Eyeon Wp Strategic Scenario Planning

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An EyeOn research report in association with Tilburg University

Strategic Scenario PlanningLinking Performance Indicatorsto Bottom-Line Results

Planning & control in leading organisations


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Loek Lemmens, partner EyeOn

Renske Munsters, consultant EyeOn

September 2007

An EyeOn research reportAn EyeOn research report in association with Tilburg University

Strategic Scenario PlanningLinking Performance Indicatorsto Bottom-Line Results


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ContentsContents 2

1 ManagementSummary 3

2 Introduction 4

3 StrategicscenarioplanningaspartofCPM 63.1 Introduction 63.2 Identifying risk 73.3 Strategic risk management in practice 73.4 Strategic risk management by scenario analysis 8

4 EyeOnapproachtostrategicscenarioplanning 94.1 Key Value Drivers (KVD) 94.2 Key Performance Indicators (KPI) 94.3 KVD Simulation Model 104.3.1 The generic KVD Simulation Model 104.3.2 From the generic model to the specific model 13

5 Case:Organon 145.1 Introduction to Organon 145.2 Scope 145.3 The KVD Approach at Organon 155.3.1 The results of the KVD Approach 155.3.2 The performance indicators and mathematical relations 165.4 KVD Simulation Model for Organon 165.4.1 Implementation at Organon 165.4.2 Scenario definition 17

5.5 Results 175.6 Conclusion Organon 18

6 Sevengoldenrulesforstrategyscenarioplanning 20

Definitions 22References 23


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1 Management Summary

Many large companies are quite capable ofconstructing an adequate strategic plan.

They do however, struggle when trying toimplement their strategic plan accordingly. Asa consequence, monitoring progress towardsreaching strategic objectives is an ineffectiveprocess. That raises the question how toassess and control the future performance ofan organization. After all, this should be thepart of the planning process and fit within theoverall corporate performance strategy of thecompany.

One way to solve this issue is simulation.Simulation is an appropriate tool to analyzeand support business activities on differentlevels in an organization. Simulation can beused on an operational level, on a tacticallevel or on a strategic level and can thereforebe of great value. However, it is a fact thatmany organizations fail to see the benefitsof simulation in the context of corporateperformance management (CPM). In general,simulation is deemed time-consuming,complex, and quite expensive to develop.However, in practice, simulation provides anexcellent method to study future performance

without actually having to wait for realfigures.

To support organizations in their searchhow to assess and to manage future results,EyeOn has developed the KVD SimulationModel as a practical way to investigate howthe future performance of an organizationcan be determined by means of a what-if tactical simulation. The purpose of theKVD Simulation Model is twofold. Firstly, itprovides a very useful and well-organized

framework to evaluate an organizationsstrategic direction. Secondly, it can provide atool to align an organizations strategic planwith its behaviour and can therefore be ofgreat value to any organization interested inachieving what it is aiming for.

The development of the KVD SimulationModel is part of the KVD Methodology. Thismethodology provides a consistent top-downmethod and terminology to bridge the gap

between an organizations strategic plan andthe appropriate execution of its strategic

direction. This approach helps to improve theorganizations performance by aligning theorganization in the execution of the strategy.

The first step in the KVD Methodologyconsists of the KVD Approach. The KVDApproach has been developed by EyeOnas a method to align strategy with tacticsand focuses on the aspects that determinewhether or not strategic objectives will bemet and those aspects that determine futureprofitability. The KVD Approach facilitatesstrategy deployment throughout theorganization and aligns the organization inexecuting strategy.

The KVD Simulation Model appliesmathematical relations between the KPIs, asthey result from the KVD Approach, and theEconomic Value Added (EVA) Tree to simulatethe effect of various tactical planningpossibilities on the feasibility of certainstrategic objectives. The EVA Tree representsthe bottom-line result of a company.

The model is generic and can be viewedas a template that can easily be adjustedto represent the explicit characteristicsof an organization. The KVD Method hasbeen validated at Organon, part of thepharmaceutical business unit of AkzoNobel. After performing the KVD Approachat Organon and constructing the KVD-KPIFramework, the KVD Simulation Modelhas been adjusted representing the explicitcharacteristics of Organon.

It is the first time I have seen the production

targets translated into P&L statements as in

the KVD Simulation Model. - Mart ijn Poels

(Controller Global Manufacturing) -

The KVD Simulation Model is an excellent

method to perform what-if analyses on the

bottom-line result. - Robbert van Heekeren

(Executive Director Finance and Control) -


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Many executives acknowledge that the processof planning is often more important than the

end result. Planning provides managementwith a platform to discuss the following basicquestions. Where are we going? And how arewe going to get there? What-if things do notturn out as planned? Simulation can providethe answers to these questions.

Simulation models can also enhance thevalidation of targets while building trust andconsensus on plans and forecasts. Becausefuture plans are actually calculated, themanagement feels comfortable taking decisionsregarding these future plans. As importantas the simulation results is the process ofbuilding the simulation model and the businessscenarios. Since a simulation model shouldreflect the actual organization, it requiresextensive knowledge of which aspects drivevalue and the assumptions that lie underneath.Simulation can support the analysis of risksand opportunities that can occur during aforecast or budgeting period and during alonger planning horizon.

The white paper is the joint result of a research

study performed by EyeOn and TilburgUniversity, the validation and implementationof the simulation model by Organon as afinal step during the research study. Manythanks go to Prof. Jalal Ashayeri, MSc PhD(Econometrics and Operational Research,Tilburg University), Robbert van Heekeren(Executive Director Finance and Control,Organon), Jan-Remt Mellema (Global Strategy &Portfolio Management, Organon), Martijn Poels(Controller Global Manufacturing, Organon),and Edward Stelmakh (Area Sales Controller,

Organon) for their participation.

