330.228 Risk-based Performance Management
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2021W, VU, 2.0h, 3.0EC, to be held in blocked form

Course evaluation


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to:

  • Explain the frameworks under which risk and performance as well as their interdependency can be managed in the energy market
  • Read the related academic literature 
  • Understand the Value at Risk and apply it to novel investment decisions 
  • Carry out optimizations given empirical data, performance constraints and attitudes towards risk
  • Work within the Minimum Exceedance probability framework and its applications in R

Subject of course

To deliver a good performance (e.g. good grades in a course) is hardly achieved only by luck. A more promising way is to establish a solid performance management system. Where in its planning system the targeted performance is set and the future actions to be taken are planned and in its control system the actual performance is measured, compared to the targets and resulting deviations are taken to adjust the performance management systems for a better performance in the next period. In the context of socio-technical systems many different types of risks show up, which are not present in purely technical systems. In “Risk-based Performance Management” these risks are explicitly integrated into the performance management systems in order to deliver a better performance (e.g. better grades in a course).

In the practical part of this lecture an investor’s budgeting perspective is taken within the renewable energy domain in order to determine optimal investment volumes for direct investments. For this purpose the fundamentals of energy markets and their distinctions from financial markets are shown. The direct investment decisions are non-trivial since they are dealing with problems which are stochastic (uncertain) and intertemporal (dynamic) in nature. For instance, wind (a stochastic source of energy) does not power the turbines in an energy park on demand. Moreover, direct investment decisions include preferences of management (performance or risk criteria imposed by managers) and therefore need to combine technical with economic aspects of decision-making. To shed light on how direct investment decisions are made and work a case study research project is performed in order to acquire the necessary skills for being able to carry out independent risk-based performance management tasks.

Tentative course contents: 

  • Lesson 1: Risk and Performance Management: Introduction
  • Lesson 2: Risk and Performance Management: Literature Overview
  • Lesson 3: Risk Type Profile-Contingency of Management Control Systems
  • Lesson 4: Contingency Study Research
  • Lesson 5: Why firms implement risk governance?
  • Lesson 6: Structural Equation Modeling (SEM-Study Research)
  • Lesson 7: Features of (renewable energy) markets
  • Lesson 8: Basic Computational Techniques (in R)
  • Lesson 9: Numerical and empirical optimization for risk and performance management (in R) in the energy context
  • Lesson 10: The Minimum-Exceedence Probability (MEP) Framework
  • Lesson 11: Applications of the MEP
  • Lesson 12: Applications of the MEP

Teaching methods

Risk-based Performance Management is comprised of two parts:

  • In the first part, we study the scholarly literature for understanding how different types of risks can be integrated into traditional performance mangement systems. Next to that we also investigate research methodologies (i.e. contingency analysis and structural equation modeling) that are applied in risk and performance management research. This should be helpful for students especially when they are thinking about the "research methodology" in writing their master thesis proposals. 
  • In the second part, we apply risk and performance management systems with a hands-on approach. We discuss the properties of data collected from renewable energy technologies and carry out numerical optimizations with these datasets in the statistics language R in order to get to know how risk and performance can be managed. 

Mode of examination




Course dates

Wed08:30 - 12:0006.10.2021 Online - Information in TUWEL! (LIVE)Lesson 1 & 2
Wed08:30 - 12:0013.10.2021 Online - Information in TUWEL! (LIVE)Lesson 3 & 4
Wed08:30 - 12:0020.10.2021 Online - Information in TUWEL! (LIVE)Lesson 5 & 6
Wed08:30 - 12:0027.10.2021 Online - Information in TUWEL! (LIVE)Lesson 7 & 8
Wed08:30 - 12:0003.11.2021 Online - Information in TUWEL! (LIVE)Lesson 9 & 10
Wed08:30 - 12:0010.11.2021 Online - Information in TUWEL! (LIVE)Lesson 11 & 12
Course is held blocked

Examination modalities

  • Proactive preparation of weekly reading assignments
  • Project assignment
  • Final exam

Course registration

Begin End Deregistration end
15.09.2021 00:00 05.10.2021 23:59 05.10.2021 23:59


Study CodeSemesterPrecon.Info
066 482 Mechanical Engineering - Management STEOP
Course requires the completion of the introductory and orientation phase
066 926 Business Informatics


No lecture notes are available.

Previous knowledge

  • Budgetary planning and control systems
  • Proactive double loop management systems
  • Software package R (see R in a nutshell-document)

Preceding courses


  • Attendance Required!