2B-3-3F Fuzzy Eco-Design Product Development by Using Quality Function Deployment Tsai-Chi Kuo Department ofIndustrial Engineering and Management Ming Hsin University ofScience and of Technology 304, Hsinchu, TAIWAN, R. 0. C. E-mail:
[email protected] Wu Hsin-Hung Department ofBusiness National Changhua University ofEducation Changhua, TAIWAN, R.0.C. Email:
[email protected]
Abstract
Keywords: Eco Design, Quality Function Deployment, Green desig, Life Cycle Analysis, Multi-
Recently, resource optimization (energy and material) and environmental issues in life-cycle context are taken very seriously by both the general public and
objective Analyses
government agencies. Also, some governments have set up official eco-labeling schemes, which are used to
1. Introduction
inform consumers of Eco (ecology and economic) design Recently, resource optimization (energy and products. In the past, several environmental impact material) and environmental issues in the life-cycle analyses and evaluation tools are significantly developed context are taken very seriously by both the general to apply on the Eco design products. However, these Eco public and government agencies. These activities urge design products are not favorable in the market place as companies to set up environmental friendly production expected even though they sound more environmental technologies, which aim at the avoidance of emissions in friendly and economical. This may be due to they are to air, water and soil (Dask et al., 2000, Kuo et al., 2000, focused solely on environmental impact analysis without Zhang et al., 1995). Also, some governments have set up regarding to the customer need and the cost official eco-labeling schemes, which are used to inform consideration. As a procedure to incorporate customer consumers of Eco (ecology and economic) design needs into product concepts in product planning, Quality products (Owen, 1993, CGP, 1990, HR, 1991). In the Function Deployment (QFD) is a proven methodology to past, several environmental impact analyses and achieve total customer satisfaction. Therefore, a fuzzy evaluation tools are significantly developed to apply on theoretic modeling approach to the Eco QFD is the Eco design products. Health hazard scoring (HHS) is developed and illustrated to aid the design team in an evaluation method for health hazard assessment choosing target levels for engineering characteristics (Barzilai and Golany 1994). Munoz and Shen (1995) based on the environmental concern in this research. presented a model for analyzing the energy, time, and Unlike two valued Boolean logic, fuzzy logic is multimass of chips, lubricant, and tool waste streams. Kuo valued. It deals with degrees of membership and degrees (2000) presented a disassembly planning for the end-ofof truth. The Eco design product development problem life products during the design stage. Sage (1993) was formulated as a fuzzy multi-objective model based on provided the sustainable process index based on an the QFD planning. This model not only considers the operational definition of sustainability, which relies not overall customer satisfaction, but also enterprise only the environmental risk, but also on the economic and satisfaction with the environment committed to the technical feasibility as well as political compromise. product. With the interactive approach, the best balance Horvath et al. (1995) developed an approach to track between environmental satisfaction and overall customer toxic releases and associated risks over time based on the satisfaction can be obtained. Finally, a case study data of the Toxic Release Inventory (TRI) and relevant illustrating the appliance of the proposed model is also toxic indices. Costic et al. (1996) estimated the given. environmental performance of conventional lead-based
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company. To solve the above problems, a fuzzy theoretic modeling approach to the Eco QFD is developed and illustrated to aid the design team in choosing target levels for engineering characteristics based on the environmental concern in this research. By introducing the environmental factor, reducing, reusing, and recycling, the optimizing model is developed and compared in this paper. This model not only considers the overall customer satisfaction, but also enterprise satisfaction with the environment committed to the product. With the interactive approach, the best balance between environmental satisfaction and overall customer satisfaction can be obtained. Finally, a case study illustrating the appliance of the proposed model is also given.
solders and their substitutes using LCA. The HewlettPackard (Korpalski, 1996) provides design for environment (DfE) tools for the company's sue such as DFE guidelines, product assessments, and product stewardship metrics. However, these Eco design products are not favorable in the market place as expected even though they sound more environmental friendly and economical. This may be due to they are focused solely on environmental impact analysis without regarding to the customer need and the cost consideration. In the other words, the key issue for a successful Eco design product is that it should not only satisfy the marketing demand, cost effectives, and multi-functionality, but also meet the environmental objectives, such as resource and energy conservations, and environmental burden reduction (Lee et al., 2001, Kuo et al., 2000, Yu et al.,
2. Fuzzy & Eco QFD planning
2000).
