Sustainable Supplier Evaluation and Selection in Developing Countries; An Integrated Fuzzy Framework

Evaluation and selection of suppliers play a vital role in improving their sustainability performance. In this regard, evaluating the sustainability performance of suppliers is much more critical in developing countries. These countries are among the best destinations for supplying raw materials and workforce utilization because of lower costs. In recent years, various multi-criteria decision aiding (MCDA) frameworks utilized to evaluate suppliers based on sustainable aspects. However, few studies analyzed the evaluation and selection of suppliers' sustainability in an integrated fuzzy MCDA model regarding the supplier selection process and developing nations. This research presents a comprehensive, integrated fuzzy decision model for sustainable supplier evaluation and selection using an integrated FDelphi-FDEMATEL-FANP-FVIKOR by considering sustainability (social, economic, and environmental). Also, this research provides a comprehensive analysis of MCDA techniques' literature in terms of the main steps of sustainable supplier selection (e.g. formulation of criteria, handling of criterion's interaction, weight calculation, and final selection. The previous research rarely considered the evolution of suppliers' sustainability according to its steps in the literature. This framework assists supply chain managers and practitioners better understand the extraordinary role of these steps on businesses. This research's proposed framework is validated by a numerical example taken from Iran's automotive industry. 

Keywords: Supply Chain Management; Sustainable Supplier Selection; Fuzzy MCDM; Developing Countries

Sustainability has increasingly become essential to organizations in recent years due to the rapid decrease of natural resources, growing awareness of our environment, and concerns about corporate social responsibility (Govindan, Khodaverdi, et al., 2013; Kannan, 2018; Luthra et al., 2017). It enables firms to integrate three sustainable development aspects, social, economic, and environmental, to gain a competitive advantage in their supply chain (SC)(Hashemi et al., 2015). Sustainability of SC management includes the "managing of material, capital flows, and information, as well as improving long-lasting cooperation among partners"(Brandenburg et al., 2014; Grimm et al., 2014; Seuring, 2013). Furthermore, accurate selection of suppliers has a striking impact on the profitability of companies (A. H. I. Lee et al., 2009) and optimization of quality, price, and timelines of their products and services (Fallahpour et al., 2017; Luo et al., 2009; Yazdani et al., 2017; Rashidi et al, 2020; Khojasteh-Ghamari, 2020). This process is especially crucial for developing SC's sustainability (Govindan, Rajendran, et al., 2015; Song et al., 2017; Giannakis et al, 2020). As addressed by Hsu & Hu (2009), supplier evaluation and management play a strategic role in specifying firms' competitiveness and improving their sustainability performance (Hsu & Hu, 2009).

Various multi-criteria decision making (MCDM) frameworks utilized to evaluate the suppliers based on sustainable aspects (Chang et al, 2021; Costa et al., 2018; Igarashi et al., 2013); like VIKOR (Akman, 2015), ANP (Tseng et al., 2015; Giannakis et al ,2020), AHP (Chiouy et al., 2011; Shen et al., 2015), TOPSIS (Govindan, Khodaverdi, et al., 2013), DEMATEL (Su et al., 2016), and ELECTRE (Costa et al., 2018). Some of these studies have been implemented in developed countries (Genovese et al., 2013; Luthra et al., 2015). However, the analysis of environmental and sustainability criteria in the "Process of Supplier Selection" (PoSS) is much more critical in developing countries (Kannan et al., 2014; Mani et al., 2014). Because many multinational companies have concentrated on procurement in developing countries due to lower costs in numerous areas (Akamp & Müller, 2013), these countries are among the best regions in supplying raw materials (Genovese et al., 2013). The need to implement sustainability practices in the automotive supply chain in developing countries is becoming acute. On the other hand, developing a comprehensive evaluation framework to select a network of skillful and sustainable suppliers in an unstable and dynamic competitive environment and when only limited resources are available can reduce the environmental and legal risks and improve the competitiveness of a firm across the supply chain (Girubha et al., 2016; Tavana et al., 2013; Yazdani et al., 2017).Therefore, one of the most critical issues in suppliers' sustainability evaluation in developing countries is structuring and designing an effective supplier selection process (Banaeian et al., 2018; Tavana et al., 2013). Thus, this study's primary aim is to provide a comprehensive decision framework for supplier evaluation by considering SSCM capabilities that allow for a broad range of industries in the developing nations. The automotive industry is one of the most environmentally sensitive industries and is experiencing high pressure from national governments, international customers, and partners (Wang et al., 2015). Similarly, Iran is one of the most significant automobile producers in the Middle East (Reintegrating Iran with the West: Challenges and Opportunities, 2015). Economic globalization and Iranian automobile manufacturing companies' goal to arrive at the World Trade Organization (WTO) are the main elements stimulating Iranian companies to enhance SC's sustainability (Diabat et al., 2013; Hashemi et al., 2015; Kannan et al., 2013).

