A fuzzy Analytical Network Process for SWOT analysis (Case Study: Drug Distribution Company)

Technical Journal of Engineering and Applied Sciences Available online at www.tjeas.com 2013 TJEAS Journal-2013-3-18/2317-2326 ISSN 2051-0853 2013 TJEAS A fuzzy Analytical Network Process for SWOT analysis (Case Study: Drug Distribution Company) Alireza ArshadiKhamseh 1*, Mohammad Fazayeli 2 1. Assistant Professor, Industrial Engineering Department, Kharazmi University, Tehran, Iran 2. M.S. Student, Industrial Engineering Department, Kharazmi University, Tehran, Iran Corresponding author: Alireza ArshadiKhamseh ABSTRACT: Nowadays companies are in competition situations and need to have good knowledge about their business to help them to stay in their market. Distribution companies also encountered with their business problems. This study presents a quantitative fuzzy Analytic Network Process (Fuzzy ANP) based on SWOT analysis to set priorities among SWOT factors in our distributer company(case Study).SWOT matrix is an essential tool for top managers to compare the alternative strategies and as a result for determining the best way for their own business. The ANP method is an improved version of the AHP method. It is more useful in complicated situations when some factors have reactions on each other. Because of Qualitative decision making often encountered by ambiguity and uncertainty, in this study we use fuzzy logic with ANP to overcome both ambiguity and criteria effects in our pairwise comparison and finally The proposed SWOT fuzzy ANP methodology was implemented and tested for the Distribution Company. Keywords: Strategic Management, SWOT, Fuzzy ANP, MCDM, Distribution Company INTRODUCTION Attention Simultaneously to long-term goal, the organization's mission and internal and external information of organization is essential to identify effective strategies. Strategists never consider all possible options and ways that benefit the organization. They only consider some practical and useful ways (David, 2007).Many approaches and techniques can be used to analyze strategic cases in the strategic management process. Among them, Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis, which evaluates these factors of an organization together, is the most common one(yuksel and Dagdeviren, 2007). SWOT analysis involves systematic thinking and comprehensive diagnosis of factors relating to a new product, technology, management, or planning. Moreover, the chosen strategy has also to be in line with the current and future purposes of the decision makers(göreneret al., 2012).SWOT matrix presents a mechanism for considering the linkage among company strengths and weaknesses (internal factors), and threats and opportunities (external factors) in the marketplace, furthermore provides a framework for identifying and formulating strategies. Matching key internal and external factors is the hardest and challenging part of generating a SWOT matrix and requires the discretion of the practitioners (Sevkli et al., 2012). Output of SWOT matrix are four types of strategies)so, WO, ST and WT strategies(.swot matrix can be constructed by tools such as Internal Factors Evaluation (IFE)matrix, External Factors Evaluation (EFE) matrix or Competitive Profile Matrix(CPM)(David, 2007). However, the result of SWOT analysis is often merely a listing or an incomplete qualitative examination of the internal and external factors (Yuksel & Dagdeviren, 2007).Researchers combine some qualitative and quantitative techniques with SWOT to have more efficiencies in developed models, such as incorporating Analytic Hierarchy Process (AHP) in SWOT (Kahraman et al., 2007; Kurttila et al., 2000; Shrestha et al., 2004; Görener et al., 2012; Şeker and Özgürler, 2012). In addition, Yuksel and Dagdeviren have expanded a more Chartered model with an Analytic Network Process(ANP) to consider potential interaction effects, dependencies and feedback between the elements of SWOT matrix. Sevkli, Oztekin, Uysal, Torlak, Turkyilmaz and Delen (2012) in their studied used Analytic Network Process (ANP), in this paper we introduce combined fuzzy analytical network process by SWOT as below:

