products and processes; and lower warranty. All these benefits can be realized if Engineering and product development leadership of automotive and manufacturing organizations leverage the power of using Robust Optimization as a competitive weapon.
The overarching benefit, however, is to create a group of technical employees with skills that produce results which dazzle your customers and the general public. Become the organization that produces the highest quality products at the lowest cost. Become the organization that is the first or second choice of every top-ranked engineering graduate in the world. Become the organization that is featured in the trade magazines as the innovator with rock solid quality and a winning value proposition for your customers.
The main objective of this book is to introduce engineering executives and leaders to the technical management strategy of Robust Optimization. In the first three chapters, we will discuss what the strategy entails, Eight Steps for Robust Optimization and Robust Assessment, and how to lead it in a technical organization with an implementation strategy. Another objective of this book is to demonstrate the application of Robust Optimization to automotive applications using real-life case studies from leading automotive organizations.
In this book, we define Robust Optimization and demonstrate how these techniques can be applied to build into manufacturing organizations especially with automotive industry applications; that Robust Optimization creates the flexibility that minimizes product development cost, reduces product time-to-market, and increases overall productivity. For the past 40 years thousands of companies throughout the world have been using the methodology ‘Robust Optimization’ of the late Dr. Genichi Taguchi, the quality pioneer, and have obtained positive results. Some organizations have integrated this new powerful methodology into their corporate culture. The benefits these organizations have achieved are phenomenal.
In this groundbreaking book, we have organized 19 successfully implemented best case studies from automotive original equipment manufacturers such as General Motors, Ford, Chrysler, Nissan, Isuzu, and Mazda as well as automotive suppliers like Bosch, Delphi, and Alps Electric. We have been working for past decades with all types of clients in automotive, manufacturing, healthcare, food, aerospace and other industries. Our firm ASI Consulting Group, LLC, headquartered in Bingham Farms, Michigan, USA, is very fortunate in that clients have been continuously putting their trust in our team. Most importantly, clients share their success stories at our annual client conferences. We are also fortunate to have other organizations from Europe and Asia to attend our annual conference and present their success stories on the application of Robust Optimization. ASI has thousands of case studies in its database and it is therefore impossible to feature all case studies in this book. However, we have included some of the very best of these case studies, keeping in mind the variety of applications of Robust Optimization.
There are many books available, mostly in English and Japanese, which include some automotive case studies. However, there is no book to date which presents best practices from around the globe on Robust Optimization in the automotive industry, and manufacturing industry in general. This is the first book to focus on the automotive application of Robust Optimization. In the organizations where Robust Optimization is extremely successful, senior leadership has an understanding of the significant impact of the applications and therefore support for implementation is highly encouraged or mandatory. In the United States, Asia, and Europe, hundreds of organizations have unfortunately been using Robust Optimization incorrectly, but those that have used the methodology correctly have been saving millions of dollars. Those organizations that have not been utilizing this powerful method may have not being doing so because of their lack of management understanding and misconceptions about its complexity. This book will therefore be a must read for any engineering manager or engineer because of its ability to clarify these generalized misconceptions.
This is the first book that features case studies from all four critical areas of Robust Optimization of an automotive organization:
Vehicle Level Optimization
Subsystems Level Optimization by Original Equipment Manufacturers (OEMs)
Subsystems Level Optimization by Suppliers
Manufacturing Process Optimization.
We also hope that this book provides direct learning techniques to the vast variety of industries and educational institutions and that it provides a formula for instant knowledge on areas that apply to the reader and his/her organization.
This book is for engineers and managers who are working in the design, product, manufacturing, mechanical, electrical, process, and quality areas; all levels of management especially in the product development area; and research and development personnel and consultants. Almost all the case studies featured in this book make it suitable as a training and education guide, as well as serving both students and teachers in engineering colleges. We strongly feel that all libraries with technical sections will greatly benefit from having this book in their collection.
Subir ChowdhuryShin TaguchiJuly 31, 2015
Acknowledgments
The authors gratefully acknowledge the efforts of all who assisted in the completion of this book:
To all of the contributors and their organizations for sharing their successful case studies.
