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It usually happens in the middle of a presentation. Someone puts annual revenue on a slide, multiplies it by the market out-of-stock rate, and the number that appears is big enough to stop the room. Then the finance director asks a one-line question, and the whole thing falls apart.
The question is this: does that rate count items or does it count money?
It counts items. And that is why most estimates of out-of-stock losses presented in meetings do not survive the first serious challenge. The right calculation exists, it is simple, and it has three terms. Almost every version you see in the wild gets at least one of them wrong.
This article lays out the formula, where each term comes from, the three most common errors and, at the end, the number that tends to be far more useful in a meeting than an estimated loss. If your question is still how to measure out-of-stocks rather than how to turn them into money, start with how to measure out-of-stocks.
The definition, the types and the rate formula are gathered in the out-of-stock guide.
The formula for the cost of out-of-stocks
An out-of-stock is the absence of the product at the point where the customer reaches out for it, and the out-of-stock rate is the percentage of items sold by the store that should be on sale and are not. It is a count of products.
To turn that into money, the calculation has three factors:
lost sales = revenue × sales-weighted out-of-stock rate × the share that is never recovered
The first term is yours and needs no discussion. The other two are where everything happens.
The second term is not the rate you are handed
This is where most calculations break, and the problem lies in how the indices themselves are defined.
Published indicators count missing items against the store catalog. The Neogrid out-of-stock index states this plainly in its methodology: the index is calculated over each store's assortment and does not take sales history or demand into account. It counts products, not revenue.
Multiplying revenue by a product count assumes, without saying so, that every item in the store sells the same. The measurements show how far off that assumption is.
In a three-year project published in 2020, Rekik, Syntetos and Glock analyzed for ECR Retail Loss around one million items across roughly 100 stores belonging to seven of the largest retailers in Europe. In grocery and general merchandise, the split came out like this:
- fast movers: 19.39% of the assortment, 70.30% of sales
- medium movers: 25.91% of the assortment, 19.98% of sales
- slow movers: 54.69% of the assortment, 9.73% of sales.
Half the assortment accounts for less than a tenth of sales. A 10% out-of-stock rate concentrated in slow movers and a 10% rate concentrated in fast movers cost wildly different amounts, and a count-based index cannot tell the two apart.
What belongs in the formula, then, is the out-of-stock rate weighted by the sales of the items that went missing, not a count of them.
The third term is customer behavior
Not every out-of-stock becomes a lost sale. A customer who cannot find the product does one of five things, and only some of them hurt the retailer.
The 2002 study by Gruen, Corsten and Bharadwaj, carried out for the Grocery Manufacturers of America and the Food Marketing Institute, measured this across 71,000 consumers in 29 countries. The worldwide average:
- buys the item at another store: 32%
- switches brand: 20%
- switches size or type within the same brand: 20%
- delays the purchase: 17%
- does not buy the item at all: 11%.
Two of those lines cost the retailer a sale: buying elsewhere and not buying at all. Added together, they give the study's conclusion, which is the third term of the formula: the retailer faces an average direct loss of 43% of the potential sale.
Note that the figure for the manufacturer is different. The manufacturer loses on the 20% who switch brand and the 11% who do not buy, which comes to 31%. Retailers and manufacturers lose in different situations, and that is one reason the two sides argue so much about the size of the problem without ever agreeing.
It is also worth noting what happens when the customer substitutes. The same study records that the tendency is to pick the smaller or cheaper option, which means even substitution, which sits outside the 43%, usually comes with a partial loss of revenue.
The shortcut, if you do not measure out-of-stocks yet
Few chains have a sales-weighted out-of-stock rate calculated. For them, the same study already hands over the product of the last two terms: the average worldwide sales loss attributed to out-of-stocks is 3.9%, in a narrow range of 3.7% to 4.0% across regions.
Twenty-four years later, by an independent route, the IHL Group lands close by. As we showed when covering the scale of the problem in global retail, inventory distortion, which combines shortages and overstocks, is equivalent to 6.2% of the sector's global sales.
