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Erschienen in:
2023 | OriginalPaper | Buchkapitel
13. Six Sigma
verfasst von : Fugee Tsung, Kai Wang
Erschienen in: Springer Handbook of Engineering Statistics
Verlag: Springer London
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Abstract
Six Sigma, which was first launched by Motorola in the late 1980s, has become a successful standard-quality initiative to achieve and maintain excellent business performance in today’s manufacturing and service industries. In this chapter, we provide a systematic and principled introduction of Six Sigma from its various facets. The first part of this chapter describes what Six Sigma is, why we need Six Sigma, and how to implement Six Sigma in practice. A typical business structure for Six Sigma implementation is introduced, and potential failure modes of Six Sigma are also discussed.
The second part describes the core methodology of Six Sigma, which consists of five phases, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC). Specific operational steps in each phase are described in sequence. Key tools to support the DMAIC process including both statistical tools and management tools are also presented. The third part highlights a specific Six Sigma technique for product development and service design, Design for Six Sigma (DFSS), which is different from DMAIC. DFSS also has five phases: Define, Measure, Analyze, Design, and Verify (DMADV), spread over product development. Each phase is described, and the corresponding key tools to support each phase are presented.
In the fourth part, a real case study on printed circuit board (PCB) improvement is used to demonstrate the application of Six Sigma. The company and process background are provided. The DMAIC approach is specifically followed, and key supporting tools are illustrated accordingly. At the end, the financial benefit of this case is realized through the reduction of cost of poor quality (COPQ). The fifth part provides a discussion of Six Sigma in current Big Data background. A brief introduction of Big Data is first given, and then the tremendous opportunities offered by Big Data analytics to the core methodology of Six Sigma, i.e., DMAIC, are outlined in detail. The capabilities of each phase that would be greatly enhanced are emphasized. Finally, the last part is given to conclusions and a discussion of prospects of Six Sigma.