Modern availability and/or reliability studies of industrial systems have to take into account many considerations related to complexity (different abstraction levels; vast array of units, components, etc); wide range of failure modes; arbitrary failure and repair distributions; functional and/or technical dependencies among components; Data not available, etc. Taking into account these considerations, the opportunity to carry out system availability assessments through analytical models, will be many times very restrictive. 
A general approach to this problem is based in Monte Carlo (stochastic) simulation. The simulation of the system’s life process will be carried out in the computer, and estimates will be made for the desired measures of performance. The simulation will be then treated as a series of real experiments, and statistical inference will then be used to estimate confidence intervals for the performance metrics. In this paper we will use the continuous time simulation technique to model and assess the availability and reliability of a system.