Introduction to Statistical Process Control

A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Controldescribes many recent SPC methods that improve upon the more established techniques. The author—a leading researcher on SPC—shows how these methods can handle new applications.

After exploring the role of SPC and other statistical methods in quality control and management, the book covers basic statistical concepts and methods useful in SPC. It then systematically describes traditional SPC charts, including the Shewhart, CUSUM, and EWMA charts, as well as recent control charts based on change-point detection and fundamental multivariate SPC charts under the normality assumption. The text also introduces novel univariate and multivariate control charts for cases when the normality assumption is invalid and discusses control charts for profile monitoring

Table of Contents

Introduction 
Quality and the Early History of Quality Improvement 
Quality Management 
Statistical Process Control 
Organization of the Book

Basic Statistical Concepts and Methods 
Introduction 
Population and Population Distribution 
Important Continuous Distributions
Important Discrete Distributions 
Data and Data Description 
Tabular and Graphical Methods for Describing Data 
Parametric Statistical Inferences 
Nonparametric Statistical Inferences

Univariate Shewhart Charts and Process Capability 
Introduction 
Shewhart Charts for Numerical Variables 
Shewhart Charts for Categorical Variables 
Process Capability Analysis 
Some Discussions

Univariate CUSUM Charts 
Introduction 
Monitoring the Mean of a Normal Process 
Monitoring the Variance of a Normal Process 
CUSUM Charts for Distributions in Exponential Family 
Self-Starting and Adaptive CUSUM Charts 
Some Theory for Computing ARL Values 
Some Discussions

Univariate EWMA Charts 
Introduction 
Monitoring the Mean of a Normal Process 
Monitoring the Variance of a Normal Process
Self-Starting and Adaptive EWMA Charts 
Some Discussions

Univariate Control Charts by Change-Point Detection 
Introduction 
Univariate Change-Point Detection 
Control Charts by Change-Point Detection 
Some Discussions

Multivariate Statistical Process Control 
Introduction 
Multivariate Shewhart Charts 
Multivariate CUSUM Charts 
Multivariate EWMA Charts 
Multivariate Control Charts by Change-Point Detection 
Multivariate Control Charts by LASSO 
Some Discussions

Univariate Nonparametric Process Control 
Introduction 
Rank-Based Nonparametric Control Charts 
Nonparametric SPC by Categorical Data Analysis 
Some Discussions

Multivariate Nonparametric Process Control 
Introduction 
Rank-Based Multivariate Nonparametric Control Charts 
Multivariate Nonparametric SPC by Log-Linear Modeling
Some Discussions

Profile Monitoring 
Introduction 
Parametric Profile Monitoring 
Nonparametric Profile Monitoring 
Some Discussions

Appendix A: R Functions for SPC 
Appendix B: Datasets Used in the Book

Bibliography

Index

Write a review