Predictive maintenance in fleets: how it works

A major breakdown of an articulated truck stranded on the roadside costs on average between €3,500 and €6,000 once you add up the tow truck, the workshop, the held-up goods and the service you can’t bill. And it usually comes without warning: it happens on the day it hurts most. Predictive maintenance changes that equation at the root.

In this article we explain how predictive maintenance works technically in fleets, what data it needs, what kinds of breakdowns it anticipates and how much a real fleet saves by implementing it. No “magic AI” mysticism, and with market figures.

What predictive maintenance is

Predictive maintenance is the practice of anticipating breakdowns before they happen by combining three elements:

1. Real-time vehicle data: engine readings, battery, oil pressure, temperature, manufacturer DTC codes, driver behavior.
2. Accumulated history: months or years of telemetry from the same vehicle and from similar vehicles.
3. Trained AI models: algorithms that detect patterns invisible to the human eye and issue alerts with an explicit time horizon (“battery <30 days,” “clutch <90 days”).

Compared with corrective maintenance (fix it when it breaks) and preventive maintenance (replace parts every X km), predictive maintenance replaces parts when the data says they’re going to fail. Not before, not after.

What data AI needs to predict breakdowns

Prediction quality depends directly on the quantity and quality of data the system receives. Depending on the combination, the Teltonika devices we use at Trackia collect:

Plug & play OBD tracker (FMM003 / FMC003)

• Continuous GPS position (multi-constellation GNSS), speed, acceleration and braking.
• Engine on and off, operating hours, prolonged idling.
• Vehicle battery voltage and disconnection detection.
• OEM data via OBD: actual fuel, actual odometer and engine DTC codes.
• Built-in crash detection and jamming detection.

That’s enough to predict battery problems, injection failures, overheating, fuel anomalies and problematic driving patterns. The difference between the FMM003 (LTE Cat M1) and the FMC003 (LTE Cat 1) is how often events are sent to the server.

Eye Bluetooth accessories

• Eye Sensor (BTSMP1): temperature, humidity, movement and opening/closing in the trailer, warehouse or refrigerated box. Useful for predicting cold chain problems and detecting tampering.
• Eye Beacon (BTSID1): passive identification of the driver or asset (tool, crate, container), linked to the vehicle each time it’s used.

When these are combined with the tracker, the AI correlates vehicle data with who’s driving, what cargo is on board and under what conditions, which noticeably improves alert accuracy.

More details on the Teltonika hardware combination that fits each fleet in differences between the Teltonika FMM003, FMC003 and Eye accessories.

What types of breakdown can be anticipated

Not everything can be predicted with the same reliability. In order of maturity:

These ranges are typical in the market for mature systems based on CAN-bus data. Any provider promising “we predict everything” should raise an eyebrow.

How much predictive maintenance actually saves

Industry data compiled by Motor16 in 2025 puts the typical savings in fleets with AI-based predictive maintenance at:

• A 40% reduction in major breakdowns (the ones that take the vehicle out of operation).
• A 30% reduction in annual maintenance cost per vehicle.
• A 22% reduction in total workshop spend in the first year (Appvizer 2026).

For a fleet of 50 vans with an average maintenance spend of €3,200 per vehicle per year, the aggregate savings exceed €48,000 a year, not counting the indirect cost of vehicles off the road (which is usually 2-3 times the direct workshop cost).

Real anonymized case

A food distribution fleet with 35 rigid trucks reported the following after nine months of predictive maintenance:

• 21 major breakdowns anticipated and repaired in a scheduled workshop visit.
• 4 major breakdowns not anticipated (an acceptable range).
• A 78% reduction in the cost of tow trucks and roadside service.
• Net maintenance savings: €142,000 in the first year.

How predictive maintenance fits into day-to-day operations

Implementing predictive maintenance isn’t a matter of plugging in a device and waiting. The realistic operational flow is:

1. Hardware installation (15-40 minutes per vehicle depending on the model).
2. Calibration phase (2-4 weeks) in which the system builds up baseline data.
3. Activation of predictive alerts once the model has enough history.
4. Integration with the workshop management system (in-house or external): when an alert is triggered, a scheduled work order is generated automatically.
5. Monthly review of the hit rate and threshold adjustments.

The better the integration with the workshop (in-house or external), the more savings materialize. A prediction that isn’t acted on is just one more email in the fleet manager’s inbox.

Coordination with the workshop

Success depends on three clear roles:

• System (Trackia): issues predictive alerts with a horizon and a priority.
• Fleet manager: validates the alert and books a workshop appointment.
• Workshop: closes the incident and sends feedback to the system (correct prediction / false positive).

Without the third step, the system doesn’t improve.

What predictive maintenance doesn’t solve

Technical honesty requires us to say it: predictive maintenance doesn’t anticipate everything.

• Failures due to manufacturing defects: there’s no prior pattern to learn from.
• Damage from accidents or vandalism: unpredictable by definition.
• Driver negligence (impacts, misuse): the system can detect it but not prevent it.
• Components with very little data history (poorly instrumented auxiliary electrical systems).

The realistic promise is to eliminate 40% of major breakdowns and cut aggregate maintenance cost by 22% to 30%. Any higher figure should be verified.

Conclusion

Predictive maintenance is the AI component with the fastest, most measurable ROI in a fleet: savings of 22% to 30% in the first year, a 40% reduction in major breakdowns and, above all, vehicles that stop breaking down at the worst possible moment.

The technical condition is having the right hardware (CAN-bus on heavy vehicles, OBD-II on light ones) and software with trained models, not static rules. The operational condition is integrating predictions with the workshop so that every alert is closed with a real work order.

Want to see how much Trackia would reduce your fleet’s breakdowns? Book a personalized demo →

We’ll show you cases similar to yours in industry and size and send you a 12-month savings estimate within 24 hours. Learn more on our predictive maintenance page or explore all of Trackia’s AI capabilities.