NPV is then recalculated, and the sensitivity of the NPV based on the change in assumptions is determined. Depending on one’s confidence in the assumptions, one can determine how potentially variable its projections of risk can be.
Scenario analysis takes sensitivity analysis a step further. Rather than just looking at the sensitivity of its assumptions to the variables, scenario analysis also looks at the probability distribution of the variables.
Like sensitivity analysis, scenario analysis starts with the construction of a base-case scenario. From there, other scenarios are considered, known as the “best-case scenario” and the “worst-case scenario.” Probabilities are assigned to the scenarios and computed to arrive at an expected value. Given its simplicity, scenario analysis is one of the most frequently used risk-analysis techniques.
Monte Carlo simulations spit out numerous calculations of expected values given a number of constraints. Constraints are added and the system generates random variables of inputs. From there, metrics like NPV are calculated. Rather than generating just a few iterations, the simulation repeats the process many times. From the numerous results, the expected value is then calculated.
In short, almost every major decision in an organization – to drive revenue, control costs or mitigate risk, for instance – can be infused with risk analytics. The can transform a company’s risk-based decisions by infusing both historical and future risk information into the decision-making process.
John Bugalla is a principal with ermINSIGHTS and Kristina Narvaez is president and CEO of ERM Strategies LLC.