Find out how, in a project with Schwäbische Werkzeugmaschinen GmbH in the area of discrete manufacturing at automotive supplier Schabmüller, scrap and rework has been reduced by 85 % by means of Xplain Data algorithms.
Applying the Causal Discovery methods, previously unknown causes for production failures quickly became evident.
”Artificial Intelligence helps us to identify cause and effect relationships in complex data”, that is what Dr.-Ing. Mathias Kammüller, Chief Digital Officer and Board Member of Trumpf said after a successful project with Xplain Data. The mission of the project was to identify causes for machine failures based on data collected during assembly and operation of machines (root cause analysis). In continuation, the algorithms will now be deployed to constantly monitor for emerging new causes and alert related departments.
The project report was published in the quality management magazine QZ-online in August 2022 (German).