International Journal of Production Research · 2020 · 249 citations · 92 references
ManagementOperations ManagementSupply Chain ManagementBig Data AnalyticsInventory ManagementLogistics ManagementLogistics AnalyticsSupply Chain Decision AnalyticsSupply Chain Decision Support SystemsSupply Chain DigitizationSupply Chain InformaticsSupply Chain Performance MeasurementDigital Supply Chain ManagementInventory AnalysisLogisticsSupply Chain Data CollectionSupply Chain Data IntegrationSupply Chain Performance Monitoring
Data is increasingly central to research and business, and the rapid growth of data across sectors has made its management a major challenge; Big Data Analytics offers decision‑makers a powerful solution, particularly in supply chain, logistics, and inventory management, where it optimizes operations by analyzing customer behavior. This paper reviews the literature on Big Data Analytics in supply chain, logistics, and inventory management from 2015 to 2019. The authors identified 58 relevant studies from an initial set of 260 papers retrieved from the Web of Science database. The review provides a comprehensive inventory of advances and outstanding challenges in applying Big Data Analytics to supply chain, logistics, and inventory management.
It is a widely accepted fact that almost every research or business revolves around Data. Data from various business sectors has been growing sharply and the management of this massive amount of data is the biggest professional crunch these days. The notion of Big Data Analytics (BDA) is a prominent facet that delivers the best possible solution to decision-makers for efficiently handling the problems related to huge data. The key role of BDA in the area of Supply Chain Management (SCM), Logistics Management (LM), and Inventory Management (IM) is of utmost significance as it optimises the business operations by analyzing customer behaviour. Motivated with the promising paybacks of the BDA, a recent review from the year 2015–2019 is presented in this paper. Further, the significance of BDA in SCM, LM, and IM has been highlighted by studying 58 papers, which have been sorted after a detailed study of 260 papers, collected through the Web of Science (WoS) database. The findings and observations give state-of-the-art insights to scientists and business professionals by presenting an exhaustive list of the progress made and challenges left untackled in the field of BDA in SCM, LM, and IM.
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Gang Wang, Angappa Gunasekaran, Eric W.T. Ngai · International Journal of Production Economics · 2016 · 1.5K citations · Full text
Management, Management Information Systems, Supply Chain Management +16