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Demand & Revenue Management

Backgroundrm

The term Revenue Management (RM) is often used to describe the management of demand by means of control on the price and/or on product availability. In a wider sense, it encompasses also other areas of demand management, for example demand estimation and forecasting. While controlling demand via pricing has always been practiced since the beginning of trade, RM adds a new twist by allowing scientific decision making based on large data bases. The practice developed in the airline industry when American Airlines succeeded in fending off competition by implementation of the first RM system. In principle, such a system works as illustrated in the picture: a customer's availability request for a particular journey is sent to a reservation system, is recorded in a large database that may also collect information on current prices offered etc. Demand forecasts are regularly updated using this database and analyst input, and these forecasts represent the foundation upon which the optimisation process takes place. The latter process returns the controls that tell the reservation system what availability/pricing information to return to the customer.

A next-generation retailer will be able to track the behavior of individual customers from Internet click streams, update their preferences, and model their likely behavior in real time.

Are you ready for the era of ‘big data’?, McKinsey Quarterly, Oct 2011

Most airlines have adopted such systems, and many other industries followed such as hotels, car rentals, cruise lines, train, casinos and many more. There are also many applications in B2B markets where RM is successfully used. Today's most prominent challenges for forecasting and revenue management include making use of large data sets, e.g. to model customer behavior, and to derive insights to make decisions on pricing, promotions etc. This includes data arising from customer relationship systems and even social media.

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Recent Publications

  • Yang, X., A. Strauss, Currie, C and Eglese, R. Choice-Based Demand Management and Vehicle Routing in E-fulfilment. Transportation Science 50 (2016): 473-488
  • J. Meissner, A. Strauss, K. Talluri. An Enhanced Concave Program Relaxation for Choice Network Revenue Management. Production and Operations Management 22 (2013): 71-87 [doi]
  • P. Kemmer, A. Strauss, T. Winter. Dynamic Simultaneous Fare Proration for Large-Scale Network Revenue Management. Journal Of The Operational Research Society 63 (2012) (Published):1336-1350 . [doi] (joint work with Lufthansa Systems)