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Developing a Circular Business Model for Machinery Life Cycle Extension by Exploiting Tools for Digitalization

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  • Federica Cappelletti

    (Dipartimento di Ingegneria e Scienze Matematiche, Università Politecnica delle Marche, 60121 Ancona, Italy)

  • Silvia Menato

    (Dipartimento di Tecnologie Innovative, Scuola Universitaria Professionale della Svizzera Italiana, Polo Universitario Lugano—Campus Est, 6962 Lugano, Switzerland)

Abstract

Digitalization technologies have been identified as enablers for the adoption of circular economy practices. The machinery-value chain addressed in this study is affected by the introduction of digital technologies that enable real-time monitoring of data on product condition and control optimization, the deployment of predictive analytics techniques, as well as offering circular-based services. Machinery-lifetime extension can be digitally enabled on both old and new machines. The research objectives were to investigate how digital technologies enable the adoption of circular economy-based business models by manufacturing companies and provide answers regarding (i) which Life Cycle Extension Strategy is suitable for digital circular-business model adoption and (ii) how digitalization of machines enables manufacturing companies to innovate their business models. The correlation matrix is the tool developed from the proposed approach and it aims to support manufacturers in their first contact with circular business models. In the European RECLAIM project context, two manufacturers have applied the approach. The next steps are expected to introduce quantitative indicators to define thresholds for the steps toward circularity without replacing the qualitative approach, as this guarantees its applicability in a context that has never considered circularity yet.

Suggested Citation

  • Federica Cappelletti & Silvia Menato, 2023. "Developing a Circular Business Model for Machinery Life Cycle Extension by Exploiting Tools for Digitalization," Sustainability, MDPI, vol. 15(21), pages 1-24, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15500-:d:1271861
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    References listed on IDEAS

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    1. Schiavone, Francesco & Leone, Daniele & Caporuscio, Andrea & Lan, Sai, 2022. "Digital servitization and new sustainable configurations of manufacturing systems," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    2. Ghosh, Swapan & Hughes, Mat & Hodgkinson, Ian & Hughes, Paul, 2022. "Digital transformation of industrial businesses: A dynamic capability approach," Technovation, Elsevier, vol. 113(C).
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