Technology advances such as machine learning; new data management systems; data analytics and Artificial Intelligence, hold the promise for owners to significantly reduce their cost of ownership through operational benefits. For instance, this could reflect in greater asset longevity, improved safety and increased site productivity.
However, many organisations experience limitations with these new technologies because they are hamstrung by the variable quality of their asset data and the lack of focus on the highest value decisions.
At Aurecon, we’re talking to clients about a new way of approaching asset portfolio management, where business strategies and decision-making are re-established at the apex of the leadership function, and technology is used as a tool to fill data gaps or convert data into useful information relevant to making key asset decisions.
We start by asking a client “What outcomes do you need to achieve? What are your corporate and strategic goals?”
From this starting point, leaders can nominate the outcomes they see as the most critical. The task is then to access the right data sets and manipulate them into the right format to facilitate effective decision-making processes.
Good asset management is about finding the right balance between costs, risks and asset performance. Along with assessing existing available data, we also evaluate risks and the decisions that must be made in relation to them.
Life-cycle models that show the interaction between cost and risk for a given level of performance, supported by the right data, provide insights into the future. With these models, decision makers can test various scenarios that are built from options-analyses and ensure that their compliance, safety, maintenance and productivity obligations are met. Managers can also explore alternative approaches to ensure that they are not overspending on their assets.
Aurecon is recognised for its ability to build strategic life-cycle costing models using simple and easy to access platforms. Why do we do it this way? So that they are easy to understand, use and modify. Designing them like this also enables our clients to have the potential to repurpose them for projects in other parts of the business, or across the entire organisation.
For example, we have built life-cycle costing models for an infrastructure intensive organisation that operates ship-loaders to export materials.
The infrastructure requires large maintenance budgets, in particular painting. The loaders are built of steel and they operate by the sea, consequently controlling corrosion is a large part of managing the risk and ensuring good safety and productivity outcomes over the asset life cycle. This is typical for port infrastructure where corrosion management and painting can be a significant component of the asset budget.
Utilising existing asset data, including condition assessment reports (by Aurecon and others), we developed hypotheses in consultation with the client. By running models to evaluate different scenarios, the client could select an optimum strategy to minimise risk and life-cycle costs.
The final version of the life-cycle costing model integrated an economic forecasting model and a physical condition model. The economic model delivers budget estimates, which managers can easily build into their future financial planning process. The physical model estimates asset deterioration using available condition assessment data and the results of the maintenance strategies deployed.
While the overall life-cycle costing model was developed using sophisticated theoretical approaches and knowledge, it is validated using actual results so that the hypotheses are continually tested, and the model accuracy is enhanced.
In this case, the client realised operational cost savings, improved safety, productivity and peace of mind.
We have built a similar life-cycle cost model for a freeway operator in a major Australian city that was looking at upgrading a freeway surface. Our client wanted to know if it was a better cost-benefit scenario to replace the tarmac road surface rather than patching the road with tarmac on an ‘as needs basis’; or replacing the surface with concrete.
As with most civil infrastructure, it’s more than just cost and quality: the owners needed a ‘multi-criteria analysis’ model, integrating data from stakeholders, regulators, environmental and safety requirements and user-satisfaction measures. Due to the critical nature of the asset, we incorporated an ‘operational security’ criterion into our model (common for civil infrastructure), which included a ‘fatal flaw’ test. The test presupposes a critical asset has reached a point of failure where it cannot support its assigned capability or poses a safety risk to an organisation.
This approach was also used to build a condition assessment tool for aviation bases, covering the infrastructure, capacity and compliance of the bases.
All these models used existing data to reduce asset life-cycle costs and risk. The data had to be checked for accuracy and currency and sometimes restructured into the right format. However, this effort was manageable as we only had to work on the targeted data required to make the relevant decisions rather than ‘boiling the ocean’.
In conclusion, the primary goal of asset management is to enable leaders to make good life-cycle decisions about the assets and we use the emerging digital tools as required to achieve this outcome. Technology alone is not the answer.
Newcastle Lead, Asset Management, Neil Naismith, is a leader in Aurecon’s asset management advisory business. He has 10 years’ experience in professional asset management, complemented by 25 years of operations management experience within the engineering, resources, and energy sectors.
During Neil’s career he has managed a broad range of asset management projects and initiatives including: asset compliance audits, asset management strategies and plans, and asset management outsourcing projects, among others.