Método para identificar la cantidad necesaria de personal en oficinas de atención al cliente
| dc.contributor.advisor | Figueroa Garcia, Juan Carlos | |
| dc.contributor.author | Cepeda Cepeda, Wilson Alfonso | |
| dc.contributor.orcid | Figueroa Garcia, Juan Carlos [0000-0001-5544-5937] | |
| dc.contributor.other | García Barreto, Germán Alberto (Catalogador) | |
| dc.date.accessioned | 2025-11-22T15:59:51Z | |
| dc.date.available | 2025-11-22T15:59:51Z | |
| dc.date.created | 2025-06-11 | |
| dc.description | El trabajo presenta un método para identificar la cantidad necesaria de personal en oficinas de atención al cliente, asegurando el cumplimiento regulatorio del nivel de servicio mayor o igual al 80%, se emplea un enfoque ex post-facto, apoyado en análisis estadísticos no paramétricos, y modelos de clasificación como árbol de decisión, donde el ajuste dinámico de la distribución de personal, promover la atención en la jornada de la mañana para equilibrar la carga, asegurar tiempos adecuados de atención, mejorar el control de ausencias y la gestión de recursos humanos, permite a las organizaciones tomar decisiones de forma estratégica, que mejoren la experiencia del cliente en el proceso de atención de sus servicios, por consiguiente, la propuesta aporta de forma significativa a la gestión y es aplicable a contextos similares. | |
| dc.description.abstract | The study presents a method for identifying the required number of personnel in customer service offices, ensuring regulatory compliance with a service level equal to or greater than 80%. An ex post facto approach is employed, supported by non-parametric statistical analyses and classification models such as decision trees. The dynamic adjustment of personnel distribution promoting morning shift coverage to balance workload, ensuring appropriate service times, improving absence control, and enhancing human resource management enables organizations to make strategic decisions that improve the customer experience throughout the service process. Consequently, the proposed method makes a significant contribution to organizational management and is applicable to similar contexts. | |
| dc.format.mimetype | ||
| dc.identifier.uri | http://hdl.handle.net/11349/99902 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad Distrital Francisco José de Caldas | |
| dc.relation.references | Aniyeri, R., & Nadar, R. (2018). Passengers queue analysis in international airports terminals in Kerala using multiphase queuing system. In Int. J. Mathematics in Operational Research (Vol. 12, Issue 1). | |
| dc.relation.references | Arias, M. B., & Bae, S. (2017). Prediction of electric vehicle charging-power demand in realistic urban traffic networks. Applied Energy, 195, 738–753. https://doi.org/10.1016/j.apenergy.2017.02.021 | |
| dc.relation.references | Borst, S. C., & Combé, M. B. (1993). An M/G/1 queue with customer collection. Communications in Statistics. Stochastic Models, 9(3), 341–371. https://doi.org/10.1080/15326349308807270 | |
| dc.relation.references | Buchholz, P., & Kriege, J. (2017). Fitting correlated arrival and service times and related queueing performance. Queueing Systems, 85(3–4), 337–359. https://doi.org/10.1007/s11134-017-9514-5 | |
| dc.relation.references | Cantor, D. E., & Jin, Y. (2019). Theoretical and empirical evidence of behavioral and production line factors that influence helping behavior. Journal of Operations Management, 65(4), 312–332. https://doi.org/10.1002/joom.1019 | |
| dc.relation.references | Chacón, H., Koppisetti, V., Hardage, D., Choo, K.-K. R., & Rad, P. (2023). Forecasting call center arrivals using temporal memory networks and gradient boosting algorithm. Expert Systems with Applications, 224. https://doi.org/10.1016/j.eswa.2023.119983 | |
