Research on the Optimization of Green Suppliers Based on AHP and GRA

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1 Journal of Information & Computational Science 9: 1 (2012) Available at Research on the Optimization of Green Suppliers Based on AHP and GRA Jianliang Peng Contemporary Business and Trade Research Center, Zhejiang Gongshang University Hangzhou , China Abstract The optimization of green suppliers is a key step in green supply chain management. First of all, in the green supply chain management and green procurement characteristics of the premise is proposed based on green supply chain management supplier evaluation index system construction method. Secondly, according to green supply chain management theory Analytical Hierarchy Process (AHP) and Grey Relational Analysis (GRA) are given the green supply chain management model supplier evaluation index system, combined with the characteristics of the indicator system proposed the concept of green adjustment factors, and gives the calculation of the specific steps and methods. Finally, a numerical example shows that this algorithm is scientific and reasonable. Keywords: Green Supplier; Analytical Hierarchy Process (AHP); Grey Relational Analysis (GRA); Optimization; Green Factor 1 Introduction As more and more environmental protection regulations were issued, and more intense public awareness of environmental protection, today s enterprises lay more emphasis on environmental issues to survive in the global market. Environmental management is playing an increasingly important role in business management. So many scholars put forward a number of solutions by studying, such as Total Quality Environmental Management (TQEM), Green Supply Chain Management (GSCM), ISO14000 standard, low-carbon economy. GSCM was first proposed by the American Manufacturing Research Institute at Michigan State University in Key to the implementation of GSCM is located in the procurement of upstream supply chain, green suppliers role in cost savings and environmental protection can be passed to downstream through the supply chain in all aspects, bring competitive advantages to the whole supply chain [1]. Therefore, how to choose the best green supplier is the company s major strategic issue that needs scientific, rational and efficient selection criteria and methods. Project supported by the Contemporary Business and Trade Research Center of Zhejiang Gongshang University which is the Key Research Institute of Social Sciences and Humanities Ministry of Education (No. 10JDSM09YB). address: pengjl@mail.zjgsu.edu.cn (Jianliang Peng) / Copyright 2012 Binary Information Press January 2012

2 174 J. Peng / Journal of Information & Computational Science 9: 1 (2012) In the Green Supply Chain Management, to select green supplier is the key. Present results of their research focused on the green supplier evaluation and selection of green suppliers. In the evaluation indexes, Handheld, et al. [2], Lee et al. [3], Hoek [4], Zhu et al. [5], Liu et al. [6] carried out extensive researches, take these results together, these studies show that the main consideration of green supplier selection are product quality, price, delivery lead time, and environmental capacity. In the evaluation methods, Lee et al. used AHP and fuzzy theory to take research on the green supplier selection of high-tech industry. Kuo et al. [7] took electronics industry as an example and applied ANN and MADA algorithm to have green supplier selection research. Sun [8] established a supplier s green evaluation and selection architecture based on the rough set theory. Xu, et al. [9] thank that an enterprise should evaluate and select green suppliers on the basis of product life cycle assessment, and control them according to the strategy of grading. Wu et al. [10] proposed a new method to choose logistics service suppliers based on a fuzzy quality function deployment, and then elaborated the steps of the method that integrated QFD and fuzzy set theory to select modern third-party logistics suppliers. One major characteristic of the study is adopting SERVQUAL model to select appropriate selection index system of logistics service suppliers. According to the connotation of green supply chain and the standards of environmental management, Li et al. [11] designed the green partner selecting measurement systems composed of some main factors such as green message. And the authors applied the method of fuzzy evaluation mode to the green partner selecting measurement system. Liu et al. [12] proposed a model with grey relational analysis and fuzzy quality function deployment. With this method, enterprises can choose the green supplier with high customer satisfaction quickly and accurately. This paper is to build a vendor evaluation system based on green supply chain management, and integrate Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA) to solve problem of green supplier evaluation, combined with the green adjustment factor to modify the selected plan and to meet the need of scientific, rational selection of green suppliers, has great practical significance. 2 Establishment of Green Supplier Evaluation Index System To make a comprehensive evaluation of the supplier under supply chain management system, we must have a rounded, scientific, comprehensive and integrated evaluation index system. In order to evaluate and select suppliers effectively, under the guidance of the principles like system comprehensive, concise scientific, stable comparability and flexible operability, this paper constructs a three-level comprehensive evaluation index system. The first layer is the target layer; the specific factors which impact the selection of supplier are based on the second layer of the index system, that is the rule layer, the testing goals we focus on are the four aspects as the enterprise ability, cooperation degree, service level, environmental factors; the third layer is project hierarchy, that is, the segmenting factors which relevant to suppliers. It should be particular emphasis on environmental factors, because in the laws and regulations of the end-product s sales territory, has clearly stated some indexes such as the content of harmful materials, the lowest rate of materials harmless, so these indicators must be reflected in the supplier s selection criteria. Therefore, this article here on particular selects some indexes such as the energy consuming ratio and the lowest rate of materials harmless. In this way, you can finish basically the survey on supplier environmental management capacity in the study following case. The green supplier evaluation index