Recently, the development of integrated MCDA models has become progressively significant (Mardani et al., 2015). In the PoSS, decision frameworks can be merged with other models to reinforce their flexibility and quality (Chai et al., 2013; Nielsen et al., 2014). Integrative decision models can be used in different circumstances, compensate for deficiencies (Zimmer et al., 2016), and merge individual models' various advantages (Karsak & Dursun, 2016). One of these models' advantages is that they can simplify the "upfront process" in supplier selection and decrease the number of criteria and alternatives by any filtration process (Govindan, Rajendran, et al., 2015). In the literature of the PoSS under sustainability, most of the studies have used techniques based on single models, and there is narrow literature in the context of integrative models. Meanwhile, De Boer et al. (2001) and Wu & Barnes (2011) presented a holistic framework for the PoSS (De Boer et al., 2001; H. H. Wu & Chang, 2015). They believe that the "Formulation of criteria" stage is vital in supplier selection (SS). Although the "Formulation of criteria" stage has received much less attention in the literature of PoSS under the sustainable aspects, few systematic approaches are presented to support this problem (Nielsen et al., 2014; Zimmer et al., 2016). Furthermore, most aspects are indirectly ordirectly linked and possess a degree of interdependency (Chen & Chen, 2010; Su et al., 2016). In the literature, some studies have evaluated relationships among criteria (Hashemi et al., 2015) using the techniques such as DEMATEL (Abdollahi et al., 2015; Govindan, Kannan, et al., 2015; Govindan, Khodaverdi, et al., 2015; Su et al., 2016), Interpretive Structural Modeling (ISM) (Govindan, Kannan, et al., 2013; Luthra et al., 2017; Soni et al., 2014) and ANP (Hashemi et al., 2015; Giannakis et al.,(2020). However, no studies have simultaneously considered inner and outer interactions of sustainability aspects in the PoSS, and considering such relationships will provide essential insights for organizations (Igarashi et al., 2013; Seuring, 2013; Wang et al., 2015; Giannakis et al.,2020).

As cited, most of the research used various MCDA methods to evaluate sustainable suppliers. However, no study analyzed the evaluation and selection of suppliers' sustainability in an integrated fuzzy MCDA model in supplier selection and developing nations. Development, in this regard, could enhance the literature of sustainable supplier evaluation. These gaps have inspired us to develop a systematic model for the same. 

In this sense, this research intends to answer the following questions: 

• How to identify the main criteria for sustainable supplier selection (SSS)?

• How to identify inside and outside interrelationships among these criteria?

• How to prioritize the weights of these criteria?  

• How to rank and select the best sustainable alternatives from a set of suppliers?

To answer the questions mentioned above, an FDelphi, FDEMATEL, FANP, and FVIKOR is proposed to evaluate and select sustainable suppliers based on a new integrated framework. The FDelphi technique (Dalkey & Helmer, 1963) utilizes experts' responses to determine the final evaluation criteria. This method is a highly structured method that overcomes ambiguity in experts' opinions (Susana Garrido Azevedo et al., 2016; Zhao & Li, 2015). Indeed, thePoSS is a kind of multiple criteria decision-making (MCDM) problem, including various criteria, usually conflicting ones (Hsu & Hu, 2009; Tadić et al., 2014; Zavadskas et al., 2014). MCDM is specified as the procedure of detecting the most favourable choice among a set of feasible alternatives by providing a detailed and robust recommendation for decision-makers (Chai et al., 2013; Dalalah et al., 2011). The DEMATEL technique (Gabus & Fontela, 1972)proposed establishing the precise inner interrelationships, determining the ultimate priorities of the aspects in SSCM, and helping decisions with FANP. This method is one of the most efficient tools to analyze the cause and effect relationships between dimensions and criteria (Gölcük & Baykasoʇlu, 2016; Govindan, Khodaverdi, et al., 2015; Hsu & Hu, 2009). Moreover, it assesses each criterion (Govindan, Khodaverdi, et al., 2015). Besides, the Fuzzy DEMATEL method investigates the condition of fuzziness (R. J. Lin, 2013). It can have more accuracy and flexibility in various situations. The fuzzy theory was initially introduced by Zadeh and has been successfully applied in various fields. Because human preferences and judgments are frequently complex and ambiguous, as a result, the fuzzy theory is presented into the suggested framework of MCDA (Govindan, Khodaverdi, et al., 2013). FANP (Saaty, 2006) is applied to prioritize the evaluation criteria of sustainability. It offers a non-linear network structure of MCDA problems by considering interdependencies and feedback (R. J. Kuo et al., 2015; C. Lin et al., 2015). Eventually, the FVIKOR method (Opricovic & Tzeng, 2007) used to select the best sustainable supplier. The fuzzy VIKOR method developed by Opricovic (2007) devised to optimize complex systems, rank a set of alternatives with conflicting criteria for various problems, and propose compromise solutions in terms of nearness ideal solutions in a fuzzy setting (Awasthi & Kannan, 2016; Opricovic & Tzeng, 2007). In the end, this research presents some principal contributions as follows:

1. To develop a fuzzy comprehensive and strategic framework (FDelphi, FDEMATEL, FANP, FVIKOR) to evaluate and select sustainable suppliers in a developing economy. 

2. This research provides a comprehensive analysis of MCDA techniques' literature in terms of the main steps of sustainable supplier selection (e.g. formulation of criteria, handling of criterion's interaction (inside and outside), weight calculation, and final selection), integrated fuzzy frameworks, and developing nations. 

The previous research considered only the development of fuzzy models and rarely considered the evolution of suppliers' sustainability according to its steps in the literature. As a result, this framework assists supply chain managers and practitioners better understand the extraordinary role of these steps of PoSS on businesses in developing countries.

A literature review is a systematic, comprehensive survey utilized to investigate scientific research in urgent fields and assist in future guidance directions (Govindan, Rajendran, et al., 2015; Zimmer et al., 2016). From the sustainable supply chain viewpoint, various methodologies have been suggested for the literature review. The literature review has been considered from two viewpoints in this study: (1) Methods and (2) Criteria. 

