Towards fuzzy preference relationship based on decision making approach to access the performance of suppliers in environmental conscious manufacturing domain

Accepted Manuscript Towards fuzzy preference relationship based on decision making approach to access the performance of suppliers in environmental conscious manufacturing domain Amit Kumar Sinha, Ankush Anand PII: DOI: Reference:

S0360-8352(16)30511-3 http://dx.doi.org/10.1016/j.cie.2016.12.033 CAIE 4585

To appear in:

Computers & Industrial Engineering

Received Date: Revised Date: Accepted Date:

11 May 2016 5 December 2016 25 December 2016

Please cite this article as: Kumar Sinha, A., Anand, A., Towards fuzzy preference relationship based on decision making approach to access the performance of suppliers in environmental conscious manufacturing domain, Computers & Industrial Engineering (2016), doi: http://dx.doi.org/10.1016/j.cie.2016.12.033

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Towards fuzzy preference relationship based on decision making approach to access the performance of suppliers in environmental conscious manufacturing domain Amit Kumar Sinha1*, Ankush Anand1 1: Department of Mechanical Engineering, Shri Mata Vaishno Devi University, Katra, India-182320 *: Corresponding Author: [email protected]

Amit Kumar Sinha Amit Kumar Sinha is an Assistant Professor in Mechanical Engineering at Shri Mata Vaishno Devi University, Katra, India-182320. He is also a Ph.D scholar from the same University. He received MS in Human and Systems Engineering from Ulsan National Institute of Science & Technology, South Korea. He has B.Tech Degree in Manufacturing Engineering from National Institute of Science & Technology, Ranchi, India. He has more than 5 years of teaching and research experience at different levels. He works in the area of Evolutionary Computing, applications, modelling and Simulation of Manufacturing System, Supply Chain Management, Planning and Scheduling of Automated Manufacturing System etc. He has published around 5 articles in leading international journals and is serving as a Reviewer of five International Journals including Computers and Industrial Engineering.

Ankush Anand Ankush Anand is an Assistant Professor in Mechanical Engineering at Shri Mata Vaishno Devi University, Katra, India-182320. He has a Ph.D Degree in Mechanical Engineering from the same University. He has more than 10 years of teaching and research experience at different levels. He works in the area of Sustainable Design, Design Optimization, Life Cycle Engineering, Tribology etc. He has published around 10 articles in leading international journals and is serving as a Reviewer in reputed International Journal.

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Towards fuzzy preference relationship based on decision making approach to access the performance of suppliers in environmental conscious manufacturing domain

ABSTRACT Environmental consciousness has become a significant aspect at various levels of product design. In the recent years, by and large industries are focusing towards environment conscious products. Quantitative analysis of energy consumption and waste generation from any product is directly related to the supply chain management of the product. Hence, the selection of most suitable supplier from the pool of available suppliers is emerged as an important multi attribute (multi objective, multi criteria, multi factor) decision making (MADM) problem. This research paper addresses the supplier selection problem from the perspective of supply analysis, logistic analysis, process analysis, use analysis and, recycle analysis for calculating environmental impact of a product. Based on the associated environmental importance of a product a procedure based on fuzzy framework is used for ranking and selecting the best suitable supplier from its environmental perspective. A multi preference fuzzy relationship model is introduced, to incorporate the uncertainty in the decision making by the decision maker. Finally, a solution procedure based on OWA (Ordered Weighted Averaging) aggregation operator is developed. The proposed methodology has been discussed along with an illustrative examples.

Key words: Environmental Conscious Manufacturing (ECM), Environmental Impact (EI), Multi Attribute Decision Making (MADM), Decision Support System, Green Supply Chain, Fuzzy Systems,

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1. Introduction In the last few decades, environmental problems have gained increased attention of government, non-government organizations, customers, institutions, researchers and practitioners (McAleer et al., 2000). Protection of the environment has become an important issue at all levels of society. Nowadays, the focus of environment protection strategy has been shifting from various components of earth (air, water, soil) to issues like acid rain, climate change, etc. (Smith et al., 2013). For example, Netherland which is suffering from a lot of environmental problems which include change in climate, depletion of ozone layer, dispersion of toxic substances, disposal of solid waste and, eutrophication (Ketting, 2011). Pollution is nothing more than a form of waste. Under 1976 Resource Conservation and Recovery Act (RCRA), the US Environmental Protection Agency (USEPA) has estimated about 8 billion tons of industrial waste per annum. However, the most interesting fact during this study was that more than 214 million tons of wastes are hazardous in nature (Mike Ewall., 2007). This alarming amount of waste is generated at every stage of the production process, consumption and disposal of manufactured products. In the recent years, most of the industries are inching towards manufacturing environmental friendly products (Mike Ewall., 2007). To respond for the environmental consciousness product, manufacturing companies have carried out a vast number of environmental programs. Great efforts have recently resulted in increasing the environmental performance of industrial products (Bovea and Wang, 2003). Environmental consciousness/friendliness is one of the most important criteria apart from quality, cost and, time-to-market for assessing the performance of suppliers (Agarwal & Vijayvargy, 2012). General Motors has launched a special drive coined as “WE CARE” (Waste Elimination and Cost Awareness Reward Everyone) which involves cooperation with suppliers on specific measures like reduction in waste of packaging material and, increase in their recycling ability (Barry et al., 1993). Noci (1997) categories these environmental programs into three main phases: First phase was till 1970 where purchasing managers (decision makers) introduced the concept of end-of-pipe programs so that the air emission, solid wastes, waste water and energy consumption of the plant can be reduced. Time period of second phase is in between 1970 and 1980s. During second phase, the purchasing managers introduced the concept of clean technologies programs aimed at reducing the company’s impact on the state of natural resources by changing major steps in the production process. Third phase started from the 3

beginning of 1990s where the purchasing managers changed the operating procedures and introduced the concept of eco-auditing framework. Nowadays, it is a time of fourth phase according to which environmental consciousness manufacturing companies are developing eco-friendly programs aimed at organizing their supply value chains according to eco-efficiency perspective (Hass, 1996; Noci, 1995; Noci 1997). Prior to 1980, the purchasing function was typically viewed as being primarily clerical, but today more analytics are employed to assess the environmental impact while addressing purchasing issues (Gupta, 1995). Therefore, purchasing manager must procure goods and services from that supplier(s), which are able to manufacture the products at the lowest cost, highest quality, shortest lead-time and greater flexibility and with minimum environmental impact (Wetzstein et al., 2016; Dobos & Vörösmarty, 2014). Recent literatures (Awasthi et al., 2010; Gupta, 1995; Sarkis, 2003) on supplier selection address two important questions: (1) How to spot the preferred solution(s) based on environmental and business concerns? (2) How to improve the understanding of the trade-offs between these two dimensions? Literature survey reveals that there is a dearth of multi criteria and multi attribute decision making problem in the perspective of environmental conscious supplier selection domain (Genovese et al., 2013; Jabbour & Jabbour, 2009). Although, many researchers (Bhattacharya et al., 2014; Humphreys et al., 2006) incorporated environmental consciousness issues into supply chain management. However, what is missing is an overall view of the overall environmental impact of any product in a whole supply chain so that any decision maker can take action to select those suppliers who has less overall environmental impact in whole supply chain management (Igarashi et al. 2013). In addition, most of the early literatures lack in exploring the broad environmental criteria either quantitatively or qualitatively with specific references to environmental cost coupled with production process, product and, management systems (Appolloni et al. 2014). Handfield et al. (2002) suggested an AHP model for assessing and selecting environmentally conscious supplier. This model has however certain issues which needs further research in terms of critical weights, inappropriateness of the crisp ratio representation, rank reversal problem and, the difficulty in the comparing many criteria. Evaluation of supplier performance on the basis of environmental consideration is a complex problem. The complexity of such problems is characterized by the difficulty of finding a unique quantitative measure. It is 4

