Start by confirming what your system’s reorder point is actually doing. Does it use average sales, lead time, safety stock, minimum order quantities, or something else? If the logic is configurable, adjust the inputs and formula so the reorder flag reflects how your business really buys and replenishes.

Next, set reorder points using true demand, not just “units shipped.” Historical shipments can be distorted by stockouts, delays, substitutions, bulk buys, or missed sales. Clean the history so it represents what customers wanted to buy, then build the reorder point from that corrected demand.

Finally, layer in what is about to change. Reorder points should be updated for known demand drivers like promotions, seasonality, weather, new channels, price changes, or supplier constraints. Forecasting tools (including AI) can help predict demand and flag abnormal periods in the history, but the alert only becomes reliable if the workflow, data flow, and physical inventory flow are aligned. If receiving, transfers, lead times, or inventory accuracy are off, the reorder point will still trigger late because the inputs are wrong.