Prediction is Not Guesswork: Tools for the HORECA Sector that Turn Data into Margin and Sustainability.

Every night, in hundreds of Spanish restaurants, someone makes a decision that defines the month’s outcome: how much to buy tomorrow, how many servers to call in, what preparations to get a head start on. For decades, this decision has been based on intuition, experience, and a bit of luck. Today, that logic is changing.

This isn’t about replacing a manager’s judgment, but about transforming what’s already happening in the business—every sale, every reservation, every behavioral pattern—into useful information. Demand forecasting tools in HORECA are not science fiction; they are already being implemented in real restaurants, bars, and hotels, and their impact is not what many imagine. They don’t sell more just for the sake of selling more. They reduce waste, protect margins, and align operations with the day’s reality. For businesses with tight margins, this can be the difference between closing down or growing.

Demand Forecasting: From the Till to Operational Decisions

The starting point is simple yet powerful: a restaurant’s transaction history is data. Each transaction records what was sold, when, and to whom. However, most hospitality businesses don’t use this information beyond the daily closing of the register. Tools like the one presented by Controliza are changing that paradigm: machine learning algorithms analyze past sales patterns and, with increasing accuracy, anticipate demand for the coming days or weeks.

Real value emerges across three operational layers:

• Tighter Purchasing: If the software predicts lower foot traffic on a Tuesday, it’s possible to buy less and avoid inventory that will end up as waste. In sectors with perishable goods, this translates directly to profit.

• Staffing Planning: Knowing how many people will arrive allows for precise shift scheduling, reducing both unnecessary overstaffing and team overload. Platforms like 7shifts use AI precisely for this purpose: anticipating staffing needs and assigning shifts in real-time.

• Anticipatory Preparation: With demand forecasts, the kitchen and front-of-house teams can prepare more effectively, improving service times and reducing operational stress.

What distinguishes useful solutions is that they don’t rely solely on sales history. The best ones integrate external variables: weather, holidays, local events, market trends, even social media data or internal marketing campaigns. A restaurant with a terrace, for example, needs to anticipate that a sunny weekend is likely to bring more customers than a rainy one. A bar in a university district that’s unaware of upcoming exams or a major party next Friday is operating blind.

Food Waste: The Hidden Impact

Here’s the often-overlooked factor. Reducing food waste isn’t just a sustainable headline; it’s pure profitability turned into purpose.

• Food waste in the hospitality sector represents between 4% and 10% of the Cost of Goods Sold (COGS), according to industry analyses.
• More accurate demand forecasting directly reduces over-purchasing, lowering both waste and capital tied up in inventory.
• Companies that have implemented demand forecasting systems report waste reductions of between 15% and 25%, which translates into percentage points of profitability for the hospitality sector.

(Source: Report on Food Waste in the Restaurant Sector, Cáritas España, 2023)

This connects two worlds that often remain separate: sustainability and profitability. It’s not a trade-off. When a restaurant better anticipates what it will sell, it buys better, wastes less food, cuts costs, and improves its carbon footprint. All at the same time.

Real Implementation: Why “Decorative Software” Exists

Herein lies the risk we identify when working with small and medium-sized hospitality businesses: investing in technology without first defining the problem it solves.

SoftDoit, specializing in hospitality solutions, emphasizes something critical: useful automation is measurable automation. Before implementing a forecasting system, it’s essential to define key indicators: what is your current waste level? What is your inventory turnover? What is your labor cost as a percentage of sales? Without this baseline, it’s impossible to validate the ROI afterward.

The implementation logic should be:

1. Diagnose: Map current processes and specific pain points (not “we need to be more digital”).
2. Define Metrics: What do we want to improve, and how will we measure it?
3. Select Tool: Not the most visually appealing, but the one that aligns with that specific metric.
4. Measure and Adjust: Adoption is not an event; it’s a process.

Many restaurants purchase comprehensive management software, the tool collects data for months, and then no one uses it because they don’t know what decisions to make with that information. This is “decorative software.” The solution is to start small: implement demand forecasting as a component, learn to use the data, and scale from there.

Aspect	Traditional Approach	Data-Driven Approach
Decision Basis	Intuition and experience	Historical data + external variables
Purchasing	Generic estimate	Adjusted to forecast
Staffing	Fixed shifts	Dynamic based on customer traffic
Waste	Accepted as normal	Measured reduction as a goal

Conclusion: Operational Resilience, Not Just Efficiency

As experts who guide hospitality businesses in operational innovation, we identify that demand forecasting is, in reality, a lever for both economic and environmental resilience simultaneously. When a restaurant more accurately predicts its needs, it doesn’t just “save money”; it structures its business differently: it buys better, hires better, and wastes less food.

In a sector where margins are tight and volatility is increasing—due to labor costs, fluctuations in food commodities, and changes in consumer behavior—the ability to anticipate and adjust is the difference between being reactive and being resilient.

The technology is ready. The question is: is your business ready to truly use it?

Sources and References

• Controliza (2024): Machine Learning Demand Forecasting Tool for Hospitality.
• 7shifts (2024): Staff Management and Shift Forecasting Platform for HORECA.
• SoftDoit (2023): Guide to Useful Digitalization in Restaurants: Automating Key Processes.
• Cáritas España (2023): Report on Food Waste in the Restaurant and Catering Sector.
• Global Hospitality Insights (2024): Technology and Sustainability Trends in European HORECA.

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