Interpretable Multivariate Process Monitoring Using MEWMA and Explainable Machine Learning

Monitoring the stability of multivariate quality processes is essential for ensuring product conformity and process reliability in industrial systems. Multivariate Exponentially Weighted Moving Average (MEWMA) control charts are widely used to detect small and persistent shifts in correlated quality characteristics. However, although MEWMA can identify out-of-control (OOC) conditions, it does not directly indicate which variables contribute to the detected signal. To address this limitation, this study reformulates multivariate control chart interpretation as an explainable supervised learning problem using data from an automotive production process. Several machine learning classifiers, including XGBoost, Random Forest, Support Vector Machines (SVM), LightGBM, Logistic Regression, CatBoost, and K-Nearest Neighbors (KNN), were trained using In-Control (IC)/OOC labels generated from MEWMA monitoring outcomes. Statistical tests were conducted to examine whether the observed performance differences among classifiers were statistically significant, while the computational efficiency of the framework was evaluated through a per-observation timing experiment. Among the evaluated models, XGBoost provided the most balanced overall classification performance and was further examined using the SHapley Additive exPlanations (SHAP) method. SHAP analysis enabled both global and local interpretations of model predictions by quantifying each variable’s contribution to OOC classifications. The findings indicate that combining MEWMA-based monitoring with explainable machine learning offers a practical and interpretable complement to analytical decomposition approaches in multivariate process monitoring. The proposed approach offers a practical, data-driven framework for explaining MEWMA-based IC/OOC classification decisions and identifying the relative contributions of variables in industrial quality-monitoring applications.