kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2024-21-3-374-383kaz29-1400Research ArticleЭКОНОМИКА И БИЗНЕСECONOMY AND BUSINESSПРИМЕНЕНИЕ НЕТРАДИЦОННЫХ МОДЕЛЕЙ МЕТОДА ОСВОЕННОГО ОБЪЕМА В ПРОЕКТНОЙ АНАЛИТИКЕAPPLICATIONS OF NON-TRADITIONAL EARNED VALUE MANAGEMENT MODELS IN PROJECT ANALYTICShttps://orcid.org/0000-0002-8970-5802КапонеК.CaponeC.

MBA, исследователь 

100000, г. Ташкент

MBA, Researcher 

100000, Tashkent

chr.capone@gmail.comhttps://orcid.org/0009-0007-2616-3042АхласовЮ. С.AkhlassovY. S.

магистр экономики, научный сотрудник 

050057, г. Алматы

MSc in Economics, Researcher 

050057, Almaty

elaman0510kz@mail.ruhttps://orcid.org/0009-0007-6749-3363ИбраевО. С.IbrayevO. S.

магистр экономики, научный сотрудник 

010000, г. Астана

MSc in Economics, Researcher 

010000, Astana

olzhasibraev001@gmail.comБританский университет менеджментаУзбекистанBritish Management UniversityUzbekistanНаучно-исследовательский институт «Алматыгенплан»КазахстанScientific Research Institute “Almatygenplan”KazakhstanАО «Фонд развития промышленности»Казахстан“Industrial Development Fund” JSCKazakhstan202403102024213374383Copyright © Капоне К., Ахласов Ю.С., Ибраев О.С., 20242024Капоне К., Ахласов Ю.С., Ибраев О.С.Capone C., Akhlassov Y.S., Ibrayev O.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/1400

Эффективное управление финансовыми ресурсами в проектах имеет решающее значение для их успешного выполнения. Часто проблемы с финансовыми ресурсами, такие как перерасход бюджета, приводят к неблагоприятным последствиям, которые непосредственно влияют на успешное завершение проекта, качество результата и удовлетворенность заинтересованных сторон. Поэтому идентификация и разработка инструментов для эффективного управления финансовыми ресурсами имеет первостепенное значение. Цель данного исследования – применить нетрадиционные модели управления освоенным объемом (EVM), основанные на машинном обучении, для прогнозирования затрат на проект. Для достижения целей исследования был проанализирован предыдущий опыт в данной области, подготовлен набор данных по прошлым проектам и применена модель машинного обучения. Исследование показало, что нетрадиционные модели, такие как алгоритм регрессии AdaBoost, продемонстрировали результаты, близкие к фактическим затратам. Результаты исследования свидетельствуют о том, что разработанная модель может стать незаменимым инструментом для управления проектами и принятия бизнес-решений, так как она демонстрирует способность адаптироваться к различным условиям и делать точные прогнозы.

Effective management of financial resources in projects is crucial for project success. Often, difficulties with financial resources, such as budget overruns, lead to unfavorable consequences that directly impact the successful completion of the project, the quality of the outcome, and stakeholder satisfaction. Therefore, identifying and developing tools for the effective management of financial resources, is of paramount importance. The purpose of this study is to apply non-traditional earned value management (EVM) models based on machine learning to predict project costs. To achieve the research objectives, previous literature on the topic was analyzed, a dataset of past projects was prepared, and a machine learning model was applied. The study found that non-traditional models, such as the regression algorithm AdaBoost, produced results close to the actual costs. The research indicates that the developed model could become an indispensable tool for project management and business decision-making, as it demonstrates the ability to adapt to various conditions and make accurate forecasts.

