Supply Chain ML
Demand forecasting, inventory optimisation and anomaly detection built on your own sales and stock history.
6–10 weeks
Forecasting models trained on your actual history rather than a generic template, tuned to the trade-off you care about — stockouts versus holding cost — and delivered where your planners already work.
What this solves
- Stockouts on fast movers and capital tied up in slow ones
- Purchase planning done on spreadsheets and instinct
- Seasonal and festival demand missed every year
- Anomalies spotted only after the month closes
What you get
- Demand forecasting models on your sales history
- Reorder point and replenishment recommendations
- Anomaly detection on sales and stock movement
- Integration into your ERP or planning workflow
- Backtested accuracy reporting
Technology
PyTorchscikit-learnPostgreSQLFastAPI
Industries
Talk to the engineers who build it
Tell us the problem and we'll tell you whether supply chain ml is the right answer — and what it would take.