Empirical Analysis Of Logistics Demand In The Central Region

With the economic globalization and infomationization accelerating continuously, the logistics has been developing rapidly throughout the word since the middle of 1980s. It is considered that the logistics is one artery and a basic industry of the national economic develops, and its development level becomes one of the important symbols to weight the modernization degree and comprehensive national strength of a nation.As the national intimate region, the central region holds a very important position in the national socio-economic develops. Its development of economic and its construction of the logistics system have the material influence to the development of national economy. At present the logistics industry in the central region is developing quickly, but there are still some problems in its plan. In order to give the reasonable basis for its plan, this article gives an empirical analysis of the logistics demand in the central region.Firstly, it analyzed the logistics demand in the central region in the present situation, and pointed out that it has the regional characteristic. Through Gray Relational Analysis, it analyzed the importance of the factors which influenced the logistics demand in the central region.Secondly, it introduced the logistics market demand forecasting index system and forecasting methods which are used commonly, and made simple comparison and analysis. Then, according to the characteristics of logistics market in the central region and considered the feasibility of obtaining the data, this paper designed the demand forecasting index system, and selected the applicable forecasting methods. It made use of the related economic date in central region, to make empirical analysis of the forecasting model, and to forecast the logistics demand in central region.Because the logistic market system is indefinite, this paper chose the Gray Relational Analysis to analyze the factors which influence the logistics demand, and mainly used the Gray Forecasting Model to forecast it. For the one-sidedness to use only one forecasting method, this article chose the combined forecast model and considered the standard errors to calculate the average forecasting value, which improved the forecast result.