A framework for sustainable product design: a hybrid fuzzy approach based on Quality Function Deployment for Environment

Accepted Manuscript A Framework for Sustainable Product Design: A Hybrid Fuzzy approach Based on Quality Function Deployment for Environment Mojdeh Younesi, Emad Roghanian PII:

S0959-6526(15)01249-4

DOI:

10.1016/j.jclepro.2015.09.028

Reference:

JCLP 6116

To appear in:

Journal of Cleaner Production

Received Date: 7 December 2014 Revised Date:

8 July 2015

Accepted Date: 8 September 2015

Please cite this article as: Younesi M, Roghanian E, A Framework for Sustainable Product Design: A Hybrid Fuzzy approach Based on Quality Function Deployment for Environment, Journal of Cleaner Production (2015), doi: 10.1016/j.jclepro.2015.09.028. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT A Framework for Sustainable Product Design: A Hybrid Fuzzy approach Based on Quality Function Deployment for Environment Mojdeh Younesi *, Emad Roghanian Department of Industrial Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran ABSTRACT

1. INTRODUCTION

TE D

M AN U

Keywords: Sustainable product design Quality function deployment for environment Fuzzy DEMATEL Maximum mean de-entropy algorithm Fuzzy logarithmic least squares method Fuzzy analytic network process

The modern manufacturing organizations are focused on making sustainable products by means of costs reduction and prevention of environmental problems. To achieve this, several tools are available to the organizations such as Quality Function Deployment for Environment (QFDE) which is the integration of voice of customer, voice of environment and quality characteristics. In this paper, an integrated QFDE, fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy Analytic Network Process (FANP) is proposed for sustainable product design to help companies identify the best design criteria for a specific product. Therefore, the DEMATEL method is used to assume the interdependence of customer attributes and, in addition, fuzzy Maximum Mean deEntropy (MMDE) algorithm is utilized to choose the best and practical threshold value in DEMATEL process. Moreover, FANP will be integrated into QFDE as a prioritization technique and also fuzzy Logarithmic Least Squares Method (LLSM) is employed to find weights during the FANP process. An integrated QFDE methodology is appropriate to use in early design, since it does not require detailed information about the product. In order to examine the practicality of the proposed model, a case study is carried out in Iran Transfo Corporation which attracts a significant interest due to its undeniable impacts on the environment.

RI PT

Article history: Received Received in revised form Accepted Available online

SC

ARTICLE INFO

AC C

EP

Product design had various logics and principles during the time. In other words, many different concepts in product design paradigm are traceable. For the first time, in 1986, the term “sustainability” is used by the World Commission on the Environment and Development (WCED) as the capability to meet the current needs without compromising the ability of future generations to meet their own needs (Umeda et al., 2012). Nowadays, sustainable product design has received notable attentions from around the world. According to the most recent Green Brand Survey of 9000 consumers in the US, Australia, China, Brazil, France, Germany, India, and UK more than 50% of consumers have environmental concerns and the need for adding environmental requirements into the design of products is concerned as an essential issue (Chen et al., 2012). The design of sustainable products leads engineers not only to consider environmental objectives, but also to add cost, quality and social aspects at the early design phases (Fargnoli and Kimura, 2006). Companies that pay attention to the quality and customer needs can continue to survive in this competitive business world. To achieve this, several tools are available to the organizations such as Quality Function Deployment (QFD), Life Cycle Assessment (LCA) and Theory of Inventive Problem Solving (TRIZ) (Vinodh et al., 2014). QFD is one of the most important management tools which are useful for the design and product development that aims to translate consumers’ requirements into the design targets and major quality assurance points to be used throughout the production phase (Vinodh and Rathod, 2010). Quality Function Deployment for Environment (QFDE), which is derived from traditional QFD, is one of the significant tools developed by Masui et al. (2003). It considers economic, social and environmental aspects with other product design requirements. The QFDE method will be utilized in order to contemplate several user requirements to produce

ACCEPTED MANUSCRIPT

RI PT

an environmental product to fit them in an early design stage. The purpose of this method is to identify and prioritize criteria for customer satisfaction and also to help engineers who are unfamiliar with environmental issues (Bereketli and Genevois, 2013). In addition, to solve the interactions among criteria, the Analytic Network Process (ANP), as a Multi Criteria Decision Making (MCDM) method, was proposed by Saaty (1996). The ANP is a mathematical theory that can deal with all kinds of dependences systematically. Furthermore, Decision Making Trial and Error Laboratory (DEMATEL) is a tool which can establish cause and effect relationship between customer needs and the product requirements to make an intelligible structural model of the system (Jassbi et al., 2011). Although these methods have been used individually by several researchers for specific purposes, a few of these studies are integrated to fill the mentioned gaps and, also, had environmental concerns.

M AN U

SC

As a consequence, the novelty of the proposed approach in this paper is that it integrates QFDE, fuzzy DEMATEL and Fuzzy Analytic Network Process (FANP) for sustainable product design to help companies identify the best design for a specific product. The drawback of QFDE is omitting the correlation between Customer Attributes (CAs) and Technical Requirements (TRs), as a roof, in the house of quality (HoQ) matrix. Hence, in this paper the DEMATEL method is used to fill this gap and the FANP is integrated into QFDE as a prioritization technique because of the importance of finding the realistic TRs weights. In this article, various papers are used in order to provide a comprehensive literature review of sustainable product design and QFDE. Then, the experts were asked to answer the questionnaires about transformer design and also Excel, GAMS and Matlab software have been used during this research.

TE D

Since environmentally friendly product design is one of the most important factors for survival in today's competitive environment, designing and creating products based on environmental criteria with the aim of combining economic, quality and environmental aspects is critical. Accordingly, to examine the practicality of the proposed model, a case study is carried out in the Iran Transfo Corporation in Iran and a cast resin dry-type distribution transformer is selected due to ease of design in electronic industries which is produced based on customer’s demand. A transformer is an electrical device that transfers energy between two or more circuits through electromagnetic induction. The cast resin dry-type distribution transformer is a kind of distribution transformers which is safe, ecological and environmentally friendly and also has no fire hazard. From the other point of view, it has disadvantages such as high load losses and price so it is important to analyze this product to improve its environmental performance.

