4th International Controlling Conference in Croatia.
Zagreb, 08. November 2016 Karl-Heinz Steinke
Member of Board ICV
International Controller´s Association
Controlling in the Age of Uncertainty and
Disruption – how Controlling can cope with new
Challenges
Examples of new Challenges
New Business Models
Volatility &
Uncertainty
Digital Disruption
Ethics & Changing Values
Globalization
Market Dynamics
Market Regulation
Enterprise
Risk as a measurable Uncertainty
GDP
Money Exchange Rates
Inflation
Commodity Prices (Oil)
Geopolitical risks and crisis
Markets and Competition
Technology Break Thru´s
Event Risks
Controlling Action
o Diversification
o Hedging
o Long Term Contracts
o Capacity Flexibility
o Buffer Management
o Joint Ventures
o Evaluate Driving Forces
o Search Early Warning Signals
o Define Risk Strategy
o Develop Scenarios
o Calculate Sensitivities
o Planning in Corridors
o Develop „Rolling Targets“
To do´s
Instruments
Scenario/Corridor Planning
P&L Statement
Operating Revenue ('000 USD) Jan-04 Feb-04 Mar-04 Apr-04 May-04 Jun-04 Jul-04 Aug-04 Sep-04 Oct-04 Nov-04 Dec-04
North America 124,865 143,595 109,132 118,954 154,640 100,516 80,413 128,661 135,094 124,286 159,086 133,632
Europe 108,796 125,115 95,088 103,646 134,739 87,581 70,064 112,103 117,708 108,292 138,613 116,435
Asia Pacific 43,689 50,242 38,184 41,621 54,107 35,170 28,136 45,017 47,268 43,486 55,663 46,757
Latin America 18,754 21,567 16,391 17,866 23,226 15,097 12,078 19,324 20,290 18,667 23,894 20,071
Middle East/Africa 8,571 9,857 7,491 8,165 10,615 6,900 5,520 8,832 9,273 8,531 10,920 9,173
Group Revenue 304,675 350,376 266,286 290,252 377,327 245,263 196,210 313,936 329,633 303,262 388,176 326,068
Operating Costs (Less DA)
North America 89,903 103,388 78,575 85,647 111,341 72,372 57,897 92,636 97,267 89,486 114,542 96,215
Europe 78,333 90,083 68,463 74,625 97,012 63,058 50,446 80,714 84,750 77,970 99,802 83,833
Asia Pacific 31,456 36,174 27,493 29,967 38,957 25,322 20,258 32,412 34,033 31,310 40,077 33,665
Latin America 13,503 15,528 11,802 12,864 16,723 10,870 8,696 13,913 14,609 13,440 17,204 14,451
Middle East/Africa 6,171 7,097 5,394 5,879 7,643 4,968 3,974 6,359 6,677 6,143 7,862 6,604
Group Operating Costs 219,366 252,271 191,726 208,981 271,676 176,589 141,271 226,034 237,336 218,349 279,487 234,769
Group EBITDA 85,309 98,105 74,560 81,270 105,652 68,674 54,939 87,902 92,297 84,913 108,689 91,299
P&L Statement
Operating Revenue ('000 USD) Jan-04 Feb-04 Mar-04 Apr-04 May-04 Jun-04 Jul-04 Aug-04 Sep-04 Oct-04 Nov-04 Dec-04
North America 124,865 143,595 109,132 118,954 154,640 100,516 80,413 128,661 135,094 124,286 159,086 133,632
Europe 108,796 125,115 95,088 103,646 134,739 87,581 70,064 112,103 117,708 108,292 138,613 116,435
Asia Pacific 43,689 50,242 38,184 41,621 54,107 35,170 28,136 45,017 47,268 43,486 55,663 46,757
Latin America 18,754 21,567 16,391 17,866 23,226 15,097 12,078 19,324 20,290 18,667 23,894 20,071
Middle East/Africa 8,571 9,857 7,491 8,165 10,615 6,900 5,520 8,832 9,273 8,531 10,920 9,173
Group Revenue 304,675 350,376 266,286 290,252 377,327 245,263 196,210 313,936 329,633 303,262 388,176 326,068
Operating Costs (Less DA)
North America 89,903 103,388 78,575 85,647 111,341 72,372 57,897 92,636 97,267 89,486 114,542 96,215
