Analysis of Customer Satisfaction Using Quality Function Deployment

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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6340(Print), ISSN 0976 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) IAEME

7

ANALYSIS OF CUSTOMER SATISFACTION USING QUALITY

FUNCTION DEPLOYMENT

Parul Guptaa

, R.K. Srivastavab

aAssociate Professor, Department of Mechanical Engineering,

Moradabad Institute of Technology, Moradabad-244001,Uttar Pradesh,India

b Professor, Motilal Nehru National Institute of Technology, Allahabad,E-mail: [email protected], E-mail: [email protected]

ABSTRACT

QFD is a tool that bridges the distance between an organization and its customers. To accomplish that

goal it is important to know the customers needs or requirements (Customer Voice) so that they canbe involved from the early phases of the planning process. This implies implementing technological

solutions by specialists (Technician Voice) to determine the customers requirements.QFD provides many benefits for an organization during product development. The most important of

these benefits are a strong focus on the customer, improved communication, and better teamworkacross the organization. This paper present three modified quality function deployment model and

illustrative examples

Keywords:- Customer Satisfaction, Kano Model, Quality function deployment (QFD), House of

Quality, Customer Satisfaction.

I. INTRODUCTION

Quality function deployment (QFD) is defined by Cecilia Temponi, John Yen and W.AmosTiao as a multiattribute measurement method that brings together major components of an

organization and the complex task of capturing customers expectations and ultimately delivering

customer satisfaction.Quality function deployment is a customer driven tool in implementing total quality

management. Among lots of TQM methods, QFD has been used to translate customer needs and

wants into technical design requirements by integrating marketing, design engineering,manufacturing, and other relevant functions of an organization.(Akao, 1990)

As an approach to design, QFD is a concept that Akao introduced in Japan in 1966. It was first putinto use at Mitsubishis Kobe shipyard site in 1972,and then Toyota and its suppliers developed itfurther for a rust prevention study. After the concept of QFD was introduced in the US by King in

1983, many US firms, such as Procter&Gamble, Raychem, Digital Equipment, Hewlett-Packard,AT&T, ITT, GM and Ford applied QFD to improving communication, product development, and

measurement of processes and systems (Park,1998).Customer satisfaction has been a matter of concern to most of the companies. Satisfaction

ratings are being used as an indicator of the performance of services and products and help to form

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING ANDTECHNOLOGY (IJMET)

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ISSN 0976 6359 (Online)

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ulate strategies of the companies. Hanan and Karp have stated that Customer satisfaction is theultimate objective of every business: not to supply, not to sell, not to service, but to satisfy the needs

that drive customers to do business. Market success of a product is also important from the

environment point of view, since a product which is not sold, becomes the most useless product fromboth economical and environmental point of view. It has environmental impacts without having any

value for the customer .

II.QFD PROCESS

QFD employs several matrices to clearly establish relationships between company functions

and customer satisfaction. These matrices are based on the ``what-how'' matrix, which is called HOQ.QFD is an iterative process performed by a multifunctional team. The team will use the matrices to

translate customer needs to process step specifications. The matrices explicitly relate the dataproduced in one stage of the process to the decisions that must be made at the next process stage.

Product planning is the first matrix. Customers desires, in customers' own words (whats), aredetermined and translated into technical description (hows) or proposed performance characteristics

of the product. The second QFD matrix relates potential product features to the delivery ofperformance characteristics. Process characteristics and production requirements are related to

engineering and marketing characteristics with the third and fourth matrices. (Temponi,1998)

Figure 1- Quality Function Deployment Process

III.HOUSE OF QUALITYS GENERAL DESCRIPTION AND PROCESS

House of Quality, introduced by Hauser and Clausing, is the most commonly used matrix intraditional QFD methodology in order to translate the desires of customers into product design or

engineering characteristics and subsequently into product characteristics, process plans and

production requirements. The house of quality is applied for identifying customer requirements and

establishing priorities of design requirements to satisfy CRs. The aim is providing right products for

the right customers.The house is made up of three main parts: the customer attributes or customer requirements

(horizontal section); engineering characteristics or design requirements (vertical section) and the

center of the house.


