Optimal Contractor Selection in Construction Industry: The Fuzzy Way

Abstract

A purely price-based approach to contractor selection has been identified as the root cause for many serious project delivery problems. Therefore, the capability of the contractor to execute the project should be evaluated using a multiple set of selection criteria including reputation, past performance, performance potential, financial soundness and other project specific criteria. An industry-wide questionnaire survey was conducted with the objective of identifying the important criteria for adoption in the selection process. In this work, a fuzzy set based model was developed for contractor prequalification/evaluation, by using effective criteria obtained from the percept of construction professionals, taking subjective judgments of decision makers also into consideration. A case study consisting of four alternatives (contractors in the present case) solicited from a public works department of Pondicherry in India, is used to illustrate the effectiveness of the proposed approach. The final selection of contractor is made based on the integrated score or Overall Evaluation Score of the decision alternative in prequalification as well as bid evaluation stages.

Introduction

Project construction must be managed in an effective manner considering the growing demand from clients, competition, and regulatory agencies [1]. However, a failure to properly manage these challenges can lead to problems for the entire project and construction team as well. One of the potential risks in construction projects is the selection of unsuitable contractors which have a considerable impact on client goals such as financial resources, cost, quality, project duration and increasing chances of project success [2]. Thus, contractor selection is viewed as a multifaceted decisive problem in the process of construction management due to the involvement of number of criteria and their interdependence. Therefore, the capability of the contractor to implement the project should be evaluated using a multiple set of selection criteria including reputation, past performance, performance potential, financial soundness and other project specific criteria [3]. Generally, clients tend to select contractors purely on the basis of lowest bid price although tender conditions stipulate several other evaluation criteria. Some researchers opined that contractors should not be selected based on lowest price, but it should be attributed to the highest weight in view of the involved project delivery problems [4]. Both the selected criteria and a sound evaluation methodology are essential for contractor selection, including prequalification, in order to guarantee the simultaneous achievement of time, cost, and quality specifications [2]. Further, contractor selection is a group decision making process under multiple criteria wherein human judgment and crisp data are not adequate to model human preferences with an exact numerical value. Hence, a more realistic approach may be to use linguistic assessments instead of numerical values. Therefore, the ratings and weights of criteria in the problem are to be assessed by means of linguistic variables, which are useful in dealing with too complex or ill-defined situations [3, 5]. Thus, Fuzzy set based approach that makes use of linguistic variables and has the advantage of simultaneously considering multiple criteria, multiple decision makers and handling vague and imprecise data. This paper outlines a Fuzzy Set Theory (FST) based methodology, for optimal contractor selection, to handle the uncertainty involved in decision making.

Literature Review

Different researchers and client organizations used varying sets of contractor selection criteria to assess the capability of the candidate contractors [6]. Russell et al. [7] have considered financial stability, past performance, experience, and key personnel availability as selection criteria. Holt et al. [8, 14] considered contractor’s current workload, past experience in terms of size of projects completed, management resources in terms of formal training regime, past performance and time of year weather; whereas Hatush and Skitmore [9, 15] used financial soundness, technical ability, management capability, and health and safety performance as Contractor selection criteria. Waara and Bröchner [10] investigated the Price and Non price Criteria for Contractor Selection. Krishna Rao et al. [11] conducted a questionnaire survey with construction professionals of the Indian construction industry and proposed criteria set for contractor evaluation in the Indian context. A few researchers worked on contractor selection and proposed different methodologies for bid evaluation [12]. Nguyen [13] proposed a model based on fuzzy sets to tender evaluation. Holt et al. [8, 14] provided example application of multi-attribute analysis (MAA) to the evaluation of construction bidders. Hatush and Skitmore [9, 15] applied program evaluation and review technique (PERT) to evaluate contractor data against client goals (time, cost and quality). Russell et al. [16] developed a rule-based expert system called ‘Qualifier-2’ for contractor pre-qualification while Sonmez et al. [17] adopted evidential reasoning theory to prequalify contractors. McCabe et al. [18] established a contractor prequalification model using data envelopment analysis (DEA). Hanna et al. [19] and Lam et al. [20] applied neural networks to contractor prequalification. Singh and Tiong [21, 22] identified contractor selection criteria (CSC) relevant to Singapore construction industry and developed a fuzzy decision framework for contractor selection. Morote and Ruz-Vila [23] illustrated the use of a systematic prequalification procedure, based on Fuzzy Set Theory, for evaluation of five contractors for the rehabilitation project of a building at Technical University of Cartagena. Krishna Rao et al. [24] proposed multiplicative approach of multi-attribute utility theory for contractor Prequalification and illustrated the same considering the case study of construction of a multistoried building. Paul et al. [25] compared the contractor selection process using TOPSIS and Extended TOPSIS models. Krishna Rao [26] pointed out that the current contractor selection system awards the contract to the prequalified contractor having the lowest bid price, ignoring the contractor’s merit in prequalification score. A modification to the present system, combining the prequalification score and bid price score was suggested by Krishna Rao [26], for the final selection of the contractor in his proposed fuzzy set based approach.

