The urgency of addressing global challenges such as climate change, resource scarcity, and environmental degradation has positioned the Circular Economy (CE) as a crucial strategy for sustainable development. Industry 4.0 (I4.0) technologies have been recognized as key enablers of CE. However, a significant knowledge gap exists regarding how organizations can effectively integrate these technologies with CE strategies. Existing integration frameworks often focus narrowly on specific industries, technologies, or the traditional 3R model, neglecting the broader 10R framework and, hence, offering limited guidance. This paper addresses these gaps by developing a comprehensive 10R-based integration framework, providing practical guidance on how I4.0 technologies can support the full range of CE strategies. Using a literature review based on keywords’ clusters analysis, this study explores how I4.0 can support both implementation and decision-making in the CE 10Rs, providing a practical guide for businesses and supporting a broader shift towards sustainable business models. The results show that IoT, Big Data, and Digital Twins effectively support Rs related to smarter product use and manufacturing processes. Additive Manufacturing, Augmented/Virtual Reality, and Cognitive Twins are crucial in extending the lifespan of products or components. IoT, Artificial Intelligence, Blockchain, and human-robot collaboration can improve recycling practices and material recovery. The study reveals that while ’Reduce’ and ’Recycle’ dominate the literature, integrating I4.0 technologies with lesser-explored strategies like ’Reuse,’ ’Repurpose,’ ’Refurbish,’ and ’Remanufacture’ offers significant potential for future research. It also stresses the need to assess the energy and environmental impacts of I4.0 technologies themselves in the CE context.

Industry 4.0 technologies in support of circular Economy: A 10R-based integration framework / Ciano, M. P.; Peron, M.; Panza, L.; Pozzi, R.. - In: COMPUTERS & INDUSTRIAL ENGINEERING. - ISSN 0360-8352. - 201:(2025). [10.1016/j.cie.2025.110867]

Industry 4.0 technologies in support of circular Economy: A 10R-based integration framework

Panza L.;
2025

Abstract

The urgency of addressing global challenges such as climate change, resource scarcity, and environmental degradation has positioned the Circular Economy (CE) as a crucial strategy for sustainable development. Industry 4.0 (I4.0) technologies have been recognized as key enablers of CE. However, a significant knowledge gap exists regarding how organizations can effectively integrate these technologies with CE strategies. Existing integration frameworks often focus narrowly on specific industries, technologies, or the traditional 3R model, neglecting the broader 10R framework and, hence, offering limited guidance. This paper addresses these gaps by developing a comprehensive 10R-based integration framework, providing practical guidance on how I4.0 technologies can support the full range of CE strategies. Using a literature review based on keywords’ clusters analysis, this study explores how I4.0 can support both implementation and decision-making in the CE 10Rs, providing a practical guide for businesses and supporting a broader shift towards sustainable business models. The results show that IoT, Big Data, and Digital Twins effectively support Rs related to smarter product use and manufacturing processes. Additive Manufacturing, Augmented/Virtual Reality, and Cognitive Twins are crucial in extending the lifespan of products or components. IoT, Artificial Intelligence, Blockchain, and human-robot collaboration can improve recycling practices and material recovery. The study reveals that while ’Reduce’ and ’Recycle’ dominate the literature, integrating I4.0 technologies with lesser-explored strategies like ’Reuse,’ ’Repurpose,’ ’Refurbish,’ and ’Remanufacture’ offers significant potential for future research. It also stresses the need to assess the energy and environmental impacts of I4.0 technologies themselves in the CE context.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3002017
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