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World Applied Sciences Journal 22 (8): 1066-1079, 2013 ISSN 1818-4952 © IDOSI Publications, 2013 DOI: 10.5829/idosi.wasj.2013.22.08.631

Segmenting Critical Success Factors for ERP Implementation Using an Integrated Fuzzy AHP and Fuzzy DEMATEL Approach 1

1

Saeed Rouhani, 2Amir Ashrafi and 2Samira Afshari

Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Tehran, Iran 2 Department of Information Technology Management, Allameh Tabataba’i University, P.O. Box 14155-6476, Tehran, Iran

Abstract: Enterprise Resource planning (ERP) Systems as standard software packages have been welcomed in most of industries. Although implementation of these systems needs considering issues to achieve the success and planned aspiration levels. In order to identifying and classifying these issues as ERP Critical Success Factors (CSFs), various researches have been done and the importances of these factors were discussed. As considerable area, the degree of influence of factors involved in ERP project is a room for investigation. In this paper, with considering the degree of influence factors on each other, a hybrid model was proposed based on fuzzy analytical hierarchy process analysis and fuzzy DEMATEL to evaluate and assess the critical factors in ERP project implementation. Finding the cause and effect relationship between factors and the influence degree on each other was the main contribution of case study of current research on an Iranian steel company. In the case company the cause and grounds factor were identified as: “clear project plan”, “training and education”, “project champion”, “project team competence” and “organizational culture” by utilizing FAHP-DEMATEL hybrid model. Key words: Critical Success Factors (CSF) Enterprise Resource Planning (ERP) Process (AHP) Fuzzy DEMATEL INTRODUCTION In response to deal with radically changing external environment, many firms have changed their information systems (IS) strategies by operating application software packages rather than in-house development [1-3]. Furthermore, the increasing pace of technology in daily operation’s inducing organizations to keep their information accurate and available in real time [4]. These aforementioned environmental features increases the pressure on companies to decline total costs in the entire supply chain, shorten throughput times, decrease inventories, expand product choice, provide more trustworthy delivery dates and better customer service, improve quality and efficiently coordinate global demand, supply and production. Hence, to accomplish these objectives, companies are increasingly turning to enterprise resource planning (ERP) systems [5].

Analytical Hierarchy

ERP is a standard software package [6], that integrate all information and processes to determine how to access, store, summarize, interpret and use information, within a coalesced system [7]. In fact, ERP systems are technological tools that provide a way to coordinate many facets of a company and also enabling resources to be allocated more efficiently [8]. However, in the past research, pressure from competitors [9], requests from partners or customers in the supply chain for linkage or system upgrades [10] and need to make changes to its business processes and practices [11], are the most important reasons to adopt the ERP systems by organizations, nonetheless, three stimulus include productivity improvements, competitive benefit and customers demand have greater share in this regard [12]. The importance of these systems, has introduced them as the most important in the corporate use of information technology in the 1990’s [13]. According to the previous definitions, the adoption of ERP systems is

Corresponding Author: Saeed Rouhani, Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Tehran, Iran.

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extremely valuable in today's competitive environment and in such circumstances there is no way to escape from its lack. Accordingly, several studies have been conducted in the area of?? Enterprise Resource Planning including: risk management issues [14, 15], choosing the right software [16], appropriate organizational structure [17, 18] and critical success factors [19, 20]. Akkermans and van Helden [21], described the impact of CSFs on project performance in ERP implementation. Further, king and burgess [22], developing a new dynamic model in order to achieve success in ERP implementation by considering CSFs in center. Additionally, work on critical success factors (CSFs) should encourage more fitting implementation practice. In regard to critical success factor, various techniques have been used particularly the multi-criteria decision making techniques often tide to ranking and determining the importance of factors have been identified. But what has been less attention among, is the degree of influence factors involved in project implementation on each other and also on the overall project. Hence, it seems to be essential for employing new decision making method in order to resolve the problem either in context of prioritizing and influence degree of each factor. This study proposed a hybrid model based on fuzzy set theory to deal with ambiguity of human judgment, the analytical hierarchy process to determine the rank of each factor and the DEMATEL technique to evaluate the critical factors in context of importance level and the influence degree of each factor on overall performance. In general, the main objective of this study is shown as follows: (1) determine the weight and rank of each factor in terms of dimensions; (2) construct causal relation diagram and specify cause and effect group. The rest of this paper is organized as follows. In section 2, some of the prior literature related to the critical factors of ERP implementation is reviewed. In section 3, research methodology is presented in detail. In section 4, an empirical study is illustrated. Finally, in section 5 according to the finding of this research, suggestions for future research are discussed. Literature Review Erp Implementation: The term enterprise resource planning (ERP) was proposed in 1990s by the Gartner Group [23]. An ERP is an information system that manages, through integration, all aspects of a business including production planning, purchasing, manufacturing, sales, distribution, accounting and

