Navigating the human element: Unveiling insights into workforce dynamics in supply chain automation through smart bibliometric analysis

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dc.contributor.authorAngielsky, Melanie
dc.contributor.authorMelanie, Lukas
dc.contributor.authorMadzik, Peter
dc.contributor.authorFalat, Lukas
dc.date.accessioned2024-12-04T11:41:32Z
dc.date.available2024-12-04T11:41:32Z
dc.date.issued2024
dc.description.abstract-translatedThis study aims to create a scientific map of supply chain automation research focusing on human resources management, which will be applicable in practice and widen the knowledge in theory. It introduces the scientific articles, subject areas and dominant research topics related to supply chain automation, focusing on human resources management. In this study, 509 publications retrieved from the Scopus database were analyzed by a novel methodological approach – a smart bibliometric literature review using Latent Dirichlet Allocation with Gibbs sampling. The study processes scientific articles with automated tools. It uses a novel machine-learning-based methodological approach to identify latent topics from many scientific articles. This approach creates the possibility of comprehensively capturing the areas of supply chain automation focusing on human resources management and offers a science map of this rapidly developing area. This kind of smart literature review based on a machine learning approach can process a large number of documents. Simultaneously, it can find topics that a standard bibliometric analysis would not show. The authors of the study identified six topics related to supply chain automation, focusing on human resources management, specifically (1) network design, (2) sustainable performance and practices, (3) efficient production, (4) technology-based innovations and changes, (5) management of business and operations, and (6) global company strategies. The study’s results offer key insights for decision-makers, illuminating essential themes related to automation integration in the supply chain and the vital role of human resources in this transformation. The limitations of this study are the qualitative level of results provided by the machine learning approach, which does not contain manual analysis of documents and the subjectivity of the expert process to set the appropriate number of topics.en
dc.format16 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.15240/tul/001/2024-5-011
dc.identifier.issn1212-3609 (print)
dc.identifier.issn2336-5064 (online)
dc.identifier.urihttp://hdl.handle.net/11025/57906
dc.language.isoenen
dc.publisherTechnická univerzita v Libercics
dc.rightsCC BY-NC 4.1en
dc.rights.accessopenAccessen
dc.subjectautomatizacecs
dc.subjectchytrá výrobacs
dc.subjectprůmysl 4.0cs
dc.subjectdodavatelský řetězeccs
dc.subjectkvalifikacecs
dc.subjectpracovní sílycs
dc.subject.translatedautomationen
dc.subject.translatedsmart manufacturingen
dc.subject.translatedindustry 4.0en
dc.subject.translatedsupply chainen
dc.subject.translatedqualificationen
dc.subject.translatedworkforceen
dc.titleNavigating the human element: Unveiling insights into workforce dynamics in supply chain automation through smart bibliometric analysisen
dc.typečlánekcs
dc.typearticleen
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen
local.files.count1*
local.files.size2848413*
local.has.filesyes*

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