Decision-Making in Government Contractor Selection

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Abstract

This study was conducted to demonstrate the reliability of using the multi-criteria decision-making method to accurately select the best contractor among different contractors seeking to be pre-qualified for government construction projects. Applying the analytical hierarchical (AHP) process is specifically appropriate for decision-making for the government because of its suitability in the construction industry as compared with traditional methods that use price as the only pre-qualification criteria. The AHP method can be used to overcome the problems inherent in traditional method such as delays, cost overruns, poor quality work, and other project delivery problems.

The study was driven by the research objectives of analyzing the application of multi-criteria decision-making (MCDM) process in the construction industry and contractor selection using the analytic hierarchy process. A methodology that applied a six point criteria for decision-making based on ranking of contractors using pairwise comparisons in the decision-making process for accurate pre-qualification of contractors. The results showed that the AHP decision-making process factors both qualitative and quantitative data for decision-making, implying higher levels of reliability for the relevant ministries in decision-making. However, additional research needs to be conducted on the role of expert opinion and stakeholders in accurate decision-making in the contractor pre-qualification process.

Introduction

Effective identification and selection of appropriate contractors to undertake government projects within the desired quality metrics in the UAE is vital for a streamlined procurement process. Saaty (1980) notes that the traditional methods often considered the lowest bidder as the most appropriate to award a project. However, Saaty (1980) along with Vaidya and Kumar (2006) argue that the approach often functions within those parameters that fail to consider the quality attributes of a construction project by exclusively considering cost for the evaluation process.

According to Saaty (1980), Ishizaka and Labib (2011), and Al-Harbi (2001) among other authors, lacking a standard methodology or selection criteria that factors efficiency, cost, social, economic and cultural elements for identifying and qualifying contractors results into construction projects that lack the quality component of the built environment. The approach contributes significantly to the rise in problems with public safety and serious project failures. Ishizaka and Labib (2011) note that lack of professionalism becomes clearly pronounced in such projects that clearly defines the decision-making process for contractor pre-qualification in the UAE. According to Saaty (1980), such projects have been characterized by various problems such as poor quality, extensive delays, and increases in the number of litigations and claims besides significant cost overruns.

This calls for greater commitment in decision-making as a vital link for making accurate decisions in the procurement process when deciding on the most appropriate and competent contractor to award a construction project (Saaty, 1980). Among the proposed methods and decision-making models is the multi-criteria decision-making (MCDM) technique. Also, known as multiple-criteria decision analysis (MCDA), the method factors both qualitative and quantitative elements by using evaluating alternative solutions (Ishizaka & Labib, 2011). When confronted with a decision, the MCDA technique can be used to make an appropriate decision. The results depend on how the problem is represented in the decision space, criterion space or in the weighted space (Ishizaka & Labib, 2011).

However, a qualifying technique that promises better decision-making approach which uses an integrated model that factors both the MCDM and AHP (Analytic hierarchy process) elements into the decision-making process have been proposed in the study. Typically, Saaty (1980) considers AHP as a method which enables one to organize and analyze a complex decision in a structured manner using relative rankings of alternatives with relative weights used for the decision-making criteria (Ishizaka & Labib, 2011). According to Saaty (1980), the complex decision-making process in contactor selection begins with a subjective judgment that creates quantitative data for decision-making. AHP is appropriate for this case because it is simple, appropriate, and practical for the selection problem (Ishizaka & Labib, 2011).

The technique uses a scale with values ranging from 1 to 9. In this case, a complex problem is reduced into simpler and unstructured components that are later arranged into variables that are organized in a hierarchical manner (Saaty, 1980). According to Ishizaka and Labib (2011), the relative importance of each subjective judgment is assigned a numerical value for the problem in the solution space and the solution determined by analyzing the weights based on estimating the consistency ratio (Cheng & Li, 2004). AHP is simple, flexible, applies deductive problem solving methods, easy to understand, and provides the decision maker with the opportunity to make the best decisions consistent with the project owner’s goals.

