Green Supplier Selection in the Automobile Industry: A Comprehensive Framework for Multi-Criteria Decision Analysis

doi:10.21203/rs.3.rs-2468222/v1

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Green Supplier Selection in the Automobile Industry: A Comprehensive Framework for Multi-Criteria Decision Analysis

https://doi.org/10.21203/rs.3.rs-2468222/v1

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This research proposes a comprehensive framework to evaluate green suppliers. A real-world green supplier selection (GSS) problem is considered in which three India-based automobile manufacturing organizations, namely Supplier 1, Supplier 2, and Supplier 3 are selected for the evaluation. Initially, fourteen important criteria are extracted from literature and experts’ suggestion, which encompasses both the environmental, economic, and operational aspects of sustainability. Firstly, data is collected through the development of a questionnaire, formation of an expert committee, and carrying out interviews with the experts. Secondly, an integrated multi-criteria decision-making (MCDM)-multivariate approach is proposed in which the item analysis is used to examine the internal consistency of data, the principal component analysis (PCA) is used to calculate the weights of the parameters. Finally; the simple additive weighting (SAW) method is used to determine the performance scores of the case organizations for ranking and benchmarking purpose. The result reveals that Supplier 3 is the benchmark supplier as it gains the highest performance score. The result also recommends that collaboration with suppliers for green purchasing is the most influential parameter for GSS. This research may assist policymakers to strategically select the optimum green supplier for sustainability.

Green supplier selection

green supply chain management

principal component analysis

multi-criteria decision-making

sustainability

The socio-economic fabric of any nation explicitly depends upon the outgrowth of its manufacturing industries [1]. Among various manufacturing industries, automobile industry plays a key role in various fields of human civilization, including the field of sustainability. The automobile industry is one of the world’s fastest growing economic sectors. Aforesaid industry is rapidly evolving in several directions, including technological innovation, the deployment of stronger regulatory norms, and structural reforms in response to ever-changing customer requirements [2]. In recent times, emerging economies such as China, India, and Brazil have experiencing drastic economic growth which can be witnessed from the radical expansion of the worldwide market of their domestic automobile sectors [3]. Basically, automobile industry is a resource and carbon intensive industry, which dispenses a huge number of offensive pollutants and hazardous waste into the environment that are potentially harmful to the environment as well as human hygiene [4]. Another side of the narrative is that the road transportation sector is accountable for around 25% of entire global carbon emissions from fossil fuels and 50% of global oil consumption [5]. It is anticipated that by 2030, this sector will have the greatest influence on climate change [6]. This emphasizes the world's current technological and environmental imbalance. Due to the thriving environmental consciousness and escalating pressures from government, stakeholders, and market competitors, it becomes imperative for automobile manufacturing organizations to consider green supply chain management (GSCM) principles into their organizational policies [7].

By incorporating various environmentally-friendly practices, GSCM aims to reduce environmental footprints and eliminate waste during supply chain activities, starting from procurement, design, manufacturing, packaging, transporting, distributing, and finally product disposal after end-user consumption [8]. However, prior research proved that both financial and environmental performance can be improved significantly by implementing various GSCM practices, such as green purchasing, green design, green manufacturing, green transportation, green marketing, and so on [9, 10, 62]. An organization’s GSCM performance depends on not only its internal green initiatives but also its suppliers’ green practices [11]. As organizations tend to outsource a variety of jobs, GSS, as an integral part of GSCM, has a critical role in guiding organizations to maintain their strategic stance in market [12]. It has been proven that GSS can noticeably reduce the environmental burdens of organizations in terms of waste generation and carbon dioxide emissions [13]. Therefore, evaluation and selection of green suppliers is a pressing task in the automobile industry.

Sometimes, GSS involves many complex variables like multiple conflicting criteria, large number of alternatives, and unstructured environment, forming a typical multi-objective optimization problem wherein, MCDM is considered to be one of the suitable options for solving those problems [14]. Solution of MCDM problems comprises three stages: development of an evaluation framework, determination of criteria weight, and assessment of the performances of alternatives [15]. In case of GSS framework, rigorous literature review and experts’ opinion is required to identify the most suitable evaluation criteria, followed by application of various screening methods (such as the Delphi method [16], PCA [17], and data mining [18] etc.). In MCDM, calculation of criteria weight is of utmost important as it influences the alternative ranking significantly. Basically, the methods for calculating criteria weight can be classified into two types. The first type is subjective method, which calculates weight using several computing approaches carried out through experts’ interview. Few common methods in this segment include the analytic hierarchy process (AHP) [19], best-worst method (BWM) [20], and decision-making trial and evaluation laboratory (DEMATEL) [21]. The second type is objective method, in which criteria weight is determined through various mathematical calculations. In this case, the performance ratings of the alternatives are utilized to train a set of criteria weight, called objective weight. Few popular methods in this category include, Shannon’s entropy method [22], criteria importance through intercriteria correlation (CRITIC) [23] etc. On the other hand, literature has witnessed various alternative performance measurement methods such as; technique for order preference by similarity to an ideal solution (TOPSIS) [24], complex proportional assessment (COPRAS) [25], multi-objective optimization on the basis of ratio analysis (MOORA) [26], Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) [27], and so on.

In spite of the research being conducted by various researchers to address GSS problems, there are still a few limitations. Two major limitations that must be addressed are the very time-consuming nature of their methods, as well as the inconsistency issue that appears as the number of alternative and criteria increases. Unfortunately, no significant effort has been imparted in the extant literature to evaluate and improve the consistency and reliability of research data used for GSS. In a noteworthy contribution, Ray et al. [28] argued that in order to attain accurate decision, research data has to be consistent, complete, relevant, and trustworthy. Relying on random, redundant, and irrelevant research data; leads to inclusion of those factors which do not even meet the evaluation standards. Hence, researchers and practitioners should appreciate the augmentative role of supplier evaluation alongside the consistency assessment. Although, in past few years GSS problem has been addressed rigorously by several authors in different industrial segments (such as the iron and steel industry [29], agro-food industry [30], construction industry [31], oil and natural gas industry [32], textile industry [33], automobile industry [34], pharmaceutical industry [35], energy sector [46]), empirical analysis in automobile sector is still insufficient, particularly in the context of developing countries. Despite the abundance of studies related to GSS models based on economic criteria, studies that simultaneously consider environmental, economic, and operational criteria are rather limited. In addition, till date, no article has developed GSS model considering overall GSCM performance based on environmental, economic, and operational criteria in automobile industry. While prior studies concentrated mainly on the developed nations [14, 36–43], very limited research attention has been imparted to developing countries [3, 34]. To bridge those knowledge gaps, this study attempts to answer the following research questions (RQs):

RQ.1 How to check the consistency of the research data in GSS problems?

