Impact of Drivers for the Implementation of Green Supply ... · companies to engage in environmental management. ... the Tamilnadu leather manufacturing environment and then are analyzed

ADVANCES in NATURAL and APPLIED SCIENCES

ISSN: 1995-0772 Published BY AENSI Publication EISSN: 1998-1090 http://www.aensiweb.com/ANAS

2016 October 10(15): pages 31-42 Open Access Journal

To Cite This Article: G. Kathiresan and Dr. S. Ragunathan., Impact of Drivers for the Implementation of Green Supply Chain Management in Leather Industries of Northern Tamilnadu. Advances in Natural and Applied Sciences. 10(15); Pages: 31-42

Impact of Drivers for the Implementation of Green Supply Chain Management in Leather Industries of Northern Tamilnadu

1G. Kathiresan and 2Dr. S. Ragunathan 1G. Kathiresan, Associate Professor, Department of Mechanical Engineering, SSM College of Engineering, Komarapalayam-638183, Namakkal District, Tamilnadu, India. 2Dr. S.Ragunathan, Principal & Professor, Department of Mechanical Engineering, AVS Engineering College, Military Road, Ammapet, Salem District-636006, Tamilnadu, India.

Address For Correspondence: G.KATHIRESAN, Associate Professor, Department of Mechanical Engineering, SSM College of Engineering, Komarapalayam-638183, Namakkal District, Tamilnadu, India.

Copyright © 2016 by authors and American-Eurasian Network for Scientific Information (AENSI Publication). This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

ABSTRACT Background: Green supply chain management (GSCM) is an influential weapon to distinguish one concern from the others and it can significantly control the success of any plan. Green supply chain intents at restraining the wastages within the industrial structure for preserving the energy and prohibit the dissipation of hazardous substances in to environment. With elevated attentiveness about corporate onus and the decisive to meet the strings of the environmental policy, green supply chain management (GSCM) is becoming more and more important for Indian firms. Objective: The main objective of this research is to identify pivotal and obtrusive drivers for espousing “greenness” in supply chain management system of leather industries to originate eco-friendly environment by diminishing pollution. This work also interprets the impact of pinpointed drivers (such as technological, organizational and environmental) in the adoption of green supply chain management using structural equation modeling (SEM), VIKOR and Fuzzy VIKOR. Results: Hence SEM, VIKOR and Fuzzy VIKOR provide a base line to analyze and understand the importance of parameter weightage and ranking pertaining to GSCM implementation in leather industry. Conclusion: From the results, weightage of each drivers and its ranking were found. Among all identified drivers it is perceived that External Stakeholder Cooperation demeanour has high impact for approbation of green concept in supply chain management in Tamilnadu leather industries

KEYWORDS: Structural Equation Modeling, VIKOR, Green Supply Chain, Fuzzyness, Tannery,

INTRODUCTION

Environmental degradation is a growing global concern in today’s competitive scenario. In 2010, the

world’s greenhouse gas emission was the highest ever in history. The implications this will have is

still unknown, but research done leaves no doubt that the climate changes facing today is a consequence of

the increased amounts of gases that circulates in our atmosphere due to increased human activity following

the industrialization. Researchers are saying that if the global middle-temperature rises with more than 2

degrees Celsius until 2100 there is a large potential for dangerous climate change.

The expected consequences of climate change is that it will cause a more uneven distribution of the

world’s resources than what is the case today, and thus leading to mass migrations and conflicts [1].

Increasing environmental awareness amongst the customer and government rules getting stricter has increased

the pressure on the industries to become more eco-friendly. Increased production and consumption has resulted

in increased use of raw material and energy, which has led to the depletion of natural resources. Additionally,

the waste production and pollution has also increased significantly. Hence, there is an utmost need of

32 G. Kathiresan and Dr. S. Ragunathan., 2016/ Advances in Natural and Applied Sciences. 10(15) October 2016, Pages: 31-42

improvement in industrial processes for product manufacturing. Thus, the companies are being subjected to the

dual challenge of responding to competitive and environmental demands simultaneously. The Green or

Sustainable Supply Chain is an approach which seeks to minimize an organization’s ecological footprint, and

is an extremely broad area of study. GSCM has caught the attention of most of the multinational industry and

SME (Small and Medium industry) in last two decades for the sake of environmental factors. Due to civic

alertness, fiscal, ecological or legislative reasons, the necessity of GSCM has increased [2].

