This article investigates the synergistic integration of Large Language Models (LLMs) and Digital Twins (DTs) to enhance production logistics and assembly operations in manufacturing. Grounded in the principles of Industry 5.0, we adopt a scenario-based qualitative methodology supported by a structured evaluation framework to explore how this fusion can drive new capabilities and value across industrial systems. We examine macro-level application areas, such as workforce training, human-machine interfaces, and quality assurance, and micro-level areas including adaptive scheduling and intra logistics coordination. The analysis highlights key opportunities, including semantic interpretability, real-time decision support, and context-aware automation, while also identifying critical challenges such as hallucinated outputs, cybersecurity risks, and computational constraints. To support comparative insights, we synthesize the capabilities and value propositions of LLMs across application areas in a structured summary table. By articulating the transformative potential and limitations of LLM-DT convergence, the article offers strategic guidance for phased implementation and emphasizes the need for hybrid, explainable, and human-centered AI systems in manufacturing.