TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol.12, No.6, Jun
e
201
4, pp. 4368 ~ 4
3
7
8
DOI: 10.115
9
1
/telkomni
ka.
v
12i6.547
0
4368
Re
cei
v
ed
De
cem
ber 2
1
, 2013; Re
vi
sed
Feb 8, 2014;
Acce
pted Fe
brua
ry 21, 20
14
Design of Enterprise Production and Sales measures
and Forecast In
formation System Based on WEB
Gao Shuzhi
1
, Zhao Na
2
School of Infor
m
ation En
gi
ne
erin
g, Shen
ya
n
g
Univ
ersit
y
of Chemic
al T
e
chnol
og
y,
Shen
ya
n
g
110
142, Ch
in
a
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: szg686
8@1
2
6
.com
1
, 3291
7
409
5@q
q
.com
2
A
b
st
r
a
ct
T
he enter
prise
supp
ly of raw
mater
i
als
an
d
prod
ucti
on, sa
l
e
s, prod
uct inf
o
rmatio
n
inte
gr
ation
as a
researc
h
obj
e
c
t, on the basis of J2EE architecture
a
n
d
the Sprin
g
framew
ork, buil
d
in
g W
eb Bas
e
d
Enterpris
e
pro
ductio
n
an
d di
stributio
n and
pred
iction
of
mu
lti-tier arch
itecture ERP system. First, use
sprin
g
+
h
ib
erna
te imp
l
e
m
e
n
tati
on of MVC des
ign
mod
e
; and,
w
i
th mvc mod
e
imple
m
entati
on of J2EE Mult
i
- Application S
ystem schema, developm
ent
function
perfect enterpr
ise s
a
les
m
a
nagement system
;
last,
use
move
aver
age
law
,
a
n
d
T
r
end
F
o
recast
law
,
and
i
ndex
smooth
law
,
a
nd
lin
e typ
e
th
e
most X
i
a
opi
n
g
meth
od a
nd cu
rve type the most Xiaop
ing
method F
i
ve kin
d
of algorith
m
avera
ge to sal
e
s accurate re
al -
time
forec
a
st, decisi
o
n
secto
r
can
dev
elo
p
ed
accurate
pr
oducti
on
pl
an t
o
effective
gu
i
danc
e pr
od
ucti
on
job.
Ke
y
w
ords
:
MVC mo
de, J2E
E
framew
ork, e
n
terpri
s
e
resou
r
ce pla
nni
ng, s
a
les pr
edict.
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
．
Introdu
cti
on
Produ
ction
o
f
the ch
emi
c
al indu
stry i
s
a
co
ntinuo
us
pro
c
e
s
s i
ndu
stry, pro
ductio
n
planni
ng, re
al
-time sch
edul
ing, real
-time
monito
ri
ng
system ha
s a
very importa
n
t
position, o
n
a
contin
uou
s
prod
uctio
n
p
r
ocess-ori
ent
ed ente
r
p
r
ise, guarante
e
the sta
b
l
e
, full capa
city
prod
uctio
n
lin
e is the prem
ise an
d key t
o
decre
ase t
he co
nsumpti
on of pro
d
u
c
ts, red
u
ce co
sts
and ne
ed
s o
f
the enterp
r
i
s
e safety in prod
ucti
o
n
, e
n
vironm
ental
prote
c
tion.ERP (Enterpri
s
e
Re
sou
r
ce Plannin
g
) a
s
a mean
s of
enterp
r
ise deci
s
io
n su
p
port sy
stems and tech
no
logy
manag
eme
n
t, has bee
n growin
g re
cog
n
i
tion and use of domesti
c a
nd foreig
n en
terpri
se
s [1. 2].
Acco
rdi
ng to
statistics, 8
0
%
of the
wo
rl
d's to
p 5
00
compani
es ha
ve implem
ent
ed e
r
p; e
r
p
u
s
ers
in China have exceeded 2000, and
sales reached 2000 in
10
billion.
According to
presentations,
in Hai
c
an
g so far in 1994
, the Group
adopte
d
ER
P
system
s, with averag
e an
nual be
nefits of
more than 10 million; bosoar grou
p ERP
system, lower
product cost
s 5%-15%
, 10% reduction in
stock fund
s-4
0
%, produ
ctivity 5%-15%.
Sales m
ana
g
e
ment in m
o
dern
ente
r
pri
s
e m
anag
em
ent occu
pie
s
an impo
rtant
positio
n;
a sale
s man
a
gement alm
o
st determi
ne
s the level of
the enterpri
s
e'
s economi
c
li
feline. With the
developm
ent
of sociali
s
t market eco
nomy, sa
le
s are increa
singly importa
nt in enterprise
prod
uctio
n
an
d mana
gem
e
n
t activities, p
r
odu
ction
and
sale
s of the
prod
uct o
n
ly throu
gh in
ord
e
r
to reali
z
e
the
value of
co
mpen
sation
after the
pro
ductio
n
cost,
creating
a certain amou
n
t
of
profit. The
r
ef
ore, e
n
terp
ri
ses m
u
st
stre
n
g
then th
e
ma
nagem
ent of
sale
s, ma
rket
fore
ca
sts, h
o
ld
sale
s, promo
t
e the enterp
r
ise
org
ani
ze
prod
uction
and sale
s by market dem
and, efforts t
o
redu
ce
sal
e
s co
sts a
nd i
n
crea
se e
c
o
nomic
efficie
n
cy. Base
d
on this, the
enterp
r
i
s
e m
u
st
establi
s
h
a set of sale
s f
o
re
ca
sting, p
l
annin
g
,
acco
unting, supe
rvision,
contro
l, analysi
s
a
nd
deci
s
io
n
sup
port
ca
pabilit
ies i
n
o
n
e
sales ma
nage
ment
system
to
sup
port
enterp
r
i
s
e
s
f
o
r
effective man
ageme
n
t of sales [3].
This a
r
ticle
mainly desi
g
ned and d
e
velope
d a pro
ductio
n
enterprise ba
sed
on ERP
sale
s and fo
reca
sting
syst
em, the whol
e system is
t
he platform o
n
the Internet
, using BS mode
while u
s
ing
SQL se
rver,
j2ee, mvc technolo
g
y as a deve
l
opment tool
. Finally, in
the
developm
ent and sales m
anag
ement o
f
enterpri
s
e
ERP system
a sale
s fore
cast modul
e, and
validation.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
De
sign of Enterp
rise Prod
u
c
tion an
d Sales m
easu
r
e
s
and Fo
re
ca
st Inform
ation…
(Gao Shu
z
hi
)
4369
2. Ke
y
Technolog
y
and Soft
w
a
re
Ar
chitec
ture o
f
ERP Sy
stem
2.1. Ke
y
Tec
hnolog
y
ERP System
Develo
pment
use the
curre
n
t internation
a
l po
pula
r
th
ree laye
r BS
(bro
wser
serve
r
)
stru
cture [
4
]; we
u
s
e i
s
by jsp+strut
s
+ejb va
rious compo
n
ents
co
mpo
s
ed of te
ch
nol
ogy;
whi
c
h,
JSP a
nd u
s
e
r
inte
ra
ctive; Struts
resp
on
sibl
e
fo
r receive from
JSP p
a
ss
co
me data,
will
its
pass to EJB
components,
the sam
e
time, it can will from datab
ase or processi
ng data pass to
JSP page; EJB the main respon
sibl
e for and d
a
tab
a
se inte
ra
ctive, to data incre
a
se, and
by
deleting, an
d cha
nge o
pera
t
ion.
2.2. Soft
w
a
r
e
Archi
t
ec
tu
re
This
system
is the u
s
e
of MVC de
sig
n
pa
ttern
s[5][6
], both model
s (m
odel
) --
A view
(view) -- Th
e controlle
r (Co
n
trolle
r). It is mandat
o
r
y to sepa
rate the
input, processing a
nd outp
u
t
of the appli
c
ation, and St
ruts i
s
a typical impl
eme
n
tation of MVC frame
w
orks, whi
c
h will
b
e
at
the front de
sk of the b
r
owser, a mi
ddle tier
, Co
ntrolle
r, and
J2EE EJB comp
one
nts
that
impleme
n
t the busi
n
e
ss lo
gic an
d datab
ase lin
ks up, will be de
scri
bed sepa
ratel
y
below.
(1) MVC
mode
MVC appli
c
at
ions into thre
e parts
of the model
, view, Controlle
r, as sho
w
n in Fig
u
re 1:
Even
t
Sta
t
us cha
nge
V
i
e
w
selection
U
s
er
re
qu
e
s
t
s
No
ti
ce of
chan
ge
Sta
t
us q
uer
y
Model
N
o
ti
fi
e
s
the
vi
ew
chan
ges
Re
s
p
on
s
e
pr
og
ra
m f
e
at
u
r
e
s
R
e
sp
ond
to sta
t
us i
n
qui
r
i
e
s
statu
s
Packa
ge a
ppl
i
c
ati
o
n stat
e
View
Select
the
vi
e
w
o
f
th
e respo
n
se
Use
r
a
c
ti
on i
s
m
a
p
ped
in
to a
m
ode
l
upd
ate
D
e
f
i
n
e
ap
pl
i
c
ati
on
beh
avi
o
r
Sel
e
ct
Vi
ew
per
m
i
t
cont
rol
p
r
o
ducts
T
r
ansm
i
tti
ng u
s
er
i
npu
t to
the
contr
o
l
l
e
r
M
ode
l
upd
ate
re
que
st
In
ter
p
r
e
ta
ti
on m
o
d
e
l
Control
Me
tho
d
of
deb
ugg
in
g
Figure 1. The
MVC Pattern
of each Part
of the Relatio
n
shi
p
and Fu
nction
(2)
Struts
Fra
m
e
w
or
k
Since
the
beg
inning
of th
e
90, Strut
s
fra
m
e
stru
cture wa
s the first
to implement the MVC
pattern, after nearly 10 y
ears of devel
opment, Stru
ts has m
a
tured, it has a
compl
e
te set of
softwa
r
e architecture,
by
Cent
ral Co
ntrol co
mp
o
nent pe
rfe
c
t com
b
ination
of the vari
ous
comp
one
nts
[
6
]. Is an essential asp
e
ct
of busine
ss
Web
site soft
ware. The frame structu
r
e is
s
h
ow
n
in
F
i
gu
r
e
2
.
