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A Methodology to Improve Higher Education Quality using the QualityFunction Deployment and Analytic Hierarchy ProcessHendry Raharjoab; Min Xiea; Thong Ngee Gohab; Aarnout C. Brombacherbc

a Department of Industrial and Systems Engineering, National University of Singapore, b Centre forDesign Technology, National University of Singapore, c Technische Universiteit Eindhoven,Eindhoven, The Netherlands

To cite this Article Raharjo, Hendry , Xie, Min , Goh, Thong Ngee and Brombacher, Aarnout C.(2007) 'A Methodology toImprove Higher Education Quality using the Quality Function Deployment and Analytic Hierarchy Process', TotalQuality Management & Business Excellence, 18: 10, 1097 — 1115To link to this Article: DOI: 10.1080/14783360701595078URL: http://dx.doi.org/10.1080/14783360701595078

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A Methodology to Improve HigherEducation Quality using the QualityFunction Deployment and AnalyticHierarchy Process

HENDRY RAHARJO�,��, MIN XIE,� THONG NGEE GOH�,�� & AARNOUTC. BROMBACHER��,†

�Department of Industrial and Systems Engineering, National University of Singapore;� �Centre for Design Technology, National University of Singapore; †Technische Universiteit Eindhoven,

Eindhoven, the Netherlands

ABSTRACT In order to formulate an effective strategic plan in a customer-driven educationcontext, it is important to recognize who the customers are and what they want. Using QualityFunction Deployment (QFD), this information can be translated into strategies to achievecustomer satisfaction. Since the final strategic plan relies heavily on the way QFD is used, thispaper will first describe the existing problems in its use and then propose a better way to improveit. In this paper, the customers are divided into two major parties, namely, the internal and theexternal customer. The internal customer comprises of the lecturers and the students, while theexternal customer is the employers of the graduates. After collecting the Voice of Customer(VOC), the Analytic Hierarchy Process (AHP) technique was employed to generate the prioritiesof the VOC for each group of customers. Then, the results were used as the input for formulatingstrategies or Quality Characteristics (QCs) to meet the Demanded Qualities (DQs) using QFD. Asimple case study is provided to demonstrate the usefulness of the methodology. A sensitivityanalysis was also conducted to anticipate the changes in the DQs that will affect the output of theQFD. This is useful for providing a better strategic planning for the education institution to meetthe future needs of its customers.

KEY WORDS: Quality of education, quality function deployment, internal customer, externalcustomer, analytic hierarchy process, sensitivity analysis, future needs of customers

Introduction

In this era of global education, it is imperative for a higher education institution continu-

ously assure and improve its quality. The word ‘quality’ in education can be interpreted in

Total Quality Management

Vol. 18, No. 10, 1097–1115, December 2007

Correspondence Address: Hendry Raharjo, Department of Industrial and Systems Engineering, National Univer-

sity of Singapore, Singapore, 119260. Email: [email protected]

1478-3363 Print/1478-3371 Online/07/101097–19 # 2007 Taylor & FrancisDOI: 10.1080/14783360701595078

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various ways, but in order to provide better education quality, the education institution has

to strive for offering a particular competitive advantage to its customers by recognizing,

fulfilling and exceeding their requirements. This is in line with the concept of Total

Quality Education (TQE) proposed by Dahlgaard et al. (1995): ‘An education culture

characterized by increased customer satisfaction through continuous improvement, in

which all employees and students actively participate’.

A large number of higher education institutions teach Total Quality Management

(TQM) to their students; unfortunately only some of them practice what they preach.

The implementation of TQM in education required a customer-driven focus (Pitman

et al., 1996; Hwarng & Teo, 2000, 2001; Sa & Saraiva, 2001; Sahney et al., 2006). There-

fore, it is of great importance to listen to the Voice of the Customer (VOC) (Griffin &

Hauser, 1993) to effectively and efficiently deliver values to customers. The Quality Func-

tion Deployment (QFD) is an alternative way to recognize or listen to the Voice of Cus-

tomer (VOC) better in manufacturing industries as well as in service industries. The QFD

is known as one of the most powerful tools in TQM. It has been applied mostly to indus-

tries (Chan & Wu, 2002a), but the underlying concept of a customer-driven process may,

to a certain extent, apply to the education field.

The first crucial step that makes the efforts effective is to identify who the customers are

and what they demand from the institution. The customers/stakeholders of the university

may involve faculties/lecturers, students, employer of graduates, alumni, administrative

staffs, parents, government, local community, and so on (see Reavill, 1997). For the sake

of simplicity, we will only focus on three main parties, namely, students (Sirvanci, 1996;

Wallace, 1999; Sakthivel & Raju, 2006), lecturers, and employers of graduates (Willis &

Taylor, 1999). Focusing on the students is the center pedagogical principle in the construc-

tivism approach. On the other hand, students may also be regarded as the product of higher

education (Pitman et al., 1996; Bailey & Bennett, 1996; Bier & Cornesky, 2001). Using a

different point of view, the classification of customers may differ from one to another

(see Madu et al., 1994; Ermer, 1995; or Duffuaa et al., 2003). In this study, only two

major categories are considered, namely, the internal and the external customer.

The internal customer consists of the lecturers and the students, while the external cus-

tomer is the employers of the graduates. The students are considered as customers in terms

of the services they receive from the institution, for example, the efficiency in modules

registration/administration problem, the organizational or non-academic activity, the

class punctuality and lecturer’s attendance frequency, food hygiene/service, and so on.

They are not deemed suitable to judge more on the content of the education. The lecturers

are regarded as the designer as well as the main player in education, thus the education

institution should provide them with the necessary facilities for education purposes. The

employers of graduates are considered as the customer because their feedback towards

the graduates would provide a valuable measure of the Quality of Performance (QP)

(Mergen et al., 2000; Widrick et al., 2002) of the respective higher education. Their feed-

back may also reflect the learning outcome or competency of the graduates.

