Abstract

In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivariate control chart (MCC) for monitoring correlated quality variables of different types. A recent study by Huang et al. (2023), which develops a Shewhart type MCC, is the first attempt to tackle such a challenge. The proposed chart of Huang et al. (2023), which utilizes the step-down multiple testing procedure of Holm (1979), not only can monitor correlated variables of different types, but also can provide instantaneous diagnostics of which parameters are out of control when the chart signals. In this study, we adapt and extend the methodology of Huang et al. (2023) to develop an exponentially weighted moving average (EWMA) chart specifically for the case when the sample size is one. The proposed chart is shown to be effective in detecting parameter changes as well as diagnosing which parameters are out of control when the chart signals.

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