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Integration of environmental considerations in quality function deployment by using fuzzy logic
Expert Systems with Applications 36 (2009) 7148–7156
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Integration of environmental considerations in quality function deployment by using fuzzy logic Tsai-Chi Kuo a,*, Hsin-Hung Wu b, Jiunn-I Shieh c a
Department of Industrial Engineering and Management, Ming Hsin University of Science and of Technology, 304 Hsinchu, Taiwan, ROC Department of Business Administration, National Changhua University of Education, Changhua, Taiwan, ROC c Department of Information Science and Applications, Asia University, Taichung, Taiwan, ROC b
a r t i c l e
i n f o
Keywords: Eco-designed product Quality function deployment Fuzzy group method
a b s t r a c t Recently, resource optimization (energy and material) and environmental issues in the life-cycle context are taken very seriously by both the general public and government agencies. These activities urge governments and companies alike to set up environmental friendly production technologies, which aim to avoid harmful emissions into air, water and soil. However, these Eco-designed products have not been achieved a favorable position in the marketplace as expected even though they appear to be more environmental friendly and economical. This may be due that they are focused solely on environmental impact analysis without regard for customer needs and cost considerations. In this research, an Eco-quality function deployment (Eco-QFD) is developed to aid a product design team in considering environmental concerns since QFD is a proven quality systems tool to achieve total customer satisfaction. A fuzzy group method is applied to Eco-QFD for product development planning to reduce the vagueness and uncertainty in a group decision-making process. This fuzzy multi-objective model not only considers the overall customer satisfaction but also encourages enterprises to produce an environmentally friendly product. With an interactive approach, the optimal balance between environmental acceptability and overall customer satisfaction can be obtained. Finally, a case study illustrating the application of the proposed model is also provided. 2008 Elsevier Ltd. All rights reserved.
1. Introduction Recently, resource optimization (energy and material) and environmental issues in the life-cycle context are taken very seriously by both the general public and government agencies. These activities urge governments and companies alike to set up environmental friendly production technologies, which aim to avoid harmful emissions into air, water and soil (Kuo, Chang, & Huang, 2000b; Kuo, Zhang, & Huang, 2000a; Yu, Jin, Zhang, Ling, & Barnes, 2000). Also, some governments have set up official Eco-labeling schemes, which are used to inform consumers of Eco (ecology and economic) design products (CGP, 1990; HR, 1991; Owen, 1993). In the past, several environmental impact analyses and evaluation tools have been developed for Eco-design products. For example, health hazard scoring (HHS) is an evaluation method for health hazard assessment (Barzilai & Golany, 1994). Kuo (2000) presented a disassembly planning method for the end-of-life products during the initial design stage. Sage (1993) offered sustainable process indices based on an operational definition of sustainability, * Corresponding author. Fax: +886 35455149. E-mail addresses: [email protected] (T.-C. (H.-H. Wu), [email protected] (J.-I. Shieh).
Kuo),
[email protected]
0957-4174/$ - see front matter 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2008.08.029
which relies not only on environmental risk, but also includes economic and technical feasibility as well as political compromise. Horvath, Hendrickson, Lave, and McMichael (1995) developed an approach to track toxic releases and associated risks over time based on the data of the Toxic Release Inventory (TRI) and relevant toxic indices. Costic, Sullivan, Bryant, and Shangguan (1996) estimated the environmental performance of conventional lead-based solders and their substitutes using life cycle analysis (LCA). Hewlett–Packard provided design for environment (DfE) tools for companies, such as DfE guidelines, product assessments, and product stewardship metrics (Korpalski, 1996). However, these Eco-design products are not favorable in the market place as expected even though they sound more environmental friendly and economical. This situation may be due to they are focused solely on environmental impact analysis without paying much attention to customer needs and cost considerations. In other words, the key issue for a successful Eco-Design product is not only to meet environmental objectives such as resource and energy conservation and environmental burden reduction but also to take into account cost effectiveness, market demand, and multi-functionality requirements (Lee, Lye, & Khoo, 2001). To further incorporate customer needs into product design concepts and product planning, quality function deployment (QFD) is
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considered to be one of the very effective quality systems to achieve total customer satisfaction (Akao, 1990; Chan and Wu, 2002–2003; Clausing, 1994; Wu, 2002–2003; Wu, 2002). The major purpose of QFD is to collect and analyze the voice of the customer and then deploy those voices into high quality products to meet or even exceed customer requirements. That is, product should be designed to meet customer needs so the marketing department, design engineers, and manufacturing staffs should work closely together from the time the product is first conceived (Hauser & Clausing, 1988; Tan & Shen, 2000). Generally speaking, QFD is an overall concept that provides a means of translating customer requirements into appropriate engineering characteristics or technical attributes for each stage of product development and production (Sullivan, 1986; Tan & Shen, 2000). The most commonly used QFD is composed of four major phases, i.e., product planning (also known as house of quality (HoQ)), parts deployment, process planning, and production planning (Chan and Wu, 2002–2003; Hauser & Clausing, 1988; Wu, Liao, & Wang, 2005). The applications of QFD have been expanded to a wide variety of areas, such as design planning, engineering, management, teamwork, timing, costing, to name a few (Chan & Wu, 2002; Kuo & Wu, 2001, 2003). Moreover, Cristopher, Deshmukh, and Wang (1996) developed the Green QFD (GQFD) method to integrate life cycle analysis and QFD to evaluate products using environmental considerations. Furthermore, Zhang, Wang, and Zhang (2000) presented the GQFD II that integrates life cycle assessment, life cycle costing and QFD into an efficient tool that deploys customer, environmental, and cost requirements throughout the entire product development process. In Japan, a QFD for the Environment (QFDE) tool is also used to design an environmental friendly product (JEMAI, 2001; Masui, Sakao, Aizawa, & Inaba, 2001). QFDE, depicted in Fig. 1, is carried out in four phases. Phases I and II allow the user to identify environmentally significant components (component parts and devices) of the product. Phases III and IV allow the user to choose the most environmentally friendly design from alternative design proposals. However, loosely defined and structured, QFD sometimes becomes more of an art than a science, which makes it most difficult for practitioners to utilize (Chan & Wu, 2002). The reasons are as follows: First, the weighting factors are normally described using vague linguistic type language, such as ‘serious’, ‘moderate’, and ‘low’ (Hui, He, & Dang, 2002). Second, customer information is gathered in a subjective ad hoc manner, since it comes from a vari-
Step 1: Arrange conventional and environmental VOCs in order Step 2: Prioritize the VOCs Phase I Step 3: Specify enginnering metrics (EMs) Step 4: Determine the relevance of the VOCs to the EMs Step 1: Present the components in an orderly requirements Step 2: Determine the relationships between specific EMs
Phase II
Environmentally significant components Step 1: Assess project costs Step 2: Present design proposal
Phase III
Step 1: Evaluate alternative design proposals Step 2: Choose one from the proposals
Phase IV
Design the proposal that is the most environmentally friendly Fig. 1. Flow of QFDE-based DfE (JEMAI, 2001).
