Overall Equipment Effectiveness with decentralized AI
Increase availability, performance & quality holistically - Set the stage for intelligent overall equipment effectiveness (OEE) for your machines in the field with our platform. Eliminate the root causes of the Six Big Losses.
Get to know the platformIncrease machine availability
The most important dimension of overall equipment effectiveness is the availability factor of a machine. A high availability factor is the key to economical manufacturing processes. The most frequent losses of availability are caused by machine failures, malfunctions and employee absences. Planned setup times for tool change and tool setting also reduce the availability of a machine. With our platform you can detect these events much earlier. With cross-enterprise optimizations, the data basis can be improved significantly. With Katulu's unique approach this is possible without having to sharing data from the shopfloor. As a result, many losses can be detected and avoided. Read also Predictive Maintenance.
Increase performance
The performance factor, as a further dimension of overall equipment effectiveness, has the special feature that it is not necessarily linear. In particular, decreasing batch sizes pose a challenge for industrial production, since the net running time of a machine decreases due to increasing setup time. In addition, short downtimes of less than three minutes (micro stops) are caused by poorly adjusted tools, short cleaning operations, incorrect programming and blocked sensors. With our platform , micro stops become predictable, making it possible to either eliminate them or prepare for them in time. To make this possible, process and machine data from many machines of the same type are required. With Katulu, it becomes possible to analyze this data decentrally at the user's site using AI, without data leaving the user's production environment. This will also make it possible to identify performance losses due to slow run times, i.e. when a machine is running slower than the theoretical maximum speed.
Quality is Key
As the third dimension of overall system effectiveness, quality is decisive for customer satisfaction. The quality of the manufacturing process is always given when the produced component exactly meets the customer's requirements - and this directly during the first process run. In particular, non-compliance with specific quality criteria leads to losses in quality. Compared to the other dimensions of OEE, quality behaves more consistently in the overall view. However, if a product has a quality defect, this loss is disproportionately serious, as 100% scrap is produced. In addition to rejects during production, quality losses also occur during startup, e.g. due to heating, startup and early manufacturing phases. Quality losses can be eliminated by optimal adjustment of the machine in connection with the detection and correction of quality losses in the production phase. This requires the right database and continuous AI-based optimizations. Our platform is the right way to do this. Read also Inline Quality Monitoring.
