ORIGINAL ARTICLE
Forecasting the Course of Cumulative Cost Curves for
Different Construction Projects
1
Wroclaw University of Science and Technology, Faculty of Civil Engineering, Department of Building Engineering
Acceptance date: 2023-05-05
Online publication date: 2023-07-11
Publication date: 2023-07-11
Civil and Environmental Engineering Reports 2023;33(1):71-89
KEYWORDS
TOPICS
ABSTRACT
Planning the course of cumulative cost curves and effectively monitoring the implementation process and the incurred financial outlays are still significant problems in the management of construction projects. This is particularly noticeable during the execution phase of construction works. Therefore, it is worthwhile to correctly determine the shape of the cost curve before starting this stage and to periodically examine its fitting to the scheduled course of the budgeted cost curve, the envelope of cost curves characterised by the best-fit curve. There are many methods of forecasting and estimating the costs of construction works, but they are very often complicated and require the decision-maker to use and elaborate mathematical tools. The aim of the research was to determine the shape and course of the cost curves for selected construction projects. Based on the analysis of the collected data on investment projects in 3 facilities research groups (collective housing, hotels and retail service facilities), an original attempt was made to determine the best fit curve and the area of the curve, which in turn indicates the limits of the correct planning of the cumulative costs of construction projects. The Three Sigma rule was applied, correlations and determinants were determined, and the area of the cost curves was described with a third degree polynomial. The conducted research showed that: 1. the optimal formula for determining the best-fit curve, which allow to determine the cost and time of construction works, is a 3-degree polynomial; 2. cost curves, within a certain bounding box, determine the area of the most likely cash flow; 3. when planning the course of a cost curve, it is advisable to use the bounding box of cost curves rather than a single, model, theoretical, or empirical mathematical expression describing the cost curve.
REFERENCES (64)
1.
Szafranko, E and Harasymiuk, J 2022. Modelling of Decision Processes in Construction Activity. Applied Sciences 12, 3797.
2.
Bozejko, W, Hejducki, Z, Uchroński, M. and Wodecki, M 2014. Solving resource-constrained construction scheduling problems with overlaps by metaheuristic. Journal of Civil Engineering and Management 20, 649–659.
3.
Bozejko, W, Hejducki, Z and Wodecki, M 2019. Flowshop scheduling of construction processes with uncertain parameters. Archives of Civil and Mechanical Engineering 19, 194–204.
4.
Plebankiewicz, E, Zima, K and Wieczorek, D 2021. Modelling of time, cost and risk of construction with using fuzzy logic. Journal of Civil Engineering and Management 27, 412–426.
5.
Kowacka, M, Skorupka, D, Duchaczek, A, Waniewska, A and Dudziak-Gajowiak, D 2019. Risk analysis in surveying works related to roads construction. Scientific Review Engineering and Environmental Sciences 28 , 377–382.
6.
Radziszewska-Zielina, E, Adamkiewicz, D, Szewczyk, B and Kania, O 2022. Decision-making support for housing projects in post-industrial areas. Sustainability 14, 3573.
7.
Hsieh, T-Y, Hsiao-Lung Wang, M and Chen, C-W 2004. A case study of S-curve regression method to project control of construction management via T-S fuzzy model. Journal of Marine Science and Technology 12, 209–216.
8.
Sztubecka, M, Skiba, M, Mrówczyńska, M and Mathias M 2020. Noise as a Factor of Green Areas Soundscape Creation. Sustainability 12(3), 999.
9.
Konior, J 2019. Fuzziness over randomness in unforeseen works of construction projects. Civil Engineering and Architecture 7 , 42–48.
10.
Konior, J and Szóstak M 2020. Methodology of planning the course of the cumulative cost curve in construction projects. Sustainability 12 , 2347.
11.
Mrówczyńska, M, Skiba, M, Bazan-Krzywoszańska, A, Bazuń, D and Kwiatkowski M 2018. Social and infrastructural conditioning of lowering energy costs and improving the energy efficiency of buildings in the context of the local energy policy. Energies 11, 2302.
12.
Leśniak, A and Plebankiewicz, E 2015. Modeling the decision-making process concerning participation in construction bidding. Journal of Management in Engineering 31(2).
13.
Kasprowicz, T 2017. Quantitative assessment of construction risk. Archives of Civil Engineering 63, 55–62.
14.
Kasprowicz, T 2017. Quantitative identification of construction risk. Archives of Civil Engineering 63, 63–75.
