TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol.12, No.4, April 201
4, pp. 2914 ~ 2
9
2
3
DOI: http://dx.doi.org/10.11591/telkomni
ka.v12i4.4804
2914
Re
cei
v
ed Se
ptem
ber 13, 2013; Revi
se
d No
vem
ber
8, 2013; Acce
pted No
vem
b
er 23, 201
3
A Novel Balanced Scorecard Design Based on
Fuzzy
Analytic Network Process and
its Application
Cao Yuhon
g
*
1
, You Jianxin
2
Schoo
l of Econ
omics an
d Man
agem
ent T
ongj
i Univers
i
t
y
, Sh
ang
hai, C
h
in
a
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: ange
lca
oni
un
iu@si
na.com
1
, hu
w
e
i3
32
0@si
na.com
2
A
b
st
r
a
ct
In this
pap
er,
w
e
prop
ose
a
nove
l
b
a
l
ance
d
scor
e
card
d
e
sig
n
b
a
se
d o
n
fu
zz
y
ana
lyti
c netw
o
r
k
process
an
d th
en c
o
n
duct p
e
r
f
orma
nce
ev
al
uatio
n thr
oug
h
a cas
e
stu
d
y.
After ana
ly
z
i
n
g
the r
e
l
a
ted w
o
rks
abo
ut bal
anc
e
d
scorecar
d
d
e
sig
n
an
d the
alg
o
rith
m of
fu
zz
y
a
nalytic n
e
tw
ork proces
s, w
e
illustrate the
improve
d
b
a
la
nced sc
orecar
d des
ign.
F
i
rs
tly, the basic
conce
p
ts for the ba
la
nced
scorecar
d
s a
r
e
introd
uced. Se
cond
ly, four p
e
rspec
tiv
e
s of
the bal
anc
ed
scorecards
d
e
sig
n
are pr
ov
ide
d
. T
h
irdly, the
meth
od t
o
pr
o
m
ote
the
perf
o
rmanc
e of b
a
l
anc
ed sc
or
ec
ard d
e
si
gn thr
oug
h the
fu
zzy ana
lytic n
e
tw
ork
process
is d
e
m
o
n
strated. In
the pr
op
osed
desi
gn,
a fu
zz
y
nu
mb
er is
r
epres
ente
d
by
the l
e
ft an
d ri
gh
t
formati
on of e
a
ch de
gre
e
of me
mb
ersh
ip i
n
fu
zz
y
a
n
a
l
yti
c
netw
o
rk process. F
u
rthermor
e
, the de
g
r
e
e
possi
bil
i
ty for a
convex fu
zz
y
nu
mb
er to b
e
l
a
rger
th
an
a gi
ven co
nvex fu
zz
y
n
u
m
b
e
rs ca
n be r
epr
esent
e
d
by an
effective
sche
m
e. Parti
c
ularly, th
e ba
sic struct
ure i
n
the fu
zz
y
an
a
l
ytic netw
o
rk p
r
ocess
mo
del
i
s
orga
ni
z
e
d
hi
er
archic
ally, a
n
d
the loca
l w
e
i
ghts of
the strategi
es, bal
an
ced scorec
a
rd
perspectiv
e
s
an
d
perfor
m
a
n
ce i
ndic
a
tors can
be o
b
tain
ed
b
y
matrix co
mp
uting. F
i
n
a
lly,
a case stu
d
y of colle
ge E
n
g
lis
h
classro
o
m tea
c
hin
g
q
uantit
ative ev
al
uatio
n
is giv
en t
o
d
e
monstrate
th
e perfor
m
anc
e
of the
prop
o
s
ed
bal
ance
d
scor
e
card
desi
gn.
Experi
m
ental r
e
sults sh
ow
that the pro
pose
d
bal
anc
ed sc
orecar
d des
ign
i
s
quite effective.
Ke
y
w
ords
:
ba
l
ance
d
scorec
a
rd, fu
zz
y
an
alyt
ic netw
o
rk proc
ess, fu
zz
y
nu
mber, case study
, index w
e
ig
ht
Copy
right
©
2014 In
stitu
t
e o
f
Ad
van
ced
En
g
i
n
eerin
g and
Scien
ce. All
rig
h
t
s reser
ve
d
.
1. Introducti
on
The Balan
c
e
d
Scorecard is propo
se
d in
1992
by p
r
of
essor
Ro
bert
S. Kaplan an
d David
P. Norton, a
nd it has
be
come
one
of the
most ex
pedie
n
t man
ageme
n
t met
hod
s on
sev
e
ral
resea
r
ch field
s
. At the
high
est
con
c
e
p
tu
al level,
b
a
la
nce
d
score
c
a
r
d refers
to
a
frame whi
c
h
can
be u
s
ed to
h
e
lp the o
r
g
a
n
i
zation
s to tra
n
sp
ose the
st
rategy into
o
peratio
nal o
b
j
e
ctives, in
order
to direct both
the organi
za
tion performa
n
ce an
d beh
avior. A success factor in i
m
pleme
n
ting the
Balanced Score
c
a
r
d is rep
r
esented by t
he use of ded
icated
softwa
r
e tools [1].
The bal
an
ce
d scorecard
refers to a
perfo
rma
n
c
e me
asure
m
ent syste
m
whi
c
h
sup
p
leme
nts traditio
nal
system
s
wit
h
the
crit
eri
a
that m
e
a
s
ure
pe
rform
ance from t
h
ree
addition
al
pe
rspe
ctives,
in
cluding 1) cu
st
omer pe
rs
pe
ctive, 2) inte
rnal bu
sin
e
ss
perspe
c
tive a
n
d
3) inn
o
vation
and le
arni
ng
perspe
c
tive [2-4]. Fu
rthermore, Kapl
an
and
Norto
n
prop
osed a t
h
ree
layered
struct
ure fo
r the fo
ur p
e
rspe
ctives: 1
)
mi
ssi
o
n
(to b
e
come
the cust
om
ers’ mo
st p
r
eferre
d
sup
p
lier), 2
)
obje
c
tives (to provid
e t
he
cu
stome
r
s
with n
e
w
prod
uct
s
) an
d 3) me
asures
(pe
r
centag
e of
turn
over g
enerat
ed
by
new p
r
odu
cts). The
b
a
lan
c
ed
scorecard
is d
e
si
gne
d t
o
be
a pe
rforman
c
e
mea
s
u
r
e
m
ent
system
and
a
pla
n
n
ing and
co
ntrol device.
