## What is Flow Simulation?

Flow simulation, or Discrete Event Simulation (DES), is a powerful technique used to analyze the flow within a system. This flow could represent various scenarios such as a manufacturing process in a factory, logistics within a warehouse, or even the movement of people in a hospital.

Flow simulation serves multiple purposes. It’s often used to calculate or evaluate specific parameters, but it’s also effective for gaining insights into a system or for project guidance.

###### CALCULATIONS IN DYNAMIC SYSTEMS

One of the unique aspects of flow simulation is its ability to perform calculations on dynamic systems. For instance, it can answer questions like:

- What will the throughput/h be in the new production system?
- How many storage spaces are required for each variant?
- How many nurses are needed per shift?

###### FLOW SIMULATION MODEL

To perform these calculations, a Flow Simulation model is built. This model is essentially a simplified digital replica of reality. The Flow Simulation model is usually limited to covering only the area needed to answer the current questions.

###### THE FLOW SIMULATION MODEL USUALLY CONSISTS OF:

- Static objects that usually represent some form of equipment or process.
- Moving objects that usually symbolize a product, resource, or work order.
- Flows that outline how the moving objects can move between the different static objects.
- Logic that dictates exactly how the moving objects move in different situations or how equipment or resources make decisions.

###### DYNAMIC INPUT DATA AND DISTRIBUTIONS

Input data from either an existing or future reality is fed into the Flow Simulation model. This input data is often dynamic – for example a manual cycle time in an assembly station or the time interval between two patients arriving at a hospital. The dynamic input data is not described with constant values but with the help of a distribution (e.g., A normal distribution with an average value and a standard deviation).

###### RANDOM REPLICATIONS

When the simulation model runs, values are randomly generated from the distribution which creates a potential scenario and gives a result for each unique run (often called replication or observation). To get a reliable (sufficiently accurate) value, a replication analysis is done to find out how many replications need to be run.

###### STATISTICAL ANSWERS

The answers from all replications together provide a statistical answer to the question. For example:

- With 95% certainty, the true mean value lies between 10.3 parts/h and 10.7 parts/h. So there is a confidence level in the answer (95%) and a confidence interval (10.3 – 10.7).”