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Among the critical factors of project success is the proper selection of qualified contractors. More owners are adopting Best Value (BV) procurement which combines the technical qualifications with fee proposals to rank bidders. However, the successful application of BV depends on two integral factors: ranking of contractor’s qualifications using multiple criteria and the weights assigned to each selection criterion. Many criteria are qualitative, leading to difficulties in objectively quantifying a contractor’s rank. Thus, this paper provides owners with a simplified decision-support methodology that quantifies criteria that owners might consider in their decisions, reduces the number of comparisons that owners must perform, and reduces bias in defining criteria weights. This was achieved by first collecting the criteria commonly used to assess contractors’ competence. Then, criteria were analyzed and clustered into separate, quantifiable groups. Third, pairwise comparison was employed to devise a criteria-weighting method based on the clustered groups and determine the scores of bidding contractors. The proposed methodology could help identify the areas of strengths and weaknesses of each contractor in comparison with other bidders. The method presents a scientific and practical approach and, thus, can be considered for future applications.

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The Last Planner System (LPS) has long been used in construction projects to promote reliable planning and enhance productivity. However, despite various attempts to evaluate LPS implementation efforts, the human aspect of the evaluation attempts has not been given enough attention. This issue may be tackled through Fuzzy Inference Systems (FIS) to capture more information regarding the gradual and intricate changes in scoring systems. Therefore, this paper aims to offer a standardized diagnosis model for LPS performance in construction projects. This model employs an FIS that analyzes the results of an LPS implementation for a more accurate investigation of the implementation. First, a thorough literature review is conducted to select the most prominent factors influencing the LPS implementation process, followed by expert panel questionnaire development and distribution among LPS experts to rank the selected factors. The obtained questionnaire results are then used to develop the FIS. The objective of this paper is hereby twofold: (1) to allow assessing expected LPS benefits through the qualitative assessment of the performance in the four LPS phases, and (2) to facilitate comparing past, current, and future performances throughout the organization's LPS implementation process to ensure continuous improvement.