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ety of sources, using surveys, focus groups, interviews, listening to salepeople, talking to trade show exhabitors, reading journals, and reviewing existing data on warranty and customer complaints (Bossert, 1991). Third, over 50% of the QFD efforts are spent on capturing the voice of the customer-performance characteristics and relative importance characteristics (Bosserman, 1992). Several research methodologies have been developed to determine the weighting factors for different customer needs, such as the well know AHP (Satty, 1980), conjoint analysis (Green & Srinivasna, 1978, 1990), the benchmarking of products from other companies (Park & Kim, 1998). Wasserman (1993) formulated the QFD planning process as a linear programming model to select the matrix of design features, which would result in the highest level of customer satisfaction. For an attribute’s importance evaluation, it is usually expressed in linguistic terms such as ‘unimportant’ and ‘very important’ and then transferred in the form of concise numbers. However, it is also well recognized that people’s assessments of the qualitative attributes of a product are always subjective and thus imprecise, and the linguistic terms that people use to express their feelings or judgments are vague in nature. To solve the above problems, a fuzzy approach to the Eco-QFD is proposed and illustrated to aid a design team in choosing target levels for technical attributes based on environmental concerns. Unlike two valued Boolean logic, fuzzy logic is multi-valued, which deals with degrees of membership and degrees of truth. Therefore, the Eco-design product development problem was formulated as fuzzy multiobjective model based on the QFD planning. By applying the matrix with a simplified house of quality, the importance that customers assign to each product area can be ascertained. Besides, by introducing environmental factors, an optimizing model is developed and illustrated. This model not only fulfill overall customer satisfaction but also provides a means of planning successful environmentally friendly products. With an interactive approach, the optimal balance between environmental acceptability and overall customer satisfaction can be obtained. Finally, a case study illustrating the application of the proposed model is also provided.
2. Identify customer requirements and technical attributes in QFD Gathering and analyzing the voice of the customer is critically important in QFD in order to provide customer-oriented products. The voice of the customer may come from a wide variety of sources, such as surveys, focus groups, interviews, trade shows, complaints, and even expert opinions (Griffin & Hauser, 1993; Gryna, 2001). Later, customer needs should be translated into technical attributes. In reality, customer needs are fulfilled by completing those specified technical attributes. To incorporate the environmental concerns (ECs) in QFD, the philosophy generated by the Japan Environmental Management Association for Industry is used (JEMAI, 2001). That is, the voice of the customer consists of traditional and environmental customer needs. It is essential to capture the marketing needs from the customers’ perspective. Typically, due to the limited experience and lack of precise information, customers may not be able to describe what they really need precisely and clearly. Moreover, the expressions tend to be subjective, qualitative, and vague. Sometimes, expert opinions are preferred since these opinions are more technical, objective, and easy to be understood. To further consider environmental concerns, the voice of environmental concerns should be identified as well. To summarize customer requirements as well as environmental concerns systematically, a tree-like hierarchical structure, depicted in Fig. 2, can be constructed in HoQ. When customer needs and environmental concerns are identified, a cross-functional team is needed to translate those needs into
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Fig. 2. The integration of life cycle analysis and house of quality.
appropriate technical attributes. When the environmental concerns are taken into consideration, the term of technical attributes should consist of both general technical attributes and environmental attributes. To better identify environmental attributes, a systematic method, life cycle analysis, can be applied from the raw material, manufacturing, distribution, use, and recycling stages. As the environmental attributes are considered in HoQ, the next step is to evaluate the relationship between each of both traditional and environmental customer needs and each of both traditional and environmental technical attributes. Typically, the relationship is determined qualitatively, such as strong, medium, weak, and no relation with appropriate weights by a cross-functional team. The framework of HoQ in considering both traditional and environmental aspects is depicted in Fig. 3. Since customer needs as well as environmental concerns are complicated and often imprecise, fuzzy approaches, such as fuzzy sets, fuzzy arithmetic, and fuzzy defuzzification methods, can be applied (Vanegas & Labib, 2001; Wang, 1999; Zhou, 1998). Moreover, the relationship between customer needs and technical attributes is determined based upon the past experience due to the lack of precise information in the cross-functional team. To determine the relationship objectively, fuzzy methods can be implemented (Fung, Popplewell, & Xie, 1998; Temponi, Yen, & Tiao, 1999).
bers, linguistic variables, and fuzzy preference relation, are briefly reviewed. 2.2. Fuzzy sets The fuzzy set, originally proposed by Zadeh (1965), is defined as follows: In a universe of discourse Ux, a fuzzy subset A of Ux is characterized by a membership function fA(x), where fA:Ux ? [0, 1] and the membership function associates with each member x of Ux a number fA(x) is in the interval [0, 1], representing the grade of membership of x in A. The larger fA(x) represents the stronger degree of membership of x in A. The height of the fuzzy set means the maximum degree of membership. If there exists at least an element with the height of one, then the fuzzy set is normalized. ~ # R whose A fuzzy number is a convex, normalized fuzzy set A membership function is at least segmentally continuous and has the functional value lA(x) = 1 at precisely one element. In this study, the triangular fuzzy numbers defined by Dubois and Prade (1978) are used. A fuzzy number A in the real line R is a triangular fuzzy number, denoted by A = (a, b, c) and presented in Fig. 4, if its membership function fA:R ? [0, 1] is equal to
2.1. Fuzzy sets, fuzzy operations, and fuzzy preference relation
8 xa > < ba ; a 6 x 6 b; xc fA ðxÞ ¼ bc ; b 6 x 6 c; > : 0; otherwise;
In this section, the concepts of fuzzy sets, fuzzy numbers, fuzzy operations, particularly the operations of triangular fuzzy num-
with a 6 b 6 c. Let A and B be two normal, convex fuzzy subsets of R with piecewise continuous membership function fA(x) and fB(y), "x, y R,
Technical Attributes
Life Cycle Analysis
Customer Needs
Importance
Customer requirements without environmental concerns Customer requirements with
Weight
Relationship between customer requirements and technical attributes
environmental concerns EC: environmental concern
Target Value
Fig. 3. The planning of HoQ by further considering environmental concerns.
ð1Þ
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μA (x) 1 0.8
μ (x) =
0.6
x ≤ a or x ≥ c
0 x − a
a≤ x ≤ b
b − a c − x
b≤ x ≤c
c − b
0.4 0.2 x a
c
b
Fig. 4. Triangular membership functions.
