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Revenue Management > Revenue Management > Flashcards

Revenue Management Flashcards

1
Q

RM Objectives

A

Maximize Revenue.

Right Product to right customer at right time and right price.

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2
Q

RM Conditions

A

1 perishable product
2 low marginal cost
3 limited capacity
4 market segmentation

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3
Q

Classical Segmentation

A

Tourists
-price sensitive
book early
-low service demands

Business

  • not price sensitive
  • book late
  • high service demands
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4
Q

Stages of RM process

A

1 Forecast
2 Optimization
3 Reservations

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5
Q

Why is RM getting more complex?

A

networked customers
alliances and competition
automatization

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6
Q

Traditional RM Model

and modern extensions

A

Independent Demand

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7
Q

Conditions for Price Differentiation

A

Multiple demand functions
supplies maximizes revenue
market not perfectly transparent or competetive

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8
Q

Degrees of price differentiation

A 
1st:
perfect differentiation
2nd:
by self selection (find highest prices maintaining fencing)
3rd:
by customer characteristics
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9
Q

Capacity Oriented Pricing

A

Price per unit dependent on number of units bought.

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10
Q

Segment Oriented Pricing

A

geographical
temporal
personal
product-oriented

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11
Q

Auctions

A

-price determined through bids
-increase with number of customers and valuation
RM potential:
near perfect 1st degree
no need for estimation

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12
Q

Name Your Own Price

A

Based on Auctions
Bids succesful if exceeding bid price
can be combined with opaque selling

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13
Q

Booking Limits

A

Maximum Number of units to be sold per class.

  • partitioned
  • nested
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14
Q

Protection Limits

A

Number of units to be reserved per class

  • partitioned
  • nested (C includes A)
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15
Q

Theft Nesting

A

Bookings in valuable classes reduce limit of other classes.

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16
Q

Leg Based RM with independent demand

A

Littlewood’s Rule and EMSR-b

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17
Q

Littlewood’s Rule

A

two classes
2 arrives before 1
p2 < p1
sell in 2 as long as p2>p1*P(D>x)

derives protection limits.

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18
Q

EMSR-b

A 
1 aggregate demand for class j and more expensive
2 calculate avg price for class j and more expensive
3 apply littlewood's rule
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19
Q

Bid Price

A

Minimum Revenue to earn with next booking on resource.
=1 -> all classes are available
=inf -> no capacity left

opportunity cost of seeling an additional unit of capacity.

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20
Q

Bid Prices

  • well-suited for?
  • control what?
  • dynamic?
  • customers?
A
  • > Products that require multiple resources
  • > availability of tariffs rather than booking classes
  • > re-calculated, updated frequently
  • > don’t see bid prices, nor full set of available classes
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21
Q

Dynamic Demand Arrivals

A

possibility of product request arriving at particular time

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22
Q

Stochastic Dynamic Programming Model

A
  • sales horizon partitioned (most one request)
  • independent demand
  • product i requires a(hi) resources of h
  • state described by original capacity C and left-over capacity c(h) per resource h.
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23
Q

Demand Estimation

needs to know?

A
  • how many potential customers
  • in which demand segment
  • which choices?
  • -in the past
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24
Q

Demand Forecast asks?

A

How much demand in the future?

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25
Q

Faulty demand forecasts lead to?

A

Spoilage: selling too little, too late
Spill: Selling too much, too early

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26
Q

Forecast Data

A 
Sales Transaction Data
Controls Data (bid prices)
Auxiliary Data (tax, demographic, weather)
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27
Q

Partial Data

A

If sales horizon and observations overlap.

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28
Q

Poisson Intensity indicates…

A

probability of a customer arriving and requesting class i

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29
Q

Forecasting Methods

A

Structural: descriptive, HW, function

Time-Series: ARIMA model!

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30
Q

Time Series Methods

A

1 Hypothesis about process generating data
2 estimate process parameters
3 apply best method for model

-> can exploit correlations in data

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31
Q

Pick Up Forecasting

A

Used for overlapping observations of subsequent offerings.

