Revenue Management Flashcards
1
Q
RM Objectives
A
Maximize Revenue.
Right Product to right customer at right time and right price.
2
Q
RM Conditions
A
1 perishable product
2 low marginal cost
3 limited capacity
4 market segmentation
3
Q
Classical Segmentation
A
Tourists
-price sensitive
book early
-low service demands
Business
- not price sensitive
- book late
- high service demands
4
Q
Stages of RM process
A
1 Forecast
2 Optimization
3 Reservations
5
Q
Why is RM getting more complex?
A
networked customers
alliances and competition
automatization
6
Q
Traditional RM Model
and modern extensions
A
Independent Demand
7
Q
Conditions for Price Differentiation
A
Multiple demand functions
supplies maximizes revenue
market not perfectly transparent or competetive
8
Q
Degrees of price differentiation
A
1st: perfect differentiation 2nd: by self selection (find highest prices maintaining fencing) 3rd: by customer characteristics
9
Q
Capacity Oriented Pricing
A
Price per unit dependent on number of units bought.
10
Q
Segment Oriented Pricing
A
geographical
temporal
personal
product-oriented
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
12
Q
Name Your Own Price
A
Based on Auctions
Bids succesful if exceeding bid price
can be combined with opaque selling
13
Q
Booking Limits
A
Maximum Number of units to be sold per class.
- partitioned
- nested
14
Q
Protection Limits
A
Number of units to be reserved per class
- partitioned
- nested (C includes A)
15
Q
Theft Nesting
A
Bookings in valuable classes reduce limit of other classes.
16
Q
Leg Based RM with independent demand
A
Littlewood’s Rule and EMSR-b
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.
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
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.
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
21
Q
Dynamic Demand Arrivals
A
possibility of product request arriving at particular time
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.
23
Q
Demand Estimation
needs to know?
A
- how many potential customers
- in which demand segment
- which choices?
- -in the past
24
Q
Demand Forecast asks?
A
How much demand in the future?
25
Faulty demand forecasts lead to?
Spoilage: selling too little, too late
Spill: Selling too much, too early
26
Forecast Data
```
Sales Transaction Data
Controls Data (bid prices)
Auxiliary Data (tax, demographic, weather)
```
27
Partial Data
If sales horizon and observations overlap.
28
Poisson Intensity indicates...
probability of a customer arriving and requesting class i
29
Forecasting Methods
Structural: descriptive, HW, function
| Time-Series: ARIMA model!
30
Time Series Methods
1 Hypothesis about process generating data
2 estimate process parameters
3 apply best method for model
-> can exploit correlations in data
31
Pick Up Forecasting
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)
32
Independent Demand Model
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
33
How to make demand (more) independent?
Fencing
- advance purchase restrictions
- minimum or weekend stay restrictions
- cancellation fees
34
Danger of Assuming Independent Demand?=
Spiral Down, Buy down
35
Dependent Demand Model
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
Relevant Questions for Dependent Demand?
1 Products targeting same customers?
2 Qualitative differences between products?
3 How much demand per product?
4 How much overlap?
37
FRAT 5
new fare ratio retains 50% of demand
38
Random Utility Models account for
1 not observable relevant variables
| 2 inconsistent customer choice
39
MNL assumptions
utility= representative component and random component
random component is Gumbel distributed
Indepdence from irrelevant alternatives
40
MNL scaling parameter
0 -> deterministic
| inf -> entirely random
41
IAA example
in MNL the number of products influences choice even if products dont differ on relevant attributes.
42
Preference Lists
- how modeled?
- what kind of model?
directed acyclic graph
| non-parametric
43
Estimation vs Forecasting
descriptive vs predictive
| past vs future
44
Non-parametric vs parametric Estimation
no particular distribution or model VS assumes underlying distributin or model
shape vs parameters
45
Estimation Objectives
unbiased - expected value of estimator equals actual
efficient - unbiased and lowest possible variance
consistent- converges to true value as sample size increases
46
Estimation Challenges
Endogeneity
Constrained Observations
Biased Observations
Small Sample
47
Regression Estimators are...
causal predictions.
