FINAL_Quality Function Deployment Implementation Based to Enhance Aesthetics Interaction

8/3/2019 FINAL_Quality Function Deployment Implementation Based to Enhance Aesthetics Interaction

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Quality Function Deployment Implementation Basedon Fuzzy Kano Model An Application to Enhance AestheticsProduct

Yunia Dwie Nurcahyanie

Departement of Industrial Engineering, Universitas PGRI Adi Buana Surabaya

Abstract

To win the market has driven enterprises to produce wider variety of products to meet

consumers¶ needs, producers need to developing new products and executing innovative processes. The main function process is to develop a system which ensures customer satisfaction

and enhancing aesthetics matters. One of the important system developments is to take customer requirements into consideration. Quality function deployment (QFD) has been used for years; it

is one of the structured methodologies that are used to translate customer needs into specificquality development. In traditional QFD approach, each element¶s interdependence and

customer requirements are usually not systematically treated. Additionally, the Kano model caneffectively classify customer demand attributes, but to make Kano model more objective in the

course of weighing, we have also included Fuzzy mode in our discussion. This approach can fulfill two objectives, First, through the Kano model with the Fuzzy mode, it will not only

discriminate out options for the required attributes it is much more accurate with the aid of theambiguous questionnaire response method. Second, combining the Kano model and QFD, can

not only provide a new way to optimize the product design but can also enhance customer satisfaction and loyalty, and minimize dissatisfaction.

Keywords: Fuzzy Kano Model, Product Development, QFD

IntroductionIn product development stage, customer

requirements are generally not treatedsystematically. Even if customer

requirements are collected before the design

phase, they tend to be disregarded andfinally vanish during the construction phase.Because of the lack of attention paid to

customer requirements collection at thesestages, problems in terms of design ability,

delays due to incomplete designs,misunderstandings of customer expectations,

rework, etc. are observed. To clearly specify

customer wants and needs, Quality Function

Deployment (QFD) framework that iscommonly discussed in the quality

management literature can be used as a proactive approach to encounter quality

issues instead of taking the passive approachof lunching customer complaints (Akao,

1990). The basic concept of QFD is totranslate the desires of customer or voice of

customer (VOC), into product technicalrequirements or engineering characteristics,

and subsequently into parts characteristics, process plans and production requirements



QFD Implementation Based on Fuzzy Kano Model An Application to Enhance Aesthetics Product

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by using a chart called House of Quality(HOQ).

As competition for new markets andcustomers increases, level of customer

satisfaction also became a key factor for

long-term business success. Satisfiedcustomers are loyal customers and ensure alasting cash flow in the future. According to

Reichfeld and Sasser (1990), a 5% increasein customer loyalty can increase the profit of

a business by 100% due to the fact thatsatisfied customers purchase the products

more often and in greater quantities.Generally, satisfied customers are less price-

sensitive and more inclined to spend moreon products they have tried and tested. The

Kano model of customer satisfaction candetermine µµattractive¶¶ or µµmust-be¶¶

requirements which can be used in the QFDmatrix to assure that most critical needs are

translated into the next phases of productdevelopment (Tan & Shen, 2000). However,

the selection of weights is very subjective.As a consequence, this study will use

the Fuzzy mode to improve subjectivelinguistic scale in Kano¶s two dimensional

quality element. The use of Fuzzy mode isfor respondents to express themselves about

the extent of correct attribution bymembership and any numeric value.

The reminder of this paper is organized asfollows. Section 1 discuss about the product

development and the importance in takingvoice of customer into consideration ,

Section 2 discuss about literature review.Section 3 discusses the formulation in more

detail. Section 4 provides an illustrativeexample of the application in development

procedure. Finally, Section 5 providesconcluding remarks. This study presents a

novel approach for determining customer requirements. First, the requirements in

discussion are considered as attributes whichare categorized as attractive, must-be or one

dimensional by using the Kano modelmethod to make the requirements more

objective when weighing the requirements by Fuzzy mode. Second, we design the

product by integrating a decision-makingtechnique to incorporate the dependencies

inherent in the QFD process.

