Calculate the buffer inventory that protects you from demand spikes and late deliveries — and see the reorder point it produces.
Safety stock = (max daily usage × max lead time) − (average daily usage × average lead time)
Busiest realistic day
Typical day, from sales history
Worst delivery you have seen
Typical order-to-delivery time
Safety Stock
160 units
Buffer against spikes and delays
Resulting reorder point
300 units
(20 × 7 lead-time demand) + 160 safety stock
Interpretation: Hold 160 units as a buffer. If demand spikes to 30 units/day while a delivery runs 10 days, this buffer covers the gap that your average-based planning (20 units/day, 7-day lead time) would miss.
Safety Stock = (Max daily usage × Max lead time) − (Avg daily usage × Avg lead time)
Your worst realistic case: the busiest sales day you actually see, combined with the slowest delivery a supplier has actually made. Use real history, not hypothetical disasters.
What your reorder point already plans for. Safety stock is exactly the gap between the two scenarios — the demand your average-based plan cannot see.
| Max daily usage × max lead time | 30 × 10 = 300 units |
| Avg daily usage × avg lead time | 20 × 7 = 140 units |
| Safety stock | 300 − 140 = 160 units |
| Reorder point (140 + 160) | 300 units |
Go deeper with the safety stock guide (including the Z-score method), find your trigger level with the reorder point calculator, and size your orders with the EOQ calculator. In StockZip, low-stock alerts fire automatically when stock crosses the level you set — free for 100 items.
The formula, the Z-score method, how much is too much, and which items need a buffer.
The most practical formula for small businesses is: Safety Stock = (maximum daily usage × maximum lead time) − (average daily usage × average lead time). For example, if you normally sell 20 units a day with a 7-day lead time, but your worst realistic day is 30 units and your worst delivery took 10 days: (30 × 10) − (20 × 7) = 300 − 140 = 160 units of safety stock. It covers the gap between planning on averages and living through worst cases.