How it works
From signals to smarter orders
Chopp’s AI forecasting combines a store’s sales history with the outside forces that move demand, then turns the result into a view of what will sell, so stores can stock accordingly.
Four steps, in plain language
This is the general shape of demand forecasting as Chopp describes it: time series analysis enriched with real-world signals.
- 01
Learn from sales history
Past sales per product and store are the foundation. They reveal trend and recurring seasonal patterns.
- 02
Add real-world signals
Weather patterns, local events, traffic flows, seasonality trends, holidays and schedules explain much of the variation history alone cannot.
- 03
Predict sales
Advanced time series analysis produces a forecast of what is likely to sell in the period ahead.
- 04
Optimise inventory
Stores use the forecast to order and stock the right products in the right quantities, cutting both waste and stockouts.
Why outside signals matter
History tells you what usually happens. Signals tell you what is different this time.
A sunny Saturday, a home match at the local stadium or a holiday that falls on a Monday can push demand far from the average. Forecasts that ignore these drivers are systematically wrong on exactly the days that matter most.
That is why Chopp analyses various data points, not just sales, before it predicts.
Client to provide · Onboarding and integration
Describe how a store gets started with Chopp: which data is needed, how it is connected, how long set-up takes and how forecasts are delivered (dashboard, export, ordering-system integration). Not recoverable from the archived site.
Ready to cut away the waste?
Tell us about your store or chain, and we will show you how AI sales forecasting can help you order the right products in the right quantities.