Analyzing the drivers of green manufacturing with fuzzy approach

Journal of Cleaner Production xxx (2014) 1e12

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Analyzing the drivers of green manufacturing with fuzzy approachq Kannan Govindan a, *, Ali Diabat b, K. Madan Shankar c a

Department of Business and Economics, University of Southern Denmark, Odense M-5230, Denmark Department of Engineering Systems and Management, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates c Department of Mechanical Engineering, P.T.R College of Engineering & Technology, Madurai, Tamilnadu, India b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 September 2013 Received in revised form 19 February 2014 Accepted 24 February 2014 Available online xxx

Green issues have gained more importance in contemporary globalization. Recent years have seen manufacturing processes understand the green issues due to the social and environmental concerns involved. The drivers of green manufacturing, however, have not been thoroughly investigated. Thus, this paper investigates the responsibility of identifying twelve common drivers of green manufacturing from the combined assistance of existing literature, industrial managers, and expert opinion in the relevant field. A questionnaire on these common drivers was circulated among 120 leading firms in south India, and aided by their replies; a pair-wise comparison was made among the drivers. The pair-wise comparison is used as an input data and the drivers were analyzed on its basis. The analysis resorted to the use of a fuzzy Multi Criteria Decision Making (MCDM) approach. The obtained results are validated by a two-stage sensitivity analysis, namely: (1) using different de-fuzzification methods that are further evaluated through the Spearman coefficient and (2) assigning varying weight to the essential top priority drivers of green manufacturing among all common drivers. This study helps firms to stimulate an essential driver for quick and better adoption of green manufacturing. Finally, the paper concludes with some insight into the future path of green manufacturing in developing countries and an acknowledgment of the study’s own limitations. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: Green manufacturing Drivers Fuzzy AHP EMS

1. Introduction Due to scare resources and increasing population, the conservation of environmental quality has become essential. In many aspects, environmental problems have affected regional and global cooperation and have even prompted conflicts (Chen, 2005). Hence, the practice of green activities has become mandatory to balance these conflicts; even manufacturing processes cannot make an exception. While green concerns have been endorsed by some individuals for decades, the 1987 report of the World Commission on Environment and Development (WECD) revealed that current environmental patterns have altered the planet and its living organisms, including human beings (Sarkis and Rasheed, 1995). In this connection, many green strategies were evolved and integrated in our real life operations and management. Likewise, the

q This paper is the extended version of paper titled “Evaluation of Essential drivers of green manufacturing using Fuzzy approach” presented in 4th International workshop / Advances in Cleaner Production held in Sao Paulo, Brazil, from 22nd to 24th of May, 2013. * Corresponding author. Tel.: þ45 65503188. E-mail address: [email protected] (K. Govindan).

integration of green activities in manufacturing has emerged as an important research topic in recent years. While existing research generally defines green manufacturing from their own perspectives through their experiments and experiences, the most referred explanation is provided by Melnyk and Smith (1996), who define green manufacturing (GM) as “a system that integrates product and process design issues with issues of manufacturing planning and control in such a manner to identify, quantify, assess, and manage the flow of environmental waste with the goal of reducing and ultimately minimizing environmental impact while trying to maximize resource efficiency”. More simply, green manufacturing includes environmental consciousness in manufacturing. Generally, the three “R”s (remanufacture, reduce, and reuse/recycle) are one main strategy of green manufacturing, which includes activities such as reducing hazardous waste volume, minimizing coolant consumption while machining, and calculating proper energy mixes to ensure a sustainable energy source (Dornfeld et al., 2013). To report cleaner production, industries must undertake environmentally conscious policies for operations such as product development, manufacturing, service and distribution, and end-of-life activities in addition to the growing awareness of sustainable issues (Subramoniam et al., 2009). GM helps a firm financially by reducing

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waste, sustaining utilization of materials, and minimizing pollution which harms the environment. Existing environmentally concerned literature exposed environmental gains for businesses (Mudgal et al., 2010; Sarkis et al., 2011; Perron, 2005; Shipeng and Linna, 2011; Kannan et al., 2008; Carter and Rogers, 2008).The importance of green manufacturing serves as the basis for this paper with the study being conducted in industries that focus on South India. The strict policies and regulations in developed nations (like U.S and EU) result in effective GM system implementation in those countries. But in developing scenarios, GM is still in initial stages; recent studies on GM in India by Rehman and Shrivastava (2013a,b) revealed that the majority of the population don’t have enough awareness on GM in India. In addition to this, India is one of the largest populated nations, ranking in 4th position on CO2 emissions in 2013 (Trends in Global CO2 Emissions, 2013). Effective GM implementation creates job opportunities by balancing the economic crisis with practicing the efficient use of resources, which are obvious needs for a developing nation like India. When we compare the Indian scenario with other developing contexts, it is evident that the other contexts are entirely different and unrelated to one another, because economic capacity ultimately decides whether GM will be effectively implemented. It is a known factor that every developing nation’s green investments are different from one another which makes the nations heterogeneous in GM implementation. Also, poor environmental concerns in developing nations impact the whole global chain. Hence, there is a fundamental urge to analyze green manufacturing in an Indian context which also acts as a pioneering approach for all developing nations. In this regard, to reveal the importance of the GM, to encourage awareness, and to address the existing research gap, this study assumes the responsibility to analyze the drivers of GM in an Indian scenario. Generally, GM is initiated by the pressures of some factors e external, internal, societal, committal e called drivers. These drivers help to adapt green manufacturing to industries either voluntarily or mandatorily. This paper aims to collect these common drivers of green manufacturing from various sources and to analyze them with the help of Analytic hierarchy process (AHP) in fuzzy environments which will reduce the vagueness of the results. We seek to provide the priority and to identify the essential driver among common drivers which were framed through references. Analytic Hierarchy Process (AHP) is a MCDM tool which helps to solve complex problems by separating them into simple problems by implementing a level of hierarchies; each level represents a set of criteria or attributes connected to those simple sub problems (Sambasivan and Fei, 2008; Saaty, 1980, 1990). Due to this flexibility, AHP has been chosen as the solution methodology for this problem. In this paper, Section 2 defines the literature review that plays a major role in data collection. The problem description is placed in Section3. Section 4 provides the methodology of the study, and Sections 5 and 6 point out the application of the proposed model, and our results with respective discussions. Section 7 concludes the paper.

2. Literature review The literature review is organized into four different sub sections. The first provides an overview and details of current attempts made by researchers in the field of green manufacturing. We further extend the discussion with our pinpoint focus on an Indian scenario in the second sub section. The third sub section explores the drivers of green manufacturing in existing literature. Finally, the fourth sub section reveals the gaps in the existing attempts and presents the highlights of this research. These sub categories ensure an improved understanding of the theory behind the title.

