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Business process simulation is a well-known approach to estimate the impactof changes to a process with respect to time and cost measures -- a practiceknown as what-if process analysis. The usefulness of such estimations hinges onthe accuracy of the underlying simulation model. Data-Driven Simulation (DDS)methods leverage process mining techniques to learn process simulation modelsfrom event logs. Empirical studies have shown that, while DDS models adequatelycapture the observed sequences of activities and their frequencies, they failto accurately capture the temporal dynamics of real-life processes. Incontrast, generative Deep Learning (DL) models are better able to capture suchtemporal dynamics. The drawback of DL models is that users cannot alter themfor what-if analysis due to their black-box nature. This paper presents ahybrid approach to learn process simulation models from event logs wherein a(stochastic) process model is extracted via DDS techniques, and then combinedwith a DL model to generate timestamped event sequences. An experimentalevaluation shows that the resulting hybrid simulation models match the temporalaccuracy of pure DL models, while partially retaining the what-if analysiscapability of DDS approaches.

Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning