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Selecting Engineering Partner for EPC Projects Using a Fuzzy AHP Approach

Selecting Engineering Partner for EPC Projects Using a Fuzzy AHP Approach

Mehdi Ravanshadnia ¹, Hamid R. Abbasian ² and Hossein Rajaie ¹ +

¹ Amirkabir University of Technology, Tehran, Iran

 (Received xxx 2006, accepted xxx 2006,  will be set by the editor)

Abstract. In today's competitive environment, it is necessary to deliver construction projects on time and within budget. One of the solutions towards this achievement is integrated construction and engineering efforts in the form of engineering procurement construction (EPC) joint ventures.

In an integrated system, the planning for both design and construction can proceed almost simultaneously. This system results mutual responsibility, facilitates innovations in construction technology, and minimizes project’s cost and time.

This paper describes a fuzzy analytical hierarchy (AHP) model which constitute a complete quantitative, estimation methodology that can be used to select engineering partner for construction companies. A numerical case with a sensitivity analysis is presented to demonstrate the model application in real cases. The model can be used widely by key decision makers of construction companies bidding EPC projects.

 

Keywords: joint-venture, partner selection, EPC project, fuzzy AHP

 

 

1.    Introduction

Over the past decades, the nature of construction industry business environment changed enormously. Emerging project delivery systems, such as design-build, engineering-procurement-construction (EPC) and management contracting (MC) are increasingly being adopted by the clients.

Owners are attracted to design-build and EPC delivery systems as they normally speed up project completion, reduce project cost, simplify contracts, increase constructability, and create a single point of responsibility. On the other hand, contractors were attracted to those delivery systems because of their flexibility and profitability potential. But, EPC and design-build contracts contain three main challenges in construction companies:

1)               Partnership with an engineering company is mostly the prerequisite of EPC projects’ bid. Most of the EPC projects are implemented by project oriented joint ventures. It potentially provides an adversarial environment that may be full of dispute risks.

2)               Few engineering firms have significant experience in managing construction projects. Most engineering firms consider various ways to reduce their liability by deliberately avoiding the responsibility in construction means, methods, and techniques.

3)               Merged engineering and construction activities change the scope of engineer’s liability. The hallowed standard of care is replaced by warranties, guaranties, and even strict liability. Design er engineers must recognize the practical effect of this liability change.

In general, new delivery systems reduce the duration and the expenses of project execution in comparison with traditional construction delivery systems. Against, these new delivery systems have their own considerations for all of the project stakeholders particularly for partners in construction joint venture.

According to the definition of Geringer, a joint venture (JV) involves at least two parent organizations that contribute equity and resources to a semiautonomous legally separate entity, of which they participate in the decision-making process (Geringer and Hebert 1989). JVs occur when two or more legally separate bodies form a jointly owned entity in which they invest and engage in various decision-making activities (Geringer 1991). The increasing magnitudes, complexities, and risks associated with major construction projects have brought together organizations with diverse strengths and weaknesses form JVs to collectively bid for, and execute projects (Kumaraswamy et al. 2000).

Joint ventures are means of accessing resources held by other organizations, including competitors, on a limited basis; organizations are able to avoid committing substantial capital in development or acquisition of those resources. In EPC projects the search for a suitable complementary partner is usually new by a contractor who is interested in using the engineering capabilities of a consultant (an engineering company).

Globally, international joint ventures are reported to have performed poorly with estimated rates of instability and unsatisfactory performance ranging from 37 to over 70% (Geringer 1991; Park and Ungson 1997). The partner selection process influences the whole project progress procedure.

Partnership with an engineering company is a prerequisite of bidding EPC projects. The problem of picking up appropriate engineering company is a multi criteria decision. The major idea of this research is to propose an appropriate model that select engineering partner.

This research proposes a fuzzy AHP model for engineering partner selection. Developed model applies Buckley’s fuzzy AHP method in a multi stage partner selection problem. The model uses trapezoidal fuzzy membership functions and derives fuzzy weights and performance scores from geometric mean method. Developed fuzzy AHP engineering partner selection model has been implemented in a case study and experts showed enthusiasm for applying the model.