2.1. Green manufacturing The intervention of external auditing certification by British standard 7750 in the early 1990’s resulted in the integration of corporate environmental policies with programs such as GM (Green Manufacturing) or EMS (Environment Management System) getting more attention over the last decade (Morrow and Rondinelli, 2002). The International Standards Organization (ISO) published the first EMS standard internationally in 1996 known as ISO 14001 (Agan et al., 2013). Due to the rapid development of green manufacturing, many researchers focused their attention on this theme and conducted various studies with extensions. Richards (1994) studied the life cycle approach and design guidance with the EMS along with its barriers and challenges for effective implementation. Handfield et al. (1997) explored green practices in the furniture manufacturing industry through interviews conducted with five environmental managers. Vachon (2007) revealed that green supply chain practices have a higher impact on suppliers than on customers in his discussion of the green supply chain practices from both perspectives (suppliers and customers). Pun et al. (2002) identified success factors in EMS adoption and implementation and also investigated the relationship between environmental strategies and environmental management. They also provided best practices through their examination of several successful cases. Matthews (2003) recommended new changes in internal benchmarking of the EMS system through suggested changes in the benchmarking cycles of plan, do, check, and act. Azzone and Noci (1998) defined green manufacturing strategies and ways to implement them with regard to operations management; they also identified the most effective performance measuring system for deployment of green manufacturing strategies. Despeisse et al. (2013) worked in the research fields of sustainable manufacturing and provided an approach to systemize identification opportunities in factories. They also introduced the tactics for resource efficiency manufacturing. Chin et al. (1999) provided an overview of strategic issues and attributes involved in implementing EMS by adopting the ISO 14001 and by evaluating success factors using AHP to implement ISO 14001ebased EMS. Searcy et al. (2012) found the colloquium on ISO 14001 and revealed the challenges in implementing the ISO 14001 in environment management systems. Sangwan (2006) presented the multi attribute decision model to justify green manufacturing by performance value analysis attributes identified from strategic, tactical, and operational issues. Gutowski et al. (2005) observed the status of environmentally benign manufacturing from three main nations including Japan, Europe, and the United States; this committee evaluated research questions and provided solution methodologies to relevant research questions. Hui et al. (2001) carried out a survey in Hong Kong to analyze the current status of the nation by investigating the critical factors considered for implementing GM/GMS. In addition, a monthly review explored the status of green manufacturing around their region. For instance, Business Horizons and the Harvard Business Review published some pieces on environmental management issues explaining the importance of EMS (Handfield et al., 1997). In Business Horizons, Sarkis and Rasheed (1995) published a seminal paper concerned with greening manufacturing functions. It explored all the needs, motivations, and obstacles of green manufacturing practices. Lun (2011) discussed the impact of GM practices on organizational performance with a perspective on the GM elements using relevant models and indicators. Li et al. (2010) evaluated six major objectives (quality, time, service, cost, environmental impact, and resource consumption) after the implementation of green manufacturing with the assistance of proposed methodology. Vijayaraghavan and Helu (2013) discussed the various technologies which assured

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GM and they ended with a case study which focuses and demonstrates energy monitoring. Onsrud and Simon (2013) discussed the concept of sustainability in terms of GM with its metrics, standards, and best practices. 2.2. Green manufacturing in Indian scenario In an Indian context, GM is in the preliminary stage as we have established in earlier sections. Hence, in recent years some researchers focused on the status of GM implementation in an Indian origin. Specifically, Sangwan (2011) explored the qualitative and quantitative positive impacts of GM in Indian SME’s (Small and Medium scale Enterprises) with the assistance of the empirical study where 198 SME’s were surveyed and the data was analyzed through statistical software (SPSS). The virtual status of GM implementation in Indian context was revealed by Rehman and Shrivastava (2013a) with their study from the Vidharba region of Maharashtra (India) with the assistance of their survey instrument. Their study clearly defined that there is a substantial gap between virtual and literature resources; their study also confirmed that more effort is needed to establish GM concepts. Digalwar et al. (2013) made an empirical investigation among Indian manufacturing firms to find the performance measures of green manufacturing. In this study, they approached 400 industrial managers and got a response rate of 27%. They found several performance measures, including top management commitment, employee empowerment, knowledge management, employee training, green product and process design, environmental health and safety, production planning and control, suppliers and materials management, quality, cost, customer environment performance requirement, customer responsiveness, and company growth. Rehman and Shrivastava (2013b) made a review of 123 green manufacturing papers over past 15 years and explored state of the art methods through these papers which have been published in 73 reputable journals. 2.3. Drivers of green manufacturing Many researchers focus on the general concept of green manufacturing alone and not on the drivers of green manufacturing. A few researchers have turned their attention towards the drivers, barriers, and pressures of EMS/GM. Ammenberg and Sundin (2005) explored the drivers, barriers, and experiences of environmental management systems. This paper focuses mainly on “Design For Environment” (DFE) and Product-Oriented Environment Management Systems (POEMS). Morrow and Rondinelli (2002) projected the motivations or drivers of environmental management systems based on ISO 14001 and (Eco Management and Audit Scheme) EMAS certification. Sarkis et al. (2010) established the stakeholder pressures in the adoption of environmental practices and also expressed the mediating effect of training; their work is the only paper which takes only one driver as a base of the paper. Zhou et al. (2012) proposed the analytic and simulation model to evaluate green strategies. Santolaria et al. (2011) explored the main drivers of integration of eco-design with perceptions and predictions illustrated in a Spanish environment. Massoud et al. (2010) investigated the drivers, barriers, and incentives to implementing environmental management systems with the assistance of a case study in the Lebanon food industry. Drivers of sustainability in the wine industry were established by Gabzdylova et al. (2009) with a case study of the New Zealand wine industry. Wu and Wirkkala (2009) pointed out that drivers of green manufacturing deal with federal and state regulations and pressures from customers, investors, and competitors. Routroy (2009) explored the drivers and antecedents for green supply chain

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management implementation in the manufacturing environment. He mainly argued that top management commitment, government initiatives, green sourcing, green design, green operations, green packaging, reverse logistics, environmental management system, green innovation, and customer awareness are the drivers of the green supply chain in a manufacturing context, very similar to green manufacturing. Pun et al. (2002) averred that customer requirements, competitive pressures, and resource conservation opportunities are drivers of green business strategy. Chin et al. (1999) explained the various attributes involved in the implementation of green practices in manufacturing. The attributes include management attitude, organizational change, external aspects, social aspects and technical aspects. These attributes are further classified into relevant subordinate issues. Agan et al. (2013) described that state rules and regulations, customers, internal motivation, and firm performance are the drivers of GM. Santolaria et al. (2011) discussed the drivers of GM as business efficiencies, innovation, cost, brand positioning, and business communication. Chuang and Yang (2013) explored the performance of GM system with the finding of key success factors based on a three-layer assessment model composed of green design, green manufacturing processes, and green packaging. 2.4. Research gap & highlights It is concluded from the above review that there is no work analyzing the drivers of green manufacturing. Some researchers explore the drivers but they are limited with preliminary drivers such as customers, stakeholders, and regulations. Also most studies do not examine the Indian manufacturing origin. Current research does not prioritize the drivers nor do they reveal the essential driver of green manufacturing. Hence, this study attempts to fill this gap by identifying the common drivers of green manufacturing and by evaluating its essential driver. The highlights of this research are mentioned below.  Common drivers of green manufacturing are identified through literature review and experts’ recommendations.  A framework model has been proposed with MCDM approach; with its help the essential driver and the priorities among drivers of green manufacturing are identified.  The obtained results are further explored through a two-stage sensitivity analysis and through feedback from industrial experts. 3. Problem description The possible adverse effects of GM/EMS implementation on profit margins are concentrated on by industrialists even though implementation provides a competitive edge and other benefits (Hui et al., 2001). Due to the increasing significance of environmental concerns, the company has to adapt GM as a mandatory process. But there is no proper work which examines the drivers of GM in an Indian scenario. Generally in India, green manufacturing practices are implemented through three main strategies: green energy, green products, and green processes in business operations. In India, CII (Confederation of Indian Industry, 2011) is a non-profit organization that promotes GM in Indian manufacturing sectors. According to the CII, green manufacturing is entirely different from conventional manufacturing by its “aims to reduce the amount of natural resources needed to produce finished goods through more energy, and materials efficient manufacturing processes that also reduce the negative externalities associated with waste and pollution”. The “1st green manufacturing summit” was held on March, 2011 where several objectives toward GM were made and

