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E-raamat: Robust Optimization: World's Best Practices for Developing Winning Vehicles

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  • ISBN-13: 9781119212089
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Robust Optimization: World's Best Practices for Developing Winning VehiclesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 20-Jan-2016
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119212089
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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 Vehiclesis 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.

Preface xxi
Acknowledgments xxv
About the Authors xxvii
1 Introduction to Robust Optimization
1(16)
1.1 What Is Quality as Loss?
2(2)
1.2 What Is Robustness?
4(1)
1.3 What Is Robust Assessment?
5(1)
1.4 What Is Robust Optimization?
5(12)
1.4.1 Noise Factors
8(1)
1.4.2 Parameter Design
9(4)
1.4.3 Tolerance Design
13(4)
2 Eight Steps for Robust Optimization and Robust Assessment
17(40)
2.1 Before Eight Steps: Select Project Area
18(1)
2.2 Eight Steps for Robust Optimization
19(33)
2.2.1 Step 1: Define Scope for Robust Optimization
19(1)
2.2.2 Step 2: Identify Ideal Function/Response
20(3)
2.2.2.1 Ideal Function: Dynamic Response
20(1)
2.2.2.2 Nondynamic Responses
21(2)
2.2.3 Step 3: Develop Signal and Noise Strategies
23(9)
2.2.3.1 How Input M is Varied to Benchmark "Robustness"
23(1)
2.2.3.2 How Noise Factors Are Varied to Benchmark "Robustness"
23(9)
2.2.4 Step 4: Select Control Factors and Levels
32(6)
2.2.4.1 Traditional Approach to Explore Control Factors
32(1)
2.2.4.2 Exploration of Design Space by Orthogonal Array
33(1)
2.2.4.3 Try to Avoid Strong Interactions between Control Factors
33(3)
2.2.4.4 Orthogonal Array and its Mechanics
36(2)
2.2.5 Step 5: Execute and Collect Data
38(1)
2.2.6 Step 6: Conduct Data Analysis
38(11)
2.2.6.1 Computations of S/N and β
39(4)
2.2.6.2 Computation of S/N and β for L18 Data Sets
43(1)
2.2.6.3 Response Table for S/N and β
43(5)
2.2.6.4 Determination of Optimum Design
48(1)
2.2.7 Step 7: Predict and Confirm
49(1)
2.2.7.1 Confirmation
50(1)
2.2.8 Step 8: Lesson Learned and Action Plan
50(2)
2.3 Eight Steps for Robust Assessment
52(3)
2.3.1 Step 1: Define Scope
52(1)
2.3.2 Step 2: Identify Ideal Function/Response and Step 3: Develop Signal and Noise Strategies
52(1)
2.3.3 Step 4: Select Designs for Assessment
52(1)
2.3.4 Step 5: Execute and Collect Data
52(1)
2.3.5 Step 6: Conduct Data Analysis
52(1)
2.3.6 Step 7: Make Judgments
53(1)
2.3.7 Step 8: Lesson Learned and Action Plan
53(2)
2.4 As You Go through Case Studies in This Book
55(2)
3 Implementation of Robust Optimization
57(6)
3.1 Introduction
57(1)
3.2 Robust Optimization Implementation
57(8)
3.2.1 Leadership Commitment
58(1)
3.2.2 Executive Leader and the Corporate Team
58(2)
3.2.3 Effective Communication
60(1)
3.2.4 Education and Training
61(1)
3.2.5 Integration Strategy
62(1)
3.2.6 Bottom Line Performance
62(1)