 

2.    Literature Survey

Although construction joint venture contracts are more than ever being used in the construction industry, there are relatively a few researches in the field. The lack of academic works is more severe when engineering partner selection is considered. Amongst all works, the following works are found to be more relevant to the existing paper.

Bing and Tiong (1999) studied the effective risk management measures of international construction joint ventures through case studies to achieve validity for the risk management model. They considered 14 risk mitigation measures in start-up phase in four stages; partner selection, agreement, employment, and control. In the other paper, They identified the risk factors associated with international JVs from and integrated perspective. They grouped risk factors into three main groups: (1) Internal; (2) Project specific; and (3) External.

Askar and Gab-Allah (2002) investigated the potential problems facing parties involved in the Egyptian construction environment. The main conclusion of this study is that picking up the right project, competitive financial proposal; and special features of bid are critical success factors essential for the success of BOT projects in Egypt.

Sherif Mohamed (2003) surveyed the performance in international construction joint ventures formed and operated by Australian and British contracting organizations. Based on the empirical results, he advocated that selecting a suitable complementary local partner and adopting a proactive risk management strategy are vital antecedents to successful venture performance in international projects.

Sillars et al. (2004) exposes factors, which may be observed at joint venture inceptions, those are predictive of organizational success within the joint venture. The survey research of U.S. architecture/engineering/construction (A/E/C) firms indicated that smaller partners in a joint venture experience are more market growth and are more successful. Also, a firm with strength in legitimacy (client trust) is more likely to gain in short-term income but will likely suffer some long-term market loss in comparison to its less-legitimized partners. It is further concluded that culture match among partners’ plays a significant role in ensuring profitable joint venture returns.

Tang et al. (2006) conducted a study that developed and tested a partnering model which revealed the relationships between the critical success factors of partnering and demonstrated their importance to construction.

Ozorhon et al. (2007) developed an analytic network process (ANP) model to examine the links between the determinants of performance and to observe the influences of the factors on the international construction joint ventures performance. The performance of the model is tested on eight real construction projects and satisfactory results are obtained.

Ye and Li (2009) proposed a TOPSIS method for group decision making with interval values to solve partner selection problem for virtual enterprise under incomplete information.

This paper proposes a fuzzy AHP model for engineering partner selection in EPC projects. The earliest work in the fuzzy AHP appeared in Van Laarhoven and Pedrycz (1983), which compared fuzzy ratios described by triangular membership functions. Recently, fuzzy AHP has been applied and developed by various researchers. But, the most relevant work is done by Zahang and Zou (2007). They set up a hierarchy structure of the risks and then developed a fuzzy analytical hierarchy process model for the appraisal of the risk condition pertaining to the JVs to support the rational decision making of project stakeholders.

 

 

3.    Fuzzy AHP Partner Selection Model

Presenting the results of an academic research, this paper proposes a fuzzy AHP model for the problem of partner selection for EPC project. The lack of the ability of AHP to deal with the imprecision data in the pair-wise comparison process has been improved in fuzzy AHP. Instead of a crisp value, fuzzy AHP uses a range of value to incorporate the decision maker’s uncertainty (Kuswandari, 2004). The proposed process is phased as follows:

Step 1) Establishing Criteria for Evaluating Partners

For more criteria adjustment authors ask a group of experts to establish the criteria of evaluating partners. This team can be constituted top managers of a contractor company, project managers, operation groups, and some experts in tendering activities.

In this study, firstly based on current researches some factors like financial capability, relationship with the government, influence in local communities, experience, reputation, and particular strengths to undertake such a project were identified. At last the team agreed on the following criteria as the most important criteria in the engineering partner selection problem.

1. Partner’s financial resources and managerial competence (PFRMC)
2. Partner’s familiarity with the nature of the project (PFNP)
3. Partner’s reputation and its connection with the client (PRCC)
4. Partner’s relationship with the contractor (PRC)
5. Partner’s proposed commission fee (PPCF)

Step2. Creating the Hierarchical Structure

Presenting the decision making hierarchy, the goal is placed at the top, the general criteria are placed at the second level and the third level of hierarchical structure is the list of alternatives.

