Slide 1

The definition of Simulation which you see here is from Merriam Webster and I thought it reflected well on today’s subject matter.

Slide 2

I am pleased to present this subject matter to you. As a member of FENG and a former CFO and Assistant Treasurer before that, I recognize the importance of forecasting and I can assure you that I have used the technique that I am about to show you to help me make better forecasts and, in the long run, better decisions.

Slide 3

Let’s first take a look at the landscape in which we plan.

Donald Rumsfeld said, “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns.

These are things we don't know we don't know.”

Stephen Covey said, “If there's one thing that's certain in business, it's uncertainty.”

Claude Shannon said, “Information is the resolution of uncertainty.”

Slide 4

In the face of this uncertainty, one would ask, “Why do we plan?” I say that it is fundamentally because of this uncertainty that we plan!

• You can’t get to where you want to go without a road map!

• You may have objectives for Net Sales, Net Earnings and Returns on Equity and/or Capital Employed. How are they going to be achieved within the national or global economic climate? In the next 6-12 months it is likely that we will see higher market interest rates; investors seeking greater liquidity; and “lumpy” demand by customers.

• You may also have objectives for conserving Cash for future investments. How will this be done?

• You have an obligation to your stakeholders to protect and enhance their interests

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Slide 6

Osborne is a:

• Manufacturer of widgets

• Purchases partially assembled product

• Adds manufactured components

• Sells nationally

• 5 year old profitable company with Retained Earnings of $110,760

• C Corporation for Income Tax purpose

It is Monday, August 15th and the management of Osborne is having their weekly staff meeting. In attendance is the CEO, the CMO, the GM and the CFO.

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Slide 8

The only item on the agenda for this meeting is next year’s plan. The CEO says that the market indicates that we can’t raise prices so let me hear your forecasts of Unit Volume.

The CMO says that with the new production line functioning well and the positive reaction to the new widget design, my most likely case scenario is that we ought to be able to sell 989,300 units.

The GM leaps to his feet and says that’s way too high for the shop to handle. My best guess is 950,000 units.

Despite the inputs from Marketing and Production, the CEO, who is largely reacting to what the street is expecting, says – let’s shoot for the moon. I say 995,000.

Slide 9

As this dialog is unfolding, the CFO is deep in thought:

How will he create a single financial projection with three disparate projections of unit volume?

• No mention has been made of purchased assemblies required to achieve those projections while maintaining inventory

• They would have to purchase a minimum of 685,000; maximum of 700,000; most likely 690,000

• These assemblies are sourced from several suppliers whose prices range from $.375 to $.48 cents each depending on supplier availability

Slide 10

The GM interrupts the CFO’s thoughts when he mentions

• There has been a lot of talk in the press about the possibility of an increase in the minimum wage

• I have 9 people in the shop

• 6 of them earn at the minimum wage of $8.25 per hour

• Therefore there are 1,896 production hours for each, without overtime, that I need to put an hourly rate on

• What are the odds of an increase and to what amount?

After some discussion, there is group consensus of

• A 30% likelihood of the minimum wage going to $10.50 an hour

Slide 11

At this point, the CFO’s eyes are rolling when the CEO turns to him and says

• Take these figures that we have discussed and put together our most likely case scenario

• We’ll meet again next Monday and go over what you come up with

• By the way, don’t forget about that mandatory $20,000 bank loan repayment

• There should be no problem meeting that requirement based on the numbers discussed, RIGHT?

CFO says it appears that way but let me see what the numbers look like.

Slide 12

The CFO hurries back to his office.

• He can’t wait to put pencil to paper

• He also has a glimmer of an idea that he would like to try, but first he has to create the “most likely case scenario”

• He comes up with the following

Slide 13

Assumptions for the Most Likely Case Scenario:

Unit Volume - 989,300 Raw Material Units Purchased –

690,000 Unit Price of Raw Materials - $0.40 No consideration given to any change in

the minimum wage

Slide 14

From these assumptions, he creates an Income Statement which shows Net Sales of $989,300 and a Net Income of $47,812.

Slide 15

Supporting that Income Statement are Schedules of Cost of Goods Sold and Direct Labor. Note that the Purchases component of the Cost of Goods Sold schedule is $276,000 (represented by 690,000 Units at $0.40 per Unit). Also note that the Direct Labor schedule reflects 6 workers at the current minimum wage level of $8.25.

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He also creates a Balance Sheet with an ending Cash Balance of $22,360 and a Current Ratio of 2.52 to 1 after making the mandatory debt payment of $20,000.

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The accompanying Cash Flow reflects just enough Cash Flow from Operations to cover that debt payment.

