Your fuel report may show that the fleet averaged 28 litres per 100 km last month. What it usually cannot explain is why truck 47 consumed 15% more than truck 48 on the same route.

It cannot show whether the difference comes from driver behaviour, inconsistent source data, fuel sensor drift or a developing technical issue. Standard FMS fuel data gives you a number, but not the operational context needed to act on it.

The challenge becomes even greater in mixed fleets. DAF, Mercedes, Volvo and Scania do not always calculate or expose fuel-related data in the same way. That makes direct comparison unreliable and limits the value of reporting.

Squarell goes beyond standard FMS by accessing deeper OEM-specific signals related to fuel consumption, idle behaviour, throttle input, cruise control usage and aftertreatment status. These signals are then harmonised into a consistent data model across brands.

This allows your platform to compare vehicles more fairly, analyse consumption with more confidence and benchmark performance across mixed commercial fleets without relying on inconsistent OEM calculations.

Instead of adding another dashboard, Squarell delivers the data into your own platform, analytics environment or customer reporting flow, so you stay in control of the user experience.

When fuel and behaviour data becomes more accurate, it becomes more useful. You can identify which drivers are creating unnecessary fuel cost, which vehicles are underperforming and which patterns may point to a mechanical inefficiency.

Fuel anomalies often signal maintenance issues. See Uptime for predictive maintenance data.

This supports more targeted coaching, stronger customer reporting and earlier intervention when
efficiency drops. It also helps platforms move beyond static reporting and provide recommendations
based on real vehicle behaviour rather than assumptions.

For telematics providers and fleet platforms, this turns raw vehicle signals into practical operational intelligence.