an uphill task to compare the environmental damage parameters including, cost to hazardous impact etc. The decision-making problem handles the uncertainty which account for determining the relative importance of each criterion and their sub-criteria. Sometimes, decision-making problem becomes more critical when the available information is incomplete, imprecise or vague in nature. Apart from these things, decision makers have to handle both qualitative and quantitative criteria for evaluating the performance of environmental conscious supplier. This motivates the authors to propose a novel approach to solve a complex multi criteria decision making problem for a supply chain to assess the performance of suppliers in environmentally conscious manufacturing domain. This research paper altogether presents a new paradigm shift in research area by two ways. In the first way, the authors have incorporated environmental criteria as a key factor for supplier selection at each stage of supply chain. We have introduced five analyses namely supply analysis, logistic analysis, process analysis, use analysis and recycle analysis and for each and every analysis we have calculated six different types of environmental impact for a product on the basis of global warming potential, ozone depletion potential, photochemical ozone creation potential, acidific potential, nutrient enrichment potential and, volatile organic compound potential. In this paper, we propose a novel approach called multi preference fuzzy relationship based multi-criteria decision making (MPFRMCDM) model for assessing the performance of suppliers in environmental conscious manufacturing domain. The aim of the paper is to develop a decisionmaking support tool for sustainable supplier selection under the domain of manufacturing industries. Framework of environmental conscious or sustainable supplier selection is another salient feature of this paper. An illustrative example is used to illustrate how the fuzzy preference relationship has been implemented. A sensitivity analysis is conducted to evaluate the influence of criteria weights on the environmental performance evaluation of suppliers. The framework of the proposed MPFRMCDM approach encompasses various established decision making techniques to make the overall approach competitive and compatible with the problem at hand. Moreover, the MPFRMCDM uses an external repository concept to preserve all effective set of suppliers. MPFRMCDM approach incorporates a fuzzy based feedback mechanism

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which iteratively uses the information to determine the compromise solution. Finally, this paper has been structured with the following objectives: (1) To validate the efficacy and application of the proposed model on complex multi criteria decision making problem for assessing the performance of suppliers in environmental conscious manufacturing domain. (2) To solve highly constrained multi criteria decision making problem and obtain manageable and compromise solution. (3) To present fuzzy preference relationship that has been applied to even out the data so as to emulate the human reasoning process and making decision based on vague and imprecise data. (4) To eliminate the human effects in decision making process. Therefore, the present paper attempts to select a most suitable supplier in the fuzzy framework of multi-criteria decision-making (MCDM) process, which has the least total environmental impact. A multi preference fuzzy relationship model has been adopted here for selecting the best supplier. The reason for using fuzzy relationship in the subjective evaluation is to incorporate the uncertainty in the decision by a particular decision maker. Akin to AHP, we have modeled the problem of environmental conscious supplier selection in a hierarchy order. The decision maker has opined himself in multi-criteria fuzzy preference relationship. Next, on the basis of global information about the alternative we prepare a list of alternatives rank wise, from which the set of solution alternative is obtained. The OWA operator (Yager, 1988) has been utilized to calculate the global ranking of suppliers. This paper investigates the feasibility of applying a multi criteria fuzzy preference relation model in an environmental conscious supplier selection environment. The rest of the paper is arranged as follows. In the next section, the literature review on decision making model for assessing the performance of suppliers in environmental conscious manufacturing domain is reviewed. Section 3 details the proposed approach for development of an environmental conscious supplier selection system. This section also deals with four levels of environmental emission hierarchy framework for the selection of supplier. In section 4, a background of O.W.A operator and fuzzy multiplicative preference relationship is shown. In section 5, we propose an algorithm for the evaluation of total environmental impact. In section 6,

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an illustrative example with result and discussions is presented. Section 7 presents concluding remarks and its future scope.

2. Literature review In general, manufacturing industries are not only concerned about manufacturing products but, also focuses on the environmental friendliness of the products. Say for example, quality, cost, delivery, and environment is the Sony’s procurement policy with their suppliers (Handfield et al.2002). FIAT and BMW in the automotive industries and IBM as an electronic industry have involved suppliers in their new product development (NPD) process so that they can develop more effective green product design concepts and hence, to respond to the growing societal demand for more environmentally friendly products (Noci, 1997). Zhang et al. (2003) defined environmental conscious supplier management as a process that integrates supplier environment systems into manufacturer’s own management system, through activities such as supplier selection, monitoring, evaluation and feedback to improve the manufacturer’s environmental performance. Today, the majority of manufacturing industries incorporate environmental work in their own processes (Butlin 1989). However, day by day the environmental work related to suppliers is becoming weaker (O’Brien 2014). There is no real tool to practically measure a supplier’s environmental status. From the literature survey we can arrive at one simple question. “Why have purchasing departments not seen the opportunities for a better environmental tool?” The reason is also very simple (Igarashi et al. 2013). The first reason is that manufacturing industries have gone forward with one’s own processes, while environmental questions are relatively new, and that usable methods and analytical tools have been lacking. Other reasons for the status of environmental work are its complexity, that clear research results on the industries level are lacking, and that there are varying attitudes to the value of the efforts and the effects of environmental investment. An environmental measure can often have a reverse side. Enarsson (1998) cited very simple example of this is powdered paint, which is considered very environment-friendly since its use reduces the use of solvents. But the production of powder paints is highly demanding on energy, and therefore puts a strain on the environment. Therefore, assessment of supplier on the basis of environmental consciousness issues is open and challenging research issue not only in the design and manufacturing field, but also, in the 7

research are of business strategy because most industries have their own rules and operation strategies for their supplier selection in the domain of supply chain management. On the basis of Ishikawa’s fishbone diagram, Enarsson (1998) proposed a method for evaluating suppliers from an environmental perspective. Green vendor rating system has been developed by Noci (1997) for assessing the supplier’s environmental performance. Humphreys et al. (2003a), proposed a multi-stage framework for incorporating environmental criteria with supplier selection process and checking environmental performance of a supplier against legal requirements. Humphreys et al. (2003a) carried out a research study based on evaluation of supplier environmental performance using case based reasoning approach. Anand and Wani (2010) prposed a life cycle design based environment related product design model which takes due consideration of environment in product design. Chan (2003) proposed a model named Interactive Selection Model (Somuyiwa and Mcism 2012)) with AHP to handle the supplier selection process (SSP) systematically and quantitatively. Literature reveals that several techniques were developed say for example Weight Point Method (WPM), Cost-Ratio Method (CRM), Categorical Method (CM) has been utilized by researchers and practitioners for accessing the performance of suppliers (Dobler et al., 1990; Talluri and Sarkis, 2002). Fuzzy methodology appears to be one of the most promising tools for managing the ambiguity and in determination that characterizes the evaluation of suppliers’ contribution to product development (Jain et al., 2004; Nassimbeni and Battain, 2003). Bellman and Zadeh (1970) introduced Fuzzy set theory which is used to model vagueness and uncertainty in decision making processes arising due to lack of complete information. Chan et al. (1999) integrated fuzzy and entropy theories into the House of Quality to obtain the competitive priority rating of the customer needs. Multi-agent based fuzzy decision making method has been discussed by Zhang et al. (2003) for solving environmetally concious supplier selection problem. Some fuzzy membership function has been modeled for solving sustainable supplier selection problem (Humphreys et al. 2003b). Chan et al. (2008) carried out a research study using fuzzy based Analytic Hierarchy Process (fuzzy-AHP) for effective selection of global supplier by considering both quantitative and qualitative decision factors. Tuzkaya et al. (2009) presented a hybrid fuzzy multi-criteria decision approach for evaluating environmental performance of suppliers. Bai and Sarkis (2010) have utilized the concept of grey system and rough set methodologies for considering sustainability as criteria for supplier selection. Calabrese et 8

al.(2016) proposed a fuzzy-AHP based methodology for assessing structure materiality based on Global Reporting Initiative Guidelines in sustainability. Although, many researchers are trying to incorporate environmental conscious supplier selection issues into supply chain management, but literature survey reveals that there is need to carry out research studies which gives an effective and efficient solution to environmetal concious supplier selection problem. This is due to the fact that it is hard to access the perofrmance of suppliers on the basis of the environmental impact in the supply chain management under the domain of manufacturing industry (Coskun et al., 2016). Therefore, the objective of the paper is (i) to identify the appropriate environmental impact and, (ii) to access the suppliers on the basis of environmental impact in a supply chain of manufacturing environment. 3.

Development of an environmental conscious supplier selection model

This paper provides a supplier selection model using fuzzy framework of multi-criteria decision making process to consider environmental issues. The model is formulated in a generic form of supplier selection. Humphreys et al. (2006) have suggested six supplier selection factors including environmental issues, which are most important criteria. They also suggested five subcriteria of environmental issues which are also divided into different sub-criteria (shown in Figure 1).

