基于BP神经网络的最小库存量预测模型设计——以广东省某文具制造企业为例  

Design of Minimum Inventory Forecasting Model Based on BP neural network:a Case Analysis

作　　者：许四化[1] 

机构地区：[1]广东纺织职业技术学院经济管理系,广东佛山528041

出　　处：《物流技术》2011年第8期77-80,共4页Logistics Technology

摘　　要：文具制造行业的产品种类繁多,且需求变化大,库存水平的高低对于该行业中的企业有着重要的战略地位。由于企业实际的采购量制定的特殊性,以文具制造原材料纸张的最小实际库存为研究重点,用已有最小实际库存值及其影响因素的历史数据训练BP神经网络,然后用构建稳定的网络进一步预测某月的最小实际库存值,并且以此可以适当调整安全库存值。Products of stationery manufacturing industries are varied and numerous whose demand can change greatly over a short period of time.Because of the specificity of actual enterprise purchasing amounts and varieties,the paper mainly studies the minimal actual inventory of paper,the most typical raw material of stationery manufacturing,utilizex the historical data of the minimal actual inventory and its influencing factors to train the BP neural network,and then builds a stable network to further forecast the actual minimum inventory amount of a certain month,facilitating the enterprise to make appropriate adjustment to their safety stock level.

关 键 词：BP神经网络 最小实际库存 文具制造企业 

分 类 号：F253[经济管理—国民经济]