Contractor Prequalification Research Articles

Relevance of Contractors’ Prequalification Criteria to Time Performance of Civil Engineering Project

Civil engineering project completion within predetermined time schedule is usually the desire of the client and every other stakeholder. This has been a mirage because project in this case often fails to meet time target as a result of challenges emanating from its various peculiar characteristics. Civil engineering project completion within estimated time target can be achieved by adopting appropriate contractors’ selection criteria. This research therefore reflected Quantity Surveyors’ and Civil/Structural Engineers’ perception on the relevance of contractors’ prequalification criteria to time performance of civil engineering project. The purpose was to determine the impact of contractors’ selection criteria on time performance of civil engineering project, revealed the contractor prequalification criteria impacting most on time performance of civil engineering project. The primary data used were obtained from construction professionals in consulting, client and contracting organizations engaging in both private and public projects through a well designed questionnaire. Regression analysis and Karl Pearson’s coefficient of correlation were employed to evaluate the impact as well as relationship between contractors’ prequalification criteria and time performance of civil engineering project. Contractors’ prequalification criteria had both positive and negative relationship with time performance of civil engineering project at varying degree of significance. Also, contractors’ prequalification criteria significantly impact on and differently predict time performance of civil engineering project. Therefore, prioritizing reputation and past performance as well as strict adherence to the impact order of the set of criteria for contractors’ prequalification listed in this study would lead to achievement of civil engineering project that meet time target.

Factors Influencing Contractor Prequalification Processes in Developing Countries

Factors Influencing Contractor Prequalification Processes in Developing Countries

A fuzzy multiple-criteria decision-making model for contractor prequalification

Selecting an appropriate contractor can improve the likelihood of project success. This study intends to presents a model for selecting contractors. The model is based on a fuzzy multiple criteria model for contractor prequalification. First, a field study with experts and a review of the literature is performed to generate 22 sub-criteria along with six criteria used to evaluate contactor prequalification. Second, analytic hierarchy process (AHP) and triangular fuzzy numbers are integrated to develop a fuzzy evaluation model which prioritises the relative weights of contractor prequalification criteria. Then, the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is used to rank the contractors. The results show financial stability, technical capacity and experience are the most important criteria for contractor selection. Moreover, Alamut and Folman are in the first two positions in terms of ranking for contractor selection. The proposed model and results can provide useful insights for contractor selection.

Contractor Prequalification in Taiwan

It is very important for the project owner to screen out potential failure contractors from the bidding process. Contractor prequalification has been established as a standard practice in solving this problem. This study uses a prequalification that is a financial ratios model to assess the contractor's financial capability in Taiwan. Do not consider non-quantifiable factor, the model is effective in assessing the financial ability of construction contractors' selection.

A Meta Classification and Analysis of Contractor Selection and Prequalification

In order to attain the objectives of a construction project all of the resources should be used effectively and efficiently. Therefore, the success of a construction project is directly related to organize the project team and to select the building production process participants accurately who will use these resources. Selection of the contractor, as a main participant of a construction project, is the most important and challenging decision process for a client.The objectives of this paper are (1) to introduce the findings of the literature review about the contractor selection and pre- qualification models and systems studied between 1992 – 2013 (2) to classify academic studies dealing with pre-qualification and selection criteria of contractors using a Meta classification method. In this paper, 133 peer-reviewed academic studies have been analyzed and classified in the domain of contractor selection, contractor pre-qualification and weighting criteria. A meta- classification system is adapted and used in order to present the state of the art of contractor pre-qualification and selection challenge. It is obtained that the statistical models, fuzzy set theory and AHP are the most preferred methods in order to solve contractor selection problem. Moreover, an increment in the studies of contractor selection in the last period is determined. Then, based on the findings of literature review, supporting the available contractor pre-qualification and selection models and systems with rapidly changing information technology applications such as agent-based systems is discussed

Contractor financial prequalification using simulation method based on cash flow model

Abstract Although construction contractor prequalification highly prioritizes the financial capacity of the construction contractors, a special assessment of contractors' financial capacities has seldom been performed in the construction industry. Cash flows largely reflect a contractor's capacity to meet its financial obligations, this study assesses the credit qualities of construction contractors by using a cash flow based credit model (CFB credit model). The model uses historical continuously free cash flow to firm of construction contractors to simulate possible future cash flow paths. The credit quality scores of construction contractors are then assessed using the CFB credit model. The results demonstrate the effectiveness of assessment of the credit quality of construction contractors, and recommend the clients to adopt it in prequalification of the contractors' financial abilities.

