Journal of Industrial Management Perspective

Journal of Industrial Management Perspective

Modeling the Impact of the Covid-19 Risks on Global Supply Chains using Text Mining Methods: A Case Study of the Food Supply Chain

Document Type : Original Article

Authors
Navid Nezafati 1
Mohammad Reza Sheikhattar 2
1 Assistant Professor, Shahid Beheshti University.
2 Ph.D Candidate, Shahid Beheshti University.
10.52547/jimp.12.3.141
Abstract
The first step in analyzing and evaluating supply chain risks is to identify these risks. Conventional analysis methods are based on manual filters or data-driven automated methods. Manual filtering methods suffer from validation problems due to sampling limitations, and automated data analysis methods, on the other hand, perform poorly in analyzing risk data that is complex and ambiguous. To fill the research gap, in this study, an interactive framework between the analyst and the machine is presented to analyze a large volume of risk data in the field of food supply chain using topic modeling techniques, word embedding, term correlation analysis, and knowledge map. The purpose of the supply chain risk monitoring system is to assist managers in food companies to monitor and identify crisis risks and provide a decision support information system to create a sustainable food supply chain. The results of the topic analysis of metadata showed a knowledge map in five areas of "harvest", "agriculture", "food retail", "food services", "distribution" and "consumption", which was approved by the expert panel. The results show that the risk analysis model is useful in extracting knowledge units related to the field of food supply chain crisis management.
Keywords
Supply Chain Crisis Management
Supply Chain Risk Monitoring
Pandemic Crisis
Topic Analysis Model
Text Mining
Subjects
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