Demand Forecasting System Based Auto Parts Industry Chain Collaboration Platform Research And Implementation

With the rapid development of China’s automobile industry, car service is rapidly rising.Auto parts procurement is an important part of the car service. Reasonable auto parts demand forecast can significantly improve the reliability of auto parts purchases and increase aftermarket parts management level, thus improving enterprise efficiency.In turn, the missing link in the forecast, is likely to cause a waste of money purchases, reducing the efficiency of the industry chain, thus affecting the overall coordination automobile industry chain.Because demand for auto parts affected by many factors, the traditional single forecasting model because it does not consider all aspects of the situation, leading to predict the effect is not ideal.Objective of this paper is to establish a reasonable auto parts demand forecasting system in order to establish a more effective relationship between supply and demand in automobile parts manufacturer and service provider.Firstly, the existing parts management platform for auto parts business status quo and demand forecasting, analysis of existing problems and characteristics, so as to arrive demand auto parts demand forecasting system. Then, we design a demand forecasting system is based on the automobile industry chain collaboration platform accessories.Secondly, according to the characteristics of the existing platform, we design the combination forecasting method GM(1,N) model and BP neural network algorithm combines auto parts demand forecast.And for the needs analysis, the author of the historical data available from the platform parts requirements, and use of data warehouse technology, the automobile industry chain collaboration platform for integration of various data sources, thus improving the quality of data.Finally, the author achieve the combination forecasting model by using the data of chain collaboration platform for the automotive industry as a data source. The authors also achieved function modules on the parts demand forecasting, which could provide users with decision support on accessories demand planning proposal, and apply them on the requirements management between parts factories and other business units.In this paper the author combine the business data with the data warehouse technology and forecasting techniques to improve the technical feasibility of the system, and apply the forecasting system to the automotive industry chain collaboration platform that improves the value of the system.