Embed Size (px)
DESCRIPTION
Research on green supply chain management by professor K. Mathiyazhagan.It is analysis of supply chain in indian industry.
Citation preview
lable at ScienceDirect
Journal of Cleaner Production 83 (2014) 391e403
Contents lists avai
Journal of Cleaner Production
journal homepage: www.elsevier .com/locate/ jc lepro
Analysis of enablers for implementation of sustainable supply chainmanagement e A textile case
Ali Diabat a, *, Devika Kannan b, K. Mathiyazhagan c
a Department of Engineering Systems and Management, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emiratesb Department of Mechanical & Manufacturing Engineering, Aalborg University, Copenhagen, Denmarkc Mechanical Engineering, ITM University, Gurgaon, India
a r t i c l e i n f o
Article history:Received 28 April 2014Received in revised form13 June 2014Accepted 13 June 2014Available online 9 July 2014
Keywords:Sustainable Supply Chain ManagementEnabler analysisInterpretive Structural Modelling
* Corresponding author.E-mail addresses: [email protected] (A. Diab
(D. Kannan).
http://dx.doi.org/10.1016/j.jclepro.2014.06.0810959-6526/© 2014 Elsevier Ltd. All rights reserved.
a b s t r a c t
Industries currently face pressure on environmental initiatives from both government regulations andglobal competition in addition to customer pressure. Hence, organizations are forced to implementsustainable practices to improve their environmental performance over economic performance. TheSustainable Supply Chain Management (SSCM) system is a concept which ensures environmentallyfriendly practices in traditional supply chains. Industries in developing countries such as India facepressure from various perspectives to adopt SSCM in Traditional SCM. In this regard, the objective of thisstudy has been fixed to analyze the enablers for implementing SSCM into Indian industries. This study isessential for Indian industries, and especially for textile industries, to market products in the World TradeOrganization and huge market opportunities. There are many enablers for adopting SSCM into TSCM, butthese enablers do not ensure similar impact in all industries and countries; in fact, where SSCM isadopted the system varies according to culture and the country's regulations. Hence, industries essen-tially need to identify influential enablers to adopt SSCM. This study aims to identify influential enablersfor SSCM by using Interpretive Structural Modelling (ISM) from 13 recommended enablers in five Indiantextile units located in south India. ISM results reveal that five enablers dominate an industry's practices,and those five enablers include Adoption of safety standards, Adoption of green practices, Communityeconomic welfare, Health and safety issues, and Employment stability. The result of this study shows thatsafety perspective enablers provide additional motivation when compared to the other enablers forSSCM adoption.
© 2014 Elsevier Ltd. All rights reserved.
1. Introduction
Pagell and Shevchenko (2014) state that over the past twodecades, the topic of “sustainability” has received substantialattention in supply chain management and has been the subject ofmuch research. Current researchers and practitioners give specialattention to environmental issues to achieve environmental andbusiness needs (Caniato et al., 2011). Businesses certainly do facenew challenges and opportunities for adopting good environ-mental practices (Sezen and Turkkantos, 2013). Globally, sustain-ability has unique issues related to market competition, globallimitations of energy, the availability of raw and virgin material,environmental protection crises, and increasing global population(Bajaj et al., 2013). In this view, Hart and Milstein (2003) analyzed
at), [email protected]
the global challenges associated with sustainable development;they sought to identify the strategies and practices that contributeto a more sustainable world while simultaneously driving share-holder values. According to Veleva and Ellenbecker's (2001)statement, many industries started to adopt differing practicestowards sustainable development, which they defined as “thecreation of goods and services using processes and systems thatare non-polluting, conserve energy and natural resources, areeconomically viable, safe and healthy for employees, communitiesand consumers, socially and creatively rewarding for all workingpeople.” Sustainable development is one solution for reducingwaste by effective resource utilization. In this view, SustainableSupply Chain Management (SSCM) is an activity that helps toensure Sustainable Development (SD). Hart and Milstein (2003)point out that a truly sustainable development enterprise is onethat simultaneously achieves social, economic, and environmentalbenefits. These three achievements are called the triple bottomline.
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403392
Kuik et al. (2010) demonstrate that traditional supply chainsshould develop into Sustainable Supply Chain Management (SSCM)regarding social, economic, and environmental benefits. Morespecifically, SSCM can be defined as the “integration of social,economic, and environmental practices within a global supplychain that provides green products, excellent services and accurateinformation (Xie and Allen, 2013), sharing those benefits with allemployees, shareholders, business partners and the wider com-munity” (Kuik et al., 2010). In India, the concept of SSCM focuses onindustries through integrating sustainability in supply chain man-agement. Aslan et al. (2012) said that many industries began toconsider sustainable development due to India's entry into theWorld Trade Organization (WTO).
SSCM implementation results in motivational activities called“enablers” which industries must consider. Grzybowska (2012)defines an enabler as ‘‘one that enables another to achieve anend’’ where the word “enable” means to make able; to give power,means, competence, or ability. An enabler is considered a variablethat motivates the attainment of SSCM. But in practice, all enablersdo not ensure a similar impact for sustainable adoption in in-dustries. Industries have to identify the best initial enabler to beconsidered for SSCM adoption. Only then can they adopt full-fledged sustainable practices in traditional activities (Santos et al.,2013). For this reason, it is essential to analyse enablers for SSCMadoption to ensure an industry's sustainable development. Manyresearchers have analysed enablers for SSCM implementation fromtheir country's perspective. For instance, Faisal (2010) analysed 16enablers for SSCM adoption from a Qatar perspective; Hussain(2011) analysed 21 enablers for SSCM from a Canadian perspec-tive, and Walker and Jones (2012) investigated enablers and bar-riers for SSCM from a United Kingdom perspective. Analysingenablers for SSCM adoption is important and should be pursuedglobally from each country's unique perspective. Based on this, thisresearch aims to analyse and to identify influential enablers foradoption of SSCM in Indian textile industries.
Specially, the identification of influential enablers for SSCM inthe textile sector is important because India has a current popu-lation of over 1 billion, and it is anticipated that India will likelyovertake China as the most populous country with around 1.6billion population by 2050 (Hubacek et al., 2007). Due to the highpopulation, textile industries have a tremendous opportunity toproduce a large quantity of materials to meet customer needs.Generally, textile industries use hazardous materials and disposeuntreated waste into the surrounding environment. Based onpressures from government regulations, recently textile industrieshave increased their awareness of environmental issues and havestarted to incorporate sustainable concepts in their TSCM byadopting SSCM practices. Supply chain management has opera-tional activities starting from the procurement of raw materials tothe delivery of finished products (forward supply chain) (Dattaet al., 2013; Dweiri, and Khan, 2012; Jaggi and Verma, 2009;Maleki and CruzeMachado, 2013; Tewari and Misra, 2013). Col-lecting used products from customers is also a part of SCM (Reversesupply chain management) (Charkha and Jaju, 2014; Topcu et al.,2013). There is limited research in Indian textile industries from aperspective of sustainability (as explained in the Research Gapsection). Influential enablers are identified from a set of 13 rec-ommended enablers for SSCM adoption with the help of Interpre-tive Structural Modelling (ISM) in five textile units, based on theirinterest, in Tamilnadu, South India. The results impact environ-mental adoption, making it easier for the adoption of SSCM in In-dian textile industries which then can be extended to all industriesin India. This study also helps industries to improve overall sus-tainable performance, from procurement of raw material to finalproduct, by identifying leading or dominant enablers to adopt
SSCM in traditional activities. The discussions and conclusions arefrom an extensive survey, site visits, and interviews. This paperbrings to clear view about the essential needs to analyze the en-ablers for SSCM adoption in an Indian textile sector context whichhas not been done previously. This original research helps man-agers of textile industries to improve their environmentalperformance.
The paper is organized as follows: a literature review and theresearch gaps are given in Section 2, and a description of the studyfollows in Section 3. The methodology (ISM) of the study isdescribed in Section 4 and our research design is summarized inSection 5. The results we obtained, along with a discussion, aredescribed in Section 6. Finally, our conclusions, recommendations,limitations, and future scope of the study are given in Section 7.
