Int
ern
at
i
onal
Journ
al of Ele
ctri
cal
an
d
Co
mput
er
En
gin
eeri
ng
(IJ
E
C
E)
Vo
l.
10
,
No.
5
,
Octo
be
r
2020
,
pp.
4992
~
5
000
IS
S
N:
20
88
-
8708
,
DOI: 10
.11
591/
ijece
.
v
10
i
5
.
pp4
992
-
50
00
4992
Journ
al h
om
e
page
:
http:
//
ij
ece.i
aesc
or
e.c
om/i
nd
ex
.ph
p/IJ
ECE
Optimat
ion ec
onomic o
rder quant
ity meth
od
for a supp
or
t
syst
em re
order poin
t stock
Li
nda
Per
dan
a
W
anti
1
,
Rati
h H
af
s
ar
ah
M
aharra
ni
2
, Nu
r Wa
c
hid
Ad
i
Prase
tya
3
,
Eka
Tr
ipus
tik
as
ar
i
4
,
G
anja
r
N
d
aru Ikh
tia
gu
n
g
5
1,2,3
Depa
rtment
o
f
Inform
at
i
cs,
Po
li
te
kn
ik
Neg
eri
Cil
acap, Indone
s
ia
4
Depa
rtment of I
nform
at
ion
S
y
s
t
em,
Univer
si
ta
s
Am
ikom Pur
wo
ker
to, Indone
si
a
5
Depa
rtment of
El
e
ct
ri
ca
l
Eng
in
ee
ring
,
Poli
te
kn
i
k
Nege
ri
Ci
la
c
ap
,
Indone
si
a
Art
ic
le
In
f
o
ABSTR
A
CT
Art
ic
le
history:
Re
cei
ved
Ja
n 2
1,
2020
Re
vised
Ma
r 2
8
,
2020
Accepte
d
Apr 11
, 202
0
The
succ
ess
of
a
compan
y
is
m
ea
sured
b
y
th
e
abi
l
ity
to
pro
vide
goods
and
services
at
the
right
t
ime
and
pla
c
e.
B
e
sides,
one
of
t
he
factors
of
the
compan
y
's
progre
ss
is
al
so
supported
b
y
inv
ent
or
y
m
ana
gement
which
func
ti
ons
to
cont
rol
th
e
compan
y
'
s
inv
ent
or
y
b
y
n
ei
th
er
hoar
ding
lot
s
of
produc
ts
nor
running
out
of
produ
ct
s.
The
dev
elopm
ent
of
te
chno
log
y
req
u
ire
s
a
compan
y
'
s
inve
ntor
y
m
an
a
gement
that
is
f
ast,
pre
ci
se
and
a
cc
ur
at
e
to
support
i
ts’
pe
rform
anc
e.
In
f
ac
t
,
som
e
comp
ani
es
h
av
e
diffi
cu
lty
in
de
te
rm
ini
ng
the
s
toc
k
of
their
g
oods
produc
ti
on
so
tha
t
it
impedes
the
fu
l
fil
lment
of
con
sum
er
nee
ds
in
the
eve
nt
of
h
igh
m
ark
et
demand.
Thi
s
stud
y
is
to
det
er
m
ine
the
reo
rde
r
point
stock
of
a
compa
n
y
tha
t
de
te
rm
ine
s
the
num
ber
of
purc
hase
s
an
d
sale
s
of
the
compan
y
'
s
produc
ts,
the
r
ef
ore
th
e
amount
of
exp
ense
s
an
d
inc
om
e
ca
n
b
e
pre
sen
te
d
to
the
bo
ard
o
f
dire
c
tors
to
be
foll
owed
u
p
quic
kl
y
and
ac
cur
a
t
ely
.
The
m
et
hod
u
sed
is
a
sta
ti
s
ti
c
al
app
roa
ch
to
the
ec
onom
ic
quan
tit
y
m
odel
where
sa
fety
stock
an
alys
is
is
first
per
fo
rm
ed.
Thi
s
m
ethod
is
used
to
put
the
comp
an
y
'
s
produc
ts
t
o
the
inve
n
tor
y
so
tha
t
the
r
e
are
no
excess
or
eve
n
short
age
s
of
produc
ts.
S
y
stem
deve
lopme
nt
m
et
hod
used
is
a
use
r
ce
nt
ere
d
design
,
which
is
the
m
ost
suita
ble
t
o
the
stud
y
.
Th
e
output
of
the
ac
t
ivi
t
y
is
in
form
at
ion
in
the
form
of
advice
to
th
e
compan
y
leade
rs
i
n
m
aki
ng
decisio
ns
about
produ
ct
ion
p
la
nn
ing,
cont
rolling
sto
c
k
inve
nto
r
y
,
det
a
il
ing
m
ark
et
demand
quic
k
l
y
,
pre
ci
sel
y
and
ac
cu
ratel
y
and
deve
lop
ing
a
decision
support
s
y
stem
that
is
m
ade
b
y
t
aking
int
o
account
the
detail
s
of
user
n
ee
ds.
Ke
yw
or
d
s
:
Eco
no
m
ic
o
r
de
r qu
a
ntit
y
Ma
rk
et
dem
and
Pr
od
uctio
n plann
i
ng
Re
order p
oin
t
st
ock
Stock co
ntr
ol
Suppor
t
syst
em
Copyright
©
202
0
Instit
ut
e
o
f Ad
vanc
ed
Engi
n
ee
r
ing
and
S
cienc
e
.
Al
l
rights re
serv
ed
.
Corres
pond
in
g
Aut
h
or
:
Lind
a
Perda
na Wanti
,
Dep
a
rtm
ent o
f Info
rm
at
ic
s,
Po
li
te
kn
ik
Nege
ri Ci
la
cap,
Dr
.
S
oetom
o
S
t.,
N
o. 1, Si
dakay
a,
Ci
la
cap, 5
3212,
C
e
nt
ral J
ava
, I
ndonesi
a
.
Em
a
il
:
l
ind
a_
pe
rd
a
na@p
nc.
a
c.id
1.
INTROD
U
CTION
Com
petition
in
the
global
m
ark
et
le
ads
com
pan
ie
s
to
look
f
or
op
portu
niti
es
by
m
ini
m
izing
pro
du
ct
io
n
c
ost
s
but
increa
sing
i
nv
e
stm
e
nt
with
out
re
du
ci
ng
pro
duc
t
qu
al
it
y.