2.1. The methodologies used for the selection and evaluation of sustainable supplier

In recent years, the number of articles and research concerning supplier selection in sustainability has increased. Bai & Sarkis (2010) Introduced a framework by using rough theory and a grey system for SSS (Bai & Sarkis, 2010). Baskaran et al. (2012) employed the grey approach as a supportive methodology for evaluating sustainability-focused suppliers in the Indian textile industry (Baskaran et al., 2012). The researchers concluded that pollution and unfair competition have top priorities. Büyüközkan & Çifçi (2011) proposed the fuzzy ANP method under incomplete preferences to evaluate the suppliers with the criteria of sustainability (Büyüközkan & Çifçi, 2011). Amindoust et al. (2012) recommended the fuzzy inference system (FIS) model for SSS. One of the advantages of their model is the mitigation of subjectivity in the process of decision-making (Amindoust et al., 2012). Govindan et al. (2013) presented the fuzzy numbers and FTOPSIS model considering the Triple Bottom Line (TBL) approach to evaluate a supplier's sustainability (Govindan, Khodaverdi, et al., 2013). Bai & Sarkis (2014) evaluated the relative performance of sustainable suppliers (SS) using the neighbourhood rough set theory and the DEA method (Bai & Sarkis, 2014). The proposed two-stage method can decrease data sets and computational requirements. Grimm et al. (2014) identified F (CSF) in the context of SSCM for sub-supplier management in food SC (Grimm et al., 2014). They presented fourteen CSFs that positively affect managing sub-supplier compliance with corporate sustainability standards (CSS).Chardine-Baumann & Botta-Genoulaz (2014) propound a framework in terms of sustainability to assess the supply chain. Their suggested framework applied twenty-six criteria (Chardine-Baumann & Botta-Genoulaz, 2014). Ghadimi & Heavey (2014) used the Fuzzy Inference System (FIS) method to select sustainable selection (SS) in the medical device industry (Ghadimi & Heavey, 2014).Sarkis & Dhavale (2015)discuss a novel model under the Bayesian approach and Monte Carlo Markov chain (MCMC) simulation to SSS (Sarkis & Dhavale, 2015).The advantage of this method is that it can use missing or smaller data sets. Lin et al. (2015) adopted the fuzzy ANP method on the TBL approach to SS in the Taiwan electronics industry (C. Lin et al., 2015). They found that green supply chain management and product design for sustainability is the essential SS criteria. Su et al. (2016) applied the hierarchical FDEMATEL to prioritizing SSCM criteria and suppliers in the Taiwan electronics industry (Su et al., 2016). Luthra et al. (2015) recognized twenty-six critical success factors (CSFs) to perform sustainability-focused GSCM in the mining industry by using the ISM method. They identified "scarcity of natural resources" as the most important practice to implement GSCM to develop businesses' sustainability (Luthra et al., 2015). They suggested an integrated framework to evaluate SS by using AHP and VIKOR methods. They listed twenty-two SSS criteria into three sustainability dimensions. The criteria of the environmental cost, quality, price, environmental competencies, and occupational health systems were the most influencing SSS Criteria (Luthra et al., 2017). Lin & Tseng (2016) recommended a hierarchical structure using interval-valued triangular fuzzy numbers to evaluate competitive priorities under SSCM in the Taiwan electronics industry. They understood that innovation is the most crucial criterion for SSCM (Y. H. Lin & Tseng, 2016). By reviewing the research literature, some of the most rated research conducted in SSS areas introduced in Table 1. This table shows the authors, methods, and integrated fuzzy frameworks used three dimensions of sustainability, developing nations, and the process of sustainable supplier selection problems. 

Table 1 demonstrates that the number of researches about supplier selection, considering the simultaneous three sustainability dimensions, has been growing in developing countries. It also shows that there is an increasing trend in the literature using integrative non-fuzzy MCDA models. However, there is also narrow literature in the context of integrative fuzzy MCDA frameworks.

Moreover, the process of supplier selection consists of multiple tasks (Zimmer et al., 2016). Such as "Formulation of criteria", "Evaluation and qualification", and "Final selection". As illustrated in Table 1, the step formulation of criteria has less regard in supplier sustainability literature. It can be concluded from the literature that a few research pieces have tried to support this problem by proposing a systematic method. Thus, it is clear that more attention must be paid to the formulation of criteria for enhancing the supplier selection process. The traditional Delphi technique cannot converge anticipation values only at an optimal cost and shorter. A fuzzy Delphi technique proposed properly grabbing the uncertainty and vagueness nature from experts' subjective opinions and improving traditional Delphi capability as an efficient model to solve the above challenges (Kannan, 2018). 

Furthermore, step evaluation and qualification is the second step of the process of supplier selection. This step also consists of two sub-steps: depicting criterion's interaction (Inner and outer) and relative calculating weights. According to Table 1, the concept of criterion interaction is little considered in the literature. Moreover, it is a critical research subject for making intelligent decisions within MCDA ( Nielsen et al., 2014; Gölcük & Baykasoʇlu, 2016; Song et al., 2017; Giannakis et al., 2020). By reviewing the literature, it is evident that some techniques have emerged for modeling inner interactions or outer dependencies between the criteria of sustainability. However, no attempt has been made to recommend a fuzzy integrated, comprehensive, strategic framework for solving sustainable supplier evaluation problems, analyzing the inner and outer interactions between criteria and sub-criteria of SSCM. The interaction among aspects has a striking impact on computing the weights of criteria (Girubha et al., 2016). Also, to overcome the difficulties of ANP modeling, scholars used the DEMATEL method to escalate the modeling capabilities of ANP. The hybridization of DEMATEL and ANP capture substantial attention in recent years, and it is one of the most helpful tools to handle criterion's interaction in an MCDA arena (Gölcük & Baykasoʇlu, 2016). From the technical viewpoint, one of our method's principal contributions is in the way it simultaneously analyzes inner and outer interactions of sustainability criteria employing hybridization of FDEMATEL and FANP. However, the proposed framework considers criterion interaction as necessary inputs for weight calculation (Song et al., 2017).

