Abstract
Most process mining techniques are backward-looking, i.e., event data are used to diagnose performance and compliance problems. The combination of process mining and simulation allows for forward-looking approaches to answer “What if?” questions. However, it is difficult to create fine-grained simulation models that describe the process at the level of individual events and cases in such a way that reality is captured well. Therefore, we propose to use coarse-grained simulation models (e.g., System Dynamics) that simulate processes at a higher abstraction level. Coarse-grained simulation provides two advantages: (1) it is easier to discover models that mimic reality, and (2) it is possible to explore alternative scenarios more easily (e.g., brainstorming on the effectiveness of process interventions). However, this is only possible by bridging the gap between low-level event data and the coarse-grained process data needed to create higher-level simulation models where one simulation step may correspond to a day or week. This paper provides a general approach and corresponding tool support to bridge this gap. We show that we can indeed learn System Dynamics models from standard event data.
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References
van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
van der Aalst, W.M.P.: Process mining and simulation: a match made in heaven! In: Proceedings of the 50th Computer Simulation Conference, SummerSim 2018, pp. 4:1–4:12 (2018)
Camargo, M., Dumas, M., González, O.: Automated discovery of business process simulation models from event logs. Decis. Support Syst. 134, 113284 (2020)
Duggan, J.: A comparison of Petri net and system dynamics approaches for modelling dynamic feedback systems. In: 24th International Conference of the Systems Dynamics Society (2006)
Khodyrev, I., Popova, S.: Discrete modeling and simulation of business processes using event logs. In: Proceedings of the International Conference on Computational Science, pp. 322–331 (2014)
Munoz-Gama, J.: Conformance Checking and Diagnosis in Process Mining - Comparing Observed and Modeled Processes. Lecture Notes in Business Information Processing, vol. 270. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-49451-7
Pourbafrani, M., van der Aalst, W.M.P.: PMSD: data-driven simulation using system dynamics and process mining. In: Proceedings of Demonstration at the 18th International Conference on Business Process Management, pp. 77–81 (2020), http://ceur-ws.org/Vol-2673/paperDR03.pdf
Pourbafrani, M., van Zelst, S.J., van der Aalst, W.M.P.: Scenario-based prediction of business processes using system dynamics. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C.A., Meersman, R. (eds.) OTM 2019. LNCS, vol. 11877, pp. 422–439. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33246-4_27
Pourbafrani, M., van Zelst, S.J., van der Aalst, W.M.P.: Semi-automated time-granularity detection for data-driven simulation using process mining and system dynamics. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds.) ER 2020. LNCS, vol. 12400, pp. 77–91. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62522-1_6
Pourbafrani, M., van Zelst, S.J., van der Aalst, W.M.P.: Supporting automatic system dynamics model generation for simulation in the context of process mining. In: Abramowicz, W., Klein, G. (eds.) BIS 2020. LNBIP, vol. 389, pp. 249–263. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53337-3_19
Pourbafrani, M., van Zelst, S.J., van der Aalst, W.M.P.: Supporting decisions in production line processes by combining process mining and system dynamics. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds.) IHSI 2020. AISC, vol. 1131, pp. 461–467. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39512-4_72
Rosenberg, Z., Riasanow, T., Krcmar, H.: A system dynamics model for business process change projects. In: International Conference of the System Dynamics Society, pp. 1–27 (2015)
Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Inf. Syst. 34(3), 305–327 (2009)
Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decis. Support Syst. 46(1), 300–317 (2008)
Sterman, J.D.: Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill, New York (2000)
Sterman, J.D.: All models are wrong: reflections on becoming a systems scientist. Syst. Dyn. Rev. J. Syst. Dyn. Soc. 18(4), 501–531 (2002)
Acknowledgments
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy– EXC 2023 Internet of Production- Project ID: 390621612. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.
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Pourbafrani, M., van der Aalst, W.M.P. (2021). Extracting Process Features from Event Logs to Learn Coarse-Grained Simulation Models. In: La Rosa, M., Sadiq, S., Teniente, E. (eds) Advanced Information Systems Engineering. CAiSE 2021. Lecture Notes in Computer Science(), vol 12751. Springer, Cham. https://doi.org/10.1007/978-3-030-79382-1_8
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