RQ.2 What are the important evaluation criteria for GSS in automobile industry?

RQ.3 Which criteria seems to be more influential for GSS?

RQ.4 How the performance of alternatives (supplier organizations) be evaluated and which one is the benchmark supplier?

In order to address the aforesaid research questions, this study aims to put forward a comprehensive GSS framework by considering both environmental, economic, and operational criteria. Initially, fourteen important GSS criteria are identified from literature and experts’ suggestions. Thereafter, an integrated multivariate-MCDM approach is applied, in which the item analysis is used to check the internal consistency of the dataset, PCA is used to determine the weight of the criteria, and SAW method is utilized to calculate the performance score of the supplier organizations for final ranking. Three India-based automobile manufacturing organizations are selected to check the competency of the proposed GSS framework. The reason behind combining those methods are: (i) the amalgamation of multivariate analysis and MCDM approaches can deal with complex decision-making problems more effectively than any standalone approach because of its capability of examining more than one element of independent variables that impact the variability of dependent variables, (ii) the integrated approach has less computational complexity and can accommodate any number of criteria and alternatives where item analysis checks the consistency of the data prior to the actual analysis, which ensures that the decision is reliable. Although the integrated approach can provide a legible, rational, and accurate solution for real-life decision-making problems which has not been used previously by any of the author. This research attempts to fill this gap by utilizing this approach for the evaluation of green suppliers in the context to automobile industry.

The remainder of this research is organized as follows: research background is depicted in section 2. Section 3 narrates research design. Section 4 shows the application of the proposed framework along with the interpretation of results and discussions. Comparison of the key findings with the existing research is delineated in section 5. Finally, section 6 concludes the paper by highlighting research implications, limitations and future scope of this research.

Regarding the scope of this research, research background section has three main sub-sections; namely green supplier selection, green supplier evaluation criteria, and green supplier selection methods respectively.

2.1 Green supplier selection

Supply chain management consist of various stages from the raw-material purchasing to delivery of final products and in each stage, it requires potential suppliers. Selection of potential suppliers is one of the decisive variables for organizational sourcing decisions [36]. The most crucial decision-making problem often confronted by the purchase department is the appropriate evaluation and selection of suppliers which satisfies the basic business needs [37]. Therefore, supplier selection (SS) must fulfill multiple business requirements providing a competitive stance to minimize cost, enhance quality or reduce detrimental environmental impact [38]. Traditional SS primarily focuses on profit maximization, with less priority given to environmental issues [39]. But in recent times, due to rising concern about the rapid depletion of natural resources, organizations become more committed to offering their products, processes, and services in a sustainable way [40]. GSS is often recognized as a construct that can assess suppliers’ performance along with green technical standards [41]. Literature [42, 43] affirmed that GSS plays a pivotal role for manufacturing organizations to achieve competitive advantages and long-term environmental sustainability. However, GSS can significantly reduce environmental footprints from supply chain operations whilst improving financial progress [44]. Zhang et al. [45] systematically reviewed the literature of various models, methods, and applications in the field of GSS over the period of 2009–2020 and claimed that the theme of GSS has gained considerable attention over the last decade as corporations began to focus on environmental performances in their purchasing decision due to stricter legislation from government and pressure from several stakeholders.

2.2 Evaluation criteria for green supplier selection

The choice of evaluation criteria plays a critical role in GSS. Selection of the optimum supplier involves numerous criteria that are both qualitative and quantitative in nature. It was observed by Zhang et al. [45] that GSS requires a combination of traditional supplier selection criteria and green supplier evaluation criteria.

2.2.1 Traditional criteria for supplier selection

During the time period 1966 to 1990, majority of the prior research considered quality, capacity, cost, delivery time as the popular criteria for SS [47]. According to Cheraghi et al. [48], aforesaid timeframe can be deduced into three periods: in the early 1980s, ‘cost’ was the dominant criteria in SS because the soul objective of the organizations was to gain profit. Later, in the early 90s, ‘delivery’, ‘lead time’, and ‘consumer demand’ became the significant criteria. Afterwards, in the late 90s, the focus is shifted to criteria like ‘service level’, ‘quality’, ‘flexibility’, ‘production capacity’, and ‘reliability’. After summing up the traditional criteria, it is found that ‘cost’, ‘quality’, and ‘delivery’ remain the frequently used criteria in the literature.

2.2.2 Green criteria for supplier selection

Although, conventional criteria are dominant in the SS literature, green criteria are gaining research interest now-a-days. Noci et al. [49] integrated green thinking into SS process for the first time and suggested that both the environmental and economic performance of the suppliers should be assessed simultaneously while making GSS decision. Afterwards, green criteria are being used as a supplement to the traditional criteria in the SS process [50]. While, traditional criteria are mainly economy-focused; green criteria consider various environmental criteria (emission control systems, hazardous waste disposal, environmental management system, ISO certification, utilization of resources), economic criteria (total energy consumption, product cost, investment in research and development), and operational criteria (eco-design, green manufacturing technologies, process optimization, minimizing inventory) concurrently. One of the research works by Govindan et al. [50] revealed that ‘environmental management system’, ‘total carbon emission’, ‘green technical competence’, and ‘recycling and remanufacturing; are the frequently used criteria in GSS. Some of the evaluation criteria considered by previous researchers along with their application area is depicted in Table 1.

