A Fuzzy Decision Framework for Contractor Selection

Contractor selection is the process of selecting the most appropriate contractor to deliver the project as specified so that the achievement of the best value for money is ensured. Construction clients are becoming more aware of the fact that selection of a contractor based on tender price alone is quite risky and may lead to the failure of the project in terms of time delay and poor quality standards. Evaluation of contractors based on multiple criteria is, therefore, becoming more popular. Contractor selection in a multicriteria environment is, in essence, largely dependent on the uncertainty inherent in the nature of construction projects and subjective judgment of decision makers (DMs). This paper presents a systematic procedure based on fuzzy set theory to evaluate the capability of a contractor to deliver the project as per the owner’s requirements. The notion of Shapley value is used to determine the global value or relative importance of each criterion in accomplishing the overall objective of the decision-making process. The research reported upon forms part of a larger study that aims to develop a fuzzy decision model for construction contractor selection involving investigating multiple criteria selection tendencies of construction clients, relationship among decision criteria, and construction clients’ preferences of criteria in the contractor selection process. An illustration with a bid evaluation exercise is presented to demonstrate the data requirements and the application of the method in selecting the most appropriate contractor for the project under uncertainty. The proposed model is not intended to supplant the work of decision-making teams in the contractor selection process, but rather to help them make quality evaluations of the available candidate contractors. One major advantage of the proposed method is that it makes the selection process more systematic and realistic as the use of fuzzy set theory allows the DMs to express their assessment of contractors’ performance on decision criteria in linguistic terms rather than as crisp values.     

Performance Modeling for Contractor Selection

This paper proposes a contractor selection system that incorporates the contractor’s performance prediction as one of the criteria for selection. This research was developed working with an owner organization that was interested in developing a framework for evaluating contractors for future work. A modeling framework, developed in previous research, was used to develop a conceptual model of a project that depicts a causal structure of the variables, risks, and interactions that affect a contractor’s performance for a specific project from the owner’s point of view. The conceptual model helps to identify information needed for a comprehensive evaluation; some information can be readily available from historical records, while other can be unavailable and can be replaced by estimates based on experience. Ideally, over time, the owner should collect most of the information required for future evaluations. A mathematical component of the model can generate predictions of multiple project performance outcomes for each contractor under evaluation; these predictions and a contractors’ bid prices are then used for contractor evaluation purposes.     

Contractor Performance Evaluation for the Best Value of Superpave Projects

The best-value (BV) procurement process uses other key factors as well as bid price in the evaluation and selection of the best-performing contractor for the job. Contract time, lane rental, warranty, and quality of delivered product are examples of the key factors that indicate the contractor-expected performance. Literature on best value shows a need for analyzing the past performance of the contractor in similar jobs as an indicator of his/her qualification trend. This paper addresses this issue and proposes a methodology to incorporate quality of delivered product in the BV procurement system of asphalt construction. The paper uses past quality control (QC) testing results and utilizes Monte Carlo simulation to estimate the probability that the contractor gets full payment as an indication of qualification trend. The QC data were obtained from the Nebraska Department of Roads for a number of Superpave pavement projects. The results show the possibility of assigning a quality score for the contractor based on the past performance. This paper contributes to the current practice of best value with a new approach of employing QC as part of the selection process.     

Contractor Selection Criteria: Investigation of Opinions of Singapore Construction Practitioners

The construction industry has witnessed the failure of many contractors due to varying reasons such as financial problems, poor performance, or accidents arising from the lack of adequate safety consideration at worksites. All these incidents have led to the impression that the current system of awarding the contracts is inefficient in selecting the contractor capable of meeting the demands and challenges of present times and hence needs to be reviewed accordingly. Therefore, in an attempt to investigate the current situation of the Singapore construction industry a questionnaire survey was conducted for accruing the data required to identify the important contractor selection criteria (CSC) and to draw upon construction practitioners’ opinions regarding the importance of those CSC in assessing the capabilities of the candidate contractors during the selection process. The research reported upon forms part of a larger study that aims to develop a computer-interactive multicriteria decision system for contractor selection involving identification of CSC for inclusion in the system, investigation of CSC preferences of construction practitioners, and establishment of weights for those CSC from their perceived importance determined through the questionnaire survey of Singapore construction practitioners. The study highlights that there are statistically significant differences in opinions regarding the degree of importance assigned to some CSC among public clients, private clients, and contractors. Findings from the study may act as an aid in improving the Singapore construction industry by helping construction clients identify multiple CSC apart from cost which should always be considered when assessing the capability of candidate contractors during the selection process, by assisting contractors in improving their attributes in line with clients’ preferences and by facilitating Singapore construction clients and researchers to develop a contractor selection system capable of assessing multiple attributes of the candidate contractors so that the risk of the project failure due to the selection of an inappropriate contractor is minimized.     

