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CHAPTER 4
FUZZY AHP, EXTENDED BROWN-GIBSON MODEL AND
FUZZY QUALITY FUNCTION DEPLOYMENT
COMBINED MODEL
4.1 INTRODUCTION
The AHP-EBG-QFD combined model (discussed in Chapter-3) to
measure and improve the service performance using cost, time and service
quality as dimensions has certain limitations. The AHP process is similar to
the human thinking process, and it turns the complex decision-making process
into simple comparisons and rankings. However, while considering the
relative importance of one criterion or alternative, the decision makers often
face uncertainty and fuzziness. Hence it will be beneficial if FAHP is used, in
which the uncertain comparison ratios are expressed as fuzzy sets or fuzzy
numbers.
Similarly, in traditional QFD, using crisp values for assessing the
importance of customer needs, degree of relationship between customer needs
and design requirements, and degree of relationship among the design
requirements have been criticized by several authors (Chan and Wu 2002;
Bai and Kwong 2003; Karsak 2004; Fung et al 2006; Bevilacqua et al 2006;
Kwong et al 2007). The imprecise design information can be represented
effectively by linguistic variables and triangular fuzzy numbers. Using fuzzy
set theory, the value of a linguistic variable can be quantified and extended to
mathematical operations.
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In order to consider the imprecision and vagueness in determining the subjective assessment, FAHP-EBG-FQFD model is proposed in this chapter. FAHP is used instead of traditional AHP for measuring service quality and FQFD is used instead of traditional QFD for addressing the relationships between customer needs and design requirements in a better way.
4.2 FAHP-EBG-FQFD COMBINED MODEL The proposed methodology provides a framework for service performance management, as illustrated in Figure 4.1. The proposed methodology has the following two phases:
4.2.1 Service Performance Measurement The service performance measurement has the following three steps: Step 1 : Identification of the performance dimensions and data collection
This step has been explained in step 1 of Section 3.2.1 in detail. The same procedure has used.
Step 2 : Service quality measurement using FAHP
The purpose of AHP is to capture the expert’s knowledge through pairwise comparison matrix. Over the years, there have been criticisms related to the AHP’s reflection of human thinking style and its inability to accommodate uncertainty in the decision making process (Haq et al 2006; Jaganathan et al 2007). Therefore, FAHP, a fuzzy extension of AHP, is developed to solve the hierarchical fuzzy problems. In the FAHP, triangular fuzzy numbers are utilized to improve the scaling scheme in the judgment matrices, and interval arithmetic is used to solve the fuzzy eigen vector. The six-step-procedure based on Ayag and Ozdemir (2006) is given below:
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Figure 4.1 FAHP-EBG-FQFD combined model
EBG model for Service Performance measurement
Satisfied with Performance
score FQFD
Determine the optimum set of service requirements to be included in the new
design
Deploy the new service design
Review the implementation plan
NO YES
FAHP
Subjective factor measure
Tangible data from service managers
Customer feedback
Objective factor measure
Identify service performance dimensions by conducting brainstorming sessions with customers and service managers
Performance management objectives
Classify service dimensions into 1. Objective factors (cost and time dimensions) 2. Subjective factors (service quality factors)
Conduct a structured survey at the work place at predetermined time intervals
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i. The service quality factors which influence the decision are
identified and the service quality factors based on their
interdependence have been grouped. A hierarchical structure
with the objective function i.e., the best service system has
been arranged in the top level with the criteria in the
intermediate level and alternatives at the lower levels.
ii. The fuzzy pair wise comparison matrix A~ ( ija ) for the service
quality parameters using triangular fuzzy numbers (~1 ,
~3 ,
~5 ,
~7 ,
~9 )
is obtained from the experts (service managers). Table 4.1
gives the definition and membership function of the fuzzy
numbers.
Table 4.1 Definition and membership function of fuzzy numbers
Intensity of importance
Fuzzy number Definition Membership
function
1 1~ Equally important (1, 1, 2)
3 3~ Moderately more important (2, 3, 4)
5 5~ Strongly more important (4, 5, 6)
7 7~ Very strongly more important (6, 7, 8)
9 9~ Extremely more important (8, 9, 10)
Then α- cuts fuzzy comparison matrix for the service quality
parameters has been generated by defining the upper and lower limit of fuzzy
numbers using the following equations (Ayag and Ozdemir 2006):
63
~1 α = [1, 3- 2 α]
~1 α
-1 = [1/(3- 2 α), 1]
~3 α = [1+ 2 α, 5- 2 α]
~3 α
-1 = [1/(5- 2 α), 1/ (1+ 2 α)]
~5 α = [3+ 2 α, 7- 2 α]
~5 α
-1 = [1/(7- 2 α), 1/ (3+ 2 α)]
~7 α = [5+ 2 α, 9- 2 α]
~7 α
-1 = [1/(9- 2 α),1/ (5+ 2 α)]
~9 α = [7+ 2 α, 11- 2 α]
~9 α
-1 = [1/(11- 2 α), 1/(7+2 α)]
iii. The fuzzy comparison matrix of the alternatives with respect
to each service quality factor is obtained based on the
feedback from the customers. Then generate α- cuts fuzzy
comparison matrix for all service quality parameters.
