Spare Parts Inventory Optimization
Probabilistic Stockpiling using Poisson & Service Level Models
Identifying the right balance between carrying costs and downtime costs is a critical engineering challenge. Overstocking wastes capital, while understocking leads to catastrophic operational delays. For parts with random, low-frequency failure rates, the Poisson Distribution provides the most accurate mathematical model for demand prediction.
Service Level confidence
The target probability that a spare will be available when a failure occurs. High-criticality assets typically target 95% to 99%.
Expected Demand ()
Calculated as: . This represents the mean number of failures during the replenishment window.
Interactive Inventory Optimizer
Input your asset details and replenishment lead time to determine the mathematically optimal number of spare parts needed to achieve your target availability.
Variable Inputs
Assumes failures are independent and random (Phase II - Useful Life).
Probability Density Function (Poisson)
The Cost of "Stock-out"
When calculating spares for critical path components (e.g., backbone routers or primary UPS modules), the cost of a stock-out can exceed the purchase price of the spare by orders of magnitude. The Poisson model helps justify the insurance cost of inventory by providing a quantifiable confidence interval.