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Figure 2- A typical HOQ matrix with a 1-3-9 rating scheme

Customer requirements section indicates the voice of customers. It shows the requirement of

the customers and what they think is important in the product and also relative importance of the

different customer attributes. Design requirements section records the technical aspects of designing aproduct. It indicates, How the customer wants can be met. The objectives and targets section

(basement of the house) indicates the relative importance of the different engineering characteristicsand also indicates target levels or measures of effectiveness for each. The roof of the house indicates

the positive and negative relationships between the design requirements. (Hauser and Clausing,

1988).The center of the house describes the correlation between the design requirements and the

customer attributes. The strength and direction of each relationship is represented by a graphical

symbol creating a matrix of symbols indicating how well each engineering characteristic meets eachcustomer attribute (Hauser and Clausing, 1988).

In conventional QFD applications, a cell (i, j) in the relationship matrix of HOQ( i.e., ith row

and jth column of HOQ) is assigned 1, 3, 9 (or 1, 5, 9) to represent a weak, medium, or strongrelationship between ith CR (called Cri) and jth DR called DRj) , respectively. The absolute and

relative importance of DRs are computed using the relative importance of CRs and the relationship

ratings (i.e., 139 or 159) . For each DR, the absolute importance rating is computed as:

=

=

m

i

ijij RWAI1

where AIj =absolute technical importance rating of DRj, j=1, . . . ,n, Wi =degree of importance

(i.e.,importance weight) of CRi , i=1, . . . ,m, Rij =relationship rating representing the strength of therelationship between CRi and DRj.

The absolute importance rating can then be transformed into the relative importance rating, RIjis shown as

=

=n

k

k

j

j

AI

AIRI

1


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The larger the RIj, the more important is DRj. Thus without consideration of any other

constraints (e.g., cost and time), DRs should be incorporated into the product of interest in the order of

their relative importance rating to achieve more customer satisfaction.

IV. THREE EXTENSIONS OF QFD

QFD provides many benefits for an organization during product development. The most

important of these benefits are a strong focus on the customer, improved communication, and better

teamwork across the organization (Bossert, 1991). The process of linking houses together especially

benefits the development process by maintaining the "voice of the customer" throughout the entireprocess, establishing clear relationships between multiple groups, and providing a means for

incorporating more and more levels of detail into the process (Hauser and Clausing, 1988).Besides these advantages many researchers express some deficiencies and disabilities of QFD

in product development stage. Researcher has generally focused on potential lacks of QFD and HOQand some of them describe possible alternatives to overcome these problems. Next sections in this

paper present three modified quality function deployment model and illustrative examples.

1. A new integrative decision model for prioritizing design requirements

The conventional HOQ employs a rating scale (e.g. 1-3-9,1-3-5 or 1-5-9) to indicate the degree

of strength between (weak-medium-strong) customer requirements and design requirements.

Although conventional HOQ approach, presented by Hauser and Clausing, it is easy to understandand use, there are several methodological issues associated with it, namely;

Determination of the degree of importance of CRSAssignment of the relationship ratings between CRs and DRs,Adjustment of the relationship ratings between CRs and DRs, called normalization, in order

to insure a more meaningful representation of the DR priorities

Incorporation of the correlations between DRs to a decision process for determiningappropriate DRs

Consideration of cost trade-offs among DRs.Some research has been done to resolve these methodological issues. Lu and Armacost applied

the Analytical Hierarchy Process (AHP) to determine the degree of importance of CRs. Wassermanpresented a linear integer programming model for maximizing customer satisfaction subject to a cost

constraint with a linear function and a procedure for normalizing the relationship ratings between CRsand DRs. However, Taeho Park and Kwang-Jae Kim thought that main problem is the usage ofconventional rating scheme. Therefore, they realized the necessity of development of a better

relationship rating scheme between CRs and DRs and integration of the correlations between DRs to adecision model for determining appropriate DRs to satisfy CRs.

Taeho Park and Kwang-Jae Kim state three problems of conventional rating scheme.

1. Choice of rating scale is very subjective and there are no scientific bases for any of thechoices.

2. The conventional relationship rating scheme primarily shows ordinal ranks of relationshipbetween CRs and DRs rather than a continuum of rating values indicating a sliding scale ofrelationship strength. As a result, the absolute importance ratings of DRs in the conventional HOQ

present ordinal importance ranks of DRs in their contribution to customer satisfaction rather thanmore meaningfully, showing the differences of DRs in contributing to customer satisfaction in their

magnitude.


