Best of LinkedIn: Smart Manufacturing CW 51 - 02

00:00:00: This episode is provided by Thomas Allgeier and Frennis based on the most relevant LinkedIn posts about smart manufacturing in calendar weeks, fifty one to two.

00:00:09: Frennis is a B to B market research company that supports enterprises across the smart manufacturing industry.

00:00:14: with the market, customer and competitive insights they need to navigate dynamic markets and drive customer centric product development.

00:00:22: Welcome back.

00:00:24: For today's deep dive, our mission is pretty targeted.

00:00:27: We're taking all the top smart manufacturing insights from LinkedIn over the holidays and the start of twenty twenty six.

00:00:32: Right.

00:00:33: So that's content from calendar weeks.

00:00:35: fifty one through two.

00:00:36: And we're going to distill it into something you can actually use.

00:00:39: And what's really fascinating in this stack of sources is how much the conversation has, you know, matured.

00:00:45: It really has.

00:00:45: We've definitely moved past the whole.

00:00:47: what if phase of industry?

00:00:49: four point oh now it's all about the how to execute.

00:00:51: So what are the big anchors you're seeing?

00:00:53: I'd say there are three.

00:00:54: First is just disciplined execution.

00:00:57: Second, this rapid convergence of AI and digital twins, but in real time.

00:01:02: And maybe the most important is how things like sustainability and the talent shortage are forcing all these digital projects to deliver actual tangible value.

00:01:12: Anchoring them in reality, not just theory.

00:01:15: Exactly.

00:01:15: Okay, so let's unpack that execution mindset first.

00:01:19: It really seems like manufacturers are moving away from this idea of a giant Big Bang transformation.

00:01:24: They have to.

00:01:25: They're looking at it more like a journey of, you know, manageable wins, step by step.

00:01:29: Jeff Winter had a great way of putting this.

00:01:31: He said industry four point.

00:01:32: oh isn't a light switch.

00:01:33: No, it's a dimmer dial.

00:01:34: Yeah.

00:01:35: I love that analogy.

00:01:36: This idea of taking industry four point oh baby steps.

00:01:39: is so critical because we've all seen it.

00:01:41: companies get stuck in analysis paralysis.

00:01:43: They

00:01:43: try to do everything at once and end up doing nothing.

00:01:45: Precisely.

00:01:46: You have to start small, show return, and then you can advance with purpose.

00:01:49: But where you start from matters, I saw she how Andy you made a foundational point about this.

00:01:54: The old rule.

00:01:55: Lean first, then smart.

00:01:56: Right, because otherwise you just end up digitizing your own waste.

00:02:00: Making your problems faster, not solving them.

00:02:02: It's so true.

00:02:03: And it gets to a core question, you know.

00:02:04: Yeah.

00:02:05: If your plant is already inefficient, is smart tech really going to find the waste for you?

00:02:09: Or just add complexity?

00:02:11: So it has to start with a real diagnosis of your plant's unique pain points.

00:02:16: It has to be business led.

00:02:18: Every single use case has to deliver clear, non-gocible value.

00:02:22: Which brings up sequencing.

00:02:24: Arthur Sariman highlighted this as basically risk management.

00:02:28: It is.

00:02:28: You just cannot have... multiple, really high-load digital projects hitting the same team at the same time.

00:02:35: It's a recipe for burnout.

00:02:36: Performance just drops off a cliff.

00:02:38: And her point was key.

00:02:40: Don't start with a technology shopping list.

00:02:42: Start with asking, where are we losing money or capacity right now?

00:02:45: Then you find the tech to fix that.

00:02:47: And this all ties back to the human element, the mindset.

00:02:49: Absolutely.

00:02:50: Bill Sheetz put it perfectly.

00:02:51: Industry four point zero is a mindset, not a software package.

00:02:55: Success starts on the floor with stable connected processes at the machine.

00:02:59: But then you connect that to the bigger picture, and Stefan Wasko highlights this strategic problem.

00:03:05: Yeah, a dilemma, especially for companies in the West and particularly in Europe.

