Towards improving the Quality of Quality Function Deployment Model and available Software packages

8/22/2019 Towards improving the Quality of Quality Function Deployment Model and available Software packages

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September 2001

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Towards improving the Quality of Quality Function Deployment Model

and available Software packages

ATH. P. SYNODINOS AND A. W. LABIB

Abstract

Although Quality Function Deployment (QFD) was successfully used as a tool for

defining new products, as well as for diagnosing and improving existing products, it has

some drawbacks that may lead not only to wrong results but also to the waste of time and

effort. During the last decade, several people have introduced methods for improving QFD

by solving some of its weaknesses mainly by using Fuzzy logic/Sets and Analytic

Hierarchy Process (AHP). However, there is much more space for improving QFD.The authorss located nine drawbacks of QFD and presented them, and discussed the

work done so far concerning those problems. The weakness analyzed in the present work is

the fact that the house of quality chart, which is the main tool of QFD, can get extremely

large under normal conditions; if a House chart contains 20 CAs and 30 ECs, which is a

reasonable size, more than 1000 relationships must be analysed if every cell is to be

addressed (relations between CAs and ECs plus relations between ECs and themselves).

Three exclusive, new methods are proposed in the present work for simplifying a

house of quality chart:

1. Simplification by importance.

2. Simplification by decomposition.

3. Simplification by competitors.

The last method mentioned above, is considered to be robust, mainly because while

putting a lot of effort to improve other CAs, there is the danger to maintain inadequately the

ignored CA. Thus, one implementing this method must be sure that this will not have a

negative effect on the CAs ignored. Nevertheless, this approach is also a way of simplifying

successfully a HoQ chart and should be examined further.

The other two methods mentioned above, are not only more reliable than the first

one but are also effective, according to the Monte Carlo simulation carried out by the

authors. The authors explain in detail the procedure and the algorithms of all methods

proposed in this article, and examples are also given.


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Ath. P. Synodinos: Towards improving the quality of QFD

2

A software package was also developed that simplifies house of quality charts by

the methods proposed. However, before building this package, the most common of the

existing software concerning QFD were evaluated, presented and criticised. The main

drawback of all of them is that they serve only for constructing a house of quality chart and

none of them proposes a method for improving the QFD method. Although newer ones

were very user friendly the authors of this article think that this is not enough to meet the

users requirements, since software packages are implementations of a method, QFD, that

has inherit complexities.

1. Importance of QFD and Identification of some of its drawbacks

In the first half of the twentieth century, trade blocks such as the European Union(EU), the North American Free Trade Association (NAFTA) and the Association of East

Asian Nations (ASEAN) were formed to allow free commerce on a region basis. This had a

result to diminish monopoly and to raise competition; companies are now exposed to a

wider range of competitors and thus they should sell quality products. However, quality is

no more a question of defect free products. Constraints such as maintenance cost,

attractiveness and customer requirements are also contributing in the quality of a product.

Quality Function Deployment (QFD) serves for making the product specification so

as to satisfy what customer really want. Yoji Akao in Japan introduced the concept of

Quality Function Deployment in 1966. However he published his approach in the West in

October 1983 in the United States in a short article that appeared in Quality Progress, the

monthly journal of the American Society for Quality Control (ASQC) (Kim and Shin

2000). Now, over 100 companies are believed to use QFD in the United States successfully,

including the Budd Corporation, the Kelsey Hayes Corporation, Motorola, DEC, Hewlett

Packard, Xerox, ITT, NASA, Ford, General Motors and U.S. housing industry (Kim and

Shin 2000).

The main target of Quality Function Deployment (QFD) within concurrent

engineering is to translate the customer attributes into manufacturing processes and/or

quality characteristics. In simpler words, QFD aims to convert the customer whats into

engineering hows. QFD can be defined as deployment of quality through deployment of

quality functions. Companies that used QFD claimed that they achieved benefits such as:

Improved customer satisfaction

Improved internal communications


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Better documentation of key issues

Brought together large amounts of verbal data

Brought together multi-functional teams

Reduced development time by 50%

Reduced start-up and engineering cost by 30%

No money wasted.

