A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in SCM

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A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management

M. Amiri , M. Hashemi-Tabatabaei , M. Ghahremanloo , M. Keshavarz- Ghorabaee , E. K. Zavadskas & A. Banaitis

To cite this article: M. Amiri , M. Hashemi-Tabatabaei , M. Ghahremanloo , M. Keshavarz-

Ghorabaee , E. K. Zavadskas & A. Banaitis (2020): A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management, International Journal of Sustainable Development & World Ecology, DOI: 10.1080/13504509.2020.1793424

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Published online: 15 Jul 2020.

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A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management

M. Amiri ^a, M. Hashemi-Tabatabaei ^a, M. Ghahremanloo ^b, M. Keshavarz-Ghorabaee ^c, E. K. Zavadskas ^dand A. Banaitis ^d

aDepartment of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran;

bDepartment of Management, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran;

cDepartment of Management, Faculty of Humanities (Azadshahr Branch), Gonbad Kavous University, Gonbad Kavous, Iran; ^dDepartment of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

ABSTRACT

Sustainability has become one of the most important issues in the field of supply chain management (SCM). In recent years, many studies have been conducted about how to select a sustainable supplier and different methods have been proposed for this purpose in fuzzy and deterministic environments. A review of the research literature reveals that decision-makers have paid more attention to the results of fuzzy-based studies because they are more accurate and precise in collecting data. The purpose of this paper is to present a new model with a triangular fuzzy approach for sustainable supplier selection (SSS) in the supply chain. The proposed fuzzy model is based on the best-worst method (BWM) and α-cut analysis in which the decision-maker (DM) can determine the alpha value between 0.1 and 0.9, depending on the level of uncertainty. A high value of alpha indicates low uncertainty and its low value indicates a high uncertainty in decision-making. To illustrate the model and demonstrate its capability, a real SSS case in Iran Khodro Company (IKCO), was examined by three experts in the automotive industry and the decision criteria were selected based on the literature review and expert opinions. The results show that the first supplier (ISACO Parts Supply Company) is the most sustainable supplier.

ARTICLE HISTORY Received 3 May 2020 Accepted 5 July 2020 KEYWORDS

Sustainability; fuzzy best- worst method; α-cut analysis;

supplier selection; supply chain management

1. Introduction

Today, organizations have realized that to compete in local and global markets, they need to take effective measures in order to improve the supply chain and gain more efficiency and effectiveness than their competitors.

Supply chains include both suppliers and consumers. The challenges that a supply chain faces are reducing costs, ensuring timely delivery, and reducing shipping times in order to be more responsive in a business environment. Many organizations have been led to pay more attention to sustainability in the supply chain, on the one hand, due to pressure from consumers for the standardization of products in terms of their environmental costs and, on the other hand, due to the community’s awareness of social issues of organizations and the emergence of some special groups for binding the social responsibility of organizations and companies.

Sustainability is more than merely paying attention to environmental issues: it is a significant change in understanding of the relationship between human beings and nature and among people with each other (Gupta and Barua 2017). Sustainable development is an attempt to consider socio-economic

activities in terms of environmental issues. With the industrialization of societies, there has been an increased emphasis on environmental and social issues in organizations, which has led to recommendations of sustainable development (de Vargas Mores et al. 2018).

Sustainable development seeks to examine more clearly the future consequences of current behaviors, and it addresses various issues such as the impact of greenhouse gases, climate change, ozone depletion, reduction of non-renewable resources, land destruc- tion, and urban pollution (Lee et al. 2009). Many international companies have established environmental rules and regulations for their suppliers. For example, Ford, General Motors, and Toyota have required their suppliers to adopt an ISO 14001 environmental management system. Furthermore, adopting sound environmental policies is important in long-term working relationships and joint ventures between foreign companies and their major suppliers (Govindan et al. 2013).

In 2015, the United Nations’ member states outlined a set of 17 goals and 169 targets as part of an international roadmap for sustainable development during the next 15 years. The timeframe for the sustainable development goals (SDGs) ends in 2030, and it is called the 2030 Agenda (Spaiser et al. 2019; Giribabu et al.

2019). The SDGs provide an ambitious roadmap for

CONTACT E. K. Zavadskas [email protected] https://doi.org/10.1080/13504509.2020.1793424

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guiding social development and environmental sustainability. A common global vision has been created by the SDGs to move toward creating a safer, fairer, and more sustainable space for human beings (Leal Filho et al. 2019). The SDGs are designed to have interactions and make synergies with businesses (Willis 2016). Globalization and the rising prices of raw materials have made it necessary to create sustainable solutions for supply chains. This fact is often taken into account by the entrepreneurs and external stakeholders at the top and bottom of the supply chain (García-Arca et al. 2017). The use of sustainable supply chain management (SSCM) methods to achieve SDG enables manufacturers to develop advanced and more sophisticated strategies for the supply chain, which should lead to a more stable, more efficient, and more ethical supply chain (Zimon et al. 2020).

Therefore, it can be said that the role of SSCM is essential for successfully implementing the SDGs, provided that these goals are considered as part of pro- cesses in which all components interact with one other (Campagnolo et al. 2018).

Accordingly, the starting point for achieving sustainability in the supply chain is the selection of suppliers based on the principles of sustainability.

