This article examines how digital twin simulation technology is revolutionizing inventory management across complex retail networks. Digital twins create continuously updated virtual replicas of entire retail ecosystems, ingesting real-time data from multiple sources to mirror physical operations with unprecedented fidelity. These sophisticated simulations leverage advanced machine learning models and physics-inspired engines to predict demand patterns and evaluate countless “what-if” scenarios. The article explores how deep learning predicts item-level demand while reinforcement learning agents discover optimal replenishment strategies that balance competing objectives. It investigates implementation outcomes across various retail contexts, documenting substantial improvements in safety stock requirements, on-shelf availability, and operational resilience. Furthermore, the article analyzes how digital twins transform supply chain management by creating data-driven laboratories that accelerate innovation cycles and enable risk-free experimentation. By capturing emergent behaviors in complex systems and facilitating cross-functional collaboration, digital twins enable retailers to transition from reactive to proactive inventory management, ultimately delivering competitive advantages through operational excellence and capital efficiency in increasingly volatile market environments
Keywords: digital twin simulation, inventory optimization, reinforcement learning, retail technology, supply chain resilience
