EXECUTIVE SUMMARY

After a journey to understand the maintenance backlog across 2,200 schools the NSW Department of Education (DoE) can now confidently project requirements for current and future funding of maintenance and has committed to wiping the maintenance backlog to zero by the end of the 2019/20 financial year. 

Extending to an IoT (Internet of Things) pilot will enable NSW DoE to predict relevant changes to maintenance strategies during the operating phase of the assets with a more real-time view.

The result is a methodology that creates a degradation model that works on a different time scale. Over time, the Dynamic Utilisation Factor can enable predictive utilisation and inform performance optimisation opportunities in future asset design and construction.

PROJECT MISSION

NSW DoE has utilised forecasting intelligence using qualitative data points across the design and orientation of their asset base. The next phase of progress is the next generation of degradation modeling using live IoT data within NSW Schools. This aligns with the Primary objective of NSW DoE’s Vision statement: To be Australia’s best education system and one of the finest in the world.

This trial has seen NSW DoE expand into IoT to inform strategic business decisions for learning environments. Onsite sensors have been installed to monitor classroom conditions such as lighting, temperature, humidity, C02 levels, movement and vibration. The project seeks to investigate, analyse, benchmark and elevate the quality of the learning environment for all stakeholders, including principals, teachers, parents and students.

IoT data aims to alleviate the burden of ensuring the classroom is an optimal learning space for students, as well as providing greater insight into understanding NSW school assets moving into the future. By extending the IoT within NSW Schools, NSW DoE has ultimately been able to provide greater insight into understanding NSW school assets moving into the future. NSW DoE is considering a proposal to extend the trial across a larger number of schools.

THE IoT PROJECT

PROGRAM AND PROJECT MANAGEMENT

The NSW DoE has embarked on a research project with AssetFuture to investigate the possibility of calibrating degradation models with sensor-based data.

Selection of the schools (one secondary and one primary) was made based on proximity to each other so that sensors could be installed and maintained easily.

The project team from AssetFuture managed the installation of the sensors in collaboration with the Principals and teachers, taking into account of parameters and constraints including school times, class times, teacher availability, student safety (Working with Children compliance). Once installed, these sensors provided zero disruption to the classrooms.

Figure 1 – IoT with Department of Education

57 sensors were deployed in 2 schools from June 2019 and is still active.

Sensor data was then analysed using the Dynamic Utilisation Factor.

THE DYNAMIC UTILISATION FACTOR

The Dynamic Utilisation Factor is a new concept that will enable NSW DoE to further understand how and when an asset is used. This data, which will be collected over time, will allow NSW DoE to enhance the accuracy of predictive utilisation and inform performance optimisation opportunities. By extending this project into IoT devices NSW DoE will use the Dynamic Utilisation Factor to significantly improve the feedback loop of individual assets within rooms and buildings to enhance future asset forecasting.

Utilisation is defined as:

Equation 1 – Dynamic Utilisation Factor

where:

Equation 2 – Relative Occupancy Rate

and:

Equation 3 – Relative Room Capacity

Additionally, the area per person according to use is as follows (Australian Building Codes Board 2020):

Table 1 – Area per person according to use

Type of use	Area per person
Early childhood centre	4 m2
School – general classroom	2 m2
School – multi-purpose hall	1 m2
School – staff room	10 m2
School – trade and practical – primary	4 m2
School – trade and practical – secondary	30 m2

 

 

RESULTS

The total number of people entering each distinct area (Area A, B, C & D) between May 2019 and December 2019 was used for the following work examples below. To avoid outliers skewing the results, data points with dates falling on a weekend or during the school holiday period were excluded from the sample dataset.

Using the formulas described in methodology, the results have been calculated as Utilisation and normalised as Adjusted Utilisation in Table 2:

Table 2 – Utilisation rate of each area

Area	Area Type	Utilisation	Adjusted Utilisation
A	Classroom	0.69	0.83
B	Classroom	1.92	1.33
C	Classroom	1.26	1.12
D	Classroom	0.78	0.88

It can be observed in Table 2 that Area A and D have an 83% and 88% utilisation rate respectively.  Therefore, assets in Area A and D should have their Design Life increased by 17% and 12% respectively.  Whereas, assets in Area B and C are overutilised which means their Design Life should decrease by 33% and 12% respectively.

