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 |
Files in This Item:
File | Description | Size | Format | |
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SYM-AM-25-363.pdf | SYM Paper | 2.45 MB | Adobe PDF | View/Open |
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