Assessment and evaluation of contractor data against client goals using PERT approach

Zedan Hatush and Martin Skitmore

Construction Management and Economics, 1997, vol. 15, issue 4, 327-340

Abstract: A methodology for assessing and evaluating contractor data for the purpose of prequalification and bid evaluation is presented. The PERT approach is used to develop a linear model for the assessment of contractor data. The model incorporates a multiple ratings permitting the uncertainty in contractor data to be evaluated. An empirical study investigating the importance of different contractor criteria is described. A lexicographical ordering with aspiration levels and risk analysis with sensitivity methods are used to evaluate and select or rank-order contractors against the main client goals of time, cost, and quality. A literature review is reported regarding client goals and current evaluation strategies. The assumptions, advantages and disadvantages of this work as well as an example are also presented.

Keywords: Prequalification; Bid Evaluation; Pert; Criteria; Client Goals; Contractor; Data (search for similar items in EconPapers)
Date: 1997
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/014461997372881 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:conmgt:v:15:y:1997:i:4:p:327-340

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RCME20

DOI: 10.1080/014461997372881

Access Statistics for this article

Construction Management and Economics is currently edited by Will Hughes

More articles in Construction Management and Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().