Table 1 illustrates a comparison between companys function before and after QFD

implementation according to ASI1

(ASI Quality Systems, UK), which were the first to

introduce Taguchi Methods and Quality Function Deployment (QFD) to the west.

Table 1 Before and after QFD, according to ASI

BEFORE QFD AFTER QFD

Individual Work Cross-functional Teams

Some Customer Focus Intense Customer Focus

"Over the Wall" Development Supports Simultaneous Engineering

Poor Documentation Supports Integrated Product Development

Poor Communications Better Communication/ Documentation

The house of quality

The primary design element of QFD is the house of quality. The house of quality

has been used successfully in Japan, first by Toyota and then by other manufacturers of

consumer electronics, home appliances, clothing, integrated circuits, construction

equipment and agricultural appliances. The House of Quality can be used as a stand alone

tool to solve a particular development problem. However it can also be applied within a

more complex system in which a series of tools are used. The "Clausing Four-Phase

Model" (Clausing & Pugh, 1991) (fig. 1) is the most widely known tool for using in those

complex systems. It translates customer requirements through several stages into

production equipment settings; it uses three QFD matrices and a table for planning

production requirements (Y. Akao 1991).

Once the first chart is complete, the Engineering Characteristics of it are transferred

as Whats to the second matrix where the Hows are part characteristics. The third matrix

relates the part characteristics to key process operations (or critical parameters of process

1ASI Quality Systems is the UK representative of the American Supplier Institute (ASI) of Michigan,

USA.


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operations). The final table maps the key process operations to the operational mechanisms

and controls. The procedure of constructing a HoQ chart is illustrated in figure 2.

Figure 1 The "Clausing Four-Phase Model"2

Figure 2 Procedure of building a HoQ

Problems with QFD

Although QFD is a straightforward method of quality management it has some

problems that can lead to wrong assumptions or results. For most of these several works has

been already done. Some of those QFD problems are tabulated in Table 2.

2Source: Clausing& Pugh 1991

DetermineCAs

Importance

Identify

Competingproductsattributes

List allECs

Draw CAs vs.ECs matrix

Identifyrelationshipsbetween ECSand CAs

Identifyrelationshipsbetween ECS

Set target values


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Table 2 Problems of QFD and work done for each so far

I.D. Problem Who worked on it Years Solution

1 What customer really mean (for

example, what is meant by easy to fit,

or 'I don't like the shape').

Fung et al. 1998

Surveys, indepth qualitative

interviews, experts involvement by

the marketing team

2

Prioritising (Ranking) Engineering

Characteristics is fuzzy.

It sometimes leads to wrong ranking.

WangPark and Kim

Masud and Dean

Zhou

Kalargeros Gao

Andrew Ellis

19991998

1993

1998

1998

2001

Use of triangular fuzzy numbersthen FWS then defuzzification

AHP, swing method, integration of

matrix

Fuzzy Set, AHP and FWA

Software

3

Target values are fuzzy.

Zhou

Moskowitz and Kim

Fung et al

Libardo Vanegas

Vanegas and Labib

1998

1997

1998

1999

2000

Obtained by experience or intuition

Integrated mathematical

programming

AHP, fuzzy logic

Fuzzy sets, FAHP

FQFD

4

The strenght by which an EC affects a

CA is determined using linguistic

expressions such as positive, negative,

immaterial.

Park and Kim

Temponi et al

Kalargeros Gao

Moskowitz and Kim

Libardo Vanegas

Vanegas and Labib

1998

1997

1998

1997

1999

2000

Fuzzy logic/set method

Use of linguistic terms (weak,

strong).

Relates each value of EC to the

degree to which a CA is satisfied

Fuzzy Set that represents the cust.

