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A framework based on fuzzy Delphi and DEMATEL for sustainable product development: A case of Indian automotive industry
Journal Pre-proof A framework based on fuzzy Delphi and DEMATEL for sustainable product development: A case of Indian automotive industry
Prashant Kumar Singh, Prabir Sarkar PII:
S0959-6526(19)33861-2
DOI:
https://doi.org/10.1016/j.jclepro.2019.118991
Reference:
JCLP 118991
To appear in:
Journal of Cleaner Production
Received Date:
07 May 2019
Accepted Date:
20 October 2019
Please cite this article as: Prashant Kumar Singh, Prabir Sarkar, A framework based on fuzzy Delphi and DEMATEL for sustainable product development: A case of Indian automotive industry, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.118991
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Literature review
Identify various ecodesign practices for sustainable product development
Expert opinion
Collect opinion of the experts about each practice using a linguistic scale
Refer Table 3
Phase 1 fuzzy Delphi
Finalize ecodesign practices using fuzzy Delphi Technique Construct the initial relation matrix using expert input
Refer Table 4
Construct average relation matrix (A) by aggregating inputs of all experts Construct the normalized relation matrix (D)
No
If sum of each column < 1
Yes
Use revised DEMATEL Necessary modifications
Feasibility of DEMATEL, then Construct total relation matrix (T)
Determine cause and effect factors
Set the threshold value (α)
Develop a causal diagram for ecodesign practices
Establish inter-relationship among ecodesign practices
Obtain the most significant ecodesign practices for sustainable product development
Phase 2 DEMATEL
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A framework based on fuzzy Delphi and DEMATEL for sustainable product development: A case of Indian automotive industry Prashant Kumar Singh* and Prabir Sarkar Department of Mechanical Engineering, IIT Ropar, Rupnagar-140001, Punjab, India E-mail of corresponding author - [email protected]
Abstract Constantly increasing environmental problems has led the governments worldwide to impose strict regulations and policies on industries for producing sustainable products. Industries adopt various ecodesign practices (EPs) to achieve the goal of sustainable product development. However, the significance of these EPs in the production of environmentally friendly products is not clearly understood by the industries, especially in developing economies such as India. In this study, a hybrid framework based on fuzzy Delphi and Decision Making Trial and Evaluation Laboratory (DEMATEL) approach is proposed to identify various EPs and analyze the causal (i.e. cause and effect) relationships among these EPs to obtain most significant practices for sustainable product development. The proposed framework is illustrated by taking a case of Indian automotive industry. A panel of fifteen experts from Indian automotive companies and academy is involved in this study. This study helps the designers not only to find EPs relevant to automotive products but also to understand the complex relationship among these EPs. Results show that ‘Using alternative manufacturing techniques’, ‘Enhancing durability and reliability’ and ‘Ensuring easier maintenance and repair’ are the three most significant EPs for sustainable development of automotive products. Keywords: Ecodesign practice (EP); sustainable product development; fuzzy Delphi; DEMATEL; automotive industry
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1. Introduction Various industries indulged in the production of different consumer and non-consumer products are one of the key contributors to the increasing environmental issues. Increasing population has led to a rapid growth in the production and consumption of goods that will only aid to the increasing environmental problems such as resource depletion and increasing waste. One way to deal with this problem is the adoption of the concept of ‘Ecodesign’. This is an approach that considers the integration of environmentally friendly practices in the development of products throughout the entire product life cycle from raw material to the end of life (ISO, 2011). It consists of a set of practices, known as Ecodesign Practices (EPs), that leads to design and develop a sustainable product (Charter and Tischner, 2001). These practices can be adopted to design and produce products which are safe and sustainable and to ensure that the end of products’ life is the birth of something new, whenever possible (P.K. Singh and Sarkar, 2019). Nowadays, a growing number of companies have started implementing ecodesign practices for producing environmentally conscious products and services to achieve the goal of sustainable product development (Ghazilla et al., 2015). The successful implementation of EPs is still an issue, especially in small and medium enterprises of developing economies due to some significant barriers (P. K. Singh and Sarkar, 2019). Implementing all available EPs is a big challenge for small enterprises because they have limited personnel and financial resources (Biondi et al., 2002; Hadjimanolis and Dickson, 2000; Hillary, 2004; P. K. Singh and Sarkar, 2019). Therefore, it becomes important to analyze these EPs to find out the most significant EPs. One way to find significant EPs is to identify the set of most influential EPs i.e. those which affect most of the other EPs due to their interdependencies. The adoption of EPs depends on the type of product. For example; a set of EPs relevant to sustainable production of food products may not be relevant to sustainable production of electronic products and vice versa. Therefore, the study should be focused to a particular industry. In this work, the focus is on automotive industry. 1.1 Current status of automotive industry in India Indian automotive industry is growing rapidly and recently became the 4th largest manufacture in the world which was at 7th place in 2017. Indian automotive industry is expected to reach up to 18 trillion INR (i.e. approx. 282 billion US$) at the end of 2026. Only in financial year 2018 (FY18),
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29 million vehicles were manufactured in India (“Indian Automobile Industry Analysis,” 2019). Such a rapid growth has created concerns for sustainable production and utilization of vehicles in India. These concerns are not only limited to the production of the vehicles but also spread across the entire life cycle from raw material to the end of life of a vehicle. These challenges can be dealt easily through the implementation of ecodesign practices (EPs) in the design and development of automotive products. However, EPs must be considered by the designers at very beginning i.e. during conceptual design phase because more than 80% of the environmental load is determined in this phase (Tischner, 2000). 1.2 Research objective The objective of this research is threefold: i. To identify various EPs suggested by the researchers for sustainable product development through a review of the literature. ii. Finalize the EPs which are relevant to automotive products. iii. Establish and examine the causal relationships between the finalized EPs to find the significant EPs for sustainable production of automotive products. A fuzzy Delphi technique is used to finalize the EPs relevant to automotive products whereas a DEMATEL approach is used to obtain the causal relationship between EPs which shows the type of influence that one EP has on the other EP. Causal relationship is generally demonstrated with the help of a causal diagram that divides the factors under study into cause and effect factors. A cause factor offers some influence on the system and an effect factor receives this influence. DEMATEL approach also helps to develop an interaction matrix that shows the inter-relationship among EPs. An EP which is a cause factor and having an inter-relationship with most of other EPs will have higher potential to improve the development of sustainable automotive products. Rest of the article is structured as follows: Section 2 provides a review of the literature on various EPs suggested by researchers for sustainable product development. Section 3 includes a description of the research methodology of this study. The proposed framework based on fuzzy Delphi and DEMATEL is explained in Section 4. Application of the proposed framework is provided in Section 5 which is followed by the conclusions and limitations of this study given in Section 6.
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2. Literature review This section presents a focused review of the literature to find various ecodesign practices suggested by the researchers for sustainable product development. There are some EPs related to the auto industries which are suggested by the researchers. Borchardt et al. (2009) suggested that using recycled composite materials, renewable bio-fuels and synthetic lubricating oils having high durability are the key EPs for greening the automotive industry. Selection of renewable and recycled materials is an important strategy for improving the environmental friendliness of automotive products (de Medina, 2006). Imposing a restriction on the use of hazardous materials can be a significant approach for automotive industry. The materials can be divided in three different lists viz. ‘white list,’ ‘gray list’ and ‘black list’ (Luttropp and Lagerstedt, 2006; Tingström and Karlsson, 2006). Green manufacturing, green purchasing, green distribution and environmentally conscious disposal are crucial practices that must be adopted by the auto industry in the production of the products as well as in various supply chain activities (Borchardt et al., 2011; Miguel Sellitto et al., 2015; Vanalle et al., 2017; Zhu et al., 2007; Zhu and Sarkis, 2007). Sarkis et al. (2010) suggested that EPs to be adopted by the auto industry can be divided in three categories viz. EPs related to the type of material (Keldmann and Olesen, 1994), EPs related to the design stage of the products (Gonzalez et al., 2002; van Hemel and Cramer, 2002) and EPs related to the internal organization for environmental management (Banerjee, 2001; Nakashima et al., 2002). ‘Car sharing’ can be considered as an ecofriendly practice because sharing of one car can reduce the usage of 9 to 13 vehicles (Martin et al., 2010). The available EPs related to the auto industry are limited and there is a need to explore more EPs to identify those which are relevant to automotive industry (Schiavone et al., 2008). Researchers have suggested a significant number of EPs which are independent of any specific sector. These studies are given in Table 1. Fiksel (1996) suggested a number of EPs for creating eco-efficient products and process with a consideration of all phases of the product life cycle. A set of thirty-three EPs were presented by van Hemel and Brezet (1997). These EPs were distributed among eight different strategies and the entire system is known as ‘Ecodesign Strategy Wheel’ or ‘LiDS (Life cycle Design Strategy) wheel’ with strategies representing eight spokes of the wheel. All EPs were further classified at three different levels viz. product component level, product structure level and product system level. In another study, Stuart and Sommerville (1998) suggested EPs for selecting materials and distinguished them between component level and
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product level. Further, it was illustrated how the material selection decisions affect the other life cycle phases of a product. Lewis and Gertsakis (2001) proposed a global guide to design green products. They recommended a set of EPs to be adopted at an early stage of the design process to meet the expected quality, cost, manufacturability and minimum impact on the environment. They also emphasized on the selection of appropriate EP to consider the trade-offs. For example, the influence of a lightweight material on its recyclability. It means, a lightweight material is not only easy to dissemble but also transported easily to the recycling plant. Thus, the time and cost of recycling is reduced. Popularly known as ‘12 principles of green engineering’ were identified by Anastas and Zimmerman (2003) to design environmentally benign products and processes. These principles are related to the material, manufacturing and the end of life aspects of products. These principles provide a framework to optimize unsustainable products, processes and systems. Wimmer and Züst (2003) developed a tool which is known as ‘Ecodesign Pilot’ where Pilot stands for product investigation, learning and optimization tool for sustainable product development. This tool is based on a set of nineteen EPs distributed among the various phases of a product life cycle. This study further recommends the implementation of EPs to design a product which is intensive to a certain phase of the product life cycle such as manufacturing intensive, use intensive or end of life intensive product. McLennan (2004) suggested some common EPs that can be implemented in different design disciplines such as product design, architecture, urban planning etc. to eliminate negative environmental impacts of products. Giudice et al. (2006) proposed certain EPs for the designers to achieve eco-efficiency in each phase of a product life cycle. ‘Ten golden rules’ were identified by Luttropp and Lagerstedt (2006) for integrating environmental aspects in product development. These golden rules are basically guidelines related to the selection of materials, manufacturing techniques, usage and the end of life activities. Telenko et al. (2008) compiled a set of EPs to be adopted in conceptual and embodiment design stages to achieve substantial environmental improvements. Vezzoli and Manzini (2008) provided a deep insight to various EPs to achieve environmental sustainability through design. In this study, the author also tried to understand the interrelationship between EPs. The main emphasis was given to the selection of resources, materials and end of life activities. Belletire et al. (2012) identified a set of 39 EPs related to the entire lifecycle of the products. They suggested that environmental performance of a product life cycle can be conceptualize on the basis of these EPs. Murray (2013) proposed a topic
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guide that is based on EPs and guides the product design and development team to initiate and develop a process for embedding sustainability in product design in R&D programmes. Although there are about 300 EPs suggested by different studies as mentioned above but there is a high degree of commonality among these EPs. For example: in one research article, an EP is mentioned as “Avoid toxic materials” and in another article an EP is mentioned as “Choose non-toxic materials”. Technically, both EPs has common meaning and therefore all identified EPs are studied and common EPs are eliminated by a team of three postdoctoral researchers having PhD degree in the field of product sustainability. Thus, a total of 41 individual EPs are obtained as given with a short description in Table 2. These EPs are classified in the form of a hierarchy under five different life cycle stages, as shown in Figure 1. Since the concept of ecodesign is based on the product life cycle approach, hence it is reasonable to classify EPs in different life cycle stages. 2.1 Research gap The available EPs related to the auto sector are limited and there is a need to explore more EPs to identify those which are relevant to automotive industry (Schiavone et al., 2008). Some researchers have suggested that most of the environmentally friendly practices are closely correlated (Anastas and Zimmerman, 2003; Giudice et al., 2006) but there is a lack of studies that provide a comprehensive understanding of these relationships. The relationship between EPs directly affects the functional performance of products and their components (Giudice et al., 2006). There is a list of practices, principles and guidelines reported in the literature, but a rigorous and qualitative validation of many of these principles is still lacking (Telenko et al., 2008). Therefore, a study should be carried out that does not only demonstrate the relationship among various EPs but also can show the capability of each EP to influence the other EPs for sustainable development of various products and processes. 3. Research methodology This research is based on a hybrid fuzzy Delphi and DEMATEL approach. First, various ecodesign practices that can be implemented in the development of sustainable products are identified through extensive literature search. There was a high degree of commonality among the ecodesign
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practices suggested by various researchers. All identified practices are studied and segregated to eliminate the commonality by a team of three postdoctoral researchers working in the field of product sustainability. Thus, a total of 41 individual ecodesign practices are obtained. Then, a fuzzy Delphi technique is used to finalize ecodesign practices relevant to automotive industry (details are given in Section 5.1). Although traditional Delphi technique can be used to finalize the criteria but the vagueness and uncertainty in expert opinion still persisted in this method. Therefore, a fuzzy based Delphi technique is adopted in this study. Once the ecodesign practices are finalized, a DEMATEL approach is utilized to understand the inter-relationship among these ecodesign practices and also to identify the cause and effect factors by developing a causal diagram. Although there are other decision making methods such as Analytical Network Process (ANP), Elimination and Choice Expressing Reality (ELECTRE), Interpretive Structural Modeling (ISM) etc. but these methods are only able to prioritize the factors and fail to identify the cause and effect factors. An understanding of the cause and effect factors helps the stakeholders in a comprehensive decision making. In this study, the aim is to identify the EPs which are relevant to auto industry as well as to understand the causal relationship among these relevant EPs. It requires a hybrid methodology to fulfill these objectives. Therefore, a combination of fuzzy Delphi and DEMATEL is used in this study. Fuzzy Delphi finalizes the factors by selecting the EPs which are relevant to auto industry and DEMATEL helps to analyze the causal relationships among these relevant EPs to find the key EPs which can play a significant role in the sustainable growth of auto industry in India.
3.1 Fuzzy Delphi Delphi technique was first developed by RAND Corporation (Dalkey and Helmer, 1963). It is a qualitative technique for collecting the opinion of a distributed group of people related to a specific area (Bouzon et al., 2016; Kapse et al., 2018). Decision making through traditional Delphi technique was associated with some vagueness and uncertainty therefore a fuzzy based Delphi technique was introduced by Ishikawa (1993). Fuzzy Delphi method has ability to capture the vagueness and uncertainty associated to the data. It has widely been used in different applications
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such as supplier selection (Tahriri et al., 2014), green supply chain performance (Bhattacharya et al., 2014), assessing consumers’ motivation to purchase remanufactured products (Vafadarnikjoo et al., 2018) and technology selection (Hsu et al., 2010). Fuzzy Delphi technique is based on the theory of fuzzy sets. If U is a universal set then a fuzzy set of U is defined by a membership function A which is given as:
A ( x) [0,1] , x U Fuzzy sets are of two types i.e. Type – I and Type – II. The type – II fuzzy set can be any subset in the range [0,1]. It consists of the primary membership and corresponding to each primary membership, there is a secondary membership (which can also be in [0,1]) that defines the possibilities for the primary membership. A type-1 fuzzy set is a special case of a type-2 fuzzy set. Its secondary membership function is a subset with only one element which is unity. A triangular membership function is used in this study which is defined as:
x p q p , p x q r x A ,q x r r q 0 where p, q and r are the triangular fuzzy numbers (TFNs) and denoted as (p, q, r). If A (l1 , m1 , n1 ) and B (l 2 , m2 , n2 ) are two sets of TFNs then operational laws are defined as: Addition : (l1 , m1 , n1 ) (l 2 , m2 , n2 ) (l1 l 2 , m1 m2 , n1 n2 ) Multiplication : (l1 , m1 , n1 ) (l 2 , m2 , n2 ) (l1 l 2 , m1 m2 , n1 n2 ) Similarly, the subtraction and division laws can also be determined. Various steps involved in fuzzy Delphi are as follows (Ishikawa et al., 1993; Kumar et al., 2019): Step 1: In this step, various criteria related to the area under investigation are identified and tabulated. Step 2: After the identification of the criteria, the document containing these criteria is provided to the experts in the form of a questionnaire. Experts provide their linguistic inputs using a
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linguistic scale which is given in Table 3. Input of experts for each criterion are converted into fuzzy numbers. A fuzzy number corresponding to the jth criteria provided by ith expert is represented as: z ij ( pij , qij , rij ) for i = 1, 2, 3,….n and j = 1, 2, 3,….m.
(1)
where n is the number of experts and m is the number of criteria. Then the fuzzy weights of criteria ~ p j are given as follows: ~ p j ( p j , q j , r j ) where p j min( pij ),
n q j (qij ) i 1
1/ n
(2)
r j max(rij ) where i = 1, 2, 3,….n and j = 1, 2, 3,….m
Step 3: In this last step, the mean method is used to calculate the defuzzification value S j as given below: S j ( p j q j r j ) / 3,
j = 1, 2, 3,….m
(3)
A threshold ( ) is set to select or reject the criteria. If S j , then select the criteria. If S j < , then reject the criteria.
3.2 DEMATEL method DEMATEL approach was first introduced by Battelle Memorial Institute of Geneva in 1976. The main characteristic of this approach is that it helps to understand the complex relationship among selected criteria. DEMATEL approach is based on digraphs which are able not only to show interrelationship among criteria but also to show the direction of relationship (Kumar and Dash, 2016; Wu and Tsai, 2012). The highlights of DEMATEL method are: i) this method is based on graph theory and facilitates the analysis of difficult problems by using a visualization method; ii) it develops cause and effect relationships among different factors which makes it easy to understand the mutual influence of the factors and iii) this method is able to know the strength of
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the relationships between or among the factors which is not possible in other multi-criteria decision making methods. This approach has widely been used in various fields such as sustainable supply chain (Mangla et al., 2014), knowledge management (Wu, 2012), online reputation management (Kumar and Dash, 2017) and redistributed manufacturing (Luthra et al., 2019). Various steps involved in this approach are as follows (Kumar and Dash, 2016; Tzeng et al., 2007): Step 1: In the first step, an initial relation matrix is constructed for the criteria with the help of expert opinion. The opinion of the experts is collected with the help of a linguistic scale given in Table 4. Step 2: Construct the average relation matrix after receiving the inputs of all experts. For each expert, a non-negative matrix of the order n × n is constructed as X k [ xijk ] where k indicates the kth expert with 1 ≤ k ≤ H, and n indicates the number of factors. X1, X2, ..., XH are the matrices obtained through H experts. The average relation matrix A can be established as: A [aij ]
1 H
H
x K 1
k ij
(4)
where k indicates the kth expert and H represents the total number of experts. Step 3: Compute the normalized direct relation matrix (D) as D=m×A
1 1 where m min , n n max i aij max j aij i 1 j 1
(5)
(6)
i, j 1,2,3,....n . It is important to note that the sum of each column of the normalized relation matrix must be less than one for the feasibility of DEMATEL approach (Kumar et al., 2017). Step 4: Calculate total relation matrix (T) as
T D( I D) 1
(7)
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where I represent identity matrix. Step 5: Find out the sum of rows (r) and columns (c) of total relation matrix as follows: n r [ri ] n1 t ij j 1 n1
(8)
n c [ci ]1n t ij i 1 1n
(9)
Then, obtain ‘prominence’ i.e. (r + c) and ‘relation’ i.e. (r - c) for each criteria. Prominence of a criteria is a measure of its importance for the entire system. (r - c) values represent the type of relation among the criteria i.e. cause or effect relation. If (r - c) value of a criteria is positive, then it becomes a cause factor and if (r - c) is negative then it known as an effect factor or receiver. Cause factors directly influence the other factors of a system whereas effect factors receive these effects. Thus, cause factors are the key for a system. Step 6: Set a threshold value (α) to avoid minor effects as follows; n
n
[t i 1 j 1
ij
]
N
(10)
Where N is the number of elements in matrix T The elements of total relation matrix which are greater than the threshold value form an interaction matrix that represents inter-relationship between the criteria.