respectively. Let be a classic operator, such as +, , . . . , ^, _, etc., and let () be the corresponding fuzzy operator. The extended operation on A and B, denoted by A()B, is defined by
fAðÞB ðtÞ ¼ sup ffA ðxÞ ^ fB ðyÞg:
dj ¼ ðLdj ; Mdj ; Udj Þ ¼
ð2Þ
P P P 1X 1X 1X Ldqj ; Mdqj ; Udq P q¼1 P q¼1 P q¼1 j
! ð9Þ
x;y:t¼xy
2.3. Operations of triangular fuzzy numbers The fuzzy arithmetic operations of triangular fuzzy numbers are described as follows (Liang & Wang, 1993). If two triangular fuzzy numbers are Ai = (Li, Mi, Ui) and Aj = (Lj, Mj, Uj), (Ai > 0 and Aj > 0), then those operators are defined as
Addition : Ai Aj ¼ ðLi þ Lj ; M i þ M j ; U i þ U j Þ;
ði; j NÞ;
ð3Þ
Negation of Ai : HAi ¼ ðU i ; M i ; Li Þ;
ð4Þ
Subtraction H : Ai H Aj ¼ ðLi U j ; Mi Mj ; U i Lj Þ;
ð5Þ
Step 3: Aggregate fuzzy performance ratings through all experts by means of fuzzy addition and scalar multiplication to form a comprehensive performance matrix S, where per2j Pj formance rating Sji ¼ ð1=PÞ ðS1j i Si ; . . . ; Si Þ with P experts is a triangular fuzzy number of the form
Sji ¼ ðLSji ; MSji ; USji Þ ¼
P P P 1X 1X 1X LSqj MSqj USqj i ; i ; P q¼1 P q¼1 P q¼1 i
! ð10Þ
Multiplication : h ðL; M; UÞ ¼ ðhL; hM; hUÞ; Ai Aj ðLi Lj ; M i Mj ; U i U j Þ; A1 i
ðU i U j P 0Þ;
ð6Þ
1 1 ðU 1 i ; M i ; Li Þ;
Inverse of Ai : ðU i > 0Þ; ð7Þ Division £ : Ai £ Aj ðLi =U j ; Mi =Mj ; U i =Lj Þ; ðU i P 0; U j > 0Þ: ð8Þ 2.4. Linguistic variables Linguistic variables, proposed by Zadeh (1975a, 1975b, 1976), are variables whose values are words or sentences in a natural or artificial language. For example, {very poor, poor, fair, good, very good} can be expressed. Basic variables can be defined as [0, 1] in order to evaluate suitable situation of the criteria (attributes), where ‘‘1” can be regarded as the optimal suitable situation, while ‘‘0” can be regarded as the poorest situation. 2.5. Fuzzy preference relation Li (1999) has proposed a fuzzy preference relation, and a fuzzy model associated with the solution algorithm is suggested based on an a-level weighted. A stepwise algorithm of the fuzzy model used in this study is presented as follows: Step 1: Define the linguistic terms as triangular fuzzy numbers. Step 2: Determine the weight for each customer requirement (CR) by P experts. Then, w1j ; w2j , . . ., and wPj are the weight for the jth CR. To fuzzify these weights described in Step 1, the fuzzy weights are d1j ; d2j , . . ., and dPj for the jth CR. Final, aggregate weights through all experts by means of fuzzy addition and scalar multiplication for the jth CR to form an average value triangular fuzzy number by the following equation:
Step 4: Aggregate fuzzy performance ratings with fuzzy weights by means of fuzzy multiplication to form an importance weighted, comprehensive decision matrix E:
Eji ¼ Sji di :
ð11Þ
Step 5: Compute the ranking values for each CR under each technical attribute (TA) by taking defuzzification for each Eji . In this study, center-of-gravity (COG) is used for defuzzification since COG is one of the most common forms of defuzzification. The formula is as follows: Let Aji ðxÞ are membership functions associated with the fuzzy number Eji s for each iRand each j. Then the correspondent crisp b
x
j
xAi ðxÞdx
values uji are Rx a
j
Ai ðxÞdx
for each i and each j, where a is
the lower bound of support of the fuzzy number Eji , and b is the upper bound of support of the fuzzy number Eji . Step 6: Rank the TAs in accordance with the sum of the ranking values uji . That is, uj1 þ uj2 þ þ uj17 for each j.
3. An integrated framework Customer needs and environmental concerns can be generated by a group of expertise. The weight for each customer need and environmental concerns can also be evaluated and determined by this group of expertise. Assume a certain product with m CRs (without environmental concerns) and n m CRs (with environmental concerns) have been identified and integrated, denoted by CR1, CR2, CR3,. . ., CRm, (without environmental concerns) and
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CRm+1, CRm+2, CRm+3,. . ., CRn (with environmental concerns) by a group of P experts. Let wq ¼ ½wq1 ; wq2 ; . . . ; wqm ; wqmþ1 ; . . . ; wqn T be the matrix of the weight for each CR evaluated by the qth expert, and dq ¼ ðdq1 ; dq2 ; . . . ; dqm ; dqmþ1 ; . . . dqn ÞT be the fuzzy output vector based on the fuzzy numbers for each CR evaluated by the qth expert. Therefore, if these P experts are to evaluate CRm, then the weight for CRm is expressed as wqm in crisp value, where q = 1,2,3,. . .,P. The wqm value can be transformed as a triangular fuzzy number dqm , where q = 1,2,3,. . .,P, by fuzzy inference techniques, discussed in Fig. 5. The fuzzy number dqm is represented as ðLdqm ; Mdqm ; Udqm Þ, where Ldqm ; Mdqm , and Udqm represent the lower bound, average value, and the upper bound, respectively. For example, if the first expert gives CRm rating for the mth customer requirement with w1m ¼ 5, the value which implies wm is ‘medium’ is assigned by a
Q1: Very Unimportant (VU) Q2: Unimportant (U) Q3: Midium (M) Q4: Important (I) Q5: Very Important (VI)
triangular fuzzy number (TFN) Q3 = ‘approximately 5’ = (4, 5, 6), i.e., d1m ¼ ð4; 5; 6Þ. Each customer requirement will be evaluated and transformed as fuzzy output by experts based on Eq. (1). For instance, the mth CR can be transformed as dm by qth expert. The average fuzzy number dm is computed and presented as dm = (Ldm, Mdm, Udm), where P P Ldm = Pq¼1 Ldqm =p (fuzzy lower bound), Mdm = Pq¼1 Mdqm =p (averPP q age), and Udm = q¼1 Udm =p (fuzzy upper bound), and P is the number of experts. For example, there are three experts who rate the weight of the mth CR by w1m ; w2m , and w3m . The weight for each CR is a crisp value such that fuzzification is needed. The crisp values of the weight for the mth CR is Q1, Q2, and Q3. If the corresponding values for the crisp values of Q1, Q2, and Q3 are 1, 3, and 5, respectively, the respective TFN are (1, 1, 2), (2, 3, 4), and (4, 5, 6). Note that d1m ¼ ðLd1m ; Md1m ; Ud1m Þ = (1, 1, 2), d2m ¼ ðLd2m ; Md2m ; Ud2m Þ = (2, 3, 4), and
1.0 0.8 0.6 0.4 0.2
1 Q1
3 Q2
5 Q3
7 Q4
9 Q5
0.0 0 1 Q1
2
3 Q2
4
5 Q3
Fig. 5. Crisp values and triangular fuzzy numbers.
Table 1 The fuzzy result for weighting importance for each customer requirement (a = 0.5)
6
7 Q4
8
9 Q5
Table 2 The Eco-QFD result for one of the experts’ opinions
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Table 3 The Eco-QFD table
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d3m ¼ ðLd3m ; Md3m ; Ud3m Þ. Therefore, the aggregated value of the TFN value is computed by the following:
dm ¼ ðLdm ; Mdm ; Udm Þ ¼
1þ2þ4 1þ3þ5 2þ4þ6 ; ; 3 3 3
¼ ð2:3; 3; 4Þ: When the weight for each customer requirement and environmental concern is determined, the relationship between customer requirements and technical attributes is to be evaluated. The relationship is typically evaluated by the notations of }, s, and D to represent strong, medium, and weak relationships, while the blank shows no relationship. However, as the dimension of customers requirements increase, the number of potentially related design variables becomes much larger. One of the main problems associated with a large relationship matrix is the difficulty in setting priorities and importance using an accurate number. This is due to the fact that the value setting process is typically vague or imprecise in practice. Second, the data available for product design is often limited, inaccurate, or vague at best particularly when developing an entirely new product. To solve these problems, the fuzzy logic technique is typically used. Let S be the relationship matrix with the order of n r between CRs and TAs assuming that there are r TAs based upon life cycle analysis. The element Sji represents the relationship between the ith CR and jth TA. The relationship between CRs and TAs, Sji , is evaluated by a group of P experts and can be fuzzied and transformed by the fuzzy inference techniques with Sji ¼ ðLSji ; MSji ; USji Þ, where LSji ; MSji , and USji are the lower bound, average value, and the higher bound, respectively. The ranking value between ith CR and jth TA would be Sji multiplying di. Therefore, the ranking value, denoted by Aj, for the jth TA is Z j ¼ uj1 uj2 ; . . . ; ujn , where uji is a crisp value associated with Eji ¼ Sji di by COG defuzzification. The aim of this Eco-QFD is to prioritize the importance of TAs based on the LCA. In fact, the Eco-design product development problem can be formulated as a fuzzy multi-objective model based P Pn j j j on the QFD by Max Z j ¼ m i¼1 ui þ i¼mþ1 ui , where Z is the sum of the potential importance impact of the jth TA for j = 1, 2, 3, . . ., r, and uji is the ranking value between ith CR and jth TA, with i = 1, 2, 3, . . . ,m without environmental concerns, and i = m + 1, m + 2, . . . , n with environmental concerns.