Satisfaction

FQFD

5EC target levels should be determined

based on constraints (customer

satisfaction, costs of improvement,

market position technical difficulty).

But are focused on customer

satisfaction.

Wang

Dawson and Askin

Park and Kim

Zhoo

Libardo Vanegas

Vanegas and Labib

1999

1997

1998

1999

1999

2000

Only Libardo Vanegas and Labib

took into account all the constraints.

He used fuzzy sets and the NFWA

theory

FQFD

6

Complexity (house can get too big).

Moskowitz and Kim

Kim and Shin

Zhang

Kim and Shin

1996

1996

1996

2000

Software

Factor analysis

ANN

Decomposition

7

Prioritising CAs.

Khoo and Ho

Shirland and Jesse

Park and Kim

Zhou

Fung et al.

1996

1997

1998

1998

1998

Use of correlation matrix of CAs

Use of CAA

AHP

8Target values are not feasible. Libardo Vanegas 1999

Just mentioned this problem but

didnt propose any solutions.

9QFD is inadequate. Prasad B. 2000

Concurrent Function Deployment.

An emerging alternative to QFD

FWS=Fuzzy Weighted Sum

CAA=Comparative Attribute Analysis

AHP=Analytic Hierarchy Process

FAHP=Fuzzy Analytic Hierarchy Process

FQFD=Fuzzy Quality Function DeploymentANN = Artificial neural Networks

Simplification of QFD

In general, a CA is affected by many ECs either positively or negatively. This

however, leads to huge houses of quality, as sometimes the number of CAs exceeds 40. If a

House chart contains 20 CAs and 30 ECs, which is a reasonable size, more than 1000

relationships must be analysed if every cell is to be addressed (relations between CAs and


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ECs plus relations between ECs). This implies the need for a huge amount of time and

effort. Consequently, the simplification of the house is vital and could save time and money

if achieved.

2. Software presentation

To construct a QFD (Quality Function Deployment) table one need to input a lot of

information and this might lead not only to errors but also to waste of time. During the last

decade QFD Software was developed in order to simplify the method and make it faster.

The features of some of those programs are tabulated in table 3.

Table 3 Existing QFD Software comparison

Package nameQFD Capture

Prof. v4.02

QFD

Designer

v4.00

Qualica

QFD

v2.2

QFD

2000

v1.00

Meta

QFD by

S.

Rampton

QFD

by An.

Ellis

Broach

design

Company nameInt.TechneGroup

Inc.

Quali

SoftQualica

Total

Quality

Soft.

UMIST UMIST UMIST

Features

Arrow-keys/ Tab Button Enabled Y Y Y N Y Y Y

Creates Graphs Y Y Y Y Y Y Y

Creates new Symbols Y Y Y Y U N N

Customize Colors Y Y Y Y U N N

Customize HoQ Chart N Y Y Y U N N

Data entered directly into matrix Y N N N Y Y Y

Data entered into separate tables N N Y Y N N N

Drop-down Lists Y Y Y Y Y Y N

Export as document Y Y Y Y U Y N

Export as image N N N Y U N Y

Export to the Internet N N Y N U N N

Help Option N Y Y Y Y Y Y

HoQ improvement Y N N Y N Y N

Icons Y Y Y Y Y Y N

Perfoms weighting Calculations Y N Y N N N N

Print/Save Option Y Y Y Y Y N Y

Project Roadmap N N Y Y N N N

Ready templates Y Y Y Y N N N

Relationships as symbols N Y Y Y Y N N

Roof as a triangle N N Y N Y N N

Starting up Wizards N Y Y Y Y N N

Stores Further Information Y N N Y Y N Y

Supports Calculation Y Y Y Y N N N

Template generation Y Y Y Y N N N

Tool Bar N Y Y N Y N N

Web Support N Y Y Y Y N N

Windows API compatible N Y Y Y Y N N


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Table 4, illustrates another comparison of the packages with respect to Williams and

Davis criteria (Williams and Davis 1994). Williams and Davis made use of their case study

experience and software selection criteria suggested by other authors (Holder 1990, Pidd

1989) in order to develop a list of eight criteria which reflect the issues that need to be

addressed when choosing manufacturing software and simulation packages in particular.

