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
References
1. Alhares, T., Budayan, С. (2019). Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects. Symmetry, 11(2), 180-190. https://doi.org/10.3390/sym11020190
2. Alvarez-Dionisi. L.E., Turner. R., Mittra. M. (2015). Global project management trends. International Journal of Information Technology Project Management, 7(3), 54-73.
3. Algiev, S. (2014). Current state and trends in the development of project management systems in state programs of the Republic of Kazakhstan. News of the National Academy of Sciences of the Republic of Kazakhstan, 1, 28-34.
4. Anicic, D. & Anicic, J. (2019). Cost Management Concept and Project Evaluation Methods. Journal of Process Management, 7( 2), 54-60. https://doi.org/10.5937/jouproman7-21143
5. Barber, C., Dacre, N. & Dong, H. (2021). Reframing project management process paralysis: an autoethnographic study of the UK Fire Service. Advanced Project Management, 2(121), 21-43. https://doi.org/10.2139/ssrn.3830416
6. Bento, S., Pereira, L., Gonçalves, R., Dias, Á. and Lopes da Costa, R. (2022). Artificial intelligence in project management: systematic literature review. International Journal of Technology Intelligence and Planning, 13(2), 143–163. https://doi.org/10.1504/IJTIP.2022.1005040
7. Chou, J.-S., Tai, Y. & Chang, L.-J. (2010). Predicting the Development Cost of TFT-LCD Manufacturing Equipment with Artificial Intelligence Models. International Journal of Production Economics, 128(1), 339-350. https://doi.org/10.1016/j.ijpe.2010.07.031
8. Chou, J. S., Lin, C. W., Pham, A. D., & Shao, J. Y. (2015). Optimized Artificial Intelligence Models for Predicting Project Award Prices. Automation in Construction, 54, 106-115. https://doi.org/10.1016/j.autcon.2015.02.006
9. Dacre, N., Kockum, F. & Senyo P.K. (2020). Transient Information Adaptation of Artificial Intelligence Towards Sustainable Data Processes in Complex Projects. British Academy of Management, 2, 234-246. http://dx.doi.org/10.2139/ssrn.3813559
10. Derici, S. & Doğan, N.Ö. (2019). Project Management and Efficiency of the Projects in the Industry 4.0 Era. In the book: Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution. IGI Global, US, 188-209. https://doi.org/10.4018/978-1-5225-7865-9.ch010
11. Elkhatib, M., Al Hosani, A., Al Hosani, I., & Albuflasa, K. (2022). Agile Project Management and Project Risks Improvements: Pros and Cons. Modern Economy, 13, 1157–1176. https://doi.org/10.4236/me.2022.139061
12. Elmousalami, H. H. (2020). Data on Field Canals Improvement Projects for Cost Prediction Using Artificial Intelligence. Data in Brief, 31(1), 105-116. https://doi.org/10.1016/j.dib.2020.105688
13. Fildes, R., Lusk, E.J. (1984). The choice of a forecasting model. Omega, 12(5), 427-435.
14. Fridgeirsson, T. V., Ingason, H. T., Jónasson, H. I., & Jónsdóttir, H. (2021). An Authoritative Study on the Near Future Effect of Artificial Intelligence on Project Management Knowledge Areas. Sustainability, 13(4), 2345-2443 https://doi.org/10.3390/su13042345.
