Modeling Long-Term Product and Pricing Decisions in the Automobile Market:

An Agent-Based Approach

Jie Cheng

J.D. Power and Associates

14th Face-to-Face DBD Open Workshop Meeting2002 ASME International Design Engineering Conference

Montreal, Canada, September 29th, 2002

Other JDPA Contributors

• Dr. Irina Ionova

• Dr. Jorge Silva-Risso

• Dr. Jie Du

• Dr. Wei Fan

Outline• Background

– Long term product/pricing decisions in the automotive industry

• Problem Description• Approach

– Agent-based Simulation incorporating a consumer choice MNL model

• Application– California Upper Middle Car Market (model years ‘97-’00)

• Summary and Next Steps

Long Term Strategic Decisions

• Types of Decisions– Platform/vehicle model introduction/exit

– Vehicle freshening and feature upgrade

– Vehicle quality improvement

– Vehicle pricing strategy

– Vehicle incentive strategy

• Financial impact ranging from hundreds of millions to billions of dollars of investment or opportunity cost

• Needs for market simulation tools to assess the effectiveness of decisions under different scenarios

Focal Point of Study

• What are the effects of product content/ feature upgrade on market share/profitability?

• What are the effects of product quality improvement on market share/profitability?

• What are the pricing leverage with improved product features or quality?

Study Approach

• An agent-based simulation framework for the modeling of market players and their dynamic interactions

• A disaggregate MNL model for the estimation of random utility coefficients which determine consumers’ vehicle purchase choices

• Data Source: – Automotive retail sales data (JDPA/Polk)– Automotive retail production data (JDPA)– Automotive retail sales transaction data (JDPA)– Vehicle quality surveys (JDPA’s APEAL, IQS, VDI)– Consumer demographic data (JDPA, Census Database)

Research Work on Agent-based Market Simulation

• A large number of social simulations using interactive agents have been reported, especially in the area referred to as Agent-Based Computational Economics [Tes98]

• Three types of exploration [Tak00]– Simulation of primitive society such as “sugarscape” and

“mechanism of emergence and collapse of money” [EA96][Yas95]

– Simulation of specific markets, such as “stock market” and “foreign exchange market” [PAH94] [ITT99][IO96]

– Simulation of the entire economic society such as “Agent-Based Keynesian Economics” and “ASPEN”[Bru97] [NB98]

Agent-based Simulation of the Automotive Market

• An individual-based simulation framework

• “Agent” means “actor” or “individual” in the artificial market; market consists of a lot of agents

• Four types of agents: Manufacturers, Dealers, Lenders, and Consumers

• Each agent group has its unique view of the market and a set of behavioral rules with common parameters

• Agents interact through retail purchase/finance transactions or inventory replenishment order fulfillment transactions

Agent Interactions in the Market

Manufacturers

Consumers

Captive LendersDealers

Vehicles

Incentives

Incentives

Payments

Incentive subsidy

VehiclesLoan/Lease

Payments

• Consumers arrive at the market each week following a Poisson distribution

• Individual consumers are “generated” based on a pre-determined distribution of age, gender, income, etc.

• Each consumer selects and purchases a vehicle offered in the “market” in a week and then leaves the “market”

• The probability for a vehicle brand to be chosen by a consumer is proportional to the relative utility of that brand, which is also a function of the demographic profile of that consumer

Consumer Agents

Consumers’ Decision Rules

A consumer of type h selects a vehicle brand i with probability

j

U

Uht h

ti

hti

e

eiP

,

,

)(

htii

htii

htii

tititihi

hi

iiiihti

incomegenderage

APRrebatepriceLMakeLMod

VDIIQSAPEALU

,,11,,10,,9

,8,7,654

321,

J.D. Power & Associates’Automotive Performance,Execution, And Layout Index

Measures consumers’ satisfaction about a newvehicle’s styling, engine, ride, comfort, seats, sound,cockpit, and HVAC

Initial Quality SurveyMeasuring Things-Gone-Wrong per 100 vehicles

Vehicle DependabilityIndex - Measuring Things-Gone-Wrong for 4-5 years old vehicles

1 if trade-in vehicle is thesame model as the purchasevehicle; 0 otherwise

1 if trade-in vehicle has thesame make as the purchasevehicle and not the same model; 0 otherwise

Estimation of Random Utility Coefficients

• Based on point-of-sale retail transaction data collected by J.D. Power & Associates

• Only one transaction per household

• A total of 122,546 transactions during 1997-2000 for the California market

• A total of seven vehicles in the Upper Middle car segment

• Disaggregate Multinomial logit choice model

In t e rc e p t A g e F e m a le In c o m eA c c o rd 3 . 1 2 1 6 -0 . 0 4 5 2 0 . 3 2 4 4 0 . 0 0 8 8C a m ry 1 . 4 8 6 1 -0 . 0 1 5 6 0 . 3 2 9 2 0 . 0 1 1 1

C e n t u ry -4 . 6 7 7 0 . 0 5 9 1 0 . 1 6 8 6 0 . 0 0 0 3L u m in a / Im p a la -0 . 9 5 1 0 . 0 0 3 9 -0 . 1 6 7 8 -0 . 0 0 6 3

In t re p id 0 . 5 3 3 9 -0 . 0 3 3 2 -0 . 0 6 6 7 0 . 0 0 2 4M a x im a 1 . 3 0 1 3 -0 . 0 4 3 1 0 . 0 0 1 7 0 . 0 0 9T a u ru s 0 0 0 0

A P E A L V D I L o g (P ric e ) L o g (R e b a t e ) A P R0 . 5 0 4 2 -0 . 1 5 0 7 -0 . 5 9 7 5 0 . 0 2 8 8 -0 . 0 2 9 1

