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Utilizing reliability modeling to analyze United States Air Force officer retention
•Considered thousands of officer personnel records form the United States Air Force.•Models for retention over several dozen career fields.•Retention models focused on a 20-year time frame.•Developed methods to combine retention models.•Devised new metric for managerial use that provides insights in...
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Published in: | Computers & industrial engineering 2018-03, Vol.117, p.171-180 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •Considered thousands of officer personnel records form the United States Air Force.•Models for retention over several dozen career fields.•Retention models focused on a 20-year time frame.•Developed methods to combine retention models.•Devised new metric for managerial use that provides insights into career fields requiring special attention.
The United States Air Force organizes its workforce around rank structure and work specialty codes (Air Force Specialty Codes (AFSCs)). Unlike civilian organizations, all active duty Air Force personnel start at the entry level ranks. The challenge is to develop and manage personnel to fill a variety of skill sets at a variety of ranks over a 20–30 year planning horizon. The Air Force uses sustainment lines to accomplish many of its manpower management goals. However, the current methodology for developing these sustainment lines is not statistically defensible based on the actual retention data and does not provide management a means to identify specialty codes of concern. Leveraging methods from reliability theory, we utilize survivability functions constructed from historical retention data to develop and demonstrate a statistically defensible methodology for creating the sustainment lines at the core of the Air Force personnel management system and provide a tool for managers to focus attention on potential workforce problem areas. |
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ISSN: | 0360-8352 1879-0550 |
DOI: | 10.1016/j.cie.2018.01.013 |