Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Phillip M. LaCasse, PhD

Abstract

The Army’s recruiting landscape has changed markedly in recent years, raising questions about whether forecasting methods of Army contracts remain robust. This thesis recreates the presented models in Joshua McDonald’s 2015 thesis. It replicates and evaluates the models with updated data (2018–2023) to assess their current validity and compare them to novel alternative approaches, such as simpler regression models or neural networks. While the 2015 model remains a valuable baseline, results suggest that either refining its variables or adopting alternative methods can improve predictive accuracy and interpretability. Ultimately, the United States Army Recruiting Command has many options regarding how it examines its recruiting mission distribution and any insight into that endeavor can prove valuable.

AFIT Designator

AFIT-ENS-MS-25-M-165

Comments

An embargo was observed for posting this thesis on AFIT Scholar.

This work is marked as Distribution A - Approved for public release. Distribution Unlimited. PA clearance case number on file.

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