Abstract
Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures -- a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods combine automated process discovery and enhancement techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to capture the temporal dynamics of real-life processes. In contrast, parallel work has shown that generative Deep Learning (DL) models are able to accurately capture such temporal dynamics. The drawback of these latter models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted from a log using automated process discovery and enhancement techniques, and this model is then combined with a DL model to generate timestamped event sequences (traces). An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while retaining the what-if analysis capability of DDS approaches.
Abstract (translated)
URL
https://arxiv.org/abs/2103.11944