A comprehensive literature review revealed that there is a need to select a potential contractor based on a set of multiple decision criteria, both price and non-price related and evolve a method that considers group decision making. The contractor selection should be made in two—stages viz; (1) Prequalification—examining the contractors for the desired minimum requisites of project implementation, and (2) Evaluation of selected price bids of contractors. Traditional models of contractor evaluation lean to ignore vagueness, fuzziness and human behaviour inherent in the very nature of construction projects.

Research Significance

The concept of integrating prequalification/technical score with bid price score is gaining popularity for selecting an optimal contractor to enable the owners/clients to successfully complete the projects in terms of time, cost and quality. The present work addresses this aspect using Fuzzy Set Theoretic approach for the selection of the contractor. In the present work, this methodology is demonstrated through a case study considering 15 contractor selection criteria (CSC), excepting bid price. Further, relative priority values of criteria are worked out based on the perceptions of construction professionals (Contractors, Public and Private Clients), along with the criteria preferences of Decision Makers (DMs) employed for contractor evaluation in order that the selection process becomes more effective in realizing its objective in true sense. In this method, decision makers evaluate the fuzzy weights of the criteria and contractors assessment on a particular criterion using linguistic variables to consider the uncertainty associated with the mapping of human perception to a numerical value.

Fuzzy Set Approach to Decision Making

Linguistic Variables and Membership Function

The fuzzy set is a kind of mathematical expression which deals with some phenomenon with vagueness.

A mapping on the Universe X can be given as

$$\upmu_{\text{A}} :X \to \left[ {0,1} \right]$$

(1)

$$x \to\upmu_{\text{A}} \left( x \right)$$

(2)

where, µ_A represents a fuzzy subset A on the Universe with µ_A as the membership of A, and µ_A(x) as the grade of membership. The membership grades are very often represented by real-number values ranging in the closed interval between 0 and 1. The grade of membership is usually expressed in terms of trapezoidal and triangular membership functions.

The fuzzy set provides the concepts of membership function, linguistic variables, and so on for describing a vague concept. The decision makers can evaluate the criteria or alternatives in terms of linguistic variables such as Very Important/Very Good, Good/Important, Above Average, Average, Below Average, poor and very Poor. For each linguistic variable, there is a corresponding fuzzy number.

Fuzzy Weights Calculation

The average Fuzzy weights can be calculated according to the fuzzy number of each linguistic variable. Let A = {a₁, a₂, a₃, a₄}, B = {b₁, b₂, b₃, b₄} be any two positive trapezoidal fuzzy numbers and ⊕ is the symbol for fuzzy plus operation, then fuzzy plus operation (fuzzy addition) is expressed as:

$${\text{A}} \oplus {\text{B}} = \left\{ {{\text{a}}_{1} ,{\text{a}}_{2} ,{\text{a}}_{3} ,{\text{a}}_{4} } \right\} + \left\{ {{\text{b}}_{1} ,{\text{b}}_{2} ,{\text{b}}_{3} ,{\text{b}}_{4} } \right\} = \left\{ {{\text{a}}_{1} + {\text{b}}_{1} ,{\text{a}}_{2} + {\text{b}}_{2} ,{\text{a}}_{3} + {\text{b}}_{3} ,{\text{a}}_{4} + {\text{b}}_{4} } \right\}$$

