Javier Jiménez - ATPCO | We fuel the future of air travel. · · 2017-11-17Javier Jiménez CCO...

Javier Jiménez

CCO

Airnguru

Javier JiménezAirnguru

Javier Jiménez

• Introduction

• Evidence #1: Time to Market reports

• Evidence #2: RBD Mapping and the Longest Common Subsequence

• Conclusion

“The reason why it is so difficult for existing firms to capitalize on disruptive innovations is that their processes and their business model that make them good at the existing business actually make them bad at competing for the disruption.

— Clayton Christensen

— Sun Tzu , The Art of War

Information Build-up

Time to collect data and produce relevant competitive

and market information

Response Design

Time to design the price adjustment and react to

the changes in the market

Approval & Publishing

Time to approve, upload and publish the new prices

and rules of the fares

t2t1 t3

t1+t2+t3 = Time to Market

• The average time to market that we have seen is 20.5 [hrs], but there is a great deviation from airline to airline

• These differences can be caused by differences in any of the steps in the pricing process

• Improvements in time to market reduce exposure to losses in market share and dilution.

13.2213.82

18.518.6318.6718.77

23.7225.1125.2725.8226.2627.5127.83

30.9565.42

78.9881.9

94.02

0 20 40 60 80 100

I?B?A?L?A?L?D?Q?U?A?KLC?L?J?

A?E?E?D?

Time to Market ranking [hrs]

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

0

10

20

30

40

50

60

Tho

usa

nd

s

Time to Market by DOW [hrs]

Records Time to Market

0

500

1,000

1,500

2,000

2,500

0

10

20

30

40

50

60

70

80

0:0

0

1:0

0

2:0

0

3:0

0

4:0

0

5:0

0

6:0

0

7:0

0

8:0

0

9:0

0

10

:00

11

:00

12

:00

13

:00

14

:00

15

:00

16

:00

17

:00

18

:00

19

:00

20

:00

21

:00

22

:00

23

:00

Tho

usa

nd

s

Time to Market by TOD [hrs]

Records Time to Market

• The average time to market that we have seen is 20.5 [hrs], but there is a great deviation from airline to airline

• These differences can be caused by differences in any of the steps in the pricing process

• Improvements in time to market reduce exposure to losses in market share and dilution.

13.22

13.82

18.518.63

18.67

18.77

23.72

25.1125.27

25.82

26.26

27.5127.83

30.95

65.42

78.98

81.994.02

0 20 40 60 80 100

I?B?

AA

L?

ACLHD?

Q?

U?

A?K?

C?

L?

J?

A?E?

E?

D?

Time to Market ranking [hrs]

What is Cystic Fibrosis?

Cystic fibrosis (CF) is a genetic disorder that affects mostly the lungs, but also the pancreas, liver, kidneys, and intestine. Long-term issues include difficulty breathing and coughing up mucus as a result of frequent lung infections.

Unaffected

“Carrier” FatherUnaffected

“Carrier” Father

Unaffected

1 in 4

chance

Unaffected

“Carrier“

1 in 4 chance

Affected

1 in 4

chance


In the 80’s a group of scientists tried to find the gene that produced the cystic fibrosis. They managed to narrow the search to a small region in chromosome 7, but at the time genetic mapping techniques were limited, so they couldn’t narrow the search to a specific gene. ?

Chromosome 7


To solve the problem they selected known genes and compared them with chromosome 7 using Sequence Comparison

But how did they do it?

Sequence 1: G A A T T C A G T T A

Sequence 2: G G A T C G A

27.7% match…Not very similar

✓✕ ✓ ✓✕✕ ✓ ⎯ ⎯ ⎯ ⎯

63.4% match…much better…

G A A T T C A G T T A

G G A T C G A✓ ✕ ✓✓ ⎯ ✓ ⎯ ✓ ⎯ ⎯ ✓

?

