Demand Forecasting and Revenue Management
MSCI 381
Value: 0.5
Lecturers:
Zhan Pang (z.pang@lancaster.ac.uk)
Nikos Kourentzes (n.kourentzes@lancaster.ac.uk)
Course Website http://luvle.lancs.ac.uk/11-12/msci/msci381.nsf
Pre-Requisites
The course requires knowledge of elementary statistics and Excel on the level of MSCI 103 or MSCI 224.
Term Taught: Michaelmas
ECTS: 8
Introduction
Every firm eventually has to sell its products. Questions that arise in this context are, for example: What sales channels should the firm use? How should a product be priced in the different channels? How can the firm prevent cannibalization across channels? And how should prices be adjusted due to seasonality or after initial demand has been observed? In this course, we focus on two elementary parts of this decision process namely, how to forecast the arising demand and how to set the best prices for the offered products.
Forecasting is used throughout most organisations. There are many approaches to producing forecasts, some of which rely on the judgement of individuals, whilst other methods are more formal and are based on statistical models. This course introduces the two most common statistical approaches: extrapolation, where the history of the variable being forecast is the singular element used to produce a forecast, and causal modelling, which seeks an explanation for changes.
Revenue Management focuses on how a firm should set and update pricing and product availability decisions across its various selling channels in order to maximize its profitability. The most familiar example probably comes from the airline industry, where tickets for the same flight may be sold at many different fares throughout the booking horizon depending on product restrictions as well as the remaining time until departure and the number of unsold seats. The use of such strategies has transformed the transportation and hospitality industries, and is increasingly important in retail, telecommunications, entertainment, financial services, health care and manufacturing. In parallel, pricing and revenue optimization has become a rapidly expanding practice in consulting services, and a growing area of software and IT development.
Course Objectives and Learning Outcomes
The course aims to give students an appreciation of modern business forecasting methods. More explicitly, it aims to ensure that the successful student is capable of developing a validated quantitative set of forecasts using both extrapolative and causal forecasting methods. By the end of the course students should be able to apply a simple forecasting method to support demand and revenue management. Students will also develop an appreciation for the use of spreadsheets in forecasting.
In the second part of the course, you learn to identify and exploit opportunities for revenue optimization in different business contexts. You review the main methodologies that are used in each of these areas, discuss legal issues associated with different pricing strategies, and survey current practices in different industries. As the course outline reveals, most of the topics covered in the course are either directly or indirectly related to pricing issues faced by firms that operate in environments where they enjoy some degree of market power. Within the broader area of pricing theory, the course places particular emphasis on tactical optimization of pricing and capacity allocation decisions, tackled using quantitative models of consumer behavior (e.g., captured via appropriate price-response relations), demand forecasts and market uncertainty, and the tools of constrained optimization -- the two main building blocks of revenue optimization systems.
Teaching Methods
The course is primarily lecture based together with a number of computer workshops where students carry out various exercises and receive help on aspects of their assignments.
Contact Hours: 20 lectures, 9 drop-in workshops
Course Assessment
There are two pieces of assessed coursework (CWA), equally weighted. They both require roughly 10 hours work. The first is a forecasting exercise, which will be given as group work. The second is a revenue management problem set; this will be an individual exercise.
Reading and Lecture Notes
- Lecture notes will be posted at LUVLE and there will be Microsoft Excel templates to support the forecasting aspect and labs of the course. In addition, students should read:
- Makridakis, Spyros, Wheelwright, Steven, and Hyndman, Rob. 1998. Forecasting: Methods and Application. Wiley, New York. (On short loan in the library)
- Material and lecture notes for the Revenue Management part of the course will be posted on LUVLE or distributed in class. Further, students must read the following book: Phillips, Robert: Pricing and Revenue Optimization, Stanford University Press 2005. This useful and moderately priced textbook is mandatory. There are a few copies on short loan in the library as well. Students may also find it useful to read Tim J. Smith. Pricing Strategy. South-Western Cengage Learning. 2011.
Related Information on the Web
Lancaster Centre for Forecasting
INFORMS Section on Revenue Management and Pricing
Course Management
The course will use LUVLE for posting lecture notes, assignments and answering queries.
The undergraduate secretary for the Management Science department is Helena Greenwood, in A68 Management School. Her office hours are 10 - 12 and 2.30 - 4.30.
Further Information
There is also a departmental webboard giving answers to frequently asked questions at: https://luvle.lancs.ac.uk/11-12/MNGT/MSCIFAQ.nsf
