Mostrar el registro sencillo del ítem
Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector
dc.contributor.author | Fuentes, Jairo | |
dc.contributor.author | Aguilar, Jose | |
dc.contributor.author | Montoya, Edwin | |
dc.contributor.author | Pinto, Ángel | |
dc.date.accessioned | 2024-04-09T16:23:39Z | |
dc.date.available | 2024-04-09T16:23:39Z | |
dc.date.issued | 2024-02-05 | |
dc.identifier.uri | https://repositorio.ufps.edu.co/handle/ufps/6867 | |
dc.description.abstract | In this paper, we propose autonomous cycles of data analysis tasks for the automation of the production chains aimed to improve the productivity of Micro, Small and Medium Enterprises (MSMEs) in the context of agroindustry. In the autonomous cycles of data analysis tasks, each task interacts with the others and has different functions, in order to reach the goal of the cycle. In this article, we identify three industrial-automation processes within the production chain, in which autonomous cycles can be applied. The first cycle is responsible to identify the type of input to be transformed—such as quantity, quality, time, and cost—based on information from the organization and its context. The second cycle selects the technological level used in the raw-material transformation, characterizing the platform of plant processing. The last cycle identifies the level of specialization of the generated product, such as the quality and value of the product. Finally, we apply the first autonomous cycle to define the type of input to be transformed in a coffee factory. | eng |
dc.format.extent | 21 Páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | Information (Switzerland) | spa |
dc.relation.ispartof | Information 2024, 15, 86. https://doi.org/10.3390/info15020086 | |
dc.rights | under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | eng |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | spa |
dc.source | https://www.mdpi.com/2078-2489/15/2/86 | spa |
dc.title | Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector | eng |
dc.type | Artículo de revista | spa |
dcterms.references | Solleiro, J.; Del Valle, M. El cambio Tecnológico en la Agricultura y las Agroindustrias en México; Siglo, Ed.; Siglo XXI: Yucatán, México, 1996; p. xxi. | spa |
dcterms.references | Solleiro-Rebolledo, J.L.; García-Martínez, M.B.; Castañón-Ibarra, R.; Martínez-Salvador, L.E. Smart specialization for building up a regional innovation agenda: The case of San Luis Potosí, Mexico. J. Evol. Stud. Business-JESB 2020, 5, 81–115. [CrossRef] | spa |
dcterms.references | Sánchez, M.; Aguilar, J.; Cordero, J.; Valdiviezo-Díaz, P.; Barba-Guamán, L.; Chamba-Eras, L. Cloud Computing in Smart Educational Environments: Application in Learning Analytics as Service. In New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing; Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2016; Volume 444, pp. 993–1002. | spa |
dcterms.references | Aguilar, J.; Garces-Jimenez, A.; Gallego-Salvador, N.; De Mesa, J.A.G.; Gomez-Pulido, J.M.; Garcia-Tejedor, A.J. Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings. IEEE Access 2019, 7, 123402–123415. [CrossRef] | spa |
dcterms.references | Candia, G. Industry 4.0 and its aberrations. ˙Ιnformasiya Cəmiyyəti Probl. 2022, 1, 48–57. [CrossRef] | spa |
dcterms.references | Eisavi, V.; Homayouni, S.; Yazdi, A.M.; Alimohammadi, A. Land cover mapping based on random forest classification of multitemporal spectral and thermal images. Environ. Monit. Assess. 2015, 187, 291. [CrossRef] | spa |
dcterms.references | Sanchez, M.; Exposito, E.; Aguilar, J. Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context. Comput. Ind. 2020, 121, 103247. [CrossRef] | spa |
