<![CDATA[Big data analytics (BDA) in supply chain management (SCM) is growing in attention. This is because BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and highlights avenues for future research. Big data analytics is a combination of tools, processing systems, and algorithms that can interpret insights from data. Traditionally, SCM relied on ERP and other disparate storage systems for data. Big Data assists in reconfiguring the numerous flexible sections of the supply chain, optimizing available resources (space, tools, materials, human resources, and so on), and maximizing productivity throughout implementation. Through our research, we want to know the scope of Big Data in Supply Chain.]]>
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