Optimising asset lifecycle planning requires the right level of data granularity to be collected during condition assessments. The granularity of asset data directly influences decision-making, ensuring maintenance and renewal efforts are based on actual conditions rather than broad assumptions. Many organisations struggle with inflated renewal backlogs due to assessments conducted at a high level, leading to overestimated funding needs and inefficient resource allocation.
By adopting a more detailed, component-level approach, asset managers can refine backlog estimates, optimise spending, and prevent unnecessary renewals. This distinction is crucial for financial planning, as targeted interventions—such as replacing only the worn sections of floor coverings rather than an entire floor—can significantly reduce costs.
For local governments managing extensive facility portfolios, the financial implications of granular data are substantial. By improving the accuracy of condition assessments, councils can unlock substantial savings, redirecting funds toward critical infrastructure, compliance, and community enhancements.
The presentation will draw on the authors experience leading both condition assessment and strategic lifecycle analysis projects for Australian clients. The author will demonstrate examples of successful data collection frameworks, and how the collected data can be transformed into powerful lifecycle models that can enhance asset sustainability, optimise investment efficiency, and ensure renewal budgets are allocated where they are truly needed.