Dat
a
accur
acy
be
com
es
an
i
m
po
rta
nt
so
urce
of
a
com
pan
y
to
determ
ine
sal
es
pr
edi
ct
ion
s
in
acco
r
dan
ce
with
m
a
rk
et
dem
and
.
On
e
of
the
com
pan
y'
s
strat
egies
to
increase
be
ne
fits
is
to
m
a
nag
e
al
l
of
it
s
asset
s.
The
com
pan
y'
s
inv
ento
ry
m
anag
em
ent
sh
ould
pr
ov
i
de
accurate
i
nform
at
ion
ab
out
detai
l
inv
e
ntor
y
of
go
ods
a
nd
se
rv
ic
es
,
es
pe
ci
al
ly
la
rg
e
c
om
pan
ie
s
with
m
any
hi
gh
v
al
ua
ble goods.
T
he
in
form
ation
acc
ur
a
cy
about o
r
der
i
ng
or
r
el
easi
ng
good
s
al
so
af
fects
th
e
com
pan
y'
s
per
f
or
m
ance.
Ri
sk
s
ta
ke
n
by
t
he
c
om
pan
y
if
the
data
pr
e
se
nted
does
no
t
m
at
ch
the f
ie
ld
d
at
a
on the
pr
ocurem
ent and
release
of
goods, w
hich
ca
n
ca
us
e c
om
pan
ie
s to
l
ose
m
on
ey
.
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N:
20
88
-
8708
Op
ti
m
atio
n
ec
onomic
order
quanti
ty
m
et
ho
d
fo
r
a
s
upport
system r
eor
de
r point st
ock
(
L
inda Per
dana
Wa
nti
)
4993
In
this
re
ga
rd,
this
stud
y
ai
m
s
to
analy
ze
and
create
a
syst
e
m
that
can
be
us
ed
by
com
pan
ie
s
to
pro
vid
e
decisi
on
s
relat
ed
to
the
proc
ur
em
ent
of
goods.
A
syst
em
will
be
de
velo
pe
d
to
help
c
om
pan
ie
s
determ
ine
the
a
m
ou
n
t
of
goods
ordere
d
by
op
ti
m
iz
ing
the
m
e
tho
d
that
w
il
l
be
us
e
d,
na
m
el
y
econ
om
i
c
orde
r
qu
a
ntit
y
(EOQ
)
[1]
.
By
optim
iz
ing
the
E
OQ
m
et
ho
d,
c
om
pan
y
can
dete
r
m
ine
the
nu
m
ber
of
orde
rs
rel
at
ed
to
how
m
uch
ra
w
m
at
erial
s
re
qu
e
ste
d
to
s
up
pliers
an
d
reor
der
points
re
la
te
d
to
the
ti
m
e
of
proc
ur
em
ent
of
goods,
as
well
as
re
portin
g
on
ra
w
m
at
erial
inv
e
ntory
[
2]
.
T
he
E
O
Q
m
et
ho
d
is
al
s
o
us
e
d
to
fi
gure
ou
t
the
rem
ai
nin
g
raw
m
at
erial
i
nv
e
ntory
as
a
pr
e
ve
ntife
m
ea
su
re
f
or
t
he
c
om
pan
y
in
m
anag
in
g
ra
w
m
ater
ia
ls,
in
or
der
to
m
i
nim
iz
e
the
co
st
of
purch
a
sing
ra
w
m
at
erial
s
to
s
upplier
s
base
d
on
da
ta
on
the
num
ber
of
com
pan
y p
rod
uction o
r
der
s
[
3]
.
The
EO
Q
m
eth
od
is
reco
m
m
end
ed
in
pur
chasin
g
go
od
s
to
su
ppli
ers
a
s
well
as
fo
r
s
el
f
-
pr
oduce
d
goods
[
4]
.
H
oweve
r
there
is
a
sli
gh
t
diff
e
re
n
ce,
go
ods
that
are
sel
f
-
pr
oduc
ed
are
us
i
ng
econom
ic
lot
s
i
ze
[5]
.
A
sig
nifica
nt
diff
e
re
nce
is
in
the
ec
onom
ic
lot
siz
e
whic
h
re
qu
i
res
orde
rin
g
c
os
ts,
includi
ng
the
orde
r
pr
e
par
at
io
n
c
ost
and
m
achine
pr
e
par
at
io
n
c
ost
wh
ic
h
is
us
e
d
to
pr
oduce
goods.
In
t
he
o
t
her
hand,
ec
on
om
i
c
order
quantit
y
is
m
os
tl
y
us
ed
to
fin
d
out
th
e
qu
al
it
y
of
order
s
with
m
ini
m
u
m
storag
e
c
os
ts
an
d
in
ve
rs
e
cost
order
i
ng
[6
,
7]
.
Decisi
on
m
aking
is
us
e
d
to
so
lve
pro
blem
s
face
d
by
ta
ki
ng
i
nto
acco
unt
the
crit
eria
us
e
d
[8
,
9]
.
In
this
stu
dy
the
decisi
on
s
upport
co
ncep
t
is
us
ed
to
fi
gure
ou
t
the
am
ou
nt
of
inv
e
nt
or
y
that
m
us
t
be
prov
i
de
d
by
the
com
pany
by
no
ti
ci
ng
m
ark
et
dem
and
an
d
s
upply
of
ra
w
m
at
erial
s
.
The
need
of
r
aw
m
at
erial
s
b
ased
on
the
m
ark
et
de
m
and
will
be
determ
ined
with
the
hel
p
of
a
s
upport
syst
e
m
reo
r
de
r
point
stoc
k.
Syste
m
requirem
ents
are
de
fine
d
acc
ordin
g
to
us
er
necessit
y,
be
cause
the
a
naly
sis
and
desi
gn
of
the
syst
em
is
or
ie
nted
to
wards
us
e
r
input
[
10
,
11]
.
Re
ord
er
po
i
nt
stock
is
fo
un
d
base
d
on
the
num
ber
of
ra
w
m
at
er
ia
ls
pur
c
hases
in
accor
da
nce
with
m
ark
et
de
m
and
,
t
her
e
fore
the
c
om
pan
y'
s
rev
e
nu
e
f
igures
ca
n
be
know
n
qu
ic
kly, preci
s
el
y and
ac
cu
ratel
y.
2.
RESEA
R
CH MET
HO
D A
ND LIT
ER
A
TURE
REV
I
EW
2.1.
