Applying the Grey Prediction Model to Regional Logistics Demand Scale

Regional logistics forecasting is the key step in regional logistics planning and logistics resources rationalization. This paper takes advantage of the high predictable power of the first-order one-variable grey differential equation model(abbreviated an GM(1,1) model) for a prediction of regional logistics demand scale. The prediction model is proposed by residual modification and Markov-chain estimation. As an example, this paper use the statistical data of retail sales of social consumer goods in Linyi city, Shandong Province from 1998 to 2006 for a validation of the effectiveness of the GM(1,1) model. At the same time this paper tests the results of prediction with residual error. The result shows that the model is higher performance than autoregressive model, moving average model, and exponential smoothing method.