In the following sections, a literature review on the use of the QFD in education will be

given and followed with some existing technical and practical problems which motivated

the proposed methodology (the second section). Then, in the third section, the basic QFD

model, its use and terms will be described. Several reasons to choose the Analytic Hier-

archy Process (AHP) in combination with the QFD will also be elaborated. In the

fourth section, a methodology to improve higher education quality using the QFD and

1098 H. Raharjo et al.


AHP is proposed using a step-by-step procedure and a flowchart. A real-world case study

was also used to illustrate the idea (the fifth section). A sensitivity analysis was also con-

ducted, based on the case study data, to deal with the dynamics of the education systems as

well as to anticipate the future needs of the customers. Lastly, a brief conclusion of the

study is provided in the sixth section.

QFD in Education and some Problems

Since the 1980s, higher education institutions have begun to adopt and apply quality man-

agement to the academic domain owing to its success in industry (Grant et al., 2002) and

they have also benefited from the application of TQM (Kanji & Tambi, 1999; Owlia &

Aspinwall, 1998). QFD, as one of the most powerful TQM tools, has also been used

quite extensively in academia. Jaraiedi & Ritz (1994) applied QFD to analyze and

improve the quality of the advising and teaching process in an engineering school.

Koksal & Egitman (1998) used QFD to improve industrial engineering education

quality at the Middle East Technical University. Lam & Zhao (1998) suggested the use

of the QFD and the Analytic Hierarchy Process (AHP) to identify appropriate teaching

techniques and to evaluate their effectiveness in achieving an education objective. Bier

& Cornesky (2001) critically analyzed and constructed a higher education curriculum to

meet the needs of the customers and accrediting agency using QFD.

Adopting the constructivist’s point of view, Chen & Chen (2001) introduced a QFD-

based approach to evaluate and select the best-fit textbook based on the VOC. Kauffmann

et al. (2002) also used QFD to select courses and topics that enhance a master of engin-

eering management program effectiveness. They further pointed out the additional

benefit of QFD in the academic context, that is, to develop collegial consensus by provid-

ing an open and measurable decision process. Brackin (2002) wrote the analogy of the use

of QFD in the industry with the assessment of engineering education quality by breaking

down the assessment items into a set of WHATs and HOWs following the four phases of

QFD. Duffuaa et al. (2003) applied QFD for designing a basic statistics course. More

recently, Sahney et al. (2004, 2006) used QFD, in combination with SERVQUAL as

well as Interpretive Structural Modeling and Path Analysis, to identify a set of

minimum design characteristics to meet the needs of the student as an external customer

of the educational system. Chen & Yang (2004) explored the possibility to use Internet

technology by developing a Web-QFD model. They gave a real-world example of an edu-

cation system in Taiwan and argued that the Web-QFD may not only provide a more effi-

cient way of using the QFD in terms of cost, time and territory, but also may facilitate a

better group decision making process. Aytac & Deniz (2005) used QFD to review and

evaluate the curriculum of the Tyre Technology Department at the Kocaeli University

Kosekoy Vocational School of Higher Education.

It is clear that QFD has been extensively used in improving education quality. However, if

one takes a closer look at how QFD was implemented in education, one discovers some pro-

blematic areas that need improvement. In this paper, five major problems will be highlighted.

They can be divided into two major categories, namely, the technical problems (the first, the

second, and the third problems) and the practical ones (the fourth and the fifth problems).

The first problem is the use of absolute values to assign the degree of customer import-

ance. As pointed out by Chuang (2001), the customers will tend to assign a high degree

of importance to most of their requirements, thus resulting in values near the highest possible

A Methodology to Improve Higher Education Quality 1099


score. These values will have no significant meaning (Cohen, 1995) and will later produce

somewhat arbitrary and inaccurate results for prioritizing technical response. Some

examples for using a set of discrete values can be found in Jaraiedi & Ritz (1994), Ermer

(1995), Chen & Chen (2001), Kaminski et al. (2004), and Chou (2004). Therefore, relative

measurement among the customer requirements is suggested to be a better alternative.

The second problem is the technique that is used to obtain the priorities of a group’s

preference. Some of the studies simply proposed the use of an arithmetic mean or weighted

arithmetic mean for obtaining the preference of the customer group, which seems arbitrary

and not robust. This case can be found in Bier & Cornesky (2001), Hwarng & Teo (2001),

Duffuaa et al. (2003), Kaminski et al. (2004), or Aytac & Deniz (2005). A better approach

would be to use a geometric mean that also formed the foundation of a group preference

method in the AHP (Ramanathan & Ganesh, 1994; Forman & Peniwati, 1998).

The third problem is the difficulty in identifying a true relationship between customer

requirements and design/technical attributes. It seems quite unrealistic if all customer

requirements are related to design/technical attributes so that the QFD correlation

matrix will be full blocked. A less severe condition would be that all the technical attri-

butes correlate with a particular customer requirement. It simply shows that the QFD

team has difficulty in assigning more discriminating relationship values between them.

Examples of this case can be found in Duffuaa et al. (2003) or Lam & Zhao (1998),

which used a full blocked relationship matrix.

The fourth problem is that the flexibility in using QFD in education should be enhanced,

resting on the assumption that it is not just a ‘plug-and-play decision machine’ (see

Govers, 2001). There are two points to highlight. First, the number of HOQs does not

have to be strictly four (Hauser & Clausing, 1988). Based on the necessity of the deploy-

ment process, the QFD team may decide how many HOQs to use. An example given by

Brackin (2002) to follow the four phases showed the inflexibility. Second, the true VOC

should come from the proper and right customers. Several researchers in education do not

include the students since they may have unnecessary wants and be considered too imma-

ture to judge the content of education. On the other hand, Sa & Saraiva (2001) attempted to

include kindergarten children as the customers. This approach seems to be overconfident

and risky.