SuretyAssist: Fuzzy Expert System to Assist Surety Underwriters in Evaluating Construction Contractors for Bonding

In construction, many owners mitigate the risk of unforeseen contractor default by accepting only bonded contractors who must endure a rigorous evaluation process by surety brokers and surety underwriters. This evaluation process includes a financial analysis and a review of work on hand and past performance, all of which have reliable structured methods for their evaluation. Additionally, a number of subjective criteria are considered that are more difficult to capture and assess objectively but which can be modeled effectively using fuzzy logic. The purpose of this paper is to illustrate how fuzzy logic and expert systems can be combined to provide a structured approach to evaluating contractors for surety underwriting purposes. Fuzzy logic is used to model both the objective and subjective factors considered in contractor evaluation using linguistic terms, and expert rules are used to capture the surety experts’ reasoning process. A fuzzy expert system, SuretyAssist, is presented that can be used to provide an initial evaluation of general contractors as well as periodic reviews to determine whether or not to accept them as clients for bonding. SuretyAssist was validated using 31 actual cases of contractor evaluation and found to be accurate in 81% of the cases.     

Quality-Based Contractor Rating Model for Qualification and Bidding Purposes

A quality contractor on every construction project would make project management simpler for Department of Transportation (DOT) project engineers and strategic planning more accurate for DOT executives. A qualification model that includes the quality of a contractor’s past work would be invaluable in assuring that each project had a quality contractor. Including the quality of past work in the bidding process would further help in this goal. Implementation of such a model for both its potential uses would require business process reengineering for the DOT in two important areas: qualification of contractors and contract award procedures. The research team has produced an innovative model, called the Quality-Based Performance Rating (QBPR) system. This model receives inputs from traditional subjective sources and integrates them with totally objective data input from the results of tests of the project’s materials and workmanship, then uses these inputs to produce a score for each project that is further used by the system to generate an index for each contractor reflecting that contractor’s quality of work over a specified time frame.     

Contractor Process Improvement for Enhancing Construction Productivity

This paper presents a regression model that relates job site productivity to process improvement initiatives (PIIs) executed both before and during construction. Applied during early project stages, this model helps industry practitioners to predict the expected value of labor productivity based on certain inputs related to preconstruction planning and construction execution. The model demonstrates the strong relationship of project performance to a variety of PIIs including design completeness, definition of a project vision statement, testing oversight, and project manager experience and dedication. The correlational research methodology targeted 75 projects representing approximately $274.53 million in civil construction. The data collection effort considered 45 PIIs (independent variables) using quantitative and qualitative measures. The modeling technique involved the use of multiple linear regression, a method that exploits available data from multiple, independent sources to focus on specific outcomes. The model was developed directly from contractor specific information and subjected to rigorous statistical analysis. The model provides project managers as front line industry practitioners with a deliberate yet practical approach to project management and productivity enhancement. The modeling results include verification analysis and a discussion of the model’s usefulness and limitations.     

Contractor Selection for Design∕Build Projects

As traditional construction procurement approaches are found to be inadequate in meeting the demands and challenges of recent times, alternative procurement routes such as through management contracting or build-operate-transfer are increasingly being adopted. Although design∕build is one of the more popular alternative procurement methods that also has a long history, it does not appear to have well-established contractor selection procedures. This paper focuses on developing a model for contractor prequalification and bid evaluation in design∕build projects. For this purpose, it presents a comparative overview of some international practices in the design∕build contractor selection process. The overall objective is to identify the core aspects of selecting a suitable bidder in order to achieve the best “value for money.” The strengths and weaknesses of current practices of contractor selection are highlighted while identifying some of the best practices followed in design∕build projects by various clients.     

Optimizing Owner∕Contractor Core Competencies for Capital Programs

This paper proposes a process to help managers of capital project organizations, within owner companies, select key competencies for their capital programs and, where appropriate, leverage contractor personnel and expertise. The process involves evaluation of owner and contractor competencies and determination of the best possible sourcing strategy for each competency required in the owner's capital program. The sourcing strategy is defined in terms of work relationships that clearly identify primary and secondary roles and responsibilities. The resulting work structure helps owners and contractors avoid gaps and eliminate overlaps in their work relationships. The proposed process also provides an approach to evaluate alignment of work structures between owner's corporate objectives and their capital programs. Work structure and alignment indicators consider project delivery approaches, contract types, organizational relationships, and other relevant factors such as proprietary technology. Thus, this paper provides a decision process that can help managers optimize owner and contractor competencies for capital program success.     

