The five P’s of industrial AI that power digital twins

Over the past 20 years, artificial intelligence (AI) has significantly transformed industry, taking an organisation’s ability to optimise processes and proactively detect and solve problems to a whole new level. As a result of the increasing adoption of digital transformation, AI continues to provide benefits across a range of industrial processes. This has resulted in the extensive use of digital twins – virtual representations of physical objects, systems or factories that are created through data gathered from Internet of Things (IoT) devices, advanced computer systems and digital processes.

AI is the brain behind the digital twin. By applying various forms of AI – such as neural networks, computer vision, and machine learning – in different ways, it can create targeted solutions presented in the form of analytics. Once a digital twin has been put into operation, AI analytics provide insights than can help everything from enhancing operations for safe and profitable processes, through to automating monitoring and control processes to ensure safety and performance.

From an industrial perspective, these can be broken down into five categories: predictive, performance, prescriptive, prognostic and perceptive analytics.

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Predictive analytics is one of the most common advanced technologies used by industry, utilising big data and machine learning to spot anomalies in processes and assets. This can highlight current inefficiencies, enabling workers to optimise processes, but also warn of future equipment failure days, weeks or even months in advance. Thanks to this information, businesses are able to schedule maintenance repairs well in advance of equipment failure, limiting operational risk and saving costs by avoiding unplanned downtime.

Duke Energy, for example, was able to avoid costs of over $34 million by detecting a sophisticated turbine problem that would have resulted in a catastrophic failure, potential injury to workers and extensive downtime had it occurred.


By combining industry and asset-specific algorithms, AI is able to not only identify anomalies that help an organisation discover and rectify faults before they occur, but also optimise processes for improved yield and/or operational efficiency.


Prescriptive analytics takes things beyond simply alerting you to an issue – it also identifies and recommends the best course of action to resolve it.

It does this through root cause analysis and risk-based decision support, analysing the criticality and urgency of an issue to recommend actions that will optimise efficiency and profitability by minimising downtime and avoiding costly delated.

Wanting to use digital transformation to boost efficiency and sustainability throughout its operations, Ontario Power Generation (OPG) established over 100 predictive and prescriptive operating maintenance models by harnessing data from thousands of sensors throughout its plants.

This allowed the organisation to reduce risk and increase operational efficiency throughout the fleet, as well as saving $400,000 and $200,000 in two separate early warning catches.

Predictive and prescriptive analytics also enabled OPG to reduce annual maintenance hours by 3,000, freeing up stuff to work on higher value corrective tasks.

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With prognostic analytics, neural networks, deep- and reinforcement learning enable you to forecast events, such as operational performance degradation or the remaining useful life of an asset. This can help organisations to manage risk, maximise profitability and improve sustainability.

Prognostic AI can be used to optimise operations and maintenance strategies, providing risk-based insights into decisions such as whether or not an operation should attempt to run until the next planned maintenance outage or if work needs to take place more urgently. It can also help to identify specific areas for improvement.


Finally, perceptive analytics is all about how intelligent machines interact with their surrounding environments. Advanced technologies such as vision and audio AI, and natural language processing (NLP) are used to automatically detect relationships between sensors and devices.

For example, Schneider Electric used perceptive analytics to not only detect a fault in the main drive chain at its Lexington factory, but also an issue on the motor that runs this chain, which corresponded with the appearance of the former problem.

Through this perceptive analytics, the company was able to avoid factory downtime and related costs and has been able to reconfigure equipment to avoid similar problems occurring in the future.

Benefits of a smart factory

After recently transforming this brownfield site into a smart factory by digitising plant-wide operations, Schneider Electric has seen many benefits from its advanced AI and analytics. This has included optimisation of processes, faster, smarter decision making, improvements in labour productivity, a 6% reduction in unplanned downtime, 26% energy reduction, 78% CO2 reduction and a 20% water use reduction.

This led to the factory being awarded Advanced Lighthouse status from the World Economic Forum and becoming a showcase factory for the business: one that’s being replicated at other Schneider Electric facilities around the world.

“We feel like we’re only scratching the surface on the benefits that can accrue as a result of these new digitisation tools. We’re exploring areas that we’ve never had the opportunity to look at before,” said Mike Labhart, senior manager, GSC North America smart factory innovation at Schneider Electric.

“This opens the door to new ways of thinking about our facility and will reveal ways to improve productivity and efficiency, not just in Lexington but in other plants around the world.”

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Don’t get left behind

An increasing number of industrial companies are following in the footsteps of Schneider Electric to actively leverage the benefits of AI, digital twins and analytics.

In many cases AI is no longer just an option, but rather a requirement to remain competitive, profitable and sustainable. Benefits grow with the addition of new capabilities added by the five P’s of industrial AI, which – amongst other things – help detect and prevent problems faster, better maintain operations, and optimise and enhance processes. As a result, industrial operations continue to improve in new and exciting ways.

The opportunities to benefit from the five P’s of industrial AI appear virtually limitless. As AI continues to advance with every passing year, we’re excited to see what the future will hold.

Written by Jim Chappell, global head of AI and advanced analytics at AVEVA

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