In this case goal hierarchical structure is selection of engineering partner and its criteria are as is mentioned in step 1 (Figure.1).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fig. 1- Decision making hierarchical structure

 

Step3. Defining Linguistic Terms

A linguistic variable is a variable which is expressed in linguistic terms. The concept of a linguistic variable is very useful to describe the situation that is too complex or has vagueness. Five linguistic terms, “Very Unimportant (VU)”, “Important (I)”, “Equally Important (EI)”, “Important (I)”, and “Very Important (VI)” are defined for expert judgments linguistic transformation.

 

Step4. Constituting the Evaluating Matrices

Experts can evaluate alternatives, criteria and sub-criteria in linguistic terms according to the previous step. Evaluation matrix  can be constituted for the  attribute as in Eq.1.

 

 

where ij ; for i=1,2,3,...,n , j=1,2,3,...,n are linguistic variables and n is the number of alternatives.

 

Step5 .Transform Linguistic Evaluating Matrices into Fuzzy Number Evaluating Matrix

Zadeh in 1965 cited that these linguistic variables can be expressed in fuzzy numbers form. Triangular, trapezoidal, S-shaped, Gaussian, Sigmoid curve membership functions are the most popular fuzzy numbers. In this paper trapezoidal fuzzy number is used. The trapezoidal fuzzy numbers can be denoted as à = (a₁, a₂, a₃, a₄) where a₂ and a₃ are the intervals that judgment certainty is full (μ_ã(x)=1), a₁ is the left spread and a₄ is the right spread (see Table.1). It’s a conceptual schema and mathematical form shown by Eq. (2).

 

After constructing evaluation matrix linguistic variables can be transformed into fuzzy number as below table:

 

Table.1- Trapezoidal fuzzy conversion scale

 

 

Step 6. The Weights of Alternatives

Main MADM researchers had suggested some approaches to solve fuzzy AHP problem like Van Laarhoven and Pedrycz (1983), Buckley (1985), Chen (1992), Chang (1992, 1996). This paper applies Buckley’s fuzzy AHP approach. Buckley pointed out that Van Laarhoven and Pedrycz’s(1983) method was subject to two problems. First, the linear equations of obtained equations do not always have a unique solution. Second, they insist on obtaining triangular fuzzy numbers for their weights. (Chen et al. , 1992) Buckley uses the geometric mean method to derive fuzzy weights and performance scores. This method has following steps:

a) Calculating geometric mean of each row as below:

b) Calculating fuzzy weight  as below:

where the sign  is for fuzzy multiplication, and  is for fuzzy addition.

The following will detail the derivation of fuzzy weight. Let the left leg and the right leg of  be defined as follows respectively:

Furthermore, let:

Similarly, we can define , , and . Finally the weight of the  alternative (, is determined as:

Where the membership function  is defined as follows. Let x be a real number on the horizontal axis. The  can summarized as:

 

Table.2- Entities in a pair-wise comparison matrix

 

When , the  is calculated as:

When , the  is calculated as:

 

Step b is repeated for all the fuzzy performance scores.

c) The fuzzy weights and fuzzy performance scores are aggregated. The fuzzy utilities are obtained based on:

The multiplication and addition of fuzzy numbers is done by below equations:

 

 

 

Mentioned step and its formula is used for opinion of one expert so if there are more than one expert, the mean opinion of experts can be used that it extracts from below formula:

 

 

Where  is calculated as below:

Where n is the number of experts, and , i=1,2,3,...,n ,is the opinion of  expert.

Consider that in the comparison step when the criterion B is “Very Important” denoted by the fuzzy number
, so that negative judgment,” very unimportant”, is described by .

 

Step7. Deffuzified Membership Function:

After the calculation of membership function for comparing them together, they should be deffuzified in the correct approach. In this paper centroid defuzzification is used. Its formula is as below:

where  is the horizontal axis value and  is the value of membership function in .

