Modelo de inventario fractal para la toma de decisiones hotelera

Authors

DOI:

https://doi.org/10.29105/vtga6.1-557

Keywords:

Modelo de inventario, Geometría Fractal, Hotel

Abstract

This article develops a future data model for a fourstar hotel in the city of Pachuca, Hidalgo. The model
is developed under the technique of complex
systems and chaos theory. The results show that the
series is multi-fractal, that is, it presents a behavior
related to power laws. The series is also antipersistent, presenting positive increases followed by
equal increases. The breakpoint in the series is
determined in data 12. The forecast parameters show
the exponent H = 0.087 and a Range = 0.56, with this
information the inventory model is developed for
one of the inputs through the graphical method and a
sensitivity analysis is carried out for the decision making of the company case study.

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References

Akal, M. (2004). Forecasting Turkey’s tourism revenues by ARMAX model. Tourism Management, 25(5), 565–580. https://doi.org/10.1016/j.tourman.2003.08.001 DOI: https://doi.org/10.1016/j.tourman.2003.08.001

Al Shehhi, M., & Karathanasopoulos, A. (2020). Forecasting hotel room prices in selected GCC cities using deep learning. Journal of Hospitality and Tourism Management, 42, 40–50. https://doi.org/10.1016/j.jhtm.2019.11.003 DOI: https://doi.org/10.1016/j.jhtm.2019.11.003

Alvarez, E., & Brida, J. G. (2019). An agent-based model of tourism destinations choice. International Journal of Tourism Research, 21(2), 145–155. https://doi.org/10.1002/jtr.2248 DOI: https://doi.org/10.1002/jtr.2248

Ampountolas, A. (2018). Forecasting hotel demand uncertainty using time series Bayesian VAR models: Tourism Economics. https://doi.org/10.1177/1354816618801741 DOI: https://doi.org/10.1177/1354816618801741

Arbelo-Pérez, M., Arbelo, A., & Pérez-Gómez, P. (2017). Impact of quality on estimations of hotel efficiency. Tourism Management, 61, 200–208. https://doi.org/10.1016/j.tourman.2017.02.011 DOI: https://doi.org/10.1016/j.tourman.2017.02.011

Assaf, A. G., & Tsionas, M. G. (2019). Forecasting occupancy rate with Bayesian compression methods. Annals of Tourism Research, 75, 439–449. https://doi.org/10.1016/j.annals.2018.12.009 DOI: https://doi.org/10.1016/j.annals.2018.12.009

Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29(1), 19–31. https://doi.org/10.1016/j.tourman.2007.04.009 DOI: https://doi.org/10.1016/j.tourman.2007.04.009

Balankin, A. S., Morales Matamoros, O., Gálvez, E., & Pérez, A. (2004). Crossover from antipersistent to persistent behavior in time series possessing the generalyzed dynamic scaling law. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 69(3 Pt 2), 036121. https://doi.org/10.1103/PhysRevE.69.036121 DOI: https://doi.org/10.1103/PhysRevE.69.036121

Banxico. (2020). SIE - Inflación. https://www.banxico.org.mx/tipcamb/main.do?page=inf&idioma=sp

Bi, J.-W., Liu, Y., Fan, Z.-P., & Zhang, J. (2020). Exploring asymmetric effects of attribute performance on customer satisfaction in the hotel industry. Tourism Management, 77, 104006. https://doi.org/10.1016/j.tourman.2019.104006 DOI: https://doi.org/10.1016/j.tourman.2019.104006

Cang, S., & Yu, H. (2014). A combination selection algorithm on forecasting. European Journal of Operational Research, 234(1), 127–139. https://doi.org/10.1016/j.ejor.2013.08.045 DOI: https://doi.org/10.1016/j.ejor.2013.08.045

Castro, J. A. V., Coria, E. C., & Martínez, E. E. V. (2018). Cooperación empresarial para el fomento de la innovación en la pyme turística. Revista de ciencias sociales, 24(3), 9–20. DOI: https://doi.org/10.31876/rcs.v24i3.24918

Chan, C. K., Witt, S. F., Lee, Y. C. E., & Song, H. (2010). Tourism forecast combination using the CUSUM technique. Tourism Management, 31(6), 891–897. https://doi.org/10.1016/j.tourman.2009.10.004 DOI: https://doi.org/10.1016/j.tourman.2009.10.004

Chang, C.-L., Sriboonchitta, S., & Wiboonpongse, A. (2009). Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation. Mathematics and Computers in Simulation, 79(5), 1730–1744. https://doi.org/10.1016/j.matcom.2008.09.006 DOI: https://doi.org/10.1016/j.matcom.2008.09.006

Chu, F.-L. (2008). Analyzing and forecasting tourism demand with ARAR algorithm. Tourism Management, 29(6), 1185–1196. https://doi.org/10.1016/j.tourman.2008.02.020 DOI: https://doi.org/10.1016/j.tourman.2008.02.020

