VENDOR RATING AND SELECTION IN MANUFACTURING INDUSTRY

International Journal of Decision Making in Supply Chain and Logistics (IJDMSCL) Volume 2, No. 1, January-June 2011, pp. 15-23, International Science Press (India), ISSN: 2229-7332 VENDOR RATING AND SELECTION IN MANUFACTURING INDUSTRY T. HEMAVATHI ABSTRACT Supplier selection, which is the first step of the activities in the product realization process of supply chain management from purchasing of material till to the end of delivering the products, is evaluated as a critical factor for the companies desiring to be successful in nowadays. Traditionally companies consider factors like quality, cost, delivery, Service etc. when evaluating supplier performance. However, environmental pressure is increasing, resulting in many companies beginning to consider environmental issues and rating of their supplier s by considering environmental criteria also. A method aiming at the usage of AHP (Analytical Hierarchy process) is developed owning to the evaluation of supplier and selecting the best supplier. Keywords: Supplier Selection, AHP, Expert Choice, Sensitivity Analysis. 1. INTRODUCTION Supply chain is a network of departments, which is involved in the manufacturing of a product from the procurement of raw materials to the distribution of the final products to the customer. Purchasing commands a significant position in most organizations since purchased parts, components, and supplies typically represent 40 to 60 percent of the sales of its end products. This means that relatively small cost reductions gained in the acquisition of materials can have a greater impact on profits than equal improvements in other cost-sales areas of the organization. The purchasing function has gained great importance in the supply chain management due to factors such as globalization, increased value added in supply, and accelerated technological change. Purchasing involves buying the raw materials, supplies, and components for the organization. A key and perhaps the most important process of the purchasing function is the efficient selection of suppliers, because it brings significant savings for the organization. The objective of the supplier selection process is to reduce risk and maximize the total value for the buyer, and it involves considering a series of strategic variables. Department of Industrial Engineering, Anna University, Chennai 25, Tamil Node, India. E-mail: hemavathi24@gmail.com 1, hemavathi82@gmail.com 2

16 / T. HEMAVATHI Increasingly, purchasing managers are being asked not only to transform purchasing into a more strategic function. But also to integrate environmental issues in their decisions. Introducing the environmental dimension into purchasing decisions embeds a new set of trade-offs in the decision, complicating the decision-making process with both qualitative and quantitative factors. In the last few years green movements, institutions and governments have forced many companies to improve their environmental performance. As a consequence of this growing interest in the environment, many firms established integrated relationships with their suppliers to design new green products. Green supply chain management (GSCM) is generally understood to involve screening suppliers based on their environmental performance and doing business only with those that meet certain environmental regulations or standards. Hence, this paper integrates environment criteria into other 4 criteria like (cost, service, delivery, quality) and corresponding their sub criteria for selecting best supplier using Analytical Hierarchy process [2]. 2. LITERATURE REVIEW Ali kokangul et al (2009) have developed an integrated approach of AHP and nonlinear integer and multiobjective programming with some constraints such as quantity discounts, capacity, and budget to determine the best suppliers and to place the optimal order quantities among them. This integration based multicriteria decision making methodology takes into account both qualitative and quantitative factors in supplier selection. While AHP matches item characteristics with supplier characteristics, nonlinear integer programming model analytically determines the best suppliers and the optimal order quantities among the determined suppliers. The objectives of the mathematical models constructed are maximizing the total value of purchase (TVP) and minimizing the total cost of purchase (TCP). Jing-Rung Yu et al (2008) proposed a framework which integrates the analytic hierarchy process (AHP) and integer programming to rate suppliers performance regarding incoming raw materials in the context of supplier management and then to allocate periodical purchases. Cemalettin kubat et al (2006) has integrated AHP, Fuzzy AHP and GA to determine the best suppliers. Fuzzy set has been utilized to linguistic factor to organize criteria and sub criteria weight, pair wise comparison with fuzzy AHP where it is utilized to organize all factors and which was assigned weighting for related factors. Finally, a hypothetical supplier selection problem was solved by GA algorithm. Ozden Bayazit et al (2005) for the first time discussed a comprehensive application of AHP for a real-world case along with sensitivity analysis to choose the best supplier. They proposed an AHP model to choose the best supplier and place the order quantities among them for a construction company.