Table 4 The ranking difference when consider the environment concern (less is better)
w/o EC: without environmental concern. With EC: with environmental concern.
4. A case study A real-life case for the toner cartridge design of the printer is used in this study. To implement the Eco-QFD, a panel of three experts from recycling cartridge manufacturer, original cartridge designer, and environmental expert was formed. Based on the LCA, the product stages of raw material, manufacturing, distribution, and disposal processes are evaluated and analyzed. The experts have identified 17 customer requirements by the company’s sales network and marketing surveys as shown in Table 1. These 17 customer requirements can be further categorized into four levels with cost, function, appearance, and environment. To evaluate the weight for each CR, a scale of 1–3–5–7–9 is used, where 9 means the strongest relation. Each expert gives a crisp value for each CR. Then, the crisp value for each CR given by each expert is fuzzified. For example, the computation of the weight for CR17 is as follows:
P3
u u¼1 Ld17
d17 ¼
3
¼
P3 ;
u u¼1 Md17
3
P3 ;
u u¼1 Ud17
!
3
8þ2þ2 9þ3þ3 9þ4þ4 ; ; ¼ ð4; 5; 5:67Þ; 3 3 3
where d117 ¼ ðLd117 ; Md117 ; Ud117 Þ ¼ ð8; 9; 9Þ; d217 ¼ ðLd217 ; Md217 ; Ud217 Þ ¼ ð2; 3; 4Þ, and d317 ¼ ðLd317 ; Md317 ; Ud317 Þ ¼ ð2; 3; 4Þ. Table 1 summarizes the fuzzy results of determining the weight for each customer requirement. Technical attributes are deployed and evaluated based on the life cycle analysis from the raw material, manufacturing, distribution, use, and recycling stages. Owing to the space, this paper only shows the fuzzy results in Tables 2 and 3. For instance, the crisp values of carbon in TA column are the numbers in the columns multiplying the respective weights in CR rows as depicted in Table 3. The numerical figure is 516.6, which is accounted for 8.83%. To draw conclusions based on Table 3, material supply and design and manufacturing are the most dominant factors, accounted for 70.34%, that influence customer’s purchasing decisions. This indicates that the product development team can prioritize all the TAs and tackle the problems from the most important to the least important when resources and time are limited. A comparison be-
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tween with and without environmental concerns is summarized in Table 4 for each TA with less is better. The results show that disassembly easily (TA14) and toner recycling (TA17) should be taken into consideration with highest priorities from the viewpoints of environmental concerns. 5. Conclusions This paper develops an Eco-QFD to provide a framework for designing Eco-products by integrating the LCA into QFD throughout the entire product development process. The major advantages of the Eco-QFD framework are summarized as follows. First, the Eco-QFD is a useful tool to integrate not only the environmental concerns but also quality, cost, and customer needs to improve the product design process. It is essentially important to satisfy customer needs from a wide variety of considerations if the Ecoproduct is to be successful in the market place. Second, by applying fuzzy theory in the Eco-QFD, the product development team can overcome the vagueness and uncertainty faced in group decisionmaking processes. Finally, various technical attributes and environmental concerns can be prioritized such that the product development team can concentrate their limited resources on critical issues to develop customer-oriented environmentally friendly products. References Akao, Y. (Ed.). (1990). Quality function deployment. Cambridge, MA: Productivity Press. Barzilai, J., & Golany, B. (1994). AHP rank reversal. Normalization and Aggregation Rules, INFOR, 32, 57–63. Bosserman, S. (1992). Quality function deployment: The competitive advantage. Motorola: Private Trunked Systems Division. Bossert, J. L. (1991). Quality function deployment: A practitioner’s approach. Milwaukee, WI: ASQC Quality Press. CGP (1990). Canada green plan. Ottawa, Ontario: Ministry of Supply and Services. Chan, L. K., & Wu, M. L. (2002). Quality function deployment a literature review. European Journal of Operational Research, 143, 463–497. Chan, L. K., & Wu, M. L. (2002–2003). Quality function deployment: A comprehensive review of its concepts and methods. Quality Engineering, 15(1), 23–35. Clausing, D. (1994). Total quality development: A step-by-step guide to world-class concurrent engineering. New York: ASME Press. Costic, M., Sullivan, J., Bryant, B., & Shangguan, D. (1996). LCI for automotive electronic systems: Substitution assessment of Ag–Sn for Pb–Sn solder at Ford Motor Company. In Proceedings of IEEE international symposium on electronics and the environment (pp. 58–63). Cristopher, M., Deshmukh, A., & Wang, B. (1996). Green quality function deployment. In Proceeding of the 4th international conference on environmentally conscious design and manufacturing (pp. 297–304). Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9(6), 613–626. Fung, R. Y. K., Popplewell, K., & Xie, J. (1998). An intelligent hybrid system for customer requirements analysis and product attribute targets determination. International Journal of Production Research, 36(1), 13–34. Green, P. E., & Srinivasna, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5, 103–123. Green, P. E., & Srinivasna, V. (1990). Conjoint analysis in consumer research: New developments and direction. Journal of Marketing, 54(4), 3–19. Griffin, A., & Hauser, J. (1993). Voice of the customer. Marketing Science, 12, 419–425. Gryna, F. M. (2001). Quality planning and analysis: From product development through use (4th ed.). London: McGraw-Hill International Edition. Hauser, J. R., & Clausing, D. (1988). The house of quality. Harvard Business Review, 66(3), 63–73. Horvath, A., Hendrickson, C. T., Lave, L. B., & McMichael, F. C. (1995). Performance measurement for environmentally – Conscious manufacturing. Manufacturing Science and Engineering, MED-Vol. 2–2/MH-Vol. 3–2, 855–860. HR (1991). House resolution 2845. United States Congress: House Committee on Energy and Commerce. Hui, I. K., He, L., & Dang, C. (2002). Environmental impact assessment in an uncertain environment. International Journal of Production Research, 40(2), 375–388.
