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bstract
Industrial operations could emit harmful pollutants and degrade natural environment, thereby posing a threat to human beings and
wildlife (polar bears, panda, penguins, turtles, whales, walrus etc.). Globally manufacturers must ensure that the operations be
done as safely and responsibly as possible keeping in line with the three dimensions of triple bottom line. We develop a framework
which analyses the various complex relationships involved in a sustainable supply chain with the aid of interpretive structural
modeling. The key factors inuencing sustainable supply chain were identied based on a thorough literature review and in
consultation with rubber industry experts. Further MICMAC analysis was applied to identify the autonomous, linkage, dependent
and independent factors.
Keywords: Sustainable Supply Chain Management (SSCM), Indian Rubber Industry, Interpretative Structural Modeling, MICMAC.
A Framework for the Analysis of Sustainable
Supply Chain Management: An Insight from
Indian Rubber Industry
Surajit Bag*, Neeraj Anand**, K.K. Pandey***
*PhD Scholar, College of Management and Economics Studies, University of Petroleum& Energy Studies,
Uttarakhand, Dehradun, India. Email: surajit.bag@gmail.com
**Professor, College of Management and Economics Studies, University of Petroleum& Energy Studies
Uttarakhand, Dehradun, India. Email: nanand@ddn.upes.ac.in.
***Associate Professor, College of Management and Economics Studies, University of Petroleum& Energy
Studies, Uttarakhand, Dehradun, India. Email: kkpandey@ddn.upes.ac.in
India’s economy has grown very rapidly in recent
years. Since 1991 it has been among the top 10% of the
world’s countries in terms of economic growth. Before
the liberalization of its economy began in 1991, India
had been one of the most over-regulated and closed
economies in the world. But with the fast pace growth
of Indian economy has led to innite damages in the
environment due to industrial operations. Successful
environmental policies can contribute to efciency
by encouraging, rather than inhibiting, technological
innovation. However, little research to date has focused
on the design and implementation of sustainable supply
chains that ensure productivity improvements in the face
of increasing stringency of environmental regulations.
Reducing and mitigating carbon emissions, the culprit of
global warming and climate change, is an increasingly
important concern for both industry and government
(IPCC, 2007). The United Nations, the European Union,
and many countries have enacted legislations or designed
mechanisms, such as carbon taxes, carbon offset, clean
development, cap and trade, carbon caps, and made
joint implementation to curb the total amount of carbon
emissions. Firms worldwide, in response to such
mechanisms and legislations or to concerns raised by
their own customers, are undertaking initiatives to reduce
their carbon footprints.
However, these initiatives have largely focused on
investment in new technology, developing energy-
efcient equipment and facilities, nding less polluting
sources of energy, and implementing energy-saving
programmes. While such efforts are valuable, they tend to
ignore a potentially more signicant source of emissions
- one driven by business practices, production economics,
operational policies, interaction, and coordination, where
the ow of products to consumers engages multiple
rms in long and complex supply chains (NSF, 2010). It
is therefore necessary to address the problem of carbon
emissions reduction from a supply chain and logistics
perspective.
The Indian rubber products manufacturing sector draws
its strength and stability from the rapidly growing demand
A Framework for the Analysis of Sustainable Supply Chain Management: an Insight from Indian Rubber Industry 69
for the products in both domestic and overseas market.
The exports are well over 85 countries including US,
Russia, UK, Bangladesh, Afghanistan, Italy, Germany,
France, Saudi Arabia, UAE, Canada and the African
countries. The chemicals and the allied products export
promotion council co-ordinates activities connected with
the export of rubber products.
Indian rubber goods manufacturing sector faces major
challenge from environmental degradation resulting
from its various operations. Workers are exposed to these
hazards through inhalation and skin absorption during
rubber processing and product manufacturing. Risk of
cancer and other adverse health effects are high among
rubber products workers, DHHS (NIOSH), 93(106),
Sept 1993.CPCB has categorized this sector in the high
polluting RED category due to GHG emission and solid
waste generation and throughout the supply chain this
sector is trying hard to reduce their carbon emissions.
We have presented statistics of natural rubber and
synthetic rubber production, consumption, import and
export in Fig. 1 and 2. In Fig. 3 we have presented the
statistics of rubber goods exports.
The purpose of conducting literature review is to
understand whatsoever work has been carried out by
past researchers in the area of sustainable supply chain
management in the last decade.
Various secondary sources were considered to extract the
information and seminal papers from leading journals
such as IJPE, IJPR, Transportation Research, EJOR,
DSS, EJPSM, JOM and JPSM were referred to prepare
the groundwork for further research.
The summary of the literature on SSCM has been
tabulated in Table 1.
The review reveals various insights and gaps in the
existing GSCM literature.
