Simulate. Optimize. Succeed: Integrating Digital Twin Technology for Real-Time Risk Detection and Decision Support in IT Projects Management Lifecycle

This study evaluates the role of Digital Twins in IT project management lifecycles, intending to enhance predictive analytics, decision-making, and real-time workflow optimisation. Drawing upon complexity theory, decision support systems, and Agile methodologies, the study proposes a modular DTT framework capable of simulating project conditions, forecasting risks, and adapting to dynamic changes. A hybrid qualitative methodology comprising a structured literature review, case study analysis, conceptual framework design and simulation evaluation was adopted. Workflow modelling tools such as Draw.io and Figma were used to visualise IT processes and lifecycle design, and an interactive dashboard prototype. Simulated Agile scenarios, including project delays, resource bottlenecks, and testing constraints, were used to validate the conceptual framework. The simulation demonstrated over 90% risk detection accuracy, AI-driven recommendations with response times under three seconds, and a potential reduction of up to five working days in project delivery. In practical terms, the model offers IT teams faster decision-making, improved visibility into risks, and enhanced delivery timelines, making it highly applicable for fast-paced IT environments with possible integration capabilities with tools like Jira, to enable real-time updates during project reviews. This study provides a flexible, data-based Digital Twin Technology (DTT) model that helps teams work continuously, see project issues more clearly, and make decisions early. It shows how IT projects can shift from reacting to problems to predicting and preventing them.