Manufacturing and AI - a solution for the skills gap

A lot is being said about the effect of AI on the workforce, and especially how it takes some jobs off the market. There is no doubt it already happening and probably much faster than anticipated in fields like entry level software programing, etc. where LLMs are shining.

When looking at global manufacturing, though, we see a different trend: there is a growing shortage in skilled workforce. The experienced workforce is leaving, mainly retiring or advancing to other positions, and not enough people see manufacturing as an attractive career option (Did you know that there is a shortage of about 80,000 professional welders in the US, and about 300,000 in the EU?) .  The common forecast is that by 2033, about 1.9 million skilled manufacturing positions will not be filled in the US alone.

Indeed, manufacturing positions are being eliminated by advance technology, but this is a natural modernization process which I would argue is due to good old automation, and not necessarily AI. Especially because AI is still not utilized in large scale on factories floors.

I would argue that there is an opportunity here. The use of AI in order to optimize manufacturing processes is ONE WAY to mitigate the skills gap. (The ones who read my opinions on Additive Manufacturing will know that I am not in favor of over-hyped silver bullet solutions when it come to manufacturing...). Education and training toward manufacturing cannot be avoided, with or without AI.

Still, AI can and should be used for things like:

Supporting production lines diagnostics and maintenance, to mitigate the shortage in skilled service engineers.

Finding the correct operation parameters to handle materials in AM, to support the gap in experienced AM machinery operators

Performing QA/QC for raw materials and manufactured goods, before, during and at the end of the manufacturing process (most of the effort is currently directed at finished goods)

Also, training AI based algorithms by professional workers is a great way for companies to preserve their manufacturing organizational memory.

As always when it comes to the development of hardware based manufacturing technologies, it's not going to be easy. But it is definitely fun.

(Disclaimer: Although I used AI tools to research for this post, I did in fact wrote it myself. I guess there is a limit to how many new tricks you can teach this old dog 🙂 )