Supply chain risk identification: a real-time data-mining approach

Abstract

Purpose

The global pandemic COVID-19 unveils transforming the supply chain (SC) to be more resilient against unprecedented events. Identifying and assessing these risk factors is the most significant phase in supply chain risk management (SCRM). The earlier risk quantification methods make timely decision-making more complex due to their inability to provide early warning. The paper aims to propose a model for analyzing the social media data to understand the potential SC risk factors in real-time.

Design/methodology/approach

In this paper, the potential of text-mining, one of the most popular Artificial Intelligence (AI)-based data analytics approaches for extracting information from social media is exploited. The model retrieves the information using Twitter streaming API from online SC forums.

Findings

The potential risk factors that disrupt SC performance are obtained from the recent data by text-mining analyses. The outcomes carry valuable insights about some contemporary SC issues due to the pandemic during the year 2021. The most frequent risk factors using rule mining techniques are also analyzed.

Originality/value

This study presents the significant role of Twitter in real-time risk identification from online SC platforms like “Supply Chain Dive”, “Supply Chain Brain” and “Supply Chain Digest”. The results indicate the significant role of data analytics in achieving accurate decision-making. Future research will extend to represent a digital twin for identifying potential risks through social media analytics, assessing risk propagation and obtaining mitigation strategies.

Citation

Deiva Ganesh, A. and Kalpana, P. (2022), "Supply chain risk identification: a real-time data-mining approach", Industrial Management & Data Systems, Vol. 122 No. 5, pp. 1333-1354. https://doi.org/10.1108/IMDS-11-2021-0719