Garg's Principle: A Predictive Framework for Safety Stock Optimization and Over-Stock Reduction Across Forecast Horizons

Traditional safety stock methods implemented in enterprise resource planning systems rely on static, service-level-based formulas derived from historical demand variability, enforcing uniform inventory buffers across planning periods without explicitly evaluating future time-phased feasibility. This results in excess inventory accumulation when future feasibility is assured, while stock-outs persist during demand regime changes, leading to frequent manual planner overrides and reduced trust in system-generated plans. Garg's Principle introduces a deterministic, horizon-based safety stock framework that reframes safety stock as a conditional feasibility control rather than a static inventory parameter. The framework evaluates projected inventory stress across the full planning horizon using Dynamic Net Availability and introduces inventory protection only when unavoidable shortages are mathematically projected. Safety stock is injected as an averaged supply adjustment across periods preceding the projected stock-out lead-time window through a pre-shortage injection rule, avoiding blanket buffering. Artificial intelligence is deliberately limited to demand verification, producing bounded correction factors applied to forecasts prior to planning calculations, while all safety stock logic remains fully deterministic, explainable, and auditable within existing enterprise systems.