Practitioners Unplugged

About Practitioners Unplugged

With the help of AVEVA and Schneider Electric, this show explores the principles of Industry 4.0 through the insights of industry practitioners. Take an in-depth look at leveraging smart manufacturing technologies to drive industry innovation. Hear about firsthand experiences in implementing real-world manufacturing solutions. Our hope is that you will gain valuable knowledge about the challenges and successes encountered from their journeys, offering practical lessons for applying these insights to your own digital transformation efforts.

Publisher

IndustrialSage

Category

business · news · technology

Language

en

Explicit

No

First episode

8 Aug 2024

Latest episode

24 Sept 2026

Latest episodes

1 of 22 episodes have a transcript.

1. 

   24 Sept 2026

   Most Manufacturing Digital Maturity Models Are Theory. This One Runs 110 Plants

   Key Takeaways ofi’s manufacturing digital maturity model has three levels: digital, integrated, and autonomous. Level one is simply removing paper and Excel from the shop floor. The central team never arrives with a solution already chosen. It interviews plant leaders first. Four value pillars run in parallel, not in sequence, and each site weights them differently. A central digital catalogue turns one plant’s working solution into a repeatable deployment. Technology does not resolve problems. It makes existing process and capability gaps visible. Adaptability is now the top hiring criterion, ahead of any specific technical skill. Get future Practitioners Unplugged episodes Join manufacturing executives getting new conversations and key insights in their inbox. Subscribe Now Equipment risk reduction has always been part of HSB’s business. What’s changed is how the company gets there. John Stokes, SVP of Technology Risk Solutions at HSB, joins Practitioners Unplugged (https://www.industrialsage.com/practitioners-unplugged-by-industrialsage/) hosts Dante Vaccaro and Sree Hameed. Together, they explain why real-time equipment data is turning industrial insurance from a backward-looking payout into a genuine prevention strategy. An Engineering Company First, an Insurance Company Second HSB, short for Hartford Steam Boiler, marks its 160th anniversary this year. The company’s founding story has little to do with underwriting. In 1865, steam boilers were exploding at a rate of roughly once every four days across American industry. Most people considered it an act of God, comparable to an earthquake or a hurricane. That changed when a boiler explosion aboard the steamship Sultana (https://en.wikipedia.org/wiki/Sultana_(steamboat)) on the Mississippi River killed more than 1,100 passengers. It remains the deadliest maritime disaster in U.S. history, and it became a tipping point for boiler safety nationwide. In response, HSB’s founders created the Hartford Standards: specifications for how boilers should be designed, manufactured, and maintained. Those standards became the foundation of the codes that steam boiler and pressure vessel operators still follow today. Notably, HSB issued its first insurance policy only after establishing itself as an engineering and standards organization. That order has never reversed. HSB employs nearly 3,000 people worldwide, and more than half hold a technical or engineering background. Inspection and engineering come first. Insurance comes second. From Actuarial Underwriting to Real-Time, Observable Risk For most of the insurance industry’s history, risk has been priced through actuarial underwriting. That means measuring risk based on how losses trend over time. It’s a backward-looking method, and one that depends on getting eyes on equipment in person, which is slow and expensive. In fact, Stokes describes a shift already underway, one where connected equipment and real-time data start to replace estimation with observation. “We’re moving away from this model where insurers are estimating risk only through these actuarial principles, and now we’re onto these more observational, observable and measurable risk through real-time insights from connected equipment and data.” John Stokes, SVP, Technology Risk Solutions, HSB That shift matters for claims, too. Richer data, Stokes says, will improve the experience for every stakeholder in the chain: the insured, the OEM, and the insurer itself. Equipment Risk Reduction in Practice: Cutting Risk by Up to 90 Percent The clearest evidence for equipment risk reduction through technology comes from HSB’s own performance guarantee work. Stokes points to vibration monitoring on rotating equipment as a concrete example. Picture a motor driving a pump in a wastewater facility, or an industrial fan in a data center. Traditional vibration monitoring flags a problem only after it already exists, usually forcing an unplanned overhaul, repair, or replacement. Machine learning applied to that same data works differently. It catches the first anomaly and turns a catastrophic failure into a minor maintenance task instead. “We’ve done performance guarantees where it’s been in our assessment that the presence of technology can reduce that risk on a single piece of equipment by as much as 90 percent. That is strong and really impactful when it comes to insurance costs.” John Stokes, SVP, Technology Risk Solutions, HSB That number changes the calculation for manufacturers weighing a technology investment against its return. Factor in what that level of equipment risk reduction saves on insurance costs and maintenance budgets. The technology can pay for itself well before the depreciation schedule says it should. The “Uber for Machines” Model: Selling Outcomes, Not Equipment Stokes draws a direct comparison to Uber’s disruption of transportation. The technology, not