Table 4 Assessment of packages with respect to Williams and Davis criteria

Package name

QFD

Capture

Prof. v4.02

QFD

Designer

v4.00

Qualica

QFD

v2.2

QFD

2000

v1.00

QFD by

An. Ellis

Meta

QFD by

S.

Rapton

Broach

design

Company name Int. TechneGroup Inc.

QualiSoft

Qualica TotalQuality

Soft.

UMIST UMIST UMIST

Criterion

Cost U U U U N/A N/A N/A

Comprehensiveness of

the system

Flex. Med High High High Med Med Med

Interest Low Med High Med Med Med Med

Integration with other systems Low Low Med Low Low Med Low

Documentation Med High High Med Med High Low

Training Low Med Low Med N/A N/A Low

Ease of use (by usertype)

expert Med High High High High High High

new Low Low Med Med Low Low Lowregular Med Med High High High Med Med

End Low Med Med Med Med Med Med

Hardware and Installation Low Low Med Med Low Med Low

Confidence-related issues High Med High High Low Low Low

U = Unavailable

N/A = Not Available

All the packages evaluated here were capable enough of constructing successfully a

House of Quality chart. Qualica QFD and QFD 2000 are the newer ones and thus have

better user interface. However all packages aimed only in constructing the HoQ. None of

them presented something new such as simplification of the HoQ chart or prioritization

using Fuzzy logic to name a few. Qualica QFD offers the option to sort columns or rows

depending on the users needs but again this is not a great tool.

The authors of this article believe that new packages must be released solving

problems of QFD such as prioritization of CAs or ECs, complexity and vague descriptions

of the relationships.


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Proposed method for simplifying a HoQ Chart

Introduction

The method proposed for simplifying the HoQ chart includes three different paths:

I. Simplify the chart by CAs importance

II. Simplify the chart by decomposition

III. Simplify the chart by competitors performance

Simplify the chart by CAs importance

The concept of this method is that if the importance factor of a CA is relatively low

then this CA can be ignored in order to obtain a smaller house. If for example the

importance factor scale starts from 1 (lowest) and ends at 9 (upper) then CAs with

importance factor of 1 or 2 can be ignored. The lower acceptable importance factor can be

set as needed, for example in a longer scale, CAs with importance factor of 3 could also be

ignored. And so on. Although this is a very simple approach, it can be very useful if an

optimal lowest acceptable importance factor is selected.

The lowest acceptable importance factor depends on the following:

1. The number of CAs. It is obvious that such a simplification would be feasible only in a

HoQ with many CAs.

Example:

Consider a HoQ with only 3 CAs having relevant importance 5, 6 and 3 respectively. In

such a small house, there is no point in ignoring the third CA just because it has the

lowest importance factor.

2. The lowest and highest importance factor and the average importance factor. The method

is not feasible when lowest and highest importance factors are close to each other.

Example:

If a HoQ consist of five CAs and four of them have an importance factor of five and the

other has six, then it is unfeasible to simplify the chart by importance, because the new

house would have only one CA.


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The algorithm of the proposed method

This method is described with the following equation.

=

=

+=

otherwise,1.1

if,0

if,0

max.

min.

min ff

ff

fMAIFrndav

rndav

(4.1)

Where:

MAIF= Minimum Acceptable Importance Factor

fmin= Minimum importance factor

fmax= Maximum importance factor

fav.rnd= Rounded to closest integer average importance factor

Each case of equation 4.1 is explained with examples below, where it is also

explained why the added value in case 3 is 1.1 and not 1.

The concept of the method proposed is that all the CAs that have an importance

factorlower

3

than the MAIF will be ignored in the simplified, new House of Quality chart.

