2IMI30 - Business process simulation

Organizations are constantly trying to improve the way their businesses perform. To this end, managers have to take decisions on changes to the operational processes. However, these changes are never without consequences and often high costs are involved. Therefore, it is of the utmost importance that these decisions are supported by a thorough analysis of all possible consequences on the organization.

To gain insights into the consequences of decisions on an operational process, one often resorts to simulation studies. In these studies, simulation models are made of the operational process under consideration, taking into account the necessary elements, such as costs, resources and activities. These simulation models are then executed with different parameters, to gain insights into the consequences of different decisions on the basis of which a final decision is made.

It is clear that the construction of simulation models of an operational process is a far from trivial task. Deciding which elements of the operational process to take into account and which not is key to getting useful simulation results.

In this course, we use a discrete event simulation tool called Arena to execute simulations. This tool allows for the graphical definition of a simulation model, together with complex definitions of queue types, resource availability and so on.


The focus of the business process simulation course is on the construction of simulation models, the execution of these models and the interpretation of the simulation results. After the course, students:

  • have a thourough understanding of the workings of a discrete event simulation,
  • understand (pseudo) random number generation,
  • understand distribution fitting and are capable of analyzing input data for use in simulation experiments,
  • are capable of performing ¿sanity checks¿ on their models,
  • can translate a description of an operational process to a simulation model with the purpose of solving a given problem,
  • can interpret simulation results, compare simulations of the same system with different parameters and recommend a solution to a given problem.

Staff involved