Europe 78,333 90,083 68,463 74,625 97,012 63,058 50,446 80,714 84,750 77,970 99,802 83,833
Asia Pacific 31,456 36,174 27,493 29,967 38,957 25,322 20,258 32,412 34,033 31,310 40,077 33,665
Latin America 13,503 15,528 11,802 12,864 16,723 10,870 8,696 13,913 14,609 13,440 17,204 14,451
Middle East/Africa 6,171 7,097 5,394 5,879 7,643 4,968 3,974 6,359 6,677 6,143 7,862 6,604
Group Operating Costs 219,366 252,271 191,726 208,981 271,676 176,589 141,271 226,034 237,336 218,349 279,487 234,769
Group EBITDA 85,309 98,105 74,560 81,270 105,652 68,674 54,939 87,902 92,297 84,913 108,689 91,299
Mid-Longterm Planning
Integrated Riskmodell/
Monte Carlo Simulation
Effects on Profit/Cashflows
DisributionCash Flow/ Earnings
0%
10%
20%
30%
40%
50%
Financial Counterparty Competition Facility Disruption Asset Integration
P&L Statement
Operating Revenue ('000 USD) Jan-04 Feb-04 Mar-04 Apr-04 May-04 Jun-04 Jul-04 Aug-04 Sep-04 Oct-04 Nov-04 Dec-04
North America 124,865 143,595 109,132 118,954 154,640 100,516 80,413 128,661 135,094 124,286 159,086 133,632
Europe 108,796 125,115 95,088 103,646 134,739 87,581 70,064 112,103 117,708 108,292 138,613 116,435
Asia Pacific 43,689 50,242 38,184 41,621 54,107 35,170 28,136 45,017 47,268 43,486 55,663 46,757
Latin America 18,754 21,567 16,391 17,866 23,226 15,097 12,078 19,324 20,290 18,667 23,894 20,071
Middle East/Africa 8,571 9,857 7,491 8,165 10,615 6,900 5,520 8,832 9,273 8,531 10,920 9,173
Group Revenue 304,675 350,376 266,286 290,252 377,327 245,263 196,210 313,936 329,633 303,262 388,176 326,068
Operating Costs (Less DA)
North America 89,903 103,388 78,575 85,647 111,341 72,372 57,897 92,636 97,267 89,486 114,542 96,215
Europe 78,333 90,083 68,463 74,625 97,012 63,058 50,446 80,714 84,750 77,970 99,802 83,833
Asia Pacific 31,456 36,174 27,493 29,967 38,957 25,322 20,258 32,412 34,033 31,310 40,077 33,665
Latin America 13,503 15,528 11,802 12,864 16,723 10,870 8,696 13,913 14,609 13,440 17,204 14,451
Middle East/Africa 6,171 7,097 5,394 5,879 7,643 4,968 3,974 6,359 6,677 6,143 7,862 6,604
Group Operating Costs 219,366 252,271 191,726 208,981 271,676 176,589 141,271 226,034 237,336 218,349 279,487 234,769
Group EBITDA 85,309 98,105 74,560 81,270 105,652 68,674 54,939 87,902 92,297 84,913 108,689 91,299
Riskinformation Riskquantification
Finanziell Kontrahenten Wettbewerb Anlagen-
integrität
Betriebs-
unterbrechungen
0,0%
1,0%
2,0%
3,0%
4,0%
5,0%
(816) (474) (75) 324 666 1.008
50%
40%
30%
20%
10%
0%
-750 -500 -250 250 500 750
Streik
CAPEX
Treibstoffpreise
Zinssätze
Wechselkurse
Operative
Risks
FinancialRisi
ken
Slot-Verfügbarkeiten
Wettbewerber-
Aktionen
BIP-Entwicklung
Steuern
OPEX
Verzögerung
Flugzeuglieferungen
Marktkapazität
Regulatorische
Risiken: Sicherheit
Strategic
Risks
Regulatorische
Risiken: Umwelt
Sicherheit
Netzwerkrisiken
Ticketabgaben
Kreditrisiken
Katastrophen/Krieg
Price
Modell
Yield/
demand
Modell
FX
- Modell … Risikopyramide
Market/Riskparameter
GDP/Demand
Volumes&Capacities
Prices
Forex
Events
…
Simulation Unternehmensrisiken Illustrative
Profit&Loss
Statement
(Lufthansa interne Darstellung in Anlehnung an Price Waterhouse Coopers, 2011)
Hedging and Diversification
Logistic
s
Passenger Business MRO
Business
0,90
1,00
1,10
1,20
1,30
1,40
1,50
1,60
1,70
1,80
pro
.02
srp
.03
vlj
.04
ruj.
