.png)
"How much do we actually throw away?" Most bakeries can only give a rough estimate in response to this question. Yet the return rate is one of the most important metrics in the business—and one of the easiest to calculate, if you know how.
This article provides a step-by-step guide on how to calculate your return rate, what figures are typical in the industry, and how your business compares.
Here's how to calculate the return rate
The basic formula is simple:
Return rate = unsold quantity ÷ quantity produced × 100
Here’s an example: You make 200 croissants, and by the end of the day, 30 are left over. The return rate is 15%. The key is to choose a consistent metric: Use either unit count or merchandise value, but stick with one. Merchandise value is more meaningful because a leftover braided loaf costs more than a roll.
Resolution is key. An overall rate of 12% sounds solid—but it can mask the fact that three items consistently exceed 35%. Therefore, always calculate the rate on three levels: overall, per store, and per item. Only the item level reveals where you can take action.
What are the normal values?
Industry-standard benchmarks for bakeries range from 10% to 20%—with significant variations depending on the product assortment, location type, and planning practices. Fresh pastries and sandwiches tend to have higher return rates, while bread has a lower rate. Stores with extended evening hours structurally have more returns than those that close early.
More important than comparing yourself to the industry is comparing yourself to your own past performance: How is your sales rate changing over the weeks? Which store is planning most accurately? Which items are losing value? If you track your sales rate weekly, you’ll spot problems before they become costly.
Why a low quota alone is not the goal
A 2% return rate is not a success if it comes at the cost of empty shelves. Those who systematically underproduce may have few returns—but they also miss out on sales and end up with disappointed customers. The trick is to optimize both returns and availability at the same time.
That’s exactly why precise forecasts are needed. GoNina calculates the expected demand per item and location for the next seven days—taking into account weather, holidays, school breaks, and seasonal patterns. The dashboards show returns and availability in real time for each store and item, without Excel and without manual exports.
Businesses that use GoNina reduce their excess inventory by up to 52%—while improving inventory availability and increasing sales by up to 6%.
Frequently Asked Questions
How often should I analyze the return rate?
Take a quick look at outliers every day, and conduct a weekly analysis by item and store. It’s worth comparing monthly trends to the previous year to properly assess seasonal effects.
Which items should I tackle first?
The ten items with the highest return value in francs—not in units. That’s where the greatest financial leverage lies.
Do I need software for this?
In theory, Excel is sufficient for the calculations. The real effort lies in the daily maintenance and analysis—which is exactly what GoNina automates, including the forecasts that actively reduce the rate.
Conclusion
The return rate is easy to calculate—the figure is derived from regular analysis by item and store. If you know your numbers, you can take targeted action. And if you plan using AI forecasts, you can measurably reduce the return rate without sacrificing inventory availability. Read the in-depth article to learn how to reduce returns in practice.
Our comprehensive guide to AI sales forecasting for bakeries provides a complete overview.
