Demand Forecasting and Revenue Management
Course Website https://mle.lancs.ac.uk/course/view.php?id=4929
The course requires knowledge of elementary statistics and Excel. Students should have successfully completed MSCI 103 or MSCI 224.
Term Taught: Michaelmas
This course includes two separate submodules: Forecasting and Demand Planning; and Pricing and Revenue Management. These two modules are taught and assessed separately, however both provide a unique skill-set to the students.
Forecasting and Demand Planning
Forecasting is ubiquitous in business operations and decision making. Most actions and decisions in companies are based on some implicit or explicit forecast. Recently with the advent of high powered computing, digital information exchange and data warehousing in organizations, analytical predictive modelling is gaining prevalence. How can we forecast what will be the demand of a product in the next period? What will be the effect of a new government policy or promotion to our operations? How do special events affect our sales and profitability? Such questions are just an example of common and important issues that organisations would like to know for improving its decision making. This module will explore how we can tackle such questions, what statistical forecasting can offer and what are its limitations.
On successful completion of the module students will be able to:
- Produce reliable and accurate business forecasts and design reliable implementations for real applications.
- Identify the different forecasting objects and relate to the forecasting process and the notions of uncertainty, stochasticity and forecastability.
- Understand, develop and use univariate and multivariate forecasting methods for business applications.
- Evaluate forecasts and develop monitoring and continuous improvement schemes for forecasting applications.
- Identify external drivers that affect your forecasting target and quantify their impact.
Pricing and Revenue Management
Pricing is one of the most powerful but least understood business disciplines within most commercial organizations. It is a double-edged sword: A good pricing strategy will help a firm to gain competitive advantage, but a poor pricing strategy will destroy a firm’s value. Warren Buffett, the legendary value investor, identified pricing power as the single most important criterion in evaluating a business. Advances of information technologies have enabled firms to manage their prices and demand in a dynamic fashion with the access to large amount of high quality data. Dynamic pricing (DP) has been proved to be one of most effective marketing strategies to help firms to manage their profitability in dynamic market environments. Revenue Management (RM), also referred to as Yield Management (YM), is a special area of pricing. It is a growing new business discipline that integrates demand-side management (e.g., segmentation, pricing and availability) and supply-side management (e.g., capacity allocation and inventory control) in competitive market environments. Starting from Airline industry in 1970’s, it has grown into a mainstream business practice in varieties of service industries (e.g., Walt Disney Land, hotels, car rentals) and some manufacturing industries (e.g., Ford). It has also created its own supporting industry with established consulting firms, IT solution providers. Major airlines (e.g., AA, BA, Continental, Lufthansa and SAS) have large numbers of staffs of IT and OR analysts working on revenue management.
In the Pricing and Revenue Management submodule, you will learn how to address the following questions:
- How to identify pricing and revenue management problems in practice?
- How to estimate price-responsive demand models and customer willingness-to-pay?
- How to model pricing and capacity allocation decision making problems?
- How to optimize the pricing and capacity allocation decisions?
- How to measure the performance of the strategy you develop and demonstrate its value?
- How to use data and decision analysis to provide business insight and develop strategies?
Course educational aims
The general educational aims of this module are to:
- Equip participants with analytical thinking and skills that are high valued and demanded in modern society and career.
- Improve the statistical and mathematical literacy of the students, in particular with respected to applying that knowledge in real business problems.
- Expand the understanding and experience of the participants on what are best practices in the use of analytics in modern organisations and improve their career perspectives.
- Prepare students with the necessary skills to communicate complex analytical methods in lay business terms, supporting the decision making of individuals, teams and organisations.
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, 3 drop-in workshops for forecasting and 3 drop-in workshops for revenue management.
There are two pieces of assessed coursework (CWA), equally weighted.
The first is a demand planning/forecasting group course work (50%).The students will be expected to solve a real forecasting problem. The second coursework is a revenue management coursework; this will be an individual coursework (50%).
Reading and Lecture Notes
Lecture notes will be posted to Moodle and there will be Microsoft Excel templates to support the forecasting aspect and labs of the course.
Recommended material for the Forecasting and Demand Planning submodule:
- Ord, J. Keith & Fildes, Robert, Principles of Business Forecasting South-Western Cengage Learning, 2012
- Hyndman, J. Rob & Athanasopoulos, George, Forecasting Principles and Practice, 2013, https://www.otexts.org/book/fpp
- Makridakis, Spyros, Wheelwright, Steven, and Hyndman, J. Rob, Forecasting: Methods and Application. Wiley, New York, 1998.
- Tashman, L. J., 2000. Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting 16 (4), 437 – 450.
- Makridakis, S., Hibon, M., 2000. The M3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451–476.
- Hyndman, R. J., Koehler, A. B., 2006. Another look at measures of forecast accuracy. International Journal of Forecasting 22 (4), 679–688.
- Gardner, E. S., 2006. Exponential smoothing: The state of the art - part II. International Journal of Forecasting 22 (4), 637 – 666.
Recommended books for Pricing and Revenue Management submodule:
- Phillips, Robert: Pricing and Revenue Optimization, Stanford University Press 2005.
Related Information on the Web
The course will use Moodle 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.
There is also a departmental webboard giving answers to frequently asked questions at: https://mle.lancs.ac.uk/course/view.php?id=5712