In the evolving landscape of asset management, leveraging data-driven models across the entire asset lifecycle is paramount for optimising performance and minimising risks. This presentation explores the integration of predictive analytics and advanced asset failure models, such as Conditional Weibull models, in each lifecycle stage of the investments. We highlight how digitised and systematised portfolio management, enhanced by real-time data capture and machine learning, enables continuous monitoring and precise failure predictions, leading to strategic investment choices and optimised network reliability.

Through practical case studies, we illustrate the application of these models in the asset management portfolio optimisation. Attendees will gain insights into how to incorporate resource constraints in portfolios to maximise return on investment. By embracing data-driven decision-making throughout the asset management lifecycle, organisations can improve performance, reduce risks, and ensure optimal resource utilisation.

Some key take-aways include

1. Smart Asset Management: Employ data-driven models throughout the asset lifecycle, from acquisition to disposal, to optimise performance and reduce risks
2. Predictive Maintenance: Leverage real-time data and machine learning to predict asset failures, allowing proactive planning for repairs and minimising downtime
3. Strategic Decision-Making: Integrate data models into investments, enhance portfolio optimisation, and manage risks and budgets based on resource constraints