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Application of Fuzzy Topsis for Ranking Suppliers of Supply Chain in Automobile Manufacturing Companies in Iran

Application of Fuzzy Topsis for Ranking Suppliers of Supply Chain in Automobile Manufacturing... Fuzzy Inf. Eng. (2011) 4: 433-444 DOI 10.1007/s12543-011-0096-3 ORIGINAL ARTICLE Application of Fuzzy Topsis for Ranking Suppliers of Supply Chain in Automobile Manufacturing Companies in Iran H. Aghajani · M. Ahmadpour Received: 26 January 2011/ Revised: 20 June 2011/ Accepted: 15 October 2011/ © Springer-Verlag Berlin Heidelberg and Fuzzy Information and Engineering Branch of the Operations Research Society of China Abstract Evaluating, ranking and selecting of good supplier play an important role in decreasing of buying risk and increasing of efficiency and effectiveness in value chain and competitive ability of organizations. The goal of this paper is determina- tion and localization of criteria, ranking and selecting suppliers in Automobile Man- ufacturing Companies in Iran. Based on literature review, 27 criteria were selected and localized, then using factor analysis, they were decreased to 6 items including quality, delivery, technical skill, after sales services, investment and product design. Ultimately, 4 suppliers including Tavan, Borna, Saba and Niroogostaran have been assessed and ranked by fuzzy topsis technique. The results of the research state that score of Borna is better than others with coefficient of 0.52, and from this view, Tavan, Saba and Niroogostaran are in the next ranking with coefficient of 0.45, 0.41, 0.31, respectively. Finally, it concludes some remarks including discussion, summary of implications for managers about how they can use this research results for select- ing the best supplier and promoting the competitive ability of their organization, and directions for their further work too. Keywords Topsis· Ranking supply chain · Automobile 1. Introduction Supplying chain management and selecting suppliers are important subjects for com- panies. So many producers want to create effective relation with suppliers to improve their performance and competitive ability. The theme of buying and supplying pro- cess is discussed as a strategic issue and has caused a lot of attention to buyer and H. Aghajani () Department of Industrial Management, University of Mazandaran, Babolsar, Iran email: aghajani@umz.ac.ir M. Ahmadpour () Department of Art, University of Mazandaran, Babolsar, Iran email: mj.ahmadpour@umz.ac.ir 434 H. Aghajani· M. Ahmadpour seller relationships in organizations, thus supplier selection process issue has been suggested as one of the most important issues for the establishment of an effective supply chain. The goals of decreasing buying risk and increasing efficiency and effec- tiveness of the value chain can lead to the creation of a strong durable and long-term relationship between buyer and supplier [6]. Crosby believed that 50 of the quality problems of companies are due to unsuitable supplier selection and management. Therefore it is important that we select only suppliers that can supply buying needs of different administrative zones (Monczka et al, 2008). Although different studies (Astom & Golhar, 1993; Dickson, 1996; Bharadwaj, 2004; Hoha & Krishna, 2007; Lin & Chang, 2007; Chen et al, 2006) have been conducted about supply chain management, in spite of this, the purpose of present study is determining the criteria assessment for automobile suppliers in Iran, and assessing and ranking battery suppliers in the automobile industry. 2. Background and Questions The term of supply chain management was developed by Oliver and Webber in 1982. Since the advent, this concept, mainly was used to explain the benefits of integra- tion of the organization functional areas such as purchasing, production, logistics and marketing. But the history of its rise is on 50 and 60 decades. production resources planning (MRPII) was introduced in the 1970. The concept of materials management went one step forward and distribution and transportation operations added to it by intensifying global competition in 1980, and finally led to the concept of integrated support that is known as supply chain management. Development of supply chain management was continued by introducing three main process, including logistics management, relationship management and information management in 1990. Seven principles of supply chain management system were introduced that include customer classification based on the services they needed, setting supporting network, demand planning by using continuous projections and optimal allocation of resources, product design and production based on customer desire and enhancing the speed of adapting to the change, the strategic management of supply chain in order to reduce the cost of materials and components and related services, supply chain design strategy that can support different levels of decisions, selecting performance comprehensive criteria to measure success rate in achieving efficient and effective to the ultimate consumer demands (Steadler et al, 2003; Teimoori, 1999; Riazi, 2000; Hathmouth & Cristofer, 2007; Giannoccaro & Pontrandolfo, 2002). The first studies on the evaluation and selection criteria for suppliers have been done by Dickson since 1966. He introduced summary involving at least 50 different factors that were expressed by other writer- s (Dickson, 1996). Wilson (1990) and Hatson considered three effective categories of criteria, including financial, tactical and services on the supply selection process. Weber (1999) knew the pure price as the most important criteria for choosing the sup- plier. Dempsey (1978) introduced twenty supplier assessing criteria and believes that the importance of the selection criteria are more important than other parts of sup- pliers process. He has divided evaluating criteria and selecting of vendors to the two categories of explicit and implicit. Criterias in other studies were divided into 60 cas- es and six categories (Ghodsypour & Brien, 1998), 64 cases and 12 groups including Fuzzy Inf. Eng. (2011) 4: 433-444 435 company’s public information, management capabilities, personnel capabilities, cost structure, total quality management philosophy, technological capability, accepting and complying regulations about environmental depollution, financial ability, pro- duction control and scheduling systems, information systems capabilities, potential of the long-term relationship, strategies and policies (Monczka et al, 1998), 8 cas- es including supplier background, cost management/financial capability, customer to service experience, relationships and contracts, operational capacity, quality assur- ance programs, technical ability, materials management planning (Banfield, 1999), 23 items including net price, timely delivering, quality