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ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

Grocery-Specific Data Models

A data foundation built for grocery

Before any AI model can forecast demand or write an order, it has to understand what’s happening in your stores and DCs—but most models can’t even decode grocery data.

Afresh built a foundation that can, so you can actually use AI to make your teams' lives easier and decisions more accurate.

Before any AI model can forecast demand or write an order, it has to understand what’s happening in your stores and DCs—but most models can’t even decode grocery data.

Afresh built a foundation that can, so you can actually use AI to make your teams' lives easier and decisions more accurate.

Request a demo

Small data errors become shrink and empty shelves

Grocery data is messy by default. One product carries different IDs at the vendor, DC, store, and register; gets bought by the pallet and sold by the pound; or gets cut or cooked into new SKUs. When those relationships are mapped wrong, the error travels straight into the order.

One product, many records

Your POS, item master, order guide, DC, and vendors each keep their own codes. Vendor changes and other IDs get added to each item. Your systems of record can’t agree on which products are the same.

Ordered one way, sold another

The DC buys by the pallet, the store orders by the case, and the register sells by each or by the pound. Most systems can’t translate the math between all of them.

Items that change form

Primals are cut down in store and sold by weight, and whole fruit becomes a cut fruit cup. Each has its own sales pattern, recipes, and yield.

How Afresh builds your clean data foundation

These models sit underneath every Afresh forecast, order, and plan to ensure accurate business planning.

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

One identity for every item

Afresh normalizes each record’s shorthand and pulls out the attributes that matter: sell size, organic status, and whether it sells by each or by weight.

Every code that describes the same product, from vendor and DC records to store and register codes, resolves to a single Afresh item. Records that look alike but are different products stay separate, so their demand forecasts stay accurate.

Afresh normalizes each record’s shorthand and pulls out the attributes that matter: sell size, organic status, and whether it sells by each or by weight.

Demand traced back through the recipe

Afresh links each prepared item to its ingredients—noting yield, trim, pack size, and vendor—and attributes every sale back to the whole items you actually order.

The granular demand signals improve the accuracy of every store order, production plan, and DC purchase order.

Afresh links each prepared item to its ingredients—noting yield, trim, pack size, and vendor—and attributes every sale back to the whole items you actually order.

The granular demand signals improve the accuracy of every store order, production plan, and DC purchase order.

Smarter demand-driven decisions

Every pound of prepared chicken wings sold from the hot case is also demand for raw chicken wings. Afresh connects prepared items to their ingredients and attributes sales back to the items you actually order.

Recipes mapped to Afresh items

The ingredient in a recipe and the item available to order aren’t always a one-to-one match. A store may receive chicken breasts from different vendors and can use any of them in a recipe that calls for chicken.

Afresh maps the recipe input to the orderable items that can satisfy it and allocates demand across them. That relationship stays intact when the vendor or pack changes.

Your data, maintained every day

Afresh runs your data through our cleaning, item mapping, recipe attribution, and other data models daily to catch errors or anomalies and map item relationships.

We flag anything that needs your team’s input so it gets fixed at the source. These processes keep the foundation reliable as assortments, vendors, and codes change across stores and DCs.

Afresh runs your data through our cleaning, item mapping, recipe attribution, and other data models daily to catch errors or anomalies and map item relationships.

We flag anything that needs your team’s input so it gets fixed at the source. These processes keep the foundation reliable as assortments, vendors, and codes change across stores and DCs.

Built with your team, run by ours

Afresh works directly with your IT, merchandising, and supply chain teams to ingest the required data, establish mappings, validate the results, and prepare for go-live.

After launch, Afresh keeps processing and maintaining that data as vendors, assortments, and recipes change. We run on top of the systems you already have, which means no ERP replacement and no second item master for your team to update.

12.5 B

grocery items modeled

12,800+

departments

10 yrs

of grocery data

Every decision downstream inherits this foundation

If a platform can't solve for when the same product is split across several IDs or trace prepared food demand back to the ingredient, the problem shows up in every decision.

Afresh gets the relationships right upstream to give our demand, inventory, and perishability models an accurate picture of what is actually happening across a grocer's business.

With better item identity, attributed demand, and clean data, the models inform more accurate store orders, production plans, and DC buys.

If a platform can't solve for when the same product is split across several IDs or trace prepared food demand back to the ingredient, the problem shows up in every decision.

Afresh gets the relationships right upstream to give our demand, inventory, and perishability models an accurate picture of what is actually happening across a grocer's business.

With better item identity, attributed demand, and clean data, the models inform more accurate store orders, production plans, and DC buys.

If a platform can't solve for when the same product is split across several IDs or trace prepared food demand back to the ingredient, the problem shows up in every decision.

Afresh gets the relationships right upstream to give our demand, inventory, and perishability models an accurate picture of what is actually happening across a grocer's business.

With better item identity, attributed demand, and clean data, the models inform more accurate store orders, production plans, and DC buys.

A data foundation built for grocery

A data foundation built for grocery

A data foundation built for grocery