330.228 Risk-based Performance Management
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2021W, VU, 2.0h, 3.0EC, wird geblockt abgehalten


  • Semesterwochenstunden: 2.0
  • ECTS: 3.0
  • Typ: VU Vorlesung mit Übung
  • Format der Abhaltung: Distance Learning


Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage...

  • Explain the frameworks under which risk and performance as well as their interdependency can be managed in in the and energy market
  • Read the related acdemic 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

Inhalt der Lehrveranstaltung

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


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. 



Vortragende Personen


LVA Termine

Mi.08:30 - 12:0006.10.2021 Online - Information in TUWEL! (LIVE)Lesson 1 & 2
Mi.08:30 - 12:0013.10.2021 Online - Information in TUWEL! (LIVE)Lesson 3 & 4
Mi.08:30 - 12:0020.10.2021 Online - Information in TUWEL! (LIVE)Lesson 5 & 6
Mi.08:30 - 12:0027.10.2021 Online - Information in TUWEL! (LIVE)Lesson 7 & 8
Mi.08:30 - 12:0003.11.2021 Online - Information in TUWEL! (LIVE)Lesson 9 & 10
Mi.08:30 - 12:0010.11.2021 Online - Information in TUWEL! (LIVE)Lesson 11 & 12
LVA wird geblockt abgehalten


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


Von Bis Abmeldung bis
15.09.2021 00:00 05.10.2021 23:59 05.10.2021 23:59


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Lehrveranstaltung erfordert die Erfüllung der Studieneingangs- und Orientierungsphase STEOP
066 926 Business Informatics


Es wird kein Skriptum zur Lehrveranstaltung angeboten.


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

Vorausgehende Lehrveranstaltungen

Weitere Informationen

  • Anwesenheitspflicht!