원문정보
초록
영어
Cold chain is crucial to ensure product safety and quality, but the risk is higher than tradi-tional supply chain due to the complexity of the operation, such as the need for strict temper-ature control and reliance on specialized equipment. Therefore, implementing strategic measures to maintain temperature control, product quality, and safety while effectively managing and carefully monitoring these risk factors are essential components of cold chain management. This study focuses on exploring the potential hazards inherent in cold chain processes for perishable food and medicine products, which directly affect the quality of human life. We used web crawling techniques to meticulously collect from Google News articles that are related to cold chain risks. Using data collected from the Google News platform from January 2015 to April 2022, we leveraged latent Dirichlet allocation (LDA) topic modeling to systematically extract risk factors in the fresh food and medicine cold chains domain. We then calculated the importance of each topic based on word frequency probabilities. In doing so, we comparatively analyzed the differences and similarities of cold chain elements in the two industries and empirically validated them using non-parametric statistical techniques. The results of this study provide important insights for understanding and mitigating supply chain risks within the cold chain industry.
목차
Ⅰ. Introduction
Ⅱ. Conceptual Background
2.1. Cold Chain Risk Factors
2.2. Big Data Analysis in Supply Chain Risk Management
2.3. LDA Topic Modeling in Cold Chain Risk Studies
Ⅲ. Methodology
3.1. Data Collection and Preprocessing
3.2. Our LDA Models
Ⅳ. Result
4.1. Topic Naming
4.2. Topic Matching
Ⅴ. Discussion
5.1. Risk Factor Level Comparisons
5.2. Topic Level Comparisons
5.3. Risk Mitigation Strategy
Ⅵ. Conclusion
6.1. Theoretical and Practical Contributions
6.2. Limitations and Future Research Directions
Appendix