04
tra.
05
stu.0
5
lip
.06
sij.
07
kol.
07
ožu.08
lis.
08
svi.
09
pro
.09
srp
.10
vlj
.11
ruj.
11
tra.
12
stu.1
2
lip
.13
sij.
14
US
D /
EU
R
Reducing Volatility by Rolling
Foward Contracts in $
U…U…
Diversification
Quelle: Lufthansa interne Darstellung (in Anlehnung an Krystek & Müller-Stewens 1993,)
Uncertainty as an unmeasurable
Risk
Urheber: <a href='http://de.123rf.com/profile_pixelsaway'>pixelsaway / 123RF Lizenzfreie Bilder</a>
Levels of Uncertainty
A clear enough future Demand factors knowable, singel forcast sufficient for strategy decisions, knowing where and haw to
compete, DCF-modell applicable
Alternative futures Discrete scenarios to establish probabilities, to and fro decisions, wait and see strategy, i.e. for
regulatory or legislative changes
A range of futures A limited range of futures defining a range of probabilities, but key variables/trigger events can be
defines, i.e. entry in new and unknown markets
Radical Uncertainty (at least true ambiguity) Impossibility to find a range of potential outcomes, not all variables knowable, freshfield undertakings
i.e. Russia investments after 1992, qualitative analysis, looking out for analogous situations/patterns
Hugh g. Courtney, Jane Kirkland, S.Patrick Viguerie, „The changing LandscapeHarvard business Review, Nov/Dec 1997
Organizational Resilience
Controlling Action
Resilience Transformation Early Warning
Ability to cope with
unexpected situations
Preparing the
organization for
necessary transformation
processes
Precautionary Measures:
-Postponent of decisions
- Liquidity reserves
- Fix costs reduction
- Insurance
- Risk sharing
Ability to adjust
capacities, output and
costs according new
situations
Creating flexibility
corridors for recource
usages
Identification of conversion
drivers
Modularity, compability,
mobility, scalability,
universality
Control of the
transformation processes
Identification of early
warning signals
Quelle: „Vorsprung vor Boom und Krise – das Controlling volatilitätsfest machen, Ideenwerkstatt des ICV“,
in: Controlling Heft 10/2013Anforderungen Zeit Flexibilitätskorridor f1 Flexibilitätskorridor f2 Flexibilitätskorridor f3 Wandlungs-fähigkeit Wandlungsfähigkeit
Wandlungsbefähiger Beschreibung Beispiele (in Anlehnung an Nyhuis, Rein-hart & Abele 2008, S. 28).
Digital Disruption
• Digitization in all Businesses
and Branches
• New Value Networks
• Data Explosion
• New scientific or technological
• Knowledge
New Value Network
(Source: Kagermann et al. 2013)
New Business Models Airbnb
Hilton 93 years to establish 678.000 rooms in 91 countries
Airbnb 4 years same number of rooms in 192 countries; 2014: guests+25.000.000
Uber
6 years in 277 cities; 2 billlion. $ turnover
No own driver, no own cars
Coursera
3 years more than 25 million students from 190 countries
240.000 students parallel in the course which is most liked
Quelle: Losbichler, Heimo :Vortrag auf dem Controller Congress 2014
Shared/Collaborative Economy
Fragmentizing of „Economies of Scale“ within Community
Individuals can perform decentralized the same as big hierarchical companies
Procurement
Production.
Sales
A B C
You
have!
I need!
Radical change in consumption, production, financing and learning
Direkct not redundant
Common use
High degree of transparency
High usage of resslources
Gatekeeper not providing value will be eliminatet
Banking vs. Crowdfunding
Gault Millau vs. TripAdvisor
New Busines Models with great speed ((legally suspicious)
Quelle: Losbichler, Heimo :Cortrag auf dem Controller Congress 2014
Data Explosion as a Challenge
1986 1993
2000
2007
Analog:
(Papier, Film, Audio)
2,62* 1018 Bytes
Digital:
1986: 0,02 * 1018 Bytes
(Taschenrechner, ...)