Literature Review:

Literature review on Green Supply Chain Management (GSCM):

GSCM is considered as a process of integrating the environmental concerns, values and thinking into

supply chain [3]. GSCM is a spectacle where eco-friendly novelties disperse from a consumer firm to a

purveyor firm, with ecological innovation defined as being yields, procedure, technics or method developed to

trim down environ effect [4]. It is not astonishing that green supply chain management meets its explication in

the supply chain management. Accumulating the green module to supply chain management comprises

inscribing the power and appositeness of supply chain management to the connatural environment [5]. The

concept of GSCM can be holistically represented in following fig.1 [6].

Fig. 1: Schematic Representation of GSCM

Literature Review on GSCM Research:

GSCM is a very good practice to be done for dealing and minimizing with the environmental issues that we

are facing today [7]. Many researchers contribute in this topic in various respects. Several researchers have

given various definitions of GSCM in different perspective. Number of definitions of GSCM exist [8]. Similar

to the concept of supply chain management, the boundary of GSCM is dependent on researcher goals and the

problems at hand, e.g., should it be just the procurement stage or the full logistics channel that is to be

investigated [9]. Detailed review of the work carried out by Srivastava et al., [10-11] reveals that the key themes

from the perspective of practice and performance of GSCM that came out in the prose over the last two decades

are the conception of: green configuration, green operations, reverse logistics, offal management and green

production. The green supply chain concept came into context in 1989. Kelle and Silver’s [12] article was the

pioneer of this fiction that extended an optimal forecasting structure for organizations to use to anticipate

products that can be potentially be reused. According to Chen et al., [13] the customer is the essence of any

business. Businesses must design and manufacture products and provide services that meet customers’ needs

and expectations. Environmental consciousness of consumers is one of the most significant driving forces for

companies to engage in environmental management. If consumers are ‘green’, then it will be profitable for

business to become ‘green’ [14].

Research Gap:

It is evident from the literature review that extensive research has been in the field of GSCM. It is

unfortunate that Indian manufacturing sector is not yet embracing the concept of GSCM enthusiastically. It is a

well-known fact that leather industrial sector plays a vital part of manufacturing industry next to textile

industrial segment in the northern region of Tamilnadu. Tanneries in Tamilnadu are getting revolutionized

rapidly but are still reluctant to adapt to the concept of GSCM. It is thus imperative to identify the factors that

will motivate the tanneries to adopt the concept of GSCM.

Problem Description:

Due to environmental issues, GSCM find a great need in the society. A lot of work has been done on it but

the main drawback is that implementation of GSCM has not been seen active in full form. Today many

industries are following “greenness” in their business but at the same time many industries are there which are

not following GSCM. It not only causes problem for them but also to the society. According to the project

undertaken by Central Leather Research Institute (Chennai) and Pollution control authorities, leather industry

sector fared badly in the green rating program, and supply chain has the major contribution in reducing the green


rating of the company [15]. So it is importunate to identify the drivers to help the industry to achieve

environmental sustainability.

This Thesis is the comprehensive study of various such drivers which motivates and encourages the

industries to transit from conventional supply chain to an ecofriendly supply chain in order to reduce the

environmental footprint of the organization. In this study various driving factors are identified by referring to the

literature and by consulting various experts from academia and industry. The identified drivers are then tested in

the Tamilnadu leather manufacturing environment and then are analyzed by using 3 methods in order to identify

the relative importance of the drivers and it is adjudged by the relation between the identified drivers.

The factors are extensively analyzed using 3 different methods

1. Structural Equation Modeling (SEM)

2. VIKOR and

3. Fuzzy VIKOR.

Solution Methodology

From the literature reviews and expert opinions, the drivers are spotted which motivate and encourage the

implementation of GSCM in tanneries [16-17]. The drivers are basically assorted into 4 categories.

They are

1. Technology context

2. External Stake-Holders Context

3. Organizational context

4. Performance context

Fig. 2: Identified Drivers

Synthesis of Consolidated Data:

A meticulously designed set of questions were prepared which involved all intricate factors which would

aid in appreciating and recognizing the feasibility of change of the existing supply chain system to green supply

chain system. The prepared questionnaires were sent to all the industrial experts and were thoroughly briefed to

rank the factors in a five point grade system. Based on the feedback collected, different methods like SEM,

VIKOR and Fuzzy VIKOR were adopted to analyse the information.

