提高国防部生命周期成本估算精度的宏观随机模型

Erin T. Ryan, Christine M. Schubert Kabban, D. Jacques, J. Ritschel
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引用次数: 6

摘要

作者提出了一个预测成本模型,该模型被证明可以为国防部项目提供更准确的生命周期成本估计。与当前的成本估算方法不同,该模型不依赖于固定计划基线的假设。相反,这里提出的模型采用了一种随机方法来处理项目的不确定性,试图识别并合并估算误差的顶层(即“宏观”)驱动因素,以产生一个成本估算,该估算可能在不断变化的项目基线的现实世界中更准确。这种宏观随机成本模型所提供的估计精度的预测改进转化为国防部投资组合中的数千亿美元。此外,提高成本估算的准确性可以降低实际生命周期成本和/或使国防采办官员能够在更准确的价值和可负担性评估的基础上做出更好的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Macro-Stochastic Model for Improving the Accuracy of Department of Defense Life Cycle Cost Estimates
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.
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