Published on the 07/05/2026 | Written by Heather Wright

Under-leveraged systems leaving money on job sites…

At a time when every dollar counts, local construction, transport and commercial vehicle companies are leaving money in the ground with under-utilised machines, incomplete data and fragmented systems quietly eroding margins – even as utilisation emerges as one of the most under-used levers for improving productivity, cost control and project profitability across equipment-intensive industries.

New research from Teletrac Navman (those of a certain vintage may recall that Navman was a Kiwi company founded by Sir Peter Maire back in the 1980s) shows the issue is not a lack of technology investment. In fact, 84 percent of fleets have invested in tracking, telematics and integrated equipment platforms to some degree. The problem, Teletrac Navman construction solution specialist James French told iStart, is what happens next – or more accurately, what doesn’t, with many deployments remaining incomplete, creating pockets of insight, rather than organisation-wide intelligence.

“Without all the data… you can be profitable overall, but you’re leaving money in the dirt.”

The report, which is based on a quantitative online survey of 600 companies operating commercial vehicle fleets, including 200 across Australia and New Zealand, 75 percent are still relying on manual logs either as a primary tracking method or alongside digital systems. Fewer than one-third have fully implemented utilisation technology.

French says while most operators recognise utilisation is important – and believe equipment may be underutilised – the actual picture many businesses are working from is incomplete.

“The utilisation information is often incomplete,” he says, describing fleets where some equipment is tracked, while others aren’t, or where telematics is applied only to specific projects, rather than the entire fleet. The result is what he calls a partial utilisation picture – and decisions made on partial data rarely end well.

French says utilisation initiatives commonly stall because projects are left half finished. GPS might be rolled out to part of the fleet, but not all. Maintenance systems live somewhere else. Third-party equipment has its own tracking platform. Acquisitions add yet another layer of complexity, with multiple systems running side by side because no one wants to disrupt what the newly acquired business is comfortable using.

“You’ve got disparate systems,” he says. “GPS sits here, maintenance sits somewhere else, fuel lives somewhere else again.”

Without pulling those datasets together, organisations miss the opportunity to build a full picture of how assets are actually performing – and what they really cost to run.

French says once utilisation data is integrated with maintenance, fuel and financial systems, businesses can calculate their true total cost of ownership per machine, per hour. That’s where the money starts to surface – and sometimes, where uncomfortable truths appear.

“If it costs $250 an hour to run a machine and you’re charging $220 an hour, you’re losing money,” he says. “Without all the data, you don’t know it. You can be profitable overall, but you’re leaving money in the dirt.”

Idling away

One of the headline findings from the research is that equipment is sitting idle up to 50 percent of the time.

French admits calculating idle is fraught with technical challenges.

Basic GPS can tell operators where machines are and when engines are on or off. More accurate utilisation comes when systems are connected into engine data via CAN (controller area network) bus – the vehicle standard designed to allow microcontrollers and electronic control units such as the engine, brakes and lights, to communicate with each other – pulling details such as RPM to distinguish between productive work and an engine running without doing anything useful.

Machine-level integration also matters. A crane truck may be stationary with the engine running, but if the crane is operating, that’s productive use, not idle time. Without that visibility, utilisation data risks being misleading.

Manual logs are also fraught with issues. French points to a real-world productivity study he worked on in a previous role, comparing manually recorded data with machine-generated data.

“We found about a 15 percent difference,” he says.

Human error, transposed numbers, lost paperwork and best-guess reporting cause further issues in manual reporting. With digital systems, French says, the data becomes a single source of truth that can be audited, retrieved and compared across years.

The double-pay issue

Nowhere is the financial impact more visible than in equipment hoarding.

The report found that two-thirds of organisations admit assets are sometimes held onsite but unused, largely due to uncertainty around maintenance and scheduling. French says the behaviour is understandable, but costly.

“If I ring another site asking if they’ve got a 24‑tonne excavator, they’ll often say they’re flat out,” he says. “Sometimes that’s fear they won’t get it back. Sometimes it’s that they don’t actually know whether it’s being used or not.”

Without utilisation data, businesses turn to renting an additional machine – and double-paying.

“If I think it’s being used and I go and rent another machine, now I’m paying for one excavator sitting idle and another one working,” French says. “One is internal money. The rental is external money. That costs more.”

The fix does not require sophisticated analytics from day one. French says even fleet‑wide GPS coverage can immediately change behaviour.

“If I can ring a site and say, ‘I can see that machine hasn’t worked in a week – we’re sending a truck to move it,’ that saves money immediately.”

He cites the example of one client who reported saving roughly $30,000 in just 40 days by identifying idle plant and redeploying assets based on evidence rather than assumption.

For Australian companies, there’s another big win waiting: Fuel tax credits. He says many companies are leaving cash unclaimed simply because they can’t prove how, where and when equipment is used.

GPS and utilisation data can provide an accurate picture of when vehicles and equipment are operating on ‘non-gazetted’ roads where full fuel tax rebates can be claimed.

“GPS gives a very accurate picture of what percentage you’re off road,” he says, pointing to the ability to use geofencing and location data to distinguish between on‑road travel and off‑road work.

The distinction matters across a wide range of real-world scenarios, from trucks and light vehicles operating on job sites as opposed to driving to those sites, to concrete agitators running engines while unloading, washing and equipment once they enter a site. Forestry operations are another area French highlights, with vehicles transitioning from public roads onto private land and becoming eligible for different treatment.

“In Australia there are billions of dollars sitting in unclaimed fuel tax credits and this is one of the areas you make a quick gain.”

Where utilisation delivers fastest value

French says the first integration point for utilisation data should be maintenance. With live hour data, service intervals become predictable, automated and defensible – an operational and compliance win.

From there, fuel data becomes critical, particularly as costs rise and supply remains uncertain. Understanding where and how fuel is being consumed allows operators to narrow down why similar machines are costing more on one site than another – whether that’s environment, operator behaviour or simply the wrong machine for the job.

Critically, this feeds decision-makers beyond the fleet team.

“When it all feeds into a BI platform,” French says, “the CFO can see what machines cost, what they generate, and what happens if you remove assets that aren’t being used.”

At that point, utilisation stops being a reporting metric and starts behaving like what it actually is: One of the most powerful – and under‑leveraged – drivers of financial performance in asset‑heavy businesses.