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- W3175255090 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." @default.
- W3175255090 created "2021-07-05" @default.
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- W3175255090 date "2021-01-01" @default.
- W3175255090 modified "2023-09-23" @default.
- W3175255090 title "Local Post-Hoc Explanations for predictive Process Monitoring in manufacturing." @default.
- W3175255090 hasPublicationYear "2021" @default.
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