Abstract
To reduce variation in manufacturing processes, traditional statistical process control (SPC) techniques can be applied to monitor automatic process control (APC) controlled processes for detecting assignable cause process variation. In this paper we compare the monitoring of process output and the monitoring of the control action of Minimum-Mean-Squared-Error- and Proportional-Integral-Controlled processes. We show that the robustness property of the PI controller makes it difficult to detect unanticipated mean shifts when the process output is being monitored. We illustrate how the signal-to-noise ratios developed in Jiang, Tsui, and Woodall (2000) can be used to predict the SPC chart performance and help select the appropriate chart for monitoring.
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