and so on. Policies for guaranty goods (Weber, 1999), 14 cases including quality, reliability, timely delivery, suppli- ers response to changes, easy contact with supplier, supplier satisfaction, competitive pricing, supplier financial stability, supplier technical skill, application of statistical control by supplier, exact number of shipments, delivery times, supplier activity in product design, supplier major investment, proximity/adjacency provider (Astom & Golhar, 1993), seven cases related to Peugeot companies of France, including or- ganization, process, quality control of inputs and final product, after-sales services, incentive systems, knowledge and skills of personnel, past performance or supplier’s quantitative and qualitative Background (Teimoori, 1381), 11 cases in four categories including signs of cooperation, communicative behavior, problem solving techniques and selection processes of supplier/products (Peterson et al, 1998), 19 cases in three categories including finance, strategy, service (Wilson & Hatson, 1990), 51 cases in eight groups, including quality, planning, facilities (machines), control, seller desire and interest, tools of supplier, organization and supplier management, commitment, and responsibility (Bache et al, 1987), 36 cases (Simson, 1998 ), 10 cases in four groups, including delivery, products quality, services, and prices (Bhardwage, 2004), the hexagonal model related to the Svjdak French company including quality, logis- tics, competitiveness, research and development ability, international relations, stabil- ity (Teimoori, 1999), five cases including coordination and percent of conflicts, close relations of cooperation, buyer status, the risk percentage of the non-receivable ac- counts, percentage of termination without agreement, as well as using fuzzy methods for presenting buying model (Lin & Chang, 2007), 29 cases and uses of multi model approach (Hoha & Krishna, 2007), five cases including profitability for supplier, per- centage of conflicts, technological capability, quality, effective communication and using a fuzzy mathematical decision model (Chen et al, 2006), four cases including reliability, competitive prices, technological capabilities, supports and services (Con- stantine et al, 2004), 29 cases divided in to five groups according to the integration level of buyer and seller (Ghodsypour & Brien, 1998). Hampry and others have divid- ed environmental factors while reviewing and developing knowledge based systems to main and subsidiary factors (Humphreys et al, 2003). Dulmin and Mininno (2003) presented a mathematical method of multi-objective decision making, and considered both qualitative and quantitative aspects in the evaluation and selection of suppliers. In order to optimize revenue from the supply chain, it requires selecting criteria and model based on contingency view, then appropriate supplier can be selected, while we can use a non-linear mixed integer model for optimization of supply chain imple- mentation or supplier selection (Ghodsi poor and others, 2007). Based on literature, 436 H. Aghajani· M. Ahmadpour the mentioned in the present study the following questions will be answered: ∗- Which is evaluation criteria and ranking suppliers of automobile in Iran? ∗- Which is the top supplier of battery in automobile in Iran? 3. Research Methodology 3.1. Sample Our sample has 127 experts in the automobile industry of Iran (Iran Khodro, Saipa and Bahman Group). In this sample, 65 percent is BA and the rest is Master and Doctorate, 94 percent has more than 10 years of experience. These samples have pro- vided the required data during the four stages through completing of questionnaires, specialized interviews and having team work. 3.2. Data and Scale Data is collected during the four stages with a separate method. In first phase, based on the literature review, we identify 32 criteria for assessing and ranking supplier- s through a questionnaire with standard components (Ghodsypour & Brien, 1998; Bache et al, 1987; Weber, 1999; Wilson & Hatson, 1990; Teimoori, 1999; Simson, 1998; Peterson et al, 1998; Handfield & Monczka, 1999; Banfield, 1999; Humphreys et al, 2003; Bharadwaj, 2004; Chen et al, 2006; Lin & Chang, 2007; Hoha & Krish- na, 2007) that has given to the experts for being ensure that criteria are related to the industry. At the end of this phase, 27 criteria were identified appropriately and relat- ed. In the second phase, these 27 criteria were evaluated by experts by using Likert scale and were reduced to six factors by using Factor Analysis and we discovered the relationship among them. In the third phase, we decided on the importance of coeffi- cients by meeting experts. Finally, in the fourth phase, in order to evaluate suppliers based on selected criteria, we designed forms that a decision maker team allocates Trapezoidal fuzzy number for weighting and evaluating to suppliers . 3.3. Validity In order to create validity, variables were extracted based on the literature review, and then we make it indigenous by using experts opinion and also primary samples (Hult & Ferrel, 1997; Bazargan and others, 1377, 171-166; Sarokhany, 1383, 139). Pretest- ing the questionnaire was given to the 11 academic experts for modification, further, it was given to the 28 persons in primary samples. Ultimately, the final questionnaire was designed and it was used for gathering data. 3.4. Reliability There are several methods determining reliability of instrument, that one of them is measuring internal consistency (Conca et al, 2004). The internal consistency can be measured by Alfa Cronbach (Churchill, 1979; Cronbach, 1951). This method has used in many studies (Peterson, 1994). Although the minimum acceptable value for this ratio should be 0.7, but values of 0.6 and even 0.55 is also acceptable (Van de ven & Ferry, 1979: 38; Nunnally, 1978: 62). In this study, the Alfa Cronbach was 0.89. 4. Findings Fuzzy Inf. Eng. (2011) 4: 433-444 437 4.1. Determining Criteria Based on literature review, 27 criteria from 32 criteria for evaluating and ranking sup- pliers were extracted as follows: 1. quality, 2. delivery on time, 3. delivery times, 4. the exact number of delivered shipments, 5. knowledge of human resources, 6. co- operating relationships, 7. technical skills (technical ability), 8. competitive pricing, 9. statistical control of process, 10. product design capabilities, 11. communications (communicating system), 12. flexibility, 13. capacity and facilities for production, 14. desirability for transaction with supplier, 15. supplier management and organiza- tion, 16. after-sales services, 17. packaging ability, 18. reputation, 19. geographical location (proximity and adjacency), 20. future prospects, 21. small shipments, 22. transportation method, 23. commitment, 24. past performance, 25. policies and guarantees for product warranty, 26. financial situation (major investment), 27. moti- vational system. The questionnaire by these criteria was distributed among samples. After collecting and analyzing data, three criteria that had very low importance (less score 2 or less important) have been removed. Then by Factor Analysis those fac- tors reduced to six including: quality, timely delivery, technical capability, financial status, design ability, and after sales services. 