Dynamic Modeling for Analyzing Cost Overrun Risks in Residential Projects
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 3
Abstract
The primary concern in the residential projects industry is to implement a project with the optimal quality within the planned schedule and planned budget. Simulation is considered an effective tool for analyzing the construction process in residential projects because it considers behavior of feedback as well as the frequent changes with time. This study aimed to develop a system dynamics (SD) model to estimate the overall cost overrun risks in addition to allowing decision makers to anticipate the feedback of indefinite numbers of what-if questions considering highly dynamic uncertainty and dynamic risk interactions influencing residential project performance that traditional methods may miss. Major risks that can cause cost overruns in residential projects were initially identified through a review of related literature, a questionnaire survey, and several workshop rounds. Then, the model boundaries’ variables were identified, and a qualitative model was created through a causal loop diagram (CLD). Afterward, mathematical equations and relationships between variables were established in a stock-flow diagram. The proposed model consists of five subsystems to simulate how changes in dynamics of inspecting the completed works from the contractor’s and the consultant’s points of view; the probability of scope change; the material resources management process; the workforce hire-quit cycle; and the work-rework cycle can influence the cost performance of a residential project. The model was tested and validated using data derived from a residential project located in Egypt, and testing results showed that the simulated behavior of the model is the same as the actual behavior of the project. The sensitivity analysis results showed that increasing workforce more than the desired, scope changes, and rework rate are the parameters that cause the largest cost performance regression in residential projects.
Practical Applications
Simulation is considered an effective tool for analyzing the construction process in residential projects because it considers the behavior of feedback as well as frequent changes with time. In this research, a SD model is presented to estimate the overall cost overrun risks in addition to allowing decision makers to anticipate the feedback of indefinite numbers of what-if questions. The whole model is divided into five subsystems, which simplified the whole system into some more understandable ones. The model provides a framework for uncertain events that may influence cost performance in the construction industry such as the impact of global events (e.g., Russia–Ukraine war) on the supplies of building materials and their prices. The proposed system dynamics model provided a material-resource subsystem to simulate those kinds of global events and their influence on cost performance on construction projects.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
References
Al-Kofahi, Z. G., A. Mahdavian, and A. Oloufa. 2022. “System dynamics modeling approach to quantify change orders impact on labor productivity 1: Principles and model development comparative study.” Int. J. Construct. Manage. 22 (7): 1355–1366. https://doi.org/10.1080/15623599.2020.1711494.
Alzraiee, H., T. Zayed, and O. Moselhi. 2015. “Dynamic planning of construction activities using hybrid simulation.” Autom. Constr. 49 (Part B): 176–192. https://doi.org/10.1016/j.autcon.2014.08.011.
Ansari, R. 2019. “Dynamic simulation model for project change-management policies: Engineering project case.” J. Constr. Eng. Manage. 145 (7): 05019008. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001664.
Badawy, M. 2021. “Second-order confirmatory factor analysis model for estimating the overall risk of change orders in road projects.” J. Eng. Des. Technol. https://doi.org/10.1108/JEDT-09-2020-0383.
Badawy, M., F. Alqahtani, and H. Hafez. 2022. “Identifying the risk factors affecting the overall cost risk in residential projects at the early stage.” Ain Shams Eng. J. 13 (2): 101586. https://doi.org/10.1016/j.asej.2021.09.013.
Badawy, M., A. Hussein, S. M. Elseufy, and K. Alnaas. 2021. “How to predict the rebar labours’ production rate by using ANN model?” Int. J. Construct. Manage. 21 (4): 427–438. https://doi.org/10.1080/15623599.2018.1553573.
Barlas, Y. 1996. “Formal aspects of model validity and validation in system dynamics.” Syst. Dyn. Rev. J. Syst. Dyn. Soc. 12 (3): 183–210. https://doi.org/10.1002/(SICI)1099-1727(199623)12:3%3C183::AID-SDR103%3E3.0.CO;2-4.
Bekr, G. A. 2015. “Causes of delay in public construction projects in Iraq.” Jordan J. Civ. Eng. 9 (2): 149–162.
Charoenngam, C., and E. Sriprasert. 2001. Assessment of cost control systems: A case study of Thai construction organizations.” Eng. Constr. Architect. Manage 8 (5–6): 368–380. https://doi.org/10.1108/eb021197.
