Abstract

This study explores the application of digital twin technology in procurement and supply chain management to enhance resilience and support predictive cost avoidance. Digital twins are virtual replicas of physical assets, processes, and supply networks, which allow real-time monitoring, scenario simulation, and predictive analytics for operational decision-making. The paper synthesizes existing literature on digital twins, supply chain risk management, and predictive cost models, proposing a conceptual architecture for integrating digital twins into procurement and logistics operations. Key functionalities include real-time data acquisition, simulation-based scenario analysis, predictive risk alerts, and resilience metrics. The study highlights how digital twins facilitate early detection of disruptions, optimize procurement strategies, and reduce unnecessary costs, while aligning operational decisions with strategic objectives. The findings contribute to theory by extending digital twin applications beyond manufacturing to procurement and supply chain domains, and to practice by providing a framework for organizations seeking predictive and resilient supply chain operations.