Supply Chain Risk Prevention and Control Based on Fuzzy Influence Diagram and Discrete Hopfield Neural Network

Abstract

Efficient and reasonable supply chain management helps enterprises improve their efficiency, reduce costs, shorten cash flow times, and reduce enterprise risks. Risk prevention and control is a safety symbol for supply chains. To explore different influence degrees of multirisk factors and multilinks on enterprises, we propose a supply chain risk prevention and control model based on a fuzzy influence diagram and Hopfield neural network. Using the model that both calculates the risk size and occurrence probability of the supply chain and allows identifying various risk prevention and control levels, the supply chain risk is evaluated both objectively and fairly. We analyzed the theoretical and practical properties of supply chain risk prevention and control models and used it in the H company to illustrate this model.