A machine learning study to improve the reliability of project cost estimates
Narbaev T. Hazir Ö. Khamitova B. Talgat S.
2024Taylor and Francis Ltd.
International Journal of Production Research
2024#62Issue 124372 - 4388 pp.
Project managers need reliable predictive analytics tools to make effective project intervention decisions throughout the project life cycle. This study uses Machine learning (ML) to enhance the reliability in project cost forecasting. A XGBoost forecasting model is developed and computational experiments are conducted using real data of 110 projects representing 1268 cost data points. The developed model performs better than some Earned value management (EVM), ML (Random forest, Support vector regression, LightGBM, and CatBoost), and non-linear growth (Gompertz and Logistic) models. The model produces more accurate estimates at the early, middle, and late stages of the project execution, allowing for early warning signals for more effective cost control. In addition, it shows more accurate estimates in most projects tested, suggesting consistency when repeatedly used in practice. Project forecasting studies mainly used ML to estimate the project duration; a few ML studies estimated the project cost at the project’s conceptual stage. This study uses real data and EVM metrics, proposing an effective XGBoost model for forecasting the cost throughout the project life cycle.
Cost forecasting , earned value management , machine learning , non-linear growth model , project monitoring and control , The XGBoost model
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Business School, Kazakh-British Technical University, Almaty, Kazakhstan
Department of Supply Chain Management and Information Systems, Rennes School of Business, Rennes, France
Business School
Department of Supply Chain Management and Information Systems
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