| dc.relation.references | Dudin, A. N., Klimenok, V. I., & Vishnevsky, V. M. (2019). The theory of queuing systems with correlated flows. In The Theory of Queuing Systems with Correlated Flows. https://doi.org/10.1007/978-3-030-32072-0 | |
| dc.relation.references | Dudina, O., Kim, C., & Dudin, S. (2013). Retrial queuing system with Markovian arrival flow and phase-type service time distribution. Computers and Industrial Engineering, 66(2), 360–373. https://doi.org/10.1016/j.cie.2013.06.020 | |
| dc.relation.references | Efrat-Treister, D., Moriah, H., & Rafaeli, A. (2020). The effect of waiting on aggressive tendencies toward emergency department staff: Providing information can help but may also backfire. PLoS ONE, 15(1). https://doi.org/10.1371/journal.pone.0227729 | |
| dc.relation.references | Fendick, K. W., Saksena, V. R., & Whitt, W. (1991). Investigating Dependence in Packet Queues with the Index of Dispersion for Work. IEEE Transactions on Communications, 39(8), 1231–1244. https://doi.org/10.1109/26.134013 | |
| dc.relation.references | FREDERICK S. HILLER, & GERALD J. LIEBERMAN. (2010). Introduction to Operations Research (McGraw-Hill Interamericana de España S.L., Ed.; 9 Ed). | |
| dc.relation.references | Gauthier, T. D. (2001). Detecting Trends Using Spearman’s Rank Correlation Coefficient. Environmental Forensics, 2(4), 359–362. https://doi.org/10.1006/ENFO.2001.0061 | |
| dc.relation.references | George C. Canavos. (1988). Applied Probability and Statistic Methods (Limusa, Ed.; Primera Ed). | |
| dc.relation.references | HAMDY A. TAHA. (2012). Operations Research An Introduction 9a. (Pearson, Ed.; Pearson Education, Trans.; 9 Ed). Prentice Hall. | |
| dc.relation.references | Hoseinpour, P. (2021). Improving service quality in a congested network with random breakdowns. Computers and Industrial Engineering, 157. https://doi.org/10.1016/j.cie.2021.107226 | |
| dc.relation.references | Ibrahim, R., L’Ecuyer, P., Shen, H., & Thiongane, M. (2016). Inter-dependent, heterogeneous, and time-varying service-time distributions in call centers. European Journal of Operational Research, 250(2), 480–492. https://doi.org/10.1016/j.ejor.2015.10.017 | |
| dc.relation.references | Imgbemena, C. E., Mgbemena, C. O., & Chinwuko, E. C. (2011). A regression analysis approach to queueing system modelling: A case of banks. Journal of Applied Sciences Research, 7(3), 200–212. | |
| dc.relation.references | Ioannidou, O., & Erduran, S. (n.d.). Beyond Hypothesis Testing Investigating the Diversity of Scientific Methods in Science Teachers’ Understanding. https://doi.org/10.1007/s11191-020-00185-9 | |
| dc.relation.references | Ishizaki, F., Takine, T., & Hasegawa, T. (1995). Analysis of a discrete-time queue with gated priority. Performance Evaluation, 23(2), 121–143. https://doi.org/10.1016/0166-5316(95)90861-F | |
| dc.relation.references | Kanawaty, George. (1992). Introduction to Work Study (Limusa S.A. De C.V., Ed.; 4 Edicion). OIT. | |
| dc.relation.references | Kim, B., & Sohraby, K. (2006). Tail behavior of the queue size and waiting time in a queue with discrete autoregressive arrivals. Advances in Applied Probability, 38(4), 1116–1131. https://doi.org/10.1239/aap/1165414594 | |
| dc.relation.references | Kuaban, G. S., Kumar, R., Soodan, B. S., & Czekalski, P. (2020). A multi-server queuing model with balking and correlated reneging with application in health care management. IEEE Access, 8, 169623–169639. https://doi.org/10.1109/ACCESS.2020.3024259 | |
| dc.relation.references | Liu, Y., & Whitt, W. (2012). The Gt/GI/st+GI many-server fluid queue. Queueing Systems, 71(4), 405–444. https://doi.org/10.1007/s11134-012-9291-0 | |