The
r
efo
r
e, some
comp
anie
s
fo
und th
at the
measures on
a
balan
ce
d
score
c
a
r
d
ca
n be
u
s
e
d
a
s
the
co
rne
r
stone
of a manag
e
m
ent system
that commu
nicate
s st
rat
egy, aligns i
ndividual
s an
d teams to the
strategy, e
s
ta
blish
e
s l
ong t
e
rm
strate
gic target
s, align
s
initiatives,
a
llocate
s lo
ng
and
sho
r
t term
resou
r
ces a
n
d
finally, provides fee
dba
ck and learning
about the st
ra
tegy [1].
On the oth
e
r
hand, the
an
alytic network pro
c
e
ss i
s
a
more
gen
era
l
form of the
analytic
hierarchy process
whi
c
h is utilized
in m
u
lti-criteria de
cisi
on analysi
s
. Analytic hi
erarchy process
stru
ctures a
deci
s
io
n p
r
ob
lem into
a
hi
era
r
chy with
a go
al, de
ci
si
on
crite
r
ia, a
nd alte
rnativ
es,
while th
e an
alytic network p
r
ocess
st
ructu
r
e
s
it a
s
a n
e
two
r
k.
Both then
use
a sy
ste
m
of
pairwise com
pari
s
on
s
to measure
the
weig
hts of
th
e comp
onent
s of
the
structure, a
nd th
e
n
to
ran
k
th
e alte
rnatives i
n
th
e
de
cisi
on [
5
-8
]. In
this pap
er, we propo
sed
a
novel
analytic network
process and
utilize it is in t
he design of
balan
ced scorecard.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
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046
A N
o
v
e
l Balanc
ed Sc
or
ec
ar
d D
e
s
i
gn Bas
ed on
Fuzzy Analy
t
ic
Netw
ork
Pr
oc
es… (
C
ao Yuhong)
2915
The main in
n
o
vations of th
is pap
er lie in
the following
asp
e
ct
s:
(1) A m
odifi
ed fuzzy an
alytic network p
r
o
c
e
ss i
s
prop
osed t
o
ma
ke the
balan
ce
d
s
c
o
r
ec
ar
d
des
ig
n
.
(2) We ch
oo
se
bala
n
ced scorecard
pe
rspe
ctiv
es
an
d the pe
rform
ance indi
cato
rs
ba
sed
on the
fou
r
perspe
c
tives,
incl
udin
g
1
)
Cu
stom
er
p
e
rspe
ctive, 2
)
Internal
bu
sine
ss p
r
o
c
e
s
s
perspe
c
tive, 3) Lea
ning a
n
d
gro
w
th pe
rspective, and
4) Fina
nci
a
l p
e
rspe
ctive.
(3) T
he ba
sic
stru
cture the fu
zzy analytic
netwo
rk p
r
o
c
ess model in
a hiera
r
chi
c
al
form.
(4) The
lo
cal weights
of the
strat
egie
s
, bala
n
c
ed
sco
r
e
c
a
r
d p
e
rsp
e
cti
v
es a
n
d
perfo
rman
ce i
ndicators ca
n
be comp
uted
by
pairwi
s
e compa
r
ison m
a
trice
s
effe
ctively.
(5) Th
e inn
e
r dep
end
en
ce
matrix of
ea
ch
persp
ectiv
e
is dete
r
min
ed by th
e fu
zzy scal
e
according to the pro
p
o
s
ed f
our bal
an
ced
score
c
a
r
d p
e
rspe
ctives.
The rest of t
he pa
per i
s
o
r
gani
ze
d a
s
the follo
wing
se
ction
s
. Section 2 intro
d
u
c
e
s
the
related
wo
rks. Section 3 ill
ustrate
s
the
prop
os
e
d
s
c
he
me
fo
r
ba
lan
c
ed
sc
or
ec
ar
d
d
e
s
i
gn
b
a
se
d
on fuzzy an
alytic network process. In se
ct
ion
4, experim
ent
s are cond
u
c
ted to ma
ke
perfo
rman
ce
evaluation
wi
th comp
ari
s
o
n
to ot
her existing meth
od
s. Finally, we
con
c
lu
de th
e
whol
e pape
r i
n
se
ction 5.
2. Related Works
Balanced score
ca
rd belo
ngs to one o
f
the most particul
a
r met
hod
s for perf
o
rma
n
ce
measurement
in seve
ral a
pplication fiel
ds. Ba
lan
c
e
d
score
c
a
r
d
propo
se
s a ge
neral f
r
ame
w
ork
whi
c
h h
a
s be
en u
s
ed
mo
re preci
s
ely b
y
many re
se
a
r
ch
ers. Mo
re
over, bal
an
ce
d sco
r
e
c
ard a
s
a
gene
ral fram
ewo
r
k h
a
s b
e
en adapte
d
b
y
many practi
tioners to spe
c
ific implem
e
n
tation are
a
s.
