Poisson in the Supply Chain: Turning Random Demand into Smart Inventory

In this episode, we demystify Poisson distributions and show how they model random events with a steady average rate—like daily demand, arrivals, or failures. Using real-world supply chain examples (coats sold per day, reorder points with lead times, staffing, machine maintenance), we explain how to estimate lambda, compute probabilities, and set stocking levels. We also cover limitations (independence, seasonality) and how to layer in seasonal patterns and expert judgment, plus how to handle black-swan events with data-driven tools. A practical, accessible tour of the math behind everyday operations.

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