Design and Analysis of Extended Exponentially Weighted Moving Average Signed-Rank Control Charts for Monitoring the Process Mean

Abstract

In the real world, a nonparametric control chart is a powerful substitute for enhancing outcome quality, although the fundamental procedure characteristic often fails to match the distribution assumptions. This study aims to construct and evaluate an extended exponentially weighted moving average control chart based on signed-rank statistics (EEWMA-SR) for recognizing changes in procedures. According to the study, the proposed chart proves more potent recognition of shifts in the process mean, predominantly small shifts, than the other control charts by the average run length in Monte Carlo simulation. Applying the proposed control chart to an actual dataset yielded events that corroborated the study discoveries.