The normal or bell curve distribution is far more common in statistics textbooks than it is in real factories, where processes follow non-normal and often highly skewed distributions. Statistical Process Control for Real-World Applications shows how to handle non-normal applications scientifically and explain the methodology to suppliers and customers.
The book exposes the pitfalls of assuming normality for all processes, describes how to test the normality assumption, and illustrates when non-normal distributions are likely to apply. It demonstrates how to handle uncooperative real-world processes that do not follow textbook assumptions. The text explains how to set realistic control limits and calculate meaningful process capability indices for non-normal applications. The book also addresses multivariate systems, nested variation sources, and process performance indices for non-normal distributions.
Preface : Why This Book?
About the Author
Introduction
Chapter 1 : Traditional Control Charts
Chapter 2 : Nonnormal Distributions
Chapter 3 : Range Charts for Nonnormal Distributions
Chapter 4 : Nested Normal Distributions
Chapter 5 : Process Performance Indices
Chapter 6 : The Effect of Gage Capability
Chapter 7 : Multivariate Systems
Glossary
Appendix A : Control Chart Factors
Appendix B : Simulation and Modeling
Appendix C : Numerical Methods
References
