A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research

Big data has become a global phenomenon with companies in almost all industries trying in some way to identify and exploit this is untapped asset. The big data application in supply chain management (SCM) has also caught the management’s attention, and with the high influx of data being generated at different points in supply chain can be used to stimulate data driven decisions, build supply chain flexibility, adaptability and agility. With the inception and wide adoption of the Industry 4.0 technologies like Internet of things (IoT), cloud computing (CC), Smart manufacturing (SM), Artificial intelligence (AI), the need for integration of big data and analytics has been felt more than ever. The purpose of this survey is to investigate the applications of predictive analytics in different supply chain areas and provide a classification based on the different techniques/algorithm used at various supply chain areas, detect gaps and propose the future direction for research. The review also investigates the application of big data analytics (specifically, predictive analytics) along with these disruptive technologies in the SCM areas. The survey review indicated that manufacturing and demand forecasting are the two major areas with the most predictive analytics application, whereas the clustering, regression, and artificial neural networks are the more commonly used algorithms. The new SCM areas identified for Big data analytics applications integrated with the emerging technology are smart manufacturing and intelligent logistics management. Furthermore, the immediate need for future studies in other SCM areas like product development and inventory management are pointed out due to its immense potential benefits for the supply chain management