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the Indian government extended their investments in green power and related activities. Hence, there is a need to establish the vital role of GM to the professionals. Hence, in this study, drivers of green manufacturing are chosen as the pedestal. This paper attempts to work on this problem by collecting both the common drivers from existing literature and from experts’ opinions. From the common drivers, an essential driver and the priority of drivers are identified by the Fuzzy AHP. A pair-wise comparison is obtained with the help of manufacturing managers in leading firms situated in various parts of south India. The questionnaire comprising common drivers with the sequence of a nine-point Likert scale was provided to production managers. Collection of common drivers by literature support and expert opinions are shown in Table 1. 3.1. Framework for the study From the review of existing literature and experts’ opinion, common drivers of green manufacturing were identified. From the common drivers, the essential driver and the priority among drivers are concluded by fuzzy approach based on data provided by

firms located in various parts of south India. Fig 1 shows the proposed study’s framework. 4. Solution methodology Generally, MCDM is a tool for solving complex engineering problems through modeling and methodological approaches (Rouyendegh and Erkan, 2012). In these MCDM tools, AHP is one of the pioneering methods which was first developed by Saaty in 1971 to solve decision making problems in order to identify the best alternative based on human judgments (Saaty, 1980). AHP identifies the weight of the alternatives based on human judgments among all alternatives. Earlier, numerical values of linguistic variables were used in the evaluation of the alternatives (Aktepe and ERSOZ, 2011). The AHP decision making process strengthens the firm’s comprehensiveness and reasonableness. (Chen, 2000; Chen et al., 2006; Govindan et al., 2014). While AHP is the best decision making tool, it depends purely on human judgments which may cause biases and vagueness in results (Ozdagoglu and Ozdagoglu, 2007). Utilizing Fuzzy AHP increases accuracy in the decision making process

Table 1 Common drivers of green manufacturing. S. No

Drivers

Explanation

Sources

1

Financial benefit (D1)

2

Company image (D2)

3

Environmental conservation (D3)

4

Compliance with regulations (D4)

5

Stakeholders (D5)

Dornfeld et al. (2013); Wu and Wirkkala (2009); Searcy et al. (2012); Chin et al. (1999); Agan et al. (2013); Deif (2011); Gabzdylova et al. (2009); Zhu and Sarkis (2006); Gutowski (2001); Agan et al. (2013); Gabzdylova et al. (2009); Searcy et al. (2012); Pun et al. (2002); Zhu and Sarkis (2006) Dornfeld et al. (2013); Wu and Wirkkala (2009); Searcy et al. (2012); Pun et al. (2002); Gabzdylova et al. (2009); Zhu and Sarkis (2006) Dornfeld et al. (2013); Wu and Wirkkala (2009); Pun et al. (2002); Chin et al. (1999); Agan et al. (2013); Despeisse et al. (2013); Tseng et al. (2013); Deif (2011); Gabzdylova et al. (2009) Dornfeld et al. (2013); Searcy et al. (2012); Despeisse et al. (2013); Gabzdylova et al. (2009); Zhu and Sarkis (2006)

6

Green innovation (D6)

7

Supply chain requirement (D7)

8

Customers (D8)

The economic urge and crisis pressures to adapt GM because GM practices impact on the optimal resource and energy usage which increases the financial benefit of the firm. Reputation plays a vital role in any firm’s growth. Hence to retain the company image, the practice of GM is mandatory. Depletion of natural resources and concerns on environmental conservation motivate GM implementation. To comply with regulations like ISO 14001 and other certifications, firms are pressured to maintain green environment activities throughout the processes. Investors, media, government, etc. are considered as stakeholders who have the power to make a direct impact on the firm’s decisions. Recently, pressures for firms to adopt GM systems have been dominant. Recent year’s innovation exists in all forms; hence due to the momentous importance of green activities, green innovations are emerging in the current realm which forces the manufacturers to adopt new sustainable tactics. Reverse logistics, reverse supply chain, etc. are becoming vital areas that expand day by day. Supply chain requirements pressure the manufacturers to design products with green concerns; for instance, easy dismantling, usage of recyclable material in the product manufacturing, etc. Customer awareness on green concerns pressures the manufacturers to make their product as green sensitive.

9

Employee demands (D9)

10

Internal motivations (D10)

11

Market trend (D11)

12

Competitors (D12)

Some of the operations may pollute the environment as well as the safety of the employees; thus, employees’ demand the firm to practice GM systems. Practicing of GM exhibits some positive vibration among the employees which augments employee commitment. In recent trends, green products are popular and gain support from all external stakeholders. This trend pressures the manufacturers to produce green products via green manufacturing activities. To stay in the market, manufacturers need to compete with their competitors by introducing innovative new launches. Often, green ideas help the manufacturers.

Dornfeld et al. (2013); Routroy (2009); Tseng et al. (2013); Zhu and Sarkis (2006);

Dornfeld et al. (2013); Zhu and Sarkis (2006); Chien and Shih (2007)

Dornfeld et al. (2013); Wu and Wirkkala (2009); Routroy (2009); Searcy et al. (2012); Pun et al. (2002); Chin et al. (1999); Agan et al. (2013); Massoud et al. (2010); Gabzdylova et al. (2009) Searcy et al. (2012); Gabzdylova et al. (2009); Zhu and Sarkis (2006)

Wu and Wirkkala (2009); Searcy et al. (2012); Agan et al. (2013); Gabzdylova et al. (2009) Dornfeld et al. (2013); Searcy et al. (2012); Pun et al. (2002); Chin et al. (1999); Agan et al. (2013); Deif (2011); Massoud et al. (2010); Gabzdylova et al. (2009) Dornfeld et al. (2013); Wu and Wirkkala (2009); Sangwan (2006); Searcy et al. (2012); Pun et al. (2002); Chin et al. (1999); Agan et al. (2013); Tseng et al. (2013); Deif (2011); Gabzdylova et al. (2009)

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Literature Review

5

Opinions of Industrial managers

Experts’ notion

Identification of Common driver

Feedback Feedback

Questionnaire with common driver (based on Likert Scale) - circulated to various industries in southern part of India

Industries Identification of essential driver and ranking drivers with fuzzy approach based on data provided by industries

Validation of results by using two stage sensitivity analysis (validation of defuzzification methods using Spearman’s Coefficient and varying weights of essential driver) and feedback from industries

Conclusion with essential driver and priority among driver

Fig. 1. Proposed framework for the study.