JEMAI (2001). Study on the introduction and promotion of environmentally conscious business activities – Design for environment. Japan Environmental Management Association for Industry, FY 2000 Report. Korpalski, T. (1996). The role of the product steward in advancing design for environmental in Hewlett–Packard’s computer products organization. In Proceedings of IEEE international symposium on electronics and the environment (pp. 37–41). Kuo, T. C., Zhang, H. C., & Huang, S. H. (2000a). Disassembly analysis for electromechanical products: A graph-based heuristic approach. International Journal of Production Research, 38(5), 993–1007. Kuo, T. C., Chang, S. H., & Huang, S. H. (2000b). Fuzzy multi-attribute decisionmaking in green engineering. In Proceedings of the fifth international conference on industrial engineering – Theory, applications and practice (Vol. 278). Kuo, T. C. (2000). Disassembly sequence and cost analysis for electromechanical products. Robotics and Computer Integrated Manufacturing, 16, 43–54. Kuo, T. -C., & Wu, H. -H. (2001). Using a green quality function deployment to develop green products. In Proceedings of the international conference on production research ICPR-16 (No. 0189). Kuo, T.-C., & Wu, H.-H. (2003). Green products development by applying grey relational analysis and green quality function deployment. International Journal of Fuzzy Systems, 5(4), 229–238. Lee, S. G., Lye, S. W., & Khoo, M. K. (2001). A multi-objective methodology for evaluating product end-of-life options and disassembly. International Journal of Advanced Manufacturing Technology, 18, 148–156. Liang, G.-S., & Wang, M.-J. J. (1993). A fuzzy multi-criteria decision-making approach for robot selection. Robotics and Computer Integrated Manufacturing, 10(4), 267–279. Li, R. J. (1999). Fuzzy method in group decision making. Computers and Mathematics with Applications, 38, 91–101. Masui, K., Sakao, T., Aizawa, S., & Inaba, A. (2001). Quality function deployment for environment (QFDE) to support design for environment (DfE). In Symposia on design for manufacturability, international mechanical engineering congress and exposition (IMECE), November 11–16, New York, NY. Owen, J. V. (1993). Environmentally conscious manufacturing. Manufacturing Engineering, 111(4), 44–55. Park, T., & Kim, K.-J. (1998). Determination of an optimal set of design requirements using house of quality. Journal of Operations Management, 16, 569–581. Sage, J. (1993). Industrielle Abfallvermeidung und deren Bewertung am Beispiel der Leiterplattenherstellung (Graz: dbv/Technische Universitat Graz). Satty, T. L. (1980). The analytic hierarchy process: Planning priority setting, resource allocation. New York: McGraw Hill. Sullivan, L. P. (1986). Quality function deployment. Quality Progress, 19(6), 39–50. Tan, K. C., & Shen, X. X. (2000). Integrating Kano’s model in the planning matrix of quality function deployment. Total Quality Management, 11(8), 1141–1151. Temponi, C., Yen, J., & Tiao, W. A. (1999). House of quality: A fuzzy logic-based requirements analysis. European Journal of Operational Research, 117, 340–354. Vanegas, L. V., & Labib, A. W. (2001). A fuzzy quality function deployment (FQFD) model for deriving optimum targets. International Journal of Production Research, 39(1), 99–120. Wang, J. (1999). Fuzzy outranking approach to prioritize design requirements in quality function deployment. International Journal of Production Research, 37(4), 899–916. Wasserman, G. S. (1993). On how to prioritize design requirement during the QFD planning process. IIE Transactions, 25(3), 59–65. Wu, H.-H. (2002). Implementing grey relational analysis in quality function deployment to strengthen multiple attribute decision making processes. Journal of Quality, 9(2), 19–39. Wu, H. -H. (2002–03). A comparative study of using grey relational analysis in multiple attribute decision making problems. Quality Engineering (Vol. 15(2), pp. 209–217). Wu, H.-H., Liao, A. Y. H., & Wang, P.-C. (2005). Using grey theory in quality function deployment to analyse dynamic customer requirements. International Journal of Advanced Manufacturing Technology, 25, 1241–1247. Yu, Y., Jin, K., Zhang, H. C., Ling, F. F., & Barnes, D. (2000). A decision-making model for materials management of end-of-life electronic products. Journal of Manufacturing Systems, 19(2), 94–107. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353. Zadeh, L. A. (1975a). The concept of a linguistic variable and its application to approximate reasoning (I). Information Sciences, 8, 199–249. Zadeh, L. A. (1975b). The concept of a linguistic variable and its application to approximate reasoning (II). Information Sciences, 8, 301–357. Zadeh, L. A. (1976). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 9, 43–80. Zhang, Y., Wang, H.-P., & Zhang, C. (2000). Green QFD-II: A life cycle approach for environmentally conscious manufacturing by integrating LCA and LCC into QFD matrices. International Journal of Production Research, 37(5), 1075–1091. Zhou, M. (1998). Fuzzy logic and optimization models for implementing QFD. Computers and Industrial Engineering, 35, 237–240.
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Integration of environmental considerations in quality function deployment by using fuzzy logic Tsai-Chi Kuo a,*, Hsin-Hung Wu b, Jiunn-I Shieh c a
Department of Industrial Engineering and Management, Ming Hsin University of Science and of Technology, 304 Hsinchu, Taiwan, ROC Department of Business Administration, National Changhua University of Education, Changhua, Taiwan, ROC c Department of Information Science and Applications, Asia University, Taichung, Taiwan, ROC b
a r t i c l e
i n f o
Keywords: Eco-designed product Quality function deployment Fuzzy group method
a b s t r a c t Recently, resource optimization (energy and material) and environmental issues in the life-cycle context are taken very seriously by both the general public and government agencies. These activities urge governments and companies alike to set up environmental friendly production technologies, which aim to avoid harmful emissions into air, water and soil. However, these Eco-designed products have not been achieved a favorable position in the marketplace as expected even though they appear to be more environmental friendly and economical. This may be due that they are focused solely on environmental impact analysis without regard for customer needs and cost considerations. In this research, an Eco-quality function deployment (Eco-QFD) is developed to aid a product design team in considering environmental concerns since QFD is a proven quality systems tool to achieve total customer satisfaction. A fuzzy group method is applied to Eco-QFD for product development planning to reduce the vagueness and uncertainty in a group decision-making process. This fuzzy multi-objective model not only considers the overall customer satisfaction but also encourages enterprises to produce an environmentally friendly product. With an interactive approach, the optimal balance between environmental acceptability and overall customer satisfaction can be obtained. Finally, a case study illustrating the application of the proposed model is also provided. 2008 Elsevier Ltd. All rights reserved.
1. Introduction Recently, resource optimization (energy and material) and environmental issues in the life-cycle context are taken very seriously by both the general public and government agencies. These activities urge governments and companies alike to set up environmental friendly production technologies, which aim to avoid harmful emissions into air, water and soil (Kuo, Chang, & Huang, 2000b; Kuo, Zhang, & Huang, 2000a; Yu, Jin, Zhang, Ling, & Barnes, 2000). Also, some governments have set up official Eco-labeling schemes, which are used to inform consumers of Eco (ecology and economic) design products (CGP, 1990; HR, 1991; Owen, 1993). In the past, several environmental impact analyses and evaluation tools have been developed for Eco-design products. For example, health hazard scoring (HHS) is an evaluation method for health hazard assessment (Barzilai & Golany, 1994). Kuo (2000) presented a disassembly planning method for the end-of-life products during the initial design stage. Sage (1993) offered sustainable process indices based on an operational definition of sustainability, * Corresponding author. Fax: +886 35455149. E-mail addresses: [email protected] (T.-C. (H.-H. Wu), [email protected] (J.-I. Shieh).