dcterms.references | Valencia-Cárdenas, M.; Restrepo-Morales, J.A.; Día-Serna, F.J. Big Data Analytics in the Agribusiness Supply Chain Management. AiBi Rev. Investig. Adm. Ing. 2021, 9, 32–42. [CrossRef] | spa |
dcterms.references | Flórez, D. Prospective research guidelines for the production chain of sugarcane—(focus on panela, not centrifuged sugar). Tecnura 2013, 17, 72–86 | spa |
dcterms.references | Chaves, J.; Díaz, R.; Hernández, A.; Hidalgo, O. Cadenas productivas agroindustriales y competitividad: Definición de políticas y estrategias en el meso nivel. Econ. Soc. 2000, 13, 5–18. | spa |
dcterms.references | Isaza, J. Cadenas productivas. Enfoques y precisiones conceptuales. Sotavento 2008, 11, 8–25. | spa |
dcterms.references | Sen, D.; Ozturk, M.; Vayvay, O. An Overview of Big Data for Growth in SMEs. Procedia-Soc. Behav. Sci. 2016, 235, 159–167. [CrossRef] | spa |
dcterms.references | Li, Y.; Jiang, W.; Yang, L.; Wu, T. On neural networks and learning systems for business computing. Neurocomputing 2018, 275, 1150–1159. [CrossRef] | spa |
dcterms.references | Marinagi, C.; Skourlas, C.; Galiotou, E. Advanced information technology solutions for implementing information sharing across supply chains. In ACM International Conference Proceeding Series, Proceedings of the PCI ‘18: 22nd Pan-Hellenic Conference on Informatics, Athens, Greece, 29 November–1 December 2018; ACM: New York, NY, USA, 2018; pp. 99–102. [CrossRef] | spa |
dcterms.references | Lopez, H.A.G.; Cisneros, M.A.P. Industry 4.0 & Internet of Things in Supply Chain. In Proceedings of the CLIHC ‘17: 8th Latin American Conference on Human-Computer Interaction, Antigua Guatemala, Guatemala, 8–10 November 2017; pp. 1–4. [CrossRef] | spa |
dcterms.references | Luque, A.; Peralta, M.E.; Heras, A.d.L.; Córdoba, A. State of the Industry 4.0 in the Andalusian food sector. Procedia Manuf. 2017, 13, 1199–1205. [CrossRef] | spa |
dcterms.references | García, E.; Vieira, M. Estudo de caso de mineração de dados multirelacional: Aplicação do algoritmo connetionblock em um problema da agroindústria. In Proceedings of the Simpósio Brasileiro de Bancos de Dados, Campinas, Brazil, 13–15 October 2008; pp. 224–237. | spa |
dcterms.references | Meyer, M.; Dykes, J. Criteria for Rigor in Visualization Design Study. IEEE Trans. Vis. Comput. Graph. 2019, 26, 87–97. [CrossRef] [PubMed] | spa |
dcterms.references | Bader, F.; Rahimifard, S. Challenges for Industrial Robot Applications in Food Manufacturing. In Proceedings of the ISCSIC ‘18: The 2nd International Symposium on Computer Science and Intelligent Control, Stockholm, Sweden, 21–23 September 2018. | spa |
dcterms.references | Kakhki, F.D.; Freeman, S.A.; Mosher, G. Evaluating machine learning performance in predicting injury severity in agribusiness industries. Saf. Sci. 2019, 117, 257–262. [CrossRef] | spa |
dcterms.references | Borghesan, F.; Zagorowska, M.; Mercangöz, M. Unmanned and Autonomous Systems: Future of Automation in Process and Energy Industries. IFAC-Pap. 2022, 55, 875–882. [CrossRef] | spa |
dcterms.references | Uygun, Y. Autonomous Manufacturing-Related Procurement in the Era of Industry 4.0. In Digitalisierung im Einkauf; Schupp, F., Wöhner, H., Eds.; Springer: Gabler, Wiesbaden, 2023. | spa |
dcterms.references | Kephart, J.; Chess, D. The vision of autonomic computing. Computer 2003, 36, 41–52. [CrossRef] | spa |
dcterms.references | Papetti, A.; Gregori, F.; Pandolfi, M.; Peruzzini, M.; Germani, M. Iot to enable social sustainability in manufacturing systems. Adv. Transdiscipl. Eng. 2018, 7, 53–62. | spa |
