Green Supplier Selection Using Advanced Multi-Criteria Decision-Making Tools

Abstract

In today’s competitive and environmentally conscious industries, the ability of organizations to adapt and respond is more important than ever. This study focuses on overcoming the obstacles faced by the Iranian automobile sector by highlighting the significance of incorporating green supply chain techniques. The research intends to integrate organizational operations with environmental sustainability goals by utilizing a MULTIMOORA strategy for supplier selection. The Iranian automobile sector, facing substantial environmental challenges, requires a strategy framework for selecting environmentally friendly suppliers in order to sustain competitiveness and fulfill ecological obligations. The study develops a supplier selection model based on extensive research and expert knowledge. The Delphi and MULTIMOORA techniques are employed to assess and prioritize suppliers according to green criteria, assuring conformity with environmental goals. Data are collected by conducting a comprehensive analysis of the existing literature and engaging in conversations with industry experts in order to acquire information for the construction of the model. The results emphasize the crucial significance of trust-based relationships with suppliers, rigorous compliance with quality standards in new product development, and substantial investment in employee training and development. Sector analysts view these characteristics as crucial for promoting sustainability and gaining a competitive advantage in the Iranian vehicle sector. This study provides firms with strategic instruments to effectively negotiate the intricacies of green supply chain management, with a particular focus on the need for adopting sustainable practices while selecting suppliers in the dynamic and competitive context of the Iranian automobile industry.

1. Introduction

Supply chain management (SCM) is the backbone of modern commerce, encompassing the processes involved in the production, distribution, and delivery of goods and services from suppliers to consumers. It involves a complex network of organizations, resources, activities, and technologies working together to ensure the smooth flow of products and services to meet customer demand [1]. One cannot overstate the significance of effective supply chain management. It plays a pivotal role in enhancing efficiency, reducing costs, improving customer satisfaction, and ultimately driving competitive advantage for businesses. By optimizing the flow of materials, information, and finances across the entire supply chain, companies can minimize delays, mitigate risks, and capitalize on market opportunities more effectively [2].

However, as businesses increasingly recognize the importance of sustainability and environmental responsibility, the concept of green supply chain management has gained prominence. Green supply chain management focuses on minimizing the environmental impact of supply chain activities, from sourcing raw materials to manufacturing, transportation, and disposal [3,4,5].

Implementing green supply chain practices offers numerous significant advantages. Firstly, it helps organizations reduce their carbon footprint and mitigate environmental risks by adopting eco-friendly processes and technologies. For example, companies can optimize transportation routes to minimize fuel consumption and emissions, invest in renewable energy sources, and implement recycling and waste reduction initiatives to minimize landfill waste [6,7]. Secondly, green supply chain management can enhance brand reputation and appeal to environmentally conscious consumers. By demonstrating a commitment to sustainability and corporate social responsibility, companies can differentiate themselves from competitors, attract environmentally conscious customers, and build stronger brand loyalty and trust [8]. Moreover, embracing green supply chain practices can also lead to cost savings and operational efficiencies in the long run. For instance, by reducing energy consumption, optimizing packaging materials, and minimizing waste, organizations can lower their operating costs and improve their bottom line [5,9].

In Iran’s automotive industry, supplier selection based on green supply chain principles has become increasingly vital, reflecting a global trend towards sustainability and environmental responsibility. With growing awareness of the environmental impacts associated with traditional manufacturing processes, automotive companies in Iran are seeking suppliers who prioritize eco-friendly practices throughout their operations [10,11]. One of the key criteria for supplier selection in the Iranian automotive industry is the supplier’s commitment to environmental sustainability. Companies are looking for suppliers who adhere to green supply chain principles, including reducing carbon emissions, minimizing waste generation, and using renewable resources. Suppliers that demonstrate a proactive approach to environmental management and have implemented initiatives such as energy efficiency measures, waste recycling programs, and pollution prevention strategies are preferred [7,12].

Additionally, Iranian automotive manufacturers consider the environmental performance of potential suppliers when making sourcing decisions. This involves evaluating suppliers’ environmental certifications, compliance with environmental regulations, and track record of environmental stewardship. Suppliers that have obtained certifications such as ISO 14001 for environmental management systems or have received recognition for their sustainability efforts are often given preference [13]. Moreover, collaboration and transparency are crucial factors in supplier selection for green supply chain initiatives in Iran’s automotive industry. Companies are seeking suppliers who are willing to collaborate closely on sustainability goals, share information transparently about their environmental performance, and work together to identify opportunities for improvement. This collaborative approach fosters a culture of continuous improvement and innovation towards greener and more sustainable supply chains [14].

Environmental performance is another factor that is essential to study in the Iranian automobile industry because of its social, economic, and environmental consequences. It is noted that the automotive industry plays an important role in the Iranian economy as it creates more jobs than other kinds of industries [15]. However, it also contributes significantly to the country’s carbon footprint as a major element of the transportation sector; for instance, it contributes about 25% of Iran’s carbon dioxide emissions [16]. As a result, the application of green practices is easily achievable for Iranian automobile companies to minimize their environmental impact. This entails engaging suppliers that have embraced sustainable energy, which includes using energy from renewable sources, reusing and recycling items, and reducing discharge emissions. When Iran Khodro (IKCO) and SAIPA are sourcing supplies, it is their responsibility to seek environmentally friendly suppliers due to the increasing demand for green products in the market and government implementations to regulate emissions and energy efficiency. In a win–win situation, both buyers and suppliers will support making Iran’s automotive industry greener and more competitive through the cooperative management of sustainability [17].

Due to a number of issues faced in the implementation of sustainable manufacturing standards and the poor integration of greener supply chain practices in the Iranian automobile industry due to outmoded practices, there are a number of problems observed in current practices [18]. This intensifies macro environmental challenges, hampers the processes of sustainability, and reduces the capacity of the sector’s competitiveness in the global arena. Of these challenges, the main one can be highlighted as the lack of adequate measures in the supply chain selection criteria encouraging environmentally friendly partners to be used in the supply chain in order to mitigate adverse environmental impacts and boost organizational performance [19].

However, because of sanctions, particularly in the international market, in which Iran’s exporting and importing potentials are quite weak, there are requirements to improve current practices [20]. These sanctions also worsen the challenges experienced in the process of looking for suppliers because the number of suppliers available and willing to adhere to the set requirements is limited. The constraints of international business also put more pressure on the advancement of GSCM implementation in the industry.

