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Accession Number ADA586225
Title Quantifying Uncertainty for Early Life Cycle Cost Estimates.
Publication Date Apr 2013
Media Count 20p
Personal Author B. Ferguson D. Goldenson D. Zubrow J. McCurley R. Stoddard
Abstract Extensive cost overruns in major defense programs are common, and studies have identified poor cost estimation as a main contributor. Research and experience have identified several factors associated with poor cost estimates. These include the following: (1) optimistic expectations about the program's scope and technology such that it can be delivered on schedule and within budget; (2) the enormous amount of unknowns and uncertainty that exist when these estimates are made about large-scale, unprecedented systems that take years to develop and deploy; and (3) the heavy reliance, of necessity, on expert judgment. In this paper, we describe a new, integrative approach for pre- Milestone A cost estimation called quantifying uncertainty in early life cycle cost estimation (QUELCE). QUELCE synthesizes scenario building, Bayesian belief network modeling, and Monte Carlo simulation into an estimation method that quantifies uncertainties, allows subjective inputs, visually depicts influential relationships among change drivers and outputs, and assists with explicit description and documentation underlying an estimate. We use scenario analysis and dependency structure matrix techniques to limit the combinatorial effects of multiple interacting program change drivers to make modeling and analysis more tractable. Finally, we describe results and insights gained from applying the method retrospectively to a major defense program.
Keywords Bayes theorem
Bayesian belief network modeling
Computerized simulation
Cost estimates
Cost overruns
Department of defense
Dependency structure matrix techniques
Life cycle costs
Major defense programs
Military procurement
Monte carlo method
Monte carlo simulation
Pre milestone a cost estimation
Quantitative analysis
Quelce(Quantifying uncertainty in early life cycle cost esti
Scenario analysis
Scenario building
Scenarios
Symposia
Uncertainty


 
Source Agency Non Paid ADAS
NTIS Subject Category 96 - Business & Economics
72F - Statistical Analysis
62B - Computer Software
74E - Logistics, Military Facilities, & Supplies
Corporate Author Carnegie-Mellon Univ., Pittsburgh, PA. Software Engineering Inst.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1405
Contract Number N/A

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