customer service and facilitate the flow of information within an organization [3, 24]. Through the integration of these diverse systems, organizations can gain a competitive advantage in the rapidly changing age. Further, it is a key part of the information infrastructure of modern businesses [8]. A successful ERP can be the backbone of business intelligence for an organization, giving management a unified view of its processes [20]. According to this definition, it seems that ERP packages are one of the important software purchases of medium and small organizations that will have fundamental changes in the organization [4, 25]. Over the past few years, the speed of organizations to implement ERP systems has been phenomenal. A global provider of market intelligence and advisory services for IT and consumer technology markets, International Data Group (IDC), found that global spending on ERP systems increased at an aggregate annual rate of 13.5% between 2001 and 2006, reaching $187 billion in 2006 while a later study anticipated a 5% annual growth from 2006 to 2010 [11]. While, implementation of enterprise resource planning (ERP) systems will result in many benefits, such as reduction of cycle time, increasing the speed of transactions [25, 26], coalesced information systems and business processes reengineering [27, 28]. But it must be acknowledged that an ERP project involves several components of software and business systems, thereby raising organizational problems such as employee resistance, time overruns [12], asymmetry in current business processes [29] and the hidden costs of implementation [1], causing hesitance of organizations to use these systems [15]. Despite the promised benefits, many companies have been plagued by a high failure rate, estimated to be between 60% and 90% [30]. Many researchers in previous studies have reported a high percentage of failure in ERP projects [31, 32]. The Standish Group report on ERP implementation projects expressed that these projects were, on average, 178% over budget, took 2.5 times as long as intended and delivered only 30% of promised benefit [33]. So, we can easily get that ERP implementation projects are among the most difficult systems development projects [34, 35]. Hence, a complete understanding on ERP concept and also the affecting factors in implementation process is essential and inevitable. In conjunction with enterprise resource planning systems problem many studies with different approaches are presented. Bueno and Salmeron [36] presented a

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technology acceptance model for ERP systems success. Xu and Ma [37] focused on knowledge transfer in ERP implementation process. According to Cebeci [16] a new fuzzy decision support system in order to select appropriate ERP software was developed. Also, Wu [38] applied rough set theory to segment and mine the ERP user’s perceived benefits. And finally Chang et al. [39] has adopted a multi-criteria decision making method to measuring the success possibility of ERP implementation. Critical Success Factors Definition: The basic concept of critical success factors was developed by Rockart (1979). Critical success factors include very few key areas of performance in which favorable results are absolutely necessary for a particular manager to reach his or her goals [40]. These include limited areas which derived to satisfactory results and success in organizational performance. The cases are characteristics, conditions or variables that can significantly affect on the success of an organization [41]. Thus, identifying them can help to clarify the nature and scope of resources and prevents to concentrate on the loss cases of the project time [42]. Due to inherent complexity of ERP systems, full control and prediction of the problems and opposite obstacles are impossible. Therefore, the need to be prepared to deal with the disorder will naturally impact on the project and corrective measures to struggle with these problems is essential for every organizations [34, 43]. Since the appearance of enterprise resource planning systems, extensive studies have been carried out in the area of critical success factors [2, 20, 44-50]. In the following, we will introduce the identified factors in the research literature. Identify the Critical Success Factors: In this study, with reviewing extensive literature in terms of ERP critical success factors, about 15 factors which cited more frequently were identified. Then, to validate these selected criteria in context of case environment,

interview with 9 experts includes 3 academic experts in information systems field, 3 ERP implementation consultants, 2 information technology (IT) managers and 1 project manager were set. The outcomes of these interviews revealed that just 9 factors could be considered as CSFs in terms of steel industry to implement ERP systems. Further, through the consulting process, the specified factors were grouped into 3 major dimension include strategic, project management and context. Below, brief descriptions about each criterion are presented. Also, the author’s selected criteria and core dimensions in ERP implementation are briefly shown in Tables 1-2. Top Management Support: In general, one of the first ERP initiative is to gain full commitment from senior executives [51]. Top management could be a useful role in settling disputes and in providing clear direction [33]. According to the Motwani et al. [20], top management needs to identify the project as a top precedence. As Somers [44] states, it has the highest priority in terms of importance. Moreover, as the project progresses, active participation of management remains critical in terms of progressing the project [49]. Thus, top management team must be considered as one of the strategic implications of ERP implementation. More information can be found in [52, 53]. Business Plan and Vision: Effective implementation requires an appropriate business vision that establishes the goals and the business model behind the implementation project [49]. It is as the clarity of the business model behind the project implementation [52]. Furthermore, because ERP implementations usually exceed the time frame for a typical business project, a business plan and vision are needed to lead organizational effort [47]. Planning effort refers to all factors that have to be addressed in the planning stages before the start of the project [46]. According to the importance level of this issue, it can be classified in strategic dimension.