This paper presents a generic approach todriver-based modelling and simulation tosupport strategic and tactical planning andmanagerial decision-making. It is called theKVD Simulation Model where KVD stands forKey Value Drivers. The KVD Simulation Modelis a powerful method for managers to gaininsight on the factors that drive bottom-lineresults. Not only can they prepare important

However originally used in a military context,this quote by Sun Tzu, a Chinese general of500 BC, pinpoints the problem that manyorganizations are facing: how to make surethe organizations strategic objectives areobtained by defining and implementing theright tactics or plans.

Planning is not about creating a singularprojection of the future. The added valueof any planning activity is to review andanalyze the impact of risk on assumptions,initiatives and targeted results. Plans mustexplicitly deal with uncertainty. A rangeof scenarios can provide managementwith a basis of assessing the feasibility ofalternative strategies. Simulation can beconsidered an adequate tool to determine ifcertain projections or expectations will beachievable. The difference between scenarioplanning and simulation is not alwaysclear. Scenario planning considers different

business alternatives, whereas simulation is atechnique that can perform scenario analysis.Moreover, simulation enables sensitivity andwhat-if analysis to further assess uncertainty.

In practice few companies systematicallyintegrate scenario planning and simulationinto the planning process. There appear to be3 reasons for this: Simple (behavioural) fear of the unknown.

Many people and managers react touncertainty with denial and unconsciously

take deterministic views of the future. Lack of time. Most organizations work up tothe last minute simply to complete a singleplan and view of action. There is no time leftto evaluate alternate courses of actions.

Lack of adequate training in developingscenario plans and techniques. Using theoutcome of analytical techniques andmodels for taking decisions in inherentlyuncertain situations requires both educationand experimentation.

2 Introduction

Strategy without tactics is the slowest route

to victory. Tactics without strategy is the noise

before defeat. (Sun Tzu, 500 BC.)


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decisions in the planning process, but theycan also rely on actual figures while doing so.

The next chapter elaborates on the subjectof strategic risk, the main trigger behind thedevelopment of the KVD Simulation Model.

Chapter 4 considers the EyeOn Approachand briefly addresses the KVD Approach,after which the position of simulation andthe structure of the generic KVD SimulationModel are discussed. Chapter 5 contains acase study of Organon, the pharmaceuticalbusiness of Akzo Nobel*. The explicit KVDSimulation Model estimates the bottom-line result of Organon based upon its KeyValue Drivers (KVDs) and Key PerformanceIndicators (KPIs) corresponding to Organonsstrategic objectives. Using this model, it ispossible to perform what-if scenario analysesto measure the effect of changes in the KVDsand/or KPIs on the Economic Value Added.Chapter 6 presents the seven requirements fora good simulation model based on conclusionsdrawn in this research.

*) In March 2007, Akzo Nobel announced that theyhad accepted an offer for the purchase of itswholly owned subsidiary Organon BioSciencesfrom Schering-Plough. This transaction isexpected to close in the 2nd half of 2007.


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3.1 Introduction

Organizations have to create a link betweendeveloping strategic objectives and makingan adequate tactical planning to ensure thesestrategic objectives are met. In fact this is partof strategy implementation. The figure belowpositions the results of the KVD approachand the scenario planning and analysis inthe Corporate Performance Management(CPM) framework. This enables a closed-loopprocess that starts with understanding wherethe organization is today, what the strategicdirection is, what targets should be set andhow resources must be allocated to achievethese targets.

Strategy implementation is a topic that canbe explored extensively. Many companies arecapable of defining their strategic objectivesquite explicitly, but fail to implement themsuccessfully.

3 Strategic scenario planning as part of CPM

Target Settingon KVDs/KPIs

OperationalPlanning

CorrectiveActions

Realisation

Non-FinancialTargets

Reporting& Analysis

Forecasting Consolidate

ContextAnalysis

KVD-KPI FrameworkStrategic Dashboard

Scenario Planningand Analysis

Tactical dashboard

Operations

O

perations

Strat

egy

Strategic Planning

Planning/Budgetting

DO

CHECK

PLAN

ACT

StrategicPlan

Figure 3.1: Positioning of the KVD approach and scenario

planning and analysis in CPM framework

The ratio of books in print on the subject of

strategic planning to those on the subject of

strategic implementation is 70:1 (McKnight,2001)

It is not only crucial to translate strategy intoa tactical plan adequately, it is also highlyimportant to proactively manage corporateperformance.

By performing scenario analyses at thetop level of an organization, strategicdash boarding and clear target setting onvalue drivers and performance indicatorsare facilitated throughout the entireorganization. It is not only possible to reacton environmental or internal factors thatinfluence bottom-line results, but also toanticipate or even prevent negative effects.

Besides the fact that strategy implementationis difficult to manage, there is another


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factor that makes the process of strategyimplementation more complex. Since thetransformation of strategic objectives intotactical settings is not straightforward, theimplementation of strategy evokes risks. It isinevitable that risk occurs when translating a

strategy of a company into a tactical plan. Ofcourse, for a company it is highly important tomeasure and, if possible, to manage the riskthe company faces while it is trying to reachits strategic objectives.

The concept of risk results from therecognition of future uncertainty. It is quiteimpossible to know what the future will bringin response to a given action today. The actionperformed today can have different outcomesand that in fact creates risk.

3.2 Identifyingrisk

The concept of risk can be reviewed in manydifferent ways. In this paper we divide riskinto two views, external and internal risk.Risks within the control of a company arereferred to as internal risk, whereas risksoutside the control of a company are referredto as external risk.