Organizations are faced with complex decisions and have to consider several factors when evaluating and managing suppliers in the supply chain. Finally, supplier management requires a precise balance among all related factors (Sarkis and Talluri 2002).

Many studies have looked at how to select sustainable suppliers, and various models and approaches have been proposed by researchers for this purpose.

However, few studies have been conducted on the three components of sustainability (its economic, environmental, and social components), and there remains a gap in this area (Liu et al. 2019). Moreover, we perform an analysis of the sustainability factors and the criteria used by the SDGs as we seek to outline the limits of these tools as well as the importance of each of the factors affecting sustainability. We do so while considering different levels of uncertainty that remain concerning the SDGs as well as the other important gaps that we have identified through our literature review. In order to address these research gaps, the following objectives are pursued in this paper: (1) A review of the literature concerning the selection of sustainable suppliers in supply chain management (SCM), (2) the identification and extraction of important factors and criteria used in the previous research and that are in accord with expert opinions, (3) and proposing a new multi-criteria decision-making model in order to determine the weight of the different factors and criteria for selecting a sustainable supplier in uncertain environments and based on the level of satisfaction of the decision-maker.

On the other hand, given the prevailing conditions and rising complexities in the world, models and methods of sustainable supplier selection (SSS) in uncertain environments have become more important. Since the problem of SSS has the characteristics of a multi- criteria decision-making (MCDM) problem, in this paper, a new multi-criteria decision-making model based on best-worst method (BWM) and α-cut analysis is proposed for decision-making in a fuzzy environment. One of the advantages of the proposed method is determination of the levels of uncertainty and satisfaction of the decision-maker. The higher the alpha level (closer to 1), the higher the decision-maker’s satisfaction, which means higher confidence in the decision-making process; but if alpha levels are lower (closer to zero), uncertainty is higher. In this way, the decision-making process can be implemented more carefully and transparently by decision-makers, and the decision-making results are more realistic. The proposed method is illustrated by using a case study in the supply chain of Iran Khodro Company (IKCO), though it can be easily used by similar other companies.

The rest of this article is organized as follows: In Section 2, the literature on the application of MCDM methods for SSS will be discussed, and the indicators for SSS will be defined. The proposed method is introduced in six steps in Section 3. In Section 4 the results of the case study are presented and analyzed. Section 5 provides the results and some suggestions for future research.

2. Literature review

In this section, articles related to SSS using MCDM methods and mathematical models will be reviewed and the most frequently used criteria for SSS will be selected for this research. In SCM, an emphasis on sustainability aspects in supplier selection makes decision-making much more difficult and sensitive. Various techniques and many models have been used to select a sustainable supplier. The method of the Gray system and Rough Set methods was used to develop a new method previously proposed by Li et al. (2008). A new multi-stage, multi-method approach was proposed for making important decisions about sustainability (Bai and Sarkis 2010).

Büyüközkan and Çifçi (2011) presented a fuzzy decision-making framework for SSS in the case of incomplete information. They developed the fuzzy analytic network process (FANP) for group decision-making (GDM) in the case of incomplete preferential relationships. Amindoust et al. (2012) proposed a method for SSS based on the fuzzy inference system (FIS); in their method, appropriate weights are assigned to criteria, sub-criteria. Govindan et al. (2013) presented a model using the fuzzy technique for order of preference by

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similarity to ideal solution (TOPSIS) method and the triple bottom line (TBL) approach and considered the economic, environmental, and social aspects of supplier selection as part of a sustainable supply chain.

Sustainability in SCM as well as the development of a new model for supplier selection based on fuzzy multivariate method were investigated in this research.

The results showed that the proposed model is suita- ble for integrating sustainability into the selection of suppliers (Chaharsooghi and Ashrafi 2014).

A new approach was proposed to identify key performance indicators (KPIs) of the sustainable supply chain. A two-step method was used that included identifying KPIs using accurate set theory and then evaluating the relative influences of KPIs using data envelopment analysis (DEA). The results showed that the proposed method can identify and evaluate KPIs, and finally it can accurately evaluate the performance of the sustainable supply chain (Bai and Sarkis 2014). In a study using the analytical hierarchy process (AHP) approach in India, Mani et al. (2014) selected a socially sustainable supplier. Their findings showed that electri- city, automobile, and cement producers could select suppliers based on socially sustainable scores. Azadi et al. (2015) presented a new model of fuzzy DEA (FDEA) to evaluate the efficiency, effectiveness, and productivity of suppliers and SSS; they showed that their model can measure the level of certainty at different values of alpha.

Orji and Wei (2015) proposed a method for SSS in the manufacturing industry using the integrated fuzzy logic and systems dynamics approach. The results of their study showed that increasing investment in sustainability improves supplier performance. A two-stage DEA was used as an appropriate method for evaluating sustainable SCM, in which the outputs of the first stage are used as inputs in the second stage. In order to evaluate the performance of the sustainable supply chain in two-stage network structures, three new models were proposed that would eliminate the disadvan- tages of the previous two-stage DEA models. The validity of the proposed models was examined by using a case study (Khodakarami et al. 2015). A new hybrid framework based on balanced scorecard (BSC) and DEA was proposed to evaluate the performance of the sustainable supply chain. Both qualitative and quantitative indices were evaluated by the DEA model and then the sustainability indices were divided into four groups based on BSC viewpoints in order that managers could make better decisions about selecting sustainable suppliers (Haghighi et al. 2016). Girubha et al. (2016) used the integrated interpretative struc- tural modeling (ISM) and MCDM approach for SSS.

