NSW Department of Education & AssetFuture – Innovation Through Technology


In 2013, the NSW Department of Education (DoE) commenced a journey to understand the maintenance backlog across 2,200 schools. By July 2018, NSW DoE were able to confidently project requirements for current and future funding of maintenance and document functionality across all schools using machine intelligence.

Using Asset Management best practice around data acquisition and interpretation methods, the DoE created a condition and functionality index for all NSW school assets. The DoE has improved its ability to project current and future funding needs, prioritising investment decisions. In early 2019, the NSW Government committed to wiping the maintenance backlog to zero by June 2020.



Best practice asset information management is underpinned by information and data standards that provide a consistent understanding of the requirements to support collection, storage and interpretation of data to support strategic decisions. This understanding allows a clear line of sight between strategy and operations. Working with the AssetFuture platform DoE was provided with a clear set of in-built data standards consistent across the entire technology ecosystem. These standards cover data fields including:

  • Asset hierarchy
  • Asset naming convention
  • Condition & Functional ‘Fit-for-Purpose’ assessment (i.e. is an existing classroom teaching area fit for purpose of teaching 30 school age children?)
  • Criticality & Risk assessment
  • Minimum required condition standards


The data acquisition team were trained in the application of the condition measurement methodology which varied based on the type of asset being captured. A predefined condition scale was calibrated in-line with specific degradation characteristics which were also referenced within the data capture device. This reduced the variation caused by human judgement when assessing asset condition, leading to increased quality of the data acquired.


Having a consistent method to assess condition, risk and service levels for each school within the portfolio, enabled comparisons to be made across NSW schools for the first time. This provided the confidence to make budget allocation decisions across an extensive asset portfolio. AssetFuture’s Asset Condition and Criticality Index (ACCRI) methodology made comparison of risks and service levels across an extensive set of asset classes a simple process, and was only possible through accurate asset information.


Prior to this project, upkeep of asset data over time was extremely challenging leading to a partially informed view. Implementing the right governance arrangements in the form of platform access rights as well as stakeholder accountability and monitoring compliance with data standards, enabled a level of data currency to efficiently carry out annual planning and budgeting activity as well as adjusting annual plans based on the latest “live” information. Best practice has been implemented for data management, ensuring that interpretation of data is giving a clear line of sight between strategy and operations:

  • Data framework and governance
  • Data ecosystem to maintain currency (storage and maintenance)

Additionally, data currency has moved to a position where all data is now captured and updated in the AssetFuture platform to maintain a live and current dataset in the ecosystem to enable accuracy around decision making.


The outcomes of this project were linked to the interpretation of quality data sets. Using AssetFuture’s Lifecycle prediction algorithms to inform decision making for each schools’ portfolio of assets, was a key outcome. DoE now has a comprehensive interpretation of:

  • Degradation Modelling
  • Lifecycle Prediction
  • Functional Performance
  • LCC Condition Profile & Projection
  • LCC Maintenance & Condition Projection
  • Scenario Budget Limit Analysis
  • Using the data across the entire asset lifecycle, from Capital & Cluster Planning through to disposal and refurbishment, for Asset Management Planning best practice


The scale of the level of data acquisition required meant that creative solutions were applied to existing data sets and acquisition of new data. The DoE utilised many internal bespoke systems such as AMS (Asset Management System) for spatial management, RPM (Routine Planned Maintenance) for works maintenance, and new Asset Intelligence with AssetFuture. This data was merged, and additional data collected using different data capture techniques, with a consistent approach across all asset classes to support evidence-based decision making. Drone imagery was used to assess defects and the creation of a new data set that was complete to align modelling with business strategy:

  • Utilisation
  • Acceptable condition ratings
  • Geographic location
  • Climate
  • Importance and criticality by Fit-category and element type
  • Functional fit-for-purpose index baseline against international criteria


In a project of this size with a large number of assets, stakeholders numbered in the thousands. Stakeholders included more than 2,200 Principals of geographically dispersed schools, teachers, and over 750,000 students. Skillful management of these specialised stakeholders was critical to minimise disruption, and was achieved in the following ways:

  • Dedicated Communications Strategy Team – the team was developed so each member had a clear understanding of which sites would be visited when.
  • Precision project management – cost factors for time were transparent
  • The original assessment had to be completed to meet immovable Treasury submission timelines
  • Multifactor parameters/constraints to enable execution were taken into consideration by Project Management, and communicated with the Communications Strategy Team. Examples of these parameters included school times, class times, teacher availability, student safety (Working With Children compliance)


Previously it was very difficult to align annual budgeting to the needs of students across NSW. The state-wide funding allocation model is now based on best practice Asset Management principles, having full consideration for asset cost, risk and performance. For NSW DoE, application to Treasury for funding is based on real-time interpretation of quality data ensuring:

  • Understand current and future funding requirements
  • Use information in Strategic Planning documentation
  • Decision making is aligned with department objectives line of sight, supporting regional planning and principal decision making
  • Justified and accepted minimum investment required to maintain portfolio to acceptable standard
  • Use data and intelligence to prioritise expenditure OPEX and CAPEX programs
  • Using the data across the entire asset lifecycle, from Capital & Cluster Planning through to disposal and refurbishment

The implementation of the Lifecycle technologies has been one of the key factors in budget increases of more than $1 billion over the forward estimates for students attending schools in NSW. There has also been a reduction in risk from poorly maintained learning spaces and enhanced learning environments. International research has shown there is a direct link between the condition of assets and learning outcomes.



The success of the project over two assessment cycles is largely due to the bringing together of a highly motivated team of subject matter experts who were able to use their expertise:

  • The DoE Business systems unit was able to provide detailed information on the school assets in a form that smoothly integrated with the AssetFuture requirements
  • The AssetFuture software had been developed and improved over a significant period before engaging with DoE
  • This enabled the team to quite quickly adapt performance parameters to suit the unique DoE asset base
  • The DoE project team members had extensive networks within the portfolio to ensure that there was appropriate buy-in to the new system
  • The project team included members with expertise in educational planning which assisted the development of functionality questions based on factors known to have an impact on educational outcomes
  • The project was supported at the highest level by the Minister and the Executive of DoE so that entry to schools to undertake assessments during school hours was assured
  • Appropriate funding support was provided by DoE
  • Dedicated project managers from DoE and AssetFuture ensured that the project proceeded according to time, quality and budget constraints
  • The DoE project team members undertook additional quality assurance and ‘reality’ checks in addition to the detailed quality assurance of the data undertaken by AssetFuture
  • A formal process has been adopted to ensure the impact of any maintenance or capital works is captured and the life cycle costing data base managed by AssetFuture is updated accordingly
  • DoE has provided ongoing funds to ensure this occurs and that the system is adequately supported by AssetFuture



Interpretation of data from the AssetFuture platform enabled DoE to confidently project the funding requirements for 2,200 schools in NSW. Scenario planning was used for multiple planning parameters, as well as functional performance indices. Drone technology was utilised for defect and condition assessment for roof surveys.

LCC Maintenance Projection – Current and Future Maintenance Projection (sample of schools). Shows liability by year and asset class.

Scenario Analysis – Ability to vary planning parameters to simulate various funding/maintenance scenarios.

Functional Performance – Functionality indices allow investment to be prioritized across asset classes.

Drone Technology – Drone technology for better resolution of maintenance issues on high roofs.

Trees Validation – Validation of tree audits as requested by the Coroners court.

Scenario ACCRI – Risk matrix to assist investment planning decisions.

Data Transfer – Maintaining currency of data by updating after all maintenance and capital works projects.

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