Prioritization of Supplier Selection Criteria: A Fuzzy-AHP Approach

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1 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp Prioritization of Supplier Selection Criteria: A Fuzzy-AHP Approach 34 Rajeev Jain Mechanical Engineering Department Kalaniketan Polytechnic College MNNIT, Allahabad-2004, INDIA jainrajeev@rediffmail.com A.R. Singh Mechanical Engineering Department MNNIT, Allahabad-2004, INDIA P.K. Mishra Mechanical Engineering Department MNNIT, Jabalpur-48200, INDIA ABSTRACT Now a day the worldwide industry is developing as single economy and culture. As an effect the manufacturing industry is moving towards higher value added activities and the requirements for suppliers might undergo significant changes. The present work deals with the problem of selection of suitable and viable suppliers. Initially, major categories of supplier selection criteria have been identified. Criteria are then categorizes into qualifying criteria, selection criteria and additional factors that companies uses through the selection process. A Fuzzy-AHP method is employed to determine the relative importance of the criteria and to assign the weight to the criteria. These in turn help to indentify the preferences of purchasers in selecting their suppliers in the context of Indian manufacturing industry. For the purpose, a survey was made to identify the supplier selection criteria and assign the weight through questionnaire. Keywords: Supplier selection, Fuzzy AHP, Criteria prioritization.. INTRODUCTION Supply chain management and strategic sourcing have been the fastest growing area of concern to the management particularly in last two decades. A supply chain is a network that consists of departments, such as marketing, planning, purchasing and finance etc. The purchasing department s activities are increasingly seen as a strategic issue in an organization. The purchasing issues, strategies, and tactics are just as important as marketing, finance, accounting, and operational issues even though purchasing is first in the value chain and furthest from the actual delivery of the product or service to the customer. Purchasing can have a significant impact on quality, customer satisfaction, profitability and market share [5,9]. Suppliers are the critical link to any supply chain and consequently sourcing decision is one of the important decisions to be taken at the planning stage. Over the past decade, the need to gain global competitiveness on the supply side has increased substantially for any organization. With in new strategies for purchasing and manufacturing, suppliers play a vital role in achieving corporate competition. Hence, selecting the right suppliers is a vital component of these strategies. In practice, vagueness and imprecision of the goals, constraints and parameters in this problem make the decisionmaking complicated. Consequently, the issue of supplier selection has attracted much attention within the field of SCM and most of the approaches examine the problem based on selection criteria. In practice, there could be several criteria used by a firm for its supplier selection decision, such as price offered, part quality, on-time delivery, after-sales services, supplier location and supplier s financial status. Apparently, supplier selection is a multi-criteria problem that includes both quantitative and qualitative factors. It is necessary to make trade-off between these tangible and intangible factors while considering a suitable supplier. 2. LITERATURE REVIEW The problem of supplier selection has been amatter of concern to the researchers since the published work of Dickson [0]. The quantum of work is voluminous that may beobvious from the four articles that reviews the literature onthis area only [8,9,2,25]. The literature deals with supplier evaluation and selection models, methods and tools. Supplier selection is defined as the process of finding the suppliers being able to provide the buyer with the right quality products and/or services at the right price, at the right quantities and at the right time [22]. The supplier selection process is generally described in the literature consists of five stages: () Identification of the need for a new supplier; (2) Identification and elaboration of selection criteria; (3) Initial screening of potential suppliers from a large set; (4) Final supplier selection; and (5) Continuous evaluation and assessment of selected suppliers [7,8].

