Post on 01-Jan-2016
description
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OPSM 405 Service Management
Class 15:
Yield management: introduction
Koç University
Zeynep Aksinzaksin@ku.edu.tr
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Service Delivery SystemCustomer Demand
Limited Capacity
Fundamental Problem:
Variable Usage
Services cannot be produced in advance and stored for later consumption; they must be produced at the time of consumption.
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Matching supply and demand in services Management options
• reject demand• inventory excess demand (queueing)• modulate capacity (add facililities, scheduling,
resource allocation)• modulate demand (pricing, yield management)
Primary considerations• return on assets• operating costs• revenue losses (opportunity costs)• service levels
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Successful implementations
American Airlines
– $1.4B additional revenue over three-year period
– “Selling the right capacity to the right customer at the right price”
Hertz
– 1-5% revenue increase annually ($10-50M per year) Marriott Hotels
– $25-35M additional revenue in 1991 Royal Caribbean Cruise Line
– $20M+ additional revenue per year
Source: Arthur D. Little, 1992
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When is this strategy appropriate?
Limited flexibility in supply Variable/uncertain demand Price flexibility/segmentation possible Available data Examples:
– airlines– hotels/resorts/theme parks– car/equipment rental – cruise ships– freight shipping– theater/performing arts– broadcasting (TV, radio,etc.)– utilities (elec., telecom)
1970s
1980s
1990s
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Yield Management System
Reservation System
Forecasting
Overbooking Levels
Discount Allocation
current demandcancellations
cancellation rate estimates
overbooking levels
futuredemandestimates
fare class allocations
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What is revenue/yield management?Two Perspectives:
1) A Market Segmentation Strategy (capture consumer surplus)
p
q
p
q
p0 p2p1
Create separate“fare products”
Intelligently allocate fixedcapacity to products
NOTE: Segmentation may make sense even with static allocation! Segmentation can also provide value (e.g. cancellation option)
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Segmentation/product design
Ideally, we would like to discriminate (sort) customers based on their actual willingness-to-pay (reservation price).
$0.00
$50.00
$100.00
$150.00
$200.00
$250.00
C4 C2 C3 C1 C5
Cust. Res. PriceC1 $120C2 $180C3 $167C4 $230C5 $ 45
=====$742
ConsumerSurplus = $742
But willingness-to-pay is usually unobservable!
Ex:
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So we try to find a variable that is correlated with willingness-to-pay (a “sorting mechanism”)
Cust. Res. Price Adv. Purchase?C1 $120 YESC2 $180 NOC3 $167 YESC4 $230 NOC5 $ 45 YES
Create two produce (advance/late purchase) with two prices: Adv: $100 Late: $150
$0.00
$25.00
$50.00
$75.00
$100.00
$125.00
$150.00
C4 C2 C3 C1 C5
ConsumerSurplus = $500
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Sorting mechanisms Time of purchase/usage
– advanced/spot purchase– day-of-week/season
Purchase restrictions– cancellation options– minimum term– Saturday night stay
Purchase volume (individual vs. group) Duration of usage (single night/weekly rate) Customer affiliation
– corporate– contract user
Finding a good sorting mechanism is an art and requires a certain amount of trial and error.
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2) Matching Price to Demand (peak-load pricing)
High Low
Discount
Full Fare
Demand
Pricexx
xxxxxxxx
xxxxxxxxxx
x
Create a small number of“price points”
Allocate more capacityto low price points if demandis weak; allocate more capacityto high price points if demand isstrong
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Example: Using capacity controls for peak load pricing
Capacity = 100 seats
Demand Rev. Demand Rev.
$50 fare 30 $1,500 150 $5,000
$25 fare 80 $2,000 20 $500$75 fare 2 $150 80 $6,000
$2,150 $6,500
Off-Peak Day Peak Day
SinglePrice
TwoPrices
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Example
2 FlightsCapacity = 3 seats$800
$700
$400
$300
$200
Ex: 5 customers with different valuations
NOTE: We usually cannot observe these valuations in practice
8:00 AM 1:00 PM
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Best single price: $700Revenue: 2 x $700 = $1400
Maximum obtainable revenue$800 + $700 + $400 + $300 +$ 200 = $2400
Only 58% of maximum achieved!
$700
$400
$800
$300
$200
priced out
$700 $700
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Discrimination via a “sorting mechanism”
$800
$700
$400
$300
$200
Customers returning by Saturday
Customers staying a Saturday
A trait that is correlated with willingness to pay allows for discrimination
- Saturday night stay - Advance purchase req. - Distribution channel (e.g. internet)
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Price discrimination: SA stay: $400 No SA stay: $700Revenue: 2 x $700 + 1 x $400 = $1800
Maximum revenue$2400
Now 75% of maximum achieved!
$700
$400
$800
$300
$200
priced out
$700 $400 $700 $400
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$400
Implement dynamic pricing
Capacity-controlled fares can be used to dynamically adjust the “effective price” of each departure.
$700
$800
$300$200
$700
$400
8:00 AM 1:00 PM
$700
$400
Priced outWe would like to price the empty flight to attract more traffic! How?
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$400
$700
$800
$300
$200
$700
$400
$200
8:00 AM 1:00 PM
$700
$400
$200 XNo seats available
Revenue = 2 x $700 + 1 x $400 + 2 x $200 = $2200
2 seats available
92% of maximum!
Capacity-controlled deep discount
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Example summary:2 Flights3 Seats each
1) Single price $1400 (+0%)
2) Two prices w/ sortingmechanism $1800 (+29%)
3) Two prices w/ sortingmech. & capacity-controlleddeep discount $2200 (+57%)
Forecasting demand
Data requirements– high-level of detail (origin-destination, fare-class, day-of-week,
departure time)– quantities tracked
• demand for each rate category/fare-class/departure• cancellation rates• no-show rates/ go-show rates
– daily processing Forecasting issues
– seasonalities– trends– special events
Good forecasting and accurate data are essential