Demand Information Distortion and
Bullwhip Effect
Chopra: Chap. 17
Assignment 2 is released.
Lessons of the Game
• Such oscillations are common
– Bullwhip effect (demand distortion)
• Everyone blames others - but problem is
with the structure
–
Bullwhip EffectBullwhip Effect
Manufacturers Regional Local Local Local
Distributors Wholesalers Retailers Customers
Bullwhip effect: increased demand variability up the SC
Cambell Chicken Soup
Bullwhip effect in the US PC supply chain
Semiconductor
1995 1996 1997 1998 1999 2000 2001
-40%
-20%
0%
20%
40%
60%
80%
PC
Semiconductor
Equipment
Changes in
demand
Semiconductor
1995 1996 1997 1998 1999 2000 2001
-40%
-20%
0%
20%
40%
60%
80%
PC
Semiconductor
Equipment
Changes in
demand
Annual percentage changes in demand (in $s) at three levels of the semiconductor
supply chain: personal computers, semiconductors and semiconductor manufacturing
equipment.
4L 5L 4L 5L
HP- Shipment Wholesaler-see-
thru
HP Laser: L –Series
5L
Elek Tek
Micro Electronic
PC Warehouse
Comp USA
Office
Depot
Best Buy
OfficeMax
Staple
120002000 6000
Sel
l –o
ut
Std
Dev
Order Std Dev
• Causes for Poor SC Performance
– Demand uncertainty ( how to cope with it?)
– Product variety ( -- )
– Information distortion along the SC -- bullwhip ( -- )
Safety stock
Better forecast.
Better plan.
Curses of Bullwhip EffectCurses of Bullwhip Effect
• Curses
–
–
–
X
= 350= 350
Svc Level = .95Svc Level = .95
P(Stockout) = .05P(Stockout) = .05
FrequencyFrequency
xx = ?= ?
= 10= 10
Safety Stock = Safety Stock = xx --
Causes of Bullwhip EffectCauses of Bullwhip Effect
• Key causes
– Demand forecasts update (by different parties)
• “information distortion”
– Leadtimes
– Price promotion - forward buying
– Order synchronization
– Batch ordering practice
– Shortage “Gaming”
Not in the
game
Psychological effect?
• Each location forecasts demand to determine shifts in
the demand process
• How should a firm respond to a “high” demand obs?
– Is this a signal of higher future demand or just random
variation in current demand?
• If the firm’s inventory is low, hedge by assuming this signals higher
future demand, i.e., order more than usual
• How should a firm respond to a “low” demand obs?
– If the firm’s inventory is high, be more conservative and
wait to see if demand has really shifted, i.e., no order now
• Rational reactions at one level propagate up the SC
Demand forecast updating – by
Intuition
Forecast Updating - 121 SC:
An Example – Order-up-to Level
Forecast Updating - 121 SC:
An Example – Order-up-to Level
Period t t-1 t-2 t-3 t-4 t-5
Demand 64 40 45 35 40
Forecast 64 40 45 35 40
Order Upto 128 80 90 70 80
Order q 112 30 65 25
Assumptions: retailer uses Dt-1 to forecast future demand
Dt, as Ft = Dt-1; order-up-to-level 2 Ft.
No Safety stock?
