Technical Explanation: Stochastic Inventory Control
In supply chain engineering, inventory buffers exist to absorb two distinct types of variance: demand volatility (how much customers buy) and supply lead time volatility (how long replenishment takes).
Assuming both demand and lead time follow independent normal distributions, the total variance during lead time equals the sum of the variances. Multiplying this pooled standard deviation by the normal distribution Z-factor yields the safety stock required to satisfy the chosen cycle service level.
How to Use This Calculator
- Average Demand (D): Enter expected units consumed or sold per period (day/week/month).
- Demand Std Dev (σ_D): Quantify the periodic demand fluctuations.
- Average Lead Time (L): Input supplier transit and processing duration in matching periods.
- Lead Time Std Dev (σ_L): Quantify delivery unreliability or variance.
- Service Level (%): Select target order fulfillment probability to determine the Z-score.
The Exponential Cost of Higher Service Levels
Because the standard normal curve flattens at the extremes, increasing service levels from 95% (Z ≈ 1.645) to 99.9% (Z ≈ 3.09) almost doubles the necessary buffer stock. Engineering teams must strike an optimal balance between stockout costs and working capital holding costs.