In
the following part
s
,
we
will intr
oduce t
he related works about
t
he appli
c
ations
of bal
anced
s
c
o
r
ec
ar
d
Cattinelli
et al
. sho
w
e
d
the
potential
of th
e
p
r
opo
se
d
method
s th
ro
ugh ill
ustrative results
derived
from
the analy
s
is
of BSC data
of 109
FME
clinics in th
ree
co
untrie
s
. T
he auth
o
rs
were
able to
ide
n
tify the pe
rform
ance
d
r
ivers f
o
r
spe
c
ific g
r
oup
s of
c
lini
c
s a
nd to
di
stingui
sh
betwe
en
cou
n
trie
s
wh
ose
pe
rform
a
nce
s
a
r
e
likel
y to impr
ove
from th
ose
wh
ere
a de
clin
e
in pe
rform
a
n
c
e
might be exp
e
cted [9].
Na
sser et al.
dealed
with a ca
se stu
d
y that
took pla
c
e in a nut
rio
nal thera
p
y company
from Janu
ary
to Novem
b
e
r
20
10. Fo
r a
nalysi
s
of
the
learni
ng a
n
d
gro
w
th p
e
rspective all
45
of
the compa
n
y's
collab
o
rators
were
con
s
id
ered
an
d for
client a
nalysi
s
1
24 h
o
me
-care
clie
nts
we
re
con
s
id
ere
d
. The study sa
mple co
nsi
s
t
ed of 39
coll
aborators an
d 44 client
s partici
pating i
n
the
research [10].
Lin et
al. ex
plore
d
the
u
s
e of a
ma
na
gem
ent
tool: balan
ce
d sco
r
ecard (BSC),
whi
c
h
facilitates ma
nage
rs to me
et multiple strategic
g
oals,
and fuzzy linguisti
c
metho
d
for evaluati
ng
OR p
e
rfo
r
ma
nce. BSC i
s
a strate
gic
pl
annin
g
and m
anag
ement system
that
is use
d
exten
s
ively
in bu
sine
ss a
nd ind
u
st
ry, govern
m
ent
and n
onp
ro
fit
org
ani
zation
s. First, a m
o
del is devel
o
ped
for mea
s
uri
n
g the accepta
b
le perfo
rma
n
ce of O
R
ba
sed o
n
the intera
ction finan
cial, cu
stome
r
s,
internal
bu
si
ness
process a
nd l
e
a
r
ni
ng a
nd
grow
th pe
rspe
ctive. Afte
r that,
BSC
stru
ct
ure
integrate
d
wit
h
fuzzy ling
u
i
s
tic is p
r
op
osed
for mea
s
u
r
ing an
d improving the se
rvice [11].
Mutale et al. applie
d the concept of bal
anced
sco
r
e
c
ard to de
scri
be the ba
seli
ne statu
s
of three inte
rvention di
stricts in Za
mb
ia.To as
se
ss the baselin
e status
of the pa
rticipati
n
g
distri
cts, the
authors u
s
e
d
a
modified
balan
ce
d score
ca
rd ap
proach followi
n
g
the dom
ai
ns
highlighte
d
in
the MOH 20
11 Strategi
c Plan [12].
Hwa et al. set out to develop a BSC as part of
a strate
gic plan
ning i
n
itiative. Based on a
need
s a
s
sessment of the
University
of California, Sa
n Fra
n
ci
sco,
Divisio
n
of Hospital M
edi
ci
ne,
missi
on and vision statem
ents we
re de
veloped.
T
h
e
autho
rs eng
aged
re
pre
s
e
n
tative facult
y to
develop
strat
egic
obje
c
tives a
nd d
e
termine p
e
rf
o
r
m
ance metri
cs acro
ss
4 B
S
C pe
rspe
ctives
[13].
Ja
ksi
c et al. prop
osed an
appro
a
ch to
integrate th
e Balanced
Scorecard m
odel and
Analytic net
work process i
n
the
ca
se of
sele
cted fin
a
n
cial i
n
stitutio
ns in S
e
rbi
a
.
The
subj
ect
o
f
analysi
s
will be op
en-end
ed inve
stmen
t
funds of p
r
o
perty value g
r
owth. T
he re
aso
n
s
whi
c
h l
e
d
the authors i
n
sele
cting th
ese no
n-dep
osit financi
a
l in
stitution
s
are first of all, their impo
rtan
ce
for the devel
opment of o
v
erall finan
ci
al and
real
sector, a
s
wel
l
as wea
k
d
o
mesti
c
po
rtfolio
manag
eme
n
t pra
c
tice
s
of these in
stitutions, which
resulted in the
e
x
tremely high
yield de
cline
in
the last four y
ears [14].
Evaluation Warning : The document was created with Spire.PDF for Python.
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046
TELKOM
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Vol. 12, No. 4, April 2014: 2914 – 2
923
2916
Maurer et al.
sho
w
ed that
the BSC ca
n be
used fo
r the com
p
re
hen
sive co
ntrol of
a
radiol
ogy d
e
partme
n
t an
d
thus p
r
ovid
es
a m
eani
n
g
ful contri
but
ion in
o
r
ga
ni
zing
the
vari
ous
diagn
osti
c a
n
d
treatm
ent
service
s
, the
manag
eme
n
t
of co
mplex
cli
n
ical
environ
ment an
d
can
be
of help with the tasks in
re
sea
r
ch and te
achi
ng [15].
Javier
et al. illustrate
d the
desi
gn of a b
a
lan
c
ed
scoreca
r
d fo
r ma
nagin
g
an e
m
erg
e
n
c
y
depa
rtment i
n
a tertiary care unive
rsity teac
hing h
o
spital; data
derived by i
m
pleme
n
ting
the
score
c
a
r
d
system are al
so pre
s
e
n
ted. The proje
c
t wa
s ca
rri
ed
out
in the followin
g
pha
se
s: 1)
sele
ction
of i
ndicators
of
activity and
q
uality
of p
r
o
c
esse
s a
n
d
o
u
tcome
s
for the
scorecard
,
2)
validation
of the in
dicators, 3) analy
s
is o
f
indi
cators from 20
07 th
ro
ugh
200
9, an
d 4)
con
c
lu
si
on
s
rega
rdi
ng clin
ical pe
rform
a
nce in relatio
n
to
the indicators th
at make up the
sco
r
ecard [16].
