Discrete event simulation provides a dynamic view of the value stream as well the ability to run “what-if” experiments to predict the effectiveness of changes. As such, discrete event simulation is a very powerful tool to maximize operational and investment efficiency and to find robust solutions that mitigate risk.

A discrete event simulation is built by connecting modeling elements (machines, conveyors, buffers, parts, people, etc) in the process flow logic. Next the performance of each element is described with variables such as cycle times, downtimes (MTBF or MCBF and MTTR), changeover times, conveyor min/max/floats, buffer sizes, shift times, etc. A Value Stream Map organizes most of the data required to build a discrete event simulation model.

Uncertainty in any performance variable can be captured by fitting a probability distribution around a mean value. For example, a cycle time might average 60 seconds but vary between 54 and 66 seconds. This variation is simulated by sampling the appropriate probability distribution with a random number stream. By using a different random number stream for each probability distribution, the events in the model are independent of each other…just like in the real world.

At a constant time interval, the simulation software “engine” assigns a random number to each element in the model. Next, all the elements try to advance one step. Then the elements all report back on their status: up, down, blocked, starved, etc. The simulation software notes the status of each element in the model and then repeats the process over and over until the experiment is complete. At the end of the experiment, the discrete event simulation software collates the results and generates the desired reports.

What-if experiments are easily performed by making changes to the input data set (typically an external Excel spreadsheet) and then re-running the model. Discrete event simulation is a versatile tool equally applicable to both manufacturing and business processes.