Literature review

QFD and its application in productdesign

The origins of QFD can be traced toMitsubishi¶s Heavy Industries Kobe

shipyard in Japan in the late 1960s whenQFD was first used to facilitate cross-

functional product development process

(Day, 1993).QFD is an overall concept that providesmeans of translating customer requirements

into appropriate technical requirements for each stage of product development and

production (i.e., marketing strategies, planning, product design and engineering,

process development). The need for QFDwas driven by two related objectives

(Gonzalez, Quesada, Picado, & Ecklman,2004). These objectives started with the user

(or customers) of a product and ended with product producers. To satisfy the objective,

the Voice of the Customer is translated intothe Voice of the Engineer through a matrix,

which is named House of Quality (HOQ).Hauser and Clausing (1988), Day (1993),

Fung, Chen, and Tang (2006) illustrated thatthe basic format of the HOQ consists of sixsections: (1) obtaining customer attributes

and their relative importance; (2) developingdesign requirements responsive to customer

attributes; (3) planning matrix; (4)interrelationships between customer

requirements and design requirements; (5)design requirement correlation; (6) action

plan. QFD can improve product quality anddeliver products at a lower cost, and

consequently can increase the market share(Kim, 1993). QFD can also facilitate




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continuous information system improvementwith an emphasis on the impact of

organization learning on innovation (Partovi& Corredoira, 2002; Partovi, 2006). QFD

has also been extended and modified to

make it more comprehensive and applicablesuch as enhanced QFD (Clausing, 1994) andintelligent information system for QFD

(Harding, Popplewell, Fung, & Omar,2001). Bossert (1991) and Sarkis et al.

(1994) indicated that QFD was used todetermine the expectations of the potential

users of the information systems. Potentialusers included people who would be

implementing, using,

Kano model and its applications inQFD

Kano, Seraku, Taka hashi, and Tsuji(1984) developed a model which has been

used by others to categorize the attributes of the product or service based on how well

they are able to satisfy customer requirements, for example, King (1995),

CQM (1993), Clausing (1994), and Cohen(1995). As Fig. 1 shows, the extent to which

a quality element is provided is indicated onthe x-axis. The further the arrow moves

towards the right, the greater the extent towhich the quality element is provided, while

the further the arrow moves towards the left,the less the left, the less the extent to which

the quality element is provided. Customer satisfaction is indicated on the y-axis. The

higher the arrow, the higher customer dissatisfaction will be; on the other handle

the lower the arrow, the higher customer dissatisfaction.

Based on these axes, the following are the popularly named Kano customer

requirement categories (seeFig. 1): The must-be or basic quality element:

customers believe that this quality is anecessity; when it is not present, customers

will be dissatisfied.

The attractive quality element: when present, customers will be satisfied; yet

when it is not present, customer would stillaccept without dissatisfaction.

Figure. 1. Kano¶s two-dimensional quality

model and f ive types of quality element.

The one-dimensional quality element:customers satisfaction is proportional to the

level of fulfillment ± the higher the level of fulfillment, the higher customer satisfaction,

and vice versa. The indifferent quality element: customers

satisfaction will not be affected no matter whether this quality is provided or not.

The reverse quality element: customerswill be dissatisfied if this quality element is

provided; otherwise, they will be satisfied.Two-dimensional quality was initially used

in the development of the manufactured product quality (Kano et al., 1984) in a

survey conducted on TV or decorativeclocks. The survey results show that user

conceptions of quality are not one-dimensional but two-dimensional; thus, the

one-dimensional quality is unable to cover user quality conceptions. The Kano model

has been applied not only to new product

development (Matzler & Hinterhuber, 1998) but also to new service creation (NSC)(Bhattacharyya & Rahman, 2004). The

importance of the Kano model is that itinvolves little mathematical computation

and relevant information can be quicklyobtained. Recently, this technique has been

applied to the development of a variety of




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online services, such as web site (Zhang &von Dran, 2001), internet community

(Szmigin & Reppel, 2004), and onlineticketing ( Nilsson- Witell & Fundin, 2005).

