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Modeling Long-Term Product and Pricing Decisions in the Automobile Market: An Agent-Based Approach Jie Cheng J.D. Power and Associates 14 th Face-to-Face DBD Open Workshop Meeting 2002 ASME International Design Engineering Conference Montreal, Canada, September 29 th , 2002

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modeling long term product and pricing decisions in the automobile market an agent based approach

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 Meeting

2002 ASME International Design Engineering Conference

Montreal, Canada, September 29th, 2002

other jdpa contributors
Other JDPA Contributors
  • Dr. Irina Ionova
  • Dr. Jorge Silva-Risso
  • Dr. Jie Du
  • Dr. Wei Fan
outline
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
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
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
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
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
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
Agent Interactions in the Market

Manufacturers

Incentive subsidy

Payments

Vehicles

Dealers

Captive Lenders

Incentives

Payments

Vehicles

Incentives

Loan/Lease

Consumers

consumer agents
Consumer Agents
  • 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
consumers decision rules

Measures consumers’

satisfaction about a new

vehicle’s styling, engine,

ride, comfort, seats, sound,

cockpit, and HVAC

J.D. Power & Associates’

Automotive Performance,

Execution, And Layout Index

Initial Quality Survey

Measuring Things-Gone-

Wrong per 100 vehicles

Vehicle Dependability

Index - Measuring Things-

Gone-Wrong for 4-5 years old vehicles

1 if trade-in vehicle has the

same make as the purchase

vehicle and not the same model;

0 otherwise

1 if trade-in vehicle is the

same model as the purchase

vehicle; 0 otherwise

Consumers’ Decision Rules

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

estimation of random utility coefficients
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
manufacturer agents m agents
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
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
vehicle apeal scores
Vehicle APEAL Scores

* Lumina was replaced by Impala for 2000 model year

apeal elasticity
APEAL Elasticity
  • Effects on market share percent change with a 1% improvement
  • in APEAL scores
vdi elasticity
VDI Elasticity
  • Effects on market share percentage change with a 1%
  • improvement in VDI scores
simulation to assess the effects of apeal improvement
Simulation to Assess the Effects of APEAL Improvement

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

Case 3: Accord’s APEAL

improve by 1%

Case 1: Taurus’ APEAL

improve by 1%

Case 0: Base

results of simulation apeal
Results of Simulation (APEAL)

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

Taurus: +0.21ppt

Accord: +0.90ppt

Case 3: Accord’s APEAL

improve by 1%

Taurus: -0.06ppt

Accord: +0.93ppt

Case 1: Taurus’ APEAL

improve by 1%

Taurus: +0.41ppt

Accord: -0.17ppt

Case 0: Base

Taurus: 6.9%

Accord: 41.2%

summary
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
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