Pharma inventory and out-of-stock risk prediction

Stock-outs start weeks before the shelf is empty

A stock-out in life sciences can mean a patient waits for therapy or a production line stops for a missing material. Two common causes are records that show stock that is not really there, and safety stock that was set once and never revisited.

Your planners watch thousands of products, sites and suppliers. Demand shifts, lead times slip and a supplier delivery arrives late, and the signal often reaches the planner after it is too late to act. Spreadsheets and fixed reorder rules cannot keep up with that volume; planners need to know which few products need attention today.

How out-of-stock risk prediction works

Step 1: Bring the supply data together

Start with demand history, inventory positions, open supplier and production orders, reorder points and delivery history for each product and location.

Step 2: Forecast demand and lead times

H2O Driverless AI builds forecasting and regression models for demand, consumption rate and supplier lead time, adding calendar effects, order patterns and supplier history through automatic feature engineering.

Step 3: Score out-of-stock risk and flag gaps

A classification model scores each product and location for stock-out risk, and anomaly detection flags inventory records that do not match movement, a common sign of stock that exists only on paper.

Step 4: Suggest actions for planners

Planners receive configurable alerts for projected late orders and supplier deliveries, and suggested changes to reorder points and safety stock. Each suggestion carries reason codes, and the planner approves or rejects it.

Reorder points, safety stock and supplier risk

The same forecasting and risk models cover both sides of supply planning: current stock status and replenishment, where safety stock matches real demand instead of a fixed rule, and lead time, supplier delivery risk and on-time-in-full tracking for open orders.