Research themes in machine learning applications in supply chain management using bibliometric analysis tools

What is it about?

Modern day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent machine learning algorithms. This paper conducts a systematic literature review of machine learning in Supply Chain Management through bibliometric and network analysis that enables grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure. Using the comprehensive tools of bibliometric analysis, we identify influential authors, sources, regions, affiliations, and papers. In addition, the use of network analysis tools from bibliometrics, emerging research cluster analysis, topological analysis, key research topics, interrelation and collaboration networks and their patterns. Finally, the optimum number of clusters is decided for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis. The findings of this paper can serve as an effective guideline for future studies in this area.

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