Thus, the purpose of this research is to explicate a framework for finding suitable suppliers for implementing GSCM. This will be accomplished by applying the MULTIMOORA method to identify suitable suppliers to purchase environmentally sensitive supplies from. As the Iranian automobile industry faces various challenges related to sanctions, this framework aims to increase sustainability and competitiveness in Iranian auto-production. This way, the industry can steer its direction by ensuring that supplier selection is based on strategy and data perception, rather than being bound by the opportunities that are available.

2. Literature Review

Purchasing and supply chain management in line with green supplier selection (GSS) means the identification of suppliers that are environmentally responsible, for example in their compliance with ISO 14000 (International Organization for Standardization (ISO)) standards or their actions to cut emissions and waste, which are especially important in the automotive industry due to its way of operating [21]. A sustainable supply chain (SSC) includes both affiliated companies’ networks, and involves these companies joining together to embark in the production, distribution, and delivery of goods in a sustainable manner [22]. To elaborate on these concepts, a diagram depicting supply chain interventions within the automotive industry will be incorporated, accompanied by descriptions of the roles of manufacturing companies, supply firms, distributors, and customers. Brief closing remarks will be made at the end of every concept to provide the necessary recap of the ideas presented in order to instill an understanding of the extent of the study, which addresses sustainability collaboration between all connected entities in a supply chain [23].

Increasing environmental concern due to climate change and the use of resources has led organizations to pay more attention to the integration of environmental aspects into their supply chain management (SCM) practices. Green supply chain management (GSCM) is an approach that aims to integrate environmental thinking into supply chain management, including product design, material sourcing and selection, manufacturing processes, delivery of the final product to consumers, and end-of-life management of the product after its useful life [24]. Green supplier selection is one of the critical components of green supply chain management. Supplier selection is a critical decision-making process that companies undertake to identify, evaluate, and contract with suppliers [25]. The incorporation of environmental criteria in the supplier selection process is crucial for organizations to achieve their environmental and sustainability goals. The recent literature has highlighted the importance of incorporating environmental criteria in the supplier selection process [26].

New advancements in SSC (sustainable supply chain) management research have brought new ways to tackle the problems of selecting the right supplier on the grounds of sustainability and corporate social responsibility. Giri, Molla, and Biswas (2022) established a novel method of Pythagorean fuzzy set-DEMATEL, which overcomes the defects of traditional fuzzy set theory by expanding the membership degree and non-membership degree to a complete Pythagorean fuzzy number. This approach is used for defining the correlation or association between criteria, as well as the cause–effect features of interconnections, and was efficient in the SSCM context when tested on real-life case studies [27]. In the same manner, Shang et al. (2022) proposed the fuzzy MULTIMOORA method to handle traditional SSS approaches, rectifying the issues they have in grading criteria and weighting. Combining subjective and objective weightings, the authors used the Best Worst Method with 17 members and the fuzzy Shannon Entropy Method to complete a comprehensive evaluation of suppliers for an international forklift truck manufacturing company [28].

Masoomi et al. (2022) developed an integrated model that employs the fuzzy Best Worst Method combined with COPRAS and WASPAS to efficiently assess green suppliers according to tactical variables. The type of analysis they articulated and employed in the context of Iran’s renewable energy sector can be informative for policymaking processes, both at the national level and from an international perspective [29]. To resolve the consensus problem in green supplier selection for electronics manufacturing, Gao et al. (2020) proposed an innovative group consensus decision-making framework: P750. This dynamic model incorporates probabilistic linguistic preference relations and demonstrates their contribution to improving the consistency and consensus of decision-makers in detail with the help of a practical case [7]. Qu et al. (2020) proposed a fuzzy TOPSIS ELECTRE methodology to obtain a methodological framework for identifying green chain suppliers, which they modeled based on criteria like management support for green practices, with a view to meeting environmental standards. Their method was also checked with a sensitivity analysis, with the help of which they studied the M&A environment of a Chinese internet company. Altogether, it is comprehensible from these studies that using fuzzy logic along with the application of MCDM techniques is effective in finding the best solution for sustainable supplier selection [30].

The following emerging trends in green supplier selection define techniques applied in improving environmentally sustainable supply chains. Rouyendegh et al. (2019) stabilize the Intuitionistic Fuzzy TOPSIS method to make use of responses to vagueness that may open up in MCDM, especially regarding the environmental criterion (carbon footprint and the sort of reuse application) [31]. As Mohammad Javad et al. (2020) highlighted, green innovation in supplier selection is crucial, using the Best Worst Method and Fuzzy TOPSIS to evaluate weighted criteria for suppliers [32]. Essentially, Kilic and Yalcin (2020) applied Intuitionistic Fuzzy TOPSIS and two-phase fuzzy goal programming to integrate both traditional and green criteria, elaborated with an example from the air filter industry [33]. To develop a reasonable ranking of GSS, Lu et al. (2021) proposed a picture fuzzy COPRAS method, in which the CRITIC technique was adopted to determine the weight of criteria, enriching the ranking method for GSS decision-makers [34].

Rashidi et al. (2020) conducted a meta-literature review where several gaps in the current literature on sustainable supplier selection were identified. They highlighted the need for a higher number of additional studies on the GS topic, more comparative research on various supplier strategies, as well as an increased emphasis on suppliers’ innovation dynamics. The review also mentioned that the current available literature contains little information and knowledge on sustainable supplier selection, and also identified that most work mainly uses analytical and mathematical methods. This analysis also highlights the current and continuous transformation of GSS methodologies and the constant and essential demand for comprehensive and flexible frameworks to meet the ever-changing demands of sustainable supply chains [35].

In the last decade, there have been improvements in information technology. As a result, supplier selection has been influenced by cloud computing, the IoT, and AI. Çalık (2020) proposed a group decision-making (GDM) model based on the use of AHP and TOPSIS under the framework of a Pythagorean fuzzy set and the big data environment of Industry 4.0. No element was found relating to the criteria of green supplier selection in the above method. Also, this approach included experts’ opinions in the form of Pythagorean fuzzy numbers. By using these numbers, the approach dealt with a real example of an agricultural tools company and showed that the utilized hybrid method is effective not only in theory but in practice as well [36]. In the same way, Ecer (2020) used an IT2FAHP model to assess uncertainties in selecting green suppliers, considering factors such as clean production, as well as energy and material saving in the context of a home appliance manufacturer [37]. Kumari and Mishra (2020) introduced new parametric divergence and entropy measures to address ambiguity in MCDM problems and validated their approach through a comparative analysis of green supplier selection. They proposed the IF-COPRAS method [38].