To the ASI Consulting Group, LLC and all its employees and partners worldwide.
To our colleagues and friends, Alan Wu, Brad Walker, Michael O'Ship, Matt Gajda, Michael Holbrook, Brian Bartnick, Bill Eureka, Jay Eleswarpu, Joe Smith, and Francois Pelka for effectively promoting Robust Optimization each day.
To our colleague Jodi Caldwell for her hard work on the preparation of the manuscript.
To our two retired colleagues and friends, Jim Quinlan and Barry Bebb for enriching us over the past two decades.
To Paul Petralia, our editor at Wiley, for his dedication and guidance to make the book better.
To Liz Wingett, our project editor at Wiley, for her hard work making the book published on time.
To Martin Noble, our copy editor at Wiley, for his dedicated efforts toward refining the manuscript.
To Anne Hunt, Associate Commissioning Editor at Wiley for her work on the book.
To Sandra Grayson, Associate Book Editor, for her work on the book.
Finally, this book never would have been materialized without the continuous support of our wonderful wives Malini Chowdhury and Junko Taguchi.
About the Authors
Subir Chowdhury has been a thought leader in quality management strategy and methodology for more than 20 years. Currently Chairman and CEO of ASI Consulting Group, LLC, he leads Six Sigma and Quality Leadership implementation, and consulting and training efforts. Subir's work has earned him numerous awards and recognition. The New York Times cited him as a “leading quality expert”; BusinessWeek hailed him as the “Quality Prophet.” The Conference Board Review described him as “an excitable, enthusiastic evangelist for quality.”
Subir has worked with many organizations across diverse industries including manufacturing, healthcare, food, and nonprofit organizations. His client list includes major global corporations and industrial leaders such as American Axle, Berger Health Systems, Bosch, Caterpillar, Daewoo, Delphi Automotive Systems, Fiat-Chrysler Automotive, Ford, General Motors, Hyundai Motor Company, ITT Industries, Johns Manville, Kaplan Professional, Kia Motors, Leader Dogs for the Blind, Loral Space Systems, Make It Right Foundation, Mark IV Automotive, Procter & Gamble, State of Michigan, Thomson Multimedia, TRW, Volkswagen, Xerox, and more. Under Subir's leadership, ASI Consulting Group has helped hundreds of clients around the world save billions of dollars in recovered productivity and increased revenues.
Subir is the author of 14 books, including the international bestseller The Power of Six Sigma (Dearborn Trade, 2001), which has sold more than a million copies worldwide and been translated into more than 20 languages. Design for Six Sigma (Kaplan Professional, 2002) was the first book to popularize the “DFSS” concept. With quality pioneer Dr. Genichi Taguchi, Subir co-authored two technical bestsellers Robust Engineering (McGraw Hill, 1999) and Taguchi's Quality Engineering Handbook (Wiley, 2005).
His book, the critically acclaimed The Ice Cream Maker (Random House Doubleday, 2005) introduced LEO® (Listen, Enrich, Optimize), a flexible management strategy that brings the concept of quality to every member of an organization. The book was formally recognized and distributed to every member of the 109th Congress. The LEO process continues to be implemented in many organizations. His most recent book, The Power of LEO (McGraw-Hill, 2011) was an Inc. Magazine bestseller. A follow-up to The Ice Cream Maker, the book shows organizations how the LEO methodology can be integrated into a complete quality management system.
London, UK based Thinkers50 named Subir as one of the “50 Most Influential Management Thinkers in the World” in 2011, 2013 and 2015. Subir is a recipient of the Society of Manufacturing Engineers' Gold Medal, the Society of Automotive Engineers' (SAE) Henry Ford II Distinguished Award for excellence in Automotive Engineering and the American Society of Quality's first Philip Crosby Medal for authoring the most influential book on Quality. The US Department of Homeland Security presented the “Outstanding American by Choice Award” to Subir for his contributions to the field of quality and management.
In 2014, the University of California at Berkeley established the Subir & Malini Chowdhury Center for Bangladesh Studies. The Center will award graduate fellowships, scholarships, and research grants that focus on ways to improve the quality of life for the people of Bangladesh.
