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Business Simulation Process Revised

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Business Simulation Process Revised
Business Process Simulation Revisited
Wil M.P. van der Aalst
Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, NL-5600 MB, The Netherlands w.m.p.v.d.aalst@tue.nl Abstract. Computer simulation attempts to “mimic” real-life or hypothetical behavior on a computer to see how processes or systems can be improved and to predict their performance under different circumstances. Simulation has been successfully applied in many disciplines and is considered to be a relevant and highly applicable tool in Business Process Management (BPM). Unfortunately, in reality the use of simulation is limited. Few organizations actively use simulation. Even organizations that purchase simulation software (stand-alone or embedded in some BPM suite), typically fail to use it continuously over an extended period. This keynote paper highlights some of the problems causing the limited adoption of simulation. For example, simulation models tend to oversimplify the modeling of people working part-time on a process. Also simulation studies typically focus on the steady-state behavior of business processes while managers are more interested in short-term results (a “fast forward button” into the future) for operational decision making. This paper will point out innovative simulation approaches leveraging on recent breakthroughs in process mining.

1

Limitations of Traditional Simulation Approaches

Simulation was one of the first applications of computers. The term “Monte Carlo simulation” was first coined in the Manhattan Project during World War II, because of the similarity of statistical simulation to games of chance played in the Monte Carlo Casino. This illustrates that that already in the 1940s people were using computers to simulate processes (in this case to investigate the effects of nuclear explosions). Later Monte Carlo methods were used in all kinds of other domains ranging from finance and telecommunications to games and workflow



