15 of 15 people found the following review helpful:
5.0 out of 5 stars
An Introduction to More Effective Experimentation, February 20, 2002
This review is from: Design of Experiments Using The Taguchi Approach: 16 Steps to Product and Process Improvement (Hardcover)
The challenge that all research directors and senior scientists face is that virtually all scientists are originally trained to believe that the only proper way to conduct an experiment is to vary only one factor at a time while holding all other variables fixed. This belief, which can be shown mathematically to be totally incorrect, has been a drag on the scientific community for decades, causing experimental science to be much less productive than it should be. In fact, by properly designing experiments in which several factors are varied in a carefully thought out, predetermined pattern, much more information can be obtained with far fewer experiments.
What Dr. Roy has achieved in his book on experimental design is to clearly explain why this is so and to provide the tools which allows his readers to overcome their ingrained beliefs and adopt a more effective method of designing their own experimental programs.
Of particular interest is his discussion of experimental design using orthogonal arrays (Chapter 4) for it is in this part of the book that the full power of modern approaches to experimentation become most evident. Here, he illustrates how a set of as few as eight experiments can be used to determine how three presumable independent factors, such as time, temperature and concentration, can be studied to obtain detailed information not only on how each factor individually effects the quality of the final product, but also on the extent to which the factors interact.
Historically, learning this type of technique for designing experiments has been a daunting task since somewhat tedious mathematically manipulations are required for both the design of the experiment and the analyses of the resulting data. However, included with Dr. Roy's book is complete software which eliminates the need for the experimenter to either manually solve the handful of equations needed to extract the results in useful form, or to develop custom spreadsheets in an attempt to automate the process. The software is easy to use and includes all of the tables and data which are used in the book to illustrate the principles of experimental design. The software is also capable of assisting the experimenter in designing sets of up to eight concurrent experiments, rapidly analyzing the data and generating graphical and tabular presentations which greatly aid in the interpretation of the results.
This is an extremely useful book which can have a major beneficial effect on the productivity of any laboratory engaged in experimental process research such as crystal growth, chemical synthesis or manufacturing. By allowing the reader to overcome his innate aversion to varying more than one experimental factor at a time, the book makes it possible for the reader to become a much more productive scientist or engineer and become a role model for his coworkers to emulate.
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7 of 7 people found the following review helpful:
5.0 out of 5 stars
Review Of Dr. Ranjit K. Roy's Book,, May 22, 2001
This review is from: Design of Experiments Using The Taguchi Approach: 16 Steps to Product and Process Improvement (Hardcover)
Ranjit Roy is one of the best instructors that anyone is likely to meet. This is especially remarkable considering that Dr. Roy specializes in a very difficult subject: making Design of Experiments (DoE) easy to understand and accessible to everyone. His software, Qualitek 4, based upon his experience in doing real experiments and teaching other engineers to do experiments, established the benchmark in how to make this type of software useful and user friendly. While traditional classes in designed experiments spend considerable time teaching engineers how to do the mathematical analysis of these studies, Ranjit likes to teach engineers how to do experiments using software that automatically designs the experiment, analyzes the data, and prints reports (with graphics) that can immediately be shared in management or team meetings.
This book is written for people who want to learn a method for designing and analyzing simple experiments with the goal of helping the reader apply these techniques in their own work environment. I believe this book will be particularly helpful to engineers (Black Belts or Green Belts) trying to learn the technique of designed experiments as taught in the Six Sigma process. This book is a "must have" for anyone who currently uses, or who wants to learn to use, Ranjit's software. Others will want to purchase this book for the very plentiful and helpful case studies taken from real life experiences. Dr. Roy provides resources for three key elements of the learning process: brief discussions of applicable theory, step-by-step application of the concepts, and fully functional software to use with the stories/examples in the book.
Explanations of theory are simple and elegant. For example, in explaining to the reader the concept of "Degrees Of Freedom", Ranjit writes, "Degrees Of Freedom (DOF) is an indication of the amount of information contained in a data set.
Dr. Roy provides sixteen steps to learn designed experiments and apply the methods to any problem. These steps include topics such as: Dr. Taguchi's philosophy, the definition of quality and how to measure it, the logic behind experimental design analysis methods, the use of orthogonal arrays, two-level experiments, three-level experiments, Analysis of Variance (ANOVA), interactions, mixed level factors, combination designs, strategies for robust design, signal-to-noise ratios, experiments with multiple criteria, how to quantify improvement, experiment preparation and planning, and even more case studies (a special chapter of added examples in addition to the plentiful examples provided in each of the above mentioned topics). Each step is a chapter, and each chapter: starts with a learning objective, provides learning by going through examples using software, and then ends with: a "question and answer" dialogue based upon Ranjit's teaching experience, a chapter summary, additional exercises, and software solutions for these exercises. Readers will especially appreciate material in the book that is original with Ranjit himself: the development of overall evaluation criteria for studies with multiple responses, and the use of a "severity index" to quantify the strength of an interaction.
The real heart of this book is the combination of software with examples. The book takes the reader through the software frame by frame, showing you where to click, what the output looks like, and how to interpret this output. The software provided with the book is fully functional with the data from the examples used in the book. However, when it comes time for the reader to do their own experiments, they are only allowed to use the portion of the software that deals with two-level experiments in an L8 orthogonal array (unless, of course, you purchase the complete software package).
Ranjit makes it clear that this book is intended for beginners who want to get started. He only covers Taguchi's static robustness studies (even though the software is capable of dynamic studies), and tells the reader that, "As you gain application experience, you will find it necessary to refer to other books to develop clearer concepts of the mathematical treatments." Readers check assumptions in their analysis by doing a confirmation experiment to see if the result of setting factors at the perceived best levels falls within a confidence interval provided by the software.
No book is perfect. Critics may say that some of the definitions are weak (for example, "system design" is defined as the "design of a product or process using special Taguchi techniques"), that some areas are too simplified (not enough emphasis on assumptions used in the analysis, no explanation of the distinction between data replication and repetition, no mention that the Cpk analysis is only applicable if the process is stable), and that in certain situations the software is weak (Dr. Roy warns the reader that the software will not correctly calculate confidence intervals if the Degrees Of Freedom are less than three, and that in some situations the software will only allow you to use an 80% confidence interval). No doubt, the biggest criticism of the book is that the reader will learn how to use many features of the software that they will not be able to later apply unless they purchase the full software package.
Nevertheless, a beginner can learn designed experiments with this book, and can make a great deal of progress using L8 arrays (in fact, the technique of designed experiments is so powerful that readers may be able to purchase software for their entire company based upon the savings associated with the application of one good L8 experiment!). So, if you have ever thought about doing experiments but have never quite felt comfortable enough to get started … get this book and get going. We have Ranjit to thank for taking the fear out of the process and providing engineers with simple explanations, examples, and software to quickly build the self-confidence needed to get real results.
Larry R. Smith Quality and Reliability Manager, Ford Motor Company
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