The Study Of Retailing Incorporation Inventory Control Model

With the rapid economic development, the scientific research that based on the competitiveness of enterprises has become the forefront of management, reduced inventory costs based on improving the quality of services and increase customer satisfaction is the important means that to improve the competitiveness of enterprises, so, inventory control has become a key factor to improve the competitiveness of enterprises. The retail group who directly face consumer groups ,its levels of inventory management not only direct impact on the retail enterprise competitiveness, but also affect the upper reaches of corporate decision-making production, so ,the retail inventory control has become the very important link in the whole supply chain. the key of inventory control is to accurately forecast for the future demand . Papers finished the preparations for the sample data, completed the analysis of different types commodities, established 1 for each type goods, accurately forecast through inventory control mode will eliminate the phenomenon of high inventory,reduce inventory costs and improve customer service levels.First of all, paper introduced the concept of the retail inventory control, pointed out category of the retail and problems in the traditional retail inventory control, gave the method to solve the problem; described application of relevant technology in inventory control and the status quo on the domestic and international retail inventory control. Secondly, Goods were divided into three categories through cluster analysis—the festive goods and seasonal commodities, random demand goods, then analyzed the characteristics of each type goods. Identify the demand forecasting model of each type commodity. Festival goods used grey model GM(1,1); Seasonal goods used the combination of BP neural network and grey model GM(1,1); Random commodity demand forecast used moving average,grey model GM(1,1),ARMA model, through comparison of fitting, found a model whose fitting is the best and whose error is Minimum to be the random demand goods model.