According to the process of supplier selection, after completing the sub-step criterion's interaction, a precise evaluation of relative weights takes place among the chosen criteria. As depicted in Table 1, it is evident that the ANP and AHP were widely applied for the computation of criteria weight in the qualification stage of the selection process. However, based on the literature, there are other methodologies for computation of weights include Entropy (Erol et al., 2011; Faisal et al., 2017; Khan et al., 2018), QFD (Tavana et al., 2017; Yazdani et al., 2017), DEMATEL (Hsu et al., 2013; J. Lee et al., 2015; Su et al., 2016) TOPSIS (Azimifard et al., 2018; Govindan, Khodaverdi, et al., 2013; Kannan et al., 2014) ITARA (Chang et al., 2020). However, ANP is one of the most common MCDA methods for evaluation and qualification of criteria weight.

In the end, the final step selection was triggered by an ultimate evaluation of sustainable supplier's performance within the process of supplier selection (Zimmer et al., 2016). However, some researchers utilized hierarchical models in the literature, namely, ANP, AHP, and the DEA model, to rank sustainable suppliers. The DEA restricts the adoption of criteria, and the outcomes are considerably influenced by extreme value (Zhao & Li, 2015). Thus, there are some restrictions on the number of alternatives and criteria (Banaeian et al., 2018). Moreover, when so many candidates are proposed, the ANP and AHP methods cannot rank them efficiently (Abdollahi et al., 2015). They also have some shortcomings: lack of ability to accommodate many alternatives due to tediousness and rise in complexity and rank reversal (Girubha et al., 2016). To defeat these problems, some practitioners emphasized the potential of outranking models, namely, ELECTRE or PROMETHEE, and compromising techniques, such as VIKOR or TOPSIS. It can be observed that VIKOR and TOPSIS are more extensively applied in the literature on supplier sustainability. There are some advantages to VIKOR compared to other outranking and compromising techniques. Compared to the TOPSIS method, VIKOR can ultimately reflect the decision maker's subjective preferences (Rostamzadeh et al., 2015), and it is suitable when criteria have conflicting nature (Luthra et al., 2017). In VIKOR, the best candidate is the nearest to the ideal situation, while in TOPSIS, the best candidate is the best according to the ranking index, which is not always the nearest to the ideal situation (Tadić et al., 2014). However, the ELECTRE is not often able to present a complete ranking of the alternatives. Thus, ELECTRE can be regarded as more helpful for decision problems defined by not several alternatives and criteria (Caterino et al., 2009). Also, PROMETHEE requires a considerable amount of mathematical knowledge (Girubha et al., 2016; Kafa et al., 2018).

2.2. Criteria used for the selection of sustainable supplier

Supply Chain Management of Sustainability (SCMS) has become an essential concern for many firms and industries in recent decades (Ahi & Searcy, 2015; Ivanov et al., 2017; Giannakis et al., 2020;Rashidi et al., 2020). One implication of SCMS is integrating a broader set of criteria that has to be joined, mostly entitled the Triple Bottom Line (TBL) approach (Brandenburg et al., 2014). Along with economic performance, the TBL approach proposes that environmental and social elements should be engaged in SSCM (Su et al., 2016). Simultaneous consideration of three dimensions of the TBL approach can help organizations attain long-term sustainability (Wang et al., 2015).Several studies have tried to incorporate the TBL approach into the PoSS (Baskaran et al., 2012). Based on the literature, the SS's most critical economic criteria are price, quality, flexibility, delivery, and technical capability (Song et al., 2017; Zimmer et al., 2016). Also, the most commonly utilized environmental criteria in the selection process of the green supplier areenvironmental management system (EMS), resource consumption, and design for the environment (Fallahpour et al., 2017; Govindan, Rajendran, et al., 2015). Literature review of the selection and evaluation of suppliers regarding social sustainability reveals factors like stakeholders' role in health and safety (Ahi & Searcy, 2015; Zimmer et al., 2016).A total of 18 initial criteria of three dimensions (social, economic, and environmental) were identified through in-depth interviews with experts and a detailed literature analysis.The initial criteria were presented in Table 2.

2.3. Research Methodology

This study proposes a novel integrated decision model under FDelphi-FDEMATEL-FANP-FVIKOR to evaluate and select the best suppliers in an automotive company.

2.3.1. Fuzzy Set Theory (FST)

In many processes, real-world judgments may not be accomplished precisely because of vagueness, imprecision, and the nature of human thinking (Govindan & Sivakumar, 2016). In this study, fuzzy triangular numbers are utilized to evaluate the preferences because it is simple for decision-makers to use.

2.3.2. FDelphi method

The Delphi technique can determine and rank the ultimate evaluation criteria and indicators (Zimmer et al., 2016). A formalized communication technique is devised to extract unbiased information and acquire the experts' most reliable consensus. It has significant advantages such as providing opportunities to receive feedback, evaluating alternatives quantitatively under uncertainty and complex situations, and improve past opinions through repetitive rounds of consulting (Susana Garrido Azevedo et al., 2016; Zhao & Li, 2015). Therefore, in this study, the FDelphi is utilized to recognize the most critical ultimate evaluation criteria and propose a systematic framework for the "formulation of criteria" stage in SS.

2.3.3. Fuzzy DEMATEL

It is a functional, structural tool for analyzing the influential relationships between complex and intertwined elements (R. J. Lin, 2013; Su et al., 2016). The goal of DEMATEL is to convert these relationships from a complex, involved system to an intelligible structural framework of that system (Patil & Kant, 2014). The advantages of DEMATEL are the ability to reduce the number of criteria to evaluate aspects' effectiveness and to depict the interrelations among criteria (Kahraman et al., 2015). When developing the inner interdependencies of elements, the DEMATEL model does not need all elements' analogy. As a result, the comparisons are meaningfully reduced (Tadić et al., 2014). By incorporating ANP with DEMATEL, the complexity of the problems can be decreased. In this study, the FDEMATEL technique is applied to determine the precise inner interrelationships among the criteria in SSCM.

