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Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. At the same time, robust estimators are of vital importance in order to estimate population parameters. Median absolute deviation (MAD) and quantiles are such estimators for population standard deviation. In this study, alternative control charts to the Tukey control chart based on the robust estimators are proposed. To monitor the control chart's performance, the ARL values are compare for many symmetric and skewed distributions. The simulation results show that the in-control ARL values of proposed control charts are higher than Tukey's control chart in all cases and more efficient to detect the process mean. However, the out- of- control ARL values for the all control charts are worse when the probability distribution is non-normal. As a result, it is recommended to use control chart based on the estimator Qn for the process monitoring performance when data are from normal or non-normal distribution. An application example using real-life data is provided to illustrate the proposed control charts, which also supported the results of the simulation study to some extent.

Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. At the same time, robust estimators are of vital importance in order to estimate population parameters. Median absolute deviation (MAD) and quantiles are such estimators for population standard deviation. In this study, alternative control charts to the Tukey control chart based on the robust estimators are proposed. To monitor the control chart's performance, the ARL values are compare for many symmetric and skewed distributions. The simulation results show that the in-control ARL values of proposed control charts are higher than Tukey's control chart in all cases and more efficient to detect the process mean. However, the out- of- control ARL values for the all control charts are worse when the probability distribution is non-normal. As a result, it is recommended to use control chart based on the estimator Qn for the process monitoring performance when data are from normal or non-normal distribution. An application example using real-life data is provided to illustrate the proposed control charts, which also supported the results of the simulation study to some extent. [ABSTRACT FROM AUTHOR]