What matters for this calculation is the split inside that number, which tends to go unnoticed: IHL attributes 65.6% of the distortion to out-of-stocks and the remainder to overstocks. Applying that proportion, you arrive at somewhere around 4.1% of sales lost to out-of-stocks. That last piece of arithmetic is ours, done on the two figures they publish.
Two independent measurements, more than two decades apart, converging close to 4% of sales. It is the most solid benchmark available for anyone who does not yet measure their own sales-weighted rate, and it holds up in a meeting far better than multiplying revenue by a product count.
The three most common errors in the calculation
Multiplying revenue by a count-based index
Explained above, and by far the most frequent. The index answers how many items are missing. It does not answer how much money is missing. A finance director takes that calculation apart with the question this article opened on.
Discounting twice
Anyone who uses the 3.9% shortcut and then applies a recovery percentage on top is counting the same discount twice. The 3.9% is already the result of multiplying the extent of out-of-stocks by the 43% of responses that hurt the retailer. The shortcut has the third term baked in. Use the full formula or use the shortcut, never both.
Treating the result as a ceiling
It is a floor. The authors of the global study record that the research does not measure the effect of out-of-stocks on permanent store switching, and that there is little reliable research on the subject.
What they did observe is that consumers reduce substitution as they run into repeated out-of-stocks, meaning they start meeting that need somewhere else. The calculation captures the sale lost that day. It does not capture the customer who stopped coming back.
To run this estimate on your own numbers, our out-of-stock calculator builds the figure from the number of stores, the revenue and the rate in use.
The number that works better in a meeting
An estimated loss is a weak argument, because it is a model, and every model can be argued with. A measured gain is a strong one, because it is an experiment.
The ECR Retail Loss study did not stop at the diagnosis. The researchers set up a test and control design: stores that were subjected to a stock count, and comparable stores that were not, matched on size, location, assortment and prior sales.
Correcting the inventory records increased sales at all seven retailers, in a range of 3.83% to 8.38%, with an average of 5.98%. The authors make a point of noting that the gain showed up even at the chains already considered particularly accurate.
And the gain concentrates where the discrepancy is largest:
- items with high discrepancy: sales increase above 14%
- items with medium discrepancy: a little over 7%
- items with low discrepancy: 2.11%.
In a conversation with the board, that is the sentence that shifts the framing. The question stops being how much am I losing, which is an estimate, and becomes how much comes back if I act, which seven large chains have already measured against a control group.
Where to concentrate the effort to reduce out-of-stocks
If counting everything is expensive, the next question is what to count. The same study answers with concentration data, and the answer is more favourable than people expect.
At two of the seven retailers, 2.25% and 1.90% of items caused 70% of all discrepancies by value. At the other five, that share ranged between 13.14% and 22.82%. Even in the worst case, a relatively small number of items generates most of the problem.
Cross that with the sales-velocity data and the priority draws itself: start with the fast movers that show high discrepancy. It is a small set, it carries the largest sales gain per item corrected, and it avoids expensive routines that count the entire portfolio.
That also leads to the report's most provocative recommendation, which is about the calendar. The sector runs stocktakes in the quietest period of the year, treating the count as an audit obligation and an interruption to the work. The authors argue the opposite: since counting lifts sales, it should happen before the peak season, and its cost should be weighed against the sales gain before any decision is taken.
A word of caution about the numbers in circulation
It is worth being careful when researching this subject, because it attracts stray figures.
Percentages considerably higher than the ones in this article turn up regularly, presented with no source or with a source that points to another page with no source. When you trace the origin, you almost always end up at a commercial blog that reworded an old figure.
The numbers here come from three places you can open and check for yourself: the 2002 study by the Grocery Manufacturers of America and the Food Marketing Institute, which triangulated 52 studies; the ECR Retail Loss measurement across seven European retailers, published in 2020; and IHL Group's 2026 data.
One honest caveat about the first of those. The study of causes and consumer behavior is from 2002, and it is old. We looked for a more recent replacement with the same rigour and did not find one. It remains the reference the later literature cites, and the percentages circulating today as the global average are usually its own, reused without fresh measurement. We would rather cite the original source, with the date in plain view, than pass along a second-hand number.