Data Analysis:

Structural Equation Modeling (SEM):

Structural Equation Modeling has its sources in alley (or) path analysis, which was formulated by the

geneticist Sewall Wright [18]. It is still habitual to start a SEM analysis by illustrating a path diagram. A path

diagram comprises of boxes and circles, which are coupled by arrows. In Wright’s notation, examined variables

are depicted by a rectangle or square box, and concealed factors by a circle or ellipse. Single headed arrows are

used to characterize casual rapports in the model, with the fickle at the tail of the arrow causing the variable at

the point. Double headed arrows specify covariances or correlations, without a casual explanation. Statistically,

the single headed arrows or paths symbolize retrogression coefficients and double headed arrows covariances

[19].

SEM concept is contemplated in this work since there is no intricacy in postulate testing as it takes the

affirmatory technique rather than the exploratory technique. The SEM pattern comprises of two pleats – one is

the lower order structure and the other is the higher order structure. In the lower order framework, the

information acquired through the investigation for the parameter was given as the input. The erect score attained

through the lower order model are inputted to the higher order model. This construct score acts as an ascertained

fickle data for the higher order model. This is shown in following Fig 3 & 4.


nnnY

Y

Y

Y

Y

Y

4

3

2

1

4

3

2

1

4

3

2

1

Fig. 3: Lower order SEM Model Fig. 4: Higher Order SEM Model

Then, the factor loading of each scale on GSCM are analyzed for significance and the importance of each

gauge, in spite of the symbol, will give the impact of those drivers on GSCM. These values are cashed for

attaining at the proportionate weightage of traits. To execute the aforementioned technique, SEM model with

LISREL representation can be developed by assuming the relationship between the observed variables and their

underlying factors.

Fig. 5: Conceptual Model of Lower Order Fig. 6: Conceptual Model of Higher Order

Equation Formation:

The lower order and higher order factor structure can be written in equation or statement which briefs its

configuration.

The Lower order factor structure equations are

Y1 = λ11 + 1, Y2 = λ21 + 2,

Y3 = λ31 + 3, Y4 = λ41 + 4 ….

Yn = λnn + n

and the equation can be written in vector form as:

Also, the above lower order system can be compiled as:

YY


η = Γ ξ +

Aj = γj / ∑ γj

Where, the Lower- order factor loadings and ε is assessment fallacy terms.

The higher order constituent system equations are

η1 = 1 ξ + 1, η2 = 2 ξ + 2,

η3 = 3 ξ + 3, η4 = 4 ξ + 4,

η5 = 5 ξ + 5, η6 = 6 ξ + 6,

η7 = 7 ξ + 7, η8 = 8 ξ + 8,

η9 = 9 ξ + 9, η10 = 10 ξ + 10

and the equation can be written in vector form as

Where, 1, 2,3,4… 10 - Higher- order factor loadings,

1,2,3,4 … 10 - Assessment fallacy terms

Also, the above higher order structure can be abridged as:

Where, Γis Higher- order element loadings and is Remnant (i.e. Residual) error terms.

From the higher order element structure, important factors were recognized. Based on that, relative

weightage for the attributes are deliberated using the subsequent expression.

Comparative weightage for trait (Latent factors):

Where γj is the High order element loading of the “j” th trait and

∑ γj is the summation of all the High order element loadings of the traits.

Thus relative influence or weightage of attributes on driver selection can be established by Structural

Equation Model (SEM). The achieved results from LISREL for lower order and higher order model are depicted

in fig respectively.

Fig. 7: Lower Order GSCM Driver Model Fig. 8: Higher Order GSCM Driver Model

nnn

4

3

2

1

4

3

2

1

4

3

2

1


From the above diagrams it is evident that the loading of SHC i.e., Stake holders Cooperation has highest

impact for the adoption of green supply chain in leather industries.

VIKOR:

VIKOR method can be used to resolve multiple criteria decision making (MCDM) problems with

contradictory and non-proportionate (different units) factors, portraying that relinquishing is agreeable for

contrast resolution, the decider wants a solution that is the intimate to the optimal, and the alternates are

valuated according to all constituted criteria. This technique focus on sorting and selecting from a set of

alternates in the presence of conflicting criteria and on suggesting conciliate solution [20].

