Abstract
Using process mining actionable insights can be extracted from the event data stored in information systems. The analysis of event data may reveal many performance and compliance problems, and generate ideas for performance improvements. This is valuable, however, process mining techniques tend to be backward-looking and provide little support for forward-looking approaches since potential process interventions are not assessed. System dynamics complements process mining since it aims to capture the relationships between different factors at a higher abstraction level, and uses simulation to predict the effects of process improvement actions. In this paper, we propose a new approach to support the design of system dynamics models using event data. We extract a variety of performance parameters from the current state of the process using historical execution data and provide an interactive platform for modeling the performance metrics as system dynamics models. The generated models are able to answer “what-if” questions. Our experiments, using event logs including different relationships between parameters, show that our approach is able to generate valid models and uncover the underlying relations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Aalst, W.M.P.: Business process simulation survival guide. In: vom Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management 1. IHIS, pp. 337–370. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-642-45100-3_15
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., Dustdar, S.: Process mining put into context. IEEE Internet Comput. 16, 82–86 (2012)
Binder, T., Vox, A., Belyazid, S., Haraldsson, H., Svensson, M.: Developing system dynamics models from causal loop diagrams. In: Proceedings of the 22nd International Conference of the System Dynamics Society, Oxford, Great Britain, 25–29 July 2004 (2004)
van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle time prediction: when will this case finally be finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88871-0_22
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)
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Process and deviation exploration with inductive visual miner. In: Proceedings of the BPM Demo Sessions 2014 Co-Located with the 12th International Conference on Business Process Management (BPM 2014), Eindhoven, The Netherlands, 10 September 2014, p. 46 (2014) http://ceur-ws.org/Vol-1295/paper19.pdf
de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016). https://doi.org/10.1016/j.is.2015.07.003
Mannhardt, F., de Leoni, M., Reijers, H.A.: The multi-perspective process explorer. In: Proceedings of the BPM Demo Session 2015 Co-Located with the 13th International Conference on Business Process Management (BPM 2015), Innsbruck, Austria, 2 September 2015, pp. 130–134 (2015). http://ceur-ws.org/Vol-1418/paper27.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.: 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
Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)
Pruyt, E.: Small system dynamics models for big issues: triple jump towards real-world complexity. TU Delft Library (2013)
Ratzer, A.V., et al.: CPN tools for editing, simulating, and analysing coloured Petri nets. In: van der Aalst, W.M.P., Best, E. (eds.) ICATPN 2003. LNCS, vol. 2679, pp. 450–462. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-44919-1_28
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 colored Petri nets from event logs. STTT 10(1), 57–74 (2008). https://doi.org/10.1007/s10009-007-0051-0
Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Inf. Syst. 34(3), 305–327 (2009). https://doi.org/10.1016/j.is.2008.09.002
Rozinat, A., Wynn, M.T., van der Aalst, W.M.P., ter Hofstede, A.H.M., Fidge, C.J.: Workflow simulation for operational decision support. Data Knowl. Eng. 68(9), 834–850 (2009). https://doi.org/10.1016/j.datak.2009.02.014
Schober, P., Boer, C., Schwarte, L.A.: Correlation coefficients: appropriate use and interpretation. Anesth. Analg. 126(5), 1763–1768 (2018)
Sterman, J.D.: Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill, New York (2000)
Tax, N., Teinemaa, I., van Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018). http://arxiv.org/abs/1811.00062
Tax, N., Verenich, I., Rosa, M.L., Dumas, M.: Predictive business process monitoring with LSTM neural networks. CoRR abs/1612.02130 (2016). http://arxiv.org/abs/1612.02130
Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. TKDD 13(2), 17:1–17:57 (2019). https://doi.org/10.1145/3301300
Van Dongen, B.F. (Boudewijn): BPI challenge 2017 (2017). https://doi.org/10.4121/UUID:5F3067DF-F10B-45DA-B98B-86AE4C7A310B
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pourbafrani, M., van Zelst, S.J., van der Aalst, W.M.P. (2020). Supporting Automatic System Dynamics Model Generation for Simulation in the Context of Process Mining. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-53337-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-53336-6
Online ISBN: 978-3-030-53337-3
eBook Packages: Computer ScienceComputer Science (R0)