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

Nazir, Affaf (2020) A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research. [Dissertation (University of Nottingham only)]

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Abstract

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.

Item Type: Dissertation (University of Nottingham only)
Keywords: supply chain management, predictive analytics, smart manufacturing, IoT, Cloud computing
Depositing User: Nazir, Affaf
Date Deposited: 08 Jun 2021 11:29
Last Modified: 08 Jun 2021 11:29
URI: http://eprints.nottingham.ac.uk/id/eprint/63292

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