2. Literature review
Today, environmental issues attract the concern of global supplychain practitioners (Muduli et al., 2013). Ahi and Searcy (2013)mention that due to increasing recognition of environmental is-sues in traditional activities, organizations need to address theproblem of sustainability in their operations. Seuring (2013) offereda modeling technique for SSCM through 300 papers published inthe past fifteen years on the topic of green or sustainability (for-ward) supply chains. Also, he summarized research on quantitativemodels for forward supply chains in order to achieve the mostsubstantial review of the field. Seuring and Gold (2013) summa-rized that effective integration of sustainability into industries thatrequired action beyond the organizational boundaries. Boundary-spanning activities are increasingly being taken up by corporateaction and are spurred, accompanied, and reflected in a growingbody of academic literature. For this reason, recently, environ-mental issues have received special attention from researchersglobally. To offset these emerging environmental issues, industrypractitioners expressed keenness to explore a solution to reducewaste generated from current supply chain practices (Kuik et al.,2010). Govindan et al. (2014) mentioned that manufacturing in-dustries started to implement environmental practices in theirexisting supply chain management. Ahi and Searcy (2013) identi-fied and analyzed definitions of Green Supply Chain Management(GSCM) and SSCM. They show that researchers worldwideexpressed much interest about research in environmental practiceslike GSCM (Salimifard and Raeesi, 2014) and SSCM. SustainableDevelopment (SD) is one of the better solutions for waste reduc-tion. It improves industries' environmental performance. Theconcept of sustainability was defined in 1987 in the Brundtlandreport and then adopted by the United Nations World Commissionon Environment and Development (WCED): “sustainability meansbeing able to satisfy current needs without compromising thepossibility for future generations to satisfy their own needs” (WorldCommission on Environment and Development, 1987). AlexandreTorres Romiguer and Alexandre (2011) states that there are variousdefinitions for “sustainable development” favoring the user. Forinstance, the “Forum for the Future Organization” defines sustain-able development as a dynamic process, which enables all people torealize their potential and improve their quality of life in wayswhich simultaneously protect and enhance the Earth's life supportsystems. Gladwin et al., 1995, p. 878, p. 897 and Carter and Rogers(2008) inferred that Sustainable Development (SD) must encom-pass the concept of security, which “demands safety from chronicthreats and protection from harmful disruption” including “biodi-versity loss, climate change, freshwater scarcity, food insecurity(KhalilieDamghani et al., 2012; Rajkumar, 2013), and populationgrowth.” In recent years, researchers focused on a combination ofscientific research with supply chain management (SCM) through
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403 393
Sustainable Development (SD). In modern industries, traditionalSCM is relevant not only from an economic perspective (Ramaaet al., 2013), but also because of its social and environmentalimpact (Kleindorfer et al., 2005; Elkington, 1994; Zhu et al., 2008;Caniato et al., 2011). Crals and Vereeck (2005) inferred that termslike corporate social responsibility, ethical funds, eco-efficiency,highlight these impacts. While these terms indicate different con-cepts, they point to various aspects of sustainable development.Sikdar et al. (2012) mentions sustainability analysis as a system ofinterest, to which products, or processes, or corporations, or evenecosystems can relate.
Sustainability is a concept of the triple bottom line (Elkington,1998; Kleindorfer et al., 2005) advanced to a leading area withinacademic research. Govindan et al. (2013) analyzed the sustainablesupply chain initiatives and determined the problem of identifyingan effective model based on the Triple Bottom Line (TBL) approach(economic, environmental, and social aspects) for supplier selec-tion operations in the supply chain with the help of a fuzzy multicriteria approach. Grimm et al. (2014) summarized the research onthe theory of Critical Success Factors (CSFs) by applying the theoryto sustainability and sub-supplier management perspectives in thefood industry. Tseng and Hung (2014) proposed a strategicdecision-making model for SSCM that accounts for both the oper-ational costs and social costs caused by carbon dioxide emissionsfrom operating such a supply chain network. It shows that all overthe world, researchers have started to analyze sustainability issuesin all directions in order to promote Sustainable Development (SD).Ugwua and Haupt (2007) state that Sustainable Development (SD)attained international focus to reduce pollution globally; they notethat it is used to make decisions about various projects. Heilig(1997) stated that the concept of sustainability involves proceduresthat should not unbalance the ecosystem of which we are a part,but that this is precisely what the human species has always beendoing. In industries, sustainability involves becoming aware of andmanaging risks associated with scarcity in natural resources; theserisks are used as inputs to the supply chain and help to identifyfluctuations in energy costs. In addition, proactive engagement insustainable practices lowers the risk of the introduction of new andcostly regulations (Shrivastava, (1995a, p. 955); Porter and van derLinde, 1995; Carter and Rogers, 2008; Shen et al., 2013). White andLee (2009) and Sarkis (2012) state that when a sustainable supplychain term is used, the default perspective is typically environ-mental or ecological sustainability.
Alexandre Torres Romiguer and Alexandre (2011) determinesthat the seven most critical environmental requirements and eco-nomic aspects for sustainable practice are as follows: reduction inwaste and emissions; reduction in energy intensity of goods andservices; use of renewable and sustainable energy resources;maximum use and re-use of recycled components and materials;measurement and assessment of business impact on ecosystems;standard measures for evaluating sustainability performance; andenvironmental consciousness pervading the culture of an organi-zation. Alexandre Torres Romiguer and Alexandre (2011) pleads forincluding social requirements in Corporate Social Responsibilitiesbecause it is rare for management literature to examine social andeconomic responsibilities; most existing studies on organizationalsustainability focus on ecological sustainability. Van Hoof and Thiell(2014) have tested a theoretical model of collaboration capacity as amulti-dimensional organizational construct to gauge cleaner pro-duction adoption within supply chains from the perspective ofsustainable supply chains in Mexican small and medium-sizedenterprises. In this view, Wiengarten et al. (2013) has exploredthe differences in Environmental Management System imple-mentation and investments from North America and WesternEurope perspectives to achieve a greater understanding of
industries' environmental motivations. Also, this study analyseddifferences in ISO 14000 certification and environmental supplychain investment levels between Western European and NorthAmerican industries. Besk et al. (2014) describe how SSCM prac-tices allow companies to maintain control over their supply chainand to gain better benefits with the adoption of dynamic capabil-ities from the perspective of Germany. Marshall et al. (2005) &Chahal and Sharma (2006) identify both internal and externaldrivers for SSCM implementation. External drivers include cus-tomers' demand for such products (Nilakantan, 2013); pressuresfrom investors, community groups, the public, and competitors;and compliance with regulations. Internal drivers are normallyconnected to managerial thoughts, employees' demands, organi-zational culture, internal pressure on business managers, and socialdevelopment activities (Haigh and Jones, 2006). From a financialperspective there are three major enablers (drivers), namely: costsaving, greater efficiency, and increased profits (Berry andRondinelli, 1998; Bhaskaran et al., 2006 and Porter and van derLinde, 1995). Beske et al. (2014) investigate SSCM practices anddynamic capabilities in the food industry.
Faisal (2010) describes the steps in SSCM implementation. Thesupply chain considers the product from the initial processing ofraw material to delivery to the customer (Kongar and Gupta, 2009;Kassem and Dawood, 2013; Patil and Kant, 2014). From Faisal'sresearch, one infers the effective adoption of sustainable practicesin a supply chain by understanding the dynamics connecting arange of enablers aiding the conversion of a supply chain into atruly sustainable entity. Bagheri and Hjorth (2007) and Faisal(2010) state that sustainability is not a predetermined ideal, butan evolutionary progression of developing the management ofsystems through enhanced understanding and knowledge. Hussain(2011) discovered 21 enablers for SSCM and found the interactionbetween them through ISM from a Canadian perspective. Zheng(2010) evaluated environmentally friendly conditions inmanufacturing supply chains by using the Fuzzy analytical hierar-chy process (AHP) approach. Faisal (2010) also analyzed enablersfor SSCM, but only 10 enablers were considered; this study focusedon economic and environmental perspectives rather than on thesocial perspective. Singh and Debnath (2012) analyse the benefitsof sustainability through a Clean Development Mechanism (CDM)relevant to India using ISM. Government regulations, competitiveadvantages, and corporate responsibility emerged as importantdrivers for initialization of sustainability in today's businesses(Houda and Said, 2011). De Brito et al. (2008) concluded thatinitially corporations get involved with sustainability due to pres-sures from legislation. Dehghanian et al. (2011) created a frame-work to determine an integrated index to assess sustainability ofthe supply chain of a product of AHP. Vinodh (2010) indicated amajor improvement of agility and sustainability in the design anddevelopment of knobs. Similarly, Berkes et al. (2000) discussedcomparative aspects of land-use sustainability in two areas, whichoffer physical and ecological similarities and cultural and historicalcontrasts between India and Canada. Aslan et al. (2012) stated thattextile industries started to adopt e-sustainability through groupactivities.
Dehghanian et al. (2011) stated that industries started engagingin various activities involving sustainable developments.Ammenberg and Hjelm (2003) also pointed out that small andmedium enterprises (SME) globally contribute to more than halfthe global economy and environmental impact, but are oftenneglected in research as to how and to what extent they affect theenvironment (Von Geibler et al., 2004; Preuss, 2005; Michelsen andFet, 2010). India has numerous SMEs producing various productsboth for home use and for export. Many sectors also grew to thenext level to enter the WTO. The textile industry is a sector which
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403394
started to improve its performance from the perspective of envi-ronmentally friendly practices with economic improvements. Thepresent study focuses on analysing enablers for SSCM adoptionfrom environmental, economic, and social perspectives. Initially,sustainable supply chain management articles were collected fromvarious international publications such as Science direct, Taylorsand Francis, Emerald, Wiley, Springer, and other openly availablematerials. We sought a fit for sustainable adoption of traditionalsupply chain by using the terms “sustainable supply chain man-agement; enablers for sustainable supply chain and sustainablesupply chain management in textile industry.” Based on theseterms, more than 100 papers are downloaded which are then fittedto the sustainable supply introduction and enablers for SSCManalysis based on the objective of our studies. From these papers,initially 25 enablers were identified and shortlisted into 13 enablersafter a discussion with textile industry experts. A detaileddescription about the selection of the 13 enablers from the 25 en-ablers for SSCM adoption is given in the Phase 1: Preliminary sur-vey section; there we identify common enablers. Next, these 13enablers were categorized based on the three perspectives essen-tial to the triple bottom line of sustainability: environmental, eco-nomic, and social. In Table 1, we identify the enablers from theliterature and show both the sources and roles of each element ofthe triple bottom line.