<> Environmental evaluation is a fuzzy concept which involves understanding the transformation of many natural variables by different segments of society. In the last decade, there has been an increased concern about environmental quality. Nowadays, everyone has an individual perception about pollution. A multi preference fuzzy relationship model has been adopted here for selecting the best supplier. Environmental performance of supplier is evaluated on the basis of environmental impact Handfield et al. (2002). Albino et al. (1998) clearly discussed that price cannot be the only accessing criteria for supplier selection. In other words, environmental impact would be an important criterion for supplier selection. The researchers proposed an environmental consensus supplier strategy development process which is illustrated in Figure 2. (Monczka et al., 2008). 9

<>

Decision makers collect information from new and existing suppliers about their products. Such information may include data based on current performance and new potential suppliers’ performance in terms of environmental record. Additional information may be provided in the form of products or technical specification/statement of work based on these. After that the decision maker gathers information on new government emission standards or new environmentally friendly technologies about the threshold level of environmental pollution. In the next stage, this information is used to identify, quantify, assess, and select the best suppliers from the in terms of environmental performance (reducing waste and maximizing resource efficiency). In this stage, we need a decision support tool which can judge the potential of the supplier in terms of environmental impact. In general, environmental friendly products create impact in following ways: (A) Impact on Supply Chain: Environmental issues play an important role in the activities of companies. Decision with respect to production planning, logistics, location, reallocation and inventory control are changing due to the legal requirements or consumer pressure to reduce waste and emission. Therefore, there is a need to select a supplier that has a lowest overall environmental impact. (B) Impact on Environmental Chain: The amount of waste and level of emission generated in the supply chain leads to a number of serious environmental issues which includes global warming, acid rain, etc. Frequently, these environmental issues are global and also complex in nature. Therefore, the supplier that has a lowest environmental impact should be assigned as a higher priority. Figure 3 discuss the framework of general environmental supply chain network. It shows the way by which, any product gives environmental impact. Products produced from industry moves for packing and thereafter, through transportation reached up to end customers. After use of the products some form of waste come out. Some parts of the waste appear as an emission waste which is a byproduct of carbon dioxide, acetylene, sulfur dioxide, isoprene, etc. These byproducts pollute water, air, soil, and sometimes appear as a blast (noise). Primary resources 10

effected from these pollutants and indirectly these polluted primary resources appear as a form of raw materials and in this way again and again this close loop circle is running day by day and creating the environmental problems.

<>

The supply chain involves the integration of production planning, dispatch, order generation and, logistic planning, apart from general logistics, which preliminary includes distribution (Mukhopadhyay and Barua, 2003). During each step in the supply chain, from the extraction of product material to processing steps, emission takes place which can badly affect people, plants, animals and eco-systems. Hence, decisions required to be taken to minimize the waste flows. Legal requirements and changing preferences increasingly make suppliers and manufacturers responsible for their products, even beyond their sale and delivery. To comply with these newer regulations, producers have to apply cradle-to grave product management covering the entire supply chain. Therefore, there is a need to take a decision at the highest level approach to select those suppliers whose environmental impact is minimum. (Chan et al., 1999) integrated fuzzy and entropy theories into the House of Quality to obtain the competitive priority rating of the customer needs. The most accepted technique to evaluate the environmental profile of a product is based on the environmental impact analysis. In this paper, we proposed a five-stage environmental analysis framework to evaluate the environmental impact of the product. The five-stage environmental analysis (shown in figure 4.) consists of (1) supply analysis; (2) logistic analysis; (3) process analysis; (4) use analysis; and (5) recycle analysis. In the first stage analysis (supply analysis) the information about supplier’s environmental metrics (energy used, water used, emission generated, and hazardous waste generated) are collected and analyzed on the basis of environmental performance of the supplier. The rest of the four stages analyze the effect of the product purchased to the logistics, manufacturing process of the manufacture, and the consuming and recycling of the product. Based on these inputs the outputs from all the five stages are aggregated with the help of fuzzy preference modeling which we will discuss in next section of this article.

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In Figure 5, a model of environmental analysis in each stage is illustrated (Ross and Evans, 2002; Zhang et al., 2003). In each operation in the five stages, we have six different types of byproduct (hazardous material) such as Carbon dioxide (CO2), Oxides of Nitrogen (NO), Hydro Chlorofluorocarbon (HCFC), Oxides of Sulpher (SO), Hazardous Substance (Harz) and Volatile Organic Compound (VOC) that are generated as a part of by products (Alvarez et al., 2016). Their overall impact on the environment is categorized into six categories namely: (1) Global Warming Potential (GWP); (2) Ozone depletion Potential (ODP); (3) Photochemical Ozone Creation Potential (POCP); (4) Acidification Potential (AP); (5) Nutrient Enrichment potential (NEP); and (6) Volatile Organic Compound Potential (VOCP) (Legarth, 1997; Wenzel et al., 2000).

<> (1) Global Warming Potential (GWP): Its main reference material is carbon dioxide (CO2). It is applied to measure the potential heat from escaping into the universe, in terms of unit weight of CO2 emitted. (2) Ozone depletion Potential (ODP): Its main constituent is Chlorofluorocarbon -11 (CFC-11). (3) Photochemical Ozone Creation Potential (POCP): Acetylene (C2H4) is the main constituent material. (4) Acidification Potential (AP): Here the main constituent material is sulfur dioxide (SO2). (5) Nutrient Enrichment potential (NEP): Its constituent material is NOx (mostly NO3 ) (6) Volatile Organic Compound Potential (VOCP): Mainly Isoprene and Monoterpene are its main constituent materials. In Figure 6, we have proposed a four level of environmental emission hierarchy framework for the selection of the supplier. Hierarchical representation of the problem is an important tool dealing with large systems, which is usually complex in nature. This involves identifying the elements of the problem, grouping the elements into homogenous sets and, arranging these sets in different level. Each set of elements occupy a level of the hierarchy. The top level consists of information about suppliers’ environmental metrics such as emission, energy used, water used, 12

and the hazardous waste. Second level is represented by five different stages of analysis such as supply analysis, logistic analysis, process analysis, use analysis and, recycle analysis. The third level represents six different types of environmental impacts which are Global Warming Potential (GWP), Ozone Depletion Potential (ODP), Photochemical Ozone Creation Potential (POCP), Acidification Potential (AP), Nutrient Enrichment Potential (NEP), Volatile Organic Compound Potential (VOCP). Finally, the set of suppliers is shown in last level.

<>

In general, let us assume following notations: s :Set of suppliers  s  S  h :Set of hazardous materials (Byproducts)  h  H  m :Set of environmental metrics  m  M  a :Set of environmentalanalysis  a  A e :Set of environmentalimpacts  e  E 

X  x1 , x2 , x3 ,...xi , x j ,...xn 

yhams :Total amount of hazardous material ' h 'of ath environmental analysis with mth set of environmental metrices for s th supplier e  hams : eth environmentalimpact produce by per unit of

hazardous material ' h 'of a th environmental analysis with mth set of environmental metrics for s th supplier

eams : Importance (weight) of eth environmentalimpact of ath environmental analysis with mth set of environmental metrics for sth supplier

Total environmental impact for supplier ‘s’ = TEIs

 M A E  M A E H  e  yhams   =   eams      hams   m1 a 1 e1 h 1   m1 a 1 e1





…(1)

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The decision maker has to select that supplier who has minimum overall environmental impact. Objective function can be written as:

Select minimum (TEI s )

…(2)

Next section deals with the basic background of OWA (order weight average) operator and four evaluation structure of preference which is used in multi-criteria decision making problem.

4. Back ground for multi-criteria decision making problem In this section, we present a multi criteria fuzzy preference relationship model. During the evaluation of total environmental impact in five-stage analysis, decision maker gives his opinion in fuzzy multiplicative preference relation. In this paper, we have considered the fuzzy preference relation as our base to information representation, as it incorporates the uncertainty in the decision taken by decision-maker. Once the information is put forth, the selection process is applied, which includes “Aggregation phase” and “Exploitation phase” (Chiclana et al., 1998, 2001). In the aggregation phase, we get collective fuzzy preference relationship which gives an indication about the global preference between alternatives on the basis of pair wise comparison matrix. The pair wise comparison matrix is formed on the basis of the majority of the expert’s opinion. Global/collective fuzzy preference relationship can be developed by using OWA (Order Weighted Averaging) operator and it is calculated by using equation 3. Pc   Pijc 

…(3)

Pc can be calculated on the basis of the average aggregation of individual fuzzy preference relationship among the sets of alternatives (P1, P2, …,Pz) and collective preference between pair of ith and jth alternatives is calculated on the basis of equation 4.



