A fuzzy gap analysis model for evaluating the performance of engineering consultants

Evaluating the performance of Engineering Consultants (ECs) is a major challenge as the process involves a number of subjective elements. However, as performance is an important dimension for prequalifying and selecting ECs, it would be helpful if relevant data can be solicited from various clients. The diversity in the Consultant Performance Evaluation (CPE) criteria and the interpretation of performance levels amongst various assessors would undoubtedly undermine the reliability and transparency of CPE. In this paper, a fuzzy gap analysis model is proposed to improve the practice of CPE. The study begins by identifying a list of CPE criteria and their corresponding quantitative indicators for the design stage of a project. Considering the performance levels for each of the quantitative indicators could be interpreted by assessors differently, an empirical survey is conducted to identify a range of possible values within each of the ‘poor’, ‘average’, ‘good’, ‘very good’ and ‘excellent’ performance levels. The fuzzy gap analysis CPE model is proposed to facilitate assessors to compare the client's expectation and actual service quality of an EC. The model proposed in this paper is not only novel but could also improve the fairness and transparency of this type of decision under an increasingly collaborative environment.