Kuo),
[email protected]
0957-4174/$ - see front matter 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2008.08.029
which relies not only on environmental risk, but also includes economic and technical feasibility as well as political compromise. Horvath, Hendrickson, Lave, and McMichael (1995) developed an approach to track toxic releases and associated risks over time based on the data of the Toxic Release Inventory (TRI) and relevant toxic indices. Costic, Sullivan, Bryant, and Shangguan (1996) estimated the environmental performance of conventional lead-based solders and their substitutes using life cycle analysis (LCA). Hewlett–Packard provided design for environment (DfE) tools for companies, such as DfE guidelines, product assessments, and product stewardship metrics (Korpalski, 1996). However, these Eco-design products are not favorable in the market place as expected even though they sound more environmental friendly and economical. This situation may be due to they are focused solely on environmental impact analysis without paying much attention to customer needs and cost considerations. In other words, the key issue for a successful Eco-Design product is not only to meet environmental objectives such as resource and energy conservation and environmental burden reduction but also to take into account cost effectiveness, market demand, and multi-functionality requirements (Lee, Lye, & Khoo, 2001). To further incorporate customer needs into product design concepts and product planning, quality function deployment (QFD) is
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considered to be one of the very effective quality systems to achieve total customer satisfaction (Akao, 1990; Chan and Wu, 2002–2003; Clausing, 1994; Wu, 2002–2003; Wu, 2002). The major purpose of QFD is to collect and analyze the voice of the customer and then deploy those voices into high quality products to meet or even exceed customer requirements. That is, product should be designed to meet customer needs so the marketing department, design engineers, and manufacturing staffs should work closely together from the time the product is first conceived (Hauser & Clausing, 1988; Tan & Shen, 2000). Generally speaking, QFD is an overall concept that provides a means of translating customer requirements into appropriate engineering characteristics or technical attributes for each stage of product development and production (Sullivan, 1986; Tan & Shen, 2000). The most commonly used QFD is composed of four major phases, i.e., product planning (also known as house of quality (HoQ)), parts deployment, process planning, and production planning (Chan and Wu, 2002–2003; Hauser & Clausing, 1988; Wu, Liao, & Wang, 2005). The applications of QFD have been expanded to a wide variety of areas, such as design planning, engineering, management, teamwork, timing, costing, to name a few (Chan & Wu, 2002; Kuo & Wu, 2001, 2003). Moreover, Cristopher, Deshmukh, and Wang (1996) developed the Green QFD (GQFD) method to integrate life cycle analysis and QFD to evaluate products using environmental considerations. Furthermore, Zhang, Wang, and Zhang (2000) presented the GQFD II that integrates life cycle assessment, life cycle costing and QFD into an efficient tool that deploys customer, environmental, and cost requirements throughout the entire product development process. In Japan, a QFD for the Environment (QFDE) tool is also used to design an environmental friendly product (JEMAI, 2001; Masui, Sakao, Aizawa, & Inaba, 2001). QFDE, depicted in Fig. 1, is carried out in four phases. Phases I and II allow the user to identify environmentally significant components (component parts and devices) of the product. Phases III and IV allow the user to choose the most environmentally friendly design from alternative design proposals. However, loosely defined and structured, QFD sometimes becomes more of an art than a science, which makes it most difficult for practitioners to utilize (Chan & Wu, 2002). The reasons are as follows: First, the weighting factors are normally described using vague linguistic type language, such as ‘serious’, ‘moderate’, and ‘low’ (Hui, He, & Dang, 2002). Second, customer information is gathered in a subjective ad hoc manner, since it comes from a vari-
Step 1: Arrange conventional and environmental VOCs in order Step 2: Prioritize the VOCs Phase I Step 3: Specify enginnering metrics (EMs) Step 4: Determine the relevance of the VOCs to the EMs Step 1: Present the components in an orderly requirements Step 2: Determine the relationships between specific EMs
Phase II
Environmentally significant components Step 1: Assess project costs Step 2: Present design proposal
Phase III
Step 1: Evaluate alternative design proposals Step 2: Choose one from the proposals
Phase IV
Design the proposal that is the most environmentally friendly Fig. 1. Flow of QFDE-based DfE (JEMAI, 2001).
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ety of sources, using surveys, focus groups, interviews, listening to salepeople, talking to trade show exhabitors, reading journals, and reviewing existing data on warranty and customer complaints (Bossert, 1991). Third, over 50% of the QFD efforts are spent on capturing the voice of the customer-performance characteristics and relative importance characteristics (Bosserman, 1992). Several research methodologies have been developed to determine the weighting factors for different customer needs, such as the well know AHP (Satty, 1980), conjoint analysis (Green & Srinivasna, 1978, 1990), the benchmarking of products from other companies (Park & Kim, 1998). Wasserman (1993) formulated the QFD planning process as a linear programming model to select the matrix of design features, which would result in the highest level of customer satisfaction. For an attribute’s importance evaluation, it is usually expressed in linguistic terms such as ‘unimportant’ and ‘very important’ and then transferred in the form of concise numbers. However, it is also well recognized that people’s assessments of the qualitative attributes of a product are always subjective and thus imprecise, and the linguistic terms that people use to express their feelings or judgments are vague in nature. To solve the above problems, a fuzzy approach to the Eco-QFD is proposed and illustrated to aid a design team in choosing target levels for technical attributes based on environmental concerns. Unlike two valued Boolean logic, fuzzy logic is multi-valued, which deals with degrees of membership and degrees of truth. Therefore, the Eco-design product development problem was formulated as fuzzy multiobjective model based on the QFD planning. By applying the matrix with a simplified house of quality, the importance that customers assign to each product area can be ascertained. Besides, by introducing environmental factors, an optimizing model is developed and illustrated. This model not only fulfill overall customer satisfaction but also provides a means of planning successful environmentally friendly products. With an interactive approach, the optimal balance between environmental acceptability and overall customer satisfaction can be obtained. Finally, a case study illustrating the application of the proposed model is also provided.
2. Identify customer requirements and technical attributes in QFD Gathering and analyzing the voice of the customer is critically important in QFD in order to provide customer-oriented products. The voice of the customer may come from a wide variety of sources, such as surveys, focus groups, interviews, trade shows, complaints, and even expert opinions (Griffin & Hauser, 1993; Gryna, 2001). Later, customer needs should be translated into technical attributes. In reality, customer needs are fulfilled by completing those specified technical attributes. To incorporate the environmental concerns (ECs) in QFD, the philosophy generated by the Japan Environmental Management Association for Industry is used (JEMAI, 2001). That is, the voice of the customer consists of traditional and environmental customer needs. It is essential to capture the marketing needs from the customers’ perspective. Typically, due to the limited experience and lack of precise information, customers may not be able to describe what they really need precisely and clearly. Moreover, the expressions tend to be subjective, qualitative, and vague. Sometimes, expert opinions are preferred since these opinions are more technical, objective, and easy to be understood. To further consider environmental concerns, the voice of environmental concerns should be identified as well. To summarize customer requirements as well as environmental concerns systematically, a tree-like hierarchical structure, depicted in Fig. 2, can be constructed in HoQ. When customer needs and environmental concerns are identified, a cross-functional team is needed to translate those needs into
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Fig. 2. The integration of life cycle analysis and house of quality.
