A Macro-Stochastic Model for Improving the Accuracy of Department of Defense Life Cycle Cost Estimates

Abstract

The authors present a prognostic cost model that is shown to provide significantly more accurate estimates of life cycle costs for Department of Defense programs. Unlike current cost estimation approaches, this model does not rely on the assumption of a fixed program baseline. Instead, the model presented here adopts a stochastic approach to program uncertainty, seeking to identify and incorporate top-level (i.e., “macro”) drivers of estimating error to produce a cost estimate that is likely to be more accurate in the real world of shifting program baselines. The predicted improvement in estimating accuracy provided by this macro-stochastic cost model translates to hundreds of billions of dollars across the Department of Defense portfolio. Furthermore, improved cost estimate accuracy could reduce actual life cycle costs and/or allow defense acquisition officials the ability to make better decisions on the basis of more accurate assessments of value and affordability.

Acknowledgments

The authors welcome opportunities to share the data used to formulate this model. Please contact the primary author for any questions on this paper or to obtain source data.

Notes

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1. “AUC” is not a standard DoD acronym; the authors have coined it for convenience in the context of this application. Further, AUC should not be confused with the APUC (Average Procurement Unit Cost).