Uncertainties Prevailing in Construction Bid Documents and Their Impact on Project Pricing through the Analysis of Prebid Requests for Information
Publication: Journal of Management in Engineering
Volume 39, Issue 6
Abstract
Construction bid documents may contain uncertain or incomplete information that can affect project pricing as well as project performance, if not addressed prior to bidding. To resolve the uncertainties and clarify project requirements, the risk and uncertainties prevailing in the document should be identified at an early stage of the project life cycle. In this study, pre-bid request for information (RFI) is utilized as a key clue to quantify project ambiguities and uncertainties of a bid document, as pre-bid RFI is generated by bidders when any ambiguous or incomplete information is encountered in the bid document. Despite the significance of pre-bid RFI in quantifying project uncertainty, studies considering pre-bid RFI to identify project uncertainty are limited. Driven by document-based analysis, this study aims to investigate what uncertainties are frequently encountered in bid documents and how they affect project pricing. To achieve the research goal, this study will (1) identify the prevailing risks/uncertainties in the bid document; (2) determine the most common risks/uncertainties and their impacts on bid price; and (3) verify the significance of pre-bid RFIs in bid uncertainty prediction models. To achieve these objectives, public project data from US state Departments of Transportation (DOTs) were collected and used for frequency analysis, correlation testing, and machine learning-based prediction models. The results of uncertainty prediction models showed that uncertainties driven by pre-bid RFI analysis can improve the project risk prediction up to 15%, verifying the significance of RFIs in the bid price prediction model. This study will contribute to the construction management body of knowledge by clarifying the likelihood of errors and uncertainties that should be checked before bidding, thereby proactively preventing future design changes, claims, and dispute risks.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding authors upon reasonable request.
References
Abotaleb, I. S., and I. H. El-adaway. 2017. “Construction bidding markup estimation using a multistage decision theory approach.” J. Constr. Eng. Manage. 143 (1): 04016079. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001204.
Brogan, E., B. Shrestha, C. M. Clevenger, and P. P. Shrestha. 2022. “State transportation agencies’ current practices in providing design information for design-build projects during procurement.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 14 (1): 03721004. https://doi.org/10.1061/(ASCE)la.1943-4170.0000521.
Carr, P. G. 2005. “Investigation of bid price competition measured through prebid project estimates, actual bid prices, and number of bidders.” J. Constr. Eng. Manage. 131 (11): 1165–1172. https://doi.org/10.1061/ASCE0733-93642005131:111165.
Cunningham, P., and S. J. Delany. 2021. “k-Nearest neighbour classifiers—A tutorial.” ACM Comput. Surv. 54 (6): 1–25. https://doi.org/10.1145/3459665.
Daoud, O. E., and E. N. Allouche. 2003. “Bid queries as a gauge for quality control of documents.” In Proc., Annual Conf.—Canadian Society for Civil Engineering, 17–25. Surrey, BC, Canada: Canadian Society for Civil Engineering.
Dosumu, O. S. 2018. “Perceived effects of prevalent errors in contract documents on construction projects.” Constr. Econ. Build. 18 (1): 1–26. https://doi.org/10.5130/AJCEB.v18i1.5663.
Duzkale, A. K., and G. Lucko. 2016. “Exposing uncertainty in bid preparation of steel construction cost estimating: I. Conceptual framework and qualitative C-I-V-I-L classification.” J. Constr. Eng. Manage. 142 (10): 04016049. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001117.
Iowa DOT. 2022. “Plans and estimation proposals.” Accessed November 5, 2022. https://iowadot.gov/contracts/plans-and-estimation-proposals.
Jarkas, A. M., S. A. Mubarak, and C. Y. Kadri. 2014. “Critical factors determining bid/no bid decisions of contractors in Qatar.” J. Manage. Eng. 30 (4): 05014007. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000223.
Laryea, S. 2011. “Quality of tender documents: Case studies from the UK.” Construct. Manage. Econ. 29 (3): 275–286. https://doi.org/10.1080/01446193.2010.540019.
Lee, J., and J.-S. Yi. 2017. “Predicting project’s uncertainty risk in the bidding process by integrating unstructured text data and structured numerical data using text mining.” Appl. Sci. 7 (11): 1141. https://doi.org/10.3390/app7111141.
