A neural network bid/no bid model: the case for contractors in Syria

Mohammed Wanous, Halim Boussabaine and John Lewis

Construction Management and Economics, 2003, vol. 21, issue 7, 737-744

Abstract: Despite the crucial importance of the 'bid/no bid' decision in the construction industry, it has been given little attention by researchers. This paper describes the development and testing of a novel bid/no bid model using the artificial neural network (ANN) technique. A back-propagation network consisting of an input buffer with 18 input nodes, two hidden layers and one output node was developed. This model is based on the findings of a formal questionnaire through which key factors that affect the 'bid/no bid' decision were identified and ranked according to their importance to contractors operating in Syria. Data on 157 real-life bidding situations in Syria were used in training. The model was tested on another 20 new projects. The model wrongly predicted the actual bid/no bid decision only in two projects (10%) of the test sample. This demonstrates a high accuracy of the proposed model and the viability of neural network as a powerful tool for modelling the bid/no bid decision-making process. The model offers a simple and easy-to-use tool to help contractors consider the most influential bidding variables and to improve the consistency of the bid/no bid decision-making process. Although the model is based on data from the Syrian construction industry, the methodology would suggest a much broader geographical applicability of the ANN technique on bid/no bid decisions.

Keywords: ANN; ANN bidding model; 'bid/no bid' criteria; construction; Syria (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (7)

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DOI: 10.1080/0144619032000093323

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