2.3.4. Fuzzy ANP

ANP method is a generalized form of the method (Y. H. Lin & Tseng, 2016). Although the AHP method uses a unidirectional hierarchical structure in the evaluation process (Büyüközkan & Çifçi, 2011; Tadić et al., 2014), it does not consider the interdependency of criteria among decision-making levels (Hashemi et al., 2015; R. J. Kuo et al., 2015) and also not comprise the feedback loops between the factors (Önüt et al., 2009). ANP employs interdependencies between clusters using a non-linear network structure (Abdollahi et al., 2015). On the other hand, because of imprecise and uncertain decisions with conventional ANP, the FANP method is advantageous (Vinodh et al., 2011). It is a more flexible method that provides more accurate results in a decision environment (Tadić et al., 2014). In this study, the FANP method is applied to prioritize the evaluation criteria relative weights of sustainability to SS.

2.3.5. FVIKOR:

The VIKOR was extended for multi-criteria optimization of systems (generally complicated), and it is based on sorting and choosing from a set of alternatives (Akman, 2015; Rostamzadeh et al., 2015). In this method, the computational procedure is straightforward, and it offers a systematic and logical approach to gaining the best decision (Chatterjee et al., 2009).

The evaluation framework proposed in this research was utilized in a case study of an Iranian automotive company. A real-world case of ABD company (the name is changed for unknown reasons) is presented here. ABD Company was established in 1985 and is located in Tehran province, Iran. It is the first and one of the largest automobile parts manufacturing and engineering company that manufactures, designs, and manufactures automobile parts for several great Iranian automotive companies. ABD has a nationwide supply chain network currently, including more than 500 suppliers. It currently provides the critical parts for producing over 350,000 vehicles each year and is planning to increase this figure to 450,000 in the next two years. This company makes diverse parts, like engines, gearboxes, brake pads, water pumps, shock absorbers, clutch discs.  It aims to improve the structure of parts to enhance their quality, promoting reliable delivery of products, improving working conditions free of harmful chemicals, and lowering resource consumption.

Moreover, ABD is interested in exporting its products to foreign countries and new markets and is interested in evaluating its top-ranked suppliers' relative performance based on sustainability. Hence, this company is elected because it has a nationwide supply network of automobile parts suppliers to demonstrate the applicability of our proposed framework. In this paper, industry experts with related experience were invited to determine the suitability of proposed criteria and choose qualified suppliers through semi-structured interviews and detailed literature analysis. Interviews of multiple brainstorming sessions were registered and analyzed to obtain the final list of criteria. After consulting, a committee of experts was formed to evaluate eleven qualified suppliers of SSCM. A brief profile of the experts in terms of experience, designation, department, and qualification is presented in Table 3. 

3.1. Statement of the problem

Iran has one of the biggest automotive industries in the Middle East. Iran's automotive industry size is rated as 18th in the world. With nearly 1.2 million cars in 2014, this figure is anticipated to rise to over 1.7 million in 2018. It compares favourably with market growth rates seen in China, India, and Taiwan. Automotive companies in Iran include companies like Iran Khodro Industrial Group and Saipa, accounting for about 94% of domestic production. These companies are all among the prominent companies in the region. There are some comparative advantages in Iran's automotive industry, such as low energy costs, low land values, and competitive wage rates, making Iran an attractive destination for automakers in industrialized countries (Wilman & Bax, 2015). However, there are some challenges in developing countries like Iran, such as increasing regional warming, the lack of adequate resources, increasing pollution, public pressures, and subsequent environmental and social issues that have made automotive industries rethink integrating sustainability issues into their traditional activities (Yazdani et al., 2017). In the climate change conference in Paris (2015), Iran dedicated a policy for reducing CO2 up to 12% until 2030. In Iran, so many operations are not professionally and compatible with the environment. Also, technologies are almost outdated, and new investments should be regarded for Iran's general economic structure (Yazdani et al., 2017). In recent decades the Iranian automotive industry plays a remarkable role in the Iranian economy. They also experience high pressure from international customers and national governments to decrease emissions in their industrial activities. Despite the high significance of sustainability issues in automotive industries, it is still a relatively new Iran (Govindan, Khodaverdi, et al., 2015). Thus, there is no way for them to escape the implementation of sustainable development practices. In this regard, there are few pare of researches to develop a comprehensive, proper, and strategic framework to evaluate suppliers' sustainability in the Iranian automotive industry. Since most automobile materials and components are outsourced to external suppliers, first-tier supplier selection plays a vital role in improving sustainability performance (Ghadimi & Heavey, 2014; Hashemi et al., 2015). Besides, some studies have rarely used actual data of suppliers for SSS. We have provided a real case study of an automotive company in Iran to implement the suggested model.

3.2. The computational steps of the proposed hybrid framework:  

In this study, a five-stage methodology was used to select sustainable suppliers based on the integrated FDelphi, FDEMATEL, FANP, FVIKOR:

1. Identifying initial criteria through in-depth interviews with experts and analysis of the literature

2. Identifying and determining the final evaluation criteria for sustainable supplier selection

3. Establishing the inner dependencies and determining the final weights of the criteria for SSS

4. Establishing the outer dependencies and prioritizing the relative weights of evaluation criteria for SSS

5. Ranking the suppliers based on the criteria mentioned and selecting the best sustainable supplier selection  

The general view of the proposed framework is presented in Figure 1.

3.3. Identifying and determining initial evaluation criteria of sustainability:

A total of eleven experts were selected to participate based on their experiences and knowledge in the automotive industry and their understanding of the SSCM. Firstly, initial criteria were identified by reviewing the literature and consulting experts. The initial criteria were presented in Tables 2 and 3. In this step, three main dimensions and 18 criteria of sustainability are identified. Eleven suppliers were also selected as alternatives (through conducting interviews and discussing with the company's experts).

Figure 2 comprises three levels: dimensions, criteria, and alternatives. This study aims to select a sustainable supplier by considering social, environmental, and economic criteria in a developing country. The first level is comprised of three dimensions (social, environmental, and economic). In the second and third levels, criteria based on each dimension and alternatives (eleven suppliers) are presented.  

