7.8 billion units of food moved in 2025
More orders mean more data, more edge cases, more anomalies, and better trained models.
Afresh's data models continuously score, heal, and clean every input, so demand forecasts and inventory positions reflect real store conditions.
Accurate demand forecasts and inventory positions are critical — but the models are only as good as the data that goes into them. Building a reliable data foundation is especially difficult in grocery, where one item may have a different name and unit of measure across several systems.
Afresh’s data models are trained on billions of real grocery signals to handle these scenarios. They automatically score and correct every input to build a reliable decision-making engine and improve downstream forecasts and orders.
More Accurate Forecasts
Models reflect what's actually selling, shipping, and sitting in store.
Less Data Cleanup for Your Team
Data healing runs automatically, so teams don't have to investigate every anomaly.
Confidence in Every Recommendation
Orders, counts, and plans rest on data verified against real store conditions.
With Afresh, scoring, healing, and cleaning aren't one-time setup steps. They run continuously on every input, every day, in an always-on cycle that keeps the data layer tied to operational reality.
Scoring
Each input is validated against real conditions before it goes into the Afresh Intelligence Engine
Afresh's data engine reviews each input across orders, shipments, sales, and inventory counts, then flags any mismatches to determine what isn't true. If a store sells more cherries in a week than were shipped or could plausibly be in back stock, our data scoring catches it before that input reaches the demand model.
Healing
Most data issues are repaired without manual intervention
Many data problems aren't actually about incorrect data, but about data that needs to be normalized across systems. Afresh can handle the majority of fresh data issues without requiring human investigation.
Cleaning
When data can't be healed, it gets pulled before it skews the model
Some inputs can't be reconciled. A sales feed went down or an upstream file is just wrong. Cleaning removes those inputs from use until it's fixed and flags the issue for corporate follow-up, so models stay accurate even when source systems hiccup.
Our scoring process surfaces that a store sold more cherries in a week than were shipped or could plausibly be sitting in back stock. The next step depends on what's causing the gap.
When the gap is a mapping issue, healing takes over. Investigation shows that cherry tomato sales are being attributed to the cherry item ID at the register. Healing updates the item mapping, splits the sales correctly between cherries and cherry tomatoes, and feeds the corrected data back through scoring before it reaches the demand model.
When the gap can't be reconciled—if there’s no item mapping issue, no out-of-stock event, no clear source—the next step is cleaning. Cleaning removes the affected cherry sales from the upcoming forecast and flags the issue to Afresh for follow-up, so a broken feed doesn't pull the model off course.
Validated data is the foundation, not the finish line. Once an input passes scoring, healing, or cleaning, it feeds into Afresh's probabilistic models for inventory, demand, and perishability. Those models feed the decision engine that simulates and selects the best order, production quantity, or PO recommendation to maximize profits.
Learn more about demand forecasting →
Discover why Afresh is the cornerstone solution for leading retailers.