Study Report on Numerical Kano Model for Compliance Customer Needs

Description
Study Report on Numerical Kano Model for Compliance Customer Needs with Product Development:- In business and engineering, new product development (NPD) is the complete process of bringing a new product to market. A product is a set of benefits offered for exchange and can be tangible (that is, something physical you can touch) or intangible

Study Report on Numerical Kano Model for
Compliance Customer Needs with Product
Development

Abstract. Functional form and dysfunctional form of Kano model are considered as customer need regarding
attribute of product. Both functional and dysfunctional forms are: Like, Must-be Neutral, Live-with and Dislike.
The answers of customer regarding a product of functional and dysfunctional forms have been applied for
selection of customer needs regarding product attribute (Kano evaluation). Filling-up and returning the
Questionnaires by the individuals are essential for determining Kano evaluation. But many Questionnaires have
not been returned in that case. Moreover, many possible consumers could not get opportunity to fill-up
questionnaire. These uncertain or unknown consumers' opinions are also essential for product development. The
choices of Kano evaluations have been outlined by: Attractive, One-dimensional, Must-be, Indifferent and
Reverse. In this study, choices of evaluation of unknown customer are considered uniform cumulative vector
probability (scenario 1). This study is based on the Monte Carlo simulation method, concept of probability and
Kano model. This model has also been tested for its soundness and found fairly consistent including existing
Kano model (scenario 2) and case survey for headlight of bicycle (scenario 3).

Keywords: Kano Model, Probability, Product Attributes, Customer Satisfaction and Dissatisfaction

1. INTRODUCTION

This study is an endeavor for quantitative approach
to further develop the well-known Kano Model. It is use-
ful for the research in capturing and quantifying the cus-
tomer requirements in new product development process
as well as consequent quality assurance (Rashid, 2010
and Rashid et al., 2010). The authors investigate into the
effects of customer needs, regarded as the important at-
tribute in product development. The study examines
these needs by relating them to identifying both func-
tional and dysfunctional forms of Kano Model. The pa-
per contributes to the development of a proposed nu-
merical Kano model, incorporating the compliance cus-
tomer needs and evaluation of uncertain or unknown
customers' opinions for product development. It also
provides some empirical testing results on validating the
efficacy of the proposed model and comparing it with
existing Kano model.
The paper addresses the technical aspects in terms

of three scenarios, as advocated in the Abstract. Monte
Carlo Simulation method coupling with probability con-
cepts is used to expand the existing Kano model to the
numerical model.
The testing of the proposed model is illustrated
with the setting of simulation scenarios, expressed in
equations and figures. The technical correctness of the
paper is objectively demonstrated with numerical results.
For this purpose, section 2 is illustrated for literature review,
section 3 for a numerical method of using Monte Carlo
simulation method, section 4 for a study on Kano model,
section 5 for inputs of the model events, probability vec-
tor and cumulative probability, section 6 for result and
discussions.

2. LITERATURE REVIEW

The most appropriate leveraging strategy is essen-
tial for product development with respect to the target
141

market segments considering the customer trends (Weck
et al., 2005). Product development is an integrated result
of design, manufacturing, research and development,
and compliance with Voice of Customers (VOC). Prod-
uct development is considered main challenge to comply
among satisfaction, affordability of customer, produc-
tion rate, technical ability, human error, production cost,
shorter reaction time, selling price, organizational com-
plexity and bureaucracy, value chain and competitor of
manufacturer in various customer segments (Browing,
2003; Prasad, 2000; Burlikowska and Szewieczek, 2009;
Willcox and Wekayama, 2003; Matt, 2009).Various chal-
lenges are raised from different customer segments ac-
cording to their individual customer needs. In this re-
spect, manufacturers are following laws of consumer
needs, customer pain points (Handfield and Steininger,
2005), and attention of changing customer needs by
adapting design requirements (Hintersteiner, 2000). An-
other challenge of product development is to an unstable
and diversified market behavior (Cochran et al., 2000)
and the demographic and psychographic factors of cus-
tomers. Thus, VOC, organizational aspects, peripheral
aspects, methods and tools are considered appropriately
for product development, (Fujita and Matsuo, 2006).
Systems development society is working for integrating
VOC into product development. For instance, Transi-
tional Business Model (TBM) is developed to incorpo-
rate the customer needs into the concept generation
processes for aerospace product development (Guenov
et al., 2006). Data mining techniques are identified for
product development by the researchers Jiao et al., 2007.
A knowledge management model is developed by Fager-
ström and Olsson, 2002 for using Soft System Method-
ology (SSM) and emphasized the need for effective col-
laboration between main supplier and customers for
adding value to a product development process. Identi-
fied factors are explained or significantly contributed to
successful launch of product development of an innova-
tion by another research group Haapaniemi and Sep-
panen, 2008. Integrated design knowledge is applied for
reuse framework, bringing together elements of best
practice reuse, design rationale capture and knowledge-
based support in a single coherent framework by Baxter
et al., 2007. A formal basis for the creation of an auto-
mated reasoning system is also supported for creative
engineering design by Sushkov et al., 1995. Mannion
and Kaindle, 2008 developed a formal logic-based ap-
proach to deal with the VOC in term of product re-
quirement. Sivaloganathan et al., 2000 carried out a
study for the effectiveness of systematic and conven-
tional approaches to design. A stepwise procedure based
on quantitative life cycle assessment is integrated of
environment aspects in product development by Nielsen
and Wenzel, 2002. A model is developed for coexisting
product and process design. There are various design
concepts to evaluate in order to identify the 'Best' con-
cept with application of fuzzy logic for design evalua-
tion and proposes an integrated decision-making model