15.
Kerzner, H 2003 Project Management: A systems approach to planning, scheduling, and controlling, John Wiley&Sons, Inc., New York, USA.
16.
Połoński, M 2018. Management of construction investment process, Wydawnictwo SGGW (in Polish).
17.
Jaśkowski, P, Biruk, S and Krzemiński, M 2020. Planning repetitive construction processes to improve robustness of schedules in risk environment. Archives of Civil Engineering 66, 643–657.
18.
Skrzypczak, I, Oleniacz, G, Leśniak, A, Zima, K, Mrówczyńska, M and Kazak, J. 2022. Scan-to-BIM method in construction: Assessment of the 3D buildings model accuracy in terms inventory measurements. Building Research & Information 50(8), 859-880.
19.
Leśniak, A, Kubek, D, Plebankiewicz, E, Zima, K and Belniak, S 2018. Fuzzy AHP application for supporting contractors’ bidding decision. Symmetry 10, 642.
20.
Leśniak, A 2015. Classification of the Bid/No bid criteria-factor analysis. Archives of Civil Engineering 61(4), 79-90.
21.
Konior, J 2015. Enterprise’s risk assessment of complex construction projects. Archives of Civil Engineering 61, 63–74.
22.
Lo, W and Chen, Y-T 2007. Optimization of contractor’s S-curve. 24th International Symposium on Automation & Robotics in Construction (ISARC 2007), 417–420.
23.
Project Management Institute 2017. A guide to the project management body of knowledge (PMBOK guide) 6th Edition, Project Management Institute (PMI).
25.
Chen, H-L, Chen, W-T and Lin, L 2016. Earned value project management: Improving the predictive power of planned value. International Journal of Project Management 34, 22–29.
26.
Przywara, D and Rak, A 2021. Monitoring of time and cost variances of schedule using simple Earned Value Method indicators. Applied Sciences 11, 1357.
27.
Konior, J 2019. Monitoring of construction projects feasibility by Bank Investment Supervision approach. Civil Engineering and Architecture 7, 31–35.
28.
Dziadosz, A, Kapliński, O and Rejment, M 2014. Usefulness and fields of the application of the Earned Value Management in the implementation of construction projects. Budownictwo i Architektura 13, 357–364.
29.
Czarnigowska, A 2008. Earned value method as a tool for project control. Budownictwo i Architektura 3, 15–32.
30.
Czarnigowska, A 2009. Monitoring of project progress using the Earned Value. Przegląd Budowlany (in Polish) 80, 50–55.
31.
Howes, R 2000. Improving the performance of Earned Value Analysis as a construction project management tool, Engineering. Construction and Architectural Management 7, 399–411.
32.
Lipke, W, Zwikael, O, Henderson, K and Anbari, F 2009. Prediction of project outcome. The application of statistical methods to earned value management and earned schedule performance indexes. International Journal of Project Management 27, 400–407.
33.
Chen, Z, Demeulemeester, E, Bai, S and Guo, Y 2020. A Bayesian approach to set the tolerance limits for a statistical project control method. International Journal of Production Research 58, 3150–3163.
34.
Salari, M, Bagherpour, M and Reihani, M 2015. A time-cost trade-off model by incorporating fuzzy earned value management: A statistical based approach. Journal of Intelligent & Fuzzy Systems 28, 1909–1919.
35.
Salari, M and Khamooshi, H 2016. A better project performance prediction model using fuzzy time series and data envelopment analysis. Journal of the Operational Research Society 67, 1274–1287.
36.
Hajali-Mohamad, M-T, Mosavi, M-R and Shahanaghi, K 2016. Optimal estimating the project completion time and diagnosing the fault in the project. DYNA 83, 121–127.
37.
Yaseen, Z-M, Ali, Z-H, Salih, S-Q and Al-Ansari N 2020 Prediction of risk delay in construction projects using a hybrid artificial intelligence model. Sustainability 12, 1514.
38.
Chao, L-C, and Chen, H-T 2015. Predicting project progress via estimation of S-curve’s key geometric feature values. Automation in Construction 57, 33–41.
39.
Cristóbal, J 2017. The S-curve envelope as a tool for monitoring and control of projects. Procedia Computer Science 121, 756–761.
40.
Tijanić, K and Car-Pušić D 2017. Application of S-curve in EVA Method. 13th International Conference “Organization, Technology and Management in Construction, 103–115.
