Evaluation on risks of sustainable supply chain based on optimized BP neural networks in fresh grape industry

Abstract

In order to improve the risk evaluation and management in fresh grape supply chain and enhance the sustainable level of the supply chain, this study applied neural network to evaluate the risk of fresh grape supply chain from the perspective of sustainable development. Firstly, the possible risk factors in the supply chain were identified and the risk evaluation index system were proposed; then risk evaluation models based on single BP and optimized BP (GABP and PSO-BP) neural network were established; and then the models were trained, tested and evaluated using data set from supply chain survey. The survey and analysis results showed that the risk of fresh grape supply chain was at a low level but the risk in each link was discrepant, the biggest risk was the risks among the links in the chain (R0), and the high risk dimensions were the economic risk, social risk and cooperation risk; most risky events were located in the second quadrant (small probability & high damage risk events). The results of models training and testing indicated that the optimized model was superior to single BP neural network for risk assessment in grape sustainable supply chain, and the PSO-BP model was more accurate and suitable with less evaluation errors and a bigger R2. The results also extracted the risk factors that contributed most to the overall risk of grape sustainable supply chain. This paper enriches the method of supply chain risk assessment theoretically, and provides practical suggestions for risk prevention, stable operation and sustainability improvement of fresh grape supply chain.