00:03:09: He calls it the two-year ROI prison.

00:03:12: The two-year ROI prison.

00:03:14: That's a powerful phrase.

00:03:16: What does that actually mean in practice?

00:03:18: It means you're constantly forced to choose between a quick win, like say optimizing predictive maintenance in one area and the much slower, more necessary infrastructure build out.

00:03:29: Things like standardized central data structures.

00:03:32: Things that don't pay off in two years.

00:03:34: Not

00:03:34: even close.

00:03:36: Companies with a long-term vision like you see in China can afford a ten-year view.

00:03:41: But if you're stuck in that quarterly cycle, you can't justify a big infrastructure investment upfront.

00:03:46: So it's a clash between finance and being ready for the future.

00:03:49: It's a fundamental conflict, and it's crippling long-term competitiveness.

00:03:53: Okay, speaking of costs, let's talk about unplanned downtime and what Jeff Winter calls the hidden factory.

00:03:59: The hidden factory is such a great concept.

00:04:01: It's all that time and energy wasted on workarounds.

00:04:04: The heroics.

00:04:05: The heroics, exactly.

00:04:06: The late night saves, the spreadsheets, the tribal knowledge, all the things that compensate for broken systems.

00:04:13: And the numbers are staggering.

00:04:15: Historically, Feigenbaum estimated it eats up twenty to forty percent of a company's capacity.

00:04:21: And you can't fix what you don't measure.

00:04:25: So how do you start to measure it?

00:04:27: Well, the practical suggestions are things like using smart metrics from an MES system tracking KPIs and just rigorously streamlining those handoffs between shifts or departments.

00:04:37: And that's where you move beyond just reactive maintenance.

00:04:40: Yuri Piskunomich noted that IoT sensors are changing the game.

00:04:43: They really are.

00:04:44: You shift the goal from fix it when it breaks to

00:04:46: fix it before it breaks.

00:04:48: That's

00:04:48: it.

00:04:48: You catch a heat anomaly, a vibration drift, early tool wear, and you improve quality and safety long before a catastrophic failure.

00:04:56: And

00:04:56: now that's happening in the virtual world

00:04:57: too.

00:04:58: Totally.

00:04:58: Joe Bache shared a great case study from the auto supplier, Bros.

00:05:02: They use Siemens plant simulation to model and prevent bottlenecks.

00:05:06: In welding and logistics specifically.

00:05:08: Right.

00:05:09: The complex stuff.

00:05:10: It lets them trial before implementation.

00:05:13: They can test changes virtually before spending a dime on the physical floor.

00:05:17: But if you're connecting all these sensors and digital twins, you're also expanding your digital risk surface.

00:05:23: You are.

00:05:24: Which brings us to OT cybersecurity.

00:05:27: Patrick Ho calls it the connective tissue of modern manufacturing.

00:05:30: And the shift is away from just visibility, right?

00:05:33: Right.

00:05:33: It's now about active resilient defense.

00:05:37: We're seeing trends like mandating secure by design hardware in RFQs, applying zero trust principles from IT to the physical world.

00:05:45: Interesting.

00:05:47: But Neyman Taldar pointed out there's a trade-off here, especially with extreme security like data diodes.

00:05:52: For those who don't know, what exactly is a data diode and what's the trade-off?

00:05:56: So a data diode is hardware.

00:05:58: It enforces a one-way data flow.

00:06:00: Data can go out of your OT network, but nothing can come back in.

00:06:03: So it's incredibly secure.

00:06:04: Unparalleled security.

00:06:06: Yeah.

00:06:06: But you instantly lose that bi-directional agility you need for advanced digital transformation.

00:06:11: You can't have your cloud AI sending real-time adjustments back to the machines.

00:06:15: So you have to choose between total protection and innovation.

00:06:19: You have to find that intelligent balance.

00:06:21: Total isolation kills innovation.

00:06:23: Okay, so let's move up from the shop floor to the strategic backbone of all this.

00:06:29: the digital thread.

00:06:30: Right.