The calculation is there to decide with, not to impress
An estimate of losses that cannot survive one question from finance helps nobody. It burns the subject for a year.
A defensible calculation is more modest and more useful. It says where each term comes from, admits what it does not capture, and comes with a measured gain that gives the person across the table something concrete to approve.
It was with that kind of conversation in mind that we built INTEGRA, our autonomous replenishment platform, which handles each type of out-of-stock differently. It cross-checks expected sales against actual sales and inventory balance to find the item that is sitting in the store and not selling, which is the signature of a shelf out-of-stock, and it projects demand against supplier lead time to warn the buyer before the gap appears. If you want to understand what out-of-stocks cost in your operation, talk to one of our specialists and we will run that calculation on your own numbers.
Frequently asked questions
How do I calculate what out-of-stocks cost in my chain?
The calculation is revenue multiplied by the sales-weighted out-of-stock rate and then by the proportion that is not recovered. The second term needs to be weighted by the sales of the items that were missing, and not by a count of them, because fast-moving items are 19.39% of the assortment and generate 70.30% of sales. The third term is 43%, the sum of customers who buy at another store, 32%, with those who do not buy at all, 11%. If you do not yet measure a sales-weighted out-of-stock rate, use the shortcut: the average worldwide loss is 3.9% of sales according to the 2002 global study, and around 4.1% by the calculation derived from IHL Group's 2026 data.
Can I multiply my revenue by the published out-of-stock rate?
No, and this is the most common error. The published indices count missing items against the store catalog, without considering sales history or demand, as Neogrid itself describes in its methodology. Multiplying revenue by that number assumes every item sells the same, while the ECR Retail Loss measurement across around one million items shows that half the assortment accounts for less than 10% of sales.
How much of an out-of-stock actually becomes a lost sale?
According to the global study with 71,000 consumers, the retailer loses 43% of the potential sale, which is the sum of the 32% who buy the item at another store with the 11% who do not buy at all. The others respond by switching brand, with 20%, switching size or type within the same brand, with 20%, or delaying the purchase, with 17%. For the manufacturer the calculation is different and comes to 31%, because the manufacturer loses when the customer switches brand.
What is the average sales loss from out-of-stocks?
The 2002 global study calculates 3.9% of sales, in a range of 3.7% to 4.0% across regions. From IHL Group's 2026 data, which measures inventory distortion at 6.2% of global retail sales and attributes 65.6% of that to out-of-stocks, you arrive at around 4.1%. These are two independent methodologies more than twenty years apart converging close to 4% of sales.
Does this calculation include the customer who never comes back?
No, and it is important to be clear about that. The result is a floor, not a ceiling. The authors of the global study record that the research does not measure the effect of out-of-stocks on permanent store switching and that there is little reliable research on the subject. They observed that consumers reduce substitution as they face repeated out-of-stocks, meaning they start meeting that need somewhere else.
Is it worth investing in stock counting?
ECR Retail Loss measured this with a test and control experiment at seven of the largest European retailers. Correcting the records increased sales by between 3.83% and 8.38%, with an average of 5.98%, at every participant, including those already considered accurate. For items with high discrepancy the increase went beyond 14%.
Which items should the count start with?
With the fast movers that show high discrepancy. In the ECR study, at two of the seven retailers just 2.25% and 1.90% of items caused 70% of the total discrepancies by value, and at the others that share ranged between 13.14% and 22.82%. Since fast-moving items are 19.39% of the assortment and generate 70.30% of sales, that is where correction pays off most.
When is the best time to run a stocktake?
The sector usually runs it in the quietest period of the year, treating the count as an audit obligation. The authors of the ECR study argue the opposite: since counting demonstrably lifts sales, it should happen before the high season, and its cost should be compared with the sales gain before the decision.
This content was produced by the Integrity-UX team, an IT consultancy specializing in infrastructure, cloud, security and business continuity for medium and large companies.
