Abstract
Business process simulation is a methodology that enables analysts to run the process in different scenarios, compare the performances and consequently provide indications into how to improve a business process. Process simulation requires one to provide a simulation model, which should accurately reflect reality to ensure the reliability of the simulation findings. This paper proposes a framework to assess the extent to which a simulation model reflects reality and to pinpoint how to reduce the distance. The starting point is a business simulation model, along with a real event log that records actual executions of the business process being simulated and analyzed. In a nutshell, the idea is to simulate the process, thus obtaining a simulation log, which is subsequently compared with the real event log. A decision tree is built, using the vector of features that represent the behavioral characteristics of log traces. The tree aims to classify traces as belonging to the real and simulated event logs, and the discriminating features encode the difference between reality, represented in the real event log, and the simulation model, represented in the simulated event logs. These features provide actionable insights into how to repair simulation models to become closer to reality. The technique has been assessed on a real-life process for which the literature provides a real event log and a simulation model. The results of the evaluation show that our framework increases the accuracy of the given initial simulation model to better reflect reality.
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Symbol \(\uplus \) indicates the union of multisets where duplicates are retained.
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Given two sequences s and \(s'\), \(s' \subseteq s\) indicates that \(s'\) is a sub-sequence of s.
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scikit-learn: https://scikit-learn.org/stable/, PM4py: https://pm4py.fit.fraunhofer.de/.
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The event log is available at http://fluxicon.com/academic/material/ while the accordant simulation model is available at https://github.com/AdaptiveBProcess/Simod.
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Meneghello, F., Fracca, C., de Leoni, M., Asnicar, F., Turco, A. (2022). A Framework to Improve the Accuracy of Process Simulation Models. In: Guizzardi, R., Ralyté, J., Franch, X. (eds) Research Challenges in Information Science. RCIS 2022. Lecture Notes in Business Information Processing, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-05760-1_9
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