Fleet Fuel Efficiency Software That Compares Like With Like
A raw litres-per-hundred-kilometres league table punishes the driver with the worst route. KO Fleetz normalises consumption against load, terrain and duty cycle, separates idling from moving, and shows where the burn rate is genuinely improvable rather than merely high.
The league table everyone knows is unfair
Somebody builds the efficiency report. It ranks drivers by litres per hundred kilometres, and the bottom of the list is the driver who runs the hill route out to the quarry with a full trailer, in traffic, in summer. He has been at the bottom for three years. He knows why. Everyone in the room knows why. The report gets discussed and then ignored, which is the worst of both outcomes.
Meanwhile the actual waste is invisible. A vehicle idling ninety minutes a day at a loading bay covers no distance, so it never appears in a per-kilometre figure at all — it just quietly makes every other number on that vehicle look slightly worse. The same goes for a slipping habit, a dragging brake or a set of underinflated tyres, all of which show up as a vague sense that the truck is thirsty.
And the manufacturer's figure is no help as a baseline. It was measured on a test cycle by a professional at a steady speed with no load, and quoting it to a driver hauling twenty-six tonnes through a city is a good way to lose the room. Without a fair comparison, fuel efficiency is a conversation about blame rather than about litres.
Normalise first, then compare
Comparison only means anything within a duty cycle. KO Fleetz groups trips by lane, payload band, vehicle class and terrain profile before it ranks anything, so the question stops being 'who is worst in the fleet' and becomes 'of the eleven drivers who ran this lane at this weight, why does one use noticeably more'. That question has an answer somebody can act on.
Idle burn is pulled out and counted on its own clock. Litres consumed while stationary with the engine running are reported as hours and litres, not folded into a distance-based rate, because the two behaviours have completely different fixes — one is a driving technique problem and the other is usually a scheduling or loading-bay problem.
The module also refuses to produce a single fleet-wide efficiency score. A number that averages a refrigerated trailer, a service van and a tipper is arithmetic without meaning, and it invites decisions that make the fleet worse. Nor does it flag unexplained losses; a vehicle that is being drained will look inefficient here, and separating that case is what fuel theft detection is for.
Capabilities
What KO Fleetz fuel efficiency gives your team
Like-for-like trip grouping
Consumption is compared only within matching lane, load band and vehicle class, so route difficulty stops masquerading as driver behaviour.
Idle burn separated
Stationary engine hours and the litres they consume are reported on their own, rather than dissolved into a per-kilometre rate.
Driver comparison on one lane
Ranks the drivers who actually ran the same corridor at similar weight, which is the only ranking a driver will accept as fair.
Payload-aware consumption
Litres per tonne-kilometre for vehicles with weight data, so a fully loaded run is not penalised against a half-empty one.
Vehicle drift detection
KO Fleetz tracks each vehicle against its own history, surfacing the slow climb in consumption that usually means a mechanical problem, not a driver.
Elevation and terrain context
Elevation gained across a trip is carried with the consumption figure, so a mountain leg reads differently from a motorway leg.
Behaviour correlation
Links harsh acceleration, over-speeding and heavy braking events to the fuel burned on the same segment of the same trip.
Depot and class breakdown
KO Fleetz rolls efficiency up by depot, vehicle class and contract, keeping the comparison inside groups where the roll-up still means something.
How it works
How KO Fleetz does it
Step 1: Establish each vehicle's own baseline
Consumption history is built per vehicle across its real work, giving every asset a personal reference point instead of a fleet target it could never meet.
Step 2: Segment the work
Trips are sorted into comparable groups by corridor, load band, terrain and vehicle class. Anything without a peer group is reported on its own.
Step 3: Separate idle from motion
Fuel burned standing still is separated from fuel that moved a load, and each is attributed to the shift, site or bay where it happened.
Step 4: Act on the gap
The output is a short list: this driver on this lane, this vehicle drifting against itself, this loading bay generating idle hours. Each has its own owner and fix.
Outcomes
What changes
- Comparison within matching duty cycles
- Peer group
- Standing burn counted on its own clock
- Idle split out
- Each vehicle measured against its own history
- Self-referenced
- Findings that name an owner, not a culprit
- Actionable
Frequently asked questions
They answer different questions. Fuel monitoring asks where the litres went and whether the books balance — it is accounting. Fuel efficiency asks whether the litres that were legitimately burned needed to be, and that requires normalising for the work the vehicle did. A fleet can have perfect fuel accounting and terrible efficiency. The ledger is the input here; this module is the analysis layer sitting on it.
By never comparing across duty cycles. A driver is only ranked against others who ran a comparable corridor at a comparable weight, and the group is shown alongside the ranking so anyone can check the comparison is fair. Where no peer group exists — a one-off long haul, a unique vehicle — KO Fleetz reports the trip against that vehicle's own history instead. A driver who can see why the comparison is fair will argue about the cause rather than the method.
Software does not burn less fuel. It identifies where fuel is being burned without producing work, and who can change that. Idle hours at a loading bay get fixed by scheduling. A vehicle drifting against its own baseline gets fixed in the workshop. A driver with a heavier right foot than his peers on the same lane gets fixed by coaching. We will not quote you a saving figure, because the honest answer depends entirely on which of those three you have and how much of it.
It helps a great deal and is not required. Without payload data, comparison still works within a lane and vehicle class, and idle analysis, drift detection and behaviour correlation are unaffected. What you lose is litres per tonne-kilometre, which is the fairest metric available for a fleet whose loads vary run to run. If you have axle load sensors or a weighbridge feed, it is worth connecting.
Long enough to build peer groups, which depends on how repetitive your work is. A distribution fleet running the same lanes daily produces useful comparisons within a few weeks. A general haulage operation where no two jobs match takes considerably longer, and some vehicles will never get a peer group at all — those are reported against their own history from the start, which is available as soon as a baseline exists.
Sometimes. Dragging brakes, underinflated tyres, a clogged filter and an ageing injector all present as a slow climb against a vehicle's own baseline. So does a change of driver, a change of route, a heavier season of loads, and a fuel sensor drifting out of calibration. The module shows the trend and the context around it; deciding which cause applies is a workshop conversation, and it is usually worth checking the calibration before booking the bay.
Find out where the litres are actually going
Pick one lane your fleet runs every week, and KO Fleetz will show you the spread between drivers on it and what explains the gap.