In "Risk-based Performance Management" the focus lies on the integration of risk management considerations into the performance management at different management levels (business management, enterprise management and strategic management). After finishing the course the students should know how the different performance management systems are working and how which risk management concepts are included. Next to this knowledge the students also get skills and competences for performing practical projects by their own using the statistics software R at the business and enterprise level and the conceptual approach at the strategic level.
Risk-based performance management is established as a proactive closed double loop management system, which is considered predominantly within the Gaussian stochastic process model. The Gaussian model allows closed form solution for the business and enterprise performance MGT. Next to the analytic solution also simulation studies are performed in the statistics package R. This allows extended stochastic process models with all kinds of random variables. Finally, the R package is used for implementing structural equation models (SEM) in order to analyze manifest and latent variables that are collected with the ERM-Maturity Assessment-(ERMMA-)Tool.
The lecture consists of 3 parts. In all parts students have to contribute by solving problems, making presentations and documenting their results. At the end of the course there is a final exam that covers all parts of the lecture.
Part A) consists of 2 sub-parts: 1) Forecasting, which is of essential importance in risk-based performance management systems, is discussed in the sales context. The calculations are performed in the software package R so that the students get the practical skills for performing projects on their own. 2) With the ERMMA-Monitoring-Tool progressive maturity levels for the ERM system implementation are assessed. The collected maturity levels are latent variables that are statistically analyzed with the “lavan” R Package together with manifest exploratory variables in causal models.
Part B) The risk-based performance management systems are discussed in the Return on Risk adjusted (RoRaC) context. The starting point is a single period RoRaC optimization. In order to assess the dynamic stability of the static optimization schema a dynamic analysis of the optimization results is performed. Both, the static optimization and the dynamic investigation are performed in R for the same reason as in part A.
Part C) At the strategic management level the concepts of the Boston Consulting Group (BCG) are presented by senior BCG staff to give the students insights into the professional state of the art in the strategic management domain.
