Enhancing Product Planning via Utilizing Quality Function

Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
Enhancing Product Planning via Utilizing Quality Function Deployment
with Fuzzy Logic
Hussein Salem Ketan, 2Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
Department of Production and Metallurgy Engineering, University of Technology, Baghdad,
Iraq, mohammedraof415@yahoo.com
1
1,2
doi:10.4156/jdcta.vol5.issue3. 9
Abstract
The main problem of this paper is to improve product planning through the distinction between
engineering specifications of the product, and the nomination of the basic engineering specifications
that are depend in the development process, according to the opinions of different designers and
customers, that take into account more than one goal to achieve the principle of competition for the
product. To achieve this goal, a new hybrid methodology has been built. This new methodology utilized
Quality Function Deployment (QFD) with fuzzy logic for improving product planning. The
methodology was conducted in six models that include four different of House of Quality (HOQ); two
of them are in traditional state, and the other in fuzzy state. These models are current crisp QFD model,
virtual crisp QFD model, initial fuzzy QFD model, final fuzzy QFD model, optimum fuzzy QFD model
and optimum crisp QFD model. Application results show new values and ranking for the new
engineering specifications of the desired product to be developed, and it was possible to use the values
of these specifications by the designer in its crisp format rather than the fuzzy. Thus were able to find
out the critical and important specifications for improving product planning which should be
considered in product development process.
Keywords: Quality Function Deployment, House of Quality, Fuzzy logic, Product planning,
Critical specifications
1. Introduction
Manufacturing companies facing competition in the global markets realize that to efficiently
manufactured products preferred by customers at a competitive cost within shorter timeframe over
those offered by competitors is critical to their survival. Therefore the organizations direct their efforts
towards meeting their customer requirements, development cycle times shortened, market penetration
increased with improved product quality, gaining better customer satisfaction and higher income [1.2,
3].
A widespread practice in industry is to cope with global competition by the adoption of QFD. In the
traditional, QFD most of the input variables are assumed to be precise and are treated as crisp
numerical data. However, linguistic variables expressed in fuzzy numbers seem more appropriate for
describing those inputs in QFD. Research on fuzzy QFD has received considerable attentions. There is
a need to develop a hybrid system to incorporate the principles of QFD to determine design targets [2].
In order to make the development process of the product successful by using the QFD and getting
better outcomes, there is need to have the important inputs. And one of the inputs to HOQ in QFD is
the customer's needs and the extent of the importance of these needs. Also the engineering
specifications of the product and the range of their effects on meeting the customer's requirements are
also important inputs. In addition competitive analysis includes some of the essential inputs in the
market in order to approach high accuracy in technical importance rating of the newly developed
product. There shall be the need to propose a new hybrid method that differs from the traditional
method of QFD, as this method would include the principles of QFD together with fuzzy logic so as to
improve the product planning that will lead to development the current product, under the following
conditions:
1. Possibility of selling the current product in original market and competitiveness markets.
2. Take into account the opinions of different designers and customers.
3. The improvement is done without the need to make large investments in the manufacturing
processes and without the customer's loss in original market.
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
2. Literature Review
QFD is part of Total Quality Management (TQM) that lead the companies to the success. QFD is one
of these important systems for improving product planning that leads to develop the product. Over
three decades have passed since Japanese academics and industries began to formalize the QFD
technique in the late 1960s and early 1970s [4].
Dr. Mizuno, professor at Tokyo Institute of Technology is credited with initiating the QFD system. The
first application of QFD was at Mitsubishi Heavy Industries Ltd. and in the Kobe Shipyard, Japan in 1972.
After four years of case study development, refinement, and training, QFD was successfully implemented in the production of mini-vans by Toyota. Using 1977 as a base, a 20% reduction in startup
costs was reported in the launch of the new van in 1979, a 38% reduction by 1982, and a cumulative
61% reduction by 1984. QFD was first introduced in the United States in 1984 by Dr. Clausing. QFD can
be applied to practically any manufacturing or service industry. It has become a standard practice by most
leading organizations, which also require it of their suppliers [5].