for design evaluation at developing a computer tool for
evaluation process to aid decision-making (Green and
Mamtami, 2004). A design structure matrix (DSM) is
provided by Browing, 2003 a simple, compact, and vis-
ual representation of a complex system that supports
innovative solution to decomposition and integration
problems for product development. The rapid change of
technology has been led to shorter product life cycles for
many products most particularly in consumer
electronics.A product definition and customization
system (PDCS) is established to meet rapid change of
competitive and globalised business climate
(Minderhond and Fraser, 2005; Chen et al., 2005).
Moreover, an information technology (IT) framework is
solved the product devel- opment problem through
automatic generation of infor- mation (Dean et al.,
2008). Other than information can- not be summed for
decoupled designs and overcome the problem was
applied joint probability density function and uniformly
distributed design parameters (Frey et al., 2000). A
deliberate business process is involved hun- dreds of
decisions and supported by knowledge and tools for
product development, where a new composi- tion of
fuzzy relations which is defined by using the drastic
product development (Krishnan and Ulrich, 2001). The
products model is developed for technical and marketing
purpose (Meyer, 1992). Reused design is applied by Ong
et al., 2008 for product development modeling and
analysis and optimization. Integrated de- sign of
products and their underlying design processes are
provided for a systematic fashion, motivating the
extension of product life cycle management (PLM)
(Panchal et al., 2004). 'Validation Square' is validated by
testing its internal consistency based on logic in addi- tion
to testing its external relevance based on its useful- ness
with respect to a purpose (Pedersen et al., 2000). The
concept of Lean has influenced the research of VOC
and its implementation. The focuses of all activi- ties are
turned to customer needs rather than job-at-hand
(Oppenheim, 2004). Browning, 2003 recommend that
removing one activity or changing its focus as because it
is a non-value adding activity does not help improve
overall value of a product. Sireli et al., 2007 developed a
method to integrate Kano model with QFD. Chen and
Chuang, 2008 integrated Kano model with the concept
of robust design. Li et al., 2009 integrated Kano model
to make AHP (Analytical Hierarchy Process) and rough-
set based calculations. Xu et al., 2009 developed a vari-
ant of Kano model called "analytical Kano model". As a
result, the Kano model has been appeared into one of the
most popular quality models now a day since its intro-
duction in 1984. Kano's model of attractive quality
(Kano et al., 1984) has been taken the researchers of
industries for quality product development (Berger et al.,
1993; Matzler and Hinterhuber, 1998; Kai, 2007; Fuchs
and Weiermair, 2004). Based on the information from
Kano questionnaire, it provides a quantitative approach
to observe and follow the change over time (Raharjo et
al., 2009). An investigation is done for 3G mobile ser-
142

vices perceive on the market (Baek et al., 2009). The
major difference in contrast to other wide spread quality
models, such as the technical and functional quality
model (Gronroos, 1984) or the Gap model (Parasuraman
et al., 1985), is that Kano's model is based on the as-
sumption of existence of nonlinear and asymmetric rela-
tionships between attribute-level performance of prod-
ucts/services and overall customer satisfaction (OCS).
Nevertheless, the empirical studies (Chen and Chuang,
2008; Li et al., 2009; Xu et al., 2009; Sireli et al., 2007)
of Kano model are in a sense helpful in materializing the
issues that have been emphasized by the holistic frame-
works of product development (Fagerström and Olsson,
2002; Browning, 2003; Oppenheim, 2004; Guenov et al.,
2006). Kano model is able to identify a set of product
attributes satisfying a set of customer needs (Kano et al.,
1984; Berger et al., 1993; Matzler and Hinterhuber,
1998; Kai, 2007). The above review guides to develop a
numerical Kano model for unknown customer need
analysis. Moreover, Ullah and Tamaki, 2009 have de-
veloped a method of 25 individuals; only 14 of them
submitted a Kano questionnaire with their answers on
time. 11 individuals, i.e. 44% of the answers were un-
known or technically uncertain. Their study was con-
strained in this specific area to know the 11 unknown
people's answer. According to above previous research-
ers' discussion it is found that generic unknown custom-
ers' evaluation is not studied. For this reason in this re-
gard Ullah and Tamaki made a proposition in their next
work (Ullah and Tamaki, 2010), that unknown custom-
ers are considered uniform cumulative vector probabil-
ity. According to this proposition, the proposed model is
developed for unknown or uncertain customer evalua-

tion regarding product attribute to follow above guide-
line. Regarding Kano model based numerical simulation
model is crucial for unknown customer need analysis
with product attribute i.e. Kano evaluation or customer
evaluation.

3. METHODS

This section explains the common settings of the
simulation method. Before introducing the general set-
tings, a particular case of simulation (i.e., simulation of
three mutually exclusive events from given proabilities)
are described for better understanding.
The simulation process of three mutually exclusive
events denoted by A, B, and C with known probabilities
is schematically illustrated in Fig. 1. The explanation of
the simulation process is as follows:
Suppose that A, B, and C are three mutually exclu-
sive events and Pr(A), Pr(B), and Pr(C) are their prob-
abilities, respectively, so that Pr(A) + Pr(B) + Pr(C) = 1.
Using these probabilities, the cumulative probabilities
(CPr(.)) can be calculated in the following manner:
CPr(A) = Pr(A), CPr(B) = Pr(A) + Pr(B), and CPr(C) =
Pr(A) + Pr(B) + Pr(C). Three mutually exclusive inter-
vals can be derived using the cumulative probabilities,
as follows: [0, CPr (A)), [CPr (A), CPr (B)), and [CPr
(B), CPr(C)]. Suppose that r
k
, is a random number in the
interval [0, 1] for all k = 1,., N.
Consider the following rule to simulate A: "If r
i
e [0,
CPr(A)) Then S
k
= A." This rule ensures that if r
i
is a

Figure 1. Simulation of three mutually exclusive events.
143

value in the interval [0, CPr (A)), then S
k
becomes A.
Similarly, consider two more rules to simulate B and C,
as follows: "If r
i
e [CPr (A), CPr (B)) Then S
k
= B" and
"If r
i
e [CPr (B), CPr(C)] Then S
k
= C." Therefore, if
these three rules are repeated N times, each time S
k
will
become A, B, or C depending on the value of r
i
. As such,
if S is the vector of N simulated events S = (S
1
,., S
k
,.,
S
N
), then S
k
e {A, B, C} for all i = 1,., N. If the simula-
tion process is perfect the relative frequencies of A, B,
and C in S should be equal to Pr (A), Pr (B), and Pr(C),
respectively. For example, if Pr (A) = 0.85, Pr (B) = 0.1,
and Pr(C) = 0.05, then out of 100 iterations (N = 100) 85
iterations will result A, 10 iterations will result B, and 5
iterations will result C, i.e., relative frequencies of A, B,

However, the above result also implies that irre-
spective of the fact that an event is most likely to occur
(the top side case in Fig. 2) or all events are equally
likely to occur (the bottom side case in Fig. 2). The
aforementioned three-event simulation process can be
generalized for n-event simulation process, as defined
by (1). In (1), E=(E
1
, ., E
n
) is the event vector, P = (Pr
(E
1
), Pr (E
n
)) is the probability vector, and S = (S
1
, S
N
)
is the simulated event vector. Other symbols in (1) have
the same meaning as explained in the above.

