Rapid digital transformation and increasing complexity in trade environments have created challenges for optimizing operational efficiency and maintaining competitiveness. Smart trade ecosystems, featuring interconnected platforms, real-time data, and dynamic supply chains, require advanced tools to monitor, simulate, and optimize processes. Traditional methods struggle with integrating diverse data sources and predicting operational issues. Digital twins, as real-time virtual replicas of physical systems, offer enhanced visibility, predictive capabilities, and process optimization, though their practical impact is not yet fully understood. This research explores the role of digital twins in improving business processes within smart trade environments, identifying how they enhance efficiency, decision accuracy, and overall performance. By combining theories from information systems, supply chain management, and operations research, the study presents a comprehensive view of digital twin adoption and strategic value. Using a mixed-method approach that includes literature review, expert interviews, and quantitative surveys analyzed through structural equation modeling, the findings show that digital twins significantly improve process transparency, enable predictive decision-making, and enhance coordination across trade networks, resulting in better organizational adaptability and performance. These results provide both theoretical insights and practical guidance for managers aiming to leverage digital twins for competitive advantage in smart trade operations.