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HOW INDUSTRY 4.0 RESHAPES THE WORLD: RECOMMENDATIONS BASED ON COMPLEX GRAPH NETWORK ANALYSIS

Published online by Cambridge University Press:  27 July 2021

Rongyan Zhou  and
Julie Stal-Le Cardinal
Rongyan Zhou*
Affiliation:
Université Paris-Saclay, CentraleSupélec
Julie Stal-Le Cardinal
Affiliation:
Université Paris-Saclay, CentraleSupélec
*
Zhou, Rongyan, Université Paris-Saclay, CentraleSupélec, Industrial Engineering Research Department(LGI), France, rongyan.zhou@centralesupelec.fr

Abstract

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Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed.

Keywords

Industry 4.0Machine learningBusiness models and considerationsDecision makingBig data
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1755 - 1764
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Alcácer, V. and Cruz-Machado, V. (2019) ‘Scanning the industry 4.0: A literature review on technologies for manufacturing systems’, Engineering Science and Technology, an International Journal. Elsevier, 22(3), pp. 899–919.CrossRefGoogle Scholar
Alzahrani, T. and Horadam, K. J. (2016) ‘Community Detection in Bipartite Networks: Algorithms and Case studies BT - Complex Systems and Networks: Dynamics, Controls and Applications’, in Lü, J. et al. (eds). Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 25–50. https://dx.doi.org/10.1007/978-3-662-47824-0_2.Google Scholar
Blondel, V. D. et al. (2008) ‘Fast unfolding of communities in large networks’, Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing, 2008(10), p. P10008. https://dx.doi.org/10.1088/1742-5468/2008/10/p10008.CrossRefGoogle Scholar
Bodkhe, U. et al. (2020) ‘Blockchain for Industry 4.0: A Comprehensive Review’, IEEE Access, 8, pp. 79764–79800. https://dx.doi.org/10.1109/ACCESS.2020.2988579.CrossRefGoogle Scholar
Cai, H., Zheng, V. W. and Chang, K. C. (2018) ‘A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications’, IEEE Transactions on Knowledge and Data Engineering, 30(9), pp. 1616–1637. https://dx.doi.org/10.1109/TKDE.2018.2807452.CrossRefGoogle Scholar
Fortunato, S. (2010) ‘Community detection in graphs’, Physics Reports, 486(3), pp. 75–174. doi: https://doi.org/10.1016/j.physrep.2009.11.002.CrossRefGoogle Scholar
Fortunato, S. and Hric, D. (2016) ‘Community detection in networks: A user guide’, Physics Reports, 659, pp. 1–44. doi: https://doi.org/10.1016/j.physrep.2016.09.002.CrossRefGoogle Scholar
Glawe, L. and Wagner, H. (2020) ‘The Middle-Income Trap 2.0: The Increasing Role of Human Capital in the Age of Automation and Implications for Developing Asia’, Asian Economic Papers. MIT Press, 19(3), pp. 40–58. https://dx.doi.org/10.1162/asep_a_00783.CrossRefGoogle Scholar
Jebabli, M. et al. (2018) ‘Community detection algorithm evaluation with ground-truth data’, Physica A: Statistical Mechanics and its Applications, 492, pp. 651–706. doi: https://doi.org/10.1016/j.physa.2017.10.018.CrossRefGoogle Scholar
Jung, J. H. and Lim, D.-G. (2020) ‘Industrial robots, employment growth, and labor cost: A simultaneous equation analysis’, Technological Forecasting and Social Change, 159, p. 120202. doi: https://doi.org/10.1016/j.techfore.2020.120202.CrossRefGoogle Scholar
Kamble, S. S., Gunasekaran, A. and Gawankar, S. A. (2018) ‘Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives’, Process Safety and Environmental Protection. Elsevier, 117, pp. 408–425.CrossRefGoogle Scholar
Kanavos, A. et al. (2018) ‘Emotional community detection in social networks’, Computers & Electrical Engineering, 65, pp. 449–460. doi: https://doi.org/10.1016/j.compeleceng.2017.09.011.CrossRefGoogle Scholar
Kovacs, O. (2018) ‘The dark corners of industry 4.0 – Grounding economic governance 2.0’, Technology in Society, 55, pp. 140–145. doi: https://doi.org/10.1016/j.techsoc.2018.07.009.CrossRefGoogle Scholar
Luthra, S. et al. (2020) ‘Industry 4.0 as an enabler of sustainability diffusion in supply chain: an analysis of influential strength of drivers in an emerging economy’, International Journal of Production Research. Taylor & Francis, 58(5), pp. 1505–1521. https://dx.doi.org/10.1080/00207543.2019.1660828.CrossRefGoogle Scholar
von Luxburg, U. (2007) ‘A tutorial on spectral clustering’, Statistics and Computing, 17(4), pp. 395–416. https://dx.doi.org/10.1007/s11222-007-9033-z.CrossRefGoogle Scholar
Makarov, R. I. and Khorosheva, E. R. (2019) ‘Salient Aspects of the Implementation of Digital Economics in Glass Plants in Russia’, Glass and Ceramics, 75(11), pp. 438–440. https://dx.doi.org/10.1007/s10717-019-00107-4.CrossRefGoogle Scholar
Matsukawa, H., Minner, S. and Nakashima, K. (2020) ‘Editorial: Industry 4.0 and Production Economics’, International Journal of Production Economics, 226, p. 107666. doi: https://doi.org/10.1016/j.ijpe.2020.107666.CrossRefGoogle Scholar
Rosvall, M., Axelsson, D. and Bergstrom, C. T. (2009) ‘The map equation’, The European Physical Journal Special Topics, 178(1), pp. 13–23. https://dx.doi.org/10.1140/epjst/e2010-01179-1.CrossRefGoogle Scholar
Rosvall, M. and Bergstrom, C. T. (2008) ‘Maps of random walks on complex networks reveal community structure’, Proceedings of the National Academy of Sciences, 105(4), pp. 1118 LP – 1123. https://dx.doi.org/10.1073/pnas.0706851105.CrossRefGoogle ScholarPubMed
Schneider, P. (2018) ‘Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field’, Review of Managerial Science, 12(3), pp. 803–848. https://dx.doi.org/10.1007/s11846-018-0283-2.CrossRefGoogle Scholar
Stentoft, J. and Rajkumar, C. (2020) ‘The relevance of Industry 4.0 and its relationship with moving manufacturing out, back and staying at home’, International Journal of Production Research. Taylor & Francis, 58(10), pp. 2953–2973. https://dx.doi.org/10.1080/00207543.2019.1660823.CrossRefGoogle Scholar
Wu, J. et al. (2019) ‘Unsupervised graph association for person re-identification’, in Proceedings of the IEEE International Conference on Computer Vision, pp. 8321–8330.CrossRefGoogle Scholar
Zhou, R. and Le Cardinal, J. (2019) ‘Exploring the Impacts of Industry 4.0 from a Macroscopic Perspective’, in Proceedings of the Design Society: International Conference on Engineering Design. Cambridge University Press, pp. 2111–2120.CrossRefGoogle Scholar
Zhou, R. and Stal-Le Cardinal, J. (2020) ‘The main trends for multi-tier supply chain in Industry 4.0 based on Natural Language Processing’, Computers in Industry.CrossRefGoogle Scholar