Application of the Bass diffusion model for estimating the lifecycle of a retail store

This article presents a practical application of Lifecycle estimation using the Bass Diffusion Model in the case of a retail store. The results from the application of the model show that the probability that a person will buy driven by advertising is 5%, whereas the probability of buying based on t...

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Autores:
Coronado Hernández, Jairo Rafael
Romero Conrado, Alfonso Rafael
Calderon Perez, Ricardo Rafael
Uribe Martes, Carlos Javier
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/2033
Acceso en línea:
https://hdl.handle.net/11323/2033
https://repositorio.cuc.edu.co/
Palabra clave:
Diffusion model
business lifecycle
Bass
estimation
Modelos de difusión
ciclo de vida de un negocio
estimación
Rights
openAccess
License
http://purl.org/coar/access_right/c_abf2
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/2033
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Application of the Bass diffusion model for estimating the lifecycle of a retail store
dc.title.translated.spa.fl_str_mv Aplicación del modelo de difusión de Bass para estimar el ciclo de vida de una tienda minorista
title Application of the Bass diffusion model for estimating the lifecycle of a retail store
spellingShingle Application of the Bass diffusion model for estimating the lifecycle of a retail store
Diffusion model
business lifecycle
Bass
estimation
Modelos de difusión
ciclo de vida de un negocio
estimación
title_short Application of the Bass diffusion model for estimating the lifecycle of a retail store
title_full Application of the Bass diffusion model for estimating the lifecycle of a retail store
title_fullStr Application of the Bass diffusion model for estimating the lifecycle of a retail store
title_full_unstemmed Application of the Bass diffusion model for estimating the lifecycle of a retail store
title_sort Application of the Bass diffusion model for estimating the lifecycle of a retail store
dc.creator.fl_str_mv Coronado Hernández, Jairo Rafael
Romero Conrado, Alfonso Rafael
Calderon Perez, Ricardo Rafael
Uribe Martes, Carlos Javier
dc.contributor.author.spa.fl_str_mv Coronado Hernández, Jairo Rafael
Romero Conrado, Alfonso Rafael
Calderon Perez, Ricardo Rafael
Uribe Martes, Carlos Javier
dc.subject.spa.fl_str_mv Diffusion model
business lifecycle
Bass
estimation
Modelos de difusión
ciclo de vida de un negocio
estimación
topic Diffusion model
business lifecycle
Bass
estimation
Modelos de difusión
ciclo de vida de un negocio
estimación
description This article presents a practical application of Lifecycle estimation using the Bass Diffusion Model in the case of a retail store. The results from the application of the model show that the probability that a person will buy driven by advertising is 5%, whereas the probability of buying based on the recommendation of another customer is 23%. According to the sales Lifecycle results, the store’s monthly sales have stabilized and its market share is near its peak.
publishDate 2018
dc.date.issued.none.fl_str_mv 2018-12-13
dc.date.accessioned.none.fl_str_mv 2019-01-22T20:43:42Z
dc.date.available.none.fl_str_mv 2019-01-22T20:43:42Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Coronado Hernández, J., Romero-Conrado, A., Uribe-Martes, C., & Calderón-Pérez, R. (2018). Aplicación del modelo de difusión de Bass para estimar el ciclo de vida de una tienda minorista. IJMSOR: International Journal of Management Science & Operation Research, 3(1), 5-10.
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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identifier_str_mv Coronado Hernández, J., Romero-Conrado, A., Uribe-Martes, C., & Calderón-Pérez, R. (2018). Aplicación del modelo de difusión de Bass para estimar el ciclo de vida de una tienda minorista. IJMSOR: International Journal of Management Science & Operation Research, 3(1), 5-10.
