The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Big data analytics in supply chain management: A state-of-the-art literature review
Open accessAbstract
The rapid growing interest from both academics and practitioners towards the application of Big Data Analytics (BDA) in Supply Chain Management (SCM) has urged the need of review up-to-date research development in order to develop new agenda. This review responds to this call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structured based on the content analysis method of Mayring (2008), addressing four research questions on (1) what areas of SCM that BDA is being applied, (2) what level of analytics is BDA used in these application areas, (3) what types of BDA models are used, and finally (4) what BDA techniques are employed to develop these models. The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.
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Citations
Cites
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.031 · Cited by 1,905
Wang, 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
Big Data computing and clouds: trends and future directions
Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A. S., & Buyya, R. (2015) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2014.08.003 · Cited by 901
Babiceanu, R. F., & Seker, R. (2016) · Computers in Industry · doi:10.1016/j.compind.2016.02.004 · Cited by 461 · cited 5×
“…value, which represent the rigorousness of Big Data Analytics (BDA), are particularly important because without data analysis, other BD processing aspects such as collection, storage, and management would not create much value (Huang et al., 2015; Chen and Zhang, 2014; Babiceanu and Seker, 2016).”
BackgroundInfluentialBig Data and supply chain management: A review and bibliometric analysis
Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2018) · Annals of Operations Research · doi:10.1007/s10479-016-2236-y · Cited by 382
Tourism analytics with massive user-generated content: a case study of Barcelona
Marine-Roig, E., & Anton Clavé, S. (2015) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2015.06.004 · Cited by 356
Big data reduction framework for value creation in sustainable enterprises
Rehman, M. H. U., Chang, V., Batool, A., & Wah, T. Y. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.05.013 · Cited by 279
Hofmann, E. (2017) · International Journal of Production Research · doi:10.1080/00207543.2015.1061222 · Cited by 220
Cited by
Ivanov, D., Dolgui, A., & Sokolov, B. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1488086 · Cited by 2,055
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2018.05.009 · Cited by 1,440
Digital Supply Chain: Literature review and a proposed framework for future research
Büyüközkan, G., & Göçer, F. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.02.010 · Cited by 1,205
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.05.022 · Cited by 1,015
Li, Y., Dai, J., & Cui, L. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107777 · Cited by 922
Metadata
- Authors
- Nguyen, T., ZHOU, L., Spiegler, V., Ieromonachou, P., & Lin, Y.
- Venue
- Computers & Operations Research
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 98
- Pages
- 254-264
- ISSN
- 0305-0548 (ISSN-L)
- OpenAlex
- W2728975105
- Cited by
- 565
- References
- 9
- FWCI
- 66.28
- Type
- journal article
- Language
- en
- Open access
- green
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- Nguyen, T., ZHOU, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research. https://doi.org/10.1016/j.cor.2017.07.004
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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.
