Materialisation Forecasting: A Data Mining Perspective
Have you ever wondered why airlines overbook planes? Or have you wondered why seat prices seem extremely variable, almost to the extent that you feel you could name the price? All of these facts come from dealing with a perishable commodity.
The airline’s dilemma is that, to survive in business, it must sell each seat, at the maximum margin, before departure. Once the door closes on a flight an unoccupied seat represents a liability. Why? Well, the cost of flying a plane is dictated by a set of fixed costs, the bulk of which is fuel and crew. Contrary to popular belief, the crew is there for safety reasons; other services, like delivering food to your seat and distributing sick bags are a matter of choice, which, in a business context, can be thought of as tailoring the product. Therefore, you cannot offload crew just to complement the number of passengers and save costs. Other costs are take-off and landing fees, ground handling, check-in, and baggage handling. Catering can be adjusted a little. Bottom-line: ensuring full planes is key to running a successful airline. But the essence of being successful in the airline business is to manage the greatest possible spread between costs and revenues. Doing so means executing effective yield management.
This chapter discusses a new approach to forecasting some of the key components in the yield management equation.
Keywordsdata mining multi layer perceptron neural networks yield management
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