Each year the Subir Chowdhury Fellowship on Quality and Economics is awarded by both Harvard University and London School of Economics and Political Science to a doctoral student to research and study the impact of quality in the economic advancement of a nation. The SAE International established the “Subir Chowdhury Medal of Quality Leadership,” an annual award that recognizes those individuals who promote innovation and expand the impact of quality in mobility engineering, design and manufacturing.
Subir received his undergraduate degree in Aeronautical Engineering from the Indian Institute of Technology (IIT), Kharagpur, India and his graduate degree in Industrial Management from Central Michigan University, Mt. Pleasant, Michigan. He has received Distinguished Alumnus Awards from both universities, as well as an honorary doctorate of engineering from the Michigan Technological University.
Subir lives with his wife, Malini and two children, Anandi and Anish, in Los Angeles, California.
Shin Taguchi is Chief Technical Officer (CTO) for ASI Consulting Group, LLC. He is a Master Black Belt in Six Sigma and Design for Six Sigma (DFSS) and was one of the world authorities in developing the DFSS program at ASI-CG, an internationally recognized training and consulting organization, dedicated to improving the competitive position of industries. He is the son of Dr. Genichi Taguchi, developer of new engineering approaches for robust technology that have saved American industry billions of dollars.
Over the last thirty years, Shin has trained more than 60 000 engineers around the world in quality engineering, product/process optimization, and robust design techniques, Mahalanobis-Taguchi System, known as Taguchi Methods™. Some of the many clients he has helped to make products and processes Robust include: Ford Motor Company, General Motors, Delphi Automotive Systems, Fiat-Chrysler Automotive, ITT, Kodak, Lexmark, Goodyear Tire & Rubber, General Electric, Miller Brewing, The Budd Company, Westinghouse, NASA, Texas Instruments, Xerox, Hyundai Motor Company, TRW and many others. In 1996, Shin developed and started to teach a Taguchi Certification Course. Over 360 people have graduated to date from this ongoing 16-day master certification course.
Shin is a Fellow of the Royal Statistical Society in London, and is a member of the Institute of Industrial Engineering (IIE) and the American Society for Quality (ASQ); Shin is a member of the Quality Control Research Group of the Japanese Standards Association (JSA) and Quality Engineering Society of Japan. He is an editor of the Quality Engineering Forum Technical Journal and was awarded the Craig Award for the best technical paper presented at the annual conference of the ASQ. Shin has been featured in the media through a number of national and international forums, including Fortune Magazine and Actionline (a publication of AIAG). Shin co-authored Robust Engineering, published by McGraw Hill in 1999. He has given presentations and workshops at numerous conferences, including ASQ, ASME, SME, SAE, and IIE. He is also a Master Black Belt for Design for Six Sigma (DFSS).
Shin holds a Bachelor of Science degree in Industrial Engineering and Statistics from the University of Michigan and trains and consults with many major corporations worldwide.
Shin lives with his wife, Junko and three children, Hana, Yumi and Miki, in West Bloomfield, Michigan.
1Introduction to Robust Optimization
The automotive industry is very dynamic and the product is continuously changing. The competition is so cut-throat that it is becoming increasingly important to deliver quality products at all times. The customers are demanding the highest quality product at a cheaper price. Robust optimization is the mantra for automotive product development organizations both for original equipment manufacturers (OEMs) and their suppliers, especially in this competitive environment. Dr. Genichi Taguchi's Robust Optimization idea is simply revolutionary. To practice robust optimization correctly, product development and manufacturing organizations need to change the way they work, the way work is done needs to change, the way work is managed needs to change, knowledge and skills need to change, the way organizations are led needs to change. Obviously, all of these take time. Not accepting this reality will be more devastating in the future for any organization that wants to win customers' hearts by consistently delivering highest quality products.
Dr. Genichi Taguchi talked about quality as loss to society and how that loss is estimated using a “Quality Loss Function.” He talked about robustness – the functional stability of products or processes in the face of ubiquitous variation in the usage conditions (noise factors). He talked about a product development process involving system, parameter and tolerance design steps. He suggested that engineers focus less on meeting requirements and more on discovering combinations of design variable values that (1) stabilize the function and (2) control the adjustment or “tuning” of that function. He talked about ideal functions.
