# Dynamic Inventory Optimization

Inventory optimization is a balancing act: too much inventory ties up cash, consumes warehouse space, and risks obsolescence; too little causes stockouts, lost sales, and damaged customer relationships. The traditional approach uses fixed reorder points and safety stock formulas that are set once and rarely updated. AI makes these parameters dynamic, adjusting them continuously as conditions change.

The Cost of Static Parameters

Most organizations set reorder points using a formula like: Reorder Point = Average Daily Demand × Lead Time + Safety Stock. Safety stock is calculated from a historical variability measure. These parameters are reviewed quarterly or annually — if at all.

The problem: demand and lead times are not static. Seasonal demand swings can be 3-5× between peak and trough. Supplier lead times fluctuate with their capacity utilization and global logistics conditions. A reorder point calculated in January using summer data will cause stockouts in winter.