Annals of Operations Research

, Volume 213, Issue 1, pp 27–65 | Cite as

A new approach for quantitative risk analysis

  • Stefan Creemers
  • Erik DemeulemeesterEmail author
  • Stijn Van de Vonder


Project risk management aims to provide insight into the risk profile of a project as to facilitate decision makers to mitigate the impact of risks on project objectives such as budget and time. A popular approach to determine where to focus mitigation efforts, is the use of so-called ranking indices (e.g., the criticality index, the significance index etc.). Ranking indices allow the ranking of project activities (or risks) based on the impact they have on project objectives. A distinction needs to be made between activity-based ranking indices (those that rank activities) and risk-driven ranking indices (those that rank risks). Because different ranking indices result in different rankings of activities and risks, one might wonder which ranking index is best. In this article, we provide an answer to this question. Our contribution is threefold: (1) we set up a large computational experiment to assess the efficiency of ranking indices in the mitigation of risks, (2) we develop two new ranking indices that outperform existing ranking indices and (3) we show that a risk-driven approach is more effective than an activity-based approach.


Project risk management Risk mitigation Ranking index 



This article has benefited from our collaboration with the Belgian Building Research Institute (BBRI) and was funded by IWT-grant 070665.


  1. Abdelkader, Y. H. (2010). Adjustment of the moments of the project completion times when activity times are exponentially distributed. Annals of Operations Research, 181, 503–514. CrossRefGoogle Scholar
  2. Bendavid, I., & Golany, B. (2009). Setting gates for activities in the stochastic project scheduling problem through the cross entropy methodology. Annals of Operations Research, 172, 259–276. CrossRefGoogle Scholar
  3. Cho, J. G., & Yum, B. J. (1997). An uncertainty importance measure of activities in PERT networks. International Journal of Production Research, 35(10), 2737–2758. CrossRefGoogle Scholar
  4. Cui, W., Jiajun, Q., & Chaoyuan, Y. (2006). Criticality measurement in PERT networks. In IEEE international conference on systems, man, and cybernetics, Taipei, Taiwan. Google Scholar
  5. Demeulemeester, E., & Herroelen, W. (2002). Project scheduling: a research handbook. Boston: Kluwer Academic. Google Scholar
  6. Elmaghraby, S. (2000). On criticality and sensitivity in activity networks. European Journal of Operational Research, 127(2), 220–238. CrossRefGoogle Scholar
  7. Elmaghraby, S. (2005). On the fallacy of average in project risk management. European Journal of Operational Research, 165, 307–313. CrossRefGoogle Scholar
  8. Georgieva, K., Farooq, A., & Dumke, R. R. (2009). Analysis of the risk assessment methods—a survey. In Proceedings of the international conferences on software process and product measurement, Amsterdam, The Netherlands. Google Scholar
  9. Goldratt, E. M. (1997). Critical chain. Great Barrington: The North River Press Publishing Corporation. Google Scholar
  10. Hagstrom, J. N. (1988). Computational complexity of PERT problems. Networks, 18(2), 139–147. CrossRefGoogle Scholar
  11. Hubbard, D. W. (2008). The failure of risk management: why it’s broken and how to fix it. New York: Wiley. Google Scholar
  12. Hulett, D. T. (2009). Practical schedule risk analysis. England: Gower Publishing Limited. Google Scholar
  13. Kelley, J. E. Jr. (1963). The critical-path method: resource planning and scheduling. In J. F. Muth & G. L. Thompson (Eds.), Industrial scheduling. Englewood Cliffs: Prentice-Hall. Google Scholar
  14. Kolisch, R., & Sprecher, A. (1996). PSPLIB—a project scheduling problem library. European Journal of Operational Research, 96, 205–216. CrossRefGoogle Scholar
  15. Sadeghi, N., Fayek, A. R., & Pedrycz, W. (2010). Fuzzy Monte Carlo simulation and risk assessment in construction. Computer-Aided Civil and Infrastructure Engineering, 25(4), 238–252. CrossRefGoogle Scholar
  16. Schatteman, D., Herroelen, W., Boone, B., & Van de Vonder, S. (2008). A methodology for integrated risk management and proactive scheduling of construction projects. Journal of Construction Engineering and Management, 134(11), 885–895. CrossRefGoogle Scholar
  17. The Project Management Institute (2008). Guide to the project management body of knowledge (PMBOK guide). Newton Square: The Project Management Institute. Google Scholar
  18. The Standish Group (2009). Chaos report. Boston: The Standish Group. Google Scholar
  19. Van de Vonder, S. (2006). Proactive-reactive procedures for robust project scheduling. PhD Dissertation, K.U. Leuven, Leuven. Google Scholar
  20. Van Slyke, R. M. (1963). Monte Carlo methods and the PERT problem. Operations Research, 11(5), 839–860. CrossRefGoogle Scholar
  21. Vanhoucke, M. (2010). Using activity sensitivity and network topology information to monitor project time performance. Omega, 38(5), 359–370. CrossRefGoogle Scholar
  22. Welch, B. L. (1947). The generalization of “Student’s” problem when several different population variances are involved. Biometrika, 34(1–2), 28–35. Google Scholar
  23. Williams, T. M. (1992). Criticality in stochastic networks. Journal of the Operational Research Society, 43(4), 353–357. Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Stefan Creemers
    • 1
  • Erik Demeulemeester
    • 2
    Email author
  • Stijn Van de Vonder
    • 2
  1. 1.IESEG School of ManagementLilleFrance
  2. 2.Department of Decision Sciences and Information Management, Research Center for Operations ManagementKU LeuvenLeuvenBelgium

Personalised recommendations