Transforming Big Data Analytics into Supply Chain Resilience through Dynamic Capabilities and Management Practice

Abstract

Supply chains are exposed to disruptions of growing frequency and severity, ranging from pandemics and geopolitical conflict to climate-related shocks, which has pushed Supply Chain Resilience (SCR) to the center of operations and management research. In parallel, organizations are investing heavily in Big Data Analytics (BDA) capability, the ability to acquire, integrate, and exploit large and varied data resources for decision-making. Although a growing body of empirical work links BDA capability to superior supply chain outcomes, the literature remains fragmented: Resilience research emphasizes network design and buffer capacity, analytics research emphasizes performance and efficiency gains, and few studies theorize the mechanism through which data-driven capabilities are converted into resilience. This conceptual paper addresses that gap by developing an integrative framework, termed Data-Driven Supply Chain Resilience (DDSCR), that combines the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and the emerging literature on Artificial Intelligence (AI) and Industry 4.0 (I4.0) technologies in supply chain management. The framework positions BDA capability as a strategic resource that, through the dynamic-capability processes of sensing, seizing, and reconfiguring, is converted into three resilience capacities, anticipation, adaptation, and recovery, with digital-technology intensity and supply chain risk exposure acting as boundary conditions that shape the strength of this conversion. Nine theoretically grounded propositions are advanced to specify these relationships and their contingencies, and a three-stage analytics-resilience maturity roadmap is proposed to guide implementation. The paper contributes a parsimonious, testable model that reconciles previously disconnected research streams, offers managers a diagnostic vocabulary for assessing analytics-driven resilience maturity, and outlines a structural equation modeling agenda for future empirical validation. The paper closes with a discussion of the framework's theoretical and managerial implications, its boundary conditions, and its limitations, together with directions for future research.