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.
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Research Questions
- Which features and indicators inform retention outcomes?
- What models can be developed to help predict retention outcomes?
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.
Key Findings
- The USAF has access to rich historical information on many factors that the established research literature links to turnover.
- The most significant gap in turnover-related information available to REWS is the lack of information on member attitudes and perceptions.
- Machine-learning algorithms can increase the accuracy of individual-level predictions, and these improvements could result in more-accurate group-level estimates for separation rates.
- The REWS decision workflow operationalizes these predictions so that various USAF planners can generate customizable warnings, understand potential drivers, and assess the policy response required to preempt emerging problems.
- Simplified data inputs offer a way to refresh predictions with minimal resources, and longer-term efforts will enable improvements in data inputs, model accuracy, and functionality.
Recommendations
- Gather feedback on REWS from human resource managers to guide decisionmaking refinements.
- Improve survey data collection in order to enhance REWS' ability to anticipate retention trends.
- Use simplified data inputs to refresh predictions.
- View the development of the REWS prototype as a down payment on a longer-term, continually improving business intelligence capability.
Table of Contents
Chapter One
Introduction
Chapter Two
What Information Is Most Relevant to Predicting Retention?
Chapter Three
Available Sources of Information for Predicting Air Force Retention
Chapter Four
Modeling Approaches and Performance Levels
Chapter Five
How Retention Predictions Can Be Used to Generate Warnings
Chapter Six
Next Steps for Further Development and Implementation
Appendix A
Creating the Analytic Data File
Appendix B
Machine Learning Algorithms
Appendix C
Decomposition Methodology
Appendix D
Literature Review Methodology
Appendix E
Considerations and Challenges in Applying Data Science to Air Force Human Resource Problems
Appendix F
Policy Impact Methodology
Research conducted by
The research reported here was commissioned by the Directors of Force Development (AF/A1D) and Military Force Management Policy (AF/A1P) and conducted by the Workforce, Development, and Health Program within RAND Project AIR FORCE.
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