Service Analytics Research Group

Computational Business Modelling

Lecture and exercise course for master students

Prof. Dr. Catherine Cleophas (lecture)
Leif Feddersen (exercise course)



Computational business modelling considers the design and implementation of algorithms to automate or simulate business processes. By modelling processes and decision rules and implementing them in code, programmers can tap into computational resources to make tasks such as handling and analysing business data more efficient and consistent. By systematically modelling business interactions and implementing these models in dynamic simulations, modellers can evaluate assumptions about, for example, the behaviour of customers and service personnel, as well as implications of process changes. To enable this, this course introduces the topics of process modelling, software system design, programming and testing.


The course will cover the following basic topics as well as selected advanced methods:
  • Process modelling in UML and ARIS
  • General algorithmic concepts such as variables, loops, and conditional tests
  • Specific programming concepts based on the language Python
  • Approaches to software systems design and testing


Participants gain theoretical background knowledge in the following areas:
  • Process modelling approaches
  • Algorithm design
  • Simulation modelling
They also gain hands-on experience in applying these concepts to defined tasks in
  • Drawing process diagrams
  • Implementing and running Python code
  • Testing and debugging code



  • None
  • The basics of Python are taught during the course. Hence, no prior programming knowledge is required.



  • P. Barry. Head First Python. O’Reilly, 2017.
  • M. Schedlbauer. The Art of Business Process Modeling: The Business Analyst's Guide to Process Modeling with UML & BPMN, 2010.
  • Selected papers as announced during the module


Further information:


Detailed information, room numbers, times and dates of lectures, tutorials and exams will be published on UnivIS and OpenOLAT.