Lowe, D J; Parvar, J and Emsley, M W (2004) Development of a decision support system (DSS) for the contractor's decision to bid: Regression analysis and neural networks solutions. Journal of Financial Management of Property and Construction, 9(1), pp. 27-42. ISSN 1366-4387
Abstract
The decision whether to bid or not for a project is extremely important to construction 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 model data collected from the bid/no-bid decision makers of a UK construction company for 115 historical bid opportunities. 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | bidding, decision support system, decision to bid, neural networks |
| Index terms: | construction contractor, bidding, bid/no-bid, decision to bid, neural network, construction company, artificial neural network, decision support, regression analysis, propagation, decision process, resource allocation, regression model |
| Subjects: | decision analysis, statistical analysis, modelling and simulation, resource management, artificial intelligence, engineering process, bidding, organization, practitioner |
| Topics: | Risk Management, Procurement, Engineering Principles, Site Management, Digital Applications, Roles and Professions, Business Strategy, Research Practice |
| Descriptive scope: | 3 PCA |
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