4. Proposed framework based on fuzzy Delphi and DEMATEL The proposed framework based on fuzzy Delphi and DEMATEL consists of two phases as shown in Figure 2. These two phases are: i) Identifying and finalizing the ecodesign practices relevant to automotive industries and ii) Analyzing the finalized ecodesign practices to obtain their interrelationship and cause-effect factors. First phase deals with the identification of EPs available in the literature which is followed by the selection of the relevant EPs to automotive industry through the opinion of the experts using fuzzy Delphi approach. In second phase, EPs relevant to
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automotive industry are analyzed using a DEMATEL approach to find their interrelationship and a causal (i.e. cause - effect) diagram is developed which is based on cause and effect factors. Same panel of experts is involved in this phase. In the end, the most significant EPs are obtained for sustainable development of automotive products.
5. Application of the proposed framework 5.1 Data collection and analysis A total of 30 experts were contacted from various automotive based companies and academia for the collection of data. Out of 30 experts, 20 responded and finally 15 experts agreed to be the part of this study. 12 experts belonged to different companies based on design and manufacturing of automotive components such as pistons, cylinder heads, frames, chassis and accessories for three and four wheelers. These experts were product designers, environmental engineers and production
managers each having an experience of more than 10 years. Additionally, 3 experts belonged to the academia having indulged in the research and teaching on sustainability for more than 8 years. The demographic information of these experts is provided in Table 5. The data is collected and analyzed in two phases as discussed below:
Phase 1: Identifying and finalizing the ecodesign practices relevant to automotive industries In this phase, the fuzzy Delphi technique was used to reduce the number of EPs by selecting only those EPs which are important in an auto industry whereas the irrelevant EPs were rejected. This analysis is carried out in consultation with experts from auto industry. A panel of 15 experts was formed to finalize the ecodesign practices which were identified through literature search. This panel of experts included 6 product designers, 4 environmental engineers, 2 production managers and 3 academicians each having experience of more than 8 years. In the first step of the fuzzy Delphi method, a total of 41 EPs identified from the literature were listed. In second step, a questionnaire based on the identified ecodesign practices was prepared and sent to each expert. Experts were asked to provide rating to each ecodesign practice on the basis of its significance to automotive industry using a rating scale as given in Table 3. Once the response of each expert
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received, the ratings provided by the experts for each EP were converted into Triangular Fuzzy Numbers (TFN) using Table 3. Then, these responses were aggregated to obtain the fuzzy weight of each EP using Equation 2. Let us take the example of EP1 (i.e. Selection of non-toxic material). For EP1, Expert 1 gave a rating ‘4’ which TFN is (0.5, 0.7, 0.9) [See Table 3]. In similar manner we find TFN for each rating given by all 15 experts with respect to EP1, as given below: Expert 1 – (0.5, 0.7, 0.9) [Rating 4]; Expert 2 – (0.5, 0.7, 0.9) [Rating 4]; Expert 3 – (0.7, 0.9, 0.9) [Rating 5]; Expert 4 – (0.5, 0.7, 0.9) [Rating 4]; Expert 5 – (0.5, 0.7, 0.9) [Rating 4]; Expert 6 – (0.5, 0.7, 0.9) [Rating 4]; Expert 7 – (0.7, 0.9, 0.9) [Rating 5]; Expert 8 – (0.7, 0.9, 0.9) [Rating 5]; Expert 9 – (0.7, 0.9, 0.9) [Rating 5]; Expert 10 – (0.7, 0.9, 0.9) [Rating 5]; Expert 11 – (0.5, 0.7, 0.9) [Rating 4]; Expert 12 – (0.5, 0.7, 0.9) [Rating 4]; Expert 13 – (0.5, 0.7, 0.9) [Rating 4]; Expert 14 – (0.7, 0.9, 0.9) [Rating 5]; Expert 15 – (0.7, 0.9, 0.9) [Rating 5] Now, the fuzzy weight of EP1 (Selection of non-toxic material), as given in ‘Table 6’ is (0.50, 0.79, 0.90). It was obtained as follows: First entry of fuzzy weight i.e. 0.50 was obtained by taking the minimum value among all 15 first entries given above i.e., min (0.5, 0.5, 0.7, 0.5, 0.5, 0.5, 0.7, 0.7, 0.7, 0.7, 0.5, 0.5, 0.5, 0.7, 0.7) = 0.5 Second entry of fuzzy weight i.e. 0.79 was obtained by taking the geometric mean of all 15 second entries given above i.e., (0.7 × 0.7 × 0.9 × 0.7 × 0.7 × 0.7 × 0.9 × 0.9 × 0.9 × 0.9 × 0.7 × 0.7 × 0.7 × 0.9 × 0.9)1/15 = 0.79 Third entry of fuzzy weight i.e. 0.90 was obtained by taking the maximum value among all 15 third entries given above i.e., max (0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9) = 0.9
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Similarly, the fuzzy weight was obtained for all 41 EPs. In third step of fuzzy Delphi, the defuzzification value of each EP was carried out by taking the mean of fuzzy weight (see Equation 3). For example; the defuzzification value of EP1 with a fuzzy weight (0.50, 0.79, 0.90) is obtained as (0.50+0.79+0.90)/3 which come to be ‘0.73’, as given in Table 6. Similarly, the defuzzification value was obtained for all 41 EPs. The threshold defuzzification value ‘α’ is set to 0.60 for selecting or rejecting an EP. This value i.e. 0.60 is taken from the linguistic scale (given in Table 3) and is the average of the minimum value of the linguistic variable “important” (0.5) and the maximum value of the linguistic variable “normal” (0.7) [See Table 3] (Kumar et al., 2017). The threshold value act as a criterion to select or reject an EP. On the basis of this threshold value of defuzzification (i.e. 0.60), a total of 22 EPs are selected which have a defuzzification value greater than 0.60 and 19 EPs are rejected having a defuzzification value less than 0.60. All the selected and rejected EPs are given in Table 6. Phase 2: Analyzing the finalized ecodesign practices to obtain their interrelationship and causeeffect factors In this phase, a DEMATEL approach was utilized to obtain the interrelationship as well as the causal (i.e. cause and effect) relationship among the finalized ecodesign practices. The same panel of 15 experts was involved in this phase. Implementation of various steps of DEMATEL approach is discussed as follows: Step 1: Experts were asked to construct a direct relation matrix for ecodesign practices using a linguistic scale given in Table 4. An initial relation matrix provided by expert 1 is provided in Table 7. Initial relation matrices provided by other experts are not included to avoid excess number of Tables. Step 2: An average relation matrix (A) was obtained by aggregating the inputs of all 15 experts. It is given in Table 8. Step 3: The normalized relation matrix (D) was formed by using Equations 5-6 as given below: First we calculate a scaler value ‘m’ using Equation 6 as,
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1 1 1 ' 1 0.0366 m min , n n 25.139 27.339 max i a max j a ij ij i 1 j 1 Then, m = 0.0366 was multiplied to each elements of average relation matrix (A) to obtain the normalized relation matrix (D). It can be observed in Table 9 that the sum of each column of normalized matrix is less than one which proves the feasibility of DEMATEL approach for this study. Step 4: Total relation matrix (T) was computed using Equation 7. Matrix (T) is given in Table 10. Step 5: Sum of the rows ‘r’ and columns ‘c’ of total relation matrix was determined using Equation 8 and Equation 9, respectively. Further, (r + c) and (r - c) values were calculated and cause and effect factors were identified based on (r - c) values of each ecodesign practice, as given in Table 11. Then, a causal diagram was developed by putting (r + c) data set on abscissa and (r - c) data set on ordinate, as shown in Figure 3. Step 6: A threshold value (α) was computed using Equation 10 as, n
n
[t i 1 j 1
N
ij
]
38.2851 0.0791 484
Here, N = 484 (number of elements in total relation matrix). The elements of total relation matrix which are greater than α (i.e. 0.0791) form an interaction matrix that represents inter-relationship between ecodesign practices. This interaction matrix is given in Table 12. 5.2 Result and discussion A framework based on fuzzy Delphi and DEMATEL approach is used in this study to identify and analyze various ecodesign practices significant to automotive industry for ecofriendly product development. A total of 22 EPs are selected through fuzzy Delphi technique. Then, a DEMATEL approach provides the prominence of each EP and divide them into cause and effect factors. The prominence i.e. importance or each EP based on their (r + c) values (given in Table 11) in a
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descending order is obtained as: EP6 > EP8 > EP9 > EP17 > EP5 > EP7 > EP4 > EP2 > EP1 > EP10 > EP3 > EP18 > EP15 > EP13 > EP16 > EP21 > EP20 > EP22 > EP14 > EP19 > EP11 > EP12. Further, these EPs are divided into cause and effect factors based on their positive and negative values of (r - c), respectively. Out of twenty-two EPs, thirteen EPs i.e. Selection of non-toxic materials (EP1); Selection of low energy content materials (EP2); Selection of recycled materials (EP3); Avoid variety of materials (EP4); Choosing materials that require no or less post processing (EP5); Using alternative manufacturing techniques (EP6); Ensuring easier maintenance and repair (EP8); Enhancing durability and reliability (EP9); Reducing total volume of the product (EP13); Reducing energy consumption during usage (EP14); Stimulating recycling of materials (EP18); Emphasizing a modular product structure (EP20) and Optimizing the functionality of product (EP22) fall under cause factors. Nine EPs are the effect factors or receivers that include Reducing production steps (EP7); Installing protection against release of pollutants and hazardous substances (EP10); Less and reusable packaging (EP11); Energy efficient mode of transport (EP12); Clean and low energy consumption for production (EP15); Minimizing production waste (EP16); Stimulating
remanufacturing and refurbishing (EP17); Ensuring safe incineration with energy recovery (EP19) and Targeting a classic design (EP21). It is clear from Table 11 that ‘Using alternative manufacturing technique (EP6)’ has the highest importance among all EPs. Also, it is a cause factor (as shown in Figure 3) and having highest inter-relationship with 18 EPs, as shown in the 7th row of interaction matrix (Table 12). On the other hand, ‘Energy efficient mode of transport (EP12)’ has the least importance (see Table 11) and also has no inter-relationship with any other EP, as shown in the network diagram (Figure 4). Thus, EP12 is the most independent factor among all EPs. In other words, EP12 neither affects nor get affected by any other EP. ‘Enhancing durability and reliability (EP9)’ is the main cause factor which is shown in Figure 3. It can be observed in causal diagram (i.e. cause and effect diagram) that all the EPs laying in the 1st quadrant of the causal diagram are not only the cause factors but also have significant importance (i.e. higher value of (r + c)) in comparison to other EPs. Also, this set of EPs has interrelationship with most of the EPs as evident from Table 12 and Figure 4. Thus, it is evident that these EPs (i.e. laying in 1st quadrant) are the most significant EPs and if implemented successfully
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in an automotive based company then they will lead the company very effectively towards achieving the goal of sustainable product development. Now, it is understood that the level of significance of an EP is governed by following three criteria: i) on the basis of its importance to the system (i.e. (r + c) values), ii) whether it is a cause or effect factor, and iii) its inter-relationship with other EPs. Depending on these three criteria, all the EPs which are divided in four quadrants in the causal diagram can be presented in the descending order of their significance to sustainable product development in automotive industry as: (EPs)quadrant I > (EPs)quadrant IV > (EPs)quadrant II > (EPs)quadrant III
5.3 Implications of the research This research offers the designers and managers with a set of ecodesign practices which can help them to develop environmentally conscious automotive products. However, all the 22 ecodesign practices selected for this study have a positive impact on sustainable development of automotive products but there are certain practices that influence the other ecodesign practices significantly. Therefore, these practices are considered as the most important among 22 practices. These are shown in the first quadrant of the causal diagram in Figure 3. These practices with their various advantages and direct relationship with other ecodesign practices are discussed as follows: Selection of non-toxic materials (EP1): The selection of non-toxic material (EP1) will cause lesser impact on environment and human health during its extraction, manufacturing of product, usage and during end of life activities such as recycling, incineration or land filling. The advantage with selection of non-toxic material is that it will reduce the production steps (EP7) as the purification to remove the toxicity will not be required. It will stimulate recycling of material (EP18) and ensure safe incineration (EP19). It may also enhance the reliability of the product (EP9). It will also eliminate the need to install protection against release of harmful substances (EP10). Selection of low energy content materials (EP2): Low energy content material (EP2) does not only require less energy for its extraction but also ensures less consumption of energy during production (EP15) because processing of such materials is less energy intensive. This characteristic of a material will also stimulate its recycling (EP18) at the end of life. Generally,
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metals are more energy intensive in comparison to polymers therefore designers should try to replace metals by polymers wherever possible in automotive components. Selection of recycled materials (EP3): Recycled materials (EP3) are easy to process that will reduce production steps (EP7) which ultimately will result in a low energy consumption during production (EP15). It can minimize production waste up to some extent (EP16). Also, recycled materials save the energy and resources that might be consumed in the mining or extraction of virgin materials. Also, it ensures that the energy consumed in the extraction of the material is not lost. Avoiding variety of materials (EP4): A variety of materials may require different type of machine tools for their processing. The processing of different materials may take much time that will result in reduced productivity as well as unnecessary consumption of energy and resources (EP15). A variety in materials may lead to choose alternative manufacturing techniques (EP6). The main advantage with avoiding variety of material is that it will facilitate recycling of materials (EP18). Also, it may reduce production steps (EP7) and can ensure easier maintenance and repair (EP8). Selecting materials that require no or low post processing (EP5): Materials with no/low post processing (EP5) will reduce production steps (EP7) and ultimately may result in low energy consumption during production (EP15). For example; plastic materials generally do not require any post processing such as finishing, coloring or cleaning. Also, the elimination of post processing such as finishing of metals will result in less production waste (EP16). Using alternative manufacturing techniques (EP6): Alternative manufacturing techniques (EP6) may result in low energy consumption during production (EP15). For example; bending instead of welding and fastening instead of soldering. It may also help in minimizing the production waste (EP16). For example; using additive manufacturing technique rather than subtractive techniques. It may also result in reduced production steps (EP7). Ensuring easier maintenance and repair (EP8): Ensuring easier maintenance and repair (EP8) directly influence the durability and reliability of a product (EP9). An easy and periodic maintenance of automotive products reduces the emission of greenhouse gases to the atmosphere.
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Enhancing durability and reliability (EP9): The durability and reliability (EP9) of a product can be enhanced by targeting a classic design (EP21) of the automobile rather than trendy so that its aesthetic life matches with its technical life. Thus, a classic design will enhance the durability and reliability of the automobile. Longer the life of a product, lesser will be the need to manufacture, recycle or dispose. Stimulating recycling of materials (EP18): Recycling is an important phenomenon in automotive industry because a huge amount of material remains available at the end of life of an automobile. If these materials are recycled (EP3), then can be used in various applications. An efficient recycling system will ensure minimum waste (EP16). Companies can have a dedicated recycling system (closed-loop recycling) in place. It will minimize the impact that occurs through the transportation of retired products from company to the recycling plants.
6. Conclusion and limitations of the study A two-phase framework is used in this study. In the first phase, various EPs relevant to sustainable development of automotive products are identified and finalized through literature survey and opinion of the experts using a fuzzy Delphi approach. In second phase, a DEMATEL approach is used to analyze the EPs to understand their importance for sustainable development of automotive products by establishing a cause and effect (i.e. causal) relationship among EPs. According to the findings of this study, ‘Using alternative manufacturing techniques (EP6)’, ‘Ensuring easier maintenance and repair (EP8)’ and ‘Enhancing durability and reliability (EP9)’ are the three most significant EPs for sustainable production of automotive products. These three practices cause the maximum influence on the other EPs. Apart from that, there are other EPs viz. ‘Selection of nontoxic materials (EP1)’, ‘Selection of low energy content materials (EP2)’, ‘Selection of recycled materials (EP3)’, ‘Avoiding variety of materials (EP4)’, ‘Selecting materials that require no or low post processing (EP5)’ and ‘Stimulating recycling of materials (EP18)’ which can have a significant impact in auto industry. The findings of this study can be used by the designers as a benchmark for producing more sustainable automotive products in India. There are certain limitations associated to this study. Although, a significant number of experts are involved in this study but still there might be some biasness in the opinion of the experts. Also,
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this study represents an Indian perspective however other countries can have a little different perspective on EPs for environmentally friendly development of automotive products. A fuzzy DEMATEL approach can be used in future to deal with the vagueness and uncertainty that may be associated to the opinion of the experts. Also, based on the cause-effect relationships among the analyzed EPs, a hypothesis can be developed and validated using a larger sample. Another future aspect of this work is to prioritize the EPs that may facilitate the decision making of designers for sustainable development of automotive products. References Anastas, P., Zimmerman, J., 2003. Design through 12 principles of green engineering. ENVIRONMENTAL SCIENCE & TECHNOLOGY 37, 94A-101A. Banerjee, S.B., 2001. Corporate environmental strategies and actions. Management Decision 39, 36–44. https://doi.org/10.1108/EUM0000000005405 Belletire, S., St Pierre, L., White, P., 2012. Okala ecodesign strategy wheel app [WWW Document]. Okala Practitioner. URL http://www.okala.net/downloadapp.html (accessed 10.11.19). Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., 2014. Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach. Production Planning & Control 25, 698–714. https://doi.org/10.1080/09537287.2013.798088 Biondi, V., Iraldo, F., Meredith, S., 2002. Achieving sustainability through environmental innovation: the role of SMEs. International Journal of Technology Management 24, 612. https://doi.org/10.1504/IJTM.2002.003074 Borchardt, M., Poltosi, L.A.C., Sellitto, M.A., Pereira, G.M., 2009. Adopting ecodesign practices: Case study of a midsized automotive supplier. Environ. Qual. Manage. 19, 7– 22. https://doi.org/10.1002/tqem.20232 Borchardt, M., Wendt, M.H., Pereira, G.M., Sellitto, M.A., 2011. Redesign of a component based on ecodesign practices: environmental impact and cost reduction achievements. Journal of Cleaner Production 19, 49–57. https://doi.org/10.1016/j.jclepro.2010.08.006 Bouzon, M., Govindan, K., Rodriguez, C.M.T., Campos, L.M.S., 2016. Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resources, Conservation and Recycling 108, 182–197. https://doi.org/10.1016/j.resconrec.2015.05.021 Charter, M., Tischner, U., 2001. Sustainable Solutions: Developing Products and Services for the Future. Greenleaf Publishing Limited., Sheffield, UK. Dalkey, N., Helmer, O., 1963. An experimental application of the Delphi method to the use of experts. Management Science 9, 458–467.
