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Slide 1In search of the bullwhip effect: Cachon,

In search of the bullwhip effect

Grard P. Cachon

The Wharton SchoolUniversity of Pennsylvania

Presented

September 9, 2006

Northwestern University

Glen M. Schmidt

McDonough School of BusinessGeorgetown University

Taylor Randall

David Eccles School of BusinessUniversity of Utah


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The bullwhip effect

Demand variability increases as you move up the supply chain fromthe customer towards supply

CustomerRetailerDistributorFactoryTier 1 SupplierEquipment


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Campbells Chicken Noodle Soup

Time (weeks)

Cases

Shipments

Consumption

0

1000

2000

3000

4000

5000

6000

7000

DecJan

Feb

Mar

Apr

MayJunJul

Aug

Sep

Oct

Nov

C

ases

One retailers buy


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The bullwhip at Barilla pasta

Downstream variability at

DC: mean demand is about

300, the standard deviation

is about 75

Upstream variability at CDC is

much higher


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Autos to machine tools

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

%changeind

emand

GDP = solid line

Source:Anderson, Fine and Parker (1996)

Autos Machine tools


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U.S. PC industry

Semiconductor

1995 1996 1997 1998 1999 2000 2001

-40%

-20%

0%

20%

40%

60%

80%

PC

SemiconductorEquipment

Changes indemand

Semiconductor

1995 1996 1997 1998 1999 2000 2001

-40%

-20%

0%

20%

40%

60%

80%

PC

SemiconductorEquipment

Changes indemand

Annual percentage changes in demand (in $s) at three levels of the semiconductor

supply chain: personal computers, semiconductors and semiconductor manufacturing

equipment.


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Explanations for the bullwhip effect

Fixed cost to produce/order, (s,S) models, order synchronization:Blinder (1981), Caplin (1985), Caballero and Engel (1999), Mosser (1991), Lee, Padmanabhanand Whang (LPW) (1997), Cachon (1999)

Positive serial correlation of demand shocks: Kahn (1987), LPW (1997),Graves (1999), Chen, Drezner, Ryan, Simchi-Levi (2000).

Price fluctuations/cost shocks: LPW (1997)

Non-convex production: Ramey (1991)

Demand can be backlogged: Kahn (1987)

Shortage gaming: LPW (1997), Cachon and Lariviere (1997)

Misperception of feedback/irrational behavior: Sterman (1989)


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Empirical evidence of production smoothing

Blinder and Maccini (91,92): Data: 1959-1986, monthly, seasonally adjusted, constant 1982 dollars

Production is more variable than sales in 17 of 20 two-digit manufacturing industries

the basic facts to be explained are 1) production is more variable than sales in mostindustries.

Blanchard (1983): in the automobile industry, inventory behavior is destabilizing: the variance of production

is larger than the variance of sales.

Miron and Zeldes (1988): The overall assessment of this model is quite negative: there is little evidence that

manufacturers hold inventories of finished goods to smooth production.

Eichenbaum (1989): We find overwhelming evidence against the production-level smoothing model we

conclude that the variance of production exceeds the variance of sales in mostmanufacturing industries.

Other negative results: West (1986), Ramey (1991), Mosser (1991), Kahn (1992)


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A measure of the bullwhip effect: the amplification ratio

Amplification ratio = V[Y] /V[D].

If demand is not available, use sales as a proxy for demand.

We say the bullwhip effect is present in an industry if its amplificationratio is greater than 1.

Demand, D

Inventory

Firm

Sales

(outflow), S

Orders

Production

(inflow), Y


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Our data

Sources:

Census Department, Bureau of Economic Analysis.

Data:

U.S., 1992-2006, monthly.

50 manufacturing industries: Sales, inventory.

In a subset of 23 manufacturing industries: Demand.

16 wholesale industries: Sales, inventory.

6 retail industries: Sales, inventory.

Data manipulations:

1) Adjust Demand and Sales series for margins and price.

2) Adjust Inventory series for price.

3) For each industry evaluate a Production series: Yt= S

t+ I

t

4) Log and first difference the Production, Demand and Sales series.


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General merchandise stores margin and price adjusted

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

55,000

Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06

Production Sales


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General merchandise stores margin and price adjustedplus logged and first differenced

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6


Production Sales


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Telecom margin and price adjusted

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000


Production Demand


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Telecom margin and price adjusted plus logged and firstdifferenced

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8


Production Demand

c


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Research questions

To what extent does the bullwhip effect exist in U.S. industry leveldata?

Are amplification ratios greater than 1?

Do manufacturers experience the highest demand variability andretailers the lowest?

Understand variation in the amplification ratios:

What explains variation in the amplification ratio across industries? Have amplification ratios been decreasing over time?


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Prevalence of the bullwhip effect

Aggregate series Amplification Ratio

Retail 0.50

Wholesale 1.14

Manufacturing 0.55

Seasonally

unadjusted

Seasonally

adjusted

Retail 16% (1 of 6) 100% (6 of 6)Wholesale 88% (14 of 16) 100% (16 of 16)

Manufacturing 40% (20 of 50) 74% (37 of 50)

Percentage of industries that exhibitthe bullwhip effect


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Demand variability at different levels of the supply chain

0.000

0.005

0.010

0.015

0.0200.025

0.030

0.035

0.040

0.045

0.050

Retail Wholesale Manufacturing

Varianceofdemand


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Trends in amplification ratios

0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

1 2 3 4

Time Period

1=1992-95, 2=1996-98, 3=1999-2001, 4=2002-05

AmplificationRatio

All Industries

Retailers

Wholesalers

Manufacturers


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Future research

Investigate the bullwhip effect at different levels of aggregation firm, category, sku.

Investigate the bullwhip effect at different levels of time aggregation daily, weekly, quarterly.

Obtain better order and demand data.

Do firms/supply chains that better manage the bullwhip effectperform better financially?

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