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dc.contributor.author
Sánchez, Marisa Analía  
dc.contributor.author
Rossit, Daniel Alejandro  
dc.contributor.author
Tohmé, Fernando Abel  
dc.date.available
2025-02-17T10:47:12Z  
dc.date.issued
2024-11-21  
dc.identifier.citation
Sánchez, Marisa Analía; Rossit, Daniel Alejandro; Tohmé, Fernando Abel; Enhancing production system resilience with digital twin-driven management; Taylor & Francis Ltd; International Journal Of Computer Integrated Manufacturing; 21-11-2024; 1-20  
dc.identifier.issn
0951-192X  
dc.identifier.uri
http://hdl.handle.net/11336/254551  
dc.description.abstract
In the last decade, significant advancements in digital technologies have revolutionized production systems, leading to the development of Cyber-Physical Systems (CPS) and Digital Twins (DT). This paper proposes a design of a production management system that leverages a Digital Twin associated with the shop floor. The objective is to enhance the resilience of production systems by providing real-time monitoring and enabling cloud-based outsourcing during production line failures. The methodology involves creating a Digital Twin model of the CPS, which is used to monitor the production process and visualize key performance indicators through a business intelligence tool. The contribution of this study lies in addressing the growing need for resilient production systems capable of withstanding disruptions and unexpected events. The DT provides accurate information useful for making informed decisions during disruptions. This research contributes to improving management practices by integrating production and enterprise data, facilitating decision-making processes aligned with company objectives, and enhancing the overall resilience of production systems. These findings illustrate the potential of cloud production services to maintain service levels. Among the academic contributions of this paper, is the use of System Dynamics as a suitable approach to modeling the behavior of a digital twin.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Taylor & Francis Ltd  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
DIGITAL TWIN  
dc.subject
RESILIENCE  
dc.subject
CYBER-PHYSICAL SYSTEMS  
dc.subject
INDUSTRY 4.0  
dc.subject
SMART MANUFACTURING  
dc.subject.classification
Otras Ingenierías y Tecnologías  
dc.subject.classification
Otras Ingenierías y Tecnologías  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
Enhancing production system resilience with digital twin-driven management  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2024-12-26T12:21:37Z  
dc.journal.pagination
1-20  
dc.journal.pais
Reino Unido  
dc.journal.ciudad
Londres  
dc.description.fil
Fil: Sánchez, Marisa Analía. Universidad Nacional del Sur. Departamento de Ciencias de la Administración; Argentina  
dc.description.fil
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina  
dc.description.fil
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina  
dc.journal.title
International Journal Of Computer Integrated Manufacturing  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/0951192X.2024.2428686  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/0951192X.2024.2428686