Big Data and Cross-Border Supply Chain Risk Management

The cross-border supply chains are now more intricate, interdependent, and prone to numerous disruptions such as pandemics and geopolitical tensions, cyberattacks, and climate-related occurrences. At the same time, the ever-increasing size of big data and sophisticated analytics provides unparalleled chances to detect, evaluate, trace, and mitigate the risks within a supply chain, which are not confined to national borders. This conceptual article constructs a synthesising theoretical framework that would combine the capabilities and potentials of the big data with cross border supply chain risk management (SCRM) processes. With reference to dynamic capabilities theory, information processing theory, and relational view of the firm, the paper suggests a multi-layered model which elucidates how big data analytics can be used to augment risk identification, risk assessment, risk mitigation and risk monitoring in global supply chain. The paper also includes the moderating functions of data governance, institutional distance and maturity of digital infrastructure in determining the success of big data-based SCRM. There is progression of theoretical propositions, and suggestions made on future empirical studies and managerial practise.