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Big data is of great importance in manufacturing, since knowing the diverse origin of underlying causes of problems is completely necessary for managing continuous improvement. As manufacturers are shifting towards digital transformation driven by big data, business analytics is becoming a dominant methodology for strategic decision-making in business management research. In response to this emerging phenomenon, the purpose of the current study is to provide a thorough literature review of the applicability of big data in manufacturing, with a perspective to exploring various research trends in this field and identifying the scope of potential investigations in the future. This study uses bibliometric and visual analysis approaches to systematically identify and analyse research articles from leading business journals in the Scopus database. The study sample included 89 research articles published in ABDC A*/A category journals to map thematic evolution and conceptual clusters related to keywords of ‘big data’, ‘business analytics’ and ‘manufacturing’. Using factorial analysis in Biblioshiny software, the study presents three research clusters in which researchers shall be encouraged to expand the big data/business analytics research in the context of manufacturing.

Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management