Research motivation

The inspiration for this study is to find an alternative solution to the contractor selection criteria that has been a persistent problem that adversely affects the selection of contractors in the construction industry in the UAE for many years. Here, traditional methods fail to factor various contractor parameters. Ishizaka and Labib (2011) argue that traditional methods of selecting contractors to work on government projects relied on cost for pre-qualification. Efficient and effective utilization of resources in the built environment in the UAE is vital using the multiple criteria decision-making (MCDM) elements promises a locally tailored contractor selection technique that is accurate, reliable, and robust.

Research questions or purpose or objectives of study

1. Analyzing the application of MCDM selection technique in the construction industry
2. Contractor selection using the Analytic Hierarchy Process?

Review of Relevant Literature

Overview of selection models

A critical analysis of the application of different multicriteria decision-making methods for the last 12 years in the area of renewable energy to solve risk prioritization and selection problems aimed at minimizing operational risks of energy assets demonstrated a wide range of the methods that are simple and straightforward. Specific methods such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) originally developed by Hwang and Yoon (1981) based on the concept of the best alternative is the nearest positive ideal solution. Weights for each criterion were arrived at through a compensatory aggregation that increase or decrease monotonically by considering the shortest distance from the positive ideal solution.

Hwang and Yoon (1981) gathered data from 400 articles to determine the similarities and differences among the articles based on the TOPSIS method. Multiple qualitative and quantitative criteria were used. Besides, stochasticity of inputs was considered in another study based on a methodology to evaluate the structural strength of the floating support structure configurations of wind turbines with the goal of selecting the best turbines. In general, the TOPSIS methodology is defined by the definition of the criteria and alternatives, decision matrix, normalized matrix, weighted and normalized matrix, positive and negative ideal solutions, relative closeness, and solution ranking as shown in figure 1.

Chen and Fang (1992) used the methodology to define n criteria and m alternatives for decision-making. The rationale was to establish a normalized matrix that could be transformed to a normalized scale. It is imperative to note that if x_ij score of option i exist in regard to criterion j, the resulting matrix could be X = (x_ij) m×n. If the j is the benefit attribute and j’ the negative attribute, the resulting normalized data could be expressed as: r_ij = x_ij/ √(∑x²_ij) for i = 1, …, m; j = 1, …, n _i. the resulting ideal solution could be expressed as A* = { v₁^* , …, v_n^*}, where v_j^* ={ max (v_ij) if j ∈ J ; min (v_ij) if j ∈ J’ }. The following is a typical example where TOPSIS was applied to determine the relative closeness to the ideal solution based on: Ci* = S’_i / (S_i^* +S’i). The objective was to establish the model of a vehicle to purchase among the Civic, Saturn, Ford, and Mazda. The best alternative based on the selection criteria was the Civic model. Price, durability, flue efficiency, and aesthetical features were considered and the criteria to use.

Table 1

Evidential reasoning approach (ER)

Hwang andYoon (1981) conducted a study using the ER method to determine the best contractor from among several prospective contractors. The study involved assigning numerals to the assessment grades as follows: worst-0, poor -0.4, average-0.7, good-0.85 and excellent-1. The results were based on contractor’s maximum or minimum utilities with the average of the two extremes leading to average utility. This approach resulted in the ranking of contractors on the average utility. The method has been widely used to make appropriate decisions in contractor selection in a wide range of disciplines including engineering, safety, and management among others.

Saaty (1994) detailed its application in industry by making use of graphically designed decision support software that allows the decision maker to input and run raw data on the software for decision-making. ER has a graphical user interface that allows the system to easily capture multidisciplinary data. Once the data had been accepted into the system, various assessment grades were applied that cut across the main criteria consisting of the bid amount, management capability, reputation, health and safety records, technical ability, and financial soundness. The objective was to select the best contractor to be awarded a government construction project. ER is the process of associating the bottom level criterion with the top level criterion for decision-making. The conversion of lower level criteria to higher level criterion is illustrated in figure 2.