 

4.    Model Implementation

After establishing the decision criteria and constructing the hierarchical structure, the weight of each alternative should be extracted. In the present case a major international contractor is preparing for an EPC project bid in a Middle Eastern country. The project is a rapid mass transit 400km length railway that is to be implemented in 30 months. One of the requirements of the tender is to nominate a qualified consultant as engineering partner. The contractor has four choices:

Engineering Company No.1 (EC1): which is an international consultant that has good experience in such projects; EC1 has a very large annual turnover and great financial capability; experienced and well supporting managerial team; one experience of works in the host country; EC1 has no previous cooperation with the contractor; EC1’s proposed fee is 10.2 percent of the project cost

Engineering Company No.2 (EC2): which is a local consultant that has not done the same project before; EC2 has limited financial flexibilities; relatively good supporting managerial team; local and good relations with the client; EC2 has done one joint project done with the contractor; EC2’s proposed fee is 12.5 percent of the project cost

Engineering Company No.3 (EC3): which is a local consultant that has executed too many railway projects but not the rapid ones; EC3 has a good financial capability; relatively experienced managerial team; many local projects are done by the company; the company has tight relations with the client; EC3’s managers have good relations with the contractor staff; EC3’s proposed fee is 8.9 percent of the project cost

Engineering Company No.4 (EC4): which is an international consultant that is from the original country of the contractor but not from the project one; has executed too many railway projects and two well done experiences of rapid transit railways; EC4 has a moderate financial capability; relatively experienced managerial team; EC4 has not any work experience in the host country; EC4’s managers have tight relation with the contractor staff and management; EC4’s proposed fee is 9.2 percent of the project cost

For deriving alternative weights each expert must evaluate criteria together and alternative for each attribute. Following table shows the opinions of one expert. Consider these tables show transformation expert’s idea into the fuzzy numbers. Because of this model was implemented in one of the contractor companies in Iran and based on the policy which is governed in that company, the managing director is a person who defines the strategy of the firm, therefore, the model was implemented by his point of view. Tables 3 to 8 present the opinion of managing director regarding the decision criteria weights and the preference of alternatives in a reciprocal fuzzy AHP logic. The abbreviations of decision criteria mentioned in section 3 are used in the following tables.

Table.3- Reciprocal evaluation of the partner selection criteria

 

	PFRMC				PFNP				PRCC				PRC				PPCF
PFRMC	1.00	1.00	1.00	1.00	0.14	0.17	0.20	0.25	0.08	0.08	0.09	0.10	0.10	0.11	0.13	0.14	0.06	0.07	0.07	0.08
PFNP	4.00	5.00	6.00	7.00	1.00	1.00	1.00	1.00	0.10	0.11	0.13	0.14	0.14	0.17	0.20	0.25	0.10	0.11	0.13	0.14
PRCC	10.00	11.00	12.00	13.00	7.00	8.00	9.00	10.00	1.00	1.00	1.00	1.00	4.00	5.00	6.00	7.00	0.08	0.08	0.09	0.10
PRC	7.00	8.00	9.00	10.00	4.00	5.00	6.00	7.00	0.14	0.17	0.20	0.25	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10
PPCF	13.00	14.00	15.00	16.00	10.00	11.00	12.00	13.00	7.00	8.00	9.00	10.00	7.00	8.00	9.00	10.00	1.00	1.00	1.00	1.00

 

Table.4- Reciprocal evaluation of alternatives with respect to PFRMC

	EC1				EC2				EC3				EC4
EC1	1.00	1.00	1.00	1.00	10.00	11.00	12.00	13.00	7.00	8.00	9.00	10.00	1.00	1.00	1.00	1.00
EC2	0.08	0.08	0.09	0.10	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10	0.06	0.07	0.07	0.08
EC3	0.10	0.11	0.13	0.14	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10
EC4	1.00	1.00	1.00	1.00	13.00	14.00	15.00	16.00	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00

 

Table.5- Reciprocal evaluation of alternatives with respect to PFNP

	EC1				EC2				EC3				EC4
EC1	1.00	1.00	1.00	1.00	4.00	5.00	6.00	7.00	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00
EC2	0.14	0.17	0.20	1.00	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10	0.06	0.07	0.07	0.08
EC3	0.08	0.08	0.09	0.10	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10
EC4	1.00	1.00	1.00	1.00	13.00	14.00	15.00	16.00	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00

 

 

 

 

 