2.1. Research Gap
In the past two decades, researchers have published more than100 papers about the operations and supply chain managementfields, dealing with special concerns related to Green Supply ChainManagement (GSCM) and sustainable supply chains topics(Mathiyazhagan et al., 2014; Jayaraman et al., 2007; Linton et al.,2007; Seuring et al., 2008; Sarkis, 2012; Xu et al., 2014; Kaliyanet al., 2013; Govindan et al., 2013b). Generally, different industrieshave differing opinions about a single factor based on their natureof manufacturing activities, culture, and country. Specially, manyresearchers pointed out that different industries have differentopinions about their environmental factors (Govindan et al., 2014;Mathiyazhagan and Haq, 2013; Zhu and Sarkis, 2006). Followingthis, from the initial survey (mail and face to face interview), weobserved that experts from different industries give varied opinionsfor each enabler for SSCM adoption (described in the Phase 1:Preliminary survey to identify common enablers sections).
From the literature review we see that industries (MNCs andsome SMEs) globally are aware about industrial pollution (Junioret al., 2014) and have started to adopt SSCM in their TSCM. In thisregard, there are many enablers for SSCM adoption. Many re-searchers focused on SSCM performance and other perspectives
Table 1Enablers for the Sustainable Supply Chain Management adoption with references.
Sl. No Enablers Resources
1. Employment stability (E1) Kuik et al., 20Bhaskaran et
2. Health and safety issues (E2) Carter et al., 23. Community economic welfare (E3) Gabzdylova e4. Adoption of safety standards (E4) Carter et al., 25. Adoption of green purchasing (E5) Sarkis, 2012;6. Adoption of green practices (E6) Sarkis, 2012;7. Eco-design (E7) Zhu et al., 208. Government regulations (E8) Zhu et al., 209. Hazard management (E9) Waheed et al10. Customer satisfaction (E10) Hussain, 201111. Environmental cost (E11) Carter and Ro12. Economic input to infrastructural development (E12) Kleindorfer e13. Improvement of product characteristics (E13) Hussain, 2011
(Faisal, 2010; Vinodh, 2010; Dehghanian et al., 2011; Hussain, 2011;Zheng, 2010) but there is not much work on the investigation ofenablers for adoption of SSCM from an Indian scenario. Similarstudies were undertaken in countries such as Poland and Canada(Hussain, 2011; Grzybowska, 2012). There is a large research gap inthe identification of influential enablers for adoption of SSCM inIndian textile industries. The Indian textile sector is the secondlargest employment provider; it ensures direct employment to over35 million people in the country. Hence, it is essential to think ofenvironmental adoption in the textile sector compared to othersectors. For these reasons, this issue was chosen for this study. Thispaper addresses the gap in the identification of dominant enablersto implement SSCM through a two-phase research approach. Phase1 is an initial survey to identify the enablers for SSCM, and Phase 2is the identification of the leading enabler by ISM approach.
3. Description of the study
As Pagell and Shevchenko (2014) state, all industries haven'tshown much interest to improve sustainability in supply chainmanagement without any external motivations. With sustainabilityissues in the textiles sector, the main focus is often on social issuessuch as child labor, working conditions (e.g. contract, payment,representation), and workers' health and safety. But environmentalaspects of production are increasingly attracting attention. In aglobal economy, an SME business sector's contribution is substan-tially large. Hence, it is necessary to adopt environmentally friendlypractices in SMEs also. Most textile units come under the SMEscategory. The textile industry, one of the largest global industriesafter the oil industry, is also one of the most polluting. Caniato et al.(2011) point out that many fashion, apparel, and textile companiesare SMEs. He also suggests that SMEs are able to reshape theirsupply chain and to identify different practices that large com-panies were unable to pursue. Kruse and Storm Rasmussen (2012)mention that fashion and textile industries are the most importantpolluters of the environment, in supply chains, in production, inmanual labor, and finally to the consumer. The fashion industry isglobal and it is one of the most polluting and socially challengedindustries in the world (Nordic Fashion Association, 2012). Specif-ically, the textile sector is responsible for significant environmentalproblems associated with the production process due to the use oftoxic chemicals, which adversely impact the natural environmentand human health (Pesticide Action Network, 2012).
The basic reasons for the analysis of enabler issues in SSCM aresummarized as follows:
� Due to limited availability of resources and pollution, Indianindustries are under heavy stress to focus on waste reduction
Environmental Economic Social
10; Gabzdylova et al., 2009;al., 2006
✓ ✓
007; Carter and Rogers, 2008 ✓ ✓
t al., 2009 ✓ ✓ ✓
007; Carter and Rogers, 2008 ✓ ✓
Mudgal et al., 2009 ✓ ✓
Govindan et al., 2013a ✓ ✓ ✓
06; Vojdani and Lootz, 2012 ✓ ✓
06 ✓ ✓
., 2009 ✓ ✓
; Faisal, 2010 ✓ ✓
gers, 2008 ✓ ✓ ✓
t al., 2005; Elkington, 1994 ✓ ✓
✓ ✓
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403 395
and consumption of less energy, thereby ensuring sustainablesupply chains.
� Due to government regulations and customers' environmentalconsciousness, Indian industries have started to adopt sustain-ability in supply chains to keep their customers and to sustaintheir positions in the industrial environment.
So, adopting sustainable concepts in traditional supply chainmanagement serves as a strong motivation to reduce hazards inindustries and to work towards ensuring a pollution-freeenvironment.
Presently, environmental pollution in textile industries inTamilnadu, India is a very serious issue. Every day, the media,including newspapers, social networks such as Facebook andTwitter focus on this issue, and the result is that the governmentissues stricter regulations. The public also has a heightenedawareness about the hazards of pollution. Due to these reasons,industries face pressure to update their current environmentaltechnology (SSCM). Almost all Indian textile industries have startedto think about and adopt the new concept (SSCM) to reduce the useof hazardous materials in their activities due to stricter governmentregulations. This study was helpful for industries to improve theirsustainable performance by identifying leading or dominant en-ablers to adopt SSCM in traditional activities. This research wascarried out in five textile units in Tamilnadu as described in the nextsection.
4. Methodology of the study
The steps of the solutionmethodology followed in this study areshown in Fig. 1.
5. Research design
Before mailing our survey to the industry, we visited more than50 industries involved in spinning and other related textile
Fig. 1. Flowchart of study for five textile units located in Tamilnadu.
industries located in South India. Hence, we selected South India forthe current research. Coimbatore is a hub of textile and spinningindustries in Tamilnadu. After our initial visit, we shortlisted 15industries for this research. These 15 industries were selected basedon their involvement in environmental practices through theConfederation of Indian Industry (CII). CII works to make and sus-tain an environment favorable for industrial growth in India(Govindan et al., 2014). After this, we mailed 15 textile industries.The mail contained the objectives and the necessity for research.Following frequent phone calls and mails, we received a responsefrom eight industries after two weeks. Finally, we shortlisted fivetextile industries from those eight industries, based on the avail-ability of experts. Experts in three industries were too busy withproduction targets, so we eliminated those three industries fromour study. Finally, this research was carried out in five textile unitslocated in Tamilnadu. These five were finalized and identified afterdirect visits to their plants. Based on their interest to improve theirenvironmental performance, the five industries were allotted timeand expertise (those with more than ten years' experience onenvironmental issues) from different departments of each industry.The selected industries produce a variety of apparel for men,women, and children. Of the five industries, a particular unit wasselected based on interest. The chosen industry has more than 100showrooms in Indian cities and exports to 17 countries. These fiveindustries have more experience in this field, and the managers ofthese five industries have more in-depth knowledge about em-ployees, environmental issues, and recent technologies. Because ofthese reasons and due to the availability of industry interests, wefixed five experts from each of five industries, giving a total data-base of 25 experts, a reasonable cross-section for this study. Hence,this study does not attempt to validate a hypothesis with the help ofStructural Equation Modeling (SEM). We conducted a one-dayworkshop for data collection for this study. Data collection wasdone through two phases: Phase 1: Preliminary survey to identifycommon enablers, and Phase 2: Identification of influential en-ablers for SSCM adoption.
5.1. Phase 1: preliminary survey to identify common enablers
Before this phase, we discussed which enabler was availablewith experts and which enabler provided motivation for SSCMadoption. From this, common acceptable enablers for SSCM adop-tion were deduced. At the end of this session, 15 experts from fiveindustries were asked to give their opinion, rated on their self-interest (1 e No enabler; 2 e Enabler; 3 e Moderate enabler; 4 e
Important enabler and 5 e Very important enabler) from the rec-ommended list of the 25 enablers based on extensive literature.From the recommended list of 25 enablers, 10 enablers receivedless than 2.5 (average) and 2 obtained in between 2.6 and 3.5(average). The remaining 13 enablers obtained more than 3.5(average). Finally, the experts recommended the 13 enablers whichobtained more than 3.5 rating (average) for this research. Morethan 3.5 rating denoted that 13 enablers are important and veryimportant as per experts' opinion. Enablers with lower than a 3.5rating were ignored. For these reasons, we fixed enablers withmore than 3.5 average rates, and these were proposed for the nextphase of the study. Of the 25 enablers 13 obtained more than 3.5(average) ratings. Finally, the 13 enablers selected a commonacceptable enabler from the workshop.