Table 1: ERP critical factors Dimension Strategic (D1) Project management (D2)

Context (D3)

Factors

Sources

Top management support (F1)

[20, 33, 44, 47, 49, 51-53]

Business plan and vision (F2)

[45-48, 62]

Training and education (F3)

[3, 32, 45, 47, 54, 55]

Effective communication (F4)

[2, 3, 22, 52, 61]

Project champion (F5)

[2, 19, 56, 57, 63]

Project team competence (F6)

[21, 22, 48, 49, 53, 58]

Effective cooperation (F7)

[17, 19-21, 33, 49, 59]

Organizational culture (F8)

[2, 20, 33, 45, 60]

Change management (F9)

[2, 19, 20, 32, 47, 49, 61]

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The linguistic variables

TFN

9

Absolutely importance (Ab)

(7,9,9)

(1/9,1/9,1/7)

7

Very strongly important (Vs)

(5,7,9)

(1/9,1/7,1/5)

5

Essentially important (Es)

(3,5,7)

(1/7,1/5,1/3)

3

Weakly important (Wk)

(1,3,5)

(1/5,1/3,1)

1

Equally important (Eq)

(1,1,3)

(1/3,1,1)

Training and Education: The user is the people who produce results and should be held accountable for making the system perform to expectations [33]. This criterion refers to the process of providing management and employees with concepts of ERP system. Thus, people can have a better understanding of how their jobs are related to other functional areas within the company. Due to the inherent complexity of ERP packages, training users is critical [51]. So, this issue can be found in project management dimension. Training and education in ERP implementation is addressed by [32, 54, 55]. Effective Communication: Communication prepares the path to share necessary information for making successful implementation through personnel in different functional areas. The high levels of communication all over the project can facilitate the troubleshooting process [52]. This criterion should be complete and open to ensure honesty [47]. Generally, effective communication will lead to the development of trust and exchange of information required for the acceptance of the technology [54]. Hence, many organizations have developed a communication plan to keep users well informed and aware of the system’s impact on their charges [49]. Based on importance of communication throughout the implementation project, it can be considered in project management dimension. Project Champion: Mostly, the success of technological innovations has been linked to the presence of a champion who carry out the decisive functions of transformational leadership, facilitation and marketing the project to the users [44, 49]. Project champion have greater importance in ERP implementation than other information systems [56]. Also, it must be the main sponsor of the project and constantly manage resistance to change during the implementation process [57]. Thus, in this study based on past studies and experts consensus the project champion have been settled in project management dimension.

Reciprocal of TFN

Project Team Competence: Organizations must be assured that team members in ERP project possess not only broad technological competence, but also knowledge regarding the business requirements involved in terms of h the steering committee and the entire ERP project. Indeed, it refers to the amount of knowledge and understanding the various team members have with respect to the ERP system as well as the business operation process. [58]. The skills and knowledge of the project team are so important in providing expertise in areas where team members suffer lack knowledge [49]. Like above, this issue is classified in project management dimension. More information is represented in [21, 22, 48]. Effective Cooperation: Since ERP is a highly integrated IS, its design, implementation and operation needs to have the complete cooperation of line and staff members from all areas [33]. As ERP systems cross-functional and departmental boundaries, the cooperation of all people involved is necessary [49]. It means that, ERP requires interdependence, close coordination and cooperation among the functional departments and units of an organization to perform tasks effectively [17]. With regard to this fact, this issue is located in project management dimension. The effective cooperation is considered as CSF by [21, 59]. Organizational Culture: As ERP projects are accompanied by many enterprise wide changes, the organizational culture plays an important role in the implementation phase. It can be a facilitator or a major barrier to change. In addition, since an ERP system brings a new way of working and communicating, the success and acceptance of the system is heavily dependent on the organizational culture [2]. As Allen et al. [45] stated, organizational culture had a significant impact on the implementation. So, adapting the implementation to the prevailing cultural style was one of the main cause of project implementation failures [33]. According to the above mentioned, the organizational culture is considered as one of the criteria of context dimension. This issue has also been discussed in [20, 60].

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Change Management: If the ERP systems not managed in an effective manner, it will introduce large-scale change that can cause resistance, redundancies and errors. This factor is one of the basic human related issues to the successful implementation of ERP systems that enterprises are often encountered [2, 32]. According to Motwani et al. [20], a favorable change management program with an appropriate ERP implementation, can be efficient for the organization. Due to underestimating the efforts involved in change management, many ERP implementations fails to achieve expected benefits [49]. Change management is also considered as a CSF for ERP implementation by [19, 47, 61].