Leśniak, A., and E. Plebankiewicz. 2015. “Modeling the decision-making process concerning participation in construction bidding.” J. Manage. Eng. 31 (2): 04014032. https://doi.org/10.1061/(ASCE)me.1943-5479.0000237.
Liu, M., and Y. Y. Ling. 2005. “Modeling a contractor’s markup estimation.” J. Constr. Eng. Manage. 131 (4): 391–399. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:4(391).
Moselhi, O., and T. Hegazy. 1993. “Markup estimation using neural network methodology.” Comput. Syst. Eng. 4 (2–3): 135–145. https://doi.org/10.1016/0956-0521(93)90039-Y.
Occhipinti, A., L. Rogers, and C. Angione. 2022. “A pipeline and comparative study of 12 machine learning models for text classification.” Expert Syst. Appl. 201 (Sep): 117193. https://doi.org/10.1016/j.eswa.2022.117193.
Polat, G., B. N. Bingol, A. P. Gurgun, and B. Yel. 2016. “Comparison of ANN and MRA approaches to estimate bid mark-up size in public construction projects.” Procedia Eng. 164 (Jan): 331–338. https://doi.org/10.1016/j.proeng.2016.11.627.
Rastegar, H., B. Arbab Shirani, S. H. Mirmohammadi, and E. Akhondi Bajegani. 2021. “Stochastic programming model for bidding price decision in construction projects.” J. Constr. Eng. Manage. 147 (4): 04021025. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002008.
Rumsey, D. J. 2021. “Dummies: A Wiley brand 2021.” Accessed December 25, 2022. https://www.dummies.com/article/academics-the-arts/math/statistics/how-to-interpret-a-correlation-coefficient-r-169792/.
Shafahi, A., and A. Haghani. 2014. “Modeling contractors’ project selection and markup decisions influenced by eminence.” Int. J. Project Manage. 32 (8): 1481–1493. https://doi.org/10.1016/j.ijproman.2014.01.013.
Shrestha, B., P. P. Shrestha, R. Maharjan, and D. Gransberg. 2022. “Cost, change order, and schedule performance of highway projects.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 14 (1): 04521044. https://doi.org/10.1061/(ASCE)la.1943-4170.0000523.
Shrestha, P. P., and R. Maharjan. 2018. “Effects of change orders on cost growth, schedule growth, and construction intensity of large highway projects.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 10 (3): 04518012. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000264.
Shrestha, R., and J. Lee. 2022. “Investigation on uncertainty in construction bid documents.” In Proc., 9th Int. Conf. on Construction Engineering and Project Management (ICCEPM 2022), 67–73. Las Vegas, NV: International Conference on Construction Engineering and Project Management.
Sunday, D. O., and A. O. Afolarin. 2013. “Causes, effects and remedies of errors in Nigerian construction documents.” Organ. Technol. Manage. Constr. 5 (1): 676–686. https://doi.org/10.5592/otmcj.2013.1.4.
Takano, Y., N. Ishii, and M. Muraki. 2018. “Determining bid markup and resources allocated to cost estimation in competitive bidding.” Autom. Constr. 85 (Jan): 358–368. https://doi.org/10.1016/j.autcon.2017.06.007.
Williams, T. P., and J. Gong. 2014. “Predicting construction cost overruns using text mining, numerical data and ensemble classifiers.” Autom. Constr. 43 (Jul): 23–29. https://doi.org/10.1016/j.autcon.2014.02.014.
Information & Authors
Information
Published In
Journal of Management in Engineering
Volume 39 • Issue 6 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jan 23, 2023
Accepted: Jun 16, 2023
Published online: Aug 23, 2023
Published in print: Nov 1, 2023
Discussion open until: Jan 23, 2024
ASCE Technical Topics:
- Bids
- Business management
- Construction engineering
- Construction management
- Continuum mechanics
- Contracts and subcontracts
- Data analysis
- Disaster risk management
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Life cycles
- Methodology (by type)
- Motion (dynamics)
- Practice and Profession
- Pricing
- Project management
- Research methods (by type)
- Risk management
- Solid mechanics
- Uncertainty principles
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Yasser Jezzini, Rayan H. Assaad, Modeling the Impact of Low-Carbon Procurement on Bidding Dynamics, Journal of Management in Engineering, 10.1061/JMENEA.MEENG-5997, 40, 4, (2024).