English
Received 19.07.2024, Revised 30.10.2024, Accepted 05.12.2024
The increasing complexity of construction projects and growing competitive pressure in the industry have created a critical need to enhance planning methods and resource allocation in construction management. The purpose of this study was to develop a methodology for improving the efficiency of construction process planning through the integration of digital technologies and algorithmic optimisation methods. The study was based on an analysis of the implementation of digital planning methods in seven leading international construction companies in 2023-2024, using data from technical documentation for deployed systems and financial statements. The adoption of integrated digital platforms reduced planning time by 45-52% and minimised equipment downtime by 38-40%. The use of graph databases optimised the sequencing of construction operations, while predictive analytics improved forecasting accuracy for work deadlines to 92%. The integration of artificial intelligence (AI) and Internet of Things (IoT) technologies lowered logistics costs by 25-33% and reduced warehouse inventories by 42%. The implementation of building information modelling (BIM) technologies, combined with collaborative planning systems, increased productivity by 25% and reduced schedule deviations by 32%. Furthermore, the development of a digital twin system enabled process modelling at the planning stage, facilitating early detection of potential issues and reducing error correction costs by 28%. The application of predictive analytics allowed a shift from reactive to proactive resource management through risk forecasting and automatic plan adjustments. The proposed phased methodology for digital solution implementation, incorporating a technology audit and personnel development programmes, can be applied by construction companies of various sizes to optimise planning processes and enhance operational efficiency
digital twins; intelligent systems; graph databases; manufacturing processes; collaborative planning
[1] Aizenberg, T. (2024). Modern trends in the application of AI tools in international human resource management. Economics and Society, 65. doi: 10.32782/2524-0072/2024-65-48.
[2] Althoey, F., Waqar, A., Alsulamy, S.H., Khan, A.M., Alshehri, A., Falqi, I.I., Abuhussain, M., & Abuhussain, M.A. (2024). Influence of IoT implementation on resource management in construction. Heliyon, 10(15), article number e32193. doi: 10.1016/j.heliyon.2024.e32193.
[3] Amer, F., Koh, H.Y., & Golparvar-Fard, M. (2021). Automated methods and systems for construction planning and scheduling: Critical review of three decades of research. Journal of Construction Engineering and Management, 147(7), article number 03121002. doi: 10.1061/(ASCE)CO.1943-7862.0002093.
[4] Bakker, H., Dunke, F., & Nickel, S. (2020). A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice. Omega, 96, article number 102080. doi: 10.1016/j.omega.2019.06.006.
[5] Balfour Beatty. (2025). Results, reports and presentations. Retrieved from https://www.balfourbeatty.com/investors/results-reports-and-presentations/.
[6] Bouygues Construction. (2025). Integrated report. Retrieved from https://www.bouygues.com/en/integrated-report/.
[7] Bründl, P., Stoidner, M., Nguyen, H.G., Abrass, A., & Franke, J. (2024). Optimization of process, knowledge, and manufacturing management in customized production: A graph-based approach for manufacturing planning. IFAC-PapersOnLine, 58(19), 1174-1179. doi: 10.1016/j.ifacol.2024.09.103.
[8] Bugrov, O.V. (2024). Management, expertise, and audit of construction projects: Lecture notes. Kyiv: Kyiv National University of Civil Engineering and Architecture. Retrieved from https://repositary.knuba.edu.ua/handle/123456789/13331.
[9] Deloitte. (2025). Integrated Annual Report. Retrieved from https://www.deloitte.com/global/en/services/audit.html.
[10] Ghandour, O., El Kafhali, S., & Hanini, M. (2024). Adaptive workload management in cloud computing for service level agreements compliance and resource optimization. Computers and Electrical Engineering, 120, article number 109712. doi: 10.1016/j.compeleceng.2024.109712.
[11] Guo, K., & Zhang, L. (2022). Multi-objective optimization for improved project management: Current status and future directions. Automation in Construction, 139, article number 104256. doi: 10.1016/j.autcon.2022.104256.
[12] Hachaturyan, O.S. (2020). Management in construction. Kropyvnytskyi: Central Ukrainian National Technical University. Retrieved from https://dspace.kntu.kr.ua/handle/123456789/10388.
[13] Hochtief. (2025). Financial reports and presentations. Retrieved from https://www.hochtief.com/investor-relations/financial-reports-and-presentations.
[14] ISO 29481-1:2016 “Building Information Models – Information Delivery Manual. Part 1: Methodology and Format”. (2016). Retrieved from https://www.iso.org/standard/67557.html.
[15] ISO/IEC/IEEE 15288:2015 “Systems and Software Engineering – System Life Cycle Processes”. (2015). Retrieved from https://www.iso.org/standard/63711.html.
[16] Jiang, Y., Su, S., Zhao, S., Zhong, R.Y., Qiu, W., Skibniewski, M.J., Brilakis, I., & Huang, G.Q. (2024). Digital twin-enabled synchronized construction management: A roadmap from construction 4.0 towards future prospects. Developments in the Built Environment, 19, article number 100512. doi: 10.1016/j.dibe.2024.100512.
