In this talk, we underline the interest in moving from the typical reliability binary world where components and systems are either up or down, to a multi-variate one, where the up state is decomposed into several performance levels. This is also called a performability view of the system. After discussing a very representative metric in this class, called resilience in the area, we briefly describe a Monte Carlo approach where instead of trying to reduce the variance of the estimators, we focus on reducing their time complexities. This view allows a first straightforward way of exploring resilience and related metrics. It also allows performing a sensitivity analysis of them with respect to the individual reliabilities of the components, without a significant overhead of the procedure that estimates the resilience metric alone. We limit the development to a static context. Numerical examples illustrate these procedures.