00:06:31: And we're seeing a huge catalyst for change here.

00:06:34: It's the end of support deadlines for a lot of legacy systems.

00:06:36: We saw a ton of posts about Oracle Agile PLM reaching its end of support.

00:06:41: Alex Allison and Christine Schoss both highlighted this.

00:06:45: And it's not just a maintenance headache.

00:06:46: It's forcing manufacturers to stop and really reassess their whole strategy.

00:06:51: It's a chance to rethink how work flows across the entire company.

00:06:54: Exactly.

00:06:55: They have to think about long-term digital continuity, multi-cad integration, moving to modern platforms like Team Center.

00:07:02: But

00:07:03: that strategic shift needs everyone on the same page internally.

00:07:06: Oh,

00:07:06: absolutely.

00:07:07: Breon Carroll made a great point that aligning PLM that's R&D in engineering with ERP operations and finance is an ownership challenge.

00:07:15: Not a technology problem.

00:07:16: It's not a technology problem.

00:07:18: It's about silers.

00:07:19: When those departments are siloed, every engineering change is a potential disaster on the shop floor.

00:07:25: The goal is to get everyone working off one shared, trusted digital fabric.

00:07:31: And when you achieve that, the value is huge.

00:07:33: Stephen B. Chavez talked about how the digital thread ensures that trusted bi-directional flow.

00:07:38: Data from the R&D desk, straight to the machine, and then back again.

00:07:42: We even saw some real-world examples like Watts Water Technologies.

00:07:47: They sped up their new product introduction using Siemens Process Preparation X.

00:07:51: Ashish Kumar Sahu positioned PLM plus the cloud as the ultimate business transformation platform.

00:07:57: It enables that agility and innovation everyone is chasing.

00:08:00: Okay, let's take that digital thread and stretch it all the way into the industrial metaverse.

00:08:05: This feels like the ultimate convergence point.

00:08:07: It really is.

00:08:08: It's where digital twins, AI, and real-time data all come together.

00:08:12: Dale Tutt had a really effective definition for this.

00:08:14: Who did?

00:08:15: He called the industrial metaverse a seamless, live-connected digital layer.

00:08:20: It uses executable digital twins and AI to model factories in real time.

00:08:25: And the key part is time-aligned insights.

00:08:28: That's crucial.

00:08:29: It means the twin keeps pace with the physical factory.

00:08:31: it's modeling.

00:08:33: That's what allows for real predictive optimization.

00:08:35: And we saw some of the new tools making this happen at CES- Twenty-Twenty-Six.

00:08:39: Right.

00:08:40: Clary Chow and Roland Locker highlighted the Siemens Digital Twin Composer, which is built on NVIDIA Omniverse.

00:08:46: And companies like PepsiCo are using it to virtually test whole new factory layouts.

00:08:51: Before

00:08:52: construction even starts, It's fail-fast, but done completely in the virtual world.

00:08:58: Imagine the capital risk that saves.

00:09:00: But right after that excitement, you get the reality check.

00:09:02: Of course.

00:09:03: Victor Alvarez stressed a huge point.

00:09:05: He said, while these tools are great for virtual commissioning, you know, validating the design, matching that pristine twin to the chaotic reality of actual production is still the hard part.

00:09:16: What's the risk there?

00:09:17: The risk is feeding what he called dirty water.

00:09:21: Messy sensor data.

00:09:22: noise drift from the shot floor into your perfect digital model.

00:09:25: So if the model assumes perfect input, any real world deviation just breaks the twin.

00:09:31: It breaks the twin.

00:09:32: It becomes useless for prediction.

00:09:33: It just reverts to being a static three D model, not a live diagnostic tool.

00:09:38: So data hygiene has to be solved first.

00:09:41: Still, the strategic benefit is undeniable.

00:09:43: Shankar Aman noted that using this lets engineers optimize the product and the production system at the same time really early on.

00:09:51: Which eliminates so many costly downstream risks and just shrinks those critical MPI timelines.

00:09:56: OK, let's talk about the engine driving all of this.

