OPM 661 (OPM 6610): Business Analytics: Robust Decisions
no offering in fall
Contents
Business Analytics helps to optimize decisions for the design and management of operations systems and production processes. A major driver of the performance of operations systems is stochastic variability. For example, production systems often operate in an uncertain environment due to uncertain demand, unreliable machines, or random processing capacities.
In order to support robust decisions, we apply analytical solution approaches based on techniques from predictive and prescriptive analytics. The basic concepts of the analysis of Markovian queueing systems are explained in detail and performance evaluation approaches are implemented in Python. To create digital twins of operating systems, simulation techniques are introduced and implemented. This allows to analyze the sensitivity of system parameters on the main performance measures. Advanced topics such as queueing systems with general distributions, heterogeneities, and time-dependent input parameters are covered. Additionally, general managerial insights, for example economies of scale and the value of flexible capacities are discussed. Methods and performance measures of robust planning and optimization are introduced. Students become familiar with concepts and tools for predictive and prescriptive business analytics.
Moreover, we will implement those concepts using the programming language Python to perform sensitivity analyses and to develop managerial insights for stochastic operations systems. During the course the students will work on several case studies and assignments (individual and in groups).
Learning outcomes
Students learn to understand the impact of stochastic variability in operations systems. After this course students are familiar with the theory and practice of the analysis of stochastic systems. They learn to implement, adapt and to apply methods and tools from business analytics e.g. analytical approximations, simulation, and robust planning methods to support managerial decisions.
Necessary prerequisites
Basic knowledge in Python (OPM 560 or IS 557 or „Schlüsselqualifikation1: Programmierkurs Python“ – Angebot der WIM) and knowledge in modelling in operations (OPM 561 or OPM 501).
In exceptional cases and by prior agreement, other modules may also be accepted as prerequisites.
Recommended prerequisites
Participants should be familiar with the fundamentals of production and operations management. The course further assumes a basic knowledge in mathematics (including linear programming) and in statistics (probability distributions).
| Forms of teaching and learning | Contact hours | Independent study time |
|---|---|---|
| Lecture with integrated exercise | 4 SWS | 9 SWS |
| Exercise class | 2 SWS | 8 SWS |
| ECTS credits | 8 |
| Graded | yes |
| Workload | 240h |
| Language | English |
| Form of assessment | Assignments and presentations (70%), written exam (45 min) or oral exam (30%) |
| Restricted admission | yes |
| Further information | – |
Examiner Performing lecturer |  | Prof. Dr. Raik Stolletz Prof. Dr. Raik Stolletz |
| Frequency of offering | Fall semester |
| Duration of module | 1 semester |
| Range of application | M.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL, M.Sc. Wirt. Inf., M.Sc. Wirt. Math., M.Sc. MMFACT, M.Sc. MMOSCM |
| Preliminary course work | – |
| Program-specific Competency Goals | CG 1, CG 4 |
| Literature | Will be announced during the lecture |
| Course outline | Please note that this outline will be subject to changes: Introduction to performance evaluation
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