Dr. Taguchi asked engineers and engineering leadership to look at technical work in an entirely different light.
What happened?
Well, since the word “quality” was part of the “Quality Loss Function,” the quality experts in the organization took over that concept.
Robustness sounded like product performance in the field. So robustness was delegated to the reliability and validation engineers. Noise factors seemed similar to best case and worst case conditions, so that, too, was a good fit to reliability and validation engineering.
His recommended product development system sounded a lot like existing concurrent engineering and optimization methodologies. System engineers looked at Dr. Taguchi's comments and said, “We already do this – there's nothing new here!”
Parameter design was seen as setting design variable values at levels that met requirements in all conditions. Since parameter design borrowed orthogonal arrays from design of experiments, Taguchi's methods were often seen as a form of Design of Experiment. In most engineering organizations, Designed Experiments were organized by a quality expert when engineering had a problem. Parameter design was delegated to quality and product engineering. Often, an experiment was conducted only if a problem of sufficient magnitude presented itself. Taguchi's parameter design methods were roundly criticized by statisticians for, among many other things, a lack of statistical rigor. Even today, “Taguchi Designs” remain a subset of most statistical computer programs. A subset only “recommended” for preliminary, screening experiments.
1.1 What Is Quality as Loss?
One of our client engineers once had a car with a noisy transmission. He took it to the dealer because the noise bothered him. The dealer attached a machine to the transmission. It printed out a report.
“Your transmission is within specification,” the dealer said.
There was nothing more to be done. He drove the car for a couple of years. He was glad when he could replace it with a new one. He never bought that brand of car again – even though their transmission was in specification. The dealer's machine and the printout said so.
Dr. Genichi Taguchi defines quality as “Quality may be assessed as the minimum loss imparted by the product to society from the time the product is shipped.” The larger the loss, the poorer is the quality. This kind of thinking says that there is a difference among products even if they are within specification.
The “ideal” amount of noise from an automotive transmission is zero (yes, it's impossible to achieve). As the noise from the transmission increases it will bother some people more than others. But when the noise bothers someone enough, he or she will suffer a loss. They have to take the time to drive to the dealer and wait while the service technician conducts a diagnosis. There will be a dollar value for his time. The drive, diagnosis and report out will take about two hours. Two hours at that time in this person's life is probably worth about $250. Is that the total loss? What about the company's loss of a future sale? How much is that worth? What is the profit the company would make from the sale? The loss suffered by the company who made the noisy transmission is certainly more than $250.
If an automotive manufacturer makes a very, very noisy transmission, a customer might insist that it be replaced. It doesn't matter if the transmission is in or out of specification. The customer wants it replaced. The total loss to society is probably around $3500 (including customer inconvenience). It doesn't matter whether the transmission is under warranty or not. If under warranty, the manufacturer pays; if not, the customer pays. Either way “society” is out $3500 for each transmission that is so noisy it needs to be replaced.
Using this type of data, the quality in regards to audible noise of any transmission can be estimated. The actual amount of audible noise in decibels could be placed along the bottom axis. Dr. Genichi Taguchi is suggesting that every transmission that makes any noise at all contributes a slight amount of loss to society.
The redefinition of quality that you, as the technical leader of your organization, need to embrace is that producing parts within specification is absolutely necessary. However, only producing parts that meet requirements is no longer competitive.
For long-term success in the marketplace, we must focus on producing low-cost products that lower the loss to society. The average dollars lost by society due to audible transmission noise can be estimated for the transmissions made by your company versus the transmissions made by your competition. The long-term competitive position of your company correlates well with such estimates. Products with lower quality loss to society do better over time in the market. Where do your products rate?
While automobiles provide value to society such as transportation and pleasure of driving, automobiles are producing significant amounts of losses. Those losses include emissions, global warming, and automobile accidents. Dr. Taguchi always dreamt about accident-free automobiles and automobiles that clean air.
