The Research And Application Of Grey Data Mining Model On Logistics Enterprises

With the rapid development of modern logistics informationization, higher demand of information management of logistics enterprises, namely demand of decision support, is becoming more and more. The relevant data of enterprises is incomplete and scattered, which limits the application of some data mining model. Grey system model is especially efficient in modeling of system that is short of data and has incomplete information. This article introduces grey theory into logistics trade, and builds data mining model based on grey theory to solve the practical problem in logistics enterprises' management and decision.Firstly this article studies the basic theories of grey system theory, concluding grey system model building theory, grey associating analysis, grey clustering and grey forecasting model. The model of GM(1,1) is carefully studied on data series forming, building of model and checking-up method. By analysis of mechanism of error, thisarticle advances two improved GM(1,1) model------parameter advanced GM(1,1)(GOM(1,1)) and border condition advanced GM(1,1), and strict prove is made from the basic parameter package.On the base of theory study, this article applies grey forecasting model and grey clustering model into the practical management and decision of logistics enterprises, realizing the grey forecasting of uncertain demand and the grey clustering analysis of supply chain partner selection. In forecasting model of demand, GM(1,1), GOM(1,1) and advanced GM(1,1) is used respectively. The monthly quantity shipping out of the stock which comes from the statistics report of logistics information system, is used to forecasting the demand in the future, and the results are checked up. This article made a contrast analysis of classic GM(1,1) model and improved model and used time series method to confirm the result. In the partner selection problem grey clustering method is used to analyze the factor index of supply chain partner, and the results optimize the choice and support the decision. The application of grey data mining model in the management and decision of logistics enterprises has proved that the grey forecasting model and clustering model is effective and of practical value.