Pijc  Q Pij1 ,..., Pijz



…(4)

Where, Pc: Global/collective preference P1: Individual fuzzy preference for alternative ‘1’

Q : OWA operator Pijc : Collective preference between pair of ith and jth alternatives

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The second phase which is known as exploitation phase, transform the global/collective preference into corresponding ranking of the alternatives. In another words, in the exploitation phase we get ranking of the alternatives and on the basis of the preferred ranking, we can select the alternatives from the pull of alternatives. Although, transform is based on the OWA operator, however, in the proposed model, two choices of degree namely quantifier-guided dominance degree (QGDD) and the quantifier-guided non-dominance degree (QGNDD). Equation 5 and 6 represent QGDD and QGNDD for alternative xi respectively.



QGDDi  G Pi1c ,..., Pinc





QGNDDi  G 1  Pi1s ,...,1  Pnis

… (5)



Where, Pjis  max Pjic  Pijc , 0

… (6) … (7)

Pjis : The degree to which xi is strictly dominated by x j In this research, QGDD has been used for ranking of alternatives. QGDD acts like eigen vectors of analytical hierarchy process (AHP). In the next subsection, a background of OWA operator is introduced. 4.1 OWA (Order Weight Average) Operator

Ordered Weighted Averaging (Orlovsky et al. 1978) is an aggregation operator which is introduced by Yager (1988, 1993; and 1994). OWA operator can be defined as: F : un  v

… (8)

Here, n

F  x1 , x2 ,...xn    wi bi

… (9)

i 1

Or n

Fw ( x)   wi bi i 1

bn   u, v 

…

(10) w 0,1

n

bi: Largest element in x1 , x2 ,

… (11)

... xn 15

n

w i 1

i

1

… (12)

i  i 1  wi  Q    Q   n  n 

…(13)

Q: Non decreasing quantifier In MCDM (Multi criteria decision making) process, the expert’s opinion regarding alternatives can be expressed in four ways namely fuzzy preference relationship, preference ordering of the alternatives, multiplicative preference relationship and, utility function (Chiclana et al. 2001; Herrera et al., 2001) see Table 1. <> Next section deals with the proposed solution methodology for multipurpose decision making problem under the domain of environmental conscious supplier selection, when experts are providing different evaluation structures of preferences.

5. Solution Methodology In this research paper, multi preference fuzzy relationship based methodology (MPFRBM) has been proposed to solve the multi-criteria decision making model for assessing the performance of suppliers in environmental conscious manufacturing domain. MPFRB model provides effective and efficient solution for supplier selection in multi-criteria decision making problem. The presence of uncertainty and ambiguity in multi criteria decision making problem for supplier selection is a challenging task for practitioner and researchers and it is hard to solve Environmental Conscious Supplier Selection Problem (ECSSP) by using Analytical Hierarchy Problem (AHP). Therefore, a new model is necessary to address the ECSSP. Linguistic assessment and subjective judgment of expert’s opinion about single or multiple attributes/criteria leads towards vagueness in decision making problem which cannot be incorporated by AHP. Therefore, in this paper we proposed a MPFRB model. In this methodology, we get expert’s opinion about the alternatives which can be expressed in four ways which include: fuzzy preference relationship, preference ordering of the alternatives, multiplicative preference relationship, and utility function. This opinion will form the basis for input to the MPFRB model. In order to achieve a common representation of the preference from 16

the entire expert’s for same alternatives we need transformation function for getting homogeneous information which is necessary for taking decision about supplier selection in decision making problem. By using a suitable transformation function, we can convert all other preferences into fuzzy preference relationship (Chiclana et al., 1998, 2001; Van de Walle et al., 1998). After getting homogenous information (fuzzy preference relationship) about various alternatives, we use a selection process which consist of aggregation and exploitation phase (Chiclana et al., 1998; Roubens, 1997). Finally, we use the concept of five point rating system (outstanding, good, average, fair, and poor) to evaluate the performance of suppliers on the basis of lower environmental impact. In this research paper, we have developed a decision making tool which will be utilized by manufacturing industry and academia for supplier selection problem under the domain of environmental impact. The solution methodology of the proposed model has been described in the form of algorithmic facilitate in the decision making process. We have proposed a “model” whose algorithm has been described in the following ways: (1) Generate the preference structure of the respective decision maker in any one of the following: (i)

A preference ordering of the alternatives.

(ii)

A fuzzy preference relation.

(iii)

A multiplicative relation.

(iv)

A utility function.

(2) Transform these three – preference structures (A preference ordering, A multiplicative relation, A utility function) into a fuzzy preference relation. (3) Aggregation phase: In the aggregation phase, we have developed fuzzy preference relationship by making homogenous expert’s opinion about the alternatives. Global preference fuzzy relationship can be developed by using OWA operator (see equation 3 and 4). Pc   Pijc 

… (14)



Pijc  Q Pij1 ,...Pijz



… (15)

Here, 17

Q: Fuzzy linguistic quantifier

Pij1 : Individual expert’s opinion about alternatives ‘i’ & ‘j’ z: No of experts’ opinion (4) Exploitation phase: In this phase, we have developed global ranking on the basis of OWA operator and fuzzy preference relationship (Yager, 1988). Quantifier guided dominance degree (QGDD) will provide degree of alternatives for the supplier selection problem. (5) Calculate Quantifier Guided Dominance degree (QGDD) for each alternatives Xa by utilizing OWA operator (see equation 16)



QGDDa   Q M ajt , j  1, 2,..., n



… (16)

Here, n: Represents the number of alternatives. Q: Represents fuzzy linguistics quantifier. Fuzzy linguistics gives us idea about how to calculate weight vector

W   w1 , w2 ,...wn 

… (17)

Such that n

w i 1

i

1

… (18)

According to Herrara et al. (2001), the membership function of a non-decreasing relative quantifier can be represented as 0 r  a  Q r    b  a 1

if r  a

   if a  r  b   if r  b 

… (19)

Here,

a, b, r  0,1; and a  b In general, “most”, “almost all”, “a few”, “as many as possible”, “at least half”, “nearly half”, etc. are the most common linguistic quantifiers which is used by experts in providing opinion about alternatives. Figure 7 explain some of the proportional quantifier where the parameters (a, b) are (0.2, 0.9), (0, 0.50) and, (0.5, 1) respectively.

18

<>

(6) Assign rating to each supplier for considering all attributes. (7) Calculate the final priority of environmental impact for each supplier by considering every attribute. (8) For rating purpose the following scale is used: a. P: Poor b. F: Fair c. A: Average d. G: Good e. O: Outstanding (9) Enumerate the over-all total environment impact for each supplier. (10) Select the supplier, which has “least” overall environmental impact. The proposed solution methodology is also explained in the form of flow chart which is shown in Figure.8.

<>

6. Illustrative Example This framework proposed in this research work may be used for new as well as existing supplier selection under the domain of environmental conscious manufacturing domain. For this purpose, two examples are explained in this section. Example 6.1 is of environmental conscious supplier selection network in which we select that supplier who contributes least overall environmental impact. Example 6.2 is considered from the literature Lee et al. (2009) to validate the proposed methodology on an existing decision making process. 6.1 Illustrative Example-1 In this section, we consider an environmental conscious supplier selection network, which is illustrated in Figure 9. In this example for considering simplicity of the network, we have considered only two suppliers “A” and “B”. Two Suppliers (A and B) are taken as the decision

19

alternatives for the decision maker. The proposed supply chain network consists of suppliers, who supply raw materials for the manufacturing plant and after shipping, the product reaches up to the end customers. When end customer uses the product, we consider that six pollution emitted substance/byproduct/hazardous material namely Carbon dioxide (CO2), Oxides of Nitrogen (NO), Hydro Chlorofluorocarbon (HCFC), Oxides of Sulphur (SO), Hazardous substance (Harz), and Volatile Organic Compound (VOC) are generated in this network. These byproducts are responsible for creating six different types of environmental impacts such as Global warming potential (GWP), Ozone depletion potential (ODP), Photochemical ozone creation potential (POCP), Acidific potential (AP), Nutrient enrichment potential (NEP), and Volatile organic compound potential (VOCP) which play a role of attributes for decision maker. In this work, we are considering five major stages of analysis for supply chain network where performance of each supplier will be evaluated in terms of total environmental impact. These analyses of stages include: Supplier analysis, Logistic analysis, Process analysis, Use analysis and Recycle analysis. For each and every supply chain analysis we will calculate total environmental impact of each supplier with respect to each alternative. As a consequence, we will select that supplier who contributes least overall environmental impact (Wang et al., 2016). The raw data for supplier “A” for different stage (Supplier analysis, Logistic analysis, Process analysis, Use analysis and Recycle analysis) is provided in Table 2 (Biennial, 1995; Stranddorf et. al. 2005). Similarly the raw data of supplier “B” for different stages are provided in Table 3 (Biennial, 1995; Stranddorf et. al. 2005).

