1. Proceedings
  2. I3M
  3. 2022
  4. MAS
  5. 10.46354/i3m.2022.mas.013

Repacking line simulation for a small enterprise supplier of the dairy industry

  • a Jorge Luis Torres Miranda ,
  • b Azahed Arturo Hernández Cuéllar ,
  • c Cristina Delval González,
  • d Sergio Iván Lozada Reyes
  • a Universidad Nacional Autónoma de México, México
Cite as
Miranda J.L.T., Cuéllar A.A.H., González C.D., and Reyes S.I.L., (2022).,Repacking line simulation for a small enterprise supplier of the dairy industry. Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS 2022). , 013 . DOI: https://doi.org/10.46354/i3m.2022.mas.013

Abstract

This work is applied in a small company with problems of delays in its deliveries since they don’t have the parameters that allow them to decide whether to carry out their processes by hand or by machine, and the number of operators that should work based on the size of their demand. To solve this problem, the objective is to obtain the production capacity in both lines, which is the key performance indicator, through a limited data collection solved with the implementation of a triangular distribution model, and through discrete events simulation using the FlexSim® software. The results of the simulation thrown in this work will allow to graphically visualize the bottlenecks, adjust the number of operators required in each section of the process, redistribute the plant to reduce said bottlenecks, and give an analytical way by implementing the "experiments module" that provides a certain number of replicas of manual and hopper filling events; finally allowing to obtain the productive capacity of the business, and propose a model that meet variable demands without compromising unnecessary deployment of machinery and personnel.

References

  1. Behdani, B. (2012). Evaluation of paradigms for modeling supply chains as complex sociotechnical systems. In Proceedings of the 2012 Winter Simulation Conference, Berlin, Germany.
  2. Cox, D. R. (1972). Regression models and life tables (with Discussion). J. R. Statist. Soc. B, 34:187–220.
  3. Díaz, M., Zárate, R., & Román, R. (2018). Simulación FlexSim, una nueva alternativa para la ingeniería hacia la toma de decisiones en la operación de un sistema de múltiples estaciones de prueba. Científica, 22(2), 97-104.
  4. Fan, J. and Peng, H. (2004). Nonconcave penalized likelihood with a diverging number of parameters. Ann. Statist., 32:928–61.
  5. Flores, A., López A., Ambrís F., Loyo, J., Delgado, J., Montenegro O., López R. (2020). Análisis de la capacidad de la producción de papas fritas mediante el programa de simulación FlexSim. Tecnológico Nacional de México en Celaya, México.
  6. Ghosh, B. K., Bowden, R., Gladwin, B., & Harrell, C. (2012). Simulation Using ProModel. New York: McGraw-Hill.
  7. González, E. V., & Chiatchoua, C. (2021). Supervivencia de las micro, pequeñas y medianas empresas mexicanas frente al COVID-19. Revista Latinoamericana de Investigación Social, 4(1), 1-17.
  8. Heard, N. A., Holmes, C. C., and Stephens, D. A. (2006). A quantitative study of gene regulation involved in the immune response of Anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves. J. Am. Statist. Assoc., 101:18–29.
  9. Law, A. M., Kelton, W. D., & Kelton, W. D. (2007). Simulation modeling and analysis (Vol. 3). New York: McGraw-Hill.
  10. Morales, A., Rojas, J., Hernández, L., Morales, A., Jiménez, M. (2015). Modelo de un sistema de producción esbelto con redes de Petri para apoyar la toma de decisiones. Ingeniare, Revista de Ingeniería, Chile.
  11. Pernía, Y.; Ramírez, V., Márquez, R. (2011). Un modelo de simulación del proceso de producción de la Línea DTC-2000 de Alimentos Pepsico. Universidad de los Andes, Venezuela.
  12. Thensen, A., Travis, L. (2001). Introduction to Simulation. Proceedings of the 1991 Winter Simulation Conference, Phoenix Arizona, USA.
  13. Dunna, E. G., Reyes, H. G., & Barrón, L. E. C. (2012). Simulación y análisis de sistemas con ProModel. Mexico: Pearson Education.