Time Phasing Aircraft R&D Using the Weibull and Beta Distributions
Journal of Cost Analysis and Parametrics
Early research on time phasing primarily focuses on the theoretical foundation for applying the cumulative distribution function, or S-curve, to model the distribution of development expenditures. Minimal methodology prior to 2002 provides for estimating the S-curves parameter values. Brown et al. (2002) resolved this shortcoming through regression analysis, but their methodology is not specific to aircraft and does not consider aircraft-specific variables, such as first flight. Using a sample of 26 Department of Defense aircraft programs, we build upon Brown et als work by examining whether a model driven by aircraft-specific variables can more accurately predict budget requirements. As a baseline, we compare our model to the commonly cited 60/40 rule of thumb, which assumes 60% expenditures at 50% schedule. We discover that our developed Weibull model explains 74.6% of total variation in annual budget, improving the estimation of budgets by 6.5%, on average, over the baseline 60/40 model.
Gregory E. Brown is a cost analyst at the Air Force Life Cycle Management Center, Wright-Patterson Air Force Base, Ohio.
Dr. Edward D. White is a Professor of Statistics in the Department of Mathematics and Statistics at the Air Force Institute of Technology. His primary research interests include statistical modeling, simulation, and data analytics.
Dr. Jonathan D. Ritschel is an Assistant Professor of Cost Analysis at the Air Force Institute of Technology. His research interests include public choice, institutional analysis, and cost analysis.
Michael J. Seibel is a senior cost analyst at the Air Force Life Cycle Management Center, Wright-Patterson Air Force Base, Ohio.