# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 3 Results
Edge and fog computing extend the data space to shop-floor devices, while cloud computing and cloud manufacturing provide scalable off-site processing. Operational use-cases--condition monitoring, predictive maintenance, and resilient manufacturing--show how data space platforms enable real-time analytics and adaptive control. \"Shared production\" and \"digitalization of industry\" capture how data spaces reshape manufacturing workflows and requisite skills. **Socio-Technical Governance of Industrial Data Spaces (Orange):** at the conceptual core of data space research, this cluster articulates frameworks for collective stewardship and policy. Central concepts--data space, data ecosystem(s), decentralized distributed symbiotic sharing--merge with governance models like platform governance, network governance, self-sovereignty, and shared ownership. Methodologies (e)value modeling, systematic literature review, expert interviews) validate these socio-technical designs. FAIR principles (findable, accessible, interoperable, reusable) and \"openness\" ensure data space services remain transparent and equitable, while domain tags (battery manufacturing, continuous-process industry) demonstrate how governance adapts to sectoral needs. **Formal Modeling and Standards for Data Spaces (Green):** interoperability in data spaces hinges on rigorous architectures and engineering methods. The Asset Administration Shell and SysML v2, along with MBSE practices, define meta-models for data space components. Ontologies, requirements, and specifications translate these models into implementable standards, while \"manufacturing data spaces\" and \"digital product passport\" ensure traceability across the product lifecycle. Collaboration patterns and \"value networks\" bind diverse stakeholders into coherent data space ecosystems, enabling closed-loop supply chains and sustainable practices. **Agro-Food Design Frameworks in Data Spaces (Red):** this cluster tailors data space principles to agriculture and food systems. \"Agricultural data space\" and \"agricultural data ecosystem\" describe domain-specific instances, while \"agro-food supply chain\" highlights end-to-end data flows. Core design principles and \"data sharing and exchange\" protocols ensure interoperability among sensors, platforms, and regulatory databases. By embedding farming and logistics into data-space infrastructures, this cluster addresses food-safety, traceability, and efficiency imperatives. **Provenance and Knowledge Management in Data Spaces (Purple):** trust in a federated data space arises from tamper-proof provenance and semantic integration. Blockchain constructs--Asmuth-Bloom delegated proof of stake, smart contract, IPFS--securely anchor transaction history. Knowledge graphs, data-association mining and \"industrial knowledge management\" enable the fusion of heterogeneous datasets. Anchored in use cases like the automotive and food supply chains, \"trusted edge\" and \"semantic interoperability\" ensure data space participants can verify, query, and leverage shared intelligence without sacrificing sovereignty. **Brownfield Integration and Real-Time Data Spaces (Brown):** legacy facilities pose unique challenges for data space adoption. This cluster spotlights \"brownfield\" scenarios where big-data stream analytics, data lakes, and data-quality tools retrofit existing lines. Applications such as injection molding 4.0, predictive maintenance 4.0, intra-logistics optimization, and SUMA 4.0 trials demonstrate how data spaces can overlay non-disruptively, providing continuous monitoring, scheduling, and iterative experimentation. **Federated Data Space Architectures and Marketplaces (Pink):** underpinning industrial data spaces are shared infrastructures and trading venues. Initiatives like Gaia-X and Catena-X, connectors such as the Eclipse Dataspace Connector, and platforms like FIWARE define reference architectures for secure interoperability.