2 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp Supplier selection decisions are thorny by the fact that huge number of criteria should be considered for making a decision. The analysis of criteria has been the focus in literature since seminal work of Dickson in 966 [0]. Dickson [0] identified 23 important criteria of vendor selection and ranked them with respect to their importance observed in the beginning of sixties. At that time the most significant criteria were found to be quality, delivery, performance history and warranty policies of supplier.it has been found that 23 criteria as suggested by Dickson [0] have been considered for evaluation of the suppliers. However, the evolution of industrial environment modified the degrees of relative importance of these criteria. Weber et al. [25] observed that price, delivery, quality and production capacity and location are the criteria that have been most often treated in the literature. They reviewed annotated and classified 74 articles that have appeared in the literature since 966. This review was entirely based on literature and limited to the review to selection of supplier for industrial purchases. It was reported that the approach that have more attention is linear weighting model. Linear programming, mixed integer programming and goal programming methods were most popular one.it was also mentioned that net price, delivery and quality have received a considerable amount of attention in the literature that have been published in last five years. On the other hand, warranties and claim policies, communication system, impression, labor relation record, amount of past business and reciprocal agreements, bidding procedural compliance, desire for business, operating controls, packing ability, training aids, performance history, financial position, reputation and position in industry have not received due attention. Factors like production facilities and capacity, geographical location, financial position and management and organization generated moderate attention. Ho et al. [2] have reviewed 78 articles that are dedicated to multi-criteria decision-making approaches for supplier evaluation and selection. These articles were published in between 2000 to They have analyzed the prevalently applied approaches, evaluating criteria and inadequacy of the approaches. They have reported that quality, delivery, price/cost, manufacturing capability, service, management, technology, research and development, finance, flexibility, reputation, relationship, risk and safety and environment have been found as most popular criteria in order.it has also been observed that the individual approaches were slightly more popular than the integrated approaches. The most popular individual approach was Data Envelopment Analysis (DEA), followed by mathematical programming, Analytical Hierarchy Process (AHP), Case Based Reasoning (CBR), Analytical Network Process (ANP), Fuzzy Set Theory (FST), Simple Multi Attribute Rating Technique (SMART) and Genetic Algorithm (GA). DEA has attracted more attention mainly because of their robustness. It was emphasized that the integrated AHP approaches are more prevalent. The wide applicability is due to its simplicity, ease of use, and great flexibility []. AHP has been integrated with other techniques, e.g. ANN, bi-negotiation, DEA, FST, Goal Programming (GP), Grey Relational Analysis (GRA) and multi-objective programming. Comparatively, the integrated AHP GP approach has been found to be more prevalent. The most popular criterion has been noticed as quality, followed by delivery, price/cost, manufacturing capability, service, management, technology, research and development, finance, flexibility, reputation, relationship, risk, and safety and environment in order. There has been considerable amount of work published after Jain et al. [3] reported that fuzzy based integrated methods are more prevalent now and fuzzy theory is integrated with other techniques, including AHP, ANP, linear programming (LP) with strengths, weakness, opportunities, threats (SWOT), quality function deployment (QFD), decision making trial and evaluation laboratory (DEMATEL), dempster, Technique For Order preference BY Similarity to Ideal Situation (TOPSIS), DEA, GP, neural network (NN), vlsekriterijumskaoptimizacijakompromisnoresenje (VIKOR), elimination and choice expressing reality (ELECTRE), gray relational analysis (GRA), principal component analysis (PCA), adaptive resonance theory (ART) and mathematical programming. Again, many criteria have been proposed, but there is an increase on emphasis of the criteria like flexibility, environment, risk and corporate social responsibility in this literature. While considering the views of literature and of the experts and decision makers from areas of academics, finance and production and material management, the cost, quality, delivery, service, flexibility, risk, environmental safety and social responsibilities has been considered for supplier selection problem in the present work. 3. CRITERIA CLASSIFICATION In order to evaluate the importance of the supplier selection criteria for the Indian manufacturing industry, a comprehensive set of questionnaire has been developed. The experts were requested to answer regarding each criterion along with the importance of that criterion on 5 point Likert scale. A total of 53 responses have been received out of 80 and results are depicted in Table. The results show that the criteria cost, quality, delivery and flexibility are classified in must be categories and these will be considered as most important criteria of supplier selection. The criteria service, risk environmental practices and social responsibility are reported as attractive criteria. Initially suppliers are evaluated and preferred on the basis of must be criteria. Thereafter attractive criteria may be used to have relative rating between preferred suppliers for assignment of orders.