2539-5416 electrónico
2539-5416
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/2033
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofseries.spa.fl_str_mv IJMSOR; Vol. 3, Núm. 1 (2018)
dc.relation.ispartofjournal.spa.fl_str_mv IJMSOR: International Journal of Management Science & Operation Research
IJMSOR: International Journal of Management Science & Operation Research
dc.relation.references.spa.fl_str_mv [1] J. Morrison, “Lyfe-Cycle Approach to new Product Forecasting,” J. Bus. Forecast. Methods Syst., vol. 14, no. 2, pp. 3–5, 1995. [2] N. Meade and T. Islam, “Modelling and forecasting the diffusion of innovation - A 25-year review,” International Journal of Forecasting Twenty five years of forecasting, vol. 22, no. 3. pp. 519–545, 2006. [3] R. Scitovski and M. Meler, “Solving parameter estimation problem in new product diffusion models,” Appl. Math. Comput., vol. 127, no. 1, pp. 45–63, 2002.https:// doi.org/10.1016/S0096-3003(00)00164-8 [4] J. R. Coronado-Hernández, J. P. García-Sabater, J. P. Maheut, and J. J. Garcia-Sabater, “Modelo de optimización estocástica para la planificación de cadenas de suministro para productos con ciclo de vida cortos,” in 4th International Conference On Industrial Engineering and Industrial Management, 2010. [5] J. R. Coronado-Hernández, “Análisis del efecto de algunos factores de complejidad e incertidumbre en el rendimiento de las Cadenas de Suministro. Propuesta de una herramienta de valoración basada en simulación.,” Universitat Politècnica de València, Valencia (Spain), 2016.https://doi.org/10.4995/Thesis/10251/61467 [6] A. R. Romero-Conrado, “Algoritmo heurístico basado en listas tabú para la planificación de la producción en sistemas multinivel con listas de materiales alternativas y entornos de coproducción,” Universidad de la Costa, 2018. [7] Z. L. Chen, S. Li, and D. Tirupati, “A scenario-based stochastic programming approach for technology and capacity planning,” Comput. Oper. Res., vol. 29, no. 7, pp. 781–806, 2002. https://doi.org/10.1016/S0305- 0548(00)00076-9 [8] E. M. Rogers, “Social Structure and Social Change,” Am. Behav. Sci., vol. 14, no. 5, pp. 767–782, 1971. https://doi.org/10.1177/000276427101400508 [9] F. Bass, “A New Product Growth for Model Consumer Durables,” Manage. Sci., vol. 15, no. 5, pp. 215–227, 1969. https://doi.org/10.1287/mnsc.15.5.215 [10] V. Mahajan, E. Muller, and F. Bass, “Diffusion of New Products: Empirical Generalizations and Managerial Uses,” Mark. Sci., vol. 14, no. 3, pp. 79–88, 1995. https://doi.org/10.1287/mksc.14.3.G79 [11] E. M. Rogers and T. F. Press, Diffision of Innovations. New York, 1983. [12] C. V Trappey and H. Y. Wu, “An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles,” Adv. Eng. Informatics, vol. 22, no. 4, pp. 421– 430, 2008. https://doi.org/10.1016/j.aei.2008.05.007 [13] M. Lawrence, P. Goodwin, M. O’Connor, and D. Ínkal, “Judgmental forecasting: A review of progress over the last 25áyears,” International Journal of Forecasting Twenty five years of forecasting, vol. 22, no. 3. pp. 493–518, 2006. [14] V. Mahajan and S. Sharma, “A simple algebraic estimation procedure for innovation diffusion models of new product acceptance,” Technol. Forecast. Soc. Change, vol. 30, no. 4, pp. 331–345, 1986. https://doi. org/10.1016/0040-1625(86)90031-4 [15] V. Srinivasan and C. H. Mason, Nonlinear least squares estimation of the Bass diffusion model of new product acceptance. Graduate School of Business, Stanford University, 1984. [16] Rd. C. Team, “R: A language and environment for statistical computing,” ISBN 3-900051-07-0. R Foundation for Statistical Computing. Vienna, Austria, 2013. url: http://www. R-project. org, 2005. [17] M. Valero-Herrero, J. P. Garcia-Sabater, J. R. CoronadoHernandez, and J. P. Maheut, “Planteamiento dinámico del problema de secuenciación en líneas de montaje con mezcla de modelos,” in XV Congreso de Ingeniería de Organización // 5th International Conference on Industrial Engineering and Industrial Management, 2011, pp. 288–296. [18] A. R. Romero-Conrado, L. J. Castro-Bolaño, J. R. Montoya-Torres, and M. Á. Jiménez Barros, “Operations research as a decision-making tool in the health sector: A state of the art,” DYNA, vol. 84, no. 201, p. 129, May 2017. [19] C. Saavedra Sueldo, S. Urrutia, D. Paravié, C. Rohvein, y G. Corres, Una propuesta metodológica para la determinación de capacidades estratégicas en pymes industriales, INGE CUC, vol. 10, n.º 2, pp. 43 - 50, dic. 2014. [20] C. Ayala, Desarrollo de Estrategias de Responsabilidad Social Universitaria, Módulo Arquitectura CUC, vol. 13, n.º 1, pp. 67-86, jul. 2014.
