You can use Process Modelling to model a dynamic system (a system changing over time) or a steady-state system (a system working when all the processes have been coupled together and equilibrated). 

In dynamic system process modelling, the system is constantly changing and therefore the variables are never constant, sometimes changing drastically and at a high frequency. This means that dynamic systems are influenced by variability, and in the context of a new manufacturing line this could mean that you are modelling a process which is constantly changing. An example of this is when you have a manufacturing line with frequent product switches.

Another example of such a dynamic process is when you want to understand the impact of transient behaviours such as when product or resources changeover is carried out, or what happens during peak times. In this case Discrete Event Simulation (DES) is the modelling type which is mostly used. In the example of the manufacturing line you are essentially modelling the flow of products manufactured (you can model from raw material state all the way to a finished good), but also factoring in elements such as the people, the behaviours, the resources, the constraints, and then simulate multiple what-if scenarios. So, you are essentially modelling a real-life situation, or in this case a manufacturing line, but in a digitalised format. You can “play” around or test multiple scenarios, in a safe digital space, until you are ready to implement in real life, once the optimum settings have been found.

A steady-state system, on the other hand, is modelling a system which is already calibrated and everything is running in a stable state. So, for instance in manufacturing this would be a system where it is running at a constant rate, such as when you are focused on chemical or thermal processes. There are no changes being made to the system, and thus there are no changes to the system’s output. Imagine if we were to run multiple tests of chemical reactions taking place in a chamber (therefore, without any interference to the process). What we will model is a system in a digital environment which will have no transient behavioural elements, so once all of the coupled systems have converged we will know how the system will perform. In this case the modelling techniques used may vary. 

So, dynamic models factor in changes, including human-interaction and behaviour, as well as constant or frequent changes to the process. On the other hand, steady-state is when there are no changes made to a process, thus the process should reach a stable operation. 

In certain industries dynamic models are more used in discrete manufacturing where there is an element of resource usage, frequent changes over time, human-interaction element, coordination between automation and manual processes or settings/environmental changes. Dynamic models are more operational.

Steady-state models, on the other hand are not into the operational aspect, but rather how a system behaves when the variables are not changing. 

In a model, whether that is dynamic or a steady-state, you can add as many variables as you need. Some simple examples of these are costs, throughputs, chemical reactions, mixing and even random events (for a dynamic system) and many more, depending on the model you are building and the problem/question you are trying to answer. These variables do not have to be modelled in isolation, but all of these variables can be coupled and modelled together all at once in one larger model. This gives you a holistic picture of the system, how it works when calibration takes place, in either a dynamic system or a steady-state system.