References: 1. W.M.P. van der Aalst, A.H.M. ter Hofstede, B. Kiepuszewski, and A.P. Barros. Workflow Patterns. Distributed and Parallel Databases, 14(1):5–51, 2003. 2. W.M.P. van der Aalst, J. Nakatumba, A. Rozinat, and N. Russell. Business Process Simulation: How to get it right? In J. vom Brocke and M. Rosemann, editors, Handbook on Business Process Management, International Handbooks on Information Systems, pages 317–342. Springer-Verlag, Berlin, 2010. 3. W.M.P. van der Aalst, M. Pesic, and M. Song. Beyond Process Mining: From the Past to Present and Future. In B. Pernici, editor, Advanced Information Systems Engineering, Proceedings of the 22nd International Conference on Advanced Information Systems Engineering (CAiSE’10), volume 6051 of Lecture Notes in Computer Science, pages 38–52. Springer-Verlag, Berlin, 2010. 4. W.M.P. van der Aalst, H.A. Reijers, A.J.M.M. Weijters, B.F. van Dongen, A.K. Alves de Medeiros, M. Song, and H.M.W. Verbeek. Business Process Mining: An Industrial Application. Information Systems, 32(5):713–732, 2007. 5. W.M.P. van der Aalst, M. Rosemann, and M. Dumas. Deadline-based Escalation in Process-Aware Information Systems. Decision Support Systems, 43(2):492–511, 2007. 6. W.M.P. van der Aalst, M.H. Schonenberg, and M. Song. Time Prediction Based on Process Mining. BPM Center Report BPM-09-04, BPMcenter.org, 2009. 7. W.M.P. van der Aalst, A.J.M.M. Weijters, and L. Maruster. Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering, 16(9):1128–1142, 2004. 8. W.M.P. van der Aalst and. B. van Dongen, C.W. G¨nther, A. Rozinat, E. Verbeek, u and T. Weijters. ProM: The Process Mining Toolkit. In A.K.A. de Medeiros and B. Weber, editors, Business Process Management Demonstration Track (BPMDemos 2009), volume 489 of CEUR Workshop Proceedings, pages 1–4. CEUR-WS.org, 2009. 9. J.A. Buzacott. Commonalities in Reengineerd Business Processes: Models and Issues. Management Science, 42(5):768–782, 1996. 10. F. Curbera, Y. Doganata, A. Martens, N. Mukhi, and A. Slominski. Business Provenance: A Technology to Increase Traceability of End-to-End Operations. In R. Meersman and Z. Tari, editors, Proceedings of the 16th International Conference on Cooperative Information Systems, CoopIS 2008, OTM 2008, Part I, volume 5331 of Lecture Notes in Computer Science, pages 100–119. Springer-Verlag, Berlin, 2008. 11. S. Davidson, S. Cohen-Boulakia, A. Eyal, B. Ludaescher, T. McPhillips, S. Bowers, M. Anand, and J. Freire. Provenance in Scientific Workflow Systems. Data Engineering Bulletin, 30(4):44–50, 2007. 12. B.F. van Dongen, R.A. Crooy, and W.M.P. van der Aalst. Cycle Time Prediction: When Will This Case Finally Be Finished? In R. Meersman and Z. Tari, editors, Proceedings of the 16th International Conference on Cooperative Information Systems, CoopIS 2008, OTM 2008, Part I, volume 5331 of Lecture Notes in Computer Science, pages 319–336. Springer-Verlag, Berlin, 2008. 14 13. A.H.M. ter Hofstede, W.M.P. van der Aalst, M. Adams, and N. Russell. Modern Business Process Automation: YAWL and its Support Environment. SpringerVerlag, Berlin, 2010. 14. A.K. Alves de Medeiros and W.M.P. van der Aalst. Process Mining towards Semantics. In T.S. Dillon, editor, Advances in Web Semantics I, volume 4891 of Lecture Notes in Computer Science, pages 35–80. Springer-Verlag, Berlin, 2008. 15. J. Nakatumba and W.M.P. van der Aalst. Analyzing Resource Behavior Using Process Mining. In S. Rinderle-Ma, S. Sadiq, and F. Leymann, editors, BPM 2009 Workshops, Proceedings of the Fifth Workshop on Business Process Intelligence (BPI’09), volume 43 of Lecture Notes in Business Information Processing, pages 69–80. Springer-Verlag, Berlin, 2010. 16. H.A. Reijers and W.M.P. van der Aalst. Short-Term Simulation: Bridging the Gap between Operational Control and Strategic Decision Making. In M.H. Hamza, editor, Proceedings of the IASTED International Conference on Modelling and Simulation, pages 417–421. IASTED/Acta Press, Anaheim, USA, 1999. 17. H.A. Reijers and W.M.P. van der Aalst. The Effectiveness of Workflow Management Systems: Predictions and Lessons Learned. International Journal of Information Management, 25(5):458–472, 2005. 18. A. Rozinat and W.M.P. van der Aalst. Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems, 33(1):64–95, 2008. 19. A. Rozinat, R.S. Mans, M. Song, and W.M.P. van der Aalst. Discovering Simulation Models. Information Systems, 34(3):305–327, 2009. 20. A. Rozinat, M. Wynn, W.M.P. van der Aalst, A.H.M. ter Hofstede, and C. Fidge. Workflow Simulation for Operational Decision Support. Data and Knowledge Engineering, 68(9):834–850, 2009. 21. N. Russell, W.M.P.van der Aalst, A.H.M. ter Hofstede, and D. Edmond. Workflow Resource Patterns: Identification, Representation and Tool Support. In O. Pastor and J. Falcao e Cunha, editors, Proceedings of the 17th Conference on Advanced Information Systems Engineering (CAiSE’05), volume 3520 of Lecture Notes in Computer Science, pages 216–232. Springer-Verlag, Berlin, 2005. 22. H. Schonenberg, B. Weber, B.F. van Dongen, and W.M.P. van der Aalst. Supporting Flexible Processes Through Recommendations Based on History. In M. Dumas, M. Reichert, and M.C. Shan, editors, International Conference on Business Process Management (BPM 2008), volume 5240 of Lecture Notes in Computer Science, pages 51–66. Springer-Verlag, Berlin, 2008. 23. C.D. Wickens. Engineering Psychology and Human Performance. Harper, 1992. 24. M. Wynn, A. Rozinat, W.M.P. van der Aalst, A.H.M. ter Hofstede, and C. Fidge. Chapter 17: Process Mining and Simulation. In Modern Business Process Automation: YAWL and its Support Environment, pages 437–457. Springer-Verlag, Berlin, 2010. 15

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