Rese
arch
me
t
ho
d
The
fi
rst
thin
g
the
a
uthor
do
e
s
is
gat
heri
ng
al
l
in
form
at
ion
relat
e
d
to
the
functi
on
al
need
s
of
the
syst
em
an
d
no
n
-
functi
onal
syst
em
s
t
o
be
buil
t.
T
he
syst
em
wil
l
be
buil
t
us
i
ng
t
he
us
er
c
entere
d
desig
n
(
UC
D)
syst
e
m
dev
el
opm
ent
m
od
el
.
UCD
m
od
el
is
of
te
n
us
e
d
bec
ause
the
m
od
el
can
acc
omm
o
date
al
l
the
need
s
a
nd
wish
es
of
t
he
use
r
i
n
eac
h
pro
cess
[
12]
.
Users
are
al
ways
i
nvol
ved
in
eve
r
y
ste
p
t
hat
is
c
arr
ie
d
ou
t
in
detai
l
an
d
in
a
str
uctu
re
d
m
ann
er
[
13]
.
In
t
his
m
od
el
the
us
e
r
can
al
so
pr
ov
i
de
input
after
the
sys
tem
is
bu
il
t
an
d
the
i
nput
is
us
e
d
by
the
dev
el
op
e
r
to
im
pr
ov
e
t
he
syst
e
m
,
becau
se
the
syst
em
is
act
ually
create
d
by
def
i
ning all
the
w
ish
es
of
t
he user
[
14]
.
The
first
sta
ge
carried
o
ut
i
n
t
his
stu
dy
is
t
o
determ
ine
and colle
ct
data.
B
y
com
bin
ing
th
e
co
nce
pt
of
UCD
syst
em
dev
el
opm
ent,
nam
el
y
figu
rin
g
ou
t
us
er
co
nt
ext,
dete
rm
ini
ng
a
nd
colle
ct
ing
data
ca
n
be
m
ade
into
one
co
nce
pt
or
one
ste
p.
Howe
ver
the
re
are
two
sta
ges
passed,
nam
ely
determ
ining
bo
t
h
t
he
bac
kgrou
nd
of
the
de
velo
pi
ng
syst
em
us
er,
colle
ct
ing
t
he
data
an
d
then
pr
e
pa
rin
g
al
l
the
necessary
equ
ipm
ent
du
ri
ng
the syst
em
d
evelop
m
ent p
r
oc
ess.
The
ne
xt
ste
p
is
the
li
te
ratu
re
stud
y,
w
hich
is
to
st
ud
y
eve
ryt
hin
g
relat
ed
to
eco
no
m
ic
ord
er
qu
a
ntit
y,
and
it
s
a
pp
li
cat
ion
to
decisi
on
su
pp
or
t
syst
e
m
s.
Then
the
a
uthor
e
nter
t
he
us
er
ce
ntere
d
desig
n
sta
ge
,
s
uch
a
s
plan
ning
t
he
UCD
proc
ess,
de
fining
the
backg
rou
nd
of
the
syst
e
m
use
r,
ex
plainin
g
the
us
e
r'
s
nee
ds
a
nd
the
data
us
e
d.
The
ne
xt
ste
p
is
m
aking
a
d
esi
gn
of
t
he
create
d
syst
e
m
dev
el
op
m
ent
and
t
he
c
oncept
of
decisi
on
sup
port
us
in
g
the
ec
onom
ic
or
der
quan
ti
ty
m
et
ho
d.
Af
te
r
al
l
the
proces
ses
are
ca
rr
ie
d
out,
the next
is
the
desig
n
eval
uation
that
has
been
im
ple
m
e
nted
in
the
syst
e
m
dev
el
op
m
ent
process
an
d
wait
ing
f
or
the
inp
ut
from
the
us
er
a
bout
the
syst
e
m
design
t
hat
is
bein
g
im
plem
ented
-
wh
et
he
r
it
sti
ll
need
im
pr
ov
em
ent
or
is
it
in
accor
da
nce
with
the
nee
ds
a
nd
wish
es
of
th
e
us
e
r.
If
t
he
de
sign
nee
ds
a
n
i
m
pr
ovem
ent
/
input
from
the
us
e
r,
then
the
pr
oce
ss
is
rep
eat
ed
i
n
sta
ges
ba
sed
on
the
ev
al
uat
ion
of
the
use
r
wh
et
he
r
it
sta
rts
from
the
sta
ge
of
def
i
ning
us
e
r
con
te
xt,
from
the
sta
ge
of
def
ini
ng
us
er
need
s
,
or
pe
rh
a
ps
f
ro
m
the
sta
ge
of
de
sign
i
ng
the
syst
em
.
Aft
er
al
l
the
sta
ge
s
in
t
he
us
er
centere
d
des
ig
n
m
et
ho
d
are
carried
ou
t,
th
e
proces
s
c
on
ti
nu
e
s
to
the
sta
ges
of
drawi
ng
co
nclu
sion
s
f
r
om
the
wh
ole
pr
oces
s.
User
te
stim
on
ia
ls
bec
om
e
an
i
m
po
rtant
par
t
of
how
m
any
perce
ntages
of
use
r
nee
ds
are
po
ur
e
d
int
o
the
s
yst
e
m
;
the
gr
e
at
er
the
per
ce
nt
age
m
eans
the
syst
e
m
has
su
cce
ssf
ully
acco
m
m
od
at
ed
an
d
i
m
plem
ented
the
use
r'
s
need
s
an
d
wish
es.
Fi
gur
e
1
sh
ow
each
sta
ge
carried
out i
n
t
he
syst
em
d
eve
lop
m
ent p
r
oces
s u
si
ng the
UC
D
m
od
el
[
15]
.
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8708
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t J
Elec
&
C
om
p
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V
ol.
10
, No
.
5
,
Oct
ob
e
r
2020
:
4
9
9
2
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5
0
0
0
4994
Figure
1. Re
se
arch
m
et
ho
d w
it
h
fase
of
us
er
centere
d desi
gn
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N:
20
88
-
8708
Op
ti
m
atio
n
ec
onomic
order
quanti
ty
m
et
ho
d
fo
r
a
s
upport
system r
eor
de
r point st
ock
(
L
inda Per
dana
Wa
nti
)
4995
2.2.
Li
tera
t
ure re
vi
ew
2.2.1.
