Digital Twins for Real-Time Operations Monitoring and Optimization in Oman’s Logistics Sector

Abstract

Digital twins (DTs) are transforming supply chain management and logistics operations by improving operational efficiency, enabling data-driven decision-making, and allowing for real-time. However, many current DTs implementations lack cybersecurity frameworks, predictive analytics, and dynamic risk assessment, relying instead on static data evaluation and key performance indicator (KPI) monitoring. This research addresses these gaps by suggesting an AI-based DT framework designed to enhance the resilience, security, and operational performance of logistics systems. The study explores how integrating AI, the Internet of Things (IoT), and big data analytics can enable the integration of DTs—virtual representations of physical resources and processes—to support real-time operations management, informed decision-making, and the mitigation of vulnerabilities in logistics management. The research employs a qualitative methodology and illustrative case studies, the research explores how the adoption of DTs aligns with the ongoing logistics transformations of Industry 4.0 and AI. The findings demonstrate a practical method that leads to boosting logistics efficiency, reducing delays, and promoting environmental sustainability. This work provides strategic guidance to promote the adoption this technology in the sector and offers valuable insights for researchers and practitioners seeking to leverage digital twins to deliver secure, adaptive, and efficient digital supply chain solutions.