Abstract: Inventory scheduling plays a critical role in modern supply chains, with its primary objective being to balance production and demand through appropriate inventory allocation. As market demand becomes increasingly uncertain and product attributes more diverse, traditional experience-based inventory scheduling methods often struggle to effectively integrate product characteristics with demand fluctuations. Moreover, their decision outcomes lack interpretability, making it difficult to trace the rationale behind inventory schedules and reducing stakeholders’ trust. The semantic understanding and knowledge-reasoning capabilities of pre-trained large language models, when combined with abundant real-world multi-source external data and multi-category demand data, offer opportunities to enhance existing LLM-based inventory scheduling methods. To capitalize on these opportunities, LLM-based inventory scheduling decision tasks have to face the following challenges: 1) how to effectively embed the inventory scheduling workflow into a multi-step reasoning process, yielding accurate and coherent reasoning trajectories; 2) how to balance the interpretability of allocation decisions with decision accuracy, avoiding a decline in interpretability caused by excessive focus on accuracy. To this end, we propose a novel multi-step reasoning framework for inventory scheduling based on large language models, termed InvLLM, with following primary contributions: the design of a self-calibrating multi-step reasoning method based on the inventory scheduling workflow and a multi-objective balanced optimization method for inventory scheduling decisions. Additionally, to address distortion in the reasoning fine-tuning phase, a “planning-token” supervised fine-tuning approach is proposed, combined with a generative reward model, achieving a balance between high accuracy and logical consistency. Experimental evaluations on a large-scale real-world dataset provided by JD.com demonstrate that, compared with existing inventory scheduling methods, the proposed InvLLM framework increases service rate by 3.24% and improves overall inventory evaluation scores by 1.55%, while reducing inventory rate by 1.33%, thereby validating its effectiveness and practical applicability.