Quality Improvement Through Statistical Methods

This book is based on the papers presented at the International Conference 'Quality Improvement through Statistical Methods' in Cochin, India during December 28-31, 1996. The Conference was hosted by the Cochin University of Science and Technology, Cochin, India; and sponsored by the Institute for Improvement in Quality and Productivity (IIQP) at the University of Waterloo, Canada, the Statistics in Industry Committee of the International Statistical Institute (lSI) and by the Indian Statistical Institute. There has been an increased interest in Quality Improvement (QI) activities in many organizations during the last several years since the airing of the NBC television program, "If Japan can ... why can't we?" Implementation of QI meth­ ods requires statistical thinking and the utilization of statistical tools, thus there has been a renewed interest in statistical methods applicable to industry and technology. This revitalized enthusiasm has created worldwide discussions on Industrial Statistics Research and QI ideas at several international conferences in recent years. The purpose of this conference was to provide a forum for presenting and ex­ changing ideas in Statistical Methods and for enhancing the transference of such technologies to quality improvement efforts in various sectors. It also provided an opportunity for interaction between industrial practitioners and academia. It was intended that the exchange of experiences and ideas would foster new international collaborations in research and other technology transfers.

Table of Contents:
1. Scientific Learning.- 2. Industrial Statistics and Innovation.- 3. Understanding QS-9000 With Its Preventive and Statistical Focus.- 4. A Conceptual Framework for Variation Reduction.- 5. The Role of Academic Statisticians in Quality Improvement: Strategies of Small and Medium Sized Companies.- 6. Developing Optimal Regulation and Monitoring Strategies to Control a Continuous Petrochemical Process.- 7. Capability Indices when Tolerances are Asymmetric.- 8. Robustness of On-line Control Procedures.- 9. An Application of Filtering to Statistical Process Control.- 10. Statistical Process Control and Its Applications in Korean Industries.- 11. Multivariate Statistical Process Control of a Mineral Processing Industry.- 12. Developing Objective Strategies for Monitoring Multi-Input/Single-Output Chemical Processes.- 13. Properties of the Cpm Index for Seemingly Unstable Production Processes.- 14. Process Capability Indices for Contaminated Bivariate Normal Population.- 15. Quality Improvements by Likelihood Ratio Methods for Surveillance.- 16. Properties of the Taguchi Capability Index for Markov Dependent Quality Characteristics.- 17. Fast Model Search for Designed Experiments with Complex Aliasing.- 18. Optimal 12 Run Designs.- 19. Robustness ofD-Optimal Experimental Designs for Mixture Studies.- 20. A Bivariate Plot Useful in Selecting a Robust Design.- 21. Process Optimization Through Designed Experiments: Two Case Studies.- 22. Technological Aspects of TQM.- 23. On Robust Design for Multiple Quality Characteristics.- 24. Simultaneous Optimization of Multiple Responses Using a Weighted Desirability Function.- 25. Evaluating Statistical Methods Practiced in Two Important Areas of Quality Improvement.- 26. Applications of Generalized Linear Models in Reliability Studiesfor Composite Materials.- 27. Bivariate Failure Rates in Discrete Time.- 28. A General Approach of Studying Random Environmental Models.- 29. Testing for Change Points Expressed in Terms of the Mean Residual Life Function.- 30. Empirical Bayes Procedures for Testing the Quality and Reliability With Respect to Mean Life.- 31. On a Test of Independence in a Multivariate Exponential Distribution.- 32. Random Walk Approximation of Confidence Intervals.- 33. A Study of Quality Costs in a Multicomponent and Low Volume Products Automated Manufacturing System.- 34. Estimating Dose Response Curves.- 35. On the Quality of Preterm Infants Formula and the Longitudinal Change in Mineral Contents in Human Milk.