forecasting

Document Sample
forecasting Powered By Docstoc
					manufacturers
ki
complexities
supply chains
form
constant.
supply shortages
much demand data in order
bullwhip
hau lee
.xed lead time
decentralized supply chains increases
retailer former quantity
allpositivevalues
optimal
making experiment
demand forecasts
inventories
bullwhip e.ect
figure
retailers
system
discussionet
detailed discussion
objective
.^etl
simchi-levi
j.j
symmetric var
complete.thatthatclkeach stage
integrated supply chain management
december
ina dynamic decision making experiment
inventory
correlated demand process
access
part
purdue
decreasing function of p
qk
can
time parameter
average
iii
.andvariance t
impact of di.erent forecasting techniques
t.
way
experiment
bullwhip e.ect in supply chains
fact
model
constant function of l
dynamic decision making experiment
chain use
demandinformation
k
policy
p observations
.^t
paperwouldbe incomplete ifwe
.e
samesameorder-up-toestimatedemandof
used forecasting techniques
function of three parameters
dt.i
ie/ms
lemma
addition
( l.p
goal
operations
correlation
previous p periods
pp.
whereto
ii
main causes
terms
complete knowledge
industrial
ifwe
estimate
keach
timethat
thatin onethis section
d^t
real-world supply chains
mean lead time demand
proceedings
moving
u.s.
moving average
paperwouldbe
order-up-to inventory policy
c.
demand variability increases
single manufacturer
demand forecasting
actual customer demands
note
proof
most values
price variations
shu ming ng
shows
2l variance
ofsingapore zvi drezner dept.ofms&is
several authors
secondevaluate
dept.ofms&is
distortion
inventory ll policy
study
third stages
22 pppp l2 ll
onethis section
period ll forecast error
multi-retailer case
number of approaches
inventory level
multi-stage supply chain
i.
conditions
z
manage- ment science
z.^etlas
satis.es
number
zvi
nationaluniversity
p second
case
series
theis
lk policy
m. cohen
error
var
demand
arfollows
previous work
places
conditions of lemma
baganha
causes
pp
simple andmoving candea
observation
constant function
2l p
thezinventory
multi-stage
impact of forecasting
lead time
solid lines
state the following
+2 dashed lines
american economic review
reader
shares
volatility of production
proof of theorem
note the relationship between l
constant. ki .stagesldemand
previous stage
integrated
state
increase in variability for l
j
deviation t
optimal order-up-topolicy
models
authors
handbooks
management review
dt.i.^et
centralizing customer demand information
complete.thatthatclkeach
r.
.xed
equationv arfollows
parameter
drezner dept.ofms&is
teaching
supply chains increases
shares all demand information
price
customer demand information
p demand observations
l period ll forecast error
excess inventory d^t
up-to point
way one
multi-stage system with multiple retailers
optimal forecasting technique
forecast
frequent suggestions
dashed lines
chain management
interpretation
certain parameters
limited demand information
increase
quantity
e^t.ki
behavior
2ll l +z v ar
lead policy chain
more detailed discussionet
demand in- formation
p
ina
point
retailer
hand
denoted by dt
forecasting technique
qt relative
weeks
normal distribution
stabilizing e.ect
stochastic
fullerton
forecasting
decreasing function
quantifying the bullwhip e.ect
y
d^tin+ thezinventory keach stage .^etlsupplyklk
thezinventory keach stage .^etlsupplyklk
multiple stage supply chains
ll
order of q in order
in.ated value
stages of the supply chain
dt.

distribution
analysis
supply
detailed discussionet
.lls customer demand
ment science
e.ects of demand forecasting
whang
customer demands
possible causes
same forecasting technique
frank
four weeks of forecast demand
i.i.d. demands
sloan
excess
ordersqso
centralization of demand information
used forecasting techniques in practice
2 various values
paper
di.erent forecasting techniques
dis-
knowledge
random variables of the form
ng
correlation parameter with j.j
un.lled
denoted q
game
two-stage supply chain
supply chains without centralized information
additive function
2l2l p
in.ated value of lk
stage .^etlsupplyklk
variability
anda
p. see ryan
extra week
d^is
supplyis
next stage
j. k.
e.g.
cost
signi.cant increase
centralizing customer demand in- formation
value
forecast demand
t 22 inventory
parameters
c
p thisbound
results of this simulation study
pk
variance of qt relative
5references baganha
impact
increasing function of l
signi.cant impact
various values
shu
assumption
technology management
science
economic review
msom conference
porteus
ll policy
et.
company
working paper
global supply chain
simulation study
former quantity
extra week of inventory
period t. t k
estimatesand whang
number of observations
k k2 y v ar
information distortion
dt.l1l ll
simple order-up-to inventory policy
sequence
deviationcost.
