Who Risks and Wins? - Simulated Cost Variance in Sustainable Construction Projects

[EN] More and more construction projects are closed before they ever start. Among the most significant reasons for project failures is cost risk. Construction companies have many problems with reliable cost management. Rising demands of the key market players insist on making construction projects more sustainable according to the simultaneous improvement of the economic, environmental and social responsiveness dimensions. In order to investigate these problems, a four-phase research methodology has been followed consisting of: (1) literature review to identify research trends and gaps, (2) survey to construction experts to detect their subjective perspectives about risk costs and analyse the corresponding costs structure for the investment in sustainable projects, (3) simulations based on Monte Carlo simulation with an author's methodology for calculating the cost risk with an additional statistical analysis, (4) ending questionnaire to obtain the final feedback from the experts and the validation of obtained results. A contribution to the development of knowledge about cost risk is the observation that the changing probability distributions of individual cost-generating components may include both economic as well as technological and organizational aspects. Thus, with the proposed approach, often complex, global challenges of sustainable construction projects can be tackled in an accessible way.Statutory research at the UTP University of Science and Technology, Bydgoszcz, Poland.Górecki, J.; Díaz-Madroñero Boluda, FM. (2020). Who Risks and Wins? - Simulated Cost Variance in Sustainable Construction Projects. Sustainability. 12(8):1-31. https://doi.org/10.3390/su12083370S131128Wong, J. M. W., Thomas Ng, S., & Chan, A. P. C. (2010). Strategic planning for the sustainable development of the construction industry in Hong Kong. Habitat International, 34(2), 256-263. doi:10.1016/j.habitatint.2009.10.002Sobotka, A. (2017). Innovative solutions in engineering of construction projects. Procedia Engineering, 208, 160-165. doi:10.1016/j.proeng.2017.11.034Kaplinski, O. (2013). Risk Management of Construction Works by Means of the Utility Theory: A Case Study. Procedia Engineering, 57, 533-539. doi:10.1016/j.proeng.2013.04.068Diekmann, J. E., & Featherman, W. D. (1998). Assessing Cost Uncertainty: Lessons from Environmental Restoration Projects. Journal of Construction Engineering and Management, 124(6), 445-451. doi:10.1061/(asce)0733-9364(1998)124:6(445)Špačková, O., Novotná, E., Šejnoha, M., & Šejnoha, J. (2013). Probabilistic models for tunnel construction risk assessment. Advances in Engineering Software, 62-63, 72-84. doi:10.1016/j.advengsoft.2013.04.002Wang, W.-C., Wang, S.-H., Tsui, Y.-K., & Hsu, C.-H. (2012). A factor-based probabilistic cost model to support bid-price estimation. Expert Systems with Applications, 39(5), 5358-5366. doi:10.1016/j.eswa.2011.11.049Alwan, Z., Jones, P., & Holgate, P. (2017). Strategic sustainable development in the UK construction industry, through the framework for strategic sustainable development, using Building Information Modelling. Journal of Cleaner Production, 140, 349-358. doi:10.1016/j.jclepro.2015.12.085Chen, Y., Okudan, G. E., & Riley, D. R. (2010). Sustainable performance criteria for construction method selection in concrete buildings. Automation in Construction, 19(2), 235-244. doi:10.1016/j.autcon.2009.10.004Opoku, D.-G. J., Ayarkwa, J., & Agyekum, K. (2019). Barriers to environmental sustainability of construction projects. Smart and Sustainable Built Environment, 8(4), 292-306. doi:10.1108/sasbe-08-2018-0040Freire-Guerrero, A., Alba-Rodríguez, M. D., & Marrero, M. (2019). A budget for the ecological footprint of buildings is possible: A case study using the dwelling construction cost database of Andalusia. Sustainable Cities and Society, 51, 101737. doi:10.1016/j.scs.2019.101737Cheng, W., Sodagar, B., & Sun, F. (2017). Comparative analysis of environmental performance of an office building using BREEAM and GBL. International Journal of Sustainable Development and Planning, 12(03), 528-540. doi:10.2495/sdp-v12-n3-528-540Wang, G. B., He, G. Y., & Bian, L. (2011). Sustainable Construction Project under Lean Construction Theory. Advanced Materials Research, 250-253, 3345-3349. doi:10.4028/www.scientific.net/amr.250-253.3345Zhong, Z. Y., & Chen, Y. G. (2011). Principles of Sustainable Construction Project Management Based on Lean Construction. Advanced Materials Research, 225-226, 