Grocery AI models increase accuracy across replenishment and production planning
Afresh built proprietary grocery AI models that handle perishability, variable weights, and messy data better than any other solution.
Every fresh department can finally operate with advanced intelligence to order smarter, plan better, and stand out to customers—improving your bottom line.
Unit of Measure Normalization
Fresh products often sell in multiple forms. Whole watermelons and cut watermelon ring up as different items, but they come from the same case order.
Afresh maps every sellable unit back to its source so recommendations reflect total movement, not fragmented signals across SKUs.
Ingredient Batching
When multiple prepared items share a common ingredient (like apples in mixed fruit cups, snack packs, and salads) Afresh makes one consolidated prep recommendation.
Instead of generating a separate step to slice apples for each SKU, Afresh calculates one prep recommendation for the shared ingredient that makes enough for every item.
Meat Cutting Optimization
A single cut can come from different primals (like stir-fry strips from a chuck roll or a top round).
Afresh tracks what's been opened and analyzes real-time signals and sales patterns to recommend using a remaining primal or open a new one.
Yield Awareness
Afresh learns real yield performance from recipes to account for trimming loss, so orders reflect what actually makes it to the shelf.
If a bone-in chicken breast recipe assumes 75% usable yield after trimming, Afresh will pull a higher case count than one based on raw weight.
Intraday Production
Customers don't buy everything at 8 AM, so prep shouldn't happen all at once either.
Afresh schedules production throughout the day to match actual buying patterns, so items like rotisserie chickens get roasted across three runs tied to peak lunch, afternoon, and dinner windows.
Ingredient Batching
When multiple products share a base recipe, Afresh combines them into a single batch recommendation.
That means marinara for baked ziti, chicken parm, and meatball subs gets calculated as one large batch rather than estimated separately per item, cutting both prep time and ingredient waste.
Multi-Step Plans
Some items, like croissants, need prep steps with lead times. Afresh builds schedules backward from sell time so every step lands when it needs to.
Croissants need overnight proofing, so Afresh tasks the closing associate with pulling them from the freezer the night before.
Ingredient Batching
As with Deli, when multiple bakery products share a base recipe, Afresh rolls them into one batch recommendation.
Shared base batter across three muffin varieties becomes a single large batch instead of three small sequential ones, making the process more efficient.
See why Afresh is the cornerstone solution for fresh across leading retailers.