A Control Chart Approach to Monitor and Improve Production Processes: Maximum Exponentially Weighted Moving Average using Auxiliary Variable (AV) and Multiple Measurement (ME)

Control charts are fundamental tools in Statistical Process Control (SPC), employed to monitor and improve production processes. This study aims to evaluate the performance of the Maximum Exponentially Weighted Moving Average (Max-EWMA) chart by incorporating Auxiliary Variables (AV) and Multiple Measurements (ME). This study also evaluating the covariate method, a multiple measurement framework, and scenarios involving linearly increasing variance. The evaluation focuses on the analysis of both Type I and Type II error rates, using simulation-based methodologies. The results indicate that the Multiple Measurement approach consistently outperforms the Covariate method, exhibiting lower Type II error rates and higher robustness in detecting process shifts. An increase in parameter A enhances the chart’s sensitivity to mean shifts, whereas parameter B shows negligible influence. Additionally, linearly increasing variance contributes to improved detection capability, particularly under conditions of high correlation. Overall, the Multiple Measurement method demonstrates strong effectiveness and reliability across a variety of conditions, underscoring its practical utility in process control applications.