Part One Vehicle Level Optimization 63(32)
4 Optimization of Vehicle Offset Crashworthy Design Using a Simplified Analysis Model
65(14)
Chrysler
4.1 Executive Summary
65(1)
4.2 Introduction
66(1)
4.3 Stepwise Implementation of DFSS Optimization for Vehicle Offset Impact
67(10)
4.3.1 Step 1: Scope Defined for Optimization
67(1)
4.3.2 Step 2: Identify/Select Design Alternatives
67(1)
4.3.3 Step 3: Identify Ideal Function
68(1)
4.3.4 Step 4: Develop Signal and Noise Strategy
69(1)
4.3.4.1 Input and Output Signal Strategy
69(1)
4.3.5 Step 5: Select Control/Noise Factors and Levels
70(1)
4.3.5.1 Simplified Spring Mass Model Creation and Validation
70(1)
4.3.5.2 Control Variable Selection
72(1)
4.3.5.3 Control Factor Level Application for Spring Stiffness Updates
73(1)
4.3.6 Step 6: Execute and Conduct Data Analysis
73(1)
4.3.7 Step 7: Validation of Optimized Model
74(3)
4.4 Conclusion
77(1)
4.4.1 Acknowledgments
77(1)
4.5 References
77(2)
5 Optimization of the Component Characteristics for Improving Collision Safety by Simulation
79(16)
Isuzu Advanced Engineering Center
5.1 Executive Summary
79(1)
5.2 Introduction
80(1)
5.3 Simulation Models
81(1)
5.4 Concept of Standardized S/N Ratios with Respect to Survival Space
82(4)
5.5 Results and Consideration
86(8)
5.6 Conclusion
94(1)
5.6.1 Acknowledgment
94(1)
5.7 Reference
94(1)
Part Two Subsystems Level Optimization By Original Equipment Manufacturers (OEMs) 95(140)
6 Optimization of Small DC Motors Using Functionality for Evaluation
97(16)
Nissan Motor Co
Jidosha Denki Kogyo Co.
6.1 Executive Summary
97(1)
6.2 Introduction
98(1)
6.3 Functionality for Evaluation in Case of DC Motors
98(1)
6.4 Experiment Method and Measurement Data
99(1)
6.5 Factors and Levels
100(1)
6.6 Data Analysis
101(3)
6.7 Analysis Results
104(1)
6.8 Selection of Optimal Design and Confirmation
104(3)
6.9 Benefits Gained
107(1)
6.10 Consideration of Analysis for Audible Noise
108(2)
6.11 Conclusion
110(3)
6.11.1 The Importance of Functionality for Evaluation
110(1)
6.11.2 Evaluation under the Unloaded (Idling) Condition
110(1)
6.11.3 Evaluation of Audible Noise (Quality Characteristic)
111(1)
6.11.4 Acknowledgment
111(2)
7 Optimal Design for a Double-Lift Window Regulator System Used in Automobiles
113(20)
Nissan Motor Co
Ohi Seisakusho Co
7.1 Executive Summary
113(1)
7.2 Introduction
114(1)
7.3 Schematic Figure of Double-Lift Window Regulator System
114(1)
7.4 Ideal Function
114(2)
7.5 Noise Factors
116(1)
7.6 Control Factors
117(2)
7.7 Conventional Data Analysis and Results
119(1)
7.8 Selection of Optimal Condition and Confirmation Test Results
120(2)
7.9 Evaluation of Quality Characteristics
122(2)
7.10 Concept of Analysis Based on Standardized S/N Ratio
124(1)
7.11 Analysis Results Based on Standardized S/N Ratio
125(2)
7.12 Comparison between Analysis Based on Standardized S/N Ratio and Analysis Based on Conventional S/N Ratio
127(5)
7.13 Conclusion
132(1)
7.13.1 Acknowledgments
132(1)
7.14 Further Reading
132(1)
8 Optimization of Next-Generation Steering System Using Computer Simulation