Figure 2. Struts MVC Fra
m
ewo
r
k Implemented
a)
View: mainly
gene
rated
b
y
the JSP pa
ge
complete
view, Struts
JSP tag lib
ra
ries
provide a
rich
.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 6, June 20
14: 4368 – 4
378
4370
b)
Model: rep
r
e
s
ent
s the
ap
plicatio
n's bu
sine
ss
logi
c, model exists in
the
fo
rm
of
one
or more Java
Bean.
c)
Controlle
r: struts-config th
rough th
e pre
v
ious figu
re,
you ca
n se
e
an XML file. XML
is a
s
soci
ated
with the Co
ntrolle
r, in Struts, hol
ding
the Co
ntrolle
r in the MVC
role i
s
a
serv
let
whi
c
h is
call
ed actio
n
serv
let. Actionse
r
vlet is a com
m
on control
comp
one
nt. The control
unit
providin
g ha
n
d
led all
HTTP
requ
est
s
sen
t
to the
strut
s
entry point. It intercepts an
d dist
ribute th
e
requ
est
s
to the approp
riate
action
d)
Process: in Struts, the use
r
's
req
u
e
s
t
General to. Do
as the nam
e
of the reque
sted
servi
c
e. Do
reque
sts a
r
e
links to actio
n
sevlet,
actio
n
sevlet un
de
r strut
s
-co
n
fig. In the XML
config
uratio
n
informatio
n, the u
s
er
re
qu
est is
pa
ckag
ed a
s
a fo
rm
bean
with the
spe
c
ified
na
me,
and a
c
tion
b
ean i
s
the fo
rm be
an to
specify t
he n
a
m
e, by actio
nbea
n thro
ug
h the bu
sin
e
ss
operation
s
, t
hese
strut
s
-config.In the
XML configu
r
ation. a
c
tion
sevlet,action
s
e
v
let strut
s
-co
n
fig
is
the c
o
re - t
he c
o
re of the Struts
.
(3) J2EE
overview
The $lite
r
al pl
atform u
s
ing
multi - layer d
i
st
ribute
d
ap
p
lication m
odel
. Application
of logic
based on thei
r feature
s
into
multiple com
pone
nts,
a variety of comp
onent
s distri
b
u
ted acro
ss t
he
different j2ee
prog
ram d
e
p
end
s on the l
e
vel of machi
ne [7].
Systems Usi
ng j2ee - logi
c layer on th
e serv
e
r
, usin
g both EJB compon
ent as a data -
pro
c
e
ssi
ng compon
ents, Enterpri
se
Ja
va
Bean refe
rre
d to
as EJB, it run
s
o
n
EJB serve
r
, i
s
a
non-vi
sual
cli
ent call the remote obje
c
t
.
EJB has
a seri
es of ag
reement
s allo
w itself to be
a
remote acce
ss
or
i
n
stall or deploy
on a
p
a
rticul
ar
se
rver. EJB tra
n
saction m
e
cha
n
ism p
r
ovide
d
a
very com
p
re
hen
sive, but
the do
wn
side
is that
p
e
rfo
r
man
c
e deg
radation asso
ciated with
t
he
transactio
n
m
e
ch
ani
sm. EJB suppo
rt pa
rtial appli
c
at
i
on system. In the three d
i
fferent types
o
f
enterp
r
i
s
e
de
fined in
the E
J
B b
ean:
se
ssion
be
an,
e
n
t
ity bean, me
ssage
- drive
n
be
an. G
ene
ral
Applicatio
n Server i
s
EJB serve
r
[8].
3. Sales Man
a
gemen
t
Sy
stem Based
on ERP
3.1. Introduc
tion of Sales
Manageme
n
t
Sy
stem
Sales
mana
gement
syst
em is an
i
m
porta
nt su
bsyste
ms
of Enterp
rise
Re
sou
r
ce
Planning
(ERP), sale
s ma
nagem
ent ha
ve a direct i
m
pact on th
e
Enterpri
se G
l
obal. Sales
are
the startin
g
p
o
int of busin
e
ss a
c
tivity, produ
ct
ion, fina
nce, pe
rsonn
el, and othe
r manag
eme
n
t of
enterp
r
i
s
e
s
h
a
s a d
e
ci
sive
role. Sale
s system and
di
rectly to cu
sto
m
ers; the Enterp
rise win
d
o
w
,
embodi
ment
of co
rp
orate
i
m
age; it fo
r
b
u
sin
e
ss
de
cision
m
a
kers to provide ma
rket informati
on
and
sal
e
s;
an
d p
r
ovide
info
rmation
for th
e p
r
odu
ction
forecast
an
d
prod
uctio
n
pl
annin
g
syste
m
;
cu
stome
r
fee
dba
ck for q
u
a
lity manage
ment system
s;
provide
ke
y financial da
ta for the financial
system. Sale
s mana
gem
e
n
t system an
d other
sub
s
ystem
s
as
sho
w
n in Figu
re
3:
Figure 3. Sales Man
age
m
ent System and other Su
b
s
ystem
s
3.2.
