How equipment OEMs can boost today’s machine performance

The construction equipment industry has no shortage of data. Machines on sites across the globe are streaming location, usage, health, and energy metrics every working second of the day.  Yet, for many Original Equipment Manufacturers (OEMs), this data remains an untapped resource sitting in servers or dashboards, under-analyzed and undervalued. 

The industry is data-rich but insight-poor. For OEMs, this represents both a challenge and an opportunity.  In a market where margins are tight and customers' demands are rising, turning data into actionable insights is not optional, it is necessary. 

The good news is that OEMs don't need massive digital transformations or experimental AI projects to start seeing results. By focusing on a few core indicators, manufacturers can boost forecasting accuracy, production efficiency, and machine performance across the board.  

Measure the machines at work 

One of the simplest and most powerful signals to track is the number of machines currently in operation. Tracking this month-over-month or year-over-year provides a clear signal of market activity if activity in the market is growing or shrinking.  

This data also shows whether demand is broad-based or concentrated in fewer, larger projects. For example, while single-family housing construction in the US has slowed, large-scale infrastructure and electrification projects are booming. That shift means fewer contractors overall, but larger sites such as EV factories, data centers, and battery plants are driving machine utilization. 

Understanding where demand is focused allows OEMs to adjust production plans, sales strategies, and support models accordingly. 

Look beyond activity, track machine hours 

Knowing how many machines are in the field is one thing and knowing how hard they're working is another. Total machine hours give OEMs a clearer view of real demand. 
Machines running light-duty cycles for short durations paint a different picture than those logging 10-hour days under heavy load. Pairing machine count with total hours worked allows OEMs to distinguish between a modest uptick in activity and sustained, high-intensity demand. 
Machine hours also help detect anomalies: 

• A sudden drop in hours might signal weather disruptions, regulatory changes, or macroeconomic slowdowns. 
• A spike in usage 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. 

Track energy consumption as a performance proxy 

If machine count and hours reflect volume, energy consumption reveals intensity and efficiency. Fuel and electricity data show how hard machines are being pushed, whether hybrid models are delivering real-world savings, or if electric units are being used differently than their diesel counterparts. 

For OEMs, this has huge significance, not just for R&D and engineering, but for marketing and sales as well. Demonstrating superior fuel efficiency under load backed by telematics data provides a powerful competitive advantage. It also highlights potential training gaps, maintenance needs, or mismatches between machine specs and site demands. 

Use real-time inventory deployment data 

Historically, OEMs relied on dealer forecasts, territory reports or delayed feedback to gauge market demand, a slow process often shaped by assumptions. 

Telematics now enables a more precise view: how many machines are in use, where they're operating, and how recently they began accumulating hours. This data offers an immediate and unbiased measure of sell-through, helping OEMs identify whether inventory is moving as expected or beginning to back up. 

By analyzing deployment data in real time, OEMs can rebalance stock, adjust production, and respond to demand shifts before they impact financial performance. 

From lagging reports to leading indicators 

Many OEMs still pay consultants to deliver insights that already exist in their own data. By embedding telematics into monthly S&OP processes, OEMs can answer critical questions themselves:

• Which products are selling faster than we can build them?
• Where is demand shifting by region or application?
• Which machines are underperforming, and why?
• What early signs point to overstock or underutilisation?

When COVID first started, many OEMs instinctively assumed demand was collapsing and considered halting production. However, utilization data told a different story: machines were still active, and demand remained stable in key sectors. Those who relied on data, rather than instinct, made smarter, faster decisions.  

Embedding data into monthly reporting

By integrating machine utilisation reporting into the monthly S&OP process, OEMs can validate or challenge demand forecasts in real time. Instead of reacting months later, they can course-correct immediately.

Focusing on four key metrics: active machine count, total hours, energy consumption, and deployed inventory creates a clearer demand signal and enables OEMs to build the right machines, in the right quantities, for the right markets.

In an industry built on iron and engineering, it's this kind of intelligence that moves markets forward.