41.
Cheng, M-Y, Tsai, H-C, and Liu, C-L 2009. Artificial intelligence approaches to achieve strategic control over project cash flows. Automation in Construction 18, 386–393.
42.
Kozień, E 2017. Application of approximation technique to on-line updating of the actual cost curve in the earned value method. Czasopismo Techniczne 9, 181–195.
43.
Mohagheghi, V, Meysam Mousavi, S and Vahdani, B 2017. An assessment method for project cash flow under interval-valued fuzzy environment. Journal of Optimization in Industrial Engineering 22, 79–80.
44.
Hsieh, T-Y, Wang, M, Chen, C-W, Chen, C-Y, Yu, S-E, Yang, H-C and Chen, T-H 2006. A new viewpoint of s-curve regression model and its application to construction management. International Journal on Artificial Intelligence Tools 15, 131–142.
45.
Chao, L-C, and Chien, C-F 2010. A model for updating project S-curve by using neural networks and matching progress. Automation in Construction 19, 84–91.
46.
Wang, K-C, Wang, W-C, Wang, H-H, Hsu, P-Y, Wu, W-H and Kung, C-J 2016. Applying building information modeling to integrate schedule and cost for establishing construction progress curves. Automation in Construction 72, 397–410.
47.
Maravas, A and Pantouvakis, J-P 2012. Project cash flow analysis in the presence of uncertainty in activity duration and cost. International Journal of Project Management 30, 374–384.
48.
Mohamad, H-M, Mohamad, M-I, Saad, I, Bolong, N, Mustazama, J and Razali, S-N-M 2021. A case study of s-curve analysis: Causes, effects, tracing and monitoring project extension of time. Civil Engineering Journal (Iran) 7, 649–661.
49.
Barraza, G-A, Back, W-E and Mata, F 2004. Probabilistic forecasting of roject performance using stochastic S curves. Journal of Construction Engineering and Management 130, 25–32.
50.
Yao, J-S, Chen, M-S and Lu, H-F 2006. A fuzzy stochastic single-period model for cash management. European Journal of Operational Research 170, 72–90.
51.
Kim, B-C and Reinschmidt, K 2007. An S-curve Bayesian model for forecasting probability distributions on project duration and cost at completion. 25th Inaugural Construction Management and Economics: 'Past, Present and Future' Conference, CME 2007 - Reading, United Kingdom, 1449–1459.
52.
Kim, B and Reinschmidt, KF 2009. Probabilistic forecasting of project duration using Bayesian inference and the beta distribution. Journal of Construction Engineering and Management 135, 178–186.
53.
Blyth, K and Kaka, A 2006. A novel multiple linear regression model for forecasting S-curves, Engineering. Construction and Architectural Management 13, 82–95.
54.
Banki, M-T and Esmaeeli, B 2008. Using historical data for forecasting s-curves at construction industry. IEEE International Conference on Industrial Engineering and Engineering Management, 282–286.
55.
Chao, L-C and Chien, C-F 2009. Estimating project S-curves using polynomial function and neural networks. Journal of Construction Engineering and Management 135, 169–177.
56.
Jiang, A, Issa, R and Malek, M 2011. Construction project cash flow planning using the Pareto optimality efficiency network model. Journal of Civil Engineering and Management 17, 510–519.
57.
Konior, J and Szóstak, M 2020. The S-curve as a tool for planning and controlling of construction process-case study. Applied Sciences 10.
58.
Szóstak, M 2021. Planning the time and cost of implementing construction projects using an example of residential buildings. Archives of Civil Engineering 67, 243–259.
59.
Ostojic-Skomrlj, N and Radujkovic, M 2012. S-curve modelling in early phases of construction projects. Gradevinar 64, 647–654.
60.
Peer, S 1982. Application of Cost-Flow Forecasting Models. Journal of the Construction Division 108, 226–232.
61.
Miskawi, Z 1989. An S-curve equation for project control. Construction Management and Economics 7(2), 115-124.
62.
Boussabaine, A-H and Elhag, T 1999. Applying fuzzy techniques to cash flow analysis. Construction Management and Economics 17(6), 745–755.
63.
Cioffi, D 2005. A tool for managing projects: an analytic parameterization of the S-curve. International Journal of Project Management 23, 215–222.
64.
Szóstak, M 2023. Best fit of cumulative cost curves at the planning and performed stages of construction projects. Buildings 13, 13.
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