Table 1

Evaluation criteria for GSS
Author(s)	Area of Application	Criteria considered
Hendiani et al. [14]	Large refineries in southern Iran	Employee and stakeholders’ rights, health and safety, staff training, cost, quality, supplier capacity, green involvement, energy conservation, environmental management and policies etc.
Liu et al. [51]	Agri-food supply chain in France	Environmental management system, pollution control, resource consumption, pollution, production, eco-design, green competence, health and safety, stakeholder’s influence
Gupta et al. [2]	Indian automotive industry	Environmental Management System (EMS), pollution control, green image, staff environment training, eco-design, resource consumption, price, quality, service level
Li et al. [38]	Chinese state-owned energy sector	Quality improvement, cost reduction, on time delivery, green design, green purchasing, green logistics, safety measures, community activities etc.
Memari et al. [52]	Catalytic converter manufacturer	Cost, quality, environmental efficiency, green image, pollution reduction, green competencies, safety and health, employment practices, service performance.
Guarnieri et al. [36]	Textile industry in Brazil	Cost, quality, on-time delivery, flexibility, environmental impact, reverse logistics, environmental management, eco-friendly packaging etc.
Alikhani et al. [53]	Iranian supermarket chain	Cost, quality, technology capabilities, green design, environmental management system, environmental competence, interests and rights of employees, social management commitment
Kumar et al. [54]	Indian automotive industry	Emission minimization, packaging improvements, renewable energy, waste minimization, reverse logistics, green purchasing, green designing
Hussain et al. [40]	Service firms in UAE	Environmental management, social responsibility, green product, technology standards, health and safety management, competitiveness.
Faisal et al. [55]	Large white goods manufacturers	Quality, cost, lead time, flexibility, waste reduction, recycling, environmental purchasing, reverse logistics, ethics, health and safety etc.
Chen et al. [39]	Luminance enhancement film industry in Taiwan	Cost, quality, delivery, technology, flexibility, financial capability, cultural innovativeness, relationship, pollution production, pollution control, resource consumption, eco-design, environmental management system, green image, green competencies, green product, staff environmental training, management commitment, green technology
Luthra et al. [34]	Indian automobile industry	Price, quality, flexibility, lead time, transportation cost, green design, green manufacturing, green packaging, waste management, environmental competencies, occupational health and safety etc.
Freeman et al. [56]	Electronics industry in Hangzhou	Cost, green competency, clean production technologies, quality, on-time delivery, waste management, iso 14001 certification etc.
Banaeian et al. [57]	Food and packaging industry	Capital, transportation cost, communication system, lead time, quality, production capacity, eco-friendly material, environmental planning, technical capabilities etc.
Orji et al. [58]	Gear manufacturing company	Price, on time delivery, quality, flexibility, green design, recycling, resource consumption, green product, pollution control etc.
Bhattacharya et al. [41]	UK based carpet manufacturing company	Reduction of emission, reduction of usage of harmful material, reduction of accidents, recycle of materials, energy consumption, cost of procurement, water usage, reduction of disposal cost, reduction of waste, optimum design, minimum inventory, improved quality
Awasthi et al. [42]	City logistics projects in France	Use of environment friendly materials, management commitment, adherence to environmental policies, staff training, design for environment, environmental certification, pollution control initiatives

2.3 Methods for selecting green suppliers

After reviewing the numerous studies related to decision-making problems in GSS, it is found that various methods have been employed to solve complex decision-making problems, in which every method has certain advantages and drawbacks [50]. The main limitation in most of the cases is that the outcomes are basically compromising the multiple objectives and are not the optimum solution due to the dynamic and uncertain nature of the problem. On the contrary, the notable benefit of the selected methods is the capability to consider multi-faceted, conflicting, and uncertain cases of decision-making explicitly [59]. Selecting the optimal green suppliers is often considered as a MCDM problem and in recent times, a lot of GSS models based on MCDM approaches have been developed. Various GSS methods are classified in the following categories.

2.3.1 Independent methods

Chai et al. [60] found that almost 80% of the published decision-making models are based on single or individual methods due to (i) the complexity involved with modeling and computation of integrated approaches, (ii) the relatively lower provenance of hybrid methods. Independent methods are relatively easy to understand and implement. These methods primarily consider limited number of variables and alternatives for evaluation. Some of the popular methods in this category are: AHP [19], analytic network process (ANP) [55], data envelopment analysis (DEA) [61], TOPSIS [62], quality function deployment (QFD) method [63], preference ranking organization method for enrichment of evaluations (PROMETHEE) [64], weighted aggregated sum product assessment (WASPAS) method [37], elimination et choice translating reality (ELECTRE) [65], VIKOR [66], etc. Most of the methods in this category are qualitative in nature. But the main disadvantage of those methods is that they integrate decision makers’ (DMs) subjective preferences for evaluation. Therefore, vagueness cannot be purely eliminated by these methods. However, these methods require pairwise comparisons at various levels, continuous user interface and consistency check.

2.3.2 Hybrid methods

Under this category, more than one stand-alone method is integrated in order to achieve more precise and valid outcomes. Some of the widely used methods are AHP-grey relational analysis (GRA) [67], entropy-TOPSIS [7], AHP-Entropy-TOPSIS [56], AHP-VIKOR [34], AHP-ELECTRE [36], AHP-WASPAS-multi-attributive border approximation area comparison (MACBAC) [2], ANP-grey-decision making trial and evaluation laboratory (DEMATEL) [68], entropy-COPRAS-GRA [11], entropy-TOPSIS-COPRAS-GRA [8], BWM-WASPAS-COPRAS [69], and so on. Unfortunately, the main disadvantage of such methods is failing to cope with various kinds of uncertainties, especially human bias that is common in practice.

2.3.3 Fuzzy methods

Owing to the presence of vagueness and ambiguity, GSS has become much complicated task for decision makers. Previous authors attempted to handle this uncertainty by amalgamating fuzzy set theory [70] with the existing MCDM methods [43], such as fuzzy-DEMATEL [71], fuzzy-best worst method (BWM) [14], fuzzy-AHP [54], fuzzy-TOPSIS [42], fuzzy-ANP [41], fuzzy-AHP-TOPSIS [12], fuzzy-ELECTRE-VIKOR [72], fuzzy-TODIM [73], and so on. Although fuzzy extensions of classical MCDM methods are considered as effective solutions for eliminating imprecision and subjectivity due to human interference in decision-making process, it has some drawbacks like its computational complexity and huge mathematical relationships.

2.3.4 Mathematical methods

Models in this category optimize the tradeoff and interaction among several aspects of interest taking into account restrictions and concerns such as lot sizing [74], dynamic pricing [75], single or multiple sourcing [76], order allocation [77], and quantity discounts [78]. Some of the approaches are linear programming [79], mixed-integer non-linear programming [80], goal programming [81], structural equation modeling (SEM) [40], and so on. The main drawback of such method is if a model result leads to conclusions that aren't similar to the real-world situation, the equations are amended to make up for the differences as much as feasible.