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3. The information of correlations between DRs was not used in calculating priorities of theDRs and determining appropriate DRs for a design problem. It is necessary to devise a mechanism for

accommodating the dependencies of DRs in calculating importance ratings of DRs, and to incorporate

the correlation between DRs into the decision process of determining appropriate DRs subject to someorganizational constraints, such as cost and time. For example, when two DRs with a high correlation

are selected at the same time, there may be cost savings in installing them in a product.In order to overcome these problems, Park and Kim presented a modified HOQ model to

determine an optimal set of DRs. Park and Kim integrates two aspects into Wassermanns QFD

planning process and Lus integrative HOQ model: (1) Employing a new rating scheme for the

relationship between CRs and DRs, using a most commonly used multi-attribute decision method

(swing method). (2) Considering correlation between DRs for selecting an optimal set of DRs. Phasesof the new integrative HOQ model of Taeho Park and Kwang-Jae Kim are shown below:

Figure 3- Phases of new integrative HOQ model

In phase 1, the swing method,which is a part of the SMART (Simple Multi-Attribute Rating

Technique) is used by Park and Kim to obtain the relationship ratings between CRs and DRs.

A detailed step-by-step procedure for assessing the relationship between 2CR and DRs of HOQ

using the swing method is illustrated below. It is presumed that 1DR , 2DR and 4DR have important

effects on the customer satisfaction of 2CR , whereas 3DR is not related to 2CR as manifested by the

symbols recorded in the second row of the chart.

Step 1: Show the design team two alternatives: one leads to the worst consequence with respect

to 2CR ( i.e., 0421 === DRDRDR ) , and the other one leads to the best design condition (i.e., DR

1421 === DRDRDR ).


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Step 2: Ask the design team to imagine the worst design condition and choose a DR that wouldbest improve the design condition if its level changes from 0 to 1 (that is called a swing). Suppose

the design team answers that they would swing 4DR first because it is believed to have the most

significant impact on 2CR .

Step 3: Assign 100 to 4DR , which was chosen in Step 2. Rate all other DR swings on a scale

of 0100. Suppose the design team rates the contribution of changing the levels of 2DR and 1DR

from 0 to 1 to be 60 and 40, respectively, with regard to 2CR . The rating for 3DR should remain

zero because it is irrelevant to improving 2CR .

Step 4: Normalize the ratings so that they add up to one. The normalized ratings can be used as

the relationship ratings in the HOQ chart. The relationship ratings Rij s associated with 2CR are

normalized as follows:

21R = 40/(40+60+0+100) =0.2

22R = 60/(40+60+0+100)= 0.3

23R = 0/ (40+60+0+100) = 0.0

24R =100/(40+60+0+100)=0.5

The same procedure can be employed to assess the relationship ratings of other cells in the

relationship rating matrix of HOQ. The intermediate relationship ratings, which are output of Steps 2and 3 and the normalized ones, are summarized in a table shown below:

CRs DRs

Relationship ratings Normalized relationship ratings

DR1 DR2 DR3 DR4 DR1 DR2 DR3 DR4

CR1 100 0 50 0 0.67 0.00 0.33 0.00

CR2 40 60 0 100 0.20 0.30 0.00 0.50CR3 0 0 100 0 0.00 0.00 1.00 0.00

CR4 0 60 100 0 0.00 0.38 0.62 0.00CR5 50 70 0 100 0.23 0.32 0.00 0.45

After obtaining all necessary data and calculate the degree of importance of CRS by using theeigenvector method, relationship ratings must be normalized. Taeho Park and Kwang-Jae Kim used

normalization procedure presented by Wasserman (1993)which can accommodate correlationsbetween DRs:

= =

==

n

j

n

k

jkik

n

k

kjik

norm

ij

YR

YR

R

1 1

1for i = 1,..,m;j

where Ykj denotes an element of the correlation matrix representing the correlation between

DRs.In Phase 5, Park and Kim states an integer programming model for maximizing customer

satisfaction by selecting appropriate DRs. The formulation of this model is formulated as follows:

Max f(x)= =

n

j

jjxAI1

0)( xgk

for k=1,.l


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where =jAI absolute technical importance rating of jDR , jx =01 decision variable for jDR

(i.e., if jDR is selected, 1=jx . Otherwise, it is 0), x=adecision variable vector, }{ jx , j=1, . . . ,n,

kg (x)=kth organizational resource constraint, l=number of organizational resource

constraints(Park,1997).

