Construction And Application Of Short-term "Blowout" E-commerce Logistics Demand Forecasting Model

With the expansion of online shopping transactions in China,large e-commerce enterprises such as Jingdong and Taobao have emerged.The majority of consumers formally regard online shopping as one of the main ways of consumer shopping.Because consumer shopping is mainly concentrated on holidays,major e-commerce enterprises use holidays and create various shopping festivals to carry out marketing,which makes the transaction amount and logistics business of e-commerce enterprises blowout in a short period of time.The large and uncertain logistics demand leads to the failure of the third party logistics enterprises to effectively integrate and allocate limited logistics resources,resulting in unreasonable distribution of logistics resources,business diversion plans divorced from reality,and high logistics costs.Based on this research background and related research results at home and abroad,this paper uses forecasting theory of grey system to construct a combined optimized grey verhulst model to study the demand forecasting problem of short-term "blowout" e-commerce logistics brought about by holiday promotion,so as to provide a basis for the third party logistics enterprises to formulate business diversion plans and integrate logistics resources.On the basis of systematically analyzing the current situation and demand characteristics of e-commerce logistics in China,this paper summarizes and analyses the demand characteristics of short-term "blowout" e-commerce logistics.At the same time,effectively combine the short-term "blowout" e-commerce logistics status and the statistical characteristics of logistics demand of Tmall Double Eleven to find the law ofshort-term "blowout" e-commerce logistics demand.Then,combined with the analysis of the modeling principles of the initial value,background value,time response and grey derivative of the classical grey Verhulst model,three combinatorial optimization prediction models are constructed,namely,grey Verhulst prediction model based on initial value and background value optimization,grey Verhulst prediction model based on background value and time response optimization and grey Verhulst prediction model based on initial value and grey derivative optimization.Through the analysis of three prediction models and the comparison of error test,it is found that the grey Verhulst prediction model based on the optimization of initial and background values has the lowest relative error and the highest forecasting accuracy.Therefore,the gray Verhulst prediction model based on initial value and background value optimization can be used to accurately predict the logistics demand of Tmall Double Eleven in the next five years.Finally,according to the effective forecasting results,combined with the reasonable allocation relationship between the logistics demand and the distribution and utilization of logistics resources,the logistics service countermeasures for short-term "blowout" e-commerce logistics demand are put forward from five aspects: storage resources integration,transportation and distribution resources integration,facilities and equipment resources integration,human resources integration and information resources sharing.