+ can explain abrupt changes
+offers behavioral explanation
-hard to define all relevant variables and relationships
48
Probit Model
probability of one event
49
Multionomial logit/probit
set of discrete probabilities
50
Regression Estimator vs ML Estimator
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
Demand Segments.
| What to estimate?
How many?
What share?
What behaviour?
52
Behaviour of a demand segment.
Classical?
Modern?
classical:
one segment per booking class
modern:
random choice
different utility models
non-parametric (preference lists)
53
Methods to unconstrain demand?
```
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
Bid Prices under dependent demand. Problems:
| and Solution.
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
Nested Capacity Controls or Bid Prices that increase monotonously, RM ensures increasing prices over sales horizon.
Why would this not hold?
Sales Interventions
Forecast Updates
Competitor Matching
56
```
Dependent Demand Problem:
Strategic Customers (time-lag-spiral-down)
```
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
Dependent Demand Problem:
| Parallel Flights
```
Customers switching booking class use same resource.
Intractable to optimize dependencies in choice modelling.
```
58
Dependent Demand Problem:
| Competition
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
Capacity Controls.
1 single resource and independent demand?
2 multiple resources and independent demand
3 dependent demand
1 EMSR-b
2 Bellman Function and Bid Prices
3 Bellman Function and adjusted Fares
60
Considered Trade-Off when Overbooking?
Service Degree vs Incurred Cost
61
Service Degree Type 1
Probability that given overbooking limit and capacity all customers can be serviced as promised
62
Service Degree Type 2
Percentage of Customers that did not receive service as promised
63
Overbooking gone wrong.
| Components of Costs?
1 actual monetary compensation
2 reputation demage
->often increased as exponentially increasing
64
Cancellation vs No Shows
no shows forego service without announcing in advance.
65
Static Overbooking Models
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
Dynamic Overbooking Model...does what?
updates constantly
67
Conditions for Dynamic Pricing
1 prices can be adjusted quickly without high transactional costs
2 customers and policymakers do not expect fixed prices
3 models remain solvable
68
Dangerous simplyfing assumptions:
myopic customers
time-independent demand
monopoly
69
Deterministic Dynamic Pricing
KKT
1 MR constant across periods
2 either MR=0 or C sold out
3 if demand >C; MR>0
70
3 Types of Uncertainty
1 Ignorance
2 Incertitude (probs unknown)
3 Risk
71
RM Indicators
```
Revenue
Yield (revenue per passenger)
Bookings
Capacity Utilization
Forecasted Demand
Inventory Controls
Interventions
```
72
MAPE
1/n * SUM [abs(FCi-OBSi)/OBSi]
73
RMSE
root(1/n*SUM[(FCi-OBSi)^2])
74
Challenges to Evaluating RM Performance
```
1 dynamic economy
2 evaluating effect of competition
3 what went wrong and where
4 how good is good
5 identifying comparable markets
```
75
Revenue Opportunity Model
Problematic: assumes demand prediction was absolutely accurate
76
Air Cargo Paper
- two dimensional problem: weight and volume
- uncertainty makes overbooking neccessary
- itineraries only defined by start and end
- decomposition heuristic performs best
77
Attended Home Delivery Paper
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
Risk Averse RM Paper
Why?
low number of repetitions
critical impact
management constraints
Bid Price with Factor performed best
79
Non-Traditional Settings
Classical Practices not entirely transferable
inventory sensitive vs price sensitive demand
golf courses with many early bookings and conversion management performed best
80
Trade In or Upgrade Paper
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
Competition in Airlines Paper
vertical competition
multiple airlines, one leg, one company
horizontal competition
multiple companies per leg
82
Dangerous simplifying assumptions for dynamic pricing
- myopic demand
- time independent demand
- monopoly
83
Integrating Overbooking and Capacity Controls
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
Assumption of exact OB models:
cancellation and no shows probabilities:
- same for all customers
- iid among customers
- independent of booking time
85
Integrating Overbooking and Capacity Controls MEANS...
balancing the value of accepting requests with the potential costs of denied boardings.
86
Advantages of Price-based RM
- better demand curve fitting
- more flexible
- more intuitive
87
Advantages of Capacity-based RM
- well established-algorithms
- probably better fit with existing technology
- fewer simplifying assumptions
