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Management Errors and System Reliability: A Probabilistic Approach and Application to Offshore Platforms

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  • M. Elisabeth Paté‐Cornell
  • Robert G. Bea

Abstract

Probabilistic risk analysis, based on the identification of failure modes, points to technical malfunctions and operator errors that can be direct causes of system failure. Yet component failures and operator errors are often rooted in management decisions and organizational factors. Extending the analysis to identify these factors allows more effective risk management strategies. It also permits a more realistic assessment of the overall failure probability. An implicit assumption that is often made in PRA is that, on the whole, the system has been designed according to specified norms and constructed as designed. Such an analysis tends to overemphasize scenarios in which the system fails because it is subjected to a much higher load than those for which it was designed. In this article, we find that, for the case of jacket‐type offshore platforms, this class of scenarios contributes only about 5% of the failure probability. We link the PRA inputs to decisions and errors during the three phases of design, construction, and operation of platforms, and we assess the contribution of different types of error scenarios to the overall probability of platform failure. We compute the benefits of improving the design review, and we find that, given the costs involved, improving the review process is a more efficient way to increase system safety than reinforcing the structure.

Suggested Citation

  • M. Elisabeth Paté‐Cornell & Robert G. Bea, 1992. "Management Errors and System Reliability: A Probabilistic Approach and Application to Offshore Platforms," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 1-18, March.
    • Handle: RePEc:wly:riskan:v:12:y:1992:i:1:p:1-18
    DOI: 10.1111/j.1539-6924.1992.tb01302.x
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    References listed on IDEAS

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    1. March, James G., 1988. "Variable risk preferences and adaptive aspirations," Journal of Economic Behavior & Organization, Elsevier, vol. 9(1), pages 5-24, January.
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    1. Selvik, Jon T. & Bellamy, Linda J., 2020. "Addressing human error when collecting failure cause information in the oil and gas industry: A review of ISO 14224:2016," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    2. Khorsandi, Jahon & Aven, Terje, 2017. "Incorporating assumption deviation risk in quantitative risk assessments: A semi-quantitative approach," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 22-32.
    3. Dean M. Murphy & M. Elisabeth Paté‐Cornell, 1996. "The SAM Framework: Modeling the Effects of Management Factors on Human Behavior in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 16(4), pages 501-515, August.
    4. M. Elisabeth Paté‐Cornell & Linda M. Lakats & Dean M. Murphy & David M. Gaba, 1997. "Anesthesia Patient Risk: A Quantitative Approach to Organizational Factors and Risk Management Options," Risk Analysis, John Wiley & Sons, vol. 17(4), pages 511-523, August.
    5. Dimitrina S. Dimitrova & Vladimir K. Kaishev & Shouqi Zhao, 2015. "Modeling Finite‐Time Failure Probabilities in Risk Analysis Applications," Risk Analysis, John Wiley & Sons, vol. 35(10), pages 1919-1939, October.
    6. George M. Gray & Jon C. Allen & David E. Burmaster & Stuart H. Gage & James K. Hammitt & Stanley Kaplan & Ralph L. Keeney & Joseph G. Morse & D. Warner North & Jan P. Nyrop & Alina Stahevitch & Richar, 1998. "Principles for Conduct of Pest Risk Analyses: Report of an Expert Work hop," Risk Analysis, John Wiley & Sons, vol. 18(6), pages 773-780, December.
    7. Armacost, Robert L. & Pet-Edwards, Julia, 1999. "Integrative risk and uncertainty analysis for complex public sector operational systems," Socio-Economic Planning Sciences, Elsevier, vol. 33(2), pages 105-130, June.
    8. Christian C. Blanco & Felipe Caro & Charles J. Corbett, 2019. "Managing Safety‐Related Disruptions: Evidence from the U.S. Nuclear Power Industry," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2197-2213, October.

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