Zhuhai College of Science and Technology, Zhuhai, Guangdong 519040, China.
Comput Intell Neurosci. 2022 Apr 15;2022:3821642. doi: 10.1155/2022/3821642. eCollection 2022.
As a new form of foreign trade, cross-border e-commerce has huge development potential. Although the development prospect of cross-border e-commerce is good, the management of global supply chain is very important in order to gain a place in the fierce competition and develop steadily. The traditional forecasting of purchasing volume adopts time series, and the forecasting model is relatively simple. The purchase volume of the platform is related to the various consumption behaviors of consumers, such as the number of product reviews, the number of product collections, and whether there are tax subsidies. The sales volume in the next few days is predicted by the item number, time, and sales quantity. The four-layer BP neural network model is used, and the MATLAB neural network toolbox is used to draw the training error curve and the correlation coefficient curve. After network training, the training correlation coefficient reaches 95.823%, and the prediction accuracy obtained at this time is higher. Further, using the established model based on BP algorithm, the traditional BP algorithm is optimized to obtain the purchase quantity of commodities. The method is applied to the forecast of commodity purchase volume of a cross-border e-commerce platform, and the results show that the average error rate of this method is 5.9%, which has high practical application value. The research results show that this paper considers multiple influencing factors and selects an appropriate forecasting method, which can effectively improve the accuracy of the company's commodity sales forecast, so as to better formulate procurement plans and optimize inventory structure, which has certain implications for the actual operation of cross-border e-commerce platforms.
作为一种新的外贸形式，跨境电商具有巨大的发展潜力。虽然跨境电商的发展前景良好，但为了在激烈的竞争中获得一席之地并稳定发展，全球供应链的管理非常重要。传统的采购量预测采用时间序列，预测模型相对简单。平台的采购量与消费者的各种消费行为有关，如产品评论数量、产品收藏数量、是否有税收补贴等。通过商品编号、时间和销售量来预测未来几天的销量。使用四层 BP 神经网络模型，并使用 MATLAB 神经网络工具箱绘制训练误差曲线和相关系数曲线。在网络训练之后，训练相关系数达到 95.823%，此时获得的预测精度更高。进一步地，使用基于 BP 算法建立的模型，对传统的 BP 算法进行优化，得到商品的采购量。该方法应用于跨境电商平台商品采购量的预测，结果表明该方法的平均误差率为 5.9%，具有较高的实际应用价值。研究结果表明，本文考虑了多个影响因素并选择了合适的预测方法，可以有效提高公司商品销售预测的准确性，从而更好地制定采购计划和优化库存结构，这对跨境电商平台的实际运营具有一定的意义。
