This PhD research focuses on inventory control within high-tech manufacturing supply chains. The research investigates both the manufacturing process and the spare parts network, addressing the complexities and stochastic nature of the supply chain. Various solution techniques will be explored, including the enhancement of traditional methods, development of new heuristics, and application of diverse optimization techniques. A key focus will be on the potential of Deep Reinforcement Learning (DRL) as a solution methodology. DRL, an integration of neural networks with reinforcement learning, offers a generic approach to solving complex decision-making problems under uncertainty. This research aims to benchmark DRL against current practices, given its promising application in inventory management, to provide innovative solutions for supply chain challenges in high-tech manufacturing industry.