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Examining the use of bid information in predicting the contractor's performance

Examining the use of bid information in predicting the contractor's performance Purpose – The purpose of this paper is to examine the use of bid information, including both price and non‐price factors in predicting the bidder's performance. Design/methodology/approach – The practice of the industry was first reviewed. Data on bid evaluation and performance records of the successful bids were then obtained from the Hong Kong Housing Department, the largest housing provider in Hong Kong. This was followed by the development of a radial basis function (RBF) neural network based performance prediction model. Findings – It is found that public clients are more conscientious and include non‐price factors in their bid evaluation equations. With the input variables used the information is available at the time of the bid and the output variable is the project performance score recorded during work in progress achieved by the successful bidder. It was found that past project performance score is the most sensitive input variable in predicting future performance. Research limitations/implications – The paper shows the inadequacy of using price alone for bid award criterion. The need for a systemic performance evaluation is also highlighted, as this information is highly instrumental for subsequent bid evaluations. The caveat for this study is that the prediction model was developed based on data obtained from one single source. Originality/value – The value of the paper is in the use of an RBF neural network as the prediction tool because it can model non‐linear function. This capability avoids tedious “trial and error” in deciding the number of hidden layers to be used in the network model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Financial Management of Property and Construction Emerald Publishing

Examining the use of bid information in predicting the contractor's performance

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References (40)

Publisher
Emerald Publishing
Copyright
Copyright © 2008 Emerald Group Publishing Limited. All rights reserved.
ISSN
1366-4387
DOI
10.1108/13664380810898122
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to examine the use of bid information, including both price and non‐price factors in predicting the bidder's performance. Design/methodology/approach – The practice of the industry was first reviewed. Data on bid evaluation and performance records of the successful bids were then obtained from the Hong Kong Housing Department, the largest housing provider in Hong Kong. This was followed by the development of a radial basis function (RBF) neural network based performance prediction model. Findings – It is found that public clients are more conscientious and include non‐price factors in their bid evaluation equations. With the input variables used the information is available at the time of the bid and the output variable is the project performance score recorded during work in progress achieved by the successful bidder. It was found that past project performance score is the most sensitive input variable in predicting future performance. Research limitations/implications – The paper shows the inadequacy of using price alone for bid award criterion. The need for a systemic performance evaluation is also highlighted, as this information is highly instrumental for subsequent bid evaluations. The caveat for this study is that the prediction model was developed based on data obtained from one single source. Originality/value – The value of the paper is in the use of an RBF neural network as the prediction tool because it can model non‐linear function. This capability avoids tedious “trial and error” in deciding the number of hidden layers to be used in the network model.

Journal

Journal of Financial Management of Property and Construction – Emerald Publishing

Published: Aug 22, 2008

Keywords: Hong Kong; Construction industry; Neural nets; Modelling; Bid offer spreads

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