Forecasting Performance: Leveraging Machine Learning on Earned Value Data for Proactive Control
DOI:
https://doi.org/10.5281/zenodo.15278924Keywords:
machine learning, earned value management, project performance forecasting, proactive control, regression analysis, decision trees, neural networks, project monitoring, risk management, data-driven decision-makingAbstract
This study investigates the utilisation of machine learning methodologies in the context of earned value management (EVM) data, aiming for the anticipatory prediction and regulation of project outcomes. This research seeks to utilise machine learning frameworks, including regression analysis, decision trees, and neural networks, to forecast upcoming project outcomes, pinpoint possible risks, and improve the decision-making process. The study illustrates how the incorporation of sophisticated algorithms alongside conventional EVM data can yield enhanced, immediate insights into cost and schedule effectiveness. The document further explores the ramifications of this methodology for project leaders, providing a comprehensive structure for enhancing project oversight and regulation via insights derived from data.
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Copyright (c) 2025 Rohit Shinde
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Research Articles in 'Applied Science and Engineering Journal for Advanced Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