Hou
c
k et al. introdu
ced the con
c
e
p
t of the balan
ced sco
r
e
c
a
r
d into the laborato
r
y
manag
eme
n
t enviro
n
ment
. The b
a
lan
c
ed
score
c
a
r
d
is
a pe
rformance m
e
a
s
urem
ent mat
r
ix
desi
gne
d to
captu
r
e fin
a
n
c
ial a
nd
non
-finan
cial m
e
trics that p
r
o
v
ide insi
ght i
n
to the
criti
c
al
su
ccess fa
ct
ors for an
organi
zatio
n
, effectivel
y alignin
g
orga
nization
strategy to
key
perfo
rman
ce obje
c
tives
[17
]
.
Wu et al. pro
posed a ne
w hiera
r
chical
stru
cture for the BSC with placi
ng both
finance
and
cu
stome
r
at the to
p, i
n
ternal
proce
s
s at
the
next, and lea
r
nin
g
and
growth
at the botto
m.
Empirical ex
amination
ha
s fou
nd th
e i
m
porta
nce of
the n
e
w BS
C
stru
cture i
n
a
s
sessin
g
IT
investment
s. Lea
rnin
g a
nd g
r
o
w
th pl
ays the i
n
itial drive
r
for rea
c
hi
ng b
o
t
h cu
stome
r
and
financi
a
l perf
o
rma
n
ce thro
ugh the medi
ator of in
tern
al pro
c
e
ss. T
h
is can provide deep in
si
ght
into effectively managing I
T
resou
r
ce
s i
n
the hospital
s
[18].
Next, some typical p
ape
rs
related to the fuzz
y analytic
netwo
rk a
r
e il
lustrate
d as f
o
llows.
Yu et al. pro
posed a t
w
o-stage fu
zzy logarit
h
m
ic preferen
ce pro
g
rammi
ng with
multi-
crite
r
ia de
ci
si
on-m
a
ki
ng, in
orde
r to de
ri
ve the
prio
rities of
comp
arison m
a
tri
c
e
s
in the analyt
ic
hiera
r
chy p
r
oce
s
s (A
HP) and th
e an
alytic net
work p
r
o
c
e
s
s (ANP). Th
e
Fuzzy Prefe
r
ence
Programmin
g
(FPP) p
r
opo
sed
by Mikh
a
ilov and Sing
h
is suitable f
o
r de
riving
weights in i
n
terval
or fuzzy co
mpari
s
o
n
matrice
s
, espe
cially those displ
a
ying in
con
s
i
s
ten
c
ie
s. Howeve
r, the
wea
k
n
e
ss of
the FPP is th
at it obtains
p
r
ioritie
s
of
co
mpari
s
o
n
mat
r
ice
s
by a
ddit
i
ve con
s
trai
nts,
and ge
nerate
s
different p
r
i
o
rities by p
r
o
c
e
ssi
ng up
pe
r and lo
we
r triangul
ar jud
g
m
ents [19].
Isalou et al. d
e
velope
d an integrate
d
fuzzy logic a
nd
analytic net
work p
r
o
c
e
s
s to locate
a suitabl
e location for landf
illing muni
cip
a
l solid
wa
ste
s
gen
erate
d
in Kahak T
o
wn, Qom, Iran. In
this p
ape
r, th
e auth
o
rs fin
d
ing
s
reveale
d
that inte
gra
t
ion of fu
zzy l
ogic and
ANP can
give
b
e
tter
idea
comp
are
d
with oth
e
r
model
s like A
H
P, fuzzy
log
i
c, and A
N
P (individually).
Therefore, thi
s
model can be
applied in
site sele
ction fo
r landfill of other si
milar pl
ace
s
[20]
.
Moalag
h et
al. pro
p
o
s
ed
a p
r
a
c
tical
frame
w
ork fo
r a
s
se
ssin
g
a firm’
s
E
R
P po
st-
impleme
n
tation su
cce
ss u
t
ilising curren
t models thro
ugh a fuzzy analytic net
work p
r
o
c
e
s
s. The
con
s
tru
c
t of
ERP su
cce
s
s is broken d
o
w
n into th
ree
main pa
rts, i
n
clu
d
ing m
a
n
ageri
a
l succe
ss,
orga
nisationa
l su
ccess, an
d individual
succe
ss.
Usi
n
g this fra
m
e
w
ork, the firm’s ERP
system
su
ccess can be determi
ne
d and the req
u
ired imp
r
ov
e
m
ent proje
c
ts can be prop
ose
d
to prom
ote
the su
ccess l
e
vel [21].
Pang et al. d
e
velope
d a
supplie
r evalu
a
ti
on ap
pro
a
c
h b
a
sed o
n
the analytic
netwo
rk
pro
c
e
ss
(ANP) and fuzzy
synthetic ev
aluation
u
n
d
e
r a fuzzy e
n
vironm
ent. The impo
rta
n
ce
weig
hts of va
riou
s criteri
a
are
con
s
ide
r
e
d
as ling
u
isti
c variable
s
. Th
ese lin
gui
stic
rating
s can b
e
expre
s
sed in
triangul
ar fu
zzy n
u
mbe
r
s by usi
ng th
e fuzzy extent analysi
s
. Fuzzy synth
e
tic
evaluation
is
use
d
to
sel
e
ct a suppli
e
r
al
ternat
ive a
nd
the Fu
zzy ANP (FANP
)
m
e
thod i
s
a
pplie
d
to calculate the impo
rtan
ce of
the criteri
a
weig
hts [22
]
.
