OIT 542:
Price and Revenue Optimization
This is the Advanced Application option in the menu of courses that satisfy the Management Foundations requirement in Modeling for Optimization and Decision Support (MODS). Three core modeling topics are covered in rapid-review fashion - model representation in a spreadsheet environment, optimization theory, and stochastic models - but primary emphasis is on the application domain described immediately below. OIT 542 is a two-unit course, with nine class sessions plus a final exam. nnnSystems for price and revenue optimization - also called yield management, dynamic pricing, or revenue management - combine the use of information technology, statistical forecasting, and mathematical optimization to make tactical decisions about pricing and product availability. A familiar example is the passenger airline industry, where a carrier may sell seats on the same flight at many different fares, with fare availability changing as time advances and uncommitted capacity declines. Over the last 25-30 years, revenue optimization practices have transformed the transportation and hospitality industries, where fixed capacity and advance reservations by customers are important structural factors. But model-based, data-driven pricing systems are increasingly common in other industries that have different structures, such as financial services and retail clothing.nnnIn this course students learn about the model structures and modelling techniques that underlie systems for price and revenue optimization. Two topics are given roughly equal emphasis: model-based tactical pricing, including customized pricing and retail markdown management; and classical revenue management, where automated logic is used for booking control (that is, to make yes-or-no decisions in response to booking requests from customers), rather than to set prices explicitly.nnnOIT 542 is tailored to students who already have command of basic modelling techniques and wish to learn about their application in an important business context. To be specific, a prior college course on optimization modelling is assumed as background. (Typically, such courses focus on linear programming, or linear optimization, with secondary coverage of non-linear programming and discrete optimization.) Various aspects of optimization theory will be covered in quick-review format, along with the basics of spreadsheet model representation and stochastic modelling, in order to standardize terminology and establish certain conventions that facilitate grading. In exceptional cases, for students who have strong math background and high mathematical aptitude but no prior coursework on optimization, the background knowledge assumed in OIT 542 may be acquired through self-study; appropriate study materials will be suggested by the instructor upon request. The course is entirely appropriate for second-year MBA students who have completed either OIT 245 or OIT 247 as their MODS menu selections. nnnOIT 542 draws heavily on knowledge acquired and skills developed in two other GSB foundational areas: Data and Decisions (OIT 265) and Microeconomics (MGTECON 200 or 203); it is positioned in the spring quarter so that first-year students can complete those courses beforehand. Students are required to construct and analyze at least one model for every class session, to make in-class presentations on some of those models, and to hand in four of them for grading.
Terms: Spr
| Units: 2
| Repeatable
2 times
(up to 4 units total)