Informally known as “S-Curves”, these lines (which represent all the values that a variable or a result can adopt for each level of inverse cumulative probability) are particularly useful to compare; different projects, different technical approaches or different locations of the same project.
As we descend on the “Y” axis, the probability of occurrence is higher until we reach 100% of the cases, which will have a value less than or equal to the intersection on the “X” axis. The greater the part of a curve, to the right of another curve, the better the performance of the alternative. A curve that in all its length appears to the right of the others represents the absolutely winning alternative. Perhaps, providing a trivial decision or perhaps, the need to work harder, in a more innovative way, exploring bolder options until a certain level of risk is exposed.
That is precisely what happens when an alternative is better than its closest competitor, in certain parts of the curve but worse in others. Now, the decision is not so simple and the insights will drive further analysis that will bring even more and better insights.
Why does one alternative perform better than another in one part of the curve and worse in the other part? Is the outcome consistent with our understanding of the project or does it “surprise” us? What level of risk are we willing to accept in exchange for the upside offered by one of the alternatives? Will it be possible to design a new alternative that captures the best of the other alternatives originally designed?
The “S-curves” will not always give the decision executives the final answer to the challenges they face. But they are an excellent starting point to complement with other decision criteria such as the fit with the company´s strategy, operational or logistic synergies or the total desired level of risk and reward.