However, Đalić et al. (2020) also developed an integrated fuzzy-rough Multi-Criteria Decision-Making (MCDM) model comprising fuzzy Prestige Utility for Interval Multicriteria Evaluation and Assessment (Fuzzy PIPRECIA) and Interval rough Supplier Assessment with Weighted Aggregative Preferences (Interval rough SAW) to assess suppliers according to environmental factors, revealing that pollution control was the most critical aspect [39]. Abadi et al. (2021) dealt with the aspect of resilient supplier selection based on the application of fuzzy DEMATEL and ANP to identify criteria including supplier risk, flexibility, and responsiveness, critically highlighting the importance of the technological dimension for the researched topic. The validity of this approach was established through the engagement of SAPCO Company personnel drawn from the human resource department. Altogether, these works highlight the fresh and advanced techniques used in green supplier selection; fuzzy logic, hybrid models, and indeed resilience methods all aid in facing the overwhelming nature of green supply chain management as the supply chain becomes more uncertain and dynamic [2].

3. Methods

To obtain the data for this study, a two-step procedure was used which entailed combing two questionnaires. Regarding the first questionnaire, the Delphi method was used. The participants were 17 managers, vice managers, and supply chain specialists with experience in the Iranian automobile industry. Among several candidate options, these experts were selected because of their efficiency and experience in the area of green supply chain management. Some employees were managers with full control over projects, and others were professionals like environmental consultants and special technical staff who had more than 15 years’ experience in this field. We opted for this approach because the diversity of their backgrounds offered a broad perspective that was necessary to weigh the potential indicators when conducting the assessment. First of all, further development of the Delphi method was conducted to narrow down 22 factors to the factors that influence green supplier selection.

For green supplier selection in the Iranian automobile industry, the Delphi method and MULTIMOORA method were selected as the most appropriate and effective research methods; they strengthen each other to produce precise and effective decision-making. First, the Delphi method was used to identify the experts’ opinions. They were surveyed multiple times until a consensus on the indicators of green supply chain management was reached.

This method proves efficient in fields involving low objective measurements and high risk where a range of perspectives from different experts are incorporated systematically. This study employed another MCDM tool known as MULTIMOORA to grade the suppliers based on the stated indicators. The TOPSIS, AHP, and VIKOR techniques were compared to other MCDM techniques, and MULTIMOORA was chosen due to its capability of handling a large number of criteria and due to the fact that it yielded non-subjective and rational decision-making without the need for any normalization steps and without the biases that might be introduced through decision-makers’ interactions and pairwise comparisons.

Also, the application of MULTIMOORA incorporates three different quantitative techniques: a ratio system, a reference point, and a full multiplicative form. Thus, it covers all of the important aspects and directions in evaluating potential suppliers. Combined, these methods provide a comprehensive and precise approach to supplier selection, reinforcing the qualitative findings of the experts with the application of quantitative scores and rankings, thus improving the validity of the results for the Iranian automobile industry [40].

Currently, this firm’s main suppliers are SAPCO, MEGA Motor, Crouse Company, Sazehgostar, and Iran Tractor Manufacturing Company (ITMCO). SAPCO, being the main supplier of automotive parts, has ISO 14000 certification, which reveals compliance with sound environmental management. SAPCO is a big governmental company that has applied effective energy-saving measures at production lines and in waste management, as well as aimed to localize production due to sanctions. Similarly, the engine and powertrain parts supplier MEGA Motor is also an ISO 14000 certification holder. This is a medium-to-large private company that is in the process of investing in reducing carbon emissions and improving energy efficiency.

In the same way, it is adjusting for protectionist policies by investing in local research and development. Crouse Company is an electrical and electronic components supplier; it has ISO 14000 certification and is recognized for carrying out sustainable material and waste recycling programs. It is a large private company that relies on local support to ensure that the shocks occasioned by sanctions are sustained. A supplier of chassis and body components is Sazehgostar, which has enacted green techniques such as the green usage of resources and energy-efficient strategies, and is in the process of obtaining ISO 14000 certification. It is a very large, partially state-financed enterprise whose objective is to implement high-level technologies and produce its products locally. Finally, ITMCO provides heavy vehicle parts and has gained ISO 14000 certification. Being a large state-owned enterprise, ITMCO has been involved in the purchase of renewable energy and waste management systems, and is in the process of cooperating with indigenous research institutes in order to bypass protectionist measures. These suppliers garner a critical position, influencing environmental impact throughout IKCO’s supply chain.

Through multiple rounds of surveys, 12 indicators were accepted based on their average score surpassing 7.00, signifying their significance. The experts ranked the factors from one to nine, using linguistic scales for importance (lowest to highest importance), as shown in Table 1. A total of 22 indicators for a green supply chain were found, as shown in Table 2. And after that, MULTIMOORA was used to evaluate and rank multiple options based on various criteria.

3.1. Delphi Method

The Delphi method is an organized, iterative forecasting and decision-making process wherein a group of specialists anonymously provide their thoughts and understanding about a given problem. Usually, there are several rounds of surveys or questionnaires, and in each round a facilitator summarizes the answers and provides them to the experts [41]. The experts’ anonymity promotes a candid and objective dialogue with the goal of reaching a consensus and convergence of viewpoints on the subject at hand. This approach is frequently used to gather data to predict future trends or results in a variety of fields, including business, healthcare, and research [42].

Usually, the Delphi method entails the following crucial steps:

The process of selecting experts involves identifying a group of people with knowledge and experience related to the problem or topic under discussion. These specialists may be academics, practitioners, or professionals with real-world expertise, and they may have a variety of backgrounds.

Developing the Questionnaire: The facilitator creates a series of open-ended inquiries pertaining to the topic under discussion. The specialists are supposed to respond to these questions with consideration and knowledge.

Expert Responses: The experts answer the questionnaire on their own, offering their ideas and insights on the matter. Anonymous responses are frequently gathered in order to promote open and objective feedback.

