The Research On Collaborative Forecasting And Optimal Inventory Based On CPFR

Collaborative Planning, forecasting and replenishment (CPFR) is an important issue of supply chain management currently. The real-time information which is acquired through collaboration can adopt to reduce uncertain influence factor and boost forecasting accuracy. Moreover, decreasing unnecessary inventory is the main goal of many enterprises. This paper concentrates on studying the collaborative forecasting step and collaborative replenishment step based on CPFR. The main research contents include:1,This paper constructs the mixed collaborative sale forecasting model based on CPFR via integrating time series forecasting, multivariate regression and ridge regression. In addition, the model takes sale information as explanation variable. The experimentation performance show forecasting effect of this model is better than traditional method.2,The collaborative order forecasting model established in the paper chooses collaborative information as explanation variable. Hence, the model can represent order feature. The simulated results verify forecasting accuracy of this model is superior to traditional time series method or linear regression model. As a result, this method presented in the paper can use in predicting the fact order data.3,Mixed forecasting models include many explanation variables. The forecasting accuracy will be fail if there is linearity between variables. Ridge regression analysis is modified least-squares estimation. It can offer a stable forecasting when there is strong correlation between variables.4,The minimal total cost of object function based on Cetinkyay&Lee's math model can be obtained by employing evolution strategy. Besides, the optimal transport period and optimal inventory can be gained, too. The calculation results under difference parameters are same as the fact.This paper research on the collaborative forecasting step and collaborative replenishment step based on CPFR . The study results can supply theoretical basis and practical experience for implement CPFR.