(3)

The average fuzzy weight is the arithmetical average of all fuzzy weights for factor C_ij given by all decision makers which can be expressed as

$${\text{A}}_{\text{ij}} = \left\{ {{\text{a}}_{\text{ij}} ,{\text{b}}_{\text{ij}} ,{\text{c}}_{\text{ij}} ,{\text{d}}_{\text{ij}} } \right\}$$

(4)

where, ${\text{a}}_{\text{ij}} = \frac{{\mathop \sum \nolimits_{k = 1}^{P} ak}}{P},{\text{b}}_{\text{ij}} = \frac{{\mathop \sum \nolimits_{k = 1}^{P} bk}}{P}$, ${\text{c}}_{\text{ij}} = \frac{{\mathop \sum \nolimits_{k = 1}^{P} ck}}{P}\;{\text{and}}\;{\text{d}}_{\text{ij}} = \frac{{\mathop \sum \nolimits_{k = 1}^{P} dk}}{P}$i = 1, 2 …n and j = 1, 2…m.

A ^k_ij represents the fuzzy weight assigned to factor C_ij by expert/decision maker, k;

A_ij = {a_ij, b_ij, c_ij, d_ij} represents the average fuzzy weight assigned to factor C_ij; and P represents the number of decision makers involved in the process.

Defuzzification

Defuzzification is an operation of producing a crisp value that adequately represents the degree of satisfaction of the aggregated fuzzy number. For a trapezoidal membership function, the defuzzified value, e_ij for the average fuzzy weight of factor C _ij is given by the following equation

$${\text{e}}_{\text{ij}} = \left( {aij + bij + cij + dij} \right)/4$$

(5)

where e_ij represents the defuzzified value for the average fuzzy weight of factor, C_ij.

The defuzzified values are normalized and the weight of factor, C_ij is obtained by using the following equation.

$$\mu \left( {{\text{C}}_{\text{ij}} } \right) = \frac{eij}{\sum eij}$$

(6)

where, µ(C_ij) represents weight of factor, C_ij.

Optimal Contractor Selection

In the present study as explained above the Fuzzy Set Theory is used for the selection of an optimal contractor. A real case of construction of a multi-storey building for housing quarters, located in Pondicherry in India, with an estimated contract value (ECV) of INR 360,000,000 is considered in this paper. The project completion time is 25 months. Four bidders namely Contractor P, Contractor Q, Contractor R and Contractor S have participated in tendering process. The bid prices quoted by contractors P, Q, R and S for the project under consideration are INR 363,223,423; INR 389,243,765; INR 426,798,887; and INR 385, 678,459 respectively.

Identification of Contractor Selection Criteria (CSC)

An initial list of 108 criteria, apart from tender price, was selected from the published literature and on the basis of popularity of their use in the context of UK, USA, Hong Kong, Australia, Singapore and Indian Construction industries. In order to identify the significant criteria for contractor selection in Indian context, ten experienced construction practitioners, experienced in tender evaluation exercise, from public and private sectors were involved. Based on their input, 68 contractor selection criteria (CSC), covering 6 main criteria, were chosen for inclusion in the final version of the questionnaire. The relevant and important CSC, in addition to tender price, selected from preliminary round of interviews were categorized into A Contracting Company’s attributes, B Experience record, C Past performance of the contractor, D Financial capability of the contractor, E Performance potential of the contractor and F Project specific criteria. Respondents were asked to indicate their opinion, on the level of importance of criteria in assessing the capabilities of the contractor, on a six-point Likert scale (0–5). The 68 criteria of the questionnaire included in the “Appendix” were weighed by experienced construction practitioners, ranging from public to private sectors, based on the scale mentioned. The questionnaire data were analyzed on the basis of Relative Rank Index (RRI) or Relative Importance Index (RII) technique [27, 28]. In the present study, the top 15 criteria having RRI value more than 0.80, obtained from the ALL respondent perception i.e. Perceptions of 3 groups of respondents (public clients, private clients and contractors) taken together, are considered for the contractor evaluation process as it reflects the polarized view point of respondents. Those top 15 criteria with RRI > 0.8, the bench mark adopted for deciding the significant criteria, are considered to be significant in contractor evaluation and the same are enlisted in Table 1. Therefore, the criteria set shown in Table 1 could be adopted for use in contractor evaluation [11].

