Artificial Intelligence Models for Predicting Budget Expenditures
https://doi.org/10.47703/ejebs.v68i1.331
Abstract
This study develops and tests a machine learning (ML)-based cost forecasting model against traditional earned value management (EVM) techniques. Utilizing Python for ML implementation, the research applies algorithms to a dataset of completed projects globally, evaluating their performance with metrics like mean absolute percentage error (MAPE) and percentage error (PE). The results confirmed that ML give more accurate results than the traditional methods. Thus, the initial rate showing that XGBoost is more accurate than the traditional method using Index-2 is 88%. In 23 of 25 randomly selected projects, this algorithm was more accurate. At the middle stage, the same frequency is 92.6%; later stage, the selected criterion further confirms that the ML algorithm is more accurate than the traditional method, accounting for 75% of 21 projects out of 28. By introducing ML into project management forecasting, managers could spend less time on the technical tasks in their projects. Despite its effectiveness, the study's scope is limited by a small sample size of 110 projects and the testing of only three algorithms. Future research is suggested to expand the dataset and explore additional algorithms, including neural networks and tree-based methods, to enhance forecasting precision.
Keywords
Economics,
Business,
Project Management,
Cost Forecasting,
Earned Value Method,
Artificial Intelligence,
Machine Learning,
Machine Learning Models,
Python
Supporting agencies: The study was carried out within the framework of funding by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313)
About the Authors
Christian Capone
Tashkent State University of Economics, Tashkent, Uzbekistan.
Uzbekistan
Uzbekistan
PhD Candidate, Senior Lecturer, Email: capone.ch@gmail.com
Sayazhan Talgat
Institute of Advanced Research and Sustainable Development, Almaty, Kazakhstan.
Kazakhstan
Kazakhstan
Mr. Sc. (Econ.), Researcher, Email: t.sayazhan2000@gmail.com
Oncu Hazir
Rennes Business School, Rennes, France.
France
France
PhD, Associate Professor, Email: oncu.hazir@tedu.edu.tr
Kuralay Abdrashova
Kazakh-British Technical University, Almaty, Kazakhstan.
Kazakhstan
Kazakhstan
Mr. Sc. (Econ.), Junior Researcher, Email: k.abdrashova.26@gmail.com
Assel Kozhakhmetova
Institute of Applied Sciences and Information Technology, Almaty, Kazakhstan.
Kazakhstan
Kazakhstan
PhD, Associate Professor, Email: a.kozhakhmetova@kbtu.kz
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For citations:
Capone Ch., Talgat S., Hazir O., Abdrashova K., Kozhakhmetova A. Artificial Intelligence Models for Predicting Budget Expenditures. Eurasian Journal of Economic and Business Studies. 2024;68(1):32–43. https://doi.org/10.47703/ejebs.v68i1.331
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