Example

Case 1: MAIF=fmin + 0, iffav.rnd= fmin

If fav.rnd = fmin then the CAs must all have importance factors close to the

lowest one. For example:

Table 2 Importance factors for each CA for case 1

CA name CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10

Importance factor 2 3 2 2 3 2 3 2 2 2

In such a case, again, a simplification by CAs importance is not recommended.

However, if applied, the CAs with importance factor equal to two (2) should be ignored.

3Important: lower than NOT lower or equal than


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Figure 3 Importance factor of CAs for case 1

Thus: MAIF= fmin + 0MAIF= 2 + 0 = 2.

Case 2: MAIF=fmin + 0, iffav.rnd= fmax

If fav.rnd = fmax then the CAs must all have importance factors close to the

highest one. For example:




In such a case, again, a simplification by CAs importance is not recommended.

However, if applied, the CAs with importance factor equal to six (6) should be

ignored.

Thus: MAIF= fmin + 0MAIF= 6 + 0 = 6.

Case 3: MAIF=fmin + 1.1, otherwise

This case is the most common one. This is the equation applied to a typicalHoQ with CAs having importance factors as shown in table 3.

0

1

2

3

4

CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10

Importancefactor


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In such a case, a simplification by CAs importance is recommended because

CAs with importance factors equal or less than 2.1 are ignored. However, if applied,

the CAs with importance factor equal or less than two (2) are ignored.

Thus: MAIF= fmin + 1.1MAIF= 1 + 1.1 = 2.1

Note: The reason the value added to the minimum importance factor is 1.1 and not 1 is

that this way the value 2 will be also ignored. The CAs ignored should have importance

factor less but NOT equal to the MAIF.




3

4

5

6

7

8

9


Importancefac

tor


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4.1.1 Simplify the chart by decomposition

The main idea of decomposition is to decompose the whole design problem into

sub-problems that can be easily solved independently. Kim and Shin (2000) were the firstwho applied this method in order to simplify a HoQ chart. However this approach is not

robust because several entries of the original chart are ignored in the simplified one.

The authors of this article propose another method of decomposition. Although it is

much simpler and it is not 100% reliable, it prevents wrong results due to entries ignored.

The concept of this approach is that both CAs and ECs are sorted with respect to their

overall influence in the chart. In this method the signs + and -of the relationships are

disregarded; only the strength of each relationship is considered.

The final sorting is simple: CAs that have the most influence in the chart (i.e. the

ones related with most ECs) are placed in the upper cells of the chart. Similarly, ECs that

have the most influence in the chart (i.e. the ones related with most ECs) are placed in the

left cells of the chart. Thus, in the new chart, CAs and ECs with the most influence in the

chart are placed at the first cells of the chart, all together and the ones with no or little

influence are placed at the bottom. Next, the user is free to decide how many of the last

entries can be ignored. If this ranking is not providing with acceptable results then this

simplification can be neglected.

0

1

2

3

4

5

6

7

8

9


Importancefacto

r


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4.1.1.1The algorithm of the method

Suppose a HoQ chart with n CAs and m ECs, then the absolute sum of the

relationship strengths in each line would be:

=

=

=

mj

j jira

1|| (4.2)

and the absolute sum of the relationship strengths in each row would be:

=

=

=

ni

i ij rb 1 || (4.3)

Where:

ri,j = the relationship strengths.

From equation 4.2 and 4.3 integers 1, 2... n and b1, b2,bm are derived. Then,

CAs are sorted subject to 1, 2... n and ECs are sorted subject to b1, b2,bm. There are

four possible final solutions that are illustrated in the following table:

Table 8 Possible Solutions

Original HoQ Chart Solution 1 Solution 2 Solution 3

CAs Not sorted Sorted Sorted Not sorted

ECs Not sorted Sorted Not sorted Sorted

Where:

CAs Sorted = CAs are sorted subject to 1, 2... n

ECs Sorted = ECs are sorted subject to b1, b2,bm

CAs Not sorted = CAs position in the HoQ chart is not changed

ECs Not sorted = ECs position in the HoQ chart is not changed


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Normally, the optimal solution derives when both CAs and ECs are sorted.

