煤炭企业设备备件需求预测研究  

Research on Demand Forecast of Equipment Spare Parts in Coal Enterprises

作　　者：段永峰[1] 顼文晴 DUAN Yongfeng;XU Wenqing(School of Economics and Management,Inner Mongolia University of Science and Technology,Baotou 014010,China)

机构地区：[1]内蒙古科技大学经济与管理学院,内蒙古包头014010

出　　处：《物流科技》2021年第9期28-31,共4页Logistics Sci-Tech

摘　　要：为解决煤炭企业机械设备备件库存积压、备件管理效率低问题,对国内外备件需求预测方法进行了研究。根据以往备件需求预测方法的优缺点,提出一种将BP神经网络模型与GM1,1模型相结合的备件需求预测方法。结果表明,与以往仅用BP神经网络模型预测相比,文章的BP神经网络模型与GM1,1模型相结合的备件需求预测方法更好,平均相对误差降低了16.59%,精度更高。In order to solve the problems of overstocking and low efficiency of spare parts management of mechanical equipment in coal enterprises,the demand forecasting methods of spare parts at home and abroad were studied.According to the advantages and disadvantages of the previous spare parts demand forecasting methods,a spare parts demand forecasting method combining BP neural network model with GM1,1 model is proposed.The results show that,compared with BP neural network model,the method of combining BP neural network model with GM1,1 model is better,the average relative error is reduced by 16.59%,and the accuracy is higher.

关 键 词：备件需求预测 BP神经网络 GM(1 1)模型 

分 类 号：F272[经济管理—企业管理]