Causal financial models lend themselves situations where inputs to the model are well known. Sustaining innovation falls into this category. But what kind of models should one use for disruptive innovations? This post will demonstrate risk based models intended for disruptive innovations where model inputs are not well defined.
Jose Briones uses the following Project Categorization in his Beyond Stage-Gate presentation:
Jose then categorizes the financial analysis into three levels going from the top right corner to the bottom left:
- Level 1 - Reverse Income Statement/Real Options
- Level 2 - Probabilistic Decision Analysis
- Level 3 - NPV/DCF
The message is clear, the more certain your innovation, the more you can use traditional tools. Larry McKeough addresses Level 3 in his Rocky Mountain Product Camp 2010 Presentation. However, Larry's DCF spreadsheet tool considers assumptions, so even his tool recognizes the presence of risk in low uncertainty innovations.
Risk Caused By Disruptive Innovation
Christensen uses the following model of disruptive innovations as shown in my Rocky Mountain Product Camp 2010 Presentation:
There are two risky places to innovate. The first is the low-end or new-market disruption. Both of these have considerable market and technical uncertainty, as shown by the circles below. Each of these strategies involve new value networks, new customers, and new definitions of value.
The second place is when sustaining innovation pushes performance beyond user needs and the supply chain begins to reconfigure itself to supply flexibility and speed to the market. When platforms disaggregate and margins shift between players, look out!
DCF is not well suited for these situations, especially the low-end and new-market disruptions. A better tool is reverse income statement and assumption management. The remainder of this post will walk through a reverse financials statement. A follow on post will address assumption and risk management.
Let's proceed by building out a spreadsheet step by step. I will roughly follow McGrath's example from his book Discovery Driven Growth. Assume the product is a $100K machine used in manufacturing lines. First, we will frame and scope, then work on deliverables, and finish with the reverse financials.
Framing and Scoping
Management has stipulated $1M in operating profits with a 17% return on sales (ROS) and a 20% return on assets (ROA). This results in a $5M allowance for assets, and requires sales to be approximately $6M. Given a $100K selling price, this implies selling 5 systems a month. Two questions follow:
- Can manufacturing build 5 systems a month?
- Can ROS/ROA be maintained?
The benchmark range of return on sales is 25% to 3%. The data came from the public financials of companies in the same product category. This should immediately send shivers down your spine! You should start asking yourself questions like: how do we insure our product lands in the good side of the range?
Notice what has happened so far. Instead of creating a bottom up plan that results in a ROA/ROS figure, we start with a required ROA/ROS and ask, what does this imply? It implies a sales level of sales of 5 systems per month. It implies assumptions about ROA/ROS that may not be real. Not only does the benchmark data have a wide range, but if this is a disruptive innovation, benchmarks may not even apply.
Now have to take the next step, which is the deliverables specification. We have to start breaking down the overall assumptions into smaller assumptions we can manage.
Starting with assumption F10/A3, the sales team has predicted the cost of sales and marketing is 15%, with a range of 13% to 17%. The left side (F10) says that to meet the original ROS/ROA, we require 15%. The right side (A3) says that the possible range is 13% to 17%. Therefore, we have to manage the assumptions on the right side to either meet the requirements on the left, kill the project, or improve some other assumption to compensate.
Using the data from the deliverables, a reverse income statement and reverse balance sheet are created. The left side shows a return on sales (F40) of 25% and a return on assets (F47) of 44%. This is better than the original 17% and 20% requirement. Therefore, if the left side of the spreadsheet holds, we have a project. But as my father used to say, IF is the biggest word in the dictionary.
Look at the worst case on the right side. return on sales is -5% and return on assets is -6%. Assumptions matter!
If this were a sustaining innovation, the assumptions would be reasonably accurate with small ranges, and DCF would be a great tool. Diffusion curves could be used to estimate revenues. An IRR could be generated and compared to a weighted cost of capital. Small uncertainties could be accounted for by running a Monte Carlo analysis on the DCF.
We basically framed/scoped management requirements for ROS and ROA. We then worked backwards and created financial deliverables that meet those numbers, and we treated the deliverable components as assumptions with ranges of values. Finally, we created reverse financials that show the best and worst case ROS/ROA.
- Don't use the wrong tools. Your classic MBA tools don't always work.
- Work backwards from goal to assumptions.
- Manage assumptions.
Where do we go from here?
The next post will discuss basic risk management concepts and address how to deal with the assumptions of reverse financial statements.