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For more than a decade, consultants and academics have been touting real options valuation (ROV) as a means of improving the decision making that goes into a project. To date, however, ROV has not been widely adopted as a planning tool. Many project managers worry that the esoteric Black-Scholes equations frequently used to evaluate real options would require the addition of expensive software and a specially trained finance expert to the project team.
But Black-Scholes is not the only valuation tool available. The familiar decision-tree framework is well suited to many of the contingencies that arise over the course of a project. When used as a strategic planning tool, decision analysis can help managers address issues such as how to allocate resources to ensure that the project meets specific deadlines, when to scale up or delay investments, and when to exit a project.
Much like a stock option, which gives the holder the right to purchase stock at a future date or at a set price, a real option gives managers a set of choices about capital investment that can be made as business conditions evolve. Think of it as a road map that optimizes decision making by enabling you to take multiple contingencies into account, plan your responses to them as they unfold, and phase your investments accordingly. When compared to net present value (NPV), the traditional formula for analyzing financial decisions, ROV has obvious advantages.
The problems with NPV
NPV assumes conditions of low uncertainty: The market conditions are known, the costs to completion of the project are predictable, the technologies involved are reliable, and the odds of winning any necessary regulatory approval are favorable. Whatever uncertainty exists is not enough for managers to contemplate changing the strategic plan in response to any of the outcomes. Opportunities are evaluated based on current information, and the NPV calculation of the projected cash flow of the investment under consideration results in one of two choices: go or no-go.
| By assigning a quantifiable value to uncertainty, ROV enables decision makers to gauge and react to risk over time. |
| — Fabian D'Souza |
Most business decisions, however, are not of the now-or-never variety. Rather, their strategic plans change in accordance with the magnitude of the uncertainty. By assigning a quantifiable value to uncertainty, ROV enables decision makers to gauge and react to risk over time—quite a boon in a world besieged by constant price shifts, fluctuating interest rates, fickle consumer tastes, and emerging technologies.
Is the nature of the project you're managing such that you can alter your investment or resource-allocation decisions as the uncertainty is revealed or resolved? If you can, then ROV can play a valuable role in developing a strategic map to guide you through the decision-making process. A second question helps you determine the appropriateness of the decision-tree approach to ROV: Is the uncertainty occasional or ongoing? In some arenas—energy and currency markets, for example—volatility is high and the future unfolds as an almost infinite number of possible outcomes. In most service industries and R&D-intensive industries, however, the uncertainties related to the management of a project tend to be milestone-driven. They arise as a result of a series of discrete choices presented under a limited number of scenarios; the decision-tree framework is best suited to such uncertainties.
Deciding on in-house
A simple decision such as whether to develop a new technology in-house or acquire it from an outside party illustrates the utility of the decision-tree framework. In-house development requires three years and leads to three possible outcomes. In two of these outcomes, the firm expects to create significant value. But there's also a 25 percent chance that the in-house development would fail; obviously, this outcome would have no payoff. Figure 1 shows this decision using a decision-tree framework. The probabilities of the three outcomes are based on a combination of managers' experience and judgment.
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After calculating the value of each alternative, the manager is able to pick the highest-valued alternative. For the acquisition alternative, subtracting the $10 million cost of acquisition from the $20 million payoff yields a value of $10 million. For each of the three outcomes in the in-house development alternative, you have to subtract the cost from the payoff and then multiply the result by the probability of success. Thus, for the most successful of the three outcomes, the expected value would be:
($25 million - $7 million) ¥ .35 = $6.3 million
An expected value calculation—the weighted average of the outcomes, with the probabilities used as weights—is used to blend the value of the three outcomes into a single number. A 10 percent cost of capital is used as the discount rate. Performing this calculation reveals the value of the in-house alternative to be $7.14 million, or less than 75 percent of the value of acquiring the technology from outside.
| Make sure that you involve both business managers and technical personnel in creating the decision-tree diagrams. |
| — Fabian D'Souza |
The decision-tree framework is useful not only for "organizing multistage projects that are subject to uncertainty," it can also help you redesign projects "for even higher value," writes management consultant Martha Amram in her new book, Value Sweep. Let's say that a manufacturing company is considering a $20 million investment to upgrade its existing plant so that it can introduce a new product line. This investment requires an additional $16 million in market research. If the research yields positive results, the company will proceed to launch the new product line. That launch is valued at $94 million (based on a discounted cash flow calculation). Both the infrastructure investment and the market acceptance have uncertain outcomes; those probabilities and a decision-tree diagram of the decision are shown in the top half of Figure 2.
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Doing the calculations yields a negative NPV of $3.3 million for the project according to this initial design, which means that it's not worth doing. Another option is to redesign the project by running a smaller pilot market test while the infrastructure is being developed. Results from this pilot will help to resolve some of the market risk before the next decision point. If the infrastructure is successfully deployed, and the subsequent, comprehensive market research is successful, the project can move to product launch, saving time and money over the initial project design. The bottom half of Figure 2 shows a decision-tree diagram of the project after it has been redesigned along these lines.
The redesign enables the value of the launch and revised marketing plan to be folded back into the initial investment decision. The result, when you do the calculations, is an increase in the value of the project from a negative $3.3 million to a positive $5.5 million. In addition, with the redesign there is only a 37 percent chance that the project will be terminated, whereas the original design had a 65 percent chance of being scrapped.
Redesigning the project enables managers to learn more about the market at an earlier stage, thereby creating an opportunity to modify the marketing plan and increase the chance of market success. Under the revised plan, the project value increases because the follow-on investment is determined after some of the uncertainty has been resolved. The beauty of this decision-tree approach to ROV, therefore, is that it takes advantage of risk and uncertainty by tying expenditures more closely to the maturation of the opportunity. Breaking up the one market-research investment bet into two smaller investment bets enables the project manager to use options to improve his allocation of resources to the project as new information becomes available.
Process concerns
Decision analysis is not without its implementation problems. For example, it can be difficult to get the relevant scientific and technical personnel to agree on the probabilities of failure or success for each stage of the project. In particular, managers who are invested in the success of the project often believe that the probability of success is close to 100 percent. Moreover, when a project is up and running, teams are frequently unwilling to discuss potential exit scenarios. This problem is particularly acute when managers have incentives to meet deadlines and milestones at any cost. The result is that midstream discussions about project closure are often biased.
To avoid these difficulties, make sure that you involve both business managers and technical personnel in creating the decision-tree diagrams. This will improve the buy-in that the project receives from both groups and will also make it easier to discuss plans for exiting the project if the outcomes are unsuccessful. Make sure that the two groups' incentives are aligned so that they are jointly accountable for the profitability of the project and the overall ROI of the portfolio of projects under way in your group. For instance, by giving rewards to project members for killing unsuccessful projects sooner rather than later, you increase the likelihood that even team members who have a strong personal investment in a particular project will agree to pull the plug if it's failing.
With these structural fixes in place, you're much better positioned to reap the chief benefit of the decision-tree approach to ROV: the improved coordination of spending with the potential outcomes of active learning.