INN353 Modelling and Control of Business Processes
Showing course contents for the educational year 2021 - 2022 .
Course responsible: Joachim Scholderer
ECTS credits: 5
Faculty: School of Economics and Business
Teaching language: EN
Limits of class size:
Teaching exam periods:
This course will be offered first time in autumn 2022. This course starts in the Autumn parallel. This course has teaching/evaluation in the Autumn parallel.
Course frequency: Annually
First time: Study year 2021-2022
- Study specialisations where this course is mandatory have priority: digital business transformation (M-EI), business analytics (M-TDV) and business analytics (M-ØA)
- Students from data science (M-DV and M-TDV), entrepreneurship and innovation (M-EI), industrial economics and technology management (M-IØ) and business administration (M-ØA) who have passed all mandatory and recommended prerequisites for the course
In this course, participants will learn classic and modern techniques for the modelling and control of business processes, and how these techniques can be used for automated process monitoring, anomaly detection and exception handling.
- Introduction to process modeling and process control
- Data-driven modeling of business processes, based on event log data
- Conformance checking
- Statistical process control
- Capability analysis
- Automation of anomaly detection and ex
- Implementation in the company's information systems
The course takes a data-driven approach. We will work with event log data from ERP, CRM and SCM systems. Advanced process analysis techniques are demonstrated in Celonis, Python, R and SAS.
Practical work with real cases is an important part of the course. Participants will work in teams on a semester-long case project.
- Understand the theoretical foundations of classical and modern techniques for process modelling and control
- Be able to use appropriate algorithms for data-driven business process modelling
- Be able to create and use important types of control diagrams
- Be able to perform process capability analyses
- Be able to use appropriate techniques for automated monitoring of business processes
- Be able to use appropriate techniques for automated anomaly detection and exception handling
- Be able to work in cross-functional project structures
- Be able to contribute constructively in process automation projects
Lectures, exercises with data and software, case workshops under supervision, independent group work related to the project assignment
Canvas, Microsoft Teams
Montgomery, D. C. (2019). Introduction to statistical quality control (8th Ed.). Hoboken, NJ: Wiley.Van der Aalst, W. (2016). Process mining: Data science in action (2nd Ed.). Berlin: Springer.Selected journal articles and book chapters
- INF120 Programming and data processing
- BUS240 Operations management or IND210 Industrial management
Combined assessment, consisting of an individual midway home exam (weight. 50%) and a project assignment conducted in groups of four participants (weight: 50%). No re-sit examination will be arranged in this course.
Bachelor's degree (or equivalent)
Reduction of credits:
INN350 (5 ECTS)
Type of course:
- Lectures and case workshops: 20 hours
- Exercises with data and software: 5 hours
- Project assignment: 50 hours
- Self-study/syllabus literature: 50 hours
This course will start in the autumn of 2022.
External examiner will control the quality of the syllabus, questions for the examination, and principles for the assessment of the examination answers.
Examination details: :