Monte Carlo simulation for project risk prioritisation.

Acebes, F., Curto, D., González-Varona, J. M., & Pajares, J. (2024). Monte Carlo simulation for project risk prioritisation. In Proceedings of the 17th International Conference on Industrial Engineering and Industrial Management (pp. 455–460). Springer. https://doi.org/10.1007/978-3-031-57996-7_78

This research paper explores the integration of Monte Carlo simulations into the project risk prioritization process to move beyond traditional, qualitative assessments. The authors argue that standard risk matrices often fail to capture the probabilistic nature of project uncertainties, which can lead to suboptimal resource allocation. By employing stochastic modeling, the study provides a more robust framework for quantifying both risk impact and probability, allowing project managers to identify high-consequence events with greater statistical confidence. The significance of this work lies in its ability to bridge the gap between complex theoretical risk modeling and practical application in industrial engineering. It provides a scalable methodology that helps decision-makers justify risk responses based on data-driven simulations rather than intuition alone. Ultimately, the work contributes to the field by enhancing the accuracy of risk registers in complex environments, ensuring that prioritization reflects the actual variability of modern project lifecycles.