By David Jenkins
Artificial intelligence can be a game changer in leveraging data to drive better performance across organisations and assets, and yet the asset management world has been a relatively slow adopter.
There has been a wave of digital twinning projects, and their benefits are well understood across the asset management industry. However, AI presents as a significant enabler that can accelerate the capabilities of many existing digital initiatives.
AI’s ability to interrogate and process vast quantities of data can deliver value across various operational areas. Yet, a recent survey by PwC and Mainnovation has shown that implementation levels are still relatively low.
The research involved 127 companies in Belgium, Germany, the Netherlands, Norway and South Africa, spanning industries such as heavy processing, discrete manufacturing, infrastructure and fleet, food, pharmaceuticals and service providers.
Five digital trends were investigated: predictive maintenance 4.0, mobile maintenance, augmented reality, digital twins, and 3D printing. The researchers also assessed awareness of digitalisation and the critical factors necessary for success.
The key finding from Northwest Europe, a region that might be expected to be ahead of the pack, is that while there is growing interest in these next-generation digital solutions, implementation levels continue to lag.
Only 44% of the surveyed companies have plans to implement these solutions or are in the pilot phase, which seems unexpectedly low. Among the five solutions, mobile maintenance is the innovation with the highest implementation level or 39% across the organisations surveyed, while augmented reality and 3D printing had the lowest implementation levels at 8% each.
There are, of course, successful case studies. Separate to the PwC report we have the example of the New South Wales government, which has a project to collect data from sensors and cameras fitted to vehicles operated by local councils.
Captured data is analysed by an AI platform to log road defects into a data base, which enables councils to detect potholes and cracks in roads before they deteriorate to a point where they affect road users and impact safety.
The project targets an efficiency gain of 10% in managing public assets, measured as a reduction in the maintenance backlog after 12 months of use.
An initial trial was completed and once proven, the technology can be rolled out to other councils across NSW.
The PwC report outlines some other international use cases which demonstrate AI’s transformative power in maintenance to build trust and deliver outcomes.
AI and the related areas of machine and deep learning can improve the performance of already existing systems by leveraging deeper insights from more significant quantities of data.
At aircraft manufacturer Airbus, predictive models were used to address the challenge of missing parts and optimise work priorities.
Historical data and patterns were analysed, and algorithms were trained to predict issues around missing parts and production bottlenecks.
The result was more proactive decision-making, which resulted in better resource allocation and fewer delays.
The German materials handling company Kion Group developed a mobile application that integrates a conversational AI assistant to support maintenance technicians.
The assistant delivered real-time guidance through speech—and text-based chatbots, which helped streamline the execution of maintenance jobs and improved documentation and information retrieval.
In both cases, AI and machine learning capabilities were connected and added to existing systems. These were not greenfield projects that required new infrastructure or the retirement of legacy systems, with all the complications involved.
PwC’s assessment of the survey results is that while it showed that companies are adopting AI, they could be moving faster, and there is room for improvement.
Clear roadmaps are essential to delivering value on these projects, but in most cases, organisations will be optimising systems and data they already have and not starting with a blank sheet of paper.
This makes improvements in outcomes and asset performance more accessible, helping to build business cases and hopefully persuade more organisations to press the ‘go’ button.
There is enough experience and evidence from case studies in the real world to encourage more organisations to progress to the next stage of digitalisation.
In many cases, the experience of others can be adapted, tailored and replicated with confidence.
The technology tools are out there, and many organisations should be bolder in taking that next step to transformation.
