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Paper Number
1213
Abstract
This study proposes an innovative explainable predictive quality analytics solution to facilitate the 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 the 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.
Recommended Citation
Mehdiyev, Nijat and Fettke, Peter, "LOCAL POST-HOC EXPLANATIONS FOR PREDICTIVE PROCESS MONITORING IN MANUFACTURING" (2021). ECIS 2021 Research Papers. 35.
https://aisel.aisnet.org/ecis2021_rp/35
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