Fig. 1: Membership function of the triangular fuzzy number Ã

Research Methodology Fuzzy Sets: To deal with the vagueness of human thought, Zadeh (1965) introduced fuzzy set theory based on rationality in the context of uncertainty due to the vagueness or imprecision. With different daily decision making problems of diverse intensity, the results can be misleading if the fuzziness of human decision making is not taken into account [64]. Fuzzy sets theory providing a more widely frame than classic sets theory, has been contributing to capability of reflecting real world [65]. A fuzzy set is a class of objects with a continuum of grades of membership [66]. In fuzzy set theory, the degree of membership to each of the objectives can be assigned a number between 0 and 1 is unlike CRISP set theory. Hence, fuzzy set theory can express and handle vague or imprecise judgments mathematically [67]. Fuzzy Numbers: Fuzzy numbers are fuzzy subsets of the real numbers that reveal the spread of the confidence interval [68]. A tilde ‘~’ will be placed above a symbol if the symbol represents a fuzzy set. The following mathematic concept builds on [68] and [69]. A fuzzy number à on R to be a TFN if its membership functions A ( x ) : R → [0,1] is equal to:

A

 ( x − l ) / (m − l ), ( x )= ( u − x ) / ( u − m ) ,  

0,

l≤x≤m m≤ x ≤u

(1)

otherwise

Where l and u stands for the lower and upper bounds of the fuzzy number à and m as the modal value (Fig. 1) The Linguistic Variables: Linguistic variables are variables whose values are not numbers but words or sentences in a natural or artificial language.

In fact, the concept of a linguistic variable provides a means of approximate characterization of phenomena which are too complex or too ill-defined to be amenable to description in conventional quantitative terms [69, 70]. For example, about the age can be defined the linguistic variables as "young", "too young", "very very young," "old," "too old," "very very old," and.... Variables and fuzzy numbers used in this paper is based on the study of [71]. Defuzzification: Any fuzzy aggregation method always needs to contain a defuzzification method because the results of human judgments with fuzzy linguistic variables are fuzzy numbers [72]. Defuzzification is selection of a specific crisp element based on the output fuzzy set [73]. There are several useful defuzzification methods that may be divided into two classes by considering either the vertical or the horizontal representation of possibility distribution [74].The most frequently used defuzzification method is the Centroid (Center-of-gravity) method [75], but this does not distinguish between two fuzzy numbers which have the same crisp value in spite of different shapes. Therefore, we here adopt the CFCS (Converting Fuzzy data into Crisp Scores) defuzzification method for our fuzzy aggregation procedure. This is because the CFCS method can give a better crisp value than the Centroid method [76]. According to [73], CFCS method are based on the left and right scores by maximum and minimum of fuzzy numbers. Also, the final score is determined as a weighted average based on membership functions Zijk = lijk , mijk , rijk represent fuzzy assessments of

(

)

evaluator k (k=1,2,...,p) about the degree to which the criterion i affects the criterion j. The CFCS method includes five steps algorithms described as follows: (1) Normalization

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(

)

xlijk = lijk − min lijk / ∆ max min

(

)

(2)

xmijk = mijk − min lijk / ∆ max min

(

)

xlijk =− rijk min lijk / ∆ max min

Where ∆ max = max rijk − min lijk min

Fig. 2: Membership function of linguistics variables for comparing two criteria

(2) Calculate the normal values ??of left and right

and uncertainty in the pair-wise comparison process [80]. To overcome all these shortcomings, FAHP was developed to solve hierarchy problems. This paper proposes the use of FAHP to determining weights of the main criteria. Computational process FAHP based on study of Hsieh et al [68] is as follows:

( ) xrijk / (1 + xrijk − xmijk )

xlijk= xmijk / 1 + xmijk − xlijk , xrsijk=

(4)

(3) Calculate the final normalized crisp value

(

)

k  xls k 1 − xls k + xrs k xrs k  1 − xls k + xls k  xij= ij ij ij   ij ij   ij

(5)

(4) Calculate the crisp value (6)

= zijk min lijk + xijk ∆ max min

(5) The sum of all crisp values zij=

(

1 1 zij + zij2 + ....zijp p

)

Step 1: Build pairwise comparison matrices. Through expert questionnaires, each expert is asked to assign linguistic terms by TFN (as shown in Table 2 and Fig 2) to the pairwise comparisons among all the elements/criteria in the dimensions of the hierarchy system. The hierarchical structure adopted in this study to deal with the problems of ERP key factors is shown in Fig3.