iv. Using the equation (4.1), the fuzzy eigen vector has been
calculated for all comparison matrices:
ija~ = µ
ijua + (1- µ) ijla , µ [0,1] (4.1)
where, µ is the index of optimism. The value of µ can be determined by
service team (service managers).
v. The Consistency Ratio (CR), the ratio between consistency
index and the random index is determined using the following
equation (4.2)
CR = CI / RI (4.2)
where,
CI = Consistency index
RI = Random index
n = order of the comparison matrix
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The consistency index, CI = (λ max − n)/ (n-1). When the CR value
is 10% or less, the matrix is accepted as consistent.
vi. The overall service quality measure (SQM) for ‘m’
competitors are calculated using the following equation (4.3)
( )
1
nSQ M a ttr ib ute w eight x evaluation ratingi imi
(4.3)
where i = 1, 2, . . ., n (n: total number of attributes)
Step 3 : Performance measurement using EBG model
The service performance is measured by considering both objective
and subjective factors using EBG model as explained in step 3 of Section
3.2.1. The SQM is obtained through FAHP methodology. The objective
factors and the service quality factors are then converted into consistent and
dimensionless indices to measure the service performance. When the
evaluated SSPM value falls below the satisfactory level, then the existing
services are redesigned using FQFD.
4.2.2 Service Quality Improvement
In building HoQ, deriving the rankings of DCs from input variables
and the translation of CRs into DCs are the crucial steps. Since the relationships
between CRs and DCs are often vague or imprecise, Kwong et al (2007)
methodology has been used to derive the relationship. The approach has the
following steps:
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i. Identifying the customer requirements. This step is carried out in
service quality measurement using FAHP.
ii. Identifying the service design characteristics.
iii. Determination of fuzzy relation measures among the customer
requirements and service design requirements using fuzzy expert
system approach.
iv. Conducting an evaluation of competing service providers.
v. Evaluating the service design characteristics and development
of targets.
Triangular membership functions are used to determine the
relationships between CRs and DCs. The results from FAHP are used to
determine the importance of CRs. The importance of CRs is fuzzified and
transformed into fuzzy sets. Due to this transformation, importance weights are
converted into respective degrees of membership against relevant membership
functions of the corresponding linguistic variables in the fuzzy sets. The degrees of
membership with their input linguistic descriptors of relationship are regarded
as the basic ‘facts’ of the fuzzy inference process. Fuzzy ‘if-then’ rule format
is used to describe and represent the relationships among the importance of
CRs and the relationships between CRs and DCs. The aggregated importance
value of each service design characteristic is derived and is normalized. The
service design characteristics are ranked and are deployed further for service
process improvement.
4. 3 CASE STUDY- II
A case study from automobile repair shops has been presented to
illustrate the potential applications of the proposed model. The case study has
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been carried out among the leading eight identical car repair shops at two
cities namely Erode and Coimbatore respectively.
4.3.1 Service Performance Measurement using FAHP and EBG Model
The service performance measurement in the eight repair shops is
carried out using the following steps:
Step 1 : Identification of the performance dimensions and data collection
The data pertaining to cost and time dimensions used in Section
3.3.1 have been used in the present case study also. Data related to service
quality parameters are collected using suitable questionnaire (Appendix 2).
Step 2 : Service quality measurement using FAHP
FAHP is used for service quality measurement in this study. In the
FAHP approach, triangular fuzzy numbers are introduced into the
conventional AHP matrix in order to improve the degree of judgments of
decision makers. The α - cut values and index of optimism µ incorporated into
FAHP matrix take care of the accuracy of the service quality measurement.
For a neutral person, α and µ will be 0.5.
i. The fuzzy comparison matrix is obtained from the experts and
the service quality parameters are compared with respect to
each other. The comparison matrix (AF) is obtained as shown
in Table 4.2.
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ii. The α-cuts fuzzy comparison matrix for the service quality
parameter is obtained and is shown in Table 4.3. For example,
the fuzzy number provided by the expert when comparing
SQ2 with SQ1 is 7~ and the corresponding membership function
is (6, 7, 8). With α = 0.5, the α-cut value is [5+ 2 (0.5), 9- 2
(0.5)] i.e., [6, 8].
