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Every day, bakeries face the same question: How much should they produce? Producing too much leads to returns, food waste, and unnecessary costs. Producing too little results in empty shelves, lost sales, and disappointed customers. In most bakeries, this decision is still made based on experience and gut instinct—often under time pressure and without a complete set of data.
AI-based sales forecasts are fundamentally changing this. They automatically analyze sales data, weather, holidays, school breaks, and other influencing factors, and provide specific production recommendations for each product and location. In this guide, we explain how it works, what benefits it offers, and what bakeries should keep in mind when implementing it.
What are AI sales forecasts?
An AI-based sales forecast uses machine learning to predict future demand based on historical sales data and external factors. Unlike simple average calculations, AI identifies complex patterns and correlations that are barely visible to humans.
For example: On a Wednesday in March, with a temperature of 14°C and light rain, two days after a holiday, Store A typically sells 23 pretzel rolls and 45 croissants—but Store B sells only 12 pretzel rolls and 60 croissants. A human cannot calculate such location- and product-specific patterns for hundreds of products every day. AI can.
GoNina one such AI platform. It was developed specifically for bakeries, restaurants, and other businesses that handle perishable goods, and is currently in use in Switzerland, Germany, Austria, France, and other European markets.
The Problem: Why Manual Planning Has Reached Its Limits
Demand for baked goods is influenced by a variety of factors. The weather affects purchasing behavior—on hot days, more cold drinks and lighter snacks are sold, while on cold days, more bread and pastries are sold. School vacations alter customer traffic, especially at neighborhood bakeries. Holidays shift demand, local events bring in additional walk-in customers, and weekdays have their own patterns.
In der Praxis ist es für Filialleiter oder Produktionsleiter unmöglich, all diese Faktoren täglich für jedes Produkt an jedem Standort zu berücksichtigen. Die meisten Bäckereien arbeiten deshalb mit Erfahrungsregeln: "Montags weniger Gipfeli", "Bei Regen mehr Brot", "Vor Ostern 20% mehr". Diese Faustregeln helfen, sind aber zu grob. Das Ergebnis sind Retouren am Ende des Tages oder zu früh ausverkaufte Produkte - beides kostet Geld.
Besonders komplex wird es für Filialbäckereien. Wer fünf, zehn oder zwanzig Standorte mit jeweils 50 bis 200 Artikeln plant, kann das nicht mehr sinnvoll manuell abbilden. Hier wird die Produktionsplanung zum Vollzeitjob - und trotzdem bleibt sie oft ungenau.
How do AI sales forecasts work in practice?
The process can be broken down into three steps:
Schritt 1 - Datenanbindung: Die KI-Plattform verbindet sich mit dem Kassensystem der Bäckerei. Bei GoNina geschieht das unter anderem über eine direkte Schnittstelle zu HS-Soft, ProtecData, Lightspeed und anderen gängigen POS-Systemen. Dabei werden Verkaufs-, Artikel- und Filialstammdaten automatisch importiert. Zusätzlich werden externe Datenquellen angebunden: Wetterprognosen, Feiertags- und Ferienkalender sowie lokale Eventdaten.
Schritt 2 - Analyse und Lernen: Die KI analysiert die historischen Verkaufsdaten und erkennt Muster. Sie lernt, wie sich Wetter, Wochentag, Saison und Standort auf den Verkauf einzelner Produkte auswirken. Dabei passt sich das Modell laufend an: Neue Produkte, Sortimentsänderungen oder veränderte Kundenströme werden automatisch berücksichtigt.
Schritt 3 - Prognose und Bestellvorschlag: Auf Basis der Analyse erstellt die KI eine tägliche Absatzprognose pro Produkt und Standort für die nächsten 7 bis 30 Tage. Diese Prognosen werden als konkrete Bestellvorschläge oder Produktionsmengen zurück ins System gespielt. Der Betrieb kann die Vorschläge direkt übernehmen oder manuell anpassen.
The result: Instead of making estimates every day, the bakery receives data-driven recommendations that are automatically updated.
