ML Demand Forecasting
ML Demand Forecasting
Machine learning forecasts for every product at every location, built from your sales history and the sell-through your partners share.
How it works
Vintaflow fits a machine learning (ML) forecast to every product at every warehouse. Each model learns seasonality and trend from that item's own sales history and is scored against past data, so planners can see how far to trust a number before they act on it.
Wine and spirits demand is seasonal, uneven, and slow to show up upstream. When a partner reports a week late, Vintaflow nowcasts the current week instead of waiting. When retailers and distributors share sell-through, that demand signal travels up to importers and producers, offset by the real lead time on each lane.
People stay in charge
Planners can override any forecast for a promotion, a new listing, or a known event, and the override is kept next to the model's number. Demand attribution shows which downstream buyers are driving each week, so an unusual figure can be explained instead of rebuilt in a spreadsheet.
The forecast feeds everything after it: inventory planning levels, AI-enabled replenishment, and the reports your partners see.
What it changes
- A distributor sees a seasonal peak early enough to order ahead of it instead of chasing it.
- An importer plans container orders from what retailers are selling, not from last month's orders.
- A producer sees demand from several markets in one view and plans bottling and allocation earlier.
Frequently Asked Questions
- Does Vintaflow use machine learning (ML) for demand forecasting?
- Yes. Vintaflow fits a machine learning forecast to each product at each warehouse, learning seasonality and trend from its sales history. Each model is scored against past data, and planners can review and override any forecast.
- What data does Vintaflow need to forecast demand?
- Product identifiers such as UPC or EAN, your locations, and stock and order history. Most teams start with Excel or CSV exports. Partners can also share their sell-through, which improves the demand signal upstream.
- How does Vintaflow handle partners that report sales late?
- For accounts that report a week late, Vintaflow estimates the current week's demand so the plan stays current while the actual figures arrive.
Guides and answers
Put ML Demand Forecasting into practice.
Related features
Other capabilities in the same operating layer.
Next Step
Bring your current planning model into the conversation.
Tell us which part of the supply chain this feature would affect first and we can work outward from there.