售后服务数据的运用

提出了商业销售中企业售后服务数据分析这个国际上尚未完全解决的难题的初步解决办法.首先分析了该表中存在的不合理数据,有两类不合理的数据,包括:使用了的轿车数据还没有反馈,轿车销售需要一定时间.再对该表的制作方法提出建议.第二问中,对于0205批次使用月数18时的千车故障数预测,我们假设该模型服从WeibullDistribution函数,根据该批次使用月数0至12的数据,利用WeibullDistribu-tion函数进行拟合,得到拟合函数,再由拟合函数得到0205使用18个月的千车故障数,为44.08.而对于0306批次使用月数为9时千车故障数的预测,因为每批次的千车故障数符合WeibullDistribution,所以多元线性回归模型,利用前面使用了1,2个月的数据对后面的9月的故障数进行回归计算,结果为:18.1114.对于0310使用月数12的预测,则根据数据反馈的滞后与质量管理关系模型从纵向上对其进行预测,得结果为:6.44.第3问,从销售服务过程质量管理的角度,提出7个方面的质量管理决策与咨询,并给出相应的数学模型.第4问,分别从生产组织管理和配送及物流链管理两个方面提出了想法和建议.

Data Analysis of Corporate After-Service in Business Selling

This paper gives basal solutions of data analysis of corporate after-service in business selling, which remains to be the globally unsolved problem. ;It starts with analyzing the illogical data in the table. There are two kinds of illogical data: absence of the feedback data of the used saloon cars and existence of time lag in saloon car selling. And then this paper gives suggestions of methods of presenting this table. In the 2nd question, with regard to the numbers per one thousand 0205-type cars having been used for 18 months and meeting accidents, the paper supposes that the data obey the Weibull Distribution, and fits the data of cars that have been used for 0 to 12 months to the distribution, from which the function comes out. Therefore, the result can be calculated, which is 4408. ;As the data of the number per one thousand of the 0306-type car having used for 9 months obey the Weibull Distribution, the paper uses the multiple linear regression model and the data of that having used for 1 and 2 months to get the prediction result, which is 181114.;For the delay of the data fed-back and the quality management relationship model, the paper predict the result of that of 0310-type having used for 12 months is 644.;The third question, from the angle of quality control during the selling service process, gives decision and consultation on quality control from seven aspects. And then the corresponding mathematical models are presented. ;The fourth question brings forward ideas and suggestions from the aspects of manufacture organization management and logistic & supply chain management respectively.