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A new approach for quantitative risk analysis

Author

Listed:
  • S. Creemers

    (LEM - Lille - Economie et Management - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

  • E. Demeulemeester
  • S. van de Vonder

Abstract

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. Copyright Springer Science+Business Media New York 2014
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • S. Creemers & E. Demeulemeester & S. van de Vonder, 2013. "A new approach for quantitative risk analysis," Post-Print hal-00846528, HAL.
    • Handle: RePEc:hal:journl:hal-00846528
    DOI: 10.1007/s10479-013-1355-y
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    1. is not listed on IDEAS
    2. Crispim, José & Fernandes, Jorge & Rego, Nazaré, 2020. "Customized risk assessment in military shipbuilding," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    3. Cui, Nanfang & Demeulemeester, Erik & Bie, Li, 2016. "Incorporation of activity sensitivity measures into buffer management to manage project schedule riskAuthor-Name: Hu, Xuejun," European Journal of Operational Research, Elsevier, vol. 249(2), pages 717-727.
    4. Jun Zhou & Yanjuan Tang & Yong Tian, 2025. "Multi-Objective Trade-Offs for Construction Projects with Dual Constraints of Schedule Risk and Resources Under a Risk-Driven Perspective," Sustainability, MDPI, vol. 17(5), pages 1-34, February.
    5. F. Acebes & J. M. González-Varona & A. López-Paredes & J. Pajares, 2024. "Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    6. Tao, Liangyan & Wu, Desheng & Liu, Sifeng & Lambert, James H., 2017. "Schedule risk analysis for new-product development: The GERT method extended by a characteristic function," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 464-473.
    7. Xuejun Hu & Erik Demeulemeester & Nanfang Cui & Jianjiang Wang & Wendi Tian, 2017. "Improved critical chain buffer management framework considering resource costs and schedule stability," Flexible Services and Manufacturing Journal, Springer, vol. 29(2), pages 159-183, June.
    8. Ali Namazian & Siamak Haji Yakhchali & Vahidreza Yousefi & Jolanta Tamošaitienė, 2019. "Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk," IJERPH, MDPI, vol. 16(24), pages 1-19, December.
    9. Hermans, Ben & Leus, Roel & Looy, Bart Van, 2023. "Deciding on scheduling, secrecy, and patenting during the new product development process: The relevance of project planning models," Omega, Elsevier, vol. 116(C).
    10. Junguang Zhang & Dan Wan, 2021. "Determination of early warning time window for bottleneck resource buffer," Annals of Operations Research, Springer, vol. 300(1), pages 289-305, May.

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