Quantifying Uncertainty for Early Life Cycle Cost Estimates
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2013
- Accession Number
- ADA586225
Entities
People
- Bob Ferguson
- David Zubrow
- Dennis Goldenson
- Jim Mccurley
- Robert W. Stoddard
Organizations
- Carnegie Mellon University