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

• Main Authors: Adolfo Crespo Marquez, Juan Francisco Gomez Fernandez, Pablo Martínez-Galán Fernández, Antonio Guillen Lopez
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Energies
• Subjects: intelligent assets management systems; industrial IoT; predictive analytics; asset data model
• Online Access: https://www.mdpi.com/1996-1073/13/15/3762

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 <i>strategic</i> 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.