Impact of Forecasting on BEImpact of Forecasting on BE
• The BE is due, in part, to the need to forecast
demand & hold safety stock
• Moving ave and exponential smoothing are “bad”
• The fancier the method, the worse the BE
• Smoother demand forecasts can reduce the
bullwhip effect (MA & ES methods)
• The longer the leadtime, the higher the BE
• Centralised information sig reduces the BE
Forecast Updating - 121 SCForecast Updating - 121 SC
.25.21.5 )(DVar / )(qVar
:5.1 ,0),(,exampleFor
)(qVar
)(DVar
)(qVar
)]D ,(D Cov - [2 z
)D - (DVar z )(qVar
)(
:quantity ordering
z : tperiodfor and
z :1- tperiodfor Thus,
. D
:method gforecastin naive a follows and
level to-upOrder :Retailer ).,(: :DemandMkt
2
tt
21
2
2
t
t
t
22
2-t1-t
22
2-t1-t
2
t
2111
1
211
1-T
2
zDDCov
z
z
DDzDSSq
z DFS
z DF S
F
zFS
D
tt
tttttt
ttt
t-tt-
T
TT
T
RetailerRetailer
DDTT
qqT, T, L=0L=0
If all updating If all updating
their forecasts, their forecasts,
variability variability
amplifies amplifies
exponentiallyexponentially
in [in [-- , +, +]]
Assume: extra inventory
can be returned without
any cost
Avoiding Demand Forecast Updates
• BE resulted from the chain effect along the SC
– Repetitive multiple forecast updating
• Share demand information so that every one can
obs demand shifts without distortions:
– Demand forecasts should be based on final sales to
consumers
Bullwhip can occur within a firm
Sales
We need to promote and get
rid of these green cars
Production
All green cars are sold out,
time for replenishement
Volvo Green Cars
Avoiding Demand Forecast Updates
• Channel Alignment
– VMI - vendor managed inventory scheme
– Consumer direct
– Discount for information sharing, including
plan of promotion activities
• Operational Efficiency
– Leadtime reduction
– Echelon-based inventory control
Order Synchronization
• Synchronized ordering occurs when
retailers tend to order at the same time:
– end of the week orders
– beginning of the month orders
– end of the quarter orders
Order batching
• Retailers may be required to
order in integer multiples of
some batch size, e.g., case
quantities, pallet quantities, full
truck load, etc.
• The graph shows simulated
daily consumer demand (solid
line) and supplier demand
(squares) when retailers order
in batches of 15 units, i.e.,
every 15th demand a retailer
orders one batch from the
supplier that contains 15 units. 0
10
20
30
40
50
60
70
T ime (e a c h p e rio d e q u a ls o n e d a y )
Un
its
• Smaller min order quantity (lower Q), so retailers
order more frequently
• Unsynchronize retailer order intervals
– Retailers may order every T periods
– Min batch size Q=1, so no min order Q restriction
– Retailers are placed on balanced schedules s.t. average
demand per period is held constant
• e.g., 100 identical retailers and T=5 implies 20
retailers may order each period
Order batching solutions
Trade Promotion Trade Promotion
• Why trade promotion?
• Consequences of trade promotion?
Trade promotions and forward buying
• Supplier gives retailer a temporary discount, called a trade promotion.
• Retailer purchases enough to satisfy demand until the next trade
promotion.
• Example: Campbell’s Chicken Noodle Soup over a one year period:
One retailer’s buy
T im e (w e e ks)
Ca
se
s
Shipm e nts
C onsum ption
0
1000
2000
3000
4000
5000
6000
7000
De
c
Ja
n
Fe
b
Ma
r
Ap
r
Ma
y
Ju
n
Ju
l
Au
g
Se
p
Oc
t
No
v
Ca
se
s
Total shipments and consumption
0
500
1000
1500
2000
2500
3000
Time (measured in weeks)
On
-han
d in
ven
tory
(u
nit
s)
• Retailers submit orders for delivery in a
future period
• Supplier might not be able to fill all orders
– He might not get enough components
– His production yield might not be as high as
expected
• Phantom orders
– Reatilers order more than they think they need
to make sure they get a good allocation if
demand is high or if capacity is tight
Shortage game
• Supplier allows retailers to cancel order or accepts
returns
• High retailer profit margin, i.e., costly to not have
goods
• Retailer demand expectations positively
correlated(i.e., if one retailer has high demand
expectation, the other retailers probably do too.)
• Retailer competition (if retailer A takes more
inventory, retailer B has less to sell)
• Capacity is expensive, so the supplier will not
build unlimited cap
When is shortage game likely?
Classic Bullwhip Effect:
Semiconductor Industry, 1995
• Perception: Demand for semiconductors
would have a tremendous increase
• Result: Customers, worried about a supply
shortage, tripled their orders
• Reality: Semiconductor companies
scrambled to meet demand, realized
information was inflated and suffered huge
losses
• Bad:
– supplier can’t use initial orders to forecast
demand, so it builds the wrong level of capacity
– allocation among retailers is poor: some
retailers get more than they need, others are
starved
• Good:
– Reduces idle cap., assuming the retailers
actually take and sell the product
Shortage gaming: bad and good
• Don’t let retailers cancel orders
• Don’t offer retailers generous return
policies
• Share cap. And inventory data prevent false
scares
• Prioritize retailers (customers, e.g., by past
sales)
How to stop phantom ordering