Slide 18

Upon completion, the CFO sits back and looks at the Financial Statements he has just created:

• Satisfied but somewhat concerned that the

• Mandatory Debt Repayment is barely covered

• Dissatisfied that the work he just completed

• Did not address wide discrepancy in unit volume forecasts

• Did not address wide discrepancy in raw material unit purchases

• Did not address wide range of prices for the raw materials

• Did not even consider the 30% likelihood of an increase in the minimum wage

Conclusion: We need a forecasting process that will give us sensitivity of key drivers and probability of outcomes!

Slide 19

The foregoing is a classic example of Risk Modeling: The Old Way! What he has created is a single point estimate. Every figure has to materialize exactly that way in order for this forecast to materialize. Ask yourself this question: Would you make a multi-million dollar decision based on one number?

OK. Let’s say you create three forecasts. A best case, a worst case and a most likely case. Would you make a multi-million dollar decision based on three numbers?

Take it a step further. Assuming you have unlimited staff and unlimited resources, would you mandate that your analysts run hundreds or thousands of scenarios for your million dollar decision? How much will that cost and will you get your analyses on time?

What if something changes? What if many things change and change at different times? Managing risk becomes cumbersome, time consuming and error laden.

What’s the answer?

Slide 20

Risk Modeling: The New Way! Use probability distributions. These distributions are the direct result of something called Monte Carlo simulation and are necessary for making competitive business decisions and for balancing risk and return.

Probability distributions furnish you with the full range of possible outcomes, how likely those outcomes are to occur, and identify those items that impact your bottom line most significantly and by how much.

Slide 21

What is Monte Carlo simulation?!?!

It’s really not rocket science.

At its core, Monte Carlo simulation is a virtual experiment – repeated hundreds, thousands, even millions of times – all the while generating random samples, bound by a set of parameters that you define, from each repetition of that experiment.

Those random samples are collected and then organized and analyzed to help you understand the behavior of a simple or complex system or process.

Slide 22

The CFO decides to utilize this technique on the model he just created.

• He uses Monte Carlo simulation software which is commercially available.

• He gives effect to the discrepancies missing from that scenario by assigning probability distributions to the key model drivers.

• You may already be familiar with the normal or “bell shaped” curve distribution. There are many others.

• Which distribution to use is a question that always arises.

• Historical data, expert opinion or management “gut feel” are all acceptable answers to that question

• With available historical data, it is possible to use software to analyze the data and determine the most appropriate distribution to use

In this case, the CFO is using his own judgement.

Slide 23

The text appearing to the right applies to the following nine (9) pages of graphic material:

What you are looking at is the same model that the CFO created in response to the CEO’s request – except that he has added some probability distributions to the key drivers.

CFO decides that he wants to see outputs on Net Income and Net Change in Cash.

He further decides to run 100,000 iterations of the model since that is likely to produce a greater level of confidence in the results. Each such iteration is effectively answering the question – what-if this is the result that occurs. All of those answers are collected and put into buckets, allowing the simulation to quantify the probability of any of those results occurring.

Now let’s actually run the simulation.

Here is the first of several “aha moments.” Browsing the results, here is a Histogram Chart of Net Income. If I set the Net Income value to the $47,812 in the single point model, you can see that there is almost a 90% chance that Net Income will not achieve that result. Doing the same thing in Net Change in Cash, there is almost a 90% chance that Net Change in Cash will be less than $1,275. These results are far more revealing than those obtained in the single point model which the CEO had asked him to create.

Here is the second “aha moment.” This is called a Tornado chart which shows the sensitivity of each of the model’s drivers on Net Income. There is also one for Net Change in Cash. This capability provides a road map to the areas to concentrate on in order to mitigate risk. As you can see, the Unit Price of Raw Materials is the area causing the greatest pain.

Back to the slides.

Slide 24

Net Income observations:

• 90% of the results fall between a Net Loss of $16,303 and a Net Income of $53,351

• 10% of the results are outside of these values

• The single most damaging impact to Net Income was the Unit Price of Raw Material Purchases

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Net Change in Cash observations:

• Model suggested that the company could really lose its shirt cash-wise, with 90% of the results falling between a cash shortfall of $62,840 and a cash gain of only $6,814. With all of the results considered, the cash flow ranges from a negative $94,146 to a positive $18,391. The mean cash flow is a negative $26,522.

• The $20,000 mandatory debt repayment could be in serious jeopardy

• In fact, the bank and mortgage holder might be uncomfortable enough to foreclose against the company

Slide 26

Conclusions

• Single point presentations do not provide an in depth picture of risk. Using Monte Carlo simulation gives you much greater insight into an uncertain future and points the way to areas where your risk can be mitigated.

• If you have not begun to collect history, it is a good thing to do. The past can be a useful resource for selecting the right probability distribution.

• The use of statistical techniques like Monte Carlo simulation can actually support gut feel and lead to more confidence and better, more insightful forecasts in the future. In the example shown here today, it led to the structuring of fixed price contracts with the company’s raw material providers.

• Thank you for your time and attention

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