A methodology for predicting the performance of construction contractors

This thesis addresses a fundamental decision problem, encountered by U. K. construction clients faced with a construction contract to assign: the judicious selection of a contractor. Initially, the inadequacies of current selection practices are confirmed. These findings influence the development of a new selection model, with emphasis on promoting a rationalised, quantitative technique able to identify the potential (project) performance of those contractors evaluated. This approach contrasts with present trends which promote subjectivity and rely heavily upon practitioner experience /judgment. A nationwide survey of practitioners and client groups identifies discriminating criteria essential to contractor selection, whilst also facilitating the knowledge of their importance (via weighting indices) within the selection process. The multiattribute analysis (MAA) technique embraces these criteria and is employed for its ability to aid decision making in the presence of multiple, often conflicting objectives, as characterised by this `real life' decision problem. Within the model contractor's attributes are measured, the resulting scores serving as multiplicands for the aforementioned weighting indices. The aggregate resultant yields a comparison measure. Utility values are also exercised to mirror client preferences and thereby influence optimal choice. The new technique is fully elucidated by worked example with validity being achieved by application to live selection situations. Finally, the potential for any change to existing tendering practice is investigated, via nationwide survey of U. K. construction contractors. The author has to some extent encompassed building and civil engineering, but the emphasis of this work is on the building sector.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Wolverhampton

Year dc:date.issued

1995

Author dc:creator

• Holt, Gary D.

Licence

• https://wlv.dspace7.openrepository.com/bitstreams/8907c77a-b7f1-44cf-8f32-4904188e60f7/download