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Programmatic Risk Analysis for Critical Engineering Systems Under Tight Resource Constraints

Author

Listed:
  • Robin L. Dillon

    (McDonough School of Business, Georgetown University, Washington, DC 20057)

  • M. Elisabeth Paté-Cornell

    (Department of Management Science and Engineering, Stanford University, Stanford, California 94305)

  • Seth D. Guikema

    (Department of Management Science and Engineering, Stanford University, Stanford, California 94305)

Abstract

Managers of complex engineering development projects face a challenge when deciding how to allocate scarce resources to minimize the risks of project failure. As resource constraints become tighter, balancing these failure risks is more critical, less intuitive, and can benefit from the power of quantitative analysis. This paper describes the Advanced Programmatic Risk Analysis and Management model (APRAM), a decision-support framework for the management of the risk of failures of dependent engineering projects within programs. Our goal is to guide the management of the design, the development, and the budget of dependent projects. Considering first a single project, our approach is to optimize the use of the budget reserves and of the funds dedicated to the system itself for each possible budget allocation. This phase involves separate optimizations of the system design and a strategy for resolving development problems based on a chosen objective function. The model also allows checking that specified thresholds of maximum acceptable risks are met, and if not, indicates how much is required to satisfy them. It is then extended to include project dependencies within a program. Finally, it allows checking that the level of resources available is appropriate by computing the shadow “risk cost” of the budget constraint. The NASA Jet Propulsion Laboratory has supported the development of this model, so each step of APRAM is illustrated by the schematic case of an unmanned space program involving two dependent projects. Also, where applicable, we discuss our experiences working with the APRAM concepts for the management of unmanned space missions.

Suggested Citation

  • Robin L. Dillon & M. Elisabeth Paté-Cornell & Seth D. Guikema, 2003. "Programmatic Risk Analysis for Critical Engineering Systems Under Tight Resource Constraints," Operations Research, INFORMS, vol. 51(3), pages 354-370, June.
    • Handle: RePEc:inm:oropre:v:51:y:2003:i:3:p:354-370
    DOI: 10.1287/opre.51.3.354.14961
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    2. Cameron MacKenzie & Hiba Baroud & Kash Barker, 2016. "Static and dynamic resource allocation models for recovery of interdependent systems: application to the Deepwater Horizon oil spill," Annals of Operations Research, Springer, vol. 236(1), pages 103-129, January.
    3. Robin L. Dillon & M. Elisabeth Paté‐Cornell, 2005. "Including technical and security risks in the management of information systems: A programmatic risk management model," Systems Engineering, John Wiley & Sons, vol. 8(1), pages 15-28.
    4. Russ Garber & Elisabeth Paté‐Cornell, 2012. "Shortcuts in Complex Engineering Systems: A Principal‐Agent Approach to Risk Management," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 836-854, May.
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    8. Dadsena, Krishna Kumar & Sarmah, S.P. & Naikan, V.N.A. & Jena, Sarat Kumar, 2019. "Optimal budget allocation for risk mitigation strategy in trucking industry: An integrated approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 37-55.
    9. Dean A. Shepherd & Melissa S. Cardon, 2009. "Negative Emotional Reactions to Project Failure and the Self‐Compassion to Learn from the Experience," Journal of Management Studies, Wiley Blackwell, vol. 46(6), pages 923-949, September.
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    12. E. Borgonovo & C. L. Smith, 2011. "A Study of Interactions in the Risk Assessment of Complex Engineering Systems: An Application to Space PSA," Operations Research, INFORMS, vol. 59(6), pages 1461-1476, December.
    13. Guikema, Seth D., 2009. "Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 855-860.
    14. Cameron A. MacKenzie & Hiba Baroud & Kash Barker, 2016. "Static and dynamic resource allocation models for recovery of interdependent systems: application to the Deepwater Horizon oil spill," Annals of Operations Research, Springer, vol. 236(1), pages 103-129, January.
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