Sufficient statistics process control:An empirical Bayes approach to process control |
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| Authors: | GEORGE W. STURM STEVEN A. MELNYK CAROL A. FELTZ JAMES F. WOLTER |
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| Affiliation: | 1. Grand Valley State University , Allendale, Michigan, 49401, USA;2. Department of Management , Michigan State University , East Lansing, Michigan, 48824, USA;3. Northern Illinois University , De Kalb, Illinois, 60115-2888, USA |
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| Abstract: | High volume, highly automated, information intensive, short cycle manufacturing systems severely tax most conventional statistical process control techniques. To meet this new manufacturing domain's control requirements, a new approach is needed. This paper presents such a process control procedure, sufficient statistics process control (SSPC). By drawing on empirical Bayes techniques, SSPC models the time sequence of the process while simultaneously reducing to a few sufficient statistics the large volume of incoming data. As a result, it provides real time, on-line quality control. The paper discusses the conceptual and mathematical foundations for SSPC. Its operation is illustrated through an example. Finally, the paper concludes with a discussion of the limitations of SSPC. |
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