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Friday lunchtime, 32 degrees Celsius, last day of school before the summer holidays. In the bakery around the corner, the refrigerated shelves with cold drinks and frozen products are emptying by the hour, while the baked goods counter attracts little interest. In the supermarket opposite, the ice cream freezer is already half empty by the afternoon. And in the food court at the train station, suddenly twice as many people are queuing as usual – families on their way to vacation.
Three completely different businesses, one common problem: Customer behavior changes radically overnight – and last week's order is no longer relevant.
Two mutually reinforcing effects
Heat drives up sales of individual product categories. This is not a guess, but proven: In 2022 alone, ice cream sales in Germany increased by 13.4% to 2.65 billion Euros due to the warm summer months – in the gastronomy sector, ice cream sales even rose by 42.5%. Those who under-plan on such days lose revenue within hours. Those who over-plan produce goods that end up in the trash in the evening – unnecessarily burdening their own inventory costs.
The start of holidays simultaneously changes who is even around. Regular customers go on vacation, commuters disappear, schoolchildren are no longer around for lunch breaks – and because summer holidays in Germany are staggered across 16 federal states and vary each year, this doesn't affect all branches simultaneously or to the same extent. A branch in a residential area reacts completely differently than a branch at a train station or in the city center.
Each effect would be a challenge on its own. Together – heat and the start of holidays almost always coincide – they create a complex situation that can hardly be reliably calculated using past experience.
Why traditional forecasting is no longer sufficient here
Traditional planning and forecasting are usually based on past experience from recent weeks or the previous year. This works well as long as the general conditions hardly change. However, this is precisely not the case in summer – whether in bakeries, system catering, or supermarkets:
• Weather changes cannot be accounted for with a fixed ordering rhythm – a hot day in the middle of a rainy week is simply not recognized.
• Regional holiday staggering means that a central calculation value does not work for all branches.
• Public holidays and bridge days additionally shift the frequency pattern of individual weekdays, regardless of the weather.
Anyone who has tried to manually adjust inventory knows the outcome: either shelves and counters that are too full in the evening, or empty shelves in the middle of the afternoon when demand would actually be at its peak.
How Artificial Intelligence Optimizes Inventory Management
An AI-based sales forecast processes precisely the factors that traditional experience cannot capture: weather data, holiday calendars by federal state, public holidays, and the individual behavior of each branch – not as a rough estimate, but updated daily and specific to each location. This way, the forecast knows that the branch at the main train station needs more foot traffic on the first day of the holidays, while the branch in the residential area requires significantly less product at the same time. And it reacts to a heatwave before it becomes a problem – not just afterwards.
For bakeries, chain restaurants, and supermarkets, this means less food waste on days when too much was produced or ordered, and more revenue on days when there wasn't enough product available. This is precisely where foodforecast's AI software comes in.
Summer is a Good Test for Any Forecast
If you want your purchasing and production plans to function reliably in the coming weeks, it's worth taking a look at your current forecasting software: How well are weather, holidays, and regional differences currently being considered to make your bakery, chain restaurant, or supermarket more efficient? We would be happy to show you how foodforecast incorporates precisely these factors into its daily forecast and delivers an accurate sales prediction.
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