AC C

EP

The research questions can be summarized as follows: How to use a fuzzy approach in QFDE to choose CAs and TRs based on environmental concerns? How to determine the interdependences in CAs? How to avoid subjective judgments and uncertainty in DEMATEL method? What are the priorities in CAs and TRs to design an environmentally friendly transformer? Hence, a model is designed to answer the aforementioned questions which its main contributions can be highlighted as considering the issue of sustainable product design using QFDE in a cast resin dry-type distribution transformer which Propose a hybrid model in sustainable product design by considering DEMATEL (to evaluate the interdependences in CAs), MMDE (Maximum Mean deEntropy) to find the best threshold value and LLSM (Logarithmic Least Squares Method) in FANP analysis. For this purpose, sustainable product design is essential for companies to survive in today’s environment. Companies may choose to simply obey the basic requirements of governments, or they may aim to achieve a higher standard as a form of social responsibility. The proposed framework is applied to solve the sustainable design problem in a transformer corporation to show the practicality of the framework. The paper is then organized as follows: in the next section, the review of literature and the research gap are presented. In section 3, the research framework and method are developed in order to conceptualize design processes. The test results are discussed in section 4. Managerial implications and conclusion are stated in sections 5 and 6, respectively. 2. Review of literature The basis of current QFD matrix originally referred to the same tables which were used at shipyard industry by Professor Yoji Akao in 1970. The first step in the four-phase QFD is quality deployment which is inspired by HoQ. HoQ’s rows represent the customer expectations and columns show the product components (Puglieri et al., 2011). In fact, it is an endpoint for many of the actual QFD project. Over the years, a number of practices

ACCEPTED MANUSCRIPT

RI PT

have considered the improvement in environmental aspects of products based on QFD. In this way, researchers combine environmental requirements with QFD method to assess, improve and develop criteria (Masui et al., 2003; Fargnoli and Kimura, 2006; Bereketli and Genevois, 2013). Cristofari et al. (1996) is developed GreenQFD which integrates LCA and QFD and considers two phases. In phase I, there are three houses: the Quality House (QH) that considers the QFD methodology, the Cost House (CH) which includes Life Cycle Cost (LCC) and the Green House (GH) to assume LCA methodology. In phase II, the MCDM technique is applied to find the best conceptual design which integrates all those criteria (Bovea and Wang, 2005). Davidsson (1998) has stated the Environmental-QFD (E-QFD) which is consisted of three main parts. In the phase I and II, he has tried to obtain information about environmental impacts of products and acquire stakeholder expectations in the house of quality throughout the product life cycle. In the phase III, he has evaluated the TRs and their relations to stakeholder-weighted environmental expectations. Environmentally Conscious Quality Function Deployment (ECQFD) is introduced by Vinodh and Rathod (2010) and consisted of environmental Voice of Customer (VOC) and the Environmental Matrix (EM) includes the TRs which are valued obtaining the final score for each item. Then, LCA is applied to assess and calculate the impact of the product and the process.

EP

TE D

M AN U

SC

Integrated approach for sustainable product design is based on three parts: customer need, environmental performance and economic. QFDE includes these considerations to simultaneously handle environmental and traditional product quality requirements (Otani and Yamada, 2011). QFDE which is proposed by Masui et al. (2001) is one of the most cited methods in the subject of environmental QFD. In their research paper, VoC and environmental Engineering Metrics (EMS) are incorporated into QFD to design an environmentally product in the early stages of product design. Moreover, in order to achieve various options to improve the production, QFDE integrates VoC, Voice of Environment (VoE) and Quality Characteristics (QC). QFDE also consists of four phases. In Phase I, VoC, VoE and QC for traditional and environmental qualities are correlated, while QC and components (such as function units or part characteristics) are obtained in the same way as phase II, and then analyze which of the design changes among the various candidates are most effective on environmental improvement through the phases III and IV. Bereketli and Genevois (2013) have used QFDE with fuzzy AHP (Analytic Hierarchy Process) to provide an integrated approach to product development with regard to the requirements of economic, environmental and quality. Fargnoli et al. (2014) have provided a specific design for sustainability procedure based on QFDE. They have reviewed environmental criteria and customer requirements with considering the risk factor. Sakao (2007) has presented a model based on LCA, TRIZ and QFDE to identify the features and environmental qualities of the product and to search solutions for it. He applied his methodology for a hair dryer to effectively support the product planning and conceptual design stages for product environmental activities. LCA is utilized in the early stage of product design and followed by the first and second phases of QFDE. TRIZ is used in the third phase to find design solutions so the steps of QFDE are then implemented. Eventually, LCA is applied again to appraise the environmental improvements.

AC C

Apart from the environmental assessment and life cycle techniques, QFD is also integrated with MCDM methods to design an environmentally friendly product. Lin et al. (2010) have presented a model based on fuzzy environmental QFD and sustainable aspects for a company in Taiwan. They combined ANP with QFD where CAs are sustainable production indicators and TRs are social, economic and environmental production requirements. Lin et al. (2015) have integrated Fuzzy Interpretive Structural Modeling (FISM), FANP, QFD and Functional Failure Mode and Effects Analysis (FFMEA) for a green and low carbon TFT-LCD panel. Firstly, they have applied the FISM to determine both the interdependence among CAs/TRs and the effects of TRs on CAs. The results are used to construct the HoQ which are implemented in FANP process to find priority vectors. Then FFMEA is utilized to discover potential failures and propose suggestions for improvement, and finally the priorities of TRs with respect to risk control are calculated. Fuzzy approaches can be applied to formulate the relationships between CAs and TRs. Fuzzy sets were first introduced by Zadeh in 1965 as a means of representing and data that was neither precise nor complete. The differences between the fuzzy QFD and the traditional one is that the QFD relevant data are represented as linguistic terms which they are processed by algorithms in the system’s internal environment (Zaim et al., 2014). The ANP has been proposed as a suitable Multi Criteria Decision Analysis (MCDA) tool to evaluate multiple alternatives during the conceptual planning and design. The ANP technique allows for more complex and interdependent relationships and feedbacks among elements in the hierarchy (Saaty, 1996) which uses

