Defining the Success Status of Construction Projects Based on Quantitative Performance Metrics Thresholds
Publication: Journal of Management in Engineering
Volume 39, Issue 2
Abstract
Despite the high spending and high employment, the construction industry struggles to agree how to go about project performance assessment and what constitutes a successful project. This does not allow stakeholders to address the issues in and drawbacks of their respective projects. Hence, this research outlines performance metrics upper limits, which can be used in defining a successful project. Data were collected from Construction Industry Institute (CII) members and multiple construction firms through University of Wisconsin–Madison active research projects. Two classification and regression tree (CART) models were developed using 4-fold randomized cross-validation to provide the metrics thresholds of success with a classification accuracy of 81% and 85%, respectively. CART models revealed that a successful project can be defined as having construction schedule growth less than 10.4%, construction cost growth less than 9.8%, requests for information (RFIs) per $million less than or equal to 8.6, and RFI processing time less than or equal to 7 days. Additional thresholds showed that a high-performing successful project would have a change orders per $million of less than 0.39 and rework of less than 1.5%. These thresholds can serve as a step toward a quantitative definition of success by translating qualitative success status into quantitative success status. Additionally, the established thresholds can be used as benchmarks for acceptable construction performance, which can then be adopted as risk thresholds or tolerances during project planning.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author upon reasonable request (Python code and raw data).
Acknowledgments
This work was partially funded by Construction Industry Institute Project No. RT-DCC-06, “Updating Project Delivery and Contract Strategies.” The authors would like to thank the industry experts who generously provided project data, participated in the project, and assisted the authors in testing and validating the survey.
References
Abas, M., S. B. Khattak, T. Habib, and U. Nadir. 2022. “Assessment of critical risk and success factors in construction supply chain: A case of Pakistan.” Int. J. Construct. Manage. 22 (12): 2258–2266. https://doi.org/10.1080/15623599.2020.1783597.
Aboseif, E. 2021. “Reaching a consensus among construction stakeholders: Defining success and benchmarking performance.” Master’s thesis, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin–Madison.
Aboseif, E., and R. Khallaf. 2020. “A framework for last planner system implementation in Egypt.” In Vol. 54 of Proc., CIGOS 2019, Innovation for Sustainable Infrastructure. Lecture Notes in Civil Engineering, edited by C. Ha-Minh. Singapore: Springer. https://doi.org/10.1007/978-981-15-0802-8_163.
Ahmed, M., and M. Garvin. 2022. “Review of critical success factors and key performance indicators in performance assessment of P3 transportation projects.” J. Manage. Eng. 38 (5): 04022045. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001070.
Alruqi, W. M., and M. R. Hallowell. 2019. “Critical success factors for construction safety: Review and meta-analysis of safety leading indicators.” J. Constr. Eng. Manage. 145 (3): 04019005. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001626.
Banihashemi, S., M. R. Hosseini, H. Golizadeh, and S. Sankaran. 2017. “Critical success factors (CSFs) for integration of sustainability into construction project management practices in developing countries.” Int. J. Project Manage. 35 (6): 1103–1119. https://doi.org/10.1016/j.ijproman.2017.01.014.
Barbosa, D., J. Woetzel, J. Mischke, M. J. Ribeirinho, M. Sridhar, M. Parsons, N. Bertram, and S. Brown. 2017. Reinventing construction: A route to higher productivity. Houston: McKinsey & Company.
Breiman, L. 1984. Classification and regression trees. Boca Raton, FL: CRC Press.
Chan, A. P. C., D. Scott, and A. P. L. Chan. 2004. “Factors affecting the success of a construction project.” J. Constr. Eng. Manage. 130 (1): 153–155. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(153).
Changali, S., A. Mohammad, and M. V. Nieuwland. 2015. The construction productivity imperative. Houston: McKinsey & Company.
Chen, W., X. Xie, J. Wang, B. Pradhan, H. Hong, D. T. Bui, Z. Duan, and J. Ma. 2017. “A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility.” Catena 151 (Apr): 147–160. https://doi.org/10.1016/j.catena.2016.11.032.
Chen, Y. Q., Y. Zhang, and S. J. Zhang. 2014. “Impacts of different types of owner–contractor conflict on cost performance in construction projects.” J. Constr. Eng. Manage. 140 (6). https://doi.org/10.1061/(ASCE)CO.1943-7862.0000852.
Cochran, W. G. 1977. Sampling techniques. New York: Wiley.
