01 · Upstream intelligence

AI before the factory gate

Supplier, commodity, inbound and technology signals become data objects that can be modelled, related and translated into operational exposure.

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01 · Upstream

Sense the industrial environment before it becomes a factory constraint.

The upstream research agenda treats suppliers, commodity prices, logistics and emerging technologies as continuously observed data spaces. The research challenge is to translate external signals into explicit operational exposure.

Commodity intelligence

Price, harvest, energy and freight signals become recipe- and SKU-level risk estimates.

Knowledge graphs

Supplier, ingredient, location and capability relationships become queryable networks.

Inbound anomaly detection

Shipment events are scored for deviations from expected lead-time and route behaviour.

Technology embeddings

Patents, vendor texts and papers are mapped against internal manufacturing problems.

Probabilistic ETA models

Arrival times are represented as distributions rather than single promised dates.

Scenario services

Sourcing changes can be evaluated against recipe, capacity, cost and quality consequences.

Cookie-factory question

If cocoa availability deteriorates, which cookies are exposed, which suppliers or substitute ingredients exist, how much production is at risk, and which downstream commitments should be changed first?

Data representation

A supplier is more than one row in ERP.

Graph representation

(Supplier A)-[:PROVIDES]->(Cocoa 72%) (Cocoa 72%)-[:USED_IN]->(Recipe C17) (Recipe C17)-[:MAKES]->(SKU PremiumCookie) (Supplier A)-[:SHIPS_VIA]->(Port X) (Port X)-[:HAS_RISK]->(Congestion)

Programmatic decision chain

Eventprice spike / delayed vessel
Exposure querygraph + BOM + demand plan
Actionbuy / switch / reformulate / escalate