 1 a12  a n1  a 1  a n2  21 A = =         (7) a n1 a n2  1 

a12  1 1 a 1  21     1 a n1 1 a n2

 a n1   a n2       1 

(8)

Where (9)

Fuzzy Analytical Hierarchy Process: Analytical Hierarchy Process (AHP) is a decision-making tool that by decomposition of a complex problem into a multi-level hierarchical structure Include the objectives, criteria, subcriteria and alternatives, describes overall performance of the decision. Saaty presented AHP method to determine the priority of the relative importance of options and features in multi-criteria decision issues in 1977 [77]. AHP method to repeatedly is used in the research literature [78]. One of the strength points of the AHP model is ease of use. In addition, the AHP method can effectively handle both qualitative and quantitative data simultaneously in the decision-making process [16]. Though the purpose of AHP is to capture the expert’s knowledge, the conventional AHP still cannot reflect the human thinking style [79], because it is unrealistic to assign crisp values ??for a subjective judgment especially when information is vague or imprecise [69]. AHP method is often criticized due to its use of unbalanced scale of judgments and inability to resolve the inherent ambiguity

      1,3,5,7,9   aij =  1  1 −1,3 −1,5 −1,7 −1 ,9 −1 

criterion i is relative importance to criterionj i=j criterion i is relative importance to criterionj

Step 2: To use geometric mean technique to define the fuzzy geometric mean and fuzzy weights of each criterion by Buckley as follows: ri=

( ai1 ⊗ ai 2 ⊗ ... ⊗ ain )

1

n

−1 w i = ri ⊗ ( r1 ⊗ r2 ⊗ .... ⊗ r1 )

(9) (10)

According to the above formula, ain is fuzzy comparison value of criterion i to criterion n. Also, ri is comparison of the geometric mean value of criterion i to each criterion and w i is the fuzzy weight of ith criterion.

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World Appl. Sci. J., 22 (8): 1066-1079, 2013 Table 3: The correspondence of linguistic terms and values Linguistic Variable

Linguistic Value

Very high Influence (VH) High Influence (H) No Influence (No) Low Influence (L) Very low Influence (VL)

(0.75,1,1) (0.5,0.75,1) (0,0,0.25) (0.25,0.5,0.75) (0,0.25,0.5)

Step 1: Construct the initial direct-relation matrix. Identifying the decision goal and forming a committee of experts in order to evaluate the direct affect between each pair of elements. Generate the initial direct-relation matrix A = [aij] by converting linguistic assessment into the crisp value. The initial direct-relationship matrix is a n×m matrix and aij represents a direct impact of factor i on factor j and when i = j the diagonal axis aij = 0.

Fig. 3: The hierarchical structure for critical factors in ERP projects Fuzzy DEMATEL Framework: Graph theory has grown greatly in recent years, largely due to the usefulness of graphs as models for computation and optimization. In graph theory, we can easily visually discover things inside the complex problem, because the graph displays mathematical results with visualization clearly and unambiguously. The Decision Making Trial and Evaluation Laboratory (DEMATEL) can separate involved factors into cause group and effect group [67]. According to [81-83], the main purpose of DEMATEL is to find direct and indirect causal relationships and strength of influence between all variables of a complicated system based on matrix calculation. The DEMATEL technique originated from the Geneva Research Center of the Battelle Memorial Institute in 1973 [84]. DEMATEL technique is one of the multi-criteria decision making methods but it differs from other methods by using graph theory and determine the impact of degree and factors relationship on each other. So, in this construction each element can impact all elements are on the same level, higher level or lower level and mutually accepted by all of them. So all factors are evaluated relative to each other and in this case what is are importance of each factor are all the elements. Therefore, this method can discover the cause and effect relationship and thereby provide a plausible structural model [72]. The outcome of DEMATEL method offers Information about the impact of each factor on the overall project. So, it is the most important feature in the field of relationship making multi-criteria decision and factors structure [85]. Fuzzy DEMATEL technique is described in the following steps:

Step 2: Normalize the initial direct-relations matrix. The normalized direct-relation matrix D = [dij] is calculated through Eq. (11). All elements of the matrix D are between D and all the elements on the principal diagonal elements are equal to 0. D=

1 n

max 0≤ x≤1 ∑ j =1 aij

A

(11)

Step 3: The total-relation matrix T can be acquired by using Eq. (12) in which I is an n×m identity matrix. Each element in this matrix represents the indirect effect of a factor on the others. So, the matrix T can reflect the total relation between each pair of system factors. T = D(I-D)

1

(12)

Step 4: Compute the sum of rows and sum of columns of matrix T through the Eqs. (13-14). ri = ∑1≤i ≤ n tij

(13)

ci = ∑1≤i ≤ n tij

(14)