Kiris
et al. p
r
opo
sed
a fu
zzy an
alytic n
e
twor
k
proce
s
s to d
e
termi
ne the
wei
g
h
t
s of the
crite
r
ia a
nd th
e scores
of the inventory it
ems
we
re
d
e
termin
ed with simple additiv
e
wei
ghting
b
y
usin
g lingui
sti
c
term
s. Appl
ying fuzzy ANP to a mu
lti
-
criteria i
n
ven
t
ory cla
ssifi
ca
tion pro
b
lem
is
the novelty of
this
study in
the
rel
a
ted lit
eratu
r
e. In a
d
d
ition, t
he a
p
p
licatio
n a
r
ea
of the p
r
obl
em
whi
c
h i
s
the
manag
eme
n
t of the en
gin
eerin
g vehi
cl
es’ item
s in
a co
nst
r
u
c
tio
n
firm i
s
diffe
rent
from the othe
r studie
s
[23].
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TELKOM
NIKA
ISSN:
2302-4
046
A N
o
v
e
l Balanc
ed Sc
or
ec
ar
d D
e
s
i
gn Bas
ed on
Fuzzy Analy
t
ic
Netw
ork
Pr
oc
es… (
C
ao Yuhong)
2917
3. The Propo
sed Scheme
Before illustrating the
pro
posed
schem
e of bal
anced scor
ecard
desi
gn based on fuzzy
analytic
net
work process, basi
c
con
c
ep
ts
of
bal
an
ce
d scorecard are d
e
scribed
in advan
ced.
As
is sh
own in F
i
gure 1,
we choo
se an exa
m
ple of
a bu
siness p
r
oble
m
to introdu
ce the main id
eas
of balan
ced
scorecard de
si
gn.
Figure 1. Basic Co
ncepts f
o
r the Balan
c
ed Sco
r
e
c
ard
s
In Figure
1, we present how the
business profitability is evaluated
from many aspects
whi
c
h
can
be mea
s
u
r
ed
through
out finan
cial and
non-fin
a
n
c
ial
indicators, an
d then they are cl
assified
in
the
follo
wing
cla
s
ses: 1)
Fi
nan
cial persp
ective,
2
)
Client pe
rspe
ctive, 3)
Inte
rna
l
persp
ective,
4)
Develo
pment
, 5) Le
arning
persp
ective.
Particul
ar
ly,
the frame
of
balan
ce
d sco
r
ecard is ba
sed
on four p
r
o
c
e
s
ses
whi
c
h bi
nd the sh
ort term a
c
tivities to long term o
b
jective
s
.
As is sho
w
n
in Fig
u
re
1
,
there
are four
pe
rspecti
ves in
the d
e
sig
n
of b
a
l
anced
s
c
o
r
ec
ar
ds
as
fo
llo
ws
.
(1) Cu
stome
r
perspe
c
tive.
As the com
p
anie
s
ca
n cre
a
te value through
cu
stom
ers, ma
kin
g
it
clear that ho
w these
comp
anie
s
view pe
rformance is
regarded
a
s
an import
ant pro
b
lem
of perform
ance
measurement
.
(2) Inte
rnal b
u
sin
e
ss process persp
ecti
ve.
The intern
al busi
n
e
ss p
r
o
c
e
ss p
e
rspe
ctive c
ould executive to identify the key internal
pro
c
e
s
ses in
whi
c
h the org
anization mu
st overbe
ar.
(3) L
eani
ng a
nd gro
w
th pe
rspe
ctive.
The l
eanin
g
and
gro
w
th
perspe
c
tive i
n
the
bala
n
ced
score
c
a
r
d
ca
n di
sting
u
i
sh th
e
infrast
r
u
c
ture
which the o
r
gani
zation sh
ould be co
n
s
tructed to cre
a
te a long-te
rm gro
w
th an
d
improvem
ent.
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02-4
046
TELKOM
NI
KA
Vol. 12, No. 4, April 2014: 2914 – 2
923
2918
(
4
)
F
i
na
nc
ia
l
p
e
r
s
p
ec
tive
.
In the desig
n
of balanced
score
c
a
r
d, fin
anci
a
l perfo
rmance ca
n in
dicate if the strategy,
impleme
n
tation, and exe
c
ution of a co
mpany
ca
n contribute to b
o
ttom line improvem
ent.
In the following se
ction,
we will de
m
ons
trate ho
w to enhan
ce
the perform
ance of
balan
ce
d sco
r
ecard de
sign
through the
f
u
zzy analytic
netwo
rk p
r
o
c
ess.
F
i
r
s
tly, su
p
pos
in
g
12
{,
,
,
}
n
X
xx
x
be a
set of obj
ect, a
n
d
12
{,
,
,
}
m
Uu
u
u
be a
set of
goal. Afterwa
r
ds,
m
extende
d an
alyzin
g v
a
lue
s
for e
a
ch obj
ect
co
ul
d calculated,
whi
c
h
ca
n b
e
r
e
pr
es
e
n
t
ed
a
s
fo
llow
s
:
12
,,
,
,
{
1
,
2
,
,
}
m
gi
gi
gi
M
MM
i
n
(1)
W
h
er
e e
a
c
h
j
g
i
M
is b
e
lon
ged
to tria
ngul
ar fu
zzy
num
ber,
and th
e val
u
e
of fu
zzy
synt
hetic
extent
whi
c
h is relat
ed to the
th
i
object is defin
ed
as follows:
1
11
1
kn
k
jj
ig
i
g
i
ji
j
SM
M
(2)
In orde
r to achieve the re
sults of
1
k
j
g
i
j
M
, the
fuzzy ad
ditio
n
operation o
f
the
k
exten
t
analysi
s
valu
es for a given
matrix
shoul
d be execute
d
as follo
ws.
11
1
1
{,
,
}
kk
k
k
j
g
ij
j
j
jj
j
j
M
lk
u
(3)
The degree of possi
bility of
22
2
2
1
1
1
1
(,
,
)
(
,
,
)
M
lk
u
M
l
k
u
can be com
puted a
s
follo
ws.