Many QFD applications and studies have been reported due to its effectiveness in product
development and quality management. Literature review saw the publication of papers and articles and
explains the history of researches on projects in QFD and its objectives. QFD and Fuzzy logic are
connected together, and these subjects are used as a master key words in paper.
From literature survey, the QFD plays an important role in industrial application where with this
tool one can obtain new development of a product. The survey shows the following important points:
1. The Analytic Network Process (ANP) procedure, Analytic Hierarchy Process (AHP) technology,
zero–one goal programming methodology and conjoint analysis are employed in QFD
implementations to get the best design targets with which the detail design is completed [6, 7, 8].
2. Mixed Integer Linear Programming (MILP), Kano model, argumentation systems and graph tool
such as Computer Aided Design (CAD) that are related to computational services are used with
QFD to optimize the product development and decision-making [9,10,11,12].
3. QFD is used with Kansei engineering methodology and Kano model to satisfy customer needs [13, 14].
4. Fuzzy logic is applied with QFD and other tools such as Linear Programming (FLP) model,
competitive analysis, Least-Squares Regression (FLSR) approach, Failure Modes and Effect
Analysis (FMEA) and nonlinear programming models for maximizing customer satisfaction,
optimizing the values of engineering characteristics and finding the optimal target values of the
engineering characteristics [15, 16, 17].
The researcher has presented many researches about improving quality, product planning,
cost, consumer needs, reliability, maintenance, sales, decision making and other. But they did not take
into consideration the need to develop a product to compete with other competition products in its
other markets, under the conditions of development permitted in the company without the need for
additional investment, in addition to the maintaining on the satisfactions of customers in its market and
competition in it at the same time.
3. Proposed Methodology Models
In the formalizing and constructing the proposed methodology which shown in figure (1). The
condition (A) (percentage of permitted change) in engineering specifications of current product
should not exceed the percentage, that determined by the technical expert of QFD team in the factory,
which is required the development of the current product for the purpose of competition with similar
products in other markets.
The main reasons of choosing the fuzzy logic in improving the product planning with QFD:
1. To get the optimum flexibility of technical ratings for each product specification.
2. To convert the fuzzy values of new engineering specifications to a crisp values.
3. To nomination the basic engineering specifications (critical) which should be considered in
product development.
4. To reduce the design uncertainties.
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
Symmetrical triangular fuzzy numbers is suitable for using in QFD. In this research, a special program
has been done for the purpose of calculating all the mathematical operations in the practical application
for six models in the proposed methodology through the use of MATLAB.
3.1. Current Crisp QFD Model
In this model, traditional status by using the crisp numbers so to obtain a technical rating for each
engineering term of the product, in order to find out which of the engineering specifications of the
product is necessary, and should be considered and improved for competition purposes in the original
market(a). The engineering specifications of little importance that could be disregarded in development
process. This model consists of (13) steps as shown in figure (2).
Figure 2. Steps of current crisp QFD model
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
Figure 2. Steps of current crisp QFD model
3.2. Virtual Crisp QFD Model
In this model, the traditional QFD principles will be implemented on the competitive products in the
competitive markets, where a first competing market will be marked with the letter (b) and the second
competing market with (c), to find out the values of the engineering specifications that should be
provided in the product which are required to be developed to meet the customer's requirements in the
markets (b) and (c) together. The benefit of this model is to obtain an engineering specifications for
virtual product, which meet the requirements of the customer in the markets where competition is only
required in these markets. This model consists of (12) steps as shown in figure (3).
Figure 3. Steps of virtual crisp QFD model
3.3 Initial Fuzzy QFD Model
Based on comparison results of the previews models (model-1, model-2), there are two possibilities:
First, there is no need to utilize fuzzy logic model when the Percentage Difference between first
and second model (DI) of HOWs is (≤) than Percentage of permitted change in engineering
specifications (A) for specifications of current product in the original factory.
- Second, when (DI) of HOWs is (>) than (A), there is a need for applications of fuzzy logic models.
Model three shown in figure (4) and consists of (6) steps.
-
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
Figure 4. Steps of initial fuzzy QFD model
3.4. Final Fuzzy QFD Model
In this model, a fuzzy model will be proposed for the markets where the competition is required to
get new technical rating to the virtual product in fuzzy state, which suggested in the area of competitors
markets. The intention from that is to find out the values of the new engineering specifications that
must to be achieved in the new product to meet the customers' requirements in the competitors markets.
This model includes four steps shown in figure (5).
Figure 5. Steps of final fuzzy QFD model
3.5. Optimum Fuzzy QFD Model
The fuzzy logic principles are applied to identify the optimum flexibility values in engineering
specifications for the newly developed product, when obtain at shared area between the triangles of
technical rating in model-3 and model-4. The fuzzy final technical rating for the newly developed
product will establish in model-5, in order to obtain the engineering specifications of new developed
product, that should be reached with short time and achieve the customer's desires in the markets (a),
(b) and (c) together. In this model, one of the operations that is related with fuzzy logic, which an
intersection operation is applied. Where a shared area will be found from resulting values from the
third and fourth models to reach the new triangular fuzzy numbers for each HOW, and these numbers
are considered the limits of optimum value for each HOW. This model will be applied in conditions,
involving the use of three values for confidence levels (α- cut) in membership function; they are (0.8,
0.5 and 0.2) which represent good confidence, average confidence and low confidence respectively,
that has been limited to these three values only. Thus, with these three values, all the other confidences
have been covered in membership function. This model consists of (4) steps, that shown in figure (6):
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
Identify the Optimum (α- cut) that take two conditions (first: intersection between
model 3&4) and (second: highest confidence)
Calculate Lower Limit Point (LL) of Optimum Fuzzy Technical Rating (OFTR)
Calculate Crisp Limit Point (CL) of Optimum Fuzzy Technical Rating (OFTR)
Calculate Upper Limit Point (UL) of Optimum Fuzzy Technical Rating (OFTR)
Figure 6. Steps of optimum fuzzy QFD model
Step-1: Identify the optimum of (α- cut) that equals at the highest value of intersection levels (α- cut)
between triangles (FITR) a and (FFTR) b+c as shown in figure (7), by using the following equation [18].
μT (FITR) a ∩ μT (FFTR) b + c ≠ 0
(1)
where:
μT = value of (α- cut) intersection level at (0.2, 0.5 or 0.8)
Figure 7. OFTR Triangle
Step-2: Calculate the lower limit point for the Optimum Fuzzy Technical Ratings Triangle (OFTR) by
using the following slope equation.
(OFTR)LL= (α-cut). [(CTR) a-(FITR) a LL]+ (FITR) a LL
(2)
Where:
LL= Lower limit point
Step-3: Calculate the crisp limit point for OFTR from the following slope equations.
α- cut CL= (CL - (FITR) a LL) / [(CTR) a - (FITR) a LL]
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(3)
Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
α- cut CL= (CL - (FFTR) (b + c) UL)/ [(CTR) (b + c) - (FFTR) (b + c) UL)]
(4)
where:
CL= Crisp limit point for OFTR
UL= Upper limit point
Step-4: Calculate the Upper limit point for OFTR from the following slope equation.
(OFTR)UL= (α-cut). [(CTR) (b+c)-(FFTR) (b+c) UL]+ (FFTR) (b+c) UL
(5)
3.6. Optimum Crisp QFD Model
In this model the optimum fuzzy model will be transferred to optimum crisp model, to obtain crisp
numbers which enable designers and engineers to use it during the operation of improving the product
planning phase in the process of realization the new product. The operation of transferring the optimum
fuzzy technical ratings to optimum crisp technical ratings is done by using one of the defuzzified
methods, namely the centroid method. This model consists of (5) steps, shown in figure (8):
Calculate Optimum Crisp Technical Rating (OCTR) by using Centoid Method
Calculate Normalized Optimum Crisp Technical Rating (NOCTR)
Calculate Scaled Optimum Crisp Technical Rating (SOCTR)
Calculate Percentage Difference (DF) between (NCTR) for model-1 and (NOCTR) for model-6
Calculate New Product Specification (NPS)
Figure 8. Steps of optimum crisp QFD model
Step-1: Defuzzify (OFTR) to obtain the Optimum Crisp Technical Ratings (OCTR) by using Centroid
method equation (6) [19]:
R
R
(OCTR) j = ∑ d j. µ j / ∑ µ j
r =1
(6)
r =1
where:
d = crisp value for triangle OFTR
µ = degree of membership function for OFTR
r = 1,2,…..,R
R = No. of fuzzy values in OFTR
j = 1, 2,………., p
p = No. of HOWs
Application of this formula (6) on a triangle OFTR as shown in the following:
(OCTR) j = [(OFTRLL.µLL) + (CL.α-cutCL) + (OFTRUL.µUL)]/ [µLL+α-cutCL+µUL]
Step-2: Calculate the Normalized Optimum Crisp Technical Ratings (NOCTR) for each HOW by
using the following equation [20].