4.2. Data Collection In order to use these criteria for evaluating and ranking suppliers, information of each suppliers must be collected about each of these criteria so that each supplier can be assessed based on these criteria (Thus, after providing the required information item- s, required information from suppliers, visits letters and interviews with some of the suppliers). Meanwhile, the technicians simultaneously review the systems of each supplier. After collecting the required information, we arranged meeting with supe- rior managers, and we asked them allocate weight to the criteria, and each supplier is evaluated in each criteria by using received information and fuzzy numbers. To do this, they were given a form for evaluating any supplier with regard to six criteria selected by fuzzy numbers. The result had been fuzzy decision matrix. 4.3. Ranking Suppliers In order to use Fuzzy Topsis, we define four sets for assessing and ranking suppliers: 1) Set of decision maker: D = {D , D ,··· , D }. 1 2 k 2) Set of qualified and available suppliers (option): A = {A , A ,··· , A }. 1 2 m 3) Criteria that each suppliers will be assessed based on it: C = {C , C ,··· , C }. 1 2 n 4) Set of the result of assessing supplier A with C criteria: i j X = {X , i = 1,··· , m, j = 1,··· , n}. ij In order to form fuzzy decision matrix and change fuzzy numbers into trapezoid fuzzy numbers for weighting criteria and assessing alternatives, trapezoid fuzzy num- bers are defined as follows: Fuzzy Topsis steps are: Step 1: Formation of fuzzy decision matrix after combination decision makers opinions using trapezoid fuzzy numbers and definition of criteria weights. 438 H. Aghajani· M. Ahmadpour very bad bad semi bad intermediate semi good good very good 0 1 2 3 4 5 6 7 8 9 10 Fig. 1 Linguistic variables and trapezoid fuzzy numbers for the suppliers’ evaluation very little little semi little intermediate semi much much very much 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Fig. 2 Linguistic variables and trapezoid fuzzy numbers for weighting criteria Step 2: Normalizing fuzzy decision matrix so that criteria can be divided into positive and negative performance. Then, by using the following equation, the matrix of fuzzy decision will be: R = [r ] . ij m×n According to above equation, B is the set of the criteria with positive performance, and C is the set of the criteria with negative performance: ⎛ ⎞ ⎜ ⎟ ⎜ a b c d ⎟ ij ij ij ij ⎜ ⎟ ∗ ⎜ ⎟ r = ⎜ , , , ⎟ , j ∈ B, d = max d , j ∈ B, ij ij ⎝ ⎠ j ∗ ∗ ∗ ∗ d d d d j j j j ⎛ ⎞ − − − − a a a a ⎜ ⎟ j j j j ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ r = , , , , j ∈ C, a = min a , j ∈ C. ij ⎝ ⎠ ij d c b a ij ij ij ij Step 3: Weighting decision fuzzy matrix and making weighted fuzzy matrix by using the following equation: V = [v ] , i = 1, 2,··· , m, j = 1, 2,··· , n, v = r · w . ij m×n ij ij ij Step 4: Definition of fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) by using the following equation: + + + + + A = (v , v ,··· , v ), v = max v , i = 1, 2,··· , m, j = 1, 2,··· , n, {ij} 1 2 n j − − − − − A = (v , v ,··· , v ), v = min v , i = 1, 2,··· , m, j = 1, 2,··· , n. {ij} 1 2 n j Step 5: Calculating the distance of suppliers in each index from positive and neg- ative ideals by using the following equation: n n + + − − d = d (v , v ), d = d (v , v ), i = 1, 2,··· , m. v j v j j j j j j=1 j=1 Fuzzy Inf. Eng. (2011) 4: 433-444 439 Step 6: Calculate the adjacency coefficients of each supplier by using the following equation: CC = . + − d + d i i Step 7: Adjacency coefficients will be sorted descending, and supplier that has the largest coefficient is the best option that will be chosen. 4.4. Case Study In order to test the research model, a test has been done based on data in automo- bile industry and cooperation of Sapco as a big and expertise company in Iran. The suppliers were Tavan Battery (A ), Borna Battery (A ), Saba Battery (A ) and Niroo 1 2 3 Gostaran (A ). Step 1: Formation of fuzzy decision matrix by using the data of Tables 1, 2: Table 1: Matrix of determination the importance & weights of criteria. Decision makers Criteria Symbols D D - D 1 2 i Quality C Very much Very much - - Delivery on time C Very much Much - - Technical ability C Much Semi little - - Financial situation C Very much Intermediate - - Design capabilities C Much Semi little - - After sales services C Very much Semi much - - Step 2: Normalizing fuzzy decision matrix. Since all criteria which are used in this study are positive, thus, the mentioned matrix is shown in Table 6. Step 3: Weighted fuzzy decision matrix that is shown in Table 6. Step 4: Definition of fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) as follows: A = [(1, 1, 1, 1), (1, 1, 1, 1), (0.9, 0.9, 0.9, 0.9), (1, 1, 1, 1), (0.9, 0.9, 0.9, 0.9), (1, 1, 1, 1)]. A = [(0.09, 0.09, 0.09, 0.09), (0.08, 0.08, 0.08, 0.08), (0.03, 0.03, 0.03, 0.03), (0.18, 0.18, 0.18, 0.18), (0.02, 0.02, 0.02, 0.02), (0.06, 0.06, 0.06, 0.06)]. 440 H. Aghajani· M. Ahmadpour Table 2: Matrix of suppliers’ assessment. Criteria Suppliers Decision makers Criteria Symbols Symbols D D - D 1 2 i A Intermediate Semi bad - - Quality C 1 A Semi good Intermediate - - A Intermediate Bad - - A Bad Bad - - A Good Semi good - - Delivery on 2 A Very good Semi good - - time A Intermediate Semi bad - - A Semi good Semi bad - - A Intermediate Semi bad - - Technical 3 A Semi good Intermediate - - ability A Semi good Intermediate - - A Bad Bad - - A Good Semi good - - Financial A Good Intermediate - - situation A Semi good Intermediate - - A Good Semi good - - A Intermediate Semi bad - - Design 5 A Good Semi bad - - capabilities A Intermediate Semi bad - - A Semi bad Bad - - A Semi good Semi good - - After sales A Good Intermediate - - services A Intermediate Bad - - A Semi bad Bad - - Step 5: Calculate distance between suppliers in each index from positive and neg- ative ideals, as shown at following tables: Fuzzy Inf. Eng. (2011) 4: 433-444 441 Table 3: Distance of A (i = 1, 2, 3, 4) to A based on each criteria. Quality Delivery Technical Financial Design After sales Distance of on time ability situation capabilities services to A C C C C C C 1 2 3 4 5 6 d(A , A ) 0.58 0.56 0.56 0.52 0.64 0.46 d(A , A ) 0.39 0.51 0.51 0.56 0.57 0.50 d(A , A ) 0.66 0.65 0.53 0.58 0.64 0.63 d(A , A ) 0.79 0.65 0.69 0.51 0.71 0.75 Table 4: Distance of A (i = 1, 2, 3, 4) to A based on each criteria. Quality Delivery Technical Financial Design After sales Distance of on time ability situation capabilities services A to A C C C C C C 1 2 3 4 5 6 d(A , A ) 0.40 0.50 0.50 0.47 0.34 0.58 d(A , A ) 0.64 0.58 0.53 0.46 0.50 0.60 d(A , A ) 0.35 0.42 0.51 0.40 0.34 0.49 d(A , A ) 0.15 0.42 0.29 0.48 0.26 0.28 Step 6: Calculate the adjacency coefficient of each supplier (Table 5). Table 5: Adjacency coefficient of each supplier. + − + − A d d d + d CC i i A 3.32 2.80 6.12 0.46 A 3.03 3.30 6.34 0.52 A 3.67 2.51 6.17 0.41 A 4.09 1.88 5.97 0.31 Step 7: According to calculated adjacency coefficients, the supplier that has the biggest coefficient is the best option. In this research, the adjacency coefficients of 4 suppliers are: (A = 0.52), (A = 0.46), (A = 0.41), (A = 0.31). Thus A is 2 1 3 4 2 identified as the best supplier, and then A , A and A , are placed respectively. 