As a procedure to incorporate customer needs into product concepts in product planning, Quality Function Deployment (QFD) is a proven methodology to achieve total customer satisfaction (Akao 1990, Clausing, 1994). QFD was originally proposed, through collecting and analyzing the voice of the customer, to develop products with higher quality to meet the customer's needs. Christopher et al. (1996) developed the Green QFD (GQFD) method to integrate the life cycle analysis (LCA) and QFD to evaluate the products from environmental consideration. Furthermore, Zhang et al. (2000) presented the GQFD II that integrates LCA (Life Cycle Assessment), LCC (Life Cycle Costing) and QFD into an efficient tool and deploys customer, environmental and cost requirements throughout the entire product development process. In Japan, a QFD for Environment (QFDE) tool is also used to the environmental friendly
In this research, the Eco QFD model would enhance the QFD by integrating customer, environmental and costing requirements through the entire product Furthermore, as the development processes. environmental concers are integrated into the QFD, the size of house of quality becomes large. To reduce the size and uncertainty of Eco QFD, the model is expressed in fuzzy terms to accommodate the imprecision and u i defined or incomplete understanding of the relationship betwee a incseto ustomer ru ementsoCs)in technical attributes (TAs) as Fig. 2.
2.1 Identify customer requirements and technical
measurements
Since the QFD is to transform the customer expectations into concrete technical attributes, this step is to identify the CRs and TAs with the environmental
product (JEMAI, 2001, Masui, 2001).
However, loosely defined and structured, QFD sometimes becomes an art more than a science, which makes it difficult for practitioners to use QFD (Chan, 2002). This is because, first, the weighting factors are normally given in vague linguistic language, such as 'serious', 'moderate', and 'low' (Hui et al., 2002). Seod the cutoe inomto is acoplse in a a er inf sice Itis comes from a vanety subjective ad hoc manner, of sources, including survey, focus groups, interviews, listening to salepeople, trade shows, and journals, existing data on warranty and customer complaints (Bossert, 1991). And third, over 50% of the QFD effort is spent on in capturing the voice of the customerperformance characteristics, relative importance characteristics (Bosserman,n,19) Several rarc hav been developed to determine the welghtig for different customer needs, such as ' the well know AHP (Satty, 1990), conjoint analysis (Green and Srinivasan, 1978, 1990), the benchmarking of a product from another
suecthveacusto
performanccharacteristicsBoss
concers (ECs).
Step 1. CRs identification.
For the CRs, it is
critically important to capture the customers' perspective in the corporate language. Traditionally, the customer requirements mostly focused on the cost, function, quality
acomplishdiiny
and on time delivery during the design stage. However, terms such as environmentally-conscious design and manufacturing, sustainable design, environmental design, cleans technologies, and green designs have been increasing customer's awareness. Therefore, the enterpis shoul ras consi. Therenvroental enrpiesol asocsdrth evrnmtl concerncomes into from the product the customer design. Generally, a variety of sources, including surveys, voice focus groups, interviews, listening to salespeople, and exsigdt-fcsoe opans(osr ).) data of customer complaints ~~~~~~~existing (B3ossert Customer's requrements are also too general or too detailed to be directly used. Therefore, the best way iS to
reverl iarthance
423
by TA,, where j=1,2,..., r in Room 2. Let , ** ww, ]T) be the matrix of W(= [wI, W2 ..., W. m+I
construct the HoQ at different levels as a tree-like hierarchical structure (Figure 3).
D,.te,q,.,t-q
Ent_Z=
-E5;~ --7
nI Fp_
| +C. E. %taw __ _ t
Thoge 4
vocs
Fu-f-
1
I
the weight for each CR. Also, let U be the relationship n x r matrix between CRs and TAs with elements ULj, indicating the strength of the impact of jth TA towards the ith CR. Fig. 4 depicts the planning of HoQ. ?.INS _ 0'0045'iL I 0 00j t
Figure 4. The construction of Green HoQ |D.ssgn lrvetntnt
Figure 2. System Architecture for the GQFD and Fuzzy
*.* r
Logic
Step 2. TAs determinations. This process of setting the TAs in practice is usually accomplished in a subjective, ad hoc manner, for example, through a team consensus. As the environmental attributes are considered to the TAs, the translation will become more complex and uncertain. Therefore, it is better to use a systematic method - life cycle analysis that includes raw material, manufacturing, distribute, use, and recycle stages to find the TAs.
