Date of Award

3-23-2018

Document Type

Thesis

Degree Name

Master of Science in Operations Research

Department

Department of Operational Sciences

First Advisor

Seong-Jong Joo, PhD.

Second Advisor

Raymond R. Hill, PhD.

Abstract

Personnel retention is a very important topic in both the private and public sectors. Not only do companies need to make sure they have the right people, they need to have the right amount of people. Within the public sector, specifically the US Air Force, maintaining appropriate manning comes in two phases; bringing in the right amount of people each year, and retaining enough people from year to year. These two aspects go hand in hand; if the Air Force knows how many people they will lose in a given year, they can bring in the exact number of people they need to make sure they maintain their end strength requirements. As part of the effort to ensure proper military accessions, the Air Force uses retention models to assist in predicting the future retention patterns. Not only does the Air Force want to make sure they meet their end strength requirements, they want to make sure they bring in the correct amount of people to each career field. The career fields, Air Force Specialty Codes (AFSCs), have a personnel requirement each year in order to accomplish that AFSCs mission. In this study, semiparametric survival analysis was used to determine the significant factors in predicting the rated officer career retention rates. The variables considered were sex, marital status, whether or not an officer had dependents, whether or not an officer was prior enlisted, whether or not an officer graduated as a distinguished graduate, and the institution from where the officer was commissioned. All of these factors were significant for the rated officer career field, which was validated using survival analysis. All of these factors are included in the survival analysis, which took the variables and created a survival curve fit to a specific distribution; the log-logistic. This survival curve was compared to previously done survival analysis to determine the best decision for use in manpower retention prediction for the United States Air Force.

AFIT Designator

AFIT-ENS-MS-18-M-136

DTIC Accession Number

AD1056367

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