The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
An investigation into emerging industry 4.0 technologies as drivers of supply chain innovation in Australia
Abstract
Abstract As supply chains recover from the impact of COVID-19, a sudden acceleration of interest in digitalization and automation is expected, as firms increasingly look towards digital technologies as sources of innovation in the wake of an extreme disruption. The purpose of this study is to utilize the experience of supply chain practitioners, to ascertain the current level of adoption of a number of key Industry 4.0 technologies, understand what preparatory measures are being taken by firms to ensure they are digitally-ready to utilise Industry 4.0 technologies, recognise how and where these technologies are likely impact supply chains, and investigate whether organisational size is a factor in technology adoption. This empirical study utilises primary data from a descriptive survey of supply chain practitioners working across a range of industry sectors and different stages in the supply chain. Whilst the findings from this research indicate that some Industry 4.0 technologies are still in the early stages of adoption, amongst Australian supply chain organisations, they clearly show which technologies are anticipated to have the greatest impact, what sectors that impact will most likely occur in, and which specific improvements they are expected to drive. Larger firms were found to be more digitally-ready than smaller firms, and a number of significant gaps were identified between expected impact and expected investment, meaning little spend is currently projected for certain technologies that are expected to have a significant impact.
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Cited by
Ghobakhloo, M., Iranmanesh, M., Grybauskas, A., Vilkas, M., & Petraitė, M. (2021) · Business Strategy and the Environment · doi:10.1002/bse.2867 · Cited by 462
Tsolakis, N., Schumacher, R., Dora, M., & Kumar, M. (2023) · Annals of Operations Research · doi:10.1007/s10479-022-04785-2 · Cited by 325
Varriale, V., Cammarano, A., Michelino, F., & Caputo, M. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02244-8 · Cited by 79
Metadata
- Authors
- Hopkins, J. L.
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 125
- Pages
- 103323
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W3113028001
- Cited by
- 179
- References
- 18
- FWCI
- 16.62
- 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).
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