1. Existing literature is not capable to explain the un-
derlying relationships among key variables in u-
encing SSCM practices in Indian Rubber Industry.
2. Lack of SSCM model for Indian Rubber Industry.
Fig. 1: Production, consumption, import and export
of natural rubber
Fig. 2: Production, consumption, import and export
of synthetic rubber
Fig. 3: Export of Rubber products
Source: Rubber Board
70 Journal of Supply Chain Management Systems Volume 3 Issue 1 January 2014
Table 1: Literature Review
AUTHOR(S), YEAR OBJECTIVE OF STUDY KEY FINDINGS METHODOLOGY
APPLIED
VARIABLES IDEN-
TIFIED
Brandenburg et al.,
(2014)
To understand and review
mathematical models focus-
ing on environmental/social
factors in the forward supply
chains.
Major publications and models were
found in a limited set of six journals
(JCLP, IJPR, IJPE, TRE, EJOR &
DSS). AHP, ANP and LCA were
commonly used tool for developing
the models.
Review
Environmental, Social
and Economic dimen-
sions.
Mirhedayatian et al.,
(2014)
To propose a novel network
DEA model for evaluating
the GSCM in the presence of
dual role factors, undesirable
outputs and fuzzy data.
The proposed model can be easily
computerized so as to serve as a
decision making tool in decision
making.
Quantitative model-
ing
Cost of quality, Sup-
plier exibility, Car-
bon dioxide emission,
Satisfaction, Facility
technology level
Suering, S., (2013)
To review research on quan-
titative models for green or
sustainable supply chains.
Different kinds of models are
applied in this area but the social
dimension is not taken into consider-
ation. On the modeling side there are
three popular methods: Equilibrium
models, MCDM & AHP.
Review
Environmental, Social
and Economic dimen-
sions
Zhu and Lai. (2013) To develop and empirically
test a theoretical model.
The research contributes to the
literature on institutional theory in
corporate environmental practices.
Empirical
Institution pressure:
Coercive, Normative,
Competitive
Reefke and Trocchi
(2013)
To develop a framework to
facilitate a balanced approach
to performance measurement
for SSCM.
A scorecard design customized for
sustainable supply chain is proposed
along with the development and
implementation process
Theoretical
Cost savings, Pro t,
customer satisfaction,
quality management
Liu et al. (2012) To integrate green marketing
and SSCM.
Development of a new hub and
spoke integration model. Drivers:
Improve company’s sustainable sup-
ply chain capabilities; reach green
customers before competitors; gov-
ernment regulations; green custom-
ers demand; community expectation
Empirical
Products, Promotion,
Planning, Process,
People and Project
Hoejmose et al.
(2012)
To understand the general
engagement with GSCM in
both B2B and B2C supply
chains.
Firms in B2B market is generally
less engaged with green practices
compared to rms in B2C markets.
Developing Trust with supply chain
partners and top management com-
mitment is a crucial GSCM driver
among rms in B2B markets.
Empirical Trust, Top manage-
ment commitment
Ageron et al. (2012)
To develop a Sustainable
Supply Management frame-
work.
External pressures have positive im-
pact on the development of SSM;
Waste reduction programmes have
greater impact on greening supply
chains; MNC and SMEs’ have dif-
ferential impact on SSM; Financial
barriers have more impact on SSM;
Top management support is a critical
success factor in SSM; Key bene ts
such as customer satisfaction, sup-
plier innovation, quality and capacity
have greater positive impact on SSM.
Empirical
Top Manage-
ment Commitment,
Govt. regulatory re-
quirements, Qual-
ity, Flexibility, Waste
Reduction,Clean
Programs, Reducing
carbon footprint, Fi-
nancial costs, ROI,
Green Investments,
Customer Satisfaction,
Innovativeness
A Framework for the Analysis of Sustainable Supply Chain Management: an Insight from Indian Rubber Industry 71
AUTHOR(S), YEAR OBJECTIVE OF STUDY KEY FINDINGS METHODOLOGY
APPLIED
VARIABLES IDEN-
TIFIED
Bose and Pal (2012)
To investigate the in uence
of GSCM initiatives on stock
prices of rms.
Firms observe greater positive
change in stock price by undertak-
ing green initiatives. Firms with high
R&D expenses show strong positive
impact. Early adopters of GSCM
show greater positive impact.
Event Study R&D, Size of rm,
Stock prices
Dekker et al. (2012) To review research on green
logistics.
The review highlighted the contribu-
tion of operations research to green
logistics
Review
Mode choice, In-
termodal transport,
Equipment choice and
ef ciency, fuel choice
and carbon intensity
Chaabane et al. (2012)
To present a generic math-
ematical model to assist deci-
sion makers in designing sus-
tainable supply chains over
their entire life cycle.