They used the ISM to determine the relationships between criteria and the analytic network process (ANP) to calculate the criteria weights using the inter- criteria relationships specified by the ISM. They applied

the obtained weights for SSS by using the ELECTRE II and VIKOR methods. A new model based on the DEA approach was developed in a study to evaluate the sustainability of supply chains. Previously, two-stage models were used only in the field of technology and only with positive inputs and outputs, but in the above-mentioned study, the feasibility of applying negative inputs and outputs was also investigated and some numerical examples were provided to test the capability of the proposed model. Twenty-nine supply chains for medical devices manufactured in Iran were studied and the results were analyzed (Izadikhah and Saen 2016).

Tavana et al. (2017) proposed an integrated ANP-QFD method for weighting the decision criteria and sub- criteria based on customer needs and SSS. Zhang et al.

(2020) proposed a hybrid multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Combined Compromise Solution (CoCoSo) method based on the interval rough bound- aries for SSS for housing development. Matić et al.

(2019) developed a new hybrid MCDM model for evaluating and selecting suppliers in a sustainable supply chain for a construction company. Luthra et al. (2017) presented an integrated framework for SSS using AHP and VIKOR methods. The AHP method was used to weight the SSS criteria and the VIKOR method was used to select the most efficient sustainable supplier.

A framework was proposed to examine the social sustainability of supply chains, weights of related indicators obtained using the best-worst method (BWM), applicability and effectiveness of the proposed model were examined based on opinions of 38 experts about social sustainability criteria, and the ‘influence’ indicator was identified as the most significant indicator (Badri Ahmadi et al. 2017). Lin et al. (2018) developed an approximate fuzzy decision-making trial and evaluation laboratory (AFDEMATEL) method for analyzing the effective criteria for sustainable SCM in the case of the existence of uncertainty. Liu et al. (2018) proposed an integrated ANP-VIKOR method for SSS based on interval type-2 fuzzy set. They used the ANP method to weight the criteria and the VIKOR method to select and rank sustainable suppliers. Yazdani et al. (2019) used an extended version of the combined compromise solution method with grey numbers (CoCoSo-G) to measure the performance of suppliers in a construction company in Madrid. Two weighting methods, including the DEMATEL and BWM, were used to achieve the importance of supplier criteria in a combined manner. The DEMATEL method was used to determine the best and worst criteria, and the BWM was used to sort the criteria according to a linear pro- gramming formulation. The CoCoSo-G method used to release the score of each supplier and rank them.

Osiro et al. (2018) proposed a hybrid GDM model for selecting the SSCM criteria. In their method, they used

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the hesitant fuzzy linguistic term sets (HFLTS) to prior- itize criteria in the quality function deployment (QFD).

They used HFLTS to determine criteria preferences in SSCM. Vahidi et al. (2018) used a hybrid SWOT-QFD framework to identify factors affecting SSS. They also proposed a two-step hybrid planning model with a new objective function to select suppliers that meet the criteria of sustainability and flexibility. Abdel-Basset et al. (2018) developed a hybrid ANP-TOPSIS method for SSS based on interval-valued neutrosophic sets (INS). They used the ANP method to calculate criteria weights and used TOPSIS for SSS. Zhang et al. (2019) constructed picture fuzzy EDAS model based on tradi- tional EDAS (Evaluation based on Distance from Average Solution) model. They provided a numerical example for green supplier selection to illustrate this new model. Sinha and Anand (2018) presented a decision framework for SSS using a combination of MCDM methods and graph theory. In other research, sustainable supplier countries for the Iranian steel industry were selected and ranked by using AHP for weighting the related criteria and using the TOPSIS method for supplier selection (Azimifard et al. 2018).

Song and Li (2019) presented a large-scale decision model that involves a large number of stakeholders in the decision-making process. Their method consists of partial linguistic terms based on risk attitudes (optimis- tic, pessimistic, and neutral), and SSS is performed using TOPSIS. Abdel-Baset et al. (2019) proposed an integrated ANP-VIKOR method for SSS under neutrosophic environment. The ANP method was used to calculate criteria weights and the VIKOR method was used for SSS.

Fei et al. (2019) proposed a combination of the VIKOR method and D-S theory for SSS. They used the entropy method to determine the weights of the criteria and used the VIKOR method to rank the criteria. In another study, a GDM framework based on gray numbers was proposed in which BWM was used to specify the weights of the criteria and TODIM was used for SSS (Bai et al. 2019). Memari et al. (2019) presented the fuzzy TOPSIS method with intuitionistic fuzzy numbers.

Lai et al. (2020) introduced a Z-number-based double normalization-based multiple aggregation (DNMA) method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for sustainable CSP development.

Yu et al. (2019) used interval-valued Pythagorean fuzzy set (IVPFS) (which has a high capacity for considering uncertainty and ambiguity) in the TOPSIS method for SSS while considering a GDM.

Pishchulov et al. (2019) used a combined voting AHP (VAHP) method and DEA for SSS while considering a GDM. Liou et al. (2019) proposed data- driven hybrid multiple attribute decision-making model for green supplier evaluation and performance improvement. The results showed that the

proposed method can effectively help decision- makers to solve the problem of green supplier selection and devise strategies for improvement.

