A Hybrid Framework for Digital Twin Adoption to Enhance Supply Chain Resilience

Supply chain resilience is essential for sustaining operational performance amid unexpected disruptions. Recent advances in Digital Twin (DT) technology offer promising avenues for simulating and optimizing supply chain operations; however, widespread implementation is hindered by challenges such as inadequate stakeholder understanding, data quality issues, and the complexity of integrating DTs into existing systems. This study proposes an innovative framework for effective DT adoption in industrial supply chains. A novel three-stage hybrid methodology is introduced to systematically assess, rank, and prioritize key factors influencing DT integration. The analysis identifies major opportunities, including enhanced data accuracy, improved service performance, economic sustainability, and more effective supplier-manufacturer-customer interactions, as well as significant challenges, such as model calibration difficulties, intricate network complexities, and variability in local and regional practices. The empirical evaluation demonstrates that while DT technology can markedly transform supply chain operations, overcoming integration barriers is critical for realizing its full potential. The findings contribute to the theoretical understanding of disruptive technology adoption in supply chain management and provide actionable insights for engineering managers seeking to design more resilient and efficient systems. Future research directions are discussed to refine DT applications in complex industrial settings further.