On Multistage Statistical Process Control

論多階製程之統計品質管制

10.29977/JCIIE.200301.0001

舒連傑(Lian-Jie Shu)；宗福季(Fu-Gee Tsung)

統計製程管製 ； 多重品質管制 ； 多階製程 ； statistical process control ； multiple quality control ； multistage processes

工業工程學刊

20卷1期（2003 / 01 / 01）

1 - 8

英文

今日多數的製程與服務業皆爲多階段的製程（過程），然而傳統的統計品質管制手法主要強調單一製程的監測，控制與改善。本文中將探討如何將傳統的單一製程品管手法推廣至多階段製程，從而藉多階段之信息共享以提升統計品管的功效。自相關與多變量之製程環境也將於本文中討論。

Most products and services today are the results of several process stages and steps. With the current emphasis in industry on improved quality, control charts are widely used in process monitoring. However, conventional statistical process control (SPC) techniques focus mostly on individual stages in a process and do not consider disseminating information throughout the multiple stages of the process. Such techniques are ineffective in analyzing multistage processes. The cause-selecting chart (CSC), based on output adjusted for the effect of the incoming quality is an effective SPC tool for analyzing multistage processes. This paper discusses several important problems associated with conventional CSCs. First, the model relating input and output measures often needs to be estimated in practice before the CSC is implemented. Little is known about the performance of the CSC when the model parameters are estimated. Second, the simple linear regression model widely discussed in the CSC is insufficient to capture the stochastic behavior of the output. In practice, the process disturbance in a multistage process can be autocorrelated. To deal with autocorrelated disturbance, the cumulative score (Cuscore) chart and the triggered Cuscore chart are proposed. Finally, extensions of the CSC to multivariate CSCs are discussed.

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