Chu, F.-L. (2011). A piecewise linear approach to modeling and forecasting demand for Macau tourism. Tourism Management, 32(6), 1414–1420. https://doi.org/10.1016/j.tourman.2011.01.018 DOI: https://doi.org/10.1016/j.tourman.2011.01.018

Chu, F.-L. (2014). Using a logistic growth regression model to forecast the demand for tourism in Las Vegas. Tourism Management Perspectives, 12, 62–67. https://doi.org/10.1016/j.tmp.2014.08.003 DOI: https://doi.org/10.1016/j.tmp.2014.08.003

Costa, C., Bakas, F. E., Breda, Z., Durão, M., Carvalho, I., & Caçador, S. (2017). Gender, flexibility and the ‘ideal tourism worker.’ Annals of Tourism Research, 64, 64–75. https://doi.org/10.1016/j.annals.2017.03.002 DOI: https://doi.org/10.1016/j.annals.2017.03.002

Danese, P., & Kalchschmidt, M. (2011). The role of the forecasting process in improving forecast accuracy and operational performance. International Journal of Production Economics, 131(1), 204–214. DOI: https://doi.org/10.1016/j.ijpe.2010.09.006

De la Peña, M. R., Núñez-Serrano, J. A., Turrión, J., & Velázquez, F. J. (2016). Are innovations relevant for consumers in the hospitality industry? A hedonic approach for Cuban hotels. Tourism Management, 55, 184–196. https://doi.org/10.1016/j.tourman.2016.02.009 184 DOI: https://doi.org/10.1016/j.tourman.2016.02.009

Furenes, M. I., Øgaard, T., & Gjerald, O. (2017). How face-to-face feedback influences guest outcome evaluation of co-production: Changing or shaping guest experiences? Tourism Management Perspectives, 21, 59–65. https://doi.org/10.1016/j.tmp.2016.11.004 DOI: https://doi.org/10.1016/j.tmp.2016.11.004

Gunter, U., & Önder, I. (2015). Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data. Tourism Management, 46, 123–135. https://doi.org/10.1016/j.tourman.2014.06.017 DOI: https://doi.org/10.1016/j.tourman.2014.06.017

Hassani, H., Silva, E. S., Antonakakis, N., Filis, G., & Gupta, R. (2017). Forecasting accuracy evaluation of tourist arrivals. Annals of Tourism Research, 63, 112–127. https://doi.org/10.1016/j.annals.2017.01.008 DOI: https://doi.org/10.1016/j.annals.2017.01.008

Ho, C.-I., & Lee, Y.-L. (2007). The development of an e-travel service quality scale. Tourism Management, 28(6), 1434–1449. https://doi.org/10.1016/j.tourman.2006.12.002 DOI: https://doi.org/10.1016/j.tourman.2006.12.002

Hodari, D., & Sturman, M. C. (2014). Who’s in Charge Now? The Decision Autonomy of Hotel General Managers: Cornell Hospitality Quarterly. https://doi.org/10.1177/1938965513518839 DOI: https://doi.org/10.1177/1938965513518839

Hu, B., & Mao, J.-M. (1987). Fractal dimension and degeneracy of the critical point for iterated maps. Journal of Physics A: Mathematical and General, 20(7), 1809–1818. https://doi.org/10.1088/0305-4470/20/7/026 DOI: https://doi.org/10.1088/0305-4470/20/7/026

Johnson, P. A., & Sieber, R. (2009). Agent-Based Modelling: A Dynamic Scenario Planning Approach to Tourism PSS. In S. Geertman & J. Stillwell (Eds.), Planning Support Systems Best Practice and New Methods (pp. 211–226). Springer Netherlands. https://doi.org/10.1007/978-1-4020- 8952-7_11 DOI: https://doi.org/10.1007/978-1-4020-8952-7_11

Kotler, P. (1997). Marketing Management. Prentice Hall. https://books.google.com/books/about/Marketing_Management.html?id=4ViTPwAACAAJ

Kourentzes, N., & Athanasopoulos, G. (2019). Cross-temporal coherent forecasts for Australian tourism. Annals of Tourism Research, 75, 393–409. https://doi.org/10.1016/j.annals.2019.02.001 DOI: https://doi.org/10.1016/j.annals.2019.02.001

Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75, 410–423. https://doi.org/10.1016/j.annals.2019.01.014 DOI: https://doi.org/10.1016/j.annals.2019.01.014

Li, G., Wu, D. C., Zhou, M., & Liu, A. (2019). The combination of interval forecasts in tourism. Annals of Tourism Research, 75, 363–378. https://doi.org/10.1016/j.annals.2019.01.010 DOI: https://doi.org/10.1016/j.annals.2019.01.010

McGuire, K. A. (2016). The Analytic Hospitality Executive: Implementing Data Analytics in Hotels and Casinos. John Wiley & Sons. DOI: https://doi.org/10.1002/9781119162308

Morales‐ Matamoros, O., Tejeida‐ Padilla, R., & Badillo‐ Piña, I. (2010). Fractal behaviour of complex systems. Systems Research and Behavioral Science, 27(1), 71–86. https://doi.org/10.1002/sres.984 DOI: https://doi.org/10.1002/sres.984

Piccoli, G., Lui, T.-W., & Grün, B. (2017). The impact of IT-enabled customer service systems on service personalization, customer service perceptions, and hotel performance. Tourism Management, 59, 349–362. https://doi.org/10.1016/j.tourman.2016.08.015 DOI: https://doi.org/10.1016/j.tourman.2016.08.015

Puška, A., Šadić, S., Maksimović, A., & Stojanović, I. (2020). Decision support model in the determination of rural touristic destination attractiveness in the Brčko District of Bosnia and Herzegovina: Tourism and Hospitality Research. https://doi.org/10.1177/1467358420904100 DOI: https://doi.org/10.1177/1467358420904100

Qiu, S., Dooley, L. M., & Xie, L. (2020). How servant leadership and self-efficacy interact to affect service quality in the hospitality industry: A polynomial regression with response surface analysis. Tourism Management, 78, 104051. https://doi.org/10.1016/j.tourman.2019.104051 DOI: https://doi.org/10.1016/j.tourman.2019.104051

Rivera, R. (2016). A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data. Tourism Management, 57, 12–20. https://doi.org/10.1016/j.tourman.2016.04.008 DOI: https://doi.org/10.1016/j.tourman.2016.04.008

SECTUR. (2018). Visión global del turismo a México, análisis de mercados, perspectivas del turismo mundial. https://www.datatur.sectur.gob.mx/Documentos%20compartidos/VisionGlobalTurismoAMex Abr2018.pdf 185

SECTUR. (2019). Estrategia Nacional De Turismo 2019-2020. gob.mx. http://www.gob.mx/sectur/prensa/estrategia-nacional-de-turismo-2019-2024-tendra-un sentido-democratico-miguel-torruco

Shen, S., Li, G., & Song, H. (2011). Combination forecasts of International tourism demand. Annals of Tourism Research, 38(1), 72–89. https://doi.org/10.1016/j.annals.2010.05.003 DOI: https://doi.org/10.1016/j.annals.2010.05.003

Song, H., Gao, B. Z., & Lin, V. S. (2013). Combining statistical and judgmental forecasts via a web based tourism demand forecasting system. International Journal of Forecasting, 29(2), 295–310. https://doi.org/10.1016/j.ijforecast.2011.12.003 DOI: https://doi.org/10.1016/j.ijforecast.2011.12.003

Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338–362. https://doi.org/10.1016/j.annals.2018.12.001 DOI: https://doi.org/10.1016/j.annals.2018.12.001

Sornette, D., & Andersen, J. V. (2000). Increments of Uncorrelated Time Series Can Be Predicted With a Universal 75% Probability of Success. International Journal of Modern Physics C, 11(04), 713–720. https://doi.org/10.1142/S0129183100000626 DOI: https://doi.org/10.1142/S0129183100000626

Wong, K. K. F., Song, H., & Chon, K. S. (2006). Bayesian models for tourism demand forecasting. Tourism Management, 27(5), 773–780. https://doi.org/10.1016/j.tourman.2005.05.017 DOI: https://doi.org/10.1016/j.tourman.2005.05.017

Xiang, Z. (2018). From digitization to the age of acceleration: On information technology and tourism. Tourism Management Perspectives, 25, 147–150. https://doi.org/10.1016/j.tmp.2017.11.023 DOI: https://doi.org/10.1016/j.tmp.2017.11.023

Xie, L., Guan, X., & Huan, T.-C. (2019). A case study of hotel frontline employees’ customer need knowledge relating to value co-creation. Journal of Hospitality and Tourism Management, 39, 76–86. https://doi.org/10.1016/j.jhtm.2019.02.002 DOI: https://doi.org/10.1016/j.jhtm.2019.02.002

Yang, Y., & Zhang, H. (2019). Spatial-temporal forecasting of tourism demand. Annals of Tourism Research, 75, 106–119. https://doi.org/10.1016/j.annals.2018.12.024 DOI: https://doi.org/10.1016/j.annals.2018.12.024

Published

2020-07-01

How to Cite

Briones-Juárez, A., Velázquez-Castro, J. A., & Cruz-Coria, E. (2020). Modelo de inventario fractal para la toma de decisiones hotelera . Vinculategica Efan, 6(1), 174–186. https://doi.org/10.29105/vtga6.1-557