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It has the potential to be a “gamechanger,” according to a recent report published by the Capgemini Research Institute entitled Scaling AI in Manufacturing Operations: A Practitioner’s Perspective.
However, many companies haven’t embarked on their digital manufacturing journey as they don’t know where to begin. This is a subject close to the heart of Craig Stevens, who recently took up the role of vice-president of digital engineering at Capgemini, but prior to that worked as a chief digital engineer at the Manufacturing Technology Centre (MTC) in Coventry.
“At the MTC ‘Making Digital a Reality’ was my strapline and we worked with companies on driving their digital agenda,” said Stevens. “We’d initially carry out a digital diagnostic assessment to see where they are on their Industry 4.0 journey and some instead of being 4.0 were more like 0.4 as they hadn’t grasped it or hadn’t had the opportunity to look at some of these digital technologies like AI. But there is some low-lying fruit that can help them.”
Kick-start the journey
This ‘low-lying fruit’ is highlighted in the Capgemini report in the form of three use cases. The report identified these use cases as the most likely to help manufacturers kick-start their AI journey as they offer clear business value, can be most easily implemented and would deliver the best return on investment.
“Targeting three areas where AI can be easily applied is extremely helpful. These three all offer an ideal starting point,” said Stevens. The first is preventative or intelligent machine maintenance: using data from sensors on machinery and equipment the technology can predict problems and identify when parts need to be replaced.
The second is product quality inspection: through the utilisation of high-resolution cameras and powerful image recognition technology, realtime in-line inspection can be carried out.
The third is demand planning and forecasting: using machine learning manufacturers can predict changes in consumer demand and behaviour, which then enables them to make the necessary adjustments to production schedules, leading to more accurate forecasts.
But, as the Capgemini study points out, to really tap into the manifold benefits that AI can bring companies need to move beyond pilots and proofs of concept to deploy AI solutions at scale. The extent to how successfully companies are doing this is the subject of Capgemini’s latest report, The AI-powered Enterprise: Unlocking the Potential of AI at Scale.
Quality data fundamental
Having surveyed 950 organisations for this report, the research revealed that 53% have moved beyond pilots and proofs of concept. However, despite this, the report does reveal that scaling AI isn’t easy as only 13% have rolled out multiple AI use cases.
The report highlights that one of the key criteria to scaling AI, which is proven by those ‘AI-at-scale leaders’ which make up the 13%, is investing in laying down a strong foundation of data and then a focus on improving data quality.
This is a point that Stevens resolutely agrees with as he said: “Key for organisations embarking on any AI journey is to have a bedrock of some data science capability.
Even if you’re a small firm starting out with a simple predictive maintenance use case, there are a few caveats such as how easily you can get the data out to do the analysis.”
But wherever an organisation is on their AI journey, it is worth pursuing, as proven by the Capgemini study which reports that 97% of the AI-at-scale leaders are reaping benefits from their AI deployments.
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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.