a better vehicle, made that model work. He argues the same shift is underway in industrial equipment, with OEMs moving from selling machines to selling outcomes. “Think about an Uber for machines model where OEMs are not just selling equipment, they’re selling outcomes. Equipment as a service. No capital expense required upfront. The customer is really paying for that equipment based upon how it performs and what it ultimately produces.” John Stokes, SVP, Technology Risk Solutions, HSB Rolls-Royce proved this concept decades ago with its Power-by-the-Hour model (https://www.rolls-royce.com/media/press-releases-archive/yr-2012/121030-the-hour.aspx) for jet engines. Airlines paid a flat hourly rate rather than buying engines outright. Stokes sees the same logic extending into manufacturing plants and logistics centers, especially as robotics make automated production lines more common. The model only works if three things are true. The equipment must be connected, the data must be available, and someone on the other end must know what that data actually means. Without all three, Stokes is direct: it doesn’t work financially for anyone in the chain. “Hope Is Not a Strategy”: Why Prevention Beats Payout In practice, the obstacle to wider adoption of these models is rarely the technology itself. “We’ve all heard hope is not a strategy, right? And there’s a lot of ways to plan for the bad things that are gonna happen, and I think insurance is just one of those. The basic principle of insurance is that it responds when something bad’s already happened. Mitigation and prevention are much more important, and that’s where technology plays a role in many use cases, if not all.” John Stokes, SVP, Technology Risk Solutions, HSB Relatively small technology investments, Stokes notes, aren’t always easy to implement. That’s largely because of a cultural divide: OEMs and their customers have operated the same way for decades. Evolving that mindset, from “bad things are gonna happen, that’s why I need insurance” to “I have things at my disposal to keep those bad things from happening,” takes time. The CFO Is the Real Catalyst for This Shift Stokes is direct about where the decision to adopt these new models ultimately gets made: the CFO’s office. The conversation has to pencil out financially before it goes anywhere else, and it can’t only be about day-to-day operations. It has to show an improvement to the top and bottom line. That means more revenue from producing more with new technology, without shelling out significant capital. It also means lower ongoing operational costs for the equipment itself. Notably, insurance has a role in that financial case, too, not just paying for unexpected equipment damage but standing behind the overall financial model of an as-a-service arrangement. As more OEMs and end users test these waters together, equipment risk reduction stops being a technology story. It becomes a finance story, and that’s exactly the audience Stokes believes will ultimately drive this shift. Frequently Asked Questions HSB and Equipment Risk Reduction What is HSB, and how is it connected to insurance? HSB (Hartford Steam Boiler) is a specialty insurer and inspection company founded in 1865, now part of Munich Re. It provides equipment breakdown insurance and other technical risk coverage, with roots in industrial engineering and safety standards rather than traditional underwriting. How much can technology reduce equipment risk, according to HSB? In HSB’s own performance guarantee assessments, proven, connected technology has reduced the risk profile on a single piece of equipment by as much as 90 percent, according to John Stokes, SVP of Technology Risk Solutions at HSB. Why is insurance moving from actuarial underwriting to real-time risk monitoring? Actuarial underwriting prices risk based on historical loss trends, a backward-looking approach. Connected equipment and IoT sensors now let insurers like HSB observe risk in real time, enabling more accurate pricing and earlier intervention before failures occur. Equipment-as-a-Service Business Models What is the “Uber for machines” model in manufacturing? It describes a shift where OEMs sell equipment outcomes rather than the equipment itself, similar to Rolls-Royce’s Power-by-the-Hour model for jet engines. Customers pay based on performance rather than upfront capital expense, and the model depends entirely on connected equipment data. What does “equipment as a service” mean for manufacturers? Equipment as a service shifts ownership away from the manufacturer’s balance sheet. Instead of a capital purchase, the manufacturer pays based on the equipment’s output or uptime, while the OEM retains responsibility for keeping it running. What’s the biggest barrier to adopting predictive, technology-driven insurance models? According to Stokes, the barrier is cultural, not technical. OEMs and end users have operated the same way for decades, and shifting from a reactive mindset to a preventive one takes time, even when the financial case is strong. Keep practicing, keep learning, keep transforming. To follow more of HSB’s work, visit HSB’s site (https://www.munichre.com/hsb/en.html) or connect with HSB on LinkedIn (https://www.linkedin.com/company/hsb). For another Practitioners Unplugged conversation on shifting from reactive to predictive operations, see Episode 15 on transforming water infrastructure (https://www.industrialsage.com/episode-15-from-run-to-failure-to-predictive-operations-transforming-water-infrastructure/). To submit a request for a new episode topic from Practitioners Unplugged, visit our contact page. (https://www.industrialsage.com/contact/) Explore more about Schneider Electric (https://www.se.com/us/en/) & AVEVA (http://www.aveva.com/en/)

