煤炭供应链牛鞭效应量化研究

1.西安科技大学 管理学院,陕西 西安 710054; 2.西安科技大学 能源经济与管理研究中心,陕西 西安 710054; 3.延安大学 经济管理学院,陕西 延安 716000; 4.西安科技大学 理学院,陕西 西安 710054

牛鞭效应; 煤炭供应链; 季节性

Quantitative research on bullwhip effectin the coal supply chain
YUN Xiao-hong1,2,ZHANG Jin-suo3,JIN Hao2,4

(1.College of Management,Xi'an University of Science and Technology,Xi'an 710054,China; 2.Research Center for Energy Economic and Management,Xi'an University of Science & Technology,Xi'an 710054,China; 3.School of Economics and Management,Yan'an University,Yan'an 716000,China; 4.College of Sciences,Xi'an University of Science and Technology,Xi'an 710054,China)

bullwhip effect; coal supply chain; seasonality

DOI: 10.13800/j.cnki.xakjdxxb.2016.0417文章编号:1672-9315(2016)04-0560-07

备注

考虑一个煤炭勘探开采企业和一个煤炭销售运输企业组成的两级煤炭供应链,建立了煤炭市场需求服从SARMA时间序列过程,煤炭销售运输企业采用MA技术预测市场需求和Order-up-to库存策略的煤炭供应链牛鞭效应量化模型,并对该模型的影响因素进行理论分析和算例验证。研究表明:0<ρ<1,0<θ<1时,煤炭供应链必会产生牛鞭效应; L≤k≤S时,牛鞭效应值较小; 季节性自回归系数、季节性移动平均系数、季节性循环周期、订货提前期和历史数据个数5个参数对煤炭供应链牛鞭效应有直接影响。具体而言,季节性循环周期的增大有助于减少煤炭供应链牛鞭效应,季节性自回归系数的增大并不总是能减少煤炭供应链牛鞭效应,而季节性移动平均系数和订货提前期的减少有助于减少煤炭供应链牛鞭效应。

In the case of considering a two stage coal supply chain which is composed of a coal exploration and mining enterprise and a coal sales and transport enterprise,the coal market demand follows SARMA time series process,where the coal sales and transport enterprise uses MA technique to predict the market demand and order-up-to inventory policy to determine the coal quantity.The paper not only establishes a quantitative bullwhip effect model in two stage supply chain,but also theoretically analyzes and validates the size of the bullwhip effect including its influential factors.The research shows that:Firstly,when0<ρ<1,0<θ<1,the bullwhip effect in the coal supply chain must happen.Then,whenL≤k≤S,the bullwhip effect is smaller.Last,the five parameters have direct effects on the bullwhip effect in the coal supply chain,such as the seasonal autoregressive coefficient,seasonal moving average coefficient,seasonal cycle,order lead time and historical data number.Specifically,the enlargement of the seasonal cycle will help to reduce the bullwhip effect,while the seasonal autoregressive coefficients is not always can reduce the bullwhip effect,the reduction of the seasonal moving average coefficients and order lead time will help to reduce the bullwhip effect.