Busines
s
Process
The system
The main for
a ch
emi
c
al sale
s se
ctor bu
sine
ss
d
e
si
gn,
sales
Th
e
Dep
a
rtme
nt of resp
on
sible
for ch
emical
prod
uct
s
s
a
le
s,
cu
st
ome
r
af
t
e
r kn
ow p
r
odu
ct
s de
ci
si
on
purcha
s
e
pro
duct
s
, ca
n b
e
sig
ned
con
t
ract, sales
sector
acco
rdi
ng to c
ontra
ct content o
r
d
e
r,
customer in
contract
term
inate
date to financial
sector paymen
t,
payment financial
sector
will
cu
stome
r
kno
t
para
g
ra
ph i
n
formatio
n to
sale
s
se
ct
or,
sale
s se
ctor according
to
knot
p
a
ragra
p
h
singl
e to
cu
st
omer op
en
bi
ll of ladi
ng,
custome
r
by
B
ill of La
ding
to warehou
se
take
of.Sales to
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
De
sign of Enterp
rise Prod
u
c
tion an
d Sales m
easu
r
e
s
and Fo
re
ca
st Inform
ation…
(Gao Shu
z
hi
)
4371
accou
n
ting, sales d
a
ta, sal
e
s data from
an existi
ng fore
ca
st for the next year the sal
e
s of the
prod
uct, fo
r p
o
licyma
k
e
r
s to give th
e a
c
curate p
r
o
d
u
c
tion
plan
nin
g
the
pro
d
u
c
tion
se
ctor. M
o
re
b
u
s
in
es
s
pr
oc
es
se
s
as
sho
w
n
in
F
i
gu
re 4
.
(1) Info
rmatio
n flow pro
c
e
s
s : custo
m
er
orde
r informa
t
ion by sale
s se
ctor to warehou
se
manag
eme
n
t se
ctor, an
d
prod
uctio
n
plan secto
r
,
and pu
rcha
se se
ctor, the
same time
the
Trea
su
ry inventory informat
ion pa
ss to sales
se
ct
or; p
r
odu
ction pl
a
n
se
ctor by Issue
d
pro
d
u
c
tion
plan,
will Informatio
n pa
ss to
pro
d
u
c
tion
se
ct
or, th
e trea
su
ry m
anag
ement
sector;
pu
rcha
se
se
ctor from
sale
s secto
r
,
the trea
sury
manag
em
en
t secto
r
a
c
ce
ss to p
u
rcha
se info
rmatio
n;
sale
s
se
ct
or
f
r
om f
i
na
nci
a
l se
ct
o
r
a
c
ce
ss
t
o
c
u
st
omer
c
r
edit
i
n
f
o
rmat
io
n;
s
a
les
se
ct
o
r
t
h
e
openi
ng of Bill of Lading to ship
ping p
e
rsonn
el, shi
pping p
e
rson
nel held bill
of lading to the
Trea
su
ry take
delivery of.
(2)Ca
p
ital flo
w
s:
The
cust
omer is orde
ring
a down payment, cont
rac
t
fees
, the c
o
s
t
of
the
p
r
oje
c
t and other
fi
nan
cial se
ct
ors;
finan
ci
al
se
cto
r
n
e
e
d
s to
p
r
ovid
e fund
s fo
r the
pro
c
u
r
eme
n
t dep
artme
n
t, or di
re
ct pa
yments to
supplie
rs,
pu
rcha
sin
g
n
o
rmal to
ma
ke
the
purcha
s
in
g d
epartm
ent.
(3) Mate
rial fl
ow
pro
c
e
s
s:
prod
uctio
n
d
epar
tm
ent of
the Trea
sury
unde
r the
pro
ductio
n
plan to prov
ide materi
als, raw mate
ri
als, pr
o
d
u
c
tion of cu
sto
m
er produ
ct
and pro
d
u
c
tion
depa
rtment
s will
p
r
od
uce prod
uct
s
accordin
g
to
Pro
ductio
n
Plan
stora
g
e
for shippin
g
; extra
c
tion
of custo
m
ers
holdin
g
bills o
f
lading
as
schedul
ed to the treasury produ
cts.
Figure 4. Logi
stics, Capital
Flow an
d Information Flo
w
Relatio
n
ship
3.3. Sy
stem
Function
s
The whole system de
si
gn
for 8
m
odule:
syste
m
logs on,
the und
erl
y
ing data
manag
eme
n
t, co
ntra
ct m
anag
ement,
orde
r m
ana
g
e
ment, shipp
i
ng ma
nag
e
m
ent, data
and
statistics, sal
e
s fore
ca
stin
g, privilege
s on the
syste
m
manage
m
ent module,
the unde
rlying
data,
inclu
d
ing p
r
o
duct ma
nag
e
m
ent, cu
sto
m
er ma
nag
e
m
ent and
sa
les ma
nag
e
m
ent, use
of the
function mo
d
u
le diag
ram i
n
Figure 5.
Sales m
anag
ement is i
n
te
gration i
n
to ERP
co
ncept, prom
oting
sci
entific man
a
g
e
ment,
for the
man
a
gement
of th
e imple
m
ent
ation of th
e
strategy to
p
r
ovide
a b
e
tter
se
rvice
a
n
d
sup
port
.
S
u
p
p
ly
sale
s sy
st
em t
o
c
o
mplete the following features
.
(1) Real
- ti
me auto
m
ati
c
colle
ction:
use
of
comp
uter te
chn
o
lo
gy and
com
m
unication
techn
o
logie
s
,
will e
n
ter the
mana
gem
ent
of Intelligent
Instrum
ent - related
param
eters colle
ctio
n
netwo
rk.
(2) Advan
c
e
d
data
man
ageme
n
t: re
que
sted by
the u
s
er to
provid
e a
variety of
para
m
eters a
nd histo
r
ical data, and forms
of data re
porting, op
erating re
co
rd
s.
