Abstract
Data analytics is increasingly becoming fundamental, especially for manufacturing companies which are asked to improve their sustainable-related performances to compete on the market. In this context, hence, Industry 4.0 enabling technologies integrated with specific industrial communication protocols appear as opportunities to upgrade obsolete machines by capturing value through data collection and analysis. Data analytics implies for manufacturing companies a better monitoring of resources consumptions during the production process and an enhanced decision-makers support on both short-term and long-term decisions. Nevertheless, the extant literature shows limited attention over industrial asset circular management and the opportunity to exploit data collection from the shopfloor. Therefore, this research contribution aims at understanding how to support manufacturing companies, which are currently operating with old and close-to-be obsolete machines, in the digital transition to make them be able to encounter the eco-efficiency and circular economy principles through asset lifecycle extension. In this research it has been focused the attention on the machinery department of a manufacturing company producing agricultural machinery having problem of obsolescence of industrial assets which generates lots of avoidable scraps and increased energy consumption. Through the installation of the MT Connect protocol on obsolete industrial assets it was possible to start extracting data during the production activities enabling a deep data analysis which led to the introduction of specific maintenance activities and the optimization of the production activities. Based on these corrections it was possible to decrease the material used, due to fewer scraps, and the energy consumed. This research study enabled the company to extend obsolete assets lifecycle extracting value from them thanks to data analytics techniques and sensors.
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Acerbi, F., Pasanisi, D., Pesenti, V., Verpelli, L., Taisch, M. (2023). Capturing Value by Extending the End of Life of a Machining Department Through Data Analytics: An Industrial Use Case. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-031-43688-8_28
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DOI: https://doi.org/10.1007/978-3-031-43688-8_28
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