In contemporary project management practices, one of the most pressing challenges is the establishment of a reliable and adaptable project schedule that accounts for potential variations and allows sufficient time for revisions prior to implementation. Traditional scheduling techniques often fall short of providing structured mechanisms for dealing with schedule uncertainty, feedback loops, and control adjustments. This paper introduces a novel and practical methodology that integrates Monte Carlo simulation with Pre-control chart techniques to enhance planning precision and risk-informed decision-making. The approach is further supported by the capabilities of Primavera Risk Analysis software, enabling the identification of critical risk zones, evaluation of control intervals, and visualization of project performance against predetermined control boundaries. The innovation of this research lies in the application of univariate pre-control charts—initially designed for industrial quality control—into the domain of project scheduling and monitoring, offering project managers a scientifically grounded and visually interpretable framework for schedule adjustment and control. The proposed methodology not only facilitates early detection of deviations but also provides a structured algorithm for schedule revision, ensuring that corrective actions can be implemented proactively. A case-based illustration is presented to demonstrate the practical implications and advantages of this integrated method in a real-world project context. The results indicate that the use of pre-control charts within a probabilistic scheduling environment leads to improved schedule reliability, better resource alignment, and enhanced preparedness for execution. This study lays the foundation for future research on extending pre-control logic to multi-objective project management domains, such as cost and quality control, thereby contributing to the development of integrated and adaptive control strategies for complex projects.