<> <> <> Expert presents their opinion about supplier “A” in terms of fuzzy preference relationship for conducting five stages of analysis (Supplier analysis, Logistic analysis, Process analysis, Use analysis and Recycle analysis). The views of experts for all the analysis are listed in Table 4, Table 5, Table 6, Table 7, and Table 8 respectively. <> <> 20

<> <> <> In a similar way, the expert presents their opinion about supplier “B” in a fuzzy preference relationship for conducting five stages of analysis (Supplier analysis, Logistic analysis, Process analysis, Use analysis and Recycle analysis). The views of experts for all the analysis are listed in Table 9, Table 10, Table 11, Table 12, and Table 13 respectively. <> <> <> <> <>

As mentioned in Step 2 of the Algorithm, we made homogenize the opinion of the expert. According to step 3 (suggested in our proposed algorithm) we implement selection process that considers into two phases (Chiclana et al., 1998; Roubens, 1997) (a) Aggregation Phase and (b) Exploitation Phase The OWA aggregation operator (Yager, 1988) and different types of Fuzzy linguistic quantifier are “most”, “at least”, “many as possible” defined by the parameters (0.2, 0.9), (0, 0.5) and, (0.5, 1) respectively. However, we have applied Fuzzy linguistic quantifier “as many as possible” with the pair (0.5, 1) and, weight vector W = (1/3, 1/3, 1/3, 0, 0, 0,). In the exploitation phase, we have applied the Quantifier Guided Dominance Degree (QGDD) that quantifies the dominance that all others in a Fuzzy majority sense. Quantifier Guidance Dominance Degree for all the five stages for supplier “A” and “B” is calculated and shown in Table 14 and 15 respectively. The element of first row of Table 14 is calculated on the basis of Table 4. In the similar way the element of second row, third row, fourth row, and fifth row is calculated on the basis of Table 5, Table 6, Table 7 and, Table 8 respectively. The calculation of the element of first raw of Table 14 is as follows: 21

(0.5*1/3+1.074*1/3+1.024*1/3+1.074*0+1.181*0+1.111*0)= 0.866 (-0.074*1/3+0.5*1/3+0.393*1/3+0.5*0+0.607*0+0.565*0)= 0.273 (-0.024*1/3+0.550*1/3+0.5*1/3+0.550*0+0.657*0+0.616*0)=0.342 (-0.074*1/3+0.5*1/3+0.393*1/3+0.5*0+0.607*0+0.565*0)= 0.273 (-0.181*1/3+0.393*1/3+0.342*1/3+0.393*0+0.5*0+0.458*0)=0.184 (-0.140*1/3+0.434*1/3+0.383*1/3+0.434*0+0.514*0+0.5*0)=0.225 In the similar way, element of Table 15 is calculation for supplier “B” by using Table 9, Table 10, Table 11, Table 12 and Table 13. <> <> The global priority weights are determined for all the five stages and are enumerated by using the quantifier guided dominance degree (QGDD). A five point rating scale: outstanding (O), good (G), average (A), fair (F) and, poor (P) are considered and their priority weights are decided by the decision maker. In this case, we have considered the scoring value of 0.513, 0.261, 0.129, 0.063, 0.063 and, 0.034 for respectively outstanding, good, average, fair, and poor. Individual Impact is calculated by multiplying global impact with score (see Table 16). <> Finally, we get the overall impact of environmental performance of supplier “A’ as 3.0781 and, analysis for “B” is conducted in the same way and the overall impact of environmental performance of supplier “B” comes out to be 3.2938. Therefore, supplier “B” has more environmental impact as compared to supplier “A”. Hence, supplier “A” is selected as its environmental impact is minimum. 6.1 Illustrative Example-2 An example of the environmental conscious supplier selection problem is considered here for finding out the supplier who can manufacture environmental friendly TFT-LCD module. Various criteria and sub criteria and other details as available from the literature of the environmental conscious supplier problem are given in Lee et al. (2009). The researchers analyzed three different suppliers namely: Supplier A, Supplier B and, Supplier C. The necessary information about these concepts is given in Lee et al. (2009). The objective of all these decision making techniques is to identify the most suitable supplier who has least over all environmental impact. The hierarchy of green supplier selection is illustrated in Lee et al. (2009). In this hierarchy they 22

have identified six different criteria: Quality (Q), Technological Capability (TC), Pollution Control (PC), Environmental Management (EM), Green Product (GP) and, Green Competencies (GC). On the basis of the experts opinion, a fuzzy integrated matrix for Supplier A, B, and C are illustrated in Table 17, Table 18 and, Table 19 respectively. <> <> <> However, we have applied Fuzzy linguistic quantifier “as many as possible” with the pair (0.5, 1) and, weight vector W = (1/3, 1/3, 1/3, 0, 0, 0,). In the exploitation phase, we have applied the Quantifier Guided Dominance Degree (QGDD) that quantifies the dominance that all others in a Fuzzy majority sense. Quantifier Guidance Dominance Degree for all the criteria for supplier “A” , “B” and, “C” is calculated and shown in Table 20. <>

The global priority weights are determined for all the five stages and are enumerated by using the quantifier guided dominance degree (QGDD). A five point rating scale: outstanding (O), good (G), average (A), fair (F) and, poor (P) are considered and their priority weights are decided by the decision maker. In this case, we have considered the scoring value of 0.513, 0.261, 0.129, 0.063, 0.063 and, 0.034 for respectively outstanding, good, average, fair, and poor. Individual Impact is calculated by multiplying global impact with score (see Table 21). <> Finally, we get the overall impact of environmental performance of supplier “A”, “B” and “C” are 1.204, 2.314 and, 3.908 respectively. Therefore, supplier “B” and “C” has more environmental impact as compared to supplier “A”. Hence, supplier “A” is selected as its environmental impact is minimum.

7. Concluding remarks and future research Supplier selection for an environmental friendly product is an important issue. When we are interested to calculate the overall environmental impact of the product, a quantitative approach is necessary. Any type of supplier environmental analysis can be demonstrated into the suggested 23

five analysis procedure and finally, by aggregating the expert opinion about the suppliers, we can select environmentally conscious supplier from the pool of suppliers. Another aspect of the proposed model is that it provides most accepted approach which evaluates the environmental impact. In this research paper, we have considered fuzzy preference relation based approach to handle the supplier selection problem. To emulate the human decision making process in an uncertain situation we utilize fuzzy preference relationship for evaluating structure of preferences. This research paper gives us an idea to examine the manner by which managers/decision makers can select the more efficient supplier performance on the basis of environmental impact. This research explores the selection of supplier, which is environmental friendly in the multiattribute decision making process with less requirement of skill of decision maker. The objective of this research paper is to improve the quality and reduction of time in taking decisions. This research work also focuses on resolving trade-offs and the challenge of assessing as they pertain to the environmental responsibility of suppliers. The proposed methodology can be applied and tested in other areas like technology selection, machine selection, an advertising media selection, a waste disposal method selection, project selection and for various other types of decision-making problem.

Acknowledgement The authors would like to thank the Editor and the anonymous reviewers for their constructive and valuable comments.

