Dynamic Decision Management –Strategische Entscheidungen bei Flexibilität undUnsicherheit
Gastvorlesung TU Berlin, Lehrstuhl für Finanzierung & Investition, Prof. Dr. Hirth
Berlin, 5. Juni 2012
A.T. Kearney 10/05.2012/43216d 2
Agenda
■ About A.T. Kearney
■ Dynamic Decision Management
• Methodology
• Long case: Rent a Power Plant
• Short case: Bidding Process
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About A.T. Kearney
A.T. Kearney 10/05.2012/43216d 4
"Our success as consultants will depend upon theessential rightness of the advice we give and our
capacity to convince those in authority that it is good."
Andrew Thomas Kearney
A.T. Kearney 10/05.2012/43216d 5
Our expertise spans a wide range of industries and servicesM
ark
et
Te
am
s
Automotive Industries
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Utilities
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Competence Teams
About A.T. Kearney
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Where we are
• Amsterdam• Berlin• Brussels• Bucharest• Copenhagen• Düsseldorf• Frankfurt• Helsinki• Istanbul
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• Global footprint: 54 Offices in major business centers in 39 countries• Resources: More than 3,000 employees• Success: Consulting fees of more than US$1 bn in 2011• Recognition: Ranked 11th of 236 firms in "Best Firms to Work For 2009"
About A.T. Kearney
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• Methodology
• Long case: Rent a Power Plant
• Short case: Bidding Process
Dynamic Decision Management
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The current economic challenges require fast and widestrategic decisions
Challenges
Business environment
• Scarce financial resources due to the crisis
• Reduction of investment volumes required
• Reprioritization of strategy or investmentdecisions respectively
• Highly volatile markets
• Uncertain future
Shortfalls of traditional decisionmaking approaches
• High planning complexity due to a wide rangeof dynamic parameters
• Often, modeling of selected scenarios donewithout explicit statement on probability
• No consideration of future decision alternativesin selected scenarios
• Value of entrepreneurial decision alternativescurrently not identified
Dynamic Decision Management: Methodology
Improved decision making and higher decision qualityfor strategic decisions
Objective
Source: A.T. Kearney
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Our Dynamic Decision Management methodology helps ourclients to answer four fundamental questions
Key client propositions: Dynamic Decision Management
Source: A.T. Kearney
Questions
Scenariodevelopmentand analysis
Strategic optionsdevelopment
Total valuecalculation
Trigger pointbased
strategy process
Which potentialfutures
are there?
What are strategicalternatives to
react?
Which set ofalternatives
provides highestvalue?
How can strategicoptions bemanaged
dynamically?
A DCB
Overview
Overallapproach
Dynamic Decision Management: Methodology
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Components of total value
• The DDM-Approach builds upon traditionalvaluation, i.e. the calculation of the NPV ofexpected cash flows
• Additionally, risks and uncertainties, e.g.external market conditions, are considered
• Furthermore, the value of decision alternativesis quantified
• As a next generation scenario approach,DDM evaluates the complete space ofpotential futures/scenarios
• A.T. Kearney approach successfully appliedin several cases with high uncertainty anddecision alternatives, e.g. energy deliverycontracts, power plant mothballing andinvestment decisions, high speed railwayinvestments, and M&A company valuation
Traditionalvaluation
The Dynamic Decision Management approach accounts foruncertainty and flexibility in strategic decision making
Unique selling proposition: DDM approach
Flexibility Total valueNPV ofexpectedcashflows
Risk
Riskvalue
Flexvalue
Totalvalue
Key considerations
Source: A.T. Kearney
Dynamic Decision Management: Methodology
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There are stages of excellence, on how to incorporate risk inyour decision making
• Higher risk meansproject-specific higherWACC
• Risk enhancement leadsto lower NPV
• Thus chancesdiscriminated versusrisks
• Diverse risks ofindividual cash flowelements not considered
WACC8%
WACC10%
risk
• Mapping of uncertainfuture cash flows usingdiscrete scenarios
• Completeness ofdecision alternativesresp. scenarios notprovided
• No statement onprobabilities
NPV
• Discrete scenarios withrespective risk aresubjectively/generallyallocated to probabilities
• Lack of fully analyticalderivation of probabilities
NPV
Probability (P)
Scenario 1
Scenario 2
Scenario 3
• Expected value andvolatilities mean: stabledistribution of cash flowsand their probabilities
• Completeness of allcash flow points throughanalytical methodologyinstead of intuition
• Risk considered throughprobabilities
NPV
Probability (P) Probability (P)
Total Value
• Consideration of activemanagement (flexibility)in the future
• Value optimizationthrough activemanagement = exerciseof decision alternatives
• NPV plus value offlexibility = Total Value
NPV with riskreduction
Scenario-basedNPV
NPV with lumpsum probabilities
NPV with specificprobabilities
Total value1 2 3 4 5
Scenariobundle 1
Scenariobundle 2
Scenariobundle 3
NPV
Risk Risk
Value enhancementthrough
investmentdecision
Probability toachieve project
value
Source: A.T. Kearney
Stages of Excellence in accounting for risk
Dynamic Decision Management
Valuation approachincl. Risk
Valuation approach incl.Risk and Flexibility
Dynamic Decision Management: Methodology
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Different types of decision alternatives have to be differentiatedto model realistic business implications
Decisiontype
Description Example
Categorize types of decision alternatives
Source: A.T. Kearney
Abandon
Activate
Choose
Combine
Terminate a project or an ongoinginvestment
Carry out a project once a certaintrigger has been activated.
