Many systems are affected by different random factors and stochastic processes, significantly complicating their reliability analysis. In general, the performance of complicated systems may gradually, suddenly, or continuously be downgraded over times from perfect functioning to breakdown states or may be affected by unexpected shocks. In the literature, analytic reliability assessment examined for especial cases is restricted to applying the Exponential, Gamma, compound Poisson, and Wiener degradation processes. Consideration of the effect of non-fatal soft shock makes such assessment more challenging which has remained a research gap for general degraded stochastic processes. Through the current article, for preventing complexity of analytic calculations, we have focused on applying a simulating approach for generalization. The proposed model embeds both the stochastic degradation process as well randomly occurred shocks for two states, multi-state, and continuous degradation. Here, the user can arbitrarily set the time to failure distribution, stochastic degradation, and time to occurrence shock density function as well its severity. In order to present the validity and applicability, two case studies in a sugar plant alongside an example derived from the literature are examined. In the first case study, the simulation overestimated the system reliability by less than 5%. Also, the comparison revealed no significant difference between the analytic and the simulation result in an example taken from an article. Finally, the reliability of a complicated crystallizer system embedding both degradation and soft shock occurrence was examined in a threecomponent standby system.