1 3 4 442 H. Aghajani· M. Ahmadpour Tables 6: Fuzzy decision matrix and weights of criteria. Normalize. Weighted. Criteria Sym. A A A A W A A A A A A A A 1 2 3 4 j 1 2 3 4 1 2 3 4 2 4 1 1 0.8 0.2 0.4 0.1 0.1 0.3 0.4 0.1 0.1 Quality C 4.3 6.3 3.3 2 0.9 0.5 0.7 0.4 0.2 0.5 0.7 0.4 0.2 4.7 6.7 3.7 2 1 0.5 0.7 0.4 0.2 0.5 0.7 0.4 0.2 6 9 6 3 1 0.7 1 0.7 0.3 0.7 1 0.7 0.3 4 5 2 2 0.40 0.4 0.5 0.2 0.2 0.4 0.5 0.2 0.2 Delivery on C 6.3 7 4.7 4.7 0.73 0.6 0.7 0.5 0.5 0.6 0.7 0.5 0.5 time 6.7 8 5.3 5.3 0.77 0.7 0.8 0.5 0.5 0.7 0.8 0.5 0.5 9 10 8 8 1 0.9 1 0.8 0.8 0.9 1 0.8 0.8 2 4 4 1 0.20 0.3 0.5 0.5 0.1 0.3 0.5 0.5 0.1 Technical 4.7 5.7 5.3 2.3 0.57 0.6 0.7 0.7 0.3 0.6 0.7 0.7 0.3 ability 5.3 6.3 5.7 2.7 0.63 0.7 0.8 0.7 0.3 0.7 0.8 0.7 0.3 8 8 8 5 0.40 0.6 0.4 0.4 0.6 1 1 1 0.6 5 4 4 5 0.40 0.6 0.4 0.4 0.6 0.6 0.4 0.4 0.6 Financial 6.7 6.3 5.7 7.3 0.63 0.7 0.7 0.6 0.8 0.7 0.7 0.6 0.8 situation 7.3 6.7 6.3 7.7 0.67 0.8 0.7 0.7 0.9 0.8 0.7 0.7 0.9 9 9 8 9 1 1 1 0.9 1 1 1 0.9 1 2 2 2 1 0.20 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.1 Design capabili- C 3.7 3 3.7 2.3 0.57 0.4 0.6 0.4 0.3 0.4 0.6 0.4 0.3 ties 4.3 5.7 4.3 2.7 0.63 0.5 0.6 0.5 0.3 0.5 0.6 0.5 0.3 6 9 6 5 0.90 0.7 1 0.7 0.6 0.7 0.1 0.7 0.6 5 4 1 1 0.50 0.6 0.4 0.1 0.1 0.6 0.4 0.1 0.1 After sale C 6 6 4.3 2.3 0.80 0.7 0.7 0.5 0.3 0.7 0.7 0.5 0.3 abilities 7 6 4.7 2.7 0.90 0.8 0.7 0.5 0.3 0.8 0.7 0.5 0.3 8 9 8 5 1 0.9 1 0.9 0.6 0.9 0.1 0.9 0.6 Tavan Battery (A ), Borna Battery (A ), Saba Battery (A ) and Niroo Gostaran (A ). 1 2 3 4 5. Discussion, Conclusions and Implications In this research, based on literature review, it is presented criteria for assessing and ranking suppliers and selecting the best supplier regarding to environmental and situ- ational circumstances of Iran automobile industry using introduction and implemen- tation of Fuzzy Topsis multi-criteria decision making model. That is, in additional to presentation of comprehensive and localized criteria for assessment of suppliers, it is used decision making mathematical model simultaneously. In general, supplier- s’ assessment criteria are usually developed based on situation and circumstances of economical, social, political and cultural of developed countries, which are not com- patible with the circumstance of Iran, therefore, in this research it is extracted and Fuzzy Inf. Eng. (2011) 4: 433-444 443 localized these criteria based on Iran’s situation and circumstances. These criteria were 32 item at first, and they ultimately are declined to 6 item. Most researches in criteria fields as Dickson (1996), Teimoori (1999), and Riazi (2000), only introduce criteria, but this research also introduce localized criteria, that is explained as an assessment technique. Chen et al (2007) presented four criteria for assessment and selection of suppliers in supply chain management as unreal and mental, and tried to present a method for using Fuzzy Topsis Technique, but this research, by extraction of assessment criteria related to Iran automobile industry, has implemented the technique in real environment of Iran. The process presented in this research can be implemented in automobile manufac- turing companies. Doing this and selecting the best suppliers, companies managers can be sure about this selection and it’s future consequences. Other companies in- volved with the selection of suppliers also can use the methods and criteria of this research, of course with according to modifications required their organization strat- egy. Moreover, some implications for real worlds are: ∗- Exact definition of assessment, ranking and selection process suppliers in format of rules. ∗- Writing criteria of assessment and ranking suppliers based on organization’s strategies and periodical review of them. ∗- Forming expert teams for assessing and ranking, and using mentioned tech- niques in this research for assessing and ranking suppliers. For further research implications are: ∗- Repeat investigation of suppliers’ assessment criteria of this research for updat- ing them regarding to environmental and world market changes. ∗- Ranking this research suppliers using another models and comparison of their results with this research results to find the best model. Acknowledgements We appreciate Mr. Fatehnezhad in Sapco company for his full cooperation and sup- port during data collection, sessions and analysis. References 1. Astom K, Golhar D Y (1993) JIT purchasing: Attribute classification and literature review. Prod. Planning Control 4(3): 273-282 2. Bazargan A, Hejazi E, Sarmad Z (1998) Research methods in behavioral sciences. Tehran: Agah Publishing Co (In Persian) 3. Banfield H (1999) Harnessing value in the supply chain strategic sourcing in action. John Wiley & Sons Inc 4. Bharadwaj N (2004) Investigating the decision criteria used in electronic components procurement. Industrial Marketing Management 33: 317-323 5. Constantine S, Katsikeas N G, Katsikea P E (2004) Supply source selection criteria: The impact of supplier performance on distributor performance. Industrial Marketing Management 33: 755-764 6. Chen C T, Lin C T, Huang S F (2006) A fuzzy approach for supplier evaluation and selection in supply chain management. Int. J. 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Application of Fuzzy Topsis for Ranking Suppliers of Supply Chain in Automobile Manufacturing Companies in Iran

Fuzzy Information and Engineering , Volume 3 (4): 12 – Dec 1, 2011

Application of Fuzzy Topsis for Ranking Suppliers of Supply Chain in Automobile Manufacturing Companies in Iran

Abstract

AbstractEvaluating, ranking and selecting of good supplier play an important role in decreasing of buying risk and increasing of efficiency and effectiveness in value chain and competitive ability of organizations. The goal of this paper is determination and localization of criteria, ranking and selecting suppliers in Automobile Manufacturing Companies in Iran. Based on literature review, 27 criteria were selected and localized, then using factor analysis, they were decreased to 6 items...