(Kwong and Bai, 2002). Therefore, conventional AHP is modified to a fuzzy approach. In conventional AHP, pair-wise comparisons are made by transferring preferences into weights as denoted from 1to 9. These numbers provide the corresponding relative weight, clearly explained by Saaty. An unbalanced scale of judgments, the inability to handle uncertainty, and reduced accuracy in pair-wise comparison were the reasons for AHP criticism and the associated development of fuzzy AHP. Due to this inconvenience, Chang (1996) first discussed triangular fuzzy numbers in his study. Our problem is also analyzed through triangular fuzzy numbers. Triangular fuzzy numbers, as shown in Fig 2, have three numbers, namely l, m, and u which denote the smallest value, the most promising value, and the largest value, respectively, to describe a fuzzy event. In this regard, fuzzy numbers are crisp numbers for simplifying problems using defuzzification methods. The evaluating procedure for this methodology is described as follows: (modified from Kwong and Bai, 2002) Step 1: Identify the common attributes (drivers) related to green manufacturing with the combined assistance of existing literature, field and industrial experts’ opinions.

1.0

M 0.0

l

m

u

Fig. 2. Triangular fuzzy number (Tseng et al., 2013).

Step 2: Set up the pair-wise comparison matrix among the common drivers, one over another under fuzzy environment based on the replies from the industrial experts in a Likert ninepoint scale which gets converted into a Saaty scale to identify the most influential driver and to establish priority among them. The matrix formation of pair-wise comparison is shown in Equation (1). The fuzzy matrix Ã. (aij) is summarized below:

2

1 a12 a13 6 a21 1 a23 6 6 .: .: .: ~ 6 A¼6 .: .: .: 6 4 aðn  1Þ1 aðn  1Þ2 aðn  2Þ3 an1 an2 an3

3 .: a1ðn  1Þ a1n .: a2ðn  1Þ a2n 7 7 7 .: .: .: 7 7 .: .: .: 7 .: 1 aðn  1Þn 5 .: anðn  1Þ 1 (1)

where

~ij ¼ a

8 > > > < > > > :

i ¼ j  1 1 1 1 1; 3; 5; 7; 9 or 1 1; ; ; ; 3 5 7 9 isj

Step 3: Fuzzy numbers are to be defuzzified into crisp numbers through various defuzzification methods. Step 4: Estimate the global weights of each driver through arithmetic operations by formal procedures. The priority of the criteria should be checked for its consistency to prove the validation of the result. The steps for consistency check

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are provided below and it is a cyclic process until the consistency index (C.R) is less than 0.1. The following steps provide the consistency check for the pairwise comparison matrix (Haq and Kannan, 2006):

combination of literature reviews, experts’ opinions and industrial managers’ views.

1. Calculate the eigenvector or relative weights and lmax for each matrix of order n 2. Compute the consistency index for each matrix of order n by the formulae:

The next phase is the identification of the essential drivers and the determination of the priority among common drivers of green manufacturing. This objective is achieved by two sub-phases, namely (i) questionnaire development and data collection and (ii) MCDM approach.

CI ¼ ðlmaxn Þ=ðn  1Þ

(2)

3. The consistency ratio is then calculated using the formulae:

CR ¼ CI=RI

(3)

Table 2 shows the random index used in the consistency check. This index depends on ‘n’ which denotes the number of criteria. For instance, in this problem twelve drivers are investigated; hence, n ¼ 12 and the corresponding (Random Index) R.I is 1.48. 5. Application of proposed framework The proposed framework is validated through a two-phase approach. The first phase is to identify the common drivers of GM, and the second phase is to determine not only the essential drivers and but also the priority among drivers using the Fuzzy AHP method. Both phases are detailed below. 5.1. Identification of common drivers In this first phase, to identify the common drivers of GM, we address this objective through two approaches: from existing literature and from the industrial and field experts. First, the existing literature in the fields of green manufacturing, environmentally conscious manufacturing, and green supply chain were reviewed and shortlisted by our research team. Further, the drivers were collected and analyzed based on several rounds of discussions. For this collection of drivers from literature, we used the search terms “green manufacturing,” “environmentally conscious manufacturing,” “drivers of green manufacturing,” and “Green manufacturing in Indian scenario” from leading journals such as Elsevier, Springer, Emerald, Taylor & Francis, etc. Secondly, to get the assistance from the field and industrial experts, a one-day program was organized in our place on ‘green manufacturing,’ where we invited 120 industrial production managers. Of the 120 invited, 90 production managers attended. The program established the concepts and validated the need for the study, especially in Indian origin, to the participating experts. Once the theme of the problem was explained, discussions were held with such industrial managers. After several rounds of discussions and clarifications, the common drivers of green manufacturing were identified, as shown in Table 1. To bridge the gap between the virtual world and our literature resources, this two-way approach, using both literature review and professional experts, was made. Finally, twelve common drivers of green manufacturing were identified based on a Table 2 Random index for corresponding number of criteria. Step 5: The drivers of GM gets prioritized and the most influential driver is identified with the results of the weights. 1

2

3

4

5

6

7

8

9

10

11

12

0.00

0.00

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

1.51

1.48

5.2. Identification of essential drivers and priority among drivers

5.2.1. Questionnaire development and data collection A questionnaire was prepared with the twelve common drivers (which were identified from the combined assistance of experts, industrial managers and literature in the previous steps) based on a Likert 9 point scale and circulated to over 120 different industrial sectors through mail, telephone enquiries, direct meetings, and managerial interviews. The classification of sectors is disclosed below in Table 3. A total of 90 firms responded. Of the returned questionnaires, 50 were complete and 40 were incomplete. According to Malhotra and Grover (1998), a 20% response rate is enough for any survey and in this case the response rate is about 45%, so this survey can be judged as bona fide and acceptable. The pair-wise comparison was made based on this response from among drivers of green manufacturing with the assistance of the AHP methodology. Table 4 shows the fundamental scale absolute numbers as described by Saaty in 2008. 5.2.2. MCDM approach The data obtained from various industries is analyzed with the assistance of MCDM tool, Fuzzy AHP which is represented in Fig 3. As per the four steps methodology discussed in previous sections, the priority among common drivers is identified. Step 1: Common drivers of GM in Indian scenario were collected and identified. Step 2: The pair-wise comparison was made among the common drivers of GM as shown in Equation (1) which is based on the replies collected from the industries through the questionnaire survey. It depends on the Likert scale which is further converted into the Saaty scale and is shown in Table 5. Step 3: Fuzzy numbers are converted into crisp numbers which are commonly known as defuzzification. With the help of this linguistic defuzzification (Table 6), the fuzzy pair-wise comparison is converted into a crisp pair-wise comparison as presented in Table 7. Step 4: The global weights of the drivers are estimated through various numerical and arithmetic operations under AHP which is shown in Table 8.