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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Ecodesign Practices
Raw Material
Selection of non-toxic materials (EP1) Avoid caustic and/or flammable materials Selection of renewable materials Selection of low energy content materials (EP2) Selection of recycled materials (EP3) Avoid variety of material (EP4) Choosing recyclable materials Choosing biodegradable material Choosing materials that require no or less post processing (EP5) Reducing the material requirement
Manufacturing
Using alternative manufacturing techniques (EP6) Reducing production steps (EP7) Less and clean consumables for production Installing protection against hazardous substances (EP10) Clean and low energy consumption for production (EP15)
Packaging & Distribution
Use
Less and reusable packaging (EP11)
Ensuring easier maintenance and repair (EP8)
Stimulating reuse of the entire product
Choosing recyclable packaging
Enhancing durability and reliability (EP9)
Stimulating remanufacturing and refurbishing (EP17)
Choosing lightweight materials for packaging
Reducing energy consumption during usage (EP14)
Stimulating recycling of material (EP18)
Energy efficient mode of transport (EP12) Energy efficient logistics Reducing total volume of the product (EP13)
Choosing a clean source of energy for usage Reducing the amount of consumables during usage
Minimizing production waste (EP16)
Ensuring no wastage of energy during usage
Emphasizing a modular product structure (EP20)
Shared use of the product
Targeting a classic design (EP21) Reducing number of parts or components Ensuring integration of product functions Optimizing the functionality of product (EP22)
Ensuring a strong productuser relation
End of Life
Ensuring closed-loop recycling Ensuring safe incineration with energy recovery (EP19) Ensuring safe disposal of product remain
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Literature review
Identify various ecodesign practices for sustainable product development
Expert opinion
Collect opinion of the experts about each practice using a linguistic scale
Refer Table 3
Phase 1 fuzzy Delphi
Finalize ecodesign practices using fuzzy Delphi Technique Construct the initial relation matrix using expert input
Refer Table 4
Construct average relation matrix (A) by aggregating inputs of all experts Construct the normalized relation matrix (D)
No
If sum of each column < 1
Yes
Use revised DEMATEL Necessary modifications
Feasibility of DEMATEL, then Construct total relation matrix (T)
Determine cause and effect factors
Set the threshold value (α)
Develop a causal diagram for ecodesign practices
Establish inter-relationship among ecodesign practices
Obtain the most significant ecodesign practices for sustainable product development
Phase 2 DEMATEL
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Cause factors
Effect factors
EP1
EP2
EP3
EP7
EP4
EP10
EP5
EP11
EP6
EP12
EP8
EP15
EP9
EP16
EP13
EP17
EP14
EP19
EP18
EP20
EP22
EP21
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Highlights
This study explores various ecodesign practices for sustainable product development.
Ecodesign practices relevant to automotive products are finalized through fuzzy Delphi.
Analyze causal relationships among finalized practices using DEMATEL approach.
Provides noticeable implications to implement ecodesign practices in automotive industries of developing economies.
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Table 1. Ecodesign practices suggested by researchers Life cycle stages
Number of suggested practices
Authors
Focused Area
Fiksel (1996)
Design for environment
All
48
van Hemel and Brezet (1997)
Ecodesign
All
33
Stuart and Sommerville (1998)
Life cycle design for materials
Material selection
10
Lewis and Gertsakis (2001)
Ecodesign
Material, Manufacturing, Usage, End of life
13
Anastas and Zimmerman (2003)
Green engineering
Material, Manufacturing, End of life
12
Wimmer and Züst (2003)
Ecodesign
All
19
McLennan (2004)
Green architecture
Material, Manufacturing, End of life
08
Giudice et al. (2006)
Design for environment
All
28
Luttropp and Lagerstedt (2006)
Ecodesign
Material, Manufacturing, Usage, End of life
10
Telenko et al. (2008)
Design for environment
All
47
Vezzoli and Manzini (2008)
Life cycle design
Material, End of life
32
Belletire et al. (2012)
Ecodesign
All
39
Murray (2013)
Ecodesign
All
15
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Table 2. List of ecodesign practices with short description S. N.
Ecodesign practices
Description
1.
Selection of non-toxic materials
Avoid materials that contain hazardous substances such as asbestos, lead, lithium etc.
2.
Avoid caustic and/or flammable materials
It is important to ensure the safety by avoiding the flammable materials such as ethanol, rubber, acetone etc.
3.
Selection of renewable materials
Avoid the materials which are derived from the sources which are not replenished naturally or require very long time period to replenish. These materials are: minerals, tropical wood, fossil fuel, zinc, platinum, copper and tin
4.
Selection of low energy content materials
Some materials consume very high amount of energy during their extraction and production. Therefore, such materials should be avoided.
5.
Selection of recycled materials
Try to use materials which have already been used in some other products so that the energy consumed in the extraction of these materials is not lost
6.
Avoid variety of material
Using variety of materials in a product does not only hinder the recycling process but also may reduce the productivity because different materials may have different methods of processing
7.
Choosing recyclable materials
Choose such a material that ensures a significant amount of material recovery through recycling. For example; Aluminium
8.
Choosing biodegradable material
If a product has short life span, then it is preferable to use biodegradable materials such as Polyesters and Polyanhydrides
9.
Choosing materials that require no or less post processing
Avoid materials which require post processing such as painting, surface finishing, cleaning etc. because it will result in increased production steps
10.
Reducing the material requirement
Try to reduce the material requirement by optimizing the size of the product. Thus, try to avoid overdimensioning of the product
11.
Using alternative manufacturing techniques
Choose a manufacturing technique which has lesser environmental impact. Also, it should ensure the efficient use of the material
12.
Reducing production steps
Try to reduce the number of steps in production of the products. It should be considered during the selection of materials and manufacturing techniques
13.
Ensuring easier maintenance and repair
Indicate the parts that require frequent inspection Components which are prone to wear and tear should be easy to dismantle or replace
14.
Enhancing durability and reliability
Reliability and durability are the vital characteristics of a product which must be taken care by the designers. Use methods like failure mode and effect analysis
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15.
Less and clean consumables for production
Ensure that less amount of supporting materials (i.e. consumables) is required during production process. If required then it should not contain hazardous substances
16.
Installing protection against release of pollutants and hazardous substances
It is important to ensure that there is no or minimum release of substances such as harmful gases and hazardous liquid or solid consumables
17.
Less and reusable packaging
Lesser the packaging, higher will be the saving of material and energy during transportation. Thus, it reduces the possibility of waste and emission
18.
Choosing recyclable packaging
Choose packaging materials that can be recycled easily and have high percentage of recyclability. For example; plastics and cardboards
19.
Choosing lightweight materials for packaging
Lightweight materials for packaging does not only reduce the volume to be transported but also stimulates easy take back of packaging materials
20.
Energy efficient mode of transport
Transport through ships or trains is most favorable followed by road transport. Air transport causes the highest impact on environment
21.
Energy efficient logistics
Ensure that the transport is loaded efficiently and at the same time distribution logistic is efficient too
22.
Reducing total volume of the product
Lesser volume will ensure more number of products to be transported at a time. Also, it will be easier to maintain and repair
23.
Reducing energy consumption during usage
If a product has a heating component, then it must be well insulated. Each energy intensive product during usage must have a default power down mode
24.
Clean and low energy consumption for production
Choose those energy sources that cause less consumption and less emission. For example; in case of coal, choose coal with less Sulphur content (anthracite)
25.
Minimizing production waste
Optimize the production system in such a way that production wastes can be minimized inside the company
26.
Choosing a clean source of energy for usage
A clean source of energy ensures lesser emission of harmful substances in the environment especially in case of a high energy consuming product
27.
Reducing the amount of consumables during usage
28.
Ensuring no wastage of energy or consumables during usage
Design the product in such a way that it requires only a few consumables for its proper functioning. For example; use permanent filters rather than paper filters in coffee makers Design the product to enhance the user’s ability to use it as per the need to avoid the wastage. Use techniques such as eco-feedback or forced functionality
29.
Shared use of the product
It is found that when a product or service is used or shared by a number of people than the product is used more efficiently
Journal Pre-proof
30.
Stimulating reuse of the entire product
This approach tends to reuse the product either for the same purpose or for some other purpose. The environmental benefits are better if the product retains its original features
31.
Stimulating remanufacturing and refurbishing
Design the product so that working components or subassemblies can be taken out easily to use in other assemblies If required, these components can be cleaned or pained to give them a pleasant look
32.
Stimulating recycling of material
If possible, use same type of material for the entire product If not possible then use materials having compatibility with each other
33.
Ensuring closed-loop recycling
Closed loop recycling ensures no or minimum wastage of material and the recycled material can be used to manufacture the same product again
34.
Ensuring safe incineration with energy recovery
If reuse and recycling is not possible, next option is to incinerate the product or its remain along with energy recovery. Certain metals are also recovered during this process. It is also known as ‘thermal recycling’
35.
Emphasizing a modular product structure
Design the product in different modules so that it can very easily adopt the required changes. For example; additional memory slots in computers
36.
Ensuring safe disposal of product remain
If it is not possible to recycle or recover energy from the product remain then it should be disposed safely. Generally, land filling is a preferred way to achieve it
37.
Targeting a classic design
Design the product for significant aesthetic life at least up to its technical life because people tend to change the product if it goes out of fashion
38.
Reducing number of parts or components
Lesser the number of parts or component, easier will be the assembly and disassembly. It will facilitate easy maintenance and repair
39.
Ensuring a strong product-user relation
Add certain features in the design of a product that develop a strong relation between the product and the user
40.
Ensuring integration of product functions
Integration of various product features in a single product results in the saving of material and space
41.
Optimizing the functionality of product
Sometimes unnecessary features are added to products to achieve lucrative appearance. Try to eliminate these unnecessary features in the design
Journal Pre-proof
Table 3. Linguistic scale for fuzzy Delphi approach Linguistic variable
Rating
Corresponding TFN
Extremely unimportant
1
(0.1, 0.1, 0.3)
Unimportant
2
(0.1, 0.3, 0.5)
Normal
3
(0.3, 0.5, 0.7)
Important
4
(0.5, 0.7, 0.9)
Extremely important
5
(0.7, 0.9, 0.9)
Journal Pre-proof
Table 4. Linguistic scale for DEMATEL approach Linguistic variable
Code
Score
No influence
NI
0
Low influence
LI
1
Medium influence
MI
2
High influence
HI
3
Very high influence
VI
4
Journal Pre-proof
Table 5. Demographic information of the experts
Male
Highest Qualification B. Tech.
Work experience (Years) 21
Product Designer
Male
B. Tech.
18
Expert 3
Product Designer
Male
B. Tech.
16
Expert 4
Product Designer
Male
M. Tech.
14
Expert 5
Product Designer
Male
B. Tech.
14
Expert 6
Product Designer
Male
B. Des.
11
Expert 7
Male
PhD
16
Male
B. Tech.
16
Male
M.S.
12
Expert 10
Environmental Engineer Environmental Engineer Environmental Engineer Environmental Engineer
Female
PhD
11
Expert 11
Production Manager
Male
B. Tech.
20
Expert 12
Production Manager
Male
MBA
13
Expert 13
Professor
Male
PhD
15
Expert 14
Associate Professor
Male
PhD
10
Expert 15
Associate Professor
Female
Post-Doctoral
9
Experts
Position
Gender
Expert 1
Product Designer
Expert 2
Expert 8 Expert 9
Table 6. Finalizing ecodesign practices using fuzzy Delphi S. No.
Ecodesign practices
Fuzzy weight
Defuzzification
Select/Reject
Code
1.
Selection of non-toxic materials
(0.50, 0.79, 0.90)
0.730
Select
EP1
2.
Avoid caustic and/or flammable materials
(0.10, 0.32, 0.70)
0.373
Reject
-
3.
Selection of renewable materials
(0.10, 0.40, 0.70)
0.400
Reject
-
4.
Selection of low energy content materials
(0.30, 0.74, 0.90)
0.647
Select
EP2
5.
Selection of recycled materials
(0.50, 0.81, 0.90)
0.737
Select
EP3
6.
Avoid variety of material
(0.30, 0.68, 0.90)
0.627
Select
EP4
7.
Choosing recyclable materials
(0.10, 0.54, 0.90)
0.513
Reject
-
8.
Choosing biodegradable material
(0.10, 0.32, 0.70)
0.373
Reject
-
9.
Choosing materials that require no or less post processing
(0.30, 0.74, 0.90)
0.647
Select
EP5
10.
Reducing the material requirement
(0.10, 0.59, 0.90)
0.530
Reject
-
11.
Using alternative manufacturing techniques
(0.50, 0.86, 0.90)
0.753
Select
EP6
12.
Reducing production steps
(0.30, 0.78, 0.90)
0.660
Select
EP7
13.
Ensuring easier maintenance and repair
(0.50, 0.81, 0.90)
0.737
Select
EP8
14.
Enhancing durability and reliability
(0.50, 0.83, 0.90)
0.743
Select
EP9
15.
Less and clean consumables for production
(0.10, 0.60, 0.90)
0.533
Reject
-
16.
Installing protection against release of pollutants and hazardous substances
(0.30, 0.66, 0.90)
0.620
Select
EP10
17.
Less and reusable packaging
(0.30, 0.68, 0.90)
0.627
Select
EP11
18.
Choosing recyclable packaging
(0.10, 0.47, 0.70)
0.423
Reject
-
19.
Choosing lightweight materials for packaging
(0.10, 0.35, 0.70)
0.383
Reject
-
20.
Energy efficient mode of transport
(0.30, 0.64, 0.90)
0.613
Select
EP12
21.
Energy efficient logistics
(0.10, 0.41, 0.70)
0.403
Reject
-
22.
Reducing total volume of the product
(0.30, 0.66, 0.90)
0.620
Select
EP13
23.
Reducing energy consumption during usage
(0.30, 0.74, 0.90)
0.647
Select
EP14
24.
Clean and low energy consumption for production
(0.50, 0.79, 0.90)
0.730
Select
EP15
25.
Minimizing production waste
(0.30, 0.78, 0.90)
0.660
Select
EP16
26.
Choosing a clean source of energy for usage
(0.10, 0.63, 0.90)
0.543
Reject
-
27.
Reducing the amount of consumables during usage
(0.10, 0.46, 0.70)
0.420
Reject
-
28.
Ensuring no wastage of energy or consumables during usage
(0.10, 0.60, 0.90)
0.533
Reject
-
29.
Shared use of the product
(0.10, 0.35, 0.70)
0.383
Reject
-
30.
Stimulating reuse of the entire product
(0.10, 0.63, 0.90)
0.543
Reject
-
31.
Stimulating remanufacturing and refurbishing
(0.30, 0.82, 0.90)
0.673
Select
EP17
32.
Stimulating recycling of material
(0.50, 0.83, 0.90)
0.743
Select
EP18
33.
Ensuring closed-loop recycling
(0.10, 0.47, 0.70)
0.423
Reject
-
34.
Ensuring safe incineration with energy recovery
(0.30, 0.74, 0.90)
0.647
Select
EP19
35.
Emphasizing a modular product structure
(0.30, 0.82, 0.90)
0.673
Select
EP20
36.
Ensuring safe disposal of product remain
(0.10, 0.59, 0.90)
0.530
Reject
-
37.
Targeting a classic design
(0.50, 0.79, 0.90)
0.730
Select
EP21
38.
Reducing number of parts or components
(0.10, 0.60, 0.90)
0.533
Reject
-
39.
Ensuring a strong product-user relation
(0.10, 0.35, 0.70)
0.383
Reject
-
40.
Ensuring integration of product functions
(0.10, 0.54, 0.90)
0.513
Reject
-
41.
