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Predicting project performance through neural networks

Cheung, Sai On, Wong, Peter Shek Pui, Fung, Ada S. Y., & Coffey, Vaughan (2006) Predicting project performance through neural networks. International Journal of Project Management, 24(3), pp. 207-215.

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Abstract

Successful project delivery of construction projects depends on many factors. With regard to the construction of a facility, selecting a competent contractor for the job is paramount. As such, various approaches have been advanced to facilitate tender award decisions. Essentially, this type of decision involves the prediction of a bidderÕs performance based on information available at the tender stage. A neural network based prediction model was developed and presented in this paper. Project data for the study were obtained from the Hong Kong Housing Department. Information from the tender reports was used as input variables and performance records of the successful bidder during construction were used as output variables. It was found that the networks for the prediction of performance scores for Works gave the highest hit rate. In addition, the two most sensitive input variables toward such prediction are ‘‘Difference between Estimate’’ and ‘‘Difference between the next closest bid’’. Both input variables are price related, thus suggesting the importance of tender sufficiency for the assurance of quality production.

Impact and interest:

14 citations in Scopus
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ID Code: 29150
Item Type: Journal Article
Keywords: Bid evaluators, Performance scores, Prediction by neural networks
DOI: 10.1016/j.ijproman.2005.08.001
ISSN: 0263-7863
Subjects: Australian and New Zealand Standard Research Classification > BUILT ENVIRONMENT AND DESIGN (120000) > BUILDING (120200) > Building Construction Management and Project Planning (120201)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Urban Development
Copyright Owner: Copyright 2006 Elsevier
Deposited On: 08 Dec 2009 12:48
Last Modified: 29 Feb 2012 23:31

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