Chritamara, S., S. O. Ogunlana, and N. Bach. 2002. System dynamics modeling of design and build construction projects.” Constr. Innovation 2 (4): 269–295. https://doi.org/10.1108/14714170210814801.
Dabirian, S., S. Abbaspour, M. Khanzadi, and M. Ahmadi. 2022. “Dynamic modelling of human resource allocation in construction projects.” Int. J. Constr. Manage. 22 (2): 182–191. https://doi.org/10.1080/15623599.2019.1616411.
Ford, D. N. 1995. The dynamics of project management: An investigation of the impacts of projects process and coordination on performance. Boston: Massachusetts Institute of Technology.
Forrester, J. W. 1997. “Industrial dynamics.” J. Oper. Res. Soc. 48 (10): 1037–1041. https://doi.org/10.1057/palgrave.jors.2600946.
Forrester, J. W. 2009. Some basic concepts in system dynamics. Cambridge, MA: Sloan School of Management Massachusetts Institute of Technology.
Fung, H. P., and H. Siow. 2013. “Relationship between team satisfaction and project performance as perceived by project managers in Malaysia: A mixed methods study.” Open J. Soc. Sci. Res. OJSSR 1 (9): 238–249. https://doi.org/10.12966/ojssr.12.02.2013.
García-Fornieles, J. M., I. S. Fan, A. Perez, C. Wainwright, and K. Sehdev. 2003. “A cost engineering approach to manage unplanned work—An example from aircraft modification.” AIAA J. 147 (8): 1121–1129.
Gregoriades, A., and B. Karakostas. 2004. “Unifying business objects and system dynamics as a paradigm for developing decision support systems.” Decis. Support Syst. 37 (2): 307–311. https://doi.org/10.1016/S0167-9236(03)00004-6.
Habibi, M., S. Kermanshachi, and E. Safapour. 2018. “Engineering, procurement and construction cost and schedule performance leading indicators: State-of-the-art review.” In Proc., Construction Research Congress, 2–4. Reston, VA: ASCE. https://doi.org/10.1061/9780784481271.037.
Han, S., P. Love, and F. Peña-Mora. 2013. “A system dynamics model for assessing the impacts of design errors in construction projects.” Math. Comput. Modell. 57 (9–10): 2044–2053. https://doi.org/10.1016/j.mcm.2011.06.039.
Hartley, J. R. 2017. Concurrent engineering: Shortening lead times, raising quality, and lowering costs. New York: Routledge. https://doi.org/10.1201/9781315140186.
Jiang, Z., D. Fang, and M. Zhang. 2015. “Understanding the causation of construction workers’ unsafe behaviors based on system dynamics modeling.” J. Manage. Eng. 31 (6): 04014099. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000350.
Jin, X., X. Xu, X. Xiang, Q. Bai, and Y. Zhou. 2016. “System-dynamic analysis on socio-economic impacts of land consolidation in China.” Habitat Int. 56 (Aug): 166–175. https://doi.org/10.1016/j.habitatint.2016.05.007.
Khodeir, L. M., and A. El Ghandour. 2019. “Examining the role of value management in controlling cost overrun [application on residential construction projects in Egypt].” Ain Shams Eng. J. 10 (3): 471–479. https://doi.org/10.1016/j.asej.2018.11.008.
Kim, S., S. Chang, and D. Castro-Lacouture. 2020. “Dynamic modeling for analyzing impacts of skilled labor shortage on construction project management.” J. Manage. Eng. 36 (1): 04019035. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000720.
Li, C. Z., J. Hong, C. Fan, X. Xu, and G. Q. Shen. 2018a. “Schedule delay analysis of prefabricated housing production: A hybrid dynamic approach.” J. Cleaner Prod. 195 (Sep): 1533–1545. https://doi.org/10.1016/j.jclepro.2017.09.066.
Li, C. Z., X. Xu, G. Q. Shen, C. Fan, X. Li, and J. Hong. 2018b. “A model for simulating schedule risks in prefabrication housing production: A case study of six-day cycle assembly activities in Hong Kong.” J. Cleaner Prod. 185 (Jun): 366–381. https://doi.org/10.1016/j.jclepro.2018.02.308.