Table 4.2 Fuzzy comparison matrix (AF) for service quality parameters
Service
quality
parameters
SQ1 SQ2 SQ3 SQ4 SQ5 SQ6 SQ7 SQ8
SQ1 1 17~ 11~ 1~ 13~ 1~ 17~ 17~
SQ2 7~ 1 5~ 9~ 5~ 9~ 11~ 1~
SQ3 1~ 15~ 1 3~ 11~ 5~ 15~ 15~
SQ4 11~ 19~ 13~ 1 15~ 1~ 19~ 17~
SQ5 3~ 15~ 1~ 5~ 1 5~ 15~ 13~
SQ6 11~ 19~ 15~ 11~ 15~ 1 19~ 19~
SQ7 7~ 1~ 5~ 9~ 5~ 9~ 1 1~
SQ8 7~ 11~ 5~ 7~ 3~ 9~ 11~ 1
iii. Using the equation (4.1), the fuzzy eigenvector for the
comparison matrix is calculated. The value of µ determined
by service team (service managers) is µ = 0.5. The calculated
eigen vector for comparison matrix of the service quality
parameters is shown in Table 4.4.
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Table 4.3 The α-cuts fuzzy comparison matrix for the service quality
parameters (α = 0.5)
Service quality parameters
SQ1 SQ2 SQ3 SQ4 SQ5 SQ6 SQ7 SQ8
SQ1 1 [1/8,1/6] [1/2,1] [1,2] [1/4,1/2] [1, 2] [1/8,1/6] [1/8,1/6]
SQ2 [6, 8] 1 [4,6] [8,10] [4,6] [8,10] [1/2,1] [1,2]
SQ3 [1, 2] [1/6,1/4] 1 [2,4] [1/2,1] [4,6] [1/6, 1/4] [1/6,1/4]
SQ4 [1/2, 1] [1/10,1/8] [1/4,1/2] 1 [1/6, 1/4] [1, 2] [1/10, 1/8] [1/8, 1/6]
SQ5 [2,4] [1/6,1/4] [1, 2] [4,6] 1 [4,6] [1/6, 1/4] [1/4,1/2]
SQ6 [1/2,1] [1/10, 1/8] [1/6, 1/4] [1/2,1] [1/6, 1/4] 1 [1/10, 1/8] [1/10, 1/8]
SQ7 [6, 8] [1,2] [4,6] [8,10] [4,6] [8,10] 1 [1,2]
SQ8 [6, 8] [1/2,1] [4,6] [6, 8] [2, 4] [8,10] [1/2, 1] 1
Table 4.4 The eigen vector for comparison matrix of the service quality
parameters
Service quality
parameters SQ1 SQ2 SQ3 SQ4 SQ5 SQ6 SQ7 SQ8
Priority Vector
SQ1 1 0.146 0.75 1.5 0.375 1.5 0.146 0.146 0.0366
SQ2 7 1 5 9 5 9 0.75 0.75 0.2447
SQ3 1.5 0.208 1 3 0.75 5 0.208 0.208 0.0654
SQ4 0.75 0.112 0.375 1 0.208 1.5 0.112 0.146 0.0275
SQ5 3 0.208 1.5 5 1 5 0.208 0.375 0.0893
SQ6 0.75 0.112 0.208 0.75 0.208 1 0.112 0.112 0.0231
SQ7 7 1.5 5 9 5 9 1 1.5 0.2919
SQ8 7 0.75 5 7 3 9 0.75 1 0.2214
λmax = 8.47496 CR = 0.0457
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iv. The fuzzy comparison matrix of the alternatives with respect to
each service quality factor is obtained from the customers. The
repair shops (unit A, unit B… unit H) are compared with
respect to each service quality factor (SQ1, SQ2, …., SQ8) at
the lowest level of the hierarchy using customers' opinion as
shown in Table 4.5 to Table 4.12. The largest eigen vector, λmax
and CR are mentioned below each table.
