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Incorporating psychological influences in probabilistic cost analysis
Author(s) -
Kujawski Edouard,
Alvaro Mariana L.,
Edwards William R.
Publication year - 2004
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.20004
Subject(s) - cost contingency , probabilistic logic , cost estimate , cost engineering , risk analysis (engineering) , operations research , probabilistic analysis of algorithms , computer science , engineering , systems engineering , business , artificial intelligence
Today's typical probabilistic cost analysis assumes an “ideal” project that is devoid of the human and organizational considerations that heavily influence the success and cost of real‐world projects. In the real world “Money Allocated Is Money Spent” (MAIMS principle); cost underruns are rarely available to protect against cost overruns while task overruns are passed on to the total project cost. Realistic cost estimates therefore require a modified probabilistic cost analysis that simultaneously models the cost management strategy including budget allocation. Psychological influences such as overconfidence in assessing uncertainties, dependencies among cost elements, and risk are other important considerations that are generally not addressed. It should then be no surprise that actual project costs often exceed the initial estimates and are delivered late and/or with a reduced scope. This paper presents a practical probabilistic cost analysis model that incorporates recent findings in human behavior and judgment under uncertainty, dependencies among cost elements, the MAIMS principle, and project management practices. Uncertain cost elements are elicited from experts using the direct fractile assessment method and fitted with three‐parameter Weibull distributions. The full correlation matrix is specified in terms of two parameters that characterize correlations among cost elements in the same and in different subsystems. The analysis is readily implemented using standard Monte Carlo simulation tools such as @Risk and Crystal Ball ® . The analysis of a representative design and engineering project substantiates that today's typical probabilistic cost analysis is likely to severely underestimate project cost for probability of success values of importance to contractors and procuring activities. The proposed approach provides a framework for developing a viable cost management strategy for allocating baseline budgets and contingencies. Given the scope and magnitude of the cost‐overrun problem, the benefits are likely to be significant. © 2004 Wiley Periodicals, Inc. Syst Eng 7: 000–000, 2004