It's GIGO time! Let's consider an queueing system with Exp( ) interarrivals and Exp( ) FIFO services at a single server. You may recall from some class (either this one or stochastic processes) that the steady-state expected cycle time (i. e., the time that the customer is in the system, including wait + service) is . If you were to try this out in Arena, let's say with interarrivals and services (note the notation change between my usual "Exp" and Arena's ), then we'd get . Go ahead, see for yourself in Arena, but make sure that you run the system for 100,000 or so customers so that you can be sure that you're in steady-state! Finally, here's the GIGO question, which will show what can happen when you mismodel a component of your process: What is the (approximate) steady-state expected cycle time if you have i. i.d. interarrivals instead of interarrivals? Note that both interarrival distributions have the same mean (10), but that doesn't necessarily imply that they'll have the same expected cycle times. Hint: You may want to use Arena as described above.