Decissi
on sup
po
r
t
s
ys
te
m
Decisi
on
sup
port
syst
em
(D
SS)
is
a
syst
e
m
that
pr
ovide
s
reco
m
m
end
a
ti
on
s
f
or
so
l
vin
g
a
pro
blem
by
accom
m
od
at
ing
the
c
rite
ria
us
e
d
[
16
,
17]
.
DS
S
is
a
com
pu
te
rized
inform
at
ion
syst
e
m
fo
r
sup
ports
decisi
on
m
aki
ng
act
ivit
ie
s
by
organ
iz
in
g
c
rite
ria/
inform
ation
by
cal
cula
ti
ng
the
weig
ht
of
eac
h
al
te
r
native
involve
d
[18
,
19]
. In othe
r words
,
the
outp
ut
of
t
he
c
om
pu
te
r
-
base
d decisi
on s
upport syst
e
m
and
the
resu
l
ts o
f
the
decisi
on
c
an
be
use
d
a
s
a
reco
m
m
end
a
ti
on
to
so
l
ve
t
he
prob
le
m
s
faced
[20
-
22]
.
The
sta
ges
in
m
aking
a d
eci
sio
n
[23]
, consist
of
:
-
The
fi
rst
sta
ge
is
the
intel
li
gen
ce
sta
ge
,
w
hi
ch
is
lo
ok
i
ng
f
or
i
nfor
m
at
ion
on
t
he
crit
eria
us
e
d
to
pro
d
uc
e
decisi
ons a
nd
determ
ine the
weig
ht of
pr
e
fe
ren
ces
for ea
ch
of the
crite
ria.
-
The
seco
nd
sta
ge
is
the
design
sta
ge
by
m
akin
g
the
sta
ge
s
of
the
deci
sion
pr
ocess
m
ade,
dev
el
opin
g
the stages
of ea
ch deci
sio
n
m
a
king
process
, a
nd an
al
yz
in
g
t
he deci
sio
ns
th
at
will
b
e ta
ken.
-
The
thir
d
sta
ge
is
the
cho
ic
e
sta
ge,
w
hich
is
choosi
ng
the
a
vaila
ble
decisi
on
s
base
d
on
the
weig
hte
d
va
lue
for
eac
h
rec
omm
end
ed
decisi
on
t
o
s
olv
e
a
pro
blem
.
The
de
ci
sion
with
th
e
highest
weig
ht
is
the
on
e
to
be
i
m
ple
m
ented.
-
The
f
ourth
or
f
i
nal
sta
ge
is
the
i
m
ple
m
entat
i
on
sta
ge,
wh
e
r
e
the
reco
m
m
e
nd
e
d
decisi
on
with
the
highe
st
weig
ht is a
pp
li
ed
/ t
ake
n
t
o
s
ol
ve
the
pro
ble
m
.
The
intel
li
gence,
desig
n
a
nd
cho
ic
e
sta
ge
are
the
init
ia
l
ste
ps
in
decisi
on
m
aking,
an
d
it
is
ende
d
with
a
decisi
on
rec
om
m
end
a
ti
on
[
23
,
24]
.
These
rec
omm
end
at
io
ns
will
be
us
e
d
at
the
i
m
ple
m
entat
io
n
sta
ge
wh
e
re
t
he
rec
omm
end
ed
de
ci
sion
is
im
ple
m
ented
an
d
us
e
d
to
so
l
ve
a
pro
blem
[25
,
26]
.
T
o
determ
ine
the
weig
ht
value
f
or
each
c
r
it
erion
wh
ic
h
is
us
ed
as
al
te
rn
at
ive
so
l
ution
i
s
by
fi
gur
ing
ou
t
t
he
hi
gh
e
st
weig
hted
al
te
r
native
as
a
rec
omm
end
ed
de
ci
sion
bas
ed
on
the
hi
gh
est
weig
ht
value
[
27
]
.
S
om
e
m
eth
ods
are
of
te
n
us
e
d
in
de
ci
sion
s
upport
con
ce
pts
suc
h
as
AHP
(
a
naly
ti
cal
hierar
ch
y
pr
oce
ss
)
in
r
esearch
t
hat
ha
s
been
cond
ucted
by
[
28
-
30]
,
TO
PS
I
S
m
et
ho
ds
in
the
fo
ll
owin
g
s
tud
ie
s
[
31
-
33]
,
as
well
as
EO
Q
(
eco
nom
ic
or
de
r
qu
a
ntit
y
)
in t
his stu
dy
[
34,
35]
.
2.2.2.
Ec
onomic
o
r
d
er qu
an
ti
ty
Eco
no
m
ic
or
de
r
qua
ntit
y
(EOQ)
m
et
hod
is
use
d
to
m
ake
the
volum
e
or
num
ber
of
orde
rs
best
s
uite
d
to
the
nee
ds
th
at
are
im
ple
m
e
nted
at
each
ti
m
e
of
purc
has
e.
By
m
ini
m
iz
i
ng
the
cost
of
order
i
ng
go
ods
duri
ng
the
purc
hasin
g
tim
e,
the
costs
can
be
re
du
ce
d
as
eco
no
m
icall
y
as
po
ssible
[3
5]
.
The
EO
Q
m
e
tho
d
ca
n
al
so
be
us
e
d
to
stream
li
ne
raw
m
at
er
ia
ls
in
a
pr
oduc
ti
on
proces
s
com
par
ed
to
the
one
with
out
us
ing
the
m
e
th
od
.
The
us
e
of
ec
onom
ic
or
der
quantit
y
can
be
m
axi
m
iz
ed
if
t
he
orde
r
tim
e
and
order
qu
a
nt
it
y
are
known
[36]
.
The
ti
m
e
of
the
m
essage
(lea
d
tim
e),
nam
ely
the
tim
e
wh
en
the
order
is
m
ade
and
t
he
tim
e
wh
en
the
order
i
s
receive
d
[
37]
.
The
le
a
d
ti
m
e
is
known
an
d
i
s
co
ns
ta
nt
or
s
te
ady
eve
ry
ti
m
e
an
or
der
is
m
ade
[38]
.
Wh
erea
s
the num
ber
of
econom
ic
o
rd
e
rs
that m
igh
t
be
su
it
ed
to
t
his
m
et
ho
d
ca
n be
cal
culat
ed by t
he fo
rm
ula
[39
]
:
EOQ
Me
t
ho
d
=
√
2DS
H
(
1
)
w
it
h
:
EOQ
:
nu
m
ber
of it
em
s in
each order
D
:
annual
dem
and
for raw
m
at
e
rial
inv
e
ntory
S
:
costs
require
d pe
r order
H
:
the
fee
r
e
quir
ed fo
r
st
or
a
ge per
unit
ann
ually
2.2.3.
