# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 3 Results
\"Marketplace\" and \"platform economy\" concepts translate data space connectivity into commercial exchanges, while \"design science\" and \"standards\" ensure these federated environments scale. Embedding CO\({}_{2}\) footprint metrics further aligns data space marketplaces with sustainability goals. **Blockchain-Enabled Trust Mechanisms in Data Spaces (Gray):** a focused look at distributed-ledger tools within data spaces. Blockchain, smart contracts, and IPFS provide immutable registries that enforce provenance, while \"traceability\" and \"data ownership\" frameworks ensure participants can audit and control data flows. This trust layer is indispensable for high-assurance applications--where data space collaborations cross company and regulatory boundaries. **Circular-Economy and Cross-Company Data Spaces (Olive):** this cluster explores how data spaces drive circularity and inter-firm exchange. B2B data exchange and cross-company data exchange models enable collaborative recycling, remanufacturing, and resource sharing. Digital twins and product-lifecycle management tools integrate into data space workflows, while purchasing and supply management functions leverage international data space standards. Sectoral examples--such as the footwear industry--illustrate how industrial marketplaces foster co-innovation and sustainable development. **Shop-Floor IoT and Learning in Data Spaces (Cyan):** at the operational frontier, this cluster fuses IoT sensing with data space analytics. FIWARE-based platforms, elemental correlation analysis, and knowledge-learning algorithms transform raw sensor feeds into actionable patterns. Case studies in semiconductor wafer manufacturing systems and shop-floor scheduling exemplify how data spaces enable continuous learning loops--turning each production trial into an insight generator within the \"Industry of Things\". Together, these ten color-coded clusters map a rich tapestry of governance, infrastructure, analytics and application domains--illustrating how industrial data spaces have evolved into a multidimensional research frontier. What Are the Main Thematic Contributions and Conceptual Perspectives in the Existing Scientific Literature on Data Spaces in This Context? To address RQ2, the reviewed literature was systematically classified based on several key dimensions relevant to the implementation of data spaces in manufacturing and supply chains. The classification framework considers the industrial domains addressed, the operational objectives pursued, the type of integration implemented, the technologies integrated, the nature of shared data, and the use of data-sharing standards or architectures. These dimensions, summarized in Table 11, provide the basis for the following analysis. **Automotive dominates sectoral coverage in the literature:** as shown in Appendix A Table 11, the automotive sector is the most frequently examined, with five purely automotive cases [40; 41; 42; 43; 44] plus one cross-sector study with electronics [45]. Beyond this, food (three studies [46; 47; 48]), machining (three studies [49; 50; 51]), and battery manufacturing (two studies [52; 53]) also appear. Niche domains--such as aerospace [54], footwear [55], nuclear energy [56; 57], semiconductor processing [58], home appliances [59], and heavy equipment [60]--round out the landscape. **Resilience is the most common operational objective driving data space adoption:** resilience leads as the primary objective, addressed in half the sample. Efficiency follows in 13 studies [54; 50; 51; 55; 56; 58; 59; 61; 62; 63; 64; 65], and sustainability in 10 studies [40; 42; 43; 46; 47; 52; 65; 66; 67; 68]. Multi-objective approaches include resilience + sustainability (four studies [40; 42; 66; 68]), efficiency + resilience (two studies [51; 59]), and efficiency + sustainability (one study [65]). **Vertical integration is the predominant form of data sharing, while horizontal and sector-level integration are rare:** Two-thirds of the works focus on cross-enterprise (within-supply-chain) integration.