Optimizing the functionality of product
(0.30, 0.64, 0.90)
0.613
Select
EP22
Table 7. Initial relation matrix using linguistic input of expert 1 EP1
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
EP11
EP12
EP13
EP14
EP15
EP16
EP17
EP18
EP19
EP20
EP21
EP22
EP1
NI
LI
NI
LI
MI
LI
MI
LI
MI
HI
NI
NI
NI
LI
HI
MI
MI
MI
HI
NI
LI
NI
EP2
NI
NI
LI
LI
MI
LI
MI
MI
LI
LI
NI
LI
NI
MI
VI
MI
MI
HI
LI
NI
LI
MI
EP3
LI
LI
NI
NI
LI
MI
HI
MI
LI
LI
NI
NI
NI
LI
HI
HI
MI
MI
MI
NI
LI
NI
EP4
LI
NI
LI
NI
MI
HI
MI
HI
LI
LI
NI
NI
LI
MI
MI
MI
LI
VI
LI
MI
MI
MI
EP5
LI
NI
MI
NI
NI
MI
VI
MI
LI
MI
LI
NI
LI
LI
VI
MI
MI
LI
NI
LI
MI
MI
EP6
LI
MI
LI
HI
MI
NI
HI
MI
MI
MI
LI
NI
MI
MI
VI
HI
MI
LI
NI
LI
MI
MI
EP7
LI
MI
HI
LI
VI
HI
NI
NI
LI
NI
NI
NI
LI
NI
VI
LI
LI
NI
NI
LI
LI
MI
EP8
MI
LI
LI
HI
LI
MI
LI
NI
VI
NI
NI
LI
NI
MI
LI
LI
MI
MI
MI
HI
LI
MI
EP9
MI
MI
MI
MI
MI
LI
LI
HI
NI
MI
NI
NI
LI
LI
MI
MI
HI
MI
MI
MI
HI
HI
EP10
HI
MI
LI
MI
MI
MI
LI
LI
MI
NI
NI
NI
LI
LI
MI
LI
NI
LI
HI
NI
NI
LI
EP11
LI
NI
LI
LI
NI
MI
NI
NI
LI
NI
NI
MI
HI
LI
NI
NI
LI
LI
NI
NI
LI
LI
EP12
NI
LI
NI
NI
LI
LI
NI
LI
NI
LI
LI
NI
MI
LI
LI
NI
NI
MI
LI
NI
NI
NI
EP13
LI
LI
LI
NI
LI
LI
NI
LI
LI
LI
HI
MI
NI
LI
MI
MI
MI
MI
NI
LI
MI
MI
EP14
LI
MI
LI
MI
NI
MI
LI
MI
MI
NI
MI
LI
MI
NI
LI
LI
NI
LI
NI
LI
MI
MI
EP15
HI
HI
HI
LI
VI
HI
VI
NI
LI
MI
LI
MI
LI
MI
NI
MI
MI
LI
NI
LI
NI
NI
EP16
LI
LI
HI
MI
MI
VI
NI
LI
MI
MI
NI
LI
MI
LI
MI
NI
MI
MI
LI
MI
LI
LI
EP17
MI
MI
HI
MI
LI
HI
LI
LI
HI
NI
LI
NI
MI
LI
MI
MI
NI
LI
NI
LI
MI
MI
EP18
LI
HI
HI
VI
MI
NI
LI
MI
LI
LI
NI
LI
LI
LI
NI
HI
MI
NI
LI
LI
MI
MI
EP19
HI
NI
LI
NI
LI
LI
NI
MI
MI
HI
LI
MI
LI
NI
LI
MI
LI
MI
NI
NI
LI
NI
EP20
LI
LI
LI
MI
NI
LI
LI
VI
MI
LI
LI
NI
NI
MI
LI
MI
LI
LI
LI
NI
MI
MI
EP21
MI
LI
NI
LI
MI
MI
NI
MI
VI
NI
NI
NI
MI
LI
NI
LI
MI
MI
LI
MI
NI
HI
EP22
NI
MI
LI
MI
MI
LI
MI
LI
HI
LI
LI
LI
LI
MI
NI
NI
LI
MI
NI
MI
MI
NI
NI – No Influence, LI – Low Influence, MI – Medium Influence, HI – High Influence, VI – Very-High Influence
Table 8. Average relation matrix for ecodesign practices EP1
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
EP11
EP12
EP13
EP14
EP15
EP16
EP17
EP18
EP19
EP20
EP21
EP22
Sum
EP1
0
0.8
1.07
0.867
0.733
0.933
0.667
1.13
1.07
2.87
0.733
0.667
0.467
0.533
1.26
0.933
0.867
1.07
1.53
0.467
0.867
0.667
20.201
EP2
0.733
0
0.867
0.933
1.13
1.07
0.867
2.93
0.667
1.07
0.467
0.4
0.4
1.07
0.933
1.07
1.4
1.2
1.07
0.4
0.467
0.333
19.477
EP3
1.2
0.733
0
0.933
1.07
0.8
1.26
1.13
1.6
1.07
0.333
0.267
0.333
0.467
0.867
0.4
1.07
3.2
0.867
0.4
0.533
0.333
18.866
EP4
0.933
0.733
1.07
0
0.467
0.933
1.73
1.13
0.933
0.533
0.4
0.267
0.533
0.4
0.933
1.07
1.53
3.07
0.933
0.867
0.933
1.07
20.468
EP5
0.933
1.2
1.07
0.667
0
1.13
3.2
2.87
1.2
1.07
0.667
0.333
0.533
0.333
0.933
0.867
1.4
0.933
0.467
0.4
0.867
0.4
21.473
EP6
0.733
0.867
0.667
0.867
1.07
0
2.8
2.87
3
1.73
0.733
0.4
0.933
0.733
1.2
0.933
2.8
0.867
0.733
1.4
1.07
0.933
27.340
EP7
0.667
0.467
1.2
1.6
2.8
2.8
0
2.2
0.933
1.13
0.267
0.333
0.267
0.267
0.333
0.4
0.867
0.4
0.333
0.467
0.533
0.4
18.664
EP8
1.07
3.2
0.933
1.07
3.2
2.8
3.2
0
0.933
2.2
0.267
0.267
0.333
0.267
0.333
0.267
1.07
0.867
0.333
0.267
0.333
0.333
23.543
EP9
0.933
1.2
0.933
1.07
0.933
3.2
1.07
0.867
0
1.6
0.333
0.267
0.933
0.333
0.4
0.267
0.467
0.333
0.267
0.467
0.267
0.267
16.407
EP10
2.8
1.07
0.933
0.533
0.667
1.07
1.13
1.07
1.87
0
0.267
0.333
0.267
0.4
0.267
0.333
0.467
0.4
0.333
0.267
0.333
0.267
15.077
EP11
0.667
0.4
0.4
0.333
0.667
0.533
0.333
0.467
0.267
0.333
0
0.267
1.6
0.533
0.333
0.267
0.333
0.933
0.333
0.267
0.333
0.667
10.266
EP12
0.267
0.4
0.333
0.333
0.267
0.333
0.467
0.333
0.267
0.267
0.333
0
0.533
0.267
0.333
0.267
0.267
0.867
0.733
0.333
0.267
0.333
7.800
EP13
0.333
0.333
0.4
0.533
0.467
0.867
0.467
0.333
1.13
0.333
1.87
0.933
0
1.07
0.933
1.26
1.07
0.733
0.667
0.933
1.07
1.2
16.935
EP14
0.4
0.933
0.333
0.267
0.533
1.07
0.467
0.333
0.333
0.933
0.667
0.533
1.2
0
0.867
0.333
0.4
0.333
0.267
0.333
0.667
0.733
11.935
EP15
1.07
0.467
0.867
0.533
0.467
1.07
0.533
0.467
0.867
0.667
0.333
0.267
0.733
0.867
0
0.4
0.333
0.4
0.333
0.267
1.07
0.333
12.344
EP16
0.867
0.933
0.667
0.667
0.933
0.533
0.333
0.333
0.4
1.13
0.333
0.267
1.26
0.267
2.8
0
0.933
0.333
0.267
0.333
0.867
0.267
14.723
EP17
0.933
1.6
0.667
1.2
1.6
1.6
0.667
0.867
1.07
0.867
0.533
0.333
0.4
0.533
1.07
1.07
0
0.4
0.333
0.867
0.4
0.333
17.343
EP18
1.6
1.6
3.2
3.2
0.867
0.667
0.333
0.4
0.4
0.867
0.533
0.533
0.733
0.333
0.533
0.733
0.333
0
0.267
0.4
0.333
0.4
18.265
EP19
1.2
0.867
0.667
0.733
0.467
0.533
0.333
0.333
0.533
1.07
0.333
0.333
0.733
0.267
0.267
0.333
0.333
0.267
0
0.333
0.4
0.267
10.602
EP20
0.4
0.4
0.333
0.467
0.667
1.2
0.667
0.333
0.467
0.333
0.4
0.333
0.933
0.533
1.07
2.8
0.933
0.733
0.267
0
0.933
0.867
15.069
EP21
0.333
0.4
0.333
0.867
0.867
1.13
0.467
0.4
0.333
0.4
0.533
0.533
0.533
0.533
1.2
0.933
0.733
0.533
0.4
0.867
0
0.933
13.261
EP22
0.467
0.333
0.4
1.13
0.467
0.867
0.333
0.333
0.4
0.267
0.933
0.333
1.6
0.933
1.07
1.07
0.4
0.533
0.267
0.533
0.867
0
13.536
Sum
18.539
18.936
17.343
18.803
20.339
25.139
21.324
21.129
18.673
20.74
11.268
8.199
15.257
10.939
17.935
16.006
18.006
18.405
11
10.868
13.41
11.336
Table 9. Normalized relation matrix for ecodesign practices EP1
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
EP11
EP12
EP13
EP14
EP15
EP16
EP17
EP18
EP19
EP20
EP21
EP22
EP1
0.0000
0.0293
0.0392
0.0317
0.0268
0.0341
0.0244
0.0414
0.0392
0.1050
0.0268
0.0244
0.0171
0.0195
0.0461
0.0341
0.0317
0.0392
0.0560
0.0171
0.0317
0.0244
EP2
0.0268
0.0000
0.0317
0.0341
0.0414
0.0392
0.0317
0.1072
0.0244
0.0392
0.0171
0.0146
0.0146
0.0392
0.0341
0.0392
0.0512
0.0439
0.0392
0.0146
0.0171
0.0122
EP3
0.0439
0.0268
0.0000
0.0341
0.0392
0.0293
0.0461
0.0414
0.0586
0.0392
0.0122
0.0098
0.0122
0.0171
0.0317
0.0146
0.0392
0.1171
0.0317
0.0146
0.0195
0.0122
EP4
0.0341
0.0268
0.0392
0.0000
0.0171
0.0341
0.0633
0.0414
0.0341
0.0195
0.0146
0.0098
0.0195
0.0146
0.0341
0.0392
0.0560
0.1124
0.0341
0.0317
0.0341
0.0392
EP5
0.0341
0.0439
0.0392
0.0244
0.0000
0.0414
0.1171
0.1050
0.0439
0.0392
0.0244
0.0122
0.0195
0.0122
0.0341
0.0317
0.0512
0.0341
0.0171
0.0146
0.0317
0.0146
EP6
0.0268
0.0317
0.0244
0.0317
0.0392
0.0000
0.1025
0.1050
0.1098
0.0633
0.0268
0.0146
0.0341
0.0268
0.0439
0.0341
0.1025
0.0317
0.0268
0.0512
0.0392
0.0341
EP7
0.0244
0.0171
0.0439
0.0586
0.1025
0.1025
0.0000
0.0805
0.0341
0.0414
0.0098
0.0122
0.0098
0.0098
0.0122
0.0146
0.0317
0.0146
0.0122
0.0171
0.0195
0.0146
EP8
0.0392
0.1171
0.0341
0.0392
0.1171
0.1025
0.1171
0.0000
0.0341
0.0805
0.0098
0.0098
0.0122
0.0098
0.0122
0.0098
0.0392
0.0317
0.0122
0.0098
0.0122
0.0122
EP9
0.0341
0.0439
0.0341
0.0392
0.0341
0.1171
0.0392
0.0317
0.0000
0.0586
0.0122
0.0098
0.0341
0.0122
0.0146
0.0098
0.0171
0.0122
0.0098
0.0171
0.0098
0.0098
EP10
0.1025
0.0392
0.0341
0.0195
0.0244
0.0392
0.0414
0.0392
0.0684
0.0000
0.0098
0.0122
0.0098
0.0146
0.0098
0.0122
0.0171
0.0146
0.0122
0.0098
0.0122
0.0098
EP11
0.0244
0.0146
0.0146
0.0122
0.0244
0.0195
0.0122
0.0171
0.0098
0.0122
0.0000
0.0098
0.0586
0.0195
0.0122
0.0098
0.0122
0.0341
0.0122
0.0098
0.0122
0.0244
EP12
0.0098
0.0146
0.0122
0.0122
0.0098
0.0122
0.0171
0.0122
0.0098
0.0098
0.0122
0.0000
0.0195
0.0098
0.0122
0.0098
0.0098
0.0317
0.0268
0.0122
0.0098
0.0122
EP13
0.0122
0.0122
0.0146
0.0195
0.0171
0.0317
0.0171
0.0122
0.0414
0.0122
0.0684
0.0341
0.0000
0.0392
0.0341
0.0461
0.0392
0.0268
0.0244
0.0341
0.0392
0.0439
EP14
0.0146
0.0341
0.0122
0.0098
0.0195
0.0392
0.0171
0.0122
0.0122
0.0341
0.0244
0.0195
0.0439
0.0000
0.0317
0.0122
0.0146
0.0122
0.0098
0.0122
0.0244
0.0268
EP15
0.0392
0.0171
0.0317
0.0195
0.0171
0.0392
0.0195
0.0171
0.0317
0.0244
0.0122
0.0098
0.0268
0.0317
0.0000
0.0146
0.0122
0.0146
0.0122
0.0098
0.0392
0.0122
EP16
0.0317
0.0341
0.0244
0.0244
0.0341
0.0195
0.0122
0.0122
0.0146
0.0414
0.0122
0.0098
0.0461
0.0098
0.1025
0.0000
0.0341
0.0122
0.0098
0.0122
0.0317
0.0098
EP17
0.0341
0.0586
0.0244
0.0439
0.0586
0.0586
0.0244
0.0317
0.0392
0.0317
0.0195
0.0122
0.0146
0.0195
0.0392
0.0392
0.0000
0.0146
0.0122
0.0317
0.0146
0.0122
EP18
0.0586
0.0586
0.1171
0.1171
0.0317
0.0244
0.0122
0.0146
0.0146
0.0317
0.0195
0.0195
0.0268
0.0122
0.0195
0.0268
0.0122
0.0000
0.0098
0.0146
0.0122
0.0146
EP19
0.0439
0.0317
0.0244
0.0268
0.0171
0.0195
0.0122
0.0122
0.0195
0.0392
0.0122
0.0122
0.0268
0.0098
0.0098
0.0122
0.0122
0.0098
0.0000
0.0122
0.0146
0.0098
EP20
0.0146
0.0146
0.0122
0.0171
0.0244
0.0439
0.0244
0.0122
0.0171
0.0122
0.0146
0.0122
0.0341
0.0195
0.0392
0.1025
0.0341
0.0268
0.0098
0.0000
0.0341
0.0317
EP21
0.0122
0.0146
0.0122
0.0317
0.0317
0.0414
0.0171
0.0146
0.0122
0.0146
0.0195
0.0195
0.0195
0.0195
0.0439
0.0341
0.0268
0.0195
0.0146
0.0317
0.0000
0.0341
EP22
0.0171
0.0122
0.0146
0.0414
0.0171
0.0317
0.0122
0.0122
0.0146
0.0098
0.0341
0.0122
0.0586
0.0341
0.0392
0.0392
0.0146
0.0195
0.0098
0.0195
0.0317
0.0000
Sum
0.6785
0.6931
0.6348
0.6882
0.7444
0.9201
0.7805
0.7733
0.6834
0.7591
0.4124
0.3001
0.5584
0.4004
0.6564
0.5858
0.6590
0.6736
0.4026
0.3978
0.4908
0.4149
Table 10. Total relation matrix for ecodesign practices EP1
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
EP11
EP12
EP13
EP14
EP15
EP16
EP17
EP18
EP19
EP20
EP21
EP22
EP1
0.0723
0.0984
0.1015
0.0971
0.0998
0.1224
0.1046
0.1193
0.1098
0.1763
0.0619
0.051
0.064
0.0553
0.1023
0.0832
0.0947
0.1006
0.092
0.0539
0.0751
0.0601
EP2
0.0963
0.0778
0.0967
0.1026
0.123
0.1337
0.1208
0.1864
0.0967
0.1196
0.0534
0.042
0.0618
0.0743
0.0931
0.0894
0.1178
0.1072
0.0765
0.0526
0.0623
0.0493
EP3
0.1118
0.098
0.0706
0.1068
0.1136
0.1197
0.1253
0.1206
0.1254
0.1156
0.0477
0.037
0.0578
0.0517
0.0872
0.065
0.1018
0.1753
0.0685
0.0515
0.0624
0.048
EP4
0.1039
0.1
0.1101
0.0778
0.0978
0.1277
0.1425
0.1224
0.1047
0.0988
0.0529
0.0387
0.0692
0.0523
0.096
0.0937
0.1215
0.1741
0.0723
0.0708
0.0801
0.0767
EP5
0.1102
0.1268
0.1118
0.1036
0.0985
0.1534
0.2116
0.1994
0.125
0.1299
0.0642
0.0428
0.0705
0.0529
0.0988
0.0881
0.1269
0.105
0.06
0.0577
0.0813
0.0559
EP6
0.1202
0.1329
0.1115
0.1258
0.1528
0.1397
0.2159
0.2151
0.2035
0.1698
0.0765
0.0521
0.0983
0.0764
0.1234
0.1061
0.1896
0.1148
0.077
0.1027
0.0998
0.0842
EP7
0.0952
0.0952
0.109
0.1264
0.1825
0.1971
0.1028
0.1734
0.1148
0.1248
0.048
0.04
0.0578
0.0471
0.0742
0.0693
0.1081
0.0852
0.0528
0.0587
0.0675
0.054
EP8
0.1275
0.2035
0.1182
0.1273
0.2176
0.2215
0.2312
0.128
0.1332
0.1812
0.0568
0.0449
0.0695
0.0575
0.0876
0.0776
0.1324
0.1148
0.0639
0.0605
0.0709
0.0596
EP9
0.095
0.1057
0.0898
0.098
0.1038
0.1953
0.1205
0.1141
0.0727
0.1291
0.0465
0.0349
0.0751
0.0463
0.0684
0.0582
0.843
0.072
0.0463
0.0541
0.0523
0.0452
EP10
0.1527
0.0958
0.0854
0.0742
0.0878
0.1162
0.1094
0.1088
0.1256
0.0704
0.0395
0.0348
0.0471
0.0439
0.0579
0.0537
0.0724
0.0675
0.0464
0.0408
0.0487
0.0399
EP11
0.0574
0.0494
0.0476
0.0476
0.0612
0.0642
0.0531
0.0573
0.046
0.0506
0.0222
0.0253
0.0826
0.0395
0.0436
0.0383
0.0464
0.0662
0.0324
0.0302
0.0367
0.0449
EP12
0.0358
0.041
0.0378
0.0395
0.0384
0.0461
0.0468
0.0424
0.0362
0.0386
0.027
0.0111
0.0382
0.0242
0.0349
0.0305
0.0348
0.0557
0.0412
0.0269
0.0274
0.0269
EP13
0.062
0.0636
0.0613
0.0702
0.0725
0.0995
0.0753
0.0701
0.0917
0.0682
0.0966
0.0552
0.0428
0.068
0.0825
0.0871
0.0877
0.0738
0.0526
0.0637
0.0746
0.0735
EP14
0.0533
0.0719
0.0477
0.048
0.0621
0.09
0.0642
0.0607
0.0549
0.0765
0.0478
0.0362
0.0721
0.0236
0.0667
0.0441
0.0543
0.0486
0.0327
0.0356
0.0519
0.0496
EP15
0.0796
0.0595
0.0697
0.0607
0.0636
0.0953
0.0707
0.0683
0.0766
0.0733
0.0365
0.0278
0.0567
0.0546
0.0386
0.0475
0.0549
0.0553
0.0372
0.0348
0.0674
0.037
EP16
0.0802
0.0809
0.0687
0.0702
0.0847
0.0831
0.0694
0.0701
0.0665
0.0939
0.0402
0.0303
0.0789
0.0386
0.143
0.0385
0.0803
0.0579
0.0382
0.0399
0.0659
0.0375
EP17
0.0939
0.1199
0.0809
0.102
0.1255
0.1397
0.1033
0.1108
0.1044
0.1023
0.0526
0.0368
0.058
0.0532
0.0942
0.0871
0.064
0.0738
0.048
0.0661
0.0571
0.0465
EP18
0.1216
0.1203
0.1741
0.1761
0.0993
0.1052
0.0914
0.0941
0.0834
0.1038
0.0542
0.0453
0.0698
0.0475
0.0778
0.077
0.0782
0.0767
0.051
0.0511
0.0565
0.0515
EP19
0.08
0.0677
0.0579
0.0617
0.0564
0.0674
0.0562
0.0573
0.0586
0.0808
0.033
0.0276
0.0516
0.0303
0.0426
0.0412
0.0487
0.0463
0.0227
0.033
0.0394
0.0307
EP20
0.0623
0.0639
0.0576
0.0659
0.0781
0.1067
0.081
0.0695
0.068
0.0671
0.0433
0.0328
0.0719
0.0472
0.0898
0.1393
0.0831
0.0703
0.0371
0.0293
0.0694
0.0595
EP21
0.0548
0.0583
0.0526
0.074
0.0785
0.0977
0.0702
0.067
0.0584
0.0626
0.0444
0.0373
0.0528
0.0443
0.0843
0.0699
0.0706
0.0607
0.0391
0.0569
0.0319
0.059
EP22
0.0583
0.0546
0.0541
0.0823
0.0629
0.0872
0.0627
0.0615
0.0595
0.0569
0.0604
0.0313
0.0909
0.059
0.0803
0.0743
0.0583
0.0616
0.035
0.0455
0.0631
0.0274
Journal Pre-proof
Table 11. Calculation to find cause and effect factors r
c
r+c
Importance
r-c
Cause/Effect
EP1
1.9956
1.9243
3.9199
9
0.0713
Cause
EP2
2.0333
1.9851
4.0184
8
0.0482
Cause
EP3
1.9613
1.8146
3.7759
11
0.1467
Cause
EP4
2.084
1.9378
4.0218
7
0.1462
Cause
EP5
2.2743
2.1604
4.4347
5
0.1139
Cause
EP6
2.7881
2.6088
5.3969
1
0.1793
Cause
EP7
2.0839
2.3289
4.4128
6
-0.245
Effect
EP8
2.5852
2.3166
4.9018
2
0.2686
Cause
EP9
2.5663
2.0156
4.5819
3
0.5507
Cause
EP10
1.6189
2.1901
3.809
10
-0.5712
Effect
EP11
1.0427
1.1056
2.1483
21
-0.0629
Effect
EP12
0.7814
0.8152
1.5966
22
-0.0338
Effect
EP13
1.5925
1.4374
3.0299
14
0.1551
Cause
EP14
1.1925
1.0877
2.2802
19
0.1048
Cause
EP15
1.2656
1.7672
3.0328
13
-0.5016
Effect
EP16
1.4569
1.5591
3.016
15
-0.1022
Effect
EP17
1.8201
2.6695
4.4896
4
-0.8494
Effect
EP18
1.9059
1.8634
3.7693
12
0.0425
Cause
EP19
1.0911
1.1229
2.214
20
-0.0318
Effect
EP20
1.4931
1.1163
2.6094
17
0.3768
Cause
EP21
1.3253
1.3417
2.667
16
-0.0164
Effect
EP22
1.3271
1.1169
2.444
18
0.2102
Cause
Table 12. Interaction matrix for ecodesign practices EP1 EP1 EP2 EP3 EP4 EP5 EP6 EP7 EP8 EP9 EP10
* * * * * * * * *
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
*
* *
* * *
* * * * * * * * * *
* * * * * * * * * *
* * * * * * * * * *
* * * * * * * * * *
* * * * * * * *
* * * * * * * * *
* * * * * * * *
* * * * * * *
* * * * *
EP11
EP12
EP13
*
EP14
EP15
EP16
EP17
EP18
EP19
* * * * * *
* *
* * * * * * * *
*
* * *
* * * * * * * * *
*
*
*
* *
EP11 EP12 EP13 EP14 EP15 EP16 EP17 EP18 EP19
* * * * *
* * *
* *
* *
EP20 EP21 EP22
*
* * *
* * * * * * * * *
*
* *
* *
* *
*
*
* * * *
* *
*
Here * shows the interrelationship between ecodesign practices
*
* * *
* *
*
*
EP20
EP21
EP22
*
* * *
*
Prashant Kumar Singh, Prabir Sarkar PII:
S0959-6526(19)33861-2
DOI:
https://doi.org/10.1016/j.jclepro.2019.118991
Reference:
JCLP 118991
To appear in:
Journal of Cleaner Production
Received Date:
07 May 2019
Accepted Date:
20 October 2019
Please cite this article as: Prashant Kumar Singh, Prabir Sarkar, A framework based on fuzzy Delphi and DEMATEL for sustainable product development: A case of Indian automotive industry, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.118991
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Journal Pre-proof
Literature review
Identify various ecodesign practices for sustainable product development
Expert opinion
Collect opinion of the experts about each practice using a linguistic scale
Refer Table 3
Phase 1 fuzzy Delphi
Finalize ecodesign practices using fuzzy Delphi Technique Construct the initial relation matrix using expert input
Refer Table 4
Construct average relation matrix (A) by aggregating inputs of all experts Construct the normalized relation matrix (D)
No
If sum of each column < 1
Yes
Use revised DEMATEL Necessary modifications
Feasibility of DEMATEL, then Construct total relation matrix (T)
Determine cause and effect factors
Set the threshold value (α)
Develop a causal diagram for ecodesign practices
Establish inter-relationship among ecodesign practices
Obtain the most significant ecodesign practices for sustainable product development
Phase 2 DEMATEL
Journal Pre-proof
A framework based on fuzzy Delphi and DEMATEL for sustainable product development: A case of Indian automotive industry Prashant Kumar Singh* and Prabir Sarkar Department of Mechanical Engineering, IIT Ropar, Rupnagar-140001, Punjab, India E-mail of corresponding author - [email protected]
Abstract Constantly increasing environmental problems has led the governments worldwide to impose strict regulations and policies on industries for producing sustainable products. Industries adopt various ecodesign practices (EPs) to achieve the goal of sustainable product development. However, the significance of these EPs in the production of environmentally friendly products is not clearly understood by the industries, especially in developing economies such as India. In this study, a hybrid framework based on fuzzy Delphi and Decision Making Trial and Evaluation Laboratory (DEMATEL) approach is proposed to identify various EPs and analyze the causal (i.e. cause and effect) relationships among these EPs to obtain most significant practices for sustainable product development. The proposed framework is illustrated by taking a case of Indian automotive industry. A panel of fifteen experts from Indian automotive companies and academy is involved in this study. This study helps the designers not only to find EPs relevant to automotive products but also to understand the complex relationship among these EPs. Results show that ‘Using alternative manufacturing techniques’, ‘Enhancing durability and reliability’ and ‘Ensuring easier maintenance and repair’ are the three most significant EPs for sustainable development of automotive products. Keywords: Ecodesign practice (EP); sustainable product development; fuzzy Delphi; DEMATEL; automotive industry
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1. Introduction Various industries indulged in the production of different consumer and non-consumer products are one of the key contributors to the increasing environmental issues. Increasing population has led to a rapid growth in the production and consumption of goods that will only aid to the increasing environmental problems such as resource depletion and increasing waste. One way to deal with this problem is the adoption of the concept of ‘Ecodesign’. This is an approach that considers the integration of environmentally friendly practices in the development of products throughout the entire product life cycle from raw material to the end of life (ISO, 2011). It consists of a set of practices, known as Ecodesign Practices (EPs), that leads to design and develop a sustainable product (Charter and Tischner, 2001). These practices can be adopted to design and produce products which are safe and sustainable and to ensure that the end of products’ life is the birth of something new, whenever possible (P.K. Singh and Sarkar, 2019). Nowadays, a growing number of companies have started implementing ecodesign practices for producing environmentally conscious products and services to achieve the goal of sustainable product development (Ghazilla et al., 2015). The successful implementation of EPs is still an issue, especially in small and medium enterprises of developing economies due to some significant barriers (P. K. Singh and Sarkar, 2019). Implementing all available EPs is a big challenge for small enterprises because they have limited personnel and financial resources (Biondi et al., 2002; Hadjimanolis and Dickson, 2000; Hillary, 2004; P. K. Singh and Sarkar, 2019). Therefore, it becomes important to analyze these EPs to find out the most significant EPs. One way to find significant EPs is to identify the set of most influential EPs i.e. those which affect most of the other EPs due to their interdependencies. The adoption of EPs depends on the type of product. For example; a set of EPs relevant to sustainable production of food products may not be relevant to sustainable production of electronic products and vice versa. Therefore, the study should be focused to a particular industry. In this work, the focus is on automotive industry. 1.1 Current status of automotive industry in India Indian automotive industry is growing rapidly and recently became the 4th largest manufacture in the world which was at 7th place in 2017. Indian automotive industry is expected to reach up to 18 trillion INR (i.e. approx. 282 billion US$) at the end of 2026. Only in financial year 2018 (FY18),
