The problem it solves

Forecasting, stocking, and shipping are usually optimized separately, by different teams with different spreadsheets. The forecast doesn't know freight costs; the stocking plan doesn't know tomorrow's demand shift. Each function optimizes locally — and the company pays globally in expedites, transfers, and dead stock.

What it does for your business

One optimization, full picture

Demand forecasts, warehouse inventory positions, and transportation costs feed a single optimization — so every stocking and shipping decision accounts for all three.

Positions inventory where demand will be

Multi-location stocking recommendations that move product ahead of demand instead of chasing it with transfers and expedited freight.

Plans purchasing with real constraints

Reorder recommendations respect supplier lead times and MOQs, truck and container capacity, and your working-capital limits — not just a reorder point formula.

Shows the trade-offs

Service level vs. carrying cost vs. freight spend is an explicit dial your team controls — with the projected impact of each setting shown before you commit.

How it works

ML demand forecasting combined with mathematical optimization (operations-research solvers) over your network — locations, lanes, lead times, and cost structures — integrated with your ERP as the system of record.

What we need from you

Order history, current inventory and location data, supplier lead times and MOQs, and freight cost structures. An architecture review maps your network before modeling begins.

Talk through Supply Chain Optimization with our team

A 30-minute conversation on your architecture and goals — with live demos of the underlying models where they exist today.

Request a Demo