In part 2 we introduce ANP, fuzzy logic in part 3, we have fuzzy ANP in section 4, and after that introduce our method for problems in section 5 and in part 6, we solve a problem (case study) by our method and finally by comparison the results in FANP by ANP, AHP, FAHP, we have conclusions in last section. Table 1. Explanation of the pair-wise comparison scale Intensity of importance Explanation definition 1 Equal importance Two activities equally contribute to the object 3 Moderate importance Experience and judgment slightly favour one activity over the other 5 Strong importance Experience and judgment strongly favour one activity over the other 7 Very strong importance An activity is very strongly favoured over the other, its dominance is demonstrated inpractice 9 Extreme importance The evidence favouring one activity over the other is of the highest possible order ofaffirmation 2, 4, 6, 8 For compromise Sometimes one needs to interpolate a compromise judgment between the above values numerically becausethere is no good word to describe it Analytic Network Process (ANP) Among the various Multi-Criteria Decision-Making (MCDM) techniques proposed, the Analytic Hierarchy Process (AHP) proposed by Saaty (1980) is very popular and has been applied in wide variety of areas including planning, selecting the best alternative, resource allocation and resolving conflicts(subramanian and Raman than, 2012). However, in the application of AHP, the most important research restriction or limitation is that researchers have to assume that criteria are independent with no interaction in the decision process(hao-wei Yang and Kuei- Feng Chang, 2011). To overcome this shortcoming in AHP, Saaty (2003) developed Analytic Network Process (ANP) as a new and comprehensive decision theory The AHP assuming independence between the different levels of the hierarchy which will be a special case of the ANP. The ANP is the most comprehensive framework for the analysis of corporate decisions. It allows both dependence within clusters of elements (inner dependence) and between clusters (outer dependence).the elements in a cluster may influence other elements in the same cluster and those in other clusters with respect to each of several properties (Onutet al., 2011). In ANP, similar to AHP, the relative importance of a given element is determined using pairwise comparison with a scale of 1-9. The Scale is shown in Table 1 (Saaty, 1994).All of these relations are evaluated as pairwise comparisons. A reciprocal value is assigned to the inverse comparison. However, ANP must evaluate interdependencies within levels of clusters and mutually dependent elements in a cluster. To complete this evaluation, Saaty has developed a square matrix super matrix whose size is the number of all elements in the network. The final analysis of ANP is to derive the overall weight of each element. These overall weights are usually calculated by raising the super matrix to a sufficiently large power until the weights have converged and can remain stable (Guneriet al., 2009).A comparison the AHP and ANP methods are shown in Figure1. The super matrix is composed of sub-matrixes, where each of them is a set of relations dealing with two levels in the network model. Let the clusters of a decision system be ; i=1,2,3,, n and each cluster has elements, denoted by Iftwoclusters and are chosen and compare all elements topairwisethefirst element the matrix D is obtained. Eigenvector D is equal to Eq. (2), Pairwise may be not being meaningful in this case, the Eigenvector will be zero (no dependencies).if there is any dependence amongst the factors, then desired estimate would be a non-zero matrix. If all elements compare with all elements, then, Matrix is obtained as eigenvector. 2318

Then, the local Eigen vectors obtained are grouped and placed in the appropriate positions in a supermatrix based on the flow of influence from one cluster to another, or from a cluster to itself. A standard form for a super matrix is as shown in Eq. (4). To obtain convergence on the final weights of the alternatives, the weighted super matrix is raised to the power of, Where k is large enough; the new matrix is called the limit super matrix. The limit super matrix(equation 5) has the same form as the weighted supermatrix, but all the columns are the same. If all dependencies in a hierarchy are considered, the priority weights of the alternatives can be found in the column of alternatives in the normalized supermatrix. The largest overall priority of the calculations made using the matrix operation is selected as the best alternative (Yuksel and Dagdeviren, 2007). Fuzzy Numbers Fuzzy set theory by Zadeh (1965, 1976)was introduced first. Fuzzy set theory with regard to the ambiguity and uncertainty(rather than delete and ignore)and promoting multi-valued logic instead of two-valued logic (true, false) allows a closer Check at the issues. Human judgment is generally characterized by vague language, like equally, moderately, strongly, very strongly, extremely and a significant degree. Using such language, decision makers quantify uncertain events and objects. Fuzzy theory enables decision makers to tackle the ambiguities involved in the process of the linguistic assessment of the data (Mohanty et al., 2005).A fuzzy set theory is defined to be a class of objects with a continuum of grades of membership, which assigns to each object a membership level, ranging between zero and one. A Triangular Fuzzy Number (TFN) is represented by (l/m,m/u) or (l,m,u), where. The parameters, and refer to the smallest possible value, the most promising value, and the largest possible value that describes a fuzzy number. Each TFN has linear representations on its left and right side such that its membership function can be defined as Eq.6 (Yukseland Dagdeviren, 2010). (6) { A TFN is shown in Fig. 2. Fuzzy ANP Fuzzy ANP(FANP) is very useful in situations where there is a high degree of interdependence between various attributes of the alternatives. In this approach, pairwise comparison matrices are formed between various criteria of each level with the help of triangular fuzzy numbers. FANP can easily accommodate the 2319

