Redesigned reorder-point logic across three SKU categories using a demand variability analysis — cutting excess inventory by 18% without moving the stockout rate.
Warehouse carrying costs were climbing because reorder points were set from average demand rather than true demand variability. The same static rule governed every SKU — overstocking slow movers while leaving fast movers exposed.
The result was a system that looked balanced on paper but was quietly wrong in both directions at once.
Pulled 12 months of sales and lead-time data, then clustered SKUs by coefficient of variation (CoV) in demand. Each volatility segment got its own reorder point and safety stock calculation, replacing the single blanket rule.
The new model was tested against three months of live order data before rollout, confirming it held up against real ordering patterns rather than just historical averages.
Excess inventory across the three SKU categories dropped 18%, translating to an estimated $42,000 in freed-up working capital. Stockout rate stayed at 1.8% — essentially flat from baseline — confirming the leaner inventory didn't come at the cost of service.