IFS: Moving towards asset performance management using AI
Artificial Intelligence (AI) is being increasingly used by manufacturing companies and asset-intensive organisations today. In manufacturing alone, the global AI market was estimated at US$2.6 billion in 2022, and is expected to register a compound annual growth rate (CAGR) of 44.5% over the ten-year period to 2032, according to a recent study by Emergen Research.
Typically, though, despite these projections of ongoing dynamic growth, AI has so far primarily been used to solve specific problems in isolation, such as demand forecasting, supply chain optimisation, schedule optimisation or natural language processing (NLP) driven customer service bots.
That’s changing today, and it’s changing fast. Now, ERP tools with comprehensive AI capabilities embedded within them can collate and analyse data from potentially every facet of an organisation, helping businesses to accurately plan ahead, optimise processes, and reduce waste. It helps businesses transform their operations – and it is one of the main factors behind the emergence of asset performance management (APM).
In the past, asset management was often focused on the single goal of improving maintenance and creating an efficient and reliable asset operation. Today, that focus has broadened considerably. This is thanks, in part, to the rapidly changing external factors that influence the choices asset managers need to make based on the strategic objectives of their organisation and the global market they operate in. As such, we have a more holistic view of APM which highlights how it enables organisations to realise value from their assets and achieve their organisational objectives.
In line with this, we are seeing businesses increasingly looking to enhance not just the APM process itself but the way their entire organisation operates as they look to apply AI to their data management and real time data analysis processes. These kinds of approaches have the potential to completely transform the way organisations operate.
But for many businesses, that is still a long way off. Just being able to stream their data in near real time and receiving flags that a process is going wrong or is outside a boundary is still a big step forward for a lot of companies in terms of how they think about and maintain their assets, as well as how they manage their processes generally. And this is far from being AI-driven.
Taking it stage by stage
So, for most companies, there is no need as yet to leap ahead to a completely AI-driven world.
Many organisations remain in a position to take advantage of standard data-driven changes to their process, based on receiving intelligence a little earlier than before or in an aggregated form they can understand. For the time being, we would expect that to remain the status quo.
However, the next stage will be to bring in AI to find the more complex relationships where there is more value. CIOs and other technology-focused board members have a key role to play here in being champions of these advanced new systems and helping to get them rolled out across the business.
Putting theory into practice
Today, we are starting to see more practical applications of the use of AI to drive APM forwards. For instance, the use of AI-driven APM can already help organisations ensure asset reliability and reduce reactive repairs with predictive maintenance.
Businesses can, in this context, use AI together with Industrial Internet of Things (IIoT) capability to automate the ingestion of machine measurements and readings, and then display real-time data about their asset’s performance, schedule and its surrounding conditions.
Other use cases are rapidly coming on stream. We are, for example, seeing the advent of IoT sensors for monitoring cargo and equipment such as engines. AI and machine learning (ML) is also being more widely used today for optimising routes and maintenance needs.
We are witnessing greater use of robotic process automation for automating administrative repetitive tasks and greater application of digital twin technology for simulating and optimising assets. A further AI-driven approach increasingly applied to APM today is the use of remote assistance using mixed reality and augmented reality on a mobile platform to streamline asset repairs.
Added to all this, smart technology is also being more widely used to help employees perform their tasks more efficiently through mobile solutions that help to create the connected worker and remote assistance using mixed and augmented reality. All this capability can be, and is best, delivered via a single cloud-based platform, running on a single database.
Reaping the rewards
The potential to realise organisational benefits from an advanced AI-driven approach to APM adoption will only grow as assets increase in complexity and smarter devices continue to proliferate.
This makes real-time asset information more attractive as a tool for decision support, and it will also become more apparent that assets play a central role in the ability of an organisation to deliver on essential company-wide KPIs.
One of these business targets is likely to relate to ensuring the highest possible levels of uptime, ideally making it certain that the business can deliver five nines availability – in other words, it is able to remain fully operational 99.999% of the time.
By employing AI-driven predictive and prescriptive maintenance techniques, businesses can anticipate when and why a piece of equipment is likely to break down and take pre-emptive action to fix it, providing instant cost savings and boosting productivity at the same time. This model is evolving all the time. We are seeing more active moves towards resilient asset management, where asset performance is actively managed for real-time optimisation.
AI-driven APM can also drive efficiencies by improving workforce scheduling. Its capacity to achieve a birds-eye view of the whole operation means that it can help organisations to more easily allocate the right engineer to the right job at the right time, helping to optimise costs, while raising staff morale and levels of customer satisfaction.
There may still be some way to go before AI is widely used by organisations across multiple sectors, but the process is now well underway and already bringing benefits to businesses large and small.
As AI continues to evolve so too will APM and the benefits businesses can glean from using it. We would expect to see the range of applications growing rapidly over time. As we look to the future, we are creating a world where AI-driven APM can transform whole businesses, making them more operationally efficient and raising their levels of productivity to drive competitive edge.
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