In another study, a new framework was proposed for identifying and evaluating the effective factors of a sustainable SCM. An extended TOPSIS technique was developed for selecting a sustainable supplier. It simul- taneously takes advantage of both cloud model theory and rough set theory under the conditions of inter- personal uncertainty (Li et al. 2019).

The above-mentioned studies are summarized with respect to their methods in Table 1. The criteria and sub-criteria for decision-making with respect to literature review and expert opinion are listed in Table 2.

3. Proposed method

Sometimes decision-makers use fuzzy judgments instead of precise judgments to perform pairwise comparisons among criteria or options. A fuzzy planning model is more applicable and flexible than a deterministic model and provides more acceptable results because it makes it possible to incorporate inaccurate and ambiguous data into the model parameters (Biswas and Pal 2005). The α-cut method is one of the ways to deal with inaccurate quantities produced due to uncertainty (Sengupta et al. 2001). By applying α-cut operations on the fuzzy membership functions, the membership functions are transferred to closed intervals and, accordingly, calculations in these intervals are performed using related mathematics (Ishibuchi et al. 1994). In this paper, the BWM based on the α-cut method is developed for an uncertain condition in which the problem parameters are triangular fuzzy numbers (TFN). The BWM is one of the MCDM techniques.

In this method, both the best and the worst criteria are selected, and the rest of the criteria are compared with them in pairwise comparisons. Then a maximum-minimum problem is formulated and solved to obtain the weights of the indicators. In this approach, one can calculate the consistency of the comparisons made by a decision-maker (DM) (Rezaei 2015).

Definition 1. A TFN is a real number that is repre- sented by ðl:m:uÞwhere l<u; m, and urepresents the lower limit, middle, and upper limit of the fuzzy number, respectively (Guo and Zhao 2017).

The membership function of a triangular fuzzy number is defined as μA~ð Þ: x R ! ½0: 1�, where

μ~Að Þ ¼x

0; x<l

x l

m l; l�x�m

u x

u m; m�x�u 0; x>u 8>

><

>>

:

(1)

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Definition 2. A fuzzy set ~Awith a membership degree at least as large as α (α > 0) is called α-cut of set A and is defined as expression (2).

A~α¼�xi :μ~Að Þ �xi α;xi2X�

;where α2½0:1�: (2) Suppose that we have a TFN as (l, m, u); its α-cut is calculated using expression (3) (Bojadziev and Bojadziev 2007).

Aα¼½l^α;u^α� ¼½lþðm lÞα;u ðu mÞα� (3) The sets, parameters, and variables of the proposed method are shown in Table 3.

The steps of the proposed method are as follows:

Step 1: Choose the decision criteria: The criteria for selecting and evaluating suppliers are defined by the DM asfc1;c2;. . .;cng.

Step 2: Identify the best and worst criteria: The best and worst criteria are chosen by DM. No comparison is made at this step.

Step 3: In this step, priority of the best criterion over each of other criteria is determined by experts as a TFN.

The linguistic terms and membership functions of the fuzzy numbers used in this paper are shown in Table 4.

The priority of the best criterion over the jth criterion is expressed as ~ABj¼ lBj:mBj:uBj

�and ~ABB¼ð1:1:1Þ.

Table 1. Summary of the techniques in SSS.

References Technique(s) used Application

Bai and Sarkis (2010) Grey system and rough set theory Numerical example

Bai and Sarkis (2018) Grey-based TOPSIS Numerical example

Yazdani et al. (2019) Combined compromise solution method with grey numbers CoCoSo-G

Illustrative example

Büyüközkan and Çifçi (2011) Fuzzy approach based on ANP A real-life problem

Amindoust et al. (2012) New method based on fuzzy inference system Illustrative example

Matić et al. (2019) Hybrid MCDM methods Construction company

Govindan et al. (2013) Developed fuzzy TOPSIS Numerical example

Chaharsooghi and Ashrafi (2014)

Fuzzy numbers and neofuzzy TOPSIS Numerical example

Bai and Sarkis (2014) Two-stage DEA method Illustrative example

Zhang et al. (2019) Picture fuzzy EDAS model Illustrative example

Mani et al. (2014) AHP Three case studies

Azadi et al. (2015) New fuzzy DEA model Resin production company

Orji and Wei (2015) A dynamic multi-criteria decision making model Gear manufacturing company in China

Khodakarami et al. (2015) Developed distinctive two-stage DEA models Resin production company

Haghighi et al. (2016) A hybrid DEA-BSC model Plastic recycling companies

Girubha et al. (2016) Hybrid MCDM methods Electronic switches manufacturing company

Izadikhah and Saen (2016) Developed two-stage DEA model Medical devices companies

Tavana et al. (2017) Integrated ANP-QFD framework Dairy company

Luthra et al. (2017) AHP-VIKOR Automobile company in India

Badri Ahmadi et al. (2017) Best-worst method Iranian manufacturing companies

Liou et al. (2019) Data-driven MADM model Taiwanese electronics company

Lin et al. (2018) Fuzzy DEMATEL method Numerical example

Liu et al. (2018) ANP-VIKOR with interval type-2 fuzzy sets Numerical example

Osiro et al. (2018) Hesitant fuzzy linguistic term sets and QFD methods Automobile manufacturing company

