1. 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

2. Other JDPA Contributors Dr. Irina Ionova Dr. Jorge Silva-Risso Dr. Jie Du Dr. Wei Fan

3. 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

4. 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

5. 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?

6. 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)

7. 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]

8. 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

9. Agent Interactions in the Market Manufacturers Consumers Captive LendersDealers Vehicles Incentives Payments Incentive subsidy Vehicles Loan/Lease Payments

10. 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

11. Consumers’ Decision Rules A consumer of type h selects a vehicle brand i with probability J.D. Power & Associates’ Automotive Performance, Execution, And Layout Index Measures consumers’ satisfaction about a new vehicle’s styling, engine, ride, comfort, seats, sound, cockpit, and HVAC 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 is the same model as the purchase vehicle; 0 otherwise 1 if trade-in vehicle has the same make as the purchase vehicle and not the same model; 0 otherwise

12. 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

13. 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

14. M-Agents’ Incentive Rules Rules applied at the end of each sales week to determine rebate levels for the following week

15. Lender Agents (L-Agents) L-Agents’ parameters of interest –Federal Primary Interest Rate –Current Market APR offerings L-Agents offers financing for consumers w One L-Agent for each M-agent (Captive Lenders) Offering different APR rates on a weekly basis APR rates change as a function of both federal primary interest rates and OEM marketing strategies

16. 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

17. D-Agents’ Pricing and Ordering Rules Rules applied at the end of each sales week to determine prices for the following week

18. Market Dynamics: Transaction Prices

19. Market Dynamics: Market Shares

20. Market Dynamics: Days-of-Supply

21. Vehicle APEAL Scores * Lumina was replaced by Impala for 2000 model year

22. APEAL Elasticity Effects on market share percent change with a 1% improvement in APEAL scores

23. APEAL Component Measures

24. APEAL Engine Component Elasticity Effects on market share percent change with a 1% improvement in Engine Component APEAL scores

25. Vehicle VDI Scores

26. VDI Elasticity Effects on market share percentage change with a 1% improvement in VDI scores

27. Simulation to Assess the Effects of APEAL Improvement Case 0: Base Case 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%

28. Detailed Market Share Changes

29. Results of Simulation (APEAL) Case 0: Base Taurus: 6.9% Accord: 41.2% Case 1: Taurus’ APEAL improve by 1% Taurus: +0.41ppt Accord: -0.17ppt Case 3: Accord’s APEAL improve by 1% Taurus: -0.06ppt Accord: +0.93ppt Case 2: Both Accord’s and Taurus’ APEAL improve by 1% Taurus: +0.21ppt Accord: +0.90ppt

30. Results of Simulation (VDI) Case 0: Base Taurus: 6.9% Accord: 41.2% Case 1: Taurus’ VDI improve by 5% Taurus: +0.33ppt Accord: -0.11ppt Case 3: Accord’s VDI improve by 5% Taurus: -0.04ppt Accord: +0.52ppt Case 2: Both Accord’s and Taurus’ VDI improve by 5% Taurus: +0.05ppt Accord: +0.11ppt

31. Results of Simulation (APEAL vs Price) Case 0: Base Taurus: 6.9% Case 1: Taurus’ APEAL improve by 2% Taurus: +0.40ppt Case 3: Taurus’s price increase by 2% Taurus: -0.57ppt Case 2: APEAL improve by 2% and price increase by 2% Taurus: +0.07ppt

32. 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

33. 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