Supply chain and procurement teams use AI to forecast demand and optimise routing before disruptions hit, compare and monitor supplier risk automatically, and replace manual product matching and cataloguing work. This library documents 39 real deployments across 5 distinct patterns, spanning retail, manufacturing, healthcare, agriculture and 7 other industries.
Supply chains rarely fail on a quiet day. The patterns evidenced in this library, from demand forecasting to network risk simulation, are built to hold up when a supplier misses a decommit, a route gets disrupted, or spend data is scattered across a dozen systems. We bring the data engineering and forecasting expertise needed to model that volatility, across industries as different as retail, manufacturing, agriculture and healthcare logistics.
A lot of what looks like a supply chain problem is actually a data integration problem. Supplier data lives in five formats, spend data has never been reconciled, or a catalogue matching process is still done by hand. We treat that integration work as part of the build rather than a separate project, so the forecasting or automation layer on top has something real to run on.

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