G公司汽车售后备件需求预测
发布时间：2018-11-12 07:09
【摘要】：摘要：汽车保有量近年来迅速扩大,由此引发的汽车售后服务市场将随着汽车市场的迅猛发展呈现出前所未有的活力。精确的备件需求预测对于汽车售后服务企业的物资管理非常重要,直接决定着企业的库存水平和顾客服务水平。售后服务的好坏将直接影响品牌效应,是影响其市场占有率的重要因素之一。 对于汽车售后备件的需求预测研究,国内外已经有很多需求预测方法,其中被广泛应用的主要有30多种,包括移动平均法、指数平滑法、回归分析法、人工神经网络预测法等。但是,由于汽车售后备件需求发生机理具有自身的特性,不能简单将以上各种基于时间序列预测的方法应用在汽车售后备件需求的预测上。 本文综合现有的预测方法,实地调研并分析了G公司售后服务备件需求的特性及G公司售后备件库存的情况,提出通过建立基于售后备件生命周期的动态需求预测模型,对处在售后维修期内的车辆备件随时间发生故障的分布规律进行分析,得到处于不同生命周期的备件的故障率,再将预测时间段内新销售的车辆备件数量加入到动态需求预测模型中,进而得到G汽车公司的售后备件不同时间阶段内的需求预测,提高G汽车公司售后备件需求预测的准确率,为售后备件的库存管理优化提供较为科学的数据支持,从而降低库存及物流成本,减少库存的滞后和积压,提高售后服务质量及总体利润。
[Abstract]:Absrtact: with the rapid expansion of automobile ownership in recent years, the automobile after-sales service market will take on unprecedented vitality with the rapid development of automobile market. Accurate prediction of spare parts demand is very important to the material management of automobile after-sales service enterprises, which directly determines the inventory level and customer service level of enterprises. The quality of after-sales service will directly affect the brand effect, is one of the important factors affecting its market share. There are many demand forecasting methods at home and abroad, including moving average method, exponential smoothing method, regression analysis method, artificial neural network method and so on. However, due to its own characteristics, the above methods based on time series prediction can not be used to predict the demand of spare parts after sale. Based on the existing prediction methods, the characteristics of after-sales spare parts demand in G Company and the inventory of after-sales spare parts in G Company are investigated and analyzed in this paper, and a dynamic demand forecasting model based on the life cycle of after-sale spare parts is proposed. This paper analyzes the distribution of vehicle spare parts in the period of after-sale maintenance and gets the failure rate of spare parts in different life cycles. Then the number of new vehicle spare parts sold in the forecast period is added to the dynamic demand forecasting model, and then the demand forecast of the after-sale spare parts of G Automobile Company in different time stages is obtained. To improve the accuracy of demand forecast of after-sale spare parts in G Automobile Company, and to provide more scientific data support for the optimization of after-sale spare parts inventory management, thus reducing inventory and logistics costs, and reducing the lag and backlog of inventory. Improve after-sales service quality and overall profit.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F274;F426.471
本文编号：2326411
[Abstract]:Absrtact: with the rapid expansion of automobile ownership in recent years, the automobile after-sales service market will take on unprecedented vitality with the rapid development of automobile market. Accurate prediction of spare parts demand is very important to the material management of automobile after-sales service enterprises, which directly determines the inventory level and customer service level of enterprises. The quality of after-sales service will directly affect the brand effect, is one of the important factors affecting its market share. There are many demand forecasting methods at home and abroad, including moving average method, exponential smoothing method, regression analysis method, artificial neural network method and so on. However, due to its own characteristics, the above methods based on time series prediction can not be used to predict the demand of spare parts after sale. Based on the existing prediction methods, the characteristics of after-sales spare parts demand in G Company and the inventory of after-sales spare parts in G Company are investigated and analyzed in this paper, and a dynamic demand forecasting model based on the life cycle of after-sale spare parts is proposed. This paper analyzes the distribution of vehicle spare parts in the period of after-sale maintenance and gets the failure rate of spare parts in different life cycles. Then the number of new vehicle spare parts sold in the forecast period is added to the dynamic demand forecasting model, and then the demand forecast of the after-sale spare parts of G Automobile Company in different time stages is obtained. To improve the accuracy of demand forecast of after-sale spare parts in G Automobile Company, and to provide more scientific data support for the optimization of after-sale spare parts inventory management, thus reducing inventory and logistics costs, and reducing the lag and backlog of inventory. Improve after-sales service quality and overall profit.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F274;F426.471
【参考文献】
相关期刊论文 前10条
1 冯杨;尹迪;罗兵;;基于SES的不常用备件需求预测模型[J];兵工自动化;2011年02期
2 徐能伟,包凡彪;4S汽车专营店零部件库存理论应用分析[J];北京汽车;2005年03期
3 宋国强;陈超;许玲;杨磊;查贵庭;;贝叶斯在地铁备件库存预测中的应用研究[J];装备制造技术;2008年08期
4 马秀红,宋建社,董晟飞;基于回归分析的备件故障率预测模型[J];计算机仿真;2003年11期
5 颜卫军,邵晨曦,孙新波;汽配汽修企业资源管理软件设计[J];计算机应用与软件;2003年02期
6 李保华;杨云;;备件需求预测模型研究[J];航空维修与工程;2008年05期
7 黄远兵;蔡启明;杨玮龙;黄燕美;;基于支持向量回归的服务备件需求量预测研究[J];物流科技;2006年10期
8 赵敏,崔南方;备件的联合库存模式研究[J];中国设备工程;2004年06期
9 宋华,王虎;汽车维修及服务企业信息化战略[J];武汉理工大学学报(信息与管理工程版);2004年06期
10 于孝生;ABC分类管理方法在汽车维修业的应用研究[J];物流技术;2005年10期
相关硕士学位论文 前2条
1 张洪;现代汽车售后服务管理体系的研究[D];广东工业大学;2005年
2 明谦;H汽车公司供应链环境下整车物流管理的研究[D];电子科技大学;2005年
，本文编号：2326411
本文链接：https://www.wllwen.com/guanlilunwen/shengchanguanlilunwen/2326411.html