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
P
(NOCTR) j = (OCTR) j / ∑ (OCTR) j
(7)
j =1
Step-3: Calculate the Scaled Optimum Crisp Technical Ratings (SOCTR) to find optimum ranking for
each HOW in markets (a, b and c) together by using the following equation [21].
(SOCTR) j = (OCTR) j / max. (OCTR) j
(8)
Step-4: Make a comparison between the results from actual crisp model for current product in market
(a) and optimum crisp model for future products in markets (a, b and c) together in order to
obtain the percentage difference (DF) between (NCTR) a and (NOCTR) by using equation
(9).
(DF) j = [(NCTR) j a - (NOCTR) j]. 100 % / (NCTR) j a
(9)
Step-5: Translate the optimum crisp technical ratings (NOCTR) for HOWs in markets (a, b and c) to
new values of the target design that represent the New Product Specifications (NPS) to
obtain new development product by using equation (10):
- (NCTR) a represents the current product specifications.
- NOCTR represents the new product specifications.
Equation (10) is obtained from using equation (9)
NPS j =PS j – [PS j. DF j]
(10)
where:
- NPS = new product specifications
- PS = current product specifications
4. Case Study
To enhance the product planning by using QFD with fuzzy logic, the proposed methodology was
used. The selected product was (Woolen Blanket) as one of the products of the state company for Wool
Industries at Al-Fattah Factory in Baghdad. The condition (A) percentage of permitted change in the
engineering specifications of current blanket should not exceed (±5%) that is determined by the
technical expert in the AL-Fattah factory. The following models explain the implementation of model5.
There is a team of multi occupations including several persons, each of them represents one of the
existing sections in the factory, starting with the design department and ending with the marketing
department. This team is called the QFD team. WHATs and HOWs of this methodology are presented
in tables (1 and 2) that depend on experience of QFD team and the especial questionnaire for customer
of this product. (FITR)a represents output of model-3 and (FFTR)b+c represent output of model-4 as
shown in table (3). Model-5 is intersection between two models (3 and 4). A stepwise-described
algorithm of model-5 is simply presented as follow:
Step 1: Determine the optimum of (α- cut) between three values (0.2, 0.5 or 0.7) by using equation (1),
as shown in the following.
For (H1) by using figure (9):
1- At α- cut= 0.8, There is no intersection between (FITR) a and (FFTR)b+c as shown in the following.
μ0.8 (FITR) a ∩ μ0.8 (FFTR) b + c = 0
μ0.8 (0.2280, 1.7500) a ∩ μ0.8 (0.1934, 1.1180) b + c = 0
2- At α- cut= 0.5, There is intersection between (FITR) a and (FFTR) b +c as shown in the following.
μ0.5 (FITR) a ∩ μ0.5 (FFTR) b + c ≠ 0
μ0.5 (0.2280, 1.7500) a ∩ μ0.5 (0.1934, 1.1180) b + c ≠ 0
3- At α- cut= 0.2, There is intersection between (FITR) a and (FFTR) b +c as shown in the following.
μ0.5 (FITR) a ∩ μ0.5 (FFTR) b + c ≠ 0
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
μ0.2 (0.2280, 1.7500) a ∩ μ0.2 (0.1934, 1.1180) b + c ≠ 0
But the result in the previous point-3, this intersection that is shown in figure (9) are not used
because these result do not represent the (OFTR), and the confidence level at (α- cut= 0.2) is lower
than confidence level at (α- cut= 0.5) in degree of membership function. Therefore (α- cut= 0.5) is used
to obtain (OFTR) triangle that consists from three point which named (LL, CL and UL).
