Exploring the application of analytics in supply chain during COVID-19 pandemic: a review and future research agenda

Abstract

Purpose This study aims to comprehend the application of analytics in the supply chain during the ongoing COVID-19 crisis and identify the emerging themes. Design/methodology/approach The author downloaded a list of research articles on the application of analytics to the supply chain from SCOPUS, conducted a systematic literature review for exploratory analysis and proposed a framework. Notably, the author used the topic modeling technique to identify research themes published during the ongoing COVID-19 crisis and thereby underscore some future research directions. Findings The author found that artificial intelligence, machine learning, internet of thing and blockchain are trending topics. Additionally, the author identified five themes by topic modeling, including the theme “Social Media information in Supply chain.” Research limitations/implications The results were derived from a data set extracted from SCOPUS. Thus, the author excluded all studies not listed in SCOPUS from the analysis. Future research with articles indexed in other databases should be investigated to get a more holistic perspective of specific themes. Practical implications This study provides a deeper understanding and proposes a framework for applications of analytics in the supply chain that researchers could use for future research and industry practitioners to implement in their organizations to make a more sustainable and resilient supply chain. Originality/value This study provides exploratory information from published articles on the use of analytics in the supply chain during the COVID-19 crisis and generates themes that help understand the emerging and underpinned area of research.