A contractor pre-qualification model based on the quality function deployment method

Contractors play a significant role in construction projects and selection of the most appropriate contractor for the project is a critical decision for clients. The pre-qualification of construction contractors is accounted as a very important step in contractor selection for every project. The main purpose of pre-qualification is to identify an array of eligible contractors, which is required for post-qualification steps and further considerations. Although there have been numerous models for contractor pre-qualification, there are inadequate researches to present a contractor pre-qualification model with consideration of the project objectives or the project owner’s requirements. A new contractor pre-qualification model was developed with the aim of resolving this issue. This model employs the quality function deployment (QFD) method, and considers both the project owner’s requirements and the contractor’s abilities. The results reveal that consideration of the project objectives or the project owner’s requirements and expectations can influence contractor pre-qualification.

Prequalification of Egyptian construction contractors using fuzzy‐AHP models

Purpose – Contractor prequalification is a typical multiple criteria decision‐making problem that includes both quantitative and qualitative criteria. The conditions surrounding the prequalification decision are often imprecise, subjective and uncertain; assessments are consequently made using linguistic approximations. Fuzzy set theory is specifically designed to handle qualitative and linguistic data based on approximations and provides a method of representing in numerical form the linguistic approximations used to describe the decision‐maker judgments. However, fuzzy set theory has a weakness in identification of the relative weights of the decision criteria. On the other hand, one of the most accurate and easy methods for identifying the relative weights is the analytic hierarchy process (AHP). This paper seeks to address these issues.Design/methodology/approach – The main objective of this paper is to develop a new integrated decision model composed of fuzzy set theory and analytic hierarchy process...

Financial Prequalification for a Contractor by using a Dynamic Threshold Cash Flow Based Model

Prequalification plays a crucial role in selecting a capable contractor in construction project. Contractor prequalification studies seldom address contractor financial risk, despite the importance of contractor's evaluation in successful project completion. Based on a cash flow based structural model using the dynamic threshold by Liao, Chen and Su, this study evaluates the credit quality of construction contractor. Via uses of the area under curve (AUC), the discriminatory performance of the cash flow model in ranking the credit qualities of construction contractors for three year is evaluated, in whichS & P issuer credit ratings are used as the benchmarks. Empirical results indicate that the proposed model has an excellent discriminatory power under AUC. Result of this study demonstrates that the proposed model is highly effective in evaluating the credit risk of construction contractors. Importantly, the proposed model only requires financial statement, making it applicable to both listed and private construction contractors.

Forecasting contractor's deviation from the client objectives in prequalification model using support vector regression

Contractor prequalification (CP) is a very complex decision-making process with nonlinearity, uncertainty and imprecision in inputs containing both subjective and objective data. The failure to perform CP can lead to large losses, delays or severe loss of project quality. Although the most reliable approach identified in the literature is currently artificial neural network (ANN), it has weaknesses that negatively affect CP. In this study, a new approach called support vector machines (SVM) has been used to forecast a contractor's deviation from a client's objectives. In order to test the model, CP for 250 virtual contractors was solved. The proposed model had a great generalization in linear, nonlinear, noisy and inductive environments. The Results showed that SVM could reliably perform even with a small amount of training data. Also when compared to ANN, SVM showed an overall better performance.

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.

A fuzzy sets based contractor prequalification procedure

Abstract Contractor prequalification makes it possible to admit for tendering only competent contractors. The paper presents a proposal for contractor prequalification schema involving two stages of prequalification: “on a standing list” and “per project”. A model of prequalification employing the theory of fuzzy sets to evaluate the “per project” contractors is precisely described. A simple numerical example illustrates the model operation and a description of a program supporting the prequalification procedure follows.