VENDOR RATING AND SELECTION IN MANUFACTURING INDUSTRY / 17 Robert Hadfield (2002) illustrated the use of the Analytical Hierarchy Process (AHP) as a decision support model to help managers understand the trade-offs between environmental dimensions and how AHP can be used to evaluate the relative importance of various environmental traits and to assess the relative performance of several suppliers along these traits. 3. THE ANALYTICAL HEIRARCHY PROCESS (AHP) The AHP decomposes the decision process as a hierarchical structure and also deals with quantifiable and intangible criteria by using the pair wise comparison matrices, in which qualitative data is normalized after pair wise comparison matrix and quantitative data is dealt with reverse normalization. The AHP divides the decision problem into the four following steps [10]: (1) Considering all alternatives in reaching the goal, the hierarchical structure of decision-making process is constructed by identifying all related criteria, including quantitative and qualitative prime criteria and sub-criteria. (2) Collect pair wise comparison matrices. The primary criteria are pair wisely compared in terms of their relative importance to the decision goal, and the sub-criteria are evaluated pair wisely based on their importance to their parental primary criteria. (3) The eigenvalues for each matrix are generated and are converted into relative weights of the decision criteria. Meanwhile, the comparison data of alternatives are converted into scores. (4) The weights of the decision criteria are aggregated and the composite priority of each element at each level is obtained. Consequently, a numerical ranking of all alternatives is generated. A consistency index for each criterion measures the degree of consistency inherent in the decision-makers ranking of alternatives. The consistency ratio (CR), is defined as CR = CI/RI, where CI = (λ Max N)/(N 1), λ Max is the largest eigenvalue of the pair wise comparison matrix, and RI is similar to CI but based on random matrices, each with the same dimension as the matrix. A value above 0.1 for criteria indicates inconsistency in the corresponding matrix, thus pair wise comparison judgment needs to be inspected again. A detailed description of the AHP can be found in Saaty (1980, 1994). Herein, we adopt Expert Choice TM to generate the priorities. Expert Choice TM, commercial AHP software, provides simple step-by-step instructions for the data entry of pair wise comparison[1]. It performs all matrix calculations and obtains all eigenvalues efficiently. After that, both local and global weights or priorities and the consistency index for each level of hierarchy are generated easily.

18 / T. HEMAVATHI 4. MODEL DEVELOPMENT The selection of potential suppliers is done by Experts or decision makers using analytical hierarchy process. The overall objective of the Analytic Hierarchic Process (AHP) is to prioritize the best supplier who meets the criteria s and sub criteria s framed by the experts or decision maker. This Paper develops a hierarchical structure consists of four hierarchical levels in which the overall objective is located at the top of the hierarchy. Five criteria are located at the second level. At the third level each criteria has their corresponding four sub criteria. At the bottom of the hierarchy, n suppliers are located. Relationship among criteria s, sub criteria s and alternatives [2, 4] are shown in Figure 1. Fig. 1: AHP Heirarchy for Supplier Selection 5. SUPPLIER EVALUATION Supplier selection is evaluated by using the questionnaire. The questionnaire is formed to make pair wise comparison of the various suppliers attributes of criteria and

VENDOR RATING AND SELECTION IN MANUFACTURING INDUSTRY / 19 subcriteria. This comparison is made by saaty scale in Appendix 1, to give the relative importance of one attribute with respect to another. The evaluation and pair wise comparison are done by the experts or decision makers of the supply chain management team. In this step, criteria s, sub criteria and alternatives are compared with each other and among themselves with using pair wise comparisons questionnaire sheet. An example of questionnaire and pair wise comparison is given in Appendix 2 and Appendix 3.respectively, doing pair wise comparison matrix an unweighted matrix is obtained and then the weighted matrix is obtained using normalization. Next the potential short listed supplier alternatives are evaluated with respect to each decision criteria and sub criteria.then local weights are calculated using the weights generated during Pair wise comparison matrix from which the global weights are generated and then potential suppliers are ranked in ascending order by summing up the global weights. The supplier with highest score will be the best supplier decided by the decision makers Hence, the decision making system helps in the process of decision making/prioritizing the short listed suppliers which have highest priority as shown in Appendix 4. Then the consistency of the matrix of order n is evaluated for examining the decision makers data. If this consistency index fails to reach a required level then answers to comparisons may be re-examined and the results of computation for the consistency. 6. SENSITIVITY ANALYSIS A series of sensitivity analyses were conducted to investigate the impact of changing the priority of the criteria on the suppliers ranking[4]. First, Dynamic sensitivity of Expert choice was performed. Dynamic sensitivity analysis is used to dynamically change the priorities of the criteria to determine how these changes affect the priorities of the alternative choices (Saaty, 2001). We investigated the impact of changing the priority of five main criteria on overall results. As shown in Figures 2, the results indicate that the suppliers ratings with respect to criteria and its weight. When the importance of cost is increased S2 is the best supplier (figure 2). We performed a second sensitivity analysis. Where the relative importance of quality was increased from 0.31 to 0.43(figure 3); supplier ratings do not change although the superiority of the best alternative is changed. 7. CONCLUSION Analytic Hierarchic Approach is used to select qualitatively and quantitatively evaluate the potential supplier and the ranking was done to prioritize them. The decision makers are the team members in supply chain management to decide which the best supplier is, and expert choice package is used for sensitive analysis part.

