Dynamic Forecast Of Demand For Short Life Cycle Footwear Products Based On Improved BASS Model

The ability of big data processing and analysis has been greatly improved with the rapid development of information technology.While storing and collecting data,more and more companies have begun to pay attention to and value data analysis and mining,and use it to assist companies in making relevant decisions.Therefore,this article comprehensively uses data analysis and mining related methods to forecast the demand for short life cycle shoe products.The main work of this paper is as follows:(1)The lack of historical sales data for shoe products.First,a large number of SKU were classified by sales volume through clustering algorithm.Secondly,the theory of fuzzy cluster-rough set and the principle of system similarity are described.Based on this,the similarity measurement of shoe products is carried out to solve the problem of lack of historical sales data of shoe products,so as to improve the prediction accuracy.(2)Construction of demand prediction model for shoe products.When Bass model is used to forecast the demand of shoe products,the randomness factor in the actual sales process is comprehensively considered.An improved Bass model was constructed,which added the impact of product price changes,holiday travel factors and seasonal factors.(3)The lack of adaptability of the model leads to low prediction accuracy.Simulated annealing algorithm,nonlinear least squares and genetic algorithm were used to estimate the model parameters.In addition,when new products generate real-time sales data,rolling update of parameters is carried out to improve the accuracy of shoe product demand prediction.Finally,an empirical study was conducted with the offline store consumption data of a shoe company.The prediction of new product sales volume was compared with the standard BASS model,and the results showed that the improved BASS model significantly improved the effect of shoe product demand prediction method,indicating the effectiveness of the model thinking.The accurate prediction of the sales volume of new products has practical guiding significance for the quantity of products produced,the storage arrangement and the distribution arrangement of stores in each commercial area.