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spelling Coronado Hernández, Jairo RafaelRomero Conrado, Alfonso RafaelCalderon Perez, Ricardo RafaelUribe Martes, Carlos Javier2019-01-22T20:43:42Z2019-01-22T20:43:42Z2018-12-13Coronado Hernández, J., Romero-Conrado, A., Uribe-Martes, C., & Calderón-Pérez, R. (2018). Aplicación del modelo de difusión de Bass para estimar el ciclo de vida de una tienda minorista. IJMSOR: International Journal of Management Science & Operation Research, 3(1), 5-10.2539-5416 electrónicohttps://hdl.handle.net/11323/20332539-5416Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This article presents a practical application of Lifecycle estimation using the Bass Diffusion Model in the case of a retail store. The results from the application of the model show that the probability that a person will buy driven by advertising is 5%, whereas the probability of buying based on the recommendation of another customer is 23%. According to the sales Lifecycle results, the store’s monthly sales have stabilized and its market share is near its peak.En el presente artículo se presenta una aplicación práctica para la estimación del ciclo de vida de una tienda minorista a través de la implementación del modelo de difusión de Bass. Al aplicar el modelo se obtiene que la probabilidad que una persona compre impulsado por publicidad es del 5%, mientras que la que compre por recomendación de otro cliente es del 23%. Al analizar el ciclo de venta del negocio se observa que este llegó a un nivel estable de ventas mensuales y se encuentra en auge dentro de la cuota de mercado que se tiene hasta el momento.Coronado Hernández, Jairo Rafael-d8e63bd7-6965-4dae-bf59-1489a821811c-0Romero Conrado, Alfonso Rafael-0000-0003-4603-0785-600Calderon Perez, Ricardo Rafael-386ad9fe-9b11-43ca-8036-1818136bd08c-0Uribe Martes, Carlos Javier-0000-0001-5466-4134-600application/pdfengFundación I+D+IIJMSOR; Vol. 3, Núm. 1 (2018)IJMSOR: International Journal of Management Science & Operation ResearchIJMSOR: International Journal of Management Science & Operation Research[1] J. Morrison, “Lyfe-Cycle Approach to new Product Forecasting,” J. Bus. Forecast. Methods Syst., vol. 14, no. 2, pp. 3–5, 1995. [2] N. Meade and T. Islam, “Modelling and forecasting the diffusion of innovation - A 25-year review,” International Journal of Forecasting Twenty five years of forecasting, vol. 22, no. 3. pp. 519–545, 2006. [3] R. Scitovski and M. Meler, “Solving parameter estimation problem in new product diffusion models,” Appl. Math. Comput., vol. 127, no. 1, pp. 45–63, 2002.https:// doi.org/10.1016/S0096-3003(00)00164-8 [4] J. R. Coronado-Hernández, J. P. García-Sabater, J. P. Maheut, and J. J. Garcia-Sabater, “Modelo de optimización estocástica para la planificación de cadenas de suministro para productos con ciclo de vida cortos,” in 4th International Conference On Industrial Engineering and Industrial Management, 2010. [5] J. R. Coronado-Hernández, “Análisis del efecto de algunos factores de complejidad e incertidumbre en el rendimiento de las Cadenas de Suministro. Propuesta