This report introduces the Aim Point Investment Model, which allocates portfolio-level resources based on programs' utility and existing Army data sources, subject to constraints on the U.S. Army's ability to employ those resources productively. This model allows decisionmakers to quickly understand the implications of changing operational prioritization and budget constraints in terms of real effects on actual program-level funding.
Optimizing Portfolio-Level Modernization Investment
An Overview of the Aim Point Investment Model (APIM)
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Research Questions
- What are the characteristics of a decision support tool and associated decisionmaking framework that would aid U.S. Army leaders in navigating the enormous complexities of resource allocation decisions, which require consideration of hundreds of interdependent programs with changing values across many years?
- Can existing Army data sources be used to inform utility functions and constraints across investment programs?
This report introduces the Aim Point Investment Model, an optimization model for portfolio-level resource allocation across U.S. Army programs and time. The report is intended to provide a technical overview of the model and its capabilities while also detailing the motivation for creating the model and recommendations from related research. The recommendations should be of interest to decisionmakers and those interested in improving decision support tools for asset allocation problems.
In brief, the project's objective was to develop a method and tool to support quick-turn exploration of modernization investment portfolios in light of changing budget constraints and operational priorities in order to develop rough-order optimal investment strategies across a preestablished set of investment options and set of budget and requirement assumptions. Given the enormous complexity of the decision space, some sort of automated decision support tool was required. To develop that decision support tool, the authors explored alternative approaches to extracting the information needed about programs' relative utility and any constraints on the Army's ability to procure the capability from existing Army data sources. This report describes one of these approaches, which uses Army prioritization guidance—synthesized from several sources—combined with plausible constraints to produce resource allocation solutions that are consistent with the Army's stated modernization strategy.
Key Findings
- A force package construct helps differentiate value across Army programs while addressing the issue of interdependency.
- While existing Army data can provide information on value categories and constraints, additional value categories and constraints at the program level could improve the efficacy of a decision support tool.
- Optimization analysis requires better information about objectives and constraints.
Recommendations
- Continue to use a force package construct because such a construct is very helpful in addressing the issue of interdependencies and can help simplify identification of priority needs.
- Define value categories for Army programs across procurement and research, development, test, and evaluation.
- Define constraints, such as budgetary and production constraints, to limit the decision space.
- Develop an iterative approach to help decisionmakers explore alternatives across broad priorities and specific programs.
Research conducted by
The research described in this report was sponsored by the United States Army and conducted by the Strategy, Doctrine, and Resources Program within RAND Arroyo Center.
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