# Supply Chain Data Analytics for Digital Twins:
###### Abstract  
The latest research highlights the need for circularity in modern industrial supply chains, which is reflected in the decisions of European and global policymakers, as well as in the strategies of major stakeholders. Digital Twins are considered a principal catalyst in the transition to circularity, while real-world, accurate and timely data is a key factor in these supply chains. This emphasis on data highlights the central role of data analytics in extracting key insights and utilizing machine learning to propose sustainability initiatives in decentralized production ecosystems. In consequence, commercial solutions are being developed; however, a single solution might not address all requirements. In this work we present a comprehensive modular, scalable and secure analytics architecture, designed to expand the available components in commercial solutions by providing an intelligent layer to Digital Twins. Our approach integrates with the latest standards for international data spaces, interoperability and process models in distributed environments where multiple actors engage in co-opetition. The proposed architecture is implemented in a market-ready solution and demonstrated in two case studies, in Spain and in Greece. Validation results confirm that the analytics service delivers accurate, timely and actionable insights, while following open communication standards and sustainability guidelines. Our research indicates that companies implementing digital twin solutions using standardized connectors for interoperability can benefit by customizing the proposed solution and avoiding complex developments from scratch. digital twins; data analytics; decentralised architecture; machine learning; scalable systems; interoperability; industrial supply chains +
Footnote †: journal: Applied sciences  
00295156939 Adopting a circular economy can help mitigate these pressing issues while creating new paths for economic growth. A key approach to increasing circularity is embracing the Three R's: Reduce, Reuse, and Recycle. The transition toward circularity presents a significant economic opportunity, particularly through the implementation of digitalization tools. These technologies enable companies to measure, track, and optimize circularity within their processes, ensuring more efficient resource utilization. In this context, Digital Twins have emerged as a fundamental tool in Industry 4.0, enabling real-time monitoring and optimization of industrial operations by integrating IoT devices, cloud computing, and AI-driven analytics; Digital Twins provide real-time insights and predictive analytics. Their contribution to circularity includes extending product lifespans (e.g., predictive maintenance in manufacturing), optimizing material use and recycling (e.g., tracking rare-earth metals in electronic devices), reducing waste in production processes, enhancing energy efficiency, and closing the loop in supply chains (e.g., monitoring end-of-life products for reuse or recycling). Digital twins trace their origins to NASA's Apollo program, where they were developed as a means to remotely monitor key spacecraft systems. These physical models enabled engineers to predict and address potential issues in real-time [4]. In 2002, the concept was introduced into Product Lifecycle Management (PLM) as a virtual representation of physical assets spanning their entire lifecycle. The application of digital twins quickly expanded into various fields, including industry, health, and logistics [5]. The significance of digital twins grew alongside the Industry 4.0 revolution, driven by the explosion of data from diverse sources, such as logistics, manufacturing devices, and the Internet of Things (IoT). The proliferation of real-time data streams facilitated the development of digital twins that accurately represent physical systems. This new representation enabled the implementation of advanced analytics and artificial intelligence models within digital replicas, enhancing their capabilities and applications.