OPM 682: Revenue Management – Analytics for Effective Resource Allocation and Value Creation

Revenue Management (RM) is concerned with demand-management decisions (such as product availability decisions, price optimization, assortment optimization and product line design) in face of resource constraints. Since resources to create and offer products are usually limited and often perishable, the effectiveness of market-related decisions is highly interrelated with resource allocation decisions.
Over the last decades, RM tools and systems have helped businesses in a variety of industries to ensure economic viability and to allocate scarce resources more effectively. Robert Crandall, former Chairman and CEO of American Airlines, has called RM “the single most important technical development in transportation management since we entered deregulation.” While airlines have the longest history of development in classical RM, applications have rapidly diffused beyond airlines to industries such as retailing, hospitality, railways, car rental, telecommunications and financial services, electric utilities, consumer goods production and even MTO manufacturing.
For outside observers, RM may seem often like an art. But finally, the most important pillar of RM is analytics – including systematic data analysis, understanding value-to-the-customer and forecasting demand, and powerful optimization that allows taking the relevant market- and resource-related factors jointly into account. With a focus on prescriptive analytics, this course discusses managerial concepts, optimization modelling approaches and state-of-the-art solution tools for RM. We pursue an active-learning approach including lecture-style class sessions, case discussions, exercises, guest lectures, and hands-on computer implementations using e.g. the Excel solver and easy-to-learn but powerful professional mathematical optimization tools.
The methods and tools taught in the course have become invaluable aids to informed decision making in order to save resources, create value-to-the-customer, and improve firm profitability.

Learning outcomes
Students will gain insights into practical applications of Revenue Management and get familiar with the underlying models and methods, thereby enhance their analytics skills.

Necessary prerequisites
At least one of the modules OPM 501, 502, 560, 561, 581, 582, or 591 (parallel attendance possible); further modules may be accepted by Professor upon request.

Recommended prerequisites
Participants should be familiar with Operations Management and enjoy analytics, such as mathematical modeling and optimization.

Forms of teaching and learningContact hoursIndependent study time
Lecture2 SWS8 SWS
Exercise class2 SWS5 SWS
ECTS credits6
Graded yes
Form of assessmentWritten exam (90 min)
Restricted admissionno
Further information
Performing lecturer
Prof. Dr. Cornelia Schön
Prof. Dr. Cornelia Schön
Frequency of offeringSpring 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 2, CG 3
LiteratureSee syllabus published on ILIAS
Course outlineTopics include, e.g.,
  • Revenue Management Overview
  • Predictive vs. Prescriptive Analytics for RM
  • Static and Dynamic Resource Allocation Models
  • Demand Response Modeling
  • Product Line Design and Assortment Optimization
  • Dynamic Pricing under Resource Constraints
  • AI-Powered Revenue Management
  • Ethical Considerations in RM
  • Case Studies, Guest Lectures, Practice Insights