The fifth problem lies in pooling the needs of several different customers into one group.

This might possibly lead to a fallacious conclusion since one stakeholder may have a

unique need that others may not consider, or even a conflicting need with respect to

other customers. An example for this case can be found in Koksal & Egitman (1998),

which combined three different stakeholders into one.

Therefore, in view of these problems, this paper will attempt to fill in the gap by provid-

ing a better methodology by using QFD and AHP to improve higher education quality. It is

hoped that this will help higher education institutions, in general, improve their quality in

the future by providing the best education program for their nation.

Using QFD with AHP

The Basic QFD Model

The Quality Function Deployment (QFD) technique was first developed in the late 1960s

by Yoji Akao and Shigeru Mizuno (Akao & Mazur, 2003). It is generally defined as a



structural framework to translate customer requirements into appropriate technical

requirements for product/service development to satisfy customers. There are a lot of

good texts on the fundamental understanding of QFD, for example, Cohen (1995), Shillito

(1994), ReVelle et al. (1997) or a recent one by Xie et al. (2003). A recent comprehensive

literature review on the QFD and how to use it can be found in Chan & Wu (2002b), and a

synopsis of recent methodological enhancement on QFD can be found in Kim et al.

(2003).

The House of Quality (HOQ) is the central component in constructing QFD (Hauser &

Clausing, 1988). A typical HOQ chart is shown in Figure 1. The HOQ comprises of several

standard main components as follows:

. Whats/Demanded Quality (DQ): a list of the customer requirements.

. Hows/Quality Characteristic (QC): a list of ways for achieving the Whats, which is

called the design/technical attributes/requirements.

. Importance Ratings: the relative importance scores of each Demanded Quality which, in

this paper, is derived using the AHP method.

. Relationship Matrix: a matrix that shows the relationship level between Whats and

Hows. In this paper, a five-level correlation value is used to accommodate better

the QFD team in assigning more accurate portrayal of DQ and QC relationships.

The five levels used are: Extreme (9), Strong (7), Moderate (5), Some (3), and

Weak (1).

. Correlation Matrix: a matrix that shows the relationship between Hows.

. Customer Competitive Assessment: a review of competitive products/service charac-

teristics in comparison with ours.

. Score: a series of computed numbers for indicating the importance of each How.

. Rank: a series of ordinal numbers that denote the rank of the QCs.

Figure 1. Basic model of the House of Quality (HOQ)



Throughout this paper, DQ and QC will be used to refer to Demanded Quality/Whats and

Quality Characteristic/Hows, respectively. Assume that there are m DQs and n QCs, then

the score of each QCs will be computed as in equation (1):

ASj ¼Xm

i¼1

Rij � IRi, i ¼ 1, 2, 3, . . . , m; j ¼ 1, 2, 3, . . . , n (1)

where

ASj ¼ Absolute Score of QCj

Rij ¼ the weights assigned to the relationship matrix

IRi ¼ Importance Rating of DQi

The QFD output, which is a set of priorities of the QCs, can be obtained by assigning a

sequence of discrete ascending numbers (1, 2, 3, . . ., n) to each QC from the highest

score until the lowest one. A relative measure of the absolute score can also be used.

The Use of AHP in QFD

In the QFD matrix, the AHP is used to fill the left-hand part of the HOQ, that is, the

Importance Ratings (IR) block (refer to Figure 1). There are two main reasons for using

the AHP to elicit the degree of importance of the DQs. First, the use of absolute values

to identify the degree of importance of DQ in the traditional QFD, for example, 1 to 5,

with 5 denoting the most important, may lead to a tendency for the customers to assign

values near to the highest possible score, and thus may lead to somewhat arbitrary and

inaccurate results for prioritizing the QCs (Cohen, 1995; Chuang, 2001). Second, the

use of the traditional QFD may lead to inconsistency in quantifying customers’ judgment

(Akao, 1990, Lu et al., 1994; Armacost et al., 1994).

It is therefore necessary to use a more comprehensive and accurate technique to provide

an effective framework for determining the priorities of DQs in the QFD. The AHP is known

to be a widely used tool to elicit the relative priorities of a set of objects using a pairwise

comparison, which is considered as the most accurate way for humans to perfectly

compare many criteria or alternatives two at a time. Despite the fact that it has a few short-

comings, for example, the cost and time required to do the pairwise comparison as the items

get larger (Wang et al., 1998) and the rank reversal phenomenon (Belton & Gear, 1985;

Saaty & Vargas, 1984; Saaty, 1990; Raharjo & Endah, 2006), its exceptional strength in

quantifying intangible aspects, relative measurement, and consistency of decision makers

outweighs other decision tools. Therefore, it is chosen for the purpose of this study.

Generally, the AHP allows the decision maker to structure his problem hierarchically, to

make a comparison of the elements with respect to a common property, and finally to syn-

thesize all the judgments according to the hierarchy to obtain overall priorities of the set of

objects. Furthermore, the AHP, relying on the fact that human judgment is known to be

inconsistent to some extent and cannot be forced to be perfectly consistent in all cases,

offers the advantage of doing a systematic check on the consistency of decision makers’

judgment and admits inconsistency in at a certain level that is less than or equal to 10%

(Saaty, 1988). This property also makes the AHP a more flexible and realistic approach

for decision making as a descriptive theory (Saaty, 2006). For more details of the technical

aspects, interested readers may refer to any vastly available AHP literature, for example,



Saaty (1980, 1988). One may also simply use the Expert Choice software for using the AHP

to derive priorities from a set of objects. Note that the question posed in the AHP question-

naire does not use a particular reference point, and thus it is purely subjective.

