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 Control describes 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. All computations in the examples are solved using R, with R functions and datasets available for download on the author's website. Offering a systematic description of both traditional and newer SPC methods, this book is ideal as a primary textbook for a one-semester course in disciplines concerned with process quality control, such as statistics, industrial and systems engineering, and management sciences. It can also be used as a supplemental textbook for courses on quality improvement and system management. In addition, the book provides researchers with many useful, recent research results on SPC and gives quality control practitioners helpful guidelines on implementing up-to-date SPC techniques.
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 Exercises appear at the end of each chapter.
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