基于指数加权分位数回归预测的CPFR成本模型  

Study on Forecasting Models with CPFR-Exponentially Weighted Quantile Regression

作　　者：戢守峰[1] 黄英健[1] 何家强[1] 张川[1] 

机构地区：[1]东北大学工商管理学院,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2011年第7期1053-1056,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(70872019)

摘　　要：针对某些商品的高易变性、不对称性的需求模式,基于预测方法高精确度的要求,采用计量经济学前沿预测研究方法指数加权分位数回归预测法,建立了由零售商、制造商的成本模型和供应链系统总成本模型构成的CPFR供应链系统成本模型,为基于多层次CPFR的三级库存协调与优化研究中提高需求预测精度探索新的视角.该模型通过直接预测销售序列的分位数,避免既存研究中基于假设的预测失误,使预测结果更加贴近需求模式的真实值.数值分析表明指数加权分位数回归预测模型的预测精度较高.Considering the high volatility and skewness of real sales series,the latest forecasting method in statistics,exponential weighted quantile regression,was applied to get higher forecast accuracy.The cost model for supply chain system based on CPFR was built,which includes the retailer＇s cost,the manufacturer＇s cost and the supply chain total cost.This model forecasts directly the quantile of the sales series,which not only avoids the forecast mistakes based on hypothesis of research at present,but also makes the forecast results approach the real results of the demand model.The numerical analysis illustrated that quantile regression forecast is better than traditional methods in the demand forecast by giving real examples.

关 键 词：协同计划、预测和补货 指数加权分位数回归预测法 需求预测 信息熵 

分 类 号：C931[经济管理—管理学]