| Autor: |
Gleison Hidalgo Martins, Fernando Deschamps, Silvana Pereira Detro, Pablo Deivid Valle |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IET Collaborative Intelligent Manufacturing, Vol 4, Iss 2, Pp 74-86 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2516-8398 |
| DOI: |
10.1049/cim2.12051 |
| Popis: |
Abstract Industry 4.0 driven by the internet of things (IoT) is changing the way of producing and has been offering smart manufacturing systems with support technologies for the digital transformation of manufacturing plants seeking improvements in productivity, in control over the process, and customisation of production, among others. Due to these technological developments, small and medium‐sized industries have been identified as a weak link in adapting their processes and resources, where they are usually the biggest victims in the transition to industry 4.0. The evidence points out that the excess data inserted in the databases of the manufacturing system of the industries influences the decision‐making process of managers, making the process more complex and dynamic. This research focuses on a systematic literature review to assess how data‐based performance measurements for machines are being handled in the context of industry 4.0. The methodological approach follows the application of the PROKNOW‐C (Knowledge Development Process‐Constructivist) method used to build a Bibliographic Portfolio in a structured way in line with the research theme. The results presented in the Bibliometric Analysis enabled the construction of a performance measurement model based on the sources of the researched articles. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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