(3) System
s
with high
se
curity and reli
a
b
ilit
y to protect the 100 n
o
r
mal commu
n
i
cation
s,
data mana
ge
ment and u
s
e
r
mana
geme
n
t strict spe
c
ification
s
.
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TELKOM
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KA
Vol. 12, No. 6, June 20
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378
4372
(4)
Reali
z
atio
n form of a flow chart o
r
char
t, you ca
n
browse th
e data in real ti
me or
Histo
r
ic
al Dat
a
.
(5) F
o
r Use
r
rating system
acce
ss ma
na
gement me
ch
anism
This sy
stem
con
s
i
s
ts of re
al - time Da
ta Acqui
sition
System and manual e
n
try system,
real
- time
da
ta acq
u
isitio
n
from
Captu
r
e ca
rd
directl
y
from turni
n
g into a
relati
onal d
a
taba
se in
the databa
se
in real time.
Figure 5. System Functio
n
Module
3.4.
D
e
ve
l
o
p
m
e
n
t
E
n
vi
r
o
n
m
e
n
t
The Inform
ation Te
chn
o
lo
gy of proje
c
t
im
plementati
on units th
ro
ugh the yea
r
s, have
develop
ed a
more
co
mprehen
sive n
e
twork of
sy
st
ems, in
cludi
n
g
local area
netwo
rks, E
R
P
system
s, and
interco
nne
ct
ed with the Interne
t, in the area of produ
ction dat
a, enabling d
sc
(PLC) - Real
- time databa
se -
d
a
taba
se
- lan ch
ann
el
. The netwo
rk topology dia
g
ram a
s
sho
w
n
in figure 6, throu
gh a frie
ndly interface
and rea
s
o
n
able produ
cti
on and sale
s data for onli
n
e
publi
s
hing,
in
cre
a
si
ng
the t
r
an
spa
r
e
n
cy
of produ
ction
and
lay
a g
o
od fou
ndatio
n
for
ente
r
pri
s
es
to.
Figure 6. Netwo
r
k T
o
p
ogra
phie
s
The
system
uses th
e
Spring
frame
w
ork
mvc d
e
sig
n
patte
rn, after
re
se
arch a
n
d
comp
ari
s
o
n
o
n
the cu
rre
nt popul
ar devel
opment
to
ols,
determine th
e develo
p
me
nt enviro
n
me
nt
for:
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
De
sign of Enterp
rise Prod
u
c
tion an
d Sales m
easu
r
e
s
and Fo
re
ca
st Inform
ation…
(Gao Shu
z
hi
)
4373
JDK:Sp
ring
+Hibe
r
nate; Developme
n
t environm
ent: NetBean
5.5;
The ba
ck
- en
d databa
se:
SQL Server 2
000;
Web
se
rver: Tomcat 5.5.1; System platform: Win
d
o
w
s xp.
3.5. Realiza
t
i
on of ERP Sales Meas
ur
ement Ma
na
gement Sy
stem
(1) T
he main
interface of the system
Start the Tomcat se
rver,
access http:loc
al
ho
st:808
0 sellma
nag
e
m
entlogin.
JS
P, when
user
enters
a user name and
password
by calling the l
ogin.
T
o
judge whether the
content
entere
d
by th
e user
do the
right, the
rig
h
t to enter
th
e main i
n
terfa
c
e of th
e sy
stem main.
J
SP, as
s
h
ow
n
in
F
i
gu
r
e
7
.
Figure 7. The
System Main
Interface
The main inte
rface of the
system co
nsi
s
t
s
of three fun
c
tional a
r
ea
s:
a) A fun
c
tion
Module
button: Ch
oo
se
a diffe
rent m
odule;
click o
n
the mod
u
le
s button,
the workspa
c
e will enter th
e corre
s
p
ondi
ng functio
n
in
terface.
b)
Wo
rksp
ace: wo
rkspace
for
different
module
s
of fu
nction
ality, informatio
n in
pu
t, and a
list of spe
c
ific information a
nd re
co
rd
s to
add, delete, q
uery, the re
su
lts displ
a
y are
a
.
c)
Other to
ols: use
r
s to l
og
in, shutting
d
o
wn th
e sy
stem and
modif
y
their own p
a
ssword
feature sho
r
tcuts.
(2) Ba
sic d
a
ta manag
eme
n
t
a) m
ana
gem
ent of p
r
od
uct: When
a u
s
er ente
r
s th
e m
a
in
interfa
c
e
of Prod
uct
Manag
eme
n
t, as sho
w
n i
n
figure 8, you ca
n add
prod
uct
s
, qu
erie
s, and p
r
odu
ct inventory
interval q
u
e
r
ies, to
edit, de
lete, que
ry to
the produ
ct a
nd imp
o
rt i
n
to
Excel T
able,
and if th
e u
s
e
r
want
s to
print
on
a query t
o
the
dat
a for the
report, you
will
click on
" import data into the Excel
table " button.
b)
Cu
stome
r
i
n
formatio
n m
anag
ement:
Cu
stome
r
inf
o
rmatio
n ma
n
ageme
n
t, and
pro
d
u
c
t
informatio
n manag
ement capabilitie
s si
milar to also
add, modify, delete, que
ry in the table
and
import data to
Excel functio
n
.