Input :
E = (E
1
, L, E
n
) //event ve ctor
P = (Pr (E
1
), L, Pr (E
n
)) //probabil ity vector
and C become equal to the given probabilities. In reality
N //number of iterations
this does not happen because of the limitation of the
computer-generated random number r
i
. Therefore, an
error occurs. This yields an error function Error = |Pr (A)
- Pr ?(A)| + |Pr (B) - Pr ?(B)| + |Pr(C) - Pr?(C)|. Here, Pr
?(A), Pr ?(B), and Pr?(C) denote the relative frequencies of
A, B, and C in S, respectively. Thus, the objective is to
keep the value of Error close to zero. One of the ways to
Calculate :
For i = 1, L, n
CPr (E
i
) = Pr (E
1
)+ L + Pr (E
i
) // cumulative probabilit y
End For
Simulate :
For k = 1, L, N
generate r
k
//r
k
is a random number in the interval |0, 1|
achieve this objective is to increase the number of itera-
tions N. Figure 2 shows two plots of Error against num-
If
Else
r
k
e |0, CPr (E
1
)) Then
S
k
= E
1
ber of iterations N. The left hand side plot corresponds to
Pr (A) = 0.8, Pr (B) = 0.15, and Pr(C) = 0.05 (i.e., one of
the event is most likely to occur), whereas the right hand
For i = 2, L, n ÷ 1
If r
k
e |CPr (E
i
÷
1
), CPr (E
i
)) Then
End For

S
k
= E
i
(1)
side plot corresponds to Pr (A) = Pr (B) = Pr(C) = 1/3 If r
k
e |CPr (E
n
÷
1
), CPr (E
n
)|Then S
k
= E
n

(i.e., all events are equally likely to occur). As seen from End For
Fig. 2, for both cases the Error is as low as 5%, if the Output :
number of iteration is at least 2000. This critical number
of iterations (i.e., N is 2000 or above will make sure Er-
ror less than 5%) is valid only for simulating three events.
For other cases, it is important to construct similar plots
of Error versus N and then determine the critical number
of iterations.
S = (S
1
, L , S
k
,L , S
N
) //simulat ed event vect or

Probability (strictly speaking the relative frequency) of
events E
1
,., E
N
in S denoted by Pr?(.) can be determined
using the formulation defined by (2).

Input :
S = (S
1
,L ,S
k
,L,S
N
)//simulated event vector
Calculate :
For i = 1,L,n
count
i
= 0
For k = 1,L, N
If
S
k
= E
i
Then count
i
= count
i
+ 1
(2)
End For
Pr?(E
i
) = count
i
//probability of E
i
in S
N
End For
Output :
P? = (Pr?(E
1
),L, Pr?(E
n
))//simulated probability vector

Therefore, simulation Error (summation of absolute
difference between given and simulated probabilities of
each event) can be defined by the expression in (3).

n
Error
=
¿ Pr ( E
i
) ÷ Pr?( E
i
) (3)
Figure 2. Relationship between simulation error and num- i=1
144

4. A STUDY ON KANO MODEL Table 2. Five categories of product attributes based on
Kano et al. (1984).
4.1 Introduction of Kano Model

Kano model of customer satisfaction defines the rela-
tionship between product attribute and customer satis-
faction and provides five types of product attributes: 1)
Must-be, 2) One-dimensional, 3) Attractive, 4) Indiffer-
ent, and 5) Reverse, as schematically illustrated Fig. 3
and Table 1. The combination of functional and dysfunc-
tional answers is then used to identify the status of the
attribute in term of: 1) Must-be, 2) One-dimensional, 3)
Attractive, 4) Indifferent, or 5) Reverse from Table 1.
Type of Attribute
Perception
One-dimensional
Must-be
Attractive
Indifferent
Reverse
When attribute is
present?
Satisfied
No feeling
Satisfied
No feeling
Dissatisfied
When attribute is
absent?
Dissatisfied
Dissatisfied
No feeling
No feeling
Satisfied
Kano questionnaire for headlight of bicycle is High
satisfaction (Delighted)
shown in Table 3.

Attractive (A)

Indifferent (I)

One-dimensional (O)

Table 3. Kano questionnaire.

Customer Needs (CN)

Your bicycle has a headlight
Performancefullyabsent
(Dysfunctional)
Performancefullypresent
(Functional)
Must be (M)

Reverse (R)
?Like
?Must-be ?
Neutral
?Live-with
?Dislike

Lowsatisfaction (Disgusted)
Figure 3. Kano model for customer satisfaction.

All possible combinations of customer answers and
the corresponding type of product attribute are summa-
rized in Table 1. As seen from Table 1, besides the above
mentioned five types of attribute in Table 1, there is one
more type of attribute called Questionable.

Table 1. Kano Evaluation.
Dysfunctional Answer (DFA)
Functional

Your bicycle don't have a headlight
?Like
?Must-be ?
Neutral
?Live-with
?Dislike

But real answer of customer feedback is sum-
marized in Table 4 and Table 5.
It is important that Table 4 shows individuals opinion
or customer answer the Kano model-based questionnaire
(Table 3). Table 4, encompassing respondents (column 1),
Answer (FA)
Lik
e
Must-be Neutral Live-with Dislike
Functional Answer (column 2), Dysfunctional Answer

Like (L)
Must-be (M)
Neutral (N)
Live-with (Lw)
Dislike (D)
(L
)
Q
R
R
R
R
(M
)
A
I
I
I
R
(N
)
A
I
I
I
R
(Lw
)
A
I
I
I
R
(D
)
O
M
M
M
Q
(column 3). As seen from Table 3, a customer (respon-
dent) can to select one of the states out of Like, Must-be,
Neutral, Live-with, and Dislike from the functional side
stating his/her level of satisfaction, if the attribute is
added to the product.
The customer also can to select one of the states
(out of the same choices) from the dysfunctional side
stating his/her level of satisfaction, if the attribute is not
Attractive (A), Indifferent(I), Must-be(M), One-dimensional (O),
Questionable (Q) Reverse (R)

This occurs (Questionable) when one selects Like
or Dislike from both functional and dysfunctional sides
(i.e., when an answer does not make any sense). Kano
model is helpful for integrating the VOC into product
development.
added to the product. As an example, a customer can
selects "Like" from the functional side (your bicycle has
a headlight) and "Live-with" from the dysfunctional side
(your bicycle has a headlight). As result, for spe- cific
this makes the headlight attribute of bicycle an
Attractive attribute. Where 27 respondents answer is
illustrated in Table 4. According to their answer and
Kano evaluation Table 1, Evaluation answer is shown in
145

Table 5. Majority individuals are considered headlight Table 6. Simplification form of Kano evaluation.
attribute of bicycle is Must-be. Thus, this survey result
is focused headlight of bicycle as a Must-be.

