On Advanced Business SimulationsConverging Operational and Strategic Levels

Marc Drobek, Wasif Gilani, David Redlich, Thomas Molka, Danielle Soban


Business Dynamics (BD) enables strategic Key Performance Indicator (KPI) predictions to monitor the health status of companies and support the decision making process. Nevertheless, a very important factor, which is generally overlooked, is that the top level strategic KPIs are highly influenced by the operational level business processes. These two domains are, however, mostly segregated and examined as silos with different solutions. In this paper, we are proposing a framework for advanced business simulations, which converges the two domains by utilising Ontologies and process execution data. Establishing this connection enables drilling down from a high level KPI perspective into the underlying operational level details to discover hidden bottlenecks and pre-emptively apply corrective actions.


  1. Ann, R., Chase, G., Omar, R., Taylor, J., and von Rosing, M. (2011). Applying Real-World BPM in an SAP Environment. Galileo Press, Bonn.
  2. Binder, T., Vox, A., Belyazid, S., Haraldsson, H. V., and Svensson, M. (2004). Developing System Dynamics models from Causal Loop Diagrams. Technical report, University of Luebeck, Germany; Lund University, Sweden.
  3. Brockwell, P. J. and Davis, R. A. (2006). Time Series: Theory and Methods. Springer, second edition.
  4. Burns, J. R. (1977). Converting signed digraphs to Forrester schematics and converting Forrester schematics to Differential equations. IEEE Transactions on systems, man, and cybernetics, 10:695-707.
  5. Del-Rio-Ortega, A., Resinas, M., and Ruiz-Cortes, A. (2010). Defining Process Performance Indicators : An Ontological Approach. On the Move to Meaningful Internet Systems: OTM 2010, 6426:555-572.
  6. Drobek, M., Gilani, W., and Soban, D. (2013). Parameter estimation and equation formulation in Business Dynamics. In Third International Symposium on Business Modeling and Software Design, Noordwijkerhout. ScitePress.
  7. Ford, A. (1999). Modeling the environment: An Introduction to System Dynamics Models of Environmental Systems. Island Press, Washington, D.C.
  8. Ford, D. N. and Sterman, J. D. (1998). Expert knowledge elicitation to improve formal and mental models. System Dynamics Review, 14(4):309-340.
  9. Forrester, J. W. (1961). Industrial Dynamics. MIT Press; currently available from Pegasus Communications; Waltham, MA, Cambridge, MA.
  10. Forrester, J. W. (1991). System Dynamics and the Lessons of 35 Years. pages 1-35.
  11. Fritzsche, M., Picht, M., Gilani, W., Spence, I., Brown, J., and Kilpatrick, P. (2009). Extending BPM Environments of Your Choice with Performance Related Decision Support. In Business Process Management, pages 97-112. Springer.
  12. Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3):424-438.
  13. Hecking, M. and Schroder, C. (2013). Current Implementation Level of Business Process Management in Corporate Practice: A Quantitative Analysis. GRIN Verlag.
  14. Heilig, B. and Möller, M. (2014). Business Process Management mit SAP NetWeaver BPM. Galileo Press Gmbh, 1st edition.
  15. Howson, C. and Newbould, E. (2012). SAP BusinessObjects BI 4.0 The Complete Reference 3/E. McGrawHill Osborne, 3rd edition.
  16. Intalio (2013). BPMS designer; http://www.intalio.com/ products/bpms/overview/.
  17. Ko, R. K. L., Lee, S. S. G., and Lee, E. W. (2009). Business process management (BPM) standards: a survey. Business Process Management Journal.
  18. Porzucek, T., Kluth, S., Fritzsche, M., and Redlich, D. (2010). Combination of a Discrete Event Simulation and an Analytical Performance Analysis through Model-Transformations. In IEEE ECBS, pages 183- 192.
  19. Redlich, D. and Gilani, W. (2011). Event-Driven ProcessCentric Performance Prediction via Simulation. In BPM Workshops.
  20. Richmond, B. and isee systems (Firm) (2008). An Introduction to Systems Thinking: STELLA Software.
  21. Robinson, S. (1964). Simulation - The Practice of Model Development and Use. John Wiley & Sons.
  22. SoftwareAG (2014). Software AG: webMethods, last accessed: April 2014; http://www.softwareag.com/ corporate/products/wm/bpm/overview/default.asp.
  23. Steiner, T., Verborgh, R., Troncy, R., Gabarro, J., and Walle, R. V. D. (2012). Adding Realtime Coverage to the Google Knowledge Graph. In Proceedings of the ISWC 2012.
  24. Sterman, J. D. (2000). Business Dynamics: Systems thinking and modeling for a complex world. McGraw-Hill, New York, NY.
  25. The W3C SPARQL Working Group (2013). SPARQL Query Language for RDF; http://www.w3.org/TR/ rdf-sparql-query/.
  26. Van Der Aalst, W. (2011). Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer.
  27. Woods, D. and Word, J. (2004). SAP Netweaver for Dummies. Wiley Hoboken.
  28. Zhang, L. (2002). Knowledge Graph Theory and Structural Parsing. Ph.d. thesis, University of Twente.

Paper Citation

in Harvard Style

Drobek M., Gilani W., Redlich D., Molka T. and Soban D. (2014). On Advanced Business SimulationsConverging Operational and Strategic Levels . In Proceedings of the Fourth International Symposium on Business Modeling and Software Design - Volume 1: BMSD, ISBN 978-989-758-032-1, pages 166-171. DOI: 10.5220/0005425601660171

in Bibtex Style

author={Marc Drobek and Wasif Gilani and David Redlich and Thomas Molka and Danielle Soban},
title={On Advanced Business SimulationsConverging Operational and Strategic Levels},
booktitle={Proceedings of the Fourth International Symposium on Business Modeling and Software Design - Volume 1: BMSD,},

in EndNote Style

JO - Proceedings of the Fourth International Symposium on Business Modeling and Software Design - Volume 1: BMSD,
TI - On Advanced Business SimulationsConverging Operational and Strategic Levels
SN - 978-989-758-032-1
AU - Drobek M.
AU - Gilani W.
AU - Redlich D.
AU - Molka T.
AU - Soban D.
PY - 2014
SP - 166
EP - 171
DO - 10.5220/0005425601660171