Enhanced monitoring of process anomalies: exponentially weighted moving average max multivariate (EWMA Max-M) control chart

Ensuring product quality in manufacturing is crucial for customer satisfaction and cost minimization. Traditional Max-M control charts, while foundational for multivariate individual data, are less sensitive to small process shifts. This study introduces an enhanced Exponentially Weighted Moving Average Max Multivariate (EWMA Max-M) control chart, designed to improve the early detection of process anomalies. By integrating the EWMA process with the Max-M chart, the proposed methodology performs better in detecting small shifts, as demonstrated through Average Run Length (ARL) comparisons. Key factors such as the number of quality characteristics (p), the correlation between characteristics (ρ), and the mean vector shift (λ) were considered. Simulation and real-world applications, including cement data analysis, confirm the effectiveness of the EWMA Max-M chart in enhancing process monitoring.