The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Agile manufacturing practices: the role of big data and business analytics with multiple case studies
Open accessAbstract
The purpose of this study was to examine the role of big data and business analytics (BDBA) in agile manufacturing practices. Literature has discussed the benefits and challenges related to the deployment of big data within operations and supply chains, but there has not been a study of the facilitating roles of BDBA in achieving an enhanced level of agile manufacturing practices. As a response to this gap, and drawing upon multiple qualitative case studies undertaken among four UK organisations, we present and validate a framework for the role of BDBA within agile manufacturing. The findings show that market turbulence has negative universal effects and that agile manufacturing enablers are being progressively deployed and aided by BDBA to yield better competitive and business performance objectives. Further, the level of intervention was found to differ across companies depending on the extent of deployment of BDBA, which accounts for variations in outcomes.
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Cites
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Self-citationHow ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study
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How to improve firm performance using big data analytics capability and business strategy alignment?
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016) · International Journal of Production Economics · doi:10.1016/j.ijpe.2016.08.018 · Cited by 1,624
Self-citationWang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016) · International Journal of Production Economics · doi:10.1016/j.ijpe.2016.03.014 · Cited by 1,545
Self-citationCan big data and predictive analytics improve social and environmental sustainability?
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.06.020 · Cited by 860
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Ivanov, D., Dolgui, A., & Sokolov, B. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1488086 · Cited by 2,055
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Dolgui, A., Ivanov, D., & Sokolov, B. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1774679 · Cited by 501
Dubey, R., Gunasekaran, A., & Childe, S. J. (2019) · Management Decision · doi:10.1108/md-01-2018-0119 · Cited by 480
Self-citationBig data analytics capabilities and performance: Evidence from a moderated multi-mediation model
Rialti, R., Zollo, L., Ferraris, A., & Alon, I. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.119781 · Cited by 420
Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.03.181 · Cited by 381
Metadata
- Authors
- Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., & Papadopoulos, T.
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2017-10-31
- Volume
- 56
- Issue
- 1-2
- Pages
- 385-397
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2765397828
- Cited by
- 215
- References
- 9
- FWCI
- 26.96
- Type
- journal article
- Language
- en
- Open access
- green
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- Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., & Papadopoulos, T. (2018). Agile manufacturing practices: the role of big data and business analytics with multiple case studies. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1395488
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
