Best of LinkedIn: Smart Manufacturing CW 37/ 38

00:00:00: This episode is provided by Thomas Allgeier and Frennus based on the most relevant LinkedIn posts about smart manufacturing in calendar weeks, thirty seven and thirty eight.

00:00:09: Frennus is a B to B market research company that supports enterprises across the smart manufacturing industry with the market customer and competitive insights they need to navigate dynamic markets and drive customer centric product development.

00:00:23: And we're here to give you that shortcut.

00:00:24: Because well if you've been on LinkedIn at all recently in the last couple weeks were pretty much dominated by one huge event Emo handover.

00:00:33: Yeah, the buzz coming out of Hanover was just immense.

00:00:35: We've sifted through a ton of posts and our goal today is really to cut through that noise.

00:00:40: We're pulling out the key momentum points, you know, the big strategic shifts in smart manufacturing, how AI is actually being used pragmatically and how the industry is tackling resilience, efficiency and, um, sustainability all at once.

00:00:54: It's not just talk anymore, is it?

00:00:55: Like Tony Gunn said, manufacturers are actually showing the future running live on the floor.

00:01:00: But, uh, you even get to the machines, you need the right foundations.

00:01:04: Partnerships.

00:01:05: Data.

00:01:07: that digital backbone.

00:01:08: Which takes us straight into, I think, the biggest strategic news.

00:01:12: This massive ecosystem move really focused on building the data foundations needed for high accuracy industrial AI.

00:01:20: Pretty fascinating the scale of it.

00:01:22: Roland Bush from Siemens, he launched an AI alliance right there at Emo.

00:01:26: And it's really a who's who of industry heavyweights and academia trying to crack a fundamental problem.

00:01:31: Yeah, I saw the list.

00:01:32: It's not just tech firms.

00:01:33: You've got Trump.

00:01:34: L.A.F.

00:01:35: GROB, Kyron Group SC, RWPA's Aiken, Renishaw, Heller, Voith.

00:01:39: DMG, more even join.

00:01:41: Machine Builders Software, research, all in one place.

00:01:43: And the why behind it is key.

00:01:45: Bush basically said that general AI, you know, the chat GPT's of the world, they just aren't built for the shop floor.

00:01:50: Right.

00:01:51: And consumer stuff, maybe eighty percent accuracy is okay.

00:01:53: But if you're machining a critical part for, say, aerospace, eighty percent is, well, it's failure.

00:01:58: You need, like, ninety five percent plus accuracy.

00:02:01: So general AI is like a jack of all trades, but manufacturing needs a master.

00:02:06: Exactly.

00:02:06: They're focused on building what they call an industrial foundation model, an IFM.

00:02:12: specifically trained on manufacturing data to hit that super high accuracy benchmark.

00:02:16: It makes sense.

00:02:17: And Mathias Tolkma confirmed it really hinges on shared data.

00:02:21: The I of M is the outcome, sure, but that shared data vision, that's the real cornerstone.

00:02:26: Without shared contextualized data, you just don't get reliable AI.

00:02:30: And David Greenfield really hammered home what that means practically.

00:02:33: You can't just chuck sensor data into a data lake and hope for insights.

00:02:38: You need that context.

00:02:39: Right.

00:02:39: Which means deep integration between your MES, your manufacturing execution systems, and your IoT setup.

00:02:45: That's how you get real industry.

00:02:47: four point oh intelligence.

00:02:48: That's

00:02:49: the plumbing, basically.

00:02:50: And we saw it again and again in the post.

00:02:53: None of this optimization, especially sharing data across partners, none of it works unless you can move data securely.

00:02:59: Security.

00:03:00: Always crucial.

00:03:01: Absolutely.

00:03:02: Secure edge-to-cloud patterns, compliance, IP protection.

00:03:08: It has to be baked in from the start.

00:03:10: Otherwise, that whole ecosystem idea, it just doesn't fly because nobody trusts it.

00:03:14: Okay, so that digital backbone, that secure foundation, that enables the next level, right?

00:03:19: The actual, tangible stuff we saw on display.

00:03:22: Moving from strategy down to the shop floor.

00:03:25: Ima was when just talking future, it was building it.

00:03:28: Definitely.

00:03:28: Yeah.

00:03:28: And one of the really big shifts conceptually was around additive manufacturing.

00:03:33: A.M.

00:03:33: Carsten Hoyser pointed out, it's not really seen as the separate niche thing.

00:03:37: Interesting.

00:03:37: Yeah, it's becoming fully integrated into, you know, advanced manufacturing workflows.

00:03:41: Got any examples?

00:03:42: Well, the Rolls Royce one was pretty stunning.

00:03:44: They used generative design, combined it with A.M.

00:03:46: and redesigned to hydraulic pump housing.

00:03:48: I mean, at thirty two percent lighter.