5.2. Phase 2: identification of influential enablers for SSCMadoption
In this phase, experts were asked for their opinions of the 13enabler's interrelationship. This phase is described in Section 5.1.
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403396
5.3. Interpretive Structural Modelling (ISM)
This study used the ISM technique to identify the dominantenabler for SSCM. ISM was primarily proposed as a group learningprocess, but can also be used individually. ISM is a known tech-nique to solve industrial decision-making problems. ISM trans-forms unclear, poorly articulated mental models of systems intovisible, well-defined models useful for many purposes (Sage, 1977;Diabat and Govindan, 2011). ISM incorporates judgments of ex-perts in a systematic manner and establishes causal relationshipsamong variables which improve upon the internal validity of theresults (Thakkar et al., 2008). ISM is a qualitative and interpretivemethod which generates solutions for complex problems throughdiscourses based on structural mapping of complex in-terconnections of elements (Malone, 1975; Watson, 1978; Pfohl,2011). The method supports identification and ordering of com-plex relations between elements of a system so that the influencecan be analysed between elements. ISM has been applied to avariety of problems (Mandal and Deshmukh, 1994; Jharkharia andShankar, 2005; Thakkar et al., 2005; Pfohl, 2011; Kannan et al.,2009; Govindan et al., 2012).
Attri et al. (2013) mentions the many advantages of ISM meth-odology. Based on these many advantages, we selected the ISMmethodology because it suits the study and also because Indianresearchers have applied ISM to many problems to analyse theirdominant factors. These applications are summarized in Table 2.There is no work to analyse enablers for sustainability adoption inan Indian perspective with the help of ISM, but similar studies havebeen conducted in Poland and Canada (Hussain, 2011; Grzybowska,2012). Generally, every industry and every country has differentopinions about sustainable enablers based on their own culture andenvironmental regulations (Zhu and Sarkis, 2006). For this reason,we chose this problem from an Indian perspective. Recently au-thors analyzed sustainable indicators with a hybrid approach. Forexample, Tseng (2013) proposed a novel approach where fuzzy settheory and ISM were adopted to address the analytical objective.
The various steps involved in ISM methodology are as follows(Kannan et al., 2009; Diabat and Govindan, 2011):
Table 2Applications of ISM in India.
Application Authors
Higher education programplanning
Hawthorne and Sage (1975)
Energy conservation in theIndian cement industry
Saxena et al. (1992)
Vendor selection criteria Mandal and Deshmukh (1994)Adoption of knowledge
management in Indianindustries
Singh et al. (2003)
Strategic decision makingin managerial groups
Bola~nos and Nenclares (2005)
Barriers for GSCM Mudgal et al. (2010)Drivers for GSCM Diabat and Govindan (2011)Barriers of reverse logistics Ravi and Shankar (2005)Third party reverse logistic
providerGovindan et al. (2013)
Project management analysis Ahuja et al. (2009)Information sharing enablers Khurana et al. (2010)Flexible manufacturing system
enablers in Indian companiesRaj et al. (2008)
Future objectives for wastemanagement in India
Sharma et al. (1995)
Selection of green suppliers Kannan et al. (2008)Selection of reverse logistics
providerKannan et al. (2009)
Analysis of barriers for adoptionof GSCM
Mathiyazhagan et al. (2013)
Step 1: The Enablers (criteria) examined for the system underconsideration are listed.Step 2: From the enablers identified in Step 1, a contextualrelationship is established to identify which pairs of variablesshould be examined.Step 3: A Structural Self-Interaction Matrix (SSIM) is developedfor enablers, which indicates pair-wise relationships amongthem to the system under consideration.Step 4: Reachability matrix is developed from SSIM and it ischecked for transitivity. The transitivity of contextual relation isa basic assumption in ISM. It states that if a variable A is relatedto B and B is related to C, then A is necessarily related to C.Step 5: The reachability matrix in Step 4 is partitioned todifferent levels.Step 6: Based on the above relationship, a directed graph isdrawn and transitive links removed.Step 7: The resultant digraph is converted into an ISM, byreplacing variable nodes with statements.Step 8: The ISM model developed in Step 7 is reviewed to checkfor conceptual inconsistency and necessary modifications. Theabove steps are shown in Fig. 2.
5.4. Questionnaire development
5.4.1. Data collectionISM methodology suggests the use of expert opinions based on
various management techniques like brainstorming, nominal tech-nique, etc., to develop a contextual relationship among variables.Thus, in this research to identify contextual relationships amongenablers, experts fromfive textile units inTamilnaduwere consulted.For analysing enablers, a contextual relationship of “leads to” type ischosen. Thismeans thatonevariable leads to another. Basedon this, acontextual relationship between variables was developed.
5.4.2. Structural Self-Interaction Matrix (SSIM)Keeping in mind the contextual relationship for each variable,
the existence of a relation between any two barriers (i and j) andthe associated direction of the relation is questioned. Four symbolsare used to denote the direction of the relationship between en-ablers (i and j):
V: Enabler i influence to enabler j;A: Enabler j influence to enabler i;X: Enablers i and j will influence each other; andO: Enablers i and j are unrelated.
The SSIM for enablers in adoption of SSCM is given in Table 3.
5.4.3. Initial reachability matrixIn this step, a reachability matrix is developed from SSIM. The
SSIM format is initially converted into an initial reachability matrixformat by transforming information of each SSIM cell into binarydigits (i.e., ones or zeros) in the initial reachability matrix. Thistransformation is done with the following rules (Kannan et al.,2009):
� If an entry in the cell (i, j) in the SSIM is V, then cell (i, j) entrybecomes 1 and cell (j, i) entry becomes 0 in the initial reach-ability matrix.
� If an entry in the cell (i, j) in SSIM isA, then cell (i, j) entry becomes0 and cell (j, i) entry becomes 1 in the initial reachability matrix.
� If an entry in the cell (i, j) in SSIM is X, then entries in both cells[(i, j) and (j, i)] become 1 in the initial reachability matrix.
� If an entry in the cell (i, j) in SSIM is O, then the entries in bothcells [(i, j) and (j, i)] become 0 in the initial reachability matrix.
Fig. 2. Flow diagram to prepare the ISM model for enabler analysis in five textile industries.
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403 397
Following these rules, the initial reachability matrix is given inTable 4.
The final reachability matrix for enablers, shown in Table 5, isobtained by incorporating transitivities as enumerated in Step 4 ofthe ISMmethodology. The final reachability matrix then consists of
Table 3Structural Self-Interaction Matrix (SSIM).
Enablers 13 12 11 10 9 8 7 6 5 4 3 2
1 V V V V O V V X O X X X2 O O V O V O X X X X X3 O O O O X O X X X X4 X V X O X O X X O5 A A A A A O O X6 O O O O A O O7 V O X O A O8 O V O V O9 A O A O10 A X O11 X O12 O
some entries from pair-wise comparisons and some inferredentries.
5.4.4. Level partitionsIn the study, the 13 enablers, along with their reachability set,
antecedent set, intersection set and levels, are presented in Table 6.
Table 4Initial reachability matrix.
Enablers 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
1. 1 1 1 1 0 1 1 1 0 1 1 1 12. 1 1 1 1 1 1 1 0 1 0 1 0 03. 1 1 1 1 1 1 1 0 1 0 0 0 04. 1 1 1 1 0 1 1 0 1 0 1 1 15. 0 1 1 0 1 1 0 0 0 0 0 0 06. 1 1 1 1 1 1 0 0 0 0 0 0 07. 0 1 1 1 0 0 1 0 0 0 1 0 18. 0 0 0 0 0 0 0 1 0 1 0 1 09. 0 0 1 1 1 1 1 0 1 0 0 0 010. 0 0 0 0 1 0 0 0 0 1 0 1 011. 0 0 0 1 1 0 1 0 1 0 1 0 112. 0 0 0 0 1 0 0 0 0 1 0 1 013. 0 0 0 1 1 0 0 0 1 1 1 0 1
Table 5Final reachability matrix.
Enablers 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
1. 1 1 1 1 1 1 1 1 1 1 1 1 12. 1 1 1 1 1 1 1 1 1 1 1 1 13. 1 1 1 1 1 1 1 1 1 1 1 1 14. 1 1 1 1 1 1 1 1 1 1 1 1 15. 1 1 1 1 1 1 1 0 1 0 1 0 06. 1 1 1 1 1 1 1 1 1 1 1 1 17. 1 1 1 1 1 1 1 0 1 1 1 1 18. 0 0 0 0 1 0 0 1 0 1 0 1 09. 1 1 1 1 1 1 1 0 1 0 1 1 110. 0 1 1 0 1 1 0 0 0 1 0 1 011. 1 1 1 1 1 1 1 0 1 1 1 1 112. 0 1 1 0 1 1 0 0 0 1 0 1 013. 1 1 1 1 1 1 1 0 1 1 1 1 1
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403398
Level identification process of these enablers is completed in fouriterations. From Table 6, it shows that Adoption of green practices(E5) is found in Level I. Thus, it would be positioned at the top of theISM model.
The iterations are continued till the levels of each variable areobtained. The identified levels aid in building digraph and the finalISM model.