Examples of internal risk are production risks

and risks in the area of planning and control.Examples of external risk are demand risks,supply risks and environmental risks likee.g. union strikes, terrorist attacks and a treelimb falling onto a power line. Some risks canbe predicted, managed or even prevented. Atypical example of an external risk that canbe adequately responded to is the currencyexchange risk. Fluctuations cannot becontrolled, but a company can anticipate thenegative effects of these fluctuations.

When discussing risk management withina company, the major focus is often howinternal risk affects the financial bottom-lineresults of that particular company. Externalrisk however, requires a somewhat broaderperspective. To be able to adequately respondto these risks it is relevant that these risks areidentified.

After identifying potential risks, they have tobe quantified and assessed. It is importantto be able to assess risks in order to take

appropriate actions when necessary. In thecontext of the KVD Simulation Model, riskscan be assessed and measured by the keyperformance indicators as they result fromthe KVD Approach.

3.3Strategicriskmanagementinpractice

Two different kinds of risk have beenidentified. Now it is highly important tomanage these kinds of risk to optimallysupport business activities.

InternalriskWhen dealing with internal risks it is highlyimportant to tackle the causes of these risks.Since these risks occur in the companyscontrol, it is best to prevent these risksinstead of waiting for the consequences. Thereare different ways to tackle internal risks.

Research (Friedman and Essaye, 2005) showsthat many CEOs do not develop their riskmanagement policies themselves but let thefinancial guys do it. However, it is advisablethat strategy risk management is not justdelegated to the financial department, but isacknowledged as a high level priority within acompany and is adequately responded to.

The realization that risk management isa crucial factor determining the successof a company is more and more presentin companies struggling to implementstrategy. Integrated risk management hasrecently become a practical way to mitigaterisks within a company. Integrated riskmanagement is the identification andassessment of the collective risks that affectfirm value and the implementation of afirm-wide strategy to manage those risks

(Meulbroek, 2002).

Although indeed a company is likely to benefitfrom a firm-wide risk management strategy,research (Noy and Ellis, 2003) indicates thatrisk strategy is not always consistent acrossall organizational activities within a company.It is a surprising result that corporate riskstrategies vary across activities in the samecompany.


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ExternalriskBecause of increased globalization, wideningpolitical reach and the market producing andconsuming economies, external risks areincreasingly present in every company.

As opposed to internal risks, external riskscannot be prevented. However, it certainly ispossible to mitigate external risks. The mainobjective after identifying external risks is toreduce the negative effects of these risks.

Hillman (Hillman, 2006) lists severalstrategies and approaches to mitigateexternal risk while improving a companysperformance. Many solutions addressinventory levels as a critical component of asupply chain strategy that can help reducerisk. Some other elements are hedgingstrategies, forecasting techniques and theleverage of contract-management tools.

3.4 Strategicriskmanagementbyscenarioanalysis

In general, one of the most importantelements of good strategic risk managementis the analysis of risks. When risks areidentified the organization should analysethe risks by understanding the effect on the

business. Risks can be analyzed in variousways. An important distinction is the divisionof analysis tools in subjective and objectivemethods.

Subjective methods like SCOR (Supply ChainOperations Reference Model) and utilitytheory are examples of known and widelyincorporated tools to analyze risks.

Besides subjective methods, objectivemethods can also be used to analyze risks.

Simulation is a typical example of anobjective method.

The major advantage of using simulationas opposed to subjective methods or otherobjective methods is the fact that simulationis a dynamic method instead of a static one.Simulation cannot only consider time aspects,but can also incorporate and reflect morecomplex modelling issues like variability,uncertainty, and interdependencies betweenprocesses.

In the context of strategy riskmanagement, simulation can certainlyprovide an advantage when trying tomitigate or even prevent risks whileimplementing strategy. Especially what-if simulation is useful to recognize and

understand how the different risks affectthe bottom-line results of a company.What-if simulations apply scenarioanalyses to determine what will happenif circumstances change. A scenarioanalysis is basically a process to analyzepossible future events by consideringalternative possible outcomes, whichare defined in the different scenarios.Scenario analyses facilitate improveddecision-making because outcomes andimplications of decisions can be carefullyconsidered.

Before a scenario analysis can beperformed, first several scenarioshave to be defined. Scenarios rely onidentifying and examining the driversof strategy and uncertainty thosefactors that are essential to accomplishstrategy. For each of the main driversof strategy several scenarios can bedefined. Scenarios for an economicaldriver like growth are for example:rapid growth, moderate growth or slow

growth. A scenario could also be basedon a business case like e.g. new productintroduction.

When evaluating scenarios, two aspectshave to be kept in mind: the probabilitythat a particular scenario will occurand the impact of that scenario on thebusiness. Moreover, scenarios shouldbe typically based on goals, key valuedrivers of the business that are usedin the strategic and tactical planning

process. Therefore, the approach todevelop a model for scenario analysisshould start with identifying the valuedrivers based on the strategic objectives.This first step to scenario analysis in asimulation model is further explained inthe next chapter.


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The EyeOn approach clarifies the strategyimplementation process in a highly structured

way. Before the actual simulation model canbe developed, the aspects that drive valuefirst have to be identified. Therefore, the firststep in the EyeOn approach is to conduct theKVD Approach. The KVD Approach identifiesthe relevant key value drivers and keyperformance indicators that are of importancein corporate performance management. To beable to support tactical decisions, the secondstep is to develop a model that is able toquantify the future result of a specific actionor decision taken today.

4.1 KeyValueDrivers(KVD)

In practice, many organizations do not faceserious problems while setting their strategicobjectives. In fact, organizations are oftenquite experienced in determining theirlong-term goals. However, translating theirstrategic plan into a tactical planning thatachieves their strategic objectives has provento be very difficult. Therefore, EyeOn hasinitiated and developed the KVD Approach.

