Construction Bidding Markup Estimation using a Multistage Decision Theory Approach

Abstract

Determining an optimum bid value maximizes the probability of winning a construction project while realizing proper profit. Thus, this issue has been one of the important research topics in construction-related research. Various models have provided different methodological approaches for bid pricing using statistical analysis of competitors' prior bids. However, the accuracy of these models is compromised in cases where the data set of competitors' historic bids is not complete and/or where such competitors utilize a dynamic behavior (i.e., having bidding schemes that change significantly with time). Through a multistage decision theory approach, this paper presents a more advanced model for construction bidding markup estimation that uses a Bayesian analytic framework. To this effect, a three-stage research methodology was utilized. First, the authors established a systematic procedure to fit competitors' historical data into appropriate Bayesian prior density functions while taking the stochastic variability of cost estimates into consideration. Second, the authors developed stochastic likelihood functions through the most recent observation(s). Third, the authors created the posterior distributions from which the joint probability of winning and the expected profit can be calculated. To this end, the use of the Bayesian statistics in the model enables it to draw sound statistical inferences even in cases of data incompleteness and dynamic behaviors of competitors, thus tackling two important weak spots in the previous models. The proposed model was applied to two case studies from the literature with different scenarios to demonstrate its use and to illustrate the effect of different parameters on the resulting optimum markup. It was shown that the more recent bidding strategies of competitors play a significant role in predicting the future ones. Also, as the contractor becomes more certain about its competitor's behavior, both its probability of winning and optimum bidding markup increase. This research should be beneficial for the construction stakeholders to better understand the bidding decision-making processes and consequently help create a healthy contracting environment.

Department(s)

Civil, Architectural and Environmental Engineering

Keywords and Phrases

Bayesian statistics; Construction bidding; Contracting; Decision theory; Markup estimation

International Standard Serial Number (ISSN)

0733-9364; 1943-7862

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 American Society of Civil Engineers (ASCE), All rights reserved.

Publication Date

01 Jan 2017

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