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Case Study: Request-Approved Fuel Automation in a Logistics Fleet (Vega-Trans Lojistik A.Ş.)

PetroDATA20 Ağustos 20264 min read
Case Study: Request-Approved Fuel Automation in a Logistics Fleet (Vega-Trans Lojistik A.Ş.)

A 57-tractor-unit fleet was drawing ~12,500 liters a day from a single 20,000-liter tank and a twin pump — all logged on paper. Here is the automation built with fingerprint pairing, Arvento integration and a bespoke pre-approved request engine.

In logistics, fuel is the largest and most volatile operating cost line. A haulage company's margin is often decided in cents per liter, which makes knowing where, when and how much fuel goes a profitability question rather than an accounting one. This case study covers the automation installed across the Vega-Trans Lojistik A.Ş. fleet and the request engine developed specifically for the customer.

Project profile

Component Value
Fleet 57 tractor units (standard routes)
Fixed fuel tank 20,000 liters (20 tons)
Fuel pump 1 twin pump
Supply point A single point on site

Operation scale in numbers

Period Volume
Daily average ~12,500 liters
Monthly (30 days) ~375,000 liters
Yearly (365 days) ~4.5 million liters

The 20,000-liter tank holds about 1.6 days of consumption. In other words the tank fills and empties roughly every other day; at that turnover, any lag in stock reconciliation also delays the moment a loss is noticed.

The challenges

Before the system was installed, all of that volume was tracked by hand on paper records.

  • End-of-day compilation. Which vehicle took how much and when could only be assembled from paper at the end of the day.
  • Reconciliation taking hours. The gap between tank level and fill records turned into an investigation every time.
  • Wrong odometer, wrong average. In a fleet running standard routes, consumption deviation is the most reliable audit signal. But because odometer values were entered manually, liters-per-100 km averages came out wrong, and operational leakage could not be told apart from genuine route conditions.
  • A pump with no upper limit. Since the pump ran without any cap, nothing technically prevented a fill larger than planned.

The solution installed

1. Site automation system

The 20,000-liter tank was placed under level monitoring and the twin pump was automated through a control unit. Every fill is recorded automatically with vehicle, driver, quantity and time.

2. Central management system

All fuel draws and stock movements are streamed to a single center in real time. Tank stock and total fills are now continuously reconciled, and manual reconciliation ended entirely.

3. RFID + biometric identification

Vehicles were assigned RFID key fobs, and drivers were enrolled by fingerprint and paired with their vehicles. The pump does not run until the driver's fingerprint is read and the system confirms the match with the RFID fob. Every fill is therefore tied to a verified vehicle–driver pair.

Bespoke development: the pre-approved request engine

The distinguishing component of the project is the request module built at the customer's request. It works simply:

  1. The driver or the logistics center opens a fuel request before fuelling.
  2. The request is approved and the approved number of liters is passed to the pump.
  3. The pump dispenses only the pre-approved volume.
  4. With no approved request on file, the pump stays completely closed to fuelling.

This moves control from after-the-fact auditing to permission before the record. In classic automation an oversized fill shows up later in a report; here an unplanned fill simply cannot happen. Abuse is closed off, and planning discipline settles into the field: every fill is tied to a request, and every request to a trip.

Arvento integration: live odometer data

Instead of being typed in, odometer values are read live from the vehicle through an Arvento fleet-tracking integration. This detail alone determines the accuracy of consumption analysis: a single mistyped odometer reading makes that vehicle's monthly average meaningless.

Because net fill volume and live odometer data arrive together, each vehicle's liters/100 km average is calculated without error and can be compared route by route. In a fleet on standard routes this is the strongest audit tool available: a persistent difference between two tractor units running the same line is now a measurable deviation.

Value delivered

  • Real-time tank stock and fill tracking ended manual reconciliation entirely.
  • Combining live odometer data with net fill volumes produced accurate per-vehicle consumption averages (liters/100 km).
  • The pre-approved request engine technically blocks unplanned fills and operational leakage.
  • Every fill is tied to a verified vehicle–driver pair; unregistered vehicles and unauthorized users cannot draw fuel.

Conclusion

The Vega-Trans project shows the difference a customer-specific business rule makes on top of standard automation. Tank level monitoring, pump control and identification make the site visible; the pre-approved request engine turns that visibility directly into authorization. Live odometer data from the Arvento integration then makes the whole structure measurable. The result is a ~12,500-liter-a-day operation managed transparently, reconciled and auditable from a single center.

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