By applying VIKOR method, the ranking for each identified drivers were found and mentioned below

Table 1: Initial Criteria Matrix

Criteria c1(.20) c2(.17) c3(.23) c4(.14) c5(.26)

Organizational Context 3 3 4 3 2 Technology Context 4 4 4 3 3

Regulatory Requirement 3 4 3 4 3

Stakeholders Cooperation 4 4 4 3 4 Environmental Impact 4 3 3 4 3

Economic Performance 2 3 3 2 2

Table 2: Result Matrix

S.No Organi

zational

Context

Techno

logy

Context

Regulatory

Require

ments

Stake

holders

Cooperation

Environ

mental

Impact

Economic

Performance

1 S 3 2 4 1 5 6

2 R 4 1 5 6 3 Q 4 1 5 6

Table 3: Result of Sub-Criteria Matrix

S.No Ranking A7 A8 A9 A10 A11 A12

1 Sj 6 1 2 5 4 3

2 Rj 6 1 2 5 4 3

3 Qj 6 1 2 5 4 3

Fuzzy VIKOR:

From the findings of Tien-Chin Wang and Tsung-Han Chang [21], it reveals that when the info in a

decision making system is undefined, indecipherable, and imprecise or represented in linguistic terms, this leads

to the study of a new decision analysis field i.e., fuzzy decision analysis. The fundamental principle of VIKOR

is that each alternate can be appraised by each destined function; avow ranking can be presented by relating the

degree of propinquity to the irreproachable alternative [22-23-24].

According to Tien-Chin Wang, the following steps are proposed as a procedure of fuzzy VIKOR method

underneath fuzziness environment.

Step 1. Shape a clique of decision makers, and then clarify the assessment criteria and viable alternatives.

Step 2. Pinpoint the pertinent linguistic variables for evaluating the significance weight of criteria, and the

grading of alternatives.

Step 3. Drag the decision makers’ notions to get the accumulated fuzzy precedence weight of criteria, and

aggregated fuzzy rating of alternatives. If there are k persons in a decision making group, the prominence weight

of criteria and rating of each alternative can be computed by:

k

jjjj WWWk

W~

...~~1~ 21

k

ijijijij xxxk

x ~...~~1~ 21

Step 4. Construct a fuzzy decision matrix, and then find out the fuzzy best value *~, jfFBV and fuzzy

worst value jfFWV~

, of all criteria functions.

Bjxf iji

j ,~max~*

Cjxf iji

j ,~min~

Where B is related with the benefit criteria, C is associated to the expense criteria.


Step 5. Enumerate the index iS~

and iR~

jjijj

n

j

ji ffxfwS~~

/~~~~ **

1

jjijjjj

i ffxfwR~~

/~~~max~ **

where iS~

denotes to the segregation span of Ai from the fuzzy best value, similarly, iR~

is the separation

span of Ai from the fuzzy worst value, and jw~ is the weight of each criterion.

