⚙️ Optimize OEE: Uncovering hidden downtimes

⚙️ OEE and technical breakdown: understanding the actual downtimes

Overall Equipment Effectiveness (OEE) is a key performance indicator for measuring production efficiency that takes three factors into account: Availability, performance and quality. A key component of availability is technical downtime, often referred to as technical breakdown. But does this data really reflect the actual downtime caused by technical faults?

🔍What counts as technical downtime?

The production line usually only recognizes that it is not running, but often cannot identify the exact cause. For example, some companies record all downtimes over 10 minutes as technical problems, even though the downtime can consist of several factors.

⏳ Net and gross downtime: what’s the difference?

Experience shows that the technical downtime should be divided into two parts:

1. Net technical downtime: The time during which the technician actually works on the problem – e.g. error analysis, replacement of spare parts or test operation. (often technical problems)
2. Gross Technical Downtime: The total technical downtime, including waiting times for the technician, searching for spare parts or other inefficient times. (often organizational problems)

Many companies fail to recognize this distinction, which can distort OEE metrics. For example, if a company sees a technical downtime of over 10%, the actual technical breakdown could only be 5%, while the remaining 5% is due to waiting times for the technician.

🎯 The real cause of the problem: Not always on the production line

As Einstein said, the cause of a problem is not always where it occurs. If we take a closer look at technical downtime, we see that the real cause is often not the production line itself, but the organization of technical support, the maintenance strategy or a lack of resources.

👨‍🔧The role of technicians and the influence of priorities

In a larger production company with more than 15 production lines, there are often only a few technicians. This means that technicians have to carry out repairs in a certain order of priority, which means that less critical production lines have to expect longer waiting times. However, this waiting time is not a technical malfunction, but a capacity limitation.

🚀How can the analysis be improved?

1. Detailed analysis of downtime: If we divide the technical downtime into smaller sections (e.g. waiting for the technician, spare parts procurement, diagnosis, repair, etc.), we can more easily identify where most of the time is lost.
2. Reduction of waiting times: If the analysis shows that the majority of the problems are not due to the technical defect but to waiting times, the following measures can be considered:
3. More accurate OEE data: If the production line detects that it is not working but cannot accurately categorize the causes, more detailed downtime detection can help to better understand the actual causes.
4. Optimization of the maintenance strategy: Preventive and predictive maintenance can help to reduce unplanned downtimes and distribute the workload of technicians more efficiently.

✅ Conclusion

Understanding the actual technical downtime is crucial to improving production efficiency. If the OEE figures indicate a high technical downtime, it is worth breaking down the downtime into net and gross times to understand where the actual time losses occur. If it turns out that the main time loss is not due to technical faults but to waiting times, the maintenance processes need to be optimized – not the machines.

A more detailed data analysis can ensure that the technical downtime is actually due to machine failures and not to organizational deficiencies. Automated systems, improved maintenance strategies and optimized resource allocation help to increase efficiency and ensure a smooth production process.

🚀 Result: More efficiency, fewer delays, more stable production!

Attila Jezsó

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