3.4. Application of FDelphi to identify the final criteria of sustainability

In this study, the questionnaires of FDelphi are designed based on the work of Kannan, 2018, Azevedo et al., 2013 & 2012, And consulting experts (Susana G. Azevedo et al., 2012, 2013; Kannan, 2018). Also, each criterion is evaluated on the fuzzy number. The FDelphi method utilized in this study is comprised of two rounds. In the first round, the respondents were asked to give their opinions about the criteria' importance and register them to the SSCM. In the subsequent round, the respondents provided consolidated results of the prior round and invited them to modify their choices. The consolidated fuzzy numbers are defuzzified. Some researchers have tried to define consensus and have recognized two main factors: stability and convergence. Stability is the consistency of responses across rounds. Convergence is the degree of agreement achieved by a group of experts. For stability and convergence, two conditions were selected for this research:

1. Stability of rankings across rounds, and

2. significant Kendall coefficient of concordance (W) given a significance level p < 0.05 for group ranking.

 The level of a significance test is the probability of a difference as large as that obtained or more considerable given the null hypothesis of no association. By using the MegaStat application for Excel, Kendall's Coefficient of concordance was calculated for related results. Looking at Table 4, it is concluded that there are stability and convergence of answers across rounds. Regarding Table 4, the rating of economic, environmental, and social criteria is relatively constant across the two rounds. This means that the responses are stable. Besides, looking at Kendall's Coefficient of concordance, it can be understood that there is a convergence between the results obtained from the group of experts because W was enhanced from the first to the second round (0.10, 0.31), and its value is statistically significant for p < 0.05.  In this paper, the most important criteria are selected in terms of mean rating (having values MS Excel computes more than 3.5 in mean rating, namely medium good and the mean rating). After two rounds of FDelphi, the most critical economic criteria are cost, quality, technology capability, and delivery reliability. Furthermore, the experts underlined the following three criteria as the keys to environmental aspects: Eco-design, Pollution control, and Resource consumption. Also, in the context of the social dimension, three dominant criteria are occupational safety and health, respect for stakeholders' policy, and rights. The rankings of importance for each criterion are presented in Table 4.  

Cronbach's alpha is computed for a questionnaire using SPSS v.19, and results are shown in Table 5. 

3.5. Application of Fuzzy DEMATEL for establishing the inner dependencies between criteria of sustainability

In this step, seven experts (Table 3) were adopted to participate. Using fuzzy DEMATEL, the experts determine the inner relations of the final criteria (from the FDelphi technique) within the dimensions (economic, environmental, and social). The fuzzy direct-relationship matrix is shaped for each dimension, and a normalized fuzzy direct-relationship matrix is computed for each dimension. These matrixes are produced from the fuzzy direct-relationship matrix, and the Fuzzy total-relation matrix is obtained for each dimension. These matrixes are acquired from the normalized fuzzy direct-relationship matrix and through MATLAB software. Now we have a defuzzified matrix, thereby creating the inner dependence matrix. The normalized inner dependence matrix for the economic dimension is shown in Table 6. The normalized inner dependencies for the rest of the environmental and social dimensions are acquired in the same manner. 

In the next step, setting up a threshold value equals to the average of all elements defuzzified total-relation matrix, and acquiring network relationship map (NRM) for each dimension. The Network Relationship Maps for each dimension are shown in Figures 3, 4, and 5.

As mentioned, the interaction among criteria has a considerable impact on computing the weights of the aspects. Some conceptual inner interaction models (three conceptual models) were formulated for evaluating sustainable suppliers as one of the critical inputs for a step of weight calculation.

3.6. Application of Fuzzy ANP for determining the outer dependencies and prioritizing the weights of evaluation criteria of sustainability

According to the ANP method and the importance of the criteria and sub-criteria, the clusters' elements' outer interactions are also necessary (Hashemi et al., 2015). In this research, a structured questionnaire was designed to determine outer dependencies of the social, environmental, and economic criteria in SS underworks of Hashemi et al. (2015) and Warfield (1974) (Hashemi et al., 2015; Warfield, 1974). In this aggregated relation matrix, the experts were requested to provide their opinions on the interrelationships between social, environmental, and economic criteria, where number 1 represents a relationship between two criteria, and 0 does not show any relationship. For aggregating various opinions, it is assumed that there is a relationship between two criteria if three or more experts confirmed the existence of such a relationship. Figure 6 represents the aggregated relation matrix's output for social, environmental, and economic criteria.

In summary, it can be concluded that using an aggregated relation matrix and a conceptual outer interaction model illustrated as another essential input for the step of relative weight calculation (see Figure 6). Fuzzy comparison matrices were constructed based on interdependent relationships among economic, environmental, and social criteria and prioritized the evaluation criteria relative weights of sustainability. By applying triangular fuzzy scales recommended by Ayag & Ozdemir (2009), the seven DMs are asked to respond to pairwise comparisons constructed on the sustainability factors. Also, the geometric mean is applied to aggregate the experts' judgments. Next, the lower limit and upper limit of the fuzzy numbers are calculated. 

According to the center of the maxima method, the defuzzified values are obtained[1]. After completing pairwise comparisons, the local weights are computed through the SuperDecisions software. These weights are utilized to form the supermatrix. The local weights related to economic, environmental, and social dimensions are presented in Table 7. As can be seen, the experts gave the highest priority (0.606) to the economic cluster, followed by the social cluster (0.264) and environmental (0.128). 

