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15:08, 04 February 2026
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Russia Automates Load Monitoring for Dump Trucks

The system tracks multiple parameters in real time, boosting vehicle efficiency.

Photo: iStock

The Tsifra Group has developed and tested a Russian-made load-monitoring system for road dump trucks, the company said.

The digital solution, known as SKZ-DS, tracks cargo weight, the number of trips, vehicle technical condition, and compliance with load limits in real time.

The system can be integrated into existing operational workflows. It is expected to improve vehicle efficiency, productivity, and road safety, while saving millions of rubles per year by reducing fines, downtime losses, repair costs, and fuel expenses.

Tested and Ready for Deployment

Testing under harsh climatic conditions and in areas with limited infrastructure confirmed the technology’s accuracy. The system is now ready for industrial deployment. In a pilot phase, it will be rolled out on 20 road dump trucks, with plans to scale the solution across the Sakhalin Oblast.

“We have been successfully developing load and fuel monitoring systems for mining equipment in the extractive industry for several years and see strong demand for these solutions,” said Nikolai Godunov, executive director of Tsifra Group’s Mining Division. “Transparent trip accounting and automated load control will allow us to increase equipment productivity. Experience shows that precise control of dump-truck loading, fuel consumption, and careful equipment operation are relevant not only in mining but also in other sectors, such as construction and transport logistics.”

Scaling the Experience

The use of digital technologies in heavy industry continues to accelerate, with online monitoring systems for production processes playing a particularly important role.

Previously, diamond producer ALROSA tested the Horus Mine digital control system at its Udachny mining and processing plant. The system automates the loading of mining dump trucks by analyzing rock volume and fragmentation using machine vision, lidar, and neural networks.

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