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Fuzzy Inf. Eng. (2011) 4: 433-444 DOI 10.1007/s12543-011-0096-3 ORIGINAL ARTICLE Application of Fuzzy Topsis for Ranking Suppliers of Supply Chain in Automobile Manufacturing Companies in Iran H. Aghajani · M. Ahmadpour Received: 26 January 2011/ Revised: 20 June 2011/ Accepted: 15 October 2011/ © Springer-Verlag Berlin Heidelberg and Fuzzy Information and Engineering Branch of the Operations Research Society of China Abstract Evaluating, ranking and selecting of good supplier play an important role in decreasing of buying risk and increasing of efficiency and effectiveness in value chain and competitive ability of organizations. The goal of this paper is determina- tion and localization of criteria, ranking and selecting suppliers in Automobile Man- ufacturing Companies in Iran. Based on literature review, 27 criteria were selected and localized, then using factor analysis, they were decreased to 6 items including quality, delivery, technical skill, after sales services, investment and product design. Ultimately, 4 suppliers including Tavan, Borna, Saba and Niroogostaran have been assessed and ranked by fuzzy topsis technique. The results of the research state that score of Borna is better than others with coefficient of 0.52, and from this view, Tavan, Saba and Niroogostaran are in the next ranking with coefficient of 0.45, 0.41, 0.31, respectively. Finally, it concludes some remarks including discussion, summary of implications for managers about how they can use this research results for select- ing the best supplier and promoting the competitive ability of their organization, and directions for their further work too. Keywords Topsis· Ranking supply chain · Automobile 1. Introduction Supplying chain management and selecting suppliers are important subjects for com- panies. So many producers want to create effective relation with suppliers to improve their performance and competitive ability. The theme of buying and supplying pro- cess is discussed as a strategic issue and has caused a lot of attention to buyer and H. Aghajani () Department of Industrial Management, University of Mazandaran, Babolsar, Iran email: aghajani@umz.ac.ir M. Ahmadpour () Department of Art, University of Mazandaran, Babolsar, Iran email: mj.ahmadpour@umz.ac.ir 434 H. Aghajani· M. Ahmadpour seller relationships in organizations, thus supplier selection process issue has been suggested as one of the most important issues for the establishment of an effective supply chain. The goals of decreasing buying risk and increasing efficiency and effec- tiveness of the value chain can lead to the creation of a strong durable and long-term relationship between buyer and supplier [6]. Crosby believed that 50 of the quality problems of companies are due to unsuitable supplier selection and management. Therefore it is important that we select only suppliers that can supply buying needs of different administrative zones (Monczka et al, 2008). Although different studies (Astom & Golhar, 1993; Dickson, 1996; Bharadwaj, 2004; Hoha & Krishna, 2007; Lin & Chang, 2007; Chen et al, 2006) have been conducted about supply chain management, in spite of this, the purpose of present study is determining the criteria assessment for automobile suppliers in Iran, and assessing and ranking battery suppliers in the automobile industry. 2. Background and Questions The term of supply chain management was developed by Oliver and Webber in 1982. Since the advent, this concept, mainly was used to explain the benefits of integra- tion of the organization functional areas such as purchasing, production, logistics and marketing. But the history of its rise is on 50 and 60 decades. production resources planning (MRPII) was introduced in the 1970. The concept of materials management went one step forward and distribution and transportation operations added to it by intensifying global competition in 1980, and finally led to the concept of integrated support that is known as supply chain management. Development of supply chain management was continued by introducing three main process, including logistics management, relationship management and information management in 1990. Seven principles of supply chain management system were introduced that include customer classification based on the services they needed, setting supporting network, demand planning by using continuous projections and optimal allocation of resources, product design and production based on customer desire and enhancing the speed of adapting to the change, the strategic management of supply chain in order to reduce the cost of materials and components and related services, supply chain design strategy that can support different levels of decisions, selecting performance comprehensive criteria to measure success rate in achieving efficient and effective to the ultimate consumer demands (Steadler et al, 2003; Teimoori, 1999; Riazi, 2000; Hathmouth & Cristofer, 2007; Giannoccaro & Pontrandolfo, 2002). The first studies on the evaluation and selection criteria for suppliers have been done by Dickson since 1966. He introduced summary involving at least 50 different factors that were expressed by other writer- s (Dickson, 1996). Wilson (1990) and Hatson considered three effective categories of criteria, including financial, tactical and services on the supply selection process. Weber (1999) knew the pure price as the most important criteria for choosing the sup- plier. Dempsey (1978) introduced twenty supplier assessing criteria and believes that the importance of the selection criteria are more important than other parts of sup- pliers process. He has divided evaluating criteria and selecting of vendors to the two categories of explicit and implicit. Criterias in other studies were divided into 60 cas- es and six categories (Ghodsypour & Brien, 1998), 64 cases and 12 groups including Fuzzy Inf. Eng. (2011) 4: 433-444 435 company’s public information, management capabilities, personnel capabilities, cost structure, total quality management philosophy, technological capability, accepting and complying regulations about environmental depollution, financial ability, pro- duction control and scheduling systems, information systems capabilities, potential of the long-term relationship, strategies and policies (Monczka et al, 1998), 8 cas- es including supplier background, cost management/financial