William A. Levinson


December 21, 2010 by CRC Press
Reference - 272 Pages - 136 B/W Illustrations
ISBN 9781439820001 - CAT# K11055


Features

• Explains pitfalls in assuming normality for all processes
• Explores instances in which the lognormal, gamma, and Weibull distributions might occur in real-world processes
• Illustrates how to deal with nested variation sources that are common in batch manufacturing operations
• Discusses what process capability indices are, how they relate to Six Sigma, and how to calculate them and their confidence limits for non-normal applications
• Shows how to handle uncooperative real-world processes that do not follow textbook assumptions
  
  
  
  

Summary

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.

The book includes examples from Minitab®, StatGraphics® Centurion, and MathCAD and covers how to use spreadsheets to give workers a visual signal when an out of control condition is present. The included user disk provides Visual Basic for Applications functions to make tasks such as distribution fitting and tests for goodness of fit as routine as possible. The book shows you how to set up meaningful control charts and report process performance indices that actually reflect the process' ability to deliver quality.

Table of Contents

Traditional Control Charts
Variation and Accuracy
Statistical Hypothesis Testing
Control Chart Concepts
Setup and Deployment of Control Charts
Interpretation of x‑bar/R and x‑bar/s Charts
X (Individual Measurement) Charts
Average Run Length (ARL)
z Chart for Sample Standard Normal Deviates
Acceptance Control Chart
Western Electric Zone Tests
Process Capability and Process Performance
Goodness-of-Fit Tests
Multiple Attribute Control Charts
Exercises
Solutions
Endnotes

Nonnormal Distributions
Transformations
General Procedure for Nonnormal Distributions
The Gamma Distribution
The Weibull Distribution
The Lognormal Distribution
Measurements with Detection Limits (Censored Data)
Exercises
Solutions
Endnotes

Range Charts for Nonnormal Distributions
Traditional Range Charts
Range Charts with Exact Control Limits
Range Charts for Nonnormal Distributions
Exercises
Solutions
Endnote

Nested Normal Distributions
Variance Components: Two Levels of Nesting
Exercise
Solution

Process Performance Indices
Process Performance Index for Nonnormal Distributions
Confidence Limits for Normal Process Performance Indices
Confidence Limits for Nonnormal Process Performance Indices
Exercise
Solution
Endnotes

The Effect of Gage Capability
Gage Accuracy and Variation
Gage Capability and Statistical Process Control
Gage Capability and Process Capability
Gage Capability and Outgoing Quality
Exercises
Solutions

Multivariate Systems
Multivariate Normal Distribution
Multivariate Control Chart
Deployment to a Spreadsheet: Principal Component Method
Multivariate Process Performance Index
Control Charts for the Covariance Matrix
Endnotes

Glossary
Appendix A: Control Chart Factors
Appendix B: Simulation and Modeling
Appendix C: Numerical Methods
References

William A. Levinson-Statistical Process Control for Real-World Applications -CR.pdf
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