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29 million vehicles were manufactured in India (“Indian Automobile Industry Analysis,” 2019). Such a rapid growth has created concerns for sustainable production and utilization of vehicles in India. These concerns are not only limited to the production of the vehicles but also spread across the entire life cycle from raw material to the end of life of a vehicle. These challenges can be dealt easily through the implementation of ecodesign practices (EPs) in the design and development of automotive products. However, EPs must be considered by the designers at very beginning i.e. during conceptual design phase because more than 80% of the environmental load is determined in this phase (Tischner, 2000). 1.2 Research objective The objective of this research is threefold: i. To identify various EPs suggested by the researchers for sustainable product development through a review of the literature. ii. Finalize the EPs which are relevant to automotive products. iii. Establish and examine the causal relationships between the finalized EPs to find the significant EPs for sustainable production of automotive products. A fuzzy Delphi technique is used to finalize the EPs relevant to automotive products whereas a DEMATEL approach is used to obtain the causal relationship between EPs which shows the type of influence that one EP has on the other EP. Causal relationship is generally demonstrated with the help of a causal diagram that divides the factors under study into cause and effect factors. A cause factor offers some influence on the system and an effect factor receives this influence. DEMATEL approach also helps to develop an interaction matrix that shows the inter-relationship among EPs. An EP which is a cause factor and having an inter-relationship with most of other EPs will have higher potential to improve the development of sustainable automotive products. Rest of the article is structured as follows: Section 2 provides a review of the literature on various EPs suggested by researchers for sustainable product development. Section 3 includes a description of the research methodology of this study. The proposed framework based on fuzzy Delphi and DEMATEL is explained in Section 4. Application of the proposed framework is provided in Section 5 which is followed by the conclusions and limitations of this study given in Section 6.
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2. Literature review This section presents a focused review of the literature to find various ecodesign practices suggested by the researchers for sustainable product development. There are some EPs related to the auto industries which are suggested by the researchers. Borchardt et al. (2009) suggested that using recycled composite materials, renewable bio-fuels and synthetic lubricating oils having high durability are the key EPs for greening the automotive industry. Selection of renewable and recycled materials is an important strategy for improving the environmental friendliness of automotive products (de Medina, 2006). Imposing a restriction on the use of hazardous materials can be a significant approach for automotive industry. The materials can be divided in three different lists viz. ‘white list,’ ‘gray list’ and ‘black list’ (Luttropp and Lagerstedt, 2006; Tingström and Karlsson, 2006). Green manufacturing, green purchasing, green distribution and environmentally conscious disposal are crucial practices that must be adopted by the auto industry in the production of the products as well as in various supply chain activities (Borchardt et al., 2011; Miguel Sellitto et al., 2015; Vanalle et al., 2017; Zhu et al., 2007; Zhu and Sarkis, 2007). Sarkis et al. (2010) suggested that EPs to be adopted by the auto industry can be divided in three categories viz. EPs related to the type of material (Keldmann and Olesen, 1994), EPs related to the design stage of the products (Gonzalez et al., 2002; van Hemel and Cramer, 2002) and EPs related to the internal organization for environmental management (Banerjee, 2001; Nakashima et al., 2002). ‘Car sharing’ can be considered as an ecofriendly practice because sharing of one car can reduce the usage of 9 to 13 vehicles (Martin et al., 2010). The available EPs related to the auto industry are limited and there is a need to explore more EPs to identify those which are relevant to automotive industry (Schiavone et al., 2008). Researchers have suggested a significant number of EPs which are independent of any specific sector. These studies are given in Table 1. Fiksel (1996) suggested a number of EPs for creating eco-efficient products and process with a consideration of all phases of the product life cycle. A set of thirty-three EPs were presented by van Hemel and Brezet (1997). These EPs were distributed among eight different strategies and the entire system is known as ‘Ecodesign Strategy Wheel’ or ‘LiDS (Life cycle Design Strategy) wheel’ with strategies representing eight spokes of the wheel. All EPs were further classified at three different levels viz. product component level, product structure level and product system level. In another study, Stuart and Sommerville (1998) suggested EPs for selecting materials and distinguished them between component level and
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product level. Further, it was illustrated how the material selection decisions affect the other life cycle phases of a product. Lewis and Gertsakis (2001) proposed a global guide to design green products. They recommended a set of EPs to be adopted at an early stage of the design process to meet the expected quality, cost, manufacturability and minimum impact on the environment. They also emphasized on the selection of appropriate EP to consider the trade-offs. For example, the influence of a lightweight material on its recyclability. It means, a lightweight material is not only easy to dissemble but also transported easily to the recycling plant. Thus, the time and cost of recycling is reduced. Popularly known as ‘12 principles of green engineering’ were identified by Anastas and Zimmerman (2003) to design environmentally benign products and processes. These principles are related to the material, manufacturing and the end of life aspects of products. These principles provide a framework to optimize unsustainable products, processes and systems. Wimmer and Züst (2003) developed a tool which is known as ‘Ecodesign Pilot’ where Pilot stands for product investigation, learning and optimization tool for sustainable product development. This tool is based on a set of nineteen EPs distributed among the various phases of a product life cycle. This study further recommends the implementation of EPs to design a product which is intensive to a certain phase of the product life cycle such as manufacturing intensive, use intensive or end of life intensive product. McLennan (2004) suggested some common EPs that can be implemented in different design disciplines such as product design, architecture, urban planning etc. to eliminate negative environmental impacts of products. Giudice et al. (2006) proposed certain EPs for the designers to achieve eco-efficiency in each phase of a product life cycle. ‘Ten golden rules’ were identified by Luttropp and Lagerstedt (2006) for integrating environmental aspects in product development. These golden rules are basically guidelines related to the selection of materials, manufacturing techniques, usage and the end of life activities. Telenko et al. (2008) compiled a set of EPs to be adopted in conceptual and embodiment design stages to achieve substantial environmental improvements. Vezzoli and Manzini (2008) provided a deep insight to various EPs to achieve environmental sustainability through design. In this study, the author also tried to understand the interrelationship between EPs. The main emphasis was given to the selection of resources, materials and end of life activities. Belletire et al. (2012) identified a set of 39 EPs related to the entire lifecycle of the products. They suggested that environmental performance of a product life cycle can be conceptualize on the basis of these EPs. Murray (2013) proposed a topic
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guide that is based on EPs and guides the product design and development team to initiate and develop a process for embedding sustainability in product design in R&D programmes. Although there are about 300 EPs suggested by different studies as mentioned above but there is a high degree of commonality among these EPs. For example: in one research article, an EP is mentioned as “Avoid toxic materials” and in another article an EP is mentioned as “Choose non-toxic materials”. Technically, both EPs has common meaning and therefore all identified EPs are studied and common EPs are eliminated by a team of three postdoctoral researchers having PhD degree in the field of product sustainability. Thus, a total of 41 individual EPs are obtained as given with a short description in Table 2. These EPs are classified in the form of a hierarchy under five different life cycle stages, as shown in Figure 1. Since the concept of ecodesign is based on the product life cycle approach, hence it is reasonable to classify EPs in different life cycle stages. 2.1 Research gap The available EPs related to the auto sector are limited and there is a need to explore more EPs to identify those which are relevant to automotive industry (Schiavone et al., 2008). Some researchers have suggested that most of the environmentally friendly practices are closely correlated (Anastas and Zimmerman, 2003; Giudice et al., 2006) but there is a lack of studies that provide a comprehensive understanding of these relationships. The relationship between EPs directly affects the functional performance of products and their components (Giudice et al., 2006). There is a list of practices, principles and guidelines reported in the literature, but a rigorous and qualitative validation of many of these principles is still lacking (Telenko et al., 2008). Therefore, a study should be carried out that does not only demonstrate the relationship among various EPs but also can show the capability of each EP to influence the other EPs for sustainable development of various products and processes. 3. Research methodology This research is based on a hybrid fuzzy Delphi and DEMATEL approach. First, various ecodesign practices that can be implemented in the development of sustainable products are identified through extensive literature search. There was a high degree of commonality among the ecodesign
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practices suggested by various researchers. All identified practices are studied and segregated to eliminate the commonality by a team of three postdoctoral researchers working in the field of product sustainability. Thus, a total of 41 individual ecodesign practices are obtained. Then, a fuzzy Delphi technique is used to finalize ecodesign practices relevant to automotive industry (details are given in Section 5.1). Although traditional Delphi technique can be used to finalize the criteria but the vagueness and uncertainty in expert opinion still persisted in this method. Therefore, a fuzzy based Delphi technique is adopted in this study. Once the ecodesign practices are finalized, a DEMATEL approach is utilized to understand the inter-relationship among these ecodesign practices and also to identify the cause and effect factors by developing a causal diagram. Although there are other decision making methods such as Analytical Network Process (ANP), Elimination and Choice Expressing Reality (ELECTRE), Interpretive Structural Modeling (ISM) etc. but these methods are only able to prioritize the factors and fail to identify the cause and effect factors. An understanding of the cause and effect factors helps the stakeholders in a comprehensive decision making. In this study, the aim is to identify the EPs which are relevant to auto industry as well as to understand the causal relationship among these relevant EPs. It requires a hybrid methodology to fulfill these objectives. Therefore, a combination of fuzzy Delphi and DEMATEL is used in this study. Fuzzy Delphi finalizes the factors by selecting the EPs which are relevant to auto industry and DEMATEL helps to analyze the causal relationships among these relevant EPs to find the key EPs which can play a significant role in the sustainable growth of auto industry in India.
3.1 Fuzzy Delphi Delphi technique was first developed by RAND Corporation (Dalkey and Helmer, 1963). It is a qualitative technique for collecting the opinion of a distributed group of people related to a specific area (Bouzon et al., 2016; Kapse et al., 2018). Decision making through traditional Delphi technique was associated with some vagueness and uncertainty therefore a fuzzy based Delphi technique was introduced by Ishikawa (1993). Fuzzy Delphi method has ability to capture the vagueness and uncertainty associated to the data. It has widely been used in different applications
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such as supplier selection (Tahriri et al., 2014), green supply chain performance (Bhattacharya et al., 2014), assessing consumers’ motivation to purchase remanufactured products (Vafadarnikjoo et al., 2018) and technology selection (Hsu et al., 2010). Fuzzy Delphi technique is based on the theory of fuzzy sets. If U is a universal set then a fuzzy set of U is defined by a membership function A which is given as:
A ( x) [0,1] , x U Fuzzy sets are of two types i.e. Type – I and Type – II. The type – II fuzzy set can be any subset in the range [0,1]. It consists of the primary membership and corresponding to each primary membership, there is a secondary membership (which can also be in [0,1]) that defines the possibilities for the primary membership. A type-1 fuzzy set is a special case of a type-2 fuzzy set. Its secondary membership function is a subset with only one element which is unity. A triangular membership function is used in this study which is defined as:
x p q p , p x q r x A ,q x r r q 0 where p, q and r are the triangular fuzzy numbers (TFNs) and denoted as (p, q, r). If A (l1 , m1 , n1 ) and B (l 2 , m2 , n2 ) are two sets of TFNs then operational laws are defined as: Addition : (l1 , m1 , n1 ) (l 2 , m2 , n2 ) (l1 l 2 , m1 m2 , n1 n2 ) Multiplication : (l1 , m1 , n1 ) (l 2 , m2 , n2 ) (l1 l 2 , m1 m2 , n1 n2 ) Similarly, the subtraction and division laws can also be determined. Various steps involved in fuzzy Delphi are as follows (Ishikawa et al., 1993; Kumar et al., 2019): Step 1: In this step, various criteria related to the area under investigation are identified and tabulated. Step 2: After the identification of the criteria, the document containing these criteria is provided to the experts in the form of a questionnaire. Experts provide their linguistic inputs using a
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linguistic scale which is given in Table 3. Input of experts for each criterion are converted into fuzzy numbers. A fuzzy number corresponding to the jth criteria provided by ith expert is represented as: z ij ( pij , qij , rij ) for i = 1, 2, 3,….n and j = 1, 2, 3,….m.
(1)
where n is the number of experts and m is the number of criteria. Then the fuzzy weights of criteria ~ p j are given as follows: ~ p j ( p j , q j , r j ) where p j min( pij ),
n q j (qij ) i 1
1/ n
(2)
r j max(rij ) where i = 1, 2, 3,….n and j = 1, 2, 3,….m
Step 3: In this last step, the mean method is used to calculate the defuzzification value S j as given below: S j ( p j q j r j ) / 3,
j = 1, 2, 3,….m
(3)
A threshold ( ) is set to select or reject the criteria. If S j , then select the criteria. If S j < , then reject the criteria.
3.2 DEMATEL method DEMATEL approach was first introduced by Battelle Memorial Institute of Geneva in 1976. The main characteristic of this approach is that it helps to understand the complex relationship among selected criteria. DEMATEL approach is based on digraphs which are able not only to show interrelationship among criteria but also to show the direction of relationship (Kumar and Dash, 2016; Wu and Tsai, 2012). The highlights of DEMATEL method are: i) this method is based on graph theory and facilitates the analysis of difficult problems by using a visualization method; ii) it develops cause and effect relationships among different factors which makes it easy to understand the mutual influence of the factors and iii) this method is able to know the strength of
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the relationships between or among the factors which is not possible in other multi-criteria decision making methods. This approach has widely been used in various fields such as sustainable supply chain (Mangla et al., 2014), knowledge management (Wu, 2012), online reputation management (Kumar and Dash, 2017) and redistributed manufacturing (Luthra et al., 2019). Various steps involved in this approach are as follows (Kumar and Dash, 2016; Tzeng et al., 2007): Step 1: In the first step, an initial relation matrix is constructed for the criteria with the help of expert opinion. The opinion of the experts is collected with the help of a linguistic scale given in Table 4. Step 2: Construct the average relation matrix after receiving the inputs of all experts. For each expert, a non-negative matrix of the order n × n is constructed as X k [ xijk ] where k indicates the kth expert with 1 ≤ k ≤ H, and n indicates the number of factors. X1, X2, ..., XH are the matrices obtained through H experts. The average relation matrix A can be established as: A [aij ]
1 H
H
x K 1
k ij
(4)
where k indicates the kth expert and H represents the total number of experts. Step 3: Compute the normalized direct relation matrix (D) as D=m×A
1 1 where m min , n n max i aij max j aij i 1 j 1
(5)
(6)
i, j 1,2,3,....n . It is important to note that the sum of each column of the normalized relation matrix must be less than one for the feasibility of DEMATEL approach (Kumar et al., 2017). Step 4: Calculate total relation matrix (T) as
T D( I D) 1
(7)
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where I represent identity matrix. Step 5: Find out the sum of rows (r) and columns (c) of total relation matrix as follows: n r [ri ] n1 t ij j 1 n1
(8)
n c [ci ]1n t ij i 1 1n
(9)
Then, obtain ‘prominence’ i.e. (r + c) and ‘relation’ i.e. (r - c) for each criteria. Prominence of a criteria is a measure of its importance for the entire system. (r - c) values represent the type of relation among the criteria i.e. cause or effect relation. If (r - c) value of a criteria is positive, then it becomes a cause factor and if (r - c) is negative then it known as an effect factor or receiver. Cause factors directly influence the other factors of a system whereas effect factors receive these effects. Thus, cause factors are the key for a system. Step 6: Set a threshold value (α) to avoid minor effects as follows; n
n
[t i 1 j 1
ij
]
N
(10)
Where N is the number of elements in matrix T The elements of total relation matrix which are greater than the threshold value form an interaction matrix that represents inter-relationship between the criteria.
