Fulfill the practical potential of DOE-with a powerful, 16-step approach for applying the Taguchi method Over the past decade, Design of Experiments (DOE) has undergone great advances through the work of the Japanese management guru Genechi Taguchi. Yet, until now, books on the Taguchi method have been steeped in theory and complicated statistical analysis. Now this trailblazing work translates the Taguchi method into an easy-to-implement 16-step system. Based on Ranjit Roy's successful Taguchi training course, this extensively illustrated book/CD-ROM package gives readers the knowledge and skills necessary to understand and apply the Taguchi method to engineering projects-from theory and applications to hands-on analysis of the data. It is suitable for managers and technicians without a college-level engineering or statistical background, and its self-study pace-with exercises included in each chapter-helps readers start using Taguchi DOE tools on the job quickly. Special features include: * An accompanying CD-ROM of Qualitek-4 software, which performs calculations and features all example experiments described in the book * Problem-solving exercises relevant to actual engineering situations, with solutions included at the end of the text * Coverage of two-, three-, and four-level factors, analysis of variance, robust designs, combination designs, and more Engineers and technical personnel working in process and product design-as well as other professionals interested in the Taguchi method-will find this book/CD-ROM a tremendously important and useful asset for making the most of DOE in their work.

In the completely revised second edition, additional chapters and more case studies add to the clear, simple, and essentially non-mathematical presentation of the basic concepts, techniques, and applications of the renowned Taguchi approach. This practical guide introduces the fundamentals of Taguchi experimental design and shows engineers how to design, analyze, and interpret experiments for a wide range of common products and processes. What Readers Are Saying "...a clear, step-by-step guide to the Taguchi design of experiments method. The careful descriptions, calculations, and examples demonstrate the versatility of these practical and powerful tools." —Fred Schenkelberg, Consultant, FMS Reliability, Los Gatos, California "Dr. Roy presents the theory and relates it to practical examples, explaining difficult concepts in an understandable manner. This is an easy-to-read, right-on-the-mark guide to understanding and applying Taguchi robust design and DOE. Readers will find these techniques extremely useful, practical, and easily applied to the daily job." —George Li, Process Improvement Manager, Research in Motion, Waterloo, Ontario, Canada "The book has a detailed discussion of Taguchi methods that are not covered in great detail in many books on DOE." —Frederick H. Long, President, Spectroscopic Solutions, LLC, Randolph, New Jersey "Dr. Roy's name is instantly associated with Taguchi methodologies in the manufacturing industries. His skill set is also being recognized for project management instruction. The new edition includes more easy-to-follow descriptions and examples." —Andrea Stamps, Engineering Specialist, Six Sigma Master Black Belt, General Dynamics, Southfield, Michigan "Research engineers, process development engineers, pilot plant engineers, design engineers, national research labs and academic research laboratories should use this book extensively. It's a practical textbook on how to maximize output with minimal use of resources." —Dr. Naresh Mahamuni, Research Associate, North Carolina A&T University, Greensboro, North Carolina "Dr. Roy has many years of practical experience helping engineers understand and improve their engineering, reliability, and problem-solving skills using Dr. Taguchi's ideas. He anticipates questions engineers would ask and provides information exactly when it is needed." —Larry R. Smith, Quality and Reliability Manager (retired), Ford Motor Co., Dearborn, Michigan "A large number of examples support the contents. Case studies are enumerated, which is a strength of the book." —Dr. Pradeep Kumar, Professor and Head, Dept. of Mechanical and Industrial Engineering, IIT Roorkee, Uttarakhand, India "Dr. Roy's book lists many application examples that can help engineers use the Taguchi method effectively." —Dr. Side Zhao, Control Engineer, NACCO Materials Handling Group, Portland, Oregon "The author's experience on the topic is what makes this book very useful as a principal reference in teaching the Taguchi method in quality engineering." —Dr. Carlos Diaz Ramos, Research Professor, Instituto Tecnologico de Orizaba and Universidad Veracruzana, Mexico "The author is able to explain concepts in a very knowledgeable yet down-to-earth and systematic manner. The material is very well organized." —Kush Shah, Manager, Alternative Propulsion Technology Quality, General Motors, LLC, Pontiac, Michigan "This book is a valuable introductory text in Taguchi methods with a number of illustrative examples and case studies that make the concepts clearer than books with theory only." —Dr. R. Mahalinga Iyer, Senior Lecturer, Queensland University of Technology, Brisbane, Queensland, Australia.

Design of experiments (DOE) is an off-line quality assurance technique used to achieve best performance of products and processes. This book covers the basic ideas, terminology, and the application of techniques necessary to conduct a study using DOE. The text is divided into two parts—Part I (Design of Experiments) and Part II (Taguchi Methods). Part I (Chapters 1–8) begins with a discussion on basics of statistics and fundamentals of experimental designs, and then, it moves on to describe randomized design, Latin square design, Graeco-Latin square design. In addition, it also deals with statistical model for a two-factor and three-factor experiments and analyses 2k factorial, 2k-m fractional factorial design and methodology of surface design. Part II (Chapters 9–16) discusses Taguchi quality loss function, orthogonal design, objective functions in robust design. Besides, the book explains the application of orthogonal arrays, data analysis using response graph method/analysis of variance, methods for multi-level factor designs, factor analysis and genetic algorithm. This book is intended as a text for the undergraduate students of Industrial Engineering and postgraduate students of Mechtronics Engineering, Mechanical Engineering, and Statistics. In addition, the book would also be extremely useful for both academicians and practitioners KEY FEATURES : Includes six case studies of DOE in the context of different industry sector. Provides essential DOE techniques for process improvement. Introduces simple graphical methods for reducing time taken to design and develop products.

"This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book." - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University "It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion by showing how tailor-made, optimal designs can be effectively employed to meet a client's actual needs. It should be required reading for anyone interested in using the design of experiments in industrial settings." —Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities? While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain.

This textbook provides the tools, techniques, and industry examples needed for the successful implementation of design of experiments (DoE) in engineering and manufacturing applications. It contains a high-level engineering analysis of key issues in the design, development, and successful analysis of industrial DoE, focusing on the design aspect of the experiment and then on interpreting the results. Statistical analysis is shown without formula derivation, and readers are directed as to the meaning of each term in the statistical analysis. Industrial Design of Experiments: A Case Study Approach for Design and Process Optimization is designed for graduate-level DoE, engineering design, and general statistical courses, as well as professional education and certification classes. Practicing engineers and managers working in multidisciplinary product development will find it to be an invaluable reference that provides all the information needed to accomplish a successful DoE.

In today's high-technology world, with flourishing e-business and intense competition at a global level, the search for the competitive advantage has become a crucial task of corporate executives. Quality, formerly considered a secondary expense, is now universally recognized as a necessary tool. Although many statistical methods are available for