12Distribution & Wholesale · AI solutions
Demand forecasting for a fast-moving goods distributor
Purchase planning informed by seasonality and trend rather than last month's figure.
- Per-item demand forecast
- Seasonality detection
- Promotion-aware adjustment
- Buyer planning view
The problem
Planning used a rolling average that lagged demand. Seasonal lines were bought late and promotional spikes were treated as the new normal.
What we built
We produce forecasts per item from sales history with seasonality and known promotions, surfaced as a planning suggestion a buyer works from.
Modules delivered
- Per-item demand forecast
- Seasonality detection
- Promotion-aware adjustment
- Buyer planning view
- Forecast accuracy tracking
- Slow-mover identification
What changed
What the delivered system does differently from the process it replaced.
- Planning starts from a forecast rather than an average
- Seasonal lines are ordered ahead of the season
- Forecast accuracy is measured over time
Built with
The stack chosen for this build.
- Go
- PostgreSQL
- REST API
- Python
Why this customer is not named
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