Owner–Contractor Work Structures: Process Approach

This paper describes a process to assist project managers, within owner companies, define work relationships between owners and their contractors for capital project development, and execution. The owner–contractor work structure process was developed by the Construction Industry Institute as a step-by-step process for making rational decisions about the most appropriate owner–contractor work structure for capital projects. A work structure is described by a set of project competencies and the extent of involvement of the owner and contractor in performing, leading, and/or providing input with respect to those project competencies. The process is described using a formal process modeling technique. Three case studies, conducted to validate the owner–contractor work structure process, are discussed. The results from these case studies suggested some basic changes to the process that would enhance its use in practice. A modified owner–contractor work structure process is then presented.     

Knowledge-Driven ANP Approach to Vendors Evaluation for Sustainable Construction

This paper presents a multicriteria decision-making approach to evaluate contractor candidates in the open competition of construction project procurement by means of a knowledge-framed analytic network process (KANP), which embeds a construction knowledge framework into a formal analytic network process procedure. An applied KANP model named KANP.BID is set up and used in an experimental case study. The paper concludes that the KANP.BID model is a viable and capable tool for selecting the most appropriate tender alternative under criteria of sustainable construction. It is expected that the proposed KANP.BID model can attract interest from both practitioners and researchers in terms of further research and development.     

Predicting Construction Contractor Default with Option-Based Credit Models—Models’ Performance and Comparison with Financial Ratio Models

Construction contractor evaluation is a critical issue in successfully completing a project. It is important for project owners and other stakeholders to identify potentially failing contractors and to avoid awarding them contracts. Previous studies developed construction contractor default prediction models incorporating managerial or economic variables into traditional financial ratio models to enhance predicting power. However, managerial variables are subjective and qualitative, and both economic variables and financial ratios are only available periodically and may not provide the necessary information in time. This study predicts contractor default by employing three option-based credit models (BSM, CB, and BS) based on stock market information, and the empirical results show that all of the models have strong discriminatory power in ranking contractors from riskiest to safest. The misclassification rates of the three models are BSM: 10%, CB: 10%, and BS: 12.7%, all of which are smaller than that of the enhanced ratio model developed by Russell and Zhai (22%), and two of which are smaller than that of the model developed by Severson and colleagues (12.5%). The results show that option-based credit models are good alternatives for construction contractor default prediction.     

Quantifying the Impact of Schedule Compression on Labor Productivity for Mechanical and Sheet Metal Contractor

In a typical construction project, a contractor may often find that the time originally allotted to perform the work has been severely reduced. The reduction of time available to complete a project is commonly known throughout the construction industry as schedule compression. Schedule compression negatively impacts labor productivity and consequently becomes a source of dispute between owners and contractors. This paper examines how schedule compression affects construction labor productivity and provides a model quantifying the impact of schedule compression on labor productivity based on data collected from 66 mechanical and 37 sheet metal projects across the United States. The model can be used in a proactive manner to reduce productivity losses by managing the factors affecting productivity under the situation of schedule compression. Another useful application of the model is its use as a litigation avoidance tool after the completion of a project.     

Discriminant Analysis for Predicting Ranges of Cost Variance in International Construction Projects

Unanticipated market conditions as well as project-related risks can easily lead to cost overruns in international construction projects. For a contractor to be financially successful in international projects, a careful examination of the project is a prerequisite to understanding the cost variance characteristics. Based on the reasonably accurate characterization of the cost performance, the markup or contingency amount is determined to ensure both a decent level of profit and a good chance of winning the contract. This paper presents a classification model to categorize international construction projects, particularly faced by Korean contractors, into five cost-variation classes: extreme cost overrun, moderate cost overrun, neutral, moderate cost saving, and extreme cost saving. The model is able to characterize an international project for its cost performance prediction in comparison to the contractor’s initial cost estimate. A linear discriminant analysis is utilized to develop the predictive classification model with the support of the bootstrap method. Tests show that the proposed model is able to help cost estimators determine a proper level of cost contingency before bidding on an international project.     

Predicting Performance of Design-Build and Design-Bid-Build Projects

Design-build (DB) and design-bid-build (DBB) are two principal project delivery systems used in many countries. This paper reports on models constructed to predict performance of DB and DBB projects on 11 areas, using project-specific data collected from 87 building projects. The study included collecting, checking, and validating industry data, and the statistical development of multivariate linear regression models for predicting project performance. Robust models are developed to predict construction and delivery speeds of DB and DBB projects. Gross floor area of the project is the most significant factor affecting speed. Besides this, for DBB projects, contractors’ design ability, and adequacy of plant and equipment would ensure speedy completion of the projects. For DB projects, if the contract period is allowed to vary during tender evaluation, this would slow down the project. Robust models to predict turnover and system quality of DB projects are also constructed. A DB contractor’s track record is an important variable. They must have completed past projects to acceptable quality and have ability in financial, health and safety management.     

