In search of the bullwhip effect

Gérard P. Cachon

Taylor Randall

Glen M. Schmidt

The Wharton School University of Pennsylvania

David Eccles School of Business University of Utah

McDonough School of Business Georgetown University

Presented September 9, 2006 Northwestern University

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

Slide 1

The bullwhip effect ƒ

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

Equipment Tier 1 Supplier

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

Factory

Slide 2

Distributor

Retailer

Customer

Campbell’s Chicken Noodle Soup

Cases

Shipments

Consumption

One retailer’s buy 7000 6000

Time (weeks) Cases

5000 4000 3000 2000 1000

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

Slide 3

Nov

Oct

Sep

Aug

Jul

Jun

May

Apr

Mar

Feb

Jan

Dec

0

The bullwhip at Barilla pasta Upstream variability at CDC is much higher

Downstream variability at DC: mean demand is about 300, the standard deviation is about 75 In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Slide 4

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

Slide 5

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

Slide 6

Autos to machine tools Machine tools

Autos

80% 60%

% change in demand

40% 20% 0% -20% -40% -60%

GDP = solid line

-80% Source:Anderson, Fine and Parker (1996)

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

Slide 7

U.S. PC industry Changes in demand 80%

60% Semiconductor Equipment

40%

20%

PC

0%

-20%

Semiconductor

-40% 1995

1996

1997

1998

1999

2000

2001

Annual percentage changes in demand (in $s) at three levels of the semiconductor supply chain: personal computers, semiconductors and semiconductor manufacturing equipment. In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Slide 8

Explanations for the bullwhip effect … ƒ

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)

ƒ

Positive serial correlation of demand shocks:

Kahn (1987), LPW (1997),

Graves (1999), Chen, Drezner, Ryan, Simchi-Levi (2000).

ƒ

Price fluctuations/cost shocks: LPW (1997)

ƒ

Non-convex production: Ramey (1991)

ƒ

Demand can be backlogged: Kahn (1987)

ƒ

Shortage gaming:

ƒ

Misperception of feedback/irrational behavior: Sterman (1989)

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

LPW (1997), Cachon and Lariviere (1997)

Slide 9

Empirical evidence of production smoothing ƒ

Blinder and Maccini (91,92): ⎯ ⎯ ⎯

ƒ

Blanchard (1983): ⎯

ƒ

“…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): ⎯

ƒ

“… in the automobile industry, inventory behavior is destabilizing: the variance of production is larger than the variance of sales.”

Miron and Zeldes (1988): ⎯

ƒ

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”.

“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)

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

Slide 10

A measure of the bullwhip effect: the amplification ratio Demand, D Orders

Production (inflow), Y

Firm

Inventory

Sales (outflow), S

ƒ

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

ƒ

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

ƒ

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

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

Slide 11

Our data ƒ

Sources: ⎯

ƒ

Data: ⎯ ⎯

⎯ ⎯

ƒ

Census Department, Bureau of Economic Analysis.

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) 2) 3) 4)

Adjust Demand and Sales series for margins and price. Adjust Inventory series for price. For each industry evaluate a Production series: Yt = St + ∆ It Log and first difference the Production, Demand and Sales series.

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

Slide 12

General merchandise stores – margin and price adjusted 55,000 50,000 Production

Sales

45,000

40,000 35,000 30,000 25,000

20,000 15,000 10,000 Jan-92

Jan-94

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

Jan-96

Jan-98 Slide 13

Jan-00

Jan-02

Jan-04

Jan-06

General merchandise stores – margin and price adjusted plus logged and first differenced 0.6 Production

Sales

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

-1 Jan-92

Jan-94

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

Jan-96

Jan-98 Slide 14

Jan-00

Jan-02

Jan-04

Jan-06

Telecom – margin and price adjusted 9,000

8,000 Production

Demand

7,000

6,000

5,000

4,000

3,000

2,000

1,000 Jan-92

Jan-94

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

Jan-96

Jan-98

Slide 15

Jan-00

Jan-02

Jan-04

Jan-06

Telecom – margin and price adjusted plus logged and first differenced 0.8 Production

Demand

0.6

0.4

0.2

0 c

-0.2

-0.4

-0.6

-0.8 Jan-92

Jan-94

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

Jan-96

Jan-98

Slide 16

Jan-00

Jan-02

Jan-04

Jan-06

Research questions ƒ

To what extent does the bullwhip effect exist in U.S. industry level data? ⎯ ⎯

ƒ

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?

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

Slide 17

Prevalence of the bullwhip effect Aggregate series Retail Wholesale Manufacturing

Retail Wholesale Manufacturing In search of the bullwhip effect: Cachon, Randall, Schmidt 9/06

Amplification Ratio 0.50 1.14 0.55 Percentage of industries that exhibit the bullwhip effect Seasonally Seasonally unadjusted adjusted 16% (1 of 6) 100% (6 of 6) 88% (14 of 16) 100% (16 of 16) 40% (20 of 50) 74% (37 of 50) Slide 18

Demand variability at different levels of the supply chain 0.050 0.045 Variance of demand

0.040 0.035 0.030 0.025 0.020 0.015 0.010 0.005 0.000 Retail

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

Wholesale

Slide 19

Manufacturing

Trends in amplification ratios

1.9

Amplification Ratio

1.7 1.5 All Industries

1.3

Retailers 1.1

Wholesalers

0.9

Manufacturers

0.7 0.5 1

2

3

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

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

Slide 20

4

Future research ƒ

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

ƒ

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

ƒ

Obtain better order and demand data.

ƒ

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

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

Slide 21