[17] Lin, J.C.-W., Lv, Q., Yu, D., Srivastava, G., & Chen, C.-H. (2022). Optimized scheduling of resource constraints in projects for smart construction. Information Processing & Management, 59(5), article number 103005. doi: 10.1016/j.ipm.2022.103005.
[18] Maruthi, S., Dodda, S.B., Yellu, R.R., Thuniki, P., & Reddy, S.R. (2022). Automated planning and scheduling in AI: Studying automated planning and scheduling techniques for efficient decision-making in artificial intelligence. African Journal of Artificial Intelligence and Sustainable Development, 2(2), 14-25.
[19] Nesterov, V. (2023). Integration of artificial intelligence technologies in data engineering: Challenges and prospects in the modern information environment. Bulletin of Cherkasy State Technological University, 28(4), 82-92. doi: 10.62660/2306-4412.4.2023.82-90.
[20] Pariafsai, F., & Behzadan, A.H. (2021). Core competencies for construction project management: Literature review and content analysis. Journal of Civil Engineering Education, 147(4), article number 04021010. doi: 10.1061/(ASCE)EI.2643-9115.0000051.
[21] Parsamehr, M., Perera, U.S., Dodanwala, T.C., Perera, P., & Ruparathna, R. (2023). A review of construction management challenges and BIM-based solutions: Perspectives from the schedule, cost, quality, and safety management. Asian Journal of Civil Engineering, 24(1), 353-389. doi: 10.1007/s42107-022-00501-4.
[22] Polusmiak, Y., Pavliuk, T., & Kosach, I. (2024). Project management in the construction industry: Identifying logistics problems and ways to optimize them. Management and Entrepreneurship: Trends of Development, 4(30), 69-78. doi: 10.26661/2522-1566/2024-4/30.
[23] Radwan, K., Elhakeem, A., & Elbeltagi, E. (2024). Resource assignment optimization in design firms. Ain Shams Engineering Journal, 15(4), article number 102612. doi: 10.1016/j.asej.2023.102612.
[24] Rane, N. (2023). Integrating Building Information Modelling (BIM) and Artificial Intelligence (AI) for smart construction schedule, cost, quality, and safety management: Challenges and opportunities. doi: 10.2139/ssrn.4616055.
[25] Royal BAM Group. (2025). Annual reports. Retrieved from https://www.bam.com/en/investors/annual-reports.
[26] Sarker, E., Halder, P., Seyedmahmoudian, M., Jamei, E., Horan, B., Mekhilef, S., & Stojcevski, A. (2021). Progress on the demand side management in smart grid and optimization approaches. International Journal of Energy Research, 45(1), 36-64. doi: 10.1002/er.5631.
[27] Sbiti, M., Beddiar, K., Beladjine, D., Perrault, R., & Mazari, B. (2021). Toward BIM and LPS data integration for lean site project management: A state-of-the-art review and recommendations. Buildings, 11(5), article number 196. doi: 10.3390/buildings11050196.
[28] Skanska. (2025). Reports & publications. Retrieved from https://group.skanska.com/investors/reports-publications/.
[29] Strabag. (2025). Results and reports. Retrieved from https://www.strabag.com/en/investor-relations-en/financial-publications/results-and-reports.
[30] Taghaddos, M., Mousaei, A., Taghaddos, H., Hermann, U., Mohamed, Y., & AbouRizk, S. (2024). Optimized variable resource allocation framework for scheduling of fast-track industrial construction projects. Automation in Construction, 158, article number 105208. doi: 10.1016/j.autcon.2023.105208.
[31] Tang, L., Wu, T., & Li, Q. (2024). Construction and optimization strategy for collaborative governance of construction waste resource utilization. KSCE Journal of Civil Engineering, article number 100034. doi: 10.1016/j.kscej.2024.100034.
[32] Vinci Construction. (2025). Reports and presentations. Retrieved rom https://www.vinci.com/finances/investisseurs/information-financiere/rapports-et-presentations?f%5B0%5D=annee%3A2023&f%5B1%5D=financial_type%3AA.
[33] Wang, L., Zhao, Y., & Yin, X. (2023). Precast production scheduling in off-site construction: Mainstream contents and optimization perspective. Journal of Cleaner Production, 405, article number 137054. doi: 10.1016/j.jclepro.2023.137054.
[34] Wang, Z., & Xu, H. (2024). Scheduling redundancy optimization for precast production to enhance disruption management in prefabricated construction. Computers & Industrial Engineering, 192, article number 110251. doi: 10.1016/j.cie.2024.110251.
[35] Xu, M., Mei, Z., Luo, S., & Tan, Y. (2020). Optimization algorithms for construction site layout planning: A systematic literature review. Engineering, Construction and Architectural Management, 28(10), 2956-2978. doi: 10.1108/ECAM-08-2019-0457.
[36] Yu, H., Zhang, X., Yu, S., & Yang, Z. (2021). Evolutionary game model for optimization of urban land use structure in smart cities. Journal of Cleaner Production, 280, article number 124929. doi: 10.1016/j.jclepro.2020.124929.
Українська