Abstract
Many organizations employ an information system that supports the execution of their business processes. During the execution of these processes, event data are stored in the databases that support the information system. The field of process mining aims to transform such data into actionable insights, which allow business owners to improve their daily operations. For example, a process model describing the actual execution of the process can be easily extracted from the captured event data. Most process mining techniques are “backward-looking” providing compliance and performance information. Few process mining techniques are “forward-looking”. Therefore, in this paper, we propose a novel scenario-based predictive approach that allows us to assess and predict future behavior in business processes. In particular, we propose to use system dynamics to allow for “what-if” questions. We create a system dynamics model using variables trained on the basis of the past behavior of the process, as captured in the event log. This model is used to explore the effect of possibly applied changes in the process as well as roles of external factors, e.g., human behavior. Using real event data, we demonstrate the feasibility of our approach to predict possible consequences of future decisions and policies .
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Acknowledgement
This work is partially funded by the German Research Foundation (Deutsche Forschungsgemeinschaft –DFG) in the context of the Cluster of Excellence Internet of Production (EXC 2023, 390621612).
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Pourbafrani, M., van Zelst, S.J., van der Aalst, W.M.P. (2019). Scenario-Based Prediction of Business Processes Using System Dynamics. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C., Meersman, R. (eds) On the Move to Meaningful Internet Systems: OTM 2019 Conferences. OTM 2019. Lecture Notes in Computer Science(), vol 11877. Springer, Cham. https://doi.org/10.1007/978-3-030-33246-4_27
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