Table 4.5 Fuzzy comparison matrix (BF1) with respect to SQ1
Unit A B C D E F G H Eigen Vector
A 1 15~ 13~ 19~ 15~ 15~ 13~ 13~ 0.0259
B 5~ 1 3~ 15~ 11~ 1~ 1~ 1~ 0.1100
C 3~ 13~ 1 19~ 13~ 13~ 11~ 11~ 0.0488
D 9~ 5~ 9~ 1 5~ 5~ 7~ 7~ 0.4371
E 5~ 1~ 3~ 15~ 1 1~ 3~ 3~ 0.1426
F 5~ 11~ 3~ 15~ 11~ 1 1~ 1~ 0.1013
G 3~ 11~ 1~ 17~ 13~ 11~ 1 11~ 0.0641
H 3~ 11~ 1~ 17~ 13~ 11~ 1~ 1 0.0699 λmax= 8.607 CR = 0.058
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Table 4.6 Fuzzy comparison matrix (BF2) with respect to SQ2
Unit A B C D E F G H Eigen Vector
A 1 15~ 11~ 19~ 15~ 15~ 11~ 11~ 0.0346
B 5~ 1 3~ 15~ 1~ 11~ 5~ 3~ 0.1337
C 1~ 13~ 1 17~ 13~ 13~ 3~ 1~ 0.0574
D 9~ 5~ 7~ 1 5~ 5~ 9~ 9~ 0.4321
E 5~ 11~ 3~ 15~ 1 11~ 5~ 3~ 0.1242
F 5~ 1~ 3~ 15~ 1~ 1 5~ 3~ 0.1439
G 1~ 15~ 13~ 19~ 15~ 15~ 1 11~ 0.0290
H 1~ 13~ 11~ 19~ 13~ 13~ 1~ 1 0.0448 λmax= 8.647 CR = 0.062
Table 4.7 Fuzzy comparison matrix (BF3) with respect to SQ3
Unit A B C D E F G H Eigen Vector
A 1 15~ 1~ 19~ 13~ 13~ 11~ 11~ 0.0392
B 5~ 1 5~ 15~ 1~ 1~ 3~ 3~ 0.1458
C 11~ 15~ 1 19~ 15~ 15~ 11~ 11~ 0.0317
D 9~ 5~ 9~ 1 5 5 9~ 9~ 0.4457
E 3~ 11~ 5~ 15~ 1 11~ 3~ 3~ 0.1162
F 3~ 11~ 5~ 15~ 1~ 1 3~ 3~ 0.1255
G 1~ 13~ 1~ 19~ 13~ 13~ 1 1~ 0.0499
H 1~ 13~ 1~ 19~ 13~ 13~ 11~ 1 0.0456 λmax= 8.567 CR = 0.055
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Table 4.8 Fuzzy comparison matrix (BF4) with respect to SQ4
Unit A B C D E F G H Eigen Vector
A 1 15~ 13~ 19~ 19~ 15~ 15~ 17~ 0.0206
B 5~ 1 1~ 15~ 13~ 11~ 1~ 11~ 0.0824
C 3~ 11~ 1 17~ 15~ 11~ 11~ 13~ 0.0523
D 9~ 5~ 7~ 1 1~ 5~ 5~ 3~ 0.3313
E 9~ 3~ 5~ 11~ 1 3~ 3~ 1.5 0.2181
F 5~ 1~ 1~ 15~ 13~ 1 1~ 11~ 0.0894
G 5~ 11~ 1~ 15~ 13~ 11~ 1 11~ 0.0758
H 7~ 1~ 3~ 13~ 11~ 1~ 1~ 1 0.1296 λmax= 8.482 CR = 0.046
Table 4.9 Fuzzy comparison matrix (BF5) with respect to SQ5
Unit A B C D E F G H Eigen Vector
A 1 17~ 13~ 19~ 15~ 15~ 13~ 11~ 0.0263
B 7~ 1 5~ 13~ 1~ 1~ 3~ 5~ 0.1783
C 3~ 15~ 1 17~ 13~ 13~ 11~ 1~ 0.0508
D 9~ 3~ 7~ 1 5~ 5~ 7~ 9~ 0.4059
E 5~ 11~ 3~ 15~ 1 1~ 1~ 3~ 0.1177
F 5~ 11~ 3~ 15~ 11~ 1 1~ 3~ 0.1087
G 3~ 13~ 1~ 17~ 11~ 11~ 1 3~ 0.0752
H 1~ 15~ 11~ 19~ 13~ 13~ 13~ 1 0.0367 λmax= 8.563 CR = 0.054
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Table 4.10 Fuzzy comparison matrix (BF6) with respect to SQ6
Unit A B C D E F G H Eigen Vector
A 1 17~ 13~ 19~ 15~ 15~ 15~ 17~ 0.0217
B 7~ 1 3~ 13~ 1~ 1~ 1~ 11~ 0.1314
C 3~ 13~ 1 17~ 11~ 11~ 11~ 13~ 0.0570
D 9~ 3~ 7~ 1 5~ 5~ 5~ 3 0.3674
E 5~ 11~ 1~ 15~ 1 1~ 1~ 11~ 0.0983
F 5~ 11~ 1~ 15~ 11~ 1 1~ 11~ 0.0905
G 5~ 11~ 1~ 15~ 11~ 11~ 1 13~ 0.0771
H 7~ 1~ 3~ 13~ 1~ 1~ 3~ 1 0.1563 λmax= 8.509 CR = 0.049
Table 4.11 Fuzzy comparison matrix (BF7) with respect to SQ7
Unit A B C D E F G H Eigen Vector
A 1 11~ 11~ 19~ 15~ 15~ 11~ 15~ 0.0317
B 1~ 1 1~ 19~ 13~ 13~ 1~ 13~ 0.0499
C 1~ 11~ 1 19~ 13~ 15~ 11~ 13~ 0.0392
D 9~ 9~ 9~ 1 5~ 5~ 9~ 5~ 0.4457
E 5~ 3~ 3~ 15~ 1 11~ 3~ 11~ 0.1162
F 5~ 3~ 5~ 15~ 1~ 1 3~ 1~ 0.1458
G 1~ 11~ 1~ 19~ 13~ 13~ 1 13~ 0.0456
H 5~ 3~ 3~ 15~ 1~ 11~ 3~ 1 0.1255 λmax= 8.567 CR = 0.055
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Table 4.12 Fuzzy comparison matrix (BF8) with respect to SQ8
Unit A B C D E F G H Eigen Vector
A 1 15~ 11~ 19~ 13~ 15~ 11~ 15~ 0.0294
B 5~ 1 3~ 15~ 1~ 1~ 5~ 1~ 0.1338
C 1~ 13~ 1 19~ 13~ 13~ 1~ 13~ 0.0424
D 9~ 5~ 9~ 1 7~ 5~ 9~ 7~ 0.4530
E 3~ 11~ 3~ 17~ 1 11~ 3~ 11~ 0.0875
F 5~ 11~ 3~ 15~ 1~ 1 3~ 1~ 0.1145
G 1~ 15~ 11~ 19~ 13~ 13~ 1 13~ 0.0363
H 5~ 11~ 3~ 17~ 1~ 11~ 3~ 1 0.1026 λmax= 8.641 CR = 0.062
v. The service quality measure has been arrived from the
principal eigen vector of the comparison matrix AF and
