Modern supply chain networks exhibit complex interdependencies where localized disruptions can cascade through global logistics systems, creating substantial operational and financial impacts. Traditional risk assessment methodologies rely on linear models that fail to capture the interconnected nature of port-to-port relationships, trade flow dependencies, and multimodal transportation networks. This limitation results in reactive rather than predictive risk management approaches.

This talk will demonstrate a graph-based predictive analytics framework using Neo4j to model supply chain risk prediction. The constructed model enables sophisticated risk computation algorithms by representing maritime infrastructure as nodes with quantitative performance attributes (e.g., congestion indices, berth efficiency, infrastructure capacity) and modeling shipping routes as weighted edges with risk metrics (e.g., disruption probabilities, weather delays, piracy threats).

The implementation demonstrates major ports across countries connected by shipping routes, leveraging data from the U.S. Bureau of Transportation Statistics Port Performance Program, World Port Index, and international trade databases. Risk analysis algorithms include critical chokepoint identification, network centrality analysis, and emergency rerouting scenarios.

The presentation will demonstrate constructing a data model, discovering patterns for network analysis, and dynamic visualization techniques for risk assessment workflows.

Attendees will learn mathematical foundations of graph-based supply chain modeling, data modeling strategies for large-scale network construction, and algorithmic approaches to optimization problems in logistics networks. This session targets professionals working with network data, transportation optimization, and predictive modeling applications in supply chain domains.