El Asmar, M., A. S. Hanna, and W.-Y. Loh. 2016. “Evaluating integrated project delivery using the project quarterback rating.” J. Constr. Eng. Manage. 142 (1): 04015046. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001015.
Elbashbishy, T. S., O. A. Hosny, A. F. Waly, and E. M. Dorra. 2022. “Assessing the impact of construction risks on cost overruns: A risk path simulation–driven approach.” J. Manage. Eng. 38 (6): 04022058. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001090.
Franz, B., R. Leicht, K. Molenaar, and J. Messner. 2017. “Impact of team integration and group cohesion on project delivery performance.” J. Constr. Eng. Manage. 143 (1): 04016088. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001219.
Garg, S., and S. Misra. 2021. “Distribution of rework issues in various reinforced concrete building components.” J. Perform. Constr. Facil. 35 (4): 04021033. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001596.
Hanna, A. S. 2016. “Benchmark performance metrics for integrated project delivery.” J. Constr. Eng. Manage. 142 (9): 04016040. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001151.
Hanna, A. S., and K. A. Iskandar. 2017. “Quantifying and modeling the cumulative impact of change orders.” J. Constr. Eng. Manage. 143 (10): 04017076. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001385.
Holt, G. D., and D. J. Edwards. 2005. “Domestic builder selection in the UK housing repair and maintenance sector: A critique.” J. Constr. Res. 6 (1): 123–137. https://doi.org/10.1142/S1609945105000298.
Ibrahim, M., A. Hanna, and D. Kievet. 2020. “Quantitative comparison of project performance between project delivery systems.” J. Manage. Eng. 36 (6): 04020082. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000837.
Ibrahim, M. W. 2016. “Modeling, benchmarking, and maximizing project delivery performance.” Master’s thesis, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin–Madison.
Jalloul, H., A. Hanna, and W. Lotfallah. 2022. “POP: A data-based construction project overall performance model.” In Proc., Construction Research Congress 2022, 734–743. Reston, VA: ASCE. https://doi.org/10.1061/9780784483978.075.
Khandelwal, M., D. J. Armaghani, R. S. Faradonbeh, M. Yellishetty, M. Z. A. Majid, and M. Monjezi. 2017. “Classification and regression tree technique in estimating peak particle velocity caused by blasting.” Eng. Comput. 33 (1): 45–53. https://doi.org/10.1007/s00366-016-0455-0.
Labib, Y. N., W. B. Lotfallah, A. S. Hanna, and N. W. Boulos. 2021. “Development and application of performance index for comparative assessment of public capital projects.” J. Constr. Eng. Manage. 147 (2): 04020175. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001992.
Lam, E. W. M., A. P. C. Chan, and D. W. M. Chan. 2007. “Benchmarking the performance of design-build projects: Development of project success index.” Benchmarking 14 (5): 624–638. https://doi.org/10.1108/14635770710819290.
Lattouf, M. G., F. J. Srour, and I. M. Srour. 2014. “Construction workforce management strategies to reduce absenteeism: A survey study.” In Proc., Construction Research Congress 2014, 827–836. Reston, VA: ASCE.
Le, N., O. Chong, and D. Kashiwagi. 2020. “Success factors for project risk management in construction projects: A Vietnam case study.” J. Adv. Perform. Inf. Value 12 (2): 63–80. https://doi.org/10.37265/japiv.v12i2.126.
Love, P. E. D., P. Teo, F. Ackermann, J. Smith, J. Alexander, E. Palaneeswaran, and J. Morrison. 2018. “Reduce rework, improve safety: An empirical inquiry into the precursors to error in construction.” Prod. Plann. Control 29 (5): 353–366. https://doi.org/10.1080/09537287.2018.1424961.
Luo, L., Q. He, J. Xie, D. Yang, and G. Wu. 2017. “Investigating the relationship between project complexity and success in complex construction projects.” J. Manage. Eng. 33 (2): 04016036. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000471.
Mpofu, B., E. G. Ochieng, C. Moobela, and A. Pretorius. 2017. “Profiling causative factors leading to construction project delays in the United Arab Emirates.” Eng. Constr. Archit. Manage. 24 (2): 346–376. https://doi.org/10.1108/ECAM-05-2015-0072.
Myles, A. J., R. N. Feudale, Y. Liu, N. A. Woody, and S. D. Brown. 2004. “An introduction to decision tree modeling.” J. Chemom. 18 (6): 275–285. https://doi.org/10.1002/cem.873.
Olawumi, T. O., and D. W. M. Chan. 2019. “Critical success factors for implementing building information modeling and sustainability practices in construction projects: A Delphi survey.” Sustainable Dev. 27 (4): 587–602. https://doi.org/10.1002/sd.1925.
