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

Adolfo Crespo Marquez, Juan Francisco Gomez Fernandez, Pablo Martínez-Galán Fernández and Antonio Guillen Lopez
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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

Energies, 2020, vol. 13, issue 15, 1-19

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.

Keywords: intelligent assets management systems; industrial IoT; predictive analytics; asset data model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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