An Evaluation of a Bayesian Approach to Compute Estimates-at-Completion for Weapon System Programs.

摘要

The Bayesian model developed to predict costs-at-completion on weapon system programs is an extension of research done by M. Zaki El-Sabban. The model assumes cost is a random variable and is normally distributed. Budgeted costs are used to develop the prior probability distribution. Actual cost information is used for the Bayesian updating of the probability distribution. The mean of the updated probability distribution is the new estimated cost-at-completion for the program. The model was compared with a non-linear regression model and a linear extrapolation model on five weapon system programs. On three of the programs the non-linear regression model estimated the final cost the greater percentage of the time. On the remaining two programs the Bayesian model estimated the final cost the greater percentage of the time. The Bayesian model demonstrated several advantages over previous models: use at the beginning of the program, inclusion of subjective information, and giving weight to future program budgets.