Step 5: A causal diagram can be obtained by mapping the dataset of (D + R, D - R), where the horizontal axis (D + R) is made by adding D to R and the vertical axis (D - R) is made by subtracting D from R. The complete steps of the research method are shown in Fig. 4. A Numerical Example Problem Descriptions: Shahrood steel company with more than 500 tons of steel products per day and more

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Converting linguistic variable to the fuzzy numbers based on linguistic scale is shown in Table 2. Computing the components of synthetic pairwise comparison matrix by using the geometric mean method suggested by Buckley [86] that is:

Fig. 4. Research steps than 350 personnel is one of the most important steel manufacturers in Iran. Due to the growing steel industry in Iran and the entrance of new competitors into the market, competition in such field is increasing. Therefore, based on the company strategy for maintain their position and share in the market and then outshine from competitors, they are trying to implement an enterprise resource planning system is for this major. According to the literature, implementing of enterprise resource planning system is associated with many risks. Therefore, identification of factors and factors involved in the project to reduce operational risk and its successful implementation is extremely important. Thus, according to the literature and also get advice from the project team, nine factors were identified and extracted. The degree of importance of each factor is evaluated by using fuzzy AHP method and then by using fuzzy DEMATEL method cause and effect relations diagram extracted and also most influential factors were identified and introduced. Applications of Proposed Method: In the following section, an empirical study with application of a hybrid fuzzy model is presented to structure and evaluate ERP critical factors in project implementation process. In step1, the committee defined the objectives to employ fuzzy AHP and fuzzy DEMATEL simultaneously. In step2, the formed committee, construct a list of the identified factors and evaluate them based on fuzzy linguistic variable assessment. In step3, the fuzzy AHP method is used to assess the weights of each dimension and criteria. In this step, the computational process is presented as follows: According to the 9 representatives in the project committee about the importance of the identified factors, then the pairwise comparison matrices of dimension will be acquired.

a12 as an example

aij =

1 ( a1i ⊗ ai 2 ⊗ .... ⊗ ain ) n , for

= a12

( (1,3,5) ⊗ (1,1,3) ⊗ .... ⊗ (5,7,9) )=9 ( 2.735,4.622,6.548) .

1

It can be obtained the remaining matrix elements by the same computational procedure. (4) The computational actions to evaluate fuzzy weights of each dimension illustrated as follows: rD1 =

( a11 ⊗ a12 ⊗ .... ⊗ a13 )

1 13 =

(1.451, 2.011, 3.012 ) , ,

Similarly, the other rD , that is, rD 2 = ( 0.471, 0.608, 0.957 )

rD 3 = ( 0.499, 0.815, 0.1.163 ) ,

The weight of each dimension is calculated as follows: w1 = r1 ⊗ ( r1 ⊗ r2 ⊗ .... ⊗ rn )

−1

−1

= ( 0.0.80, 0.176, 0.403) ,

−1

= ( 0.096, 0.237, 0.290 ) ,

w 2 = r2 ⊗ ( r1 ⊗ r2 ⊗ .... ⊗ rn ) w 3 = r3 ⊗ ( r1 ⊗ r2 ⊗ .... ⊗ rn )

= ( 0.281, 0.585, 1.271) ,

(5) To adopt the COA method to calculate the BNP value of the fuzzy weights of each dimension: BNPw1 (U w1 − Lw1 ) + ( M w1 − Lw1 ) 3 + Lw1 = 0.712

The complete computational procedures are presented in Table 4. In step 4, we employ fuzzy DEMATEL method to construct causal diagram and indicate the relationship between the causes and effects of factors into an intelligible structural model of the system. The computational procedures are displayed as follows: (1) Build the initial direct relation matrix based on the decision-makers assessments. For example, the assessment data of the General Manager are shown in Table 5. In order to construct comparison matrices we use linguistic terms, like “Very high influence,”

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World Appl. Sci. J., 22 (8): 1066-1079, 2013 Table 4: Weights of dimensions and criteria Dimensions and factors

Local weights

Strategic

(0.281,0.585,1.271)

Top management support

(0.117,0.261,0.600)

Clear project plan

(0.057,0.118,0.303)

Project management

(0.080,0.176,0.403)

Training and education

(0.028,0.069,0.176)

Effective communication Project champion

Overall weights

BNP

Rank

0.712

1

(0.032,0.152,0.762)

0.326

1

(0.016,0.069,0.385)

0.159

3

0.219

3

(0.002,0.012,0.070)

0.091

8

(0.028,0.064,0.173)

(0.002,0.011,0.069)

0.088

9

(0.046,0.120,0.275)

(0.003,0.021,0.110)

0.147

4

Project team competence

(0.027,0.071,0.183)

(0.002,0.012,0.073)

