Machine Learning-Enabled Recommendations for the Air Force Officer Assignment System, Volume 5
Abstract
The Department of the Air Force (DAF) continues to undergo a digital transformation involving numerous efforts designed to overhaul digital architectures, systems, and processes. These wide-ranging efforts include the human resource management (HRM) domain. Specifically, the officer assignment system, which seeks to assign the right officer to the right position at the right time to meet AF mission requirements, is undergoing a bold transformation (DAF, 2021). Along with its sister services, the U.S. Air Force (USAF) has shifted from Industrial Age, top-down personnel management procedures to a model that is closer to a digital talent marketplace (Department of the Navy, U.S. Marine Corps, 2021). This shift means that officers may apply for any available position and advertise special skills to position owners. Position owners, in turn, may review information about officers who are eligible for assignment. A shift to a true talent marketplace model, in which these preferences are the major determinant of assignments, could be a win-win, reducing the oversight burden while delivering better and more-transparent assignment matches. In addition, by reducing oversight burden, a true talent marketplace could enable more transformational change, such as allowing the USAF to move off a regular assignment cycle model. However, implementing a marketplace approach to officer assignments comes with certain trade-offs that may limit their functionality from the point of view of the USAF. One concern is the ability of officers to discern how each assignment will contribute to their development. Another concern is whether position owners have the bandwidth to vet a potentially longer list of candidates during each assignment cycle if the system encourages more people to apply.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2024
- Accession Number
- AD1223200
Entities
People
- Avery Calkins
- Claude M. Setodji
- David Schulker
- Matthew E. Walsh
- Monique Graham
Organizations
- RAND Corporation