Nevertheless, after testing the method in several HoQ charts, it was concluded that

sometimes one of the two other solutions provided, the results were more stable.

Suppose the selected solution of the three possible ones gives a new HoQ chart

whose size is G and consists ofs lines and w rows. Suppose that after sorting the CAs

subject to 1, 2... n, each CA is named as follows:

CA placed at the upper cell of the chart = CAg1

CA placed at the second upper cell of the chart = CAg2

CA placed at the lowest cell of the chart = CAgn

Then the CAs of the new chart would be:

CAg1, CAg2 CAgs, s < n

Suppose that after sorting the ECs subject to b1, b2... bm, each EC is named as

follows:

EC placed at the first left cell of the chart = ECg1

EC placed at the next cell of the chart = ECg2

EC placed at the last cell of the chart = ECgm

Then the ECs of the new chart would be:

ECg1, ECg2 ECgw, w < m

Example

Suppose the original house looks like the one in figure 6. The relationships are

represented by numbers, as shown in the table below:

Table 5 Relationship numbers meaning

Number Meaning

-2 Strong negative

-1 Negative

Empty box No relationship

1 Positive2 Strong Positive


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Figure 6 The original House of Quality

If Kim and Shins method is applied the new simplified house of quality will look like the

one of figure 7.

Figure 7 Kim and Shins Solution

Neglected

entry


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The thick lines in figure 7 represent the three new houses that derived by Kim and

Shins optimal solution (described in chapter two). The grouping seems to be very well

formed. Nevertheless, it is obvious that some strong relationships are neglected.

Furthermore, the authors of this article believe that such an effective decomposition is

possible under extreme circumstances. In the authors opinion, this solution can give good

results to only few special cases.

Figure 8 The Authors Solution

The authors proposal is illustrated in figure 8. Here, its up to the user to decide

which relationships will be neglected. The thick line represents the more efficient (but

risky) solution. The interrupted lines represent other solution that one can choose. This

method is flexible and errors due to entries ignorance are neglected.

Figure 9 Successful Simplification; Original chart


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However, this method is not 100% feasible. After testing this method by

simplifying several HoQ charts, the following two extreme cases were obtained:

Case 1: Successful Simplification.The original house of quality is illustrated in figure 9.

The chart after implementing the proposed decomposition method is demonstrated in figure

10. The thick line represents the solution obtained. In this solution, the chart simplification

percentage was 90% successful.

Figure 10 Successful Simplification; Simplified chart

Case 2: Simplification infeasible

The original house of quality is illustrated in figure 11.

Figure 11 Non feasible simplification; Original chart


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The chart after implementing the proposed decomposition method is demonstrated

in figure 12. It is obvious that the solution obtained was not successful. The new HoQ chart

does not contain any particular area that could be grouped. Thus, this solution was

unsuccessful.

Figure 12 Non feasible simplification

Simplify the chart by competitors performance

The concept of this approach is that if our company has the best value in a CA

compared to its competitors then this CA can be ignored. However this method is not

robust, mainly because while putting a lot of effort to improve other CAs, there is the

danger to maintain inadequately the ignored CA. Thus, if this method is implemented it

must be certain that this wont have a negative effect on the CAs ignored. Nevertheless, this

approach is also a way of simplifying successfully a HoQ chart and should be examined

further.

Example

Suppose that the performance values achieved by each company for each CA are as

shown in table 10. In this case, CA1, CA5 and CA9 can be ignored. Thus the new,

simplified HoQ chart will have three less CAs. However, as claimed above, it is risky to

apply this method. In this example, although our company has the higher performance


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value in CA9, the competitors values are also high and thus our company should not

neglect this CA.

