At present, China’s cross-border e-commerce has ushered in a golden period of development. When developing cross-border e-commerce, enterprises should first assess the market climate of the target country and reasonably select the target country. Based on the PESTEL theory, an evaluation index system is established for China’s cross-border e-commerce overseas strategic climate. Taking “One Belt, One Road” as the opportunity and background, the overseas strategic climate of cross-border e-commerce in 62 countries along the “One Belt, One Road” is selected as the research object, and the Decision Tree and Adaptive Boosting classification methods in machine learning are applied to train and predict the established index system. Finally an overall picture of the overseas strategic climate of the 62 countries is obtained. The results are compared and analysed in depth to identify the most suitable countries for cross-border e-merchants and to provide reference for cross-border e-merchants investors.
China’s cross-border e-commerce will usher in a new golden age of development. Based on seven countries which include the Russian Federation, Mongolia, Ukraine, Kazakhstan, Tajikistan, Kyrgyzstan and Belarus along the “Belt and Road”, an evaluation system for cross-border e-commerce investment climate indicators is established in this study. This research applied the entropy method twice to evaluate the investment climate of seven countries based on 5 years panel data comprehensively and these countries are then classified into politics-oriented and industry-oriented countries, and then the weight of indicators for each category is analyzed. In addition, cross-border e-commerce investors are proposed to prioritize industry-oriented countries. Back propagation neural network algorithm is used to map the existing data and optimize the evaluation index system in combination with the genetic algorithm. This research denotes the effort to find out the index evaluation combination corresponding to the best overall score, make the established evaluation index system applicable to other countries, and provide reference for cross-border e-commerce investors when evaluating the investment climate in each country. This study provides the important practical implications in the sustainable development of China’s cross-border e-commerce environment.
The theory of constraint suggests the application of a demand-pull replenishment strategy combined with buffer management (DPBM) in order to effectively manage inventory. A demand-pull strategy caters to the customers’ demand to drive inventory replenishment, while buffer management is designed to adjust target inventory levels (buffer size). However, there is very limited literature looking into the parameters of buffer management in depth, such as the timing and the amount of buffer adjusted. Therefore, the objective of this study was to explore the product demand characteristics that affect the parameters of choice in buffer management when applying the DPBM strategy. This study first used a DPBM strategy (implemented via simulation, based on historical demand data) under different buffer management parameters to simulate the inventory replenishment for multiple products. The products were then grouped according to the simulated results by statistical analysis. A decision tree was applied to find the critical demand pattern factors that determined the product groups. An appropriate DPBM strategy is suggested for each product group. This study uses real demand data for 21 products, provided by a wafer foundry company located in Taiwan, to demonstrate the feasibility and effectiveness of the proposed method. The results show that it is possible to determine whether a product is suitable for DPBM strategy application after analyzing only its historical demand data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.