,t
K,
lX t8jj Fi.i lS,. :. E U040 Ui w
d Zns
j t4i5i Figure 5. The matrices ofthe construction of HoQ 2.3 multi-objective model based on the QFD
u4
fuzzy
planning
Max
777771m Z4 = wjUJ + ZW1U,, n
j=1...r ~ ~ ~
3 _______
T-V .
i
for
=1i=+
Where Z is the sum of the impact potential importance selected TAs 00of 0 : il 0tiitif l:00 7;f0 t 0 is the 0 ;wi weight ofthe ith CRs _________________ _______________ ;0-0 ik 004000 Uj is the relative importance between CRs and
Tas
Figure 3. LCA & HoQ
CR1,.,CRm,(without enviromnmental concern) CRm+j,
The aim of Eco-QFD is to find the most important of the TAs based on the LCA. Therefore, the Eco-design product development problem is fornulated as fuzzy multi-objective model based on the QFD planning, which is shown in the above.
there are r TAs based on the life cycle analysis, denoted
3. Fuzzy
2.2 Quantitative presentafion of the Eco QFD Assnuaing a certain product has m CRs without environmental concerns and n-m CRs with environmental concerns have been identified and integrated, denoted by ...,CR, (with envirommental concern) in Room 1. Also,
424
technique
in
the
relative
(1975). The Mamdani fuzzy inference process is performed in four steps: (1) fuzzification of the input
importance between CRs and TAs
variables, (2) rule evaluation, (3) aggregation of the e rule outputs, and finally (4) defizzification.
After the group of experts has found, empirically, the next part is to rate the importance of the CRs, TAs, and the relative importance. The success of the Eco QFD matrix depends on how well the translation of CRs into appropriate TAs. In traditional, the evaluation is executed by using © with a rating of 9 (very important), 0 with a rating of 3 (important), and A with a rating of I (slightly important). However, as the dimension of customers needs increase, the number of potentially related design variables becomes large. One of the main problems associated with a large dimension of the relational matrix is the difficulty of setting priorities and importance in an accurate number. This is due to the value setting process is typically vagueness or impreciseness (that is, fuzziness) 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 those problems, the fuzzy logic and threshold techniques are used.
Figure 5. Schematic diagram of a fuzzy inference system (adopted from Kulkarni, 2000) Step 1. Fuzzification of the input variables. The first step is to take inputs xi and determine the degree to which they belong to each of the appropriate fuzzy sets
of fuzzy types triangular, Various via membership function. 3.1Basicconcept off fuzzy logic set ogic setmembership 3.1 Basic concept including are usused, functions an ed, Gausi bel gene trae id defined X is of A set crisp a set In classical theory, ' generalized bell shaped, T -shaped, Gaussian ~~~~~~~~trapezoidal, curves, polynomial curves, and sigmoid functions. In as function fA (x) called the characteristic function of A. these membership function, the most popular one is the This set maps universe Xto a set of two elements, 0 and I. triangular fuzzy membership function which is denoted as where fA (X) X -. 0,l a S S = if x E A number, which representing a fuzzy set or concept fA (x) = A='approximately b'. L0, x A (2) owi In the fuzzy set theory, fuzzy set A of universe X is 1 0, defined by function UA (X) called the membership function of set A. ji..0 a ' where UA(X) X [0,1]
F1,
,u (a,b,c), where a b c. It is special fuzzy
if
IHA (X) = 1, if x is totally in A
PA(X) = 0, if x is not in A
0 < PA (x) < 1, if x is partly in A
Figure 6. Triangular membership functions
A = (a,b,c)
(3)
Step 2. Rule evaluation. This process is to take the fuzzified inputs PA (x) and apply them to the
Since the fuzzy set theory lies the idea of linguistic variables, a linguistic variable carries with it the concept of fuzzy set qualifiers, called hedges. Hedges are terms that modify the shape of fuzzy sets which include adverbs such as very, somewhat, quite, more or less, and slightly. Based on the fuzzy set theory, the rating of weight of CRs and the relative importance between CRs and TAs are transformed into fuzzy inference problems. In the fuzzy set theory, the most commonly used fuzzy inference technique is called Mamdani method
antecedents of the fuzzy rules. The rule base contains linguistic rules that are provided by experts. Once the rules have been established, the fuzzy inference system could be viewed as a system that maps an input vector to an output vector. Step 3. Aggregation. Aggregation is the process of unification of the outputs of all rules. In other words, it takes the membership functions of all rules consequents previously clipped or scaled and combine them into a
425
single fuzzy set, such as addition, subtraction, multiplication, and division. Table I represented the
Q2 P*V0** 0.2
aggregation process for the fuzzy set theory.
_____i_ iLl::_X__t_ rP lQ; 3p0 ;
Table 1. The fuzzy arithmetical operations Arithmetical operation Addition(+) (a, bl+[c, dl = [a+c, b+d] Subtraction(-) [a, bl-[c, d] = [a-c, b-d] Scaled k[a, b] = [ka, kb] multiplicaton Multiplication (.)
Division(/)
P
P.
(a sp : : Figure 8. Crisp value and Triangular fuzzy number
Based on the above the fuzzy techniques, the results are rated by the q experts in the group that includes salesmen, designers, and engineers, etc. These people are responsible to identify and determine the CRs, and TAs. For each criteria, the initial rating is evaluated by each expert. Then, the corresponding results for the triangular fuzzy members are transformed as Figure 8. Furthermore, the sum of the symmetrical crisp value and TFN are represented as Table 2. For example, there are 3 experts who rate the mth CR criterion, wm. The crisp value of the weights are Q1, Q2, and QJ, respectively. Therefore, the corresponding values for the crisp value of QJ, Q2, and Q1 are 1, 3, and 1. On the other hand, the corresponding values for the TFNs are [1, 1, 2], [2, 3, 4], and [1, 1, 2]. Therefore, the aggregated vale for the crisp and TFN values are
l/cl
Step 4. Defuzzification. In order to obtain a crisp output, a defuzzification process is needed. Given a fuzzy set that encompasses a range of output values, the defuzzifier returns one number, thereby moving from a fuzzy set to a crisp number. Many defuzzification techniques have been used in practice, including centroid, maximum, means of maxima, height, and modified height method. In this research, the defuzzification method, will be used to calculate and return the center of gravity (COG) of the aggregated fuzzy set. The numerical representation is shown as in Eq. 4. (
P
[i,iZW12,41 0[4,5] W [829.91
[a, b]. [c, d] = [min(ac, ad, bc, bd), max(ac, ad, bc, bd)] [a, b]4c, d] = [a, b].[l/d,
J
0
8
t0p|( 00
t;0:t
:;;ili;; 0
(1+ 3+1) lU=,w.u : 3=,;;#0z) tw1Crispvalue -1.6
TFN Figure 7. Defuzzification - Center of gravity
, = (i
Furthermore, let the weight of CRs, z Wm,Wm+i, ...W xr , be the input . W = (Wi,W2,... *
value q
)=1
q
=
q
(1+2+1 1+3+1 2+4+2)(366 = ~TI= 1.3,1.6,2.6) 3 , 3 ,
m a m+i ..* * vector, and 8 = (51,X . be the fuzzy output vector based on the fuzzy numbers. Based on the above fuzzy inference techniques, the qualitative assessment of CRs are evaluated and fuzzified to be the triangular fuzzy number (Fig. 8). If a customer gives the CR rating w, as 5, which implies that wn is 'mediate', then the value will be assigned as a triangular fuzzy number Q3='approximately 5' = (4,5,6). Therefore,
For each CR, the TFN number is next defuzzified based on the Eq. 4. Therefore, the corresponding weight of ih CR ( Wi ) is then calculated as 2.08, where c
(
f/A (x)xdx X/-8A_x__1 (X (26-1x3)x Wi= c c 2 fUA (x)dx = ,PA (X) a x=a a
im = (4,5,6).
=
__x_a
Similarly, the calculation of the matrices of the relationship between CRs and TAs is the same as the weight of CR. Assume the 3 experts evaluate the relationship between between ih CR andjA TA, Uy, are Q3, Q3, and Q3 that are corresponding to TFNs as (4, 4,
426
8), (5, 5, 9), and (8, 9, 9). Thus, the relative importance of Ui. is Li5 9 = 6.3 = 33 Crisp value UU Y TFNs
U1i
c
c
fPA(x)xdx IA (5(66-4) WX = (5.66-4) x=a x-=a = ~~-~~x5=4.16 W17 = a ~~~~ 17 juxA(x fu()x
a
Furthermore, the TAs are deployed and evaluated
4+4+85+5+98+9+9)
=
2
C PA (X)
x=a
kb]). Therefore, the corresponding of the relationship between ifh CR andjt TA is equal to: Crisp Value Z* =1.6x6.3=10.08
Z = 2.08 x (5.3,6.3,8.6) =(11.02,13.10,17.89) Based on the above calculation, the fuzzy number technique will present the range of the technical requirement. It can reduce the uncertainty and vagueness of the experts.
based on the life cycle analysis, raw material, manufacturing, distribute, use, and recycle stages. Table shows the Eco QFD evaluation for an expert. In the Table, the crisp value of the expert's opinion is also fuzzified based on the fuzzy inference techniques. For example, the crisp values of TA - package easily for CRs the yield rate reduction, cost reduction, zero damage, and delivery on time are (1, 7, 1, 1). Then these values are fuzzified as (0, 1, 1), (6,7,8), (0,1,1), (0,1,1), separately. The four group numbers of the columns are multiplied and summarized to get the row scores that is shown in the Table. Owing to the space problems, this paper will only show the fuzzy results. Finally, these scores can be compared and evaluated.
4. Illustrative example
Table The fuzzy result for the weighting importance for
= (5.3,6.3,8.6).
the
3
3
3
Furthermore, the weighting of CRs are applied to
Uij by using the scaled multiplication (k4a,
b] = [ka
A case study for the toner cartridge design of the printer is selected based on the methodology in this study. To implement the Eco QFD, 3 experts are acquired that include (recycling cartridge manufacturer, onginal cartridge designer, and environmental expert). Base on the LCA, the product stages of raw material, manufacturing, distribution, and disposal process are evaluated and analyzed. At first, the experts identify 17 CRs from the company's sale network or through market survey that is shown in the Table. By using the cluster method, these 17 CRs are categorized into 4 level, cost, function, appearance, and environment. Furthermore, to calculate the weights for different CRs, the fuzzy inference techniques are used. The relative scale 1-3-5-79 is used for ranking the importance. In Table, the three experts evaluate and give the crisp value for each CRs, and the crisp value are fuzzified. Furthermore, the fuzzy value are defuzzified according to the Eq. For example, the calculation of the weighting importance CR17 is calculated as Eq. Also, the other weighting factor are calculated and shown in Table.
817
(u=
q
=1
q
u=
QG$0W
c
0R44 :2e* (6.70>' 7' (:W47W ( 0>' 1.00' y *c,e7t (U (U 9 (U)).' 9.00"' ma 3.66'.' N7 (1.1.0>' 7$6 6.39' 5 ( 7. (6. ; cWM0>' 7. T4(4.1.6.39. .0. 9> U9' ()> ~ 00'.' 7 (78 cR.' ¢ ..l .i e ? ..> (56' 50'' CRJ'ic n.(.26 . 5 .5X (4.5 c&,.' i . i*.5.67 7 .' 5 A a 4. s 0'w' it# V a 5.00'.' ., .S czw' 1.ih* im '5 .1' R'D6 Tme (.>3(..4'.(23>' In Table, the environmental concern for each TAs are compared (less is better). There is not very significantly difference when the product is considered environment concern except for the toner recycling attribute.
Table The difference
3
3
3
each customer requirements
q
=8+2+2=(,9+3+3 , 9+4+ 4 = 3 3 3 J
, 4556).'
)=~~~~~(4,5,5.66)
i :
427
;:d.::0 SiS:
f
:~II
t :
:w10f : : t
X
1
121
The other data of the Figure, it also could be found that TAs could be categorized as 5 groups from A to E. It shows that the toner manufacturers could focus on the A and B groups of TAs.
6.
5. Conclusions
7.
This paper develops an Eco QFD to provide a framework for designing Eco products. The CRs are properly translated into TAs, and the relationships are carefully examined as well as the importance of TAs. Also the Eco QFD integrates the LCA into QFD throughout the entire product development process. The major advantages of the Eco QFD are summarized as follows. * Eco QFD is a useful tool to integrate the environment, quality, cost, and other customer requirements to improve the design process. * Eco QFD could reduce the vagueness and uncertainty of the customer requirements. *
8.
9. 10. 11.
by using computer. Various requirements can be prioritized, that are a guide for product development teams to focus their limited resources or time on critical issues.
12.
13.
Thefmuture work will focus on the development of computer software. Also the other uzzytechniques will
be explored and analyzed.
14.
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