c) Sal
e
s inf
o
rmatio
n ma
nagem
ent: Sales m
e
mbe
r
inform
ation
manag
eme
n
t and
prod
uct
s
info
rmation m
a
n
ageme
n
t fun
c
tion Simila
r, the sam
e
With add, modi
fy, delete, query
and
will data
import to E
x
cel table
in
the fun
c
tion
, different i
s
the mod
u
le
can
be to
sa
les
membe
r
A ye
ars the mo
nths
sale
s
statistics, user
cli
ck on
t
h
e
"
s
a
les memb
er sale
s st
at
ist
i
c
s
",
in pull li
st in t
he Sele
ct to
statistics ye
ar and
sa
l
e
s
m
e
mbe
r
na
me,
click
on th
e "
Statistics ", will
by result
s to colum
n
- sha
ped Figu
re di
splay.
(3) Sale
s c
o
n
t
ract man
age
ment
Whe
n
the u
s
er
cli
c
ks o
n
“Man
agem
ent of
the sale
s contra
ct ", you will enter
contract_
m
ai
n. JSP p
age,
as sho
w
n i
n
Figu
re
8, yo
u can
add
to
the
cont
ract,
que
ry, impo
rt,
Excel Tabl
e, for “co
n
tra
c
t st
atus " for
un
resolve
d
contract to mo
dify, delete, view
the detail
s
, a
n
d
make a n
e
w
orde
r op
erati
on, addin
g
a new
cont
ract
with a statu
s
of “not pro
c
e
s
sed "
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 6, June 20
14: 4368 – 4
378
4374
Figure 8. Salesma
n
Sale
s Statistics
4. Designs o
f
Sales Fore
castin
g Sy
st
em
4.1. Sales Forecas
ting Pr
ocess
S
a
les f
o
re
ca
s
t
is t
o
ma
ke
c
o
rrect ju
dgm
e
n
ts of the
pro
duct d
e
velop
m
ent tre
nds so as to
make
ri
ght p
r
odu
ction
an
d ma
rketing
deci
s
io
ns,
th
rough
detaile
d an
alysi
s
a
nd
study of
the
prod
uct re
qui
reme
nt base
d
on relevan
t
information
.
The sale
s f
o
re
ca
st
ing pr
oce
s
s sho
w
n
in
Figure 9 ca
n be divided int
o
four ba
sic
stages:
(1) In a
c
cordan
ce
with
the ne
eds o
f
the no
rma
l
mana
geme
n
t de
cisio
n
-makin
g,
determi
ne the
objective
s an
d conte
n
t of the fore
ca
st;
(2)
co
mplete
the stati
s
tic Survey, dat
a coll
ectio
n
,
analysi
s
a
n
d
judgm
ent a
nd data
evaluation a
n
d
assumptio
n
s
;
(3)
Choo
se a
pplicable fore
ca
st method from
the judg
ments an
d assumptio
n
s, ju
dge the
forecast detai
ls, and then f
o
re
ca
st;
(4)
Dra
w
the
predi
cted
re
sults, and che
c
ks an
d feed
back.
Figure 9. Sales Fo
re
ca
stin
g Process
4.2. Sales Forecas
ting Me
thods
The sale
s forecast
can b
e
con
d
u
c
ted
by qua
litative forecast o
r
quantitative fore
ca
st
method.
(1) Qualitative forecast
Qualitative fo
recast i
s
a m
e
thod in
which the
q
ualitati
v
e app
roa
c
h i
s
a
pplied
to rese
arch
and d
e
termi
n
e the devel
op
ing natu
r
e
an
d estim
a
ted d
egre
e
of the f
u
ture
events.
This m
e
thod i
s
mainly based
on subje
c
tive judgme
n
t and intuitiv
e data, and is suitable
for produ
cts that are
initially laun
ched into
ma
rket a
nd n
e
w
techn
o
logie
s
with fe
w ma
stered
data. S
e
lling q
ualitat
ive
f
o
rec
a
st
i
n
cl
u
de:
t
y
pical
su
rv
ey
,
sampl
e
su
rv
ey
meth
od, the di
re
ct su
rvey
meth
od, the indi
re
ct
survey meth
o
d
, expert su
rvey.
(2) Quantitative predi
ction
Quantitative
predi
ction
is bas
ed on certai
n statistical
info
rmat
ion,
usi
ng
v
a
riou
s
mathemati
c
al
formula
s
to
predi
ct
sale
s to deter
mine
the de
gre
e
of develop
m
ent of the fut
u
re
events. Qua
n
t
itative prediction
of sale
s is gen
erally di
vided into the
following five
steps:
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
De
sign of Enterp
rise Prod
u
c
tion an
d Sales m
easu
r
e
s
and Fo
re
ca
st Inform
ation…
(Gao Shu
z
hi
)
4375
a)
Analyze p
a
st
data. The
chang
es
ca
used
by differe
nt factors ne
ed to be
anal
yzed
and di
stingui
shed.
b)
Adopt mathe
m
atic metho
d
for fore
ca
sti
ng. Adopt m
a
thematic m
e
thod to mea
s
ure
future data by
using the p
a
s
t analyzed d
a
ta.
