Graph Data Science for Supply Chains – Part 2: Creating Informative Metrics and Analyzing Performance in Python discusses how to use Neo4j Graph Data Science and the Python client to calculate graph centrality and community metrics. The authors demonstrate how to run algorithms such as degree, betweenness, eigenvector, and Louvain community detection on a supply chain dataset. They interpret the results of these algorithms and show how they can be used in downstream statistical modeling to estimate their association with the probability of delays in business processes. The study finds that centrality scores are significant predictors of delays, with out-degree centrality having a positive effect and in-degree centrality having a negative one. The authors conclude by highlighting the potential of Neo4j Graph Data Science for supply chain analytics and look forward to exploring route optimization and what-if scenarios in the next part of the series.