As the global trade competition becomes further intensified, Supply Chain Management (SCM) technology has become critical to maintaining competitive advantages for enterprises. Two Deep Reinforcement Learning (DRL) based methods are proposed to solve multi-period capacitated supply chain optimization problems under demand uncertainty (Peng et al., 2019). Intuition-based approaches are replaced by supply chain computerized solutions such as inventory management, warehousing, allocation, and replenishment. Hachaïchi et al. (2020) aim at building a reinforcement learning agent capable of placing optimal orders for the sake of constructing a replenishment plan for next period. Current supply chain efficiency management methods cannot effectively control the risk caused by inefficient supply chain management. In order to study improvement in supply chain efficiency management supported by machine learning and neural network technology, Han and Zhang (2020) built a supply chain risk management model based on learning and neural networks. However, the cost control ability of the model is poor. For this reason, Guan and Yu (2021) designed a supply chain resource distribution allocation model based on deep learning. Huang and Tan (2021) introduced the strategy research of supply chain management order based on a reinforcement learning algorithm. The supply chain order management process involves conducting questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm.