J. Software Engineering & Applications, 2010, 3: 268-272
doi:10.4236/jsea.2010.33032 Published Online March 2010 (http://www.SciRP.org/journal/jsea)
A Study on Development of Balanced Scorecard
for Management Evaluation Using Multiple
Attribute Decision Making
Kwang Mo Yang1, Young Wook Cho2, Seung Hee Choi3, Jae Hyun Park4, Kyoung Sik Kang5
1Department of Industrial Engineering, Yuhan University, Puchoen, Korea; 2Department of Technology & Systems Management, Induk
Institute of Technology, Seoul, Korea; 3Department of Industrial & Manufacturing Engineering, Pennsylvania State University, Penn-
sylvania, USA.; 4Department of Qualification Trend Analysis, Human Resource Development Service, Seoul, Korea; 5Department of
Industrial & Management Engineering, Myongji University, Yongin, Korea.
Received September 22nd, 2009; revised October 12th, 2009; accepted October 20th, 2009.
ABSTRACT
Recently, most businesses have introduced a system for improving their responsibility to the customers in terms of job
improvement. For example, small-quantity batch production increases cost but improve efficiency of management.
Companies have been introduced the balanced scorecard to evaluate their management as part of improvement, while
they suffer from many trials and errors. Many businesses still have difficulty in introducing balance scorecard concept in
their process, but we suggest a method to successfully introduce the balance scorecard. This study aims to suggest a new
performance measurement model reflecting relative importance of the key performance indicators for each factor. Our
model is applied to several companies in real-world to validate the new model. Also, our study proposes a methodology
for an adequate performance measurement using multiple attribute decision making.
Keywords: Balanced Scorecard, Multiple Attribute Decision Making, Management Evaluation
1. Introduction
A large number of small and medium enterprises have
realized it is necessary to employ a management evalua-
tion system for increasing competitiveness, renovating
their business system, and decreasing the cost. Unfortu-
nately, the efforts and investment on the system do not
seem to lead to the output. Consequently, it is necessary to
develop a new methodology for efficient implementation
and maintenance of a management evaluation system for
reflecting both the department objective and the entire
business goal. Balanced Scorecard (BSC) is a deliberately
selected balanced set of measures derived from the vision
and strategies that represent a tool for leaders to use in
communicating strategies to the organization and moti-
vating change [1]. Multiple attribute decision making
(MADM) is one of the decision making methods to
choose the alternative under multiple attributes [2]. If the
BSC measuring achievement is applied to MAMD, a
business could consider each attribute based on not the
each department, but the vision and strategy of the entire
business. Thus, this paper will suggest the method using
BSC enabling to evaluate a management for insuring
productivity in the real MAMD problem including more
alternatives and attribute.
2. Related Work
2.1 Balanced Scorecard (BSC)
The current business environment is an era of mega-
competition absolutely requiring a great measurement
process and an excellent management method of admini-
stration performance. Measurement is a key factor in
management. Kaplan and Norton [1] emphasized that the
importance of performance measure by saying “You
cannot control what you cannot measure.” Balanced Score-
card is a deliberately selected balanced set of measures
derived from the vision and strategies that represent a
tool for leaders to use in communicating strategies to the
organization and motivating change [1]. The concept of
performance measure is accepted by private companies,
first, and then, it has been spread to public institutions
and non-profit organizations. The performance measure-
ment system was traditionally limited to appearance em-
phasizing and growth-oriented aspects and financial
measurement factors. However, the performance measure-