In order to obtain a meaningful group preference for each party, it is assumed that each

party is a collection of synergistic individuals who act together rather than separate indi-

viduals. This is also very reasonable in the context of an education institution with each of

the involved parties working towards a common goal. On the basis of this assumption, the

Aggregating Individual Judgment (AIJ) approach is the most suitable method, compared

with the Aggregating Individual Priorities (AIP) approach (Forman & Peniwati, 1998).

The AIJ approach, which uses the geometric mean, can be expressed as follows:

aGij ¼

Yn

k¼1

akij

" #1=n

(2)

where:

n ¼ the number of decision makers,

aijG ¼ the group judgment of the (i,j ) element in the reciprocal matrix.

Equation (2) assumes that the individuals are of equal importance; otherwise, one may

use the weighted geometric mean. In this study, the students, lecturers and employers of

graduates are assumed to be of equal importance. The weight of each DQ is calculated

through pairwise comparison questionnaires given to every decision maker. Owing to

the large number of DQs, the comparisons will be very tedious. Therefore, clustering

can be used to reduce the number of comparisons. The DQs are classified into primary

DQ group and secondary DQ group using the affinity diagram approach; as an example,

the complete students’ party hierarchy is shown in Figure 2. This affinity diagram is

similar to the method of clustering and will later help reduce the number of pairwise com-

parisons in the AHP. In other words, increasing the level of hierarchy can minimize the

workload of using the AHP (Armacost et al., 1994).

Figure 2. An example of students’ party hierarchy



Finally, a reciprocal matrix is constructed to evaluate the consistency level. The consist-

ency level of the judgments can be easily checked using the Expert Choice software;

however, if the consistency level goes beyond 10%, a resurvey should be conducted on

each of the decision makers.

Proposed Methodology

In this section, the proposed methodology of using the QFD with AHP will be elabo-

rated. The aim of this methodology is to advance the use of QFD in improving

higher education quality. This methodology has overcome the technical as well as the

practical problems of using the QFD mentioned previously. Here, the AHP will be

used to obtain relative measurement, obtain group preference, and check the inconsis-

tency of decision makers’ judgments. A method proposed by Nakui (1991) was

employed to ensure that no superfluous DQs/QCs are being included while still main-

taining the significant relationships between DQ and QC. Each of the customers uses a

separate HOQ. Note that the number of HOQs used can be adjusted according to the

need of the deployment process.

For a clearer description of the idea, a step-by-step approach will be presented. This pro-

cedure applies for each customer. A flowchart of the step-by-step procedures can be seen

in Figure 3.

Step 1. Conduct a pilot survey of customer needs. In other words, this is an in-the-field

observation in order to collect the VOC from the true source of information. A

variety of methods, such as contextual inquiry, direct observation, focus group,

questionnaires, and so on, can be employed. After the survey, the QFD team

should sort out and organize the preliminary results. This will provide the QFD

team with the big picture of the customers’ needs.

Step 2. Conduct one-on-one in-depth interviews with the customers. In this step, adopt-

ing the Garbage-In-Garbage-Out (GIGO) philosophy, it is very crucial to select

some knowledgeable decision makers who are also representative of each of the

parties involved. Note that it is important to select the right students to be inter-

viewed in order to avoid unnecessary and self-centered wants.

Step 3. Use an Affinity Diagram to classify or sort out the DQs and construct a hierarchy

based on the grouping. The higher the hierarchy, the less the effort to obtain the

important values. This hierarchy also serves as the AHP hierarchy.

Step 4. Explore each Demanded Quality hierarchically by a tree diagram and translate it

into an appropriate Quality Characteristic. The QC is defined as the strategy/way to achieve the DQ. One DQ may be related to some QCs, and vice versa.

Step 5. Verify whether the DQs and the respective QCs listed are already valid, other-

wise, the QFD team should carry out the interview again.

Step 6. Ask the selected decision makers to make the AHP pairwise comparisons in

order to derive the priorities of the DQs. The QFD team may explain to decision

makers who are not familiar with the AHP mode of questioning.

Step 7. Obtain the group preference using Aggregating Individual Judgment (AIJ).

Then, check whether there is a need to resurvey the decision makers owing to

inconsistent judgments. The Expert Choice software can be used to obtain the

priorities of DQs as well as to do the inconsistency check.



Step 8. Construct the House of Quality of each customer. The minimum set for con-

structing the HOQ should exist, such as the DQ, the Importance Rating, the

QC, and the QC’s score/ranks. Other components (the roof, competitive assess-

ment, etc) might be added as necessary. Microsoft-Excel software would be a

good alternative to do the HOQ analysis.

Figure 3. The proposed methodology using the QFD and AHP



Step 9. Verify the completed HOQ components. Some rules to check the correlation

matrix as proposed by Nakui (1991) can be used. For example, if a DQ has no

corresponding QC at all, then this DQ should be taken away.

Step 10. Compute the QCs’ scores, and obtain their rankings. The QFD team may evalu-

ate whether there is a need to extend the deployment process by using another

HOQ. If there is a need to use another HOQ, a similar process can again be con-

ducted (Step 8).

Step 11. Conduct sensitivity analysis to provide a sense of how robust is the decision

made by the QFD team if there is a change in the input data. This is also

useful to anticipate future needs of customer and variability in the DQs.

Step 12. Other downstream analysis, such as gap analysis, SWOT analysis, and so forth,

can be added accordingly.

A Case Study

The House of Quality of the Customers

These real-world case study data were taken from Raharjo & Dewi (2003). The purpose is

to illustrate the use of the proposed methodology. The data for the students’ party were

obtained from several representative students from each academic year who were still

studying in the university. The students’ representatives had a minimum GPA of 3.0

out of 4.0. A number of employers of the graduates were interviewed by the QFD team

using questionnaires with the help of the graduates themselves, while all the lecturers

were interviewed on a one-on-one basis since there was a relatively small number of lec-

turers in the department. Some samples of the Quality Function Deployment charts that

were produced from the process are shown in Figures 4, 5 and 6. The alternative solutions

or QCs to each customer parties were derived from their respective Houses of Quality.

