Determinación de la Influencia de la temperatura en la precisión de los pronósticos de la demanda : Caso de estudio en una empresa de consumo masivo de café soluble liofilizado
| dc.contributor.advisor | Antero Arango, Jaime | |
| dc.contributor.author | Carreño Muñoz, Juan David | |
| dc.date.accessioned | 2025-03-14T19:42:51Z | |
| dc.date.available | 2025-03-14T19:42:51Z | |
| dc.date.issued | 2025 | |
| dc.description | graficas, tablas | spa |
| dc.description.abstract | Influencia de la temperatura en la precisión de los pronósticos de la demanda: Caso de estudio en una empresa de consumo masivo de café soluble liofilizado. El objetivo principal de este trabajo final de maestría fue determinar la influencia de la temperatura en la precisión de los pronósticos de demanda de café soluble liofilizado para un proveedor colombiano en el mercado mexicano. Se recopilaron datos históricos de ventas mensuales de café soluble y temperaturas promedio (2004-2019) en México, con el propósito de analizar la correlación entre la temperatura promedio y las ventas de café soluble en cada estado mediante el coeficiente de Pearson, encontrando una relación negativa fuerte (-0.9 promedio). Los resultados evidencian que las ventas tienden a incrementarse en estaciones más frías (invierno y otoño) y a disminuir en periodos cálidos (primavera y verano). Por otro lado, se logró mejorar la precisión de los pronósticos de la empresa objeto de estudio al implementar modelos estadísticos que incorporan la estacionalidad como una variable determinante. Se concluye que la temperatura es un factor clave en el comportamiento de las ventas de café soluble en México, impactando así mismo el abastecimiento desde su proveedor colombiano. Se recomienda replicar para futuros trabajos el estudio en otros mercados y la exploración de métodos alternativos para integrar variables exógenas adicionales que puedan influir en las ventas (Texto tomado de la fuente). | spa |
| dc.description.abstract | Influence of Temperature on the Accuracy of Demand Forecasts: A Case Study in a Mass-Consumption Freeze-Dried Instant Coffee Company. The main objective of this master's thesis was to determine the influence of temperature on the accuracy of demand forecasts for freeze-dried instant coffee for a Colombian supplier in the Mexican market. Historical data on monthly coffee sales and average temperatures (2004-2019) in Mexico were collected to analyze the correlation between average temperature and coffee sales in each state using Pearson's coefficient, revealing a strong negative relationship (average of -0.9). The results show that sales tend to increase during colder seasons (winter and autumn) and decrease during warmer periods (spring and summer). Additionally, the accuracy of the company’s forecasts improved by implementing statistical models that consider seasonality as a key variable. It is concluded that temperature is a critical factor in the sales behavior of instant coffee in Mexico and, therefore, in the sales of its Colombian supplier. It is recommended to replicate this study in other markets for future research and explore alternative methods to integrate additional exogenous variables that may influence sales. | eng |
| dc.description.curriculararea | Administración.Sede Manizales | spa |
| dc.description.degreelevel | Maestría | spa |
| dc.description.degreename | Magíster en Administración | spa |
| dc.description.researcharea | Gestión de operaciones | spa |
| dc.format.extent | xiii, 56 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.instname | Universidad Nacional de Colombia | spa |
| dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
| dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/87657 | |
| dc.language.iso | spa | spa |
| dc.publisher | Universidad Nacional de Colombia | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Manizales | spa |
| dc.publisher.faculty | Facultad de Administración | spa |
| dc.publisher.place | Manizales, Colombia | spa |
| dc.publisher.program | Manizales - Administración - Maestría en Administración | spa |
| dc.relation.references | Andrés Méndez Giraldo, G. (2014). Methodology to demand forecastingunder multiproduct environments and high variability (Vol. 18, Issue 40). | spa |
| dc.relation.references | Bahng, Y., & Kincade, D. H. (2012). The relationship between temperature and sales: Sales data analysis of a retailer of branded women’s business wear. International Journal of Retail & Distribution Management, 40(6), 410–426. https://doi.org/10.1108/09590551211230232 | spa |
| dc.relation.references | Bala, M., & Kumar, D. (2011). Supply chain performance attributes for the fast moving consumer goods industry. Journal of Transport and Supply Chain Management, 5(1). https://doi.org/10.4102/jtscm.v5i1.19 | spa |
| dc.relation.references | Basson, L. M., Kilbourn, P. J., & Walters, J. (2019). Forecast accuracy in demand planning: A fast-moving consumer goods case study. Journal of Transport and Supply Chain Management, 13. https://doi.org/10.4102/jtscm.v13i0.427 | spa |
