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The Case for Graphs in Supply Chains
This document discusses using graph databases and graph modeling for supply chain management. It begins by explaining how supply chains are naturally connected networks that can be represented as graphs. It then outlines four key steps for innovating with connected data: data capture, data modeling and storage, processing and analytics, and applications and insights. Several examples are provided of how graph queries, algorithms and analytics could be applied to problems in supply chain management. The document promotes modeling the entities and relationships in a supply chain as a graph to allow for more sophisticated analysis that accounts for network effects and connections between entities. It positions graph databases as enabling more effective supply chain optimization and risk mitigation in the global economy.
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Introduction to the significance of graphs in supply chains, emphasizing connectivity and the risks of failure to adapt.
Exploration of how successful organizations leverage graph technology to navigate complex, connected environments.
Illustration of supply chain components as a graph, outlining the advantages of graph technology over traditional systems.
Discusses innovative approaches to data capture, modeling, and analytics within the supply chain context.
Key elements and relationships in modeling a supply chain graph and the importance of structure for insights.
Focus on the core algorithms for shortest path detection in supply chains and their applications.
Examination of centrality measures and similarity algorithms in analyzing nodes within supply chains.
Overview of analytics pipelines, AI/ML applications, and tools necessary for implementing graph solutions.
Details on a training program designed to educate about graph usage in supply chain contexts with practical approaches.
Conclusion of the presentation, extending an invitation for further communication.
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The Case for Graphs in Supply Chains
- 1. The Case forGraph in Supply Chain Alessandro Svensson Head of Neo4j Innovation Lab A look under the hood when innovating with graphs July 28, 2020
- 2. Neo4j Innovation Lab Everythingis Naturally Connected Your Organization Context of Behavior Logistics DNA-strings Customers Supply Chain Health Causes Insurance Fraud Purchase Patterns People Events Proteins Traffic Light Patterns Weather Conditions Materials Systems of Records IT-infrastructure Home appliances Knowledge
- 3. Neo4j Innovation Lab Theorganizations that understand and leverage how everything is connected in the context of their domain, enjoy tremendous opportunity
- 4. Neo4j Innovation Lab Failureto see or shift to adapting your organization to how everything is connected, means your operating at a deficit — and puts you at risk of disruption
- 5. Neo4j Innovation Lab CaseStudy: The Consumer Web C 34,3%B 38,4%A 3,3% D 3,8% 1,8% 1,8% 1,8% 1,8% 1,8% E 8,1% F 3,9%
- 6. Neo4j Innovation Lab Theconnected world requires a new way of thinking
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- 9. Neo4j Innovation Lab Thecompanies that succeed in the connected world do so with graphs
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- 11. Supply Chain isa Graph Neo4j Innovation Lab
- 12. • Customers • Employee •Suppliers • Materials • Products • Plants • Distribution Centers • Shipments • Etc… Supply Chain is a Graph Neo4j Innovation Lab
- 13. Why Supply ChainMatters Supply Chain Management with Graphs • In the global economy, companies must stabilize their supply chains by working with multiple suppliers, boosting inventories, diversifying customers, and investing in omni-channel distribution. • Effective supply chain management is crucial to mitigate both supply and demand side risk and… • …ultimately as a strategy to optimize revenue.
- 14. Data represented asin a relational database Supply Chain Management with Graphs Traditional technology and optimization models cannot account for chain-reactions triggered by major disruptions, because of its inability to handle connections between entities sufficiently. Why graphs?
- 15. Data represented asa graph Supply Chain Management with Graphs Traditional technology and optimization models cannot account for chain-reactions triggered by major disruptions, because of its inability to handle connections between entities sufficiently. Why graphs?
- 16. How to innovatesuccessfully in the in the age of connected data?
- 17. 1. Data Capture 2.Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights Consider these 4 steps: (Collecting the most relevant data for the use case) (Choosing the right technology for the right job) (Queries and Algorithms) (Tangible, end-results)
- 18. 1. Data Capture2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights Because of its connected nature, supply chain is a massive data challenge
- 19. What are theessential data- points and behaviors relevant to your use case to capture? 1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights Demand Supply Manufacturing Warehousing Order Fulfillment Transportation Weather Geospatial Third Party On Prem Data Lakes Data Warehouse Cloud IT infrastructure
- 20. Disparate Silos Cross-Silo Connections PropertyGraph Model 1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights Demand Supply Manufacturing Warehousing Order Fulfillment Transportation Weather Geospatial Third Party
- 21. 1. Data Capture2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights Modeling a Supply Chain Graph • Carrier • Shipping Site • Material • Material Group • Location • Customer • DC • Plant • Shipment • Delivery ~16 in the model • ON_DELIVERY • SHIP_CARRIER • SHIP_MODE • CONTAINS • IS_IN_GROUP • SHIPPED_TO • SHIPPED_FRO • SOURCE • HAS_LOCATION Structural Elements Behavioral Elements Relationship Types Temporal Elements • TimeTree — Facilitates point in time queries and versioning
- 23. Query (e.g. Cypher/Python) Youknow what you’re looking for and make a decision in real-time Local Patterns Graph Algorithms You’re learning the overall structure of a network, updating data, and predicting Global Computation 1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights Input data
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- 25. Supply Chain Software Prototype (SupplyChain Software) Sign in What’s going on under the hood?