Quelle: Philip Evans, Ted-Talks, 11/2013
Digital:
276 * 1018 Bytes
Mit IP-Adresse
verlink-, auswertbar
(Big Data)
CD
DVD
Blu-ray
2011
PC-Storage
Server
Connected
via Internet 35*1021 Bytes by 2020
Analog:
18,86* 1018 Bytes
Mapping of Online-Discussions
Semantisches Mapping verteilter Online-Diskussion (Tool: complexium galaxy)); entnommen: Dreamcar Bericht Internationaler
Controller Verein: Big Data – Potential für Controller
.
PM ONE: Vortrag auf dem41. Controller Congress in München 2016
Real Time Analysis
From descriptive Analytics
to prescriptive Analytics
Controlling Action
Develop higher
analytical-Capabilities
Focus on Prognosis and
Optimization
Automation and Integration
Organizational Adaptions
Mehrdimensionales
Predictive-Modell
Automatische Generierung
und Bereitsstellung der
Forecast-Informationen
Mas
chin
eM
ensc
h
Anpassung
Einflüsse/Effekte
Maßnahmen
Kostenveränderungen
…
Bereitstellung
Forecast
Ist-Daten als Ausgangsbasis "Predictive"
JAN FEB MÄR APR MAIJAN FEB MÄR APR MAI JUN JUL AUG SEP OKT NOV DEZ
Vorjahr Abschnitt lfd. Jahr
JUN JUL AUG SEP OKT NOV DEZ
F & E KLR Steuerung
Planung Reporting
Beschaffung Produktion Logistik Vertrieb
Lieferanten
/Zulieferer-
integration
Integration
Logisik-
dienstleister
Endkunden-
informationen
B2B
Integration
Integrierter Sales Forecast (Predictive)
Interne
Daten
Externe
Daten
Stark ausgeprägt Durchlässig
Proaktiv
Prognostizierend
Explorativ
Agil
Data Scientist
Automatisiert
Predictive
Szenarienbasiert Schnell
Digitalisierung der
Steuerung
Treibermodelle
Flexibel
Chief Data Officer
Numerische Vorhersage Web Mining
Text MiningKlassifikation
AssoziationsanalyseClusteranalyse
Business Analytics
Anwendungsklassen
Abbildung Horvath&Partners in : Dreamcar Bericht Internationaler Controllerverein 2016 „Advance Analytics
The Road to Data-Driven Corporate Performance Management
Controller as a Data Scientist ?
Controlling Action
Preparation of data
Creation of predictions
Visualization of results
Speaks and understands
the language of business Pattern recognition
Dialogue in
natural language Predictive analytics
for the unpredictable
One-stop shop for
business analytics
Dreamcar Bericht Internationaler Controllerverein 2016 „Advance Analytics
The Road to Data-Driven Corporate Performance Management
Understanding the Chain of Effects
Global/Regional
Level
Industries
Enterprises Volatility
Change in
Orders
Exchange Rates $/€ GDP USA GDP Europe
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Volatilität AE Trumpf Volatilität Dollar
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Volatilität AE Trumpf Volatilität GDP USA
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Volatilität AE Trumpf UE Volatilität
0
0,1
0,2
0,3
0,4
0,5
0,6
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
UE Volatilität
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Volatilität AE Trumpf UE Volatilität
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Volatilität AE Trumpf UE Volatilität
Turnover Production
Internationaler Controller Verein, Ideenwerkstatt, Bericht: „Vorspung vor Boom und Krisse – Das
Controlling volatilitätsfest machen“, 2013 (Abb (in Anlehnung an Wiendahl et al. 2005, S. 56).
Roadmap
Evaluate
Business
Challenges
Set Goals
and
Priorities
Identify potentially
Competitive
Advantages and
Value Contibution
Clarfiy Information
and technological
Needs
Recrute and qualify
Employees
Develop
Recommendations
for Action Start Project
Develop the structural
and organizational
environment
Adaptive Leadership
Develop your organizations „Signal“ advantage
Increase the agility to correct yourself
Cultivate a diversity of perspectives to generate a multiplicity of
options
Build platforms for colllaboration
No rigid rules, but learning and adapting to change over time
“The more complex the
world, the more
important is controlling"
Thank you for your attention