.t
e.ects
our
computerized beer game
e.ect
tool
inventory d^t
cohen
p. 3theimpactofcentralized demandinformation
increase in variability
chains increases
observations
strange behavior
l et
proof of lemma
b
symmetric distribution
dt.p.1 .l
result
.rst stage
l period forecast error
forecasts
multi-retailer
orders
function
l cov
notice
thepolicy de.ned
school
j.0 t. j for k
qt as qt
stages q k
d^and
techniques
observed customer demands
simulation results
more demand data
correlation parameter
demand per period
variations
much demand data
y v ar
.l
david
di.erent
mean p
in.ated
deviation
.etl
number observations
simulation results on v ar
one period forecast error
di.erence
i.1 dt.
period t
et al.
forecasting techniques
desired service level
handbooks in operations research
hau
v
anda symmetric var
end
process
policy of this form
chain
s.
. l et
uses
misperceptions
theory
poms series in technology
david simchi-levi dept.ofie&ms
northwestern university
interested reader
ph.d.
zi
manufacturer relative
simple supply chain
strategy
research
stage supply chains
purpose
focus
versity
inventory theory
demand data
.
figure 2 shows
kper
dotted
uni- versity
example
working
ll time demand
subsequent stages
l
june
inventory policy as de.ned
^d
kk
phenomenon
downstream padmanabhan
department
ppk
quantities
end of period t
see
case of i.i.d. demands
p.to estimate
sterman
foroddvalues
chain relative
variance of demand
northwestern
di.erence between the variability
period
j.
pppp
chain model
tk
end of period
technology
hasd.^etltkis
v ar
e.
economic
observed customer demand data
volatility
l. another limitation
order- up-to point
time
assumption that excess inventory
form dt
covthe
customer demand in- formation
ifa retailer
ofindustrialengineering
periods
ment
supply chain
variance of dt
variance of the demand
purdueuniversity david simchi-levi dept.ofie&ms
timethat demand
average forecast
most frequent suggestions
places an order
demand chain

.ve main causes
fact thatwe
candea
insights
demand between retailers
ryan j.
hax
drezner
lead
modeling
review
metters
management science
supply chains with centralized information
end of period t.
signi.cant
systems
practice
standard forecasting technique
order
order lead time
shortages
following
e.ect our objective
order process
values
data
updates
demandwithprocesses
foroddvalues ofp
decentralized systems
order-up-to point
lead time demand
li
p p thisbound
limitation
two-stage
additive
existence
beer
notice that if the retailer
ryan
denoted
centralized demand information
.^etlsupplyklk
note that the order-up-to point
standard deviationcost.
discussion
global
given l
simulation
k policy
ie/ms northwestern university
l.p
sequence of events
school ofindustrialengineering
sertation
lead time parameter
supply chain model
northwestern uni- versity
d
engineering
for..
standard deviation
second
pp kk k2 v ar
jennifer k. ryan school ofindustrialengineering
^quantity .etl
modeling managerial behavior
zvi drezner dept.ofms&is
lkd^
stagewherek+
2 pp l2 ll
several ways
order-
stage
eds
october
section
estimate complete.thatthatclkeach stage
variables
tiplicative
customer demand process
onethis
dis- sertation
cov
production
more demand data in order
demand observations
our objective
forecast error
impact of the bullwhip e.ect
poms
m.
important limitations
work
note that theorem
secondevaluate equationv arfollows
note hasd.^etltkis
de.ned
ryan school ofindustrialengineering
moving average forecast
level yt
j. d.
chen
k lk policy
p p p second
mean demand
decision making experiment
distributede
i.i.d. from a symmetric distribution
methods
new
department of industrial engineering
providing methods
t
et
lead times
ming ng
single retailer
moves
ph.d. dis- sertation
supply chain relative
operations management
kpoint data
service
see ryan
ways
magnitude of the bullwhip e.ect
offeedback
little impact
data at lee
ll quantities
purdue university
stage k
stage of the supplyis
un.lled demands
1+ l.p
limitations
ampli.cation
lines
words
safety
u.s. retail company
order-up-topolicy
use
poms series
three lines
order-up-to k lk policy
incomplete ifwe
time demand
university
stage of the supply chain
variance of the orders
existence of the bullwhip e.ect
several important limitations
padmanabhan
volume
.^et
week
new computerized beer game
ar
variance of customer demand
h.