1.2 What Is Robustness?
What is robustness? You may have to dust off some of your old textbooks (or go online), but you can do it. The ideas aren't that complicated for a technically trained person like you. Let's define robustness as the ability of a product or process to function consistently as the surrounding uncontrollable or uncontrolled factors vary.
An example is the power window system in the driver's side door of your car. Does it perform today as well as it did the day you took delivery of it? On an extremely cold morning? On a hot summer day? When you are sitting in the car with the motor off? At 50 mph? Has the window ever stopped working entirely?
If two window systems are being compared, the more robust window system is the one that performs most consistently over a large number of cycles, at low and high temperatures, when running on battery power, or when the car is moving a high speed.
Higher robustness means that a product will last longer in the field, that is, in the hands of the customer. No matter how old the vehicle, no customer should have to awkwardly open the door of her car on a cold winter day to pay and pick up her order at the drive-through window. Only window systems with high levels of robustness can meet that requirement.
Robustness is easy to understand. We appreciate the chain of coffee stores that provides a cup of coffee with consistent taste, aroma, and temperature, regardless of whether we buy it in Seattle or Shanghai. We gravitate toward products that perform consistently over a long useful life. A carpenter needs a circular saw that will last for years of hard use after being thrown into the back of a pickup truck. The expensive two-fuel stove in our kitchen shouldn't have the control panel fail in the first month we own it.
One common misunderstanding about robustness is that more expensive products tend to be more robust. We think that we have to pay for robustness. But is a luxury brand car more robust than a small traditional sedan of one-quarter of the price? In many regards, probably not. More importantly, robust optimization provides methods by which high robustness can be achieved at low cost.
1.3 What Is Robust Assessment?
Robustness is a measurement, not a requirement to be reached. Robustness is only meaningful in comparison. Is my product more or less robust than my competitor's? By how much? Is the new design more or less robust than the old design? By how much? The measure or robustness is the signal-to-noise ratio (S/N ratio). The higher the S/N ratio, the more robust the product or process.
Use the creativity of your people to develop methods to assess (estimate) the robustness of your products in 15 minutes! Usually no more than six measurements are needed to estimate robustness. Most companies that use these ideas strategically develop special fixtures to help engineers estimate robustness quickly and efficiently.
After learning and applying Robust Assessment, an Engineering Vice President at Ricoh said, “From now on, our assessment on a paper handling system will take only two sheets of paper.” At Nissan, a robust assessment technique was developed that takes only 15 minutes to assess robustness of a power window system with a high confidence level.
John Elter, a former VP of Engineering at Xerox, said that engineering labs used to be filled with prototype copy machines running continuously for life test and to estimate failure rate. After Robust Assessment, they are filled with jigs and fixtures to measure functions and robustness; functions include paper feeding, toner dispensing, toner charging, toner transfer, fusing, etc.
1.4 What Is Robust Optimization?
Robust optimization, a concept as familiar as it is misunderstood, will be clarified in this chapter. We conduct robust optimization by following the two-step process: (1) Minimize variability in the product or process, and (2) adjust the output to hit the target. In other words, first optimize performance to get the best out of the concept selected, then adjust the output to the target value to confirm whether all the requirements are met. The better the concept can perform, the greater our chances to meet all requirements. In the first step we try to kill many birds with one stone, that is, to meet many requirements by doing only one thing. How is that possible?
We start by identifying the ideal function, which will be determined by the basic physics of the system, be it a product or process. In either case, the design will be evaluated by the basic physics of the system. When evaluating a product or a manufacturing process, the ideal function is defined based on energy transformation from the input to the output. For example, for a car to go faster, the driver presses down on the gas pedal, and that energy is transformed to increased speed by sending gas through a fuel line to the engine, where it is burned, and finally to the wheels, which turn faster.
When designing a process, energy is not transformed, as in the design of a product, but information is. Take the invoicing process, for example. The supplier sends the company an invoice, and that information starts a chain of events that transforms the information into various forms of record-keeping and results, finally, in a check being sent to the supplier.
In either case, we first define what the ideal function for that particular product or process would look like; then we seek a design that will minimize the variability of the transformation of energy or information, depending on what we are trying to optimize.