A decision support system for contractor prequalification for surety bonding

In the construction bonding business, a complex and comprehensive prequalification or assessment process is done to evaluate contractor, project, and contractual risks. Previous studies have focused mainly on contractor prequalification from the owner's or consultant's perspective. There remains a need for a model to evaluate the contractor and project-specific aspects (i.e., project team and contractual risks) from the surety bonding perspective. This paper identifies and classifies the most relevant evaluation criteria that surety underwriters and brokers consider when evaluating a specific construction project for bonding purposes. Several data collection techniques (questionnaires, one-on-one interviews, and interacting group meetings), with highly experienced surety experts, were conducted to compile a comprehensive and detailed list of the evaluation criteria. Both fuzzy logic and expert systems are combined to develop a decision support system (DSS) for use in contractor and project evaluation. An approach for fuzzy membership function estimation is presented using a traditional membership function estimation technique, integrated with data of contractor prequalification cases. Thirty-eight alternative system configurations are investigated to determine the most accurate one. The system is validated using 32 prequalification cases, and the accuracy of the system is found to be 84.0%. ► A model was developed to evaluate both contractor- and project-specific aspects from the surety bonding perspective. ► The model provides a systematic and structured approach for contractor prequalification. ► A comprehensive and detailed list of the important criteria for contractor and project prequalification was complied. ► A new approach for fuzzy membership function estimation is presented. ► The model decreases the subjectivity in the contractor prequalification process and makes its logic easy to trace.

A Human Perspective of Contractor Prequalification

The contractor prequalification topic has had much written on it over the last two decades. In the past it has been primarily viewed in terms of the appropriate selection of contractors for construction projects. There has been extensive research interest into the functional and process driven aspects of contractor prequalification. However, there has been little research taking a more holistic view of contractor prequalification in terms of the human aspects that surround it. Hence, gaining a wider appreciation on how prequalification systems affect the major stakeholders seems warranted. The use of soft systems thinking allowed for an in-depth and contextual analysis of prequalification issues. This necessitated unstructured in-depth interviews to be carried out with senior industry practitioners. Sifting through their respective experiences allowed for an alternative view of contractor prequalification systems. It allowed a very human perspective of prequalification to emerge. The research suggests that viewing prequalification systems from a human perspective provides a fuller picture of their true benefits and limitations.

A multiple kernel learning-based decision support model for contractor pre-qualification

Due to the complex nature of the contractor pre-qualification such as subjectivity, non-linearity and multi-criteria, advanced model should be required for achieving a high accuracy of this decision-making process. Previous studies have been conducted to build up quantitative decision models for contractor pre-qualification, among them artificial neural network (ANN) and support vector machine (SVM) have been proved to be desirable in solving the pre-qualification problem with regards to their higher accuracy and efficiency for solving the non-linear problem of classification. Based on the algorithm of SVM, multiple kernel learning (MKL) method was developed and it has been proved to perform better than SVM in other areas. Hence, MKL is proposed in this research, the capability of MKL was compared with SVM through a case study. From the result, it has been proved that both SVM and MKL perform well in classification, and MKL is more preferable than SVM, with a proper parameter setting. Therefore, MKL can enhance the decision making of contractor pre-qualification.

Structural equation model for assessing impacts of contractor's performance on project success

Contactor's expertise and performance play a significant role in successful delivery of a project. While clients strive hard to make the best decisions in selecting the right contractor for the right job, a clear understanding of the underlying attributes associated with contractors' selection in the context of achieving successful project outcomes is critical. In an attempt to understand these pre-emptive qualification criteria and their links to contractors' performance on a project, a hierarchical structural model is established. By employing the structural equation modelling technique, the model adapts a total of 29 technical attributes across five confirmatory factors namely, soundness of business and workforce (SBW), planning and control (PC), quality performance (QP), past performance (PP) and overall project success (OPS). Based on the survey data collected across medium size construction projects in Australia, the results of the model confirmed that technical planning and controlling expertise of contractor is key in achieving success on projects. With a clear understanding of the significance of these factors in the context of contractors' performance, these findings could potentially contribute to the development of a company's procedures or to enhance existing knowledge of contractor prequalification practices.

