To NPV or Not to NPV: That Is the Question
Warren Buffett once suggested that the concepts of time value of money and discounted cash flow (DCF) model were introduced more than 2500 years ago. In about 600 B.C. oracle Aesop formulated his investment insight "a bird in the hand is worth two in the bush" and since that time the model for calculating the value of an asset as the present value of the cash generated by this asset throughout its life has remained unchanged.
However, being one of the first and mandatory concepts taught in finance classes all over the world, the DCF model has recently become the subject of debates and discussions in regards of its bias towards innovation. Facing highly uncertain innovation opportunities, companies tend to use ubiquitous NPV as an easy-to-understand analytical tool that can help quantify uncertain future and assess outcomes. However, cautious estimates of the future cash flows and oversimplified application of the tool itself lead to underinvestment in innovative projects.
A related idea is articulated by the information-action paradox which describes the problem many companies face while investing in projects with high level of uncertainty, including innovation. For such initiatives the availability of information is inversely correlated with freedom to act, or, in other words, the ability to act decreases with the increase of data. This concept is built on the assumption that the market is not static: existing competitors and new entrants are continuously building capabilities to gain their positions on the market. However, when the information about their moves becomes available, it limits the freedom of grasping the same opportunities as the capabilities developed by the competitors are difficult and/or expensive to imitate.
One of the illustrations of this paradox is constantly emerging disruptors in mature sectors which not only successfully enter the markets but also establish strong positions threatening incumbents. The explanation of this phenomenon is in their entrepreneurial spirit and low threshold of proof. Starting from scratch they are not preoccupied by the impact new initiative may have on their existing activities and are more likely to take risks. As for established market players, they demonstrate much higher threshold of proof. They tend to avoid losses and prefer to keep the things as they are rather than invest in risky innovation.
From the FP&A standpoint, which may also lack that entrepreneurial perspective, the use of the traditional tools of financial analysis based on the discounted cash flows not only perfectly supports this kind of behavior, but also justifies it.
While assessing innovation initiatives analysts commit two errors which result in a biased anti-innovation decision:
Future cash streams from innovation are difficult to predict which causes errors of estimation in NPV calculations. Analysts usually build their financial models for the first 5 years of the investment and then add terminal value for all the years coming thereafter which may contribute up to 50% of NPV. As for these distant years the level of uncertainty is very high while the level of precision is low, terminal value is calculated based on the numbers of the last projected year, therefore it accumulates and magnifies all the errors in assumptions made for the first years of the model.
Projected cash flow from innovation is usually compared with 0 representing the scenario of doing nothing where the company’s current business and financial standing are supposed to be unchanged. However, firm’s competitors continue to make their investments, including innovation, which will probably put pressure on prices, sales volumes and market shares. That’s why the most likely scenario to compare against will be the one reflecting a decline in performance as the current situation may deteriorate due to the factors the company does not control.
As mentioned, it is extremely difficult to build accurate forecast of the cash streams from innovation and it seems impossible to estimate potential deterioration of the company’s financial performance in case when an investment in innovation is not made. One of the solutions here can be the use of scenario analysis or simulation modeling. The former focuses on a few scenarios reflecting the most probable extent of decline in performance according to analyst’s point of view, while the latter can generate thousands of random scenarios based on the ranges of various parameters (price, cost, volumes, timeframe of the impact, etc.) and help identify most probable outcomes.
Are there any alternatives that could overcome described drawbacks of DCF model?
Real options
NPV, reflecting the risks of uncertainty associated with the project, does not capture neither the possibility that actual cash flows may be much higher under certain conditions, nor the value of management flexibility. Valuation experts point out that many projects, such as R&D, geographical expansion, investments in new capacities are naturally structured in stages which can be pursued or abandoned based on the results of the previous stage or after getting new information. These projects can be seen as “real” options, as opposed to financial options—in which investors have the right but not the obligation to invest in a specific asset.
This comparison brings experts to the application of option-pricing models in different variations to assess investment projects: Black-Scholes-Merton model, binomial model, or a combination of DCF and real options, with a goal to find the way to consider the value of flexibility in a decision-making process. Flexibility represents the ability to make decisions during the project which will maximize returns and minimize losses, for instance, the opportunity to expand operations in case of favorable market conditions, abandon the project, defer investments in time, restart operations after a temporary shutdown, etc.