The model can serve as a tool that fa-
cilitates the understanding of optimal
SC strategies under different envi-
ronmental policies.
Quantitative model-
ing
Economic: Cost, reve-
nue, taxes, transfer En-
vironmental variables:
Carbon footprint, Raw
material use, Energy
use, Social variables:
Noise, pollution
Dubey and Bag (2013)
To explore sustainable manu-
facturing practices that im-
proves environmental and
business performance.
Green Purchasing, SRM, Green lo-
gistics and regulatory norms are
positive determinants of rms busi-
ness performance and Environmental
performance.
Empirical
Green Purchasing,
SRM, Green logistics
and Regulatory norms
Gunasekaran
and Spalanzani
(2012)
To bring important issues re-
lated to sustainable business
development in both manu-
facturing and services sector.
Developed a framework for sustain-
able development along with strate-
gies, techniques and tools.
Conceptual
Sustainable Prod-
uct and process de-
sign, sustainability
in supply operations,
sustainability in pro-
duction operations,
sustainability in dis-
tribution operations,
sustainability through
reverse logistics
Gimenez et al. (2012)
To analyse the impact of en-
vironmental programmes on
each dimension of the triple
line.
Internal environmental programmes
have a positive impact on the three
components of the triple bottom line.
Social initiatives have a positive im-
pact only on two components: social
and environmental performance .
Empirical
Environmental,Social
and Economic perfor-
mance, Internal and
external action pro-
grams
Hassisni et al. (2012)
To review literature on sus-
tainable supply chains from
2000-2010.
Based on the ndings authors have
developed a framework for sustain-
able supply chain metrics.
Review and Case
Market Forces, Poli-
cyand Regulations,
Scienceand Technol-
ogy, Product Devel-
opment, Process Ca-
pability, Sourcingand
Operations, Logistics,
Marketing andPR, So-
cial issues
Barari et al. (2012)
To provide integrated and ho-
listic conceptual framework
that combines the practical
aspects of green supply chain
with the objective of pro t
maximization.
With the help of evolutionary game
theory it has been possible to derive
the strategy set that not only prom-
ises maximum economic bene t and
presents a win-win situation.
Quantitative model-
ing
Green tax, green bur-
den
72 Journal of Supply Chain Management Systems Volume 3 Issue 1 January 2014
AUTHOR(S), YEAR OBJECTIVE OF STUDY KEY FINDINGS METHODOLOGY
APPLIED
VARIABLES IDEN-
TIFIED
Kang et al. (2012)
To establish the framework
for strategy development to
construct the sustainable sup-
ply chain.
Identi ed the factors. Theoretical
Leadership for knowl-
edge sharing, Innova-
tion of product and
process
Wanget al. (2011)
To provide a multi objective
mixed integerformulation for
the supply chain network de-
sign with environmental con-
cerns.
The model can be effectively used in
the strategic planning for green sup-
ply chain.
Quantitative model-
ing
Demand and Supply
of Product, Carbon
dioxide emission, ca-
pacity of facility, envi-
ronmental investment
cost, environmental
protection, Transpor-
tation cost, Handling
cost for products
Wu and Pagell (2011)
To understand how organ-
isations balance short term
pro tability and long term
environmental sustainability
when making supply chain
decisions under uncertainty.
Factors contributing to the uncertain
decision environment are as under:-
Uncertainty about environmental
outcomes and future regulation, the
saliency of each environmental issue
to multiple stakeholders, lack of vis-
ibility and in uence in one’s supply
chain.
Theoretical Operating principles,
Technical standard
Gupta and Desai
(2011)
To review the current state of
academic research in sustain-
able supply chain manage-
ment.
Authors developed an integrative
framework summarizing the exist-
ing literature under four broad cat-
egories: strategic considerations,
decisions at functional interfaces,
regulation and government policies,
integrative models and decision sup-
port tools.
Review
Product design
and product life
cycle,Regulation and
government policies
Azevedo et al. (2011)
To investigate the relation-
ships between green prac-
tices and supply chain perfor-
mance.
A conceptual model of the relation-
ships between green practices and
SC performance was developed.
Case Study
Environmental friend-
ly practices in pur-
chasing, Environmen-
tal collaboration with
suppliers, Minimizing
waste, Environmen-
tal collaboration with
customers, Reverse
logistics
Pereira (2009)
To understand how IT can
foster information manage-
ment and help sort out supply
chain problems.
IT must be used to develop SC strate-
gies to make supply chains more ro-
bust and resilient.
Conceptual Information Technol-
ogy
Longo and Mirabelli
(2008)
To present an advance mod-
eling approach and a simula-
tion model and provide a de-
cision making tool for supply
chains.
An advance model is proposed based
on programming code, tables and
event generators and provides the
user with a simulator capable of high
ef ciency for executing simulation
runs.
