Professur für Service Analytics

Computational Business Modeling

Vorlesung mit Übung für Masterstudierende

Prof. Dr. Catherine Cleophas (Vorlesung)
Lena Hörsting und Peyman Kazemi (Übung)



Computational business modeling considers the design and implementation of algorithms to automate or simulate business processes. By modeling 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 modeling business interactions and implementing these models in dynamic simulations, modelers 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 modeling, software system design, programming and testing.


The course will cover the following basic topics as well as selected advanced methods:
  • Process modeling 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 modeling approaches
  • Algorithm design
  • Simulation modeling
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


Weitere Informationen:


Semesterspezifische Informationen, Ort und Zeit der Veranstaltungen sowie die Prüfungen werden im UnivIS sowie in OpenOLAT bekannt gegeben.