Beyond Joins – Supply Chain Optimierung & Next-Gen AI mit Graphen

EBCONT

Join us at EBCONT Connect to learn why relational data hits its limits in supply chains—and how graphs and GraphRAG enable smarter AI.

Techno Z | Gebäude 3

Put an end to rigid SQL joins in complex supply chains. Without buzzwords, we show how graph databases bring clarity to deep dependencies and take your AI projects (GraphRAG) to the next level. Your Highlights:<ul><li>Performance check: Why graphs outperform SQL for highly connected data</li><li>Supply chain: Finally get a grip on risks and impact analyses</li><li>Smart AI: Graphs as a factual anchor for reliable LLM answers</li><li>Hands-on: Direct comparison of models and queries</li></ul> Learn how to place the graph as an intelligence layer on top of your existing IT landscape. Register now and exchange ideas with experts during networking!

Agenda

Doors Open

From the relational model to graphs: why deep SQL joins become slow and hard to manage in connected supply chains, how the “join problem” emerges in questions like “who supplies the supplier of my supplier,” and why index-free adjacency lets graph traversals run in constant or linear time instead of exploding in complexity. A direct comparison of modeling a bill of materials in SQL versus a graph makes the core difference clear: in graphs, relationships are first-class citizens; in SQL, they are only metadata.

 Intro: The Paradigm Shift

Practical application: using graphs to bring transparency, compliance, and risk control to supply chains that function as networks, not linear chains. Examples include end-to-end traceability across bills of materials, simpler and faster queries compared to recursive SQL, and impact analyses that show which products are affected by disruptions. Graphs also expose single points of failure and enable route optimization based on cost, CO₂, and time.

Deep Dive: Supply Chain Management (SCM) with Graphs

Coffee Break

From hype to application: how graphs make AI smarter by adding structure and context to RAG. Vector-only search often misses relationships when answers depend on linking multiple facts. GraphRAG combines unstructured text with structured knowledge graphs, using the graph as memory and fact-checker to reduce hallucinations. In a supply chain scenario, vectors find documents about a strike, while the graph connects logistics providers, parts, products, and revenue impact—allowing the LLM to deliver a grounded, business-relevant answer.

Beyond Joins – Supply Chain Optimierung & Next-Gen AI mit Graphen

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Beyond Joins – Supply Chain Optimierung & Next-Gen AI mit Graphen

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