Course code INN353

INN353 Modelling and Control of Business Processes

Norsk emneinformasjon

Search for other courses here

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
(NO=norsk, EN=Engelsk)
Limits of class size:
70
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
Preferential right:
  • 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
Course contents:

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.

Learning outcome:

Knowledge

  • Understand the theoretical foundations of classical and modern techniques for process modelling and control

Skills

  • 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

General competence

  • Be able to work in cross-functional project structures
  • Be able to contribute constructively in process automation projects
Learning activities:
Lectures, exercises with data and software, case workshops under supervision, independent group work related to the project assignment
Teaching support:
Canvas, Microsoft Teams
Syllabus:
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
Prerequisites:
INN265 Analysis of business processes

Recommended prerequisites:
  • INF120 Programming and data processing
  • BUS240 Operations management or IND210 Industrial management
Assessment:
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.
Nominal workload:
125 hours
Entrance requirements:
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
Note:
 This course will start in the autumn of 2022.
Examiner:
External examiner will control the quality of the syllabus, questions for the examination, and principles for the assessment of the examination answers.
Examination details: :