The COVID-19 pandemic has caused fundamental consumer behavior changes, supply chains, and routes to markets. We need to accelerate transforming fragile value chains with systems that leverage highly interdependent supply chains. 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 session, you’ll learn:
• What a knowledge graph is and how it plays a salient role in supply chain
• 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 3600 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 3600 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 US Army’s use of knowledge graphs for what-if analysis to enable a more agile supply chain.
• Maya Natarajan, Program Manager, Knowledge Graphs at Neo4j
• Amy Hodler, Director, Neo4j Graph Analytics & AI Programs at Neo4j