interrelationships existing among the functional activities. However, human perceptions and judgments are often vague and uncertain, and needs to combine the model by fuzzy theory. There are many fuzzy AHP methods proposed by various authors (Buckley, 1985; Chang, 1992, 1996; Cheng, 1997; Deng, 1999; Leung and Cao, 2000; Mikhailov, 2004; Laarhoven and Pedrycz, 1983). In this study, we use Chang (1992, 1996) extent analysis method. This method is based on the TFN by using improved saaty s normalization method. The steps of Chang s (1992, 1996)extent analysis approach are as follows: Let be an object set, and be a goal set. According to the method of Chang s (1992)extent analysis, each object is taken and extent analysis for each goal,, is performed, respectively. Therefore, extent analysis values for each object can be obtained, with the following signs: (7) Where all the are TFNs. The steps of Chang s extent analysis can be given in the following: Step 1: The value of fuzzy synthetic extent with respect to the i th object is defined as * + (8) To obtain ; perform the fuzzy addition operation of m extent analysis values for a particular matrix such that ( ) (9) And to obtain * +,perform the fuzzy addition operation of valuesuch that (10) And then compute the inverse of the vector in Eq. (6) such that [ ] ( ) (11) Step 2:The degree of possibility of * ( )+(12) And can be equivalently expressed as follows: is defined as { (13) Whered is the ordinate of the highest intersection point D between and (see Figure 3).To compare and, we need both the values of and. Step 3: The degree possibility for a convex fuzzy number to be greater than can be defined by convex fuzzy numbers Assume that Then the weight vector is given by (16) (14) (15) 2320

Where are elements. Step 4: Via normalization, the normalized weight vectors are (17) Where W is a non-fuzzy number. The fuzzy scale regarding relative importance to measure the relative weights(sevkli et al.,2012) is given in table 2.This scale will be used in Chang s fuzzy AHP method. To evaluate of the decision maker preferences, pairwise comparison matrices are structured by using triangular fuzzy numbers(l,m,u).the triangularfuzzymatrixcanbegivenasfollows: (18) ( ) The element represents the comparison of component (row element) with component (column element).if is pairwise comparison matrix,it is assumed that it is reciprocal,and the reciprocal value,i.e.1/, is assigned to the element. is alsoa triangularfuzzy pairwisecomparisonmatrix. Table 2. Definition of TFN-linguistic scale for importance Triangular fuzzy scale Linguistic scale for importance TFN Using FANP for SWOT The general supermatrix notation for SWOT model used in this study which given in Figure 4 is as follows: [ ] The interdependency is exhibited by the presence of the matrix element of the supermatrix algorithm is as follows:(sevkli et al., 2012), the proposed 2321

Step 1:Identify SWOT sub-factors and forming SWOT Matrix and determine the alternative strategies according to them. Step 2:Determine the importance degrees of the SWOT factors by using the scale, with assuming that there is no dependency among the SWOT factors ( ). Step 3:Determine, by Using the scale, the inner dependence matrix of each SWOT factor with respect to other factors and consider schematic affiliation them ( ). Step 4:Determine the final importance of the SWOT factors with respect to the dependencies( ). Step 5:By Using the scale, Determine the local importance degrees of the SWOT sub-factors (calculate ) Step 6:By using the final importance of the SWOT factors and the local importance degrees of the SWOT sub-factors, determine the global importance degrees of the SWOT sub-factors (calculate ). Step 7:Determine the importance degrees of the alternative strategies with respect to each SWOT subfactor with a fuzzy scale (calculate ). Step 8: Determine the overall importance of the alternative strategies, reflecting the interrelationships within the SWOT factors(calculate ). The application of SWOT fuzzy ANP methodology in the Distribution Company This section presents a case study that was implemented in the Distribution Company for drug industries to select the best strategy by using SWOT fuzzy ANP methodology by considering vision, mission, targets and internal and external environment of this company. We have 6 alternative strategies based on SWOT analysis as bellows: Development of a new package of products (SO1) Creating a rewarding system for customers (SO2) Decreasing of work load (WO1) Using new technologies in ordering (ST1) Modification in distribution Dep. to have better delivery system(st2) Ready money (cash) protection (WT1) External factors Opportunities (O) ew health ministry recommendation on health(o1) here is no new package for selling by competitors(o2) Government special support in year 2012(O3) Threats (T) nline order system by competitor (T1) anction increasing(t2) ew conditions of competitors for better settlement (T3) Table 3. SWOT Matrix for the Distribution Company Internal factors Strengths (S) Weaknesses (W) Refund products in distribution xclusive representative for distribution of products(s1) outing system for marketing(s2) overing all drugstores in Tehran (S3) Employee cooperation in decision making for new product development (S4) SO strategies evelopment of a new package of products (SO1) Creating a rewarding system for customers (SO2) ST strategies sing new technologies in ordering (ST1) Modification in distribution Dep. to have better delivery system(st2) N T Dep. (W1) ore varietyin products(w2) ate settlement(w3) Low sale(w4) WO strategies Decreasing of work load (WO1) O WT strategies Ready money (cash) protection S (WT1) N E R C D U 2322