References Agarwal, G., Vijayvargy, L. 2012. Green supplier assessment in environmentally responsive supply chains through analytical network process. In: Proceedings of the 2010 Int. Multi Conference of Engineers and computer scientists (Vol. 2): Citeseer. Albino, V., Garavelli, A., Okogbaa, O., 1998. Vulnerability of production systems with multisupplier network: a case study. International Journal of Production Research 36, 3055-3066. Alvarez, S., Carballo-Penela, A., Mateo-Mantecón, I., Rubio, A., 2016. Strengths-WeaknessesOpportunities-Threats analysis of carbon footprint indicator and derived recommendations. Journal of Cleaner Production 121, 238-247. Anand, A., Wani, M., 2010. Product life-cycle modeling and evaluation at the conceptual design stage: A digraph and matrix approach. Journal of Mechanical Design 132, 091010-091019. 24

Appolloni, A., Sun, H., Jia, F. and Li, X., 2014. Green Procurement in the private sector: a state of the art review between 1996 and 2013. Journal of Cleaner Production, 85, 122-133. Awasthi, A., Chauhan, S.S., Goyal, S., 2010. A fuzzy multicriteria approach for evaluating environmental performance of suppliers. International Journal of Production Economics 126, 370-378. Bai, C., Sarkis, J., 2010. Integrating sustainability into supplier selection with grey system and rough set methodologies. International Journal of Production Economics 124, 252-264. Barry, J., Girard, G., Perras, C., 1993. Logistics planning shifts into reverse. Journal of European Business 5, 1-34. Bellman, R.E., Zadeh, L.A., 1970. Decision-making in a fuzzy environment. Management Science 17, 141-164. Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., 2014. Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach. Production Planning & Control 25, 698714. Biennial, R.A., 1995. Report (Washington DC). Environmental Protection Agency. Bovea, M., Wang, B., 2003. Identifying environmental improvement options by combining life cycle assessment and fuzzy set theory. International Journal of Production Research 41, 593609. Butlin, J., 1989. Our common future. By World commission on environment and development. (London, Oxford University Press), 1987, 284-287. Calabrese, A., Costa, R., Levialdi, N., Menichini, T., 2016. A fuzzy Analytic Hierarchy Process method to support materiality assessment in sustainability reporting. Journal of Cleaner Production 121, 248–264. Chan, F.T.S., 2003. Interactive selection model for supplier selection process: an analytical hierarchy process approach. International Journal of Production Research 41, 3549-3579. Chan, F.T.S., Kumar, N., Tiwari, M., Lau, H., Choy, K., 2008. Global supplier selection: a fuzzy-AHP approach. International Journal of Production Research 46, 3825-3857. Chan, L., Kao, H., Wu, M., 1999. Rating the importance of customer needs in quality function deployment by fuzzy and entropy methods. International Journal of Production Research 37, 2499-2518. 25

Chiclana, F., Herrera, F., Herrera-Viedma, E., 1998. Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations. Fuzzy sets and Systems 97, 33-48. Chiclana, F., Herrera, F., Herrera-Viedma, E., 2001. Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations. Fuzzy Sets and Systems 122, 277-291. Coskun, S., Ozgur, L., Polat, O., Gungor, A., 2016. A model proposal for green supply chain network design based on consumer segmentation. Journal of Cleaner Production 110, 149-157.

Dobler, D.W., Burt, D.N., Lee, L., 1990. Purchasing and materials management: Text and cases. McGraw-Hill New York, NY. Dobos, I. & Vörösmarty, G., 2014. Green supplier selection and evaluation using DEA-type composite indicators. International Journal of Production Economics, 157, 273-278. Enarsson, L., 1998. Evaluation of suppliers: how to consider the environment. International Journal of Physical Distribution & Logistics Management 28, 5-17. Gupta, M.C., 1995. Environmental management and its impact on the operations function. International Journal of Operations & Production Management 15, 34-51. Genovese, A., Lenny Koh, S., Bruno, G. & Esposito, E., 2013. Greener supplier selection: state of the art and some empirical evidence. International Journal of Production Research, 51(10), 2868-2886. Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., 2002. Applying environmental criteria to supplier assessment: A study in the application of the Analytical Hierarchy Process. European Journal of Operational Research 141, 70-87. Hass, J. (1996). Greening the supply value chain: A case study and the development of a conceptual model. In:

Proceedings of the Third Conference of the Nordic Business

Environmental Management Network, Aahrus, March (Vol. 28). Herrera, F., HerreraViedma, E., Chiclana, F., 2001. Multiperson decision-making based on multiplicative preference relations. European Journal of Operational Research 129, 372-385.

26

Humphreys, P., McCloskey, A., McIvor, R., Maguire, L., Glackin, C., 2006. Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process. International Journal of Production Research 44, 2379-2419. Humphreys, P., McIvor, R., Chan, F., 2003a. Using case-based reasoning to evaluate supplier environmental management performance. Expert Systems with Applications 25, 141-153.

Humphreys, P., Wong, Y., Chan, F., 2003b. Integrating environmental criteria into the supplier selection process. Journal of Materials Processing Technology 138, 349-356. Igarashi, M., de Boer, L. and Fet, A.M., 2013. What is required for greener supplier selection? A literature review and conceptual model development. Journal of Purchasing and Supply Management, 19(4), 247-263. Jain, V., Tiwari, M., Chan, F., 2004. Evaluation of the supplier performance using an evolutionary fuzzy-based approach. Journal of Manufacturing Technology Management 15, 735744. Jabbour, A.B.L. & Jabbour, C.J. (2009). Are supplier selection criteria going green? Case studies of companies in Brazil. Industrial Management and Data Systems, 109(4), 477-495. Ketting, N., 2011. Annual Reports by the Netherlands Commission for Environmental Assessment. Legarth, J.B., 1997. Environmental decision making for recycling options. Resources, Conservation and Recycling 19, 109-135. Lee, A.H., Kang, H.Y., Hsu, C.F. & Hung, H.C., 2009. A green supplier selection model for high-tech industry. Expert Systems with Applications, 36(4), 7917-7927. McAleer, E., McIvor, R., Humphreys, P., McCurry, L., 2000. What multinational corporations with manufacturing plants in Northern Ireland and the Republic of Ireland are demanding from their suppliers. Journal of Small Business and Enterprise Development 7, 363-373. Mike Ewall., K.N., 2007. Hazardous waste and time incineration in the U.S. and Mexicon cement industries: Environmental and Health Problems. Energy Justice Network. Monczka, R.M., Handfield, R.B., Giunipero, L., 2008. Purchasing and supply chain management. South-Western Pub. Mukhopadhyay, S., Barua, A.K., 2003. Supply chain cell activities for a consumer goods company. International Journal of Production Research 41, 297-314. 27

Nassimbeni, G., Battain, F., 2003. Evaluation of supplier contribution to product development: Fuzzy and neuro-fuzzy based approaches. International Journal of Production Research 41, 2933-2956. Noci, G., 1997. Designing ‘green’vendor rating systems for the assessment of a supplier's environmental performance. European Journal of Purchasing & Supply Management 3, 103114. Noci, G., 1995. Supporting decision‐making for recycling‐based investments. Business Strategy and the Environment, 4(2), 62-72. O'Brien, J., 2014. Supplier Relationship Management: Unlocking the Hidden Value in Your Supply Base. Kogan Page Publishers. Orlovsky, S., 1978. Decision-making with a fuzzy preference relation. Fuzzy Sets and Systems 1, 155-167. Ross, S., Evans, D., 2002. Use of life cycle assessment in environmental management. Environmental Management 29, 132-142. Roubens, M., 1997. Fuzzy sets and decision analysis. Fuzzy sets and Systems 90, 199-206. Sarkis, J., 2003. A strategic decision framework for green supply chain management. Journal of Cleaner Production 11, 397-409. Smith, P., Ashmore, M.R., Black, H.I., Burgess, P.J., Evans, C.D., Quine, T.A., Thomson, A.M., Hicks, K. & Orr, H.G., 2013. REVIEW: the role of ecosystems and their management in regulating climate, and soil, water and air quality. Journal of Applied Ecology, 50(4), 812-829. Somuyiwa, A., Mcilt, Mcism., 2012. Firm's competitiveness through supply chain responsiveness and supply chain management practice in Nigeria. British Journal of Arts and Social Science, 10(1), 42-52. Stranddorf, H, J., Leif, H., Anders, S., 2005. Impact categories, normalisation and weighting in LCA. Danish Ministry of the Environment, Environmental Protection Agency, Environmental News, No.78. Talluri, S., Sarkis, J., 2002. A model for performance monitoring of suppliers. International Journal of Production Research 40, 4257-4269. Tuzkaya, G., Ozgen, A., Ozgen, D., Tuzkaya, U., 2009. Environmental performance evaluation of suppliers: A hybrid fuzzy multi-criteria decision approach. Int. J. Environ. Sci. Tech 6, 477490. 28

Van de Walle, B., De Baets, B., Kerre, E., 1998. Characterizable fuzzy preference structures. Annals of Operations Research 80, 105-136. Wang, Q., Zhao, D., He, L., 2016. Contracting emission reduction for supply chains considering market low-carbon preference. Journal of Cleaner Production 120, 72-84. Wenzel, H., Hauschild, M.Z., Alting, L., 2000. Environmental Assessment of Products: Volume 1: Methodology, tools and case studies in product development. Springer. Wetzstein, A., Hartmann, E., Benton Jr, W. & Hohenstein, N.O., 2016. A systematic assessment of supplier selection literature–State-of-the-art and future scope. International Journal of Production Economics, 182, 304-323. Yager, R.R., 1988. On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man and Cybernetics,18, 183-190. Yager, R.R., 1993. Families of OWA operators. Fuzzy Sets and Systems 59, 125-148. Yager, R.R., 1994. Aggregation operators and fuzzy systems modeling. Fuzzy Sets and Systems 67, 129-145. Zhang, H.C., Li, J., Merchant, M., 2003. Using fuzzy multi-agent decision-making in environmentally conscious supplier management. CIRP Annals-Manufacturing Technology 52, 385-388.