0.093

7

Effective collaboration

(0.022,0.075,0.190)

(0.001,0.013,0.076)

0.095

5

Context

(0.096,0.237,0.490)

0.274

2

Organizational culture

(0.049,0.136,0.330)

(0.004,0.032,0.161)

0.171

2

Change

(0.028,0.072,0.182)

(0.002,0.017,.0890)

0.094

6

Table 5: The assessment data of the General Manager F1

F2

F3

F4

F5

F6

F7

F8

F9

F1

No

L

H

L

L

H

H

L

VH

F2

L

No

L

H

L

L

H

L

L

F3

L

L

No

L

L

L

H

H

VH

F4

H

L

L

No

L

L

H

H

VH

F5

L

L

L

L

No

L

L

L

L

F6

H

H

H

H

H

No

H

L

H

F7

L

L

H

H

L

L

No

H

VH

F8

L

L

H

H

L

L

H

No

VH

F9

H

L

H

L

L

L

H

H

No

Table 6: The initial direct relation matrix F1

F2

F3

F4

F5

F6

F7

F8

F9

F1

0.000

0.396

0.655

0.630

0.448

0.346

0.785

0.292

0.578

F2

0.863

0.000

0.422

0.318

0.681

0.344

0.733

0.240

0.759

F3

0.837

0.267

0.000

0.707

0.889

0.344

0.786

0.811

0.889

F4

0.474

0.204

0.418

0.000

0.452

0.292

0.915

0.340

0.785

F5

0.429

0.163

0.526

0.733

0.000

0.111

0.811

0.266

0.707

F6

0.941

0.760

0.630

0.474

0.292

0.000

0.734

0.318

0.655

F7

0.604

0.137

0.552

0.863

0.137

0.189

0.000

0.630

0.889

F8

0.837

0.215

0.604

0.915

0.344

0.267

0.863

0.000

0.707

F9

0.578

0.682

0.733

0.655

0.370

0.163

0.837

0.604

0.000

Table 7: The normalized direct relation matrix F1

F2

F3

F4

F5

F6

F7

F8

F9

F1

0.000

0.066

0.109

0.105

0.074

0.057

0.131

0.048

0.096

F2

0.144

0.000

0.070

0.053

0.113

0.057

0.122

0.040

0.126

F3

0.139

0.044

0.000

0.118

0.148

0.057

0.131

0.135

0.148

F4

0.079

0.040

0.069

0.000

0.075

0.048

0.152

0.056

0.131

F5

0.071

0.027

0.087

0.122

0.000

0.018

0.135

0.044

0.118

F6

0.157

0.126

0.105

0.079

0.048

0.000

0.122

0.053

0.109

F7

0.100

0.022

0.092

0.144

0.022

0.031

0.000

0.105

0.148

F8

0.139

0.035

0.100

0.152

0.057

0.044

0.144

0.000

0.118

F9

0.096

0.113

0.122

0.109

0.061

0.027

0.139

0.100

0.000

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World Appl. Sci. J., 22 (8): 1066-1079, 2013 Table 8: The total-relation Matrix F1 F2 F3 F4 F5 F6 F7 F8 F9

F1

F2

F3

F4

F5

F6

F7

F8

F9

0.253 0.390 0.450 0.311 0.292 0.435 0.338 0.408 0.371

0.197 0.143 0.218 0.168 0.148 0.275 0.158 0.187 0.250

0.320 0.298 0.290 0.276 0.281 0.352 0.303 0.343 0.355

0.359 0.324 0.452 0.251 0.351 0.373 0.388 0.435 0.392

0.242 0.282 0.354 0.230 0.155 0.248 0.193 0.248 0.252

0.156 0.159 0.185 0.141 0.110 0.120 0.131 0.158 0.142

0.422 0.426 0.515 0.423 0.397 0.461 0.301 0.474 0.463

0.232 0.229 0.359 0.230 0.212 0.262 0.278 0.210 0.297

0.375 0.409 0.501 0.388 0.366 0.428 0.410 0.430 0.320

Table 9: The values of (R+C) and (R-C) R C F1 2.556 3.248 F2 2.660 1.744 F3 3.324 2.818 F4 2.418 3.325 F5 2.312 2.204 F6 2.954 1.302 F7 2.500 3.882 F8 2.893 2.309 F9 2.842 3.627

