Intellectual Core in Supply Chain Analytics: Bibliometric Analysis and Research Agenda

Nitin Singh (), Kee-Hung Lai () and Justin Zuopeng Zhang
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Nitin Singh: Operations Management, Information Systems and Business Analytics Indian Institute of Management Ranchi, India
Kee-Hung Lai: Faculty of Business, Hong Kong Polytechnic University, Hong Kong, SAR, Hong Kong
Justin Zuopeng Zhang: Coggin College of Business, University of North Florida, USA

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 02, 539-567

Abstract: Supply chain management has evolved from local and regional purchasing and supply activities prior to the industrial revolution to the current form of technology-led, data-driven, collaborative, and global supply network. Data-driven technologies and applications in supply chain management enable supply chain planning, performance, coordination, and decision-making. Although the literature on procurement, production, logistics, distribution, and other areas within the supply chain is rich in their respective areas, systematic analyses of supply chain analytics are relatively few. Our objective is to examine supply chain analytics research to discover its intellectual core through a detailed bibliometric analysis. Specifically, we adopt citation, cocitation, co-occurrence, and centrality analysis using data obtained from the Web of Science to identify key research themes constituting the intellectual core of supply chain analytics. We find that there has been increasing attention in research circles relating to the relevance of analytics in supply chain management and implementation. We attempt to discover the themes and sub-themes in this research area. We find that the intellectual core of SCA can be classified into three main themes: (i) introduction of big data in the supply chain, (ii) adoption of analytics in different functions of operations management like logistics, pricing and location, and (iii) application of analytics for improving performance and business value. The limitations of this study and related future research directions are also presented.

Keywords: Supply chain analytics; bibliometric analysis; centrality; citation and cocitation analysis; co-occurrence analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023300021

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