Digital Twins (DTs) are data-driven virtual replicas of physical systems that enable real-time monitoring and decision-making. Unlike traditional simulation tools, DTs operate through continuous synchronization with physical systems, allowing predictive and prescriptive capabilities that are essential in dynamic supply chain (SC) environments. This paper presents an overview, based on a structured literature review, on the application of DTs in SC management. First, we conduct a quantitative analysis of peer-reviewed journal articles from the Scopus database, which highlights a marked growth in scholarly interest on the topic. Then, we categorize the applications according to the key processes of the SCOR model: Plan, Source, Make, Deliver, and Return. The analysis reveals that DTs are primarily employed in managing production processes (Make), supporting smart manufacturing and inventory optimization, and in strategic planning (Plan), facilitating supply chain design and collaboration. Emerging uses in procurement (Source) and reverse logistics (Return) are also discussed. We conclude by highlighting some challenges in the large-scale adoption of DTs in SCs, positioning DTs as fundamental enablers of efficient, flexible, resilient, and sustainable SCs in the era of Industry 4.0.