Contractor Perspective on Factors for Evaluating Installation Options for Small-Diameter Utilities

Buried municipal infrastructure is rapidly expanding due to increased urbanization. Subsequently, engineers and contractors are faced with having to designate the most effective method of installing new buried infrastructure while being mindful of cost considerations. In order to determine the most feasible underground construction method, various risks and cost factors must be assessed and analyzed. A comprehensive survey was conducted to solicit input from 28 contractors in various geographical regions throughout the United States and Canada. The intent was to determine specific risk factors and their inherent impacts, as well as to better understand cost comparison between horizontal directional drilling and traditional open-cut construction methods. This paper is relevant to practitioners and researchers because it identifies and describes risks and cost factors from a contractor perspective that can be used for evaluating installation options for small diameter pipelines in an urban environment.     

Managing Owner’s Risk of Contractor Default

The objective of the study presented in this paper is to provide owners with a decision-making mechanism that will free them from automatically taking the typical “transfer the risk to a surety” option and will allow them to make intelligent and economical decisions that include retaining or avoiding the risk of contractor default. The methodology involves using artificial neural network (ANN) and a genetic algorithm (GA) training strategies to predict the risk of contractor default. Prediction rates of 75 and 88% were obtained with the ANN and GA training strategies, respectively. The model is of relevance to owners because once the likelihood of contractor default is predicted and the owner’s risk behavior is established, the owner can make a decision to retain, transfer, or avoid the risk of contractor default. It is of relevance to surety companies too as it may speed up the process of bonding and of reaching more reliable and objective bond/not bond decisions. The comparative use of the ANN and GA training strategies is of particular relevance to researchers.     

Stochastic Modeling for Pavement Warranty Cost Estimation

This paper presents a cost estimation model for long-term pavement warranties with multiple distress indicators. One application area for such warranties involves performance-based specifications (PBSs). In contrast to traditional approaches, PBS gives contractors the flexibility to select construction methods, materials, and even design. However, the contractors then must warrant the performance of their work for a specified period of time. Therefore, an accurate estimation of the risks associated with the warranty is a significant cost issue for any contractor to cover potential risks while still being competitive in bidding. Quantitative evaluation of the cost of risk incurred by the warranty has several difficulties. The deterioration of a highway project is a complex process, which is affected by pavement structure, material, traffic load, and weather conditions. Based on a probabilistic risk analysis of failures of performance indicators, the resulting model can estimate the warranty cost at a detailed level. The application of the model has been demonstrated via a numerical case study using long-term pavement performance data.     

New Project Procurement Process

In the United Kingdom, partnering was proposed by Sir Michael Latham as a means of achieving 30% cost savings in a suitable organizational climate. How this is achieved within the construction industry involves not only a mutuality of purpose between project personnel, but appropriately applied methods of process. This paper addresses the issues surrounding the way forward in partnering. By applying systematic processes to selecting partners on a short-term construction project, a case study demonstrates that cooperative working can be developed and conflict can be avoided. A unique and rigorous selection methodology known as the project delivery process was adopted for the Forton Lake Opening Bridge Millennium project in the United Kingdom, and demonstrates how significant improvements may be achieved in line with worldwide improvement targets.     

Reconciliation of Owner and Contractor Views in Heavy Construction Projects

Delay and loss of productivity are the two main types of damage experienced by the contractor when the owner issues a change order. Courts have recognized critical path method schedule analysis as the preferred method of identifying and quantifying critical delays. As for the inefficiency damages, there is no direct way of measuring inefficiency due to its qualitative nature and the difficulty of linking the cause of the productivity loss to the damage. Most of the scholarly work published in this area was based on data supplied by the contractors; and that explains why there are discrepancies between what the contractor asks for and what the owner believes the contractor is entitled to. This study addresses the need for a statistical model to quantify the productivity loss from verifiable site data such as owner’s daily reports, change orders, drawings, and specifications, rather than rely solely on contractor surveys. A model is developed and validated to quantify the productivity loss in pipe work in roadway projects due to the change orders. The productivity loss study analyzed two sets of data that include: (1) variables that predict which of the two parties, the owner and the contractor, contributed to the productivity loss; and (2) variables that predict, from the legal viewpoint, productivity losses which only the owner is responsible for. The study showed the difference between what the contractor asked for and what he/she is actually entitled to. This model can be used by both the owner and the contractor to quantify the productivity loss due to change orders, and to offer an objective approach to reconcile their differences. This study concludes with an example to demonstrate the use of the model.