3 J. Peng / Journal of Information & Computational Science 9: 1 (2012) system shows in Table 1. Table 1: Green supplier evaluation index system Target Layer Rule Layer Project Hierarchy Evaluation of green supplier, A Enterprise ability, B1 Service level, B2 Cooperation degree, B3 Environmental factors, B4 Volume flexibility, C1 Scale of production, C2 Information level, C3 Price rate, C4 Delivery time, C5 Delivery-check qualified rate, C6 On-time delivery rate, C7 Average order completion ratio, C8 Cooperation degree, C9 Content of hazardous substances, C10 Energy consumption, C11 Harmless rate, C12 3 Calculation Steps of Green Supplier Selection Model Based on AHP and GRA 3.1 Analytic Hierarchy Process (AHP) U.S. operations researcher A. L. Saaty put forward the Analytical Hierarchy Process (referred to as the AHP method) in the 1970s; it is a decision analysis method which combine the qualitative and quantitative. It is a process which make decision-makers thought for a complex system modeling and quantitative. Applying this approach, decision-makers decompose the complex problem into a number of levels and a number of factors, through this way, make simple comparison and calculations between the factors, then we can get the weights of different programs, can provide the basis for the selection of the best option. As a tool combine the qualitative and quantitative, AHP has been widely used in many areas. Application procedures of AHP are as follows [13]: Step 1 Confirming problems. Step 2 Building hierarchy structure. Step 3 Establishing multiple comparisons judgment matrix. Step 4 Checking consistency. The consistency index of judgment matrix is CI (Consistency Index). Its expression is: CI = λ max n n 1 And the greater the value of consistency index CI is, the greater the degree of the judgment matrix deviate from complete consistency will be; the smaller the value of CI is, the closer

4 176 J. Peng / Journal of Information & Computational Science 9: 1 (2012) the judgment matrix and complete consistency will be. Generally, the greater the order (n) of judgment matrix is, the greater the value of CI which shows the deviation from complete consistency caused by human will be; the smaller the n is, the smaller the value of CI which shows the deviation from complete consistency caused by human will be. In this paper, we use ANC to have consistency test. Step 5 Total taxis of hierarchy. Arranging weights which come from the factors in same level shows the relative importance of the top-level (overall objective); known as total taxis of hierarchy, this process is from high level to low level and step by step. The total taxis of hierarchy which come from the lowest level (program level) are the total order of all evaluation schemes. 3.2 Grey System Theory Model Grey system theory is a new method to study the problems with little research data and information uncertainty. Because the data obtained by the questionnaire, so we have very less information, therefore we combine the AHP and gray system theory method to have evaluation of green suppliers and to improve the accuracy and objectivity of evaluation. When making decisions by grey relational analysis in grey decision-making theory, can be divided into the following steps [14]: Step 1 Establish decision-making situation. Make a i as the event to be decided, b j as the corresponding jth countermeasure, then we call the double combination b j for a i as the situation, note as s ij = (a i,b j ) = (event, strategy). Step 2 Establish evaluation system sample based on the target of evaluation situation. Note the sample results from situation s ij under the target as u p ij, note U as the sample results matrix under the target P : U = u p 11 u p 1n..... u p m1 u p mn Step 3 According to the target polarity, does the effect measure transformation. Make M eff as the effect transformation measurement, u p ij as the sample results from situation s ij under the target P, r p ij as the image from up ij under M eff, make M eff : u p ij rp ij, M eff is the effect transformation measurement or the effect transformation, it satisfies: (i) r p ij has positive polarity; (ii) r p ij 0, 1. We note rp ij as the effect measurement from situation s ij under the target P, effect transformation measurement criteria: Maximum objective transforming calculation formula (calculation formula of upper limit measure of effect): the calculation formula of effect transformation under the target of maximum value is: M eff (u p ij ) = u p ij u p ij max i max j u p r p ij = ij max i max j u p ij

