random effect model_panel data case study

8/11/2019 Random Effect Model_Panel Data Case Study

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BizModellingCase of PRARIES Airlines

Team BizWizards

Deepak Himani Valecha Shekhar Mandal


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ASSUMPTIONS

Cost /Km is constantThe route list for the given cities is exhaustive

The Airline wants to scale up and expand its operations in thegiven 39 cities only

ABOUT THE DATA

No. of Cities : 39No. of Routes : 202

Data Available for four yearsAttributes Given : Distance , Passenger , Concentration ratio

Linear relation of Passenger and Concentration ratio with FARENon Linear Relation of Distance with Fare

Low MulticollinearityHetereoscedasticity


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600610620630640650660670

680690700710

1997 1998 1999 2000

Avg Passenger

Avg Passenger

Descriptive Graphs

0200400600800

100012001400160018002000

" N E W

Y O R K

, N Y "

" N E W

Y O R K

, N Y "

" N E W

Y O R K

, N Y "

" N E W

Y O R K

, N Y "

" N O R F O L K

, V A

"

" O A K L A N D

, C A

"

" O A K L A N D

, C A

"

" O K L A H O M A C I T Y

, O K "

" O M A H A

, N E "

" O N T A R I O

, C A

"

" O N T A R I O

, C A

"

" O R L A N D O

, F L "

" O R L A N D O

, F L "

" O R L A N D O

, F L "

" O R L A N D O

, F L "

" P H I L A D E L P H I A

, P A

"


, P A

"


, P A

"

" P H O E N I X

, A Z "

" P H O E N I X

, A Z "

" P H O E N I X

, A Z "

" P I T T S B U R G H

, P A

"

" P I T T S B U R G H

, P A

"

" P O R T L A N D

, O R "

" P O R T L A N D

, O R "

" P R O V I D E N C E

, R I "

" R A L E I G H

/ D U R H A M

, N C "

" R E N O

, N V

"

" S A C R A M E N T O

, C A

"

" S A C R A M E N T O

, C A

"

" S A L T L A K E C I T Y

, U T "

" S A N A N T O N I O

, T X "

" S A N D I E G O

, C A

"

" S A N D I E G O

, C A

"

" S A N F R A N C I S C O

, C A

"

" S A N J O S E

, C A

"

" S A N T A A N A

, C A

"

" S E A T T L E

, W A

"

" S T . L O U I S

, M O

"

" S T . L O U I S

, M O

"

" T U C S O N

, A Z "

FARE IN DIFFERENT ROUTES OVER YEARS

1997 1998 1999 2000

0

500

1000

15002000

2500

3000

1 3 7 7 3 1 0 9

1 4 5

1 8 1

2 1 7

2 5 3

2 8 9

3 2 5

3 6 1

3 9 7

4 3 3

4 6 9

5 0 5

5 4 1

5 7 7

6 1 3

6 4 9

6 8 5

7 2 1

7 5 7

7 9 3

F a r e

Distance

Distance vs Fare


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Key Attributes Affecting AIRFARE

Methodology: Random Effect Model

Lfare = 0 + 1*y98+ 2*y99+ 3*y00 + 4*lpassen - 5*concern + 6*

ldistsq

Distance square accounts for the nonlinear trend being followed indistance and fare

Fare has been increasing over theyears

Passengers are theMost significant

factor

Distance ( +ve)Concentration

(+ve)Passengers

( -ve)


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Most ProfitableRoutes based on

