This paper proposes a method to make future failure predictions from input data on population age distribution and failure rates, using a Monte Carlo approach. In contrast to many methods used today, the method in this paper is designed to address multiple properties and assumptions simultaneously, which makes the task complicated. For example, the component population is allowed to be divided into both age and different types. The time-dependent failure rates are defined separately for each individual type, can consist of a combination of multiple different failure rates for separate modes, and can be of practically any shape. Furthermore, a volatility measure for the failure rates is introduced and used to model the uncertainties in failure rate estimates. The method handles investment and reinvestment scenarios as well as different restoration models, such as replacing a failed component with a new component of a different type. As a part of the project, a stand-alone software tool was developed and presented in the paper. In the included case study, the method and the tool are shown to be useful when investigating reinvestment strategies to renew the population and decrease the expected number of future failures. The paper gives the reader useful information and understanding on how the problem of predicting the reliability of the future power system can be addressed and solved.