Discrete event simulation: Optimize workflow with a virtual process model

Discrete event simulation simulates the flow of materials, components, and finished products through a production facility to analyze bottlenecks and explore ways to optimize production in a risk-free virtual environment.

A discrete event simulation models a process where actions or procedures along the chain of events change the system in only one degree. That could be, for instance, a conveyor belt junction sending an object one way or another or a robot applying a screw or bolt where there wasn’t one before.

Discrete event simulation assumes that there is no change in state between a departure event and the following arrival event, so it models the behavior or process of a workflow in a compressed time and form for fast and accurate analysis.

Those analyses might predict performance results, discovering the variables resulting from the interaction between events or statistical modeling.

Every position or workstation along the workflow of a factory floor can be an event where the system undergoes a state change.

That’s not to say that the space required for a manufacturing production line is never considered in a discrete event simulation. In some cases, the state change might be in (or include) the transport system itself—for example, where it turns to fit adequately on the factory floor.

Manufacturing factory floor processes are an everyday use of discrete event simulation because they contain many relatively simple events—or can be broken into relatively simple parts with little or no state change between events.

However, discrete event simulation applies to many other areas. One example is queuing processes when customers interface with your company on a technical support, sales call, or in-person.

Discrete event simulation is also popular in medicine. Health care systems are adapted individually because they often contain complex steps and interactions between different providers and organizations, all with multiple variables that are hard to predict.

Health care interventions can use discrete event simulation to introduce different behaviors and decisions, ultimately identifying the best way forward when a patient presents with symptoms.