Optimization And Variation Reduction In Quality

This book gives the reader tools for optimizing performance and reducing variation in manufacturing. Statistical process control and Taguchi methods are featured in the book along with other less published techniques. The book covers the product life cycle from design to manufacturing and is aimed at improving industrial practice. Methods are shown through case studies and extensive graphics are used to illustrate points.

Optimization in Quality Control presents a broad survey of the state of the art in optimization in quality, and focuses on industrial and national competitiveness. Each chapter has been carefully developed and refereed anonymously by experts in the area of optimization in quality control. Some of the topics covered in this volume include: fundamentals of optimization techniques contemporary approaches to optimization models in process control economic design of control charts determining optimal target values in multiple criteria economic selection models examining quality improvement schemes by trading off between expected warranty servicing costs and increasing manufacturing costs designing optimal inspection plans. This book will serve as an important reference source for academics, professionals and researchers.

Reducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product. The authors have structured this book around an algorithm for reducing process variation that they call "Statistical Engineering." The algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. The fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. A key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation. The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. A unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches. Also includes case studies, chapter exercises, chapter supplements, and six appendices. PRAISE FOR Statistical Engineering "I found this book uniquely refreshing. Don't let the title fool you. The methods described in this book are statistically sound but require very little statistics. If you have ever wanted to solve a problem with statistical certainty (without being a statistician) then this book is for you. - A reader in Dayton, OH "This is the most comprehensive treatment of variation reduction methods and insights I’ve ever seen."- Gary M. Hazard Tellabs "Throughout the text emphasis has been placed on teamwork, fixing the obvious before jumping to advanced studies, and cost of implementation. All this makes the manuscript !attractive for real-life application of complex techniques." - Guru Chadhabr Comcast IP Services COMMENTS FROM OTHER CUSTOMERS Average Customer Rating (5 of 5 based on 1 review) "This is NOT a typical book on statistical tools. It is a strategy book on how to search for cost-effective changes to reduce variation using empirical means (i.e. observation and experiment). The uniqueness of this book: Summarizes the seven ways to reduce variation so we know the goal of the data gathering and analysis, present analysis results using graphs instead of P-value, and integrates Taguchi, Shainin methods, and classical statistical approach. It is a must read for those who are in the business of reducing variation using data, in particular for the Six Sigma Black Belts and Master Black Belts. Don't forget to read the solutions to exercises and supplementary materials to each chapter on the enclosed CD-ROM." - A. Wong, Canada

This volume presents recent research in reliability and quality theory and its applications by many leading experts in the field. The subjects covered include reliability optimization, software reliability, maintenance, quality engineering, system reliability, Monte Carlo simulation, tolerance design optimization, manufacturing system estimation, neural networks, software quality assessment, optimization design of life tests, software quality, reliability-centered maintenance, multivariate control chart, methodology for measurement of test effectiveness, imperfect preventive maintenance, Markovian reliability modeling, accelerated life testing, and system availability assessment. The book will serve as a reference for postgraduate students and will also prove useful for practitioners and researchers in reliability and quality engineering.

In this volume of the Six Sigma and Beyond series, quality engineering expert D.H. Stamatis focuses on how Statistical Process Control (SPC) relates to Six Sigma. He emphasizes the "why we do" and "how to do" SPC in many different environments. The book provides readers with an overview of SPC in easy-to-follow, easy-to-understand terms. The author reviews and explains traditional SPC tools and how they relate to Six Sigma and goes on to cover the use of advanced techniques. In addition, he addresses issues that concern service SPC and short run processes, explores the issue of capability for both the short run and the long run, and discusses topics in measurement.

In the world of modern engineering, rigorous and definite design methodologies are needed. However, many parts of engineering design are performed in either an ad-hoc manner or based on the intuition of the engineer. This is the first book to look at both stages of the design process – conceptual design and detailed design – and detail design methodologies for every step of the design process. Case studies show how practical design problems can be solved with analytic design methods. This book is an excellent introduction to the subject. The book’s practical focus will make the book useful to practicing engineers as a practical handbook of design.

As quality becomes an increasingly essential factor for achieving business success, building quality improvement into all stages—product planning, product design, and process design—instead of just manufacturing has also become essential. Quality Engineering: Off-Line Methods and Applications explores how to use quality engineering methods and other modern techniques to ensure design optimization at every stage. The book takes a broad approach, focusing on the user’s perspective and building a well-structured framework for the study and implementation of quality engineering. Starting with the basics, this book presents an overall picture of quality engineering. The author delineates quality engineering methods such as DOE, Taguchi, and RSM as well as computational intelligence approaches. He discusses how to use a general computational intelligence approach to improve product quality and process performance. He also provides extensive examples and case studies, numerous exercises, and a glossary of basic terms. By adopting quality engineering, the defect rate during manufacturing shows noticeable improvement, the production cost is significantly lower, and the quality and reliability of products can be enhanced. Taking an integrated approach that makes the methods of upstream quality improvement accessible, without extensive mathematical treatments, this book is both a practical reference and an excellent textbook.