Modelling and Simulation of Quality Risk Forecasting in a Supply Chain

In recent years, the quality risks in supply chain are frequently encountered by Chinese's manufacturing enterprises, the aim of the paper is to present a manufacturer perspective methodology of forecasting quality risks in supply chain. To do it, firstly, we innovatively propose achieving factors of quality risks by the house of quality of quality function deployment. Secondly, an optimal selection approach regarding support vector machine parameters is suggested based on chaos particle swarm optimization, and then the forecasting model based on support vector machine is advanced. Finally, the experimental simulation of the model should be carried on some sample data. The results show that the forecasting accuracy and generalization ability of the proposed model is higher than particle swarm optimization-support vector machine on the same data sets. Therefore, the proposed method can be considered as a promising alternative method for forecasting quality risks in a supply chain from the perspective of manufacturers in China. (Received, processed and accepted by the Chinese Representative Office.)