Now, let's delve into the Poisson distribution, which is similar to the binomial distribution but with a critical difference. According to Albright & Winston (2017), in the Poisson distribution, the random variable represents a count of the number of successes, not a fixed number of trials. Instead, it's a fixed interval of time or space for the number of successes to be recorded. This unique characteristic makes Poisson distributions a fundamental tool in many applied management science models. They find practical applications in probabilistic inventory, queueing (the study of waiting lines in a fixed period), and predicting the number of occurrences of an event in a fixed period.

can you add to this discussion and include reference?