00:09:59: Applied industrial AI.

00:10:01: We're finally seeing it move beyond the height.

00:10:04: Oh, yeah.

00:10:05: This is embedded intelligence that is driving real tangible results.

00:10:09: You can see it in those big industry partnerships.

00:10:12: David Morley described the Siemens and Rolls Royce demo at Emo twenty twenty five.

00:10:16: Right.

00:10:16: With the AI powered NXCAM co-pilot.

00:10:19: And it cut programming time for complex jet engine parts by a reported eighty percent.

00:10:24: Eighty

00:10:24: percent.

00:10:24: I mean that is just a radical immediate efficiency game.

00:10:27: It's closing the gap between the design intent and the final machine program.

00:10:31: But even with that high level success, it still comes down to the fundamentals.

00:10:35: It always does.

00:10:36: Jean Roque reminds us that machine learning success in manufacturing is all about data quality, and having core predictive modeling regression classification integrated directly into your MES, the foundation has to be solid.

00:10:51: So what's next, Jen?

00:10:52: I saw David Rogers mentioned something called the instructed retriever for manufacturing searches.

00:10:56: Yeah,

00:10:57: this is really specific but powerful.

00:10:59: It uses system level reasoning to cut through all the dense technical jargon in old manuals.

00:11:05: So the maintenance engineer finds the exact troubleshooting data they need instantly.

00:11:09: Exactly.

00:11:10: And that time saved searching... translates directly into less downtime.

00:11:14: And then there's agentic AI.

00:11:16: Right.

00:11:16: Victor M. and Biju Kalapili were discussing this.

00:11:19: It's about moving systems toward genuine reasoning, planning, and adapting.

00:11:23: So an agentic AI could manage a task on its own based on a high-level goal you give it.

00:11:28: That's the idea.

00:11:28: Instead of just following pre-programmed rules, but, and this is big, but Victor M. stresses a crucial point here.

00:11:34: What's the catch?

00:11:35: You can't automate accountability.

00:11:38: Not when decisions affect safety, quality, or profit margins.

00:11:42: Even as AI gets smarter, a human has to own the decision.

00:11:46: That's essential for responsible scaling.

00:11:48: Right.

00:11:49: So let's shift to automation itself.

00:11:51: It's becoming much more flexible through something called software-defined automation, or SDA.

00:11:57: Oscar El described SDA as basically virtualizing your automation controls.

00:12:02: This gives you huge gains in agility and interoperability.

00:12:05: especially using standards like IEC-Six-One-Four-Ninety-Nine.

00:12:10: And

00:12:10: FebresterDot confirms that open platforms like Schneider's EcoStruxure are key to getting that multi-vendor harmony you need.

00:12:16: Right.

00:12:17: You can't be locked into one ecosystem anymore.

00:12:18: We're seeing some powerful examples of this in the e-mobility sector.

00:12:22: Definitely.

00:12:22: Victor Malita noted that scenomorphic robots are being used for really precise CNC machining on big parts, like battery trays.

00:12:29: By unifying the robot control with the CNC accuracy?

00:12:32: Exactly.

00:12:33: That flexibility is critical when you have customized or rapidly changing designs.

00:12:37: And then you have just sheer methodology innovation, like Tesla's giga casting.

00:12:41: That update in Berlin, yeah.

00:12:42: Daniel Keper highlighted it.

00:12:44: It's a method that gives them about a thirty-five percent cost reduction and ten percent weight reduction.

00:12:49: All through fully automated casting for structural subframes.

00:12:52: It's a huge market advantage.

00:12:54: And the tooling to validate this is becoming mainstream.

00:12:57: Marcus Ramela mentioned, three-D laser scanners are now standard tools right on the shop floor.

00:13:03: Not just for quality inspection, but for reverse engineering old parts too.

00:13:07: Right, when you don't have the original CAD data, it's a game changer.

00:13:11: Okay, switching gears a bit.

00:13:12: Let's talk about sustainability.

00:13:14: It feels like the conversation has shifted from it being a cost to it being a profit lever.

