"Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines.Adopting a balanced approach to traditional and modern methods, this text includes coverage of SQC techniques in both industrial and non-manufacturing settings, providing fundamental knowledge to students of engineering, statistics, business, and management sciences.A strong pedagogical toolset, including multiple practice problems, real-world data sets and examples, provides students with a solid base of conceptual and practical knowledge."--
Provides instructions for establishing quality-control laboratories; presents the techniques & methods required tominimize in-plant sampling errors efficiently; discusses the current status of federal environmental legislation pertinent to the chemical industry.
A statistical approach to the principles of quality control and management Incorporating modern ideas, methods, and philosophies of quality management, Fundamentals of Quality Control and Improvement, Third Edition presents a quantitative approach to management-oriented techniques and enforces the integration of statistical concepts into quality assurance methods. Utilizing a sound theoretical foundation and illustrating procedural techniques through real-world examples, this timely new edition bridges the gap between statistical quality control and quality management. The book promotes a unique "do it right the first time" approach and focuses on the use of experimental design concepts as well as the Taguchi method for creating product/process designs that successfully incorporate customer needs, improve lead time, and reduce costs. Further management-oriented topics of discussion include total quality management; quality function deployment; activity-basedcosting; balanced scorecard; benchmarking; failure mode and effects criticality analysis; quality auditing; vendor selection and certification; and the Six Sigma quality philosophy. The Third Edition also features: Presentation of acceptance sampling and reliability principles Coverage of ISO 9000 standards Profiles of past Malcolm Baldrige National Quality Award winners, which illustrate examples of best business practices Strong emphasis on process control and identification of remedial actions Integration of service sector examples The implementation of MINITAB software in applications found throughout the book as well as in the additional data sets that are available via the related Web site New and revised exercises at the end of most chapters Complete with discussion questions and a summary of key terms in each chapter, Fundamentals of Quality Control and Improvement, Third Edition is an ideal book for courses in management, technology, and engineering at the undergraduate and graduate levels. It also serves as a valuable reference for practitioners and professionals who would like to extend their knowledge of the subject.
Modern Industrial Statistics The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approaches Modern Industrial Statistics is a leading reference and guide to the statistics tools widely used in industry and services. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on computer age statistical analysis, can be used in general courses on analytics and statistics. Part B is focused on industrial statistics applications. The fully revised third edition covers the latest techniques in R, MINITAB and JMP, and features brand-new coverage of time series analysis, predictive analytics and Bayesian inference. New and expanded simulation activities, examples, and case studies—drawn from the electronics, metal work, pharmaceutical, and financial industries—are complemented by additional computer and modeling methods. Helping readers develop skills for modeling data and designing experiments, this comprehensive volume: Explains the use of computer-based methods such as bootstrapping and data visualization Covers nonstandard techniques and applications of industrial statistical process control (SPC) charts Contains numerous problems, exercises, and data sets representing real-life case studies of statistical work in various business and industry settings Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, and downloadable appendices Provides an author-created R package, mistat, that includes all data sets and statistical analysis applications used in the book Part of the acclaimed Statistics in Practice series, Modern Industrial Statistics with Applications in R, MINITAB, and JMP, Third Edition, is the perfect textbook for advanced undergraduate and postgraduate courses in the areas of industrial statistics, quality and reliability engineering, and an important reference for industrial statisticians, researchers, and practitioners in related fields. The mistat R-package is available from the R CRAN repository.
This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on the basic tools and principles of process control, methods of statistical process control (SPC), and multivariate SPC. Next, the authors explore the design and analysis of experiments, quality control and the Quality by Design approach, computer experiments, and cyber manufacturing and digital twins. The text then goes on to cover reliability analysis, accelerated life testing, and Bayesian reliability estimation and prediction. A final chapter considers sampling techniques and measures of inspection effectiveness. Each chapter includes exercises, data sets, and applications to supplement learning. Industrial Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. In addition, it can be used in focused workshops combining theory, applications, and Python implementations. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Modern Statistics: A Computer-Based Approach with Python. It covers topics such as probability models and distribution functions, statistical inference and bootstrapping, time series analysis and predictions, and supervised and unsupervised learning. These texts can be used independently or for consecutive courses. The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/IndustrialStatistics/. "This book is part of an impressive and extensive write up enterprise (roughly 1,000 pages!) which led to two books published by Birkhäuser. This book is on Industrial Statistics, an area in which the authors are recognized as major experts. The book combines classical methods (never to be forgotten!) and "hot topics" like cyber manufacturing, digital twins, A/B testing and Bayesian reliability. It is written in a very accessible style, focusing not only on HOW the methods are used, but also on WHY. In particular, the use of Python, throughout the book is highly appreciated. Python is probably the most important programming language used in modern analytics. The authors are warmly thanked for providing such a state-of-the-art book. It provides a comprehensive illustration of methods and examples based on the authors longstanding experience, and accessible code for learning and reusing in classrooms and on-site applications." Professor Fabrizio RuggeriResearch Director at the National Research Council, ItalyPresident of the International Society for Business and Industrial Statistics (ISBIS)Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)
About the Book Industrial Statistics is about a new technology to replace the traditional statistical process control method. Quality decisions are made based on inspection data of one variable, this is a data dependent process. Industrial statistics is based on artificial intelligence or a mathematical model. This hybrid technology helps to plan and tailor manufacturing processes with quality attributes that deliver quality products consistently every time. There is no need for separate data analysis, if needed there is a provision for data analysis using the inspection data by the operator during the production process. With both customers and users in mind, Industrial Statistics sheds light on various components of the manufacturing industry and how the latest technology can be used to make quality products for reduced cost. Using the artificial intelligence of industrial statistics does not require any data from the manufactured products. It is a proactive process control method, and this is the first book in the world to detail such a method. About the Author Babu is graduated from Government of Kerala, India, Department of Technical Education, Diploma in Engineering (Automobile) and has more than 35 plus years of experience in various manufacturing industries. After retiring, he has decided to do something for the industrial community that will help them to ease the manufacturing process with freedom to do their assigned work with pride in quality and workmanship. This passion drove him to invent industrial statistics. Babu believes all his activities in both professional and spiritual life are guided by the Holy Spirit. His special interest is to serve the community in whatever ways possible. He served as a senior citizen commissioner in the city of Cypress for almost four years, conducting a survey to determine the needs of the seniors and proposed suggestions to the council for action. He also was the 3rd Vice Chairman of the SME, Orange County Chapter #119 and he was selected as the instructor to teach the manufacturing students at the California State University, Fullerton, who are taking tests as certified Manufacturing Engineers, and continued that position for three years until the program was discontinued by the State government. He has also worked to organize annual picnics with activities for children and adults alike as well as charity work to raise money to help those in need after a disaster. He has a wife and three children and five grandchildren and his wife is a retired nursing supervisor from the local hospital.