Reo
r
der p
oin
t
st
ock
Re
order
point
stock
can
be
in
te
rp
rete
d
as
an
ap
pro
pr
ia
te
ti
m
e
to
re
order
[
40
]
.
I
n
oth
e
r
w
ords
R
OP
i
s
a
per
io
d
in
w
hi
ch
orde
rs
m
us
t
be
re
-
m
ade.
ROP
is
al
so
rela
te
d
to
le
ad
tim
e
and
saf
et
y
st
ock
[41]
.
Be
cause
to
m
ake
the
RO
P
re
qu
ir
ed
t
he
rig
ht
le
ad
ti
m
e
is
wh
en
the
saf
et
y
stoc
k
ha
s
thin
ne
d
or
is
al
m
os
t
gone
[6]
.
To
cal
c
ulate
th
is reor
der
point
stoc
k
the
for
m
ula used
is
[
42]
:
=
(
∗
)
+
(
2
)
w
it
h:
ROP
:
orders
m
us
t b
e re
-
done
d
: t
he
num
ber
of d
ai
ly
n
ee
ds
L
:
le
ad
ti
m
e
/wai
ti
ng
ti
m
e fo
r
t
he
orde
r
to
b
e
r
e
tur
ned
SS
:
safety
stoc
k
/suffi
ci
ent am
ount of st
ock at o
ne
ti
m
e
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
10
, No
.
5
,
Oct
ob
e
r
2020
:
4
9
9
2
-
5
0
0
0
4996
2.2.4.
Sa
f
et
y
s
to
c
k
The
sa
fety
in
ven
t
or
y
se
rv
e
s
to
pr
otect
th
e
com
pan
y
a
gainst
a
c
ondi
ti
on
w
her
e
the
c
om
pan
y
exp
e
riences
a
s
hortage
of
raw
m
a
te
rial
s,
dela
ys
in
the
sup
ply
of
orde
re
d
ra
w
m
at
erial
s
that
hinder
pro
duct
io
n
act
ivit
ie
s
or
a
s
urge
i
n
dem
and
th
at
is
no
t
pr
edict
ed,
so
the
com
pan
y
m
us
t
increase
pro
du
ct
ion
t
o
m
eet
m
ark
et
needs
[
43
]
. In
gen
e
ral,
c
om
pan
y
m
anag
em
en
t
m
us
t
find
out
how
m
uch
ra
w
m
at
erial
s
are
sti
ll
ob
ta
ined
r
el
at
ed
to sto
rag
e
or st
or
a
ge
c
os
ts,
so
the co
m
pan
y
m
us
t al
so
d
et
er
m
ine the tolera
nce lim
i
ts
[44]
.
To
ca
lc
ulate
th
e
value
of
sa
fe
ty
stock,
the
first
thin
g
t
o
know
is
that
t
he
a
m
ou
nt
of
ra
w
m
a
te
rial
that
has
bee
n
us
ed
in
the
pre
vious p
eri
od
an
d
t
he
est
i
m
at
ed
a
m
ou
nt of
ra
w
m
ater
ia
l
that
will
be
us
e
d
i
n
that p
eri
od
is cal
culat
ed p
er year.
T
hen the
value
is
an
a
l
yz
ed
us
i
ng the
stat
ist
ic
al
f
orm
ula as f
ollows
[
5]
:
∶
√
∑
(
−
)
2
(
3
)
w
it
h
:
X
: t
he
act
ual am
ount
of r
a
w
m
a
te
rial
u
sag
e
Y
: t
he
est
im
a
te
d
a
m
ou
nt
of r
a
w m
at
erial
s u
sag
e
N
: t
he
am
ou
nt
of d
at
a
3.
RESU
LT
S
AND A
N
ALYSIS
Figure
2
e
xpla
ins
the
f
ram
e
work
of
the
sy
stem
being
cre
at
ed,
w
her
e
t
he
center
of
t
he
cy
cl
e
is
in
the
de
sig
n
of
syst
e
m
s
that
relat
e
to
al
l
processes
,
f
r
om
the
analy
sis
st
age
to
the
det
erm
inati
on
of
bot
h
functi
onal
an
d
non
-
f
un
ct
io
nal
requirem
ents
of
th
e
sys
te
m
.
The
decisi
on
of
us
i
ng
t
he
eco
no
m
ic
or
de
r
quantit
y
as
a
m
et
ho
d
f
or
cal
culat
in
g
the
decisi
on
s
upport
syst
em
is
to
fig
ur
e
ou
t
the
nu
m
ber
of
order
s
that
m
us
t
be
requested
by
the
com
pan
y,
t
o
determ
ine
the
le
ad
tim
e
and
safety
stoc
k
therefor
e
the
com
pan
y
ca
n
m
ake
pr
e
ve
ntive
act
ion
s
w
hen
t
he
stock
has
thi
nned
a
nd
the
use
r
centere
d
de
sign
m
et
ho
d
a
s
syst
e
m
dev
el
op
m
ent
m
et
ho
d,
since
the
syst
em
is
t
ai
lored
to
al
l
t
he
needs
of
use
rs.
I
n
this
stu
dy,
the
com
pa
ny
is
the
Ma
c
kar
el
Com
pan
y.
The
design
cy
cl
e
al
so
gets
fe
ed
ba
ck
from
the
us
er
durin
g
the
proces
s
of
desig
n
evaluati
on.
It
is
to
i
m
pr
ove
the
sy
stem
that
has
been
co
rr
ect
e
d
by
the
us
e
r.
U
sers
can
reque
st
i
m
pr
ov
em
ents
to
syst
e
m
mo
dule
s
that are
not s
uitable
to
t
he nee
ds
.
Figure
2
.
The
fram
ewo
rk of i
nfor
m
at
ion
sys
tem
r
esearch
In
t
his
re
searc
h,
a
sto
rag
e
fe
e
of
I
DR
60,000
pe
r
ye
ar
is
us
e
d
wit
h
a
book
i
ng
fee
of
I
DR
10
5,0
00
,
or
a
n
a
ver
a
ge
of
ID
R
8,7
50.