The objective function of this formula is to maximize a total absolute technical importancerating from selected DRs, which represents the magnitude of customer satisfaction. When selecting

DRs to implement, the conventional QFD doesnt take into account trade-offs between the amount ofcustomer satisfaction achieved from the selected DRs and the use of organizational resources, such as

cost and time.

King and Wasserman developed simple linear cost constraint function which for g(x) to selectthe most appropriate DRs under a limitation of a given target cost. Function called as Knapsack

problem approach is illustrated as follows:

0.....)( 11 ++= Bxcxcxg nn

This means that DRs should be selected in a decreasing order of the technical importance

rating/cost ratios until the total cost of selected DRs doesnt exceed the limited repair budget.Park and Kim state that in the case where correlations exist among some DRs, some savings in

resource consumption are most likely expected when two or more correlated DRs are simultaneouslyinstalled into a product or service design. Since most practical QFD problems, as Wasserman 1993addressed, involve some degree of dependencies among DRs, they think it is more appropriate to

express the g(x)function in a quadratic form such that

Bxxsxcxg ji

n

j

n

i

n

j

ijjj = = = >1 1 1

)(

where ijs is saving of resource (e.g., cost) usage associated with simultaneous

implementation ofith andjth DRs.

Case study: Application to building indoor air quality improvement

Taeho Park and Kwang-Jae Kim has been applied proposed decision model to an indoor air

quality improvement problem as an illustrative example.After a study conducted in 2012 problems caused by poor indoor air quality identified as

follows: (1) stuffiness, (2) temperature, (3) dust particles, (4) ventilation, (5) odors, (6) housekeeping,and (7) flies. Then a customer study was conducted using a pair wise comparison method in the AHP

data collection process. Since a group of secretaries working daily in the BT building participated inthe survey, a geometric mean which is an 8th root of the product of judgments provided by eight

individuals was used to combine group judgments. Following table shows the results.

Temperature Dust Ventilation Odors House Flies

Stuffiness 1.0 2.7 2.2 1.1 1.1 2.4

Temperature 3.5 1.2 1.2 1.6 1.2Dust Particles 0.82 0.54 0.63 1.4

Ventilation 1.8 1.3 1.8

Odors 2.0 1.7Housekeeping 2.4

Eigen values of the judgment matrix in the table that are the importance weights of CRs, arethen calculated as 0.202, 0.187, 0.085, 0.152, 0.157, 0.132 and 0.084, respectively. Figure 4 presents

an HOQ matrix for the BT building indoor air quality problem, including (1) degrees of importance of

CRs as obtained from the AHP analysis, (2) normalized relationship ratings between CRs and DRsobtained using swing method and normalization of relationship ratings (3) correlation between DRs,

and (4) cost required to install the DRs.


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Figure 4- HOQ matrix for the indoor air quality problem for the BT building

According to the results of prioritization of DRs, it is found that upgrading an air delivery

system (DR6) is most important for improving building occupants satisfaction with indoor airquality, and the installation of a CO monitoring station with sensors (DR14) is least important.

If a repair budget is enough to complete all recommendations, the problem will become very

trivial. However, however, when available organizational resources are limited, a further analysis isnecessary to select which DRs should be completed; so Park and Kim found the cost savings that is

occurred when two related DRs are installed at the same time. For example, upgrading air plenumwalls (DR1) and replacing all fan plenum door seals with new ones (DR2) require Rs.18000 and

Rs.12000 respectively, when each of them is completed separately. When both of them are included

in a repair contact, Rs.4500 out of Rs.30000 is discounted because of savings in time. Therefore, theyform this quadratic integer programming technique;

Objective function: Max f(x)= =

16

1j

jjxAI

Budget constraint :

Bxxsxxsxxsxxsxxsxxsxcxc ++ 151215,1212612.611611,6929,210110,1212,1161611 .....


