Revenue Management for Airline Industry

The First NIDA Business Analytics and Data Sciences Contest/Conferenceวันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์

•What is Revenue Management? •Revenue Management in Airline Industry-Market segmentation -Capacity allocation/Booking control-Overbooking - Forecasting •Research topics & Future trends

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รศ.ดร.กาญจ์นภา อมรัชกุลสาขาวิชา Logistic Managementคณะสถิติประยุกต์ NIDA

นวมินทราธิราช 3003วันที่ 2 กันยายน 2559 9.30-10.00 น.

AIRLINE REVENUE MANAGEMENT

Kannapha Amaruchkul

Logistics Management Program,

School of Applied Statistics, NIDA

2016-09-02 9:30-10:30 Rm3003

WHAT IS REVENUE MANAGEMENT (RM)?

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WHAT RM?

Other synonymous names are

Yield management

Pricing and revenue optimization

Demand-chain management

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Credit: http://en.wikipedia.org/wiki/Demand_chain

LOGISTICS NETWORK (SUPPLY CHAIN)

5Source: Simchi-Levi, D., & Kaminsky, P., & Simchi-Levi, E. (2007). Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies. Boston: McGraw-Hill.

SUCCESS OF AIRLINE RM

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In 1985, American Airlines (AA) was threatened on its

core routes by the low-fare carrier PeopleExpress, after

deregulation

AA developed RM program

PeopleExpress lack of a strong computer reservation

system and went out of business.

The AA team won 1991 Edelman Prize for best

application of management science.

Source: http://airwaysnews.com/html/timetable-and-route-maps/

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PRICE OPTIMIZATION

Price-response function

d(p) = 1000 – 2p

ตน้ทุนต่อหน่วย c = 30 THB

1. Single-price opt ก ำไร 110,540

2. Two-price ก ำไร 130,600

ก ำไรท่ีไดจ้ำกกำรตั้งสองรำคำท่ี 300 และ 230 บำท

3. Three-price ก ำไร 138,450

ก ำไรท่ีไดจ้ำกกำรตั้งสำมรำคำท่ี 300, 265, 230 บำท

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กำญจน์ภำ อมรชักุล. (27 ธนัวำคม 2557). Traditional Pricing ของตัว๋โดยสำรรถทวัร ์versus Dynamic Pricing ของตัว๋โดยสำรเครื่องบิน Low Cost หน่ึงกลยุทธเ์ชิงรำคำของกำรจดักำรรำยได ้(Pricing & Revenue Optimization), [ระบบออนไลน์],

managerOnline: http://www.manager.co.th/Daily/ViewNews.aspx?NewsID=9570000149055

MARKET SEGMENTATION IN AIRLINE INDUSTRY

9Source: Simchi-Levi, D., & Kaminsky, P., & Simchi-Levi, E. (2007). Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies. Boston: McGraw-Hill.

Sensitivity to

duration/flexibility

High ? ?

Low ? ?

Leisure

Business

Low High Sensitivity

to price

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Pricing and Revenue

Optimization

Pricing

Price Optimization

Price Differentiation

Revenue Management

Capacity Allocation

(Single-Resource)

Network Management

Overbooking

“FENCING” BY RESTRICTIONS

Business travelers typically do not want to stay over Saturday night, so they will not buy M or Q

RM system determines how many seats to protect for Y and B

Impose booking limits on M and QMIT Open Courseware (http://ocw.mit.edu/) Airline RM: Flight Leg and Network Optimization. P. Belobaba.

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CAPACITY ALLOCATION: EXPECTED MARGINAL SEAT REVENUE (EMSR) Full fare 𝑝1 = 987 Discount fare 𝑝2 = 650

Full-fare demand 𝐷1 Poisson with mean 45. Discount-fare demand 𝐷2 Poisson with mean 75

Find an optimal protection level. Capacity = 100 seats.

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Protection level,

y 𝑃 𝐷1 ≥ 𝑦 )

Expected Marginal

revenue for class 1,

𝑝1𝑃(𝐷1 ≥ 𝑦)20 1.0000 986.99

30 0.9927 979.76

40 0.7916 781.33

41 0.7445 734.78

42 0.6927 683.69

43 0.6372 628.94

44 0.5792 571.66

45 0.5198 513.07

46 0.4605 454.48

47 0.4024 397.16

48 0.3468 342.29

49 0.2947 290.84

50 0.2468 243.59

60 0.0187 18.41

70 0.0003 0.33

Yes: keep y units for class 1

No: Sell to class 2 now

𝒑𝟏 = 𝟗𝟖𝟕

Increase protection

level from 29 to 30?

𝑷(𝑫𝟏 ≥ 𝟑𝟎)

𝑫𝟏 ≥ 𝟑𝟎

0

𝒑𝟐 = 𝟔𝟓𝟎

𝑷(𝑫𝟏 < 𝟑𝟎)

Increase protection level for class 1, 𝑦1, as long as 𝑝2 < 𝑝1𝑃 𝐷1 ≥ 𝑦1

NETWORK RM

A carrier might have 1000 daily departures.

A320 with 200 seats per flight leg.

200*1000 = 200,000 seats per network day.

365 network days

At any given time, airline inventory is

365*200,000 = 73,000,000 seats.

Managing inventory is challenging due to volume

http://img.photobucket.com/albums/v94/kyrul/airliners/airasia.gif13

BID PRICE NETWORK CONTROL

ODF Bid price 350 2010 1690

Product

bid price AvailabilityOrigin Destination

Fare

Class Fare AB BC CD

B C Y 4500 0 1 0 2010 Yes

B C M 3200 0 1 0 2010 Yes

B C B 2250 0 1 0 2010 Yes

B C Q 1950 0 1 0 2010 No

A C Y 5200 1 1 0 2360 Yes

A C M 3430 1 1 0 2360 Yes

A C B 2600 1 1 0 2360 Yes

A C Q 2300 1 1 0 2360 No

A D Y 5820 1 1 1 4050 Yes

A D M 3790 1 1 1 4050 No

A D B 3020 1 1 1 4050 No

A D Q 2690 1 1 1 4050 No

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BID PRICE CALCULATION: DETERMINISTIC LP

ODF Fare Leg1 Leg2

1 AB full fare 145 1 0

2 AB discount fare 100 1 0

3 BC full fare 130 0 1

4 BC discount fare 75 0 1

5 AC full fare 255 1 1

6 AC discount fare 170 1 1

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Remaining capacities: Leg 1, 10; Leg 2 12

Forecast demand is shown on the RHS

FUTURE TRENDS

Big data “For the airline industry, big data is cleared for take-off”

Intelligence market segmentation

Amazon and Netflix have been renowned for successful recommendation algorithms E-commerce

Increased personalization in inflight merchandising and advertising.

Create value propositions based on real-time information about customer preferences and needs

Local transport from/to airport

Special occasion

For business travelers, Priority immigration clearance, WiFi, extra legroom

For family-vacation booking, entertainment bundle

Predictive analyticsUnbundled revenue, strongly pushed by the price-competitive low-cost carriers (LCCs)

Optimize the total revenue, including ancillaries.

Predict customer behavior on the two decisions: 1) airfare and 2) ancillary purchases

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RM APPLICATIONS: BUSINESS ENVIRONMENT

Traditional application areas

Airline

Hotel

Car rental

Tour operators

Cargo

Cruise

Non-traditional application areas

Broadcast

Healthcare

Manufacturing

Apparel

Restaurants

Golf

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Capacity is limited and immediately perishable.

Commitments need to be made when future demand is uncertain.

Firm can differentiate among customer segments.

The same unit of capacity can be used to deliver many different products or services.

Firms are profit-oriented and have broad freedom of action.