Moving from a deterministic to a probabilistic schedule: increase schedule reliability and manage stakeholder expectations by applying Monte Carlo techniques to planning

About

Large-scale, first-of-a-kind scientific projects such as ITER involve unprecedented complexity, long timelines, and significant technical uncertainties. Traditional deterministic scheduling methods often fail to capture the inherent variability in task durations, the uncertain outcomes of prototyping and testing and the cascading impact of risks, leading to unrealistic milestone forecasts and limited decision-making support. Monte Carlo scheduling techniques offer a robust alternative by incorporating probabilistic modelling of uncertainties and risk factors into project schedules. Through simulation of thousands of potential scenarios, these methods provide a distribution of possible completion dates rather than a single deterministic estimate. This probabilistic approach enables project managers to quantify confidence levels for major milestones, identify critical risk drivers, and prioritize mitigation strategies effectively. By presenting a range of outcomes, Monte Carlo analysis enhances transparency and strengthens stakeholder confidence in delivery forecasts and supports informed decision-making under uncertainty. This presentation will clearly describe how Monte Carlo modelling has been used for the schedule of a key component that has long determined the critical path of the ITER project. It will show how using a "Monte Carlo schedule" has led to a more accurate and realistic timeline, and, just as importantly, how it has become a valuable tool for communicating with stakeholders and managing expectations about the project schedule.

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