  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.
