摘要
在自回归移动平均(ARMA)模型的基础上,建立需求过程为季节性自回归移动平均(SARMA)的时间序列,零售商采用最小均方差(MMSE)预测技术预测市场需求,库存采用补充订货至目标库存(order-up-to)策略的简单两级季节性供应链牛鞭效应量化模型,并对模型牛鞭效应的大小及其影响因素进行理论分析和实例验证,不仅刻画出各种情形下牛鞭效应存在的辨别条件和属性,而且实证结果表明煤炭供应链采用SARMA模型度量牛鞭效应优于ARMA模型.
In the case of considering the seasonal autoregressive moving average （SARMA） time series based on autoregressive moving average （ARMA） model, retailers use minimum mean variance technique to predict the market demand and order-up-to inventory policy to determine the quantity of goods. 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 influence factors. The paper not only depicts the condition for the existence of the bullwhip effect and properties under various situations but also proves that the coal enterprise application SARMA model to measure bullwhip effect is more optimal than ARMA model.
出处
《数学的实践与认识》
北大核心
2015年第21期99-106,共8页
Mathematics in Practice and Theory
基金
国家自然科学基金(71273207
71473194
71103143)
陕西省自然科学基金(2014JM9362)
西安科技大学哲学社会科学繁荣发展计划(2013SZ02)