Sireli, Kauffmann, and Ozan (2005) applied

a similar methodology to cockpit weather information system (CWIS) design.Additionally, in the QFD literature, the

Kano model is applied by assigning weightsto different customer attributes. Islam and

Liu (1995) indicated that customer needscan be divided into three subgroups, i.e.,

basic, one-dimensional and excitement. For each requirement, the raw importance is

adjusted by multiplying a weight that iscalculated by an analytic hierarchy process.

Similarly, using the dual importance grid,Robertshaw (1995) classified the type of

Kano element and suggested that customer needs should be re-prioritized: the first

priority is to deliver what is expected; thesecond is what is specified; the last is to

provide the attractive elements. Gerson(2003) showed a modified Kano method to

determine the degree to which an attribute isconsidered attractive or must-be by

customers and how to integrate into the planning matrix of the QFD. Tan, Tang, and

Forrester (2004) analyzed customer satisfaction based on the Kano model and

pointed out the importance of productinnovation in exceeding customer

satisfaction.

Classify two-dimensionalquality elements by Fuzzy mode

QFD practitioners should not only know

what customers want the most, but shouldalso understand how much attention is

needed for each customer attribute in order to achieve the desired customer satisfaction

level. This section proposes a procedureshowing Kano¶s model which can be

integrated to adjust the raw importance.However, traditional Kano¶s two-

dimensional quality classifications are toconduct functional and dysfunctional

questionnaires with a singular scale. It is toclassify the quality attribution from

respondents answer in the questionnaire

item. However, the affective perceptionlevel of one¶s evaluation towards each itemin questionnaires cannot be fully expressed

with singular scale or numeric value. Thisdeficiency is due to each respondent¶s

complicity, subjective mentality anddifference of preference during the

answering process (Hsu & Chen, 2001).Uncertainties exist in both the human

mentality and the languages.Simultaneously, modes of acknowledgement

exhibited by the interviewees vary from oneanother. Feelings associated with the tone of

the linguistic implications in response to thequestionnaire are also vague. The expression

in one single numeric value is sometimesnot suitable. Therefore, Zadeh (1973)

believed the application of the Fuzzylinguistic in the uncertain issues will be a

relatively better tool. Questionnaireinterviewees apply the linguistic remarks

such as never, rarely, sometimes, frequently,and always to express their degree of

acknowledgement of the issues. However,the Fuzzy linguistic is able to evaluate the

interviewee¶s response to the linguisticremark one step further based on the

strength of feeling exhibited by theevaluation at different linguistic implication

interval, which is much more reflective of the factual circumstances and is enabled to

acquire much more accurate results.As a consequence, this study will use

Fuzzy mode to improve the subjectivelinguistic scale in Kano¶s two dimensional

quality element. Our method allowsrespondents to express themselves about the

extent of correct attribution by membershipand any numeric value. With this method, it

can pass on the overall correct mentality in




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reasonable sense under unknowncircumstances.

Suppose U is a universe of discourse,

and

are the Kano¶s

functional and dysfunctional questions

located in U . is the respondent of one set Kano¶s Fuzzy mode questionnaire.

Meanwhile, by using Fuzzy set relations, we

have defined respondent normalized

membership and

corresponding to

linguistic variable and to

calculate two-dimensional Fuzzy relation perception level. The possible classification

will be defined byKano¶s two-dimensional quality element

according to references. All the qualityelement classification meeting the

significant level will also be calculated

under the significant membership

level. Among all, and with

maximum value in Kano¶s Fuzzy quality

element classification is defined as Kano¶sFuzzy mode (KFM). If there are more than

two sets of Kano¶s Fuzzy quality element

classification with the same value then

this set of data is called with multi-Fuzzymode or multi-consensus. If the finalscoring

is equal, the greatest impact on the productis determined in the following order M > O

> A > I (CQM, 1993). The procedure of Kano¶s Fuzzy mode classification in this

study is shown as follows:

