An Enhanced Double EWMA Chart for Monitoring the Process Mean Shifts

Exponentially weighted moving average (EWMA) chart is one of the well-known memory-type charts that used in detecting small to moderate disturbances in the process mean. In literature, the EWMA chart with auxiliary information concept (EWMA-AIC) was shown to outperform the traditional EWMA chart. On similar lines, to further enhance the shift detection performance of the EWMA-AIC chart, we propose the double exponentially weighted moving average chart with auxiliary information concept (DEWMA-AIC) in monitoring the process mean. The Monte Carlo simulation is used to compute the run length characteristics of the DEWMA-AIC chart, which include the average run length (ARL), standard deviation of the run length (SDRL) and expected average run length (EARL). The results reveal that the DEWMA-AIC chart is more sensitive than the traditional DEWMA and EWMA-AIC charts in shift detection.