Introduction
In spare parts management, certain components are used very rarely, yet their availability is critical. The Poisson model is ideal for forecasting such rare events, providing a statistically grounded approach to service-level planning and inventory control.
Understanding the Poisson Model
The Poisson model predicts the number of events (demand occurrences) in a fixed period based on historical averages. The probability of observing kk events is given by:
Applications in Spare Parts
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Service level optimization: Determines the probability of fulfilling demand without stockouts.
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Reorder point calculation: Defines inventory thresholds to trigger replenishment.
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Inventory planning for critical parts: Ideal for emergency or backup components with unpredictable usage.
Advantages
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Simple, yet powerful for low-frequency demand items.
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Provides quantifiable risk of stockouts, supporting service-level agreements.
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Scales easily across a large portfolio of rare-use components.
Implementation Tips
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Use a rolling window of historical data to estimate λ\lambda.
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Adjust for seasonal variations if spare parts usage fluctuates cyclically.
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Combine with safety stock calculations for optimal replenishment.
