Abstract: In the refining industry, timely decisions are paramount, as delays can significantly impact profitability. Effective decision-making relies heavily on simulation, which facilitates outcome prediction, scenario evaluation, and bottleneck identification – ultimately guiding investment, maintenance scheduling, and production planning. Traditional simulation approaches, however, are often hampered by lengthy setup processes, delaying critical decision-making. To address this challenge, Saudi Aramco developed the Process Simulation Twin [PST], an innovative application that automates data collection, reconciliation, and model calibration to provide an up-to-date, refinery-wide simulation framework for techno-economic assessment. By leveraging the PST, users gain instant access to current process simulations and can focus on higher-value activities, such as troubleshooting operational issues and opportunity identification. The PST offers consistent, real-time estimates for immeasurable key performance indicators [KPIs], monitors planning model accuracy, and provides regular planning model updates. Furthermore, it lays the groundwork for integrating Artificial Intelligence [AI] and Machine Learning [ML] techniques, including reinforcement learning and predictive analytics, to drive informed decision-making and unlock sustainable value creation across the organization.