Abstract
The digital transformation of supply chain operations demands manufacturing systems capable of autonomous decision-making in response to dynamic production fluctuations. However, existing inventory management approaches suffer from limited adaptability to plan variations and delayed response times, primarily due to the absence of tight coupling between inventory control and real-time production demand. To address these challenges, this study proposes AISDBM-Agent, an LLM-enabled autonomous decision-making prototype system for intelligent supply chain inventory optimization. The framework is structured into four functional modules: the perception module receives and translates external multi-source information; the memory module integrates, categorizes, and stores the processed information to mitigate the risks arising from outdated knowledge or contextual inconsistencies; the strategy module utilizes the processed input from the perception module and the knowledge provided by the memory module to perform reasoning and decision-making, automatically generating optimal procurement decisions, employing the Chain of Thought (CoT) method to enhance the transparency and interpretability of these decisions; the execution module interacts with external systems and obtains execution feedback to ensure information timeliness, thereby resolving the issue of delayed response in inventory management. A case study on steel scrap inventory management is conducted to validate the effectiveness of the proposed framework. The results demonstrate that AISDBM-Agent significantly enhances the system’s ability to respond to fluctuations in demand, enabling the smooth management of stock levels.