RAND Project Air Force was tasked with developing a new capability for planners: a retention early warning system (REWS) that alerts policymakers when a subgroup of U.S. Air Force (USAF) military members is at risk for future shortages. The goal of the research project was to develop a forecasting model for retention, operationalized within a prototype decision-support application, that can alert decisionmakers to emerging problems and thus allow them enough time to consider adjusting accession and retention policies before shortages occur. The authors' overall approach to designing the system drew on widely used paradigms for solving data science problems. These paradigms emphasize understanding the business problem, drawing on a wide array of data sources and types, testing several flexible prediction approaches to optimize performance, and operationalizing the information for decisionmaking. To gain an understanding of the data sources that would be desirable for this application, the authors performed an extensive review of the turnover literature and identified gaps in existing USAF data collection efforts.
Tasked with developing a new capability for U.S. Air Force human resources planners, the authors have developed an initial prediction prototype tool that can be used to alert decisionmakers of emerging problems and thus allow them enough time to consider adjusting accession and retention policies before shortages occur.
The authors examine the development of an early warning system that alerts decisionmakers when a subgroup of U.S. Air Force military members is at risk for future shortages, thereby enabling policy adjustments before shortages occur.