de una herramienta de valoración basada en simulación.,” Universitat Politècnica de València, Valencia (Spain), 2016.https://doi.org/10.4995/Thesis/10251/61467 [6] A. R. Romero-Conrado, “Algoritmo heurístico basado en listas tabú para la planificación de la producción en sistemas multinivel con listas de materiales alternativas y entornos de coproducción,” Universidad de la Costa, 2018. [7] Z. L. Chen, S. Li, and D. Tirupati, “A scenario-based stochastic programming approach for technology and capacity planning,” Comput. Oper. Res., vol. 29, no. 7, pp. 781–806, 2002. https://doi.org/10.1016/S0305- 0548(00)00076-9 [8] E. M. Rogers, “Social Structure and Social Change,” Am. Behav. Sci., vol. 14, no. 5, pp. 767–782, 1971. https://doi.org/10.1177/000276427101400508 [9] F. Bass, “A New Product Growth for Model Consumer Durables,” Manage. Sci., vol. 15, no. 5, pp. 215–227, 1969. https://doi.org/10.1287/mnsc.15.5.215 [10] V. Mahajan, E. Muller, and F. Bass, “Diffusion of New Products: Empirical Generalizations and Managerial Uses,” Mark. Sci., vol. 14, no. 3, pp. 79–88, 1995. https://doi.org/10.1287/mksc.14.3.G79 [11] E. M. Rogers and T. F. Press, Diffision of Innovations. New York, 1983. [12] C. V Trappey and H. Y. Wu, “An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles,” Adv. Eng. Informatics, vol. 22, no. 4, pp. 421– 430, 2008. https://doi.org/10.1016/j.aei.2008.05.007 [13] M. Lawrence, P. Goodwin, M. O’Connor, and D. Ínkal, “Judgmental forecasting: A review of progress over the last 25áyears,” International Journal of Forecasting Twenty five years of forecasting, vol. 22, no. 3. pp. 493–518, 2006. [14] V. Mahajan and S. Sharma, “A simple algebraic estimation procedure for innovation diffusion models of new product acceptance,” Technol. Forecast. Soc. Change, vol. 30, no. 4, pp. 331–345, 1986. https://doi. org/10.1016/0040-1625(86)90031-4 [15] V. Srinivasan and C. H. Mason, Nonlinear least squares estimation of the Bass diffusion model of new product acceptance. Graduate School of Business, Stanford University, 1984. [16] Rd. C. Team, “R: A language and environment for statistical computing,” ISBN 3-900051-07-0. R Foundation for Statistical Computing. Vienna, Austria, 2013. url: http://www. R-project. org, 2005. [17] M. Valero-Herrero, J. P. Garcia-Sabater, J. R. CoronadoHernandez, and J. P. Maheut, “Planteamiento dinámico del problema de secuenciación en líneas de montaje con mezcla de modelos,” in XV Congreso de Ingeniería de Organización // 5th International Conference on Industrial Engineering and Industrial Management, 2011, pp. 288–296. [18] A. R. Romero-Conrado, L. J. Castro-Bolaño, J. R. Montoya-Torres, and M. Á. Jiménez Barros, “Operations research as a decision-making tool in the health sector: A state of the art,” DYNA, vol. 84, no. 201, p. 129, May 2017. [19] C. Saavedra Sueldo, S. Urrutia, D. Paravié, C. Rohvein, y G. Corres, Una propuesta metodológica para la determinación de capacidades estratégicas en pymes industriales, INGE CUC, vol. 10, n.º 2, pp. 43 - 50, dic. 2014. [20] C. 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