2. 

   28 Jul 2026

   Insurance Companies Are Engineering Firms First

   Key Takeaways HSB was founded in 1865 as an engineering and standards organization, not an insurer, and it still leads with inspection and engineering before underwriting today. Equipment risk reduction is driving a shift in insurance, moving the industry from backward-looking actuarial underwriting toward real-time, observable risk powered by connected equipment data. In HSB’s own performance guarantee assessments, proven, connected technology has reduced the risk profile on a single piece of equipment by as much as 90 percent. The “Uber for machines” model, in which OEMs sell equipment outcomes instead of the equipment itself, depends entirely on connected data and an insurer willing to help absorb the financial risk. Insurance has always responded after a loss. Technology is what finally makes prevention affordable and scalable enough to change that. Equipment-as-a-service adoption is limited more by culture than by technology, and the CFO’s financial case is often what tips an organization toward change. Equipment risk reduction has always been part of HSB’s business. What’s changed is how the company gets there. John Stokes, SVP of Technology Risk Solutions at HSB, joins Practitioners Unplugged (https://www.industrialsage.com/practitioners-unplugged-by-industrialsage/) hosts Dante Vaccaro and Sree Hameed. Together, they explain why real-time equipment data is turning industrial insurance from a backward-looking payout into a genuine prevention strategy. An Engineering Company First, an Insurance Company Second HSB, short for Hartford Steam Boiler, marks its 160th anniversary this year. The company’s founding story has little to do with underwriting. In 1865, steam boilers were exploding at a rate of roughly once every four days across American industry. Most people considered it an act of God, comparable to an earthquake or a hurricane. That changed when a boiler explosion aboard the steamship Sultana (https://en.wikipedia.org/wiki/Sultana_(steamboat)) on the Mississippi River killed more than 1,100 passengers. It remains the deadliest maritime disaster in U.S. history, and it became a tipping point for boiler safety nationwide. In response, HSB’s founders created the Hartford Standards: specifications for how boilers should be designed, manufactured, and maintained. Those standards became the foundation of the codes that steam boiler and pressure vessel operators still follow today. Notably, HSB issued its first insurance policy only after establishing itself as an engineering and standards organization. That order has never reversed. HSB employs nearly 3,000 people worldwide, and more than half hold a technical or engineering background. Inspection and engineering come first. Insurance comes second. From Actuarial Underwriting to Real-Time, Observable Risk For most of the insurance industry’s history, risk has been priced through actuarial underwriting. That means measuring risk based on how losses trend over time. It’s a backward-looking method, and one that depends on getting eyes on equipment in person, which is slow and expensive. In fact, Stokes describes a shift already underway, one where connected equipment and real-time data start to replace estimation with observation. “We’re moving away from this model where insurers are estimating risk only through these actuarial principles, and now we’re onto these more observational, observable and measurable risk through real-time insights from connected equipment and data.” John Stokes, SVP, Technology Risk Solutions, HSB That shift matters for claims, too. Richer data, Stokes says, will improve the experience for every stakeholder in the chain: the insured, the OEM, and the insurer itself. Equipment Risk Reduction in Practice: Cutting Risk by Up to 90 Percent The clearest evidence for equipment risk reduction through technology comes from HSB’s own performance guarantee work. Stokes points to vibration monitoring on rotating equipment as a concrete example. Picture a motor driving a pump in a wastewater facility, or an industrial fan in a data center. Traditional vibration monitoring flags a problem only after it already exists, usually forcing an unplanned overhaul, repair, or replacement. Machine learning applied to that same data works differently. It catches the first anomaly and turns a catastrophic failure into a minor maintenance task instead. “We’ve done performance guarantees where it’s been in our assessment that the presence of technology can reduce that risk on a single piece of equipment by as much as 90 percent. That is strong and really impactful when it comes to insurance costs.” John Stokes, SVP, Technology Risk Solutions, HSB That number changes the calculation for manufacturers weighing a technology investment against its return. Factor in what that level of equipment risk reduction saves on insurance costs and maintenance budgets. The technology can pay for itself well before the depreciation schedule says it should. The “Uber for Machines” Model: Selling Outcomes, Not Equipment Stokes draws a direct comparison to Uber’s disruption of transportation. The technology, not a better vehicle, made that model work. He argues the same shift is underway in industrial equipment, with OEMs moving from selling machines to selling outcomes. “Think about an Uber for machines model where OEMs are not just selling equipment, they’re selling outcomes. Equipment as a service. No