3 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp Table : Classification of supplier selection criteria Criteria Category Importance (mean) Cost Quality Delivery Flexibility Service Risk Environmental practices Social responsibility M M M M A A A A Q: Must be criterion, A: Attractive criterion AHP, FUZZY SET THEORY AND FAHP In previous section the criteria for two phases of suppler selection have been categorized. Before ranking the suitable suppliers there is a need to identify the relative weights of these criteria. In view of wide applications of Fuzzy Analytic hierarchy process (FAHP) and its ability to deal with vague data linguistics nature, it has been used to prioritize the different criteria and to calculate the relative weights of criteria and their sub-criteria. AHP is used for choosing the most suitable alternative that fulfils the entire set of objectives in multi-attribute decisionmaking problem [24]. AHP allows a set of complex issues, to be compared with the importance of each issue relative to its impact on the solution to the problem. Since the inception of AHP, numerous applications have been published in the literature [6,5,20]. AHP is used as a decision-making tool for considering the priority for different strategies, with the assumption that multiple-criteria problems can be completely expressed in a hierarchical structure. The data acquired from the decision-makers are compared pair-wise to evaluate the relative importance of each criteria, or the degree of preference of one factor to another with respect to each criterion. However, the perception and judgments of human are represented by linguistic and vague for a complex problem. However, Cheng et al. [4] have summarized the limitation of AHP as: The AHP method is mainly used in nearly crisp decision applications, The AHP method creates and deals with a very unbalanced scale of judgment, The AHP method does not take into account the uncertainty associated with the mapping of human judgment to a number, The ranking of the AHP method is rather imprecise; and the subjective judgment, selection and preference of decision-makers have great influence on the AHP results. Several researchers have integrated fuzzy set theory with AHP to consider the uncertainty and many of the shortcomings as depicted by Cheng et al. [4]. Van-laarhoven and Pedrycz [23] have compared fuzzy ratios described by triangular membership functions and the logarithmic least squares method to obtain element sequencing. Buckley [] has determined fuzzy priorities of comparison ratios whose membership functions were trapezoidal. Chang [3] has introduced a new approach for handling fuzzy AHP with the use of triangular fuzzy numbers for pair-wise comparison scale of fuzzy AHP, and use of the extent analysis method to demonstrate by an example. Mon et al. [7] have presented a method for evaluating weapon systems using fuzzy AHP based on entropy weight calculations. Chang [3] has reported an algorithm for evaluating naval tactical missile systems by the fuzzy AHP method and entropy concepts to evaluate aggregate weights of criteria of selection. These aforementioned studies have calculated fuzzy priorities based on arithmetic operations for fuzzy triangular or trapezoidal numbers. Karsak and Kuzgunkaya [4] have presented a fuzzy multiple objective programming approach to facilitate decision making in the selection of a flexible manufacturing system. Yu [27] has incorporated an absolute term linearization technique and a fuzzy rating expression into a goal programming-ahp model for solving group decisionmaking fuzzy AHP problems. In contrast to current fuzzy AHP methods, the GP-AHP method can concurrently tackle the pair-wise comparison involving triangular, general concave and concave-convex mixed fuzzy estimates under a group decision-making environment. Kuo et al. [6] have developed a decision support system for locating a new convenience store through fuzzy AHP. Weck et al. [26] have presented a method to evaluate different production cycle alternatives adding the mathematics of fuzzy logic to the classical AHP. Any production cycle evaluated in this manner yields a fuzzy set. The outcome of the analysis can finally be defuzzified by forming the surface center of gravity of any fuzzy set, and the alternative production cycles investigated can be ranked in order in terms of the main objective set. Chan et.al [2] has reported that most decision-makers tend to give assessments based on their knowledge, past experience and subjective judgments. Importance of different strategies for mitigating risks contains ambiguity and multiplicity of meaning as these descriptions are usually linguistic and vague. It is also recognized that human assessment on qualitative attributes is always subjective and thus imprecise. In order to simplify the fuzzy AHP process for industry from the practical and feasible