Table 4. Real Customer Answer for bicycle headlight.
Your bicycle has a headlight
S
l
1
2
3
F
A
Lik
e
Lik
e
Lik
e
DF
A

Like
Must-be
Neutral
Combination of FA
and DFA
Like Like
Like Must-be
Like Neutral
KE
Questionable (Q)
Attractive (A)
Attractive (A)
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Functional Answer
Must-be
Live-with
Must-be
Must-be
Like
Like
Must-be
Like
Must-be
Must-be
Neutral
Must-be
Must-be
Like
Must-be
Must-be
Like
Must-be
Must-be
Like
Dysfunctional Answer
Dislike
Live-with
Dislike
Dislike
Dislike
Dislike
Live-with
Dislike
Dislike
Dislike
Neutral
Dislike
Dislike
Must-be
Neutral
Dislike
Must-be
Dislike
Dislike
Dislike
4
5
6
7
8
9
1
0
1
1
1
2
1
3
1
4
1
5
1
6
1
7
1
8
1
9
2
0
2
1
2
2
2
3
2
4
2
5
Like
Like
Must-be
Must-be
Must-be
Must-be
Must-be
Neutral
Neutral
Neutral
Neutral
Neutral
Live-with
Live-with
Live-with
Live-with
Live-with
Dislike
Dislike
Dislike
Dislike
Dislike
Live-with
Dislike
Like
Must-be
Neutral
Live-with
Dislike
Like
Must-be
Neutral
Live-with
Dislike
Like
Must-be
Neutral
Live-with
Dislike
Like
Must-be
Neutral
Live-with
Dislike
Like Live-with
Like Dislike
Must-be Like
Must-be Must-be
Must-be Neutral
Must-be Live-with
Must-be Dislike
Neutral Like
Neutral Must-be
Neutral Neutral
Neutral Live-with
Neutral Dislike
Live-with Like
Live-with Must-be
Live-with Neutral
Live-with Live with
Live-with Dislike
Dislike Like
Dislike Must-be
Dislike Neutral
Dislike Live-with
Dislike Dislike
Attractive (A)
One-dimensional (O)
Reverse (R)
Indifferent (I)
Indifferent (I)
Indifferent (I)
Must-be (M)
Reverse (R)
Indifferent (I)
Indifferent (I)
Indifferent (I)
Must-be (M)
Reverse (R)
Indifferent (I)
Indifferent (I)
Indifferent (I)
Must-be (M)
Reverse (R)
Reverse (R)
Reverse (R)
Reverse (R)
Questionable (Q)
21
22
23
24 25 26
27
Must-be
Like
Like
Like
Must-be Must-be Must-be
D
i
s
l
i
k
e
N
e
u
t
r
a
l
Liv
e-
wit
h
D
i
s
l
i
k
e
D
i
s
l
i
k
e
D
i
s
l
i
k
e
D
i
s
l
i
k
e

T
able 6
is a
straig
htfor
ward
outlin
e of
Kano
model
. This
is a
real
pictur
e of
relatio
nship
amon
g FA,
DFA
and
KE. It
is also
show
n
frequ
ency
25 for
each
FA,
DFA
and
KE
regard
ing
events
,
which
are
define
d in
Table
s 7-8.
This
rule is
applie
d for
select
ion of
the
simul
ated
KEe{
A, O,
M, I, R,
Q}
from
simulat
ed FA
and
DFA.
P
robabi
lity
provid
es the
real
knowle
dge
when
out-
come
of
events
is
uncerta
in. In
the
present
study,
events

Table 5. Compile the Customer Answer from Table 4.

Evaluation of Answer
Functional Answer Dysfunctional Answer Attractive (A) 4
Like 9 Like 0 Indifferent (I) 4
Must-be 16 Must-be 2 Must-be (M) 14
Neutral 1 Neutral 3 One-dimensional (O)
5Live-with 1 Live-with 3 Questionable (Q) 0
Dislike 0 Dislike 19 Reverse ( R ) 0
146

probabilities are equivalent to relative frequency of those Table 8. Mutual Exclusive Probability of the Events of
events. Generally, an event is a set of outcome to which a Kano Evaluation (KE)/ inputs of scenario 2.
probability is assigned. Events of FA, DFA and KE are
Your bicyclehas aheadlight
considered from above Table. These are described in
Tables 7-8. Following table shows both FA and DFA
events, mutually exclusive probability vector Pr (.) and
cumulative probability CPr (.):

Table 7. Probability of the events of FA and DFA FA/DFA.

Frequency, f Probability, Pr (.) Cumul)ative Probability,
Event (Ei)

Attractive
Indifferent
Must-be
One-dimensional
Fre
-
quency
4
4
14
5
f(.)

0.14815
0.14815
0.51852
0.18519
LL(.)

L
L
L
L
SL
L
L
TV(.)

0.3
0.3
0.5
0.3
Pr(.)

0.204638472
0.204638472
0.34106412
0.204638472
CPr(.)

0.204638472
0.409276944
0.750341064
0.954979536
Events(E)

Like (L)
Must-be (M)
5
5
0.2
0.2
CPr (.
0.2
0.4
Questionable
Reverse
0
0
0
0
V
U
V
U
0.033
0.033
0.022510232
0.022510232
0.977489768
1
Neutral (N)
Live-with (Lw)

Dislike (D)
5
5

5
0.2
0.2

0.2
0.6
0.8

1
According to the Kano events, the following model
is proposed for considering as a scenario 2:

4.2 Kano Rule

The following table represents FA, DFA and KE

Table 9. A Kano rule with events probability in tabular form.
Dysfunc-
Frequency, Functional Cumulative Cumulative
Sl. Customer Kano Evaluation (KE) Probability tional An- Probability f Answer (FA) Probability Probability
No. swer (DFA)
1 Attractive 1 Like 0.333 1 Live-with 0.333 0.333
2 Attractive 1 Like 0.333 Must-be 0.333 0.666
3 Attractive 1 Like 0.333 Neutral 0.333 1
Frequency for Attractive = 3
4 One-dimensional 1 Like 1 Dislike 1 1
Frequency for One-dimensional= 1
5 Must-be 1 Live-with 0.333 0.333 Dislike 0.333
6 Must-be 1 Must-be 0.333 0.666 Dislike 0.333
7 Must-be 1 Neutral 0.333 1 Dislike 0.333
Frequency for Must-be = 3
8 Indifferent 1 Live-with 0.111111111 0.3333 Live-with 0.11111111
9 Indifferent 1 Live-with 0.111111111 Must-be 0.11111111
10 Indifferent 1 Live-with 0.111111111 Neutral 0.11111111
11 Indifferent 1 Must-be 0.111111111 0.666 Live-with 0.11111111
12 Indifferent 1 Must-be 0.111111111 Must-be 0.11111111
13 Indifferent 1 Must-be 0.111111111 Neutral 0.11111111
14 Indifferent 1 Neutral 0.111111111 1 Live-with 0.11111111 0.333
15 Indifferent 1 Neutral 0.111111111 Must-be 0.11111111 0.666
16 Indifferent 1 Neutral 0.111111111 Neutral 0.11111111 1
Frequency for Indifferent = 9
17 Reverse 1 Dislike 0.142857143 0.571428571 Live-with 0.14285714 0.142857143
18 Reverse 1 Dislike 0.142857143 Must-be 0.14285714 0.285714286 19 Reverse
1 Dislike 0.142857143 Neutral 0.14285714 0.428571429
20 Reverse 1 Dislike 0.142857143 Like 0.14285714
21 Reverse 1 Live-with 0.142857143 0.714285714 Like 0.14285714
22 Reverse 1 Must-be 0.142857143 0.857142857 Like 0.14285714
23 Reverse 1 Neutral 0.142857143 1 Like 0.14285714 1
Frequency for Reverse = 7
24 Questionable 1 Dislike 0.5 0.5 Dislike 0.5 0.5
25 Questionable 1 Like 0.5 1 Like 0.5 1
Frequency for Questionable = 2
Total Kano Evaluation = 25
147

