# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 4 Discussion  
The evolving landscape of data spaces in manufacturing and supply chains has been examined in this paper by addressing three research questions: _(1) what are the bibliometric trends and patterns in scholarly publications on data spaces within these contexts; (2) which thematic contributions and conceptual perspectives emerge from that literature; and (3) what practical insights can be drawn from ongoing European initiatives deploying data spaces in industry?_ To answer these questions, a comprehensive bibliometric analysis has been conducted--tracking publication volumes, authorship and outlet distributions, geographic and institutional affiliations, and keyword co-occurrence networks--alongside a structured content classification of thematic and technological contributions and a targeted review of European data-space projects. Findings indicate a sharply increasing scholarly interest since 2020, characterized by both peer-reviewed journal articles and conference proceedings. Thematic clustering reveals a progression from foundational governance and federated-sharing principles through trust mechanisms and core platform infrastructures (e.g., Gaia-X, Catena-X) to application domains such as sustainability, circular-economy frameworks, and AI-enhanced factory operations. The automotive and electronics sectors lead real-world implementations, while Internet of Things, digital twins, and machine-learning techniques emerge as the predominant associated technologies. International Data Spaces remains the principal reference architecture, although complementary standards (AAS, OPC UA, Eclipse Data-space Connector) show varied uptake. European initiatives predominantly occupy implementation stages, supported mainly by EU funding and joint-innovation business models, yet inconsistencies in reporting and fragmented standard adoption highlight the need for greater harmonization. The analysis of the current academic and practical landscape of data spaces in manufacturing and supply chains thus highlights promising developments. However, several critical opportunities for further research can be identified:Developing standardized design taxonomies for data spaces: future research should aim to establish a unified and widely accepted taxonomy that classifies the fundamental components of data space architectures. This includes roles, system components, governance models, and data categories. A shared design vocabulary would enhance comparability across projects, facilitate replication of successful configurations, and support the transfer of best practice across sectors. **Expanding the use of advanced empirical research methodologies:** with a growing number of real-world data space projects entering the operational phase, researchers are encouraged to adopt empirical methods such as longitudinal case studies, field experiments, and benchmarking analyses. These methodologies would enable a deeper understanding of performance outcomes, implementation challenges, and long-term impacts. **Operationalizing federated learning and decentralized AI within data spaces:** there is strong alignment between data spaces' principles--such as data sovereignty and distributed governance--and techniques in federated and privacy-preserving AI. Future research should focus on developing and validating AI models that can operate in decentralized environments, respecting data usage constraints while enabling advanced analytics across organizational boundaries. **Integrating unstructured and textual data into data-sharing ecosystems:** as manufacturing and supply chain operations generate increasing volumes of unstructured data--such as service logs, technical documentation, and communication records--research should explore how such data can be effectively shared and utilized within data spaces. This includes defining metadata standards, secure exchange protocols, and use cases for training large language models or enhancing semantic interoperability.