In statistical process control, control charts are one tool for monitoring the control status of a process. One such type of chart is the cumulative sum (CUSUM) chart which has advantages over other styles of control chart. A study of the economic design of CUSUM control charts is undertaken via a comparative study of long-run hourly cost (LRHC) and a computational search algorithm is used to minimize LRHC for a CUSUM chart using nine parameters confined to their respective feasible parameter spaces as defined by the chart designer. Savings over similarly designed two stage Xbar charts are discovered and presented.

This book provides insights into important new developments in the area of statistical quality control and critically discusses methods used in on-line and off-line statistical quality control. The book is divided into three parts: Part I covers statistical process control, Part II deals with design of experiments, while Part III focuses on fields such as reliability theory and data quality. The 12th International Workshop on Intelligent Statistical Quality Control (Hamburg, Germany, August 16 – 19, 2016) was jointly organized by Professors Sven Knoth and Wolfgang Schmid. The contributions presented in this volume were carefully selected and reviewed by the conference’s scientific program committee. Taken together, they bridge the gap between theory and practice, making the book of interest to both practitioners and researchers in the field of quality control.

This contributed book focuses on major aspects of statistical quality control, shares insights into important new developments in the field, and adapts established statistical quality control methods for use in e.g. big data, network analysis and medical applications. The content is divided into two parts, the first of which mainly addresses statistical process control, also known as statistical process monitoring. In turn, the second part explores selected topics in statistical quality control, including measurement uncertainty analysis and data quality. The peer-reviewed contributions gathered here were originally presented at the 13th International Workshop on Intelligent Statistical Quality Control, ISQC 2019, held in Hong Kong on August 12-14, 2019. Taken together, they bridge the gap between theory and practice, making the book of interest to both practitioners and researchers in the field of statistical quality control.

This book explores the domain of reliability engineering in the context of machine tools. Failures of machine tools not only jeopardize users' ability to meet their due date commitments but also lead to poor quality of products, slower production, down time losses etc. Poor reliability and improper maintenance of a machine tool greatly increases the life cycle cost to the user. Thus, the application area of the present book, i.e. machine tools, will be equally appealing to machine tool designers, production engineers and maintenance managers. The book will serve as a consolidated volume on various dimensions of machine tool reliability and its implications from manufacturers and users point of view. From the manufacturers' point of view, it discusses various approaches for reliability and maintenance based design of machine tools. In specific, it discusses simultaneous selection of optimal reliability configuration and maintenance schedules, maintenance optimization under various maintenance scenarios and cost based FMEA. From the users' point of view, it explores the role of machine tool reliability in shop floor level decision- making. In specific, it shows how to model the interactions of machine tool reliability with production scheduling, maintenance scheduling and process quality control.

Mathematical and Statistical Approaches in Food Science and Technology offers an accessible guide to applying statistical and mathematical technologies in the food science field whilst also addressing the theoretical foundations. Using clear examples and case-studies by way of practical illustration, the book is more than just a theoretical guide for non-statisticians, and may therefore be used by scientists, students and food industry professionals at different levels and with varying degrees of statistical skill.