Quantitative model-
ing
Inventory Control,
Lead Time, Demand
Intensity, Demand
variability
Linton et al. (2007)
To prepare a background to
better understand current
trends in the area of sustain-
able supply chains.
Research on Sustainable supply
chain is still at a infant stage.It is
strong links with government policy.
Review
Product life extension,
Product design, Re-
covery process at end
of life.
A Framework for the Analysis of Sustainable Supply Chain Management: an Insight from Indian Rubber Industry 73
1. What are the key variables in uencing SSCM
practices and the nature of relationship existing
between the variables in context to Indian Rubber
Industry?
2. Can a SSCM model be developed for Indian
Rubber Industry?
Literature review has given a direction to identify the
research gaps and to develop the below two speci c
research objectives for the present study are as follows:-
1. To identify the key factors in uencing SSCM prac-
tices and understand the relationships in context to
Indian rubber goods manufacturing sector.
2. To develop a SSCM model for Indian rubber goods
manufacturing sector.
Phase I:
Based on the synthesis of literature review and ex-
perts opinion from rubber industry; the key variables
in uencing SSCM practices have been identi ed.
Phase II:
GSCM model has been developed by using ISM
technique which was further re ned using MICMAC
analysis.
The research variables have been derived from the above
literature review. To reduce the redundancy and check
their relevancy in present Indian context the pretesting
has been carried out among ten selected experts who are
having more than 20 years of work experience. The nal
shortlisted variables are as under:-
1. Supplier Relationship Management (SRM)
2. Customer Relationship (CR)
3. Top Management Commitment (TMC)
4. Regulatory Pressures (RP)
5. Market Pressures (MP)
6. Green Technology Adoption (GTA)
7. Total Quality Management (TQM)
8. Flexible operations (FO)
9. Technology Innovativeness (TI)
10. Cleaner Production (CP)
11. Environmental and Social Responsibility (ESR)
12. Carbon Emissions Reduction (CER)
13. Export Sales (ES)
14. Market Share (MS)
15. Pro t (PR)
ISM is a proven and popular methodology for
understanding relationships among speci c items that
de ne a problem. ISM is useful to achieve the objective
in presence of large number of directly and indirectly
related elements and complex interactions among them
AUTHOR(S), YEAR OBJECTIVE OF STUDY KEY FINDINGS METHODOLOGY
APPLIED
VARIABLES IDEN-
TIFIED
Zhu and Sarkis (2004)
To determine the economic
and environmental relation-
ships of GSCM practices
among Chinese rms.
Signi cant win-win opportunities
exist for Chinese rms practicing
GSCM. Strong relationship exists
between GSCM practice and positive
economic performance.
Empirical
Commitment of
GSCM from senior
managers, Total qual-
ity environmental
management, Envi-
ronmental compliance
and auditing pro-
grams, Supplier rela-
tionship, green design
Croom et al. (2000) To conduct literature review.
Lack of theoretical work in the eld
as compared to empirical based stud-
ies
Review
Sourcing strategy, at-
titude and commit-
ment to collaborative
improvement pro-
grammes
74 Journal of Supply Chain Management Systems Volume 3 Issue 1 January 2014
which may or may not be expressed in a proper manner.
ISM plays a vital role in this kind of situation and helps
in understanding a structure within a system. The ISM
model depicts the structure of a complex problem in a
carefully designed pattern.
ISM has been used in the past by several researchers due
to multiple bene ts. It guides and records the results of
group response on complex issues in an ef cient and
systematic manner, (Source: Attri et al., 2013; War eld
1994, 1974).ISM has been applied in different areas of the
supply chain starting from purchasing to production and
logistics management. Dubey et al., (2013) have applied
ISM to understand the contextual relationship among
antecedents of truck freight.Sushil (2012) hascontributed
in the ISM literature by providing directions to interpret
the links in ISM using the tool of interpretive matrix.
ISM steps are as follows:
1. Developing the structural self interaction matrix
(SSIM)
For developing SSIM, the below symbols have been used
to denote the direction of relationships between variables
(i and j):
V: i leads to j but j does not lead to i
A: i does not lead to j but j leads to i
X: i leads to j and j leads to i
O: i and j are unrelated to each other
2. Develop Reachability Matrix
The SSIM has been converted into a binary matrix i.e.,
the reachability matrix (Table 4) by substituting V, A, X
and O by 1 and 0. The substitutions of ‘1’ and ‘0’ have
been done as below:
i. If the (i, j) entry in the SSIM is V, then the (i.j) en-
try in the reachability matrix becomes ‘1’ and (j,i)
entry becomes ‘0’
ii. If the (i, j) entry in the SSIM is A, then the (i.j) en-
try in the reachability matrix becomes ‘0’ and (j,i)
entry becomes ‘1’
iii. If the (i, j) entry in the SSIM is X, then the (i.j) en-
try in the reachability matrix becomes ‘1’ and (j,i)
entry also becomes ‘1’
iv. If the (i, j) entry in the SSIM is O, then the (i.j) en-
try in the reachability matrix becomes ‘0’ and (j,i)
entry also becomes ‘0’
Matrice d’ Impacts croises multiplication appliqué an
classment (cross-impact matrix multiplication applied to
classi cation) is abbreviated as MICMAC. The objective
of MICMAC analysis is to analyze the drive power and
dependence power of factors. Based on the drive power
and dependence power the factors have been classi ed
into four factors: autonomous factors, linkage factors,
dependent and independent factors.