управление проектомискусственный интеллектмашинное обучениеметод освоенного объемапрогнозирование затратProject managementArtificial intelligenceMachine learningEarned Value ManagementCost forecastingReferencesInan T., Narbaev T., & Hazir O. A Machine learning study to enhance project cost forecasting. IFACPapersOnLine, 2022, vol. 55, no. 10, pp. 3286–3291. https://doi.org/10.1016/j.ifacol.2022.10.127.Inan T., Narbaev T., & Hazir O. A Machine learning study to enhance project cost forecasting. IFACPapersOnLine, 2022, vol. 55, no. 10, pp. 3286–3291. https://doi.org/10.1016/j.ifacol.2022.10.127.Gil J., Martinez Torres J., & González-Crespo R. The application of artificial intelligence in project management research: A review. International Journal of Interactive Multimedia and Artificial Intelligence, 2021, no. 6, pp. 54–66. https://doi.org/10.9781/ijimai.2020.12.003.Gil J., Martinez Torres J., & González-Crespo R. The application of artificial intelligence in project management research: A review. International Journal of Interactive Multimedia and Artificial Intelligence, 2021, no. 6, pp. 54–66. https://doi.org/10.9781/ijimai.2020.12.003.Bhavsar K., Shah V., & Gopalan S. Business Process Reengineering: A Scope of Automation in Software Project Management Using Artificial Intelligence. International Journal of Engineering and Advanced Technology (IJEAT), 2019, vol. 9, no. 2, pp. 3589–3595. https://doi.org/10.35940/ijeat.B2640.129219.Bhavsar K., Shah V., & Gopalan S. Business Process Reengineering: A Scope of Automation in Software Project Management Using Artificial Intelligence. International Journal of Engineering and Advanced Technology (IJEAT), 2019, vol. 9, no. 2, pp. 3589–3595. https://doi.org/10.35940/ijeat.B2640.129219.Taboada I., Daneshpajouh A., Toledo N., & de Vass T. Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences, 2023, vol. 13, no. 8, p. 5014. https://doi.org/10.3390/app13085014.Taboada I., Daneshpajouh A., Toledo N., & de Vass T. Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences, 2023, vol. 13, no. 8, p. 5014. https://doi.org/10.3390/app13085014.Elmas Ç., & Babayev J. Artificial Intelligence Techniques Used in Project Management. Advances in Artificial Intelligence Research, 2021, vol. 1, no. 1, pp. 1–5.Elmas Ç., & Babayev J. Artificial Intelligence Techniques Used in Project Management. Advances in Artificial Intelligence Research, 2021, vol. 1, no. 1, pp. 1–5.Elmousalami H.H. Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis. IEEE Transactions on Engineering Management, 2020, PP(99), pp. 1–14. https://doi.org/10.1109/TEM.2020.2972078.Elmousalami H.H. Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis. IEEE Transactions on Engineering Management, 2020, PP(99), pp. 1–14. https://doi.org/10.1109/TEM.2020.2972078.Balali A., Valipour A., Antucheviciene J., & Šaparauskas J. Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects. Symmetry, 2020, vol. 12, p. 1745. https://doi.org/10.3390/sym12101745.Balali A., Valipour A., Antucheviciene J., & Šaparauskas J. Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects. Symmetry, 2020, vol. 12, p. 1745. https://doi.org/10.3390/sym12101745.Narbaev T., Hazir Ö., Khamitova B., & Talgat S. A machine learning study to improve the reliability of project cost estimates. International Journal of Production Research, 2023, vol. 62, no. 12, pp. 4372–4388. https://doi.org/10.1080/00207543.2023.2262051.Narbaev T., Hazir Ö., Khamitova B., & Talgat S. A machine learning study to improve the reliability of project cost estimates. International Journal of Production Research, 2023, vol. 62, no. 12, pp. 4372–4388. https://doi.org/10.1080/00207543.2023.2262051.Ottaviani F.M., & De Marco A. Multiple linear regression model for improved project cost forecasting. Procedia Computer Science, 2022, pp. 808–815. https://doi.org/10.1016/j.procs.2021.12.079.Ottaviani F.M., & De Marco A. Multiple linear regression model for improved project cost forecasting. Procedia Computer Science, 2022, pp. 808–815. https://doi.org/10.1016/j.procs.2021.12.079.Warfield D., IS/IT research: a research methodologies review. Journal of theoretical & applied information technology, 2010, no.13.Warfield D., IS/IT research: a research methodologies review. Journal of theoretical & applied information technology, 2010, no.13.Operations Research & Scheduling Research Group (2023). Retrieved February 21, 2024, from: https://www.projectmanagement.ugent.be/research/data/realdataOperations Research & Scheduling Research Group (2023). Retrieved February 21, 2024, from: https://www.projectmanagement.ugent.be/research/data/realdataPMI, Project Management Body of Knowledge (PMBOK Guide). 