Extrapolating this calculation over time coupled with an underlying degradation model will produce a significantly more accurate representation of degradation unique to actual room utilisation.  This can be seen in Figure 3 below where dynamic utilisation is applied over time:

Figure 3 – Impact of dynamic utilisation over and assets lifespan

Due to the relatively lower utilisation once the asset is beyond Poor (Condition 4) state, the item exceeded Design Life despite having higher than expected utilisation between Good (Condition 2) to Poor (Condition 4).  This equates to an increase of 3.35 years prior to replacement at End of Life (Condition 5).  Applying Dynamic Utilisation at scale over time to performance critical assets would substantially improve predicting degradation and subsequently the actual point of condition intervention.

IMPACT

Dynamic Utilisation Factor significantly improves the accuracy and feedback loop of individual assets within rooms to further understand how and when an asset is used.  Over time, collection of utilisation data can enable predictive utilisation and inform performance optimisation opportunities in future asset design and construction.

PROJECT APPLICATIONS TO BEST PRACTICE

FIT FOR PURPOSE

Customer needs are at the forefront of this project, and here the primary customer is NSW School students. Ensuring that the facilities available perform to their maximum potential is critical for achieving NSW DoE’s vision. Additionally, teachers, parents and principals are active stakeholders in the quality of the learning environment.

REDUCING COMPLEXITY

Conventional data capture tends to be subjective and would usually be sourced by someone within the classroom (student / parent / teacher / principal). IoT sensor data is objective, and when coupled with conventional data, effectiveness is optimised. With objectivity comes transparency and enabling organisational barriers to break down as facts drive evidence-based decision making.

MOVE FROM REACTIVE TO PROACTIVE

Data monitoring in real-time will accelerate the feedback loop, giving an exponentially deeper understanding of assets and how they perform.

LOWEST COST OF OWNERSHIP

Used effectively, this data will be able to inform future schools and classrooms, and measure effectiveness of deployment and revisions that should be made. It will enable NSW DoE to gain a deeper understanding of the status-quo and can in turn inform decisions on whether to acquire, upgrade and maintain such assets.

DATA STANDARDS

NSW DoE has already employed best practice asset information management. Information and data standards 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. NSW DoE provided a clear set of in-built data standards consistent across the entire technology ecosystem. These standards, which IoT fits into, covers 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

DATA MAINTENANCE – GOVERNANCE AND UPKEEP

IoT enables a whole-of-life approach to asset management, by automating the capture and collection of data. All stages of asset life can be brought together and updated on an ongoing basis, creating value through accessible and meaningful data. This enables more effective design and planning, through to maintenance and eventually replacement.

Data monitoring in real-time will accelerate the feedback loop, giving an exponentially deeper understanding of assets and how they perform. 

INTERPRETATION OF DATA / RESULTS

The outcomes of this pilot were linked to the dynamic interpretation of quality data sets. By utilising Extended Lifecycle Prediction algorithms to inform decision making for each schools’ portfolio of assets, was a key outcome. Building on NSW DoE’s previous interpretation of degradation modeling, lifecycle prediction and data continuity, Dynamic Utilisation Factors significantly improve the accuracy and feedback loop of individual assets within rooms to further understand how and when an asset is used.  Over time, collection of utilisation data can enable predictive utilisation and inform performance optimisation opportunities in future asset design and construction.

VALUE FOR NSW DoE AND STUDENTS

NSW DoE will utilise Asset Intelligence to combine IoT and modeling to enhance evidence-based decisions, transforming the data collected to better learning outcomes for NSW School Students. These outcomes are driven by a richer understanding of variations in classrooms that affect the overall asset lifecycle.

By extending the pilot, a further 250 schools will be involved in the deployment of dynamic lifecycle models. Increased IoT data will enhance environmental correlation and improve machine learning models, a further 1000 sensor devices will be installed in learning areas all in order to increase the educational outcomes for students in NSW Schools.

References

Australian Building Codes Board 2020, National Construction Codes, viewed 20 February 2020, https://abcb.gov.au/