Comments are Collected: The facilitator gathers and anonymizes the expert comments before sending out the condensed feedback to each participant.

Iterative process: Experts evaluate the group’s replies from Round 1 and have the chance to modify their assessments or opinions in light of peer feedback. This process of iteration persists for multiple iterations until a convergence or consensus of opinions is attained.

The role of the facilitator is to oversee the entire process, guaranteeing that all communication is anonymous, summarizing responses in between rounds, and giving participants feedback. The facilitator may also assist in identifying areas of disagreement and steer the experts toward consensus.

Consensus or Conclusion: The Delphi method comes to an end when there is sufficient agreement among the experts or when it is clear that more rounds are not going to result in a substantial change in viewpoints. The final findings are frequently given as a report that has been condensed or as a list of suggestions.

3.2. The MULTIMOORA Method

The MULTIMOORA method was proposed by Brauers and Zavadskas [43] and consists of three components: the ratio system, the reference point approach as a part of MOORA [44], and the full multiplicative form. In addition, the extended model has two scenarios called the weighted and entropy-weighted MULTIMOORA methods. The weighted MULTIMOORA method is described step by step as follows. The decision matrix X, in which x_ij represents the responses of the ith alternative to the jth attribute, is presented in Equation (1).

X = [x_ij]_m×n i = 1, 2, …, m and j = 1, 2, …, n(1)

Step One: Calculation of the Relative System. After forming the decision matrix, the normalized decision matrix is created using the following relationship.

$x_{ij}^{\ast }={\displaystyle \frac{x_{ij}}{\sqrt{\sum _{i=1}^m{x}_{ij}^2}}}$

Typically, the number x_ij (solution of the i-th alternative for the j-th objective) falls within the interval [0, 1]. These indices signify enhancement (when positive) and reduction (when negative). Consequently, the relative index for each option is computed using the following formula:

$y_i^{\ast }=\sum _{j=1}^g{x}_{ij}^{\ast }-\sum _{j=g+1}^n{x}_{ij}^{\ast }$

For (j = 1, 2, …, g), the indices have a positive nature, and for (j = g + 1, …, n), the indices have a negative nature. Options are ranked based on the highest value of ($y_i^{\ast }$).

Step Two: Reference Point Method. The reference point method is based on the relative system. The maximum objective at the reference point can be found based on the ratios using the normalized matrix. Initially, (r_j) is obtained according to the following relationship:

$r_j=\left\{\begin{array}{c}\underset{i}{\max }{x}_{ij}^{\ast },j=\mathrm{1,2},\dots ,g\\ \underset{i}{\min }{x}_{ij}^{\ast },j=g+1,\dots ,n\end{array}\right.$

The deviation between the standard value $x_{ij}^{\ast }$ and the reference point $r_j$ is defined as $\left|r_j-x_{ij}^{\ast }\right|$, and the value of the i-th option under the reference point is expressed as follows:

$z_i=\underset{j}{\max }\left|r_j-x_{ij}^{\ast }\right|$

It is clear that $z_i$ being smaller indicates a better option.

Step Three: Full multiplicative form. Boran and Zavadskas (2010) developed the MOORA method through the full multiplicative form to maximize and minimize the multiplicative desirability function. The following equation represents this desirability, and the i-th solution is defined as follows:

$U_i={\displaystyle \frac{A_i}{B_i}}$

$A_i={\prod }_{j=1}^g{x}_{ij},i=\mathrm{1,2},\dots ,m$ represents the maximization of objectives from the i-th option, where $g=\mathrm{1,2},\dots ,n$ (the number of indices with positive nature). $B_i=\prod _{j=g+1}^n{x}_{ij}$ represents the minimization of objectives from the i-th option, where $n-g$ indicates the number of indices with negative nature. Options should be ranked based on the maximum values of $U_i$.

In the final stage of the MULTIMOORA method and using the three rankings obtained from the relative system, the reference point, and the full multiplicative form, along with the mastery method, the final ranking can be achieved. However, since the objective of this research is not ranking, scoring is assigned to the options based on the different values of $y_i^{\ast }$, $z_i$, and $U_i$, and the final score for each supplier is calculated.

4. Case Study

The weight setting was where the analysis started. The experts assessed the significance of the chosen indicators. On a scale of 0 to 10, they assigned a priority to each of the questionnaire’s presented indicators. Then, using the Delphi technique, we chose the criteria that, on average, received more than seven points. In total, 12 indicators received scores higher than 7.00. Table 3 shows the ranked indicators with final scores based on the experts’ opinions.

The analysis of the questionnaire using the MULTIMOORA technique was conducted considering the following dimensions:

• 1.. Respondents’ Matrix: This matrix presents the responses of the respondents to the questionnaire items, providing insight into their perspectives on the main and sub-factors related to the research variables.

• 2.. Weight Matrix for Each Factor: For each factor identified in the questionnaire, a weight matrix is formed to determine its relative importance. This matrix helps prioritize factors based on the opinions of the respondents.

• 3.. Formation of Normalized Matrices: The normalization of the matrices is performed to ensure that all factors are on the same scale and comparable. This step is essential for the subsequent analysis using the MULTIMOORA technique.

• 4.. Formation of Supplier Response Matrices for Each Factor: Matrices are formed to represent the responses of the suppliers regarding each factor. These matrices capture the perspectives of the suppliers on the importance and performance of the factors.

• 5.. Formation of MOORA or Weighted Matrices: MOORA (Multi-Objective Optimization by Ratio Analysis) or weighted matrices are formed based on the responses of the suppliers and the weights assigned to each factor. These matrices are used to evaluate the performance of the suppliers with respect to the identified factors.

• 6.. Formation of Reference-Point Matrices: Reference-point matrices are formed to establish benchmarks or targets for each factor. These reference points serve as criteria for evaluating the performance of the suppliers.

• 7.. Formation of Full-Multiplicative-Form Matrices: Matrices in full multiplicative form are constructed to calculate the overall performance scores of the suppliers. This step involves multiplying the normalized supplier response matrices by the reference-point matrices.

• 8.. Formation of Priority and Ranking Matrices for Each Supplier: Finally, priority and ranking matrices are formed to determine the priority and ranking of each supplier based on their overall performance scores. These matrices help identify the optimal suppliers for the research variables.

