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Selection of technical risk responses for efficient contingencies

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  • Edouard Kujawski

Abstract

The primary goal of good project risk management should be to successfully deliver projects for the lowest cost at an acceptable level of risk. This requires the systematic development and implementation of a set of Risk Response Actions (RRAs) that achieves the lowest total project cost for a given probability of success while meeting technical performance and schedule. We refer to this set as the “efficient RRA set.” This work presents a practical and mathematically sound approach for determining the efficient RRA set. It builds on some of Markowitz's portfolio selection principles and introduces several conceptual and modeling differences to properly treat project technical risks. The set of RRAs is treated as whole and not just individual risks. The efficient RRA set is determined based on “Outcome Cost vs. Probability of Success.” The risks and RRAs are characterized using scenarios, decision trees, and cumulative probability distributions. The analysis provides information that enables decision‐makers to select the efficient RRA set that explicitly takes their attitude toward project risk into account. Decision‐makers should find it both useful and practical for sound decision‐making under uncertainty/risk and efficiently optimizing project success. The computations are readily performed using commercially available Monte Carlo simulation tools. The approach is detailed using a realistic but simplified case of a project with two technical risks. © 2002 Wiley Periodicals, Inc. Syst Eng 5, 194–212, 2002

Suggested Citation

  • Edouard Kujawski, 2002. "Selection of technical risk responses for efficient contingencies," Systems Engineering, John Wiley & Sons, vol. 5(3), pages 194-212.
    • Handle: RePEc:wly:syseng:v:5:y:2002:i:3:p:194-212
    DOI: 10.1002/sys.10025
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    References listed on IDEAS

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    1. Muzaffar A. Shaikh, 1998. "Project schedule recomputation after risk inclusion," Systems Engineering, John Wiley & Sons, vol. 1(3), pages 242-249.
    2. Stanley Kaplan, 1981. "On The Method of Discrete Probability Distributions in Risk and Reliability Calculations–Application to Seismic Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 1(3), pages 189-196, September.
    3. Edouard Kujawski, 2002. "Why projects often fail, even with high cost‐contingencies," Systems Engineering, John Wiley & Sons, vol. 5(2), pages 151-155.
    4. Charles N. Haas, 1999. "On Modeling Correlated Random Variables in Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 19(6), pages 1205-1214, December.
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    Cited by:

    1. Edouard Kujawski, 2016. "A Probabilistic Game‐Theoretic Method to Assess Deterrence and Defense Benefits of Security Systems," Systems Engineering, John Wiley & Sons, vol. 19(6), pages 549-566, November.
    2. Edouard Kujawski & Gregory A. Miller, 2007. "Quantitative risk‐based analysis for military counterterrorism systems," Systems Engineering, John Wiley & Sons, vol. 10(4), pages 273-289, December.
    3. Dengsheng Wu & Xiaoqian Zhu & Jie Wan & Chunbing Bao & Jianping Li, 2019. "A Multiobjective Optimization Approach for Selecting Risk Response Strategies of Software Project: From the Perspective of Risk Correlations," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 339-364, January.
    4. Mahsa Parsaei Motamed & Shahrooz Bamdad, 2022. "A multi-objective optimization approach for selecting risk response actions: considering environmental and secondary risks," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 266-303, March.
    5. Zhang, Yao & Zuo, Fei & Guan, Xin, 2020. "Integrating case-based analysis and fuzzy optimization for selecting project risk response actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    6. Edouard Kujawski & Diana Angelis, 2010. "Monitoring risk response actions for effective project risk management," Systems Engineering, John Wiley & Sons, vol. 13(4), pages 353-368, December.
    7. Ruth Y. Dicdican & Yacov Y. Haimes, 2005. "Relating multiobjective decision trees to the multiobjective risk impact analysis method," Systems Engineering, John Wiley & Sons, vol. 8(2), pages 95-108.

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