Table 3 Profile of the respondent e Indian manufacturing firms. Industry

Chemical Food Iron & Steel Automotive components Textile & Apparel Paper Electrical/Electronics

Total

15 15 20 20 20 20 10

Nature of the firm (employees)

Turnover/annum (Rs. Crores)

Enterprise

Large

Enterprise

Large

8 8 10 10 10 10 10

7 7 10 10 10 10 10

8 8 10 10 10 10 10

7 7 10 10 10 10 10

Considering with employees Considering with turnover. >3000 (Enterprise) >200 (enterprises). 2001e-3000 (Large) 170e200 (Large).

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Table 4 Fundamental scale absolute numbers (Saaty, 2008). Intensity of importance

Definition

Explanation

1 2 3 4 5 6 7

Equal importance Weak or slight Moderate importance Moderate plus Strong importance Strong plus Very strong or demonstrated importance

Two activities contribute equally to the objective

8 9

Very, very strong Extreme importance

Reciprocals of above

If activity i has one of the above non-zero numbers a ssigned to it when compared with activity j, then j has the reciprocal value when compared with i If the activities are very close

1.1e1.9

Step 5: According to the Equation (3), a consistency check was made for the input. If the C.R is 0.099, which is less than predefined C.R., and then our result is consistent and acceptable. 6. Results and discussion Table 8 shows the result of the study, in which it is revealed that compliance with regulations is the essential driver among drivers of green manufacturing. The drivers are a helping factor in implementation of green manufacturing. According to this study, common drivers are analyzed through Fuzzy AHP. In this analysis, the priority of the drivers of green manufacturing was on the basis of Fuzzy pair-wise comparison was made with assistance of industrial managers

Defuzzification (based on linguistic method) Pair-wise comparison in crisp numbers (results of defuzzification) No Consisten cy Check

Yes Essential driver of GM (linguistic defuzzification method)

Stage 1: Sensitivity Analysis I (Validation of defuzzification methods (Spearman Correlation coefficient)

Results

Stage 2: Sensitivity Analysis II (Varying weight of most influential GM driver from the validated defuzzification method obtained from Sensitivity analysis I)

Fig. 3. Steps involved in fuzzy AHP.

Final Results (indicating most essential driver of GM implementation)

Experience and judgment slightly favor one activity over another Experience and judgment strongly favor one activity over another An activity is favored very strongly over another; its dominance demonstrated in practice The evidence favoring one activity over another is of the highest possible order of affirmation A reasonable assumption

May be difficult to assign the best value but when compared with other contrasting activities the size of the small numbers would not be too noticeable, yet they can still indicate the relative importance of the activities

their relative weights and corresponding ranks. The rank of the drivers are posted in decreasing order, which is that M4 > M5 > M8 > M1 > M12 > M11 > M2 > M3 > M7 > M6 > M10 > M9. The compliance with regulations obtained the highest relative weight of 0.14382 when compared to other drivers. Our team discussed the results with industry mangers and top level management actors from various industries, who confirmed that our results coincided with their activities. They accepted that they followed environmentally conscious practices only during the time of auditing, certifications, or inspections. For instance, solid waste from production called scrap is dumped with polythene covering to prevent chemicals seeping into the soil in the scrap yard. Although this is the proper procedure for dumping waste, some industries dump waste as it is, in the scrap yard without any polythene covering to save the cost of the polythene cover. Hence, even in multinationals, regulations are the main driver for the implementation of green manufacturing. Next to compliance with regulations, stakeholder and customer pressures rank second and third among other drivers, respectively. Due to pressure from stakeholders, companies are forced to adopt green practices in manufacturing as they provide the necessary financial support to the firm. Many researchers discovered that stakeholder pressure is the main driver for implementing green manufacturing. The recent increase in environment awareness among customers and their expectation of suppliers based on environmental consciousness is now high. Companies unwilling to lose their core customers are thus forced to adopt green practices in manufacturing. Financial benefit ranks fourth. As material reuse is cheaper than the cost of original material, this practice ensures savings in addition to recycling being of tremendous benefit to the economy. EU nations recycle material in tons through Waste Electrical and Electronic Equipment (WEEE). Because of such reasons, this driver holds the fifth position in priority rating. Other drivers also help in implementing green manufacturing in industries due to their relevant benefits. To know the effect of our study’s results, after three months, our research team approached the industrial managers through mails, telephonic enquiries and direct meetings to check whether there have been any positive changes due to our obtained results. In this connection, managers demonstrated they were highly appreciative of our work; they revealed that their green activities had improved from previous performances. We obtained these details from internal green auditors, which validate many positive effects like less waste generation, less resources consumption, positive motivations among internal employees, satisfaction among top level management etc.

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Table 5 Pair-wise comparison of drivers.

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

M1

M2

M3

M4

M5

M6

M7

M8

M9

M10

M11

M12

(1,1,1) (2,3,4) (1,2,3) (1/5,1/4,1/3) (1/6,1/5,1/4) (1/4,1/3,1/2) (1/4,1/3,1/2) (1,2,3) (1/6,1/5,1/4) (1/6,1/5,1/4) (1,2,3) (1,2,3)

(1/4,1/3,1/2) (1,1,1) (1/3,1/2,1) (1,2,3) (1,2,3) (1/5,1/4,1/3) (1/4,1/3,1/2) (1,2,3) (1/8,1/7,1/6) (1/6,1/5,1/4) (1,2,3) (1,2,3)

(1/3,1/2,1) (1,2,3) (1,1,1) (1,2,3) (1,2,3) (1/3,1/2,1) (1/3,1/2,1) (2,3,4) (1/5,1/4,1/3) (1/4,1/3,1/2) (1,2,3) (1,2,3)

(3,4,5) (1/3,1/2,1) (1/3,1/2,1) (1,1,1) (1,1,1) (1/6,1/5,1/4) (1/5,1/4,1/3) (1/3,1/2,1) (1/10,1/9,1/8) (1/6,1/5,1/4) (1/4,1/3,1/2) (1/3,1/2,1)

(4,5,6) (1/3,1/2,1) (1/3,1/2,1) (1,1,1) (1,1,1) (1/6,1/5,1/4) (1/5,1/4,1/3) (1/3,1/2,1) (1/9,1/8,1/7) (1/9,1/8,1/7) (1/3,1/2,1) (1/3,1/2,1)

(2,3,4) (3,4,5) (1,2,3) (4,5,6) (4,5,6) (1,1,1) (1,2,3) (4,5,6) (1/3,1/2,1) (1/3,1/2,1) (3,4,5) (3,4,5)

(2,3,4) (2,3,4) (1,2,3) (3,4,5) (3,4,5) (1/3,1/2,1) (1,1,1) (3,4,5) (1/4,1/3,1/2) (1/4,1/3,1/2) (2,3,4) (2,3,4)

