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Selection of technical risk responses for efficient contingencies
Author(s) -
Kujawski Edouard
Publication year - 2002
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.10025
Subject(s) - set (abstract data type) , computer science , schedule , selection (genetic algorithm) , portfolio , project portfolio management , project management , risk analysis (engineering) , cumulative prospect theory , operations research , risk management , outcome (game theory) , project risk management , expected utility hypothesis , engineering , machine learning , systems engineering , mathematics , statistics , medicine , management , mathematical economics , financial economics , economics , programming language , operating system
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