FORECASTING ARMY RESERVE OFFICER TRAINING CORPS COMMISSIONS USING MACHINE LEARNING TECHNIQUES
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Authors
Hudalla, Daniel R.
Subjects
forecasting
attrition
Cadets
ROTC
USACC
commission
machine learning
random forest
attrition
Cadets
ROTC
USACC
commission
machine learning
random forest
Advisors
Koyak, Robert A.
Date of Issue
2019-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The U.S. Army Cadet Command (USACC) produces about two–thirds of the Army officer corps in any given year. The command currently forecasts commissions 24 months before the completion of a fiscal year despite limited ability to influence production within this timeframe. Consequently, USACC must make recruiting decisions to shape its cohorts at least six months before current forecasts begin. This study explores the use of statistical machine learning models to forecast the number of currently enrolled cadets who will commission in a cohort nearly three years out. The developed forecasts can be used to determine the number of new cadets USACC must recruit to accomplish future recruiting missions. We find that a machine learning model can identify predictors that increase or decrease the likelihood of commissioning, and offer insight related to scholarship and contracting policies based on model outputs.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
Identifiers
NPS Report Number
Sponsors
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.