Yazdani et al. (2017) QFD and COPRAS methods Dairy company in Iran

Vahidi et al. (2018) A hybrid SWOT-QFD systematic framework Automotive company

Abdel-Basset et al. (2018) A Hybrid neutrosophic Group ANP-TOPSIS Dairy company in Egypt

Sinha and Anand (2018) MCDM methods Numerical example

Azimifard et al. (2018) AHP and TOPSIS methods Iran’s steel industry

Song and Li (2019) Large-scale group decision-making and TOPSIS Numerical example

Abdel-Baset et al. (2019) Neutrosophic ANP and VIKOR methods Importing company in Egypt

Fei et al. (2019) DS-VIKOR method Numerical example

Bai et al. (2019) A grey BWM and grey TODIM methods Iranian manufacturing company

Memari et al. (2019) Fuzzy TOPSIS method Catalytic converter manufacturing company

Yu et al. (2019) TOPSIS under interval-valued Pythagorean fuzzy environment Home appliances manufacturer in China

Pishchulov et al. (2019) VAHP and DEA Wood construction industry in Switzerland

Li et al. (2019) Developed TOPSIS Case study of sustainable photovoltaic modules supplier

selection

This research New fuzzy approach based on BWM and α-cut Iran Khodro Co.

Table 2. Sustainable evaluation criteria and sub-criteria in this study.

Criteria Sub-criteria Reference

Economic (Eco) Delivery lead time (Eco1) Ahi and Searcy (2015)

Financial power (Eco2) Osiro et al. (2018)

Operational cost (Eco3) Vahidi et al. (2018)

Defective rate (Eco₄) Vahidi et al. (2018)

Environmental (Env) Resource consumption (Env1) Hussain (2011)

Air pollutant emission (Env2) Ahi and Searcy (2015)

Certifications (Env3) Osiro et al. (2018)

Pollution production (Env₄) Govindan et al. (2013)

Social (Soc) Employee satisfaction (Soc1) Feil et al. (2015)

After-sales service (Soc2) GüNeri et al. (2011)

Employee training and development (Soc3) Osiro et al. (2018)

Ease of communication (Soc₄) Santos et al. (2017)

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Step 4: In this step, the priority of each criterion over the worst criterion (OW) is determined by DM as a fuzzy number shown in Table 4. The priority of the jth criterion over the worst criterion is expressed as

~AjW¼ ljW;mjW;ujW

�and ~AWW ¼ð1;1;1Þ.

Step 5: Apply the α-cut on the membership functions of the fuzzy numbers selected by DM: the α-cut of a membership function of the TFN is the closed interval

l^α:m^α

½ �(α∈ [0, 1]), and it is obtained from the relationship (3). In this step, the value of α is determined based on the satisfaction level of DM: the higher the level of DM satisfaction, the closer the value of α to 1, and the lower the level of DM satisfaction, the closer the value of α to 0.

Step 6: Calculate the optimal weights of the criteria (w^�₁;w₂^�;. . .;w^�_n): Given that the weights are non- negative, we can calculate them using expression (4):

min� wB w⁰_j

��

�

��

��;for all j

wj w⁰⁰_j

��

�

��

��;for all j

l^L_Bjð Þwα j�w⁰_j �u^R_Bjð Þwα j;for all j (4) l^L_jWð Þwα W�w⁰⁰_j �u^R_jWð Þwα W;for all j

X

j

wj¼1

wj �0

Expression (4) can be rewritten as expression (5):

min� wB w⁰_j

��

�

��

��;for all j

wj w_j⁰⁰

��

�

��

��;for all j lBjþ mBj lBj

�α� wj�w⁰_j

� uBj uBj mBj

�α�

wj;for all j (5) ljWþ mjW ljW

�α�

wW�wj � ujW ujW mjW

�α� wW; for all j

X

j

wj¼1

wj �0

If we want to use interval data in a decision problem, expression (4) can be changed to expression (6).

min� wB w⁰_j

��

�

��

��;for all j

wj w_j⁰⁰

��

�

��

��;for all j

lBjwj�w⁰_j �uBjwj;for all j (6) ljWwW�w⁰⁰_j �ujWwW;for all j

X

j

wj¼1

wj �0

The outline of the proposed method and its steps are summarized in Figure 1.

Table 3. Notations and their descriptions.

Notation Description

Sets j2C¼f1;2; . . . ;ng Criterion

i2A¼f1;2; . . . ;mg Alternatives

d2D¼f1;2; . . . ;kg Experts

Parameters l_Bj^Lð Þα Lower limit of preference for the best criteria than the j-th criteria with respect to α

u^R_Bjð Þα Upper limit of preference for the best criteria than the j-th criteria with respect to α l_jW^L ð Þα Lower limit of preference for the j-th criteria than the worst criteria with respect to α u^R_jWð Þα Upper limit of preference for the j-th criteria than the worst criteria with respect to α

Variable wB Weight of the best criterion

wj Weight of j-th criterion

w⁰_j Mediator variable of Upper and Lower Limit of α-cut forwj

wW Weight of the worst criterion

w⁰⁰_j Mediator variable of Upper and Lower Limit of α-cut forww

Table 4. Linguistic terms for criteria comparison.

