The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond
Abstract
In the Industry 4.0 era, automation and data analytics emerge as the major forces to enhance efficiency in operations management (OM). Disruptive technologies, such as artificial intelligence, robotics, blockchain, 3D printing, 5G, Internet‐of‐Thing, digital twins, and augmented reality, are widely applied. They potentially will bring a radical change to real world operations. In this study, we first explore several major disruptive technologies, examine the corresponding OM studies, and highlight their current applications in the industry. Then, we discuss the pros and cons associated with the use of these technologies and uncover the potential human–machine conflicting areas. After that, we propose measures which may be able to achieve human–machine reconciles in the coming Industry 5.0 era. A concept of “sustainable social welfare” which includes worker welfare, privacy, etc. is proposed and the roles played by policy makers are also discussed. Finally, a future research agenda, which covers topics in both the Industry 4.0 and Industry 5.0 eras, is established.
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Citations
Cites
Three pillars of sustainability: in search of conceptual origins
Purvis, B., Mao, Y., & Robinson, D. (2019) · Sustainability Science · doi:10.1007/s11625-018-0627-5 · Cited by 3,279 · cited 1×
“Traditionally, in OM, lots of studies focus on three pillars, namely corporate benefits, environment, and society (including consumer welfare) and put them with equal weighting (Purvis et al. 2019).”
BackgroundWu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.109 · Cited by 2,410
Incomplete Contracts and Renegotiation
Hart, O., & Moore, J. (1988) · Econometrica · doi:10.2307/1912698 · Cited by 1,876
Ivanov, D., & Dolgui, A. (2021) · Production Planning & Control · doi:10.1080/09537287.2020.1768450 · Cited by 1,790
Incomplete Contracts: Where do We Stand?
Tirole, J. (1999) · Econometrica · doi:10.1111/1468-0262.00052 · Cited by 1,193
Big Data Analytics in Operations Management
Choi, T., Wallace, S. W., & Wang, Y. (2018) · Production and Operations Management · doi:10.1111/poms.12838 · Cited by 1,027
Self-citationDigital servitization business models in ecosystems: A theory of the firm
Kohtamäki, M., Parida, V., Oghazi, P., Gebauer, H., & Baines, T. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.06.027 · Cited by 995
Foundations of Incomplete Contracts
Hart, O., & Moore, J. (1999) · Review of Economic Studies · doi:10.1111/1467-937x.00080 · Cited by 960
Blockchain for Supply Chain Traceability: Business Requirements and Critical Success Factors
Hastig, G. M., & Sodhi, M. S. (2020) · Production and Operations Management · doi:10.1111/poms.13147 · Cited by 901
Babich, V., & Hilary, G. (2020) · Manufacturing & Service Operations Management · doi:10.1287/msom.2018.0752 · Cited by 782
Cited by
Industry 5.0: Prospect and retrospect
Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., & Wang, L. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.09.017 · Cited by 1,300
Ivanov, D. (2023) · International Journal of Production Research · doi:10.1080/00207543.2022.2118892 · Cited by 833
Dynamic digital capabilities and supply chain resilience: The role of government effectiveness
Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., Foropon, C., & Papadopoulos, T. (2023) · International Journal of Production Economics · doi:10.1016/j.ijpe.2023.108790 · Cited by 473
Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2309309 · Cited by 441
Ivanov, D., Dolgui, A., & Sokolov, B. (2022) · Transportation Research Part E: Logistics and Transportation Review · doi:10.1016/j.tre.2022.102676 · Cited by 398
Metadata
- Authors
- Choi, T., Kumar, S., Yue, X., & Chan, H.
- Venue
- Production and Operations Management
- Publisher
- SAGE Publications
- Published
- 2022-01
- Volume
- 31
- Issue
- 1
- Pages
- 9-31
- ISSN
- 1059-1478 (ISSN-L)
- 1937-5956
- OpenAlex
- W3212055144
- Cited by
- 655
- References
- 72
- FWCI
- 46.72
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
