Stockout Probability Model
Analyze inventory risk and stockout probability
Days of stock: 8 days
CV: 25.0% demand variability
Stockout probability is the likelihood that customer demand will exceed available inventory during a specific period (typically lead time). It quantifies the risk of being unable to fulfill orders.
A 10% stockout probability means there's a 1 in 10 chance that demand will exceed your inventory during lead time, resulting in unfulfilled orders.
The model assumes demand follows a normal (Gaussian) distribution, characterized by:
- Mean (μ): Average demand during lead time
- Std Dev (σ): Demand variability around the mean
where z = (I - μ) / σ
- Service Level: % of cycles without stockout
- Fill Rate: % of demand fulfilled from stock
- Z-Score: Standard deviations from mean
Demand Variability
Fluctuation in daily demand
Higher variability = higher stockout risk
Improve demand forecasting
Lead Time Variability
Supplier delivery uncertainty
Unreliable suppliers increase risk
Work with reliable suppliers
Inventory Level
Current stock on hand
Lower inventory = higher stockout risk
Maintain adequate safety stock
Safety Stock
Buffer against variability
More safety stock = lower risk
Calculate optimal safety stock
| Risk Level | Probability Range | Description | Recommended Action |
|---|---|---|---|
| Low | < 5% | Minimal risk; inventory adequate for most scenarios | Maintain current inventory policy |
| Medium | 5% - 15% | Moderate risk; stockout possible in high demand | Consider increasing safety stock |
| High | 15% - 30% | Significant risk; stockout likely during lead time | Urgently review inventory policy |
| Critical | > 30% | Severe risk; stockout highly probable | Expedite replenishment immediately |
- •Use historical data to accurately estimate demand mean and standard deviation
- •Account for seasonality - adjust parameters during peak demand periods
- •Monitor both service level and fill rate as complementary KPIs
- •Use what-if analysis to stress-test inventory policies
- •Differentiate service levels by product importance (ABC analysis)
- •Review and update parameters regularly (at least quarterly)
- ✗Underestimating demand variability (using point forecasts only)
- ✗Ignoring lead time variability from suppliers
- ✗Setting the same service level for all products
- ✗Not updating parameters when conditions change
- ✗Confusing service level with fill rate
- ✗Using outdated historical data for parameter estimation