capital expense required upfront. The customer is really paying for that equipment based upon how it performs and what it ultimately produces.” John Stokes, SVP, Technology Risk Solutions, HSB Rolls-Royce proved this concept decades ago with its Power-by-the-Hour model (https://www.rolls-royce.com/media/press-releases-archive/yr-2012/121030-the-hour.aspx) for jet engines. Airlines paid a flat hourly rate rather than buying engines outright. Stokes sees the same logic extending into manufacturing plants and logistics centers, especially as robotics make automated production lines more common. The model only works if three things are true. The equipment must be connected, the data must be available, and someone on the other end must know what that data actually means. Without all three, Stokes is direct: it doesn’t work financially for anyone in the chain. “Hope Is Not a Strategy”: Why Prevention Beats Payout In practice, the obstacle to wider adoption of these models is rarely the technology itself. “We’ve all heard hope is not a strategy, right? And there’s a lot of ways to plan for the bad things that are gonna happen, and I think insurance is just one of those. The basic principle of insurance is that it responds when something bad’s already happened. Mitigation and prevention are much more important, and that’s where technology plays a role in many use cases, if not all.” John Stokes, SVP, Technology Risk Solutions, HSB Relatively small technology investments, Stokes notes, aren’t always easy to implement. That’s largely because of a cultural divide: OEMs and their customers have operated the same way for decades. Evolving that mindset, from “bad things are gonna happen, that’s why I need insurance” to “I have things at my disposal to keep those bad things from happening,” takes time. The CFO Is the Real Catalyst for This Shift Stokes is direct about where the decision to adopt these new models ultimately gets made: the CFO’s office. The conversation has to pencil out financially before it goes anywhere else, and it can’t only be about day-to-day operations. It has to show an improvement to the top and bottom line. That means more revenue from producing more with new technology, without shelling out significant capital. It also means lower ongoing operational costs for the equipment itself. Notably, insurance has a role in that financial case, too, not just paying for unexpected equipment damage but standing behind the overall financial model of an as-a-service arrangement. As more OEMs and end users test these waters together, equipment risk reduction stops being a technology story. It becomes a finance story, and that’s exactly the audience Stokes believes will ultimately drive this shift. Frequently Asked Questions HSB and Equipment Risk Reduction What is HSB, and how is it connected to insurance? HSB (Hartford Steam Boiler) is a specialty insurer and inspection company founded in 1865, now part of Munich Re. It provides equipment breakdown insurance and other technical risk coverage, with roots in industrial engineering and safety standards rather than traditional underwriting. How much can technology reduce equipment risk, according to HSB? In HSB’s own performance guarantee assessments, proven, connected technology has reduced the risk profile on a single piece of equipment by as much as 90 percent, according to John Stokes, SVP of Technology Risk Solutions at HSB. Why is insurance moving from actuarial underwriting to real-time risk monitoring? Actuarial underwriting prices risk based on historical loss trends, a backward-looking approach. Connected equipment and IoT sensors now let insurers like HSB observe risk in real time, enabling more accurate pricing and earlier intervention before failures occur. Equipment-as-a-Service Business Models What is the “Uber for machines” model in manufacturing? It describes a shift where OEMs sell equipment outcomes rather than the equipment itself, similar to Rolls-Royce’s Power-by-the-Hour model for jet engines. Customers pay based on performance rather than upfront capital expense, and the model depends entirely on connected equipment data. What does “equipment as a service” mean for manufacturers? Equipment as a service shifts ownership away from the manufacturer’s balance sheet. Instead of a capital purchase, the manufacturer pays based on the equipment’s output or uptime, while the OEM retains responsibility for keeping it running. What’s the biggest barrier to adopting predictive, technology-driven insurance models? According to Stokes, the barrier is cultural, not technical. OEMs and end users have operated the same way for decades, and shifting from a reactive mindset to a preventive one takes time, even when the financial case is strong. Keep practicing, keep learning, keep transforming. To follow more of HSB’s work, visit HSB’s site (https://www.munichre.com/hsb/en.html) or connect with HSB on LinkedIn (https://www.linkedin.com/company/hsb). For another Practitioners Unplugged conversation on shifting from reactive to predictive operations, see Episode 15 on transforming water infrastructure (https://www.industrialsage.com/episode-15-from-run-to-failure-to-predictive-operations-transforming-water-infrastructure/). To submit a request for a new episode topic from Practitioners Unplugged, visit our contact page. (https://www.industrialsage.com/contact/) Explore more about Schneider Electric (https://www.se.com/us/en/) & AVEVA (http://www.aveva.com/en/)

