【摘要】 需求预测不准确可导致跨境电商企业库存管理成本偏高。文章以出口至欧美市场的中国跨境家用百货零售电子商务公司为例,在现有的预测模型基础上,使用季节系数调整和优化经典报童和(s,S)模型,并通过Python编程使得产品信息变动可得到新的最优订购策略。使用需求仿真对比不同模型的改善效果,通过相关性检验,发现适合不同季节属性产品和最优订货量有一定相关性。更多还原

【Abstract】 Inaccurate demand forecast could result in high inventory management costs for cross-border e-commerce companies. Taking a Chinese cross-border e-commerce retailer exporting to Europe and the United States as an example,this paper adopts seasonal coefficients to optimize the classic( s,S) model based on the existing forecasting models. Using a Python program,product information changes lead to a new optimal ordering strategy. We run demand simulation to compare the improvement effects of different models through the correlation test,and find that there is a certain correlation between the seasonal products and the optimal order quantity.更多还原