How can you monitor machine downtime?

Are you looking for a way to effectively reduce breakdowns and eliminate unplanned downtime? Instead of wasting time manually logging problems, plants are increasingly turning to automation and modern CMMS systems . This gives you ongoing control over production, easily measuring MTTR and MTBF metrics, and quickly identifying bottlenecks. Automated reports give you an immediate overview of what’s actually happening on the shop floor. This allows you to react quickly and ensure machines are running smoothly.

This ongoing monitoring is essential if you want to improve your OEE . However, it’s worth remembering that each sector has its own standards – for example, in the food industry, the average OEE is 74% , while in mechanical workshops it’s around 69% .

Sustainable integration and digital data flow

The foundation for effective downtime tracking is a digital IT architecture based on proven standards, such as the 5- level ISA-95 framework . Modern monitoring systems continuously synchronize machine operation and ensure error-free information flow. In practice, this works as follows:

• PLC controllers themselves send current operating statuses, which almost completely eliminates errors resulting from manual data entry,
• any exceedance of the standard cycle time is immediately and fully automatically recorded in the database.

This allows operators to focus on their work, and you receive reliable and consistent data for repair planning.

How to interpret the OEE indicator in practice?

Accurately recording downtime is essential for reliable OEE calculations. It faithfully reflects the situation on the line, taking into account machine availability, work rate, and the quality of finished products. Automation ensures error-free reporting.

However, it’s important to remember that there’s no single optimal OEE value – what we consider a great result depends on the specifics of a given industry. Understanding these differences allows you to calmly assess daily production fluctuations. Instead of making hasty decisions, you can base your development on verified facts and sensibly plan for growth.

The true scale of production problems

Traditional data collection methods often distort reality. Sometimes, a minor tape jam lasting five seconds is treated the same way as a major failure that halted the line for five hours . Professional software immediately sorts events, assigning them appropriate severity and specific error codes retrieved directly from the machine. Maintenance teams then know what to address first. This precision shortens diagnosis time and allows for planning preventative measures based on hard data.

Distinguishing breakdowns from planned outages

For statistics to truly improve management, the system must accurately distinguish between sudden failures and scheduled interruptions . Advanced filters detect and exclude pauses resulting from shift staffing shortages or scheduled maintenance. A downtime caused by a conscious decision to halt the line doesn’t necessarily mean a hardware failure. Excluding such factors from the final reports provides a clear and reliable picture of the situation. This allows you to know exactly what on the production floor actually requires technical improvement.

A practical example – less downtime for a food industry leader

It’s important to back up theory with practice, as exemplified by the German company Settele. This renowned manufacturer produces over 120 tons of products daily , requiring efficient maintenance. Therefore, the company opted for full digitalization and the implementation of a CMMS system. The software allowed for centralized recording of all failures, tasks, and maintenance schedules , as well as precise management of necessary spare parts .

As the company’s management notes, the mobile app significantly saves technicians time in their daily work. Transparent machine records and a complete repair history not only increased cost control but, above all, guaranteed higher efficiency and a significant reduction in unplanned downtime on the production floor.

Combating micro-downtime at the Konkol Poultry Slaughterhouse

In food processing, every minute of downtime translates into significant losses. A prime example of optimization is the Konkol Poultry Slaughterhouse, which ensures the daily, trouble-free operation of over 200 machines spread across two production halls. Instead of paper reports, a mobile CMMS system was implemented for immediate fault reporting directly from production. This allows the company to precisely measure even the shortest micro-downtimes, lasting as little as one minute, which are often managed by operators themselves. Accurately tracking repair times helped identify hidden bottlenecks and create a fair reward system for the technical department.

The digital future of maintenance

Effective downtime tracking is the foundation of profitable production today. As market examples demonstrate, switching from paper reports to automated CMMS systems dramatically changes workflows. This provides reliable OEE, MTTR, and MTBF metrics, as well as complete control over your machinery.

Automatic fault categorization and efficient filtering of planned outages finally provide a clear picture of the situation on the shop floor. Ultimately, investing in digital information management is the fastest way to eliminate hidden losses. By relying on hard data, you stop guessing about the causes of downtime and start taking preventative measures , permanently securing the continuity and efficiency of the entire factory.