Step 1: To assure the number of respondents

and to establish the initiating feeling set ,

and in functional and dysfunctional

question:

=

=

where h represents the hth expert; s, t

represents the scale used in the

questionnaire; represents the

s initial weighted of the hth expert.Step 2: To normalize the initiating

evaluation value:

=

=

Step 3: To structure Fuzzy relationship set

:

x =

where h = 1,2, ...,p ; s = 1,2,...,n; t = 1,2,...,m

Step 4: To define Kano¶s two-dimensional

quality element classification set

and = =

supp

where

and represent the Kano¶s

two-dimensional quality element Fuzzyclassification

µµmust-be¶¶, µµone-dimensional¶¶,µµattractive¶¶, µµindifferent¶¶, µµreverse¶¶, and

µµquestionable¶¶, respectively.

Step 5: To calculate the extent of each

Fuzzy quality element :

= =

where h = 1, 2,. . . ,p

Step 6: To defuzzificate Kano¶s significant

classification level (- cut) P :

= =

,

where h = 1,2,..., p ; d = 1,2,...,6; = 0

Step 7: To obtain Kano¶s Fuzzy mode set(KFM):




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Step 8: The relationship between customer

satisfaction improvement ratio andimportance increment ratio is not treated as

linear. According to Tan and Shen (2000),for attractive attributes, k > 1; for

onedimensionalattributes, k = 1; for must-be attributes, 0 < k

< 1. It can be further converted to thefollowing

equation:

where s, represents customer satisfaction, c,is a constant, p, represents the product or service performance. is the adjusted

improvement ratio where is the originalimprovement ratio and k is the adjustment

for each Kano category. The possible valuesfor k are µ1/2¶, µ1¶, and µ2¶ for must-be,

one-dimensional and attractive attributes,respectively.

Step 9: Readjust the raw importance of eachCR. In addition, the customer requirement

weights of the customers requirement unitsCRi (i = 1, 2, . . . , I) are denote by

wCR(CRi), and defined as follows:

=

Categorize customer requirements

The first step is identification of

customer requirements. Data were obtained by interviewing high-tech exerts in Surabayaand reviewing the literatures. A total of 30

high-tech experts from Product Design andengineering were interviewed, including

five professors from the InformaticsDepartment of the domestic universities and

colleges, According to the reference, thestudy would conclude that using Fuzzy

mode classification is more objective thantraditional mode. Taking the customer

requirements ± automate the changemanagement procedure as an example, the

double-sided Kano questionnaire was used

to comprehend customer needs; However,the classical questionnaire response methodonly provided a single selection option.

According to the questionnaire results, theutilization of the attribute analysis table

based on the Kano model it could beidentified that 30 experts considered the

customer requirement to be one-dimensional, 18 (=4 + 6 + 8) experts

categorized it as attractive, one expert wasclassified as must-be, and one expert was

considered to be indifferent. Based on theclassical questionnaire results, it could be

discovered that the attribute of the customer requirement ± automate the change

management procedure ± could beconsidered one-dimensional. The Fuzzy

Mode questionnaire provides scores inaccordance with variations in magnitude.

After the questionnaires are collected andanalyzed through formula for example the

single-expert response, revealed thatdisplays its magnitude toward this customer

requirement, which is automate the changemanagement procedure.

Simultaneously, these results, by thesimilar application of the attribute analysis

table based on the Kano model , couldidentify the attribute weights assigned by the

single expert to each customer requirementas attractive: 0 + 0.15 + 0.15 = 0.3, one-

dimensional = 0.2, must-be = 0.16, reverse =0, and indifferent = 0.12. Because of the a =

0.3, the automate the change management procedure of this expert belong to the

attractive. Finally, the results of each expertquestionnaire were statistically analyzed.

Thirty-four experts were selected.Attractive, fifteen were classified as one-

dimensional, and one belonged to must-be.customer requirements thus were considered




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attractive. We have excluded qualityelements that are indifferent, reverse and

questionable. We find that 15 of the 50customer requirements selected as the most

significant factors were related to customer

satisfaction.In 15 service quality elements,there are six that are must-be, seven that are

one-dimensional and two that are attractive.

