Leveraging knowledge graphs to determine enterprise asset management systems hierarchies
The need for real-time business insights using rapidly growing IoT-generated data points and demand for efficient low latency queries fuel the need for a more efficient data structure, such as knowledge graphs. Asset management poses the challenge of overseeing a network of intertwined assets, where understanding their interdependencies is vital for effective planning and decision-making. Asset hierarchies simplify this task by organizing assets into a relational structure based on subsystems and components. Examining this structure allows organizations to pinpoint critical assets with significant impacts on system performance. Such relationship analysis enables organizations to discern critical paths, anticipate maintenance needs and prevent failures by addressing issues proactively.
Data integration, optimization and geospatial capabilities of the enterprise asset management system
This amalgamation facilitates real-time reports and visualizations, aiding proactive asset management strategies. It centralizes information, eradicating data silos and providing decision-makers with accurate and current asset information. Integration with additional platforms, such as purchase management and weather systems, enables the consideration of external factors impacting asset performance.
Spatial data integration in asset management enables geospatial asset visualization, enhancing decision-making via geographic information system (GIS) technology. This spatial perspective optimizes asset placement, boosts utilization and refines maintenance operations. Furthermore, it aids in routing optimization for maintenance crews, promoting operational efficiency, reducing response times and leading to cost savings.
A balance of preventive and predictive maintenance minimizes disruption and optimizes resource allocation. Detection of patterns indicating potential failures prompts proactive asset maintenance, preventing costly breakdowns, reducing unplanned downtime and prolonging asset life-span.
Contextualizing worker notes using natural language processing
There is increased awareness and popularity of using natural language processing (NLP) methods to improve business outcomes. NLP market size is projected to grow from US$24.10 billion in 2023 to US$112.28 billion by 2030, at a CAGR of 24.6%,³ indicating a massive rate of adoption among organizations across industry segments. The true value of maintenance records emerges from its contextualization, enriching raw figures with additional information, such as procedures, technician notes and environmental conditions. This contextualized data can be leveraged to glean insights into performance trends, recurrent issues and maintenance patterns, allowing decision-makers to optimize processes, cut costs and pinpoint training needs. Furthermore, contextual insights can evaluate personnel performance and boost maintenance operations overall. This rich detailing of activities facilitates information sharing across teams, promoting collaboration and informed decision-making, thereby utilizing the organization’s collective knowledge and experience to its fullest extent.