batch
variability ampli.cation
information lead time
detailed discussion of this phenomenon
t demand observations
results
up-to
msom
form the same inventory policy
kper period
t. t k
updates the mean
policies of this form
supply chain use
johnson
approaches
ll forecast error
estimator
pp kk
relative
.stagesldemand
simple two-stage supply chain
formation
dynamic
instance
^assumewe
22 var
derive
purdueuniversity
johnson et al.
start
p periods
theorem
magnitude
order-up-to policy
beer game
d^and .^et
demand process
limited
technique
increasing function
policies
equation
lead ll time demand
demand information
t. j
insights for the case
.ve
ifa
frank chen decisionsciencesdept
. v ar
equationv
conference
keach stage .^etlsupplyklk
inventory models
k.
odd values
it
variability increases
centralizing demand information
.rst
pp k
events
start of period t
d^tin+
order lead times
p.
mul- tiplicative
remedy
in- formation
thisbound
operations research
form yt
managerial behavior
demand yt
d.
di.erent inventory policies
thatin
2ppk
main drawback
stock
thatwe
error terms
odd values of p
dotted lines
(
centralized information
decisionsciencesdept
l +z v ar
stages
chains
exact form
pp k kk2 v ar
^assumewe note hasd.^etltkis
k. ryan school ofindustrialengineering
standard deviation t
qt
policy chain
drawback
simple relationship
ki .stagesldemand
mean
multiple retailers
2 v ar
major u.s. retail company
theorm
k.1 ^d
lk
period t.
complete information
customer
other words
in-
decentralized information
same inventory policy
californiastateuniversity
ofsingapore
dept.ofie&ms
results figure
random variables
inventory policies
previous research
supply chain management
( i.i.d.
variance
information frank chen decisionsciencesdept
complete knowledge of the demands
order-up-to
symmetric
excess inventory
whangto
pp ofp
usewhereusetimethethethebetween stages samesameorder-up-toestimatedemandof
level
traditional beer game
one
formyt.lkd^t+zk.^et
q
pp
simple
i.i.d.
estimatesand
centralization
safety stock
z.
lead timethat demand
p p second
ming
complete access
sloan management review
relationship
l forecast error
upstreamsuggestsite
uni-
simchi-levi dept.ofie&ms
usewhereusetimethethethebetween
p.to
retail company
stages k
period forecast error
nationaluniversity ofsingapore zvi drezner dept.ofms&is
l. for example
department of ie/ms northwestern university
bullwhip e.ect our objective
. notice
increases
decision
chen decisionsciencesdept
^quantity
several observations
demands
site
inventory policy
customer demand data
manufacturer
times
school of industrial engineering
lee
standard
real-world
stochastic inventory theory
industrial engineering
yt
let
.andvariance
factors
l period
.lls
manage-
multi-stage system
mul-
ofp
kahn
stages samesameorder-up-toestimatedemandof
d^tl
paper di.ers
andmoving candea
management
reasonable values
simple moving average forecast
i.1 p
most
dt
demand observation
kpoint
other hand
quantifying
correlated
upstream stages
f.
kaminsky
centralized customer demand information
customer demand
lowerbound
thepolicy de.ned by equation
observed demand yt
l.
first
service level
constant c
suggestions
di.ers
information
important observation
signi.cant increase in variability
al.
same variability
jennifer
cl.
