Study On The Sales Forecasting Of Clothing And Inventory Problem Based On The Grey Theory

Clothing retail occupies an important position in wholesale and retail trade in China and has a major role in promoting the development of the economy.With the textile and clothing industry continues to develop and become more mature, clothing market has experienced significant change from expanding the whole quantity to upgrade the structure restructuring and clothing production has changed from high-volume and low frequency to low-volume and multi-frequency. These changes have brought inventory overstock or out. In order to solve the problem, Clothing companies need to make sales forecasts for products. Clothing companies also make the reasonable distribution decisions and transfer and replenishment decisions based on the predictions. It can improve the overall earnings of enterprises, brand awareness of business and the service levels based on the results.The clothing product has a short life cycle, wide range of products, strong fashion and high risk of product devaluation. A lot of prediction methods has been considerate and studied, at last,we find GM(1,1) model can solve the issues which we has little dates of clothing. However, GM(1,1) model has some flaws. In order to solve these problems, We optimized the initial value and the background value of GM(1,1) model and introduced influencing factors of products selling and interference factor, then establish the improved GM(1,1) forecasting model for clothing product.In this paper, we established distribution model based on improved GM(1,1)model, which researches on clothing category and depends on the clothing productions which has been sold. The paper is mainly research the combination of clothing and decided distribution decisions of the store, which means that the research is going to decide the category and the shipments of distribution of category. Product distribution based on the reasonable forecast for dynamic product sales by improved GM(1,1) model and the combination of the characteristics of the industry can better meet the needs of consumers and seize the market share. it also can improve the service quality levels and optimize the inventory management.Consumers often encounter the phenomenon of out of stock. In this case,themajority of consumers do not choose to wait for the coming of the clothing. They will choose substitute products or abandon the purchase of clothing. In order to achieve a high degree of consumer satisfaction and higher income, enterprises should try to avoid out of stock and maintain proper inventory levels. In order to solve the shortage problem, this paper use the improved GM(1,1) model to forecast sales which is based on historical data, then use the result of sales forecasts and inventory of products,taking into account the cost of products to establish transfer and replenishment model of the garment and explain its rationality and universality.To further illustrate the model is reasonable and practical, this paper statistic and analyzes the sales data in one store of a female clothing brand in Hangzhou, and use the sales data of two weeks as a statistic unit which is began form November, and then select sales data of five categories such as a single skirt, pants, sweaters, jackets,dresses to verify the distribution model. The results indicate that this improved GM(1,1) model can apply to predict of fashion sales with relatively high accuracy. To conclude, the improved GM(1,1) model is a useful theory and a tool for fashion companies to make distribution decisions. At the same time, the paper use the sales data of areas a female clothing brand in Hangzhou form May to mid-June and then use improved GM(1,1) model to do the forecast of a single paragraph apparel,then use the predicted value and stock value to do the transfer and replenishment decisions to verify the correctness of the transfer and replenishment model. The research has shown that the transfer and replenishment model of clothing based on sales forecast of improved GM(1,1) has better universality and practicality, it is possible to provide a theoretical basis and support for enterprises to adjust transfer and replenishment decisions.