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Figure 1: Environmental framework for incorporating environmental criteria into the supplier selection process (Humphreys et al. 2006)

30

Figure 2: Environmental consensus supplier selection (ECSS) strategy development process

Figure 3: Framework for environmental supply chain network

31

Figure 4: Five-stage environmental analysis

Figure 5: Model of each stage analysis in environmental supply chain network

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Figure 6: Environmental emission hierarchy framework

Figure 7: Proportional fuzzy quantifier

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Figure 8: Flow diagram of solution methodology

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Figure 9: Environmental conscious supplier selection network

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Table 1: Different types of preference structures for collecting information Preference ordering of the alternatives Supplier Sk preference on alternative “X”



A utility function



T k  t k (1), t k (2),...t k (n) U k  uik , i  1, 2,...n k

t (n): Permutation function

uik   0,1

k i

u : Utility evaluation (Luce and Suppes, 1965)

Scaling system

A multiplicative preference relationship

A fuzzy preference relationship

Bk  X  X

Ck  X  X

B k  bijk 

C : X  X   0,1 k

bij : Ratio of preference intensity for alternative xi

Where,

C  x1 , x2   cijk k

to x j Miller (1956) suggested 1-9 scale for If,

bijk  1 indifference between and x j

»

Kacprzyk Roubens(1988); Kitainik(1993) suggested If,

and

1 » indifference 2 between xi and x j cijk 

xi

cijk  1

»

xi

is

bijk  9 » xi is absolutely preferred to xj absolutely to xj

bijk   2,8 Intermediate evaluation

1 » xi is 2 » preferred to x j cijk 

Orlovsky (1978) suggested that

cijk  c kji  1 and ciik 

1 2

36

Table 2. The raw data for supplier “A” (Unit: metric ton/year) Stages/Analysis Supply Analysis Logistic Analysis Process Analysis Use Analysis Recycle Analysis

GWP 1000 2 2.7 10 4

ODP 80 1 0.2 4.2 2

POCP 100 0.15 0.1 0.32 3

AP 80 0.02 0.1 1.3 4

NEP 50 0.03 0.023 0.86 0.2

VOCP 60 0.006 0.4 0.002 1

NEP 40 0.06 0.01 1.2 0.1

VOCP 70 0.08 0.3 0.001 1

Table 3. The raw data for supplier “B” (Unit: metric ton/year) Stages/Analysis Supply Analysis Logistic Analysis Process Analysis Use Analysis Recycle Analysis

GWP 500 1 2 8 4

ODP 90 0.01 0.1 5 1

POCP 50 0.18 0.3 0.8 3

AP 60 0.13 0.02 2.6 3

Table 4. Preference ordering of D.M for supplier “A” in the First stage (Supplier) analysis in Fuzzy preference relationship GWP ODP POCP AP NEP VOCP

GWP 0.5 -0.074 -0.024 -0.074 -0.181 -0.140

ODP 1.074 0.5 0.550 0.5 0.393 0.434

POCP 1.024 0.393 0.5 0.393 0.342 0.383

AP 1.074 0.5 0.550 0.5 0.393 0.434

NEP 1.181 0.607 0.657 0.607 0.5 0.514

VOCP 1.111 0.565 0.616 0.565 0.458 0.5

Table 5: Preference ordering of D.M for supplier “A” in The Second stage (Logistic) analysis in Fuzzy Preference Relationship GWP ODP POCP AP NEP VOCP

GWP 0.5 0.342 0.434 -0.547 0.068 -0.821

ODP 0.657 0.5 0.068 -0.390 -0.297 -0.664

POCP 1.089 0.931 0.5 0.040 0.133 -0.232

AP 1.547 1.390 0.988 0.5 0.592 0.226

NEP 1.455 1.297 0.866 0.407 0.5 0.133

VOCP 1.821 1.664 1.232 0.773 0.866 0.5

37

Table 6: Preference ordering of D.M for supplier “A” in The Third stage (Process) analysis in Fuzzy Preference Relationship GWP ODP POCP AP NEP VOCP

GWP 0.5 0.092 -0.250 -0.250 -0.598 0.065

ODP 1.092 0.5 0.342 0.342 0.007 0.657

POCP 1.25 0.657 0.5 0.5 0.165 0.815

AP 1.25 0.657 0.5 0.5 0.165 0.815

NEP 1.584 0.992 0.834 0.834 0.5 1.149

VOCP 0.934 0.342 0.184 0.184 -0.151 0.5

Table 7: Preference ordering of D.M for supplier “A” in The Fourth stage (Use) analysis in Fuzzy Preference Relationship GWP ODP POCP AP NEP VOCP

GWP 0.5 0.302 -0.283 -0.035 -0.058 -1.438

ODP 0.697 0.5 -0.86 0.232 0.138 -0.756

POCP 1.283 1.085 0.5 0.188 0.724 -0.664

AP 0.964 0.766 0.180 0.5 0.405 -1.071

NEP 1.058 0.860 0.274 0.593 0.5 -0.914

VOCP 2.438 2.240 1.654 1.973 1.879 0.5

Table 8: Preference ordering of D.M for supplier “A” in The Fifth stage (Recycle) analysis in Fuzzy Preference Relationship GWP ODP POCP AP NEP VOCP

GWP 0.5 0.342 0.434 0.5 -0.181 0.184

ODP 0.657 0.5 0.592 0.657 -0.024 0.342

POCP 0.565 0.407 0.5 0.565 -0.116 0.25

AP 0.5 0.342 0.434 0.5 -0.181 0.184

NEP 0.181 1.024 1.116 1.181 0.5 0.866

VOCP 0.815 0.657 0.75 0.815 0.133 0.5

Table 9: Preference ordering of D.M for supplier “B” in The First stage (Supply) analysis in Fuzzy Preference Relationship GWP ODP POCP AP

GWP 0.5 0.109 -0.023 0.017

ODP 0.890 0.5 0.366 0.407

POCP 1.023 0.633 0.5 0.541

AP 0.982 0.592 0.458 0.5

NEP 1.074 0.684 0.550 0.592

VOCP 0.947 0.557 0.423 0.464 38

NEP VOCP

0.074 0.052

0.315 0.054

0.449 0.576

0.407 0.534

0.5 0.627

0.372 0.5

Table 10: Preference ordering of D.M for supplier “B” in The Second stage (Logistic) analysis in Fuzzy Preference Relationship GWP ODP POCP AP NEP VOCP

GWP 0.5 -0.547 0.109 0.035 -0.1402 -0.074

ODP 1.547 0.5 1.157 1.083 0.907 0.451

POCP 0.890 -0.160 0.5 0.4255 0.249 0.315

AP 0.964 -0.086 0.573 0.5 0.323 0.389

NEP 1.1402 0.091 0.75 0.675 0.5 0.565

VOCP 1.074 0.026 0.684 0.610 0.434 0.5

Table 11: Preference ordering of D.M for supplier “B” in The Third stage (Process) analysis in Fuzzy Preference Relationship GWP ODP POCP AP NEP VOCP

GWP 0.5 -0.181 0.068 -0.547 -0.705 0.068

ODP 1.181 0.5 0.75 0.133 -0.023 0.75

POCP 0.931 0.249 0.5 -0.118 -0.276 0.5

AP 1.547 0.866 1.116 0.5 0.342 1.116

NEP 1.705 1.023 1.273 0.657 0.5 1.273

VOCP 0.931 0.249 0.5 -0.118 -0.276 0.5

Table 12: Preference ordering of D.M for supplier “B” in The Fourth stage (Use) analysis in Fuzzy Preference Relationship GWP ODP POCP AP NEP VOCP