In the final step, to obtain global priorities, the priorities found by FDEMATEL and FANP are entered in the proper columns of a supermatrix (unweighted supermatrix[1]). By raising the supermatrix to a sufficiently large power 2p+1, the limit supermatrix is formed. In this step, the limit supermatrix is calculated by using the SuperDecisions software. Priority weights of criteria for sustainable supplier selection have been shown in limited supermatrix. Table 8 shows that "cost" (0.096) was found as the most crucial criterion based on the economic dimension, followed by "technological capability" (0.079), "delivery reliability" (0.053), and "quality" (0.029). Also, "rights of stakeholders" (0.176), respect for policy (0.156), occupational safety, and health (0.149) have been evaluated as the most important criteria under the social dimension. Moreover, resource consumption has been identified as the most critical criterion in the environmental dimension.

In the next step, the FVIKOR method is used to select the best sustainable supplier based on the weights computed by FANP.

3.7. Application of Fuzzy VIKOR for ranking the best sustainable supplier:

In the final step, FVIKOR was used to rank a sustainable supplier. The suppliers' fuzzy aggregate evaluation matrix concerning the criteria is constructed using a scale of linguistic variables. Then the fuzzy ideal values and the fuzzy nadir values of all criteria are obtained.

The ranking of the suppliers in terms of defuzzified values of  in descending order are determined. Because the condition C1 is not satisfied, then a set of compromise solutions are presented, and by considering the relation Q (A (6))-Q (A (11)) < 0.1, maximum m is determined 6 (the positions of these alternatives are "in closeness"). Based on the Q index and by taking into account the relation Q(A(m))-Q (A (1)) < DQ, the ranking of the suppliers (in terms of "in closeness") in descending order is specified as S11 > S7 > S10 > S3 > S6 and the best supplier is found supplier 11.

The proposed integrated framework presents a five-stage systemic analytical methodology for the evaluation and improvement of sustainable suppliers. In the first step, 18 initial criteria of three dimensions (social, environmental, and economic) were identified through in-depth interview with experts and a detailed analysis of the literature.Next, the final criteria have been determined by using the FDelphi technique. Regarding Table 4, the ranking of the main criteria of sustainability is presented under the mean rating. After two rounds of FDelphi, technology capability, cost, delivery reliability, and quality are the most important criteria based on the economic dimension. The experts have also selected the following three criteria as the most important keys to the environmental dimension: Pollution control, eco-design, and resource consumption. Moreover, in the context of the social dimension, results also showed that three dominant criteria are occupational safety and health, respect for the policy, and the rights of stakeholders. In the third step, using FDEMATEL, the experts determine the final criteria' inner relations within the dimensions. For example, the Network Relationship Map for the economic dimension (Figure 3) indicates that quality, delivery reliability, and technology capability is affected by the cost. Hence, it demonstrates that firms should pay specific attention to the criterion of cost as a crucial factor in establishing a long-term, mutually beneficial partnership with suppliers. Similarly, based on NRM, cost, quality, and technology capability are influenced by delivery reliability. Traditionally, efficient supplier selection in SCM was dependent upon the supplier able to meet the delivery schedule. High delivery reliability is a substantial element in practising the SSCM process (Y. H. Lin & Tseng, 2016). This criterion was given the "most important" in evaluating suppliers by (Beikkhakhian et al., 2015; Chang et al., 2011; Ho et al., 2010). Furthermore, the direction of influence within the environmental dimension (Figure 4) indicates that the Eco-design will affect resource consumption and pollution control. A poor design could lead to unnecessary material and energy consumption (Liou et al., 2016). Besides, the utilization of eco-design mobilizes the whole supply chain to diminish environmental impacts, minimize the application of non-renewable resources, and decrease the volume of toxic emissions from raw materials to the finished product (Govindan, Kannan, et al., 2013; R. J. Lin, 2013; Tseng et al., 2015). 

Moreover, within the third dimension (Figure 5), occupational safety and health, stakeholders' rights, and respect for the policy are affected by each other. After specifying the criteria's inner relationship structures, this study used FANP to calculate the criteria' weights. Although criteria like cost and quality have commonly presented the highest priority in the supplier selection problems, these criteria' real importance is much dependent on other criteria. Using the aggregated relation matrix, the experts determine the outer interrelationships between social, environmental, and economic criteria. As shown in Figure 6, the findings of this model were compatible with Hashemi et al. (2015) and Kuo et al. (2010) (Hashemi et al., 2015; R. J. Kuo et al., 2010). After that, using FANP, weights of the criteria are computed. From table 7, the ranking of the main dimensions of sustainability is given as economic (0.606), social (0.264), environmental (0.128). Furthermore, the ranking of the criteria of sustainability based on their main dimensions is also calculated. Based on limited supermatrix, presented in Table 8, stakeholders' rights, resource consumption, respect for the policy, occupational safety & health, and cost criteria have been rated as the top five criteria. The criterion of rights of stakeholders obtains the highest rank. Sustainability is affected by risks that arise from negative stakeholders' reactions, and attention must be paid to the direct role that key stakeholders have played over a sustainable supply chain (Tseng et al., 2015; Zimmer et al., 2016). Furthermore, resource consumption has been identified as the second important criterion to improve the supply chain's sustainable performance. Nowadays, natural resource scarcity has forced governments and enterprises to implement environmental management measures (Luthra et al., 2015). Moreover, there is a huge scarcity of resources in developing countries due to the frequent exploitation of those resources (Govindan, Kannan, et al., 2015). Because of the rapid decrease of natural resources and increasing international pressures, Iranian industrial companies need to consider resource consumption's importance in enhancing the supply chain's sustainability. The next ranked criterion is respect for policy. In developing countries like Iran, supportive economic and environmental policies must be designed to boost the supply chain's sustainability when it comes to its automobile industry. These policies can make the firm's appearance more transparent towards the community and strengthen trust, economic benefits, and good relations. The next criterion is occupational safety & health. It is related to the safety, welfare, and health of people engaged in the work environment. This criterion aims to maintain and promote the highest degree of workers' mental, physical, and social health and hinder occupational accidents for working conditions (Chardine-Baumann & Botta-Genoulaz, 2014). This criterion was identified as the most significant SSCM practice by Luthra et al. (2017), Azadnia et al. (2012), Erol et al. (2011) (Azadnia et al., 2012; Erol et al., 2011; Luthra et al., 2017). Lastly, the cost criterion obtains the fifth rank in the ranking list. Although organizations' objective is to maximize profit, the single criterion approach based upon the lowest cost is no longer the most significant in current SCM. However, the suppliers are expected to prepare goods and services at the lowest cost for their sustainability actions. In the final step, FVIKOR is used to rank a sustainable supplier. According to FVIKOR, the suppliers' ranking in descending order is specified as S11 > S7 > S10 > S3 > S6, and the best supplier is found to be supplier 11. 