Saaty (1994) notes that the criterion used consisted of financial soundness and financial stability. The results indicate that contractors can be ranked in order of preference by quantifying the grades to void situations where certain contractors might have similar scores.

Multiple Objective Decision-making

This decision-making approach integrates additional attributes such as market share, future growth, corporate good will in addition to profit maximization (Ishizaka & Labib, 2011). The decision environment consists of several elements from which decisions are made. In each case, a set of attributes exist for each attribute and selection criteria. Ishizaka and Labib (2011) investigated the use of multiple objective decision-making in selecting a contractor for the construction of a water distribution system. The results showed that the multi-objective framework promoted to a greater extent the inclusion of participants in the decision-making process. Besides, the method factors a wide range of alternative objectives for decision-making compared with other methods.

Requirements of the selection model

Extant literature on the optimum utilization of different selection models of suppliers and contractors to execute government projects based on different criteria has been done. Outstanding features of the selection criteria have been investigated. Laumanns, Thiele, Deb and Zitzler (2002) identified completeness, non-redundancy, appropriate precision, reliability, and mutual exclusiveness as the qualifying factors have been discussed. According to Laumanns et al. (2002), the suitability of completeness as decision-making criteria within different procurement and supply chain systems is critical for making impartial decisions.

The study established that completeness constitutes all the attributes of the system criteria that define the aspects decision makers use to evaluate the suitability of the contractor for a government project. Laumanns et al. (2002) noted mutual exclusiveness as the criteria for measuring the aspects of the problem that is not measured by other criterion to avoid duplication. The study involved an assessment of the awarding criteria of 80% for the companies working for government projects in five countries and concluded that reliability had a significant implication on the accuracy of the criteria used for decision-making. A study by De Boer, Labro, and Morlacchi, (2001) revealed that the objective of accounting for a complete consideration of the problem and other interconnections criterion was necessary for defining an appropriate criterion for effective decision-making in contractor selection.

A study by De et al. (2001) on non-redundancy as a criterion to use for effective contractor evaluation and selection was verified by several authors including Laumanns et al. (2002) in a study involving the procurement of turbine construction materials when used to make appropriate decisions by evaluating different pairs of alternatives. The results concluded redundancy to be a critical aspect of the decision-making criteria. In the discourse by Laumanns et al. (2002), appropriate precision constitutes another critical aspect of the decision-making criterion. In each case, the decision-making criterion should not only reflect the rank order of the criterion, but also the preferences of the decision maker. Cheng and Li (2004) applied a decision-matrix in the selection of a house to purchase by considering seven alternatives that were evaluated by using five criteria.

Reasons for using Analytic Hierarchy Process (AHP)

Developed by Saaty (1980), AHP is the most qualified decision-making method that was adopted for this study. Appropriate decision-making entails accurate estimation and use of pertinent data. Al-Harbi (2001) and Cheng and Li (2004) outlined pertinent reasons that underpin the use of the AHP method. AHP has been widely used in different industries with well proven results. The conclusion is based on results from thousands of organizations that have tested the method for decision-making with proven results.

Researchers have noted that AHP accommodates a broad set of applications for decision-making in a wide range of industries which include project prioritization, vendor selection, recruitment, and technology selection among other applications. Another reason for the choice of AHP as an appropriate decision-making method is because it is intuitive and easy to use. The method allows the use to break a problem into alternatives, criteria, and explicit goals irrespective of the level of complexity of the problem being solved. It is evident that most methods that have been developed recently are complex despite using software to make decisions.it is imperative for the decision maker to understand the method before using it in the software context. Besides, AHP accommodates collaborative decision-making very well.

Analytic Hierarchy Process (AHP) was designed to accommodate various expert views when there are different sources of data. A typical example is the situation that arises where contradictory goals of minimizing price and maximizing quality prevail in decision-making. AHP enables a decision maker to develop a hierarchy of the criteria in order to make the right decision. De Boer et al. (2001) established that AHP enables a decision maker to consider inputs from different groups using a pairwise method that allows for collaboration among different participants. It is established that the AHP method allows for the validation of data for consistency using an appropriate algorithm check.

