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The Role of Statistical Design of Experiments in Six Sigma: Perspectives of a Practitioner T. N. Goh * Industrial and Systems Engineering Department, National University of Singapore, Singapore 119260 ABSTRACT Six Sigma has now been well recognized as an effective means of attaining excellence in the quality of products and services. It entails the use of statistical thinking as well as management and operational tools to bring about fundamental improvements. This article explains, in a nonmathematical language, the rationale and mechanics of design of experiments as seen in its deployment in Six Sigma. It also outlines the way in which the design of experiments has been utilized in the past for quality improvement, culminating in its important role in Six Sigma. An appreciation of the changing scope of experimental design applications over the years and in the future would provide useful perspectives on the significance of Six Sigma in an organization’s quest for quality excellence. Key Words: Six Sigma; Design of experiments; Robust design; Taguchi methods; Design for Six Sigma; Statistical quality control INTRODUCTION Six Sigma as a quality improvement initiative has been now well recognized, with an increasing amount of literature explaining its rationale, implementation, and impact on the quality profession (1–5). As the pervasiveness of personnel training and commitment to Six Sigma grows, a good appreciation of the method- ologies deployed in Six Sigma would contribute to the effectiveness of its application. In this article, a brief review is given of the major tools used in Six Sigma, leading to an explanation of the practical role played by statistical design of experiments. A number of versions of design of experiments used by quality practitioners since the “pre-Six Sigma” years are then summarized and contrasted, highlighting the motivation and approach associated with each framework of experimental design and analysis. It is seen that regardless of the specific 659 Copyright q 2002 by Marcel Dekker, Inc. * E-mail: [email protected] Quality Engineering, 14(4), 659–671 (2002)
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format used, “improvement” is always the impetus for, and impact of, experimental design applications. The likely development of this important statistical tool in industrial and business circles in the future is discussed in the concluding section. SIX SIGMA AND DESIGN OF EXPERIMENTS Six Sigma as it is practiced today takes the form of projects conducted in phases generally recognized as Define–Measure–Analyze–Improve–Control, or DMAIC. Generally, after the project definition phase, key process characteristics are identified and bench- marked in the Measure and Analyze phases; this is followed by the Improve phase where a process is changed for better performance, then the Control phase aimed at monitoring and sustaining the gains. The common thread through these phases is the use of statistical thinking (6,7) in which measured data is an indispensable proxy for realities and facts. This can also
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