(1/3,1/2,1) (1/3,1/2,1) (1/4,1/3,1/2) (1,2,3) (1,2,3) (1/6,1/5,1/4) (1/5,1/4,1/3) (1,1,1) (1/10,1/9,1/8) (1/9,1/8,1/7) (1/3,1/2,1) (1/3,1/2,1)

(4,5,6) (6,7,8) (3,4,5) (8,9,10) (7,8,9) (1,2,3) (2,3,4) (8,9,10) (1,1,1) (1,2,3) (7,8,9) (7,8,9)

(4,5,6) (4,5,6) (2,3,4) (4,5,6) (7,8,9) (1,2,3) (2,3,4) (7,8,9) (1/3,1/2,1) (1,1,1) (5,6,7) (5,6,7)

(1/3,1/2,1) (1/3,1/2,1) (1/3,1/2,1) (2,3,4) (1,2,3) (1/5,1/4,1/3) (1/4,1/3,1/2) (1,2,3) (1/9,1/8,1/7) (1/7,1/6,1/5) (1,1,1) (1,1,1)

(1/3,1/2,1) (1/3,1/2,1) (1/3,1/2,1) (1,2,3) (1,2,3) (1/5,1/4,1/3) (1/4,1/3,1/2) (1,2,3) (1/9,1/8,1/7) (1/7,1/6,1/5) (1,1,1) (1,1,1)

7. Sensitivity analysis

Table 6 Linguistic terms and the corresponding triangular fuzzy numbers (S¸en and Çınar, 2010). Linguistic term

Fuzzy number

Positive triangular fuzzy scale (l,m,u)

Extreme unimportance Intermediate value Very unimportant Intermediate value Essential unimportance Intermediate value Moderate unimportance Intermediate value Equally important Intermediate value Moderate importance Intermediate value Essential importance Intermediate value Very vital importance Intermediate value Extremely vital importance

91 81 71 61 51 41 31 21 1 2 3 4 5 6 7 8 9

(1/10,1/9,1/8) (1/9,1/8,1/7) (1/8,1/7,1/6) (1/7,1/6,1/5) (1/6,1/5,1/4) (1/5,1/4,1/3) (1/4,1/3,1/2) (1/3,1/2,1) (1,1,1) (1,2,3) (2,3,4) (3,4,5) (4,5,6) (5,6,7) (6,7,8) (7,8,9) (8,9,10)

To validate and explore the obtained results, a two stage sensitivity analysis was performed. Sensitivity analysis is nothing but the process of checking the robustness of the obtained output. In stage 1, the different defuzzification methods were validated using the Spearman Correlation coefficient. In stage 2 the weight of the essential (highest weighted) driver from stage 1 above, previously identified in sensitivity analysis I, is positioned to know the impacts of the essential drivers on all other drivers of GM. 7.1. Stage 1: sensitivity analysis I e implementation and validation of different defuzzification methods using Spearman Correlation coefficient This paper used linguistic defuzzification to analyze the priority of drivers and to identify essential drivers of green manufacturing. Three different types of defuzzification are used to check the accuracy of the results in this section. There are many types of defuzzification methods (Rose, 1995; Ganesh, 2006). This paper considers only four common defuzzification methods, which are as follows:

If we compare the obtained results with developed nations like EU and US, then according to Gutowski et al. (2005), cost reduction, risk, reputation and regulations are considered as the major motivation of practicing the GM systems. This result is contrary to our results, but it is mainly due to the nature of the geography. Developed nations are highly aware of the benefits of GM, but in the developing regions there is a huge lack of awareness of GM benefits; they simply are not aware of the common success factors for its implementation. In developed nations, reputation is one of the most critical criteria which play a major role in GM implementation. But in our study reputation captures 7thposition, so the wellknown statement “One size does not fit all,” is applicable; every region needs its own GM implication tactics.

   

Linguistic method Centroid Method or (COA Method) Graded Mean Integration Representation (GMIR) Method Median or Signed distance method or Area of compensation method

Table 9 shows the comparative results of different defuzzification methods, and Fig 4 graphs the difference in the relative weights results from the various defuzzification methods. A deviation in the results e obtained from different defuzzification methods e is identified. The results of the Centroid (COA)

Table 7 Pair-wise comparison of drivers in crisp values: (Linguistic method).

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

M1

M2

M3

M4

M5

M6

M7

M8

M9

M10

M11

M12

1 3 2 0.25 0.2 0.333333 0.333333 2 0.2 0.2 2 2

0.333333 1 0.5 2 2 0.25 0.333333 2 0.142857 0.2 2 2

0.5 2 1 2 2 0.5 0.5 3 0.25 0.333333 2 2

4 0.5 0.5 1 1 0.2 0.25 0.5 0.111111 0.2 0.333333 0.5

5 0.5 0.5 1 1 0.2 0.25 0.5 0.125 0.125 0.5 0.5

3 4 2 5 5 1 2 5 0.5 0.5 4 4

3 3 2 4 4 0.5 1 4 0.333333 0.333333 3 3

0.5 0.5 0.333333 2 2 0.2 0.25 1 0.111111 0.125 0.5 0.5

5 7 4 9 8 2 3 9 1 2 8 8

5 5 3 5 8 2 3 8 0.5 1 6 6

0.5 0.5 0.5 3 2 0.25 0.333333 2 0.125 0.166667 1 1

0.5 0.5 0.5 2 2 0.25 0.333333 2 0.125 0.166667 1 1

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K. Govindan et al. / Journal of Cleaner Production xxx (2014) 1e12 Table 8 Relative weights and rank of drivers. S. No

Drivers of green manufacturing

Relative weights

Rank

1 2 3 4 5 6 7 8 9 10 11 12

Financial benefit(M1) Company image (M2) Environmental conservation (M3) Compliance with regulations (M4) Stakeholders (M5) Green innovation (M6) Supply chain requirement (M7) Customers (M8) Employee demands (M9) Internal motivations (M10) Market trend (M11) Competitors (M12)

0.131379607 0.09184063 0.060945927 0.143832814 0.139697824 0.025156901 0.035011256 0.138181279 0.012775003 0.017235008 0.101208283 0.102735467

4 7 8 1 2 10 9 3 12 11 6 5

the ranks; R ¼ 0 represents no association between the ranks; R ¼ 1 represents perfect disagreement between the ranks. As per the Equation (4) the Spearman Correlation co-efficient (R) for different defuzzification methods is shown in Table 10. According to this result, it is clearly evident that the Linguistic method is perfectly correlated with all three methods; similarly, GMIR defuzzification method is also perfectly correlated. Hence, it is considered that the Linguistic method has high reliability when compared to the other defuzzification methods. 7.2. Stage 2: sensitivity analysis II e varying the weight of essential criterion obtained from linguistic method

method are different from the other two methods where competitors, market trend, and company image obtained 5, 6, and 7 rankings, respectively. This sequence is totally changed in the Centroid method, which reveals that company image ranks 5th, competitors rank 6th, and market trends rank 7th. There are only slight changes in the value of relative weights between other three methods, but the ranking is equivalent. It is clearly revealed that defuzzification methods have an impact in the results by the above discussions. In this connection, the identification of best defuzzification method is mandatory to validate the reliability of the results. Hence, the Spearman Correlation co-efficient was performed for these four different defuzzification methods. 7.1.1. Spearman correlation co-efficient (R) In this study four defuzzification methods were considered: linguistic, Centroid, GMIR, and Median for first stage sensitivity analysis. As mentioned, it is important to check the reliability of the methods. Here, the Spearman Correlation co-efficient (R) is used to find the statistical significance between the different defuzzification methods. According to Fink (1995) and Li et al. (2012), the Spearman Correlation co-efficient is used frequently and effectively to relate the two ordinal characteristics. The Spearman’s coefficient is defined as (Kannan et al., 2014; Raju and Kumar, 1999)