c)
Comp
are th
e data
fore
ca
st result
with
p
e
rso
n
a
l
intuitive ex
perie
nce. If
th
e
predi
catio
n
a
c
cura
cy is
n
o
t up to sta
ndard, the p
a
st an
alyzed
data nee
d to be
checked and the forecast method
will
be
ch
anged t
o
forecast
ag
ain. besides,
we
can al
so
ado
pt the mean
value of the fo
re
ca
st re
sults
by different math
em
atic
method.
d) Con
s
id
er
oth
e
r
si
gnificant
factors. Math
ematical
pre
d
i
ct
ion
starts f
r
om the premi
s
e
that assume the mathem
atical mod
e
l of the
histo
r
ical data co
ntinue
s to exist in the
future. T
herefore,
sig
n
ifica
n
t facto
r
s no
n-
existe
nt in
the p
a
st
shoul
d be
con
s
ide
r
e
d
in the actual
work.
e)
Applicatio
n o
f
the p
r
edi
ct
re
sult. Sale
s f
o
re
ca
st
ing
re
sult
s a
r
e
t
he ba
si
s f
o
r
t
he
prep
aration o
f
sales
plan
s.
In the enterp
r
is
e
s
b
a
si
ng
prod
uctio
n
on
sale
s prospe
cts,
prod
uct
sale
s plan directly
determi
ne th
e pro
d
u
c
tion
and finan
cial
plan
s. If forecast
result is
satisfactory, the p
l
an should
b
e
put
into p
r
actice. On th
e co
ntra
ry, if the
predi
cted
re
sult is not satisfacto
ry, cou
n
terme
a
sure
s sho
u
ld be ta
ken im
mediat
ely
to block the p
r
edi
ction.
Sales of qua
n
t
itative foreca
sting metho
d
s
mainly co
ntain moving a
v
erage m
e
tho
d
, trend
forecastin
g m
e
thod, expo
n
ential smooth
i
ng meth
od, li
near lea
s
t sq
uare
s
m
e
thod
and
cu
rve type
least squa
re
s method.
(a) Movin
g
averag
e metho
d
Moving avera
ge method i
s
to make u
s
e
of the past few years
;
a
c
tu
al market sal
e
s data
to calculate the avera
ge value an
d move it back
in ti
me as the me
thod to fore
ca
st the annu
al
market sal
e
s
volume. The
cal
c
ulatio
n formula i
s
:
12
3
1
nn
XX
X
X
X
X
n
(1)
The di
sadva
n
t
age of the m
o
ving averag
e met
hod i
s
t
o
avera
ge th
e trend
of ch
ange i
n
each year, in many ca
se
s, may cau
s
e la
rge e
rro
rs.
(b) T
r
en
ds fo
reca
st method
Tren
d fore
ca
sting meth
od
is a math
em
atical meth
od
to make
use
of the actual
market
sale
s data i
n
the past fe
w years to
observe
the
developm
ent
trend of the
m
and forecast
obje
c
tive ann
ual market sales. You ca
n take th
re
e, four or five averag
e, dep
endin
g
on the
spe
c
ific
circu
m
stan
ce
s. Th
e disa
dvanta
ge of tr
end f
o
re
ca
sting m
e
thod is: in t
he use of the
pre
averag
es a
n
d
trend ave
r
age
s the re
cent c
han
ge
s and lo
ng-t
e
rm chan
ge
s will be in the
integratio
n. As a matter of fact, that the impac
t of re
ce
nt data and forwar
d data o
n
predi
cting the
future is
different.
[]
n
XA
B
C
(2)
Here A is the last numb
e
r o
f
the earlier fi
ve
average
s; B repre
s
e
n
ts
the anural distance
of present year to fore
ca
st year; C indi
ca
tes
the last n
u
mbe
r
of the three tre
nd a
v
erage
s.
(c) Exponenti
a
l smoothi
ng
Exponential
smoothi
ng m
e
thod i
s
to
adopt a
gra
dually de
cayi
ng un
equ
al
weig
hted
method to ha
ndle all hi
storical data, foll
owin
g t
he pri
n
cipl
e of "focus ne
arly, ign
o
re far
“ra
ngi
ng
weig
hted ap
p
r
oa
ch to data
pro
c
e
ssi
ng p
r
edictio
n meth
od
[9]. Bas
i
c
formula is
:
1
(1
)
ii
i
F
AF
(3)
Here i is
the current time;
ɑ
is the expon
ential smo
o
th
ing co
efficient
; A
i
is the demand
value of i; F
i
is the predicte
d
value; F
i +1
is the i+1 p
r
ed
ictive value.
(d) Li
nea
r lea
s
t squ
a
re
s m
e
thod
Linea
r lea
s
t squ
a
re
s met
hod is a m
a
thematic
al me
thod to obtai
n a a strai
g
h
t
line of
tenden
cy cha
nging b
a
sed
on the info
rmation of the
different pe
riods in th
e p
a
st, so th
at the
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TELKOM
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KA
Vol. 12, No. 6, June 20
14: 4368 – 4
378
4376
distan
ce of e
a
ch p
o
int of this st
raig
ht line dist
a
n
ce to the co
rre
sp
ondin
g
point
of the actual
data
line is the min
i
mum. Accord
ing to the re
g
r
essio
n
line, the stan
dard formul
a [10]:
i
na
b
x
y
(4)
2
i
ax
b
x
x
y
(5)
x
is
a
c
o
mmon fac
t
or. In order to
s
i
mp
li
fy the cal
c
ul
ation, man
a
g
e
to ta
ke
x
as
zero. Make n be odd, x = 0 at the middle
of the data. The st
anda
rd form
u
l
a whe
n
x
= 0
reg
r
e
ssi
on lin
e:
2
i
i
yn
a
x
yb
x
(6)
Formul
a tran
spo
s
ition of si
multaneo
us e
quation
s
can
be obtain
ed:
2
i
i
y
a
n
x
y
b
x
(7)
Obtain a, b, and su
bstitute
into the equat
ion to obtain the pre
d
ictio
n
value.
