摘要
牛鞭效应是供应链运营管理中客观存在的现象。企业为了减少由实际需求和计划数量的偏差造成的生产不稳定,提高安全库存数量从而保证正常的生产活动,在此情况下需求逐级放大引发了牛鞭效应。精准预测是缓解牛鞭效应的重要手段,但是传统的时序预测在复杂的环境中并没有很好的预测效果。基于以上问题,从理论层面论证了需求预测、安全库存、牛鞭效应之间的关系,提出能够优化预测结果的ARIMA-BP模型。以某制造商企业近两年的产品订单数据为研究对象,分别用不同的预测模型对订单进行预测分析,再与该企业原预测模型下的牛鞭效应仿真结果进行对比。结果表明,ARIMA-BP的模型预测精度更高,能够有效地缓解牛鞭效应。
The bullwhip effect is an objective phenomenon in supply chain operation management.In order to reduce the production instability caused by the deviation between the actual demand and the planned quantity,companies increase the number of safety stocks to ensure normal production activities.In this case,the gradual increase in demand has triggered the bullwhip effect.Accurate prediction is an important means to mitigate the bullwhip effect,but traditional time series prediction does not have a good prediction effect in a complex environment.Based on the above problems,this paper demonstrated the relationship between demand forecasting,safety stock,and bullwhip effect from a theoretical level,and then proposed an ARIMA-BP model that can optimize the forecasting results.We took the product order data of a manufacturer company in the past two years as the research object,and used different forecasting models to predict and analyze the orders,and then compared the simulation results with the bullwhip effect under the company’s original forecasting model.The results show that the ARIMA-BP model has higher prediction accuracy and better suppression effect on the bullwhip effect.
作者
杨光
陈佳
徐斌
Yang Guang;Chen Jia;Xu Bin(School of Maritime Economics and Management,Dalian Maritime University,Dalian 116026,Liaoning,China)
出处
《计算机应用与软件》
北大核心
2022年第3期96-101,共6页
Computer Applications and Software