R ¼ 1

9

The results revealed from our study were purely dependent upon individual human judgments. While this approach may seem unstable and vague, according to Chang et al. (2007) small changes in the relative weights do have an impact on the whole system’s final rankings. Hence, with these considerations in place, and in order to increase the stability and biases of the obtained results, a second sensitivity analysis (II) was also performed in this study. In sensitivity analysis II, the “compliance with regulations” (M4) driver has been selected due to its highest weight obtained from the Linguistic defuzzification method. Recall that the Linguistic method was found as the best method in Sensitivity analysis I (shown in Tables 8 and 9). Further, M4’s value varied from the range of 0e0.9 with 0.1 as increment. This variation impacts the whole system, as shown in Table 11 and as illustrated in Fig 5. Due to this weights variation, the ranking criteria are ultimately determined. At 0 of compliance with regulations criterion, driver M5 holds the first position and M4 holds the last position and at 0.1 of compliance with regulations criterion, M5 holds the first position and M9 holds the last position. At 0.2e0.9 of compliance with regulations criterion, the results are the same as in the normal condition, in which Compliance with Regulations (M4) holds first position and Employee demands (M9) holds last position. This ranking clearly establishes that if the Compliance with Regulations driver is motivated then we can implement effective green manufacturing practices. 8. Managerial implications

P 6 Aa ¼ 1 D2a   ; A A2  1

(4)

where, a ¼ number of alternatives; A ¼ total number of alternatives; Da ¼ difference between ranks obtained through two different methods. R ¼ 1 represents perfect association between

This research also explores some managerial implications. It helps the production manager to reveal the importance of green manufacturing to all team members and also induces their firms to conduct greening programs. It also helps increase the adeptness of green manufacturing by stimulating the essential driver which is

Table 9 Comparison of relative weights and rank obtained from different defuzzification methods. S. No

1 2 3 4 5 6 7 8 9 10 11 12

Drivers of green manufacturing

Financial benefit (M1) Company image (M2) Environmental conservation (M3) Compliance with regulations (M4) Stakeholders (M5) Green innovation (M6) Supply chain requirement (M7) Customers (M8) Employee demands (M9) Internal motivations (M10) Market trend (M11) Competitors (M12)

Relative weights

Rank

Linguistic method

Centroid method

GMIR method

Median method

L.M

C.M

GMIR

M.M

0.1313 0.0918 0.0609 0.1438 0.1396 0.0251 0.0350 0.1381 0.0127 0.0172 0.1012 0.1027

0.13474 0.09954 0.06475 0.14073 0.13679 0.02638 0.03545 0.13532 0.01335 0.01727 0.09707 0.09856

0.13391 0.09771 0.0638 0.14148 0.13749 0.02609 0.03533 0.13605 0.01321 0.01725 0.09806 0.09956

0.13307 0.09582 0.06287 0.14225 0.13821 0.02578 0.03521 0.13677 0.01307 0.01724 0.09907 0.10058

4 7 8 1 2 10 9 3 12 11 6 5

4 5 8 1 2 10 9 3 12 11 7 6

4 7 8 1 2 10 9 3 12 11 6 5

4 7 8 1 2 10 9 3 12 11 6 5

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K. Govindan et al. / Journal of Cleaner Production xxx (2014) 1e12

Fig. 4. Comparison chart for different methods of defuzzification.

8.3. Customer pressure

Table 10 Spearman Correlation co-efficient (R).

Linguistic method Centroid method GMIR method Median method

Linguistic method

Centroid method

GMIR method

Median method

1

0.979021 1

1 0.979021 1

1 0.979021 1 1

Customer pressure plays a major role in the implementation of green practices. Companies involved in green manufacturing programs are highly communicative with their customers based on such green practices. Industries will able to adapt these processes, this study discovered. 9. Conclusions

explored in this study. The following data obtained from study results are presented as the three “C”s: 8.1. Compliance with regulations Compliance with Regulations has top priority in green manufacturing practices. This criterion ensures that implementation of green manufacturing is mandatory. For instance, internal and external audits and certification are used to cultivate green practices. 8.2. Commitment Commitment from the top management is considered as the most critical factor in the implementation of green manufacturing. Top level management includes members such as stakeholders and external bodies who are supportive in terms of investment. The implementation of green manufacturing in industries takes place because of pressures from stakeholders as conformed by our study.

Environmental issues play a major part in strategic manufacturing decisions (Welford, 1995; Noci 1997; Azzone and Noci, 1998; Pun et al., 2002). Hence the study of green technologies in manufacturing and its strategies became mandatory. Implementation of EMS/GM is also a significant issue for production managers and company authorities. Additionally, this study provides a brief view of the drivers which play a crucial role in the implementation of greening throughout manufacturing sectors. This study, through literature resources and experts’ support, identified twelve common drivers which include financial benefits, company image, environmental conservation, compliance with regulations, stakeholders, green innovation, supply chain requirements, customers, employee demands, internal motivations, market trends, and competitors. From these common drivers, the study provides the essential driver and determines the ranking priority among the others for implementation of green actions in the manufacturing strategy. From these results, this paper argues

Table 11 Ranking for drivers when increasing Compliance with Regulations criterion value from 0 to 0.9 by sensitivity analysis. Drivers

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Compliance with regulations criterion values in sensitivity analysis 0

0.1

0.1438 (normal)

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

3 6 7 12 1 9 8 2 11 10 5 4 0

3 7 8 6 1 10 9 2 12 11 5 4 0.1

4 7 8 1 2 10 9 3 12 11 6 5 Normal (0.1438)

4 7 8 1 2 10 9 3 12 11 6 5 0.2

4 7 8 1 2 10 9 3 12 11 6 5 0.3

4 7 8 1 2 10 9 3 12 11 6 5 0.4

4 7 8 1 2 10 9 3 12 11 6 5 0.5

4 7 8 1 2 10 9 3 12 11 6 5 0.6

4 7 8 1 2 10 9 3 12 11 6 5 0.7

4 7 8 1 2 10 9 3 12 11 6 5 0.8

4 7 8 1 2 10 9 3 12 11 6 5 0.9

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Fig. 5. Ranking for drivers when increasing Compliance with Regulations driver value by Sensitivity analysis.

