Grocery Store Food Waste Starts at the Order

Grocery store food waste is usually reported as a disposal number, which is the last point at which it can be measured and the last point at which anyone can do anything about it. The number that changes a decision sits one step upstream: how many units of a perishable item came into the store against how many scanned before they had to come off the shelf.

That comparison is available to any operator holding both its order quantities and its point-of-sale, and almost nobody runs it at item level. This page works it through on six items, keeps the published waste-volume statistics separate and attributed, and points at the two method pages rather than restating them.

Key takeaways

• Waste is an ordering outcome. By the time a case is in the back room, the decision that produced the loss was made days earlier.
• Measure sell-through per item inside its shelf life, not per department per week. A department average hides the two items doing most of the damage.
• Rank by dollars at cost, not by percentage. A 68% sell-through on a cheap item loses less money than a 90% sell-through on rotisserie chicken.
• Separate markdown scans from dumps before claiming a saving. The gross number finds the item; the net number sizes the fix.
• Shrink and perishable loss are different entities with different owners. One buys audits, the other changes order quantities.

What the published grocery store food waste numbers say

These are worth knowing and worth quoting correctly, with their period attached. They are not our numbers and they are not your store's.

Two things stand out. Retail is a small share of the national total, 5.7%, which is a useful corrective to the assumption that supermarkets are the centre of the problem. And within retail, date label concerns are the single largest bucket at 45.8%, roughly two and a half times spoilage. A store throwing away product that is still good because a date printed on it made it unsellable is a labelling and rotation problem, and it is a different fix from ordering.

What none of those figures can do is tell you which item in your produce department is over-ordered on a Tuesday. National tonnage sizes a problem. It does not locate one.

Shrink and perishable loss are not the same entity

Retail shrink is theft, damage and administrative error across the whole store, measured as the gap between book inventory and counted inventory. Perishable loss overlaps it and is not the same thing: it is product that arrived, sat, and expired, and it is fully explained by a demand forecast and an order quantity.

Keeping them apart matters operationally, because they route to different people. A shrink programme buys cameras, audits and receiving discipline. A perishable loss programme changes what the store orders on a Monday. Rolling both into one number on a P&L is how a produce manager ends up in a loss-prevention meeting about something loss prevention cannot fix.

The measurable driver: order quantity against sell-through

Here is the read, worked through nine supermarkets over 12 weeks, stated per store per week. Ordered units are what came into the store. Sold units are what scanned at full price inside the item's shelf life. Everything else is unsold, and the cost column is what those unsold units cost to buy.

Six items, 467 units ordered, 386 sold, 81 unsold, an 82.7% sell-through and $202.90 a store a week at cost. Across nine stores that is $1,826 a week and about $95,000 a year, from six items in a store that carries thousands.

That figure is gross exposure rather than net loss, and the distinction is where most waste reporting either overstates or gives up. Of the 81 unsold units, 34 scanned at a markdown at an average ring of $1.40, recovering $47.60 against the $202.90 those units cost. The net is $155.30 a store a week, about $72,700 a year across the nine stores. Report the gross to find the item and the net to size the fix.

The milk row is in the table as a control. At 95.4% sell-through it loses $9.90 a week and is doing its job, which is what a well-ordered perishable looks like. It also explains why the department average is useless as a management unit: five of the six items sit below 85% and one sits above 95%, and the average of those is a number that describes no item on the list.

Note also that fresh-cut melon has the worst sell-through at 67.5% and only the third largest dollar loss, while rotisserie chicken has the second best sell-through at 84.5% and the largest loss at $53.30. Ranking by percentage sends the buyer to the wrong item.

Sizing the right order, not the smallest one

The instinct after a table like that is to cut every order. That trades a known loss for an unknown one, because a stockout on Saturday afternoon costs the sale, some part of the trip, and occasionally the customer.

Work the melon row properly. Its sales are concentrated Friday through Sunday. Ordering 32 units instead of 40, with the delivery weighted to the weekend, would have sold 26 rather than 27 and left 6 unsold rather than 13. Cost of unsold units falls from $33.80 to $15.60, a saving of $18.20 a store a week. The one lost sale costs $2.39 of margin at a $4.99 retail. Net saving $15.81, and the item stays in stock through the peak, because the eight units came out of the Monday-to-Wednesday order rather than the Friday one.

That is the whole method: cut the day, not the week. An order cut applied flat across seven days produces the same waste on the slow days and a stockout on the fast ones, which is why blunt order reductions tend to get reversed within a month.

Running the check yourself

• Join order quantity to scanned units at item, store and week. If you hold only one side of that join, this analysis is not available to you and no amount of disposal reporting substitutes for it.
• Compute sell-through inside the item's shelf life window rather than the calendar week. A five-day item ordered on Thursday is not measurable against a Sunday-to-Saturday week.
• Rank by cost of unsold units, then look at percentage. Percentage finds the sloppiest item; dollars find the expensive one.
• Split the week. Most over-ordering is a day-of-week pattern, and the fix is a delivery profile rather than a smaller total.
• Separate markdown scans from dumps and from donations before claiming a saving. All three are unsold at full price and only one of them recovers nothing.
• Re-measure four weeks after the change, and watch the stockout side as closely as the waste side. A waste programme with no in-stock measurement will quietly buy its savings out of sales.

What the fix looks like

The mechanics of the fix are covered in two places rather than repeated here. How to prevent over-ordering in retail covers order accuracy and the review cadence that keeps a corrected order from drifting back. How to reduce overstock covers what to do about the inventory you already hold.

The connective tissue between those pages and the table above is the shelf life constraint. Overstock in a shelf-stable category is capital sitting still; the same overstock in a perishable category is a dated write-off, so the acceptable days of supply is set by the product, not by a chainwide policy. Replenishment logic that treats a 5-day fresh-cut item like a 90-day canned good will over-order the first one every single week. See replenishment planning for the parameter side of that.

Where Scout fits

Scout ingests store-level point-of-sale and order data and compares ordered units to scanned units by item, store and day, so the six-row table above is a standing view rather than a quarterly project. Items where the order consistently runs ahead of sell-through inside shelf life surface as flagged over-ordering, with a recommended order quantity attached and the day-of-week profile behind it.

The boundary, stated plainly: Scout recommends the order and flags the over-ordering. The purchasing system still raises and transmits the purchase order, and Scout does not hold an order guide or carry an EDI connection to your suppliers.

Related: inventory management, store-level inventory visibility, and Invafresh for the fresh item management category.