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16 - The Engineering Risk-Analysis Method and Some Applications

Published online by Cambridge University Press:  05 June 2012

By
M. Elisabeth Paté-Cornell
Edited by
Ward Edwards ,
Ralph F. Miles Jr.  and
Detlof von Winterfeldt
M. Elisabeth Paté-Cornell
Affiliation:
Department of Management Science and Engineering, Stanford University
Ralph F. Miles Jr.
Affiliation:
California Institute of Technology
Detlof von Winterfeldt
Affiliation:
University of Southern California
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Summary

ABSTRACT. Engineering risk analysis methods, based on systems analysis and probability, are generally designed for cases in which sufficient failure statistics are unavailable. These methods can be applied not only to engineered systems that fail (e.g., new spacecraft or medical devices), but also to systems characterized by performance scenarios including malfunctions or threats. I describe some of the challenges in the use of risk analysis tools, mainly in problem formulation, when technical, human, and organizational factors need to be integrated. This discussion is illustrated by four cases: ship grounding due to loss of propulsion, space shuttle loss caused by tile failure, patient risks in anesthesia, and the risks of terrorist attacks on the US. I show how the analytical challenges can be met by the choice of modeling tools and the search for relevant information, including not only statistics but also a deep understanding of how the system works and can fail, and how failures can be anticipated and prevented. This type of analysis requires both imagination and a logical, rational approach. It is key to proactive risk management and effective ranking of risk reduction measures when statistical data are not directly available and resources are limited.

Engineering Risk Analysis Method: Imagination and Rationality

Risk analysis for well-known, well-documented and steady-state systems (or stable phenomena) can be performed by methods of statistical analysis of available data. These include, for example, maximum likelihood estimations, and analyses of variance and correlations.

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Advances in Decision Analysis
From Foundations to Applications
 , pp. 302 - 324
Publisher: Cambridge University Press
Print publication year: 2007

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