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

criteria feedback and interrelationship. Also it is enabling the pair-wise comparisons of the sub-criteria under main criteria (Zaim et al., 2014). Lee and Lin (2011) have adopted fuzzy Delphi method (FDM) to select the critical factors, and combine QFD and FANP to choose the most important criteria in new product development. Büyüközkan and Berkol (2011) have presented a framework using ANP, QFD and zero-one goal programming models to determine the design requirements for a sustainable supply chain. Lee et al. (2010) have integrated QFD with the supermatrix approach of ANP and the fuzzy set theory to calculate the priorities of TRs, and applied multi-choice goal programming to consider QFD results with other additional goals. FANP is commonly adopted to accommodate the complicated interdependence among criteria, but they might be infeasible in which multiple criteria appear on a hierarchy. To overcome the difficulties, we use DEMATEL to construct a fuzzy MCDM based QFDE. Fuzzy DEMATEL is utilized to incorporate correlations among criteria. DEMATEL was developed by the Science and Human Affairs Program of the Battelle Memorial Institute of Geneva Research Centre (Jassbi et al., 2011), which is able to visualize the complex interdependency relationship among all evaluation criteria. Not only can convert the relations between cause and effect of criteria into a visual structural model, but also can be used as a wise way to handle the inner dependences within a set of criteria. The DEMATEL is based on digraphs which can separate factors into cause and effect group. Wang and Chen (2012) have used fuzzy MCDM based QFD which integrates FDM and fuzzy DEMATEL with linear integer programming to reduce the gap between CAs and product development and to execute collaborative product design and optimal selection of module mix. Aliei and Rafiean (2014) have used fuzzy AHP and fuzzy DEMATEL to rank critical success factors of the corporate entrepreneurship in Iranian institutes. Although QFDE, FANP, and fuzzy DEMATEL have been used individually by several researchers for specific purposes, these approaches have not been integrated for sustainable product design. A sustainability framework for the identification of the pertinent eco-design improvement should attend as a basic conceptual structure for decision makers in design phases with a multi-aspect approach and should include an integrated methodology which is able to combine the required aspects. Hence, the following hybrid methodology is suggested to fill the aforementioned research gaps. 3. METHOD

AC C

EP

TE D

The aim of this study is to identify the improvement strategies to accomplish sustainable product design. This research is followed by the selection of suitable approach for the case study. The list of CAs and TRs are recognized for a dry transformer, and they were outlined from the relevant literature. Therefore, we propose the QFDE method, which allows engineers to make strategic decisions in the early product design stage. DEMATEL is used to find correlations among CAs and FANP will be integrated into QFDE in order to identify the weights of the technical requirements. This proposed framework allows experts to identify options using linguistic expressions and proposes a hybrid approach based on a fuzzy DEMATEL, fuzzy QFDE and FANP techniques to find the possible design options. Since all decision makers in this research are not completely familiar with dry-type transformers and using average method which would have led to a long process, the data used in comparisons matrices and the house of quality matrix are based on the most used items or mode (Farsijani and Torabande, 2013). The systematic hybrid procedure contains steps as follows: Step1. Obtain CAs and TRs which are collected from literature and selected by designers (Table 2 and Table 3) (Lebot, 2002; Barnes et al., 1996; IEC 60076-11:2004, 2006; Farsijani and Torabande, 2013; Mouhamad and Lauzevis, 2013; Yurekten et al., 2013). Considering cost, quality and environmental traits, as basic required aspects, is very highly significant in sustainable design. Step 2. To determine the interdependence among the CAs, a fuzzy DEMATEL (By adopting a fuzzy triangular number) with five linguistic terms as {Very high, High, Low, Very low, No} is created. These linguistic terms are shown in Table 1. Table 1. Linguistic terms using in fuzzy DEMATEL method Linguistic terms

Very High Influence (VH)

High Influence (H)

Low Influence (L)

Very Low Influence (VL)

No Influence (No)

Linguistic values

(8, 9, 9)

(6, 7, 8)

(4, 5, 6)

(2, 3, 4)

(1, 1, 1)

ACCEPTED MANUSCRIPT The questionnaire is prepared to ask the relationship between any two CAs. The experts are responsible to rate the importance degrees of them. Therefore, the fuzzy matrix (1):

(inner independence) is produced which is shown as

z%12 L z%1n   0 z%2 n  M  z% n 2 0

(1)

RI PT

 0  z% z% =  21 M   z%n1

z%

It is called initial direct-relation fuzzy matrix and z%ij = (lij , mij , uij ) is a triangular fuzzy number that has three elements. If x%ij is the element of normalized direct-relation matrix ( defined as:

z%ij r

=(

lij mij uij , , ) r r r

SC

x%ij =

n

j =1

T% = lim( X% 1 + X% 2 + ... + X% k ) k →∞

M t%n 2 L

t%1 n   t%2 n  M   t%nn 

t%ij = (lij′′, mij′′ , uij′′ )

[l ij′′ ] = X l × ( I − X l ) −1

[mij′′ ] = X m × ( I − X m )−1

(3)

(4)

(5)

(7) (8) (9)

EP

[uij′′ ] = X u × ( I − X u ) −1

(2)

(6)

TE D

Where

t%12 L t%22 L

M AN U

r = max l ≤i ≤ n (∑ uij )

 t%11 % t T% =  21 M   t%n 1

X% ), total-relation fuzzy matrix (T) is

AC C

The interrelationship of each factor can be showed as the directed graphs on a two-dimensional plane after a certain threshold value is set. Only those factors which have an effect on matrix T, greater than the threshold value, should be selected. The experts are usually asked to set the threshold value and as a result, it is timeconsuming and hard to aggregate their ideas and make a constant decision. We use the MMDE algorithm to find a threshold value for describing the impact-relations map or a binary matrix. In this algorithm, by using the entropy approach, Li and Tzeng (2009) have defined another information measure which searches for the threshold value by nodes or vertices. First step contains of transforming total relation matrix (T) into an ordered set T = { t1 1 , t1 2 , ..., t , t 2 2 , ..., t n n } and rearrange them from large to small. Every element of T should be 21

considered as an ordered triplet

(tij , xi , x j ) as influence value, dispatch node and receive node, respectively. By D

taking the second element (dispatch node), we make a new set ( T ). For an ordered dispatch-node set (or an ordered receive-node set), we can count the frequency of the different elements of the set. If the finite cardinality of an order dispatch-node set (or an ordered receive node set) is m and also n denotes the cardinal number of different elements as well as the frequency of element xi is k, the corresponding probability of pi is D

assigned k/m. After that, the probability of different elements ( H ) is assigned and de-entropy can be D

calculated by (10) which N (T ) is the cardinal number of different elements. We choose the MMDE and

ACCEPTED MANUSCRIPT perform these steps for the receive node again. As a result, the minimum influence value in set could be introduced as the threshold value then the impact-relations map or binary matrix can be structured (Li and Tzeng, 2009).

