26/08/2026
๐๐ฒ๐๐ผ๐ป๐ฑ ๐๐ต๐ฒ ๐๐ฒ๐ฎ๐ฑ๐น๐ถ๐ป๐ฒ
We recently came across an interesting analysis by the Manufacturing Leadership Council: 89% of manufacturers surveyed say that data has improved their decision-making. At the same time, 63% use less than half of the production data actually available to them.
https://manufacturingleadershipcouncil.com/data-mastery-and-analytics/
That is quite a striking combination. It suggests that, in many cases, the problem is not a lack of data, but the difficulty of bringing the right information together at the right time.
This is hardly surprising when information about machines, orders and processes is spread across different systems, older equipment makes data difficult to access, or different sources first need to be brought onto a common footing. Collecting even more data does not necessarily solve the problem.
Things become more interesting when existing information can be connected: Which order is currently running? What is happening on the machine? Which process values are changing? Was there already an indication of a deviation? This is where individual data points gradually become something people can actually work with.
AI can also help at this stage by identifying connections more quickly or deriving the next sensible step from the information available. But simply giving AI access to a mountain of data is only part of the equation. Its real value grows when that information can be related to the machines, orders and processes behind it.
This is exactly the idea behind ๐บ๐๐๐ฏ and the ๐ฆ๐๐๐๐ฒ๐บ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐ ๐๐ฎ๐๐ฎ module: bringing existing production data together from different sources and connecting it to the machines, orders and processes it belongs to.
https://www.nuveon.de/en/systemprocessdata
P.S.
This is also where ๐๐ ๐ฏ๐ ๐ก๐๐๐ฒ๐ผ๐ป comes into play: it can build on this foundation and turn connected information into understandable context and concrete actions.
https://www.nuveon.de/en/ai