Choose the most valuable amongseveral projects
Choose the best combination ofpossible independent projects
A chemical company has started to commercializea new product. As it realizes that the business isnot profitable, it stops losses by terminating theproject
A reseller wins another sales contract if certainsales targets are met
A city plans to extend its public transportationsystem to a suburb. It can do so either by buildinga new tram line OR by buying new buses andbuilding the infrastructure for bus stops.
The investment plan to build a new marina com-plex includes an option to add several facilitiessuch as a supermarket AND/OR a fashion shopAND/OR a boat chandlery AND/OR a restaurant.
Switch Switch back and forth betweendifferent projects or operation modes
An automotive manufacturing line can switch toproduce different kinds of cars.
Dynamic Decision Management: Methodology
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• Identify macro drivers
• Determine most likely scenario & implications
• Estimate future cash flow of most likely scenario
• Derive probability distribution (risk value)• Derive value of flexibility (flex value)• Calculate total value
• Identify and prioritize strategic alternatives in „space ofpotential futures“ spanned by scenario analysis
• Implement trigger points and model decision paths
Based on a scenario analysis, relevant strategic decisionalternatives are identified, evaluated and managed by DDM
Source: A.T. Kearney
Methodology: Identification, valuation and management of strategic decisions
Methodology built on well-known cash flow models
Clear picture of strategy‘smost probable Total Value
Key elements Benefits
Demonstration thatmanagement will activelyinfluence value of strategy
C
A
• Identify trigger points with highest value impact• Develop trigger-point-based strategic radar• Implement dynamic strategy review process
Dynamic DecisionManagement fullyavailably
D
As a next generation scenario approach, DDM evaluates the complete space ofpotential futures/scenarios and allows for their quantification.
B
Dynamic Decision Management: Methodology
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Client examplePortfolio of strategic business topics for utilities
A matrix helps identify business issues with high un-certaintiesand a high degree of entrepreneurial flexibility
Business opportunities with high uncertainties AND a high business flexibilitycreate a substantial number of options with a potentially high real option value
"Classic"scenario-basedDCF assessment
External and internal“uncertainties"
Entrepreneurial "Flexibility"
low
high
low high
Gridstrategypower
Tradingstrategy
Waterstrategy
PHEVstrategy
Salesstrategy
M&A-Strategy
Smartmetering-strategy
"Total Value" approachbased on real optionsanalysis: additionalassessment of the value ofstrategic options
Gridstrategygas
Heatstrategy
Fossilgenerationstrategy
Renewablegenerationstrategy
Shared servicestrategy
Technical servicestrategy
Mothballingstrategy
Dynamic Decision Management: Methodology
Source: A.T. Kearney
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• Methodology
• Long case: Rent a Power Plant
• Short case: Bidding Process
Dynamic Decision Management
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Current situation and main questions
Source: Partner 1; A.T. Kearney
Long case: Rent a power plant
Current situation
• “Partner 2” and Partner 1 have acapacity utilization contract for “Powerplant” that will expire at the end of 2012
• When the contract expires, “Partner 2”has the exclusive right to extend thecontract
• The duration of the next contract issubject of negotiation between Partner 1and “Partner 2”
• Partner 1 can start negotiations withother companies only if “Partner 2”decides not to extend the contract
Main questions
• What value does the “Power plant”contract most probably have from“Partner 2’s” perspective ?
• How can uncertainties of the businesscase be considered in the evaluation?
• What value does “Partner 2’s” flexibilityto extend or terminate the contract atcertain points in the future have?
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The base and pessimistic case have been modeled based ondifferent price assumptions
• EEX base and peak prices have beenestimated based on the assumptionthat nuclear energy is not beingphased out
• It is assumed that “Power plant” isonline if forecasted price develop-ments result in a positive CDS; thisresults into a specific demandforecast
• CO2-price is assumed to remainconstant from 2013 onwards
• Dollar exchange rate is assumed tobe constant at 1.39 USD/€
• Installed capacity will increasegradually
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Base price €/MWh 51.8 58.4 61.6 65.9 63.2 67.3 70.2 73.6 75.7 78.6
Peak price €/MWh 69.4 78.8 83.1 90.3 87.4 93.1 97.1 101.8 104.7 108.8
MWh Tsd.MWh
422 469 439 520 598 664 716 765 779 845
API#2 US$/t 99.9 104.5 107.0 109.0 110.0 112.2 114.4 116.7 119.1 121.4
CO2 €/EUA 16.8 20.5 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0
Dollarexchangerate
US$/€ 1.39 1.39 1.39 1.39 1.39 1.39 1.39 1.39 1.39 1.39
Installedcapacity
MW 170.0 180.0 190.0 200.0 235.0 259.2 259.2 259.2 259.2 259.2
Main assumptions Partner 1
Source: Partner 1; A.T. Kearney
Example
For the pessimistic case, price levels and CO2-price levels are lower than in thebase case. All other assumptions remain equal.