Additive: incremental gain per time-slice
Multiplicative:
relative increase per time slice (assumes correlation of future bookings with current bookings)

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32
Q

Independent Demand Model

A

1 customer only requests products defined for his segment
2 customers from different segments do not request same product
3 if product is sold out, segment stopy buying
4 pricing of not demanded products irrelevant

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33
Q

How to make demand (more) independent?

A

Fencing

  • advance purchase restrictions
  • minimum or weekend stay restrictions
  • cancellation fees
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34
Q

Danger of Assuming Independent Demand?=

A

Spiral Down, Buy down

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35
Q

Dependent Demand Model

A

1 customers buy preferentially in their segment
2 different segments can request same product
3 if sold out, customers may be other product
4 pricing of one product may affect product choices of other segments

36
Q

Relevant Questions for Dependent Demand?

A

1 Products targeting same customers?
2 Qualitative differences between products?
3 How much demand per product?
4 How much overlap?

37
Q

FRAT 5

A

new fare ratio retains 50% of demand

38
Q

Random Utility Models account for

A

1 not observable relevant variables

2 inconsistent customer choice

39
Q

MNL assumptions

A

utility= representative component and random component

random component is Gumbel distributed
Indepdence from irrelevant alternatives

40
Q

MNL scaling parameter

A

0 -> deterministic

inf -> entirely random

41
Q

IAA example

A

in MNL the number of products influences choice even if products dont differ on relevant attributes.

42
Q

Preference Lists

  • how modeled?
  • what kind of model?
A

directed acyclic graph

non-parametric

43
Q

Estimation vs Forecasting

A

descriptive vs predictive

past vs future

44
Q

Non-parametric vs parametric Estimation

A

no particular distribution or model VS assumes underlying distributin or model

shape vs parameters

45
Q

Estimation Objectives

A

unbiased - expected value of estimator equals actual
efficient - unbiased and lowest possible variance
consistent- converges to true value as sample size increases

46
Q

Estimation Challenges

A

Endogeneity
Constrained Observations
Biased Observations
Small Sample

47
Q

Regression Estimators are…

A

causal predictions.
+ can explain abrupt changes
+offers behavioral explanation

-hard to define all relevant variables and relationships

48
Q

Probit Model

A

probability of one event

49
Q

Multionomial logit/probit

A

set of discrete probabilities

50
Q

Regression Estimator vs ML Estimator

A

Regression (OLS):
minimize squared error resulting from estimated paramters across all observations

MLE:
Maximize probability of observing the prediction given by estimated paramters multiplied across all observations

51
Q

Demand Segments.

What to estimate?

A

How many?
What share?
What behaviour?

52
Q

Behaviour of a demand segment.
Classical?
Modern?

A

classical:
one segment per booking class

modern:
random choice
different utility models
non-parametric (preference lists)

53
Q

Methods to unconstrain demand?

A 
Naive
Projection Detruncation
-estimate mean and sd
-replace closed with heuristial solution
-test for convergence
Estimation Maximisation
-estimate mean and sd
-test for convergence
-replace xconst by xk via ML estimation
54
Q

Bid Prices under dependent demand. Problems:

and Solution.

A

demand may be dependent on offers with buy-dwon between classes (customers dont spend as much as they might have)

bid prices may decrease over time - cheaper classes become available

->Solution: Adjusted Fares
Bid Prices adjusted to account for buy -down

55
Q

Nested Capacity Controls or Bid Prices that increase monotonously, RM ensures increasing prices over sales horizon.
Why would this not hold?

A

Sales Interventions
Forecast Updates
Competitor Matching

56
Q 
Dependent Demand Problem:
Strategic Customers (time-lag-spiral-down)
A

Customers may delay purchase, expecting price dip in the future (as they have learned from past observations)
->updating forecasts can induce a time-laag-spiral-down effect

57
Q

Dependent Demand Problem:

Parallel Flights

A 
Customers switching booking class use same resource.
Intractable to optimize dependencies in choice modelling.
58
Q

Dependent Demand Problem:

Competition

A

Automated RM system would have to forecast demand AND competitor otffers -hardly feasible

Prisoner’s Dilemma:
underbidding is dominant
leads to total buy-down

->need thresholds

59
Q

Capacity Controls.
1 single resource and independent demand?