133(14)
Nissan Motor Co
8.1 Executive Summary
133(1)
8.2 Introduction
134(1)
8.3 System Description
134(1)
8.4 Measurement Data
135(1)
8.5 Ideal Function
136(1)
8.6 Factors and Levels
136(1)
8.6.1 Signal and Response
136(1)
8.6.2 Noise Factors
136(1)
8.6.3 Indicative Factor
137(1)
8.6.4 Control Factors
137(1)
8.7 Pre-analysis for Compounding the Noise Factors
137(1)
8.8 Calculation of Standardized S/N Ratio
138(3)
8.9 Analysis Results
141(1)
8.10 Determination of Optimal Design and Confirmation
141(1)
8.11 Tuning to the Targeted Value
142(2)
8.12 Conclusion
144(3)
8.12.1 Acknowledgment
145(2)
9 Future Truck Steering Effort Robustness
147(26)
General Motors Corporation
9.1 Executive Summary
147(1)
9.2 Background
148(6)
9.2.1 Methodology
148(1)
9.2.2 Hydraulic Power-Steering Assist System
149(3)
9.2.3 Valve Assembly Design
152(1)
9.2.4 Project Scope
153(1)
9.3 Parameter Design
154(18)
9.3.1 Ideal Steering Effort Function
154(3)
9.3.2 Control Factors
157(1)
9.3.3 Noise Compounding Strategy and Input Signals
157(2)
9.3.4 Standardized S/N Post-Processing
159(6)
9.3.5 Quality Loss Function
165(7)
9.4 Acknowledgments
172(1)
9.5 References
172(1)
10 Optimal Design of Engine Mounting System Based on Quality Engineering
173(14)
Mazda Motor Corporation
10.1 Executive Summary
173(1)
10.2 Background
174(1)
10.3 Design Object
174(1)
10.4 Application of Standard S/N Ratio Taguchi Method
175(3)
10.5 Iterative Application of Standard S/N Ratio Taguchi Method
178(3)
10.6 Influence of Interval of Factor Level
181(3)
10.7 Calculation Program
184(1)
10.8 Conclusions
185(1)
10.8.1 Acknowledgments
186(1)
10.9 References
186(1)
11 Optimization of a Front-Wheel-Drive Transmission for Improved Efficiency and Robustness
187(22)
Chrysler Group
Consulting Group
11.1 Executive Summary
187(1)
11.2 Introduction
188(1)
11.3 Experimental
189(1)
11.3.1 Ideal Function and Measurement
189(1)
11.4 Signal Strategy
190(1)
11.5 Noise Strategy
191(1)
11.6 Control Factor Selection
192(1)
11.7 Orthogonal Array Selection
193(3)
11.8 Results and Discussion
196(10)
11.8.1 S/N Calculations
196(4)
11.8.2 Graphs of Runs
200(1)
11.8.3 Response Plots
201(1)
11.8.4 Confirmation Run
201(2)
11.8.5 Verification of Results
203(3)
11.9 Conclusion
206(1)
11.9.1 Acknowledgments
207(1)
11.10 References
207(2)
12 Fuel Delivery System Robustness
209(14)
Ford Motor Company
12.1 Executive Summary
209(1)
12.2 Introduction
210(1)
12.2.1 Fuel System Overview
210(1)
12.2.2 Conventional Fuel System
211(1)
12.2.3 New Fuel System
211(1)
12.3 Experiment Description
211(2)
12.3.1 Test Method
211(1)
12.3.2 Ideal Function
211(2)
12.4 Noise Factors
213(1)
12.4.1 Control Factors
213(1)
12.4.2 Fixed Factors
214(1)
12.5 Experiment Test Results
214(1)
12.6 Sensitivity (β) Analysis
214(3)
12.7 Confirmation Test Results
217(6)
12.7.1 Bench Test Confirmation
217(1)
12.7.1.1 Initial Fuel Delivery System
217(1)
12.7.1.2 Optimal Fuel Delivery System
218(1)
12.7.2 Vehicle Verification
218(1)
12.7.2.1 Initial Fuel Delivery System