appropriate technical attributes. When the environmental concerns are taken into consideration, the term of technical attributes should consist of both general technical attributes and environmental attributes. To better identify environmental attributes, a systematic method, life cycle analysis, can be applied from the raw material, manufacturing, distribution, use, and recycling stages. As the environmental attributes are considered in HoQ, the next step is to evaluate the relationship between each of both traditional and environmental customer needs and each of both traditional and environmental technical attributes. Typically, the relationship is determined qualitatively, such as strong, medium, weak, and no relation with appropriate weights by a cross-functional team. The framework of HoQ in considering both traditional and environmental aspects is depicted in Fig. 3. Since customer needs as well as environmental concerns are complicated and often imprecise, fuzzy approaches, such as fuzzy sets, fuzzy arithmetic, and fuzzy defuzzification methods, can be applied (Vanegas & Labib, 2001; Wang, 1999; Zhou, 1998). Moreover, the relationship between customer needs and technical attributes is determined based upon the past experience due to the lack of precise information in the cross-functional team. To determine the relationship objectively, fuzzy methods can be implemented (Fung, Popplewell, & Xie, 1998; Temponi, Yen, & Tiao, 1999).
bers, linguistic variables, and fuzzy preference relation, are briefly reviewed. 2.2. Fuzzy sets The fuzzy set, originally proposed by Zadeh (1965), is defined as follows: In a universe of discourse Ux, a fuzzy subset A of Ux is characterized by a membership function fA(x), where fA:Ux ? [0, 1] and the membership function associates with each member x of Ux a number fA(x) is in the interval [0, 1], representing the grade of membership of x in A. The larger fA(x) represents the stronger degree of membership of x in A. The height of the fuzzy set means the maximum degree of membership. If there exists at least an element with the height of one, then the fuzzy set is normalized. ~ # R whose A fuzzy number is a convex, normalized fuzzy set A membership function is at least segmentally continuous and has the functional value lA(x) = 1 at precisely one element. In this study, the triangular fuzzy numbers defined by Dubois and Prade (1978) are used. A fuzzy number A in the real line R is a triangular fuzzy number, denoted by A = (a, b, c) and presented in Fig. 4, if its membership function fA:R ? [0, 1] is equal to
2.1. Fuzzy sets, fuzzy operations, and fuzzy preference relation
8 xa > < ba ; a 6 x 6 b; xc fA ðxÞ ¼ bc ; b 6 x 6 c; > : 0; otherwise;
In this section, the concepts of fuzzy sets, fuzzy numbers, fuzzy operations, particularly the operations of triangular fuzzy num-
with a 6 b 6 c. Let A and B be two normal, convex fuzzy subsets of R with piecewise continuous membership function fA(x) and fB(y), "x, y R,
Technical Attributes
Life Cycle Analysis
Customer Needs
Importance
Customer requirements without environmental concerns Customer requirements with
Weight
Relationship between customer requirements and technical attributes
environmental concerns EC: environmental concern
Target Value
Fig. 3. The planning of HoQ by further considering environmental concerns.
ð1Þ
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μA (x) 1 0.8
μ (x) =
0.6
x ≤ a or x ≥ c
0 x − a
a≤ x ≤ b
b − a c − x
b≤ x ≤c
c − b
0.4 0.2 x a
c
b
Fig. 4. Triangular membership functions.
respectively. Let be a classic operator, such as +, , . . . , ^, _, etc., and let () be the corresponding fuzzy operator. The extended operation on A and B, denoted by A()B, is defined by
fAðÞB ðtÞ ¼ sup ffA ðxÞ ^ fB ðyÞg:
dj ¼ ðLdj ; Mdj ; Udj Þ ¼
ð2Þ
P P P 1X 1X 1X Ldqj ; Mdqj ; Udq P q¼1 P q¼1 P q¼1 j
! ð9Þ
x;y:t¼xy
2.3. Operations of triangular fuzzy numbers The fuzzy arithmetic operations of triangular fuzzy numbers are described as follows (Liang & Wang, 1993). If two triangular fuzzy numbers are Ai = (Li, Mi, Ui) and Aj = (Lj, Mj, Uj), (Ai > 0 and Aj > 0), then those operators are defined as
Addition : Ai Aj ¼ ðLi þ Lj ; M i þ M j ; U i þ U j Þ;
ði; j NÞ;
ð3Þ
Negation of Ai : HAi ¼ ðU i ; M i ; Li Þ;
ð4Þ
Subtraction H : Ai H Aj ¼ ðLi U j ; Mi Mj ; U i Lj Þ;
ð5Þ
Step 3: Aggregate fuzzy performance ratings through all experts by means of fuzzy addition and scalar multiplication to form a comprehensive performance matrix S, where per2j Pj formance rating Sji ¼ ð1=PÞ ðS1j i Si ; . . . ; Si Þ with P experts is a triangular fuzzy number of the form
Sji ¼ ðLSji ; MSji ; USji Þ ¼
P P P 1X 1X 1X LSqj MSqj USqj i ; i ; P q¼1 P q¼1 P q¼1 i
! ð10Þ
Multiplication : h ðL; M; UÞ ¼ ðhL; hM; hUÞ; Ai Aj ðLi Lj ; M i Mj ; U i U j Þ; A1 i
ðU i U j P 0Þ;
ð6Þ
1 1 ðU 1 i ; M i ; Li Þ;
Inverse of Ai : ðU i > 0Þ; ð7Þ Division £ : Ai £ Aj ðLi =U j ; Mi =Mj ; U i =Lj Þ; ðU i P 0; U j > 0Þ: ð8Þ 2.4. Linguistic variables Linguistic variables, proposed by Zadeh (1975a, 1975b, 1976), are variables whose values are words or sentences in a natural or artificial language. For example, {very poor, poor, fair, good, very good} can be expressed. Basic variables can be defined as [0, 1] in order to evaluate suitable situation of the criteria (attributes), where ‘‘1” can be regarded as the optimal suitable situation, while ‘‘0” can be regarded as the poorest situation. 2.5. Fuzzy preference relation Li (1999) has proposed a fuzzy preference relation, and a fuzzy model associated with the solution algorithm is suggested based on an a-level weighted. A stepwise algorithm of the fuzzy model used in this study is presented as follows: Step 1: Define the linguistic terms as triangular fuzzy numbers. Step 2: Determine the weight for each customer requirement (CR) by P experts. Then, w1j ; w2j , . . ., and wPj are the weight for the jth CR. To fuzzify these weights described in Step 1, the fuzzy weights are d1j ; d2j , . . ., and dPj for the jth CR. Final, aggregate weights through all experts by means of fuzzy addition and scalar multiplication for the jth CR to form an average value triangular fuzzy number by the following equation:
Step 4: Aggregate fuzzy performance ratings with fuzzy weights by means of fuzzy multiplication to form an importance weighted, comprehensive decision matrix E:
Eji ¼ Sji di :
ð11Þ
Step 5: Compute the ranking values for each CR under each technical attribute (TA) by taking defuzzification for each Eji . In this study, center-of-gravity (COG) is used for defuzzification since COG is one of the most common forms of defuzzification. The formula is as follows: Let Aji ðxÞ are membership functions associated with the fuzzy number Eji s for each iRand each j. Then the correspondent crisp b
x
j
xAi ðxÞdx
values uji are Rx a
j
Ai ðxÞdx
for each i and each j, where a is
the lower bound of support of the fuzzy number Eji , and b is the upper bound of support of the fuzzy number Eji . Step 6: Rank the TAs in accordance with the sum of the ranking values uji . That is, uj1 þ uj2 þ þ uj17 for each j.
