Proactive error prevention through AI saves costs and increases efficiency in production
Artificial intelligence is not only interested in production and quality data. Process data, which is often available in large quantities, forms the basis for finding dependencies between values and therefore anomalies.
The more data is acquired for a process, the greater the possibilities for analysis by artificial intelligence. Where humans can only manually access mass data on a situation-specific basis, neural networks are formed instead, which artificial intelligence can use to identify connections and relationships between the data.
This enables a contextual and semantic analysis of problems and causes. And, of course, early detection, which means reacting to problems before they affect the process. And thus preventing downtimes, tool breakages, poor quality, increased energy consumption and the like from occurring in the first place!