SOURCE-LINKED INTELLIGENCE
A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics
Efficient utilization of supply chain analytics for decision making remains a significant challenge for planners, as critical tasks such as database querying, key performance indicator (KPI) analysis, demand forecasting, and performance diagnosis require heterogeneous expertise spanning data engineering, operations research, and domain knowledge. In this work, we propose an agentic system for supply chain analytics that bridges the gap between business decision-making and technical expertise, where a coordinator agent interprets user intent and delegates sub-tasks to specialized agents. The sy
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-11T21:59:30.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.