PRAISE FOR THE FIRST EDITION
"This book is a significant addition to the literature on statistical practice should be of considerable interest to those interested in these topics." International Journal of Forecasting
Recent research has shown that monitoring techniques alone are inadequate for modern Statistical Process Control (SPC), and there exists a need for these techniques to be augmented by methods that indicate when occasional process adjustment is necessary. Statistical Control by Monitoring and Adjustment, Second Edition presents the relationship among these concepts and elementary ideas from Engineering Process Control (EPC), demonstrating how the powerful synergistic association between SPC and EPC can solve numerous problems that are frequently encountered in process monitoring and adjustment.
The book begins with a discussion of SPC as it was originally conceived by Dr. Walter Shewart and Dr. W. Edwards Deming. Subsequent chapters outline the basics of the new integration of SPC and EPC, which is not available in other related books. Thorough coverage of time series analysis for forecasting, process dynamics, and non–stationary models is also provided, and these sections have been carefully written so as to require only an elementary understanding of mathematics. Extensive graphical explanations and computational tables accompany the numerous examples that are provided throughout each chapter, and a helpful selection of problems and solutions further facilitates understanding.
Statistical Control by Monitoring and Adjustment, Second Edition is an excellent book for courses on applied statistics and industrial engineering at the upper–undergraduate and graduate levels. It also serves as a valuable reference for statisticians and quality control practitioners working in industry.
Chapter 1: Introduction and Statistics Revision.
Chapter 2: Standard Control Charts: A First Approximation.
Chapter 3: What Can Go Wrong and What Can We Do About It?
Chapter 4: Introduction to Forecasting and Process Dynamics.
Chapter 5: Non–Stationary Time Series Models for Process Disturbances.
Chapter 6: Repeated Feedback Adjustment.
Chapter 7: Periodic Adjustment.
Chapter 8: Control of a Process with Inertia.
Chapter 9: Explicit Consideration of Monetary Cost.
Chapter 10: Cuscore Charts: Looking for Signals in Noise.
Chapter 11: Monitoring an Operating Feedback System.
Chapter 12: A Brief Review of Time Series Analysis.
Bibliography and Further Reading.
GEORGE E. P. BOX, PHD, is Ronald Aylmer Fisher Professor Emeritus of Statistics and Industrial Engineering at the University of Wisconsin Madison. He is a Fellow of the Royal Society of London and the American Academy of Arts and Sciences. He is an honorary Fellow and Shewart and Deming Medalist of the American Society for Quality and an honorary member of the International Statistical Institute. He is also the recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association and the Guy Medal in Gold of the Royal Statistical Society. Dr. Box is the coauthor of Statistics for Experimenters: Design, Innovation, and Discovery, Second Edition; Response Surfaces, Mixtures, and Ridge Analyses, Second Edition; Evolutionary Operation: A Statistical Method for Process Improvements; Improving Almost Anything: Ideas and Essays, Revised Edition; Time Series Analysis: Forecasting and Control, Fourth Edition; and Bayesian Interface and Statistical Analyses, all published by Wiley. ALBERTO LUCEÑO, PHD, is Professor in the ETS de Ingenieros de Caminos at the University of Cantabria, Spain. He is an Associate Editor of the Journal of Quality Technology and Statistical Modelling: An International Journal. MARÍA DEL CARMEN PANIAGUA–QUIÑONES, PHD, is Visiting Assistant Professor in the School of Industrial Engineering at Purdue University. Her research interests include experimental design, quality productivity, and engineering management and control process improvement. She was the recipient of the Brumbaugh Award of the American Society for Quality for Best Technical Paper in 2007.