Table 2: Review on ISM application
Author and Year ISM Application
Hawthrone and Sage(1975) Higher education program planning
Sage (1977) Modeling complex situations
Jedlica and Meyer (1980) Exploring factors involved in a cross cultural context
Saxena et al. (1992) Determining the hierarchy and class of elements in cement industry
Mandal and Desmukh (1993) Vendor selection
Kanungo et al. (1999) Developing an IS effectiveness framework
Ravi and Shankar (2004) Explore reverse logistics barriers
Jharkaria and Shankar (2005) Enablers of IT implementation in SC
Ravi et al. (2005) Indentify key reverse logistics variables
Faisal et al. (2006) Modeling the enablers for supply chain risk mitigation
Thakkar et al. (2006) Integrated approach with ISM and ANP to develop a balanced scorecard
Source: Diabat et al. 2013
A Framework for the Analysis of Sustainable Supply Chain Management: an Insight from Indian Rubber Industry 75
Table 3: Structural self -interaction matrix (SSIM)
15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
1 V O V V A V V V V V A A A O
2 V V V A A A A X V A O O A
3 V V V V V V V V V V A A
4O O O V V V V O O V X
5O O O V V V V V V V
6V V V V X V A A A
7 V V V V X V V V
8V O O O O A A
9V O O V A V
10 V O V V A
11 O V V V
12 V V V
13 V V
14 V
15
Table 4: Reachability Matrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DRIVING POWER (Y)
1 1 0 0 0 0 1 1 1 1 1 0 1 1 0 1 9
2 0 1 0 0 0 0 1 1 0 0 0 0 1 1 1 6
3 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 13
4 1 0 1 1 1 1 0 0 1 1 1 1 0 0 0 9
5 1 0 1 1 1 1 1 1 1 1 1 1 0 0 011
60 1 0 0 0 1 0 0 0 1 1 1 1 1 1 8
70 0 0 0 0 1 1 1 1 1 1 1 1 1 1 10
8 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 4
9 0 1 0 0 0 1 0 1 1 1 0 1 0 0 1 7
10 0 1 0 0 0 0 0 1 0 1 0 1 1 0 1 6
11 1 1 0 0 0 1 1 0 1 1 1 1 1 1 0 10
12 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 5
13 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 3
14 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2
15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
DEPENDENCE POWER (X) 5 8 3 2 2 9 6 8 7 9 6 10 8 8 12
Cluster 1: Autonomous variables
These factors have a weak drive power and weak
dependence power. In this cluster we do not have any
variable.
Cluster 2: Dependence variables
These factors have a weak drive power but strong
dependence power. In this cluster we have seven variables,
i.e, 2 (Customer Relationship),8 (Flexible operations),10
(Cleaner Production),12 (Carbon Emissions Reduction),13
(Export Sales), 14 (Market Share) and 15(Pro t)
Cluster 3: Linkage variables
76 Journal of Supply Chain Management Systems Volume 3 Issue 1 January 2014
Table 5: Transivity
TRANSIVITY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
DRIVING
POWER (Y)
1 1 1* 0 0 0 1 1 1 1 1 1* 1 1 1* 1
2 0 1 0 0 0 1* 1 1 1* 1* 1* 1* 1 1 1
