U.S. Air Force Enlisted Classification and Reclassification Potential Improvements Using Machine Learning and Optimization Models
Abstract
Recent trends in initial skills training (IST) for Air Force specialties (AFSs) indicate that the number of United States Air Force (USAF) enlisted personnel reclassified into other occupational specialties has increased in recent years, with a steady rise having occurred between fiscal years 2013 and 2017. Career field reclassification can result in a wide range of negative outcomes, including increased costs, delayed manning, training schedule challenges, and decreased morale. To understand and address the challenge of IST reclassification, the authors considered options for improving processes to classify and reclassify enlisted active-duty, nonprior service airmen for IST. In this report, they outline key findings from a 2019 study that employed qualitative and quantitative analyses, including machine learning (ML) models, to assess predictors of IST success (and failure). They also describe their test of an optimization model designed to identify opportunities for revising reclassification decisions in order to not only reduce the numbers of reclassified airmen but also to achieve greater job satisfaction and productivity for airmen and improve USAF retention rates.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2022
- Accession Number
- AD1163511
Entities
People
- Jonathan W. Welburn
- Joshua Snoke
- Kimberly Curry Hall
- Kirsten M. Keller
- Louis T. Mariano
- Maria. C. Lytell
- Matthew E. Walsh
- Owen Hall
- Patrick S. Roberts
- Sean Robson
- Vikram Kilambi
Organizations
- RAND Corporation