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In recent years, the abundant availability of data has allowed for more data-driven decision-making. However, this data has primarily been utilized for building CAPEX investment cases from the ground up. It’s only very recently that significant technological advancements have converged to revolutionize investment and maintenance planning within the energy and utilities sector.<\/p>\r\n\r\n\r\n\r\n
In the realm of energy and utilities, a powerful platform is now at your disposal, enabling companies to conduct intricate “what if…?” scenarios for strategic investment planning. This platform allows for the manipulation of various parameters, unveiling the impacts of diverse investment decisions at an enterprise level. Moreover, it aids in operational optimization by employing simulations to plan fleet capacity and downtime based on the outcomes of different scenarios.<\/p>\r\n\r\n\r\n\r\n
When the platform’s simulations merge with the capabilities of Machine Learning (ML) and Artificial Intelligence (AI), it becomes possible to identify anomalies and deviations in asset behavior, predicting potential failures or risks in assets and plants. By using ML and AI algorithms to uncover correlations and integrating them into the platform, one can precisely chart the trajectory of predicted failures or risks within simulations.<\/p>\r\n\r\n
The key advantages are a reduction in downtime and cost savings achieved through proactive asset repair, maintenance, and replacement.<\/p>\r\n\r\n\r\n\r\n
\r\nEnhanced CAPEX Allocation, Extended Component Lifespans, Reduced Downtime, Enhanced Efficiency, and Lower Operating Costs.<\/p>\r\n<\/blockquote>\r\n\r\n\r\n\r\n
Embracing the Transition to Condition-Based Maintenance<\/h3>\r\n\r\n
But how attainable is this vision? If you currently rely solely on schedule-based maintenance and aspire to shift toward condition-based practices, it’s imperative to secure high-quality process data. Initiate a comprehensive data strategy to ensure data availability and governance. Begin by identifying which components hold the utmost significance and create maintenance strategy categories as a foundation for progress.<\/p>\r\n\r\n
For those grappling with CAPEX planning challenges, commence with asset investment planning. Many companies initiate their journey by addressing the high costs associated with asset unavailability, as it is often the most straightforward to tackle using real-time data. Within six months, you can begin leveraging ML and AI to train models and yield initial results. Any unusual patterns and deviations that elude your engineers can serve as early indicators of potential failures or risks.<\/p>\r\n\r\n\r\n\r\n
Proven Benefits in the Energy & Utilities Sector<\/h3>\r\n\r\n
Promising outcomes have emerged from the application of this platform, infused with ML, within the energy and utilities industry. These include enhanced CAPEX allocation, prolonged component lifespans, minimized downtime, maximized efficiency, and reduced operating costs. In a recent implementation, a digital model was enriched with real-time data and expert knowledge to create a platform offering a 360\u00b0 view of assets and predicting failures. This led to a 20% improvement in Mean Time to Response (MTTR), a 12% reduction in operational transportation expenses, and a 0.2% increase in asset availability.<\/p>\r\n\r\n
While the specifics of asset management may differ for power plants, wind turbines, or water stations, the overarching challenges of achieving high performance, resilience, and cost-efficiency remain consistent across all sectors within energy and utilities.<\/p>\r\n\r\n
<\/p>\r\n
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