  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.