Fig. 5: The causal diagram

R+C 5.804 4.404 6.142 5.743 4.516 4.256 6.382 5.202 6.469

R–C -0.692 0.916 0.506 -0.907 0.108 1.652 -1.382 0.584 -0.785

R+C

“High influence,”“Low influence,” “Very low influence,” and “No influence,” as shown in Table 3. Then aggregate these assessment data using CFCS method based on Eqs. (3-7) was produced the initial direct relation matrix Table 6. (2) The normalized direct relation matrix (Table 7) was acquired through Eq. (11) based on the initial direct relation matrix. Also, the total relation matrix (Table 8) was obtained using Eq. (12). Then using Eqs. (13) and (14), the causal and effect diagram (Fig. 5) could be obtained by mapping a dataset of (R+C, R-C) which horizontal axis (R+C) represents the importance of criteria and (R-C) classify the identified factors into cause group as shown in Table 9. Looking at this causal diagram, it is clear that these factors were visually divided into the cause group, including F2, F3, F5, F6 and F8 while the effect group was composed of such factors as F1, F4, F7 and F9.

According to the result of Fuzzy AHP and Fuzzy DEMATEL for arrangement of these factors, several implications can be derived as follows: Based on the proposed method, the identified factors are divided into two sets of cause and effect group. The cause group includes “clear project plan” (F2), “training and education” (F3), “project champion” (F5), “project team competence” (F6) and “organizational culture” (F8) and the other factors including “top management support” (F1), “effective communication” (F4), “effective cooperation” (F7) and “change” (F9) were classified into the effect group. The cause group factors have clear and evident effect on performance of the entire system, Because the factors in cause group represent the influencing factors, whereas the effect group factors stand on the influenced factors [84]. Further, the cause factors have net impact on the entire system as their performance can seriously influence the overall target. Hence, it is acknowledged that factors in cause group should be paid more attention. Further, through this causal diagram (Fig. 5) several valuable clues can be acquired for making profound decisions. In brief, the factors in effect group are tending to be easily impacted by others, which make effect factors unsuitable to be a critical success factor. For example, among all these factors, “change” (F9) is the most important factor because it has the highest intensity of relation to other factors. Moreover, it has the highest importance between the other factors. But as shown in Table 9, the (r-c) value of this factor is negative. So, this factor has less effect on the others. From the cause-effect relationship diagram, we can see that “project team competence” (F6) is a cause factor with (r-c) as 1.652 more than zero which ranks first place among all causal factors. Besides, Table 9 shows that the degree of influential impact of F6 2.954. It is suggests that F6 has remarkable impact on other factors and that improvement of F6 can lead to the development of the whole system.

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The (r+c) score of “training and education” (F3) shows that it is the important factor among all cause factors. Furthermore, the degree of influential impact of F3 ranks the forth place in all 9 factors. It is indicated that F3 can be considered as a significant factor in improving the implementation process. Also, the (r) score of F3 is 3.324, which indicates it has notable impact on the other factors. Among all 9 factors, “collaboration” (F7) has the lowest (r-c), which indicates it is the most influenced factor for the implementation of ERP project. Moreover, it has a (r+c) as high as 6.382, which ranks in the second place among all 9 factors. Additionally, the factor having the second highest priority in terms of (r-c) is factor F4, “effective communication”. Also, its rank in the degree of importance (r+c) is in the fifth place. Finally, the (r) score of “project champion” F5 is 2.312, which places in the lowest level among all 9 factors in terms of degree of influential impact. Therefore, it has no significant effect on the other factors and the whole system. Thus, it can be influenced easily by the other 8 factors. CONCLUSION As the business world moves ever closer to a completely collaborative model and competitors upgrade their capabilities, to remain competitive, organizations must improve their own business practices and procedures. During these years, organizations have invested remarkable resources in the implementation of Enterprise Resource Planning (ERP) systems. In this study, with considering the degree of influence factors involved in ERP implementation project a hybrid model was proposed based on fuzzy hierarchy analysis and fuzzy DEMATEL to evaluate and assess the critical factors in ERP project implementation. Further, finding the cause and effect relationship between factors and the influence degree on each other is the main contribution of case study of current research on Shahrood Steel Company. In the case company, the cause and grounds factor were “clear project plan” (F2), “training and education” (F3), “project champion” (F5), “project team competence” (F6) and “organizational culture” (F8). However, the study has some limitation. First, this study just conducted a case study; thus the findings should not be generalized to other enterprises. Second, it should be considered that different enterprises may have different concerns in terms of criteria for ERP implementation. Third, we examine only nine factors as CSFs. In this sense, it is worthwhile to perform more cases study with developing new criteria for use.

Although the provided numerical example on case study demonstrated the usefulness of the model for factors arrangement, we believe that room still remains for future validation and improvement. So, further research is necessary to fine tune the proposed model and assess its validity in others cases. Applying other MCDM methods in a fuzzy environment to arranging ERP implementation CSFs and comparing the results of these methods is also recommended for future research. Also, mathematical models or meta-heuristics can be combined with the existing method. REFERENCES 1.

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