The Poisson Distribution is a statistical probability distribution that is commonly used in supply chain management to model the occurrence of rare events such as machine breakdowns or defects in production processes. The Poisson Distribution is characterized by its discrete nature and is defined by a single parameter, the mean. In supply chain management, the Poisson Distribution is often used to model the frequency of rare events, which can be used to calculate the likelihood of disruptions and plan for contingency measures. For example, if a manufacturing process experiences machine breakdowns that follow a Poisson Distribution with a mean of 2 breakdowns per week, then a company might plan for a contingency measure such as a spare machine to ensure that production can continue without interruption. Similarly, if a transportation network experiences traffic accidents that follow a Poisson Distribution with a mean of 1 accident per month, then a company might choose to build in a buffer of safety lead time to ensure that deliveries are not delayed. The Poisson Distribution is an important tool in supply chain management for modeling rare events and managing risk, and can be used to make informed decisions about contingency planning and risk management.
Random arrivals - orders, trucks, failures - follow the Poisson distribution when events are independent.
Set the rate; watch counts per interval scatter around the mean with variance equal to the mean.
Plan buffers for the spread, not the average: variance = mean is the Poisson signature.
