FAIR for digital twins

Abstract

The continuing drive towards digitization in manufacturing leads to an increasing number of digital twins for monitoring and controlling all kinds of processes. While these capture crucial data of all individual steps and allow for analysis and optimization, more often than not the underlying models are confined to individual systems or organizations. This hinders data exchange, especially across institutional borders and thus represents an important barrier for economic success. Similar challenges in the scientific community led to the emergence of the FAIR principles (Findable, Accessible, Interoperable, and Reusable) as guidelines towards a sustainable data landscape. Despite the growing presence within academia, their transfer to industry has not yet received similar attention. We argue that the existing efforts and experiences in science can be exploited to address current data management challenges in industry as well. An improved data exchange within organizations and beyond can not just lower costs, but also opens up new opportunities ranging from discovering new suppliers or partners to improving existing value chains.