15. García, J.A.L., Peña, A.B. & Pérez, P.Y.P. (2017). Project Control and Computational Intelligence: Trends and Challenges. International Journal of Computational Intelligence Systems, 10(1), 320–335. https://doi.org/10.2991/ijcis.2017.10.1.22
16. Haenlein, M. & Kaplan, A. (2019). A Brief History of Artificial Intelligence: on the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
17. Javed, F., Siddique, A., Munir, A. & Lali M. I. (2020). Discovering Software Developer’s Coding Expertise Through Deep Learning. IET Software, 14(3), 213 – 220. https://doi.org/10.1049/iet-sen.2019.0290
18. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Yilong, Dong, Q., Shen, H. & Wang, Yongjun. (2017). Artificial Intelligence in Healthcare: Past, Present and Future. Stroke and vascular neurology, 2, 230–243. https://doi.org/10.1136/svn-2017-000101
19. Ju, X., Ferreira, F.A.F & Wang, M. (2020). Innovation, Agile Project Management and Firm Performance in a Public Sector-Dominated Economy: Empirical Evidence from High-Tech Small and Medium-Sized Enterprises in China. Socio-Economic Planning Sciences, 72, 1–14. https://doi.org/10.1016/j.seps.2019.100779
20. Kim, B.C. (2015). Probabilistic Evaluation of Cost Performance Stability in Earned Value Management. Journal of Management in Engineering, 32(1), 401-425. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000383
21. Kyläheiko, K., Puumalainen, K., Pätäri, S. & Jantunen A. (2017). How do Firm and Industry-Specific Factors Affect Innovation and Financial Performance? International Journal of Technology Intelligence and Planning, 11(3), 230–251. https://doi.org/10.1504/IJTIP.2017.085494
22. Li, W., Duan, P., & Su, J. (2021). The effectiveness of project management construction with data mining and blockchain consensus. Journal of Ambient Intelligence and Humanized Computing, 2(121), 321-340. https://doi.org/10.1007/s12652-020-02668-7
23. Lok, K. L., So, A., Opoku, A. & Song, H. (2019). Globalized Service Providers’ Perspective for Facility Management Outsourcing Relationships: Artificial Neural Networks. Management Decision, 2(134), 421-435. https://doi.org/10.1108/MD-01-2019-0102
24. Lok, K. L., So, A., Opoku, A., & Chen, C. (2021). A Sustainable Facility Management Outsourcing Relationships System: Artificial Neural Networks. Sustainability, 13(9), 4740. https://doi.org/10.3390/su13094740 .
25. Mahoto, N., Iftikhar, R., Shaikh, A., Asiri Y., Alghamdi A., Rajab K. (2021). An Intelligent Business Model for Product Price Prediction Using Machin eLearning Approach. Intelligent Automation & Soft Computing, 29(3),147–159. https://doi.org/10.32604/iasc.2021.018944
26. Mahmood, A. A. Marzooqi, M. El. Khatib, & H. Ameemi, Al. (2021). How Artificial Intelligence can Leverage Project Management Information System (PMIS) and Data Driven Decision Making in Project Management. International Journal of Business Analytics and Security, 3(1), 180-193. https://doi.org/10.54489/ijbas.v3i1.215
27. Mingxue, Ma, Vivian, Tam, W. Y., Khoa, N., Osei-Kyei, R. (2023). A Systematic Literature Review on Price Forecasting Models in Construction Industry. International journal of construction management, 4, 1-10. https://doi.org/10.1080/15623599.2023.2241761
28. Mentzas, G., (1994). Towards Intelligent Organisational Information Systems. International Transactions in Operational Research, 1, 169–187. https://doi.org/10.1111/1475-3995.d01-19
29. Narbaev, T. (2015). Project management knowledge discovery in Kazakhstan: Co-word analysis of the field. The 12th International Conference on Intellectual Capital, Knowledge Management and Organizational Learning, 169-175. Bangkok, November 5-6, Academic Conferences and Publishing International Press.
30. Narbaev, T. & De Marco, A. (2017). Earned Value and Cost Contingency Management: A Framework Model for Risk Adjusted Cost Forecasting. The Journal of Modern Project Management, 4(3), 12-19. https://doi.org/10.19255/JMPM228
31. Ong, S. & Uddin, S. (2020). Data science and artificial intelligence in project management: the past, present and future. Journal of Modern Project Management, 7(4), 1–8. https://doi.org/10.19255/jmpm02202
32. Pellerin, R. & Perrier, N. (2019). A review of methods, techniq,ues and tools for project planning and control. International Journal of Production Research, 57(7), 2160-2178. https://doi.org/10.1080/00207543.2018.1524168
33. Ribeiro, J., Lima, R., Eckhardt, T. & Paiva, S., 2021. Robotic Process Automation and Artificial Intelligence in Industry 4.0 - A Literature review. Procedia Computer Science, 181, 51–58. https://doi.org/10.1016/j.procs.2021.01.104
34. Tsekhovoy, A.F. (2010). Concept for the development of project management in the Republic of Kazakhstan for 2010–2020. Innovation Committee of the People's Democratic Party "Nur Otan". (In Russ).
35. Wang, M., Lee, C. H., & Austin, S. B. (2021). Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus. Journal of Management in Engineering, 2(23), 49-57. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763
36. Wauters, M. & Vanhoucke, M. (2016). A comparative study of artificial intelligence methods for project duration forecasting. Expert Systems with Applications, 46, 249–261. https://doi.org/10.1016/j.eswa.2015.10.008
37. Yitmen, I., Sepehr A., Akıner, I. & Ernur, A. (2021). An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management. Applied Sciences, 11(9), 4276. https://doi.org/10.3390/app11094276
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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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