Abstract
Several decision points exist in business processes (e.g., whether a purchase order needs a manager’s approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than €500 needs a manager approval). Decision mining in process mining aims to describe/predict the routing of a process instance at a decision point of the process. By predicting the decision, one can take proactive actions to improve the process. For instance, when a bottleneck is developing in one of the possible decisions, one can predict the decision and bypass the bottleneck. However, despite its huge potential for such operational support, existing techniques for decision mining have focused largely on describing decisions but not on predicting them, deploying decision trees to produce logical expressions to explain the decision. In this work, we aim to enhance the predictive capability of decision mining to enable proactive operational support by deploying more advanced machine learning algorithms. Our proposed approach provides explanations of the predicted decisions using SHAP values to support the elicitation of proactive actions. We have implemented a Web application to support the proposed approach and evaluated the approach using the implementation.
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The experimental results are reproducible in https://github.com/aarkue/eXdpn/tree/main/quantitative_analysis along with the corresponding process model.
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The authors would like to thank the Alexander von Humboldt (AvH) Stiftung for funding this research.
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Park, G., Küsters, A., Tews, M., Pitsch, C., Schneider, J., van der Aalst, W.M.P. (2023). Explainable Predictive Decision Mining for Operational Support. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_6
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