The chaos support vector machine forecasting using in supply chain management

Supply chain management(SCM) measurements such as demand volume in order management, product quality in manufacturing processes, capacity usage in production management, and traffic costs in transportation management are very important in SCM. The accuracy of the forecasting is very important for the application of effective SCM. According to the chaotic and non-linear characters of activities required data in SCM, the model of support vector machines (SVM) based on Lyapunov exponents is established. The time series matrix is established according to the theory of phase-space reconstruction, and then Lyapunov exponents is computed to determine time delay and embedding dimension. Then support vector machines algorithm is used to predict activities required in SCM. In order to prove the rationality of chosen dimension, another two random dimensions were selected to compare with the calculated dimension. A computerized system is developed to implement the forecasting functions and is successfully running in real glass manufacturing.