capability, customer to service experience, relationships and contracts, operational capacity, quality assur- ance programs, technical ability, materials management planning (Banfield, 1999), 23 items including net price, timely delivering, quality and so on. Policies for guaranty goods (Weber, 1999), 14 cases including quality, reliability, timely delivery, suppli- ers response to changes, easy contact with supplier, supplier satisfaction, competitive pricing, supplier financial stability, supplier technical skill, application of statistical control by supplier, exact number of shipments, delivery times, supplier activity in product design, supplier major investment, proximity/adjacency provider (Astom & Golhar, 1993), seven cases related to Peugeot companies of France, including or- ganization, process, quality control of inputs and final product, after-sales services, incentive systems, knowledge and skills of personnel, past performance or supplier’s quantitative and qualitative Background (Teimoori, 1381), 11 cases in four categories including signs of cooperation, communicative behavior, problem solving techniques and selection processes of supplier/products (Peterson et al, 1998), 19 cases in three categories including finance, strategy, service (Wilson & Hatson, 1990), 51 cases in eight groups, including quality, planning, facilities (machines), control, seller desire and interest, tools of supplier, organization and supplier management, commitment, and responsibility (Bache et al, 1987), 36 cases (Simson, 1998 ), 10 cases in four groups, including delivery, products quality, services, and prices (Bhardwage, 2004), the hexagonal model related to the Svjdak French company including quality, logis- tics, competitiveness, research and development ability, international relations, stabil- ity (Teimoori, 1999), five cases including coordination and percent of conflicts, close relations of cooperation, buyer status, the risk percentage of the non-receivable ac- counts, percentage of termination without agreement, as well as using fuzzy methods for presenting buying model (Lin & Chang, 2007), 29 cases and uses of multi model approach (Hoha & Krishna, 2007), five cases including profitability for supplier, per- centage of conflicts, technological capability, quality, effective communication and using a fuzzy mathematical decision model (Chen et al, 2006), four cases including reliability, competitive prices, technological capabilities, supports and services (Con- stantine et al, 2004), 29 cases divided in to five groups according to the integration level of buyer and seller (Ghodsypour & Brien, 1998). Hampry and others have divid- ed environmental factors while reviewing and developing knowledge based systems to main and subsidiary factors (Humphreys et al, 2003). Dulmin and Mininno (2003) presented a mathematical method of multi-objective decision making, and considered both qualitative and quantitative aspects in the evaluation and selection of suppliers. In order to optimize revenue from the supply chain, it requires selecting criteria and model based on contingency view, then appropriate supplier can be selected, while we can use a non-linear mixed integer model for optimization of supply chain imple- mentation or supplier selection (Ghodsi poor and others, 2007). Based on literature, 436 H. Aghajani· M. Ahmadpour the mentioned in the present study the following questions will be answered: ∗- Which is evaluation criteria and ranking suppliers of automobile in Iran? ∗- Which is the top supplier of battery in automobile in Iran? 3. Research Methodology 3.1. Sample Our sample has 127 experts in the automobile industry of Iran (Iran Khodro, Saipa and Bahman Group). In this sample, 65 percent is BA and the rest is Master and Doctorate, 94 percent has more than 10 years of experience. These samples have pro- vided the required data during the four stages through completing of questionnaires, specialized interviews and having team work. 3.2. Data and Scale Data is collected during the four stages with a separate method. In first phase, based on the literature review, we identify 32 criteria for assessing and ranking supplier- s through a questionnaire with standard components (Ghodsypour & Brien, 1998; Bache et al, 1987; Weber, 1999; Wilson & Hatson, 1990; Teimoori, 1999; Simson, 1998; Peterson et al, 1998; Handfield & Monczka, 1999; Banfield, 1999; Humphreys et al, 2003; Bharadwaj, 2004; Chen et al, 2006; Lin & Chang, 2007; Hoha & Krish- na, 2007) that has given to the experts for being ensure that criteria are related to the industry. At the end of this phase, 27 criteria were identified appropriately and relat- ed. In the second phase, these 27 criteria were evaluated by experts by using Likert scale and were reduced to six factors by using Factor Analysis and we discovered the relationship among them. In the third phase, we decided on the importance of coeffi- cients by meeting experts. Finally, in the fourth phase, in order to evaluate suppliers based on selected criteria, we designed forms that a decision maker team allocates Trapezoidal fuzzy number for weighting and evaluating to suppliers . 3.3. Validity In order to create validity, variables were extracted based on the literature review, and then we make it indigenous by using experts opinion and also primary samples (Hult & Ferrel, 1997; Bazargan and others, 1377, 171-166; Sarokhany, 1383, 139). Pretest- ing the questionnaire was given to the 11 academic experts for modification, further, it was given to the 28 persons in primary samples. Ultimately, the final questionnaire was designed and it was used for gathering data. 3.4. Reliability There are several methods determining reliability of instrument, that one of them is measuring internal consistency (Conca et al, 2004). The internal consistency can be measured by Alfa Cronbach (Churchill, 1979; Cronbach, 1951). This method has used in many studies (Peterson, 1994). Although the minimum acceptable value for this ratio should be 0.7, but values of 0.6 and even 0.55 is also acceptable (Van de ven & Ferry, 1979: 38; Nunnally, 1978: 62). In this study, the Alfa Cronbach was 0.89. 