Table 1. The traditional Kano questionnaire

CR 1 like must be neutrallive

withdislike

Do you

comfortable

enough with

your computer

table, is it

has enoughspace?

¥

Do you feel

not

comfortable

enough withyour

computer

table, and it

is not hasenough

space?

¥

Table 2. The Fuzzy Kano model questionnaireCR 1 like must be neutral

livewith

dislike

Do you

comfortable

enoughwith your

computer table, is ithas enough

space?

0.5 0.3 0.2 0 0

Do you feel

notcomfortable

enoughwith your computer

table, and it

is not has

enoughspace?

0 0 0.3 0.3 0.4

Table 3. The attribute analysis based on the

Fuzzy Kano model Do you

comfortableenough withyour

computer

table, is ithas enough

space?

Do you feel not comfortable enough with your computer

table, and it is not has enough space?

like must be neutrallivewith

dislik

like 0,5 x 0 0,5 x 0 0,5 x0,3

0,5 x0,3

0,5 x 0

must be 0,2 x 0 0,2 x 0 0,2 x0,3

0,2 x0,3

0,2 x 0

neutral 0,2 x 0 0,2 x 0 0,2 x

0,3

0,2 x

0,3

0,2 x 0

livewith

0 x 0 0 x 0 0 x 0,3 0 x 0,3 0 x 0,3

dislike 0 x 0 0 x 0 0 x 0,3 0 x 0,3 0 x 0,3

Table 4. Customer requirements category

compared with tradional Kano

PRODUCT REQUIREMENTTRADITIONAL

CATEGORY

QUALIT

CATEGO

Comfortable, with enough surface area M OCorner of the table is not sharp A A

Strong and durable M O

Easy to move O O

Easy to install M O

Easy to arrange according to officespace

O O

Save place O A

Strong table covering (not easily to

peel)I M

Design is support with the latestcomputer technology

I M

Design can be arranged according tothe needs of product support

I M

Desk drawer I

MCupboard storage I M

There is a special place for thekeyboard

A A

There is a special place for the CPU I M

There is a special place for the CD I M

There is a special place for the printer I M

There is a special place for Stavolt I M

Fixed cable tidy O O

Height of a table can be adjusted A A

Can be converted into office desk I M

Materials used are environment-friendly

A A

Conclusion

With the rapid developments in scienceand technology, customer requirements

regarding products are constantly changing.Therefore, from the standpoint of system

designers, it is obligated to maintainconstant contact with customers, strive to




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comprehend changes in customer needs, andattempt to satisfy those needs, if it is desired

to be in an unchallengeable position in thesevere market competition. Accurate

analytical methods are required to help

designers understand current information oncustomer requirements and obtain the futurerequirement prediction information. This

study describes a method for moreobjectively analyzing customer requirements

and thus enabling the manufacture of products that meet customer demands. As a

customer-oriented, QFD involves numerousdata from both customer and QFD team

members. Depending on their perspective background, people give information about

their personal performances in manydifferent ways. As the determination of CR

priorities is the key concept in QFD, we believe that greater emphasis has to be

placed on analyzing and merging individualassessments in different formats in order to

provide a systematic decision procedure for system design within the QFD process,

which has been traditionally based on expertopinions.

For improving the weaknesses of theclassical QFD, the contributions of this

study primarily have the following focuses:

first, the questionnaire design was conductedusing the Fuzzy linguistic method for moreaccurately verifying customer requirements.

Upon understanding customer requirements,these requirements were simultaneously

categorized with the aid of the Kano model.If the customer requirements were

considered attractive, must-be, or one-dimensional, it indicated that it should be

listed as design item with top priority duringthe system development process. On the

contrary, if it was classified as theindifferent or reverse attribute, the

development could be postponed or removed under the circumstances where the

development costs and customer satisfactionwere taken into account and; second, with

regard to the system function and system.

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FINAL_Quality Function Deployment Implementation Based to Enhance Aesthetics Interaction

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