viewpoints, the fuzzy AHP based on the fuzzy interval arithmetic with triangular fuzzy numbers and confidence index α with interval mean approach to determine the weights for evaluative elements have been proposed. The flow chart has been divided into five phases: planning, fuzzyfication, fuzzy operations, defuzzification and analysis and confirmation (Figure ). From the analytic results of this integrated model, a relative importance ranking for each evaluation hierarchy is obtained. In order to model such kind of uncertainty in human preference, fuzzy sets can be incorporated with the pair wise comparison as an extension of AHP. Since fuzziness and vagueness are common characteristics in many decision-making problems, the fuzzy AHP approach allows a more accurate description of the decision-making process. A major contribution of fuzzy set theory is its capability of representing vague data, as it allows

4 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp Figure : Fuzzy AHP flow chart mathematical operators and programming to apply to the fuzzy domain. A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function that assigns a grade of membership ranging between zero and one to each object. In the present study the authors have adopted the procedure of FAHP described in Singh et al.[2]. In this study, triangular fuzzy numbers, to 9, have been used to represent subjective pair wise comparisons of criteria of supplier selection. A tilde ~ is placed above a symbol if the symbol represents a fuzzy set. In order to take the imprecision of human qualitative assessments into consideration; the five triangular fuzzy numbers are defined with the corresponding membership function as shown in Figure 2 and Table 2. Computational Procedure of Fuzzy AHP The AHP method is also known as an eigenvector method. It indicates that the eigenvector corresponding to the largest eigen value of the pair wise comparisons matrix provides the relative priorities of the factors, and preserves ordinal preferences among the alternatives. This means that if an alternative is preferred to another, its eigenvector component is larger than that of the other. A vector of weights obtained from the pair wise comparisons matrix reflects the relative performance of the various factors. In the fuzzy AHP triangular fuzzy numbers are utilized to improve the scaling scheme in the judgment matrices, and interval arithmetic is used to solve the fuzzy eigenvector []. The procedure of this the approach is as follows: Step : Construct the hierarchy structure model. Step 2: Comparing the performance score: Triangular fuzzy numbers (, 357 and 9 ) are used to indicate the relative strength of each pair of elements in the same hierarchy. Step 3: Constructing the fuzzy comparison matrix: By using triangular fuzzy numbers, via pair wise comparison, the fuzzy judgment matrix A is constructed as equation 4.4;

5 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp Figure 2: The membership functions of triangular fuzzy numbers, 357 and 9 Table 2: Definition and membership function of fuzzy numbers Intensity of Importance Fuzzy number Definition Equally important/ preferred 3 3 Moderately more important/preferred 5 5 Strongly more important/preferred 7 7 Very strongly more important/preferred 9 9 Extremely more important/preferred Membership function (,, 3) (, 3, 5) ( 3, 5, 7) (5, 7, 9) (7, 9,) A = () i j Where a ij = ÏÔ π Ì or ÓÔ, 3, 7, 9, 3, 5, 7, 9 i π j Step 4: Estimating the degree of optimism for A Degree of satisfaction for the judgment matrix A is estimated by the index of optimism μ. The larger value of the μ indicates the higher degree of optimism. The index of optimism is a linear convex combination defined as: a a a a ij = maiju + ( - u) aij, " m Œ[ 0, ] (2) While α is fixed, following crisp judgment matrix can be obtained after setting the index of optimism, μ, in order to estimate the degree of satisfaction Step 5: Solving fuzzy eigen value A fuzzy eigen value, λ is a fuzzy number solution to Ax = l x (4) (3) where is A n n fuzzy matrix containing fuzzy numbers a ij and x is a non-zero n x, fuzzy vector containing fuzzy number xi.to perform fuzzy multiplications and additions by using the interval arithmetic and α cut, the equation 4 becomes equivalent to a a a a a a a a a [ ail xl, aiu au ]... [ ainlxnl, ainuanu ] = [ lxil, lzi a u ] (5) where t A = [ a ], x = ( x,..., xn ) ij a a A = A A a a a a [, ], x = [ x, x ], ij ijl iju i a a a l = [ l, lu ] il For 0 < α and all i, j, where i=, 2,..,n and j=,2,..,n Step 6: Determining the weights for criteria The Eigen value method is used for calculating the eigenvector or weighting vector for each pair-wise matrix. The eigenvector is calculated by fixing the μ value and identifying the maximal Eigen value [8]. λ max is calculated then Normalization of both the matrix of paired comparisons and evolution of priority weights (approximate