The KVD Approach provides organizationswith a framework designed to align strategicand tactical planning with the managementreporting process. Furthermore, the KVDApproach results in improved business andcompany performance as strategies have beensuccessfully implemented.

The KVD Approach consists of three basicsteps. In all steps, interviews are conductedwith senior executives of several functionalareas of the organization to determine the

companys specific strategic objectives, keyvalue drivers, and key performance indicators.The interviews result in a KVD Overview,clearly listing all relevant strategic objectives,value drivers and performance indicators.

After the interviews, a KVD-KPI Frameworkis constructed that links the performanceindicators to EVA. The KVD Simulation Modeluses this framework to deduct the changesfrom the financial result when altering one ormore strategic objectives.

For a more detailed description of the KVD

Approach, see the EyeOn Whitepaper FromStrategy to Action Planning for Value.

4.2KeyPerformanceIndicators(KPI)

The EyeOn approach uses the EVA Treeto reflect the bottom-line results of anorganization. The KVD Simulation Model usesmathematical relations between the KPIsand the EVA Tree to simulate the effect ofvarious tactical planning possibilities on the

feasibility of the strategic objectives.

4 EyeOn approach to strategic scenario planning

StrategicObjective

StrategicObjective

Key PerformanceIndicator



Key Value Driver

Key Value Driver

Key Value Driver



Strategy(Strategic Plan)

Figure 4.1: Part of the KVD Overview


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A distinction has to be made betweenmathematical and related KPIs. Themathematical performance indicators havea direct mathematical relation to the EVATree: the financial consequence of changecan be calculated. The related KPIs alsoaffect the financial results, but not in a direct

mathematical relation. They merely influencethe mathematical performance indicators orhave an ambiguous relation to elements of theEVA Tree.

It is not necessary to include all KPIs in theKVD Simulation Model. A selection needsto be made to discern the most importantand most relevant KPIs. One of the possiblemethods that can be applied to rate therelevance of each individual KPI is theAnalytic Hierarchy Process (AHP).

The AHP is an appropriate prioritizationapplication and in essence is a multi-criteriadecision-making method that was originallydeveloped by Prof. Thomas L. Saaty. In short,it is a method that derives ratio scales frompaired comparisons. The comparisons canbe made on an objective basis, but also ona subjective basis. The method starts withthe structuring of a problem. Since the KVDApproach has already provided a hierarchicalstructure in the KVD-KPI Framework, this

step can be discarded. The next step in theAHP methodology uses pairwise comparisonsin combination with the scale of relativeimportance to assess the relevance of all KPIs.In the context of the KVD Simulation Model,the AHP method uses subjective comparisonsto determine the relative weights of the KPIs.

Only the most relevant KPIs are implementedin the KVD Simulation Model.

4.3KVDSimulationModel

4.3.1ThegenericKVDSimulationModel

The generic KVD Simulation Model consistsof a simulation menu, an input module, anoutput module, and a module displaying anoverview of the main inputs and outputs.

Each module contains several sheets.

SimulationmenuThe first sheet of the file containing theKVD Simulation Model is in fact a simulationmenu. This menu enables the user to choose arequired module. By selecting the name of thedesired module, that module pops up.

InputmoduleAfter the simulation menu, the first sheetof the input module shows the KVD-KPI

EVA Tree

Sales

Related KPI

Cost ofGoods Sold

ContributionMargin

Cost ofOrganisation

EBIT

Taxes

EVA

NOPAT

CapitalCost

WACC

Net Invested

CapitalWorkingCapital

FixedCapital

Mathematical KPI

Mathematical KPI

Mathematical KPI

Mathematical KPI

Mathematical KPI

Mathematical KPI

Mathematical KPI

Related KPI

Related KPI

Related KPI

Related KPI

Related KPI

Figure 4.2: The performance indicators linked to EVA


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Framework to graphically represent theoutcome of the KVD Approach. The KVD-KPIFramework also clearly depicts the relationsbetween KPIs, and between KPIs and the EVATree.

The following sheets of the input moduledepict the input parameters for variouskinds of scenarios. In general, these sheetsof the input module are divided into threemain parts. The first part depicts the What-IfParameters that will be used for the scenarioplanning: this section displays the values ofthose parameters that the user would liketo analyse the effect of on the bottom-lineresults. These values can be altered. Thesecond part consists of Static Parameters.Static parameters are not variable in thesimulation. However, they are necessary tocalculate the different elements of the EVATree. If the user does not want to include Taxchanges in the different business scenarios,Tax is an example of a static parameter. Thisis an input necessary to calculate the NOPATand EVA, but it is fixed and therefore notinteresting for e.g. a sensitivity analysis of theinputs. When all values of input parametersare determined, the different elements thatconstruct the EVA Tree have to be calculated.The last section of a sheet of the input module

therefore denotes the calculation.The second sheet of the input moduledepicts the input parameters of the base casescenario of an organization. This sheet iscalled Base Case. The user cannot alter thevalues on this sheet. The base case scenario

reflects the expectations if everything goes asplanned.

After the base case sheet there are a fewsheets that denote the values of the inputparameters per scenario. The user can namethese sheets. The user can also add or deleteinput sheets depending on the number ofscenarios the user wants to evaluate. Thevalues of the input parameters of eachdifferent scenario can be altered in such away that the values represent the businessscenario that the user wants to analyse.

In the generic model, one specific scenariois already incorporated. This is a dynamicscenario. This scenario is similar to the basecase scenario, but a level of uncertaintyis included. Whereas the base case hasan uncertainty level of zero, the dynamicscenario can introduce different levels ofuncertainty. I.e. the longer the time horizon,the less likely it is that the base case scenariorepresents the true values for future results.