00
m
on
thly
,
and
a
n
a
ver
a
ge
or
der
of
5
to
6
ti
m
es
a
m
essage
pe
r
m
on
th.
The
a
ver
a
ge
a
m
ou
nt
of
ra
w
m
a
te
rial
pu
rc
h
ases
per
m
on
th
reac
hes
290
kg.
T
he
a
verage
m
on
thly
cost
of
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N:
20
88
-
8708
Op
ti
m
atio
n
ec
onomic
order
quanti
ty
m
et
ho
d
fo
r
a
s
upport
system r
eor
de
r point st
ock
(
L
inda Per
dana
Wa
nti
)
4997
purc
hasin
g
ra
w
m
a
te
rial
s
that
m
us
t
be
incurred
by
the
com
pan
y
is
ID
R
10
,
141,0
00.
00.
These
c
os
t
s
will
be
us
e
d
to
cal
culat
e
the
aver
ag
e
raw
m
a
te
rial
need
ed
in
a
per
io
d
of
1
m
on
th
and
to
cal
culat
e
the
safety
requirem
ents,
nam
ely
the
sto
ck
of
safe
ra
w
m
at
erial
s
in
supp
li
es
per
m
onth
an
d
t
he
nee
d
for
raw
m
at
e
rial
s
in
the
wait
in
g
peri
od
.
By
us
in
g
t
he
f
orm
ula
that
was
e
xpla
ined
in
the
previ
ous
cha
pter,
this
cha
pte
r
will
lo
ok
f
or
the v
al
ues
of th
ese nee
ds
o
ne by o
ne.
Starti
ng
by
ca
lc
ulati
ng
the
value
of
orde
r
quantit
y
com
pan
y
us
in
g
t
he
EO
Q
m
et
ho
d
wit
h
(
1),
the r
es
ult i
s:
:
√
2
∗
8750
∗
5
.
83
∗
290
5000
∶
76
.
91
(
4
)
Af
te
r
knowin
g
the
value
of
th
e
orde
r
quantit
y,
the
n
the
value
is
us
e
d
to
fi
nd
the
re
orde
r
po
i
nt
value
us
i
ng
(
2),
bu
t
t
he
a
ver
a
ge
ra
w
m
at
erial
re
qu
irem
ents
per
m
on
th
m
us
t
be
know
n
i
n
a
dv
a
nce,
as
well
as
the
va
lue
of
safety
re
qu
i
re
m
ents
an
d
t
he
needs
durin
g
t
he
wait
ing
tim
e
f
or
orde
rin
g
raw
m
at
erial
s
befor
e
it
com
e.
A
fte
r
the
cal
culat
ion,
it
is
found
t
hat
the
a
ver
a
ge
value
of
ne
eds
per
m
on
th
is
141.0
1
kg,
the
val
ue
of
safety
requirem
ents
cal
culat
ed
by
(
3)
with
a
tole
ra
nce
lim
it
of
10%
is
360
kg
a
nd
t
he
ra
w
m
a
te
rial
s
need
e
d
durin
g
wait
ing
ti
m
e resu
lt
ed
i
n
re
ord
er
values p
o
int
is 6
9.26
kg.
Figure
3
e
xp
la
ins
the
relat
ionship
bet
ween
EOQ,
re
order
point
a
nd
safe
ty
stock
i
n
a
di
agr
am
with
the
value
s
obta
ined
in
pr
e
vi
ou
s
cal
c
ulati
ons.
From
the
analy
sis,
it
can
be
con
cl
ud
e
d
th
at
the
com
pan
y
will
reord
e
r
w
he
n
the
am
ou
nt
of
raw
m
at
erial
is
on
ly
69.
26
kg
le
ft.
Order
s
m
ade
by
the
c
om
pan
y
a
m
ou
nted
to
76.91
kg
to
i
nc
rease
ra
w
m
at
erial
inv
e
nto
r
y.
Wh
il
e
the
s
afety
stock
val
ue
is
360
kg,
i
n
this
case
the
safety
stock
va
lue
is
la
rg
e
beca
us
e
it
is
us
ed
to
m
ai
ntain
the
sta
bili
ty
of
the
com
pan
y'
s
su
pp
ly
as
a
pr
e
ve
ntive
m
easur
e in
the
even
t
of a s
urg
e in m
ark
et
d
e
m
and
or a s
hor
ta
ge
of
raw m
a
te
rial
su
pply
due to
v
a
rio
us
f
act
or
s.
Figure
3.
The
re
la
thion
s
hip be
tween
of E
OQ,
r
e
orde
r po
i
nt,
and sa
fety
sto
ck
4.
CONCL
US
I
O
N
Ba
sed
on
t
he
r
esults
of
the
a
naly
sis
that
ha
s
bee
n
done
a
nd
ex
plai
ne
d
i
n
the
previ
ous
discu
ssio
n,
the
co
ncl
us
io
n
that
can
be
dra
wn
f
ro
m
this
st
ud
y
is
that
the
econom
ic
or
de
r
qu
a
ntit
y,
re
order
point
value
an
d
s
a
f
e
t
y
s
t
o
c
k
v
a
l
u
e
a
r
e
t
h
e
m
o
s
t
o
p
t
i
m
a
l
t
o
s
up
p
o
r
t
t
h
e
c
om
pa
n
y
i
n
s
o
l
v
i
n
g
p
r
o
b
l
e
m
s
r
e
g
a
r
d
i
n
g
t
h
e
p
r
o
c
u
r
e
m
e
n
t
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
10
, No
.
5
,
Oct
ob
e
r
2020
:
4
9
9
2
-
5
0
0
0
4998
o
f
r
a
w
m
a
t
e
r
i
a
l
s
f
o
r
i
n
v
e
n
t
o
r
y
a
n
d
p
r
o
d
u
c
t
i
o
n
b
y
c
o
n
s
i
d
e
r
i
n
g
a
n
d
e
s
t
i
m
a
t
i
n
g
m
a
r
k
e
t
d
e
m
a
nd
a
n
d
w
a
i
t
i
n
g
t
i
m
e
s
.
Ra
w
m
at
erial
s
are
use
d
by
the
com
pan
y
f
or
t
he
producti
on
process
.
Re
ord
er
point
value
is
69
kg,
E
OQ
i
s
77
kg
an
d
safety
stock
is
35
kg.
From
the
cal
culat
ion
s
pe
rfo
r
m
ed,
the
m
os
t
econom
ic
al
tot
al
cost
of
in
ve
ntory
has
been
obta
ined
.
