Industry 4.0 and lean production are two key topics in manufacturing in the last decades, and they have attracted a great interest both from practitioners and researchers, as it is testified by the great number of studies and projects on those two topics. The diffusion of lean principles has revolutionized the industrial context, both from a manufacturing and from a logistics point of view. Several studies suggest that the right way to achieve the main goals of lean, i.e. reducing wastes and increasing value through the five concepts that aim at perfection, lies in the integration of physical machines and electronic devices through networks of sensors and software to forecast, plan, manufacture, and control products in a ‘digitised’ way. It is generally agreed that the digitisation of firms needs the adoption of key enabling technologies. What is not clear is which of these key enabling technologies are ‘lean enablers’, in the correctly redesigned business processes of a lean organisation. The objective of this article is to identify and analyse which key enabling technologies can be considered in a lean-oriented future state process or, alternatively, which other context of application is suitable for those technologies. The analysis is based on published literature, and it makes use of a quantitative systematic method known as meta-analysis, aimed at identifying future topics and research trends concerning Industry 4.0, its key enabling technologies and their implementations as possible enablers of lean thinking and lean manufacturing.

A meta-analysis of industry 4.0-related technologies that are suitable for lean manufacturing / Bertolini, M.; Esposito, G.; Neroni, M.; Rizzi, A.; Romagnoli, G.. - In: ...SUMMER SCHOOL FRANCESCO TURCO. PROCEEDINGS. - ISSN 2283-8996. - 1:(2019), pp. 150-156. ((Intervento presentato al convegno 24th Summer School Francesco Turco, 2019 tenutosi a ita nel 2019.

A meta-analysis of industry 4.0-related technologies that are suitable for lean manufacturing

Bertolini M.;
2019

Abstract

Industry 4.0 and lean production are two key topics in manufacturing in the last decades, and they have attracted a great interest both from practitioners and researchers, as it is testified by the great number of studies and projects on those two topics. The diffusion of lean principles has revolutionized the industrial context, both from a manufacturing and from a logistics point of view. Several studies suggest that the right way to achieve the main goals of lean, i.e. reducing wastes and increasing value through the five concepts that aim at perfection, lies in the integration of physical machines and electronic devices through networks of sensors and software to forecast, plan, manufacture, and control products in a ‘digitised’ way. It is generally agreed that the digitisation of firms needs the adoption of key enabling technologies. What is not clear is which of these key enabling technologies are ‘lean enablers’, in the correctly redesigned business processes of a lean organisation. The objective of this article is to identify and analyse which key enabling technologies can be considered in a lean-oriented future state process or, alternatively, which other context of application is suitable for those technologies. The analysis is based on published literature, and it makes use of a quantitative systematic method known as meta-analysis, aimed at identifying future topics and research trends concerning Industry 4.0, its key enabling technologies and their implementations as possible enablers of lean thinking and lean manufacturing.
24th Summer School Francesco Turco, 2019
ita
2019
1
150
156
Bertolini, M.; Esposito, G.; Neroni, M.; Rizzi, A.; Romagnoli, G.
A meta-analysis of industry 4.0-related technologies that are suitable for lean manufacturing / Bertolini, M.; Esposito, G.; Neroni, M.; Rizzi, A.; Romagnoli, G.. - In: ...SUMMER SCHOOL FRANCESCO TURCO. PROCEEDINGS. - ISSN 2283-8996. - 1:(2019), pp. 150-156. ((Intervento presentato al convegno 24th Summer School Francesco Turco, 2019 tenutosi a ita nel 2019.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1200078
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