Growing pressure for transparency, flexibility and trustworthy data exchange is reshaping how manufacturing networks collaborate. A recent paper by FLEX4RES, “Advancing the Collaboration in Manufacturing Networks: A Systematic Literature Review on Implementations of Asset Administration Shells and Data Spaces”, explores how two key technologies — the Asset Administration Shell (AAS) and data spaces — are beginning to address these demands.

Addressing the challenges of digital collaboration

Despite decades of digitalisation, many manufacturers still operate with fragmented information systems that restrict cooperation across partners. The review identifies three persistent hurdles: dismantling data silos, ensuring semantic interoperability and protecting sensitive knowledge when sharing data beyond company boundaries. The combined use of AAS for structured digital representation and data spaces for sovereign exchange offers a credible route to overcoming these barriers.

What current implementations reveal

Eighteen implementations were examined, giving a clear picture of the current maturity level. Most remain in early development phases, where prototypes validate concepts rather than support full-scale industrial operations. Even so, progress is visible. AAS is increasingly used to model machines, products and processes, supported by frameworks such as BaSyx, FA3ST and Eclipse AASX. Data spaces typically build on IDS or Eclipse Dataspace Components, though alignment with broader Gaia-X principles is still developing.

The review also uncovers ongoing challenges, including inconsistent semantic standards, limited out-of-the-box interoperability and uncertainty around governance in shared environments. Yet real use cases — from digital product passports to shared production scenarios — show clear benefits in traceability, integration speed and trust.

Laying the groundwork for future adoption

The authors highlight that the next step is moving from promising concepts to deployable industrial solutions. Stronger semantic alignment, clearer terminology and higher readiness levels will be essential for scaling collaborative data ecosystems across manufacturing networks.