The ingredient movement your ordering system never sees

POS data is a key component in nearly every ordering decision retailers make, and for most of the store, that might work well enough. Fresh departments are more complicated: What a customer buys at the register isn’t always the same thing as what a store actually needs to order.

In fresh, many items become ingredients for different sellable item. Raw chicken breast might sell as-is in the meat case, get seasoned and packaged, or go into pre-assembled chicken and veggie kebab skewers sold in deli. Three different SKUs, three different sales histories, all pulling from the same raw ingredient on the order guide.

The problem: fragmented demand signals

Most ordering systems get three different sales numbers from the POS data and miss the connection between them. Nothing tells the system that all three trace back to one ingredient, or how much of that chicken goes into daily deli sales.

If a system can't see that the same raw chicken is also the seasoned chicken breasts and the chicken skewers, it can't add those sales signals back together into one real number to forecast how much raw chicken the entire store needs to order.

Most forecast data only considers what happened at the register. It doesn't account for how an ingredient moved or transformed before it got there.

Why fragmented demand leads to bad orders

When a system can't add those three items back together, it under-orders. Stores run short and miss sales, associates override too high and end up with a full backroom, or experienced order writers spend extra time manually correcting order numbers because they have extra context that the system can’t capture. That means the ordering system is missing an entire layer of demand it was never built to see, and stores can’t automate accurate replenishment.

The missing link

Grocers need an ordering system that links the ingredient they ordered to every finished item it sells as and knows how much of each raw ingredient goes into the final item. With that link in place, total consumption becomes visible, not just the portion that passed through a register. That's what closes the gap between POS sales and real demand: knowing what a store actually consumed, one recipe at a time. And that’s the difference between an automated ordering system your associates constantly override and one they trust.

How Afresh closes the gap

Afresh was built specifically to create these crucial digital links between fresh items and their recipes to better model demand, inventory, perishability, and generate order and production plan recommendations.

By ingesting recipe data, Afresh tracks and connects demand for the raw and pre-seasoned chicken breasts in the meat department and the pre-assembled chicken skewers in deli. It knows the recipes and yields that tie them together (e.g. how much raw chicken it actually takes to produce a certain number of kebab skewers).

By attributing and aggregating demand more accurately, Afresh gives DCs and suppliers a five-week forecast and generates order recommendations that ensure the store has the right amount of chicken at the right time to cover every department’s needs.

Fresh departments have always shared ingredients across department lines, but replenishment systems were never designed to see it. Choosing an ordering solution that can see the full picture and make context-aware decisions is what will finally drive trust, adoption, and results.