interoperability

AI has long been capable of analyzing production processes, yet why is it still so difficult to bring its insights back into production without intermediate steps? In automotive body-in-white mass production, the challenge is less a lack of data than the absence of holistic integration concepts that extend all the way to the machines. This paper demonstrates why bidirectionally communicative information systems are critical to addressing this challenge and identifies the design principles required to effectively integrate AI-generated results into production processes in the future.

From automotive supply chains to smart factories, data ecosystems promise seamless collaboration across company boundaries. But how can industries build the necessary infrastructure without creating new dependencies? This article explores how open-source software can serve as a foundation and impactful tool for sovereign, interoperable technologies and standards. Yet, open-source software is not a silver bullet; rather, it poses challenges for digital sovereignty in burgeoning data ecosystems.

The networking meetings of the research funding program “Dynamics of Digitally Networked Value Creation Systems (DynaVer)” have shown that different disciplines disagree on what interoperability is and whether it is ultimately desirable. This article aims to shed light on the difficulties involved in developing a universally applicable and, at the same time, legally sound definition of interoperability. Furthermore, drawing on insights from competition economics, the article assesses whether and under what circumstances interoperability is a desirable outcome of regulation.