Abstract
The decision whether to bid or not for a project is extremely important to contractors; besides the issues of resource allocation, the preparation of a bona fide tender commits the organisation to considerable expenditure, which is only recovered if the bid is successful. There is, therefore, a potential financial benefit to be realised through the adoption of an effective and systematic approach to the decision to bid process. Artificial neural network and regression techniques are used to produce a rational and optimal model for the bid/no-bid decision process. While the regression model is ultimately rejected, the selected back-propagation network, comprising 21 input nodes, 3 hidden layers and 4 output nodes is used to support a DSS for the decision to bid process. The results obtained demonstrate that the model functions effectively in predicting the decision process.
Original language | English |
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Title of host publication | Proceedings of the First International Symposium on Commercial Management, University of Manchester, 7th April 2005 |
Editors | David Lowe, Margaret Emsley |
Place of Publication | United Kingdom |
Publisher | University of Manchester Institute of Science and Technology (UMIST) |
Pages | 122-135 |
Number of pages | 14 |
ISBN (Print) | 0-9547918-1-1 |
Publication status | Published - 7 Apr 2005 |
Keywords
- Bidding
- construction
- decision support system
- decision to bid
- neural networks