AverageRevenue/KM

Growth in AvgRevenue/Km

Total Revenue 0

1000

2000

30004000

9 6 3

9 6 4

1 0 6 7

1 1 2 3 9 6

0 1 1 1 5

1 1 0 9

1 0 5 7 9 6

9 9 9 4

Revenue Per KM (in $/KM)Profit

0%

50%

100%

150%

200%

250%

300%

350%

9 5 3

1 0 4 6

1 1 2 7 9 5

4

1 0 3 8 9 9

4 9 7 4

1 0 8 9

1 1 0 9

1 1 3 2

1 0 2 2

1 1 2 4

1 1 2 8

1 0 9 6

1 0 7 7

1 0 6 5

1 0 7 3

1 1 0 1

1 0 6 3 9 7

6

Growth in Revenue/KM

Growth in Profit

0

2,000

4,000

6,000

8,000

10,000

9 5 2

9 6 3

1 1 2 2 9 6

4 9 6 0

9 5 7

9 5 1

1 0 2 9 9 5

8

1 0 0 0

1 1 1 7

1 0 4 7

1 1 3 3

1 0 1 9

1 1 0 8 9 5

4 9 5 3

1 1 1 4

1 1 1 0

1 1 2 3

Total Revenue

Total Rev

MOST PROFITABLE ROUTES (1/2)(Total No. of Routes: 202)


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Route Id Route Name 963 "NEW YORK, NY" To "WASHINGTON, DC"

952 "NEW YORK, NY" To "SAN FRANCISCO, CA"

1123 "SAN JOSE, CA" To "SANTA ANA, CA"

975 "OAKLAND, CA" To "SANTA ANA, CA" 1109 "SAN DIEGO, CA" To "SAN JOSE, CA"

953 "NEW YORK, NY" To "SAN JOSE, CA"

1108 "SAN DIEGO, CA" To "SAN FRANCISCO, CA"

969 "OAKLAND, CA" To "ONTARIO, CA"

974 "OAKLAND, CA" To "SAN DIEGO, CA"

994 "ONTARIO, CA" To "SACRAMENTO, CA"

MOST PROFITABLE ROUTES (2/2)(Total No. of Routes: 202)

Flights connecting to NEWYORK, Oakland ,San Diego and San Jose

are highly profitable due to high Revenue/KM

Based on Total Revenue, Revenue/ KM and Growth inRevenue/KM, Below is the list of profitable routes


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Distance Revenue

Average Rev/KMRevenue Growth

HIGH PERFORMING CITIES(Total No. of Cities : 39)


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CITY Distance Passenger Revenue Avg Rev/KM Frequency Growth

"NEW YORK, NY" HIGH HIGH HIGH HIGH HIGH ABOVE AVERA

"SAN FRANCISCO, CA" HIGH HIGH HIGH HIGH HIGH HIGH

SEATTLE HIGH HIGH HIGH AVERAGE HIGH HIGH

WASHINGTON HIGH HIGH HIGH HIGH HIGH HIGH

AVERAGE 45225 KM 27570 $4.3 Millions 81/KM 10.3 19%

% of TOTAL 26.80% 31.80% 42.50% 21.30%

Benchmark : AVERAGE

HIGH : Twice of Average ( Distance, Passenger, Revenue)>120% of Average (Avg Rev/KM, Frequency, Growth)

Frequency of Flight Revenue Growth

HIGH PERFORMING CITIES(Total No. of Cities : 39)


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Competitive Rivalry

- Less Product

Differentiation-Same Supplier

- No brand loyalty bycustomers.

Bargaining power ofBuyers(customers)

- Low Switching Cost

Bargaining power ofSuppliers of aircraft

eqpuipment

- High Switching cost

- High Brand Value ofSuppliers

Threat of Substitutes-other LCC and FSC

- Road

- Rail

- Marine

Threat of New Entrants

- High Capital

- Low Growth rate

- Strict Regulations

Porters 5 Forces Analysis


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- Aggressive Fuel price hedging

- Operation enhancements at profitable routes

- Only one kind of aircraft to reduce engineering cost

- No Travel Agent only direct booking

- Paid meals, Snacks and Beverages

- Fast turnaround unload a flight, cleaning andreloading

- Fixed timetables, a unionizes 24/7 shift operatinghighly skilled workforce

- Time Value Relationship for seats(earlier thebooking more will be the discount).

- Frequent Flyer program for regular travellers toretain their loyalty

- Dedicated Promotions

-Special Tariff plans for off and festive seasons

Recommendations


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THANK YOU!!!

QUESTIONS

random effect model_panel data case study

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