2.3.5 Intelligent methods

These methods embed new frontier and emerging computer technologies into analytical approaches for smart decision-making. Such types of models are fed by historic data or experts’ input. Some models under this category are machine learning and optimization models [82], genetic algorithm [83], artificial intelligence-based models [84], fuzzy inference system [85], support vector machine [86], big data and cloud computing-based models [87], artificial neural networks [88]. The main disadvantage of these methods is that the system's complexity makes it difficult for organizations to deal with the problem satisfactorily without a high level of expertise in complicated computer programming.

2.4 Criticism on existing literature

A thorough reviewing of the literature reveals that there is plenty of research in the field of GSS. But majority of those research are limited to developed countries only. Till date, there is notable deficit of GSS literature in the context of developing countries. The investigation shows that manufacturing enterprises in developed nations have already gained several competitive advantages by implementing GSS and various GSCM initiatives. On the contrary, manufacturing enterprises in the developing countries show too much fragility towards environmental sustainability [41]. However, GSCM implementation is at a very early stage in developing countries due to the presence of several barriers [34]. In current scenario, sustainability has become a major issue especially for the automotive industry, because it is responsible for a large portion of global environmental pollution. Therefore, GSS is becoming an inevitable option for strategic decision-making of the automobile manufacturing organizations [2]. Yet there is a handful of research available on the web where the introduction of GSCM aspects in SS problem has been studied explicitly in the context of automobile industry. This is probably due to the fact that there is a notable paucity of facile frameworks for evaluating green suppliers. Whatever GSS frameworks have been proposed till date, they are conceptual and lacks empirical evidences. In most of the cases, vagueness of information is present and research data is subjective in nature. Furthermore, very rare attempts by the researchers have been made to examine the consistency of data. For instance, while evaluating the suppliers based on various GSCM criteria, majority of the research employed various MCDM tools to determine the weight of evaluation criteria and rank the alternatives accordingly. But little attention has been paid to the reliability assessment of the research data prior to the actual evaluation. To the best of authors’ knowledge, research effort to evaluate green suppliers using combined multivariate analysis and MCDM method is almost non-existent. Most of the existing studies evaluated green suppliers based on the environmental or/and economic dimensions only and integration of environmental, operational, and economic aspects for GSS have been ignored. There is a plenty of research related to GSS in different industrial segments, which explored various performance indicators, evaluation criteria, improvement measures. Yet they do not provide adequate information about the benchmark industry, its strategies, and the most influential criteria for GSS.

The afore-mentioned knowledge gaps clearly corroborate the objectives of the current research.

This research applies an integrated MCDM-multivariate approach as a solution methodology to evaluate green suppliers. In this research, item analysis is used to examine the internal consistency of data, PCA is used to determine the weight of the criteria, and SAW method is applied to determine the performance scores of the supplier organizations and rank them accordingly. A brief overview of these methods and proposed research framework are shown in following sub-sections.

3.1 Item analysis

Item analysis is intended to improve the reliability of dataset within any experiment by assessing different responses to individual test items or parameters [89]. This research uses Cronbach’s alpha (${\alpha }$) method as a measure of internal consistency of the dataset [90]. It is considered as a coefficient of reliability. [28]. The ${\alpha }$ value ranges from 0 to 1. It is recommended that a good ${\alpha }$ value should lie between 0.75 to 0.85 or even higher in many cases [17]. If ${\alpha }$ value is less than 0.75 then the dataset may not be reliable for further analysis, and requires further investigation. A low ${\alpha }$ value could be because of poor inter-relation between items. The $\alpha$ value can be improved by eliminating or introducing more items in the existing dataset. Cronbach’s alpha is calculated using the following equation:

$${\alpha }=\frac{\text{K}}{\text{K}-1}\left[1-\frac{{\sum }_{\text{i}=1}^{\text{N}}{{\sigma }}_{\text{i}\text{i}}}{\sum _{\text{i}=1}^{\text{N}}\sum _{\text{j}=1}^{\text{N}}{{\sigma }}_{\text{i}\text{j}}}\right]$$

where, $\text{K}$ implies the number of test items, ${{\sigma }}_{\text{i}\text{j}}$ is the covariance between ${\text{i}}^{\text{t}\text{h}}$ item and ${\text{j}}^{\text{t}\text{h}}$ item and ${{\sigma }}_{\text{i}\text{i}}$ is the variance of the ${\text{i}}^{\text{t}\text{h}}$ item.

3.2 PCA method

PCA is a dimensionality-reduction tool typically used convert a large number of correlated variables into a smaller number that retains the majority of the information in the dataset [91]. PCA uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components (PCs). There are as many PCs as there are variables in the dataset [28]. If there is a dataset with $p$ variables, there will be $p$ eigenvalues. DMs can select the first $n$ eigenvalues and discard the remainder to reduce the dimensionality. PCs are built in such a way that the first PC explains the majority of the variation within dataset [17]. Likewise, the second PC is computed in a orthogonal way to the first PC and it accounts for the next largest variation. This procedure is iterated until a total of $p$ main components, equal to the original number of variables. If the cumulative proportion of the variable is less than 0.60, then the further assessment is required. If the proportion is greater than 0.60, then the first PC is responsible for a large proportion of the total variance. If only the first PC is considered, variable weight are calculated by squaring the eigenvalues of the variables corresponding to the first PC. Detail about PCA method can be found in the study by Bhowmik et al. [92].

3.3 SAW method

The SAW method is the simplest and the most commonly used MCDM tool [93]. According to Karni et al. [94], as compared to other prominent MCDM approaches, such as ELECTRE and AHP, the ranking by SAW method does not differ much in real-world practices. If more than one method is selected to evaluate multiple criteria, SAW offers a level ground to interpret and compare [95]. Besides this, it is a common straightforward approach used in various decision-making process. Additionally, SAW outputs are very comprehensive for managers as compared to other similar approaches [94]. The procedural steps of the SAW method are delineated below.

Step 1: Form a decision matrix ( $X$ )

This method begins with the formation of a decision matrix (Eq. 2), including the criteria and alternatives. First, a decision matrix ($\text{m}\times \text{n}$) that includes $\text{m}$ alternatives (organizations) and $\text{n}$ attributes (criteria) is constructed. The performance value of each alternative with respect to each criterion in an MCDM problem is expressed in the following manner:

for (i = 1, 2, …., m; j = 1, 2, ……. n),

where, ${\text{x}}_{\text{i}\text{j}}$ implies the performance measures of the ${\text{i}}^{\text{t}\text{h}}$ alternative on the ${\text{j}}^{\text{t}\text{h}}$criteria; hence, ${\text{x}}_{\text{i}\text{j}}$ is the performance value of the ${\text{i}}^{\text{t}\text{h}}$ alternative with respect to the ${\text{j}}^{\text{t}\text{h}}$criteria.