766-770. doi:10.4028/www.scientific.net/amr.225-226.766Rafindadi, A. D., Mikić, M., Kovačić, I., & Cekić, Z. (2014). Global Perception of Sustainable Construction Project Risks. Procedia - Social and Behavioral Sciences, 119, 456-465. doi:10.1016/j.sbspro.2014.03.051Solís-Guzmán, J., Rivero-Camacho, C., Alba-Rodríguez, D., & Martínez-Rocamora, A. (2018). Carbon Footprint Estimation Tool for Residential Buildings for Non-Specialized Users: OERCO2 Project. Sustainability, 10(5), 1359. doi:10.3390/su10051359Baldry, D. (1998). The evaluation of risk management in public sector capital projects. International Journal of Project Management, 16(1), 35-41. doi:10.1016/s0263-7863(97)00015-xRanasinghe, M. (1994). Contingency allocation and management for building projects. Construction Management and Economics, 12(3), 233-243. doi:10.1080/01446199400000031Plebankiewicz, E., Zima, K., & Wieczorek, D. (2016). Life Cycle Cost Modelling of Buildings with Consideration of the Risk. Archives of Civil Engineering, 62(2), 149-166. doi:10.1515/ace-2015-0071Heralova, R. S. (2014). Life Cycle Cost Optimization Within Decision Making on Alternative Designs of Public Buildings. Procedia Engineering, 85, 454-463. doi:10.1016/j.proeng.2014.10.572Hwang, B.-G., Shan, M., Phua, H., & Chi, S. (2017). An Exploratory Analysis of Risks in Green Residential Building Construction Projects: The Case of Singapore. Sustainability, 9(7), 1116. doi:10.3390/su9071116Lee, J. K., Han, S. H., Jang, W., & Jung, W. (2017). «Win-win strategy» for sustainable relationship between general contractors and subcontractors in international construction projects. KSCE Journal of Civil Engineering, 22(2), 428-439. doi:10.1007/s12205-017-1613-7Artto, K. A., Lehtonen, J.-M., & Saranen, J. (2001). Managing projects front-end: incorporating a strategic early view to project management with simulation. International Journal of Project Management, 19(5), 255-264. doi:10.1016/s0263-7863(99)00082-4Walȩdzik, K., & Mańdziuk, J. (2018). Applying hybrid Monte Carlo Tree Search methods to Risk-Aware Project Scheduling Problem. Information Sciences, 460-461, 450-468. doi:10.1016/j.ins.2017.08.049Van Slyke, R. M. (1963). Letter to the Editor—Monte Carlo Methods and the PERT Problem. Operations Research, 11(5), 839-860. doi:10.1287/opre.11.5.839Chau, K. W. (1995). Monte Carlo simulation of construction costs using subjective data. Construction Management and Economics, 13(5), 369-383. doi:10.1080/01446199500000042Beeston *, D. (1986). Combining risks in estimating. Construction Management and Economics, 4(1), 75-79. doi:10.1080/01446198600000005Górecki, J., & Płoszaj, E. (2019). Cost risk of construction of small hydroelectric power plants. MATEC Web of Conferences, 262, 07004. doi:10.1051/matecconf/201926207004Zhang, H. Y., & Yang, G. B. (2011). Review of Study on Risk Management for the Construction Project. Advanced Materials Research, 243-249, 6404-6409. doi:10.4028/www.scientific.net/amr.243-249.6404Xia, N., Zou, P. X. W., Griffin, M. A., Wang, X., & Zhong, R. (2018). Towards integrating construction risk management and stakeholder management: A systematic literature review and future research agendas. International Journal of Project Management, 36(5), 701-715. doi:10.1016/j.ijproman.2018.03.006Siraj, N. B., & Fayek, A. R. (2019). Risk Identification and Common Risks in Construction: Literature Review and Content Analysis. Journal of Construction Engineering and Management, 145(9), 03119004. doi:10.1061/(asce)co.1943-7862.0001685Díaz-Madroñero, M., Mula, J., & Peidro, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research, 52(17), 5171-5205. doi:10.1080/00207543.2014.899721Díaz-Madroñero, M., Peidro, D., & Mula, J. (2015). A review of tactical optimization models for integrated production and transport routing planning decisions. Computers & Industrial Engineering, 88, 518-535. doi:10.1016/j.cie.2015.06.010Li, B., Akintoye, A., Edwards, P. J., & Hardcastle, C. (2005). Perceptions of positive and negative factors influencing the attractiveness of PPP/PFI procurement for construction projects in the UK. Engineering, Construction and Architectural Management, 12(2), 125-148. doi:10.1108/09699980510584485Zou, P. X. W., Zhang, G., & Wang, J. (2007). Understanding the key risks in construction projects in China. International Journal of Project Management, 25(6), 601-614. doi:10.1016/j.ijproman.2007.03.001Mohamed, F. D. (2012). Integrating Risk Assessment in Planning for Sustainable Infrastructure Projects. ICSDEC 2012. doi:10.1061/9780784412688.042Taylan, O., Bafail, A. O., Abdulaal, R. M. S., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116. doi:10.1016/j.asoc.2014.01.003Chou, J.