Concept of Simulation

Owned by Gunjan Tulwani (Unlicensed)
Last updated: Jul 02, 2015
2 min read

Simulation is used to model a real-life situation on a computer so that it can be studied to see how the system works. By analyzing the situation and changing variables, predictions may be made about the behavior of the process model.
In context with business process management, simulation can be considered to be of two types:

  • Monte Carlo: This is a method for iteratively evaluating a deterministic model using sets of random numbers as inputs. It is often used when the model is complex, nonlinear, or involves more than just a couple of uncertain parameters.
  • Discrete event: This is a way of building up models to observe the time-based (or dynamic) behavior of a system. It is a formal method for building simulation models and ensuring that they are credible. During the experimental phase, the models are executed (run over time) in order to generate results. The results can then be used to provide insight into a system and a basis to make decisions on.


Randomness is the key feature in simulation. It provides a way of matching the built scenario with that of the real world. It is defined using a type of distribution (e.g. Normal, Exponential, and Poison etc.) and a degree of randomness (e.g. Standard Deviation, Degree of Freedom etc.).

The simulation of business processes involves the following steps:

  1. Business Process Model is created as defined in the prior section.
  2. Resources are identified and assigned to each of the steps (activities) of the process model where necessary, since some steps may be automated or may not have any defined resources.
  3. Timing information is assigned to the steps in the process model.
  4. Probabilities are assigned to the different paths out of decision nodes.
  5. Simulation Scenarios are executed.
  6. When the simulation finishes, the simulation results can be analyzed.
  7. Based on the simulation results, the process model can be changed or resources reallocated or business rules improved and then the simulation is run again to compare the new results with the earlier results.
  8. This fine tuning is repeated a number of times to help the analysts understand the implications of the changes in the process model until the optimally designed model is ready.


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