5.4.5. Formation of ISM based modelFrom the final reachability matrix, a structural model is gener-
ated and given in Fig. 3. The relationship between enablers j and i isshown by an arrow from i to j. The resulting graph is called adigraph. After removing transitivities as described in ISM meth-odology, the digraph is finally converted into an ISM model for theenabler.
5.4.6. MICMAC AnalysisMatriced Impacts ‘croises-multiplication applique’ and class-
ment (cross-impact matrix multiplication applied to classification)is abbreviated to MICMAC. The MICMAC principle is based on themultiplication properties of matrices (Sharma et al., 1995; Diabatand Govindan, 2011; Kannan et al., 2009). It is a graphical repre-sentation of enablers into four clusters, namely: Independent,Linkage, Autonomous, and Dependent. The purpose of MICMACanalysis is to analyse the enablers' drive power and dependencepower. This is done to identify the key enablers that drive thesystem in various categories. Based on their drive power anddependence power, enablers, in the present case, were classifiedinto four categories as follows (Kannan et al., 2009):
1. Autonomous enablers: These have weak driving power andweak dependence. They are relatively disconnected from thesystem, with which they have few links, which may be verystrong. These enablers are represented in Quadrant e I.
Table 6Level partitions for enablers.
Enabler Reachability set Antecedent set
5 1 2 3 4 5 6 7 9 11 1 2 3 4 5 6 7 8 9 10 1110 2 3 6 10 12 1 2 3 4 6 7 8 10 1112 2 3 6 10 12 1 2 3 4 6 7 8 9 10 117 1 2 3 4 6 7 9 11 13 1 2 3 4 5 6 7 98 8 1 2 39 1 2 3 4 6 7 9 11 13 1 2 3 4 5 6 7 911 1 2 3 4 6 7 9 11 13 1 2 3 4 5 6 7 913 1 2 3 4 6 7 9 11 13 1 2 3 4 6 7 91 1 2 3 4 6 1 2 3 4 5 6 7 92 1 2 3 4 6 1 2 3 4 5 6 7 9 10 113 1 2 3 4 6 1 2 3 4 5 6 7 94 1 2 3 4 6 1 2 3 4 5 6 7 96 1 2 3 4 6 1 2 3 4 5 6 7 9 10 11
2. Dependent enablers: This category includes enablers whichhave weak drive power, but strong dependence power and areplaced in Quadrant e II.
3. Linkage enablers: These have strong driving power and strongdependence and are placed in Quadrant e III. They are unstableand so action on them does not affect others. It also includes afeedback effect on them.
4. Independent enablers: These have strong driving power butweak dependence power. These are represented in Quadrant eIV. It is observed that a variable with a very strong driving po-wer, called a key variable, falls into the category of independentor linkage criteria. The driver power and dependence power ofeach of these barriers are shown in Table 7. More details of thefinal ISM model for the enabler are given in Fig. 4.
Subsequently, the diagram of driving power vs. dependencepower for the enablers is constructed as shown in Fig. 4. As illus-trated, it is observed from Table 6 that there is one enabler, Gov-ernment regulations (E8) in Quadrant - I. In Quadrant e II,Customer satisfaction (E10) and Economic input to infrastructuraldevelopment (E12) are evident. Similarly, the remaining enablersare positioned according to their driving and dependence power.
6. Result and discussion
Indian researchers started analysing SSCM and GSCM practicesin the Indian industries (Mathiyazhagan et al., 2013; Mudgal et al.,2010; Luthra et al., 2011; Govindan et al., 2014). The textile industryis a sector which adopted SSCM practices due to pressures fromSSCM enablers. Enablers for SSCM, collected from literature, wereput into ISM to investigate interactions between them. The driver-dependence power diagram obtained from MICMAC analysis givesan insight into the relative importance and interdependencies be-tween enablers. Fig. 4 indicates the dependence and driving powerof enablers. The present research with ISM shows the followinginterpretations:
� The driver and dependence power diagram shows four quad-rants. In the first quadrant (Quadrant I), Government regulations(E8) enabler appears because it has a driving power of 4 anddependence power of 6. It shows that government regulationsenabler has less driving and dependence power. Generally,autonomous enablers are weak drivers and weak dependentswithout much influence on sustainable supply chain imple-mentation in the textile industry. Guenther et al. (2011) statesthat environmental legislation and regulations can hold backinnovation by prescribing best available techniques and settingunreasonable deadlines. From this result, it can be inferred thatthe government regulation enabler serves a minor role and does
Intersection set Iteration no. & level
12 13 1 2 3 4 5 6 7 9 11 I12 13 2 3 6 10 12 II12 13 2 3 6 10 12 II11 13 1 2 3 4 6 7 9 11 13 III4 6 8 8 III11 13 1 2 3 4 6 7 9 11 13 III11 13 1 2 3 4 6 7 9 11 13 III11 13 1 2 3 4 6 7 9 11 13 III11 13 1 2 3 4 6 IV12 13 1 2 3 4 6 IV11 13 1 2 3 4 6 IV11 13 1 2 3 4 6 IV12 13 1 2 3 4 6 IV
Fig. 3. ISM based Model for Enablers of sustainable management.
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403 399
not provide a tremendous impact to the adoption of SSCM inindustries. But, Mathiyazhagan and Haq (2013) have identifiedthat automotive component sectors are facing more pressurefrom central governmental environmental regulations andregional environmental regulations due largely to their cus-tomers' pressure. In this view, we need more strict regulationsfor the textile industries to adopt the environmental practiceswhich are available at present.
� The second quadrant (Quadrant II), is also called a dependentquadrant with low driving power and high dependence power.As per our research, Customer satisfaction (E10) and Economicinput to infrastructural development (E12) enablers appear inthis Quadrant. Enabler E10 has a dependence power of 11 anddriving power of 6 showing that customers provide limitedpressure or motivation for implementation of SSCM in textileindustries. Still, from the customer's side, there is a need toprovide more motivation towards improving sustainabilitypractices in the textile industries. Customers also need to enactmore pressure for environmentally friendly products (Zhu andSarkis, 2006). But, Ellen et al. (2006) say that organizationsneed to attract their customers by means of engaging in sus-tainable behaviors. Similarly, Enabler E12 has high dependence
Table 7Dependence power and driving power.
Enablers 1. 2. 3. 4. 5. 6. 7.
1. 1 1 1 1 1 1 12. 1 1 1 1 1 1 13. 1 1 1 1 1 1 14. 1 1 1 1 1 1 15. 1 1 1 1 1 1 16. 1 1 1 1 1 1 17. 1 1 1 1 1 1 18. 0 0 0 0 1 0 09. 1 1 1 1 1 1 110. 0 1 1 0 1 1 011. 1 1 1 1 1 1 112. 0 1 1 0 1 1 013. 1 1 1 1 1 1 1Dependence power 10 12 12 10 13 12 10
power (12) and less driving power, which shows that industrieshave less interest in assigning money to develop their infra-structure for sustainability.
� Ten enablers appear in Quadrant III. Enablers in this quadranthave strong driving and strong dependence power. They areunstable. Any action on themwill affect others and will providea feedback effect on them. They can disturb thewhole system. Inthis quadrant, Adoption of green purchasing (E5) enabler haslowest driving power (9) and high dependence power (12).From this, it is inferred that implementing green practice pro-vides reducedmotivation for the adoption of SSCM as it dependson other practices; meaning that the green practice enablerneeds the co-operation of other enablers, without which, E5 isdifficult to practice. Guenther et al. (2010) pointed out for thewhole environment in industries, green procurement activitywill ensure a tremendously effective way to develop the entireenvironmental performance. The next lowest value enabler isHazard management (E9). It has a driving power of 11 anddependence power of 10. Currently, every industry is involved inreducing their usage of hazardous materials in their operationsand many have also started to find alternatives for such mate-rials. This enabler is important for textile industries because for
8. 9. 10. 11. 12. 13. Driving power
1 1 1 1 1 1 131 1 1 1 1 1 131 1 1 1 1 1 131 1 1 1 1 1 130 1 0 1 0 0 91 1 1 1 1 1 130 1 1 1 1 1 121 0 1 0 1 0 40 1 0 1 1 1 110 0 1 0 1 0 60 1 1 1 1 1 120 0 1 0 1 0 60 1 1 1 1 1 126 10 11 10 12 9
Fig. 4. Driving power and dependence power diagram.
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403400
color coating and other fabric related purposes, color agents areneeded. These color agents are harmful to human health andsurroundings. The Hazard management (E9) ISM value showsthat textile industries have started to reduce hazardous mate-rials used in their industry through special attention.
Improvement of product characteristics (E13) enabler appears inthe next position in Quadrant III. Compared to previous enablers(E8; E10; E12; E5; E9) E13 enabler has a high driving power (12) andless dependence power (9). This shows that textile industries havestarted to improve their products by reducing the use of hazardoussubstances in clothes and by improving apparel characteristics. Butthis step needs the co-operation of other enablers such as Hazardmanagement (E9); Government regulations (E8); Economic inputto infrastructural development (E12); and Adoption of green pur-chasing (E5). Two enablers appear in the next position of this re-gion, namely, Eco-design (E7) and Environmental cost (E11). Thesetwo enablers have equal driving power and dependence power (12,10). For adoption of any system in the industry, it should alterexisting methodological activities by design. The design depart-ment has a notable role in every organization.We need to re-designindustrial activities towards ecological activities, especially forenvironmental issues. Past researchers have observed that it isessential for industries to design the process and products relatedto environmentally friendly activities to sustain their performance(Bhaskaran et al., 2006; Mudgal et al., 2010; Hussain, 2011;Grzybowska, 2012; Houda and Said, 2011; Singh and Debnath,2012).