21
21
1
2
12
22
1
1
1,
()
0
,
,
if
k
k
is
sa
tisf
i
e
d
P
M
M
i
f
l
u
i
s
s
a
tisfie
d
lu
ot
herw
i
s
e
ku
k
l
(4)
Particul
arly, a
fuzzy nu
mbe
r
ca
n be
rep
r
ese
n
ted by th
e left and ri
g
h
t formation
of each
degree of me
mbershi
p
, wh
ich is
sho
w
n i
n
Figure 2.
Figure 2. Illustration of
a Triangul
ar Fuzzy Number
The d
egree
possibility for a co
nvex fuzzy n
u
mbe
r
t
o
be la
rge
r
t
han
k
convex
fuzzy
numbe
rs
,(
{
1
,
}
)
i
M
ik
can
be rep
r
e
s
ent
ed as follo
ws:
l
m
M
()
ry
M
M
1.
0
0.0
()
ly
M
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
A N
o
v
e
l Balanc
ed Sc
or
ec
ar
d D
e
s
i
gn Bas
ed on
Fuzzy Analy
t
ic
Netw
ork
Pr
oc
es… (
C
ao Yuhong)
2919
12
1
2
(,
,
,
)
(
)
()
(
)
mi
n
(
)
,
{
1
,
2
,
,
}
k
k
i
PM
M
M
M
P
M
M
MM
MM
PM
M
i
k
(5)
The lin
gui
stic variabl
es’
m
e
mbe
r
ship fu
nction
s
ca
n b
e
dete
r
min
e
d
by the
proce
s
s which
is sh
own in Figure 3.
Based
on the
above formal
descri
p
tion, the
bal
an
ced
score
c
a
r
d
ca
n be d
e
si
gne
d by the
fuzzy an
alytic network p
r
o
c
ess as the foll
owin
g step
s.
(1)
Con
s
tru
c
ting
a pe
rforman
c
e evalu
a
tion t
eam
whi
c
h
is
made
up
of e
x
perts an
d d
e
t
erminatio
n
of busin
ess vision.
(2)
Cho
o
si
ng the
strategie
s
to
be pursu
ed in
orde
r to obta
i
n the busi
n
e
ss visi
on.
(3)
Selecting
bal
anced sco
r
e
c
ard p
e
rspe
cti
v
es and
the
perfo
rman
ce
indicators ba
sed o
n
the
s
e
per
spe
c
t
i
v
e
s.
(4)
Orga
nizi
ng th
e basi
c
st
ruct
ure the fu
zzy
analytic net
work p
r
o
c
e
s
s model hie
r
a
r
chically.
(5)
Cal
c
ulating t
he local wei
ghts of the
stra
tegi
es, b
a
lan
c
ed
scoreca
r
d p
e
rsp
e
ctives a
n
d
perfo
rman
ce i
ndicators thro
ugh pai
rwi
s
e
comp
ari
s
o
n
matrices.
(6)
Determine
th
e fuzzy
scale
,
the inn
e
r d
epen
den
ce
matrix of
ea
ch pe
rspe
ctive acco
rdi
ng to
other bal
an
ce
d score
c
a
r
d p
e
rspe
ctives.
(7)
Comp
uting th
e global
weig
hts for the pe
rforma
nce ind
i
cators.
(8)
Measuri
ng th
e perfo
rman
ce indicators.
(9)
Determing th
e bu
sine
ss
perfo
rman
ce
for a
spe
c
if
ic pe
riod
of time by usin
g the glo
bal
weig
hts calcu
l
ated in the seventh step.
4
.
Case s
t
ud
y
In this se
ction, we will
give a case
study of college Engl
i
s
h
classroom t
eaching
quantitative e
v
aluation to
demon
strate
the pe
rf
orm
a
nce of
the
p
r
opo
sed bala
n
ce
d
sco
r
e
c
a
r
d
desi
gn. Firstl
y, a hierarchy
stru
cture of the index
syst
em for co
lleg
e
English cla
s
sroo
m tea
c
h
i
ng
quality evalu
a
tion i
s
give
n
in T
able
1.
The
pro
p
o
s
ed
h
i
er
ar
ch
y str
u
c
t
ur
e
o
f
the
in
de
x s
y
s
t
em is
made u
p
of two mai
n
sect
ions: 1
)
Evaluation of
tea
c
he
rs
and
2)
Evaluation of
stude
nts. Ba
sed
on the a
bove
Hierarchy
structure
of the
index sy
stem
, twenty one
i
ndexe
s
for
qu
ality evaluatio
n
are p
r
e
s
ente
d
.
Figure 3. Membershi
p
Fun
c
tion
s of the Lingui
stic Va
riable
s
to Rep
r
esent the Re
lative
Importanc
e
In this
se
ction
,
we ma
ke
pe
rforma
nce ev
aluation fo
r th
e propo
sed
a
ppro
a
ch by th
e ca
se
of colle
ge En
glish
cla
s
sro
o
m
teachi
ng q
uality eval
uation. Furth
e
rm
ore, 17 i
ndex
es a
r
e u
s
e
d
i
n
this expe
rime
nt, therefore,
we
sh
ould t
e
st the
co
ntri
bution
rate fo
r ea
ch i
ndex.
20
cla
s
ses
are
arrang
ed to make
our p
e
rforman
c
e ev
aluation, a
n
d
we test the
weig
ht of each index for e
a
ch
cla
ss.
Th
e re
sult
s a
r
e
sho
w
n in Tabl
e 2
.
Based
on
th
e re
sult
s of
Table
2, the
contri
bution
rate
for ea
ch index
i
s
calculated by
averagi
ng th
e index
weig
ht (sho
wn in
Figure 4
)
,
which ill
ust
r
ate
s
the
co
ntrib
u
tion de
gree
for
each index in
the case of college Engli
s
h cla
s
sroo
m teaching q
uali
t
y evaluation.
