A machine learning study to improve the reliability of project cost estimates

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 exper...

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Bibliographic Details
Published in:International journal of production research 2024-06, Vol.62 (12), p.4372-4388
Main Authors: Narbaev, Timur, Hazir, Öncü, Khamitova, Balzhan, Talgat, Sayazhan
Format: Article
Language:English
Subjects:
Cost analysis
Cost control
Cost estimates
Cost forecasting
Data points
Decision analysis
Decision trees
earned value management
Forecasting
Machine Learning
Mathematical models
non-linear growth model
Predictive analytics
Project management
project monitoring and control
Reliability
Statistics
Support vector machines
The XGBoost model
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Summary: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.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2023.2262051