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Cost savings occurring when two DRs are completed at the same time illustrated below.

Pair of DRs Cost saving from simultaneous installation

DR1 and DR2 Rs.4,500

DR1 and DR10 Rs.10,200DR2 and DR9 Rs.4,050




Taeho Park and Kwang Jae-Kim solved above quadratic programming module by assumingthat repair budget of Rs.200000and they found out;

1. Objective value function of the total importance rating: 0.84842. Decision variables: DR1=.......DR9=1; DR10=DR11=0;DR12=.......DR16=13. Total cost required:Rs.1,98,700If the budget is at least Rs.450,000 (Rs.513,000(

=

16

1j

jc )- Rs.63,000 ( = >

16

1

16

j ij

ijs )),all DRs can be

installed, resulting in the objective function. Therefore, 84.5% customer satisfaction can be achieved

only 44.2% (198,700/450,000) of total investment required.

Park and Kim present these results in a sensitivity analysis shown below.

Figure 5- Sensitivity and performance analysis for customer satisfaction improvement over

budget increment

In this graph, the achieved level of customer satisfaction increased as a higher budget wasallowed, with increments of Rs.25,000.However,the marginal rate of increase diminished as the level

of baseline budget become higher. For instance; the increase of the budget from Rs.100,000 toRs.125,000 increased the customer satisfaction by 9.4% (66.2-56.8) while the increase caused by the

budget change from Rs.200,000 to Rs.225,000 was only 1.4%. Therefore, they stated that as the


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customer satisfaction level increases by investment in technology, equipment and training, more effortand investment are required to achieve the same level of additional customer satisfaction. In this case,

the customer satisfaction level will remain at 88.5% without DR11, which will replace the existing

standard profile control system with a direct digital control DDC System. To improve the levelfurther, a considerable amount of budget (Rs.211,500=Rs.240,000(cost for DR alone)-Rs.28,500

(savings) is required. However, the control system conversion will improve the customer satisfactionlevel by 11.5%.

The proposed model is compared with a Knap-sack model shown in Wasserman that does not

take cost savings into account. Since he doesnt take into account an organizational constraint of cost

savings, it cant allow for installing additional DRs, which might be selected with cost savings. Thus

the Knapsack model results in no greater customer satisfaction than the proposed model.In conclusion, Taeho Park and Kwang-Jae Kim stated that The new relationship rating scheme

using the swing method measures decision-makers opinions on the relationship between CRs andDRs more systematically and accurately than the conventional relationship rating scale used in HOQ.

Since the new relationship rating scheme relies on a simple additive multi-attribute model, it is easy touse; thus, it is a very handy and useful tool for practitioners. In addition, it converts decision-makers

thoughts of the relationship between CRs and DRs into a continuum of rating values so that the QFDproblem can be formulated into a mathematical programming problem subject to limited resources

e.g., budget in an organization. As a result, the QFD problem could be extended to resource allocation

problems in the operations management field. In other words, the investment will be justified with abetter working environment, more customer satisfaction and more market share resulting from better

decision making.

2.Integrating Kanos model in the planning matrix of QFD

K.C.Tan and X.X.Shen state in their articles that the quality of a product or service is ultimatelyjudged in terms of customer satisfaction. Focusing on listening to the voice of the customer (VOC),

quality function deployment has been used as a quality improvement and product developmenttechnique in many fields. In order to achieve total customer satisfaction in an effective way, QFD

practitioners should go beyond listening to the VOC. Therefore, Tan and Shen recommended thatKanos model (which will be described below briefly) should be incorporated into the planning matrix

of QFD to help accurately and deeply understand the nature of the VOC.Review of Kanos modelFirst, Professor N.Kano has developed a very useful diagram for characterizing customer needs

in 1984. Then King, Clausing and Cohen developed this model, which divides products or servicefeatures into three distinct categories, each of which affect customers in a different way. (Matzler,

1998)

One-dimensional attributes: With regard to one-dimensional attributes, customersatisfaction is proportional to the level of fulfillment. It means that it result in customer satisfactionwhen fulfilled and dissatisfaction when not fulfilled. The higher the level of fulfillment, the higher the

customers satisfaction. These attributes are usually explicitly demanded by the customer. For

example, when customers want to buy a new car, mileage may be such an attribute.

