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EspañolAnticipating what will happen in the market is no longer a competitive advantage — it has become a matter of survival. In times of tight margins, high product turnover, and increasingly demanding consumers, accurately forecasting demand is essential to keep operations healthy and cash flow in the black. Even so, many companies still base their decisions on intuition or generic models, far removed from the reality of their business.
But what if it were possible to use artificial intelligence to turn historical data into reliable forecasts — and, more than that, into better decisions?
The Challenge: Predicting the Future When the Present Is Already Complex
Companies dealing with logistics and inventory management know that demand forecasting is one of the biggest operational challenges. Mistakes in this process lead to significant losses, whether due to overstocking or stockouts. This was the scenario that led one of our clients — a company responsible for purchasing and logistics for large retail chains — to reach out to us.
Their traditional model was based on simple statistical forecasts. It worked within a standard pattern, but couldn’t keep up with market fluctuations, seasonality, or regional behavior.
As a result, around R$10 million worth of inventory remained stagnant — representing at least 33% of total volume — hindering decision-making agility and the strategic use of resources.
These numbers clearly showed the operational impact. Perishable products lost their ideal sales window. Other items sold out sooner than expected. Planning became reactive, and management was forced to make last-minute adjustments constantly.
The Solution: Artificial Intelligence Applied to Predictability
Our role was to develop a sales prediction solution based on artificial intelligence. The goal was clear: increase accuracy in purchasing and replenishment decisions using real data and continuous learning.
The process was divided into four main stages:
- Data Collection: We gathered historical data on purchases, sales, seasonality, regional variations, and other relevant factors.
- Pre-processing: We cleaned, organized, and structured the data to effectively feed the models.
- Model Training: We implemented an advanced deep learning architecture specialized in sequential data processing, capable of learning complex patterns across multiple time series simultaneously. The system automatically integrates external variables that influence demand, such as seasonality, regional events, and market trends, continuously adapting as new data is incorporated.
- Forecast Generation: We produced projections for 30, 60, 90, and 120-day windows, taking into account the particularities of each store, region, and product.
This approach allowed the model to learn from past patterns and from the behavior of external variables like supplier lead times and stockout history.
The Results: From Uncertainty to Intelligence
With the application of AI, we transformed the client’s decision-making process. Predictability began to guide buying and selling actions, directly impacting various areas:
- 50% reduction in idle inventory, from R$10 million to R$5 million, without affecting operations.
- Increased efficiency in financial resource allocation.
- Reduced losses and waste, especially with perishables.
- Greater predictability in logistics planning.
This project demonstrates how combining a company’s existing business knowledge with new AI technologies creates a powerful competitive edge.
The company further improved its planning process by complementing managerial expertise with data-driven insights, increasing accuracy in supply decisions and reducing stock variations.
The Strategic Importance of Forecasting
Although this solution was initially applied to sales forecasting, it has the potential to impact other areas of management. The model can be extended to:
- Commercial target projections
- Strategic KPI performance estimates
- Anticipation of operational needs
- Personalized shopping experiences
- Supply chain optimization
- Product lifecycle management
- Workforce planning
- Predictive equipment maintenance
- Quality control
- Production capacity planning
- Dynamic pricing optimization
Consumer trend detection - Customer flow forecasting
Product lifecycle forecasting
By turning data into actionable intelligence, we make business operations faster, more efficient, and more competitive.
It’s Not Just About Technology — It’s About Results
This project shows that when well applied, artificial intelligence is no longer just a technical differentiator — it becomes a strategic asset. In this case, technology didn’t just automate processes; it created a new standard for decision-making, driven by accurate, data-backed predictions.
This is where innovation connects with business: less waste, more profitability, better decisions.
If your company also needs more accurate forecasts to plan with greater confidence, it may be time to turn your data into a real competitive advantage. Get in touch to learn more about this solution.
HOW TO IMPROVE YOUR EGU SCORE?
If your EGU score is lower than expected, don’t worry! The system itself helps you improve. Here are some actions you can take:
✅ Regularly analyze your indicators and input data correctly.
✅ Follow the improvement suggestions provided within EGU.
✅ Engage your team in using management tools.
✅ Use the “Improve EGU” feature for targeted actions.
Now that you know the impact of EGU on your efforts, make the most of all available resources and take your goals to the next level!
