The challenge
Universities are asset-intensive organizations that manage complex building portfolios, ranging from highly specialized research and teaching buildings, sports facilities, archives and libraries to student accommodation centers. Not only do they face the same challenges in maintaining their asset portfolio as any other asset-intensive organization, but it has been widely acknowledged that Australia’s tertiary education sector was among the hardest financially hit by the COVID-19 pandemic.
As a research-intensive university, the University of Adelaide (UoA) needed to balance maintaining comfortable facilities, providing specialized conditions for research activities and storage archives, and overall business continuity within the allocated budget. However, like many of its peers, it faced significant funding challenges and needed to either reduce or delay its planned asset renewals.
Another challenge was making the right capital and maintenance expenditure decisions. Given the university is nearly 150 years old, its building stock varies significantly in age, complexity and condition state. If left unmanaged, building portfolio degradation could significantly impact services supported by the buildings.
The university wanted to move away from its previous practice of investment planning, which relied heavily on the opinions and perceptions of building managers and occupants without solid evidence to back up the decisions being made.
Instead, UoA sought to better understand the short-, medium- and long-term impacts of different investment levels on the overall building portfolio based on real data. It also wanted a way to objectively prioritize buildings and building component renewals based on their importance in supporting the overall vision and ambitions of the university.
Lastly, it needed to be able to communicate this information in a simple and effective manner to UoA’s leadership so they could easily understand the consequences of their decision making.
The solution
UoA partnered with Brightly — which is now a part of Siemens Asset Management — to create a 25-year asset investment plan (AIP) that included ”what-if” scenarios to predict the deterioration of its buildings and condition levels given various funding levels.
To develop the plan, the university adopted Predictor, a prediction modeling and decision support tool for the long-term planning of infrastructure assets. The Predictor models combined snapshots of asset data with asset lifecycle and financial strategies to produce options for capital works investment in the future.
The “what-if” scenarios allowed UoA to improve its understanding of tipping points beyond which risks to business continuity would be unacceptable. Through this, UoA was able to identify an optimum short-term reduction in asset investment funding with an acceptable level of deterioration to its building stock in 5–10 years.
Using the program, UoA was also able to create a target level of service for each building priority to assess performance and contribution to the university’s strategic objectives. It placed buildings into four priority categories for maintenance and operational purposes, from high priority through to low priority. Capital works intervention levels were then designed to reflect building priority and building component criticality, allowing UoA to mitigate extreme or high risks involved with failing critical components in high-priority buildings.
Using visualizations to tell the story was a crucial part of communicating the predictive insights to the key stakeholders. A visualization platform was developed using Power BI to present modeling analysis outcomes in a clear and impactful way, using metrics and graphs that could be understood by everyone.
The results
The AIP approach has been groundbreaking for UoA and within its peer group. Since its development, data-driven AIP has become an integral part of UoA’s asset management, allowing its leadership to compare different investment options for their building portfolio and make more informed decisions on what to invest in and when.
At each iteration of the modeling, multiple funding strategies are presented to aid with planning and decision making. A key improvement has been refining the cost of building component renewal. While the initial modeling adopted component replacement value as the renewal (treatment) cost, over the subsequent modeling iterations, this was refined by applying a percentage of component replacement values which are continuously validated against the latest building capital works.
Stakeholder engagement in each modeling iteration has also allowed them to better understand many of the assumptions that have been made during the modeling process and to adopt improvements going forward.
The university recognizes that achieving desired outcomes should not be a one-off process but a journey involving all stakeholders. Equipped with a better understanding of the modeling process, UoA continues to enhance its AIP by broadening its input parameters, improving data accuracy and output visualizations and embedding the AIP model outputs within the critical decision-making process of its leadership. For UoA, evidence-based asset investment planning has become a way of life.