no offering in fall
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. Advanced topics such as queueing systems with general distributions, heterogeneities, and time-dependent input parameters are covered. Additionally, managerial insights, for example economies of scale and the value of flexible capacities are discussed. Several 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, students will practice the concepts by implementing these with the programming language Python. The implementations are used to perform sensitivity analyses 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).
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 or robust planning methods to support managerial decisions.
OPM 560 and at least one of the modules OPM 501, 502, 561 (recommended), 581, 582 or 591 (parallel attendance possible).
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||2 SWS||8 SWS|
|Exercise class||2 SWS||5 SWS|
|Form of assessment||Assignments and presentations (70%), written exam (45 min.) or oral exam|
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. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., M.Sc. Bus. Math.|
|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 queueing systems
Performance analysis of Markovian systems