individual factor comparison matrices (BF1-BF8). A sample
calculation related to unit A is shown below.
SQMA = (0.0366 * 0.0259) + (0.2447 * 0.0346) + (0.0654 *
0.0392) + (0.0275 * 0.0206) + (0.0893 * 0.0263) +
(0.0231 * 0.0217) + (0.2919 * 0.0317) + (0.2214 *
0.0294)
= 0.031157
In a similar way, SQM for the remaining seven units have been
calculated and are listed in Table 4.13.
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Table 4.13 SQM for case study-II
Units A B C D E F G H
SQM 0.0311 0.1116 0.0460 0.4351 0.1152 0.1292 0.0446 0.0863
Step 3 : Performance measurement using Extended Brown-Gibson model
The SSPM score of various units are calculated as discussed in
section 3.3.1. The calculated SSPM is shown in Table 4.14.
Table 4.14 SSPM for case study -II
Unit A B C D E F G H
SSPM 0.045 0.100 0.057 0.324 0.161 0.175 0.062 0.076
From SSPM values, it has been found that the service performance
of unit D has been significantly high in comparison to other units. The service
performance of unit A is low. Hence, the services offered by unit A have to be
redesigned using FQFD.
4.3.2 Service Performance Improvement using FQFD
The services offered by the car repair shops have to be improved
and FQFD has been employed to facilitate this process. The FQFD procedure
deals with building the HoQ. Building the HoQ for unit A consists of the
following five steps:
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i. Identifying the customer requirements.
The HoQ matrix starts with identifying the customer requirements.
This step is carried out in service quality measurement using FAHP. The
identified customer requirements are as follows: promptness of service
advisor in attending to the customer (SQ1); understanding the problem in the
vehicle (SQ2); attention to modifications demanded by the customer (SQ3);
mechanics’ trustworthiness (SQ4); timely delivery of vehicle (SQ5); value for
money service (SQ6); ability to fix the problem in the first visit (SQ7) and
quality of service done (SQ8).
ii. Identifying the service design characteristics
The QFD team identifies service design requirements that are most
needed to meet the customer requirements. The service design requirements
identified are as follows: trained service executive at the reception (DC1);
trained service mechanic (DC2); rewards and recognition scheme for the
employees (DC3); service reporting (DC4); man power planning (DC5); use of
genuine parts for service (DC6); rechecking of complaints at the time of
service completion and delivery (DC7); response to customer feedback (DC8).
iii. Determination of fuzzy relation measures among the customer
requirements and service design characteristic using fuzzy expert
system approach
Fuzzy expert system (FES) is used to determine the fuzzy relation
measures between CRs and DCs. Two fuzzy inputs namely “Customer
Normalized Rating (CNR)” and “Relationship measure” are used in
determining one fuzzy output value “Fuzzy Relationship Input (FRI)”. Figure
4.2 provides the membership functions of CNR, Relationship measure and
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FRI. CNR and Relationship measure are to be fuzzified initially using the
membership functions shown in the Figure 4.2. The linguistic values and their
corresponding fuzzy numbers of CNR and Relationship measure are defined
in the Table 4.15 and 4.16 respectively. The linguistic values and their
corresponding fuzzy numbers of FRI are defined in the Table 4.17. Twenty
fuzzy rules are developed to form a fuzzy rule base of the FES (CRs–DCs).