Employment stability (E1) and Adoption of safety standards (E4)enablers appear in the same position. It shows that these two en-ablers strongly influence other enablers. Employment is a majorconcern for every organization because without labor cooperation,an industry cannot achieve its goals (Kuik et al., 2010). The adoptionof safety standards will improve environmental performance bymeans of placing standardized norms on the environmental issues.
ISO 14001 certification provides safety standards for industries(Carter et al., 2007; Carter and Rogers, 2008). This result shows thatthe textile industry feels that these two enablers provide goodmotivation for SSCM adoption.
In this quadrant, three enablers appear in the same position,namely: Health and safety issues (E2), Community economic wel-fare (E3), and Adoption of green purchasing (E6). In the dependencepower and the driving power diagram, these enablers have highdependence and driving power (12, 13). Also, this result proves thatenablers E2, E3 and E6 have mutual interactions. Health and safetyare serious issues in every industry, because workers want a safeworking environment. Organizations also consider this enablerimportant as without workers' involvement, industry cannotbenefit. Thus, this enabler is important to motivate SSCM adoption.The Health and safety issues (E2) enabler has a dependence powerof 12 and a driving power of 13. Community economic welfare (E3)is placed next. This enabler gives economic support to employeesand increases worker involvement. In this quadrant, Adoption ofgreen purchasing (E6) is the final enabler. It also has same drivingand dependence power as E2 and E3. All production starts fromprocurement of raw materials. Clearly, industries need to adoptsustainability concepts in their procurement. Green purchasing isthe best concept to make an industry sustainable (Zhu et al., 2006;Mudgal et al., 2010).
� In the fourth Quadrant (IV), no enabler appears. Of these 13enablers, none has an independent character, which proves thatall enablers are dependent in textile industries for SSCMadoption.
7. Conclusion
Sustainable development has grown to be a generally used termthat goes beyond uncomplicated economic security to include
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403 401
issues of environmental impact and resource use, together withsocial effects (Konstantinos et al., 2011). Moving towards sustain-ability in the TSCM requires more motivation (enablers). Customersalso expect more environmental friendliness than traditional op-erations. As the present Indian scenario demonstrates, industriesneed more motivations (enablers) for SSCM adoption to improvetheir environmental performance. From this study, we observedthat textile organizations have notable environmental awarenessand also are interested in retaining their customers by improvingenvironmental performance (adopting SSCM). Identifying leadingenablers for SSCM creates considerable challenges for researchersand industrial experts. Based on the inputs from experts in fivetextile industries and an academician, a Structural Self-InteractionMatrix (SSIM) the basis for ISM was formed.
From the ISM framework, we inferred that adoption of greenpurchasing (E5) enabler occupies the top level (iteration I). For thetextile industry, this enabler (E5) provides less impact compared tothe other recommended enablers as almost all industries havestarted adopting green purchasing. For this reason, green pur-chasing practice enabler gets less weight. In iteration II, two en-ablers appear: namely, Customer satisfaction (E10) and Economicinput to infrastructural development (E12). These enablers occupythe next position to the E5 enabler. Similarly, in iteration III, fiveenablers are placed: namely, Improvement of product characteris-tics (E13); Environmental cost (E11); Hazard management (E9);Government regulations (E8), and Eco-design (E7). From iterationIII, we infer that these motivations occur from different directions.For example, Government regulations are external enablers, butEco-design is an internal enabler for industries. These five enablersare more important than the previous iterations' (I and II) enablers.Similarly, another five enablers appear in the lower level (iterationIV). These five enablers deal with the involvement of employees toSSCM: namely, Employment stability (E1); Health and safety issues(E2); Community economic welfare (E3); Adoption of safety stan-dards (E4), and Adoption of green practices (E6). These enablersplay a dominant role in implementing SSCM in textile industries.This result shows that textile industries feel that employees'involvement, stability and community economic enablers are moreimportant than the other enablers, because without employees'involvement, industry cannot achieve its goals. There is a need formore interest from the employees' side, especially in SSCMimplementation.
7.1. Recommendation
➢ It is evident from the results that identification of the leadingenablers in textile industries is helpful for easy implementationof an effective SSCM.
➢ It also improves environmental performance and creates a greenenvironmental zone. The result of this study shows that fiveenablers play a dominant role. Textile industries need to focusmore on other recommended enablers.
➢ The conclusion of this study is useful to implement SSCM in atextile industry in an Indian scenario. Industries find it difficultto identify dominant enablers, but this study provides animproved solution for this problem by using ISM.
➢ Industries need to identify the enablers with important rolesand those with less important roles during SSCM adoption. Assummarized above; this study is one of the better research tasksto identify the principal enabler for SSCM adoption.
➢ The purpose of this paper is to provide a solid framework forenabling SSCM in textile industries, because it is vital for suchindustries to offer accountability when it comes to environ-mental consciousness.
7.2. Limitations and future scope
This study was conducted only in the textile sector where fiveunits were selected. Involving more industries might provide moreinsight into enablers. More sectors can also be considered forsimilar analysis. Only 13 enablers were considered in this research,but in reality more enablers exist and their identification ispossible. This study identifies the most dominant enabler throughthe use of the ISM methodology. In future studies, fuzzy theory(Bansal et al., 2014; Patil and Kant, 2014) can be utilized in thetraditional ISM. In addition, Decision Making Trial and EvaluationLaboratory (DEMATEL) can be used to identify the dominantenabler (Patil and Kant, 2014), and AHP can be utilized to prioritizethe enablers.
Acknowledgment
The authors would like to thank Prof. Kannan Govindan, Uni-versity of Southern Denmark, Denmark for providing an opportu-nity to work on the network project “Sustainable supply chainmanagement: A step towards Environmental and Social Initiatives”(2211916) between partner countries from China, India & Denmarkwhich is supported by a grant from Forsknings e og Innovations-styrelsen, Denmark.
References
Ahi, P., Searcy, C., 2013. A comparative literature analysis of definitions for green andsustainable supply chain management. J. Clean. Prod. 52, 329e341.
Ammenberg, J., Hjelm, O., 2003. Tracing business and environmental effects ofenvironmental management systems e a study of networking small and me-dium sized enterprises using a joint environmental management system. Bus.Strategy Environ. 12 (3), 163e174.
Aslan, I., Çınar, O., Kumpikait _e, V., 2012. Creating strategies from tows matrix forstrategic sustainable development of Kipas Group. J. Bus. Econ. Manag. 13 (1),95e110. http://dx.doi.org/10.3846/16111699.2011.620134.
Bagheri, A., Hjorth, P., 2007. A framework for process indicators to monitor forsustainable development: practice to an urban water system. Environ. Dev.Sustain. 9 (2), 143e161. http://dx.doi.org/10.1007/s10668-005-9009-0.
Bajaj, S., Jha, P.C., Aggarwal, K.K., 2013. Singleesource, singleedestination, multiproduct EOQ model with quantity discount incorporating partial/full truckloadpolicy. Int. J. Bus. Perform. Supply Chain Model. 5 (2), 198e220.
Bansal, A., Kumar, P., Issar, S., 2014. Evaluation of a 3PL company: an approach offuzzy modelling. Int. J. Adv. Operat. Manag. 6 (2), 131e161.
Berkes, F., Gardner, J.S., Sinclair, A.J., 2000. Comparative aspects of mountain landresources management and sustainability: case studies from India and Canada.Int. J. Sustain. Dev. World Ecol. 7 (3), 75e90.
Berry, M., Rondinelli, D., 1998. Proactive corporate environmental management: anew industrial revolution. Acad. Manag. Exec. 12 (2), 38e50.
Beske, P., Land, A., Seuring, S., 2014. Sustainable supply chain management practicesand dynamic capabilities in the food industry: a critical analysis of the litera-ture. Int. J. Prod. Econ. 152, 131e143.
Bhaskaran, S., Polonsky, M., Cary, J., Fernandez, S., 2006. Environmentally sustain-able food production and marketing: opportunity or hype? Br. Food J. 108 (8),677e690.
Caniato, F., Caridi, M., Crippa, L., Moretto, A., 2011. Environmental sustainability infashion supply chains: an exploratory case based research. Int. J. Prod. Econ.,659e670.
Carter, Craig, R., Rogers, Dale S., 2008. A framework of sustainable supply chainmanagement: moving toward new theory. Int. J. Phys. Distrib. Logist. Manag. 38(5), 360e387.
Carter, C.R., Ellram, L.M., Tate, W.L., 2007. Structure and influence: a logisticsmanagement application of social network analysis. J. Bus. Logist. 28 (1),137e168.
Chahal, H., Sharma, R.D., 2006. Implications of corporate social responsibility onmarketing performance: a conceptual framework. J. Serv. Res. 6 (1), 205e216.
Charkha, P.G., Jaju, S.B., 2014. Supply chain performance measurement system: anoverview. Int. J. Bus. Perform. Supply Chain Model. 6 (1), 40e60.