1.
0
0.
0
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ISSN: 23
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KA
Vol. 12, No. 4, April 2014: 2914 – 2
923
2920
Table 1. The
Index System used in o
u
r this Experim
e
n
t
O
b
jects of
evaluation
Index of
evaluation
Evaluation
of teachers
Teaching aims
C1:Combination
of
language kno
w
ledg
e and lang
uage application
Teaching appr
oa
ches
C2:Emphasis on application
C3:Prope
r choice of teaching me
dia
Teaching design
C4:Adaptation to
students’ present level
C5:The o
p
timal design w
h
ich c
an
realize the teaching aims
C6:Prope
r allocation of time
Evaluation of
students
Learning attitude
C7:Students focu
s on learning tasks at hand
C8:Students hav
e curiosity
fo
r an
d interest in study e
x
ploration
Participation in E
nglish
learning
C9:Students’ active participation
in listening, speaking, reading,
writing and tra
n
slation
C10:Students’ ea
gerness to fi
nish task-based activities
C11:Students’ pr
actice of language know
led
ge bas
ed on dail
y
communication
Learning Meth
od
s
C12:Prope
r choice of learning me
thods
C13:Practice and
application
C14:Students’ pr
ogress and impr
ovement
Learning Effect
C15:Satisfaction w
i
th lear
ning atm
o
sphere
C16:Satisfaction w
i
th lear
ning res
u
lts
C17:Satisfaction w
i
th lear
ning met
hods
Figure 4. Con
t
ribution Rate
for Each Ind
e
x
Figure 5. The
Norm
alized
Score of Qual
ity Evaluation for Different
Cla
s
ses
0
0.
02
0.
04
0.
06
0.
08
0.
1
0.
12
C
1
C2
C3
C4
C5
C6
C7
C8
C9
C1
0
C
1
1
C1
2
C
1
3
C1
4
C
1
5
C1
6
C
1
7
Co
n
t
r
i
b
u
t
i
o
n
Ra
t
e
0.
5
0.
5
5
0.
6
0.
6
5
0.
7
0.
7
5
0.
8
0.
8
5
0.
9
0.
9
5
R
e
su
l
t
s
o
f
st
u
d
e
n
t
s
e
v
a
l
u
a
t
i
o
n
R
e
s
u
l
t
s
of
t
h
e
pr
op
os
e
d
a
p
pr
oa
c
h
N
o
rm
a
l
i
zed
s
c
o
r
e
o
f
q
u
a
l
i
t
y
ev
a
l
u
a
t
i
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
A N
o
v
e
l Balanc
ed Sc
or
ec
ar
d D
e
s
i
gn Bas
ed on
Fuzzy Analy
t
ic
Netw
ork
Pr
oc
es… (
C
ao Yuhong)
2921
Table 2. Inde
x Weight for
Each Cl
ass
Evaluation of teachers
Evaluation of students
C1
C2 C3 C4 C5 C6 C7 C8 C9
C10 C11 C12 C13
C14
C15 C16 C17
Class
1
0.
07
0.
08 0.
04 0.
05 0.
08 0.
02 0.
10 0.
07 0.
08 0.
02 0.
05 0.
09 0.
04
0.
07
0.
01 0.
09 0.
04
Class
2
0.
11
0.
04 0.
06 0.
08 0.
04 0.
03 0.
05 0.
00 0.
07 0.
04 0.
11 0.
04 0.
03
0.
08
0.
03 0.
07 0.
11
Class
3
0.
07
0.
07 0.
04 0.
10 0.
01 0.
03 0.
10 0.
03 0.
04 0.
10 0.
07 0.
01 0.
03
0.
07
0.
05 0.
10 0.
09
Class
4
0.
09
0.
02 0.
08 0.
02 0.
01 0.
02 0.
06 0.
09 0.
11 0.
07 0.
04 0.
09 0.
05
0.
06
0.
08 0.
02 0.
10
Class
5
0.
05
0.
07 0.
08 0.
10 0.
00 0.
09 0.
03 0.
05 0.
02 0.
09 0.
08 0.
06 0.
05
0.
10
0.
05 0.
07 0.
01
Class
6
0.
06
0.
05 0.
07 0.
04 0.
00 0.
10 0.
09 0.
11 0.
04 0.
07 0.
12 0.
05 0.
00
0.
04
0.
07 0.
05 0.
02
Class
7
0.
01
0.
08 0.
00 0.
10 0.
11 0.
11 0.
09 0.
08 0.
01 0.
01 0.
05 0.
01 0.
06
0.
03
0.
09 0.
10 0.
07
Class
8
0.
11
0.
04 0.
06 0.
07 0.
07 0.
08 0.
06 0.
09 0.
06 0.
10 0.
10 0.
03 0.
06
0.
01
0.
02 0.
01 0.
03
Class
9
0.
07
0.
09 0.
04 0.
06 0.
08 0.
06 0.
03 0.
06 0.
08 0.
07 0.
01 0.
08 0.
07
0.
07
0.
06 0.
05 0.
02
Class
10
0.
03
0.
04 0.
01 0.
06 0.
12 0.
01 0.
12 0.
08 0.
11 0.
08 0.
05 0.
02 0.
10
0.
03
0.
09 0.
00 0.
04
Class
11
0.
07
0.
07 0.
07 0.
03 0.
05 0.
05 0.
08 0.
08 0.
05 0.
06 0.
07 0.
05 0.
05
0.
05
0.
05 0.
06 0.
06
Class
12
0.
04
0.
03 0.
09 0.
07 0.
05 0.
03 0.
09 0.
08 0.
09 0.
03 0.
02 0.
09 0.
03
0.
09
0.
02 0.
06 0.
