CHAPTER 4 FUZZY AHP, EXTENDED BROWN-GIBSON ...shodhganga.inflibnet.ac.in/bitstream/10603/31925/9/09...59 CHAPTER 4 FUZZY AHP, EXTENDED BROWN-GIBSON MODEL AND FUZZY QUALITY FUNCTION

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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):

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~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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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



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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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



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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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



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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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


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


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


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


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.

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CHAPTER 4 FUZZY AHP, EXTENDED BROWN-GIBSON ...shodhganga.inflibnet.ac.in/bitstream/10603/31925/9/09...59 CHAPTER 4 FUZZY AHP, EXTENDED BROWN-GIBSON MODEL AND FUZZY QUALITY FUNCTION