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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References
Ruessmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M.: Industry 4.0: the future of productivity and growth in manufacturing. Boston Consult. Group 9, 54–89 (2015)
Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. J. Manuf. Syst. 48, 157–169 (2018). https://doi.org/10.1016/j.jmsy.2018.01.006
Usuga Cadavid, J.P., Lamouri, S., Grabot, B., Pellerin, R., Fortin, A.: Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J. Intell. Manuf. 31, 1531–1558 (2020). https://doi.org/10.1007/s10845-019-01531-7
Waller, M.A., Fawcett, S.E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logist. 34, 77–84 (2013). https://doi.org/10.1111/jbl.12010
Rainer, C.: Data mining as technique to generate planning rules for manufacturing control in a complex production system. Springer (2013). https://doi.org/10.1007/978-3-642-30749-2
Schuh, G., Reinhart, G., Prote, J.P., Sauermann, F., Horsthofer, J., Oppolzer, F., Knoll, D.: Data mining definitions and applications for the management of production complexity. In: 52nd CIRP Conference on Manufacturing Systems, pp. 874–879. Elsevier B.V., Ljubljana (2019). https://doi.org/10.1016/j.procir.2019.03.217
Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35, 137–144 (2015). https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Mayo, M.: The Data Science Puzzle - 2020 edn. https://www.kdnuggets.com/2020/02/data-science-puzzle-2020-edition.html
Mayo, M.: The data science puzzle, explained. https://www.kdnuggets.com/2016/03/data-science-puzzle-explained.html/2
Sharp, M., Ak, R., Hedberg, T.: A survey of the advancing use and development of machine learning in smart manufacturing. J. Manuf. Syst. 48, 170–179 (2018). https://doi.org/10.1016/j.jmsy.2018.02.004
Bevilacqua, M., Ciarapica, F.E., Marcucci, G.: Supply chain resilience research trends: a literature overview. IFAC-PapersOnLine 52, 2821–2826 (2019). https://doi.org/10.1016/j.ifacol.2019.11.636
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall Press, Harlow (2009)
Tiwari, S., Wee, H.M., Daryanto, Y.: Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Comput. Ind. Eng. 115, 319–330 (2018). https://doi.org/10.1016/j.cie.2017.11.017
Wang, C., Jiang, P.: Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops. J. Intell. Manuf. 29, 1485–1500 (2018). https://doi.org/10.1007/s10845-016-1194-1
Leong, P.H., Goh, O.S., Kumar, Y.J.: An embodied conversational agent using retrieval-based model and deep learning. Int. J. Innov. Technol. Explor. Eng. 8, 4138–4145 (2019). https://doi.org/10.35940/ijitee.L3650.1081219
Grabot, B.: Rule mining in maintenance: analysing large knowledge bases. Comput. Ind. Eng. 139, 1–5 (2018). https://doi.org/10.1016/j.cie.2018.11.011
Dhungana, S.: On building effective data science teams. https://medium.com/craftdata-labs/on-building-effective-data-science-teams-4813a4b82939. Accessed 16 May 2020
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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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