GWP 0.5 0.393 -0.023 0.244 0.068 -1.554

ODP 0.06 0.5 0.082 0.351 0.175 -1.438

POCP 1.023 0.971 0.5 0.768 0.592 -1.021

AP 0.755 0.648 0.231 0.5 0.323 -1.292

NEP 0.931 0.824 0.407 0.675 0.5 -1.114

VOCP 2.545 2.438 2.021 2.289 2.113 0.5

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Table 13: Preference ordering of D.M for supplier “B” in The Fifth stage (Recycle) analysis in Fuzzy Preference Relationship GWP ODP POCP AP NEP VOCP

GWP 0.50 0.184 0.434 0.434 -0.339 -0.184

ODP 0.185 0.5 0.75 0.75 -0.024 0.5

POCP 0.565 0.25 0.5 0.5 -0.274 0.25

AP 0.565 0.25 0.5 0.5 -0.274 0.25

NEP 1.339 1.024 1.274 1.274 0.5 1.024

VOCP 0.815 0.5 0.75 0.75 -0.024 0.5

Table 14. Calculated QGDDa for supplier “A” in all the Stages analysis Stages/Analysis Supply Analysis Logistic Analysis Process Analysis Use Analysis Recycle Analysis

GWP 0.866 0.748 0.947 0.826 0.574

ODP 0.273 0.591 0.355 0.629 0.416

POCP 0.342 0.334 0.197 0.043 0.508

AP 0.273 -0.299 0.197 0.338 0.574

NEP 0.184 -0.032 -0.142 0.268 -0.107

VOCP 0.225 -0.572 0.512 -0.952 0.258

NEP 0.279 0.3386 -0.334 0.278 -0.212

VOCP 0.229 0.230 0.439 -1.337 0.188

Table 15. Calculated QGDDa for supplier “B” in all the Stages analysis Stages/Analysis Supply Analysis Logistic Analysis Process Analysis Use Analysis Recycle Analysis

GWP 0.804 0.979 0.870 0.709 0.626

ODP 0.414 -0.207 0.189 0.603 0.311

POCP 0.281 0.588 0.439 0.186 0.561

AP 0.321 0.514 -0.532 0.454 0.561

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Table 16. Overall Impact of environmental performance of supplier “A” as well as of supplier “B”

First stage Analysis

Second stage Analysis

Third stage Analysis

Fourth stage Analysis

Fifth stage Analysis

Supplier “A” Supplier “B” Pollutant Global Rating Score Individual Global Rating Score Individual Impact Impact Impact Impact GWP 0.866 O 0.513 0.4444 0.804 O 0.513 0.4124 ODP POCP AP NEP VOCP GWP

0.273 0.342 0.273 0.184 0.225 0.748

F A F F F O

0.063 0.129 0.063 0.063 0.063 0.513

0.0171 0.0441 0.0171 0.0115 0.0141 0.3837

0.414 0.281 0.321 0.279 0.227 0.979

A F A F F O

0.129 0.063 0.129 0.063 0.063 0.513

0.0534 0.0177 0.0414 0.0175 0.0143 0.5022

ODP POCP AP NEP VOCP GWP

0.591 0.334 -0.299 -0.032 -0.572 0.947

G A P P P O

0.261 0.129 0.034 0.034 0.034 0.513

0.1542 0.0430 -0.0101 -0.0010 -0.0194 0.4858

-0.207 0.588 0.524 0.3386 0.230 0.870

P G G A F O

0.034 0.261 0.261 0.129 0.063 0.513

-0.0070 0.1534 0.1341 0.0436 0.0144 0.4463

ODP POCP AP NEP VOCP GWP

0.335 0.197 0.197 -0.142 0.512 0.826

A F F P G O

0.129 0.063 0.063 0.034 0.261 0.513

0.0457 0.0124 0.0124 -0.0048 0.1336 0.4237

0.189 0.439 -0.532 -0.334 0.439 0.709

F A P P A O

0.063 0.129 0.034 0.034 0.129 0.513

0.0119 0.0566 -0.0180 -0.0113 0.0566 0.3637

ODP POCP AP NEP VOCP GWP

0.629 0.043 0.338 0.268 -0.952 0.574

O F A F P G

0.513 0.063 0.129 0.063 0.034 0.261

0.3226 0.0027 0.0436 0.0168 -0.0323 0.1498

0.603 0.186 0.454 0.278 -1.337 0.626

O F A F P O

0.513 0.063 0.129 0.063 0.034 0.513

0.3093 0.0117 0.0585 0.0175 -0.0454 0.3211

ODP 0.416 A 0.129 POCP 0.508 G 0.261 AP 0.574 G 0.261 NEP -0.107 P 0.034 VOCP 0.258 F 0.063 Overall Impact of supplier “A” = 3.0781

0.0536 0.1325 0.1498 -0.0036 0.0162

0.311 A 0.129 0.0401 0.561 G 0.261 0.1464 0.561 G 0.261 0.1464 -0.212 P 0.034 -0.0072 0.188 F 0.063 0.0118 Overall Impact of supplier “B” = 3.2938

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Table 17. Preference ordering of D.M for supplier “A” in Fuzzy preference relationship Q TC PC EM GP GC

Q 1 0.16 0.26 0.29 0.58 0.45

TC 2.79 1 0.26 0.25 0.31 0.26

PC 1.23 1.15 1 0.37 0.24 0.26

EM 0.90 1.15 0.80 1 0.40 0.39

GP 0.60 0.90 1.56 0.74 1 0.33

GC 0.50 1.15 0.94 0.67 1.04 1

Table 18. Preference ordering of D.M for supplier “B” in Fuzzy preference relationship Q TC PC EM GP GC

Q 1 0.25 0.55 0.50 1.36 0.99

TC 3.95 1 0.62 0.54 0.58 0.62

PC 1.83 1.61 1 0.82 0.52 0.54

EM 1.99 1.85 1.22 1 0.87 0.90

GP 0.74 1.73 1.93 1.15 1 0.87

GC 1 1.61 1.85 1.11 1.15 1

Table 19. Preference ordering of D.M for supplier “C” in Fuzzy preference relationship Q TC PC EM GP GC

Q 1 0.36 0.81 1.11 1.68 1.99

TC 6.16 1 0.87 0.87 1.11 0.87

PC 3.83 3.79 1 1.25 0.64 1.07

EM 3.49 4.04 2.70 1 1.36 1.50

GP 1.73 3.27 4.21 2.51 1 0.96

GC 2.24 3.79 3.75 2.59 3.01 1

Table 20. Calculated QGDDa for supplier “A”, “B” and, “C” Stages/Analysis Supplier “A” Supplier “B” Supplier “C”

Q 1.673 2.26 3.663

TC 0.77 0.953 1.71

PC 0.506 0.723 0.893

EM 0.303 0.62 1.076

GP 0.376 0.82 1.143

GC 0.323 0.716 1.31

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Table 21. Overall Impact of environmental performance of supplier “A”, “B” and “C” Criteria Supplier Global “A” Impact Rating Score Individual Impact Supplier Global “B” Impact Rating Score Individual Impact Supplier Global “C” Impact Rating Score Individual Impact

Q 1.673

TC 0.77

PC 0.506

EM 0.303

GP 0.376

GC 0.323

O 0.513 0.858

G 0.261 0.200

A 0.129 0.065

F 0.063 0.019

F 0.063 0.023

F 0.063 0.020

2.26 O 0.513 1.1593 3.663 O 0.513 1.879

0.953 O 0.513 0.488 1.71 O 0.513 0.877

0.723 G 0.261 0.188 0.893 G 0.261 0.233

0.62 A 0.129 0.079 1.076 G 0.261 0.280

0.82 G 0.261 0.214 1.143 G 0.261 0.298

0.716 G 0.261 0.186 1.31 G 0.261 0.341

Overall Impact of supplier “A” = 1.204 Overall Impact of supplier “B” = 2.314 Overall Impact of supplier “C” = 3.908

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Highlights The state of the knowledge of the environmental consciousness supplier selection reviewed. Environmentally conscious supplier selection model is proposed. A fuzzy framework of multi attribute decision making process is proposed. Importance of environmental impact in supply chain management are studied

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