00:03:49: Thirty two percent.

00:03:50: That's

00:03:51: huge.

00:03:52: Massive efficiency gain.

00:03:53: And it's only possible because they fully integrated additive into their existing process.

00:03:57: And it's not just for the huge companies, is it?

00:04:00: I saw Greg Knox highlighted a trend.

00:04:02: Machine integrated automation is now viable even for really small lots, like one or two pieces.

00:04:08: Exactly.

00:04:08: Automation used to be all about high volume, same part over and over.

00:04:12: Now it's proving it's worth even in high mix, low volume.

00:04:16: That's a potential game changer for smaller job shops.

00:04:19: I also found the focus on just removing friction interesting.

00:04:24: Nadine Krawals shared some data showing teams can waste, what, up to twenty percent of their time just looking for tools?

00:04:30: Twenty percent.

00:04:31: just searching or waiting because the tools weren't managed right for the next job.

00:04:35: That's crazy when you think about it.

00:04:37: It is.

00:04:38: And companies like ToolHive, CribWise, TDM Systems, they're really focused on digital tool management to eliminate exactly that waste.

00:04:45: And then you had the really cool physical demos.

00:04:47: Brent M. Donaldson described how Soreloose showed off their vibration-canceling tech, the DAF system.

00:04:53: Oh yeah,

00:04:53: for milling.

00:04:54: Instead of a boring graph that used ping-pong balls, shaking like crazy under lights when the system was off.

00:04:59: And then totally still when they switched it on.

00:05:01: Yeah,

00:05:01: that really shows you the impact.

00:05:03: Very clever.

00:05:04: That mix of power and incredible precision was kind of everywhere.

00:05:07: Cameron Pinder commented on the scale, you know, from massive Pama machines, right down to the tiny stuff.

00:05:13: Like what?

00:05:13: Like a .

00:05:14: one millimeter end mill from NS Tool.

00:05:18: And a submicron hand wheel from Yazda Precision Tools.

00:05:22: mind-blowing precision, big and small.

00:05:24: And specific machines are getting smarter too.

00:05:26: Dinesh Mishra noted WFL mill turns innovations in mill turn tech and Leo Lin flagged Takasawa Taiwan's heavy-duty turn mill machine.

00:05:36: The hardware is definitely keeping up.

00:05:38: Absolutely.

00:05:39: Which flows nicely into our next theme, right?

00:05:41: How that physical capability is being boosted by the digital twin and embedded intelligence.

00:05:46: Yeah.

00:05:47: These are moving from just buzzwords to actual practical tools changing how work gets.

00:05:51: Yeah,

00:05:52: becoming real force multipliers.

00:05:53: Rahul Garg, via Modern Machine Shop, made that point.

00:05:56: It's not about replacing engineers, it's helping them work faster.

00:05:59: They mentioned NXTM co-pilots cutting programming time by like up to eighty percent.

00:06:03: Eighty percent.

00:06:04: Think about what that means.

00:06:06: One programmer handles five times the work, or maybe tackles five really complex jobs instead of just one.

00:06:13: It really changes the value equation for human skills.

00:06:16: And crucially, it seems like this tech is getting more, uh, democratic.

00:06:21: David Morley and Tainer, okay, why?

00:06:23: We're really stressing that the digital twin isn't just for the bowings and airbuses of the world.

00:06:29: It's for

00:06:29: every shot.

00:06:30: Yeah, that's a key message.

00:06:32: You can simulate everything, fixtures, parts, the whole process, catch problems, cut scraps, speed things up, all before you touch a piece of metal.

00:06:39: It saves time and money no matter your size.

00:06:41: And the smarts are moving closer to the machine too, right?

00:06:43: To the edge.

00:06:44: Exactly.

00:06:45: Ralph Ammon highlighted a really interesting development with AWS IoT site-wise.

00:06:50: They built a native anomaly detection.

00:06:51: Okay, what does that mean in practice?

00:06:53: How does it lower the barrier?

00:06:55: Well, it means maintenance teams can jump straight to predictive maintenance.

00:06:58: They can use as little as, say, fourteen days of historical data.

00:07:02: And the system starts flagging weird behavior without needing specialist machine learning engineers or data scientists.

00:07:08: Ah, so it makes predictive maintenance much more accessible.

00:07:10: Huge

00:07:11: win, especially for smaller places that don't have those dedicated data teams.

00:07:14: That sounds like a really pragmatic approach, which actually ties into that whole MES elephant problem that Moosa Rat Hussain brought up.

00:07:21: Ah yes, the elephant in the room, or rather on the shop floor.

00:07:25: Uh-huh, right.

00:07:26: His point was that lots of smaller manufacturers, SMEs, get sold these massive, complex, expensive MES systems, the elephants, when all they really need is basic visibility.