We concentrate on the transformation of energy or information because all problems, including defects, failures, and poor reliability, are symptoms of variability in the transformation of energy or information. By optimizing that transformation – taking out virtually all sources of “friction” or noise along the way – we strive to meet all the requirements at once.
To understand fully this revolutionary approach, let's first review how quality control has traditionally worked. Virtually since the advent of commerce, a “good” or acceptable product or process has been defined simply as one that meets the standards set by the company. But here's the critical weakness to the old way of thinking: It has always been assumed that any product or process that falls anywhere in the acceptable range is equal to any other that falls within that range.
Picture the old conveyer belt, where the products roll along the line one by one until they get to the end, where an inspector wearing goggles and a white coat looks at each one and tosses them either into the “acceptable” bin or the “reject” bin. In that case, there are no other distinctions made among the finished products, just “okay” or “bad.”
If you were to ask that old-school inspector what separates the worst “okay” specimen from the best reject – in other words, the ones very close to the cutoff line – he'd probably say something like, “It's a hair difference, but you've got to draw the line somewhere.” But the inspector treats all acceptable samples the same: He just tosses them in the “okay” bin, and the same with the rejects. Even though he knows there are a million shades of gray in the output, he separates them all into black or white.
Now if you asked a typical consumer of that product if there was any difference between a sample that barely met the standards to make into the “okay” bin and one that was perfect, she'd say, “Yes, absolutely. You can easily tell the difference between these two.”
The difference between the inspector's and the customer's viewpoints can be clarified further with the following analogy: If both people were playing darts, the inspector would only notice whether or not the dart hit the dartboard, not caring if it landed near the edge of the board or right on the bull's-eye. But to the customer, there would be a world of difference between the dart that landed on the board's edge and the one that pierced the bull's-eye. Although she certainly wouldn't want any dart not good enough to hit the board, she would still greatly prefer the bull's-eye to the one just an inch inside the board's edge. The point is: With the old way of inspecting products, the manufacturer or service provider made no distinctions among acceptable outputs, but the consumer almost always did, which made the company out of step with the customer's observations and desire.
This dissonance between these two perspectives demonstrates that the traditional view of quality – “good enough!” – is not good enough for remaining competitive in the modern economy. Instead of just barely meeting the lowest possible specifications, we need to hit the bull's-eye. The way to do that is to replace the oversimplified over/under bar with a more sophisticated bull's-eye design, where the goal is not merely to make acceptable products, but to reduce the spread of darts around the target.
The same is also true on the other side of the mark. In the old system, once you meet the specification that was that. No point going past it. But even if we're already doing a good job on a particular specification, we need to look into whether we can do it better and, if so, what it would cost. Would improving pay off?
Robust optimization requires you to free your employees – and your imaginations – to achieve the optimum performance by focusing on the energy/information transformation described earlier. This notion of having no ceiling is important, not just as a business concept, but psychologically as well. The IRS, of course, tells you how much to pay in taxes, and virtually no one ever pays extra. Most taxpayers do their best to pay as little as legally possible. Charities, on the other hand, never tell their donors what to pay – which might explain why Americans are by far the most generous citizens around the world in terms of charitable giving.
The point is simple: Don't give any employee, team, or project an upper limit. Let them optimize and maximize the design for robustness. See what's possible, and take advantage of the best performances you can produce! Let the sky be the limit and watch what your people can do! A limitless environment is a very inspiring place to work.
The next big question is: Once the energy/information transformation is optimized, is the design's performance greater than required? If so, you've got some decisions to make. Let's examine two extreme cases.
When the optimum performance exceeds the requirements, you have plenty of opportunities to reduce real cost. For example, you can use the added value in other ways, by using cheaper materials, increased tolerances, or by speeding up the process. The objective of robust optimization is to improve performance without increasing costs. Once you can achieve that, you can take advantage of the opportunities that cost reduction can create.
On the flip side, if the optimum performance comes in below the requirements, it's time to rethink the concept and come up with something better. The problem is that, in most corporate cultures, it is very difficult to abandon a concept because so many people have already spent so much time and effort on the project.
