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Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain
Open accessAbstract
Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.
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Cited by
Big data analytics in supply chain management between 2010 and 2016: Insights to industries
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2017.11.017 · Cited by 582
Management challenges in creating value from business analytics
Vidgen, R., Shaw, S., & Grant, D. B. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.02.023 · Cited by 493
Big 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
Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.11.009 · Cited by 347
Big data analytics: transforming data to action
Bumblauskas, D., Nold, H., Bumblauskas, P., & Igou, A. (2017) · Business Process Management Journal · doi:10.1108/bpmj-03-2016-0056 · Cited by 130
Metadata
- Authors
- Waller, M. A., & Fawcett, S. E.
- Venue
- Journal of Business Logistics
- Publisher
- Wiley
- Published
- 2013-12
- Volume
- 34
- Issue
- 4
- Pages
- 249-252
- ISSN
- 0735-3766 (ISSN-L)
- 2158-1592
- OpenAlex
- W2020963941
- Cited by
- 163
- References
- 0
- FWCI
- 20.92
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Waller, M. A., & Fawcett, S. E. (2013). Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain. Journal of Business Logistics. https://doi.org/10.1111/jbl.12024
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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.