Contractor’s bid pricing strategy: a model with correlation among competitors’ prices

Abstract The approach used by construction companies to determine bid prices is an element of their strategy used to win jobs in competitive tenders. Such strategies build upon an analysis of the contactor’s potential and capabilities (am I able to deliver? am I eligible to participate in the tender?), and the analysis of the economic environment, including the expected behavior of competitors. The tender strategy sets out both the guidelines and the procedure in deciding whether or not to bid as well as the rules for determining the price. The price, on the one hand, should be high enough to cover expected direct and indirect costs as well as risk-adjusted profit. On the other hand, it needs to be low enough to be considered most attractive (typically: the lowest) among the prices offered by the competitors. The paper focuses on the price definition component of the bidding strategy. It provides a brief overview of the existing methods that support bidding decisions by comparing their demand for input and limitations in practical applications and presents a simulation-based method supporting the determination of the profit ratio. This probabilistic method assumes the existence of a positive correlation between the prices offered by the competitors. Its application is illustrated by means of a numerical example. The outcomes of the simulation prompt the amount of the profit margin that maximizes the expected value of the contractor’s profit.

[1]  Mark E. Johnson,et al.  Multivariate Statistical Simulation , 1989, International Encyclopedia of Statistical Science.

[2]  L. Friedman A Competitive-Bidding Strategy , 1956 .

[3]  K. Simmonds Competitive Bidding: Deciding the Best Combination of Non-Price Features , 1968 .

[4]  Paul D. Boughton,et al.  The competitive bidding process: Beyond probability models , 1987 .

[5]  Ustalanie wskaźnika narzutu zysku w strategii przetargowej przedsiębiorstwa budowlanego , 2009 .

[6]  Kwong Wing Chau,et al.  Monte Carlo simulation of construction costs using subjective data , 1995 .

[7]  Alan Mercer,et al.  The optimum markup when bidding with uncertain costs , 1990 .

[8]  Heng Li,et al.  Neural network models for intelligent support of mark‐up estimation , 1996 .

[9]  G. M. Jenkins,et al.  A Systems Approach to Management , 1968 .

[10]  Craig B. Borkowf,et al.  Random Number Generation and Monte Carlo Methods , 2000, Technometrics.

[11]  Tarek Zayed,et al.  Best-Value Model Based on Project Specific Characteristics , 2008 .

[12]  Huifen Chen,et al.  Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations , 2001, INFORMS J. Comput..

[13]  Xian-Xun Yuan Bayesian method for the correlated competitive bidding model , 2012 .

[15]  A. Touran Probabilistic Cost Estimating with Subjective Correlations , 1993 .

[16]  Liang Y Liu,et al.  ANALYTICAL MODEL FOR ANALYZING CONSTRUCTION CLAIMS AND OPPORTUNISTIC BIDDING , 2004 .

[17]  Enrique F Schisterman,et al.  Estimation of the correlation coefficient using the Bayesian Approach and its applications for epidemiologic research , 2003, BMC medical research methodology.

[18]  Xian-Xun Yuan A correlated bidding model for markup size decisions , 2011 .

[19]  Aminah Robinson Fayek,et al.  COMPETITIVE BIDDING STRATEGY MODEL AND SOFTWARE SYSTEM FOR BID PREPARATION , 1998 .

[20]  Gul Polat,et al.  Comparison of ANN and MRA Approaches to Estimate Bid Mark-up Size in Public Construction Projects , 2016 .

[21]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[22]  Larry G. Crowley Friedman and gates: Another look , 2000 .

[23]  Robert I. Carr,et al.  General Bidding Model , 1982 .

[24]  Edyta Plebankiewicz,et al.  Modelling decision-making processes in bidding procedures with the use of the fuzzy sets theory , 2014 .

[25]  Ali A. Shash,et al.  Factors affecting a contractor's mark-up size decision in Saudi Arabia , 1992 .

[26]  Ossama Hosny,et al.  Simulating the winning bid: A generalized approach for optimum markup estimation , 2012 .

[27]  Bo Chen,et al.  An Investigation of the Average Bid Mechanism for Procurement Auctions , 2015, Manag. Sci..

[28]  D. V. Gokhale,et al.  Assessment of a Prior Distribution for the Correlation Coefficient in a Bivariate Normal Distribution , 1982 .

[29]  Anthony N. Pettitt,et al.  Gates' bidding model , 2007 .

[30]  Min Liu,et al.  Modeling a Contractor’s Markup Estimation , 2005 .

[31]  Sou-Sen Leu,et al.  Average-Bid Method—Competitive Bidding Strategy , 1993 .

[32]  Sungbin Cho,et al.  An exploratory project expert system for eliciting correlation coefficient and sequential updating of duration estimation , 2006, Expert Syst. Appl..

[33]  Sofia Lundberg,et al.  Tender evaluation and supplier selection methods in public procurement , 2013 .

[34]  P. Jaśkowski,et al.  Modelling contractor’s bidding decision , 2017 .

[35]  M. Skitmore,et al.  Scoring rules and abnormally low bids criteria in construction tenders: a taxonomic review , 2015 .