Abstract
Modern multivariate control charts that use spatial signed rank are sensitive to the detection of small shifts under non-normal or gamma distributions. In this paper, Monte Carlo simulation is used to compare the performances of multivariate control charts based on the average run length. The results show that the spatial signed-rank multivariate exponentially weighted moving average (SSRM) control chart outperforms the multivariate exponentially weighted moving average (MEWMA) control chart, the double-MEWMA control chart, and the spatial signed-rank double multivariate exponentially weighted moving average control chart when detecting small shifts in the process mean. SSRM is appropriate for data from a non-normal distribution, which is valuable for the manufacturing industry when detecting waste. Moreover, SSRM is an excellent method suitable for most industrial processes, and therefore, is a very powerful tool.
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