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Revenue management in Hotel industry - Essay Example

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Revenue management in Hotel industry
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Revenue Management in the Hotel Industry Revenue management in basic terms concerns the technique for optimizing income revenue realized from fixed, perishable inventory. Its origin could be traced back to the airline industry in the 1980s when Ultimate Super Saver airline fares were launched by American airlines to give them competitiveness against PeopleExpress, considered to be a low cost carrier. The need was to fill the minimum seats to cover the airline’s fixed operating expenses then the remaining seats would be sold at higher rates so as to maximize profits and revenue.

Salerno (2012) identified three conditions which would be necessary for revenue management: there should be adequate fixed resources for sale; the resources should be perishable; and customers should be willing to pay different prices for the resources. Hotels sell rooms as fixed inventory which are highly perishable and attract different prices depending on size, location and availability of unique features. Appropriate forecast enhances identification of cost drivers leading to development of appropriate measures of performance.

It would enable an organization understand how operational drivers affect its financial performance outcomes. As budget forecasts are highly dependent on demand and supply, hotels should consider their room occupancy and the charged rates should increase with increase in reservations. But in practice, this does not happen and Salerno (2012) argues that most hoteliers would blindly set rates for future and then get disappointed.Poor accuracy of budgets still remains a problem in many organizations.

When forecasts are made way above or below the budget, the organization risks making bad decisions based on the incorrect projections. This would also cause the management to pay less attention to budgets as they become unreliable and not trusted. Generally, inaccurate forecasts significantly affect revenue management system performance as the organization will suffer lack of proper planning (Weatherford & Kimes, 2003). When the forecasts are set so high, Hayes and Miller (2011) argue that the forecast then becomes a motivational tool for increased performance and not a revenue management tool.

However, the authors note that no organization should seek to make inaccurate forecasts.These inaccuracies would be a result of deriving facts from poor tools of budgeting such as spreadsheets fed with wrong formulae. Limited time for employees to come up with good projections and their subsequent lack of motivation could also be a reason. Additionally, sidelining key employees from the budget process in a top-down budgeting process would lead to inaccurate budgeting.Therefore, Salerno (2012) proposes that hotels should forecast their room occupancy at least six months in advance though a one year forecast would be sufficient enough.

Weatherford and Kimes (2003) affirm the importance of referring back to history when making accurate forecasts. Improving the accuracy of forecasts and budgets would make planning and execution of operations by management easier. It enables managers understand the past and current demand for their hotel rooms and makes it easier to determine the interventions needed to meet the performance targets of their businesses and make informed decisions when targets are not met. Hayes and Miller (2011) note that accurate forecast enable hotel managers efficiently schedule their staff and purchase the needed supplies in correct quantities.

ReferencesHayes, D. K. & Miller, A. (2011). Revenue Management for the Hospitality Industry. Hoboken, New Jersey: John Wiley & Sons Inc.Salerno, N. (2012). What the Heck is Hotel Revenue Management, Anyway? A Hotel Marketer’s Guide to Revenue Management. Retrieved 27 April 2012 from http://www.hotelmarketingcoach.com/What%20the%20heck%20is%20Revenue%2 0Management.htmWeatherford, L.R. & Kimes, S. E. (2003). A Comparison of Forecasting Methods for Hotel Revenue Management. International Journal of Forecasting, vol. 19, no. 3, 401 – 415.

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