Manufacturer Agents (M-Agents)• M-Agents’ parameters of interest

– Sales Volume and Market Share

– Inventory (Days-of-Supply or DOS)

– Prices, Revenue, Costs, and Profits

• M-Agents’ Decision Rules– Pricing (annually)

– Production volume (weekly)

– Incentives (weekly)

• M-Agents used in simulation– Honda, Toyota, Buick, Chevrolet, Dodge, Nissan, Ford

Dealer Agents (D-Agents)

• D-Agents represent franchised dealers selling vehicles of a particular brand

• D-Agents’ parameters of interest– Vehicle inventory (Days-of-Supply)

– Vehicle transaction prices and sale volume

– Vehicle replenishment orders

– Revenue, costs, and profits

• One D-Agent generated for each M-Agent

Market Dynamics: Transaction Prices

$10,000$12,000$14,000$16,000$18,000$20,000$22,000$24,000$26,000$28,000

1 18 35 52 69 86 103 120 137 154 171 188

Week Number (1=1st week in 1997)

Accord

Camry

Century

Lumina

Intrepid

Maxima

Taurus

Market Dynamics: Market Shares

0%

10%

20%

30%

40%

50%

60%

1 18 35 52 69 86 103 120 137 154 171 188 205

Week Number (1=1st week in 1997)

Accord

Camry

Century

Lumina

Intrepid

Maxima

Taurus

Market Dynamics: Days-of-Supply

0

1020

3040

50

6070

80

1

11 21 31 41 51 61 71 81 9110

111

112

1

131

141

151

161

171

181

191

Week Number (1=1st week of 1997)

Day

s of

Sup

ply

Accord

Camry

Century

Lumina

Intrepid

Maxima

Taurus

Vehicle APEAL Scores

5.65.8

66.26.46.66.8

77.27.47.67.8

1997 1998 1999 2000

Model Year

AP

EA

L/1

00

Accord

Camry

Century

Intrepid

Lumina

Maxima

Taurus

* Lumina was replaced by Impala for 2000 model year

APEAL Elasticity

Accord Camry Century Lumina Intrepid Maxima Taurus

Accord 2.03 -1.41 -0.04 -0.06 -0.08 -0.23 -0.21

Camry -1.35 2.05 -0.10 -0.01 -0.09 -0.22 -0.21

Century -0.79 -1.44 3.11 -0.33 0.00 -0.18 -0.21

Lumina -0.92 -1.24 -0.05 2.51 -0.03 -0.15 -0.13

Intrepid -1.63 -1.62 -0.04 0.00 2.99 -0.63 -0.59

Maxima -1.40 -1.38 -0.11 -0.25 -0.17 2.84 -0.19

Taurus -1.06 -1.38 0.00 -0.06 0.11 -0.11 3.14

• Effects on market share percent change with a 1% improvementin APEAL scores

Vehicle VDI Scores

0

1

2

3

4

5

6

1997 1998 1999 2000

Model Year

VD

I/10

0

Accord

Camry

Century

Intrepid

Lumina

Maxima

Taurus

VDI Elasticity

Accord Camry Century Lumina Intrepid Maxima Taurus

Accord 0.22 -0.16 -0.02 -0.01 -0.04 -0.03 -0.08Camry -0.12 0.24 0.00 0.00 0.00 -0.03 -0.05

Century -0.30 -0.33 0.45 -0.09 -0.10 -0.05 0.00Lumina -0.21 -0.21 -0.05 0.46 0.00 -0.06 0.00Intrepid 0.00 -0.02 0.00 0.00 0.87 0.00 0.00Maxima -0.14 -0.06 0.00 0.00 0.00 0.47 0.00Taurus -0.23 -0.29 -0.08 -0.19 -0.28 -0.16 0.38

• Effects on market share percentage change with a 1%improvement in VDI scores

Simulation to Assess the Effects of APEAL Improvement

Case 0: BaseCase 1: Taurus’ APEAL improve by 1%

Case 3: Accord’s APEAL improve by 1%

Case 2: Both Accord’s and Taurus’ APEAL improve by 1%

Detailed Market Share Changes

30.0%

35.0%

40.0%

45.0%

50.0%1 15

29

43

57

71

85

99

11

3

12

7

14

1

15

5

16

9

18

3

19

7

Week Number (1=1st Week in 1997)

Market

Sh

are

Accord0

Accord3

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

1

14

27

40

53

66

79

92

10

5

11

8

13

1

14

4

15

7

17

0

18

3

19

6

20

9

Week Number (1=1st week in 1997)

Market S

hare

Taurus0

Taurus1

Results of Simulation (APEAL)

Case 0: Base

Taurus: 6.9%Accord: 41.2%

Case 1: Taurus’ APEAL improve by 1%

Taurus: +0.41pptAccord: -0.17ppt

Case 3: Accord’s APEAL improve by 1%

Taurus: -0.06pptAccord: +0.93ppt

Case 2: Both Accord’s and Taurus’

APEAL improve by 1%

Taurus: +0.21pptAccord: +0.90ppt

Summary

• An agent-based market simulation framework for the assessment of Manufacturers’ long term quality decisions

• Consumer agents’ behavior is governed by the results of a disaggregate MNL consumer demand model

• Manufacturer, Lender, and Dealer agents make tactical marketing decisions on a weekly basis based on a set of parameterized production rules for potential self-learning

• Major results include market share elasticity with respect to vehicle design and quality

Summary (cont’d)

• APEAL (representing perceived styling and functionality) has dominant effects on market share changes of vehicles

• VDI (representing perceived vehicle quality, durability, and reliability based on previous ownership experience or word-of-mouth) has significant effects on market share changes

• A pricing leverage can be determined for each quality improvement by controlling the same market share as before