A Single EWMA Chart for Monitoring Process Mean and Process Variance

Abstract

Traditionally, an X¯ -chart is used for detecting an assignable cause that shifts the process mean, and the joint X¯ and R chart is used for detecting assignable cause(s) that shift the process mean and/or the process variability. However, these charts are not sensitive in detecting small changes in process parameters. An alternative to these charts is the exponentially weighted moving average (EWMA) control chart which is quite effective in detecting small changes in process parameters. However, for simultaneous monitoring of the process mean and the process variance, two EWMA charts are generally used. One is used for the process mean and the other for the process variance. This is obviously time consuming. In this paper, we propose a single EWMA control chart for monitoring the mean and variance of a process. The proposed chart is based on only one statistic and is more effective than the joint X¯ and R chart in detecting assignable cause(s). It is an extension to the chart studied by Chen et al. [2]. It is shown also that the proposed EWMA chart performs better than the combination of the EWMA X -chart and the EWMA ln(S2) -chart, except the case when the assignable cause promotes small shifts in the process mean without changing the process variability.

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