Figure 4.2 Membership functions of CNR, relationship and FRI
Mem
bers
hip
degr
ee
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Table 4.15 Linguistic values and corresponding fuzzy numbers for CNR
Linguistic value Description Fuzzy number
VN Not Very Important (0, 0, 10, 30)
N Not Important (10, 30, 50)
M Moderate (30, 50, 70)
I Important (50, 70, 90)
VI Very Important (70, 90, 100, 100)
Table 4.16 Linguistic values and corresponding fuzzy numbers for
relationship measure
Linguistic value Description Fuzzy number
VW Very Weak (0, 0, 20, 40)
W Weak (20, 40, 60)
M Moderate (40, 60, 80)
S Strong (60, 80, 100, 100)
Table 4.17 Linguistic values and corresponding fuzzy numbers for FRI
Linguistic value Description Fuzzy number
MW Most Weak (0, 0, 5, 20)
VW Very Weak (5, 20, 35)
W Weak (20, 35, 50)
M Moderate (35, 50, 65)
S Strong (50, 65, 80)
VS Very Strong (65, 80, 95)
MS Most Strong (80, 95, 100, 100)
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The relationship measures are then derived by comparing each customer requirement with each service design characteristic and the relationship matrix is constructed as shown in Table 4.18. The CNR for each unit is also provided in the Table 4.18. For example, when the customer requirement “Quality of service done” (SQ8) is compared with the service design characteristic “manpower planning (DC5)”, the relationship measure is “strong”. These relationship measures and CNR are used as inputs to derive the output FRI. For example, the customer normalized rating is equal to 21.9 and the relationship measure is equal to 70 are fuzzified according to the corresponding membership functions. After the fuzzy rules reasoning, all the rules are executed and the output “FRI” between the inputs “SQ8” and “DC5” is calculated by the system (= 50.9). Figure 4.3 provides the process of max-min fuzzy inferencing for calculating fuzzy output of FRI and Figure 4.4 provided the output surface of the fuzzy inference system. The last row of the output column shows the results of max-min inferencing for the various fuzzy rules used. Centroid method of defuzzification is used in this study. Similarly, all FRI values between the CRs and DCs are calculated and summarized in Table 4.19.
Table 4.18 The relationship matrix between CR and DC
Customer Requirements
CNR % from Fuzzy
AHP
Service Design Characteristics
DC1 DC2 DC3 DC4 DC5 DC6 DC7 DC8
SQ1 3.56 ● ● ○ □ □ ▲ Δ Δ SQ2 27.26 ● ● Δ ● Δ Δ ○ Δ SQ3 6.43 ● ○ Δ Δ Δ Δ ● Δ SQ4 2.66 ○ ● ○ ● Δ ○ ○ ○ SQ5 8.65 ● ○ ○ ○ ● Δ Δ ○ SQ6 2.25 □ □ ▲ □ ▲ ● ○ □ SQ7 27.26 ○ ● Δ ● ○ ▲ ● □ SQ8 21.90 ● ● ● ● ○ ● ● ●
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where,
● Most Strong, 90
○ Strong, 70
□ Moderate, 50
Δ Weak, 30
▲ Most Weak, 10
Figure 4.3 Fuzzy inferences for inducing FRI between SQ8 and DC5
Figure 4.4 Output surface of Fuzzy inference system
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After obtaining all fuzzy relationship input values, the importance of
DCj is calculated by the following equation.
Ij = FRIjCNRjM
i
1
, j= 1,2,….N (4.4)
For example, the importance of “DC5” is calculated as follows:
27.5 x 0.0356 + 26.4 x 0.2726 + 15.4 x 0.0643 + 15.4 x 0.0266 +
50 x 0.0865 + 6.72 x 0.0225 + 54.6 x 0.2726 + 50.9 x 0.219 = 40.08.
Importance values of all the other DCs are calculated and are
summarized in Table 4.20 along with the normalized importance.
