Metodología de gestión de la demanda basado en forecast: solución para pronóstico erróneo de materiales en industria hotelera
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Advisors
Ramirez Valdivia, Cesar MarcialIssue Date
2021-02-08Keywords
Industria hoteleraGestión de la demanda
Pronóstico erróneo
Hotel industry
Demand management
Wrong forecast
Forecast
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Demand management methodology based on forecast: solution for wrong forecast of materials in hotel industryAbstract
El pronóstico erróneo de huéspedes y materiales en los hoteles de Huancayo es un problema que conlleva a generar gastos ocasionados por rotura de stock y exceso de stock, que a su vez conlleva a pérdida de clientes. De esta forma se diseñó un modelo el cual mediante cuatro métodos de pronóstico puede sugerir al hotel qué cantidad de huéspedes van a arribar al hotel y cuántos productos se debe de comprar para evitar costos adicionales. En esta investigación se dará a conocer los resultados obtenidos con la aplicación de un modelo de pronóstico de la demanda basado en forcast en el hotel “Rey” ubicado en la provincia de Huancayo del departamento de Junín. En el presente artículo se pronosticó cantidades para el año 2020 y se comparan mediante el error MAPE, así se logra sugerir un pronóstico con un porcentaje de error reducido y mejor a comparación con el que se tenía antes de la implementación.The wrong forecast of guests and materials in Huancayo’s hotels is a big problem that leads to the generation of expenses caused by stock breakage and excess stock, which in turn leads to loss of customers. In this way, a model was designed which, through four forecasting methods, can suggest to the hotel how many guests will arrive at the hotel and how many products should be purchased to avoid additional costs. In this research, the results obtained with the application of a demand forecasting model based on forcast in the “Rey” hotel, located in Huancayo, will be disclosed. In this article, quantities were predicted for the year 2020 and they are compared using the MAPE error, in this way is possible to suggest a forecast with a reduced and better error percentage compared to the one that was had before implementation.
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info:eu-repo/semantics/bachelorThesisRights
info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 International
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- Creative Commons