Table 1 Contractor Selection/Evaluation Criteria (with RRI > 0.80)

Contractor Prequalification using Fuzzy Set Theoretic Approach

In the fuzzy set based model for contractor Prequalification/selection, the linguistic variables are used by decision makers to evaluate the fuzzy weights of the criteria and contractors on a particular criterion. The linguistic variables and the corresponding fuzzy numbers for the trapezoidal membership function (Fig. 1) chosen by the decision maker, to evaluate the importance of the criteria and the ratings of contractor alternatives with respect to qualitative criteria are as presented in Table 2. Three decision makers DM-1, DM-2 and DM-3 were employed to evaluate the candidate contractors on the criteria considered, using the linguistic variables shown in Table 2. Tables 3, 4, 5, 6, 7, 8, 9 and 10, explain the fuzzy evaluation of criteria, calculation and ranking of prequalified contractors by calculating the crisp score of the decision alternatives (contractors in this case).

Fig. 1

Linguistic Variables for Rating Criteria and Contractor

Table 2 Linguistic variables and fuzzy numbers for rating criteria and contractor

Table 3 Decision makers fuzzy evaluation of importance of main criteria

Table 4 Fuzzy calculations for main criteria

Table 5 Relative importance of sub criteria—ALL respondents

Table 6 Decision makers fuzzy evaluation of contractors

Table 7 Average fuzzy scores of contractors

Table 8 Defuzzified values (crisp scores) of contractor assessment

Table 9 Normalized defuzzified values of contractor assessment

Table 10 Overall priority values (OPV) of contractors using ALL respondents’ RRI

The decision makers’ preferences, in respect of various main criteria (A–F) and the decision alternatives (contractors), expressed in linguistic variables are presented in Tables 3 and 6 (decision- makers’ fuzzy evaluation of importance of criteria and contractors) respectively. The linguistic variables assigned to criteria are then converted into corresponding fuzzy numbers as shown in Table 3.

To normalize the differences existing in different decision makers’ preferences on a criterion or a decision alternative (contractor in this context), a simple average of fuzzy numbers (average fuzzy score) is calculated to subsequently determine the weights or priorities. The average fuzzy scores for the main criteria and decision alternatives are calculated using Eq. 4 and are shown in Tables 4 and 7 respectively. After determining the average fuzzy score, defuzzification is done using Eq. 5, to obtain the defuzzified or the crisp values for various main criteria as shown in Table 4.

From the defuzzified values of main criteria, normalized crisp scores or normalized crisp values have been computed using Eq. 6 and are also presented in Table 4. Table 5 shows the Relative importance of sub criteria that is worked out by integrating the preferences of DMs considered in the present work i.e. Priority (P _i ) of main criterion and the Relative Rank/Importance Index (RRI/RII) of sub-criterion (r _i ) obtained from the questionnaire survey conducted with Construction professionals, i.e. Contractors, Public and private clients in Construction Industry [26]. Tables 6 and 7 show the details of the decision makers’ evaluation and the average fuzzy scores of the contractors.

Tables 8 and 9 respectively show the defuzzified values (crisp scores) and the normalized defuzzified values of contractor assessment (calculated using Eqs. 5 and 6 respectively). Normalized defuzzified values provide the priority of criterion and of the alternative (contractor in this case) for the criterion under consideration.

