This study addresses the integrated optimization of production, Preventive Maintenance (PM), inspection, and quality control decisions in a multi-period and multi-product manufacturing system subject to two distinct types of stochastic failures. Type I failure represents a shift from an in-control to an out-of-control state, leading to the production of nonconforming items, which can only be identified through inspection. Type II failure is a complete system breakdown that halts production and requires corrective maintenance. The proposed model incorporates multiple levels of PM, each associated with different costs, durations, and age-reduction effects on the production system. To reflect real-world market variability, three demand scenarios are considered, representing high, medium, and low demand levels with different associated lost-order costs. The objective is to maximize the expected total profit by jointly determining production quantities, inspection schedules, PM levels, and inventory and sales decisions. The problem is formulated as a mixed-integer nonlinear programming model. Due to its complexity, a step-by-step solution procedure and a genetic algorithm are developed and compared with exact solutions obtained via GAMS for smaller instances. Computational results indicate that increasing the number of inspections up to a certain threshold improves profitability by reducing nonconforming output and failure-related losses, while the optimal PM level depends on the interaction between maintenance costs, production requirements, and demand conditions. The findings provide practical insights for managers in knowledge-intensive and high-mix manufacturing environments seeking to balance equipment reliability, product quality, and customer service under uncertain demand.