Manufacturing industries are increasingly focused on enhancing energy efficiency while maintaining high levels of production throughput and product quality. However, most existing energy-saving control (EC) methods overlook the influence of production quality on overall energy performance. To address this challenge, this paper proposes a dynamic EC method for multistage manufacturing systems with product quality scrap. The method utilizes a Markov decision process (MDP) framework to dynamically control the operational states of all machines based on real-time system conditions. Specifically, for two-stage manufacturing systems, the dynamic EC problem is formulated as an MDP, and the optimal EC policy is obtained by a dynamic programming algorithm. For multistage manufacturing systems, to address the curse of dimensionality, an aggregation procedure is proposed to approximate the optimal EC policy for each machine based on the results of two-stage manufacturing systems. Finally, numerical experiments are performed to demonstrate the effectiveness of the proposed dynamic EC method. For a five-stage manufacturing system, the proposed dynamic EC policy achieves a 13.55% reduction in energy consumption costs and a 3.02% improvement in system throughput compared to the baseline. Extensive case studies demonstrate that the dynamic EC policy consistently outperforms three well-studied methods: the station-level EC policy, the upstream-buffer EC policy, and the energy saving opportunity window policy. Moreover, the results confirm the effectiveness of the proposed method in capturing the influence of product quality scrap on the system energy efficiency. This study presents a sensor-integrated methodology for EC, contributing to the advancement of smart manufacturing practices in alignment with Industry 4.0 initiatives.