SiloConnect
Cement production sites have been optimized through automation and sensors for years, but logistics remains the sector’s biggest opportunity for efficiency gains. In most organisations, it still runs on phone calls, spreadsheets, and last-minute adjustments.
Artificial Intelligence (AI) is making a significant impact today, functioning not as a futuristic concept but as an effective layer of intelligence that sits on top of connected silo data and helps logistics teams act earlier and more precisely.
AI interprets real-world data, identifies patterns in consumption, and enables proactive actions before issues emerge. In an industry where millions of tons are transported each month, this transformation represents a genuine change in operations.
Why traditional logistics still struggles
For decades, dispatch operations have relied on reactive communication: phone calls, spreadsheets, and manual coordination between plants and transporters.
Even a short delay in stock information triggers a chain reaction:
- Emergency deliveries
- Unplanned truck dispatches
- Overtime costs and idle time at plants
According to the European Logistics Association (2024), nearly 30% of heavy-truck mileage in construction materials transport is still driven empty or inefficiently, due to poor data synchronisation between plants and sites.
And the problem isn't just operational. When dispatch decisions are based on stale or incomplete data, every inefficiency compounds. A missed replenishment becomes an emergency order. An emergency order becomes an unplanned trip. An unplanned trip eats margin.
AI doesn't fix this by replacing human judgment. It gives dispatchers and planners better information, earlier.
Turning data into logistics foresight
SiloConnect collects continuous strain data from the silo's structure, measuring micro-deformations on one of its metallic legs.
That data reflects the actual weight of stored material, even under harsh industrial conditions: dust, temperature variations, and humidity.
What makes it different from a standard sensor is what happens with the data.
SiloConnect's AI algorithms, patented by Nanolike, learn the unique behavior of each silo over time. Instead of delivering theoretical precision, the system provides stable, decision-ready data that logistics teams can rely on.
“Our AI doesn’t predict, it learns. Each silo has its own fingerprint, and the algorithm adapts to it,” explains Lilian Beuneche, Data Analyst and AI expert at Nanolike.
In practice, that translates to:
- Reliable consumption insights without manual measurement
- Automatic detection of irregular stock evolution
- Smarter replenishment planning across a full network of silos
"We train a dedicated AI model for every silo. That means our solution adapts to each use case, automatically, and at no extra cost to the client." - Lilian Beuneche, Data Analyst and AI expert at Nanolike.
From monitoring to operational decisions
Once the data is clean and continuous, logistics teams stop guessing. But the real shift isn't just visibility. It's what teams do with that visibility.
With SiloConnect, AI acts as a decision support layer, not an autopilot. Machine learning algorithms flag anomalies, such as unusual consumption or irregular refill timing, and help teams understand the impact of corrective actions.
Over time, this creates a continuous improvement cycle where each delivery informs better planning for the next.
What changes day-to-day for logistics teams
- Automatic low-level alerts before a stockout happens, not after
- Higher truck occupancy rates
- Fewer emergency calls between dispatchers and plant operators
- More balanced delivery schedules across the day
Results from the field
The deployment of AI-based silo monitoring is already producing measurable results across major markets.
- Holcim Mexico: +24% transport efficiency, −51% idle time, and −11% logistics cost per ton.
- Lafarge France: optimized truck fleet size by redistributing deliveries more evenly across the day.
- Holcim Spain: halved last-minute orders and achieved zero stockouts.
"With SiloConnect, we managed to smooth out our deliveries throughout the day, reducing the morning rush and optimising truck usage. Fewer last-minute changes, less time on the phone, and greater flexibility in planning our routes," says Nathalie Herrero, Logistics and Planification at Holcim France.
These results demonstrate that AI-driven insights can drastically reduce logistical friction, not by replacing human decision-making, but by empowering it with better timing and visibility.
Conclusion
AI in cement logistics is operational today. Not experimental. Not theoretical.
The combination of connected silo data and adaptive algorithms gives logistics directors, dispatchers, and planners a clearer view of what's happening across their network, earlier than before, and with enough reliability to act on it.
The question isn't whether AI belongs in cement logistics. The results at Holcim France, Spain, and Mexico already answer that. The question is how quickly your operations can benefit from it.
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