The COVID-19 pandemic has caused fundamental changes in consumer behavior, supply chains, and routes to markets. Only knowledge graphs that natively capture and store vast amounts of data relationships can help us outmaneuver uncertainty and thrive.  

Knowledge graphs are adept at mapping complex, interconnected data and maintaining high performance with vast volumes of data. Their inherent relationship-centric approach enables companies to better manage, read, visualize, and analyze data. Graph data science uses the predictive power of relationships for analytics and machine learning that play an important role in logistics, forecasting, and production planning.  

With a combination of knowledge graphs and graph-based analytics, supply chain companies can bring complex products to market on schedule, proactively take action to remediate potential issues, and mitigate risks through greater end-to-end visibility.


In this 45-minute session, you’ll learn:

  • What a knowledge graph is and how it plays a salient role in supply chains
  • How knowledge graphs and graph data science analytics are essential for a robust and flexible supply chain
  • How various global companies are using graph technology from product 360˚ to predictive maintenance and for “what-if” analyses for their supply chains

Join us to hear how Lockheed Martin utilizes a knowledge graph for a 360˚ view of their entire product lifecycle. We’ll also look at how Caterpillar combines knowledge graphs and machine learning for predictive maintenance and improving equipment lifespan. Finally, we will discuss the U.S. Army’s use of knowledge graphs for what-if analysis to enable a more agile supply chain.

Amy Hodler
Director, Neo4j Graph Analytics & AI Programs, Neo4j

Amy Hodler is the Graph Analytics & AI program director at Neo4j. She loves seeing how the community uses graph analytics to reveal structures within real-world networks and infer behavior.


Amy is the co-author of the O'Reilly book Applied Graph Algorithms in Apache Spark and Neo4j, published in early 2019 and updated July 2020.


Dr. Maya Natarajan
Program Manager, Knowledge Graphs, Neo4j

Dr. Maya Natarajan is Neo4j’s Program Manager for Knowledge Graphs. She is passionate about bringing different technologies together to solve complex problems. At Neo4j, Maya is championing the use of knowledge graphs to bring context to various systems. Maya has positioned technologies from Blockchain to Predictive & User-Based Analytics to Machine Learning to Deep Learning to Search to BPM and beyond in a myriad of industries at various small and large companies.

 

Maya started her career in the biotechnology area where she was in R&D focusing on cardiovascular drugs, and she has five patents to her name.


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