There is need to calculate the three intersection points between the two triangular numbers for
(FITR) a and (FFTR) b + c for H1 at (α- cut= 0.5) that represents the OFTR.
Step 2: (OFTR) LL is calculated at (α- cut= 0.5) by using equation (2) as shown in table (4).
For H1:
(OFTR) LL1 = o.5 [(0.8820- 0.2280] + 0.2280 = 0.5550
Step 3: CL is calculated by using equations (3), (4) as shown in table (4).
For H1:
α- cut CL= (CL- 0.2280)/ (0.8820-0.2280)
(3)''
α- cut CL= (CL- 1.1180)/ (0.6228- 1.1180)
(4)''
(α- cut) CL = 0.7745
CL = 0.7345
Step 4: Calculate (OFTR) UL at (α- cut= 0.5) by using equation (5) as shown in table (4).
For H1:
(OFTR) UL = o.5 [0.6228- 1.1180] + 1.1180= 0.8704
Therefore, three points of OFTR triangle are (0.5550, 0.7345, and 0.8704) that shown in table
(5). Then calculate (OFTR) for other HOWs according to above steps.
The last model in the proposed methodology is the processes for translating the optimum
fuzzy model to optimum crisp model. In order to get real numbers which can understand by the
designers that concerned with developing the product.
Table 1. WHATs of QFD
No.
1
2
3
Customer
WHATs
No
dimensions
needs
(W)
.
Keep warm
W1
Performance
Not dying skin or
clothes
W3
light weight
W4
Easy to clean
W5
Smoothness
W6
Price
W7
Keeping the color
W8
O
W
1
Dimensions
ً◌Length
2250 mm
H1
Width
1750 mm
H2
852 gm/m2
H3
Thickness
5 mm
H4
Width of Selvedge
40 mm
H5
Percentage of Wool
55 %
H6
Percentage of Polyester
15 %
H7
Percentage of Cotton
25%
H8
2400 ID
H9
Square Metric Weight
Features
Conformance
Reliability
Long life
W9
no defects of
W10
fabric-sewing
No changes in
measurements
Nice finish
Uniformity in the
color of blanket
Surface uniformly
5
H
Product Specifications (PS)
W2
Healthy for use
after washing
4
Table 2. HOWs of QFD
Quality
Aesthetics
Texture
uniformly
Different color
options
Packaging
W11
2
Components
3
Economic
Profit per unit
W12
Percentage of White Yarn in
W13
Blanket
Percentage of dark Color Yarn in
4
W14
Surface
Design
40 %
80 %
Between Yarns
W15
W16
Blanket
Percentage of Colors Matching
30 %
5
W17
Chemical
Properties
Fixed Degree of Color
85 %
Temperature of washing
90 c○
H1
0
H1
1
H1
2
H1
3
H1
4
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
Table 3. (FITR) a and (FFTR) b + c
H
Fuzzy Initial
Technical Rating
(FITR) a
Fuzzy Final
Technical Ratings
(FFTR) b + c
1
(0.2280 , 1.7500)
(0.1934 , 1.1180)
2
(0.3560, 1.9420)
(0.2908 , 1.2478)
3
(3.8920, 9.1040)
(2.5465 , 5.3438)
4
(2.8920, 7.0680)
(1.5306 , 3.8816)
5
(0.1880, 2.1080)
(0.0667 , 1.1065)
6
(4.8220, 10.7980)
(2.8087 , 6.1151)
7
(1.6600, 5.3360)
(0.9755 , 3.0179)
9
(4.1060, 9.6680)
(2.3532 , 5.4074)
10
(1.2820, 3.9100)
(0.7834 , 2.2154)
11
(1.9360, 5.1660)
(1.1078 , 2.8892)
12
(1.6340, 4.8920)
(0.8663 , 2.6843)
13
(1.7260, 4.3380)
(1.1114 , 2.6042)
14
(2.9160, 6.6940)
(1.8349 ,4.0563)
Figure 9. OFTR for H1
Table 4. (OFTR) LL , CL, α- cut CL and (OFTR) UL for each HOW
H
(CTR) a
(FITR) a LL
(OFTR) LL
(FFTR)(b+c)UL
(CTR)(b+c)
α- cut CL
CL
(OFTR) UL
1
0.8820
0.2280
0.5550
1.1180
0.6228
0.7745
0.7345
