AI is already being used to help realize sustainable digital factories

In Finland, we have an expression – “silent knowledge” – that means all the things that are so obvious to you where you work that nobody has ever bothered to write them down, let alone systematize them.

In factories, there tends to be a lot of silent knowledge. Manufacturing operations often need a few tweaks that people on the lines find out about over time. That knowledge gets passed from operator to operator and shift to shift, so things work.

Or it would be better to say there used to be a lot of silent knowledge. It was all in the heads of a generation now passing through retirement. As they disappear from payrolls, many factory management teams struggle to recruit younger factory workers willing to take up the same roles.

You might think me unduly optimistic, but maybe this is an opportunity.

First, the manufacturing industry is under extreme pressure to adopt new, sustainable production methods that reduce resource consumption and cut CO2 emissions. Circularity demands an entirely new way of thinking about manufacturing purposes – or, indeed, the purpose of manufacturing.

Second, the generation of automated production technology under the umbrella term “OT” is also nearing the end of its useful life. Patching up today’s OT (which most factories can’t consider just scrapping) to work with modern IT systems requires a new way of thinking.

Looking through these two prisms, a change in workforce generation may be quite helpful in fostering new ways of thinking.

Another way AI is opening doors for manufacturers is by using video from the many cameras that are probably already in their factories.

By analyzing camera data, AI can identify where processes are not being performed properly or safely. Computer Vision and AI can recognize what kind of work is being done, such as sorting, packing, and grinding, from a factory’s camera or video footage. This makes assessing and improving work processes possible, ensuring greater factory safety and eliminating difficulties, inefficiencies, and differences between workers.

Applying AI computer vision to automate QC is another use case for video, where it can cut down on raw materials wastage. Fujitsu has recently tested AI computer vision for a high-volume producer of metal ingots. It checks for surface defects as minor as 1mm2 during live production on materials that are too large and too numerous to expect human operators to be able to function efficiently and continuously. The system helps the manufacturer identify trends and patterns regarding when and where faults develop. The business impact of these insights is the ability to introduce targeted and effective interventions, resulting in fewer customer complaints, fewer compensation payments, heightened customer satisfaction, and an increasing brand reputation for quality.