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Supply chains (SC) are increasingly complex and if the resulting complexity is not managed effectively, it could lead to adverse consequences for the firm. The effect big data analytics (BDA) can have on managing distinct types of SC complexity is not well understood in the extant literature. Based on a sample of 166 firms from Pakistan, this study empirically investigates the effects of BDA, and of structural and dynamic SC complexities, on SC resilience. The study also investigates the role of BDA as a mediator between SC complexities and SC resilience. We find that structural SC complexity positively affects SC resilience, while there doesn't seem to be a significant impact for dynamic SC complexity. We also find a mediating effect of BDA for structural and dynamic SC complexities on SC resilience. Our results contribute to the extant literature investigating BDA and SC resilience by offering a more nuanced understanding of distinct types of SC complexities. We establish a more critical understanding of the role of BDA in mediating the critical link between the two types of SC complexity and SC resilience. The proposed model highlights that there are both direct and indirect effects between structural SC complexity and SC resilience, however dynamic SC complexity only influences SC resilience via BDA. These findings provide strategic insights for SC executives as to where to invest in BDA to build much needed SC resilience.

The ripple effect refers to disruption propagation across the supply network affecting its global performance. To cope with it, supply networks should be resilient. This study investigates the drivers of supply network resilience, viewed as adaptive capacity to disruptions, focusing on trust and investigating the moderating role of network topology on the relationship between trust and resilience. We first develop an NK agent-based model of the supply network to simulate resilient performance. Then, a simulation analysis is carried out, to assess the effect of trust on the resilience of supply networks displaying different complex topologies. Our results confirm that trust positively affects supply network resilience; however, across the different topologies, the beneficial effect of trust varies. In particular, we find that trust is beneficial at most for the following topologies: local, small-world, block-diagonal, and random. For centralised, diagonal, and hierarchical topologies improving trust increases resilience at a moderate level. We also find that, as the frequency of disruptions rises, the positive effect of trust on resilience decreases. Managerial implications of the main findings are finally discussed.

In recent times, the literature has seen considerable growth in research at the intersection of digital innovation, data analytics, and supply chain resilience. While the number of studies on the topic has been burgeoning, due to the absence of a comprehensive literature review, it remains unclear what aspects of the subject have already been investigated and what are the avenues for impactful future research. Integrating bibliometric analysis with a systematic review approach, this paper offers the review of 262 articles at the nexus of innovative technologies, data analytics, and supply chain resiliency. The analysis uncovers the critical research clusters, the evolution of research over time, knowledge trajectories and methodological development in the area. Our thorough analysis enriches contemporary knowledge on the subject by consolidating the dispersed literature on the significance of innovative technologies, data analytics and supply chain resilience thereby recognizing major research clusters or domains and fruitful paths for future research. The review also helps improve practitioners’ awareness of the recent research on the topic by recapping key findings of a large amount of literature in one place.

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