Please use this identifier to cite or link to this item: https://dair.nps.edu/handle/123456789/5415
Title: AI-Based DPCAP FAR/DFARS Change Support Tool
Authors: Jose Ramirez-Marquez
Joshua Gorman
Akram Amer
Douglas Buettner
Brian Maye
Patrick Butler
Naren Ramakrishnan
Bradley Freedman
Keywords: Natural Language Processing
NLP
National Defense Authorization Act
NDAA
Defense FAR Supplement
DFARS
Issue Date: 12-May-2025
Publisher: Acquisition Research Program
Citation: APA
Series/Report no.: Acquisition Management;SYM-AM-25-363
Abstract: "The Department of Defense’s Defense Pricing, Contracting, and Acquisition Policy Contract Policy Directorate in the Office of the Assistant Secretary of Defense is responsible for periodic updates to the Federal Acquisition Regulation (FAR) and Defense FAR Supplement (DFARS) based on changes in the National Defense Authorization Act (NDAA), Small Business Administration rule changes, U.S. Department of Labor rule changes, or from executive orders. Reading through and assessing these documents for changes that require corresponding changes to acquisition regulations is labor-intensive. Further, when rule changes are proposed to the public for comments, reading and summarizing these public comments can range from straightforward to very labor-intensive. In this paper, we report our initial research results to greatly improve the efficiency of analyzing the NDAA language for required updates of the FAR and DFARS, and issuance of memoranda and guidance using artificial intelligence, including large language models and advanced natural language processing techniques to provide an improvement in staff efficiency for these laborious tasks."
Description: SYM Paper
URI: https://dair.nps.edu/handle/123456789/5415
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

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