00:13:19: A

00:13:19: fundamental one.

00:13:20: One that actually drives resilience.

00:13:22: Yom

00:13:22: Ligurich and Nik Lilek really emphasize that connection.

00:13:26: Smart and sustainable manufacturing go hand-in-hand.

00:13:28: They drive both resilience and ROI.

00:13:31: And digital twins are a huge part of that.

00:13:33: They're helping companies optimize new EV plants for energy use right from the start.

00:13:37: And we've got some amazing real-world examples to back this up.

00:13:41: We do.

00:13:42: Gianluca Casanova shared how ABB's hundred-and-ten-year-old factory in Oyardson achieved zero operational emissions.

00:13:49: And a forty-two.

00:13:52: That's incredible.

00:13:53: It is.

00:13:53: Done through electrification and digital energy management.

00:13:56: And then Sabine Vermeerish-Goodwin points out that the digital thread is key for a circular economy.

00:14:02: It gives you the lifecycle transparency you need to reuse and repair things.

00:14:06: And we're seeing this proven out by third parties.

00:14:08: Pasquale Peretti mentioned multiple Schneider electric sites.

00:14:12: In Naples, Leeds.

00:14:13: While

00:14:14: achieving smart factory certifications, it proves that adopting IOT and real-time analytics gives you measurable benefits for both efficiency and sustainability.

00:14:23: The two goals support each other.

00:14:25: So finally, let's bring it all home to the most critical element, the people.

00:14:29: the workforce, the skills, the leadership mindset.

00:14:32: Because none of this tech works without the right people.

00:14:35: This is where it all comes together.

00:14:36: I think we should start with a positive message from Tony Gunn.

00:14:39: He framed manufacturing as this really engaging field.

00:14:43: Yes.

00:14:44: More like engineering meets gaming.

00:14:46: That framing is so crucial.

00:14:49: It helps move the perception of manufacturing away from being dirty and repetitive.

00:14:53: And toward being data driven and problem solving.

00:14:56: Which is exactly what you need.

00:14:58: to attract modern talent.

00:14:59: But you have to invest in that talent.

00:15:01: Mike Nager stressed that the ROI on smart equipment depends entirely on having a skilled workforce that can actually use it.

00:15:07: A hundred percent.

00:15:08: And Hans Schubert echoed this, saying, leadership has to invest in education, build those talent pipelines to bring in the next generation.

00:15:17: And

00:15:17: we know there's a documented skills gap here.

00:15:19: A

00:15:19: big one, yeah.

00:15:20: WUMSIC Dr.

00:15:21: Sue identified it in cyber physical systems and IoT.

00:15:25: To bridge it, Robert Little highlighted programs like the Siemens Mechatronics System certification.

00:15:30: He said it's really effective for workforce readiness, especially when it's paired with a degree.

00:15:34: Right.

00:15:34: And ultimately, Allison Payne reminds us this isn't about technology replacing people.

00:15:39: It's

00:15:39: technology empowering people, workforce augmentation.

00:15:42: And that requires frontline leadership to be the bridge between the high level corporate strategy and what's actually happening on the factory floor.

00:15:51: The human element has to be at the center

00:15:53: of it all.

00:15:54: So when you look across all of these sources, what's the big takeaway for you?

00:15:58: It's the shift from talking about potential to demonstrating disciplined execution.

00:16:03: That's what stands out.

00:16:04: We're seeing this convergence where AI, digital twins, these software-defined systems, they're just becoming standard infrastructure.

00:16:12: But every single success story has a focus on execution, on solving messy data problems, and on making that long-term investment in people.

00:16:20: If you enjoyed this deep dive, new episodes drop every two weeks.

00:16:23: Also check out our other editions on digital construction and digital power tools.

00:16:27: So, if the biggest barrier to transformation isn't the technology, but that short-term, two-year ROI prison mindset, what necessary foundational investment will you prioritize this quarter that won't actually pay off for three years?

00:16:40: Thank you for diving deep with us.

00:16:42: Be sure to subscribe.