Evaluating the viability of a new business venture
In this tutorial we are going to use Monte Carlo simulation to forecast the profitability
of a new business venture. For this example we are going to look to evaluate if opening a new comic shop would prove
profitable over a three year period. The technique works equally well on any type of venture whether it be a new business or a new
product launch and for any time period.
Were going to look at initial set-up costs, running costs for three years and then
revenue over the same three year period. Let us consider the following a set-up costs
for the venture.
|
Set-Up Costs |
|
Mortgage or Lease Deposit
|
|
Inventory |
|
Store Fixtures Signs & Equipment |
|
Office Supplies & Store Use Items |
|
Professional Fees |
|
Utilities Connection |
In addition we have the following running costs
|
Running Costs |
|
Rent/Mortgage Payment
|
|
Inventory
|
|
Wages |
|
Insurance |
|
Utilities |
And finally we also have the proposed revenue over the period.
Next we have to make some predictions about
what each of these items are likely to be in the future. In using Monte Carlo simulation, rather than assigning one single estimate we assign three estimates.
We assign a low, a medium and a high estimate.
In addition to this we also choose a probability for falling between the ranges i.e. how likely
is it that each cost will be between the low and medium
estimate or the medium and high estimate. So let us do this for our first cost, Mortgage or Lease Deposit.
For this example let us say that the Mortgage or Lease Deposit is going to be around
10% of the property value and were looking at buying a property worth $250,000. So
for this cost let us say the medium estimate is $25,000. We may however be able
to find a property for $200,000 although we could end up spending $300,000 on the shop.
As these are costs they will be represented as negative values so the low estimate
would be -$30,000 and the high estimate -$20,000. Now we consider how likely it is that we will
spend between our low estimate -$30,000 and our medium estimate
-$25,000. This is usually
done by drawing on past experience
or using judgement. In our example let us assume that we have looked at a few properties and we feel there are not many properties
around our target of $250,000 so we think it is 80%
likely that we will get a property between $250,000 and $300,000 and therefore the
chance of the value being between medium and high estimates is therefore 20%.
We then have the following.
|
Set-Up Costs |
Low Estimate |
Medium Estimate |
High Estimate |
Confidence |
|
Mortgage or Lease Deposit
|
-$30,000 |
-$25,000 |
-$20,000 |
80% |
We then repeat for each cost assigning low, medium and high estimates along with the probability of each falling between
the low and medium estimates. Let us use the following figures.
|
Set-Up Costs |
Low Estimate |
Medium Estimate |
High Estimate |
Confidence |
|
Mortgage or Lease Deposit
|
-$30,000 |
-$25,000 |
-$20,000 |
80% |
|
Inventory |
-$30,000 |
-$20,000 |
-$10,000 |
70% |
|
Store Fixtures, Signs & Equipment |
-$18,000 |
-$12,000 |
-$8,000 |
50% |
|
Office Supplies & Store Use Items |
-$7,000 |
-$4,000 |
-$2,500 |
75% |
|
Professional Fees |
-$8,000 |
-$5,000 |
-$2,000 |
50% |
|
Utilities Connection |
-$3,000 |
-$2,000 |
-$1,000 |
40% |
Next we can assign estimates for the ongoing running costs. It may be the case that some costs are
fixed. For example we may know that we can fix the insurance
costs at $200 a
month so the total costs over 3 years would be $7,200 (36 months x $200). In this
case we can apply the same costs for each element so that there is no variability.
|
Running Costs |
Low Estimate |
Medium Estimate |
High Estimate |
Confidence |
|
Insurance |
-$7,200 |
-$7,200 |
-$7,200 |
100% |
In others there may be only a low or high cost for example we may need one or two full-time
staff but we will never need between one and two. In this example we can
provide just low and a high estimates and leave the medium
empty. The confidence
here relates to the probability we will use the low value. In the example below we are saying we are 60% confident
that we will need two staff member's costing $180,000 over three years and therefore
40% confident that we will need one staff costing a total of $90,000.
|
Running Costs |
Low Estimate |
Medium Estimate |
High Estimate |
Confidence |
|
Wages |
-$180,000 |
- |
-$90,000 |
60% |
We can also have two estimates, either low and medium
or medium and high, the same. For example we may need between
$20,000 and $40,000 of inventory, but we definitely will not need more than $40,000
so the low and medium estimates can both be set to $40,000 and the high estimate
set to $20,000. Let us
use a confidence factor of 20%. Here were saying 80% of the
time we will need between $20,000 and $40,000 and 20% we will use $40,000.
|
Running Costs |
Low Estimate |
Medium Estimate |
High Estimate |
Confidence |
|
Inventory |
-$40,000 |
-$40,000 |
-$20,000 |
20% |
Continuing with the remaining running costs for three years we will use the following.
