Recent major global events such COVID-19 pandemic, the Russia-Ukraine conflict, and the US-China trade war brought to light the vulnerability of many supply chains. In this context, it is critical to understand ways through which firms can build defensive mechanisms against supply chain disruptions. The extant literature has highlighted supply chain robustness as a notable disruption mitigation capability, identifying it as a proactive approach to dealing with supply chain disruptions. Supply chain robustness refers to the ability of an organization to proceed with its planned operation despite a disruption (Simchi‐Levi et al., 2018). Arguably, big data analytics is a key resource organizations can orchestrate to develop robustness capabilities for surviving disruptions. The complexity of supply chain decision-making increases significantly when organizations confront uncertainties. Consequently, the existing body of research emphasizes the importance of data-driven approaches in managing supply chain disruptions. The use of big data analytics is one such data-driven approach that empowers decision-makers to swiftly analyze and interpret information, enabling them to make rapid decisions in response to changing events and disruptions. Despite its increase in popularity, the role of big data analytics in enhancing supply chain robustness remains poorly understood in theory and its application. Further, scholarly discussions on the impact of big data analytics on supply chain robustness remain anecdotal with very limited empirical studies. This lacuna becomes increasingly significant, particularly in light of the highly disruptive scenario brought about by the unprecedented implications of the COVID-19 pandemic, which may hinder the applicability of lessons learned from previous disruptive events. Accordingly, this study aims to examine how firms may leverage big data analytics capability to enhance supply chain robustness. The study draws from the sense-seize-transform (SST) conceptualization of the dynamic capabilities theory to propose a research model. The hypotheses of this study will be tested using data collected from firms operating in Ghana, a sub-Saharan African country. The structural equation modeling technique will be employed to test the study’s research model. The anticipated results of this study are in three folds: (i) to determine the direct relationship between big data analytics and supply chain robustness; (ii) to understand the nomological network of associations between supply chain analytics and dynamic capabilities (sense, seize, and transform (SST)) in fostering robust supply chains; (iii) to highlight the hierarchy of capabilities that provides the foundation required to build supply chain robustness from analytics. The study will offer useful theoretical contributions while offering noteworthy managerial implications.