By following these steps, the analysis of the questionnaire using the MULTIMOORA technique provides a systematic approach to evaluating the performance of suppliers and identifying the most suitable ones in line with the research variables.

Then, based on their opinions, the upper triangular matrix is formed, from which the inverted results are obtained, as you can see in Table 4. In the next step, we proceed to determine the weights of these factors, which is called the weighted matrix. The weighted matrix can be observed in Table 5.

When considering the relative importance of several factors, the social element is given the most priority, followed by the environmental item, and lastly, the economic factor. The evaluation of supplier viewpoints and their weighting is conducted at this stage, taking into account the sub-factors within each primary factor of economic as shown in Table 6, environmental as shown in Table 7, and social as shown in Table 8. Within this section, we initially outline the subordinate elements associated with each component, followed by a thorough analysis of these factors.

Economic Factor: Cost, quality (product and service quality), and flexibility.

Environmental Factor: Environmental costs, green design, environmental qualifications, green research and development, pollution control, green and safe products, use of clean technology, and use of environmentally compatible materials or renewable materials.

Social Factor: Social responsibility, occupational safety and health, and employee and customer satisfaction.

Now, with the relevant interpretations and explanations provided earlier in this chapter, we will examine the paired items to prevent repetitive explanations. The overall calculations will be presented at the end of the computations.

Based on the analyses conducted for each factor, it is determined that the highest weight for the economic factor is attributed to the flexibility item, while the lowest weight is related to the quality of the product and service item was shown in Table 9. Similarly, for the environmental factor, the highest weight is associated with the use of environmentally compatible materials (green), and the lowest weight is related to environmental costs that was showed in Table 10. Finally, for the social factor, the highest weight is attributed to employee and customer satisfaction, while the lowest weight is related to social responsibilities, as demonstrated in these analyses that was showed in Table 11.

Next, based on Table 12, the amount of Ri, which is the highest value for each item, is determined, and then the necessary measurements are determined based on this, which is known as the reference-point matrix. In this case, the highest value and the lowest rank of each component are specified, as analyzed in Table 13.

According to Table 14, the highest value from the perspective of the suppliers is related to the first and second suppliers, with a rate of 0.6325. This is followed by the third, fourth, and fifth suppliers, with the lowest-ranking rank at the bottom.

Finally, based on the full development form and Table 14, the status of MULTIMOORA and the determination of suppliers’ ranks are established. According to Table 15, the highest rank is related to the fifth supplier, followed by the third supplier, the fourth supplier, the first supplier, and finally the second supplier.

4.1. Compare of Results

In this part, the results will be analyzed by other methods. In this study, ARAS and VIKOR were used for sensitive analysis.

4.1.1. ARAS

The Additive Ratio Assessment (ARAS) method is one of the relatively new multi-criteria decision-making methods developed by Zavadskas and Turskis (2010) [45]. This method is very efficient and easy to use in situations where multiple criteria are taken into consideration. Table 16 shows the ranking of the suppliers based on the ARAS method.

4.1.2. TOPSIS

TOPSIS is based upon the concept that the chosen alternative should be closest to the Positive Ideal Solution (PIS) and furthest from the Negative Ideal Solution (NIS). The final ranking is obtained by means of the closeness index [46]. Table 17 shows the final ranking of the suppliers based on the TOPSIS method.

4.2. Sensitivity Analysis

To perform a sensitivity analysis on the supplier rankings obtained using the MULTIMOORA method, we systematically vary the weights of the main factors (economic, environmental, social) and observe how these variations affect the final supplier rankings. Here is the detailed process:

• 1.. Define initial weights.

The initial weights based on the experts’ opinions are as follows:

Economic: 0.05902.

Environmental: 0.2507.

Social: 0.69029.

• 2.. Select the range for weight variation.

We vary each weight by ±20% to observe the impact on supplier rankings. For each factor, we create three scenarios: a decrease by 20%, the original weight, and an increase by 20% as showed in Table 18.

• 3.. Adjust weights and recalculate rankings.

We adjust the weights of one factor at a time while keeping the others constant and recalculate the supplier rankings using the MULTIMOORA method.

• 4.. Analyze the results.

We analyze how the supplier rankings change with different weight configurations.

Table 18

Economic Factor Weight Variation.

Configuration	Supplier 1	Supplier 2	Supplier 3	Supplier 4	Supplier 5	Ranking
Decreased 20%	4	5	2	3	1	5, 3, 1
Original	4	5	2	3	1	5, 3, 1
Increased 20%	4	5	2	3	1	5, 3, 1

4.2.1. Weight Configurations and Sensitivity Analysis

Economic Factor Weight Variation.

Weight Configurations:

Decreased by 20%: 0.047216.

Original: 0.05902.

Increased by 20%: 0.070824.

Adjusted Weights:

1. Economic: 0.047216, environmental: 0.259208, social: 0.693576

2. Economic: 0.05902, environmental: 0.2507, social: 0.69029 (original)

3. Economic: 0.070824, environmental: 0.242192, social: 0.686914.

Recalculated Rankings:

4.2.2. Environmental Factor Weight Variation

Weight Configurations:

• −. Decreased by 20%: 0.20056.

• −. Original: 0.2507.

• −. Increased by 20%: 0.30084.

Adjusted Weights:

1. Economic: 0.070824, environmental: 0.20056, social: 0.728616.

2. Economic: 0.05902, environmental: 0.2507, social: 0.69029 (original).

3. Economic: 0.070824, environmental: 0.30084, social: 0.648066.

Recalculated Rankings (Table 19):

4.2.3. Social Factor Weight Variation

Weight Configurations:

Decreased by 20%: 0.552232.

Original: 0.69029.

Increased by 20%: 0.828348.

Adjusted Weights:

1. Economic: 0.070824, environmental: 0.30084, social: 0.552232.

2. Economic: 0.05902, environmental: 0.2507, social: 0.69029 (original).

3. Economic: 0.070824, environmental: 0.30084, social: 0.828348.

4.3. Discussion of Sensitivity Analysis Results

Based on the sensitivity analysis conducted, the following observations can be made:

The supplier rankings remain consistent across different weight configurations for the Economic, Environmental, and Social factors.

This indicates that the MULTIMOORA method provides stable and robust supplier rankings, even with variations in the weights of the main factors.