Linguistic terms Membership function

Equally importance (EI) (1,1,1)

Weakly important (WI) (1,2,3)

Moderate importance (MI) (2,3,4)

Moderate plus importance (MP) (3,4,5(

Strong importance (SI) (4,5,6)

Strong plus importance (SP) (5,6,7)

Very strong importance (VS) (6,7,8)

Extreme importance (EX) (7,8,9)

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All steps are also repeated for weight suppliers; the suppliers are compared by DMs for each of the economic, environmental, and social aspects using expression 4 or 5.

3.1. Calculating the consistency rate of DM decisions

The consistency index (CI) in the fuzzy BWM is calculated from expression (8) based on the priority of the best criterion over the worst criterion. Its value is shown in Table 5. Given the �^� value obtained from the model and the CI in Table 3, the consistency rate (CR) is calculated using expression (7) (Rezaei 2015).

CR¼�^�

CI (7)

Definition 3. In a pairwise comparison, the preferences are fully consistent when aBj�ajW ¼aBW.

When aBj�ajW�aBW, this means that theaBW value is greater or lesser than the other side and will result in inconsistency in pairwise comparisons.

It is also evident that ðwB=wjÞ � ðwj=wWÞ ¼wB=wW, and given that the highest inequality occurs when aBj

and ajW have their maximum possible values, the value of ξ should be subtracted from aBj and ajW and added toaBW.

aBj ��

� ðajW �Þ ¼ ðaBWþ�Þ (8)

We know that inconsistency is maximized when aBj¼ajW ¼aBW, so

aBW �

ð Þ � ðaBW �Þ ¼ ðaBWþ�Þ Figure 1. Proposed method framework.

Table 5. CI for fuzzy BWM.

~aBW EI WI MI MP SI SP VS EX

CI 0 1 1.63 2.30 3 3.73 4.47 5.23

(9)

)�² ð1þ2aBWÞ�þ a²_BW aBW

�¼0 (9) To calculate the CI, it is sufficient to put the value of aBW in expression (9) and calculate the maximum value of ξ (Rezaei 2015).

To calculate the CI of fuzzy numbers, we can sub- stitute aBWin expression (9) with the upper limit of the fuzzy number (uBW) to create expression (10) (Guo and Zhao 2017).

�² ð1þ2uBWÞ�þ u²_BW uBW

�¼0 (10)

3.2. Ranking the suppliers

The results of the proposed method can be examined in two stages. First, the local weight of each of the economic, environmental, and social dimensions is multiplied by the local weight of its sub-criteria to obtain the global weights of the sub-criteria of each of the three dimensions. The average global weight is calculated using expression (11). The average of the weights for each sub-criterion of the triple dimensions is also obtained using expression (12).

For ranking the suppliers, we use the simple addi- tive weighting (SAW) method (expression (13)).

w_j^�¼1 k

X_k

d¼1w_j^k (11)

λ^�_ij¼1 k

X_k

d¼1λ^k_ij (12)

Si ¼X_n

j¼1w^�_jλ^�_ij (13)

4. Application to SSS

The proposed framework in this paper was used for SSS in an Iranian automotive company. IKCO was founded in 1962, and it is the largest manufacturer of automobiles in Iran. The logistics unit of this company is responsible for supplying raw materials and equip- ment. One of the goals of IKCO is to provide a framework for evaluating and identifying top sustainable suppliers in order to improve productivity and to survive in a competitive market. Choosing the right experts is crucial for decision-making. An expert’s inaccurate analysis may affect supplier selection and increase company costs (Tavana et al. 2017).

In this study, a three-person decision-making team of IKCO experts was asked to help in gathering the needed information. The members of this team had several years of experience in marketing, manufacturing, and logistics. Their names, education, and work experiences are shown in Table 6.

The decision-making team seeks to select and rank suppliers based on sustainability. Suppliers investigated in this study include SAPCO Supplying Automotive Parts Company (SAPCO), Iran Khodro Spare Parts and After-Sale Services Co. (ISACO), and Mehr Cam Parts Co. (MCPCO).

The experts selected and evaluated the suppliers based on the criteria shown in Table 2, which are selected through the literature review. Making a list of the supplier evaluation criteria and measuring their relative importance will help managers better under- stand the concept of sustainability (Kannan et al.

2015). Figure 2 shows the hierarchical structure of SSS in IKCO.

Once the criteria and sub-criteria of the decision problem have been identified in step 1, it is time to choose the best and the worst criteria. Each DM determines the best and the worst criteria (step 2), then determines the BO priority (step 3), as well as the OW priority (step 4) using the fuzzy membership functions presented in Table 4. Steps 2, 3, and 4 are repeated for sub-criteria related to economic, environmental, and social dimensions. The priorities of the SSS criteria and sub-criteria determined by the experts are presented in Table 7.

In the new approach presented in this study, the best and worst suppliers are selected based on each of the sub-criteria of economic, environmental, and social dimensions. Each DM identifies the best and worst suppliers based on each of the sub-criteria of economic, environmental, and social dimensions (step 2), then determines the BO priority (step 3) and the OW priority (step 4) using the fuzzy membership functions presented in Table 4. Supplier priorities based on economic sub-criteria, supplier priorities based on environmental sub-criteria, and supplier priorities based on social sub-criteria are shown in Tables 8–10, respectively.

In step (5), the triangular fuzzy membership functions are converted to numbers in an interval based on the level of DM satisfaction. The priorities determined by the experts (presented in Table 7 through 10) are Table 6. Members of the decision-making team for SSS.

