Development of Media Text Analysis Based Supply Chain Risk Monitoring System

Recently, companies are exposed to various supply chain risks such as intensified trade conflicts, epidemics, economic and geopolitical uncertainties, and natural disasters. Thus there is increasing importance in monitoring information related to supply chain risks. Analyzing real-time media texts, such as news articles, can be utilized for monitoring up-to-date information supply chain risks. However, researches regarding analyzing supply chain risk related text are in early stages, and researches to apply modern AI techniques such as deep learning-based natural language processing to supply chain risk texts are scarce. This study aims to develop a supply chain risk monitoring system that monitors and extracts information related to supply chain risks by analyzing news articles. To collect supply chain risk related articles a filtering model based on KoBERT is developed, of which risk types are identified based on LDA topic modeling to be utilized as the train data. To predict news articles’ risk types, two deep learning- based risk classification models are developed using BOW(Bag of Words) and KoBERT. The results showed high accuracy of KoBERT based model in filtering supply chain risk-related articles, and in the classification of supply chain risk types also KoBERT based model showed better performance than BOW based model.