Not a single day goes by without machine learning being mentioned in the news or digital media. Innovation has advanced self-driving vehicle technology or defeated the world chess champion in just a few years. But machine learning in food service is not a well-known concept despite the importance it brings to restaurants.
But what about machine learning in the food service sector? First of all, you will be surprised to learn that machine learning is already helping restaurants in many ways:
- Forecasting sales with greater accuracy
- Identifying product disappearances
- Assigning customers to restaurants based on their preset taste profile
- Teaching robots to cook
- Identifying food content at the molecular level
In this article, we will provide a brief definition of machine learning and discuss other ways it can help the food service sector, specifically by forecasting sales with greater accuracy.
What is machine learning?
Machine learning describes the ability of algorithms to learn from data processing. It is one of the branches of artificial intelligence that enables the automatic learning of machines. Machine learning is responsible for extracting information from stored data, since data alone is nothing more than bits. The more data they process, the more they learn and the more accurate they become in answering a question.
How can the machine learn?
To start, a simple and interesting question to ask an algorithm might be: Is this object a car, a pedestrian or a tree?
When algorithms try to answer a problem, they estimate a solution taking into account several parameters. In our previous example, the algorithm might examine an image and estimate the type of object in the image taking into account shape, color and movement. This is the example of an algorithm that estimates traffic.
The algorithm learns by testing what was predicted against what actually happened. This type of machine learning is called supervised learning. In each round, the algorithm modifies internal parameters or parts of its structure based on initial errors and tries again. This process continues, which includes discarding changes that reduce the algorithm's accuracy and keeping changes that increase accuracy. The algorithm is said to have "learned" when new images are presented and correctly classified.
How does machine learning help food service?
To understand how machine learning helps with restaurant sales forecasting, we need to know the algorithms that can be used.
Basic algorithms that do not use machine learning
Basic algorithms can forecast a restaurant's future sales based on simple variables such as last week's and last year's sales, as well as taking into account holidays, weather, etc.
For example, to predict tomorrow's sales, a basic algorithm considers:
- Calculates the average between last year's sales on the same day and last week's sales on the same day
- Increase sales by 20% if it is a holiday
- Increase sales by 20% if it is a sunny day
However, all these variables do not have the same impact for all restaurants. For example, weather can be a major factor for an ice cream shop by the beach, but a minor factor for a pizzeria in a shopping center. Therefore, a basic algorithm cannot personalize the forecast and understand that each restaurant is different. Restaurants that do not use machine learning will never achieve an accurate sales forecast.
Algorithms that use machine learning
Algorithms can help increase forecast accuracy over time by personalizing each variable for each location, meaning they learn while processing data about which parameter has the greatest impact on sales for a specific location. What influences the quality of the prediction is the amount of observations used to train the algorithm. That is, one month of data is not the same as three years of accumulated data.
The most relevant variables and information for restaurant sales forecasting are:
- Historical data (i.e., seasonal data, weekly data, as well as the growth trend)
- Weather (temperature, precipitation, sunshine hours)
- Calendar (e.g., holidays, semester, Mother's Day)
- Custom events (e.g., football matches, theater performances)
- Promotions or marketing actions that influence demand
- Reviews (positive and negative)
- Diner reservations at each location
Improving sales forecast accuracy with machine learning
At Controliza we have combined different algorithms that work locally, combining some of the variables, and globally, combining the result of each local prediction. To always provide the most appropriate predictions, our algorithm interprets each location's environment as a hidden variable particular to each one. This way it is able to anticipate with just a couple of consecutive observations and thus know how much the intensity of the change will affect the forecast and act by quickly adapting to the new environment. The more data we process, the more we learn and the more accurate we will be in our next forecast.
From forecasting to action: how to apply machine learning in day-to-day operations
The real challenge in foodservice isn’t just predicting how many sales you’ll make tomorrow, but turning that forecast into operational decisions that protect your margin. If data arrives too late, if purchasing is based on intuition, or if each site works with different criteria, waste, overstocking, stockouts, and a food cost that drifts off target start to appear before anyone identifies the root cause in time. That’s where machine learning delivers real value: not as a standalone technology, but as an intelligence layer that connects forecasting, purchasing, inventory, production, and traceability so every daily decision is based on useful data.
In practice, a machine learning model can detect patterns that manual analysis cannot clearly identify. It doesn’t just take sales history into account, but also variables such as seasonality, weather, holidays, time-slot behaviour, promotions, local events, or differences between channels. With that context, forecasting stops being a generic estimate and becomes an operational guide: how much to buy, what volume to produce, which items will rotate more slowly, and where avoidable waste may occur. This is especially important for HORECA chains, where small repeated errors across many outlets end up generating significant losses in purchasing, recipe costing, and stock control.
The challenge lies in bringing that intelligence into the day-to-day running of the business. Many companies have data scattered across ERP, POS, spreadsheets, delivery notes, and inventory systems, but lack a unified view that allows them to act quickly. Controliza solves this with an AI-powered operational intelligence platform designed for HORECA groups, where data is shared across departments and products. This means that from Forecasting you can anticipate demand more accurately and connect that information with purchasing, inventory, or kitchen operations to adjust orders, reduce surplus, and improve execution. The result is not just better forecasting, but a more coordinated operation, with less friction and greater responsiveness.
When machine learning is integrated into operational management, the impact becomes measurable. You can reduce food cost by between 3% and 5%, cut waste by more than 20%, and speed up decision-making with clearer traceability from procurement through to production. In addition, by cross-checking forecasts with actual consumption, recipe costing, and delivery note receipts, it becomes easier to detect variances, correct purchasing habits, and standardise criteria across sites. Instead of spending time chasing errors, your team can focus on improving profitability, consistency, and service, which is where competitive advantage is really won in foodservice.
From sales forecasts to operational decisions
Accurate forecasting is only useful if you can turn it into action. This is where many restaurant groups struggle: they may predict demand, but they still lose margin through waste, poor purchasing timing, stock deviations and weak traceability. In practice, a forecast should not stay in a report. It should guide what you buy, what you produce and how you protect food cost every day.
Controliza connects prediction with execution through a shared data platform. With Forecasting, your teams can anticipate demand and link it to purchasing, recipe costing, delivery notes and inventory movements. This makes it easier to adjust production, detect unusual consumption patterns and react before waste turns into margin loss.
For HORECA chains, the impact is operational as well as financial. When machine learning is connected to real processes, you can reduce food cost by 3-5%, cut waste by more than 20% and improve traceability without adding manual work. Instead of making decisions based on intuition, you use live data to standardize control across the business.
Conclusion
Machine learning in food service has many benefits for restaurants, from helping cooks in kitchens to forecasting future sales, which in turn helps calculate labor needs, inventory management and planning for both staffing and marketing actions.
Additionally, machine learning also helps restaurants operate more efficiently, for example by contributing to reducing restaurant food waste and allowing restaurateurs to focus on the areas where they can add the most value.
Therefore, machine learning takes artificial intelligence (AI) to the next level by enabling a system to learn, and it is now that restaurants are beginning to benefit from this technology.
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