# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 1 Introduction  
Nowadays, manufacturing and supply chains have become globally distributed and highly interconnected, involving a wide range of autonomous actors [1, 2, 3]. However, despite advancements in digital technologies, a fundamental challenge persists: the fragmentation of data across organizational and technological boundaries [4, 5]. Manufacturers often lack visibility into upstream constraints or downstream demand changes, while suppliers remain unaware of production schedules or quality issues further along the chain. This lack of integrated data exchange undermines coordination, reduces responsiveness, and limits innovation potential across the value chain. Poor data integration can lead to overproduction, inventory imbalances, delayed deliveries, and reactive decision-making [6, 7], undermining strategic sustainability and operational initiatives in small- and medium-sized manufacturers [8] and affecting sustainable performance through supply chain visibility [9]. This challenge also intersects with broader sustainability goals, as transparent and integrated data flows have been shown to shape stakeholder perceptions of sustainable development, mediate green behavior and corporate social responsibility interactions, and enhance environmental performance through operational transparency in manufacturing contexts [10, 11, 12]. Moreover, the absence of real-time, standardized, and contextualized data can become particularly dangerous in regulated or complex industries, where delayed recalls or compliance failures can result in substantial financial and reputational damage. Lastly, in an increasingly AI-driven environment [13, 14, 15, 16, 17, 18], limited accessto high-quality, cross-organizational data hinders the performance of manufacturing and supply chain predictive models, impeding opportunities for automation, optimization, and intelligent decision support. Given the increasing complexity and volatility of global supply chains, the ability to share and utilize data across organizational boundaries has become critical for industrial competitiveness and systemic resilience. However, achieving seamless and secure data exchange remains a significant challenge due to a combination of technical, organizational, legal, and trust-related barriers [19; 20; 21]. A major obstacle is the heterogeneity of systems and data models across different organizations [22]. Manufacturing stakeholders typically use diverse platforms with incompatible formats and terminologies. This lack of interoperability leads to costly, fragile, and often manual integration efforts that are difficult to scale and maintain. The distributed and hierarchical structure of supply chains further complicates matters [23]. Limited data sharing between these tiers impedes end-to-end visibility and delays the detection of disruptions or quality issues. Trust is another critical barrier [24]. Companies are often reluctant to share sensitive data due to concerns over misuse, competitive risks, and lack of control. The absence of standardized frameworks for data usage rights, consent management, and access control across organizational boundaries exacerbates these concerns. Legal and regulatory constraints--such as data privacy laws and cross-border compliance requirements--further complicate data exchange, especially when ownership, responsibility, and liability remain ambiguous [25]. Finally, existing solutions based on bilateral integration or centralized platforms do not scale effectively in large, dynamic ecosystems. As each new partnership requires a custom integration effort, complexity and cost increase exponentially. As a result, even firms with well-integrated internal data systems often struggle to extend those capabilities to external partners. Over the past two decades, various technological solutions have thus been proposed to address data fragmentation in manufacturing and supply chains, including centralized platforms, EDI systems, blockchain, and industrial cloud solutions [26].