Graph Data Science for Supply Chain, Logistics, and Transportation

This repository contains worked examples for applying Neo4j Graph Data Science to supply chain, logistics, and transportation problems.

Each subdirectory contains its own analytical workflow and worked example (or series of worked examples).

• modeling-and-visualization-c2k provides materials to replicate the analysis in part I of the Graph Data Science for Supply Chains Blog Series which focuses on

  1. Graph data modeling and ingest for supply chains
  2. Visualising and exploring supply chains processes
  3. Identifying and inspecting critical stages and interdependent process using low-code/no-code graph algorithms

• Metrics-and-performance-analytics-c2k compliments part II of the Graph Data Science for Supply Chains Blog Series and focuses on understanding and analysing supply chain performance using graph.

• route-optimzation-c2k contains examples of using Neo4j Graph Data Science for route optimization and recommendation

• transportation-network-london-underground demonstrates how to use Neo4j Graph Data Science with Neo4j Bloom to explore anf better understand a transportation network. The same methods can also be used for supply chain and logistics networks.