Current Volume 9
Monte Carlo Simulation and Bayesian Inference in Project Management: A Comprehensive Review
Abstract
Project management operates within environments characterized by inherent uncertainty, where traditional deterministic approaches often fail to capture the complexity of real-world scenarios. This comprehensive review examines the application of Monte Carlo Simulation (MCS) and Bayesian inference as predictive tools for effective project delivery. Through systematic analysis of 47 peer-reviewed studies published between 2018 and 2024, this paper synthesizes current knowledge on how these probabilistic methodologies address uncertainty in project scheduling, cost estimation, and risk assessment. Monte Carlo Simulation provides robust frameworks for modeling probability distributions of project variables through stochastic processes, while Bayesian inference offers dynamic updating mechanisms that incorporate new evidence into predictive models. Our analysis reveals that despite widespread adoption in specialized sectors, significant gaps remain in understanding the conditions under which each method performs optimally, the barriers to practical implementation, and the theoretical frameworks guiding their selection. This review identifies three critical research frontiers: the contextualization of method selection based on project characteristics, the integration of these tools with emerging digital technologies, and the development of standardized implementation protocols. The findings contribute to both theoretical advancement and practical application by providing project management scholars and practitioners with a critical assessment of current capabilities, limitations, and future research directions for probabilistic decision-making tools.
Keywords
Monte Carlo Simulation, Bayesian Inference, Project Management, Risk Assessment, Uncertainty Quantification, Predictive Analytics
Citations
IRE Journals:
Emmanuel Adie, John Wasiu, Ibrahim Abdulrazaq "Monte Carlo Simulation and Bayesian Inference in Project Management: A Comprehensive Review" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 2607-2632 https://doi.org/10.64388/IREV9I6-1712870
IEEE:
Emmanuel Adie, John Wasiu, Ibrahim Abdulrazaq
"Monte Carlo Simulation and Bayesian Inference in Project Management: A Comprehensive Review" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712870
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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