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Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models

Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain
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Energies 2020, 13(15), 3762; https://doi.org/10.3390/en13153762
Received: 7 May 2020 / Revised: 13 July 2020 / Accepted: 15 July 2020 / Published: 22 July 2020
(This article belongs to the Special Issue Advances in Maintenance Management)
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. View Full-Text
Keywords: intelligent assets management systems; industrial IoT; predictive analytics; asset data model intelligent assets management systems; industrial IoT; predictive analytics; asset data model
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MDPI and ACS Style

Crespo Marquez, A.; Gomez Fernandez, J.F.; Martínez-Galán Fernández, P.; Guillen Lopez, A. Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models. Energies 2020, 13, 3762. https://doi.org/10.3390/en13153762

AMA Style

Crespo Marquez A, Gomez Fernandez JF, Martínez-Galán Fernández P, Guillen Lopez A. Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models. Energies. 2020; 13(15):3762. https://doi.org/10.3390/en13153762

Chicago/Turabian Style

Crespo Marquez, Adolfo, Juan F. Gomez Fernandez, Pablo Martínez-Galán Fernández, and Antonio Guillen Lopez. 2020. "Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models" Energies 13, no. 15: 3762. https://doi.org/10.3390/en13153762

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