A DSS Reshapes Revenue Management in Railway Networks : A DSS Reshapes Revenue Management in Railway Networks Ting Li Department of Decision and Information Sciences Rotterdam School of Management, Erasmus University Pre-ICIS SIG-DSS Workshop 2006 December 10, 2006, Milwaukee, Wisconsin, USA


Outline: Outline Research background and questions Research studies and methodology Impact of smart card adoption on RM -- multiple case study Customer behavioral responses to differentiated pricing -- stated preference experiment (SP) RM DSS -- simulation Future work and discussion Outline


Motivation: Motivation Business needs Diffuse the concentration of peak load Increase capacity utilization Advancement of ICT Problem: information and decision imbalancing, lack of reservation system / booking data Smart card adoption makes it possible Increased application of Revenue Management “Selling the right capacity to the right type of customers at the right time for the right price as to maximize revenue.” Great success: American Airlines ($500 million/y), National Car Rental ($56 million/y) Privatization of Public Transport Motivation


Research Questions: Research Questions Research Questions Research Objective Assess the possibilities of revenue management in contribution of customer data provided by a nation-wide smart card adoption in the Netherlands Research Questions What type of differentiated pricing fare scheme is sensible & feasible? How customers respond to various forms of differentiated pricing? What are the impacts to the transportation network yield? Research Approach Develop a Revenue Management Decision Support System (RM-DSS) prototype for Public Transport Operators


Previous Research: Previous Research Previous Research Information system research Dynamic pricing benefits consumers (Bakos, 1997). RM increases performance enterprises (increased customer information) Revenue management literature Increased dynamic pricing strategies due to (Elmaghraby et.al., 2003) Increased availability of demand data Ease of changing prices due to new technologies Availability of decision support tools for analyzing demand Conditions: Perishable inventory, relatively fixed capacity, ability to segment market, fluctuating demand, high production cost and low marginal cost, flexible pricing structure and ICT capability


RM DSS: RM DSS Revenue Management DSS


World-wide Smart Card Implementation: World-wide Smart Card Implementation World-wide Smart Card Implementation


Differentiated Pricing Strategy: Differentiated Pricing Strategy Differentiated Pricing Strategy Uniform pricing vs. Dynamic pricing Customer-oriented pricing (direct-segmentation) Profile-based pricing (e.g. 65+, student) Usage-based pricing (e.g. bundle) Journey-oriented pricing (indirect-segmentation) Time-based pricing (time-of-day, day-of-week) Route / region-based pricing Origin-destination based pricing Mode-based pricing (e.g., transfer, P&R)


Framework: Framework Public Transport Operators’ rational Effects to Customers Data / information sources needed Fare media (Potential ICT) Framework RM DSS


Behavior Responses to Differentiated Pricing: Behavior Responses to Differentiated Pricing Behavior Responses to Differentiated Pricing Traveler Frequent Traveler Infrequent Traveler Single / Return Ticket Reduction Card Season Card Reduction Card Differentiated price: 30% higher between 16:00-18:00 than off-peak price How do customers respond to it? Departure time change (18:00) Mode change (alternative: car) No change


Stated Preference Experiment: Stated Preference Experiment Stated Preference Experiment Focus group interview Quantitative survey Stated preference experiment June and July 2006 13,000 invitations to panel members 4571 responses received (35% response rate) Each respondent is presented with 8 choice sets Each choice set contains two alternative products: one more expensive with less restrictions & less expensive with more restrictions.


Estimation Results: Estimation Results Estimation Results RM DSS


Modeling of Demand: Modeling of Demand Modeling of Demand Model of demand is the key … rather than asking “how much demand should we accept/ reject for each product” as airlines used to do, it is now natural to ask “which alternatives should we make available to our customers in order to profitably influence their choices” -- van Ryzin (2005) Computer simulation is an often-used methodology to study travel behavior as a cost effective alternative to field studies. Solving consumer optimization problems analytically are beyond computational ability Benefits concerning the magnitude of the price differences Multi agent micro-simulation


Modeling of Travel Behavior: Modeling of Travel Behavior


RM DSS: Passenger Railway Networks Simulation => Evaluate dynamic pricing strategies on the transportation network yield RM DSS


Conclusion and Future work: Conclusion and Future work Conclusion and Future Work Understand customer behavior is the key What they say is what they will do? RM DSS Framework “Big brother” issue Sensitivity analysis Case study: High Speed Train (A’dam-Brussels-Paris)