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- 1. Proprietary + Confidential Building Digital Twins with Google Cloud and Neo4j Christopher Upkes, Neo4j Simon Floyd, Industry Director, Manufacturing, Google Cloud June 7, 2022. GRAPHCONNECT 2022 +
- 2. Proprietary + Confidential Table of Contents Trends and opportunities The Digital Thread Top use cases Google Cloud approach Our solutions with Neo4j Building the Graph for the Digital Thread 01 02 03 04 05 06
- 3. Proprietary + Confidential Trends & Opportunities Electrifying everything for sustainability, compliance, and new levels of reliability. Activating wearable computing with private 5G networks Using AI when human cognition is insufficient or low-value add Creating revenue streams over the lifetime of product ownership Building digital resilience with a manufacturing ontology Connecting everything with the digital thread
- 4. Proprietary + Confidential The Digital Thread: connecting digital twins and data throughout the product lifecycle Manufacturing Operations Supply Chain & Logistics Products & Customers
- 5. Proprietary + Confidential OWNERSHIP IoT PPM PROTOTYPING PLM ENGINEERING DMS SALES CRM OMNICHANNEL FSM MAINTENANCE ERP SOURCING MES PRODUCTION Data Cloud for Manufacturers AI Analytics Predictions Visualization Collaboration REMANUFACTURING MES Optimal inventory placement and promotions. Customer personalization and upsell selling. Product enhancement and digital services. Intelligence for design and innovation. Supply chain risk mitigation and optimization. Manufacturing and quality improvement. Intelligent Products Manufacturing Data Engine Supply Chain Twin/Pulse … Digital Thread can solve for complex, data intensive use cases across the value chain
- 6. Proprietary + Confidential Simulation NPI and PPM So ware requirements Product de nition Use cases diagrams Product structure Suppliers / AML / AVL So ware development Design definition EXAMPLE CHALLENGES We’re solving for context and meaning in the connection between data types. Features: relationship between the market requirements, and product requirements. Manufacturability: relationship between design, procurement and supply chain constraints. Functionality: relationship between use cases, hardware capabilities, and software operation.
- 7. Proprietary + Confidential Our solutions connect key data from the field to the enterprise and enable advanced analytics and AI Products Connected, personalized digital experiences with AI powered features, analytics and commerce. Intelligent Products Essentials Create new or update capabilities with AI or ML to enhance the ownership experience and enable monetization. Customer 360 Create customer-centric omnichannel and personalized product experiences backed with advanced analytics. Interaction Insights Manufacturing End-to-end connectivity from shop floor to cloud with AI to reduce waste, increase quality and increase productivity. Visual Inspection AI Use AI to identify cosmetic defects and assembly conformance with high accuracy. Manufacturing Data Engine Connect and analyze factory equipment and processes at scale, optimize operations, e.g., predictive maintenance. Supply Chain Build a digital representations of physical supply chains by organizing and orchestrating critical data. Supply Chain Pulse Empowers business users to manage end to end supply chains in real time via visibility, alerting, collaboration and using AI. Supply Chain Twin Digital representation of the physical supply chain that can be used for planning and operations decisions. Shipment +48 hours vs. plan Firebase Cloud for Marketing Vertex AI BigQuery Looker Cloud Bigtable Apigee API Platform Cloud Storage Visual Inspection AI Dataflow Pub/Sub Graph Database Management System
- 8. Proprietary + Confidential The top use cases that can be solved with a digital thread Full Context Decision Making Knowledge Transfer Design, Supply or Manufacturing Variance Impact and influence of people, parts, process Warranty Claim Validity Product Liability Authenticity Verification Recall Resolution Analyze information with context, relationships, and impact Gain an understanding of a product through the data Quickly determine the difference between products; parts, suppliers Quantify and visualize the impact of people, parts or processes Determine if a warranty claim is valid or fraudulent Trace the root cause of a liability issue Identify if a product is authentic or if the parts are OEM Perform rapid what-if scenarios with data context and access
