Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Digital twin technology is a next-generation technology in supply chain management that portrays advanced capabilities in predicting capacity, performance optimization, and responsiveness. Predictive modeling and decision-making can be supported using digital twins by applying real-time data from physical assets and virtualized environments to enhance operational efficiency and flexibility. The methodological basis, technological frameworks, and industry-focused implementations of digital twin models in supply chain capacity forecasting will be examined in this review. It also describes how it enhances logistics, manufacturing, humanitarian endeavors, and food systems, especially emphasizing the significance of incorporating artificial intelligence, generative algorithms, and sustainability-focused analytics. The analysis exposes pitfalls such as data inconsistency, cybersecurity risks, and scalability issues that make wide adoption difficult. In addition, future trends, including blockchain, quantum computing, and edge technologies, are mentioned in the review as enablers of simpler and more sustainable capacity forecasting. Such findings point to the need to align technological innovation with organizational preparedness and governance systems as a way of realizing the full potential of digital twin–driven forecasting systems.