Application areas of the Analytic Hierarchy Process (AHP)

Al-Harbi (2001) points pointed out case studies where AHP was applied for accurate decision making in selecting the appropriate cares for use in the construction industry. With a wide range of models to select from, it was determined that a wide range of factors such as the lift radii, stability, capacity, and set up requirements. According to Al-Harbi (2001), the cranes were selected based on a hierarchy of goals, objectives and alternative. Three types of cranes were evaluated for selection. The results showed that one crane had a 90% compliance with the rest of the cranes 10% compliant.

The Analytic Hierarchy Process (AHP)

Pre-qualification and bid evaluations have been suggested as key stages in the contractor selection criteria. Researchers link pre-qualification with past performance, financial stability, and past experience. Vaidya and Kumar (2006) observed that a proper pre-qualification method could be appropriate to accurately select the right contractor who meets the desired project quality expectations, time, cost, and schedule. Vaidya and Kumar (2006) evaluated the two stage multicriteria decision-making approach with specific focus on the AHP process and established that both quantitative and qualitative methods were necessary to avoid shortcomings that could be evident in decision-making.

One of the key fundamental points that were suggested includes the weights criteria. Often, decision makers conduct contractor evaluation on project specific attributes by aggregating them to identify an optimum choice. The analytic hierarchy process (AHP) process has been widely used in this context. Among the advantages with the method is its reliability in enabling decision-making in unstructured complex situations that constitute contain multiple attributes where decision made fail to meet the criteria for a linear framework. The unstructured nature of the technique allows for the use of both psychological and physical elements in the decision-making process.

Figure 3 is a detailed description of contractor pre-qualification using the AHP method. In the discourse, an expert opinion is sought to determine the suitability of a contractor to fulfill construction project execution requirements before attaining the status of getting issued with bidding documents. In figure 1, the contractor selection decision is made according to Saaty (1994) by defining the problem and developing the criteria to solve the problem. Pre-qualification of the contractor is defined on criteria that exclude other participants who do not meet set criteria for decision-making. It is demonstrated that contractor data is collected and used in decision-making. The overall goal is to evaluate different sets of data based on the established criteria.

Here, it is imperative to note that the AHP approach diminishes a highly unstructured and complex situation into a more robust and structured problem. It is evident in the discourse that the description of the problem is a complex process involving a hierarchy of criteria and sub criteria (Ishizaka & Labib, 2011). This is followed by attaching priorities to a problem and calculating the results based on the AHP criteria. Multiple decisions are made and rated according to the data for each contractor. In theory, this stage calls for the use of a pairwise comparison matrix of size (n-1) that uses a selection approach of dominating element that qualify to be selected from the set of elements in the matrix. The approach is consistent with the criteria that was developed by Saaty and allows the decision maker to avoid problems such as lack of planning, focus, cost distractions, and the inability to make correct choices. Table 2A consists of both numerical and verbal rating of Pair-wise comparison scale of AHP preferences.

Table 2.

In most cases, the hierarchy of elements is generalized into the criteria that define contractor performance, project approach, capacity to accomplish the project, and the firm’s background. Successive elements uniquely define the sub-criteria for each contractor with the requisite rating to each sub-criterion for each alternative. The basis of the ranking is based on pairwise comparisons. The dominating elements are used in decision-making under the pairwise comparisons method. Elements defining the relative importance of the criteria include the nature of the organization, sum of the project, size, and the project type. A typical study involving pre-qualification and selection of companies involving 27 categories was conducted by Ishizaka and Labib (2011) that classified the firms into three types. The size of the firm was used as the classifying criteria in Hong Kong where the study was conducted. According to Ishizaka and Labib (2011), the companies were classified as under 75, 75-150 and above 150. Another study conducted in another setting used the criteria and sub-criteria elements in table 3.

Table 3.

