Christian D. Ovalle
ABSTRACT
Without a doubt one of the greatest strengths that a country has is the production and exportation of raw materials and also finished products, but none of this would be possible without an optimized and automated supply chain in relation to logistics management, what this research aims to do is to present a smart supply chain model through a predictive algorithm of machine learning. After applying the predictive model, the obtained result showed that sales increased according to the type of car; Sedan 48.60%, Wagon 11.73% and Hatchback 32.52%. On the other hand, it is also observed that there is a marked relationship between variables 1 and 2 of 80.2%, and it is also observed that there is a relationship between variables 1 and 3 of 78.9%, and we also observed a relationship between variables 2 and 3 of 82.3%.
Supplemental Material
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- J. M. Velásquez, M. Khakifirooz, and F. Mahdi, Large Scale Optimization in Supply Chains and Smart Manufacturing, vol. 149. 2019.Google Scholar
Cross Ref
- Y. Leal, “Gestión Logística De Materiales En La Industria Petrolera Venezolana,” Rev. Enfoques, vol. 2, no. 5, pp. 16–34, 2018, doi: 10.33996/revistaenfoques.v2i5.27.Google ScholarCross Ref
- A. Calatayud and R. Katz, “Cadena de suministro 4.0: Mejores prácticas internacionales y hoja de ruta para América Latina,” Cadena Suminist. 4.0 Mejor. prácticas Int. y hoja ruta para América Lat., 2019, doi: 10.18235/0001956.Google ScholarCross Ref
- R. Toorajipour, V. Sohrabpour, A. Nazarpour, P. Oghazi, and M. Fischl, “Artificial intelligence in supply chain management: A systematic literature review,” J. Bus. Res., vol. 122, no. September 2020, pp. 502–517, 2021, doi: 10.1016/j.jbusres.2020.09.009.Google ScholarCross Ref
- A. Sener, M. Barut, A. Oztekin, M. Y. Avcilar, and M. B. Yildirim, “The role of information usage in a retail supply chain: A causal data mining and analytical modeling approach,” J. Bus. Res., vol. 99, pp. 87–104, Jun. 2019, doi: 10.1016/j.jbusres.2019.01.070.Google ScholarCross Ref
- K. Butner, The smarter supply chain of the future, vol. 38, no. 1. 2010.Google Scholar
- A. K. Chávez Valdivia, “Redesigning the ownership of artworks: Artificial and robotic intelligence,” Rev. Chil. Derecho y Tecnol., vol. 9, no. 2, pp. 153–185, 2021, doi: 10.5354/0719-2584.2020.57674.Google Scholar
- C. A. Paz, “Legal challenges for artificial intelligence in Chile,” Rev. Chil. Derecho y Tecnol., vol. 9, no. 2, pp. 257–290, 2021, doi: 10.5354/0719-2584.2020.54489.Google Scholar
- P. Ezanno , “Research perspectives on animal health in the era of artificial intelligence,” Vet. Res., vol. 52, no. 1, pp. 1–15, 2021, doi: 10.1186/s13567-021-00902-4.Google ScholarCross Ref
- M. Vázquez-Marrufo, E. Sarrias-Arrabal, M. García-Torres, R. Martín-Clemente, and G. Izquierdo, “A systematic review of the application of machine-learning algorithms in multiple sclerosis,” Neurologia, no. xxxx, 2021, doi: 10.1016/j.nrl.2020.10.017.Google Scholar
- D. J. Benavides, P. Arévalo, L. G. González, and L. Hernández, “Method of monitoring and detection of failures in PV system based on machine learning,” Rev. Fac. Ing. Univ. Antioquia, no. 102, pp. 26–43, 2021, doi: 10.17533/udea.redin.20200694.Google ScholarCross Ref
- S. J. Nawaz, S. K. Sharma, S. Wyne, M. N. Patwary, and M. Asaduzzaman, “Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future,” IEEE Access, vol. 7, no. Ml, pp. 46317–46350, 2019, doi: 10.1109/ACCESS.2019.2909490.Google ScholarCross Ref
- R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke, “Explainable Machine Learning for Scientific Insights and Discoveries,” IEEE Access, vol. 8, pp. 42200–42216, 2020, doi: 10.1109/ACCESS.2020.2976199.Google ScholarCross Ref
