The Autonomous Era in Ready-Mix Logistics in Northern Cyprus: How Korman Beton Ltd. Reduced Operational Downtime to Zero with PetroDATA
A 154-vehicle mixed fleet was running on an automation system that kept failing. Here is the migration: 100% autonomous vehicle identification, Arvento integration and a local + central architecture.
Introduction and company profile
Ready-mix concrete is a product whose countdown starts the moment it is produced. A mixer leaving the plant must reach the pour site within a fixed window; a delay is not just a delivery problem but a direct risk of losing the product. That is why in ready-mix logistics, refuelling is the most critical link in fleet movement — and the one least tolerant of delay.
Korman Beton Ltd. operates in ready-mix concrete production and construction logistics in Northern Cyprus. Its fleet is large and mixed, spanning 154 vehicles:
| Vehicle class | Count |
|---|---|
| Mixers (transit mixers) | 100 |
| Heavy machines | 27 |
| Cars | 17 |
| Concrete pumps | 10 |
| Total | 154 |
With four different classes fed from a single supply point, uninterrupted fuel management is effectively synonymous with the operation itself.
Defining the problem: the hidden cost of a system that does not work
Before moving to PetroDATA, Korman Beton was using another automation system on site. It could manage fuel processes only at a basic level while producing constant hardware and software failures. Seeking a more stable and professional solution to replace a structure that ran inefficiently and disrupted operations, the company approached PetroDATA Bilişim.
This is a situation frequently underestimated in automation projects: a system that does not work costs more than no system at all.
- A pause means a site delay. A mixer waiting at the pump means the pour is pushed back. In ready-mix, time cannot be recovered.
- Unreliable data is not data. A consumption report drawn from a faulty record chain cannot support decisions, so the fleet ends up managed by estimate again.
- Maintaining the system consumes labor. Constant fault-chasing pulls the operations team away from its own work.
Here the issue is not a missing module but stability. That is the reasoning behind the migration decision.
The engineering solution
Local + central architecture
A two-layer structure was installed at the concrete plant campus:
- 1 local site automation system — the layer that manages fuelling on site and keeps working by holding records locally even if the connection drops.
- 1 cloud / central management system — the layer where all data is monitored, reported and reconciled in real time.
This pairing is what stability looks like architecturally: fuelling on site is not made dependent on a central connection, while the center sees the site continuously.
100% autonomous vehicle identification
The entire 154-vehicle fleet — mixers, concrete pumps, heavy machines and cars — was equipped with RFID vehicle identification. Full coverage is a deliberate choice: when part of a fleet stays outside the system, that gap invalidates both the report and the audit.
The result is a fuelling loop stripped of the human factor. Manual forms, pump-operator discretion and mistyped plates or quantities are out of the chain; fuel is dispensed only to a registered vehicle, on an automatic record.
Arvento telematics integration
The system was integrated with the Arvento GPS and fleet-tracking platform at API level. Current odometer and operating-hour data are pulled autonomously from the vehicles themselves and matched with fuel consumption analysis.
This is the only way to use the right metric across a mixed fleet:
| Vehicle class | Natural metric |
|---|---|
| Mixers · cars | liters/100 km |
| Concrete pumps · heavy machines | liters/hour |
The haulage time of the mixers and the active operating hours of the concrete pumps are combined with odometer data from Arvento to produce per-vehicle efficiency reports.
Operational impact
1. Uninterrupted operation. The frequently failing legacy system was replaced with industrial-grade PetroDATA hardware and software, and system downtime was reduced to zero. In a ready-mix operation racing against time, site delays caused by fuelling stoppages were eliminated.
2. Zero human error. Installing vehicle identification and telematics integration together created an autonomous — unmanned — fuel management loop. Errors from manual data entry disappeared entirely.
3. Comprehensive consumption analysis. Per-vehicle liters/hour and liters/100 km efficiency reports became available. For the first time, a 154-vehicle mixed fleet appears in a single comparison table: a mixer deviating from its own average, or a pump running beyond expectation, is now a measurable signal.
4. Loss and leakage control. Because no fuel can be dispensed to an unregistered vehicle and every fill is captured automatically, tank level stays continuously reconciled with total fills.
Conclusion
The Korman Beton Ltd. project shows that the value of an automation investment is measured not by a feature list but by running without interruption in the field. Migrating from a failure-prone system to an industrial-grade architecture — separating the local and central layers, bringing 100% of the fleet under vehicle identification and making measurement autonomous through Arvento integration — turned a 154-vehicle ready-mix operation into one managed by data rather than estimate.
This installation in Northern Cyprus is also a concrete example of PetroDATA's field experience abroad: migration projects prove that moving to a more stable structure is possible without halting the running operation.
