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

Author

Listed:
  • Adolfo Crespo Marquez

    (Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain)

  • Juan Francisco Gomez Fernandez

    (Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain)

  • Pablo Martínez-Galán Fernández

    (Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain)

  • Antonio Guillen Lopez

    (Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain)

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.

Suggested Citation

  • Adolfo Crespo Marquez & Juan Francisco Gomez Fernandez & Pablo Martínez-Galán Fernández & Antonio Guillen Lopez, 2020. "Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models," Energies, MDPI, vol. 13(15), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3762-:d:387888
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    References listed on IDEAS

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    1. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    2. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
    3. Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
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    Cited by:

    1. Fausto Pedro García Márquez, 2022. "Special Issue on Advances in Maintenance Management," Energies, MDPI, vol. 15(7), pages 1-4, March.
    2. Saihi, Afef & Ben-Daya, Mohamed & As'ad, Rami, 2023. "Underpinning success factors of maintenance digital transformation: A hybrid reactive Delphi approach," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Martínez-Galán Fernández, Pablo & Guillén López, Antonio J. & Márquez, Adolfo Crespo & Gomez Fernández, Juan Fco. & Marcos, Jose Antonio, 2022. "Dynamic Risk Assessment for CBM-based adaptation of maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Sandra Giraldo & David la Rotta & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortúa, 2021. "Digital Transformation of Energy Companies: A Colombian Case Study," Energies, MDPI, vol. 14(9), pages 1-14, April.
    5. Damjan Maletič & Matjaž Maletič & Basim Al-Najjar & Boštjan Gomišček, 2020. "An Analysis of Physical Asset Management Core Practices and Their Influence on Operational Performance," Sustainability, MDPI, vol. 12(21), pages 1-20, October.
    6. Fco. Javier García-Gómez & Víctor Fco. Rosales-Prieto & Alberto Sánchez-Lite & José Luis Fuentes-Bargues & Cristina González-Gaya, 2021. "An Approach to Sustainability Risk Assessment in Industrial Assets," Sustainability, MDPI, vol. 13(12), pages 1-23, June.

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