Li, Q., L. Zhang, L. Zhang, and S. Jha. 2021. “Exploring multi-level motivations towards green design practices: A system dynamics approach.” Sustainable Cities Soc. 64 (Jan): 102490. https://doi.org/10.1016/j.scs.2020.102490.
Love, P. E., P. Mandal, J. Smith, and H. Li. 2000. “Modelling the dynamics of design error induced rework in construction.” Construct. Manage. Econ. 18 (5): 567–574. https://doi.org/10.1080/014461900407374.
Love, P. E. D. 2002. “Auditing the indirect consequences of rework in construction: A case based approach.” Manage. Auditing J. 17 (3): 138–146. https://doi.org/10.1108/02686900210419921.
Motawa, I., C. Anumba, S. Lee, and F. Peña-Mora. 2007. “An integrated system for change management in construction.” Autom. Constr. 16 (3): 368–377. https://doi.org/10.1016/j.autcon.2006.07.005.
Nguyen, L. D., and S. O. Ogunlana. 2005. “Modeling the dynamics of an infrastructure project.” Comput.-Aided Civ. Infrastruct. Eng. 20 (4): 265–279. https://doi.org/10.1111/j.1467-8667.2005.00392.
Ogunlana, S. O., H. Li, and F. A. Sukhera. 2003. “System dynamics approach to exploring performance enhancement in a construction organization.” J. Constr. Eng. Manage. 129 (5): 528–536. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:5(528).
Oliva, R. 2003. “Model calibration as a testing strategy for system dynamics models.” Eur. J. Oper. Res. 151 (3): 552–568. https://doi.org/10.1016/S0377-2217(02)00622-7.
Ozcan-Deniz, G., and Y. Zhu. 2016. “A system dynamics model for construction method selection with sustainability considerations.” J. Cleaner Prod. 121 (May): 33–44. https://doi.org/10.1016/j.jclepro.2016.01.089.
Rodrigues, L. L., N. Dharmaraj, and B. S. Rao. 2006. “System dynamics approach for change management in new product development.” Manage. Res. News. https://doi.org/10.1108/01409170610692824.
Shafiei, I., E. Eshtehardian, F. Nasirzadeh, and S. Arabi. 2020. “Dynamic modeling to reduce the cost of quality in construction projects.” Int. J. Construct. Manage. 1–14. https://doi.org/10.1080/15623599.2020.1845425.
Sing, M. C., P. E. Love, D. Edwards, and J. Liu. 2016. “Dynamic modeling of workforce planning for infrastructure projects.” J. Manage. Eng. 32 (6): 04016019. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000463.
Sonawane, R. 2004. Applying system dynamics and critical chain methods to develop a modern construction project management system. Kingsville, TX: Texas A&M Univ.
Sterman, J. 2000. Business dynamics: System thinking and modeling for complex world. Boston: McGraw-Hill.
Vorakulpipat, C., Y. Rezgui, and C. J. Hopfe. 2010. “Value creating construction virtual teams: A case study in the construction sector.” Autom. Constr. 19 (2): 142–147. https://doi.org/10.1016/j.autcon.2009.11.016.
Wang, J., and H. Yuan. 2017. “System dynamics approach for investigating the risk effects on schedule delay in infrastructure projects.” J. Manage. Eng. 33 (1): 04016029. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000472.
Xu, X., J. Wang, C. Z. Li, W. Huang, and N. Xia. 2018. “Schedule risk analysis of infrastructure projects: A hybrid dynamic approach.” Autom. Constr. 95 (Nov): 20–34. https://doi.org/10.1016/j.autcon.2018.07.026.
Yuan, H., L. Shen, J. J. Hao, and W. Lu. 2011. “A model for cost–benefit analysis of construction and demolition waste management throughout the waste chain.” Resour. Conserv. Recycl. 55 (6): 604–612. https://doi.org/10.1016/j.resconrec.2010.06.004.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 3 • September 2022
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© 2022 American Society of Civil Engineers.
History
Received: Feb 2, 2022
Accepted: May 8, 2022
Published online: Jul 13, 2022
Published in print: Sep 1, 2022
Discussion open until: Dec 13, 2022
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