On revenue management and the use of occupancy forecasting error measures

Koupriouchina, Larissa; Rest, Jean‐Pierre van der; Schwartz, Zvi

doi:10.1016/j.ijhm.2014.05.002

Cited by 39 publications

(49 citation statements)

References 56 publications

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“…The demand for hotel accommodation is measured by a variety of variables, from different perspectives. Some variables relate to the scale of demand, such as guest arrivals (Guizzardi and Stacchini, 2015), the number of nights stayed (Falk, 2014;Lim et al, 2009), the number of rooms sold (such as Corgel et al, 2013;Song et al, 2011b), and occupancy rates (Koupriouchina et al, 2014;Wu et al, 2010).…”

Section: Measurement Of Hotel Demandmentioning

confidence: 99%

“…Relatively few studies have focused on hotel modeling and forecasting. Koupriouchina et al (2014) More recently, studies with the theme of tourism and hotel modeling and forecasting have continued to appear in academic journals related to not only tourism and hospitality, but also some other fields, indicating growing interest in the research area.…”

Section: Introductionmentioning

confidence: 99%

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New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

255

217

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Design/Methodology/approachArticles on tourism and hotel demand modeling and forecasting published in both Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) journals were identified and analyzed. FindingsThis review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior, and consumer confidence indicators, among others. More sophisticated techniques such as Research limitations/implicationsThe main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implicationsThis review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/valueThe value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

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Section: Measurement Of Hotel Demandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

255

217

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“…To ensure optimal parameter estimation, we apply a multi-start technique that initializes the neural network three times for different initial random values returning the best result. Using as a criterion the performance on the validation set, the results correspond to the selection of the best topology and the best spread in the case of the RBF neural networks.Following the suggestions made by Koupriouchina et al (2014), we evaluate our predictions at a number of forecasting horizons. Therefore, forecasts for 1, 3…”

mentioning

confidence: 99%

A new forecasting approach for the hospitality industry

Clavería

Monte

Porras

2015

International Journal of Contemporary Hospitality Management

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Purpose – This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. To do so, the authors developed a multivariate setting that allows the incorporation of the cross-correlations in the evolution of tourist arrivals from visitor markets to a specific destination in neural network models.\ud Design/methodology/approach – This multiple-input-multiple-output approach allows the generation of predictions for all visitor markets simultaneously. Official data of tourist arrivals to Catalonia (Spain) from 2001 to 2012 were used to generate forecasts for one, three and six months ahead with three different networks.\ud Findings – The study revealed that multivariate architectures that take into account the connections between different markets may improve the predictive performance of neural networks. Additionally, the authors developed a new forecasting accuracy measure and found that radial basis function networks outperform the rest of the models.\ud Research limitations/implications – This research contributes to the hospitality literature by developing an innovative framework to improve the forecasting performance of artificial intelligence\ud techniques and by providing a new forecasting accuracy measure.\ud Practical implications – The proposed forecasting approach may prove very useful for planning purposes, helping managers to anticipate the evolution of variables related to the daily activity of the\ud industry.\ud Originality/value – A multivariate neural network framework has been developed to improve forecasting accuracy, providing professionals with an innovative and practical forecasting approachPeer ReviewedPostprint (author's final draft

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“…As Talluri and van Ryzin (2004a, p.407) observe: "a revenue management system requires forecasts of quantities such as demand, price sensitivity, and cancellation probabilities, and its performance depends critically on the quality of these forecasts". While there is an ample research on forecasting, a major weakness of work in hotel revenue management is its focus on the model selection aspect of hotel forecasting, with notable exceptions such as Schwartz and Hiemstra (1997), Kimes (1999), Schwartz (2003), Schwartz and Cohen (2004), Bendoly (2013) and Koupriouchina et al (2014). Forecasting comprises multiple facets including • problem definition • information gathering • preliminary (exploratory) data analysis • choosing and fitting models…”

Section: Introductionmentioning

confidence: 99%

“…As Li et al (2005) and Song and Li (2008) identified in two comprehensive literature reviews, 451 studies on tourism demand modelling and forecasting were published during the period 1960-2008. The hospitality literature has traditionally paid little attention to forecasting in hotel revenue management with the exception of Law (1998), Weatherford et al (2001), Cranage (2003), Law (2004), Lim et al (2009), Yang et al (2014 and Koupriouchina et al (2014). In the operations research literature, a stream of forecasting applications in hotels can be observed with work from Rajopadhye et al (2001), Baker et al (2002), Brännäs et al (2002), Weatherford and Kimes (2003), Aghazadeh (2007), Chen and Kachani (2007), Yüksel (2007), Bermúdez et al (2009), Guadix et al (2010, Haensel and Koole (2011a), Zakhary et al (2011) and Lee (2012).…”

Section: Introductionmentioning

confidence: 99%

A call for exploratory data analysis in revenue management forecasting: a case study of a small and independent hotel in The Netherlands

Sierag

Rest

Koole

et al. 2017

IJRM

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Using five years of data collected from a small and independent hotel this case study explores RMS data as a means to seek new insights into occupancy forecasting. The study provides empirical evidence on the random nature of group cancellations, an important but neglected aspect in hotel revenue management modelling. The empirical study also shows that in a local market context demand differs significantly per point of time during the day, in addition to seasonal monthly and weekly demand patterns. Moreover, the study presents evidence on the nonhomogeneous Poisson nature of the probability distribution that demand follows, a crucial characteristic for forecasting modelling that is generally assumed but not reported in the hotel forecasting literature. This implies that demand is more uncertain for smaller than for larger hotels. The paper concludes by drawing attention to the critical and often overlooked role of exploratory data analysis in hotel revenue management forecasting.

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On revenue management and the use of occupancy forecasting error measures

Cited by 39 publications

References 56 publications

New developments in tourism and hotel demand modeling and forecasting

New developments in tourism and hotel demand modeling and forecasting

A new forecasting approach for the hospitality industry

A call for exploratory data analysis in revenue management forecasting: a case study of a small and independent hotel in The Netherlands

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