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

TL;DR

This paper introduces a hybrid method combining process mining and deep learning to create more accurate and flexible business process simulation models that better capture temporal dynamics while supporting what-if analysis.

Contribution

It proposes a novel hybrid approach that integrates data-driven process discovery with deep learning to improve temporal accuracy and maintain interpretability in process simulation models.

Findings

Hybrid models match the temporal accuracy of pure DL models.

Hybrid models retain some interpretability and flexibility of DDS models.

Experimental results demonstrate improved simulation accuracy.

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 leverage process mining 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 accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL 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 via DDS techniques, and then combined with a DL model to generate timestamped event sequences. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches.