Level 2 (Self)
TECHNOLOGY AREAS
Information Systems
MODERNIZATION PRIORITIES
Mission Readiness & Disaster Preparedness
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Nuclear
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Sustainment & Logistics
KEYWORDS
Artificial Intelligence, SFFAS, Audit Readiness, Supply Chain Risk Management, Financial Analysis, Related Party, Economic Dependency, Natural Language Processing
OBJECTIVE
Develop an innovative, AI-driven tool to automate the assessment of economic dependency for vendors within the Defense Logistics Agency's (DLA) supply chain. This capability will enable DLA to proactively identify relationships and analyze potential related-party transactions and economic dependencies in compliance with federal accounting standards and audit recommendations, including specifically Statements of Federal Financial Accounting Standards 47, thereby enhancing supply chain resilience, financial stewardship, and audit compliance.
ITAR
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.
DESCRIPTION
DLA's global mission relies on a vast and diverse industrial base. Ensuring financial transparency and mitigating supply chain risk requires a comprehensive understanding of the economic relationships between DLA and its key suppliers. Current methods for this analysis are manual, time-consuming, and cannot effectively scale across thousands of vendors and millions of transactions.
This SBIR topic seeks the development of an AI-powered tool to automate this process. The desired solution would integrate with DLA's business systems to identify significant vendor relationships and automatically retrieve publicly available financial data (e.g., from SEC filings). Using this data, the tool will apply a defined criterion for economic dependency (e.g., percentage of a vendor's revenue derived from DLA) to flag potential related parties. The solution should also be capable of assessing risk based on contract type (e.g., cost-reimbursement vs. fixed-price). The final tool must be designed to operate in a secure government environment and provide auditable, traceable results
PHASE I
Conduct a feasibility study to demonstrate the core concepts. This includes developing a proof-of-concept tool that can successfully identify a universe of vendors from sample contract data, retrieve public financial information from sources like the SEC's EDGAR system, commercial public 10K reports, SAM.gov, and apply the economic dependency criteria. The study must address SFFAS 47. The final report should include the prototype design, preliminary results, established golden dataset to base subsequent review and analysis upon, and a detailed plan for a Phase II effort. The Phase I award will not exceed $100,000 over a 12-month period.
PHASE II
Develop a scalable prototype of the AI tool within a government-approved development environment that fits DLA tech stack. The prototype must demonstrate the ability to process a large volume of vendor and contract data, accurately retrieve external information to support the evaluation of relationships and economic dependencies, assess and map/categorize risk and materiality, and provide transparent evidence for DLA assertions and/or disclosures with deep reasoning anchored to SFFAS 47. Phase II will include rigorous testing to validate the accuracy and efficiency of the tool and will produce a detailed transition plan for integration into DLA's operational environment. The Phase II award will not exceed $1,000,000 over a 24-month period.
PHASE III DUAL USE APPLICATIONS
A successful solution will be transitioned from development to production for operational use within DLA's environment and technology stack to support ongoing audit readiness and supply chain risk management. This technology has applicability for other DoW components and federal agencies seeking to automate financial oversight and identify concentration risk within their own supply chains or those using Defense Capital Working Funds (DCWF) to support investment analysis, strategic supply chain management, budgetary projections, and financial compliance, where identifying and understanding economic dependencies on the DLA and DoW is critical to fiscal stewardship.
REFERENCES
OMB Memorandum M-24-10: Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence (March 2024)
NDAA FY2024, Section 1542: Strategy and Guidance for the Use of AI in Defense Business Systems (December 2023)
NDAA FY2024, Section 1002: Plan for Attainment of Unqualified Audit Opinion (December 2023)
Executive Order 14110: Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (October 2023)
SFFAS 62: Transitional Amendment to SFFAS 54, Leases (May 2023)
NDAA FY2021, Section 881: Promotion of Artificial Intelligence Capabilities (January 2021)
NDAA FY2020, Section 847: Mitigating Risks Related to Foreign Ownership, Control, or Influence (FOCI) (December 2019)
SFFAS 54: Leases (April 2018)
OMB Circular A-123: Management's Responsibility for Enterprise Risk Management and Internal Control (July 2016)
SFFAS 47: Reporting Entity (December 2014, Effective FY2018)