Table 3 provides a summary of some criteria and sub-criteria used for contractor pre-qualification. However, the study established that environmental safety and health, contractor experience and performance, resources, quality management, technical ability, and financial stability were the key elements that were most widely accepted. It is evident that two sets of problems are solved that are common in project management, procurement, and supply chain situations.

Research Methodology

The problem of accurate selection of a contractor was best solved by considering a multiple criteria approach known as MCDM that accommodates both qualitative and quantitative data. The analytic hierarchy process (AHP) was applied to solve the decision-making problem. AHP consists of various stages that can be summarized into a two stage process model consisting of problem definition and restructuring using some definite criteria with possible solutions in which the process involved creating a hierarchy for the decision criteria (Ishizaka & Labib, 2011).

The decision-making criteria were defined by the decision maker by choosing the alternative options using the AHP method. In the discourse, availability of resources such as expertise and other personnel were considered besides relevant equipment, contractor’s experience, and financial stability. Inclusive of the alternative contractors in the study were A, B, C, D, E, and F as shown in figure 2 (Ishizaka & Labib, 2011). This is consistent with the theoretical approach where an alternative scale defining the intensity of importance with accompanying explanations as well as the scale shown in table 4 shows a ranking of items between 1 and 9.

Table 4.

In practice, a pairwise comparison of the appropriate matrix elements, A, with column and rows entries having element a_ij assigned relative importance of i^th factor with respect to j^th factor was applied in the study. A scale of integer-values between 1and 9 was used in the pairwise comparisons in an Eigen value method.

The matrix method using Eigen values allows for the transformation of subjective judgment into numerical judgments with preferences and weights attached to them (Cheng & Li, 2004). It is evident from the table that the pairwise comparisons were done using the element that dominated the order or influence. It is imperative to note that a value of 1 indicates a strong level of influence of the company and 9 indicates the level of one criterion on the other criterion is very important (Al-Harbi, 2001). The approach allowed for the use of n (n – 1)/ judgments. This approach is based on the use of hierarchical synthesis to weight the eigenvectors based on the weights of the criteria. The sum in each column in the comparison matrix is calculated to find the total of each weighted eigenvectors to establish those values that correspond to each of the lower levels of the hierarchy after the consistence ration was calculated using the (λ_max) eigenvalue. In theory, the (λ_max) eigenvalue can be calculated as follows.

The λ_max takes the maximum eigenvalue defined by positive integers or natural numbers that are used in the judgment matrix with associated weight and factors.

Data Source and Discussion

Questionnaires were administered on the respondents who work in the respective ministry to collect data for analysis. However, subjective judgment was obtained by interviewing respective respondents. The rationale was that the respondents have worked for the UAE in the respective ministry and were deemed appropriate to provide correct data for analysis and decision-making. The goal was to determine the appropriateness or accuracy of the selection criteria. Six contractors were involved in the study and the pre-qualification criteria were based on financial soundness, past experience, manpower resources criteria, and relevance of equipment for the construction project.

Based on the hierarchy tree developed according to the AHP method, the questionnaire that was developed had the attributes that define the pairwise comparisons at each level of the hierarchy. A Saaty’s 1-9 scale was used to rank the pairwise comparisons that provide clear indicators of the strength of the preferences by assigning numerical values to the subjective judgment of the decision maker (Al-Harbi, 2001). To ensure clarity and focus, several questionnaires were administered with the data that was used for input into AHP for analysis. The resulting data for pairwise comparisons from the respondents is tabulated in table 5.

Table 5: Calculating the Pair-wise comparison matrix for experience.