- M. Garbulowski , “R.ROSETTA: an interpretable machine learning framework,” BMC Bioinformatics, vol. 22, no. 1, pp. 1–18, 2021, doi: 10.1186/s12859-021-04049-z.Google ScholarCross Ref
- Y. Matsuzaka and Y. Uesawa, “A molecular image-based novel quantitative structure-activity relationship approach, deepsnap-deep learning and machine learning,” Curr. Issues Mol. Biol., vol. 42, pp. 455–472, 2021, doi: 10.21775/cimb.042.455.Google Scholar
- J. M. Velásquez, M. Khakifirooz, and F. Mahdi, Large Scale Optimization in Supply Chains and Smart Manufacturing, vol. 149. 2019.Google ScholarCross Ref
- Y. Leal, “Gestión Logística De Materiales En La Industria Petrolera Venezolana,” Rev. Enfoques, vol. 2, no. 5, pp. 16–34, 2018, doi: 10.33996/revistaenfoques.v2i5.27.Google ScholarCross Ref
- A. Calatayud and M. F. Millan, “ALC 2030: Construyendo las cadenas de suministro del futuro,” ALC 2030 Construyendo las cadenas Suminist. del Futur., 2019, doi: 10.18235/0001969.Google ScholarCross Ref
- R. Toorajipour, V. Sohrabpour, A. Nazarpour, P. Oghazi, and M. Fischl, “Artificial intelligence in supply chain management: A systematic literature review,” J. Bus. Res., vol. 122, no. September 2020, pp. 502–517, 2021, doi: 10.1016/j.jbusres.2020.09.009Google ScholarCross Ref
- A. Sener, M. Barut, A. Oztekin, M. Y. Avcilar, and M. B. Yildirim, “The role of information usage in a retail supply chain: A causal data mining and analytical modeling approach,” J. Bus. Res., vol. 99, pp. 87–104, Jun. 2019, doi: 10.1016/j.jbusres.2019.01.070.Google ScholarCross Ref
- K. Butner, The smarter supply chain of the future, vol. 38, no. 1. 2010.Google Scholar
- A. K. Chávez Valdivia, “Redesigning the ownership of artworks: Artificial and robotic intelligence,” Rev. Chil. Derecho y Tecnol., vol. 9, no. 2, pp. 153–185, 2021, doi: 10.5354/0719-2584.2020.57674.Google Scholar
- C. A. Paz, “Legal challenges for artificial intelligence in Chile,” Rev. Chil. Derecho y Tecnol., vol. 9, no. 2, pp. 257–290, 2021, doi: 10.5354/0719-2584.2020.54489.Google Scholar
- P. Ezanno , “Research perspectives on animal health in the era of artificial intelligence,” Vet. Res., vol. 52, no. 1, pp. 1–15, 2021, doi: 10.1186/s13567-021-00902-4.Google ScholarCross Ref
- M. Vázquez-Marrufo, E. Sarrias-Arrabal, M. García-Torres, R. Martín-Clemente, and G. Izquierdo, “A systematic review of the application of machine-learning algorithms in multiple sclerosis,” Neurologia, no. xxxx, 2021, doi: 10.1016/j.nrl.2020.10.017.Google Scholar
- D. J. Benavides, P. Arévalo, L. G. González, and L. Hernández, “Method of monitoring and detection of failures in PV system based on machine learning,” Rev. Fac. Ing. Univ. Antioquia, no. 102, pp. 26–43, 2021, doi: 10.17533/udea.redin.20200694.Google ScholarCross Ref
- S. J. Nawaz, S. K. Sharma, S. Wyne, M. N. Patwary, and M. Asaduzzaman, “Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future,” IEEE Access, vol. 7, no. Ml, pp. 46317–46350, 2019, doi: 10.1109/ACCESS.2019.2909490.Google ScholarCross Ref
- R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke, “Explainable Machine Learning for Scientific Insights and Discoveries,” IEEE Access, vol. 8, pp. 42200–42216, 2020, doi: 10.1109/ACCESS.2020.2976199.Google ScholarCross Ref
- . Garbulowski , “R.ROSETTA: an interpretable machine learning framework,” BMC Bioinformatics, vol. 22, no. 1, pp. 1–18, 2021, doi: 10.1186/s12859-021-04049-z.Google ScholarCross Ref
- Y. Matsuzaka and Y. Uesawa, “A molecular image-based novel quantitative structure-activity relationship approach, deepsnap-deep learning and machine learning,” Curr. Issues Mol. Biol., vol. 42, pp. 455–472, 2021, doi: 10.21775/cimb.042.455.Google Scholar
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