Being a promising alternative to NPV, real options valuation has not been widely adopted by the companies neither in its initial version, nor in modified ones. Though there are some successful examples of the use of real option models, lack of understanding the option theory and difficulty to explain its assumptions when assessing projects remain the main obstacles for application.
Discovery driven planning
Another weakness of the DCF model not discussed previously in this article is its sensitivity to assumptions. Project teams seeking for funding can easily define the “right” set of parameters to justify the project by merely changing a couple of initial assumptions. This point counterbalances abovementioned drawbacks but does not add value to using NPV to measure innovation. However, Rita Gunther McGrath and Ian MacMillan suggest taking advantage of this cheating tip in their discovery-driven planning approach which implies the reverse sequence of analytical activities when assessing project’s potential.
If project teams already know what numbers should be reached to win the funding, there is no need to randomly “play” with assumptions to figure out the acceptable set of parameters. It’s better to define minimally acceptable revenues, profits and cash flows and then identify the assumptions that must prove true to make those financials happen.
Thus, the first step in the discovery-driven planning is to create a reverse income statement which defines how the success should look like. Unlike traditional planning starting from revenues and going down to the profits, discovery-driven planning begins with required profits and work its way in the income statement up to the revenues expected to deliver this profit considering all the associated cost. Key metrics should then be benchmarked against the market and/or competitors to check whether they are realistic or not.
Next step is the construction of detailed operational requirements (production, selling, distribution, etc.) which will be part of the allowable cost.
All critical assumptions of the first two steps must be discussed and, if any of them proved unrealistic or invalid, the process is pushed back to the reverse income statement and looped until the performance requirements and industry benchmarks can be met and the set of assumptions is plausible. If it’s not possible, the project should be killed.
In case of positive outcome on the previous step, key assumptions are carefully documented in the checklist to be validated for the project to succeed, and the plan of when and how these assumptions will be tested is created. The assumptions should be tested at project’s milestones when the company makes a decision whether to invest more, redirect or abandon the project based on the information gained so far.
Discovery-driven planning are commonly used by startups, but, unfortunately, are not adopted by established companies. Application of conventional methods to assess innovation within large corporations often comes together with untested hypotheses treated as unquestionable facts, linear trends in projections, upfront funding and little opportunities for maneuvers when new information is available. This may lead to project failures. Discovery-driven planning establishes learning culture in the organization and helps to uncover assumptions that may put project associated with high level of uncertainty in danger.
No one is perfect, project assessment methodologies are of no exception. Commonly used discounted cash flow model and its NPV metric are efficient in well-understood and predictable projects which can be assess based on the information from past experiences. However, this approach may underestimate the value of potential innovation associated with uncertainty. This is especially true for well-established corporations which tend to avoid risks and therefore may lose new market opportunities.
Discovery-driven planning, already adopted by startups, can become a game changer for large corporations offering the way to overcome many drawbacks of conventional project valuation.
Sources:
Warren E. Buffett, Chairman’s Letter to the Shareholders. Berkshire Hathaway, 2000.
Persuade Your Company to Change Before It’s Too Late: How to make the case when the evidence isn’t yet clear by Pontus M.A. Siren, Scott D. Anthony, and Utsav Bhatt. Harvard Business Review, January-February 2022.
Innovation Killers: How Financial Tools Destroy Your Capacity to Do New Things by Clayton M. Christensen, Stephen P. Kaufman, and Willy C. Shih. Harvard Business Review, January 2008.
The real power of real options by Keith Leslie and Max Michaels. McKinsey Quarterly, June, 1, 2001.
A Real-World Way to Manage Real Options by Tom Copeland and Peter Tufano. Harvard Business Review, March 2004.
Making Real Options Really Work by Alexander B. van Putten and Ian MacMillan. Harvard Business Review, December 2004.
Discovery-Driven Planning by Rita Gunther McGrath and Ian MacMillan. Harvard Business Review, July–August 1995.