Application of Big Data Analysis Technology in the Construction of Cross-Border E-Commerce Supply Chain Platform

Abstract

AbstractWith the development of the era of big data, the global e-commerce market is constantly developing, and the cross-border e-commerce (CBEC) market is also booming. At the same time, big data brings massive data resources. If CBEC wants to realize its own value in massive data, it needs to carry out data analysis and build supply chain platform (SCP) management. The competition of CBEC in the current era is not only the competition between individual enterprises, but also the competition between supply chains. With the increasing demand of CBEC market and the increasing diversity of customer demand, lean production and precision manufacturing is the key to the development of CBEC enterprises. This paper mainly studies the application of big data analysis technology (BDAT) in the construction of CBEC SCP. This paper expounds the characteristics of CBEC supply chain, analyzes and understands the construction methods and steps of SCP, and makes choices among downstream construction methods, upstream construction methods and comprehensive construction methods. Then this paper analyzes the application of BDAT in the construction of SCP, and realizes the demand positioning of supply chain by completing the data analysis steps. In this paper, through BDAT to collect and sort out data, we know the scale of CBEC market from 2015 to 2019, understand its transaction scale and growth rate, and understand the overall proportion and change of B2B mode and B2C mode of CBEC platform. The research results show that the transaction scale of CBEC has been expanding from 2015 to 2019. In 2015, the transaction scale of CBEC was 3.9 trillion yuan. In 2019, the transaction scale of CBEC expanded to 9.1 trillion yuan. In terms of growth rate, the growth rate of CBEC began to slow down, from 25.8% in 2015 to 18.4% in 2019.KeywordsSupply chain platformCross-border e-commerceBig data analysis technologySupply chain management