Advertisement

Business Trend Analysis by Simulation

  • Helen Schonenberg
  • Jingxian Jian
  • Natalia Sidorova
  • Wil van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6051)

Abstract

Business processes are constantly affected by the environment in which they execute. The environment can change due to seasonal and financial trends. For organisations it is crucial to understand their processes and to be able to estimate the effects of these trends on the processes. Business process simulation is a way to investigate the performance of a business process and to analyse the process response to injected trends. However, existing simulation approaches assume a steady state situation. Until now correlations and dependencies in the process have not been considered in simulation models, which can lead to wrong estimations of the performance. In this work we define an adaptive simulation model with a history-dependent mechanism that can be used to propagate changes in the environment through the model. In addition we focus on the detection of dependencies in the process based on the executions of the past. We demonstrate the application of adaptive simulation models by means of an experiment.

Keywords

Business Process Reference Model Business Process Management Adaptive Model Process Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    ProM Nightly Builds (2006), http://prom.win.tue.nl/tools/prom/nightly/
  2. 2.
    van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering 47(2), 237–267 (2003)CrossRefGoogle Scholar
  3. 3.
    Agresti, A.: Categorical Data Analysis, 2nd edn. Wiley Series in Probability and Statistics. Wiley-Interscience, Hoboken (2002)zbMATHGoogle Scholar
  4. 4.
    Baccelli, F., Konstantopoulos, P.: Estimates of Cycle Times in Stochastic Petri Nets. Rapport de recherche 1572, INRIA, Rocquencourt (1992)Google Scholar
  5. 5.
    Barros, A.P., Decker, G., Grosskopf, A.: Complex Events in Business Processes. In: Abramowicz, W. (ed.) BIS 2007. LNCS, vol. 4439, pp. 29–40. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Desel, J., Esparza, J.: Free Choice Petri Nets. Cambridge Tracts in Theoretical Computer Science, vol. 40. Cambridge University Press, Cambridge (1995)zbMATHGoogle Scholar
  7. 7.
    Devore, J., Farnum, N.: Applied Statistics for Engineers and Scientists, 1st edn. Duxbury, Boston (1999)Google Scholar
  8. 8.
    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, Part I. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Ferson, S., Burgman, M.A.: Correlations, Dependency Bounds and Extinction Risks. Biological Conservation 73(2), 101–105 (1995); Applications of Population Viability Analysis to Biodiversity Google Scholar
  10. 10.
    Gladwin, B., Tumay, K.: Modeling Business Processes with Simulation Tools. In: WSC 1994: Proceedings of the 26th conference on Winter simulation, San Diego, CA, USA, pp. 114–121. Society for Computer Simulation International (1994)Google Scholar
  11. 11.
    Jensen, K., Kristensen, L.M., Wells, L.: Coloured Petri Nets and CPN Tools for Modelling and Validation of Concurrent Systems. International Journal on Software Tools for Technology Transfer 9(3-4), 213–254 (2007)CrossRefGoogle Scholar
  12. 12.
    Jian, J.: Mining Simulation Models with Correlations. Master’s thesis, Eindhoven University of Technology, Eindhoven, The Netherlands (2009)Google Scholar
  13. 13.
    R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2006), ISBN 3-900051-07-0Google Scholar
  14. 14.
    Regev, G., Wegmann, A.: Why Do We Need Business Process Support? Balancing Specialization and Generalization with BPS Systems (Introductory note). In: CAiSE Workshops (2003)Google Scholar
  15. 15.
    Reijers, H.A.: Case Prediction in BPM Systems: A Research Challenge. Journal of the Korean Institute of Industrial Engineers 33, 1–10 (2006)Google Scholar
  16. 16.
    Reisig, W., Rozenberg, G. (eds.): APN 1998. LNCS, vol. 1491. Springer, Heidelberg (1998)zbMATHGoogle Scholar
  17. 17.
    Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering Simulation Models. Inf. Syst. 34(3), 305–327 (2009)Google Scholar
  18. 18.
    Schonenberg, M.H., Sidorova, N., van der Aalst, W.M.P., van Hee, K.M.: History-Dependent Stochastic Petri Nets. In: Voronkov, A. (ed.) PSI 2009. LNCS, vol. 5947, pp. 366–379. Springer, Heidelberg (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Helen Schonenberg
    • 1
  • Jingxian Jian
    • 1
  • Natalia Sidorova
    • 1
  • Wil van der Aalst
    • 1
  1. 1.Department of Mathematics & Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations