Unbalanced supply and demand, bottleneck of transport capacity, seasonal cycle, and other factors lead to fragile supply chain of fresh agricultural products led by the platform, impeding smooth operation of the supply chain and even causing disruption risk. This paper studies the short-term and long-term vulnerability of the platform leading fresh agricultural product supply chain under the influence of logistics capital flow and information flow, defines its structure and the meaning of its vulnerability, analyzes the vulnerability of each link, and finds out the existing weak links in the supply chain through empirical research. The probability of accident is quantitatively analyzed by using the Bayesian Network. Firstly, the bow-tie model is used to identify the cause and consequence of the accident, and then it is transformed into the Bayesian Network model; then, the “Precursor Incident” information and prior probability are introduced to derive the posterior accident occurrence probability, and the probability of accident occurrence changing with time is quantitatively analyzed; finally, the dynamic risk calculation of fresh agricultural product trading center dominated by a certain platform was carried out. The results show that, with the increase of supply chain operation time and Precursor Incident, the probability of short-term supply chain vulnerability and accident risk present a significant increase trend, while the probability of long-term supply chain vulnerability and accident risk present a significant decrease trend. Therefore, it is suggested that enterprises should establish a dynamic risk evaluation system to monitor and predict the probability of event vulnerability, pay attention to “Precursor Incident,” and take measures to reduce it, such as effective integration of supply chain principal information, timely improvement of information integrated technologies, and comprehensive training on food safety and moral credibility.
Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier
