Navigating the human element: Unveiling insights into workforce dynamics in supply chain automation through smart bibliometric analysis
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Date
2024-07-02
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Abstract
This 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.