Industry: Supply Chain

Knowledge graphs for supply chain.

Supply chain knowledge graphs that quantify recall exposure in hours instead of weeks, surface tier 3 concentration risks invisible to procurement dashboards, and give ESG reports fact level lineage back to supplier disclosures. Multi tier visibility, sanctions screening, and grounded buyer assistants on GS1, GDSN, and SCOR.

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Your supply chain is a graph. Your tools are treating it like a spreadsheet.

Direct Supplier Visibility Isn't Enough Anymore

Every disruption of the last five years (from pandemic port closures to single supplier semiconductor shocks) exposed the same gap: enterprises can see their direct suppliers, but cannot walk beyond them. When a tier 3 supplier goes offline, procurement finds out from the news.

A Graph Turns the Network Into Queries

A knowledge graph fixes this at the data layer. Suppliers, parts, products, shipments, tariffs, sanctions, and ESG disclosures all become nodes and edges. Recall exposure that took weeks to reconstruct takes seconds. Concentration risks that lived in a supply chain analyst's head become queries anyone can run.

Why AI Assistants Compound the Value

Once the graph exists, planners and buyers get grounded assistants: answers about supplier alternatives, past PO history, or ESG exposures cite the specific supplier scorecard or contract clause the answer came from. Same substrate, different retrieval layer.

Six proven supply chain use cases

Multi tier supplier visibility

Model your extended supply network (direct suppliers, their suppliers, and beyond) as a graph. Answer 'which of my products depend on a single tier 3 source' before that supplier disrupts.

•Discover tier 2 and tier 3 concentration risks invisible to procurement dashboards
•Map dual source and single source status at part level, not just supplier level
•Faster response when a geopolitical or ESG event hits a specific region

Part to recall traversal

When a component is recalled, trace it through every product, batch, and customer shipment in seconds: not the weeks a spreadsheet reconciliation takes.

•Recall exposure quantified in hours, not weeks
•Downstream customer notifications generated from graph queries
•Audit ready lineage for regulators and insurers

Sanctions and trade compliance screening

Transitive screening across suppliers, beneficial owners, and vessels catches indirect exposures that name only lists miss: critical under OFAC, EU sanctions, and export control regimes.

•Catch shell company and beneficial ownership evasions
•Continuous re screening as lists and ownership change
•Explainable results: the exact path that triggered a flag

ESG and Scope 3 lineage

Emissions, labour, and provenance data attached to nodes and rolled up through the network. Every reported ESG figure links back to the specific supplier disclosures that fed it.

•Defensible Scope 3 reporting against CSRD, SEC climate rules, and voluntary frameworks
•Supplier level intervention prioritization based on graph impact
•Buyer facing product level footprint queries

Demand signal propagation

A change in downstream demand or a disruption at one node propagates through the graph to show which suppliers and inventories are affected: before the ripple hits your production line.

•Earlier detection of bullwhip effects
•Automated re planning proposals grounded in real network topology
•Better inventory position decisions

GraphRAG for buyer and planner assistants

Ground an LLM assistant in the supply chain graph plus contracts, SOPs, and supplier scorecards. Answers cite the specific supplier, contract clause, or purchase order they used.

•Faster buyer research and RFQ preparation
•Grounded answers to 'why is this order late' and 'what are our alternatives'
•Reduced onboarding time for new planners

Frequently asked questions

How do you get multi tier data: suppliers don't share their suppliers.+

Combination of your direct data (POs, ASNs, contracts), supplier self disclosure programs, third party data providers (Interos, Everstream, Sayari, S&P), customs and bill of lading feeds, and inferred edges from public disclosures and news. The graph makes the inferred vs verified provenance of each edge explicit so you know how much to trust it.

How does this integrate with our ERP and PLM?+

We ingest from SAP, Oracle, Infor, Microsoft Dynamics, and PLM systems (Teamcenter, Windchill, Enovia) via standard connectors or change data capture. The graph is a semantic layer over them: nothing is replaced.

Which industry standards do you align to?+

GS1 (GTIN, GLN, SSCC), GDSN for master data, SCOR for process modelling, PLIB for parts libraries, and industry specific ontologies (IATA for cargo, GS1 Healthcare, IPC for electronics).

What about IoT and real time telemetry?+

Time series data lives in its native store (Timescale, InfluxDB); the graph holds the asset topology and references the time series ranges. Queries that mix 'which asset' with 'what happened when' hop between both.

What's a realistic first engagement?+

A 6 week multi tier visibility pilot over one product family with your existing direct supplier data plus one third party dataset is a common start. Recall traversal and ESG lineage are usually phase 2 once the underlying graph exists.

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