Objective: Calculate the required personnel and resources needed to fulfill the service promise agreed with the customer. Methods and materials: This paper presents a discrete event simulation (DES) model developed to select and implement a Point of Sale (POS) for a company providing financial products. First, the paper shows the characterization of the system components and times per process. Then, hypothesis testing and goodness-of-fit statistics are estimated. Subsequently, the simulation scenarios assess the times between arrivals and the number of commercial advisers. Results and discussion: This model allows us to assess the allocation of resources to fulfill the service promise, which is that 80 % of customers must be served within one hour or less. This paper provided the service isoquants allowing us to observe the behavior of the performance metrics (service promise fulfillment) among different scenarios. Conclusions: The use of DES techniques allows for the evaluation of the assignment of personnel to achieve the fulfillment of the service promise, including facilities, equipment, and the evaluation of related processes. These methods can be extended to the analysis of resource allocation in the development of other processes, observing the relationship between service quality and operating costs.
Discrete event simulation, isoquants, resource allocation, service planning, waiting linessimulación de eventos discretos, isocuantas, planeación de servicios, asignación de recursos, líneas de espera
 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.
This work is licensed under a Creative Commons Attribution 4.0 International License.