2 multiple resources and independent demand

3 dependent demand

A

1 EMSR-b

2 Bellman Function and Bid Prices

3 Bellman Function and adjusted Fares

60
Q

Considered Trade-Off when Overbooking?

A

Service Degree vs Incurred Cost

61
Q

Service Degree Type 1

A

Probability that given overbooking limit and capacity all customers can be serviced as promised

62
Q

Service Degree Type 2

A

Percentage of Customers that did not receive service as promised

63
Q

Overbooking gone wrong.

Components of Costs?

A

1 actual monetary compensation
2 reputation demage

->often increased as exponentially increasing

64
Q

Cancellation vs No Shows

A

no shows forego service without announcing in advance.

65
Q

Static Overbooking Models

A

1 Determine overbooking limit
2 use result for capacity allocation as if it was real
3 update overbooking limit and allocations as actual bookings and cancellations come in

66
Q

Dynamic Overbooking Model…does what?

A

updates constantly

67
Q

Conditions for Dynamic Pricing

A

1 prices can be adjusted quickly without high transactional costs
2 customers and policymakers do not expect fixed prices
3 models remain solvable

68
Q

Dangerous simplyfing assumptions:

A

myopic customers
time-independent demand
monopoly

69
Q

Deterministic Dynamic Pricing

A

KKT
1 MR constant across periods
2 either MR=0 or C sold out
3 if demand >C; MR>0

70
Q

3 Types of Uncertainty

A

1 Ignorance
2 Incertitude (probs unknown)
3 Risk

71
Q

RM Indicators

A 
Revenue
Yield (revenue per passenger)
Bookings
Capacity Utilization
Forecasted Demand
Inventory Controls
Interventions
72
Q

MAPE

A

1/n * SUM [abs(FCi-OBSi)/OBSi]

73
Q

RMSE

A

root(1/n*SUM[(FCi-OBSi)^2])

74
Q

Challenges to Evaluating RM Performance

A 
1 dynamic economy
2 evaluating effect of competition
3 what went wrong and where
4 how good is good
5 identifying comparable markets
75
Q

Revenue Opportunity Model

A

Problematic: assumes demand prediction was absolutely accurate

76
Q

Air Cargo Paper

A
  • two dimensional problem: weight and volume
  • uncertainty makes overbooking neccessary
  • itineraries only defined by start and end
  • decomposition heuristic performs best
77
Q

Attended Home Delivery Paper

A

Trade-Off:
high service degree vs efficient logistical operations
->dynamic management

offer time slot if generated profit > opportunity costs

using generalized attraction model to capture dissatisfaction if preferred slot not offered

78
Q

Risk Averse RM Paper

A

Why?
low number of repetitions
critical impact
management constraints

Bid Price with Factor performed best

79
Q

Non-Traditional Settings

A

Classical Practices not entirely transferable
inventory sensitive vs price sensitive demand
golf courses with many early bookings and conversion management performed best

80
Q

Trade In or Upgrade Paper

A

Result: cash payout and refund should reversely depend on inventory level of refurbished product

low: both trade in options offers
medium: both with medium payout
high: no trade in for cash, upgrade only

81
Q

Competition in Airlines Paper

A

vertical competition
multiple airlines, one leg, one company
horizontal competition
multiple companies per leg

82
Q

Dangerous simplifying assumptions for dynamic pricing

A
  • myopic demand
  • time independent demand
  • monopoly
83
Q

Integrating Overbooking and Capacity Controls

A

Iterative:
1 calculate ob-limit 2 allocate capacity
Integrative
static
1 calculate current ob-limit 2 allocate capacity during time slice
dynamic
1 calculate service degree 2 decide request acceptance

84
Q

Assumption of exact OB models:

A

cancellation and no shows probabilities:

  • same for all customers
  • iid among customers
  • independent of booking time
85
Q

Integrating Overbooking and Capacity Controls MEANS…

A

balancing the value of accepting requests with the potential costs of denied boardings.

86
Q

Advantages of Price-based RM

A
  • better demand curve fitting
  • more flexible
  • more intuitive
87
Q

Advantages of Capacity-based RM

A
  • well established-algorithms
  • probably better fit with existing technology
  • fewer simplifying assumptions

Revenue Management (1 decks)

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