# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 3 Results
Company-level implementations appear in 11 studies [56; 50; 53; 56; 57; 58; 60; 62; 65; 62; 66; 69; 70], only one study adopts a sector-wide scope [54], and one bridges both scopes [60]. **IoT, digital twins, and machine learning are the most frequently integrated technologies:** Internet of Things is the most integrated technology, followed by machine learning in 10 studies [45; 46; 48; 49; 53; 56; 57; 62; 70; 71], digital twins in 7 [40; 42; 52; 58; 63; 70; 71], big-data analytics in 4 [45; 48; 50; 56], and blockchain in 3 [44; 47; 64]. Notably, 11 studies report no specific technology integration [41; 43; 51; 54; 59; 60; 61; 65; 66; 67; 69]. **Shared data types primarily relate to production, quality, and product lifecycle:** production metrics are the most exchanged data type, with product status next in 11 studies [40; 43; 46; 47; 49; 51; 52; 54; 56; 61; 68]. Maintenance records appear in seven studies [42; 43; 45; 50; 56; 57], quality measures in four [44; 45; 50; 65], sustainability metrics in two [52; 66], demand data in one [66], inventory in one [44], and energy in two [44; 62]. **International Data Spaces (IDS) is the most cited reference architecture, but standard adoption remains fragmented:** The IDS architecture leads standardization, followed by the Asset Administration Shell in 11 studies [40; 44; 49; 51; 52; 53; 59; 66; 67; 70; 71] and Gaia-X in 10 [40; 41; 46; 48; 51; 54; 67; 68; 70; 71]. Other standards--OPC UA, EDC, FIWARE, Catena-X, IDS-RAM--appear only once or twice, and 10 studies cite no formal interoperability framework [43; 47; 56; 57; 58; 60; 62; 65; 69]. **Reporting practices vary, limiting cross-study comparability and synthesis:** While general patterns emerge across sectors, technologies, and operational goals, significant inconsistencies in reporting persist. Many studies lack detail regarding integration types, data semantics, or technical implementation frameworks, making it difficult to compare approaches or replicate findings. Several studies omit reference architectures entirely, while others do not clearly define shared data types or technological components. These gaps underscore the need for consistent taxonomies, standardized descriptors, and transparent reporting practices to enable robust cross-study learning and methodological clarity in future research on industrial data spaces. What Initial Evidence and Practical Insights Can Be Drawn from Ongoing European Initiatives That Are Developing and Implementing Data Spaces in Manufacturing and Supply Chains? In order to assess RQ3, the evolution and current state of real-world initiatives related to data spaces in the European context from the IDSA platform were analyzed. Figure 7 presents two radar plots illustrating the distribution of identified initiatives across five maturity stages--Exploratory, Preparatory, Implementation, Operational, and Scaling--indicating their progression along the data space development lifecycle. According to Figure 6(a), the documented data spaces are most heavily concentrated in the Preparatory and Implementation stages. Figure 6(b), which maps the use cases associated with these data spaces, similarly shows a predominance in the Implementation and Operational phases. This juxtaposition suggests that, although a considerable number of projects remain in preparatory planning, a substantial share has already moved into implementation and practical deployment. The presence of several data spaces in the Exploratory stage reflects an ecosystem in which foundational infrastructure and governance arrangements are still taking shape. Concurrently, the clustering of use cases toward the Operational phase indicates that many applications have progressed beyond pilot testing into live environments. Collectively, these patterns illustrate a clear shift from theoretical exploration to real-world execution in European data space development, demonstrating that reference architectures such as IDS and Gaia-X have evolved from abstract concepts into active, functioning systems.