# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 5 Conclusions  
This study addressed the growing relevance of data spaces in the context of manufacturing and supply chains, where fragmented data exchange continues to hinder interoperability, responsiveness, and innovation across organizational boundaries. Although digital integration tools have long been the subject of academic inquiry, the existing literature has not adequately captured the emerging paradigm of decentralized and federated data sharing enabled by data spaces. This gap is particularly striking given the increasing policy and industry attention to establishing secure and sovereign data ecosystems in Europe. To fill this gap, the study conducted a systematic literature review combined with a document-based analysis of ongoing European initiatives. The goal was twofold: to synthesize scholarly knowledge on data spaces in manufacturing and supply chains, and to contextualize it through observation of real-world implementations. Three research questions guided the investigation, examining bibliometric patterns, thematic trends in the academic discourse, and practical insights from European projects. The results show that academic interest in data spaces has accelerated since 2022, with increasing contributions from journals and conferences, particularly in industrial informatics and digital manufacturing. Germany and China emerged as the most active countries in terms of scholarly output, reflecting their institutional and industrial engagement with the topic. Content-wise, the literature remains concentrated on production and product lifecycle activities within vertically integrated value chains, with limited coverage of horizontal collaboration models. While IoT, digital twins, and machine learning are frequently discussed, the explicit adoption of data-sharing standards and architectures such as IDS or Gaia-X is inconsistent. Moreover, reporting practices vary significantly across studies, hindering comparability and knowledge transfer. On the other hand, the European data space initiatives review revealed a maturing ecosystem, with most projects situated in the implementation or operational phase. These initiatives strongly reflect the design principles of interoperability, sovereignty, and shared innovation. Catena-X, in particular, stands out as both a technical and governance benchmark, having influenced a significant portion of the academic literature through use cases involving circular economy, platform governance, and Manufacturing-as-a-Service. Other projects, such as COOPERANTS and Boost 4.0, highlight the applicability of data spaces beyond traditional manufacturing, including aerospace and advanced smart services. Despite these advances, several limitations were identified. First, the academic literature still lacks a standardized design taxonomy, which would facilitate replication and cross-domain learning. Second, empirical validation remains limited, with few studies leveraging real-world performance data or advanced evaluation methodologies. Third, opportunities to integrate decentralized AI techniques, manage unstructured data, or promote horizontal and cross-sector data sharing remain largely untapped. Additionally, our methodological choices--namely, the focused keyword string, exclusive reliance on the Scopus database, and concentration on projects catalogued by the International Data Spaces Association--may have constrained the scope of our review and excluded relevant work documented elsewhere. Future research should focus on consolidating design frameworks, expanding empirical inquiry, and exploring technical enablers that align with the federated nature of data spaces. Addressing these gaps will be critical for realizing the full potential of data-driven collaboration in industrial value networks.