2006Market Size (mln EUR)

2006 2007 uncert.Market Size Growth

Uncertainty over time

2008 2009 2010 2011

2006 2007 uncert.Market Share (mln EUR)

Uncertainty over time

2008 2009 2010 2011

2006 2007 uncert.Non Key Products 2008 2009 2010 2011

2006 2007 uncert.Cost of Organization 2008 2009 2010 2011

What-if Parameters (Example)

2006 2007Obsolete Stock 2008 2009 2010 2011

2006 2007Direct Material Cost 2008 2009 2010 2011

2006 2007Mfg. Operational Cost 2008 2009 2010 2011

2006 2007Working Capital 2008 2009 2010 2011

Static Parameters (Example)

Current Liabilities

Receivables

Total Inventory

2006 2007EVA Tree 2008 2009 2010 2011Fixed Capital

Taxes

WACC

Figure 4.3: Part of the Input Module


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EBIT and EVA. Four graphs are shown peroutput variable. For example, for sales theydepict the absolute sales per scenario overthe various years, the cumulative sales perscenario per year, the difference betweensales of a particular scenario with respect tothe sales of the base case per year, and thedifference between the cumulative sales of aparticular scenario and the cumulative salesof the base case.

4.3.2Fromthegenericmodeltothespecificmodel

The generic KVD Simulation Model serves asa template for any organization. The genericKVD Simulation Model is able to incorporatethe characteristics of almost every companyby adding company specific information.The case study at Organon including thedevelopment of the KVD Simulation Modelspecifically for Organon provides an excellentexample.

2006 2007

Sales

Cost of Goods Sold

Contribution Margin

Cost of Organization

EBITTaxes

NOPAT

Fixed CapitalWorking Capital

Invested Capital

WACC

Capital Cost

EVA

2008 2009 2010 2011

Output (Example)

Calculation EVA 2011 EVA Tree 2011

Sales

Cost ofGoods Sold

ContributionMargin

Cost ofOrganisation

EBIT

Taxes

EVA

NOPAT

CapitalCost

WACC

Net InvestedCapital

WorkingCapital

FixedCapital

Besides, uncertainty of different inputparameters can be included in this scenario.

OutputmoduleSubsequently, the output module depictsthe outputs of each different scenario. Theseoutput sheets show the user the values of theEVA Tree as they are calculated by using thevalues of the different input sheets. E.g. thesheet Output Base Case shows the EVA Treeas it is calculated by the values of the inputparameters in the sheet Base Case.

OverviewoutputmoduleThe last module Overview Output providesa brief overview of a few output variableslike e.g. Sales, EBIT and EVA. This modulealso gives an overview of the most relevantinput parameters of all scenarios. Besides intables, the output variables are also depictedin graphs. The various graphs depict severalvalues of the three output variables Sales,

2007

SalesEBITEVA

Output: 2008 2009 2010 2011

Base Case (Example) Dynamic Scenario (Example)

Cum.

2007Market Share

Product AProduct Betc....

Input: 2008 2009 2010 2011

2007

SalesEBITEVA

Output: 2008 2009 2010 2011 Cum.

2007Market Share

Product AProduct Betc....

Input: 2008 2009 2010 2011

Figure 4.5: Part of the Overview Output Module

Figure 4.4: Part of the Output Module


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The generic Model does not yet include anydata, only the basic mathematical relations ofthe EVA Tree that hold for any organization.For the model to become specific, theKVD Approach first has to be conducted

to understand the key value drivers andtheir key performance indicators. The keyperformance indicators are in fact the highlycompany specific information that the modelneeds to create a link between the strategicobjectives and the EVA Tree. Althoughthe mathematical relations linking keyperformance indicators to the EVA Tree can becompany specific, the basic structure and set-up of the model is to a large extent generic.Therefore, the generic model can be used as atemplate for any organization.


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5.1 IntroductiontoOrganon

Organon, the pharmaceutical business unitof Akzo Nobel, is the largest innovativebiopharmaceutical company originating inthe Netherlands. Since the foundation in1923, Organon has always been stronglyscientifically oriented. Organon researchesand investigates innovative medical products.It sells products in more than 100 countriesall over the world and is represented bysubsidiary companies in more than fifty ofthem. Moreover, Organon strives to registerits products all over the world.

In 2006 Organon realized a turnover ofE 2.611million, which is 69 % of the total turnovergenerated by the pharmaceutical businessunit of Akzo Nobel. Momentarily, Organonhas more than 14.100 employees, spread overmore than 60 countries. In the Netherlands,approximately 5.000 employees are active. TheNetherlands also accommodate a large part ofthe main office of Organon as well as severalof its largest production- and R&D-sites.

In March 2007, Akzo Nobel announced that

they had accepted an offer for the purchaseof its wholly owned subsidiary OrganonBioSciences from Schering-Plough. Thistransaction is expected to close in the 2ndhalf of 2007.

Organon presents itself as being an innovativeorganization:

Therefore, the R&D department of Organonis an important aspect of the entire

organization. The structure of the R&Dpipeline of Organon can be seen in Figure 5.1.

5.2Scope

It is very important to determine the scope ofthe KVD methodology. This is due to the factthat while one executive can decide to list hisstrategic objectives, KVDs and KPIs for thewhole organization, the other can considere.g. only one business unit. Therefore, thescope of the KVD Methodology has to bedetermined explicitly before the interviewsof the KVD Approach are conducted. Therepresentatives of Organon have agreed onthe following scope:

The scope of the KVD Methodology includes that

decisions or actions that affect the bottom-line

results of Organon in the next five or six years.

