缺陷汽车召回管理预警体系研究

发布时间：2018-02-17 00:09

本文关键词： 缺陷汽车产品召回管理预警系统指标体系因子分析法灰色预测法　出处：《长安大学》2014年硕士论文　论文类型：学位论文

【摘要】：近年来中国经济迅猛发展，带动各行各业尤其是汽车产业飞速发展，但汽车质量管理还缺乏核心竞争力，导致汽车召回现象呈现级数增长趋势；而缺陷汽车产品召回管理是提高企业核心竞争力和经济效益的关键因素。研究如何有效评估缺陷汽车召回管理预警体系并制定出切实可行的事前召回对策具有重要的理论和实际意义。在对国内外缺陷汽车产品召回管理预警体系相关理论研究的基础上，分析缺陷产品召回管理影响因素；结合我国召回管理特点及其指标体系构建原则，初步建立缺陷汽车召回管理预警指标体系有4个一级指标及对应的22个二级指标；设计调查问卷，运用SPSS18.0软件中的因子分析法分析调研数据，最终确定缺陷汽车召回管理预警评价指标体系有4个一级指标并对应9个二级指标，4个一级指标由安全因素、社会因素、公众认知因素和消费者相关因素构成，，9个二级指标为材料因素、产品对消费者的危害程度影响、抽检合格率、结构因素、警示性因素、消费者对产品的满意度因素、现存行业标准因素、产品返修率因素、销售量。借助MATLAB软件，采用灰色预测理想点逼近法确定各指标与正负理想点的灰关联度和相对贴近度，判断预警状态并提出改进建议。实例验证表明，所建模型能使企业及时决策是否实施召回而避免或缓解给企业带来巨大损失，使企业召回预警管理更加完善，同时提高企业的竞争力和经济效益。
[Abstract]:In recent years, the rapid development of China's economy has led to the rapid development of various industries, especially the automobile industry, but the lack of core competitiveness of automobile quality management has led to the phenomenon of automobile recall showing a progressive growth trend. The recall management of defective automobile products is the key factor to improve the core competitiveness and economic benefit of enterprises. It is very important to study how to effectively evaluate the early warning system of defective automobile recall management and formulate feasible prior recall countermeasures. The theoretical and practical significance. On the basis of the theoretical research on the recall management early warning system of defective automobile products at home and abroad, this paper analyzes the influencing factors of the recall management of defective products, and combines the characteristics of recall management in China and the construction principles of its index system. There are 4 primary indexes and 22 secondary indexes in the early warning index system of defective automobile recall management, the questionnaire is designed and the investigation data are analyzed by factor analysis method in SPSS18.0 software. Finally, it is determined that the early warning evaluation index system of defective automobile recall management has four first-grade indexes and corresponding nine second-class indexes, four first-grade indicators are composed of safety factors, social factors, and so on. The composition of public cognition factors and consumer related factors, 9 secondary indicators are material factors, the harm degree of products to consumers, the qualified rate of spot check, structural factors, warning factors, consumer satisfaction factors, With the help of MATLAB software, the grey prediction ideal point approximation method is used to determine the grey correlation degree and relative closeness between each index and positive or negative ideal point. The example shows that the model can make the enterprise decide whether to implement recall in time and avoid or alleviate the huge loss to the enterprise, and make the enterprise recall warning management more perfect. At the same time, improve the competitiveness of enterprises and economic benefits.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.471;F252;F713.2

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