Note that the roof was not emphasized in this case; however, it can be added if it is

deemed necessary (see Wasserman, 1993).

Sensitivity Analysis

Since the DQs are based on the survey of a certain party at one point in time, it is very

likely that the condition will change at another point in time in the near future, see

Raharjo et al. (2006) or Kim et al. (2007) for further description of DQ’s dynamics. There-

fore, a sensitivity analysis would be advantageous in dealing with the dynamics of DQs as

well as in anticipating the future needs of the customers. Xie et al. (1998) wrote a study on

the sensitivity of VOC in the QFD and showed that small changes in the customer require-

ment weights will not affect the rank in the technical attributes because the relationship

matrix is discrete in nature. However, if the variability in the DQ is relatively large,

then the prioritization in the QC might still be affected.

In this study, the objectives of the sensitivity analysis are to anticipate the future

changes of customer interest and to investigate the most sensitive component of the

DQ. For the first purpose, there are two cases to be analyzed for each customer party.

The first case (Case I) is to assign equal weights to each primary Demanded Quality,

and the second case (Case II) is when one particular primary Demanded Quality outweighs

the rest of the other requirements. An example for employers’ party importance rating

change is shown in Table 1.



Figure 4. Trimmed part of HOQ for students’ party

Figure 5. Trimmed part of HOQ for lecturers’ party



As a result of this change of weight in the primary DQ in Case I and Case II, the order of

priority of the strategies to be taken has also changed accordingly. This can also be

observed by the reversal in the QC’s prioritization rank from the QFD chart. As an

example, for the employers’ party, the initial alternative solutions, consecutively from

the most important QCs, were ‘to give more team assignment’, ‘arrange leadership train-

ing’, ‘get involved in committee activities’, and so on. For Case I, a few of the QCs’ ranks

were reversed, while in Case II the priority reversal occurred more often as shown in

Table 2.

To investigate the most sensitive component of the DQs, an experimental study was

conducted for each of the customers. The most sensitive component is interpreted as

the component that causes the largest number of differences in rank due to rank reversals

of the QC when the corresponding DQ is changed. Table 3 gives the design of the exper-

iment that will be carried out. There are two factors to be analyzed, namely, the Primary

Figure 6. Complete HOQ for employers’ party

Table 1. IR Change for employers’ party sensitivity analysis

Academic qualification Leadership skill Interpersonal skill Prob.solving skill

Case I 0.25 0.25 0.25 0.25Case II 0.05 0.85 0.05 0.05



DQ (PDQ) and the Importance Rating (IR). The PDQ has p elements of DQ [DQ1, DQ2,

DQ3, . . ., DQp] of each customer, and the IR has four elements f2, 3, 4, 5g. A cell of the ith

PDQ and the jth IR is defined as a collection of r variables, where r is the number of QCs in

the HOQ. For instance, fY1,1,1, Y1,1,2, Y1,1,3, . . . ,Y1,1,rg describes the ranks generated when

DQ1 is two-time relatively more important than DQ2, DQ3, . . . , DQp.

The response variable of interest is the number of differences between the initial state rank

(Xk) and the rank generated from the experiment design cell (Yi,j,k). The initial state was set

up under the circumstance that each DQ had equal weight. If the initial state rank is denoted

by fX1, X2, X3, . . . , Xrg and the generated rank from one of the experiment cells (i,j) is

denoted by fYi, j,1, Yi, j,2, . . . , Yi, j,rg, then the rank difference, Dijk can be expressed as below:

Dijk ¼ Xk � Yijk

�� (3)

where

Dijk ¼ the absolute rank difference in the ith PDQ and jth IR for the kth QC.

Xk ¼ initial rank of the kth QC.

Yijk ¼ generated rank in the ith PDQ and jth IR of the kth QC.

i ¼ 1, 2, 3, . . . , p, where p denotes the number of PDQs.

j ¼ 1, 2, 3, 4 (IR ¼ 2-time, 3-time, 4-time, and 5-time, respectively)

k ¼ 1, 2, 3, . . . , r, where r denotes the number of QCs in the HOQ.

Table 2. Alternative solution for employers’ party

Rk. Initial Rk. Case I Rk. Case II

1 Give more teamassignments



2 Leadership training 2 Leadership training 2 Get involved incommittee activities

3 Get involved incommittee activities

3 Get involved incommittee activities

3 Leadership training

4 Intensify discussionand presentations



5 Provide ethics andreligion courses

5 Give assignment withtime limitation


6 EQ training 6 Provide foreignlanguage classes

6 Provide foreignlanguage classes


7 Teach more mostly-used comp.prog


8 Provide foreignlanguage classes

8 Invite guest lecturersfrom industries






10 Make more reasoningproblems



11 EQ training 11 EQ training

12 Give additionalcourses





To test if there is a significant difference among the mean of Dijk in each cell, a two-

sample t-test with unequal variance was employed. The comparison analysis was con-

ducted in pairs to determine the level of sensitivity of each DQ. The higher the mean of

Dijk, the more sensitive the corresponding DQ. In support of the inferential analysis that

was carried out for each party, a visual inspection method was also used. The boxplots

suggest very strongly the conjecture that the means of rank difference are not all the

same. As an example, Figure 7 shows one of the students’ party DQs (Facility) boxplot

that reveals the higher the IR level, the higher the mean and variance of Dijk become.

This simply implies that the IR level has a significant effect on the mean of Dijk.