T
his
stu
dy
aim
s
to
op
ti
m
iz
e
the
EOQ
m
et
ho
d
a
nd
to
fig
ur
e
out
the
op
ti
m
u
m
reo
r
de
r
point
,
safety
stoc
k
a
nd E
OQ v
al
ues by ap
plyi
ng it
to
the
d
eci
si
on
su
pp
or
t sy
ste
m
. Suggesti
ons
f
or
f
ur
t
her
resea
rch
is
to
de
velo
p
de
ci
sion
m
aking
t
o
determ
ine
safety
stock,
re
ord
er
points
a
nd
e
conom
ic
or
de
r
qu
a
ntit
y
us
in
g
oth
e
r
m
et
ho
ds so
the
r
es
ults can
b
e
com
par
ed.
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est
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r
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iv
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20
18.
[20]
J.
Sains,
L.
Sop
ia
nti,
and
N.
B
aht
i
ar,
“
Students
Major
Dete
rm
ina
ti
on
De
ci
sion
Support
Sy
st
e
m
s
U
sing
Profile
Matc
hing
Metho
d
with
SM
S Gat
ewa
y
Im
ple
m
entati
on
,
”
J. Sai
ns
Dan Mat
.
,
vol
.
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3,
no
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,
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-
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–
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2015
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[21]
I.
Kaba
shkin
a
nd
J.
Lu
či
na
,
“
Deve
lopme
nt
of
the
m
odel
of
decision
su
pport
for
al
t
er
nat
iv
e
cho
ic
e
i
n
the
tra
nsporta
ti
o
n
tra
nsi
t
s
y
s
te
m
,
”
Tr
ansp.
Tel
ec
o
mm
un.
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72
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F.
Kitsios
and
M.
Kam
ari
otou,
“
Dec
ision
support
s
y
stems
and
business
strat
e
g
y
:
A
con
ce
p
tu
al
fra
m
ewo
rk
fo
r
strat
eg
ic
informati
on
s
y
st
ems
pla
nning,”
2016
6
th
Int
.
Conf
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IT
Conve
rg.
S
ec
ur.
ICITCS
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B.
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K.
Jones,
B.
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nnar
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S.
Nw
auba
ni,
“
A
val
id
at
ed
low
c
a
rbon
office
bui
ld
ing
interve
n
ti
on
m
odel
base
d
on
struct
ura
l
equ
ati
on
m
odel
li
ng
,
”
J
.
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ean. P
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M.
Al
Shobaki
and
S.
Abu
Nas
er,
“
Requi
remen
ts
for
Apply
ing
Dec
ision
Support
Sy
st
ems
in
Pale
stini
an
Highe
r
Educ
a
ti
on
Institutions
-
Applie
d
Stud
y
on
Al
-
Aqs
a
Univer
sit
y
i
n
Gaz
a,”
E
conStor
Open
Ac
ce
ss
Arti
c
.
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–
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Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N:
20
88
-
8708
Op
ti
m
atio
n
ec
onomic
order
quanti
ty
m
et
ho
d
fo
r
a
s
upport
system r
eor
de
r point st
ock
(
L
inda Per
dana
Wa
nti
)
4999
[25]
L.
A.
R
.
W
ina
n
da,
A.
Arif
in,
F
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Arrofiqi
,
T.
W
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Adi,
and
N
.
Anw
ar,
“
A
design
conc
ep
t
of
fuz
z
y
dec
ision
suppor
t
s
y
stem for const
ruc
ti
on
workers
safe
t
y
m
onit
or
in
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”
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“
Dec
is
ion
Support
S
y
st
em for
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ai
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Dec
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Support
S
y
stems
(DS
S)
Capa
b
il
i
ti
es
a
nd
Com
pet
enc
i
es
Im
pac
t
on
Firm
Perform
anc
e:
A
Media
ti
ng
Rol
e
of
Abs
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ve
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c
ity
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Int.
J
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B
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“
Te
sting
an
AH
P
m
odel
for
ai
rcr
aft
spar
e
p
art
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P
la
n.
Control
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vo
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“
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a
ti
on
of
t
he
Anal
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t
ic
a
l
Hi
era
rch
y
Proc
ess
(AH
P)
to
Multi
-
Crit
eria
Anal
y
si
s
for
Contractor
S
el
e
ct
ion
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”
Am. J
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Ind. Bus. Mana
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,
vol. 5, no. 9,
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,
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K.
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M.
Laaz
iri
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S.
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M.
L.
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A
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E
l
Yam
ami,
“
AH
P
-
base
d
Approa
ch
for
Ev
al
u
at
ing
Ergonomic
Cri
teria,”
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dia
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S.
A.
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“
Eff
ic
ie
n
c
y
ran
k
ing
of
decision
m
aki
ng
unit
s
in
dat
a
enve
lopmen
t
anal
y
s
is
b
y
usi
ng
TOPS
IS
-
DE
A
m
et
hod,
”
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Re
s
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rci
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B.
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z
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“
Ergonomic
Room
S
el
e
ct
ion
with
Int
uit
ive
Fuz
z
y
TOPS
IS
Me
thod,
”
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dia
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y
-
bas
ed
fuz
z
y
TOPS
IS
fra
m
ework
for
sele
ct
i
on
of
a
sus
ta
inable
buil
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m
a
te
ri
a
l,
”
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K.
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s,
B.
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evi
ć
,
“
Neuro
-
fu
zzy
infe
r
enc
e
s
y
stems
appr
o
ach
to
dec
ision
support
s
y
st
em for
e
con
om
ic
orde
r
quan
ti
t
y
,
”
Ec
on
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Re
s.
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N.
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y
a
,
“
Support
Sy
st
em
for
Dete
rm
ini
ng
Dec
ision
of
Raw
Mate
ria
l
Inve
ntor
y
(Case
Stu
d
y
:
PT.
Maka
ss
ar
Mega
prima)
(in
Indone
sia:
Sis
te
m
Pendukung
Keputusa
n
Penent
uan
Persediaa
n
baha
n
Baku
(Studi
Kasus
:
P
T.
Maka
ss
ar
Mega
prima)
)
,
”
InfoSys
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“
Ec
o
nom
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orde
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and
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pla
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ct
i
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s,”
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“
Introduc
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ic
Ord
er
Quantit
y
Mo
del
for
Inv
ent
or
y
Control
in
W
eb
base
d
Point
of
Sale
Appli
cations
and
Com
par
at
iv
e
Anal
y
s
is
of
Techni
ques
for
De
m
and
Forec
asti
n
g
in
Inve
n
tor
y
Ma
nage
m
ent
,
”
In
t.
