AI-powered demand forecasting for grocery
AI forecasting models trained on grocery data that reflect the constantly changing real world
Most forecasting systems struggle when demand gets unpredictable—especially in fresh. Afresh’s proprietary forecasting models are built to handle that uncertainty, creating more accurate demand signals across fresh and the full store so every downstream decision starts from a stronger forecast.
Key benefits
Handle uncertainty in demand
Captures the variability from seasonality, holidays, and weather, so recommendations hold up when conditions shift.
Forecast new items from day one
Afresh finds similar items already in your system, then generates a reliable forecast for the new SKU before its first day on the shelf.
Forecast across every form an item sells in
The same item often sells under multiple SKUs after in-store transformation. Afresh rolls all those forms up into one demand signal, so the forecast reflects total demand instead of fragmenting across SKUs.
Respond to trends
Incorporate trends and unexpected events into demand forecasts, allowing more agile adjustments in ordering when spikes or drops hit.
Every grocery decision relies on a good forecast
Every store order, production plan, and DC purchase starts with a forecast of what shoppers will buy. When that demand forecast misses, the cost shows up in two places: shrink dollars on overstocked items and lost sales on understocked ones. Grocers move so many items that even a slight forecast error can compound into millions in losses.
Most F&R systems rely on rigid models that don’t work for grocery
Traditional forecasting and replenishment systems tend to rely on forecasted point estimates that require strong historical information to accurately predict future demand. These forecasts break down in highly variable departments like fresh—where product demand is constantly changing by season, price, and availability.
Afresh built smarter logic for fresh—proven across the whole store
Afresh started with fresh because it was the hardest test of the underlying technology. Shelf life, random weight, recipe attribution, and data noise all break generic forecasters. Afresh built the data and forecasting foundation around those constraints, then extended it across center store, frozen, and general merchandise.
01 — INPUT
Better data in, better forecasts out
Creates a more reliable foundation for every forecast.
Afresh checks incoming data for quality, corrects inaccurate or mismatched values, and fills gaps where needed. The result is a cleaner picture of what is actually happening in each store.
02 — FORECAST
Forecast the range, not just one number
Models what could sell—and how likely each outcome is.
Instead of predicting a single number, Afresh estimates the probability of different demand levels for every item, store, and day. Instead of saying “10 cases of mushrooms will sell,” the model determines how likely it is that a range of quantities will sell. That range captures uncertainty and informs a more realistic view of future demand.
03 — DECISION
Turn the forecast into the best decision
Balances demand with inventory, freshness, and profitability.
Demand is only one part of the decision. Afresh also considers inventory, shrink, display needs, and perishability, then evaluates possible order quantities to find the option that best supports availability while protecting margin.
How probabilistic demand forecasts power the Afresh platform
Store Ordering
The forecast gives every item a full range of likely demand, not a single number. Our store ordering policy weighs that range against shelf life and on-hand inventory to recommend the order quantity most likely to balance freshness and margin.
Production Planning
The forecast predicts demand by daypart, not just by day, so production plans show quantity recommendations for different timed runs that match each peak.
DC Buying
The forecast aggregates 35 days of store-level demand into a DC view that already accounts for retail price, promotion lifecycle, and seasonality.