Competitive Bidding Strategy Model and Software System for Bid Preparation
Publication: Journal of Construction Engineering and Management
Volume 124, Issue 1
Abstract
This paper presents a competitive bidding strategy model for use in setting a margin (markup) for civil engineering and building construction projects. The goal of this model is to help a company to achieve its objectives in bidding. The model provides more than 90 factors that may influence the choice of margin size, and it enables the decision-maker to assess the impact of those that are relevant to his or her bid situation. The use of fuzzy set theory allows assessments to be made in qualitative and approximate terms, which suit the subjective nature of the margin-size decision. The model has been implemented in the form of a prototype software system named PRESTTO, which is described. One conclusion of this paper is that fuzzy set theory can be applied successfully to model the margin-size decision. A second conclusion is that use of this competitive bidding strategy model can improve the quality of the decision-making process used in setting a margin and can help contractors to gain a competitive edge in bidding. The competitive bidding strategy model has been validated with actual project bids collected from a survey of the Australian construction industry; validation will be described in a companion paper.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Ahmad, I., and Minkarah, I.(1988). “Questionnaire survey on bidding in construction.”J. Mgmt. in Engrg., ASCE, 4(3), 229–243.
2.
Bacarreza, R. R. (1973). “The construction project markup decision under conditions of uncertainty,” PhD thesis, Dept. of Civ. Engrg., Stanford Univ., Stanford, Calif.
3.
Benjamin, N. B. H.(1972). “Competitive bidding: The probability of winning.”J. Constr. Div., ASCE, 98(9), 313–330.
4.
Berenji, H. R. (1992). “Fuzzy logic controllers.”An introduction to fuzzy logic applications in intelligent systems, R. R. Yager and L. A. Zadeh, eds., Kluwer Academic Publishers Group, Norwell, Mass., 69–96.
5.
Dixon, W. J., and Massey, F. J. Jr. (1969). “Analysis of variance.”Introduction to statistical analysis. McGraw-Hill Book Co., Inc., New York, N.Y., 150–192.
6.
Fayek, A. (1996a). “A competitive estimating and tendering strategy model for use in the civil engineering construction industry,” PhD thesis, Dept. of Civ. and Envir. Engrg., Univ. of Melbourne, Melbourne, Victoria, Australia.
7.
Fayek, A. (1996b). “PRESTTO: an integrated estimating and tendering software system. User Manual.”Tech. Rep. No. RR/PROJ MGT/01/96, Dept. of Civ. and Envir. Engrg., Univ. of Melbourne, Melbourne, Victoria, Australia.
8.
Fayek, A., Duffield, C. F., and Young, D. M. (1995). “The design and implementation of an integrated computer-aided estimating and tendering system that utilises fuzzy logic.”Building for the 21st Century: Proc., Fifth East Asia-Pacific Conf. on Struct. Engrg. and Constr., Griffith Univ., Southport, Qld., Australia, Vol. 1, 859–864.
9.
Ferguson, N. S., Langford, D. A., and Chan, W. M.(1995). “Empirical study of tendering practice of Dutch municipalities for the procurement of civil-engineering contracts.”Int. J. Proj. Mgmt., Oxford, England, 13(3), 157–161.
10.
Friedman, L.(1956). “A competitive-bidding strategy.”Operations Res., 4(1), 104–112.
11.
Hegazy, T., and Moselhi, O.(1995). “Elements of cost estimation: A survey in Canada and the United States.”Cost Engrg., 37(5), 27–33.
12.
Klir, G. J., and Folger, T. A. (1988). Fuzzy sets, uncertainty, and information. Prentice-Hall Inc., Englewood Cliffs, N.J.
13.
Nguyen, V. U.(1985). “Tender evaluation by fuzzy sets.”J. Constr. Engrg. and Mgmt., ASCE, 111(3), 231–243.
14.
Odusote, O. O., and Fellows, R. F.(1992). “An examination of the importance of resource considerations when contractors make project selection decisions.”Constr. Mgmt. and Economics, London, England, 10, 137–151.
15.
Russell, A. D., and Fayek, A.(1994). “Automated corrective action selection assistant.”J. Constr. Engrg. and Mgmt., ASCE, 120(1), 11–33.
16.
Saaty, T. L.(1977). “A scaling method for priorities in hierarchical structures.”J. Math. Psychology, 15(1), 234–281.
17.
Sanders, S. R., and Cooper, T. (1990). “Factors affecting contractor profitability.”Trans., Am. Assoc. of Cost Engrs., American Association of Cost Engineers, Morgantown, W. Va., K.9.1–K.9.7.
18.
Schmucker, K. J. (1984). Fuzzy sets, natural language computations, and risk analysis. Computer Science Press, New York, N.Y.
19.
Shash, A. A.(1993). “Factors considered in tendering decisions by top U.K. contractors.”Constr. Mgmt. and Economics, London, England, 11, 111–118.
20.
Shash, A. A.(1995). “Competitive bidding system.”Cost Engrg., 37(2), 19–20.
21.
Shash, A. A., and Abdul-Hadi, N. H.(1992). “Factors affecting a contractor's mark-up size decision in Saudi Arabia.”Constr. Mgmt. and Economics, London, England, 10, 415–429.
22.
Stark, R. M., and Rothkopf, M. H.(1979). “Competitive bidding: A comprehensive bibliography.”Operations Res., 27(2), 364–390.
23.
Ting, S. C., and Mills, A. (1996). “Analysis of contractors' bidding decisions.”Proc., CIB W92 Int. Symp. on Procurement Sys., Univ. of Natal, Durban, South Africa, 53–65.
24.
Zadeh, L. A.(1965). “Fuzzy sets.”Info. and Control, 8(3), 338–353.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 124 • Issue 1 • January 1998
Pages: 1 - 10
Copyright
Copyright © 1998 American Society of Civil Engineers.
History
Published online: Jan 1, 1998
Published in print: Jan 1998
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.