that compliance with regulations is the essential driver for implementation of green manufacturing. It coincides with existing literature; many researchers focus on this criterion as a leading driving factor for green manufacturing. The introduction of the ISO 14001 standard is another significant reason for industries to implement the EMS in their manufacturing strategies to reduce the impact of their operations on the environment (Chin et al., 1999). From the above reference, it is justified that the implementation of green manufacturing is primarily to satisfy governmental regulations. A 2011ISO report revealed that until that date, 22,000 organizations worldwide had chosen to certify their EMS with ISO 14001. Many researchers focus on different issues connected with the implementation of EMS based on standards (Zobel, 2013). This study explores the priority of drivers of green manufacturing. According to the results, stakeholders and customers comprise the second and third essential drivers of green manufacturing, respectively. This result can be justified by literature (Morrow and Rondinelli, 2002; Sarkis et al., 2010; Vachon and Klassen, 2008). The relative weights and the rank of all drivers of green manufacturing are provided in the results section. This study identified the priority among drivers of green manufacturing successfully and also explored the essential driver for implementation of green manufacturing. Without exception, this study has some limitations. In case of Small and Medium Enterprises (SMEs), these drivers may fail. For instance, in developing countries such as India, all SMEs do not have green practices; they manage with other means. There are more than 1 lakh of house hold industries in India. They do not even know the basic rules of green manufacturing and in such regions, this study was inactive. In the future, this problem can be extended by adding other common drivers of green manufacturing. Also this study was able to analyze the obstacles against implementing green practices in SMEs and house hold industries. This study may also be conducted with the introduction of different MCDM tools rather than AHP. This research will help contribute to a better understanding of the phenomenon behind green manufacturing and will also explore the differences between green activities of Multi-National Corporations (MNCs) and SMEs. Acknowledgment This research was supported by a Grant from Forsknings-og Innovationsstyrelsen for project 12-132697. References Agan, Y., Acar, M.F., Borodin, A., 2013. Drivers of environmental processes and their impact on performance; A study of Turkish SMEs. J. Clean. Prod.. http:// dx.doi.org/10.1016/j.jclepro.2012.12.043. Aktepe, A., ERSOZ, S., 2011. A fuzzy analytic hierarchy process model for supplier selection and a case study. Int. J. Res. Dev. 3 (1), 33e37.

Ammenberg, J., Sundin, E., 2005. Products in environmental management systems: drivers, barriers and experiences. J. Clean. Prod. 13, 405e415. Azzone, G., Noci, G., 1998. Identifying effective PMSs for the deployment of “green” manufacturing strategies. Int. J. Oper. Prod. Manag. 18 (4), 308e335. Carter, C.R., Rogers, D.S., 2008. A framework of sustainable supply chain management: moving toward new theory. Int. J. Phys. Distrib. Logist. Manag. 38 (5), 360e387. Chang, D.Y., 1996. Applications of the extent analysis method on fuzzy AHP. Eur. J. Operat. Res. 95, 649e655. Chang, C.W., Wu, C.R., Lin, C.T., Chen, H.-C., 2007. AnapplicationofAHPand sensitivity analysis for selecting the best slicing machine. Comput. Ind. Eng. 52 (2), 296e307. Chen, C.T., 2000. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114, 1e9. Chen, C.C., 2005. Incorporating green purchasing into the frame of ISO 14000. J. Clean. Prod. 13, 927e933. Chen, Y., Lai, S., Wen, C., 2006. The influence of green innovation performance on corporate advantage in Taiwan. J. Bus. Ethics 67 (4), 331e339. Chien, M.K., Shih, L.H., 2007. An empirical study of the implementation of green supply chain management practices in the electrical and electronic industry and their relation to organizational performances. Int. J. Environ. Sci. Technol. 4 (3), 383e394. Chin, K.S., Chiu, S., Tummala, V.M.R., 1999. An evaluation of success factors using the AHP to implement ISO 14001-based EMS. Int. J. Qual. Reliab. Manag. 16 (4), 341e361. Chuang, S.P., Yang, C.L., 2013. Key success factors when implementing a greenmanufacturing system. Prod. Plan. Control, 1e15 (ahead-of-print). CII, 2011. Energy, Products and Processes. http://www.cii.in/webcms/Upload/BCGCII%20Green%20Mfg%20Report.pdf (accessed 23.08.13.). Deif, A.M., 2011. A system model for green manufacturing. J. Clean. Prod. 19, 1553e 1559. Despeisse, M., Oates, M.R., Ball, P.D., 2013. Sustainable manufacturing tactics and cross-functional factory modeling. J. Clean. Prod. 42, 31e41. Digalwar, A.K., Tagalpallewar, A.R., Sunnapwar, V.K., 2013. Green manufacturing performance measures: an empirical investigation from Indian manufacturing industries. Meas. Bus. Excell. 17 (4), 1. Dornfeld, D., Yuan, C., Diaz, N., Zhang, T., Vijayaraghavan, A., 2013. Introduction to Green manufacturing. In: Green Manufacturing. Springer US, pp. 1e23. Fink, A., 1995. How to Analyze Survey Data. SAGE Publications, London. Gabzdylova, B., Raffensperger, J.F., Castka, P., 2009. Sustainability in the New Zealand wine industry: drivers, stakeholders and practices. J. Clean. Prod. 17, 992e 998. Ganesh, M., 2006. Introduction to Fuzzy sets and Fuzzy logic. Prentice-Hall of India, New Delhi. Govindan, K., Kaliyan, M., Kannan, D., Haq, A.N., 2014. Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. Int. J. Prod. Econ. 147, 555e568. Gutowski, 2001. Environmentally benign manufacturing and eco-materials; product induced material flows. J. Adv. Sci. 13 (3), 43. Gutowski, T., Murphy, C., Allen, D., Bauer, D., Bras, B., Piwonka, T., Wolff, E., 2005. Environmentally benign manufacturing: observations from Japan, Europe and the United States. J. Clean. Prod. 13 (1), 1e17. Handfield, R.B., Walton, S.V., Seegers, L.K., Melnyk, S.A., 1997. ‘Green’value chain practices in the furniture industry. J. Operat. Manag. 15 (4), 293e315. Haq, A.N., Kannan, G., 2006. Fuzzy analytical hierarchy process for evaluating and selecting a vendor in a supply chain model. Int. J. Adv. Manufactur. Technol. 29 (7e8), 826e835. Hui, I.K., Chan, A.H.S., Pun, K.F., 2001. A study of the environmental management system implementation practices. J. Clean. Prod. 9, 269e276. Kannan, G., Haq, A.N., Sasikumar, P., Arunachalam, S., 2008. Analysis and selection of green suppliers using interpretative structural modeling and analytic hierarchy process. Int. J. Manag. Decis. Mak. 9 (2), 163e182. Kannan, D., Jabbour, A.B.L.D.S., Jabbour, C.J.C., 2014. Selecting green suppliers based on GSCM practices: using fuzzy TOPSIS applied to a Brazilian electronics company. Eur. J. Operat. Res. 233 (2), 432e447.

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Please cite this article in press as: Govindan, K., et al., Analyzing the drivers of green manufacturing with fuzzy approach, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.02.054