OPM 661: Business Analytics: Robust Planning in Stochastic Systems

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).

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 or robust planning methods to support managerial decisions.

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

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 learningContact hoursIndependent study time
Lecture2 SWS8 SWS
Exercise class2 SWS5 SWS
ECTS credits6
Graded yes
Form of assessmentAssignments and presentations (70%), written exam (45 min.) or oral exam
Restricted admissionno
Further information
Performing lecturer
Prof. Dr. Raik Stolletz
Prof. Dr. Raik Stolletz
Frequency of offeringFall semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., M.Sc. Bus. Math.
Preliminary course work
Program-specific Competency GoalsCG 1, CG 4
LiteratureWill be announced during the lecture
Course outlinePlease 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