1. I.5 Taguchi’s Philosophy  Some Important Aspects  Loss Functions  Exploiting Nonlinearities  Examples  Taguchi - Comments and Criticisms

2. Some Important Aspects  Uses DOE to Make Rugged Products and Processes – DOE Is Used As a Tool “For Reducing the Effects of Variation” – Traditional DOE Had “Focused More on Optimizing Average Product Performance Than on Considering the Effects of Variation”

3. Some Important Aspects Loss Functions  For Squared Error Loss, Loss = Variance + (Bias) 2  Minimizing This Loss Involves – Reducing Variation – Targeting The Process

4. Some Important Aspects Loss Functions  Minimizing This Loss −May Involve Conflicting Goals  You may not be able to simultaneously – Optimally target the process and reduce variation  Taguchi tries to resolve the conflict through signal to noise performance measures

5. Some Important Aspects Loss Functions  Just Meeting Specs versus Squared Error Loss

6. Some Important Aspects Loss Functions Sony USA vs Sony JAPAN

7. Some Important Aspects  The competitive race is never ending – Deming/Shewhart PDSA Cycle – Juran “Managerial Breakthrough” – Kaizen – Continual Improvement – Improvement Occurs When Variation Is Reduced (Mostly Effected at The Product and Process Design Stage)

8. Some Important Aspects Reduce The Effects Of Variation! How?  “ By Exploiting The Nonlinear Effects of Product Parameters On The Performance Characteristics”  Use DOE – To Search For Interactions Between Control Factors and Noise Factors. If There Is An Interaction, It May Be Useful For Mitigating The Effect Of The Noise Factor – To Identify The Design Parameters That Have The Most Effect On Product Performance.

9. Some Important Aspects Example 3: Improving a Process  Which Factors Affect – Accuracy? – Precision?

10. Some Important Aspects Exploiting Nonlinearities  To Understand This Concept Let’s Consider an Example On Estimating Angles

11. Some Important Aspects Exploiting Nonlinearities - Other Examples  INA Tile  Plasticity of Caramel  Electric Circuit

12. Some Important Aspects Exploiting Nonlinearities To Fix This Idea Let’s  See How To Keep a Hubcap From Falling Off!

13. Taguchi Comments  Developed A Comprehensive Model of Quality Engineering  Quality Engineering Philosophy Is Fundamentally Sound – Exploiting Nonlinearities To Mitigate Noise Factors Is Novel – Loss Functions

14. Taguchi Criticisms Taguchi Criticisms  There Is Room For Improvement In His Methodology By The Use Of More Sound Statistical Ideas – Better Designs May Be Available – S/N Ratio Application May Be Better Analyzed If Viewed As A Bivariate Response (S,N) Problem  S/N Can Mask Factor Effects – Ignores Sequential Experimentation – EVOP and Response Surface Techniques – Adaptive design

15. Taguchi Criticisms Taguchi Criticisms  Traditional DOE Terminology and Methodology Is Modified Which Leads To Unnecessarily Complications – Linear Graphs rather than Alias Structure for Choosing Designs

16. Taguchi Criticisms Taguchi Criticisms  The Term “Taguchi Methodology”* – Is Objectionable – Ignores the Major Contribution of Others to This Endeavor

17. Taguchi Criticisms Taguchi Criticisms  The Term “Taguchi Methodology”* – ”Taguchi himself has said that he does not like the use of that term, which to his embarrassment has been used by others, ignorant of statistical history, to include such tools as analysis of variance, fractional factorials, orthogonal arrays, and so forth.” Box et al (1988)

18. Part I References  G.E.P. Box, W.G. Hunter and J.S.Hunter (1978). Statistics for Experimenters, John Wiley & Sons, N.Y.  G.E.P. Box, S. Bisgaard and C. Fung (1988). “An Explanation and Critique of Taguchi's Contributions to Quality Engineering,”University of Wisconsin Center for Quality and Productivity Improvement, Report #28.  C. Daniel (1976). Applications of Statistics to Industrial Experimentation, John Wiley & Sons, N.Y.  H. Karatsu (1988). TQC Wisdom of Japan, Productivity Press, Cambridge, MA.  R. Snee (1990). Statistical Thinking and Its Contribution to Total Quality, The American Statistician, 44, 116-121.