Picture this:
You’re managing a logistics hub with forklifts, sorters, conveyors–the whole mechanical orchestra. Suddenly, one machine throws an error. Then another. You check the OEM dashboard. It’s lighting up like a Christmas tree with error codes.
Sounds like a sophisticated system, right?
Now imagine that none of those blinking lights talk to each other, and worse, they’re all screaming in different languages.
Welcome to industrial maintenance in 2025.
A Flood of Data (And the Life Rafts are Mostly Empty)
OEM dashboards and maintenance platforms collect thousands of error codes. Every beep, blink, and failure has a log. But this data is siloed, non-standardized, and underused. Think of it like a library where every book is written in a different language.
To make matters worse, many OEMs change the error codes from model to model. That means that one machine may use one set of codes, but the newer model of the same machine uses a completely different set. There’s often no standardization and no consistency, even within the same manufacturer and product line.
The result is that most of this rich information ends up serving just one purpose: reactive firefighting. A failure happens. The system logs it. A technician is dispatched.
Rinse and repeat!
Europe’s Maintenance Crunch: A Pressure Cooker of Expectations
Industrial operators in Europe aren’t just dealing with aging assets. They’re also navigating a maze of external pressures:
- Environmental, social, and governance (ESG) disclosures demanding uptime transparency and environmental impact tracking.
- Contractual service level agreements (SLAs) holding companies accountable for every hiccup in performance.
- Operational risk mandates that expect you to see problems before they happen.
Yet in most facilities, predictive maintenance is still calendar-based, not condition-based. That’s like changing your car’s tires every six months, no matter how much or how little you drive.
The Cost of Doing Nothing
Only ~10% of maintenance events are truly reactive. But these failures…well, they’re costly.
Like, 2.5–4× more expensive costly.
Why? Things like emergency labor. Unplanned downtime. Over-ordering parts because no one trusts the inventory numbers.
And the problems don’t stop there:
- No pattern recognition. Logs aren’t mined for repeat issues or recurring root causes.
- No memory. Free-text technician notes are unstructured, non-searchable, and vanish into the void.
- Over-assigned teams. Three techs show up to fix something two could’ve handled—because no one has task-level benchmarks.
- Broken parts logic. Some stock thresholds were set two decades ago and never updated, leading to wasted CAPEX.
If you’re picturing a digital system run on tribal knowledge and educated guesses, you’re not far off.
Siloed Systems, Fragmented Truths
From SAP PM to Oracle and Maximo, most operators rely on siloed Computerized Maintenance Management Systems/Enterprise Asset Management platforms that don’t play nicely together. Many original equipment manufacturers have their own code languages, their own data structure, their own idea of what a “fault” means.
So while major OEMs like Sandvik, Komatsu, and Alcoa pour money into asset-level traceability and lifecycle forecasting, the lack of cross-OEM standardization means it’s hard for anyone to get the full picture.
Each site, each machine, each generation speaks a slightly different dialect. It’s like trying to build a maintenance strategy out of a Tower of Babel.
The Domino Effect
Take mining, for example. Sequential machines (like excavators, mobile crushers, belt wagons) need to stay perfectly aligned to avoid spillage, collisions, and unplanned downtime.
One misalignment at a transfer point, and the whole operation grinds to a halt. But there’s no centralized intelligence pulling historical data to say: “Hey, this exact failure happened three months ago, and here’s what caused it.”
Instead, a planner spends a few hours calculating, goes digging through old failure notes, maybe makes a call, and maybe hazards a guess. And the wheel spins on.
A System Built to Forget
Right now, industrial maintenance systems are collecting data like hoarders collect newspapers. There’s value buried in the pile, but people either don’t have the tools or aren’t utilizing them enough to make sense of it.
Reactive failures cost more. Parts get overstocked or go missing. Technicians get dispatched based on gut feel. And all the while, mountains of error data sit idle, quietly recording preventable problems.
It’s not that machines aren’t talking.
It’s that they’re not talking to each other.
Coda: What if no one wants a solution?
There’s a final uncomfortable twist to this tale.
OEMs simply might not want to play ball.
Sharing error logs means surrendering a piece of their intellectual territory. That’s data that protects aftermarket service revenues, parts sales, and competitive edge. Why would they hand it over to a neutral platform that might make their machinery interchangeable?
Instead, maybe each OEM builds its own DIY translator, maybe even drops a branded LLM on top of its maintenance dashboard. Suddenly you’ve got KomatsuGPT, SandvikBERT, and Alcoa.ai all interpreting their own equipment in their own walled gardens.
It’s a solution, sure. But it wouldn’t be close to true orchestration.
We’ll get better at translating errors within ecosystems, but without cross-OEM collaboration, the bigger opportunity–true interoperability, optimized resource planning, and system-level intelligence–remains just out of reach.
So the big question may not be can we fix it.
It might be will anyone let us?
Interested in how this kind of operational mess turns into a founder opportunity?
At Beam, we work with entrepreneurs who want to bring real innovation to heavy industries like logistics, manufacturing, and mining. If you’re itching to solve the hard stuff—this is your invitation.
