Fleet buyers have long known that quoted fuel-consumption figures do not always match what happens once a vehicle enters service (they don’t need the AAA telling them).
A ute fitted with a tray, tools, payload, roof racks and other accessories, then operated across regional roads, construction sites or congested metro routes, is unlikely to deliver the same result as a standard vehicle on a controlled test cycle.
The problem is not a lack of fuel data. Most fleets have plenty of it. The challenge is comparing that data in a way that helps Fleet Managers make better replacement and vehicle-selection decisions.
Summit Fleet Leasing and Management believes AI can make that process much easier.
During a demonstration of its AI Fleet Insights platform, Technology and Business Systems Manager Jon Bates asked the system to compare the fuel economy of Toyota HiLux and Ford Ranger vehicles operating in a customer fleet.
The system identified the number of vehicles in each group, compared average and median fuel consumption, highlighted variation across the fleet and identified higher-consumption pockets in regional areas.
For the example fleet, the tool found only a small average difference between the two models. But the more useful outcome was the ability to see where the numbers changed and where further investigation may be needed.
The gap between the brochure and the job
Manufacturer fuel-consumption figures remain useful as a starting point. They give Fleet Managers a consistent point of comparison during procurement, but they cannot reflect every operational variable that fleet vehicles face.
During the demonstration, the discussion turned to a common fleet purchasing question: what happens when a vehicle quoted at 8.9L/100km is fitted with a tray and then operated in real-world conditions?.
The value of fleet fuel data is not simply proving that a vehicle uses more fuel than its quoted figure. It is understanding the circumstances behind the result.
A vehicle working in metropolitan stop-start traffic may deliver a very different outcome from the same model travelling regional highways. The same applies to payload, towing, idling, terrain, accessory fitment, driver behaviour and the type of work undertaken.
AI can help Fleet Managers move beyond a fleet-wide average and identify which of those variables are influencing actual fuel use.
Comparing like with like
A traditional fuel report can show litres, kilometres and cost. It can be harder to quickly compare similar vehicles across different locations, operating groups and time periods.
In the Summit demonstration, AI Fleet Insights was able to compare more than 1,300 Ford Rangers with 109 Toyota HiLux vehicles in minutes. It assessed average consumption, median fuel use and the range of results across the two models.
Bates said the system could also identify outliers and areas requiring more attention.
“It’s identified some outliers, like high consumption pockets in regional areas,” he said.
That matters when a Fleet Manager is choosing the next vehicle model. A model may perform well in one part of the organisation but be less suitable for another operating environment.
The answer may not be to select a different vehicle altogether. It may be to change the specification, review payload, modify driver training, improve route planning or examine whether a vehicle is being used for work it was not intended to do.
Turning fuel data into a procurement tool
When manufacturer-stated fuel consumption is considered alongside actual fleet fuel use, the vehicle-selection discussion becomes much more practical.
Rather than choosing a model based on purchase price, quoted consumption and anecdotal feedback, a fleet can ask more useful questions:
- How does this model perform when fitted with our standard accessories?
- What happens when it carries the equipment our teams use every day?
- Does it deliver different results across metro, regional and remote work?
- Which specification gives us the best operating outcome over the vehicle lifecycle?
- Are some models better suited to certain roles, routes or operating conditions?
These questions matter when fuel costs are under pressure and organisations are expected to do more with less.
A fleet may find that the lowest claimed consumption figure does not necessarily translate into the lowest operating cost. A vehicle that uses slightly more fuel but is better suited to the work, needs fewer modifications or reduces downtime may still be the stronger lifecycle choice.
From raw transactions to better decisions
The opportunity is not just to produce another fuel report.
AI can help Fleet Managers turn raw transactions into a more useful decision tool by identifying patterns, comparing vehicle groups and highlighting where further investigation will have the greatest value.
It will not replace a proper vehicle trial or the judgement of experienced Fleet Managers. But it can make fleet data more relevant at the point when a replacement decision is being made.
For organisations buying hundreds or thousands of vehicles, even a small improvement in fuel efficiency or vehicle fit-for-purpose can have a significant impact on operating cost.
The most useful fuel figure may no longer be the one printed in a brochure. It may be the one generated from the fleet’s own real-world data.