Step 6. Figure out the index iQ~

**** ~~/

~~1

~~/

~~~RRRRvSSSSvQ iii

,~

max~

,~

min~*

iii SiSSS

Where

iiii RRRR~

max~

,~

min~*

The index ii S~

min is with a summit majority rule, and ii R~

min is with a modicum individual regret of

foe. And v is presented as the weight in scheme of the maximum group utility, usually v = 0.5.

Step 7. Defuzzification for triangular fuzzy number iQ~

.

The technique of transforming a fuzzy number into a curt value is called defuzzification. In this work,

Chen’s method of maximizing set and minimizing set is applied.

The maximizing set is defined as:

RxxfxM M , With the membership function:

otherwise

xxxxxxxxfM

,0

,,/ 21121

By contrast, the minimizing set is defined as: ,, RxxfxG G with the membership function:

otherwise

xxxxxxxxfG

,0

,,/ 21212

Then the right utility iM FU and left utility iG FU can be denoted as:

xfxfFU MFix

iM ^sup

xfxfFU GFix

iG ^sup

As a result, the crisp value can be obtained by combining the right and left utilities.

2/1 FiUFUFU GiMiT

Step 8. Rank the alternatives by the crisp value iQ

The index iQ implies the separation measure of iA from the best alternative. That is, the smaller value

indicates the better performance of an alternative.

Step 9. Propose a compromise solution (a’) by the index Q, if the condition ‘A’ is satisfied.

A. Acceptable advantage: DQaQaQ '"

MMDQ ,1/1 is the number of alternatives 4,25.0 MifDQ , and a” stands for the

alternative with second position ranked by index Q. If condition ‘A’ is not satisfied, maaa ,.......,",' are

compromise solutions. The best alternative is the one with the minimum of Q.

The lacuna observed in the VIKOR method were overcome by following the procedure detailed above and

for every factor and its related sub factors ,analysis were carried out and their respective rankings were found

which have been tabulated below.


Table 4: Initial Criteria Matrix

Criteria c1(.20) c2(.17) c3(.23) c4(.14) c5(.26)

Organizational Context 3 3 4 3 2 Technology Context 4 4 4 3 3

Regulatory Requirement 3 4 3 4 3

Stakeholders Cooperation 4 4 4 3 4 Environmental Impact 4 3 3 4 3

Economic Performance 2 3 3 2 2

Table 5: Weight Matrix of Criteria

Wt OC TC RR SHC

Env.

Impact

Eco.

Impact

c1 0.2 L 3 4 3 4 4 2

M

R c2 0.17 L 3 4 4 4 3 3

M

R c3 0.23 L 4 4 3 4 3 3

M

R

c4 0.14 L 3 3 4 3 4 2

M

R c5 0.26 L 2 3 3 4 3 2

M

R

Table 6: Final Ranking of Criteria

Organizational Context

Technology Context

Regulatory Requirement

Stakeholders Cooperation

Environment

al Impact

Economic Impact

Sj^l

Sj^m Sj^r

S

Rj^l Rj^m

Rj^r

R qj^l

qj^m

qj^r Q

0.415

0.551 0.356

0.468

0.182 0.26

0.182

0.221 0.835

1.203

0.849 1.0225

0.16

0.2 0.12

0.17

0.104 0.13

0.078

0.11 0.287

0.359

0.215 0.305

0.315

0.394 0.236

0.335

0.131 0.164

0.098

0.139 0.539

0.675

0.403 0.573

0.056

0.07 0.042

0.028

0.056 0.07

0.042

0.0595 0

0

0 0

0.332

0.415 0.249

0.353

0.131 0.164

0.098

0.359 0.555

0.695

0.415 0.59

0.684

0.886 0.591

0.762

0.182 0.26

0.182

0.222 0.884

1.516

1.08 1.249

Ranking

S

R Q

5

5 5

2

2 2

3

3 3

1

1 1

4

4 4

6

6 6

Once the ranking for drivers generated, the ranking for sub-factors also need to generate.

Table 7: Sub-Factors

A11 Works Closely with Government and Local Authority

A12 Financial Benefits from Government for Green Initiatives

A13 Loans From Financial Institutions for Green Practices A14 Supplier Trainings to allow Capability Building in Meeting the Environmental Standards

A15 Awareness Seminar

A16 Technical Advice and Consultation Services from External Parties

Table 8: Weights and Sub-Factors

Stakeholders

Cooperation (SHC) 1 2 3 4 5

Weights 0.2 0.17 0.23 0.14 0.26

A11 3 4 4 2 3

A12 4 5 4 4 5 A13 4 4 5 4 4

A14 3 4 3 4 3

A15 3 3 4 3 4 A16 4 4 5 3 4


Table 9: Defuzzification and Ranking of Sub-Criteria Matrix

S^l

S^m

S^r

crisp S

0.517

0.739

0.518

0.628

0.092

0.115

0.069

0.097

0.201

0.251

0.15

0.213

0.526

0.754

0.529

0.641

0.439

0.609

0.414

0.518

0.261

0.351

0.231

0.298

0.526

0.754

0.529

0.092

0.115

0.069

R^ R*

R^l R^m

R^r

crisp R Q^l

Q^m

Q^r crisp Q

0.182 0.26

0.182

0.221 0.986

1.519

1.141 1.291

0.092 0.115

0.069

0.097 0

0

0 0

0.104 0.13

0.078

0.11 0.191

0.239

0.143 0.203

0.182 0.26

0.182

0.222 0.996

1.537

1.154 1.306

0.104 0.143

0.114

0.126 0.463

0.721

0.644 0.637

0.104 0.13

0.079

0.111 0.26

0.353

0.241 0.302

0.182 0.26

0.182

0.092 0.115

0.069

Ranking

S

R Q

5

5 5

1

1 1

2

2 2

6

6 6

4

4 4

3

3 3

RESULTS AND DISCUSSIONS

By VIKOR:

S.No. Organizational

Context

Technology

Context

Regulatory

requirements

Stakeholders

Cooperation

Environmental

Impact

Economic

Performance

1 S 3 2 4 1 5 6

2 R 4 2 3 1 5 6

3 Q 4 2 3 1 5 6

By Fuzzy VIKOR:

Ranking

Organizational

Context

Technology

Context

Regulatory

Requirement

Stakeholders

Cooperation

Environmental

Impact

Economic

Impact

S Q

R

5 5

5

2 2

2

3 3

3

1 1

1

4 4

4

6 6

6

By SEM:

As per results mentioned in the tabular column (6 and 9), the drivers are arranged in terms of the calibre of

their prominence based on their respective rank. Stakeholders Cooperation driver has the highest degree of

importance followed by Technology Context, Regulatory Requirement, Organizational Context, Environmental

Performance and Economic Performance. In annexation to this, considering the rank, the valuation factors

namely Regulatory Requirements (i.e., Government Regulations and Principles), Organizational context (i.e.,


Top Management Commitment, Employee Interest and Involvement), Stakeholders role are classified into root

cluster drivers. While the Technological Context (i.e., Implementation of New Technology and Practices),

Economical impact and Environmental impact are comes under sequel cluster.

The root drivers are very vibrant because of their direct influence on the entire system [25]. Thus, it would

be substantial to focus on the root set drivers. Among all the source factors, stakeholders’ cooperation has the

highest potential, which denotes that it has more impression in sculpting the organizational perception towards

the efficacious implementation of GSCM. Consequently, the drivers in the root cluster needs to be focused as

per their priority while implementing GSCM for achieving the anticipated goal.

Drivers in the sequel cluster tend to be smoothly controlled by other factors. However, these group factors

doesn’t have a direct impact on the GSCM structure, but still, makes a substantial contribution [25]. So, these

drivers need to be deliberated to find out their share in the global manner. In all the upshot drivers, technological

context attains a maximum score, which infers that this factor receives the maximum sway from all other

factors. This study is an exertion to understand and valuate the GSCM plan form industrial circumstances. It

proffers numerous substantial apportion to both the theory and practice in the sphere of GSCM. The present

research work has offered six main success factors based on literature review and experts’ opinion which are

related to successful implementation of GSCM in leather industry sector.

Conclusion:

In the epoch of globalization, ecological sustainability and green disputes have an increasing fame among

researchers and supply chain practisers [26-27]. There are numerous factors allied with assimilation of the green

initiatives in supply chain management [28]. It will be beneficial for organizations to know those drivers to

increase their economic-environ performances [29]. The companies are still belligerent to recognize the

technique for effective implementation of this notion in their relevant spaces [30-31]. In that path, this work

aims to evaluate the drivers correlated to the fruitful implementation of GSCM. This paper helps to shape a

structural model for investigating the kinships among drivers relevant to GSCM implementation. Using

literature and expert’s persuasion, six key factors with its appropriate sub factors, were identified. These drivers

were further evaluated using the Structural Equation Modeling (SEM), VIKOR and Fuzzy VIKOR method. This

approaches identified viable lean alternatives for achieving a defined set of criteria. SEM explores a set of

relationships between one or more Independent variables (IV) and one or more Dependent Variables (DV). The

VIKOR method is stationed on the accumulating ambiguous merit that characterizes how afar an alternative

from the optimal solution. The fuzzy deeds and procedures for ranking fuzzy numbers are spent in emerging

VIKOR algorithm. Fuzzy VIKOR technique aims on ranking and electing from a group of alternates in a blurred

atmosphere having conflicting criteria.

These approaches also aid to divide the factors into root (or) source and sequel cluster. It would be major to

focus on the source drivers in the commencement, and the criteria in the effect set need to be deliberated to find

out their part in the overall manner. As per result of this paper, it is ascertained that, external stakeholder

cooperation (SHC) is the paramount criterion and has preeminent assertive power for the implementation of

green supply chain in Tamilnadu leather Industries. Adoption of greenness in supply chain will assist the

organization to accomplish environmental and economic benefits

Future Scope of the work:

Concerning future work in this field of research, the structural model is based on SEM, VIKOR and Fuzzy

VIKOR methodology, which has its own precincts. Second one could be the driver selection for the successful

implementation of GSCM, as only six key drivers with its related sub factors were identified. Other vital factors

for effective GSCM execution have not been identified and categorized. Likewise the effect of dubiety in

evaluating the drivers has not been considered in this work. It could be targeted as an area of research in future

work. The future study can be directed to realize the hierarchical interrelated relationship among the drivers

using Interpretive Structural Modeling, Analytical Network Process and Fuzzy Analytical Network Process.

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Impact of Drivers for the Implementation of Green Supply ... · companies to engage in environmental management. ... the Tamilnadu leather manufacturing environment and then are analyzed