Step 2: Normalize the decision matrix [ $R= {\left({r}_{ij}\right)}_{m\times n}$ ]

As the data for the decision matrix ($\text{X}$) comes from multiple sources, it is necessary to normalize it to transform it into a dimensionless matrix to allow various criteria to be compared. The normalized decision matrix ($\text{R}$) represents the relative rating of the alternatives. In general, normalization is carried out separately for beneficial (Eq. 4) and non-beneficial criteria (Eq. 3). The benefit criterion implies that a higher value is preferable, while the non-beneficial criterion means that a lower value is better.

For non-beneficial criteria, ${\text{r}}_{\text{i}\text{j}}=\frac{{\text{x}}_{\text{i}}^{\text{m}\text{i}\text{n}}}{{\text{x}}_{\text{i}\text{j}}}$ with i = 1, 2, …., m; j = 1, 2, ……. n (3)
For beneficial criteria, ${\text{r}}_{\text{i}\text{j}}=\frac{{\text{x}}_{\text{i}\text{j}}}{{\text{x}}_{\text{i}}^{\text{m}\text{a}\text{x}}}$ with i = 1, 2, …., m; j = 1, 2, ……. n (4)

Step 3: Calculate the weighted normalized decision matrix [ $C= {\left({C}_{ij}\right)}_{m\times n}$ ]

The weighted normalized decision matrix ($\text{C}$) is calculated (Eq. 5) by multiplying the decision matrix ($\text{R}$) by the weight vector ($\text{W}$). The weight vector $\text{W}$ is composed of the individual weight ${\text{w}}_{\text{j}}$ ($\text{j}=1, 2,\dots \dots , \text{n})$ for each criterion satisfying${\sum }_{\text{j}=1}^{\text{n}}{\text{w}}_{\text{j}}=1$. In this case, ${\text{w}}_{\text{j}}$ is obtained from the PCA. ${\text{C}}_{\text{i}\text{j}}={\text{w}}_{\text{j}}\times {\text{r}}_{\text{i}\text{j}}$ (5)

Step 4: Calculate the performance score ( ${P}_{i}$ ) of the alternatives

The performance scores of the alternatives are calculated using Eq. (6):

$${\text{P}}_{\text{i}}={\sum }_{\text{j}=1}^{\text{m}}{\text{C}}_{\text{i}\text{j}}, \text{i}=1, 2, 3, \dots \dots \dots , \text{n}$$

Step 5: Select the optimal alternative (${\text{O}\text{A}}_{\text{S}\text{A}\text{W}}$)

Finally, the optimal alternative that has the maximum ${\text{P}}_{\text{i}}$ value is selected (Eq. 7):

$${\text{O}\text{A}}_{\text{S}\text{A}\text{W}}=\text{m}\text{a}\text{x}{\left.{(\text{P}}_{\text{i}}\right|}_{\text{i}=1}^{\text{n}})$$

where ${\text{O}\text{A}}_{\text{S}\text{A}\text{W}}$ is the optimal alternative in the SAW method and ${\text{P}}_{\text{i}}$ is the performance score. The performance scores of the alternatives are used for the final ranking.

3.4 Proposed research framework

This research is manifested in three steps as shown in Fig. 1. In the first step, important criteria for GSS are identified from the literature and the case organizations are selected for GSCM performance evaluation. Next, a questionnaire is developed in order to extract necessary information for this research. In Step 2, an expert committee is formed which consists of various industry professionals and executive persons from the selected organizations. Then, industry visit and structured interviews are carried out in order to gather relevant data. In Step 3, the collected data is analyzed using an integrated decision-making approach as shown in Fig. 2.

This research considers a real-world GSS problem in Indian automobile industry to show the aptness of the proposed research framework. The Indian automobile industry is a prime driver of macroeconomic growth and technological advancement. India is expected to emerge as the world’s third-largest automobile market in terms of volume by 2026 [96]. In the financial year 2020-21, the total production of vehicles including passenger vehicles, commercial vehicles, three-wheelers, and four-wheelers in India was around 22.70 million units. The Indian automotive industry is expected to reach US$ 300 billion by 2026. The Indian automobile industry turnover encompasses 7.1% of national GDP, 49% of total manufacturing output and provides about 37 million direct and indirect employments [97]. This radical expansion of the Indian automobile industry has put a lot of burden on the environment because vehicle is the core of the automobile industry and they alone can produce 80% of the entire life cycle pollution [2]. Thus, it is the high time for Indian automobile manufacturing organizations to make their supply chain greener with the help of GSS.

The GSS problem formulated in this study considers three India-based automobile manufacturing organizations based on their environmental awareness (ISO 9001, ISO 14001). Initially ten automobile manufacturing organizations were considered but only three organizations shared their enthusiasms for this research. It is worth mentioning that the number of case organizations is not a major issue here. In other words, as the GSS process is carried out using MCDM methods, any number of organizations can be considered for evaluation. In practice, these organizations are striving to attain sustainability in their business by taking several green initiatives, such as, utilizing renewable sources, recycling scrap and wastes, installing emission control devices, using eco-friendly materials, and waste water treatment. The names, locations, and other details of the organizations are kept anonymous in this study due to the privacy policy. Hence, they are termed as ‘Supplier 1’, ‘Supplier 2’, and ‘Supplier C3’ respectively. The brief business profile of the selected organizations is shown in Table 2.