Example: Base case
Long case: Rent a power plant
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Two NPV-scenarios have been calculated to determine thevalue of the “Power plant” contract
Base Case -2.0
PessimisticCase
-32.8
Basic NPV scenarios “Power plant” contract(in € mn)
• The two scenarios are based on specificsimulations of several cash flowcomponents from “Partner 2”-perspective
– Revenue
– Energy rate (CO2, coal, oil, operatingsupplies)
– Capacity charge
• Cash flows are highly uncertain due to thevolatility of their underlying price andquantity assumptions
Simple scenario calculations do not consider volatility of markets and“Partner 2’s” flexibility to extend the contract.
Long case: Rent a power plant
Source: Partner 1; A.T. Kearney
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Historic volatilities and correlations
Volatilities and correlations for the provided yearly cash flowshave been used as input factors for the model
Cashflow component Volatility
Revenue 34%
Cost of coal 32%
Cost of CO2 31%
Cost of oil and operatingsupplies
36%
Volatilities Correlations
Cashflowcomponent
Reve-nue
Cost ofcoal
Cost ofCO2
Cost ofoil & op.supply
Revenue 1.00 -1.00 -0.99 -1.00
Cost of coal 1.00 0.98 1.00
Cost of CO2 1.00 0.98
Cost of oil &op. supplies
1.00
Volatilities have been estimated based onyearly cashflows from “Partner 1’s” basecase scenario (2011-2020)
Correlations between revenue, cost of coal,cost of CO2, and cost of oil & operating sup-plies have been estimated based on yearlycashflows from “Partner 1’s” base casescenario (2011-2020)
Long case: Rent a power plant
Simplified
Source: Partner 1; A.T. Kearney
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We assume that “Partner 2” has the flexibility to extend thecontract in 2012 and 2015
Modeling of strategic decision alternatives
Valuation of flexibility: What is the value of “Partner 2’s” combined flexibility toextend the contract in 2012 and 2015?
2012 2015 2020
Source: Partner 1; A.T. Kearney
2011
End of currentcontractduration
Assumedend of
duration ofcontract 1
Current contract New contract 1 New contract 2
“Contractextension”
“Contracttermination”
“Activecontract”
“Contractextension”
“Contracttermination”
“Contracttermination”
Assumedend of
duration ofcontract 2
Assumption
Long case: Rent a power plant
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The value of the flexibility amounts to €31.2 mn, leading to aTotal Value of €29.3 mn compared to NPV of €-2.0 mn
• Result: Total Value amounts to €29.3 mn –compared to a NPV of €-2.0 mn
• The value of possibility to extend the contractin 2012 including the possibility to furtherextend in 2015 has a current value of€31.2 mn
Total value – base case
Components of Total Value
-2.0
Total Value
29.3
Flex Value
31.2
NPV
Source: Partner 1; A.T. Kearney
Probability Distribution
Contractextension
69%
• An extension in 2012 incl. the option to furtherextend from 2015 to 2020 shows a positivevalue in 69% of all scenarios.
• The value of the possibility to extend the con-tract in 2015 was calculated to be €23.0 mn
Example 2012
Long case: Rent a power plant
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• Methodology
• Long case: Rent a Power Plant
• Short case: Bidding Process
Dynamic Decision Management
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• Development of optimal sourcing strategy: closing of a fix-price long-term power supply contract is most attractive in terms of value (NPV) andrisk
• Value (NPV): long-term contract is equally attractive as sourcing at futuremarket prices as expected by client
• Risk: Lost market chances due to long-term fix-price log-in are smallerthan avoided market risks by securing the price via log-in by the contract.
For a major Swiss power off-taker, we organized a biddingprocess for power delivery
• Client analysis identified a 800 GWhwinter base load gap in its portfolio
• Client wants to source “green electricity”with delivery point in Germany orSwitzerland for 10 years
• Client is risk averse and has limited ex-perience in professional electricity trading
• Organize bidding process for long-termdelivery contract with fixed price; collectdata on EEX futures and asset dealopportunities
• Analyze value and risk profile of sourcingvia market (EEX) vs. full or partial closingof the gap via long-term delivery contract
Client situation
Project content
Approach and main results
Risk valuation: bidding process for 10 year energy delivery
Short case: Bidding Process
Source: A.T. Kearney
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The successful bidding process reduced cost and risksignificantly.
Source: Projektteam Analyse und Vergleich von Hedging-Alternativen
1300120011001000900700600500400
60 %
100%
90 %
80 %
70 %
50 %
800
40 %
30 %
20 %
10 %
0 %
NPV total cost in Mio. €
150014003000
EEX
50/50
Bidder A
Co
nfi
nd
en
ce
leve
l
Estimated risk through priceincrease in the market
Reduced Chance to potentiallysource energy at lower cost
€ 270 Mio.
€ 76 Mio.
Increasing costDecreasing cost
Short case: Bidding Process
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Questions?
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