dcterms.references | Aguilar, J.; Jerez, M.; Exposito, E.; Villemur, T. CARMiCLOC: Context Awareness Middleware in Cloud Computing. In Proceedings of the 2015 XLI Latin American Computing Conference (CLEI), Arequipa, Peru, 19–23 October 2015. | spa |
dcterms.references | Morales, L.; Ouedraogo, C.A.; Aguilar, J.; Chassot, C.; Medjiah, S.; Drira, K. Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform. Serv. Oriented Comput. Appl. 2019, 13, 199–219. [CrossRef] | spa |
dcterms.references | Verdouw, C.; Sundmaeker, H.; Tekinerdogan, B.; Conzon, D.; Montanaro, T. Architecture framework of IoT-based food and farm systems: A multiple case study. Comput. Electron. Agric. 2019, 165, 104939. [CrossRef] | spa |
dcterms.references | Yadav, S.; Luthra, S.; Garg, D. Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks. Environ. Sci. Pollut. Res. 2021, 28, 16633–16654. [CrossRef] | spa |
dcterms.references | Ramírez-Valverde, B. “Gerardo Torres Salcido y Rosa María Larroa Torres (coord): Sistemas agroalimentarios localizados: Desarrollo conceptual y diversidad de situaciones” (Reseña). Agric. Soc. Desarro. 2013, 10, 133–137 | spa |
dcterms.references | Nonaka, I. The knowledge creating company. Harv. Bus. Rev. 1991, 85, 162–171. | spa |
dcterms.references | Castellanos, O.; Rojas, J. Conceptualización y papel de la cadena productiva en un entorno de competitividad. Innovar 2001, 18, 87–98. | spa |
dcterms.references | Fletes, H.; Ocampo, G.; Valdiviezo, G. Agroindustry dynamism in the Corredor Costero, Chiapas, Mexico. Coordination and territorial competitivity. Mundo Agrar. 2016, 17, e038. | spa |
dcterms.references | Organización de Cooperación y Desarrollo Económicos, ocde. Manual de Oslo: Guía Para la Recogida e Interpretación de Datos Sobre Innovación, 3rd ed.; Traducción española Grupo Tragsa: Madrid, España, 2005; p. 188. | spa |
dcterms.references | Salimbeni, S.; Redchuk, A.; Rousserie, H. Quality 4.0: Technologies and readiness factors in the entire value flow life cycle. Prod. Manuf. Res. 2023, 11, 2238797. [CrossRef] | spa |
dcterms.references | Bell, M.; Pavitt, K. The development of technological capabilities. In Trade, Technology, and International Competitiveness; Haque, I., Ed.; Economic Development Institute, The World Bank: Washington, DC, USA, 1995; pp. 69–101. | spa |
dcterms.references | Roukh, A.; Fote, F.; Mahmoudi, S.; Mahmoudi, S. WALLeSMART: Cloud Platform for Smart Farming. In Proceedings of the ACM International Conference Proceeding Series, SSDBM ‘20: 32nd International Conference on Scientific and Statistical Database Management, Vienna, Austria, 7–9 July 2020; pp. 1–4. [CrossRef] | spa |
dc.identifier.doi | 10.3390/info15020086 | |
dc.relation.citationedition | Vol.15 No.86 (2024) | spa |
dc.relation.citationendpage | 21 | spa |
dc.relation.citationissue | 86 (2024) | spa |
dc.relation.citationstartpage | 1 | spa |
dc.relation.citationvolume | 15 | spa |
dc.relation.cites | : Fuentes, J.; Aguilar, J.; Montoya, E.; Pinto, Á. Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector. Information 2024, 15, 86. https://doi.org/10.3390/ info15020086 | |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.creativecommons | Atribución 4.0 Internacional (CC BY 4.0) | spa |
dc.subject.proposal | production-chain | eng |
dc.subject.proposal | agroindustry | eng |
dc.subject.proposal | autonomous computing | eng |
dc.subject.proposal | artificial intelligence | eng |
dc.subject.proposal | data analysis | eng |
dc.subject.proposal | machine learning | eng |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |