| Summary: | 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.
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