Table.6- Reciprocal evaluation of alternatives with respect to PRCC

	EC1				EC2				EC3				EC4
EC1	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10	0.10	0.11	0.13	0.14	10.00	11.00	12.00	13.00
EC2	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00	4.00	5.00	6.00	7.00	13.00	14.00	15.00	16.00
EC3	7.00	8.00	9.00	10.00	0.14	0.17	0.20	0.25	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10
EC4	0.08	0.08	0.09	0.10	0.06	0.07	0.07	0.08	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00

 

 

Table.7- Reciprocal evaluation of alternatives with respect to PRC

	EC1				EC2				EC3				EC4
EC1	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10	0.10	0.11	0.13	0.14	10.00	11.00	12.00	13.00
EC2	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00	0.08	0.08	0.09	0.10	13.00	14.00	15.00	16.00
EC3	7.00	8.00	9.00	10.00	10.00	11.00	12.00	13.00	1.00	1.00	1.00	1.00	13.00	14.00	15.00	16.00
EC4	0.08	0.08	0.09	0.10	0.06	0.07	0.07	0.08	0.06	0.07	0.07	0.08	1.00	1.00	1.00	1.00

 

 

Table.8- Reciprocal evaluation of alternatives with respect to PPCF attributes

	EC1				EC2				EC3				EC4
EC1	1.00	1.00	1.00	1.00	4.00	5.00	6.00	7.00	0.08	0.08	0.09	0.10	0.10	0.11	0.13	0.14
EC2	0.14	0.17	0.20	0.25	1.00	1.00	1.00	1.00	0.06	0.07	0.07	0.08	0.08	0.08	0.09	0.10
EC3	10.00	11.00	12.00	13.00	13.00	14.00	15.00	16.00	1.00	1.00	1.00	1.00	7.00	8.00	9.00	10.00
EC4	7.00	8.00	9.00	10.00	10.00	11.00	12.00	13.00	0.10	0.11	0.13	0.14	1.00	1.00	1.00	1.00

 

 

After this step the geometric mean for each parameter should be calculated. For the first reciprocal matrix, the geometric mean is:

 

Hence   

Similarly, it can get  and ,  and , and and . Finally , , and for the first attribute are as follows:

The performance scores are  , j=1, 2, 3 and 4 can be obtained as:

The other performance scores and can be obtained as:

 

After the calculation of performance scores, the weight vectors of criteria can be calculated. Table.8 presents  for each alternatives:

 

Table.9- the values of

 

According to Table.8, the membership function value of  can be calculated.

Final Results:

Finally, the membership function of each alternative is extracted as is presented in figure.2.

 

Fig. 2- Final alternative scores in fuzzy membership function

The final step of the model is to rank alternatives via membership functions’ defuzzification. Table.10 presents the results of difuzzification.

 

 

 

Table.10- Defuzzified scores of each alternative

 

Based on the results, the contractor should select Engineering Company No.3 which is a local consultant that proposed appropriate price as well.

A sensitivity analysis has been provided to check the robustness of the decision to changes in the criteria weights. In the following charts, for each criterion, the score of alternatives has been calculated with changing the weights of criteria.

	Fig3. Sensitivity with respect to the PFRMC
			Fig4. Sensitivity with respect to the PFNP

 

 

 

 

	Fig5. Sensitivity with respect to the PRCC
			Fig6. Sensitivity with respect to the PRCC

 

 

 

 

 

 

 

 

	Fig7. Sensitivity with respect to the PPCF

5.    Conclusion

Selecting an appropriate engineering partner is one of the key critical success factors of a joint venture. The development of a quantitative method helps practitioners in logical decision making. Based on the findings of the research, main influencing criteria where partner’s financial capability (PFRMC), it’s experience (PFNP), reputation and being known by the client (PRCC), the relationship with the contractor (PRC), and the engineering and supervision proposed prices (PPCF).

This paper contains a multi stage conceptual framework, and applies a well know mathematical multi criteria decision making method for partner selction. It sets up a hierarchy structure of the decision criteria and develops a fuzzy analytical hierarchy process (AHP) model for the appraisal of the candidate engineering companies for joint ventures (JVs).

To ascertain the efficiency of the proposed model, a real world case study is demonstrated and a sensitivity analysis is developed. It is concluded that the fuzzy AHP model is effective in tackling the problem of engineering partner selection for EPC projects.

 

 

 

6.    References

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