Table 10 Companies Performance on each CA

CA nameOur

company Company 1 Company 2 Company 3 Company 4

CA1 10 9 6 3 7

CA2 4 3 5 7 6

CA3 5 4 5 2 6

CA4 4 5 7 7 7

CA5 6 5 3 4 5

CA6 8 5 4 9 6

CA7 6 6 6 6 5CA8 2 9 7 5 9

CA9 10 9 7 9 9

CA10 7 9 4 6 2

Summary

The proposed method for simplifying the HoQ chart includes three different

techniques, which are: Simplifying the chart by CAs importance, by competitors

performance and by decomposition. All of those techniques are exclusive work of the

authors. Nevertheless, Kim and Shin (2000) have proposed another way of decomposing

the House of Quality chart, but the authors judge that it lacks of practicality. Although the

methods proposed are not always applicable to all HoQ charts, reliable results were

obtained when it was possible to implement them.

The Software

Program features:

Simplify the chart by CAs importance

Simplify the chart by competitors performance

Simplify the chart by decomposition

Apply the Clausing Four Phase Model


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Save HoQ Chart

Print HoQ Chart

Send HoQ Chart as E-mail attachment

Enter up to 60 CAs and 100 ECs Among the solutions offered, the user is able to choose the one that fits his needs.

Note: is the OR logical expression

Figure 13 The flow chart

Insert CAs

Insert ECs

Next: Insert

Competitors

Insert

Importance

Next? Next Child

HoQ

Chart

New House 1 level up

Decomposition

ImportanceCompetitors

ContinueEndNo

Yes

Start


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Figure 14 The toolbar

Figure 15 Inserting ECs

Figure 16 Competitors information


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Figure 17 The original HoQ chart

Figure 18 HoQ simplified by Importance


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About the simulation

The effectiveness of two of the methods proposed was investigated by Monte Carlo

simulation. First the simplification of a HoQ chart by importance was tested and then the

decomposition method was also tested. In both simulations, the results obtained were not

only accurate but also similar to the ones expected by the authors. Neither of the two

methods proposed lead to wrong results or unfeasible simplification. However, it must be

noted once again, that the methods proposed are not panacea; the user must also use

his/hers experience and human logic to avoid mistakes and to take the most of the methods

proposed.

Conclusions

Although simplifying by competitors is risky it may be helpful in some house of

quality chart types. The other two methods can be implemented in a bigger variety of charts

and results of this method are reliable, providing that engineers experience is employed.

The effectiveness of the last two methods proposed was proved by simulating.

Results confirmed that 5 to 25% simplification of a chart can be achieved when simplifying

by importance. In that case no errors where reported and thus the method is consistent.

Kim and Shin (2000) were the first who applied the decomposition method in order

to simplify a HoQ chart. However the authors believe that this approach is risky because

several entries of the original chart are ignored in the simplified one.

The authors proposed another method of decomposition. Although it is much

simpler and it is not 100% reliable, it prevents wrong results due to entries ignored. After

the simulation, the proposed simplification by decomposition was also proved to be reliable

mainly because the user has the ability to visualise the pattern of the chart to be ignored.

The results obtained from the simulation were adequate for a simplification by 30% and

better for a simplification of 16%.

Future work

Although the methods proposed for simplifying a house of quality chart gave

trustable results, the authors believe that those can be improved in terms of being applicable


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to a larger range of chart types. Especially the decomposition method can be enhanced with

more options or more decent mathematics in order to raise the simplification percentage of

the chart.

The software package developed by the authors can also be improved, first by being

re-programmed in a more professional computer language such as Microsoft C++. The

authors judge that not only would this program be faster but it would also be more flexible

if it was programmed in C++. Furthermore, a new program can be developed that would

simplify a house of quality chart by all the three methods proposed simultaneously. The

results obtained by such a program may be even better than the ones obtained in the

simulation carried out in chapter six.

Documents

Towards improving the Quality of Quality Function Deployment Model and available Software packages