Machine learning driven optimization of supply chain network for export cross-border E-commerce.

For the uncertainties of demand, exchange rates and tariffs in export cross-border trade, the machine learning driven supply chain network optimization of export cross-border E-commerce enterprises is studied by utilizing rich-data in E-commerce. A two-stage stochastic programming model including overseas warehouse, border warehouse location and inventory decisions is developed. Based on historical data, the machine learning techniques are adopted to construct stochastic trading scenarios required by the proposed model. Thus, the 'predictive to perspective' study is realized. Moreover, the model is applied to an apparel enterprise who sells its products to Southeast Asian. The sensitivity analysis is performed with the consideration of "Regional Comprehensive Economic Partnership (RCEP)" which comes into operation recently. The results show that the random forest algorithm outperforms other machine learning techniques in our case and the implementation of RCEP can bring the significant decrease of the cross-border logistics costs and the development of overseas warehouses. [ABSTRACT FROM AUTHOR]