New EWMA charts for process variance

Abstract

The EWMA chart is effective in detecting small shifts in the process mean or process variance. Numerous EWMA charts for the process variance have been suggested in the literature. In this article, new one‐sided and two‐sided EWMA charts are developed for monitoring the variance of a normal process. In developing these new EWMA charts, first, new unbiased estimators of the process variance are developed, followed by incorporating the developed estimators into the new EWMA charts' statistics. The Monte Carlo simulation method is adopted to evaluate the zero‐state and steady‐state run‐length performances of the proposed EWMA variance charts, in comparison with that of three existing EWMA variance charts and the weighted adaptive CUSUM variance chart. The findings reveal that the proposed charts generally perform better than the existing charts. An example of application is given to show the implementation of the proposed and existing charts in detecting increases or decreases in the process variance.

A variable sample size side-sensitive synthetic coefficient of variation chart

“…Control charts efficiently detect special causes that result in abnormal variation in a process by detecting changes in specific statistical parameters. In most cases, shifts in the mean ( μ ) and standard deviation ( σ ) are monitored (see, for instance, Abbas et al, 2020; Amin et al, 2021; Haq et al, 2023; Javaid et al, 2023; Mehmood et al, 2021; Shafqat et al, 2021; and Umar et al, 2019). However, such an approach was found to result in erroneous conclusions when the process does not have a consistent μ , in addition to having a σ that varies with μ .…”