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Cited by 341

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Industry 4.0 vs. Industry 5.0: Co-existence, Transition, or a Hybrid

Open access
Golovianko, Mariia, Terziyan, Vagan, Branytskyi, Vladyslav, Malyk, Diana·2023·Procedia Computer Science

Abstract

Smart manufacturing is being shaped nowadays by two different paradigms: Industry 4.0 proclaims transition to digitalization and automation of processes while emerging Industry 5.0 emphasizes human centricity. This turn can be explained by unprecedented challenges being faced recently by societies, such as, global climate change, pandemics, hybrid and conventional warfare, refugee crises. Sustainable and resilient processes require humans to get back into the loop of organizational decision-making. In this paper, we argue that the most reasonable way to marry the two extremes of automation and value-based human-driven processes is to create an Industry 4.0 + Industry 5.0 hybrid, which inherits the most valuable features of both - efficiency of the Industry 4.0 processes and sustainability of the Industry 5.0 decisions. Digital cognitive clones twinning human decision-making behavior are represented as an enabling technology for the future hybrid and as an accelerator (as well as resilience enabler) of the convergence of the digital and human worlds.

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Metadata

Authors
Golovianko, M., Terziyan, V., Branytskyi, V., & Malyk, D.
Venue
Procedia Computer Science
Publisher
Elsevier BV
Published
2023
Volume
217
Pages
102-113
ISSN
1877-0509 (ISSN-L)
OpenAlex
W4315778741
Cited by
341
References
45
FWCI
154.27
Type
conference paper
Language
en
Open access
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CoChat. (2026). Citation check: Industry 4.0 vs. Industry 5.0: Co-existence, Transition, or a Hybrid. https://app.cochat.ai/papers/10.1016/j.procs.2022.12.206
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Golovianko, M., Terziyan, V., Branytskyi, V., & Malyk, D. (2023). Industry 4.0 vs. Industry 5.0: Co-existence, Transition, or a Hybrid. Procedia Computer Science. https://doi.org/10.1016/j.procs.2022.12.206

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.