00:07:38: OEE monitoring.

00:07:40: The guard dog approach, he called it.

00:07:42: Don't buy the giant enterprise system that costs a fortune and takes ages to implement.

00:07:47: if all you need is something simple and reliable to track overall equipment effectiveness OEE and see what's happening in production.

00:07:53: focus on the function.

00:07:54: Yeah, focus on practical throughput.

00:07:56: And that ties into automation too.

00:07:57: Yeah.

00:07:58: Rodrigo Assisa Manzano detailed leavers flexible automation systems, the PHS and RLS, they can apparently boost efficiency up to ninety percent.

00:08:07: Ninety percent.

00:08:08: That's how you get to serious lights out manufacturing.

00:08:11: That combination of focused smart tools, the guard dogs and flexible automation seems to be where things are heading.

00:08:17: Okay, so we've got AI foundations, incredible shop floor innovation, practical digital tools, But there's always a but, isn't there?

00:08:24: There always is.

00:08:25: And consistently, across the board, the sources came back to the same thing.

00:08:31: The human element, the culture, all this amazing tech.

00:08:35: It hits a wall if the culture isn't ready.

00:08:37: Maggie

00:08:37: Sloick put it really well, talking about industry five point oh, it's about people with AI, not people versus AI, using tech to empower the workforce you have.

00:08:45: But, and this is a big, but you have to nail the basics first.

00:08:49: James Smith and John Hobgud both emphasize this.

00:08:51: Technology just amplifies what you've already got.

00:08:54: Meaning.

00:08:54: Meaning if your processes are a mess, AI will just help you make mistakes faster or produce the wrong things more efficiently.

00:09:01: You've got to fix the foundational processes.

00:09:03: Empower your frontline people first before you layer on fancy tech.

00:09:07: Get your house in order.

00:09:08: Makes sense.

00:09:09: Which kind of pushes this whole digital transformation thing beyond nice to have, doesn't it?

00:09:14: That's what Johannes Mann was questioning.

00:09:17: Has it stopped being optional?

00:09:18: He argued the industry needs to shift from seeing digitalization as just a cool idea to understanding it's a fundamental business imperative now.

00:09:26: It's about staying competitive.

00:09:27: And Neil Smith linked that imperative to other big goals too, like cybersecurity and sustainability.

00:09:33: Digital tools are needed not just for efficiency, but to manage security risks and actually measure and hit green targets.

00:09:40: It's all connected.

00:09:41: It really is.

00:09:42: But again, the tech only works if leadership truly buys in and drives the change.

00:09:46: Anna Olson.

00:09:47: Francesco Calore.

00:09:49: They highlighted that successful AI isn't just about the algorithm.

00:09:53: It needs leaders who break down silos, get people involved early, the ones who actually use it, and commit to the necessary cultural shift.

00:10:00: You can't just email everyone saying, we have AI now.

00:10:02: Exactly.

00:10:03: You have to evolve how the whole place operates.

00:10:06: The bottleneck isn't the tech anymore.

00:10:08: It's the change management needed to make it stick.

00:10:10: So that really sums it up then.

00:10:11: The big picture from Emo in these last couple of weeks seems to be, one, a real move towards collaborative industrial grade AI.

00:10:20: Two, automation embedding itself everywhere, even in small batches.

00:10:23: And three, the absolute necessity of sorting out the culture and processes to actually make it all work.

00:10:29: If you enjoyed this deep dive, new ones drop every two weeks.

00:10:32: Also check out our other editions on digital construction and digital power tools.

00:10:36: And just as we wrap up.

00:10:38: thinking about this incredible pace of automation we've been discussing.

00:10:41: Let me leave you with a slightly more provocative thought.

00:10:44: It builds on an idea from Patrick Hellerman.

00:10:47: Okay, I'm listening.

00:10:48: We need to start thinking very differently about automation and society.

00:10:52: Western economies should seriously consider integrating robots and automation into their pension strategies.

00:10:59: Robots and pensions, how does that work?

00:11:00: Call it the robot pension concept.

00:11:02: Instead of just seeing automation as a threat to jobs, frame it as a societal investment.

00:11:08: If we invest heavily in robotics and automation today, maybe future generations can retire earlier, healthier, and more comfortably.

00:11:15: It flips the script from, oh no, robots will take jobs and we'll have to work till seventy to let's actively fund automation.

00:11:22: so maybe we can retire well at fifty-five.

00:11:25: That's a really interesting long-term perspective.

00:11:28: Turning automation into an ally for future economic and social sustainability, not just an efficiency play.

00:11:34: Exactly.

00:11:35: just something for you to think about.

00:11:36: Definitely

00:11:37: food for a thought.

00:11:38: If you want more insights like this, make sure you subscribe to The Deep Dive.

00:11:42: Thanks for tuning in.