|
Running Costs |
Low Estimate |
Medium Estimate |
High Estimate |
Confidence |
|
Rent/Mortgage Payment
|
-$70,000 |
-$60,000 |
-$50,000 |
80% |
|
Inventory |
-$40,000 |
-$40,000 |
-$20,000 |
20% |
|
Wages |
-$180,000 |
- |
-$90,000 |
60% |
|
Insurance |
-$7,200 |
-$7,200 |
-$7,200 |
100% |
|
Utilities |
-$30,000 |
-$25,000 |
-$20,000 |
50% |
Next we need to consider revenue. Based on our sales predictions over the three
year period we create the following estimates. Here we think we will create revenue of $300,000 but it could be as high as $500,000 or as low as $200,000
and we believe
it's more likely to be between $300,000 and $500,000 rather than
between $200,000 and $300,000 so we set the confidence at 20%.
|
Running Costs |
Low Estimate |
Medium Estimate |
High Estimate |
Confidence |
|
Revenue |
$200,000 |
$300,000 |
$500,000 |
20% |
If we were to enter these into the simulation tool it would look like the following.
You can see we have to use negative values to represent costs and we have had to
order the estimates so that the low estimate is always less than the high estimate.
Note the confidence is only required for the percentage chance of being between
the low and the medium estimate. The confidence of being between the medium and
high estimate
is simply derived. Also note there is no need to key in $ as the simulation could be
for any units, as long as you use the same for each item.
Now we have all the data entered to run the simulation we press the "Perform Analysis" button.
However let us step through what actually happens during the simulation process.
To get a forecast we simulate the business venture running many times so we can
see the probability of achieving a profit after 3 years. It does
this by taking each item in turn and assigning
it a value based on the data entered. So firstly
we take Mortgage or Lease Deposit and generate
a simulated result, which is a random number. Based on the data we entered this will be between $30,000 and $25,000 80% of the time and between $25,000 and $20,000 20%
of the time. For the first simulation,
suppose we get $27,346 as the result. We then move
through all the tasks until we have values for each and then sum them up. In this example the first simulation generated the following values.
|
Activity |
Value |
|
Mortgage or Lease Deposit
|
-$27,346 |
|
Inventory |
-$23,874 |
|
Store Fixtures, Signs & Equipment |
-$8,572 |
|
Office Supplies & Store Use Items |
-$6,432 |
|
Professional Fees |
-$2,705 |
|
Utilities Connection |
-$1,854 |
|
Rent/Mortgage Payment
|
-$68,698 |
|
Inventory
|
-$32,983 |
|
Wages |
-$180,000 |
|
Insurance |
-$7,200 |
|
Utilities |
-$23,954 |
|
Revenue |
$314,430 |
|
Total |
-$69,188 |
This simulation generated a forecast loss of $69,188 in the first three years of trading.
We then repeat the process many
times, each time producing a different total. After running many times we can
view how well the various results distribute. The free Monte
Carlo simulation tool runs for 100 iterations. For up to 100,000 iterations simply purchase a license key. Running
a simulation with 100,000 iterations will vastly increase the reliability of the results.
Now having entered the data and ran the simulation let us take a look at the results.
The table below shows the forecast profits of running the business for three years
100,000 times.
It divides the results into 20 separate ranges and shows a count of each time a
simulation falls within a given
range. It also provides the cumulative number of
simulations falling up to the current range and also expresses this as a percentage
of the total
number of simulations.
You can see that the business venture completed 27 times in the range -$223,200
to -$198,575. You can see that between -$100,070 and -$75,445, there were 4,882
simulations observed, and that up to
-$75,445 a total of 14,587
simulations had completed, which is 14.6%
of the time.
The chart below presents the same information graphically with each bar representing
the number of times the result completed within the range. The line represents
the cumulative % of results which were observed by each
of the range segments.
.
Now we have the results we can use these to aid our decision making process. We can see from the results that
with this forecast we would only make a profit of
more than $23,059 43% of the time. So for over half the time, 57%, we would make
a loss or very little profit. Should we progress with the venture? At this stage
you could look to alter some of the estimates, would we increase the chance of success
by making sure we only employed one person? Perhaps we should restrict the property
budget to $250,000. Perhaps progressing with a 43% chance of profitability is acceptable.
Whilst this example is based on a simple venture it demonstrates
the value of the technique. You can see by examining the results you have far
more information to base your judgement on and can decide a course of action accordingly.
Whilst you can't see the future by using Monte Carlo analysis the potential risks
and uncertainty have been taken into account enabling you to predict it with a greater degree of confidence.
Simulations are a very powerful tool, and it is important to understand all of the
variables, inputs and outputs involved. The information that comes out is only as
good as the information that goes in. It is important to collect data from people
who understand the specific items involved. Also it is important to test the model
to ensure realistic results are provided.
This tutorial should have demonstrated the value of Monte Carlo simulation however
don't take our word for it, try it with your own data. Simply go to the
Simulation Data Entry
and see the results for yourself.
To see another example of the technique in use for establishing a project budget click
here.
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