Comparison of Cumulative Average to Unit Learning Curves: A Monte Carlo Approach
Journal of Cost Analysis and Parametrics
Cumulative average and unit cost learning curve methodologies dominate current learning curve theory. Both models mathematically estimate the structure of costs over time and under particular conditions. While cost estimators and industries have shown preferences for particular models, this article evaluates model performance under varying program characteristics. A Monte Carlo approach is used to perform analysis and identify the superior method for use under differing programmatic factors and conditions. Decision charts are provided to aide analysts learning curve model selection for aircraft production and modification programs. Overall, the results indicate that the unit theory outperforms the cumulative average theory when more than 40 units exist to create a prediction learning curve or when the data presents high learning and low variation in the program; however, the cumulative average theory predicts unit costs with less error when few units to create the curve exists, low learning occurs, and high variation transpires.
Trevor P. Miller, M.S., is a cost analyst for Air Force Space Command concentrating on SBIRS and Space Fence programs. His educational background and his research interests focus on simulation and statistical modelling.
Austin W. Dowling, M.S., is a Cost Analyst for the B-2 program with the Aeronautical Systems Center. His educational background and his research interests focus on simulation and statistical modelling.
David J. Youd, M.S., is a the Cost Chief for the Space and Missiles Systems Center’s developmental planning directorate (SMC/XR). His educational background is in Accounting, Management, and Governmental Cost Estimating.
Dr. Eric Unger is the Cost Estimating Branch Chief at Space and Missile Systems Center. He received a B.A. in Mathematics and Economics from Northwestern University, an M.S. in Acquisition Management from the Air Force Institute of Technology, and a Ph.D. in Policy Analysis from the Pardee RAND Graduate School. He served previously as the Director of the Cost Analysis Graduate Program at the Air Force Institute of Technology and Chief of Cost of MILSATOM at Los Angeles Air Force Base. His research focuses on the policy impact of quantitative cost analysis.
Edward D. White, III, Ph.D., is an Associate Professor of Statistics within the Department of Mathematics and Statistics at the Air Force Institute of Technology. His teaching and research interests are in design of experiments, linear and nonlinear regression, and statistical consulting.