Abstract
Data science enables harnessing data to improve manufacturing processes and supply chains. This has attracted attention from both research and industrial communities. However, there seems to be a lack of consensus in scientific literature regarding the definitions for some data-related concepts, which may hinder their understanding by practitioners. Furthermore, these terms tend to have definitions evolving through time. Thus, this study explores the use of six data science concepts in research under the framework of Industry 4.0 and supply chain management. To achieve this objective, a text mining approach is employed to both contribute to disambiguation of these terms and identify future research trends. Main findings suggest that even if concepts such as machine learning, data mining and artificial intelligence are often used interchangeably, there are key differences between them. Regarding future trends, topics such as blockchain, internet of things and digital twins seem to be attracting recent research interest.
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Nguyen, A., Usuga-Cadavid, J.P., Lamouri, S., Grabot, B., Pellerin, R. (2021). Understanding Data-Related Concepts in Smart Manufacturing and Supply Chain Through Text Mining. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Lamouri, S. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2020. Studies in Computational Intelligence, vol 952. Springer, Cham. https://doi.org/10.1007/978-3-030-69373-2_37
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DOI: https://doi.org/10.1007/978-3-030-69373-2_37
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