A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes

Renaud De Landtsheer, Gustavo Ospina, Philippe Massonet, Christophe Ponsard, Stephan Printz, Lasse Härtel, Johann Philipp von Cube


Nowadays supply chains have to face an increasing number of risks related to the globalisation, especially impacting the procurement processes. Even though tools are available to help companies in addressing those risks, most companies, even larger ones, still have problems to adequately quantify the risks and assess to what extend an alternative could address them. The aim of our work is to provide companies with a software supported methodology to quantify such risks and elaborate adequate risk mitigation strategies at an optimal cost. Based on a survey conducted about the risk management practices and needs within companies, we developed a tool that enables a constant focus on risks by enabling the easy expression of key risks together with the process model and hence help to focus the granularity of the model at the right level. A model-based simulator can then efficiently evaluate these risks thanks to well-known Monte-Carlo simulation techniques. Our main technical contribution lies in the development of an efficient discrete event simulation (DES) engine together with a query language which can be used to measure business risks based on simulation results. We demonstrate the expressiveness and performance of our approach by benchmarking it on a set of cases originating from the industry and covering a large set of risk categories.


  1. Almeder, C., Preusser, M., and Hartl, R. F. (2009). Simulation and optimization of supply chains: alternative or complementary approaches? In Günther, H. O. and Meyr, H., editors, Supply Chain Planning. SpringerVerlag.
  2. AnyLogic (2015). AnyLogic Multimethod Simulation Software. http://www.anylogic.com.
  3. Arenas, A. E., Massonet, P., and Ponsard, C. (2015). Goaloriented requirement engineering support for business continuity planning. In Proceedings of MReBA'15, Stockholm, Sweden.
  4. Artikis, C. and Artikis, P. (2015). Probability Distributions in Risk Management Operations. Springer, London.
  5. Brailsford, S., Churilov, L., and Dangerfield, B. (2014). Discrete-Event Simulation and Systems Dynamics for Management Decision Making. Wiley.
  6. Byong-Kyu, C. and Donghun, K. (2013). Modeling and Simulation of Discrete-Event Systems. Wiley.
  7. Deleris, L. and Erhun, F. (2005). Risk management in supply networks using Monte-Carlo simulation. In 2005 Winter Simulation Conference, Orlando, USA.
  8. Finke, G. R., Schmitt, A., and Singh, M. (2010). Modeling and simulating supply chain schedule risk. In 2010 Winter Simulation Conference, Baltimore, USA.
  9. Gleißner, W. (2012). Quantitative methods for risk management in the real estate development industry. In Journal of Property Investment & Finance, volume 30(6), pages 612-630.
  10. Klimov, R. A. and Merkuyev, Y. A. (2006). Simulationbased risk measurement in supply chains. In 20th European Conference on Modelling and Simulation (ECMS 2006), Bonn, Germany.
  11. OscaR (2012). OscaR: Scala in OR. https://bitbucket.org/oscarlib/oscar.
  12. Printz, S., von Cube, J. P., Vossen, R., Schmitt, R., and Jeschke, S. (2015a). Ein kybernetisches modell beschaffungsinduzierter störgößen. In Exploring Cybernetics - Kybernetik im interdisziplinren Diskurs. Springer Spektrum.
  13. Printz, S., von Cube, P., and Ponsard, C. (2015b). Management of procurement risks on manufacturing processes - survey results. http://simqri.com/uploads/media/Survey Results.pdf.
  14. Rockwell Automation (2015). Arena Simulation Software. https://www.arenasimulation.com.
  15. Romeike, F. (2004). Der prozess der risikosteuerung und - kontrolle. In Romeike, F., editor, Erfolgsfaktor RisikoManagement, pages 236-243. Gabler, Wiesbaden.
  16. Schmitt, A. and Singh, M. (2009). Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation. In 2009 Winter Simulation Conference, Austin, USA.
  17. Siemens (2015). Plant Simulator. http://goo.gl/NK7yWg.
  18. Siepermann, M. (2008). Risikokostenrechnung: Erfolgreiche Informationsversorgung und Risikoprävention. Erich Schmidt, Berlin.
  19. SimQRi (2015). Online SimQRi tool. https://simqri.cetic.be.
  20. Sutton, I. (2015). Process Risk and Reliability Management. Elsevier, second edition.
  21. von Cube, J. P., Abbas, B., Schmitt, R., and Jeschke, S. (2014). A monetary approach of risk management in procurement. In 7th Int. Conf. on Production Research Americas' 2014, pages 35-40, Lima, Peru.
  22. Zio, E. (2013). The Monte Carlo Simulation Method for System Reliability and Risk Analysis. Springer, London.
  23. Zsidisin, G. A. and Ritchie, B. (2009). Supply Chain Risk: A Handbook of Assessment, Management, and Performance. Springer.

Paper Citation

in Harvard Style

Landtsheer R., Ospina G., Massonet P., Ponsard C., Printz S., Härtel L. and Cube J. (2016). A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes . In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-171-7, pages 313-322. DOI: 10.5220/0005752403130322

in Bibtex Style

author={Renaud De Landtsheer and Gustavo Ospina and Philippe Massonet and Christophe Ponsard and Stephan Printz and Lasse Härtel and Johann Philipp von Cube},
title={A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes},
booktitle={Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},

in EndNote Style

JO - Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes
SN - 978-989-758-171-7
AU - Landtsheer R.
AU - Ospina G.
AU - Massonet P.
AU - Ponsard C.
AU - Printz S.
AU - Härtel L.
AU - Cube J.
PY - 2016
SP - 313
EP - 322
DO - 10.5220/0005752403130322