| dc.relation.references | Beverland, M., Lindgreen, A., Napoli, J., Kotler, P., & Pfoertsch, W. (2007). Being known or being one of many: The need for brand management for business-to-business (B2B) companies. Journal of Business & Industrial Marketing, 22(6). https://doi.org/10.1108/08858620710780118 | spa |
| dc.relation.references | Cassettari, L., Bendato, I., Mosca, M., & Mosca, R. (2017). A new stochastic multi source approach to improve the accuracy of the sales forecasts. Foresight, 19(1). https://doi.org/10.1108/FS-07-2016-0036 | spa |
| dc.relation.references | Chase, R., Jacobs, R., & Aquiliano, N. (2017). Operations and supply management. In Gestion-Calidad.com | spa |
| dc.relation.references | Colin D. Lewis. (1982). Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. Butterworth Scientific, June 1981 | spa |
| dc.relation.references | Cooper, L. G., Baron, P., Levy, W., Swisher, M., & Gogos, P. (1999). PromoCastTM: A new forecasting method for promotion planning. Marketing Science, 18(3). https://doi.org/10.1287/mksc.18.3.301 | spa |
| dc.relation.references | Croxton, K. L., Lambert, D. M., García-Dastugue, S. J., & Rogers, D. S. (2002). The Demand Management Process. The International Journal of Logistics Management, 13(2). https://doi.org/10.1108/09574090210806423 | spa |
| dc.relation.references | Daros, W. R. (2002). ¿Qué es un marco teórico? Enfoques, XIV, númer. https://doi.org/10.4067/S0718-09342002005100014 | spa |
| dc.relation.references | Euromonitor. (2023). Private label: Evolution of premium in food and beverages | spa |
| dc.relation.references | Farizal, F., Dachyar, M., Taurina, Z., & Qaradhawi, Y. (2021). Disclosing fast moving consumer goods demand forecasting predictor using multi linear regression. Engineering and Applied Science Research, 48(5), 627–636. https://doi.org/10.14456/easr.2021.64 | spa |
| dc.relation.references | Garavito, K. (2021). Implementación de un método para el pronóstico de demanda de computadores portátiles. Universidad Nacional de Colombia | spa |
| dc.relation.references | Grewal, R., & Lilien, G. L. (2012). Business-to-business marketing: Looking back, looking forward. In Handbook of Business-to-Business Marketing. https://doi.org/10.4337/9781781002445.00008 | spa |
| dc.relation.references | Guru, B. K., & Das, A. (2021). COVID-19 and uncertainty spillovers in Indian stock market. MethodsX, 8, 101199. https://doi.org/10.1016/j.mex.2020.101199 | spa |
| dc.relation.references | Hernandez Sampieri, R., Fernandez Collado, C., & Baptista Lucio, M. del P. (2010). Definición del alcance de la investigación a realizar: exploratoria, descriptiva, correlacional o explicativa. In Metodología de la investigación | spa |
| dc.relation.references | Hewage, H. C., Perera, H. N., & De Baets, S. (2022). Forecast adjustments during post- promotional periods. European Journal of Operational Research, 300(2), 461–472. https://doi.org/10.1016/j.ejor.2021.07.057 | spa |
| dc.relation.references | Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4). https://doi.org/10.1016/j.ijforecast.2006.03.001 | spa |
| dc.relation.references | Kilger, C., & Wagner, M. (2008). Demand Planning. In Supply Chain Management and Advanced Planning (pp. 133–160). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74512-9_8 | spa |
| dc.relation.references | Koutsandreas, D., Spiliotis, E., Petropoulos, F., & Assimakopoulos, V. (2022). On the selection of forecasting accuracy measures. Journal of the Operational Research Society, 73(5). https://doi.org/10.1080/01605682.2021.1892464 | spa |
| dc.relation.references | Liu, Y., Li, M., & Zhu, Z. (2013). Simulated annealing sales combining forecast in FMCG. Proceedings - 2013 IEEE 10th International Conference on e-Business Engineering, ICEBE 2013, 230–235. https://doi.org/10.1109/ICEBE.2013.35 | spa |
| dc.relation.references | LMC. (2023). Coffee market Insight | spa |
| dc.relation.references | Makridakis, S., Fry, C., Petropoulos, F., & Spiliotis, E. (2022). The Future of Forecasting Competitions: Design Attributes and Principles. INFORMS Journal on Data Science, 1(1), 96–113. https://doi.org/10.1287/ijds.2021.0003 | spa |
| dc.relation.references | Martínez-de-Albéniz, V., & Belkaid, A. (2021). Here comes the sun: Fashion goods retailing under weather fluctuations. European Journal of Operational Research, 294(3), 820–830. https://doi.org/10.1016/j.ejor.2020.01.064 | spa |
| dc.relation.references | Mehdiyev, N., Enke, D., Fettke, P., & Loos, P. (2016). Evaluating Forecasting Methods by Considering Different Accuracy Measures. Procedia Computer Science, 95, 264–271. https://doi.org/10.1016/j.procs.2016.09.332 | spa |
| dc.relation.references | Nicolai, A., & Seidl, D. (2010). That’s relevant! different forms of practical relevance in management science. Organization Studies, 31(9–10). https://doi.org/10.1177/0170840610374401 | spa |
| dc.relation.references | Nikolopoulos, K., & Fildes, R. (2013). Adjusting supply chain forecasts for short-term temperature estimates: A case study in a Brewing company. IMA Journal of Management Mathematics, 24(1), 79–88. https://doi.org/10.1093/imaman/dps006 | spa |
| dc.relation.references | Parker, P. M., & Tavassoli, N. T. (2000). Homeostasis and consumer behavior across cultures. International Journal of Research in Marketing, 17(1). https://doi.org/10.1016/s0167-8116(00)00006-9 | spa |
| dc.relation.references | Ramanathan, U., & Muyldermans, L. (2010). Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK. International Journal of Production Economics, 128(2), 538–545. https://doi.org/10.1016/j.ijpe.2010.07.007 | spa |
| dc.relation.references | Sarmiento, A. T., & Soto, O. C. (2014). New product forecasting demand by using neural networks and similar product analysis. DYNA, 81(186). https://doi.org/10.15446/dyna.v81n186.45223 | spa |
| dc.relation.references | Sivillo, J., & Reilly, D. (2004). Forecasting Consumer Product Demand With Weather Information: a Case Study. Journal of Business Forecasting | spa |
| dc.relation.references | Skinner, W. (1969). Manufacturing-missing link in corporate strategy. Harvard Business Review, 47(3) | spa |
| dc.relation.references | Stevenson, W. (2007). Operations Management (8th ed.). McGraw-Hill | spa |
| dc.relation.references | Tarallo, E., Akabane, G. K., Shimabukuro, C. I., Mello, J., & Amancio, D. (2019). Machine learning in predicting demand for fast-moving consumer goods: An exploratory research. IFAC-PapersOnLine, 52(13), 737–742. https://doi.org/10.1016/j.ifacol.2019.11.203 | spa |
| dc.relation.references | Tian, X., Cao, S., & Song, Y. (2021). The impact of weather on consumer behavior and retail performance: Evidence from a convenience store chain in China. Journal of Retailing and Consumer Services, 62. https://doi.org/10.1016/j.jretconser.2021.102583 | spa |
| dc.relation.references | Vivares-Vergara, J. A., Sarache, W., & Naranjo-Valencia, J. C. (2015). Estrategia de manufactura: Explorando el contenido y el proceso. Informacion Tecnologica, 26(3). https://doi.org/10.4067/S0718-07642015000300013 | spa |
| dc.relation.references | Wang, J. (2013). Pearson Correlation Coefficient. In Encyclopedia of Systems Biology. https://doi.org/10.1007/978-1-4419-9863-7_372 | spa |
| dc.relation.references | Wang, Z. X., Wu, J. M., Zhou, C. J., & Li, Q. (2020). Identifying the factors of China’s seasonal retail sales of consumer goods using a data grouping approach–based GRA method. Grey Systems, 10(2), 125–143. https://doi.org/10.1108/GS-11-2019-0055 | spa |
| dc.relation.references | Yau, A., Berger, N., Law, C., Cornelsen, L., Greener, R., Adams, J., Boyland, E. J., Burgoine, T., de Vocht, F., Egan, M., Er, V., Lake, A. A., Lock, K., Mytton, O., Petticrew, M., Thompson, C., White, M., & Cummins, S. (2022). Changes in household food and drink purchases following restrictions on the advertisement of high fat, salt, and sugar products across the Transport for London network: A controlled interrupted time series analysis. PLoS Medicine, 19(2). https://doi.org/10.1371/journal.pmed.1003915 | spa |
| dc.relation.references | Yohannes, M. F., & Matsuda, T. (2016). Weather Effects on Household Demand for Coffee and Tea in Japan. Agribusiness, 32(1). https://doi.org/10.1002/agr.21434 | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
| dc.subject.ddc | 600 - Tecnología (Ciencias aplicadas)::606 - Organizaciones | spa |
| dc.subject.proposal | Planeación de la demanda | spa |
| dc.subject.proposal | Pronóstico | spa |
| dc.subject.proposal | Temperatura | spa |
| dc.subject.proposal | Estaciones | spa |
| dc.subject.proposal | Café soluble liofilizado | spa |
| dc.subject.proposal | Demand planning | eng |
| dc.subject.proposal | Forecasting | eng |
| dc.subject.proposal | Temperature | eng |
| dc.subject.proposal | Seasons | eng |
| dc.subject.proposal | Freeze-Dried Instant Coffee | eng |
| dc.subject.unesco | Café | spa |
| dc.subject.unesco | Demanda | spa |
| dc.subject.unesco | Consumo de alimentos | spa |
| dc.title | Determinación de la Influencia de la temperatura en la precisión de los pronósticos de la demanda : Caso de estudio en una empresa de consumo masivo de café soluble liofilizado | spa |
| dc.title.translated | Determining the influence of temperature on the accuracy of demand forecasts : A case study in a mass-consumption freeze-dried instant coffee company | eng |
| dc.type | Trabajo de grado - Maestría | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| dcterms.audience.professionaldevelopment | Administradores | spa |
| dcterms.audience.professionaldevelopment | Bibliotecarios | spa |
| dcterms.audience.professionaldevelopment | Estudiantes | spa |
| dcterms.audience.professionaldevelopment | Investigadores | spa |
| dcterms.audience.professionaldevelopment | Maestros | spa |
| dcterms.audience.professionaldevelopment | Público general | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
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