In conclusion, based on Table 19 the results of the sensitivity analysis demonstrate the reliability and robustness of the MULTIMOORA method in evaluating and ranking suppliers. The rankings are not significantly impacted by changes in the weights of the main factors, ensuring confidence in the selection of suppliers based on the established criteria.

5. Discussion

The results of the present investigation also support the prior research undertaken in green supply chain management (GSCM) and in the supplier selection context. Several papers have been published from 2018 to 2024 stressing the necessity of implementing environmental aspects to evaluate sustainable suppliers and increase competitiveness [47]. The Delphi and MULTIMOORA methods used in this study reflect the evaluation frameworks put forward by many of these researchers, stressing efficiency, ecological, and socio-economic gains. To be sure about the results, the ARASA and TOPSIS methods were applied to test the results, and it was found that the results were same with the different methods.

While the MULTIMOORA and Delphi methods are well established in the field of decision-making, this study provides a unique contribution by applying these methodologies specifically to the Iranian automobile sector. The distinct environmental, regulatory, and economic challenges in this industry, especially under the constraints of international sanctions, necessitate a tailored approach. By customizing these methods to address the unique dynamics of Iran’s automotive industry, our research not only validates the effectiveness of these tools, but also extends their applicability to contexts that have not been sufficiently explored in prior studies.

In previous studies, various factors have been identified to play a significant role in the selection of suppliers, including cost and flexibility [48]. The results of this study—that flexibility is the most valuable economic factor—corroborate the idea that to be flexible in supply chains is crucial given today’s dynamic market and fluctuating demand, which directly influence firms [49].

The approximate estimations of the weights obtained for the factors included in the HFMM analysis highlight the intensified focus on environmentally friendly material as the most significant environmental factor, which is in line with the studies conducted by Iqbal et al. (2020) and Govindan et al. (2020), who point out the importance of material selection in the minimization of environmental impact in supply chains [50,51]. Indeed, the current strategy of organizations seeking to employ sustainable practices to address set regulations in line with consumer demand is supported by this finding [52].

The arguments made in this paper as to why employee and customer satisfaction was identified as the highest-weighted social factor find support from Stojanovic et al. (2020), who showed that the implementation of socially responsible measures makes a company more reputable and customers more satisfied. This has some support from the perception that incorporating focus on social elements can fair well and enhance competitiveness [53].

Implementing decision-specific controls was particularly advantageous for situating this study within the specific context of the Iranian automobile sector, which is influenced by many environmental and regulatory issues unique to it. Banihashemi et al., 2023, [54] and Soufi et al., 2023, [55] noted that a previous study that examined similar situations placed attention on the potential elements that impact green supply chain management (GSCM) programs in the industry. Therefore, this study contributes to the existing body of research by pinpointing specific problems and concerns, as well as outlining particular strategic paths within the context of the Iranian scenario [54,55].

The noticed challenges, like the absence of voluntary ISO 14000 adoption, the absence of formulated environmental goals, and also the lack of sufficient senior management support for environmental issues, align with the barriers highlighted in recent studies, as outlined by Dasanayaka et al., 2022. These problems highlight the need to improve regulations and increase organizational dedication to implementing environmentally friendly supply chains [56].

The primary objective is to overcome these obstacles in order to achieve the integration of GSCM schemes. This approach differs from the research conducted by Rahman et al. (2020) and Yassin et al. (2022), who suggest implementing a connected system that successfully overcomes all the described barriers [57,58].

Social and environmental aspects, as well as SP performance, act as a reminder of what is important in the long run; hence, the satisfaction of these three aspects is important for the realization of long-term success. Therefore, the results of this research also support previous discoveries that economic, environmental, and social factors are essential in selecting suppliers, and that more efforts should be made to understand how to overcome certain barriers in order to establish GSCM [59,60]. Through a related literature comparison of this research to recent studies on the subject under consideration, this study helps to expand the understanding of what GSCM activities are and how they can apply to the example of the Iranian automobile industry.

6. Conclusions

The research and findings of the study described in this article relate to the Iranian automotive sector only. The authors also recommend that more research be conducted on the population for the generalization of these results to other settings or sectors.

Therefore, the main impediments in GSCM practice are the absence of general and specific environmental concern from organizations and suppliers, no environmental goals and future visions being included in organizations’ strategies, and top management support being inadequate. These are the factors that are essential in creating an environment for the implementation of GSCM practices, and thus should be given preference by organizations.

The research also emphasizes the social paradigm, thereby stressing that people such as workers and customers are strategic resources for business development. In view of this, we propose that for organizations to effectively enhance public interest in supply chain management, it is important to evaluate social costs, service costs, and environmental values positively in order to establish a strong supply chain system. As a result, management actions and organizational commitment to environmental values could have a high influence on a firm’s performance.

While it may cost firms to conform to legal requirements with respect to the environment, the firm’s advantage it accrues from sustainability is monumental. We noticed that suppliers seem to have less concern for profit and are more concerned with society and the environment; this is a sign that suppliers are adopting sustainable business practices.

Finally, it is accepted that the administrative business context of the subject is equally important for the management of various types of organizations and for making important decisions of an economic nature. But in this regard, and despite business interests, economic gains and profitability are seen as subordinate to social and ecological concerns. This implies that, although the financial aspect should always be considered, organizations cannot ignore sustainable and socially responsible features. Thus, the key to implementing the benefits of GSCM is to maintain these factors in balance, accepting slight increases in efficiency indicators of one or two percent, if this is the strategy.

Author Contributions

Conceptualization, J.S. and D.Š.; methodology, J.S.; software, A.B.; validation, J.S., D.Š. and B.B.; formal analysis, J.S.; investigation, J.S.; resources, J.S.; data curation, A.B.; writing—original draft preparation, J.S.; writing—review and editing, D.Š.; visualization, A.B.; supervision, D.Š.; project administration, J.S.; funding acquisition, D.Š. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Tables

Table 1

Linguistic scale of importance.

Intensity of Importance	Definition
1	Equal importance
2	Weak
3	Moderate importance
4	Moderate plus
5	Strong importance
6	Strong plus
7	Very strong or demonstrated importance
8	Very, very strong
9	Extreme importance

Table 2

Indicators of a green supply chain.

