The latest version 2.1:This revision adds a brief discussion examining the behavioral effects of guardrail-centric fine-tuning on executive-level reporting. Using a controlled comparison in which all data, calculations, and decision logic were held constant, the new section shows that constraining model reasoning influences not only decision correctness but also how risks and trade-offs are communicated. The addition demonstrates that guardrail-centric fine-tuning systematically moderates explanatory framing—preserving full risk disclosure while avoiding disproportionate or alarmist language—thereby improving interpretability and governance without altering underlying outcomes.This paper introduces Guardrail-Centric Fine-Tuning, a novel paradigm for safely deploying large language models (LLMs) in deterministic, constraint-heavy operational decision systems, using inventory replenishment in a distribution environment as a practical testbed. Rather than fine-tuning models on item-specific outcomes—which often leads to brittle generalization, loss of reasoning capability, and silent failures—the approach aligns a quantized Qwen2.5-Coder-14B model to approximately fifty generalized, domain-agnostic behavioral guardrails that enforce strict reasoning boundaries, constraint hierarchies, and audit requirements. Paired with a deterministic Python enforcement layer handling all numerical calculations and hard rules, this hybrid architecture separates probabilistic reasoning from exact execution, yielding stable, explainable, and auditable ordering recommendations across diverse product catalogs. Empirical results demonstrate enhanced robustness, preservation of general capabilities, and elimination of common fine-tuning pitfalls (such as trigger-target confusion or degraded states), underscoring that constraining how models reason—rather than dictating what outcomes they produce—is a more reliable strategy for enterprise-grade AI deployment in high-stakes domains like supply chain management.