Agentic AI and Big Data Framework for Real-Time Wholesale Food Supply Chain Optimization
Keywords:
Wholesale Perishable Food Supply Chains, Supply Chain Optimization, Agentic Decision-Making Architecture, Sustainable Supply Chain Systems, Privacy-Preserving Data Ingestion, Distributed Data Ecosystems, Agentic Orchestration Layer, Agent-Based Modelling, Event-Driven Orchestration, Trigger-Action Workflows, Social Computing Protocols, Big Data Analytics, Real-Time Demand Forecasting, Inventory Allocation Optimization, Path-Dependence Theory, Complex Adaptive Systems, Human-Computer Interaction Design, Cloud And On-Premise Integration, Polybase Big Data Technologies, Value-Sharing Sentiment Analysis.Abstract
Supply chain optimization in the wholesale perishable food sector is complicated by the challenge of minimizing waste and profit loss from missed demand. An agentic, sustainable, and privacy-preserving architecture to ingest private data from distributed members is outlined, which supports the orchestration of decision-making workflows either directly in Cloud frameworks or privately in members’ premises. The agentic orchestration layer integrates an Agent-Based Modelling architecture with event-driven and trigger-action orchestration. Social-Computing protocols enable external agent coordination.
The framework uses Big Data modelling and Agents to support six decision-making workflows for real-time optimization of wholesale perishable food supply chains. These workflows forecast total demand from private Big Data events and optimize inventory allocation, sensing, and fulfillment. Path-Dependence Theory, complex adaptive systems, and Human-Computer Interaction design the architecture. Built with Big Data technologies and Polybase integration of SQL Server, Azure SQL Data Warehouse, and Hadoop, the framework seeks to stimulate supply chain member investment through value-sharing sentiment analysis.
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