PLEASE SCROLL DOWN FOR ARTICLE This article was downloaded by: [TÜBİTAK EKUAL] On: 1 October 2010 Access details: Access Details: [subscription number 786636097] Publisher Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37- 41 Mortimer Street, London W1T 3JH, UK Total Quality Management & Business Excellence Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713447980 A Methodology to Improve Higher Education Quality using the Quality Function Deployment and Analytic Hierarchy Process Hendry Raharjo ab ; Min Xie a ; Thong Ngee Goh ab ; Aarnout C. Brombacher bc a Department of Industrial and Systems Engineering, National University of Singapore, b Centre for Design 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 to Improve Higher Education Quality using the Quality Function Deployment and Analytic Hierarchy Process', Total Quality Management & Business Excellence, 18: 10, 1097 — 1115 To link to this Article: DOI: 10.1080/14783360701595078 URL: http://dx.doi.org/10.1080/14783360701595078 Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
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PLEASE SCROLL DOWN FOR ARTICLE
This article was downloaded by: [TÜBİTAK EKUAL]On: 1 October 2010Access details: Access Details: [subscription number 786636097]Publisher RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Total Quality Management & Business ExcellencePublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713447980
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
Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf
This article may be used for research, teaching and private study purposes. Any substantial orsystematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply ordistribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss,actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directlyor indirectly in connection with or arising out of the use of this material.
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-
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
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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
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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
1100 H. Raharjo et al.
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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)
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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,
1102 H. Raharjo et al.
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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
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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.
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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
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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.
1106 H. Raharjo et al.
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Figure 4. Trimmed part of HOQ for students’ party
Figure 5. Trimmed part of HOQ for lecturers’ party
A Methodology to Improve Higher Education Quality 1107
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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