Replace uncertain planning assumptions with probability distributions and then utilize Monte Carlo simulation to visualize the range of potential outcomes.

1. Slide 1
The definitionof Simulationwhichyousee here
isfrom MerriamWebsterand I thoughtit
reflectedwell ontoday’ssubjectmatter.
Slide 2
I am pleasedtopresentthissubjectmatterto
you.As a memberof FENG and a formerCFO
and AssistantTreasurerbefore that,Irecognize
the importance of forecastingandI can assure
youthat I have usedthe technique thatIam
aboutto show you to helpme make better
forecastsand,inthe longrun,betterdecisions.
Slide 3
Let’sfirsttake a lookat the landscape inwhich
we plan.
DonaldRumsfeldsaid, “There are known
knowns.These are thingswe know thatwe
know.There are knownunknowns.Thatisto
say,there are thingsthatwe know we don't
know.But there are alsounknownunknowns.
These are thingswe don't know we don't
know.”
StephenCovey said, “If there'sone thingthat's
certaininbusiness,it'suncertainty.”
Claude Shannon said, “Informationisthe
resolutionof uncertainty.”

2. Slide 4 In the face of thisuncertainty,one wouldask,
“Why do we plan?”I say thatit isfundamentally
because of thisuncertaintythatwe plan!
• You can’t get towhereyou want to go withouta road
map!
• You may have objectives for NetSales, NetEarnings
and Returns onEquity and/or CapitalEmployed. How
are they going to beachieved within the national or
global economic climate? In the next6-12months itis
likely that wewillsee higher marketinterestrates;
investors seeking greater liquidity; and “lumpy”
demand by customers.
• You may also haveobjectives for conserving Cash for
future investments.How willthis be done?
• You have an obligation toyour stakeholders to protect
and enhance their interests
Slide 5
Slide 6
Osborne isa:
• Manufacturerofwidgets
• Purchases partiallyassembledproduct
• Adds manufactured components
• Sells nationally
• 5 year old profitablecompany with Retained Earnings
of$110,760
• C Corporationfor IncomeTax purpose
It isMonday, August15th
andthe management
of Osborne ishavingtheirweeklystaff meeting.
In attendance isthe CEO,the CMO, the GM and
the CFO.

3. Slide 7
Slide 8
The onlyitemon the agendafor thismeetingis
nextyear’splan.The CEO saysthat the market
indicatesthatwe can’traise pricesso letme
hearyour forecastsof Unit Volume.
The CMO saysthat withthe new productionline
functioningwell andthe positive reactiontothe
new widgetdesign, mymostlikelycase scenario
isthat we ought to be able to sell 989,300 units.
The GM leapstohisfeetandsays that’swaytoo
highfor the shopto handle.Mybestguessis
950,000 units.
Despite the inputsfromMarketingand
Production, the CEO,whoislargelyreactingto
whatthe streetisexpecting,says – let’sshoot
for the moon.I say 995,000.
Slide 9
As thisdialogisunfolding,the CFOisdeepin
thought:
 How will he create a single financialprojectionwith
three disparate projections ofunit volume?
• No mention has beenmadeofpurchasedassemblies
requiredto achievethoseprojections while
maintaining inventory
• They would have topurchasea minimum of
685,000; maximum of700,000;most likely
690,000
• These assemblies aresourced fromseveral
suppliers whoseprices rangefrom $.375 to
$.48 cents eachdepending on supplier
availability

4. Slide 10
The GM interruptsthe CFO’sthoughtswhenhe
mentions
• There has been a lotoftalk inthepress about the
possibilityofan increasein the minimum wage
• I have 9 peoplein theshop
• 6 ofthem earnattheminimumwageof
$8.25 perhour
• Therefore thereare 1,896production hours
for each, without overtime, thatI need to
put an hourly rateon
• What aretheodds ofan increase andto
what amount?
Aftersome discussion,there isgroupconsensus
of
• A 30% likelihood oftheminimumwage going to$10.50
an hour
Slide 11
At thispoint,the CFO’seyesare rollingwhen
the CEO turns to himand says
• Take thesefigures thatwehave discussed andput
together our mostlikely case scenario
• We’ll meetagainnext Monday and go over whatyou
come up with
• By the way, don’t forget about that mandatory
$20,000bank loan repayment
• There should beno problem meeting thatrequirement
based onthenumbers discussed, RIGHT?
CFO saysit appearsthat waybut letme see
whatthe numberslooklike.

