Control charts are effective tools for signal detection in manufacturing processes. As much of the data in industries come from processes having non-normal or unknown distributions, the commonly used Shewhart variable control charts cannot be appropriately used, because they depend heavily on the normality assumption. The average run length (ARL) is generally used to measure the detection performance of a process when using a control chart, but it is biased for the monitoring statistic with an asymmetric distribution. That is, the ARL-biased control chart leads to take longer to detect the shifts in parameter than to trigger a false alarm. To overcome this problem, we herein propose an ARL-unbiased exponentially weighted moving average proportion (EWMA-p) chart to monitor the process variance for process data with non-normal or unknown distributions. We further explore the procedure to determine the control limits and to investigate the out-of-control variance detection performance of the ARL-unbiased EWMA-p chart. With a numerical example involving non-normal service times from a bank branch in Taiwan, we illustrate the applications of the proposed ARL-unbiased EWMA-p chart and also compare the out-of-control detection performance of the ARL-unbiased EWMA-p chart, the arcsin transformed symmetric EWMA variance, and other existing variance charts. The proposed ARL-unbiased EWMA-p chart shows superior detection performance. Thus, we recommend the ARL-unbiased EWMA-p chart for process data with non-normal or unknown distributions.
Quality and Reliability Engineering International, Vol.32, No.3, pp.1227–1235