A Predictive Machine Learning Approach to Cost Risk Management in Major Defense Acquisition Programs

This Praxis aims to create a decision support tool by analyzing data collected by the Government Accountability Office for the Department of Defense’s Major Development Acquisition Programs over the past two decades. The study aims to tackle the cost overruns highlighted in the Government Accountability Office’s 2003 – 2023 report by exploring data analytics, sentiment analysis, and machine learning techniques. By examining performance metrics, this study seeks to develop a model that provides insights and actionable strategies for proactively mitigating cost risks by stakeholders in the Department of Defense and Prime Contractors. Furthermore, this research has practical significance as it presents an approach to risk management in defense acquisition. It is organized into five chapters that delve into the development of the tool, its methodology, and its practical application for enhancing decision-making and operational efficiency in defense acquisition endeavors. This research contributes to academia and is a resource for enhancing cost performance in major defense programs.

Author

• Hensiek, Levi Dale

Language

• en

Date created

• 2024

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Campos-Nanez, Enrique

Committee Member(s)

• Blackford, Jeffery
• Biemer, Steve

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/m039k572c