A Study on Development of Balanced Scorecard for Management Evaluation using Multiple Attribute Decision Making269
ment of an organization based on financial factors has
showed a limitation as a means for delivering informa-
tion on the quality and performance of administration.
Many researchers have studied performance measure-
ment based on financial factors to overcome the limita-
tion. Currently, many companies have noted the BSC
proposed by Kaplan and Norton [1] and have gradually
applied and operated the BSC. For example, the research
on the administration performance measurement method
and management has been studied, actively, and there
were remarkable development. However, the applied
area is still limited to human resource organization. In
this study, we estimate the weight reflecting the adequate
importance of the BSC for lower Key Performance Indi-
cators (KPI) (Figure 1) by using a Multiple Attribute
Decision Making (MADM) based on the analysis of ad-
ministration environments of company. In other words,
we consider the weight reflecting practical features and
suggest a desirable performance measurement model
based on the weight. This study aims to suggest a new
performance measurement model reflecting relative im-
portance for the KPI for each aspect and apply the new
performance measurement model to real business envi-
ronment to validate the effect of the new model, identify
any limitation, and suggest a methodology for proper
performance measurement.
2.2 Multiple Attribute Decision Making
(MADM)
Multiple attribute decision making (MADM) is one of
the decision making methods to choose the alternative
under multiple attributes [3]. An MADM problem could
occur when we understand the management situation.
Since a number of conflict factors are caused by the lim-
ited resources, MADM allows a decision maker to de-
termine the factor among the variables with multi-attribute
or the optimal environment to operation situation. To
solve an MADM problem with a numerical approach,
Barron and Schmidt [4] attempted to solve a problem
with distance or fuzzy index. Dyer and Sarin [5], French
[6], Haimes and Chankong et al. [7] suggested the inter-
active approach to improve the method using multi-
objective liner programming. However, it was hard to
keep the consistency and to guarantee the optimal solu-
tion. Analytic Hierarchy Process (AHP) [8,9] and Elimi-
nation Et Choice Translating Reality(ELECTRE) [10]
became more complicated because the more attributes,
the more coefficient by geometric progression. Cho [3]
described the method to determine the optimal plant in
the MADM problem having mixed attributes, such as
nominal-the-better type, smaller-the-better type, and lar-
ger-the-better type. Although the method can not only
decide the optimal plant but also solve the MADM hav-
ing mixed attributes, it is possible only if each attribute is
independent. In this paper, we put the priority on the
management variables having the high mean value and
the low difference of the weights of a certain factor by
experts to understand management situation with Process
Capability Index. Thus, we will suggest the method using
BSC enabling to evaluate a management for insuring
productivity in a real MADM problem including more
alternatives and attributes.
3. Management Evaluation Formula
Management evaluation method is based on balanced
scorecard with multiple attribute. It is consider the sub-
jective and objective attributes (Figure 2). The objective
attributes are the element calculated with the target data
and real data observed. The subjective attributes are the
sub variables under the basic four aspects in the BSC.
The v is defined as decision making matrix, having each
Figure 1. Example of KPI and four aspects of the BSC

A Study on Development of Balanced Scorecard for Management Evaluation using Multiple Attribute Decision Making 270
Figure 2. BSC checklist form (per department)
department of m and reconsideration attribute of l con-
nected with this as following:
where, Ai = ith department, Xj = jth attribute, xij = evalua-
tion value Xj of for attribute in department Ai.
3.1 Subjective Attribute Formula
3.1.1 Weighted Decision to Each Subjective Attribute
It is very difficult to assign the weight to each attribute in
an MADM problem. Since last selection crystallization
can change according to the weight given, the weight
should be assigned, deliberately. In this paper, we decide
the weight based on the suggestion of experts, and the
method determining the weight could be used for process
capacity index. Process capability is the process charac-
teristic ability that reflects how identical product can be
produce according to manufacturing process established
in product design process, which means uniformity of the
product. To estimate characteristic ability, various statis-
tical methods have suggested. Evaluating process capacity
by variables of process and specification limit of product
is known as process capacity analysis, and the process
capacity analysis can be expressed in terms of process
capability index (Cp). The process capability index is
developed based on the concept of 6σ and applied first to
industry field.
p66
USL LSLT
C



 (1)
For a single specification, the limit is defined as fol-
lowing.
For an upper specification, the limit is:
3
p
USL
C



