I regularly sit in initial meetings with production managers who tell me: “Our OEE is at 68 percent. We know that's too low. But we don't know exactly why.”
That's the real problem — and it can be solved. But it doesn't start with a new machine, an expensive MES project or a management consultant giving a PowerPoint presentation on lean manufacturing. It starts with data transparency.
What OEE really measures — and what it doesn't
OEE stands for Overall Equipment Effectiveness and combines three factors: availability, performance and quality. A machine with 95% availability, 90% performance and a 98% quality rate achieves an OEE of around 84% — a figure many manufacturers name as their target.
But this is exactly where the first problem lies: most companies that give me an OEE figure haven't measured it. They've estimated it — from Excel sheets, shift reports and the experience of long-serving staff.
An estimated OEE isn't a management tool. It's an opinion.
The transparency problem in practice
Let's take a concrete example. A machine builder in Austria that we worked with ran five CNC lines and three presses. Their own estimate of OEE was “somewhere around 72 percent”. Planned maintenance times were known. Unplanned breakdowns were only noted at the shift handover — often 4 to 8 hours after the fact.
What that means in practice: if a tool breaks on line 3 at 2:30 pm and it only shows up in the shift handover at 10 pm, the production manager has had 7.5 hours with no chance to intervene. No rescheduling, no countermeasures, no heads-up to the maintenance team.
Once the machines were connected via OPC UA and MQTT and a real-time dashboard was in place, the team saw for the first time where the time was really being lost:
- 42% of unplanned stoppages came down to three recurring fault patterns
- The mean time to acknowledge (MTTA) averaged 38 minutes — not 8
- Two machines were permanently running 15–20% below target speed
The measured OEE was 61% — not 72%. And suddenly it was clear where the biggest levers were.
Why the urge to optimise straight away leads in the wrong direction
When production managers come under pressure, the reflex is often: we need a new MES. We need a lean project. We need more people in maintenance.
Those may well be the right answers — but only if you know what the question is. Without reliable data on the causes of losses, every optimisation measure is a shot in the dark. You treat symptoms, not causes.
One company we spoke to before a digitalisation project had just invested heavily in additional maintenance capacity — because “the machines break down too often”. The data analysis showed that 60% of the stoppages weren't machine faults at all, but material shortages and badly scheduled changeovers. More maintenance wouldn't have solved the problem.
Three levels of OEE transparency
Data transparency isn't a binary concept — it comes in stages. From what we see in practice, there are three levels:
Level 1 — Reactive transparency
Shift reports, Excel sheets, manual recording. The data exists, but with a delay of hours or days. No way of reacting in real time.
Level 2 — Operational transparency
Machine data is captured automatically and a dashboard shows OEE live. Shift supervisors see problems within minutes. The first data-driven decisions become possible.
Level 3 — Predictive transparency
Anomalies are detected before they turn into stoppages. Trend analyses show which machines are going to cause problems in the coming days. Maintenance is planned based on condition.
Most of the mid-sized manufacturers we work with are at level 1. The step to level 2 is technically manageable and delivers measurable results within weeks. Level 3 is the medium-term goal.
What it actually takes
Real-time OEE transparency doesn't come from a big ERP project. What you need is:
- Machine data in real time: OPC UA, MQTT or simple sensors — depending on the machine generation. Modern machines deliver their data natively via OPC UA. Older machines need an adapter.
- Consistent loss classification: Stoppages have to be categorised: tool change, machine fault, material shortage, changeover. Without that, the analysis is worthless.
- A dashboard shift supervisors actually use: Not a BI tool with 20 pages of reports. One view that shows in 10 seconds what's going wrong, and where.
- Alerts when OEE drops below target: If OEE on a line falls below 70%, someone has to be informed immediately — not in the next shift report.
What the machine builder in Austria achieved in the end
Three months after the real-time dashboard went live, the team had the following results:
The crucial point: no new machine was bought. No process was fundamentally changed. The only change was that the right people saw the right data at the right time.
That's transparency. And it's the first problem you need to solve — before you think about optimisation, automation or AI.
Conclusion
An OEE of 68%, 72% or 75% isn't an abstract problem. It's money left on the table every single day — in the form of unused machine capacity, unnecessary scrap and reactive maintenance.
The first step isn't the solution — it's fully understanding the problem. And that's only possible with data that's available in real time.
If you want to know where your biggest losses are and what the path to level-2 transparency would look like for you: get in touch. The initial consultation costs nothing.