219(1)
12.7.2.2 Optimal Fuel Delivery System
219(1)
12.8 Conclusion
220(3)
13 Improving Coupling Factor in Vehicle Theft Deterrent Systems Using Design for Six Sigma (DFSS)
223(12)
General Motors Corporation
13.1 Executive Summary
223(1)
13.2 Introduction
224(1)
13.3 Objectives
225(1)
13.4 The Voice of the Customer
225(1)
13.5 Experimental Strategy
225(2)
13.5.1 Response
225(1)
13.5.2 Noise Strategy
226(1)
13.5.3 Control Factors
226(1)
13.5.4 Input Signal
227(1)
13.6 The System
227(1)
13.7 The Experimental Results
228(1)
13.8 Conclusions
229(8)
13.8.1 Summary
233(1)
13.8.2 Acknowledgments
234(1)
Part Three Subsystems Level Optimization By Suppliers 235(152)
14 Magnetic Sensing System Optimization
237(12)
ALPS Electric
14.1 Executive Summary
237(2)
14.1.1 The Magnetic Sensing System
238(1)
14.2 Improvement of Design Technique
239(2)
14.2.1 Traditional Design Technique
239(1)
14.2.2 Design Technique by Quality Engineering
239(2)
14.3 System Design Technique
241(5)
14.3.1 Parameter Design Diagram
241(1)
14.3.2 Signal Factor, Control Factor, and Noise Factor
242(2)
14.3.3 Implementation of Parameter Design
244(1)
14.3.4 Results of the Confirmation Experiment
244(2)
14.4 Effect by Shortening of Development Period
246(1)
14.5 Conclusion
246(1)
14.5.1 Acknowledgments
247(1)
14.6 References
247(2)
15 Direct Injection Diesel Injector Optimization
249(30)
Delphi Automotive Systems
15.1 Executive Summary
249(1)
15.2 Introduction
250(3)
15.2.1 Background
250(1)
15.2.2 Problem Statement
250(1)
15.2.3 Objectives and Approach to Optimization
251(2)
15.3 Simulation Model Robustness
253(4)
15.3.1 Background
253(4)
15.3.2 Approach to Optimization
257(1)
15.3.3 Results
257(1)
15.4 Parameter Design
257(11)
15.4.1 Ideal Function
257(1)
15.4.2 Signal and Noise Strategies
258(1)
15.4.2.1 Signal Levels
258(1)
15.4.2.2 Noise Strategy
258(1)
15.4.3 Control Factors and Levels
259(1)
15.4.4 Experimental Layout
259(1)
15.4.5 Data Analysis and Two-Step Optimization
259(4)
15.4.6 Confirmation
263(1)
15.4.7 Discussions on Parameter Design Results
264(1)
15.4.7.1 Technical
264(1)
15.4.7.2 Economical
264(4)
15.5 Tolerance Design
268(7)
15.5.1 Signal Point by Signal Point Tolerance Design
269(1)
15.5.1.1 Factors and Experimental Layout
269(1)
15.5.1.2 Analysis of Variance (ANOVA) for Each Injection Point
269(1)
15.5.1.3 Loss Function
269(1)
15.5.2 Dynamic Tolerance Design
270(1)
15.5.2.1 Dynamic Analysis of Variance
271(1)
15.5.2.2 Dynamic Loss Function
273(2)
15.6 Conclusions
275(3)
15.6.1 Project Related
275(2)
15.6.2 Recommendations for Taguchi Methods
277(1)
15.6.3 Acknowledgments
278(1)
15.7 Reference and Further Reading
278(1)
16 General Purpose Actuator Robust Assessment and Benchmark Study
279(20)
Robert Bosch
16.1 Executive Summary
279(1)
16.2 Introduction
280(1)
16.3 Objectives
280(6)
16.3.1 Robust Assessment Measurement Method
281(1)
16.3.1.1 Test Equipment
281(1)
16.3.1.2 Data Acquisition
284(1)
16.3.1.3 Data Analysis Strategy
285(1)
16.4 Robust Assessment
286(10)
16.4.1 Scope and P-Diagram