Figure 4. A developed numerical Kano model.

events and probability of Kano model. Accordingly sec-
ond column of Table 9 represents the customer Kano
evaluation and then next column shows the frequency of
Kano evaluation.
4
th
~6
th
column show the Functional answer (FA)
and 7
th
~8
th
column show the dysfunctional answer (DFA)
with probability and cumulative probability of respective
Kano evaluation (KE).
According Table 9 with following figure 5 is
framed a Kano rule in graphical form. This rule is
guided functional and dysfunctional answer from given
Kano evaluation, likes E = (A, M, I, O, R, Q). These
rules are used to develop a numerical Kano model.

4.3 Simulation Process for Selection FA and DFA
from KE

In this simulation process, event vectors, probabil-
ity vector, cumulative probability has been applied.
Their applications are shown in Figures 4 and 5 accord-
ing to steps 1~8. These figures show a customer need
analysis model for the proposed simulation process and
representation of the relationship among KE, FA and
DFA of Kano model. The proposed simulation process
is constructed for the selection of simulated FA and

simulated DFA from the simulated KE; as described
below:

Input Steps:
Step 1: Choices of events and probability vector of
Kano evaluation (KE),
E
e (A, M, I, O, R, Q)
according to scenarios 1~3 and figures 4~5.
Step 2: Determine the number of iterations (a set
of random number).
Calculate:
Step 3: Generate a set of random inputs in the in-
terval [0, 1].
Step 4: Applied the concept of cumulative prob-
ability of the Events.
Step 5: Simulated events vector according to Eq. 1.
Output: Outputs-1~3
Step 6: Simulated events of KE of customer ac-
cording to Eqs. 1~2 (Output-1).
Step 7: Simulated events of FA from output 1 of
customer according Kano rule and
Eqs. 1~2(Output-2)
Step 8: Simulated events of DFA from output 1 of
customer according Kano rule and Eqs.
1~2 (Output-3)
148

E = (A, M, I, O, R, Q)

Simulate Kano Evaluation(KE)

Attractive (A)

Simulate Functional Answer (FA)

E = (A, M, I, O, R, Q)

Simulate Kano Evaluation (KE)

Must-be (M)
Simulate Functional Answer (FA)
KanoRule KanoRule
Like
Attractive Must-be/Neutral/Live-with
FA/KE=1 Must-be
Simulate Dysfunctional Answer (DFA) FA/KE=1/3Like
Simulate Dysfunctional Answer (DFA)
All=DFA/FA=1/3 Must-be Neutral Live-with
Must-be/Neutral/Live-with
Must-be Neutral Live-with All=DFA/FA=1/3 Dislike
Dislike

E=(A,M,I,O,R,Q)

SimulateKanoEvaluation(KE)

Indifferent (I)

KanoRule

Indifferent

All=FA/KE=3/9

Simulate Functional Answer (FA)

Must-be/Neutral/Live-with

Simulate Dysfunctional Answer (DFA)

E = (A, M, I, O, R, Q)

Simulate Kano Evaluation

One-dimensional (O)

KanoRule
One-dimensional
FA/KE=1

Simulate Functional Answer

Like/Dislike

Simulate Dysfunctional Answer
Must-be

All=DFA/FA=1/3
Must-be
Neutral

Neutral
Live-with

Live-with
Must-be/Neutral/Live-with
Lik
e
DFA/FA=1
Dislike

Like/Dislike

E = (A, M, I, O, R, Q) E = (A, M,
I, O, R, Q)

Simulate Kano Evaluation (KE)
Simulate Kano Evaluation(KA)

Reverse (R)
Questionable (Q)

KanoRule

All=FA/KE=1/2

Questionable

Simulate Functional Answer (FA)

Like/Dislike

Simulate Dysfunctional Answer (DFA)
Kano Rule

All=FA/KE=1/7

Reverse

FA/KE=4/7
Simulate Functional Answer (FA)

Must-be/Neutral/Live-with/Dislike

Simulate Dysfunctional Answer (DFA)
Like Dislike Must-be Neutral Live-with Dislike Like/Must-be/Neutral/Live-with
All=DFA/FA=1/2
Like Dislike
Like/Dislike All=DFA/FA=1/7
Like Must-be Neutral Live-with

Figure 5. Graphical forms of the Kano rule
149

Generic individuals are considered in step 1 and it is
expected that these individuals opinion are enough for
product design information. These individuals are rede-
fined with vector in Eq. 1. Choices of Evaluation
Ee{A, O, M, I, R, Q} of generic individuals (known
and unknown customers) are considered uniform event
probability vector, while cumulative vector probability
is considered in Eq. 1. According to step 2, a set of ran-
dom number inputs has been generated by using the
RAND (). A set of numbers was generated between 0
and 1 by using Eq. 1. The graphical rules are described
in previous subsection of both functional and dysfunc-

nario 3 in the following Table 11. The relative fre-
quency is turning to probability through Fuzzy method
(Ullah and Tamaki, 2010); as described next 5 steps:
Step 1: Determine relative frequencies of the states
of known answers.
Step 2: Determine Linguistic Likelihood.
Step 3: Determine Truth Values. Step 4:
Determine Probability.
Step 5: Determine Cumulative Probability.

Table 11. Input of the system for scenario 3.

Your bicycle has a headlight
tional answer separation from Kano evaluation. There-
fore, a system is developed to implement the simulation.

5. INPUTS OF THE MODEL

First scenario 1 is considered as uniform vector of
KE. For the scenario following table acts as an input of
the system. It shows the generic system of unknown
Event (Ei)
Attractive
Indifferent
Must-be
One-dimensional
Questionable
Reverse
Frequency
4
4
14
50
0
f(.)
0.14815
0.14815
0.51852
0.18519
0
0
LL(.)
LL
LL
SL
LL
V
U
VU
TV(.)
0.3
0.3
0.5
0.3
0.033
0.033
Pr(.)
0.204638472
0.204638472
0.34106412
0.204638472
0.022510232
0.022510232
CPr(.)
0.204638472
0.409276944
0.750341064
0.954979536
0.977489768
1
customer needs analysis on the system input equal
probability vector (0.16667). A unique probability dis-
tribution may be hard to identify, when information is
scarce, vague, or conflicting (Autonsson and Otto, 1995;
Coolen et al., 2010). In that case probability represents
the real knowledge, and provides tools for modeling
and work weaker states of information. As a result, the
unknown customers' choices of evaluation i.e. Attrac-
tive (A), Indifferent (I), Must-be (M), One-dimensional
(O), Questionable (Q), Reverse (R) is generally un-
known, i.e., scarce, vague etc.
It is facilitated to consider equal probability of
choice. This formulation also guarantees that the sum-
mation of all choices probabilities is equal to 1 (i.e., the
axiom of Normality as required by the concept of clas-
sical probability). This system input is straight forward
demonstrated in Table 10.

