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Price recalculation model of construction contracts

Abstract

In recent years, the world economy has experienced rapid economic change in the construction sector and record inflation. The rapidly rising prices of construction materials and machinery in the construction market are also pushing up the cost of construction services. The main problem is that construction projects have been drawn up and contracts between clients and contractors have been awarded at previous prices, and it is therefore necessary to look for opportunities to index the price of construction contracts. The aim of the study is to propose a mathematical model and a smartphone application for the price recalculation of construction projects. The success of a construction project depends on the correct decisions taken at the stage of preparation and implementation of the procurement documents for construction contracts, a complex process requiring a lot of legal and technological knowledge. The proposed mathematical model would help clients and contractors to carry out the calculation of the price according to the construction price index quickly and without the need for extensive specialist technical expertise. The mathematical model is adapted to smartphones with Android software.


Article in Lithuanian.


Statybos rangos sutarčių kainos perskaičiavimo modelis


Santrauka


Pastaraisiais metais pasaulio ekonomika patyrė spartų statybos šakos ekonomikos pokytį bei rekordinę infliaciją. Statybų rinkoje sparčiai kilo statybinių medžiagų bei mechanizmų kainos, didėjo statybinių paslaugų kainos. Didžiausia problema tai, kad statybos projektai parengti ir rangos darbų sutartys tarp užsakovų ir rangovų sudarytos ankstesnėmis kainomis, todėl būtina ieškoti galimybių statybos rangos sutarčių kainai indeksuoti. Tyrimo tikslas – pasiūlyti matematinį statybos projekto kainos perskaičiavimo modelį ir programėlę išmaniesiems telefonams. Statybos projekto sėkmė priklauso nuo teisingų sprendimų, priimtų statybos rangos darbų pirkimų dokumentų rengimo ir įgyvendinimo etape. Tai yra sudėtingas ir daug teisinių, technologinių žinių reikalaujantis procesas. Siūlomas matematinis modelis padėtų užsakovams ir rangovams greitai ir be didelės specialistų techninės kompetencijos atlikti kainos perskaičiavimą pagal statybos kainų indeksą. Matematinis modelis pritaikytas išmaniesiems telefonams su „Android“ programine įranga.


Reikšminiai žodžiai: statybos rangos sutartis, statybos rangos darbų kaina, kainos perskaičiavimas, matematinis modelis.

Keyword : construction contract, purchase price of construction contracts, price recalculation, mathematical model

How to Cite
Vilkonis, A., & Antuchevičienė, J. (2024). Price recalculation model of construction contracts. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 16. https://doi.org/10.3846/mla.2024.19221
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May 28, 2024
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References

Alavipour, S. M. R., & Arditi, D. (2018). Optimizing financing cost in construction projects with fixed project duration. Journal of Construction Engineering and Management, 144(4), 04018012-1–04018012-13. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001451

Arditi, D., & Pattanakitchamroon, T. (2008). Analysis methods in time-based claims. Journal of Construction Engineering and Management, 134, 242–252. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:4(242)

Chou, J. S., Pham, A. D., & Wang, H. (2013). Bidding strategy to support decision-making by integrating fuzzy AHP and regression-based simulation. Automation in Construction, 35, 517–527. https://doi.org/10.1016/j.autcon.2013.06.007

Elsayegh, A., Dagli, C. H., & El-Adaway, I. H. (2020). An agent-based model to study competitive construction bidding and the winner’s curse. Procedia Computer Science, 168, 147–153. https://doi.org/10.1016/j.procs.2020.02.278

Huang, Z. X. (2016). Modeling bidding decision in engineering field with incomplete information: A static game–based approach. Advances in Mechanical Engineering, 8(1), 1–8. https://doi.org/10.1177/168781401562483

Hwang, B. G., Zhao, X., & Goh, K. J. (2014). Investigating the client-related rework in building projects: The case of Singapore. International Journal of Project Management, 32, 698–708. https://doi.org/10.1016/j.ijproman.2013.08.009

Kainodara. (2022). https://www.kainodara.lt

Li, H. (1996). Neural network models for intelligent support of mark-up estimation. Engineering, Construction and Architectural Management, 3(1/2), 69–81. https://doi.org/10.1108/eb021023

Lorentziadis, P. L. (2016). Optimal bidding in auctions from a game theory perspective. European Journal of Operational Research, 248(2), 347–371. https://doi.org/10.1016/j.ejor.2015.08.012

Love, P. E. D., Wang, X., Sing, C., & Tiong, R. L. K. (2013). Determining the probability of cost overruns. Journal of Construction Engineering and Management, 139, 321–330. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000575

