Modern cities like Shanghai and Singapore have long-term infrastructure plans.
COMMENT:
Whether it's enabling goods and services to flow from businesses to consumers, ensuring good health outcomes through clean drinking water and reliable sewerage systems, or enhancing social interactions through reliable transportation networks, infrastructure (physical and virtual) plays a key role our national wellbeing.
Like most of the developed world,
however, New Zealand has an ageing infrastructure base suffering from a backlog of investment, and an asset management workforce that is ageing, not growing fast enough to keep up with demand, and in some areas needs to upskill.
The high profile failures we've seen in the water sector are symptomatic examples of how this plays out for the end-user or consumer.
Fortunately, new technological solutions born out of Industry 4.0 (think the connection of objects, or elements of system, via the internet to enable the transfer of data on a real-time basis and the use of this data to drive insight for organisations) offer the potential to help address these challenges.
In an ideal world, so called 'insight driven organisations (IDO)' use data to manage their assets in a more cost effective and efficient way, improve service and information availability for customers, and target their investments in the areas that will deliver the greatest improvements and value for money.
In an infrastructure and asset management context data can be deployed in a number of ways, including predictive maintenance, digital twinning and optimisation.
Predictive maintenance is the continuous monitoring of asset performance, supported by analysis of historical performance and failures. It allows asset operators to predict when a failure might occur and replace the relevant component before it fails. For example, by monitoring the flows in a water supply network a utility operator might see tell-tale signs of a failure before it actually occurs allowing the faulty part to be replaced before the end user is impacted.
Building a digital version of a physical object is called digital twinning and can be used to help improve the performance of our infrastructure. By developing a digital twin of an asset or system, infrastructure companies can test and understand the impact that improvement projects could have on performance.
Optimisation involves using data to improve network performance by changing the configuration of the system or asset. For example, the use of sensors to collect and transmit data on traffic flows can help transport operators to reduce congestion by directing traffic more effectively and commuters can use this information to plan their routes.
The logic for using these technologies is clear. They can help support improved decision making through a near real time understanding of asset performance, provide a faster feedback loop when testing operational changes and empower customers to take control of their experience.
However, many infrastructure companies have large asset bases and are operating with constrained finances, meaning that investments in networks of internet enabled devices may need to be targeted to where the greatest value can be realised.
Alternatively, companies that have started down the path towards being an IDO may be capturing lots of data from their large asset base but are unsure about what to do with it. So, how can companies in these situations take advantage of data driven solutions? There are some simple approaches that could help deliver meaningful benefits.
Typically, companies starting the journey towards becoming an IDO are faced with challenges, questions and uncertainty around whether their existing data is able to provide business value. Often this data has been collected over a long period of time, may have been passed between companies and migrated through a number of systems, raising questions about its integrity and quality.
High levels of data quality aren't necessary in the search for data driven insights, and in fact it is more important to have a good understanding of the strengths and weaknesses of your data. An understanding of data quality will help organisations decide when to use the data, and identify particular pain points or gaps in their data that could be the focus of initial quality improvement initiatives.
Infrastructure companies often have extensive data records that haven't been used to their full effect. Despite having collected large amounts of asset data over a long period of time this data is often left unused or is used solely for operational purposes. Repurposing this data to support asset management decision making can have an immediate impact. For example, data held on file about the location of assets could be used to assess the impact of other information (e.g. soil type, ground water acidity) on the useful life of underground pipes and allow maintenance regimes to be adjusted accordingly.
For infrastructure companies that already have extensive records, or have started their transition to more automated data collection, the challenge might be knowing where to start – data overload is a risk. Organisations need to treat data in a smart way and explore it when there is a particular business question to answer. Value lies in discovering the small parts of the collected data which are relevant to the question at hand, to deliver meaningful improvements to the business. Building the capability to analyse and interrogate data in the appropriate way will be the key to success and avoiding time-consuming analysis that doesn't deliver benefit.
The adoption of new data driven technologies by infrastructure organisations doesn't have to follow a 'big bang' approach. There are incremental steps that can be taken to exploit existing data through an improved understanding of data quality and by asking the right questions of the data that is available. These incremental steps can ultimately make a big difference in the end user or consumer experience.
John Marker is a Deloitte New Zealand partner and national infrastructure and capital projects lead