AI models trained on grocery data for forecasts that reflect the constantly changing real world
Most forecasting systems — even modern ones — rely on point estimates that need strong historical patterns to be accurate. That means they’re often inaccurate when dealing with the variability and inconsistency of changing shopper demand, especially in fresh departments.
Afresh built proprietary demand forecast models that handle the uncertainty of fresh and the scale of the full store, so every downstream decision starts from a more accurate signal.
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 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 what's overstocked and lost sales on what isn't. 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 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.
Data Enhancement
Afresh scores every data input for quality, cleans inaccurate or mis-matched values, and redacts bad inputs so estimates downstream reflect real store conditions.
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Probabilistic Demand Modeling
All demand forecasters aim to find trends between data inputs. However, most models learn one item at a time and predict a single number for each item. That works in stable categories, but falls short when shopper behavior shifts, items rotate through promotion, or a new SKU has no history to learn from.
Afresh's forecaster takes a different approach. The model predicts a full range of likely outcomes for every item, at every store, every day. Instead of "10 cases of mushrooms will sell," the forecast model determines how likely it is that different quantities will sell. That full range feeds into Afresh’s decision making engine.
Learn more about Intelligent Inventory →
Holistic Decision Making
Demand forecast is only one input into Afresh’s broader decision optimization engine that also models inventory and perishability. This engine then simulates orders across in-stocks, shrink, display sizes, and freshness, analyzing thousands of possible orders to find the single best one that maximizes profits.
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.
Discover why Afresh is the cornerstone solution for leading retailers.