OPM 661: Business Analytics: Robust Planning in Stochastic Systems

kein Angebot im HWS

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).

Lern- und Qualifikations­ziele
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.

Notwendige Voraussetzungen
OPM 560 and at least one of the modules OPM 501, 502, 561 (recommended), 581, 582 or 591 (parallel attendance possible).

Inhaltliche Voraussetzungen
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).

Vorlesung2 SWS8 SWS
Übung2 SWS5 SWS
Prüfungs­form und -umfangAssignments and presentations (70%), written exam (45 min.) or oral exam
Informationen zur Anmeldung
Geprüft durch
Durchführende Lehr­kraft
Prof. Dr. Raik Stolletz
Prof. Dr. Raik Stolletz
Dauer des Moduls 1 Semester
VerwendbarkeitM.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL, M.Sc. Wirt. Inf., M.Sc. Wirt. Math.
Programm­spezifische KompetenzzieleCG 1, CG 4
Benotung Ja
LiteraturWill be announced during the lecture
GliederungPlease note that this outline will be subject to changes:
Introduction to queueing systems
Performance analysis of Markovian systems
  • Introduction to stochastic processes and Markov chains
  • Aggregated performance analysis
  • Analysis of production networks
Impact of variability
  • Queueing systems with general distributions
  • Time-dependent analysis of queueing systems
  • Heterogeneous queueing systems
Robust planning and optimization
  • Robust planning with scenarios
  • Simulation and optimization
Practical insights
  • Guest lecture by Business Analytics professionals
  • Field trip