Determinants of Data Analytics Capability for Resilient Supply Chain in Manufacturing Companies: A Conceptual Model

The COVID-19 epidemic has made manufacturers more susceptible to supply chain disruptions due to unforeseen changes in demand, material shortages, and a shortage of labor. Prior studies have examined supply chain resilience as an organization's capacity to respond to unanticipated interruptions and provide solutions to ensure operational continuity. However, there is a limited attempt at exploring the significance of integrating shared information with other resources to increase data processing capacity for a reliant supply chain. In addressing this issue, this study combines the resource-based view (RBV) with organizational information processing theory (OIPT) to clarify the antecedents of big data analytics capability for resilient supply chains. Following the two-stage mixed-methods explanatory approach, the proposed model will be tested using data gathered from surveys and interviews with supply chain managers in Malaysian manufacturing companies. The expected findings will provide insights into how manufacturers can leverage big data analytics to build a resilient supply chain. Specifically, managers will better understand how to maximize data processing capacity for a resilient supply chain by fusing shared information with other organizational resources. This study's importance comes primarily from its contribution to theory and practice. It examines how firms build resilient supply chains by combining shared information with other organizational resources. Additionally, it adds to the growing body of literature on the foundations of big data analytics for a resilient supply chain. This subject is becoming increasingly crucial in the era of post COVID-19 pandemic.