MDEtD =

(10)

HD N (T D )

(11)

RI PT

H ( p 1 , p 2 , ..., p n ) = − ∑ p i lo g p i

1 1 H D = H  ,...,  − H ( p1 , p2 ,..., pn ) n n

(12)

[( u ij − l ij ) + ( m ij − l ij )] 3

+ l ij

(13)

M AN U

d ij =

SC

After using the defuzziation (Dehghani et al., 2013) method (13) on T% , according to the data in Table 6, the inner relationship between CAs are set based on the threshold value to determine whether there is a relation between each two factors. These inner relationship matrices for CAs construct Impact-relations map (Wang and Chen, 2012).

a%ij = (lij , mij , uij ) = ( n

Min

J =∑

n

∑

(14)

(ln wiL − ln wUj − ln lij )2 + (ln wiM − ln wMj − ln mij ) 2 + (ln wiU − ln wLj − ln uij ) 2 

AC C

i =1 j =1, j ≠ i

wiL wiM wiU , , ) wUj wMj w Lj

EP

If

TE D

Step 3. The pair-wise comparison matrices have then constructed based on the Impact-relations map. In this phase, the consistency of the decision maker’s judgments has to be checked. In the next step, Wang et al. (2006) have calculated the relative importance of CAs by using modified fuzzy LLSM method. Different kinds of methods have been improved to handle fuzzy comparison matrices. For instance, Buckley (1985) has utilized the geometric mean method to find fuzzy weights. Chang (1996) proposed an extent analysis method, which derives crisp weights for fuzzy comparison matrices. Nevertheless, this method is found unable to derive the true weights from a fuzzy or crisp comparison matrix and could not present the relative importance of alternatives at all. This problem can be determined by using the modified fuzzy LLSM (15) which derives the priorities of the triangular fuzzy comparison matrices. The modified fuzzy LLSM is developed as a nonlinear optimization model, and normalized triangular fuzzy weights for triangular fuzzy comparison matrices can be driven by it (Wang et al., 2008).

Subject to:

wiL +

n

∑

j =1, j ≠ i n

∑

wiU +

j =1, j ≠ i

n

∑w i =1

M i

n

∑ (w i =1

L i

wUj ≥ 1 wLj ≤ 1

=1 +wiU ) = 2

wiU ≥ wiM ≥ wiL > 0

i=1, 2, n

(15)

ACCEPTED MANUSCRIPT By using LLSM algorithm, the normalized weight vector for environmental CAs (W weight ) with respect to the goal is calculated. From those comparison matrices, the normalized priority vectors of CAs’ inner relations (WCA) are obtained and ,consequently, final priorities of CAs is prepared by: WFinal = WCA ×W weight

(16)

M AN U

Fig. 1. House of Quality

SC

RI PT

Step 4. In final step, the house of quality (Fig. 1) is developed. Based on the results from step 3, providing the fuzzy comparison matrices for TRs with respect to each CA and , finally, using LLSM to prepare normalized weight vectors, a HOQ is constructed to comprise the CAs and TRs to calculate the priorities of the TRs. This can be seen clearly in the case study in Fig .2.

4. RESULTS

EP

TE D

We carried out a case study in the Iran Transfo Corporation to deal with power generation and distribution transformers. The company is the only manufacturer of transformers in Iran and the Middle East. We chose the cast resin dry-type distribution transformer as the case study. Transformers alter voltages from one level to another. Most commonly, this change includes very high power line transmission voltages being reduced to the much lower levels used in heavy industry and households. Dry type transformers complete this function securely and efficiently that they can be utilized for indoor applications where other types are too risky. All cast resin drytype transformers are designed, produced and tested in accordance with IEC 60076-11:2004 or any other international and national standards upon request. This type of transformer offers various benefits such as: no environmental pollution, no toxic substances, low noise level, no fire hazard, reduction of expenses of electricity distribution and installation as close as possible to the center of load and consumption. From the other point of view, it includes disadvantages like high cost and increased losses. We try to make a more environmentally friendly product. Furthermore, in consequence of the limited number of experts and specialists that are completely familiar with specific transformer design and the lack of direct relationship with customers, the CAs or TRs of QFDE are prepared by literatures. According to the identifications of criteria which are discussed in this research, the DEMATEL questionnaires are designed based on the lists of customer attributes and environmental and technical requirements as follows:

AC C

Table 2. Customer Attributes CAs

CA1: Cheap (Cost) CA2: Easy to Maintain (Quality, Environment) CA3: Voltage Variations (Quality) CA4: Environmentally Safe (Environment) CA5: Light Weight (Quality, Environment) CA6: Long Life Time (Quality, Environment) CA7: Less Volume (Quality, Environment) CA8: Preparation Time Packaging/Transportation/Delivery(Quality) CA9: Energy Saving (Environment, Cost) CA10: Quiet (Quality) CA11: Easy to Recycle (Environment, Cost) CA12: Free of Hazardous Substances (Environment) CA13: Less Material Usage (Environment)

Table 3. Technical Requirements TRs TR1: Materials (Product) TR2: Energy Consumption (Product, Environment) TR3: Power (Product) TR4: Load Losses (Product, Environment) TR5: No Load Losses (Product, Environment) TR6: Volume (Product, Environment) TR7: Weight (Product, Environment) TR8: Physical Life Time (Product, Environment) TR9: Noise and Vibration (Product) *Product= Product related parameter *Environment= Environment related parameter

ACCEPTED MANUSCRIPT First, the received questionnaires (Table 4) are analyzed by the DEMATEL to identify the inner dependence among CAs in this study. Table 4. The inner independence matrix based on DEMATEL CA2

CA3

CA4

CA5

CA6

CA7

CA8

CA9

CA10

CA11

CA12

CA13

No

VL VL H VL

(0,0,0)

VL VL

No L No

(0,0,0)

No L L

VL No

(0,0,0)

VL No No VL

VL

(0,0,0)

No H H H No

H No No No VL

L VL No VL H

No VL VH L No

No L VL VL No

No H L VH VL

No No VL No No

No No VL No VH

VL No L L No H H H

VL No No No VL H No No

VL No VL VL VL VL VL VL

L No VL L No H H VL

No VL L No No L No VH

(0,0,0)

No

No No H No VH VH L

(0,0,0)