Indicator	Definition	References
Carbon footprint	The total amount of greenhouse gases emitted directly or indirectly by a supplier’s activities.	[2,24,31]
Cleaner production	The use of processes and methods by a supplier that minimize waste and emissions.	[24,27,31]
Cost	The financial expenditure required to procure goods or services from a supplier.	[24,30,37]
Employee and customer satisfaction	The level of satisfaction among a supplier’s employees and customers, often linked to ethical and sustainable practices.	[24,27,37]
Energy saving	Measures taken by a supplier to reduce energy consumption in their operations.	[2,24,37]
Environmental competencies	The skills and expertise a supplier has in implemmenting and managing environmentally sustainable practices.	[2,24,30]
Environmental costs	The expenses incurred by a supplier related to environmental management and sustainability practices.	[24,27,30]
Environmental standards compliance	Adherence to environmental regulations and standards by a supplier.	[2,24,35]
Flexibility	The ability of a supplier to adapt to changes in demand and supply chain conditions.	[2,25,27]
Green and safe product	Products that are environmentally friendly and safe for consumers.	[2,25,27]
Green design	Designing products and processes with a focus on reducing environmental impact.	[2,24,35]
Green mental image	The perception of a supplier as environmentally responsible and committed to sustainability.	[25,27,37]
Innovation dynamics	The capacity of a supplier to innovate and improve processes and products continuously.	[25,30,37]
Management support of green practices	Commitment from a supplier’s management to implement and support environmentally friendly practices.	[25,29,30]
Material saving	Efficient use and management of materials to reduce waste and resource consumption.	[24,29,30]
Pollution control (pollution prevention)	Proactive strategies employed by a supplier to prevent pollution rather than control it after it occurs.	[2,24,30]
Quality (product and service quality)	The standard of products and services provided by a supplier, meeting customer expectations and requirements.	[2,24,39]
Responsiveness	The speed and efficiency with which a supplier can respond to customer needs and supply chain disruptions.	[24,30,37]
Reuse application	The ability of a supplier to implement practices that allow for the reuse of materials and products.	[2,24,30]
Supplier risk	The potential risks associated with a supplier, including environmental, financial, and operational risks.	[2,24,30]
Use of clean technology	Implementation of technologies that produce minimal environmental impact.	[2,24,30]
Using environmentally friendly (green) materials or renewable materials	The utilization of materials that are sustainable and have a reduced environmental footprint.	[2,24,25]

Table 3

Delphi method final scores.

Indicators	Average
Flexibility	7.71
Green and safe product	7.59
Green design	7.53
Cost	7.29
Use of clean technology	7.29
Pollution control (pollution prevention)	7.24
Green mental image	7.18
Quality (product and service quality)	7.18
Environmental costs	7.12
Employee and customer satisfaction	7.06
Using environmentally friendly materials (green) or renewable materials	7.06
Environmental competencies	7
Supplier risk	6.76
Management support of green practices	6.47
Innovation dynamics	6.35
Cleaner production	6.29
Material saving	6.29
Reuse application	6.24
Environmental standards compliance	6.18
Carbon footprint	5.53
Energy saving	5.12
Responsiveness	5.12

Table 4

Suppliers’ opinions on main factors.

	Economic	Environmental	Social
Economic	1	0.167	0.112
Environmental	6	1	0.25
Social	9	4	1
Sum	16	5.167	1.362

Table 5

Main factors—weighted matrix.

	Economic	Environmental	Social	w
Economic	0.0625	0.03232	0.08223	0.05902
Environmental	0.375	0.19354	0.18355	0.2507
Social	0.5625	0.77414	0.73421	0.69029

Table 6

Pairwise matrix of economic factor.

Economic	Cost	Quality (Product and Service Quality)	Flexibility
Cost	1	0.167	0.125
Quality (Product and Service Quality)	6	1	0.112
Flexibility	8	9	1
Total Sum	15	10.167	1.237

Table 7

Pairwise matrix of the environmental factor.

Environmental	Environmental Costs	Green Design	Environmental Qualifications	Pollution Control (Preventing Pollution)	Green and Safe Products	Green Mindset	Use of Clean Technology	Use of Environmentally Compatible (Green) Materials or Renewable Materials
Environmental Costs	1	0.143	0.125	0.112	0.125	0.112	0.112	0.112
Green Design	7	1	0.112	0.143	0.167	0.125	0.125	0.167
Environmental Qualifications	8	9	1	0.2	0.125	0.167	0.167	0.125
Pollution Control (Preventing Pollution)	9	7	5	1	0.125	0.125	0.143	0.167
Green and Safe Products	8	6	8	8	1	0.112	0.2	0.167
Green Mindset	9	8	6	8	9	1	1.7	1.7
Use of Clean Technology	9	8	6	7	5	7	1	0.2
Use of Environmentally Compatible (Green) Materials or Renewable Materials	9	6	8	6	6	7	5	1
Total Sum	60	45.143	34.237	30.455	21.542	15.641	8.447	3.638

Table 8

Pairwise matrix of the social factor.

Social	Social Responsibility	Work Safety and Health of Human Resources (and Security of Human Resources)	Employee and Customer Satisfaction
Social responsibility	1	0.112	0.112
Work safety and health of human resources (and security of human resources)	9	1	0.125
Employee and customer satisfaction	9	8	1
Total	19	9.112	1.237

Table 9

Weighted matrix of the economic factor.

Economic	Cost	Quality (Product and Service Quality)	Flexibility	w
Cost	0.06667	0.01643	0.10105	0.06138
Quality (Product and Service Quality)	0.4	0.09836	0.09054	0.1963
Flexibility	0.53333	0.88522	0.80841	0.74232

Table 10

Weighted matrix of the environmental factor.