6th edition. (PMI Publications, Newtown Square, 2017).PMI, Project Management Body of Knowledge (PMBOK Guide). 6th edition. (PMI Publications, Newtown Square, 2017).Wauters M., & Vanhoucke M. A comparative study of Artificial Intelligence methods for project duration forecasting. Expert Systems with Applications, 2016, vol. 46, pp. 249–26. https://doi.org/10.1016/j.eswa.2015.10.008.Wauters M., & Vanhoucke M. A comparative study of Artificial Intelligence methods for project duration forecasting. Expert Systems with Applications, 2016, vol. 46, pp. 249–26. https://doi.org/10.1016/j.eswa.2015.10.008.Shanmugasundar G., Vanitha M., Čep R., Kumar V., Kalita K., & Ramachandran M. A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining. Processes, 2021, vol. 9, p. 2015. https://doi.org/10.3390/pr9112015Shanmugasundar G., Vanitha M., Čep R., Kumar V., Kalita K., & Ramachandran M. A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining. Processes, 2021, vol. 9, p. 2015. https://doi.org/10.3390/pr9112015Shoushtari F., Daghighi A., & Ghafourian E. Application of Artificial Intelligence in Project Management. International journal of industrial engineering and operational research, 2024, vol. 6, no. 2, pp. 49–63.Shoushtari F., Daghighi A., & Ghafourian E. Application of Artificial Intelligence in Project Management. International journal of industrial engineering and operational research, 2024, vol. 6, no. 2, pp. 49–63.Radhoush S., Whitaker B.M., & Nehrir H. An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks. Energies, 2023, vol. 16, no. 16, p. 5972. https://doi.org/10.3390/en16165972.Radhoush S., Whitaker B.M., & Nehrir H. An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks. Energies, 2023, vol. 16, no. 16, p. 5972. https://doi.org/10.3390/en16165972.PMI, The Standard for Earned Value Management. 2nd Edition. (PMI Publications, Newtown Square, 2019).PMI, The Standard for Earned Value Management. 2nd Edition. (PMI Publications, Newtown Square, 2019).Gao C., Min X., Fang M., Tao T., Zheng X., Liu Y., Wu X., & Huang Z. Innovative Materials Science via Machine Learning. Advanced Functional Material, 2022, vol. 32, p. 2108044. https://doi.org/10.1002/adfm.202108044.Gao C., Min X., Fang M., Tao T., Zheng X., Liu Y., Wu X., & Huang Z. Innovative Materials Science via Machine Learning. Advanced Functional Material, 2022, vol. 32, p. 2108044. https://doi.org/10.1002/adfm.202108044.Capone C., & Narbaev T. Estimation of Risk Contingency Budget in Projects using Machine Learning. IFAC-PapersOnLine, 2022, vol. 55, no. 10, pp. 3238–3243. https://doi.org/10.1016/j.ifacol.2022.10.140.Capone C., & Narbaev T. Estimation of Risk Contingency Budget in Projects using Machine Learning. IFAC-PapersOnLine, 2022, vol. 55, no. 10, pp. 3238–3243. https://doi.org/10.1016/j.ifacol.2022.10.140.IPMA P. Artificial intelligence impact in project management, 2020. [Electronic resource]. From https://ipma.world/assets/IPMA_PwC_AI_Impact_in_PM_-_the_Survey_Report.pdf.IPMA P. Artificial intelligence impact in project management, 2020. [Electronic resource]. From https://ipma.world/assets/IPMA_PwC_AI_Impact_in_PM_-_the_Survey_Report.pdf.Mahesh B., Machine Learning Algorithms – A Review. International Journal of Science and Research, 2020, vol. 9, pp. 381–386. https://doi.org/10.21275/ART20203995.Mahesh B., Machine Learning Algorithms – A Review. International Journal of Science and Research, 2020, vol. 9, pp. 381–386. https://doi.org/10.21275/ART20203995.Capone C., Talgat S., Hazir O., Abdrasheva K., Kozhakhmetova A. Artificial Intelligence Models for Predicting Budget Expenditures. Eurasian Journal of Economic and Business Studies, 2024, vol. 68, no. 1, pp. 32–43. https://doi.org/10.47703/ejebs.v68i1.331.Capone C., Talgat S., Hazir O., Abdrasheva K., Kozhakhmetova A. Artificial Intelligence Models for Predicting Budget Expenditures. Eurasian Journal of Economic and Business Studies, 2024, vol. 68, no. 1, pp. 32–43. https://doi.org/10.47703/ejebs.v68i1.331.

The authors declare that there are no conflicts of interest present.