Supply Chain Risk Predictions with Neo4j Graph Data Technology
summary
Graph model encodes ports as nodes with congestion, berth efficiency and capacity attributes and shipping lanes as weighted edges with disruption probabilities; data ingested via Cypher from the U.S. Bureau of Transportation Statistics Port Performance Program, World Port Index and trade databases into Neo4j. Queries compute a combined disruption score across 77 major trade roads, identify central chokepoints, and run emergency rerouting scenarios that return results in seconds; analysis flags regions with average delays of 32 vs 12 hours and ranks alternatives by capacity and risk.
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chapters (12)
- 0:00Introduction to supply chain graphs
- 1:52Graphs vs tables performance
- 2:35Supply chain data modeling
- 4:00Relationships in shipping networks
- 5:46Importance of risk prediction
- 6:50Highest risk road identification
- 8:14Port performance comparison
- 9:22Global network capacity analysis
- 10:28Capacity and performance tradeoffs
- 11:13Emergency rerouting scenarios
- 12:45Emergency planning benefits
- 13:27Series of follow-up videos
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