J.
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and
A.
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stedt
,
“
Ec
onom
ic
Order
Quanti
tie
s
in
produc
ti
on:
From
Harri
s
to
Ec
onom
ic
Lo
t
Schedul
ing
Problems
,
”
In
t. J
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E.
A.
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,
“
Com
par
at
ive
Anal
y
sis
of
R
e
-
Order
Point
Calculations
(in
In
donesia
:
Ana
li
si
s
Perba
ndinga
n
Pe
rhit
ungan
Re
-
Or
der
Point
)
,
”
Bi
nu
s B
us. Rev.
,
vol
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288
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300,
2014
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[40]
N.
K.
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al
and
D.
K.
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h
ar,
“
Optimiza
ti
o
n
of varia
b
le
demand
fuz
z
y
e
conomic
orde
r
quantit
y
i
nvent
or
y
m
odel
s
without
and
with
bac
kord
eri
ng
,
”
Comput.
Ind
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g.
,
vol. 78, pp. 1
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[41]
S.
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“
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c
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ic
Order
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M.
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R.
L.
La
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,
“
Econom
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ity
(EOQ),
”
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cl
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and
A.
R.
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ij
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a
,
“
Dete
rm
ini
n
g
Safe
t
y
Stock
,
Reorde
r
Points
and
Order
Quan
ti
t
y
of
Produc
ti
o
n
Mac
hine
Parts
Based
on
Unc
er
ta
inty
of
Dem
an
d
and
L
ea
d
Ti
m
e
in
Manuf
actur
ing
Com
pani
es
(
Case
Stud
y
a
t
P
T
W
ij
a
y
a
Kar
y
a
B
et
on
PP
B
Bo
y
ol
al
i)
(
in
Indone
si
a:
Pen
en
tua
n
Saf
ety
Stock
,
Reord
er
Point
dan
Ord
er
Quantit
y
Suku
Cada
ng
Mesin
Produks
i
Berda
sa
rka
n
Ket
ida
kp
asti
an
Dem
and
da
n
Lead
Ti
m
e
p
a
da
Perusaha
an
Manufa
ktur
(
Stu
d
i
Kasus
di
PT
W
ij
a
y
a
K
ar
y
a
B
et
o
n
PP
B
Boy
olali
)
)
,
”
in
Seminar
Nasional
Tekni
k
Industri
UG
M
2015
,
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91
–
99
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C.
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şanu
,
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y
Ma
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m
ent
,
Serv
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eve
l
and
Sa
fety
Stock
,
”
J
.
Publ
ic
Adm.
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in
anc.
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–
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53,
2016
.
BIOGR
AP
H
I
ES
OF
A
UTH
ORS
Lind
a
Pe
rdana
Wanti
was
born
in
Ban
y
um
as,
Octobe
r
1
0,
1
988.
Curre
nt
l
y
working
as
a
le
ct
u
rer
at
th
e
Cil
acap
Stat
e
Po
l
y
technic
sinc
e
2019.
She
rec
ei
v
e
d
the
Master
of
Com
pute
r
Scie
nc
e
(M.
Kom
.
)
degr
ee
in
In
form
at
ic
s
Engi
n
ee
ring
from
Univer
sita
s
Am
ikom
Yoy
ak
ar
t
a
in
2013.
Her
rese
arc
h
in
te
r
ests
inc
lude
m
ac
hi
ne
le
arn
ing
for
dec
ission
support
s
y
stem,
dat
ab
ase
and ex
per
t
s
y
stem.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
10
, No
.
5
,
Oct
ob
e
r
2020
:
4
9
9
2
-
5
0
0
0
5000
Ratih
Hafsarah
Maharr
ani
ha
s
a
Master
of
C
om
pute
r
Scie
n
c
e
(M.Kom
)
in
I
nform
at
ic
s
Engi
ne
eri
ng
fro
m
Dian
Nus
wantor
o
Univer
sit
y
Sem
ara
ng
in
2
010.
Her
r
ese
ar
ch
intere
st
s
inc
lud
e
m
ac
h
ine l
e
arn
ing
for
d
ecision
support
s
y
s
te
m
s,
and
data
m
ini
ng.
Nu
r
Wachid
A
di
Pr
aset
y
a
was
born
in
W
onogiri
,
Novem
ber
15
,
1988.
Curre
n
tly
working
as
a
lectur
er
at
the
Cil
acap
State
Pol
y
technic
si
nce
2019.
He
g
rad
uated
from
Inform
at
ics
Engi
ne
eri
ng
a
t
UIN
Sunan
Kali
ja
ga
Yog
y
ak
art
a
in
2011
and
earned
a
Master
o
f
Com
pute
r
Scie
nc
e
(M.
Kom
)
from
the
Ma
ster
of
Info
rm
at
i
on
Engi
n
ee
ring
at
the
Univ
ersity
of
Islam
Indone
sia
in
201
8.
He
has
rese
ar
ch
int
er
ests
in
the
fie
ld
of
image
proc
essing
and
i
nform
at
ion
s
y
stems
design.
Ek
a
Tri
pu
stika
sari
has
a
Maste
r
of
Scie
nce
(M.
Si)
in
Ec
onom
ics
from
Jende
ral
Soedirman
Univer
sit
y
Purw
oker
to in
2011
.
Her
rese
arc
h
intere
sts in
cl
ude
m
ana
gement
and
l
ea
der
ship
.
Ganjar
Nd
aru
Ik
htiagu
ng
was
born
in
Pekal
o
ngan,
Jul
y
28,
1
983.
Curre
n
tly
working
a
s
a
lectur
er
at
the
Cil
acap
Sta
te
Po
l
y
technic.
He
gr
adua
t
ed
from
Econom
ic
s
Facu
lty
at
Unnes
in
2006
and
ea
r
ned
a
Master
of
Mana
gement
(M.M)
f
rom
the
Master
of
Mana
gement
a
t
the
Diponegor
o
Univer
sit
y
,
Se
m
ara
ng,
Indone
sia
in
2015.
He
has
rese
arc
h
int
er
ests
in
m
ana
gement and
ec
onom
ic.
Evaluation Warning : The document was created with Spire.PDF for Python.