I inherited spareparts manufacturing firm for aftermarket

VW stopped increment for all its workers , there exports are all time low

Chinese cars are flooded to the market with cheaper prices and more features

They have made more cars than demand

I saw there autoparts manufacturers are not being paid from past 8 months

Where is the world heading towards and how do I keep track of it

Please show some lights of expertise and experience.

Why does 3M put their branding on everything they make. I know a lot of their business is B2B (Business to Business) I don’t get why the need to add their branding if a manufacturer uses one of their products. There has to be a thousand companies that manufacture the same things as 3M. from adhesive for sticky notes, to insulation for gloves / beanies. Surely 3M doesn’t need branding as they mainly rely on the business from other businesses. The only thing I can think of is if a customer sees 3M in quality products, the customer will think 3M is good and will buy more 3M cerated products. Even if 3M only have a small part in production of the final product .

I only kinda realised I’m might be on track for the answer in the last sentence. Let me know your thoughts!!

Everyone in manufacturing likes to talk about the AI and automation boom, but after reading an article with a roundup of what plant managers actually think, the data tells a much more realistic story and it is the same story I keep seeing in manufacturing AI rollouts:

1. Pilots are everywhere; results are not A majority of U.S. manufacturers have experimented with AI or automation in some form. Wide scale adoption and a solid ROI are still the exception.
2. Knowledge transfer is the real use case as experienced people retire, AI can pull sensor data, SOPs, maintenance history and institutional knowledge into something a less experienced employee can use: shorter downtime, faster troubleshooting, more consistency. AI should support people, not remove them, less time mining data, more time driving improvements.
3. Precision beats AI everywhere: start where people lose time or complexity creates mistakes. Don't jump from "AI is exciting" to "AI everywhere." Tie the case to downtime, throughput, yield, scrap, maintenance cost, labor, quality or energy.
4. Projects die on fit, not models: nearly 90% of AI projects don't survive year one, usually because nobody figured out how to fit AI into how the plant actually works.

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Article Here

If you run a plant or a line: are you success with AI Rollouts? Or are we still years away from something that has a real impact?