首页|Data on Machine Learning Reported by Researchers at Comenius University (Navigat ing the Human Element: Unveiling Insights Into Workforce Dynamics In Supply Chai n Automation Through Smart Bibliometric Analysis)
Data on Machine Learning Reported by Researchers at Comenius University (Navigat ing the Human Element: Unveiling Insights Into Workforce Dynamics In Supply Chai n Automation Through Smart Bibliometric Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting originating from Bratislava, Slovakia, b y NewsRx correspondents, research stated, “This study aims to create a scientifi c map of supply chain automation research focusing on human resources management , which will be applicable in practice and widen the knowledge in theory. It int roduces the scientific articles, subject areas and dominant research topics rela ted to supply chain automation, focusing on human resources management.” Our news editors obtained a quote from the research from Comenius University, “I n this study, 509 publications retrieved from the Scopus database were analyzed by a novel methodological approach - a smart bibliometric literature review usin g Latent Dirichlet Allocation with Gibbs sampling. The study processes scientifi c articles with automated tools. It uses a novel machine-learning-based methodol ogical approach to identify latent topics from many scientific articles. This ap proach 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. Simultaneou sly, it can find topics that a standard bibliometric analysis would not show. Th e authors of the study identified six topics related to supply chain automation, focusing on human resources management, specifically (1) network design, (2) su stainable performance and practices, (3) efficient production, (4) technology-ba sed innovations and changes, (5) management of business and operations, and (6) global company strategies. The study’s results offer key insights for decision-m akers, illuminating essential themes related to automation integration in the su pply chain and the vital role of human resources in this transformation.”
BratislavaSlovakiaEuropeCyborgsE merging TechnologiesMachine LearningComenius University
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