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 optimistic expectations about the program scope and technology that can be delivered on schedule and within budget; 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 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 (BBN) 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 (DSM) 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.