Don’t Roll the Dice on your Business Case

So the business case for your new product is completed and you are ready to present it to your execs. As suggested in a previous post, you have consulted with the key decision-makers prior to the presentation and of course they disagree with your numbers and/or your assumptions. Your ability to drive everyone to a consensus will be critical to making your business case presentation meeting a success. But how will you make this happen when no one can agree on the key variables and assumptions?

When uncertainty is involved, as is always the case, the Monte-Carlo analysis can be used to better understand your chances of success or failure.

Armed with the results of an objective analysis, you can then go back to your execs and show the impact of their input. If there is little or no impact, you are in good shape. If there is significant impact at least you are no longer clueless about the key variables and are better equipped to address them.

The beauty of the Monte-Carlo Analysis is that all the known uncertainties you are aware of can be taken into account together to give you a full picture of your risk.

Implementing a Monte-Carlo analysis may sound complicated at first. However given rudimentary spreadsheet knowledge and one uninterrupted day any product manager can pull it off. For this post I will review the usefulness of the Monte-Carlo analysis. Future posts will talk about how this gets done with a spreadsheet, as well as how to use Sensitivity analysis to help isolate your issues.

The principle of a Monte-Carlo analysis is quite simple – it is to incorporate projected levels of uncertainty into a probabilistic analysis. If one or more of your figures is uncertain, then you create a variable for the figure and assign a random value to it that simulates the anticipated variation. For example, if fixed cost is projected to be $10 with a variance of +/- 10%, the random value will make the cost vary from $9 to $11. This is repeated for all known variables within the business case. You then run the numbers each time to see how it affects your objectives, such as NPV, cash flow or rate of return. The results of each run will return one out of an infinite possibility of outcomes. By repeating this exercise thousands of times, and this can be done in a spreadsheet in real-time, you can obtain a more realistic idea of your ability to meet your objectives.

Let’s use an example.  The figure below shows a summary table of your business case. Your objective is to reach a15% Net Present Value of $1M by the end of year 5. You get no revenue in year 1 as you are building the product and training the company during that time.

So far so good. However, after collaborative review, the following input was received from your key decision-makers:

1)      Your CEO thinks there is a 30% chance of a competitor entering your space in Y2 which, if this occurs, will force you to reduce your price by 20% starting Y2

2)      R&D is telling you that since this is a new product, your fixed cost figure really depends on the project going according to the conservative plan. Any unforecasted difficulty will impact the cost figure. Your CTO estimates that there is a 50% chance that the fixed cost may vary uniformly +/- 40%

3)      Your VP of sales’ quantity figures do not match what industry influencers think the market can bear. Your best estimate is that the quantities may vary uniformly +/-  20%

Now how do you account for the estimated variation in the business case? How will the combination of all these factors impact your NPV?

Let’s jump directly to the results of the Monte-Carlo analysis. Please note first that Monte-Carlo analysis requires multiple simulation runs involving lots of random numbers, so every simulation run provides similar yet different results.

After running 2048 simulations on my spreadsheet, the calculations return the following results:

The first thing to notice by looking at the Minimum NPV is that you could be losing money. A Monte-Carlo analysis was not required to tell you this. The theoretical worst case scenario could have been calculated by increasing costs to 40%, reducing prices and quantities by 20% respectively directly in the spreadsheet. The question truly is: what is the likely probability the worst case scenario will come to pass? A probability calculation provided by the Monte-Carlo analysis is required to provide this information.

The Maximum NPV shows that you can theoretically make more than the $1M NPV. The approach described above can be used to calculate the theoretical best case scenario – and the same applies with respect to probability of its occurrence.

The Mean NPV value is worrisome: on average, by running 2048 simulations, the average NPV was $667k. The probabilistic calculations provided by the Monte-Carlo analysis provide insight that your $1M NPV might be a bit idealistic. Again, what is the likelihood of that?

This brings us to the next graph. The grey bars show the % of occurrences that your NPV will be within a given $ bracket indicated in the X axis at the bottom of the graph. The blue line shows the cumulative distribution of % of occurrences.

The resulting Monte-Carlo analysis of the business case strongly suggests that more work is required to meet the stated objectives. In review of probability of outcome as depicted by the results of the analysis:

1)      You have a greater than 12% chance of realizing a negative NPV (extreme left bar). Your management must be prepared for this eventuality.

2)      You have an 80% chance of missing your $1M NPV objective, so you need to investigate further what needs to be done to bring back your chances within an acceptable range. We will see how this can be done in a future post using Sensitivity analysis.

3)      You have a 44% of chance that your realized NPV will fall between $500k and $1.1M. In other words, your business case is all over the place as reflected by a high degree of variance in the probabilistic analysis. Pretty much anything could happen to your new product in terms of profitability, and therefore your business case truly does not hold water.

This is bad news, but you have identified significant issues when no decision has been made and little money spent. It is better to know that you may crash before you board the plane!

Stay tuned to read about how we developed the results, and importantly, how primary issues can be identified through Sensitivity analysis. The resulting analysis will enable you to identify the problems that can be fixed.