Table 2

Business characteristics of organizations
Suppliers	Turnover (US $- billion)	Type of vehicles produced	Number of associate suppliers	Total number of plants across the country	Number of workers	Total vehicles produced/ year (million)
Supplier 1	26	Two wheelers, CNG Auto	675	6	44,330	10
Supplier 2	32	Commercial vehicles, Trucks	1270	4	14,980	5.4
Supplier 3	40	Heavy duty vehicle, Passenger bus	2150	9	65,780	7.6

The stepwise application of the proposed research framework is discussed below:

Step-1

In this step, essential evaluation criteria of GSS are determined. An extensive literature review is carried out to identify the most important and relevant criteria. A precise GSS process will occur only if an appropriate set of relevant evaluation criteria are identified, verified, and adopted. This is justified because different organizations have distinct supply chain characteristics and the selected criteria must comply with the supply chain characteristics of the case organizations. In order to do so, the rough list of GSS process was discussed with the experts in this domain through some random meetings. The preliminary list of GSS criteria was validated using different decision measures. Referring to the meetings, the experts examined these criteria with their experience in real-time SS process. Although the primary list of evaluation criteria is prepared from the existing literature, the final list is enriched with the experience of experts for solving real-world problems more feasibly. In this research, fourteen important GSCM criteria are extracted from the extant literature and verified by the experts for final evaluation. As discussed earlier, this research intends to incorporate Environmental, economic, and operational criteria into the GSS process, which are enlisted in Table 3.

Table 3

List of criteria
Criteria	Notation	Dimension	Source
Increase in investment for GSCM	P1	Economic	Hendiani et al. [14]
Savings in total cost	P2	Economic	Memari et al. [52]
Effective waste management system	P3	Environmental	Jain et al. [29]
Use of emission control systems	P4	Environmental	Hussain et al. [40]
Use of energy-efficient technologies	P5	Operational	Guarnieri et al. [36]
Reuse of material	P6	Operational	Yazdani et al. [63]
Reduction of scrap materials	P7	Operational	Gupta et al. [2]
Collaboration with suppliers for green purchasing	P8	Environmental	Li et al. [78]
Environmental planning	P9	Environmental	Pourjavad et al. [43]
Application of robust environment policy	P10	Environmental	Ray et al. [28]
Generation of hazardous wastes	P11	Environmental	Aslani et al. [37]
Use of eco-friendly packaging material	P12	Environmental	Weber et al. [47]
Top management commitment for GSCM implementation	P13	Environmental	Sahoo et al. [10]
Design for proper utilization of resources	P14	Operational	Ghosh et al. [11]

Thereafter, a structured questionnaire [28] is prepared to capture a specific set of queries regarding the GSS problem. It comprises of three parts namely, the general part, the technical part, and the miscellaneous part. The general part contains questions about the basic information, such as the nature of the business, total number of employees, turnover, and so on. The technical part includes queries about different GSCM activities carried out by the organizations and their performance measures. The last part deals with miscellaneous information, such as various constraints and GSCM strategies.

Step-2

After selection of case organizations, detail information regarding research objectives are sent to the corresponding industry officials. An expert committee is constituted, which consists of 20 members from each organization (a total of 60 members), among which 5 members from the strategic level, 5 members from the tactical and 10 members from the operational level are selected. The selected members are highly skilled in their respective domains and they have more than 20 years of corporate expertise related to supply chain operations. Communications with the industry experts are established through e-mails, telephone calls, and frequent site visits. Then, interviews with the experts are carried out separately and individual responses are collected very carefully to avoid any kind of possible personal as well as professional bias. During the interview process, the questionnaire is distributed among the experts, who, individually assign their preference ratings in view of the importance of criteria and also provided their opinion. Experts’ ratings are measured using a 9-point linguistic scale [17], where a rating of 1 means the corresponding criteria is less significant and 9 means extremely significant. Since the data is gathered from 20 individual responses of the experts from each organization; therefore, the mean value of those responses is considered for a single criterion [28]. After carrying out the interviews, individual responses are compiled together, and finally a data matrix with criterion value for each alternative is constructed. The data matrix is formed after sorting the raw data according to the requirements without any kind of manipulation, and shown in Table 4.

Table 4

Data matrix
Suppliers	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14
Supplier 1	8	6	9	6	7	8	7	7	8	7	5	7	6	6
Supplier 2	7	5	7	5	4	6	8	5	7	6	6	8	3	4
Supplier 3	7	8	7	6	8	7	7	8	8	6	8	7	5	7

Step-3

The data analysis is performed using TIBCO STATISTICA software. First, the item analysis is carried out to check the consistency of the data. The Cronbach’s value is obtained using Eq. (1). In this analysis, the value is found to be 0.8519, which is sufficiently high and meets the consistency criterion of item analysis. It also implies that the items (criteria) are highly correlated with each other and the dataset is consistent and reliable for making any strong decision. Table 5 shows the omitted item statistics, which represents the modified values after omitting the corresponding items from the dataset. It is used to determine whether eliminating an item from the dataset or adding an extra item to the dataset improves the consistency or not. From the table, it can be seen that the value has not changed significantly after omitting each item from the dataset. For example, if the first item or criteria (P1) is eliminated, the modified value becomes 0.8504, which is very much close to the original i.e., 0.8519. Similarly, it can be been seen for the remaining items also. Therefore, the dataset is reliable and the criteria are highly correlated with each other. However, there is no need to add or eliminate any item (criteria) from the dataset as the omitted item statistics suggests that they all measure the same characteristics.

Table 5

Omitted item statistics
Omitted item	P1	P2	P3	P4	P5	P6	P7
Modified alpha value	0.8504	0.8233	0.8529	0.8324	0.7992	0.8289	0.8846
Omitted item	P8	P9	P10	P11	P12	P13	P14
Modified alpha value	0.8061	0.8324	0.8504	0.8690	0.8846	0.8137	0.8061

After the aforesaid analysis the well-known PCA method is applied. The number of PCs is equal to the number of criteria considered in this study (i.e., 14). Therefore, the number of PCs to be considered is determined by the magnitude of the eigenvalues. Table 6 shows the PCs and their corresponding eigenvalues. Table 6 highlights only five PCs out of 14. According to the theory, PCs with the highest eigenvalues (greater than 0) should be retained. The first PC has an eigenvalue of 9.7751, the second PC has an eigenvalue of 4.2249, and the remaining items have zero eigenvalues.

Table 6

Eigenvalue table
Principal components	PC1	PC2	PC3	PC4	PC5
Eigenvalue	9.7751	4.2249	0.0000	0.0000	0.0000
Cumulative proportion	0.698	0.302	0.000	0.000	0.000

The cumulative proportion is used to estimate the amount of variance explained by the PCs. Table 6 shows that PC1 and PC2 together explain the entire variation in the dataset ($69.8+30.2=100\%$). Therefore, except PC1 and PC2, the remaining PCs have no contribution to the variation. Since PCA is employed here to determine the criterion weight, the threshold value for the cumulative proportion is set to 0.60 [28]. According to Table 6, the proportion of PC1 is 0.698, which is greater than the threshold value. Therefore, only PC1 is considered to determine the weight. Thereafter, final weight is computed by squaring the eigenvalues of the criteria corresponding to PC1. The weight matrix is shown in Table 7, which indicates P8 secure the highest weight (i.e., 0.564).