3. 

   1 Jul 2026

   The Skills Gap Is a Leadership Gap: What 300 Manufacturing Conversations Reveal

   Key Takeaways When a company has known about the skills gap long enough without acting, the problem has shifted from a talent pipeline issue to a leadership accountability issue. Change management is not a project phase with a defined start and finish. It is the continuous operating mode every manufacturer navigating digital transformation needs to internalize now. Most companies being pushed for an AI strategy are not yet ready for one. A coherent data strategy and clear organizational goals for the next three to five years come first. Young workers are not persuaded by executive pitches for manufacturing careers. They are moved by peers their own age who are visibly thriving in the industry. Lasting trust with communities, customers, and future employees is built through a bigger story: community impact, national security, and career fulfillment, not product launches and trade show schedules. Get future Practitioners Unplugged episodes Join manufacturing executives getting new conversations and key insights in their inbox. Subscribe Now Three hundred conversations across nearly a decade of manufacturing. That is the vantage point Chris Luecke brings to Practitioners Unplugged as Founder and Host of Manufacturing Happy Hour, one of the most recognized independent podcasts in the industry. Chris started the show in 2016 while working at Rockwell Automation, recording from an iPhone as a way to build credibility in a competitive sales role. What followed was a cross-industry window into what is actually happening at the plant floor and leadership team level across every corner of manufacturing. Four Patterns From 300 Manufacturing Conversations In Episode 20, Chris joins hosts Dante and Sree to break down what a decade of listening has revealed. Four patterns emerge consistently: a skills gap that has quietly become a leadership gap, a change management challenge that organizations keep solving backwards, an AI rush that is skipping a critical step, and a talent attraction problem that executives are consistently approaching the wrong way. The Shift From Skills Gap to Leadership Gap The phrase Chris carries with him from his interviews came from Jason T. Ray of Paperless Parts. Ray’s company helps machine shops and job shops with quoting, and his take was direct: “We’ve known about the skills gap for a long time, and at some point, when you’ve known about a problem long enough, it’s not a skills gap anymore. It’s a leadership gap.” Indeed, Chris has watched that observation hold up across hundreds of subsequent conversations. The pattern is consistent. Walk into a facility with strong culture, modern technology, and leaders who hold themselves accountable. The talent conversation there sounds entirely different. Those companies are not complaining about pipeline. They are asking a more useful question: how do we find people with the right character so we can train them for the skills we need? By contrast, the facilities throwing their hands up tend to share a common profile: managers instead of leaders, a culture that leads with consequences rather than development, and a workforce problem acknowledged for years without meaningful action. Ultimately, the labor market is the same for everyone. The difference is leadership. Change Management Is a Habit, Not a Phase One of the most common patterns Chris hears from manufacturers who struggled with digital transformation: change management was called in after the technology was already deployed and adoption had stalled. A central team rolled out a system, resistance followed, and someone scrambled to fix what should have been part of the plan from the beginning. His reframe is direct. The question is not whether to start change management before, during, or after your next initiative. For any company that has not already internalized it, the most important shift is making it a habit. “It’s not before, during, or after. It’s just where we are in our change management journey. We are always going to be going through one.” Chris Luecke, Founder and Host, Manufacturing Happy Hour Whether the change is a full system replacement or a software update to existing automation equipment, the goal is the same: ingrain the reality that change is continuous. Digital transformation was never a destination with a defined endpoint. It is a different way of thinking about how a business operates going forward. The organizations navigating it best are not managing through change. They have made it part of their identity. Why an AI Strategy Without a Data Foundation First Gets the Sequence Wrong As a podcaster, Chris has noticed something revealing. He receives a steady stream of requests from technology companies wanting to talk about what their AI is doing for manufacturing. Calls from manufacturers ready to describe what AI is actually doing for their operations are far fewer. In fact, that gap is not surprising. Many of the companies now rushing to define an AI strategy are working