What Does a Good Innovation Option Look Like?

We have been following a bit of a theme lately on valuation methods and selecting innovation projects. This was started with my post on some research that we have been doing on valuing innovation projects. Using surveys and quantitative analysis the study showed that traditional valuation methods such as net present value inhibited innovation. However, treating an innovation project as a real option was positively correlated with successful innovation.

Following this post, a few readers pointed us to Clay Christensen’s essay on the NPV trap, which is very supportive of the survey results. Tim subsequently wrote a blog piece on this too.

If traditional valuation methods are so flawed when selecting innovation projects, then it’s probably worth saying a bit more about real options. One reader of the blog, who runs a VC investment company specializing in high-tech ventures, said she had been using real options to make investment decisions for several years and found the outcomes to be much better than traditional methods for valuing projects (thanks Deb!).

What got me thinking about real options in the first place was a finance colleague who gave me an excellent article from the McKinsey Quarterly. It’s quite old now but I still think it’s the best introduction to real options and innovation strategy so I will use some of the ideas in this post.

The first thing to think about is how to value a financial option. I won’t go into detail on this one but some very smart people won the Nobel Prize for this and then nearly brought down the global economy when they used their equations to manage a hedge fund called Long Term Capital Management (now that really is academic impact!).

In the equation for valuing a stock option, increasing uncertainty and time to expiry increase the value of the option. The cost of exercising the option decreases its potential value, as does the revenues that we might lose by holding the option, rather than the underlying stock.

The value of a real option works in exactly the same way, as explained by this diagram from the McKinsey Quarterly overview.

The most important issue for managing innovation projects is that uncertainty and time increase the value of the project. In traditional valuation methods, these variables decrease the value of the project. Innovation always involves uncertainty and longer time frames and is therefore highly compatible with real options.

But taking a real options approach to innovation can tell us more than just valuation. It also tells us how to maximise the value of the project by constructing it as a real option where we can maximise its value by looking at the variables that make the option more attractive. For example, can we take an initial stake in the project as an option that will allow us to hold the option for a long time? Can we decrease the costs of exercising the option by finding potential partners to help us take the innovation to a bigger market?

This gets a bit abstract so I will use a recent example of an Australian oil and gas producer that is buying a ‘real option’ in producing biofuel from algae.

In this case uncertainty is high. The technology is unproven and nobody really knows what the oil or carbon price will be in five years time. Given that Beach Energy owns the tenements in the Cooper Basin where the algae farms are proposed, the potential time to expiry is very long and the exercise price is also relatively low. It’s a very good case for constructing the business case as a real option.

Beach does this by staging the investment with an alliance partner. If the initial scoping study or the pilot plant fails then all they have lost is the minimal investment in the early stage of the project. They can exercise the option by scaling up production if the industry conditions make the project economically feasible.

Again, the most important point is that there is value in embracing uncertainty and learning by trying things out.

Source: Leslie and Michaels (1997) The Real Value of Real Options. McKinsey Quarterly (3), pp. 97-108.

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