Longitudinal Study of Innovative Contracting Practices in State Departments of Transportation

Gathering content expertise both from published sources and from recognized highway construction professionals, a questionnaire was created to determine the use of 16 innovative contracting practices in the highway construction industry. The major practices include: build-own-operate-transfer, contractor prequalification, design/build, dispute resolution, guarantee/warranty, partnering, value engineering, among others. Using the questionnaire, a 10-year longitudinal study gathered information from U.S. Departments of Transportation in 1996, and then again in 2007. Statistical analysis of the 43 state responses found that 10 of these practices have seen significant increase, but the perceived benefits have increased very little, and one practice has seen a significant decrease in perceived benefit.

Framework for Performance-Based Contractor Prequalification

Performance-based contractor prequalification goes beyond the financial prequalification provided by the surety industry when it issues a bond for a public transportation project to include a contractor's performance record in the prequalification process. This paper reports the results of a survey of the status of performance-based contractor prequalification from 41 U.S. state departments of transportation (DOTs) and seven Canadian provincial ministries of transportation. These results were correlated with a content analysis of 43 DOT administrative prequalification documents and 62 sets of project-specific, performance-based prequalification documents. The findings were validated through structured interviews with contractors. The study found that performance-based contractor prequalification can be portrayed as a three-tiered system. The first tier mirrors the current administrative prequalification systems. The second tier is performance-based and includes postproject contractor evaluations, and the final tier consists of project-specific prequalification. This system constitutes a framework from which a transportation agency can design a contractor prequalification system that directly rewards good performers and encourages poor performers to improve. These features of the system are accomplished by adjusting, on the basis of a given contractor's past performance, its bidding capacity and the amount of performance bond that it is required to provide.

Https://journals.vgtu.lt/index.php/JCEM/article/download/6449/5589

Contractor prequalification makes it possible to admit for tendering only competent contractors. The undertaken decisions demand taking into consideration many criteria, including among others, experience and financial standing of the candidates. It is often difficult to be quantified. The objectives of the construction owner in a given project are also meaningful. All these factors cause difficulties in working out a mathematical prequalification model. In the paper a model based on fuzzy sets theory is proposed. It takes into consideration both different criteria, objectives and evaluations of numerous decision ‐ makers. To illustrate the model operation a simple numerical example is presented. Santrauka Išankstinis rangovų kvalifikacijos vertinimas leidžia dalyvauti konkurse tik kompetentingiems rangovams. Priimant pradinį sprendimą, reikia atsižvelgti į daugelį kriterijų, tarp jų į kandidatų patirtį ir jų finansinę padėtį. Šiuos kriterijus paprastai sunku įvertinti kiekybine išraiška. Taip pat svarbūs ir statytojo tikslai nagrinėjamame projekte. Šie išvardyti veiksniai sukelia sunkumų, rengiant matematinį išankstinio rangovų vertinimo modelį. Straipsnyje pasiūlytas neapibrėžtųjų aibių teorija pagrįstas modelis. Jis apima skirtingus kriterijus, tikslus ir sprendimus priimančių asmenų gausius vertinimus. Pateiktas nesudėtingas skaičiavimo pavyzdys rodo pasiūlyto modelio taikymą.

Efficacy of Using Support Vector Machine in a Contractor Prequalification Decision Model

Contractor prequalification is basically a nonlinear two-group classification problem. A robust contractor prequalification decision model should include the ability of handling both quantitative and qualitative data. Support vector machine (SVM) is a set of related supervised learning methods which can handle data in a high dimensional feature space for nonlinear separable problems. A new contractor prequalification decision model using SVM is proposed to assist clients to identify qualified contractors for tendering in this study. A case study was used to validate the proposed decision model and the classification ability was compared with neural networks (NNs) and principal component analysis (PCA). The results show that the proposed SVM model outperforms NN and PCA and the merits of using SVM to mitigate the limitations of using NN are elaborated. The proposed decision model is an ideal alternative for supporting clients to perform contractor prequalification decision making.