3 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1
4 1 1* 1 1 1 1 1* 1* 1 1 1 1 1* 1* 1*
5 1 1* 1 1 1 1 1 1 1 1 1 1 1* 1* 1*
6 1* 1 0 0 0 1 1* 1* 1* 1 1 1 1 1 1
7 1* 1* 0 0 0 1 1 1 1 1 1 1 1 1 1
8 0 1 0 0 0 1 1* 1 0 1* 1* 1* 1* 1* 1
9 0 1 0 0 0 1 1* 1 1 1 1* 1 1* 1* 1
10 0 1 0 0 0 1* 1* 1 0 1 0 1 1 1* 1
11 1 1 0 0 0 1 1 1* 1 1 1 1 1 1 1*
12 0 1 0 0 0 0 1* 1* 0 0 0 1 1 1 1
13 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
14 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
DEPENDENCE
POWER (X)
Table 6: Level Partitioning (Iteration 1)
VARIABLES RS AS IS LEVEL
1 1,2,6,7,8,9,10,11,12,13,14,15 1,3,4,5,6,7,11 1,6,7,11
2 2,6,7,8,9,10,11,12,13,14,15 1,2,3,4,5,6,7,8,9,10,11,12 2,6,7,8,9,10,11,12
3 1,2,3,6,7,8,9,10,11,12,13,14,15 3,4,5 3
4 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 4,5 4,5
5 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 4,5 4,5
6 1,2,6,7,8,9,10,11,12,13,14,15 1,2,3,4,5,6,7,8,9,10,11 1,2,6,7,8,9,10,11
7 1,2,6,7,8,9,10,11,12,13,14,15 1,2,3,4,5,6,7,8,9,10,11,12 1,2,6,7,8,9,10,11,12
8 2,6,7,8,10,11,12,13,14,15 1,2,3,4,5,6,7,8,9,10,11,12 2,6,7,8,10,11,12
9 2,6,7,8,9,10,11,12,13,14,15 1,2,3,4,5,6,7,9,11 2,6,7,9,11
10 2,6,7,8,10,12,13,14,15 1,2,3,4,5,6,7,8,9,10,11 2,6,7,8,10
11 1,2,6,7,8,9,10,11,12,13,14,15 1,2,3,4,5,6,7,8,9,11 1,2,6,7,8,9,11
12 2,7,8,12,13,14,15 1,2,3,4,5,6,7,8,9,10,11,12 2,7,8,12
13 13,14,15 1,2,3,4,5,6,7,8,9,10,11,12,13 13
14 14,15 1,2,3,4,5,6,7,8,9,10,11,12,13,14 14
15 15 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 15 1
A Framework for the Analysis of Sustainable Supply Chain Management: an Insight from Indian Rubber Industry 77
Table 7: Level Partitioning (Iteration 2)
VARIABLES RS AS IS LEVEL
1 1,2,6,7,8,9,10,11,12,13,14 1,3,4,5,6,7,11 1,6,7,11
2 2,6,7,8,9,10,11,12,13,14 1,2,3,4,5,6,7,8,9,10,11,12 2,6,7,8,9,10,11,12
3 1,2,3,6,7,8,9,10,11,12,13,14 3,4,5 3
4 1,2,3,4,5,6,7,8,9,10,11,12,13,14 4,5 4,5
5 1,2,3,4,5,6,7,8,9,10,11,12,13,14 4,5 4,5
6 1,2,6,7,8,9,10,11,12,13,14 1,2,3,4,5,6,7,8,9,10,11 1,2,6,7,8,9,10,11
7 1,2,6,7,8,9,10,11,12,13,14 1,2,3,4,5,6,7,8,9,10,11,12 1,2,6,7,8,9,10,11,12
8 2,6,7,8,10,11,12,13,14 1,2,3,4,5,6,7,8,9,10,11,12 2,6,7,8,10,11,12
9 2,6,7,8,9,10,11,12,13,14 1,2,3,4,5,6,7,9,11 2,6,7,9,11
10 2,6,7,8,10,12,13,14 1,2,3,4,5,6,7,8,9,10,11 2,6,7,8,10
11 1,2,6,7,8,9,10,11,12,13,14 1,2,3,4,5,6,7,8,9,11 1,2,6,7,8,9,11
12 2,7,8,12,13,14 1,2,3,4,5,6,7,8,9,10,11,12 2,7,8,12
13 13,14 1,2,3,4,5,6,7,8,9,10,11,12,13 13
14 14 1,2,3,4,5,6,7,8,9,10,11,12,13,14 14 2
Table 8: Level Partitioning (Iteration 3)
VARIABLES RS AS IS LEVEL
1 1,2,6,7,8,9,10,11,12,13 1,3,4,5,6,7,11 1,6,7,11
2 2,6,7,8,9,10,11,12,13 1,2,3,4,5,6,7,8,9,10,11,12 2,6,7,8,9,10,11,12
3 1,2,3,6,7,8,9,10,11,12,13 3,4,5 3
4 1,2,3,4,5,6,7,8,9,10,11,12,13 4,5 4,5
5 1,2,3,4,5,6,7,8,9,10,11,12,13 4,5 4,5