For Organon, this is an important distinction,because Organon has to face an R&D pipelinethat requires an average of 12 years for

a product to materialize on the market.Therefore, the KVD Approach only considersthe products in the R&D pipeline that areexpected to enter the market within the nextfive years and the products that are alreadyon the market.

Besides setting the scope, other assumptionshave to be made. The most important onesconcern market perception and market sizeapproach.

First it has to be determined what line ofapproach is used to estimate the available

5 Case: Organon

LeadOptimization

Pre-clinical/ClinicalDevelopment

Lead

DevelopmentExploratory

Discovery

Target

Research

Pre-clinical/ClinicalDevelopment

RegistrationMarketing& Sales

Full Developmentand launch

Development

Figure 5.1: Structure of the R&D pipeline of Organon

With around 19% of revenues invested in R&D,

we continue to focus heavily on our product

pipeline. (Toon Wilderbeek, General Manager

Organon, Akzo Nobel Annual Report 2006).


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market for sales. The market can beapproached from different angles. Thesedifferent views are of the whole of Organon,per therapeutic area, per treatment type, orper key product. The KVD Simulation Modelpresents the market per key product.

Organon also discerns different marketperceptions. Figure 5.2 displays a funnelvision of the various kinds of marketperceptions. The numbers in the figure arepurely fictional. First, there is the diseasearea. This is the number of people thathave a particular type of affection. Only apercentage of these people are diagnosed.This is the diagnosis rate. Of this diagnosisgroup, only a certain percentage is treated, ofwhich a particular percentage complies withthe treatment. Not all people persist in thetreatment, while others simply drop out. Whatremains is the actual patient share.

5.3TheKVDApproachatOrganon

The KVD Approach is conducted at Organonby means of interviews with several keystakeholders in different functional areaswithin Organon. The persons interviewed are:

Robbert van Heekeren, Global Finance andControl.

Dr. Jan-Remt Mellema, Global Strategy &Portfolio Management.

Martijn Poels, Global ManufacturingController.

Edward Stelmakh, Area Sales Controller.

5.3.1TheresultsoftheKVDApproach

The participants have performed the threesteps in the KVD approach individually. Eachof them has listed the strategic objectives, keyvalue drivers, and key performance indicatorshe thinks best capture the strategic directionOrganon intends to follow in the next fewyears. To demonstrate the outcomes of theKVD Approach at Organon, Figure 5.3 presentsa single strategic objective, specified into keyvalue drivers and key performance indicators.

100

50

30

10

20

5

Disease area

Diagnosis rate

Treatment rate

Persistence rate

Compliance rate

% patient share

Figure 5.2: Funnel vision of different views of market size

The simulation model takes into account thedisease area and the actual patient share. Toprevent complexity in the model, the otherperspectives are not taken into account.

Business Growth

Continue to Innovate

Market Share

Number of Patents

Revenue from Productsin New Therapeutic Fields

Growth in NewTherapeutic Fields

Business Partnershipsand Strategic Alliances

FocusedMarketing Mix

Market Share Growthper Partnership

AdvertisingEffectiveness

Strategy(Strategic Plan)

Figure 5.3: Part of the KVD Overview for Organon


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5.3.2 Theperformanceindicatorsandmathemati-calrelations

Now all strategic objectives, KVDs and KPIsare determined and presented in the KVDOverview, the key performance indicators can

be linked to the EVA Tree. Since the KVD-KPIFramework is constructed around three levels:related KPIs, mathematical KPIs and the EVATree, the distinction has to be made betweenrelated and mathematical KPIs. The relatedKPIs do not directly influence an element ofthe EVA Tree in a clear mathematical way.However, the mathematical KPIs have a directeffect on the EVA Tree. The related indicatorsare incorporated in the KVD-KPI Framework,but are discarded while constructing the KVDSimulation Model.

A part of the Organon KVD-KPI Framework isdepicted in Figure 5.4. KPIs with white labelshave a direct relation to the EVA Tree, whileblack ones do not.

5.4KVDSimulationModelforOrganon

Based on the outcomes of the KVD Approach,the KVD Simulation Model is constructedaccording to the specific characteristicsof Organon. The scenarios as defined and

presented in this white paper are examplesand do not reflect Organons expectation ofthe future!

5.4.1ImplementationatOrganon

Validation is the process of determiningwhether a simulation model is an accuraterepresentation of the system for the particularobjectives of the study, while verification isconcerned with determining whether theassumptions of the simulation model arecorrect. First the KVD Simulation Model isverified, after which the model is checked forits validity.

EVA Tree

Sales

Cost ofGoods Sold

ContributionMargin

Cost ofOrganisation

EBIT

Taxes

EVA

NOPAT

CapitalCost

WACC

Net InvestedCapital

WorkingCapital

FixedCapital

Market Share

Cost of Goods notin Standard (EUR)

R&D as %of Sales

ForecastAccuracy (%)

# Patents

Out-Licensing

In-Licensing

Figure 5.4: Part of the Organon KVD-KPI FrameworkBuilding a model using a commercialsimulation package enhances the verification

of the model, because it limits the amount

of programming that would otherwise be

required. Since the KVD Simulation Model

uses @RISK for its simulation runs, this

already partly verifies the model. A technique

that is often used to further verify a

simulation model is to run a simulation under

a variety of parameter settings and to check if

the output parameters change in a reasonable

way. This boils down to an output analysis to


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check whether or not alterations in the inputparameters affect the outcomes as expected.