4. Proposed framework based on fuzzy Delphi and DEMATEL The proposed framework based on fuzzy Delphi and DEMATEL consists of two phases as shown in Figure 2. These two phases are: i) Identifying and finalizing the ecodesign practices relevant to automotive industries and ii) Analyzing the finalized ecodesign practices to obtain their interrelationship and cause-effect factors. First phase deals with the identification of EPs available in the literature which is followed by the selection of the relevant EPs to automotive industry through the opinion of the experts using fuzzy Delphi approach. In second phase, EPs relevant to
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automotive industry are analyzed using a DEMATEL approach to find their interrelationship and a causal (i.e. cause - effect) diagram is developed which is based on cause and effect factors. Same panel of experts is involved in this phase. In the end, the most significant EPs are obtained for sustainable development of automotive products.
5. Application of the proposed framework 5.1 Data collection and analysis A total of 30 experts were contacted from various automotive based companies and academia for the collection of data. Out of 30 experts, 20 responded and finally 15 experts agreed to be the part of this study. 12 experts belonged to different companies based on design and manufacturing of automotive components such as pistons, cylinder heads, frames, chassis and accessories for three and four wheelers. These experts were product designers, environmental engineers and production
managers each having an experience of more than 10 years. Additionally, 3 experts belonged to the academia having indulged in the research and teaching on sustainability for more than 8 years. The demographic information of these experts is provided in Table 5. The data is collected and analyzed in two phases as discussed below:
Phase 1: Identifying and finalizing the ecodesign practices relevant to automotive industries In this phase, the fuzzy Delphi technique was used to reduce the number of EPs by selecting only those EPs which are important in an auto industry whereas the irrelevant EPs were rejected. This analysis is carried out in consultation with experts from auto industry. A panel of 15 experts was formed to finalize the ecodesign practices which were identified through literature search. This panel of experts included 6 product designers, 4 environmental engineers, 2 production managers and 3 academicians each having experience of more than 8 years. In the first step of the fuzzy Delphi method, a total of 41 EPs identified from the literature were listed. In second step, a questionnaire based on the identified ecodesign practices was prepared and sent to each expert. Experts were asked to provide rating to each ecodesign practice on the basis of its significance to automotive industry using a rating scale as given in Table 3. Once the response of each expert
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received, the ratings provided by the experts for each EP were converted into Triangular Fuzzy Numbers (TFN) using Table 3. Then, these responses were aggregated to obtain the fuzzy weight of each EP using Equation 2. Let us take the example of EP1 (i.e. Selection of non-toxic material). For EP1, Expert 1 gave a rating ‘4’ which TFN is (0.5, 0.7, 0.9) [See Table 3]. In similar manner we find TFN for each rating given by all 15 experts with respect to EP1, as given below: Expert 1 – (0.5, 0.7, 0.9) [Rating 4]; Expert 2 – (0.5, 0.7, 0.9) [Rating 4]; Expert 3 – (0.7, 0.9, 0.9) [Rating 5]; Expert 4 – (0.5, 0.7, 0.9) [Rating 4]; Expert 5 – (0.5, 0.7, 0.9) [Rating 4]; Expert 6 – (0.5, 0.7, 0.9) [Rating 4]; Expert 7 – (0.7, 0.9, 0.9) [Rating 5]; Expert 8 – (0.7, 0.9, 0.9) [Rating 5]; Expert 9 – (0.7, 0.9, 0.9) [Rating 5]; Expert 10 – (0.7, 0.9, 0.9) [Rating 5]; Expert 11 – (0.5, 0.7, 0.9) [Rating 4]; Expert 12 – (0.5, 0.7, 0.9) [Rating 4]; Expert 13 – (0.5, 0.7, 0.9) [Rating 4]; Expert 14 – (0.7, 0.9, 0.9) [Rating 5]; Expert 15 – (0.7, 0.9, 0.9) [Rating 5] Now, the fuzzy weight of EP1 (Selection of non-toxic material), as given in ‘Table 6’ is (0.50, 0.79, 0.90). It was obtained as follows: First entry of fuzzy weight i.e. 0.50 was obtained by taking the minimum value among all 15 first entries given above i.e., min (0.5, 0.5, 0.7, 0.5, 0.5, 0.5, 0.7, 0.7, 0.7, 0.7, 0.5, 0.5, 0.5, 0.7, 0.7) = 0.5 Second entry of fuzzy weight i.e. 0.79 was obtained by taking the geometric mean of all 15 second entries given above i.e., (0.7 × 0.7 × 0.9 × 0.7 × 0.7 × 0.7 × 0.9 × 0.9 × 0.9 × 0.9 × 0.7 × 0.7 × 0.7 × 0.9 × 0.9)1/15 = 0.79 Third entry of fuzzy weight i.e. 0.90 was obtained by taking the maximum value among all 15 third entries given above i.e., max (0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9) = 0.9
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Similarly, the fuzzy weight was obtained for all 41 EPs. In third step of fuzzy Delphi, the defuzzification value of each EP was carried out by taking the mean of fuzzy weight (see Equation 3). For example; the defuzzification value of EP1 with a fuzzy weight (0.50, 0.79, 0.90) is obtained as (0.50+0.79+0.90)/3 which come to be ‘0.73’, as given in Table 6. Similarly, the defuzzification value was obtained for all 41 EPs. The threshold defuzzification value ‘α’ is set to 0.60 for selecting or rejecting an EP. This value i.e. 0.60 is taken from the linguistic scale (given in Table 3) and is the average of the minimum value of the linguistic variable “important” (0.5) and the maximum value of the linguistic variable “normal” (0.7) [See Table 3] (Kumar et al., 2017). The threshold value act as a criterion to select or reject an EP. On the basis of this threshold value of defuzzification (i.e. 0.60), a total of 22 EPs are selected which have a defuzzification value greater than 0.60 and 19 EPs are rejected having a defuzzification value less than 0.60. All the selected and rejected EPs are given in Table 6. Phase 2: Analyzing the finalized ecodesign practices to obtain their interrelationship and causeeffect factors In this phase, a DEMATEL approach was utilized to obtain the interrelationship as well as the causal (i.e. cause and effect) relationship among the finalized ecodesign practices. The same panel of 15 experts was involved in this phase. Implementation of various steps of DEMATEL approach is discussed as follows: Step 1: Experts were asked to construct a direct relation matrix for ecodesign practices using a linguistic scale given in Table 4. An initial relation matrix provided by expert 1 is provided in Table 7. Initial relation matrices provided by other experts are not included to avoid excess number of Tables. Step 2: An average relation matrix (A) was obtained by aggregating the inputs of all 15 experts. It is given in Table 8. Step 3: The normalized relation matrix (D) was formed by using Equations 5-6 as given below: First we calculate a scaler value ‘m’ using Equation 6 as,
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1 1 1 ' 1 0.0366 m min , n n 25.139 27.339 max i a max j a ij ij i 1 j 1 Then, m = 0.0366 was multiplied to each elements of average relation matrix (A) to obtain the normalized relation matrix (D). It can be observed in Table 9 that the sum of each column of normalized matrix is less than one which proves the feasibility of DEMATEL approach for this study. Step 4: Total relation matrix (T) was computed using Equation 7. Matrix (T) is given in Table 10. Step 5: Sum of the rows ‘r’ and columns ‘c’ of total relation matrix was determined using Equation 8 and Equation 9, respectively. Further, (r + c) and (r - c) values were calculated and cause and effect factors were identified based on (r - c) values of each ecodesign practice, as given in Table 11. Then, a causal diagram was developed by putting (r + c) data set on abscissa and (r - c) data set on ordinate, as shown in Figure 3. Step 6: A threshold value (α) was computed using Equation 10 as, n
n
[t i 1 j 1
N
ij
]
38.2851 0.0791 484
Here, N = 484 (number of elements in total relation matrix). The elements of total relation matrix which are greater than α (i.e. 0.0791) form an interaction matrix that represents inter-relationship between ecodesign practices. This interaction matrix is given in Table 12. 5.2 Result and discussion A framework based on fuzzy Delphi and DEMATEL approach is used in this study to identify and analyze various ecodesign practices significant to automotive industry for ecofriendly product development. A total of 22 EPs are selected through fuzzy Delphi technique. Then, a DEMATEL approach provides the prominence of each EP and divide them into cause and effect factors. The prominence i.e. importance or each EP based on their (r + c) values (given in Table 11) in a
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descending order is obtained as: EP6 > EP8 > EP9 > EP17 > EP5 > EP7 > EP4 > EP2 > EP1 > EP10 > EP3 > EP18 > EP15 > EP13 > EP16 > EP21 > EP20 > EP22 > EP14 > EP19 > EP11 > EP12. Further, these EPs are divided into cause and effect factors based on their positive and negative values of (r - c), respectively. Out of twenty-two EPs, thirteen EPs i.e. Selection of non-toxic materials (EP1); Selection of low energy content materials (EP2); Selection of recycled materials (EP3); Avoid variety of materials (EP4); Choosing materials that require no or less post processing (EP5); Using alternative manufacturing techniques (EP6); Ensuring easier maintenance and repair (EP8); Enhancing durability and reliability (EP9); Reducing total volume of the product (EP13); Reducing energy consumption during usage (EP14); Stimulating recycling of materials (EP18); Emphasizing a modular product structure (EP20) and Optimizing the functionality of product (EP22) fall under cause factors. Nine EPs are the effect factors or receivers that include Reducing production steps (EP7); Installing protection against release of pollutants and hazardous substances (EP10); Less and reusable packaging (EP11); Energy efficient mode of transport (EP12); Clean and low energy consumption for production (EP15); Minimizing production waste (EP16); Stimulating
remanufacturing and refurbishing (EP17); Ensuring safe incineration with energy recovery (EP19) and Targeting a classic design (EP21). It is clear from Table 11 that ‘Using alternative manufacturing technique (EP6)’ has the highest importance among all EPs. Also, it is a cause factor (as shown in Figure 3) and having highest inter-relationship with 18 EPs, as shown in the 7th row of interaction matrix (Table 12). On the other hand, ‘Energy efficient mode of transport (EP12)’ has the least importance (see Table 11) and also has no inter-relationship with any other EP, as shown in the network diagram (Figure 4). Thus, EP12 is the most independent factor among all EPs. In other words, EP12 neither affects nor get affected by any other EP. ‘Enhancing durability and reliability (EP9)’ is the main cause factor which is shown in Figure 3. It can be observed in causal diagram (i.e. cause and effect diagram) that all the EPs laying in the 1st quadrant of the causal diagram are not only the cause factors but also have significant importance (i.e. higher value of (r + c)) in comparison to other EPs. Also, this set of EPs has interrelationship with most of the EPs as evident from Table 12 and Figure 4. Thus, it is evident that these EPs (i.e. laying in 1st quadrant) are the most significant EPs and if implemented successfully
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in an automotive based company then they will lead the company very effectively towards achieving the goal of sustainable product development. Now, it is understood that the level of significance of an EP is governed by following three criteria: i) on the basis of its importance to the system (i.e. (r + c) values), ii) whether it is a cause or effect factor, and iii) its inter-relationship with other EPs. Depending on these three criteria, all the EPs which are divided in four quadrants in the causal diagram can be presented in the descending order of their significance to sustainable product development in automotive industry as: (EPs)quadrant I > (EPs)quadrant IV > (EPs)quadrant II > (EPs)quadrant III
5.3 Implications of the research This research offers the designers and managers with a set of ecodesign practices which can help them to develop environmentally conscious automotive products. However, all the 22 ecodesign practices selected for this study have a positive impact on sustainable development of automotive products but there are certain practices that influence the other ecodesign practices significantly. Therefore, these practices are considered as the most important among 22 practices. These are shown in the first quadrant of the causal diagram in Figure 3. These practices with their various advantages and direct relationship with other ecodesign practices are discussed as follows: Selection of non-toxic materials (EP1): The selection of non-toxic material (EP1) will cause lesser impact on environment and human health during its extraction, manufacturing of product, usage and during end of life activities such as recycling, incineration or land filling. The advantage with selection of non-toxic material is that it will reduce the production steps (EP7) as the purification to remove the toxicity will not be required. It will stimulate recycling of material (EP18) and ensure safe incineration (EP19). It may also enhance the reliability of the product (EP9). It will also eliminate the need to install protection against release of harmful substances (EP10). Selection of low energy content materials (EP2): Low energy content material (EP2) does not only require less energy for its extraction but also ensures less consumption of energy during production (EP15) because processing of such materials is less energy intensive. This characteristic of a material will also stimulate its recycling (EP18) at the end of life. Generally,
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metals are more energy intensive in comparison to polymers therefore designers should try to replace metals by polymers wherever possible in automotive components. Selection of recycled materials (EP3): Recycled materials (EP3) are easy to process that will reduce production steps (EP7) which ultimately will result in a low energy consumption during production (EP15). It can minimize production waste up to some extent (EP16). Also, recycled materials save the energy and resources that might be consumed in the mining or extraction of virgin materials. Also, it ensures that the energy consumed in the extraction of the material is not lost. Avoiding variety of materials (EP4): A variety of materials may require different type of machine tools for their processing. The processing of different materials may take much time that will result in reduced productivity as well as unnecessary consumption of energy and resources (EP15). A variety in materials may lead to choose alternative manufacturing techniques (EP6). The main advantage with avoiding variety of material is that it will facilitate recycling of materials (EP18). Also, it may reduce production steps (EP7) and can ensure easier maintenance and repair (EP8). Selecting materials that require no or low post processing (EP5): Materials with no/low post processing (EP5) will reduce production steps (EP7) and ultimately may result in low energy consumption during production (EP15). For example; plastic materials generally do not require any post processing such as finishing, coloring or cleaning. Also, the elimination of post processing such as finishing of metals will result in less production waste (EP16). Using alternative manufacturing techniques (EP6): Alternative manufacturing techniques (EP6) may result in low energy consumption during production (EP15). For example; bending instead of welding and fastening instead of soldering. It may also help in minimizing the production waste (EP16). For example; using additive manufacturing technique rather than subtractive techniques. It may also result in reduced production steps (EP7). Ensuring easier maintenance and repair (EP8): Ensuring easier maintenance and repair (EP8) directly influence the durability and reliability of a product (EP9). An easy and periodic maintenance of automotive products reduces the emission of greenhouse gases to the atmosphere.
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Enhancing durability and reliability (EP9): The durability and reliability (EP9) of a product can be enhanced by targeting a classic design (EP21) of the automobile rather than trendy so that its aesthetic life matches with its technical life. Thus, a classic design will enhance the durability and reliability of the automobile. Longer the life of a product, lesser will be the need to manufacture, recycle or dispose. Stimulating recycling of materials (EP18): Recycling is an important phenomenon in automotive industry because a huge amount of material remains available at the end of life of an automobile. If these materials are recycled (EP3), then can be used in various applications. An efficient recycling system will ensure minimum waste (EP16). Companies can have a dedicated recycling system (closed-loop recycling) in place. It will minimize the impact that occurs through the transportation of retired products from company to the recycling plants.
6. Conclusion and limitations of the study A two-phase framework is used in this study. In the first phase, various EPs relevant to sustainable development of automotive products are identified and finalized through literature survey and opinion of the experts using a fuzzy Delphi approach. In second phase, a DEMATEL approach is used to analyze the EPs to understand their importance for sustainable development of automotive products by establishing a cause and effect (i.e. causal) relationship among EPs. According to the findings of this study, ‘Using alternative manufacturing techniques (EP6)’, ‘Ensuring easier maintenance and repair (EP8)’ and ‘Enhancing durability and reliability (EP9)’ are the three most significant EPs for sustainable production of automotive products. These three practices cause the maximum influence on the other EPs. Apart from that, there are other EPs viz. ‘Selection of nontoxic materials (EP1)’, ‘Selection of low energy content materials (EP2)’, ‘Selection of recycled materials (EP3)’, ‘Avoiding variety of materials (EP4)’, ‘Selecting materials that require no or low post processing (EP5)’ and ‘Stimulating recycling of materials (EP18)’ which can have a significant impact in auto industry. The findings of this study can be used by the designers as a benchmark for producing more sustainable automotive products in India. There are certain limitations associated to this study. Although, a significant number of experts are involved in this study but still there might be some biasness in the opinion of the experts. Also,
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this study represents an Indian perspective however other countries can have a little different perspective on EPs for environmentally friendly development of automotive products. A fuzzy DEMATEL approach can be used in future to deal with the vagueness and uncertainty that may be associated to the opinion of the experts. Also, based on the cause-effect relationships among the analyzed EPs, a hypothesis can be developed and validated using a larger sample. Another future aspect of this work is to prioritize the EPs that may facilitate the decision making of designers for sustainable development of automotive products. References Anastas, P., Zimmerman, J., 2003. Design through 12 principles of green engineering. ENVIRONMENTAL SCIENCE & TECHNOLOGY 37, 94A-101A. Banerjee, S.B., 2001. Corporate environmental strategies and actions. Management Decision 39, 36–44. https://doi.org/10.1108/EUM0000000005405 Belletire, S., St Pierre, L., White, P., 2012. Okala ecodesign strategy wheel app [WWW Document]. Okala Practitioner. URL http://www.okala.net/downloadapp.html (accessed 10.11.19). Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., 2014. Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach. Production Planning & Control 25, 698–714. https://doi.org/10.1080/09537287.2013.798088 Biondi, V., Iraldo, F., Meredith, S., 2002. Achieving sustainability through environmental innovation: the role of SMEs. International Journal of Technology Management 24, 612. https://doi.org/10.1504/IJTM.2002.003074 Borchardt, M., Poltosi, L.A.C., Sellitto, M.A., Pereira, G.M., 2009. Adopting ecodesign practices: Case study of a midsized automotive supplier. Environ. Qual. Manage. 19, 7– 22. https://doi.org/10.1002/tqem.20232 Borchardt, M., Wendt, M.H., Pereira, G.M., Sellitto, M.A., 2011. Redesign of a component based on ecodesign practices: environmental impact and cost reduction achievements. Journal of Cleaner Production 19, 49–57. https://doi.org/10.1016/j.jclepro.2010.08.006 Bouzon, M., Govindan, K., Rodriguez, C.M.T., Campos, L.M.S., 2016. Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resources, Conservation and Recycling 108, 182–197. https://doi.org/10.1016/j.resconrec.2015.05.021 Charter, M., Tischner, U., 2001. Sustainable Solutions: Developing Products and Services for the Future. Greenleaf Publishing Limited., Sheffield, UK. Dalkey, N., Helmer, O., 1963. An experimental application of the Delphi method to the use of experts. Management Science 9, 458–467.