This research presents some implications and managerial insights. 

• As it can be recognized in Table 1, the researches conducted on the evaluation of suppliers' sustainability only considered specific dimensions and processes of suppliers' sustainability. Consequently, no study in the literature provides a comprehensive framework encompassing various models' capables of  formulating criteria, handling criterions interaction (inside and outside), calculating weights, and selecting final alternatives in an integrated fuzzy MCDA environment in a developing country. 

• The research presents an integrated fuzzy hierarchical model for evaluating and selecting sustainable suppliers (Figure 2), which can be employed as a roadmap for managers and practitioners. 

• This research's proposed framework includes social, environmental, and economic criteria to evaluate and select a sustainable supplier. Managers of various industries can utilize the proposed framework to select the most qualified sustainable supplier or evaluate their sustainability. Also, this framework assists automotive managers and practitioners to allocate opportunities and resources appropriately. Furthermore, organizations can employ a suggested framework to improve sustainable products and processes. This framework can also be used as a benchmarking framework by considering all three sustainability characteristics, especially in developing countries (Figure 1). It will also help the Iranian automotive industry to prepare for the successful implementation of SSCM. 

• This study reveals suppliers' status in sustainability in a developing country such as Iran and its automobile industry. It helps research literature by proposing a practical framework to SSS in the context of developing nations. 

• This study considers the importance of the formulation stage of criteria in the research literature and fills the gap in the existing literature (De Boer et al., 2001; Igarashi et al., 2013; Luo et al., 2009; C. Wu & Barnes, 2011; Zimmer et al., 2016) to propose a systematic fuzzy method to support this problem.  

• Furthermore, the proposed framework presents some conceptual interactions models among criteria to deal with the "criterion's interaction" stage and provides a better understanding of the inner and outer relationships between three characteristics of sustainability in SSS and fills the gap in the existing literature (Igarashi et al., 2013; Luthra et al., 2017; Seuring, 2013; Wang et al., 2015). The suggested framework considers the step of criterion interaction as an essential input for weight calculation. 

• In this study, to enhance the step of qualification of the model proposed by Zimmer et al., 2015; Wu and Barnes, 2011; Luo et al., 2009; De Boer et al., 2001, it was decomposed into two sub-steps:  depicting criterion's interaction (inner and outer) and calculating relative weights (De Boer et al., 2001; Luo et al., 2009; C. Wu & Barnes, 2011; Zimmer et al., 2016). 

• In the proposed framework, by combining FDEMATEL and FANP, the ANP method's complexity can be decreased, and the comparisons are meaningfully reduced.

• In our proposed framework, the FANP helps decision-makers apply his/her own set of weights for survey items. 

Evaluation and selection of suppliers play a vital role in improving the sustainability performance of suppliers. However, evaluating the sustainability performance of suppliers is much more critical in developing countries. In this regard, this research presents a novel integrated fuzzy decision model for the evaluation and selection of sustainable suppliers by using an integrated FDelphi-FDEMATEL-FANP-FVIKOR by considering all three characteristics of sustainability in a developing country and its automobile industry. This research's proposed framework is validated by a numerical example taken from the case of Iran's automotive industry. Compared to the prior literature, the proposed framework provides several contributions to the selection of sustainable suppliers. This study's main contribution is to present a new integrated fuzzy comprehensive, strategic framework (FDelphi-FDEMATEL-FANP- FVIKOR methods) to evaluate and rank sustainable suppliers in MCDA techniques' literature terms of the process of supplier selection and developing nations.

Moreover, based on the research literature, a few papers have been presented about dealing with the "criterion's interaction" stage in the PoSS. Also, no attempt has been made to date to recommend a fuzzy integrated, comprehensive framework for solving sustainable supplier evaluation problems, which analyzes the inner and outer relationships between criteria and sub-criteria. However, along with the FDEMATEL method and an aggregated relation matrix, the conceptual interactions model among the criteria for evaluating a sustainable supplier is formulated to support this problem. Also, some studies of literature have rarely used actual supplier data for SSS. One of the advantages of the proposed framework is its practical applicability. Also, it is a useful fuzzy decision tool to deal with the evaluation problem. In the proposed FANP, decision-makers opinions can be collected separately and incorporated into the model by applying for interval numbers. It is a more flexible method compared to other fuzzy ANP models. 

This paper contains some limitations, like the shortage of qualified experts to confirm the study's validity. Furthermore, this research's findings are highly dependent upon the experts' opinions, and the experts' judgments can be biased. Also, only first-tier suppliers have regarded the PoSS. Considering the findings of this research, it becomes essential to use the other methods of MCDA for computation of weights, including fuzzy BWM, or step of computation of weights or the final selection, use intuitionistic or hesitant fuzzy sets models or grey system theory to evaluate sustainable suppliers in the future researches.

Future studies might focus on developing the proposed framework by adding the step of supplier development. Furthermore, the proposed framework can also be used in industries such as pharmaceuticals, textiles, and chemicals. Also, sensitivity analyses can be applied to prove the validity of weightings.

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