ABSTRACT In modern manufacturing environments, traditional statistical process control (SPC) methods often struggle with complex, dynamic data patterns, particularly when observations are autocorrelated. Control charts are useful tools used in SPC to detect any significant drift in a process. Thus, there is an increasing interest in improving their detection ability, regardless of the nature and complexity of the data. This paper introduces two new exponentially weighted moving average (EWMA) charts for monitoring mean drifts in a process. The first one is named, mean drift EWMA (MD‐EWMA) chart, and the second one, named windowed mean drift EWMA (WMD‐EWMA) chart, which is the enhanced version of the MD‐EWMA chart. The effectiveness of both charts in detecting moderate and large mean drifts while minimising false positives is explored under different scenarios. Furthermore, the performance of the WMD‐EWMA chart is compared with the MD‐EWMA, EWMA and CUSUM charts. The comparison results reveal the necessity of a window‐based approach in reducing false positives, particularly when specific behavioural patterns are expected. Through extensive simulations and real‐world case studies, the WMD‐EWMA chart is shown to outperform the MD‐EWMA, Shewhart, EWMA, and CUSUM charts by effectively identifying significant mean drifts and reducing false alarms.
Louw et al. (Thu,) studied this question.