These come from analysis on strengths, threats, opportunities, and weaknesses of company (see table 3). Step 1:The problem was converted into a hierarchical structure in order to transform the sub-factors and alternative strategies into a state in which they can be measured by the ANP technique. The ANP model structure for the case study is shown in Fig. 5. The aim of choosing the best strategy was placed in the first level of the ANP model and the SWOT factors(strengths, Weaknesses, Opportunities, Threats) were in the second level and the SWOT sub-factors in the third level and6 alternative strategies developed for this study were placed in the last level of the model. Step 2: At this Step, the pair-wise comparisons of the SWOT factors was conducted with assuming that there is no dependence between SWOT factors and using the scale.for example Weaknesses (W) and Threats (T)are compared using the question How important is Weaknesses (W) when it is compared with Threats (T)? and the answer Moderately preferred, which is equivalent to the triangular fuzzy numbers. Pairwise comparison matrices are analyzed by the Chang s extending analysis method and local weights are determined. The local Weights for the factors are calculated in a similar method to the fuzzy evaluationmatrices, as shown in Table 4. Table 4. Pairwise Comparison of SWOT factors without dependence among them SWOT factors S W O T Importance degrees of SWOT factors Local weights Bottom Medium Top Strengths (S) 0.195122 0.361111 0.481481 0.395112 Weaknesses (W) 0.178862 0.287037 0.407407 0.292906 Opportunities (O) 0.154472 0.203704 0.333333 0.184728 Threats (T) 0.130081 0.148148 0.296296 0.127254 From Table 4: Using these vectors: ( ),, Thus, the weight vector from Table 4 is calculated as 2323

Step 3: In this step, interdependent weights of the factors are calculated and the dependencies among the factors are considered. Dependence among the factors is determined by analyzing the impact of each factor on every other factor using pairwise comparisons (see Fig. 6.). The relative importance weights are presented in the last column of Tables 5. Table 5. The inner dependence matrix of the SWOT factors with respect to strengths Local Strengths W O T Importance degrees of SWOT weights factors Bottom Medium Top Weaknesses (W) 0.069681 0.086957 0.119222 0 Opportunities (O) 0.358362 0.608696 0.933045 0.792266 Threats (T) 0.230375 0.304348 0.466523 0.207734 Step 4: In this step, the interdependent importance of the SWOT factors are calculated as follows: Step 5: In this step, local importance of the SWOT sub-factors are calculated using the pairwise comparison matrix. 2324

Step 6: Using interdependent weights of the factors (Step 4) and local weights sub-factors (Step 3); global weights for the sub-factors are calculated in this step. Global sub-factor weights are computed by multiplying local weight of the sub-factor by interdependent weight of the factors. Step 7: In this step, the importance degrees of the alternative strategies calculated with respect to each SWOT sub-factors. Step 8: Finally, the overall priorities of the alternative strategies, reflecting the interrelationships within the SWOTfactors, are calculated as follows: The FANP analysis results indicate that is the best strategy withan overall priority value of. COMPARING THE AHP, ANP, FAHP WITH FANP RESULTS According to the FANP analysis, alternative strategies are ordered as.the same example is analyzed with the AHP and FAHP hierarchical models assuming there is no dependence among the factors. In addition, the ANP model is using to the same model. The overall priorities computed for the alternative strategies are presented below. The same pairwise comparison is used to compute the AHP, FAHP and ANP priority values. Table 10. Weights and Ranking of the Strategies with AHP and ANP Weights in AHP 0.30146 0.11989 0.08144 0.17782 0.21926 0.10014 Ranking in AHP 1 4 6 3 2 5 Weights in ANP 0.29823 0.11136 0.07254 0.18785 0.23199 0.09803 Ranking in ANP 1 4 6 3 2 5 Weights in FAHP 0.086787 0.045339 0.05766 0.138121 0.601976 0.070116 Ranking in FAHP 3 6 5 2 1 4 Weights in FANP 0.053081 0.050931 Ranking in FANP 3 5 6 2 1 4 CONCLUSIONS SWOT is a common tool for analysis threats, opportunities, weaknesses and strengths points of a company that wants to stay in its market.in real world factors are not independent and they effect on each other s and also there are many criteria for managers that must be considered together to have the best result for their company. We have discussed about ambiguity and uncertainty in decision making and design an FANP (fuzzy Analytical Network Process) to solve these difficulties, as we said; this method will be a strong one when criteria and alternatives have reactions on each other. We used our model in a drug distribution company to find which strategy will be suitable for our case study in drug distribution market and finally we compare this method with some other fuzzy and non-fuzzy MCDM methods. Our proposed solution and technique will be suitable for problem solving in any level of management. REFERENCES Chang DY. 1996.Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95 (3), 649 655. Dagdeviren M, Yuksel I, Kurt M. 2008.A Fuzzy analytic network process (ANP) model to identify faulty behavior risk (FBR) in work system. Safety Science, 46 (5),771 783. 2325

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