4. Findings Fuzzy Inf. Eng. (2011) 4: 433-444 437 4.1. Determining Criteria Based on literature review, 27 criteria from 32 criteria for evaluating and ranking sup- pliers were extracted as follows: 1. quality, 2. delivery on time, 3. delivery times, 4. the exact number of delivered shipments, 5. knowledge of human resources, 6. co- operating relationships, 7. technical skills (technical ability), 8. competitive pricing, 9. statistical control of process, 10. product design capabilities, 11. communications (communicating system), 12. flexibility, 13. capacity and facilities for production, 14. desirability for transaction with supplier, 15. supplier management and organiza- tion, 16. after-sales services, 17. packaging ability, 18. reputation, 19. geographical location (proximity and adjacency), 20. future prospects, 21. small shipments, 22. transportation method, 23. commitment, 24. past performance, 25. policies and guarantees for product warranty, 26. financial situation (major investment), 27. moti- vational system. The questionnaire by these criteria was distributed among samples. After collecting and analyzing data, three criteria that had very low importance (less score 2 or less important) have been removed. Then by Factor Analysis those fac- tors reduced to six including: quality, timely delivery, technical capability, financial status, design ability, and after sales services. 4.2. Data Collection In order to use these criteria for evaluating and ranking suppliers, information of each suppliers must be collected about each of these criteria so that each supplier can be assessed based on these criteria (Thus, after providing the required information item- s, required information from suppliers, visits letters and interviews with some of the suppliers). Meanwhile, the technicians simultaneously review the systems of each supplier. After collecting the required information, we arranged meeting with supe- rior managers, and we asked them allocate weight to the criteria, and each supplier is evaluated in each criteria by using received information and fuzzy numbers. To do this, they were given a form for evaluating any supplier with regard to six criteria selected by fuzzy numbers. The result had been fuzzy decision matrix. 4.3. Ranking Suppliers In order to use Fuzzy Topsis, we define four sets for assessing and ranking suppliers: 1) Set of decision maker: D = {D , D ,··· , D }. 1 2 k 2) Set of qualified and available suppliers (option): A = {A , A ,··· , A }. 1 2 m 3) Criteria that each suppliers will be assessed based on it: C = {C , C ,··· , C }. 1 2 n 4) Set of the result of assessing supplier A with C criteria: i j X = {X , i = 1,··· , m, j = 1,··· , n}. ij In order to form fuzzy decision matrix and change fuzzy numbers into trapezoid fuzzy numbers for weighting criteria and assessing alternatives, trapezoid fuzzy num- bers are defined as follows: Fuzzy Topsis steps are: Step 1: Formation of fuzzy decision matrix after combination decision makers opinions using trapezoid fuzzy numbers and definition of criteria weights. 438 H. Aghajani· M. Ahmadpour very bad bad semi bad intermediate semi good good very good 0 1 2 3 4 5 6 7 8 9 10 Fig. 1 Linguistic variables and trapezoid fuzzy numbers for the suppliers’ evaluation very little little semi little intermediate semi much much very much 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Fig. 2 Linguistic variables and trapezoid fuzzy numbers for weighting criteria Step 2: Normalizing fuzzy decision matrix so that criteria can be divided into positive and negative performance. Then, by using the following equation, the matrix of fuzzy decision will be: R = [r ] . ij m×n According to above equation, B is the set of the criteria with positive performance, and C is the set of the criteria with negative performance: ⎛ ⎞ ⎜ ⎟ ⎜ a b c d ⎟ ij ij ij ij ⎜ ⎟ ∗ ⎜ ⎟ r = ⎜ , , , ⎟ , j ∈ B, d = max d , j ∈ B, ij ij ⎝ ⎠ j ∗ ∗ ∗ ∗ d d d d j j j j ⎛ ⎞ − − − − a a a a ⎜ ⎟ j j j j ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ r = , , , , j ∈ C, a = min a , j ∈ C. ij ⎝ ⎠ ij d c b a ij ij ij ij Step 3: Weighting decision fuzzy matrix and making weighted fuzzy matrix by using the following equation: V = [v ] , i = 1, 2,··· , m, j = 1, 2,··· , n, v = r · w . ij m×n ij ij ij Step 4: Definition of fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) by using the following equation: + + + + + A = (v , v ,··· , v ), v = max v , i = 1, 2,··· , m, j = 1, 2,··· , n, {ij} 1 2 n j − − − − − A = (v , v ,··· , v ), v = min v , i = 1, 2,··· , m, j = 1, 2,··· , n. {ij} 1 2 n j Step 5: Calculating the distance of suppliers in each index from positive and neg- ative ideals by using the following equation: n n + + − − d = d (v , v ), d = d (v , v ), i = 1, 2,··· , m. v j v j j j j j j=1 j=1 Fuzzy Inf. Eng. (2011) 4: 433-444 439 Step 6: Calculate the adjacency coefficients of each supplier by using the following equation: CC = . + − d + d i i Step 7: Adjacency coefficients will be sorted descending, and supplier that has the largest coefficient is the best option that will be chosen. 4.4. Case Study In order to test the research model, a test has been done based on data in automo- bile industry and cooperation of Sapco as a big and expertise company in Iran. The suppliers were Tavan Battery (A ), Borna Battery (A ), Saba Battery (A ) and Niroo 1 2 3 Gostaran (A ). Step 1: Formation of fuzzy decision matrix by using the data of Tables 1, 2: Table 1: Matrix of determination the importance & weights of criteria. Decision makers Criteria Symbols D D - D 1 2 i Quality C Very much Very much - - Delivery on time C Very much Much - - Technical ability C Much Semi little - - Financial situation C Very much Intermediate - - Design capabilities C Much Semi little - - After sales services C Very much Semi much - - Step 2: Normalizing fuzzy decision matrix. Since all criteria which are used in this study are positive, thus, the mentioned matrix is shown in Table 6. Step 3: Weighted fuzzy decision matrix that is shown in Table 6. Step 4: Definition of fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) as follows: A = [(1, 1, 1, 1), (1, 1, 1, 1), (0.9, 0.9, 0.9, 0.9), (1, 1, 1, 1), (0.9, 0.9, 0.9, 0.9), (1, 1, 1, 1)]. A = [(0.09, 0.09, 0.09, 0.09), (0.08, 0.08, 0.08, 0.08), (0.03, 0.03, 0.03, 0.03), (0.18, 0.18, 0.18, 0.18), (0.02, 0.02, 0.02, 0.02), (0.06, 0.06, 0.06, 0.06)]. 