Crals, Evy, Vereeck, Lode, 2005. The affordability of sustainable entrepreneurshipcertification for SMEs. Int. J. Sustain. Dev. World Ecol. 12 (2), 173e183.
Datta, S., Samantra, C., Mahapatra, S.S., Mondal, G., Chakraborty, P.S., Majumdar, G.,2013. Selection of internet assessment vendor using TOPSIS method in fuzzyenvironment. Int. J. Bus. Perform. Supply Chain Model. 5 (1), 1e27.
De Brito, M., Carbone, V., Blanquart, C., 2008. Towards a sustainable fashion retailsupply chain in Europe: organisation and performance. Int. J. Prod. Econ. 114 (2),534e553.
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403402
Dehghanian, F., Mansoor, S., Nazari, M., 2011, December. A framework for integratedassessment of sustainable supply chain management. In: Industrial Engineeringand Engineering Management (IEEM), 2011 IEEE International Conference on.IEEE, pp. 279e283.
Diabat, A., Govindan, K., 2011. An analysis of drivers affecting the implementation ofgreen supply chain management. Resour. Conser. Recycl. 55 (6), 659e667.
Dweiri, F., Khan, S.A., 2012. Development of a universal supply chainmanagement performance index. Int. J. Bus. Perform. Supply Chain Model. 4 (3),232e245.
Elkington, J., 1994. Towards the sustainable corporation: winewinewin businessstrategies for sustainable development. Calif. Manag. Rev. 36 (2), 90e100.
Elkington, J., 1998. Cannibals with Forks: the Triple Bottom Line of the 21st Century.New Society Publishers, Stoney Creek, CT.
Ellen, P.S., Webb, D.J., Mohr, L.A., 2006. Building corporate associations: consumerattributions for corporate socially responsible programs'. J. Acad. Mark. Sci. 34(2), 147e157.
Faisal, M.N., 2010. Sustainable supply chains: a study of interaction among theenablers. Bus. Process Manag. J. 16 (3), 508e529.
Gabzdylova, B., Raffensperger, J.F., Castka, P., 2009. Sustainability in the New Zea-land wine industry: drivers, stakeholders and practices. J. Clean. Prod. 17,992e998. http://dx.doi.org/10.1016/j.jclepro.2009.02.015.
Gladwin, T.N., Kennelly, J.J., Krause, T., 1995. Shifting paradigms for sustainabledevelopment: implications for management theory and research. Acad. Manag.Rev. 20 (4), 874e907.
Govindan, K., Palaniappan, M., Zhu, Q., Kannan, D., 2012. Analysis of third partyreverse logistics provider using interpretive structural modeling. Int. J. Prod.Econ. 140 (1), 204e211.
Govindan, K., Rajendran, S., Sarkis, J., Murugesan, P., 2013. Multi criteria decisionmaking approaches for green supplier evaluation and selection: a literaturereview. J. Clean. Prod. (in press). DOI: 10.1016/j.jclepro.2013.06.046.
Govindan, K., Khodaverdi, R., Jafarian, A., 2013a. A fuzzy multi criteria approach formeasuring sustainability performance of a supplier based on triple bottom lineapproach. J. Clean. Prod. 47, 345e354.
Govindan, K., Kannan, D., Mathiyazhagan, K., Jabbour, A.B.L.D.S., Jabbour, C.J.C.,2013b. Analysing green supply chain management practices in Brazil's elec-trical/electronics industry using interpretive structural modelling. Int. J. Envi-ron. Stud. 70 (4), 477e493.
Govindan, K., Mathiyazhagan, K., Devika, K., Haq, A.N., 2014. Barriers analysis forgreen supply chain management implementation in Indian industries usinganalytic hierarchy process. Int. J. Prod. Econ. 147, 555e568.
Grzybowska, K., 2012. Sustainability in the supply chain: analysing the enablers. In:Environmental Issues in Supply Chain Management. Springer, Berlin Heidel-berg, pp. 25e40.
Günther, E., Hoppe, H., Endikrat, J., 2011. Corporate financial performance andcorporate environmental performance: a perfect match? J. Environ. Law Policy3, 279e296.
Haigh, M., Jones, M.T., 2006. The Divers of corporate social responsibility: a criticalreview. Bus. Rev. Camb. 5 (2), 245e251.
Hart, S.L., Milstein, M.B., 2003. Creating sustainable value. Acad. Manage. Exec. 17(2), 56e67.
Houda, M., Said, T., 2011. Sustainability metrics for a supply chain: the case ofsmall and medium enterprises, Logistics (LOGISTIQUA). In: 2011 4th Inter-national Conference on Res. Unit in Logistics, Ind. Manage. & Quality(LOGIQ), ISGI-Sfax, Sfax, Tunisia, pp. 109e114. http://dx.doi.org/10.1109/LOGISTIQUA.2011.5939411.
Hubacek, K., Guan, D., Barua, A., 2007. Changing lifestyles and consumption patternsin developing countries: a scenario analysis for China and India. Futures 39,1084e1096. http://dx.doi.org/10.1016/j.futures.2007.03.010.
Hussain, M., 2011. Modelling the Enablers and Alternatives for Sustainable SupplyChain Management. M.S. thesis. Dept. Information Systems Engg., ConcordiaUniversity, Montreal, Quebec, Canada.
Jaggi, C.K., Verma, M., 2009. Retailer's ordering policy for deteriorating items in asupply chain with varying deterioration rates. Int. J. Adv. Operat. Manag. 1 (2),250e266.
Jayaraman, V., Klassen, R., Linton, J.D., 2007. Supply chain management in a sus-tainable environment. J. Operat. Manag. 25 (6), 1071e1074.
Junior, H.L.D.S., Silva, G.L.D., Silva, V.L.D., 2014. Qualitative analysis of the presenceof emerging contaminants in water supplies for human use: a case study of theGuilherme de Azevedo reservoir in Caruaru (PE, Brazil). Int. J. Adv. Operat.Manag. 6 (2), 101e109.
Kaliyan, M., Govindan, K., Haq, Noorul, 2013. Barrier Analysis to Improve Green inExisting Supply Chain Management. Reverse Supply Chains Issues and Analysis.CRC Press (Taylor and Francis), pp. 273e288, 978-1-4398-9902.
Kannan, G., Noorul Haq, A., Sasi kumar, P., Arunachalam, S., 2008. Analysis andselection of green suppliers using interpretative structural modeling and ana-lytic hierarchy process. Int. J. Manage. Decision Making, 9 (2), 163e182.
Kannan, G., Pokharel, S., Sasi Kumar, P., 2009. A hybrid approach using ISM andfuzzy TOPSIS for the selection of reverse logistics provider. Resour. Conser.Recycl. 54 (1), 28e36.
Kassem, M., Dawood, N., 2013. A framework for the integration and coordination ofthe construction supply chain. Int. J. Bus. Perform. Supply Chain Model. 5 (2),115e130.
KhalilieDamghani, K., Taghavifard, M., Olfat, L., Feizi, K., 2012. Measuring agilityperformance in fresh food supply chains: an ordinal two-stage data envelop-ment analysis. Int. J. Bus. Perform. Supply Chain Model. 4 (3), 206e232.
Kleindorfer, P.R., Singhal, K., Wassenhove, L.N.V., 2005. Sustainable operationsmanagement. Prod. Operat. Manag. 14 (4), 482e492.
Kongar, E., Gupta, S.M., 2009. A multiple objective tabu search approach for end-of-life product disassembly. Int. J. Adv. Operat. Manag. 1 (2), 177e202.
Kruse, E., Storm Rasmussen, V., 2012, Maj 2. Bæredygtig Mode Er Bæredygtig Vækst(Sustainable fashion is sustainable growth). Retrieved May 12, 2012, fromBerlingske Tidende. http://www.b.dk/kommentarer/baeredygtig-mode-er-baeredygtig-vaekst.
Kuik, S.V., Nagalingam, Y., Amer, 2010. Challenges in implementing sustainablesupply chain within a collaborative manufacturing network. In: 8th Interna-tional Conference on Supply Chain Management and Information Systems,Hong Kong.
Linton, J.D., Klassen, R., Jayaraman, V., 2007. Sustainable supply chains: an intro-duction. J. Operat. Manag. 25 (6), 1075e1082. http://dx.doi.org/10.1016/j.jom.2007.01.012.
Maleki, M., CruzeMachado, V., 2013. Supply chain performance monitoring usingBayesian network. Int. J. Bus. Perform. Supply Chain Model. 5 (2), 177e197.
Malone, D.W., 1975. An introduction to the application of interpretive structuralmodelling. Proc. IEEE 63, 397e404.
Mandal, A., Deshmukh, S.G., 1994. Vendor selection using interpretive structuralmodeling (ISM). Int. J. Operat. Prod. Manag. 14 (6), 52e59.
Marshall, R., Cordano, M., Silverman, M., 2005. Exploring individual and institu-tional drivers of proactive environmentalism in the US wine industry. Bus.Strategy Environ. 14 (2), 92e109.
Mathiyazhagan, K., Govindan, K., NoorulHaq, A., Geng, Y., 2013. An ISM approach forthe barrier analysis in implementing green supply chain management. J. Clean.Prod. 47, 283e297.