Table 10. Input of the system for scenario 1.
Cumulative
6. RESULTS AND DISCUSSION

A generic simulation model is presented to know
the Kano-model-based any known and unknown cus-
tomer answer evaluation regarding product develop-
ment. Input (Table 8, Table 10, Table 11) is applied in
the model for following respective output (Table 13,
Table 12, Table 14) of simulated events probabilities of
Kano evaluation (KE), Functional Answer (FA) and
Dysfunctional Answer (DFA). All simulated Kano
evaluation (KE) probability range, 0.15815~0.17385 is
consistent of the system input value 0.166667 (lower
portion of output 1 of the scenario 1). The average
simulated functional answer, Like is 0.41799; Must-be,
Neutral and Live-with are likely equal around 0.1349,
whereas Dislike attributes range is 0.177 (top portion of
output 2 of the scenario 1). The scenario also shows that
average simulated dysfunctional answer Like attributes
is around 0.179 Must-be, Neutral and Live-with is
likely equal around 0.1355 where as Dislike attributes
range is 0.4171 (middle portion of output 3 of the sce-
Kano evaluation (KE)

Attractive (A)
Indifferent (I)
Must-be (M)
One-dimensional (O)
Questionable (Q)
Reverse (R)
Probability, Pr (.)

1/6 = 0.166667
1/6 = 0.166667
1/6 = 0.166667
1/6 = 0.166667
1/6 = 0.166667
1/6 = 0.166667
Probability,
CPr (.)
0.166667
0.333333
0.500000
0.666667
0.833333
1
nario 1). This output shows the summation of event
vector to one. The results of simulated of the scenario 2
events probabilities of KE, FA and DFA are shown in
Table 13. All simulated KE, FA and DFA average prob-
ability is consistent of Kano model. The average simu-
lated functional answer (FA) and dysfunctional answer
(DFA) Like, Must-be, Neutral and Live-with, Dislike is
occurred equally likely. It is shown a proposition for
generic unknown customer evaluation according to Ul-
lah and Tamaki, 2010. For this reason, the Kano evalua-
For scenario 2: an input is illustrated in Table 8 for
existing Kano model.
For scenario 3, a survey has been done according
to Table 3 for Kano questionnaire and obtained cus-
tomer answer in Table 5, and their evaluation is shown
in Table 4. This evaluation is considered inputs for sce-
tion of existing Kano model of the scenario 2 can be
also considered for generic unknown customer evalua-
tion. In the presented study, random inputs gave deter-
ministic result, because of Table 13 shows that simu-
lated probability range combined of Indifferent and
Reverse is 0.6361~0.6463, which is also consistent with
150

0.64 (Ullah and Tamaki, 2010). This result ensures that
the simulation provides the consistent deterministic
result not uniquely deterministic. Ullah and Tamaki,
2010 also conclude generic unknown customer evalua-
tion "Indifferent or Reverse". This study shows that
always the probability of Indifferent attribute range
0.3517~0.366 is always greater than Reverse attribute
range 0.2722~0.28535. It shows that this proposition of
Ullah and Tamaki, 2010 regarding Kano model based
generic customer evaluations is not completely appro-
priate. While, Indifferent attribute is predominated for
generic unknown customer evaluation.
Simulated results have been presented in Table 14
for the scenario 3. All simulated Kano evaluation (KE)
average probability is consistent of the system input
value of Table 11. The average simulated functional

answer (FA), Like is 0.418; Must-be, Neutral and Live-
with are likely equal around 0.186, whereas Dislike
attributes is 0.0238. The scenario also shows that aver-
age simulated dysfunctional answer like attributes is
around 0.0243 must-be, Neutral and Live-with is likely
equal around 0.14 where as Dislike attributes range is
0.555. It shows the summation of event vector to one.
The main findings from the presented simulation
model are summarized below: All scenarios show the
consistent outputs. Random inputs are furnished consis-
tent deterministic result. The summation of simulated
events vector probability for each Kano evaluation,
Functional answer and Dysfunctional is 1. The differ-
ence between maximum values and minimum value has
been found consistent with average value.
Moreover, suppose a producer is considered 0.80

Table 12. Output for the scenario 1.

