Slide 1In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

In search of the bullwhip effect

Gérard P. CachonThe Wharton School

University of Pennsylvania

PresentedSeptember 9, 2006

Northwestern University

Glen M. SchmidtMcDonough School of Business

Georgetown University

Taylor RandallDavid Eccles School of Business

University of Utah

Slide 2In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

The bullwhip effect

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

CustomerRetailerDistributorFactoryTier 1 SupplierEquipment

Slide 3In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Campbell’s Chicken Noodle Soup

Time (weeks)

Cas

es

Shipments

Consumption

0

1000

2000

3000

4000

5000

6000

7000

Dec Ja

n

Feb

Mar

Apr

May Ju

n

Jul

Aug Se

p

Oct

Nov

Cas

es

One retailer’s buy

Slide 4In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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

Slide 5In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Slide 6In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Slide 7In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Autos to machine tools

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

% c

hang

e in

dem

and

GDP = solid line

Source:Anderson, Fine and Parker (1996)

Autos Machine tools

Slide 8In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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.

Slide 9In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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, Padmanabhan and 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)

Slide 10In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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 most

industries”.

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 most manufacturing industries.”

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

Slide 11In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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 amplification ratio is greater than 1.

Demand, D

Inventory

Firm

Sales(outflow), S

Orders

Production(inflow), Y

Slide 12In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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 = St + ∆ It4) Log and first difference the Production, Demand and Sales series.

Slide 13In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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

Slide 14In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

General merchandise stores – margin and price adjusted plus logged and first differenced

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

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

Production Sales

Slide 15In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Telecom – margin and price adjusted

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

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

Production Demand

Slide 16In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Telecom – margin and price adjusted plus logged and first differenced

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

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

Production Demand

c

Slide 17In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Research questions

To what extent does the bullwhip effect exist in U.S. industry level data?⎯ Are amplification ratios greater than 1?⎯ Do manufacturers experience the highest demand variability and

retailers the lowest?

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

Slide 18In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Prevalence of the bullwhip effect

Aggregate series Amplification RatioRetail 0.50Wholesale 1.14Manufacturing 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 exhibit the bullwhip effect

Slide 19In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Demand variability at different levels of the supply chain

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

Retail Wholesale Manufacturing

Varia

nce

of d

eman

d

Slide 20In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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 Period1=1992-95, 2=1996-98, 3=1999-2001, 4=2002-05

Am

plifi

catio

n R

atio

All IndustriesRetailersWholesalersManufacturers

Slide 21In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

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 effect perform better financially?