Table 4.19 Fuzzy relationship values between the CRs and the DCs
Customer
Requirements DC1 DC2 DC3 DC4 DC5 DC6 DC7 DC8
SQ1 50 50 42.5 27.5 27.5 6.72 15.4 15.4
SQ2 62.4 62.4 26.4 62.4 26.4 26.4 54.6 26.4
SQ3 50 42.5 15.4 15.4 15.4 15.4 50 15.4
SQ4 42.5 50 42.5 50 15.4 42.5 42.5 42.5
SQ5 50 42.5 42.5 42.5 50 15.4 15.4 42.5
SQ6 27.5 27.5 6.72 27.5 6.72 50 42.5 27.5
SQ7 54.6 62.4 19.3 62.4 54.6 19 62.4 39.6
SQ8 58.6 58.6 58.6 58.6 50.9 58.6 58.6 58.6
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Table 4.20 The importance values of DCs and their normalization
Service Design Characteristics Absolute importance
Normalized Absolute importance
Trained service executive at the reception (DC1)
55.8 0.1550
Trained service mechanic (DC2) 56.99 0.1583
Rewards and recognition scheme (DC3) 32.75 0.0910
Service reporting (DC4) 54.45 0.1513
Man power planning (DC5) 40.08 0.1113
Use of genuine parts for service (DC6) 30.03 0.0834
Rechecking of complaints at the time of service completion & delivery (DC7)
51.91 0.1442
Response to customer feedback (DC8) 37.79 0.1050
Total 359.8 1
iv. Conducting an evaluation of competing service providers and
prioritizing the CRs.
The customer competitive assessment in the HoQ provides a good
way to determine whether the customer requirements have been met. It also
indicates areas to be concentrated in the next design. It contains an appraisal
of where an organization stands relative to its major competitors in terms of
each requirement. The assessment values are obtained from the FAHP
methodology.
The CRs are prioritized by calculating the absolute weight. The
absolute weight is calculated by multiplying the CNR percentage from FAHP,
scale-up factor and sales point in HoQ matrix (Figure 4.5). The prioritized
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CRs are as follows: understanding the problem in the vehicle (SQ2); ability to
fix the problem in the first visit (SQ7); quality of service done (SQ8); timely
delivery of vehicle (SQ5); attention to modifications demanded by the
customer (SQ3); mechanics’ trustworthiness (SQ4); value for money service
(SQ6); promptness of service advisor in attending the customer (SQ1).
v. Evaluating the service design characteristics and development of
targets.
In order to meet the customer requirements, the service
organisation has to prioritise the service design requirements and fix the
targets for each service design requirement. The normalized absolute
importance of each service DCs are prioritized as explained above. Similarly,
the relative importance of each DC is calculated by considering the absolute
weight for CRs and fuzzy relationship values between the CRs and the DCs.
The HoQ for unit A is constructed and is depicted in Figure 4.5. Higher
absolute and relative ratings are used to identify the areas where service
efforts need to be concentrated. The primary difference between these weights
is that the relative weight includes information on the customer scale-up
factor and sales point (Besterfield et al 2007). Figure 4.6 compares the
normalized absolute importance and normalized relative importance. Based
on both the scores, the prioritized service requirements are in the following
order: trained service mechanic (DC2); trained service executive at the
reception (DC1); service reporting (DC4); rechecking of complaints at the time
of service completion and delivery (DC7); man power planning (DC5);
response to customer feedback (DC8); rewards and recognition scheme to
employees (DC3); use of genuine parts used for service (DC6).
83
Customer
Requirements CNR %
from Fuzzy AHP
Service Design Characteristics
DC1 DC2 DC3 DC4 DC5 DC6 DC7 DC8
SQ1 3.56 ● ● ○ □ □ ▲ Δ Δ 11.3 11.39 12.84 13.17 13.97 11.01 14.15 9.82 14.15 1.25 1 4.45
SQ2 27.26 ● ● Δ ● Δ Δ ○ Δ 15.06 13.84 15.11 13.02 12.16 15.64 6.40 6.30 15.64 1.04 2 56.70
SQ3 6.43 ● ○ Δ Δ Δ Δ ● Δ 17.09 15.10 8.34 13.43 11.38 13.65 11.01 6.41 17.09 1.00 1 6.43
SQ4 2.66 ○ ● ○ ● Δ ○ ○ ○ 8.9 8.34 13.77 9.99 21.36 9.72 16.73 18.22 21.36 2.40 1 6.38
SQ5 8.65 ● ○ ○ ○ ● Δ Δ ○ 11.46 18.46 13.39 12.23 11.52 11.82 16.59 5.16 18.46 1.61 1 13.93 SQ6 2.25 □ □ ▲ □ ▲ ● ○ □ 9.46 13.61 15.01 11.07 9.63 9.84 17.00 21.97 21.97 2.32 1 5.22
SQ7 27.26 ○ ● Δ ● ○ ▲ ● □ 13.80 5.17 10.34 13.43 11.38 15.84 10.06 17.65 17.65 1.15 1.5 47.02
SQ8 21.90 ● ● ● ● ○ ● ● ● 12.81 13.86 11.16 13.65 8.57 12.45 8.02 14.43 14.43 1.13 1.5 37.12
Absolute Importance 55.8 56.99 32.75 54.45 40.08 30.03 51.91 37.79 UNIT A
UNIT B
UNIT C
UNIT D
UNIT E
UNIT F
UNIT G
UNIT H
Targ
et v
alue
Scal
e-up
fact