VL No

No VL No

No No No VL

VL L VL No No

VL No No No No VH

(0,0,0)

VL VH VL No No VH L

No

(0,0,0)

No No No VL No VH

(0,0,0)

No No No VL H

(0,0,0)

VL VL No No

RI PT

CA1 (0,0,0)

(0,0,0)

(0,0,0)

No No No

VH VH

SC

CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11 CA12 CA13

Second, the MMDE is employed to find the threshold value to omit the unnecessary relations in total relation matrix (Table 5). CA3

0.045 0.121 0.127 0.186 0.114 0.108 0.079 0.125 0.122 0.049 0.223 0.196 0.197

0.036 0.049 0.062 0.121 0.049 0.076 0.046 0.044 0.046 0.059 0.159 0.073 0.071

0.069 0.093 0.051 0.101 0.058 0.082 0.051 0.076 0.077 0.063 0.120 0.104 0.104

Table 5. The total relation fuzzy matrix (T) for CAs CA4 CA5 CA6 CA7 CA8 CA9 CA10

CA11

CA12

0.053 0.139 0.143 0.043 0.096 0.124 0.067 0.089 0.115 0.047 0.204 0.180 0.129

0.063 0.169 0.146 0.201 0.113 0.109 0.130 0.097 0.068 0.050 0.010 0.214 0.216

0.036 0.058 0.083 0.066 0.046 0.073 0.045 0.042 0.041 0.031 0.510 0.050 0.069

M AN U

CA2

0.076 0.066 0.067 0.101 0.051 0.059 0.091 0.109 0.049 0.035 0.153 0.085 0.194

0.057 0.177 0.183 0.189 0.079 0.049 0.077 0.070 0.149 0.053 0.246 0.217 0.163

0.123 0.058 0.062 0.070 0.092 0.053 0.046 0.054 0.047 0.033 0.122 0.078 0.185

TE D

CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11 CA12 CA13

CA1

0.101 0.092 0.070 0.104 0.148 0.087 0.062 0.045 0.054 0.037 0.111 0.111 0.172

0.043 0.089 0.164 0.121 0.050 0.053 0.072 0.048 0.035 0.064 0.110 0.070 0.073

0.032 0.101 0.075 0.080 0.039 0.041 0.036 0.036 0.065 0.017 0.065 0.051 0.052

CA13 0.062 0.078 0.105 0.091 0.176 0.092 0.171 0.091 0.055 0.041 0.218 0.148 0.010

EP

As a result, the threshold value could be determined as 0.051 by MMDE algorithm and then the impactrelations diagram or binary reachability matrix can be structured. The inner relationship between CAs are set based on the threshold value to determine whether there is a relation between each two factors or not and the impact-diagraph map is obtained as shown in Fig. 2, Table 6. Reachability matrix derived from T for CAs

CA2

CA 3

AC C

CA1 CA 1 CA 2 CA 3 CA 4 CA 5 CA 6 CA 7 CA 8 CA 9 CA 10 CA 11 CA 12 CA 13

0 1 1 1 1 1 1 1 1 0 1 1 1

0 0 1 1 0 1 0 0 0 1 1 1 1

1 1 0 1 1 1 0 1 1 1 1 1 1

CA 4

CA 5

CA 6

CA 7

CA 8

CA 9

1 1 1 0 1 1 1 1 1 0 1 1 1

1 1 1 1 0 1 1 1 0 0 1 1 1

1 1 1 1 1 0 1 1 1 1 1 1 1

1 1 1 1 1 1 0 1 0 0 1 1 1

1 1 1 1 1 1 1 0 1 0 1 1 1

0 1 1 1 0 1 1 0 0 1 1 1 1

CA 10 0 1 1 1 0 0 0 0 1 0 1 0 1

CA11 1 1 1 1 1 1 1 1 1 0 0 1 1

CA 12 0 1 1 1 0 1 0 0 0 0 0 0 1

CA 13 1 1 1 1 1 1 1 1 1 0 1 1 0

SC

RI PT

ACCEPTED MANUSCRIPT

Fig. 2. Impact-relations map based on the threshold value

M AN U

The questionnaire was prepared based on the pairwise comparison of elements, and decision makers were asked to do the questionnaire with linguistic terms (Table 7). Table 7: Fuzzy triangular numbers in FANP method Linguistic terms

Linguistic values

Preference with equal importance

(1, 1, 1)

(1, 3, 5) (3, 5, 7)

Preference with very strong importance

(5, 7, 9)

Preference with full and absolute importance

(7, 9, 9)

TE D

Preference with little importance

Preference with strong importance

EP

The non-uniqueness of normalized fuzzy weights brings difficulty and inconvenience for the comparison and ranking of fuzzy weights as well as the synthesis of local fuzzy weights. Therefore, we use modified fuzzy LLSM, in this paper. By using this algorithm, the normalized fuzzy weight vector for environmental CAs with respect to the goal is calculated (Table 8). Table 8. Derived normalized weights of CAs

AC C

CA1 CA2 CA3 CA4

CA5 CA6 CA7 CA8 CA9 CA10 CA11 CA12 CA13

Fuzzy number

l

m

u

0.052 0.078 0.072 0.060 0.061 0.076 0.061 0.079 0.079 0.069 0.079 0.069 0.079

0.070 0.090 0.083 0.070 0.064 0.076 0.064 0.083 0.083 0.070 0.083 0.076 0.083

0.097 0.095 0.083 0.084 0.074 0.076 0.074 0.083 0.083 0.073 0.083 0.083 0.083

Weight 0.073 0.088 0.080 0.071 0.066 0.076 0.066 0.080 0.081 0.071 0.080 0.076 0.080

Following that, the inner dependence among the CAs is determined through analyzing the impact of each CA on other CAs by using pairwise comparisons. To state it differently, each time, relative importance of all criteria is calculated for each particular criterion. Table 9 manifest the result of evaluation and relative importance weights of CAs. Then, the final priorities of the CAs are calculated by using (16) as follows:

ACCEPTED MANUSCRIPT Table 9. Relative importance weights of CAs WCA CA2

CA3

CA4

CA5

CA6

CA7

CA8

CA9

CA10

CA11

CA12

CA13

WFinal

0.073 0.088 0.080 0.071 0.066 0.076 0.066 0.080 0.081 0.071 0.080 0.076 0.080

0 0.096 0.089 0.107 0.070 0.105 0.083 0.066 0.096 0 0.096 0.105 0.082

0.062 0 0.082 0.095 0.076 0.074 0.070 0.095 0.088 0.082 0.095 0.088 0.088

0.067 0.082 0 0.090 0.076 0.082 0.082 0.076 0.112 0.071 0.093 0.082 0.082

0.071 0.082 0.082 0 0.071 0.082 0.082 0.082 0.082 0.082 0.083 0.105 0.090

0.124 0.124 0.124 0.124 0 0.124 0 0 0 0 0.140 0.111 0.123

0.090 0.083 0.082 0.083 0.076 0 0.082 0.076 0.082 0.082 0.077 0.096 0.083

0.090 0.120 0.099 0.090 0 0.099 0 0.091 0.099 0 0.099 0.108 0.100

0.004 0.007 0.008 0.004 0 0.015 0.021 0 0.004 0 0.007 0.008 0.020

0.083 0.098 0.106 0.083 0 0.083 0.090 0.090 0 0.090 0.113 0.078 0.083

0 0.140 0.123 0.111 0 0.124 0 0 0.124 0 0.124 0.124 0.124

0.067 0.082 0.082 0.096 0.076 0.082 0.076 0.082 0.082 0.082 0 0.089 0.095

0.071 0.082 0.082 0.102 0.082 0.078 0.082 0.082 0.082 0.082 0.094 0 0.072

0.071 0.082 0.082 0.102 0.082 0.078 0.082 0.082 0.082 0.082 0.094 0.072 0

0.029 0.040 0.039 0.041 0.023 0.038 0.030 0.031 0.035 0.025 0.042 0.040 0.039

SC

RI PT

CA1

M AN U

Easy to Recycle (CA11) is the most important CA with a priority of 0.042, followed by Environmentally Safe (0.041), Free of Hazardous Substances (0.040), Easy to maintain (0.040), Less Material Usage (0.039), Voltage Variations (0.039), Long Life Time (0.038), Energy Saving (0.035), Preparation Time (0.031), Less Volume (0.030). Cheap (0.029), Quiet (0.025), Light Weight (0.023). In the next step, assuming that there is no dependence among the TRs, they are compared with respect to each CA. For example, one of the matrices for determining the degree of relative importance of the TRs with respect to CA13 can be shown as Table 10. From these comparison matrices, the normalized TRs weights based on CA13 are calculated by using LLSM method, respectively (see Table 11).

Table 10. The relationship between TRs toward CA13 TR1 (1,1,1)

TR2 (1,3,5)

TR3 (3,5,7)

TR2

(0.2,0.33,1)

(1,1,1)

(1,1,1)

TR3

(0.143,0.2,0.333)

(1,1,1)

TR4

(1,1,1)

(1,1,1)

TR5

(1,1,1)

TR6 TR7 TR8

(0.2,0.333,1)

TR9

(0.143,0.2,0.333)

TR4 (1,1,1)

TR5 (1,1,1)

TR6 (1,3,5)

TR7 (1,1,1)

TR8 (1,3,5)

TR9 (3,5,7)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,3,5)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,3,5)

(0.2,0.333,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

EP

TE D

CA13 TR1

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,1,1)

(1,3,5)

(1,1,1)

(1,1,1)

(0.2,0.333,1)

(0.2,0.333,1)

(1,1,1)

(1,1,1)

(0.2,0.333,1)

(1,1,1)

AC C

CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11 CA12 CA13

Wweight

Table 11. The weighted vector of TRs with respect to CA13 Fuzzy number

CA13 TR1 TR2

l

m

u

weight

0.139

0.213

0.256

0.203

0.095

0.095

0.097

0.095

TR3

0.089

0.089

0.089

0.089

TR4 TR5

0.117

0.117

0.117

0.117

0.117

0.117

0.117

0.117

TR6 TR7

0.095

0.095

0.097

0.095

0.106

0.106

0.106

0.106

TR8

0.095

0.104

0.120

0.106

TR9

0.050

0.060

0.091

0.067

ACCEPTED MANUSCRIPT

0.110

0.122

0.122

Easy to Maintain Voltage Variations Environmentally Safe

CA2

0.040

4

0.122

0.122

0.122

0.122

CA3

0.039

6

0.122

0.122

0.099

CA4

0.041

2

0.148

0.121

Volume

Noise and Vibration

Load Losses

RI PT Power

Weight 0.099

0.122

0.110

0.099

0.090

0.099

0.110

0.110

0.090

0.099

0.122

0.099

0.110

0.101

0.099

0.122

0.121

0.110

0.098

0.109

0.090

0.109

0.090

CA5

0.023

13

0.136

0.121

0.109

0.121

0.109

0.109

0.109

0.099

0.083

CA6

0.038

7

0.135

0.099

0.149

0.109

0.109

0.099

0.099

0.099

0.099

Less Volume Preparation Time Energy Saving

CA7

0.030

10

0.122

0.122

0.110

0.110

0.110

0.110

0.110

0.110

0.091

CA8

0.031

9

0.134

0.134

0.099

0.099

0.109

0.109

0.109

0.109

0.090

CA9

0.035

8

0.100

0.110

0.110

0.110

0.100

0.100

0.122

0.122

0.122

Quiet

CA10

0.025

12

0.1206

0.120

0.091

0.108

0.108

0.089

0.089

0.134

0.134

Easy to Recycle Free of Hazardous Less Material Usage

CA11

0.042

1

0.1482

0.121

0.101

0.109

0.099

0.109

0.099

0.109

0.101

CA12

0.040

3

0.1349

0.109

0.134

0.090

0.099

0.109

0.109

0.109

0.099

CA13

0.039

5

0.203

0.095

0.089

0.117

0.117

0.095

0.106

0.106

0.067

RW

0.0350

0.03454

0.03493

0.03461

0.03455

0.03471

0.03463

0.03451

0.03460

Final Weight

0.0350

0.0344

0.0349

0.0346

0.0345

0.0348

0.0347

0.034

0.0345

1

8

2

5

7

3

4

9

6

AC C

EP

TE D

Light Weight Long Life Time

Rank

M AN U

TR9

0.122

TR8

11

TR7

0.029

TR6

TR4

CA1

SC

TR3

Cheap

TR5

TR2

Physical Life Time

TR1

Energy Consumption

Rank

No Load Losses

Weight

Materials

Then, for the remaining TRs, the normalized technical requirements weights vectors are calculated in the same way and shown in Fig. 3 placing in the body of the HoQ. The HoQ is prepared with CAs weights and the relationships between CAs and TRs calculated by using LLSM method. The total of the sum at crossing-points between CAs and TRs is the raw weight (RW). Furthermore, final weights of each TR is obtained by “the total of the sum multiplied by CAs and TRs”/ RW. The Final weight ranking is obtained according to the relationships and the weighting factors of customer requirements. To consider customer satisfaction, Materials (TR1) is the most important environmental TR with a priority of 0.035 followed by Energy consumption (TR2), Power (TR3), Load Losses (TR4), Physical Life Time (TR8), Volume (TR6), Weight (TR7), No Load Losses (TR5) and Noise and Vibration (TR9) with priorities of 0.0349, 0.0348, 0.0347, 0.0346, 0.0345, 0.0345, 0.0344, and 0.034, respectively.