Chromosome 7

Longest Common Subsequence Dynamic Programming




✓ ✓ ✓ ✓✕✕ ✓ ⎯ ⎯ ⎯ ⎯

- G A A T T C A G T T A

- 0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0

A 0

T 0

C 0

G 0

A 0


𝑆𝑖,𝑗 = max൞

𝑆𝑖−1 + 0𝑆𝑗−𝑖 + 0

𝑆𝑖−1,𝑗−1 + 1, 𝑖𝑓 𝑉𝑖 = 𝑊𝑖




✓ ✓ ✓ ✓✕✕ ✓ ⎯ ⎯ ⎯ ⎯


- 0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 4 4 5 5 5 5

A 0 1 2 2 2 2 2 5 5 5 5 6

✓=

✕=

⎯ =






✓ ✓ ✓ ✓✕✕ ✓ ⎯ ⎯ ⎯ ⎯


G G A T C G A✓ ✕ ✓✓ ⎯ ✓ ⎯ ✓ ⎯ ⎯ ✓


- 0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 4 4 5 5 5 5

A 0 1 2 2 2 2 2 5 5 5 5 6


✓=

✕=

⎯ =




✓ ✓ ✓ ✓✕✕ ✓ ⎯ ⎯ ⎯ ⎯


G G A T C G A✓ ✕ ✓✓ ⎯ ✓ ⎯ ✓ ⎯ ⎯ ✓


- 0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 4 4 5 5 5 5

A 0 1 2 2 2 2 2 5 5 5 5 6

✓=

✕=

⎯ =





✓ ✓ ✓ ✓✕✕ ✓ ⎯ ⎯ ⎯ ⎯


G G A T C G A✓ ✕ ✓✓ ⎯ ✓ ⎯ ✓ ⎯ ⎯ ✓


- 0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 4 4 5 5 5 5

A 0 1 2 2 2 2 2 5 5 5 5 6


✓=

✕=

⎯ =




✓ ✓ ✓ ✓✕✕ ✓ ⎯ ⎯ ⎯ ⎯


G G A T C G A✓ ✕ ✓✓ ⎯ ✓ ⎯ ✓ ⎯ ⎯ ✓


- 0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 4 4 5 5 5 5

A 0 1 2 2 2 2 2 5 5 5 5 6

✓=

✕=

⎯ =


They found that a similarity with ATP (an organic chemical related to energy transfer and secretion) was presented in the position q 32.2 of the chromosome.




✓ ✓ ✓ ✓✕✕ ✓ ⎯ ⎯ ⎯ ⎯


G G A T C G A✓ ✕ ✓✓ ⎯ ✓ ⎯ ✓ ⎯ ⎯ ✓


- 0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 4 4 5 5 5 5

A 0 1 2 2 2 2 2 5 5 5 5 6

63.4% match…more similar

✓=

✕=

⎯ =


Eureka! The CFTR gene

The gene they found after applying the algorithm resulted to be the gene responsible for Cystic Fibrosis

CFTR gene

Cystic Fibrosis Transmembrane Regulator gene

A

T

C

A

T

C

T

T

T

G

G

T

G

T

T

Sequence of nucleotides

in CFTR gene

Deleted in many

patients with

cystic fibrosis

Now… let’s go back to the algorithm


- 0 0 0 0 0 0 0 0 0 0 0 0

G 0 1 1 1 1 1 1 1 1 1 1 1

G 0 1 1 1 1 1 1 1 2 2 2 2

A 0 1 2 2 2 2 2 2 2 2 2 2

T 0 1 2 2 3 3 3 3 3 3 3 3

C 0 1 2 2 3 3 4 4 4 4 4 4

G 0 1 2 2 3 3 4 4 5 5 5 5

A 0 1 2 2 2 2 2 5 5 5 5 6

Now… let’s go back to the algorithm

- Y H A S K M P N G Q A

- 0 0 0 0 0 0 0 0 0 0 0 0

Y 0 1 1 1 1 1 1 1 1 1 1 1

H 0 1 1 1 1 1 1 1 2 2 2 2

L 0 1 2 2 2 2 2 2 2 2 2 2

V 0 1 2 2 3 3 3 3 3 3 3 3

M 0 1 2 2 3 3 4 4 4 4 4 4

S 0 1 2 2 3 3 4 4 5 5 5 5

Q 0 1 2 2 2 2 2 5 5 5 5 6

Market MIA-SCL - Economy (AA vs LA)

Market NYC-LON - Economy (AA vs DL)

Javier Jiménez - ATPCO | We fuel the future of air travel. · · 2017-11-17Javier Jiménez CCO...

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