Quantifying and Comparing the Benefits of Risk Reduction Programs to Prioritize Expenditures

Abstract

Many techniques are being used to quantify the costs and benefits of risk reduction programs to allow competing programs to be compared and ranked for purposes of allocating limited available resources. Often, insufficient attention is paid to selecting appropriate measures for scaling program benefits, identifying implicit value-laden assumptions associated with the methodologies, and exploring the implications of inherent uncertainties. This paper presents the results of a case study of these issues which critically evaluates an approach developed at General Motors that uses cost and actuarial data to compare the cost effectiveness of various risk reduction programs. An earlier “best estimate” application of the approach to a set of risk reduction programs addressing diverse technological risks is reexamined to explicitly characterize and factor in the associated uncertainties. The strengths and limitations of this benefits quantification and prioritization approach are discussed. Considerations which are generally applicable to comparing and ranking the costs and benefits of programs addressing risks of highly diverse origin are described, and their impact on the selection of an appropriate cost/benefit quantification and comparison methodology are presented.

Participants in the Study Group were Deborah Amaral, Felix Dayo, Theresa Donahoe-Nestor, Jennings Ellis, Conrad Eustis, Eden Fisher, Jeffrey Funk, Carol Giron, Steve Goldstein, Ronald Mamicio, David Meeker, Gil Miller, Granger Morgan, William Mura, and Giri Tayi.