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.