Course code INN353

INN353 Monitoring and Control of Business Processes

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Showing course contents for the educational year 2022 - 2023 .

Course responsible: Joachim Scholderer
Teachers: Daumantas Bloznelis
ECTS credits: 5
Faculty: School of Economics and Business
Teaching language: EN
(NO=norsk, EN=Engelsk)
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
Course contents:

In this course, participants will learn classic and modern techniques for the monitoring and control of business processes, and how these techniques can be used for automated anomaly detection and exception handling.

  • Data-driven modeling of business processes, based on event log data
  • Statistical process control
  • Capability analysis
  • Predictive process monitoring
  • Automation of anomaly detection and exception handling
  • 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 Python, R and SAS. Practical work with real cases is an important part of the course. 

Learning outcome:


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


  • 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:
Workshops on campus, video lectures, exercises with data and software, self-study
Teaching support:
Canvas, Microsoft Teams
Montgomery, D. C. (2019). Introduction to statistical quality control (8th Ed.). Hoboken, NJ: Wiley. Selected journal articles and book chapters
STAT100 Statistics INN265 Analysis of business processes

Recommended prerequisites:
  • INF120 Programming and data processing
  • BUS240 Operations management or IND210 Industrial management
Mandatory activity:

Participation in at least three of the four campus-based workshops. 

The mandatory activity is valid only for one semester, i.e., if a student would like to retake the course, the mandatory activity also need to be retaken. The mandatory activity must be approved in order to be assessed in the course.

Combined assessment, consisting of a mid-term home exam in the teaching period (weight: 20%) and a 30-hour home exam in the teaching period (weight: 80%). No re-sit examination will be arranged in this course. 
Nominal workload:
125 hours
Entrance requirements:
Study specialisations where this course is mandatory have priority: business analytics (M-ØA) and business analytics (M-TDV). 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.
Reduction of credits:
INN350 (5 ECTS) 
Type of course:
  • Video lectures: 15 hours 
  • Campus workshops: 15 hours 
  • Exercises with data and software: 15 hours 
  • Self-study/syllabus literature: 50 hours 
  • Home exam: 30 hours 
The course is in English. Incoming students can contact student advisors at the School of Economics and Business ( for admission to the course.
External examiner will control the quality of the syllabus, questions for the examination, and principles for the assessment of the examination answers.
Examination details: Combined assessment: Letter grades