286(1)
16.4.2 Ideal Function
286(4)
16.4.3 Signal and Noise Strategy
290(1)
16.4.4 Control Factors
291(1)
16.4.5 Raw Data
291(1)
16.4.6 Data Analysis
291(5)
16.5 Conclusion
296(1)
16.5.1 Acknowledgments
297(1)
16.6 Further Reading
297(2)
17 Optimization of a Discrete Floating MOS Gate Driver
299(16)
Delphi-Delco Electronic Systems
17.1 Executive Summary
299(1)
17.2 Background
300(2)
17.3 Introduction
302(1)
17.4 Developing the "Ideal" Function
302(3)
17.5 Noise Strategy
305(1)
17.6 Control Factors and Levels
305(1)
17.7 Experiment Strategy and Measurement System
306(1)
17.8 Parameter Design Experiment Layout
306(1)
17.9 Results
307(1)
17.10 Response Charts
307(4)
17.11 Two-Step Optimization
311(1)
17.12 Confirmation
312(1)
17.13 Conclusions
312(3)
17.13.1 Acknowledgments
314(1)
18 Reformer Washcoat Adhesion on Metallic Substrates
315(26)
Delphi Automotive Systems
18.1 Executive Summary
315(1)
18.2 Introduction
316(1)
18.3 Experimental Setup
317(3)
18.3.1 The Ideal Function
318(1)
18.3.2 P-Diagram
318(1)
18.3.3 Control Factors
319(1)
18.3.3.1 Alloy Composition
319(1)
18.3.3.2 Washcoat Composition
320(1)
18.3.3.3 Slurry Parameters
320(1)
18.3.3.4 Cleaning Procedures
320(1)
18.3.3.5 Preparation
320(1)
18.4 Control Factor Levels
320(1)
18.5 Noise Factors
320(2)
18.5.1 Signal Factor
320(1)
18.5.2 Unwanted Outputs
320(2)
18.6 Description of Experiment
322(1)
18.6.1 Furnace
322(1)
18.6.2 Orthogonal Array and Inner Array
323(1)
18.6.3 Signal-to-Noise and Beta Calculations
323(1)
18.6.4 Response Tables
323(1)
18.7 Two Step Optimization and Prediction
323(6)
18.7.1 Optimum Design
329(1)
18.7.2 Predictions
329(1)
18.8 Confirmation
329(5)
18.8.1 Design Improvement
329(5)
18.9 Measurement System Evaluation
334(1)
18.10 Conclusion
334(2)
18.11 Supplemental Background Information
336(4)
18.12 Acknowledgment
340(1)
18.13 Reference and Further Reading
340(1)
19 Making Better Decisions Faster: Sequential Application of Robust Engineering to Math-Models, CAE Simulations, and Accelerated Testing
341(26)
Robert Bosch Corporation
19.1 Executive Summary
341(1)
19.2 Introduction
342(3)
19.2.1 Thermal Equivalent Circuit — Detailed
343(1)
19.2.2 Thermal Equivalent Circuit — Simplified
343(1)
19.2.3 Closed Form Solution
343(2)
19.3 Objective
345(2)
19.3.1 Thermal Robustness Design Template
345(1)
19.3.2 Critical Design Parameters for Thermal Robustness
345(1)
19.3.3 Cascade Learning (aka Leveraged Knowledge)
346(1)
19.3.4 Test Taguchi Robust Engineering Methodology
346(1)
19.4 Robust Optimization
347(17)
19.4.1 Scope and P-Diagram
347(1)
19.4.2 Ideal Function
347(2)
19.4.3 Signal and Noise Strategy
349(1)
19.4.4 Input Signal
350(1)
19.4.5 Control Factors and Levels
350(1)
19.4.6 Math-Model Generated Data
351(1)
19.4.7 Data Analysis
351(3)
19.4.8 Thermal Robustness (Signal-to-Noise)
354(2)
19.4.9 Subsystem Thermal Resistance (Beta)
356(1)
19.4.10 Prediction and Confirmation
357(5)
19.4.11 Verification
362(2)
19.5 Conclusions
364(2)
19.5.1 Acknowledgments
365(1)
19.6 Further Reading
