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Lim, Chiehyeon
Service Engineering & Knowledge Discovery Lab
Research Interests
  • Smart service systems, Service-oriented data analytics, Service operations, Service design, Decision science, Personal process management

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From technological development to social advance: A review of Industry 4.0 through machine learning

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Title
From technological development to social advance: A review of Industry 4.0 through machine learning
Author
Lee, ChanghunLim, Chiehyeon
Issue Date
2021-06
Publisher
Elsevier BV
Citation
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, v.167, pp.120653
Abstract
Industry 4.0 has attracted considerable interest from firms, governments, and individuals as the new concept of future computer, industrial, and social systems. However, the concept has yet to be fully explored in the scientific literature. Given the topic's broad scope, this work attempts to understand and clarify Industry 4.0 by analyzing 660 journal papers and 3,901 news articles through text mining with unsupervised machine learning algorithms. Based on the results, this work identifies 31 research and application issues related to Industry 4.0. These issues are categorized and described within a five-level hierarchy: 1) infrastructure development for connection, 2) artificial intelligence development for data-driven decision making, 3) system and process optimization, 4) industrial innovation, and 5) social advance. Further, a framework for convergence in Industry 4.0 is proposed, featuring six dimensions: connection, collection, communication, computation, control, and creation. The research outcomes are consistent with and complementary to existing relevant discussion and debate on Industry 4.0, which validates the utility and efficiency of the data-driven approach of this work to support experts’ insights on Industry 4.0. This work helps establish a common ground for understanding Industry 4.0 across multiple disciplinary perspectives, enabling further research and development for industrial innovation and social advance.
URI
https://scholarworks.unist.ac.kr/handle/201301/50062
URL
https://www.sciencedirect.com/science/article/pii/S0040162521000858
DOI
10.1016/j.techfore.2021.120653
ISSN
0040-1625
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SME_Journal Papers
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