6 1,2,6,7,8,9,10,11,12,13 1,2,3,4,5,6,7,8,9,10,11 1,2,6,7,8,9,10,11
7 1,2,6,7,8,9,10,11,12,13 1,2,3,4,5,6,7,8,9,10,11,12 1,2,6,7,8,9,10,11,12
8 2,6,7,8,10,11,12,13 1,2,3,4,5,6,7,8,9,10,11,12 2,6,7,8,10,11,12
9 2,6,7,8,9,10,11,12,13 1,2,3,4,5,6,7,9,11 2,6,7,9,11
10 2,6,7,8,10,12,13 1,2,3,4,5,6,7,8,9,10,11 2,6,7,8,10
11 1,2,6,7,8,9,10,11,12,13 1,2,3,4,5,6,7,8,9,11 1,2,6,7,8,9,11
12 2,7,8,12,13 1,2,3,4,5,6,7,8,9,10,11,12 2,7,8,12
13 13 1,2,3,4,5,6,7,8,9,10,11,12,13 13 3
Table 9: Level Partitioning (Iteration 4)
VARIABLES RS AS IS LEVEL
1 1,2,6,7,8,9,10,11,12 1,3,4,5,6,7,11 1,6,7,11
2 2,6,7,8,9,10,11,12 1,2,3,4,5,6,7,8,9,10,11,12 2,6,7,8,9,10,11,12
3 1,2,3,6,7,8,9,10,11,12 3,4,5 3
4 1,2,3,4,5,6,7,8,9,10,11,12 4,5 4,5
5 1,2,3,4,5,6,7,8,9,10,11,12 4,5 4,5
6 1,2,6,7,8,9,10,11,12 1,2,3,4,5,6,7,8,9,10,11 1,2,6,7,8,9,10,11 4
7 1,2,6,7,8,9,10,11,12 1,2,3,4,5,6,7,8,9,10,11,12 1,2,6,7,8,9,10,11,12 4
78 Journal of Supply Chain Management Systems Volume 3 Issue 1 January 2014
VARIABLES RS AS IS LEVEL
8 2,6,7,8,10,11,12 1,2,3,4,5,6,7,8,9,10,11,12 2,6,7,8,10,11,12 4
9 2,6,7,8,9,10,11,12 1,2,3,4,5,6,7,9,11 2,6,7,9,11
10 2,6,7,8,10,12 1,2,3,4,5,6,7,8,9,10,11 2,6,7,8,10
11 1,2,6,7,8,9,10,11,12 1,2,3,4,5,6,7,8,9,11 1,2,6,7,8,9,11
12 2,7,8,12 1,2,3,4,5,6,7,8,9,10,11,12 2,7,8,12 4
Table 10: Level Partitioning (Iteration 5)
VARIABLES RS AS IS LEVEL
1 1,2,9,10,11 1,3,4,5,11 1,11
2 2,9,10,11 1,2,3,4,5,9,10,11 2,9,10,11 5
3 1,2,3,9,10,11 3,4,5 3
4 1,2,3,4,5,9,10,11 4,5 4,5
5 1,2,3,4,5,9,10,11 4,5 4,5
9 2,9,10,11 1,2,3,4,5,9,11 2,9,11
10 2,10 1,2,3,4,5,9,10,11 2,10 5
11 1,2,9,10,11 1,2,3,4,5,9,11 1,2,9,11
Table 11: Level Partitioning (Iteration 6)
VARIABLES RS AS IS LEVEL
1 1,9,11 1,3,4,5,11 1,11
3 1,3,9,11 3,4,5 3
4 1,3,4,5,9,11 4,5 4,5
5 1,3,4,5,9,11 4,5 4,5
9 9,11 1,2,3,4,5,9,11 9,11 6
11 1,9,11 1,2,3,4,5,9,11 1,9,11 6
Table 12: Level Partitioning (Iteration 7)
VARIABLES RS AS IS LEVEL
1 1 1,3,4,5 1 7
3 1,3 3,4,5 3
4 1,3,4,5 4,5 4,5
5 1,3,4,5 4,5 4,5
Table 13: Level Partitioning (Iteration 8)
VARIABLES RS AS IS LEVEL
3 3 3,4,5 3 8
4 3,4,5 4,5 4,5
5 3,4,5 4,5 4,5
Table 14: Level Partitioning (Iteration 9)
VARIABLES RS AS IS LEVEL
4 4,5 4,5 4,5 9
5 4,5 4,5 4,5 9
A Framework for the Analysis of Sustainable Supply Chain Management: an Insight from Indian Rubber Industry 79
Table 15: Position coordinates of identifi ed variables
Variables Dependence Power (X) Driving Power(Y)
1 5 9
2 8 6
3 3 13
4 2 9
5 2 11
6 9 8
7 6 10
8 8 4
9 7 7
10 9 6
11 6 10
12 10 5
13 8 3
14 8 2
15 12 1
Fig. 4: ISM Model
80 Journal of Supply Chain Management Systems Volume 3 Issue 1 January 2014
These factors have a strong drive power as well as strong
dependence power. In this cluster we have three variables, i.e., 6
(Green Technology Adoption), 7 (Total Quality Management),
9 (Technology Innovativeness)and 11(Environmental and
Social Responsibility).
