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07 January, 2020

The concept of artificial intelligence (AI) is not new – it can be  traced back to the days of “expert systems” and beyond – but it has become an inescapable aspect of almost any discussion about smart factories in recent years. But are it, and its fellow-traveller machine learning (ML), more than just buzz phrases?

A new report* from the Capgemini Research Institute suggests that some companies are already deriving real benefits from applying AI in in their manufacturing operations. For example, the yoghurt-maker Danone reports that it has reduced forecast errors by 20% and lost sales by 30% by using machine learning to predict variations in demand.

The tyre manufacturer Bridgestone is another beneficiary: it has improved the uniformity of its products by more than 15% by implementing automated quality control in a new assembly system.

And General Motors reports that it is avoiding the costs of unexpected robot failures – which can be as high as $20,000 a minute – by using an AI-based predictive maintenance system that spots signs of potential failures before they occur.

Capgemini analysed AI implementation among 300 global manufacturers – the top 75 in each of four sectors: industrial manufacturing; automotive; consumer products; and aerospace and defence. If found that more than half (51%) of manufacturers in Europe are already running at least one AI application. Nationally, Germany is the most fervent adopter with 69% of the manufacturers surveyed using AI, followed by France (on 47%) and the UK (on 33%). The US (28%) and China (11%) are lagging behind.

There are three main areas where these manufacturers are implementing AI: intelligent maintenance (which accounts for 29% of the applications reported to Capgemini); automated quality inspection and control (27%); and demand planning (20%). Not surprisingly, they are picking applications where they expect to obtain the biggest benefits, such as reduced operating costs, improved productivity and enhanced quality.

The challenge, as CapGemini points out, is whether and when they will move beyond these AI pilot projects to more widespread adoption where the potential benefits might not be quite so dramatic.

But if the attractions are as compelling as they seem to be in the real-life applications described in the report, then artificial intelligence and machine learning will no longer be regarded as fads, but as vital aspects of the manufacturing landscape.


Tony Sacks, Editor

* The 36-page report, Scaling AI in manufacturing operations: A practitioners’ perspective, can be downloaded from


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