Static reorder points cause two problems: you either run out of stock because demand spiked unexpectedly, or you over-order because the threshold was set too conservatively. Both cost money.

The Problem

Traditional inventory alerts trigger at a fixed quantity threshold regardless of demand patterns. A product selling 10 units per day and a product selling 100 units per day might both alert at 50 units remaining - which is a five-day supply for one and a half-day supply for the other.

The AI Approach

An AI model can analyze historical sales velocity, seasonal patterns, lead times, and current demand trends to calculate dynamic reorder points. Alerts trigger based on predicted days-of-supply rather than absolute quantity, accounting for upcoming demand changes.

Three-Step Build

Step 1 - Data collection. Pull historical sales data, current inventory levels, supplier lead times, and any known demand events (promotions, seasonal peaks) from your inventory management system.

Step 2 - Demand forecasting. Use a time-series model or LLM to forecast near-term demand for each SKU. Calculate days-of-supply at current inventory levels using the forecast.

Step 3 - Smart alerting. Generate alerts when predicted days-of-supply falls below the lead time plus a safety buffer. Include recommended order quantity based on forecasted demand through the next replenishment cycle.

Where It Breaks

New products without sales history have no baseline for forecasting. Sudden demand shifts from viral events or competitor stockouts are unpredictable. Supplier lead time variability adds uncertainty that static safety stock calculations handle poorly.

The Production Path

Start with your top 20% of SKUs by revenue. Compare AI-recommended reorder points against actual stockout and overstock events for a month before switching from advisory to automated ordering. Integrate with your purchasing system for one-click reorder approval.