20 / T. HEMAVATHI APPENDIX Appendix 1 AHP Measurement Scale No Option Numerical Values 1. Equal 1 2. Marginally Strong 3 3. Strong 5 4. Very Strong 7 5 Extremely Strong 9 6 Intermediate Values 2,4,6,8 7 Reflecting Dominance of Second Alternative Reciprocals Compared with the First Appendix 2 Questionnaire for Criteria No. Attribute 1 Relative Importance Attribute 2 1. Quality 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Cost 2 Quality 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Delivery 3 Quality 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Service 4 Quality 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Environment 5 Cost 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Delivery 6 Cost 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Service 7 Cost 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Environment 8 Delivery 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Service 9 Delivery 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Environment 10 Service 9 8 7 6 5 4 3 2 1 9 8 7 6 5 4 3 2 Environment Appendix 3 Pair Wise Comparision Matrix Q C D S E Q 1 2 3 3 5 C ½ 1 2 2 4 D 1/3 ½ 1 ½ 2 S 1/3 ½ 2 1 3 E 1/5 ¼ 1/2 1/3 1

VENDOR RATING AND SELECTION IN MANUFACTURING INDUSTRY / 21 Normalized Pair Wise Comparision Matrix Q C D S E Q.422.470.352.333 5 C.213.235.235.266 4 D.140.117.121.133 2 S.140.117.235.2 3 E.084.088.058.066 1 Appendix 4 Ranking of alternatives: Grand total of s1 = Σ(SG1 Q + SG1 C + SG1 D + SG1 S + SG1 E) =.296 Grand total of s2 = Σ(SG2 Q + SG2 C + SG2 D + SG2 S + SG2 E) =.298 Grand total of s3 = Σ(SG3 Q + SG3 C + SG3 D + SG3 S + SG3 E) =.193 Grand total of s4 = Σ(SG4 Q + SG4 C + SG4 D + SG4 S + SG4 E) =.20 S.No Supplier Weights Rank 1. S2.298 I 2. S1.296 II 3. S3.193 III 4. S4.20 IV Fig. 1: Weights for the Criteria

22 / T. HEMAVATHI Fig. 2: Priorities of the Supplier with Respect to Criteria Fig. 3: Priorities of the Supplier with Respect to Change in Criteria ACKNOWLEDGEMENT The author would like to thank the guide and staff members of the department, who have helped through the project and improving the quality of the contents of the paper. REFERENCES [1] Jing-Rung Yu, Chao-Chia Tsai, A Decision Framework for Supplier Rating and Purchase Allocation: A Case in the Semiconductor Industry, Computers & Industrial Engineering, 55, (2008), 634-646. [2] Ali Kokangul & Zeynep Susuz, Integrated Analytical Hierarch Process & Mathematical Programming to Supplier Selection Problem with Quantity Discount, International Journal of Applied Mathematical Modeling, (2008).

VENDOR RATING AND SELECTION IN MANUFACTURING INDUSTRY / 23 [3] Amir Sanayeia. S. Farid Mousavib, M.R. Abdic, Ali Mohaghar, An Integrated Group Decision-making Process for Supplier Selection and Order Allocation using Multi-attribute Utility Theory and Linear Programming, Journal of the Franklin Institute, 345, (2008), 731-747. [4] Ozden Bayazita, Birsen Karpak, An AHP Application in Vendor Selection, ISAHP 2005, Honolulu, Hawaii, July 8-10, 2005. [5] Paul Humphreys, Ronan McIvor, Felix Chan, Using Case-based Reasoning to Evaluate Supplier Environmental Management Performance, International Journal of Expert Systems with Applications, 2003. [6] Humphreys, Y.K. Wongb, F.T.S.Chan, Integrating Environmental Criteria into the Supplier Selection Process, Journal of Materials Processing Technology, 138, (2003), 349-356. [7] Chung, S.-H., Lee, A.H.I., Pearn, W.L., Analytic Network Process (ANP) Approach for Product Mix Planning in Semiconductor Fabricator, International Journal of Production Economics, 2005. [8] Robert Hand fields a, Steven V. Walton b,*, Robert Sroufe c, Steven A. Melnyk, Applying Environmental Criteria to Supplier Assessment, A Study in the Application. [9] Expert Choice, (1992), Expert ChoiceTMVersion 8, Pittsburg, PA: Expert Choice, Inc. [10] Saaty, T.L.(1980), The Analytic Hierarchy Process, New York: McGraw-Hill.