- 9. Proprietary + Confidential Proprietary + Confidential Neo4j graph database applications in Discrete Manufacturing Manufacturing Digital Twin Analyze Bill of Materials for compliance, supplier management, counterfeit parts, lead times, parts life cycles, etc. Supply Chain Twin Optimize the flow of goods, uncover vulnerabilities and boost overall supply chain resilience. Product 360 Personalize experiences for the complete customer life cycle
- 10. Proprietary + Confidential Building the Graph for the Digital Thread
- 11. Proprietary + Confidential Graph Model Comparison Master Graph ID Management Context Graph ● Untrusted or incomplete sources ● All attribution included in the graph ● Source of record ● Trusted and complete sources ● Bipartite graph (source IDs and clues) ● Whole graph process ● Trusted and complete sources ● Attribution required for traversal in graph ● Traversal entrypoint
- 12. Proprietary + Confidential The Context Graph In a Context Graph, unlike a Master Data Graph, key data connects disparate trusted sources, ensuring context is maintained as we traverse the graph. Often, common business tasks involve retrieval of information from numerous business applications. This requires management of multiple accounts and logins and more importantly, understanding of multiple application interfaces. Trusted Source Trusted Source Trusted Source Key Data
- 13. Proprietary + Confidential Discrete Manufacturing Context In At the center of the Manufacturing Digital Twin is the part community. This key structure connects trusted sources and ensures context as we traverse the digital thread. As an example, EBOMs, MBOMs and Batch Trees are all connected to the part community. Other business entities, such as engineers, vendors, and machinists and artifacts such as POs and QA documents are connected to the BOM structures. Engineering Manufacturing Procurement Part Community
- 14. Proprietary + Confidential Place Image Here Graph Communities ● Consist of connected trees ● Center of community generates influence ● Community outskirts are influenced ● Traverse community to understand influence
- 15. Proprietary + Confidential Place Image Here Part Communities
- 16. Proprietary + Confidential Place Image Here Part Community Model
- 17. Proprietary + Confidential The Flow of Influence on the Thread As we follow the thread from our anchor point, we travel through the community structures where we realize the influence as “pull” on the thread..
- 18. Proprietary + Confidential Use Case: Knowledge Transfer Imagine you are brand new to the engineering team at that builds sophisticated technology for your consumers. The assemblies you work on have been in production for years and some of the engineers that worked on previous versions of the product have retired or moved on.
- 19. Proprietary + Confidential Use Case: Knowledge Transfer With your Manufacturing Digital Twin, you can build your new EBOM, submit it to the graph and follow then follow the digital thread. As we traverse the thread, we can see that we are able to reach a recall through our connectivity to a particular MBOM Version and it’s associated Quality Control Artifacts. Because there is a path between the new EBOM and a recall, we can analyze the individual nodes that the BOMs are composed of and determine the repeated design flaw.
- 20. Proprietary + Confidential Place Image Here And Suddenly…Centrality
- 21. Proprietary + Confidential Place Image Here And Suddenly…Centrality
- 22. Proprietary + Confidential Centrality is a Spotlight ● People ● Vendors ● Artifacts ● Assemblies ● Parts
- 23. Proprietary + Confidential Path Analysis: An Examination of the Thread The length of our thread can help us to better understand, compare and contrast the complexity and efficiency of individual processes. Some traversals return paths of unexpected length, usually longer than expected.
- 24. Proprietary + Confidential Path Analysis: An Examination of the Thread Understanding and analyzing the expansion of our traversal provides invaluable insight. By understanding the number of unique paths available for a particular thread we can better understand complexity and the associated risk.
- 25. Proprietary + Confidential Things to Remember ● Try and solve problems with every source addition ● Architecture must support ad-hoc queries ● Data should be loaded in near-real time ● Embrace “schema-less” ● The Context Graph is typically a cache
- 26. Proprietary + Confidential Thank you. +