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公式動画＆関連する動画 [How Agentic AI Solves the $184B Supply Chain Crisis | MongoDB]
Visit the Solution Library to learn more: https://mdb.link/zsz5DJhc020
GitHub repo: https://github.com/mongodb-industry-solutions/multiagent-supply-chainegister
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Global supply chains face an estimated $184 billion annual loss due to instability, weather events, and volatile costs. While generative AI adoption in logistics is growing rapidly, traditional reactive dashboards are no longer enough. Modern systems require autonomous agentic AI that actively resolves disruptions in real time.
This video walks through an agentic AI architecture built with three specialized AI agents grounded in a unified data layer powered by MongoDB Atlas. Learn how consolidating vectors, spatial coordinates, and operational data within a single document eliminates data fragmentation and network latency.
Key Highlights:
- The Root Cause of AI Bottlenecks: How traditional relational database schemas create data fragmentation and latency.
- Data Locality with MongoDB Atlas: Unifying vectors, geospatial data, and operational records to act as persistent memory for agents.
- Disruption Analysis Agent: Converting unstructured QA text reports into actionable vectors using MongoDB Atlas Vector Search.
- Supply Chain Planning Agent: Evaluating carrier availability, route coverage, transit times, and pricing with a human-in-the-loop workflow.
- Risk Analysis Agent: Recalculating dynamic Value at Risk (VaR) based on custom risk weights, historical weather events, and border crossing data.
Chapters
00:00 - The $184B Global Supply Chain Challenge
00:37 - Moving Beyond Dashboards to Agentic AI Systems
01:14 - Eliminating Data Tax with MongoDB Atlas Data Locality
01:36 - Agent 1: Disruption Analysis with Vector Search
02:17 - Agent 2: Supply Chain Planning & Route Optimization
02:45 - Agent 3: Risk Analysis & Dynamic Value at Risk
03:28 - Architecture Summary & Solution Library Link
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