5 J. Peng / Journal of Information & Computational Science 9: 1 (2012) Minimum objective transforming calculation formula (calculation formula of lower limit measure of effect): the calculation formula of effect transformation under the target of minimum value is M eff (u p ij ) = min i min j u p ij u p r p ij = min i min j u p ij ij u p ij Moderate objective transforming calculation formula (calculation formula of moderate limit measure of effect)the calculation formula of effect transformation under the target of moderate value is: } } M eff (u p ij ) = min {u 0, u p ij max { }r p u 0, u p ij = min {u 0, u p ij ij max { } u 0, u p ij Step 4 Establish a unified measure space, find the satisfactory situation. The effect transformation of samples u p ij with different polarities into rp ij which is unified and has the same polarity, we call all of the r p ij as space of the consistency (uniform) polarity. In the space of the consistency, measure the effect in the same situation such as rij, 1 rij, 2 rij, 3, rij, l can be used for arithmetic. Make r p ij as the effect measure from situation s ij under the target P, when P =1,2,, l, then we say it as the unified effect measurement or unified measurement for s ij. For each evaluation index, we calculated the mean value of the correlation coefficient which corresponding to the indexes, in order to reflect the correlation between the evaluation objects, we call that associated order, denoted by: r i = 1 m 12 r p i p=1 If the indicators play different roles in comprehensive evaluation, we can calculate the correlation coefficient s weighted average value r i = p=1 In the formula, CW stands for the weight of indexes. r p i CW Step 5 According to the observation of very object s satisfactory situation or maximal relational order, we can get the results of comprehensive evaluation. 4 Case Study: Selection of Green Supplier Based on AHP and GRA 4.1 Background of a Case The selection of green supplier is directly related to the survival and development of enterprise, it has great significance. Company X is a comprehensive large-scale manufacturing enterprise;

6 178 J. Peng / Journal of Information & Computational Science 9: 1 (2012) its subsidiary of refrigerators will produce a group of refrigerators to exports to EU countries. Now we have four suppliers i.e. A, B, C, D to provide major components of the refrigerator compressors, reference to the local directive of the European Union, the products are divided into three categories. According to the regulations of export destinations, if the imported products that must meet standards of first-class, then they can have a tariff reduction which is roughly equivalent to 25% of the price; in line with the standard of second-class price they can have a tariff reduction which is roughly equivalent to 15% of the price; in line with the third-class they can have a tariff reduction which is roughly equivalent to 5% of the price; the products do not meet the above criteria, they will be subject to pay the 30%-50% of environmental tax. And destination for exports of such products of the highest levels of harmful substances is 0.05 liters; the minimum rate of raw materials harmless should be to 85%. Because of Company D s products do not meet the highest level of harmful substances of export destination, so Company D is eliminated at first. 4.2 Data Collection and Processing According to previous delivery trading experience and services available information, we can get the suppliers actual data of eight indicators, as shown in Table 2. Table 2: Actual data of three suppliers Candidate suppliers Supplier A Supplier B Supplier C Price rates, X4 450 YUAN/unit 500 YUAN/unit 460 YUAN/unit Delivery time, X5 16 Days 15 Days 16 Days Delivery-check qualified rate, X6 80% 90% 86% On-time delivery, X7 85% 80% 90% Average order completion rate, X8 80% 60% 70% Hazardous substances content, X Litres 0.01 Litres 0.02 Litres Energy consumption, X Harmlessness, X12 88% 98% 96% Production flexibility, production scale, the level of information and cooperation degree are four qualitative indexes, which need experts to score (score range of 0-10 points). 12 experts were asked to score on three suppliers, i.e. A, B, C, after scoring, the results are shown in Table 3. Table 3: The score of the three suppliers Candidate suppliers Supplier A Supplier B Supplier C Production flexibility, X Production scale, X Level of information, X Cooperation degree, X