Figure 8 also serves as an example of a boxplot for students’ party DQ at 5-time IR

level. The visual investigation of Figure 8 substantiates the fact that the levels of sensi-

tivity of the DQs are not all the same. The DQs of the students’ party, in the order of

the most sensitive one, were facility, lecturer, extracurricular, administration, location,

Table 3. The design of experiment for Yi,j,k and Di,j,k

IR IR

PDQ 2 times 3 times 4 times 5 times PDQ 2 times 3 times 4 times 5 times

Y1,1,1 Y1,2,1 Y1,3,1 Y1,4,1 D1,1,1 D1,2,1 D1,3,1 D1,4,1

DQ1 Y1,1,2 Y1,2,2 Y1,3,2 Y1,4,2 DQ1 D1,1,2 D1,2,2 D1,3,2 D1,4,2

. . .. . . .. . . .. . . .. . . .. . . .. . . .. . . ..Y1,1,r Y1,2,r Y1,3,r Y1,4,r D1,1,r D1,2,r D1,3,r D1,4,r

Y2,1,1 Y2,2,1 Y2,3,1 Y2,4,1 D2,1,1 D2,2,1 D2,3,1 D2,4,1

DQ2 Y2,1,2 Y2,2,2 Y2,3,2 Y2,4,2 DQ2 D2,1,2 D2,2,2 D2,3,2 D2,4,2

. . .. . . .. . . .. . . .. . . .. . . .. . . .. . . ..Y2,1,r Y2,2,r Y2,3,r Y2,4,r D2,1,r D2,2,r D2,3,r D2,4,r

. . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .Yp,1,1 Yp,2,1 Yp,3,1 Yp,4,1 Dp,1,1 Dp,2,1 Dp,3,1 Dp,4,1

DQp Yp,1,2 Yp,2,2 Yp,3,2 Yp,4,2 DQp Dp,1,2 Dp,2,2 Dp,3,2 Dp,4,2

. . .. . . .. . . .. . . .. . . .. . . .. . . .. . . ..Yp,1,r Yp,2,r Yp,3,r Yp,4,r Dp,1,r Dp,2,r Dp,3,r Dp,4,r

Figure 7. Boxplot of one students’ party DQ (facility) by IR level



external relationship and curriculum. The mean and standard deviation of each cell in the

experiment for the students’ party are provided in Table 4. A similar procedure to deter-

mine the most sensitive DQ was also applied for employers’ party and lecturers’ party.

Results and Analysis

From each of the QFD matrices analysis, the DQs can be sorted out in order of importance

so that it can be revealed what matters most for each customer, while the alternative sol-

utions to the department can be derived from reading the score/rank of the Quality

Characteristics. For example, based on the level of importance, the attribute that

matters most to the employers of the graduates was the ‘Interpersonal Skill’ of which

the subgroups, consecutively from the highest level of importance, were ‘responsibility’,

‘honesty’, ‘communication skill’, ‘personality’, and ‘loyalty’. While the primary alterna-

tive solutions for the employers’ party, namely, the QCs which have high ranks were ‘to

give more team assignment’ and ‘leadership training’, ‘get involved in committee

Figure 8. Boxplot of all students’ party DQs at 5-time IR level

Table 4. Descriptive statistics for students’ party Dijk

Students’ Party PDQ

IR Level

2 3 4 5

Mean Stdev Mean Stdev Mean Stdev Mean Stdev

Lecturer 1.81 1.97 3.19 2.77 4.06 3.29 4.58 3.69Extracurricular 1.68 1.85 2.68 2.86 3.23 3.64 3.42 3.90Curriculum 0.39 1.05 0.58 1.29 0.58 1.29 0.65 1.31Location 0.77 1.50 0.87 1.65 0.97 1.96 1.16 2.45Administration 1.03 1.80 1.71 2.65 2.26 3.14 2.65 3.53External Relationship 0.39 0.88 0.74 1.55 0.71 1.53 0.71 1.53Facility 2.65 1.85 4.39 3.08 5.23 3.49 5.87 3.83



activities’, and ‘intensify discussion and presentation’. For other parties, similar analysis

was done accordingly.

Based on the gap analysis t-test (Zeithaml et al., 1990) that was conducted to check the

significant distance between the existing and the expected condition of the customer

requirements, it was concluded that most of the requirements have a significant gap,

except for some attributes (‘lecturers’ working atmosphere’, ‘students’ campus location’

and ‘extracurricular’).

The effect of changing the DQ in the sensitivity analysis has provided an insight into the

alteration of the QC’s priority due to further changes of customers’ interest. For the stu-

dents’ party, the DQs that induced highest volatility in rank, consecutively, were ‘facility’,

‘lecturer’, ‘extracurricular’, ‘administration’, ‘location’, ‘external relationship’, and ‘cur-

riculum’. For the lecturers’ party, the DQs, in order, from the most sensitive, were ‘facil-

ity’, ‘working environment’, ‘curriculum’, and ‘bureaucracy’. While for the employers’

party, the DQs that caused highest instability in rank, consecutively, were ‘academic qua-

lification’, ‘problem solving skill’, ‘interpersonal skill’, and ‘leadership skill’. The DQs, of

which variability may significantly affect the final output/decision of the QFD team, may

provide the education institution with important information to formulate their strategy.

Furthermore, it is also useful for determining which DQs are critical to improve quality

in the future.

Conclusions

This paper has proposed a methodology for improving the quality of higher education

institutions using the QFD and AHP. Some points to highlight in improving the use of

the QFD in higher education, which have been discussed in this study, are:

. It is important to use a relative measurement rather than a set of absolute values for

representing the importance values of customer requirements in QFD, and the AHP

can be considered as a powerful tool to serve this purpose.

. Considerable attention should be paid to obtain a group preference. Using a geometric

mean would generally be better compared to using an arithmetic mean in the case where

the group acts synergistically towards a common goal.

. A careful check should be conducted to identify the true relationship between the DQs

and QCs in order to give a useful result. The QFD can be tailored to suit the particular

need of the users, for example, in determining how many HOQs to use. In addition, for

each customer, this study suggests that there should be one corresponding QFD

analysis.