366(1)
20 Pressure Switch Module Normally Open Feasibility Investigation and Supplier Competition
367(20)
Robert Bosch
20.1 Executive Summary
367(1)
20.2 Introduction
368(2)
20.2.1 Current Production Pressure Switch Module — Detailed
368(1)
20.2.2 Current Production (N.C.) Switching Element — Detailed
369(1)
20.3 Objective
370(1)
20.4 Robust Assessment
370(13)
20.4.1 Scope and P-Diagram
370(1)
20.4.2 Ideal Function
371(1)
20.4.3 Noise Strategy
372(1)
20.4.4 Testing Criteria
372(1)
20.4.5 Control Factors and Levels
373(1)
20.4.6 Test Data
374(1)
20.4.7 Data Analysis
375(4)
20.4.8 Prediction and Confirmation
379(4)
20.4.9 Verification
383(1)
20.5 Summary and Conclusions
383(6)
20.5.1 Acknowledgments
385(2)
Part Four Manufacturing Process Optimization 387(40)
21 Robust Optimization of a Lead-Free Reflow Soldering Process
389(14)
Delphi Delco Electronics Systems
ASI Consulting Group
21.1 Executive Summary
389(1)
21.2 Introduction
390(1)
21.3 Experimental
391(5)
21.3.1 Robust Engineering Methodology
391(3)
21.3.2 Visual Scoring
394(2)
21.3.3 Pull Test
396(1)
21.4 Results and Discussion
396(5)
21.4.1 Visual Scoring Results
396(4)
21.4.2 Pull Test Results
400(1)
21.4.3 Next Steps
401(1)
21.5 Conclusion
401(1)
21.5.1 Acknowledgment
402(1)
21.6 References
402(1)
22 Catalyst Slurry Coating Process Optimization for Diesel Catalyzed Particulate Traps
403(24)
Delphi Energy and Chassis Systems
22.1 Executive Summary
403(1)
22.2 Introduction
404(1)
22.3 Project Description
405(1)
22.4 Process Map
406(1)
22.4.1 Initial Performance
406(1)
22.5 First Parameter Design Experiment
406(10)
22.5.1 Function Analysis
407(2)
22.5.2 Ideal Function
409(1)
22.5.3 Measurement System Evaluation
409(2)
22.5.4 Parameter Diagram
411(1)
22.5.5 Factors and Levels
411(1)
22.5.6 Compound Noise Strategy
412(1)
22.5.7 Parameter Design Experiment Layout (1)
412(2)
22.5.8 Means Plots
414(1)
22.5.9 Means Tables
414(1)
22.5.10 Two-Step Optimization and Prediction
415(1)
22.5.11 Predicted Performance Improvement Before and After
416(1)
22.6 Follow-up Parameter Design Experiment
416(3)
22.6.1 Parameter Design Experiment Layout (2)
417(1)
22.6.2 Means Plots for Signal-to-Noise Ratios
417(1)
22.6.3 Confirmation Results in Tulsa
417(1)
22.6.4 Noise Factor Q Affect on Slurry Coating
417(2)
22.7 Transfer to Florange
419(5)
22.7.1 Ideal Function and Parameter Diagram
421(1)
22.7.2 Parameter Design Experiment Layout (3)
421(2)
22.7.3 Means Plots for Signal-to-Noise Ratios
423(1)
22.7.4 Prediction and Confirmation
423(1)
22.7.5 Process Capability
423(1)
22.8 Conclusion
424(3)
22.8.1 The Team
424(3)
Index 427
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 non-profit 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 Subirs 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 of 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.



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 MethodsTM.  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).
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