An empirical study of risk warning in supply chain based on BP neural network

From the perspective of risk indicator of supply chain, this paper makes an empirical study of risk warning system in Sanming Steel Group in Fujian province. It discusses several indicators that cause risks to supply chain in company and categorize them. Then risk model is tested with artificial neural network to testify its applicability and accuracy. It´s argued that this is a rewarding attempt to go from academic level towards practical use and explores ways of thinking for risk warning system designing.