440 H. Aghajani· M. Ahmadpour Table 2: Matrix of suppliers’ assessment. Criteria Suppliers Decision makers Criteria Symbols Symbols D D - D 1 2 i A Intermediate Semi bad - - Quality C 1 A Semi good Intermediate - - A Intermediate Bad - - A Bad Bad - - A Good Semi good - - Delivery on 2 A Very good Semi good - - time A Intermediate Semi bad - - A Semi good Semi bad - - A Intermediate Semi bad - - Technical 3 A Semi good Intermediate - - ability A Semi good Intermediate - - A Bad Bad - - A Good Semi good - - Financial A Good Intermediate - - situation A Semi good Intermediate - - A Good Semi good - - A Intermediate Semi bad - - Design 5 A Good Semi bad - - capabilities A Intermediate Semi bad - - A Semi bad Bad - - A Semi good Semi good - - After sales A Good Intermediate - - services A Intermediate Bad - - A Semi bad Bad - - Step 5: Calculate distance between suppliers in each index from positive and neg- ative ideals, as shown at following tables: Fuzzy Inf. Eng. (2011) 4: 433-444 441 Table 3: Distance of A (i = 1, 2, 3, 4) to A based on each criteria. Quality Delivery Technical Financial Design After sales Distance of on time ability situation capabilities services to A C C C C C C 1 2 3 4 5 6 d(A , A ) 0.58 0.56 0.56 0.52 0.64 0.46 d(A , A ) 0.39 0.51 0.51 0.56 0.57 0.50 d(A , A ) 0.66 0.65 0.53 0.58 0.64 0.63 d(A , A ) 0.79 0.65 0.69 0.51 0.71 0.75 Table 4: Distance of A (i = 1, 2, 3, 4) to A based on each criteria. Quality Delivery Technical Financial Design After sales Distance of on time ability situation capabilities services A to A C C C C C C 1 2 3 4 5 6 d(A , A ) 0.40 0.50 0.50 0.47 0.34 0.58 d(A , A ) 0.64 0.58 0.53 0.46 0.50 0.60 d(A , A ) 0.35 0.42 0.51 0.40 0.34 0.49 d(A , A ) 0.15 0.42 0.29 0.48 0.26 0.28 Step 6: Calculate the adjacency coefficient of each supplier (Table 5). Table 5: Adjacency coefficient of each supplier. + − + − A d d d + d CC i i A 3.32 2.80 6.12 0.46 A 3.03 3.30 6.34 0.52 A 3.67 2.51 6.17 0.41 A 4.09 1.88 5.97 0.31 Step 7: According to calculated adjacency coefficients, the supplier that has the biggest coefficient is the best option. In this research, the adjacency coefficients of 4 suppliers are: (A = 0.52), (A = 0.46), (A = 0.41), (A = 0.31). Thus A is 2 1 3 4 2 identified as the best supplier, and then A , A and A , are placed respectively. 1 3 4 442 H. Aghajani· M. Ahmadpour Tables 6: Fuzzy decision matrix and weights of criteria. Normalize. Weighted. Criteria Sym. A A A A W A A A A A A A A 1 2 3 4 j 1 2 3 4 1 2 3 4 2 4 1 1 0.8 0.2 0.4 0.1 0.1 0.3 0.4 0.1 0.1 Quality C 4.3 6.3 3.3 2 0.9 0.5 0.7 0.4 0.2 0.5 0.7 0.4 0.2 4.7 6.7 3.7 2 1 0.5 0.7 0.4 0.2 0.5 0.7 0.4 0.2 6 9 6 3 1 0.7 1 0.7 0.3 0.7 1 0.7 0.3 4 5 2 2 0.40 0.4 0.5 0.2 0.2 0.4 0.5 0.2 0.2 Delivery on C 6.3 7 4.7 4.7 0.73 0.6 0.7 0.5 0.5 0.6 0.7 0.5 0.5 time 6.7 8 5.3 5.3 0.77 0.7 0.8 0.5 0.5 0.7 0.8 0.5 0.5 9 10 8 8 1 0.9 1 0.8 0.8 0.9 1 0.8 0.8 2 4 4 1 0.20 0.3 0.5 0.5 0.1 0.3 0.5 0.5 0.1 Technical 4.7 5.7 5.3 2.3 0.57 0.6 0.7 0.7 0.3 0.6 0.7 0.7 0.3 ability 5.3 6.3 5.7 2.7 0.63 0.7 0.8 0.7 0.3 0.7 0.8 0.7 0.3 8 8 8 5 0.40 0.6 0.4 0.4 0.6 1 1 1 0.6 5 4 4 5 0.40 0.6 0.4 0.4 0.6 0.6 0.4 0.4 0.6 Financial 6.7 6.3 5.7 7.3 0.63 0.7 0.7 0.6 0.8 0.7 0.7 0.6 0.8 situation 7.3 6.7 6.3 7.7 0.67 0.8 0.7 0.7 0.9 0.8 0.7 0.7 0.9 9 9 8 9 1 1 1 0.9 1 1 1 0.9 1 2 2 2 1 0.20 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.1 Design capabili- C 3.7 3 3.7 2.3 0.57 0.4 0.6 0.4 0.3 0.4 0.6 0.4 0.3 ties 4.3 5.7 4.3 2.7 0.63 0.5 0.6 0.5 0.3 0.5 0.6 0.5 0.3 6 9 6 5 0.90 0.7 1 0.7 0.6 0.7 0.1 0.7 0.6 5 4 1 1 0.50 0.6 0.4 0.1 0.1 0.6 0.4 0.1 0.1 After sale C 6 6 4.3 2.3 0.80 0.7 0.7 0.5 0.3 0.7 0.7 0.5 0.3 abilities 7 6 4.7 2.7 0.90 0.8 0.7 0.5 0.3 0.8 0.7 0.5 0.3 8 9 8 5 1 0.9 1 0.9 0.6 0.9 0.1 0.9 0.6 Tavan Battery (A ), Borna Battery (A ), Saba Battery (A ) and Niroo Gostaran (A ). 1 2 3 4 5. Discussion, Conclusions and Implications In this research, based on literature review, it is presented criteria for assessing and ranking suppliers and selecting the best supplier regarding to environmental and situ- ational circumstances of Iran automobile industry using introduction and implemen- tation of Fuzzy Topsis multi-criteria decision making model. That is, in additional to presentation of comprehensive and localized criteria for assessment of suppliers, it is used decision making mathematical model simultaneously. In general, supplier- s’ assessment criteria are usually developed based on situation and circumstances of economical, social, political and cultural of developed countries, which are not com- patible with the circumstance of Iran, therefore, in this research it is extracted and Fuzzy Inf. Eng. (2011) 4: 433-444 443 localized these criteria based on Iran’s situation and circumstances. These criteria were 32 item at first, and they ultimately are declined to 6 item. Most researches in criteria fields as Dickson (1996), Teimoori (1999), and Riazi (2000), only introduce criteria, but this research also introduce localized criteria, that is explained as an assessment technique. Chen et al (2007) presented four criteria for assessment and selection of suppliers in supply chain management as unreal and mental, and tried to present a method for using Fuzzy Topsis Technique, but this research, by extraction of assessment criteria related to Iran automobile industry, has implemented the technique in real environment of Iran. The process presented in this research can be implemented in automobile manufac- turing companies. Doing this and selecting the best suppliers, companies managers can be sure about this selection and it’s future consequences. Other companies in- volved with the selection of suppliers also can use the methods and criteria of this research, of course with according to modifications required their organization strat- egy. Moreover, some implications for real worlds are: ∗- Exact definition of assessment, ranking and selection process suppliers in format of rules. ∗- Writing criteria of assessment and ranking suppliers based on organization’s strategies and periodical review of them. ∗- Forming expert teams for assessing and ranking, and using mentioned tech- niques in this research for assessing and ranking suppliers. For further research implications are: ∗- Repeat investigation of suppliers’ assessment criteria of this research for updat- ing them regarding to environmental and world market changes. ∗- Ranking this research suppliers using another models and comparison of their results with this research results to find the best model. 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Journal

Fuzzy Information and EngineeringTaylor & Francis

Published: Dec 1, 2011

Keywords: Topsis; Ranking supply chain; Automobile

References