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Hybrid Business Process Simulation: Updating Detailed Process Simulation Models Using High-Level Simulations

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Research Challenges in Information Science (RCIS 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 446))

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Abstract

Process mining techniques transfer historical data of organizations into knowledge for the purpose of process improvement. Most of the existing process mining techniques are “backward-looking” and provide insights w.r.t. historical event data. Foreseeing the future of processes and capturing the effects of changes without applying them to the real processes are of high importance. Current simulation techniques that benefit from process mining insights are either at detailed levels, e.g., Discrete Event Simulation (DES), or at aggregated levels, e.g., System Dynamics (SD). System dynamics represents processes at a higher degree of aggregation and accounts for the influence of external factors on the process. In this paper, we propose an approach for simulating business processes that combines both types of data-driven simulation techniques to generate holistic simulation models of processes. These techniques replicate processes at various levels and for different purposes, yet they both present the same process. SD models are used for strategical what-if analysis, whereas DES models are used for operational what-if analysis. It is critical to consider the effects of strategical decisions on detailed processes. We introduce a framework integrating these two simulation models, as well as a proof of concept to demonstrate the approach in practice.

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.

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Notes

  1. 1.

    Standard ML.

  2. 2.

    https://github.com/mbafrani/PMSD.

  3. 3.

    https://cpn-model-process-discovery-1.herokuapp.com/generate-cpn-model/.

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Authors and Affiliations

  1. Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

    Mahsa Pourbafrani & Wil M. P. van der Aalst

Authors
  1. Mahsa Pourbafrani
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  2. Wil M. P. van der Aalst
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Correspondence to Mahsa Pourbafrani .

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Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Renata Guizzardi

  2. University of Geneva, CUI, Carouge, Switzerland

    Jolita Ralyté

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Xavier Franch

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Pourbafrani, M., van der Aalst, W.M.P. (2022). Hybrid Business Process Simulation: Updating Detailed Process Simulation Models Using High-Level Simulations. 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_11

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  • DOI: https://doi.org/10.1007/978-3-031-05760-1_11

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  • Print ISBN: 978-3-031-05759-5

  • Online ISBN: 978-3-031-05760-1

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