Now the KVD Simulation Model is verified,it can be validated. A very important step invalidating a simulation model is to collect the

right data and information about the system.The aspect of gathering the right informationhas been covered by the interviews in whichthe mathematical relations were investigated.A model can also be validated by a structuredwalk-through of the simulation model. Apositive side effect of this approach is thatit promotes ownership of the model bythe management team. Since the variousparticipants involved in the validation andverification process represent differentaspects of Organon, their approval safeguardsthe quality of the model.

5.4.2 Scenariodefinition

Before a simulation can be performed, firstseveral scenarios have to be defined. Twoscenarios have already been incorporated, abase case scenario and a dynamic scenario.Below, they are explained in more detail.Besides, the two standard scenarios, one candefine other business scenarios. The KVDSimulation Model allows for these additionalscenarios to be defined and named by the

user.

The participants of Organon have indicatedthat they would like to analyse severalvariations of the dynamic scenario. Therefore,the third and fourth scenarios reflect thesame values of input parameters as thebase case scenario, but with differencesin uncertainty to determine how thesealterations influence the bottom-line results.

The first scenario depicts the base case

scenario. This scenario calculates outputsbased on the expected values of futureinputs. This scenario analyses what happensif everything occurs as expected. Thisscenario is very suitable to compare theother scenarios with to get a clear idea ofhow alterations or future uncertaintiesaffect the bottom-line results with respectto the expected course of events. Becausethe base case scenario does not incorporateuncertainty, all Monte Carlo simulationsresult in the same output values.

Since it is not very realistic to expect thateverything will go as planned, the secondscenario incorporates uncertainty. In the caseof Organon, two kinds of uncertainty havebeen incorporated in the model: uncertaintyover time and uncertainty in the markets per

key product.

In the third scenario Organon expectshigher market shares than in the other twoscenarios, and wants to see how this affectsthe bottom-line results. Therefore, thisscenario, the growth scenario, incorporatesslight changes in the values of theseparameters to see how they affect theoutcomes.

The fourth scenario distinguishes itself fromthe other scenarios because in this scenarioOrganon assumes that market launches of thethree most promising compounds are delayedand moreover, enter the market with lowermarket shares. Specifically, Organon wantsto monitor the effects of problems whileintroducing new products on the market.An appropriate name for this scenario istherefore Introduction Issues.

5.5Results

The KVD Simulation Model depicts the resultsof all scenarios clearly in tables and in graphs.The tables depict the outcomes of the threemost important output values, Sales, EBIT,and EVA, and those what-if parameters thatthe user would like to see the effects of onthe main outputs. Because these inputsand outputs are displayed for all scenarios,one can clearly see how certain parametersettings affect the bottom-line results.

The different graphs show the outcomes of all

scenarios per year, the cumulative outcomesof all scenarios per year, the differencesof all scenarios with respect to the basecase scenario per year, and the cumulativedifferences of all scenarios with respect to thebase case scenario per year. The four graphsfor the output parameter EVA are depictedin Figure 5.5. The scenarios as defined andpresented are examples and do not reflectOrganons expectation of the future!


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It is possible to compare each individualscenario to the base case scenario. Below thegraphs depict the values of all outputs for thegrowth scenario compared to the base casescenario.

Besides depicting the output valuesin histograms, it is also possible toderive various statistics, and to performsensitivity analyses and scenarioanalyses. E.g. a sensitivity analysis onSales, EBIT and EVA in 2011 has shownthat for Organon the most essentialinput parameters are the market sharesper key product, the sales of products

that are not key products, and theroyalty income Organon receives for itsout-licensing of products. Research &Development and Sales & Distributionalso heavily influence the bottom-lineresults of Organon.

5.6ConclusionOrganon

In this whitepaper a simulation modelhas been developed that can assess

whether or not strategic objectives aremet when adapting tactical settings.Besides a fully operational modelthat has been developed for Organonspecifically, a generic model has alsobeen configured that can easily beadjusted to fit the characteristics of anycompany. The KVD Simulation Modeluses the influence of key performanceindicators on Economic Value Added(EVA) to determine how tactical settingsaffect the bottom-line results of acompany.

2007 2008 2009 2010 2011 2007 2008 2009 2010 2011

2007 2008 2009 2010 2011

EVA EVA (Cum.)

EVA (Difference wrt Base Case) EVA (Cum. difference wrt Base Case)

2007 2008 2009 2010 2011

Figure 5.5: Output graphs EVA

2007 2008 2009 2010 2011

Sales

2007 2008 2009 2010 2011

EBIT

2007 2008 2009 2010 2011

EVA

Figure 5.6: Values of base case and growth scenario


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Since the KVD Simulation Model supports thedecision making process on the strategic andtactical level, and therefore has to be used atmanagement team level, the model is userfriendly and is not a difficult to understandblack box. The business scenarios are easy

to be defined in the model and simulationresults are obtained in an understandableformat.

The validation of the model at Organonhas indicated that although it is not easyto capture the essence of a company in asimplified way, the model is able to representthe basic characteristics of a companywithout going into too much detail. This isthe strength of the KVD Simulation Model.Without major complexity, the modelshows the bottom-line consequences whenimplementing a certain tactical planningand adjusting key performance indicatorsaccordingly. Although the model has notconsidered the entire pipeline of Organon dueto the restricted scope, the KVD SimulationModel generates sensible and valuable results.In the future the model could be expanded toinclude the entire pipeline of Organon to takeinto account the long time horizon typical forpharmaceutical businesses.

The KVD Simulation Model serves as anadequate starting point in the evaluation of

strategy implementation. - Robbert van Heekeren

(Executive Director Finance and Control) -

The model really captures the basic structure of

Organon without going into too much detail.

- Edward Stelmakh (Area Sales Controller) -


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1 Designacomprehensiblemodelformanagement.

