In the Industry 4.0 era, preventive maintenance plays a crucial role in ensuring system reliability and minimizing downtime. Among others, the key reasons include integration of smart technologies, increased system complexity, cost reduction, enhanced productivity and efficiency, and regulatory compliance and safety. In this context, this paper evaluates a Deterministic and Stochastic Petri Net (DSPN) model of a generic system experiencing alternating failures and repairs while undergoing preventive maintenance at regular intervals. Unlike traditional models, the proposed approach incorporates time shifts, where each failure and subsequent repair reset the countdown to the next preventive maintenance. The DSPN framework captures the stochastic nature of failures and repairs alongside deterministic scheduling constraints. Performance evaluation focuses on system availability under two scenarios: a low availability system and a high availability system. The results provide insights into how time-shifted preventive maintenance strategies impact long-term system performance. This study contributes to the overall body of knowledge about optimizing maintenance policies by balancing preventive interventions and corrective actions, supporting enhanced operational efficiency in industrial systems.