through the same underlying infrastructure problems they faced during Industry 4.0. The pattern repeats: technology gets deployed ahead of organizational readiness, and adoption stalls. “Everyone wants to have an AI strategy right now when really I think some people should still be thinking about what their data strategy is.” Chris Luecke, Founder and Host, Manufacturing Happy Hour The sequence matters. Before asking what AI could do for an operation, a manufacturer needs to understand what the organization actually wants to accomplish over the next three to five years. What would being more productive look like, and what business impact would it create? Technology executes strategy. It does not create it. Reversing that order already cost manufacturers one expensive cycle with Industry 4.0, and the companies that skipped the foundation are now being asked to skip it again. Who Actually Recruits the Next Generation The conventional manufacturing talent pitch relies on executives and industry veterans making the case to young people. Chris is direct about why this approach keeps falling short. “An 18-year-old isn’t gonna wanna listen to a 39-year-old like me or a 55-year-old saying, ‘Hey, there’s a great career in manufacturing ahead for you.’ It’s a data point.” Chris Luecke, Founder and Host, Manufacturing Happy Hour Instead, what actually moves behavior is peer influence. A 23-year-old from the same neighborhood, building a real career in manufacturing, making real money, and carrying no student debt: that is a story an 18-year-old can see themselves in. It is not a statistic. Rather, it is evidence. Therefore, Chris’s call to action for manufacturers is to stop trying to be the messenger and start enabling their youngest high performers to tell the story. The companies winning on talent are not just hiring young workers. They are creating conditions where those employees thrive visibly, so the next wave of candidates can look at that and think: that could be me. This is not a new dynamic. After all, every generation has listened to its peers first. The manufacturers who understand that are the ones building pipelines that actually fill. The Bigger Story Manufacturers Are Missing Manufacturers tend to default to a narrow content and communication strategy: announce the new product, promote the upcoming trade show, share the latest certification. Chris has no argument against any of those. But he argues consistently that manufacturers are missing a much more powerful opportunity by stopping there. The topics that build real trust over time connect manufacturing to something larger: career fulfillment, community development, national security. Those are the stories that, told consistently over years, make a company trusted rather than simply recognized. “There are too many manufacturers that miss the opportunity for more general trust-building and relationship-building in manufacturing.” Chris Luecke, Founder and Host, Manufacturing Happy Hour In turn, that trust has direct business consequences: stronger sales relationships, a more compelling employer brand, and a talent pipeline built on genuine credibility rather than recruiting budgets. The manufacturers willing to build community one relationship at a time, through honest storytelling and local presence rather than product roadshows, are the ones developing a durable competitive advantage. The call to action from Chris is straightforward: start. Share the stories that demonstrate how much you care about the industry beyond the specific thing you are currently selling. Show up in your community. Build your audience one by one. Most competitors will not put in that kind of consistent work, which is exactly why it works. Keep practicing. Keep learning. Keep transforming. To follow more of Chris Luecke’s work across manufacturing media, visit manufacturinghappyhour.com (https://manufacturinghappyhour.com/) or connect with him on LinkedIn (https://linkedin.com/in/cwluecke). To submit a request for a new episode topic from Practitioners Unplugged, visit our contact page. (https://www.industrialsage.com/contact/) Explore more about Schneider Electric (https://www.se.com/us/en/) & AVEVA (http://www.aveva.com/en/)

Inside a recent episode

Episode #5 | Transforming Manufacturing with Industrial AI

Published 5 Dec 2024 · Transcript excerpt

[…] And so no, I'm not one of those knee jerklets throughout ISA 95. I think that those layers of protection, layers of decision -making at each, you know, so the control system's making very fast decisions. And then you've got the MES. It's making slightly slower decisions or longer, you know, time frame decisions. And you get the European, then, you know, their European is working on time frames a week's months, you know, and so I think that that's the right way to approach it. And then the cloud becomes more of a support system. It becomes a way of doing offline analysis, a way of making all of those components better and then deploying it later down into the system. […]

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