09
Class
13
0.
04
0.
10 0.
04 0.
04 0.
10 0.
06 0.
07 0.
07 0.
11 0.
11 0.
02 0.
02 0.
04
0.
08
0.
05 0.
03 0.
04
Class
14
0.
07
0.
10 0.
05 0.
01 0.
06 0.
04 0.
08 0.
10 0.
10 0.
05 0.
03 0.
08 0.
06
0.
03
0.
04 0.
09 0.
02
Class
15
0.
12
0.
05 0.
11 0.
10 0.
02 0.
05 0.
05 0.
09 0.
02 0.
04 0.
02 0.
01 0.
06
0.
05
0.
05 0.
06 0.
10
Class
16
0.
08
0.
04 0.
02 0.
06 0.
00 0.
07 0.
03 0.
10 0.
06 0.
10 0.
10 0.
02 0.
06
0.
01
0.
10 0.
08 0.
06
Class
17
0.
06
0.
01 0.
07 0.
06 0.
06 0.
09 0.
10 0.
10 0.
03 0.
11 0.
04 0.
06 0.
08
0.
06
0.
01 0.
04 0.
02
Class
18
0.
09
0.
10 0.
04 0.
09 0.
08 0.
06 0.
00 0.
04 0.
09 0.
10 0.
02 0.
02 0.
09
0.
07
0.
03 0.
03 0.
04
Class
19
0.
12
0.
07 0.
04 0.
00 0.
07 0.
07 0.
05 0.
06 0.
10 0.
08 0.
07 0.
03 0.
10
0.
08
0.
01 0.
01 0.
05
Class
20
0.
04
0.
11 0.
13 0.
00 0.
01 0.
11 0.
02 0.
05 0.
02 0.
04 0.
01 0.
12 0.
05
0.
03
0.
06 0.
12 0.
09
For ea
ch
cla
ss, we co
mp
are the no
rm
aliz
e
d
score of quality evaluation b
e
tween the
results
of stu
dents’ evalua
tion
and
re
su
lts of the pro
pos
ed ap
pro
a
ch. As i
s
sh
ow in Fi
g.5, we
can
kn
ow th
at the pe
rformance
of th
e propo
sed
desi
gn i
s
very close to th
e evaluatio
n
of
stude
nts. T
h
erefo
r
e, the
con
c
lu
sio
n
s
can
be
d
r
awn that the
p
r
opo
se
d b
a
la
nce
d
score
c
ard
desi
gn i
s
q
u
i
t
e effective f
o
r the
qu
ality edu
cation
reform
orie
nte
d
college
En
glish
cla
s
sro
o
m
teachi
ng qu
a
n
titative evaluation.
From th
e ab
ove expe
rim
ental results,
it
can
be
seen that th
e
perfo
rma
n
ce of the
prop
osed de
sign is very effective. The m
a
in rea
s
o
n
s li
e in the following aspe
cts:
(1) Th
e de
sig
n
of the prop
ose
d
balan
ce
d sc
ore
c
a
r
d i
s
ba
sed on f
our processe
s whi
c
h
bind the sh
ort
term activities to long term
objective
s, includi
ng: 1. Finan
cial
pe
rsp
e
ctive, 2. Clie
nt
perspe
c
tive, 3. Internal pe
rsp
e
ctive,
4. Develo
pment
, 5. Learning
perspe
c
tive.
(2) In the pro
posed bala
n
ced sc
orecard
desi
gn, finan
cial pe
rf
orm
a
nce can express if the
strategy, im
plementatio
n, and exe
c
u
t
ion of
a company
can
contri
bute
to bottom line
improvem
ent.
(3) In this pa
per, a
fuzzy
numbe
r
can
be r
epresent
ed by the
left and
right fo
rmation of
each deg
ree
of membershi
p
in the pro
p
o
s
ed fu
zzy an
alytic netwo
rk process.
(4) Th
e deg
ree possibility
for a convex fuzzy
numb
e
r
to be large
r
than a given convex
fuzzy nu
mbe
r
s ca
n be re
prese
n
t
ed by a
n
effective scheme.
(5) The
ba
si
c stru
ctu
r
e in
the fu
zzy a
nal
ytic network
pro
c
e
s
s mod
e
l is
organi
ze
d in the
hiera
r
chi
c
al mode, and th
e local weight
s of the
strate
gies, bala
n
ce
d score
c
a
r
d p
e
rspe
ctives a
nd
perfo
rman
ce i
ndicators ca
n
be obtaine
d by pairwi
s
e
compa
r
ison m
a
trice
s
.
5. Conclusio
n
We illustrate a novel balanc
ed scorecard design
based on
fuzzy analyt
ic network
pro
c
e
ss in th
is pape
r. Th
e main innov
ations of
this paper lie in
that 1) a fu
zzy num
ber i
s
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ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 4, April 2014: 2914 – 2
923
2922
rep
r
e
s
ente
d
by the left a
nd right form
ation of
each
degre
e
of membe
r
ship
in fuzzy anal
ytic
netwo
rk process. 2)
The
d
egre
e
p
o
ssibi
lity for
a conv
ex fuzzy
num
ber to
be la
rg
er tha
n
a giv
en
convex fu
zzy
numbe
rs can
be repr
esent
ed by a
n
effe
ctive sche
me.
3) T
he
basi
c
stru
cture in th
e
fuzzy
analytic network
pro
c
e
s
s mo
del i
s
o
r
g
anized
hiera
r
chi
c
ally, and
the l
o
ca
l wei
ghts of t
he
strategi
es, b
a
lan
c
ed sco
r
eca
r
d persp
e
c
tives
a
nd p
e
rform
a
n
c
e
i
ndicators can
be
o
b
taine
d
b
y
matrix c
o
mputing.
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h
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TELKOM
NIKA
ISSN:
2302-4
046
A N
o
v
e
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