The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review

Abstract

Purpose

This paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.

Design/methodology/approach

The papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.

Findings

The previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.

Practical implications

AI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.

Originality/value

The paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.

Citation

Shah, H.M., Gardas, B.B., Narwane, V.S. and Mehta, H.S. (2023), "The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review", Kybernetes, Vol. 52 No. 5, pp. 1643-1697. https://doi.org/10.1108/K-05-2021-0423