Presentatie Stefan KLM

1. Implementing data anaytics and
data science @ KLM Finance.
Stefan van Heukelum • KLM Royal Dutch Airlines
Financial
Data
Analytics

3. KLM in numbers.
Number of destinations:
167 Net promoter score:
39 Aircraft in fleet:
204

4. KLM in numbers.

5. Financial Data Analytics.
If you could invest in these Digital Finance ‘building
blocks’ … how would you invest?

6. Why?
How?
What?

7. Why?

8. ‘Contrary to beliefs that the only purpose of management accounting is
to collect, transform and report data, its primary purpose is first and
foremost to influence behavior at all levels, from the desk of the CEO
down to each employee..
… It should do this by supporting decisions. A secondary purpose is to
stimulate investigation and discovery by signaling relevant information
(and, consequently, bringing focus) and by generating questions.’
Gary Cokins, SF magazine

9. To become the most
customer-centric, innovative
and efficient European
Network Carrier.
KLM’s audacious goal:

10. Name of presentation - Version 1.01
Old Finance
Backward-looking
Silo’s
Executing transactions
Static process
New Finance
Forward-looking
Integral
Managing exceptions
Continuous learning
Reactive Proactive
In order to support this, organisational tranformation is needed.
Creating reports Creating insights

11. How?

12. Data.

13. Team.
17
Business ITOR
Information management

14. Tools and infrastructure.

15. Definition
• Translate needs into stories
• Determine scope
• Determine planning
Data engineering
• Collection/preparation
• Transformation of data from
multiple sources
Modeling & Visualisation
• Explorative data analytics
• Modeling
• Visualisation to tell the story
Interpretation on decision
making
• Interpretation of insights
• Decision making to create value
Discovery phase: Ideation
Delivering phase: Prototyping
Deployment phase: Realization
Business impact
• Processes
• Stakeholders
Prioritisation
• Value
• Feasibility
• Creating actions that
implement value
• Deploying models in the
business
• Change management
Way of working.

16. What?

17. Source: https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/memo-to-the-cfo-get-in-front-of-
digital-finance-or-get-left-back?cid=soc-app

18. Strengthen business
partnering role (DS)
- Delivering actionable insights
through advanced data analytics
(cost or revenues)
- Risk assessments
- Cost forecasting models linked
to operational drivers
Improve finance and
control processes
- Automation and optimization of
repetitive (transactional) tasks
- Build in financial integrity
- Battle and contain fraud

19. Some examples

20. High operational
excellence, still
things can go wrong
• Operating 680 flights
• Carrying 90K pax/day
• 167 destinations
• 82.4% A15 performance
20

21. A lot of questions
arise:
- Which cost can be allocated
specifically to non-
performance?
- How do cost vary among
flights (destinations)?
- What are the main drivers
for these cost?
- How will all cost come in
and how long will it take
until all cost are in?
- …
21

22. OPS
data
Customer
data
Financial
data
22
New insights!
Add master
data to cost
data

23. How do these
insights lead to
value?
- Better cost models to
allocate cost and forecast
cost development on
multiple levels (department,
flight, pax)
- Better input for operational
optimizers
- Automation leads to
elimination of manual labor
- Elimination of manual labor
leads to freed up capacity to
use on additional deep-
dives
23

24. Making right cost
forecast for
commercial
purposes
- Engineering & Maintenance
provides services to third
parties
- In order to make a good
deal for both parties, we
need to be able to think
about costprices 10/15
years ahead
- What drives the
development of cost?
- operational area
- flight distances
- ‘tough’ landings
- high speed flying
24

25. How do these
insights lead to
value?
- Factor in all identified
drivers in cost price, f.e.
- Cost of repairs
- Quantity of repairs
- Industrialize model to
incorporate recent trends
- Be in control of margin, less
surprises
25

26. • Change is slow
• Quality of data
• Industrialization of models
• Cultural change (throughout the organisation)
Main challenges
26

27. Reporting & Data Analytics
AMS/BC
Stefan van Heukelum • KLM Royal Dutch Airlines
Thank you!