AI for Supply Chain

Supply chain is probably the area where AI hype runs hardest into the wall of operational reality. The models that demo nicely against clean datasets eventually meet the actual world (late shipments, mislabeled SKUs, weather events, port disruptions, suppliers going dark for a week at a time) and the gap between paper performance and actual performance can be brutal.

The systems that do hold up tend to be narrower in scope than the marketing copy generally suggests. Those are the ones we build.

Where it actually pays off

• Demand forecasting. Better-than-spreadsheet forecasts on SKUs and lanes where you have enough history to support them. We'll be honest about which ones qualify.
• Anomaly detection. Catching the late shipment, the price spike, or the unusual order pattern before it actually becomes an incident.
• Document automation. Pulling structured data out of bills of lading, customs forms, supplier invoices, and the rest of the paperwork that still arrives in your inbox as PDFs.
• Supplier intelligence. Watching public signals like news, financial filings, and capacity changes for risk on the suppliers that genuinely matter to you.
• Routing and inventory placement. In the cases where the variables are constrained enough that optimization actually beats good heuristics.

Where we'll talk you out of it

Anywhere the data is just too sparse for the model to learn against in a meaningful way. Anywhere the cost of being wrong is high and the variability is fundamentally outside of what the data captures. We'd genuinely rather not start a project than start something we'll eventually have to walk back from.

How a deployment looks

Plugged into the systems you already run, including your ERP, WMS, TMS, and supplier portal, and integrated into the way your operators already work day to day. Not a new dashboard they have to learn from scratch. Get in touch and we'll talk through your specific operation.