摘要
分析了BTO生产方式下建材装备制造企业产品物料的组成特点,构建了虚拟产品BOM,在此基础上利用ABC分析法对物料进行分类,并建立了基于BP神经网络的物料预测模型。根据Z公司的实际物料需求,将该模型与传统预测方法进行了对比仿真实验,实验结果表明基于BP神经网络的物料需求预测模型比传统预测方法更可靠,更加接近企业实际,能有效提高预测精度。
This paper analyzes the composition characteristics of the product material of building materials and equipment manufacturing enterprises in BTO environment and builds the V - BOM, on the base of which the materials are classified by using ABC analytical method and a material forecast model based on BP neural network is established. According to the actual material demand of Company Z, the forcast model is compared to the traditional one and the simulation experiment is conducted. The result shows that the BP neural network forecast model has a reliable forecast accuracy which is closer to the actual situation of enterprises.
出处
《湖北理工学院学报》
2013年第3期38-43,共6页
Journal of Hubei Polytechnic University
基金
国家自然科学基金项目资助(项目编号:71171154)
中央高校基本科研业务费专项资金资助(项目编号:135104004)
