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 firms 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 firms 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: Campbells Chicken Noodle Soup over a one year period:

One retailers 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 cant 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

Dont let retailers cancel orders

Dont 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