Avaliação de desempenho de sistemas produto-serviço: revisão de literatura e agenda de pesquisa

Moro, Suzana Regina; Ensslin, Sandra Rolin

doi:10.14488/1676-1901.v18i2.2864

Cited by 1 publication

(2 citation statements)

References 68 publications

(103 reference statements)

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“…The PROKNOW-C method consists of a set of procedures for the selection of a bibliographic portfolio and its bibliometric analysis in a structured and progressive way, aligned with the research theme according to the researcher's worldview, which delimits and defines the academic context regarding premises such as bibliographic databases, search axes, time windows, filters and criteria for inclusion or exclusion of publications [33][34][35][36][37]. The method allows the construction of knowledge to better understand a topic and supports the selection of publications considering their impact factor in the scientific community [38,39]. It encompasses four steps as shown in Ref.…”

Section: Research Methodology Proknow-cmentioning

confidence: 99%

“…With the selection of the Bibliographic Portfolio carried out on the research topic, the set of articles was evidenced by quantitative methods by means of counting publications, authors, citations and the database of the journals. The results of the data analysis in the bibliometrics phase were developed in four stages [38,39,[41][42][43][44][45]: (a) estimation of degree of relevance of journals; (b) estimation of scientific recognition of articles; (c) estimation of degree of relevance of the authors; and (d) estimation of most used keywords.…”

Section: Bibliometric Analysismentioning

confidence: 99%

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Performance measurement based on machines data: Systematic literature review

Martins

Deschamps

Detro

et al. 2022

IET Collab Intel Manufact

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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.

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