Big Data and supply chain management: a review and bibliometric analysis

By Deepa Mishra, Angappa Gunasekaran, Thanos Papadopoulos, Stephen J. Childe. Annals of Operations Research, 2016

330 citations recorded by the source corpus.

Subfields: Information Systems, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/131cef9d6caf65a8cf7ad6b771c531c409e8930d

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Citations in the corpus

Big data analytics capability in supply chain agility (2019, neutral): “…the importance of big data analytics in supporting the strategic goals of an organization (Davenport, 2006; Manyika et al. 2011; Prescott, 2014; Mishra et al. 2016, 2017; Roden et al. 2017; Ji-fan Ren et al. 2017; Choi et al. 2017; Fosso Wamba, 2017; Jabbour et al. 2017), but there is as yet…”
Role of big data analytics in developing sustainable capabilities (2019, background): “Moreover, big data increases a ﬁ rm's value when it is incorporated with other key organizational resources (Sanders, 2014) to impact ﬁ rms' SCM practices (Mishra et al., 2018).”
Impact of Industry 4.0 on supply chain performance (2020, background): “Another example of the application of IoT in retail can be seen in smart shelves (Mishra et al. 2016b; Heim, Wentworth Jr and Peng 2009).”
Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain (2017, background): “[39], application of BDA in supply chain management and forecasting has been gaining importance over the past decade.”
Critical analysis of Big Data challenges and analytical methods (2017, methodology): “Alterna-tively, meta-based-approaches can be used to conducting a literature review and include the work of Mishra, Gunasekaran, Papadopoulos, and Childe (2016), which adopt a bibliometric and network analysis approach to obtain and compare in ﬂ uential work in a speci ﬁ c domain (in this example,…”
Disruption risks in supply chain management: a literature review based on bibliometric analysis (2020, methodology): “…different methodologies, such as quantitative modelling (Fahimnia et al. 2015), simulations (Ivanov 2018, 2019), and empirical studies (Dubey et al. 2019a, 2019b; Ambulkar, Blackhurst, and Grawe 2015; Mishra et al. 2016), to mutually justify the correctness of the proposed managerial insights.”