Published: 19. June 2025,
Construction, Digitalization
– 5 min.
The construction equipment market is awash in data. Machines are humming away on mega projects and residential builds alike, beaming out a continuous stream of location, usage, health, and energy consumption metrics.
But for many OEMs, that data is still sitting idle — under-leveraged, under-analyzed, and under-valued. The construction industry is data rich, but insights poor.
In a sector where margins are tight, global demand is shifting fast, and customer expectations are rising, the ability to use your own data effectively isn’t just a technical upgrade, it’s a necessity.
“OEMs don’t need AI moonshots or digital overhauls to start seeing results. There are practical, data-backed steps they can take today to boost forecasting accuracy, optimize production, and improve machine performance across the board.”
The good news? OEMs don’t need AI moonshots or digital overhauls to start seeing results. There are practical, data-backed steps they can take today to boost forecasting accuracy, optimize production, and improve machine performance across the board.
Here are four key things OEMs can do right now using the telematics data already at their fingertips.
Let’s start simple. The first signal to track is whether more or fewer machines are working compared to a previous period — month-over-month, year-over-year, or even week-by-week depending on the application.
Why does it matter? Because it tells you if activity in the market is growing or shrinking. More importantly, it will show whether that activity is widely distributed or highly concentrated.
For instance, if your data shows more machines working this month than last, demand may be broad-based. If fewer machines are working, but hours are still high (more on that to come), you may be looking at a market consolidating around a fewer, yet larger projects. That has huge implications for product planning, sales strategies, and support models.
In today’s market, this isn’t a hypothetical. In the US, for example, single-family housing is down, but massive infrastructure and electrification projects are booming. That’s fewer contractors, but significantly more work, concentrated on mega-sites like EV factories, data centers, and battery plants. Understanding that shift in real time can help OEMs respond with precision.
The second indicator to monitor is total machine hours. It’s one thing to know how many units are active, it’s another to know how intensively they’re being used. This tells you the true volume of work performed in the field.
Are your machines idling through light-duty cycles, or are they pushing 10-hour days in demanding conditions?
Pair this with your machine count and you start to see a much sharper picture of demand. You might find, for example, that although fewer machines are active, each one is logging significantly more hours.
“Machine hours also help you spot anomalies. A sudden drop in hours might signal weather disruptions, regulatory changes, or macroeconomic slowdowns. A spike could suggest an emerging opportunity in a specific region or sector.”
That suggests strong, possibly pent-up demand and signals an opportunity to ramp up production of specific models.
Machine hours also help you spot anomalies. A sudden drop in hours might signal weather disruptions, regulatory changes, or macroeconomic slowdowns. A spike could suggest an emerging opportunity in a specific region or sector.
While hours and unit count show volume, energy use — fuel burn plus electricity use — tells you how hard the machines are working.
Think of it as GDP vs. GDP per capita. Raw totals matter, but efficiency and intensity matter more. Are your machines putting in long days but running at low load? Are electric models showing different usage patterns than their diesel counterparts? Are hybrid machines actually delivering fuel savings in the field?
Tracking energy use can also point to training or operator behavior issues, signal potential maintenance needs, or flag mismatches between machine spec and site demands.
For OEMs, this has huge significance, not just for R&D and engineering, but for marketing and sales as well. Imagine being able to show, with real-world data, that your machines deliver superior fuel efficiency under load compared to the competition. That’s more than a talking point, it’s a differentiator.
The fourth often overlooked data point is deployed inventory. In other words, how many of the machines you’ve produced have actually reached end users? This is where telematics adds value that traditional reporting simply can’t match.
Historically, OEMs relied on dealer forecasts, territory reports, and months of lagging feedback to gauge market demand. The process was slow, prone to bias, and frequently distorted by human assumptions along the way.
Telematics, by contrast, shows you in real time how many machines are in use, where they are, and how recently they started accumulating hours. That’s your real indicator of sell-through — not what left your factory, but what actually landed on a jobsite.
It’s also your early warning system. If you’re producing machines faster than they’re being deployed, inventory is backing up. If you see sales accelerating in one geography but stalling in another, you can pivot production or reallocate stock before the imbalance turns into a financial drag.
And because the data is so granular, you can slice it by geography, industry, or machine type — allowing for hyper-specific, hyper-relevant decisions.
One of the biggest ironies in today’s equipment industry is that OEMs often pay handsomely for consultants to deliver insights that already exist in their own data. With the right framework, they could be answering critical questions in-house:
The value of telematics isn’t just in the data, it’s in what OEMs choose to do with it. That means not just collecting machine signals, but acting on them to build smarter strategies, deploy capital more efficiently, and respond faster to change.
This is the opportunity for OEMs today: replace lag time with real-time. Replace gut instinct with hard evidence. Replace anecdotal reports with granular insights from machines in the field.
With these four core indicators — active machine count, hours worked, energy consumed, and inventory deployed — OEMs can dramatically improve their ability to forecast, plan, and perform.
“Historically, OEMs relied on dealer forecasts, territory reports, and months of lagging feedback to gauge market demand. The process was slow, prone to bias, and frequently distorted by human assumptions along the way.”
Having that kind of information on hand in real time can greatly enhance the demand signal and ensure that you are building the right machines, at the right time for the right market.
The result? Better machine availability, more accurate production runs, tighter alignment with market demand — and ultimately, better-performing machines in the hands of customers.
In an industry driven by iron and engineering, that’s the kind of intelligence that moves markets.
To turn data into insights and insights into results doesn’t require any new processes — simply embed utilization reporting as an enhancement to your demand signal as part of your monthly S&OP process.
As you analyze the demand signal coming from the regions, use utilization data to validate or challenge the trends.
As an example, when COVID hit, most OEMs had the initial gut instinct to shut down the factories and supply chains. But aa quick glance at utilization data proved otherwise with machine utilization continuing to be strong.
Want to read more from Fred? Here’s what he had to say about the growing symbiosis between construction technology and the components OEM sector after some landmark agreements this year.
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