Abstract
Simulation is a powerful tool to explore and analyze business processes and their potential improvements. Recorded event data allow for the generation of data-driven simulation models using process mining. The accuracy of existing approaches, however, remains a challenge. Various efforts are being made to improve the quality of the used data and techniques, such as extracting detailed resource performance. One of the least addressed challenges is the initial state of the simulation run. Starting from a steady state has been considered in simulation in other fields. In current process simulation approaches, the executions mostly start from an empty state. This assumption leads to initialization bias, or the startup problem, which has an impact on the early results and limits the types of analysis that can be performed. In this paper, we propose an approach to estimate a steady state of simulation models, which enables the generation of more realistic simulation results. The evaluation using real-world and synthetic event data shows the requirements for and advantages of starting from representative steady states in process simulations.
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy-EXC-2023 Internet of Production - 390621612. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research. We thank Celonis for supporting the project.
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Pourbafrani, M., Lücking, N., Lucke, M., van der Aalst, W.M.P. (2023). Steady State Estimation for Business Process Simulations. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management Forum. BPM 2023. Lecture Notes in Business Information Processing, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-41623-1_11
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