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Big data analytics in logistics and supply chain management

Big data analytics in logistics and supply chain management

In recent years, big data analytics capability has attracted significant attention from academia and management practitioners. The first paper in this SI is on the application of big data and predictive analytics (BDPA) on humanitarian supply chains by Dubey et al. (2018). This article examines what the antecedents of BDPA are. The second paper in this SI is on the application of BDPA on supply chain sustainability by Jeble et al. (2018). This article examines what the resources needed to build BDPA capability are. The third paper in this SI is on the use of large datasets to examine the impact of financial restrictions on green innovation capability in the context of the global supply chain by Song et al. (2018). The fourth paper in this SI is an exploratory study which aims to understand how supply chain practitioners view big data and its application in supply chain management by Stentoft et al. (2018). The fifth paper in this SI investigates the application of big data analytics (BDA) and IoT in logistics by Hopkins and Hawking (2018). The sixth paper in this SI is on the influence of digital divide (DD) and digital alphabetization (DA) on the big data generation in supply chain management by Gravili et al. (2018). The seventh paper in this SI attempts to develop a theoretical model, which tries to explain how the enablers of big data in operations and supply chain management are associated with each other by Lamba and Singh (2018). The eighth paper in this SI examines the role of cloud ERP on organizational performance by Gupta et al. (2018). The ninth paper in this SI examines the determinants of big data analytics (BDA) in logistics and supply chain management by Lai et al. (2018). The tenth paper in this SI examines the customer’s tolerance in the context of omnichannel retail stores via logistics and supply chain analytics by Hoehle et al. (2018). The eleventh paper of this SI examines how unstructured data in the form of tweets can be exploited to improve customer service by Bhattacharjya et al. (2018). The twelfth paper of this SI examines how big data analytics can be used for forecasting in supply chains by Hofmann and Emanuel (2018). The thirteenth paper of this SI examines the role of big data analytics in logistics and supply chain by Queiroz and Telles (2018).


Big data analytics and demand forecasting in supply chains: a conceptual analysis,Big data analytics and IoT in logistics: a case study,Big data analytics in logistics and supply chain management,Big data analytics in supply chain and logistics: an empirical approach,Big data and predictive analytics in humanitarian supply chains: enabling visibility and coordination in the presence of swift trust,Creation of unstructured big data from customer service: the case of parcel shipping companies on Twitter,Customers' tolerance for validation in omnichannel retail stores: enabling logistics and supply chain analytics,Global supply chain integration,financing restrictions,and green innovation: analysis based on 222,773 samples ,Impact of big data and predictive analytics capability on supply chain sustainability,Modeling big data enablers for operations and supply chain management,Practitioners understanding of big data and its applications in supply chain management,Role of cloud ERP on the performance of an organization: contingent resource-based view perspective,The influence of the Digital Divide on Big data generation within supply chain management,Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation

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