Research And Realization Of Auto Sale Decision Support System Based On ASP Industry Chain Collaboration Platform

As automobile industry is developing sharply in China, car sales market keeps expanding. But many manufacturers blindly spread their workshops for more and greater profits, which lead to oversupply. As a result, manufactures have to wander in a vicious circle of cutting down their price to increase sales. So, how to predict demand of the market by analysis of historical data, manufactures thus could make plans of production, sales and staffing, to avoid unnecessary waste of resources in production or sales; At the same time, how to choose new vehicle models for the market, determine the direction of investment by analysis of customer behavior for cars when new automobile models are launched to the market. These issues are brothering auto production enterprise decision makers.Therefore, this thesis aims to assist manufacturers and distributors to make appropriate decisions by establishing auto sale decision support system based on ASP industry chain collaboration platform.First of all, starting from the historical data analysis, this thesis improves the quality of data for analysis and prediction through the building of data warehouse, and achieves multi-angle, multi-level analysis of historical data by adopting online processing technology to create a multi-dimensional data set. And the results are presented to decision makers in chart form.Next, this thesis chooses gray GM (1,1) prediction model after analyzing the factors that affect car sales and the characteristics of corporate sales data and studying the current prediction technology, which is improved by adding seasonal variation index. And the prediction accuracy is improved after adopting this prediction model.Finally, evaluation index system of automobile products is established according to market research and performance parameters which used to measure the quality of a new vehicle. And evaluation model is created by integrating grey relation degree and analytic hierarchy, which could allocates evaluation index weights rationally and effectively, increase accuracy of the evaluation, and provide an important basis for precise product investment.Data warehouse, online processing, prediction and product evaluation are applied in development of system in order to enhance the technical feasibility of the system. At same time, the practical value of the decision support system increases when the system is applied in industry chain collaboration platform.