With a few notable exceptions, airlines and hospitality forecasting research has been focused so far on point predictions of customers’ bookings. However, Revenue Management decisions are subject to a much greater risk when based exclusively on point predictions. To overcome this drawback, we propose a stochastic framework that allows the construction of prediction intervals for reservation-based (pickup) forecasting methods, which are widely used in the industry. Moreover, we introduce an extension of the multiplicative pickup technique based on Generalized Linear Models. We test the proposed framework with real reservation data from a medium-sized hotel on Lake Maggiore (Italy) and we obtain more efficient prediction intervals relative to classical time series methods. Our approach can be useful to hotel revenue managers that wish to make more informed decisions, planning alternative pricing and room allocation strategies for a range of possible demand scenarios.
Fiori, A., & Foroni, I. (2019). Prediction accuracy for reservation-based forecasting methods applied in Revenue Management. INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT, 84(January 2020), 1-10.
|Citazione:||Fiori, A., & Foroni, I. (2019). Prediction accuracy for reservation-based forecasting methods applied in Revenue Management. INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT, 84(January 2020), 1-10.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||Prediction accuracy for reservation-based forecasting methods applied in Revenue Management|
|Autori:||Fiori, A; Foroni, I|
FIORI, ANNA MARIA (Corresponding)
FORONI, ILARIA (Corresponding)
|Data di pubblicazione:||2019|
|Rivista:||INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.ijhm.2019.102332|
|Appare nelle tipologie:||01 - Articolo su rivista|