The impact of contractor selection method on transaction costs: a review

The basic premise of transaction-cost theory is that the decision to outsource, rather than to undertake work in-house, is determined by the relative costs incurred in each of these forms of economic organization. In construction the "make or buy" decision invariably leads to a contract. Reducing the costs of entering into a contractual relationship (transaction costs) raises the value of production and is therefore desirable. Commonly applied methods of contractor selection may not minimise the costs of contracting. Research evidence suggests that although competitive tendering typically results in the lowest bidder winning the contract this may not represent the lowest project cost after completion. Multi-parameter and quantitative models for contractor selection have been developed to identify the best (or least risky) among bidders. A major area in which research is still needed is in investigating the impact of different methods of contractor selection on the costs of entering into a contract and the decision to outsource

Application of integrated fuzzy VIKOR & AHP methodology to contractor ranking

Contractor selection is a critical activity, which plays an important role in the overall success of any construction project. The implementation of fuzzy multiple criteria decision attribute (MCDA) in selecting contractors has the advantage of rendering subjective and implicit decision making more objective and transparent. An additional merit of fuzzy MCDA is the ability to accommodate quantitative and qualitative information. In this paper, an integrated VIKOR–AHP methodology is proposed to make a selection among the alternative contractors in one of Iranian construction industry projects. In the proposed methodology, the weights of the selection criteria are determined by fuzzy pairwise comparison matrices of AHP

A fuzzy multi-criteria decision making model for construction contractor prequalification

Selecting an appropriate contractor is essential for the success of any construction project. Contractor prequalification procedure makes it possible to admit for tendering only competent contractor. Prequalification is a multi-criteria decision problem that is, in essence, largely dependent on the uncertainty and vagueness in the nature of construction projects and subjective judgement of the decision-maker. This paper presents a systematic prequalification procedure, based on Fuzzy Set Theory, whose main differences and advantages in comparison with other models are the use of an algorithm to handle the inconsistencies in the fuzzy preference relation when pair-wise comparison judgements are used and the use of linguistic assessment or exact assessment of performance of the contractors on qualitative or quantitative criterion, respectively. Finally, a case study for the rehabilitation project of a building at Technical University of Cartagena is presented to illustrate the use of the proposed model and to demonstrate its effectiveness

An Artificial Intelligence Framework to Contractor Financial Prequalification

Financial distress in the construction industry always causes major disruptions that usually result in a rippling effect on the economy. Avoiding such defaults is a top priority for employers to meet their demands. Artificial Intelligence (AI) models have provided increased accuracy in predicting financial distress compared to statistical, fuzzy and logistic regression models, and other classification models. The main objective of this work is to support project employers in pre-qualifying contractors by predicting the status of construction contractors during a bid analysis to disqualify contractors with a high probability of experiencing financial distress during the project duration. Eight financial indicators & six macroeconomic variables were used in the analysis. The selected variables were proven to be highly correlated with the output values as provided in the literature while maintaining variables with diverse effects on the output. This work employs multiple models including artificial neural networks (ANN), support vector machines (SVM), and logistic regression using different tools (Python & NeuralTools) based on collected financial statements and macroeconomic indicators. The results show that the ANN model developed using python achieved higher performance measures than SVM (radial basis function & linear kernel functions), logistic regression & ANN developed using NeuralTools. The results also show that adding macroeconomic variables to financial ratios as input variables significantly enhance the accuracy and F-1 score of the model. Accordingly, the developed model is effective in predicting financial distress for construction companies

A review of application of multi-criteria decision making methods in construction

Construction is an area of study wherein making decisions adequately can mean the difference between success and failure. Moreover, most of the activities belonging to this sector involve taking into account a large number of conflicting aspects, which hinders their management as a whole. Multi-criteria decision making analysis arose to model complex problems like these. This paper reviews the application of 22 different methods belonging to this discipline in various areas of the construction industry clustered in 11 categories. The most significant methods are briefly discussed, pointing out their principal strengths and limitations. Furthermore, the data gathered while performing the paper are statistically analysed to identify different trends concerning the use of these techniques. The review shows their usefulness in characterizing very different decision making environments, highlighting the reliability acquired by the most pragmatic and widespread methods and the emergent tendency to use some of them in combination

Analysis and evaluation of criteria for pre-selecting contractors in the Saudi Arabian construction sector

The construction industry in Saudi Arabia is experiencing dramatic developments and expansion due to recent changes in the Kingdom’s socio-economic development policies. The selection of construction contractors is an essential component in the success of projects yet there is both a lack of skilled manpower and a lack of experience in terms of managing major projects within Saudi Arabia. Thus, appropriate tools are required to select, evaluate, measure and monitor the performance of construction contractors. This paper critically analyses and evaluates current techniques for pre-selecting contractors and identifies the most appropriate techniques and criteria that could be adopted in Saudi Arabia. This has been achieved by undertaking a critical analysis of the literature and by carrying out preliminary interviews with practitioners in Saudi Arabia. The findings of this initial research have been used to establish the scope of work that will later form the basis of PhD researc

Contractors’ selection criteria: opinions of Palestinian construction professionals

Bid awarding practice in Palestine suffers from a myriad of problems. The aggressive competition, as well as the selection of the lowest bidder, may be considered as the major causes of such problems. The aim of this paper is to investigate the opinions of Palestinian construction professionals concerning contractors’ evaluation and selection criteria. A questionnaire survey was adopted for this study, incorporating 38 factors that are believed to be related to contractors’ selection. These factors were identified through a rigorous literature review, and grouped into 10 classes. The results show that the financial evaluation of the bid is considered as the most important class, being ranked in the first position, with a weight equal to 40.10%. The remaining nine classes are all related to technical criteria, with a total weight of 59.90%. The respondents placed a very low emphasis on the health and safety criteria, indicating a

Methodology to predict construction contractors’ performance using non-price measures

Despite being one of the largest industry sectors in the world, construction continues to suffer from underperformance. Contractors are the driving force behind built assets, and selecting high-performing contractors is crucial to the success of construction projects. However, the industry lacks a systematic and purpose-driven method of assessing contractors’ performance using objective metrics. Furthermore, contractors do not have a systematic way to gauge their own performance in the pursuit of continuous improvement. Although there are numerous approaches to the measurement of contractors’ performance, the literature suggests that most are complicated and highly dependent on data that are difficult to attain. The research presented in this thesis addresses this knowledge gap by creating a model for predicting construction contractors’ performance based on directly attributable measures that are quantitatively measurable and easily accessible. The findings of this research make a number of contributions to theory and practice. The developed performance model—the Contractors’ Performance Index (CPIx) provides a performance score based on seven non-price CMoPs. As the CPIx is based on factors that are within the control of the contractor, it provides a fair and independent assessment of performance that is not influenced by other factors. In an industry significantly driven by pricebased decisions that are solely based on non-price measures, the CPIx shifts the focus towards other aspects such as quality, health and safety, sustainability and productivity when evaluating performance, leaving price based measures for commercial considerations. Contractors can use the CPIx to self-evaluate their levels of project and organisational performance. If implemented as a sector-based performance evaluator, it can then be used to develop industry benchmarks for different categories of construction. The CPIx is presented as a prototype mobile application that can be conveniently used by various stakeholders to track performance within the construction industry