5. Slide 12 The CFO hurriesbackto hisoffice.
• He can’t waitto putpencil topaper
• He alsohas a glimmerof an ideathathe
wouldlike totry,but firsthe has to
create the “most likelycase scenario”
• He comesup withthe following
Slide 13
Assumptionsforthe MostLikelyCase Scenario:
 Unit Volume - 989,300
 Raw Material UnitsPurchased –
690,000
 Unit Price of Raw Materials - $0.40
 No considerationgiventoanychange in
the minimumwage
Slide 14
From these assumptions,he createsanIncome
StatementwhichshowsNetSalesof $989,300
and a NetIncome of $47,812.
Slide 15
SupportingthatIncome Statementare
Schedulesof Costof GoodsSoldand Direct
Labor. Note thatthe Purchasescomponentof
the Cost of Goods Soldschedule is$276,000
(representedby690,000 Units at $0.40 per
Unit).Alsonote thatthe DirectLabor schedule
reflects6workersat the current minimum
wage level of $8.25.

6. Slide 16
He alsocreatesa Balance Sheetwithanending
Cash Balance of $22,360 anda CurrentRatioof
2.52 to 1 aftermakingthe mandatorydebt
paymentof $20,000.
Slide 17
The accompanyingCashFlow reflectsjust
enoughCashFlow fromOperationstocover
that debtpayment.
Slide 18 Upon completion,the CFOsitsbackandlooksat
the Financial Statementshe hasjustcreated:
• Satisfied but somewhat concernedthat the
• Mandatory Debt Repayment is barely
covered
• Dissatisfiedthat thework hejustcompleted
• Did not address widediscrepancy in unit
volume forecasts
• Did not address widediscrepancy in raw
materialunit purchases
• Did not address widerange ofprices for the
raw materials
• Did not evenconsider the 30%likelihoodof
an increasein the minimum wage
Conclusion:We needaforecastingprocessthat
will give ussensitivityof keydriversand
probabilityof outcomes!

7. Slide 19 The foregoing is a classic exampleofRisk Modeling: The OldWay!
What he has created is a singlepointestimate.Every figurehas to
materializeexactly thatway in order for this forecastto
materialize. Ask yourselfthis question:Would you makea multi-
million dollar decision basedon onenumber?
OK. Let’s sayyou createthreeforecasts.Abest case, a worst case
and a mostlikely case. Wouldyou makea multi-milliondollar
decision based on three numbers?
Take it a step further.Assuming you haveunlimitedstaffand
unlimited resources, wouldyou mandatethatyouranalysts run
hundreds or thousands of scenarios for your million dollar
decision? How much willthatcost andwill youget your analyses
on time?
What ifsomething changes? Whatifmany things changeand
change atdifferent times? Managing risk becomes cumbersome,
time consuming anderror laden.
What’s the answer?
Slide 20
RiskModeling:The New Way!Use probability
distributions.These distributionsare the direct
resultof somethingcalledMonte Carlo
simulationandare necessaryformaking
competitivebusinessdecisionsand for
balancingriskandreturn.
Probabilitydistributionsfurnishyouwiththe
full range of possible outcomes,how likely
those outcomesare to occur, and identifythose
itemsthatimpact yourbottomline most
significantlyandbyhow much.
Slide 21 What isMonte Carlosimulation?!?!
It’sreallynotrocketscience.
At itscore,Monte Carlosimulationisavirtual
experiment–repeatedhundreds,thousands,
evenmillionsof times –all the while generating
randomsamples,boundbya set of parameters
that youdefine,fromeachrepetitionof that
experiment.
Those randomsamplesare collectedandthen
organizedandanalyzedtohelpyouunderstand
the behaviorof a simple orcomplex systemor
process.