In the final step of the process, numerical priorities in terms of the Overall Priority Values (OPV) that represent the alternatives’ relative ability to achieve the decision goal are obtained for each of the decision alternatives. The Overall Priority Values of contractor (Table 10) are obtained by sum product of the criterion priority and the contractor priority for a particular alternative. The prequalified contractors are ranked based on overall priority value (OPV) and a contractor with the highest OPV is Ranked-1 and so on. From the Overall priority values (OPV) of contractors computed in Table 10, using criterion RRI values of ALL respondents’ percept, the rank order of the four prequalified contractors P, Q, R and S, based on prequalification/Technical score is 4–2–3–1.

Final Selection of Contractor

In the preceding part, the prequalified contractors have been ranked based on their technical potential (prequalification score), excepting the quoted bid price. It is generally observed that any prequalified contractor having the lowest bid price wins the contract, which underlines the contradiction of not taking the first stage prequalification scores into account in second stage evaluation (price bid evaluation) as the winner may also have the lowest prequalification score among the prequalified contractors [26]. Hence, to address this problem, in the present work it is proposed to combine the bid price score with prequalification score for final selection of the contractor, which will enable the owners or clients to complete the project optimally in terms of time, cost and quality parameters.

In this connection, the normalized technical score, for each contractor, is determined as the ratio of the technical score (OPV) of each contractor against the total technical score of all contractors as shown in Table 11.

Table 11 Final rank order of contractors

Similarly, the bid price score (B_P) which is used to evaluate the bids is computed as the ratio of the base bid price and the proposed bid price of the candidate contractor [28]. The base bid price is the client’s Estimated Cost Value (ECV) to deliver the project without compromising the quality standards (as per contractor document). Higher bid price score indicates that the contractor’s proposed bid price is closer to the base bid price and vice versa.

In the present case study, the base bid price (ECV) is given as INR 360,000,000. The proposed bid prices of contractor of contractors P, Q, R and S are INR 363,223,423, INR 389,243,765, INR 436,798,887 and INR 385,678,459 respectively. The bid scores of the four candidate contractors P, Q, R, and S are found to be 0.9913, 0.9249, 0.8435 and 0.9334 respectively. The normalized bid price score determined is as shown in the Table 11. The Overall Evaluation Score (OES) is calculated by combining the technical/prequalification score obtained in FST approach with bid price score, by giving equal weight to both the scores (Table 11). For example, the Overall Evaluation Score (OES) of Contractor P = 0.2243 + 0.2680 = 0.4923. The rank order of contractors P, Q, R and S is set based on the OES or combined score as 3–2–4–1.

It can be observed from the final rank order in Table 11 that the ranks 1 and 2 (of contractors S and Q) remained same as those based on prequalification score while there is a small variation in ranks 3 and 4 between the rankings obtained based on prequalification score and combined scores. But, Contractor-S emerged as the most desired in both cases. Further, it is to be noted that the final rank order is obtained by attributing equal importance or weight to both technical and bid price scores. Hence, FST approach can be considered as a method which could predict the potential contractor more reliably considering the uncertainties in the selection process while the combined score (OES) aids in selecting the optimal contractor. This could be visualized from the change in the final rank order of contractors as compared to the ranking based on prequalification score alone.

Conclusions

• 1. The study reveals that Fuzzy Set Theory (FST) provides a reasonable and efficient basis for contractor evaluation to address the uncertainty in rating criteria and contractors.

• 2. The FST approach offers a reliable basis for decision making as it facilitates an integration of the weights of criteria obtained from industry survey and the evaluations of decision makers. The decisions arrived are relevant as they reflect the polarized views of clients and contractors.

• 3. FST approach could predict the optimal contractor more reliably considering the combined score of the prequalification and the Bid Price Scores.

• 4. Quality decisions such as predicting the potential contractor can be made more reliably using FST approach.

• 5. The proposed methodology of FST is a fool proof method and can be customized to other sectors also.

