Understanding and predicting process performance variations of a balanced manufacturing line at Bosch

Abstract

Industry 4.0 takes advantage of data-driven approaches to improve manufacturing processes. Root cause analysis (RCA) techniques are naturally required to support the identification of reasons for (in)efficiency processes. However, RCA methods tend to be sensitive to data perturbations and outliers, compromising the confidence of the results and demanding the implementation of robust RCA approaches. Here, methods of graph theory (queue directed graphs), operational research (multi-directional efficiency analysis), machine learning (extreme gradient boosting), and game theory (Shapley analysis) are merged together, in order to obtain a robust approach that is able to benchmark the workers acting on a discrete manufacturing process, determine the relevance level of process variables regarding a worker belonging to the (in)efficient group, and predict the worker performance variation into its next working session. A use case at Bosch ThermoTechnology is analysed to show the methodology’s applicability.