Supply chains don’t fail at the node. They fail at the connection.

Global supply chains face significant vulnerabilities due to their complex, interconnected nature, with disruptions often stemming from lesser-known, tier 3 suppliers that can halt production unexpectedly. While data exists to mitigate such risks, many organizations lack systems that efficiently connect and transform this data into actionable business intelligence. The integration of Databricks' lakehouse architecture with Neo4j's graph database capabilities offers a solution by allowing supply chain managers to traverse and analyze connected data swiftly, addressing critical questions about inventory, supplier risks, and alternative sourcing. Graph algorithms enhance this approach by identifying suppliers whose failure could have widespread impacts and uncovering hidden dependency clusters. A dual architecture of Databricks and Neo4j facilitates real-time analysis and decision-making, enabling faster responses to disruptions and more robust supply chain resilience. This system is exemplified by a scenario where supply chain disruptions are rapidly assessed and resolved using a combination of operational data and graph intelligence, providing a significant advantage over traditional, manual investigation processes.