Distributions (which we will discuss later in this lesson) are a method used in ProcessModel to introduce randomness or variability. For example, you may have an activity where the processing time is not always constant due to people performing the activity, the type of entity being processed, or a variety of other reasons. To understand the importance of running replications in a simulation, let’s suppose you have 3 consecutive activities, each having a distribution in their time fields. Because the values for those times are chosen randomly each time an entity enters the processes, it is possible that the majority of the times chosen will be in the low range of possible values. That, of course, would give your process a lower than expected overall throughput time.
Now let’s say you delete one of the processes. You would expect the average cycle time to drop because there is one less activity. However, because of the randomness of the distributions, the two remaining activities could return many time values in the high range of the scale. In that case, the average cycle time would go up instead of down. Even though this is an extreme example, it demonstrates the nature of randomness. Replications allow you to run a simulation multiple times, generating different random values for each replication. Then you can see the range of possible values and the true mean. Seeing the randomness allows you to uncover potential problems and make accurate process decisions.
Open the last model used in Lesson 1, Lesson 1 – Solution 4. Click on the Simulation menu and select Options. Update the replications to 30, and simulate the model.