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Digital transition from industry 4.0 to industry 5.0 in smart manufacturing: A framework for sustainable future

Author

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  • Sarkar, Bishal Dey
  • Shardeo, Vipulesh
  • Dwivedi, Ashish
  • Pamucar, Dragan

Abstract

Digital transition to Industry 5.0 (I5.0) has finally arrived after Industry 4.0 (I4.0) was offered ten years ago to help address industry flaws in manufacturing. I5.0 is necessary in contemporary business to achieve competitive advantages for a sustainable future. However, despite the importance of I5.0, the critical success factors (CSFs) for the transition from I4.0 to I5.0 have not been explored extensively in the literature. This study develops a model to investigate potential CSFs for the smooth transition from I4.0 to I5.0. The study ensures systems flexibility, then ranks the identified CSFs based on their influential weights and explores their interrelationships. The study adopts the Bayesian best-worst method (B-BWM) to assess the perceptions of several decision-makers from a probabilistic perspective for reducing information loss. The results from this study reflect that CSFs, such as adoption of advanced technology to create value in I5.0 paradigm, high-powered analytical ability for smart manufacturing, cost and funding in transition from I4.0 to I5.0, aligned organizational strategy in transition to I5.0, and security and privacy to ensure effective digital transformation, contribute significantly towards the digital transformation from I4.0 to I.50 in smart manufacturing in the context of steel manufacturing business environment considering the developing countries. The successful implementation of CSFs can help achieve flexibility in the supply chain, contribute to flexible systems and support managers in framing strategies that help them prioritize for an easy transition towards a sustainable future.

Suggested Citation

  • Sarkar, Bishal Dey & Shardeo, Vipulesh & Dwivedi, Ashish & Pamucar, Dragan, 2024. "Digital transition from industry 4.0 to industry 5.0 in smart manufacturing: A framework for sustainable future," Technology in Society, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:teinso:v:78:y:2024:i:c:s0160791x24001970
    DOI: 10.1016/j.techsoc.2024.102649
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    2. Wang, Jianlong & Liu, Yong & Wang, Weilong & Wu, Haitao, 2024. "Does artificial intelligence improve enterprise carbon emission performance? Evidence from an intelligent transformation policy in China," Technology in Society, Elsevier, vol. 79(C).

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