Fig. 3: House of Quality of the case study

In this research, a novel framework has been developed based on QFDE technique which led us to recognize the most relevant environmental requirements for the cast resin dry type transformer. Eventually, selecting the appropriate material has the highest potential to design a sustainable transformer. The possible implementation tips to be considered by the producers should be regarded as follows: the type of material used for building the transformer core affect various parts such as load losses, energy efficiency and etc. This silicon steel technology is produced from iron bars and is one of the most recycled materials worldwide. Depending on the characteristic load profile, either load losses/no-load losses, or both should be minimized. Yurekten et al. (2013) have stated that a core made of amorphous metal considerably reduces core losses by 40-70%. Amorphous metal is produced by rapid solidification from the liquid and its one step manufacturing process reduces the production

ACCEPTED MANUSCRIPT cost. Georgilakis et al. (2011) have found that the environmental cost of the losses can reach 35% of the transformer purchasing price for high loss transformer so the environmental cost of transformer losses should incorporate into economic evaluation. Mouhamad and Lauzevis (2013) have proved that LCA of amorphous technology is the most ecofriendly one in comparison to traditional technology. Hernandez et al. (2010) have introduced a wound core technology in order to reduce weight and volume which leads to cost minimization. Consequently, the improvement strategies by considering the highest importance in QFDE are all related to materials and core. As a consequence, the environmental performance of the transformer will be improved by these considerations.

RI PT

5. Managerial Implications

M AN U

SC

QFDE enables designer engineers to choose the most productive plan using design changes. Even though there have been plentiful researches that have studied sustainable product design using QFDE with other methods, the authors, after reviewing these papers, found that there is a possibility of merging the concepts to obtain a hybrid framework. For the reason that conventional QFDE approaches have some drawbacks, the proposed framework can tackle some of the issues which were found in the literature. The research program which include awareness of environmental issues have to be conducted for managers to encourage them understand the benefits of research results. Hence, managers should be informed about the steps of QFDE and data which are needed for successful model implementation. In the next step, CAs and TRs have to be recognized and experts should be selected by top management to do questionnaires. Finally, ranking of TRs have to be derived and should be executed. The effect of the hybrid model accomplishment should be studied afterwards. The inferences obtained from the study are applicable to other manufacturing organizations.

6. CONCLUSION

EP

TE D

The modern manufacturing organizations are focused on making sustainable status by means of costs reduction and prevention of environmental problems. The practical aspects of sustainable design can be found in previous studies include environmentally safe, manufacture without using hazardous substances, easy to recycle and so on. In this study, an integrated QFDE methodology is considered cost, quality and environmental parameters for sustainable product design and helps designers to make better decisions by incorporating fuzzy decision making into QFDE in design phase. Through a comprehensive literature review, a list of CAs that satisfy customers for a dry type transformer and a list of TRs that may be necessary for it were prepared. This research proposes an extensive framework that applies fuzzy DEMATEL to determine the relationship among factors, MMDE method to choose the best and practical threshold value in DEMATEL process, LLSM method to find weights and finally uses integrated FANP-QFDE technique to obtain priority weights of the requirements. In conclusion, the proposed methodology can help designers effectively determine key CAs and TRs for designing and manufacturing sustainable products

AC C

For future studies, this model can be applied to the other stages of QFDE, and the model can be utilized by manufacturers in sustainable product design process in other industries. In addition, one of the most important factors in sustainable product design is social concerns which is missed in this research and can be used in future studies to complete sustainable product design process. Regarding to the literature review, integrating LCA, which considers the product’s whole life cycle, with the proposed method will be useful because of assessing the environmental impact of products and processes. Also, a goal programming model can be constructed to consider additional goals such as risk control or manufacturing variability in order to select the most important TRs.

ACCEPTED MANUSCRIPT REFERENCES Aliei, M., Rafiean, H., 2014. Segmenting critical success factor for generating Corporate Entrepreneurship Based on Fuzzy DEMATEL and Fuzzy AHP in Iranian Institutes. Journal of Technology Innovation. 1(1), 101123. Barnes, P.R., Van Dyke, J.W., McConnell, B.W., Das, S., 1996. Determination Analysis of Energy Conservation Standards for Distribution Transformers (No. ORNL-6847). Oak Ridge National Laboratory, Oak Ridge, Tenn.

RI PT

Bereketli, I., Genevois, M.E., 2013. An integrated QFDE approach for identifying improvement strategies in sustainable product development. Journal of Cleaner Production. 54, 188-198. Bovea, M.D., Wang, B., 2005. Green Quality Function Deployment: A methodology for integrating customer, cost and environmental requirements in product design. International Journal of Environmentally Conscious & Manufacturing. 12(4), 9-19. Buckley, J. J., 1985. Fuzzy hierarchical analysis. Fuzzy sets and Systems. 17(3), 233–247.

SC

Büyüközkan, G., Berkol, Ç., 2011. Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment. Expert Systems with Applications. 38(11), 13731–13748. Chang, D. Y., 1996. Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research. 95(3), 649–655.

M AN U

Chen, C., Zhu, J., Yu, J., Noori, H., 2012. A new methodology for evaluating sustainable product design performance with two-stage network data envelopment analysis. European Journal of Operational Research. 221(2), 348–359. Cristofari, M., Deshmukh, A., Wang, B., 1996. Green quality function deployment. In: Proceedings of the 4th International Conference on Environmentally Conscious Design and Manufacturing Cleveland. 297-304. Davidsson, B., 1998. Modified Product Quality Tools for Improved Environmental Design in Small and Medium Sized Enterprises. IIIEE Master’s theses 98:9. International Institute for Industrial Environmental Economics, Lund University, Lund.

