Spare Parts Calculator
Optimize spare parts inventory using Poisson demand modeling and MTBF-based failure forecasting
Spare Parts Calculator
Guide to Spare Parts Optimization
Poisson Demand Model
When failure events are rare and independent (constant failure rate), spare parts demand follows a Poisson distribution. The key input is the expected number of failures (λ = operating time / MTBF × number of systems).
P(X = k) = (e^(-λ) × λ^k) / k!λ = (T / MTBF) × N_systemsWhy Confidence Level Matters
- 80%: Budget-conscious, acceptable for non-critical parts with short lead times
- 90%: Standard stocking level for general industrial parts
- 95%: Recommended for critical equipment with significant downtime costs
- 99%: Safety-critical applications or very long lead time parts
FAQ
When is the Poisson model appropriate?
The Poisson model works well when failures are independent, occur at a constant rate (exponential time between failures), and the expected number per period is relatively small. For wear-out parts with increasing failure rates (Weibull β > 1), consider adjusting your MTBF to reflect the planning horizon.
How do I balance stocking cost vs. stockout risk?
Compare the cost of carrying an extra spare (unit cost × carrying rate × time) against the expected cost of a stockout (downtime cost per event × probability of need). Stock more when downtime costs are high relative to part cost.