基于BP神经网络的零部件供应商评价研究  

Research on Component Supplier Evaluation Based on BP Neural Network

作　　者：郭倩茹 唐阳山[1] GUO Qian-ru;TANG Yang-shan(School of Automobile and Traffic Engineering,Liaoning University of Technology,Jinzhou 121001,China)

机构地区：[1]辽宁工业大学汽车与交通工程学院,辽宁锦州121001

出　　处：《辽宁工业大学学报（自然科学版）》2022年第4期270-273,共4页Journal of Liaoning University of Technology(Natural Science Edition)

摘　　要：如今生产一辆汽车需要上万个零部件,为了更好地提高汽车制造企业的生产效率降低成本,选择一套合理的零部件供应商评价体系和正确的评价方法尤为重要。研究了供应商管理的相关文献针对供应商评价体系做出了优化,设置了6个一级指标分别为产品竞争力、供应商生产能力、服务能力、创新能力、运营能力及供应商环保能力,20个二级指标。使用Dematel方法对各评价指标建立权重,利用BP神经网络构建供应商评价模型,对20个仪表盘供应商数据进行仿真,通过神经网络的训练结果对供应商进行评价排序。Nowadays, tens of thousands of parts are needed to produce a car. In order to better improve the production efficiency of automobile manufacturers and reduce costs, it is particularly important to select a reasonable evaluation system and correct evaluation method for parts suppliers.The relevant literature of supplier management is studied to optimize the supplier evaluation system,including 6 first-level indicators and 20 second-level indicators. Dematel method was used to establish weights for each evaluation index, BP neural network was used to build supplier evaluation model,supplier data were trained, and suppliers were evaluated and ranked by the simulation results of neural network.

关 键 词：零部件供应商 BP神经网络 DEMATEL 评价体系 

分 类 号：F224[经济管理—国民经济]