Spurred on by the pervasiveness of the Internet of Things (IoT), the buzz around digital twins has exploded. A digital twin is a digital model of a real system, allowing us to simulate processes and predict problems with the system in real time.
Knowledge graphs are adept at mapping complex, interconnected data and maintaining high performance with vast volumes of data. Their relationship-centric approach makes them the perfect technology for building a digital twin.
In this webinar, you’ll learn how to apply this technology for supply chain management. Companies that use knowledge graphs for their digital twin can realize substantial benefits, including an end-to-end view of processes, agile problem resolution, and improved operational efficiency. Join us to find out:
- What a knowledge graph is and how to use it for your digital twin
- How to identify bottlenecks and perform “what if” scenarios in the knowledge graph
- Examples of digital twins being used for data discovery, bill of materials, and predictive maintenance in the supply chain
Don’t miss this 60-minute session with Dr. Maya Natarajan and Dr. Michael Moore, where you’ll get a demo of how to build a large-scale digital twin capable of unifying data across disparate sources, delivering rich analytics, and supporting near real-time monitoring of critical assets.
Dr. Maya Natarajan is Neo4j’s Sr. Director 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.
Michael Moore leads our global Partner enablement programs and is helping Neo4j partners build compelling services and products leveraging Neo4j’s industry-leading graph data management platform.
Trained as a scientist, Michael has over 20 years of experience delivering high-value enterprise analytics solutions and portfolios for startups, agencies, and Fortune 100 companies. Michael has been working with Neo4j graph databases and graph analytics since 2013, and in his most recent role, Michael established EY's national consulting practice in Enterprise Knowledge Graphs and AI. At EY, Michael architected and delivered full-stack graph applications for EY clients across multiple industries and use cases, including customer 360°, recommendation engines, risk analytics, KYC, fraud detection, anti-money laundering, energy trading, digital twins, capital projects total cost visibility, master data management, data lineage, and enterprise data fabrics. Prior to EY, Michael was at Microsoft where he managed a global portfolio of over 65 applications in the connected sales and marketing space for Microsoft’s B2B and partner ecosystems.
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