Abstract
This article investigates the performances of auxiliary information based X ¯ (called X ¯ − AI ) and exponentially weighted moving average (EWMA) (called EWMA-AI) charts for detecting shifts in the process mean in short production runs using the truncated average run length (TARL), truncated standard deviation of the run length (TSDRL) and expected truncated average run length (ETARL) criteria. The findings reveal that the EWMA-AI chart is better in detecting small and moderate shifts, while the two charts are comparable in detecting large shifts. An example is given to demonstrate the applications of the X ¯ − AI and EWMA-AI charts in short production runs. The significance of this article is based on the fact that it is the first article that studies the performances of auxiliary information (AI) charts in short production runs.
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