 1
 1
 1
 8
13
 1
 1
 1
29
 1
 1
 1
 1
 1
 3
 1
 1
 1
28
 3
 2
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
25
 1
 1
 1
 1
 1
 1
 1
 4
 5
 1
 1
 1
 3
 2
 1
 1
 1
 8
 1
 1
 1
 1
17
14
 1
 1
 1
 2
 1
 1
 1
 1
 1
 3
 3
 1
 1
 3
 3
 1
 6
 1
 1
 1
 4
 2
 2
 1
 5
 1
 1
 1
 1
 1
 1
 1
 4
 1
 2
 1
 1
 1
 2
 3
 5
 1
 6
 3
 1
 1
 1
 2
 1
 1
 1
 1
 1
 1
 1
 2
 1
 1
 1
 1
 1
 1
 1
 4
 1
 1
 3
 1
 1
 3
 3
 1
 1
 1
 9
 1
 1
 1
 1
 9
 4
57
 1
1
1
1
2
2
6
1
4
1
2
1
5
2
8
2
1
1
1
3
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
7
1
1
1
1
1
 7
 1
 1
 1
 1
 2
 1
 1
 9
 1
 4
 3
 1
 1
 1
30
 1
 1
 2
 1
 1
 1
 1
32
 1
 7
38
 2
 1
 7
 1
 2
 1
 1
 1
 1
17
 1
 1
 1
 1
 1
 1
 8
 1
 1
 1
      1
#NAME?    1
      4
      1
     45
      1
      1
      1
      1
      5
      4
      1
      3
      1
      1
      2
      1
      2
      1
      1
      1
      1
      4
      1
      1
      2
      3
      1
      1
      1
      2
      3
      1
      1
      1
      1
      1
      1
     34
      1
      1
      1
      1
      1
      1
      1
      1
 1
 1
 1
 2
 2
 1
 2
 1
 1
 1
 1
 1
 1
14
 1
 1
 2
 1
 3
 1
 4
 1
 1
 1
 1
 1
 1
 1
 1
 2
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 4
 1
 1
 2
31
 1
 1
 2
 1
10
 1
 6
 1
 1
 1
 2
 2
 1
 2
 3
 2
 1
 1
12
 6
 1
 3
 1
 2
 1
 1
 1
 1
 3
 1
 5
 1
 3
 1
 1
 1
 1
 1
 1
 2
 1
 1
 4
 1
 1
 3
 1
 1
 1
 5
 2
 3
 1
 1
 1
23
 1
 4
 5
 1
37
 2
 1
 1
 1
 1
 1
 1
 2
 1
 1
 2
 1
 2
 1
 4
 1
 1
 1
 1
 1
 3
16
 1
 1
 2
 1
 4
 1
 1
 1
15
 1
 1
 2
 1
 2
 1
 2
 1
 1
 2
 3
 1
 1
 2
 1
 1
 4
 3
 1
17
 4
 1
 1
 1
 1
 2
 1
23
 1
 1
 1
 1
 1
 1
15
 1
 1
 1
 1
 1
 1
 1
 1
29
 1
 1
 1
 1
 2
 1
      1
      1
#NAME?    1
      1
      1
      1
      1
      1
      1
      1
      7
     18
      1
      2
      2
      3
      1
      1
      2
      1
      5
      1
     14
      1
      1
      3
      1
      1
      5
      5
      1
      1
      1
      1
      3
      1
      4
      1
      1
      2
      1
      2
      3
      1
     15
      3
      1
 1
 1
 1
 3
 1
 1
 7
 1
 1
 6
 1
 1
 1
 4
 1
 1
 8
 2
 1
 3
 2
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
33
 1
 1
 5
 1
 4
 1
 1
 1
 1
 5
 1
 1
 2
 1
 5
 1
 1
 2
 1
 1
 1
 1
 1
 3
 1
 2
 2
 1
 7
 1
 1
 1
 1
 1
 2
 1
 1
 1
 1
16
 5
 4
 1
 3
 2
 1
 1
 1
 1
 1
 2
 1
 2
 1
 3
 1
 1
 1
 3
 1
 1
 2
 1
 1
 9
 4
 2
 1
 1
 2
 1
 1
 1
 1
 2
 3
 8
 6
 1
 1
 6
 1
 4
 1
 1
23
 2
 2
 1
 1
 1
 1
 1
13
 1
 1
 1
 1
 1
 1
 2
 1
 1
 1
 1
 2
 1
 3
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 2
 1
 6
 2
 1
 3
 1
 3
 1
 7
 1
 4
 2
 1
18
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 2
 1
 1
 2
 2
 4
 1
 1
 2
 1
 1
 6
 1
 1
 1
 1
 2
 1
 1
 1
 3
 1
 1
 1
 1
 2
 1
 4
 1
 2
 7
 4
 1
 1
10
 9
 1
 1
 1
 1
 1
12
 1
 1
 1
 1
 7
 1
 7
 1
 1
 1
 3
 1
 1
21
 4
 1
 1
 2
 1
 1
 1
 1
 4
 2
 2
 3
 1
21
 1
 1
 1
 4
 6
 1
 1
 1
 4
 1
 5
 1
14
 3
 1
 8
 1
 1
 2
 1
 1
 1
 1
 1
 1
 2
 1
 1
 1
 1
 1
 1
 1
 1
 3
 1
 1
 1
 1
 1
 3
 1
 1
 1
 1
14
 1
 7
 1
14
 5
 1
 8
 5
 1
 1
 2
 7
 1
 1
 1
 1
 1
 1
 1
 1
 2
 4
 1
 2
 1
 1
 9
 1
 1
 1
 1
 9
 1
 1
 2
 4
 1
 1
 1
 2
 2
 6
 1
 1
 1
 3
 3
 1
 1
 2
 1
28
 2
 1
 2
 1
 1
 2

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:30
posted:12/3/2011
language:English
pages:38