Increase efficiency 10-20% using OEE software – even when production seems to run well

Implementing OEE software often delivers a 10–20% increase in efficiency

Even when “everything is running smoothly”: the data often paints a different picture.

We frequently uncover critical risks – for example, systematically incorrect cycle time targets that not only reduce performance but also distort job costing. Suddenly, orders become less profitable than assumed – without anyone noticing.

Our customers almost always discover plenty of “low-hanging fruit” — potential that only becomes visible through reliable data, and can be implemented quickly and cost-effectively.

An OEE tool like Cosmino Panteo is used for:

• Full transparency: Loss times become visible to everyone. This visibility alone often leads to noticeable improvements – for example, this effect can be seen in waiting times for personnel or materials.
• Faster reaction to problems: Real-time data gives shift supervisors and teams clarity about deviations, causes, and potential, enabling them to act quickly.
• Targeted investments: Only analysis based on complete data acquisition show where investments deliver the greatest impact. Even short downtimes, cycle slowdowns, and hidden losses add up significantly.
• Reliable order costing: Completely acquired data, ideally automatically reported back to the ERP system, enables time-saving and reliable post-calculation of all orders.

50% of losses are invisible at first – here are the most common blind spots

„We hardly have any losses! “ – „Yes, we do – you just don´t see them. “ And that´s the problem.

In projects, we often encounter the same blind spots—losses that simply slip through without 100% acquisition:

• Deviations from target times: Machines appear productive, but run slower than expected. In many of our projects, cycle time deviations of up to 30% from the target times were identified. This means that orders were less profitable than assumed. However, the analyses also show products that run faster than previously calculated—here, orders can be obtained that did not previously seem profitable.
• Rework, maintenance, training, prototype production: These times often do not appear in any statistics. Without 100% data acquisition, other downtimes can also be “hidden” within them. In reality, they significantly reduce capacity.
• Setup times: Target values in master data are often inaccurate. Only through systematic OEE data acquisition does it become clear how much setup times fluctuate and where optimization is possible.
• Decommissioned machines: When operating resources are taken out of service due to defects or other reasons, it often appears to be normal. Clear and unambiguous definitions are required here; otherwise, this result in concealed efficiency losses that give the illusion of a significantly better OEE than is actually the case.
• Regularly recurring minor problems: These are sometimes accepted as part of the process, but they are avoidable losses of time.
• Quality losses: These must not be omitted from the OEE. It is important to note that poor quality is often only detected in the subsequent process, but should be attributed to the process that caused it.

Achieve flexibility with the right software and the right partner

Projects often start with skepticism: production managers fear additional work, shift teams fear micromanagement.

But experience shows that as soon as the first successes become visible, skepticism turns into enthusiasm.

We not only deliver software, but also bring decades of experience from OEE projects – working on an equal footing with your teams.

• Together, we define master data, evaluations, and interfaces—to create a scalable solution that fits your processes exactly.
• We actively accompany the entire project, contributing best practices and practical tips to avoid typical pitfalls—while also ensuring that no waste is generated in the project itself.
• In the start-up phase, we work closely with your team, jointly check data acquisition quality, discuss evaluations and optimization potential – until your team is confidently operating the solution on its own.

This is how even production that seems to be running smoothly becomes transparent, measurably more efficient, and sustainably more successful.