AI in cement logistics: how silo monitoring cuts costs

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.

TL;DR

• SiloConnect measures micro-deformations at the silo base and uses patented AI to turn that signal into reliable weight data, silo by silo
• The AI doesn't forecast, it learns each silo's individual behavior, filtering out heat, dust, and uneven loading
• Holcim Mexico cut transport cost per ton by 11% and delivery waiting times by 51% after a national rollout

Why cement logistics is still reactive

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.

➔ Keep reading about the cement logistics digitalization

How AI turns silo deformation into reliable inventory data

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
  

"Each silo has its own physical signature. Our sensors measure both the deformation at the silo foot and the local temperature. By correlating the two, our AI isolates the true weight-related deformation, filtering out environmental effects like heat or sun exposure." says Lilian Beuneche

How AI supports dispatch decisions without replacing planners

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. This matters for dispatch teams who fear losing control. The system doesn't take decisions away, it gives teams the visibility to decide earlier and with more confidence.

"Operators who used to call for orders need to feel confident that the system will do the right thing at the right time. That's why we focused heavily on visibility and control." David Poirot, Product Director at Nanolike

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: −11% transport cost per ton, +24% transport productivity (more weekly deliveries), and −51% delivery waiting times after a national rollout.
• Lafarge France: optimized truck fleet size by redistributing deliveries more evenly across the day.
  

"What began as an innovation quickly became our new logistics standard, not just a smart upgrade, but a key driver of efficiency at scale," says Juan Ignacio Chiesa, Logistics and Distribution Director at Holcim Mexico.

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.

Beyond logistics, proactive replenishment becomes a commercial asset: reliable service builds customer loyalty and sets your delivery offer apart.

The question isn't whether AI belongs in cement logistics. The results at Holcim France and Mexico already answer that. The question is how quickly your operations can benefit from it.