Majer, R., Ellingerová, H., & Gašparík, J. (2020). Methods for the calculation of the lost profit in construction contracts. Buildings, 10, Article 74. https://doi.org/10.3390/buildings10040074

Marzouk, M., & Moselhi, O. (2003). A decision support tool for construction bidding. Construction Innovation, 3(2), 111–124. https://doi.org/10.1108/14714170310814882

Mohemad, R., Hamdan, A. R., Othman, Z. A., & Noor, N. M. M. (2010). Decision support systems (DSS) in construction tendering processes. International Journal of Computer Science, 7(7), 35–45. https://arxiv.org/abs/1004.3260

Nandini, S. S., Varadharajan, R., Kumar, S. K., & Krishnaraj, L. (2022). Effective ways to handle the change management for cost in various types of contracts. In L. Y. Loon, M. Subramaniyan, K., & Gunasekaran (Eds.), Lecture notes in civil engineering: Vol. 191. Advances in construction management (pp. 501–511). Springer. https://doi.org/10.1007/978-981-16-5839-6_43

Nguyen, V. T., Do, S. T., Tran, C. N. N., & Vo, M. D. (2022). Assessing the impact of the traits of payment delay causes on subcontractor’s business performance in construction projects: A case study in Vietnam. International Journal of Construction Management, 1–11. https://doi.org/10.1080/15623599.2022.2152980

Omopariola, E. D., Windapo, A., Edwards, D. J., & Thwala, W. D. (2020). Contractors’ perceptions of the effects of cash flow on construction projects. Journal of Engineering, Design and Technology, 18, 308–325. https://doi.org/10.1108/JEDT-04-2019-0099

Plebankiewicz, E., Zima, K., & Wieczorek, D. (2021). Modelling of time, cost and risk of construction with using fuzzy logic. Journal of Civil Engineering and Management, 27(6), 412–426. https://doi.org/10.3846/jcem.2021.15255

Rashidi, A., Tamošaitienė, J., Ravanshadnia, M., & Sarvari, H. (2023). A scientometric analysis of construction bidding research activities. Buildings, 13(1), Article 220. https://doi.org/10.3390/buildings13010220

Rothkopf, M. H., & Harstad, R. M. (1994). Modeling competitive bidding: A critical essay. Management Science, 40(3), 364–384. https://doi.org/10.1287/mnsc.40.3.364

Smith, J., Edwards, D. J., Martek, I., Chileshe, N., Hayhow, S., & Roberts, C. J. (2023). The antecedents of construction project change: An analysis of design and build procurement application. Journal of Engineering, Design and Technology, 21(3), 655–689. https://doi.org/10.1108/JEDT-12-2020-0507

Sonmez, R., Ahmadisheykhsarmast, S., & Güngör, A. A. (2022). BIM integrated smart contract for construction project progress payment administration. Automation in Construction, 139, Article 104294. https://doi.org/10.1016/j.autcon.2022.104294

Vilkonis, A. (2023). Analysis of public procurement for building contracts. Mokslas – Lietuvos ateitis [Science – Future of Lithuania], 15, 1–5. https://doi.org/10.3846/mla.2023.16913

Vilkonis, A., Antucheviciene, J., & Kutut, V. (2023). Construction contracts quality assessment from the point of view of contractor and customer. Buildings, 13, Article 1154. https://doi.org/10.3390/buildings13051154

Wang, W. C., Dzeng, R. J., & Lu, Y. H. (2007). Integration of simulation‐based cost model and multi‐criteria evaluation model for bid price decisions. Computer‐Aided Civil and Infrastructure Engineering, 22(3), 223–235. https://doi.org/10.1111/j.1467-8667.2007.00480.x

Wibowo, M. A., Astana, I. N. Y., & Rusdi, H. A. (2015). An analysis of bidding strategy, project performance and company performance relationship in construction. Procedia Engineering, 125, 95–102. https://doi.org/10.1016/j.proeng.2015.11.015

Zavadskas, E. K., Vilutienė, T., Turskis, Z., & Tamošaitienė, J. (2010). Contractor selection for construction works by applying SAW-G and TOPSIS Grey techniques. Journal of Business Economic and Management, 11(1), 34–55. https://doi.org/10.3846/jbem.2010.03

Zhang, G., Zhang, G., Gao, Y., & Lu, J. (2011). Competitive strategic bidding optimization in electricity markets using bilevel programming and swarm technique. IEEE Transactions on Industrial Electronics, 58(6), 2138–2146. https://doi.org/10.1109/TIE.2010.2055770