The goal is to ensure that the right and competent contractor are selected from among different bidders. The appropriateness of the judgments based on the method which uses several decision elements has to be tested for consistency using the consistency index (CI) for every specific hierarchy structure and comparison matrix (Al-Harbi, 2001). In theory, the relation holds true which states that a_ij * a_jk = a_{jk Aijk} (for all i, j, and k) holds true for a consistent matrix. The relation CI = (λ_max – n)/n – 1is used to calculate CI where n, which is the matrix size or the elements that are compared in the matrix. The relationship CR = CI/RI = [(λ_max – n)/n – 1]/RI holds true for determining the consitency ratio, which must be less than 0.1 for the decision to be accepted. A high level of incnsistency is noted if the consistency ration is above 0.1. Once the results have been proved to be inconsistent, futher reviews are made to make the results consistent.

Data Analysis and Discussion

The results show detailed description of the most preferred criterion for accurately selecting the right contractor for government construction projects in the UAE. Among the criterion used in the selection process, experience has the highest score of 0.58215 as compared with the rest of the criterion, making experience the best criteria for making the most accurate selection of the contractor. Successive criterion scores include manpower with a score of 0.19544, relevant equipment with a score of 0.09778 and financial stability with a score of 0.12462. The decision makers in the government of the UAE use experience as a critical criterion to measure the suitability of a contractor to be awarded a construction project. Experience, which is regarded as a strong determinant of the number of successfully completed projects and can be used as an indicator of the future performance of the going through the pre-qualification process. This is one of the approaches that can be used to ensure that decision makers avoid decisions that often cause project delivery problems such as delays, cost overruns, and technical failures. Figure 3 show that manpower had a score of 0.19544, which is the second in rank when compared with the rest of the criteria. The value, 0.19544 is within the acceptable limit of the defining criteria of CR: 0.05 < 0.10, which shows a high degree of reliability.

Manpower constitutes important criteria for selecting a contractor in the UAE because it defines the level of knowledge of the employees who undertake the construction projects based on the ability to use modern equipment and technology. Qualified manpower is a strong prerequisite that measures better contractor performance. In this discussion, there is need to involve professionals in decision-making is to add value to the decisions already made using the AHP analytical method. Decision-making is possible and consistent with other models of decision-making when using the AHP model. However, calculation of pair-wise comparisons based on the entry in a matrix leads to consistent results that become more critical to apply, which enables the decision maker to bypass consensus by the professionals. Further modifications of the AHP model provides enhanced abilities in decision-making which incorporates additional economic scenarios that involves other stakeholders. This technique has been applied widely in different contractor selection environments and proved to be very successful.

Result and Discussion

It is imperative to note that AHP process provides the desired abilities for decision makers in the government to accurately select the right contractor to execute government construction projects. The selection criteria is vital to enable the decision makers overcome the problems inherent in many government construction projects. It is evident as noted by Greco et al. (2001) that the problem with price is that it increases competition and forces competing contractors to push down prices to be so low that the resulting quality of work is low.

The results show evidence of many problems that have been experienced by the government of the UAE such as poor quality work, cost overruns, health and safety problems, abandonment of projects, and completion delays. The technique has been applied in different countries and at varying capacities in different industries successfully (Greco et al., 2001). One specific approach was to use applications designed to enable sensitivity analysis for accurate decision-making. The goal of conducting sensitivity analysis is to show the effects of altering any of the parameters used as the criteria for decision-making among the four criteria used in decision-making in the AHP process.

Table 6.

The importance of each alternative is shown in table 5, but decision makers can shift the criterion bar to test to the left or to the right to determine the impact of the alternative weights used in decision-making. It is important to determine the ranking of each contractor based on changes that are introduced on the input parameters. Even small changes make an impact on the ranking of alternative contractors among the six contractors being evaluated for pre-qualification. In practice, different sensitivity analysis models have been used for decision-making and the current study used the performance sensitivity analysis (PSA) to evaluate alternative ranking of prospective contractors among contractors A, B, C, D, E, and F as shown in figure 6.

The sensitivity analysis model works by enabling the user to shift the criterion bar either to the left or to the right and observe the impact on the ranking of the contractors. Consistency of the results can be established using the what-if analysis.

