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Defining Revenue Management
as a Game
Gert Hartmans
Agifors - Berlin
2002
Objectives
• Better understanding revenue management for system development
• Breakdown processes in small sub-processes
• Represent each sub-process as a game
• Link all games to reflect total process and enable review of strategies
• Derive system implications
Conceptual view of RevMgt 2000
Forecast
Display Availability
Reservations & Departure Control
GroupDesk
Fares & Rules
CapacityOptimise
Fares CapacityCompetitor
But daily issues ... 2002
• Corporate account access/effects
• Loyalty Program redemption access
• Alliance partner access
• Interline/SPA (non-) access
• Product differentiation (Economy +)
• CRS costs
• Ticket Class / Booked Class mismatch
• Waivers by sales
Systems - ‘ modular design ’
Salescontracts
Pricing
InventoryControl
Account-ing
Loyalty
SPA’sReserva-
tions
DepartureControl
Systems - links
Pricing
InventoryControl
Account-ing
Loyalty
SPA’sReserva-
tions
Salescontracts
DepartureControl
Systems in Alliance
Pricing
InventoryControl
Account-ing
Loyalty
SPA’sReserva-
tions
Salescontracts
DepartureControl
Pricing
InventoryControl
Account-ing
Loyalty
SPA’sReserva-
tions
Salescontracts
DepartureControl
Game Concept
• Players/Rules
• Rewards/Loss
• Choices/Strategy
• Enables modeling– Expectations/Guessing– Deception/Cheating– Cooperation/Retaliation
Inventory Control - Game
• Players Objectives– Maximize Revenue
• Rules – Players set availability access per round
without prior knowledge of others steps
– Two classes: H earns 1 points, L earns 0.5 points– L books first
• Actions– Open class or Close class
Inventory Control - Game 1.1:2
• Capacity 2 per player, Demand H1 L1 per round
• Pay off matrix for Blue– Per round one H-pax books valued at 1 and one
L-pax books valued at .5
• Dominant strategy = All open
Action red
H O L O H O L C H C L C H C L O AvgH O L O 0.75 1 1.5 1.25 1.125H O L C 0.5 0.5 1 1 0.75H C L C 0 0 0 0 0H C L O 0.25 0.5 0.5 0.25 0.375
Rewards Demand CapacityH = 1 1L = 0.5 1
Act
ion
Blu
e
2
BluePay-offMatrix
Inventory Control - Game 2.1:2
• Capacity still 2, demand H2 L1, L books first• Pay off matrix for Blue
– Per round two H-pax books valued at 1 and one L-pax books valued at .5
• Dominant strategy = H Open/ L Closed
Action red
H O L O H O L C H C L C H C L O AvgH O L O 0.75 1 1.5 1.25 1.125H O L C 1.5 1 2 2 1.625H C L C 0 0 0 0 0H C L O 0.25 0.5 0.5 0.25 0.375
Rewards Demand CapacityH = 1 2L = 0.5 1
Act
ion
Blu
e
2
BluePay-offMatrix
Inventory Control - Game 1.4:2
• Capacity still 2, demand H1 L4, L books first• Pay off matrix for Blue
– Per round one H-pax books valued at 1 and four L-pax books valued at .5
• Dominant strategy = Keep L open * Reward for H needs to be > 1.14 to close L
Action red
H O L O H O L C H C L C H C L O AvgH O L O 1 1 1 1 1H O L C 1 0.5 1 1 0.875H C L C 0 0 0 0 0H C L O 1 1 1 1 1
Rewards Demand CapacityH = 1 1L = 0.5 4
Act
ion
Blu
e
2
BluePay-offMatrix
spill
Usage of concepts
– Dominant strategy best for both, can be used for guessing expected competitor action
(leads to Nash Equilibrium)
– If competitor deviates from dominant strategy alternatives do not seem more profitable,avoid copy-cat marketing
– When reality/rules differs for both players, different strategies may be advantageous(larger capacity, different fares, lower frequency etc.)
Sub-processes
• Inventory control game
• Pricing game
• Fare Rule game
• Sales game
• Sales - Agents game
• Agents - Customer game
… products … loyalty ….
Pricing - Game 2.2:2
• Capacity 2 per player, demand H2L2, L books first
• Pay off matrix for Blue– Per round two H-pax books valued at 1 and two
L-pax books valued at .5
• Dominant strategy = Offer H fares only
BluePay-offMatrix
Action red
H A L A H A L X H X L X H X L A AvgH A L A 1.5 1 1 1.5 1.25H A L X 2 1 2 2 1.75H X L X 0 0 0 0 0H X L A 0.5 1 1 0.5 0.75
Rewards Demand CapacityH = 1 2L = 0.5 2
Act
ion
Blu
e
2
Fare Rule - Game 2.2:2
• Capacity 2 per player, demand H2L2 • L books first, H books L if no rule exists• Pay off matrix for Blue
– Per round two H-pax books valued at 1 and twoL-pax books valued at .5
• Dominant strategy = Add fare rules
BluePay-offMatrix
Action red
No Rule Rule AvgNo Rule 1 1 1Rule 1.5 1.5 1.5
Rewards Demand CapacityH = 1 2L = 0.5 2
Act
ion
Blu
e
2
Sales Game 4.4:4
• Capacity 4 is shared per 2 players (sales office) based on bids H:L 3:1 or evenly 2:2 (gray area)
• Demand H2 L2 per round per player, 4 per market• Dominant strategy = bid high and sell high,
but potential for cheating
B H S H B H S L B L S L B L S H AvgB H S H 2 2 3 3 2.5B H S L 1 1 1.5 1.5 1.25B L S L 0.5 0.5 1 1 0.75B L S H 1 1 2 2 1.5
Rewards Demand CapacityH = 1 2:2L = 0.5 2:2
Act
ion
Blu
e
4
BluePay-offMatrix
Sales - Agents Game
• Commission % differs, Demand 1 per round• Pay off matrix for Blue (Sales office)• Dominant strategy = Surpass competitor
commission levels if possible
Action red
20% 10% 5% Avg20% 0.4 0.8 0.8 0.6710% 0 0.45 0.9 0.455% 0 0 0.475 0.16
Rewards Demand1 1
BluePay-offMatrix
Agents - Customer Game
• Discount differs, Demand 1 per round• Pay off matrix for Blue (agent)• Dominant strategy = Match or surpass
competitor agent
Action red
20% 10% 5% Avg20% 0.4 0.8 0.8 0.6710% 0 0.45 0.9 0.455% 0 0 0.475 0.16
Rewards Demand1 1
BluePay-offMatrix
Value Chain
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Airlines - Inventory game
Airlines - Pricing game
Airlines - Fare Rules game
Sales game
Agents -Customers game
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Action red
H O L O H O L C H C L C H C L OH O L O 1 1 1 1H O L C 1 0.5 1 1H C L C 0 0 0 0H C L O 1 1 1 1A
ctio
n B
lue
Sales - Agents game
Conclusions
• Market position is chain of games
• Organization, systems do not reflect inter-relations which are reflected in processes
• Open to cheating and deception
• System alignment and interfaces should help in achieving more optimal results
Enforcing rules
• Currently: Booking =/= Sale =/= Fare
• Commission variation, rule waivers, or over stating fares, leads to incorrect information
• Objective: 1 product = 1 contract =1 booking = 1 sale = 1 fare
Place CommercialHere