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CivilComp Proceedings
ISSN 17593433 CCP: 84
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping, G. Montero and R. Montenegro
Paper 74
A Probabilistic Model for Setting the BidCutting Limit L.C. Chao^{1} and C.N. Liou^{2}
^{1}Department of Construction Engineering, National Kaohsiung First University of Science & Technology, Taiwan
L.C. Chao, C.N. Liou, "A Probabilistic Model for Setting the BidCutting Limit", in B.H.V. Topping, G. Montero, R. Montenegro, (Editors), "Proceedings of the Fifth International Conference on Engineering Computational Technology", CivilComp Press, Stirlingshire, UK, Paper 74, 2006. doi:10.4203/ccp.84.74
Keywords: bid markup, risk analysis, cost estimate, probabilistic cost estimating, bidding model, MonteCarlo method.
Summary
Intense price competition is quite common in the construction industry because of
the predominant practice of owners awarding a contract based on the lump sum,
lowest bid method. In many markets, because of low entry barriers or historical
reasons, there are too many players going after limited jobs. In order to win enough
contracts to sustain normal operation, contractors in such markets have to cut their
bids to compete, with contract winning taking priority over profit maximization.
Hence, it is frequent to see the winning bid for a project close to a reasonably
estimated cost, i.e. a nearzero markup applied in the bid. However, pricecutting not
only gives up possible profits but also undoubtedly increases the risk of making a
loss on completing the job. To avoid suffering an unworthy loss as a result of
haphazardly submitting an inadequate bid, contractors adopting a low bid strategy
need to achieve a balance between a high chance of winning and a low loss risk.
They should evaluate the impact of bid cutting on the increase in loss risk against
the increase in the chance of winning, as a basis for bid price decision. However,
although bidding decisions have attracted much research interest over the decades,
few models for markup determination found in the literature address situations
where the need to survive is what drives bid prices.
Conventional bidding models were developed on the basis of an estimate of the probability of winning for a markup [1,2]. Typically, the optimum markup is determined based on the maximum expected profit, where the expected profit for a markup is defined as the product of the probability of winning multiplied by the markup. Although reasonable and able to achieve the highest profit in the longer term over many times of bidding, the problem is that in intense competition the recommended markup tends to result in too low a chance of winning. For example, a zero markup, which is not uncommon in the construction industry, will never be recommended, since such a markup's expected profit being zero is always less than a positive markup's. Hence, in the light of few job opportunities in the market, they cannot meet the urgent need of having a higher chance of getting a contract. Other models, such as utility theory models [3,4] and neural network models [5,6], include qualitative factors as inputs to reflect the many facets of the markup problem. They offer various methods for producing an optimum markup for a specific project, yet they are not survival oriented as they do not cater to intensely contested markets and provide a basis for bid cutting to raise the chance of winning. This paper presents an approach to setting a lower limit of the bid for a project. The purpose is to provide a basis for guiding bidcutting decisions often required in contracting environments where price competition is intense. At center is a model that evaluates the expected probability of not making a loss for a bid, considering the events of both winning and not winning. The bid achieving the highest expected probability of not making a loss will involve the minimum overall loss risk and thus can be regarded as the suggested lower limit. Even though in intense competition the chance of winning may outweigh profit, raising the chance of winning does not warrant cutting bids casually and taking unnecessary financial risk. With guidance of the proposed model, the decisions relating to bid cutting to gain competitiveness in intensely contested markets will not be made arbitrarily, but rather a rational choice made after careful calculations. An example with data from real cases is used to illustrate the approach, which is implemented using MonteCarlo simulation for the solution. References
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