Canada
Ahead of our webinar next week, the Canadian Council for Public-Private Partnerships (CCPPP) has posed Samantha Fuller, our international managing director, a few questions about the operational phase of P3s.
How IoT and AI are Reinventing the Operational Phase of P3s
Wednesday November 13th, 12 noon EST
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1) What are some specific challenges or “pain points” that O&M organizations operating in Canada’s Infrastructure Sector must manage, that AI and the Internet of Things can help with?
The complex nature of managing infrastructure projects, over long lifecycles and involving many different stakeholders, means there are lots of moving parts. As any O&M operator will testify, managing this complexity can often turn into a frustrating game of a whack-a-mole.
The fundamental pain point is a lack of meaningful, actionable data. Intelligent technologies such as Internet of Things sensors and AI-powered analytics platforms add visibility, accountability and a baseline for continuous improvement.
The data gathered by sensors can allow operators to monitor and measure asset performance, enabling them to identify trends and anomalies. And these insights help improve the accuracy and efficiency of asset management. Understanding the condition of an asset and when it’s likely to fail or even falter, for example, leads to more effective planned preventative maintenance, where a schedule can be developed that results in less under- or over-servicing, minimal operational downtime, and savings on costs associated with engineers’ travelling to site to fix things. Canada is vast – you better make sure that an engineer’s visit really counts.
Meanwhile, AI can crunch data and make calculations at a level impossible for humans or legacy IT systems to replicate. The technology can find patterns in the real-time data that don’t just highlight when an asset is close to failure but also the most ideal maintenance schedule based on multiple performance indicators such as vibration, temperature and pressure, ultimately shifting maintenance from a preventative discipline to a predictive one.
Another common pain point for the O&M operators of infrastructure projects is managing payments. Payment mechanism software helps provide reports and trend analysis of services failures, deductions and rolling threshold values. Integrating sensor data and AI with paymech software imbues a level of transparency that helps avoid deductions while ensuring fairness for all stakeholders.
Finally, it’s impossible to ignore the enormous pressures now associated with net zero, especially in public infrastructure projects. The same combination of sensor data and AI allows operators to monitor their carbon emissions, measure to make improvements, and keep a record for all regulatory requirements.
2) Can you provide an example of how leveraging this technology has impacted operations of a P3 project in Canada?
There is a great example in the Nordics. Jernhusen owns and manages several railway stations and properties across Sweden’s rail network. The company’s mission is to connect different modes of transportation and help create efficient transfer points for passengers. These aims tie in with Sweden’s Net Zero 2045 target, which the country is attempting to reach by encouraging more people to use public transit and decrease car use overall.
Station management is a challenge at the best of times, especially during winter. Wet and icy conditions can present a health and safety hazard and play havoc with key assets such as lifts and escalators. Something Canadians know all too well.
Our IoT platform and the integration of sensors has enabled proactive, AI-driven predictive maintenance on assets such as escalators, can make accurate weather predictions and helps avoid maintenance work during peak commuter times.
Sensors have drastically reduced breakdowns through accurate sensor data from sources such as escalator run time, humidity, footfall and weather conditions. Using our asset management tool, Jernhusen’s FM team can monitor the weather forecast for the days ahead and when roads are likely to be gritted as a result. Grit is easily trampled into a station and is a common cause of escalator malfunctions as it gets clogged up in the belt. From here, our system sends alerts to FM teams of when to expect grit, helping them to prepare in advance. Escalator malfunctions have dramatically decreased since adopting this AI functionality.
3) How can O&M organizations operating in Canada’s infrastructure sector determine how—and when—to implement technology such as AI or IoT with minimal disruption to existing performance measuring methods, data collection, etc.?
New technologies can be daunting, especially operators are so used to using a plethora of legacy systems with little integration. But the implementation of sensors and AI doesn’t need to be. The key is to start small by making sure all the right data is being collected and sensors are in the right places, and ensure you get feedback before scaling up or rolling these out elsewhere.
One solution is to start by focusing on just a couple of systems. Another is to set up a pilot project or site. This will allow operators to monitor the data collection and flow process, adjust where necessary and refine settings.
It’s important that operators ensure both the building’s network infrastructure and power supply can support sensors. Then it’s all about integrating these with existing building management system(s). When asset sensors are connected to a BMS, their performance can be compared to pre-established limits, allowing you to determine if it is working correctly or not. Readings outside of the limits will automatically alert the integrated CAFM software, creating a job and assigning an operative.
4) Has this evolving technology impacted the competitive landscape in Canada’s P3 sector? If so, how has the development of AI and IoT influenced operational performance expectations, contract development and more?
P3s are naturally risk averse, which means adoption and development are relatively slow. Of course, much of this conservatism stems from 25-year contract lifecycles – parties can be reluctant to make commitments over such long periods.
What these new technologies do is facilitate contract renegotiations if the terms of the deal aren’t working for some parties – say the operator underestimated the number of reactive callouts or the size of the engineering team. Introducing new technology as part of the framework can help manage the cost and expectations of the results. If elements of the contract aren’t working as expected, the parties involved can initiate a contract reset to resolve disputes and change the terms with agreement for both sides.
What’s more, it’s clear that P3 projects want to be standard-bearers for net zero, putting huge pressure on bidders to demonstrate their commitment to decarbonization and other environmental targets. Having that IoT and AI capability will only be an advantage as it gives them the ability to monitor, measure and report their efforts.