- 26. What’s going onunder the hood? (Conceptual) Prototype (Supply Chain Software) Detecting the shortest, cheapest path between a manufacturing plant (in green) and a customer (in blue) in a supply chain. Shortest Path Algorithms
- 27. Prototype (Supply Chain Software) Detectingthe shortest, cheapest path between a manufacturing plant (in green) and a customer (in blue) in a supply chain. Shortest Path Algorithms What’s going on under the hood? (Conceptual)
- 28. Detecting the shortest,cheapest path between a manufacturing plant (in green) and a customer (in blue) in a supply chain. Shortest Path Algorithms Weights to relationships that indicates cheapest paths Prototype (Supply Chain Software) What’s going on under the hood? (Conceptual)
- 29. Detecting the shortest,cheapest path between a manufacturing plant (in green) and a customer (in blue) in a supply chain. Shortest Path Algorithms Prototype (Supply Chain Software) 🔥 Weights to relationships that indicates cheapest paths What’s going on under the hood? (Conceptual)
- 30. Prototype (Supply Chain Software) Calculateall possible routes PATH_0 (green) is the shortest path through the supply chain nodes. PATH_1 (orange) and PATH_2 (blue) are the next most cost-effective paths. What’s going on under the hood? (Conceptual)
- 31. Prototype (Supply Chain Software) Calculateall possible routes PATH_0 (green) is the shortest path through the supply chain nodes. PATH_1 (orange) and PATH_2 (blue) are the next most cost-effective paths. “Best” paths based on relevant criteria What’s going on under the hood? (Conceptual)
- 32. Prototype (Supply Chain Software) CentralityAlgorithm Graph depicting the centrality scores of nodes based on incoming and outgoing shipments. What’s going on under the hood? (Conceptual)
- 33. Prototype (Supply Chain Software) CentralityAlgorithm Graph depicting the centrality scores of nodes based on incoming and outgoing shipments. What’s going on under the hood? (Conceptual)
- 34. Prototype (Supply Chain Software) NodeSimilarity I.e. Jaccard similarity, overlap similarity, cosine distance, euclidean distance etc. Centrality Algorithm Calculating centrality scores What’s going on under the hood? (Conceptual)
- 35. Under the hood Applications INPUT DATA + AnalyticsPipeline + AI/ML-pipeline 1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights Dashboards Route-planningExploration
- 36. Under the hood Applications INPUT DATA ++ Analytics Pipeline (AI/ML-pipelines) It’s what happens under the hood that determine the potential of your applications. If you have a connected data problem — make sure to solve it with graphs! 1. Data Capture 2. Data Modeling & Storage 3. Processing & Analytics 4. End user-applications & Insights
- 37. If you wantto learn more…
- 38. The Applied UseCase Training Program Learning graphs in the context of a use case Brought to you by: Neo4j Innovation Lab & Neo4j Graph Academy
- 39. 1. Data Capture2. Data Model & Storage 3. Processing & Analytics 4. Applications & Insights Micro Batches Micro Batches Real-Time Transactions Graph Algorithm Feedback Cypher Workbench Modeling & Code Gen Hop ETL Apache Kafka Event Streams GRAND Stack Dashboard Bloom & Linkurious Graph Visualizations Neo4j BI Connector SQL BI Tools Neo4j Browser SQL BI Tools DBC HDFS ETL CSV JSON Neo4j Graph Database Graph Data Science Library Cypher Keymaker Analytics Pipelines Micro Batches Micro Batches Real-Time Transactions Graph Algorithm Feedback Cypher Workbench Modeling & Code Gen Hop ETL Apache Kafka Event Streams GRAND Stack Dashboard Bloom & Linkurious Graph Visualizations Neo4j BI Connector SQL BI Tools Neo4j Browser SQL BI Tools DBC HDFS ETL CSV JSON Neo4j Graph Database Graph Data Science Library Cypher Keymaker Analytics Pipelines Neo4j Supply Chain Solutions Framework
- 40. 1. Data Ingestion 2.Data Model & Storage 3. Processing & Analytics 4. Applications & Insights Micro Batches Micro Batches Real-Time Transactions Graph Algorithm Feedback Cypher Workbench Modeling & Code Gen Hop ETL Apache Kafka Event Streams GRAND Stack Dashboard Bloom & Linkurious Graph Visualizations Neo4j BI Connector SQL BI Tools Neo4j Browser SQL BI Tools DBC HDFS ETL CSV JSON Neo4j Graph Database Graph Data Science Library Cypher Keymaker Analytics Pipelines Neo4j AML Solutions Framework
- 41. Keymaker Analytics Pipelines Cypher Workbench Modeling &Code Gen Hop ETL Apache Kafka Event Streams Neo4j Graph Database Graph Data Science Library Cypher GRAND Stack Dashboard Bloom & Linkurious Graph Visualizations Neo4j BI Connector SQL BI Tools Neo4j Browser SQL BI Tools The Property Graph Model The Case for Graphs Curriculum Module Graphs in Supply Chain Module Graph Queries & Algorithms Module Building Applications
- 42. Curriculum Module The Case forGraphs Module Graphs in Practice Module Solutions Framework • The Case for Graphs in Supply Chain • Property Graph Model • Money Queries • Modeling • Data Loading Techniques • Cypher • Graph Algorithms • Supply Chain Solutions Framework • Keymaker Analytics Pipeline • Application building examples
- 43. About the program Atraining program that teaches graphs in the context of a use case What? Enterprise developer & architect teams at large/midsize organizations Who should participate? 12 hour instructor lead curriculum combined with self- paced assessments and exercises. How? Fast, and inexpensive, way to learn how to apply graphs from best practice in preparation for a more robust POC. Why? Use Case Training POC
- 44. Please feel freeto reach out to me for more information! alessandro.svensson@neo4j.com Alessandro Svensson, Neo4j
- 45. Thank you forlistening! " alessandro.svensson@neo4j.com Alessandro Svensson, Neo4j