Benchmarking the sustainable manufacturing paradigm via automatic analysis and clustering of scientific literature: A perspective from Italian technologists

Giuseppe Ingarao, Michela Simoncini, Andrea Matta, Barbara Cimatti, Paolo C. Priarone, Michele Dassisti, Nicla Frigerio, Filippo Chiarello, Archimede Forcellese, Giampaolo Campana, Gualtiero Fantoni, Marcello Colledani

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

The number of scientific papers in the field of Sustainable Manufacturing (SM) shows a strong growth of interest in this topic in the last 20 years. Despite this huge number of publications, a clear statement of the profound meaning of Sustainable Manufacturing, or at least a strong theoretical support, is still missing. The 6R framework seems to be a first attempt to rationalize this issue, as it is an axiomatic identification of its true nature. Recognizing the pursuing of one or more of the Reduce-Recycle-Reuse-Recover-Redesign-Remanufacture principles allows users to identify if any manufacturing action is in the right direction of sustainability. In the paper, the authors speculate on the use of this framework and its possible extension by referring to all the existing scientific contributions on Sustainable Manufacturing in the SCOPUS® databases as a source of data. Starting from the measurement of the distribution of the scientific papers allocated onto the 6Rs dimensions, by using both author keywords and automatically extracted multiword from texts, the distribution of the scientific papers among the 6R was derived. A new framework is proposed based on analytical text tools to compare the affinity of the applied research activities of the Italian Technologist network SOSTENERE to sustainable manufacturing and provide also a benchmarking view to describe the Italian way to SM with respect to the rest of existing applications.
Original languageEnglish
Pages (from-to)153-159
Number of pages7
JournalProcedia Manufacturing
Volume33
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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