Agentic AI for Demand, Credit, and Inventory Intelligence in Food Supply Chains
DOI:
https://doi.org/10.5281/zenodo.20679023Keywords:
Predictive Demand, Credit Risk. Inventory Financing. Supply Chain Data Ecosystems, Agentic AI, Autonomous Analytics, Multi-Cloud Big Data, National Food Service Supply Chains,Agentic AI-Driven Multi-Cloud Big Data Architecture for Predictive Demand, Credit Risk, and Inventory Financing in National Food Service Supply Chains.Abstract
A multi-cloud big-data architecture delivers predictive services for demand forecasting, credit risk modeling, and supply chain inventory financing. Strategic partnerships provide a comprehensive range of data for a national food service domain, supported by data governance and management protocols. Predictive services facilitate collaborative demand reconciliation across heterogeneous cloud environments, deepening data interaction among competing firms, while optimizing Steering Committee members’ inventory levels and exposure to credit risk from suppliers, distributors, and retailers. Agentic Artificial Intelligence, integrating the principles of Autonomous Analytics and a multi-agency governance framework, performs the groundwork for decision signals regarding all three services.
While agentic artificial intelligence (AI) enables autonomous data preparation, predictive analytics, and deep learning, it does not remove humans from the loop. Instead, agentic AI facilitates deeper human-AI collaboration across the multi-cloud big-data architecture, with the agentic AI agents charged with examining results and providing interpretable explanations. Although solutions are focused on a national food service supply chain formed by the collaboration of food manufacturers, distributors, and both commercial and governmental businesses, the framework is readily applicable to other large supply chains requiring data preparation, predictive demand signals for collaborative reconciliation, risk assessment, and inventory financing facilitation.
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