Mathiyazhagan, K., Govindan, K., Noorul Haq, A., 2014. Pressure analysis for greensupply chain management implementation in Indian industries using analytichierarchy process. Int. J. Prod. Res. 52 (1), 188e202.
Michelsen, O., Fet, M.A., 2010. Using eco-efficiency in sustainable supply chainmanagement; a case study of furniture production. Clean. Techn Environ. Policy12, 561e570.
Mudgal, R.K., Shankar, R., Talib, P., Raj, T., 2009. Greening the supply chain practices:an Indian perspective of enablers' relationships. Int. J. Adv. Operat. Manag. 1 (2),151e176.
Mudgal, R.K., Shankar, R., Talib, P., Raj, T., 2010. Modeling the barriers of greensupply chain practices: an Indian perspective. Int. J. Logist. Syst. Manag. 7 (1),81e107.
Muduli, K., Govindan, K., Barve, A., Geng, Y., 2013. Barriers to green supply chainmanagement in Indian mining industries: a graph theoretic approach. J. Clean.Prod. 47, 335e344.
Nilakantan, K., 2013. Replenishment policies for warehouse systems under cyclicdemand. Int. J. Bus. Perform. Supply Chain Model. 5 (2), 148e176.
Nordic Fashion Association, 2012. Copenhagen Fashion Summit. Retrieved May 12,2012, from Nordic Fashion Association. http://nordicfashionassociation.com/40520/Background.
Pagell, M., Shevchenko, A., 2014. Why research in sustainable supply chain man-agement should have no future. J. Supply Chain Manag. 50 (1), 44e55.
Patil, S.K., Kant, R., 2014. Predicting the success of knowledge management adop-tion in supply chain using fuzzy DEMATEL and FMCDM approach. Int. J. Bus.Perform. Supply Chain Model. 6 (1), 75e93.
Pesticide Action Network, 2011, May 2. Problems with Conventional Cotton.Retrieved May 6, 2012, from Pan North America. http://www.panna.org/resources/cotton.
Pfohl, H.C., Gallus, Philipp, Thomas, David, 2011. Interpretive structural modeling ofsupply chain risks. Int. J. Phys. Distrib. Logist. Manag. 41 (9), 839e859.
Porter, M.E., van der Linde, C., 1995. Green and competitive: ending the stalemate.Harv. Bus. Rev. 73 (5), 120e134.
Preuss, L., 2005. Rhetoric and reality of corporate greening: a view from the supplychain management function. Bus. Strategy Environ. 14 (2), 123e139.
Rajkumar, P., 2013. Service quality measurement of the public distribution system inthe food grains supply chain. Int. J. Bus. Perform. Supply Chain Model. 5 (2),131e147.
Ramaa, A., Subramanya, K.N., Rangaswamy, T.M., 2013. Performance measurementsystem of supply chain-an empirical study. Int. J. Bus. Perform. Supply ChainModel. 5 (4), 343e360.
Romiguer, Torres, Alexandre, 2011. Sustainable Development: Objectives, Enablersand Challenges for Spanish Companies. Master thesis.
Sage, A.P., 1977. Interpretive Structural Modelling: Methodology for Large ScaleSystemsk. McGraw-Hill, New York, NY.
Salimifard, K., Raeesi, R., 2014. A green routing problem: optimising CO2 emissionsand costs from a biefuel vehicle fleet. Int. J. Adv. Operat. Manag. 6 (1), 27e57.
Santos, F.C.A., Andrade, E.M., Ferreira, A.C., Leme, P.C.S., Jabbour, C.J.C., 2013. Prac-tices of environmentally responsible reverse logistics systems in Braziliancompanies. Int. J. Bus. Perform. Supply Chain Model. 5 (1), 63e85.
Sarkis, J., 2012. A boundaries and flows perspective of green supply chain man-agement. Supply Chain Manag. Int. J. 17 (2), 202e216.
Seuring, S., 2013. A review of modeling approaches for sustainable supply chainmanagement. Decis. Support Syst. 54 (4), 1513e1520.
Seuring, S., Gold, S., 2013. Sustainability management beyond corporate bound-aries: from stakeholders to performance. J. Clean. Prod. 56, 1e6.
Seuring, S., Sarkis, J., Muller, M., Rao, P., 2008. Sustainability and supply chainmanagement e an introduction to the special issue. J. Clean. Prod. 16 (15),1545e1551.
A. Diabat et al. / Journal of Cleaner Production 83 (2014) 391e403 403
Sezen, B., Turkkantos, S., 2013. The effects of relationship quality and lean appli-cations on buyereseller relationships. Int. J. Bus. Perform. Supply Chain Model.5 (4), 378e400.
Sharma, H.D., Gupta, A.D., Sushil, 1995. The objectives of waste management inIndia: a futures inquiry. Technol. Forecast. Soc. Change 48 (3), 285e309.
Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., Diabat, A., 2013. A fuzzy multi criteriaapproach for evaluating green supplier's performance in green supply chainwith linguistic preferences. Resour. Conserv. Recycl. 74, 170e179.
Shrivastava, 1995. The role of corporations in achieving ecological sustainability.Acad. Manag. Rev. 20 (4), 936e960.
Sikdar, S.K., Sengupta, Debalina, Harten, Paul, 2012. More on aggregating multipleindicators into a single index for sustainability analyses. Clean. Techn. Environ.Policy 14 (5), 765e773.
Singh, R., Debnath, Roma Mitra, 2012. Modeling sustainable development: India'sstrategy for the future. World J. Sci. Technol. Sustain. Dev. 9 (2), 120e135.
Tewari, S.K., Misra, M., 2013. Developing supply chain evaluation frameworkthrough performance assessment approach. Int. J. Bus. Perform. Supply ChainModel. 5 (1), 28e45.
Thakkar, J., Kanda, A., Deshmukh, S.G., 2008. Interpretive structural modeling (ISM)of IT-enablers for Indian manufacturing SMEs. Inform. Manag. Comput. Secur.16 (2), 113e136.
Topcu, A., Benneyan, J.C., Cullinane, T.P., 2013. A simulationeoptimisation approachfor reconfigurable inventory space planning in remanufacturing facilities. Int. J.Bus. Perform. Supply Chain Model. 5 (1), 86e114.
Ugwua, Haupt, T.C., 2007. Key performance indicators and assessment methods forinfrastructure sustainability e a South African construction industry perspec-tive. Build. Environ. 42 (2), 665e680.
Van Hoof, B., Thiell, M., 2014. Collaboration capacity for sustainable supply chainmanagement: small and medium-sized enterprises in Mexico. J. Clean. Prod. 67,239e248.
Veleva,, V., Ellenbecker, M., 2001. Indicators of sustainable production: frameworkand methodology. J. Clean. Prod. 9, 519e549.
Vinodh, 2010. Improvement of agility and sustainability: a case study in an Indianrotary switches manufacturing organisation. J. Clean. Prod. 18 (10e11),1015e1020.
Vojdani, N., Lootz, F., 2012. Designing supply chain networks for the offshore windenergy industry. Int. J. Bus. Perform. Supply Chain Model. 4 (3), 271e284.
Von Geibler, J., Kuhndt, M., Seifert, E.K., Lucas, R., Lorek, S., Bleischwitz, R., 2004.Sustainable business and consumption strategies. In: Bleischwitz, R.,Hennicke, P. (Eds.), Eco-efficiency, Regulations and Sustainable Business.Edward Elgar, Cheltenham, pp. 116e164.
Walker, H., Jones, N., 2012. Sustainable supply chain management across the UKprivate sector. Supply Chain Manage. Int. J., 17 (1), 15e28.
Waheed, B., Khan, Faisal, Veitch, Brian, 2009. Linkage-based frameworks for sus-tainability assessment: making a case for driving force-pressure-state-exposuree effect-action (DPSEEA) frameworks. Sustainability 1, 441e463.
Watson, R.H., 1978. Interpretive structural modeling-a useful tool for technologyassessment? Technol. Forecast. Soc. Change l (11), 165e185.
White, L., Lee, G.J., 2009. Operational research and sustainable development:tackling the social dimension. Eur. J. Operat. Res. 193 (3), 683e692.
Wiengarten, F., Pagell, M., Fynes, B., 2013. ISO 14000 certification and investmentsin environmental supply chain management practices: identifying differencesin motivation and adoption levels between Western European and NorthAmerican companies. J. Clean. Prod. 56, 18e28.
World Commission on Environment and Development, 1987. Our Common Future.Oxford University Press, New York, NY.
Xie, Y., Allen, C., 2013. Information technologies in retail supply chains: a compar-ison of Tesco and Asda. Int. J. Bus. Perform. Supply Chain Model. 5 (1), 46e62.
Zheng, F., 2010. The evaluation on environmentally friendly condition ofmanufacturing Supply chain. In: 2010 3rd International Conference on Infor-mation Management, Innovation Management and Industrial Engineering,pp. 116e121.
Zhu, Q., Sarkis, J., Cordeiro, J., Lai, K.H., 2006. Firm-level correlates of emergentgreen supply chain management practices in the Chinese context. Int. J. Manag.Sci. 36 (4), 577e591.
Zhu, Q., Sarkis, J., Lai, K.H., 2008. Confirmation of a measurement model for greensupply chain management practices implementation. Int. J. Prod. Econ. 111 (2),261e273.