What results are realistic?
Betriebe, die mit GoNina arbeiten, erreichen auf Basis realer Daten aus über 50 aktiven Standorten folgende Ergebnisse:
52% weniger Überschuss: Durch präzisere Prognosen wird weniger produziert, was nicht verkauft wird. Das reduziert Retouren, Lebensmittelverschwendung und Entsorgungskosten.
6% increase in sales: More accurate forecasts also mean that popular products are less likely to sell out prematurely. Availability increases, and so does sales.
12 hours saved per month: The daily analysis of sales figures, the creation of production lists, and coordination between stores are largely eliminated. The team can focus on craftsmanship and sales.
These results vary depending on the size of the business, the product range, and the initial situation. As a general rule, the larger the product range and the more locations, the greater the effect.
Which bakeries are AI forecasts suitable for?
Whether you have two stores or twenty: Anyone who prepares fresh products in the morning and delivers them to various locations knows the daily challenge—how much of each product should go where today?
More locations = greater impact. Complexity increases with every branch. Instead of headquarters distributing orders across the board or each branch placing orders individually, GoNina provides GoNina recommendations for each branch and product. Businesses with multiple locations save the most time and reduce excess inventory the most significantly.
It’s worth it even if you have just one location. If you carry a wide assortment and have to decide every day how many braided loaves, how many rolls, and how many croissants to put in the oven, you’ll benefit from a forecast that automatically takes into account the weather, the day of the week, and the season. Fewer returns in the evening, fewer empty baskets in the afternoon.
What sets GoNina other solutions?
GoNina developed specifically for the food industry. Three things are particularly important to us:
1. Accurate Forecasts: For bakeries, one thing matters most in the end—having the right amount at the right time. That’s why we’ve developed industry-leading forecasting models that provide daily forecasts by product and location. These models are developed in collaboration with ETH Zurich and supported by Innosuisse, NVIDIA Inception, and other partners.
2. Simple for everyday use: Bakeries aren’t IT companies. GoNina is GoNina be quick to implement and easy to use. Direct integrations with POS systems such as HS Soft, ProtecData, Lightspeed, and others ensure that onboarding is completed in just a few days—without manual data entry or CSV exports.
3. Close to our customers: We respond quickly, provide personalized support in German, French, English, and Italian, and host all data in Switzerland and the EU. But above all: We listen. Customer feedback goes directly into product development—not into a queue.
Every business can try GoNina risk-free for GoNina weeks. If you’re not satisfied, you’ll get your money back.
Frequently Asked Questions
How much does it cost to use AI sales forecasting?
Thecost depends on the number of locations and the scope of theproject. GoNina transparent pricing, which can be viewed on the website. You can get started risk-free with a 4-week trial period that includes a money-back guarantee.
How long does the setup take?
Ifyou already have a POS system, onboarding usually takes just a few days. GoNina the setup and integration—the effort required to run the system is minimal.
Do I need to replace my existing systems?
No. GoNina your existing POS system and inventory management system. The forecasts are integrated into your existing workflows as order suggestions.
Does this also work for seasonal products or new items?
Yes. The AI automatically recognizes seasonal patterns and can quickly learn about new products by using similar items as a reference.
In which countries is GoNina ?
GoNina available in Switzerland, Germany, Austria, France, and other European markets. The platform supports German, French, English, and Italian.
Conclusion
KI-basierte Absatzprognosen sind kein Zukunftsthema mehr - sie sind heute verfügbar und liefern messbare Ergebnisse. Für Bäckereien bedeuten sie weniger Überschuss, mehr Umsatz, weniger Planungsaufwand und bessere Sortimentsentscheidungen. Mehr dazu, wie KI Prognosen den Planungsaufwand konkret reduzieren, liest du in unserem vertiefenden Beitrag. Und welche digitalen Werkzeuge sich für Bäckereien sonst noch lohnen, zeigt unser Überblick zur Digitalisierung. Die Technologie ist da. Die Frage ist nur, wann der eigene Betrieb den Schritt macht.
