Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing
This work addresses the need for explainable AI in manufacturing decision-making, but it is incremental as it applies existing XAI methods to a specific domain without introducing new techniques.
The study tackled the problem of making predictive process monitoring in manufacturing interpretable for domain experts by combining process mining, machine learning, and XAI methods, resulting in the application of Shapley values and ICE plots to generate local post-hoc explanations for a deep learning model predicting process outcomes.
This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and shop-floor data obtained from various enterprise information systems, a deep learning model was applied to predict the process outcomes. Since this study aims to operationalize the delivered predictive insights by embedding them into decision-making processes, it is essential to generate relevant explanations for domain experts. To this end, two complementary local post-hoc explanation approaches, Shapley values and Individual Conditional Expectation (ICE) plots are adopted, which are expected to enhance the decision-making capabilities by enabling experts to examine explanations from different perspectives. After assessing the predictive strength of the applied deep neural network with relevant binary classification evaluation measures, a discussion of the generated explanations is provided.