Table 7

Criteria weights obtained by PCA
Criteria	P1	P2	P3	P4	P5	P6	P7
Weight (${\text{w}}_{\text{j}}$)	0.024	0.021	0.045	0.096	0.032	0.010	0.001
Criteria	P8	P9	P10	P11	P12	P13	P14
Weight (${\text{w}}_{\text{j}}$)	0.564	0.101	0.020	0.028	0.024	0.014	0.208

The ranking of the criteria based on their PCA weight is as $P8>P14>P9>P4>P3>P5>P11>P12=P1>P2>P10>P13>P6>P7$. P8 gains the highest weight. Hence, ‘collaboration with suppliers for green purchasing’ is the most influential criteria for green supplier evaluation. On the contrary, P7 has the least weight. Hence, ‘reduction of scrap material’ has no significant contribution to GSS. Additionally, from Table 7, it can be said that P14 or ‘design for proper utilization of resources’, and P9 or ‘environmental planning’ are important GSCM criteria as they have relatively higher weight than other criteria.

Lastly, the SAW method is helps to calculate the performance score of the suppliers and rank them accordingly. The data matrix (Table 4) is normalized using Eqs. (3) and (4). The normalized data matrix ($\text{R}$) is shown in Table 8.

Table 8

The normalized data matrix ($\text{R}$)
Suppliers	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14
Supplier 1	0.87	0.75	1.00	1.00	0.87	1.00	0.87	0.87	1.00	1.00	1.00	0.87	1.00	0.86
Supplier 2	1.00	0.62	0.78	0.83	0.50	0.75	1.00	0.62	0.87	0.86	0.83	1.00	0.50	0.57
Supplier 3	1.00	1.00	0.78	1.00	1.00	0.87	0.87	1.00	1.00	0.86	0.62	0.87	0.83	1.00

Thereafter, the weight (ref. Table 7) is multiplied with the normalized data (ref. Table 8) in order to obtain the weighted normalized data matrix ($\text{C}$) depicted in Table 9.

Table 9

The weighted normalized data matrix ($\text{C}$)
Supplier	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14
Supplier 1	0.021	0.016	0.045	0.004	0.028	0.010	0.001	0.494	0.001	0.020	0.029	0.022	0.015	0.179
Supplier 2	0.024	0.014	0.035	0.003	0.016	0.007	0.001	0.353	0.001	0.018	0.024	0.025	0.007	0.119
Supplier 3	0.024	0.022	0.035	0.004	0.032	0.009	0.001	0.564	0.001	0.018	0.018	0.022	0.012	0.209

Then, the performance score (${\text{P}}_{\text{i}}$) of the alternatives are determined using Eq. (6) and shown in Table 10.

Table 10

Ranking of suppliers
Suppliers	Performance score	Rank
Supplier 1	0.885064	2
Supplier 2	0.647364	3
Supplier 3	0.970537	1

The ranking of the supplier organizations based on their GSCM performances, is as follows: Supplier 3 $>$ Supplier 1 $>$ Supplier 2. Supplier 3 secures the top position in ranking with the highest performance score (${\text{P}}_{\text{i}}=$0.970537) followed by Supplier 1 (${\text{P}}_{\text{i}}=0.885064)$ and Supplier 2 (${\text{P}}_{\text{i}}=0.647364)$ respectively. The performance score of Supplier 1 is satisfactory (${\text{P}}_{\text{i}}=0.885064$), whereas the performance score of Supplier 2 (${\text{P}}_{\text{i}}=0.647364$) is not up to the mark. Hence, Supplier 3 is the benchmark supplier. Other organizations should follow the strategies of Supplier 3 in order to enhance their performance.

On the basis of significant discoveries, a comparison of the current outcome with previous works is highlighted in this section. The verdicts of this study are in line with previous research findings as;

Firstly, this research finds that collaboration with suppliers for green purchasing is the most influential GSCM criteria, which is consistent with the findings of Sönnichsen et al. [98]; Pacheco-Blanco et al. [99], and Guarnieri et al. [36]. Guarnieri et al. [36] argued that organizations need collaborative alliance with suppliers in order to ensure business resilience, green innovation, and expansion. They must collaborate more closely for lower inventory levels, improved quality, lower risk, process improvements, and, of course, cost efficiency [98]. Many organizations have realized the reputational threats (environmental reputation) posed by their suppliers' [99]. To minimize the threat, organizations started to collaborate with their suppliers to ensure their operations and processes are environmentally friendly. However, in this research, the benchmark supplier maintains the healthy supplier’s relationship with more than 1500 suppliers for raw materials and sub-assemblies. The organization also encourage its suppliers to go for ISO 14001 standard. It conducts on-site supplier sustainability assessments and engagement program. It has also given targets to its suppliers to reduce their carbon footprint by 15% in the upcoming year. As a result, it has already achieved certain competitive advantages, such as; $\left(a\right)$it offers green products to consumers at a reasonable price as compared to other competitive brands in the market, $\left(b\right)$it has gained customer satisfaction and improved brand value by developing new green products and services, and $\left(c\right)$ it has been able to cope with supplier uncertainties, sudden disruptions, and various associated risks.

Secondly, findings of this research suggest that design for proper utilization of resources is the second most important criteria. This supports the argument of Foo et al. [100], who stated that organizations should consider design for scrap and waste reduction by virtue of design for durability, and design for reusability/recyclability in order to effectively implement GSCM. Environmental planning is another important criterion found in this study. This corroborates the findings of Gupta et al. [2], who argued that environmental planning plays a crucial role in decision making about environmental protection, as well as social, political, economic, and governance issues which provides a holistic framework for achieving sustainable development goals.

Most interestingly some of findings in this research disagrees the argument of previous researchers. For example; reduction of scrap material is found as the least important GSCM criteria in this research. This opposes the findings of Rupa et al. [9], who claimed that environmental impacts of supply chain operations can be minimized and GSCM performance may be enhanced through eliminating waste, reduction of scraps, and recycling activities. Again, this research diverges from the existing approaches of Awasthi et al. [42] and Tuzkaya et al. [15], who attempted to select the optimum green suppliers by evaluating their environmental performance only. Economic and operational aspects were not considered in their studies. Conversely, this research simultaneously considers the environmental, economic, and operational aspects for GSS.