Successive Simulation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Simulation Resultsof Functional Answer AveragePr(.) MaximumPr(.) MinimumPr(.)
Like 0.4192 0.42205 0.4141 0.41335 0.4147 0.4138 0.42105 0.41755 0.42025 0.421 0.42 0.41995 0.4202 0.4188 0.41865 0.4192 0.417994118 0.42205 0.41175
Must-be 0.1336 0.1344 0.13355 0.13725 0.13535 0.1361 0.1321 0.1352 0.13 0.1325 0.135 0.1336 0.1349 0.128 0.13525 0.1351 0.133958824 0.13725 0.128
Neutral 0.1349 0.1329 0.1342 0.1375 0.13385 0.1358 0.13375 0.1346 0.13665 0.1345 0.133 0.13775 0.1322 0.136 0.13625 0.13665 0.134997059 0.13775 0.1322
Live-with 0.1362 0.13355 0.1397 0.1373 0.1364 0.13345 0.13115 0.1358 0.13875 0.1341 0.133 0.13165 0.1362 0.13795 0.1305 0.13555 0.135082353 0.1397 0.1305
Dislike 0.1761 0.1771 0.17845 0.1746 0.1797 0.18085 0.18195 0.17685 0.17435 0.178 0.178 0.17705 0.1765 0.17925 0.17935 0.1735 0.177967647 0.18375 0.1735
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.0205 0.97595
Simulation Resultsof Dysfunctional Answer AveragePr(.) MaximumPr(.) MinimumPr(.)
Like 0.1748 0.18575 0.1744 0.1774 0.1793 0.1777 0.17895 0.1807 0.17765 0.1814 0.18 0.18275 0.17445 0.18115 0.1765 0.1826 0.179067647 0.18575 0.1744
Must-be 0.1354 0.1324 0.1336 0.13495 0.13405 0.1343 0.13285 0.13275 0.1361 0.1348 0.135 0.13575 0.13405 0.13985 0.1422 0.1375 0.135514706 0.1422 0.1324
Neutral 0.13315 0.1331 0.1341 0.1342 0.1356 0.13405 0.13335 0.13205 0.13305 0.1379 0.1335 0.135 0.13425 0.1319 0.13485 0.13185 0.133902941 0.13785 0.13185
Live-with 0.1348 0.1326 0.1322 0.13595 0.133 0.13745 0.13675 0.13295 0.1389 0.1348 0.14 0.133 0.1367 0.12875 0.13115 0.1316 0.134391176 0.13985 0.12875
Dislike 0.42185 0.41615 0.4257 0.4175 0.41805 0.4165 0.4181 0.42155 0.4143 0.4112 0..412 0.4125 0.42055 0.41835 0.4153 0.41645 0.417123529 0.4257 0.4112
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.03135 0.9786
Simulation Resultsof Kano Evaluation AveragePr(.) MaximumPr(.) MinimumPr(.)
Attractive 0.16765 0.1628 0.16595 0.1634 0.16725 0.16675 0.17065 0.1664 0.16865 0.1697 0.168 0.1676 0.16745 0.1679 0..17 0.16715 0.167214706 0.17065 0.1628
Indifferent 016525 0.167 0.1643 0.1723 0.1633 0.1682 0.15815 0.1622 0.16845 0.1671 0.169 0.1681 0.16535 0.161 0.1664 0.1644 0.165770588 0.1723 0.15815
Must-be 0.16925 0.16095 0.17325 0.16615 0.17005 0.1642 0.16695 0.1706 0.1659 0.1635 0.161 0.16435 0.16925 0.166 0.16585 0.16835 0.166497059 0.17325 0.16095
One-dimensional 0.17125 0.1703 0.1666 0.1686 0.16315 0.1665 0.16745 0.1668 0.1691 0.1655 0.17 0.1649 0.1701 0.1686 0.16435 0.1671 0.167352941 0.17125 0.16315
Questionable 0.16165 0.17385 0.1674 0.1641 0.16915 0.16635 0.16665 0.1685 0.1618 0.1681 0.164 0.1707 0.16385 0.16605 0.1694 0.16595 0.1667 0.17385 0.16165
Reverse 0.16495 0.1651 0.1625 0.16545 0.1671 0.168 0.17015 0.1655 0.1661 0.1663 0.169 0.16435 0.164 0.17045 0.164 0.16705 0.166464706 0.17045 0.1625
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.03175 0.9692

Table 13. Output for the scenario 2.

Successive Simulation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Simulation Resultsof Functional Answer AveragePr(.) MaximumPr(.) MinimumPr(.)
Like 0.20035 0.20125 0.20035 0.2042 0.1996 0.1972 0.20035 0.202 0.20165 0.2036 0.202 0.2045 0.1951 0.1954 0.19845 0.2003 0.200379412 0.2045 0.1951
Must-be 0.2026 0.19665 0.19555 0.19385 0.204 0.2014 0.19955 0.1965 0.1973 0.1979 0.20465 0.1997 0.20305 0.20435 0.19995 0.2016 0.200182353 0.20465
0.19385Neutral 0.20145 0.2002 0.19895 0.20185 0.1994 0.19795 0.2019 0.2037 0.1982 0.2037 0.20195 0.1968 0.2022
0.19785 0.19935 0.1967 0.200029412 0.2037 0.1968
Live-with 0.19745 0.20075 0.2022 0.1999 0.19925 0.2039 0.20245 0.19825 0.2015 0.19625 0.196 0.19895 0.2007 0.2023 0.2032 0.1982 0.200020588 0.2039
0.196Dislike 0.19815 0.20115 0.20295 0.2002 0.19775 0.19955 0.19575 0.19955 0.20135 0.19855 0.1954 0.20005
0.19895 0.2001 0.19905 0.2032 0.199388235 0.20295 0.1954
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.0197 0.97715
Simulation Resultsof Dysfunctional Answer 0 0
Like 0.20435 0.2003 0.19435 0.2075 0.20325 0.1966 0.2016 0.19945 0.20165 0.1985 0.2042 0.2024 0.19765 0.19705 0.19465 0.1962 0.199944118 0.2075
0.19435Must-be 0.1999 0.20085 0.2037 0.19935 0.1967 0.2001 0.19835 0.20255 0.1987 0.20055 0.20175 0.2037 0.1989
0.2024 0.19915 0.203 0.200420588 0.2037 0.1967Neutral 0.19945 0.20345 0.19775 0.19665 0.1989 0.2044 0.20085
0.20165 0.2012 0.2036 0.19985 0.19875 0.2056 0.1983 0.206 0.20345 0.201105882 0.206 0.19665Live-with 0.20095 0.19485
0.20565 0.1971 0.2016 0.2021 0.20295 0.2036 0.19675 0.2024 0.1985 0.19485 0.2005 0.2007 0.2024 0.19635 0.200311765 0.20565
0.19485Dislike 0.19535 0.20055 0.19855 0.1994 0.19955 0.1968 0.19625 0.19275 0.2017 0.19495 0.1957 0.2003 0.19735
0.20155 0.1978 0.201 0.198217647 0.2017 0.19275
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.02455 0.9753
Simulation Resultsof Kano Evaluation 0 0
Attractive 0.1197 0.1222 0.11965 0.12125 0.11885 0.1205 0.1208 0.1241 0.1184 0.12265 0.12205 0.1229 0.1173 0.11695 0.12065 0.12015 0.120302941 0.1241
0.11695Indifferent 0.36165 0.3578 0.3659 0.3517 0.3599 0.36455 0.3629 0.3616 0.3588 0.3618 0.36245 0.3568 0.36625
0.3647 0.36615 0.36235 0.361688235 0.36625 0.3517Must-be 0.1156 0.12085 0.11735 0.12015 0.12035 0.1191 0.11965
0.1174 0.11735 0.11805 0.11805 0.1184 0.1214 0.12055 0.12005 0.11735 0.118832353 0.1214 0.1156One-dimensional 0.04125 0.03985
0.0414 0.04065 0.0396 0.03825 0.0382 0.03795 0.04155 0.03985 0.03865 0.04035 0.03875 0.0391 0.03975 0.04165 0.039876471 0.04155
0.03795Questionable 0.0779 0.07905 0.0791 0.0809 0.08075 0.0779 0.07975 0.07735 0.0845 0.07815 0.0803 0.0828 0.07625
0.08125 0.07605 0.0805 0.079708824 0.0845 0.07605Reverse 0.2839 0.28025 0.2766 0.28535 0.28055 0.2797 0.2787 0.2816
0.2794 0.2795 0.2785 0.27875 0.28005 0.27745 0.27735 0.278 0.279591176 0.28535 0.2766
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.02315 0.97485
Indifferent
andReverse
0.64555 0.63805 0.6425 0.63705 0.64045 0.64425 0.6416 0.6432 0.6382 0.6413 0.64095 0.63555 0.6463 0.64215 0.6435 0.64035 0.0641279412 0.6463 0.63555
151

Table 14. Output for the scenario 3.

