Simulation model to implement automation equipment in the distribution center of the P & P company

Authors

DOI:

https://doi.org/10.21501/25907565.3043

Keywords:

Storage, Costs, Storage technology, Simulation, Optimization.

Abstract

Management in distribution centers have a high impact on customer satisfaction. They must responded quickly to the productive process receiving area to maintain a high reliability in the storage and to control of the products and the operating costs. The objective of this report is to present the analysis of different technologies to improve the management of the main distribution center that the Phantom Product company projects for the needs of the year 2020, through a FlexSim simulation model, to guarantee that at least in 95% of the days the production lines are not stopped due to inattention or inefficiency of the logistics area of the distribution center. This methodology is focused on the simulating and experimental design of the receiving and storage operation of the distribution center, providing a state of the art work. The results are generated from simulation, which identifies the needs and verifies the ability of the tools used to improve the operation of the distribution center, thus being able to validate, the analysis of the information, which of the equipment options for the warehouse is meeting the requirements in times of the plant receiving goods and the costs increase from the stoppage in each option.

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Author Biography

Santiago Luis Franco, Politécnico Colombiano Jaime Isaza Cadavid

Medellín-Antioquia

References

FlexSim. (2016). Manual Usuario. FlexSim. Recuperado de https://answers.flexsim.com/storage/attachments/6788-flexsim-1710-manual-arial.pdf

Gómez R. A. y Correa A. A. (2011). Mejoramiento de la recepción en una empresa de colchones utilizando simulación y diseño de experimentos. Revista Lasallista de Investigación, 8(1), 68-81. Recuperado de http://repository.lasallista.edu.co:8080/ojs/index.php/rldi/article/view/75.

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Prácticas de sistemas de fabricación. (2012). Simulación de un proceso industrial mediante el software de FlexSim. Recuperado de https://rua.ua.es/dspace/bitstream/10045/20587/1/Simulacion_de_un_proceso_industrial_mediante_FlexSim.pdf.

Vargas, J. y Giraldo, J. (2014). Modelo de predicción de costos en servicios de salud soportado en simulación discreta. Información Tecnológica, 25(4), 175-184. Doi: http://dx.doi.org/10.4067/S0718-07642014000400019.

Tutorial FlexSim. (s.f.). TutOrial FlexSim. Recuperado de https://profearias.files.wordpress.com/2013/02/tutorial_flexsimsp.pdf.

Published

2018-12-13

How to Cite

Franco, S. L., Muñoz Rodríguez, J. H., Lopera Bohórquez, F., Montoya Peláez, M., & Arango Palacio, I. C. (2018). Simulation model to implement automation equipment in the distribution center of the P & P company. Revista Universidad Católica Luis Amigó, (2), 37–54. https://doi.org/10.21501/25907565.3043

Issue

Section

Investigación