7 J. Peng / Journal of Information & Computational Science 9: 1 (2012) Green Supplier Comprehensive Evaluation Based on AHP and GRA Step 1 Determine the index weight. This paper designs a specific questionnaire, the survey objects are focus on the manager or supervisor in manufacturing companies, and some green supply chain experts. According to the calculation steps and associated formulas of AHP, through the pair wise comparison for the indexes in evaluation system, we can get the relative importance, and test consistency, thereby determine the weight of indexes. First we get the weight from a set of indicators to the index in the previous level, and finally get the target s weight order from the indexes, in particular the lowest level of indicators, and have a general sort, CW is the product between the matrix weight of target layer and the weight of rule layer, that is the weight from indexes in ultimate project hierarchy to the relative target layer, the results shown in Table 4. Table 4: The total ordering of judgment matrix Rule layer and weight project hierarchy and weight Enterprise Service Cooperation Environmental ability, B1 level, B2 degree, B3 factors, B Weight of index in project hierarchy (CW) Production flexibility, C Production scale, C Level of information, C Price rate, C Delivery time, C Delivery-check qualified rate, C On-time delivery, C Average order completion rate,c Cooperation degree, C Hazardous substances content, C Energy consumption, C Harmlessness, C Above table shows that in the program evaluation, flexible production C1 accounts for the most important position, with the total weight of 22.9%. In addition, energy consumption C11 is also very important, with the weight of 13.5%. Step 2 Grey relational analysis. Based on the calculation steps and associated formulas of grey relational analysis, and the data in Table 2 and Table 3, we can get the sample results in matrix U: U =

8 180 J. Peng / Journal of Information & Computational Science 9: 1 (2012) With the goal of the case, to select the optimal value of each index as the reference data column, and have the effect measure transformation, unifying measure space of the three suppliers r p ij is as shown in Table 5. Table 5: Unified measure space of the three suppliersr p ij Target Index r p 1 r p 2 r p 3 1 Production flexibility Production scale Level of information Price rate Delivery time Delivery-check qualified rate On-time delivery Average order completion rate Cooperation degree Hazardous substances content Energy consumption Harmlessness Combined the weight of indicators in Table 4 and three suppliers correlation coefficient of each index in Table 5, we can find the mean correlation coefficient of three suppliers. r 1 = 1 12 r 2 = 1 12 r 3 = p=1 12 p=1 12 p=1 r p 1 CW = r p 2 CW = r p 3 CW = Get the satisfactory situation: S i = max {r 1, r 2, r 3 } = max {0.0735, , } = Then the rank of the suppliers is Company A, Company B, Company C. Step 3 Adjustment of green index. Because Company A meets the standard of third-class, Company B meets the standard of firstclass, Company C meets the standard of second-class, so the use of raw material from Company C will lead to cost increased by 10%, we can conclude that α C = 1, while using products from 1.1 Company C will cause a reduction on its duty which is equivalent to the 15% of price, then Similarly, can obtain β C = = 1.15 η C = α C β C = η A = 1

9 J. Peng / Journal of Information & Computational Science 9: 1 (2012) Step 4 Selecting the best supplier. η B = According to the formulaz = r i η, we can calculate the evaluation Zafter adjustment of green index, the supplier value MAX (Z) is the best supplier in the green economy. Are calculated: Z A = Z B = Z C = Can be seen, Company B is Company X s best green supplier. 5 Conclusions The selection of green supplier is a key step in green supply chain management and the rational choice of green suppliers is very important in enhancing the competitiveness of enterprises, resource conservation and environmental protection. In this paper, we can find the best supplier by calculating based on AHP and GRA method, while adding the green adjustment factor. Through the method, we also learn that if we just take into account the ability of the enterprise itself, don t take into account the characteristics of the green supply chain under full life cycle, have not apply the green adjustment factor according to the reality, may also lead to the wrong choice. This paper demonstrates the application of this method is effective by a case. Acknowledgements The author would like to thank anonymous referees for their helpful comments and suggestions, which improved the contents and composition substantially. References [1] Xu Wang, Daoping Wang, Yan Wang, An empirical study on the green vendor selection index of China iron and steel industry based on factor analysis, Soft Science, 23 (2009), [2] Robert B. Handfield, Steve V. Walton, Lisa K. Seegers et al., Green value chain practices in furniture industry, Journal of Operations Management, 15 (1997), [3] Amy H. I. Lee, He-Yau Kang, Chang-Fu Hsu et al., A green supplier selection model for high-tech industry, Expert Systems with Applications, 36 (2009), [4] Remko I. van Hoek, From reversed logistics to green supply chains, Supply Chain Management, 4 (1999), [5] Qinghua Zhu, Joseph Sarkis, Kee-hung Lai, Green supply chain management: Pressures, practices and performance within the Chinese automobile industry, Journal of Cleaner Production, 15 (2007), [6] Bin Liu, Qinghua Zhu, The supplier selection based on green purchasing, Management Review, 17 (2005), 32-36

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