(e)
Curve
s
le
ast sq
uares
method
Whe
n
sale
s factor i
s
the
multi-facto
r
, the
sales gro
w
th
will
o
bey geomet
ric progre
s
sion,
so tend
en
cy cha
nge lin
e is not a st
rai
ght li
ne, but a quad
ratic curve. Curv
e least
squa
re
s
method i
s
a mathemati
c
method to ob
tain a tenden
cy cha
nge q
u
adrati
c
curve,
according to
the
informatio
n o
f
the differe
n
t
perio
ds in
the pa
st, so
that the
distance
of e
a
ch poi
nt on t
he
quad
ratic curve to the correspon
ding
p
o
int of the
a
c
tual data li
ne
is mini
mal.
Quad
ratic curve
equatio
n:
2
y
ab
x
c
x
(8)
Similarly, the deviation ad
d
s
of i period S
:
22
2
11
()
[
(
)
]
nn
ii
ii
Sy
y
y
a
b
x
c
x
(9)
Partial derivat
ive
S
a
、
S
b
、
S
c
. respe
c
tively, each
equal to zero,
and thus o
b
tained:
2
23
22
3
4
i
i
i
yn
a
b
x
c
x
yx
a
x
b
x
c
x
yx
a
x
b
x
c
x
(10)
Similarly, if n
is odd, and x
= 0 is plac
e
d
in the middl
e of the data perio
d, then
0
x
，
3
0
x
. thus
:
2
2
22
4
i
i
i
yn
a
c
x
yx
b
x
yx
a
x
c
x
(11)
To obtain the
values of a, b, c, and sub
s
titute into the equation
2
cx
bx
a
y
, and
obtain the pre
d
iction valu
e.
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TELKOM
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ISSN:
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046
De
sign of Enterp
rise Prod
u
c
tion an
d Sales m
easu
r
e
s
and Fo
re
ca
st Inform
ation…
(Gao Shu
z
hi
)
4377
4.3. Application of Sales Foreca
sting
Sy
stem
Sales forecasting module
can pre
d
ict th
e next years
,
prod
uct
sale
s of the enterp
r
ise XX
from 2
000
to
cu
rrent yea
r
. The fo
re
ca
st for 20
00
- 200
8
can
b
e
con
s
ide
r
ed
as a
test
of the
predi
ction m
e
thod. This m
odule i
s
to make u
s
e of
th
e first nine y
ears sales to
predi
ct the tenth
year sal
e
s, p
r
oviding five predi
ction me
thods,
nam
el
y: the least squares meth
od of the moving
averag
e met
hod, tre
nd fo
recastin
g, ex
pone
ntial sm
oothing, lin
e
a
r le
ast
sq
ua
res meth
od
and
curve type. If five methods
can n
o
t achie
v
e good p
r
edi
ction, we
ca
n
take the ave
r
age of the five
method
s as t
he be
st value
.
Exponential
smoothi
ng fo
recast i
s
use
d
to p
r
edi
ct
actual
sales.
value i
s
0.2, the
actual
sale
s, as sho
w
n in
Table 1, the
p
r
edi
cted resul
t
s are
sho
w
n
in Figure 10-11.
Table 1. Sale
s Data
Ye
a
r
s
Sales volume
(
tons
)
1991
1992
1993
1994
1995
1996
1997
1998
1999
Sales volume
(tons)
1280
1255
1273
1305
1286
1346
1399
1467
1567
Ye
a
r
s
Sales volume
(
tons
)
2000
2001
2002
2003
2004
2005
2006
2007
2008
Sales volume
(tons)
1595
1588
1622
1611
1615
1685
1789
1790
1829
Figure 10. Moving Averag
e Fore
ca
st Result
s
As is
sho
w
n i
n
the predi
ction map
s
: ev
ery metho
d
’
s error
b
e
twe
e
n
the
a
c
tual sale and
f
o
rec
a
st
sale
is on t
he
sma
ll side,
whi
c
h
can b
e
f
o
re
ca
st
sale
s of
en
t
e
rpri
se p
r
o
d
u
ct
s a
c
cu
rat
e
ly
,
taking the
averag
e value
of the fore
ca
st re
sult
s. Sa
le fore
ca
st can help th
e
deci
s
io
n-m
a
ki
ng
depa
rtment
s
develop a
c
cu
rate p
r
od
ucti
on plan
ning,
to guide the
prod
uctio
n
, g
r
eatly re
du
ce
the
backlo
g
of b
u
sin
e
ss inve
ntorie
s, prom
ote the
rapid
flow of
co
rp
orate fu
nd
s,
and b
r
in
g go
od
eco
nomi
c
be
nefits for the
enterp
r
i
s
e.
Figure 11. Expone
ntial Smoothing Fo
re
cast Re
sult
s
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