Graphs in Automotive and Manufacturing - Unlock New Value from Your Data
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Joe Depoe, Neo4j
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Graphs in Automotive and Manufacturing - Unlock New Value from Your Data
- 1. Graphs in Automotive and Manufacturing Joe Depeau Sr. Presales Consultant, UK 29th May, 2018 @joedepeau http://linkedin.com/in/joedepeau
- 2. • Introduction to Graphs and Neo4j • Automotive & Manufacturing Data Overview • Use Cases • Supply Chain Management • Warranty Analytics • Customer 360 View • Knowledge Graph • Others … • Q&A 2 Agenda
- 3. Introduction to Graphs and Neo4j 3
- 4. Relational vs. Graph Databases 4
- 5. Graphs in the Age of Connections 5
- 6. 6
- 7. 7 Car name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Anatomy of a Property Graph Database Nodes • Represent the objects in the graph • Can be labeled Relationships • Relate nodes by type and direction Properties • Name-value pairs that can go on nodes and relationships. LOVES LOVES LIVES WITH Person Person
- 9. 9 Some Examples of Typical Automotive Data Event DataProduct Data Customer DataOrganizational Data 3rd Party Data Documentation Facilities Processes Systems and Databases KPIs and Reports Personal Data Customer Relationships Documentation Processes Product Details Product Hierarchy Bills of Materials Sensor / Telematics Data Warranty Claims Customer Contact Social Media Market Data Organisational Hierarchy Corporate Data Supply Chain Data Supplier Data Logistics Data Inventory Data Dealer Data
- 11. Supply Chain Example Graph Organisational Data Customer Data Product Data Event Data 3rd Party Data Created using https://neo4j-arrows.appspot.com/ Supply Chain Data 11
- 12. 12 Supply Chain Graph Uses • Can I use the graph to help me improve my ordering and procurement processes? • Can the graph help me save money on orders? • Can I optimize my inventory using a graph? • Can the graph help me with comparative analysis of my suppliers and their products? Yes! Yes! Yes! Yes!
- 13. Organisational Data Customer Data Product Data Event Data 3rd Party Data Warranty Analytics Example Graph 13 Supply Chain Data Created using https://neo4j-arrows.appspot.com/
- 14. 14 Warranty Analytics Graph Uses • Can I find patterns in the graph indicating inappropriate claims or warranty fraud? • Can I use the graph to help predict future claims? • Can the graph help me manage my warranty and recall risk? • Can I use the graph to help me understand whether faults might be caused by supplier issues? Yes! Yes! Yes! Yes!
- 15. 15 Customer 360 View Example Gr Organisational Data Customer Data Product Data Event Data 3rd Party Data Created using https://neo4j-arrows.appspot.com/ Supply Chain Data
- 16. 16 Customer 360 View Graph Uses • Can I use the customer 360 view graph to improve my customers’ experience? • Can I use the customer 360 view graph to identify high Lifetime Value customers? • Can I use the customer 360 view graph to detect and prevent churn? • Can I use the customer 360 view graph to improve upsell and cross-sell? Yes! Yes! Yes! Yes!
- 17. 17 Organisational Data Customer Data Product Data Event Data 3rd Party Data Created using https://neo4j-arrows.appspot.com/ Supply Chain Data Knowledge Graph Exam
- 18. 18 Knowledge Graph Uses • Can I use a knowledge graph to improve my products and services? • Can a knowledge graph help improve my product time- to-market? • Can I use a knowledge graph for customer-facing use as well? Yes! Yes! Yes!
- 19. 19 Other Automotive and Manufacturing Graph Use Cases • Identity and Access Management • Infrastructure and Network Management • Master/Meta-data Management • Regulatory Compliance (i.e. GDPR)
- 21. 21 Case Study : Volvo uses Neo4j to connect every component to customer needs
- 22. 22 Case Study : U.S. Army uses Neo4j for Bill of Materials and Supply Chain Management
- 23. 23 Case Study : Schleich uses Neo4j for integrated product data management throughout the value chain
- 24. 24 Case Study : NASA uses Neo4j for a “Lessons Learned” database used by engineers and scientists MISSION TO MARS
- 25. 25 Case Study : Lockheed Martin uses Neo4j for Product 360
- 27. Q & A 27
- 28. 28 Thank you!
Editor's Notes
- This slide builds from left to right with animations. Organisational Data : data you hold within your company, and is generally only accessible within your company (or in limited ways externally). This includes your corporate documentation, data about your processes, data about your employees and organizational hierarchy, KPIs and reports, and your IT infrastructure (systems and databases). This information is often very siloed. Customer Data : this is all the data you have on your customers, whether they are corporate or individual customers. This includes their address, personal data, and documents related to them (i.e. contracts). You may also have information about relationships between your customers (either between individuals, or between individuals and companies). This may live in different systems, or be centralized in your CRM system. Products and Services Data : information about all of your products including any documentation and processes specific to these products, the details about different products, a product hierarchy (i.e. brand, vehicle line, different options available), and bills of materials related to your products. There could be different types of bills of materials – for example Manufacturing BOM, Engineering BOM, Configurable BOM, etc. You may also want to store different snapshots of a BOM … the generic design, the BOM for a product as built, and iterations of that as it’s been repaired/upgraded over time. Event Data : this is data about specific events. This could include sensor or telematics data, individual claims, and customer contacts (i.e. call centre). 3rd Party Data : data you bring in from outside, like social media data, market data, press releases, news stories, studies and surveys, etc. Data about and from your dealer network is also included here (Customer data from your dealers might also be classified here, if you want to be specific). This is not an exhaustive list of data categories, it’s more indicative of the types of data a manufacturer / automotive company could be working with.
- The Knowledge Graph example graph is very ‘green’ as it’s mostly about improving access to data for customers (this is a retail customer example). An internal knowledge graph would probably be more red, or a mix of red and green. This slide illustrates the types of data you might look at to build your knowledge graph. This customer has several products already, so the knowledge they might be seeking could need to be quite specific to the products they have. If they are looking for information about products they don’t have, then going to the general documentation is probably appropriate. Three is also information about the teams who manage specific products, in case they need to reach a real person. This knowledge graph could be used to improve search engine capability/results, as well as power a self-service support chatbot.
- Can I use knowledge graph to improve customer experience? Yes – you can use a Knowledge graph to help drive effective self-service support, for example, using a search engine. Can I use the knowledge graph with chatbots as well as search engines? Yes – the eBay shopbot example is a good one for this. Chatbots powered by a knowledge graph can be more effective. Can I use a knowledge graph internally as well? Yes – this example was for an external-facing knowledge graph, but an internal one (again, using a search engine or a chatbot) could help your employees as much as an externally-facing one could help customers!
- These are more industry-agnostic but still applicable to Manufacturing / Automotive.
- The real power of Neo4j isn’t in working with small graphs like the examples we’ve seen. It’s when your data looks like this! Being able to work with data and spot patterns at scale in real time is critical to these Banking use cases, and only Neo4j can bring that power to your business.