The fusion of green criteria in SS process is receiving more importance day by day as the sustainability is emerging as a new paradigm in engineering, management, and research. Government legislation and fierce market competitions also forced organizations to consider GSCM issues within their sourcing decisions. Thus, managers, policymakers, industry practitioners, and researchers are in tremendous pressure to establish a balance between financial outgrowth and sustainable development of business organizations across the globe. Engineering management (EM), as a widely recognized notion, is the solution for such concerns. Basically, EM brings together the technological skills of engineering and the strategic, administrative, legal, and planning abilities of management in order to solve the complex and real-world problems [77]. Therefore, to associate the EM with the research outcomes, the main problem addressed in this research is the development of a comprehensive GSS framework to facilitate the decision-making process by addressing various issues faced in procurement process by supply chain management professionals. Among several criteria to evaluate the performance of suppliers; GSS framework aims to consider environmental, economic, and operational issues, simultaneously. Hence, it is very hard to identify and design the evaluation criteria to satisfy all these perspectives. This would become more complicated once they are applied in a particular industry. Furthermore, automobile industry has its specific concern towards SS, which is incorporated into the process of developing GSS criteria in this research. So, these are some issues related to the EM perspective of this research.

This research work presents a data-driven model that provides a comprehensive insight into the performance evaluation of green suppliers using an integrated multivariate analysis-MCDM approach. So far to the authors’ knowledge, the proposed integrated framework is a novel concept and has not been proposed earlier. The efficacy of the proposed research framework is shown through a real-world case study, in which three India-based automobile manufacturing organizations are considered for evaluating the GSCM performance. Different evaluation criteria were identified and shortlisted from the literature as well as consulting with industry experts. Finally, ‘fourteen’ criteria were finalized considering environmental, economic, and operational dimensions of sustainability. Final data matrix is constructed after aggregating the responses received from the interviews with the experts’, and criteria weight is obtained based on PCA analysis. The consistency test is performed using item analysis in order to check the reliability of the research data. It is found that the dataset satisfies the consistency criteria and reliable for analysis. Consecutively, PCA analysis reveals that ‘collaboration with suppliers for green purchasing’ is the most influential criteria. ‘Design for proper utilization of resources’ and ‘environmental planning’ are the second and third most important criteria wherein ‘reduction of scrap material’ is the least important criteria for GSS respectively. So, those criteria should be predominantly taken into consideration for GSS. In final stage of the analysis, the weight obtained from PCA is utilized as an input for the SAW method in order to evaluate the optimum supplier organizations. The result reveals the priority ranking of the suppliers based on their performance. Supplier 3 is the benchmark supplier as it gains the highest performance score. Performance of Supplier 1 is satisfactory whereas performance of Supplier 2 is poor. Supplier 1 and 2 should follow the strategies of the benchmark supplier in order to improve their GSCM performance for sustainability. Hence, findings of this research consecutively address all the RQs mentioned in section 1.

6.1 Research implications

The findings of this research could be significant from both theoretical and practical perspectives respectively. Some of the upmost implications of this research are summarized as;

The proposed methodology that combines the superiority of multivariate analysis based on MCDM approach contributes to the literature of GSS by considering reliability and consistency of the dataset used in this research. However, consistency measurement and testing of reliability ensure that the experts’ judgement are neither random nor irrational.

This research develops a set of specific criteria that are most relevant to GSS in automobile industry. The relevance of the selected criteria for GSS has been confirmed by item analysis. In practice, deciding which of the evaluation criteria is more important than others is difficult. In this study, the most significant criteria is determined using PCA, which should be considered by DMs for GSS.

The benchmark organization is identified in this study. Since this research has taken into account all the aspects of sustainability, the benchmark organization's strategies have a direct impact on the development of a cleaner production system.

The proposed approach can accommodate consequences when DMs, parameters, and alternatives are large in number. Also, the whole decision-making process integrates stakeholders of all managerial levels of organizations, i.e., strategic, tactical and operational. As a result, the obtained results are in line with the group opinion.

The suggested GSS framework can assist policymakers in various industrial decision-making scenario, such as identification of priority items and constraints of the system, cost-benefit analysis, performance measurement of vendors, supplier assessment, improvement measures, and so on.

6.2 Limitations and scope for further investigation

This study has few limitations that are basically the foundation for future research avenues.

It is worth mentioning that the proposed GSS framework is specific to the focal industry, i.e., automobile industry in this study. The same framework could be replicated to other case industries such as leather, cement, plastic, steel, electronics etc. in order to test the generalizability of the results.

Identification of the important evaluation criteria is based upon the literature review. So, there is a chance of overlooking other important GSCM criteria, which can be included in future research. Applying the same framework to other types of industries might require a diverse set of criteria, which can be obtained by acquisitions of experts’ opinions.

For future research, the proposed methodology can also include the unstructured or fuzzy environment. Future research could also apply other MCDM tools like VIKOR, TOPSIS, GRA, and DEMATEL. Future research may also integrate social dimension of sustainability into the GSS process.

Furthermore, the framework can be used as a benchmarking tool by researchers in different contexts to support the GSS problem globally. It can be useful for any organization to select its suppliers with regard to the GSCM considerations.

Funding information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement: The authors confirm that the data supporting the findings of this study are available within the article.

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Green Supplier Selection in the Automobile Industry: A Comprehensive Framework for Multi-Criteria Decision Analysis

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Abstract

Figures

1. Introduction

2. Research Background

2.1 Green supplier selection

2.2 Evaluation criteria for green supplier selection

2.2.1 Traditional criteria for supplier selection

2.2.2 Green criteria for supplier selection

2.3 Methods for selecting green suppliers

2.3.1 Independent methods

2.3.2 Hybrid methods

2.3.3 Fuzzy methods

2.3.4 Mathematical methods

2.3.5 Intelligent methods

2.4 Criticism on existing literature

3. Research Design

3.1 Item analysis

3.2 PCA method

3.3 SAW method

3.4 Proposed research framework

4. Application And Result

5. Comparison Based On Key Findings

6. Discussion And Conclusion

6.1 Research implications

6.2 Limitations and scope for further investigation

Declarations

References

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