Omoush, M. M. 2020. “Assessing and prioritizing the critical success factors and delays of project management implementation: Empirical evidence at construction projects in Jordan.” Int. J. Bus. Manage. 15 (10): 117–130. https://doi.org/10.5539/ijbm.v15n10p117.
Osei-Kyei, R., and A. P. C. Chan. 2017. “Developing a project success index for public–private partnership projects in developing countries.” J. Infrastruct. Syst. 23 (4): 04017028. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000388.
Pishdad-Bozorgi, P., and J. M. de la Garza. 2012. “Comparative analysis of design-bid-build and design-build from the standpoint of claims.” In Proc., Construction Research Congress 2012, 21–30. Reston, VA: ASCE.
Proverbs, D., G. Holt, and H. Cheok. 2000. “Construction industry problems: The views of UK construction directors.” Accessed April 20, 2022. https://www.arcom.ac.uk/-docs/proceedings/ar2000-073-081_Proverbs_Holt_and_Cheok.pdf.
Sanvido, V., F. Grobler, K. Parfitt, and M. Guvenis. 1992. “Critical success factors for construction projects.” J. Constr. Eng. Manage. 118 (1): 94–111. https://doi.org/10.1061/(ASCE)0733-9364(1992)118:1(94).
Shrestha, B., J. Choi, Y. Kwak, and J. Shane. 2021. “Recipes for standardized capital projects’ performance success.” J. Manage. Eng. 37 (4): 04021029. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000926.
Shrestha, P. P., and J. D. Fernane. 2016. “Performance of design-build and design-bid-build projects for public universities.” J. Constr. Eng. Manage. 143 (3): 04016101. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001241.
Simonson, K. 2021. “Construction data.” Associated General Contractors of America. Accessed April 25, 2022. https://www.agc.org/learn/construction-data.
Tabish, S., and K. Jha. 2011. “Important factors for success of public construction projects.” In Vol. 15 of Proc., 2nd Int. Conf. on Construction and Project Management 2011. Singapore: IACSIT Press.
Tripathi, K. K., and K. N. Jha. 2018. “Determining success factors for a construction organization: A structural equation modeling approach.” J. Manage. Eng. 34 (1): 04017050. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000569.
WEF (World Economic Forum) and Boston Consulting Group. 2016. Shaping the future of construction: A breakthrough in mindset and technology. Geneva: WEF.
Williams, T. 2016. “Identifying success factors in construction projects: A case study.” Project Manage. J. 47 (1): 97–112. https://doi.org/10.1002/pmj.21558.
Wuni, I. Y., and G. Q. Shen. 2020. “Critical success factors for modular integrated construction projects: A review.” Build. Res. Inf. 48 (7): 763–784. https://doi.org/10.1080/09613218.2019.1669009.
Yan, H., H. Elzarka, C. Gao, F. Zhang, and W. Tang. 2019. “Critical success criteria for programs in China: Construction companies’ perspectives.” J. Manage. Eng. 35 (1): 04018048. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000659.
Yap, J. B. H., H. Abdul-Rahman, and W. Chen. 2017. “Design change dynamics in building project: From literature review to a conceptual framework formulation.” J. Surv. Constr. Property 8 (1): 13–33. https://doi.org/10.22452/jscp.vol8no1.2.
Yap, J. B. H., I. N. Chow, and K. Shavarebi. 2019. “Criticality of construction industry problems in developing countries: Analyzing Malaysian projects.” J. Manage. Eng. 35 (5): 04019020. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000709.
Zhang, B., Z. Wei, J. Ren, Y. Cheng, and Z. Zheng. 2018. “An empirical study on predicting blood pressure using classification and regression trees.” IEEE Access 6 (Jan): 21758–21768. https://doi.org/10.1109/ACCESS.2017.2787980.
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Published In
Journal of Management in Engineering
Volume 39 • Issue 2 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 10, 2022
Accepted: Oct 11, 2022
Published online: Nov 30, 2022
Published in print: Mar 1, 2023
Discussion open until: Apr 30, 2023
ASCE Technical Topics:
- Business management
- Business organizations
- Construction companies
- Construction engineering
- Construction industry
- Construction management
- Data collection
- Employment
- Engineering fundamentals
- Measurement (by type)
- Methodology (by type)
- Metric systems
- Model accuracy
- Models (by type)
- Organizations
- Personnel management
- Practice and Profession
- Project management
- Research methods (by type)
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