attribute weights). In order to control the results of the method, the consistency ratio for each of the matrices and overall inconsistency for the hierarchy are calculated. The deviations from consistency are expressed by the following equation: l - n CI = max n - Where CI is consistency index. The consistency ratio (CR) is used to estimate directly the consistency of pair wise comparisons.; CR iu (6) = CI (7) RI Where RI is selected from Table 3 according to the rank of the matrix. Table 3: Average Index for Randomly Generated Weights Matrix Rank RI The comparisons are acceptable if CR< 0.. If the consistency test is not passed, the original values in the pair wise comparison matrix must be revised by the decision maker. Step 7: Ranking the Criteria Ranking are provided on the basis of the final score of the enablers. Final score are calculated with the help of relative importance weight of dimension and relative important weight of enabler. Further, a survey was carried out to find out the

6 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp importance of the enablers in the industries using Likert scale (-5). The mean of data is calculated for different enablers. The final score is defined as: Where FS k = (Ad k * A k ) (8) FS k = Final score of enabler (sub criteria) k Ad k = Relative importance weight of dimension (criteria) d of enabler k A k = Relative importance weight of enabler k 5. APPLICATION OF FAHP MODEL Hierarchy Structure In previous section the criteria of suppler selection have been categorized.the criteria are prioritized and structured in different hierarchy levels to use FAHP. Initially, a hierarchy was developed. Further interviews were conducted with the experts from areas of academics, finance and production & material management to review the hierarchy of model that is feasible for application in supply chain management. It facilitated to discuss directly with the experts and hierarchy model was revised if needed. A description brochure of criteria was also submitted to the experts to check whether these descriptions in the model hierarchy were understandable or not. After revising both the hierarchy and descriptions of criteria, finally hierarchy models of both the phases were developed (Figure 3). Fuzzy Comparison Matrices Fuzzy comparison matrices were constructed for different dimensions and enablers that were precipitated during Figure 6.3: The Hierarchical Structure Model

7 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp development of hierarchical structure. The comparison matrix was to be developed with triangular fuzzy numbers. For the purpose, the experts were requested to evaluate the relative importance of the criteria based on triangular fuzzy numbers with the intension to: (i) Weight the judgments of the different criteria. (ii) Understand the views regarding applicability of FAHP. (iii) Receive their suggestions in general. These experts were also requested to express the relative importance of each dimension by pair-wise comparison. In order to account the imprecision of human qualitative assessments into consideration five triangular fuzzy numbers were defined with the corresponding membership function as shown in Figure 2 and Table 2. Their judgments for hierarchy model were analyzed using consistency ratio and the process of judging the comparison of matrix was continued till CR reached the value that is less than 0.. Fuzzy comparison matrixes and there consistency ratio for criterions and sub-criterions are shown in Table 4 to Table 8. Dimension/ criteria Cost (C) Quality (Q) Delivery (D) Flexibility (F) Table 4: Fuzzy comparison matrix of the order qualification criteria Cost (C) Quality (Q) 3 - Delivery (D) (λmax = and CR = ) Table 5: Fuzzy comparison matrix of sub-criteria of criterion cost Flexibility(F) Cost C C2 C3 C4 C5 C C2 C3 C4 C5 3 λmax = and CR = ~ Table 6: Fuzzy Comparison Matrix of Sub-criteria of Criterion Quality Quality Q Q2 Q3 Q4 Q Q2 Q3 Q4 9 λmax = and CR = Table 7: Fuzzy Comparison Matrix of Sub-criteria of Criterion Delivery Delivery D D2 D3 D4 D5 D D2 D3 D4 D5 λmax = and CR = Table 8: Fuzzy Comparison Matrix of Sub-criteria of Criterion Flexibility Quality F F2 F3 F4 F F2 F3 F4 λmax = 4.75 and CR = Defining Lower and Upper Limit of Fuzzy Numbers Through Interval of Confidence The discussion with experts yielded the relative importance of different strategies. The triangular membership function and α-cuts used to convert the subjective judgments of the experts into fuzzy judgments. After that a degree of optimism for the experts was estimated by the index of optimism µ. Individual fuzzy comparison matrices based on triangular membership function and α cuts were initially formulated. The lower limit and upper limit of the fuzzy numbers with respect to α, were defined as follows by using equation 2; ~ ~ - È a = [, 3-2a], a = Í, 3-2a Î ~ ~ È 3 a = [ + 2a, 5-2a] 3 a = Í, Î5-2a + 2a ~ ~ - È 5 a = [ 3 + 2a, 7-2a] 5 a = Í, Î7-2a 3 + 2a ~ ~ - È 7 a = [ 5 + 2a, 9-2a] 7 a = Í, 9-2a 5 + 2a Î ~ ~ - È 9 a = [ 7 + 2a, - 2a] 9 a = Í, Î - 2a 7 + 2a For α = 0.5, for above expression will yield the fuzzy comparison matrices (Table 4 to Table 8). For illustration the resulted Table: 4 converted as shown below: È È È Í,, [ 4, 6] ÎÍ 4 2 ÎÍ 4 2 Í Í [ 2, 4] [ 4, 6] [ 6, 8] FCM( a = 0. 5) = Í Í È [ 2, 4], [ 2, 4] Í Î Í6 4 Í ÍÈ È È.,, ÎÍ Î Í6 4 Î Í8 6 ÎÍ 4 2

