International Controller s Association · 4th International Controlling Conference in Croatia. ......

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


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


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


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


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



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


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


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


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



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


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


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

International Controller s Association · 4th International Controlling Conference in Croatia. ......

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