USMC Manpower Models Modernization II
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Authors
Seagren, Chad
Subjects
manpower modeling
decision support
continuous process improvement
decision support
continuous process improvement
Advisors
Date of Issue
2024-03-01
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This project builds on our previous work in support of a fiscal year (FY) 22 Naval Research Program (NRP) project (Seagren et al., 2022). The primary objective of this project is to determine the feasibility and effectiveness of a more automated process to more accurately account for all active-duty personnel gains and losses by month and facilitate the inclusion of this information in various models and processes. We develop a machine learning version of the Enlisted End-Strength Planning Model (ESPM) and compare its performance to the legacy process. Ultimately, we find that it is possible to develop and implement a machine learning approach that effectively forecasts base Non-End of Active Service attrition in a managerially useful manner. In fact, evidence suggests that the machine learning models may dramatically outperform the legacy methods and that these findings are robust across fiscal years. However, resources required to develop the machine learning models include significant time on a high-performance computer, which may not be practical for the topic sponsor. While we find that the machine learning models perform well, the benefits relative to the legacy model and process likely do not justify the effort given current constraints of computing power.
Type
Report
Description
NPS NRP Executive Summary
Series/Report No
Department
Department of Defense Management (DDM)
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
Sponsors
HQMC Manpower and Reserve Affairs (M&RA)
Funder
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Chief of Naval Operations (CNO)
Format
4 p.
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.