-S., & Le, T.-S. (2014). Probabilistic multiobjective optimization of sustainable engineering design. KSCE Journal of Civil Engineering, 18(4), 853-864. doi:10.1007/s12205-014-0373-xDziadosz, A., Tomczyk, A., & Kapliński, O. (2015). Financial Risk Estimation in Construction Contracts. Procedia Engineering, 122, 120-128. doi:10.1016/j.proeng.2015.10.015Lee, S., & Kim, K. (2015). Collar Option Model for Managing the Cost Overrun Caused by Change Orders. Sustainability, 7(8), 10649-10663. doi:10.3390/su70810649Kankhva, V. (2016). Methodic Approaches to Cost Evaluation of Innovation Projects in Underground Development. Procedia Engineering, 165, 1305-1309. doi:10.1016/j.proeng.2016.11.855Badi, S. M., & Pryke, S. (2016). Assessing the impact of risk allocation on sustainable energy innovation (SEI). International Journal of Managing Projects in Business, 9(2), 259-281. doi:10.1108/ijmpb-10-2015-0103Ayub, B., Thaheem, M. J., & Din, Z. ud. (2016). Dynamic Management of Cost Contingency: Impact of KPIs and Risk Perception. Procedia Engineering, 145, 82-87. doi:10.1016/j.proeng.2016.04.021Ali, Z., Zhu, F., & Hussain, S. (2018). Risk Assessment of Ex-Post Transaction Cost in Construction Projects Using Structural Equation Modeling. Sustainability, 10(11), 4017. doi:10.3390/su10114017Baudrit, C., Taillandier, F., Tran, T. T. P., & Breysse, D. (2018). Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models. Computer-Aided Civil and Infrastructure Engineering, 34(2), 97-115. doi:10.1111/mice.12391Flyvbjerg, B., Ansar, A., Budzier, A., Buhl, S., Cantarelli, C., Garbuio, M., … van Wee, B. (2018). Five things you should know about cost overrun. Transportation Research Part A: Policy and Practice, 118, 174-190. doi:10.1016/j.tra.2018.07.013Cantarelli, C. C., van Wee, B., Molin, E. J. E., & Flyvbjerg, B. (2012). Different cost performance: different determinants? Transport Policy, 22, 88-95. doi:10.1016/j.tranpol.2012.04.002Cantarelli, C. C., Molin, E. J. E., van Wee, B., & Flyvbjerg, B. (2012). Characteristics of cost overruns for Dutch transport infrastructure projects and the importance of the decision to build and project phases. Transport Policy, 22, 49-56. doi:10.1016/j.tranpol.2012.04.001Skamris, M. K., & Flyvbjerg, B. (1997). Inaccuracy of traffic forecasts and cost estimates on large transport projects. Transport Policy, 4(3), 141-146. doi:10.1016/s0967-070x(97)00007-3Flyvbjerg, B., Skamris holm, M. K., & Buhl, S. L. (2003). How common and how large are cost overruns in transport infrastructure projects? Transport Reviews, 23(1), 71-88. doi:10.1080/01441640309904Plebankiewicz, E. (2018). Model of Predicting Cost Overrun in Construction Projects. Sustainability, 10(12), 4387. doi:10.3390/su10124387Cavalieri, M., Cristaudo, R., & Guccio, C. (2019). On the magnitude of cost overruns throughout the project life-cycle: An assessment for the Italian transport infrastructure projects. Transport Policy, 79, 21-36. doi:10.1016/j.tranpol.2019.04.001Li, S., Lu, Y., Kua, H. W., & Chang, R. (2020). The economics of green buildings: A life cycle cost analysis of non-residential buildings in tropic climates. Journal of Cleaner Production, 252, 119771. doi:10.1016/j.jclepro.2019.119771Švajlenka, J., & Kozlovská, M. (2020). Evaluation of the efficiency and sustainability of timber-based construction. Journal of Cleaner Production, 259, 120835. doi:10.1016/j.jclepro.2020.120835Švajlenka, J., Kozlovská, M., & Pošiváková, T. (2018). Analysis of Selected Building Constructions Used in Industrial Construction in Terms of Sustainability Benefits. Sustainability, 10(12), 4394. doi:10.3390/su10124394Lei, Z., Tang, W., Duffield, C., Zhang, L., Hui, F., & You, R. (2018). Qualitative Analysis of the Occupational Health and Safety Performance of Chinese International Construction Projects. Sustainability, 10(12), 4344. doi:10.3390/su10124344Yang, Y., Tang, W., Shen, W., & Wang, T. (2019). Enhancing Risk Management by Partnering in International EPC Projects: Perspective from Evolutionary Game in Chinese Construction Companies. Sustainability, 11(19), 5332. doi:10.3390/su11195332Kapelko, M., Oude Lansink, A., & Stefanou, S. E. (2014). Assessing dynamic inefficiency of the Spanish construction sector pre- and post-financial crisis. European Journal of Operational Research, 237(1), 349-357. doi:10.1016/j.ejor.2014.01.047Sfakianaki, E., Iliadis, T., & Zafeiris, E. (2015). Crisis management under an economic recession in construction: the Greek case. International Journal of Management and Decision Making, 14(4), 373. doi:10.1504/ijmdm.2015.07401

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