In this webinar, we took a closer look at the hidden bottlenecks in production processes. We explained how OEE is built up and how you can use it to make losses in availability, performance and quality visible.
Using a virtual factory, we showed how downtime and production data are tracked and visualized in real time, so problems stand out faster and can be tackled concretely.
Finally, we showed how AI analysis can be used to detect patterns and root causes in production data, so you don't just react to problems but can also improve proactively.
Why measuring is crucial
Many manufacturing companies feel it: their lines have more to give than what actually comes out. Machines are running, orders get completed, and yet output keeps lagging behind. The question isn't whether there's loss, but where it's hiding.
For those who want to keep producing locally, efficiency is no longer a luxury. Doing more with less is only possible if you know:
- where you're losing time
- which line is underperforming
- why it's happening
Without these insights, you keep optimizing based on gut feeling.
Getting started quickly: insight with minimal setup
You don't need to launch a complex digitalization project. With just two sensors you can already discover a surprising amount.
From that, you immediately get:
- downtime: no flow means a problem
- the actual speed of your line
- quality losses (with a reject system or manual input)
This forms the basis for one of the most important KPIs in production: OEE
OEE: powerful, but often misused
OEE (Overall Equipment Effectiveness) measures how efficiently your production line runs. The formula consists of three components that are multiplied together:
Important: OEE isn't a report you review once a month. The real value lies in real-time insight per hour, per shift, per day. This lets you intervene immediately wherever things go wrong.
One number, major impact
What's often underestimated: even "good" scores lead to major losses. Just look:
That's not a small loss. It's pure profit left on the table.
Where are the hidden losses?
The biggest bottlenecks are rarely the obvious downtimes. They lie beneath the surface.
You won't see these losses in classic reports, but they cost hours per shift.
A practical example from the webinar
In a simulated production line, we analyzed a single 8-hour shift. Here's what we found:
Main causes:
- frequent calibrations
- maintenance and changeovers
- operator interventions
- technical issues (overheating, encoder problems)
Without data, this stays hidden. With data, it becomes concrete and solvable.
From data to action
A dashboard alone isn't enough. You make the difference by:
- having real-time visibility into losses
- recognizing trends (e.g. problems every morning)
- comparing lines against each other
- prioritizing bottlenecks
And by combining that production data with operator notes, quality measurements and machine data, that's what gives you the complete picture.
With AI analysis it goes one step further. What used to take weeks now happens in seconds: recognizing patterns across data sources, establishing correlations (e.g. temperature ↔ downtime), detecting root causes and proposing concrete actions, from smarter changeover planning to targeted technical interventions.
Concrete impact
Even small improvements make a big difference. A concrete example:
Improving production doesn't start on the shop floor, but with insight. Making hidden losses visible, identifying your biggest bottlenecks, taking targeted actions with direct impact. Without complex projects or lengthy implementations.