Big Data analytics and its applications in supply chain management: A literature review using SCOR model

Ghadge, Abhijeet and Mogale, Dnyaneshwar ORCID: https://orcid.org/0000-0002-7977-0360 2023. Big Data analytics and its applications in supply chain management: A literature review using SCOR model. Presented at: International Conference on Data Analytics in Public Procurement and Supply Chain (ICDAPS2022), Virtual, 10-11 June 2022. Published in: Tiwari, Manoj Kumar, Kumar, Madhu Kanjan, Rofin, T. M. and Mitra, Rony eds. Applications of Emerging Technologies and AI/ML Algorithms. Asset Analytics Singapore: Springer, pp. 57-67. 10.1007/978-981-99-1019-9_7 Item availability restricted.

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Abstract

As we move further into the 21st century, technology has played an essential role in people lives inevitably, and generated massive information called ‘Big Data (BD)'. With the ability to manage massive dataset, big data analytics can usefully extract the insight from big data and support the firm to leverage decision making. Hence, the interest in big data applications has spread to comprehends many areas of study, including Supply Chain Management (SCM). However, the academic articles studying the employment of Big Data Analytics (BDA) in SCM is limited. Besides, most of those academic papers offer less interest in the entire SC systems. Most of them prefer to study in an individual SC area. Thus, this study aims to investigate state of the art in this domain through Systematic Literature Review (SLR) and discuss future research opportunities. We found that optimisation, simulation, and visualisation tend to be the most appropriate BD tools to apply in SCM. Also, linear programming, statistics, association rule mining, fuzzy logic, and decision tree are likely to be the most suitable BDA techniques for SC operations.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Springer
ISBN:	978-981-99-1018-2
Date of First Compliant Deposit:	3 July 2023
Last Modified:	20 Feb 2024 13:58
URI:	https://orca.cardiff.ac.uk/id/eprint/160775

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