Enhancing Circular Economy Initiatives Through Data Analytics and Sustainable Practices

This study examines how Data Analytics (DA) enhances Circular Economy (CE) practices across industries. Using a mixed-method approach of liter ature review and qualitative interviews with five Swiss companies, we explore how organizations leverage DA to support CE initiatives. The findings show varying DA maturity levels, with larger firms effectively integrating DA, Internet-of-Things (IoT), and Artificial Intelligence (AI) for CE goals. Key challenges identified include difficulty in customer acceptance of IoT-enabled CE solutions, data accessibility issues, and limited in-house expertise in applying DA to Circular Economy Prac tices (CEP), particularly among smaller companies. Despite these obstacles, DA enhances resource optimization, predictive maintenance, and closed-loop supply chains. Larger organizations use DA, IoT, and AI to extend product lifespans and reduce waste, while smaller firms struggle with resource limitations and data extrac tion, highlighting a capability gap in CE implementation. This research contributes to the growing literature on technology-enabled circular economy by providing a refined understanding of DA implementation challenges and opportunities in CE contexts. We propose a first thought of a conceptual framework linking DA capabilities to specific CE outcomes such as increased resource efficiency, extended product life cycles, and closed material loops. Additionally, we offer practical recommendations for companies at different stages of DA maturity to enhance their CEP. The find ings emphasize the need for industry-specific, user-friendly DA tools, and improved stakeholder education to foster the adoption of CEP.