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|Title:||A fuzzy competence requirement (FCR) model for competitive bidding strategy|
|Publisher:||Emerald Group Publishing Limited|
|Source:||Construction innovation, 2010, v. 10, no. 1, p. 75-88 How to cite?|
|Abstract:||Purpose– The “multi‐criteria selection” has become popular practice in selecting contractors, especially for public works, which involve multiple stakeholders. In line with this development, contractors need to consider more factors apart from offering a lower bidding price in formulating a bidding strategy. The purpose of this paper is to introduce a quantitative method, namely the fuzzy competence requirement (FCR) model, for assisting a contractor to formulate a better bidding strategy by better utilising its competence for meeting the best multiple criteria imposed by clients.|
Design/methodology/approach– The fuzzy linear programming with multiple objectives technique is applied in this paper and an illustrative example is introduced to demonstrate the application of the proposed model.
Findings– The FCR model can assist in generating a range of bidding strategies by assuming different confidence levels that contractors may perceive. These strategies could be valuable references for contractors to develop their competitive bidding strategy, which enables contractors to efficiently utilise their competence to meet clients' multiple selection criteria.
Research limitations/implications– The FCR model can provide contractors with useful information for formulating their bidding strategy. However, other factors, such as the sense of the market and estimation of rivals' bidding strategy, should also be considered in the bidding decision.
Practical implications– The FCR model can help contractors make better decisions in bidding by considering their competence and meeting client's multiple criteria.
Originality/value– This paper introduces a new model – FCR model – for helping contractors improve the efficiency of their bidding decision.
|Appears in Collections:||Journal/Magazine Article|
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Citations as of Oct 17, 2017
Checked on Oct 23, 2017
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