Authors : Abel J. Stephen

Volume/Issue : Volume 9 - 2024, Issue 9 - September

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Google Scholar : https://tinyurl.com/bdh7fsdj

Scribd : https://tinyurl.com/yk6uwn5c

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP164

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This paper introduces a methodology for advanced project duration estimation, integrating the Program Evaluation and Review Technique (PERT) with Monte Carlo simulation. It employs various distributions — normal and beta — to enhance the accuracy of task duration modeling based on initial three-point estimates. This aproach refines these distributions, establishing a robust mode while maintaining a consistent 90th- percentile confidence level. The study illustrates the feasibility of the implementation using accessible tools i.e., Google Sheets and Power BI, ensuring practicality in project management. The conclusion underscores improved accuracy and reliability in project duration estimates, enhancing risk management and decision- making throughout project execution.

Keywords : Monte Carlo Simulation, PERT, Machine Learning, Artificial Intelligence, Probabilistic Modelling, Project Duration Estimation, Project Management.

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