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Modeling Long-Term Product and Pricing Decisions in the Automobile Market: An Agent-Based Approach
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Modeling Long-Term Product and Pricing Decisions in the Automobile Market: An Agent-Based Approach

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  1. 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

  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 Incentive subsidy Payments Vehicles Dealers Captive Lenders Incentives Payments Vehicles Incentives Loan/Lease Consumers

  10. 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

  11. 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

  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. 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

  15. Market Dynamics: Transaction Prices

  16. Market Dynamics: Market Shares

  17. Market Dynamics: Days-of-Supply

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

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

  20. Vehicle VDI Scores

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

  22. 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

  23. Detailed Market Share Changes

  24. 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%

  25. 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

  26. 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