|Student Name:||Capt Allen Deneve|
|Thesis:||A Macro-Stochastic Approach to Improved Cost Estimation for Defense Acquisition Programs|
|Location:||Bldg 640, Room 103|
|Date & Time:||02/20/2014 at 0930|
|Abstract:|| Inaccurate cost estimates are a recurrent problem for Department of Defense (DoD) acquisition programs, with cost overruns exceeding billions of dollars each year. These estimate errors hinder the ability of the DoD to assess the affordability of future programs and properly allocate resources to existing programs. In this research, the author employs a novel approach called “macro-stochastic” cost estimation for significantly reducing cost estimate errors in Major Defense Acquisition Programs (MDAPs). To achieve this reduction, the author first extracts and catalogues key programmatic data from 936 Selected Acquisition Reports. The author then analyzes historical trends in the data using mixed-model regression with high-level descriptive program parameters. Based on these trends, the model reduces estimate errors by 18.7% on average, when applied to a randomly selected cost estimate. However, the validated model is most beneficial when applied early in program life; when applied to the first cost estimate of each program in the database, the macro-stochastic technique reduces cost estimate error by over one third. This statistically and economically significant reduction could potentially allow for reallocation of $6.25 billion, annually, if applied consistently to the DoD’s portfolio of MDAPs.