Since the target audience of strategicscenario planning and the simulation modelconsists of managers, the models should beunderstandable. Should models be difficultto comprehend, it is hard for managementto trust the model and rely on the opinionof outsiders that the results of the model arethe expected results of the defined strategicscenarios.

2 Ensuremanagementdefinesandadaptsbusinessscenarios.

It is required that the management teamdefines the business scenarios. When aimingat a useful what-if analysis, it is necessary forthe scenarios that are simulated to be based

on events that could happen. After reviewingthe results of the defined business scenariosit must be possible to easily adapt them into anew scenario.

3 Establishanefficientdecision-makingprocess

It is important that the simulation model

is efficient. It is required that the model isable to run simulations of changed businessscenarios on the spot and results can beobtained easily. In this way the model canbe used in a management team meetingsupporting efficient scenario planning andanalysis and decision-making.

Simulation is an appropriate tool to analyseand support business activities on differentlevels in an organization. Simulationcan be used on an operational level, ona tactical level or on a strategic level andcan therefore be of great value. In the pastdecades simulation has become a widespreadmodelling approach in operations research,management science, and various otherapplication areas. However, it is a fact thatmany organizations fail to see the benefitsof simulation in the context of corporateperformance management (CPM). However,in practice, simulation provides an excellentmethod to study future performance withoutactually having to wait for real figures.

For a simulation model to be effectiveand useful for strategic scenarioplanning, however, it has to meet a fewcharacteristics. A simulation model shouldincorporate the right balance of realismand simplicity. Whereas the ideal modelshould be a close approximation of theenvironment it is representing, it should

remain understandable. More specifically, asimulation model in the context of strategyimplementation should include the basiccharacteristics of an organization, but notin too much detail. Moreover, it should bepossible for the model to be used by laymen.

Based on our experience we recommend thefollowing seven rules for effective strategyscenario planning supported by a simulationmodel; rules that guarantee a transparent andunderstandable decision-making process:

6 Seven golden rules for

strategy scenario planning


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4 Prepareunderstandablereportsandanalysis

The results of the simulation model mustbe documented in an understandable

management report and the output mustbe interpreted easily. Comparisons of thedifferent scenarios must be presentedtransparently for effective decision-making.The simulation model must also support amore detailed analysis if required.

5 EnsurereliableresultsVery important is that the model providesreliable results. The model must be as detailedas required to simulate reliable results. Onthe other hand it is important to limit themodel to the relevant performance indicatorsin order to design a comprehensible model.This trade-off is very important and can besupported by the AHP method. The reliabilityalso depends on the accuracy of the inputdata and on all mathematical relations beingdefined appropriately.

6 Designaflexiblestrategicsimulationmodel

Change is inherent in time. Therefore,the model must be a representation of a

latest known business environment; thisenvironment is subject to change. It istherefore necessary for the model to be ableto be adjusted easily.

7 PrepareasimpleuserinterfaceFor a simulation model concerning strategyimplementation it is crucial that themanagement team can easily understandit without technical support from themodel builders. Therefore, a simple userinterface certainly cannot be ignored whenconstructing the simulation model.


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DefinitionsAKeyValueDriver(KVD) is the translation

of a strategic objective into how it will beachieved.

- Variables that must be achieved orimplemented successfully to meet strategicobjectives and for the intended strategy ofthe business to succeed.

- These variables are the few key areas wherethings must go right for the business toflourish. If things should not go right inthese key areas, this would cause strategy tofail.

- Synonymous to KVD are the terms criticalsuccess factor (CFS), and key performanceareas.

KeyPerformanceIndicators(KPI) arequantitative measures to monitor KVDs.- Many things are measurable. That does

not make them key to the organizationssuccess. In selecting Key Performance

Indicators, it is critical to limit them to thosefactors that are essential to the organizationreaching its goals.

- It is also important to keep the numberof Key Performance Indicators small justto keep everyones attention focused onachieving the same KPIs.

- A mathematical KPI is related to the EVAtree whereas this relationship can beexpressed via a calculation rule or formula.

Strategicobjectives express the aspirationthat defines purpose or expected level ofachievement of the organization.

MonteCarlosimulation is a problem solvingtechnique used to approximate the probabilityof certain outcomes by running multipletrial runs, called simulations, using randomvariables


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References Axson, D.A.J. [2003]. Best practices in planning and management reporting

from data to decisions, 2003.

Friedman, B., Essaye, T., [2005]. Corporate risk: what does management really know? PublicUtilities Fortnightly, Vol. 143, No. 2, 54-58.

Hillman, M., [August 2006]. Strategies for managing supply chain risk. Supply Chain ManagementReview, Vol. 10, No. 5, 11-13.

Kouwen, E. van, Lemmens, L., Tullemans, P., [2007]. From strategy to action - Planning for value.EyeOn Research Report.

McKnight, R., [2001]. The four jobs of strategy implementation. Wynewood, PA: Richard McKnightand Associates.

McLeish, D.L., [2005]. Monte Carlo and finance: a guide for finance professionals. John Wiley andSons Ltd.

Meulbroek, L., [spring 2002]. The promise and challenge of integrated risk management. RiskManagement and Insurance Review, Vol. 5, No. 1, 55-66, Spring 2002.

Munsters, M.A.J., [2007]. Key value driver simulation modeling: Linking performance indicators tobottom-line results. Master Thesis Tilburg University.

Noy, E., Ellis, S., [2003]. Corporate risk strategy: does it vary across business activities? EuropeanManagement Journal, Vol. 21, No. 1, 119-128.


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For additional information regarding thisresearch, please contact EyeOn BV.

Drs Loek LemmensPartner++ 31 6 [email protected]

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