Attractive attributes: These attributes are the product criteria, which have the greatestinfluence on how satisfied a customer will be with a given product. These attributes neither explicitly

expressed nor expected by the customer. Although fulfilling these requirements leads to more thanproportional satisfaction, their absence doesnt cause dissatisfaction because as mentioned customers

are unaware of what they are missing.

Must be attributes: These attributes are basic criteria of a product. If the product or servicedoesnt meet the need sufficiently, the customers become very dissatisfied. On the other hand, as the

customer takes these requirements for granted, their fulfillment will not increase his satisfaction.Fulfilling the must-be attributes will only lead to a state of not dissatisfied. The customer regards the

must be attributes as prerequisites; he takes them for granted and therefore doesnt explicitly demand


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them. Must be requirements are in any case a decisive competitive factor, and if they are not fulfilled,the customer will not be interested in the product at all.

Figure 6- The Kano model

A proposed approach to using Kanos model

In this proposed approach developed by Tan and Shen, there are mainly two issues with which

QFD practitioners must be confronted; classifying customer attributes into Kano categories andchoosing the proper transformation function for customer attributes in each category. The data neededin classifying customer attributes are obtained through a Kano questionnaire that consists of a pair of

questions.They expressed the relationship between customer satisfaction and product or service

performance existing in Kanos model can be quantified by using an appropriate function with

parameters. Specifically, the relationship can be expressed as s=f(k,p), where s represents thecustomer satisfaction, p represents the product or service performance and k is the adjustment

parameter for each Kano category.

Kanos model tells us that not all customer satisfaction attributes are equal. Not only are somemore important to the customer than others, but also some are important to the customer in different

ways than others. As it is shown at graphic, the attractive attributes result more easily in customer

satisfaction than must-be attributes do. Moreover for attractive attributes, the customer satisfactionincreases progressively with the improvement of the product performance. Therefore, for attractive

attributes, we can get s/s>p/p where s and p represent the customer satisfaction degree and product

performance level. Similarly for one dimensional attributes, s/s=p/p , for must be attributes,s/s1, for one dimensional

attributes k=1, for must be attributes, 0


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International Journal of Mech

6340(Print), ISSN 0976 635

Figure 7- The VO

Tan and Shen claim that in tr

a competitive analysis and basedcustomer attribute. Adopting the st

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anical Engineering and Technology (IJMET

9(Online) Volume 3, Issue 3, Sep- Dec (2012)

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r, the adjusted importance may not accurately reprthis case is shown below.

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International Journal of Mech

6340(Print), ISSN 0976 635

Where adjIR is the adjusted

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develop as well as fill out the HO

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HOQ chart for a new product. The

anical Engineering and Technology (IJMET

9(Online) Volume 3, Issue 3, Sep- Dec (2012)

improvement ratio 0IR is the original improvemen

categories. In this equation, k is the only para

sifying the customer attributes into proper Kanothis case, Tan and Shen chose the k value , 1 and

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Han, Sang Hyun Choi, Soung Hie Kim presentede, is the large size of the HOQ. Even for a simpleast. This implies the need for a huge amount of ti

chart. Notwithstanding the rapid growth of QFD

ent of efficient methodologies for developing theese researchers suggest a knowledge-based appro

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1. Similar products have similar attributes of HOQ charts like customer requirements,engineering characteristics, and so on. If similar HOQ charts are built into a same class, managing the

HOQ charts is more efficient.2. HOQ charts in the same class arc classified into a rule tree according to their similarity

degree. The main reason is to locate more similar charts nearby for the efficient selection.3. IF-THEN typed knowledge retrieves the more similar HOQ chart from the selected class

base for a new product. Based on the retrieved HOQ charts, human experts can modify the chart with

ease. If one HOQ chart is not enough, more than two charts will be used for a new product. In that

ease, the criteria of selection is degree of similarity of a rule tree.

4. More QFD analysis is performed, the knowledge base and case base becomes more richer.

That means more suitable HOQ chart(s) may be provided for a new product.

In most cases, the QFD model is usually applicable to only one specific design problem, eventhough developing QFD model needs much time and effort from multiple functional groups.

