# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 3 Results
Figure 3: Distribution of publications on data spaces in manufacturing and supply chains by outlet: (**a**) main journal sources for research articles; (**b**) main conference proceedings for conference papers. Figure 4a shows that Germany dominates with 20 documents--nearly three times the 7 produced by China--while Greece follows with 3. Portugal, Spain, and South Korea each contributed 2 papers, and a long tail of single-document entries comes from Austria, Belgium, Finland, Iran, Ireland, Italy, Japan, Luxembourg, the Netherlands, Slovenia, Sweden, and the United States. This distribution underlines Germany's leading role--driven by its strong academic-industrial ecosystem and European data-space initiatives--alongside emerging activity in China and a broad international base. Figure 4b reveals a five-way tie at the top: Fraunhofer IPK, Beihang University, Fraunhofer ISST, INESC TEC (Institute for Systems and Computer Engineering), and Fraunhofer IOSB each published three documents. A secondary group--including Friedrich-Alexander-Universitat Erlangen-Nurnberg, Universidade do Porto, Technische Universitat Braunschweig, Siemens AG, and the University of Patras--contributed two apiece. Together, these patterns highlight both the centrality of key German and Chinese centers and a widening network of institutional expertise across Europe and beyond. Figure 5 displays the most prolific authors in the scholarly literature on data spaces applied to manufacturing and supply chains, ranked by the number of published documents. According to Figure 5, four authors--[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], [PERSON]--each contributed three documents, marking them as the core drivers of the discourse. They are followed by a second tier of six researchers--[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], [PERSON]--each with two papers. Many of these  
Figure 4: Distribution of publications on data spaces in manufacturing and supply chains by affiliation: (**a**) contributing countries based on author affiliation; (**b**) contributing institutions based on publication output. Figure 5: Most prolific authors in the field of data spaces for manufacturing and supply chain management based on the number of publications. prolific authors map directly onto the institutional hubs identified in Figure 4 (e.g., Beihang University and the Fraunhofer Institutes), while contributors like [PERSON] (University of Patras) highlight the broader European engagement. Overall, this author-level profile reveals a tight-knit group of repeat scholars shaping both the theoretical frameworks and practical case studies in industrial data spaces, against a backdrop of growing international collaboration. Lastly, Figure 6 visualizes the keyword co-occurrence network extracted from the reviewed literature. Each node represents a keyword, and links indicate the frequency of co-occurrence within the same publication. Colors correspond to clusters of semantically related terms, identified using the Louvain community detection algorithm. These clusters highlight the main thematic areas currently structuring academic discourse on data spaces in manufacturing and supply chains. In reviewing the literature, ten thematic clusters emerge--each distinguished by its color in the network map--and together they sketch the evolving contours of industrial data space research. **Advanced Data Space Intelligence and Edge Computing (Blue):** this cluster examines how data space architectures embed intelligence at the edge. Terms like artificial  
Figure 6: Keyword co-occurrence network for publications on data spaces in manufacturing and supply chains, highlighting thematic clusters and their interrelations. intelligence, federated learning, and privacy-preserving machine learning illustrate distributed model training within the industrial Internet of Things.