Palabras clave
isocuantas
planeación de servicios
asignación de recursos
líneas de espera
Cómo citar
Resumen
Objetivo: realizar el cálculo del personal requerido y los recursos necesarios para alcanzar el cumplimiento de la promesa de servicio pactada con el cliente. Materiales y métodos: este artículo presenta un modelo de simulación de eventos discretos (DES, por sus siglas en inglés), desarrollado para la implementación un punto de venta (POS) para una empresa que provee productos financieros. En la primera parte, el artículo muestra la caracterización de los componentes del sistema y los tiempos de proceso, seguido de la estimación estadística de las pruebas de hipótesis y de bondad de ajuste. Posteriormente, los escenarios de simulación evalúan los tiempos entre las llegadas y el número de asesores comerciales. Resultados y discusión: este modelo permite evaluar la asignación de recursos para cumplir la promesa de servicio, la cual es que el 80 % de los clientes deben ser atendidos en una hora o antes. Este artículo proporciona las gráficas isocuantas de servicio que permiten observar el comportamiento de la medida de desempeño (cumplimiento de la promesa de servicio) entre los diferentes escenarios. Conclusiones: Usando técnicas de DES es posible evaluar la asignación de personal para lograr el cumplimiento de la promesa de servicio, considerando las instalaciones, herramientas y evaluación de los procesos relacionados. Estos métodos se pueden extender al análisis de asignación de recursos en el desarrollo de otros procesos, observando la relación entre la calidad del servicio y los costos de operación.
M. Becerra-Fernández, E. C. G. La Rotta, M. M. H. Ramírez, and O. R. R. Quiroga, “Collaborative planning capacities in distribution centers,” in Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, L. Zhang, X. Song, and Y. Wu, Eds. Singapore: Springer Singapore, 2016, Part I, pp. 622–632.
A. Crespo, C. Bianchi, and J. Gupta, “Operational and financial effectiveness of e-collaboration tools in supply chain integration,” Eur. J. Oper. Res., vol. 159, pp. 348–363, 2004. doi: 10.1016/j.ejor.2003.08.020
K. Kloos and R. Pibernik, “Allocation planning under service-level contracts,” Eur. J. Oper. Res., vol. 280, no. 1, 2019. doi: 10.1016/j.ejor.2019.07.018
Y. Jiang and A. Seidmann, “Capacity planning and performance contracting for service facilities,” Decis. Support Syst., vol. 58, no. 1, pp. 31–42, 2014. Available: https://doi.org/10.1016/j.dss.2013.01.010
E. C. González-La Rotta and M. Becerra-Fernandez, “Cross-docking with vehicle routing problem. A state of art review,” DYNA, vol. 84, no. 200, pp. 271–280, 2017. doi: 10.15446/dyna.v84n200.60868
M. Becerra-Fernandez and R. Rodriguez-Yee, “Selection of Alternatives for the natural gas supply in Colombia using the analytic hierarchy process,” Ing., vol. 22, no. 2, May 2017. Available: http://dx.doi.org/10.14483/udistrital.jour.reving.2017.2.a02
M. Becerra-Fernandez, F. Cosenz, and I. Dyner, “Modeling the natural gas supply chain for sustainable growth policy,” Energy, vol. 205, p. 118018, 2020. Available: https://doi.org/10.1016/j.energy.2020.118018
J. B. Jun, S. H. Jacobson, and J. R. Swisher, “Application of discrete-event simulation in health care clinics: A survey,” J. Oper. Res. Soc., vol. 50, no. 2, pp. 109–123, Feb. 1999. Available: 10.1057/palgrave.jors.2600669
W. Trigueiro de Sousa Junior, J. A. Barra Montevechi, R. de Carvalho Miranda, and A. Teberga Campos, “Discrete simulation-based optimization methods for industrial engineering problems: A systematic literature review,” Comput. Ind. Eng., vol. 128, no. December 2018, pp. 526–540, 2019. Available: https://doi.org/10.1016/j.cie.2018.12.073
N. Furian, M. O’Sullivan, C. Walker, and S. Vössner, “Evaluating the impact of optimization algorithms for patient transits dispatching using discrete event simulation,” Oper. Res. Heal. Care, vol. 19, pp. 134–155, 2018. Available: https://doi.org/10.1016/j.orhc.2018.03.008
T. B. T. Nguyen, A. I. Sivakumar, and S. C. Graves, “Capacity planning with demand uncertainty for outpatient clinics,” Eur. J. Oper. Res., vol. 267, no. 1, pp. 338–348, 2018. doi: 10.1016/j.ejor.2017.11.038
A. R. Heching and M. S. Squillante, “Optimal capacity management and planning in services delivery centers,” Perform. Eval., vol. 80, no. C, pp. 63–81, 2014. Available: https://doi.org/10.1016/j.peva.2014.01.003
J. C. Chen, T. L. Chen, and H. Harianto, “Capacity planning for packaging industry,” J. Manuf. Syst., vol. 42, pp. 153–169, 2017.
F. D. Ramalho, P. Y. Ekel, W. Pedrycz, J. G. Pereira Júnior, and G. L. Soares, “Multicriteria decision making under conditions of uncertainty in application to multiobjective allocation of resources,” Inf. Fusion, vol. 49, no. March 2018, pp. 249–261, 2019.
D. J. van der Zee, “Model simplification in manufacturing simulation: Review and framework,” Comput. Ind. Eng., vol. 127, no. October 2018, pp. 1056–1067, 2019. doi: 10.1016/j.cie.2018.11.038
T. L. Garwood, B. R. Hughes, M. R. Oates, D. O’Connor, and R. Hughes, “A review of energy simulation tools for the manufacturing sector,” Renew. Sustain. Energy Rev., vol. 81, no. August 2017, pp. 895–911, 2018.
D. Mourtzis, M. Doukas, and D. Bernidaki, “Simulation in manufacturing: Review and challenges,” Procedia CIRP, vol. 25, no. C, pp. 213–229, 2014. Available: https://doi.org/10.1016/j.procir.2014.10.032
A. Greasley, “Using system dynamics in a discrete-event simulation study of a manufacturing plant,” Int. J. Oper. Prod. Manag., vol. 25, no. 6, pp. 534–548, 2005. doi: 10.1108/01443570510599700
S. C. Brailsford, T. Eldabi, M. Kunc, N. Mustafee, and A. F. Osorio, “Hybrid simulation modelling in operational research: A state-of-the-art review,” Eur. J. Oper. Res., vol. 278, no. 3, pp. 721–737, 2019. Available: https://doi.org/10.1016/j.ejor.2018.10.025
J. Orjuela, M. M. Herrera, and W. Casilimas, “Impact analysis of transport capacity and food safety in Bogota,” in 2015 Workshop Engineering Application: International Congress on Engineering (WEA). Bogotá: IEE, 2015, pp. 7–13.
Y. M. Lee et al., “Discrete event simulation modeling of resource planning and service order execution for service businesses,” in Proc. 2007 Conf. Winter Simul., S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, Eds. 2007, pp. 2227–2233. Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.172.1725&rep=rep1&type=pdf
R. Harpring, G. W. Evans, R. Barber, and S. M. Deck, “Improving efficiency in social services with discrete event simulation,” Comput. Ind. Eng., vol. 70, no. 1, pp. 159–167, 2014. Available: https://doi.org/10.1016/j.cie.2014.01.016
H. Zhang and H. Li, “Simulation-based optimization for dynamic resource allocation,” Autom. Constr., vol. 13, no. 3, pp. 409–420, 2004.
M. Afzalabadi, A. Haji, and R. Haji, “Vendor’s optimal inventory policy with dynamic and discrete demands in an infinite time horizon,” Comput. Ind. Eng., vol. 102, pp. 368–373, 2016. Available: https://doi.org/10.1016/j.cie.2016.06.024
R. Yang, S. Bhulai, R. van der Mei, and F. Seinstra, “Optimal resource allocation for time-reservation systems,” Perform. Eval., vol. 68, no. 5, pp. 414–428, May 2011. doi: 10.1016/j.peva.2011.01.003
C. Baril, V. Gascon, J. Miller, and N. Côté, “Use of a discrete-event simulation in a Kaizen event: A case study in healthcare,” Eur. J. Oper. Res., vol. 249, no. 1, pp. 327–339, 2016. Available: https://doi.org/10.1016/j.ejor.2015.08.036
V. Sharma, J. Abel, M. Al-Hussein, K. Lennerts, and U. Pfründer, “Simulation application for resource allocation in facility management processes in hospitals,” Facilities, vol. 25, no. 13–14, pp. 493–506, 2007. Available: https://doi.org/10.1108/02632770710822599
G. A. Wainer, Discrete-event modeling and simulation: a practitioner’s approach. Boca Raton, FL: CRC, 2017.
J. Banks, J. S. Carson II, B. L. Nelson, and D. M. Nicol, Discrete-event system simulation. United States: Pearson, 2005.
J. P. Davis and C. B. Bingham, “Developing theory through simulation methods,” Acad. Manag. Rev., vol. 32, no. 2, pp. 480–499, 2007. Available: https://doi.org/10.5465/amr.2007.24351453
A. M. Law and D. Kelton, Simulation Modeling and Analysis, 3rd edit. Singapore: McGraw Hill, 2000.
I. W. Gibson, “An approach to hospital planning and design using discrete event simulation,” Proc. Winter Simul. Conf., pp. 1501–1509, 2007. doi: 10.1109/WSC.2007.4419763
J. Trujillo-Díaz, M. M. Herrera, and F. N. Díaz-Piraquive, “A computer-based decision support system for knowledge management in the swine industry,” Int. J. Grid Util. Comput., vol. “In press,” 2020.
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2021 Mauricio Becerra-Fernández, PhD, Milton M. Herrera, PhD, Cristian Trejos, MSc, Olga R. Romero