 (2)
For a lower specification, the limit is:
3
p
LSL
C 


(3)
In this paper, we will use the process capability index
for a lower specification limit only. The weight for each
attribute is assigned by the data that several experts decide
to each attribute. The evaluation data of experts for each
attribute is determined by experts scoring from 9 to 1.
The mean of data (μ) that experts decide can be calcu-
lated. The lower specification limit is 1 that experts de-
cided absolute minimum, and the standard deviation that
each expert decides is σ which is following.
2
()
ˆ(p=1, 2,...., n)
1
jp
bb
sn 

  (4)
where, bjp is the mean that expert P decided data for each
attribute j. And then, in this paper, we put the priority
order on the attribute Xj having the high mean value and
the low difference of the weights by the decision of
experts. The values decided by experts are calculated by

A Study on Development of Balanced Scorecard for Management Evaluation using Multiple Attribute Decision Making271
Equation (4), normalized by Equation (5), and repre-
sented as NCp(Normalized Capability Index).
12
(...... )
pj
pj
p
pp
C
NP CC C  i

j
(5)
NCp is defined the weight for each attribute, and the
notation is replaced to w, where, w is under a certain cri-
terion, such as following:
12
1
1
,,.....,1
here, /
l
lj
j
l
jpi pi
j
www ww
wC C






(6)
3.2 Objective Attribute Formula
3.2.1 Normalization of Evaluation Value Matrix for
Objective Attribute
Evaluation value xij for attribute Xj in each department Ai
is considered as profit attribute or cost attribute by nor-
malized step, and the quantitative values of attributes are
also normalized in the same intervals. For example, the
profit attribute, high preference as evaluation value, is
normalized as following:
12
/ (......)
(1, 2,...,;1, 2,...,)
ij ijjjjmj
rxxxxix
imjl

 (7)
Otherwise, cost attribute, low preference as evaluation
value, is normalized as following:
12
(1 /)/[(1/)(1 /)...(1 /)...(1 /)
(1, 2,...,;1, 2,...,)
ij ijjjjmj
rx xxxix
imjl
 

here,0 1
ij
r (8)
And then, we can make matrix R as following based
on the normalized values.
This paper presents the process of calculation only for
the financial aspect factor, and the processes for the rest
aspects are regarded identical. If PR(F)i is preference rate
for department i, PR(F)i is weighted mean of attribute for
process i.
1
() ()
l
ij
j
PR FFwNF i


 (9)
where,
1
() 1
m
ii
PR F



NF(i)j is the normalized data of department in finan-
cial attribute j. According to the result calculated by
Equation (9) for each department, most high preference
rate had department select and then if free department is
department that had optimum the priority order as fol-
lowing:
12
max( )max(( ),( ),.....,( ))
im
PR FPRFPRFPRF

(10)
In this model, we assume each factor is independent
each other. Similarly, the data for customer aspect, in-
ternal process aspect, and learning/growing aspect can be
estimated. The proposed model has evaluated in a de-
partment performance in management environment. The
result is applied to the simulated operation of CLV (Cus-
tomer Lifetime Value) [11]. The primary evaluation crite-
rion is the BSC which consists of financial aspect, cus-
tomer aspect, internal process aspect, and learn-
ing/growing aspect. Also, in this paper, it will be able to
be applied to MADM for deciding weigh of each variable.
Therefore, we can summary the step to evaluate data for
each department as following:
Step 1) The variables to assign weight are divided into
financial aspect, customer aspect, internal process aspect,
and learning/growing aspect.
Step 2) The data rank of each decided variable is de-
termined by Group Consensus.
Step 3) Find sub-variables in the higher level variables.
The sub-variables can be changed by the condition of the
enterprise.
Step 4) Assign the weight for each factor by using
MADM.
Step 5) Decide total evaluation data for the department
by using Equation (11).
() ()
() ()
TotalEvaluation DataaPRFiPRCi
PR PiPR Li



 (11)
here
0 < α < 1, 0 < β < 1, 0 < γ < 1, 0 < δ < 1
α + β+ γ + δ = 1,
Fi > 0, Pi > 0, Li > 0, Ci > 0
where
α: financial aspest weight
β: customer aspect weight
γ: internal process aspect weight
δ: learning/growing aspect weight
PR(Fi): preference rate of financial aspect
PR(Ci): preference rate of customer aspect
PR(Pi): preference rate of internal process aspect
PR(Li): preference rate of learning/growing aspect
The result in Figure 3 is drawn by applying the sug-
gested balanced scorecard to a real company. In Figure 3,
we compare the evaluation data for each aspect and show
the total evaluation data.