A typical observation of the changes that occur when comparing the outcome of the results between figure 6 and figure 7 by adjusting different weights such as relevant equipment for the construction work from 0.10 to 0.25 and 0.05 does not show any changes in contractor ranking. The results show that contractors in the UAE have the requisite equipment that qualifies them for construction work of government projects, which shows that relevance of equipment has no impact on the ranking of the contractors.

However, when tests were done using financial stability as the criteria by shifting the testing bar from 0.15 on figure 4 to 0.25 on figure 5, changes in the rankings were detected, which showed that financial stability was a critical factor in the UAE to consider for decision makers when awarding government construction projects in the pre-qualification stage. When tests were done on the priority of financial stability by reducing priority with a margin of 0.45, no changes in ranking were observed. The rationale is that construction projects are capital intensive and require investments in the construction industry by those companies that have a good credit rating. However, changes in either side of the bar on manpower resources did not show any indication of changes, which is a clear indicator that such resources are readily available as evidence in figure 6 shows. The most important criterion to consider is financial stability.

Theory shows that decision-making should be based on the attainment of a specific goal. This is reflected in the pairwise comparison criteria that consist of the specific goal of selecting the best contractor for the government projects in the UAE. After the definition of the goal has been done, the underlying criteria include experience, manpower, relevant equipment, and financial stability as shown in figure 3. A better performing contractor avoids delays and provides good quality project outcomes, which is an indicator of a successful project. Financial stability is the next element in the criteria and constitutes key defining elements such as credit rating, company liquidity, profitability, yearly turnover, and credit history. Construction companies in the construction industry heavily rely on loans and other high risk sources of financing construction projects. The risk of becoming bankrupt and defaulting on loans is high among construction companies, which is a critical factor to consider when making decisions on the contractor selection process.

Conclusion and Managerial Implications

In conclusion, it is evident that the traditional methods used for the pre-qualification of contractors depend on the quotation price as a dominant and important criterion in decision-making. However, the problem is that the traditional approach forces contractors to reduce the bid price to be as unrealistically as low as possible, which leads to cost overruns, delays in project execution, poor quality work, project delivery problems, and financial troubles for the contractor. One of the alternative methods that are widely used in various industries for prequalifying contractors in the UAE is the analytic hierarchy process (AHP). AHP is a multi-criteria process which takes into explicit account more than one criterion in the decision-making process.

The analytic hierarchy process (AHP) enables the decision makers in the respective ministry in the UAE not only to make accurate decisions, but enables them to learn about the decision-making problem, discover the value systems of interested contractors and stakeholders, and identify and use organizational objectives that are critical in decision-making. This study focused on the use of the AHP method which is based on a matrix of pairwise comparisons for accurate decision-making. In this study, the selection criteria that were used in the selection process include available manpower resources, relevant equipment criteria, experience, and financial stability/soundness. The AHP process has been demonstrated to be appropriate for decision-making because it allows the decision maker in the relevant ministry to incorporate additional elements for decision-making as opposed to the traditional method of considering price as the main criteria. In conclusion, it is imperative to note that the AHP method provides a better method of accurately prequalifying contractors.

Study Limitations

The study used the multi-criteria decision-making (MCDM) method for accurate decision-making for contractor selection process in the UAE. However, the analytic hierarchy process (AHP) was used as the most appropriate tool for MCDM despite the existence of other methods that can be used to solve more complex decision-making problems. Here, different attributes that have been utilized in the highly structured decision-making process. It is evident that AHP is an inherently limited hierarchical decision-making process that is not inclusive of other elements used in other decision-making methods. Besides, different authors identify different levels of the contractor selection process that limits the universal nature of the method for contractor pre-qualification. However, the novelty of the results is based on the accuracy of the results in using the method for contractor selection in the government of the UAE.

References

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Greco, S., Matarazzo, B., & Slowinski, R. (2001). Rough sets theory for multicriteria decision analysis. European journal of operational research, 129(1), 1-47.

Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making Methods and Applications, Springer.. Berlin/Heidelberg/New-York: Springer Verlag.

Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert systems with applications, 38(11), 14336-14345.

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