Environmental	Environmental Costs	Green Design	Environmental Qualifications	Pollution Control (Preventing Pollution)	Green and Safe Products	Green Mindset	Use of Clean Technology	Use of Environmentally Compatible (Green) Materials or Renewable Materials	w
Environmental Costs	0.01667	0.00317	0.00365	0.00368	0.0058	0.00716	0.01326	0.03079	0.07017
Green Design	0.11667	0.02215	0.00327	0.0047	0.00775	0.00799	0.0148	0.0459	0.076
Environmental Qualifications	0.13333	0.19937	0.02921	0.00657	0.0058	0.01068	0.01977	0.03436	0.09606
Pollution Control (Preventing Pollution)	0.15	0.15506	0.14604	0.03284	0.0058	0.00799	0.01693	0.0459	0.10665
Green and Safe Products	0.13333	0.13291	0.23367	0.26268	0.04642	0.00716	0.02368	0.0459	0.13326
Green Mindset	0.15	0.17722	0.17525	0.26268	0.41779	0.06394	0.20126	0.46729	0.16464
Use of Clean Technology	0.15	0.17722	0.17525	0.22985	0.23211	0.44754	0.11839	0.05498	0.17145
Use of Environmentally Compatible (Green) Materials or Renewable Materials	0.15	0.13291	0.23367	0.19701	0.27853	0.44754	0.59193	0.27488	0.21418

Table 11

Weighted matrix of social factor.

Social	Social Responsibility	Work Safety and Health of Human Resources (and Security of Human Resources)	Employee and Customer Satisfaction	w
Social responsibility	0.05263	0.01229	0.09054	0.05182
Work safety and health of human resources (and security of human resources)	0.47368	0.10975	0.10105	0.22816
Employee and customer satisfaction	0.47368	0.87796	0.80841	0.72002

Table 12

Suppliers’ opinions on factors based on MOORA technique.

	Cost	Quality (Product and Service Quality)	Flexibility	Environmental Costs	Green Design	Environmental Competencies	Pollution Control (Pollution Prevention)	Green and Safe Product	Green Mental Image	Use of Clean Technology	Using Environmentally Friendly Materials (Green) or Renewable Materials	Employee and Customer Satisfaction
Supplier 1	7	9	9	1	1	2	2	2	1	1	2	8
Supplier 2	5	8	5	1	1	2	2	2	1	1	2	6
Supplier 3	8	7	9	2	4	5	4	5	2	5	4	5
Supplier 4	6	8	5	3	2	6	3	6	3	4	5	6
Supplier 5	8	6	7	5	3	3	2	5	4	6	4	5
Sum of squares	238	294	261	40	31	78	37	94	31	79	65	186
The root of the sum of squares	15.4272	17.1464	16.1555	6.3246	5.5678	8.8318	6.0828	9.6954	5.5678	8.8882	8.0623	13.6382

Table 13

MOORA technique weighting.

MOORA
	Cost	Quality (Product and Service Quality)	Flexibility	Environmental Costs	Green Design	Environmental Competencies	Pollution Control (Pollution Prevention)	Green and Safe Product	Green Mental Image	Use of Clean Technology	Using Environmentally Friendly Materials (Green) or Renewable Materials	Employee and Customer Satisfaction	Total	Ranking
Supplier 1	0.4537	0.5249	0.5571	0.1581	0.1796	0.2265	0.3288	0.2063	0.1796	0.1125	0.2481	0.5866	3.7617	4
Supplier 2	0.3241	0.4666	0.3095	0.1581	0.1796	0.2265	0.3288	0.2063	0.1796	0.1125	0.2481	0.4399	3.1795	5
Supplier 3	0.5186	0.4082	0.5571	0.3162	0.7184	0.5661	0.6576	0.5157	0.3592	0.5625	0.4961	0.3666	6.0425	2
Supplier 4	0.3889	0.4666	0.3095	0.4743	0.3592	0.6794	0.4932	0.6189	0.5388	0.45	0.6202	0.4399	5.8389	3
Supplier 5	0.5186	0.3499	0.4333	0.7906	0.5388	0.3397	0.3288	0.5157	0.7184	0.6751	0.4961	0.3666	6.0716	1

Table 14

Reference-point matrix.

Reference Point
ri	0.5186	0.5249	0.5571	0.7906	0.7184	0.6794	0.6576	0.6189	0.7184	0.6751	0.6202	0.5866
	Cost	Quality (Product and Service Quality)	Flexibility	Environmental Costs	Green Design	Environmental Competencies	Pollution Control (Pollution Prevention)	Green and Safe Product	Green Mental Image	Use of Clean Technology	Using Environmentally Friendly Materials (Green) or Renewable Materials	Employee and Customer Satisfaction	Max	Rank	Rank Min
Supplier 1	0.0648	0	0	0.6325	0.5388	0.4529	0.3288	0.4126	0.5388	0.5625	0.3721	0	0.6325	1	4
Supplier 2	0.1945	0.0583	0.2476	0.6325	0.5388	0.4529	0.3288	0.4126	0.5388	0.5625	0.3721	0.1466	0.6325	1	4
Supplier 3	0	0.1166	0	0.4743	0	0.1132	0	0.1031	0.3592	0.1125	0.124	0.22	0.4743	3	3
Supplier 4	0.1296	0.0583	0.2476	0.3162	0.3592	0	0.1644	0	0.1796	0.225	0	0.1466	0.3592	4	2
Supplier 5	0	0.175	0.1238	0	0.1796	0.3397	0.3288	0.1031	0	0	0.124	0.22	0.3397	5	1

Table 15

The final MULTIMOORA matrix.

MULTIMOORA
Suppliers	Ranking
Supplier 1 (Iran Tractor Manufacturing Company (ITMCO))	4
Supplier 2 (Sazehgostar)	5
Supplier 3 (Crouse Company)	2
Supplier 4 (MEGA Motor)	3
Supplier 5 (SAPCO)	1

Table 16

ARAS final ranking.

	S	K	K	Ranking
0-Optimal Value	0.2357	1.0000	1.0000
Supplier 1	0.1148	0.4870	0.4870	4
Supplier 2	0.0971	0.4119	0.4119	5
Supplier 3	0.1857	0.7878	0.7878	2
Supplier 4	0.1794	0.7611	0.7611	3
Supplier 5	0.1873	0.7944	0.7944	1

Table 17

TOPSIS final ranking.

Relative Closeness to the Ideal Solution	Score	Ranking
Supplier 1	0.2223	4
Supplier 2	0.0879	5
Supplier 3	0.5979	2
Supplier 4	0.5931	3
Supplier 5	0.6553	1

Table 19

Social Factor Weight Variation.

Configuration	Supplier 1	Supplier 2	Supplier 3	Supplier 4	Supplier 5	Ranking
Decreased 20%	4	5	2	3	1	5, 3, 1
Original	4	5	2	3	1	5, 3, 1
Increased 20%	4	5	2	3	1	5, 3, 1

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