Benchmarking and Prediction of Entities Performance on Manufacturing Processes through MEA, Robust XGBoost and SHAP Analysis

Abstract

Determining the reasons for process variability of manufacturing processes is generically quite demanding. In the era of big data and Industry 4.0, data-driven root cause analysis (RCA) techniques are required to support the identification of such reasons. However, an important issue with classical RCA methods is their sensibility to data perturbations. In fact, adversarial data perturbation is currently one of the hot topics in the literature. Such sensibility phenomena requires the implementation of robust RCA approaches. Here, methods of operational research (multi-directional efficiency analysis), machine learning (eXtreme Gradient Boosting), and game theory (Shapley values) are merged, to obtain a robust approach that can (1) benchmark entities acting on a manufacturing process, (2) determine the importance level of process variables regarding an entity belonging to the (in)efficient group, and (3) predict the performance of the entity’s future work sessions. A use case at Vista Alegre Atlantis S.A., a Portuguese leader company that manufactures porcelain tableware, high-quality glass and crystal, is analysed to show the methodology’s success.