Artificial Intelligence in Digital Twins for Adaptive Production Planning

: This study examines the role of artificial intelligence (AI) in digital twins for adaptive production planning under volatile manufacturing conditions. The purpose is to develop a conceptual approach that integrates AI into digital twin architectures to improve planning responsiveness and decision quality. The method combines a focused analytical review of recent literature, conceptual system design, and a hypothetical scenario-based validation logic. The proposed architecture integrates real-time data acquisition, system-state modelling, scenario simulation, predictive analytics, and decision support. The analysis shows that AI strengthens digital twins by enabling bottleneck prediction, disruption detection, scenario evaluation, and maintenance-aware planning adjustments. The paper concludes that integrating AI into digital twins supports a shift from static planning to adaptive, data-driven planning with improved responsiveness and system stability.