Cluster 4: Driving variables
These factors have a strong drive power but weak
dependence power. In this cluster we have four variables,
i.e., 1 (Supplier Relationship Management), 3 (Top
Management Commitment), 4 (Regulatory Pressures)
and 5 (Market Pressures)
Earlier works and reviews have a limited focus and
narrow perspective. They do not cover adequately
all the aspects and facets of SSCM. Although rub-
ber industry is of national important for the growth
of Indian economy but lack of previous SSCM em-
pirical studies related to this sector is the main rea-
son for lack of SSCM knowledge. Rubber board of
India is putting emphasis in enhancing export sales
and showing interest in GSCM practices. Literature
show that without SSCM practices it is impossible
to develop competitiveness in the global market.
The model developed by us clearly explains the
complex relationships among key variables and also
show the direct and indirect relationships in a better
fashion so that managers can easily understand the
links and devise GSCM strategies successfully.
SSCM practices in Indian rubber industry are main-
ly in uenced by Market Pressures, Carbon Emission
Reduction, Market Share and Pro t. Rubber indus-
try feel motivated in SSCM practices due to increase
in market share and pro t. Also there would be re-
duction in carbon emission and hence less market
pressure on the sector. This will increase the brand
image of the rm.
Secondly Supplier relationship management and
Green technology adoption are the linkage variables
with respect to SSCM practices in Indian rubber
industry.
Thirdly Supplier Relationship Management, Top
Management Commitment, Regulatory Pressures
and Market Pressures are the key drivers of SSCM
practices in Indian rubber industry.
Reducing emissions in the rubber industry requires
a sustained and focused effort.
Maximize energy ef ciency potential by replacing
obsolete, inef cient equipments and adopting best
available technologies and best practices.
Switching to low carbon energy sources.
Fig. 5: MICMAC analysis
Driving Power(Y),
5, 9
Driving Power(Y),
8, 6
Driving Power(Y),
3, 13
Driving Power(Y),
2, 9
Driving Power(Y),
2, 11
Driving Power(Y),
9, 8
Driving Power(Y),
Driving Power(Y),
8, 4
Driving Power(Y),
7, 7
Driving Power(Y),
9, 6
Driving Power(Y),
6, 10
Driving Power(Y),
10, 5
Driving Power(Y),
8, 3
Driving Power(Y),
8, 2 Driving Power(Y),
12, 1
Driving Power
Dependence Power
Driving Power(Y),
Driving Power(Y),
Driving Power(Y),
Driving Power(Y),
Driving Power(Y),
9, 8
9, 8
9, 8
Driving Power(Y),
Driving Power(Y),
Driving Power(Y),
Driving Power(Y),
Driving Power(Y),
Driving Power(Y),
Driving Power(Y),
Autonomous
variables Dependence
variables
Linkage
variables
Driving variables
A Framework for the Analysis of Sustainable Supply Chain Management: an Insight from Indian Rubber Industry 81
Alter product design and waste disposal protocol to
facilitate reuse and recycle in order to close the sup-
ply chain loop.
Improve benchmarking through standardized mea-
surement and data capturing protocols.
Formation of a cross functional team and prepare
the action plan for implementing GSCM in a holistic
way.
Preparation of the GSCM/environmental policy and
create awareness among all employees.
Approving the budget from top management for in-
vestment in clean technologies/best practices.
Emphasis on supplier relationship management.
Train and educate suppliers so as to implement ISO
14001.
Emphasis on TQM practices.
Monitor progress on a periodic basis is important.
We understand that every management research has its
own limitations; the present study also suffers from certain
limitations. Present study is con ned to a single sector
and need to be validated in other sector and industry.
There are three important components of ‘Unique
Contributions’ i.e., What, How and Why (Whetten,
1989). In the present study we have put effort to answer
the three vital questions in terms of variables which we
have identi ed from the synthesis of literature and experts
opinion. We have developed a contextual relationships
using ISM approach and further re ned using MICMAC
analysis.
Given the nature of this study, researcher makes three
contributions:-
First researcher provides one of the most compre-
hensive analyses of SSCM in Indian context.
Second the study furthers existing research in the
eld of SSCM, which suggests that SRM is an im-
portant factor for its successful implementation.
Researcher therefore contributes to an emergent lit-
erature, which suggests that the implementation of
SSCM is sensitive to the characteristics of buyer-
supplier relationships.
Third SSCM has been explored on a more in-depth
and theoretical level, by integrating NRBV and in-
stitutional theories, and addressing both internal and
external perspectives of the rm.
To eradicate the limitations of present research we propose
to validate the model empirically in other sectors by using
Exploratory factor analysis and further test using linear
multiple regression analysis using SEM packages such as
AMOS/LISREL.
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