Nonparametric Mixed Moving Average-Extended Exponentially Weighted Moving Average Signed-Rank Control Chart
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Control charts are strong statistical monitoring instruments extensively utilized in both manufacturing and non-manufacturing operations. In numerous ongoing processes, the concept of normality is challenging to achieve, resulting in erroneous evaluations within parametric monitoring systems. When the actual variability of a performance parameter is unknown, nonparametric control charts provide a reliable and adaptable approach to evaluating the process. The benefits of utilizing combination control charts encompass increased sensitivity, thorough monitoring, and the capacity to adapt by altering mixtures to satisfy workflow and company needs. To overcome this limitation, this work introduces a hybrid moving average-extended exponentially weighted moving average control chart utilizing the Wilcoxon signed-rank statistic, namely, the MA-EEWMA-WSR, to identify shifts in the process mean. A Monte Carlo simulation was used to estimate the average run length, and the average extra-quadratic loss (AEQL) was calculated to comprehensively assess the performance of the control chart for some selected symmetric distributions: normal, Laplace, and logistic. The study shows that the proposed technique demonstrated efficacy in detecting all alterations across various distributions, outperforming other charts, such as MA-EEWMA, EWMA-WSR, and EEWMA-WSR under the zero-state scenario. The efficiency of the proposed chart in identifying process adjustments is demonstrated in a case study of the dry bleach products dataset.
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