Appendices

Appendix: Sample Questionnaire

This questionnaire lists out several evaluation criteria, sub-criteria and their measures that are normally considered by the project owners/clients or their representatives to assess the contractors’ potential to execute the construction project under consideration. Please give your rating, by ticking appropriate one, based on your experience with contractor selection process, their relevance or level of importance in assessing the contractors’ potential to deliver the project at hand.

Scale	Meaning
IR	Particular criterion/attribute (measure) is irrelevant in assessing the contractor’s potential
VLI	It has very low importance in assessing the contractor’s potential
LI	It has low importance in assessing the contractor’s potential
MI	It has medium importance in assessing the contractor’s potential
I	It is important in assessing the contractor’s potential
VI	It is very important in assessing the contractor’s potential

Respondent Details

A	Contracting company’s attributes	IR	VLI	LI	MI	I	VI
1.	Age (experience) and registration of the contractor’s firm/company
2.	Familiarity with regulating authorities
3.	Familiarity with local working culture
4.	Company’s negotiating skill
5.	Company’s trade union record
6.	Prior business relationship
7.	Company proximity to project
8.	Health and safety record of the company
9.	Achievement of quality level (e.g., ISO: 9000:14000)
10.	Post-business attitude (e.g., claims and counter-claims)
11.	Past failures
12.	Record of firm’s social responsibility

B	Experience record	IR	VLI	LI	MI	I	VI
1.	Experience of working on similar projects
2.	Experience with owner’s organization
3.	Experience in local area
4.	Experience in similar geographical and weather conditions
5.	Type and size of projects completed in past 5 years
6.	Highest value of project executed in past 5 years

C	Past performance of the contractor	IR	VLI	LI	MI	I	VI
1.	Work quality in completed projects (i.e., third party quality certification and incentives awarded)
2.	Adherence to time schedule in past works
3.	Percentage of past works completed within the agreed contract value
4.	Percentage of works sublet in past projects
5.	Standard of sub-contractors’ works in past projects
6.	Attitude towards incomplete/correcting faulty works
7.	Cordial Relationship with past project clients/owners
8.	Relationship with sub-contractors
9.	Relationship with suppliers
10.	Relationship with regulating authorities
11.	Blacklisting in past projects
12.	Quality of service during defect liability period
13.	No. of arbitral awards or court decisions (litigation history) in past 5 years

D	Financial capability of the contractor	IR	VLI	LI	MI	I	VI
1.	Current commitments
2.	Authorized and paid-up capitals
3.	Working capital
4.	Current and fixed assets
5.	Net worth
6.	Turnover
7.	Profit generating ability of the company
8.	Liquidity status of the company
9.	Capital structure of the company (amount of debt and equity)
10.	Reference of financial institutions
11.	Balance sheet data
12.	Credit rating
13.	Financial closure (finances-arrangement) for the project

E	Performance potential of the contractor	IR	VLI	LI	MI	I	VI
1.	Qualification and experience of management staff
2.	Availability of in-house skilled labour
3.	Availability of plant and equipment resources
4.	Present work load (works on hand) and capability to support the current project
5.	Quality control and assurance program
6.	Specialized knowledge of particular construction method
7.	Availability of in-house design capacity

F	Project specific criteria	IR	VLI	LI	MI	I	VI
1.	Construction method statement
2.	Specified project time schedule
3.	Qualification and experience level of the project manager
4.	Qualification & experience of professional and technical staff
5.	Experience level of the project team on similar type of project
6.	Number of direct workers available for the project.
7.	Availability of testing equipment as quality assurance
8.	Health and safety setup for the project
9.	The Contractor’s cost and time control considerations
10.	Reputation of sub-contractors to be used for the project
11.	Type of performance bond (through bank or surety company)
12.	Payment schedule
13.	Risk sharing level of the project owner

G	Other criteria	IR	VLI	LI	MI	I	VI
1.	Bid or tender price
2.	Advance payment
3.	Quoted project duration
4.	Defect liability period