3. An integrated framework Customer needs and environmental concerns can be generated by a group of expertise. The weight for each customer need and environmental concerns can also be evaluated and determined by this group of expertise. Assume a certain product with m CRs (without environmental concerns) and n m CRs (with environmental concerns) have been identified and integrated, denoted by CR1, CR2, CR3,. . ., CRm, (without environmental concerns) and
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CRm+1, CRm+2, CRm+3,. . ., CRn (with environmental concerns) by a group of P experts. Let wq ¼ ½wq1 ; wq2 ; . . . ; wqm ; wqmþ1 ; . . . ; wqn T be the matrix of the weight for each CR evaluated by the qth expert, and dq ¼ ðdq1 ; dq2 ; . . . ; dqm ; dqmþ1 ; . . . dqn ÞT be the fuzzy output vector based on the fuzzy numbers for each CR evaluated by the qth expert. Therefore, if these P experts are to evaluate CRm, then the weight for CRm is expressed as wqm in crisp value, where q = 1,2,3,. . .,P. The wqm value can be transformed as a triangular fuzzy number dqm , where q = 1,2,3,. . .,P, by fuzzy inference techniques, discussed in Fig. 5. The fuzzy number dqm is represented as ðLdqm ; Mdqm ; Udqm Þ, where Ldqm ; Mdqm , and Udqm represent the lower bound, average value, and the upper bound, respectively. For example, if the first expert gives CRm rating for the mth customer requirement with w1m ¼ 5, the value which implies wm is ‘medium’ is assigned by a
Q1: Very Unimportant (VU) Q2: Unimportant (U) Q3: Midium (M) Q4: Important (I) Q5: Very Important (VI)
triangular fuzzy number (TFN) Q3 = ‘approximately 5’ = (4, 5, 6), i.e., d1m ¼ ð4; 5; 6Þ. Each customer requirement will be evaluated and transformed as fuzzy output by experts based on Eq. (1). For instance, the mth CR can be transformed as dm by qth expert. The average fuzzy number dm is computed and presented as dm = (Ldm, Mdm, Udm), where P P Ldm = Pq¼1 Ldqm =p (fuzzy lower bound), Mdm = Pq¼1 Mdqm =p (averPP q age), and Udm = q¼1 Udm =p (fuzzy upper bound), and P is the number of experts. For example, there are three experts who rate the weight of the mth CR by w1m ; w2m , and w3m . The weight for each CR is a crisp value such that fuzzification is needed. The crisp values of the weight for the mth CR is Q1, Q2, and Q3. If the corresponding values for the crisp values of Q1, Q2, and Q3 are 1, 3, and 5, respectively, the respective TFN are (1, 1, 2), (2, 3, 4), and (4, 5, 6). Note that d1m ¼ ðLd1m ; Md1m ; Ud1m Þ = (1, 1, 2), d2m ¼ ðLd2m ; Md2m ; Ud2m Þ = (2, 3, 4), and
1.0 0.8 0.6 0.4 0.2
1 Q1
3 Q2
5 Q3
7 Q4
9 Q5
0.0 0 1 Q1
2
3 Q2
4
5 Q3
Fig. 5. Crisp values and triangular fuzzy numbers.
Table 1 The fuzzy result for weighting importance for each customer requirement (a = 0.5)
6
7 Q4
8
9 Q5
Table 2 The Eco-QFD result for one of the experts’ opinions
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Table 3 The Eco-QFD table
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d3m ¼ ðLd3m ; Md3m ; Ud3m Þ. Therefore, the aggregated value of the TFN value is computed by the following:
dm ¼ ðLdm ; Mdm ; Udm Þ ¼
1þ2þ4 1þ3þ5 2þ4þ6 ; ; 3 3 3
¼ ð2:3; 3; 4Þ: When the weight for each customer requirement and environmental concern is determined, the relationship between customer requirements and technical attributes is to be evaluated. The relationship is typically evaluated by the notations of }, s, and D to represent strong, medium, and weak relationships, while the blank shows no relationship. However, as the dimension of customers requirements increase, the number of potentially related design variables becomes much larger. One of the main problems associated with a large relationship matrix is the difficulty in setting priorities and importance using an accurate number. This is due to the fact that the value setting process is typically vague or imprecise in practice. Second, the data available for product design is often limited, inaccurate, or vague at best particularly when developing an entirely new product. To solve these problems, the fuzzy logic technique is typically used. Let S be the relationship matrix with the order of n r between CRs and TAs assuming that there are r TAs based upon life cycle analysis. The element Sji represents the relationship between the ith CR and jth TA. The relationship between CRs and TAs, Sji , is evaluated by a group of P experts and can be fuzzied and transformed by the fuzzy inference techniques with Sji ¼ ðLSji ; MSji ; USji Þ, where LSji ; MSji , and USji are the lower bound, average value, and the higher bound, respectively. The ranking value between ith CR and jth TA would be Sji multiplying di. Therefore, the ranking value, denoted by Aj, for the jth TA is Z j ¼ uj1 uj2 ; . . . ; ujn , where uji is a crisp value associated with Eji ¼ Sji di by COG defuzzification. The aim of this Eco-QFD is to prioritize the importance of TAs based on the LCA. In fact, the Eco-design product development problem can be formulated as a fuzzy multi-objective model based P Pn j j j on the QFD by Max Z j ¼ m i¼1 ui þ i¼mþ1 ui , where Z is the sum of the potential importance impact of the jth TA for j = 1, 2, 3, . . ., r, and uji is the ranking value between ith CR and jth TA, with i = 1, 2, 3, . . . ,m without environmental concerns, and i = m + 1, m + 2, . . . , n with environmental concerns.
Table 4 The ranking difference when consider the environment concern (less is better)
w/o EC: without environmental concern. With EC: with environmental concern.
4. A case study A real-life case for the toner cartridge design of the printer is used in this study. To implement the Eco-QFD, a panel of three experts from recycling cartridge manufacturer, original cartridge designer, and environmental expert was formed. Based on the LCA, the product stages of raw material, manufacturing, distribution, and disposal processes are evaluated and analyzed. The experts have identified 17 customer requirements by the company’s sales network and marketing surveys as shown in Table 1. These 17 customer requirements can be further categorized into four levels with cost, function, appearance, and environment. To evaluate the weight for each CR, a scale of 1–3–5–7–9 is used, where 9 means the strongest relation. Each expert gives a crisp value for each CR. Then, the crisp value for each CR given by each expert is fuzzified. For example, the computation of the weight for CR17 is as follows:
P3
u u¼1 Ld17
d17 ¼
3
¼
P3 ;
u u¼1 Md17
3
P3 ;
u u¼1 Ud17
!
3
8þ2þ2 9þ3þ3 9þ4þ4 ; ; ¼ ð4; 5; 5:67Þ; 3 3 3
where d117 ¼ ðLd117 ; Md117 ; Ud117 Þ ¼ ð8; 9; 9Þ; d217 ¼ ðLd217 ; Md217 ; Ud217 Þ ¼ ð2; 3; 4Þ, and d317 ¼ ðLd317 ; Md317 ; Ud317 Þ ¼ ð2; 3; 4Þ. Table 1 summarizes the fuzzy results of determining the weight for each customer requirement. Technical attributes are deployed and evaluated based on the life cycle analysis from the raw material, manufacturing, distribution, use, and recycling stages. Owing to the space, this paper only shows the fuzzy results in Tables 2 and 3. For instance, the crisp values of carbon in TA column are the numbers in the columns multiplying the respective weights in CR rows as depicted in Table 3. The numerical figure is 516.6, which is accounted for 8.83%. To draw conclusions based on Table 3, material supply and design and manufacturing are the most dominant factors, accounted for 70.34%, that influence customer’s purchasing decisions. This indicates that the product development team can prioritize all the TAs and tackle the problems from the most important to the least important when resources and time are limited. A comparison be-
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