Design for Lean Six Sigma: A Holistic Approach to Design and Innovation
1.1 The Goal.
1.2 Robust DFSS -The State of the Art.
1.4 Guide to this book.
2. Driving Growth through Innovation.
2.1 Delivering On the Promise.
2.2 Creating Better Promise.
2.3 Ambidextrous Organization.
2.4 Platforms for Growth.
2.5 Innovation and Design.
2.5.1 Managing the Paradox of Preservation and Evolution.
3. Process for Systematic Innovation.
3.1 Balanced Innovation Portfolio.
3.2 Effective Teams for Collaboration.
3.3 Execution Process for Innovation Projects.
3.4 Techniques and Tools.
3.5 Climate for Innovation.
4. Lean Six Sigma Essentials.
4.1 Origins of Six Sigma.
4.2 Six Sigma Approach.
4.2.1 Variation is the Enemy!
4.3 Origins of Lean.
4.3.1 Waste is the Enemy!.
4.4 Lean Six Sigma: Faster, Better and Cheaper.
5. Deploying Design for Lean Six Sigma (DFLSS).
5.1 Deploying DFLSS.
6. Capturing the Voice of the Customer.
6.1 Defining Elements of Customer-Producer Relationship.
6.2 Customer Expectations.
6.3 Methods of Collecting Customer Expectations.
6.4 Research Ethics.
7. Design Axioms and Their Usefulness in DFLSS.
7.1 Design Axioms.
7.2 Domain Thinking.
7.3 Design of a Software System.
7.3.1 Designing MTS software.
7.4 Design of a system that will market sporting goods.
7.5 Design of a fan belt/pulley system.
7.6 Use of Design Principles in an Academic Department.
7.6.1 Mechanical Engineering Department at MIT.
7.6.2 FR-DP Identification.
7.6.3 Actions Taken.
7.7 Design of a University System That Will Teach Students Only Through Internet.
8. Implementing Lean Design.
8.1 Key Principle of Lean Design.
8.2 Strategies for Maximizing Value And Minimizing Costs And Harm8.3 Modular Designs.
8.4 Value Engineering.
8.5 3P Approach.
9. Theory of Inventive Problem Solving (TRIZ).
9.1Introduction to TRIZ.
9.2 TRIZ Journey.
9.2 1 TRIZ Roadmap.
9.2.2 Ideality Equation.
9.2.3 Itself method.
9.2.4 TRIZ analysis tools.
9.2.5 TRIZ database tools.
9.3 Case Examples of TRIZ.
9.3.1 Improving Process of Fluorination.
9.3.2 CMM support problem.
9.4 Robustness through inventions.
9.4.1 What is a Robustness Invention.
9.4.2 Research Methodology.
9.4.3 Results of the Patent Search.
9.4.4 Robust Invention Classification Scheme.
9.4.5 Signal Based Robust Invention.
9.4.6 Response Based Robust Invention.
9.5.7 Noise Factor Based Robust Invention.
9.4.8 Control Factor Based Robust Invention.
10. Design for Robustness.
10.1.1 Evaluation of the Function Using Energy Transformation.
10.1.2 Studying the interactions between Control and Noise Factors.
10.1.3 Use Of Orthogonal Arrays (Oas) and Signal-To-Noise Ratios To Improve Robustness.
10.1.4 Two-Step Optimization.
10.1.5 Tolerance Design using Quality Loss Function.
10.2 Additional topics in designing for robustness.
10.2.1 Parameter Diagram (P-diagram).
10.2.2. Design of Experiments.
10.2.3 Signal to Noise (S/N) Ratios.
10.3 Role of Simulations in Design for Robustness.
10.4 Example - Circuit stability design.
10.4.1 Control Factors and Noise Factors.
10.4.2 Parameter Design.
10.5 PCB Drilled Hole Quality Improvement.
10.5.2 Drilled Hole quality characteristics.
10.5.4 Experiment Description.
10.5.5 Designing the experiment.
10.6 PCB Design of A Valve-Less Micropump Using Taguchi Methods.
10.6.2 Working Principle and Finite Element Modeling.
10.6.3 Design for Robustness.
11. Robust System Testing.
11. 1 Introduction.
11.1.1 A Typical System Used in Testing.
11.2 Method of Software Testing.
11.2.1 Study of two-factor combinations.
11.2.2 Construction of Combination Tables.
11.3 MTS software testing.
11.4 Case Study.
12. Development of Multivariate Measurement System Using the Mahalanobis Taguchi Strategy.
12.1 What is Mahalanobis-Taguchi Strategy?
12.2 Stages in MTS.
12.3 Signal-to-Noise Ratio - Measure of Prediction Accuracy.
12.3.1 Types of S/N Ratios in MTS.
12.4 Medical Case Study.
12.5 Case Example 2: Auto Marketing Case Study.
12.6 Case Study 3: Improving Client Experience.
12.7 Improvement of the Utility Rate of Nitrogen While Brewing Soy Sauce.
12.8 Application of MTS For Measuring Oil In Water Emulsion.
12.8.2 Application of MTS.
12.9 Prediction of Fasting Plasma Glucose (FPG) From Repetitive Annual.
Health Check-Up Data.
12.9.2 Diabetes Mellitus.
12.9.3 Application of MTS.
Appendix A. Some Useful Orthogonal Arrays.
Appendix B. Equations for Signal-to-Noise (S/N) Ratios.
Appendix C. Related Topics of Matrix Theory.
Dr. Philip Samuel is the Chief Innovation Officer for the Breakthrough Management Group, a management consulting firm specializing in performance excellence and innovation. He has been active in the management of innovation, design, and operations areas for over twenty years. He has consulted with numerous industrial and governmental organizations including Alberta Research Council, Ameriprise Financial, AXA, Baxter BioScience, BMW, ConocoPhillips, Environment Canada, Hess Corporation, Hitachi Global Storage Technologies, Johnson Controls, Kaiser Permanente, Merrill Lynch, McKesson, National Research Council of Canada, Rhodia, Schlumberger, Saint-Gobain, and Textron. He holds a PhD from the University of Calgary and an MBA from Arizona State University.