Supply chain management is crucial for operational efficiency amid globalization and technological advances, but faces challenges like demand uncertainty and stock imbalances from poor inventory management. This study aims to analyze inventory management strategies for optimizing supply chain performance. It uses a qualitative Systematic Literature Review (SLR) method following PRISMA guidelines. The population comprises articles from databases like Scopus, Google Scholar, and ScienceDirect; the sample includes 30 selected journal articles based on inclusion criteria such as relevance and full-text availability. Instruments involve keyword searches (e.g., "inventory management," "supply chain optimization"); data analysis uses qualitative descriptive techniques, categorizing strategies and mapping tables. Results reveal five strategy categories: inventory control (EOQ, safety stock; 8 articles), demand forecasting (AI-based; 7 articles), digital systems (blockchain, IoT; 4 articles), optimization models (5 articles), and collaborative strategies (JIT, VMI; 6 articles), predominantly from Q1/Q2 Scopus journals. Effective strategies enhance efficiency and cut costs. In conclusion, integrating these strategies, especially with technology, boosts supply chain adaptability; future research should explore sector-specific applications
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