Intelligent system to support judgmental business forecasting: the case of estimating hotel room demand

Ghalia, Mounir Ben; Wang, P.P.

doi:10.1109/91.868945

Cited by 32 publications

(10 citation statements)

References 18 publications

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“…The expectation that late booking customers are willing to pay higher rates is shared across the airline, hotel and car rental industries (for example, see Belobaba, 1989 andAlstrup et al, 1986 for airline applications; Ben Ghalia and Wang, 2000;Baker andCollier, 2003 andSchwartz, 2000 for hotel applications and Carroll and Grimes, 1995 for car rental applications).…”

Section: Introductionmentioning

confidence: 99%

Do you really know who your customers are?: A study of US retail hotel demand

Lee

Garrow

Higbie

et al. 2009

J Revenue Pricing Manag

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This study uses booking data from 28 US hotels to investigate the validity of two key assumptions in hotel revenue management: (1) customers who book later are willing to pay higher rates than customers who book earlier; and (2) demand is stronger during the week than on the weekend. Empirical results based on an analysis of booking curves, average paid rates and occupancy rates for group, restricted retail, unrestricted retail and negotiated demand segments challenge the validity of these assumptions. Based

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Section: Introductionmentioning

confidence: 99%

Do you really know who your customers are?: A study of US retail hotel demand

Lee

Garrow

Higbie

et al. 2009

J Revenue Pricing Manag

View full text Add to dashboard Cite

show abstract

“…It can be seen that although fuzzy logic principles have been adopted in various industrial areas, their applications in supply and replenishment aspects, such as the stock-up management in convenience stores, still need to be explored due to inadequate literature in this area. Theoretically, fuzzy logic can improve a replenishment system through more reliable forecasting of forthcoming demands, thereby providing essential support for managers in terms of decision-making (Magdalena & Monasterio-Huelin, 1997;Ghalia & Wang, 2000). Accurate forecasting of consumer demands for utilities is important for economic planning of utility suppliers because a large forecasting error would increase operating costs and possibly adversely affect customer satisfaction (Rousselot et al, 1993;Lertpalangsunti & Chan, 1997).…”

Section: Related Studiesmentioning

confidence: 99%

On a responsive replenishment system: a fuzzy logic approach

2003

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In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7-Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a costeffective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results.

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“…Pricing decisions have been based mainly on rational choice perspective and focused solely on the efficiency of the choice (Dutta, Zbaracki, & Bergen, 2003;Ghalia & Wang, 2000;Jones, 1999;Phillips, 1999). A pricing choice is considered efficient only if it prompts an optimal decision that leads to revenue maximization.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role Of Human Judgment in Making Discount Decisions in the Lodging Industry

Lee

Croes

Rivera

2015

The Journal of Hospitality Financial Management

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This study assesses the process of making pricing decisions, specifically discounts, in the lodging industry. The study applied a qualitative technique employing structured interviews of hotel managers in the United States. The assessments included the narration of events, stages, and cycles of choices made by hotel managers. The findings of the study enhanced the understanding of how management's discount choice was constructed. In addition, this study identified the habitual management practices in the lodging industry such as "less-than-35 rule," "trial and error," and "follow suit." To confirm the findings from the interviews, a follow-up study was conducted. The survey was designed to learn what kinds of information managers rank highly when considering price adjustments in their operations. Different rankings on information attributes among departments were investigated; results show that managers from different departments rank some information elements differently. Learning the process of a discount choice provided new insights on managerial resources and capabilities required to set and change prices in the lodging industry.

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Intelligent system to support judgmental business forecasting: the case of estimating hotel room demand

Cited by 32 publications

References 18 publications

Do you really know who your customers are?: A study of US retail hotel demand

Do you really know who your customers are?: A study of US retail hotel demand

On a responsive replenishment system: a fuzzy logic approach

Exploring the Role Of Human Judgment in Making Discount Decisions in the Lodging Industry

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