Melton City Council: Unlocking Hidden Capital: Doing Best with Less by Predictive Modelling Algorithms

Melton City Council is one of the fastest growing municipalities in Australia, managing an infrastructure asset portfolio valued at over $2.5B with a planned annual increase of $220m through new build. Its population is projected to grow 182% by 2051

“The challenge is to extract more value from limited resources”

The challenge of 2021 is to preserve the current average asset health of 80% and to extract more value from the current resources to meet future demands. Melton City Council developed and implemented algorithm-based asset models across all its asset categories, enabling the improvement of service delivery at the current level of capital expenditure, making it an industry leader in the international community

Use of best practice asset management principles

The premise of the Victorian Deliberative Engagement process is the need to explore and weigh up multiple future options and funding trade-offs through community engagement and consultation. Effective public deliberation and consultation require evidence-based alternatives derived from proven methodologies for:

•             Linkages of asset interventions and customer outcomes;

•             Optimising service level outcomes across all asset types;

•             Repeatable and reliable condition assessments.

In order to maximise cost benefit Council implemented a scientific approach through the use of predictive modelling.  A desired overall asset condition was set in consultation with internal and external stakeholders, and the cost of maintaining assets at this condition was calculated in order to inform the Financial Plan on Council’s spending on asset renewal into the future.

Figure 1: The flip from Budget Driven to Service Driven Asset Management

 Currently, Melton City Council has documented 10 year Asset Plans that take into consideration different alternatives and future service level trade-offs.

This framework provides a clear link between the Corporate Plan, Asset Management Plans (AMP) and the Long Term Financial Plan (LTFP). It highlights the move from budget driven asset management, to a long-term financial plan that is informed by the understanding of Council’s assets, levels of service and the impact of different funding strategies. In essence, the AMPs inform the LTFP and not the other way around. (Figure 2)

                     Figure 2: Asset Planning Model

Performance Assessment

Condition audits on Council assets are undertaken cyclically every 4 years for each asset category (roads, pathways, etc.), and are based on IPWEA assessment guidelines. Council incorporated these guidelines into a suite of condition audit manuals, one for each category.  These were completed as part of a ‘Business Process’ improvement action in the Council’s Asset Management Strategy, in order to specify the rules around data collection and to systematically standardise condition rating to IPWEA guidelines.  

                    Figure 3: Council used IPWEA guidelines to develop its own Condition Audit Manuals for the various asset categories.

Degree of originality and ingenuity of the solution

The project delivered breakthrough outcomes in decision making, demanding a different way of thinking from all stakeholders; Engineers, Finance Officers, Councillors and service providers. Although condition is a key factor in the decision making, it is not the only factor. Other consideration include material, age, function and usage. The renewal incorporated all these factors in a pre-determine decision making matrix informed by stakeholders. Degradation strategies were also refined and recalibrated to reflect actual behaviour based on historical evidence.

In the absence of a well calibrated decision logic and comprehensive data, only assets that are in a poor condition were selected for treatment. This option was tested in a model called ‘Option 5 – Run to Failure’ while the service driven model was called ‘Option 1 – Allocated Budget’. Testing these simulations on road assets, the results were strikingly clear. The Run to Failure simulation presents high risk of a large number of Council’s Road assets reaching the asset failure zone (Figure 6) in the same year, therefore posing financial and safety risks. Figure 4 illustrates these comparisons using the road assets models as an example.

Figure 4: Increased Renewal and Maintenance cost at the “Failure Zone”

Figure 5: Comparing outcomes Year 20 – Allocated Budget Vs Run to Failure Budget

The results are clear (Figure 6). The traditional model (Option 5) not only shows a decline in asset condition but also an increased cost over 20 years as a result of late intervention. A comparison in the net strategy costs for the selected scenario shows a staggering difference of $102M over 20 years.

