Cross-border e-business has brought new opportunities but also incidences of risk that have affected the supply chain. The study aims to design a real-time supply chain risk management framework for cross-border e-commerce to improve risk identification by utilising Big Data technologies, namely Hadoop HDFS and Spark Streaming. In this study based on a survey of actual cross-border transaction data over 100,000, fundamental supply chain parameters which are time to convey the product, supplier reliability, product cost, and order delivery ratio are also assessed. The study used Spark Streaming and K-means clustering to analyse and partition suppliers in real-time to risk factors and to generate hypothetical events including supplier insolvency and augmented shipping latex. This study shows a positive impact of real-time analysis and also clustering analysis for enhancing the probabilities of risk identification and for comprehending the delay and inefficiency in shipping and supplier performance. By this, the framework enhances decision-making as it provides real-time results, which can help businesses such as Alibaba to negotiate supply chain risks in real time. In conclusion, the integration of real-time big data analytics appears very promising for orchestrating cross-border e-commerce supply chains, yet they encourage researchers to employ real data for that purpose.