dc.creator | Crespo Márquez, Adolfo | es |
dc.creator | Gómez Fernández, Juan Francisco | es |
dc.creator | Martínez-Galan Fernández, Pablo | es |
dc.creator | Guillén López, Antonio Jesús | es |
dc.date.accessioned | 2021-04-19T11:45:00Z | |
dc.date.available | 2021-04-19T11:45:00Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Crespo Márquez, A., Gómez Fernández, J.F., Martínez-Galan Fernández, P. y Guillén López, A.J. (2020). Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models. Energies, 13 | |
dc.identifier.issn | 1996-1073 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107312 | |
dc.description.abstract | Maintenance Management is a key pillar in companies, especially energy utilities, which
have high investments in assets, and so for its proper contribution has to be integrated and aligned
with other departments in order to conserve the asset value and guarantee the services. In this line,
Intelligent Assets Management Platforms (IAMP) are defined as software platforms to collect and
analyze data from industrial assets. They are based on the use of digital technologies in industry.
Beside the fact that monitoring and managing assets over the internet is gaining ground, this paper
states that the IAMPs should also support a much better balanced and more strategic view in existing
asset management and concretely in maintenance management. The real transformation can be
achieved if these platforms help to understand business priorities in work and investments. In this
paper, we first discuss about the factors explaining IAMP growth, then we explain the importance
of considering, well in advance, those managerial aspects of the problem, for proper investments
and suitable digital transformation through the adoption and use of IAMPs. A case study in the
energy sector is presented to map, or to identify, those platform modules and Apps providing
important value-added features to existing asset management practices. Later, attention is paid to the
methodology used to develop the Apps’ data models from a maintenance point of view. To illustrate
this point, a methodology for the development of the asset criticality analysis process data model is
proposed. Finally, the paper includes conclusions of the work and relevant literature to this research | es |
dc.format | application/pdf | es |
dc.format.extent | 19 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Energies, 13 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | intelligent assets management systems | es |
dc.subject | industrial IoT | es |
dc.subject | predictive analytics | es |
dc.subject | asset data model | es |
dc.title | Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I | es |
dc.relation.publisherversion | https://www.mdpi.com/1996-1073/13/15/3762 | es |
dc.identifier.doi | 10.3390/en13153762 | es |
dc.contributor.group | Universidad de Sevilla. CEI-19-TEP134: Metodología para aplicación industrial de soluciones de mantenimiento inteligente. Integración de técnicas de Analítica Predictiva y Machine Learning en plataformas IoT | es |
dc.journaltitle | Energies | es |
dc.publication.volumen | 13 | es |