InventOR: A Prompt-Configured, No-Fine-Tuning LLM Workflow for Material-Level Inventory Control

We present InventOR, a prompt-configured large-language-model (LLM) workflow that generates material-level (r, Q) inventory policies from cutoff-bounded historical CSVs and inline parameter blocks through an enterprise LLM application, without project-specific model training or fine-tuning. Deterministic parsing and post-cutoff simulation evaluate each LLM artifact against an SAP-derived and an SAP-safety-lead-time-informed operations-research comparator on a common 346-pair cohort from a three-plant industrial dataset. All 365 eligible plant-material pairs yield scoreable, capacity-feasible artifacts after retry handling. Working-day simulation reports 77.52% demand-weighted aggregate fill and 97.94% mean material fill for the LLM-emitted arm. Runs 2 and 3 show repeated completion but policy-value variation. Run 1 lacks preserved deployed prompt and runtime metadata; the contribution is a transparent descriptive evaluation protocol with explicit boundaries and current-run provenance controls for prospective evaluation.