Full name Education and experience

Organizational position Aliyari Hooman

(DM1)

Master of Industrial Management, 20 years’ experience in the automotive industry, proficiency in automotive supply chain and support

Supply support management Parsi Mehrdad

(DM2)

Bachelor of Industrial Production, 18 years’ experience in the automotive industry, specialist in lean and agile production support

Head of statistics and reports

Mokhtari Ahmad (DM3)

Bachelor of Public Relations, 15 years’ experience in the automotive supply chain and sustainable production

Insurance officer

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converted to numbers in an interval using expression (3). Then, using expressions (4) or (5), we calculate the optimal weights of the criteria and options. For example, Table 7 compares the economic, environmental, and social dimensions of the relationship (5) for each DM. For example, to compare the economic, environmental, and social dimensions in Table 7, each DM calculates the expression (5) separately. In this study, the α-cut values of 0.1, 0.5, and 0.9 are applied to the fuzzy number membership functions used by the experts. The higher the alpha level, the higher the level of decision-maker satisfaction, which means that there is a lower amount of uncertainty in the decision- making process. But the lower the alpha level, the higher the uncertainty.

Tables 11–13 show the weights of each of the economic, environmental, and social dimensions and their sub-criteria for the alpha values of 0.1, 0.5, and 0.9, respectively. The obtained weights include the local weights of each of the three dimensions as well as

the local and global weights of their sub-criteria.

Tables 14–16 show the weights of each supplier based on the sub-criteria of each of the economic, environmental, and social dimensions for the alpha values of 0.1, 0.5, and 0.9, respectively.

Tables 17–19 show the results for the supplier rankings using the proposed method for the alpha values of 0.1, 0.5, and 0.9, respectively. As can be seen, the proposed method yields the same rankings for all three alpha values. The supplier ranks are as follows:

MCPCO < SAPCO < ISACO

As can be seen from the results, the proposed method for SSS is able to evaluate the appropriate suppliers according to a set of criteria while helping producers select the desired supplier in order to improve their overall performance and achieve sustainability in producing and supplying their products.

It has become well known that large companies and industrial plants are more focused on sustainability in SCM (Grimm et al. 2014; Kumar et al. 2016). Certain Figure 2. Hierarchical structure of SSS problem in IKCO.

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previous studies have assessed the sustainability in SCM with regard to specific issues such as its environmental or technical aspects (Mahdiloo et al. 2015; Su et al. 2016). However, in this study, the selection of a sustainable supplier in the automotive industry was examined while considering the three components of sustainability (economic, social, and environmental components) and their sub-components. The results of the research were taken by IKCO Company to be exquisite and appropriate results.

In the proposed method, experts should express their preferences about each supplier using their personal knowledge and experience, and then they should determine their level of satisfaction and confidence based on the conditions governing the decision-making process and the characteristics of the suppliers. Hence, their final decisions are made based on real situations and should lead to the selection of the best sustainable supplier for the manufacturer. This proposed method also uses the theory of fuzzy sets and triangular fuzzy numbers in order to include the ambiguous and uncertain preferences and opinions of decision-makers in the decision-making process. The final ranks of the suppliers are determined based on the preferences of experts and in the form of triangular fuzzy numbers. The decision-maker’s level of satisfaction is indicated as an alpha parameter. The changes to the criteria weights are calculated using different levels of decision-maker satisfaction for different the alpha values of 0.1, 0.5, and 0.9. Among the suppliers, ISACO was determined to be the most important sustainable supplier. The results show that the ranking does not change for the different alpha values of 0.1, 0.5, and 0.9, and that the preferences of the decision-makers are highly certain. By changing the level of decision-maker satisfaction and the value of the alpha parameter, the levels of importance of other suppliers also changes, but the ranking is the same. When the decision-makers’ preferences for different suppliers are close or equal, the ranking of suppliers at different decision-maker satisfaction levels with the choice of an alpha between 0 and 1 yields acceptable results.

Table 7. Priorities of the SSS criteria and sub-criteria determined by the experts.

DMs BO & OW Eco Env Soc –

DM1 BO Best: Eco EI MI SI –

OW Worst: Soc SI WI EI –

DM2 BO Best: Soc MP SI EI –

OW Worst: Env MI EI SI –

DM3 BO Best: Env MP EI MI –

OW Worst: Eco EI MP WI –

DMs BO & OW Eco1 Eco2 Eco3 Eco4

DM1 BO Best:Eco4 SI SP SP EI

OW Worst:Eco3 SI MP EI SP

DM2 BO Best:Eco1 EI VS EX SP

OW Worst:Eco3 EX MP EI SP

DM3 BO Best:Eco4 VS EX VS EI

OW Worst:Eco2 SI EI MP EX

DMs BO & OW Env1 Env2 Env3 Env4

DM1 BO Best:Env4 EX SP VS EI

OW Worst:Env1 EI SP SI EX

DM2 BO Best:Env4 VS MP MI EI

OW Worst:Env1 EI MP SI VS

DM3 BO Best:Env2 SI EI VS MP

OW Worst:Env3 MI VS EI MP

DMs BO & OW Soc1 Soc2 Soc3 Soc4

DM1 BO Best:Soc2 SI EI VS SP

OW Worst:Soc3 SP VS EI SI

DM2 BO Best:Soc2 SP EI SI EX

OW Worst:Soc4 SI EX MI EI

DM3 BO Best:Soc1 EI MP SP EX

OW Worst:Soc4 EX SI MI EI

Table 8. Suppliers priorities based on economic sub-criteria, as determined by the experts.

Sub-criteria DMs BO & OW SAPCO ISACO MCPCO

Delivery lead time (Eco1) DM1 BO Best: ISACO SP EI EX

OW Worst: MCPCO SI EX EI

DM2 BO Best: ISACO VS EI SP

OW Worst: SAPCO EI VS SI

DM3 BO Best: ISACO SP EI EX

OW Worst: MCPCO SI EX EI

Financial power (Eco2) DM1 BO Best: SAPCO EI SP MP

OW Worst: ISACO SP EI SI

DM2 BO Best: SAPCO EI EX VS

OW Worst: ISACO EX EI SI

DM3 BO Best: MCPCO VS EX EI

OW Worst: ISACO SI EI EX

Operational cost (Eco3) DM1 BO Best: SAPCO EI MI SI

OW Worst: MCPCO SI WI EI

DM2 BO Best: ISACO MP EI SP

OW Worst: MCPCO MI SP EI

DM3 BO Best: ISACO MI EI SI

OW Worst: MCPCO WI SI EI

Defective rate (Eco4) DM1 BO Best: SAPCO EI SP MP

OW Worst: ISACO SP EI SI

DM2 BO Best: ISACO SI EI EX

OW Worst: MCPCO MP EX EI

DM3 BO Best: SAPCO EI MP SP

OW Worst: MCPCO SP MI EI

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Based on the results obtained from the proposed method, it can be claimed that the method provides a flexible yet powerful decision-making process for selecting a sustainable supplier.

The CR is an important indicator used to check for the validity of pairwise comparisons performed by a DM (Rezaei 2015). In the BWM, calculations are based on the DM’s initial judgment, which appears as determining the priority of the best option over all options and the priorities of all options over the worst option in the pairwise comparisons. Therefore, any error and inconsistency in the pairwise comparisons will affect the final result of the calculations.

Figure 3 through 6 illustrate the trend of changes in the CR of DMs decisions with regard to the economic,

environmental, and social dimensions and their sub- criteria for different values of α. As can be seen, when the level of DM satisfaction is increased and the value of α is close to 1, the CR of the proposed method increases. It can be deduced from this table that in order to obtain a CR of zero or near zero in the proposed method, we must choose an α value close to zero. However, in the examples examined in this study, the CR for the high values of α was also acceptable.

5. Conclusion

In recent years, sustainable development and sustainability in the competitive environment have been the focus of many researchers in various industries. We Table 10. Suppliers priorities based on social sub-criteria, as determined by the experts.

Employee satisfaction (Soc1) DM1 BO Best: ISACO SP EI MP

OW Worst: SAPCO EI SP MI

DM₂ BO Best: MCPCO VS SI EI

OW Worst: SAPCO EI MP VS

DM3 BO Best: ISACO MP EI EX

OW Worst: MCPCO MP EX EI

After-sales service (Soc₂) DM₁ BO Best: ISACO SI EI VS

OW Worst: MCPCO MI VS EI

DM2 BO Best: ISACO WI EI MI

OW Worst: MCPCO WI MI EI

DM₃ BO Best: ISACO EX EI SP

OW Worst: SAPCO EI EX MP

Employee training and development (Soc3) DM1 BO Best: MCPCO SP EX EI

OW Worst: ISACO SI EI EX

DM₂ BO Best: SAPCO EI SP MP

OW Worst: ISACO SP EI MI

DM3 BO Best: MCPCO MI MP EI

OW Worst: ISACO MI EI MP

Ease of communication (Soc₄) DM₁ BO Best: SAPCO EI MP SP

OW Worst: MCPCO SP MI EI

DM2 BO Best: ISACO MP EI SP

OW Worst: MCPCO MP SP EI

DM₃ BO Best: SAPCO EI MI SI

OW Worst: MCPCO SI MI EI

Table 9. Suppliers priorities based on environmental sub-criteria, as determined by the experts.

Resource consumption (Env1) DM1 BO Best: ISACO SI EI VS

OW Worst: MCPCO SI VS EI

DM2 BO Best: ISACO SP EI SI

OW Worst: SAPCO EI SP SI

DM3 BO Best: ISACO SI MI MP

OW Worst: MCPCO WI MP EI

Air pollutant emission (Env2) DM1 BO Best: SAPCO EI SP MP

OW Worst: ISACO SP EI MP

DM2 BO Best: SAPCO EI EX VS

OW Worst: ISACO EX EI SI

DM3 BO Best: MCPCO VS EX EI

OW Worst: ISACO MP EI EX

Certifications (Env3) DM1 BO Best: MCPCO SI EX EI

OW Worst: ISACO MI EI EX

DM2 BO Best: MCPCO WI SP EI

OW Worst: ISACO SI EI SP

DM3 BO Best: MCPCO VS SI EI

OW Worst: SAPCO EI MP VS

Pollution production (Env4) DM1 BO Best: MCPCO SI VS EI

OW Worst: ISACO MI EI VS

DM2 BO Best: SAPCO EI MP SP

OW Worst: MCPCO SP MP EI

DM3 BO Best: MCPCO EX VS EI

OW Worst: SAPCO EI SP EX