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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Ecodesign Practices
Raw Material
Selection of non-toxic materials (EP1) Avoid caustic and/or flammable materials Selection of renewable materials Selection of low energy content materials (EP2) Selection of recycled materials (EP3) Avoid variety of material (EP4) Choosing recyclable materials Choosing biodegradable material Choosing materials that require no or less post processing (EP5) Reducing the material requirement
Manufacturing
Using alternative manufacturing techniques (EP6) Reducing production steps (EP7) Less and clean consumables for production Installing protection against hazardous substances (EP10) Clean and low energy consumption for production (EP15)
Packaging & Distribution
Use
Less and reusable packaging (EP11)
Ensuring easier maintenance and repair (EP8)
Stimulating reuse of the entire product
Choosing recyclable packaging
Enhancing durability and reliability (EP9)
Stimulating remanufacturing and refurbishing (EP17)
Choosing lightweight materials for packaging
Reducing energy consumption during usage (EP14)
Stimulating recycling of material (EP18)
Energy efficient mode of transport (EP12) Energy efficient logistics Reducing total volume of the product (EP13)
Choosing a clean source of energy for usage Reducing the amount of consumables during usage
Minimizing production waste (EP16)
Ensuring no wastage of energy during usage
Emphasizing a modular product structure (EP20)
Shared use of the product
Targeting a classic design (EP21) Reducing number of parts or components Ensuring integration of product functions Optimizing the functionality of product (EP22)
Ensuring a strong productuser relation
End of Life
Ensuring closed-loop recycling Ensuring safe incineration with energy recovery (EP19) Ensuring safe disposal of product remain
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Literature review
Identify various ecodesign practices for sustainable product development
Expert opinion
Collect opinion of the experts about each practice using a linguistic scale
Refer Table 3
Phase 1 fuzzy Delphi
Finalize ecodesign practices using fuzzy Delphi Technique Construct the initial relation matrix using expert input
Refer Table 4
Construct average relation matrix (A) by aggregating inputs of all experts Construct the normalized relation matrix (D)
No
If sum of each column < 1
Yes
Use revised DEMATEL Necessary modifications
Feasibility of DEMATEL, then Construct total relation matrix (T)
Determine cause and effect factors
Set the threshold value (α)
Develop a causal diagram for ecodesign practices
Establish inter-relationship among ecodesign practices
Obtain the most significant ecodesign practices for sustainable product development
Phase 2 DEMATEL
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Cause factors
Effect factors
EP1
EP2
EP3
EP7
EP4
EP10
EP5
EP11
EP6
EP12
EP8
EP15
EP9
EP16
EP13
EP17
EP14
EP19
EP18
EP20
EP22
EP21
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Highlights
This study explores various ecodesign practices for sustainable product development.
Ecodesign practices relevant to automotive products are finalized through fuzzy Delphi.
Analyze causal relationships among finalized practices using DEMATEL approach.
Provides noticeable implications to implement ecodesign practices in automotive industries of developing economies.
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Table 1. Ecodesign practices suggested by researchers Life cycle stages
Number of suggested practices
Authors
Focused Area
Fiksel (1996)
Design for environment
All
48
van Hemel and Brezet (1997)
Ecodesign
All
33
Stuart and Sommerville (1998)
Life cycle design for materials
Material selection
10
Lewis and Gertsakis (2001)
Ecodesign
Material, Manufacturing, Usage, End of life
13
Anastas and Zimmerman (2003)
Green engineering
Material, Manufacturing, End of life
12
Wimmer and Züst (2003)
Ecodesign
All
19
McLennan (2004)
Green architecture
Material, Manufacturing, End of life
08
Giudice et al. (2006)
Design for environment
All
28
Luttropp and Lagerstedt (2006)
Ecodesign
Material, Manufacturing, Usage, End of life
10
Telenko et al. (2008)
Design for environment
All
47
Vezzoli and Manzini (2008)
Life cycle design
Material, End of life
32
Belletire et al. (2012)
Ecodesign
All
39
Murray (2013)
Ecodesign
All
15
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Table 2. List of ecodesign practices with short description S. N.
Ecodesign practices
Description
1.
Selection of non-toxic materials
Avoid materials that contain hazardous substances such as asbestos, lead, lithium etc.
2.
Avoid caustic and/or flammable materials
It is important to ensure the safety by avoiding the flammable materials such as ethanol, rubber, acetone etc.
3.
Selection of renewable materials
Avoid the materials which are derived from the sources which are not replenished naturally or require very long time period to replenish. These materials are: minerals, tropical wood, fossil fuel, zinc, platinum, copper and tin
4.
Selection of low energy content materials
Some materials consume very high amount of energy during their extraction and production. Therefore, such materials should be avoided.
5.
Selection of recycled materials
Try to use materials which have already been used in some other products so that the energy consumed in the extraction of these materials is not lost
6.
Avoid variety of material
Using variety of materials in a product does not only hinder the recycling process but also may reduce the productivity because different materials may have different methods of processing
7.
Choosing recyclable materials
Choose such a material that ensures a significant amount of material recovery through recycling. For example; Aluminium
8.
Choosing biodegradable material
If a product has short life span, then it is preferable to use biodegradable materials such as Polyesters and Polyanhydrides
9.
Choosing materials that require no or less post processing
Avoid materials which require post processing such as painting, surface finishing, cleaning etc. because it will result in increased production steps
10.
Reducing the material requirement
Try to reduce the material requirement by optimizing the size of the product. Thus, try to avoid overdimensioning of the product
11.
Using alternative manufacturing techniques
Choose a manufacturing technique which has lesser environmental impact. Also, it should ensure the efficient use of the material
12.
Reducing production steps
Try to reduce the number of steps in production of the products. It should be considered during the selection of materials and manufacturing techniques
13.
Ensuring easier maintenance and repair
Indicate the parts that require frequent inspection Components which are prone to wear and tear should be easy to dismantle or replace
14.
Enhancing durability and reliability
Reliability and durability are the vital characteristics of a product which must be taken care by the designers. Use methods like failure mode and effect analysis
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15.
Less and clean consumables for production
Ensure that less amount of supporting materials (i.e. consumables) is required during production process. If required then it should not contain hazardous substances
16.
Installing protection against release of pollutants and hazardous substances
It is important to ensure that there is no or minimum release of substances such as harmful gases and hazardous liquid or solid consumables
17.
Less and reusable packaging
Lesser the packaging, higher will be the saving of material and energy during transportation. Thus, it reduces the possibility of waste and emission
18.
Choosing recyclable packaging
Choose packaging materials that can be recycled easily and have high percentage of recyclability. For example; plastics and cardboards
19.
Choosing lightweight materials for packaging
Lightweight materials for packaging does not only reduce the volume to be transported but also stimulates easy take back of packaging materials
20.
Energy efficient mode of transport
Transport through ships or trains is most favorable followed by road transport. Air transport causes the highest impact on environment
21.
Energy efficient logistics
Ensure that the transport is loaded efficiently and at the same time distribution logistic is efficient too
22.
Reducing total volume of the product
Lesser volume will ensure more number of products to be transported at a time. Also, it will be easier to maintain and repair
23.
Reducing energy consumption during usage
If a product has a heating component, then it must be well insulated. Each energy intensive product during usage must have a default power down mode
24.
Clean and low energy consumption for production
Choose those energy sources that cause less consumption and less emission. For example; in case of coal, choose coal with less Sulphur content (anthracite)
25.
Minimizing production waste
Optimize the production system in such a way that production wastes can be minimized inside the company
26.
Choosing a clean source of energy for usage
A clean source of energy ensures lesser emission of harmful substances in the environment especially in case of a high energy consuming product
27.
Reducing the amount of consumables during usage
28.
Ensuring no wastage of energy or consumables during usage
Design the product in such a way that it requires only a few consumables for its proper functioning. For example; use permanent filters rather than paper filters in coffee makers Design the product to enhance the user’s ability to use it as per the need to avoid the wastage. Use techniques such as eco-feedback or forced functionality
29.
Shared use of the product
It is found that when a product or service is used or shared by a number of people than the product is used more efficiently
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30.
Stimulating reuse of the entire product
This approach tends to reuse the product either for the same purpose or for some other purpose. The environmental benefits are better if the product retains its original features
31.
Stimulating remanufacturing and refurbishing
Design the product so that working components or subassemblies can be taken out easily to use in other assemblies If required, these components can be cleaned or pained to give them a pleasant look
32.
Stimulating recycling of material
If possible, use same type of material for the entire product If not possible then use materials having compatibility with each other
33.
Ensuring closed-loop recycling
Closed loop recycling ensures no or minimum wastage of material and the recycled material can be used to manufacture the same product again
34.
Ensuring safe incineration with energy recovery
If reuse and recycling is not possible, next option is to incinerate the product or its remain along with energy recovery. Certain metals are also recovered during this process. It is also known as ‘thermal recycling’
35.
Emphasizing a modular product structure
Design the product in different modules so that it can very easily adopt the required changes. For example; additional memory slots in computers
36.
Ensuring safe disposal of product remain
If it is not possible to recycle or recover energy from the product remain then it should be disposed safely. Generally, land filling is a preferred way to achieve it
37.
Targeting a classic design
Design the product for significant aesthetic life at least up to its technical life because people tend to change the product if it goes out of fashion
38.
Reducing number of parts or components
Lesser the number of parts or component, easier will be the assembly and disassembly. It will facilitate easy maintenance and repair
39.
Ensuring a strong product-user relation
Add certain features in the design of a product that develop a strong relation between the product and the user
40.
Ensuring integration of product functions
Integration of various product features in a single product results in the saving of material and space
41.
Optimizing the functionality of product
Sometimes unnecessary features are added to products to achieve lucrative appearance. Try to eliminate these unnecessary features in the design
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Table 3. Linguistic scale for fuzzy Delphi approach Linguistic variable
Rating
Corresponding TFN
Extremely unimportant
1
(0.1, 0.1, 0.3)
Unimportant
2
(0.1, 0.3, 0.5)
Normal
3
(0.3, 0.5, 0.7)
Important
4
(0.5, 0.7, 0.9)
Extremely important
5
(0.7, 0.9, 0.9)
Journal Pre-proof
Table 4. Linguistic scale for DEMATEL approach Linguistic variable
Code
Score
No influence
NI
0
Low influence
LI
1
Medium influence
MI
2
High influence
HI
3
Very high influence
VI
4
Journal Pre-proof
Table 5. Demographic information of the experts
Male
Highest Qualification B. Tech.
Work experience (Years) 21
Product Designer
Male
B. Tech.
18
Expert 3
Product Designer
Male
B. Tech.
16
Expert 4
Product Designer
Male
M. Tech.
14
Expert 5
Product Designer
Male
B. Tech.
14
Expert 6
Product Designer
Male
B. Des.
11
Expert 7
Male
PhD
16
Male
B. Tech.
16
Male
M.S.
12
Expert 10
Environmental Engineer Environmental Engineer Environmental Engineer Environmental Engineer
Female
PhD
11
Expert 11
Production Manager
Male
B. Tech.
20
Expert 12
Production Manager
Male
MBA
13
Expert 13
Professor
Male
PhD
15
Expert 14
Associate Professor
Male
PhD
10
Expert 15
Associate Professor
Female
Post-Doctoral
9
Experts
Position
Gender
Expert 1
Product Designer
Expert 2
Expert 8 Expert 9
Table 6. Finalizing ecodesign practices using fuzzy Delphi S. No.
Ecodesign practices
Fuzzy weight
Defuzzification
Select/Reject
Code
1.
Selection of non-toxic materials
(0.50, 0.79, 0.90)
0.730
Select
EP1
2.
Avoid caustic and/or flammable materials
(0.10, 0.32, 0.70)
0.373
Reject
-
3.
Selection of renewable materials
(0.10, 0.40, 0.70)
0.400
Reject
-
4.
Selection of low energy content materials
(0.30, 0.74, 0.90)
0.647
Select
EP2
5.
Selection of recycled materials
(0.50, 0.81, 0.90)
0.737
Select
EP3
6.
Avoid variety of material
(0.30, 0.68, 0.90)
0.627
Select
EP4
7.
Choosing recyclable materials
(0.10, 0.54, 0.90)
0.513
Reject
-
8.
Choosing biodegradable material
(0.10, 0.32, 0.70)
0.373
Reject
-
9.
Choosing materials that require no or less post processing
(0.30, 0.74, 0.90)
0.647
Select
EP5
10.
Reducing the material requirement
(0.10, 0.59, 0.90)
0.530
Reject
-
11.
Using alternative manufacturing techniques
(0.50, 0.86, 0.90)
0.753
Select
EP6
12.
Reducing production steps
(0.30, 0.78, 0.90)
0.660
Select
EP7
13.
Ensuring easier maintenance and repair
(0.50, 0.81, 0.90)
0.737
Select
EP8
14.
Enhancing durability and reliability
(0.50, 0.83, 0.90)
0.743
Select
EP9
15.
Less and clean consumables for production
(0.10, 0.60, 0.90)
0.533
Reject
-
16.
Installing protection against release of pollutants and hazardous substances
(0.30, 0.66, 0.90)
0.620
Select
EP10
17.
Less and reusable packaging
(0.30, 0.68, 0.90)
0.627
Select
EP11
18.
Choosing recyclable packaging
(0.10, 0.47, 0.70)
0.423
Reject
-
19.
Choosing lightweight materials for packaging
(0.10, 0.35, 0.70)
0.383
Reject
-
20.
Energy efficient mode of transport
(0.30, 0.64, 0.90)
0.613
Select
EP12
21.
Energy efficient logistics
(0.10, 0.41, 0.70)
0.403
Reject
-
22.
Reducing total volume of the product
(0.30, 0.66, 0.90)
0.620
Select
EP13
23.
Reducing energy consumption during usage
(0.30, 0.74, 0.90)
0.647
Select
EP14
24.
Clean and low energy consumption for production
(0.50, 0.79, 0.90)
0.730
Select
EP15
25.
Minimizing production waste
(0.30, 0.78, 0.90)
0.660
Select
EP16
26.
Choosing a clean source of energy for usage
(0.10, 0.63, 0.90)
0.543
Reject
-
27.
Reducing the amount of consumables during usage
(0.10, 0.46, 0.70)
0.420
Reject
-
28.
Ensuring no wastage of energy or consumables during usage
(0.10, 0.60, 0.90)
0.533
Reject
-
29.
Shared use of the product
(0.10, 0.35, 0.70)
0.383
Reject
-
30.
Stimulating reuse of the entire product
(0.10, 0.63, 0.90)
0.543
Reject
-
31.
Stimulating remanufacturing and refurbishing
(0.30, 0.82, 0.90)
0.673
Select
EP17
32.
Stimulating recycling of material
(0.50, 0.83, 0.90)
0.743
Select
EP18
33.
Ensuring closed-loop recycling
(0.10, 0.47, 0.70)
0.423
Reject
-
34.
Ensuring safe incineration with energy recovery
(0.30, 0.74, 0.90)
0.647
Select
EP19
35.
Emphasizing a modular product structure
(0.30, 0.82, 0.90)
0.673
Select
EP20
36.
Ensuring safe disposal of product remain
(0.10, 0.59, 0.90)
0.530
Reject
-
37.
Targeting a classic design
(0.50, 0.79, 0.90)
0.730
Select
EP21
38.
Reducing number of parts or components
(0.10, 0.60, 0.90)
0.533
Reject
-
39.
Ensuring a strong product-user relation
(0.10, 0.35, 0.70)
0.383
Reject
-
40.
Ensuring integration of product functions
(0.10, 0.54, 0.90)
0.513
Reject
-
41.
