Abstract
SUMMARY 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.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