0.8704
1.0480
0.3560
0.7020
1.2478
0.7462
0.7471
0.8730
0.9970
6.4295
3.8920
4.3995
5.3438
4.0348
0.3774
4.8497
5.0820
4.9410
2.8920
3.3018
3.8816
2.7131
0.3076
3.5222
3.6479
1.0375
0.1880
0.6128
1.1065
0.4993
0.6305
0.7236
0.8029
7.7275
4.8220
5.4031
6.1151
4.5295
0.2879
5.6585
5.7979
3.3400
1.6600
1.9960
3.0179
1.9468
0.4936
2.4892
2.8037
5.6770
3.3720
3.8330
4.5874
3.2812
0.3366
4.1478
4.3261
6.7810
4.1060
4.6410
5.4074
3.9082
0.3118
4.9400
5.1076
10
2.4685
1.2820
1.5193
2.2154
1.4680
0.4826
1.8547
2.0659
11
3.4575
1.9360
2.2403
2.8892
1.9772
0.3917
2.5320
2.7068
12
3.1925
1.6340
1.9457
2.6843
1.7177
0.4159
2.2822
2.4910
13
2.9085
1.7260
1.9625
2.6042
1.8899
0.4630
2.2735
2.4613
14
4.6670
2.9160
3.2662
4.0563
3.0026
0.4066
3.6279
3.8456
2
3
4
5
6
7
8
9
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Enhancing Product Planning via Utilizing Quality Function Deployment with Fuzzy Logic
Hussein Salem Ketan, Mohammed Abdulraoof Abdulrazzaq Al-Sabbagh
International Journal of Digital Content Technology and its Applications. Volume 5, Number 3, March 2011
Table 5. Results for optimum fuzzy model in markets (a, b and c)
2
Degree of
membership
function for OFTR
(0.5000,0.7745,0.500
(0.5000,0.7471,0.500
Optim
um
α - cut
0.5
0.5
Optimum Fuzzy
Technical Ratings
OFTR
(0.5550,0.7345,0.8
(0.7020,0.8730,0.9
3
(0.2000,0.3774,0.200
0.2
(4.3995,4.8497,5.0
4
(0.2000,0.3076,0.200
0.2
(3.3018,3.5222,3.6
5
(0.5000,0.6305,0.500
0.5
(0.6128,0.7236,0.8
6
(0.2000,0.2879,0.200
0.2
(5.4031,5.6585,5.7
7
(0.2000,0.4936,0.200
0.2
(1.9960,2.4892,2.8
8
(0.2000,0.3366,0.200
0.2
(3.8330,4.1478,4.3
9
(0.2000,0.3118,0.200
0.2
(4.6410,4.9400,5.1
10
(0.2000,0.4826,0.200
0.2
(1.5193,1.8547,2.0
11
(0.2000,0.3917,0.200
0.2
(2.2403,2.5320,2.7
12
(0.2000,0.4159,0.200
0.2
(1.9457,2.2822,2.4
13
(0.2000,0.4630,0.200
0.2
(1.9625,2.2735,2.4
14
(0.2000,0.4066,0.200
0.2
(3.2662,3.6279,3.8
H
1
5. Conclusions
QFD is an improvement tool that should enable companies to achieve high competitiveness.
However, tools alone cannot provide results by themselves. The conclusions of this paper regarding the
utilizing of fuzzy logic into QFD for improving the product during the planning phase , enabling more
effective product development and obtaining real crisp values for product specifications that can use by
the product designers.. QFD is widely used in the different manufacturing companies, and can be
employed to develop a framework for the new product development process. Furthermore, customer
needs should be linked to product development to increase the product’s competitiveness. The benefit
of the proposed methodology, were able to find out the critical and important specifications for
improving product planning which should be considered in product development stage. The future
work of this paper may be applied this proposed methodology to other sectors such as service to find
out the extent of its advantage.
6. Acknowledgement
The authors are grateful to Al-Fattah Factory for woolen blanket.
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