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dc.contributor.authorFuentes, Jairo
dc.contributor.authorAguilar, Jose
dc.contributor.authorMontoya, Edwin
dc.contributor.authorPinto, Ángel
dc.date.accessioned2024-04-09T16:23:39Z
dc.date.available2024-04-09T16:23:39Z
dc.date.issued2024-02-05
dc.identifier.urihttps://repositorio.ufps.edu.co/handle/ufps/6867
dc.description.abstractIn 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.extent21 Páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherInformation (Switzerland)spa
dc.relation.ispartofInformation 2024, 15, 86. https://doi.org/10.3390/info15020086
dc.rightsunder the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).eng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.sourcehttps://www.mdpi.com/2078-2489/15/2/86spa
dc.titleAutonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sectoreng
dc.typeArtículo de revistaspa
dcterms.referencesSolleiro, 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.referencesSolleiro-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.referencesSánchez, 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.referencesAguilar, 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.referencesCandia, G. Industry 4.0 and its aberrations. ˙Ιnformasiya Cəmiyyəti Probl. 2022, 1, 48–57. [CrossRef]spa
dcterms.referencesEisavi, 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.referencesSanchez, 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.referencesValencia-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.referencesFlórez, D. Prospective research guidelines for the production chain of sugarcane—(focus on panela, not centrifuged sugar). Tecnura 2013, 17, 72–86spa
dcterms.referencesChaves, 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.referencesIsaza, J. Cadenas productivas. Enfoques y precisiones conceptuales. Sotavento 2008, 11, 8–25.spa
dcterms.referencesSen, 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.referencesLi, Y.; Jiang, W.; Yang, L.; Wu, T. On neural networks and learning systems for business computing. Neurocomputing 2018, 275, 1150–1159. [CrossRef]spa
dcterms.referencesMarinagi, 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.referencesLopez, 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.referencesLuque, 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.referencesGarcía, 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.referencesMeyer, M.; Dykes, J. Criteria for Rigor in Visualization Design Study. IEEE Trans. Vis. Comput. Graph. 2019, 26, 87–97. [CrossRef] [PubMed]spa
dcterms.referencesBader, 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.referencesKakhki, 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.referencesBorghesan, 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.referencesUygun, 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.referencesKephart, J.; Chess, D. The vision of autonomic computing. Computer 2003, 36, 41–52. [CrossRef]spa
dcterms.referencesPapetti, 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.referencesAguilar, 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.referencesMorales, 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.referencesVerdouw, 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.referencesYadav, 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.referencesRamírez-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, 133–137spa
dcterms.referencesNonaka, I. The knowledge creating company. Harv. Bus. Rev. 1991, 85, 162–171.spa
dcterms.referencesCastellanos, O.; Rojas, J. Conceptualización y papel de la cadena productiva en un entorno de competitividad. Innovar 2001, 18, 87–98.spa
dcterms.referencesFletes, H.; Ocampo, G.; Valdiviezo, G. Agroindustry dynamism in the Corredor Costero, Chiapas, Mexico. Coordination and territorial competitivity. Mundo Agrar. 2016, 17, e038.spa
dcterms.referencesOrganización 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.referencesSalimbeni, 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.referencesBell, 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.referencesRoukh, 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.doi10.3390/info15020086
dc.relation.citationeditionVol.15 No.86 (2024)spa
dc.relation.citationendpage21spa
dc.relation.citationissue86 (2024)spa
dc.relation.citationstartpage1spa
dc.relation.citationvolume15spa
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.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.subject.proposalproduction-chaineng
dc.subject.proposalagroindustryeng
dc.subject.proposalautonomous computingeng
dc.subject.proposalartificial intelligenceeng
dc.subject.proposaldata analysiseng
dc.subject.proposalmachine learningeng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
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oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa


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