| dc.relation.references | Mohd Razali, N., & Bee Wah, Y. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. In Journal of Statistical Modeling and Analytics (Vol. 2). | |
| dc.relation.references | Müller, A. (2000). On the waiting times in queues with dependency between interarrival and service times. Operations Research Letters, 26(1), 43–47. https://doi.org/10.1016/S0167-6377(99)00060-7 | |
| dc.relation.references | Román, R. E., Smida, A., Román Castillo, R. E., & Smida, A. (2017). Una reflexión ex post facto sobre la conducción de estudios multicaso para la construcción de teoría en ciencias de gestión. Innovar, 27(64), 129–144. https://doi.org/10.15446/innovar.v27n64.62373 | |
| dc.relation.references | Shunko, M., Niederhoff, J., & Rosokha, Y. (2018). Humans are not machines: The behavioral impact of queueing design on service time. Management Science, 64(1), 453–473. https://doi.org/10.1287/mnsc.2016.2610 | |
| dc.relation.references | van Diepen, M., & Franses, P. H. (2006). Evaluating chi-squared automatic interaction detection. Information Systems, 31(8), 814–831. https://doi.org/10.1016/J.IS.2005.03.002 | |
| dc.relation.references | Van Harten, A., & Sleptchenko, A. (2003). On Markovian Multi-Class, Multi-Server Queueing. Queueing Systems, 43(4), 307–328. https://doi.org/10.1023/A:1023209813523 | |
| dc.relation.references | Vinish, P., Pinto, P., & Hawaldar, I. T. (2022). PERCEIVED IDLE WAIT AND ASSOCIATED EMOTIONAL DISCOMFORT: AN ANALYSIS OF RETAIL WAITING EXPERIENCE. Innovative Marketing, 18(1), 1–11. https://doi.org/10.21511/im.18(1).2022.01 | |
| dc.relation.references | Vinish, P., Pinto, P., Hawaldar, I. T., & Munshi, M. M. (2022). Coping emotional discomfort at retail checkout: Potential distractions and implications. Innovative Marketing, 18(3), 159–169. https://doi.org/10.21511/im.18(3).2022.14 | |
| dc.relation.references | von Schéele, F., Haftor, D. M., & Pashkevich, N. (2022). Predicting delays in service operations. Service Business, 16(2), 211–226. https://doi.org/10.1007/s11628-021-00466-5 | |
| dc.relation.references | Whitt, W. (1984). Open and Closed Models for Networks of Queues. AT&T Bell Laboratories Technical Journal, 63(9), 1911–1979. https://doi.org/10.1002/j.1538-7305.1984.tb00084. | |
| dc.relation.references | Wu, C. A., Bassamboo, A., & Perry, O. (2019). Service system with dependent service and patience times. Management Science, 65(3), 1151–1172. https://doi.org/10.1287/mnsc.2017.2983 | |
| dc.rights.acceso | Abierto (Texto Completo) | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Correlación | |
| dc.subject | Árbol de decisión | |
| dc.subject | Servicios al Cliente | |
| dc.subject | Teoria de Colas | |
| dc.subject | Nivel de Servicio | |
| dc.subject.keyword | Correlation | |
| dc.subject.keyword | Decision tree | |
| dc.subject.keyword | Customer Service | |
| dc.subject.keyword | Queueing Theory | |
| dc.subject.keyword | Service level | |
| dc.subject.lemb | Maestría en Ingeniería Industrial -- Tesis y disertaciones académicas | |
| dc.subject.lemb | Servicio al cliente | |
| dc.subject.lemb | Relaciones con el cliente | |
| dc.subject.lemb | Servicio al cliente -- Árboles de decisión | |
| dc.subject.lemb | Gestión de recursos humanos | |
| dc.title | Método para identificar la cantidad necesaria de personal en oficinas de atención al cliente | |
| dc.title.titleenglish | Method to identify the required number of personnel in a customer service office | |
| dc.type | masterThesis | |
| dc.type.coar | http://purl.org/coar/resource_type/c_7a1f | |
| dc.type.degree | Investigación-Innovación | |
| dc.type.driver | info:eu-repo/semantics/bachelorThesis |
Archivos
Bloque de licencias
1 - 1 de 1
No hay miniatura disponible
- Nombre:
- license.txt
- Tamaño:
- 7 KB
- Formato:
- Item-specific license agreed upon to submission
- Descripción: