Process monitoring using statistical stability metrics: Applications to biopharmaceutical processes

Abstract

Industrial manufacturing commonly employs control charts to monitor process performance, where special-cause variation is identified with runs rules. While the sensitivity of a control chart to detect non-random variation is increased as the number of rules increases, the false positive rate also increases. Accordingly, there is no way to discern a false signal from a true shift by simply observing runs rule violations on a control chart alone. In this article, two stability metrics are utilized to identify underlying variation as “common cause” or “special cause,” when Nelson rule violations are observed in an XmR control chart in biopharmaceutical processes.

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