AI-Powered Tool for Automated Evaluation of Vendor Economic Dependency

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. 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 Keywords: Artificial Intelligence, SFFAS, Audit Readiness, Supply Chain Risk Management, Financial Analysis, Related Party, Economic Dependency, Natural Language Processing CMMC Level: Level 2 (Self)