Transcript Performance Measurement and Control in Logistics Service

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
• Research background and questions
• Research studies and methodology
Outline
– 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
Motivation
Passenger
Demand
 Business needs
Vehicle
Supply
Over-capacity
• Diffuse the concentration of peak load
• Increase capacity utilization
Missed-income
0
Motivation
 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
24
Research Questions
 Research Objective
• Assess the possibilities of revenue management in contribution
of customer data provided by a nation-wide smart card
Research Questions
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
 Information system research
•
Dynamic pricing benefits consumers (Bakos, 1997).
•
RM increases performance enterprises (increased customer
Previous Research
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
Revenue Management DSS
Smart Card
Data
Extrapolate
demand
Travel behavior
Public
Transport
System
RM DSS
Choose
pricing strategy
Yield
Report
Infrastructure /
Vehicle Data
Extrapolate
Supply
Information
Simulate
demand / supply
Evaluate
performance
Diagnosis
Demand / Supply
RM DSS
Legend:
system
Information
activities
input-output
Selected
Pricing Strategy
World-wide Smart Card Implementation
World-wide Smart Card Implementation
Year
City (Country)
Transportation (Issuing Authority)
Name of SC
1997
Hong Kong (China)
Octopus Cards Limited
Octopus*
1997
Tampere (Finland)
Tampere City Transport
Tampere Travel Card
1999
Washington D.C.
(U.S.A.)
Washington Metropolitan Area Transit
Authority
SmarTrip
2000
Taipei (Taiwan)
Taipei Smart Card Corporation
EasyCard
2001
Warsaw (Poland)
Warsaw Transport Authority
Warsaw City Card
2001
Tokyo (Japan)
East Japan Railway Company (JR East)
SUICA*
2001
Paris (France)
Régie Autonome des Transports
Parisiens (RATP)
Navigo Card
2002
Singapore
EZ-Link Private Limited
Ez-link*
2002
Chicago (U.S.A.)
Chicago Transit Authority (CTA)
Chicago Card*
2003
London (U.K.)
Transport for London (TfL)
Oyster*
2004
Seoul (South
Korea)
Korea Smart Card Co., Ltd
T-Money
2006
Beijing (China)
Beijing Municipal Administration &
Communications Card Company Limited
Yikatong*
2006
The Netherlands
Trans Link Systems (TLS)
OV-chipcard*
2007
(planned)
Toronto (Canada)
The Greater Toronto Transportation
Authority
GTA Card
Differentiated Pricing Strategy
• Uniform pricing vs. Dynamic pricing
Differentiated Pricing Strategy
• 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
• Public Transport Operators’ rational
• Effects to Customers
• Data / information sources needed
Framework
• Fare media (Potential ICT)
RM DSS
Behavior Responses to Differentiated Pricing
Behavior Responses to Differentiated Pricing
Differentiated price: 30% higher between 16:00-18:00 than
off-peak price
+30%
Differentiated Price
How do customers respond to it?
16:00 18:00
•Departure time change (<16:00 or >18:00)
•Mode change (alternative: car)
•No change
Traveler
Frequent
Traveler
Season
Card
Reduction
Card
Infrequent
Traveler
Reduction
Card
Single / Return
Ticket
Stated Preference Experiment
• Focus group interview
Stated Preference Experiment
• 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
RM DSS
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
Passenger Disposition
Modeling of Travel Behavior
Activity Schedule
•Location
•Duration
•Timing
•Purpose
Decision Window
•Departure time
•Schedule Tolerance
Max. WTP
Influenced by
•Travel purpose
•Income
Characteristics
•Age
•Income
•Education
•Car ownership
Past Experience
•Comfort
•Crowdness
•Punctuality
Passenger Choice Set
Possible Schedule
Passenger Disutility
Passenger Choice
Passenger Decision
•Departure time
•Mode
•Route
•Fare
Product and Ticket
Passenger Railway Networks Simulation
RM DSS
Dynamic Pricing
Strategy
Passenger Disposition
Infrastructure Network
Passenger Choice Set
Train Scheduling
Passenger Decision
Capacity
Demand Simulation
Category
Supply Simulation
Performance Metrics
Metrics
•Network capacity utilization (load factor)
Supply
•Spread in train loading (passenger distribution)
(train operation)
•Load factor (Peak and average load)
•Cost (per day per
=> Evaluate dynamic pricing strategies
ontrain)
the
•Passenger (#)
transportation network yield
Demand
•Journey (number of trips)
(passenger travel)
•Revenue (Euro)
•Volume (Passenger*km)
Conclusion and Future Work
• Understand customer behavior is the key
Conclusion and Future work
• 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)