Figure 6: Comparison of an optimised model vs traditional failure model

Program and project management

Unlocking hidden potential involved developing a Strategic Asset Management Framework (SAMF) to guide decision making. Key cornerstones of this process included the following:

• Reliable asset data collected cyclically through planned condition audits;
• Consultation with Stakeholders from other service units within Council through planned workshops;
• Community consultation and feedback;
• Benchmarking of Asset Life Cycle Profiles using based on reliable historical data and on industry standards;
• Benchmarking of Asset Degradation Profile based on reliable historical data and expert advice asset’s behaviour;
• Agreement on intervention triggers based on IPWEA assessment guidelines;
• Agreement on treatments based on tried and tested methods;
• Factual treatment cost estimates based on similar projects.

This was the lead up work necessary for populating the algorithms within the Assetic Predictor software. Figure 6 provides a summary this process in five main steps undertaken to develop.

Figure 7:  Collaborative Approach at every stage of the project

At each iteration of the project, a collaborative approach was adopted to clarify and refine the developed solutions to enhance its maturity to deliver project outcomes. The result of this progressive process is an agile project that is continuously improved and calibrated, consistent with key learnings:

• Prioritize – Focus on critical asset types to extract maximum value;
• Collaboration – Collaboration is key as a deliberative engagement policy with the community is adopted;
• Understand asset management needs ­– Understand the rationale behind asset provision, the type of assets required and the funding required.

Benefit/Value of the project or service to the community or organisation

This project has enabled Council to improve its risk management by identifying potential financial risks, and helped to maximise cost benefits from renewal expenditure by accumulating a saving of approximately $2.6 million per annum on the Roads asset category alone. (Table 1 below)

Using the road assets as an example, a comparison between the Allocated Budget simulation and ‘Failure/Run to Failure’ simulation reveals the risk of increased backlog.

Table 1: Annual saving on cost

	Assets in poor condition and end-of-life
Option 1: Current Budget	7.6% (6.68 +0.92)
Option 5: Run to Failure	13.14% (12.37 + 0.77)
Difference	5.54%
Roads Total Replacement Value	$938,026,265
Savings over 20 years	$51,966,655.08  (5.54% x 938,026,265)
Annual cost savings	$ 2,598,332.75  (51,966,655.08/20)

It is estimated that the average asset portfolio degrades at average 3-4% per annum. By optimising interventions in line with strategic asset management best practice, this consumption rate can be reduced by a conservative estimate of 0.16%. Given the scale of the overall asset portfolio, this could realise savings of up to $4 million per annum by applying the analytical models developed through this project.

“Sealed roads and buildings represent the largest asset classes in Melton City Council’s portfolio. By comparing the traditional scenario of only treating assets in poor condition vs a robust model with community driven levels of service, the annual benefit derived from these asset classes are significant”   

Luke Shannon, GM Planning and Development

This project has been led by the Engineering Services team, with extensive collaboration and assistance from Operations, Finance, and various Service Managers from across the organisation. Together with Assetic and IMG, we continually strive to achieve our long-term objective of sustainable, community driven outcomes relating to asset management.

Melton City Council is one of the fastest growing municipalities in Australia, managing over $2.5B worth of infrastructure assets, and more to come with future growth of demand. An objective, defendable and future proofed approach to asset management decisions is required to deliver more value and benefits to the community.

The traditional methods of allowing assets to run to failure have proven not only to lead to a decline in the level of service provided to the community, but also to increase the cost of maintaining these assets over the long term. By exploring innovative products and techniques, Council can create a balance between renewal and preservation treatments where applicable, and where cost efficient.

Predictive modelling has created an environment where these options can be explored then tested over time. It helped identify the optimal time and optimal asset to be selected for renewal, resulting in a reduction of consumption rate and significant cost savings. Had Melton not secured the quality data and run through scenario modelling it would only be able to fix what breaks. Now it predicts what will occur and can intervene earlier.

Application of best practice asset management principles through predictive modelling established the case for optimisation from an evidence base, and its contribution to the eventual outcomes. Currently, Melton City Council has documented 20 year Asset Plans that take into consideration different alternatives and future service level trade-offs.

This project has helped Melton City Council operate more cohesively and collaboratively, providing a consistent language and scientific evidence base on which to discuss asset management with a focus on customer and community outcomes. The Council has raced to the forefront of deliberative community engagement, with many hours of community and stakeholder consultations to set the desired levels of service. The result of this joint effort drives better understanding of the needs of the community, translating to better informed predictive models.