But this is where leadership comes in. Despite the heartbreak of letting an idea go, if it's not good enough, it's not good enough. So instead of spending good money on a doomed project and fighting fires later, it's best to cut your losses, reject the concept (salvaging the best ideas, if any), and move on to the next one, instead of locking yourself into a method of production that's never going to give you the results you want. Thus, it is extremely important to detect poor designs and reject them at the early stages of development.
Dr. Genichi Taguchi has built a model based on this concept that demonstrates the impact that variations from the target have on profits and costs. As the function of the product or process deviates from the target – either above or below it – the quality of the function is compromised. This in turn results in higher losses. The further from the target, the greater the monetary losses will be.
1.4.1 Noise Factors
The bugaboos that create the wiggles in the products and processes we create can be separated into the following general categories:
manufacturing, material, and assembly vitiations;
environmental influences (not ecological, but atmospheric);
customer causes;
deterioration, aging, and wear;
neighboring subsystems.
This list will become especially important to us when we look at parameter design for robust optimization, whose stated purpose is to minimize the system's sensitivity to these sources of variation. From here on, we will lump all these sources and their categories under the title of noise, meaning not just unwanted sound, but anything that prevents the product or process from functioning in a smooth, seamless way. Think of noise as the friction that gets in the way of perfect performance.
When teams confront a function beset with excessive variation caused by noise, the worst possible response is to ignore the problem – the slip-it-under-the-rug response. Needless to say, this never solves the problem, although it is a surprisingly common response.
As you might expect, more proactive teams usually respond by attacking the sources of the noise, trying to buffer them, or compensating for the noise by other means. All these approaches can work to a degree, but they will almost always add to the costs.
Traditionally, companies have created new products and processes by the simple formula design-build-test, or, essentially, trial and error. This has its appeal, of course, but is ultimately time consuming, inefficient, and unimaginative. It's physically rigorous but intellectually lazy.
1.4.2 Parameter Design
Parameter design takes a different approach. Instead of using the solutions listed above, which all kick in after the noise is discovered, parameter design works to eliminate the effect of noise before it occurs by making the function immune to possible sources of variation. It's the difference between prevention and cure, the latter being one of the biggest themes of design for six sigma.
We make the function immune to noise by identifying design factors we can control and exploiting those factors to minimize or eliminate the negative effects of any possible deviations – rather like finding a natural predator for a species that's harming crops and people. Instead of battling the species directly with pesticides and the like, it's more efficient to find a natural agent. The first step toward doing this is to discard the familiar approach to quality control, which really is a focus on failure, in favor of a new approach that focuses on success.
Instead of coming up with countless ways that a system might go wrong, analyzing potential failures, and applying a countermeasure for each, in parameter design we focus on the much smaller number of ways we can make things go right! It's much faster to think that way, and much more rewarding, too. Think of it as the world of scientist versus the world of engineers. It is the goal of scientists to understand the entire universe, inside and out. A noble goal, surely, but not a very efficient one. It is the engineer's goal to understand what he needs to understand to make the product or process he's working on work well. We need to think like engineers, looking for solutions, not like pure scientists, looking for explanations for every potential problem.
The usual quality control systems try to determine the symptoms of poor quality, track the rate of failure in the product or process, then attempt to find out what's wrong and how to fix it. It's a backward process: beginning with failure and tracing it back to how it occurred.
In parameter design we take a different tack: one that may seem a little foreign at first, but which is ultimately much more rewarding and effective. As discussed earlier, every product or process ultimately boils down to a system whereby energy is transferred from one thing to another to create that product or process. It's how electricity becomes a cool breeze pumping out of your air conditioner. In the case of software or business processes, a system transforms information, not energy, and exactly the same optimization can be applied.
In the parameter design approach, instead of analyzing failure modes of an air-conditioning unit, we measure and optimize the variability and efficiency of the energy transformation from the socket to the cool air pumping out of the unit. In other words, we optimize the quality of energy transformation.
This forces us to define each intended function clearly so that we can reduce its variability and maximize its efficiency. In fact, that's another core issue of parameter design: the shift from focusing on what's wrong and how to fix it to focusing on what's right and how to maximize it. Mere debugging and bandaging are not effective.
