Confronta i metodi

	Hotel Revenue Management	Tourism Demand Forecasting
Campo	Tourism	Tourism Hospitality
Famiglia	MCDM	Regression model
Anno di origine	1989	2008
Ideatore	Sheryl E. Kimes	Haiyan Song; Gang Li; Stephen F. Witt
Tipo	Decision model for allocating fixed perishable capacity through demand forecasting, price differentiation, and inventory control	Predictive time-series and econometric demand models
Fonte seminale	Kimes, S. E. (1989). Yield management: A tool for capacity-constrained service firms. Journal of Operations Management, 8(4), 348-363.	Song, H., & Li, G. (2008). Tourism demand modelling and forecasting - A review of recent research. Tourism Management, 29(2), 203-220.
Alias	Yield Management, Hotel Yield Management, Lodging Revenue Management, Capacity and Rate Optimization	Tourist Arrivals Forecasting, SARIMA Tourism Forecasting, Tourism Demand Modelling and Forecasting, Econometric Tourism Forecasting
Correlati	3	4
Sintesi	Hotel revenue management, also called yield management, is the decision discipline of selling the right room to the right guest at the right price at the right time to maximize revenue from a fixed, perishable inventory. Sheryl Kimes's 1989 paper crystallized the concept for capacity-constrained service firms, identifying the conditions, fixed capacity, perishable inventory, segmentable demand, low marginal cost, and advance sales, under which managing yield rather than simply chasing occupancy pays off. Because an unsold room-night is lost forever, the hotel must forecast segmented demand, erect rate fences that separate price-sensitive from price-insensitive guests, and decide how much capacity to protect for higher-paying late bookers. Enz, Canina, and Walsh further showed that performance must be judged on revenue per available room rather than misleading single averages, anchoring revenue management to the right objective.	Tourism demand forecasting predicts future tourist arrivals, overnight stays, or expenditure from historical data, supporting planning by destinations, airlines, hotels, and policymakers. The field spans two broad model families. Time-series models such as seasonal ARIMA (SARIMA) extrapolate the patterns embedded in the demand series itself — trend, seasonality, and autocorrelation — without explanatory variables. Econometric models such as autoregressive distributed lag models (ADLM) and error-correction models relate demand to drivers like income, relative prices, and exchange rates, allowing both forecasting and policy analysis. Haiyan Song and Gang Li's influential 2008 review in Tourism Management synthesized this literature, documenting the proliferation of methods since 2000 and emphasizing rigorous out-of-sample evaluation. Their work, with Stephen Witt, helped make tourism demand forecasting a methodologically mature subfield.