A Study on Development of Balanced Scorecard for Management Evaluation using Multiple Attribute Decision Making 272
Figure 3. Result of management evaluation data for each department in a company
4. Conclusions
Among the methods to solve multiple attribute decision
making with balanced scorecard for management evalua-
tion for a department, numerical approaches offer the
optimal solution, but fail to reflect the opinions of deci-
sion makers and CEO. Besides the current interactive
approaches making up for the weak point fail to keep the
consistency and to insure the optimal solution since it is
hard to consider the entire alternative and attribute, si-
multaneously. Moreover, the increase in the number of
attributes grows the amount of information due to pair
wise comparison between the alternatives. In this paper,
we propose the balanced scorecard method to assign the
attribute weight by an expert group in the multiple attrib-
ute decision making including more alternatives and at-
tributes. Also, we suggest the management evaluation
method that assigns more weight on the attribute having
the high mean weight by experts and the low difference
or consensus in the evaluation. The proposed method
could contribute to developing a good approach to re-
flecting both the optimal solution and the strategy of the
entire business.
REFERENCES
[1] R. S. Kaplan and D. P. Norton, “Transforming the balanced
scorecard from performance measurement to strategy,”
Accounting Horizons, Vol. 15, No. 2, pp. 87–104, 2001.
[2] K.-M. Yang, Y.-W. Wook, and J.-H. Park, “A study on
evaluation method for process safety using multiple
attribute decision making,” 38th International Conference
on CIE, Las Vegas, USA, pp. 2274–2278, 3–6 August 2008.
[3] Y.-W. Cho, “Selecting the optimal preferred facilities with
multiple characteristics using Loss Function,” Korea
Safety Management & Science, Vol. 2, No. 2, pp.
127–135, 2002.
[4] H. Barron and C. P. Schmidt, “Sensitivity analysis of
additive multi-attribute value models,” Operations Research,
Vol. 36, pp. 122–127, 1988.
[5] J. S. Dyer and R. K. Sarin, “Measurable multi-attribute
value functions,” Operations Research, Vol. 27, No. 4, pp.
810–822, 1979.
[6] S. French, “Decision theory: An introduction to the
mathematics of rationality,” Ellis Horwood Series in
Mathematics and its Applications, pp. 448, 1986.
[7] Y. Y. Haimes and V. Changkong, “Decision making with
multiple objectives,” Lecture Notes in Economics and
Mathematical Systems, Springer-Verlag, New York, No.
242, pp. 388–399, 1985.
[8] L. C. Lawrence and C. Dong, “On the efficacy of modeling
multi-attribute decision problems using AHP and Sinarchy,”
Eurpean Journal of Operational Research, Vol. 132, No. 1,
pp. 39–49, 2001.
[9] T. L. Saaty, “A scaling method for priorities in hierarchical
structures,” Journal of Mathematical Psychology, Vol. 15,
No. 3, pp. 234–281, 1977.
[10] P. T. Harker and L. G. Vargas, “The theory of ratio scale
estimation: Saaty’s analytic hierarchy process,” Management
Science, Vol. 33, No. 11, pp. 1383–1403, 1987.
[11] K.-M. Yang, S.-H. Choi, J.-H. Park, and K.-S. Kang,
“Development of correlation weight customer lifetime
value using analytic hierarchy process,” 36th International
Conference on CIE, Taipei, Taiwan, pp. 5461–5465,
20–23 June 2006.