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This study reveals that recent variants of the EWMA chart have problematic weighting functions. These functions assign more weight to past data values than to the most recent ones, a practice that is less advisable. What we observed is that these weighting functions have a detrimental impact on the run length properties of these modified charts. Surprisingly, it turns out that, in practice, using a standard EWMA chart often results in superior overall performance when compared to the recent variants of the EWMA chart.
The Max-Mixed EWMA Exponentially Weighted Moving Average (MM EWMA) control chart is a statistical process control technique used for joint monitoring of the mean and variance of a process. This control chart is designed to detect small and moderate shifts in the mean and variance of a process by comparing the maximum of two statistics, one based on the mean and the other on the variance. In this paper, we propose a new MM EWMA control chart. The proposed chart is compared with existing control charts using simulation studies, and the results show that the chart performs better in detecting small and moderate shifts in both the mean and variance. The proposed chart can be helpful in quality control applications, where joint monitoring of mean and variance is necessary to ensure a product’s or process’s quality. The real-life application of the proposed control chart on yogurt packing in a cup data set shows the outperformance of the MM EWMA control chart. Both simulations & real-life application results demonstrate the better performance of the proposed chart in detecting smaller shifts during the production process.
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