Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing

• URL: http://arxiv.org/abs/2009.10513v2
• Date: Thu, 10 Jun 2021 08:58:41 GMT
• Title: Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing
• Authors: Nijat Mehdiyev and Peter Fettke
• Abstract summary: This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making in manufacturing. It combines process mining, machine learning, and explainable artificial intelligence (XAI) methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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