or
Sale
s po
int
Abs
olut
e w
eigh
t
Normalized Absolute Importance 0.1550 0.1583 0.0910 0.1513 0.1113 0.0834 0.1442 0.1050 Customer competitive assessment ( from Fuzzy AHP )
Relative Importance 9935.8
10197
5765.9
9923.3
7004.8
5441
9302.6
6708.38
● MOST STRONG, 90 ○ STRONG, 70 □ MODERATE, 50 Δ WEAK, 30 ▲ MOST WEAK, 10
Normalized Relative Importance
0.155
0.159
0.09
0.154
0.109
0.085
0.145
0.104
TARGETS
Job
card
pre
para
tion
trai
ning
onc
e in
six
mon
ths
Expe
rtis
e tr
aini
ng o
nce i
n tw
o m
onth
s
Prop
er p
erfo
rman
ce
appr
aisa
l pro
cedu
re
Inte
nsiv
e tr
aini
ng in
se
rvic
e re
port
ing
proc
edur
es
Ret
aini
ng th
e ta
lent
ed p
ool
and
prop
er a
lloca
tion
of
reso
urce
s
Impl
emen
ting
prop
er
purc
hase
pro
cedu
re
Esta
blis
hing
a fo
ol-p
roof
m
echa
nism
for
re-c
heck
ing
Initi
atin
g co
rrec
tive a
ctio
ns
base
d on
fee
dbac
k an
alys
is
Figure 4.5 FQFD based house of quality
84
The targets to meet these requirements have been identified and
deployed further by the QFD team. The targets for the prioritized service DCs
are as follows: expertise training once in two months; job card preparation
training once in six months; intensive training about service reporting
procedures; establishment a fool-proof mechanism for re-checking the
customer complaints; retaining the talented pool and proper allocation of
resources; initiate corrective actions based on feedback analysis; proper
performance appraisal procedure; implement proper purchase procedure. The
redesigned services are further deployed and the implementation plans are
reviewed for continuous performance improvement.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
DC1 DC2 DC3 DC4 DC5 DC6 DC7 DC8
Design Characteristics
Impo
rtan
ce r
atin
g
Normalized absoluteimportanceNormalized relativeimportance
Figure 4.6 Comparison between normalized absolute and relative
importance
85
4.4 SCOPE AND LIMITATIONS OF FAHP-EBG-FQFD
COMBINED MODEL
The scopes of FAHP-EBG-FQFD combined model are found to be:
Using fuzzy sets for the pair wise comparison, enhances the
decision making process and allows a precise assessment of
service quality attributes.
Triangular fuzzy numbers introduced into the conventional
AHP is found to improve the degree of judgments of decision
makers. The confidence level (α) and the index of optimism
(µ) make up for the deficiency in the conventional AHP.
FQFD provides a methodology for determining the
aggregated importance of DCs. Compared with the previous
methods for determining the importance of DCs, the
aggregated importance method is found to offer a complete
measure.
The identified limitations of FAHP-EBG-FQFD combined model are:
The problem with survey questionnaires comes from the
ordinal measurement scales. The most frequently used
ordinal level scale is the Likert scale, with rankings of the
form: 1 strongly agree; 2 agree; 3 unsure; 4 disagree, and 5
strongly disagree. Clearly, someone who circles 5 disagrees
with the statement more than someone who circles 4 does.
However, the degree of difference is unclear, since an ordinal
scale indicates relative position, not the magnitude of the
difference between the choices.
86
Questionnaire measurement presents the problem that
respondents or customers have to convert their preference to
scores internally. This possibly distorts the preference being
captured. Thus, the final “scores” do not necessarily indicate
user preference, since the customers may have difficulty in
recording their preference to the response format of the five-
or nine-point scale.
4.5 CONCLUDING REMARKS
In this chapter, FAHP-EBG-FQFD combined performance
management model has been proposed. It helps in measuring the performance
of the organization exactly and prioritizing the service requirements for future
implementation. Qualitative dimensions of service are measured with FAHP
and it proves to be the best for quantifying the imprecise data.
The EBG model provides the performance measures of automobile
repair shops using service performance measure namely time, cost and service
quality. FQFD is utilized in order to redesign the services offered by various
units. Fuzzy relation measures between CRs and DCs are determined based
on fuzzy expert systems approach in QFD. The service design requirements
are then prioritized and targets are identified.
The case study has led to identify the scope and limitations of
FAHP-EBG-FQFD combined model. In order to overcome the limitations,
fuzzy logic-DEA-FFMEA has been proposed. The details are presented in the
next chapter.