This research demonstrates the effectiveness of a new control chart for identifying shifts in skewed distributions, suggesting a robust monitoring tool.
The limitations of normalcy assumptions are accommodated by a nonparametric control chart, which is user-friendly and robust. This study introduced a moving average control chart integrated with an extended exponentially weighted moving average control chart utilizing sign statistics, namely MA-EEWMA Sign. We analyzed the study for assessing the efficacy of a monitoring strategy using the average run lengths through Monte Carlo simulation. Performance comparison index (PCI), extra quadratic loss (EQL), especially overall performance are still used to evaluate the usefulness of control charts. Overall, the findings reveal that the provided chart remains the best control chart for finding moderate to minor shifts from normally distributed to skewed distribution. The effectiveness was evaluated using the following charts: moving average, exponentially weighted moving average, extended exponentially weighted moving average, and a hybrid of the latter two. The research findings were confirmed when the suggested control chart was adjusted for the actual dataset.
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Talordphop et al. (2025) studied this question.
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