TE D

Dehghani, M., Esmaeilian, M., Moghaddam, R.T., 2013. Employing Fuzzy ANP for Green Supplier Selection and Order Allocations: A Case Study. International Journal of Economy, Management and Social Sciences. 2(8), 565-575. Fargnoli, M., Kimura, F., 2006. Sustainable Design of Modern Industrial Products. In: Proceedings of the 13th CIRP International Conference on Life Cycle Engineering. 189-194.

EP

Fargnoli, M., De Minicis, M., Tronci, M., 2014. Design Management for Sustainability: An integrated approach for the development of sustainable products. Journal of Engineering and Technology Management. 34, 29-45. Farsijani, H., Torabandeh, M.A., 2013. Improvement of Efficiency in Product Designing By usage Of Fuzzy QFD & TRIZ: A Case Study on Transfo Company. Journal of Applied Science and Agriculture. 8(4), 451-461.

AC C

Georgilakis, P.S. Olivares-Galvan, J.C., Escarela-Perez, R., Fofana, I., Stefopoulos, G.K., 2011. Environmental Cost of Transformer Losses for Industrial and Commercial Users of Transformers. In: Proceedings of the North American Power Symposium (NAPS). 1-4. IEEE. Hernandez, I., Olivares-Galvan, J.C., Georgilakis, P.S., Canedo, J.M., 2010. A Novel Octagonal Wound Core for Distribution Transformers Validated by Electromagnetic Field Analysis and Comparison with Conventional Wound Core. IEEE Transactions on Magnetics. 46(5), 1251-1258. IEC 60076-11:2004 Power transformers-Part11: Dry-type transformers. Jassbi, J., Mohammadnejad, F., Nasrollahzadeh, H., 2011. A Fuzzy DEMATEL framework for modeling cause and effect relationships of strategy map. Expert Systems with Applications. 38(5), 5967-5937. Lebot, B., 2002. Energy-Efficient Distribution Transformers: A Hidden Opportunity for Large Scale Energy Savings. In: Proceedings of the 3rd International Workshop on Distribution Transformer Efficiency. 15-24. Lee, A.H.I., Kang, H.Y., Yang, C.Y., Lin, C.Y., 2010. An evaluation framework for product planning using FANP, QFD and multi choice goal programming. International Journal of Production Research. 48(13), 39773997.

ACCEPTED MANUSCRIPT Lee, A.H.I., and Lin, C.Y., 2011. An integrated fuzzy QFD framework for new product development. Flexible Services and Manufacturing Journal. 23(1), 26-47. Lin, C.Y., Lee, A.H.I., Kang, H.Y., 2015. An integrated new product development framework–an application on green and low-carbon products. International Journal of Systems Science. 46(4), 733-753. Lin, Y., Cheng, H. P., Tseng, M.L., Tsai, J. C.C., 2010. Using QFD and ANP to analyze the environmental production requirements in linguistic preferences. Expert Systems with Applications. 37(3), 2186-2196.

RI PT

Li, C.W., Tzeng, G.H., 2009. Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall. Expert Systems with Applications. 36(6), 9891-9898. Masui, K., Sakao, T., Kobayashi, M., Inaba, A., 2001. Quality Function Deployment for Environment: QFDE (1st report)-A Methodology in Early Stage of DfE. In: Proceedings of the 2nd International Symposium on Environmentally Conscious Design and Inverse Manufacturing, 852-857. Masui, K., Sakao, T., Kobayashi, M., Inaba, A., 2003. Applying quality function deployment to environmentally conscious design. International Journal of Quality & Reliability Management, 20(1), 90-106.

SC

Mouhamad, M., Lauzevis, P., 2013. Life Cycle Assessment and Inrush Currents Measurements of Amorphous Transformers. In: Proceedings of the 22nd International Conference on Electricity Distribution. 862-866.

M AN U

Otani, S., Yamada, S., 2011. Application of QFDE on greenhouse gas reduction strategy. International Journal of Quality and Service Sciences. 3(3), 285-303. Puglieri, F.N., Ometto, A., Augusto Cauchick Migue, P., 2011. Eco-design methods for developing new products based on QFD: A literature analysis. Product (IGDP). 9, 23-29. Saaty, T.L., 1996. Decision making with dependence and feedback: The analytic network process (Vol. 4922). Pittsburgh: RWS Publications. Sakao, T., 2007. A QFD-centered design methodology for environmentally conscious product design. International Journal of Production Research. 45(18-19), 4143-4162.

TE D

Umeda, Y., Takata, S., Kimura, F., Tomiyama, T, Sutherland, J.W., Kara, S., Herrmann, C., Duflou, J.R., 2012. Toward integrated product and process life cycle planning- An environmental perspective. CIRP Annals Manufacturing Technology. 61(2), 681-702. Vinodh, S., Rathod, G., 2010. Integration of ECQFD and LCA for sustainable product design. Journal of Cleaner Production. 18(8), 833-842. Vinodh, S., Rathod, G., 2010. Application of QFD for enabling environmentally conscious design in an Indian rotary switch manufacturing organization. International Journal of Sustainable Engineering. 3(2), 95-105.

EP

Vinodh, S., Kamala, V., Jayakrishna, K., 2014. Integration of ECQFD, TRIZ, and AHP for innovative and sustainable product development. Applied Mathematical Modeling. 38(11), 2758-2770.

AC C

Wang, C.H., Chen, J.N., 2012. Using quality function deployment for collaborative product design and optimal selection of module mix. Computers & Industrial Engineering. 63(4), 1030-1037. Wang, Y.M., Luo, Y., Hua, Z., 2008. On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research. 186(2), 735-747. Wang. Y.M., Elhag, T.M.S., Hua, Z., 2006. A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process. Fuzzy Sets and Systems. 157(23), 3055-3071. Yurekten, S., Kara, A., Markidyan, K., 2013. Energy Efficient Green Transformer Manufacturing with Amorphous Cores. In: proceedings of the International Conference on Renewable Energy Research and Applications, 534-536. Zaim, S., Sevkli, M., Akdag˘, H.C., Demirel, O.F., Yayla, A.Y., Delen, D., 2014. Use of ANP weighted crisp and fuzzy QFD for product development. Expert Systems with Applications. 41(9), 4464-4474.