8. Slide 22 The CFO decidestoutilize thistechniqueonthe
model he justcreated.
• He uses MonteCarlosimulationsoftwarewhichis
commercially available.
• He gives effect tothediscrepancies missing from that
scenario byassigning probability distributions tothe
key model drivers.
• You may already befamiliar withthenormal or“bell
shaped” curvedistribution. Therearemanyothers.
• Which distribution touse is a question that always
arises.
• Historical data,expert opinionor
management “gut feel”are allacceptable
answers tothat question
• With availablehistoricaldata, it is possible
to use software toanalyzethedata and
determinethemost appropriate
distributionto use
In this case, the CFO is using his own judgement.
Slide 23
The textappearingto the rightappliestothe
followingnine (9) pagesof graphicmaterial:
What you arelooking at is thesamemodelthat the CFO created
in responseto the CEO’s request–except that hehas added some
probability distributions to the key drivers.
CFO decides thathe wants to seeoutputs on Net Income andNet
Change in Cash.
He further decides torun 100,000iterations ofthemodelsince
that is likely toproduce a greaterlevelofconfidence inthe
results. Each such iteration is effectively answering thequestion –
what-ifthis is theresultthat occurs. Allofthoseanswers are
collectedand putinto buckets, allowing thesimulationto quantify
the probability ofany ofthoseresults occurring.
Now let’s actually runthesimulation.
Here is thefirst ofseveral“aha moments.” Browsing theresults,
here is a Histogram Chart ofNetIncome. IfI set the Net Income
value to the$47,812in the singlepointmodel, youcanseethat
there is almosta 90% chancethatNetIncomewillnot achieve
that result. Doing thesamething in NetChangeinCash, thereis
almosta 90% chancethatNetChangein Cash willbeless than
$1,275.Theseresults arefar morerevealing than those obtained
in the singlepoint model which the CEO had askedhimto create.
Here is thesecond“aha moment.” This is calleda Tornado chart
which shows thesensitivity ofeachofthe model’s drivers onNet
Income. There is alsoonefor NetChangein Cash.This capability
provides a roadmap to theareas to concentrateonin orderto
mitigaterisk.As you cansee, theUnit Price ofRaw Materials is
the area causing thegreatestpain.
Back to the slides.

9. Slide 24 NetIncome observations:
• 90% of the resultsfall betweenaNet
Loss of $16,303 and a NetIncome of
$53,351
• 10% of the resultsare outside of these
values
• The single mostdamagingimpactto
NetIncome wasthe UnitPrice of Raw
Material Purchases
Slide 25
NetChange inCash observations:
• Model suggestedthatthe company
couldreallylose itsshirtcash-wise,with
90% of the resultsfallingbetweena
cash shortfall of $62,840 and a cash
gainof only$6,814. Withall of the
resultsconsidered,the cashflow ranges
froma negative $94,146 to a positive
$18,391. The meancash flow isa
negative $26,522.
• The $20,000 mandatorydebt
repaymentcouldbe inseriousjeopardy
• In fact,the bankand mortgage holder
mightbe uncomfortable enoughto
foreclose againstthe company
Slide 26
Conclusions
• Single point presentations do notprovide an indepth
picture ofrisk.Using Monte Carlo simulation gives you
much greaterinsight intoan uncertainfutureand
points theway to areas whereyour risk can be
mitigated.
• Ifyou have not begunto collect history, itis a good
thing to do. The past canbe a useful resourcefor
selecting therightprobability distribution.
• The use ofstatisticaltechniques likeMonte Carlo
simulation canactually support gutfeeland lead to
more confidenceandbetter,moreinsightful forecasts
in the future. In the exampleshownhere today,it led
to the structuring offixed pricecontracts with the
company’s raw materialproviders.
• Thank you for your timeand attention

10. Slide 27