8 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp Estimating the Degree of Optimization Degree of satisfaction for the judgment matrices is estimated by the index of optimism μ. The larger value of the index μ indicates the higher degree of optimism. The index of optimism is a linear convex combination defined by Equation 2. The following crisp judgment matrix can be obtained after setting the index of optimism, μ, in order to estimate the degree of satisfaction. Here μ =0.5 are used to transform fuzzy group comparison matrices into group crisp comparison matrices, from which the importance weights were obtained. For example, group crisp comparison matrix (GCCM) is obtained as shown below after using μ = 0.5 (in Equation 2). È Í GCCM( a = 0. 5 and m = 0. 5) = Í Í ÎÍ Main Criterion (Dimensions) A dk Table 9: Prioritization of Criteria Sub-criterion (Enablers) Cost Low price Free order distribution/ logistic cost Free after sale service Discount for bulk order Discount for early payment Quality Meeting minimum standard and requirement Long durability ISO certified Low rejection/return rate Delivery 0.28 On time delivery Short delivery lead time Reliable delivery method Product received in good condition No error in product type and quantity Flexibility Demand flexibility Production flexibility Variety flexibility Service flexibility Eigen Value and Eigen Vector Le t GCCM(a = 0.5 and m = 0.5) = A. Eigen value of the matrix A can be obtained by solving the characteristic equation of A, i.e. det (A li) = 0. l = , l 2 = i, l 3 = i, l 4 = As the value of l is the largest, the corresponding eigenvectors of A can be calculated as by substituting the l in the Equation 5: X = (0.2596, , 0.350, ) T After normalization, the importance weights of the dimensions (criteria) can be determined as: Normalized weight for main criteria = 0.628, 0.565, 0.295, ). Similarly, the importance weights of the different criteria can be determined and that is shown in Table 9. A k FS k Rank Check the Consistency Property If the consistency ratio (CR = CI/RI CR = CI/RI) is less than 0., then comparison are acceptable, otherwise not. If the consistency test is not passed, the original values in the pair wise comparison matrix must be revised by the decision maker. Here CR of the matrix A can be calculated as: CR CI l n = and CI = max - RI n - For l max = 4.77, n = 4 in matrix A then CI = = For RI = 0.90 (from Table 3) the value of CR = CI = RI For matrix A as, CR < 0. so this comparison is acceptable. Fuzzy comparison matrices and there consistency ratio for criteria and sub-criterions are shown in Table 4 to Table 8. Final Score and Ranking Final score are calculated using Equation 8. Final score of various criteria are shown in Table 9. Ranking are provided according to their final score. VI. CONCLUSION Based on literature review, views of experts from areas of academics, and stakeholder of industries, criteria and sub criteria for supplier selection were selected. The criteria are then classified into two categories of requirements. Cost,

9 MIT International Journal of Mechanical Engineering, Vol. 3, No., Jan. 203, pp quality, delivery and flexibility are identified as most important criteria whereas service, risk, environmental practices and social responsibility are distinguished as attractive criteria of supplier selection. After identification of criteria, Fuzzy-AHP approach has been used to prioritize the criteria in the context of Indian industries. The relative importance of various criteria has also been assessed. In the methodology, triangular fuzzy members were introduced into the conventional AHP in order to improve the judgments of decision makers and experts. The ranking of criteria have been made according to their final scores on the basis of weight. Results indicate that Indian manufacturing industries recognize quality and delivery as the most important criteria. It was found from the pair wise comparison of main criteria the criterion quality to be of highest importance with % followed by delivery 2.8% (Table 9). The highest rank was found to be for sub criteria low rejection/return rate and meeting minimum standard and requirement respectively on the basis of the final score. In the present work, the attractive criteria and sub-criteria of supplier selection has not been analyzed. It offers the scope of future work in the area. 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