Risk and Price in the Bidding Process of Contractors

Laryea, Samuel; Hughes, Will

doi:10.1061/(asce)co.1943-7862.0000293

Cited by 67 publications

(64 citation statements)

References 27 publications

(32 reference statements)

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“…The way in which risk is accounted for in the bidding process was studied in [16]. The types of uncertainty that occur in the bidding document of construction projects were defined in [8], and the factors that cause cost overruns were identified.…”

Section: Literature Reviewmentioning

confidence: 99%

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Predicting Project’s Uncertainty Risk in the Bidding Process by Integrating Unstructured Text Data and Structured Numerical Data Using Text Mining

Lee

2017

Applied Sciences

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Abstract:The construction bidding process takes place in the early stages of a construction project. The bidding process involves many uncertainties since the construction risk factors are predicted and reflected in the bid price before construction commences. Therefore, bidders should thoroughly understand the uncertainty factors of the project before make bidding decisions. The uncertainty risk of construction projects is determined by the content that is contained in the bidding document. If the information provided in the bidding document is not accurate and is unclear, the uncertainty of the projects increases. Thus, this study is performed to predict risks in the bidding process of construction projects by analyzing the uncertainty of the bidding document and using it as factors to predict a project's bidding risk. To achieve this, bidding risk prediction modeling was conducted using the pre-bid clarification information of each project. In addition, text mining on pre-bid RFI documents, which are in an unstructured text data format, was performed and the results of text mining were used as major influencing factors for the risk prediction models. As a result, the accuracy of the risk prediction model including text data was improved (72.92%) when compared to the prediction model using only numeric data (52.08%). The results of this study are expected to strengthen the possibility of further similar studies in the future since it enhances the predictive accuracy by incorporating the uncertainty of the bidding document, which is rarely considered in previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

“…Such research can be divided into the study of model development to support bid decision making [4,5,[10][11][12][13][14] and study to determine bid cost and contingencies in consideration of bidding risks [8,15,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Project’s Uncertainty Risk in the Bidding Process by Integrating Unstructured Text Data and Structured Numerical Data Using Text Mining

Lee

2017

Applied Sciences

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“…Although other formal and analytical risk models have recently been developed to prescribe how risk is to be incorporated into bids (particularly in construction bids) [55,56,57,58], in practice, price risks are usually excluded from the final bid in order to improve competitiveness [59].…”

Section: Assumptionsmentioning

confidence: 99%

Detecting abnormal and collusive bids in capped tendering

Ballesteros‐Pérez

González-Cruz

Cañavate-Grimal

et al. 2013

Automation in Construction

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Recent developments in the area of Bid Tender Forecasting have enabled bidders to implement new types of easy-to-use tools for increasing their chances of winning contracts. Although these new tools (such as iso-Score Curve Graphs, Scoring Probability Graphs, and Position Probability Graphs) are designed for bidders in capped tendering (tenders with an upper price limit), some of their principles can also be applied by a Contracting Authority to detect which bidders do not follow a standard pattern, that is, their bids are extremely high or low. Since a collusive bid generally needs to be sufficiently high or low to make an impact on the bid distribution, any person in charge of supervising capped tenders can be alerted to any bidder that might be involved in a cartel after identifying the same abnormal behavior in a series of tenders through simple calculations and a new type of graph.

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“…Generally, the components of mark-up include profit, risk contingencies, and recovery of general overheads. The actual process of how contractors determine their bidding price, which includes mark up, is not clearly articulated in the literature (Laryea and Hughes, 2011). Selection of an appropriate mark-up for a new construction project is a very complex decision-making process (Ahmad, 1988).…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Mark-up Estimation Model for Syrian Contractors

Wanous¹,

Boussabaine²

2017

IJAEC

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. AbstractOne of the most important decisions that have to be made by construction contractors is how much to mark-up the estimated cost of a new project. The main objectives of this paper are to model the relationship between mark-up estimation and the key factors affecting it and to compare the application of regression analysis and neural network techniques on the mark-up decision making process in order to find which technique is more reliable in terms of accuracy and robustness. The most influential mark-up factors were identified through a formal questionnaire survey conducted among Syrian contractors. Subsequently, data on one hundred and eleven real-life bidding situations was collected from Syria. Ninety-six of these projects were used to develop linear, non-linear regression and neural network mark-up models. The remaining fifteen projects were randomly held-back for validating the developed models. The neural network model proved to be robust and more accurate than the regression models. Although this study was carried out in the context of the Syrian construction industry, the methodology and the findings have much broader geographical applicability.

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Risk and Price in the Bidding Process of Contractors

Cited by 67 publications

References 27 publications

Predicting Project’s Uncertainty Risk in the Bidding Process by Integrating Unstructured Text Data and Structured Numerical Data Using Text Mining

Predicting Project’s Uncertainty Risk in the Bidding Process by Integrating Unstructured Text Data and Structured Numerical Data Using Text Mining

Detecting abnormal and collusive bids in capped tendering

A Neural Network Mark-up Estimation Model for Syrian Contractors

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