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Role of Predictive Analytics in Asset Investment Planning – The bottom-up approach

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Marija Matkovic Author
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5 min Reading time
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11 Dec 2024 Published
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Many utility companies are shifting from time-based maintenance to smarter and proactive asset health management systems. These systems aim to reduce customer downtimes and manage to expand infrastructure within a budget while ensuring compliance with regulations.

Asset Investment Planning

Asset Investment planning is a process that organizations use to plan and manage resource allocation, with the main goal to maximize benefits, control costs, and mitigate risks to ensure high-value returns. We designed an AIP solution to assist our customers in planning and managing investments in high-value assets. The solution provides many capabilities and enhanced intelligence to optimize these investments’ benefits, costs, and risks. The AIP solution includes features such as Maintenance Decision Intelligence (MDI) and AIP Simulation based on predictive analytics. These help investment managers identify assets requiring replacement or maintenance, simulate various investment scenarios, and allocate funds based on risk minimization or fixed budget plans.

Predictive analytics helps make informed financial decisions based on predictive models and data. The AIP solution includes a Risk Matrix that aggregates assets on a substation and cost level, allowing users to analyze health scores, probabilities of failure, and consequences to identify critical investment areas. This data and analysis provide valuable input for strategic investment decisions.

Additionally, the IPS®APM solution supports investment planning by providing insights into asset conditions, risk assessments, and performance evaluations. Executives can use this information to prioritize investments based on asset health, criticality, and financial impact. The solution includes MDI diagrams, risk matrices, simulations, and predictive analytics to support decision-making.

How does predictive analytics work in asset investment planning?

The predictive analytics in AIP utilizes machine learning algorithms and standardized asset performance calculation logic methods. It integrates an advanced analytical model to track asset health and reliability by predicting asset aging and identifying end-of-life. Our AIP solution also includes a simulation engine that evaluates different investment options. This engine combines inputs from Asset Performance Management (APM) asset analysis indexes with data-driven algorithms to determine the preferred investment solution. The predictive models analyze health scores, probabilities of failure, and consequences to identify critical areas for investment.

The AIP Simulation feature enables users to simulate various investment scenarios by varying budget allocations, offering risk minimization and fixed budget plans. This lets users make informed financial decisions based on predictive models and data. The Risk Matrix aggregates assets on a substation and cost level, aiding in analyzing health scores, probabilities of failure, and consequences, which serve as valuable inputs for strategic investment decisions.

The data for these analyses is leveraged from multiple systems, such as enterprise asset management, asset performance management, GIS systems, and outage management systems. Additionally, the solution supports predictive analytics using historical values from visual inspections, monitoring systems, electrical test equipment, statistics (e.g., malfunction KPIs, Taylor series, Weibull functions), or any other data source. Integrated algorithms evaluate asset health and prioritize preventive maintenance approaches for all key assets over the reactive approach.

Risk Matrix

The Risk Matrix in the AIP solution plays a crucial role in aggregating assets based on their health scores, probabilities of failure, and consequences (both technical and financial). This aggregation allows users to identify critical areas for investment by analyzing these factors. The Risk Matrix helps make informed financial decisions by providing valuable input for strategic investment planning. It supports the AIP Simulation feature, enabling users to simulate different investment scenarios, minimize risks, and optimize budget allocations. The data and analysis from the Risk Matrix serve as essential inputs for the Asset Investment Planning team to make strategic decisions.

Probability of Failure

The probability of failure in the AIP solution refers to the likelihood that an asset will fail within a specified time frame. This metric is a crucial component of the AIP’s predictive analytics, helping to assess the risk associated with each asset. The probability of failure is calculated using various data inputs, including health scores, historical performance data, and condition assessments.

The AIP solution uses these probabilities, along with the consequences of failure (both technical and financial), to identify critical areas for investment. This information is integrated into the Risk Matrix, aggregating assets based on their health scores, probabilities of failure, and consequences. By analyzing these factors, the AIP solution helps users make informed decisions about where to allocate resources to minimize risks and optimize investment outcomes.

Regression budgeting

Regression budgeting in the AIP solution involves predicting future budget investments based on historical data and derived data such as the health index. This approach calculates the budget needed for investment over time for each selected asset or cluster. The engine not only predicts the required budget but also provides results such as the predicted cost of each asset and the impact of asset failures on the system.
The data science algorithms used for regression budgeting include multivariate and neural network regression algorithms. These algorithms enable the creation of simulations that show how changes in asset elements affect budgeting. By analyzing historical budget data and asset health indexes, regression budgeting helps forecast financial requirements for asset maintenance and replacement, ensuring informed and optimized investment decisions.

Maintenance Decision Intelligence

The Maintenance Decision Intelligence (MDI) feature in the AIP solution correlates with Asset Performance Management (APM) and Asset Investment Planning (AIP). This correlation enables investment managers to identify assets that require replacement or maintenance based on their current conditions and maintenance requirements. The MDI feature helps prioritize assets, ensuring cost-effective investments by providing insights into which assets need attention and when. It supports informed decision-making by highlighting the maintenance needs and conditions of assets, optimizing investment strategies, and ensuring efficient allocation of resources.

Conclusion

Predictive analytics is transforming asset investment planning in the utility sector by enabling decisions based on actual data. By leveraging AIP solution, utilities can shift from reactive to proactive strategies, optimizing asset health management and investment allocation. Features such as Risk Matrices, Maintenance Decision Intelligence, and regression budgeting empower utilities to minimize risks, prioritize critical assets, and achieve better financial outcomes. This approach not only ensures compliance and infrastructure flexibility but also helps utilities achieve long-term efficiency in their operations.