Research on risk factor filtering and rating of cold chain logistics from the perspective of root‐state risk identification

Abstract

AbstractThe natural attributes of perishable and vulnerable cold chain products make the cold chain have more risks than the general supply chain. The attribute characteristics and internal relations of various risks increase the complexity of risk analysis. The purpose of this paper is to study the horizontal and vertical assessment of various risk factors. The multi‐dimensional risk measurement model is used to integrate the assessment of multiple risk factors of human, machine, environment, and management, and the cold chain risk management is discussed from the perspective of risk factor classification. The root‐state risk identification (RSRI) method was used to identify potential risks. Based on the double standard filtering and multiple criteria, the filtering of irrelevant risks and screening of uncontrollable risks were evaluated, and the triangular fuzzy number method was used to quantitatively evaluate the controllable risk factors. A total of 223 potential risks, 18 important risks, and 6 key risks were identified, followed by inspection and quarantine reports, pesticide residues, improper loading and unloading operations, unqualified centralized environment, unqualified pre‐cooling technology of carriages, and unreasonable storage temperature. According to the analysis results, targeted control measures are proposed to better prevent risks and reduce the probability of cold chain accidents. The traditional risk assessment method can only assess the impact of a single risk factor on the system. This assessment method overcomes this limitation and provides a new perspective for cold chain risk management.Practical ApplicationThis study laid the foundation for further risk safety management of cold chain logistics.