Optimizing the functionality of product
(0.30, 0.64, 0.90)
0.613
Select
EP22
Table 7. Initial relation matrix using linguistic input of expert 1 EP1
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
EP11
EP12
EP13
EP14
EP15
EP16
EP17
EP18
EP19
EP20
EP21
EP22
EP1
NI
LI
NI
LI
MI
LI
MI
LI
MI
HI
NI
NI
NI
LI
HI
MI
MI
MI
HI
NI
LI
NI
EP2
NI
NI
LI
LI
MI
LI
MI
MI
LI
LI
NI
LI
NI
MI
VI
MI
MI
HI
LI
NI
LI
MI
EP3
LI
LI
NI
NI
LI
MI
HI
MI
LI
LI
NI
NI
NI
LI
HI
HI
MI
MI
MI
NI
LI
NI
EP4
LI
NI
LI
NI
MI
HI
MI
HI
LI
LI
NI
NI
LI
MI
MI
MI
LI
VI
LI
MI
MI
MI
EP5
LI
NI
MI
NI
NI
MI
VI
MI
LI
MI
LI
NI
LI
LI
VI
MI
MI
LI
NI
LI
MI
MI
EP6
LI
MI
LI
HI
MI
NI
HI
MI
MI
MI
LI
NI
MI
MI
VI
HI
MI
LI
NI
LI
MI
MI
EP7
LI
MI
HI
LI
VI
HI
NI
NI
LI
NI
NI
NI
LI
NI
VI
LI
LI
NI
NI
LI
LI
MI
EP8
MI
LI
LI
HI
LI
MI
LI
NI
VI
NI
NI
LI
NI
MI
LI
LI
MI
MI
MI
HI
LI
MI
EP9
MI
MI
MI
MI
MI
LI
LI
HI
NI
MI
NI
NI
LI
LI
MI
MI
HI
MI
MI
MI
HI
HI
EP10
HI
MI
LI
MI
MI
MI
LI
LI
MI
NI
NI
NI
LI
LI
MI
LI
NI
LI
HI
NI
NI
LI
EP11
LI
NI
LI
LI
NI
MI
NI
NI
LI
NI
NI
MI
HI
LI
NI
NI
LI
LI
NI
NI
LI
LI
EP12
NI
LI
NI
NI
LI
LI
NI
LI
NI
LI
LI
NI
MI
LI
LI
NI
NI
MI
LI
NI
NI
NI
EP13
LI
LI
LI
NI
LI
LI
NI
LI
LI
LI
HI
MI
NI
LI
MI
MI
MI
MI
NI
LI
MI
MI
EP14
LI
MI
LI
MI
NI
MI
LI
MI
MI
NI
MI
LI
MI
NI
LI
LI
NI
LI
NI
LI
MI
MI
EP15
HI
HI
HI
LI
VI
HI
VI
NI
LI
MI
LI
MI
LI
MI
NI
MI
MI
LI
NI
LI
NI
NI
EP16
LI
LI
HI
MI
MI
VI
NI
LI
MI
MI
NI
LI
MI
LI
MI
NI
MI
MI
LI
MI
LI
LI
EP17
MI
MI
HI
MI
LI
HI
LI
LI
HI
NI
LI
NI
MI
LI
MI
MI
NI
LI
NI
LI
MI
MI
EP18
LI
HI
HI
VI
MI
NI
LI
MI
LI
LI
NI
LI
LI
LI
NI
HI
MI
NI
LI
LI
MI
MI
EP19
HI
NI
LI
NI
LI
LI
NI
MI
MI
HI
LI
MI
LI
NI
LI
MI
LI
MI
NI
NI
LI
NI
EP20
LI
LI
LI
MI
NI
LI
LI
VI
MI
LI
LI
NI
NI
MI
LI
MI
LI
LI
LI
NI
MI
MI
EP21
MI
LI
NI
LI
MI
MI
NI
MI
VI
NI
NI
NI
MI
LI
NI
LI
MI
MI
LI
MI
NI
HI
EP22
NI
MI
LI
MI
MI
LI
MI
LI
HI
LI
LI
LI
LI
MI
NI
NI
LI
MI
NI
MI
MI
NI
NI – No Influence, LI – Low Influence, MI – Medium Influence, HI – High Influence, VI – Very-High Influence
Table 8. Average relation matrix for ecodesign practices EP1
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
EP11
EP12
EP13
EP14
EP15
EP16
EP17
EP18
EP19
EP20
EP21
EP22
Sum
EP1
0
0.8
1.07
0.867
0.733
0.933
0.667
1.13
1.07
2.87
0.733
0.667
0.467
0.533
1.26
0.933
0.867
1.07
1.53
0.467
0.867
0.667
20.201
EP2
0.733
0
0.867
0.933
1.13
1.07
0.867
2.93
0.667
1.07
0.467
0.4
0.4
1.07
0.933
1.07
1.4
1.2
1.07
0.4
0.467
0.333
19.477
EP3
1.2
0.733
0
0.933
1.07
0.8
1.26
1.13
1.6
1.07
0.333
0.267
0.333
0.467
0.867
0.4
1.07
3.2
0.867
0.4
0.533
0.333
18.866
EP4
0.933
0.733
1.07
0
0.467
0.933
1.73
1.13
0.933
0.533
0.4
0.267
0.533
0.4
0.933
1.07
1.53
3.07
0.933
0.867
0.933
1.07
20.468
EP5
0.933
1.2
1.07
0.667
0
1.13
3.2
2.87
1.2
1.07
0.667
0.333
0.533
0.333
0.933
0.867
1.4
0.933
0.467
0.4
0.867
0.4
21.473
EP6
0.733
0.867
0.667
0.867
1.07
0
2.8
2.87
3
1.73
0.733
0.4
0.933
0.733
1.2
0.933
2.8
0.867
0.733
1.4
1.07
0.933
27.340
EP7
0.667
0.467
1.2
1.6
2.8
2.8
0
2.2
0.933
1.13
0.267
0.333
0.267
0.267
0.333
0.4
0.867
0.4
0.333
0.467
0.533
0.4
18.664
EP8
1.07
3.2
0.933
1.07
3.2
2.8
3.2
0
0.933
2.2
0.267
0.267
0.333
0.267
0.333
0.267
1.07
0.867
0.333
0.267
0.333
0.333
23.543
EP9
0.933
1.2
0.933
1.07
0.933
3.2
1.07
0.867
0
1.6
0.333
0.267
0.933
0.333
0.4
0.267
0.467
0.333
0.267
0.467
0.267
0.267
16.407
EP10
2.8
1.07
0.933
0.533
0.667
1.07
1.13
1.07
1.87
0
0.267
0.333
0.267
0.4
0.267
0.333
0.467
0.4
0.333
0.267
0.333
0.267
15.077
EP11
0.667
0.4
0.4
0.333
0.667
0.533
0.333
0.467
0.267
0.333
0
0.267
1.6
0.533
0.333
0.267
0.333
0.933
0.333
0.267
0.333
0.667
10.266
EP12
0.267
0.4
0.333
0.333
0.267
0.333
0.467
0.333
0.267
0.267
0.333
0
0.533
0.267
0.333
0.267
0.267
0.867
0.733
0.333
0.267
0.333
7.800
EP13
0.333
0.333
0.4
0.533
0.467
0.867
0.467
0.333
1.13
0.333
1.87
0.933
0
1.07
0.933
1.26
1.07
0.733
0.667
0.933
1.07
1.2
16.935
EP14
0.4
0.933
0.333
0.267
0.533
1.07
0.467
0.333
0.333
0.933
0.667
0.533
1.2
0
0.867
0.333
0.4
0.333
0.267
0.333
0.667
0.733
11.935
EP15
1.07
0.467
0.867
0.533
0.467
1.07
0.533
0.467
0.867
0.667
0.333
0.267
0.733
0.867
0
0.4
0.333
0.4
0.333
0.267
1.07
0.333
12.344
EP16
0.867
0.933
0.667
0.667
0.933
0.533
0.333
0.333
0.4
1.13
0.333
0.267
1.26
0.267
2.8
0
0.933
0.333
0.267
0.333
0.867
0.267
14.723
EP17
0.933
1.6
0.667
1.2
1.6
1.6
0.667
0.867
1.07
0.867
0.533
0.333
0.4
0.533
1.07
1.07
0
0.4
0.333
0.867
0.4
0.333
17.343
EP18
1.6
1.6
3.2
3.2
0.867
0.667
0.333
0.4
0.4
0.867
0.533
0.533
0.733
0.333
0.533
0.733
0.333
0
0.267
0.4
0.333
0.4
18.265
EP19
1.2
0.867
0.667
0.733
0.467
0.533
0.333
0.333
0.533
1.07
0.333
0.333
0.733
0.267
0.267
0.333
0.333
0.267
0
0.333
0.4
0.267
10.602
EP20
0.4
0.4
0.333
0.467
0.667
1.2
0.667
0.333
0.467
0.333
0.4
0.333
0.933
0.533
1.07
2.8
0.933
0.733
0.267
0
0.933
0.867
15.069
EP21
0.333
0.4
0.333
0.867
0.867
1.13
0.467
0.4
0.333
0.4
0.533
0.533
0.533
0.533
1.2
0.933
0.733
0.533
0.4
0.867
0
0.933
13.261
EP22
0.467
0.333
0.4
1.13
0.467
0.867
0.333
0.333
0.4
0.267
0.933
0.333
1.6
0.933
1.07
1.07
0.4
0.533
0.267
0.533
0.867
0
13.536
Sum
18.539
18.936
17.343
18.803
20.339
25.139
21.324
21.129
18.673
20.74
11.268
8.199
15.257
10.939
17.935
16.006
18.006
18.405
11
10.868
13.41
11.336
Table 9. Normalized relation matrix for ecodesign practices EP1
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
EP11
EP12
EP13
EP14
EP15
EP16
EP17
EP18
EP19
EP20
EP21
EP22
EP1
0.0000
0.0293
0.0392
0.0317
0.0268
0.0341
0.0244
0.0414
0.0392
0.1050
0.0268
0.0244
0.0171
0.0195
0.0461
0.0341
0.0317
0.0392
0.0560
0.0171
0.0317
0.0244
EP2
0.0268
0.0000
0.0317
0.0341
0.0414
0.0392
0.0317
0.1072
0.0244
0.0392
0.0171
0.0146
0.0146
0.0392
0.0341
0.0392
0.0512
0.0439
0.0392
0.0146
0.0171
0.0122
EP3
0.0439
0.0268
0.0000
0.0341
0.0392
0.0293
0.0461
0.0414
0.0586
0.0392
0.0122
0.0098
0.0122
0.0171
0.0317
0.0146
0.0392
0.1171
0.0317
0.0146
0.0195
0.0122
EP4
0.0341
0.0268
0.0392
0.0000
0.0171
0.0341
0.0633
0.0414
0.0341
0.0195
0.0146
0.0098
0.0195
0.0146
0.0341
0.0392
0.0560
0.1124
0.0341
0.0317
0.0341
0.0392
EP5
0.0341
0.0439
0.0392
0.0244
0.0000
0.0414
0.1171
0.1050
0.0439
0.0392
0.0244
0.0122
0.0195
0.0122
0.0341
0.0317
0.0512
0.0341
0.0171
0.0146
0.0317
0.0146
EP6
0.0268
0.0317
0.0244
0.0317
0.0392
0.0000
0.1025
0.1050
0.1098
0.0633
0.0268
0.0146
0.0341
0.0268
0.0439
0.0341
0.1025
0.0317
0.0268
0.0512
0.0392
0.0341
EP7
0.0244
0.0171
0.0439
0.0586
0.1025
0.1025
0.0000
0.0805
0.0341
0.0414
0.0098
0.0122
0.0098
0.0098
0.0122
0.0146
0.0317
0.0146
0.0122
0.0171
0.0195
0.0146
EP8
0.0392
0.1171
0.0341
0.0392
0.1171
0.1025
0.1171
0.0000
0.0341
0.0805
0.0098
0.0098
0.0122
0.0098
0.0122
0.0098
0.0392
0.0317
0.0122
0.0098
0.0122
0.0122
EP9
0.0341
0.0439
0.0341
0.0392
0.0341
0.1171
0.0392
0.0317
0.0000
0.0586
0.0122
0.0098
0.0341
0.0122
0.0146
0.0098
0.0171
0.0122
0.0098
0.0171
0.0098
0.0098
EP10
0.1025
0.0392
0.0341
0.0195
0.0244
0.0392
0.0414
0.0392
0.0684
0.0000
0.0098
0.0122
0.0098
0.0146
0.0098
0.0122
0.0171
0.0146
0.0122
0.0098
0.0122
0.0098
EP11
0.0244
0.0146
0.0146
0.0122
0.0244
0.0195
0.0122
0.0171
0.0098
0.0122
0.0000
0.0098
0.0586
0.0195
0.0122
0.0098
0.0122
0.0341
0.0122
0.0098
0.0122
0.0244
EP12
0.0098
0.0146
0.0122
0.0122
0.0098
0.0122
0.0171
0.0122
0.0098
0.0098
0.0122
0.0000
0.0195
0.0098
0.0122
0.0098
0.0098
0.0317
0.0268
0.0122
0.0098
0.0122
EP13
0.0122
0.0122
0.0146
0.0195
0.0171
0.0317
0.0171
0.0122
0.0414
0.0122
0.0684
0.0341
0.0000
0.0392
0.0341
0.0461
0.0392
0.0268
0.0244
0.0341
0.0392
0.0439
EP14
0.0146
0.0341
0.0122
0.0098
0.0195
0.0392
0.0171
0.0122
0.0122
0.0341
0.0244
0.0195
0.0439
0.0000
0.0317
0.0122
0.0146
0.0122
0.0098
0.0122
0.0244
0.0268
EP15
0.0392
0.0171
0.0317
0.0195
0.0171
0.0392
0.0195
0.0171
0.0317
0.0244
0.0122
0.0098
0.0268
0.0317
0.0000
0.0146
0.0122
0.0146
0.0122
0.0098
0.0392
0.0122
EP16
0.0317
0.0341
0.0244
0.0244
0.0341
0.0195
0.0122
0.0122
0.0146
0.0414
0.0122
0.0098
0.0461
0.0098
0.1025
0.0000
0.0341
0.0122
0.0098
0.0122
0.0317
0.0098
EP17
0.0341
0.0586
0.0244
0.0439
0.0586
0.0586
0.0244
0.0317
0.0392
0.0317
0.0195
0.0122
0.0146
0.0195
0.0392
0.0392
0.0000
0.0146
0.0122
0.0317
0.0146
0.0122
EP18
0.0586
0.0586
0.1171
0.1171
0.0317
0.0244
0.0122
0.0146
0.0146
0.0317
0.0195
0.0195
0.0268
0.0122
0.0195
0.0268
0.0122
0.0000
0.0098
0.0146
0.0122
0.0146
EP19
0.0439
0.0317
0.0244
0.0268
0.0171
0.0195
0.0122
0.0122
0.0195
0.0392
0.0122
0.0122
0.0268
0.0098
0.0098
0.0122
0.0122
0.0098
0.0000
0.0122
0.0146
0.0098
EP20
0.0146
0.0146
0.0122
0.0171
0.0244
0.0439
0.0244
0.0122
0.0171
0.0122
0.0146
0.0122
0.0341
0.0195
0.0392
0.1025
0.0341
0.0268
0.0098
0.0000
0.0341
0.0317
EP21
0.0122
0.0146
0.0122
0.0317
0.0317
0.0414
0.0171
0.0146
0.0122
0.0146
0.0195
0.0195
0.0195
0.0195
0.0439
0.0341
0.0268
0.0195
0.0146
0.0317
0.0000
0.0341
EP22
0.0171
0.0122
0.0146
0.0414
0.0171
0.0317
0.0122
0.0122
0.0146
0.0098
0.0341
0.0122
0.0586
0.0341
0.0392
0.0392
0.0146
0.0195
0.0098
0.0195
0.0317
0.0000
Sum
0.6785
0.6931
0.6348
0.6882
0.7444
0.9201
0.7805
0.7733
0.6834
0.7591
0.4124
0.3001
0.5584
0.4004
0.6564
0.5858
0.6590
0.6736
0.4026
0.3978
0.4908
0.4149
Table 10. Total relation matrix for ecodesign practices EP1
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
EP11
EP12
EP13
EP14
EP15
EP16
EP17
EP18
EP19
EP20
EP21
EP22
EP1
0.0723
0.0984
0.1015
0.0971
0.0998
0.1224
0.1046
0.1193
0.1098
0.1763
0.0619
0.051
0.064
0.0553
0.1023
0.0832
0.0947
0.1006
0.092
0.0539
0.0751
0.0601
EP2
0.0963
0.0778
0.0967
0.1026
0.123
0.1337
0.1208
0.1864
0.0967
0.1196
0.0534
0.042
0.0618
0.0743
0.0931
0.0894
0.1178
0.1072
0.0765
0.0526
0.0623
0.0493
EP3
0.1118
0.098
0.0706
0.1068
0.1136
0.1197
0.1253
0.1206
0.1254
0.1156
0.0477
0.037
0.0578
0.0517
0.0872
0.065
0.1018
0.1753
0.0685
0.0515
0.0624
0.048
EP4
0.1039
0.1
0.1101
0.0778
0.0978
0.1277
0.1425
0.1224
0.1047
0.0988
0.0529
0.0387
0.0692
0.0523
0.096
0.0937
0.1215
0.1741
0.0723
0.0708
0.0801
0.0767
EP5
0.1102
0.1268
0.1118
0.1036
0.0985
0.1534
0.2116
0.1994
0.125
0.1299
0.0642
0.0428
0.0705
0.0529
0.0988
0.0881
0.1269
0.105
0.06
0.0577
0.0813
0.0559
EP6
0.1202
0.1329
0.1115
0.1258
0.1528
0.1397
0.2159
0.2151
0.2035
0.1698
0.0765
0.0521
0.0983
0.0764
0.1234
0.1061
0.1896
0.1148
0.077
0.1027
0.0998
0.0842
EP7
0.0952
0.0952
0.109
0.1264
0.1825
0.1971
0.1028
0.1734
0.1148
0.1248
0.048
0.04
0.0578
0.0471
0.0742
0.0693
0.1081
0.0852
0.0528
0.0587
0.0675
0.054
EP8
0.1275
0.2035
0.1182
0.1273
0.2176
0.2215
0.2312
0.128
0.1332
0.1812
0.0568
0.0449
0.0695
0.0575
0.0876
0.0776
0.1324
0.1148
0.0639
0.0605
0.0709
0.0596
EP9
0.095
0.1057
0.0898
0.098
0.1038
0.1953
0.1205
0.1141
0.0727
0.1291
0.0465
0.0349
0.0751
0.0463
0.0684
0.0582
0.843
0.072
0.0463
0.0541
0.0523
0.0452
EP10
0.1527
0.0958
0.0854
0.0742
0.0878
0.1162
0.1094
0.1088
0.1256
0.0704
0.0395
0.0348
0.0471
0.0439
0.0579
0.0537
0.0724
0.0675
0.0464
0.0408
0.0487
0.0399
EP11
0.0574
0.0494
0.0476
0.0476
0.0612
0.0642
0.0531
0.0573
0.046
0.0506
0.0222
0.0253
0.0826
0.0395
0.0436
0.0383
0.0464
0.0662
0.0324
0.0302
0.0367
0.0449
EP12
0.0358
0.041
0.0378
0.0395
0.0384
0.0461
0.0468
0.0424
0.0362
0.0386
0.027
0.0111
0.0382
0.0242
0.0349
0.0305
0.0348
0.0557
0.0412
0.0269
0.0274
0.0269
EP13
0.062
0.0636
0.0613
0.0702
0.0725
0.0995
0.0753
0.0701
0.0917
0.0682
0.0966
0.0552
0.0428
0.068
0.0825
0.0871
0.0877
0.0738
0.0526
0.0637
0.0746
0.0735
EP14
0.0533
0.0719
0.0477
0.048
0.0621
0.09
0.0642
0.0607
0.0549
0.0765
0.0478
0.0362
0.0721
0.0236
0.0667
0.0441
0.0543
0.0486
0.0327
0.0356
0.0519
0.0496
EP15
0.0796
0.0595
0.0697
0.0607
0.0636
0.0953
0.0707
0.0683
0.0766
0.0733
0.0365
0.0278
0.0567
0.0546
0.0386
0.0475
0.0549
0.0553
0.0372
0.0348
0.0674
0.037
EP16
0.0802
0.0809
0.0687
0.0702
0.0847
0.0831
0.0694
0.0701
0.0665
0.0939
0.0402
0.0303
0.0789
0.0386
0.143
0.0385
0.0803
0.0579
0.0382
0.0399
0.0659
0.0375
EP17
0.0939
0.1199
0.0809
0.102
0.1255
0.1397
0.1033
0.1108
0.1044
0.1023
0.0526
0.0368
0.058
0.0532
0.0942
0.0871
0.064
0.0738
0.048
0.0661
0.0571
0.0465
EP18
0.1216
0.1203
0.1741
0.1761
0.0993
0.1052
0.0914
0.0941
0.0834
0.1038
0.0542
0.0453
0.0698
0.0475
0.0778
0.077
0.0782
0.0767
0.051
0.0511
0.0565
0.0515
EP19
0.08
0.0677
0.0579
0.0617
0.0564
0.0674
0.0562
0.0573
0.0586
0.0808
0.033
0.0276
0.0516
0.0303
0.0426
0.0412
0.0487
0.0463
0.0227
0.033
0.0394
0.0307
EP20
0.0623
0.0639
0.0576
0.0659
0.0781
0.1067
0.081
0.0695
0.068
0.0671
0.0433
0.0328
0.0719
0.0472
0.0898
0.1393
0.0831
0.0703
0.0371
0.0293
0.0694
0.0595
EP21
0.0548
0.0583
0.0526
0.074
0.0785
0.0977
0.0702
0.067
0.0584
0.0626
0.0444
0.0373
0.0528
0.0443
0.0843
0.0699
0.0706
0.0607
0.0391
0.0569
0.0319
0.059
EP22
0.0583
0.0546
0.0541
0.0823
0.0629
0.0872
0.0627
0.0615
0.0595
0.0569
0.0604
0.0313
0.0909
0.059
0.0803
0.0743
0.0583
0.0616
0.035
0.0455
0.0631
0.0274
Journal Pre-proof
Table 11. Calculation to find cause and effect factors r
c
r+c
Importance
r-c
Cause/Effect
EP1
1.9956
1.9243
3.9199
9
0.0713
Cause
EP2
2.0333
1.9851
4.0184
8
0.0482
Cause
EP3
1.9613
1.8146
3.7759
11
0.1467
Cause
EP4
2.084
1.9378
4.0218
7
0.1462
Cause
EP5
2.2743
2.1604
4.4347
5
0.1139
Cause
EP6
2.7881
2.6088
5.3969
1
0.1793
Cause
EP7
2.0839
2.3289
4.4128
6
-0.245
Effect
EP8
2.5852
2.3166
4.9018
2
0.2686
Cause
EP9
2.5663
2.0156
4.5819
3
0.5507
Cause
EP10
1.6189
2.1901
3.809
10
-0.5712
Effect
EP11
1.0427
1.1056
2.1483
21
-0.0629
Effect
EP12
0.7814
0.8152
1.5966
22
-0.0338
Effect
EP13
1.5925
1.4374
3.0299
14
0.1551
Cause
EP14
1.1925
1.0877
2.2802
19
0.1048
Cause
EP15
1.2656
1.7672
3.0328
13
-0.5016
Effect
EP16
1.4569
1.5591
3.016
15
-0.1022
Effect
EP17
1.8201
2.6695
4.4896
4
-0.8494
Effect
EP18
1.9059
1.8634
3.7693
12
0.0425
Cause
EP19
1.0911
1.1229
2.214
20
-0.0318
Effect
EP20
1.4931
1.1163
2.6094
17
0.3768
Cause
EP21
1.3253
1.3417
2.667
16
-0.0164
Effect
EP22
1.3271
1.1169
2.444
18
0.2102
Cause
Table 12. Interaction matrix for ecodesign practices EP1 EP1 EP2 EP3 EP4 EP5 EP6 EP7 EP8 EP9 EP10
* * * * * * * * *
EP2
EP3
EP4
EP5
EP6
EP7
EP8
EP9
EP10
*
* *
* * *
* * * * * * * * * *
* * * * * * * * * *
* * * * * * * * * *
* * * * * * * * * *
* * * * * * * *
* * * * * * * * *
* * * * * * * *
* * * * * * *
* * * * *
EP11
EP12
EP13
*
EP14
EP15
EP16
EP17
EP18
EP19
* * * * * *
* *
* * * * * * * *
*
* * *
* * * * * * * * *
*
*
*
* *
EP11 EP12 EP13 EP14 EP15 EP16 EP17 EP18 EP19
* * * * *
* * *
* *
* *
EP20 EP21 EP22
*
* * *
* * * * * * * * *
*
* *
* *
* *
*
*
* * * *
* *
*
Here * shows the interrelationship between ecodesign practices
*
* * *
* *
*
*
EP20
EP21
EP22
*
* * *
*