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Machine Tool Transition from Industry 3.0 to 4.0: A Comparison between Old Machine Retrofitting and the Purchase of New Machines from a Triple Bottom Line Perspective

Author

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  • Serena Ilari

    (Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, 60131 Ancona, Italy)

  • Fabio Di Carlo

    (Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, 60131 Ancona, Italy)

  • Filippo Emanuele Ciarapica

    (Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, 60131 Ancona, Italy)

  • Maurizio Bevilacqua

    (Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, 60131 Ancona, Italy)

Abstract

The emerging scenario designed by digital technologies connected to Industry 4.0 is pushing towards increasingly sustainable companies. Access to the multiple benefits of digitalization (such as increased productivity, flexibility, efficiency, quality, lower consumption of resources, and the improvement of worker safety) is possible by purchasing new-generation machinery. However, thanks to smart retrofitting processes, companies can extend the shelf life of machinery without replacing it entirely. This work aims to present a framework to assess the sustainability of implementing a smart retrofitting process in old machines as an alternative to replacement from a triple bottom line (economic, environmental, and social) perspective. Due to the multidimensional and multidisciplinary variables that the proposed framework must consider, a multicriteria decision-making process is developed to identify the best transition solution from Industry 3.0 to 4.0. Then, we analyze a case study in which, thanks to the previously proposed methodology, two types of smart retrofitting on a column drill are compared with three replacement options for the same machine tool. In conclusion, the case study shows that retrofitting in the context of Industry 4.0 (or smart retrofitting), despite its high acquisition cost, is the best solution in terms of sustainability, and that this is because the smart retrofitting solution not only positively influences all parameters of digitization but also has a strong impact on the safety criterion.

Suggested Citation

  • Serena Ilari & Fabio Di Carlo & Filippo Emanuele Ciarapica & Maurizio Bevilacqua, 2021. "Machine Tool Transition from Industry 3.0 to 4.0: A Comparison between Old Machine Retrofitting and the Purchase of New Machines from a Triple Bottom Line Perspective," Sustainability, MDPI, vol. 13(18), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10441-:d:638900
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    References listed on IDEAS

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    1. Małgorzata Jasiulewicz-Kaczmarek & Katarzyna Antosz & Ryszard Wyczółkowski & Dariusz Mazurkiewicz & Bo Sun & Cheng Qian & Yi Ren, 2021. "Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing," Energies, MDPI, vol. 14(5), pages 1-30, March.
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    Cited by:

    1. Atiq Zaman, 2022. "Waste Management 4.0: An Application of a Machine Learning Model to Identify and Measure Household Waste Contamination—A Case Study in Australia," Sustainability, MDPI, vol. 14(5), pages 1-18, March.

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