Successive Simulation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Simulation Result of Functional Answer AveragePr(.) MaximumPr(.) MinimumPr(.)
Like 0.42225 0.418 0.4202 0.41735 0.4207 0.41915 0.424 0.4137 0.4231 0.4161 0.41645 0.419 0.4193 0.4178 0.4181 0.4178 0.4189375 0.424 0.4137
Must-be 0.1877 0.1894 0.1865 0.18645 0.1877 0.18735 0.1867 0.18935 0.18165 0.1856 0.18745 0.1839 0.1846 0.1903 0.1871 0.18775 0.18684375 0.1903 0.18165
Neutral 0.18265 0.1877 0.1825 0.1878 0.17995 0.1879 0.1811 0.18895 0.18585 0.1866 0.19245 0.187 0.19025 0.18435 0.18055 0.1875 0.18581875 0.19245 0.17995
Live-with 0.18355 0.18205 0.1851 0.1851 0.1865 0.18235 0.1838 0.185 0.18585 0.18815 0.1811 0.1871 0.1821 0.1835 0.18775 0.18325 0.1845125 0.18815 0.1811
Dislike 0.02385 0.02285 0.0257 0.0233 0.02515 0.02325 0.0244 0.023 0.02355 0.02355 0.02255 0.0231 0.02375 0.02405 0.0265 0.0237 0.0238875 0.0265 0.02255
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.0214 0.97895
Simulation Result of Dysfunctional Answer AveragePr(.) MaximumPr(.) MinimumPr(.)
Like 0.0256 0.02235 0.02485 0.02475 0.02495 0.02325 0.0265 0.02545 0.0244 0.02235 0.0247 0.0234 0.02605 0.02285 0.0235 0.02395 0.02430625 0.0265 0.02235
Must-be 0.14555 0.1411 0.1447 0.1373 0.13805 0.14155 0.14195 0.1364 0.1369 0.1381 0.139 0.1395 0.14365 0.1391 0.1447 0.1366 0.140259375 0.14555 0.1364
Neutral 0.13815 0.1402 0.1391 0.1334 0.1406 0.14 0.1368 0.1421 0.14125 0.1411 0.1416 0.1421 0.1406 0.14085 0.14035 0.1371 0.13970625 0.1421 0.1334
Live-with 0.13825 0.13915 0.14065 0.14455 0.1379 0.14235 0.14045 0.14205 0.14185 0.14115 0.1415 0.1379 0.14005 0.13745 0.14095 0.14265 0.14055 0.14455 0.13745
Dislike 0.55245 0.5572 0.5507 0.56 0.5585 0.55285 0.5543 0.554 0.5556 0.5573 0.5532 0.5572 0.54965 0.55975 0.5505 0.5597 0.555178125 0.56 0.54965
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.0187 0.97925
Simulation Result of Kano Evaluation AveragePr(.) MaximumPr(.) MinimumPr(.)
Attractive 0.20655 0.20505 0.20675 0.19915 0.2057 0.2058 0.20865 0.20375 0.2045 0.2025 0.20635 0.2035 0.2085 0.2023 0.2042 0.20195 0.2047 0.20865 0.19915
Indifferent 0.20545 0.206 0.20725 0.2061 0.20085 0.2075 0.20095 0.2074 0.20635 0.208 0.20795 0.2075 0.20575 0.20605 0.21085 0.20535 0.206203125 0.21085 0.20085
Must-be 0.3376 0.3437 0.3378 0.3435 0.34435 0.3411 0.339 0.3455 0.3374 0.344 0.34365 0.3414 0.3408 0.3432 0.33485 0.3444 0.3413875 0.3455 0.33485
One-dimensional 0.20425 0.2027 0.2014 0.2065 0.2025 0.2016 0.20315 0.19815 0.2073 0.20285 0.1983 0.2051 0.19885 0.2047 0.20345 0.20435 0.202821875 0.2073 0.19815
Questionable 0.02205 0.02105 0.02355 0.0217 0.02415 0.0219 0.02435 0.02215 0.0222 0.0212 0.02305 0.0211 0.02195 0.02265 0.02265 0.02245 0.022384375 0.02435 0.02105
Reverse 0.0241 0.0215 0.02325 0.02305 0.02245 0.0221 0.0239 0.02305 0.02225 0.02145 0.0207 0.0215 0.02415 0.0211 0.024 0.0215 0.022503125 0.02415 0.0207
Summation 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.0208 0.97475

probabilities for one dimensional and others 0.2 for a
product attribute, what happens for customer functional
answer (satisfaction) with customer dysfunctional an-
swer (dissatisfaction) for this product. This system can
to evaluate functional answer (FA) and dysfunctional
answer (DFA) regarding above product attribute (KE)
information. This system can evaluate any kind of cus-
tomer requirements (FA and DFA) from product attrib-
ute (KE).Therefore, in real life producers can use this
system to evaluate their product attribute. This system
can also compare the field survey result and proposed
standard for product decision making.
Demographic and psychographic factors of cus-
tommer are not considered in this model. In traditional
Kano model, functional answer and dysfunctional an-
swer are considered to determine customer evaluation
but in this study, customer evaluation is considered to
determine customers' satisfaction and dissatisfaction. In
built error is generated from Monte Carlo simulation
method. In the present study, Maximum value, Mini-
mum value and average value of simulated attributes
are not same due to in built generated error, which is
shown in Tables 12~14.

7. CONCLUSIONS

A numerical Kano model is developed for cus-
tomer need analysis of product development on basis of
Kano model. This model can compliance customers'
needs with product development through different angle
of probability of product attributes. Needs of Customers
are changing due to their income, profession, age and
technology etc. In this case producer can change their
product development strategy quickly to adopt this nu-

merical model to change probability of product attribute.
Kano rule then can apply to find customer satisfaction
i.e. functional answer and customer dissatisfaction i.e.
dysfunctional answer. This work is better than tradi-
tional Kano model and any computational intelligence
model for easier operation in computer with accuracy.
Anybody can operate the model regarding product de-
velopment compliance with customer needs. As a result,
it will be easily conformed with any product develop-
ment process. This model can forecast the relevant
product development. These simulations also offer eco-
nomic benefits by contributing human beings. There-
fore, a simulation model is presented to know the simu-
lated functional answer (FA) and dysfunctional answer
(DFA) from a given Kano evaluation (KE). It has also
been found that the selection of choice of generic un-
known customer evaluation is predominately indifferent
attribute than others product attributes. This study also
ensures that the simulation provides the consistent de-
terministic result.

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