Decision Support System for Consultant Evaluation and Ranking using Qualifications-Based Selection and Fuzzy TOPSIS Approach

Abstract

Consultant evaluation and selection is a subjective process due to the qualitative nature of evaluation criteria. A thorough evaluation that includes all the necessary criteria should be conducted. A suitable procurement method for determining proficient consultants was found to be Qualifications-Based Selection, which means that the price criterion is not considered. Owners often use both price and non-price criteria in the evaluation process. However, when price is one of the deciding factors, non-price criteria suffer because clients do not pay as much attention to consultant qualifications as they should and instead focus on the price. They may give all consultants the same rating for the qualifications-related criteria, and then the bid price determines which firm gets the job. The objective of this research is to develop an automated decision support system to assist clients in selecting competent consultants with minimal subjectivity and improved consistency. Through a study of industry practices and an extensive literature review, this research identifies all criteria needed to properly evaluate consultants. The statistical analysis of the documents used by clients to evaluate consultants yielded the weights of the main criteria categories, which are (1) technical and (2) managerial and organizational. For this multi-criteria decision-making problem, fuzzy TOPSIS was determined to be the most appropriate technique. Fuzzy logic, which is a subset of artificial intelligence, deals with linguistic variables, whereas TOPSIS performs mathematical operations and ranks consultants. The analytical consultant evaluation and ranking model was created using the Python programming language, which is widely used for data analysis and machine learning. iii The contributions of this research include the development of pre-evaluation inquiries for screening and shortlisting consultants before the detailed evaluation process to save the decision-maker time and effort by focusing on eligible consultants only. It also involves the identification of evaluation rules for measuring criteria, which will be checked by the decision-maker to objectively determine the rating for each criterion. By that, subjectivity is minimized, while transparency and fairness are reinforced, because, unlike traditional approaches, where the decision-maker is required to set the linguistic ratings, evaluation rules decide on these ratings. Furthermore, the developed analytical model is comprehensive with all the relevant criteria, including environmental considerations, sustainability, and innovation which have recently gained attention. The decision-maker has the choice of using the system's recommended criteria weights or entering different weights based on the project characteristics. The computerized system is flexible and adaptable, allowing the decision-maker to exclude any non-applicable evaluation rules that may not fit in some projects without affecting the model calculations.