However, these researchers often investigate that some prior knowledge from the experience ofdeveloping a QFD model can be utilized to resolve other similar QFD situations. From this

investigation, they consider a class analysis to combine the prior knowledge so that they handle a setof similar QFD situations simultaneously. Although a concrete example or definition ofsimilarity is

not found (Holtzman 1989), QFD class concept would be helpful in modeling HOQ charts in an

efficient way.Kim, Han and Choi suggest a class analysis, which regards a QFD analysis as an integrator of

QFD knowledge and treats a set of QFD having some degree of similarity as a single unit. For this

purpose, first they develop a rule tree and then suggested If-Then typed knowledge-based approach.Designing a decision class involves many trade-offs. If the decision class is too narrowly

defined, it will represent too few individual products; if it is defined in a general manner, its

corresponding class analysis will lose the benefits of domain specificity and may be prohibitedexpensively. Therefore, it is necessary to design a decision class that is neither too restrictive nor too

comprehensive.(Kim,1998)Knowledge based-methodology is consists of the following five phases:

Phase 1: Build a class of similar QFD cases.

Products are characterized by attributes like customer's age, customer's monthly income, marketregion, ere such that an individual product is characterized by its attribute values.

Phase 2: Construct a rule tree for the class

Each product has a number of attributes and can be classified into a particular subclass. STIG(Splitting Using Total Information Gain) algorithm (Kim, 1993) is used to construct a IF-THEN typed

rule tree.

Phase 3: Classification of a new QFD situation into a proper class using a rule tree. IF-THENtyped knowledge retrieves the very similar HOQ charts from the selected class base with a new

product.

Phase 4: Based on the retrieved HOQ chart, human experts can modify HOQ charts with ease.The retrieved HOQ charts have proper customer requirements and engineering characteristics for the

new product, but some part of them may be modified or deleted. New requirements and characteristics

may be added. With the retrieved HOQ chart, human expert can save time and effort at a considerable

amount.Phase 5: Updating the class base, knowledge base, and data base by adding a new generated

HOQ chart to the class for the later use.

V. CONCLUSION

Quality function is a very dynamic topic and also house of quality is a very flexible model that

many researchers have developed them in case of different subjects and areas. This paper has tried toexplain three of them; a new model for prioritizing design requirements, a proposed approach that

integrate Kanos model into QFD and a knowledge based approach to QFD. These new approaches


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may also have some deficiencies but as mentioned earlier, it is very progressive topic that furtherresearches will remove these deficiencies and make QFD applicable for different areas efficiently and

effectively.

REFERENCES

1. Determination of an optimal set of design requirements using house of quality; Taeho Park,Kwang-Jae Kim, Journal of Operations Management 16 (1998) 569-581

2. QFD not just a tool but a way of quality management, Cor P.M. Govers, International Journalof Production Economics (2001) 151-159

3. Ernzer, M., Kopp, K. (2003), Application of Kano Method to Life Cycle Design , IEEEProceedings of EcoDesign: Third Intern ational Symposium on Environmentally Conscious

Design and Inverse Manufacturing , Tokyo Japan, December 8-11,383389

4. Integrating Kanos model in the planning matrix of quality function deployment; K.C. Tan,X.X. Shen, Total Quality Management, Vol:11 No:8 (2000) 1141-1151

5. 5. Hanan, M. and Karp, P. (1989 ), Customer Satisfaction, how to M a xi mise , M eas ure and Mark et y o ur C omp a ny s U lt imate Product . New York.

6. Determination of information system development priority using Quality FunctionDeployment; Chang Hee Han, Jae Kyeong Kim, Sang Hyun Choi, Soung Hie Kim;Computers Industry Engineering (1998) Vol:35 241-244.

7. A Knowledge-Based Approach to the Quality Function Deployment; Chang Hee Han, JaeKyeong Kim, Sang Hyun Choi, Soung Hie Kim; Computers Industry Engineering (1998)Vol:35, 233-236

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9. How to make product development projects more successful by integrating Kanos model ofcustomer satisfaction into Quality Function Deployment; Kurt Matzler, Hans H. Hinterhuber;

Technovation vol:18 (1998) 25-38

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Documents

Analysis of Customer Satisfaction Using Quality Function Deployment