For the case study, it can be concluded that endeavors which the higher education

institution should take as a main priority were to develop the overall facility, re-evaluate

existing curriculum, reduce unnecessary bureaucracy, improve lecturers’ qualifications,

and provide more leadership/team training. Furthermore, in order to design effective

and efficient strategies, other subsequent/downstream analyses can be added, such as

the gap analysis, Strengths-Weaknesses-Opportunity-Threat (SWOT) analysis, optimiz-

ation with respect to cost constraint (Lai et al., 2007) and so forth.

Alternative solutions that are generated from the QFD depend fully on the level of

importance of the customer requirements. Any changes in the Demanded Quality’s



level of importance will alter the priority order of the alternative solutions. The HOQ

should be adjusted and updated when there are some changes in the existing condition,

and it is very possible to add more Quality Characteristics to the HOQ. This is another

advantage of using the QFD, that is, it is very useful in generating new or innovative

QCs to meet the customer requirements. Finally, the sensitivity analysis may serve as a

useful tool to anticipate the changes or variability in the DQ, and to provide the necessary

information to better meet the future needs of the customers, and thus it enables the

education institution to be alert, proactive, and forward thinking (Kuo, 2006).

Acknowledgement

This research is partially supported by funding from Centre for Design Technology,

National University of Singapore.

References

Akao, Y. (1990) Quality Function Deployment: Integrating Customer Requirements into Product Design

(Cambridge, MA: Productivity Press).

Akao, Y. & Mazur, G. H. (2003) The leading edge in QFD: past, present and future, International Journal of

Quality & Reliability Management, 20(1), pp. 20–35.

Armacost, R. L. et al. (1994) An AHP framework for prioritizing customer requirements in QFD: an industrial-

ized housing application, IIE Transactions, 26(4), pp. 72–79.

Aytac, A. & Deniz, V. (2005) Quality function deployment in education: a curriculum review, Quality &

Quantity, 39, pp. 507–514.

Bailey, D. & Bennett, J. V. (1996) The realistic model of higher education, Quality Progress, November, pp. 77–79.

Belton, V. & Gear, T. (1985) The legitimacy of rank reversal – a comment, Omega, 13(3), pp. 143–144.

Bier, I. D. & Cornesky, R. (2001) Using QFD to construct a higher education curriculum, Quality Progress, April,

pp. 64–68.

Brackin, P. (2002) Assessing engineering education: an industrial analogy, International Journal of Engineering

Education, 18(2), pp. 151–156.

Chan, L. K. & Wu, M. L. (2002a) Quality function deployment: a literature review, European Journal of

Operational Research, 143, pp. 463–497.

Chan, L. K. & Wu, M. L. (2002b) Quality function deployment: a comprehensive review on its concepts and

methods, Quality Engineering, 15(1), pp. 23–35.

Chen, J. & Chen, J. C. (2001) QFD-based technical textbook evaluation – procedure and a case study, Journal of

Industrial Technology, 18(1), pp. 1–8.

Chen, S. H. & Yang, C. C. (2004) Applications of web-QFD and E-Delphi method in the higher education system,

Human Systems Management, 23, pp. 245–256.

Chou, S. M. (2004) Evaluating the service quality of undergraduate nursing education in Taiwan – using quality

function deployment, Nurse Education Today, 24, pp. 310–318.

Chuang, P. T. (2001) Combining the analytic hierarchy process and quality function deployment for a location

decision from a requirement perspective, International Journal of Advanced Manufacturing Technology,

18, pp. 842–849.

Cohen, L. (1995) Quality Function Deployment: How to Make QFD Work for You (Addison-Wesley).

Dahlgaard, J. J. et al. (1995) Total quality management and education, Total Quality Management, 6(5–6),

pp. 445–455.

Duffuaa, S. O. et al. (2003) Quality function deployment for designing a basic statistics course, International

Journal of Quality & Reliability Management, 20(6), pp. 740–750.

Ermer, D. S. (1995) Using QFD becomes an educational experience for students and faculty, Quality Progress,

28(5), pp. 131–136.

Forman, E. & Peniwati, K. (1998) Aggregating individual judgments and priorities with the analytic hierarchy

process, European Journal of Operational Research, 108, pp. 165–169.



Govers, C. P. M. (2001) QFD not just a tool but a way of quality management, International Journal of Pro-

duction Economics, 69, pp. 151–159.

Grant, D. et al. (2002) Quality management in US Higher education, Total Quality Management, 13(2), pp. 207–

215.

Griffin, A. & Hauser, J. R. (1993) The voice of the customer, Marketing Science, 12(1), pp. 1–27.

Hauser, J. R. & Clausing, D. (1988) The House of Quality, Harvard Business Review, 66(3), pp. 63–73.

Hwarng, H. B. & Teo, C. (2000) Applying QFD in higher education, ASQ’s 54th Annual Quality Congress

Proceedings, pp. 255–263.

Hwarng, H. B. & Teo, C. (2001) Translating customers’ voices into operations requirements: a QFD application

in higher education, International Journal of Quality & Reliability Management, 18(2), pp. 195–225.

Jaraiedi, M. & Ritz, D. (1994) Total quality management applied to engineering education, Quality Assurance in

Education, 2(1), 32–40.

Kanji, G. K. & Tambi, A. M. B. A. (1999) Total quality management in UK higher education institution, Total

Quality Management, 10(1), pp. 129–153.

Kaminski, P. C. et al. (2004) Evaluating and improving the quality of an engineering specialization program

through the QFD methodology, International Journal of Engineering Education, 20(6), pp. 1034–1041.

Kauffmann, P. et al. (2002) Using quality function deployment to select the courses and topics that enhance

program effectiveness, Journal of Engineering Education, 91(2), pp. 231–237.

