A Robust TEWMA–MA Control Chart Based on Sign Statistics for Effective Monitoring of Manufacturing Processes

A nonparametric control chart is a type of control chart that does not rely on assumptions regarding the underlying distribution of the data. This characteristic provides greater flexibility and robustness, particularly when handling non-normal data, skewed distributions, or datasets containing outliers. The primary objective of this study is to propose a nonparametric TEWMA–MA control chart based on the sign statistic, designed to operate under both symmetric and asymmetric distributions for effective process monitoring. This chart aims to enhance the ability to quickly detect shifts in the production process. The run-length characteristics obtained through Monte Carlo simulation (MC) were employed as performance measures. In addition, overall efficiency was assessed using AEQL, RMI, and PCI. The proposed control chart was compared against MA, TEWMA, MA–TEWMA, TEWMA–MA, and MA–TEWMA sign charts. The findings indicate that the proposed chart is effective for process control and demonstrates superior detection capability compared to competing charts, particularly in identifying small to moderate shifts. Furthermore, to validate its practical utility, the proposed control chart was applied to real-world data.