Kim, K. J. et al. (2003) A synopsis of recent methodological enhancements on quality function deployment,

International Journal of Industrial Engineering, 10(4), pp. 462–466.

Kim, K. J. et al. (2007) Robust QFD: framework and a case study, Quality and Reliability Engineering Inter-

national, 23 (1), pp. 31–44.

Koksal, G. & Egitman, A. (1998) Planning and design of industrial engineering education quality, Computers &

Industrial Engineering, 35(3–4), pp. 639–642.

Kuo, W. (2006) Assessment for U.S. engineering programs, IEEE Transactions on Reliability, 55(1), pp. 1–6.

Lam, K. & Zhao, X. (1998) An application of quality function deployment to improve the quality of teaching,

International Journal of Quality & Reliability Management, 15(4), pp. 389–413.

Lai X. et al. (2007) Optimizing product design using quantitative quality function deployment: a case study,

Quality and Reliability Engineering International, 23(1), pp. 45–57.

Lu, M. et al. (1994) Integrating QFD, AHP, and benchmarking in strategic marketing, Journal of Business &

Industrial Marketing, 9(1), pp. 41–50.

Madu, C. N. et al. (1994) TQM in the university: a quality code of honor, Total Quality Management, 5(6), pp.

375–390.

Mergen, E. et al. (2000) Quality management applied to higher education, Total Quality Management, 11(3), pp.

345–352.

Nakui, S. (1991) Comprehensive QFD system, Transactions from The Third Symposium on Quality Function

Deployment, Novi, Michigan.

Owlia, M. S. & Aspinwall, E. M. (1998) Application of quality function deployment for the improvement of

quality in an engineering department, European Journal of Engineering Education, 23(1), pp. 105–125.

Pitman, G. et al. (1996) QFD application in an educational setting, International Journal of Quality & Reliability

Management, 13(4), pp. 99–108.

Raharjo, H. & Dewi, D. R. S. (2003) Application of analytic hierarchy process in quality function deployment for

improving quality at industrial engineering department university ‘X’. The 7th International Symposium on

the Analytic Hierarchy Process, Bali, Indonesia, August 7–9, 2003, Indonesia.

Raharjo, H. & Endah, D. (2006) Evaluating relationship of consistency ratio and number of alternatives on rank

reversal in the AHP, Quality Engineering, 18(1), pp. 39–46.

Raharjo, H. et al. (2006) Prioritizing quality characteristics in dynamic quality function deployment, Inter-

national Journal of Production Research, 44(23), pp. 5005–5018.

Ramanathan, R. & Ganesh, L. S. (1994) Group preference aggregation methods employed in AHP: an evaluation

and intrinsic process for deriving members’ weightages, European Journal of Operational Research, 79,

pp. 249–265.

Reavill, L. R. P. (1997) Quality assessment and the stakeholder model of higher education, Total Quality

Management, 8(2–3), pp. 246–252.

ReVelle, J. B. et al. (1997) The QFD Handbook (New York: Wiley).

Sa, P. M. E. & Saraiva, P. (2001) The development of an ideal kindergarten through concept engineering/ quality

function deployment, Total Quality Management, 12(3), pp. 365–372.



Shillito, M. L. (1994) Advanced QFD: Linking Technology to Market and Company Needs (New York: Wiley).

Saaty, T. L. (1980) The Analytic Hierarchy Process (New York: McGraw-Hill).

Saaty, T. L. (1988) Multicriteria Decision Making, The Analytic Hierarchy Process, Planning, Priority, Setting,

Resource Allocation (Pittsburgh: RWS Publications).

Saaty, T. L. (1990) An exposition of the AHP in reply to the paper Remarks on the Analytic Hierarchy Process,

Management Science, 36(3), pp. 259–268.

Saaty, T. L. (2006) Rank from comparisons and from ratings in the analytic hierarchy/network processes,

European Journal of Operation Research, 168, pp. 557–570.

Saaty, T. L. & Vargas, L. G. (1984) The legitimacy of rank reversal, Omega, 12(5), pp. 513–516.

Sahney, S. et al. (2004) A SERVQUAL and QFD approach to total quality education: a student perspective, Inter-

national Journal of Productivity and Performance Management, 53(2), pp. 143–166.

Sahney, S. et al. (2006) An integrated framework for quality in education: application of quality function deploy-

ment, interpretive structural modelling and path analysis, Total Quality Management, 17(2), pp. 265–285.

Sakthivel, P. B. & Raju, R. (2006) Conceptualizing total quality management in engineering education and

developing a TQM educational excellence model, Total Quality Management, 17(7), pp. 913–934.

Sirvanci, M. (1996) Are students the true customers of higher education, Quality Progress, October, pp. 99–102.

Wallace, J. (1999) The case for student as customer, Quality Progress, February, pp. 47–51.

Wang, H. et al. (1998) A comparative study of the prioritization matrix method and the analytic hierarchy process

technique in quality function deployment, Total Quality Management, 9(6), pp. 421–430.

Wasserman, G.S. (1993) On how to prioritize design requirements during the QFD planning process, IIE

Transactions, 25(3), pp. 59–65.

Widrick, S. M. et al. (2002) Measuring the dimension of quality in higher education, Total Quality Management,

13(1), pp. 123–131.

Willis, T. H. & Taylor, A. J. (1999) Total quality management and higher education: the employers’ perspective,

Total Quality Management, 10(7), pp. 997–1007.

Xie, M. et al. (1998). A sensitivity study of the sensitivity of ‘Customer Voice’ in QFD analysis, International

Journal of Industrial Engineering – Applications and Practices, 5(1), pp. 301–307.

Xie, M. et al. (2003). Advanced QFD Applications (Quality Press, ASQ).

Zeithaml, V. A. et al. (1990) Delivering Quality Service: Balancing Customer Perceptions And Expectations

(New York: Free Press).



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